From f0152355a30ca5c63b9d46b37c8bc5522ef91a35 Mon Sep 17 00:00:00 2001
From: Alexander Hess <alexander@webartifex.biz>
Date: Sun, 15 Sep 2019 12:20:18 +0200
Subject: [PATCH 001/242] Initial commit

Add the following files:
- LICENSE (MIT)
- README.md (empty)
- pyproject.toml (after poetry init)
---
 LICENSE        | 21 +++++++++++++++++++++
 README.md      |  1 +
 pyproject.toml | 15 +++++++++++++++
 3 files changed, 37 insertions(+)
 create mode 100644 LICENSE
 create mode 100644 README.md
 create mode 100644 pyproject.toml

diff --git a/LICENSE b/LICENSE
new file mode 100644
index 0000000..5d77230
--- /dev/null
+++ b/LICENSE
@@ -0,0 +1,21 @@
+MIT License
+
+Copyright (c) 2019 Alexander Hess [alexander@webartifex.biz]
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.
diff --git a/README.md b/README.md
new file mode 100644
index 0000000..6eece6b
--- /dev/null
+++ b/README.md
@@ -0,0 +1 @@
+# An Introduction to Python and Programming
diff --git a/pyproject.toml b/pyproject.toml
new file mode 100644
index 0000000..6303a1d
--- /dev/null
+++ b/pyproject.toml
@@ -0,0 +1,15 @@
+[tool.poetry]
+name = "intro-to-python"
+version = "0.1.0"
+description = "An introduction to Python and programming for wanna-be data scientists"
+authors = ["Alexander Hess <alexander@webartifex.biz>"]
+license = "MIT"
+
+[tool.poetry.dependencies]
+python = "^3.6"
+
+[tool.poetry.dev-dependencies]
+
+[build-system]
+requires = ["poetry>=0.12"]
+build-backend = "poetry.masonry.api"

From d2fdda7c09c7533d7f43dee95078226a12f2f9ff Mon Sep 17 00:00:00 2001
From: Alexander Hess <alexander@webartifex.biz>
Date: Mon, 16 Sep 2019 00:09:00 +0200
Subject: [PATCH 002/242] Add "develop" dependencies for the presenter

- the Jupyter notebooks are assumed to be run within an installation of
  the Anaconda Distribution (version 2019-07 with Python 3.7.3) by the
  students (assumed to be total beginners)
- for the presenter some special third-party libraries are added to
  allow for executable code cells in presentation mode (via rise)
- black / blackcellmagic provide a convenient way to style the code
  while working on the notebooks
---
 poetry.lock    | 739 +++++++++++++++++++++++++++++++++++++++++++++++++
 pyproject.toml |   5 +
 2 files changed, 744 insertions(+)
 create mode 100644 poetry.lock

diff --git a/poetry.lock b/poetry.lock
new file mode 100644
index 0000000..75af38f
--- /dev/null
+++ b/poetry.lock
@@ -0,0 +1,739 @@
+[[package]]
+category = "dev"
+description = "A small Python module for determining appropriate platform-specific dirs, e.g. a \"user data dir\"."
+name = "appdirs"
+optional = false
+python-versions = "*"
+version = "1.4.3"
+
+[[package]]
+category = "main"
+description = "Disable App Nap on OS X 10.9"
+marker = "python_version >= \"3.3\" and sys_platform == \"darwin\" or sys_platform == \"darwin\""
+name = "appnope"
+optional = false
+python-versions = "*"
+version = "0.1.0"
+
+[[package]]
+category = "main"
+description = "Classes Without Boilerplate"
+name = "attrs"
+optional = false
+python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*"
+version = "19.1.0"
+
+[[package]]
+category = "main"
+description = "Specifications for callback functions passed in to an API"
+name = "backcall"
+optional = false
+python-versions = "*"
+version = "0.1.0"
+
+[[package]]
+category = "dev"
+description = "The uncompromising code formatter."
+name = "black"
+optional = false
+python-versions = ">=3.6"
+version = "18.9b0"
+
+[package.dependencies]
+appdirs = "*"
+attrs = ">=17.4.0"
+click = ">=6.5"
+toml = ">=0.9.4"
+
+[[package]]
+category = "dev"
+description = "IPython wrapper to format cell using black."
+name = "blackcellmagic"
+optional = false
+python-versions = "*"
+version = "0.0.2"
+
+[package.dependencies]
+black = "*"
+ipython = "*"
+
+[[package]]
+category = "main"
+description = "An easy safelist-based HTML-sanitizing tool."
+name = "bleach"
+optional = false
+python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*"
+version = "3.1.0"
+
+[package.dependencies]
+six = ">=1.9.0"
+webencodings = "*"
+
+[[package]]
+category = "dev"
+description = "Composable command line interface toolkit"
+name = "click"
+optional = false
+python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*"
+version = "7.0"
+
+[[package]]
+category = "main"
+description = "Cross-platform colored terminal text."
+marker = "python_version >= \"3.3\" and sys_platform == \"win32\" or sys_platform == \"win32\""
+name = "colorama"
+optional = false
+python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*"
+version = "0.4.1"
+
+[[package]]
+category = "main"
+description = "Better living through Python with decorators"
+name = "decorator"
+optional = false
+python-versions = ">=2.6, !=3.0.*, !=3.1.*"
+version = "4.4.0"
+
+[[package]]
+category = "main"
+description = "XML bomb protection for Python stdlib modules"
+name = "defusedxml"
+optional = false
+python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*, !=3.4.*"
+version = "0.6.0"
+
+[[package]]
+category = "main"
+description = "Discover and load entry points from installed packages."
+name = "entrypoints"
+optional = false
+python-versions = ">=2.7"
+version = "0.3"
+
+[[package]]
+category = "main"
+description = "IPython Kernel for Jupyter"
+name = "ipykernel"
+optional = false
+python-versions = ">=3.4"
+version = "5.1.2"
+
+[package.dependencies]
+ipython = ">=5.0.0"
+jupyter-client = "*"
+tornado = ">=4.2"
+traitlets = ">=4.1.0"
+
+[[package]]
+category = "main"
+description = "IPython: Productive Interactive Computing"
+name = "ipython"
+optional = false
+python-versions = ">=3.5"
+version = "7.8.0"
+
+[package.dependencies]
+appnope = "*"
+backcall = "*"
+colorama = "*"
+decorator = "*"
+jedi = ">=0.10"
+pexpect = "*"
+pickleshare = "*"
+prompt-toolkit = ">=2.0.0,<2.1.0"
+pygments = "*"
+setuptools = ">=18.5"
+traitlets = ">=4.2"
+
+[[package]]
+category = "main"
+description = "Vestigial utilities from IPython"
+name = "ipython-genutils"
+optional = false
+python-versions = "*"
+version = "0.2.0"
+
+[[package]]
+category = "main"
+description = "IPython HTML widgets for Jupyter"
+name = "ipywidgets"
+optional = false
+python-versions = "*"
+version = "7.5.1"
+
+[package.dependencies]
+ipykernel = ">=4.5.1"
+nbformat = ">=4.2.0"
+traitlets = ">=4.3.1"
+widgetsnbextension = ">=3.5.0,<3.6.0"
+
+[package.dependencies.ipython]
+python = ">=3.3"
+version = ">=4.0.0"
+
+[[package]]
+category = "main"
+description = "An autocompletion tool for Python that can be used for text editors."
+name = "jedi"
+optional = false
+python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*"
+version = "0.15.1"
+
+[package.dependencies]
+parso = ">=0.5.0"
+
+[[package]]
+category = "main"
+description = "A small but fast and easy to use stand-alone template engine written in pure python."
+name = "jinja2"
+optional = false
+python-versions = "*"
+version = "2.10.1"
+
+[package.dependencies]
+MarkupSafe = ">=0.23"
+
+[[package]]
+category = "main"
+description = "An implementation of JSON Schema validation for Python"
+name = "jsonschema"
+optional = false
+python-versions = "*"
+version = "3.0.2"
+
+[package.dependencies]
+attrs = ">=17.4.0"
+pyrsistent = ">=0.14.0"
+setuptools = "*"
+six = ">=1.11.0"
+
+[[package]]
+category = "main"
+description = "Jupyter metapackage. Install all the Jupyter components in one go."
+name = "jupyter"
+optional = false
+python-versions = "*"
+version = "1.0.0"
+
+[package.dependencies]
+ipykernel = "*"
+ipywidgets = "*"
+jupyter-console = "*"
+nbconvert = "*"
+notebook = "*"
+qtconsole = "*"
+
+[[package]]
+category = "main"
+description = "Jupyter protocol implementation and client libraries"
+name = "jupyter-client"
+optional = false
+python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*, !=3.4.*"
+version = "5.3.2"
+
+[package.dependencies]
+jupyter-core = "*"
+python-dateutil = ">=2.1"
+pywin32 = ">=1.0"
+pyzmq = ">=13"
+tornado = ">=4.1"
+traitlets = "*"
+
+[[package]]
+category = "main"
+description = "Jupyter terminal console"
+name = "jupyter-console"
+optional = false
+python-versions = ">=3.5"
+version = "6.0.0"
+
+[package.dependencies]
+ipykernel = "*"
+ipython = "*"
+jupyter-client = "*"
+prompt-toolkit = ">=2.0.0,<2.1.0"
+pygments = "*"
+
+[[package]]
+category = "dev"
+description = "Common utilities for jupyter-contrib projects."
+name = "jupyter-contrib-core"
+optional = false
+python-versions = "*"
+version = "0.3.3"
+
+[package.dependencies]
+jupyter-core = "*"
+notebook = ">=4.0"
+setuptools = "*"
+tornado = "*"
+traitlets = "*"
+
+[[package]]
+category = "dev"
+description = "A collection of Jupyter nbextensions."
+name = "jupyter-contrib-nbextensions"
+optional = false
+python-versions = "*"
+version = "0.5.1"
+
+[package.dependencies]
+ipython-genutils = "*"
+jupyter-contrib-core = ">=0.3.3"
+jupyter-core = "*"
+jupyter-highlight-selected-word = ">=0.1.1"
+jupyter-latex-envs = ">=1.3.8"
+jupyter-nbextensions-configurator = ">=0.4.0"
+lxml = "*"
+nbconvert = ">=4.2"
+notebook = ">=4.0"
+pyyaml = "*"
+tornado = "*"
+traitlets = ">=4.1"
+
+[[package]]
+category = "main"
+description = "Jupyter core package. A base package on which Jupyter projects rely."
+name = "jupyter-core"
+optional = false
+python-versions = ">=2.7, !=3.0, !=3.1, !=3.2"
+version = "4.5.0"
+
+[package.dependencies]
+traitlets = "*"
+
+[[package]]
+category = "dev"
+description = "Jupyter notebook extension that enables highlighting every instance of the current word in the notebook."
+name = "jupyter-highlight-selected-word"
+optional = false
+python-versions = "*"
+version = "0.2.0"
+
+[[package]]
+category = "dev"
+description = "Jupyter notebook extension which supports (some) LaTeX environments within markdown cells. Also provides support for labels and crossreferences, document wide numbering, bibliography, and more..."
+name = "jupyter-latex-envs"
+optional = false
+python-versions = "*"
+version = "1.4.6"
+
+[package.dependencies]
+ipython = "*"
+jupyter_core = "*"
+nbconvert = "*"
+notebook = ">=4.0"
+traitlets = ">=4.1"
+
+[[package]]
+category = "dev"
+description = "jupyter serverextension providing configuration interfaces for nbextensions."
+name = "jupyter-nbextensions-configurator"
+optional = false
+python-versions = "*"
+version = "0.4.1"
+
+[package.dependencies]
+jupyter_contrib_core = ">=0.3.3"
+jupyter_core = "*"
+notebook = ">=4.0"
+pyyaml = "*"
+tornado = "*"
+traitlets = "*"
+
+[[package]]
+category = "dev"
+description = "Powerful and Pythonic XML processing library combining libxml2/libxslt with the ElementTree API."
+name = "lxml"
+optional = false
+python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*, != 3.4.*"
+version = "4.4.1"
+
+[[package]]
+category = "main"
+description = "Safely add untrusted strings to HTML/XML markup."
+name = "markupsafe"
+optional = false
+python-versions = ">=2.7,!=3.0.*,!=3.1.*,!=3.2.*,!=3.3.*"
+version = "1.1.1"
+
+[[package]]
+category = "main"
+description = "The fastest markdown parser in pure Python"
+name = "mistune"
+optional = false
+python-versions = "*"
+version = "0.8.4"
+
+[[package]]
+category = "main"
+description = "Converting Jupyter Notebooks"
+name = "nbconvert"
+optional = false
+python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*, !=3.4.*"
+version = "5.6.0"
+
+[package.dependencies]
+bleach = "*"
+defusedxml = "*"
+entrypoints = ">=0.2.2"
+jinja2 = ">=2.4"
+jupyter-core = "*"
+mistune = ">=0.8.1,<2"
+nbformat = ">=4.4"
+pandocfilters = ">=1.4.1"
+pygments = "*"
+testpath = "*"
+traitlets = ">=4.2"
+
+[[package]]
+category = "main"
+description = "The Jupyter Notebook format"
+name = "nbformat"
+optional = false
+python-versions = "*"
+version = "4.4.0"
+
+[package.dependencies]
+ipython-genutils = "*"
+jsonschema = ">=2.4,<2.5.0 || >2.5.0"
+jupyter-core = "*"
+traitlets = ">=4.1"
+
+[[package]]
+category = "main"
+description = "A web-based notebook environment for interactive computing"
+name = "notebook"
+optional = false
+python-versions = ">=3.5"
+version = "6.0.1"
+
+[package.dependencies]
+Send2Trash = "*"
+ipykernel = "*"
+ipython-genutils = "*"
+jinja2 = "*"
+jupyter-client = ">=5.3.1"
+jupyter-core = ">=4.4.0"
+nbconvert = "*"
+nbformat = "*"
+prometheus-client = "*"
+pyzmq = ">=17"
+terminado = ">=0.8.1"
+tornado = ">=5.0"
+traitlets = ">=4.2.1"
+
+[[package]]
+category = "main"
+description = "Utilities for writing pandoc filters in python"
+name = "pandocfilters"
+optional = false
+python-versions = "*"
+version = "1.4.2"
+
+[[package]]
+category = "main"
+description = "A Python Parser"
+name = "parso"
+optional = false
+python-versions = "*"
+version = "0.5.1"
+
+[[package]]
+category = "main"
+description = "Pexpect allows easy control of interactive console applications."
+marker = "python_version >= \"3.3\" and sys_platform != \"win32\" or sys_platform != \"win32\""
+name = "pexpect"
+optional = false
+python-versions = "*"
+version = "4.7.0"
+
+[package.dependencies]
+ptyprocess = ">=0.5"
+
+[[package]]
+category = "main"
+description = "Tiny 'shelve'-like database with concurrency support"
+name = "pickleshare"
+optional = false
+python-versions = "*"
+version = "0.7.5"
+
+[[package]]
+category = "main"
+description = "Python client for the Prometheus monitoring system."
+name = "prometheus-client"
+optional = false
+python-versions = "*"
+version = "0.7.1"
+
+[[package]]
+category = "main"
+description = "Library for building powerful interactive command lines in Python"
+name = "prompt-toolkit"
+optional = false
+python-versions = "*"
+version = "2.0.9"
+
+[package.dependencies]
+six = ">=1.9.0"
+wcwidth = "*"
+
+[[package]]
+category = "main"
+description = "Run a subprocess in a pseudo terminal"
+marker = "sys_platform != \"win32\" or os_name != \"nt\" or python_version >= \"3.3\" and sys_platform != \"win32\""
+name = "ptyprocess"
+optional = false
+python-versions = "*"
+version = "0.6.0"
+
+[[package]]
+category = "main"
+description = "Pygments is a syntax highlighting package written in Python."
+name = "pygments"
+optional = false
+python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*, !=3.4.*"
+version = "2.4.2"
+
+[[package]]
+category = "main"
+description = "Persistent/Functional/Immutable data structures"
+name = "pyrsistent"
+optional = false
+python-versions = "*"
+version = "0.15.4"
+
+[package.dependencies]
+six = "*"
+
+[[package]]
+category = "main"
+description = "Extensions to the standard Python datetime module"
+name = "python-dateutil"
+optional = false
+python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*"
+version = "2.8.0"
+
+[package.dependencies]
+six = ">=1.5"
+
+[[package]]
+category = "main"
+description = "Python for Window Extensions"
+marker = "sys_platform == \"win32\""
+name = "pywin32"
+optional = false
+python-versions = "*"
+version = "225"
+
+[[package]]
+category = "main"
+description = "Python bindings for the winpty library"
+marker = "os_name == \"nt\""
+name = "pywinpty"
+optional = false
+python-versions = "*"
+version = "0.5.5"
+
+[[package]]
+category = "dev"
+description = "YAML parser and emitter for Python"
+name = "pyyaml"
+optional = false
+python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*"
+version = "5.1.2"
+
+[[package]]
+category = "main"
+description = "Python bindings for 0MQ"
+name = "pyzmq"
+optional = false
+python-versions = ">=2.7,!=3.0.*,!=3.1.*,!=3.2.*"
+version = "18.1.0"
+
+[[package]]
+category = "main"
+description = "Jupyter Qt console"
+name = "qtconsole"
+optional = false
+python-versions = "*"
+version = "4.5.5"
+
+[package.dependencies]
+ipykernel = ">=4.1"
+ipython-genutils = "*"
+jupyter-client = ">=4.1"
+jupyter-core = "*"
+pygments = "*"
+traitlets = "*"
+
+[[package]]
+category = "dev"
+description = "Reveal.js - Jupyter/IPython Slideshow Extension"
+name = "rise"
+optional = false
+python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*, <4"
+version = "5.5.1"
+
+[package.dependencies]
+notebook = ">=5.5.0"
+
+[[package]]
+category = "main"
+description = "Send file to trash natively under Mac OS X, Windows and Linux."
+name = "send2trash"
+optional = false
+python-versions = "*"
+version = "1.5.0"
+
+[[package]]
+category = "main"
+description = "Python 2 and 3 compatibility utilities"
+name = "six"
+optional = false
+python-versions = ">=2.6, !=3.0.*, !=3.1.*"
+version = "1.12.0"
+
+[[package]]
+category = "main"
+description = "Terminals served to xterm.js using Tornado websockets"
+name = "terminado"
+optional = false
+python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*"
+version = "0.8.2"
+
+[package.dependencies]
+ptyprocess = "*"
+pywinpty = ">=0.5"
+tornado = ">=4"
+
+[[package]]
+category = "main"
+description = "Test utilities for code working with files and commands"
+name = "testpath"
+optional = false
+python-versions = "*"
+version = "0.4.2"
+
+[[package]]
+category = "dev"
+description = "Python Library for Tom's Obvious, Minimal Language"
+name = "toml"
+optional = false
+python-versions = "*"
+version = "0.10.0"
+
+[[package]]
+category = "main"
+description = "Tornado is a Python web framework and asynchronous networking library, originally developed at FriendFeed."
+name = "tornado"
+optional = false
+python-versions = ">= 3.5"
+version = "6.0.3"
+
+[[package]]
+category = "main"
+description = "Traitlets Python config system"
+name = "traitlets"
+optional = false
+python-versions = "*"
+version = "4.3.2"
+
+[package.dependencies]
+decorator = "*"
+ipython-genutils = "*"
+six = "*"
+
+[[package]]
+category = "main"
+description = "Measures number of Terminal column cells of wide-character codes"
+name = "wcwidth"
+optional = false
+python-versions = "*"
+version = "0.1.7"
+
+[[package]]
+category = "main"
+description = "Character encoding aliases for legacy web content"
+name = "webencodings"
+optional = false
+python-versions = "*"
+version = "0.5.1"
+
+[[package]]
+category = "main"
+description = "IPython HTML widgets for Jupyter"
+name = "widgetsnbextension"
+optional = false
+python-versions = "*"
+version = "3.5.1"
+
+[package.dependencies]
+notebook = ">=4.4.1"
+
+[metadata]
+content-hash = "431dace6d8d5c3e390b59e964116bb6219fb45c7a8c09cbfe99d70c704a54320"
+python-versions = "^3.6"
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+rise = ["5a3d529170d707ff0af42bf12f9584b5089baf401643d136d052a66cb4210052", "ba94321aee993cb2f0647c0db8688064afcff6000576f0fcb1e98652abc629e3"]
+send2trash = ["60001cc07d707fe247c94f74ca6ac0d3255aabcb930529690897ca2a39db28b2", "f1691922577b6fa12821234aeb57599d887c4900b9ca537948d2dac34aea888b"]
+six = ["3350809f0555b11f552448330d0b52d5f24c91a322ea4a15ef22629740f3761c", "d16a0141ec1a18405cd4ce8b4613101da75da0e9a7aec5bdd4fa804d0e0eba73"]
+terminado = ["d9d012de63acb8223ac969c17c3043337c2fcfd28f3aea1ee429b345d01ef460", "de08e141f83c3a0798b050ecb097ab6259c3f0331b2f7b7750c9075ced2c20c2"]
+testpath = ["46c89ebb683f473ffe2aab0ed9f12581d4d078308a3cb3765d79c6b2317b0109", "b694b3d9288dbd81685c5d2e7140b81365d46c29f5db4bc659de5aa6b98780f8"]
+toml = ["229f81c57791a41d65e399fc06bf0848bab550a9dfd5ed66df18ce5f05e73d5c", "235682dd292d5899d361a811df37e04a8828a5b1da3115886b73cf81ebc9100e", "f1db651f9657708513243e61e6cc67d101a39bad662eaa9b5546f789338e07a3"]
+tornado = ["349884248c36801afa19e342a77cc4458caca694b0eda633f5878e458a44cb2c", "398e0d35e086ba38a0427c3b37f4337327231942e731edaa6e9fd1865bbd6f60", "4e73ef678b1a859f0cb29e1d895526a20ea64b5ffd510a2307b5998c7df24281", "559bce3d31484b665259f50cd94c5c28b961b09315ccd838f284687245f416e5", "abbe53a39734ef4aba061fca54e30c6b4639d3e1f59653f0da37a0003de148c7", "c845db36ba616912074c5b1ee897f8e0124df269468f25e4fe21fe72f6edd7a9", "c9399267c926a4e7c418baa5cbe91c7d1cf362d505a1ef898fde44a07c9dd8a5"]
+traitlets = ["9c4bd2d267b7153df9152698efb1050a5d84982d3384a37b2c1f7723ba3e7835", "c6cb5e6f57c5a9bdaa40fa71ce7b4af30298fbab9ece9815b5d995ab6217c7d9"]
+wcwidth = ["3df37372226d6e63e1b1e1eda15c594bca98a22d33a23832a90998faa96bc65e", "f4ebe71925af7b40a864553f761ed559b43544f8f71746c2d756c7fe788ade7c"]
+webencodings = ["a0af1213f3c2226497a97e2b3aa01a7e4bee4f403f95be16fc9acd2947514a78", "b36a1c245f2d304965eb4e0a82848379241dc04b865afcc4aab16748587e1923"]
+widgetsnbextension = ["079f87d87270bce047512400efd70238820751a11d2d8cb137a5a5bdbaf255c7", "bd314f8ceb488571a5ffea6cc5b9fc6cba0adaf88a9d2386b93a489751938bcd"]
diff --git a/pyproject.toml b/pyproject.toml
index 6303a1d..1cd387e 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -7,8 +7,13 @@ license = "MIT"
 
 [tool.poetry.dependencies]
 python = "^3.6"
+jupyter = "^1.0"
 
 [tool.poetry.dev-dependencies]
+black = {version = "^18.3-alpha.0", allows-prereleases = true}
+blackcellmagic = "^0.0.2"
+jupyter-contrib-nbextensions = "^0.5"
+rise = "^5.5"
 
 [build-system]
 requires = ["poetry>=0.12"]

From f33af9684775f387743c2f425b4bc83f6fa2d969 Mon Sep 17 00:00:00 2001
From: Alexander Hess <alexander@webartifex.biz>
Date: Mon, 16 Sep 2019 00:14:56 +0200
Subject: [PATCH 003/242] Add a simple .gitignore file

---
 .gitignore | 11 +++++++++++
 1 file changed, 11 insertions(+)
 create mode 100644 .gitignore

diff --git a/.gitignore b/.gitignore
new file mode 100644
index 0000000..c4b1ff0
--- /dev/null
+++ b/.gitignore
@@ -0,0 +1,11 @@
+# Environments
+.env/
+env/
+.venv/
+venv/
+
+# Jupyter
+.ipynb_checkpoints/
+
+# pyenv
+.python-version

From bb2cfce3c79f1ad5890de2d5413632c1226d6f03 Mon Sep 17 00:00:00 2001
From: Alexander Hess <alexander@webartifex.biz>
Date: Mon, 16 Sep 2019 00:18:38 +0200
Subject: [PATCH 004/242] Add initial version of notebook 00

---
 00_start_up.ipynb                   | 897 ++++++++++++++++++++++++++++
 static/anaconda.png                 | Bin 0 -> 142513 bytes
 static/growth_major_languages.png   | Bin 0 -> 210392 bytes
 static/growth_smaller_languages.png | Bin 0 -> 175532 bytes
 static/logo.png                     | Bin 0 -> 7821 bytes
 static/logos.png                    | Bin 0 -> 364253 bytes
 static/terminal.png                 | Bin 0 -> 29931 bytes
 static/xkcd.png                     | Bin 0 -> 90835 bytes
 8 files changed, 897 insertions(+)
 create mode 100644 00_start_up.ipynb
 create mode 100644 static/anaconda.png
 create mode 100644 static/growth_major_languages.png
 create mode 100644 static/growth_smaller_languages.png
 create mode 100644 static/logo.png
 create mode 100644 static/logos.png
 create mode 100644 static/terminal.png
 create mode 100644 static/xkcd.png

diff --git a/00_start_up.ipynb b/00_start_up.ipynb
new file mode 100644
index 0000000..588d7ab
--- /dev/null
+++ b/00_start_up.ipynb
@@ -0,0 +1,897 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "# An Introduction to Python and Programming"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "This course is set up to be a *thorough* introduction to programming in [Python](https://www.python.org/).\n",
+    "\n",
+    "It teaches the concepts behind and the syntax of the core Python language as defined by the [Python Software Foundation](https://www.python.org/psf/) in the official [language reference](https://docs.python.org/3/reference/index.html) and introduces additions to the language as distributed with the [standard library](https://docs.python.org/3/library/index.html) that come with every installation.\n",
+    "\n",
+    "Furthermore, some very popular third-party libraries like [numpy](https://www.numpy.org/), [pandas](https://pandas.pydata.org/), [matplotlib](https://matplotlib.org/), and others are portrayed."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "source": [
+    "<img src=\"static/logo.png\" width=\"15%\" align=\"left\">"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "## Prerequisites"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "To be suitable for *total beginners*, there are *no* formal prerequisites. It is only expected that the student has:\n",
+    "\n",
+    "- a *solid* understanding of the **English language** (i.e., usage of *technical* terms with *narrow* and *distinct* meanings),\n",
+    "- knowledge of **basic mathematics** from high school (i.e., addition, subtraction, multiplication, division, and a little bit of calculus and statistics),\n",
+    "- the ability to **think conceptually** and **reason logically** (i.e., *not* just memorizing), and\n",
+    "- the willingness to **invest 2-4 hours a day for a month** (cf., \"ABC\"-rule at the end)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "## Objective"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "The course's **main goal** is to **prepare** the student **for further studies** in the \"field\" of **data science**."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "This includes but is not limited to more advanced courses on topics such as:\n",
+    "- linear algebra\n",
+    "- statistics & econometrics\n",
+    "- data cleaning & wrangling\n",
+    "- data visualization\n",
+    "- data engineering (incl. SQL databases)\n",
+    "- data mining (incl. web scraping)\n",
+    "- feature generation, machine learning, & deep learning\n",
+    "- optimization & (meta-)heuristics\n",
+    "- algorithms & data structures\n",
+    "- quantitative finance (e.g., option valuation)\n",
+    "- quantitative marketing (e.g., customer segmentation)\n",
+    "- quantitative supply chain management (e.g., forecasting)\n",
+    "- management science & decision models\n",
+    "- backend / API / web development (to serve data products to clients)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "### Why data science?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "The term **[data science](https://en.wikipedia.org/wiki/Data_science)** is rather vague and does actually *not* refer to an academic discipline. Instead the term was popularized by the tech industry who also coined non-meaningful job titles such as \"[rockstar](https://www.quora.com/Why-are-engineers-called-rockstars-and-ninjas)\" or \"[ninja developers](https://www.quora.com/Why-are-engineers-called-rockstars-and-ninjas)\". Most *serious* definitions describe the field as being **multi-disciplinary** *integrating* scientific methods, algorithms, and systems thinking to extract knowledge from (structured and unstructured) data *and* also emphasize the importance of **[domain knowledge](https://en.wikipedia.org/wiki/Domain_knowledge)**.\n",
+    "\n",
+    "Recently, this integration aspect feeds back into the academic world. The [MIT](https://www.mit.edu/), for example, created the new [Stephen A. Schwarzman College of Computing](http://computing.mit.edu) for [artifical intelligence](https://en.wikipedia.org/wiki/Artificial_intelligence) (with a 1 billion dollar initial investment) where students undergo a \"bilingual\" curriculum with half the classes in quantitative and method-centric fields (like the ones mentioned above) and the other half in domains such as biology, business, chemistry, politics, (art) history, or linguistics (cf., the [official Q&As](http://computing.mit.edu/faq/) or this [NYT article](https://www.nytimes.com/2018/10/15/technology/mit-college-artificial-intelligence.html)). Their strategists see a future where programming skills are just as naturally embedded into every students' studies as are nowadays subjects like calculus, statistics, or academic writing. Then, programming literacy is not just another \"nice to have\" skill but a prerequisite (or an enabler) to understanding more advanced topics in the actual domains studied. This could make teaching easier for top-notch researchers who use a lot of programming in their day-to-day lifes answering big questions: The student and the teacher are then more likely to \"speak\" the same language."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "## Installation"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "To follow this course, a working installation of **Python 3.6** or higher is needed.\n",
+    "\n",
+    "A popular and beginner friendly way is to install the [Anaconda Distribution](https://www.anaconda.com/distribution/) that not only ships Python and the standard library but comes pre-packaged with a lot of third-party libraries from the so-called \"scientific stack\". Just go to the [download](https://www.anaconda.com/download/) page and install the latest version (i.e., *2019-07* with Python 3.7 at the time of this writing) for your operating system.\n",
+    "\n",
+    "Then, among others, you will find an entry \"Jupyter Notebook\" in your start menu like below. Click on it and a new tab in your web browser will open where you can switch between folders as you could in your computer's default file browser."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "<img src=\"static/anaconda.png\" width=\"40%\">"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "To download the course's materials as a ZIP file, open the accompanying [GitHub repository](https://github.com/webartifex/intro-to-python) in a web browser and click on the green \"Clone or download\" button on the right. Then, unpack the ZIP file into a folder of your choosing (ideally somewhere within your personal user folder so that the files show up right away)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "### Alternative Installation"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Python can also be installed in a \"pure\" way as obtained from its core development team. However, this is somewhat too \"advanced\" for a beginner who would then also be responsible for setting up all the third-party libraries needed to actually view this document. Plus, many of the involveld steps must be done in a [terminal](https://en.wikipedia.org/wiki/Terminal_emulator) window, which tends to be a bit intimidating for most beginners."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "<img src=\"static/terminal.png\" width=\"50%\" align=\"center\">"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "For more \"couragous\" beginners wanting to learn how to accomplish this, here is a rough sketch of the aspects to know. First, all Python realeases are available for free on the official [download](https://www.python.org/downloads/) page for any supported operating system. Choose the one you want, then download and install it (cf., the [instruction notes](https://wiki.python.org/moin/BeginnersGuide/Download)). As this only includes core Python and the standard library, the beginner then needs to learn about the [pip](https://pip.pypa.io/en/stable/) module, which is to be used inside a terminal. With the command `python -m pip install jupyter`, all necessary third-party libraries can be installed (cf., more background [here](https://jupyter.readthedocs.io/en/latest/install.html)). However, this would be done in a *system-wide* fashion and is not recommended. Instead, the best practice is to create a so-called **virtual environment** with the [venv](https://docs.python.org/3/library/venv.html) module with which the installed third-party packages can be *isolated* on a per-project basis (the command `python -m venv env-name` creates a virtual enviroment called \"env-name\"). This tactic is employed to avoid a situation known as **[dependency hell](https://en.wikipedia.org/wiki/Dependency_hell)** Once created, the virtual environment must then be activated each time before resuming work in each terminal (with the command `source env-name/bin/activate`). While there exist convenience tools that automate parts of this (e.g., [poetry](https://poetry.eustace.io/docs/) or [virtualenvwrapper](https://virtualenvwrapper.readthedocs.io/en/latest/)), it only distracts a beginner from actually studying the Python language. Yet, it is still worthwhile to have heard about these terms and concepts as many online resources often implicitly assume the user to know about them."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "## Jupyter Notebooks"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "The document you are viewing is a so-called [Jupyter notebook](https://jupyter-notebook.readthedocs.io/en/stable/notebook.html), a file format introduced by the [Jupyter Project](https://jupyter.org/).\n",
+    "\n",
+    "\"Jupyter\" is an [acronym](https://en.wikipedia.org/wiki/Acronym) derived from the names of the three major programming languages **[Julia](https://julialang.org/)**, **[Python](https://www.python.org)**, and **[R](https://www.r-project.org/)**, all of which play significant roles in the world of data science. The Jupyter Project's idea is to serve as an integrating platform such that different programming languages and software packages can be used together within the same project easily.\n",
+    "\n",
+    "Furthermore, Jupyter notebooks have become a de-facto standard for communicating and exchanging results in the data science community (both in academia and business) and often provide a more intuitive alternative to terminal based ways of running Python (e.g., the default [Python interpreter](https://docs.python.org/3/tutorial/interpreter.html) as shown above or a more advanced interactive version like [IPython](https://ipython.org/)) or even a full-fledged [Integrated Development Environment](https://en.wikipedia.org/wiki/Integrated_development_environment) (e.g., the commercial [PyCharm](https://www.jetbrains.com/pycharm/) or the free [Spyder](https://github.com/spyder-ide/spyder)).\n",
+    "\n",
+    "In particular, they allow to mix plain English text with Python code cells. The plain text can be formatted using the [Markdown](https://guides.github.com/features/mastering-markdown/) language and mathematical expressions can be typeset with [LaTeX](https://www.overleaf.com/learn/latex/Free_online_introduction_to_LaTeX_%28part_1%29). Lastly, we can include pictures, plots, and even videos. Because of these features, the notebooks developed for this course come in a self-contained \"tutorial\" style that enables students to learn and review the material on their own."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "### Markdown vs. Code Cells"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "A Jupyter notebook consists of cells that have a type associated with them. So far, only cells of type \"Markdown\" have been used, which is the default way to present (formatted) text.\n",
+    "\n",
+    "The next cell below is an example of a \"Code\" cell containing a line of actual Python code: it simply outputs the text \"Hello world\" when executed. To edit an existing code cell, just enter into it with a mouse click. You know that you are \"in\" a code cell when you see the frame of the code cell turn green.\n",
+    "\n",
+    "Besides this **edit mode** there is also a so-called **command mode** that you can reach by hitting the \"Escape\" key after entering a code cell, which turns the frame's color into blue. Using the Enter\" and \"Escape\" keys you can now switch between the two modes.\n",
+    "\n",
+    "To *execute* (or *run*) a code cell, hold the \"Control\" key and press \"Enter\". Note how you do not go to the subsequent cell. Alternatively, you can hold the \"Shift\" key and press \"Enter\", which executes the cell and places your focus on the next cell right after.\n",
+    "\n",
+    "Similarly, a Markdown cell is also in either the edit or command mode. For example, simply double-click on the text you are just reading, which takes you into edit mode. Now you could change the formatting (e.g., make a word printed in *italics* or **bold** with single or double asterisks) and then \"execute\" the cell to actually render the text as specified.\n",
+    "\n",
+    "To change a cell's type, choose either \"Code\" or \"Markdown\" in the navigation bar at the top."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Hello world\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"Hello world\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Sometimes a code cell starts with an exclamation mark `!`. Then, the Jupyter notebook behaves as if you just typed the following commands right into a terminal. The cell below asks `python` to show its version number. This is actually *not* Python code but a command in the [Shell](https://en.wikipedia.org/wiki/Shell_script) language. The `!` is useful to execute short commands without leaving a Jupyter notebook."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Python 3.7.3\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!python --version"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "## Programming vs. Computer Science vs. IT"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "For this course *programming* is \"defined\" as\n",
+    "- a **structured** way of **problem solving**\n",
+    "- by **expressing** the steps of a **computation / process**\n",
+    "- and thereby **documenting** the process in a formal way\n",
+    "\n",
+    "Programming is always **concrete** and based on a **particular case**.\n",
+    "\n",
+    "Programming definitely exhibits elements of an **art** or a **craft** as we will hear programmers call code \"beautiful\" or \"ugly\" or talk about the \"expressive\" power of an application."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "That is different from *computer science*, which is\n",
+    "- a field of study comparable to (applied) **mathematics** that\n",
+    "- asks **abstract** questions (\"Is something computable at all?\"),\n",
+    "- develops and analyses **algorithms** and **data structures**,\n",
+    "- and **proves** the **correctness** of a program\n",
+    "\n",
+    "In a sense, a computer scientist does not need to know a programming language to work. In fact, many computer scientists only know how to produce \"ugly\" code in the eyes of professional programmers."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "*IT* or *information technology* is a term that has many meanings to many people. Often, it has something to do with hardware and devices both of which are out of scope for programmers and computer scientists. In fact, many people from the two aforementioned fields are more than happy if their printer and internet just work as they do not know a lot more about that than non-technical people."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "## Why Python?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "### What is Python?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "- [Guido van Rossum](https://en.wikipedia.org/wiki/Guido_van_Rossum) (Python’s **[Benevolent Dictator for Life](https://en.wikipedia.org/wiki/Benevolent_dictator_for_life)**) was bored during a week around Christmas 1989 and started Python as a hobby project \"that would keep \\[him\\] occupied\" for some days\n",
+    "- the idea was to create a **general-purpose scripting** language that would allow fast **prototyping** and would **run on every operating system**\n",
+    "- Python grew through the 90s as van Rossum promoted it via his \"Computer Programming for Everybody\" initiative that had the **goal to encourage a basic level of coding literacy** as an equal knowledge alongside English literacy and math skills\n",
+    "- to become more independent from its creator the next major version **Python 2** (released in 2000; still in heavy use as of today) was **open-sourced** from the get-go which attracted a **large and global community of programmers** that **contributed** their individual knowledge and best practices in their free time to make Python even better\n",
+    "- **Python 3** resulted from a major overhaul of the language in 2008 taking into account the **learnings from almost two decades**, streamlining the language, and getting ready for the age of **big data**\n",
+    "- the language is named after the sketch comedy group [Monty Python](https://en.wikipedia.org/wiki/Monty_Python)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "#### Summary"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "Python is a **general-purpose** programming language that allows for **fast development**, is **easy to comprehend**, **open-source**, long established, unifies the knowledge of **hundreds of thousands of experts** around the world, runs on basically every machine, and can handle the complexities of applications involving **big data**."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "### Why open-source?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Couldn't a company like Google, Facebook, or Microsoft come up with a better programming language? The following argument provides hints why this cannot really be the case.\n",
+    "\n",
+    "Wouldn't it be weird if professors and scholars of English literature and language studies dictated how we'd have to speak in day-to-day casual conversations or how authors of poesy and novels should use language constructs to achieve a certain type of mood? If you agree with that premise, it makes sense to assume that even programming languages should evolve in a \"natural\" way as users just *use* the language over time and in new and unpredictable contexts making up their own new conventions."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Loose *communities* are the main building block around which open-source software is built. Someone starts a project (like Guido) and makes it free to use for anybody (e.g., on a code-sharing platform like [GitHub](https://github.com/)). People find it useful enough to solve one of their daily problems and start using it. They see how a project could be improved and provide new use cases (via the popularized concept of a \"[pull request](https://help.github.com/articles/about-pull-requests/)\"). The project grows both in lines of code and number of people using it. After a while, people start local user groups to share their same interests and meet on a regular basis (e.g., this is a big market for companies like [Meetup](https://www.meetup.com/) or non-profits like [PyData](https://pydata.org/)). Out of these local and usually monthly meetups grow yearly conferences on the country or even continental level (e.g., the original [PyCon](https://us.pycon.org/) in the US, [EuroPython](https://europython.eu/), or [PyCon.DE](https://de.pycon.org/)). The content presented at these conferences is made publicly available via GitHub and YouTube (e.g., [PyCon 2019](https://www.youtube.com/channel/UCxs2IIVXaEHHA4BtTiWZ2mQ) or [EuroPython](http://europython.tv/)) and serves as references on what people are working on and introductions to the endless number of specialized fields."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "While these communities are rather loose and constantly changing, smaller in-groups, often democratically organized and elected (e.g., the Python Software Foundation), take care of, for example, the development of the \"core\" Python language itself.\n",
+    "\n",
+    "Interestingly, Python is just a specification (i.e., a set of rules) as to what is allowed and what not. The current version of Python can always be lookep up in the [Python Language Reference](https://docs.python.org/3/reference/index.html). In order to make changes to that, anyone can make a so-called **[Python Enhancement Proposal](https://www.python.org/dev/peps/)**, or **PEP** for short, where it needs to be specified what exact changes are to be made and argued why that is a good thing to do. These PEPs are reviewed by the [core developers](https://devguide.python.org/coredev/) and anyone interested and are then either accepted, modified, or rejected if, for example, the change introduces internal inconsistencies. This is very similar to the **double blind peer review** established in academia. In fact, many of the contributors held or hold positions in academia, one more indicator of the high quality standards in the Python community. To learn more about PEPs, check out [PEP 1](https://www.python.org/dev/peps/pep-0001/) that describes the entire process.\n",
+    "\n",
+    "In total, no one single entity can control how the language evolves and the users' needs and ideas always feed back to the language specification via a quality controlled and \"democratic\" process."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Besides being free as in **\"free beer\"**, a major benefit of open-source is that one can always **look up how something works in detail** (that is the literal meaning of *open* source). This is a huge benefit compared to commercial languages (e.g., MATLAB) since a programmer can always continue to **study best practices** or how things are done. Along this way, many **errors are uncovered** as well. Furthermore, if one runs an open-source application, one can be reasonably sure that no bad people built in a \"backdoor\". [Free software](https://en.wikipedia.org/wiki/Free_software) is consequently free of charge but brings many other freedoms with it, most notable the freedom to change the code."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "### Isn't C a lot faster?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "The \"weird\" thing is that the default Python implementation is actually written in the C language.\n",
+    "\n",
+    "[C](https://en.wikipedia.org/wiki/C_%28programming_language%29) and [C++](https://en.wikipedia.org/wiki/C%2B%2B) (check this [introduction](https://www.learncpp.com/)) are wide-spread and long established (i.e., since the 1970s) programming languages that are employed in many mission critical softwares (e.g., operating systems themselves, low latency databases and web servers, nuclear reactor control systems, airplanes, ...). They are fast mainly because the programmer not only needs to come up with the **business logic** but also manage the computer's memory \"manually\" (and the knowledge necessary to do that is not easy to learn).\n",
+    "\n",
+    "In contrast, Python automatically manages the memory for the programmer. This speeds up the development process a lot. So speed here is really a trade-off of application run time vs. engineering / development time and in many applications the program's run time is not that important (e.g., what if C needs 0.001 seconds in a case where Python needs 0.1 seconds to train a linear regression model?). When the requirements change and computing speed becomes an issue, the Python community offers many third-party libraries (usually also written in C) where specific problems can be solved in near-C time.\n",
+    "\n",
+    "**In a nutshell**: While it is of course true that C is a lot faster than Python when it comes to **pure computation time**, in many cases this does not really matter as the **significantly shorter development cycles** are the more important cost factor in a rapidly changing business world."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "### Who uses it?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "<img src=\"static/logos.png\" width=\"70%\">"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "While it is usually not the best argument to quote authorative figures like the pope, we will do this in this section anyways:\n",
+    "\n",
+    "- **[Massachusetts Institute of Technology](https://www.mit.edu/)**\n",
+    "  - teaches Python in its [introductory course](https://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-0001-introduction-to-computer-science-and-programming-in-python-fall-2016/) to computer science independent of the student's major\n",
+    "  - replaced the infamous course on the [Scheme](https://groups.csail.mit.edu/mac/projects/scheme/) language ([source](https://news.ycombinator.com/item?id=602307))\n",
+    "- **[Google](https://www.google.com/)**\n",
+    "  - used the strategy **\"Python where we can, C++ where we must\"** from its early days on to stay flexible in a rapidly changing environment ([source](https://stackoverflow.com/questions/2560310/heavy-usage-of-python-at-google))\n",
+    "  - the very first web-crawler was written in **Java and so difficult** to maintain that it was **re-written in Python** right away ([source](https://www.amazon.com/Plex-Google-Thinks-Works-Shapes/dp/1416596585/ref=sr_1_1?ie=UTF8&qid=1539101827&sr=8-1&keywords=in+the+plex))\n",
+    "  - Python and C++, Java, and Go are the only four server-side languages to be deployed to production\n",
+    "  - Guido van Rossom was hired by Google from 2005 to 2012 to advance the language there\n",
+    "- **[NASA](https://www.nasa.gov/)** open-sources a lot of its projects, many of which are written in Python and regard working with really big data ([source](https://code.nasa.gov/language/python/))\n",
+    "- **[Facebook](https://facebook.com/)** uses Python besides C++ and its legacy PHP (a language for building websites; the \"cool kid\" from the early 2000s)\n",
+    "- **[Instagram](https://instagram.com/)** operates the largest installation of the popular **web framework  [Django](https://www.djangoproject.com/)** ([source](https://instagram-engineering.com/web-service-efficiency-at-instagram-with-python-4976d078e366))\n",
+    "- **[Spotify](https://spotify.com/)** bases its data science on Python ([source](https://labs.spotify.com/2013/03/20/how-we-use-python-at-spotify/))\n",
+    "- **[Netflix](https://netflix.com/)** also runs its predictive models on Python ([source](https://medium.com/netflix-techblog/python-at-netflix-86b6028b3b3e))\n",
+    "- **[Dropbox](https://dropbox.com/)** \"stole\" Guido van Rossom from Google to help scale the platform ([source](https://medium.com/dropbox-makers/guido-van-rossum-on-finding-his-way-e018e8b5f6b1))\n",
+    "- **[JPMorgan Chase](https://www.jpmorganchase.com/)** requires new employees to learn Python as part of the onboarding process starting with the 2018 intake ([source](https://www.ft.com/content/4c17d6ce-c8b2-11e8-ba8f-ee390057b8c9?segmentId=a7371401-027d-d8bf-8a7f-2a746e767d56))\n",
+    "- ...\n",
+    "- ... this list is intentionally shortened as Python is simply very popular ..."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "As images tell more than words, here are two plots of popular languages' \"market shares\" based on the number of questions asked on [Stack Overflow](https://stackoverflow.blog/2017/09/06/incredible-growth-python/), the most relevant platform for answering programming related questions: As of late 2017, Python surpassed [Java](https://www.java.com/en/), heavily used in big corporates, and [JavaScript](https://developer.mozilla.org/en-US/docs/Web/JavaScript), the \"language of the internet\" that does everything in web browsers, in popularity. Two blog posts from \"technical\" people explain this in more depth to the layman: [Stack Overflow](https://stackoverflow.blog/2017/09/14/python-growing-quickly/) and [DataCamp](https://www.datacamp.com/community/blog/python-scientific-computing-case)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "<img src=\"static/growth_major_languages.png\" width=\"50%\">"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "As the graph below shows, neither Google's very own language **[Go](https://golang.org/)** nor **[R](https://www.r-project.org/)**, a very popular language in the niche of statistics and data science, can compete with Python's year-to-year growth."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "<img src=\"static/growth_smaller_languages.png\" width=\"50%\">"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Even popular news and media outlets notice the recent popularity of Python: [Economist](https://www.economist.com/graphic-detail/2018/07/26/python-is-becoming-the-worlds-most-popular-coding-language), [Huffington Post](https://www.huffingtonpost.com/entry/why-python-is-the-best-programming-language-with-which_us_59ef8f62e4b04809c05011b9), [TechRepublic](https://www.techrepublic.com/article/why-python-is-so-popular-with-developers-3-reasons-the-language-has-exploded/), and [QZ](https://qz.com/1408660/the-rise-of-python-as-seen-through-a-decade-of-stack-overflow/)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "## How to learn Programming"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "### The ABC Rule"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Simply put, **always be coding**.\n",
+    "\n",
+    "Programming is more than just writing code into a text file. It means reading through parts of documentation, blogs with best practices, and tutorials, or researching some problem on Stack Overflow while trying to implement some feature in the application at hand. Also, it means using command line tools to automate some part of the work, or manage different versions of a software simultaneously using software like **[git](https://git-scm.com/)**. In short, programming involves a lot of \"muscle memory\". This can only be built and kept up through near-daily usage.\n",
+    "\n",
+    "Further, many aspects of software architecture and best practices can only be understood after having implemented some requirement for the very first time. Coding also means \"breaking\" things to find out what makes them actually work to begin with.\n",
+    "\n",
+    "Coding is learned best by just doing it for some time on a daily or at least regular basis and not right before some task is due, just like learning a \"real\" language."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "### The Maker's Schedule"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "[Y Combinator](https://www.ycombinator.com/)'s co-founder [Paul Graham](https://en.wikipedia.org/wiki/Paul_Graham_%28programmer%29) wrote a very popular and often cited [article](http://www.paulgraham.com/makersschedule.html) where he divides every person into belonging to one of two groups:\n",
+    "\n",
+    "- **Managers**: People that need to organize things and command others (like a \"boss\"). Their schedule is usually organized by the hour or even by 30 minute intervals.\n",
+    "- **Makers**: People that create things (like programmers, artists, or writers). Such people think in half days or full days.\n",
+    "\n",
+    "Have you ever wondered why so many tech nerds work all the nights and sleep during \"weird\" hours? This is mainly because many programming related tasks require a certain \"flow\" state of one's mind. This is hard to achieve when one can get interupted, even if it is only for one short question. Graham describes that only knowing that one has an appointment in three hours can cause a programmer to not get into a flow state.\n",
+    "\n",
+    "As a result, do not set aside a certain amount of time for learning something but rather plan in an **entire evening** or a **rainy Sunday** where you can work on a problem in an **open end** setting. And do not be surprised any more to hear a \"I looked at it over the weekend\" from a programmer."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "### Phase Iteration - The $\\pi$ Rule"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "When being asked the above question, most programmers will answer something that can be classified into one of two broader groups.\n",
+    "\n",
+    "**1) Toy Problem / Case Study / Prototype**: Pick some problem that you want to write a programatic solution for, break it down into smaller packages, and solve them \"backwards\".\n",
+    "\n",
+    "**2) Books / Video Tutorials / Courses**: Research the best book / blog post / video tutorial for something and work it through from start to end.\n",
+    "\n",
+    "The truth is that you need to iterate between these two phases.\n",
+    "\n",
+    "Building a prototype will always reveal issues no book or tutorial can think of before. Data is never clean as it comes. Some algorithm from a text book must be adapted to a very specific but important aspect of a case study. It is important to learn to \"ship a product\", i.e., to finish some project to the very end because only then you will have looked at all the aspects.\n",
+    "\n",
+    "The major downside of this approach is that you likely learn bad \"patterns\" as whatever you learn is overfitted to the case and you do not get the big picture or mental concepts behind a solution. This is a gap well written books can fill in (e.g., check the Python / Programming books by [Packt](https://www.packtpub.com/packt/offers/free-learning/) or [O’Reilly](https://www.oreilly.com/))."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "## Contents"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "**Part 1: Expressing Logic**\n",
+    "\n",
+    "- What is a programming language?\n",
+    "  1. Structure of a Program\n",
+    "- What kind of words exist?\n",
+    "  2. Variables, Expressions, Statements, & Comments\n",
+    "  3. Functions\n",
+    "- How can we form sentences from words?\n",
+    "  4. Conditionals\n",
+    "  5. Iteration"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "**Part 2: Managing Data**\n",
+    "\n",
+    "- How is data stored in memory?\n",
+    "  6. Numbers\n",
+    "  7. Text\n",
+    "  8. Sequences\n",
+    "  9. Mappings & Sets\n",
+    "- How can we create our own data types?\n",
+    "  10. Classes & Instances"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "## xkcd Comic"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "As with every good lecture, there always has to be a [xkcd](https://xkcd.com/353/) comic somewhere."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "import antigravity"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "<img src=\"static/xkcd.png\" width=\"30%\">"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.3"
+  },
+  "livereveal": {
+   "auto_select": "code",
+   "auto_select_fragment": true,
+   "scroll": true,
+   "theme": "serif"
+  },
+  "toc": {
+   "base_numbering": 1,
+   "nav_menu": {},
+   "number_sections": false,
+   "sideBar": true,
+   "skip_h1_title": true,
+   "title_cell": "Table of Contents",
+   "title_sidebar": "Contents",
+   "toc_cell": false,
+   "toc_position": {
+    "height": "calc(100% - 180px)",
+    "left": "10px",
+    "top": "150px",
+    "width": "384px"
+   },
+   "toc_section_display": false,
+   "toc_window_display": false
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
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From 318a5a8dd39a4ab68f3a9fcbdf3711bc4630b357 Mon Sep 17 00:00:00 2001
From: Alexander Hess <alexander@webartifex.biz>
Date: Mon, 16 Sep 2019 10:56:41 +0200
Subject: [PATCH 005/242] Add a requirements.txt file

This file makes it easier to install all dependencies for users without
poetry installed
---
 requirements.txt | 56 ++++++++++++++++++++++++++++++++++++++++++++++++
 1 file changed, 56 insertions(+)
 create mode 100644 requirements.txt

diff --git a/requirements.txt b/requirements.txt
new file mode 100644
index 0000000..41753e1
--- /dev/null
+++ b/requirements.txt
@@ -0,0 +1,56 @@
+appdirs==1.4.3
+attrs==19.1.0
+backcall==0.1.0
+black==18.9b0
+blackcellmagic==0.0.2
+bleach==3.1.0
+Click==7.0
+decorator==4.4.0
+defusedxml==0.6.0
+entrypoints==0.3
+ipykernel==5.1.2
+ipython==7.8.0
+ipython-genutils==0.2.0
+ipywidgets==7.5.1
+jedi==0.15.1
+Jinja2==2.10.1
+jsonschema==3.0.2
+jupyter==1.0.0
+jupyter-client==5.3.2
+jupyter-console==6.0.0
+jupyter-contrib-core==0.3.3
+jupyter-contrib-nbextensions==0.5.1
+jupyter-core==4.5.0
+jupyter-highlight-selected-word==0.2.0
+jupyter-latex-envs==1.4.6
+jupyter-nbextensions-configurator==0.4.1
+lxml==4.4.1
+MarkupSafe==1.1.1
+mistune==0.8.4
+nbconvert==5.6.0
+nbformat==4.4.0
+notebook==6.0.1
+pandocfilters==1.4.2
+parso==0.5.1
+pexpect==4.7.0
+pickleshare==0.7.5
+prometheus-client==0.7.1
+prompt-toolkit==2.0.9
+ptyprocess==0.6.0
+Pygments==2.4.2
+pyrsistent==0.15.4
+python-dateutil==2.8.0
+PyYAML==5.1.2
+pyzmq==18.1.0
+qtconsole==4.5.5
+rise==5.5.1
+Send2Trash==1.5.0
+six==1.12.0
+terminado==0.8.2
+testpath==0.4.2
+toml==0.10.0
+tornado==6.0.3
+traitlets==4.3.2
+wcwidth==0.1.7
+webencodings==0.5.1
+widgetsnbextension==3.5.1

From 33d6d91c9acffcea45dc4a1f2caca8036adb9e67 Mon Sep 17 00:00:00 2001
From: Alexander Hess <alexander@webartifex.biz>
Date: Mon, 16 Sep 2019 10:58:33 +0200
Subject: [PATCH 006/242] Add course info to README.md

- course description (objective & prerequisites)
- installation notes for Python & third-party packages
---
 README.md | 140 ++++++++++++++++++++++++++++++++++++++++++++++++++++++
 1 file changed, 140 insertions(+)

diff --git a/README.md b/README.md
index 6eece6b..4fd2b52 100644
--- a/README.md
+++ b/README.md
@@ -1 +1,141 @@
 # An Introduction to Python and Programming
+
+The purpose of this repository is to serve as an interactive "book" for a
+thorough introductory course on programming in the
+**[Python](https://www.python.org/)** language.
+
+The course's **main goal** is to **prepare** the student for **further
+studies** in the "field" of **data science**.
+
+The "chapters" are written in [Jupyter notebooks](https://jupyter-notebook.readthedocs.io/en/stable/)
+which are a de-facto standard for exchanging code and results among data
+science professionals and researchers.
+As such they can be viewed in a plain web browser:
+
+- [00 - Start up](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/00_start_up.ipynb)
+
+However, it is recommended that students **install Python and Jupyter
+locally** and run the code in the notebooks on their own.
+This way, the student can play with the code and learn more efficiently.
+Precise **installation instructions** are either in the [00th notebook](
+https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/00_start_up.ipynb)
+or further below.
+
+Feedback is encouraged and will be incorporated.
+Open an issue in the [issues tracker](https://github.com/webartifex/intro-to-python/issues)
+or initiate a [pull request](https://help.github.com/en/articles/about-pull-requests)
+if you are familiar with the concept.
+
+
+## Prerequisites
+
+To be suitable for *total beginners*, there are *no* formal prerequisites.
+It is only expected that the student has:
+
+- a *solid* understanding of the **English language**,
+- knowledge of **basic mathematics** from high school,
+- the ability to **think conceptually** and **reason logically**, and
+- the willingness to **invest 2-4 hours a day for a month**.
+
+
+## Installation
+
+To follow this course, a working installation of **Python 3.6** or higher is
+expected.
+
+A popular and beginner friendly way is to install the [Anaconda Distribution](
+https://www.anaconda.com/distribution/) that not only ships Python but comes
+pre-packaged with a lot of third-party libraries from the so-called
+"scientific stack".
+Just go to the [download](https://www.anaconda.com/distribution/#download-section)
+section and install the latest version (i.e., *2019-07* with Python 3.7 at the
+time of this writing) for your operating system.
+
+Then, among others, you will find an entry "Jupyter Notebook" in your start
+menu like below.
+Click on it and a new tab in your web browser will open where you can switch
+between folders as you could in your computer's default file browser.
+
+<img src="static/anaconda.png" width="50%">
+
+To download the course's materials as a ZIP file, click on the green "Clone or
+download" button on the top right on this website.
+Then, unpack the ZIP file into a folder of your choosing (ideally somewhere
+within your personal user folder so that the files show up right away).
+
+
+### Alternative Installation
+
+Python can also be installed in a "pure" way as obtained from its core
+development team (i.e., without any third-party packages installed).
+However, this is somewhat too "advanced" for a beginner as it involves working
+with a terminal emulator, which looks similar to the picture below and is
+used without a mouse by typing commands into it.
+
+<img src="static/terminal.png" width="50%" align="center">
+
+Assuming that you already have a working version of Python 3.6 or higher
+installed (cf., the official [download page](https://www.python.org/downloads/)),
+the following summarizes the commands to be typed into a terminal emulator to
+get the course materials up and running on a local machine without Anaconda.
+You are then responsible for understanding the concepts behind them.
+
+First, the [git](https://git-scm.com/) command line tool is a more professional
+way of "cloning" the course materials as compared to downloading them in a ZIP
+file.
+
+- `git clone https://github.com/webartifex/intro-to-python.git`
+
+This creates a new folder *intro-to-python* with all the materials of this
+repository in it.
+
+Inside this folder, it is recommended to create a so-called **virtual
+environment** with Python's [venv](https://docs.python.org/3/library/venv.html)
+module.
+This must only be done the first time.
+A virtual environment is a way of *isolating* the third-party packages
+installed by different projects, which is considered a best practice.
+
+- `python -m venv venv`
+
+The second *venv* is the environment's name and by convention often chosen to
+be *venv*.
+However, it could be another name as well.
+
+From then on, each time you want to resume work, go back into the
+*intro-to-python* folder inside your terminal and "activate" the virtual
+environment (*venv* is the name chosen before).
+
+- `source venv/bin/activate`
+
+This may change how the terminal's [command prompt](https://en.wikipedia.org/wiki/Command-line_interface#Command_prompt)
+looks.
+
+[poetry](https://poetry.eustace.io/docs/) and [virtualenvwrapper](https://virtualenvwrapper.readthedocs.io/en/latest/)
+are popular tools to automate the described management of virtual environments.
+
+After activation for the first time, you must install the project's
+**dependencies** (= the third-party packages needed to run the code), most
+notably [Jupyter](https://pypi.org/project/jupyter/) in this project (the
+"python -m" is often left out; if you have poetry installed, you may just
+type `poetry install` instead).
+
+- `python -m pip install -r requirements.txt`
+
+
+With everything installed, you can now do the equivalent of clicking the
+"Jupyter Notebook" entry in your start menu.
+
+- `jupyter notebook`
+
+This opens a new tab in your web browser just as above.
+
+
+## About the Author
+
+Alexander Hess is a PhD student at the Chair of Logistics Management at the
+[WHU - Otto Beisheim School of Management](https://www.whu.edu) where he
+conducts research on urban delivery platforms and teaches an introductory
+course on Python (cf., [course listing](https://vlv.whu.edu/campus/all/event.asp?objgguid=0xE57C2715B01B441AAFD3E79AA05CACCF&from=vvz&gguid=0x6A2B0ED5B2B949E69957A2099E7DE2F1&mode=own&tguid=0x3980A9BBC3BF4A638E977F2DC163F44B&lang=en)).
+
+Connect him on [LinkedIn](https://www.linkedin.com/in/webartifex).

From b5382758eab7febe096c0c5bfb324490b9f612a8 Mon Sep 17 00:00:00 2001
From: Alexander Hess <alexander@webartifex.biz>
Date: Thu, 19 Sep 2019 16:03:10 +0200
Subject: [PATCH 007/242] Add initial version of notebook 01

---
 01_elements_of_a_program.ipynb | 3708 ++++++++++++++++++++++++++++++++
 1 file changed, 3708 insertions(+)
 create mode 100644 01_elements_of_a_program.ipynb

diff --git a/01_elements_of_a_program.ipynb b/01_elements_of_a_program.ipynb
new file mode 100644
index 0000000..66c8e64
--- /dev/null
+++ b/01_elements_of_a_program.ipynb
@@ -0,0 +1,3708 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "\n",
+    "# Chapter 1: Elements of a Program"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Do you remember how you first learned to speak in your mother tounge? Probably not. No one's memory goes back that far. Your earliest memory as a child should probably be around the age of three or four years old when you could already say simple things and interact with your environment. Although you did not know any grammar rules yet, other people just understood what you said. Well, most of the time.\n",
+    "\n",
+    "It is intuitively best to take the very mindset of a small child when learing a foreign language and we do so for learning the Python language as well. This first chapter introduces simplistic examples and we better just accept them as they are without knowing any of the \"grammar\" rules yet. Then, we analyze them in parts and slowly build up our understanding.\n",
+    "\n",
+    "Consequently, if parts of this chapter do not make sense right away, let's not worry too much. Besides introducing some basics (that we need to understand), it also serves as an outlook for what is to come. So, many terms and concepts referenced here will be covered in great detail in following chapters."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "## Example: Average of a Subset of Numbers"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "As our introductory example, we want to calculate the average of all even numbers from one through ten.\n",
+    "\n",
+    "While we could come up with an [analytical solution](https://math.stackexchange.com/questions/935405/what-s-the-difference-between-analytical-and-numerical-approaches-to-problems/935446#935446) (i.e., derive some equation with \"pen and paper\" from, e.g., one of [Faulhaber's formulas](https://en.wikipedia.org/wiki/Faulhaber%27s_formula)), we instead solve the task programmatically.\n",
+    "\n",
+    "We start by creating a **list** called `numbers` that holds all the individual numbers."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "scrolled": true,
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "numbers = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "To verify that something happened in our computer's memory, we simply **reference** `numbers` and observe that Python indeed knows about."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[1, 2, 3, 4, 5, 6, 7, 8, 9, 10]"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "numbers"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "So far, so good. Let's see how the desired result could be expressed as a **sequence of instructions** in Python.\n",
+    "\n",
+    "Intuitively, the line `for number in numbers` describes a \"loop\" over all the numbers in the `numbers` list, one at a time.\n",
+    "\n",
+    "The `if number % 2 == 0` may look disturbing at first sight. Both the `%` and `==` must have an unintuitive meaning here. Luckily, the **comment** in the same line after the `#` symbol has the answer: The program only does something if the current `number` is even.\n",
+    "\n",
+    "In particular, it increases `count` by $1$ and adds the current `number` onto the [running](https://en.wikipedia.org/wiki/Running_total) `total`. Both `count` and `number` were initially set to $0$ and the single `=` reads as \"... is *set* equal to ...\". It could not indicate a mathematical equation as, for example, `count` is generally not equal to `count + 1`.\n",
+    "\n",
+    "Lastly, the `average` is calculated as the ratio of the final **values** of `total` and `count`. Overall, we divide the sum of all even numbers by the count of all even numbers, which is exactly what we are looking for.\n",
+    "\n",
+    "We also observe how the lines of code \"within\" the `for` and `if` **statements** are *indented* and *aligned* with multiples of four spaces: This shows immediately how the lines relate to each other."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "count = 0  # initialize variables to keep track of the sum\n",
+    "total = 0  # so far and the count of the even numbers\n",
+    "\n",
+    "for number in numbers:\n",
+    "    if number % 2 == 0:  # only look at even numbers\n",
+    "        count = count + 1\n",
+    "        total = total + number\n",
+    "\n",
+    "average = total / count"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Yet, we do not see any **output** and obtain the value of `average` by simply referencing it again."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "6.0"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "average"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "## Generating Cell Output in a Jupyter Notebook"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Note how only two of the previous four code cells generate an **output** while two remained \"silent\" (i.e., there is no \"**Out[...]**\" after running the cell).\n",
+    "\n",
+    "By default, Jupyter notebooks show the value of a cell's last so-called **expression**. This output can be suppressed by ending the last line with a semicolon.\n",
+    "\n",
+    "To visualize something before the end of the cell, we can use the [print()](https://docs.python.org/3/library/functions.html#print) built-in function."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'I am feeling great :-)'"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "\"Hello, World!\"\n",
+    "\"I am feeling great :-)\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "\"I am invisible!\";"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Hello, World!\n",
+      "I am feeling great :-)\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"Hello, World!\")\n",
+    "print(\"I am feeling great :-)\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "## (Arithmetic) Operators"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Python comes with basic mathematical operators built in. **Operators** are built-in **tokens** that have a special meaning to the Python interpreter. Most operators either \"operate\" with the object immediately following them (= **unary** operators; e.g., negation) or somehow \"process\" the two objects \"around\" them (= **binary** operators; e.g., addition). But we will see some exceptions from that as well.\n",
+    "\n",
+    "By definition, operators have **no** permanent **side effects** in the computer's memory. Although the code cells in this section do indeed lead to *new* objects being created in memory, they are immediately \"forgotten\" as they are not stored in a **variable** (like `numbers` above). We will revisit this idea further below when we compare **expressions** with **statements**.\n",
+    "\n",
+    "Let's see some examples of operators. We start with the binary `+` and the `-` operators for addition and subtraction. Binary operators resemble what mathematicians call [infix notation](https://en.wikipedia.org/wiki/Infix_notation) and have the expected meaning."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "90"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "77 + 13"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "8"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "101 - 93"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "The `-` operator can be used as a unary operator as well. Then it just flips the sign of a number."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "-1"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "-1"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "When we compare the output of the `*` and `/` operators for multiplication and division, we note the subtle difference between the $42$ and the $42.0$. This is a first illustration of the concept of a **data type**."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "42"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "2 * 21"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "42.0"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "84 / 2"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "The so-called **floor division operator** `//` always rounds down to the next integer and is thus also called **integer division operator**. This is a first example of an operator we commonly do not know from high school mathematics."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "42"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "84 // 2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "42"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "85 // 2"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "To obtain the remainder of a division, we can use the **modulo operator** `%`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "1"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "85 % 2"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Note that the remainder is $0$ if a number is divisable by another."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "49 % 7"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Modulo division can be useful if we, for example, need to get the last couple of digits of a large integer."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "3"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "123 % 10"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "23"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "123 % 100"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "The [divmod()](https://docs.python.org/3/library/functions.html#divmod) built-in **function** combines the integer and modulo divisions into one operation. However, this is not an operator any more (but a function). Also observe that [divmod()](https://docs.python.org/3/library/functions.html#divmod) returns a \"pair\" of integers."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(4, 2)"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "divmod(42, 10)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Raising a number to a power is performed with the **exponentiation operator** `**`. Note that this is different from the `^` operator many other programming languages might use and that also exists in Python with a *different* meaning."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "8"
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "2 ** 3"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "The normal order of precedence from mathematics applies (i.e., \"PEMDAS\" rule) but parentheses help avoid confusion."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "18"
+      ]
+     },
+     "execution_count": 21,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "3 ** 2 * 2 "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "The parentheses here serve as a **delimiter**."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "18"
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "(3 ** 2) * 2  # same result as before but much clearer code"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "81"
+      ]
+     },
+     "execution_count": 23,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "3 ** (2 * 2)  # different result"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Some programmers also use \"style\" conventions. For example, we can play with the **whitespace**, which is an umbrella term that refers to any non-printable sign like spaces, tabs, or the like. However, parentheses convey a much clearer picture."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "18"
+      ]
+     },
+     "execution_count": 24,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "3**2 * 2  # bad style; it is better to use parentheses here"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "There are plenty more mathematical and non-mathematical operators that are introduced throughout this book together with the concepts they implement or support. Some of these are already shown in the next section."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "## Objects vs. Values vs. Types"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Python is a so-called **object-oriented** language, which is a paradigm of organizing a program's memory.\n",
+    "\n",
+    "An **object** can be viewed as a \"bag\" of $0$s and $1$s in a distinct memory location that not only portrayes the idea of a certain **value** but also has some associated rules as to how this value is treated and may be worked with.\n",
+    "\n",
+    "An object *always* has **three** main characteristics. Let's look at the following examples and work them out."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "a = 789\n",
+    "b = 42.0\n",
+    "c = \"Python rocks\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "### Identity / \"Memory Location\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "The [id()](https://docs.python.org/3/library/functions.html#id) built-in function shows an object's \"address\" in memory."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "140382247181072"
+      ]
+     },
+     "execution_count": 26,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "id(a)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "140382247352144"
+      ]
+     },
+     "execution_count": 27,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "id(b)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "140382247028656"
+      ]
+     },
+     "execution_count": 28,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "id(c)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "These addresses are really not that meaningful for anything other than checking if two variables actually point at the same object. This may be helpful as different objects can of course have the same value."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "d = 789"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "`a` and `d` indeed have the same value as can be checked with the **equality operator** `==`. The resulting `True` (and the `False` below) is yet another data type, a so-called **boolean**."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 30,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "a == d"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "On the contrary, `a` and `d` are different objects as can be seen with the **identity operator** `is`: they are stored at seperate addresses in the memory."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "False"
+      ]
+     },
+     "execution_count": 31,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "a is d"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "### (Data) Type / \"Behavior\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "The [type()](https://docs.python.org/3/library/functions.html#type) built-in function shows an object's type. For example, `a` is an integer (`int`) while `b` is a so-called [floating-point number](https://en.wikipedia.org/wiki/Floating-point_arithmetic) (`float`)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "int"
+      ]
+     },
+     "execution_count": 32,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "type(a)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "float"
+      ]
+     },
+     "execution_count": 33,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "type(b)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Different types imply different behaviors for the objects. The `b` object, for example, can be \"asked\" if it could also be interpreted as an `int` with the [is_integer()](https://docs.python.org/3/library/stdtypes.html#float.is_integer) \"functionality\" that comes with every `float` object.\n",
+    "\n",
+    "Formally, we call such type-specific functionalities **methods** (to differentiate them from functions) and we will eventually fully introduce them when we talk about object-orientation in Chapter 10. For now, it suffices to know that we can access them using the **dot operator** `.`. Of course `b` could be converted into an `int`, which the boolean value `True` tells us.\n",
+    "\n",
+    "Also note how the `.` operator is neiter a unary nor a binary operator as specified above."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 34,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "b.is_integer()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "For an `int` object this [is_integer()](https://docs.python.org/3/library/stdtypes.html#float.is_integer) check does not make sense as we know it is an `int` to begin with. This is why we see the `AttributeError` below as `a` does not even know what `is_integer()` means."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [
+    {
+     "ename": "AttributeError",
+     "evalue": "'int' object has no attribute 'is_integer'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-35-7db0a38aefcc>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0ma\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mis_integer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;31mAttributeError\u001b[0m: 'int' object has no attribute 'is_integer'"
+     ]
+    }
+   ],
+   "source": [
+    "a.is_integer()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "The `c` object is a so-called **string** type `str`, which we can view as Python's way of representing \"text\". Strings also come with their own behaviors, for example, to convert a text to lower or upper case."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "str"
+      ]
+     },
+     "execution_count": 36,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "type(c)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'python rocks'"
+      ]
+     },
+     "execution_count": 37,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "c.lower()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'PYTHON ROCKS'"
+      ]
+     },
+     "execution_count": 38,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "c.upper()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Python Rocks'"
+      ]
+     },
+     "execution_count": 39,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "c.title()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "### Value"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Almost trivially, every object also has a value to which it \"evaluates\" when referenced. We think of the value as the **conceptual idea** of what the $0$s and $1$s in memory mean to us humans. A machine does not really see beyond the $0$s and $1$s. At least, not yet.\n",
+    "\n",
+    "For built-in data types, Python prints out the object's value in a so-called **[literal](https://docs.python.org/3/reference/lexical_analysis.html#literals)** notation. This basically means that we can just copy & paste the output back into a code cell to create a *new* object with the *same* value."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "789"
+      ]
+     },
+     "execution_count": 40,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "a"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "42.0"
+      ]
+     },
+     "execution_count": 41,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "b"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "In this book, we follow the convention of creating strings with **double quotes** `\"` instead of the **single quotes** `'` to which Python defaults in its literal output. Both types of quotes can be used interchangebly."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Python rocks'"
+      ]
+     },
+     "execution_count": 42,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "c"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "## Formal vs. Natural Languages"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Just like the language of mathematics is good at expressing relationships among numbers and symbols, any programming language is just a formal language that is good at expressing computations.\n",
+    "\n",
+    "Formal languages come with their own \"grammatical rules\" called **syntax**."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "### Syntax Errors"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "If we do not follow the rules, the code cannot be **parsed** correctly, i.e., the program does not even start to run but raises a **syntax error** (indicated as `SyntaxError`). Computers are very dumb in the sense that the slightest syntax error leads to the machine not understanding our code.\n",
+    "\n",
+    "For example, if we wanted to write an accounting program that adds up currencies, we would have to model dollar prices as `float` objects as the dollar symbol cannot be read by Python."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "ename": "SyntaxError",
+     "evalue": "invalid syntax (<ipython-input-43-cafa82e54b9c>, line 1)",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;36m  File \u001b[0;32m\"<ipython-input-43-cafa82e54b9c>\"\u001b[0;36m, line \u001b[0;32m1\u001b[0m\n\u001b[0;31m    3.99 $ + 10.40 $\u001b[0m\n\u001b[0m         ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m invalid syntax\n"
+     ]
+    }
+   ],
+   "source": [
+    "3.99 $ + 10.40 $"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Python requires certain symbols at certain places (e.g., a `:` is missing here) ..."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "ename": "SyntaxError",
+     "evalue": "invalid syntax (<ipython-input-44-499e4d0d0cbb>, line 1)",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;36m  File \u001b[0;32m\"<ipython-input-44-499e4d0d0cbb>\"\u001b[0;36m, line \u001b[0;32m1\u001b[0m\n\u001b[0;31m    for number in numbers\u001b[0m\n\u001b[0m                         ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m invalid syntax\n"
+     ]
+    }
+   ],
+   "source": [
+    "for number in numbers\n",
+    "    print(number)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "... and relies on whitespace / indentation unlike many other programming languages. A `IndentationError` is just a special type of a `SyntaxError`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "ename": "IndentationError",
+     "evalue": "expected an indented block (<ipython-input-45-19398c5f89de>, line 2)",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;36m  File \u001b[0;32m\"<ipython-input-45-19398c5f89de>\"\u001b[0;36m, line \u001b[0;32m2\u001b[0m\n\u001b[0;31m    print(number)\u001b[0m\n\u001b[0m        ^\u001b[0m\n\u001b[0;31mIndentationError\u001b[0m\u001b[0;31m:\u001b[0m expected an indented block\n"
+     ]
+    }
+   ],
+   "source": [
+    "for number in numbers:\n",
+    "print(number)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "### Runtime Errors"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Syntax errors as above are easy to find as the code will not even run to begin with.\n",
+    "\n",
+    "However, there are also so-called **runtime errors** (also called **exceptions**) that occur if the code would run given correct input.\n",
+    "\n",
+    "This example does not work because just like in the \"real\" world, Python does not know how to divide by $0$. The syntactically correct code leads to a `ZeroDivisionError`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "ename": "ZeroDivisionError",
+     "evalue": "division by zero",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mZeroDivisionError\u001b[0m                         Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-46-bc757c3fda29>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0;36m1\u001b[0m \u001b[0;34m/\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;31mZeroDivisionError\u001b[0m: division by zero"
+     ]
+    }
+   ],
+   "source": [
+    "1 / 0"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "### Semantic Errors"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "So-called **semantic errors**, on the contrary, can be very hard to spot as they do *not* crash the program. The only way to find such errors is to run the program with test input for which we know the answer already and can then verify it. However, testing software is a whole discipline on its own and often very hard to do in practice.\n",
+    "\n",
+    "The cell below copies our introductory example from above with a \"tiny\" error."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "metadata": {
+    "code_folding": [],
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "count = 0\n",
+    "total = 0\n",
+    "\n",
+    "for number in numbers:\n",
+    "    if number % 2 == 0:\n",
+    "        count = count + 1\n",
+    "        total = total + count  # count is wrong here, it should be number\n",
+    "\n",
+    "average = total / count"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 48,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "3.0"
+      ]
+     },
+     "execution_count": 48,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "average"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Finding errors is is called **debugging**. For the history of the term, check this [link](https://en.wikipedia.org/wiki/Debugging)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "## Best Practices"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Adhering to just syntax rules is therefore *never* enough. Over time, **best practices** and common **style guides** were created to make it less likely for a developer to mess up a program and also to allow \"onboarding\" him as a contributor to an established code base (often called **legacy code**) faster. These rules are not enforced by Python itself: Badly styled and un-readable code will still run. At the very least, Python programs should be styled according to [PEP 8](https://www.python.org/dev/peps/pep-0008/) and documented \"inline\" (i.e., in the code itself) according to [PEP 257](https://www.python.org/dev/peps/pep-0257/).\n",
+    "\n",
+    "An easier to read version of PEP 8 can be found [here](https://pep8.org/). The video below features a well known \"[Pythonista](https://en.wiktionary.org/wiki/Pythonista)\" talking about the importance of code style."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 49,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/jpeg": 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\n",
+      "text/html": [
+       "\n",
+       "        <iframe\n",
+       "            width=\"60%\"\n",
+       "            height=\"300\"\n",
+       "            src=\"https://www.youtube.com/embed/Hwckt4J96dI\"\n",
+       "            frameborder=\"0\"\n",
+       "            allowfullscreen\n",
+       "        ></iframe>\n",
+       "        "
+      ],
+      "text/plain": [
+       "<IPython.lib.display.YouTubeVideo at 0x7fad49702518>"
+      ]
+     },
+     "execution_count": 49,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from IPython.display import YouTubeVideo\n",
+    "YouTubeVideo(\"Hwckt4J96dI\", width=\"60%\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "For example, while the above code to calculate the average of the even numbers from 1 through 10 is correct, a Pythonista would re-write it in a more \"Pythonic\" way and use the [sum()](https://docs.python.org/3/library/functions.html#sum) and [len()](https://docs.python.org/3/library/functions.html#len) (= \"length\") built-in functions. Pythonic code runs faster in many cases and is less error prone."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 50,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "numbers = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 51,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "evens = [n for n in numbers if n % 2 == 0]  # example for a so-called list comprehension"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 52,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[2, 4, 6, 8, 10]"
+      ]
+     },
+     "execution_count": 52,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "evens"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 53,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "average = sum(evens) / len(evens)  # built-in functions are much faster than a for-loop"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 54,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "6.0"
+      ]
+     },
+     "execution_count": 54,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "average"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "To get a rough overview of the mindsets of a typical Python programmer, check these rules by an early Python core developer that are deemed so important that they are actually included in every Python installation."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 55,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The Zen of Python, by Tim Peters\n",
+      "\n",
+      "Beautiful is better than ugly.\n",
+      "Explicit is better than implicit.\n",
+      "Simple is better than complex.\n",
+      "Complex is better than complicated.\n",
+      "Flat is better than nested.\n",
+      "Sparse is better than dense.\n",
+      "Readability counts.\n",
+      "Special cases aren't special enough to break the rules.\n",
+      "Although practicality beats purity.\n",
+      "Errors should never pass silently.\n",
+      "Unless explicitly silenced.\n",
+      "In the face of ambiguity, refuse the temptation to guess.\n",
+      "There should be one-- and preferably only one --obvious way to do it.\n",
+      "Although that way may not be obvious at first unless you're Dutch.\n",
+      "Now is better than never.\n",
+      "Although never is often better than *right* now.\n",
+      "If the implementation is hard to explain, it's a bad idea.\n",
+      "If the implementation is easy to explain, it may be a good idea.\n",
+      "Namespaces are one honking great idea -- let's do more of those!\n"
+     ]
+    }
+   ],
+   "source": [
+    "import this"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "### Jupyter Notebook Aspects to keep in Mind"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "#### The Order of Code Cells is arbitrary"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Observe that you can run the code cells in a Jupyter notebook in any arbitrary order.\n",
+    "\n",
+    "That means, for example, that a variable defined towards the bottom could accidently be referenced at the top of the notebook. This happens easily if we iteratively built a program and go back and forth between cells.\n",
+    "\n",
+    "As a good practice, it is recommended to click on \"Kernel\" > \"Restart & Run All\" in the navigation bar once a notebook is finished. That restarts the Python process forgetting any **state** (i.e., all variables) and ensures that the notebook runs top to bottom without any errors the next time it is opened."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "#### Notebooks are linear"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "While this book is built with Jupyter notebooks, it is important to understand that \"real\" programs are almost always just \"linear\" (= top to bottom) sequences of instructions but instead can take many different **flows of execution**.\n",
+    "\n",
+    "At the same time, for a beginner's course it is often easier to just code in a linear fashion.\n",
+    "\n",
+    "In real data science projects one would probably employ a mixed approach and put re-usable code into so-called Python modules (= \\*.py files) and then use Jupyter notebooks to built up a linear report or story line for a business argument to be made."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "## Variables / Names"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "**Variables** are created with the **assignment statement** `=`. As its name suggests, it is *not* an operator, mainly because of its side effect of making a **name** \"point\" to an object in memory."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 56,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "a = 20.0\n",
+    "b = 789"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "When referenced, a variable just evaluates to the value of the object it points to. Colloquially, we could say that `a` evaluates to `20.0` here but this would not be an accurate description of what is really going on in memory.\n",
+    "\n",
+    "We will see some more colloquial jargons in this section but should always remind ourselves what we better said instead."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 57,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "20.0"
+      ]
+     },
+     "execution_count": 57,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "a"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "A variable can be **re-assigned** as often as we wish. Thereby, we could also assign an object of a different type. Because this is allowed, Python is said to be a **dynamically typed** language. On the contrary, a **statically typed** language like C also allows re-assignment but only with objects of the same type. This subtle distinction is one reason why Python is slower at execution than C: As it runs a program, it needs to figure out an object's type each time it is referenced. But as mentioned before, this can be mitigated with third-party libraries."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 58,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "a = 20"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 59,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "20"
+      ]
+     },
+     "execution_count": 59,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "a"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "If we want to re-assign a variable while referencing its \"old\" (i.e., current) object, we can also **update** it using a so-called **augmented assignment statement** (*not* operator). This implicitly inserts the current \"value\" as the first token on the right-hand side."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 60,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "a *= 4  # same as a = a * 4"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 61,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "a //= 2  # same as a = a // 2, \"//\" to retain the integer type"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 62,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "a += 2  # same as a = a + 2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 63,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "42"
+      ]
+     },
+     "execution_count": 63,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "a"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Variables can be **de-referenced** (i.e., \"deleted\") with the `del` statement. This does *not* \"delete\" the object to which a variable points to. It merely removes the variable from the \"list of all variables\"."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 64,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "789"
+      ]
+     },
+     "execution_count": 64,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "b"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 65,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "del b"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "If we refer to an unknown name, a runtime exception occurs, namely a `NameError`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 66,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [
+    {
+     "ename": "NameError",
+     "evalue": "name 'b' is not defined",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-66-89e6c98d9288>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mb\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;31mNameError\u001b[0m: name 'b' is not defined"
+     ]
+    }
+   ],
+   "source": [
+    "b"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Some names magically exist when we start Python. In this introductory book, we can safely ignore such variables."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 67,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'__main__'"
+      ]
+     },
+     "execution_count": 67,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "__name__"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "To see all defined names, the built-in function [dir()](https://docs.python.org/3/library/functions.html#dir) is helpful."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 68,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['In',\n",
+       " 'Out',\n",
+       " 'YouTubeVideo',\n",
+       " '_',\n",
+       " '_10',\n",
+       " '_11',\n",
+       " '_12',\n",
+       " '_13',\n",
+       " '_14',\n",
+       " '_15',\n",
+       " '_16',\n",
+       " '_17',\n",
+       " '_18',\n",
+       " '_19',\n",
+       " '_2',\n",
+       " '_20',\n",
+       " '_21',\n",
+       " '_22',\n",
+       " '_23',\n",
+       " '_24',\n",
+       " '_26',\n",
+       " '_27',\n",
+       " '_28',\n",
+       " '_30',\n",
+       " '_31',\n",
+       " '_32',\n",
+       " '_33',\n",
+       " '_34',\n",
+       " '_36',\n",
+       " '_37',\n",
+       " '_38',\n",
+       " '_39',\n",
+       " '_4',\n",
+       " '_40',\n",
+       " '_41',\n",
+       " '_42',\n",
+       " '_48',\n",
+       " '_49',\n",
+       " '_5',\n",
+       " '_52',\n",
+       " '_54',\n",
+       " '_57',\n",
+       " '_59',\n",
+       " '_63',\n",
+       " '_64',\n",
+       " '_67',\n",
+       " '_8',\n",
+       " '_9',\n",
+       " '__',\n",
+       " '___',\n",
+       " '__builtin__',\n",
+       " '__builtins__',\n",
+       " '__doc__',\n",
+       " '__loader__',\n",
+       " '__name__',\n",
+       " '__package__',\n",
+       " '__spec__',\n",
+       " '_dh',\n",
+       " '_i',\n",
+       " '_i1',\n",
+       " '_i10',\n",
+       " '_i11',\n",
+       " '_i12',\n",
+       " '_i13',\n",
+       " '_i14',\n",
+       " '_i15',\n",
+       " '_i16',\n",
+       " '_i17',\n",
+       " '_i18',\n",
+       " '_i19',\n",
+       " '_i2',\n",
+       " '_i20',\n",
+       " '_i21',\n",
+       " '_i22',\n",
+       " '_i23',\n",
+       " '_i24',\n",
+       " '_i25',\n",
+       " '_i26',\n",
+       " '_i27',\n",
+       " '_i28',\n",
+       " '_i29',\n",
+       " '_i3',\n",
+       " '_i30',\n",
+       " '_i31',\n",
+       " '_i32',\n",
+       " '_i33',\n",
+       " '_i34',\n",
+       " '_i35',\n",
+       " '_i36',\n",
+       " '_i37',\n",
+       " '_i38',\n",
+       " '_i39',\n",
+       " '_i4',\n",
+       " '_i40',\n",
+       " '_i41',\n",
+       " '_i42',\n",
+       " '_i43',\n",
+       " '_i44',\n",
+       " '_i45',\n",
+       " '_i46',\n",
+       " '_i47',\n",
+       " '_i48',\n",
+       " '_i49',\n",
+       " '_i5',\n",
+       " '_i50',\n",
+       " '_i51',\n",
+       " '_i52',\n",
+       " '_i53',\n",
+       " '_i54',\n",
+       " '_i55',\n",
+       " '_i56',\n",
+       " '_i57',\n",
+       " '_i58',\n",
+       " '_i59',\n",
+       " '_i6',\n",
+       " '_i60',\n",
+       " '_i61',\n",
+       " '_i62',\n",
+       " '_i63',\n",
+       " '_i64',\n",
+       " '_i65',\n",
+       " '_i66',\n",
+       " '_i67',\n",
+       " '_i68',\n",
+       " '_i7',\n",
+       " '_i8',\n",
+       " '_i9',\n",
+       " '_ih',\n",
+       " '_ii',\n",
+       " '_iii',\n",
+       " '_oh',\n",
+       " 'a',\n",
+       " 'average',\n",
+       " 'c',\n",
+       " 'count',\n",
+       " 'd',\n",
+       " 'evens',\n",
+       " 'exit',\n",
+       " 'get_ipython',\n",
+       " 'number',\n",
+       " 'numbers',\n",
+       " 'quit',\n",
+       " 'this',\n",
+       " 'total']"
+      ]
+     },
+     "execution_count": 68,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "dir()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "### Who am I? And how many?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "It is important to understand that *several* variables can point to the *same* object in memory. This can be counter-intuitive in the beginning and lead to many hard to track down bugs.\n",
+    "\n",
+    "This makes `b` point to whatever object `a` points to."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 69,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "b = a  # this is different from b == a"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 70,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "42"
+      ]
+     },
+     "execution_count": 70,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "a"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 71,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "42"
+      ]
+     },
+     "execution_count": 71,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "b"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "For \"simple\" types like `int` or `float` this will never cause confusion.\n",
+    "\n",
+    "Let's \"change the value\" of `a`. Really, let's create a *new* `123` object and make `a` point to it."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 72,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "a = 123"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 73,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "123"
+      ]
+     },
+     "execution_count": 73,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "a"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "`b` \"is still the same\" as before. Really, `b` still points to the same object as before."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 74,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "42"
+      ]
+     },
+     "execution_count": 74,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "b"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "However, if a name points to an object of a more \"complex\" object, for example, of type `list`, \"weird\" things can happen."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 75,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "x = [1, 2, 3]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 76,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "list"
+      ]
+     },
+     "execution_count": 76,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "type(x)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 77,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "y = x"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Let's change the first element of `x`.\n",
+    "\n",
+    "Chapter 7 discusses lists in more depth. For now, let's just view a list as some sort of **container** object that holds an arbitrary number of pointers to other objects and treat the brackets `[...]` attached to `x` as just another operator, called the **indexing operator**. `x[0]` instructs Python to first \"follow\" the pointer from the \"global\" directory of all names to the list object. Then, it follows the first pointer it finds there to the `1` object. The indexing operator must be an operator as we merely read the first element an do not change anything in memory.\n",
+    "\n",
+    "Note how Python **begins counting at 0**. This is not the case for many other languages, for example, MATLAB, R, or Stata. To understand why this makes sense, see this short [note](https://www.cs.utexas.edu/users/EWD/transcriptions/EWD08xx/EWD831.html) by one of the all-time greats in computer science, the late [Edsger Dijkstra](https://en.wikipedia.org/wiki/Edsger_W._Dijkstra)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 78,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "1"
+      ]
+     },
+     "execution_count": 78,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "x[0]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "To change the first entry in the list, we use the assignment statement `=` again. Here, this does actually *not* create a *new* variable but only changes the object to which the first pointer in the `x` list points to. As we only change parts of the `x` list, we say that we change its **state**."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 79,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "x[0] = 99"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 80,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[99, 2, 3]"
+      ]
+     },
+     "execution_count": 80,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "x"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "The changes made to `x` can also be seen through the `y` variable."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 81,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[99, 2, 3]"
+      ]
+     },
+     "execution_count": 81,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "y"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "The illustrated difference in behavior has to do with the fact that integers and floats are **immutable** types while lists are **mutable**.\n",
+    "\n",
+    "In the first case, an object cannot be changed \"in place\" once it is created in memory. When we assigned `123` to the already existing `a`, we actually did not change the $0$s and $1$s in the object `a` pointed to before the assignment but created a new integer object and made `a` point to it while the `b` variable is *not* affected.\n",
+    "\n",
+    "In the second case, `x[0] = 99` creates a *new* integer object `99` and merely changes the first pointer in the `x` list.\n",
+    "\n",
+    "In general, the assignment statement (re-)creates a variable and makes it point to whatever object is on the right-hand side *if* the left-hand side is a *pure* variable name. Otherwise, it changes some object on the left-hand side (this is strictly not a must but we should expect it).\n",
+    "\n",
+    "In the beginning, visualizing the memory with a tool like [PythonTutor](http://pythontutor.com/visualize.html#code=x%20%3D%20%5B1,%202,%203%5D%0Ay%20%3D%20x%0Ax%5B0%5D%20%3D%2099%0Adel%20x,%20y%0Ax%20%3D%20%5B1,%202,%203%5D%0Ay%20%3D%20x.copy%28%29%0Ax%5B0%5D%20%3D%2099&cumulative=false&curInstr=0&heapPrimitives=nevernest&mode=display&origin=opt-frontend.js&py=3&rawInputLstJSON=%5B%5D&textReferences=false) will assist in understanding what is going on."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "### Naming Conventions"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "**[Phil Karlton](https://skeptics.stackexchange.com/questions/19836/has-phil-karlton-ever-said-there-are-only-two-hard-things-in-computer-science)** famously noted (during his time at [Netscape](https://en.wikipedia.org/wiki/Netscape)):\n",
+    "\n",
+    "> \"There are two hard problems in computer science: naming things and cache invalidation ... and off-by-one errors.\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Variable names may contain upper and lower case letters, numbers, and underscores (\"\\_\") and be as long as we want them to be. However, they must not begin with a number. Also, they must not be any of Python's **[keywords](https://docs.python.org/3/reference/lexical_analysis.html#keywords)**.\n",
+    "\n",
+    "Variable names are usually chosen such that they do not need any more documentation and are self-explanatory. A very common convention is to use so-called **[snake\\_case](https://en.wikipedia.org/wiki/Snake_case)**: Keep everything lowercase and use underscores to seperate words.\n",
+    "\n",
+    "See this [link](https://en.wikipedia.org/wiki/Naming_convention_%28programming%29#Python_and_Ruby) for a comparison of different naming conventions."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "#### Good examples"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 82,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "pi = 3.14"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 83,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "answer_to_everything = 42"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 84,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "my_name = \"Alexander\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 85,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "work_address = \"Burgplatz 2, Vallendar\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "#### Bad examples"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 86,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "PI = 3.14  # unless used as a \"global\" constant"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 87,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "answerToEverything = 42  # this is a style used in languages like Java"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 88,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "name = \"Alexander\"  # name of what ?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 89,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "ename": "SyntaxError",
+     "evalue": "can't assign to operator (<ipython-input-89-dfc191b6a91a>, line 1)",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;36m  File \u001b[0;32m\"<ipython-input-89-dfc191b6a91a>\"\u001b[0;36m, line \u001b[0;32m1\u001b[0m\n\u001b[0;31m    address@work = \"Burgplatz 2, Vallendar\"\u001b[0m\n\u001b[0m                                           ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m can't assign to operator\n"
+     ]
+    }
+   ],
+   "source": [
+    "address@work = \"Burgplatz 2, Vallendar\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "If a variable name collides with a built-in name, just add a trailing underscore."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 90,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "type_ = \"student\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Variables with leading and trailing double underscores (referred to as **dunder** in Python \"slang\") are used for important built-in variables. Do *not* use this style for custom variables!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 91,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'__main__'"
+      ]
+     },
+     "execution_count": 91,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "__name__"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "### The big Picture"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "This PyCon talk by [Ned Batchelder](https://nedbatchelder.com/) (a software engineer at [edX](https://www.edx.org/) and the organizer of the [Python User Group](https://www.meetup.com/bostonpython/) in Boston) summarizes all situations where some sort of variable assignment is done in Python. The content is intermediate and therefore it is ok if you do not understand everything at this point. However, the contents should be known by everyone claiming to be a Pythonista."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 92,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
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+      "text/html": [
+       "\n",
+       "        <iframe\n",
+       "            width=\"60%\"\n",
+       "            height=\"300\"\n",
+       "            src=\"https://www.youtube.com/embed/_AEJHKGk9ns\"\n",
+       "            frameborder=\"0\"\n",
+       "            allowfullscreen\n",
+       "        ></iframe>\n",
+       "        "
+      ],
+      "text/plain": [
+       "<IPython.lib.display.YouTubeVideo at 0x7fad49f57320>"
+      ]
+     },
+     "execution_count": 92,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from IPython.display import YouTubeVideo\n",
+    "YouTubeVideo(\"_AEJHKGk9ns\", width=\"60%\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "## Expressions"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "An **expression** is any syntactically correct **combination** of **variables** and **literals** with **operators**. See the [language reference](https://docs.python.org/3/reference/expressions.html) for a full list.\n",
+    "\n",
+    "In simple words, anything that can be used on the right-hand side of an assignment statement without creating a `SyntaxError` is an expression.\n",
+    "\n",
+    "What we said about individual operators above (namely that they have *no* side effects) should have been put here to begin with. The examples in the section on operators were actually all expressions.\n",
+    "\n",
+    "The simplest possible expression contains only one variable (or literal)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 93,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "123"
+      ]
+     },
+     "execution_count": 93,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "a"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 94,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "165"
+      ]
+     },
+     "execution_count": 94,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "a + b"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "The definition of an expression is **recursive**. So here the sub-expression `a + b` is combined with the literal `3` by the operator `**` to form another expression."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 95,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "4492125"
+      ]
+     },
+     "execution_count": 95,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "(a + b) ** 3"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "As before, the bracket operator `[...]` can be used for indexing."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 96,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "3"
+      ]
+     },
+     "execution_count": 96,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "y[2]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "When not used as a **delimiter**, parentheses also constitute an operator, namely the **call operator** `(...)`. We have seen this syntax above when we \"called\" (i.e., executed) built-in functions and methods."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 97,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "104"
+      ]
+     },
+     "execution_count": 97,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "sum(x)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "### Operator Overloading"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Python **overloads** certain operators. For example you can not only add numbers but also strings. This is called **string concatenation**."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 98,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "greeting = \"Hi \"\n",
+    "audience = \"class\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 99,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Hi class'"
+      ]
+     },
+     "execution_count": 99,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "greeting + audience"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Duplicate strings using multiplication."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 100,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Hi Hi Hi Hi Hi Hi Hi Hi Hi Hi Hi Hi Hi Hi Hi Hi Hi Hi Hi Hi Hi Hi Hi Hi Hi Hi Hi Hi Hi Hi Hi Hi Hi Hi Hi Hi Hi Hi Hi Hi Hi Hi '"
+      ]
+     },
+     "execution_count": 100,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "b * greeting"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "## Statements"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "A **statement** is anything that changes the state of the program's memory or has some other side effect. Statements do not just evaluate to a value like expressions; instead, they create or change values. See the [language reference](https://docs.python.org/3/reference/simple_stmts.html) for a full list.\n",
+    "\n",
+    "Most notably of course are the `=` and `del` statements."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 101,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "a = 123"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 102,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "del a"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "The [print()](https://docs.python.org/3/library/functions.html#print) function is regarded a \"statement\" as well. In fact, it used to be a real statement in Python 2 and has all the necessary properties. It is a bit of a corner case as expressions are also \"printed\" in a Jupyter notebook when evaluated last in a code cell."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 103,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "I change the display of the computer\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"I change the display of the computer\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "## Comments"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "We can use the `#` symbol to write comments in plain English right into the code.\n",
+    "\n",
+    "As a good practice, comments should not describe what happens (this should be evident by reading the code, otherwise it is most likely badly written code) but why something happens.\n",
+    "\n",
+    "Comments can be either added at the end of a line of code (by convention seperated with two spaces) or be on a line on their own."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 104,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "distance = 891  # in meters\n",
+    "elapsed_time = 93  # in seconds\n",
+    "# Calculate the speed in km/h.\n",
+    "speed = 3.6 * distance / elapsed_time"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "But let's think wisely if we really need to use a comment.\n",
+    "The second cell is a lot more \"Pythonic\"."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 105,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "seconds = 365 * 24 * 60 * 60  # = seconds in the year"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 106,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "seconds_per_year = 365 * 24 * 60 * 60"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "## TL;DR"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "We end each chapter with a summary of the main points. The essence in this first chapter is that just like a sentence in a real language like English can be decomposed into its parts (subject, predicate, objects, ...) the same can be done with programming languages."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "- program\n",
+    "  - **sequence** of **instructions** that specify how to perform a computation (= a \"recipe\")\n",
+    "  - a \"black box\" that processes **inputs** and transforms them into meaningful **outputs** in a *deterministic* way\n",
+    "  - conceptually similar to a mathematical function $f$ that maps some input $x$ to an output $y = f(x)$\n",
+    "\n",
+    "\n",
+    "- input (examples)\n",
+    "  - (numeric) data from a CSV file\n",
+    "  - text entered on a command line\n",
+    "  - (relational) data obtained from a database\n",
+    "  - ...\n",
+    "\n",
+    "\n",
+    "- output (examples)\n",
+    "  - result of a computation (e.g., statistical summary of a sample dataset)\n",
+    "  - a \"side effect\" (e.g., a transformation of raw input data into cleaned data)\n",
+    "  - a physical \"behavior\" (e.g., a robot moving or a document printed)\n",
+    "  - ...\n",
+    "\n",
+    "\n",
+    "- objects\n",
+    "  - distinct and well-contained areas / parts of the memory that hold the actual data\n",
+    "  - the concept by which Python manages the memory for us\n",
+    "  - can be classified into objects of the same **type** (i.e., same abstract \"structure\" but different concrete data)\n",
+    "  - built-in objects (incl. **literals**) vs. user-defined objects (cf., Chapter 8)\n",
+    "  - e.g., `1`, `1.0`, and `\"one\"` are three different objects of distinct types that are also literals (i.e., by the way we type them into the command line Python knows what the value and type are)\n",
+    "\n",
+    "\n",
+    "- variables\n",
+    "  - storage of intermediate **state**\n",
+    "  - **names** that point to **objects** in **memory**\n",
+    "  - e.g., `x = 1` creates the variable `x` that points to the object `1`\n",
+    "\n",
+    "\n",
+    "- operators\n",
+    "  - special built-in symbols that perform operations with objects in memory\n",
+    "  - usually operate with one or two objects\n",
+    "  - e.g., addition `+`, subtraction `-`, multiplication `*`, and division `/` all take two objects whereas the negation `-` only takes one\n",
+    "\n",
+    "\n",
+    "- expressions\n",
+    "  - **combinations** of **variables** (incl. **literals**) and **operators**\n",
+    "  - do *not* change the involved objects / state of the program\n",
+    "  - evaluate to a **value** (i.e., the \"result\" of the expression, usually a new object)\n",
+    "  - e.g., `x + 2` evaluates to the (new) object `3` and `1 - 1.0` to `0.0`\n",
+    "\n",
+    "\n",
+    "- statements\n",
+    "  - instructions that **\"do\" something** and **have side effects** in memory\n",
+    "  - re-map names to different objects and *change* the state of the program\n",
+    "  - usually work with expressions\n",
+    "  - e.g., the assignment statement `=` makes a name point to an object\n",
+    "\n",
+    "\n",
+    "- comments\n",
+    "  - **prose** supporting a **human's understanding** of the program\n",
+    "  - ignored by Python\n",
+    "\n",
+    "\n",
+    "- functions (cf., Chapter 2)\n",
+    "  - named sequences of instructions\n",
+    "  - the smaller parts in a larger program\n",
+    "  - make a program more modular and thus easier to understand\n",
+    "\n",
+    "\n",
+    "- flow control (cf., Chapter 3)\n",
+    "  - expression of **logic** or an **algorithm**\n",
+    "  - conditional execution of a small **branch** within a program (i.e., `if`-statements)\n",
+    "  - repetitive execution of parts of a program (i.e., `for`-loops and `while`-loops)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.3"
+  },
+  "livereveal": {
+   "auto_select": "code",
+   "auto_select_fragment": true,
+   "scroll": true,
+   "theme": "serif"
+  },
+  "toc": {
+   "base_numbering": 1,
+   "nav_menu": {},
+   "number_sections": false,
+   "sideBar": true,
+   "skip_h1_title": true,
+   "title_cell": "Table of Contents",
+   "title_sidebar": "Contents",
+   "toc_cell": false,
+   "toc_position": {
+    "height": "calc(100% - 180px)",
+    "left": "10px",
+    "top": "150px",
+    "width": "303.333px"
+   },
+   "toc_section_display": false,
+   "toc_window_display": false
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

From 387708f253f7040cbade6fdabaa8e1f0b5e75b14 Mon Sep 17 00:00:00 2001
From: Alexander Hess <alexander@webartifex.biz>
Date: Thu, 19 Sep 2019 16:04:19 +0200
Subject: [PATCH 008/242] Adjust content overview in notebook 00

- the old chapters 01 and 02 were merged
  -> reflect that in the contents overview in notebook 00
- streamline text
---
 00_start_up.ipynb | 51 +++++++++++++++++++----------------------------
 1 file changed, 21 insertions(+), 30 deletions(-)

diff --git a/00_start_up.ipynb b/00_start_up.ipynb
index 588d7ab..7551dcd 100644
--- a/00_start_up.ipynb
+++ b/00_start_up.ipynb
@@ -295,15 +295,7 @@
      "slide_type": "slide"
     }
    },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Hello world\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "print(\"Hello world\")"
    ]
@@ -316,7 +308,7 @@
     }
    },
    "source": [
-    "Sometimes a code cell starts with an exclamation mark `!`. Then, the Jupyter notebook behaves as if you just typed the following commands right into a terminal. The cell below asks `python` to show its version number. This is actually *not* Python code but a command in the [Shell](https://en.wikipedia.org/wiki/Shell_script) language. The `!` is useful to execute short commands without leaving a Jupyter notebook."
+    "Sometimes a code cell starts with an exclamation mark `!`. Then, the Jupyter notebook behaves as if you just typed the following command right into a terminal. The cell below asks `python` to show its version number. This is actually *not* Python code but a command in the [Shell](https://en.wikipedia.org/wiki/Shell_script) language. The `!` is useful to execute short commands without leaving a Jupyter notebook."
    ]
   },
   {
@@ -671,7 +663,7 @@
     }
    },
    "source": [
-    "### The ABC Rule"
+    "### ABC Rule"
    ]
   },
   {
@@ -728,7 +720,7 @@
     }
    },
    "source": [
-    "### Phase Iteration - The $\\pi$ Rule"
+    "### Phase Iteration"
    ]
   },
   {
@@ -756,7 +748,7 @@
    "cell_type": "markdown",
    "metadata": {
     "slideshow": {
-     "slide_type": "slide"
+     "slide_type": "skip"
     }
    },
    "source": [
@@ -767,39 +759,38 @@
    "cell_type": "markdown",
    "metadata": {
     "slideshow": {
-     "slide_type": "slide"
+     "slide_type": "skip"
     }
    },
    "source": [
     "**Part 1: Expressing Logic**\n",
     "\n",
-    "- What is a programming language?\n",
-    "  1. Structure of a Program\n",
-    "- What kind of words exist?\n",
-    "  2. Variables, Expressions, Statements, & Comments\n",
-    "  3. Functions\n",
-    "- How can we form sentences from words?\n",
-    "  4. Conditionals\n",
-    "  5. Iteration"
+    "- What is a programming language? What kind of words exist?\n",
+    "  1. Elements of a Program\n",
+    "  2. Functions & Modularization\n",
+    "- What is the flow of execution? How can we form sentences from words?\n",
+    "  3. Boolean Logic & Conditionals\n",
+    "  4. Recursion & Looping"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {
     "slideshow": {
-     "slide_type": "slide"
+     "slide_type": "skip"
     }
    },
    "source": [
-    "**Part 2: Managing Data**\n",
+    "**Part 2: Managing Data and Memory**\n",
     "\n",
     "- How is data stored in memory?\n",
-    "  6. Numbers\n",
-    "  7. Text\n",
-    "  8. Sequences\n",
-    "  9. Mappings & Sets\n",
+    "  5. Numbers\n",
+    "  6. Text\n",
+    "  7. Sequences\n",
+    "  8. Mappings & Sets\n",
+    "  9. Arrays\n",
     "- How can we create our own data types?\n",
-    "  10. Classes & Instances"
+    "  10. Object-Orientation"
    ]
   },
   {
@@ -821,7 +812,7 @@
     }
    },
    "source": [
-    "As with every good lecture, there always has to be a [xkcd](https://xkcd.com/353/) comic somewhere."
+    "As with every good lecture, there has to be a [xkcd](https://xkcd.com/353/) comic somewhere."
    ]
   },
   {

From 3ee72c08ae8da37af688311f9aa8a13c6631a9b8 Mon Sep 17 00:00:00 2001
From: Alexander Hess <alexander@webartifex.biz>
Date: Thu, 19 Sep 2019 16:05:04 +0200
Subject: [PATCH 009/242] Add review and exercises for notebook 00

---
 00_start_up_review_and_exercises.ipynb | 290 +++++++++++++++++++++++++
 1 file changed, 290 insertions(+)
 create mode 100644 00_start_up_review_and_exercises.ipynb

diff --git a/00_start_up_review_and_exercises.ipynb b/00_start_up_review_and_exercises.ipynb
new file mode 100644
index 0000000..26eb93c
--- /dev/null
+++ b/00_start_up_review_and_exercises.ipynb
@@ -0,0 +1,290 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Chapter 0: Start up"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Content Review"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Read chapter 0 of the book. Then work through the ten review questions."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Essay Questions "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Answer the following questions briefly with *at most* 300 characters per question!"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q1**: Describe the difference between the terms *programming* and *computer science*!"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q2**: Explain what is a *pull request* and elaborate how this concept fits to a *distributed* organization of work!"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q3**: In what sense are open-source communities democracies? How are they near-\"perfect\" [meritocracies](https://en.wikipedia.org/wiki/Meritocracy)? How is open-source software development similar to academia?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q4**: What is a major advantage of a \"slow\" programming language like Python over a faster one like C?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### True / False Questions"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Motivate your answer with *one short* sentence!"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q5**: Python has been the fastest growing *major* programming language in recent years."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q6**: Python is named after a snake to emphasize its agility and fast development speed right in its name."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q7**: Python was originally designed for highly intensive numerical computing, in particular for use cases from physics and astronomy."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q8**: JavaScript is a special subset of the Java language."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q9**: Python is *free software*. That means it will never cost anything."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q10**: The main purpose of PEPs is to regulate how code should be documented and/or styled."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## \"Coding\" Exercises"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Mastering Markdown"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Briefly review GitHub's guide on [Mastering Markdown](https://guides.github.com/features/mastering-markdown/) and create nicely formatted \"text\" cells below!"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q11**: Check the latest [Bundesliga standings](https://www.bundesliga.com/en/bundesliga/table) and provide a table of the top three teams with the following four columns: rank, team name, games played, and points scored. Render the rank in **bold**, make the team name a clickable link (to the team's website), and put both the games played and points scored in *italics*. The header row should be visually different from the three rows with the teams' information."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q12**: The quote \"Education is what remains after one has forgotten what one has learned in school\" is attributed to Albert Einstein. Use a special Markdown syntax to display the author and his quote appropriately!"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q13**: Integrate this image (https://i.ytimg.com/vi/-BoSRlzy9c4/maxresdefault.jpg) of the delicious dessert **milk rice** into this notebook."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " "
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.3"
+  },
+  "toc": {
+   "base_numbering": 1,
+   "nav_menu": {},
+   "number_sections": false,
+   "sideBar": true,
+   "skip_h1_title": true,
+   "title_cell": "Table of Contents",
+   "title_sidebar": "Contents",
+   "toc_cell": false,
+   "toc_position": {},
+   "toc_section_display": false,
+   "toc_window_display": false
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

From d90d90ecbb09e3cda9bb875e5227f6a4fe19d7ff Mon Sep 17 00:00:00 2001
From: Alexander Hess <alexander@webartifex.biz>
Date: Thu, 19 Sep 2019 16:05:16 +0200
Subject: [PATCH 010/242] Add review and exercises for notebook 01

---
 ...ts_of_a_program_review_and_exercises.ipynb | 386 ++++++++++++++++++
 1 file changed, 386 insertions(+)
 create mode 100644 01_elements_of_a_program_review_and_exercises.ipynb

diff --git a/01_elements_of_a_program_review_and_exercises.ipynb b/01_elements_of_a_program_review_and_exercises.ipynb
new file mode 100644
index 0000000..088001b
--- /dev/null
+++ b/01_elements_of_a_program_review_and_exercises.ipynb
@@ -0,0 +1,386 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n",
+    "# Chapter 1: Elements of a Program"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Content Review"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Read chapter 1 of the book. Then work through the ten review questions."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Essay Questions "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Answer the following questions briefly with *at most* 300 characters per question!"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q1**: Elaborate on how **modulo division** might be a very useful operation to know!"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q2**: What is a **dynamically typed** language? How does it differ from a **statically typed** language? What does that mean for Python?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q3**: Why is it useful to start counting at $0$?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q4**: What is **operator overloading**?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q5**: What are the basic **naming conventions** for variables? What happens if a name collides with one of Python's [keywords](https://docs.python.org/3/reference/lexical_analysis.html#keywords)?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q6**: Advocates of the [functional programming](https://en.wikipedia.org/wiki/Functional_programming) paradigm suggest not to use **mutable** data types in a program. What are the advantages of that approach? What might be a downside?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### True / False Questions"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Motivate your answer with *one short* sentence!"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q7**: \"**dunder**\" refers to a group of Australian (\"down under\") geeks that work on core Python."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q8**: The **Zen of Python** has a high opinion on Indian genius programmers."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q9**: When NASA famously converted some measurements to the wrong unit and lost a Mars satellite in 1999 ([source](https://www.wired.com/2010/11/1110mars-climate-observer-report/)), this is an example of a so-called **runtime error**."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q10**: [PEP 8](https://www.python.org/dev/peps/pep-0008/) suggests that developers use **$8$ spaces** per level of indentation."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Coding Exercises"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Printing Output"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q11.1**: Read about the [print()](https://docs.python.org/3/library/functions.html#print) built-in. How can you use it to print both `greeting` and `audience` *without* concatenating the two strings with the `+` operator?\n",
+    "\n",
+    "Hint: The `*objects` in the documentation implies that we can insert several comma-seperated variables."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "greeting = \"Hello\"\n",
+    "audience = \"World\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "print(...)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q11.2**: What does the `sep=\" \"` mean in the documentation? Use it to print out the three names in `first`, `second`, and `third` on one line seperated by commas with one [print()](https://docs.python.org/3/library/functions.html#print) statement."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "first = \"Anthony\"\n",
+    "second = \"Berta\"\n",
+    "third = \"Christian\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "print(...)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q11.3**: Lastly, what does the `end=\"\\n\"` mean in the documentation? Use it in the `for`-loop to print the numbers $1$ through $10$ in just one line."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "for number in [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]:\n",
+    "    print(...)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Fizz Buzz"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The kids game [Fizz Buzz](https://en.wikipedia.org/wiki/Fizz_buzz) is said to be often used in job interviews for entry level positions. However, opinions vary as to how good of a test it actually is ([source](https://news.ycombinator.com/item?id=16446774)).\n",
+    "\n",
+    "In its simplest form, a group of people start counting upwards in an alternating fashion. Whenever a number is divisible by $3$, the person must say \"Fizz\" instead of the number. The same holds for numbers divisible by $5$ when the person must say \"Buzz\". If a number is divisible by both numbers, one must say \"FizzBuzz\". Probably, this game would also make a good drinking game with the \"right\" beverages.\n",
+    "\n",
+    "With just Chapter 1, we actually do not yet know all of Python's language constructs we need to write an implementation of Fizz Buzz in a Pythonic way. Yet, we will tweak what we know a bit and make it work."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q11.1**: First, create a list `numbers` with the numbers from $1$ through $100$. You could type all numbers manually but there is of course a smarter way. The built-in [range()](https://docs.python.org/3/library/functions.html#func-range) may be useful here. Read how it works in the documentation. To make the output of [range()](https://docs.python.org/3/library/functions.html#func-range) a `list` object, you have to \"wrap\" it with the [list()](https://docs.python.org/3/library/functions.html#func-list) built-in (i.e., `list(range(...))`)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "numbers = ..."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q11.2**: Loop over the `numbers` list and replace numbers for which one of the two (or both) conditions apply with text strings `\"Fizz\"`, `\"Buzz\"`, or `\"FizzBuzz\"` using the indexing operator `[...]` and the assignment statement `=`. In the chapter we saw that Python starts indexing with `0` as the first element. So in each iteration of the `for`-loop you have to determine the *index* as well as checking the actual `number`.\n",
+    "\n",
+    "Also note that for numbers divisible by both $3$ and $5$ we need some sort of a \"third\" condition check: As we only know about the `if` statement so far (and have not heard about `elif` and `else` from Chapter 3), there will be three `if` statements in total within the loop. And the order of them matters!\n",
+    "\n",
+    "Hint: Is there a single condition that checks for both $3$ and $5$?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "for number in numbers:\n",
+    "    ..."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q11.3**: Create a loop that prints out either the number or any of the Fizz Buzz substitutes. Do it in such a way that we do not end up with $100$ lines of output here."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "for number in numbers:\n",
+    "    ..."
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.3"
+  },
+  "toc": {
+   "base_numbering": 1,
+   "nav_menu": {},
+   "number_sections": false,
+   "sideBar": true,
+   "skip_h1_title": true,
+   "title_cell": "Table of Contents",
+   "title_sidebar": "Contents",
+   "toc_cell": false,
+   "toc_position": {},
+   "toc_section_display": false,
+   "toc_window_display": false
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

From b41c5b53026678b68d8cebb719f2946bfe519c84 Mon Sep 17 00:00:00 2001
From: Alexander Hess <alexander@webartifex.biz>
Date: Thu, 19 Sep 2019 16:06:21 +0200
Subject: [PATCH 011/242] Adjust content overview in README.md

---
 README.md | 1 +
 1 file changed, 1 insertion(+)

diff --git a/README.md b/README.md
index 4fd2b52..78bdff9 100644
--- a/README.md
+++ b/README.md
@@ -13,6 +13,7 @@ science professionals and researchers.
 As such they can be viewed in a plain web browser:
 
 - [00 - Start up](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/00_start_up.ipynb)
+- [01 - Elements of a Program](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/01_elements_of_a_program.ipynb)
 
 However, it is recommended that students **install Python and Jupyter
 locally** and run the code in the notebooks on their own.

From f6068383ba28bec764345046ae113e8a1e3f8f22 Mon Sep 17 00:00:00 2001
From: Alexander Hess <alexander@webartifex.biz>
Date: Sun, 22 Sep 2019 20:19:28 +0200
Subject: [PATCH 012/242] Add numpy to the dependencies

The numpy library is briefly introduced in notebook 02
---
 poetry.lock      | 11 ++++++++++-
 pyproject.toml   |  1 +
 requirements.txt |  1 +
 3 files changed, 12 insertions(+), 1 deletion(-)

diff --git a/poetry.lock b/poetry.lock
index 75af38f..ad1610a 100644
--- a/poetry.lock
+++ b/poetry.lock
@@ -423,6 +423,14 @@ terminado = ">=0.8.1"
 tornado = ">=5.0"
 traitlets = ">=4.2.1"
 
+[[package]]
+category = "main"
+description = "NumPy is the fundamental package for array computing with Python."
+name = "numpy"
+optional = false
+python-versions = ">=3.5"
+version = "1.17.2"
+
 [[package]]
 category = "main"
 description = "Utilities for writing pandoc filters in python"
@@ -673,7 +681,7 @@ version = "3.5.1"
 notebook = ">=4.4.1"
 
 [metadata]
-content-hash = "431dace6d8d5c3e390b59e964116bb6219fb45c7a8c09cbfe99d70c704a54320"
+content-hash = "7c3d541c65a27324b49fc7ed2b84067e223eb4e20c20310ff9e485b016f65f91"
 python-versions = "^3.6"
 
 [metadata.hashes]
@@ -711,6 +719,7 @@ mistune = ["59a3429db53c50b5c6bcc8a07f8848cb00d7dc8bdb431a4ab41920d201d4756e", "
 nbconvert = ["427a468ec26e7d68a529b95f578d5cbf018cb4c1f889e897681c2b6d11897695", "48d3c342057a2cf21e8df820d49ff27ab9f25fc72b8f15606bd47967333b2709"]
 nbformat = ["b9a0dbdbd45bb034f4f8893cafd6f652ea08c8c1674ba83f2dc55d3955743b0b", "f7494ef0df60766b7cabe0a3651556345a963b74dbc16bc7c18479041170d402"]
 notebook = ["660976fe4fe45c7aa55e04bf4bccb9f9566749ff637e9020af3422f9921f9a5d", "b0a290f5cc7792d50a21bec62b3c221dd820bf00efa916ce9aeec4b5354bde20"]
+numpy = ["05dbfe72684cc14b92568de1bc1f41e5f62b00f714afc9adee42f6311738091f", "0d82cb7271a577529d07bbb05cb58675f2deb09772175fab96dc8de025d8ac05", "10132aa1fef99adc85a905d82e8497a580f83739837d7cbd234649f2e9b9dc58", "12322df2e21f033a60c80319c25011194cd2a21294cc66fee0908aeae2c27832", "16f19b3aa775dddc9814e02a46b8e6ae6a54ed8cf143962b4e53f0471dbd7b16", "3d0b0989dd2d066db006158de7220802899a1e5c8cf622abe2d0bd158fd01c2c", "438a3f0e7b681642898fd7993d38e2bf140a2d1eafaf3e89bb626db7f50db355", "5fd214f482ab53f2cea57414c5fb3e58895b17df6e6f5bca5be6a0bb6aea23bb", "73615d3edc84dd7c4aeb212fa3748fb83217e00d201875a47327f55363cef2df", "7bd355ad7496f4ce1d235e9814ec81ee3d28308d591c067ce92e49f745ba2c2f", "7d077f2976b8f3de08a0dcf5d72083f4af5411e8fddacd662aae27baa2601196", "a4092682778dc48093e8bda8d26ee8360153e2047826f95a3f5eae09f0ae3abf", "b458de8624c9f6034af492372eb2fee41a8e605f03f4732f43fc099e227858b2", "e70fc8ff03a961f13363c2c95ef8285e0cf6a720f8271836f852cc0fa64e97c8", "ee8e9d7cad5fe6dde50ede0d2e978d81eafeaa6233fb0b8719f60214cf226578", "f4a4f6aba148858a5a5d546a99280f71f5ee6ec8182a7d195af1a914195b21a2"]
 pandocfilters = ["b3dd70e169bb5449e6bc6ff96aea89c5eea8c5f6ab5e207fc2f521a2cf4a0da9"]
 parso = ["63854233e1fadb5da97f2744b6b24346d2750b85965e7e399bec1620232797dc", "666b0ee4a7a1220f65d367617f2cd3ffddff3e205f3f16a0284df30e774c2a9c"]
 pexpect = ["2094eefdfcf37a1fdbfb9aa090862c1a4878e5c7e0e7e7088bdb511c558e5cd1", "9e2c1fd0e6ee3a49b28f95d4b33bc389c89b20af6a1255906e90ff1262ce62eb"]
diff --git a/pyproject.toml b/pyproject.toml
index 1cd387e..1e69fc5 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -8,6 +8,7 @@ license = "MIT"
 [tool.poetry.dependencies]
 python = "^3.6"
 jupyter = "^1.0"
+numpy = "^1.17"
 
 [tool.poetry.dev-dependencies]
 black = {version = "^18.3-alpha.0", allows-prereleases = true}
diff --git a/requirements.txt b/requirements.txt
index 41753e1..28405c8 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -30,6 +30,7 @@ mistune==0.8.4
 nbconvert==5.6.0
 nbformat==4.4.0
 notebook==6.0.1
+numpy==1.17.2
 pandocfilters==1.4.2
 parso==0.5.1
 pexpect==4.7.0

From 22a85bc2ba80d5818b66a4ce4163c7a03067b1ac Mon Sep 17 00:00:00 2001
From: Alexander Hess <alexander@webartifex.biz>
Date: Sun, 22 Sep 2019 20:24:15 +0200
Subject: [PATCH 013/242] Streamline previous content

---
 00_start_up.ipynb                             |  49 +-
 00_start_up_review_and_exercises.ipynb        |   2 +-
 01_elements_of_a_program.ipynb                | 595 ++++++++++--------
 ...ts_of_a_program_review_and_exercises.ipynb |  10 +-
 4 files changed, 350 insertions(+), 306 deletions(-)

diff --git a/00_start_up.ipynb b/00_start_up.ipynb
index 7551dcd..506c6ad 100644
--- a/00_start_up.ipynb
+++ b/00_start_up.ipynb
@@ -19,7 +19,7 @@
     }
    },
    "source": [
-    "This course is set up to be a *thorough* introduction to programming in [Python](https://www.python.org/).\n",
+    "This book is set up to be a *thorough* introduction to programming in [Python](https://www.python.org/).\n",
     "\n",
     "It teaches the concepts behind and the syntax of the core Python language as defined by the [Python Software Foundation](https://www.python.org/psf/) in the official [language reference](https://docs.python.org/3/reference/index.html) and introduces additions to the language as distributed with the [standard library](https://docs.python.org/3/library/index.html) that come with every installation.\n",
     "\n",
@@ -58,10 +58,10 @@
    "source": [
     "To be suitable for *total beginners*, there are *no* formal prerequisites. It is only expected that the student has:\n",
     "\n",
-    "- a *solid* understanding of the **English language** (i.e., usage of *technical* terms with *narrow* and *distinct* meanings),\n",
-    "- knowledge of **basic mathematics** from high school (i.e., addition, subtraction, multiplication, division, and a little bit of calculus and statistics),\n",
-    "- the ability to **think conceptually** and **reason logically** (i.e., *not* just memorizing), and\n",
-    "- the willingness to **invest 2-4 hours a day for a month** (cf., \"ABC\"-rule at the end)"
+    "- a *solid* understanding of the **English language**,\n",
+    "- knowledge of **basic mathematics** from high school,\n",
+    "- the ability to **think conceptually** and **reason logically**, and\n",
+    "- the willingness to **invest 2-4 hours a day for a month**."
    ]
   },
   {
@@ -83,7 +83,7 @@
     }
    },
    "source": [
-    "The course's **main goal** is to **prepare** the student **for further studies** in the \"field\" of **data science**."
+    "The **main goal** of this introduction is to **prepare** the student **for further studies** in the \"field\" of **data science**."
    ]
   },
   {
@@ -94,7 +94,7 @@
     }
    },
    "source": [
-    "This includes but is not limited to more advanced courses on topics such as:\n",
+    "This includes but is not limited to topics such as:\n",
     "- linear algebra\n",
     "- statistics & econometrics\n",
     "- data cleaning & wrangling\n",
@@ -154,7 +154,7 @@
     }
    },
    "source": [
-    "To follow this course, a working installation of **Python 3.6** or higher is needed.\n",
+    "To follow this book, a working installation of **Python 3.6** or higher is needed.\n",
     "\n",
     "A popular and beginner friendly way is to install the [Anaconda Distribution](https://www.anaconda.com/distribution/) that not only ships Python and the standard library but comes pre-packaged with a lot of third-party libraries from the so-called \"scientific stack\". Just go to the [download](https://www.anaconda.com/download/) page and install the latest version (i.e., *2019-07* with Python 3.7 at the time of this writing) for your operating system.\n",
     "\n",
@@ -180,7 +180,7 @@
     }
    },
    "source": [
-    "To download the course's materials as a ZIP file, open the accompanying [GitHub repository](https://github.com/webartifex/intro-to-python) in a web browser and click on the green \"Clone or download\" button on the right. Then, unpack the ZIP file into a folder of your choosing (ideally somewhere within your personal user folder so that the files show up right away)."
+    "To download the materials accompanying this book as a ZIP file, open this [GitHub repository](https://github.com/webartifex/intro-to-python) in a web browser and click on the green \"Clone or download\" button on the right. Then, unpack the ZIP file into a folder of your choosing (ideally somewhere within your personal user folder so that the files show up right away)."
    ]
   },
   {
@@ -252,7 +252,7 @@
     "\n",
     "Furthermore, Jupyter notebooks have become a de-facto standard for communicating and exchanging results in the data science community (both in academia and business) and often provide a more intuitive alternative to terminal based ways of running Python (e.g., the default [Python interpreter](https://docs.python.org/3/tutorial/interpreter.html) as shown above or a more advanced interactive version like [IPython](https://ipython.org/)) or even a full-fledged [Integrated Development Environment](https://en.wikipedia.org/wiki/Integrated_development_environment) (e.g., the commercial [PyCharm](https://www.jetbrains.com/pycharm/) or the free [Spyder](https://github.com/spyder-ide/spyder)).\n",
     "\n",
-    "In particular, they allow to mix plain English text with Python code cells. The plain text can be formatted using the [Markdown](https://guides.github.com/features/mastering-markdown/) language and mathematical expressions can be typeset with [LaTeX](https://www.overleaf.com/learn/latex/Free_online_introduction_to_LaTeX_%28part_1%29). Lastly, we can include pictures, plots, and even videos. Because of these features, the notebooks developed for this course come in a self-contained \"tutorial\" style that enables students to learn and review the material on their own."
+    "In particular, they allow to mix plain English text with Python code cells. The plain text can be formatted using the [Markdown](https://guides.github.com/features/mastering-markdown/) language and mathematical expressions can be typeset with [LaTeX](https://www.overleaf.com/learn/latex/Free_online_introduction_to_LaTeX_%28part_1%29). Lastly, we can include pictures, plots, and even videos. Because of these features, the notebooks developed for this book come in a self-contained \"tutorial\" style that enables students to learn and review the material on their own."
    ]
   },
   {
@@ -295,7 +295,15 @@
      "slide_type": "slide"
     }
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Hello world\n"
+     ]
+    }
+   ],
    "source": [
     "print(\"Hello world\")"
    ]
@@ -351,7 +359,8 @@
     }
    },
    "source": [
-    "For this course *programming* is \"defined\" as\n",
+    "In this book *programming* is \"defined\" as:\n",
+    "\n",
     "- a **structured** way of **problem solving**\n",
     "- by **expressing** the steps of a **computation / process**\n",
     "- and thereby **documenting** the process in a formal way\n",
@@ -369,7 +378,8 @@
     }
    },
    "source": [
-    "That is different from *computer science*, which is\n",
+    "That is different from *computer science*, which is:\n",
+    "\n",
     "- a field of study comparable to (applied) **mathematics** that\n",
     "- asks **abstract** questions (\"Is something computable at all?\"),\n",
     "- develops and analyses **algorithms** and **data structures**,\n",
@@ -419,6 +429,8 @@
     }
    },
    "source": [
+    "Here is a brief history of and some background on Python:\n",
+    "\n",
     "- [Guido van Rossum](https://en.wikipedia.org/wiki/Guido_van_Rossum) (Python’s **[Benevolent Dictator for Life](https://en.wikipedia.org/wiki/Benevolent_dictator_for_life)**) was bored during a week around Christmas 1989 and started Python as a hobby project \"that would keep \\[him\\] occupied\" for some days\n",
     "- the idea was to create a **general-purpose scripting** language that would allow fast **prototyping** and would **run on every operating system**\n",
     "- Python grew through the 90s as van Rossum promoted it via his \"Computer Programming for Everybody\" initiative that had the **goal to encourage a basic level of coding literacy** as an equal knowledge alongside English literacy and math skills\n",
@@ -427,17 +439,6 @@
     "- the language is named after the sketch comedy group [Monty Python](https://en.wikipedia.org/wiki/Monty_Python)"
    ]
   },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "source": [
-    "#### Summary"
-   ]
-  },
   {
    "cell_type": "markdown",
    "metadata": {
diff --git a/00_start_up_review_and_exercises.ipynb b/00_start_up_review_and_exercises.ipynb
index 26eb93c..e1a435f 100644
--- a/00_start_up_review_and_exercises.ipynb
+++ b/00_start_up_review_and_exercises.ipynb
@@ -18,7 +18,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Read chapter 0 of the book. Then work through the ten review questions."
+    "Read Chapter 0 of the book. Then work through the ten review questions."
    ]
   },
   {
diff --git a/01_elements_of_a_program.ipynb b/01_elements_of_a_program.ipynb
index 66c8e64..c771c87 100644
--- a/01_elements_of_a_program.ipynb
+++ b/01_elements_of_a_program.ipynb
@@ -120,7 +120,7 @@
     "\n",
     "Lastly, the `average` is calculated as the ratio of the final **values** of `total` and `count`. Overall, we divide the sum of all even numbers by the count of all even numbers, which is exactly what we are looking for.\n",
     "\n",
-    "We also observe how the lines of code \"within\" the `for` and `if` **statements** are *indented* and *aligned* with multiples of four spaces: This shows immediately how the lines relate to each other."
+    "We also observe how the lines of code \"within\" the `for` and `if` **statements** are *indented* and *aligned* with multiples of **four spaces**: This shows immediately how the lines relate to each other."
    ]
   },
   {
@@ -152,7 +152,7 @@
     }
    },
    "source": [
-    "Yet, we do not see any **output** and obtain the value of `average` by simply referencing it again."
+    "We do not see any **output** yet but can obtain the value of `average` by simply referencing it again."
    ]
   },
   {
@@ -200,9 +200,7 @@
    "source": [
     "Note how only two of the previous four code cells generate an **output** while two remained \"silent\" (i.e., there is no \"**Out[...]**\" after running the cell).\n",
     "\n",
-    "By default, Jupyter notebooks show the value of a cell's last so-called **expression**. This output can be suppressed by ending the last line with a semicolon.\n",
-    "\n",
-    "To visualize something before the end of the cell, we can use the [print()](https://docs.python.org/3/library/functions.html#print) built-in function."
+    "By default, Jupyter notebooks show the value of a cell's last so-called **expression**. This output can be suppressed by ending the last line with a semicolon `;`."
    ]
   },
   {
@@ -243,6 +241,17 @@
     "\"I am invisible!\";"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "To visualize something before the end of the cell, we can use the [print()](https://docs.python.org/3/library/functions.html#print) built-in **function**."
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 7,
@@ -266,6 +275,39 @@
     "print(\"I am feeling great :-)\")"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Outside Jupyter notebooks, the semicolon `;` is actually used as a **seperator** between several statements that would otherwise have to be on a line on their own. However, it as *not* considered good practice to use it as it makes code less readable."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Hello, World!\n",
+      "I am feeling great :-)\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"Hello, World!\"); print(\"I am feeling great :-)\")"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {
@@ -285,7 +327,7 @@
     }
    },
    "source": [
-    "Python comes with basic mathematical operators built in. **Operators** are built-in **tokens** that have a special meaning to the Python interpreter. Most operators either \"operate\" with the object immediately following them (= **unary** operators; e.g., negation) or somehow \"process\" the two objects \"around\" them (= **binary** operators; e.g., addition). But we will see some exceptions from that as well.\n",
+    "Python comes with basic mathematical operators built in. **[Operators](https://docs.python.org/3/reference/lexical_analysis.html#operators)** are built-in **tokens** that have a special meaning to the Python interpreter. Most operators either \"operate\" with the object immediately following them (= **unary** operators; e.g., negation) or somehow \"process\" the two objects \"around\" them (= **binary** operators; e.g., addition). But we will see some exceptions from that as well.\n",
     "\n",
     "By definition, operators have **no** permanent **side effects** in the computer's memory. Although the code cells in this section do indeed lead to *new* objects being created in memory, they are immediately \"forgotten\" as they are not stored in a **variable** (like `numbers` above). We will revisit this idea further below when we compare **expressions** with **statements**.\n",
     "\n",
@@ -294,7 +336,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 9,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -307,7 +349,7 @@
        "90"
       ]
      },
-     "execution_count": 8,
+     "execution_count": 9,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -318,7 +360,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 10,
    "metadata": {
     "slideshow": {
      "slide_type": "-"
@@ -331,7 +373,7 @@
        "8"
       ]
      },
-     "execution_count": 9,
+     "execution_count": 10,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -353,7 +395,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 11,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -366,7 +408,7 @@
        "-1"
       ]
      },
-     "execution_count": 10,
+     "execution_count": 11,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -388,7 +430,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 12,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -401,7 +443,7 @@
        "42"
       ]
      },
-     "execution_count": 11,
+     "execution_count": 12,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -412,7 +454,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 13,
    "metadata": {
     "slideshow": {
      "slide_type": "-"
@@ -425,7 +467,7 @@
        "42.0"
       ]
      },
-     "execution_count": 12,
+     "execution_count": 13,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -447,7 +489,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 14,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -460,7 +502,7 @@
        "42"
       ]
      },
-     "execution_count": 13,
+     "execution_count": 14,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -471,7 +513,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 15,
    "metadata": {
     "slideshow": {
      "slide_type": "-"
@@ -484,7 +526,7 @@
        "42"
       ]
      },
-     "execution_count": 14,
+     "execution_count": 15,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -506,7 +548,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 16,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -519,7 +561,7 @@
        "1"
       ]
      },
-     "execution_count": 15,
+     "execution_count": 16,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -541,7 +583,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 17,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -554,7 +596,7 @@
        "0"
       ]
      },
-     "execution_count": 16,
+     "execution_count": 17,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -576,7 +618,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 18,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -589,7 +631,7 @@
        "3"
       ]
      },
-     "execution_count": 17,
+     "execution_count": 18,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -600,7 +642,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 19,
    "metadata": {
     "slideshow": {
      "slide_type": "-"
@@ -613,7 +655,7 @@
        "23"
       ]
      },
-     "execution_count": 18,
+     "execution_count": 19,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -630,12 +672,12 @@
     }
    },
    "source": [
-    "The [divmod()](https://docs.python.org/3/library/functions.html#divmod) built-in **function** combines the integer and modulo divisions into one operation. However, this is not an operator any more (but a function). Also observe that [divmod()](https://docs.python.org/3/library/functions.html#divmod) returns a \"pair\" of integers."
+    "The [divmod()](https://docs.python.org/3/library/functions.html#divmod) built-in function combines the integer and modulo divisions into one operation. However, this is not an operator any more (but a function). Also observe that [divmod()](https://docs.python.org/3/library/functions.html#divmod) returns a \"pair\" of integers."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 20,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -648,7 +690,7 @@
        "(4, 2)"
       ]
      },
-     "execution_count": 19,
+     "execution_count": 20,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -670,7 +712,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 21,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -683,7 +725,7 @@
        "8"
       ]
      },
-     "execution_count": 20,
+     "execution_count": 21,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -705,7 +747,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 22,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -718,7 +760,7 @@
        "18"
       ]
      },
-     "execution_count": 21,
+     "execution_count": 22,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -740,7 +782,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 23,
    "metadata": {
     "slideshow": {
      "slide_type": "-"
@@ -753,7 +795,7 @@
        "18"
       ]
      },
-     "execution_count": 22,
+     "execution_count": 23,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -764,7 +806,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 24,
    "metadata": {
     "slideshow": {
      "slide_type": "-"
@@ -777,7 +819,7 @@
        "81"
       ]
      },
-     "execution_count": 23,
+     "execution_count": 24,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -799,7 +841,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 25,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -812,7 +854,7 @@
        "18"
       ]
      },
-     "execution_count": 24,
+     "execution_count": 25,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -840,7 +882,7 @@
     }
    },
    "source": [
-    "## Objects vs. Values vs. Types"
+    "## Objects vs. Types vs. Values"
    ]
   },
   {
@@ -860,7 +902,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 26,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -897,7 +939,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": 27,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -907,31 +949,7 @@
     {
      "data": {
       "text/plain": [
-       "140382247181072"
-      ]
-     },
-     "execution_count": 26,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "id(a)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 27,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "-"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "140382247352144"
+       "139867825605456"
       ]
      },
      "execution_count": 27,
@@ -940,7 +958,7 @@
     }
    ],
    "source": [
-    "id(b)"
+    "id(a)"
    ]
   },
   {
@@ -955,7 +973,7 @@
     {
      "data": {
       "text/plain": [
-       "140382247028656"
+       "139867825776440"
       ]
      },
      "execution_count": 28,
@@ -963,6 +981,30 @@
      "output_type": "execute_result"
     }
    ],
+   "source": [
+    "id(b)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "139867825460912"
+      ]
+     },
+     "execution_count": 29,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "id(c)"
    ]
@@ -975,12 +1017,12 @@
     }
    },
    "source": [
-    "These addresses are really not that meaningful for anything other than checking if two variables actually point at the same object. This may be helpful as different objects can of course have the same value."
+    "These addresses are really not that meaningful for anything other than checking if two variables actually **point** at the same object. This may be helpful as different objects can of course have the same value."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
+   "execution_count": 30,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -1004,7 +1046,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 30,
+   "execution_count": 31,
    "metadata": {
     "slideshow": {
      "slide_type": "-"
@@ -1017,7 +1059,7 @@
        "True"
       ]
      },
-     "execution_count": 30,
+     "execution_count": 31,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1039,7 +1081,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 31,
+   "execution_count": 32,
    "metadata": {
     "slideshow": {
      "slide_type": "-"
@@ -1052,7 +1094,7 @@
        "False"
       ]
      },
-     "execution_count": 31,
+     "execution_count": 32,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1080,12 +1122,12 @@
     }
    },
    "source": [
-    "The [type()](https://docs.python.org/3/library/functions.html#type) built-in function shows an object's type. For example, `a` is an integer (`int`) while `b` is a so-called [floating-point number](https://en.wikipedia.org/wiki/Floating-point_arithmetic) (`float`)."
+    "The [type()](https://docs.python.org/3/library/functions.html#type) built-in function shows an object's type. For example, `a` is an integer (i.e., `int`) while `b` is a so-called [floating-point number](https://en.wikipedia.org/wiki/Floating-point_arithmetic) (i.e., `float`)."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 32,
+   "execution_count": 33,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -1098,7 +1140,7 @@
        "int"
       ]
      },
-     "execution_count": 32,
+     "execution_count": 33,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1109,7 +1151,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 33,
+   "execution_count": 34,
    "metadata": {
     "slideshow": {
      "slide_type": "-"
@@ -1122,7 +1164,7 @@
        "float"
       ]
      },
-     "execution_count": 33,
+     "execution_count": 34,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1148,7 +1190,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 34,
+   "execution_count": 35,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -1161,7 +1203,7 @@
        "True"
       ]
      },
-     "execution_count": 34,
+     "execution_count": 35,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1183,7 +1225,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 35,
+   "execution_count": 36,
    "metadata": {
     "slideshow": {
      "slide_type": "-"
@@ -1197,7 +1239,7 @@
      "traceback": [
       "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
       "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-35-7db0a38aefcc>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0ma\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mis_integer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;32m<ipython-input-36-7db0a38aefcc>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0ma\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mis_integer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
       "\u001b[0;31mAttributeError\u001b[0m: 'int' object has no attribute 'is_integer'"
      ]
     }
@@ -1214,12 +1256,12 @@
     }
    },
    "source": [
-    "The `c` object is a so-called **string** type `str`, which we can view as Python's way of representing \"text\". Strings also come with their own behaviors, for example, to convert a text to lower or upper case."
+    "The `c` object is a so-called **string** type (i.e., `str`), which we can view as Python's way of representing \"text\". Strings also come with their own behaviors, for example, to convert a text to lower or upper case."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 36,
+   "execution_count": 37,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -1232,7 +1274,7 @@
        "str"
       ]
      },
-     "execution_count": 36,
+     "execution_count": 37,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1243,7 +1285,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 37,
+   "execution_count": 38,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -1256,7 +1298,7 @@
        "'python rocks'"
       ]
      },
-     "execution_count": 37,
+     "execution_count": 38,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1267,7 +1309,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 38,
+   "execution_count": 39,
    "metadata": {
     "slideshow": {
      "slide_type": "-"
@@ -1280,7 +1322,7 @@
        "'PYTHON ROCKS'"
       ]
      },
-     "execution_count": 38,
+     "execution_count": 39,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1291,7 +1333,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 39,
+   "execution_count": 40,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -1304,7 +1346,7 @@
        "'Python Rocks'"
       ]
      },
-     "execution_count": 39,
+     "execution_count": 40,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1339,7 +1381,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 40,
+   "execution_count": 41,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -1352,7 +1394,7 @@
        "789"
       ]
      },
-     "execution_count": 40,
+     "execution_count": 41,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1363,7 +1405,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 41,
+   "execution_count": 42,
    "metadata": {
     "slideshow": {
      "slide_type": "-"
@@ -1376,7 +1418,7 @@
        "42.0"
       ]
      },
-     "execution_count": 41,
+     "execution_count": 42,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1393,12 +1435,12 @@
     }
    },
    "source": [
-    "In this book, we follow the convention of creating strings with **double quotes** `\"` instead of the **single quotes** `'` to which Python defaults in its literal output. Both types of quotes can be used interchangebly."
+    "In this book, we follow the convention of creating strings with **double quotes** `\"` instead of the **single quotes** `'` to which Python defaults in its literal output for `str` objects. Both types of quotes can be used interchangebly."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 42,
+   "execution_count": 43,
    "metadata": {
     "slideshow": {
      "slide_type": "-"
@@ -1411,13 +1453,13 @@
        "'Python rocks'"
       ]
      },
-     "execution_count": 42,
+     "execution_count": 43,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "c"
+    "c  # we defined c = \"Python rocks\" with double quotes \" above"
    ]
   },
   {
@@ -1463,14 +1505,14 @@
     }
    },
    "source": [
-    "If we do not follow the rules, the code cannot be **parsed** correctly, i.e., the program does not even start to run but raises a **syntax error** (indicated as `SyntaxError`). Computers are very dumb in the sense that the slightest syntax error leads to the machine not understanding our code.\n",
+    "If we do not follow the rules, the code cannot be **parsed** correctly, i.e., the program does not even start to run but **raises** a **syntax error** indicated as `SyntaxError` in the output. Computers are very dumb in the sense that the slightest syntax error leads to the machine not understanding our code.\n",
     "\n",
     "For example, if we wanted to write an accounting program that adds up currencies, we would have to model dollar prices as `float` objects as the dollar symbol cannot be read by Python."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 43,
+   "execution_count": 44,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -1479,10 +1521,10 @@
    "outputs": [
     {
      "ename": "SyntaxError",
-     "evalue": "invalid syntax (<ipython-input-43-cafa82e54b9c>, line 1)",
+     "evalue": "invalid syntax (<ipython-input-44-cafa82e54b9c>, line 1)",
      "output_type": "error",
      "traceback": [
-      "\u001b[0;36m  File \u001b[0;32m\"<ipython-input-43-cafa82e54b9c>\"\u001b[0;36m, line \u001b[0;32m1\u001b[0m\n\u001b[0;31m    3.99 $ + 10.40 $\u001b[0m\n\u001b[0m         ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m invalid syntax\n"
+      "\u001b[0;36m  File \u001b[0;32m\"<ipython-input-44-cafa82e54b9c>\"\u001b[0;36m, line \u001b[0;32m1\u001b[0m\n\u001b[0;31m    3.99 $ + 10.40 $\u001b[0m\n\u001b[0m         ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m invalid syntax\n"
      ]
     }
    ],
@@ -1503,7 +1545,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 44,
+   "execution_count": 45,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -1512,10 +1554,10 @@
    "outputs": [
     {
      "ename": "SyntaxError",
-     "evalue": "invalid syntax (<ipython-input-44-499e4d0d0cbb>, line 1)",
+     "evalue": "invalid syntax (<ipython-input-45-499e4d0d0cbb>, line 1)",
      "output_type": "error",
      "traceback": [
-      "\u001b[0;36m  File \u001b[0;32m\"<ipython-input-44-499e4d0d0cbb>\"\u001b[0;36m, line \u001b[0;32m1\u001b[0m\n\u001b[0;31m    for number in numbers\u001b[0m\n\u001b[0m                         ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m invalid syntax\n"
+      "\u001b[0;36m  File \u001b[0;32m\"<ipython-input-45-499e4d0d0cbb>\"\u001b[0;36m, line \u001b[0;32m1\u001b[0m\n\u001b[0;31m    for number in numbers\u001b[0m\n\u001b[0m                         ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m invalid syntax\n"
      ]
     }
    ],
@@ -1532,12 +1574,12 @@
     }
    },
    "source": [
-    "... and relies on whitespace / indentation unlike many other programming languages. A `IndentationError` is just a special type of a `SyntaxError`."
+    "... and relies on whitespace (i.e., indentation) unlike many other programming languages. An `IndentationError` is just a special type of a `SyntaxError`."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 45,
+   "execution_count": 46,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -1546,10 +1588,10 @@
    "outputs": [
     {
      "ename": "IndentationError",
-     "evalue": "expected an indented block (<ipython-input-45-19398c5f89de>, line 2)",
+     "evalue": "expected an indented block (<ipython-input-46-19398c5f89de>, line 2)",
      "output_type": "error",
      "traceback": [
-      "\u001b[0;36m  File \u001b[0;32m\"<ipython-input-45-19398c5f89de>\"\u001b[0;36m, line \u001b[0;32m2\u001b[0m\n\u001b[0;31m    print(number)\u001b[0m\n\u001b[0m        ^\u001b[0m\n\u001b[0;31mIndentationError\u001b[0m\u001b[0;31m:\u001b[0m expected an indented block\n"
+      "\u001b[0;36m  File \u001b[0;32m\"<ipython-input-46-19398c5f89de>\"\u001b[0;36m, line \u001b[0;32m2\u001b[0m\n\u001b[0;31m    print(number)\u001b[0m\n\u001b[0m        ^\u001b[0m\n\u001b[0;31mIndentationError\u001b[0m\u001b[0;31m:\u001b[0m expected an indented block\n"
      ]
     }
    ],
@@ -1579,14 +1621,14 @@
    "source": [
     "Syntax errors as above are easy to find as the code will not even run to begin with.\n",
     "\n",
-    "However, there are also so-called **runtime errors** (also called **exceptions**) that occur if the code would run given correct input.\n",
+    "However, there are also so-called **runtime errors**, often called **exceptions**, that occur whenever otherwise (i.e., syntactically) correct code does not run because of invalid input.\n",
     "\n",
     "This example does not work because just like in the \"real\" world, Python does not know how to divide by $0$. The syntactically correct code leads to a `ZeroDivisionError`."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 46,
+   "execution_count": 47,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -1600,7 +1642,7 @@
      "traceback": [
       "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
       "\u001b[0;31mZeroDivisionError\u001b[0m                         Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-46-bc757c3fda29>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0;36m1\u001b[0m \u001b[0;34m/\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;32m<ipython-input-47-bc757c3fda29>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0;36m1\u001b[0m \u001b[0;34m/\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
       "\u001b[0;31mZeroDivisionError\u001b[0m: division by zero"
      ]
     }
@@ -1628,14 +1670,14 @@
     }
    },
    "source": [
-    "So-called **semantic errors**, on the contrary, can be very hard to spot as they do *not* crash the program. The only way to find such errors is to run the program with test input for which we know the answer already and can then verify it. However, testing software is a whole discipline on its own and often very hard to do in practice.\n",
+    "So-called **semantic errors**, on the contrary, can be very hard to spot as they do *not* crash the program. The only way to find such errors is to run a program with test input for which we know the answer already and can thus check the output. However, testing software is a whole discipline on its own and often very hard to do in practice.\n",
     "\n",
-    "The cell below copies our introductory example from above with a \"tiny\" error."
+    "The cell below copies our introductory example from above with a \"tiny\" error. How fast could you have spotted it without the comment?"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 47,
+   "execution_count": 48,
    "metadata": {
     "code_folding": [],
     "slideshow": {
@@ -1657,7 +1699,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 48,
+   "execution_count": 49,
    "metadata": {
     "slideshow": {
      "slide_type": "-"
@@ -1670,7 +1712,7 @@
        "3.0"
       ]
      },
-     "execution_count": 48,
+     "execution_count": 49,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1687,7 +1729,7 @@
     }
    },
    "source": [
-    "Finding errors is is called **debugging**. For the history of the term, check this [link](https://en.wikipedia.org/wiki/Debugging)."
+    "Finding errors in a systematic way is called **debugging**. For the history of the term, see this [article](https://en.wikipedia.org/wiki/Debugging)."
    ]
   },
   {
@@ -1709,14 +1751,14 @@
     }
    },
    "source": [
-    "Adhering to just syntax rules is therefore *never* enough. Over time, **best practices** and common **style guides** were created to make it less likely for a developer to mess up a program and also to allow \"onboarding\" him as a contributor to an established code base (often called **legacy code**) faster. These rules are not enforced by Python itself: Badly styled and un-readable code will still run. At the very least, Python programs should be styled according to [PEP 8](https://www.python.org/dev/peps/pep-0008/) and documented \"inline\" (i.e., in the code itself) according to [PEP 257](https://www.python.org/dev/peps/pep-0257/).\n",
+    "Adhering to just syntax rules is therefore *never* enough. Over time, **best practices** and common **style guides** were created to make it less likely for a developer to mess up a program and also to allow \"onboarding\" him as a contributor to an established code base, often called **legacy code**, faster. These rules are not enforced by Python itself: Badly styled and un-readable code will still run. At the very least, Python programs should be styled according to [PEP 8](https://www.python.org/dev/peps/pep-0008/) and documented \"inline\" (i.e., in the code itself) according to [PEP 257](https://www.python.org/dev/peps/pep-0257/).\n",
     "\n",
     "An easier to read version of PEP 8 can be found [here](https://pep8.org/). The video below features a well known \"[Pythonista](https://en.wiktionary.org/wiki/Pythonista)\" talking about the importance of code style."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 49,
+   "execution_count": 50,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -1738,10 +1780,10 @@
        "        "
       ],
       "text/plain": [
-       "<IPython.lib.display.YouTubeVideo at 0x7fad49702518>"
+       "<IPython.lib.display.YouTubeVideo at 0x7f358405d550>"
       ]
      },
-     "execution_count": 49,
+     "execution_count": 50,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1759,12 +1801,12 @@
     }
    },
    "source": [
-    "For example, while the above code to calculate the average of the even numbers from 1 through 10 is correct, a Pythonista would re-write it in a more \"Pythonic\" way and use the [sum()](https://docs.python.org/3/library/functions.html#sum) and [len()](https://docs.python.org/3/library/functions.html#len) (= \"length\") built-in functions. Pythonic code runs faster in many cases and is less error prone."
+    "For example, while the above code to calculate the average of the even numbers from 1 through 10 is correct, a Pythonista would re-write it in a more \"Pythonic\" way and use the [sum()](https://docs.python.org/3/library/functions.html#sum) and [len()](https://docs.python.org/3/library/functions.html#len) (= \"length\") built-in functions (cf., Chapter 2) as well as a so-called **list comprehension** (cf., Chapter 7). Pythonic code runs faster in many cases and is less error prone."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 50,
+   "execution_count": 51,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -1777,7 +1819,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 51,
+   "execution_count": 52,
    "metadata": {
     "slideshow": {
      "slide_type": "-"
@@ -1785,12 +1827,12 @@
    },
    "outputs": [],
    "source": [
-    "evens = [n for n in numbers if n % 2 == 0]  # example for a so-called list comprehension"
+    "evens = [n for n in numbers if n % 2 == 0]  # example of a so-called list comprehension"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 52,
+   "execution_count": 53,
    "metadata": {
     "slideshow": {
      "slide_type": "-"
@@ -1803,7 +1845,7 @@
        "[2, 4, 6, 8, 10]"
       ]
      },
-     "execution_count": 52,
+     "execution_count": 53,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1814,7 +1856,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 53,
+   "execution_count": 54,
    "metadata": {
     "slideshow": {
      "slide_type": "-"
@@ -1827,7 +1869,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 54,
+   "execution_count": 55,
    "metadata": {
     "slideshow": {
      "slide_type": "-"
@@ -1840,7 +1882,7 @@
        "6.0"
       ]
      },
-     "execution_count": 54,
+     "execution_count": 55,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1862,7 +1904,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 55,
+   "execution_count": 56,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -1961,7 +2003,7 @@
     "\n",
     "At the same time, for a beginner's course it is often easier to just code in a linear fashion.\n",
     "\n",
-    "In real data science projects one would probably employ a mixed approach and put re-usable code into so-called Python modules (= \\*.py files) and then use Jupyter notebooks to built up a linear report or story line for a business argument to be made."
+    "In real data science projects one would probably employ a mixed approach and put re-usable code into so-called Python modules (i.e., *.py* files; cf., Chapter 2) and then use Jupyter notebooks to built up a linear report or story line for a business argument to be made."
    ]
   },
   {
@@ -1972,7 +2014,7 @@
     }
    },
    "source": [
-    "## Variables / Names"
+    "## Variables vs. Names vs. Identifiers"
    ]
   },
   {
@@ -1983,12 +2025,14 @@
     }
    },
    "source": [
-    "**Variables** are created with the **assignment statement** `=`. As its name suggests, it is *not* an operator, mainly because of its side effect of making a **name** \"point\" to an object in memory."
+    "**Variables** are created with the **[assignment statement](https://docs.python.org/3/reference/simple_stmts.html#assignment-statements)** `=`, which is *not* an operator, mainly because of its side effect of making a **[name](https://docs.python.org/3/reference/lexical_analysis.html#identifiers)** point to an object in memory.\n",
+    "\n",
+    "We will read the terms **variable**, **name**, and **identifier** used interchangebly in many Python related texts. In this book, we adopt the following convention: First, we treat *name* and *identifier* as perfect synonyms but only use the term *name* in the text for clarity. Second, whereas *name* only refers to a string of letters, numbers, and some other symbols, a *variable* refers to the combination of a *name* and a *pointer* to some object in memory."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 56,
+   "execution_count": 57,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -2008,14 +2052,12 @@
     }
    },
    "source": [
-    "When referenced, a variable just evaluates to the value of the object it points to. Colloquially, we could say that `a` evaluates to `20.0` here but this would not be an accurate description of what is really going on in memory.\n",
-    "\n",
-    "We will see some more colloquial jargons in this section but should always remind ourselves what we better said instead."
+    "When referenced, a variable evaluates to the value of the object it points to. Colloquially, we could say that `a` evaluates to `20.0` here but this would not be a full description of what is really going on in memory. We will see some more colloquial jargons in this section but should always relate this to what Python actually does in memory."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 57,
+   "execution_count": 58,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -2028,7 +2070,7 @@
        "20.0"
       ]
      },
-     "execution_count": 57,
+     "execution_count": 58,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2050,7 +2092,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 58,
+   "execution_count": 59,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -2058,12 +2100,12 @@
    },
    "outputs": [],
    "source": [
-    "a = 20"
+    "a = 20  # this makes a point to an object of a different type"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 59,
+   "execution_count": 60,
    "metadata": {
     "slideshow": {
      "slide_type": "-"
@@ -2076,7 +2118,7 @@
        "20"
       ]
      },
-     "execution_count": 59,
+     "execution_count": 60,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2093,12 +2135,12 @@
     }
    },
    "source": [
-    "If we want to re-assign a variable while referencing its \"old\" (i.e., current) object, we can also **update** it using a so-called **augmented assignment statement** (*not* operator). This implicitly inserts the current \"value\" as the first token on the right-hand side."
+    "If we want to re-assign a variable while referencing its \"old\" (i.e., current) object, we can also **update** it using a so-called **[augmented assignment statement](https://docs.python.org/3/reference/simple_stmts.html#augmented-assignment-statements)** (*not* operator), originally introduced with [PEP 203](https://www.python.org/dev/peps/pep-0203/). This implicitly inserts the currently mapped object as the first operand on the right-hand side."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 60,
+   "execution_count": 61,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -2111,7 +2153,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 61,
+   "execution_count": 62,
    "metadata": {
     "slideshow": {
      "slide_type": "-"
@@ -2124,7 +2166,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 62,
+   "execution_count": 63,
    "metadata": {
     "slideshow": {
      "slide_type": "-"
@@ -2137,7 +2179,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 63,
+   "execution_count": 64,
    "metadata": {
     "slideshow": {
      "slide_type": "-"
@@ -2150,7 +2192,7 @@
        "42"
       ]
      },
-     "execution_count": 63,
+     "execution_count": 64,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2167,12 +2209,12 @@
     }
    },
    "source": [
-    "Variables can be **de-referenced** (i.e., \"deleted\") with the `del` statement. This does *not* \"delete\" the object to which a variable points to. It merely removes the variable from the \"list of all variables\"."
+    "Variables can be **[de-referenced](https://docs.python.org/3/reference/simple_stmts.html#the-del-statement)** (i.e., \"deleted\") with the `del` statement. This does *not* delete the object to which a variable points to. It merely removes the variable from the \"list of all variables\"."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 64,
+   "execution_count": 65,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -2185,7 +2227,7 @@
        "789"
       ]
      },
-     "execution_count": 64,
+     "execution_count": 65,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2196,7 +2238,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 65,
+   "execution_count": 66,
    "metadata": {
     "slideshow": {
      "slide_type": "-"
@@ -2215,12 +2257,12 @@
     }
    },
    "source": [
-    "If we refer to an unknown name, a runtime exception occurs, namely a `NameError`."
+    "If we refer to an unknown name, a runtime error occurs, namely a `NameError`."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 66,
+   "execution_count": 67,
    "metadata": {
     "slideshow": {
      "slide_type": "-"
@@ -2234,7 +2276,7 @@
      "traceback": [
       "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
       "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-66-89e6c98d9288>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mb\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;32m<ipython-input-67-89e6c98d9288>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mb\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
       "\u001b[0;31mNameError\u001b[0m: name 'b' is not defined"
      ]
     }
@@ -2251,12 +2293,12 @@
     }
    },
    "source": [
-    "Some names magically exist when we start Python. In this introductory book, we can safely ignore such variables."
+    "Some variables magically exist when we start Python. In this introductory book, we can safely ignore them."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 67,
+   "execution_count": 68,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -2269,7 +2311,7 @@
        "'__main__'"
       ]
      },
-     "execution_count": 67,
+     "execution_count": 68,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2291,7 +2333,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 68,
+   "execution_count": 69,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -2321,15 +2363,15 @@
        " '_22',\n",
        " '_23',\n",
        " '_24',\n",
-       " '_26',\n",
+       " '_25',\n",
        " '_27',\n",
        " '_28',\n",
-       " '_30',\n",
+       " '_29',\n",
        " '_31',\n",
        " '_32',\n",
        " '_33',\n",
        " '_34',\n",
-       " '_36',\n",
+       " '_35',\n",
        " '_37',\n",
        " '_38',\n",
        " '_39',\n",
@@ -2337,17 +2379,17 @@
        " '_40',\n",
        " '_41',\n",
        " '_42',\n",
-       " '_48',\n",
+       " '_43',\n",
        " '_49',\n",
        " '_5',\n",
-       " '_52',\n",
-       " '_54',\n",
-       " '_57',\n",
-       " '_59',\n",
-       " '_63',\n",
+       " '_50',\n",
+       " '_53',\n",
+       " '_55',\n",
+       " '_58',\n",
+       " '_60',\n",
        " '_64',\n",
-       " '_67',\n",
-       " '_8',\n",
+       " '_65',\n",
+       " '_68',\n",
        " '_9',\n",
        " '__',\n",
        " '___',\n",
@@ -2425,6 +2467,7 @@
        " '_i66',\n",
        " '_i67',\n",
        " '_i68',\n",
+       " '_i69',\n",
        " '_i7',\n",
        " '_i8',\n",
        " '_i9',\n",
@@ -2447,7 +2490,7 @@
        " 'total']"
       ]
      },
-     "execution_count": 68,
+     "execution_count": 69,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2477,20 +2520,7 @@
    "source": [
     "It is important to understand that *several* variables can point to the *same* object in memory. This can be counter-intuitive in the beginning and lead to many hard to track down bugs.\n",
     "\n",
-    "This makes `b` point to whatever object `a` points to."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 69,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "slide"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "b = a  # this is different from b == a"
+    "This makes `b` point to whatever object `a` is pointing to."
    ]
   },
   {
@@ -2498,23 +2528,12 @@
    "execution_count": 70,
    "metadata": {
     "slideshow": {
-     "slide_type": "-"
+     "slide_type": "slide"
     }
    },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "42"
-      ]
-     },
-     "execution_count": 70,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
-    "a"
+    "b = a"
    ]
   },
   {
@@ -2537,6 +2556,30 @@
      "output_type": "execute_result"
     }
    ],
+   "source": [
+    "a"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 72,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "42"
+      ]
+     },
+     "execution_count": 72,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "b"
    ]
@@ -2549,14 +2592,14 @@
     }
    },
    "source": [
-    "For \"simple\" types like `int` or `float` this will never cause confusion.\n",
+    "For \"simple\" types like `int` or `float` this will never cause troubles.\n",
     "\n",
     "Let's \"change the value\" of `a`. Really, let's create a *new* `123` object and make `a` point to it."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 72,
+   "execution_count": 73,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -2569,7 +2612,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 73,
+   "execution_count": 74,
    "metadata": {
     "slideshow": {
      "slide_type": "-"
@@ -2582,7 +2625,7 @@
        "123"
       ]
      },
-     "execution_count": 73,
+     "execution_count": 74,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2604,7 +2647,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 74,
+   "execution_count": 75,
    "metadata": {
     "slideshow": {
      "slide_type": "-"
@@ -2617,7 +2660,7 @@
        "42"
       ]
      },
-     "execution_count": 74,
+     "execution_count": 75,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2634,12 +2677,12 @@
     }
    },
    "source": [
-    "However, if a name points to an object of a more \"complex\" object, for example, of type `list`, \"weird\" things can happen."
+    "However, if a variable points to an object of a more \"complex\" type (e.g., `list`), \"weird\" things can happen."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 75,
+   "execution_count": 76,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -2652,7 +2695,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 76,
+   "execution_count": 77,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -2665,7 +2708,7 @@
        "list"
       ]
      },
-     "execution_count": 76,
+     "execution_count": 77,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2676,7 +2719,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 77,
+   "execution_count": 78,
    "metadata": {
     "slideshow": {
      "slide_type": "-"
@@ -2697,14 +2740,14 @@
    "source": [
     "Let's change the first element of `x`.\n",
     "\n",
-    "Chapter 7 discusses lists in more depth. For now, let's just view a list as some sort of **container** object that holds an arbitrary number of pointers to other objects and treat the brackets `[...]` attached to `x` as just another operator, called the **indexing operator**. `x[0]` instructs Python to first \"follow\" the pointer from the \"global\" directory of all names to the list object. Then, it follows the first pointer it finds there to the `1` object. The indexing operator must be an operator as we merely read the first element an do not change anything in memory.\n",
+    "Chapter 7 discusses lists in more depth. For now, let's just view a `list` object as some sort of **container** that holds an arbitrary number of pointers to other objects and treat brackets `[]` attached to it as just another operator, called the **indexing operator**. `x[0]` instructs Python to first follow the pointer from the \"global\" directory of all names to the `x` object. Then, it follows the first pointer it finds there to the `1` object. The indexing operator must be an operator as we merely read the first element and do not change anything in memory.\n",
     "\n",
-    "Note how Python **begins counting at 0**. This is not the case for many other languages, for example, MATLAB, R, or Stata. To understand why this makes sense, see this short [note](https://www.cs.utexas.edu/users/EWD/transcriptions/EWD08xx/EWD831.html) by one of the all-time greats in computer science, the late [Edsger Dijkstra](https://en.wikipedia.org/wiki/Edsger_W._Dijkstra)."
+    "Note how Python **begins counting at 0**. This is not the case for many other languages, for example, [MATLAB](https://en.wikipedia.org/wiki/MATLAB), [R](https://en.wikipedia.org/wiki/R_%28programming_language%29), or [Stata](https://en.wikipedia.org/wiki/Stata). To understand why this makes sense, see this short [note](https://www.cs.utexas.edu/users/EWD/transcriptions/EWD08xx/EWD831.html) by one of the all-time greats in computer science, the late [Edsger Dijkstra](https://en.wikipedia.org/wiki/Edsger_W._Dijkstra)."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 78,
+   "execution_count": 79,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -2717,7 +2760,7 @@
        "1"
       ]
      },
-     "execution_count": 78,
+     "execution_count": 79,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2734,12 +2777,12 @@
     }
    },
    "source": [
-    "To change the first entry in the list, we use the assignment statement `=` again. Here, this does actually *not* create a *new* variable but only changes the object to which the first pointer in the `x` list points to. As we only change parts of the `x` list, we say that we change its **state**."
+    "To change the first entry in the list, we use the assignment statement `=` again. Here, this does actually *not* create a *new* variable (or overwrite an existing one) but only changes the object to which the first pointer in the `x` list points to. As we only change parts of the `x` object, we say that we **mutate** (i.e., change) its **state**."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 79,
+   "execution_count": 80,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -2752,7 +2795,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 80,
+   "execution_count": 81,
    "metadata": {
     "slideshow": {
      "slide_type": "-"
@@ -2765,7 +2808,7 @@
        "[99, 2, 3]"
       ]
      },
-     "execution_count": 80,
+     "execution_count": 81,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2782,12 +2825,12 @@
     }
    },
    "source": [
-    "The changes made to `x` can also be seen through the `y` variable."
+    "The changes made to the object `x` is pointing to can also be seen through the `y` variable!"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 81,
+   "execution_count": 82,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -2800,7 +2843,7 @@
        "[99, 2, 3]"
       ]
      },
-     "execution_count": 81,
+     "execution_count": 82,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2823,7 +2866,7 @@
     "\n",
     "In the second case, `x[0] = 99` creates a *new* integer object `99` and merely changes the first pointer in the `x` list.\n",
     "\n",
-    "In general, the assignment statement (re-)creates a variable and makes it point to whatever object is on the right-hand side *if* the left-hand side is a *pure* variable name. Otherwise, it changes some object on the left-hand side (this is strictly not a must but we should expect it).\n",
+    "In general, the assignment statement creates (or overwrites) a variable and makes it point to whatever object is on the right-hand side *only if* the left-hand side is a *pure* name (i.e., it contains no operators like the indexing operator in the example). Otherwise, it mutates some already existing object. And we always have to expect that the latter might have more than one variable pointing at it.\n",
     "\n",
     "In the beginning, visualizing the memory with a tool like [PythonTutor](http://pythontutor.com/visualize.html#code=x%20%3D%20%5B1,%202,%203%5D%0Ay%20%3D%20x%0Ax%5B0%5D%20%3D%2099%0Adel%20x,%20y%0Ax%20%3D%20%5B1,%202,%203%5D%0Ay%20%3D%20x.copy%28%29%0Ax%5B0%5D%20%3D%2099&cumulative=false&curInstr=0&heapPrimitives=nevernest&mode=display&origin=opt-frontend.js&py=3&rawInputLstJSON=%5B%5D&textReferences=false) will assist in understanding what is going on."
    ]
@@ -2880,7 +2923,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 82,
+   "execution_count": 83,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -2893,7 +2936,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 83,
+   "execution_count": 84,
    "metadata": {
     "slideshow": {
      "slide_type": "-"
@@ -2906,7 +2949,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 84,
+   "execution_count": 85,
    "metadata": {
     "slideshow": {
      "slide_type": "-"
@@ -2919,7 +2962,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 85,
+   "execution_count": 86,
    "metadata": {
     "slideshow": {
      "slide_type": "-"
@@ -2943,7 +2986,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 86,
+   "execution_count": 87,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -2956,7 +2999,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 87,
+   "execution_count": 88,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -2969,7 +3012,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 88,
+   "execution_count": 89,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -2977,12 +3020,12 @@
    },
    "outputs": [],
    "source": [
-    "name = \"Alexander\"  # name of what ?"
+    "name = \"Alexander\"  # name of what?"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 89,
+   "execution_count": 90,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -2991,10 +3034,10 @@
    "outputs": [
     {
      "ename": "SyntaxError",
-     "evalue": "can't assign to operator (<ipython-input-89-dfc191b6a91a>, line 1)",
+     "evalue": "can't assign to operator (<ipython-input-90-dfc191b6a91a>, line 1)",
      "output_type": "error",
      "traceback": [
-      "\u001b[0;36m  File \u001b[0;32m\"<ipython-input-89-dfc191b6a91a>\"\u001b[0;36m, line \u001b[0;32m1\u001b[0m\n\u001b[0;31m    address@work = \"Burgplatz 2, Vallendar\"\u001b[0m\n\u001b[0m                                           ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m can't assign to operator\n"
+      "\u001b[0;36m  File \u001b[0;32m\"<ipython-input-90-dfc191b6a91a>\"\u001b[0;36m, line \u001b[0;32m1\u001b[0m\n\u001b[0;31m    address@work = \"Burgplatz 2, Vallendar\"\u001b[0m\n\u001b[0m                                           ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m can't assign to operator\n"
      ]
     }
    ],
@@ -3015,7 +3058,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 90,
+   "execution_count": 91,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -3034,12 +3077,12 @@
     }
    },
    "source": [
-    "Variables with leading and trailing double underscores (referred to as **dunder** in Python \"slang\") are used for important built-in variables. Do *not* use this style for custom variables!"
+    "Variables with leading and trailing double underscores, referred to as **dunder** in Python \"slang\", are used for important built-in variables. Do *not* use this style for custom variables!"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 91,
+   "execution_count": 92,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -3052,7 +3095,7 @@
        "'__main__'"
       ]
      },
-     "execution_count": 91,
+     "execution_count": 92,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3085,7 +3128,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 92,
+   "execution_count": 93,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -3107,10 +3150,10 @@
        "        "
       ],
       "text/plain": [
-       "<IPython.lib.display.YouTubeVideo at 0x7fad49f57320>"
+       "<IPython.lib.display.YouTubeVideo at 0x7f35840cfe48>"
       ]
      },
-     "execution_count": 92,
+     "execution_count": 93,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3139,18 +3182,18 @@
     }
    },
    "source": [
-    "An **expression** is any syntactically correct **combination** of **variables** and **literals** with **operators**. See the [language reference](https://docs.python.org/3/reference/expressions.html) for a full list.\n",
+    "An **[expression](https://docs.python.org/3/reference/expressions.html)** is any syntactically correct **combination** of **variables** and **literals** with **operators**.\n",
     "\n",
     "In simple words, anything that can be used on the right-hand side of an assignment statement without creating a `SyntaxError` is an expression.\n",
     "\n",
-    "What we said about individual operators above (namely that they have *no* side effects) should have been put here to begin with. The examples in the section on operators were actually all expressions.\n",
+    "What we said about individual operators above, namely that they have *no* side effects, should have been put here to begin with. The examples in the section on operators above were actually all expressions!\n",
     "\n",
     "The simplest possible expression contains only one variable (or literal)."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 93,
+   "execution_count": 94,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -3163,7 +3206,7 @@
        "123"
       ]
      },
-     "execution_count": 93,
+     "execution_count": 94,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3174,7 +3217,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 94,
+   "execution_count": 95,
    "metadata": {
     "slideshow": {
      "slide_type": "-"
@@ -3187,7 +3230,7 @@
        "165"
       ]
      },
-     "execution_count": 94,
+     "execution_count": 95,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3204,12 +3247,12 @@
     }
    },
    "source": [
-    "The definition of an expression is **recursive**. So here the sub-expression `a + b` is combined with the literal `3` by the operator `**` to form another expression."
+    "The definition of an expression is **recursive**. So here the sub-expression `a + b` is combined with the literal `3` by the operator `**` to form the full expression."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 95,
+   "execution_count": 96,
    "metadata": {
     "slideshow": {
      "slide_type": "-"
@@ -3222,7 +3265,7 @@
        "4492125"
       ]
      },
-     "execution_count": 95,
+     "execution_count": 96,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3239,12 +3282,12 @@
     }
    },
    "source": [
-    "As before, the bracket operator `[...]` can be used for indexing."
+    "Here, the variable `y` is combined with the literal `2` by the indexing operator `[]`. The resulting expression evaluates to the "
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 96,
+   "execution_count": 97,
    "metadata": {
     "slideshow": {
      "slide_type": "-"
@@ -3257,7 +3300,7 @@
        "3"
       ]
      },
-     "execution_count": 96,
+     "execution_count": 97,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3274,12 +3317,12 @@
     }
    },
    "source": [
-    "When not used as a **delimiter**, parentheses also constitute an operator, namely the **call operator** `(...)`. We have seen this syntax above when we \"called\" (i.e., executed) built-in functions and methods."
+    "When not used as a **delimiter**, parentheses also constitute an operator, namely the **call operator** `()`. We have seen this syntax above when we \"called\" (i.e., executed) built-in functions and methods."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 97,
+   "execution_count": 98,
    "metadata": {
     "slideshow": {
      "slide_type": "-"
@@ -3292,7 +3335,7 @@
        "104"
       ]
      },
-     "execution_count": 97,
+     "execution_count": 98,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3325,7 +3368,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 98,
+   "execution_count": 99,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -3339,7 +3382,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 99,
+   "execution_count": 100,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -3352,7 +3395,7 @@
        "'Hi class'"
       ]
      },
-     "execution_count": 99,
+     "execution_count": 100,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3374,7 +3417,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 100,
+   "execution_count": 101,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -3387,7 +3430,7 @@
        "'Hi Hi Hi Hi Hi Hi Hi Hi Hi Hi Hi Hi Hi Hi Hi Hi Hi Hi Hi Hi Hi Hi Hi Hi Hi Hi Hi Hi Hi Hi Hi Hi Hi Hi Hi Hi Hi Hi Hi Hi Hi Hi '"
       ]
      },
-     "execution_count": 100,
+     "execution_count": 101,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3415,14 +3458,14 @@
     }
    },
    "source": [
-    "A **statement** is anything that changes the state of the program's memory or has some other side effect. Statements do not just evaluate to a value like expressions; instead, they create or change values. See the [language reference](https://docs.python.org/3/reference/simple_stmts.html) for a full list.\n",
+    "A **[statement](https://docs.python.org/3/reference/simple_stmts.html)** is anything that changes the state of the program's memory or has some other side effect. Statements do not just evaluate to a value like expressions; instead, they create or change values.\n",
     "\n",
     "Most notably of course are the `=` and `del` statements."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 101,
+   "execution_count": 102,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -3435,7 +3478,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 102,
+   "execution_count": 103,
    "metadata": {
     "slideshow": {
      "slide_type": "-"
@@ -3459,7 +3502,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 103,
+   "execution_count": 104,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -3506,7 +3549,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 104,
+   "execution_count": 105,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -3534,7 +3577,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 105,
+   "execution_count": 106,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -3547,7 +3590,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 106,
+   "execution_count": 107,
    "metadata": {
     "slideshow": {
      "slide_type": "-"
@@ -3655,7 +3698,7 @@
     "\n",
     "- flow control (cf., Chapter 3)\n",
     "  - expression of **logic** or an **algorithm**\n",
-    "  - conditional execution of a small **branch** within a program (i.e., `if`-statements)\n",
+    "  - conditional execution of a small **branch** within a program (i.e., `if` statements)\n",
     "  - repetitive execution of parts of a program (i.e., `for`-loops and `while`-loops)"
    ]
   }
diff --git a/01_elements_of_a_program_review_and_exercises.ipynb b/01_elements_of_a_program_review_and_exercises.ipynb
index 088001b..2207ccc 100644
--- a/01_elements_of_a_program_review_and_exercises.ipynb
+++ b/01_elements_of_a_program_review_and_exercises.ipynb
@@ -19,7 +19,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Read chapter 1 of the book. Then work through the ten review questions."
+    "Read Chapter 1 of the book. Then work through the ten review questions."
    ]
   },
   {
@@ -180,7 +180,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "**Q10**: [PEP 8](https://www.python.org/dev/peps/pep-0008/) suggests that developers use **$8$ spaces** per level of indentation."
+    "**Q10**: [PEP 8](https://www.python.org/dev/peps/pep-0008/) suggests that developers use **8 spaces** per level of indentation."
    ]
   },
   {
@@ -263,7 +263,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "**Q11.3**: Lastly, what does the `end=\"\\n\"` mean in the documentation? Use it in the `for`-loop to print the numbers $1$ through $10$ in just one line."
+    "**Q11.3**: Lastly, what does the `end=\"\\n\"` mean in the documentation? Use it in the `for`-loop to print the numbers 1 through 10 in just one line."
    ]
   },
   {
@@ -298,7 +298,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "**Q11.1**: First, create a list `numbers` with the numbers from $1$ through $100$. You could type all numbers manually but there is of course a smarter way. The built-in [range()](https://docs.python.org/3/library/functions.html#func-range) may be useful here. Read how it works in the documentation. To make the output of [range()](https://docs.python.org/3/library/functions.html#func-range) a `list` object, you have to \"wrap\" it with the [list()](https://docs.python.org/3/library/functions.html#func-list) built-in (i.e., `list(range(...))`)."
+    "**Q11.1**: First, create a list `numbers` with the numbers from 1 through 100. You could type all numbers manually but there is of course a smarter way. The built-in [range()](https://docs.python.org/3/library/functions.html#func-range) may be useful here. Read how it works in the documentation. To make the output of [range()](https://docs.python.org/3/library/functions.html#func-range) a `list` object, you have to \"wrap\" it with the [list()](https://docs.python.org/3/library/functions.html#func-list) built-in (i.e., `list(range(...))`)."
    ]
   },
   {
@@ -335,7 +335,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "**Q11.3**: Create a loop that prints out either the number or any of the Fizz Buzz substitutes. Do it in such a way that we do not end up with $100$ lines of output here."
+    "**Q11.3**: Create a loop that prints out either the number or any of the Fizz Buzz substitutes. Do it in such a way that we do not end up with 100 lines of output here."
    ]
   },
   {

From c3fc760e700cc3196f9aef186cdce0ea3f8fb12e Mon Sep 17 00:00:00 2001
From: Alexander Hess <alexander@webartifex.biz>
Date: Sun, 22 Sep 2019 20:25:40 +0200
Subject: [PATCH 014/242] Add sample_module.py to the project

The file sample_module.py serves as an example in notebook 02  as to how
to create a local module and then import it.
---
 sample_module.py | 75 ++++++++++++++++++++++++++++++++++++++++++++++++
 1 file changed, 75 insertions(+)
 create mode 100644 sample_module.py

diff --git a/sample_module.py b/sample_module.py
new file mode 100644
index 0000000..2efcda4
--- /dev/null
+++ b/sample_module.py
@@ -0,0 +1,75 @@
+"""This is a sample module.
+
+It defines three functions average(), average_evens(), and average_odds().
+The point is to show how we can put Python code in a .py file to be re-used
+in some other place.
+
+We should never forget to document the code as well, both on the module
+level (i.e., this docstring) but also in every function it defines.
+
+When imported, Python modules are executed top to bottom before the flow of
+execution returns to wherever they were imported into.
+
+An important convention is to prefix variables and functions that are not to
+be used outside the module with a single underscore "_". This way, we can
+design the code within a module in a modular fashion and only "export" what we
+want.
+
+Here, all three functions internally forward the computation to an internal
+utility function _scaled_average() that contains all the logic common to the
+three functions. Also, we define one _default_scalar variable that is used as
+the default for the scalar parameter in each of the functions.
+
+While this example is stylized, it shows how Python modules are often
+designed.
+"""
+
+_default_scalar = 1
+
+
+def _scaled_average(numbers, scalar):
+    """Internal utility function to calculate scaled averages."""
+    average = sum(numbers) / len(numbers)
+    return scalar * average
+
+
+def average(numbers, *, scalar=_default_scalar):
+    """Calculate the average of all numbers in a list.
+
+    Args:
+        numbers (list): list of numbers; may be integers or floats
+        scalar (float, optional): the scalar that multiplies the
+            average of the even numbers
+
+    Returns:
+        float: (scaled) average
+    """
+    return _scaled_average(numbers, scalar)
+
+
+def average_evens(numbers, *, scalar=_default_scalar):
+    """Calculate the average of all even numbers in a list.
+
+    Args:
+        numbers (list): list of numbers; may be integers or floats
+        scalar (float, optional): the scalar that multiplies the
+            average of the even numbers
+
+    Returns:
+        float: (scaled) average
+    """
+    return _scaled_average([n for n in numbers if n % 2 == 0], scalar)
+
+
+def average_odds(numbers, *, scalar=_default_scalar):
+    """Calculate the average of all odd numbers in a list.
+
+    Args:
+        numbers (list): list of numbers; may be integers or floats
+        scalar (float, optional): the scalar that multiplies the
+            average of the even numbers
+
+    Returns:
+        float: (scaled) average
+    """
+    return _scaled_average([n for n in numbers if n % 2 != 0], scalar)

From ec3097e220ba4a4562aa198d014bf5967229eed3 Mon Sep 17 00:00:00 2001
From: Alexander Hess <alexander@webartifex.biz>
Date: Sun, 22 Sep 2019 20:26:58 +0200
Subject: [PATCH 015/242] Add the sample_package folder to the project

The sample_package folder serves as an example in notebook 02 as to what
a Python package looks like. Its contents actually belong to the chapter
on object-orientation.
---
 sample_package/__init__.py |   5 +
 sample_package/matrix.py   | 254 +++++++++++++++++++++++++++++++++++++
 sample_package/utils.py    |  14 ++
 sample_package/vector.py   | 141 ++++++++++++++++++++
 4 files changed, 414 insertions(+)
 create mode 100644 sample_package/__init__.py
 create mode 100644 sample_package/matrix.py
 create mode 100644 sample_package/utils.py
 create mode 100644 sample_package/vector.py

diff --git a/sample_package/__init__.py b/sample_package/__init__.py
new file mode 100644
index 0000000..76b1b09
--- /dev/null
+++ b/sample_package/__init__.py
@@ -0,0 +1,5 @@
+"""This package provides Vectors and Matrices."""
+
+from .matrix import Matrix
+from .utils import norm
+from .vector import Vector
diff --git a/sample_package/matrix.py b/sample_package/matrix.py
new file mode 100644
index 0000000..c6b982f
--- /dev/null
+++ b/sample_package/matrix.py
@@ -0,0 +1,254 @@
+"""This module defines a Matrix class."""
+
+class Matrix:
+    """A standard m-by-n-dimensional matrix from linear algebra.
+
+    The class is designed for sub-classing in such a way that
+    the user can adapt the typing class attribute to change,
+    for example, how the entries are stored (e.g., as integers).
+
+    Attributes:
+        storage (callable): must return an iterable that is used
+            to store the entries of the matrix; defaults to tuple
+        typing (callable): type casting applied to all vector
+            entries upon creation; defaults to float
+        zero_threshold (float): maximum difference allowed when
+            comparing an entry to zero; defaults to 1e-12
+    """
+
+    storage = tuple
+    typing = float
+    zero_threshold = 1e-12
+
+    def __init__(self, data):
+        """Initiate a new matrix.
+
+        Args:
+            data (iterable of iterables): the matrix's entries;
+                must be provided with rows first, then column;
+                the number of column entries must be consistent across rows
+                where the first row sets the standard;
+                must have at least one element in total
+
+        Raises:
+            ValueError:
+                - if the number of columns is inconsistent across the rows
+                - if the provided data do not have enough entries
+        """
+        self._entries = self.storage(
+            self.storage(self.typing(x) for x in r) for r in data
+        )
+        for row in self._entries[1:]:
+            if len(row) != self.n_cols:
+                raise ValueError("each row must have the same number of entries")
+        if len(self) == 0:
+            raise ValueError("the matrix must have at least one entry")
+
+    @classmethod
+    def from_columns(cls, data):
+        """Initiate a new matrix.
+
+        This is an alternative constructor for data provided in column-major order.
+
+        Args:
+            data (iterable of iterables): the matrix's entries in column-major order;
+                the number of column entries must be consistent per row
+                while the first row sets the correct number;
+                must have at least one element in total
+
+        Raises:
+            ValueError:
+                - if the number of columns is inconsistent across the rows
+                - if the provided data do not have enough entries
+        """
+        return cls(data).transpose()
+
+    def __repr__(self):
+        name = self.__class__.__name__
+        args = ", ".join(
+            "(" + ", ".join(f"{c:.3f}" for c in r) + ",)" for r in self._entries
+        )
+        return f"{name}(({args}))"
+
+    def __str__(self):
+        name = self.__class__.__name__
+        first, last, m, n = self[0], self[-1], self.n_rows, self.n_cols
+        return f"{name}(({first:.1f}, ...), ..., (..., {last:.1f}))[{m:d}x{n:d}]"
+
+    @property
+    def n_rows(self):
+        """Number of rows in the matrix."""
+        return len(self._entries)
+
+    @property
+    def n_cols(self):
+        """Number of columns in the matrix."""
+        return len(self._entries[0])
+
+    def __len__(self):
+        return self.n_rows * self.n_cols
+
+    def __getitem__(self, index):
+        if isinstance(index, int):
+            if index < 0:
+                index += len(self)
+            if not (0 <= index < len(self)):
+                raise IndexError("integer index out of range")
+            row, col = divmod(index, self.n_cols)
+            return self._entries[row][col]
+        elif (
+            isinstance(index, tuple)
+            and len(index) == 2
+            and isinstance(index[0], int)
+            and isinstance(index[1], int)
+        ):
+            return self._entries[index[0]][index[1]]
+        raise TypeError("index must be either an integer or a tuple of two integers")
+
+    def rows(self):
+        """Iterate over the rows of the matrix.
+
+        Returns:
+            rows (Generator): produces Vector instances
+                representing individual rows of the matrix
+        """
+        return (Vector(r) for r in self._entries)
+
+    def cols(self):
+        """Iterate over the columns of the matrix.
+
+        Returns:
+            columns (Generator): produces Vector instances
+                representing individual columns of the matrix
+        """
+        return (
+            Vector(self._entries[r][c] for r in range(self.n_rows))
+            for c in range(self.n_cols)
+        )
+
+    def entries(self, *, reverse=False, row_major=True):
+        """Iterate over the entries of the matrix in flat fashion.
+
+        Args:
+            reverse (bool): flag to iterate backwards; defaults to False
+            row_major (bool): flag to iterate in row major order; defaults to False
+
+        Returns:
+            entries (Generator): produces the entries rows of the matrix
+                in the type set in the typing class variable
+        """
+        if reverse:
+            rows, cols = range(self.n_rows - 1, -1, -1), range(self.n_cols - 1, -1, -1)
+        else:
+            rows, cols = range(self.n_rows), range(self.n_cols)
+        if row_major:
+            return (self._entries[r][c] for r in rows for c in cols)
+        return (self._entries[r][c] for c in cols for r in rows)
+
+    def __iter__(self):
+        return self.entries()
+
+    def __reversed__(self):
+        return self.entries(reverse=True)
+
+    def __add__(self, other):
+        if isinstance(other, self.__class__):
+            if (self.n_rows != other.n_rows) or (self.n_cols != other.n_cols):
+                raise ValueError("matrices need to be of the same dimensions")
+            return self.__class__(
+                (s_col + o_col for (s_col, o_col) in zip(s_row, o_row))
+                for (s_row, o_row) in zip(self._entries, other._entries)
+            )
+        elif isinstance(other, numbers.Number):
+            return self.__class__((c + other for c in r) for r in self._entries)
+        return NotImplemented
+
+    def __radd__(self, other):
+        if isinstance(other, Vector):
+            raise TypeError("vectors and matrices cannot be added")
+        return self + other
+
+    def __sub__(self, other):
+        return self + (-other)
+
+    def __rsub__(self, other):
+        if isinstance(other, Vector):
+            raise TypeError("vectors and matrices cannot be subtracted")
+        return (-self) + other
+
+    def _matrix_multiply(self, other):
+        if self.n_cols != other.n_rows:
+            raise ValueError("matrices need to have compatible dimensions")
+        return self.__class__((rv * cv for cv in other.cols()) for rv in self.rows())
+
+    def __mul__(self, other):
+        if isinstance(other, numbers.Number):
+            return self.__class__((x * other for x in r) for r in self._entries)
+        elif isinstance(other, Vector):
+            return self._matrix_multiply(other.as_matrix()).as_vector()
+        elif isinstance(other, self.__class__):
+            return self._matrix_multiply(other)
+        return NotImplemented
+
+    def __rmul__(self, other):
+        if isinstance(other, numbers.Number):
+            return self * other
+        elif isinstance(other, Vector):
+            return other.as_matrix(column=False)._matrix_multiply(self).as_vector()
+        return NotImplemented
+
+    def __truediv__(self, other):
+        if isinstance(other, numbers.Number):
+            return self * (1 / other)
+        return NotImplemented
+
+    def __eq__(self, other):
+        if isinstance(other, self.__class__):
+            if (self.n_rows != other.n_rows) or (self.n_cols != other.n_cols):
+                raise ValueError("matrices need to be of the same dimensions")
+            for x, y in zip(self, other):
+                if abs(x - y) > self.zero_threshold:
+                    return False
+            return True
+        return NotImplemented
+
+    def __pos__(self):
+        return self
+
+    def __neg__(self):
+        return self.__class__((-x for x in r) for r in self._entries)
+
+    def __abs__(self):
+        return norm(self)
+
+    def __bool__(self):
+        return bool(abs(self))
+
+    def __float__(self):
+        if not (self.n_rows == 1 and self.n_cols == 1):
+            raise RuntimeError("matrix must have exactly one entry to become a scalar")
+        return self[0]
+
+    def as_vector(self):
+        """Cast the matrix as a one-dimensional vector.
+
+        Returns:
+            vector (Vector)
+
+        Raises:
+            RuntimeError: if not one of the two dimensions is 1
+        """
+        if not (self.n_rows == 1 or self.n_cols == 1):
+            raise RuntimeError("one dimension (m or n) must be 1")
+        return Vector(x for x in self)
+
+    def transpose(self):
+        """Transpose the rows and columns of the matrix.
+
+        Returns:
+            matrix (Matrix)
+        """
+        return self.__class__(zip(*self._entries))
+
+
+from .vector import Vector
diff --git a/sample_package/utils.py b/sample_package/utils.py
new file mode 100644
index 0000000..7929de2
--- /dev/null
+++ b/sample_package/utils.py
@@ -0,0 +1,14 @@
+"""This module provides utility functions."""
+
+
+def norm(vector_or_matrix):
+    """Calculate the Frobenius or Euclidean norm of a matrix or vector.
+
+    Args:
+        vector_or_matrix (Vector/Matrix): the entries whose squares
+            are to be summed up
+
+    Returns:
+        norm (float)
+    """
+    return math.sqrt(sum(x ** 2 for x in vector_or_matrix))
diff --git a/sample_package/vector.py b/sample_package/vector.py
new file mode 100644
index 0000000..39d87f6
--- /dev/null
+++ b/sample_package/vector.py
@@ -0,0 +1,141 @@
+"""This module defines a Vector class."""
+
+from .matrix import Matrix
+
+
+class Vector:
+    """A standard one-dimensional vector from linear algebra.
+
+    The class is designed for sub-classing in such a way that
+    the user can adapt the typing class attribute to change,
+    for example, how the entries are stored (e.g., as integers).
+
+    Attributes:
+        storage (callable): must return an iterable that is used
+            to store the entries of the vector; defaults to tuple
+        typing (callable): type casting applied to all vector
+            entries upon creation; defaults to float
+        zero_threshold (float): maximum difference allowed when
+            comparing an entry to zero; defaults to 1e-12
+    """
+
+    storage = tuple
+    typing = float
+    zero_threshold = 1e-12
+
+    def __init__(self, data):
+        """Initiate a new vector.
+
+        Args:
+            data (iterable): the vector's entries;
+                must have at least one element
+
+        Raises:
+            ValueError: if the provided data do not have enough entries
+        """
+        self._entries = self.storage(self.typing(x) for x in data)
+        if len(self) == 0:
+            raise ValueError("the vector must have at least one entry")
+
+    def __repr__(self):
+        name, args = self.__class__.__name__, ", ".join(f"{x:.3f}" for x in self)
+        return f"{name}(({args}))"
+
+    def __str__(self):
+        name, first, last, entries = (
+            self.__class__.__name__,
+            self[0],
+            self[-1],
+            len(self),
+        )
+        return f"{name}({first:.1f}, ..., {last:.1f})[{entries:d}]"
+
+    def __len__(self):
+        return len(self._entries)
+
+    def __getitem__(self, index):
+        if not isinstance(index, int):
+            raise TypeError("index must be an integer")
+        return self._entries[index]
+
+    def __iter__(self):
+        return iter(self._entries)
+
+    def __reversed__(self):
+        return reversed(self._entries)
+
+    def __add__(self, other):
+        if isinstance(other, self.__class__):
+            if len(self) != len(other):
+                raise ValueError("vectors need to be of the same length")
+            return self.__class__(x + y for (x, y) in zip(self, other))
+        elif isinstance(other, numbers.Number):
+            return self.__class__(x + other for x in self)
+        return NotImplemented
+
+    def __radd__(self, other):
+        return self + other
+
+    def __sub__(self, other):
+        return self + (-other)
+
+    def __rsub__(self, other):
+        return (-self) + other
+
+    def __mul__(self, other):
+        if isinstance(other, self.__class__):
+            if len(self) != len(other):
+                raise ValueError("vectors need to be of the same length")
+            return sum(x * y for (x, y) in zip(self, other))
+        elif isinstance(other, numbers.Number):
+            return self.__class__(x * other for x in self)
+        return NotImplemented
+
+    def __rmul__(self, other):
+        return self * other
+
+    def __truediv__(self, other):
+        if isinstance(other, numbers.Number):
+            return self * (1 / other)
+        return NotImplemented
+
+    def __eq__(self, other):
+        if isinstance(other, self.__class__):
+            if len(self) != len(other):
+                raise ValueError("vectors need to be of the same length")
+            for x, y in zip(self, other):
+                if abs(x - y) > self.zero_threshold:
+                    return False
+            return True
+        return NotImplemented
+
+    def __pos__(self):
+        return self
+
+    def __neg__(self):
+        return self.__class__(-x for x in self)
+
+    def __abs__(self):
+        return norm(self)
+
+    def __bool__(self):
+        return bool(abs(self))
+
+    def __float__(self):
+        if len(self) != 1:
+            raise RuntimeError("vector must have exactly one entry to become a scalar")
+        return self[0]
+
+    def as_matrix(self, *, column=True):
+        """Convert the vector into a matrix.
+
+        Args:
+            column (bool): if the vector should be interpreted as
+                as a column vector or not; defaults to True
+
+        Returns:
+            matrix (Matrix)
+        """
+        if column:
+            return Matrix([x] for x in self)
+        return Matrix([(x for x in self)])

From 72db877c457d592eb76c519eff235b0d14eece14 Mon Sep 17 00:00:00 2001
From: Alexander Hess <alexander@webartifex.biz>
Date: Sun, 22 Sep 2019 20:30:13 +0200
Subject: [PATCH 016/242] Add initial version of notebook 02

---
 02_functions.ipynb | 3478 ++++++++++++++++++++++++++++++++++++++++++++
 1 file changed, 3478 insertions(+)
 create mode 100644 02_functions.ipynb

diff --git a/02_functions.ipynb b/02_functions.ipynb
new file mode 100644
index 0000000..95646a8
--- /dev/null
+++ b/02_functions.ipynb
@@ -0,0 +1,3478 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "# Chapter 2: Functions & Modularization"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "In Chapter 1 we typed the **business logic** of our little program to calculate the mean of a subset of a list of numbers right into the code cells. Then, we executed them one after another. We had no way of **re-using** the code except for either re-executing the cells or copying and pasting their contents into other cells. And whenever we find ourselves doing repetitive manual work, we can be sure that there must be a way of automating what we are doing.\n",
+    "\n",
+    "At the same time, we executed built-in functions (e.g., [print()](https://docs.python.org/3/library/functions.html#print), [sum()](https://docs.python.org/3/library/functions.html#sum), [len()](https://docs.python.org/3/library/functions.html#len), [id()](https://docs.python.org/3/library/functions.html#id), or [type()](https://docs.python.org/3/library/functions.html#type)) that obviously must be re-using the same parts inside core Python every time we use them.\n",
+    "\n",
+    "This chapter shows how Python offers language constructs that let us **define** our own functions that we can then **call** just like the built-in ones."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "##  Function Definition"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "So-called **[user-defined functions](https://docs.python.org/3/reference/compound_stmts.html#function-definitions)** can be created with the `def` statement. To extend an already familiar example, we re-use the introductory example from Chapter 1 in its final Pythonic version and transform it into the function `average_evens()` below. \n",
+    "\n",
+    "A function's **name** must be chosen according to the same naming rules as for ordinary variables. In fact, Python manages function names just like variables. In this book, we further adopt the convention of ending function names with parentheses \"`()`\" in text cells for faster comprehension when reading (i.e., `average_evens()` vs. `average_evens`). These are not actually part of the name but must always be written out in the `def` statement for syntactic reasons.\n",
+    "\n",
+    "Functions may define an arbitrary number of **parameters** as inputs that can then be referenced within the indented **code block**: They are simply listed within the parentheses in the `def` statement (i.e., `numbers` below). \n",
+    "\n",
+    "The code block is often also called a function's **body** while the first line with the `def` in it is the **header** and must end with a colon.\n",
+    "\n",
+    "Together, the name and the list of parameters are also referred to as the function's **[signature](https://en.wikipedia.org/wiki/Type_signature)** (i.e., `average_evens(numbers)` below).\n",
+    "\n",
+    "A function may come with an *explicit* **[return value](https://docs.python.org/3/reference/simple_stmts.html#the-return-statement)** (i.e., \"result\" or \"output\") specified with the `return` statement: Functions that have one are considered **fruitful**; otherwise, they are **void**. Functions of the latter kind are still useful because of their **side effects** (e.g., the [print()](https://docs.python.org/3/library/functions.html#print) built-in). Strictly speaking, they also have an *implicit* return value of `None` that is different from the `False` we saw in Chapter 1.\n",
+    "\n",
+    "To maintain good coding practices, a function should define a **docstring** that describes what it does in a short subject line, what parameters it expects (i.e., their types), and what it returns (if anything). A docstring is a syntactically valid multi-line string (i.e., type `str`) defined with **triple-double quotes** (strings are covered in depth in Chapter 6). Good standards as to how to format a docstring are [PEP 257](https://www.python.org/dev/peps/pep-0257/) and section 3.8 in [Google's Python Style Guide](https://github.com/google/styleguide/blob/gh-pages/pyguide.md)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "def average_evens(numbers):\n",
+    "    \"\"\"Calculate the average of all even numbers in a list.\n",
+    "\n",
+    "    Args:\n",
+    "        numbers (list): a list of numbers; may be integers or floats\n",
+    "\n",
+    "    Returns:\n",
+    "        float: average\n",
+    "    \"\"\"\n",
+    "    evens = [n for n in numbers if n % 2 == 0]\n",
+    "    average = sum(evens) / len(evens)\n",
+    "    return average"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Once defined, a function can be referenced just like any other variable by its name (i.e., *without* the parenthesis). Its value might seem awkward at first: It consists of the location where we defined the function (i.e., `__main__`, which is Python's way of saying \"in this notebook\") and the signature."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<function __main__.average_evens(numbers)>"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "average_evens"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "A function is an **object** on its own with an **identity** (i.e., memory location) and a **type**, namely `function`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "140620654002656"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "id(average_evens)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "function"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "type(average_evens)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "The built-in [help()](https://docs.python.org/3/library/functions.html#help) function shows a function's docstring.\n",
+    "\n",
+    "Whenever we use code to analyze or obtain information on an object, we say that we **[introspect](https://en.wikipedia.org/wiki/Type_introspection)** it."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Help on function average_evens in module __main__:\n",
+      "\n",
+      "average_evens(numbers)\n",
+      "    Calculate the average of all even numbers in a list.\n",
+      "    \n",
+      "    Args:\n",
+      "        numbers (list): a list of numbers; may be integers or floats\n",
+      "    \n",
+      "    Returns:\n",
+      "        float: average\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "help(average_evens)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "In a Jupyter notebook, we can just as well add a question mark to a function's name to achieve the same. Then, a small tab opens in our browser."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "average_evens?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Two questions marks even show a function's source code."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "average_evens??"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "## Function Calls"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Once defined we can **call** (i.e., \"execute\") a function with the **call operator** `()`. The formal parameters are filled in by passing variables or expressions as **arguments** to the function within the parentheses."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "nums = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "6.0"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "average_evens(nums)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "The return value is usually assigned to a new variable for later reference. Otherwise we would loose access to it in memory right away."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "avg = average_evens(nums)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "6.0"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "avg"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "## Scoping Rules"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "### Local Scope disappears"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Notice how the parameters listed in a function's definition (i.e., `numbers`) and variables created inside it during execution (i.e., `evens` and `average`) are **local** to that function. That means they are only mapped to an object in memory while the function is being executed and de-referenced immediately when the function returns. We say they **go out of scope** once the function terminates."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "ename": "NameError",
+     "evalue": "name 'numbers' is not defined",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-12-6a54518a0c2c>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mnumbers\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;31mNameError\u001b[0m: name 'numbers' is not defined"
+     ]
+    }
+   ],
+   "source": [
+    "numbers"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [
+    {
+     "ename": "NameError",
+     "evalue": "name 'evens' is not defined",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-13-df246468d241>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mevens\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;31mNameError\u001b[0m: name 'evens' is not defined"
+     ]
+    }
+   ],
+   "source": [
+    "evens"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "ename": "NameError",
+     "evalue": "name 'average' is not defined",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-14-c3fe9b4213f6>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0maverage\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;31mNameError\u001b[0m: name 'average' is not defined"
+     ]
+    }
+   ],
+   "source": [
+    "average"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "### Global Scope is everywhere"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "On the contrary, while a function is being executed, it can \"see\" the variables of the **enclosing scope** (i.e., \"outside\" of it). This is a common source of *semantic* errors. Consider the following stylized (and incorrect) example `average_wrong()`. The error is hard to spot with eyes: The function never references the `numbers` parameter but the `nums` variable in the **global scope** instead."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "def average_wrong(numbers):\n",
+    "    \"\"\"Calculate the average of all even numbers in a list.\n",
+    "\n",
+    "    Args:\n",
+    "        numbers (list): a list of numbers; may be integers or floats\n",
+    "\n",
+    "    Returns:\n",
+    "        float: average\n",
+    "    \"\"\"\n",
+    "    evens = [n for n in nums if n % 2 == 0]  # should reference numbers, not nums\n",
+    "    average = sum(evens) / len(evens)\n",
+    "    return average"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[1, 2, 3, 4, 5, 6, 7, 8, 9, 10]"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "nums"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "6.0"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "average_wrong(nums)  # the result is correct by accident!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "6.0"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "average_wrong([123, 456, 789])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Also, observe how both `average_evens()` and `average_wrong()` use the same names for their respective parameters and variables internally. For sure, Python is smart enough to not mix them up. This is because each function call creates a temporary **[namespace](https://en.wikipedia.org/wiki/Namespace)** that *isolates* the local scope's names for usage only from within the function. As we saw in the [Zen of Python](https://www.python.org/dev/peps/pep-0020/), \"namespaces are one honking great idea\" (cf., `import this`)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "### Shadowing"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Code gets even more confusing when variables by the same name from different scopes collide. In particular, what should we expect to happen if a function changes a globally defined variable internally?\n",
+    "\n",
+    "`average_odds()` below works like `average_evens()` above except that it **[casts](https://en.wikipedia.org/wiki/Type_conversion)** (i.e., \"converts\") the elements of `numbers` as objects of type `int` with the [int()](https://docs.python.org/3/library/functions.html#int) built-in first before filtering and averaging them. In doing so, it introduces an *internal* variable `nums` whose name collides with the one in the global scope."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "def average_odds(numbers):\n",
+    "    \"\"\"Calculate the average of all odd numbers in a list.\n",
+    "\n",
+    "    Args:\n",
+    "        numbers (list): a list of numbers; must be integers\n",
+    "\n",
+    "    Returns:\n",
+    "        float: average\n",
+    "    \"\"\"\n",
+    "    nums = [int(n) for n in numbers]  # cast all numbers as integers first\n",
+    "    odds = [n for n in nums if n % 2 != 0]  # before filtering them\n",
+    "    average = sum(odds) / len(odds)\n",
+    "    return average"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "`nums` in the global scope is of course the same list from above."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[1, 2, 3, 4, 5, 6, 7, 8, 9, 10]"
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "nums"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "As good practice, let's first use inputs for which we can calculate the answer in our heads to verify that `average_odds()` is correct."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "3.0"
+      ]
+     },
+     "execution_count": 21,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "average_odds([1, 100, 3, 100, 5])  # verify the function's correctness with predictable inputs"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "To make the confusion even bigger, let's also pass the global `nums` as an argument to `average_odds()`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "5.0"
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "average_odds(nums)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Python, however, is again smart enough to keep the two `nums` variables apart. So the global `nums` is still pointing to the very same list object as before."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[1, 2, 3, 4, 5, 6, 7, 8, 9, 10]"
+      ]
+     },
+     "execution_count": 23,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "nums"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "The reason why everything works just fine is that *every time* we (re-)assign an object to a variable inside a function with the `=` statement, this is done in the local scope by default. There are ways to change variables existing in an outer scope from within a function but we save that for a later chapter.\n",
+    "\n",
+    "Variables whose names collide with the ones of variables in enclosing scopes - and the global scope is just the most enclosing scope - are said to **shadow** them.\n",
+    "\n",
+    "While this is not a problem for Python as we have observed, it may lead to less readable code for us humans and should be avoided if possible. But, as we have also heard, \"[naming things](https://skeptics.stackexchange.com/questions/19836/has-phil-karlton-ever-said-there-are-only-two-hard-things-in-computer-science)\" is often considered hard as well and we have to be prepared to encounter shadowing variables."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "## Built-in Functions"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Python comes with plenty of useful functions built in, some of which we have already seen before. The [documentation](https://docs.python.org/3/library/functions.html) has the full list. Just as core Python itself, they are implemented in C and thus very fast.\n",
+    "\n",
+    "[len()](https://docs.python.org/3/library/functions.html#len) counts the number of elements in a container object while [sum()](https://docs.python.org/3/library/functions.html#sum) adds up all the elements."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "10"
+      ]
+     },
+     "execution_count": 24,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "len(nums)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "55"
+      ]
+     },
+     "execution_count": 25,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "sum(nums)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "We can cast certain objects as a different type. For example, to \"convert\" a float or a text into an integer, we use the [int()](https://docs.python.org/3/library/functions.html#int) built-in. This actually creates a *new* object of type `int` from the provided `avg` or `\"6\"` objects who continue to exist in memory unchanged."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "6.0"
+      ]
+     },
+     "execution_count": 26,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "avg"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "6"
+      ]
+     },
+     "execution_count": 27,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "int(avg)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "6"
+      ]
+     },
+     "execution_count": 28,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "int(\"6\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Observe that casting as an integer is different from rounding with the [round()](https://docs.python.org/3/library/functions.html#round) built-in function."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "7"
+      ]
+     },
+     "execution_count": 29,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "int(7.99)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "8"
+      ]
+     },
+     "execution_count": 30,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "round(7.99)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Not all conversions are valid and *runtime* errors can occur as the `ValueError` shows."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "ename": "ValueError",
+     "evalue": "invalid literal for int() with base 10: 'six'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-31-28b6aa9c0167>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"six\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;31mValueError\u001b[0m: invalid literal for int() with base 10: 'six'"
+     ]
+    }
+   ],
+   "source": [
+    "int(\"six\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "We can also go in the other direction with the [float()](https://docs.python.org/3/library/functions.html#float) built-in function."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "42.0"
+      ]
+     },
+     "execution_count": 32,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "float(42)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "## Positional vs. Keyword Arguments"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "So far we have only specified one parameter in each of our user-defined functions. In Chapter 1, however, we saw the built-in function [divmod()](https://docs.python.org/3/library/functions.html#divmod) taking two arguments. Obviously, the order of the numbers passed in mattered. Whenever we call a function and list its arguments in a comma seperated manner, we say that we pass in the arguments by position or refer to them as **positional arguments**."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(4, 2)"
+      ]
+     },
+     "execution_count": 33,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "divmod(42, 10)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(0, 10)"
+      ]
+     },
+     "execution_count": 34,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "divmod(10, 42)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "For many functions there is a natural order to the arguments. But what if this is not the case? For example, let's create a close relative of the above `average_evens()` function that also scales the resulting average by a factor. What is more natural? Passing in `numbers` first? Or `scalar`? There is no obvious way and we continue with the first alternative for no real reason."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "def scaled_average_evens(numbers, scalar):\n",
+    "    \"\"\"Calculate a scaled average of all even numbers in a list.\n",
+    "\n",
+    "    Args:\n",
+    "        numbers (list): a list of numbers; may be integers or floats\n",
+    "        scalar (float): the scalar that multiplies the average\n",
+    "            of the even numbers\n",
+    "\n",
+    "    Returns:\n",
+    "        float: scaled average\n",
+    "    \"\"\"\n",
+    "    evens = [n for n in numbers if n % 2 == 0]\n",
+    "    average = sum(evens) / len(evens)\n",
+    "    return scalar * average"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "As with [divmod()](https://docs.python.org/3/library/functions.html#divmod), we can pass in the arguments by position."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "12.0"
+      ]
+     },
+     "execution_count": 36,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "scaled_average_evens(nums, 2)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "However, now the function call is a bit harder to comprehend as we need to always remember what the `2` means. This becomes even harder the more parameters we specify.\n",
+    "\n",
+    "Luckily, we can also reference the formal parameter names as **keyword arguments**. We can even combine positional and keyword arguments in the same function call. Each of the following does the exact same thing."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "12.0"
+      ]
+     },
+     "execution_count": 37,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "scaled_average_evens(nums, scalar=2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "12.0"
+      ]
+     },
+     "execution_count": 38,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "scaled_average_evens(numbers=nums, scalar=2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "12.0"
+      ]
+     },
+     "execution_count": 39,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "scaled_average_evens(scalar=2, numbers=nums)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Unfortunately, there are ways to screw this up with a `SyntaxError`: If positional and keyword arguments are mixed, the keyword arguments *must* come last."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "ename": "SyntaxError",
+     "evalue": "positional argument follows keyword argument (<ipython-input-40-a6b957a168d8>, line 1)",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;36m  File \u001b[0;32m\"<ipython-input-40-a6b957a168d8>\"\u001b[0;36m, line \u001b[0;32m1\u001b[0m\n\u001b[0;31m    scaled_average_evens(numbers=nums, 2)\u001b[0m\n\u001b[0m                                      ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m positional argument follows keyword argument\n"
+     ]
+    }
+   ],
+   "source": [
+    "scaled_average_evens(numbers=nums, 2)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "### Default Argument Values"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Defining both `average_evens()` and `scaled_average_evens()` is also kind of repetitive as most of their code is the same. Such a redundancy will make a code base harder to maintain in the long run as whenever we change the logic in one function we must *not* forget to do so for the other function as well.\n",
+    "\n",
+    "A better way is to design related functions in a **modular** fashion such that they re-use each other's logic.\n",
+    "\n",
+    "For example, as not scaling an average is just a special case of scaling it with `1`, we could re-define the two functions like below: In this setting, the function resembling the *special* case (i.e., `average_evens()`) simply **forwards** the call to the more *general* function (i.e., `scaled_average_evens()`) using a `scalar=1` argument."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "def scaled_average_evens(numbers, scalar):\n",
+    "    \"\"\"Calculate a scaled average of all even numbers in a list.\n",
+    "\n",
+    "    ...\n",
+    "    \"\"\"\n",
+    "    evens = [n for n in numbers if n % 2 == 0]\n",
+    "    average = sum(evens) / len(evens)\n",
+    "    return scalar * average"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "def average_evens(numbers):\n",
+    "    \"\"\"Calculate the average of all even numbers in a list.\n",
+    "\n",
+    "    ...\n",
+    "    \"\"\"\n",
+    "    return scaled_average_evens(numbers, scalar=1)  # refactored to use the logic in scaled_average_evens()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "The outcome of `average_evens(nums)` is of course still `6.0`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "6.0"
+      ]
+     },
+     "execution_count": 43,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "average_evens(nums)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "If we *assume* that scaling the average occurs rarely, we could also handle both cases in *one* function definition by providing a **default value** for the `scalar` parameter."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "def average_evens(numbers, scalar=1):\n",
+    "    \"\"\"Calculate the average of all even numbers in a list.\n",
+    "\n",
+    "    Args:\n",
+    "        numbers (list): list of numbers; may be integers or floats\n",
+    "        scalar (float, optional): the scalar that multiplies the\n",
+    "            average of the even numbers\n",
+    "\n",
+    "    Returns:\n",
+    "        float: (scaled) average\n",
+    "    \"\"\"\n",
+    "    evens = [n for n in numbers if n % 2 == 0]\n",
+    "    average = sum(evens) / len(evens)\n",
+    "    return scalar * average"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Now we can call the function either with or without the `scalar` argument.\n",
+    "\n",
+    "If `scalar` is passed in, this can be done as either a positional or a keyword argument. Which of the two versions where `scalar` is `2` is easier to comprehend in a large program?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "6.0"
+      ]
+     },
+     "execution_count": 45,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "average_evens(nums)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "12.0"
+      ]
+     },
+     "execution_count": 46,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "average_evens(nums, 2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "12.0"
+      ]
+     },
+     "execution_count": 47,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "average_evens(nums, scalar=2)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "### Keyword-only Arguments"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Since we *assumed* that scaling will occur *rarely*, we'd prefer that our new version of `average_evens()` be called with a *keyword argument* whenever `scalar` is passed in explicitly. Then, the second argument is never ambiguous as we could always read its name.\n",
+    "\n",
+    "Luckily, Python offers a **keyword-only** syntax, where all we need to do is to place the arguments for which we require *explicit* keyword use after an asterix `*`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 48,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "def average_evens(numbers, *, scalar=1):\n",
+    "    \"\"\"Calculate the average of all even numbers in a list.\n",
+    "\n",
+    "    Args:\n",
+    "        numbers (list): list of numbers; may be integers or floats\n",
+    "        scalar (float, optional): the scalar that multiplies the\n",
+    "            average of the even numbers\n",
+    "\n",
+    "    Returns:\n",
+    "        float: (scaled) average\n",
+    "    \"\"\"\n",
+    "    evens = [n for n in numbers if n % 2 == 0]\n",
+    "    average = sum(evens) / len(evens)\n",
+    "    return scalar * average"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "If we now call the function with a `scalar` argument passed in, we *must* use keyword notation."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 49,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "6.0"
+      ]
+     },
+     "execution_count": 49,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "average_evens(nums)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 50,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "12.0"
+      ]
+     },
+     "execution_count": 50,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "average_evens(nums, scalar=2)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "If we pass in `scalar` as a positional argument instead, we obtain a `TypeError`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 51,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "ename": "TypeError",
+     "evalue": "average_evens() takes 1 positional argument but 2 were given",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-51-b36617fef43e>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0maverage_evens\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnums\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m2\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;31mTypeError\u001b[0m: average_evens() takes 1 positional argument but 2 were given"
+     ]
+    }
+   ],
+   "source": [
+    "average_evens(nums, 2)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "## Anonymous Functions"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "The `def` statement is a statement because of its side effect of creating a *new* name that points to a *new* `function` object in memory.\n",
+    "\n",
+    "We can thus think of it as doing *two* things at once (i.e., either both of them happen or none). First, a `function` object is created that contains the concrete $0$s and $1$s that resemble the instructions we put into the function's body. In the context of a function, these $0$s and $1$s are also called **[byte code](https://en.wikipedia.org/wiki/Bytecode)**. Then, a name is created pointing at the new `function` object.\n",
+    "\n",
+    "Only this second aspect makes `def` a statement: Merely creating a new object in memory without making it accessible for later reference does *not* constitute a side effect. This is because the state the program is *not* changed. After all, if we cannot reference an object, how do we know that it is actually existing?\n",
+    "\n",
+    "Python provides a so-called **[lambda expression](https://docs.python.org/3/reference/expressions.html#lambda)** syntax that allows us to *only* create a `function` object in memory *without* making a name point to it.\n",
+    "\n",
+    "It starts with the keyword `lambda` followed by an optional comma seperated enumeration of parameters, a mandatory colon, and *one* expression that also is the resulting `function` object's return value.\n",
+    "\n",
+    "Because it does not create a name pointing to the object, we effectively create \"anonymous\" functions with it. In the example, we create a `function` object that adds `3` to the only argument passed in as the parameter `x` and returns that sum."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 52,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<function __main__.<lambda>(x)>"
+      ]
+     },
+     "execution_count": 52,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "lambda x: x + 3"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "If you think this is a rather pointless thing to do, you are absolutely correct!\n",
+    "\n",
+    "We created a `function` object, dit *not* call it, and Python immediately forgot about it. So what's the point?\n",
+    "\n",
+    "Just to prove that the `lambda` expression really creates a callable `function` object, we use the simple `=` statement to assign it to the variable `add_three`, which is really `add_three()` as per our convention from above."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 53,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "add_three = lambda x: x + 3  # we could and should use def instead"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Now we can call `add_three()` as if we defined it with the `def` statement to begin with."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 54,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "13"
+      ]
+     },
+     "execution_count": 54,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "add_three(10)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Alternatively, we could call any anonymous `function` object created with an `lambda` expression right away (i.e. without assigning it to a variable), which looks really weird for now as we need *two* pairs of parentheses: The first is just a delimiter whereas the second the call operator."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 55,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "42"
+      ]
+     },
+     "execution_count": 55,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "(lambda x: x + 3)(39)  # this looks weird but will become very useful"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "The main point of having functions without a name is to use them in a situation where we know ahead of time that we will use the function only once.\n",
+    "\n",
+    "Very popular contexts where we will apply lambda expressions are with the **map-filter-reduce** paradigm in Chapter 7 or when we do \"number crunching\" with **arrays** and **data frames** in Chapter 9."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "## Extending Core Python"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "So far, we have only used what we refer to as **core** Python in this book. By this, we mean all the syntactical rules as specified in the [language reference](https://docs.python.org/3/reference/) and a minimal set of about 50 built-in [functions](https://docs.python.org/3/library/functions.html). With this we could already implement any algorithm or business logic we can think of!\n",
+    "\n",
+    "However, after our first couple of programs, we would already start seeing recurring patterns in the code we write. In other words, we would constantly be \"re-inventing the wheel\" in each new project.\n",
+    "\n",
+    "Would it not be smarter to pull out the re-usable components from our programs and put them into some project independent **library** of generically useful functionalities? Then we would only need a way of including these **utilities** in our projects.\n",
+    "\n",
+    "As all programmers across all languages face this very same issue, most programming languages come with a so-called **[standard library](https://en.wikipedia.org/wiki/Standard_library)** that provides utilities to accomplish common tasks without a lot of code. Examples are making an HTTP request to some website, open and read popular file types (e.g., CSV or Excel files), do something on a computer's file system, and many more."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "### Standard Library"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Python also comes with its own [standard library](https://docs.python.org/3/library/index.html) that is structured into coherent modules and packages for given topics: A **module** is just a plain text file with the file extension *.py* that contains Python code while a **package** is a folder that groups several related modules.\n",
+    "\n",
+    "The code in the [standard library](https://docs.python.org/3/library/index.html) is contributed and maintained by many volunteers around the world. In contrast to so-called \"third-party\" packages (cf., the next section below), the Python core development team closely monitors and tests the code in the [standard library](https://docs.python.org/3/library/index.html). Consequently, we can be reasonably sure that anything provided by it works correctly independent of our computer's operating system and will most likely also be there in the next Python versions. Parts in the [standard library](https://docs.python.org/3/library/index.html) that are computationally expensive are often re-written in C and therefore much faster than anything we could code in Python ourselves. So, whenever we can solve a problem with the help of the [standard library](https://docs.python.org/3/library/index.html), it is almost always the best way to do so as well.\n",
+    "\n",
+    "The [standard library](https://docs.python.org/3/library/index.html) has grown very big over the years and we refer to the website [PYMOTW](https://pymotw.com/3/index.html) (i.e., \"Python Module of the Week\") that features well written introductory tutorials and how-to guides to most parts of the library. The same author also published a [book](https://www.amazon.com/Python-Standard-Library-Example-Developers/dp/0134291050/ref=as_li_ss_tl?ie=UTF8&qid=1493563121&sr=8-1&keywords=python+3+standard+library+by+example) that many Pythonistas keep on their shelf for reference. Knowing what is in the [standard library](https://docs.python.org/3/library/index.html) is quite valuable for solving real world tasks quickly.\n",
+    "\n",
+    "Throughout this book we will look at many modules and packages from the [standard library](https://docs.python.org/3/library/index.html) in more depth, starting with the [math](https://docs.python.org/3/library/math.html) and [random](https://docs.python.org/3/library/random.html) modules in this chapter."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "#### The [math](https://docs.python.org/3/library/math.html) Module"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "The [math](https://docs.python.org/3/library/math.html) module provides non-trivial mathematical functions like $sin(x)$ and constants like $\\pi$ or $\\text{e}$.\n",
+    "\n",
+    "To make functions and variables defined \"somewhere else\" available in our current program, we must first **[import](https://docs.python.org/3/reference/simple_stmts.html#import)** them with the `import` statement.  "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 56,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "import math"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "This creates the variable `math` that points to a **[module object](https://docs.python.org/3/glossary.html#term-module)** (i.e., type `module`) in memory."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 57,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<module 'math' from '/home/webartifex/.pyenv/versions/anaconda3-2019.07/lib/python3.7/lib-dynload/math.cpython-37m-x86_64-linux-gnu.so'>"
+      ]
+     },
+     "execution_count": 57,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "math"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 58,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "140620734513624"
+      ]
+     },
+     "execution_count": 58,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "id(math)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 59,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "module"
+      ]
+     },
+     "execution_count": 59,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "type(math)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "`module` objects serve as namespaces to organize the names inside a module. In this context, a namespace is nothing but a prefix that avoids collision with the variables already defined at the location where we import the module into.\n",
+    "\n",
+    "Let's see what we can do with the `math` module.\n",
+    "\n",
+    "The [dir()](https://docs.python.org/3/library/functions.html#dir) built-in function can also be used with an argument passed in. Ignoring the dunder-style names, `math` offers quite a lot of ... names. As we cannot know at this point in time if a listed name refers to a function or an ordinary variable, we use the more generic term **attribute** to mean either one of them."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 60,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['__doc__',\n",
+       " '__file__',\n",
+       " '__loader__',\n",
+       " '__name__',\n",
+       " '__package__',\n",
+       " '__spec__',\n",
+       " 'acos',\n",
+       " 'acosh',\n",
+       " 'asin',\n",
+       " 'asinh',\n",
+       " 'atan',\n",
+       " 'atan2',\n",
+       " 'atanh',\n",
+       " 'ceil',\n",
+       " 'copysign',\n",
+       " 'cos',\n",
+       " 'cosh',\n",
+       " 'degrees',\n",
+       " 'e',\n",
+       " 'erf',\n",
+       " 'erfc',\n",
+       " 'exp',\n",
+       " 'expm1',\n",
+       " 'fabs',\n",
+       " 'factorial',\n",
+       " 'floor',\n",
+       " 'fmod',\n",
+       " 'frexp',\n",
+       " 'fsum',\n",
+       " 'gamma',\n",
+       " 'gcd',\n",
+       " 'hypot',\n",
+       " 'inf',\n",
+       " 'isclose',\n",
+       " 'isfinite',\n",
+       " 'isinf',\n",
+       " 'isnan',\n",
+       " 'ldexp',\n",
+       " 'lgamma',\n",
+       " 'log',\n",
+       " 'log10',\n",
+       " 'log1p',\n",
+       " 'log2',\n",
+       " 'modf',\n",
+       " 'nan',\n",
+       " 'pi',\n",
+       " 'pow',\n",
+       " 'radians',\n",
+       " 'remainder',\n",
+       " 'sin',\n",
+       " 'sinh',\n",
+       " 'sqrt',\n",
+       " 'tan',\n",
+       " 'tanh',\n",
+       " 'tau',\n",
+       " 'trunc']"
+      ]
+     },
+     "execution_count": 60,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "dir(math)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Common mathematical constants and functions are now available via the dot operator `.` on the `math` object. This operator is sometimes also called the **attribute access operator**, in line with the just introduced term."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 61,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "3.141592653589793"
+      ]
+     },
+     "execution_count": 61,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "math.pi"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 62,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "2.718281828459045"
+      ]
+     },
+     "execution_count": 62,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "math.e"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 63,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<function math.sqrt(x, /)>"
+      ]
+     },
+     "execution_count": 63,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "math.sqrt"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 64,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Help on built-in function sqrt in module math:\n",
+      "\n",
+      "sqrt(x, /)\n",
+      "    Return the square root of x.\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "help(math.sqrt)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 65,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "1.4142135623730951"
+      ]
+     },
+     "execution_count": 65,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "math.sqrt(2)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Observe how the arguments passed to functions do not need to be just variables or simple literals. Instead, we can pass in any *expression* that evaluates to a *new* object of the type the function expects.\n",
+    "\n",
+    "So just as a reminder from the expression vs. statement discussion in Chapter 1: An expression is *any* syntactically correct combination of variables and literals with operators. And the call operator `()` is just ... well another operator. So both of the next two code cells are just expressions! They have no permanent side effect in memory. We can execute them as often as we want *without* changing the state of the program (i.e., this Jupyter notebook).\n",
+    "\n",
+    "So, regarding the very next cell in particular: Although the `2 ** 2` creates a *new* object `4` in memory that is then immediately passed into the [math.sqrt()](https://docs.python.org/3/library/math.html#math.sqrt) function, once that function call returns, \"all is lost\" and the newly created `4` object is forgotten again, as well as the return value of [math.sqrt()](https://docs.python.org/3/library/math.html#math.sqrt)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 66,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "2.0"
+      ]
+     },
+     "execution_count": 66,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "math.sqrt(2 ** 2)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Even the **composition** of several function calls only constitutes another expression."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 67,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "10.0"
+      ]
+     },
+     "execution_count": 67,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "math.sqrt(average_evens([99, 100, 101]))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "If we only need one particular function from a module, we can also use the alternative `from ... import ...` syntax.\n",
+    "\n",
+    "This does *not* create a module object but only makes a variable in our current location point to an object defined inside a module directly."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 68,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "from math import sqrt"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 69,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "4.0"
+      ]
+     },
+     "execution_count": 69,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "sqrt(16)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "#### The [random](https://docs.python.org/3/library/random.html) Module"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Often times, we need a random variable, for example, when we want to build a simulation program. The [random](https://docs.python.org/3/library/random.html) module in the [standard library](https://docs.python.org/3/library/index.html) often suffices for that."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 70,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "import random"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 71,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<module 'random' from '/home/webartifex/.pyenv/versions/anaconda3-2019.07/lib/python3.7/random.py'>"
+      ]
+     },
+     "execution_count": 71,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "random"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Besides the usual dunder-style attributes, the [dir()](https://docs.python.org/3/library/functions.html#dir) built-in function lists some attributes in an upper case naming convention and many others starting with a single underscore \"\\_\". To understand the former, we have to wait until Chapter 10 while the latter are explained further below."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 72,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['BPF',\n",
+       " 'LOG4',\n",
+       " 'NV_MAGICCONST',\n",
+       " 'RECIP_BPF',\n",
+       " 'Random',\n",
+       " 'SG_MAGICCONST',\n",
+       " 'SystemRandom',\n",
+       " 'TWOPI',\n",
+       " '_BuiltinMethodType',\n",
+       " '_MethodType',\n",
+       " '_Sequence',\n",
+       " '_Set',\n",
+       " '__all__',\n",
+       " '__builtins__',\n",
+       " '__cached__',\n",
+       " '__doc__',\n",
+       " '__file__',\n",
+       " '__loader__',\n",
+       " '__name__',\n",
+       " '__package__',\n",
+       " '__spec__',\n",
+       " '_acos',\n",
+       " '_bisect',\n",
+       " '_ceil',\n",
+       " '_cos',\n",
+       " '_e',\n",
+       " '_exp',\n",
+       " '_inst',\n",
+       " '_itertools',\n",
+       " '_log',\n",
+       " '_os',\n",
+       " '_pi',\n",
+       " '_random',\n",
+       " '_sha512',\n",
+       " '_sin',\n",
+       " '_sqrt',\n",
+       " '_test',\n",
+       " '_test_generator',\n",
+       " '_urandom',\n",
+       " '_warn',\n",
+       " 'betavariate',\n",
+       " 'choice',\n",
+       " 'choices',\n",
+       " 'expovariate',\n",
+       " 'gammavariate',\n",
+       " 'gauss',\n",
+       " 'getrandbits',\n",
+       " 'getstate',\n",
+       " 'lognormvariate',\n",
+       " 'normalvariate',\n",
+       " 'paretovariate',\n",
+       " 'randint',\n",
+       " 'random',\n",
+       " 'randrange',\n",
+       " 'sample',\n",
+       " 'seed',\n",
+       " 'setstate',\n",
+       " 'shuffle',\n",
+       " 'triangular',\n",
+       " 'uniform',\n",
+       " 'vonmisesvariate',\n",
+       " 'weibullvariate']"
+      ]
+     },
+     "execution_count": 72,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "dir(random)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "The [random.random()](https://docs.python.org/3/library/random.html#random.random) function generates a uniformly distributed `float` number between $0$ (including) and $1$ (excluding)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 73,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<function Random.random>"
+      ]
+     },
+     "execution_count": 73,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "random.random"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 74,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Help on built-in function random:\n",
+      "\n",
+      "random(...) method of random.Random instance\n",
+      "    random() -> x in the interval [0, 1).\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "help(random.random)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 75,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.4028601552264015"
+      ]
+     },
+     "execution_count": 75,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "random.random()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "While we could build some conditional logic with an `if` statement to map the number generated by [random.random()](https://docs.python.org/3/library/random.html#random.random) to a finite set of elements manually, the [random.choice()](https://docs.python.org/3/library/random.html#random.choice) function provides a lot more **convenience** for us. We simply call it with, for example, the `nums` list and it draws one element out of it with equal chance."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 76,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<bound method Random.choice of <random.Random object at 0x561d3c7fbba8>>"
+      ]
+     },
+     "execution_count": 76,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "random.choice"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 77,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Help on method choice in module random:\n",
+      "\n",
+      "choice(seq) method of random.Random instance\n",
+      "    Choose a random element from a non-empty sequence.\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "help(random.choice)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 78,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "8"
+      ]
+     },
+     "execution_count": 78,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "random.choice(nums)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "In order to re-produce the same random numbers in a simulation each time we run it, we can set the **[random seed](https://en.wikipedia.org/wiki/Random_seed)**. It is good practice to do this at the beginning of a program or notebook. Then every time we re-start the program, we will get the exact same random numbers again. This becomes very important, for example, when we employ certain machine learning algorithms that rely on randomization, like the infamous [Random Forest](https://en.wikipedia.org/wiki/Random_forest), and want to obtain **re-producable** results.\n",
+    "\n",
+    "The [random](https://docs.python.org/3/library/random.html) module provides the [random.seed()](https://docs.python.org/3/library/random.html#random.seed) function to do that."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 79,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "random.seed(42)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 80,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.6394267984578837"
+      ]
+     },
+     "execution_count": 80,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "random.random()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 81,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "random.seed(42)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 82,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.6394267984578837"
+      ]
+     },
+     "execution_count": 82,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "random.random()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "### Third-party Packages"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "As the Python community is based around open source, many developers publish their code, for example, on the Python Package Index [PyPI](https://pypi.org) from where anyone can download and install it for free using command line based tools like [pip](https://pip.pypa.io/en/stable/) or [conda](https://conda.io/en/latest/). This way, we can always customize our Python installation even more. Managing many such packages is actually quite a deep topic on its own, sometimes fearfully called **[dependency hell](https://en.wikipedia.org/wiki/Dependency_hell)**.\n",
+    "\n",
+    "The difference between the [standard library](https://docs.python.org/3/library/index.html) and such **third-party** packages is that in the first case the code goes through a much more formalized review process and is officially endorsed by the Python core developers. Yet, many third-party projects also offer the highest quality standards and a lot of such software is actually also relied on by many businesses and researchers.\n",
+    "\n",
+    "Throughout this book, we will look at many third-party libraries, mostly from Python's [scientific stack](https://scipy.org/about.html), a tightly coupled set of third-party libraries for storing **big data** efficiently ([numpy](http://www.numpy.org/)), \"wrangling\" ([pandas](https://pandas.pydata.org/)) and visualizing them ([matplotlib](https://matplotlib.org/) and [seaborn](https://seaborn.pydata.org/)), fitting classical statistical models ([statsmodels](http://www.statsmodels.org/)), training machine learning models ([sklearn](http://scikit-learn.org/)), and much more.\n",
+    "\n",
+    "Below, we briefly show how to install third-party libraries."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "#### The [numpy](http://www.numpy.org/) Library"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "[numpy](http://www.numpy.org/) is the de-facto standard in the Python world for handling **array-like** data. That is a fancy word for data that can be put into a matrix or vector format. We will look at it in depth in Chapter 9.\n",
+    "\n",
+    "As [numpy](http://www.numpy.org/) is *not* in the [standard library](https://docs.python.org/3/library/index.html), it must be *manually* installed, for example, with the [pip](https://pip.pypa.io/en/stable/) tool. As mentioned in Chapter 0, to execute terminal commands from within a Jupyter notebook, we just need to start a code cell with an exclamation mark.\n",
+    "\n",
+    "If you are running this notebook with an installation of the [Anaconda Distribution](https://www.anaconda.com/distribution/), then [numpy](http://www.numpy.org/) is probably already installed. Running the cell below, will just confirm that."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 83,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Requirement already satisfied: numpy in /home/webartifex/.pyenv/versions/anaconda3-2019.07/lib/python3.7/site-packages (1.16.4)\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!pip install numpy"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "[numpy](http://www.numpy.org/) is conventionally imported with the shorter **idiomatic** name `np`. The `as` in the import statement just changes the resulting variable name. It is a shortcut for the three lines `import numpy`, `np = numpy`, and `del numpy`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 84,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "import numpy as np"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "`np` can be used in the same way as `math` or `random` above."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 85,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<module 'numpy' from '/home/webartifex/.pyenv/versions/anaconda3-2019.07/lib/python3.7/site-packages/numpy/__init__.py'>"
+      ]
+     },
+     "execution_count": 85,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "np"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Let's convert the above `nums` list into a vector-like object of type `numpy.ndarray`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 86,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "vec = np.array(nums)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 87,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([ 1,  2,  3,  4,  5,  6,  7,  8,  9, 10])"
+      ]
+     },
+     "execution_count": 87,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "vec"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 88,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "numpy.ndarray"
+      ]
+     },
+     "execution_count": 88,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "type(vec)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "[numpy](http://www.numpy.org/) somehow magically adds new behavior to Python's built-in arithmetic operators. For example, we can now [scalar-multiply](https://en.wikipedia.org/wiki/Scalar_multiplication) `vec`.\n",
+    "\n",
+    "[numpy](http://www.numpy.org/)'s functions are implemented in highly optimized C code and therefore fast, especially when it comes to big data."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 89,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([ 2,  4,  6,  8, 10, 12, 14, 16, 18, 20])"
+      ]
+     },
+     "execution_count": 89,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "2 * vec"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "This scalar multiplication would \"fail\" if we used a plain `list` object like `nums` instead of an `numpy.ndarray` object like `vec`. The two types exhibit different **behavior** when used with the same operator, another example of **operator overloading**."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 90,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10]"
+      ]
+     },
+     "execution_count": 90,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "2 * nums  # surprise, surprise"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "[numpy](http://www.numpy.org/)'s `numpy.ndarray` objects integrate nicely with Python's built-in functions (e.g., [sum()](https://docs.python.org/3/library/functions.html#sum)) or functions from the [standard library](https://docs.python.org/3/library/index.html) (e.g., [random.choice()](https://docs.python.org/3/library/random.html#random.choice))."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 91,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "55"
+      ]
+     },
+     "execution_count": 91,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "sum(vec)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 92,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "1"
+      ]
+     },
+     "execution_count": 92,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "random.choice(vec)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "### Local Modules and Packages"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "For sure, we can create our own modules and packages. In the repository's main directory, there is a [*sample_module.py*](https://github.com/webartifex/intro-to-python/blob/master/sample_module.py) file that contains, among others, a function equivalent to the final version of `average_evens()`. To be realistic, this sample module is structured in a modular manner with several functions building on each other. It is best to skim over it *now* before reading on.\n",
+    "\n",
+    "To make code we put into a *.py* file available in our program, we import it as a module just as we did above with modules in the [standard library](https://docs.python.org/3/library/index.html) or third-party packages.\n",
+    "\n",
+    "The *name* to be imported is the file's name except for the *.py* part. In order for this to work, the file's name *must* adhere to the *same* rules as hold for [variable names](https://docs.python.org/3/reference/lexical_analysis.html#identifiers) in general.\n",
+    "\n",
+    "What happens during an import is conceptually as follows. When Python sees the `import sample_module` part, it first creates a *new* object of type `module` in memory. This is effectively an *empty* namespace. Then, it executes the imported file's code from top to bottom. Whatever variables are still defined at the end of this, are put into the module's namespace. Only if the file's code does *not* raise an error, will Python make a variable in our current location (i.e., `mod` here) point to the created `module` object. Otherwise, it is discarded. In essence, it is as if we copied and pasted the file's code in place of the import statement. If we import an already imported module again, Python is smart enough to avoid doing all this work all over and does nothing."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 93,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "import sample_module as mod"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 94,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<module 'sample_module' from '/home/webartifex/repos/intro-to-python/sample_module.py'>"
+      ]
+     },
+     "execution_count": 94,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "mod"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Disregarding the dunder-style attributes, `mod` defines the five attributes `_default_scalar`, `_scaled_average`, `average`, `average_evens`, and `average_odds`, which are exactly the ones we would expect from reading the [*sample_module.py*](https://github.com/webartifex/intro-to-python/blob/master/sample_module.py) file.\n",
+    "\n",
+    "An important convention when working with imported code is to *disregard* any attributes starting with an underscore \"\\_\". These are considered **private** and constitute **implementation details** the author of the imported code might change in a future version of his software. We *must* not rely on them in any way.\n",
+    "\n",
+    "In contrast, the three remaining **public** attributes are the functions `average()`, `average_evens()`, and `average_odds()` that we may use after the import."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 95,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['__builtins__',\n",
+       " '__cached__',\n",
+       " '__doc__',\n",
+       " '__file__',\n",
+       " '__loader__',\n",
+       " '__name__',\n",
+       " '__package__',\n",
+       " '__spec__',\n",
+       " '_default_scalar',\n",
+       " '_scaled_average',\n",
+       " 'average',\n",
+       " 'average_evens',\n",
+       " 'average_odds']"
+      ]
+     },
+     "execution_count": 95,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "dir(mod)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "We can use the imported `mod.average_evens()` just like `average_evens()` defined above. The advantage we get from **modularization** with *.py* files is that we can now easily re-use functions across different Jupyter notebooks without re-defining them again and again. Also, we can \"source out\" code that distracts from the storyline told in a notebook."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 96,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Help on function average_evens in module sample_module:\n",
+      "\n",
+      "average_evens(numbers, *, scalar=1)\n",
+      "    Calculate the average of all even numbers in a list.\n",
+      "    \n",
+      "    Args:\n",
+      "        numbers (list): list of numbers; may be integers or floats\n",
+      "        scalar (float, optional): the scalar that multiplies the\n",
+      "            average of the even numbers\n",
+      "    \n",
+      "    Returns:\n",
+      "        float: (scaled) average\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "help(mod.average_evens)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 97,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "6.0"
+      ]
+     },
+     "execution_count": 97,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "mod.average_evens(nums)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 98,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "12.0"
+      ]
+     },
+     "execution_count": 98,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "mod.average_evens(nums, scalar=2)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Packages are a generalization of modules and we will look at one in Chapter 10 in detail. You can, however, already look at a [sample package](https://github.com/webartifex/intro-to-python/tree/master/sample_package) in the repository, which is nothing but a folder with *.py* files in it.\n",
+    "\n",
+    "As a further references on modules, we refer to the [official tutorial](https://docs.python.org/3/tutorial/modules.html)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "## TL;DR"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "A **function** is a **named sequence** of statements that perform a computation.\n",
+    "\n",
+    "Functions provide benefits as they:\n",
+    "\n",
+    "- make programs easier to comprehend and debug for humans as they give names to the smaller parts of a larger program (i.e., they **modularize** a code base), and\n",
+    "- eliminate redundancies by allowing **re-use of code**.\n",
+    "\n",
+    "Functions are **defined** once with the `def` statement. Then, they can be **called** many times with the call operator `()`.\n",
+    "\n",
+    "They may process **parameterized** inputs, **passed** in as **arguments**, and output a **return value**.\n",
+    "\n",
+    "Arguments can be passed in by **position** or **keyword**. Some functions may even require **keyword-only** arguments.\n",
+    "\n",
+    "**Lambda expressions** create anonymous functions.\n",
+    "\n",
+    "Core Python can be extended with code from either the **standard library** or **third-party** libraries.\n",
+    "\n",
+    "Outside Jupyter notebooks, Python code is put into **modules** that are grouped in **packages**."
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.3"
+  },
+  "livereveal": {
+   "auto_select": "code",
+   "auto_select_fragment": true,
+   "scroll": true,
+   "theme": "serif"
+  },
+  "toc": {
+   "base_numbering": 1,
+   "nav_menu": {},
+   "number_sections": false,
+   "sideBar": true,
+   "skip_h1_title": true,
+   "title_cell": "Table of Contents",
+   "title_sidebar": "Contents",
+   "toc_cell": false,
+   "toc_position": {
+    "height": "calc(100% - 180px)",
+    "left": "10px",
+    "top": "150px",
+    "width": "384px"
+   },
+   "toc_section_display": false,
+   "toc_window_display": false
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

From 0eb4b70a994a5014e752e09b158c8678cc2ee26e Mon Sep 17 00:00:00 2001
From: Alexander Hess <alexander@webartifex.biz>
Date: Sun, 22 Sep 2019 20:31:17 +0200
Subject: [PATCH 017/242] Add review and exercises for notebook 02

---
 02_functions_review_and_exercises.ipynb | 397 ++++++++++++++++++++++++
 1 file changed, 397 insertions(+)
 create mode 100644 02_functions_review_and_exercises.ipynb

diff --git a/02_functions_review_and_exercises.ipynb b/02_functions_review_and_exercises.ipynb
new file mode 100644
index 0000000..647e47f
--- /dev/null
+++ b/02_functions_review_and_exercises.ipynb
@@ -0,0 +1,397 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Chapter 2: Functions & Modularization"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Content Review"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Read Chapter 2 of the book. Then work through the ten review questions."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Essay Questions "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Answer the following questions briefly with *at most* 300 characters per question!"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q1**: What property of the `def` statement makes it a **statement**? Is there a way to use an **expression** to create a function?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q2**: One of the first confusions of experienced programmers coming from other languages to Python regards the observation that **\"everything in Python is an object\"** (cf., this [discussion](https://www.reddit.com/r/learnpython/comments/8rypx9/everything_in_python_is_an_object/)). How does this relate to **functions**?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q3**: What does it mean for a variable to **go out of scope**?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q4**: How can a **global** variable be **shadowed**? Is this good or bad?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q5**: Explain the concept of **forwarding** a function **call**."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q6**: What are **keyword-only arguments** and when is it appropriate to use them?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### True / False Questions"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Motivate your answer with *one short* sentence!"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q7**: A mere function **call** is just an **expression**."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q8**: When using the `import` statement, we need to ensure that the imported attributes do **not** overwrite any already defined variables and functions."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q9:** Functions always have a name by which we can call them."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q10**: The [standard library](https://docs.python.org/3/library/index.html) is a collection of numerical tools often used in scientific computing, for example, advanced mathematical functions or utilities for simulation."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Coding Exercises"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Volume of a Sphere"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q11.1**: The [volume of a sphere](https://en.wikipedia.org/wiki/Sphere) is defined as $\\frac{4}{3} * \\pi * r^3$. Calculate this value for $r=10.0$ and round it to 10 digits after the comma. Use the [standard library](https://docs.python.org/3/library/index.html) to obtain a good approximation of $\\pi$."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q11.2**: Encapsulate the logic into a function `sphere_volume()` that takes one *positional* argument `radius` and one *keyword-only* argument `digits` defaulting to `5`. The volume should be returned as a `float` object under *all* circumstances. Document your work appropriately in a docstring according to [Google's Python Style Guide](https://github.com/google/styleguide/blob/gh-pages/pyguide.md)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\n",
+    "\n",
+    "\n",
+    "\n",
+    "\n",
+    "\n",
+    "\n",
+    "\n",
+    "\n",
+    "\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q11.3**: Evaluate the function with `radius = 100.0` and 1, 5, 10, 15, and 20 digits respectively."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q11.4**: What observation do you make?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q11.5**: Using the [range()](https://docs.python.org/3/library/functions.html#func-range) built-in, write a `for`-loop and calculate the volume of a sphere with `radius = 42.0` for all `digits` from `1` through `20`. Print out each volume on a seperate line.\n",
+    "\n",
+    "Note: This is the first task where you actually need to use the [print()](https://docs.python.org/3/library/functions.html#print) built-in function."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q11.6**: What important lesson did you learn about the `float` type?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " "
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.3"
+  },
+  "toc": {
+   "base_numbering": 1,
+   "nav_menu": {},
+   "number_sections": false,
+   "sideBar": true,
+   "skip_h1_title": true,
+   "title_cell": "Table of Contents",
+   "title_sidebar": "Contents",
+   "toc_cell": false,
+   "toc_position": {},
+   "toc_section_display": false,
+   "toc_window_display": false
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

From 464392787bac3b9de08e9a1d277fa8c768845661 Mon Sep 17 00:00:00 2001
From: Alexander Hess <alexander@webartifex.biz>
Date: Sun, 22 Sep 2019 20:35:03 +0200
Subject: [PATCH 018/242] Adjust content overview in README.md

---
 README.md | 1 +
 1 file changed, 1 insertion(+)

diff --git a/README.md b/README.md
index 78bdff9..b664a32 100644
--- a/README.md
+++ b/README.md
@@ -14,6 +14,7 @@ As such they can be viewed in a plain web browser:
 
 - [00 - Start up](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/00_start_up.ipynb)
 - [01 - Elements of a Program](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/01_elements_of_a_program.ipynb)
+- [02 - Functions & Modularization](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/02_functions.ipynb)
 
 However, it is recommended that students **install Python and Jupyter
 locally** and run the code in the notebooks on their own.

From 514b2dac40a380bb4ef82e8d33635386690d04f0 Mon Sep 17 00:00:00 2001
From: Alexander Hess <alexander@webartifex.biz>
Date: Tue, 24 Sep 2019 19:44:39 +0200
Subject: [PATCH 019/242] Streamline previous content

This includes shortening the filename of notebook 01
---
 00_start_up.ipynb                             | 19 ++++++++---
 ...ts_of_a_program.ipynb => 01_elements.ipynb | 34 +++++++++----------
 ... => 01_elements_review_and_exercises.ipynb |  0
 02_functions.ipynb                            | 12 +++----
 4 files changed, 38 insertions(+), 27 deletions(-)
 rename 01_elements_of_a_program.ipynb => 01_elements.ipynb (97%)
 rename 01_elements_of_a_program_review_and_exercises.ipynb => 01_elements_review_and_exercises.ipynb (100%)

diff --git a/00_start_up.ipynb b/00_start_up.ipynb
index 506c6ad..29175db 100644
--- a/00_start_up.ipynb
+++ b/00_start_up.ipynb
@@ -429,7 +429,7 @@
     }
    },
    "source": [
-    "Here is a brief history of and some background on Python:\n",
+    "Here is a brief history of and some background on Python (cf., also this [TechRepublic article](https://www.techrepublic.com/article/python-is-eating-the-world-how-one-developers-side-project-became-the-hottest-programming-language-on-the-planet/) for a more elaborate story):\n",
     "\n",
     "- [Guido van Rossum](https://en.wikipedia.org/wiki/Guido_van_Rossum) (Python’s **[Benevolent Dictator for Life](https://en.wikipedia.org/wiki/Benevolent_dictator_for_life)**) was bored during a week around Christmas 1989 and started Python as a hobby project \"that would keep \\[him\\] occupied\" for some days\n",
     "- the idea was to create a **general-purpose scripting** language that would allow fast **prototyping** and would **run on every operating system**\n",
@@ -439,6 +439,17 @@
     "- the language is named after the sketch comedy group [Monty Python](https://en.wikipedia.org/wiki/Monty_Python)"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "#### Summary"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {
@@ -447,7 +458,7 @@
     }
    },
    "source": [
-    "Python is a **general-purpose** programming language that allows for **fast development**, is **easy to comprehend**, **open-source**, long established, unifies the knowledge of **hundreds of thousands of experts** around the world, runs on basically every machine, and can handle the complexities of applications involving **big data**."
+    "Python is a **general-purpose** programming language that allows for **fast development**, is **easy to read**, **open-source**, long established, unifies the knowledge of **hundreds of thousands of experts** around the world, runs on basically every machine, and can handle the complexities of applications involving **big data**."
    ]
   },
   {
@@ -770,7 +781,7 @@
     "  1. Elements of a Program\n",
     "  2. Functions & Modularization\n",
     "- What is the flow of execution? How can we form sentences from words?\n",
-    "  3. Boolean Logic & Conditionals\n",
+    "  3. Conditionals & Exceptions\n",
     "  4. Recursion & Looping"
    ]
   },
@@ -813,7 +824,7 @@
     }
    },
    "source": [
-    "As with every good lecture, there has to be a [xkcd](https://xkcd.com/353/) comic somewhere."
+    "As with every good book, there has to be a [xkcd](https://xkcd.com/353/) comic somewhere."
    ]
   },
   {
diff --git a/01_elements_of_a_program.ipynb b/01_elements.ipynb
similarity index 97%
rename from 01_elements_of_a_program.ipynb
rename to 01_elements.ipynb
index c771c87..e1d8b09 100644
--- a/01_elements_of_a_program.ipynb
+++ b/01_elements.ipynb
@@ -742,7 +742,7 @@
     }
    },
    "source": [
-    "The normal order of precedence from mathematics applies (i.e., \"PEMDAS\" rule) but parentheses help avoid confusion."
+    "The normal [order of precedence](https://docs.python.org/3/reference/expressions.html#operator-precedence) from mathematics applies (i.e., \"PEMDAS\" rule) but parentheses help avoid confusion."
    ]
   },
   {
@@ -949,7 +949,7 @@
     {
      "data": {
       "text/plain": [
-       "139867825605456"
+       "139940106427216"
       ]
      },
      "execution_count": 27,
@@ -973,7 +973,7 @@
     {
      "data": {
       "text/plain": [
-       "139867825776440"
+       "139940106602344"
       ]
      },
      "execution_count": 28,
@@ -997,7 +997,7 @@
     {
      "data": {
       "text/plain": [
-       "139867825460912"
+       "139940105769456"
       ]
      },
      "execution_count": 29,
@@ -1041,7 +1041,7 @@
     }
    },
    "source": [
-    "`a` and `d` indeed have the same value as can be checked with the **equality operator** `==`. The resulting `True` (and the `False` below) is yet another data type, a so-called **boolean**."
+    "`a` and `d` indeed have the same value as can be checked with the **equality operator** `==`. The resulting `True` (and the `False` below) is yet another data type, a so-called **boolean**. We will look into that closely in Chapter 3."
    ]
   },
   {
@@ -1780,7 +1780,7 @@
        "        "
       ],
       "text/plain": [
-       "<IPython.lib.display.YouTubeVideo at 0x7f358405d550>"
+       "<IPython.lib.display.YouTubeVideo at 0x7f46584be518>"
       ]
      },
      "execution_count": 50,
@@ -2087,7 +2087,7 @@
     }
    },
    "source": [
-    "A variable can be **re-assigned** as often as we wish. Thereby, we could also assign an object of a different type. Because this is allowed, Python is said to be a **dynamically typed** language. On the contrary, a **statically typed** language like C also allows re-assignment but only with objects of the same type. This subtle distinction is one reason why Python is slower at execution than C: As it runs a program, it needs to figure out an object's type each time it is referenced. But as mentioned before, this can be mitigated with third-party libraries."
+    "A variable can be **re-assigned** as often as we wish. Thereby, we could also assign an object of a *different* type. Because this is allowed, Python is said to be a **dynamically typed** language. On the contrary, a **statically typed** language like C also allows re-assignment but only with objects of the *same* type. This subtle distinction is one reason why Python is slower at execution than C: As it runs a program, it needs to figure out an object's type each time it is referenced. But as mentioned before, this can be mitigated with third-party libraries."
    ]
   },
   {
@@ -2209,7 +2209,7 @@
     }
    },
    "source": [
-    "Variables can be **[de-referenced](https://docs.python.org/3/reference/simple_stmts.html#the-del-statement)** (i.e., \"deleted\") with the `del` statement. This does *not* delete the object to which a variable points to. It merely removes the variable from the \"list of all variables\"."
+    "Variables can be **[de-referenced](https://docs.python.org/3/reference/simple_stmts.html#the-del-statement)** (i.e., \"deleted\") with the `del` statement. This does *not* delete the object to which a variable points to. It merely removes the variable's name from the \"global list of all names\"."
    ]
   },
   {
@@ -2257,7 +2257,7 @@
     }
    },
    "source": [
-    "If we refer to an unknown name, a runtime error occurs, namely a `NameError`."
+    "If we refer to an unknown name, a *runtime* error occurs, namely a `NameError`. The `Name` in `NameError` gives a hint as to why we prefer the term *name* over *identifier*: Python just uses it more often in its error messages."
    ]
   },
   {
@@ -2293,7 +2293,7 @@
     }
    },
    "source": [
-    "Some variables magically exist when we start Python. In this introductory book, we can safely ignore them."
+    "Some variables magically exist when we start a Python process or are added by Jupyter. We can safely ignore the former until Chapter 10 and the latter for good."
    ]
   },
   {
@@ -2740,7 +2740,7 @@
    "source": [
     "Let's change the first element of `x`.\n",
     "\n",
-    "Chapter 7 discusses lists in more depth. For now, let's just view a `list` object as some sort of **container** that holds an arbitrary number of pointers to other objects and treat brackets `[]` attached to it as just another operator, called the **indexing operator**. `x[0]` instructs Python to first follow the pointer from the \"global\" directory of all names to the `x` object. Then, it follows the first pointer it finds there to the `1` object. The indexing operator must be an operator as we merely read the first element and do not change anything in memory.\n",
+    "Chapter 7 discusses lists in more depth. For now, let's just view a `list` object as some sort of **container** that holds an arbitrary number of pointers to other objects and treat the brackets `[]` attached to it as just another operator, called the **indexing operator**. `x[0]` instructs Python to first follow the pointer from the global list of all names to the `x` object. Then, it follows the first pointer it finds there to the `1` object. The indexing operator must be an operator as we merely read the first element and do not change anything in memory.\n",
     "\n",
     "Note how Python **begins counting at 0**. This is not the case for many other languages, for example, [MATLAB](https://en.wikipedia.org/wiki/MATLAB), [R](https://en.wikipedia.org/wiki/R_%28programming_language%29), or [Stata](https://en.wikipedia.org/wiki/Stata). To understand why this makes sense, see this short [note](https://www.cs.utexas.edu/users/EWD/transcriptions/EWD08xx/EWD831.html) by one of the all-time greats in computer science, the late [Edsger Dijkstra](https://en.wikipedia.org/wiki/Edsger_W._Dijkstra)."
    ]
@@ -2777,7 +2777,7 @@
     }
    },
    "source": [
-    "To change the first entry in the list, we use the assignment statement `=` again. Here, this does actually *not* create a *new* variable (or overwrite an existing one) but only changes the object to which the first pointer in the `x` list points to. As we only change parts of the `x` object, we say that we **mutate** (i.e., change) its **state**."
+    "To change the first entry in the list, we use the assignment statement `=` again. Here, this does actually *not* create a *new* variable (or overwrite an existing one) but only changes the object to which the first pointer in the `x` list points to. As we only change parts of the `x` object, we say that we **mutate** (i.e., \"change\") its **state**. To use the bag analogy from above, we keep the same bag but \"flip\" some of the $0$s into $1$s and some of the $1$s into $0$s."
    ]
   },
   {
@@ -2890,9 +2890,9 @@
     }
    },
    "source": [
-    "**[Phil Karlton](https://skeptics.stackexchange.com/questions/19836/has-phil-karlton-ever-said-there-are-only-two-hard-things-in-computer-science)** famously noted (during his time at [Netscape](https://en.wikipedia.org/wiki/Netscape)):\n",
+    "[Phil Karlton](https://skeptics.stackexchange.com/questions/19836/has-phil-karlton-ever-said-there-are-only-two-hard-things-in-computer-science) famously noted during his time at [Netscape](https://en.wikipedia.org/wiki/Netscape):\n",
     "\n",
-    "> \"There are two hard problems in computer science: naming things and cache invalidation ... and off-by-one errors.\""
+    "> \"There are *two* hard problems in computer science: *naming things* and *cache invalidation* ... and *off-by-one* errors.\""
    ]
   },
   {
@@ -3077,7 +3077,7 @@
     }
    },
    "source": [
-    "Variables with leading and trailing double underscores, referred to as **dunder** in Python \"slang\", are used for important built-in variables. Do *not* use this style for custom variables!"
+    "Variables with leading and trailing double underscores, referred to as **dunder** in Python jargon, are used for important built-in functionalities. Do *not* use this style for custom variables unless you know exactly what you are doing!"
    ]
   },
   {
@@ -3123,7 +3123,7 @@
     }
    },
    "source": [
-    "This PyCon talk by [Ned Batchelder](https://nedbatchelder.com/) (a software engineer at [edX](https://www.edx.org/) and the organizer of the [Python User Group](https://www.meetup.com/bostonpython/) in Boston) summarizes all situations where some sort of variable assignment is done in Python. The content is intermediate and therefore it is ok if you do not understand everything at this point. However, the contents should be known by everyone claiming to be a Pythonista."
+    "This PyCon talk by [Ned Batchelder](https://nedbatchelder.com/), a well-known Pythonista and the organizer of the [Python User Group](https://www.meetup.com/bostonpython/) in Boston, summarizes all situations where some sort of variable assignment is done in Python. The content is intermediate and therefore it is ok if you do not understand everything at this point. However, the contents should be known by everyone claiming to be proficient in Python."
    ]
   },
   {
@@ -3150,7 +3150,7 @@
        "        "
       ],
       "text/plain": [
-       "<IPython.lib.display.YouTubeVideo at 0x7f35840cfe48>"
+       "<IPython.lib.display.YouTubeVideo at 0x7f465852de48>"
       ]
      },
      "execution_count": 93,
diff --git a/01_elements_of_a_program_review_and_exercises.ipynb b/01_elements_review_and_exercises.ipynb
similarity index 100%
rename from 01_elements_of_a_program_review_and_exercises.ipynb
rename to 01_elements_review_and_exercises.ipynb
diff --git a/02_functions.ipynb b/02_functions.ipynb
index 95646a8..2c284f9 100644
--- a/02_functions.ipynb
+++ b/02_functions.ipynb
@@ -142,7 +142,7 @@
     {
      "data": {
       "text/plain": [
-       "140620654002656"
+       "139655430681056"
       ]
      },
      "execution_count": 3,
@@ -635,7 +635,7 @@
    "source": [
     "Code gets even more confusing when variables by the same name from different scopes collide. In particular, what should we expect to happen if a function changes a globally defined variable internally?\n",
     "\n",
-    "`average_odds()` below works like `average_evens()` above except that it **[casts](https://en.wikipedia.org/wiki/Type_conversion)** (i.e., \"converts\") the elements of `numbers` as objects of type `int` with the [int()](https://docs.python.org/3/library/functions.html#int) built-in first before filtering and averaging them. In doing so, it introduces an *internal* variable `nums` whose name collides with the one in the global scope."
+    "`average_odds()` below works like `average_evens()` above except that it **[casts](https://en.wikipedia.org/wiki/Type_conversion)** (i.e., \"converts\") the elements of `numbers` as objects of type `int` with the [int()](https://docs.python.org/3/library/functions.html#int) built-in first before filtering and averaging them. In doing so, it introduces an *internal* variable `nums` whose name collides with the one in the global scope. The **inequality operator** `!=` is just the **reversed** version of `==`."
    ]
   },
   {
@@ -658,7 +658,7 @@
     "        float: average\n",
     "    \"\"\"\n",
     "    nums = [int(n) for n in numbers]  # cast all numbers as integers first\n",
-    "    odds = [n for n in nums if n % 2 != 0]  # before filtering them\n",
+    "    odds = [n for n in nums if n % 2 != 0]  # before filtering for odd numbers\n",
     "    average = sum(odds) / len(odds)\n",
     "    return average"
    ]
@@ -2036,7 +2036,7 @@
     {
      "data": {
       "text/plain": [
-       "140620734513624"
+       "139655519584648"
       ]
      },
      "execution_count": 58,
@@ -2649,7 +2649,7 @@
     {
      "data": {
       "text/plain": [
-       "0.4028601552264015"
+       "0.12717011866176486"
       ]
      },
      "execution_count": 75,
@@ -2684,7 +2684,7 @@
     {
      "data": {
       "text/plain": [
-       "<bound method Random.choice of <random.Random object at 0x561d3c7fbba8>>"
+       "<bound method Random.choice of <random.Random object at 0x562161984ba8>>"
       ]
      },
      "execution_count": 76,

From 4e2b329fd1bf78e86efeadfd7a95281bf9bf5015 Mon Sep 17 00:00:00 2001
From: Alexander Hess <alexander@webartifex.biz>
Date: Tue, 24 Sep 2019 19:45:56 +0200
Subject: [PATCH 020/242] Add initial version of notebook 03

---
 03_conditionals.ipynb | 1805 +++++++++++++++++++++++++++++++++++++++++
 1 file changed, 1805 insertions(+)
 create mode 100644 03_conditionals.ipynb

diff --git a/03_conditionals.ipynb b/03_conditionals.ipynb
new file mode 100644
index 0000000..acd509d
--- /dev/null
+++ b/03_conditionals.ipynb
@@ -0,0 +1,1805 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "# Chapter 3: Conditionals & Exceptions"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "We analyzed every aspect of the `average_evens()` function in Chapter 2 except for the `if` part. While it seems to intuitively do what we expect it to, there is a whole lot more to be learned from taking it apart. In particular, the `if` can occur within both a **statement** as in our introductory example in Chapter 1 but also an **expression** as in `average_evens()`. This is analogous as to how a noun in a natural language is *either* the subject of *or* an object in a sentence. What is common to both versions of the `if` is that it leads to code being executed for *parts* of the input only. It is our first way of **controlling** the **flow of execution** in a program.\n",
+    "\n",
+    "After deconstructing `if` in the first part of this chapter, we take a close look at a similar concept, namely handling and raising **exceptions**."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "## Boolean Expressions"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Any expression that is either true or not is called a **boolean expression**. If you think such expressions are boring or just not so useful, read a bit on [propositional logic](https://en.wikipedia.org/wiki/Propositional_calculus) and you will quickly realize how mathematicians and originally philosophers base their rules of how to prove or disprove a conclusion on simple true-or-false \"statements\" about the world. It is the underlying principle of all of reasoning.\n",
+    "\n",
+    "A trivial example involves the equality operator `==` that evaluates to either `True` or `False` depending on its operands \"comparing equal\" or not."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 1,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "42 == 42"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "False"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "42 == 123"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Observe how `==` can handle objects of *different* type. This shows how it implements a notion of equality in line with how we humans think of things being equal or not. After all, `42` and `42.0` are totally different $0$s and $1$s for a computer and many programming languages would actually say `False` here! Technically, this is yet another example of operator overloading."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "42 == 42.0"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "There are, however, cases where even well-behaved Python does not make us happy. Chapter 5 will provide more insights on that."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "42 == 42.000000000000001"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "`True` and `False` are special built-in *objects* of type `bool`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "94709180875744"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "id(True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "94709180875712"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "id(False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "bool"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "type(True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "bool"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "type(False)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Let's not confuse the boolean `False` with `None`, another special built-in object! We saw the latter before in Chapter 2 as the *implicit* return value of a function without a `return` statement.\n",
+    "\n",
+    "We might think of `None` in a boolean context indicating a \"maybe\" or even an \"unknown\" answer. But for Python, there are no \"maybe\" or \"unknown\" objects as we will see further below!\n",
+    "\n",
+    "Whereas `False` is of type `bool`, `None` is of type `NoneType`. So, they are totally unrelated. On the contrary, as both `True` and `False` are of the same type, we could call them \"siblings\"."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "None"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "94709180862704"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "id(None)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "NoneType"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "type(None)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "`True`, `False`, and `None` have the property that they each exist in memory only *once*. Objects designed this way are so-called **singletons**. This **[design pattern](https://en.wikipedia.org/wiki/Design_Patterns)** was originally developed to keep a program's memory usage at a minimum. It may only be employed in situations where we know that an object will never mutate its value in place (i.e., to re-use the bag analogy from Chapter 1, no flipping of $0$s and $1$s in the bag is allowed). In languages \"closer\" to the memory like C we would have to code this singleton logic ourselves but Python has this already built in for *some* types.\n",
+    "\n",
+    "We can verify this with either the `is` operator or by comparing memory addresses."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "True is True"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "id(True) == id(True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "So the following expression regards *four* objects in memory: *One* `list` object holding ten pointers to *three* other objects."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[True, False, None, None, None, True, False, None, None, None]"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "[True, False, None, None, None, True, False, None, None, None]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "## Relational Operators"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "The equality operator is only one of several **relational (i.e., \"comparison\") operators** who all evaluate to a boolean object."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "False"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "42 == 123"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "42 != 123  # = \"not equal to\"; other programming languages sometimes use \"<>\" instead"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "The \"less than\" `<` or \"greater than\" `>` operators on their own mean \"strictly less than\" or \"strictly greater than\" but can be combined with the equality operator into just `<=` and `>=`. This is a shortcut for using the logical `or` operator as described in the next section."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "42 < 123"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "42 <= 123  # same as 42 < 123 or 42 == 123; cf., next section"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "False"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "42 > 123"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "False"
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "42 >= 123  # same as 42 > 123 or 42 == 123; cf., next section"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "## Logical Operators"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Boolean expressions can be combined or negated with the **logical operators** `and`, `or`, and `not` to form new boolean expressions. Of course, this may be done *recursively* as well to obtain boolean expressions of arbitrary complexity.\n",
+    "\n",
+    "Their usage is similar to how the equivalent words are used in plain English:\n",
+    "\n",
+    "- `and` evaluates to `True` if *both* sub-expressions evaluate to `True` and `False` otherwise,\n",
+    "- `or` evaluates to `True` if either one *or* both sub-expressions evaluate to `True` and `False` otherwise, and\n",
+    "- `not` evaluates to `True` if its *only* sub-expression evaluates to `False` and vice versa."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "x = 42\n",
+    "y = 87"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Relational operators have a *higher precedence* over logical operators (cf., the [reference](https://docs.python.org/3/reference/expressions.html#operator-precedence)). So the following expression means what we intuitively think it does."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "x > 5 and y <= 100"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "However, sometimes it is good to use *parentheses* around each sub-expression for clarity."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 23,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "(x > 5) and (y <= 100)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "This is especially useful when several logical operators are combined."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 24,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "x <= 5 or not y > 100"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 25,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "(x <= 5) or not (y > 100)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 26,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "(x <= 5) or (not (y > 100))  # but no need to \"over do\" it"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "For even better readability, [some practitioner](https://llewellynfalco.blogspot.com/2016/02/dont-use-greater-than-sign-in.html) suggest to never use the `>` and `>=` operators (note that the included example is written in [Java](https://en.wikipedia.org/wiki/Java_%28programming_language%29) and `&&` means `and` and `||` means `or`).\n",
+    "\n",
+    "Python allows **chaining** relational operators that are combined with the `and` operator. For example, the following two cells implement the same logic where the second is a lot easier to read."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "False"
+      ]
+     },
+     "execution_count": 27,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "(5 < x) and (x < 21)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "False"
+      ]
+     },
+     "execution_count": 28,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "5 < x < 21"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "### Truthy vs. Falsy"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "The operands of the logical operators do not actually have to be *boolean* expressions as defined above but may be *any* kind of expression. If a sub-expression does *not* evaluate to an object of type `bool`, Python automatically casts the resulting object as such.\n",
+    "\n",
+    "For example, any non-zero numeric object effectively becomes `True`. While this behavior allows writing more concise and thus \"beautiful\" code, it is also a common source of confusion."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 29,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "(x - 9) and (y < 100)  # = 33 and (y < 100)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Whenever we are unsure as to how Python will evaluate a non-boolean expression in a boolean context, the [bool()](https://docs.python.org/3/library/functions.html#bool) built-in allows us to check it ourselves."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 30,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "bool(x - 9)  # = bool(33)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "False"
+      ]
+     },
+     "execution_count": 31,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "bool(x - 42)  # = bool(0)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Keep in mind that negative numbers also evaluate to `True`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 32,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "bool(x - 99)  # = bool(-57)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "In a boolean context `None` is casted as `False`. So, `None` is really *not* a \"maybe\" answer but a \"no\"."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "False"
+      ]
+     },
+     "execution_count": 33,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "bool(None)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Another good rule to know is that container types (e.g., `list`) evaluate to `True` whenever they are not empty and `False` otherwise."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "False"
+      ]
+     },
+     "execution_count": 34,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "bool([])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 35,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "bool([False])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Pythonistas often use the terms **truthy** or **falsy** to describe a non-boolean expression's behavior when used in place of a boolean one."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "## Conditional Statements"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "In order to write useful programs, we need to control the flow of execution, for example, to react to user input.\n",
+    "\n",
+    "One major language construct to do so is the **conditional statement** or `if` **statement** (cf., the [reference](https://docs.python.org/3/reference/compound_stmts.html#the-if-statement)). It consists of:\n",
+    "\n",
+    "- *one* mandatory `if`-clause,\n",
+    "- an *arbitrary* number of `elif`-clauses (i.e. \"else if\"), and\n",
+    "- an *optional* `else`-clause.\n",
+    "\n",
+    "The `if`- and `elif`-clauses each specify one *boolean* expression, also called **condition**, while the `else`-clause serves as a \"catch everything else\" case.\n",
+    "\n",
+    "In terms of syntax, the header lines end with a colon and the code blocks are indented.\n",
+    "\n",
+    "In contrast to our intuitive interpretation in natural languages, only the code in *one* of the alternatives, also called **branches**, is executed. To be precise, it is always the code in the first branch whose condition evaluates to `True`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "metadata": {
+    "code_folding": [],
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "z = 101"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "metadata": {
+    "code_folding": [],
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "z is positive but odd\n"
+     ]
+    }
+   ],
+   "source": [
+    "if (z % 2 == 0) and (z > 0):\n",
+    "    print(\"z is even and positive\")\n",
+    "elif z % 2 == 0:\n",
+    "    print(\"z is even but negative\")\n",
+    "elif z > 0:\n",
+    "    print(\"z is positive but odd\")\n",
+    "else:\n",
+    "    print(\"z is neither even nor positive\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "In many situations, we only need a reduced form of the `if` statement.\n",
+    "\n",
+    "We could **inject** code only at random to, for example, implement some sort of [A/B testing](https://en.wikipedia.org/wiki/A/B_testing)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "import random"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "You will read this just as often as you see heads when tossing a coin\n"
+     ]
+    }
+   ],
+   "source": [
+    "if random.random() > 0.5:\n",
+    "    print(\"You will read this just as often as you see heads when tossing a coin\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "More often than not, we might model a binary choice."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "z is positive\n"
+     ]
+    }
+   ],
+   "source": [
+    "if z > 0:\n",
+    "    print(\"z is positive\")\n",
+    "else:\n",
+    "    print(\"z is negative\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "We may **nest** `if` statements to control the flow of execution in a more granular way. Every additional layer, however, makes the code less readable, in particular, if we have more than one line per code block."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "z is odd\n"
+     ]
+    }
+   ],
+   "source": [
+    "if random.random() > 0.5:\n",
+    "    if z % 2:  # no need to write out the \"== 0\"\n",
+    "        print(\"z is odd\")\n",
+    "    else:\n",
+    "        print(\"z is even\")\n",
+    "else:\n",
+    "    if z > 0:\n",
+    "        print(\"z is positive\")\n",
+    "    else:\n",
+    "        print(\"z is negative\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "A good way to make this code more readable is to introduce **temporary variables** *in combination* with using the `and` operator to **flatten** the branching logic. The `if` statement then reads almost like plain English. In contrast to many other languages, creating variables is a computationally *cheap* operation in Python and also helps to document the code *inline*. Without temporary variables, the `and` flattening could actually lead to more sub-expressions in the conditions be evaluated than necessary. Do you see why?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "z is positive\n"
+     ]
+    }
+   ],
+   "source": [
+    "check_oddness = (random.random() > 0.5)\n",
+    "is_odd = (z % 2)\n",
+    "is_positive = (z > 0)\n",
+    "\n",
+    "if check_oddness and is_odd:\n",
+    "    print(\"z is odd\")\n",
+    "elif check_oddness and not is_odd:\n",
+    "    print(\"z is even\")\n",
+    "elif not check_oddness and is_positive:\n",
+    "    print(\"z is positive\")\n",
+    "else:\n",
+    "    print(\"z is negative\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "## Conditional Expressions"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "When all we do with an `if` statement is to assign an object to a variable with respect to a single true-or-false condition (cf., binary choice above), there is a shortcut for that: We could simply assign the result of a so-called **conditional expression** or `if` expression to the variable.\n",
+    "\n",
+    "Think of a situation where we evaluate a piece-wise functional relationship $y = f(x)$ at a given $x$, for example:"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "$\n",
+    "y = f(x) =\n",
+    "\\begin{cases}\n",
+    "0, \\text{ if } x \\le 0 \\\\\n",
+    "x^2, \\text{ otherwise}\n",
+    "\\end{cases}\n",
+    "$"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "x = 3"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Of course, we could use an `if` statement as above to do the job. Yet, this is rather lengthy."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "if x <= 0:\n",
+    "    y = 0\n",
+    "else:\n",
+    "    y = x ** 2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "9"
+      ]
+     },
+     "execution_count": 45,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "y"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "On the contrary, the `if` expression fits into one line. The main downside here is a potential loss in readability, in particular, if the functional relationship is not that simple."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "y = 0 if x <= 0 else x ** 2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "9"
+      ]
+     },
+     "execution_count": 47,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "y"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "In this concrete example, however, the most elegant solution would be to use the built-in [max()](https://docs.python.org/3/library/functions.html#max) function."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 48,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "y = max(0, x) ** 2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 49,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "9"
+      ]
+     },
+     "execution_count": 49,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "y"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Conditional expressions may not only be used in the way described in this section. We already saw them as part of a list comprehension that is introduced in Chapter 7."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "## Exceptions"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "In the previous two chapters we already encountered a couple of *runtime* errors. A natural urge we might have after reading about conditional statements is to write code that somehow reacts to the occurence of such exceptions. All we need for that is a way to formulate a condition for that.\n",
+    "\n",
+    "For sure, this is such a common thing to do that Python provides its own language construct for it, namely the `try` statement (cf., the [reference](https://docs.python.org/3/reference/compound_stmts.html#the-try-statement)).\n",
+    "\n",
+    "In its simplest form, it comes with just two branches: `try` and `except`. The following basically tells Python to execute the code in the `try`-branch and if *anything* goes wrong, continue in the `except`-branch instead of **raising** an error to us. Of course, if nothing goes wrong, the `except`-branch is *not* executed."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 50,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "user_input = 0"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 51,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Something went wrong\n"
+     ]
+    }
+   ],
+   "source": [
+    "try:\n",
+    "    1 / user_input\n",
+    "except:\n",
+    "    print(\"Something went wrong\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "However, it is good practise to *not* **handle** *any* possible exception but only the ones we may expect from the code in the `try`-branch. The reasoning why this is done is a bit involved. We only remark here that the code base becomes easier to understand as we clearly communicate to any human reader what could go wrong during execution. Python comes with a lot of [built-in exceptions](https://docs.python.org/3/library/exceptions.html#concrete-exceptions) that we should familiarize ourselves with.\n",
+    "\n",
+    "Another good practise is to always keep the code in the `try`-branch short so as to not accidently handle an exception we do not want to handle.\n",
+    "\n",
+    "In the example, we are dividing numbers and may therefore expect a `ZeroDivisionError`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 52,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Something went wrong\n"
+     ]
+    }
+   ],
+   "source": [
+    "try:\n",
+    "    1 / user_input\n",
+    "except ZeroDivisionError:\n",
+    "    print(\"Something went wrong\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Often, we must have some code run independent of an exception occuring (e.g., to close a connection to a database). To achieve that, we can add an optional `finally`-branch to the `try` statement.\n",
+    "\n",
+    "Similarly, we might have some code that must be run exactly when no exception occurs but we do not want to put it in the `try`-branch as per the good practice mentioned. To achieve that, we can add an optional `else`-branch to the `try` statement.\n",
+    "\n",
+    "To showcase everything together, we look at one last example. To spice it up a bit, we randomize the input. So run the cell several times and see for yourself. It's actually quite easy."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 53,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Yes, division worked smoothly.\n",
+      "I am always printed\n"
+     ]
+    }
+   ],
+   "source": [
+    "divisor = random.choice([0, 1])\n",
+    "\n",
+    "try:\n",
+    "    1 / divisor\n",
+    "except ZeroDivisionError:\n",
+    "    print(\"Oops. Division by 0. How does that work?\")\n",
+    "else:\n",
+    "    print(\"Yes, division worked smoothly.\")\n",
+    "finally:\n",
+    "    print(\"I am always printed\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "## TL;DR"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "- **boolean expressions** evaluate either to `True` or `False`\n",
+    "- **relational operators** compare operands according to \"human\" interpretations\n",
+    "- **logical operators** combine boolean sub-expressions to more \"complex\" expressions\n",
+    "- the **conditional statement** is a *major* concept to **control** the **flow of execution** depending on some **conditions**\n",
+    "- a **conditional expression** is a short form of a conditional statement\n",
+    "- **exception handling** is also a common way of **controlling** the **flow of execution**, in particular if we have to be prepared for bad input data"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.3"
+  },
+  "livereveal": {
+   "auto_select": "code",
+   "auto_select_fragment": true,
+   "scroll": true,
+   "theme": "serif"
+  },
+  "toc": {
+   "base_numbering": 1,
+   "nav_menu": {},
+   "number_sections": false,
+   "sideBar": true,
+   "skip_h1_title": true,
+   "title_cell": "Table of Contents",
+   "title_sidebar": "Contents",
+   "toc_cell": false,
+   "toc_position": {
+    "height": "calc(100% - 180px)",
+    "left": "10px",
+    "top": "150px",
+    "width": "384px"
+   },
+   "toc_section_display": false,
+   "toc_window_display": false
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

From e1845113cf5ebd1d80b4e0934f8cad8afd292fce Mon Sep 17 00:00:00 2001
From: Alexander Hess <alexander@webartifex.biz>
Date: Tue, 24 Sep 2019 19:46:44 +0200
Subject: [PATCH 021/242] Adjust content overview in README.md

---
 README.md | 5 ++++-
 1 file changed, 4 insertions(+), 1 deletion(-)

diff --git a/README.md b/README.md
index b664a32..951bf75 100644
--- a/README.md
+++ b/README.md
@@ -1,3 +1,5 @@
+**Important**: The notebooks are being added throughout the fall semester of 2019!
+
 # An Introduction to Python and Programming
 
 The purpose of this repository is to serve as an interactive "book" for a
@@ -13,8 +15,9 @@ science professionals and researchers.
 As such they can be viewed in a plain web browser:
 
 - [00 - Start up](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/00_start_up.ipynb)
-- [01 - Elements of a Program](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/01_elements_of_a_program.ipynb)
+- [01 - Elements of a Program](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/01_elements.ipynb)
 - [02 - Functions & Modularization](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/02_functions.ipynb)
+- [03 - Conditionals & Exceptions](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/03_conditionals.ipynb)
 
 However, it is recommended that students **install Python and Jupyter
 locally** and run the code in the notebooks on their own.

From 0b067ec4b54fdad8d7db20775688a2c96cb382f6 Mon Sep 17 00:00:00 2001
From: Alexander Hess <alexander@webartifex.biz>
Date: Wed, 25 Sep 2019 09:08:31 +0200
Subject: [PATCH 022/242] Add review and exercises for notebook 03

---
 03_conditionals_review_and_exercises.ipynb | 389 +++++++++++++++++++++
 1 file changed, 389 insertions(+)
 create mode 100644 03_conditionals_review_and_exercises.ipynb

diff --git a/03_conditionals_review_and_exercises.ipynb b/03_conditionals_review_and_exercises.ipynb
new file mode 100644
index 0000000..f60bc2b
--- /dev/null
+++ b/03_conditionals_review_and_exercises.ipynb
@@ -0,0 +1,389 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n",
+    "# Chapter 3: Conditionals & Exceptions"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Content Review"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Read Chapter 3 of the book. Then work through the seven review questions."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Essay Questions "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Answer the following questions briefly with *at most* 300 characters per question!"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q1**: What is the **singleton** design pattern? How many objects does the expression `[True, False, True, False]` generate in memory?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q2**: What do we mean when we talk about **truthy** and **falsy** expressions?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q3**: Explain how the conceptual difference between a **statement** and an **expression** relates to the difference between a **conditional statement** and a **conditional expression**."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q4**: Why is the use of **temporary variables** encouraged in Python?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q5**: What does the `finally`-branch enforce in this code snippet? How can a `try` statement be useful *without* an `except`-branch?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "```python\n",
+    "try:\n",
+    "    print(\"Make a request to a service on the internet\")\n",
+    "finally:\n",
+    "    print(\"This could be clean-up code\")\n",
+    "```"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### True / False Questions"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Motivate your answer with *one short* sentence!"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q6**: The objects `True`, `False`, and `None` represent the idea of \"yes\", \"no\", and \"maybe\" answers in a natural language.\n",
+    "\n",
+    "Hint: you also respond with a code cell."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q7**: The `try` statement is useful for handling **syntax** errors."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Coding Exercises"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Discounting Customer Orders"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q8.1**: Write a function `discounted_price()` that takes the positional arguments `unit_price` (of type `float`) and `quantity` (of type `int`) and implements a discount scheme for a line item in a customer order as follows:\n",
+    "\n",
+    "- if the unit price is over 100 dollars, grant 10% relative discount\n",
+    "- if a customer orders more than 10 items, one in every five items is for free\n",
+    "\n",
+    "Only one of the two discounts is granted, whichever is better for the customer.\n",
+    "\n",
+    "The function should then return the overall price for the line item. Do not forget to round appropriately."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\n",
+    "\n",
+    "\n",
+    "\n",
+    "\n",
+    "\n",
+    "\n",
+    "\n",
+    "\n",
+    "\n",
+    "\n",
+    "\n",
+    "\n",
+    "\n",
+    "\n",
+    "\n",
+    "\n",
+    "\n",
+    "\n",
+    "\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q8.2**: Calculate the final price for the following line items of an order:\n",
+    "- $7$ smartphones @ $99.00$ USD\n",
+    "- $3$ workstations @ $999.00$ USD\n",
+    "- $19$ GPUs @ $879.95$ USD\n",
+    "- $14$ Raspberry Pis @ $35.00$ USD"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q8.3**: Re-calculate the last two line items with order quantities of $20$ and $15$. What do you observe?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " (your observation)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q8.4**: Looking at the `if`-`else`-logic in the function, why do you think the four example line items in **Q8.2** were chosen as they were?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Fizz Buzz revisited"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "When you worked on the Fizz Buzz exercise in Chapter 1, you actually did not know about the `elif` and `else` keywords yet. Well, now you do."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "numbers = list(range(1, 101))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q9**: Copy and paste your answer to **Q11.2** in Chapter 1 here and instead of three consecutive `if` statements re-write it with *one* compound `if` statement.\n",
+    "\n",
+    "This code will then be a lot more robust as the order of the three `if` statements cannot be screwed up."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\n",
+    "\n",
+    "\n",
+    "\n",
+    "\n",
+    "\n",
+    "\n",
+    "\n",
+    "\n",
+    "\n",
+    "\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.3"
+  },
+  "toc": {
+   "base_numbering": 1,
+   "nav_menu": {},
+   "number_sections": false,
+   "sideBar": true,
+   "skip_h1_title": true,
+   "title_cell": "Table of Contents",
+   "title_sidebar": "Contents",
+   "toc_cell": false,
+   "toc_position": {},
+   "toc_section_display": false,
+   "toc_window_display": false
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

From 84e08d06f49e57d8bed6b4630bb2e56067d421c4 Mon Sep 17 00:00:00 2001
From: Alexander Hess <alexander@webartifex.biz>
Date: Wed, 25 Sep 2019 14:27:03 +0200
Subject: [PATCH 023/242] Move FizzBuzz exercise from chapter 01 to 03

---
 01_elements_review_and_exercises.ipynb     | 72 ------------------
 03_conditionals_review_and_exercises.ipynb | 88 +++++++++++++++-------
 2 files changed, 61 insertions(+), 99 deletions(-)

diff --git a/01_elements_review_and_exercises.ipynb b/01_elements_review_and_exercises.ipynb
index 2207ccc..7e43b77 100644
--- a/01_elements_review_and_exercises.ipynb
+++ b/01_elements_review_and_exercises.ipynb
@@ -275,78 +275,6 @@
     "for number in [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]:\n",
     "    print(...)"
    ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Fizz Buzz"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The kids game [Fizz Buzz](https://en.wikipedia.org/wiki/Fizz_buzz) is said to be often used in job interviews for entry level positions. However, opinions vary as to how good of a test it actually is ([source](https://news.ycombinator.com/item?id=16446774)).\n",
-    "\n",
-    "In its simplest form, a group of people start counting upwards in an alternating fashion. Whenever a number is divisible by $3$, the person must say \"Fizz\" instead of the number. The same holds for numbers divisible by $5$ when the person must say \"Buzz\". If a number is divisible by both numbers, one must say \"FizzBuzz\". Probably, this game would also make a good drinking game with the \"right\" beverages.\n",
-    "\n",
-    "With just Chapter 1, we actually do not yet know all of Python's language constructs we need to write an implementation of Fizz Buzz in a Pythonic way. Yet, we will tweak what we know a bit and make it work."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**Q11.1**: First, create a list `numbers` with the numbers from 1 through 100. You could type all numbers manually but there is of course a smarter way. The built-in [range()](https://docs.python.org/3/library/functions.html#func-range) may be useful here. Read how it works in the documentation. To make the output of [range()](https://docs.python.org/3/library/functions.html#func-range) a `list` object, you have to \"wrap\" it with the [list()](https://docs.python.org/3/library/functions.html#func-list) built-in (i.e., `list(range(...))`)."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "numbers = ..."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**Q11.2**: Loop over the `numbers` list and replace numbers for which one of the two (or both) conditions apply with text strings `\"Fizz\"`, `\"Buzz\"`, or `\"FizzBuzz\"` using the indexing operator `[...]` and the assignment statement `=`. In the chapter we saw that Python starts indexing with `0` as the first element. So in each iteration of the `for`-loop you have to determine the *index* as well as checking the actual `number`.\n",
-    "\n",
-    "Also note that for numbers divisible by both $3$ and $5$ we need some sort of a \"third\" condition check: As we only know about the `if` statement so far (and have not heard about `elif` and `else` from Chapter 3), there will be three `if` statements in total within the loop. And the order of them matters!\n",
-    "\n",
-    "Hint: Is there a single condition that checks for both $3$ and $5$?"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "for number in numbers:\n",
-    "    ..."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**Q11.3**: Create a loop that prints out either the number or any of the Fizz Buzz substitutes. Do it in such a way that we do not end up with 100 lines of output here."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "for number in numbers:\n",
-    "    ..."
-   ]
   }
  ],
  "metadata": {
diff --git a/03_conditionals_review_and_exercises.ipynb b/03_conditionals_review_and_exercises.ipynb
index f60bc2b..f152b30 100644
--- a/03_conditionals_review_and_exercises.ipynb
+++ b/03_conditionals_review_and_exercises.ipynb
@@ -211,11 +211,6 @@
     "\n",
     "\n",
     "\n",
-    "\n",
-    "\n",
-    "\n",
-    "\n",
-    "\n",
     "\n"
    ]
   },
@@ -235,28 +230,36 @@
    "execution_count": null,
    "metadata": {},
    "outputs": [],
-   "source": []
+   "source": [
+    "discounted_price(...)"
+   ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
    "metadata": {},
    "outputs": [],
-   "source": []
+   "source": [
+    "discounted_price(...)"
+   ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
    "metadata": {},
    "outputs": [],
-   "source": []
+   "source": [
+    "discounted_price(...)"
+   ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
    "metadata": {},
    "outputs": [],
-   "source": []
+   "source": [
+    "discounted_price(...)"
+   ]
   },
   {
    "cell_type": "markdown",
@@ -270,14 +273,18 @@
    "execution_count": null,
    "metadata": {},
    "outputs": [],
-   "source": []
+   "source": [
+    "discounted_price(...)"
+   ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
    "metadata": {},
    "outputs": [],
-   "source": []
+   "source": [
+    "discounted_price(...)"
+   ]
   },
   {
    "cell_type": "markdown",
@@ -304,14 +311,23 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### Fizz Buzz revisited"
+    "### Fizz Buzz"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "When you worked on the Fizz Buzz exercise in Chapter 1, you actually did not know about the `elif` and `else` keywords yet. Well, now you do."
+    "The kids game [Fizz Buzz](https://en.wikipedia.org/wiki/Fizz_buzz) is said to be often used in job interviews for entry level positions. However, opinions vary as to how good of a test it actually is ([source](https://news.ycombinator.com/item?id=16446774)).\n",
+    "\n",
+    "In its simplest form, a group of people start counting upwards in an alternating fashion. Whenever a number is divisible by $3$, the person must say \"Fizz\" instead of the number. The same holds for numbers divisible by $5$ when the person must say \"Buzz\". If a number is divisible by both numbers, one must say \"FizzBuzz\". Probably, this game would also make a good drinking game with the \"right\" beverages."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q9.1**: First, create a list `numbers` with the numbers from 1 through 100. You could type all numbers manually but there is of course a smarter way. The built-in [range()](https://docs.python.org/3/library/functions.html#func-range) may be useful here. Read how it works in the documentation. To make the output of [range()](https://docs.python.org/3/library/functions.html#func-range) a `list` object, you have to wrap it with the [list()](https://docs.python.org/3/library/functions.html#func-list) built-in (i.e., `list(range(...))`)."
    ]
   },
   {
@@ -320,16 +336,20 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "numbers = list(range(1, 101))"
+    "numbers = ..."
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "**Q9**: Copy and paste your answer to **Q11.2** in Chapter 1 here and instead of three consecutive `if` statements re-write it with *one* compound `if` statement.\n",
+    "**Q9.2**: Loop over the `numbers` list and replace numbers for which one of the two (or both) conditions apply with text strings `\"Fizz\"`, `\"Buzz\"`, or `\"FizzBuzz\"` using the indexing operator `[]` and the assignment statement `=`.\n",
     "\n",
-    "This code will then be a lot more robust as the order of the three `if` statements cannot be screwed up."
+    "In Chapter 1 we saw that Python starts indexing with `0` as the first element. Keep that in mind.\n",
+    "\n",
+    "So in each iteration of the `for`-loop you have to determine an `index` variable as well as checking the actual `number` for its divisors.\n",
+    "\n",
+    "Hint: the order of the conditions is important!"
    ]
   },
   {
@@ -338,17 +358,31 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "\n",
-    "\n",
-    "\n",
-    "\n",
-    "\n",
-    "\n",
-    "\n",
-    "\n",
-    "\n",
-    "\n",
-    "\n"
+    "for number in numbers:\n",
+    "    ...\n",
+    "    ...\n",
+    "    ...\n",
+    "    ...\n",
+    "    ...\n",
+    "    ...\n",
+    "    ..."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q9.3**: Create a loop that prints out either the number or any of the Fizz Buzz substitutes. Do it in such a way that we do not end up with 100 lines of output here."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "for number in numbers:\n",
+    "    print(...)"
    ]
   }
  ],

From 250aa092093402f89f5ef564b56134d18cfb7a7f Mon Sep 17 00:00:00 2001
From: Alexander Hess <alexander@webartifex.biz>
Date: Tue, 1 Oct 2019 17:47:45 +0200
Subject: [PATCH 024/242] Streamline previous content

---
 00_start_up_review_and_exercises.ipynb     |   2 +-
 01_elements.ipynb                          | 143 +++++++++++++--------
 01_elements_review_and_exercises.ipynb     |   2 +-
 02_functions.ipynb                         |  96 ++++++++++----
 02_functions_review_and_exercises.ipynb    |   2 +-
 03_conditionals.ipynb                      |  38 +++---
 03_conditionals_review_and_exercises.ipynb |   2 +-
 7 files changed, 181 insertions(+), 104 deletions(-)

diff --git a/00_start_up_review_and_exercises.ipynb b/00_start_up_review_and_exercises.ipynb
index e1a435f..2f4c334 100644
--- a/00_start_up_review_and_exercises.ipynb
+++ b/00_start_up_review_and_exercises.ipynb
@@ -18,7 +18,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Read Chapter 0 of the book. Then work through the ten review questions."
+    "Read [Chapter 0](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/00_start_up.ipynb) of the book. Then work through the ten review questions."
    ]
   },
   {
diff --git a/01_elements.ipynb b/01_elements.ipynb
index e1d8b09..382d757 100644
--- a/01_elements.ipynb
+++ b/01_elements.ipynb
@@ -24,7 +24,7 @@
     "\n",
     "It is intuitively best to take the very mindset of a small child when learing a foreign language and we do so for learning the Python language as well. This first chapter introduces simplistic examples and we better just accept them as they are without knowing any of the \"grammar\" rules yet. Then, we analyze them in parts and slowly build up our understanding.\n",
     "\n",
-    "Consequently, if parts of this chapter do not make sense right away, let's not worry too much. Besides introducing some basics (that we need to understand), it also serves as an outlook for what is to come. So, many terms and concepts referenced here will be covered in great detail in following chapters."
+    "Consequently, if parts of this chapter do not make sense right away, let's not worry too much. Besides introducing some basics that we need to understand, it also serves as an outlook for what is to come. So, many terms and concepts used here will be covered in great detail in following chapters."
    ]
   },
   {
@@ -46,7 +46,7 @@
     }
    },
    "source": [
-    "As our introductory example, we want to calculate the average of all even numbers from one through ten.\n",
+    "As our introductory example, we want to calculate the *average* of all *even* numbers from *one* through *ten*.\n",
     "\n",
     "While we could come up with an [analytical solution](https://math.stackexchange.com/questions/935405/what-s-the-difference-between-analytical-and-numerical-approaches-to-problems/935446#935446) (i.e., derive some equation with \"pen and paper\" from, e.g., one of [Faulhaber's formulas](https://en.wikipedia.org/wiki/Faulhaber%27s_formula)), we instead solve the task programmatically.\n",
     "\n",
@@ -75,7 +75,7 @@
     }
    },
    "source": [
-    "To verify that something happened in our computer's memory, we simply **reference** `numbers` and observe that Python indeed knows about."
+    "To verify that something happened in our computer's memory, we simply **reference** `numbers` and observe that Python indeed knows about it."
    ]
   },
   {
@@ -110,17 +110,17 @@
     }
    },
    "source": [
-    "So far, so good. Let's see how the desired result could be expressed as a **sequence of instructions** in Python.\n",
+    "So far, so good. Let's see how the desired **computation** could be expressed as a **sequence of instructions** in Python.\n",
     "\n",
     "Intuitively, the line `for number in numbers` describes a \"loop\" over all the numbers in the `numbers` list, one at a time.\n",
     "\n",
     "The `if number % 2 == 0` may look disturbing at first sight. Both the `%` and `==` must have an unintuitive meaning here. Luckily, the **comment** in the same line after the `#` symbol has the answer: The program only does something if the current `number` is even.\n",
     "\n",
-    "In particular, it increases `count` by $1$ and adds the current `number` onto the [running](https://en.wikipedia.org/wiki/Running_total) `total`. Both `count` and `number` were initially set to $0$ and the single `=` reads as \"... is *set* equal to ...\". It could not indicate a mathematical equation as, for example, `count` is generally not equal to `count + 1`.\n",
+    "In particular, it increases `count` by $1$ and adds the current `number` onto the [running](https://en.wikipedia.org/wiki/Running_total) `total`. Both `count` and `number` were initially set to $0$ and the single `=` symbol reads as \"... is *set* equal to ...\". It could not indicate a mathematical equation as, for example, `count` is generally not equal to `count + 1`.\n",
     "\n",
     "Lastly, the `average` is calculated as the ratio of the final **values** of `total` and `count`. Overall, we divide the sum of all even numbers by the count of all even numbers, which is exactly what we are looking for.\n",
     "\n",
-    "We also observe how the lines of code \"within\" the `for` and `if` **statements** are *indented* and *aligned* with multiples of **four spaces**: This shows immediately how the lines relate to each other."
+    "We also observe how the lines of code \"within\" the `for` and `if` **statements** are **indented** and *aligned* with multiples of **four spaces**: This shows immediately how the lines relate to each other."
    ]
   },
   {
@@ -200,7 +200,7 @@
    "source": [
     "Note how only two of the previous four code cells generate an **output** while two remained \"silent\" (i.e., there is no \"**Out[...]**\" after running the cell).\n",
     "\n",
-    "By default, Jupyter notebooks show the value of a cell's last so-called **expression**. This output can be suppressed by ending the last line with a semicolon `;`."
+    "By default, Jupyter notebooks show the value of a cell's last **expression**. This output can be suppressed by ending the last line with a semicolon `;`."
    ]
   },
   {
@@ -249,7 +249,7 @@
     }
    },
    "source": [
-    "To visualize something before the end of the cell, we can use the [print()](https://docs.python.org/3/library/functions.html#print) built-in **function**."
+    "To visualize something before the end of the cell, we use the [print()](https://docs.python.org/3/library/functions.html#print) built-in **function**."
    ]
   },
   {
@@ -327,11 +327,13 @@
     }
    },
    "source": [
-    "Python comes with basic mathematical operators built in. **[Operators](https://docs.python.org/3/reference/lexical_analysis.html#operators)** are built-in **tokens** that have a special meaning to the Python interpreter. Most operators either \"operate\" with the object immediately following them (= **unary** operators; e.g., negation) or somehow \"process\" the two objects \"around\" them (= **binary** operators; e.g., addition). But we will see some exceptions from that as well.\n",
+    "Python comes with many **[operators](https://docs.python.org/3/reference/lexical_analysis.html#operators)** built in: They are **tokens** (i.e., \"symbols\") that have a special meaning to the Python interpreter.\n",
     "\n",
-    "By definition, operators have **no** permanent **side effects** in the computer's memory. Although the code cells in this section do indeed lead to *new* objects being created in memory, they are immediately \"forgotten\" as they are not stored in a **variable** (like `numbers` above). We will revisit this idea further below when we compare **expressions** with **statements**.\n",
+    "The arithmetic operators either \"operate\" with the number immediately following them (= **unary** operators; e.g., negation) or \"process\" the two numbers \"around\" them (= **binary** operators; e.g., addition). But we will see many exceptions from that as well.\n",
     "\n",
-    "Let's see some examples of operators. We start with the binary `+` and the `-` operators for addition and subtraction. Binary operators resemble what mathematicians call [infix notation](https://en.wikipedia.org/wiki/Infix_notation) and have the expected meaning."
+    "By definition, operators have **no** permanent **side effects** in the computer's memory. Although the code cells in this section do indeed create *new* numbers in memory (e.g., `77 + 13` creates `90`), they are immediately \"forgotten\" as they are not stored in a **variable** like `numbers` or `average` above. We will continue this thought further below when we compare **expressions** with **statements**.\n",
+    "\n",
+    "Let's see some examples of operators. We start with the binary `+` and the `-` operators for addition and subtraction. Binary operators are designed to resemble what mathematicians call [infix notation](https://en.wikipedia.org/wiki/Infix_notation) and have the expected meaning."
    ]
   },
   {
@@ -425,7 +427,7 @@
     }
    },
    "source": [
-    "When we compare the output of the `*` and `/` operators for multiplication and division, we note the subtle difference between the $42$ and the $42.0$. This is a first illustration of the concept of a **data type**."
+    "When we compare the output of the `*` and `/` operators for multiplication and division, we note the subtle *difference* between the $42$ and the $42.0$. This is a first illustration of the concept of a **data type**."
    ]
   },
   {
@@ -543,7 +545,7 @@
     }
    },
    "source": [
-    "To obtain the remainder of a division, we can use the **modulo operator** `%`."
+    "To obtain the remainder of a division, we use the **modulo operator** `%`."
    ]
   },
   {
@@ -613,7 +615,7 @@
     }
    },
    "source": [
-    "Modulo division can be useful if we, for example, need to get the last couple of digits of a large integer."
+    "Modulo division is also useful if we, for example, need to get the last couple of digits of a large integer."
    ]
   },
   {
@@ -672,7 +674,7 @@
     }
    },
    "source": [
-    "The [divmod()](https://docs.python.org/3/library/functions.html#divmod) built-in function combines the integer and modulo divisions into one operation. However, this is not an operator any more (but a function). Also observe that [divmod()](https://docs.python.org/3/library/functions.html#divmod) returns a \"pair\" of integers."
+    "The [divmod()](https://docs.python.org/3/library/functions.html#divmod) built-in function combines the integer and modulo divisions into one operation. However, this is not an operator but a function. Also observe that [divmod()](https://docs.python.org/3/library/functions.html#divmod) returns a \"pair\" of integers."
    ]
   },
   {
@@ -707,7 +709,7 @@
     }
    },
    "source": [
-    "Raising a number to a power is performed with the **exponentiation operator** `**`. Note that this is different from the `^` operator many other programming languages might use and that also exists in Python with a *different* meaning."
+    "Raising a number to a power is performed with the **exponentiation operator** `**`. This is different from the `^` operator many other programming languages use and that also exists in Python with a *different* meaning."
    ]
   },
   {
@@ -742,7 +744,7 @@
     }
    },
    "source": [
-    "The normal [order of precedence](https://docs.python.org/3/reference/expressions.html#operator-precedence) from mathematics applies (i.e., \"PEMDAS\" rule) but parentheses help avoid confusion."
+    "The normal [order of precedence](https://docs.python.org/3/reference/expressions.html#operator-precedence) from mathematics applies (i.e., \"PEMDAS\" rule)."
    ]
   },
   {
@@ -777,7 +779,7 @@
     }
    },
    "source": [
-    "The parentheses here serve as a **delimiter**."
+    "Parentheses help avoid confusion and take the role of a **delimiter** here."
    ]
   },
   {
@@ -871,7 +873,7 @@
     }
    },
    "source": [
-    "There are plenty more mathematical and non-mathematical operators that are introduced throughout this book together with the concepts they implement or support. Some of these are already shown in the next section."
+    "There are many more non-mathematical operators that are introduced throughout this book together with the concepts they implement. Some of these are already shown in the next section."
    ]
   },
   {
@@ -949,7 +951,7 @@
     {
      "data": {
       "text/plain": [
-       "139940106427216"
+       "140658972730512"
       ]
      },
      "execution_count": 27,
@@ -973,7 +975,7 @@
     {
      "data": {
       "text/plain": [
-       "139940106602344"
+       "140658972907392"
       ]
      },
      "execution_count": 28,
@@ -997,7 +999,7 @@
     {
      "data": {
       "text/plain": [
-       "139940105769456"
+       "140658972586992"
       ]
      },
      "execution_count": 29,
@@ -1017,7 +1019,7 @@
     }
    },
    "source": [
-    "These addresses are really not that meaningful for anything other than checking if two variables actually **point** at the same object. This may be helpful as different objects can of course have the same value."
+    "These addresses are really not that meaningful for anything other than checking if two variables actually **point** at the same object. This may be helpful as, for example, different objects in memory may of course have the same value."
    ]
   },
   {
@@ -1041,7 +1043,7 @@
     }
    },
    "source": [
-    "`a` and `d` indeed have the same value as can be checked with the **equality operator** `==`. The resulting `True` (and the `False` below) is yet another data type, a so-called **boolean**. We will look into that closely in Chapter 3."
+    "`a` and `d` indeed have the same value as can be checked with the **equality operator** `==`. The resulting `True` (and the `False` below) is yet another data type, a so-called **boolean**. We will look into that closely in [Chapter 3](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/03_conditionals.ipynb)."
    ]
   },
   {
@@ -1780,7 +1782,7 @@
        "        "
       ],
       "text/plain": [
-       "<IPython.lib.display.YouTubeVideo at 0x7f46584be518>"
+       "<IPython.lib.display.YouTubeVideo at 0x7fedb8113c50>"
       ]
      },
      "execution_count": 50,
@@ -1801,7 +1803,7 @@
     }
    },
    "source": [
-    "For example, while the above code to calculate the average of the even numbers from 1 through 10 is correct, a Pythonista would re-write it in a more \"Pythonic\" way and use the [sum()](https://docs.python.org/3/library/functions.html#sum) and [len()](https://docs.python.org/3/library/functions.html#len) (= \"length\") built-in functions (cf., Chapter 2) as well as a so-called **list comprehension** (cf., Chapter 7). Pythonic code runs faster in many cases and is less error prone."
+    "For example, while the above code to calculate the average of the even numbers from 1 through 10 is correct, a Pythonista would re-write it in a more \"Pythonic\" way and use the [sum()](https://docs.python.org/3/library/functions.html#sum) and [len()](https://docs.python.org/3/library/functions.html#len) (= \"length\") built-in functions (cf., [Chapter 2](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/02_functions.ipynb)) as well as a so-called **list comprehension** (cf., Chapter 7). Pythonic code runs faster in many cases and is less error prone."
    ]
   },
   {
@@ -2003,7 +2005,7 @@
     "\n",
     "At the same time, for a beginner's course it is often easier to just code in a linear fashion.\n",
     "\n",
-    "In real data science projects one would probably employ a mixed approach and put re-usable code into so-called Python modules (i.e., *.py* files; cf., Chapter 2) and then use Jupyter notebooks to built up a linear report or story line for a business argument to be made."
+    "In real data science projects one would probably employ a mixed approach and put re-usable code into so-called Python modules (i.e., *.py* files; cf., [Chapter 2](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/02_functions.ipynb)) and then use Jupyter notebooks to built up a linear report or story line for a business argument to be made."
    ]
   },
   {
@@ -2868,7 +2870,7 @@
     "\n",
     "In general, the assignment statement creates (or overwrites) a variable and makes it point to whatever object is on the right-hand side *only if* the left-hand side is a *pure* name (i.e., it contains no operators like the indexing operator in the example). Otherwise, it mutates some already existing object. And we always have to expect that the latter might have more than one variable pointing at it.\n",
     "\n",
-    "In the beginning, visualizing the memory with a tool like [PythonTutor](http://pythontutor.com/visualize.html#code=x%20%3D%20%5B1,%202,%203%5D%0Ay%20%3D%20x%0Ax%5B0%5D%20%3D%2099%0Adel%20x,%20y%0Ax%20%3D%20%5B1,%202,%203%5D%0Ay%20%3D%20x.copy%28%29%0Ax%5B0%5D%20%3D%2099&cumulative=false&curInstr=0&heapPrimitives=nevernest&mode=display&origin=opt-frontend.js&py=3&rawInputLstJSON=%5B%5D&textReferences=false) will assist in understanding what is going on."
+    "Visualizing what is going on in the memory with a tool like [PythonTutor](http://pythontutor.com/visualize.html#code=x%20%3D%20%5B1,%202,%203%5D%0Ay%20%3D%20x%0Ax%5B0%5D%20%3D%2099%0Aprint%28y%5B0%5D%29&cumulative=false&curInstr=0&heapPrimitives=nevernest&mode=display&origin=opt-frontend.js&py=3&rawInputLstJSON=%5B%5D&textReferences=false) might be helpful for a beginner."
    ]
   },
   {
@@ -2903,7 +2905,7 @@
     }
    },
    "source": [
-    "Variable names may contain upper and lower case letters, numbers, and underscores (\"\\_\") and be as long as we want them to be. However, they must not begin with a number. Also, they must not be any of Python's **[keywords](https://docs.python.org/3/reference/lexical_analysis.html#keywords)**.\n",
+    "Variable names may contain upper and lower case letters, numbers, and underscores (\"\\_\") and be as long as we want them to be. However, they must not begin with a number. Also, they must not be any of Python's built-in **[keywords](https://docs.python.org/3/reference/lexical_analysis.html#keywords)**.\n",
     "\n",
     "Variable names are usually chosen such that they do not need any more documentation and are self-explanatory. A very common convention is to use so-called **[snake\\_case](https://en.wikipedia.org/wiki/Snake_case)**: Keep everything lowercase and use underscores to seperate words.\n",
     "\n",
@@ -3150,7 +3152,7 @@
        "        "
       ],
       "text/plain": [
-       "<IPython.lib.display.YouTubeVideo at 0x7f465852de48>"
+       "<IPython.lib.display.YouTubeVideo at 0x7fedb808c518>"
       ]
      },
      "execution_count": 93,
@@ -3188,7 +3190,7 @@
     "\n",
     "What we said about individual operators above, namely that they have *no* side effects, should have been put here to begin with. The examples in the section on operators above were actually all expressions!\n",
     "\n",
-    "The simplest possible expression contains only one variable (or literal)."
+    "The simplest possible expressions contain only one variable or literal."
    ]
   },
   {
@@ -3218,6 +3220,33 @@
   {
    "cell_type": "code",
    "execution_count": 95,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "42"
+      ]
+     },
+     "execution_count": 95,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "42"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "For sure, we need to include operators to achieve something useful."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 96,
    "metadata": {
     "slideshow": {
      "slide_type": "-"
@@ -3230,7 +3259,7 @@
        "165"
       ]
      },
-     "execution_count": 95,
+     "execution_count": 96,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3247,12 +3276,12 @@
     }
    },
    "source": [
-    "The definition of an expression is **recursive**. So here the sub-expression `a + b` is combined with the literal `3` by the operator `**` to form the full expression."
+    "The definition of an expression is **recursive**. So, here the sub-expression `a + b` is combined with the literal `3` by the operator `**` to form the full expression."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 96,
+   "execution_count": 97,
    "metadata": {
     "slideshow": {
      "slide_type": "-"
@@ -3265,7 +3294,7 @@
        "4492125"
       ]
      },
-     "execution_count": 96,
+     "execution_count": 97,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3282,12 +3311,12 @@
     }
    },
    "source": [
-    "Here, the variable `y` is combined with the literal `2` by the indexing operator `[]`. The resulting expression evaluates to the "
+    "Here, the variable `y` is combined with the literal `2` by the indexing operator `[]`. The resulting expression evaluates to the third element in the `y` list."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 97,
+   "execution_count": 98,
    "metadata": {
     "slideshow": {
      "slide_type": "-"
@@ -3300,7 +3329,7 @@
        "3"
       ]
      },
-     "execution_count": 97,
+     "execution_count": 98,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3317,12 +3346,12 @@
     }
    },
    "source": [
-    "When not used as a **delimiter**, parentheses also constitute an operator, namely the **call operator** `()`. We have seen this syntax above when we \"called\" (i.e., executed) built-in functions and methods."
+    "When not used as a delimiter, parentheses also constitute an operator, namely the **call operator** `()`. We have seen this syntax above when we \"called\" (i.e., executed) built-in functions and methods."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 98,
+   "execution_count": 99,
    "metadata": {
     "slideshow": {
      "slide_type": "-"
@@ -3335,7 +3364,7 @@
        "104"
       ]
      },
-     "execution_count": 98,
+     "execution_count": 99,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3368,7 +3397,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 99,
+   "execution_count": 100,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -3382,7 +3411,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 100,
+   "execution_count": 101,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -3395,7 +3424,7 @@
        "'Hi class'"
       ]
      },
-     "execution_count": 100,
+     "execution_count": 101,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3417,7 +3446,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 101,
+   "execution_count": 102,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -3430,7 +3459,7 @@
        "'Hi Hi Hi Hi Hi Hi Hi Hi Hi Hi Hi Hi Hi Hi Hi Hi Hi Hi Hi Hi Hi Hi Hi Hi Hi Hi Hi Hi Hi Hi Hi Hi Hi Hi Hi Hi Hi Hi Hi Hi Hi Hi '"
       ]
      },
-     "execution_count": 101,
+     "execution_count": 102,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3458,14 +3487,14 @@
     }
    },
    "source": [
-    "A **[statement](https://docs.python.org/3/reference/simple_stmts.html)** is anything that changes the state of the program's memory or has some other side effect. Statements do not just evaluate to a value like expressions; instead, they create or change values.\n",
+    "A **[statement](https://docs.python.org/3/reference/simple_stmts.html)** is anything that changes the state of a program or has some other side effect. Statements do not just evaluate to a value like expressions; instead, they create or change values.\n",
     "\n",
     "Most notably of course are the `=` and `del` statements."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 102,
+   "execution_count": 103,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -3478,7 +3507,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 103,
+   "execution_count": 104,
    "metadata": {
     "slideshow": {
      "slide_type": "-"
@@ -3497,12 +3526,12 @@
     }
    },
    "source": [
-    "The [print()](https://docs.python.org/3/library/functions.html#print) function is regarded a \"statement\" as well. In fact, it used to be a real statement in Python 2 and has all the necessary properties. It is a bit of a corner case as expressions are also \"printed\" in a Jupyter notebook when evaluated last in a code cell."
+    "The built-in [print()](https://docs.python.org/3/library/functions.html#print) function is regarded a \"statement\" as well. In fact, it used to be a real statement in Python 2 and has all the necessary properties. It is a bit of a corner case as expressions are also \"printed\" in a Jupyter notebook when evaluated last in a code cell."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 104,
+   "execution_count": 105,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -3549,7 +3578,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 105,
+   "execution_count": 106,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -3577,7 +3606,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 106,
+   "execution_count": 107,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -3590,7 +3619,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 107,
+   "execution_count": 108,
    "metadata": {
     "slideshow": {
      "slide_type": "-"
@@ -3690,14 +3719,14 @@
     "  - ignored by Python\n",
     "\n",
     "\n",
-    "- functions (cf., Chapter 2)\n",
+    "- functions (cf., [Chapter 2](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/02_functions.ipynb))\n",
     "  - named sequences of instructions\n",
     "  - the smaller parts in a larger program\n",
     "  - make a program more modular and thus easier to understand\n",
     "\n",
     "\n",
-    "- flow control (cf., Chapter 3)\n",
-    "  - expression of **logic** or an **algorithm**\n",
+    "- flow control (cf., [Chapter 3](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/03_conditionals.ipynb))\n",
+    "  - expression of **business logic** or an **algorithm**\n",
     "  - conditional execution of a small **branch** within a program (i.e., `if` statements)\n",
     "  - repetitive execution of parts of a program (i.e., `for`-loops and `while`-loops)"
    ]
diff --git a/01_elements_review_and_exercises.ipynb b/01_elements_review_and_exercises.ipynb
index 7e43b77..8940f24 100644
--- a/01_elements_review_and_exercises.ipynb
+++ b/01_elements_review_and_exercises.ipynb
@@ -19,7 +19,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Read Chapter 1 of the book. Then work through the ten review questions."
+    "Read [Chapter 1](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/01_elements.ipynb) of the book. Then work through the ten review questions."
    ]
   },
   {
diff --git a/02_functions.ipynb b/02_functions.ipynb
index 2c284f9..681838a 100644
--- a/02_functions.ipynb
+++ b/02_functions.ipynb
@@ -19,7 +19,7 @@
     }
    },
    "source": [
-    "In Chapter 1 we typed the **business logic** of our little program to calculate the mean of a subset of a list of numbers right into the code cells. Then, we executed them one after another. We had no way of **re-using** the code except for either re-executing the cells or copying and pasting their contents into other cells. And whenever we find ourselves doing repetitive manual work, we can be sure that there must be a way of automating what we are doing.\n",
+    "In [Chapter 1](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/01_elements.ipynb) we typed the **business logic** of our little program to calculate the mean of a subset of a list of numbers right into the code cells. Then, we executed them one after another. We had no way of **re-using** the code except for either re-executing the cells or copying and pasting their contents into other cells. And whenever we find ourselves doing repetitive manual work, we can be sure that there must be a way of automating what we are doing.\n",
     "\n",
     "At the same time, we executed built-in functions (e.g., [print()](https://docs.python.org/3/library/functions.html#print), [sum()](https://docs.python.org/3/library/functions.html#sum), [len()](https://docs.python.org/3/library/functions.html#len), [id()](https://docs.python.org/3/library/functions.html#id), or [type()](https://docs.python.org/3/library/functions.html#type)) that obviously must be re-using the same parts inside core Python every time we use them.\n",
     "\n",
@@ -45,7 +45,7 @@
     }
    },
    "source": [
-    "So-called **[user-defined functions](https://docs.python.org/3/reference/compound_stmts.html#function-definitions)** can be created with the `def` statement. To extend an already familiar example, we re-use the introductory example from Chapter 1 in its final Pythonic version and transform it into the function `average_evens()` below. \n",
+    "So-called **[user-defined functions](https://docs.python.org/3/reference/compound_stmts.html#function-definitions)** can be created with the `def` statement. To extend an already familiar example, we re-use the introductory example from [Chapter 1](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/01_elements.ipynb) in its final Pythonic version and transform it into the function `average_evens()` below. \n",
     "\n",
     "A function's **name** must be chosen according to the same naming rules as for ordinary variables. In fact, Python manages function names just like variables. In this book, we further adopt the convention of ending function names with parentheses \"`()`\" in text cells for faster comprehension when reading (i.e., `average_evens()` vs. `average_evens`). These are not actually part of the name but must always be written out in the `def` statement for syntactic reasons.\n",
     "\n",
@@ -142,7 +142,7 @@
     {
      "data": {
       "text/plain": [
-       "139655430681056"
+       "140693945143776"
       ]
      },
      "execution_count": 3,
@@ -289,7 +289,7 @@
     }
    },
    "source": [
-    "Once defined we can **call** (i.e., \"execute\") a function with the **call operator** `()`. The formal parameters are filled in by passing variables or expressions as **arguments** to the function within the parentheses."
+    "We can **call** (i.e., \"execute\") a function with the **call operator** `()` as often as we wish. The formal parameters are filled in by passing variables or expressions as **arguments** to the function within the parentheses."
    ]
   },
   {
@@ -407,7 +407,7 @@
     }
    },
    "source": [
-    "Notice how the parameters listed in a function's definition (i.e., `numbers`) and variables created inside it during execution (i.e., `evens` and `average`) are **local** to that function. That means they are only mapped to an object in memory while the function is being executed and de-referenced immediately when the function returns. We say they **go out of scope** once the function terminates."
+    "Notice how the parameters listed in a function's definition (i.e., `numbers`) and variables created inside it during execution (i.e., `evens` and `average`) are **local** to that function. That means they only point to an object in memory *while* the function is being executed and de-referenced immediately when the function returns. We say they **go out of scope** once the function terminates."
    ]
   },
   {
@@ -485,6 +485,17 @@
     "average"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "[PythonTutor](http://pythontutor.com/visualize.html#code=nums%20%3D%20%5B1,%202,%203,%204,%205,%206,%207,%208,%209,%2010%5D%0A%0Adef%20average_evens%28numbers%29%3A%0A%20%20%20%20evens%20%3D%20%5Bn%20for%20n%20in%20numbers%20if%20n%20%25%202%20%3D%3D%200%5D%0A%20%20%20%20average%20%3D%20sum%28evens%29%20/%20len%28evens%29%0A%20%20%20%20return%20average%0A%0Arv%20%3D%20average_evens%28nums%29&cumulative=false&curInstr=0&heapPrimitives=nevernest&mode=display&origin=opt-frontend.js&py=3&rawInputLstJSON=%5B%5D&textReferences=false) visualizes what happens in memory: To be precise, in the exact moment when the function call is initiated and `nums` is passed in as the `numbers` argument, there are *two* pointers to the *same* `list` object (cf., steps 4-5 in the visualization). We also see how Python creates a *new* **frame** that holds the function's local scope (i.e., \"internal names\") in addition to the **global** frame. Frames are nothing but [namespaces](https://en.wikipedia.org/wiki/Namespace) to *isolate* the names of different **scopes** from each other. The list comprehension `[n for n in numbers if n % 2 == 0]` constitutes yet another frame that is in scope as the `list` object assigned to `evens` is *being* created (cf., steps 6-18). When the function returns, only the global frame is left (cf., steps 21-22)."
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {
@@ -504,7 +515,7 @@
     }
    },
    "source": [
-    "On the contrary, while a function is being executed, it can \"see\" the variables of the **enclosing scope** (i.e., \"outside\" of it). This is a common source of *semantic* errors. Consider the following stylized (and incorrect) example `average_wrong()`. The error is hard to spot with eyes: The function never references the `numbers` parameter but the `nums` variable in the **global scope** instead."
+    "On the contrary, while a function is being executed, it can reference the variables of **enclosing scopes** (i.e., \"outside\" of it). This is a common source of *semantic* errors. Consider the following stylized (and incorrect) example `average_wrong()`. The error is hard to spot with eyes: The function never references the `numbers` parameter but the `nums` variable in the **global scope** instead."
    ]
   },
   {
@@ -526,11 +537,22 @@
     "    Returns:\n",
     "        float: average\n",
     "    \"\"\"\n",
-    "    evens = [n for n in nums if n % 2 == 0]  # should reference numbers, not nums\n",
+    "    evens = [n for n in nums if n % 2 == 0]  # should reference numbers not nums\n",
     "    average = sum(evens) / len(evens)\n",
     "    return average"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "`nums` in the global scope is of course unchanged."
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 16,
@@ -555,6 +577,17 @@
     "nums"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Sometimes a function might return a correct solution for *some* inputs ..."
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 17,
@@ -576,7 +609,18 @@
     }
    ],
    "source": [
-    "average_wrong(nums)  # the result is correct by accident!"
+    "average_wrong(nums)  # this is correct by accident"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "... but still be wrong *in general*."
    ]
   },
   {
@@ -611,7 +655,9 @@
     }
    },
    "source": [
-    "Also, observe how both `average_evens()` and `average_wrong()` use the same names for their respective parameters and variables internally. For sure, Python is smart enough to not mix them up. This is because each function call creates a temporary **[namespace](https://en.wikipedia.org/wiki/Namespace)** that *isolates* the local scope's names for usage only from within the function. As we saw in the [Zen of Python](https://www.python.org/dev/peps/pep-0020/), \"namespaces are one honking great idea\" (cf., `import this`)."
+    "[PythonTutor](http://pythontutor.com/visualize.html#code=nums%20%3D%20%5B1,%202,%203,%204,%205,%206,%207,%208,%209,%2010%5D%0A%0Adef%20average_wrong%28numbers%29%3A%0A%20%20%20%20evens%20%3D%20%5Bn%20for%20n%20in%20nums%20if%20n%20%25%202%20%3D%3D%200%5D%0A%20%20%20%20average%20%3D%20sum%28evens%29%20/%20len%28evens%29%0A%20%20%20%20return%20average%0A%0Arv%20%3D%20average_wrong%28%5B123,%20456,%20789%5D%29&cumulative=false&curInstr=0&heapPrimitives=nevernest&mode=display&origin=opt-frontend.js&py=3&rawInputLstJSON=%5B%5D&textReferences=false) is again helpful at visualizing the error interactively: Creating the `list` object `evens` eventually points to takes *12* computational steps, namely one for setting up an empty `list` object, *ten* for filling it with elements derived from `nums` in the global scope, and one to make `evens` point at it (cf., steps 6-18).\n",
+    "\n",
+    "The frames logic shown by PythonTutor is the mechanism by which Python not only manages the names inside one function call but for potentially many calls occuring simultaneously as we will see in [Chapter 4](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/04_iteration.ipynb). It is the reason why we may re-use the same names for the parameters and variables inside both `average_evens()` and `average_wrong()` without Python mixing them up. So, as we already read in the [Zen of Python](https://www.python.org/dev/peps/pep-0020/), \"namespaces are one honking great idea\" (cf., `import this`) and a frame is just a special kind of namespace."
    ]
   },
   {
@@ -633,9 +679,9 @@
     }
    },
    "source": [
-    "Code gets even more confusing when variables by the same name from different scopes collide. In particular, what should we expect to happen if a function changes a globally defined variable internally?\n",
+    "Code gets even more confusing when variables by the *same* name from *different* scopes collide. In particular, what should we expect to happen if a function \"changes\" a globally defined variable internally?\n",
     "\n",
-    "`average_odds()` below works like `average_evens()` above except that it **[casts](https://en.wikipedia.org/wiki/Type_conversion)** (i.e., \"converts\") the elements of `numbers` as objects of type `int` with the [int()](https://docs.python.org/3/library/functions.html#int) built-in first before filtering and averaging them. In doing so, it introduces an *internal* variable `nums` whose name collides with the one in the global scope. The **inequality operator** `!=` is just the **reversed** version of `==`."
+    "`average_odds()` below works like `average_evens()` above except that it **[casts](https://en.wikipedia.org/wiki/Type_conversion)** (i.e., \"converts\") the elements of `numbers` as objects of type `int` with the [int()](https://docs.python.org/3/library/functions.html#int) built-in first before filtering and averaging them. In doing so, it introduces an *internal* variable `nums` whose name collides with the one in the global scope. To filter for odd numbers, we use the **inequality operator** `!=` that is just the **reversed** version of `==`."
    ]
   },
   {
@@ -652,7 +698,7 @@
     "    \"\"\"Calculate the average of all odd numbers in a list.\n",
     "\n",
     "    Args:\n",
-    "        numbers (list): a list of numbers; must be integers\n",
+    "        numbers (list): a list of numbers; must be integer-like\n",
     "\n",
     "    Returns:\n",
     "        float: average\n",
@@ -671,7 +717,7 @@
     }
    },
    "source": [
-    "`nums` in the global scope is of course the same list from above."
+    "`nums` in the global scope is still unchanged."
    ]
   },
   {
@@ -706,7 +752,7 @@
     }
    },
    "source": [
-    "As good practice, let's first use inputs for which we can calculate the answer in our heads to verify that `average_odds()` is correct."
+    "As good practice, let's first use inputs for which we can calculate the answer in our heads to \"verify\" that `average_odds()` is \"correct\"."
    ]
   },
   {
@@ -730,7 +776,7 @@
     }
    ],
    "source": [
-    "average_odds([1, 100, 3, 100, 5])  # verify the function's correctness with predictable inputs"
+    "average_odds([1.0, 10.0, 3.0, 10.0, 5.0])  # verify correctness with predictable inputs"
    ]
   },
   {
@@ -776,7 +822,7 @@
     }
    },
    "source": [
-    "Python, however, is again smart enough to keep the two `nums` variables apart. So the global `nums` is still pointing to the very same list object as before."
+    "Python, however, is again smart enough to keep all the involved `nums` variables apart. So the global `nums` is still pointing to the very same `list` object as before."
    ]
   },
   {
@@ -811,7 +857,9 @@
     }
    },
    "source": [
-    "The reason why everything works just fine is that *every time* we (re-)assign an object to a variable inside a function with the `=` statement, this is done in the local scope by default. There are ways to change variables existing in an outer scope from within a function but we save that for a later chapter.\n",
+    "The reason why everything just works is that *every time* we (re-)assign an object to a variable *inside* a function with the `=` statement, this is done in the *local* scope by default. There are ways to change variables existing in an outer scope from within a function but this is a rather advanced topic on its own.\n",
+    "\n",
+    "[PythonTutor](http://pythontutor.com/visualize.html#code=nums%20%3D%20%5B1,%202,%203,%204,%205,%206,%207,%208,%209,%2010%5D%0A%0Adef%20average_odds%28numbers%29%3A%0A%20%20%20%20nums%20%3D%20%5Bint%28n%29%20for%20n%20in%20numbers%5D%0A%20%20%20%20odds%20%3D%20%5Bn%20for%20n%20in%20nums%20if%20n%20%25%202%20!%3D%200%5D%0A%20%20%20%20average%20%3D%20sum%28odds%29%20/%20len%28odds%29%0A%20%20%20%20return%20average%0A%0Arv%20%3D%20average_odds%28%5B1.0,%2010.0,%203.0,%2010.0,%205.0%5D%29&cumulative=false&curInstr=0&heapPrimitives=nevernest&mode=display&origin=opt-frontend.js&py=3&rawInputLstJSON=%5B%5D&textReferences=false) shows how *two* `nums` variables exist in *different* scopes pointing to *different* objects (cf., steps 14-25) when we execute `average_odds([1.0, 10.0, 3.0, 10.0, 5.0])`.\n",
     "\n",
     "Variables whose names collide with the ones of variables in enclosing scopes - and the global scope is just the most enclosing scope - are said to **shadow** them.\n",
     "\n",
@@ -1122,7 +1170,7 @@
     }
    },
    "source": [
-    "So far we have only specified one parameter in each of our user-defined functions. In Chapter 1, however, we saw the built-in function [divmod()](https://docs.python.org/3/library/functions.html#divmod) taking two arguments. Obviously, the order of the numbers passed in mattered. Whenever we call a function and list its arguments in a comma seperated manner, we say that we pass in the arguments by position or refer to them as **positional arguments**."
+    "So far we have only specified one parameter in each of our user-defined functions. In [Chapter 1](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/01_elements.ipynb), however, we saw the built-in function [divmod()](https://docs.python.org/3/library/functions.html#divmod) taking two arguments. Obviously, the order of the numbers passed in mattered. Whenever we call a function and list its arguments in a comma seperated manner, we say that we pass in the arguments by position or refer to them as **positional arguments**."
    ]
   },
   {
@@ -2036,7 +2084,7 @@
     {
      "data": {
       "text/plain": [
-       "139655519584648"
+       "140694050824664"
       ]
      },
      "execution_count": 58,
@@ -2308,7 +2356,7 @@
    "source": [
     "Observe how the arguments passed to functions do not need to be just variables or simple literals. Instead, we can pass in any *expression* that evaluates to a *new* object of the type the function expects.\n",
     "\n",
-    "So just as a reminder from the expression vs. statement discussion in Chapter 1: An expression is *any* syntactically correct combination of variables and literals with operators. And the call operator `()` is just ... well another operator. So both of the next two code cells are just expressions! They have no permanent side effect in memory. We can execute them as often as we want *without* changing the state of the program (i.e., this Jupyter notebook).\n",
+    "So just as a reminder from the expression vs. statement discussion in [Chapter 1](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/01_elements.ipynb): An expression is *any* syntactically correct combination of variables and literals with operators. And the call operator `()` is just ... well another operator. So both of the next two code cells are just expressions! They have no permanent side effect in memory. We can execute them as often as we want *without* changing the state of the program (i.e., this Jupyter notebook).\n",
     "\n",
     "So, regarding the very next cell in particular: Although the `2 ** 2` creates a *new* object `4` in memory that is then immediately passed into the [math.sqrt()](https://docs.python.org/3/library/math.html#math.sqrt) function, once that function call returns, \"all is lost\" and the newly created `4` object is forgotten again, as well as the return value of [math.sqrt()](https://docs.python.org/3/library/math.html#math.sqrt)."
    ]
@@ -2649,7 +2697,7 @@
     {
      "data": {
       "text/plain": [
-       "0.12717011866176486"
+       "0.15268128055183228"
       ]
      },
      "execution_count": 75,
@@ -2684,7 +2732,7 @@
     {
      "data": {
       "text/plain": [
-       "<bound method Random.choice of <random.Random object at 0x562161984ba8>>"
+       "<bound method Random.choice of <random.Random object at 0x56111ba1cba8>>"
       ]
      },
      "execution_count": 76,
@@ -2733,7 +2781,7 @@
     {
      "data": {
       "text/plain": [
-       "8"
+       "2"
       ]
      },
      "execution_count": 78,
@@ -2881,7 +2929,7 @@
    "source": [
     "[numpy](http://www.numpy.org/) is the de-facto standard in the Python world for handling **array-like** data. That is a fancy word for data that can be put into a matrix or vector format. We will look at it in depth in Chapter 9.\n",
     "\n",
-    "As [numpy](http://www.numpy.org/) is *not* in the [standard library](https://docs.python.org/3/library/index.html), it must be *manually* installed, for example, with the [pip](https://pip.pypa.io/en/stable/) tool. As mentioned in Chapter 0, to execute terminal commands from within a Jupyter notebook, we just need to start a code cell with an exclamation mark.\n",
+    "As [numpy](http://www.numpy.org/) is *not* in the [standard library](https://docs.python.org/3/library/index.html), it must be *manually* installed, for example, with the [pip](https://pip.pypa.io/en/stable/) tool. As mentioned in [Chapter 0](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/00_start_up.ipynb), to execute terminal commands from within a Jupyter notebook, we just need to start a code cell with an exclamation mark.\n",
     "\n",
     "If you are running this notebook with an installation of the [Anaconda Distribution](https://www.anaconda.com/distribution/), then [numpy](http://www.numpy.org/) is probably already installed. Running the cell below, will just confirm that."
    ]
diff --git a/02_functions_review_and_exercises.ipynb b/02_functions_review_and_exercises.ipynb
index 647e47f..dcd0a9c 100644
--- a/02_functions_review_and_exercises.ipynb
+++ b/02_functions_review_and_exercises.ipynb
@@ -18,7 +18,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Read Chapter 2 of the book. Then work through the ten review questions."
+    "Read [Chapter 2](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/02_functions.ipynb) of the book. Then work through the ten review questions."
    ]
   },
   {
diff --git a/03_conditionals.ipynb b/03_conditionals.ipynb
index acd509d..6d70f07 100644
--- a/03_conditionals.ipynb
+++ b/03_conditionals.ipynb
@@ -19,7 +19,7 @@
     }
    },
    "source": [
-    "We analyzed every aspect of the `average_evens()` function in Chapter 2 except for the `if` part. While it seems to intuitively do what we expect it to, there is a whole lot more to be learned from taking it apart. In particular, the `if` can occur within both a **statement** as in our introductory example in Chapter 1 but also an **expression** as in `average_evens()`. This is analogous as to how a noun in a natural language is *either* the subject of *or* an object in a sentence. What is common to both versions of the `if` is that it leads to code being executed for *parts* of the input only. It is our first way of **controlling** the **flow of execution** in a program.\n",
+    "We analyzed every aspect of the `average_evens()` function in [Chapter 2](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/02_functions.ipynb) except for the `if` part. While it seems to intuitively do what we expect it to, there is a whole lot more to be learned from taking it apart. In particular, the `if` can occur within both a **statement** as in our introductory example in [Chapter 1](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/01_elements.ipynb) but also an **expression** as in `average_evens()`. This is analogous as to how a noun in a natural language is *either* the subject of *or* an object in a sentence. What is common to both versions of the `if` is that it leads to code being executed for *parts* of the input only. It is our first way of **controlling** the **flow of execution** in a program.\n",
     "\n",
     "After deconstructing `if` in the first part of this chapter, we take a close look at a similar concept, namely handling and raising **exceptions**."
    ]
@@ -139,7 +139,7 @@
     }
    },
    "source": [
-    "There are, however, cases where even well-behaved Python does not make us happy. Chapter 5 will provide more insights on that."
+    "There are, however, cases where even well-behaved Python does not make us happy. Chapter 5 will provide more insights on this \"bug\"."
    ]
   },
   {
@@ -189,7 +189,7 @@
     {
      "data": {
       "text/plain": [
-       "94709180875744"
+       "94163040564192"
       ]
      },
      "execution_count": 5,
@@ -213,7 +213,7 @@
     {
      "data": {
       "text/plain": [
-       "94709180875712"
+       "94163040564160"
       ]
      },
      "execution_count": 6,
@@ -281,9 +281,9 @@
     }
    },
    "source": [
-    "Let's not confuse the boolean `False` with `None`, another special built-in object! We saw the latter before in Chapter 2 as the *implicit* return value of a function without a `return` statement.\n",
+    "Let's not confuse the boolean `False` with `None`, another special built-in object! We saw the latter before in [Chapter 2](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/02_functions.ipynb) as the *implicit* return value of a function without a `return` statement.\n",
     "\n",
-    "We might think of `None` in a boolean context indicating a \"maybe\" or even an \"unknown\" answer. But for Python, there are no \"maybe\" or \"unknown\" objects as we will see further below!\n",
+    "We might think of `None` in a boolean context indicating a \"maybe\" or even an \"unknown\" answer; however, for Python, there are no \"maybe\" or \"unknown\" objects as we will see further below!\n",
     "\n",
     "Whereas `False` is of type `bool`, `None` is of type `NoneType`. So, they are totally unrelated. On the contrary, as both `True` and `False` are of the same type, we could call them \"siblings\"."
    ]
@@ -313,7 +313,7 @@
     {
      "data": {
       "text/plain": [
-       "94709180862704"
+       "94163040551152"
       ]
      },
      "execution_count": 10,
@@ -357,7 +357,7 @@
     }
    },
    "source": [
-    "`True`, `False`, and `None` have the property that they each exist in memory only *once*. Objects designed this way are so-called **singletons**. This **[design pattern](https://en.wikipedia.org/wiki/Design_Patterns)** was originally developed to keep a program's memory usage at a minimum. It may only be employed in situations where we know that an object will never mutate its value in place (i.e., to re-use the bag analogy from Chapter 1, no flipping of $0$s and $1$s in the bag is allowed). In languages \"closer\" to the memory like C we would have to code this singleton logic ourselves but Python has this already built in for *some* types.\n",
+    "`True`, `False`, and `None` have the property that they each exist in memory only *once*. Objects designed this way are so-called **singletons**. This **[design pattern](https://en.wikipedia.org/wiki/Design_Patterns)** was originally developed to keep a program's memory usage at a minimum. It may only be employed in situations where we know that an object will *not* mutate its value (i.e., to re-use the bag analogy from [Chapter 1](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/01_elements.ipynb), no flipping of $0$s and $1$s in the bag is allowed). In languages \"closer\" to the memory like C we would have to code this singleton logic ourselves but Python has this already built in for *some* types.\n",
     "\n",
     "We can verify this with either the `is` operator or by comparing memory addresses."
    ]
@@ -512,7 +512,7 @@
     }
    ],
    "source": [
-    "42 != 123  # = \"not equal to\"; other programming languages sometimes use \"<>\" instead"
+    "42 != 123  # = \"not equal to\"; other languages may use \"<>\""
    ]
   },
   {
@@ -672,7 +672,7 @@
     }
    },
    "source": [
-    "Relational operators have a *higher precedence* over logical operators (cf., the [reference](https://docs.python.org/3/reference/expressions.html#operator-precedence)). So the following expression means what we intuitively think it does."
+    "Relational operators have a **[higher precedence](https://docs.python.org/3/reference/expressions.html#operator-precedence)** over logical operators. So the following expression means what we intuitively think it does."
    ]
   },
   {
@@ -825,7 +825,7 @@
     }
    },
    "source": [
-    "For even better readability, [some practitioner](https://llewellynfalco.blogspot.com/2016/02/dont-use-greater-than-sign-in.html) suggest to never use the `>` and `>=` operators (note that the included example is written in [Java](https://en.wikipedia.org/wiki/Java_%28programming_language%29) and `&&` means `and` and `||` means `or`).\n",
+    "For even better readability, [some practitioners](https://llewellynfalco.blogspot.com/2016/02/dont-use-greater-than-sign-in.html) suggest to *never* use the `>` and `>=` operators (note that the included example is written in [Java](https://en.wikipedia.org/wiki/Java_%28programming_language%29) and `&&` means `and` and `||` means `or`).\n",
     "\n",
     "Python allows **chaining** relational operators that are combined with the `and` operator. For example, the following two cells implement the same logic where the second is a lot easier to read."
    ]
@@ -899,7 +899,7 @@
    "source": [
     "The operands of the logical operators do not actually have to be *boolean* expressions as defined above but may be *any* kind of expression. If a sub-expression does *not* evaluate to an object of type `bool`, Python automatically casts the resulting object as such.\n",
     "\n",
-    "For example, any non-zero numeric object effectively becomes `True`. While this behavior allows writing more concise and thus \"beautiful\" code, it is also a common source of confusion."
+    "For example, any non-zero numeric object becomes `True`. While this behavior allows writing conciser and thus more \"beautiful\" code, it is also a common source of confusion."
    ]
   },
   {
@@ -1028,7 +1028,7 @@
     }
    },
    "source": [
-    "In a boolean context `None` is casted as `False`. So, `None` is really *not* a \"maybe\" answer but a \"no\"."
+    "In a boolean context, `None` is casted as `False`! So, `None` is really *not* a \"maybe\" answer but a \"no\"."
    ]
   },
   {
@@ -1144,9 +1144,9 @@
     }
    },
    "source": [
-    "In order to write useful programs, we need to control the flow of execution, for example, to react to user input.\n",
+    "In order to write useful programs, we need to control the flow of execution, for example, to react to user input. The logic by which a program does that is referred to as **business logic**.\n",
     "\n",
-    "One major language construct to do so is the **conditional statement** or `if` **statement** (cf., the [reference](https://docs.python.org/3/reference/compound_stmts.html#the-if-statement)). It consists of:\n",
+    "One major language construct to do so is the **[conditional statement](https://docs.python.org/3/reference/compound_stmts.html#the-if-statement)** or `if` statement. It consists of:\n",
     "\n",
     "- *one* mandatory `if`-clause,\n",
     "- an *arbitrary* number of `elif`-clauses (i.e. \"else if\"), and\n",
@@ -1350,7 +1350,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "z is positive\n"
+      "z is odd\n"
      ]
     }
    ],
@@ -1602,7 +1602,7 @@
    "source": [
     "In the previous two chapters we already encountered a couple of *runtime* errors. A natural urge we might have after reading about conditional statements is to write code that somehow reacts to the occurence of such exceptions. All we need for that is a way to formulate a condition for that.\n",
     "\n",
-    "For sure, this is such a common thing to do that Python provides its own language construct for it, namely the `try` statement (cf., the [reference](https://docs.python.org/3/reference/compound_stmts.html#the-try-statement)).\n",
+    "For sure, this is such a common thing to do that Python provides its own language construct for it, namely the `try` [statement](https://docs.python.org/3/reference/compound_stmts.html#the-try-statement).\n",
     "\n",
     "In its simplest form, it comes with just two branches: `try` and `except`. The following basically tells Python to execute the code in the `try`-branch and if *anything* goes wrong, continue in the `except`-branch instead of **raising** an error to us. Of course, if nothing goes wrong, the `except`-branch is *not* executed."
    ]
@@ -1711,7 +1711,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Yes, division worked smoothly.\n",
+      "Oops. Division by 0. How does that work?\n",
       "I am always printed\n"
      ]
     }
@@ -1748,7 +1748,7 @@
     }
    },
    "source": [
-    "- **boolean expressions** evaluate either to `True` or `False`\n",
+    "- **boolean expressions** evaluate to either `True` or `False`\n",
     "- **relational operators** compare operands according to \"human\" interpretations\n",
     "- **logical operators** combine boolean sub-expressions to more \"complex\" expressions\n",
     "- the **conditional statement** is a *major* concept to **control** the **flow of execution** depending on some **conditions**\n",
diff --git a/03_conditionals_review_and_exercises.ipynb b/03_conditionals_review_and_exercises.ipynb
index f152b30..d39c551 100644
--- a/03_conditionals_review_and_exercises.ipynb
+++ b/03_conditionals_review_and_exercises.ipynb
@@ -19,7 +19,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Read Chapter 3 of the book. Then work through the seven review questions."
+    "Read [Chapter 3](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/03_conditionals.ipynb) of the book. Then work through the seven review questions."
    ]
   },
   {

From a91aea1f3685b2cb118c36fbe450a6f5febc1c2b Mon Sep 17 00:00:00 2001
From: Alexander Hess <alexander@webartifex.biz>
Date: Tue, 1 Oct 2019 17:48:22 +0200
Subject: [PATCH 025/242] Add initial version of notebook 04

---
 04_iteration.ipynb | 6529 ++++++++++++++++++++++++++++++++++++++++++++
 1 file changed, 6529 insertions(+)
 create mode 100644 04_iteration.ipynb

diff --git a/04_iteration.ipynb b/04_iteration.ipynb
new file mode 100644
index 0000000..755b4a5
--- /dev/null
+++ b/04_iteration.ipynb
@@ -0,0 +1,6529 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "# Chapter 4: Iteration"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "While controlling the flow of execution with an `if` statement is definitely a must-have building block in any programming language, it alone does not allow us to run a block of code repetitively and we need to be able to do exactly that in order to write useful software.\n",
+    "\n",
+    "You might think that the `for` statement shown in some examples before is the missing piece in the puzzle. However, we can actually live without it and postpone its official introduction until the second half of this chapter.\n",
+    "\n",
+    "Instead, we dive into the big idea of **iteration** by studying the concept of **recursion** first. This is quite the opposite of many introductory books on programming that only treat the latter as a nice-to-have artifact, if at all. Yet, understanding recursion sharpens one's mind and contributes to seeing problems from a different angle."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "## Recursion"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "A function that calls itself is **recursive** and the process of executing such a function it is called **recursion**.\n",
+    "\n",
+    "Recursive functions contain some form of a conditional check (e.g., `if` statement) to identify a **base case** that ends the recursion *when* reached. Otherwise, the function would keep calling itself forever.\n",
+    "\n",
+    "The meaning of the word *recursive* is similar to *circular*. However, a truly circular definition is not very helpful and we think of a recursive function as kind of a \"circular function with a way out at the end\"."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "#### Trivial Example: Countdown"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "A rather trivial toy example illustrates the important aspects concretely: If called with any positive integer as its `n` argument, `countdown()` just prints that number and calls itself with the *new* `n` being the *old* `n` minus `1`. This continues until `countdown()` is called with `n=0`. Then, the flow of execution hits the base case and the function calls stop."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "code_folding": [],
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "def countdown(n):\n",
+    "    \"\"\"Print a countdown until the party starts.\n",
+    "\n",
+    "    Args:\n",
+    "        n (int): seconds until the party begins\n",
+    "    \"\"\"\n",
+    "    if n == 0:  # base case\n",
+    "        print(\"Happy new Year!\")\n",
+    "    else:\n",
+    "        print(n)\n",
+    "        countdown(n - 1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "3\n",
+      "2\n",
+      "1\n",
+      "Happy new Year!\n"
+     ]
+    }
+   ],
+   "source": [
+    "countdown(3)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "As trivial as this seems at first sight, a lot of complexity is hidden in this implementation. In particular, the order in which objects are created and de-referenced in memory might not be obvious right away as [PythonTutor](http://pythontutor.com/visualize.html#code=def%20countdown%28n%29%3A%0A%20%20%20%20if%20n%20%3D%3D%200%3A%0A%20%20%20%20%20%20%20%20print%28%22Happy%20new%20Year!%22%29%0A%20%20%20%20else%3A%0A%20%20%20%20%20%20%20%20print%28n%29%0A%20%20%20%20%20%20%20%20countdown%28n%20-%201%29%0A%0Acountdown%283%29&cumulative=false&curInstr=0&heapPrimitives=nevernest&mode=display&origin=opt-frontend.js&py=3&rawInputLstJSON=%5B%5D&textReferences=false) shows: Each time `countdown()` is called, Python creates a *new* frame in the part of the memory where it manages all the names. This way Python *isolates* all the different `n` variables from each other. As new frames are created until we reach the base case after which the frames are destroyed in the *reversed* order, this is called a **[stack](https://en.wikipedia.org/wiki/Stack_(abstract_data_type))** of frames in computer science terminology. In simple words, a stack is a last-in-first-out (LIFO) task queue. Each frame has a single parent frame, namely the one whose recursive function call created it."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "### Recursion in Mathematics"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Recursion plays an important role in mathematics as well and we likely know about it from some introductory course, for example, in [combinatorics](https://en.wikipedia.org/wiki/Combinatorics)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "#### Easy Example: [Factorial](https://en.wikipedia.org/wiki/Factorial)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "The factorial function, denoted with the symbol $!$, is defined as follows for all non-negative integers:"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "$$0! = 1$$\n",
+    "$$n! = n*(n-1)!$$"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Whenever we can find a recursive way of formulating an idea, we can immediately translate it into Python in a *naive* way (i.e., we create a *correct* program that may *not* be an *efficient* implementation yet).\n",
+    "\n",
+    "Below is a first version of `factorial()`: The `return` statement does not have to be a function's last code line and we can certainly have several `return` statements as well."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "code_folding": [],
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "def factorial(n):\n",
+    "    \"\"\"Calculate the factorial of a number.\n",
+    "\n",
+    "    Args:\n",
+    "        n (int): number to calculate the factorial for\n",
+    "\n",
+    "    Returns:\n",
+    "        factorial (int)\n",
+    "    \"\"\"\n",
+    "    if n == 0:\n",
+    "        return 1\n",
+    "    else:\n",
+    "        recurse = factorial(n - 1)\n",
+    "        result = n * recurse\n",
+    "        return result"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "When we read such code, it is often easier not to follow every function call (i.e., `factorial(n - 1)` here) in one's mind but assume we receive a return value as specified in the documentation. Some call this approach **[leap of faith](http://greenteapress.com/thinkpython2/html/thinkpython2007.html#sec75)**. In fact, we practice this anyways whenever we call built-in functions (e.g., [print()](https://docs.python.org/3/library/functions.html#print) or [len()](https://docs.python.org/3/library/functions.html#len)) where we would have to read C code in many cases.\n",
+    "\n",
+    "To visualize *all* the computational steps of the examplary `factorial(3)`, we use [PythonTutor](http://pythontutor.com/visualize.html#code=def%20factorial%28n%29%3A%0A%20%20%20%20if%20n%20%3D%3D%200%3A%0A%20%20%20%20%20%20%20%20return%201%0A%20%20%20%20else%3A%0A%20%20%20%20%20%20%20%20recurse%20%3D%20factorial%28n%20-%201%29%0A%20%20%20%20%20%20%20%20result%20%3D%20n%20*%20recurse%0A%20%20%20%20%20%20%20%20return%20result%0A%0Asolution%20%3D%20factorial%283%29&cumulative=false&curInstr=0&heapPrimitives=nevernest&mode=display&origin=opt-frontend.js&py=3&rawInputLstJSON=%5B%5D&textReferences=false): The recursion again creates a stack of frames in memory. In contrast to the previous trivial example, each frame leaves a return value in memory after it is destroyed. This return value is then assigned to the `recurse` variable within the parent frame and used to compute `result`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "6"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "factorial(3)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "3628800"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "factorial(10)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "A Pythonista would formulate `factorial()` in a conciser way using the so-called **early exit** pattern: No `else`-clause is needed as reaching a `return` statement immediately ends a function call. Furthermore, we do not really need the temporary variables `recurse` and `result`.\n",
+    "\n",
+    "As [PythonTutor](http://pythontutor.com/visualize.html#code=def%20factorial%28n%29%3A%0A%20%20%20%20if%20n%20%3D%3D%200%3A%0A%20%20%20%20%20%20%20%20return%201%0A%20%20%20%20return%20n%20*%20factorial%28n%20-%201%29%0A%0Asolution%20%3D%20factorial%283%29&cumulative=false&curInstr=0&heapPrimitives=nevernest&mode=display&origin=opt-frontend.js&py=3&rawInputLstJSON=%5B%5D&textReferences=false) shows, this implementation is more efficient as it only requires 18 computational steps instead of 24 to calculate `factorial(3)`, an improvement of 25 percent! "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "def factorial(n):\n",
+    "    \"\"\"Calculate the factorial of a number.\n",
+    "\n",
+    "    Args:\n",
+    "        n (int): number to calculate the factorial for\n",
+    "\n",
+    "    Returns:\n",
+    "        factorial (int)\n",
+    "    \"\"\"\n",
+    "    if n == 0:\n",
+    "        return 1\n",
+    "    return n * factorial(n - 1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "6"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "factorial(3)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "3628800"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "factorial(10)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Note that the [math](https://docs.python.org/3/library/math.html) module in the standard library provides a [factorial()](https://docs.python.org/3/library/math.html#math.factorial) function as well and we should therefore *never* implement it ourselves in a real code base."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "import math"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Help on built-in function factorial in module math:\n",
+      "\n",
+      "factorial(x, /)\n",
+      "    Find x!.\n",
+      "    \n",
+      "    Raise a ValueError if x is negative or non-integral.\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "help(math.factorial)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "6"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "math.factorial(3)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "3628800"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "math.factorial(10)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "#### \"Involved\" Example: [Euclid's Algorithm](https://en.wikipedia.org/wiki/Euclidean_algorithm)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "As famous philosopher Euclid already shows in his \"Elements\" (ca. 300 BC), the greatest common divisor of two integers, i.e., the largest number that divides both integers without a remainder, can be efficiently computed with the following code. This example illustrates that a recursive solution to a problem is not always easy to understand."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "def gcd(a, b):\n",
+    "    \"\"\"Calculate the greatest common divisor of two numbers.\n",
+    "\n",
+    "    Args:\n",
+    "        a (int): first number\n",
+    "        b (int): second number\n",
+    "\n",
+    "    Returns:\n",
+    "        gcd (int)\n",
+    "    \"\"\"\n",
+    "    if b == 0:\n",
+    "        return a \n",
+    "    return gcd(b, a % b)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "4"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "gcd(12, 4)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Euclid's algorithm is stunningly fast, even for large numbers. Its speed comes from the use of the modulo operation `%`. However, this is *not* true for recusion in general, which can result in very slow programs if not applied correctly."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "9"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "gcd(112233445566778899, 987654321)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "As expected, for two [prime numbers](https://en.wikipedia.org/wiki/List_of_prime_numbers) the greatest common divisor is of course $1$."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "1"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "gcd(2, 7919)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "The [math](https://docs.python.org/3/library/math.html) module in the standard library provides a [gcd()](https://docs.python.org/3/library/math.html#math.gcd) function as well and therefore we should again *never* implement it on our own."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Help on built-in function gcd in module math:\n",
+      "\n",
+      "gcd(x, y, /)\n",
+      "    greatest common divisor of x and y\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "help(math.gcd)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "4"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "math.gcd(12, 4)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "9"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "math.gcd(112233445566778899, 987654321)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "1"
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "math.gcd(2, 7919)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "#### \"Easy at first Glance\" Example: [Fibonacci Numbers](https://en.wikipedia.org/wiki/Fibonacci_number)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "The Fibonacci numbers are an infinite sequence of non-negative integers that are calculated such that every number is the sum of its two predecessors where the first two numbers of the sequence are defined to be $0$ and $1$. For example, the first 13 numbers are:"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "$0, 1, 1, 2, 3, 5, 8, 13, 21, 34, 55, 89, 144$"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Let's write a function `fibonacci()` that calculates the $i$th Fibonacci number where $0$ will be the $0$th number. Looking at the numbers in a **backwards** fashion (i.e., from right to left), we realize that the return value for `fibonacci(i)` can be reduced to the sum of the return values for `fibonacci(i - 1)` and `fibonacci(i - 2)` disregarding the *two* base cases."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "def fibonacci(i):\n",
+    "    \"\"\"Calculate the ith Fibonacci number.\n",
+    "\n",
+    "    Args:\n",
+    "        i (int): index of the Fibonacci number to calculate\n",
+    "\n",
+    "    Returns:\n",
+    "        ith_fibonacci (int)\n",
+    "    \"\"\"\n",
+    "    if i == 0:\n",
+    "        return 0\n",
+    "    elif i == 1:\n",
+    "        return 1\n",
+    "    return fibonacci(i - 1) + fibonacci(i - 2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "144"
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "fibonacci(12)  # = 13th number"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "##### Efficiency of Algorithms"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "This implementation is *highly* **inefficient** as small Fibonacci numbers can already take a very long time to compute. The reason for this is **exponential growth** in the number of function calls. As [PythonTutor](http://pythontutor.com/visualize.html#code=def%20fibonacci%28i%29%3A%0A%20%20%20%20if%20i%20%3D%3D%200%3A%0A%20%20%20%20%20%20%20%20return%200%0A%20%20%20%20elif%20i%20%3D%3D%201%3A%0A%20%20%20%20%20%20%20%20return%201%0A%20%20%20%20return%20fibonacci%28i%20-%201%29%20%2B%20fibonacci%28i%20-%202%29%0A%0Arv%20%3D%20fibonacci%285%29&cumulative=false&curInstr=0&heapPrimitives=nevernest&mode=display&origin=opt-frontend.js&py=3&rawInputLstJSON=%5B%5D&textReferences=false) shows, `fibonacci()` is called again and again with the same `i` arguments.\n",
+    "\n",
+    "To understand this in detail, we would have to study algorithms and data structures (e.g., with [this book](https://www.amazon.de/Introduction-Algorithms-Press-Thomas-Cormen/dp/0262033844/ref=sr_1_1?__mk_de_DE=%C3%85M%C3%85%C5%BD%C3%95%C3%91&crid=1JNE8U0VZGU0O&qid=1569837169&s=gateway&sprefix=algorithms+an%2Caps%2C180&sr=8-1)), a discipline within computer science, and dive into the analysis of **[time complexity of algorithms](https://en.wikipedia.org/wiki/Time_complexity)**.\n",
+    "\n",
+    "Luckily, in the Fibonacci case the inefficiency can be resolved with a **caching** (i.e., \"re-use\") strategy from the field of **[dynamic programming](https://en.wikipedia.org/wiki/Dynamic_programming)**, namely **[memoization](https://en.wikipedia.org/wiki/Memoization)**. We will do so in Chapter 8 after introducing the [dictionaries](https://docs.python.org/3/library/stdtypes.html#dict) data type.\n",
+    "\n",
+    "Let's measure the average run times for `fibonacci()` and varying `i` arguments with the `%%timeit` [cell magic](https://ipython.readthedocs.io/en/stable/interactive/magics.html#magic-timeit) that comes with Jupyter."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The slowest run took 5.01 times longer than the fastest. This could mean that an intermediate result is being cached.\n",
+      "55 µs ± 44.1 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n"
+     ]
+    }
+   ],
+   "source": [
+    "%%timeit -n 100\n",
+    "fibonacci(12)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "1.63 ms ± 68 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n"
+     ]
+    }
+   ],
+   "source": [
+    "%%timeit -n 100\n",
+    "fibonacci(20)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "192 ms ± 0 ns per loop (mean ± std. dev. of 1 run, 1 loop each)\n"
+     ]
+    }
+   ],
+   "source": [
+    "%%timeit -n 1 -r 1\n",
+    "fibonacci(30)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "2.21 s ± 0 ns per loop (mean ± std. dev. of 1 run, 1 loop each)\n"
+     ]
+    }
+   ],
+   "source": [
+    "%%timeit -n 1 -r 1\n",
+    "fibonacci(35)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "5.62 s ± 0 ns per loop (mean ± std. dev. of 1 run, 1 loop each)\n"
+     ]
+    }
+   ],
+   "source": [
+    "%%timeit -n 1 -r 1\n",
+    "fibonacci(37)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "### Infinite Recursion"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "If a recursion does not reach its base case, it will theoretically run forever. Luckily, Python detects that and saves the computer from crashing by raising a `RecursionError`.\n",
+    "\n",
+    "The simplest possible infinite recursion is generated like so."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "def run_forever():\n",
+    "    \"\"\"Also a pointless function should have a docstring.\"\"\"\n",
+    "    run_forever()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "ename": "RecursionError",
+     "evalue": "maximum recursion depth exceeded",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mRecursionError\u001b[0m                            Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-29-62f686a9f8a1>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mrun_forever\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;32m<ipython-input-28-80575699c62c>\u001b[0m in \u001b[0;36mrun_forever\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mrun_forever\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m     \u001b[0;34m\"\"\"Also a pointless function should have a docstring.\"\"\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m     \u001b[0mrun_forever\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "... last 1 frames repeated, from the frame below ...\n",
+      "\u001b[0;32m<ipython-input-28-80575699c62c>\u001b[0m in \u001b[0;36mrun_forever\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mrun_forever\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m     \u001b[0;34m\"\"\"Also a pointless function should have a docstring.\"\"\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m     \u001b[0mrun_forever\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;31mRecursionError\u001b[0m: maximum recursion depth exceeded"
+     ]
+    }
+   ],
+   "source": [
+    "run_forever()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "However, even the trivial `countdown()` function from above is not immune to an infinite recursion. Let's call it with `3.1` instead of `3`. What goes wrong here?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "3.1\n",
+      "2.1\n",
+      "1.1\n",
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+     ]
+    },
+    {
+     "ename": "RecursionError",
+     "evalue": "maximum recursion depth exceeded while calling a Python object",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mRecursionError\u001b[0m                            Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-30-43e3ad331761>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mcountdown\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m3.1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;32m<ipython-input-1-8e67251e237b>\u001b[0m in \u001b[0;36mcountdown\u001b[0;34m(n)\u001b[0m\n\u001b[1;32m      9\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     10\u001b[0m         \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mn\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 11\u001b[0;31m         \u001b[0mcountdown\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mn\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "... last 1 frames repeated, from the frame below ...\n",
+      "\u001b[0;32m<ipython-input-1-8e67251e237b>\u001b[0m in \u001b[0;36mcountdown\u001b[0;34m(n)\u001b[0m\n\u001b[1;32m      9\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     10\u001b[0m         \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mn\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 11\u001b[0;31m         \u001b[0mcountdown\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mn\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;31mRecursionError\u001b[0m: maximum recursion depth exceeded while calling a Python object"
+     ]
+    }
+   ],
+   "source": [
+    "countdown(3.1)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "In the same way, a `RecursionError` occurs if we call `factorial()` with `3.1` instead of `3`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "ename": "RecursionError",
+     "evalue": "maximum recursion depth exceeded in comparison",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mRecursionError\u001b[0m                            Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-31-188b816be8b6>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mfactorial\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m3.1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;32m<ipython-input-6-88d8d933f651>\u001b[0m in \u001b[0;36mfactorial\u001b[0;34m(n)\u001b[0m\n\u001b[1;32m     10\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mn\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     11\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 12\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0mn\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0mfactorial\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mn\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "... last 1 frames repeated, from the frame below ...\n",
+      "\u001b[0;32m<ipython-input-6-88d8d933f651>\u001b[0m in \u001b[0;36mfactorial\u001b[0;34m(n)\u001b[0m\n\u001b[1;32m     10\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mn\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     11\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 12\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0mn\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0mfactorial\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mn\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;31mRecursionError\u001b[0m: maximum recursion depth exceeded in comparison"
+     ]
+    }
+   ],
+   "source": [
+    "factorial(3.1)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "The infinite recursions could easily be avoided by replacing `n == 0` with `n <= 0` in both functions and thereby **generalizing** them. But even then, calling either `countdown()` or `factorial()` with a non-integer number is *semantically* wrong and therefore we better leave the base cases unchanged.\n",
+    "\n",
+    "Errors as above are a symptom of missing **type checking**: By design, Python allows us to pass in not only integers but objects of any type as arguments to the `countdown()` and `factorial()` functions. As long as the arguments \"behave\" like integers, we will not encounter any *runtime* errors. This is the case here as the two example functions only use the `-` and `*` operators internally and in this context a `float` object behaves exactly like an `int` object. So, the functions keep calling themselves until Python decides with a built-in heuristic that the recursion is likely not going to end and aborts the computations with a `RecursionError`. Stricly speaking, this is of course a *runtime* error as well. The missing type checking is 100% intentional and considered a feature of rather than a bug in Python.\n",
+    "\n",
+    "Pythonistas often use the term **[duck typing](https://en.wikipedia.org/wiki/Duck_typing)** when refering to the same idea and the colloquial saying goes \"If it walks like a duck and it quacks like a duck, it must be a duck\". For example, we could call `factorial()` with the `float` object `3.0` and the recursion works out fine. So, as long as the `3.0` \"walks\" like a `3` and \"quacks\" like a `3`, it \"must be\" a `3`.\n",
+    "\n",
+    "We see a similar behavior when we mix objects of types `int` and `float` with arithmetic operators. For example, `1 + 2.0` works because Python implicitly views the `1` as a `1.0` at runtime and then knows how to do floating-point arithmetic: Here, the `int` \"walks\" and \"quacks\" like a `float`. Strictly speaking, this is yet another example of operator overloading whereas duck typing refers to the same behavior when passing arguments to function calls.\n",
+    "\n",
+    "The important lesson is that we must expect our functions to be called with objects of *any* type at runtime, as opposed to the one type we had in mind when we defined the function.\n",
+    "\n",
+    "Duck typing is possible because Python is a dynamically typed language. On the contrary, in statically typed languages like C we have to declare (i.e., \"specify\") the data type of every parameter in a function definition. Then, a `RecursionError` as for `countdown(3.1)` or `factorial(3.1)` above could not occur. For example, if we declared the `countdown()` and `factorial()` functions to only accept `int` objects, calling the functions with a `float` argument would immediately fail *syntactically*. As a downside, we would then lose the ability to call `factorial()` with `3.0`, which is *semantically* correct nevertheless.\n",
+    "\n",
+    "So, there is no black or white answer as to which of the two language designs is better. Yet, most professional programmers have very strong opinions with respect to duck typing reaching from \"love\" to \"hate\". This is another example as to how programming is a subjective art and not an \"objective\" science. Probably, Python is regarded more beginner friendly as `3` and `3.0` should intuitively be interchangeable."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "### Type Checking & Input Validation"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "We can use the built-in [isinstance()](https://docs.python.org/3/library/functions.html#isinstance) function to make sure `factorial()` is called with an `int` object passed in. We further **validate the input** by verifying that the integer is non-negative.\n",
+    "\n",
+    "Meanwhile, we also see how we can manually raise exceptions with the `raise` [statement](https://docs.python.org/3/reference/simple_stmts.html#the-raise-statement), another way of controlling the flow of execution.\n",
+    "\n",
+    "The first two branches in the revised `factorial()` function act as **guardians** ensuring that the code does not produce *unexpected* runtime errors: Errors can certainly be expected when mentioned in the docstring.\n",
+    "\n",
+    "Forcing `n` to be an `int` is a very puritan way of handling the issues discussed above. A more relaxed approach could be to also accept a `float` and use its [is_integer()](https://docs.python.org/3/library/stdtypes.html#float.is_integer) method to check if `n` could be casted as an `int`. After all, by being too puritan we can not take advantage of duck typing.\n",
+    "\n",
+    "So in essence, we are doing *two* things here. Besides checking for the correct type, we are also enforcing **domain-specific** (i.e., mathematical) rules with respect to the non-negativity of `n`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "def factorial(n):\n",
+    "    \"\"\"Calculate the factorial of a number.\n",
+    "\n",
+    "    Args:\n",
+    "        n (int): number to calculate the factorial for; must be positive\n",
+    "\n",
+    "    Returns:\n",
+    "        factorial (int)\n",
+    "\n",
+    "    Raises:\n",
+    "        TypeError: if n is not an integer\n",
+    "        ValueError: if n is negative\n",
+    "    \"\"\"\n",
+    "    if not isinstance(n, int):\n",
+    "        raise TypeError(\"Factorial is only defined for integers\")\n",
+    "    elif n < 0:\n",
+    "        raise ValueError(\"Factorial is not defined for negative integers\")\n",
+    "    elif n == 0:\n",
+    "        return 1\n",
+    "    return n * factorial(n - 1)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "The revised `factorial()` function works like the old one."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "1"
+      ]
+     },
+     "execution_count": 33,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "factorial(0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "3628800"
+      ]
+     },
+     "execution_count": 34,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "factorial(10)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Instead of running into an infinite recursion, we now receive very specifc error messages."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "ename": "TypeError",
+     "evalue": "Factorial is only defined for integers",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-35-188b816be8b6>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mfactorial\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m3.1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;32m<ipython-input-32-54b57bc2107d>\u001b[0m in \u001b[0;36mfactorial\u001b[0;34m(n)\u001b[0m\n\u001b[1;32m     13\u001b[0m     \"\"\"\n\u001b[1;32m     14\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mn\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mint\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 15\u001b[0;31m         \u001b[0;32mraise\u001b[0m \u001b[0mTypeError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Factorial is only defined for integers\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     16\u001b[0m     \u001b[0;32melif\u001b[0m \u001b[0mn\u001b[0m \u001b[0;34m<\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     17\u001b[0m         \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Factorial is not defined for negative integers\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;31mTypeError\u001b[0m: Factorial is only defined for integers"
+     ]
+    }
+   ],
+   "source": [
+    "factorial(3.1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "ename": "ValueError",
+     "evalue": "Factorial is not defined for negative integers",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-36-b06b2cada645>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mfactorial\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m42\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;32m<ipython-input-32-54b57bc2107d>\u001b[0m in \u001b[0;36mfactorial\u001b[0;34m(n)\u001b[0m\n\u001b[1;32m     15\u001b[0m         \u001b[0;32mraise\u001b[0m \u001b[0mTypeError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Factorial is only defined for integers\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     16\u001b[0m     \u001b[0;32melif\u001b[0m \u001b[0mn\u001b[0m \u001b[0;34m<\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 17\u001b[0;31m         \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Factorial is not defined for negative integers\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     18\u001b[0m     \u001b[0;32melif\u001b[0m \u001b[0mn\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     19\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;31mValueError\u001b[0m: Factorial is not defined for negative integers"
+     ]
+    }
+   ],
+   "source": [
+    "factorial(-42)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "### Theory of Computation"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "With everything *officially* introduced so far (i.e., without the introductary example in [Chapter 1](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/01_elements.ipynb) that only served as an overview), Python would be what is called **[Turing complete](https://en.wikipedia.org/wiki/Turing_completeness)**. That means that anything that could be formulated as an algorithm could be expressed with all the language features we have seen. Note that, in particular, we have *not* yet formally *introduced* the `for` and `while` statements!"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "## Looping"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "### The `while` Statement"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Whereas functions combined with `if` statements suffice to model any type of iteration with a recursion, Python comes with a `while` [statement](https://docs.python.org/3/reference/compound_stmts.html#the-while-statement) that often makes it easier to implement iterative ideas.\n",
+    "\n",
+    "It consists of a header line with a boolean expression followed by an indented code block. Before the first and after every execution of the code block, the boolean expression is evaluated and if it is (still) equal to `True`, the code block runs (again). Eventually, some variable referenced in the boolean expression is changed in the code block such that the condition becomes `False`.\n",
+    "\n",
+    "If the condition is `False` before the first iteration, the entire code block is *never* executed. As the flow of control keeps **looping** (or **iterating**) back to the beginning of the code block, this concept is also called a `while`-loop and each pass through the loop an **iteration**."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "#### Trivial Example: Countdown (revisited)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Let's rewrite the `countdown()` example in an iterative style. We also build in **input validation** by allowing the function to only be called with strictly positive integers. As any positive integer will hit $0$ at some point in time when iteratively decremented by $1$, `countdown()` is guaranteed to **terminate**. Note also that the base case is now handled at the end of the function, which commonly happens with iterative solutions to problems."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "metadata": {
+    "code_folding": [],
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "def countdown(n):\n",
+    "    \"\"\"Print a countdown until the party starts.\n",
+    "\n",
+    "    Args:\n",
+    "        n (int): seconds until the party begins; must be positive\n",
+    "\n",
+    "    Raises:\n",
+    "        TypeError: if n is not of an integer\n",
+    "        ValueError: if n is not positive\n",
+    "    \"\"\"\n",
+    "    if not isinstance(n, int):\n",
+    "        raise TypeError(\"Can only count down with whole numbers\")\n",
+    "    elif n <= 0:\n",
+    "        raise ValueError(\"n must be stricly positive\")\n",
+    "\n",
+    "    while n != 0:\n",
+    "        print(n)\n",
+    "        n -= 1\n",
+    "\n",
+    "    print(\"Happy new Year!\")  # = base case"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "3\n",
+      "2\n",
+      "1\n",
+      "Happy new Year!\n"
+     ]
+    }
+   ],
+   "source": [
+    "countdown(3)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "As [PythonTutor](http://pythontutor.com/visualize.html#code=def%20countdown%28n%29%3A%0A%20%20%20%20if%20not%20isinstance%28n,%20int%29%3A%0A%20%20%20%20%20%20%20%20raise%20TypeError%28%22...%22%29%0A%20%20%20%20elif%20n%20%3C%3D%200%3A%0A%20%20%20%20%20%20%20%20raise%20ValueError%28%22...%22%29%0A%0A%20%20%20%20while%20n%20!%3D%200%3A%0A%20%20%20%20%20%20%20%20print%28n%29%0A%20%20%20%20%20%20%20%20n%20-%3D%201%0A%0A%20%20%20%20print%28%22Happy%20new%20Year!%22%29%0A%0Acountdown%283%29&cumulative=false&curInstr=0&heapPrimitives=nevernest&mode=display&origin=opt-frontend.js&py=3&rawInputLstJSON=%5B%5D&textReferences=false) shows, there is a subtle but important difference in the way a `while` statement is treated in memory: In short, `while` statements can *not* run into a `RecursionError` as only *one* frame is needed to manage the names. After all, there is only *one* function call to be made. For common day-to-day applications this difference is, however, not important."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "#### \"Still involved\" Example: [Euclid's Algorithm](https://en.wikipedia.org/wiki/Euclidean_algorithm) (revisited)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Finding the greatest common divisor of two numbers is still not so obvious when using a `while`-loop instead of a recursion.\n",
+    "\n",
+    "The iterative implementation of `gcd()` below accepts any two strictly positive integers. As in any iteration through the loop the smaller number is subtracted from the larger one, the two decremented values of `a` and `b` will eventually be equal. Thus, this algorithm is also guaranteed to terminate. If one of the two numbers were negative or $0$ to begin with, `gcd()` would run forever and not even Python could detect this. Try this out by removing the input validation and running the function with negative arguments!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "metadata": {
+    "code_folding": [],
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "def gcd(a, b):\n",
+    "    \"\"\"Calculate the greatest common divisor of two numbers.\n",
+    "\n",
+    "    Args:\n",
+    "        a (int): first number; must be positive\n",
+    "        b (int): second number; must be positive\n",
+    "\n",
+    "    Returns:\n",
+    "        gcd (int)\n",
+    "\n",
+    "    Raises:\n",
+    "        TypeError: if a or b are not of an integer type\n",
+    "        ValueError: if a or b are not positive\n",
+    "    \"\"\"\n",
+    "    if not isinstance(a, int) or not isinstance(b, int):\n",
+    "        raise TypeError(\"Greatest common divisor is only defined for two integers\")\n",
+    "    elif a <= 0 or b <= 0:\n",
+    "        raise ValueError(\"a and b must be strictly positive\")\n",
+    "\n",
+    "    while a != b:\n",
+    "        if a > b:\n",
+    "            a -= b\n",
+    "        else:\n",
+    "            b -= a\n",
+    "\n",
+    "    return a"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "4"
+      ]
+     },
+     "execution_count": 40,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "gcd(12, 4)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "1"
+      ]
+     },
+     "execution_count": 41,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "gcd(2, 7919)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "##### Efficiency of Algorithms (revisited)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "We can also see that this implementation is a lot *less* efficient than its recursive counterpart which solves `gcd()` for the same two numbers $112233445566778899$ and $987654321$ within microseconds."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "4.76 s ± 0 ns per loop (mean ± std. dev. of 1 run, 1 loop each)\n"
+     ]
+    }
+   ],
+   "source": [
+    "%%timeit -n 1 -r 1\n",
+    "gcd(112233445566778899, 987654321)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "### Infinite Loops"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "As with recursion, we must ensure that the iteration ends. For the above `countdown()` and `gcd()` examples we could \"prove\" (i.e., at least argue in favor) that some pre-defined **termination criterion** will be reached eventually. However, this cannot be done in all cases as the following example shows."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "#### \"Mystery\" Example: [Collatz Conjecture](https://en.wikipedia.org/wiki/Collatz_conjecture)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "Let's play the following game:\n",
+    "- think of any positive integer $n$\n",
+    "- if $n$ is even, the next $n$ is half the old $n$\n",
+    "- if $n$ is odd, multiply the old $n$ by $3$ and add $1$ to obtain the next $n$\n",
+    "- repeat these steps until you reach $1$\n",
+    "\n",
+    "**Do we always reach the final $1$?**"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "The function below implements this game. Does it always reach $1$? No one has proven it so far! We include some input validation as before because `collatz()` would definitely not terminate if we called it with a negative number. Further, the Collatz sequence also works for real numbers but then we would have to study fractals (cf., [this](https://en.wikipedia.org/wiki/Collatz_conjecture#Iterating_on_real_or_complex_numbers)). So we restrict our example to integers only."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "metadata": {
+    "code_folding": [],
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "def collatz(n):\n",
+    "    \"\"\"Print a Collatz sequence in descending order.\n",
+    "\n",
+    "    Given a positive integer n, modify it according to these rules:\n",
+    "        - if n is even, the next n is half the previous one\n",
+    "        - if n is odd, the next n is 3 times the previous one plus 1\n",
+    "        - if n is 1, stop the iteration\n",
+    "\n",
+    "    Args:\n",
+    "        n (int): a positive number to start the Collatz sequence at\n",
+    "\n",
+    "    Raises:\n",
+    "        TypeError: if n is not of an integer\n",
+    "        ValueError: if n is not positive\n",
+    "    \"\"\"\n",
+    "    if not isinstance(n, int):\n",
+    "        raise TypeError(\"non-integers require some advanced math\")\n",
+    "    elif n <= 0:\n",
+    "        raise ValueError(\"n must be stricly positive\")\n",
+    "\n",
+    "    while n != 1:\n",
+    "        print(n, end=\" \")\n",
+    "        if n % 2 == 0:\n",
+    "            n //= 2  # //= so that n remains an int\n",
+    "        else:\n",
+    "            n = 3 * n + 1\n",
+    "\n",
+    "    print(n)  # = base case"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Collatz sequences do not necessarily become longer with a larger initial `n`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "100 50 25 76 38 19 58 29 88 44 22 11 34 17 52 26 13 40 20 10 5 16 8 4 2 1\n"
+     ]
+    }
+   ],
+   "source": [
+    "collatz(100)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "1000 500 250 125 376 188 94 47 142 71 214 107 322 161 484 242 121 364 182 91 274 137 412 206 103 310 155 466 233 700 350 175 526 263 790 395 1186 593 1780 890 445 1336 668 334 167 502 251 754 377 1132 566 283 850 425 1276 638 319 958 479 1438 719 2158 1079 3238 1619 4858 2429 7288 3644 1822 911 2734 1367 4102 2051 6154 3077 9232 4616 2308 1154 577 1732 866 433 1300 650 325 976 488 244 122 61 184 92 46 23 70 35 106 53 160 80 40 20 10 5 16 8 4 2 1\n"
+     ]
+    }
+   ],
+   "source": [
+    "collatz(1000)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "10000 5000 2500 1250 625 1876 938 469 1408 704 352 176 88 44 22 11 34 17 52 26 13 40 20 10 5 16 8 4 2 1\n"
+     ]
+    }
+   ],
+   "source": [
+    "collatz(10000)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "100000 50000 25000 12500 6250 3125 9376 4688 2344 1172 586 293 880 440 220 110 55 166 83 250 125 376 188 94 47 142 71 214 107 322 161 484 242 121 364 182 91 274 137 412 206 103 310 155 466 233 700 350 175 526 263 790 395 1186 593 1780 890 445 1336 668 334 167 502 251 754 377 1132 566 283 850 425 1276 638 319 958 479 1438 719 2158 1079 3238 1619 4858 2429 7288 3644 1822 911 2734 1367 4102 2051 6154 3077 9232 4616 2308 1154 577 1732 866 433 1300 650 325 976 488 244 122 61 184 92 46 23 70 35 106 53 160 80 40 20 10 5 16 8 4 2 1\n"
+     ]
+    }
+   ],
+   "source": [
+    "collatz(100000)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "### The `for` Statement"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Recursion and the `while` statement are two sides of the same coin. Disregarding that in the case of recursion Python internally faces some additional burden for managing the stack of frames in memory, both approaches lead to the *same* computational steps in memory. More importantly, we can re-formulate a recursive implementation in an iterative way and vice verca despite one of the two ways often \"feeling\" a lot more natural given a particular problem.\n",
+    "\n",
+    "So how does the `for` [statement](https://docs.python.org/3/reference/compound_stmts.html#the-for-statement) we saw in the very first example in this book fit into this picture? It is really a *redundant* language construct to provide a *shorter* and more *convenient* syntax for common applications of the `while` statement. In programming, such additions to a language are called **syntactic sugar**. Sugar makes a cup of tea taste better but we can drink tea without sugar too.\n",
+    "\n",
+    "Consider the following `numbers` list. Without the `for` statement, we would have to keep track of a temporary **index variable** `i` to loop over all its elements and also obtain the individual elements with the `[]` operator in each iteration of the loop."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 48,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "numbers = [5, 6, 7, 8, 9]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 49,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "5 6 7 8 9 "
+     ]
+    }
+   ],
+   "source": [
+    "i = 0\n",
+    "while i < len(numbers):\n",
+    "    number = numbers[i]\n",
+    "    print(number, end=\" \")\n",
+    "    i += 1"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "The `for` statement, on the contrary, makes the actual business logic more apparent by stripping all the boilerplate code away."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 50,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "5 6 7 8 9 "
+     ]
+    }
+   ],
+   "source": [
+    "for number in numbers:\n",
+    "    print(number, end=\" \")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "For sequences of integers the [range()](https://docs.python.org/3/library/functions.html#func-range) built-in makes the `for` statement even more convenient: It creates a list-like object of type `range` that generates integers \"on the fly\" and we will look closely at the underlying data types in Chapter 7."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 51,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0 1 2 3 4 "
+     ]
+    }
+   ],
+   "source": [
+    "for number in [0, 1, 2, 3, 4]:\n",
+    "    print(number, end=\" \")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 52,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0 1 2 3 4 "
+     ]
+    }
+   ],
+   "source": [
+    "for number in range(5):\n",
+    "    print(number, end=\" \")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Let's quickly verify that `range(5)` creates an object of type `range`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 53,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "range"
+      ]
+     },
+     "execution_count": 53,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "type(range(5))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "[range()](https://docs.python.org/3/library/functions.html#func-range) takes optional `start` and `step` arguments that we can use to customize the sequence of integers even more."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 54,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "1 3 5 7 9 "
+     ]
+    }
+   ],
+   "source": [
+    "for number in [1, 3, 5, 7, 9]:\n",
+    "    print(number, end=\" \")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 55,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "1 3 5 7 9 "
+     ]
+    }
+   ],
+   "source": [
+    "for number in range(1, 10, 2):\n",
+    "    print(number, end=\" \")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "#### Containers vs. Iterables"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "The important difference between the `list` objects (i.e., `[0, 1, 2, 3, 4]` and `[1, 3, 5, 7, 9]`) and the `range` objects (i.e., `range(5)` and `range(1, 10, 2)`) is that in the former case *six* objects are created in memory, one `list` holding pointers to *five* `int` objects, whereas in the latter case only *one* `range` object exists in memory that **generates** `int` objects as we ask for it.\n",
+    "\n",
+    "However, we can iterate over both of them. So a natural question to ask is why Python treats objects of *different*  types in the *same* way when used with a `for` statement.\n",
+    "\n",
+    "So far, the overarching storyline in this book goes like this: In Python, *everything* is an object. Besides its *identity* and *value*, every object is characterized by belonging to *one* data type that determines how the object behaves and what we can do with it.\n",
+    "\n",
+    "Now, just as we classify objects by their types, we also classify these **concrete data types** (e.g., `int`, `float`, or `str`) into **abstract concepts**.\n",
+    "\n",
+    "We have actually done this in [Chapter 1](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/01_elements.ipynb) already when we described a `list` object as \"some sort of container that holds [...] pointers to other objects\". So, abstractly speaking, **containers** are any objects that are \"composed\" of other objects and also \"manage\" how these objects are organized. `list` objects, for example, have the property that they model an inherent order associated with its elements. There exist, however, many other container types, many of which do *not* come with an order. So, containers primarily \"contain\" other objects and have *nothing* to do with looping.\n",
+    "\n",
+    "On the contrary, the abstract concept of **iterables** is all about looping: Any object that we can loop over is by definition an iterable. So, `range` objects, for example, are iterables taht do *not* contain any other objects. Moreover, looping does *not* have to occur in any particular order although this is the case for both `list` and `range` objects.\n",
+    "\n",
+    "Typically, containers are iterable and iterables are containers. Yet, only because these two concepts coincide often, we must not think of them as the same. Chapter 10 will finally give an explanation as to how abstract concepts are implemented and play together.\n",
+    "\n",
+    "`list` objects like `first_names` below are iterable containers. They actually implement even more abstract concepts as we will see in Chapter 7."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 56,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "first_names = [\"Achim\", \"Berthold\", \"Carl\", \"Diedrich\", \"Eckardt\"]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "The characteristic operator associated with a container type is the `in` operator which checks if a given object evaluates equal to any of the objects in the container. Colloquially, it checks if an object is \"contained\" in the container. This operation is also called **membership testing**."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 57,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 57,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "\"Achim\" in first_names"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 58,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "False"
+      ]
+     },
+     "execution_count": 58,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "\"Alexander\" in first_names"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "This shows the exact workings of the `in` operator: Although `7.0` is *not* in `numbers`, `7.0` evaluates equal to the `7` that is in it, which is why the following expression evaluates to `True`. So, while we could colloquially say that `numbers` \"contains\" `7.0`, it actually does not."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 59,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 59,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "7.0 in numbers"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Similarly, the characteristic operation of an iterable type is that it supports iteration, for example, with a `for`-loop."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 60,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Achim   Berthold   Carl   Diedrich   Eckardt   "
+     ]
+    }
+   ],
+   "source": [
+    "for name in first_names:\n",
+    "    print(name, end=\"   \")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "If we need to have an index variable in the loop's body, we use the [enumerate()](https://docs.python.org/3/library/functions.html#enumerate) built-in that takes an iterable as its argument and then generates a stream of \"pairs\" consisting of an index variable and an object provided by the iterable. There is *no* need to ever revert back to the `while` statement to loop over an iterable object."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 61,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0 > Achim   1 > Berthold   2 > Carl   3 > Diedrich   4 > Eckardt   "
+     ]
+    }
+   ],
+   "source": [
+    "for i, name in enumerate(first_names):\n",
+    "    print(i, name, sep=\" > \", end=\"   \")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "[enumerate()](https://docs.python.org/3/library/functions.html#enumerate) takes an optional `start` argument."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 62,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "1 > Achim   2 > Berthold   3 > Carl   4 > Diedrich   5 > Eckardt   "
+     ]
+    }
+   ],
+   "source": [
+    "for i, name in enumerate(first_names, start=1):\n",
+    "    print(i, name, sep=\" > \", end=\"   \")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "The [zip()](https://docs.python.org/3/library/functions.html#zip) built-in allows us to combine the elements of two or more iterables in a *pairwise* fashion: It conceptually works like a zipper for a jacket."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 63,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "last_names = [\"MĂĽller\", \"Meyer\", \"Mayer\", \"Schmitt\", \"Schmidt\"]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 64,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Achim MĂĽller\n",
+      "Berthold Meyer\n",
+      "Carl Mayer\n",
+      "Diedrich Schmitt\n",
+      "Eckardt Schmidt\n"
+     ]
+    }
+   ],
+   "source": [
+    "for first_name, last_name in zip(first_names, last_names):\n",
+    "    print(first_name, last_name)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "#### \"Hard at first Glance\" Example: [Fibonacci Numbers](https://en.wikipedia.org/wiki/Fibonacci_number) (revisited)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "In contrast to its recursive counterpart, the iterative `fibonacci()` function below is somewhat harder to read. For example, it is not so obvious as to how many iterations through the `for`-loop we need to make when implementing it. There is an increased risk of making an *off-by-one* error. Moreover, we need to track a `temp` variable along, at least until we have worked through Chapter 7. Do you understand what `temp` does?\n",
+    "\n",
+    "However, one advantage of calculating Fibonacci numbers in a **forwards** fashion with a `for` statement is that we could list the entire sequence in ascending order as we calculate the desired number. To show this, we added `print()` statements in `fibonacci()` below.\n",
+    "\n",
+    "We do *not* need to store the index variable in the `for`-loop's header line: That is what the underscore \"\\_\" indicates; we \"throw it away\". Also, we do not need to explicitly check if `i` is of type `int` as the [range()](https://docs.python.org/3/library/functions.html#func-range) built-in raises a `TypeError` if used with anything other than an `int`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 65,
+   "metadata": {
+    "code_folding": [],
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "def fibonacci(i):\n",
+    "    \"\"\"Calculate the ith Fibonacci number.\n",
+    "\n",
+    "    Args:\n",
+    "        i (int): index of the Fibonacci number to calculate\n",
+    "\n",
+    "    Returns:\n",
+    "        ith_fibonacci (int)\n",
+    "\n",
+    "    Raises:\n",
+    "        TypeError: if i is not of an integer type\n",
+    "        ValueError: if i is not positive\n",
+    "    \"\"\"\n",
+    "    # no need to check if i is an integer as range() does that\n",
+    "    if i < 0:\n",
+    "        raise ValueError(\"i must be non-negative\")\n",
+    "\n",
+    "    a = 0\n",
+    "    b = 1\n",
+    "    print(a, b, sep=\" \", end=\" \")  # added for didactical purposes\n",
+    "    for _ in range(i - 1):  # the index variable is not needed\n",
+    "        temp = a + b\n",
+    "        a = b\n",
+    "        b = temp\n",
+    "        print(b, end=\" \")  # added for didactical purposes\n",
+    "\n",
+    "    return b"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 66,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0 1 1 2 3 5 8 13 21 34 55 89 144 "
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "144"
+      ]
+     },
+     "execution_count": 66,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "fibonacci(12)  # = 13th number"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "##### Efficiency of Algorithms (revisited)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Another more important advantage is that now we can calculate even big Fibonacci numbers *efficiently*."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 67,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0 1 1 2 3 5 8 13 21 34 55 89 144 233 377 610 987 1597 2584 4181 6765 10946 17711 28657 46368 75025 121393 196418 317811 514229 832040 1346269 2178309 3524578 5702887 9227465 14930352 24157817 39088169 63245986 102334155 165580141 267914296 433494437 701408733 1134903170 1836311903 2971215073 4807526976 7778742049 12586269025 20365011074 32951280099 53316291173 86267571272 139583862445 225851433717 365435296162 591286729879 956722026041 1548008755920 2504730781961 4052739537881 6557470319842 10610209857723 17167680177565 27777890035288 44945570212853 72723460248141 117669030460994 190392490709135 308061521170129 498454011879264 806515533049393 1304969544928657 2111485077978050 3416454622906707 5527939700884757 8944394323791464 14472334024676221 23416728348467685 37889062373143906 61305790721611591 99194853094755497 160500643816367088 259695496911122585 420196140727489673 679891637638612258 1100087778366101931 1779979416004714189 2880067194370816120 4660046610375530309 7540113804746346429 12200160415121876738 19740274219868223167 31940434634990099905 51680708854858323072 83621143489848422977 135301852344706746049 218922995834555169026 "
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "218922995834555169026"
+      ]
+     },
+     "execution_count": 67,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "fibonacci(99)  # = 100th number"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "#### Easy Example: [Factorial](https://en.wikipedia.org/wiki/Factorial) (revisited)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "The iterative `factorial()` function is comparable to its recursive counterpart when it comes to readability. One advantage of calculating the factorial in a forwards fashion is that we could track the intermediate `product` as it grows."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 68,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "def factorial(n):\n",
+    "    \"\"\"Calculate the factorial of a number.\n",
+    "\n",
+    "    Args:\n",
+    "        n (int): number to calculate the factorial for, must be positive\n",
+    "\n",
+    "    Returns:\n",
+    "        factorial (int)\n",
+    "\n",
+    "    Raises:\n",
+    "        TypeError: if n is not an integer\n",
+    "        ValueError: if n is negative\n",
+    "    \"\"\"\n",
+    "    if not isinstance(n, int):\n",
+    "        raise TypeError(\"Factorial is only defined for integers\")\n",
+    "    elif n < 0:\n",
+    "        raise ValueError(\"Factorial is not defined for negative integers\")\n",
+    "\n",
+    "    product = 1  # because 0! = 1\n",
+    "    for i in range(1, n + 1):  # loop starts at 1 as 0! is already covered\n",
+    "        product *= i\n",
+    "        print(product, end=\" \")  # added for didactical purposes\n",
+    "\n",
+    "    return product"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 69,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "1 2 6 24 120 720 5040 40320 362880 3628800 "
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "3628800"
+      ]
+     },
+     "execution_count": 69,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "factorial(10)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "### The `break` Statement"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "The remainder of this chapter introduces more syntactic sugar. None of the language constructs introduced below are  actually needed but contribute to making Python the expressive and easy to read language that it is."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "#### Example: Searching an Iterable"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Let's say we have a list of numbers and we want to check if the square of at least one of its numbers is above a `threshold` of `100`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 70,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "numbers = [3, 7, 2, 9, 11, 4, 7, 9, 4, 5]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 71,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "threshold = 100  # is the square of an element in numbers greater than this?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "A first naive implementation could look like this: We loop over *every* element in `numbers` and set an **indicator variable** `is_above` to `True` once we encounter an element satisfying our search condition."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 72,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "3   7   2   9   11   4   7   9   4   5   =>   at least one number's square is above 100\n"
+     ]
+    }
+   ],
+   "source": [
+    "is_above = False\n",
+    "\n",
+    "for number in numbers:\n",
+    "    print(number, end=\"   \")  # added for didactical purposes\n",
+    "    if number ** 2 > threshold:\n",
+    "        is_above = True\n",
+    "\n",
+    "if is_above:\n",
+    "    print(\"=>   at least one number's square is above\", threshold)\n",
+    "else:\n",
+    "    print(\"=>   no number's square is above\", threshold)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "This implementation is *inefficient* as even if the *first* number in `numbers` has a square greater than `100`, we loop until the last element, which could take a long time for a big list.\n",
+    "\n",
+    "Moreover, we must initialize `is_above` before the `for`-loop and write an `if`-`else`-logic seperate from it to check for the final result. The actual business logic is not clear right away.\n",
+    "\n",
+    "Luckily, Python provides a `break` [statement](https://docs.python.org/3/reference/simple_stmts.html#the-break-statement) that let's us stop the `for`-loop in any iteration of the loop. Conceptually, it is yet another means of controlling the flow of execution."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 73,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "3   7   2   9   11   =>   at least one number's square is above 100\n"
+     ]
+    }
+   ],
+   "source": [
+    "is_above = False\n",
+    "\n",
+    "for number in numbers:\n",
+    "    print(number, end=\"   \")  # added for didactical purposes\n",
+    "    if number ** 2 > threshold:\n",
+    "        is_above = True\n",
+    "        break\n",
+    "\n",
+    "if is_above:\n",
+    "    print(\"=>   at least one number's square is above\", threshold)\n",
+    "else:\n",
+    "    print(\"=>   no number's square is above\", threshold)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "This is a computational improvement. However, the code can still be split into *three* groups: initialization, the `for`-loop, and some finalizing logic. We would prefer to convey the program's idea in *one* compound statement."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "### The `for`-`else` Clause"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "To express the logic in a prettier way, we add an `else`-clause at the end of the `for`-loop. The `else`-branch is only executed if the body in the `for`-branch is *not* stopped with a `break` statement before reaching the last iteration in the loop. The word \"else\" implies a rather unintuitive meaning and it had better been named a `then`-clause. In most scenarios, however, the `else`-clause logically goes together with some `if` statement within the `for`-loop's body.\n",
+    "\n",
+    "Overall, the expressive power of our code increases. Not many programming languages support a `for`-`else`-branching, which turns out to be very useful in practice."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "#### Example: Searching an Iterable (revisited)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 74,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "3   7   2   9   11   =>   at least one number's square is above 100\n"
+     ]
+    }
+   ],
+   "source": [
+    "for number in numbers:\n",
+    "    print(number, end=\"   \")  # added for didactical purposes\n",
+    "    if number ** 2 > threshold:\n",
+    "        is_above = True\n",
+    "        break\n",
+    "else:\n",
+    "    is_above = False\n",
+    "\n",
+    "if is_above:\n",
+    "    print(\"=>   at least one number's square is above\", threshold)\n",
+    "else:\n",
+    "    print(\"=>   no number's square is above\", threshold)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Lastly, we incorporate the `if`-`else` logic at the end into the `for`-loop and avoid the `is_above` variable alltogether."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 75,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "3   7   2   9   11   =>   at least one number's square is above 100\n"
+     ]
+    }
+   ],
+   "source": [
+    "for number in numbers:\n",
+    "    print(number, end=\"   \")  # added for didactical purposes\n",
+    "    if number ** 2 > threshold:\n",
+    "        print(\"=>   at least one number's square is above\", threshold)\n",
+    "        break\n",
+    "else:\n",
+    "    print(\"=>   no number's square is above\", threshold)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Of course, if we set the `threshold` a number's square has to pass higher, for example to `200`, we have to loop through the entire `numbers` list. There is no way to optimize this **[linear search](https://en.wikipedia.org/wiki/Linear_search)**, at least as long as we model the list of numbers with a `list` object. More advanced data types, however, exist that mitigate that downside."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 76,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "3   7   2   9   11   4   7   9   4   5   =>   no number's square is above 200\n"
+     ]
+    }
+   ],
+   "source": [
+    "threshold = 200\n",
+    "\n",
+    "for number in numbers:\n",
+    "    print(number, end=\"   \")  # added for didactical purposes\n",
+    "    if number ** 2 > threshold:\n",
+    "        print(\"=>   at least one number's square is above\", threshold)\n",
+    "        break\n",
+    "else:\n",
+    "    print(\"=>   no number's square is above\", threshold)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "### The `continue` Statement"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Often times, we process some iterable with numeric data, for example, a list of numbers as in the introductory example in [Chapter 1](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/01_elements.ipynb) or, more realistically, data from a CSV file with many rows and columns.\n",
+    "\n",
+    "Processing numeric data usually comes down to operations that can be grouped into one of the following three categories:\n",
+    "\n",
+    "- **mapping**: transform an observation according to some functional relationship $y = f(x)$\n",
+    "- **filtering**: throw away individual observations (e.g., statistical outliers)\n",
+    "- **reducing**: collect individual observations into summary statistics\n",
+    "\n",
+    "We will study this **map-filter-reduce** paradigm extensively in Chapter 7 after introducing more advanced data types that are needed to work with \"big\" data.\n",
+    "\n",
+    "In this section, we focus on *filtering out* some samples within a `for`-loop."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "#### Example: A simple Filter"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "Calculate the sum of all even numbers from $1$ through $12$ after squaring them and adding $1$ to the squares:\n",
+    "\n",
+    "- **\"all\"** => loop over an iterable\n",
+    "- **\"even\"** => *filter* out the odd numbers\n",
+    "- **\"square and add $1$\"** => apply the *map* $y = f(x) = x^2 + 1$\n",
+    "- **\"sum\"** => *reduce* the remaining and mapped numbers to their sum"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 77,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "numbers = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 78,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "2 > 5   4 > 17   6 > 37   8 > 65   10 > 101   12 > 145   "
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "370"
+      ]
+     },
+     "execution_count": 78,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "total = 0\n",
+    "\n",
+    "for x in numbers:\n",
+    "    if x % 2 == 0:  # only keep even numbers\n",
+    "        y = (x ** 2) + 1\n",
+    "        print(x, y, sep=\" > \", end=\"   \")  # added for didactical purposes\n",
+    "        total += y\n",
+    "\n",
+    "total"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "The above code is still rather easy to read as it only involves two levels of indentation.\n",
+    "\n",
+    "In general, code gets harder to comprehend the more **horizontal space** it occupies. It is commonly considered good practice to grow a program **vertically** rather than horizontally. Code complient with [PEP 8](https://www.python.org/dev/peps/pep-0008/#maximum-line-length) requires us to use *at most* 79 characters in a line!\n",
+    "\n",
+    "Consider the next example, whose implementation in code already starts to look \"unbalanced\"."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "#### Example: Nested Filters"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "Calculate the sum of every third and even number from $1$ through $12$ after squaring them and adding $1$ to the squares:\n",
+    "\n",
+    "- **\"every\"** => loop over an iterable\n",
+    "- **\"third\"** => *filter* out all numbers except every third\n",
+    "- **\"even\"** => *filter* out the odd numbers\n",
+    "- **\"square and add $1$\"** => apply the *map* $y = f(x) = x^2 + 1$\n",
+    "- **\"sum\"** => *reduce* the remaining and mapped numbers to their sum"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 79,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12]"
+      ]
+     },
+     "execution_count": 79,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "numbers"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 80,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "6 > 37   12 > 145   "
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "182"
+      ]
+     },
+     "execution_count": 80,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "total = 0\n",
+    "\n",
+    "for i, x in enumerate(numbers, start=1):\n",
+    "    if i % 3 == 0:  # only keep every third number\n",
+    "        if x % 2 == 0:  # only keep even numbers\n",
+    "            y = (x ** 2) + 1\n",
+    "            print(x, y, sep=\" > \", end=\"   \")  # added for didactical purposes \n",
+    "            total += y\n",
+    "\n",
+    "total"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "With already three levels of indentation, less horizontal space is available for the actual code. Of course, one could combine the two `if` statements with the logical `and` operator as shown in [Chapter 3](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/03_conditionals.ipynb). However, then we trade off horizontal space against a more \"complex\" `if` logic and this is not a real improvement.\n",
+    "\n",
+    "A Pythonista would instead make use of the `continue` [statement](https://docs.python.org/3/reference/simple_stmts.html#the-continue-statement) that causes a loop to jump right into the next iteration skipping the rest of the code block.\n",
+    "\n",
+    "The revised code fragement below occupies more vertical space and less horizontal space. A good trade-off."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "#### Example: Nested Filters (revisited)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 81,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "6 > 37   12 > 145   "
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "182"
+      ]
+     },
+     "execution_count": 81,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "total = 0\n",
+    "\n",
+    "for i, x in enumerate(numbers, start=1):\n",
+    "    if i % 3 != 0:  # only keep every third number\n",
+    "        continue\n",
+    "    elif x % 2 != 0:  # only keep even numbers\n",
+    "        continue\n",
+    "\n",
+    "    y = (x ** 2) + 1\n",
+    "    print(x, y, sep=\" > \", end=\"   \")  # added for didactical purposes \n",
+    "    total += y\n",
+    "\n",
+    "total"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "This is yet another illustration of why programming is an art. The two preceding code fragments do *exactly* the *same* with *identical* time complexity. However, arguably the latter it a lot easier to read for a human, at least when the business logic grows beyond more than just two nested filters.\n",
+    "\n",
+    "The idea behind the `continue` statement is conceptually similar to the early exit pattern we saw in the context of function definitions in [Chapter 2](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/02_functions.ipynb).\n",
+    "\n",
+    "The two examples can be modeled in an even better way as we will see in Chapter 7.\n",
+    "\n",
+    "Both the `break` and `continue` statements as well as the optional `else`-clause are not only supported within `for`-loops but also `while`-loops."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "### Indefinite Loops"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Sometimes we find ourselves in situations where we can *not* know ahead of time how often or until which point in time a code block is to be executed."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "#### Example: Guessing a Coin Toss"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Let's consider a game where we randomly choose a variable to be either \"Heads\" or \"Tails\" and the user of our program has to guess it.\n",
+    "\n",
+    "Python provides the built-in [input()](https://docs.python.org/3/library/functions.html#input) function that prints a message to the user, called the **prompt**, and reads in what the user typed in response as a `str` object. We use it to process the user's \"unpredictable\" input to our program (i.e., a user might type in some invalid response). Further, we use the [random()](https://docs.python.org/3/library/random.html#random.random) function in the [random](https://docs.python.org/3/library/random.html) module to model the coin toss.\n",
+    "\n",
+    "A popular pattern to approach such **indefinite loops** is to go with a `while True` statement which on its own would cause Python to enter into an infinite loop. Then, once a certain event occurs, we `break` out of the loop.\n",
+    "\n",
+    "Let's look at a first naive implementation."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 82,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "import random"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 83,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "random.seed(42)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 84,
+   "metadata": {
+    "code_folding": [],
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Guess if the coin comes up as heads or tails: Tails\n",
+      "Ooops, it was tails\n",
+      "Guess if the coin comes up as heads or tails: heads\n",
+      "Yes, it was heads\n"
+     ]
+    }
+   ],
+   "source": [
+    "while True:\n",
+    "    guess = input(\"Guess if the coin comes up as heads or tails: \")\n",
+    "\n",
+    "    if random.random() < 0.5:\n",
+    "        if guess == \"heads\":\n",
+    "            print(\"Yes, it was heads\")\n",
+    "            break\n",
+    "        else:\n",
+    "            print(\"Ooops, it was heads\")\n",
+    "    else:\n",
+    "        if guess == \"tails\":\n",
+    "            print(\"Yes, it was tails\")\n",
+    "            break\n",
+    "        else:\n",
+    "            print(\"Ooops, it was tails\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "This version has two *severe* aspects where we should improve on:\n",
+    "\n",
+    "1. If a user enters something other than \"heads\" or \"tails\", for example, \"Heads\" or \"Tails\", the program keeps running without the user knowing about the mistake!\n",
+    "2. It intermingles the coin tossing with the comparison against the user's input: Mixing unrelated business logic in the same code fragment makes a program harder to read and maintain in the long run."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "#### Example: Guessing a Coin Toss (revisited)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Let's refactor the code and make it *modular* and *comprehendable*.\n",
+    "\n",
+    "First, we divide the logic into two functions `get_guess()` and `toss_coin()` that are controlled from within a `while`-loop.\n",
+    "\n",
+    "`get_guess()` not only reads in the user's input but also implements a simple input validation pattern in that the [strip()](https://docs.python.org/3/library/stdtypes.html?highlight=__contains__#str.strip) and [lower()](https://docs.python.org/3/library/stdtypes.html?highlight=__contains__#str.lower) methods remove preceeding and trailing whitespace and lower case the input ensuring that the user can spell the input in any possible way (e.g. all upper or lower case). Also, `get_guess()` checks if the user entered one of the two valid options. If so, it returns either `\"heads\"` or `\"tails\"`; if not, it returns `None`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 85,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "def get_guess():\n",
+    "    \"\"\"Process the user's input.\n",
+    "    \n",
+    "    Returns:\n",
+    "        guess (str / NoneType): either \"heads\" or \"tails\"\n",
+    "            if the input can be parsed and None otherwise\n",
+    "    \"\"\"\n",
+    "    guess = input(\"Guess if the coin comes up as heads or tails: \")\n",
+    "    # handle frequent cases of \"misspelled\" user input\n",
+    "    guess = guess.strip().lower()\n",
+    "\n",
+    "    if guess in [\"heads\", \"tails\"]:\n",
+    "        return guess\n",
+    "    return None"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "`toss_coin()` is a simple function that models a fair coin toss when called with default arguments."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 86,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "def toss_coin(p_heads=0.5):\n",
+    "    \"\"\"Simulate the tossing of a coin.\n",
+    "\n",
+    "    Args:\n",
+    "        p_heads (optional, float): probability that the coin comes up \"heads\";\n",
+    "            defaults to 0.5 resembling a fair coin\n",
+    "\n",
+    "    Returns:\n",
+    "        side_on_top (str): \"heads\" or \"tails\"\n",
+    "    \"\"\"\n",
+    "    if random.random() < p_heads:\n",
+    "        return \"heads\"\n",
+    "    return \"tails\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Second, we rewrite the `if`-`else`-logic to explictly handle the case where `get_guess()` returns `None`: Whenever the user enters something invalid, a warning is shown, and another try is granted. Observe how we use the `is` operator and not the `==` operator as `None` is a singleton object.\n",
+    "\n",
+    "The `while` statement itself takes on the role of **glue code** that only manages how other parts of the program interact with each other."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 87,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "random.seed(42)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 88,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Guess if the coin comes up as heads or tails: invalid\n",
+      "Make sure to enter your guess correctly!\n",
+      "Guess if the coin comes up as heads or tails: Heads\n",
+      "Yes, it was heads\n"
+     ]
+    }
+   ],
+   "source": [
+    "while True:\n",
+    "    guess = get_guess()\n",
+    "    result = toss_coin()\n",
+    "\n",
+    "    if guess is None:\n",
+    "        print(\"Make sure to enter your guess correctly!\")\n",
+    "    elif guess == result:\n",
+    "        print(\"Yes, it was\", result)\n",
+    "        break\n",
+    "    else:\n",
+    "        print(\"Ooops, it was\", result)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Now, our little program's business logic is a lot easier to comprehend. More importantly, we can now easily make changes to the program. For example, we could make the `toss_coin()` function base the tossing on a probability distribution other than the uniform (i.e., replace the [random.random()](https://docs.python.org/3/library/random.html#random.random) function with another one). In general, a modular architecture leads to improved software maintenance."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "## TL;DR"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "**Iteration** is about **running blocks of code repeatedly**.\n",
+    "\n",
+    "There are two redundant approaches of achieving that.\n",
+    "\n",
+    "First, we can combine functions that call themselves with conditional statements. This concept is known as **recursion** and suffices to control the flow of execution in *every* way we desire. For a beginner, this approach of **backwards** reasoning might not be intuitive but it turns out to be a very useful tool to have in one's toolbox.\n",
+    "\n",
+    "Second, the `while` and `for` statements are alternative and potentially more intuitive ways to express iteration as they correspond to a **forwards** reasoning. The `for` statement is **syntactic sugar** that allows to rewrite common occurences of the `while` statement in a concise way. Python provides the `break` and `continue` statements as well as an optional `else`-clause that make working with the `for` and `while` statements even more convenient.\n",
+    "\n",
+    "**Iterables** are any **concrete data types** that support being looped over, for example, with the `for` statement. The idea behind iterables is an **abstract concept** that may or may not be implemented by any given concrete data type. For example, both `list` and `range` objects can be looped over. The `list` type is also a **container** as any given `list` objects \"contains\" pointers to other objects in memory. On the contrary, the `range` type does not point to any other objects but rather creates new `int` objects \"on the fly\"."
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.3"
+  },
+  "livereveal": {
+   "auto_select": "code",
+   "auto_select_fragment": true,
+   "scroll": true,
+   "theme": "serif"
+  },
+  "toc": {
+   "base_numbering": 1,
+   "nav_menu": {},
+   "number_sections": false,
+   "sideBar": true,
+   "skip_h1_title": true,
+   "title_cell": "Table of Contents",
+   "title_sidebar": "Contents",
+   "toc_cell": false,
+   "toc_position": {
+    "height": "calc(100% - 180px)",
+    "left": "10px",
+    "top": "150px",
+    "width": "384px"
+   },
+   "toc_section_display": false,
+   "toc_window_display": false
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

From 2c8ff65f57ea3efa01e327e4c9c14451db9de096 Mon Sep 17 00:00:00 2001
From: Alexander Hess <alexander@webartifex.biz>
Date: Tue, 1 Oct 2019 17:50:58 +0200
Subject: [PATCH 026/242] Adjust content overview in README.md

---
 README.md | 1 +
 1 file changed, 1 insertion(+)

diff --git a/README.md b/README.md
index 951bf75..4561afb 100644
--- a/README.md
+++ b/README.md
@@ -18,6 +18,7 @@ As such they can be viewed in a plain web browser:
 - [01 - Elements of a Program](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/01_elements.ipynb)
 - [02 - Functions & Modularization](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/02_functions.ipynb)
 - [03 - Conditionals & Exceptions](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/03_conditionals.ipynb)
+- [04 - Recursion & Looping](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/04_iteration.ipynb)
 
 However, it is recommended that students **install Python and Jupyter
 locally** and run the code in the notebooks on their own.

From 6385153e38219106efdbaced296f3175d20cc7de Mon Sep 17 00:00:00 2001
From: Alexander Hess <alexander@webartifex.biz>
Date: Tue, 1 Oct 2019 23:21:05 +0200
Subject: [PATCH 027/242] Add review and exercises for notebook 04

---
 04_iteration_review_and_exercises.ipynb | 941 ++++++++++++++++++++++++
 static/towers_of_hanoi.gif              | Bin 0 -> 197609 bytes
 2 files changed, 941 insertions(+)
 create mode 100644 04_iteration_review_and_exercises.ipynb
 create mode 100644 static/towers_of_hanoi.gif

diff --git a/04_iteration_review_and_exercises.ipynb b/04_iteration_review_and_exercises.ipynb
new file mode 100644
index 0000000..0e937b7
--- /dev/null
+++ b/04_iteration_review_and_exercises.ipynb
@@ -0,0 +1,941 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n",
+    "# Chapter 4: Iteration"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Content Review"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Read [Chapter 4](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/04_iteration.ipynb) of the book. Then work through the fourteen review questions."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Essay Questions "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Answer the following questions briefly with *at most* 300 characters per question!"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q1**: What is so \"special\" about the number **7919**?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q2**: Solving a problem with a **recursion** is not only popular in computer science and math. Name some examples from the fields of business or economics where problems are also solved in a **backwards** fashion!"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q3**: Explain what **duck typing** means! Why can it cause problems? Why it is [not a bug but a feature](https://www.urbandictionary.com/define.php?term=It%27s%20not%20a%20bug%2C%20it%27s%20a%20feature)?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q4**: What is **syntactic sugar**?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q5**: Describe in your own words why the **recursive** version of `fibonacci()`, the \"Easy at first Glance\" example in the chapter, is computationally **inefficient**! Why does the **iterative** version of `fibonacci()`, the \"Hard at first Glance\" example, run so much faster?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q6**: What is the conceptual difference between a **container** and a **list**?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q7**: What is a good use case for the `for`-loop's optional `else`-clause?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### True / False Questions"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Motivate your answer with *one short* sentence!"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q8**: When a **recursion** does **not** reach the base case, this is an example of the **early exit** strategy."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q9**: Any programming language **without** looping constructs like the `for` or `while` statements is **not** Turing complete."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q10**: A **recursive** formulation is the same as a **circular** one: The terms are **synonyms**."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q11**: Formulating a computational problem as a **recursion** results in an **efficient** implementation."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q12**: Whereas a **recursion** may accidently result in a **never ending** program, `while`-loops and `for`-loops are guaranteed to **terminate**."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q13**: Before writing **any** kind of **loop**, we **always** need to think about a **stopping criterion** ahead of time."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q14**: **Container** types such as `list` objects are characterized by their **support** for **being looped over**, for example as in:"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "```python\n",
+    "for element in container:\n",
+    "    # do something for every element\n",
+    "    ...\n",
+    "```"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Coding Exercises"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Towers of Hanoi"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "A popular example for a problem that is solved with a recursion art the **[Towers of Hanoi](https://en.wikipedia.org/wiki/Tower_of_Hanoi)**.\n",
+    "\n",
+    "In its basic version, a tower consisting of, for example, four disks with increasing radii, is placed on the left-most of **three** adjacent spots. In the following, we refer to the number of disks as $n$, so here $n = 4$.\n",
+    "\n",
+    "The task is to move the entire tower to the right-most spot whereby **two rules** must be obeyed:\n",
+    "\n",
+    "1. Disks can only be moved individually, and\n",
+    "2. a disk with a larger radius must *never* be placed on a disk with a smaller one.\n",
+    "\n",
+    "Although the **[Towers of Hanoi](https://en.wikipedia.org/wiki/Tower_of_Hanoi)** are a **classic** example, introduced by the mathematician [Édouard Lucas](https://en.wikipedia.org/wiki/%C3%89douard_Lucas) already in 1883, it is still **actively** researched as this scholarly [article](https://www.worldscientific.com/doi/abs/10.1142/S1793830919300017?journalCode=dmaa&) published in January 2019 shows.\n",
+    "\n",
+    "Despite being so easy to formulate, the game is quite hard to solve.\n",
+    "\n",
+    "Below is an interactive illustration of the solution with the minimal number of moves for $n = 4$."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<img src=\"static/towers_of_hanoi.gif\" width=\"60%\" align=\"left\">"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Watch the following video by [MIT](https://www.mit.edu/)'s professor [Richard Larson](https://idss.mit.edu/staff/richard-larson/) for a comprehensive introduction.\n",
+    "\n",
+    "The [MIT Blossoms Initative](https://blossoms.mit.edu/) is primarily aimed at high school students and does not have any prerequisites.\n",
+    "\n",
+    "The video consists of three segments the last of which is *not* necessary to have watched in order to solve the tasks below. So, watch the video until 37:55."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from IPython.display import YouTubeVideo\n",
+    "YouTubeVideo(\"UuIneNBbscc\", width=\"60%\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Video Review Questions"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q15.1**: Explain for the $n = 3$ case why it can be solved as a **recursion**!"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q15.2**: How does the number of minimal moves needed to solve a problem with three spots and $n$ disks grow as a function of $n$? How does this relate to the answer to **Q15.1**?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q15.3**: The **[Towers of Hanoi](https://en.wikipedia.org/wiki/Tower_of_Hanoi)** problem is of **exponential growth**. What does that mean? What does that imply for large $n$?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q15.4**: The video introduces the recursive relationship $Sol(4, 1, 3) = Sol(3, 1, 2) ~ \\bigoplus ~ Sol(1, 1, 3) ~ \\bigoplus ~ Sol(3, 2, 3)$. The $\\bigoplus$ is to be interpreted as some sort of \"plus\" operation. How does this \"plus\" operation work? How does this way of expressing the problem relate to the answer to **Q15.1**?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Naive Translation to Python"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "As most likely the first couple of tries will result in *semantic* errors, it is advisable to have some sort of **visualization tool** for the progam's output: For example, an online version of the game can be found **[here](https://www.mathsisfun.com/games/towerofhanoi.html)**."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Let's first **generalize** the mathematical relationship from above.\n",
+    "\n",
+    "While the first number of $Sol(\\cdot)$ is the number of `disks` $n$, the second and third \"numbers\" are actually the **labels** for the three spots. Instead of spots `1`, `2`, and `3` we could also call them `\"left\"`, `\"center\"`, and `\"right\"` in our Python implementation. When \"passed\" to the $Sol(\\cdot)$ \"function\" they take on the role of an `origin` (= $o$) and `destination` (= $d$) pair.\n",
+    "\n",
+    "So, the expression $Sol(4, 1, 3)$ is the same as $Sol(4, \\text{\"left\"}, \\text{\"right\"})$ and describes the problem of moving a tower consisting of $n = 4$ disks from `origin` `1` / `\"left\"` to `destination` `3` / `right`. As we have seen in the video, we need some `intermediate` (= $i$) spot."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In summary, the generalized functional relationship can be expressed as:\n",
+    "\n",
+    "$Sol(n, o, d) = Sol(n-1, o, i) ~ \\bigoplus ~ Sol(1, o, d) ~ \\bigoplus ~ Sol(n-1, i, d)$"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In words, this means that in order to move a tower consisting of $n$ disks from an `origin` $o$ to a `destination` $d$, we three steps must be executed:\n",
+    "\n",
+    "1. Move the top most $n - 1$ disks of the tower temporarily from $o$ to $i$ (= sub-problem 1)\n",
+    "2. Move the remaining and largest disk from $o$ to $d$\n",
+    "3. Move the the $n - 1$ disks from the temporary spot $i$ to $d$ (= sub-problem 2)\n",
+    "\n",
+    "The two sub-problems can be solved via the same recursive logic."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "$Sol(\\cdot)$ can be written in Python as a function `sol()` that takes three arguments `disks`, `origin`, and `destination` that mirror $n$, $o$, and $d$.\n",
+    "\n",
+    "Assume that all arguments to `sol()` will be `int` objects!\n",
+    "\n",
+    "Once completed, `sol()` should print out all the moves in the correct order. With **printing a move**, we simply mean a line like \"1 -> 3\", short for \"Move the top-most disk from spot 1 to spot 3\".\n",
+    "\n",
+    "Write your answers to **Q15.5** to **Q15.7** into the subsequent code cell and finalize `sol()`! No need to write a docstring or validate the input here."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def sol(disks, origin, destination):\n",
+    "\n",
+    "    # answer to Q15.5\n",
+    "    # ...\n",
+    "\n",
+    "    # answer to Q15.6\n",
+    "    # ...\n",
+    "    # ...\n",
+    "    # ...\n",
+    "    # ...\n",
+    "    # ...\n",
+    "    # ...\n",
+    "    # ...\n",
+    "    # ...\n",
+    "    # ...\n",
+    "    # ...\n",
+    "    # ...\n",
+    "\n",
+    "    # answer to Q15.7\n",
+    "    # ...\n",
+    "    # ..."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q15.5**: What is the `disks` argument when the function reaches its **base case**? Check for the base case with a simple `if` statement and return from the function using the **early exit** pattern!"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q15.6**: If not in the base case, `sol()` needs to determine the `intermediate` spot given concrete `origin` and `destination` arguments. For example, if called with `origin=1` and `destination=2`, `intermediate` must be `3`.\n",
+    "\n",
+    "Add **one** compound `if` statement to `sol()` that has a branch for **every** possible `origin`-`destination` pair that sets a variable `intermediate` to the correct temporary spot. **How many** branches will there be?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q15.7**: `sol()` needs to call itself **two more times** with the correct 2-pairs chosen from the three available spots `origin`, `intermediate`, and `destination`.\n",
+    "\n",
+    "In between the two recursive function calls, write a `print()` statement that prints out from where to where the \"remaining and largest\" disk has to be moved!"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q15.8**: Execute the code cells below and confirm that the printed moves are correct!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sol(1, 1, 3)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sol(2, 1, 3)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sol(3, 1, 3)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sol(4, 1, 3)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Pythonic Re-Factoring"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The previous `sol()` implementation does the job but the conditional statement needed in unnecessarily tedious. \n",
+    "\n",
+    "Let's create a more concise `hanoi()` function that in addition to a positional `disks` argument takes three keyword-only arguments `origin`, `intermediate`, and `destination` with default values `\"left\"`, `\"center\"`, and `\"right\"`.\n",
+    "\n",
+    "Write your answers to **Q15.9** and **Q15.10** into the subsequent code cell and finalize `hanoi()`! No need to write a docstring or validate the input here."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def hanoi(disks, *, origin=\"left\", intermediate=\"center\", destination=\"right\"):\n",
+    "\n",
+    "    # answer to Q15.9\n",
+    "    # ...\n",
+    "\n",
+    "    # answer to Q15.10\n",
+    "    # ...\n",
+    "    # ..."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q15.9**: Copy the base case from `sol()`."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q15.10**: Instead of conditional logic, `hanoi()` calls itself **two times** with the **three** arguments `origin`, `intermediate`, and `destination` passed on in a **different** order.\n",
+    "\n",
+    "Figure out how the arguments are passed on in the two recursive `hanoi()` calls!\n",
+    "\n",
+    "Also, write a `print()` statement analogous to the one in `sol()` in between the two recursive function calls. Is it ok to just copy and paste it?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q15.11**: Execute the code cells below and confirm that the printed moves are correct!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "hanoi(1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "hanoi(2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "hanoi(3)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "hanoi(4)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We could of course also use **numeric labels** for the three steps like so."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "hanoi(3, origin=1, intermediate=2, destination=3)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Passing a Value \"up\" the Recursion Tree"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Let's say, we did not know about the **analytical formula** for the number of **minimal moves** given $n$.\n",
+    "\n",
+    "In such cases, we could modify a recursive function to return a count value to be passed up the recursion tree.\n",
+    "\n",
+    "In fact, this is similar to what we do in the recursive versions of `factorial()` and `fibonacci()` in [Chapter 4](https://github.com/webartifex/intro-to-python/blob/master/04_iteration.ipynb) where we pass up an intermediate result.\n",
+    "\n",
+    "Let's create a `hanoi_moves()` function that follows the same internal logic as `hanoi()` but instead of printing out the moves returns the number of steps done so far in the recursion.\n",
+    "\n",
+    "Write your answers to **Q15.12** to **Q15.14** into the subsequent code cell and finalize `hanoi_moves()`! No need to write a docstring or validate the input here."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def hanoi_moves(disks, *, origin=\"left\", intermediate=\"center\", destination=\"right\"):\n",
+    "\n",
+    "    # answer to Q15.12\n",
+    "    # ...\n",
+    "\n",
+    "    moves = ...  # <- answer to Q15.13\n",
+    "    moves += hanoi_moves(...)  # <- answer to Q15.14 between the ()\n",
+    "    moves += hanoi_moves(...)  # <- answer to Q15.14 between the ()\n",
+    "\n",
+    "    return moves"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q15.12**: Copy the base case from `hanoi()`! What count should be returned when it is reached?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q15.13**: Initialize the variable `moves` with an appropriate count! This is the number of moves that corresponds to **one** recursive function call."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q15.14**: `moves` is updated with the counts passed up from the two recursive calls.\n",
+    "\n",
+    "Complete the two recursive function calls with the same arguments as in `hanoi()`!"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q15.15**: Write a `for`-loop that prints out the **minimum number** of moves needed to solve Towers of Hanoi for any number of `disks` from `1` through `20` to confirm your answer to **Q15.2**."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "##### Time Complexity"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Observe how quickly the `hanoi_moves()` function slows down for increasing `disks` arguments.\n",
+    "\n",
+    "With `disks` in the range from `24` through `26` the computation time roughly doubles for each increase of `disks` by 1.\n",
+    "\n",
+    "**Q15.16**: Execute the code cells below and see for yourself!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%%timeit -n 1 -r 1\n",
+    "print(\"Number of moves:\", hanoi_moves(24))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%%timeit -n 1 -r 1\n",
+    "print(\"Number of moves:\", hanoi_moves(25))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%%timeit -n 1 -r 1\n",
+    "print(\"Number of moves:\", hanoi_moves(26))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Passing a Value \"down\" the Recursion Tree (Advanced)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The above `hanoi()` prints the optimal solution's moves in the correct order but fails to label each move with an order number. This can be build in by passing on one more argument `offset` down the recursion tree. As the logic gets a bit \"involved\", `hanoi_ordered()` below is almost finished.\n",
+    "\n",
+    "Write your answers to **Q15.17** and **Q15.18** into the subsequent code cell and finalize `hanoi_ordered()`! No need to write a docstring or validate the input here."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def hanoi_ordered(disks, *, origin=\"left\", intermediate=\"center\", destination=\"right\", offset=None):\n",
+    "\n",
+    "    # answer to Q15.17\n",
+    "    # ...\n",
+    "\n",
+    "    total = (2 ** disks - 1)\n",
+    "    half = (2 ** (disks - 1) - 1)\n",
+    "    count = total - half\n",
+    "\n",
+    "    if offset is not None:\n",
+    "        count += offset\n",
+    "\n",
+    "    hanoi_ordered(..., offset=offset)  # <- answer to Q15.18 between the ()\n",
+    "    # answer to Q15.18\n",
+    "    hanoi_ordered(..., offset=count)  # <- answer to Q15.18 between the ()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q15.17**: Copy the base case from the original `hanoi()`!"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q15.18**: Complete the two recursive function calls with the same arguments as in `hanoi()` or `hanoi_moves()`! Do not change the already filled in `offset` arguments!\n",
+    "\n",
+    "Then, copy the `print()` statement from `hanoi()` and adjust it to print out `count` as well!"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q15.19**: Execute the code cells below and confirm that the order numbers are correct!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "hanoi_ordered(1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "hanoi_ordered(2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "hanoi_ordered(3)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "hanoi_ordered(4)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Lastly, it is to be mentioned that for problem instances with a small `disks` argument it is easier to collect all the moves first in a list and then add the order number with the [enumerate()](https://docs.python.org/3/library/functions.html#enumerate) built-in."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Open Question"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q15.20**: Conducting your own research on the internet (max. 15 minutes), what can you say about generalizing the **[Towers of Hanoi](https://en.wikipedia.org/wiki/Tower_of_Hanoi)** problem to a setting with **more than three** landing spots?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " "
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.3"
+  },
+  "toc": {
+   "base_numbering": 1,
+   "nav_menu": {},
+   "number_sections": false,
+   "sideBar": true,
+   "skip_h1_title": true,
+   "title_cell": "Table of Contents",
+   "title_sidebar": "Contents",
+   "toc_cell": false,
+   "toc_position": {},
+   "toc_section_display": false,
+   "toc_window_display": false
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
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From 3b28b8d507a260fb62d879907467acdc28adf319 Mon Sep 17 00:00:00 2001
From: Alexander Hess <alexander@webartifex.biz>
Date: Mon, 7 Oct 2019 22:31:06 +0200
Subject: [PATCH 028/242] Streamline previous content

---
 00_start_up.ipynb                       |  12 +-
 01_elements.ipynb                       | 523 +++++++++++++-----------
 02_functions.ipynb                      |  66 +--
 03_conditionals.ipynb                   |  68 ++-
 04_iteration.ipynb                      | 316 +++++++-------
 04_iteration_review_and_exercises.ipynb |   4 +-
 6 files changed, 493 insertions(+), 496 deletions(-)

diff --git a/00_start_up.ipynb b/00_start_up.ipynb
index 29175db..1cfaf91 100644
--- a/00_start_up.ipynb
+++ b/00_start_up.ipynb
@@ -224,7 +224,7 @@
     }
    },
    "source": [
-    "For more \"couragous\" beginners wanting to learn how to accomplish this, here is a rough sketch of the aspects to know. First, all Python realeases are available for free on the official [download](https://www.python.org/downloads/) page for any supported operating system. Choose the one you want, then download and install it (cf., the [instruction notes](https://wiki.python.org/moin/BeginnersGuide/Download)). As this only includes core Python and the standard library, the beginner then needs to learn about the [pip](https://pip.pypa.io/en/stable/) module, which is to be used inside a terminal. With the command `python -m pip install jupyter`, all necessary third-party libraries can be installed (cf., more background [here](https://jupyter.readthedocs.io/en/latest/install.html)). However, this would be done in a *system-wide* fashion and is not recommended. Instead, the best practice is to create a so-called **virtual environment** with the [venv](https://docs.python.org/3/library/venv.html) module with which the installed third-party packages can be *isolated* on a per-project basis (the command `python -m venv env-name` creates a virtual enviroment called \"env-name\"). This tactic is employed to avoid a situation known as **[dependency hell](https://en.wikipedia.org/wiki/Dependency_hell)** Once created, the virtual environment must then be activated each time before resuming work in each terminal (with the command `source env-name/bin/activate`). While there exist convenience tools that automate parts of this (e.g., [poetry](https://poetry.eustace.io/docs/) or [virtualenvwrapper](https://virtualenvwrapper.readthedocs.io/en/latest/)), it only distracts a beginner from actually studying the Python language. Yet, it is still worthwhile to have heard about these terms and concepts as many online resources often implicitly assume the user to know about them."
+    "For more \"couragous\" beginners wanting to learn how to accomplish this, here is a rough sketch of the aspects to know. First, all Python realeases are available for free on the official [download](https://www.python.org/downloads/) page for any supported operating system. Choose the one you want, then download and install it (cf., the [instruction notes](https://wiki.python.org/moin/BeginnersGuide/Download)). As this only includes core Python and the standard library, the beginner then needs to learn about the [pip](https://pip.pypa.io/en/stable/) module, which is to be used inside a terminal. With the command `python -m pip install jupyter`, all necessary third-party libraries can be installed (cf., more background [here](https://jupyter.readthedocs.io/en/latest/install.html)). However, this would be done in a *system-wide* fashion and is not recommended. Instead, the best practice is to create a so-called **virtual environment** with the [venv](https://docs.python.org/3/library/venv.html) module with which the installed third-party packages are *isolated* on a per-project basis (the command `python -m venv env-name` creates a virtual enviroment called \"env-name\"). This tactic is employed to avoid a situation known as **[dependency hell](https://en.wikipedia.org/wiki/Dependency_hell)** Once created, the virtual environment must then be activated each time before resuming work in each terminal (with the command `source env-name/bin/activate`). While there exist convenience tools that automate parts of this (e.g., [poetry](https://poetry.eustace.io/docs/) or [virtualenvwrapper](https://virtualenvwrapper.readthedocs.io/en/latest/)), it only distracts a beginner from actually studying the Python language. Yet, it is still worthwhile to have heard about these terms and concepts as many online resources often implicitly assume the user to know about them."
    ]
   },
   {
@@ -778,11 +778,11 @@
     "**Part 1: Expressing Logic**\n",
     "\n",
     "- What is a programming language? What kind of words exist?\n",
-    "  1. Elements of a Program\n",
-    "  2. Functions & Modularization\n",
+    "  1. [Elements of a Program](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/01_elements.ipynb)\n",
+    "  2. [Functions & Modularization](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/02_functions.ipynb)\n",
     "- What is the flow of execution? How can we form sentences from words?\n",
-    "  3. Conditionals & Exceptions\n",
-    "  4. Recursion & Looping"
+    "  3. [Conditionals & Exceptions](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/03_conditionals.ipynb)\n",
+    "  4. [Recursion & Looping](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/04_iteration.ipynb)"
    ]
   },
   {
@@ -796,7 +796,7 @@
     "**Part 2: Managing Data and Memory**\n",
     "\n",
     "- How is data stored in memory?\n",
-    "  5. Numbers\n",
+    "  5. [Numbers](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/05_numbers.ipynb)\n",
     "  6. Text\n",
     "  7. Sequences\n",
     "  8. Mappings & Sets\n",
diff --git a/01_elements.ipynb b/01_elements.ipynb
index 382d757..658e696 100644
--- a/01_elements.ipynb
+++ b/01_elements.ipynb
@@ -75,7 +75,7 @@
     }
    },
    "source": [
-    "To verify that something happened in our computer's memory, we simply **reference** `numbers` and observe that Python indeed knows about it."
+    "To verify that something happened in our computer's memory, we simply **reference** `numbers`."
    ]
   },
   {
@@ -120,7 +120,7 @@
     "\n",
     "Lastly, the `average` is calculated as the ratio of the final **values** of `total` and `count`. Overall, we divide the sum of all even numbers by the count of all even numbers, which is exactly what we are looking for.\n",
     "\n",
-    "We also observe how the lines of code \"within\" the `for` and `if` **statements** are **indented** and *aligned* with multiples of **four spaces**: This shows immediately how the lines relate to each other."
+    "The lines of code \"within\" the `for` and `if` **statements** are **indented** and *aligned* with multiples of **four spaces**: This shows immediately how the lines relate to each other."
    ]
   },
   {
@@ -152,7 +152,7 @@
     }
    },
    "source": [
-    "We do not see any **output** yet but can obtain the value of `average` by simply referencing it again."
+    "We do not see any **output** yet but obtain the value of `average` by simply referencing it again."
    ]
   },
   {
@@ -392,7 +392,7 @@
     }
    },
    "source": [
-    "The `-` operator can be used as a unary operator as well. Then it just flips the sign of a number."
+    "The `-` operator is used as a unary operator as well. Then it just flips the sign of a number."
    ]
   },
   {
@@ -486,7 +486,7 @@
     }
    },
    "source": [
-    "The so-called **floor division operator** `//` always rounds down to the next integer and is thus also called **integer division operator**. This is a first example of an operator we commonly do not know from high school mathematics."
+    "The so-called **floor division operator** `//` always \"rounds\" to the next integer and is thus also called **integer division operator**. This is a first example of an operator we commonly do not know from high school mathematics."
    ]
   },
   {
@@ -545,12 +545,47 @@
     }
    },
    "source": [
-    "To obtain the remainder of a division, we use the **modulo operator** `%`."
+    "Even though it appears that the `//` operator **truncates** (i.e., \"cuts off\") the decimals so as to effectively \"rounding\" down (i.e., the `42.5` became `42` in the previous code cell), this is *not* the case: The result is always \"rounded\" towards minus infinity!"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 16,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "-43"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "-85 // 2"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "To obtain the remainder of a division, we use the **modulo operator** `%`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -563,7 +598,7 @@
        "1"
       ]
      },
-     "execution_count": 16,
+     "execution_count": 17,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -580,12 +615,12 @@
     }
    },
    "source": [
-    "Note that the remainder is $0$ if a number is divisable by another."
+    "Note that the remainder is $0$ *only if* a number is *divisable* by another."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 18,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -598,7 +633,7 @@
        "0"
       ]
      },
-     "execution_count": 17,
+     "execution_count": 18,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -620,7 +655,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 19,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -633,7 +668,7 @@
        "3"
       ]
      },
-     "execution_count": 18,
+     "execution_count": 19,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -644,7 +679,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 20,
    "metadata": {
     "slideshow": {
      "slide_type": "-"
@@ -657,7 +692,7 @@
        "23"
       ]
      },
-     "execution_count": 19,
+     "execution_count": 20,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -674,12 +709,12 @@
     }
    },
    "source": [
-    "The [divmod()](https://docs.python.org/3/library/functions.html#divmod) built-in function combines the integer and modulo divisions into one operation. However, this is not an operator but a function. Also observe that [divmod()](https://docs.python.org/3/library/functions.html#divmod) returns a \"pair\" of integers."
+    "The [divmod()](https://docs.python.org/3/library/functions.html#divmod) built-in function combines the integer and modulo divisions into one operation. However, this is not an operator but a function. Also, [divmod()](https://docs.python.org/3/library/functions.html#divmod) returns a \"pair\" of integers and not just one object."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 21,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -692,7 +727,7 @@
        "(4, 2)"
       ]
      },
-     "execution_count": 20,
+     "execution_count": 21,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -714,7 +749,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 22,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -727,7 +762,7 @@
        "8"
       ]
      },
-     "execution_count": 21,
+     "execution_count": 22,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -749,7 +784,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 23,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -762,7 +797,7 @@
        "18"
       ]
      },
-     "execution_count": 22,
+     "execution_count": 23,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -784,7 +819,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 24,
    "metadata": {
     "slideshow": {
      "slide_type": "-"
@@ -797,7 +832,7 @@
        "18"
       ]
      },
-     "execution_count": 23,
+     "execution_count": 24,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -808,7 +843,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 25,
    "metadata": {
     "slideshow": {
      "slide_type": "-"
@@ -821,7 +856,7 @@
        "81"
       ]
      },
-     "execution_count": 24,
+     "execution_count": 25,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -838,12 +873,12 @@
     }
    },
    "source": [
-    "Some programmers also use \"style\" conventions. For example, we can play with the **whitespace**, which is an umbrella term that refers to any non-printable sign like spaces, tabs, or the like. However, parentheses convey a much clearer picture."
+    "Some programmers also use \"style\" conventions. For example, we might play with the **whitespace**, which is an umbrella term that refers to any non-printable sign like spaces, tabs, or the like. However, parentheses convey a much clearer picture."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 26,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -856,7 +891,7 @@
        "18"
       ]
      },
-     "execution_count": 25,
+     "execution_count": 26,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -904,7 +939,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": 27,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -941,7 +976,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": 28,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -951,31 +986,7 @@
     {
      "data": {
       "text/plain": [
-       "140658972730512"
-      ]
-     },
-     "execution_count": 27,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "id(a)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 28,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "-"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "140658972907392"
+       "140098012863472"
       ]
      },
      "execution_count": 28,
@@ -984,7 +995,7 @@
     }
    ],
    "source": [
-    "id(b)"
+    "id(a)"
    ]
   },
   {
@@ -999,7 +1010,7 @@
     {
      "data": {
       "text/plain": [
-       "140658972586992"
+       "140098013042536"
       ]
      },
      "execution_count": 29,
@@ -1007,6 +1018,30 @@
      "output_type": "execute_result"
     }
    ],
+   "source": [
+    "id(b)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "140098012714672"
+      ]
+     },
+     "execution_count": 30,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "id(c)"
    ]
@@ -1024,7 +1059,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 30,
+   "execution_count": 31,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -1043,12 +1078,12 @@
     }
    },
    "source": [
-    "`a` and `d` indeed have the same value as can be checked with the **equality operator** `==`. The resulting `True` (and the `False` below) is yet another data type, a so-called **boolean**. We will look into that closely in [Chapter 3](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/03_conditionals.ipynb)."
+    "`a` and `d` indeed have the same value as is checked with the **equality operator** `==`. The resulting `True` (and the `False` below) is yet another data type, a so-called **boolean**. We will look into that closely in [Chapter 3](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/03_conditionals.ipynb)."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 31,
+   "execution_count": 32,
    "metadata": {
     "slideshow": {
      "slide_type": "-"
@@ -1061,7 +1096,7 @@
        "True"
       ]
      },
-     "execution_count": 31,
+     "execution_count": 32,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1078,12 +1113,12 @@
     }
    },
    "source": [
-    "On the contrary, `a` and `d` are different objects as can be seen with the **identity operator** `is`: they are stored at seperate addresses in the memory."
+    "On the contrary, `a` and `d` are different objects as the **identity operator** `is` shows: they are stored at seperate addresses in the memory."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 32,
+   "execution_count": 33,
    "metadata": {
     "slideshow": {
      "slide_type": "-"
@@ -1096,7 +1131,7 @@
        "False"
       ]
      },
-     "execution_count": 32,
+     "execution_count": 33,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1129,7 +1164,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 33,
+   "execution_count": 34,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -1142,7 +1177,7 @@
        "int"
       ]
      },
-     "execution_count": 33,
+     "execution_count": 34,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1153,7 +1188,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 34,
+   "execution_count": 35,
    "metadata": {
     "slideshow": {
      "slide_type": "-"
@@ -1166,7 +1201,7 @@
        "float"
       ]
      },
-     "execution_count": 34,
+     "execution_count": 35,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1185,14 +1220,14 @@
    "source": [
     "Different types imply different behaviors for the objects. The `b` object, for example, can be \"asked\" if it could also be interpreted as an `int` with the [is_integer()](https://docs.python.org/3/library/stdtypes.html#float.is_integer) \"functionality\" that comes with every `float` object.\n",
     "\n",
-    "Formally, we call such type-specific functionalities **methods** (to differentiate them from functions) and we will eventually fully introduce them when we talk about object-orientation in Chapter 10. For now, it suffices to know that we can access them using the **dot operator** `.`. Of course `b` could be converted into an `int`, which the boolean value `True` tells us.\n",
+    "Formally, we call such type-specific functionalities **methods** (to differentiate them from functions) and we will eventually fully introduce them when we talk about object-orientation in Chapter 10. For now, it suffices to know that we access them using the **dot operator** `.`. Of course `b` could be converted into an `int`, which the boolean value `True` tells us.\n",
     "\n",
     "Also note how the `.` operator is neiter a unary nor a binary operator as specified above."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 35,
+   "execution_count": 36,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -1205,7 +1240,7 @@
        "True"
       ]
      },
-     "execution_count": 35,
+     "execution_count": 36,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1227,7 +1262,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 36,
+   "execution_count": 37,
    "metadata": {
     "slideshow": {
      "slide_type": "-"
@@ -1241,7 +1276,7 @@
      "traceback": [
       "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
       "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-36-7db0a38aefcc>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0ma\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mis_integer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;32m<ipython-input-37-7db0a38aefcc>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0ma\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mis_integer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
       "\u001b[0;31mAttributeError\u001b[0m: 'int' object has no attribute 'is_integer'"
      ]
     }
@@ -1258,12 +1293,12 @@
     }
    },
    "source": [
-    "The `c` object is a so-called **string** type (i.e., `str`), which we can view as Python's way of representing \"text\". Strings also come with their own behaviors, for example, to convert a text to lower or upper case."
+    "The `c` object is a so-called **string** type (i.e., `str`), which is Python's way of representing \"text\". Strings also come with their own behaviors, for example, to convert a text to lower or upper case."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 37,
+   "execution_count": 38,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -1276,7 +1311,7 @@
        "str"
       ]
      },
-     "execution_count": 37,
+     "execution_count": 38,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1287,7 +1322,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 38,
+   "execution_count": 39,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -1300,7 +1335,7 @@
        "'python rocks'"
       ]
      },
-     "execution_count": 38,
+     "execution_count": 39,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1311,7 +1346,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 39,
+   "execution_count": 40,
    "metadata": {
     "slideshow": {
      "slide_type": "-"
@@ -1324,7 +1359,7 @@
        "'PYTHON ROCKS'"
       ]
      },
-     "execution_count": 39,
+     "execution_count": 40,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1335,7 +1370,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 40,
+   "execution_count": 41,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -1348,7 +1383,7 @@
        "'Python Rocks'"
       ]
      },
-     "execution_count": 40,
+     "execution_count": 41,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1383,7 +1418,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 41,
+   "execution_count": 42,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -1396,7 +1431,7 @@
        "789"
       ]
      },
-     "execution_count": 41,
+     "execution_count": 42,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1407,7 +1442,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 42,
+   "execution_count": 43,
    "metadata": {
     "slideshow": {
      "slide_type": "-"
@@ -1420,7 +1455,7 @@
        "42.0"
       ]
      },
-     "execution_count": 42,
+     "execution_count": 43,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1437,12 +1472,12 @@
     }
    },
    "source": [
-    "In this book, we follow the convention of creating strings with **double quotes** `\"` instead of the **single quotes** `'` to which Python defaults in its literal output for `str` objects. Both types of quotes can be used interchangebly."
+    "In this book, we follow the convention of creating strings with **double quotes** `\"` instead of the **single quotes** `'` to which Python defaults in its literal output for `str` objects. Both types of quotes may be used interchangebly."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 43,
+   "execution_count": 44,
    "metadata": {
     "slideshow": {
      "slide_type": "-"
@@ -1455,7 +1490,7 @@
        "'Python rocks'"
       ]
      },
-     "execution_count": 43,
+     "execution_count": 44,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1514,7 +1549,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 44,
+   "execution_count": 45,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -1523,10 +1558,10 @@
    "outputs": [
     {
      "ename": "SyntaxError",
-     "evalue": "invalid syntax (<ipython-input-44-cafa82e54b9c>, line 1)",
+     "evalue": "invalid syntax (<ipython-input-45-cafa82e54b9c>, line 1)",
      "output_type": "error",
      "traceback": [
-      "\u001b[0;36m  File \u001b[0;32m\"<ipython-input-44-cafa82e54b9c>\"\u001b[0;36m, line \u001b[0;32m1\u001b[0m\n\u001b[0;31m    3.99 $ + 10.40 $\u001b[0m\n\u001b[0m         ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m invalid syntax\n"
+      "\u001b[0;36m  File \u001b[0;32m\"<ipython-input-45-cafa82e54b9c>\"\u001b[0;36m, line \u001b[0;32m1\u001b[0m\n\u001b[0;31m    3.99 $ + 10.40 $\u001b[0m\n\u001b[0m         ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m invalid syntax\n"
      ]
     }
    ],
@@ -1547,7 +1582,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 45,
+   "execution_count": 46,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -1556,10 +1591,10 @@
    "outputs": [
     {
      "ename": "SyntaxError",
-     "evalue": "invalid syntax (<ipython-input-45-499e4d0d0cbb>, line 1)",
+     "evalue": "invalid syntax (<ipython-input-46-499e4d0d0cbb>, line 1)",
      "output_type": "error",
      "traceback": [
-      "\u001b[0;36m  File \u001b[0;32m\"<ipython-input-45-499e4d0d0cbb>\"\u001b[0;36m, line \u001b[0;32m1\u001b[0m\n\u001b[0;31m    for number in numbers\u001b[0m\n\u001b[0m                         ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m invalid syntax\n"
+      "\u001b[0;36m  File \u001b[0;32m\"<ipython-input-46-499e4d0d0cbb>\"\u001b[0;36m, line \u001b[0;32m1\u001b[0m\n\u001b[0;31m    for number in numbers\u001b[0m\n\u001b[0m                         ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m invalid syntax\n"
      ]
     }
    ],
@@ -1581,7 +1616,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 46,
+   "execution_count": 47,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -1590,10 +1625,10 @@
    "outputs": [
     {
      "ename": "IndentationError",
-     "evalue": "expected an indented block (<ipython-input-46-19398c5f89de>, line 2)",
+     "evalue": "expected an indented block (<ipython-input-47-19398c5f89de>, line 2)",
      "output_type": "error",
      "traceback": [
-      "\u001b[0;36m  File \u001b[0;32m\"<ipython-input-46-19398c5f89de>\"\u001b[0;36m, line \u001b[0;32m2\u001b[0m\n\u001b[0;31m    print(number)\u001b[0m\n\u001b[0m        ^\u001b[0m\n\u001b[0;31mIndentationError\u001b[0m\u001b[0;31m:\u001b[0m expected an indented block\n"
+      "\u001b[0;36m  File \u001b[0;32m\"<ipython-input-47-19398c5f89de>\"\u001b[0;36m, line \u001b[0;32m2\u001b[0m\n\u001b[0;31m    print(number)\u001b[0m\n\u001b[0m        ^\u001b[0m\n\u001b[0;31mIndentationError\u001b[0m\u001b[0;31m:\u001b[0m expected an indented block\n"
      ]
     }
    ],
@@ -1630,7 +1665,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 47,
+   "execution_count": 48,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -1644,7 +1679,7 @@
      "traceback": [
       "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
       "\u001b[0;31mZeroDivisionError\u001b[0m                         Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-47-bc757c3fda29>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0;36m1\u001b[0m \u001b[0;34m/\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;32m<ipython-input-48-bc757c3fda29>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0;36m1\u001b[0m \u001b[0;34m/\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
       "\u001b[0;31mZeroDivisionError\u001b[0m: division by zero"
      ]
     }
@@ -1672,14 +1707,14 @@
     }
    },
    "source": [
-    "So-called **semantic errors**, on the contrary, can be very hard to spot as they do *not* crash the program. The only way to find such errors is to run a program with test input for which we know the answer already and can thus check the output. However, testing software is a whole discipline on its own and often very hard to do in practice.\n",
+    "So-called **semantic errors**, on the contrary, are hard to spot as they do *not* crash the program. The only way to find such errors is to run a program with test input for which we can predict the output. However, testing software is a whole discipline on its own and often very hard to do in practice.\n",
     "\n",
     "The cell below copies our introductory example from above with a \"tiny\" error. How fast could you have spotted it without the comment?"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 48,
+   "execution_count": 49,
    "metadata": {
     "code_folding": [],
     "slideshow": {
@@ -1701,7 +1736,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 49,
+   "execution_count": 50,
    "metadata": {
     "slideshow": {
      "slide_type": "-"
@@ -1714,7 +1749,7 @@
        "3.0"
       ]
      },
-     "execution_count": 49,
+     "execution_count": 50,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1755,12 +1790,12 @@
    "source": [
     "Adhering to just syntax rules is therefore *never* enough. Over time, **best practices** and common **style guides** were created to make it less likely for a developer to mess up a program and also to allow \"onboarding\" him as a contributor to an established code base, often called **legacy code**, faster. These rules are not enforced by Python itself: Badly styled and un-readable code will still run. At the very least, Python programs should be styled according to [PEP 8](https://www.python.org/dev/peps/pep-0008/) and documented \"inline\" (i.e., in the code itself) according to [PEP 257](https://www.python.org/dev/peps/pep-0257/).\n",
     "\n",
-    "An easier to read version of PEP 8 can be found [here](https://pep8.org/). The video below features a well known \"[Pythonista](https://en.wiktionary.org/wiki/Pythonista)\" talking about the importance of code style."
+    "An easier to read version of PEP 8 is [here](https://pep8.org/). The video below features a well known \"[Pythonista](https://en.wiktionary.org/wiki/Pythonista)\" talking about the importance of code style."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 50,
+   "execution_count": 51,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -1782,10 +1817,10 @@
        "        "
       ],
       "text/plain": [
-       "<IPython.lib.display.YouTubeVideo at 0x7fedb8113c50>"
+       "<IPython.lib.display.YouTubeVideo at 0x7f6b1c40e8d0>"
       ]
      },
-     "execution_count": 50,
+     "execution_count": 51,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1808,7 +1843,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 51,
+   "execution_count": 52,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -1821,7 +1856,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 52,
+   "execution_count": 53,
    "metadata": {
     "slideshow": {
      "slide_type": "-"
@@ -1834,7 +1869,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 53,
+   "execution_count": 54,
    "metadata": {
     "slideshow": {
      "slide_type": "-"
@@ -1847,7 +1882,7 @@
        "[2, 4, 6, 8, 10]"
       ]
      },
-     "execution_count": 53,
+     "execution_count": 54,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1858,7 +1893,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 54,
+   "execution_count": 55,
    "metadata": {
     "slideshow": {
      "slide_type": "-"
@@ -1871,7 +1906,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 55,
+   "execution_count": 56,
    "metadata": {
     "slideshow": {
      "slide_type": "-"
@@ -1884,7 +1919,7 @@
        "6.0"
       ]
      },
-     "execution_count": 55,
+     "execution_count": 56,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1906,7 +1941,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 56,
+   "execution_count": 57,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -2001,7 +2036,7 @@
     }
    },
    "source": [
-    "While this book is built with Jupyter notebooks, it is important to understand that \"real\" programs are almost always just \"linear\" (= top to bottom) sequences of instructions but instead can take many different **flows of execution**.\n",
+    "While this book is built with Jupyter notebooks, it is important to understand that \"real\" programs are almost always just \"linear\" (= top to bottom) sequences of instructions but instead may take many different **flows of execution**.\n",
     "\n",
     "At the same time, for a beginner's course it is often easier to just code in a linear fashion.\n",
     "\n",
@@ -2034,7 +2069,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 57,
+   "execution_count": 58,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -2059,7 +2094,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 58,
+   "execution_count": 59,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -2072,7 +2107,7 @@
        "20.0"
       ]
      },
-     "execution_count": 58,
+     "execution_count": 59,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2089,12 +2124,12 @@
     }
    },
    "source": [
-    "A variable can be **re-assigned** as often as we wish. Thereby, we could also assign an object of a *different* type. Because this is allowed, Python is said to be a **dynamically typed** language. On the contrary, a **statically typed** language like C also allows re-assignment but only with objects of the *same* type. This subtle distinction is one reason why Python is slower at execution than C: As it runs a program, it needs to figure out an object's type each time it is referenced. But as mentioned before, this can be mitigated with third-party libraries."
+    "A variable may be **re-assigned** as often as we wish. Thereby, we could also assign an object of a *different* type. Because this is allowed, Python is said to be a **dynamically typed** language. On the contrary, a **statically typed** language like C also allows re-assignment but only with objects of the *same* type. This subtle distinction is one reason why Python is slower at execution than C: As it runs a program, it needs to figure out an object's type each time it is referenced. But as mentioned before, this is mitigated with third-party libraries."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 59,
+   "execution_count": 60,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -2107,7 +2142,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 60,
+   "execution_count": 61,
    "metadata": {
     "slideshow": {
      "slide_type": "-"
@@ -2120,7 +2155,7 @@
        "20"
       ]
      },
-     "execution_count": 60,
+     "execution_count": 61,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2137,12 +2172,12 @@
     }
    },
    "source": [
-    "If we want to re-assign a variable while referencing its \"old\" (i.e., current) object, we can also **update** it using a so-called **[augmented assignment statement](https://docs.python.org/3/reference/simple_stmts.html#augmented-assignment-statements)** (*not* operator), originally introduced with [PEP 203](https://www.python.org/dev/peps/pep-0203/). This implicitly inserts the currently mapped object as the first operand on the right-hand side."
+    "If we want to re-assign a variable while referencing its \"old\" (i.e., current) object, we may also **update** it using a so-called **[augmented assignment statement](https://docs.python.org/3/reference/simple_stmts.html#augmented-assignment-statements)** (*not* operator), originally introduced with [PEP 203](https://www.python.org/dev/peps/pep-0203/). This implicitly inserts the currently mapped object as the first operand on the right-hand side."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 61,
+   "execution_count": 62,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -2155,7 +2190,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 62,
+   "execution_count": 63,
    "metadata": {
     "slideshow": {
      "slide_type": "-"
@@ -2168,7 +2203,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 63,
+   "execution_count": 64,
    "metadata": {
     "slideshow": {
      "slide_type": "-"
@@ -2181,7 +2216,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 64,
+   "execution_count": 65,
    "metadata": {
     "slideshow": {
      "slide_type": "-"
@@ -2194,7 +2229,7 @@
        "42"
       ]
      },
-     "execution_count": 64,
+     "execution_count": 65,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2211,12 +2246,12 @@
     }
    },
    "source": [
-    "Variables can be **[de-referenced](https://docs.python.org/3/reference/simple_stmts.html#the-del-statement)** (i.e., \"deleted\") with the `del` statement. This does *not* delete the object to which a variable points to. It merely removes the variable's name from the \"global list of all names\"."
+    "Variables are **[de-referenced](https://docs.python.org/3/reference/simple_stmts.html#the-del-statement)** (i.e., \"deleted\") with the `del` statement. This does *not* delete the object to which a variable points to. It merely removes the variable's name from the \"global list of all names\"."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 65,
+   "execution_count": 66,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -2229,7 +2264,7 @@
        "789"
       ]
      },
-     "execution_count": 65,
+     "execution_count": 66,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2240,7 +2275,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 66,
+   "execution_count": 67,
    "metadata": {
     "slideshow": {
      "slide_type": "-"
@@ -2264,7 +2299,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 67,
+   "execution_count": 68,
    "metadata": {
     "slideshow": {
      "slide_type": "-"
@@ -2278,7 +2313,7 @@
      "traceback": [
       "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
       "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-67-89e6c98d9288>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mb\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;32m<ipython-input-68-89e6c98d9288>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mb\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
       "\u001b[0;31mNameError\u001b[0m: name 'b' is not defined"
      ]
     }
@@ -2295,12 +2330,12 @@
     }
    },
    "source": [
-    "Some variables magically exist when we start a Python process or are added by Jupyter. We can safely ignore the former until Chapter 10 and the latter for good."
+    "Some variables magically exist when we start a Python process or are added by Jupyter. We may safely ignore the former until Chapter 10 and the latter for good."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 68,
+   "execution_count": 69,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -2313,7 +2348,7 @@
        "'__main__'"
       ]
      },
-     "execution_count": 68,
+     "execution_count": 69,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2335,7 +2370,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 69,
+   "execution_count": 70,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -2366,15 +2401,15 @@
        " '_23',\n",
        " '_24',\n",
        " '_25',\n",
-       " '_27',\n",
+       " '_26',\n",
        " '_28',\n",
        " '_29',\n",
-       " '_31',\n",
+       " '_30',\n",
        " '_32',\n",
        " '_33',\n",
        " '_34',\n",
        " '_35',\n",
-       " '_37',\n",
+       " '_36',\n",
        " '_38',\n",
        " '_39',\n",
        " '_4',\n",
@@ -2382,16 +2417,17 @@
        " '_41',\n",
        " '_42',\n",
        " '_43',\n",
-       " '_49',\n",
+       " '_44',\n",
        " '_5',\n",
        " '_50',\n",
-       " '_53',\n",
-       " '_55',\n",
-       " '_58',\n",
-       " '_60',\n",
-       " '_64',\n",
+       " '_51',\n",
+       " '_54',\n",
+       " '_56',\n",
+       " '_59',\n",
+       " '_61',\n",
        " '_65',\n",
-       " '_68',\n",
+       " '_66',\n",
+       " '_69',\n",
        " '_9',\n",
        " '__',\n",
        " '___',\n",
@@ -2471,6 +2507,7 @@
        " '_i68',\n",
        " '_i69',\n",
        " '_i7',\n",
+       " '_i70',\n",
        " '_i8',\n",
        " '_i9',\n",
        " '_ih',\n",
@@ -2492,7 +2529,7 @@
        " 'total']"
       ]
      },
-     "execution_count": 69,
+     "execution_count": 70,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2520,14 +2557,14 @@
     }
    },
    "source": [
-    "It is important to understand that *several* variables can point to the *same* object in memory. This can be counter-intuitive in the beginning and lead to many hard to track down bugs.\n",
+    "It is important to understand that *several* variables may point to the *same* object in memory. This may be counter-intuitive in the beginning and lead to many hard to track down bugs.\n",
     "\n",
     "This makes `b` point to whatever object `a` is pointing to."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 70,
+   "execution_count": 71,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -2538,30 +2575,6 @@
     "b = a"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": 71,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "-"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "42"
-      ]
-     },
-     "execution_count": 71,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "a"
-   ]
-  },
   {
    "cell_type": "code",
    "execution_count": 72,
@@ -2582,6 +2595,30 @@
      "output_type": "execute_result"
     }
    ],
+   "source": [
+    "a"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 73,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "42"
+      ]
+     },
+     "execution_count": 73,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "b"
    ]
@@ -2601,7 +2638,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 73,
+   "execution_count": 74,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -2614,7 +2651,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 74,
+   "execution_count": 75,
    "metadata": {
     "slideshow": {
      "slide_type": "-"
@@ -2627,7 +2664,7 @@
        "123"
       ]
      },
-     "execution_count": 74,
+     "execution_count": 75,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2649,7 +2686,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 75,
+   "execution_count": 76,
    "metadata": {
     "slideshow": {
      "slide_type": "-"
@@ -2662,7 +2699,7 @@
        "42"
       ]
      },
-     "execution_count": 75,
+     "execution_count": 76,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2679,12 +2716,12 @@
     }
    },
    "source": [
-    "However, if a variable points to an object of a more \"complex\" type (e.g., `list`), \"weird\" things can happen."
+    "However, if a variable points to an object of a more \"complex\" type (e.g., `list`), \"weird\" things happen."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 76,
+   "execution_count": 77,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -2697,7 +2734,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 77,
+   "execution_count": 78,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -2710,7 +2747,7 @@
        "list"
       ]
      },
-     "execution_count": 77,
+     "execution_count": 78,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2721,7 +2758,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 78,
+   "execution_count": 79,
    "metadata": {
     "slideshow": {
      "slide_type": "-"
@@ -2749,7 +2786,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 79,
+   "execution_count": 80,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -2762,7 +2799,7 @@
        "1"
       ]
      },
-     "execution_count": 79,
+     "execution_count": 80,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2784,7 +2821,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 80,
+   "execution_count": 81,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -2797,7 +2834,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 81,
+   "execution_count": 82,
    "metadata": {
     "slideshow": {
      "slide_type": "-"
@@ -2810,7 +2847,7 @@
        "[99, 2, 3]"
       ]
      },
-     "execution_count": 81,
+     "execution_count": 82,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2827,12 +2864,12 @@
     }
    },
    "source": [
-    "The changes made to the object `x` is pointing to can also be seen through the `y` variable!"
+    "The changes made to the object `x` is pointing to are also seen through the `y` variable!"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 82,
+   "execution_count": 83,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -2845,7 +2882,7 @@
        "[99, 2, 3]"
       ]
      },
-     "execution_count": 82,
+     "execution_count": 83,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2905,7 +2942,7 @@
     }
    },
    "source": [
-    "Variable names may contain upper and lower case letters, numbers, and underscores (\"\\_\") and be as long as we want them to be. However, they must not begin with a number. Also, they must not be any of Python's built-in **[keywords](https://docs.python.org/3/reference/lexical_analysis.html#keywords)**.\n",
+    "Variable names may contain upper and lower case letters, numbers, and underscores (i.e., `_`) and be as long as we want them to be. However, they must not begin with a number. Also, they must not be any of Python's built-in **[keywords](https://docs.python.org/3/reference/lexical_analysis.html#keywords)**.\n",
     "\n",
     "Variable names are usually chosen such that they do not need any more documentation and are self-explanatory. A very common convention is to use so-called **[snake\\_case](https://en.wikipedia.org/wiki/Snake_case)**: Keep everything lowercase and use underscores to seperate words.\n",
     "\n",
@@ -2925,7 +2962,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 83,
+   "execution_count": 84,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -2938,7 +2975,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 84,
+   "execution_count": 85,
    "metadata": {
     "slideshow": {
      "slide_type": "-"
@@ -2951,7 +2988,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 85,
+   "execution_count": 86,
    "metadata": {
     "slideshow": {
      "slide_type": "-"
@@ -2964,7 +3001,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 86,
+   "execution_count": 87,
    "metadata": {
     "slideshow": {
      "slide_type": "-"
@@ -2988,7 +3025,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 87,
+   "execution_count": 88,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -3001,7 +3038,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 88,
+   "execution_count": 89,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -3014,7 +3051,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 89,
+   "execution_count": 90,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -3027,7 +3064,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 90,
+   "execution_count": 91,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -3036,10 +3073,10 @@
    "outputs": [
     {
      "ename": "SyntaxError",
-     "evalue": "can't assign to operator (<ipython-input-90-dfc191b6a91a>, line 1)",
+     "evalue": "can't assign to operator (<ipython-input-91-dfc191b6a91a>, line 1)",
      "output_type": "error",
      "traceback": [
-      "\u001b[0;36m  File \u001b[0;32m\"<ipython-input-90-dfc191b6a91a>\"\u001b[0;36m, line \u001b[0;32m1\u001b[0m\n\u001b[0;31m    address@work = \"Burgplatz 2, Vallendar\"\u001b[0m\n\u001b[0m                                           ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m can't assign to operator\n"
+      "\u001b[0;36m  File \u001b[0;32m\"<ipython-input-91-dfc191b6a91a>\"\u001b[0;36m, line \u001b[0;32m1\u001b[0m\n\u001b[0;31m    address@work = \"Burgplatz 2, Vallendar\"\u001b[0m\n\u001b[0m                                           ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m can't assign to operator\n"
      ]
     }
    ],
@@ -3060,7 +3097,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 91,
+   "execution_count": 92,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -3084,7 +3121,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 92,
+   "execution_count": 93,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -3097,7 +3134,7 @@
        "'__main__'"
       ]
      },
-     "execution_count": 92,
+     "execution_count": 93,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3130,7 +3167,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 93,
+   "execution_count": 94,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -3152,10 +3189,10 @@
        "        "
       ],
       "text/plain": [
-       "<IPython.lib.display.YouTubeVideo at 0x7fedb808c518>"
+       "<IPython.lib.display.YouTubeVideo at 0x7f6b1c4606d8>"
       ]
      },
-     "execution_count": 93,
+     "execution_count": 94,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3186,7 +3223,7 @@
    "source": [
     "An **[expression](https://docs.python.org/3/reference/expressions.html)** is any syntactically correct **combination** of **variables** and **literals** with **operators**.\n",
     "\n",
-    "In simple words, anything that can be used on the right-hand side of an assignment statement without creating a `SyntaxError` is an expression.\n",
+    "In simple words, anything that may be used on the right-hand side of an assignment statement without creating a `SyntaxError` is an expression.\n",
     "\n",
     "What we said about individual operators above, namely that they have *no* side effects, should have been put here to begin with. The examples in the section on operators above were actually all expressions!\n",
     "\n",
@@ -3195,7 +3232,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 94,
+   "execution_count": 95,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -3208,7 +3245,7 @@
        "123"
       ]
      },
-     "execution_count": 94,
+     "execution_count": 95,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3219,7 +3256,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 95,
+   "execution_count": 96,
    "metadata": {},
    "outputs": [
     {
@@ -3228,7 +3265,7 @@
        "42"
       ]
      },
-     "execution_count": 95,
+     "execution_count": 96,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3246,7 +3283,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 96,
+   "execution_count": 97,
    "metadata": {
     "slideshow": {
      "slide_type": "-"
@@ -3259,7 +3296,7 @@
        "165"
       ]
      },
-     "execution_count": 96,
+     "execution_count": 97,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3281,7 +3318,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 97,
+   "execution_count": 98,
    "metadata": {
     "slideshow": {
      "slide_type": "-"
@@ -3294,7 +3331,7 @@
        "4492125"
       ]
      },
-     "execution_count": 97,
+     "execution_count": 98,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3316,7 +3353,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 98,
+   "execution_count": 99,
    "metadata": {
     "slideshow": {
      "slide_type": "-"
@@ -3329,7 +3366,7 @@
        "3"
       ]
      },
-     "execution_count": 98,
+     "execution_count": 99,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3351,7 +3388,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 99,
+   "execution_count": 100,
    "metadata": {
     "slideshow": {
      "slide_type": "-"
@@ -3364,7 +3401,7 @@
        "104"
       ]
      },
-     "execution_count": 99,
+     "execution_count": 100,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3392,12 +3429,12 @@
     }
    },
    "source": [
-    "Python **overloads** certain operators. For example you can not only add numbers but also strings. This is called **string concatenation**."
+    "Python **overloads** certain operators. For example, you may not only \"add\" numbers but also strings. This is called **string concatenation**."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 100,
+   "execution_count": 101,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -3411,7 +3448,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 101,
+   "execution_count": 102,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -3424,7 +3461,7 @@
        "'Hi class'"
       ]
      },
-     "execution_count": 101,
+     "execution_count": 102,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3446,7 +3483,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 102,
+   "execution_count": 103,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -3459,7 +3496,7 @@
        "'Hi Hi Hi Hi Hi Hi Hi Hi Hi Hi Hi Hi Hi Hi Hi Hi Hi Hi Hi Hi Hi Hi Hi Hi Hi Hi Hi Hi Hi Hi Hi Hi Hi Hi Hi Hi Hi Hi Hi Hi Hi Hi '"
       ]
      },
-     "execution_count": 102,
+     "execution_count": 103,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3487,14 +3524,14 @@
     }
    },
    "source": [
-    "A **[statement](https://docs.python.org/3/reference/simple_stmts.html)** is anything that changes the state of a program or has some other side effect. Statements do not just evaluate to a value like expressions; instead, they create or change values.\n",
+    "A **[statement](https://docs.python.org/3/reference/simple_stmts.html)** is anything that *changes* the *state of a program* or has some other *side effect*. Statements do not just evaluate to a value like expressions; instead, they create or change values.\n",
     "\n",
     "Most notably of course are the `=` and `del` statements."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 103,
+   "execution_count": 104,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -3507,7 +3544,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 104,
+   "execution_count": 105,
    "metadata": {
     "slideshow": {
      "slide_type": "-"
@@ -3531,7 +3568,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 105,
+   "execution_count": 106,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -3569,16 +3606,16 @@
     }
    },
    "source": [
-    "We can use the `#` symbol to write comments in plain English right into the code.\n",
+    "We use the `#` symbol to write comments in plain English right into the code.\n",
     "\n",
     "As a good practice, comments should not describe what happens (this should be evident by reading the code, otherwise it is most likely badly written code) but why something happens.\n",
     "\n",
-    "Comments can be either added at the end of a line of code (by convention seperated with two spaces) or be on a line on their own."
+    "Comments may be added either at the end of a line of code, by convention seperated with two spaces, or on a line on their own."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 106,
+   "execution_count": 107,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -3606,7 +3643,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 107,
+   "execution_count": 108,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -3619,7 +3656,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 108,
+   "execution_count": 109,
    "metadata": {
     "slideshow": {
      "slide_type": "-"
@@ -3649,7 +3686,7 @@
     }
    },
    "source": [
-    "We end each chapter with a summary of the main points. The essence in this first chapter is that just like a sentence in a real language like English can be decomposed into its parts (subject, predicate, objects, ...) the same can be done with programming languages."
+    "We end each chapter with a summary of the main points. The essence in this first chapter is that just like a sentence in a real language like English may be decomposed into its parts (subject, predicate, objects, ...) the same may be done with programming languages."
    ]
   },
   {
@@ -3727,8 +3764,8 @@
     "\n",
     "- flow control (cf., [Chapter 3](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/03_conditionals.ipynb))\n",
     "  - expression of **business logic** or an **algorithm**\n",
-    "  - conditional execution of a small **branch** within a program (i.e., `if` statements)\n",
-    "  - repetitive execution of parts of a program (i.e., `for`-loops and `while`-loops)"
+    "  - conditional execution of a small **branch** within a program (e.g., `if` statements)\n",
+    "  - repetitive execution of parts of a program (e.g., `for`-loops)"
    ]
   }
  ],
diff --git a/02_functions.ipynb b/02_functions.ipynb
index 681838a..03452c7 100644
--- a/02_functions.ipynb
+++ b/02_functions.ipynb
@@ -23,7 +23,7 @@
     "\n",
     "At the same time, we executed built-in functions (e.g., [print()](https://docs.python.org/3/library/functions.html#print), [sum()](https://docs.python.org/3/library/functions.html#sum), [len()](https://docs.python.org/3/library/functions.html#len), [id()](https://docs.python.org/3/library/functions.html#id), or [type()](https://docs.python.org/3/library/functions.html#type)) that obviously must be re-using the same parts inside core Python every time we use them.\n",
     "\n",
-    "This chapter shows how Python offers language constructs that let us **define** our own functions that we can then **call** just like the built-in ones."
+    "This chapter shows how Python offers language constructs that let us **define** our own functions that we may then **call** just like the built-in ones."
    ]
   },
   {
@@ -45,11 +45,11 @@
     }
    },
    "source": [
-    "So-called **[user-defined functions](https://docs.python.org/3/reference/compound_stmts.html#function-definitions)** can be created with the `def` statement. To extend an already familiar example, we re-use the introductory example from [Chapter 1](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/01_elements.ipynb) in its final Pythonic version and transform it into the function `average_evens()` below. \n",
+    "So-called **[user-defined functions](https://docs.python.org/3/reference/compound_stmts.html#function-definitions)** may be created with the `def` statement. To extend an already familiar example, we re-use the introductory example from [Chapter 1](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/01_elements.ipynb) in its final Pythonic version and transform it into the function `average_evens()` below. \n",
     "\n",
     "A function's **name** must be chosen according to the same naming rules as for ordinary variables. In fact, Python manages function names just like variables. In this book, we further adopt the convention of ending function names with parentheses \"`()`\" in text cells for faster comprehension when reading (i.e., `average_evens()` vs. `average_evens`). These are not actually part of the name but must always be written out in the `def` statement for syntactic reasons.\n",
     "\n",
-    "Functions may define an arbitrary number of **parameters** as inputs that can then be referenced within the indented **code block**: They are simply listed within the parentheses in the `def` statement (i.e., `numbers` below). \n",
+    "Functions may define an arbitrary number of **parameters** as inputs that are then referenced within the indented **code block**: They are simply listed within the parentheses in the `def` statement (i.e., `numbers` below). \n",
     "\n",
     "The code block is often also called a function's **body** while the first line with the `def` in it is the **header** and must end with a colon.\n",
     "\n",
@@ -92,7 +92,7 @@
     }
    },
    "source": [
-    "Once defined, a function can be referenced just like any other variable by its name (i.e., *without* the parenthesis). Its value might seem awkward at first: It consists of the location where we defined the function (i.e., `__main__`, which is Python's way of saying \"in this notebook\") and the signature."
+    "Once defined, a function may be referenced just like any other variable by its name (i.e., *without* the parenthesis). Its value might seem awkward at first: It consists of the location where we defined the function (i.e., `__main__`, which is Python's way of saying \"in this notebook\") and the signature."
    ]
   },
   {
@@ -142,7 +142,7 @@
     {
      "data": {
       "text/plain": [
-       "140693945143776"
+       "139925407773152"
       ]
      },
      "execution_count": 3,
@@ -289,7 +289,7 @@
     }
    },
    "source": [
-    "We can **call** (i.e., \"execute\") a function with the **call operator** `()` as often as we wish. The formal parameters are filled in by passing variables or expressions as **arguments** to the function within the parentheses."
+    "We **call** (i.e., \"execute\") a function with the **call operator** `()` as often as we wish. The formal parameters are filled in by passing variables or expressions as **arguments** to the function within the parentheses."
    ]
   },
   {
@@ -863,7 +863,7 @@
     "\n",
     "Variables whose names collide with the ones of variables in enclosing scopes - and the global scope is just the most enclosing scope - are said to **shadow** them.\n",
     "\n",
-    "While this is not a problem for Python as we have observed, it may lead to less readable code for us humans and should be avoided if possible. But, as we have also heard, \"[naming things](https://skeptics.stackexchange.com/questions/19836/has-phil-karlton-ever-said-there-are-only-two-hard-things-in-computer-science)\" is often considered hard as well and we have to be prepared to encounter shadowing variables."
+    "While this is not a problem for Python, it may lead to less readable code for us humans and should be avoided if possible. But, as we have also heard, \"[naming things](https://skeptics.stackexchange.com/questions/19836/has-phil-karlton-ever-said-there-are-only-two-hard-things-in-computer-science)\" is often considered hard as well and we have to be prepared to encounter shadowing variables."
    ]
   },
   {
@@ -946,7 +946,7 @@
     }
    },
    "source": [
-    "We can cast certain objects as a different type. For example, to \"convert\" a float or a text into an integer, we use the [int()](https://docs.python.org/3/library/functions.html#int) built-in. This actually creates a *new* object of type `int` from the provided `avg` or `\"6\"` objects who continue to exist in memory unchanged."
+    "We may cast certain objects as a different type. For example, to \"convert\" a float or a text into an integer, we use the [int()](https://docs.python.org/3/library/functions.html#int) built-in. This actually creates a *new* object of type `int` from the provided `avg` or `\"6\"` objects who continue to exist in memory unchanged."
    ]
   },
   {
@@ -1088,7 +1088,7 @@
     }
    },
    "source": [
-    "Not all conversions are valid and *runtime* errors can occur as the `ValueError` shows."
+    "Not all conversions are valid and *runtime* errors may occur as the `ValueError` shows."
    ]
   },
   {
@@ -1124,7 +1124,7 @@
     }
    },
    "source": [
-    "We can also go in the other direction with the [float()](https://docs.python.org/3/library/functions.html#float) built-in function."
+    "We may also go in the other direction with the [float()](https://docs.python.org/3/library/functions.html#float) built-in function."
    ]
   },
   {
@@ -1266,7 +1266,7 @@
     }
    },
    "source": [
-    "As with [divmod()](https://docs.python.org/3/library/functions.html#divmod), we can pass in the arguments by position."
+    "As with [divmod()](https://docs.python.org/3/library/functions.html#divmod), we pass in the arguments by position."
    ]
   },
   {
@@ -1303,7 +1303,7 @@
    "source": [
     "However, now the function call is a bit harder to comprehend as we need to always remember what the `2` means. This becomes even harder the more parameters we specify.\n",
     "\n",
-    "Luckily, we can also reference the formal parameter names as **keyword arguments**. We can even combine positional and keyword arguments in the same function call. Each of the following does the exact same thing."
+    "Luckily, we may also reference the formal parameter names as **keyword arguments**. We can even combine positional and keyword arguments in the same function call. Each of the following does the exact same thing."
    ]
   },
   {
@@ -1555,9 +1555,9 @@
     }
    },
    "source": [
-    "Now we can call the function either with or without the `scalar` argument.\n",
+    "Now we call the function either with or without the `scalar` argument.\n",
     "\n",
-    "If `scalar` is passed in, this can be done as either a positional or a keyword argument. Which of the two versions where `scalar` is `2` is easier to comprehend in a large program?"
+    "If `scalar` is passed in, this may be done as either a positional or a keyword argument. Which of the two versions where `scalar` is `2` is easier to comprehend in a large program?"
    ]
   },
   {
@@ -1869,7 +1869,7 @@
     }
    },
    "source": [
-    "Now we can call `add_three()` as if we defined it with the `def` statement to begin with."
+    "Now we call `add_three()` as if we defined it with the `def` statement to begin with."
    ]
   },
   {
@@ -2084,7 +2084,7 @@
     {
      "data": {
       "text/plain": [
-       "140694050824664"
+       "139925472068136"
       ]
      },
      "execution_count": 58,
@@ -2132,7 +2132,7 @@
     "\n",
     "Let's see what we can do with the `math` module.\n",
     "\n",
-    "The [dir()](https://docs.python.org/3/library/functions.html#dir) built-in function can also be used with an argument passed in. Ignoring the dunder-style names, `math` offers quite a lot of ... names. As we cannot know at this point in time if a listed name refers to a function or an ordinary variable, we use the more generic term **attribute** to mean either one of them."
+    "The [dir()](https://docs.python.org/3/library/functions.html#dir) built-in function may also be used with an argument passed in. Ignoring the dunder-style names, `math` offers quite a lot of ... names. As we cannot know at this point in time if a listed name refers to a function or an ordinary variable, we use the more generic term **attribute** to mean either one of them."
    ]
   },
   {
@@ -2354,9 +2354,9 @@
     }
    },
    "source": [
-    "Observe how the arguments passed to functions do not need to be just variables or simple literals. Instead, we can pass in any *expression* that evaluates to a *new* object of the type the function expects.\n",
+    "Observe how the arguments passed to functions do not need to be just variables or simple literals. Instead, we may pass in any *expression* that evaluates to a *new* object of the type the function expects.\n",
     "\n",
-    "So just as a reminder from the expression vs. statement discussion in [Chapter 1](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/01_elements.ipynb): An expression is *any* syntactically correct combination of variables and literals with operators. And the call operator `()` is just ... well another operator. So both of the next two code cells are just expressions! They have no permanent side effect in memory. We can execute them as often as we want *without* changing the state of the program (i.e., this Jupyter notebook).\n",
+    "So just as a reminder from the expression vs. statement discussion in [Chapter 1](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/01_elements.ipynb): An expression is *any* syntactically correct combination of variables and literals with operators. And the call operator `()` is just ... well another operator. So both of the next two code cells are just expressions! They have no permanent side effect in memory. We may execute them as often as we want *without* changing the state of the program (i.e., this Jupyter notebook).\n",
     "\n",
     "So, regarding the very next cell in particular: Although the `2 ** 2` creates a *new* object `4` in memory that is then immediately passed into the [math.sqrt()](https://docs.python.org/3/library/math.html#math.sqrt) function, once that function call returns, \"all is lost\" and the newly created `4` object is forgotten again, as well as the return value of [math.sqrt()](https://docs.python.org/3/library/math.html#math.sqrt)."
    ]
@@ -2428,7 +2428,7 @@
     }
    },
    "source": [
-    "If we only need one particular function from a module, we can also use the alternative `from ... import ...` syntax.\n",
+    "If we only need one particular function from a module, we may also use the alternative `from ... import ...` syntax.\n",
     "\n",
     "This does *not* create a module object but only makes a variable in our current location point to an object defined inside a module directly."
    ]
@@ -2537,7 +2537,7 @@
     }
    },
    "source": [
-    "Besides the usual dunder-style attributes, the [dir()](https://docs.python.org/3/library/functions.html#dir) built-in function lists some attributes in an upper case naming convention and many others starting with a single underscore \"\\_\". To understand the former, we have to wait until Chapter 10 while the latter are explained further below."
+    "Besides the usual dunder-style attributes, the [dir()](https://docs.python.org/3/library/functions.html#dir) built-in function lists some attributes in an upper case naming convention and many others starting with a single underscore `_`. To understand the former, we have to wait until Chapter 10 while the latter are explained further below."
    ]
   },
   {
@@ -2697,7 +2697,7 @@
     {
      "data": {
       "text/plain": [
-       "0.15268128055183228"
+       "0.270353921677863"
       ]
      },
      "execution_count": 75,
@@ -2732,7 +2732,7 @@
     {
      "data": {
       "text/plain": [
-       "<bound method Random.choice of <random.Random object at 0x56111ba1cba8>>"
+       "<bound method Random.choice of <random.Random object at 0x56063d20aba8>>"
       ]
      },
      "execution_count": 76,
@@ -2781,7 +2781,7 @@
     {
      "data": {
       "text/plain": [
-       "2"
+       "7"
       ]
      },
      "execution_count": 78,
@@ -2801,7 +2801,7 @@
     }
    },
    "source": [
-    "In order to re-produce the same random numbers in a simulation each time we run it, we can set the **[random seed](https://en.wikipedia.org/wiki/Random_seed)**. It is good practice to do this at the beginning of a program or notebook. Then every time we re-start the program, we will get the exact same random numbers again. This becomes very important, for example, when we employ certain machine learning algorithms that rely on randomization, like the infamous [Random Forest](https://en.wikipedia.org/wiki/Random_forest), and want to obtain **re-producable** results.\n",
+    "In order to re-produce the same random numbers in a simulation each time we run it, we set the **[random seed](https://en.wikipedia.org/wiki/Random_seed)**. It is good practice to do this at the beginning of a program or notebook. Then every time we re-start the program, we will get the exact same random numbers again. This becomes very important, for example, when we employ certain machine learning algorithms that rely on randomization, like the infamous [Random Forest](https://en.wikipedia.org/wiki/Random_forest), and want to obtain **re-producable** results.\n",
     "\n",
     "The [random](https://docs.python.org/3/library/random.html) module provides the [random.seed()](https://docs.python.org/3/library/random.html#random.seed) function to do that."
    ]
@@ -2899,7 +2899,7 @@
     }
    },
    "source": [
-    "As the Python community is based around open source, many developers publish their code, for example, on the Python Package Index [PyPI](https://pypi.org) from where anyone can download and install it for free using command line based tools like [pip](https://pip.pypa.io/en/stable/) or [conda](https://conda.io/en/latest/). This way, we can always customize our Python installation even more. Managing many such packages is actually quite a deep topic on its own, sometimes fearfully called **[dependency hell](https://en.wikipedia.org/wiki/Dependency_hell)**.\n",
+    "As the Python community is based around open source, many developers publish their code, for example, on the Python Package Index [PyPI](https://pypi.org) from where anyone may download and install it for free using command line based tools like [pip](https://pip.pypa.io/en/stable/) or [conda](https://conda.io/en/latest/). This way, we can always customize our Python installation even more. Managing many such packages is actually quite a deep topic on its own, sometimes fearfully called **[dependency hell](https://en.wikipedia.org/wiki/Dependency_hell)**.\n",
     "\n",
     "The difference between the [standard library](https://docs.python.org/3/library/index.html) and such **third-party** packages is that in the first case the code goes through a much more formalized review process and is officially endorsed by the Python core developers. Yet, many third-party projects also offer the highest quality standards and a lot of such software is actually also relied on by many businesses and researchers.\n",
     "\n",
@@ -2987,7 +2987,7 @@
     }
    },
    "source": [
-    "`np` can be used in the same way as `math` or `random` above."
+    "`np` is used in the same way as `math` or `random` above."
    ]
   },
   {
@@ -3094,7 +3094,7 @@
     }
    },
    "source": [
-    "[numpy](http://www.numpy.org/) somehow magically adds new behavior to Python's built-in arithmetic operators. For example, we can now [scalar-multiply](https://en.wikipedia.org/wiki/Scalar_multiplication) `vec`.\n",
+    "[numpy](http://www.numpy.org/) somehow magically adds new behavior to Python's built-in arithmetic operators. For example, we may now [scalar-multiply](https://en.wikipedia.org/wiki/Scalar_multiplication) `vec`.\n",
     "\n",
     "[numpy](http://www.numpy.org/)'s functions are implemented in highly optimized C code and therefore fast, especially when it comes to big data."
    ]
@@ -3292,7 +3292,7 @@
    "source": [
     "Disregarding the dunder-style attributes, `mod` defines the five attributes `_default_scalar`, `_scaled_average`, `average`, `average_evens`, and `average_odds`, which are exactly the ones we would expect from reading the [*sample_module.py*](https://github.com/webartifex/intro-to-python/blob/master/sample_module.py) file.\n",
     "\n",
-    "An important convention when working with imported code is to *disregard* any attributes starting with an underscore \"\\_\". These are considered **private** and constitute **implementation details** the author of the imported code might change in a future version of his software. We *must* not rely on them in any way.\n",
+    "An important convention when working with imported code is to *disregard* any attributes starting with an underscore `_`. These are considered **private** and constitute **implementation details** the author of the imported code might change in a future version of his software. We *must* not rely on them in any way.\n",
     "\n",
     "In contrast, the three remaining **public** attributes are the functions `average()`, `average_evens()`, and `average_odds()` that we may use after the import."
    ]
@@ -3341,7 +3341,7 @@
     }
    },
    "source": [
-    "We can use the imported `mod.average_evens()` just like `average_evens()` defined above. The advantage we get from **modularization** with *.py* files is that we can now easily re-use functions across different Jupyter notebooks without re-defining them again and again. Also, we can \"source out\" code that distracts from the storyline told in a notebook."
+    "We use the imported `mod.average_evens()` just like `average_evens()` defined above. The advantage we get from **modularization** with *.py* files is that we can now easily re-use functions across different Jupyter notebooks without re-defining them again and again. Also, we can \"source out\" code that distracts from the storyline told in a notebook."
    ]
   },
   {
@@ -3433,7 +3433,7 @@
     }
    },
    "source": [
-    "Packages are a generalization of modules and we will look at one in Chapter 10 in detail. You can, however, already look at a [sample package](https://github.com/webartifex/intro-to-python/tree/master/sample_package) in the repository, which is nothing but a folder with *.py* files in it.\n",
+    "Packages are a generalization of modules and we will look at one in Chapter 10 in detail. You may, however, already look at a [sample package](https://github.com/webartifex/intro-to-python/tree/master/sample_package) in the repository, which is nothing but a folder with *.py* files in it.\n",
     "\n",
     "As a further references on modules, we refer to the [official tutorial](https://docs.python.org/3/tutorial/modules.html)."
    ]
@@ -3464,11 +3464,11 @@
     "- make programs easier to comprehend and debug for humans as they give names to the smaller parts of a larger program (i.e., they **modularize** a code base), and\n",
     "- eliminate redundancies by allowing **re-use of code**.\n",
     "\n",
-    "Functions are **defined** once with the `def` statement. Then, they can be **called** many times with the call operator `()`.\n",
+    "Functions are **defined** once with the `def` statement. Then, they may be **called** many times with the call operator `()`.\n",
     "\n",
     "They may process **parameterized** inputs, **passed** in as **arguments**, and output a **return value**.\n",
     "\n",
-    "Arguments can be passed in by **position** or **keyword**. Some functions may even require **keyword-only** arguments.\n",
+    "Arguments may be passed in by **position** or **keyword**. Some functions may even require **keyword-only** arguments.\n",
     "\n",
     "**Lambda expressions** create anonymous functions.\n",
     "\n",
diff --git a/03_conditionals.ipynb b/03_conditionals.ipynb
index 6d70f07..f350397 100644
--- a/03_conditionals.ipynb
+++ b/03_conditionals.ipynb
@@ -19,9 +19,9 @@
     }
    },
    "source": [
-    "We analyzed every aspect of the `average_evens()` function in [Chapter 2](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/02_functions.ipynb) except for the `if` part. While it seems to intuitively do what we expect it to, there is a whole lot more to be learned from taking it apart. In particular, the `if` can occur within both a **statement** as in our introductory example in [Chapter 1](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/01_elements.ipynb) but also an **expression** as in `average_evens()`. This is analogous as to how a noun in a natural language is *either* the subject of *or* an object in a sentence. What is common to both versions of the `if` is that it leads to code being executed for *parts* of the input only. It is our first way of **controlling** the **flow of execution** in a program.\n",
+    "We analyzed every aspect of the `average_evens()` function in [Chapter 2](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/02_functions.ipynb) except for the `if` related parts. While it seems to intuitively do what we expect it to, there is a whole lot more to be learned from taking it apart. In particular, the `if` may occur within both a **statement** as in our introductory example in [Chapter 1](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/01_elements.ipynb) but also an **expression** as in `average_evens()`. This is analogous as to how a noun in a natural language is *either* the subject of *or* an object in a sentence. What is common to both versions of the `if` is that it leads to code being executed for *parts* of the input only. It is our first way of **controlling** the **flow of execution** in a program.\n",
     "\n",
-    "After deconstructing `if` in the first part of this chapter, we take a close look at a similar concept, namely handling and raising **exceptions**."
+    "After deconstructing `if` in the first part of this chapter, we take a close look at a similar concept, namely handling **exceptions**."
    ]
   },
   {
@@ -104,7 +104,7 @@
     }
    },
    "source": [
-    "Observe how `==` can handle objects of *different* type. This shows how it implements a notion of equality in line with how we humans think of things being equal or not. After all, `42` and `42.0` are totally different $0$s and $1$s for a computer and many programming languages would actually say `False` here! Technically, this is yet another example of operator overloading."
+    "The `==` operator handles objects of *different* type. This shows how it implements a notion of equality in line with how we humans think of things being equal or not. After all, `42` and `42.0` are totally different $0$s and $1$s for a computer and many programming languages would actually say `False` here! Technically, this is yet another example of operator overloading."
    ]
   },
   {
@@ -139,7 +139,7 @@
     }
    },
    "source": [
-    "There are, however, cases where even well-behaved Python does not make us happy. Chapter 5 will provide more insights on this \"bug\"."
+    "There are, however, cases where even well-behaved Python does not make us happy. [Chapter 5](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/05_numbers.ipynb) will provide more insights on this \"bug\"."
    ]
   },
   {
@@ -189,7 +189,7 @@
     {
      "data": {
       "text/plain": [
-       "94163040564192"
+       "94697002906592"
       ]
      },
      "execution_count": 5,
@@ -213,7 +213,7 @@
     {
      "data": {
       "text/plain": [
-       "94163040564160"
+       "94697002906560"
       ]
      },
      "execution_count": 6,
@@ -313,7 +313,7 @@
     {
      "data": {
       "text/plain": [
-       "94163040551152"
+       "94697002893552"
       ]
      },
      "execution_count": 10,
@@ -359,7 +359,7 @@
    "source": [
     "`True`, `False`, and `None` have the property that they each exist in memory only *once*. Objects designed this way are so-called **singletons**. This **[design pattern](https://en.wikipedia.org/wiki/Design_Patterns)** was originally developed to keep a program's memory usage at a minimum. It may only be employed in situations where we know that an object will *not* mutate its value (i.e., to re-use the bag analogy from [Chapter 1](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/01_elements.ipynb), no flipping of $0$s and $1$s in the bag is allowed). In languages \"closer\" to the memory like C we would have to code this singleton logic ourselves but Python has this already built in for *some* types.\n",
     "\n",
-    "We can verify this with either the `is` operator or by comparing memory addresses."
+    "We verify this with either the `is` operator or by comparing memory addresses."
    ]
   },
   {
@@ -523,7 +523,7 @@
     }
    },
    "source": [
-    "The \"less than\" `<` or \"greater than\" `>` operators on their own mean \"strictly less than\" or \"strictly greater than\" but can be combined with the equality operator into just `<=` and `>=`. This is a shortcut for using the logical `or` operator as described in the next section."
+    "The \"less than\" `<` or \"greater than\" `>` operators on their own mean \"strictly less than\" or \"strictly greater than\" but may be combined with the equality operator into just `<=` and `>=`. This is a shortcut for using the logical `or` operator as described in the next section."
    ]
   },
   {
@@ -641,7 +641,7 @@
     }
    },
    "source": [
-    "Boolean expressions can be combined or negated with the **logical operators** `and`, `or`, and `not` to form new boolean expressions. Of course, this may be done *recursively* as well to obtain boolean expressions of arbitrary complexity.\n",
+    "Boolean expressions may be combined or negated with the **logical operators** `and`, `or`, and `not` to form new boolean expressions. Of course, this may be done *recursively* as well to obtain boolean expressions of arbitrary complexity.\n",
     "\n",
     "Their usage is similar to how the equivalent words are used in plain English:\n",
     "\n",
@@ -1133,7 +1133,7 @@
     }
    },
    "source": [
-    "## Conditional Statements"
+    "## The `if` Statement"
    ]
   },
   {
@@ -1146,13 +1146,13 @@
    "source": [
     "In order to write useful programs, we need to control the flow of execution, for example, to react to user input. The logic by which a program does that is referred to as **business logic**.\n",
     "\n",
-    "One major language construct to do so is the **[conditional statement](https://docs.python.org/3/reference/compound_stmts.html#the-if-statement)** or `if` statement. It consists of:\n",
+    "One major language construct to do so is the **[conditional statement](https://docs.python.org/3/reference/compound_stmts.html#the-if-statement)**, or `if` statement for short. It consists of:\n",
     "\n",
     "- *one* mandatory `if`-clause,\n",
     "- an *arbitrary* number of `elif`-clauses (i.e. \"else if\"), and\n",
     "- an *optional* `else`-clause.\n",
     "\n",
-    "The `if`- and `elif`-clauses each specify one *boolean* expression, also called **condition**, while the `else`-clause serves as a \"catch everything else\" case.\n",
+    "The `if`- and `elif`-clauses each specify one *boolean* expression, also called **condition**, while the `else`-clause serves as the \"catch everything else\" case.\n",
     "\n",
     "In terms of syntax, the header lines end with a colon and the code blocks are indented.\n",
     "\n",
@@ -1236,15 +1236,7 @@
      "slide_type": "-"
     }
    },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "You will read this just as often as you see heads when tossing a coin\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "if random.random() > 0.5:\n",
     "    print(\"You will read this just as often as you see heads when tossing a coin\")"
@@ -1258,7 +1250,7 @@
     }
    },
    "source": [
-    "More often than not, we might model a binary choice."
+    "More often than not, we model a **binary choice**."
    ]
   },
   {
@@ -1293,7 +1285,9 @@
     }
    },
    "source": [
-    "We may **nest** `if` statements to control the flow of execution in a more granular way. Every additional layer, however, makes the code less readable, in particular, if we have more than one line per code block."
+    "We may **nest** `if` statements to control the flow of execution in a more granular way. Every additional layer, however, makes the code *less* readable, in particular, if we have more than one line per code block.\n",
+    "\n",
+    "The code cell below *either* checks if a number is even or odd *or* if it is positive or negative."
    ]
   },
   {
@@ -1334,7 +1328,9 @@
     }
    },
    "source": [
-    "A good way to make this code more readable is to introduce **temporary variables** *in combination* with using the `and` operator to **flatten** the branching logic. The `if` statement then reads almost like plain English. In contrast to many other languages, creating variables is a computationally *cheap* operation in Python and also helps to document the code *inline*. Without temporary variables, the `and` flattening could actually lead to more sub-expressions in the conditions be evaluated than necessary. Do you see why?"
+    "A good way to make this code more readable is to introduce **temporary variables** *in combination* with the `and` operator to **flatten** the branching logic. The `if` statement then reads almost like plain English. In contrast to many other languages, creating variables is a computationally *cheap* operation in Python and also helps to document the code *inline* with meaningful variable names.\n",
+    "\n",
+    "Flattening the logic *without* temporary variables could actually lead to more sub-expressions in the conditions be evaluated than necessary. Do you see why?"
    ]
   },
   {
@@ -1377,7 +1373,7 @@
     }
    },
    "source": [
-    "## Conditional Expressions"
+    "## The `if` Expression"
    ]
   },
   {
@@ -1388,7 +1384,7 @@
     }
    },
    "source": [
-    "When all we do with an `if` statement is to assign an object to a variable with respect to a single true-or-false condition (cf., binary choice above), there is a shortcut for that: We could simply assign the result of a so-called **conditional expression** or `if` expression to the variable.\n",
+    "When all we do with an `if` statement is to assign an object to a variable with respect to a single true-or-false condition (i.e., a binary choice), there is a shortcut for that: We could simply assign the result of a so-called **conditional expression**, or `if` expression for short, to the variable.\n",
     "\n",
     "Think of a situation where we evaluate a piece-wise functional relationship $y = f(x)$ at a given $x$, for example:"
    ]
@@ -1530,7 +1526,7 @@
     }
    },
    "source": [
-    "In this concrete example, however, the most elegant solution would be to use the built-in [max()](https://docs.python.org/3/library/functions.html#max) function."
+    "In this example, however, the most elegant solution would be to use the built-in [max()](https://docs.python.org/3/library/functions.html#max) function."
    ]
   },
   {
@@ -1578,7 +1574,7 @@
     }
    },
    "source": [
-    "Conditional expressions may not only be used in the way described in this section. We already saw them as part of a list comprehension that is introduced in Chapter 7."
+    "Conditional expressions may not only be used in the way described in this section. We already saw them as part of a list comprehension in [Chapter 1](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/01_elements.ipynb) and [Chapter 2](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/02_functions.ipynb)."
    ]
   },
   {
@@ -1589,7 +1585,7 @@
     }
    },
    "source": [
-    "## Exceptions"
+    "## The `try` Statement"
    ]
   },
   {
@@ -1600,7 +1596,7 @@
     }
    },
    "source": [
-    "In the previous two chapters we already encountered a couple of *runtime* errors. A natural urge we might have after reading about conditional statements is to write code that somehow reacts to the occurence of such exceptions. All we need for that is a way to formulate a condition for that.\n",
+    "In the previous two chapters we already encountered a couple of *runtime* errors. A natural urge we might have after reading about conditional statements is to write code that somehow reacts to the occurence of such exceptions. All we need is a way to formulate a condition for that.\n",
     "\n",
     "For sure, this is such a common thing to do that Python provides its own language construct for it, namely the `try` [statement](https://docs.python.org/3/reference/compound_stmts.html#the-try-statement).\n",
     "\n",
@@ -1652,9 +1648,9 @@
     }
    },
    "source": [
-    "However, it is good practise to *not* **handle** *any* possible exception but only the ones we may expect from the code in the `try`-branch. The reasoning why this is done is a bit involved. We only remark here that the code base becomes easier to understand as we clearly communicate to any human reader what could go wrong during execution. Python comes with a lot of [built-in exceptions](https://docs.python.org/3/library/exceptions.html#concrete-exceptions) that we should familiarize ourselves with.\n",
+    "However, it is good practice to *not* **handle** *any* possible exception but only the ones we may expect from the code in the `try`-branch. The reasoning why this is done is a bit involved. We only remark here that the code base becomes easier to understand as we clearly communicate to any human reader what could go wrong during execution. Python comes with a lot of [built-in exceptions](https://docs.python.org/3/library/exceptions.html#concrete-exceptions) that we should familiarize ourselves with.\n",
     "\n",
-    "Another good practise is to always keep the code in the `try`-branch short so as to not accidently handle an exception we do not want to handle.\n",
+    "Another good practice is to always keep the code in the `try`-branch short so as to not accidently handle an exception we do not want to handle.\n",
     "\n",
     "In the example, we are dividing numbers and may therefore expect a `ZeroDivisionError`."
    ]
@@ -1691,9 +1687,9 @@
     }
    },
    "source": [
-    "Often, we must have some code run independent of an exception occuring (e.g., to close a connection to a database). To achieve that, we can add an optional `finally`-branch to the `try` statement.\n",
+    "Often, we may have to run some code *independent* of an exception occuring, for example, to close a connection to a database. To achieve that, we add a `finally`-branch to the `try` statement.\n",
     "\n",
-    "Similarly, we might have some code that must be run exactly when no exception occurs but we do not want to put it in the `try`-branch as per the good practice mentioned. To achieve that, we can add an optional `else`-branch to the `try` statement.\n",
+    "Similarly, we may have to run some code *only if* no exception occurs but we do not want to put it in the `try`-branch as per the good practice mentioned above. To achieve that, we add an `else`-branch to the `try` statement.\n",
     "\n",
     "To showcase everything together, we look at one last example. To spice it up a bit, we randomize the input. So run the cell several times and see for yourself. It's actually quite easy."
    ]
@@ -1711,7 +1707,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Oops. Division by 0. How does that work?\n",
+      "Yes, division worked smoothly.\n",
       "I am always printed\n"
      ]
     }
diff --git a/04_iteration.ipynb b/04_iteration.ipynb
index 755b4a5..7ab75c3 100644
--- a/04_iteration.ipynb
+++ b/04_iteration.ipynb
@@ -197,9 +197,9 @@
     }
    },
    "source": [
-    "Whenever we can find a recursive way of formulating an idea, we can immediately translate it into Python in a *naive* way (i.e., we create a *correct* program that may *not* be an *efficient* implementation yet).\n",
+    "Whenever we find a recursive way of formulating an idea, we can immediately translate it into Python in a *naive* way (i.e., we create a *correct* program that may *not* be an *efficient* implementation yet).\n",
     "\n",
-    "Below is a first version of `factorial()`: The `return` statement does not have to be a function's last code line and we can certainly have several `return` statements as well."
+    "Below is a first version of `factorial()`: The `return` statement does not have to be a function's last code line and we may certainly have several `return` statements as well."
    ]
   },
   {
@@ -554,7 +554,7 @@
     }
    },
    "source": [
-    "Euclid's algorithm is stunningly fast, even for large numbers. Its speed comes from the use of the modulo operation `%`. However, this is *not* true for recusion in general, which can result in very slow programs if not applied correctly."
+    "Euclid's algorithm is stunningly fast, even for large numbers. Its speed comes from the use of the modulo operator `%`. However, this is *not* true for recusion in general, which may result in very slow programs if not applied correctly."
    ]
   },
   {
@@ -837,7 +837,7 @@
     }
    },
    "source": [
-    "This implementation is *highly* **inefficient** as small Fibonacci numbers can already take a very long time to compute. The reason for this is **exponential growth** in the number of function calls. As [PythonTutor](http://pythontutor.com/visualize.html#code=def%20fibonacci%28i%29%3A%0A%20%20%20%20if%20i%20%3D%3D%200%3A%0A%20%20%20%20%20%20%20%20return%200%0A%20%20%20%20elif%20i%20%3D%3D%201%3A%0A%20%20%20%20%20%20%20%20return%201%0A%20%20%20%20return%20fibonacci%28i%20-%201%29%20%2B%20fibonacci%28i%20-%202%29%0A%0Arv%20%3D%20fibonacci%285%29&cumulative=false&curInstr=0&heapPrimitives=nevernest&mode=display&origin=opt-frontend.js&py=3&rawInputLstJSON=%5B%5D&textReferences=false) shows, `fibonacci()` is called again and again with the same `i` arguments.\n",
+    "This implementation is *highly* **inefficient** as small Fibonacci numbers already take a very long time to compute. The reason for this is **exponential growth** in the number of function calls. As [PythonTutor](http://pythontutor.com/visualize.html#code=def%20fibonacci%28i%29%3A%0A%20%20%20%20if%20i%20%3D%3D%200%3A%0A%20%20%20%20%20%20%20%20return%200%0A%20%20%20%20elif%20i%20%3D%3D%201%3A%0A%20%20%20%20%20%20%20%20return%201%0A%20%20%20%20return%20fibonacci%28i%20-%201%29%20%2B%20fibonacci%28i%20-%202%29%0A%0Arv%20%3D%20fibonacci%285%29&cumulative=false&curInstr=0&heapPrimitives=nevernest&mode=display&origin=opt-frontend.js&py=3&rawInputLstJSON=%5B%5D&textReferences=false) shows, `fibonacci()` is called again and again with the same `i` arguments.\n",
     "\n",
     "To understand this in detail, we would have to study algorithms and data structures (e.g., with [this book](https://www.amazon.de/Introduction-Algorithms-Press-Thomas-Cormen/dp/0262033844/ref=sr_1_1?__mk_de_DE=%C3%85M%C3%85%C5%BD%C3%95%C3%91&crid=1JNE8U0VZGU0O&qid=1569837169&s=gateway&sprefix=algorithms+an%2Caps%2C180&sr=8-1)), a discipline within computer science, and dive into the analysis of **[time complexity of algorithms](https://en.wikipedia.org/wiki/Time_complexity)**.\n",
     "\n",
@@ -859,8 +859,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "The slowest run took 5.01 times longer than the fastest. This could mean that an intermediate result is being cached.\n",
-      "55 µs ± 44.1 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n"
+      "47.3 µs ± 12.1 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n"
      ]
     }
    ],
@@ -882,7 +881,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "1.63 ms ± 68 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n"
+      "1.63 ms ± 21.2 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n"
      ]
     }
    ],
@@ -904,7 +903,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "192 ms ± 0 ns per loop (mean ± std. dev. of 1 run, 1 loop each)\n"
+      "211 ms ± 0 ns per loop (mean ± std. dev. of 1 run, 1 loop each)\n"
      ]
     }
    ],
@@ -926,7 +925,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "2.21 s ± 0 ns per loop (mean ± std. dev. of 1 run, 1 loop each)\n"
+      "2.22 s ± 0 ns per loop (mean ± std. dev. of 1 run, 1 loop each)\n"
      ]
     }
    ],
@@ -948,7 +947,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "5.62 s ± 0 ns per loop (mean ± std. dev. of 1 run, 1 loop each)\n"
+      "5.81 s ± 0 ns per loop (mean ± std. dev. of 1 run, 1 loop each)\n"
      ]
     }
    ],
@@ -4072,17 +4071,19 @@
    "source": [
     "The infinite recursions could easily be avoided by replacing `n == 0` with `n <= 0` in both functions and thereby **generalizing** them. But even then, calling either `countdown()` or `factorial()` with a non-integer number is *semantically* wrong and therefore we better leave the base cases unchanged.\n",
     "\n",
-    "Errors as above are a symptom of missing **type checking**: By design, Python allows us to pass in not only integers but objects of any type as arguments to the `countdown()` and `factorial()` functions. As long as the arguments \"behave\" like integers, we will not encounter any *runtime* errors. This is the case here as the two example functions only use the `-` and `*` operators internally and in this context a `float` object behaves exactly like an `int` object. So, the functions keep calling themselves until Python decides with a built-in heuristic that the recursion is likely not going to end and aborts the computations with a `RecursionError`. Stricly speaking, this is of course a *runtime* error as well. The missing type checking is 100% intentional and considered a feature of rather than a bug in Python.\n",
+    "Errors as above are a symptom of missing **type checking**: By design, Python allows us to pass in not only integers but objects of any type as arguments to the `countdown()` and `factorial()` functions. As long as the arguments \"behave\" like integers, we will not encounter any *runtime* errors. This is the case here as the two example functions only use the `-` and `*` operators internally and, in the context of arithmetic, a `float` object behaves exactly like an `int` object. So, the functions keep calling themselves until Python decides with a built-in heuristic that the recursion is likely not going to end and aborts the computations with a `RecursionError`. Stricly speaking, a `RecursionError` is of course a *runtime* error as well.\n",
     "\n",
-    "Pythonistas often use the term **[duck typing](https://en.wikipedia.org/wiki/Duck_typing)** when refering to the same idea and the colloquial saying goes \"If it walks like a duck and it quacks like a duck, it must be a duck\". For example, we could call `factorial()` with the `float` object `3.0` and the recursion works out fine. So, as long as the `3.0` \"walks\" like a `3` and \"quacks\" like a `3`, it \"must be\" a `3`.\n",
+    "The missing type checking is *100% intentional* and considered a **[feature of rather than a bug](https://www.urbandictionary.com/define.php?term=It%27s%20not%20a%20bug%2C%20it%27s%20a%20feature)** in Python!\n",
+    "\n",
+    "Pythonistas often use the colloquial term **[duck typing](https://en.wikipedia.org/wiki/Duck_typing)** when refering to the same idea and the saying goes \"If it walks like a duck and it quacks like a duck, it must be a duck\". For example, we could call `factorial()` with the `float` object `3.0` and the recursion works out fine. So, as long as the `3.0` \"walks\" like a `3` and \"quacks\" like a `3`, it \"must be\" a `3`.\n",
     "\n",
     "We see a similar behavior when we mix objects of types `int` and `float` with arithmetic operators. For example, `1 + 2.0` works because Python implicitly views the `1` as a `1.0` at runtime and then knows how to do floating-point arithmetic: Here, the `int` \"walks\" and \"quacks\" like a `float`. Strictly speaking, this is yet another example of operator overloading whereas duck typing refers to the same behavior when passing arguments to function calls.\n",
     "\n",
-    "The important lesson is that we must expect our functions to be called with objects of *any* type at runtime, as opposed to the one type we had in mind when we defined the function.\n",
+    "The important lesson is that we must expect our functions be called with objects of *any* type at runtime, as opposed to the one type we had in mind when we defined the function.\n",
     "\n",
-    "Duck typing is possible because Python is a dynamically typed language. On the contrary, in statically typed languages like C we have to declare (i.e., \"specify\") the data type of every parameter in a function definition. Then, a `RecursionError` as for `countdown(3.1)` or `factorial(3.1)` above could not occur. For example, if we declared the `countdown()` and `factorial()` functions to only accept `int` objects, calling the functions with a `float` argument would immediately fail *syntactically*. As a downside, we would then lose the ability to call `factorial()` with `3.0`, which is *semantically* correct nevertheless.\n",
+    "Duck typing is possible because Python is a dynamically typed language. On the contrary, in statically typed languages like C we *must* declare (i.e., \"specify\") the data type of every parameter in a function definition. Then, a `RecursionError` as for `countdown(3.1)` or `factorial(3.1)` above could not occur. For example, if we declared the `countdown()` and `factorial()` functions to only accept `int` objects, calling the functions with a `float` argument would immediately fail *syntactically*. As a downside, we would then lose the ability to call `factorial()` with `3.0`, which is *semantically* correct nevertheless.\n",
     "\n",
-    "So, there is no black or white answer as to which of the two language designs is better. Yet, most professional programmers have very strong opinions with respect to duck typing reaching from \"love\" to \"hate\". This is another example as to how programming is a subjective art and not an \"objective\" science. Probably, Python is regarded more beginner friendly as `3` and `3.0` should intuitively be interchangeable."
+    "So, there is no black or white answer as to which of the two language designs is better. Yet, most professional programmers have very strong opinions with respect to duck typing reaching from \"love\" to \"hate\". This is another example as to how programming is a subjective art rather than an \"objective\" science. Python's design is probably more appealing to beginners who intuitively regard `3` and `3.0` as interchangeable."
    ]
   },
   {
@@ -4104,15 +4105,15 @@
     }
    },
    "source": [
-    "We can use the built-in [isinstance()](https://docs.python.org/3/library/functions.html#isinstance) function to make sure `factorial()` is called with an `int` object passed in. We further **validate the input** by verifying that the integer is non-negative.\n",
+    "We use the built-in [isinstance()](https://docs.python.org/3/library/functions.html#isinstance) function to make sure `factorial()` is called with an `int` object as the argument. We further **validate the input** by verifying that the integer is non-negative.\n",
     "\n",
-    "Meanwhile, we also see how we can manually raise exceptions with the `raise` [statement](https://docs.python.org/3/reference/simple_stmts.html#the-raise-statement), another way of controlling the flow of execution.\n",
+    "Meanwhile, we also see how we manually raise exceptions with the `raise` [statement](https://docs.python.org/3/reference/simple_stmts.html#the-raise-statement), another way of controlling the flow of execution.\n",
     "\n",
-    "The first two branches in the revised `factorial()` function act as **guardians** ensuring that the code does not produce *unexpected* runtime errors: Errors can certainly be expected when mentioned in the docstring.\n",
+    "The first two branches in the revised `factorial()` function act as **guardians** ensuring that the code does not produce *unexpected* runtime errors: Errors may certainly be expected when mentioned in the docstring.\n",
     "\n",
-    "Forcing `n` to be an `int` is a very puritan way of handling the issues discussed above. A more relaxed approach could be to also accept a `float` and use its [is_integer()](https://docs.python.org/3/library/stdtypes.html#float.is_integer) method to check if `n` could be casted as an `int`. After all, by being too puritan we can not take advantage of duck typing.\n",
+    "Forcing `n` to be an `int` is a very puritan way of handling the issues discussed above. A more relaxed approach could be to also accept a `float` and use its [is_integer()](https://docs.python.org/3/library/stdtypes.html#float.is_integer) method to check if `n` could be casted as an `int`. After all, being too puritan we cannot take advantage of duck typing.\n",
     "\n",
-    "So in essence, we are doing *two* things here. Besides checking for the correct type, we are also enforcing **domain-specific** (i.e., mathematical) rules with respect to the non-negativity of `n`."
+    "So in essence, we are doing *two* things here: Besides checking the type, we also enforce **domain-specific** (i.e., mathematical here) rules with respect to the non-negativity of `n`."
    ]
   },
   {
@@ -4368,7 +4369,7 @@
     "        n (int): seconds until the party begins; must be positive\n",
     "\n",
     "    Raises:\n",
-    "        TypeError: if n is not of an integer\n",
+    "        TypeError: if n is not an integer\n",
     "        ValueError: if n is not positive\n",
     "    \"\"\"\n",
     "    if not isinstance(n, int):\n",
@@ -4464,7 +4465,7 @@
     "        gcd (int)\n",
     "\n",
     "    Raises:\n",
-    "        TypeError: if a or b are not of an integer type\n",
+    "        TypeError: if a or b are not integers\n",
     "        ValueError: if a or b are not positive\n",
     "    \"\"\"\n",
     "    if not isinstance(a, int) or not isinstance(b, int):\n",
@@ -4548,7 +4549,7 @@
     }
    },
    "source": [
-    "We can also see that this implementation is a lot *less* efficient than its recursive counterpart which solves `gcd()` for the same two numbers $112233445566778899$ and $987654321$ within microseconds."
+    "We also see that this implementation is a lot *less* efficient than its recursive counterpart which solves `gcd()` for the same two numbers $112233445566778899$ and $987654321$ within microseconds."
    ]
   },
   {
@@ -4657,7 +4658,7 @@
     "        n (int): a positive number to start the Collatz sequence at\n",
     "\n",
     "    Raises:\n",
-    "        TypeError: if n is not of an integer\n",
+    "        TypeError: if n is not an integer\n",
     "        ValueError: if n is not positive\n",
     "    \"\"\"\n",
     "    if not isinstance(n, int):\n",
@@ -4668,7 +4669,7 @@
     "    while n != 1:\n",
     "        print(n, end=\" \")\n",
     "        if n % 2 == 0:\n",
-    "            n //= 2  # //= so that n remains an int\n",
+    "            n //= 2  # //= instead of /= so that n remains an int\n",
     "        else:\n",
     "            n = 3 * n + 1\n",
     "\n",
@@ -4791,9 +4792,9 @@
    "source": [
     "Recursion and the `while` statement are two sides of the same coin. Disregarding that in the case of recursion Python internally faces some additional burden for managing the stack of frames in memory, both approaches lead to the *same* computational steps in memory. More importantly, we can re-formulate a recursive implementation in an iterative way and vice verca despite one of the two ways often \"feeling\" a lot more natural given a particular problem.\n",
     "\n",
-    "So how does the `for` [statement](https://docs.python.org/3/reference/compound_stmts.html#the-for-statement) we saw in the very first example in this book fit into this picture? It is really a *redundant* language construct to provide a *shorter* and more *convenient* syntax for common applications of the `while` statement. In programming, such additions to a language are called **syntactic sugar**. Sugar makes a cup of tea taste better but we can drink tea without sugar too.\n",
+    "So how does the `for` [statement](https://docs.python.org/3/reference/compound_stmts.html#the-for-statement) we saw in the very first example in this book fit into this picture? It is really a *redundant* language construct to provide a *shorter* and more *convenient* syntax for common applications of the `while` statement. In programming, such additions to a language are called **syntactic sugar**. Sugar makes a cup of tea taste better but we may drink tea without sugar too.\n",
     "\n",
-    "Consider the following `numbers` list. Without the `for` statement, we would have to keep track of a temporary **index variable** `i` to loop over all its elements and also obtain the individual elements with the `[]` operator in each iteration of the loop."
+    "Consider the following `numbers` list. Without the `for` statement, we have to manage a temporary **index variable** `i` to loop over all the elements and also obtain the individual elements with the `[]` operator in each iteration of the loop."
    ]
   },
   {
@@ -4806,7 +4807,7 @@
    },
    "outputs": [],
    "source": [
-    "numbers = [5, 6, 7, 8, 9]"
+    "numbers = [0, 1, 2, 3, 4]"
    ]
   },
   {
@@ -4822,7 +4823,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "5 6 7 8 9 "
+      "0 1 2 3 4 "
      ]
     }
    ],
@@ -4831,7 +4832,8 @@
     "while i < len(numbers):\n",
     "    number = numbers[i]\n",
     "    print(number, end=\" \")\n",
-    "    i += 1"
+    "    i += 1\n",
+    "del i"
    ]
   },
   {
@@ -4842,7 +4844,7 @@
     }
    },
    "source": [
-    "The `for` statement, on the contrary, makes the actual business logic more apparent by stripping all the boilerplate code away."
+    "The `for` statement, on the contrary, makes the actual business logic more apparent by stripping all the **boilerplate code** away."
    ]
   },
   {
@@ -4858,7 +4860,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "5 6 7 8 9 "
+      "0 1 2 3 4 "
      ]
     }
    ],
@@ -4875,34 +4877,12 @@
     }
    },
    "source": [
-    "For sequences of integers the [range()](https://docs.python.org/3/library/functions.html#func-range) built-in makes the `for` statement even more convenient: It creates a list-like object of type `range` that generates integers \"on the fly\" and we will look closely at the underlying data types in Chapter 7."
+    "For sequences of integers the [range()](https://docs.python.org/3/library/functions.html#func-range) built-in makes the `for` statement even more convenient: It creates a list-like object of type `range` that generates integers \"on the fly\" and we will look closely at the underlying effects in memory in Chapter 7."
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 51,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "slide"
-    }
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "0 1 2 3 4 "
-     ]
-    }
-   ],
-   "source": [
-    "for number in [0, 1, 2, 3, 4]:\n",
-    "    print(number, end=\" \")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 52,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -4922,20 +4902,9 @@
     "    print(number, end=\" \")"
    ]
   },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "source": [
-    "Let's quickly verify that `range(5)` creates an object of type `range`."
-   ]
-  },
   {
    "cell_type": "code",
-   "execution_count": 53,
+   "execution_count": 52,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -4948,7 +4917,7 @@
        "range"
       ]
      },
-     "execution_count": 53,
+     "execution_count": 52,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -4965,12 +4934,12 @@
     }
    },
    "source": [
-    "[range()](https://docs.python.org/3/library/functions.html#func-range) takes optional `start` and `step` arguments that we can use to customize the sequence of integers even more."
+    "[range()](https://docs.python.org/3/library/functions.html#func-range) takes optional `start` and `step` arguments that we use to customize the sequence of integers even more."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 54,
+   "execution_count": 53,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -4992,7 +4961,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 55,
+   "execution_count": 54,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -5031,26 +5000,26 @@
     }
    },
    "source": [
-    "The important difference between the `list` objects (i.e., `[0, 1, 2, 3, 4]` and `[1, 3, 5, 7, 9]`) and the `range` objects (i.e., `range(5)` and `range(1, 10, 2)`) is that in the former case *six* objects are created in memory, one `list` holding pointers to *five* `int` objects, whereas in the latter case only *one* `range` object exists in memory that **generates** `int` objects as we ask for it.\n",
+    "The important difference between the above `list` objects, `[0, 1, 2, 3, 4]` and `[1, 3, 5, 7, 9]`, and the `range` objects, `range(5)` and `range(1, 10, 2)`, is that in the former case *six* objects are created in memory *before* the `for` statement starts running, *one* `list` holding pointers to *five* `int` objects, whereas in the latter case only *one* `range` object is created that **generates** `int` objects one at a time *while* the `for`-loop runs.\n",
     "\n",
-    "However, we can iterate over both of them. So a natural question to ask is why Python treats objects of *different*  types in the *same* way when used with a `for` statement.\n",
+    "However, we can loop over both of them. So a natural question to ask is why Python treats objects of *different*  types in the *same* way when used with a `for` statement.\n",
     "\n",
-    "So far, the overarching storyline in this book goes like this: In Python, *everything* is an object. Besides its *identity* and *value*, every object is characterized by belonging to *one* data type that determines how the object behaves and what we can do with it.\n",
+    "So far, the overarching storyline in this book goes like this: In Python, *everything* is an object. Besides its *identity* and *value*, every object is characterized by belonging to *one data type* that determines how the object behaves and what we may do with it.\n",
     "\n",
-    "Now, just as we classify objects by their types, we also classify these **concrete data types** (e.g., `int`, `float`, or `str`) into **abstract concepts**.\n",
+    "Now, just as we classify objects by their types, we also classify these **concrete data types** (e.g., `int`, `float`, `str`, or `list`) into **abstract concepts**.\n",
     "\n",
-    "We have actually done this in [Chapter 1](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/01_elements.ipynb) already when we described a `list` object as \"some sort of container that holds [...] pointers to other objects\". So, abstractly speaking, **containers** are any objects that are \"composed\" of other objects and also \"manage\" how these objects are organized. `list` objects, for example, have the property that they model an inherent order associated with its elements. There exist, however, many other container types, many of which do *not* come with an order. So, containers primarily \"contain\" other objects and have *nothing* to do with looping.\n",
+    "We actually did this already in [Chapter 1](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/01_elements.ipynb) when we described a `list` object as \"some sort of container that holds [...] pointers to other objects\". So, abstractly speaking, **containers** are any objects that are \"composed\" of other objects and also \"manage\" how these objects are organized. `list` objects, for example, have the property that they model an internal order associated with its elements. There exist, however, other container types, many of which do *not* come with an order. So, containers primarily \"contain\" other objects and have *nothing* to do with looping.\n",
     "\n",
-    "On the contrary, the abstract concept of **iterables** is all about looping: Any object that we can loop over is by definition an iterable. So, `range` objects, for example, are iterables taht do *not* contain any other objects. Moreover, looping does *not* have to occur in any particular order although this is the case for both `list` and `range` objects.\n",
+    "On the contrary, the abstract concept of **iterables** is all about looping: Any object that we can loop over is by definition an iterable. So, `range` objects, for example, are iterables that do *not* contain other objects. Moreover, looping does *not* have to occur in a *predictable* order although this is the case for both `list` and `range` objects.\n",
     "\n",
     "Typically, containers are iterable and iterables are containers. Yet, only because these two concepts coincide often, we must not think of them as the same. Chapter 10 will finally give an explanation as to how abstract concepts are implemented and play together.\n",
     "\n",
-    "`list` objects like `first_names` below are iterable containers. They actually implement even more abstract concepts as we will see in Chapter 7."
+    "So, `list` objects like `first_names` below are iterable containers. They actually implement even more abstract concepts as we will see in Chapter 7."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 56,
+   "execution_count": 55,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -5069,12 +5038,12 @@
     }
    },
    "source": [
-    "The characteristic operator associated with a container type is the `in` operator which checks if a given object evaluates equal to any of the objects in the container. Colloquially, it checks if an object is \"contained\" in the container. This operation is also called **membership testing**."
+    "The characteristic operator associated with a container type is the `in` operator: It checks if a given object evaluates equal to at least one of the objects in the container. Colloquially, it checks if an object is \"contained\" in the container. Formally, this operation is called **membership testing**."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 57,
+   "execution_count": 56,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -5087,7 +5056,7 @@
        "True"
       ]
      },
-     "execution_count": 57,
+     "execution_count": 56,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -5098,7 +5067,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 58,
+   "execution_count": 57,
    "metadata": {
     "slideshow": {
      "slide_type": "-"
@@ -5111,7 +5080,7 @@
        "False"
       ]
      },
-     "execution_count": 58,
+     "execution_count": 57,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -5128,12 +5097,12 @@
     }
    },
    "source": [
-    "This shows the exact workings of the `in` operator: Although `7.0` is *not* in `numbers`, `7.0` evaluates equal to the `7` that is in it, which is why the following expression evaluates to `True`. So, while we could colloquially say that `numbers` \"contains\" `7.0`, it actually does not."
+    "The cell below shows the *exact* workings of the `in` operator: Although `3.0` is *not* contained in `numbers`, it evaluates equal to the `3` that is, which is why the following expression evaluates to `True`. So, while we could colloquially say that `numbers` \"contains\" `3.0`, it actually does not."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 59,
+   "execution_count": 58,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -5146,13 +5115,13 @@
        "True"
       ]
      },
-     "execution_count": 59,
+     "execution_count": 58,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "7.0 in numbers"
+    "3.0 in numbers"
    ]
   },
   {
@@ -5163,12 +5132,12 @@
     }
    },
    "source": [
-    "Similarly, the characteristic operation of an iterable type is that it supports iteration, for example, with a `for`-loop."
+    "Similarly, the characteristic operation of an iterable type is that it supports being looped over, for example, with the `for` statement."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 60,
+   "execution_count": 59,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -5196,12 +5165,12 @@
     }
    },
    "source": [
-    "If we need to have an index variable in the loop's body, we use the [enumerate()](https://docs.python.org/3/library/functions.html#enumerate) built-in that takes an iterable as its argument and then generates a stream of \"pairs\" consisting of an index variable and an object provided by the iterable. There is *no* need to ever revert back to the `while` statement to loop over an iterable object."
+    "If we must have an index variable in the loop's body, we use the [enumerate()](https://docs.python.org/3/library/functions.html#enumerate) built-in that takes an *iterable* as its argument and then generates a \"stream\" of \"pairs\" consisting of an index variable and an object provided by the iterable. There is *no* need to ever revert back to the `while` statement to loop over an iterable object."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 61,
+   "execution_count": 60,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -5234,7 +5203,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 62,
+   "execution_count": 61,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -5267,7 +5236,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 63,
+   "execution_count": 62,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -5280,7 +5249,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 64,
+   "execution_count": 63,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -5291,17 +5260,13 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Achim MĂĽller\n",
-      "Berthold Meyer\n",
-      "Carl Mayer\n",
-      "Diedrich Schmitt\n",
-      "Eckardt Schmidt\n"
+      "Achim MĂĽller   Berthold Meyer   Carl Mayer   Diedrich Schmitt   Eckardt Schmidt   "
      ]
     }
    ],
    "source": [
     "for first_name, last_name in zip(first_names, last_names):\n",
-    "    print(first_name, last_name)"
+    "    print(first_name, last_name, end=\"   \")"
    ]
   },
   {
@@ -5327,12 +5292,12 @@
     "\n",
     "However, one advantage of calculating Fibonacci numbers in a **forwards** fashion with a `for` statement is that we could list the entire sequence in ascending order as we calculate the desired number. To show this, we added `print()` statements in `fibonacci()` below.\n",
     "\n",
-    "We do *not* need to store the index variable in the `for`-loop's header line: That is what the underscore \"\\_\" indicates; we \"throw it away\". Also, we do not need to explicitly check if `i` is of type `int` as the [range()](https://docs.python.org/3/library/functions.html#func-range) built-in raises a `TypeError` if used with anything other than an `int`."
+    "We do *not* need to store the index variable in the `for`-loop's header line: That is what the underscore variable `_` indicates; we \"throw it away\"."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 65,
+   "execution_count": 64,
    "metadata": {
     "code_folding": [],
     "slideshow": {
@@ -5351,11 +5316,12 @@
     "        ith_fibonacci (int)\n",
     "\n",
     "    Raises:\n",
-    "        TypeError: if i is not of an integer type\n",
+    "        TypeError: if i is not an integer\n",
     "        ValueError: if i is not positive\n",
     "    \"\"\"\n",
-    "    # no need to check if i is an integer as range() does that\n",
-    "    if i < 0:\n",
+    "    if not isinstance(i, int):\n",
+    "        raise TypeError(\"i must be an integer\")\n",
+    "    elif i < 0:\n",
     "        raise ValueError(\"i must be non-negative\")\n",
     "\n",
     "    a = 0\n",
@@ -5372,7 +5338,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 66,
+   "execution_count": 65,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -5392,7 +5358,7 @@
        "144"
       ]
      },
-     "execution_count": 66,
+     "execution_count": 65,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -5420,12 +5386,12 @@
     }
    },
    "source": [
-    "Another more important advantage is that now we can calculate even big Fibonacci numbers *efficiently*."
+    "Another more important advantage is that now we may calculate even big Fibonacci numbers *efficiently*."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 67,
+   "execution_count": 66,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -5445,7 +5411,7 @@
        "218922995834555169026"
       ]
      },
-     "execution_count": 67,
+     "execution_count": 66,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -5473,12 +5439,12 @@
     }
    },
    "source": [
-    "The iterative `factorial()` function is comparable to its recursive counterpart when it comes to readability. One advantage of calculating the factorial in a forwards fashion is that we could track the intermediate `product` as it grows."
+    "The iterative `factorial()` implementation is comparable to its recursive counterpart when it comes to readability. One advantage of calculating the factorial in a forwards fashion is that we could track the intermediate `product` as it grows."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 68,
+   "execution_count": 67,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -5514,7 +5480,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 69,
+   "execution_count": 68,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -5534,7 +5500,7 @@
        "3628800"
       ]
      },
-     "execution_count": 69,
+     "execution_count": 68,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -5584,12 +5550,12 @@
     }
    },
    "source": [
-    "Let's say we have a list of numbers and we want to check if the square of at least one of its numbers is above a `threshold` of `100`."
+    "Let's say we have a `numbers` list and want to check if the square of at least one of its elements is above a `threshold` of `100`."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 70,
+   "execution_count": 69,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -5602,7 +5568,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 71,
+   "execution_count": 70,
    "metadata": {
     "slideshow": {
      "slide_type": "-"
@@ -5621,12 +5587,12 @@
     }
    },
    "source": [
-    "A first naive implementation could look like this: We loop over *every* element in `numbers` and set an **indicator variable** `is_above` to `True` once we encounter an element satisfying our search condition."
+    "A first naive implementation could look like this: We loop over *every* element in `numbers` and set an **indicator variable** `is_above`, initialized as `False`, to `True` once we encounter an element satisfying the search condition."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 72,
+   "execution_count": 71,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -5663,16 +5629,16 @@
     }
    },
    "source": [
-    "This implementation is *inefficient* as even if the *first* number in `numbers` has a square greater than `100`, we loop until the last element, which could take a long time for a big list.\n",
+    "This implementation is *inefficient* as even if the *first* element in `numbers` has a square greater than `100`, we loop until the last element. This could take a long time for a big list.\n",
     "\n",
-    "Moreover, we must initialize `is_above` before the `for`-loop and write an `if`-`else`-logic seperate from it to check for the final result. The actual business logic is not clear right away.\n",
+    "Moreover, we must initialize `is_above` *before* the `for`-loop and write an `if`-`else`-logic *after* it to check for the result. The actual business logic is *not* clear right away.\n",
     "\n",
     "Luckily, Python provides a `break` [statement](https://docs.python.org/3/reference/simple_stmts.html#the-break-statement) that let's us stop the `for`-loop in any iteration of the loop. Conceptually, it is yet another means of controlling the flow of execution."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 73,
+   "execution_count": 72,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -5710,7 +5676,7 @@
     }
    },
    "source": [
-    "This is a computational improvement. However, the code can still be split into *three* groups: initialization, the `for`-loop, and some finalizing logic. We would prefer to convey the program's idea in *one* compound statement."
+    "This is a computational improvement. However, the code still consists *three* logical sections: some initialization *before* the `for`-loop, the loop itself, and some finalizing logic. We would prefer to convey the program's idea in *one* compound statement instead."
    ]
   },
   {
@@ -5732,9 +5698,9 @@
     }
    },
    "source": [
-    "To express the logic in a prettier way, we add an `else`-clause at the end of the `for`-loop. The `else`-branch is only executed if the body in the `for`-branch is *not* stopped with a `break` statement before reaching the last iteration in the loop. The word \"else\" implies a rather unintuitive meaning and it had better been named a `then`-clause. In most scenarios, however, the `else`-clause logically goes together with some `if` statement within the `for`-loop's body.\n",
+    "To express the logic in a prettier way, we add an `else`-clause at the end of the `for`-loop. The `else`-branch is executed *only if* the `for`-loop is *not* stopped with a `break` statement **prematurely** (i.e., *before* reaching the *last* iteration in the loop). The word \"else\" implies a rather unintuitive meaning and had better been named a `then`-clause. In most scenarios, however, the `else`-clause logically goes together with some `if` statement in the `for`-loop's body.\n",
     "\n",
-    "Overall, the expressive power of our code increases. Not many programming languages support a `for`-`else`-branching, which turns out to be very useful in practice."
+    "Overall, the code's expressive power increases. Not many programming languages support a `for`-`else`-branching, which turns out to be very useful in practice."
    ]
   },
   {
@@ -5750,7 +5716,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 74,
+   "execution_count": 73,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -5788,12 +5754,12 @@
     }
    },
    "source": [
-    "Lastly, we incorporate the `if`-`else` logic at the end into the `for`-loop and avoid the `is_above` variable alltogether."
+    "Lastly, we incorporate the finalizing `if`-`else` logic into the `for`-loop avoiding the `is_above` variable alltogether."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 75,
+   "execution_count": 74,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -5826,12 +5792,12 @@
     }
    },
    "source": [
-    "Of course, if we set the `threshold` a number's square has to pass higher, for example to `200`, we have to loop through the entire `numbers` list. There is no way to optimize this **[linear search](https://en.wikipedia.org/wiki/Linear_search)**, at least as long as we model the list of numbers with a `list` object. More advanced data types, however, exist that mitigate that downside."
+    "Of course, if we set the `threshold` an element's square has to pass higher, for example to `200`, we have to loop through the entire `numbers` list. There is *no way* to further optimize this **[linear search](https://en.wikipedia.org/wiki/Linear_search)**, at least as long as we model `numbers` as a `list` object. More advanced data types, however, exist that mitigate that downside."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 76,
+   "execution_count": 75,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -5879,15 +5845,15 @@
    "source": [
     "Often times, we process some iterable with numeric data, for example, a list of numbers as in the introductory example in [Chapter 1](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/01_elements.ipynb) or, more realistically, data from a CSV file with many rows and columns.\n",
     "\n",
-    "Processing numeric data usually comes down to operations that can be grouped into one of the following three categories:\n",
+    "Processing numeric data usually comes down to operations that may be grouped into one of the following three categories:\n",
     "\n",
-    "- **mapping**: transform an observation according to some functional relationship $y = f(x)$\n",
-    "- **filtering**: throw away individual observations (e.g., statistical outliers)\n",
-    "- **reducing**: collect individual observations into summary statistics\n",
+    "- **mapping**: transform a sample according to some functional relationship $y = f(x)$\n",
+    "- **filtering**: throw away individual samples (e.g., statistical outliers)\n",
+    "- **reducing**: collect individual samples into summary statistics\n",
     "\n",
     "We will study this **map-filter-reduce** paradigm extensively in Chapter 7 after introducing more advanced data types that are needed to work with \"big\" data.\n",
     "\n",
-    "In this section, we focus on *filtering out* some samples within a `for`-loop."
+    "In the remainder of this section, we focus on *filtering out* some samples within a `for`-loop."
    ]
   },
   {
@@ -5919,7 +5885,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 77,
+   "execution_count": 76,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -5932,7 +5898,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 78,
+   "execution_count": 77,
    "metadata": {
     "slideshow": {
      "slide_type": "-"
@@ -5952,7 +5918,7 @@
        "370"
       ]
      },
-     "execution_count": 78,
+     "execution_count": 77,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -5977,9 +5943,9 @@
     }
    },
    "source": [
-    "The above code is still rather easy to read as it only involves two levels of indentation.\n",
+    "The above code is easy to read as it involves only two levels of indentation.\n",
     "\n",
-    "In general, code gets harder to comprehend the more **horizontal space** it occupies. It is commonly considered good practice to grow a program **vertically** rather than horizontally. Code complient with [PEP 8](https://www.python.org/dev/peps/pep-0008/#maximum-line-length) requires us to use *at most* 79 characters in a line!\n",
+    "In general, code gets harder to comprehend the more **horizontal space** it occupies. It is commonly considered good practice to grow a program **vertically** rather than horizontally. Code compliant with [PEP 8](https://www.python.org/dev/peps/pep-0008/#maximum-line-length) requires us to use *at most* 79 characters in a line!\n",
     "\n",
     "Consider the next example, whose implementation in code already starts to look \"unbalanced\"."
    ]
@@ -6014,7 +5980,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 79,
+   "execution_count": 78,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -6027,7 +5993,7 @@
        "[1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12]"
       ]
      },
-     "execution_count": 79,
+     "execution_count": 78,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -6038,7 +6004,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 80,
+   "execution_count": 79,
    "metadata": {
     "slideshow": {
      "slide_type": "-"
@@ -6058,7 +6024,7 @@
        "182"
       ]
      },
-     "execution_count": 80,
+     "execution_count": 79,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -6084,11 +6050,11 @@
     }
    },
    "source": [
-    "With already three levels of indentation, less horizontal space is available for the actual code. Of course, one could combine the two `if` statements with the logical `and` operator as shown in [Chapter 3](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/03_conditionals.ipynb). However, then we trade off horizontal space against a more \"complex\" `if` logic and this is not a real improvement.\n",
+    "With already three levels of indentation, less horizontal space is available for the actual code block. Of course, one could flatten the two `if` statements with the logical `and` operator as shown in [Chapter 3](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/03_conditionals.ipynb#The-if-Statement). Then, however, we trade off horizontal space against a more \"complex\" `if` logic and this is *not* a real improvement.\n",
     "\n",
-    "A Pythonista would instead make use of the `continue` [statement](https://docs.python.org/3/reference/simple_stmts.html#the-continue-statement) that causes a loop to jump right into the next iteration skipping the rest of the code block.\n",
+    "A Pythonista would instead make use of the `continue` [statement](https://docs.python.org/3/reference/simple_stmts.html#the-continue-statement) that causes a loop to jump into the next iteration skipping the rest of the code block.\n",
     "\n",
-    "The revised code fragement below occupies more vertical space and less horizontal space. A good trade-off."
+    "The revised code fragement below occupies more vertical space and less horizontal space: A *good* trade-off."
    ]
   },
   {
@@ -6104,7 +6070,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 81,
+   "execution_count": 80,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -6124,7 +6090,7 @@
        "182"
       ]
      },
-     "execution_count": 81,
+     "execution_count": 80,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -6153,13 +6119,11 @@
     }
    },
    "source": [
-    "This is yet another illustration of why programming is an art. The two preceding code fragments do *exactly* the *same* with *identical* time complexity. However, arguably the latter it a lot easier to read for a human, at least when the business logic grows beyond more than just two nested filters.\n",
+    "This is yet another illustration of why programming is an art. The two preceding code cells do *exactly* the *same* with *identical* time complexity. However, the latter is arguably easier to read for a human, even more so when the business logic grows beyond two nested filters.\n",
     "\n",
-    "The idea behind the `continue` statement is conceptually similar to the early exit pattern we saw in the context of function definitions in [Chapter 2](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/02_functions.ipynb).\n",
+    "The idea behind the `continue` statement is conceptually similar to the early exit pattern we saw above.\n",
     "\n",
-    "The two examples can be modeled in an even better way as we will see in Chapter 7.\n",
-    "\n",
-    "Both the `break` and `continue` statements as well as the optional `else`-clause are not only supported within `for`-loops but also `while`-loops."
+    "Both the `break` and `continue` statements as well as the optional `else`-clause are not only supported with `for`-loops but also `while`-loops."
    ]
   },
   {
@@ -6181,7 +6145,7 @@
     }
    },
    "source": [
-    "Sometimes we find ourselves in situations where we can *not* know ahead of time how often or until which point in time a code block is to be executed."
+    "Sometimes we find ourselves in situations where we *cannot* know ahead of time how often or until which point in time a code block is to be executed."
    ]
   },
   {
@@ -6205,7 +6169,7 @@
    "source": [
     "Let's consider a game where we randomly choose a variable to be either \"Heads\" or \"Tails\" and the user of our program has to guess it.\n",
     "\n",
-    "Python provides the built-in [input()](https://docs.python.org/3/library/functions.html#input) function that prints a message to the user, called the **prompt**, and reads in what the user typed in response as a `str` object. We use it to process the user's \"unpredictable\" input to our program (i.e., a user might type in some invalid response). Further, we use the [random()](https://docs.python.org/3/library/random.html#random.random) function in the [random](https://docs.python.org/3/library/random.html) module to model the coin toss.\n",
+    "Python provides the built-in [input()](https://docs.python.org/3/library/functions.html#input) function that prints a message to the user, called the **prompt**, and reads in what was typed in response as a `str` object. We use it to process a user's \"unreliable\" input to our program (i.e., a user might type in some invalid response). Further, we use the [random()](https://docs.python.org/3/library/random.html#random.random) function in the [random](https://docs.python.org/3/library/random.html) module to model the coin toss.\n",
     "\n",
     "A popular pattern to approach such **indefinite loops** is to go with a `while True` statement which on its own would cause Python to enter into an infinite loop. Then, once a certain event occurs, we `break` out of the loop.\n",
     "\n",
@@ -6214,7 +6178,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 82,
+   "execution_count": 81,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -6227,7 +6191,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 83,
+   "execution_count": 82,
    "metadata": {
     "slideshow": {
      "slide_type": "-"
@@ -6240,7 +6204,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 84,
+   "execution_count": 83,
    "metadata": {
     "code_folding": [],
     "slideshow": {
@@ -6285,10 +6249,10 @@
     }
    },
    "source": [
-    "This version has two *severe* aspects where we should improve on:\n",
+    "This version exhibits two *severe* issues where we should improve on:\n",
     "\n",
-    "1. If a user enters something other than \"heads\" or \"tails\", for example, \"Heads\" or \"Tails\", the program keeps running without the user knowing about the mistake!\n",
-    "2. It intermingles the coin tossing with the comparison against the user's input: Mixing unrelated business logic in the same code fragment makes a program harder to read and maintain in the long run."
+    "1. If a user enters *anything* other than `\"heads\"` or `\"tails\"`, for example, `\"Heads\"` or `\"Tails\"`, the program keeps running *without* the user knowing about the mistake!\n",
+    "2. The code *intermingles* the coin tossing with the processing of the user's input: Mixing *unrelated* business logic in the *same* code block makes a program harder to read and, more importantly, maintain in the long run."
    ]
   },
   {
@@ -6310,16 +6274,16 @@
     }
    },
    "source": [
-    "Let's refactor the code and make it *modular* and *comprehendable*.\n",
+    "Let's refactor the code and make it *modular*.\n",
     "\n",
-    "First, we divide the logic into two functions `get_guess()` and `toss_coin()` that are controlled from within a `while`-loop.\n",
+    "First, we divide the business logic into two functions `get_guess()` and `toss_coin()` that are controlled from within a `while`-loop.\n",
     "\n",
-    "`get_guess()` not only reads in the user's input but also implements a simple input validation pattern in that the [strip()](https://docs.python.org/3/library/stdtypes.html?highlight=__contains__#str.strip) and [lower()](https://docs.python.org/3/library/stdtypes.html?highlight=__contains__#str.lower) methods remove preceeding and trailing whitespace and lower case the input ensuring that the user can spell the input in any possible way (e.g. all upper or lower case). Also, `get_guess()` checks if the user entered one of the two valid options. If so, it returns either `\"heads\"` or `\"tails\"`; if not, it returns `None`."
+    "`get_guess()` not only reads in the user's input but also implements a simple input validation pattern in that the [strip()](https://docs.python.org/3/library/stdtypes.html?highlight=__contains__#str.strip) and [lower()](https://docs.python.org/3/library/stdtypes.html?highlight=__contains__#str.lower) methods remove preceeding and trailing whitespace and lower case the input ensuring that the user may spell the input in any possible way (e.g. all upper or lower case). Also, `get_guess()` checks if the user entered one of the two valid options. If so, it returns either `\"heads\"` or `\"tails\"`; if not, it returns `None`."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 85,
+   "execution_count": 84,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -6351,12 +6315,12 @@
     }
    },
    "source": [
-    "`toss_coin()` is a simple function that models a fair coin toss when called with default arguments."
+    "`toss_coin()` models a fair coin toss when called with default arguments."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 86,
+   "execution_count": 85,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -6387,14 +6351,14 @@
     }
    },
    "source": [
-    "Second, we rewrite the `if`-`else`-logic to explictly handle the case where `get_guess()` returns `None`: Whenever the user enters something invalid, a warning is shown, and another try is granted. Observe how we use the `is` operator and not the `==` operator as `None` is a singleton object.\n",
+    "Second, we rewrite the `if`-`else`-logic to explictly handle the case where `get_guess()` returns `None`: Whenever the user enters something invalid, a warning is shown, and another try is granted. We use the `is` operator and not the `==` operator as `None` is a singleton object.\n",
     "\n",
-    "The `while` statement itself takes on the role of **glue code** that only manages how other parts of the program interact with each other."
+    "The `while`-loop takes on the role of **glue code** that manages how other parts of the program interact with each other."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 87,
+   "execution_count": 86,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -6407,7 +6371,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 88,
+   "execution_count": 87,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -6447,7 +6411,7 @@
     }
    },
    "source": [
-    "Now, our little program's business logic is a lot easier to comprehend. More importantly, we can now easily make changes to the program. For example, we could make the `toss_coin()` function base the tossing on a probability distribution other than the uniform (i.e., replace the [random.random()](https://docs.python.org/3/library/random.html#random.random) function with another one). In general, a modular architecture leads to improved software maintenance."
+    "Now, the program's business logic is expressed in a clearer way. More importantly, we can now change it more easily. For example, we could make the `toss_coin()` function base the tossing on a probability distribution other than the uniform (i.e., replace the [random.random()](https://docs.python.org/3/library/random.html#random.random) function with another one). In general, a modular architecture leads to improved software maintenance."
    ]
   },
   {
@@ -6473,11 +6437,11 @@
     "\n",
     "There are two redundant approaches of achieving that.\n",
     "\n",
-    "First, we can combine functions that call themselves with conditional statements. This concept is known as **recursion** and suffices to control the flow of execution in *every* way we desire. For a beginner, this approach of **backwards** reasoning might not be intuitive but it turns out to be a very useful tool to have in one's toolbox.\n",
+    "First, we combine functions that call themselves with conditional statements. This concept is known as **recursion** and suffices to control the flow of execution in *every* way we desire. For a beginner, this approach of **backwards** reasoning might not be intuitive but it turns out to be a very useful tool to have in one's toolbox.\n",
     "\n",
     "Second, the `while` and `for` statements are alternative and potentially more intuitive ways to express iteration as they correspond to a **forwards** reasoning. The `for` statement is **syntactic sugar** that allows to rewrite common occurences of the `while` statement in a concise way. Python provides the `break` and `continue` statements as well as an optional `else`-clause that make working with the `for` and `while` statements even more convenient.\n",
     "\n",
-    "**Iterables** are any **concrete data types** that support being looped over, for example, with the `for` statement. The idea behind iterables is an **abstract concept** that may or may not be implemented by any given concrete data type. For example, both `list` and `range` objects can be looped over. The `list` type is also a **container** as any given `list` objects \"contains\" pointers to other objects in memory. On the contrary, the `range` type does not point to any other objects but rather creates new `int` objects \"on the fly\"."
+    "**Iterables** are any **concrete data types** that support being looped over, for example, with the `for` statement. The idea behind iterables is an **abstract concept** that may or may not be implemented by any given concrete data type. For example, both `list` and `range` objects can be looped over. The `list` type is also a **container** as any given `list` objects \"contains\" pointers to other objects in memory. On the contrary, the `range` type does not point to any other objects but rather creates *new* `int` objects \"on the fly\" (i.e., when being looped over)."
    ]
   }
  ],
diff --git a/04_iteration_review_and_exercises.ipynb b/04_iteration_review_and_exercises.ipynb
index 0e937b7..8dd9025 100644
--- a/04_iteration_review_and_exercises.ipynb
+++ b/04_iteration_review_and_exercises.ipynb
@@ -420,7 +420,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "In words, this means that in order to move a tower consisting of $n$ disks from an `origin` $o$ to a `destination` $d$, we three steps must be executed:\n",
+    "In words, this means that in order to move a tower consisting of $n$ disks from an `origin` $o$ to a `destination` $d$, three steps must be executed:\n",
     "\n",
     "1. Move the top most $n - 1$ disks of the tower temporarily from $o$ to $i$ (= sub-problem 1)\n",
     "2. Move the remaining and largest disk from $o$ to $d$\n",
@@ -577,7 +577,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "**Q15.9**: Copy the base case from `sol()`."
+    "**Q15.9**: Copy the base case from `sol()`!"
    ]
   },
   {

From 4d6241cc0cdba4552c3f6938828cebeced8be365 Mon Sep 17 00:00:00 2001
From: Alexander Hess <alexander@webartifex.biz>
Date: Mon, 7 Oct 2019 22:31:45 +0200
Subject: [PATCH 029/242] Add initial version of notebook 05

---
 05_numbers.ipynb           | 6291 ++++++++++++++++++++++++++++++++++++
 static/complex_numbers.png |  Bin 0 -> 22878 bytes
 static/floating_point.png  |  Bin 0 -> 15371 bytes
 3 files changed, 6291 insertions(+)
 create mode 100644 05_numbers.ipynb
 create mode 100644 static/complex_numbers.png
 create mode 100644 static/floating_point.png

diff --git a/05_numbers.ipynb b/05_numbers.ipynb
new file mode 100644
index 0000000..2c06dc3
--- /dev/null
+++ b/05_numbers.ipynb
@@ -0,0 +1,6291 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "# Chapter 5: Numbers"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "After learning about the basic building blocks of expressing and structuring the business logic in programs, we focus our attention on the **data types** Python offers us, both built-in and available via the [standard library](https://docs.python.org/3/library/index.html) or third-party packages.\n",
+    "\n",
+    "We start with the \"simple\" ones: Numeric types in this chapter and textual data in Chapter 6. An important fact that holds true for all objects of these types is that they are **immutable**. To re-use the bag analogy from [Chapter 1](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/01_elements.ipynb#Objects-vs.-Types-vs.-Values), this means that the $0$s and $1$s making up an object's *value* cannot be changed once the bag is created in memory implying that any operation with or method on the object creates a *new* object in a *different* memory location.\n",
+    "\n",
+    "Chapters 7, 8, and 9 then cover the more \"complex\" data types including, for example, the `list` type. Finally, Chapter 10 completes the picture by introducing language constructs to create custom types.\n",
+    "\n",
+    "We have already seen many hints indicating that numbers are not as trivial to work with as it seems at first sight:\n",
+    "\n",
+    "- [Chapter 1](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/01_elements.ipynb#%28Data%29-Type-%2F-%22Behavior%22) revealed that numbers may come in *different* data types (i.e., `int` vs. `float` so far),\n",
+    "- [Chapter 3](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/03_conditionals.ipynb#Boolean-Expressions) raised questions regarding the **limited precision** of `float` numbers (e.g., `42 == 42.000000000000001` evaluates to `True`), and\n",
+    "- [Chapter 4](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/04_iteration.ipynb#Infinite-Recursion) showed that sometimes a `float` \"walks\" and \"quacks\" like an `int` whereas the reverse is true in other cases.\n",
+    "\n",
+    "This chapter introduces all the [built-in numeric types](https://docs.python.org/3/library/stdtypes.html#numeric-types-int-float-complex): `int`, `float`, and `complex`. To mitigate the limited precision of floating-point numbers, we also look at two replacements for the `float` type in the [standard library](https://docs.python.org/3/library/index.html), namely the `Decimal` type in the [decimals](https://docs.python.org/3/library/decimal.html#decimal.Decimal) and the `Fraction` type in the [fractions](https://docs.python.org/3/library/fractions.html#fractions.Fraction) module."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "## The `int` Type"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "The simplest numeric type is the `int` type. Python's integers behave like [integers in ordinary math](https://en.wikipedia.org/wiki/Integer) (i.e., the set $\\mathbb{Z}$) and support operators in the way we saw in the section on arithmetic operators in [Chapter 1](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/01_elements.ipynb#%28Arithmetic%29-Operators)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "a = 789"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Just like any other object, `789` has an identity, a type, and a value."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "140526570988784"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "id(a)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "int"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "type(a)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "789"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "a"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "A nice feature is using underscores `_` as (thousands) seperators in numeric literals. For example, `1_000_000` evaluates to just `1000000` in memory."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "1000000"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "1_000_000"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Whereas mathematicians argue what the term $0^0$ means (cf., this [article](https://en.wikipedia.org/wiki/Zero_to_the_power_of_zero)), programmers are pragmatic about this and just define $0^0 = 1$."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "1"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "0 ** 0"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "### Binary Representations"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "As computers can only store $0$s and $1$s, integers are nothing but that in memory as well. Consequently, computer scientists and engineers developed conventions as to how $0$s and $1$s are \"translated\" into integers and one such convention is the **[binary representation](https://en.wikipedia.org/wiki/Binary_number)** of **non-negative integers**. Consider the integers from $0$ through $255$ that are encoded into $0$s and $1$s with the help of this table:"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "|Bit $i$|  7  |  6  |  5  |  4  |  3  |  2  |  1  |  0  |\n",
+    "|-------|-----|-----|-----|-----|-----|-----|-----|-----|\n",
+    "| Digit |$2^7$|$2^6$|$2^5$|$2^4$|$2^3$|$2^2$|$2^1$|$2^0$|\n",
+    "|  $=$  |$128$| $64$| $32$| $16$| $8$ | $4$ | $2$ | $1$ |"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "A number consists of exactly eight $0$s and $1$s that are read from right to left and referred to as the **bits** of the number. Each bit represents a distinct number, the **digit**, as indicated in the table. For sure, we start counting at $0$ again.\n",
+    "\n",
+    "To encode the integer $3$, for example, we need to find a combination of $0$s and $1$s such that the sum of digits marked with a $1$ is equal to the number we want to encode. In the example, we set all bits to $0$ except for the first ($i=0$) and second ($i=1$) because $1 + 2 = 3$. So the binary representation of $3$ is $00~00~00~11$. To be precise, the $3$ is a linear combination of the digits where the coefficients are either $0$ or $1$: $3 = 0*128 + 0*64 + 0*32 + 0*16 + 0*8 + 0*4 + 1*2 + 1*1$. It is guaranteed that there is exactly one such combination for each number between $0$ and $255$.\n",
+    "\n",
+    "As each digit in the binary representation is one of two values, we say that this representation has a base of $2$. Often times, the base is indicated with a subscript to avoid confusion. For example, we write $3_{10} = 00000011_2$ or $3_{10} = 11_2$ for short omitting leading $0$s. A subscript of $10$ implies a decimal number as we know it from grade school.\n",
+    "\n",
+    "In Python, we use the built-in [bin()](https://docs.python.org/3/library/functions.html#bin) function to obtain an integer's binary representation. It returns a `str` object starting with `\"0b\"` indicating the binary format and as many $0$s and $1$s as are necessary to encode the integer omitting leading $0$s."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'0b11'"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "bin(3)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "We may pass such a `str` object as the argument to the [int()](https://docs.python.org/3/library/functions.html#int) built-in, together with `base=2`, to (re-)create an `int` object, for example, with the value of `3`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "3"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "int(\"0b11\", base=2)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Moreover, we may also use the contents of the returned `str` object as a **literal** instead: Just like we type, for example, `3` without quotes (i.e., \"literally\") into a code cell to create the `int` object `3`, we may type `0b11` and obtain an `int` object with the same value."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "3"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "0b11"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Another example is the integer `177` that is the sum of $128 + 32 + 16 + 1$: Its binary representation is the sequence of bits $10~11~00~01$. To use our new notation, $177_{10} = 10110001_2$."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'0b10110001'"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "bin(177)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Analogous to typing `177` into a code cell, we may type `0b10110001`, or `0b_1011_0001` to make it easier to read, to create the `int` object `177` in memory."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "177"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "0b_1011_0001"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "`0` and `255` are the edge cases where we set all the bits to either $0$ or $1$."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'0b0'"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "bin(0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'0b1'"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "bin(1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'0b10'"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "bin(2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'0b11111111'"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "bin(255)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Groups of eight bits are also called a **byte**. As a byte can only represent non-negative integers up to $255$, the table above is extended conceptually with greater digits to the left to express integers beyond $255$. The memory management needed to implement this is built into Python and we do not need to worry about anything.\n",
+    "\n",
+    "For example, the `789` from above is encoded with $10$ bits and $789_{10} = 1100010101_2$."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'0b1100010101'"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "bin(a)  # = bin(789)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "To contrast this bits encoding with the familiar decimal system, we show an equivalent table with powers of $10$ as the digits:\n",
+    "\n",
+    "|Decimal|   3  |   2  |   1  |   0  |\n",
+    "|-------|------|------|------|------|\n",
+    "| Digit |$10^3$|$10^2$|$10^1$|$10^0$|\n",
+    "|  $=$  |$1000$| $100$| $10$ |  $1$ |\n",
+    "\n",
+    "Now, an integer is a linear combination of the digits where the coefficents are one of *ten* values. For example, the number $123$ can be expressed as $0*1000 + 1*100 + 2*10 + 3*1$. So, the binary representation follows the same logic as the decimal system taught in grade school. The decimal system is intuitive to us humans mostly as we learn to count with our *ten* fingers."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "### Hexadecimal Representations"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "While in the binary and decimal systems there are two and ten distinct coefficients per digit, another convenient representation, the **hexadecimal representation**, uses a base of $16$. It is convenient as one digit stores the same amount of information as *four* bits and the binary representation quickly becomes unreadable for larger numbers. The letters \"a\" through \"f\" are used as digits \"10\" through \"15\".\n",
+    "\n",
+    "The following table summarizes the relationship between the three systems:"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "|Decimal|Hexadecimal|Binary|$~~~~~~$|Decimal|Hexadecimal|Binary|$~~~~~~$|Decimal|Hexadecimal|Binary|$~~~~~~$|...|\n",
+    "|-------|-----------|------|--------|-------|-----------|------|--------|-------|-----------|------|--------|---|\n",
+    "|   0   |     0     | 0000 |$~~~~~~$|  16   |    10     | 10000|$~~~~~~$|  32   |    20     |100000|$~~~~~~$|...|\n",
+    "|   1   |     1     | 0001 |$~~~~~~$|  17   |    11     | 10001|$~~~~~~$|  33   |    21     |100001|$~~~~~~$|...|\n",
+    "|   2   |     2     | 0010 |$~~~~~~$|  18   |    12     | 10010|$~~~~~~$|  34   |    22     |100010|$~~~~~~$|...|\n",
+    "|   3   |     3     | 0011 |$~~~~~~$|  19   |    13     | 10011|$~~~~~~$|  35   |    23     |100011|$~~~~~~$|...|\n",
+    "|   4   |     4     | 0100 |$~~~~~~$|  20   |    14     | 10100|$~~~~~~$|  36   |    24     |100100|$~~~~~~$|...|\n",
+    "|   5   |     5     | 0101 |$~~~~~~$|  21   |    15     | 10101|$~~~~~~$|  37   |    25     |100101|$~~~~~~$|...|\n",
+    "|   6   |     6     | 0110 |$~~~~~~$|  22   |    16     | 10110|$~~~~~~$|  38   |    26     |100110|$~~~~~~$|...|\n",
+    "|   7   |     7     | 0111 |$~~~~~~$|  23   |    17     | 10111|$~~~~~~$|  39   |    27     |100111|$~~~~~~$|...|\n",
+    "|   8   |     8     | 1000 |$~~~~~~$|  24   |    18     | 11000|$~~~~~~$|  40   |    28     |101000|$~~~~~~$|...|\n",
+    "|   9   |     9     | 1001 |$~~~~~~$|  25   |    19     | 11001|$~~~~~~$|  41   |    29     |101001|$~~~~~~$|...|\n",
+    "|  10   |     a     | 1010 |$~~~~~~$|  26   |    1a     | 11010|$~~~~~~$|  42   |    2a     |101010|$~~~~~~$|...|\n",
+    "|  11   |     b     | 1011 |$~~~~~~$|  27   |    1b     | 11011|$~~~~~~$|  43   |    2b     |101011|$~~~~~~$|...|\n",
+    "|  12   |     c     | 1100 |$~~~~~~$|  28   |    1c     | 11100|$~~~~~~$|  44   |    2c     |101100|$~~~~~~$|...|\n",
+    "|  13   |     d     | 1101 |$~~~~~~$|  29   |    1d     | 11101|$~~~~~~$|  45   |    2d     |101101|$~~~~~~$|...|\n",
+    "|  14   |     e     | 1110 |$~~~~~~$|  30   |    1e     | 11110|$~~~~~~$|  46   |    2e     |101110|$~~~~~~$|...|\n",
+    "|  15   |     f     | 1111 |$~~~~~~$|  31   |    1f     | 11111|$~~~~~~$|  47   |    2f     |101111|$~~~~~~$|...|"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "To show more examples of the above subscript convention, we pick three random entries from the table:\n",
+    "\n",
+    "$11_{10} = \\text{b}_{16} = 1011_2$\n",
+    "\n",
+    "$25_{10} = 19_{16} = 11001_2$\n",
+    "\n",
+    "$46_{10} = 2\\text{e}_{16} = 101110_2$\n",
+    "\n",
+    "The built-in [hex()](https://docs.python.org/3/library/functions.html#hex) function creates a `str` object starting with `\"0x\"` representing an integer's hexadecimal representation. The length depends on how many groups of four bits are implied by the corresponding binary representation.\n",
+    "\n",
+    "For `0` and `1` the hexadecimal representation is similar to the binary one."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'0x0'"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "hex(0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'0x1'"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "hex(1)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Whereas `bin(3)` already requires two digits, one is enough for `hex(3)`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'0x3'"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "hex(3)  # bin(3) => \"0b11\"; two digits needed"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "For `10` and `15` we see the letter digits for the first time."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'0xa'"
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "hex(10)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'0xf'"
+      ]
+     },
+     "execution_count": 21,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "hex(15)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "For `177` the binary representation, `\"0b10110001\"`, can be read as *two* groups of four bits $1011$ and $0001$ that are encoded as $\\text{b}$ and $1$ in accordance with the first three columns of the table above."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'0b10110001'"
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "bin(177)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'0xb1'"
+      ]
+     },
+     "execution_count": 23,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "hex(177)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "To obtain the original integer back, we call the [int()](https://docs.python.org/3/library/functions.html#int) built-in with a `base=16` argument."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "177"
+      ]
+     },
+     "execution_count": 24,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "int(\"0xb1\", base=16)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Alternatively, we could use a literal notation instead."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "177"
+      ]
+     },
+     "execution_count": 25,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "0xb1"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Hexadecimal values between $00_{16}$ and $\\text{ff}_{16}$ (i.e., $0_{10}$ and $255_{10}$) are commonly used to describe colors, for example, in web development but also in graphics editors. See this [online tool](https://www.w3schools.com/colors/colors_hexadecimal.asp) for some more background."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'0xff'"
+      ]
+     },
+     "execution_count": 26,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "hex(255)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Just like binary representations, the hexadecimals extend to the left for larger values like `789`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'0x315'"
+      ]
+     },
+     "execution_count": 27,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "hex(a)  # = hex(789)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "For completeness sake, we mention that there is also the built-in [oct()](https://docs.python.org/3/library/functions.html#oct) function to obtain an integer's **octal representation**. The logic is the same as for the hexadecimal representation where we just use *eight* instead of *sixteen* digits. That is the equivalent of viewing the binary representations in groups of three bits."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "### Negative Values"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "While there are conventions that model negative integers with the $0$s and $1$s in memory (cf., [Two's Complement](https://en.wikipedia.org/wiki/Two%27s_complement)), Python manages that for us and we do not look into this here for brevity.\n",
+    "\n",
+    "The binary and hexadecimal representations of negative integers are identical to their positive counterparts except that they start with a minus sign `-`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'-0b11'"
+      ]
+     },
+     "execution_count": 28,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "bin(-3)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'-0x3'"
+      ]
+     },
+     "execution_count": 29,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "hex(-3)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'-0b11111111'"
+      ]
+     },
+     "execution_count": 30,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "bin(-255)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'-0xff'"
+      ]
+     },
+     "execution_count": 31,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "hex(-255)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "### The `bool` Type"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Whereas the boolean objects `True` and `False` are commonly *not* regarded as numeric types, they behave like `1` and `0` in an arithmetic context."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "b = True"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "94554490840032"
+      ]
+     },
+     "execution_count": 33,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "id(b)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "bool"
+      ]
+     },
+     "execution_count": 34,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "type(b)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 35,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "b"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "c = False"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "94554490840000"
+      ]
+     },
+     "execution_count": 37,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "id(c)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "bool"
+      ]
+     },
+     "execution_count": 38,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "type(c)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "False"
+      ]
+     },
+     "execution_count": 39,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "c"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "By including booleans as operands in arithmetic expressions, Python implicitly casts them as `int` objects."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "1"
+      ]
+     },
+     "execution_count": 40,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "b + c  # = True + False"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "790"
+      ]
+     },
+     "execution_count": 41,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "a + b  # = 789 + True"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0"
+      ]
+     },
+     "execution_count": 42,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "a * c  # = 789 * False"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "We may explicitly cast `bool` objects as integers ourselves with the [int()](https://docs.python.org/3/library/functions.html#int) built-in."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "1"
+      ]
+     },
+     "execution_count": 43,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "int(True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0"
+      ]
+     },
+     "execution_count": 44,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "int(False)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Of course, their binary representations only need *one* bit of information."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'0b1'"
+      ]
+     },
+     "execution_count": 45,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "bin(True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'0b0'"
+      ]
+     },
+     "execution_count": 46,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "bin(False)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Their hexadecimal representations occupy *four* bits in memory while only *one* bit is actually needed. This is because \"1\" and \"0\" are just two of the sixteen possible digits."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'0x1'"
+      ]
+     },
+     "execution_count": 47,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "hex(True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 48,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'0x0'"
+      ]
+     },
+     "execution_count": 48,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "hex(False)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "As a reminder, `None` is a type on its own, namely the `NoneType`, and different from `False`. It *cannot* be casted as an integer as the `TypeError` indicates."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 49,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "ename": "TypeError",
+     "evalue": "int() argument must be a string, a bytes-like object or a number, not 'NoneType'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-49-af2123a46eb2>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;31mTypeError\u001b[0m: int() argument must be a string, a bytes-like object or a number, not 'NoneType'"
+     ]
+    }
+   ],
+   "source": [
+    "int(None)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "### Bitwise Operators"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Now that we know how integers are represented with $0$s and $1$s in memory, we look at ways of working with these individual bits, in particular with the so-called **[bitwise operators](https://wiki.python.org/moin/BitwiseOperators)**: As the name suggests, the operators perform some operation on a bit by bit basis. They only work with `int` objects.\n",
+    "\n",
+    "We keep this overview rather short as such \"low-level\" operations are not needed by the data science practicioner on a regular basis. Yet, it is worthwhile to have heard about them as they form the basis of all of arithmetic in computers.\n",
+    "\n",
+    "The first operator is the **bitwise AND** `&` operator: This operator looks at the bits of its two operands, `11` and `13` in the example, in a pairwise fashion and *only if* both operands have a $1$ in the same position will the resulting integer have a $1$ in this position. The binary representations of `11` and `13` have $1$s in their respective first and fourth bits, which is why `bin(11 & 13)` evaluates to `\"Ob1001\"`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 50,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'0b1011 & 0b1101'"
+      ]
+     },
+     "execution_count": 50,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "bin(11) + \" & \" + bin(13)  # to show the operands' binary representations"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 51,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'0b1001'"
+      ]
+     },
+     "execution_count": 51,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "bin(11 & 13)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Of course, `0b1001` is the binary representation of `9`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 52,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "9"
+      ]
+     },
+     "execution_count": 52,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "0b1001"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 53,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "9"
+      ]
+     },
+     "execution_count": 53,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "11 & 13"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "The **bitwise OR** `|` operator evaluates to an `int` object whose bits are set to $1$ if the corresponding bits of either *one* or *both* operands are $1$. So in the example `9 | 13` only the second bit is $0$ for both operands, which is why the expression evaluates to `\"0b1101\"`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 54,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'0b1001 | 0b1101'"
+      ]
+     },
+     "execution_count": 54,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "bin(9) + \" | \" + bin(13)  # to show the operands' binary representations"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 55,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'0b1101'"
+      ]
+     },
+     "execution_count": 55,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "bin(9 | 13)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "`0b1101` evaluates to the `int` object `13`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 56,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "13"
+      ]
+     },
+     "execution_count": 56,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "0b1101"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 57,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "13"
+      ]
+     },
+     "execution_count": 57,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "9 | 13"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "The **bitwise XOR** `^` operator is a special case of the `|` operator in that it evaluates to an `int` object whose bits are set to $1$ if the corresponding bit of exactly *one* of the two operands is $1$. Colloquially, the \"X\" stands for \"exclusive\". The `^` operator must *not* be confused with the `**` operator that does exponentiation! In the example `9 ^ 13` only the third bit differs between the two operands, which is why it evaluates to `\"0b100\"` omitting the fourth bit."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 58,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'0b1001 ^ 0b1101'"
+      ]
+     },
+     "execution_count": 58,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "bin(9) + \" ^ \" + bin(13)  # to show the operands' binary representations"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 59,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'0b100'"
+      ]
+     },
+     "execution_count": 59,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "bin(9 ^ 13)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "`0b100` evaluates to the `int` object `4`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 60,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "4"
+      ]
+     },
+     "execution_count": 60,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "0b100"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 61,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "4"
+      ]
+     },
+     "execution_count": 61,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "9 ^ 13"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "The **bitwise NOT** `~` operator, sometimes also called **inversion** operator, is said to simply \"flips\" the $0$s into $1$s and the $1$s into $0$s. However, it is based on the aforementioned [Two's Complement](https://en.wikipedia.org/wiki/Two%27s_complement) convention and `~x` is defined to be `~x = -(x + 1)` (cf., the [reference](https://docs.python.org/3/reference/expressions.html#unary-arithmetic-and-bitwise-operations)). The full logic behind this is considered out of scope in this book.\n",
+    "\n",
+    "We can at least verify the definition by comparing the binary representations of `8` and `-9`: They are indeed the same."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 62,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'-0b1001'"
+      ]
+     },
+     "execution_count": 62,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "bin(~8)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 63,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'-0b1001'"
+      ]
+     },
+     "execution_count": 63,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "bin(-(8 + 1))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "`~x = -(x + 1)` can be reformulated as `~x + x = -1`, which is slightly easier to check."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 64,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "-1"
+      ]
+     },
+     "execution_count": 64,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "~8 + 8"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Lastly, the **bitwise left and right shift** operators, `<<` and `>>`, simply shift all the bits either to the left or to the right. This corresponds to multiplying or dividing the integer by powers of $2$.\n",
+    "\n",
+    "When shifting left, $0$s are filled in."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 65,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'0b1001'"
+      ]
+     },
+     "execution_count": 65,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "bin(9)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 66,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'0b100100'"
+      ]
+     },
+     "execution_count": 66,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "bin(9 << 2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 67,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "36"
+      ]
+     },
+     "execution_count": 67,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "9 << 2"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "When shifting right, some bits are always lost."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 68,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'0b10'"
+      ]
+     },
+     "execution_count": 68,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "bin(9 >> 2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 69,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "2"
+      ]
+     },
+     "execution_count": 69,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "9 >> 2"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "## The `float` Type"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "As we have seen above, some assumptions need to be made as to how the $0$s and $1$s in a computer's memory should be translated into numbers. This process becomes a lot more involved when we go beyond integers and model [real numbers](https://en.wikipedia.org/wiki/Real_number) (i.e., the set $\\mathbb{R}$) with possibly infinitely many digits to the right of the period like $1.23$.\n",
+    "\n",
+    "The **[Institute of Electrical and Electronics Engineers](https://en.wikipedia.org/wiki/Institute_of_Electrical_and_Electronics_Engineers)** (IEEE, pronounced \"eye-tripple-E\") is one of the most important professional associations when it comes to standardizing all kinds of aspects regarding the implementation of soft- and hardware.\n",
+    "\n",
+    "The **[IEEE 754](https://en.wikipedia.org/wiki/IEEE_754)** standard defines the so-called **floating-point arithmetic** that is commonly used today by all major programming languages. The standard not only defines how the $0$s and $1$s are organized in memory but also, for example, how values are to be rounded, what happens in exceptional cases like divisions by zero, or what is a zero value to begin with.\n",
+    "\n",
+    "In Python, the simplest way to create a `float` object is to just use a literal notation with a dot `.` in it."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 70,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "d = 1.23"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 71,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "140526571166576"
+      ]
+     },
+     "execution_count": 71,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "id(d)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 72,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "float"
+      ]
+     },
+     "execution_count": 72,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "type(d)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 73,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "1.23"
+      ]
+     },
+     "execution_count": 73,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "d"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "As with integer literals above, we may use underscores `_` to make longer `float` objects easier to read."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 74,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.123456"
+      ]
+     },
+     "execution_count": 74,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "0.123_456"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "In cases where the dot `.` is unnecessary from a mathematical point of view, we either need to still end the number with it or use the [float()](https://docs.python.org/3/library/functions.html#float) built-in to cast the number as a float explicitly. [float()](https://docs.python.org/3/library/functions.html#float) can also `str` objects."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 75,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "1.0"
+      ]
+     },
+     "execution_count": 75,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "1.  # on the contrary, 1 creates an integer object"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 76,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "1.0"
+      ]
+     },
+     "execution_count": 76,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "float(1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 77,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "1.0"
+      ]
+     },
+     "execution_count": 77,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "float(\"1.000\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Floats are implicitly created as a result of dividing an integer by another with the division operator `/`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 78,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.3333333333333333"
+      ]
+     },
+     "execution_count": 78,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "1 / 3"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "In general, if we combine `float` and `int` objects in arithmetic operations, we always end up with a `float` type."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 79,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "3.0"
+      ]
+     },
+     "execution_count": 79,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "1.0 + 2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 80,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "42.0"
+      ]
+     },
+     "execution_count": 80,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "21 * 2.0"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "### Scientific Notation"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "`float` objects may also be created with the **scientifc literal notation**: We use the symbol `e` to indicate powers of $10$, so $1.23 * 10^0$ translates into `1.23e0`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 81,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "1.23"
+      ]
+     },
+     "execution_count": 81,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "1.23e0"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Syntactically, `e` needs a `float` object in its literal notation to its left and an `int` object to its right, both without a space. Otherwise, we get a `SyntaxError`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 82,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "ename": "SyntaxError",
+     "evalue": "invalid syntax (<ipython-input-82-1b5daaac0077>, line 1)",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;36m  File \u001b[0;32m\"<ipython-input-82-1b5daaac0077>\"\u001b[0;36m, line \u001b[0;32m1\u001b[0m\n\u001b[0;31m    1.23 e0\u001b[0m\n\u001b[0m          ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m invalid syntax\n"
+     ]
+    }
+   ],
+   "source": [
+    "1.23 e0"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 83,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "ename": "SyntaxError",
+     "evalue": "invalid syntax (<ipython-input-83-a236c4a30231>, line 1)",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;36m  File \u001b[0;32m\"<ipython-input-83-a236c4a30231>\"\u001b[0;36m, line \u001b[0;32m1\u001b[0m\n\u001b[0;31m    1.23e 0\u001b[0m\n\u001b[0m        ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m invalid syntax\n"
+     ]
+    }
+   ],
+   "source": [
+    "1.23e 0"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 84,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "ename": "SyntaxError",
+     "evalue": "invalid syntax (<ipython-input-84-05b9072d76be>, line 1)",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;36m  File \u001b[0;32m\"<ipython-input-84-05b9072d76be>\"\u001b[0;36m, line \u001b[0;32m1\u001b[0m\n\u001b[0;31m    1.23e0.0\u001b[0m\n\u001b[0m           ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m invalid syntax\n"
+     ]
+    }
+   ],
+   "source": [
+    "1.23e0.0"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "If we leave out the number to the left, Python raises a `NameError` as it actually tries to look up a variable named `e1`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 85,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "ename": "NameError",
+     "evalue": "name 'e1' is not defined",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-85-eff9d613b2d7>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0me1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;31mNameError\u001b[0m: name 'e1' is not defined"
+     ]
+    }
+   ],
+   "source": [
+    "e1"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "So, in order to write $10^1$ in Python, we need to think of it as $1*10^1$ and write `1e1`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 86,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "10.0"
+      ]
+     },
+     "execution_count": 86,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "1e1"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "### Special Values"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "There are also three special values representing **\"not a number\"**, called `nan`, and positive or negative **infinity**, called `inf`, that are created by passing in the corresponding abbreviation as a `str` object to the [float()](https://docs.python.org/3/library/functions.html#float) built-in. These values could be used, for example, as the result of a mathematically undefined operation like division by zero or to model the value of a mathematical function as it goes to infinity."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 87,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "nan"
+      ]
+     },
+     "execution_count": 87,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "float(\"nan\")  # also works as float(\"NaN\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 88,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "inf"
+      ]
+     },
+     "execution_count": 88,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "float(\"+inf\")  # also works as float(\"+infinity\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 89,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "inf"
+      ]
+     },
+     "execution_count": 89,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "float(\"inf\")  # by omitting the plus sign, we mean positive infinity"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 90,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "-inf"
+      ]
+     },
+     "execution_count": 90,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "float(\"-inf\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "`nan` objects *never* compare equal to *anything*, not even to themselves. This happens in accordance with the IEEE 754 standard."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 91,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "False"
+      ]
+     },
+     "execution_count": 91,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "float(\"nan\") == float(\"nan\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "On the contrary, as two values go to infinity, there is no such concept as difference any more and *everything* compares equal."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 92,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 92,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "float(\"inf\") == float(\"inf\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Adding `42` to `inf` makes no difference."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 93,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "inf"
+      ]
+     },
+     "execution_count": 93,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "float(\"inf\") + 42"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 94,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 94,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "float(\"inf\") + 42 == float(\"inf\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "We observe the same for multiplication ..."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 95,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "inf"
+      ]
+     },
+     "execution_count": 95,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "42 * float(\"inf\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 96,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 96,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "42 * float(\"inf\") == float(\"inf\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "... and even exponentiation!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 97,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "inf"
+      ]
+     },
+     "execution_count": 97,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "float(\"inf\") ** 42"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 98,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 98,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "float(\"inf\") ** 42 == float(\"inf\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Although differences become unmeaningful as we approach infinity, signs are still respected."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 99,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "inf"
+      ]
+     },
+     "execution_count": 99,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "-42 * float(\"-inf\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 100,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 100,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "-42 * float(\"-inf\") == float(\"inf\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "As a caveat, adding infinities of different signs is an *undefined operation* in math and results in a `nan` object."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 101,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "nan"
+      ]
+     },
+     "execution_count": 101,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "float(\"inf\") + float(\"-inf\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 102,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "nan"
+      ]
+     },
+     "execution_count": 102,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "float(\"inf\") - float(\"inf\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "### Imprecision"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "`float` objects are *inherently* imprecise and there is *nothing* we can do about it! In particular, arithmetic operations with two `float` objects may result in \"weird\" rounding \"errors\" if their exponents are \"too far apart\". These \"errors\" are actually stictly deterministic and occur as a consequence of the IEEE 754 standard.\n",
+    "\n",
+    "For example, let's add `1e0` to `1e15` and `1e16`, respectively. We see that in the second case, the `1e0` somehow gets \"lost\"."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 103,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "1000000000000001.0"
+      ]
+     },
+     "execution_count": 103,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "1e15 + 1e0"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 104,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "1e+16"
+      ]
+     },
+     "execution_count": 104,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "1e16 + 1e0"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Of course, we may also add the integer `1` to the `float` objects in the literal `e` notation with the same outcome."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 105,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "1000000000000001.0"
+      ]
+     },
+     "execution_count": 105,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "1e15 + 1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 106,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "1e+16"
+      ]
+     },
+     "execution_count": 106,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "1e16 + 1"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Interactions between very large and very small `float` objects are not the only source of imprecision."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 107,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "from math import sqrt"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 108,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "2.0000000000000004"
+      ]
+     },
+     "execution_count": 108,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "sqrt(2) ** 2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 109,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.30000000000000004"
+      ]
+     },
+     "execution_count": 109,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "0.1 + 0.2"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "This may become a problem if we rely on equality checks in our programs."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 110,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "False"
+      ]
+     },
+     "execution_count": 110,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "sqrt(2) ** 2 == 2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 111,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "False"
+      ]
+     },
+     "execution_count": 111,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "0.1 + 0.2 == 0.3"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "A popular workaround is to check if the difference between the two numbers is smaller than a pre-defined `threshold` *sufficiently* close to `0`, for example, `1e-15`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 112,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "threshold = 1e-15"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 113,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 113,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "(sqrt(2) ** 2) - 2 < threshold"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 114,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 114,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "(0.1 + 0.2) - 0.3 < threshold"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "The built-in [format()](https://docs.python.org/3/library/functions.html#format) function allows us to show the **significant digits** of a `float` number, as they exist in memory, to an arbitrary precision. To exemplify it, let's view a couple of `float` objects with `50` digits. This analysis reveals that almost no `float` number is precise. After $14$ or $15$ digits \"weird\" things happen. As we will see below, the \"random\" digits do *not* actually exist in memory, which is why Python shows them to us in this \"weird\" way.\n",
+    "\n",
+    "Note that the [format()](https://docs.python.org/3/library/functions.html#format) function is different from the [format()](https://docs.python.org/3/library/stdtypes.html#str.format) method on `str` objects introduced in the next chapter and that both work with the so-called [format specification mini-language](https://docs.python.org/3/library/string.html#format-specification-mini-language): `\".50f\"` is the instruction to show `50` digits of a `float` number. But let's not worry too much about these details for now."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 115,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'0.10000000000000000555111512312578270211815834045410'"
+      ]
+     },
+     "execution_count": 115,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "format(0.1, \".50f\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 116,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'0.20000000000000001110223024625156540423631668090820'"
+      ]
+     },
+     "execution_count": 116,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "format(0.2, \".50f\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 117,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'0.29999999999999998889776975374843459576368331909180'"
+      ]
+     },
+     "execution_count": 117,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "format(0.3, \".50f\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 118,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'0.33333333333333331482961625624739099293947219848633'"
+      ]
+     },
+     "execution_count": 118,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "format(1 / 3, \".50f\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "The [format()](https://docs.python.org/3/library/functions.html#format) function does *not* round a number in the mathematical sense! It just allows us to show an arbitrary number of the digits as stored in memory and it also does *not* change these.\n",
+    "\n",
+    "On the contrary, the built-in [round()](https://docs.python.org/3/library/functions.html#round) function creates a *new* numeric object that is a rounded version according to the common rules from math.\n",
+    "\n",
+    "For example, let's \"round\" `1 / 3` to five digits. We see that the obtained value for `roughly_a_third` is also *imprecise* but different from the \"exact\" represenation of `1 / 3` above."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 119,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "roughly_a_third = round(1 / 3, 5)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 120,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.33333"
+      ]
+     },
+     "execution_count": 120,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "roughly_a_third"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 121,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'0.33333000000000001517008740847813896834850311279297'"
+      ]
+     },
+     "execution_count": 121,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "format(roughly_a_third, \".50f\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Surprisingly, `0.125` and `0.25` appear to be *precise* and equality comparison works without the `threshold` workaround."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 122,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'0.12500000000000000000000000000000000000000000000000'"
+      ]
+     },
+     "execution_count": 122,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "format(0.125, \".50f\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 123,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'0.25000000000000000000000000000000000000000000000000'"
+      ]
+     },
+     "execution_count": 123,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "format(0.25, \".50f\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 124,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 124,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "0.125 + 0.125 == 0.25"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "### Binary Representations"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "In order to understand these subtleties, we need to look at the **[binary representation of floats](https://en.wikipedia.org/wiki/Double-precision_floating-point_format)** and review the basics of the **[IEEE 754](https://en.wikipedia.org/wiki/IEEE_754)** standard. On modern machines, floats are modelled in so-called double precision with $64$ bits that are grouped as in the picture below. The first bit determines the sign ($0$ for plus, $1$ for minus), the next $11$ bits represent an $exponent$ term, and the last $52$ bits resemble the actual significant digits, the so-called $fraction$ part. The three groups are put together like so:\n",
+    "\n",
+    "$$float = (-1)^{sign} * 1.fraction * 2^{exponent-1023}$$\n",
+    "\n",
+    "Observe that a $1.$ is implicitly prepended as the first digit and that both $fraction$ and $exponent$ are stored in base $2$ representation (i.e., they both are interpreted just as the integers above). As $exponent$ is consequently non-negative, between $0_{10}$ and $2047_{10}$ to be precise, the $-1023$ centers the entire $2^{exponent-1023}$ term around $1$ and allows for the period within the $1.fraction$ part to be shifted into either direction by the same amount. Floating-point numbers received their name as the period, formally called the **[radix point](https://en.wikipedia.org/wiki/Radix_point)**,\"floats\" along the significant digits. As an aside, an $exponent$ of all $0$s or all $1$s is used to model the special values `nan` or `inf`.\n",
+    "\n",
+    "As the standard defines the exponent part to come as a power of $2$, we now see why `0.125` is a *precise* float. It simply can be represented as a power of $2$, i.e., $0.125 = (-1)^0 * 1.0 * 2^{1020-1023} = 2^{-3} = \\frac{1}{8}$. In other words, the floating-point representation of $0.125_{10}$ is $0_2$, $1111111100_2 = 1020_{10}$, and $0_2$ for the three groups, respectively."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "<img src=\"static/floating_point.png\" width=\"85%\" align=\"center\">"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "The important fact for data science practitioners to understand is that mapping the *infinite* set of the real numbers $\\mathbb{R}$ to a *finite* set of bits leads to the imprecisions shown above!\n",
+    "\n",
+    "So, floats are usually good approximations of real numbers only with their first $14$ or $15$ significant digits. If more precision is required, we need to revert to other data types such as a `Decimal` or a `Fraction` as shown in the next two sections.\n",
+    "\n",
+    "This [blog post](http://fabiensanglard.net/floating_point_visually_explained/) gives another neat and *visual* way as to how to think of floats. It also explains why floats become worse approximations of the reals as their absolute values increase.\n",
+    "\n",
+    "The Python [documentation](https://docs.python.org/3/tutorial/floatingpoint.html) provides another good discussion of floats and the goodness of their approximations.\n",
+    "\n",
+    "If we are interested in the exact bits behind a `float` object, we use the [hex()](https://docs.python.org/3/library/stdtypes.html#float.hex) method that returns a `str` object beginning with `\"0x1.\"` followed by the $fraction$ in hexadecimal notation and the $exponent$ as an integer after subtraction of the $1023$ seperated by a `\"p\"`.\n",
+    "\n",
+    "Also, the [as_integer_ratio()](https://docs.python.org/3/library/stdtypes.html#float.as_integer_ratio) method returns the two smallest integers whose ratio best approximates a `float` object."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 125,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "one_eighth = 1 / 8"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 126,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'0x1.0000000000000p-3'"
+      ]
+     },
+     "execution_count": 126,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "one_eighth.hex()  # the result basically says 2 ** (-3)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 127,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(1, 8)"
+      ]
+     },
+     "execution_count": 127,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "one_eighth.as_integer_ratio()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 128,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'0x1.555475a31a4bep-2'"
+      ]
+     },
+     "execution_count": 128,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "roughly_a_third.hex()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 129,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(3002369727582815, 9007199254740992)"
+      ]
+     },
+     "execution_count": 129,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "roughly_a_third.as_integer_ratio()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "`0.0` is also a *precise* `float` number."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 130,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "zero = 0.0"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 131,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'0x0.0p+0'"
+      ]
+     },
+     "execution_count": 131,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "zero.hex()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 132,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(0, 1)"
+      ]
+     },
+     "execution_count": 132,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "zero.as_integer_ratio()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "As seen in [Chapter 1](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/01_elements.ipynb#%28Data%29-Type-%2F-%22Behavior%22), the [is_integer()](https://docs.python.org/3/library/stdtypes.html#float.is_integer) method tells us if a `float` can be converted into an `int` object without any loss in precision."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 133,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "False"
+      ]
+     },
+     "execution_count": 133,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "roughly_a_third.is_integer()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 134,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 134,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "one = roughly_a_third / roughly_a_third\n",
+    "\n",
+    "one.is_integer()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "As the exact implementation of floats may vary and be dependend on a particular Python installation, we can always use the [sys.float_info](https://docs.python.org/3/library/sys.html#sys.float_info) attribute in the [sys](https://docs.python.org/3/library/sys.html) module in the [standard library](https://docs.python.org/3/library/) to check the details. Usually, this is not necessary."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 135,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "import sys"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 136,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "sys.float_info(max=1.7976931348623157e+308, max_exp=1024, max_10_exp=308, min=2.2250738585072014e-308, min_exp=-1021, min_10_exp=-307, dig=15, mant_dig=53, epsilon=2.220446049250313e-16, radix=2, rounds=1)"
+      ]
+     },
+     "execution_count": 136,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "sys.float_info"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "## The `Decimal` Type"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "The [decimal](https://docs.python.org/3/library/decimal.html) module in the standard library provides a [Decimal](https://docs.python.org/3/library/decimal.html#decimal.Decimal) type that is used to represent any real number to a user-defined level of precision: \"User-defined\" does *not* mean an infinite or \"exact\" precision! The `Decimal` type merely allows us to work with a number of bits *different* from the $64$ as specified for the `float` type and also to customize the rounding rules and some other settings.\n",
+    "\n",
+    "Let's first import the `Decimal` type and also the [getcontext()](https://docs.python.org/3/library/decimal.html#decimal.getcontext) function from the [decimal](https://docs.python.org/3/library/decimal.html) module."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 137,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "from decimal import Decimal, getcontext"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "[getcontext()](https://docs.python.org/3/library/decimal.html#decimal.getcontext) shows us how the [decimal](https://docs.python.org/3/library/decimal.html) module is set up. By default, the precision is set to $28$ significant digits, which is roughly twice as many as with `float` objects."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 138,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Context(prec=28, rounding=ROUND_HALF_EVEN, Emin=-999999, Emax=999999, capitals=1, clamp=0, flags=[], traps=[InvalidOperation, DivisionByZero, Overflow])"
+      ]
+     },
+     "execution_count": 138,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "getcontext()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "The two simplest ways to create a `Decimal` object is to either **instantiate** it with an `int` object or a `str` object consisting of all the significant digits. In the latter case, a scientific notation is also possible."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 139,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Decimal('42')"
+      ]
+     },
+     "execution_count": 139,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "Decimal(42)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 140,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Decimal('0.1')"
+      ]
+     },
+     "execution_count": 140,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "Decimal(\"0.1\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 141,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Decimal('1E+5')"
+      ]
+     },
+     "execution_count": 141,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "Decimal(\"1e5\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Is is *not* a good idea to create a `Decimal` object from a `float` object. If we did so, we would create a `Decimal` object that internally used extra bits to store the \"random\" digits that are not stored for a `float` but \"seen\" by Python nevertheless."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 142,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Decimal('0.1000000000000000055511151231257827021181583404541015625')"
+      ]
+     },
+     "execution_count": 142,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "Decimal(0.1)  # do not create a Decimal from a float"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "With the `Decimal` type, the imprecisions in the arithmetic and equality comparisons from above are gone."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 143,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Decimal('0.3')"
+      ]
+     },
+     "execution_count": 143,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "Decimal(\"0.1\") + Decimal(\"0.2\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 144,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 144,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "Decimal(\"0.1\") + Decimal(\"0.2\") == Decimal(\"0.3\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Arithmetic operations between `Decimal` and `int` objects work as the latter are inherently precise: The results are *new* `Decimal` objects."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 145,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Decimal('42')"
+      ]
+     },
+     "execution_count": 145,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "42 + Decimal(42) - 42"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 146,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Decimal('420')"
+      ]
+     },
+     "execution_count": 146,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "10 * Decimal(42)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 147,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Decimal('1.05')"
+      ]
+     },
+     "execution_count": 147,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "Decimal(42) / 40"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "To verify the precision, we apply the built-in [format()](https://docs.python.org/3/library/functions.html#format) function to the previous code cell and compare it with the same division resulting in a `float` object."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 148,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'1.05000000000000000000000000000000000000000000000000'"
+      ]
+     },
+     "execution_count": 148,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "format(Decimal(42) / 40, \".50f\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 149,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'1.05000000000000004440892098500626161694526672363281'"
+      ]
+     },
+     "execution_count": 149,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "format(42 / 40, \".50f\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "However, mixing `Decimal` and `float` objects raises a `TypeError`: So, Python prevents us from potentially introducing imprecisions via innocent looking arithmetic."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 150,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "ename": "TypeError",
+     "evalue": "unsupported operand type(s) for *: 'float' and 'decimal.Decimal'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-150-a3cea145e6d1>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0;36m1.0\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0mDecimal\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m42\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;31mTypeError\u001b[0m: unsupported operand type(s) for *: 'float' and 'decimal.Decimal'"
+     ]
+    }
+   ],
+   "source": [
+    "1.0 * Decimal(42)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "In order to preserve the precision even for more advanced mathematical functions, `Decimal` objects come with many methods bound on them. For example, [ln()](https://docs.python.org/3/library/decimal.html#decimal.Decimal.ln) and [log10()](https://docs.python.org/3/library/decimal.html#decimal.Decimal.log10) take the logarithm while [sqrt()](https://docs.python.org/3/library/decimal.html#decimal.Decimal.sqrt) calculates the square root. In general, the functions in the [math](https://docs.python.org/3/library/math.html) module in the [standard library](https://docs.python.org/3/library/) should only be used with `float` objects."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 151,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Decimal('2')"
+      ]
+     },
+     "execution_count": 151,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "Decimal(100).log10()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 152,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Decimal('1.414213562373095048801688724')"
+      ]
+     },
+     "execution_count": 152,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "Decimal(2).sqrt()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "The [sqrt()](https://docs.python.org/3/library/decimal.html#decimal.Decimal.sqrt) method is still limited with respect to the precision of the returned value as, for example, $\\sqrt{2}$ is an **[irrational number](https://en.wikipedia.org/wiki/Irrational_number)** that *cannot* be expressed with absolute precision using *any* number of bits even in theory.\n",
+    "\n",
+    "We see this as raising $\\sqrt{2}$ to the power of $2$ results in an imprecise value as before!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 153,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Decimal('1.999999999999999999999999999')"
+      ]
+     },
+     "execution_count": 153,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "two = Decimal(2).sqrt() ** 2\n",
+    "\n",
+    "two"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "The [quantize()](https://docs.python.org/3/library/decimal.html#decimal.Decimal.quantize) method allows us to cut off a `Decimal` number at any digit that is smaller than the set precision while adhering to the rounding rules from math.\n",
+    "\n",
+    "For example, as the overall imprecise value of `two` still has a precision of $28$, we can correctly round it with respect to the first $20$ digits."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 154,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Decimal('2')"
+      ]
+     },
+     "execution_count": 154,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "two.quantize(20)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Consequently, with this little workaround $\\sqrt{2}^2 = 2$ works even in Python."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 155,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 155,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "two.quantize(20) == 2"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "The downside is that the entire expression is not as pretty as `sqrt(2) ** 2 == 2` from above."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 156,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 156,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "(Decimal(2).sqrt() ** 2).quantize(20) == 2"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "`nan` and positive and negative `inf` exist as well and the same remarks from above apply."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 157,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Decimal('NaN')"
+      ]
+     },
+     "execution_count": 157,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "Decimal(\"nan\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 158,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "False"
+      ]
+     },
+     "execution_count": 158,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "Decimal(\"nan\") == Decimal(\"nan\")  # nan's never compare equal to anything, not even to themselves"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 159,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Decimal('Infinity')"
+      ]
+     },
+     "execution_count": 159,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "Decimal(\"inf\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 160,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Decimal('-Infinity')"
+      ]
+     },
+     "execution_count": 160,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "Decimal(\"-inf\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 161,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Decimal('Infinity')"
+      ]
+     },
+     "execution_count": 161,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "Decimal(\"inf\") + 42  # Infinity is infinity, concrete numbers loose its meaning"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 162,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 162,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "Decimal(\"inf\") + 42 == Decimal(\"inf\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "As with `float` objects, we cannot add infinities of different signs: Now get a module-specific `InvalidOperation` exception instead of a `nan` value."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 163,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "ename": "InvalidOperation",
+     "evalue": "[<class 'decimal.InvalidOperation'>]",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mInvalidOperation\u001b[0m                          Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-163-87950ebbd6ef>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mDecimal\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"inf\"\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mDecimal\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"-inf\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;31mInvalidOperation\u001b[0m: [<class 'decimal.InvalidOperation'>]"
+     ]
+    }
+   ],
+   "source": [
+    "Decimal(\"inf\") + Decimal(\"-inf\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 164,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "ename": "InvalidOperation",
+     "evalue": "[<class 'decimal.InvalidOperation'>]",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mInvalidOperation\u001b[0m                          Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-164-996083f200c6>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mDecimal\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"inf\"\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0mDecimal\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"inf\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;31mInvalidOperation\u001b[0m: [<class 'decimal.InvalidOperation'>]"
+     ]
+    }
+   ],
+   "source": [
+    "Decimal(\"inf\") - Decimal(\"inf\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "For more information on decimals, see the tutorial at [PYMOTW](https://pymotw.com/3/decimal/index.html) or the official [documentation](https://docs.python.org/3/library/decimal.html)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "## The `Fraction` Type"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "If the numbers in an application can be expressed as [rational numbers](https://en.wikipedia.org/wiki/Rational_number) (i.e., the set $\\mathbb{Q}$), we may model them as a [Fraction](https://docs.python.org/3/library/fractions.html#fractions.Fraction) type from the [fractions](https://docs.python.org/3/library/fractions.html) module in the [standard library](https://docs.python.org/3/library/). As any fraction can always be formulated as the division of one integer by another, `Fraction` objects are inherently precise, just as `int` objects on their own. Further, we maintain the precision as long as we do not use them in a mathematical operation that could result in an irrational number (e.g., taking the square root).\n",
+    "\n",
+    "Let's first import the `Fraction` type from the [fractions](https://docs.python.org/3/library/fractions.html) module."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 165,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "from fractions import Fraction"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Among others, there are two simple ways to create a `Fraction` object: We either instantiate one with two `int` objects representing the numerator and denominator or with a `str` object. In the latter case, we have again two options and use either the format \"numerator/denominator\" (i.e., *without* any spaces) or the same format as for `float` and `Decimal` objects above."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 166,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Fraction(1, 3)"
+      ]
+     },
+     "execution_count": 166,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "Fraction(1, 3)  # this is now 1/3 with \"full\" precision"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 167,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Fraction(1, 3)"
+      ]
+     },
+     "execution_count": 167,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "Fraction(\"1/3\")  # this is 1/3 with \"full\" precision again"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 168,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Fraction(3333333333, 10000000000)"
+      ]
+     },
+     "execution_count": 168,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "Fraction(\"0.3333333333\")  # this is 1/3 with a precision of 10 significant digits"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 169,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Fraction(3333333333, 10000000000)"
+      ]
+     },
+     "execution_count": 169,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "Fraction(\"3333333333e-10\")  # the same in scientific notation"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Only the lowest common denominator version is maintained after creation: For example, $\\frac{3}{2}$ and $\\frac{6}{4}$ are the same and both become `Fraction(3, 2)`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 170,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Fraction(3, 2)"
+      ]
+     },
+     "execution_count": 170,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "Fraction(3, 2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 171,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Fraction(3, 2)"
+      ]
+     },
+     "execution_count": 171,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "Fraction(6, 4)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "We could also cast a `Decimal` object as a `Fraction` object: This only makes sense as `Decimal` objects come with a pre-defined precision."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 172,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Fraction(1, 10)"
+      ]
+     },
+     "execution_count": 172,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "Fraction(Decimal(\"0.1\"))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "`float` objects may *syntactically* be casted as `Fraction` objects as well. However, then we create a `Fraction` object that precisely remembers the `float` object's imprecision: A *bad* idea!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 173,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Fraction(3602879701896397, 36028797018963968)"
+      ]
+     },
+     "execution_count": 173,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "Fraction(0.1)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "`Fraction` objects follow the arithmetic rules from middle school and may be mixed with `int` objects *without* any loss of precision."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 174,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Fraction(7, 4)"
+      ]
+     },
+     "execution_count": 174,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "Fraction(3, 2) + Fraction(1, 4)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 175,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Fraction(1, 2)"
+      ]
+     },
+     "execution_count": 175,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "Fraction(5, 2) - 2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 176,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Fraction(1, 1)"
+      ]
+     },
+     "execution_count": 176,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "3 * Fraction(1, 3)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 177,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Fraction(1, 1)"
+      ]
+     },
+     "execution_count": 177,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "Fraction(3, 2) * Fraction(2, 3)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "`Fraction` and `float` objects may also be mixed syntactically. However, then the results may exhibit imprecision again, even if we do not see them at first sight!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 178,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.1"
+      ]
+     },
+     "execution_count": 178,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "10.0 * Fraction(1, 100)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 179,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'0.10000000000000000555111512312578270211815834045410'"
+      ]
+     },
+     "execution_count": 179,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "format(10.0 * Fraction(1, 100), \".50f\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "For more examples and discussions, see the tutorial at [PYMOTW](https://pymotw.com/3/fractions/index.html) or the official [documentation](https://docs.python.org/3/library/fractions.html)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "## The `complex` Type"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Some mathematical equations cannot be solved if the solution has to be in the set of the real numbers $\\mathbb{R}$: For example: $x^2 = -1 \\implies x = \\sqrt{-1}$ and the square root is not defined for negative numbers. To mitigate this, mathematicians introduced the concept of an [imaginary number](https://en.wikipedia.org/wiki/Imaginary_number) $\\textbf{i}$ that is *defined* as $\\textbf{i} = \\sqrt{-1}$ or often times as the solution to the equation $\\textbf{i}^2 = -1$. So, the solution to $x = \\sqrt{-1}$ then becomes $x = \\textbf{i}$.\n",
+    "\n",
+    "If we generalize the example equation into $(mx-n)^2 = -1 \\implies x = \\frac{1}{m}(\\sqrt{-1} + n)$ where $m$ and $n$ are constants chosen from the reals $\\mathbb{R}$, then the solution to the equation comes in the form $x = a + b\\textbf{i}$, the sum of a real number and an imaginary number, with $a=\\frac{n}{m}$ and $b = \\frac{1}{m}$ also from the reals $\\mathbb{R}$.\n",
+    "\n",
+    "Such compound numbers are called a **[complex numbers](https://en.wikipedia.org/wiki/Complex_number)** and the set of all such numbers is commonly denoted by $\\mathbb{C}$. The reals $\\mathbb{R}$ are a strict subset of $\\mathbb{C}$ with $b=0$. $a$ is referred to as the **real part** and $b$ as the **imaginary part** of the complex number.\n",
+    "\n",
+    "Complex numbers are often visualized in a plane like below where the real part is depicted on the x-axis and the imaginary part is depicted on the y-axis."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "<img src=\"static/complex_numbers.png\" width=\"25%\" align=\"center\">"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Complex numbers are part of core Python. The simplest way to create one is to write an arithmetic expression with the literal `j` notation for an imaginary number. A `j` notation is commonly used in many engineering disciplines instead of the symbol $\\textbf{i}$ from math as $I$ in engineering more often than not means [electric current](https://en.wikipedia.org/wiki/Electric_current).\n",
+    "\n",
+    "For example, the answer to $x^2 = -1$ can be written in Python as `1j` like below. This creates a `complex` object with value `1j`. The same syntactic rules apply as with the above `e` notation: No spaces are allowed between the number and the `j` and the number may be any `float` literal."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 180,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "sqrt_of_neg_one = 1j"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 181,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "140526569943600"
+      ]
+     },
+     "execution_count": 181,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "id(sqrt_of_neg_one)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 182,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "complex"
+      ]
+     },
+     "execution_count": 182,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "type(sqrt_of_neg_one)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 183,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "1j"
+      ]
+     },
+     "execution_count": 183,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "sqrt_of_neg_one"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "To verify that it solves the equation, let's just raise it to the power of $2$: the answer is $-1 + 0\\textbf{i} = -1$."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 184,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 184,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "sqrt_of_neg_one ** 2 == -1"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Often, we will write an expression of the form $a + b\\textbf{i}$."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 185,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(2+0.5j)"
+      ]
+     },
+     "execution_count": 185,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "2 + 0.5j"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Alternatively, we may use the [complex()](https://docs.python.org/3/library/functions.html#complex) built-in: This takes two parameters where the second is optional and defaults to `0`. We may either call it with one or two arguments of any numeric type or a `str` object in the format of the previous code cell without any spaces."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 186,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(2+0.5j)"
+      ]
+     },
+     "execution_count": 186,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "complex(2, 0.5)  # an integer and a float work just fine as arguments"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 187,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(2+0j)"
+      ]
+     },
+     "execution_count": 187,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "complex(2)  # omitting the second argument sets the imaginary part to 0"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 188,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(2+0.5j)"
+      ]
+     },
+     "execution_count": 188,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "complex(Decimal(\"2.0\"), Fraction(1, 2))  # the arguments may be any numeric type"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 189,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(2+0.5j)"
+      ]
+     },
+     "execution_count": 189,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "complex(\"2+0.5j\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Spaces in the text string version lead to a `ValueError`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 190,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "ename": "ValueError",
+     "evalue": "complex() arg is a malformed string",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-190-e3de6c558ade>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mcomplex\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"2 + 0.5j\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;31mValueError\u001b[0m: complex() arg is a malformed string"
+     ]
+    }
+   ],
+   "source": [
+    "complex(\"2 + 0.5j\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "The basic arithmetic rules work for `complex` numbers. They may be mixed with the other numeric types and the result is always a `complex` number again."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 191,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "e = 1 + 2j\n",
+    "f = 3 + 4j"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 192,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(4+6j)"
+      ]
+     },
+     "execution_count": 192,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "e + f"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 193,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(2+2j)"
+      ]
+     },
+     "execution_count": 193,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "f - e"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 194,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(40+2j)"
+      ]
+     },
+     "execution_count": 194,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "39 + e"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 195,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "-4j"
+      ]
+     },
+     "execution_count": 195,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "3.0 - f"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 196,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(5+10j)"
+      ]
+     },
+     "execution_count": 196,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "5 * e"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 197,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(0.5+0.6666666666666666j)"
+      ]
+     },
+     "execution_count": 197,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "f / 6"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 198,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(-5+10j)"
+      ]
+     },
+     "execution_count": 198,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "e * f"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 199,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(2.2-0.4j)"
+      ]
+     },
+     "execution_count": 199,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "f / e"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "The absolute value of a `complex` number is commonly defined as the Pythagoram Theorem where $\\lVert x \\rVert = \\sqrt{a^2 + b^2}$ and the [abs()](https://docs.python.org/3/library/functions.html#abs) built-in function works implements that in Python."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 200,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "5.0"
+      ]
+     },
+     "execution_count": 200,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "abs(3 + 4j)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "A `complex` number comes with two **attributes** `real` and `imag` that return the two parts as `float` objects on their own."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 201,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "1.0"
+      ]
+     },
+     "execution_count": 201,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "e.real"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 202,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "2.0"
+      ]
+     },
+     "execution_count": 202,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "e.imag"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Also, a conjugate() method is bound to every `complex` object. The [complex conjugate](https://en.wikipedia.org/wiki/Complex_conjugate) is defined to be the complex number with identical real part but an imaginary part reversed in sign."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 203,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(1-2j)"
+      ]
+     },
+     "execution_count": 203,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "e.conjugate()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "The [cmath](https://docs.python.org/3/library/cmath.html) module in the [standard library](https://docs.python.org/3/library/) implements many of the functions from the [math](https://docs.python.org/3/library/math.html) module such that they work with complex numbers."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "## TL;DR"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "There are three numeric types that are part of core Python:\n",
+    "- `int`: perfect model for whole numbers (i.e., the set $\\mathbb{Z}$); inherently precise\n",
+    "- `float`: standard to approximate real numbers (i.e., the set $\\mathbb{R}$); inherently imprecise\n",
+    "- `complex`: layer on top of the `float` type\n",
+    "\n",
+    "Furthermore, the [standard library](https://docs.python.org/3/library/) adds two more types that can be used as substitutes for `float` objects:\n",
+    "- `Decimal`: similar to `float` but allows customizing the precision; still inherently imprecise\n",
+    "- `Fraction`: perfect model for rational numbers (i.e., the set $\\mathbb{Q}$)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.3"
+  },
+  "livereveal": {
+   "auto_select": "code",
+   "auto_select_fragment": true,
+   "scroll": true,
+   "theme": "serif"
+  },
+  "toc": {
+   "base_numbering": 1,
+   "nav_menu": {},
+   "number_sections": false,
+   "sideBar": true,
+   "skip_h1_title": true,
+   "title_cell": "Table of Contents",
+   "title_sidebar": "Contents",
+   "toc_cell": false,
+   "toc_position": {
+    "height": "calc(100% - 180px)",
+    "left": "10px",
+    "top": "150px",
+    "width": "384px"
+   },
+   "toc_section_display": false,
+   "toc_window_display": false
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
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diff --git a/static/floating_point.png b/static/floating_point.png
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From 6bebe70e24003587afedc320249ddcb857eb0786 Mon Sep 17 00:00:00 2001
From: Alexander Hess <alexander@webartifex.biz>
Date: Mon, 7 Oct 2019 22:34:16 +0200
Subject: [PATCH 030/242] Adjust content overview in README.md

---
 README.md | 1 +
 1 file changed, 1 insertion(+)

diff --git a/README.md b/README.md
index 4561afb..57c513a 100644
--- a/README.md
+++ b/README.md
@@ -19,6 +19,7 @@ As such they can be viewed in a plain web browser:
 - [02 - Functions & Modularization](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/02_functions.ipynb)
 - [03 - Conditionals & Exceptions](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/03_conditionals.ipynb)
 - [04 - Recursion & Looping](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/04_iteration.ipynb)
+- [05 - Numbers](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/05_numbers.ipynb)
 
 However, it is recommended that students **install Python and Jupyter
 locally** and run the code in the notebooks on their own.

From dbc3a67af484b30db9f29d9cdb0a10a92d8edb25 Mon Sep 17 00:00:00 2001
From: Alexander Hess <alexander@webartifex.biz>
Date: Mon, 14 Oct 2019 14:09:28 +0200
Subject: [PATCH 031/242] Streamline previous content

- run grammarly on all notebooks
- add section on short-circuiting in chapter 03
---
 00_start_up.ipynb                          |  134 +-
 00_start_up_review_and_exercises.ipynb     |   16 +-
 01_elements.ipynb                          |  176 +-
 01_elements_review_and_exercises.ipynb     |    2 +-
 02_functions.ipynb                         |  134 +-
 02_functions_review_and_exercises.ipynb    |    6 +-
 03_conditionals.ipynb                      |  516 +++++-
 03_conditionals_review_and_exercises.ipynb |   48 +-
 04_iteration.ipynb                         |  182 +--
 04_iteration_review_and_exercises.ipynb    |   44 +-
 05_numbers.ipynb                           | 1685 +++++++++-----------
 11 files changed, 1566 insertions(+), 1377 deletions(-)

diff --git a/00_start_up.ipynb b/00_start_up.ipynb
index 1cfaf91..9ace23c 100644
--- a/00_start_up.ipynb
+++ b/00_start_up.ipynb
@@ -19,11 +19,9 @@
     }
    },
    "source": [
-    "This book is set up to be a *thorough* introduction to programming in [Python](https://www.python.org/).\n",
+    "This book is a *thorough* introduction to programming in [Python](https://www.python.org/).\n",
     "\n",
-    "It teaches the concepts behind and the syntax of the core Python language as defined by the [Python Software Foundation](https://www.python.org/psf/) in the official [language reference](https://docs.python.org/3/reference/index.html) and introduces additions to the language as distributed with the [standard library](https://docs.python.org/3/library/index.html) that come with every installation.\n",
-    "\n",
-    "Furthermore, some very popular third-party libraries like [numpy](https://www.numpy.org/), [pandas](https://pandas.pydata.org/), [matplotlib](https://matplotlib.org/), and others are portrayed."
+    "It teaches the concepts behind and the syntax of the core Python language as defined by the [Python Software Foundation](https://www.python.org/psf/) in the official [language reference](https://docs.python.org/3/reference/index.html). Furthermore, it introduces commonly used functionalities from the [standard library](https://docs.python.org/3/library/index.html) and popular third-party libraries like [numpy](https://www.numpy.org/), [pandas](https://pandas.pydata.org/), [matplotlib](https://matplotlib.org/), and others."
    ]
   },
   {
@@ -56,7 +54,7 @@
     }
    },
    "source": [
-    "To be suitable for *total beginners*, there are *no* formal prerequisites. It is only expected that the student has:\n",
+    "This book is suitable for *total beginners*, and there are *no* formal prerequisites. The student only needs to have:\n",
     "\n",
     "- a *solid* understanding of the **English language**,\n",
     "- knowledge of **basic mathematics** from high school,\n",
@@ -94,7 +92,7 @@
     }
    },
    "source": [
-    "This includes but is not limited to topics such as:\n",
+    "These include but are not limited to topics such as:\n",
     "- linear algebra\n",
     "- statistics & econometrics\n",
     "- data cleaning & wrangling\n",
@@ -108,7 +106,7 @@
     "- quantitative marketing (e.g., customer segmentation)\n",
     "- quantitative supply chain management (e.g., forecasting)\n",
     "- management science & decision models\n",
-    "- backend / API / web development (to serve data products to clients)"
+    "- backend/API/web development (to serve data products to clients)"
    ]
   },
   {
@@ -130,9 +128,9 @@
     }
    },
    "source": [
-    "The term **[data science](https://en.wikipedia.org/wiki/Data_science)** is rather vague and does actually *not* refer to an academic discipline. Instead the term was popularized by the tech industry who also coined non-meaningful job titles such as \"[rockstar](https://www.quora.com/Why-are-engineers-called-rockstars-and-ninjas)\" or \"[ninja developers](https://www.quora.com/Why-are-engineers-called-rockstars-and-ninjas)\". Most *serious* definitions describe the field as being **multi-disciplinary** *integrating* scientific methods, algorithms, and systems thinking to extract knowledge from (structured and unstructured) data *and* also emphasize the importance of **[domain knowledge](https://en.wikipedia.org/wiki/Domain_knowledge)**.\n",
+    "The term **[data science](https://en.wikipedia.org/wiki/Data_science)** is rather vague and does *not* refer to an academic discipline. Instead, the term was popularized by the tech industry, who also coined non-meaningful job titles such as \"[rockstar](https://www.quora.com/Why-are-engineers-called-rockstars-and-ninjas)\" or \"[ninja developers](https://www.quora.com/Why-are-engineers-called-rockstars-and-ninjas).\" Most *serious* definitions describe the field as being **multi-disciplinary** *integrating* scientific methods, algorithms, and systems thinking to extract knowledge from (structured and unstructured) data *and* also emphasize the importance of **[domain knowledge](https://en.wikipedia.org/wiki/Domain_knowledge)**.\n",
     "\n",
-    "Recently, this integration aspect feeds back into the academic world. The [MIT](https://www.mit.edu/), for example, created the new [Stephen A. Schwarzman College of Computing](http://computing.mit.edu) for [artifical intelligence](https://en.wikipedia.org/wiki/Artificial_intelligence) (with a 1 billion dollar initial investment) where students undergo a \"bilingual\" curriculum with half the classes in quantitative and method-centric fields (like the ones mentioned above) and the other half in domains such as biology, business, chemistry, politics, (art) history, or linguistics (cf., the [official Q&As](http://computing.mit.edu/faq/) or this [NYT article](https://www.nytimes.com/2018/10/15/technology/mit-college-artificial-intelligence.html)). Their strategists see a future where programming skills are just as naturally embedded into every students' studies as are nowadays subjects like calculus, statistics, or academic writing. Then, programming literacy is not just another \"nice to have\" skill but a prerequisite (or an enabler) to understanding more advanced topics in the actual domains studied. This could make teaching easier for top-notch researchers who use a lot of programming in their day-to-day lifes answering big questions: The student and the teacher are then more likely to \"speak\" the same language."
+    "Recently, this integration aspect feeds back into the academic world. The [MIT](https://www.mit.edu/), for example, created the new [Stephen A. Schwarzman College of Computing](http://computing.mit.edu) for [artificial intelligence](https://en.wikipedia.org/wiki/Artificial_intelligence) (with a 1 billion dollar initial investment) where students undergo a \"bilingual\" curriculum with half the classes in quantitative and method-centric fields (like the ones mentioned above) and the other half in domains such as biology, business, chemistry, politics, (art) history, or linguistics (cf., the [official Q&As](http://computing.mit.edu/faq/) or this [NYT article](https://www.nytimes.com/2018/10/15/technology/mit-college-artificial-intelligence.html)). Their strategists see a future where programming skills are just as naturally embedded into every students' curricula as are nowadays subjects like calculus, statistics, or academic writing. Then, programming literacy is not just another \"nice to have\" skill but a prerequisite, or an enabler, to understanding more advanced topics in the actual domains studied. Top-notch researchers who use programming in their day-to-day lives could then teach students more efficiently in their \"language.\""
    ]
   },
   {
@@ -154,11 +152,11 @@
     }
    },
    "source": [
-    "To follow this book, a working installation of **Python 3.6** or higher is needed.\n",
+    "To \"read\" this book in the most meaningful way, a working installation of **Python 3.6** or higher is needed.\n",
     "\n",
-    "A popular and beginner friendly way is to install the [Anaconda Distribution](https://www.anaconda.com/distribution/) that not only ships Python and the standard library but comes pre-packaged with a lot of third-party libraries from the so-called \"scientific stack\". Just go to the [download](https://www.anaconda.com/download/) page and install the latest version (i.e., *2019-07* with Python 3.7 at the time of this writing) for your operating system.\n",
+    "A popular and beginner-friendly way is to install the [Anaconda Distribution](https://www.anaconda.com/distribution/) that not only ships Python and the standard library but comes pre-packaged with a lot of third-party libraries from the so-called \"scientific stack.\" Just go to the [download](https://www.anaconda.com/download/) page and install the latest version (i.e., *2019-07* with Python 3.7 at the time of this writing) for your operating system.\n",
     "\n",
-    "Then, among others, you will find an entry \"Jupyter Notebook\" in your start menu like below. Click on it and a new tab in your web browser will open where you can switch between folders as you could in your computer's default file browser."
+    "Then, among others, you find an entry \"Jupyter Notebook\" in your start menu like below. Click on it to open a new tab in your web browser where you can switch between folders as you could in your computer's default file browser."
    ]
   },
   {
@@ -180,7 +178,7 @@
     }
    },
    "source": [
-    "To download the materials accompanying this book as a ZIP file, open this [GitHub repository](https://github.com/webartifex/intro-to-python) in a web browser and click on the green \"Clone or download\" button on the right. Then, unpack the ZIP file into a folder of your choosing (ideally somewhere within your personal user folder so that the files show up right away)."
+    "To download the materials accompanying this book as a ZIP file, open this [GitHub repository](https://github.com/webartifex/intro-to-python) in a web browser and click on the green \"Clone or download\" button on the right. Then, unpack the ZIP file into a folder of your choosing (ideally somewhere within your user folder so that the files show up right away)."
    ]
   },
   {
@@ -202,7 +200,7 @@
     }
    },
    "source": [
-    "Python can also be installed in a \"pure\" way as obtained from its core development team. However, this is somewhat too \"advanced\" for a beginner who would then also be responsible for setting up all the third-party libraries needed to actually view this document. Plus, many of the involveld steps must be done in a [terminal](https://en.wikipedia.org/wiki/Terminal_emulator) window, which tends to be a bit intimidating for most beginners."
+    "Python can also be installed in a \"pure\" way as obtained from its core development team. However, this is somewhat too \"advanced\" for a beginner who would then also be responsible for setting up all the third-party libraries needed to view this document. Plus, many of the involved steps are typed in a [terminal](https://en.wikipedia.org/wiki/Terminal_emulator) window, which tends to be a bit intimidating for most beginners."
    ]
   },
   {
@@ -224,7 +222,7 @@
     }
    },
    "source": [
-    "For more \"couragous\" beginners wanting to learn how to accomplish this, here is a rough sketch of the aspects to know. First, all Python realeases are available for free on the official [download](https://www.python.org/downloads/) page for any supported operating system. Choose the one you want, then download and install it (cf., the [instruction notes](https://wiki.python.org/moin/BeginnersGuide/Download)). As this only includes core Python and the standard library, the beginner then needs to learn about the [pip](https://pip.pypa.io/en/stable/) module, which is to be used inside a terminal. With the command `python -m pip install jupyter`, all necessary third-party libraries can be installed (cf., more background [here](https://jupyter.readthedocs.io/en/latest/install.html)). However, this would be done in a *system-wide* fashion and is not recommended. Instead, the best practice is to create a so-called **virtual environment** with the [venv](https://docs.python.org/3/library/venv.html) module with which the installed third-party packages are *isolated* on a per-project basis (the command `python -m venv env-name` creates a virtual enviroment called \"env-name\"). This tactic is employed to avoid a situation known as **[dependency hell](https://en.wikipedia.org/wiki/Dependency_hell)** Once created, the virtual environment must then be activated each time before resuming work in each terminal (with the command `source env-name/bin/activate`). While there exist convenience tools that automate parts of this (e.g., [poetry](https://poetry.eustace.io/docs/) or [virtualenvwrapper](https://virtualenvwrapper.readthedocs.io/en/latest/)), it only distracts a beginner from actually studying the Python language. Yet, it is still worthwhile to have heard about these terms and concepts as many online resources often implicitly assume the user to know about them."
+    "For more \"courageous\" beginners wanting to learn how to accomplish this, here is a rough sketch of the aspects to know. First, all Python releases are available for free on the official [download](https://www.python.org/downloads/) page for any supported operating system. Choose the latest one applicable, then download and install it (cf., the [instruction notes](https://wiki.python.org/moin/BeginnersGuide/Download)). As this only includes core Python and the standard library, the beginner then needs to learn about the [pip](https://pip.pypa.io/en/stable/) module. With the command `python -m pip install jupyter`, all necessary third-party libraries are installed (cf., more background [here](https://jupyter.readthedocs.io/en/latest/install.html)). However, this would be done in a *system-wide* fashion and is not recommended. Instead, the best practice is to create a so-called **virtual environment** with the [venv](https://docs.python.org/3/library/venv.html) module with which the installed third-party packages are *isolated* on a per-project basis (the command `python -m venv env-name` creates a virtual environment called \"env-name\"). This tactic is employed to avoid a situation known as **[dependency hell](https://en.wikipedia.org/wiki/Dependency_hell)**. Once created, the virtual environment must then be activated each time before resuming work in each terminal (with the command `source env-name/bin/activate`). While there exist convenience tools that automate parts of this (e.g., [poetry](https://poetry.eustace.io/docs/) or [virtualenvwrapper](https://virtualenvwrapper.readthedocs.io/en/latest/)), it only distracts a beginner from studying the Python language. Yet, it is still worthwhile to have heard about these terms and concepts as many online resources often implicitly assume the user to know about them."
    ]
   },
   {
@@ -250,9 +248,9 @@
     "\n",
     "\"Jupyter\" is an [acronym](https://en.wikipedia.org/wiki/Acronym) derived from the names of the three major programming languages **[Julia](https://julialang.org/)**, **[Python](https://www.python.org)**, and **[R](https://www.r-project.org/)**, all of which play significant roles in the world of data science. The Jupyter Project's idea is to serve as an integrating platform such that different programming languages and software packages can be used together within the same project easily.\n",
     "\n",
-    "Furthermore, Jupyter notebooks have become a de-facto standard for communicating and exchanging results in the data science community (both in academia and business) and often provide a more intuitive alternative to terminal based ways of running Python (e.g., the default [Python interpreter](https://docs.python.org/3/tutorial/interpreter.html) as shown above or a more advanced interactive version like [IPython](https://ipython.org/)) or even a full-fledged [Integrated Development Environment](https://en.wikipedia.org/wiki/Integrated_development_environment) (e.g., the commercial [PyCharm](https://www.jetbrains.com/pycharm/) or the free [Spyder](https://github.com/spyder-ide/spyder)).\n",
+    "Furthermore, Jupyter notebooks have become a de-facto standard for communicating and exchanging results in the data science community (both in academia and business) and often provide a more intuitive alternative to terminal-based ways of running Python (e.g., the default [Python interpreter](https://docs.python.org/3/tutorial/interpreter.html) as shown above or a more advanced interactive version like [IPython](https://ipython.org/)) or even a full-fledged [Integrated Development Environment](https://en.wikipedia.org/wiki/Integrated_development_environment) (e.g., the commercial [PyCharm](https://www.jetbrains.com/pycharm/) or the free [Spyder](https://github.com/spyder-ide/spyder)).\n",
     "\n",
-    "In particular, they allow to mix plain English text with Python code cells. The plain text can be formatted using the [Markdown](https://guides.github.com/features/mastering-markdown/) language and mathematical expressions can be typeset with [LaTeX](https://www.overleaf.com/learn/latex/Free_online_introduction_to_LaTeX_%28part_1%29). Lastly, we can include pictures, plots, and even videos. Because of these features, the notebooks developed for this book come in a self-contained \"tutorial\" style that enables students to learn and review the material on their own."
+    "In particular, they allow mixing plain English text with Python code cells. The plain text can be formatted using the [Markdown](https://guides.github.com/features/mastering-markdown/) language, and mathematical expressions can be typeset with [LaTeX](https://www.overleaf.com/learn/latex/Free_online_introduction_to_LaTeX_%28part_1%29). Lastly, we can include pictures, plots, and even videos. Because of these features, the notebooks developed for this book come in a self-contained \"tutorial\" style that enables students to learn and review the material on their own."
    ]
   },
   {
@@ -274,15 +272,15 @@
     }
    },
    "source": [
-    "A Jupyter notebook consists of cells that have a type associated with them. So far, only cells of type \"Markdown\" have been used, which is the default way to present (formatted) text.\n",
+    "A Jupyter notebook consists of cells that have a type associated with them. So far, only cells of type \"Markdown\" have been used, which is the default way to present formatted text.\n",
     "\n",
-    "The next cell below is an example of a \"Code\" cell containing a line of actual Python code: it simply outputs the text \"Hello world\" when executed. To edit an existing code cell, just enter into it with a mouse click. You know that you are \"in\" a code cell when you see the frame of the code cell turn green.\n",
+    "The next cell below is an example of a \"Code\" cell containing a line of actual Python code: it merely outputs the text \"Hello world\" when executed. To edit an existing code cell, enter into it with a mouse click. You know that you are \"in\" a code cell when you see the frame of the code cell turn green.\n",
     "\n",
-    "Besides this **edit mode** there is also a so-called **command mode** that you can reach by hitting the \"Escape\" key after entering a code cell, which turns the frame's color into blue. Using the Enter\" and \"Escape\" keys you can now switch between the two modes.\n",
+    "Besides this **edit mode**, there is also a so-called **command mode** that you can reach by hitting the \"Escape\" key after entering a code cell, which turns the frame's color blue. Using the \"Enter\" and \"Escape\" keys, you can now switch between the two modes.\n",
     "\n",
-    "To *execute* (or *run*) a code cell, hold the \"Control\" key and press \"Enter\". Note how you do not go to the subsequent cell. Alternatively, you can hold the \"Shift\" key and press \"Enter\", which executes the cell and places your focus on the next cell right after.\n",
+    "To *execute*, or \"*run*,\" a code cell, hold the \"Control\" key and press \"Enter.\" Note how you do not go to the subsequent cell. Alternatively, you can hold the \"Shift\" key and press \"Enter,\" which executes the cell and places your focus on the next cell right after.\n",
     "\n",
-    "Similarly, a Markdown cell is also in either the edit or command mode. For example, simply double-click on the text you are just reading, which takes you into edit mode. Now you could change the formatting (e.g., make a word printed in *italics* or **bold** with single or double asterisks) and then \"execute\" the cell to actually render the text as specified.\n",
+    "Similarly, a Markdown cell is also in either edit or command mode. For example, double-click on the text you are just reading, which takes you into edit mode. Now you could change the formatting (e.g., make a word printed in *italics* or **bold** with single or double asterisks) and then \"execute\" the cell to render the text as specified.\n",
     "\n",
     "To change a cell's type, choose either \"Code\" or \"Markdown\" in the navigation bar at the top."
    ]
@@ -316,7 +314,7 @@
     }
    },
    "source": [
-    "Sometimes a code cell starts with an exclamation mark `!`. Then, the Jupyter notebook behaves as if you just typed the following command right into a terminal. The cell below asks `python` to show its version number. This is actually *not* Python code but a command in the [Shell](https://en.wikipedia.org/wiki/Shell_script) language. The `!` is useful to execute short commands without leaving a Jupyter notebook."
+    "Sometimes a code cell starts with an exclamation mark `!`. Then, the Jupyter notebook behaves as if the following command were typed directly into a terminal. The cell below asks `python` to show its version number and is *not* Python code but a command in the [Shell](https://en.wikipedia.org/wiki/Shell_script) language. The `!` is useful to execute short commands without leaving a Jupyter notebook."
    ]
   },
   {
@@ -359,15 +357,15 @@
     }
    },
    "source": [
-    "In this book *programming* is \"defined\" as:\n",
+    "In this book, *programming* is \"defined\" as:\n",
     "\n",
-    "- a **structured** way of **problem solving**\n",
-    "- by **expressing** the steps of a **computation / process**\n",
+    "- a **structured** way of **problem-solving**\n",
+    "- by **expressing** the steps of a **computation/process**\n",
     "- and thereby **documenting** the process in a formal way\n",
     "\n",
     "Programming is always **concrete** and based on a **particular case**.\n",
     "\n",
-    "Programming definitely exhibits elements of an **art** or a **craft** as we will hear programmers call code \"beautiful\" or \"ugly\" or talk about the \"expressive\" power of an application."
+    "It exhibits elements of a form of **art** or a **craft** as we hear programmers call code \"beautiful\" or \"ugly\" or talk about the \"expressive\" power of an application."
    ]
   },
   {
@@ -385,7 +383,7 @@
     "- develops and analyses **algorithms** and **data structures**,\n",
     "- and **proves** the **correctness** of a program\n",
     "\n",
-    "In a sense, a computer scientist does not need to know a programming language to work. In fact, many computer scientists only know how to produce \"ugly\" code in the eyes of professional programmers."
+    "In a sense, a computer scientist does not need to know a programming language to work, and many computer scientists only know how to produce \"ugly\"-looking code in the eyes of professional programmers."
    ]
   },
   {
@@ -396,7 +394,7 @@
     }
    },
    "source": [
-    "*IT* or *information technology* is a term that has many meanings to many people. Often, it has something to do with hardware and devices both of which are out of scope for programmers and computer scientists. In fact, many people from the two aforementioned fields are more than happy if their printer and internet just work as they do not know a lot more about that than non-technical people."
+    "*IT* or *information technology* is a term that has many meanings to many people. Often, it has something to do with hardware or physical devices, both of which are out of scope for programmers and computer scientists. Many computer scientists and programmers are more than happy if their printer and internet connection work as they often do not know a lot more about that than non-technical people."
    ]
   },
   {
@@ -434,8 +432,8 @@
     "- [Guido van Rossum](https://en.wikipedia.org/wiki/Guido_van_Rossum) (Python’s **[Benevolent Dictator for Life](https://en.wikipedia.org/wiki/Benevolent_dictator_for_life)**) was bored during a week around Christmas 1989 and started Python as a hobby project \"that would keep \\[him\\] occupied\" for some days\n",
     "- the idea was to create a **general-purpose scripting** language that would allow fast **prototyping** and would **run on every operating system**\n",
     "- Python grew through the 90s as van Rossum promoted it via his \"Computer Programming for Everybody\" initiative that had the **goal to encourage a basic level of coding literacy** as an equal knowledge alongside English literacy and math skills\n",
-    "- to become more independent from its creator the next major version **Python 2** (released in 2000; still in heavy use as of today) was **open-sourced** from the get-go which attracted a **large and global community of programmers** that **contributed** their individual knowledge and best practices in their free time to make Python even better\n",
-    "- **Python 3** resulted from a major overhaul of the language in 2008 taking into account the **learnings from almost two decades**, streamlining the language, and getting ready for the age of **big data**\n",
+    "- to become more independent from its creator the next major version **Python 2** (released in 2000; still in heavy use as of today) was **open-sourced** from the get-go which attracted a **large and global community of programmers** that **contributed** their expertise and best practices in their free time to make Python even better\n",
+    "- **Python 3** resulted from a significant overhaul of the language in 2008 taking into account the **learnings from almost two decades**, streamlining the language, and getting ready for the age of **big data**\n",
     "- the language is named after the sketch comedy group [Monty Python](https://en.wikipedia.org/wiki/Monty_Python)"
    ]
   },
@@ -458,7 +456,7 @@
     }
    },
    "source": [
-    "Python is a **general-purpose** programming language that allows for **fast development**, is **easy to read**, **open-source**, long established, unifies the knowledge of **hundreds of thousands of experts** around the world, runs on basically every machine, and can handle the complexities of applications involving **big data**."
+    "Python is a **general-purpose** programming language that allows for **fast development**, is **easy to read**, **open-source**, long-established, unifies the knowledge of **hundreds of thousands of experts** around the world, runs on basically every machine, and can handle the complexities of applications involving **big data**."
    ]
   },
   {
@@ -480,9 +478,9 @@
     }
    },
    "source": [
-    "Couldn't a company like Google, Facebook, or Microsoft come up with a better programming language? The following argument provides hints why this cannot really be the case.\n",
+    "Couldn't a company like Google, Facebook, or Microsoft come up with a better programming language? The following argument provides hints on why this cannot be the case.\n",
     "\n",
-    "Wouldn't it be weird if professors and scholars of English literature and language studies dictated how we'd have to speak in day-to-day casual conversations or how authors of poesy and novels should use language constructs to achieve a certain type of mood? If you agree with that premise, it makes sense to assume that even programming languages should evolve in a \"natural\" way as users just *use* the language over time and in new and unpredictable contexts making up their own new conventions."
+    "Wouldn't it be weird if professors and scholars of English literature and language studies dictated how we'd have to speak in day-to-day casual conversations or how authors of poesy and novels should use language constructs to achieve a particular type of mood? If you agree with that premise, it makes sense to assume that even programming languages should evolve in a \"natural\" way as users *use* the language over time and in new and unpredictable contexts creating new conventions."
    ]
   },
   {
@@ -493,7 +491,7 @@
     }
    },
    "source": [
-    "Loose *communities* are the main building block around which open-source software is built. Someone starts a project (like Guido) and makes it free to use for anybody (e.g., on a code-sharing platform like [GitHub](https://github.com/)). People find it useful enough to solve one of their daily problems and start using it. They see how a project could be improved and provide new use cases (via the popularized concept of a \"[pull request](https://help.github.com/articles/about-pull-requests/)\"). The project grows both in lines of code and number of people using it. After a while, people start local user groups to share their same interests and meet on a regular basis (e.g., this is a big market for companies like [Meetup](https://www.meetup.com/) or non-profits like [PyData](https://pydata.org/)). Out of these local and usually monthly meetups grow yearly conferences on the country or even continental level (e.g., the original [PyCon](https://us.pycon.org/) in the US, [EuroPython](https://europython.eu/), or [PyCon.DE](https://de.pycon.org/)). The content presented at these conferences is made publicly available via GitHub and YouTube (e.g., [PyCon 2019](https://www.youtube.com/channel/UCxs2IIVXaEHHA4BtTiWZ2mQ) or [EuroPython](http://europython.tv/)) and serves as references on what people are working on and introductions to the endless number of specialized fields."
+    "Loose *communities* are the primary building block around which open-source software projects are built. Someone, like Guido, starts a project and makes it free to use for anybody (e.g., on a code-sharing platform like [GitHub](https://github.com/)). People find it useful enough to solve one of their daily problems and start using it. They see how a project could be improved and provide new use cases (via the popularized concept of a \"[pull request](https://help.github.com/articles/about-pull-requests/)\"). The project grows both in lines of code and people using it. After a while, people start local user groups to share their same interests and meet regularly (e.g., this is a big market for companies like [Meetup](https://www.meetup.com/) or non-profits like [PyData](https://pydata.org/)). Out of these local and usually monthly meetups grow yearly conferences on the country or even continental level (e.g., the original [PyCon](https://us.pycon.org/) in the US, [EuroPython](https://europython.eu/), or [PyCon.DE](https://de.pycon.org/)). The content presented at these conferences is made publicly available via GitHub and YouTube (e.g., [PyCon 2019](https://www.youtube.com/channel/UCxs2IIVXaEHHA4BtTiWZ2mQ) or [EuroPython](http://europython.tv/)) and serves as references on what people are working on and introductions to the endless number of specialized fields."
    ]
   },
   {
@@ -504,11 +502,11 @@
     }
    },
    "source": [
-    "While these communities are rather loose and constantly changing, smaller in-groups, often democratically organized and elected (e.g., the Python Software Foundation), take care of, for example, the development of the \"core\" Python language itself.\n",
+    "While these communities are somewhat loose and continuously changing, smaller in-groups, often democratically organized and elected (e.g., the [Python Software Foundation](https://www.python.org/psf/)), take care of, for example, the development of the \"core\" Python language itself.\n",
     "\n",
-    "Interestingly, Python is just a specification (i.e., a set of rules) as to what is allowed and what not. The current version of Python can always be lookep up in the [Python Language Reference](https://docs.python.org/3/reference/index.html). In order to make changes to that, anyone can make a so-called **[Python Enhancement Proposal](https://www.python.org/dev/peps/)**, or **PEP** for short, where it needs to be specified what exact changes are to be made and argued why that is a good thing to do. These PEPs are reviewed by the [core developers](https://devguide.python.org/coredev/) and anyone interested and are then either accepted, modified, or rejected if, for example, the change introduces internal inconsistencies. This is very similar to the **double blind peer review** established in academia. In fact, many of the contributors held or hold positions in academia, one more indicator of the high quality standards in the Python community. To learn more about PEPs, check out [PEP 1](https://www.python.org/dev/peps/pep-0001/) that describes the entire process.\n",
+    "Interestingly, Python is just a specification (i.e., a set of rules) as to what is allowed and what not. The current version of Python can always be looked up in the [Python Language Reference](https://docs.python.org/3/reference/index.html). To make changes to that, anyone can make a so-called **[Python Enhancement Proposal](https://www.python.org/dev/peps/)**, or **PEP** for short, where it needs to be specified what exact changes are to be made and argued why that is a good thing to do. These PEPs are reviewed by the [core developers](https://devguide.python.org/coredev/) and interested people and are then either accepted, modified, or rejected if, for example, the change introduces internal inconsistencies. This process is similar to the **double-blind peer review** established in academia. Many of the contributors held or hold positions in academia, one more indicator of the high quality standards in the Python community. To learn more about PEPs, check out [PEP 1](https://www.python.org/dev/peps/pep-0001/) that describes the entire process.\n",
     "\n",
-    "In total, no one single entity can control how the language evolves and the users' needs and ideas always feed back to the language specification via a quality controlled and \"democratic\" process."
+    "In total, no one single entity can control how the language evolves, and the users' needs and ideas always feed back to the language specification via a quality controlled and \"democratic\" process."
    ]
   },
   {
@@ -519,7 +517,7 @@
     }
    },
    "source": [
-    "Besides being free as in **\"free beer\"**, a major benefit of open-source is that one can always **look up how something works in detail** (that is the literal meaning of *open* source). This is a huge benefit compared to commercial languages (e.g., MATLAB) since a programmer can always continue to **study best practices** or how things are done. Along this way, many **errors are uncovered** as well. Furthermore, if one runs an open-source application, one can be reasonably sure that no bad people built in a \"backdoor\". [Free software](https://en.wikipedia.org/wiki/Free_software) is consequently free of charge but brings many other freedoms with it, most notable the freedom to change the code."
+    "Besides being free as in **\"free beer**,\" a major benefit of open-source is that one can always **look up how something works in detail**: That is the literal meaning of *open* source and different as compared to commercial languages (e.g., MATLAB) since a programmer can always continue to **study best practices** or find out how things are implemented. Along this way, many **errors are uncovered** as well. Furthermore, if one runs an open-source application, one can be reasonably sure that no bad people built in a \"backdoor.\" [Free software](https://en.wikipedia.org/wiki/Free_software) is consequently free of charge but brings many other freedoms with it, most notably the freedom to change the code."
    ]
   },
   {
@@ -541,13 +539,13 @@
     }
    },
    "source": [
-    "The \"weird\" thing is that the default Python implementation is actually written in the C language.\n",
+    "The \"weird\" thing is that the default Python implementation is written in the C language.\n",
     "\n",
-    "[C](https://en.wikipedia.org/wiki/C_%28programming_language%29) and [C++](https://en.wikipedia.org/wiki/C%2B%2B) (check this [introduction](https://www.learncpp.com/)) are wide-spread and long established (i.e., since the 1970s) programming languages that are employed in many mission critical softwares (e.g., operating systems themselves, low latency databases and web servers, nuclear reactor control systems, airplanes, ...). They are fast mainly because the programmer not only needs to come up with the **business logic** but also manage the computer's memory \"manually\" (and the knowledge necessary to do that is not easy to learn).\n",
+    "[C](https://en.wikipedia.org/wiki/C_%28programming_language%29) and [C++](https://en.wikipedia.org/wiki/C%2B%2B) (cf., this [introduction](https://www.learncpp.com/)) are wide-spread and long-established (i.e., since the 1970s) programming languages employed in many mission-critical software systems (e.g., operating systems themselves, low latency databases and web servers, nuclear reactor control systems, airplanes, ...). They are fast, mainly because the programmer not only needs to come up with the **business logic** but also manage the computer's memory \"manually\" (and the knowledge necessary to do that is not easy to learn).\n",
     "\n",
-    "In contrast, Python automatically manages the memory for the programmer. This speeds up the development process a lot. So speed here is really a trade-off of application run time vs. engineering / development time and in many applications the program's run time is not that important (e.g., what if C needs 0.001 seconds in a case where Python needs 0.1 seconds to train a linear regression model?). When the requirements change and computing speed becomes an issue, the Python community offers many third-party libraries (usually also written in C) where specific problems can be solved in near-C time.\n",
+    "In contrast, Python automatically manages the memory for the programmer. So, speed here is a trade-off between application run time and engineering/development time. Often, the program's run time is not that important: For example, what if C needs 0.001 seconds in a case where Python needs 0.1 seconds to train a linear regression model? When the requirements change and computing speed becomes an issue, the Python community offers many third-party libraries (usually also written in C) where specific problems can be solved in near-C time.\n",
     "\n",
-    "**In a nutshell**: While it is of course true that C is a lot faster than Python when it comes to **pure computation time**, in many cases this does not really matter as the **significantly shorter development cycles** are the more important cost factor in a rapidly changing business world."
+    "**In a nutshell**: While it is, of course, true that C is a lot faster than Python when it comes to **pure computation time**, often, this does not matter as the **significantly shorter development cycles** are the more significant cost factor in a rapidly changing business world."
    ]
   },
   {
@@ -580,25 +578,23 @@
     }
    },
    "source": [
-    "While it is usually not the best argument to quote authorative figures like the pope, we will do this in this section anyways:\n",
+    "While it is usually not the best argument to quote authoritative figures like the pope, we briefly look at who uses Python here and leave it up to the reader to decide if this is convincing or not:\n",
     "\n",
     "- **[Massachusetts Institute of Technology](https://www.mit.edu/)**\n",
     "  - teaches Python in its [introductory course](https://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-0001-introduction-to-computer-science-and-programming-in-python-fall-2016/) to computer science independent of the student's major\n",
-    "  - replaced the infamous course on the [Scheme](https://groups.csail.mit.edu/mac/projects/scheme/) language ([source](https://news.ycombinator.com/item?id=602307))\n",
+    "  - replaced the infamous course on the [Scheme](https://groups.csail.mit.edu/mac/projects/scheme/) language (cf., [source](https://news.ycombinator.com/item?id=602307))\n",
     "- **[Google](https://www.google.com/)**\n",
-    "  - used the strategy **\"Python where we can, C++ where we must\"** from its early days on to stay flexible in a rapidly changing environment ([source](https://stackoverflow.com/questions/2560310/heavy-usage-of-python-at-google))\n",
-    "  - the very first web-crawler was written in **Java and so difficult** to maintain that it was **re-written in Python** right away ([source](https://www.amazon.com/Plex-Google-Thinks-Works-Shapes/dp/1416596585/ref=sr_1_1?ie=UTF8&qid=1539101827&sr=8-1&keywords=in+the+plex))\n",
+    "  - used the strategy **\"Python where we can, C++ where we must\"** from its early days on to stay flexible in a rapidly changing environment (cf., [source](https://stackoverflow.com/questions/2560310/heavy-usage-of-python-at-google))\n",
+    "  - the very first web-crawler was written in **Java and so difficult to maintain** that it was **re-written in Python** right away (cf., [source](https://www.amazon.com/Plex-Google-Thinks-Works-Shapes/dp/1416596585/ref=sr_1_1?ie=UTF8&qid=1539101827&sr=8-1&keywords=in+the+plex))\n",
     "  - Python and C++, Java, and Go are the only four server-side languages to be deployed to production\n",
     "  - Guido van Rossom was hired by Google from 2005 to 2012 to advance the language there\n",
-    "- **[NASA](https://www.nasa.gov/)** open-sources a lot of its projects, many of which are written in Python and regard working with really big data ([source](https://code.nasa.gov/language/python/))\n",
+    "- **[NASA](https://www.nasa.gov/)** open-sources many of its projects, often written in Python and regarding analyses with big data (cf., [source](https://code.nasa.gov/language/python/))\n",
     "- **[Facebook](https://facebook.com/)** uses Python besides C++ and its legacy PHP (a language for building websites; the \"cool kid\" from the early 2000s)\n",
-    "- **[Instagram](https://instagram.com/)** operates the largest installation of the popular **web framework  [Django](https://www.djangoproject.com/)** ([source](https://instagram-engineering.com/web-service-efficiency-at-instagram-with-python-4976d078e366))\n",
-    "- **[Spotify](https://spotify.com/)** bases its data science on Python ([source](https://labs.spotify.com/2013/03/20/how-we-use-python-at-spotify/))\n",
-    "- **[Netflix](https://netflix.com/)** also runs its predictive models on Python ([source](https://medium.com/netflix-techblog/python-at-netflix-86b6028b3b3e))\n",
-    "- **[Dropbox](https://dropbox.com/)** \"stole\" Guido van Rossom from Google to help scale the platform ([source](https://medium.com/dropbox-makers/guido-van-rossum-on-finding-his-way-e018e8b5f6b1))\n",
-    "- **[JPMorgan Chase](https://www.jpmorganchase.com/)** requires new employees to learn Python as part of the onboarding process starting with the 2018 intake ([source](https://www.ft.com/content/4c17d6ce-c8b2-11e8-ba8f-ee390057b8c9?segmentId=a7371401-027d-d8bf-8a7f-2a746e767d56))\n",
-    "- ...\n",
-    "- ... this list is intentionally shortened as Python is simply very popular ..."
+    "- **[Instagram](https://instagram.com/)** operates the largest installation of the popular **web framework  [Django](https://www.djangoproject.com/)** (cf., [source](https://instagram-engineering.com/web-service-efficiency-at-instagram-with-python-4976d078e366))\n",
+    "- **[Spotify](https://spotify.com/)** bases its data science on Python (cf., [source](https://labs.spotify.com/2013/03/20/how-we-use-python-at-spotify/))\n",
+    "- **[Netflix](https://netflix.com/)** also runs its predictive models on Python (cf., [source](https://medium.com/netflix-techblog/python-at-netflix-86b6028b3b3e))\n",
+    "- **[Dropbox](https://dropbox.com/)** \"stole\" Guido van Rossom from Google to help scale the platform (cf., [source](https://medium.com/dropbox-makers/guido-van-rossum-on-finding-his-way-e018e8b5f6b1))\n",
+    "- **[JPMorgan Chase](https://www.jpmorganchase.com/)** requires new employees to learn Python as part of the onboarding process starting with the 2018 intake (cf., [source](https://www.ft.com/content/4c17d6ce-c8b2-11e8-ba8f-ee390057b8c9?segmentId=a7371401-027d-d8bf-8a7f-2a746e767d56))"
    ]
   },
   {
@@ -631,7 +627,7 @@
     }
    },
    "source": [
-    "As the graph below shows, neither Google's very own language **[Go](https://golang.org/)** nor **[R](https://www.r-project.org/)**, a very popular language in the niche of statistics and data science, can compete with Python's year-to-year growth."
+    "As the graph below shows, neither Google's very own language **[Go](https://golang.org/)** nor **[R](https://www.r-project.org/)**, a domain-specific language in the niche of statistics, can compete with Python's year-to-year growth."
    ]
   },
   {
@@ -686,13 +682,13 @@
     }
    },
    "source": [
-    "Simply put, **always be coding**.\n",
+    "**A**lways **b**e **c**oding.\n",
     "\n",
-    "Programming is more than just writing code into a text file. It means reading through parts of documentation, blogs with best practices, and tutorials, or researching some problem on Stack Overflow while trying to implement some feature in the application at hand. Also, it means using command line tools to automate some part of the work, or manage different versions of a software simultaneously using software like **[git](https://git-scm.com/)**. In short, programming involves a lot of \"muscle memory\". This can only be built and kept up through near-daily usage.\n",
+    "Programming is more than just writing code into a text file. It means reading through parts of the [documentation](https://docs.python.org/), blogs with best practices, and tutorials, or researching problems on Stack Overflow while trying to implement features in the application at hand. Also, it means using command-line tools to automate some part of the work or manage different versions of a program, for example, with **[git](https://git-scm.com/)**. In short, programming involves a lot of \"muscle memory,\" which can only be built and kept up through near-daily usage.\n",
     "\n",
-    "Further, many aspects of software architecture and best practices can only be understood after having implemented some requirement for the very first time. Coding also means \"breaking\" things to find out what makes them actually work to begin with.\n",
+    "Further, many aspects of software architecture and best practices can only be understood after having implemented some requirements for the very first time. Coding also means \"breaking\" things to find out what makes them work in the first place.\n",
     "\n",
-    "Coding is learned best by just doing it for some time on a daily or at least regular basis and not right before some task is due, just like learning a \"real\" language."
+    "Coding is learned best by just doing it for some time on a daily or at least a regular basis and not right before some task is due, just like learning a \"real\" language."
    ]
   },
   {
@@ -716,12 +712,12 @@
    "source": [
     "[Y Combinator](https://www.ycombinator.com/)'s co-founder [Paul Graham](https://en.wikipedia.org/wiki/Paul_Graham_%28programmer%29) wrote a very popular and often cited [article](http://www.paulgraham.com/makersschedule.html) where he divides every person into belonging to one of two groups:\n",
     "\n",
-    "- **Managers**: People that need to organize things and command others (like a \"boss\"). Their schedule is usually organized by the hour or even by 30 minute intervals.\n",
+    "- **Managers**: People that need to organize things and command others (like a \"boss\"). Their schedule is usually organized by the hour or even 30-minute intervals.\n",
     "- **Makers**: People that create things (like programmers, artists, or writers). Such people think in half days or full days.\n",
     "\n",
-    "Have you ever wondered why so many tech nerds work all the nights and sleep during \"weird\" hours? This is mainly because many programming related tasks require a certain \"flow\" state of one's mind. This is hard to achieve when one can get interupted, even if it is only for one short question. Graham describes that only knowing that one has an appointment in three hours can cause a programmer to not get into a flow state.\n",
+    "Have you ever wondered why so many tech people work during nights and sleep at \"weird\" times? The reason is that many programming related tasks require a \"flow\" state in one's mind that is hard to achieve when one can get interrupted, even if it is only for one short question. Graham describes that only knowing that one has an appointment in three hours can cause a programmer to not get into a flow state.\n",
     "\n",
-    "As a result, do not set aside a certain amount of time for learning something but rather plan in an **entire evening** or a **rainy Sunday** where you can work on a problem in an **open end** setting. And do not be surprised any more to hear a \"I looked at it over the weekend\" from a programmer."
+    "As a result, do not set aside a certain amount of time for learning something but rather plan in an **entire evening** or a **rainy Sunday** where you can work on a problem in an **open end** setting. And do not be surprised anymore to hear \"I looked at it over the weekend\" from a programmer."
    ]
   },
   {
@@ -743,17 +739,17 @@
     }
    },
    "source": [
-    "When being asked the above question, most programmers will answer something that can be classified into one of two broader groups.\n",
+    "When being asked the above question, most programmers answer something that can be classified into one of two broader groups.\n",
     "\n",
-    "**1) Toy Problem / Case Study / Prototype**: Pick some problem that you want to write a programatic solution for, break it down into smaller packages, and solve them \"backwards\".\n",
+    "**1) Toy Problem, Case Study, or Prototype**: Pick some problem, break it down into smaller sub-problems, and solve them with an end in mind.\n",
     "\n",
-    "**2) Books / Video Tutorials / Courses**: Research the best book / blog post / video tutorial for something and work it through from start to end.\n",
+    "**2) Books, Video Tutorials, and Courses**: Research the best book, blog, video, or tutorial for something and work it through from start to end.\n",
     "\n",
     "The truth is that you need to iterate between these two phases.\n",
     "\n",
-    "Building a prototype will always reveal issues no book or tutorial can think of before. Data is never clean as it comes. Some algorithm from a text book must be adapted to a very specific but important aspect of a case study. It is important to learn to \"ship a product\", i.e., to finish some project to the very end because only then you will have looked at all the aspects.\n",
+    "Building a prototype always reveals issues no book or tutorial can think of before. Data is never as clean as it should be. An algorithm from a textbook must be adapted to a peculiar aspect of a case study. It is essential to learn to \"ship a product\" because only then will one have looked at all the aspects.\n",
     "\n",
-    "The major downside of this approach is that you likely learn bad \"patterns\" as whatever you learn is overfitted to the case and you do not get the big picture or mental concepts behind a solution. This is a gap well written books can fill in (e.g., check the Python / Programming books by [Packt](https://www.packtpub.com/packt/offers/free-learning/) or [O’Reilly](https://www.oreilly.com/))."
+    "The major downside of this approach is that one likely learns bad \"patterns\" overfitted to the case at hand, and one does not get the big picture or mental concepts behind a solution. This gap can be filled in by well-written books: For example, check the Python/programming books offered by [Packt](https://www.packtpub.com/packt/offers/free-learning/) or [O’Reilly](https://www.oreilly.com/)."
    ]
   },
   {
@@ -801,7 +797,7 @@
     "  7. Sequences\n",
     "  8. Mappings & Sets\n",
     "  9. Arrays\n",
-    "- How can we create our own data types?\n",
+    "- How can we create custom data types?\n",
     "  10. Object-Orientation"
    ]
   },
diff --git a/00_start_up_review_and_exercises.ipynb b/00_start_up_review_and_exercises.ipynb
index 2f4c334..109a2fc 100644
--- a/00_start_up_review_and_exercises.ipynb
+++ b/00_start_up_review_and_exercises.ipynb
@@ -53,7 +53,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "**Q2**: Explain what is a *pull request* and elaborate how this concept fits to a *distributed* organization of work!"
+    "**Q2**: Explain what is a *pull request* and elaborate on how this concept fits a *distributed* organization of work!"
    ]
   },
   {
@@ -81,7 +81,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "**Q4**: What is a major advantage of a \"slow\" programming language like Python over a faster one like C?"
+    "**Q4**: What is a significant advantage of a \"slow\" programming language like Python over a faster one like C?"
    ]
   },
   {
@@ -109,7 +109,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "**Q5**: Python has been the fastest growing *major* programming language in recent years."
+    "**Q5**: Python has been the fastest-growing *major* programming language in recent years."
    ]
   },
   {
@@ -137,7 +137,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "**Q7**: Python was originally designed for highly intensive numerical computing, in particular for use cases from physics and astronomy."
+    "**Q7**: Python was initially designed for highly intensive numerical computing, in particular for use cases from physics and astronomy."
    ]
   },
   {
@@ -151,7 +151,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "**Q8**: JavaScript is a special subset of the Java language."
+    "**Q8**: JavaScript is a subset of the Java language."
    ]
   },
   {
@@ -165,7 +165,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "**Q9**: Python is *free software*. That means it will never cost anything."
+    "**Q9**: Python is *free software*. That means it does not cost anything."
    ]
   },
   {
@@ -179,7 +179,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "**Q10**: The main purpose of PEPs is to regulate how code should be documented and/or styled."
+    "**Q10**: The primary purpose of PEPs is to regulate how code should be documented and styled."
    ]
   },
   {
@@ -228,7 +228,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "**Q12**: The quote \"Education is what remains after one has forgotten what one has learned in school\" is attributed to Albert Einstein. Use a special Markdown syntax to display the author and his quote appropriately!"
+    "**Q12**: The quote \"Education is what remains after one has forgotten what one has learned in school\" is attributed to Albert Einstein. Display the author and his quote appropriately!"
    ]
   },
   {
diff --git a/01_elements.ipynb b/01_elements.ipynb
index 658e696..c941e43 100644
--- a/01_elements.ipynb
+++ b/01_elements.ipynb
@@ -20,11 +20,11 @@
     }
    },
    "source": [
-    "Do you remember how you first learned to speak in your mother tounge? Probably not. No one's memory goes back that far. Your earliest memory as a child should probably be around the age of three or four years old when you could already say simple things and interact with your environment. Although you did not know any grammar rules yet, other people just understood what you said. Well, most of the time.\n",
+    "Do you remember how you first learned to speak in your mother tongue? Probably not. No one's memory goes back that far. Your earliest memory as a child should probably be around the age of three or four years old when you could already say simple things and interact with your environment. Although you did not know any grammar rules yet, other people just understood what you said. Well, most of the time.\n",
     "\n",
-    "It is intuitively best to take the very mindset of a small child when learing a foreign language and we do so for learning the Python language as well. This first chapter introduces simplistic examples and we better just accept them as they are without knowing any of the \"grammar\" rules yet. Then, we analyze them in parts and slowly build up our understanding.\n",
+    "It is intuitively best to take the very mindset of a small child when learning a foreign language. This first chapter introduces simplistic examples, and we accept them as they are *without* knowing any of the \"grammar\" rules yet. Then, we analyze them in parts and slowly build up our understanding.\n",
     "\n",
-    "Consequently, if parts of this chapter do not make sense right away, let's not worry too much. Besides introducing some basics that we need to understand, it also serves as an outlook for what is to come. So, many terms and concepts used here will be covered in great detail in following chapters."
+    "Consequently, if parts of this chapter do not make sense right away, let's not worry too much. Besides introducing the basic elements, it also serves as an outlook for what is to come. So, many terms and concepts used here are deconstructed in great detail in the following chapters."
    ]
   },
   {
@@ -75,7 +75,7 @@
     }
    },
    "source": [
-    "To verify that something happened in our computer's memory, we simply **reference** `numbers`."
+    "To verify that something happened in our computer's memory, we **reference** `numbers`."
    ]
   },
   {
@@ -116,11 +116,11 @@
     "\n",
     "The `if number % 2 == 0` may look disturbing at first sight. Both the `%` and `==` must have an unintuitive meaning here. Luckily, the **comment** in the same line after the `#` symbol has the answer: The program only does something if the current `number` is even.\n",
     "\n",
-    "In particular, it increases `count` by $1$ and adds the current `number` onto the [running](https://en.wikipedia.org/wiki/Running_total) `total`. Both `count` and `number` were initially set to $0$ and the single `=` symbol reads as \"... is *set* equal to ...\". It could not indicate a mathematical equation as, for example, `count` is generally not equal to `count + 1`.\n",
+    "In particular, it increases `count` by $1$ and adds the current `number` onto the [running](https://en.wikipedia.org/wiki/Running_total) `total`. Both `count` and `number` are initially set to $0$ and the single `=` symbol reads as \"... is *set* equal to ...\". It could not indicate a mathematical equation as, for example, `count` is generally not equal to `count + 1`.\n",
     "\n",
-    "Lastly, the `average` is calculated as the ratio of the final **values** of `total` and `count`. Overall, we divide the sum of all even numbers by the count of all even numbers, which is exactly what we are looking for.\n",
+    "Lastly, the `average` is calculated as the ratio of the final **values** of `total` and `count`. Overall, we divide the sum of all even numbers by their count: This is nothing but the definition of an average.\n",
     "\n",
-    "The lines of code \"within\" the `for` and `if` **statements** are **indented** and *aligned* with multiples of **four spaces**: This shows immediately how the lines relate to each other."
+    "The lines of code \"within\" the `for` and `if` **statements** are **indented** and **aligned** with multiples of **four spaces**: This shows immediately how the lines relate to each other."
    ]
   },
   {
@@ -133,8 +133,8 @@
    },
    "outputs": [],
    "source": [
-    "count = 0  # initialize variables to keep track of the sum\n",
-    "total = 0  # so far and the count of the even numbers\n",
+    "count = 0  # initialize variables to keep track of the\n",
+    "total = 0  # running total and the count of even numbers\n",
     "\n",
     "for number in numbers:\n",
     "    if number % 2 == 0:  # only look at even numbers\n",
@@ -152,7 +152,7 @@
     }
    },
    "source": [
-    "We do not see any **output** yet but obtain the value of `average` by simply referencing it again."
+    "We do not see any **output** yet but obtain the value of `average` by referencing it again."
    ]
   },
   {
@@ -249,7 +249,7 @@
     }
    },
    "source": [
-    "To visualize something before the end of the cell, we use the [print()](https://docs.python.org/3/library/functions.html#print) built-in **function**."
+    "To visualize something before the end of the cell, we use the built-in [print()](https://docs.python.org/3/library/functions.html#print) **function**. Here, the parentheses `()` indicate that we execute code defined somewhere else."
    ]
   },
   {
@@ -283,7 +283,7 @@
     }
    },
    "source": [
-    "Outside Jupyter notebooks, the semicolon `;` is actually used as a **seperator** between several statements that would otherwise have to be on a line on their own. However, it as *not* considered good practice to use it as it makes code less readable."
+    "Outside Jupyter notebooks, the semicolon `;` is used as a **separator** between statements that must otherwise be on a line on their own. However, it is *not* considered good practice to use it as it makes code less readable."
    ]
   },
   {
@@ -327,11 +327,11 @@
     }
    },
    "source": [
-    "Python comes with many **[operators](https://docs.python.org/3/reference/lexical_analysis.html#operators)** built in: They are **tokens** (i.e., \"symbols\") that have a special meaning to the Python interpreter.\n",
+    "Python comes with many built-in **[operators](https://docs.python.org/3/reference/lexical_analysis.html#operators)**: They are **tokens** (i.e., \"symbols\") that have a special meaning to the Python interpreter.\n",
     "\n",
-    "The arithmetic operators either \"operate\" with the number immediately following them (= **unary** operators; e.g., negation) or \"process\" the two numbers \"around\" them (= **binary** operators; e.g., addition). But we will see many exceptions from that as well.\n",
+    "The arithmetic operators either \"operate\" with the number immediately following them (= **unary** operators; e.g., negation) or \"process\" the two numbers \"around\" them (= **binary** operators; e.g., addition).\n",
     "\n",
-    "By definition, operators have **no** permanent **side effects** in the computer's memory. Although the code cells in this section do indeed create *new* numbers in memory (e.g., `77 + 13` creates `90`), they are immediately \"forgotten\" as they are not stored in a **variable** like `numbers` or `average` above. We will continue this thought further below when we compare **expressions** with **statements**.\n",
+    "By definition, operators have **no** permanent **side effects** in the computer's memory. Although the code cells in this section do indeed create *new* numbers in memory (e.g., `77 + 13` creates `90`), they are immediately \"forgotten\" as they are not stored in a **variable** like `numbers` or `average` above. We continue this thought further below when we compare **expressions** with **statements**.\n",
     "\n",
     "Let's see some examples of operators. We start with the binary `+` and the `-` operators for addition and subtraction. Binary operators are designed to resemble what mathematicians call [infix notation](https://en.wikipedia.org/wiki/Infix_notation) and have the expected meaning."
    ]
@@ -427,7 +427,7 @@
     }
    },
    "source": [
-    "When we compare the output of the `*` and `/` operators for multiplication and division, we note the subtle *difference* between the $42$ and the $42.0$. This is a first illustration of the concept of a **data type**."
+    "When we compare the output of the `*` and `/` operators for multiplication and division, we note the subtle *difference* between the $42$ and the $42.0$: They are the *same* number represented as a *different* **data type**."
    ]
   },
   {
@@ -486,7 +486,7 @@
     }
    },
    "source": [
-    "The so-called **floor division operator** `//` always \"rounds\" to the next integer and is thus also called **integer division operator**. This is a first example of an operator we commonly do not know from high school mathematics."
+    "The so-called **floor division operator** `//` always \"rounds\" to an integer and is thus also called **integer division operator**. It is an example of an arithmetic operator we commonly do not know from high school mathematics."
    ]
   },
   {
@@ -615,7 +615,9 @@
     }
    },
    "source": [
-    "Note that the remainder is $0$ *only if* a number is *divisable* by another."
+    "The remainder is $0$ *only if* a number is *divisible* by another.\n",
+    "\n",
+    "A popular convention in both, computer science and mathematics, is to abbreviate \"only if\" as **iff**, which is short for \"**[if and only if](https://en.wikipedia.org/wiki/If_and_only_if)**.\" The iff means that a remainder of $0$ implies that a number is divisible by another but also that a number divisible by another implies a remainder of $0$. The implication goes in *both* directions!"
    ]
   },
   {
@@ -744,7 +746,7 @@
     }
    },
    "source": [
-    "Raising a number to a power is performed with the **exponentiation operator** `**`. This is different from the `^` operator many other programming languages use and that also exists in Python with a *different* meaning."
+    "Raising a number to a power is performed with the **exponentiation operator** `**`. It is different from the `^` operator other programming languages may use and that also exists in Python with a *different* meaning."
    ]
   },
   {
@@ -779,7 +781,7 @@
     }
    },
    "source": [
-    "The normal [order of precedence](https://docs.python.org/3/reference/expressions.html#operator-precedence) from mathematics applies (i.e., \"PEMDAS\" rule)."
+    "The standard [order of precedence](https://docs.python.org/3/reference/expressions.html#operator-precedence) from mathematics applies (i.e., \"PEMDAS\" rule)."
    ]
   },
   {
@@ -908,7 +910,7 @@
     }
    },
    "source": [
-    "There are many more non-mathematical operators that are introduced throughout this book together with the concepts they implement. Some of these are already shown in the next section."
+    "There exist many non-mathematical operators that are introduced throughout this book, together with the concepts they implement. They often come in a form different from the unary and binary mentioned above."
    ]
   },
   {
@@ -932,9 +934,9 @@
    "source": [
     "Python is a so-called **object-oriented** language, which is a paradigm of organizing a program's memory.\n",
     "\n",
-    "An **object** can be viewed as a \"bag\" of $0$s and $1$s in a distinct memory location that not only portrayes the idea of a certain **value** but also has some associated rules as to how this value is treated and may be worked with.\n",
+    "An **object** may be viewed as a \"bag\" of $0$s and $1$s in a distinct memory location. The concrete $0$s and $1$s in a bag portray the idea of the object's **value**, and there exist different **types** of bags that each come with associated rules as to how the $0$s and $1$s are interpreted and may be worked with.\n",
     "\n",
-    "An object *always* has **three** main characteristics. Let's look at the following examples and work them out."
+    "So, an object *always* has **three** main characteristics. Let's look at the following examples and work them out."
    ]
   },
   {
@@ -986,7 +988,7 @@
     {
      "data": {
       "text/plain": [
-       "140098012863472"
+       "139829040614096"
       ]
      },
      "execution_count": 28,
@@ -1010,7 +1012,7 @@
     {
      "data": {
       "text/plain": [
-       "140098013042536"
+       "139829040789352"
       ]
      },
      "execution_count": 29,
@@ -1034,7 +1036,7 @@
     {
      "data": {
       "text/plain": [
-       "140098012714672"
+       "139829040474736"
       ]
      },
      "execution_count": 30,
@@ -1054,7 +1056,7 @@
     }
    },
    "source": [
-    "These addresses are really not that meaningful for anything other than checking if two variables actually **point** at the same object. This may be helpful as, for example, different objects in memory may of course have the same value."
+    "These addresses are *not* meaningful for anything other than checking if two variables **point** at the same object. Let's create a second variable `d` and also set it to `789`."
    ]
   },
   {
@@ -1078,7 +1080,7 @@
     }
    },
    "source": [
-    "`a` and `d` indeed have the same value as is checked with the **equality operator** `==`. The resulting `True` (and the `False` below) is yet another data type, a so-called **boolean**. We will look into that closely in [Chapter 3](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/03_conditionals.ipynb)."
+    "`a` and `d` indeed have the same value as is checked with the **equality operator** `==`. The resulting `True` (and the `False` further below) is yet another data type, a so-called **boolean**. We look into them closely in [Chapter 3](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/03_conditionals.ipynb)."
    ]
   },
   {
@@ -1113,7 +1115,7 @@
     }
    },
    "source": [
-    "On the contrary, `a` and `d` are different objects as the **identity operator** `is` shows: they are stored at seperate addresses in the memory."
+    "On the contrary, `a` and `d` are different objects as the **identity operator** `is` shows: they are stored at separate addresses in the memory."
    ]
   },
   {
@@ -1220,9 +1222,7 @@
    "source": [
     "Different types imply different behaviors for the objects. The `b` object, for example, can be \"asked\" if it could also be interpreted as an `int` with the [is_integer()](https://docs.python.org/3/library/stdtypes.html#float.is_integer) \"functionality\" that comes with every `float` object.\n",
     "\n",
-    "Formally, we call such type-specific functionalities **methods** (to differentiate them from functions) and we will eventually fully introduce them when we talk about object-orientation in Chapter 10. For now, it suffices to know that we access them using the **dot operator** `.`. Of course `b` could be converted into an `int`, which the boolean value `True` tells us.\n",
-    "\n",
-    "Also note how the `.` operator is neiter a unary nor a binary operator as specified above."
+    "Formally, we call such type-specific functionalities **methods** (to differentiate them from functions) and we formally introduce them in Chapter 10. For now, it suffices to know that we access them using the **dot operator** `.`. Of course, `b` could be converted into an `int`, which the boolean value `True` tells us."
    ]
   },
   {
@@ -1257,7 +1257,7 @@
     }
    },
    "source": [
-    "For an `int` object this [is_integer()](https://docs.python.org/3/library/stdtypes.html#float.is_integer) check does not make sense as we know it is an `int` to begin with. This is why we see the `AttributeError` below as `a` does not even know what `is_integer()` means."
+    "For an `int` object, this [is_integer()](https://docs.python.org/3/library/stdtypes.html#float.is_integer) check does *not* make sense as we already know it is an `int`: We see the `AttributeError` below as `a` does not even know what `is_integer()` means."
    ]
   },
   {
@@ -1293,7 +1293,7 @@
     }
    },
    "source": [
-    "The `c` object is a so-called **string** type (i.e., `str`), which is Python's way of representing \"text\". Strings also come with their own behaviors, for example, to convert a text to lower or upper case."
+    "The `c` object is a so-called **string** type (i.e., `str`), which is Python's way of representing \"text.\" Strings also come with peculiar behaviors, for example, to convert a text to lower or upper case."
    ]
   },
   {
@@ -1411,9 +1411,9 @@
     }
    },
    "source": [
-    "Almost trivially, every object also has a value to which it \"evaluates\" when referenced. We think of the value as the **conceptual idea** of what the $0$s and $1$s in memory mean to us humans. A machine does not really see beyond the $0$s and $1$s. At least, not yet.\n",
+    "Almost trivially, every object also has a value to which it \"evaluates\" when referenced. We think of the value as the **conceptual idea** of what the $0$s and $1$s in memory mean to humans as machines cannot see beyond $0$s and $1$s.\n",
     "\n",
-    "For built-in data types, Python prints out the object's value in a so-called **[literal](https://docs.python.org/3/reference/lexical_analysis.html#literals)** notation. This basically means that we can just copy & paste the output back into a code cell to create a *new* object with the *same* value."
+    "For built-in data types, Python prints out the object's value as a so-called **[literal](https://docs.python.org/3/reference/lexical_analysis.html#literals)**: This means that we can copy and paste the output back into a code cell to create a *new* object with the *same* value."
    ]
   },
   {
@@ -1472,7 +1472,7 @@
     }
    },
    "source": [
-    "In this book, we follow the convention of creating strings with **double quotes** `\"` instead of the **single quotes** `'` to which Python defaults in its literal output for `str` objects. Both types of quotes may be used interchangebly."
+    "In this book, we follow the convention of creating strings with **double quotes** `\"` instead of the **single quotes** `'` to which Python defaults in its literal output for `str` objects. Both types of quotes may be used interchangeably."
    ]
   },
   {
@@ -1544,7 +1544,7 @@
    "source": [
     "If we do not follow the rules, the code cannot be **parsed** correctly, i.e., the program does not even start to run but **raises** a **syntax error** indicated as `SyntaxError` in the output. Computers are very dumb in the sense that the slightest syntax error leads to the machine not understanding our code.\n",
     "\n",
-    "For example, if we wanted to write an accounting program that adds up currencies, we would have to model dollar prices as `float` objects as the dollar symbol cannot be read by Python."
+    "For example, if we wanted to write an accounting program that adds up currencies, we would have to model dollar prices as `float` objects as the dollar symbol cannot be understood by Python."
    ]
   },
   {
@@ -1577,7 +1577,7 @@
     }
    },
    "source": [
-    "Python requires certain symbols at certain places (e.g., a `:` is missing here) ..."
+    "Python requires certain symbols at certain places (e.g., a `:` is missing here)."
    ]
   },
   {
@@ -1611,7 +1611,7 @@
     }
    },
    "source": [
-    "... and relies on whitespace (i.e., indentation) unlike many other programming languages. An `IndentationError` is just a special type of a `SyntaxError`."
+    "Furthermore, it relies on whitespace (i.e., indentation), unlike many other programming languages. The `IndentationError` below is just a particular type of a `SyntaxError`."
    ]
   },
   {
@@ -1656,7 +1656,7 @@
     }
    },
    "source": [
-    "Syntax errors as above are easy to find as the code will not even run to begin with.\n",
+    "Syntax errors are easy to find as the code does not even run in the first place.\n",
     "\n",
     "However, there are also so-called **runtime errors**, often called **exceptions**, that occur whenever otherwise (i.e., syntactically) correct code does not run because of invalid input.\n",
     "\n",
@@ -1709,7 +1709,7 @@
    "source": [
     "So-called **semantic errors**, on the contrary, are hard to spot as they do *not* crash the program. The only way to find such errors is to run a program with test input for which we can predict the output. However, testing software is a whole discipline on its own and often very hard to do in practice.\n",
     "\n",
-    "The cell below copies our introductory example from above with a \"tiny\" error. How fast could you have spotted it without the comment?"
+    "The cell below copies our first example from above with a \"tiny\" error. How fast could you have spotted it without the comment?"
    ]
   },
   {
@@ -1766,7 +1766,7 @@
     }
    },
    "source": [
-    "Finding errors in a systematic way is called **debugging**. For the history of the term, see this [article](https://en.wikipedia.org/wiki/Debugging)."
+    "Systematically finding errors is called **debugging**. For the history of the term, see this [article](https://en.wikipedia.org/wiki/Debugging)."
    ]
   },
   {
@@ -1788,7 +1788,7 @@
     }
    },
    "source": [
-    "Adhering to just syntax rules is therefore *never* enough. Over time, **best practices** and common **style guides** were created to make it less likely for a developer to mess up a program and also to allow \"onboarding\" him as a contributor to an established code base, often called **legacy code**, faster. These rules are not enforced by Python itself: Badly styled and un-readable code will still run. At the very least, Python programs should be styled according to [PEP 8](https://www.python.org/dev/peps/pep-0008/) and documented \"inline\" (i.e., in the code itself) according to [PEP 257](https://www.python.org/dev/peps/pep-0257/).\n",
+    "Thus, adhering to just syntax rules is *never* enough. Over time, **best practices** and **style guides** were created to make it less likely for a developer to mess up a program and also to allow \"onboarding\" him as a contributor to an established code base, often called **legacy code**, faster. These rules are not enforced by Python itself: Badly styled code still runs. At the very least, Python programs should be styled according to [PEP 8](https://www.python.org/dev/peps/pep-0008/) and documented \"inline\" (i.e., in the code itself) according to [PEP 257](https://www.python.org/dev/peps/pep-0257/).\n",
     "\n",
     "An easier to read version of PEP 8 is [here](https://pep8.org/). The video below features a well known \"[Pythonista](https://en.wiktionary.org/wiki/Pythonista)\" talking about the importance of code style."
    ]
@@ -1817,7 +1817,7 @@
        "        "
       ],
       "text/plain": [
-       "<IPython.lib.display.YouTubeVideo at 0x7f6b1c40e8d0>"
+       "<IPython.lib.display.YouTubeVideo at 0x7f2c7c4206d8>"
       ]
      },
      "execution_count": 51,
@@ -1838,7 +1838,7 @@
     }
    },
    "source": [
-    "For example, while the above code to calculate the average of the even numbers from 1 through 10 is correct, a Pythonista would re-write it in a more \"Pythonic\" way and use the [sum()](https://docs.python.org/3/library/functions.html#sum) and [len()](https://docs.python.org/3/library/functions.html#len) (= \"length\") built-in functions (cf., [Chapter 2](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/02_functions.ipynb)) as well as a so-called **list comprehension** (cf., Chapter 7). Pythonic code runs faster in many cases and is less error prone."
+    "For example, while the above code to calculate the average of the even numbers from 1 through 10 is correct, a Pythonista would re-write it in a more \"Pythonic\" way and use the [sum()](https://docs.python.org/3/library/functions.html#sum) and [len()](https://docs.python.org/3/library/functions.html#len) (= \"length\") built-in functions (cf., [Chapter 2](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/02_functions.ipynb)) as well as a so-called **list comprehension** (cf., Chapter 7). Pythonic code runs faster in many cases and is less error-prone."
    ]
   },
   {
@@ -1936,7 +1936,7 @@
     }
    },
    "source": [
-    "To get a rough overview of the mindsets of a typical Python programmer, check these rules by an early Python core developer that are deemed so important that they are actually included in every Python installation."
+    "To get a rough overview of the mindsets of a typical Python programmer, see these rules by an early Python core developer deemed so important that they are included in every Python installation."
    ]
   },
   {
@@ -2012,7 +2012,7 @@
    "source": [
     "Observe that you can run the code cells in a Jupyter notebook in any arbitrary order.\n",
     "\n",
-    "That means, for example, that a variable defined towards the bottom could accidently be referenced at the top of the notebook. This happens easily if we iteratively built a program and go back and forth between cells.\n",
+    "That means, for example, that a variable defined towards the bottom could accidentally be referenced at the top of the notebook. This happens quickly when we iteratively built a program and go back and forth between cells.\n",
     "\n",
     "As a good practice, it is recommended to click on \"Kernel\" > \"Restart & Run All\" in the navigation bar once a notebook is finished. That restarts the Python process forgetting any **state** (i.e., all variables) and ensures that the notebook runs top to bottom without any errors the next time it is opened."
    ]
@@ -2036,11 +2036,11 @@
     }
    },
    "source": [
-    "While this book is built with Jupyter notebooks, it is important to understand that \"real\" programs are almost always just \"linear\" (= top to bottom) sequences of instructions but instead may take many different **flows of execution**.\n",
+    "While this book is built with Jupyter notebooks, it is crucial to understand that \"real\" programs are almost always just \"linear\" (= top to bottom) sequences of instructions but instead may take many different **flows of execution**.\n",
     "\n",
-    "At the same time, for a beginner's course it is often easier to just code in a linear fashion.\n",
+    "At the same time, for a beginner's course, it is often easier to code linearly.\n",
     "\n",
-    "In real data science projects one would probably employ a mixed approach and put re-usable code into so-called Python modules (i.e., *.py* files; cf., [Chapter 2](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/02_functions.ipynb)) and then use Jupyter notebooks to built up a linear report or story line for a business argument to be made."
+    "In real data science projects, one would probably employ a mixed approach and put re-usable code into so-called Python modules (i.e., *.py* files; cf., [Chapter 2](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/02_functions.ipynb)) and then use Jupyter notebooks to build up a linear report or storyline for a business argument to be made."
    ]
   },
   {
@@ -2064,7 +2064,7 @@
    "source": [
     "**Variables** are created with the **[assignment statement](https://docs.python.org/3/reference/simple_stmts.html#assignment-statements)** `=`, which is *not* an operator, mainly because of its side effect of making a **[name](https://docs.python.org/3/reference/lexical_analysis.html#identifiers)** point to an object in memory.\n",
     "\n",
-    "We will read the terms **variable**, **name**, and **identifier** used interchangebly in many Python related texts. In this book, we adopt the following convention: First, we treat *name* and *identifier* as perfect synonyms but only use the term *name* in the text for clarity. Second, whereas *name* only refers to a string of letters, numbers, and some other symbols, a *variable* refers to the combination of a *name* and a *pointer* to some object in memory."
+    "We read the terms **variable**, **name**, and **identifier** used interchangebly in many Python-related texts. In this book, we adopt the following convention: First, we treat *name* and *identifier* as perfect synonyms but only use the term *name* in the text for clarity. Second, whereas *name* only refers to a string of letters, numbers, and some other symbols, a *variable* refers to the combination of a *name* and a *pointer* to some object in memory."
    ]
   },
   {
@@ -2089,7 +2089,7 @@
     }
    },
    "source": [
-    "When referenced, a variable evaluates to the value of the object it points to. Colloquially, we could say that `a` evaluates to `20.0` here but this would not be a full description of what is really going on in memory. We will see some more colloquial jargons in this section but should always relate this to what Python actually does in memory."
+    "When referenced, a variable evaluates to the value of the object it points to. Colloquially, we could say that `a` evaluates to `20.0`, but this would not be an accurate description of what is going on in memory. We see some more colloquialisms in this section but should always relate this to what Python actually does in memory."
    ]
   },
   {
@@ -2172,7 +2172,7 @@
     }
    },
    "source": [
-    "If we want to re-assign a variable while referencing its \"old\" (i.e., current) object, we may also **update** it using a so-called **[augmented assignment statement](https://docs.python.org/3/reference/simple_stmts.html#augmented-assignment-statements)** (*not* operator), originally introduced with [PEP 203](https://www.python.org/dev/peps/pep-0203/). This implicitly inserts the currently mapped object as the first operand on the right-hand side."
+    "If we want to re-assign a variable while referencing its \"old\" (i.e., current) object, we may also **update** it using a so-called **[augmented assignment statement](https://docs.python.org/3/reference/simple_stmts.html#augmented-assignment-statements)** (*not* operator), introduced with [PEP 203](https://www.python.org/dev/peps/pep-0203/): The currently mapped object is implicitly inserted as the first operand on the right-hand side."
    ]
   },
   {
@@ -2246,7 +2246,7 @@
     }
    },
    "source": [
-    "Variables are **[de-referenced](https://docs.python.org/3/reference/simple_stmts.html#the-del-statement)** (i.e., \"deleted\") with the `del` statement. This does *not* delete the object to which a variable points to. It merely removes the variable's name from the \"global list of all names\"."
+    "Variables are **[de-referenced](https://docs.python.org/3/reference/simple_stmts.html#the-del-statement)** (i.e., \"deleted\") with the `del` statement. It does *not* delete the object to which a variable points to but merely removes the variable's name from the \"global list of all names.\""
    ]
   },
   {
@@ -2294,7 +2294,7 @@
     }
    },
    "source": [
-    "If we refer to an unknown name, a *runtime* error occurs, namely a `NameError`. The `Name` in `NameError` gives a hint as to why we prefer the term *name* over *identifier*: Python just uses it more often in its error messages."
+    "If we refer to an unknown name, a *runtime* error occurs, namely a `NameError`. The `Name` in `NameError` gives a hint as to why we prefer the term *name* over *identifier*: Python uses it more often in its error messages."
    ]
   },
   {
@@ -2557,9 +2557,9 @@
     }
    },
    "source": [
-    "It is important to understand that *several* variables may point to the *same* object in memory. This may be counter-intuitive in the beginning and lead to many hard to track down bugs.\n",
+    "It is *crucial* to understand that *several* variables may point to the *same* object in memory. Not having this in mind may lead to many hard to track down bugs.\n",
     "\n",
-    "This makes `b` point to whatever object `a` is pointing to."
+    "Make `b` point to whatever object `a` is pointing to."
    ]
   },
   {
@@ -2631,9 +2631,9 @@
     }
    },
    "source": [
-    "For \"simple\" types like `int` or `float` this will never cause troubles.\n",
+    "For \"simple\" types like `int` or `float` this never causes troubles.\n",
     "\n",
-    "Let's \"change the value\" of `a`. Really, let's create a *new* `123` object and make `a` point to it."
+    "Let's \"change the value\" of `a`. To be precise, let's create a *new* `123` object and make `a` point to it."
    ]
   },
   {
@@ -2681,7 +2681,7 @@
     }
    },
    "source": [
-    "`b` \"is still the same\" as before. Really, `b` still points to the same object as before."
+    "`b` \"is still the same\" as before. To be precise, `b` still points to the *same object* as before."
    ]
   },
   {
@@ -2779,7 +2779,7 @@
    "source": [
     "Let's change the first element of `x`.\n",
     "\n",
-    "Chapter 7 discusses lists in more depth. For now, let's just view a `list` object as some sort of **container** that holds an arbitrary number of pointers to other objects and treat the brackets `[]` attached to it as just another operator, called the **indexing operator**. `x[0]` instructs Python to first follow the pointer from the global list of all names to the `x` object. Then, it follows the first pointer it finds there to the `1` object. The indexing operator must be an operator as we merely read the first element and do not change anything in memory.\n",
+    "Chapter 7 discusses lists in more depth. For now, let's view a `list` object as some sort of **container** that holds an arbitrary number of pointers to other objects and treat the brackets `[]` attached to it as just another operator, called the **indexing operator**. `x[0]` instructs Python to first follow the pointer from the global list of all names to the `x` object. Then, it follows the first pointer it finds there to the `1` object. The indexing operator must be an operator as we merely read the first element and do not change anything in memory.\n",
     "\n",
     "Note how Python **begins counting at 0**. This is not the case for many other languages, for example, [MATLAB](https://en.wikipedia.org/wiki/MATLAB), [R](https://en.wikipedia.org/wiki/R_%28programming_language%29), or [Stata](https://en.wikipedia.org/wiki/Stata). To understand why this makes sense, see this short [note](https://www.cs.utexas.edu/users/EWD/transcriptions/EWD08xx/EWD831.html) by one of the all-time greats in computer science, the late [Edsger Dijkstra](https://en.wikipedia.org/wiki/Edsger_W._Dijkstra)."
    ]
@@ -2816,7 +2816,7 @@
     }
    },
    "source": [
-    "To change the first entry in the list, we use the assignment statement `=` again. Here, this does actually *not* create a *new* variable (or overwrite an existing one) but only changes the object to which the first pointer in the `x` list points to. As we only change parts of the `x` object, we say that we **mutate** (i.e., \"change\") its **state**. To use the bag analogy from above, we keep the same bag but \"flip\" some of the $0$s into $1$s and some of the $1$s into $0$s."
+    "To change the first entry in the list, we use the assignment statement `=` again. Here, this does *not* create a *new* variable (or overwrite an existing one) but only changes the object to which the first pointer in the `x` list points to. As we only change parts of the `x` object, we say that we **mutate** (i.e., \"change\") its **state**. To use the bag analogy from above, we keep the same bag but \"flip\" some of the $0$s into $1$s and some of the $1$s into $0$s."
    ]
   },
   {
@@ -2901,11 +2901,11 @@
    "source": [
     "The illustrated difference in behavior has to do with the fact that integers and floats are **immutable** types while lists are **mutable**.\n",
     "\n",
-    "In the first case, an object cannot be changed \"in place\" once it is created in memory. When we assigned `123` to the already existing `a`, we actually did not change the $0$s and $1$s in the object `a` pointed to before the assignment but created a new integer object and made `a` point to it while the `b` variable is *not* affected.\n",
+    "In the first case, an object cannot be changed \"in place\" once it is created in memory. When we assigned `123` to the already existing `a`, we did not change the $0$s and $1$s in the object `a` pointed to before the assignment but created a new integer object and made `a` point to it while the `b` variable is *not* affected.\n",
     "\n",
     "In the second case, `x[0] = 99` creates a *new* integer object `99` and merely changes the first pointer in the `x` list.\n",
     "\n",
-    "In general, the assignment statement creates (or overwrites) a variable and makes it point to whatever object is on the right-hand side *only if* the left-hand side is a *pure* name (i.e., it contains no operators like the indexing operator in the example). Otherwise, it mutates some already existing object. And we always have to expect that the latter might have more than one variable pointing at it.\n",
+    "In general, the assignment statement creates a new name and makes it point to whatever object is on the right-hand side *iff* the left-hand side is a *pure* name (i.e., it contains no operators like the indexing operator in the example). Otherwise, it *mutates* an already existing object. And, we always must expect that the latter might have more than one variable pointing to it.\n",
     "\n",
     "Visualizing what is going on in the memory with a tool like [PythonTutor](http://pythontutor.com/visualize.html#code=x%20%3D%20%5B1,%202,%203%5D%0Ay%20%3D%20x%0Ax%5B0%5D%20%3D%2099%0Aprint%28y%5B0%5D%29&cumulative=false&curInstr=0&heapPrimitives=nevernest&mode=display&origin=opt-frontend.js&py=3&rawInputLstJSON=%5B%5D&textReferences=false) might be helpful for a beginner."
    ]
@@ -2944,7 +2944,7 @@
    "source": [
     "Variable names may contain upper and lower case letters, numbers, and underscores (i.e., `_`) and be as long as we want them to be. However, they must not begin with a number. Also, they must not be any of Python's built-in **[keywords](https://docs.python.org/3/reference/lexical_analysis.html#keywords)**.\n",
     "\n",
-    "Variable names are usually chosen such that they do not need any more documentation and are self-explanatory. A very common convention is to use so-called **[snake\\_case](https://en.wikipedia.org/wiki/Snake_case)**: Keep everything lowercase and use underscores to seperate words.\n",
+    "Variable names should be chosen such that they do not need any more documentation and are self-explanatory. A widespread convention is to use so-called **[snake\\_case](https://en.wikipedia.org/wiki/Snake_case)**: Keep everything lowercase and use underscores to separate words.\n",
     "\n",
     "See this [link](https://en.wikipedia.org/wiki/Naming_convention_%28programming%29#Python_and_Ruby) for a comparison of different naming conventions."
    ]
@@ -3092,7 +3092,7 @@
     }
    },
    "source": [
-    "If a variable name collides with a built-in name, just add a trailing underscore."
+    "If a variable name collides with a built-in name, we add a trailing underscore."
    ]
   },
   {
@@ -3116,7 +3116,7 @@
     }
    },
    "source": [
-    "Variables with leading and trailing double underscores, referred to as **dunder** in Python jargon, are used for important built-in functionalities. Do *not* use this style for custom variables unless you know exactly what you are doing!"
+    "Variables with leading and trailing double underscores, referred to as **dunder** in Python jargon, are used for built-in functionalities. Do *not* use this style for custom variables unless you exactly know what you are doing!"
    ]
   },
   {
@@ -3162,7 +3162,7 @@
     }
    },
    "source": [
-    "This PyCon talk by [Ned Batchelder](https://nedbatchelder.com/), a well-known Pythonista and the organizer of the [Python User Group](https://www.meetup.com/bostonpython/) in Boston, summarizes all situations where some sort of variable assignment is done in Python. The content is intermediate and therefore it is ok if you do not understand everything at this point. However, the contents should be known by everyone claiming to be proficient in Python."
+    "This PyCon talk by [Ned Batchelder](https://nedbatchelder.com/), a well-known Pythonista and the organizer of the [Python User Group](https://www.meetup.com/bostonpython/) in Boston, summarizes all situations where some sort of assignment is done in Python. The content is intermediate, and, thus, it might be worthwhile to come back to this talk at a later point in time. However, the contents should be known by everyone claiming to be proficient in Python."
    ]
   },
   {
@@ -3189,7 +3189,7 @@
        "        "
       ],
       "text/plain": [
-       "<IPython.lib.display.YouTubeVideo at 0x7f6b1c4606d8>"
+       "<IPython.lib.display.YouTubeVideo at 0x7f2c7c41bc50>"
       ]
      },
      "execution_count": 94,
@@ -3225,7 +3225,7 @@
     "\n",
     "In simple words, anything that may be used on the right-hand side of an assignment statement without creating a `SyntaxError` is an expression.\n",
     "\n",
-    "What we said about individual operators above, namely that they have *no* side effects, should have been put here to begin with. The examples in the section on operators above were actually all expressions!\n",
+    "What we said about individual operators above, namely that they have *no* side effects, should have been put here, to begin with. The code cells in the section on operators above are all expressions!\n",
     "\n",
     "The simplest possible expressions contain only one variable or literal."
    ]
@@ -3313,7 +3313,7 @@
     }
    },
    "source": [
-    "The definition of an expression is **recursive**. So, here the sub-expression `a + b` is combined with the literal `3` by the operator `**` to form the full expression."
+    "The definition of an expression is **recursive**. So, the sub-expression `a + b` is combined with the literal `3` by the operator `**` to form the full expression."
    ]
   },
   {
@@ -3383,7 +3383,7 @@
     }
    },
    "source": [
-    "When not used as a delimiter, parentheses also constitute an operator, namely the **call operator** `()`. We have seen this syntax above when we \"called\" (i.e., executed) built-in functions and methods."
+    "When not used as a delimiter, parentheses also constitute an operator, namely the **call operator** `()`. We saw this syntax above when we \"called\" (i.e., executed) built-in functions and methods."
    ]
   },
   {
@@ -3429,7 +3429,7 @@
     }
    },
    "source": [
-    "Python **overloads** certain operators. For example, you may not only \"add\" numbers but also strings. This is called **string concatenation**."
+    "Python **overloads** certain operators. For example, you may not only \"add\" numbers but also strings: This is called **string concatenation**."
    ]
   },
   {
@@ -3524,9 +3524,9 @@
     }
    },
    "source": [
-    "A **[statement](https://docs.python.org/3/reference/simple_stmts.html)** is anything that *changes* the *state of a program* or has some other *side effect*. Statements do not just evaluate to a value like expressions; instead, they create or change values.\n",
+    "A **[statement](https://docs.python.org/3/reference/simple_stmts.html)** is anything that *changes* the *state of a program* or has another *side effect*. Statements, unlike expressions, do not just evaluate to a value; instead, they create or change values.\n",
     "\n",
-    "Most notably of course are the `=` and `del` statements."
+    "Most notably, of course, are the `=` and `del` statements."
    ]
   },
   {
@@ -3563,7 +3563,7 @@
     }
    },
    "source": [
-    "The built-in [print()](https://docs.python.org/3/library/functions.html#print) function is regarded a \"statement\" as well. In fact, it used to be a real statement in Python 2 and has all the necessary properties. It is a bit of a corner case as expressions are also \"printed\" in a Jupyter notebook when evaluated last in a code cell."
+    "The built-in [print()](https://docs.python.org/3/library/functions.html#print) function is regarded as a \"statement\" as well. It used to be an actual statement in Python 2 and has all the necessary properties. It is a bit of a corner case as expressions are also \"printed\" in a Jupyter notebook when evaluated last in a code cell."
    ]
   },
   {
@@ -3608,9 +3608,9 @@
    "source": [
     "We use the `#` symbol to write comments in plain English right into the code.\n",
     "\n",
-    "As a good practice, comments should not describe what happens (this should be evident by reading the code, otherwise it is most likely badly written code) but why something happens.\n",
+    "As a good practice, comments should not describe *what* happens (this should be evident by reading the code; otherwise, it is most likely badly written code) but *why* something happens.\n",
     "\n",
-    "Comments may be added either at the end of a line of code, by convention seperated with two spaces, or on a line on their own."
+    "Comments may be added either at the end of a line of code, by convention separated with two spaces, or on a line on their own."
    ]
   },
   {
@@ -3637,8 +3637,8 @@
     }
    },
    "source": [
-    "But let's think wisely if we really need to use a comment.\n",
-    "The second cell is a lot more \"Pythonic\"."
+    "But let's think wisely if we need to use a comment.\n",
+    "The second cell is a lot more Pythonic."
    ]
   },
   {
@@ -3686,7 +3686,7 @@
     }
    },
    "source": [
-    "We end each chapter with a summary of the main points. The essence in this first chapter is that just like a sentence in a real language like English may be decomposed into its parts (subject, predicate, objects, ...) the same may be done with programming languages."
+    "We end each chapter with a summary of the main points. The essence in this first chapter is that just like a sentence in a real language like English may be decomposed into its parts (e.g., subject, predicate, and objects), the same may be done with programming languages."
    ]
   },
   {
@@ -3707,18 +3707,18 @@
     "  - (numeric) data from a CSV file\n",
     "  - text entered on a command line\n",
     "  - (relational) data obtained from a database\n",
-    "  - ...\n",
+    "  - etc.\n",
     "\n",
     "\n",
     "- output (examples)\n",
     "  - result of a computation (e.g., statistical summary of a sample dataset)\n",
     "  - a \"side effect\" (e.g., a transformation of raw input data into cleaned data)\n",
     "  - a physical \"behavior\" (e.g., a robot moving or a document printed)\n",
-    "  - ...\n",
+    "  - etc.\n",
     "\n",
     "\n",
     "- objects\n",
-    "  - distinct and well-contained areas / parts of the memory that hold the actual data\n",
+    "  - distinct and well-contained areas/parts of the memory that hold the actual data\n",
     "  - the concept by which Python manages the memory for us\n",
     "  - can be classified into objects of the same **type** (i.e., same abstract \"structure\" but different concrete data)\n",
     "  - built-in objects (incl. **literals**) vs. user-defined objects (cf., Chapter 8)\n",
@@ -3733,13 +3733,13 @@
     "\n",
     "- operators\n",
     "  - special built-in symbols that perform operations with objects in memory\n",
-    "  - usually operate with one or two objects\n",
+    "  - usually, operate with one or two objects\n",
     "  - e.g., addition `+`, subtraction `-`, multiplication `*`, and division `/` all take two objects whereas the negation `-` only takes one\n",
     "\n",
     "\n",
     "- expressions\n",
     "  - **combinations** of **variables** (incl. **literals**) and **operators**\n",
-    "  - do *not* change the involved objects / state of the program\n",
+    "  - do *not* change the involved objects/state of the program\n",
     "  - evaluate to a **value** (i.e., the \"result\" of the expression, usually a new object)\n",
     "  - e.g., `x + 2` evaluates to the (new) object `3` and `1 - 1.0` to `0.0`\n",
     "\n",
@@ -3747,7 +3747,7 @@
     "- statements\n",
     "  - instructions that **\"do\" something** and **have side effects** in memory\n",
     "  - re-map names to different objects and *change* the state of the program\n",
-    "  - usually work with expressions\n",
+    "  - usually, work with expressions\n",
     "  - e.g., the assignment statement `=` makes a name point to an object\n",
     "\n",
     "\n",
diff --git a/01_elements_review_and_exercises.ipynb b/01_elements_review_and_exercises.ipynb
index 8940f24..965b8be 100644
--- a/01_elements_review_and_exercises.ipynb
+++ b/01_elements_review_and_exercises.ipynb
@@ -40,7 +40,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "**Q1**: Elaborate on how **modulo division** might be a very useful operation to know!"
+    "**Q1**: Elaborate on how **modulo division** might be a handy operation to know!"
    ]
   },
   {
diff --git a/02_functions.ipynb b/02_functions.ipynb
index 03452c7..6ab7c32 100644
--- a/02_functions.ipynb
+++ b/02_functions.ipynb
@@ -19,11 +19,11 @@
     }
    },
    "source": [
-    "In [Chapter 1](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/01_elements.ipynb) we typed the **business logic** of our little program to calculate the mean of a subset of a list of numbers right into the code cells. Then, we executed them one after another. We had no way of **re-using** the code except for either re-executing the cells or copying and pasting their contents into other cells. And whenever we find ourselves doing repetitive manual work, we can be sure that there must be a way of automating what we are doing.\n",
+    "In [Chapter 1](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/01_elements.ipynb), we typed the **business logic** of our little program to calculate the mean of a subset of a list of numbers right into the code cells. Then, we executed them one after another. We had no way of **re-using** the code except for either re-executing the cells or copying and pasting their contents into other cells. And whenever we find ourselves doing repetitive manual work, we can be sure that there must be a way of automating what we are doing.\n",
     "\n",
     "At the same time, we executed built-in functions (e.g., [print()](https://docs.python.org/3/library/functions.html#print), [sum()](https://docs.python.org/3/library/functions.html#sum), [len()](https://docs.python.org/3/library/functions.html#len), [id()](https://docs.python.org/3/library/functions.html#id), or [type()](https://docs.python.org/3/library/functions.html#type)) that obviously must be re-using the same parts inside core Python every time we use them.\n",
     "\n",
-    "This chapter shows how Python offers language constructs that let us **define** our own functions that we may then **call** just like the built-in ones."
+    "This chapter shows how Python offers language constructs that let us **define** functions ourselves that we may then **call** just like the built-in ones."
    ]
   },
   {
@@ -47,17 +47,17 @@
    "source": [
     "So-called **[user-defined functions](https://docs.python.org/3/reference/compound_stmts.html#function-definitions)** may be created with the `def` statement. To extend an already familiar example, we re-use the introductory example from [Chapter 1](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/01_elements.ipynb) in its final Pythonic version and transform it into the function `average_evens()` below. \n",
     "\n",
-    "A function's **name** must be chosen according to the same naming rules as for ordinary variables. In fact, Python manages function names just like variables. In this book, we further adopt the convention of ending function names with parentheses \"`()`\" in text cells for faster comprehension when reading (i.e., `average_evens()` vs. `average_evens`). These are not actually part of the name but must always be written out in the `def` statement for syntactic reasons.\n",
+    "A function's **name** must be chosen according to the same naming rules as ordinary variables as Python manages function names like variables. In this book, we further adopt the convention of ending function names with parentheses `()` in text cells for faster comprehension when reading (i.e., `average_evens()` vs. `average_evens`). These are *not* part of the name but must always be written out in the `def` statement for syntactic reasons.\n",
     "\n",
-    "Functions may define an arbitrary number of **parameters** as inputs that are then referenced within the indented **code block**: They are simply listed within the parentheses in the `def` statement (i.e., `numbers` below). \n",
+    "Functions may define an arbitrary number of **parameters** as inputs that can then be referenced within the indented **code block**: They are listed within the parentheses in the `def` statement (i.e., `numbers` below). \n",
     "\n",
-    "The code block is often also called a function's **body** while the first line with the `def` in it is the **header** and must end with a colon.\n",
+    "The code block is also called a function's **body**, while the first line starting with `def` and ending with a colon is the **header**.\n",
     "\n",
     "Together, the name and the list of parameters are also referred to as the function's **[signature](https://en.wikipedia.org/wiki/Type_signature)** (i.e., `average_evens(numbers)` below).\n",
     "\n",
     "A function may come with an *explicit* **[return value](https://docs.python.org/3/reference/simple_stmts.html#the-return-statement)** (i.e., \"result\" or \"output\") specified with the `return` statement: Functions that have one are considered **fruitful**; otherwise, they are **void**. Functions of the latter kind are still useful because of their **side effects** (e.g., the [print()](https://docs.python.org/3/library/functions.html#print) built-in). Strictly speaking, they also have an *implicit* return value of `None` that is different from the `False` we saw in Chapter 1.\n",
     "\n",
-    "To maintain good coding practices, a function should define a **docstring** that describes what it does in a short subject line, what parameters it expects (i.e., their types), and what it returns (if anything). A docstring is a syntactically valid multi-line string (i.e., type `str`) defined with **triple-double quotes** (strings are covered in depth in Chapter 6). Good standards as to how to format a docstring are [PEP 257](https://www.python.org/dev/peps/pep-0257/) and section 3.8 in [Google's Python Style Guide](https://github.com/google/styleguide/blob/gh-pages/pyguide.md)."
+    "A function should define a **docstring** that describes what it does in a short subject line, what parameters it expects (i.e., their types), and what it returns (if anything). A docstring is a syntactically valid multi-line string (i.e., type `str`) defined within **triple-double quotes** `\"\"\"`. Strings are covered in depth in Chapter 6. Widely adopted standards as to how to format a docstring are [PEP 257](https://www.python.org/dev/peps/pep-0257/) and section 3.8 in [Google's Python Style Guide](https://github.com/google/styleguide/blob/gh-pages/pyguide.md)."
    ]
   },
   {
@@ -142,7 +142,7 @@
     {
      "data": {
       "text/plain": [
-       "139925407773152"
+       "140519730762208"
       ]
      },
      "execution_count": 3,
@@ -254,7 +254,7 @@
     }
    },
    "source": [
-    "Two questions marks even show a function's source code."
+    "Two question marks even show a function's source code."
    ]
   },
   {
@@ -337,7 +337,7 @@
     }
    },
    "source": [
-    "The return value is usually assigned to a new variable for later reference. Otherwise we would loose access to it in memory right away."
+    "The return value is commonly assigned to a new variable for later reference. Otherwise, we would lose access to it in memory right away."
    ]
   },
   {
@@ -493,7 +493,7 @@
     }
    },
    "source": [
-    "[PythonTutor](http://pythontutor.com/visualize.html#code=nums%20%3D%20%5B1,%202,%203,%204,%205,%206,%207,%208,%209,%2010%5D%0A%0Adef%20average_evens%28numbers%29%3A%0A%20%20%20%20evens%20%3D%20%5Bn%20for%20n%20in%20numbers%20if%20n%20%25%202%20%3D%3D%200%5D%0A%20%20%20%20average%20%3D%20sum%28evens%29%20/%20len%28evens%29%0A%20%20%20%20return%20average%0A%0Arv%20%3D%20average_evens%28nums%29&cumulative=false&curInstr=0&heapPrimitives=nevernest&mode=display&origin=opt-frontend.js&py=3&rawInputLstJSON=%5B%5D&textReferences=false) visualizes what happens in memory: To be precise, in the exact moment when the function call is initiated and `nums` is passed in as the `numbers` argument, there are *two* pointers to the *same* `list` object (cf., steps 4-5 in the visualization). We also see how Python creates a *new* **frame** that holds the function's local scope (i.e., \"internal names\") in addition to the **global** frame. Frames are nothing but [namespaces](https://en.wikipedia.org/wiki/Namespace) to *isolate* the names of different **scopes** from each other. The list comprehension `[n for n in numbers if n % 2 == 0]` constitutes yet another frame that is in scope as the `list` object assigned to `evens` is *being* created (cf., steps 6-18). When the function returns, only the global frame is left (cf., steps 21-22)."
+    "[PythonTutor](http://pythontutor.com/visualize.html#code=nums%20%3D%20%5B1,%202,%203,%204,%205,%206,%207,%208,%209,%2010%5D%0A%0Adef%20average_evens%28numbers%29%3A%0A%20%20%20%20evens%20%3D%20%5Bn%20for%20n%20in%20numbers%20if%20n%20%25%202%20%3D%3D%200%5D%0A%20%20%20%20average%20%3D%20sum%28evens%29%20/%20len%28evens%29%0A%20%20%20%20return%20average%0A%0Arv%20%3D%20average_evens%28nums%29&cumulative=false&curInstr=0&heapPrimitives=nevernest&mode=display&origin=opt-frontend.js&py=3&rawInputLstJSON=%5B%5D&textReferences=false) visualizes what happens in memory: To be precise, in the exact moment when the function call is initiated and `nums` passed in as the `numbers` argument, there are *two* pointers to the *same* `list` object (cf., steps 4-5 in the visualization). We also see how Python creates a *new* **frame** that holds the function's local scope (i.e., \"internal names\") in addition to the **global** frame. Frames are nothing but [namespaces](https://en.wikipedia.org/wiki/Namespace) to *isolate* the names of different **scopes** from each other. The list comprehension `[n for n in numbers if n % 2 == 0]` constitutes yet another frame that is in scope as the `list` object assigned to `evens` is *being* created (cf., steps 6-18). When the function returns, only the global frame is left (cf., steps 21-22)."
    ]
   },
   {
@@ -515,7 +515,7 @@
     }
    },
    "source": [
-    "On the contrary, while a function is being executed, it can reference the variables of **enclosing scopes** (i.e., \"outside\" of it). This is a common source of *semantic* errors. Consider the following stylized (and incorrect) example `average_wrong()`. The error is hard to spot with eyes: The function never references the `numbers` parameter but the `nums` variable in the **global scope** instead."
+    "On the contrary, while a function is *being* executed, it can reference the variables of **enclosing scopes** (i.e., \"outside\" of it). This is a common source of *semantic* errors. Consider the following stylized (and incorrect) example `average_wrong()`. The error is hard to spot with eyes: The function never references the `numbers` parameter but the `nums` variable in the **global scope** instead."
    ]
   },
   {
@@ -550,7 +550,7 @@
     }
    },
    "source": [
-    "`nums` in the global scope is of course unchanged."
+    "`nums` in the global scope is, of course, unchanged."
    ]
   },
   {
@@ -657,7 +657,7 @@
    "source": [
     "[PythonTutor](http://pythontutor.com/visualize.html#code=nums%20%3D%20%5B1,%202,%203,%204,%205,%206,%207,%208,%209,%2010%5D%0A%0Adef%20average_wrong%28numbers%29%3A%0A%20%20%20%20evens%20%3D%20%5Bn%20for%20n%20in%20nums%20if%20n%20%25%202%20%3D%3D%200%5D%0A%20%20%20%20average%20%3D%20sum%28evens%29%20/%20len%28evens%29%0A%20%20%20%20return%20average%0A%0Arv%20%3D%20average_wrong%28%5B123,%20456,%20789%5D%29&cumulative=false&curInstr=0&heapPrimitives=nevernest&mode=display&origin=opt-frontend.js&py=3&rawInputLstJSON=%5B%5D&textReferences=false) is again helpful at visualizing the error interactively: Creating the `list` object `evens` eventually points to takes *12* computational steps, namely one for setting up an empty `list` object, *ten* for filling it with elements derived from `nums` in the global scope, and one to make `evens` point at it (cf., steps 6-18).\n",
     "\n",
-    "The frames logic shown by PythonTutor is the mechanism by which Python not only manages the names inside one function call but for potentially many calls occuring simultaneously as we will see in [Chapter 4](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/04_iteration.ipynb). It is the reason why we may re-use the same names for the parameters and variables inside both `average_evens()` and `average_wrong()` without Python mixing them up. So, as we already read in the [Zen of Python](https://www.python.org/dev/peps/pep-0020/), \"namespaces are one honking great idea\" (cf., `import this`) and a frame is just a special kind of namespace."
+    "The frames logic shown by PythonTutor is the mechanism by which Python not only manages the names inside *one* function call but also for *many* potentially simultaneous* calls, as revealed in [Chapter 4](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/04_iteration.ipynb). It is the reason why we may re-use the same names for the parameters and variables inside both `average_evens()` and `average_wrong()` without Python mixing them up. So, as we already read in the [Zen of Python](https://www.python.org/dev/peps/pep-0020/), \"namespaces are one honking great idea\" (cf., `import this`), and a frame is just a special kind of namespace."
    ]
   },
   {
@@ -752,7 +752,7 @@
     }
    },
    "source": [
-    "As good practice, let's first use inputs for which we can calculate the answer in our heads to \"verify\" that `average_odds()` is \"correct\"."
+    "As good practice, let's first use inputs for which we can calculate the answer in our heads to \"verify\" that `average_odds()` is \"correct.\""
    ]
   },
   {
@@ -787,7 +787,7 @@
     }
    },
    "source": [
-    "To make the confusion even bigger, let's also pass the global `nums` as an argument to `average_odds()`."
+    "To add to the confusion, let's also pass the global `nums` as an argument to `average_odds()`."
    ]
   },
   {
@@ -857,13 +857,13 @@
     }
    },
    "source": [
-    "The reason why everything just works is that *every time* we (re-)assign an object to a variable *inside* a function with the `=` statement, this is done in the *local* scope by default. There are ways to change variables existing in an outer scope from within a function but this is a rather advanced topic on its own.\n",
+    "The reason why everything works is that *every time* we (re-)assign an object to a variable *inside* a function with the `=` statement, this is done in the *local* scope by default. There are ways to change variables existing in an outer scope from within a function, but this is a rather advanced topic on its own.\n",
     "\n",
     "[PythonTutor](http://pythontutor.com/visualize.html#code=nums%20%3D%20%5B1,%202,%203,%204,%205,%206,%207,%208,%209,%2010%5D%0A%0Adef%20average_odds%28numbers%29%3A%0A%20%20%20%20nums%20%3D%20%5Bint%28n%29%20for%20n%20in%20numbers%5D%0A%20%20%20%20odds%20%3D%20%5Bn%20for%20n%20in%20nums%20if%20n%20%25%202%20!%3D%200%5D%0A%20%20%20%20average%20%3D%20sum%28odds%29%20/%20len%28odds%29%0A%20%20%20%20return%20average%0A%0Arv%20%3D%20average_odds%28%5B1.0,%2010.0,%203.0,%2010.0,%205.0%5D%29&cumulative=false&curInstr=0&heapPrimitives=nevernest&mode=display&origin=opt-frontend.js&py=3&rawInputLstJSON=%5B%5D&textReferences=false) shows how *two* `nums` variables exist in *different* scopes pointing to *different* objects (cf., steps 14-25) when we execute `average_odds([1.0, 10.0, 3.0, 10.0, 5.0])`.\n",
     "\n",
     "Variables whose names collide with the ones of variables in enclosing scopes - and the global scope is just the most enclosing scope - are said to **shadow** them.\n",
     "\n",
-    "While this is not a problem for Python, it may lead to less readable code for us humans and should be avoided if possible. But, as we have also heard, \"[naming things](https://skeptics.stackexchange.com/questions/19836/has-phil-karlton-ever-said-there-are-only-two-hard-things-in-computer-science)\" is often considered hard as well and we have to be prepared to encounter shadowing variables."
+    "While this is not a problem for Python, it may lead to less readable code for us humans and should be avoided if possible. But, as we have also heard, \"[naming things](https://skeptics.stackexchange.com/questions/19836/has-phil-karlton-ever-said-there-are-only-two-hard-things-in-computer-science)\" is often considered hard as well, and we have to be prepared to encounter shadowing variables."
    ]
   },
   {
@@ -946,7 +946,7 @@
     }
    },
    "source": [
-    "We may cast certain objects as a different type. For example, to \"convert\" a float or a text into an integer, we use the [int()](https://docs.python.org/3/library/functions.html#int) built-in. This actually creates a *new* object of type `int` from the provided `avg` or `\"6\"` objects who continue to exist in memory unchanged."
+    "We may cast objects as a different type. For example, to \"convert\" a float or a text into an integer, we use the [int()](https://docs.python.org/3/library/functions.html#int) built-in. It creates a *new* object of type `int` from the provided `avg` or `\"6\"` objects that continue to exist in memory unchanged."
    ]
   },
   {
@@ -1170,7 +1170,7 @@
     }
    },
    "source": [
-    "So far we have only specified one parameter in each of our user-defined functions. In [Chapter 1](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/01_elements.ipynb), however, we saw the built-in function [divmod()](https://docs.python.org/3/library/functions.html#divmod) taking two arguments. Obviously, the order of the numbers passed in mattered. Whenever we call a function and list its arguments in a comma seperated manner, we say that we pass in the arguments by position or refer to them as **positional arguments**."
+    "So far, we have only specified one parameter in each of our user-defined functions. In [Chapter 1](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/01_elements.ipynb), however, we saw the built-in function [divmod()](https://docs.python.org/3/library/functions.html#divmod) take two arguments. And, the order of the numbers passed in mattered! Whenever we call a function and list its arguments in a comma separated manner, we say that we pass in the arguments by position or refer to them as **positional arguments**."
    ]
   },
   {
@@ -1229,7 +1229,7 @@
     }
    },
    "source": [
-    "For many functions there is a natural order to the arguments. But what if this is not the case? For example, let's create a close relative of the above `average_evens()` function that also scales the resulting average by a factor. What is more natural? Passing in `numbers` first? Or `scalar`? There is no obvious way and we continue with the first alternative for no real reason."
+    "For many functions, there is a natural order to the arguments. But what if this is not the case? For example, let's create a close relative of the above `average_evens()` function that also scales the resulting average by a factor. What is more natural? Passing in `numbers` first? Or `scalar`? There is no obvious way and we continue with the first alternative for no concrete reason."
    ]
   },
   {
@@ -1301,9 +1301,9 @@
     }
    },
    "source": [
-    "However, now the function call is a bit harder to comprehend as we need to always remember what the `2` means. This becomes even harder the more parameters we specify.\n",
+    "However, now the function call is a bit harder to comprehend as we always need to remember what the `2` means. This becomes harder the more parameters we specify.\n",
     "\n",
-    "Luckily, we may also reference the formal parameter names as **keyword arguments**. We can even combine positional and keyword arguments in the same function call. Each of the following does the exact same thing."
+    "Luckily, we may also reference the formal parameter names as **keyword arguments**. We can even combine positional and keyword arguments in the same function call. Each of the following does the *same*."
    ]
   },
   {
@@ -1430,11 +1430,11 @@
     }
    },
    "source": [
-    "Defining both `average_evens()` and `scaled_average_evens()` is also kind of repetitive as most of their code is the same. Such a redundancy will make a code base harder to maintain in the long run as whenever we change the logic in one function we must *not* forget to do so for the other function as well.\n",
+    "Defining both `average_evens()` and `scaled_average_evens()` is also kind of repetitive as most of their code is the same. Such a redundancy makes a codebase harder to maintain in the long run as whenever we change the logic in one function, we must *not* forget to do so for the other function as well.\n",
     "\n",
     "A better way is to design related functions in a **modular** fashion such that they re-use each other's logic.\n",
     "\n",
-    "For example, as not scaling an average is just a special case of scaling it with `1`, we could re-define the two functions like below: In this setting, the function resembling the *special* case (i.e., `average_evens()`) simply **forwards** the call to the more *general* function (i.e., `scaled_average_evens()`) using a `scalar=1` argument."
+    "For example, as not scaling an average is just a special case of scaling it with `1`, we could re-define the two functions like below: In this setting, the function resembling the *special* case (i.e., `average_evens()`) **forwards** the call to the more *general* function (i.e., `scaled_average_evens()`) using a `scalar=1` argument."
    ]
   },
   {
@@ -1483,7 +1483,7 @@
     }
    },
    "source": [
-    "The outcome of `average_evens(nums)` is of course still `6.0`."
+    "The outcome of `average_evens(nums)` is, of course, still `6.0`."
    ]
   },
   {
@@ -1557,7 +1557,7 @@
    "source": [
     "Now we call the function either with or without the `scalar` argument.\n",
     "\n",
-    "If `scalar` is passed in, this may be done as either a positional or a keyword argument. Which of the two versions where `scalar` is `2` is easier to comprehend in a large program?"
+    "If `scalar` is to be passed in, this may be done as either a positional or a keyword argument. Which of the two versions where `scalar` is `2` is more \"natural\" and faster to comprehend in a larger program?"
    ]
   },
   {
@@ -1651,9 +1651,9 @@
     }
    },
    "source": [
-    "Since we *assumed* that scaling will occur *rarely*, we'd prefer that our new version of `average_evens()` be called with a *keyword argument* whenever `scalar` is passed in explicitly. Then, the second argument is never ambiguous as we could always read its name.\n",
+    "Since we *assumed* that scaling occurs *rarely*, we'd prefer that our new version of `average_evens()` be called with a *keyword argument* whenever `scalar` is to be passed in explicitly. Then, the second argument is never ambiguous as we could always read its name.\n",
     "\n",
-    "Luckily, Python offers a **keyword-only** syntax, where all we need to do is to place the arguments for which we require *explicit* keyword use after an asterix `*`."
+    "Luckily, Python offers a **keyword-only** syntax, where all we need to do is to place the arguments for which we require *explicit* keyword use after an asterisk `*`."
    ]
   },
   {
@@ -1798,13 +1798,13 @@
    "source": [
     "The `def` statement is a statement because of its side effect of creating a *new* name that points to a *new* `function` object in memory.\n",
     "\n",
-    "We can thus think of it as doing *two* things at once (i.e., either both of them happen or none). First, a `function` object is created that contains the concrete $0$s and $1$s that resemble the instructions we put into the function's body. In the context of a function, these $0$s and $1$s are also called **[byte code](https://en.wikipedia.org/wiki/Bytecode)**. Then, a name is created pointing at the new `function` object.\n",
+    "We can thus think of it as doing *two* things at once (i.e., either both of them happen or none). First, a `function` object is created that contains the concrete $0$s and $1$s that resemble the instructions we put into the function's body. In the context of a function, these $0$s and $1$s are also called **[byte code](https://en.wikipedia.org/wiki/Bytecode)**. Then, a name pointing at the new `function` object is created.\n",
     "\n",
-    "Only this second aspect makes `def` a statement: Merely creating a new object in memory without making it accessible for later reference does *not* constitute a side effect. This is because the state the program is *not* changed. After all, if we cannot reference an object, how do we know that it is actually existing?\n",
+    "Only this second aspect makes `def` a statement: Merely creating a new object in memory without making it accessible for later reference does *not* constitute a side effect because the state the program is *not* changed. After all, if we cannot reference an object, how do we know it exists in the first place?\n",
     "\n",
     "Python provides a so-called **[lambda expression](https://docs.python.org/3/reference/expressions.html#lambda)** syntax that allows us to *only* create a `function` object in memory *without* making a name point to it.\n",
     "\n",
-    "It starts with the keyword `lambda` followed by an optional comma seperated enumeration of parameters, a mandatory colon, and *one* expression that also is the resulting `function` object's return value.\n",
+    "It starts with the keyword `lambda` followed by an optional comma separated enumeration of parameters, a mandatory colon, and *one* expression that also is the resulting `function` object's return value.\n",
     "\n",
     "Because it does not create a name pointing to the object, we effectively create \"anonymous\" functions with it. In the example, we create a `function` object that adds `3` to the only argument passed in as the parameter `x` and returns that sum."
    ]
@@ -1845,7 +1845,7 @@
     "\n",
     "We created a `function` object, dit *not* call it, and Python immediately forgot about it. So what's the point?\n",
     "\n",
-    "Just to prove that the `lambda` expression really creates a callable `function` object, we use the simple `=` statement to assign it to the variable `add_three`, which is really `add_three()` as per our convention from above."
+    "To prove that a `lambda` expression creates a callable `function` object, we use the simple `=` statement to assign it to the variable `add_three`, which is really `add_three()` as per our convention from above."
    ]
   },
   {
@@ -1869,7 +1869,7 @@
     }
    },
    "source": [
-    "Now we call `add_three()` as if we defined it with the `def` statement to begin with."
+    "Now we call `add_three()` as if we defined it with the `def` statement in the first place."
    ]
   },
   {
@@ -1904,7 +1904,7 @@
     }
    },
    "source": [
-    "Alternatively, we could call any anonymous `function` object created with an `lambda` expression right away (i.e. without assigning it to a variable), which looks really weird for now as we need *two* pairs of parentheses: The first is just a delimiter whereas the second the call operator."
+    "Alternatively, we could call an anonymous `function` object created with a `lambda` expression right away (i.e., without assigning it to a variable), which looks quite weird for now as we need *two* pairs of parentheses: The first is just a delimiter whereas the second the call operator."
    ]
   },
   {
@@ -1939,9 +1939,9 @@
     }
    },
    "source": [
-    "The main point of having functions without a name is to use them in a situation where we know ahead of time that we will use the function only once.\n",
+    "The main point of having functions without a name is to use them in a situation where we know ahead of time that we use the function *once* only.\n",
     "\n",
-    "Very popular contexts where we will apply lambda expressions are with the **map-filter-reduce** paradigm in Chapter 7 or when we do \"number crunching\" with **arrays** and **data frames** in Chapter 9."
+    "Popular applications of lambda expressions are with the **map-filter-reduce** paradigm in Chapter 7 or when we do \"number crunching\" with **arrays** and **data frames** in Chapter 9."
    ]
   },
   {
@@ -1963,13 +1963,13 @@
     }
    },
    "source": [
-    "So far, we have only used what we refer to as **core** Python in this book. By this, we mean all the syntactical rules as specified in the [language reference](https://docs.python.org/3/reference/) and a minimal set of about 50 built-in [functions](https://docs.python.org/3/library/functions.html). With this we could already implement any algorithm or business logic we can think of!\n",
+    "So far, we have only used what we refer to as **core** Python in this book. By this, we mean all the syntactical rules as specified in the [language reference](https://docs.python.org/3/reference/) and a minimal set of about 50 built-in [functions](https://docs.python.org/3/library/functions.html). With this, we could already implement any algorithm or business logic we can think of!\n",
     "\n",
     "However, after our first couple of programs, we would already start seeing recurring patterns in the code we write. In other words, we would constantly be \"re-inventing the wheel\" in each new project.\n",
     "\n",
     "Would it not be smarter to pull out the re-usable components from our programs and put them into some project independent **library** of generically useful functionalities? Then we would only need a way of including these **utilities** in our projects.\n",
     "\n",
-    "As all programmers across all languages face this very same issue, most programming languages come with a so-called **[standard library](https://en.wikipedia.org/wiki/Standard_library)** that provides utilities to accomplish common tasks without a lot of code. Examples are making an HTTP request to some website, open and read popular file types (e.g., CSV or Excel files), do something on a computer's file system, and many more."
+    "As all programmers across all languages face this very same issue, most programming languages come with a so-called **[standard library](https://en.wikipedia.org/wiki/Standard_library)** that provides utilities to accomplish everyday tasks without much code. Examples are making an HTTP request to some website, open and read popular file types (e.g., CSV or Excel files), do something on a computer's file system, and many more."
    ]
   },
   {
@@ -1991,13 +1991,13 @@
     }
    },
    "source": [
-    "Python also comes with its own [standard library](https://docs.python.org/3/library/index.html) that is structured into coherent modules and packages for given topics: A **module** is just a plain text file with the file extension *.py* that contains Python code while a **package** is a folder that groups several related modules.\n",
+    "Python also comes with a [standard library](https://docs.python.org/3/library/index.html) that is structured into coherent modules and packages for given topics: A **module** is just a plain text file with the file extension *.py* that contains Python code while a **package** is a folder that groups several related modules.\n",
     "\n",
-    "The code in the [standard library](https://docs.python.org/3/library/index.html) is contributed and maintained by many volunteers around the world. In contrast to so-called \"third-party\" packages (cf., the next section below), the Python core development team closely monitors and tests the code in the [standard library](https://docs.python.org/3/library/index.html). Consequently, we can be reasonably sure that anything provided by it works correctly independent of our computer's operating system and will most likely also be there in the next Python versions. Parts in the [standard library](https://docs.python.org/3/library/index.html) that are computationally expensive are often re-written in C and therefore much faster than anything we could code in Python ourselves. So, whenever we can solve a problem with the help of the [standard library](https://docs.python.org/3/library/index.html), it is almost always the best way to do so as well.\n",
+    "The code in the [standard library](https://docs.python.org/3/library/index.html) is contributed and maintained by many volunteers around the world. In contrast to so-called \"third-party\" packages (cf., the next section below), the Python core development team closely monitors and tests the code in the [standard library](https://docs.python.org/3/library/index.html). Consequently, we can be reasonably sure that anything provided by it works correctly independent of our computer's operating system and will most likely also be there in the next Python versions. Parts in the [standard library](https://docs.python.org/3/library/index.html) that are computationally expensive are often re-written in C and, therefore, much faster than anything we could write in Python ourselves. So, whenever we can solve a problem with the help of the [standard library](https://docs.python.org/3/library/index.html), it is almost always the best way to do so as well.\n",
     "\n",
-    "The [standard library](https://docs.python.org/3/library/index.html) has grown very big over the years and we refer to the website [PYMOTW](https://pymotw.com/3/index.html) (i.e., \"Python Module of the Week\") that features well written introductory tutorials and how-to guides to most parts of the library. The same author also published a [book](https://www.amazon.com/Python-Standard-Library-Example-Developers/dp/0134291050/ref=as_li_ss_tl?ie=UTF8&qid=1493563121&sr=8-1&keywords=python+3+standard+library+by+example) that many Pythonistas keep on their shelf for reference. Knowing what is in the [standard library](https://docs.python.org/3/library/index.html) is quite valuable for solving real world tasks quickly.\n",
+    "The [standard library](https://docs.python.org/3/library/index.html) has grown very big over the years, and we refer to the website [PYMOTW](https://pymotw.com/3/index.html) (i.e., \"Python Module of the Week\") that features well written introductory tutorials and how-to guides to most parts of the library. The same author also published a [book](https://www.amazon.com/Python-Standard-Library-Example-Developers/dp/0134291050/ref=as_li_ss_tl?ie=UTF8&qid=1493563121&sr=8-1&keywords=python+3+standard+library+by+example) that many Pythonistas keep on their shelf for reference. Knowing what is in the [standard library](https://docs.python.org/3/library/index.html) is quite valuable for solving real-world tasks quickly.\n",
     "\n",
-    "Throughout this book we will look at many modules and packages from the [standard library](https://docs.python.org/3/library/index.html) in more depth, starting with the [math](https://docs.python.org/3/library/math.html) and [random](https://docs.python.org/3/library/random.html) modules in this chapter."
+    "Throughout this book, we look at many modules and packages from the [standard library](https://docs.python.org/3/library/index.html) in more depth, starting with the [math](https://docs.python.org/3/library/math.html) and [random](https://docs.python.org/3/library/random.html) modules in this chapter."
    ]
   },
   {
@@ -2084,7 +2084,7 @@
     {
      "data": {
       "text/plain": [
-       "139925472068136"
+       "140519828111832"
       ]
      },
      "execution_count": 58,
@@ -2132,7 +2132,7 @@
     "\n",
     "Let's see what we can do with the `math` module.\n",
     "\n",
-    "The [dir()](https://docs.python.org/3/library/functions.html#dir) built-in function may also be used with an argument passed in. Ignoring the dunder-style names, `math` offers quite a lot of ... names. As we cannot know at this point in time if a listed name refers to a function or an ordinary variable, we use the more generic term **attribute** to mean either one of them."
+    "The [dir()](https://docs.python.org/3/library/functions.html#dir) built-in function may also be used with an argument passed in. Ignoring the dunder-style names, `math` offers quite a lot of names. As we cannot know at this point if a listed name refers to a function or an ordinary variable, we use the more generic term **attribute** to mean either one of them."
    ]
   },
   {
@@ -2356,7 +2356,7 @@
    "source": [
     "Observe how the arguments passed to functions do not need to be just variables or simple literals. Instead, we may pass in any *expression* that evaluates to a *new* object of the type the function expects.\n",
     "\n",
-    "So just as a reminder from the expression vs. statement discussion in [Chapter 1](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/01_elements.ipynb): An expression is *any* syntactically correct combination of variables and literals with operators. And the call operator `()` is just ... well another operator. So both of the next two code cells are just expressions! They have no permanent side effect in memory. We may execute them as often as we want *without* changing the state of the program (i.e., this Jupyter notebook).\n",
+    "So just as a reminder from the expression vs. statement discussion in [Chapter 1](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/01_elements.ipynb): An expression is *any* syntactically correct combination of variables and literals with operators. And the call operator `()` is yet another operator. So both of the next two code cells are just expressions! They have no permanent side effects in memory. We may execute them as often as we want *without* changing the state of the program (i.e., this Jupyter notebook).\n",
     "\n",
     "So, regarding the very next cell in particular: Although the `2 ** 2` creates a *new* object `4` in memory that is then immediately passed into the [math.sqrt()](https://docs.python.org/3/library/math.html#math.sqrt) function, once that function call returns, \"all is lost\" and the newly created `4` object is forgotten again, as well as the return value of [math.sqrt()](https://docs.python.org/3/library/math.html#math.sqrt)."
    ]
@@ -2489,7 +2489,7 @@
     }
    },
    "source": [
-    "Often times, we need a random variable, for example, when we want to build a simulation program. The [random](https://docs.python.org/3/library/random.html) module in the [standard library](https://docs.python.org/3/library/index.html) often suffices for that."
+    "Often, we need a random variable, for example, when we want to build a simulation program. The [random](https://docs.python.org/3/library/random.html) module in the [standard library](https://docs.python.org/3/library/index.html) often suffices for that."
    ]
   },
   {
@@ -2537,7 +2537,7 @@
     }
    },
    "source": [
-    "Besides the usual dunder-style attributes, the [dir()](https://docs.python.org/3/library/functions.html#dir) built-in function lists some attributes in an upper case naming convention and many others starting with a single underscore `_`. To understand the former, we have to wait until Chapter 10 while the latter are explained further below."
+    "Besides the usual dunder-style attributes, the built-in [dir()](https://docs.python.org/3/library/functions.html#dir) function lists some attributes in an upper case naming convention and many others starting with a *single* underscore `_`. To understand the former, we must wait until Chapter 10, while the latter is explained further below."
    ]
   },
   {
@@ -2697,7 +2697,7 @@
     {
      "data": {
       "text/plain": [
-       "0.270353921677863"
+       "0.5291407120147841"
       ]
      },
      "execution_count": 75,
@@ -2717,7 +2717,7 @@
     }
    },
    "source": [
-    "While we could build some conditional logic with an `if` statement to map the number generated by [random.random()](https://docs.python.org/3/library/random.html#random.random) to a finite set of elements manually, the [random.choice()](https://docs.python.org/3/library/random.html#random.choice) function provides a lot more **convenience** for us. We simply call it with, for example, the `nums` list and it draws one element out of it with equal chance."
+    "While we could build some conditional logic with an `if` statement to map the number generated by [random.random()](https://docs.python.org/3/library/random.html#random.random) to a finite set of elements manually, the [random.choice()](https://docs.python.org/3/library/random.html#random.choice) function provides a lot more **convenience** for us. We call it with, for example, the `nums` list and it draws one element out of it with equal chance."
    ]
   },
   {
@@ -2732,7 +2732,7 @@
     {
      "data": {
       "text/plain": [
-       "<bound method Random.choice of <random.Random object at 0x56063d20aba8>>"
+       "<bound method Random.choice of <random.Random object at 0x5609be5deba8>>"
       ]
      },
      "execution_count": 76,
@@ -2781,7 +2781,7 @@
     {
      "data": {
       "text/plain": [
-       "7"
+       "4"
       ]
      },
      "execution_count": 78,
@@ -2801,7 +2801,7 @@
     }
    },
    "source": [
-    "In order to re-produce the same random numbers in a simulation each time we run it, we set the **[random seed](https://en.wikipedia.org/wiki/Random_seed)**. It is good practice to do this at the beginning of a program or notebook. Then every time we re-start the program, we will get the exact same random numbers again. This becomes very important, for example, when we employ certain machine learning algorithms that rely on randomization, like the infamous [Random Forest](https://en.wikipedia.org/wiki/Random_forest), and want to obtain **re-producable** results.\n",
+    "To reproduce the same random numbers in a simulation each time we run it, we set the **[random seed](https://en.wikipedia.org/wiki/Random_seed)**. It is good practice to do this at the beginning of a program or notebook. Then every time we re-start the program, we get the *same* random numbers again. This becomes very important, for example, when we employ machine learning algorithms that rely on randomization, like the infamous [Random Forest](https://en.wikipedia.org/wiki/Random_forest), and want to obtain **reproducable** results.\n",
     "\n",
     "The [random](https://docs.python.org/3/library/random.html) module provides the [random.seed()](https://docs.python.org/3/library/random.html#random.seed) function to do that."
    ]
@@ -2899,11 +2899,11 @@
     }
    },
    "source": [
-    "As the Python community is based around open source, many developers publish their code, for example, on the Python Package Index [PyPI](https://pypi.org) from where anyone may download and install it for free using command line based tools like [pip](https://pip.pypa.io/en/stable/) or [conda](https://conda.io/en/latest/). This way, we can always customize our Python installation even more. Managing many such packages is actually quite a deep topic on its own, sometimes fearfully called **[dependency hell](https://en.wikipedia.org/wiki/Dependency_hell)**.\n",
+    "As the Python community is based around open source, many developers publish their code, for example, on the Python Package Index [PyPI](https://pypi.org) from where anyone may download and install it for free using command-line based tools like [pip](https://pip.pypa.io/en/stable/) or [conda](https://conda.io/en/latest/). This way, we can always customize our Python installation even more. Managing many such packages is quite a deep topic on its own, sometimes fearfully called **[dependency hell](https://en.wikipedia.org/wiki/Dependency_hell)**.\n",
     "\n",
-    "The difference between the [standard library](https://docs.python.org/3/library/index.html) and such **third-party** packages is that in the first case the code goes through a much more formalized review process and is officially endorsed by the Python core developers. Yet, many third-party projects also offer the highest quality standards and a lot of such software is actually also relied on by many businesses and researchers.\n",
+    "The difference between the [standard library](https://docs.python.org/3/library/index.html) and such **third-party** packages is that in the first case, the code goes through a much more formalized review process and is officially endorsed by the Python core developers. Yet, many third-party projects also offer the highest quality standards and are also relied on by many businesses and researchers.\n",
     "\n",
-    "Throughout this book, we will look at many third-party libraries, mostly from Python's [scientific stack](https://scipy.org/about.html), a tightly coupled set of third-party libraries for storing **big data** efficiently ([numpy](http://www.numpy.org/)), \"wrangling\" ([pandas](https://pandas.pydata.org/)) and visualizing them ([matplotlib](https://matplotlib.org/) and [seaborn](https://seaborn.pydata.org/)), fitting classical statistical models ([statsmodels](http://www.statsmodels.org/)), training machine learning models ([sklearn](http://scikit-learn.org/)), and much more.\n",
+    "Throughout this book, we will look at many third-party libraries, mostly from Python's [scientific stack](https://scipy.org/about.html), a tightly coupled set of third-party libraries for storing **big data** efficiently (e.g., [numpy](http://www.numpy.org/)), \"wrangling\" (e.g., [pandas](https://pandas.pydata.org/)) and visualizing them (e.g., [matplotlib](https://matplotlib.org/) or [seaborn](https://seaborn.pydata.org/)), fitting classical statistical models (e.g., [statsmodels](http://www.statsmodels.org/)), training machine learning models (e.g., [sklearn](http://scikit-learn.org/)), and much more.\n",
     "\n",
     "Below, we briefly show how to install third-party libraries."
    ]
@@ -2927,11 +2927,11 @@
     }
    },
    "source": [
-    "[numpy](http://www.numpy.org/) is the de-facto standard in the Python world for handling **array-like** data. That is a fancy word for data that can be put into a matrix or vector format. We will look at it in depth in Chapter 9.\n",
+    "[numpy](http://www.numpy.org/) is the de-facto standard in the Python world for handling **array-like** data. That is a fancy word for data that can be put into a matrix or vector format. We look at it in depth in Chapter 9.\n",
     "\n",
-    "As [numpy](http://www.numpy.org/) is *not* in the [standard library](https://docs.python.org/3/library/index.html), it must be *manually* installed, for example, with the [pip](https://pip.pypa.io/en/stable/) tool. As mentioned in [Chapter 0](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/00_start_up.ipynb), to execute terminal commands from within a Jupyter notebook, we just need to start a code cell with an exclamation mark.\n",
+    "As [numpy](http://www.numpy.org/) is *not* in the [standard library](https://docs.python.org/3/library/index.html), it must be *manually* installed, for example, with the [pip](https://pip.pypa.io/en/stable/) tool. As mentioned in [Chapter 0](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/00_start_up.ipynb), to execute terminal commands from within a Jupyter notebook, we start a code cell with an exclamation mark.\n",
     "\n",
-    "If you are running this notebook with an installation of the [Anaconda Distribution](https://www.anaconda.com/distribution/), then [numpy](http://www.numpy.org/) is probably already installed. Running the cell below, will just confirm that."
+    "If you are running this notebook with an installation of the [Anaconda Distribution](https://www.anaconda.com/distribution/), then [numpy](http://www.numpy.org/) is probably already installed. Running the cell below confirms that."
    ]
   },
   {
@@ -2963,7 +2963,7 @@
     }
    },
    "source": [
-    "[numpy](http://www.numpy.org/) is conventionally imported with the shorter **idiomatic** name `np`. The `as` in the import statement just changes the resulting variable name. It is a shortcut for the three lines `import numpy`, `np = numpy`, and `del numpy`."
+    "[numpy](http://www.numpy.org/) is conventionally imported with the shorter **idiomatic** name `np`. The `as` in the import statement changes the resulting variable name. It is a shortcut for the three lines `import numpy`, `np = numpy`, and `del numpy`."
    ]
   },
   {
@@ -3096,7 +3096,7 @@
    "source": [
     "[numpy](http://www.numpy.org/) somehow magically adds new behavior to Python's built-in arithmetic operators. For example, we may now [scalar-multiply](https://en.wikipedia.org/wiki/Scalar_multiplication) `vec`.\n",
     "\n",
-    "[numpy](http://www.numpy.org/)'s functions are implemented in highly optimized C code and therefore fast, especially when it comes to big data."
+    "[numpy](http://www.numpy.org/)'s functions are implemented in highly optimized C code and, therefore, are fast, especially when dealing with bigger amounts of data."
    ]
   },
   {
@@ -3236,13 +3236,13 @@
     }
    },
    "source": [
-    "For sure, we can create our own modules and packages. In the repository's main directory, there is a [*sample_module.py*](https://github.com/webartifex/intro-to-python/blob/master/sample_module.py) file that contains, among others, a function equivalent to the final version of `average_evens()`. To be realistic, this sample module is structured in a modular manner with several functions building on each other. It is best to skim over it *now* before reading on.\n",
+    "For sure, we can create local modules and packages. In the repository's main directory, there is a [*sample_module.py*](https://github.com/webartifex/intro-to-python/blob/master/sample_module.py) file that contains, among others, a function equivalent to the final version of `average_evens()`. To be realistic, this sample module is structured in a modular manner with several functions building on each other. It is best to skim over it *now* before reading on.\n",
     "\n",
     "To make code we put into a *.py* file available in our program, we import it as a module just as we did above with modules in the [standard library](https://docs.python.org/3/library/index.html) or third-party packages.\n",
     "\n",
-    "The *name* to be imported is the file's name except for the *.py* part. In order for this to work, the file's name *must* adhere to the *same* rules as hold for [variable names](https://docs.python.org/3/reference/lexical_analysis.html#identifiers) in general.\n",
+    "The *name* to be imported is the file's name except for the *.py* part. For this to work, the file's name *must* adhere to the *same* rules as hold for [variable names](https://docs.python.org/3/reference/lexical_analysis.html#identifiers) in general.\n",
     "\n",
-    "What happens during an import is conceptually as follows. When Python sees the `import sample_module` part, it first creates a *new* object of type `module` in memory. This is effectively an *empty* namespace. Then, it executes the imported file's code from top to bottom. Whatever variables are still defined at the end of this, are put into the module's namespace. Only if the file's code does *not* raise an error, will Python make a variable in our current location (i.e., `mod` here) point to the created `module` object. Otherwise, it is discarded. In essence, it is as if we copied and pasted the file's code in place of the import statement. If we import an already imported module again, Python is smart enough to avoid doing all this work all over and does nothing."
+    "What happens during an import is as follows. When Python sees the `import sample_module` part, it first creates a *new* object of type `module` in memory. This is effectively an *empty* namespace. Then, it executes the imported file's code from top to bottom. Whatever variables are still defined at the end of this, are put into the module's namespace. Only if the file's code does *not* raise an error, will Python make a variable in our current location (i.e., `mod` here) point to the created `module` object. Otherwise, it is discarded. In essence, it is as if we copied and pasted the file's code in place of the import statement. If we import an already imported module again, Python is smart enough to avoid doing all this work all over and does nothing."
    ]
   },
   {
@@ -3292,7 +3292,7 @@
    "source": [
     "Disregarding the dunder-style attributes, `mod` defines the five attributes `_default_scalar`, `_scaled_average`, `average`, `average_evens`, and `average_odds`, which are exactly the ones we would expect from reading the [*sample_module.py*](https://github.com/webartifex/intro-to-python/blob/master/sample_module.py) file.\n",
     "\n",
-    "An important convention when working with imported code is to *disregard* any attributes starting with an underscore `_`. These are considered **private** and constitute **implementation details** the author of the imported code might change in a future version of his software. We *must* not rely on them in any way.\n",
+    "A convention when working with imported code is to *disregard* any attributes starting with an underscore `_`. These are considered **private** and constitute **implementation details** the author of the imported code might change in a future version of his software. We *must* not rely on them in any way.\n",
     "\n",
     "In contrast, the three remaining **public** attributes are the functions `average()`, `average_evens()`, and `average_odds()` that we may use after the import."
    ]
@@ -3433,9 +3433,9 @@
     }
    },
    "source": [
-    "Packages are a generalization of modules and we will look at one in Chapter 10 in detail. You may, however, already look at a [sample package](https://github.com/webartifex/intro-to-python/tree/master/sample_package) in the repository, which is nothing but a folder with *.py* files in it.\n",
+    "Packages are a generalization of modules, and we look at one in detail in Chapter 10. You may, however, already look at a [sample package](https://github.com/webartifex/intro-to-python/tree/master/sample_package) in the repository, which is nothing but a folder with *.py* files in it.\n",
     "\n",
-    "As a further references on modules, we refer to the [official tutorial](https://docs.python.org/3/tutorial/modules.html)."
+    "As a further reading on modules and packages, we refer to the [official tutorial](https://docs.python.org/3/tutorial/modules.html)."
    ]
   },
   {
@@ -3461,7 +3461,7 @@
     "\n",
     "Functions provide benefits as they:\n",
     "\n",
-    "- make programs easier to comprehend and debug for humans as they give names to the smaller parts of a larger program (i.e., they **modularize** a code base), and\n",
+    "- make programs easier to comprehend and debug for humans as they give names to the smaller parts of a larger program (i.e., they **modularize** a codebase), and\n",
     "- eliminate redundancies by allowing **re-use of code**.\n",
     "\n",
     "Functions are **defined** once with the `def` statement. Then, they may be **called** many times with the call operator `()`.\n",
diff --git a/02_functions_review_and_exercises.ipynb b/02_functions_review_and_exercises.ipynb
index dcd0a9c..d0935c6 100644
--- a/02_functions_review_and_exercises.ipynb
+++ b/02_functions_review_and_exercises.ipynb
@@ -324,9 +324,9 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "**Q11.5**: Using the [range()](https://docs.python.org/3/library/functions.html#func-range) built-in, write a `for`-loop and calculate the volume of a sphere with `radius = 42.0` for all `digits` from `1` through `20`. Print out each volume on a seperate line.\n",
+    "**Q11.5**: Using the [range()](https://docs.python.org/3/library/functions.html#func-range) built-in, write a `for`-loop and calculate the volume of a sphere with `radius = 42.0` for all `digits` from `1` through `20`. Print out each volume on a separate line.\n",
     "\n",
-    "Note: This is the first task where you actually need to use the [print()](https://docs.python.org/3/library/functions.html#print) built-in function."
+    "Note: This is the first task where you need to use the built-in [print()](https://docs.python.org/3/library/functions.html#print) function."
    ]
   },
   {
@@ -349,7 +349,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "**Q11.6**: What important lesson did you learn about the `float` type?"
+    "**Q11.6**: What lesson did you learn about the `float` type?"
    ]
   },
   {
diff --git a/03_conditionals.ipynb b/03_conditionals.ipynb
index f350397..d0ed61d 100644
--- a/03_conditionals.ipynb
+++ b/03_conditionals.ipynb
@@ -19,7 +19,7 @@
     }
    },
    "source": [
-    "We analyzed every aspect of the `average_evens()` function in [Chapter 2](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/02_functions.ipynb) except for the `if` related parts. While it seems to intuitively do what we expect it to, there is a whole lot more to be learned from taking it apart. In particular, the `if` may occur within both a **statement** as in our introductory example in [Chapter 1](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/01_elements.ipynb) but also an **expression** as in `average_evens()`. This is analogous as to how a noun in a natural language is *either* the subject of *or* an object in a sentence. What is common to both versions of the `if` is that it leads to code being executed for *parts* of the input only. It is our first way of **controlling** the **flow of execution** in a program.\n",
+    "We analyzed every aspect of the `average_evens()` function in [Chapter 2](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/02_functions.ipynb) except for the `if` related parts. While it seems to do what we expect it to, there is a whole lot more we learn from taking it apart. In particular, the `if` may occur within both a **statement** or an **expression**, analogous as to how a noun in a natural language is *either* the subject of *or* an object in a sentence. What is common to both usages is that it leads to code being executed for *parts* of the input only. It is our first way of **controlling** the **flow of execution** in a program.\n",
     "\n",
     "After deconstructing `if` in the first part of this chapter, we take a close look at a similar concept, namely handling **exceptions**."
    ]
@@ -43,7 +43,7 @@
     }
    },
    "source": [
-    "Any expression that is either true or not is called a **boolean expression**. If you think such expressions are boring or just not so useful, read a bit on [propositional logic](https://en.wikipedia.org/wiki/Propositional_calculus) and you will quickly realize how mathematicians and originally philosophers base their rules of how to prove or disprove a conclusion on simple true-or-false \"statements\" about the world. It is the underlying principle of all of reasoning.\n",
+    "Any expression that is either true or not is called a **boolean expression**. It is such simple true-or-false \"statements\" about the world on which mathematicians, and originally philosophers, base their rules of reasoning: They are studied formally in the field of [propositional logic](https://en.wikipedia.org/wiki/Propositional_calculus).\n",
     "\n",
     "A trivial example involves the equality operator `==` that evaluates to either `True` or `False` depending on its operands \"comparing equal\" or not."
    ]
@@ -104,7 +104,7 @@
     }
    },
    "source": [
-    "The `==` operator handles objects of *different* type. This shows how it implements a notion of equality in line with how we humans think of things being equal or not. After all, `42` and `42.0` are totally different $0$s and $1$s for a computer and many programming languages would actually say `False` here! Technically, this is yet another example of operator overloading."
+    "The `==` operator handles objects of *different* types: It implements a notion of equality in line with how humans think of things being equal or not. After all, `42` and `42.0` are different $0$s and $1$s for a computer and other programming languages might say `False` here! Technically, this is yet another example of operator overloading."
    ]
   },
   {
@@ -139,7 +139,7 @@
     }
    },
    "source": [
-    "There are, however, cases where even well-behaved Python does not make us happy. [Chapter 5](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/05_numbers.ipynb) will provide more insights on this \"bug\"."
+    "There are, however, cases where even well-behaved Python does not make us happy. [Chapter 5](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/05_numbers.ipynb) provides more insights into this \"bug.\""
    ]
   },
   {
@@ -174,7 +174,7 @@
     }
    },
    "source": [
-    "`True` and `False` are special built-in *objects* of type `bool`."
+    "`True` and `False` are built-in *objects* of type `bool`."
    ]
   },
   {
@@ -189,7 +189,7 @@
     {
      "data": {
       "text/plain": [
-       "94697002906592"
+       "94906834637792"
       ]
      },
      "execution_count": 5,
@@ -213,7 +213,7 @@
     {
      "data": {
       "text/plain": [
-       "94697002906560"
+       "94906834637760"
       ]
      },
      "execution_count": 6,
@@ -281,11 +281,11 @@
     }
    },
    "source": [
-    "Let's not confuse the boolean `False` with `None`, another special built-in object! We saw the latter before in [Chapter 2](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/02_functions.ipynb) as the *implicit* return value of a function without a `return` statement.\n",
+    "Let's not confuse the boolean `False` with `None`, another built-in object! We saw the latter before in [Chapter 2](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/02_functions.ipynb) as the *implicit* return value of a function without a `return` statement.\n",
     "\n",
-    "We might think of `None` in a boolean context indicating a \"maybe\" or even an \"unknown\" answer; however, for Python, there are no \"maybe\" or \"unknown\" objects as we will see further below!\n",
+    "We might think of `None` in a boolean context indicating a \"maybe\" or even an \"unknown\" answer; however, for Python, there are no \"maybe\" or \"unknown\" objects, as we see further below!\n",
     "\n",
-    "Whereas `False` is of type `bool`, `None` is of type `NoneType`. So, they are totally unrelated. On the contrary, as both `True` and `False` are of the same type, we could call them \"siblings\"."
+    "Whereas `False` is of type `bool`, `None` is of type `NoneType`. So, they are unrelated. On the contrary, as both `True` and `False` are of the same type, we could call them \"siblings.\""
    ]
   },
   {
@@ -313,7 +313,7 @@
     {
      "data": {
       "text/plain": [
-       "94697002893552"
+       "94906834624752"
       ]
      },
      "execution_count": 10,
@@ -357,7 +357,7 @@
     }
    },
    "source": [
-    "`True`, `False`, and `None` have the property that they each exist in memory only *once*. Objects designed this way are so-called **singletons**. This **[design pattern](https://en.wikipedia.org/wiki/Design_Patterns)** was originally developed to keep a program's memory usage at a minimum. It may only be employed in situations where we know that an object will *not* mutate its value (i.e., to re-use the bag analogy from [Chapter 1](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/01_elements.ipynb), no flipping of $0$s and $1$s in the bag is allowed). In languages \"closer\" to the memory like C we would have to code this singleton logic ourselves but Python has this already built in for *some* types.\n",
+    "`True`, `False`, and `None` have the property that they each exist in memory only *once*. Objects designed this way are so-called **singletons**. This **[design pattern](https://en.wikipedia.org/wiki/Design_Patterns)** was originally developed to keep a program's memory usage at a minimum. It may only be employed in situations where we know that an object does *not* mutate its value (i.e., to re-use the bag analogy from [Chapter 1](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/01_elements.ipynb), no flipping of $0$s and $1$s in the bag is allowed). In languages \"closer\" to the memory like C, we would have to code this singleton logic ourselves, but Python has this built in for *some* types.\n",
     "\n",
     "We verify this with either the `is` operator or by comparing memory addresses."
    ]
@@ -523,7 +523,7 @@
     }
    },
    "source": [
-    "The \"less than\" `<` or \"greater than\" `>` operators on their own mean \"strictly less than\" or \"strictly greater than\" but may be combined with the equality operator into just `<=` and `>=`. This is a shortcut for using the logical `or` operator as described in the next section."
+    "The \"less than\" `<` or \"greater than\" `>` operators mean \"strictly less than\" or \"strictly greater than\" but may be combined with the equality operator into just `<=` and `>=`. This is a shortcut for using the logical `or` operator as described in the next section."
    ]
   },
   {
@@ -643,7 +643,7 @@
    "source": [
     "Boolean expressions may be combined or negated with the **logical operators** `and`, `or`, and `not` to form new boolean expressions. Of course, this may be done *recursively* as well to obtain boolean expressions of arbitrary complexity.\n",
     "\n",
-    "Their usage is similar to how the equivalent words are used in plain English:\n",
+    "Their usage is similar to how the equivalent words are used in everyday English:\n",
     "\n",
     "- `and` evaluates to `True` if *both* sub-expressions evaluate to `True` and `False` otherwise,\n",
     "- `or` evaluates to `True` if either one *or* both sub-expressions evaluate to `True` and `False` otherwise, and\n",
@@ -672,7 +672,7 @@
     }
    },
    "source": [
-    "Relational operators have a **[higher precedence](https://docs.python.org/3/reference/expressions.html#operator-precedence)** over logical operators. So the following expression means what we intuitively think it does."
+    "Relational operators have **[higher precedence](https://docs.python.org/3/reference/expressions.html#operator-precedence)** over logical operators. So the following expression means what we intuitively think it does."
    ]
   },
   {
@@ -707,7 +707,7 @@
     }
    },
    "source": [
-    "However, sometimes it is good to use *parentheses* around each sub-expression for clarity."
+    "However, sometimes, it is good to use *parentheses* around each sub-expression for clarity."
    ]
   },
   {
@@ -742,7 +742,7 @@
     }
    },
    "source": [
-    "This is especially useful when several logical operators are combined."
+    "This is especially the case when several logical operators are combined."
    ]
   },
   {
@@ -825,9 +825,9 @@
     }
    },
    "source": [
-    "For even better readability, [some practitioners](https://llewellynfalco.blogspot.com/2016/02/dont-use-greater-than-sign-in.html) suggest to *never* use the `>` and `>=` operators (note that the included example is written in [Java](https://en.wikipedia.org/wiki/Java_%28programming_language%29) and `&&` means `and` and `||` means `or`).\n",
+    "For even better readability, some practitioners suggest to *never* use the `>` and `>=` operators (cf., [source](https://llewellynfalco.blogspot.com/2016/02/dont-use-greater-than-sign-in.html); note that the included example is written in [Java](https://en.wikipedia.org/wiki/Java_%28programming_language%29) where `&&` means `and` and `||` means `or`).\n",
     "\n",
-    "Python allows **chaining** relational operators that are combined with the `and` operator. For example, the following two cells implement the same logic where the second is a lot easier to read."
+    "Python allows **chaining** relational operators that are combined with the `and` operator. For example, the following two cells implement the same logic, where the second is a lot easier to read."
    ]
   },
   {
@@ -897,9 +897,9 @@
     }
    },
    "source": [
-    "The operands of the logical operators do not actually have to be *boolean* expressions as defined above but may be *any* kind of expression. If a sub-expression does *not* evaluate to an object of type `bool`, Python automatically casts the resulting object as such.\n",
+    "The operands of the logical operators do not need to be *boolean* expressions as defined above but may be *any* expression. If a sub-expression does *not* evaluate to an object of type `bool`, Python automatically casts it as such.\n",
     "\n",
-    "For example, any non-zero numeric object becomes `True`. While this behavior allows writing conciser and thus more \"beautiful\" code, it is also a common source of confusion."
+    "For example, any non-zero numeric object becomes `True`. While this behavior allows writing more concise and thus more \"beautiful\" code, it is also a common source of confusion. `(x - 9)` is cast as `True` and then the overall expression evaluates to `True` as well."
    ]
   },
   {
@@ -923,7 +923,7 @@
     }
    ],
    "source": [
-    "(x - 9) and (y < 100)  # = 33 and (y < 100)"
+    "(x - 9) and (y < 100)  # = 33 and (y < 100) = 33 and True"
    ]
   },
   {
@@ -934,7 +934,7 @@
     }
    },
    "source": [
-    "Whenever we are unsure as to how Python will evaluate a non-boolean expression in a boolean context, the [bool()](https://docs.python.org/3/library/functions.html#bool) built-in allows us to check it ourselves."
+    "Whenever we are unsure as to how Python evaluates a non-boolean expression in a boolean context, the [bool()](https://docs.python.org/3/library/functions.html#bool) built-in allows us to check it ourselves."
    ]
   },
   {
@@ -1028,7 +1028,7 @@
     }
    },
    "source": [
-    "In a boolean context, `None` is casted as `False`! So, `None` is really *not* a \"maybe\" answer but a \"no\"."
+    "In a boolean context, `None` is cast as `False`! So, `None` is *not* a \"maybe\" answer but a \"no.\""
    ]
   },
   {
@@ -1063,7 +1063,7 @@
     }
    },
    "source": [
-    "Another good rule to know is that container types (e.g., `list`) evaluate to `True` whenever they are not empty and `False` otherwise."
+    "Another good rule to know is that container types (e.g., `list`) evaluate to `True` whenever they are *not* empty and `False` if they are."
    ]
   },
   {
@@ -1125,6 +1125,388 @@
     "Pythonistas often use the terms **truthy** or **falsy** to describe a non-boolean expression's behavior when used in place of a boolean one."
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "### Short-Circuiting"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "When evaluating boolean expressions with logical operators in it, Python follows the **[short-circuiting](https://en.wikipedia.org/wiki/Short-circuit_evaluation)** strategy: First, the inner-most sub-expressions are evaluated. Second, with identical **[operator precedence](https://docs.python.org/3/reference/expressions.html#operator-precedence)**, evaluation goes from left to right. Once it is clear what the overall truth value is, no more sub-expressions are evaluated, and the result is *immediately* returned.\n",
+    "\n",
+    "In summary, data science practitioners must know *how* the following two generic expressions are evaluated:\n",
+    "\n",
+    "- `x or y`: The `y` expression is evaluated *only if* `x` evaluates to `False`, in which case `y` is returned; otherwise, `x` is returned *without* even looking at `y`.\n",
+    "- `x and y`: The `y` expression is evaluated *only if* `x` evaluates to `True`. Then, if `y` also evaluates to `True`, it is returned; otherwise, `x` is returned.\n",
+    "\n",
+    "Let's look at a couple of examples."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "x = 0\n",
+    "y = 1\n",
+    "z = 2"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "We define a helper function `expr()` that prints out the only argument it is passed before returning it. With `expr()`, we can see if a sub-expression is evaluated or not."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "def expr(arg):\n",
+    "    \"\"\"Print and return the only argument.\"\"\"\n",
+    "    print(\"Arg:\", arg)\n",
+    "    return arg"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "With the `or` operator, the first sub-expression that evaluates to `True` is returned."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Arg: 0\n",
+      "Arg: 1\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "1"
+      ]
+     },
+     "execution_count": 38,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "expr(x) or expr(y)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Arg: 1\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "1"
+      ]
+     },
+     "execution_count": 39,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "expr(y) or expr(z)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Arg: 0\n",
+      "Arg: 1\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "1"
+      ]
+     },
+     "execution_count": 40,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "expr(x) or expr(y) or expr(z)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "If all sub-expressions evaluate to `False`, the last one is the result."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Arg: False\n",
+      "Arg: []\n",
+      "Arg: 0\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "0"
+      ]
+     },
+     "execution_count": 41,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "expr(False) or expr([]) or expr(x)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "With the `and` operator, the first sub-expression that evaluates to `False` is returned."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Arg: 0\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "0"
+      ]
+     },
+     "execution_count": 42,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "expr(x) and expr(y)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Arg: 1\n",
+      "Arg: 0\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "0"
+      ]
+     },
+     "execution_count": 43,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "expr(y) and expr(x)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Arg: 2\n",
+      "Arg: 1\n",
+      "Arg: 0\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "0"
+      ]
+     },
+     "execution_count": 44,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "expr(z) and expr(y) and expr(x)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "If all sub-expressions evaluate to `True`, the last one is returned."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Arg: 1\n",
+      "Arg: 2\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "2"
+      ]
+     },
+     "execution_count": 45,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "expr(y) and expr(z)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "The crucial takeaway is that Python does *not* necessarily evaluate *all* sub-expressions and, therefore, our code should never rely on that assumption."
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {
@@ -1144,9 +1526,9 @@
     }
    },
    "source": [
-    "In order to write useful programs, we need to control the flow of execution, for example, to react to user input. The logic by which a program does that is referred to as **business logic**.\n",
+    "To write useful programs, we need to control the flow of execution, for example, to react to user input. The logic by which a program does that is referred to as **business logic**.\n",
     "\n",
-    "One major language construct to do so is the **[conditional statement](https://docs.python.org/3/reference/compound_stmts.html#the-if-statement)**, or `if` statement for short. It consists of:\n",
+    "One language construct to do so is the **[conditional statement](https://docs.python.org/3/reference/compound_stmts.html#the-if-statement)**, or `if` statement for short. It consists of:\n",
     "\n",
     "- *one* mandatory `if`-clause,\n",
     "- an *arbitrary* number of `elif`-clauses (i.e. \"else if\"), and\n",
@@ -1154,14 +1536,14 @@
     "\n",
     "The `if`- and `elif`-clauses each specify one *boolean* expression, also called **condition**, while the `else`-clause serves as the \"catch everything else\" case.\n",
     "\n",
-    "In terms of syntax, the header lines end with a colon and the code blocks are indented.\n",
+    "In terms of syntax, the header lines end with a colon, and the code blocks are indented.\n",
     "\n",
     "In contrast to our intuitive interpretation in natural languages, only the code in *one* of the alternatives, also called **branches**, is executed. To be precise, it is always the code in the first branch whose condition evaluates to `True`."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 36,
+   "execution_count": 46,
    "metadata": {
     "code_folding": [],
     "slideshow": {
@@ -1175,7 +1557,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 37,
+   "execution_count": 47,
    "metadata": {
     "code_folding": [],
     "slideshow": {
@@ -1212,12 +1594,12 @@
    "source": [
     "In many situations, we only need a reduced form of the `if` statement.\n",
     "\n",
-    "We could **inject** code only at random to, for example, implement some sort of [A/B testing](https://en.wikipedia.org/wiki/A/B_testing)."
+    "We could **inject** code only at random, for example, to implement an [A/B testing](https://en.wikipedia.org/wiki/A/B_testing) strategy."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 38,
+   "execution_count": 48,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -1230,7 +1612,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 39,
+   "execution_count": 49,
    "metadata": {
     "slideshow": {
      "slide_type": "-"
@@ -1239,7 +1621,7 @@
    "outputs": [],
    "source": [
     "if random.random() > 0.5:\n",
-    "    print(\"You will read this just as often as you see heads when tossing a coin\")"
+    "    print(\"You read this just as often as you see heads when tossing a coin\")"
    ]
   },
   {
@@ -1255,7 +1637,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 40,
+   "execution_count": 50,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -1292,7 +1674,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 41,
+   "execution_count": 51,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -1303,7 +1685,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "z is odd\n"
+      "z is positive\n"
      ]
     }
    ],
@@ -1328,14 +1710,14 @@
     }
    },
    "source": [
-    "A good way to make this code more readable is to introduce **temporary variables** *in combination* with the `and` operator to **flatten** the branching logic. The `if` statement then reads almost like plain English. In contrast to many other languages, creating variables is a computationally *cheap* operation in Python and also helps to document the code *inline* with meaningful variable names.\n",
+    "A way to make this code more readable is to introduce **temporary variables** *in combination* with the `and` operator to **flatten** the branching logic. The `if` statement then reads almost like plain English. In contrast to many other languages, creating variables is a computationally *cheap* operation in Python and also helps to document the code *inline* with meaningful variable names.\n",
     "\n",
-    "Flattening the logic *without* temporary variables could actually lead to more sub-expressions in the conditions be evaluated than necessary. Do you see why?"
+    "Flattening the logic *without* temporary variables could lead to *more* sub-expressions in the conditions be evaluated than necessary. Do you see why?"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 42,
+   "execution_count": 52,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -1384,7 +1766,7 @@
     }
    },
    "source": [
-    "When all we do with an `if` statement is to assign an object to a variable with respect to a single true-or-false condition (i.e., a binary choice), there is a shortcut for that: We could simply assign the result of a so-called **conditional expression**, or `if` expression for short, to the variable.\n",
+    "When all we do with an `if` statement is to assign an object to a variable according to a single true-or-false condition (i.e., a binary choice), there is a shortcut: We assign the variable the result of a so-called **conditional expression**, or `if` expression for short, instead.\n",
     "\n",
     "Think of a situation where we evaluate a piece-wise functional relationship $y = f(x)$ at a given $x$, for example:"
    ]
@@ -1408,7 +1790,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 43,
+   "execution_count": 53,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -1432,7 +1814,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 44,
+   "execution_count": 54,
    "metadata": {
     "slideshow": {
      "slide_type": "-"
@@ -1448,7 +1830,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 45,
+   "execution_count": 55,
    "metadata": {
     "slideshow": {
      "slide_type": "-"
@@ -1461,7 +1843,7 @@
        "9"
       ]
      },
-     "execution_count": 45,
+     "execution_count": 55,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1483,7 +1865,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 46,
+   "execution_count": 56,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -1496,7 +1878,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 47,
+   "execution_count": 57,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -1509,7 +1891,7 @@
        "9"
       ]
      },
-     "execution_count": 47,
+     "execution_count": 57,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1531,7 +1913,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 48,
+   "execution_count": 58,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -1544,7 +1926,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 49,
+   "execution_count": 59,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -1557,7 +1939,7 @@
        "9"
       ]
      },
-     "execution_count": 49,
+     "execution_count": 59,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1574,7 +1956,7 @@
     }
    },
    "source": [
-    "Conditional expressions may not only be used in the way described in this section. We already saw them as part of a list comprehension in [Chapter 1](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/01_elements.ipynb) and [Chapter 2](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/02_functions.ipynb)."
+    "Conditional expressions may not only be used in the way described in this section. We already saw them as part of a *list comprehension* in [Chapter 1](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/01_elements.ipynb) and [Chapter 2](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/02_functions.ipynb) and revisit this in Chapter 7 in greater detail."
    ]
   },
   {
@@ -1596,16 +1978,16 @@
     }
    },
    "source": [
-    "In the previous two chapters we already encountered a couple of *runtime* errors. A natural urge we might have after reading about conditional statements is to write code that somehow reacts to the occurence of such exceptions. All we need is a way to formulate a condition for that.\n",
+    "In the previous two chapters, we encountered a couple of *runtime* errors. A natural urge we might have after reading about conditional statements is to write code that somehow reacts to the occurrence of such exceptions. All we need is a way to formulate a condition for that.\n",
     "\n",
-    "For sure, this is such a common thing to do that Python provides its own language construct for it, namely the `try` [statement](https://docs.python.org/3/reference/compound_stmts.html#the-try-statement).\n",
+    "For sure, this is such a common thing to do that Python provides a language construct for it, namely the `try` [statement](https://docs.python.org/3/reference/compound_stmts.html#the-try-statement).\n",
     "\n",
-    "In its simplest form, it comes with just two branches: `try` and `except`. The following basically tells Python to execute the code in the `try`-branch and if *anything* goes wrong, continue in the `except`-branch instead of **raising** an error to us. Of course, if nothing goes wrong, the `except`-branch is *not* executed."
+    "In its simplest form, it comes with just two branches: `try` and `except`. The following tells Python to execute the code in the `try`-branch, and if *anything* goes wrong, continue in the `except`-branch instead of **raising** an error to us. Of course, if nothing goes wrong, the `except`-branch is *not* executed."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 50,
+   "execution_count": 60,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -1618,7 +2000,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 51,
+   "execution_count": 61,
    "metadata": {
     "slideshow": {
      "slide_type": "-"
@@ -1648,16 +2030,16 @@
     }
    },
    "source": [
-    "However, it is good practice to *not* **handle** *any* possible exception but only the ones we may expect from the code in the `try`-branch. The reasoning why this is done is a bit involved. We only remark here that the code base becomes easier to understand as we clearly communicate to any human reader what could go wrong during execution. Python comes with a lot of [built-in exceptions](https://docs.python.org/3/library/exceptions.html#concrete-exceptions) that we should familiarize ourselves with.\n",
+    "However, it is good practice *not* to **handle** *any* possible exception but only the ones we may *expect* from the code in the `try`-branch. The reasoning why this is done is a bit involved. We only remark that the codebase becomes easier to understand as we communicate to any human reader what could go wrong during execution in an *explicit* way. Python comes with a lot of [built-in exceptions](https://docs.python.org/3/library/exceptions.html#concrete-exceptions) that we should familiarize ourselves with.\n",
     "\n",
-    "Another good practice is to always keep the code in the `try`-branch short so as to not accidently handle an exception we do not want to handle.\n",
+    "Another good practice is to always keep the code in the `try`-branch short to not *accidentally* handle an exception we do *not* want to handle.\n",
     "\n",
-    "In the example, we are dividing numbers and may therefore expect a `ZeroDivisionError`."
+    "In the example, we are dividing numbers and may expect a `ZeroDivisionError`."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 52,
+   "execution_count": 62,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -1687,16 +2069,16 @@
     }
    },
    "source": [
-    "Often, we may have to run some code *independent* of an exception occuring, for example, to close a connection to a database. To achieve that, we add a `finally`-branch to the `try` statement.\n",
+    "Often, we may have to run some code *independent* of an exception occurring, for example, to close a connection to a database. To achieve that, we add a `finally`-branch to the `try` statement.\n",
     "\n",
-    "Similarly, we may have to run some code *only if* no exception occurs but we do not want to put it in the `try`-branch as per the good practice mentioned above. To achieve that, we add an `else`-branch to the `try` statement.\n",
+    "Similarly, we may have to run some code *only if* no exception occurs, but we do not want to put it in the `try`-branch as per the good practice mentioned above. To achieve that, we add an `else`-branch to the `try` statement.\n",
     "\n",
-    "To showcase everything together, we look at one last example. To spice it up a bit, we randomize the input. So run the cell several times and see for yourself. It's actually quite easy."
+    "To showcase everything together, we look at one last example. To spice it up a bit, we randomize the input. So run the cell several times and see for yourself."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 53,
+   "execution_count": 63,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -1747,9 +2129,9 @@
     "- **boolean expressions** evaluate to either `True` or `False`\n",
     "- **relational operators** compare operands according to \"human\" interpretations\n",
     "- **logical operators** combine boolean sub-expressions to more \"complex\" expressions\n",
-    "- the **conditional statement** is a *major* concept to **control** the **flow of execution** depending on some **conditions**\n",
+    "- the **conditional statement** allows **controlling** the **flow of execution** depending on some **conditions**\n",
     "- a **conditional expression** is a short form of a conditional statement\n",
-    "- **exception handling** is also a common way of **controlling** the **flow of execution**, in particular if we have to be prepared for bad input data"
+    "- **exception handling** is also a common way of **controlling** the **flow of execution**, in particular, if we have to be prepared for bad input data"
    ]
   }
  ],
diff --git a/03_conditionals_review_and_exercises.ipynb b/03_conditionals_review_and_exercises.ipynb
index d39c551..89d7009 100644
--- a/03_conditionals_review_and_exercises.ipynb
+++ b/03_conditionals_review_and_exercises.ipynb
@@ -19,7 +19,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Read [Chapter 3](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/03_conditionals.ipynb) of the book. Then work through the seven review questions."
+    "Read [Chapter 3](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/03_conditionals.ipynb) of the book. Then work through the eight review questions."
    ]
   },
   {
@@ -68,7 +68,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "**Q3**: Explain how the conceptual difference between a **statement** and an **expression** relates to the difference between a **conditional statement** and a **conditional expression**."
+    "**Q3**: Describe in your own words the concept of **short-circuiting**! What does it imply for an individual sub-expression in a boolean expression?"
    ]
   },
   {
@@ -82,7 +82,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "**Q4**: Why is the use of **temporary variables** encouraged in Python?"
+    "**Q4**: Explain how the conceptual difference between a **statement** and an **expression** relates to the difference between a **conditional statement** and a **conditional expression**."
    ]
   },
   {
@@ -96,7 +96,21 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "**Q5**: What does the `finally`-branch enforce in this code snippet? How can a `try` statement be useful *without* an `except`-branch?"
+    "**Q5**: Why is the use of **temporary variables** encouraged in Python?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q6**: What does the `finally`-branch enforce in this code snippet? How can a `try` statement be useful *without* an `except`-branch?"
    ]
   },
   {
@@ -136,7 +150,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "**Q6**: The objects `True`, `False`, and `None` represent the idea of \"yes\", \"no\", and \"maybe\" answers in a natural language.\n",
+    "**Q7**: The objects `True`, `False`, and `None` represent the idea of *yes*, *no*, and *maybe* answers in a natural language.\n",
     "\n",
     "Hint: you also respond with a code cell."
    ]
@@ -152,7 +166,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "**Q7**: The `try` statement is useful for handling **syntax** errors."
+    "**Q8**: The `try` statement is useful for handling **syntax** errors."
    ]
   },
   {
@@ -180,7 +194,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "**Q8.1**: Write a function `discounted_price()` that takes the positional arguments `unit_price` (of type `float`) and `quantity` (of type `int`) and implements a discount scheme for a line item in a customer order as follows:\n",
+    "**Q9.1**: Write a function `discounted_price()` that takes the positional arguments `unit_price` (of type `float`) and `quantity` (of type `int`) and implements a discount scheme for a line item in a customer order as follows:\n",
     "\n",
     "- if the unit price is over 100 dollars, grant 10% relative discount\n",
     "- if a customer orders more than 10 items, one in every five items is for free\n",
@@ -218,7 +232,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "**Q8.2**: Calculate the final price for the following line items of an order:\n",
+    "**Q9.2**: Calculate the final price for the following line items of an order:\n",
     "- $7$ smartphones @ $99.00$ USD\n",
     "- $3$ workstations @ $999.00$ USD\n",
     "- $19$ GPUs @ $879.95$ USD\n",
@@ -265,7 +279,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "**Q8.3**: Re-calculate the last two line items with order quantities of $20$ and $15$. What do you observe?"
+    "**Q9.3**: Re-calculate the last two line items with order quantities of $20$ and $15$. What do you observe?"
    ]
   },
   {
@@ -297,7 +311,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "**Q8.4**: Looking at the `if`-`else`-logic in the function, why do you think the four example line items in **Q8.2** were chosen as they were?"
+    "**Q9.4**: Looking at the `if`-`else`-logic in the function, why do you think the four example line items in **Q9.2** were chosen as they were?"
    ]
   },
   {
@@ -318,16 +332,16 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "The kids game [Fizz Buzz](https://en.wikipedia.org/wiki/Fizz_buzz) is said to be often used in job interviews for entry level positions. However, opinions vary as to how good of a test it actually is ([source](https://news.ycombinator.com/item?id=16446774)).\n",
+    "The kids game [Fizz Buzz](https://en.wikipedia.org/wiki/Fizz_buzz) is said to be often used in job interviews for entry-level positions. However, opinions vary as to how good of a test it is (cf., [source](https://news.ycombinator.com/item?id=16446774)).\n",
     "\n",
-    "In its simplest form, a group of people start counting upwards in an alternating fashion. Whenever a number is divisible by $3$, the person must say \"Fizz\" instead of the number. The same holds for numbers divisible by $5$ when the person must say \"Buzz\". If a number is divisible by both numbers, one must say \"FizzBuzz\". Probably, this game would also make a good drinking game with the \"right\" beverages."
+    "In its simplest form, a group of people starts counting upwards in an alternating fashion. Whenever a number is divisible by $3$, the person must say \"Fizz\" instead of the number. The same holds for numbers divisible by $5$ when the person must say \"Buzz.\" If a number is divisible by both numbers, one must say \"FizzBuzz.\" Probably, this game would also make a good drinking game with the \"right\" beverages."
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "**Q9.1**: First, create a list `numbers` with the numbers from 1 through 100. You could type all numbers manually but there is of course a smarter way. The built-in [range()](https://docs.python.org/3/library/functions.html#func-range) may be useful here. Read how it works in the documentation. To make the output of [range()](https://docs.python.org/3/library/functions.html#func-range) a `list` object, you have to wrap it with the [list()](https://docs.python.org/3/library/functions.html#func-list) built-in (i.e., `list(range(...))`)."
+    "**Q10.1**: First, create a list `numbers` with the numbers from 1 through 100. You could type all numbers manually, but there is, of course, a smarter way. The built-in [range()](https://docs.python.org/3/library/functions.html#func-range) may be useful here. Read how it works in the documentation. To make the output of [range()](https://docs.python.org/3/library/functions.html#func-range) a `list` object, you have to wrap it with the [list()](https://docs.python.org/3/library/functions.html#func-list) built-in (i.e., `list(range(...))`)."
    ]
   },
   {
@@ -343,11 +357,11 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "**Q9.2**: Loop over the `numbers` list and replace numbers for which one of the two (or both) conditions apply with text strings `\"Fizz\"`, `\"Buzz\"`, or `\"FizzBuzz\"` using the indexing operator `[]` and the assignment statement `=`.\n",
+    "**Q10.2**: Loop over the `numbers` list and replace numbers for which one of the two (or both) conditions apply with text strings `\"Fizz\"`, `\"Buzz\"`, or `\"FizzBuzz\"` using the indexing operator `[]` and the assignment statement `=`.\n",
     "\n",
-    "In Chapter 1 we saw that Python starts indexing with `0` as the first element. Keep that in mind.\n",
+    "In [Chapter 1](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/01_elements.ipynb), we saw that Python starts indexing with `0` as the first element. Keep that in mind.\n",
     "\n",
-    "So in each iteration of the `for`-loop you have to determine an `index` variable as well as checking the actual `number` for its divisors.\n",
+    "So in each iteration of the `for`-loop, you have to determine an `index` variable as well as check the actual `number` for its divisors.\n",
     "\n",
     "Hint: the order of the conditions is important!"
    ]
@@ -372,7 +386,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "**Q9.3**: Create a loop that prints out either the number or any of the Fizz Buzz substitutes. Do it in such a way that we do not end up with 100 lines of output here."
+    "**Q10.3**: Create a loop that prints out either the number or any of the Fizz Buzz substitutes. Do it in such a way that we do not end up with 100 lines of output here."
    ]
   },
   {
diff --git a/04_iteration.ipynb b/04_iteration.ipynb
index 7ab75c3..b760a25 100644
--- a/04_iteration.ipynb
+++ b/04_iteration.ipynb
@@ -19,11 +19,11 @@
     }
    },
    "source": [
-    "While controlling the flow of execution with an `if` statement is definitely a must-have building block in any programming language, it alone does not allow us to run a block of code repetitively and we need to be able to do exactly that in order to write useful software.\n",
+    "While controlling the flow of execution with an `if` statement is a must-have building block in any programming language, it alone does not allow us to run a block of code repetitively, and we need to be able to do precisely that to write useful software.\n",
     "\n",
-    "You might think that the `for` statement shown in some examples before is the missing piece in the puzzle. However, we can actually live without it and postpone its official introduction until the second half of this chapter.\n",
+    "The `for` statement shown in some examples before might be the missing piece in the puzzle. However, we can live without it and postpone its official introduction until the second half of this chapter.\n",
     "\n",
-    "Instead, we dive into the big idea of **iteration** by studying the concept of **recursion** first. This is quite the opposite of many introductory books on programming that only treat the latter as a nice-to-have artifact, if at all. Yet, understanding recursion sharpens one's mind and contributes to seeing problems from a different angle."
+    "Instead, we dive into the big idea of **iteration** by studying the concept of **recursion** first. This order is opposite to many other introductory books that only treat the latter as a nice-to-have artifact, if at all. Yet, understanding recursion sharpens one's mind and contributes to seeing problems from a different angle."
    ]
   },
   {
@@ -45,11 +45,11 @@
     }
    },
    "source": [
-    "A function that calls itself is **recursive** and the process of executing such a function it is called **recursion**.\n",
+    "A function that calls itself is **recursive**, and the process of executing such a function is called **recursion**.\n",
     "\n",
     "Recursive functions contain some form of a conditional check (e.g., `if` statement) to identify a **base case** that ends the recursion *when* reached. Otherwise, the function would keep calling itself forever.\n",
     "\n",
-    "The meaning of the word *recursive* is similar to *circular*. However, a truly circular definition is not very helpful and we think of a recursive function as kind of a \"circular function with a way out at the end\"."
+    "The meaning of the word *recursive* is similar to *circular*. However, a truly circular definition is not very helpful, and we think of a recursive function as kind of a \"circular function with a way out at the end.\""
    ]
   },
   {
@@ -71,7 +71,7 @@
     }
    },
    "source": [
-    "A rather trivial toy example illustrates the important aspects concretely: If called with any positive integer as its `n` argument, `countdown()` just prints that number and calls itself with the *new* `n` being the *old* `n` minus `1`. This continues until `countdown()` is called with `n=0`. Then, the flow of execution hits the base case and the function calls stop."
+    "A rather trivial toy example illustrates the important aspects concretely: If called with any positive integer as its `n` argument, `countdown()` just prints that number and calls itself with the *new* `n` being the *old* `n` minus `1`. This continues until `countdown()` is called with `n=0`. Then, the flow of execution hits the base case, and the function calls stop."
    ]
   },
   {
@@ -92,7 +92,7 @@
     "        n (int): seconds until the party begins\n",
     "    \"\"\"\n",
     "    if n == 0:  # base case\n",
-    "        print(\"Happy new Year!\")\n",
+    "        print(\"Happy New Year!\")\n",
     "    else:\n",
     "        print(n)\n",
     "        countdown(n - 1)"
@@ -114,7 +114,7 @@
       "3\n",
       "2\n",
       "1\n",
-      "Happy new Year!\n"
+      "Happy New Year!\n"
      ]
     }
    ],
@@ -130,7 +130,7 @@
     }
    },
    "source": [
-    "As trivial as this seems at first sight, a lot of complexity is hidden in this implementation. In particular, the order in which objects are created and de-referenced in memory might not be obvious right away as [PythonTutor](http://pythontutor.com/visualize.html#code=def%20countdown%28n%29%3A%0A%20%20%20%20if%20n%20%3D%3D%200%3A%0A%20%20%20%20%20%20%20%20print%28%22Happy%20new%20Year!%22%29%0A%20%20%20%20else%3A%0A%20%20%20%20%20%20%20%20print%28n%29%0A%20%20%20%20%20%20%20%20countdown%28n%20-%201%29%0A%0Acountdown%283%29&cumulative=false&curInstr=0&heapPrimitives=nevernest&mode=display&origin=opt-frontend.js&py=3&rawInputLstJSON=%5B%5D&textReferences=false) shows: Each time `countdown()` is called, Python creates a *new* frame in the part of the memory where it manages all the names. This way Python *isolates* all the different `n` variables from each other. As new frames are created until we reach the base case after which the frames are destroyed in the *reversed* order, this is called a **[stack](https://en.wikipedia.org/wiki/Stack_(abstract_data_type))** of frames in computer science terminology. In simple words, a stack is a last-in-first-out (LIFO) task queue. Each frame has a single parent frame, namely the one whose recursive function call created it."
+    "As trivial as this seems, a lot of complexity is hidden in this implementation. In particular, the order in which objects are created and de-referenced in memory might not be apparent right away as [PythonTutor](http://pythontutor.com/visualize.html#code=def%20countdown%28n%29%3A%0A%20%20%20%20if%20n%20%3D%3D%200%3A%0A%20%20%20%20%20%20%20%20print%28%22Happy%20new%20Year!%22%29%0A%20%20%20%20else%3A%0A%20%20%20%20%20%20%20%20print%28n%29%0A%20%20%20%20%20%20%20%20countdown%28n%20-%201%29%0A%0Acountdown%283%29&cumulative=false&curInstr=0&heapPrimitives=nevernest&mode=display&origin=opt-frontend.js&py=3&rawInputLstJSON=%5B%5D&textReferences=false) shows: Each time `countdown()` is called, Python creates a *new* frame in the part of the memory where it manages all the names. This way, Python *isolates* all the different `n` variables from each other. As new frames are created until we reach the base case, after which the frames are destroyed in the *reversed* order, this is called a **[stack](https://en.wikipedia.org/wiki/Stack_(abstract_data_type))** of frames in computer science terminology. In simple words, a stack is a last-in-first-out (LIFO) task queue. Each frame has a single parent frame, namely the one whose recursive function call created it."
    ]
   },
   {
@@ -152,7 +152,7 @@
     }
    },
    "source": [
-    "Recursion plays an important role in mathematics as well and we likely know about it from some introductory course, for example, in [combinatorics](https://en.wikipedia.org/wiki/Combinatorics)."
+    "Recursion plays a vital role in mathematics as well, and we likely know about it from some introductory course, for example, in [combinatorics](https://en.wikipedia.org/wiki/Combinatorics)."
    ]
   },
   {
@@ -174,7 +174,7 @@
     }
    },
    "source": [
-    "The factorial function, denoted with the symbol $!$, is defined as follows for all non-negative integers:"
+    "The factorial function, denoted with the $!$ symbol, is defined as follows for all non-negative integers:"
    ]
   },
   {
@@ -199,7 +199,7 @@
    "source": [
     "Whenever we find a recursive way of formulating an idea, we can immediately translate it into Python in a *naive* way (i.e., we create a *correct* program that may *not* be an *efficient* implementation yet).\n",
     "\n",
-    "Below is a first version of `factorial()`: The `return` statement does not have to be a function's last code line and we may certainly have several `return` statements as well."
+    "Below is a first version of `factorial()`: The `return` statement does not have to be a function's last code line, and we may indeed have several `return` statements as well."
    ]
   },
   {
@@ -238,9 +238,9 @@
     }
    },
    "source": [
-    "When we read such code, it is often easier not to follow every function call (i.e., `factorial(n - 1)` here) in one's mind but assume we receive a return value as specified in the documentation. Some call this approach **[leap of faith](http://greenteapress.com/thinkpython2/html/thinkpython2007.html#sec75)**. In fact, we practice this anyways whenever we call built-in functions (e.g., [print()](https://docs.python.org/3/library/functions.html#print) or [len()](https://docs.python.org/3/library/functions.html#len)) where we would have to read C code in many cases.\n",
+    "When we read such code, it is often easier not to follow every function call (i.e., `factorial(n - 1)` here) in one's mind but assume we receive a return value as specified in the documentation. Some call this approach a **[leap of faith](http://greenteapress.com/thinkpython2/html/thinkpython2007.html#sec75)**. We practice this already whenever we call built-in functions (e.g., [print()](https://docs.python.org/3/library/functions.html#print) or [len()](https://docs.python.org/3/library/functions.html#len)) where we would have to read C code in many cases.\n",
     "\n",
-    "To visualize *all* the computational steps of the examplary `factorial(3)`, we use [PythonTutor](http://pythontutor.com/visualize.html#code=def%20factorial%28n%29%3A%0A%20%20%20%20if%20n%20%3D%3D%200%3A%0A%20%20%20%20%20%20%20%20return%201%0A%20%20%20%20else%3A%0A%20%20%20%20%20%20%20%20recurse%20%3D%20factorial%28n%20-%201%29%0A%20%20%20%20%20%20%20%20result%20%3D%20n%20*%20recurse%0A%20%20%20%20%20%20%20%20return%20result%0A%0Asolution%20%3D%20factorial%283%29&cumulative=false&curInstr=0&heapPrimitives=nevernest&mode=display&origin=opt-frontend.js&py=3&rawInputLstJSON=%5B%5D&textReferences=false): The recursion again creates a stack of frames in memory. In contrast to the previous trivial example, each frame leaves a return value in memory after it is destroyed. This return value is then assigned to the `recurse` variable within the parent frame and used to compute `result`."
+    "To visualize *all* the computational steps of the exemplary `factorial(3)`, we use [PythonTutor](http://pythontutor.com/visualize.html#code=def%20factorial%28n%29%3A%0A%20%20%20%20if%20n%20%3D%3D%200%3A%0A%20%20%20%20%20%20%20%20return%201%0A%20%20%20%20else%3A%0A%20%20%20%20%20%20%20%20recurse%20%3D%20factorial%28n%20-%201%29%0A%20%20%20%20%20%20%20%20result%20%3D%20n%20*%20recurse%0A%20%20%20%20%20%20%20%20return%20result%0A%0Asolution%20%3D%20factorial%283%29&cumulative=false&curInstr=0&heapPrimitives=nevernest&mode=display&origin=opt-frontend.js&py=3&rawInputLstJSON=%5B%5D&textReferences=false): The recursion again creates a stack of frames in memory. In contrast to the previous trivial example, each frame leaves a return value in memory after it is destroyed. This return value is then assigned to the `recurse` variable within the parent frame and used to compute `result`."
    ]
   },
   {
@@ -299,7 +299,7 @@
     }
    },
    "source": [
-    "A Pythonista would formulate `factorial()` in a conciser way using the so-called **early exit** pattern: No `else`-clause is needed as reaching a `return` statement immediately ends a function call. Furthermore, we do not really need the temporary variables `recurse` and `result`.\n",
+    "A Pythonista would formulate `factorial()` in a more concise way using the so-called **early exit** pattern: No `else`-clause is needed as reaching a `return` statement ends a function call *immediately*. Furthermore, we do not need the temporary variables `recurse` and `result`.\n",
     "\n",
     "As [PythonTutor](http://pythontutor.com/visualize.html#code=def%20factorial%28n%29%3A%0A%20%20%20%20if%20n%20%3D%3D%200%3A%0A%20%20%20%20%20%20%20%20return%201%0A%20%20%20%20return%20n%20*%20factorial%28n%20-%201%29%0A%0Asolution%20%3D%20factorial%283%29&cumulative=false&curInstr=0&heapPrimitives=nevernest&mode=display&origin=opt-frontend.js&py=3&rawInputLstJSON=%5B%5D&textReferences=false) shows, this implementation is more efficient as it only requires 18 computational steps instead of 24 to calculate `factorial(3)`, an improvement of 25 percent! "
    ]
@@ -384,7 +384,7 @@
     }
    },
    "source": [
-    "Note that the [math](https://docs.python.org/3/library/math.html) module in the standard library provides a [factorial()](https://docs.python.org/3/library/math.html#math.factorial) function as well and we should therefore *never* implement it ourselves in a real code base."
+    "Note that the [math](https://docs.python.org/3/library/math.html) module in the [standard library](https://docs.python.org/3/library/index.html) provides a [factorial()](https://docs.python.org/3/library/math.html#math.factorial) function as well, and we should, therefore, *never* implement it ourselves in a real codebase."
    ]
   },
   {
@@ -554,7 +554,7 @@
     }
    },
    "source": [
-    "Euclid's algorithm is stunningly fast, even for large numbers. Its speed comes from the use of the modulo operator `%`. However, this is *not* true for recusion in general, which may result in very slow programs if not applied correctly."
+    "Euclid's algorithm is stunningly fast, even for large numbers. Its speed comes from the use of the modulo operator `%`. However, this is *not* true for recursion in general, which may result in slow programs if not applied correctly."
    ]
   },
   {
@@ -624,7 +624,7 @@
     }
    },
    "source": [
-    "The [math](https://docs.python.org/3/library/math.html) module in the standard library provides a [gcd()](https://docs.python.org/3/library/math.html#math.gcd) function as well and therefore we should again *never* implement it on our own."
+    "The [math](https://docs.python.org/3/library/math.html) module in the [standard library](https://docs.python.org/3/library/index.html) provides a [gcd()](https://docs.python.org/3/library/math.html#math.gcd) function as well, and, therefore, we should again *never* implement it on our own."
    ]
   },
   {
@@ -765,7 +765,7 @@
     }
    },
    "source": [
-    "Let's write a function `fibonacci()` that calculates the $i$th Fibonacci number where $0$ will be the $0$th number. Looking at the numbers in a **backwards** fashion (i.e., from right to left), we realize that the return value for `fibonacci(i)` can be reduced to the sum of the return values for `fibonacci(i - 1)` and `fibonacci(i - 2)` disregarding the *two* base cases."
+    "Let's write a function `fibonacci()` that calculates the $i$th Fibonacci number where $0$ is the $0$th number. Looking at the numbers in a **backward** fashion (i.e., from right to left), we realize that the return value for `fibonacci(i)` can be reduced to the sum of the return values for `fibonacci(i - 1)` and `fibonacci(i - 2)` disregarding the *two* base cases."
    ]
   },
   {
@@ -841,7 +841,7 @@
     "\n",
     "To understand this in detail, we would have to study algorithms and data structures (e.g., with [this book](https://www.amazon.de/Introduction-Algorithms-Press-Thomas-Cormen/dp/0262033844/ref=sr_1_1?__mk_de_DE=%C3%85M%C3%85%C5%BD%C3%95%C3%91&crid=1JNE8U0VZGU0O&qid=1569837169&s=gateway&sprefix=algorithms+an%2Caps%2C180&sr=8-1)), a discipline within computer science, and dive into the analysis of **[time complexity of algorithms](https://en.wikipedia.org/wiki/Time_complexity)**.\n",
     "\n",
-    "Luckily, in the Fibonacci case the inefficiency can be resolved with a **caching** (i.e., \"re-use\") strategy from the field of **[dynamic programming](https://en.wikipedia.org/wiki/Dynamic_programming)**, namely **[memoization](https://en.wikipedia.org/wiki/Memoization)**. We will do so in Chapter 8 after introducing the [dictionaries](https://docs.python.org/3/library/stdtypes.html#dict) data type.\n",
+    "Luckily, in the Fibonacci case, the inefficiency can be resolved with a **caching** (i.e., \"re-use\") strategy from the field of **[dynamic programming](https://en.wikipedia.org/wiki/Dynamic_programming)**, namely **[memoization](https://en.wikipedia.org/wiki/Memoization)**. We do so in Chapter 8, after introducing the [dictionaries](https://docs.python.org/3/library/stdtypes.html#dict) data type.\n",
     "\n",
     "Let's measure the average run times for `fibonacci()` and varying `i` arguments with the `%%timeit` [cell magic](https://ipython.readthedocs.io/en/stable/interactive/magics.html#magic-timeit) that comes with Jupyter."
    ]
@@ -859,7 +859,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "47.3 µs ± 12.1 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n"
+      "39.9 µs ± 5.5 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n"
      ]
     }
    ],
@@ -881,7 +881,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "1.63 ms ± 21.2 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n"
+      "1.61 ms ± 8.84 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n"
      ]
     }
    ],
@@ -903,7 +903,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "211 ms ± 0 ns per loop (mean ± std. dev. of 1 run, 1 loop each)\n"
+      "199 ms ± 0 ns per loop (mean ± std. dev. of 1 run, 1 loop each)\n"
      ]
     }
    ],
@@ -925,7 +925,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "2.22 s ± 0 ns per loop (mean ± std. dev. of 1 run, 1 loop each)\n"
+      "2.21 s ± 0 ns per loop (mean ± std. dev. of 1 run, 1 loop each)\n"
      ]
     }
    ],
@@ -947,7 +947,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "5.81 s ± 0 ns per loop (mean ± std. dev. of 1 run, 1 loop each)\n"
+      "5.8 s ± 0 ns per loop (mean ± std. dev. of 1 run, 1 loop each)\n"
      ]
     }
    ],
@@ -975,7 +975,7 @@
     }
    },
    "source": [
-    "If a recursion does not reach its base case, it will theoretically run forever. Luckily, Python detects that and saves the computer from crashing by raising a `RecursionError`.\n",
+    "If a recursion does not reach its base case, it theoretically runs forever. Luckily, Python detects that and saves the computer from crashing by raising a `RecursionError`.\n",
     "\n",
     "The simplest possible infinite recursion is generated like so."
    ]
@@ -1031,7 +1031,7 @@
     }
    },
    "source": [
-    "However, even the trivial `countdown()` function from above is not immune to an infinite recursion. Let's call it with `3.1` instead of `3`. What goes wrong here?"
+    "However, even the trivial `countdown()` function from above is not immune to infinite recursion. Let's call it with `3.1` instead of `3`. What goes wrong here?"
    ]
   },
   {
@@ -4011,9 +4011,9 @@
       "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
       "\u001b[0;31mRecursionError\u001b[0m                            Traceback (most recent call last)",
       "\u001b[0;32m<ipython-input-30-43e3ad331761>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mcountdown\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m3.1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
-      "\u001b[0;32m<ipython-input-1-8e67251e237b>\u001b[0m in \u001b[0;36mcountdown\u001b[0;34m(n)\u001b[0m\n\u001b[1;32m      9\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     10\u001b[0m         \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mn\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 11\u001b[0;31m         \u001b[0mcountdown\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mn\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;32m<ipython-input-1-6001bec3d961>\u001b[0m in \u001b[0;36mcountdown\u001b[0;34m(n)\u001b[0m\n\u001b[1;32m      9\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     10\u001b[0m         \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mn\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 11\u001b[0;31m         \u001b[0mcountdown\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mn\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
       "... last 1 frames repeated, from the frame below ...\n",
-      "\u001b[0;32m<ipython-input-1-8e67251e237b>\u001b[0m in \u001b[0;36mcountdown\u001b[0;34m(n)\u001b[0m\n\u001b[1;32m      9\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     10\u001b[0m         \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mn\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 11\u001b[0;31m         \u001b[0mcountdown\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mn\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;32m<ipython-input-1-6001bec3d961>\u001b[0m in \u001b[0;36mcountdown\u001b[0;34m(n)\u001b[0m\n\u001b[1;32m      9\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     10\u001b[0m         \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mn\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 11\u001b[0;31m         \u001b[0mcountdown\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mn\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
       "\u001b[0;31mRecursionError\u001b[0m: maximum recursion depth exceeded while calling a Python object"
      ]
     }
@@ -4069,21 +4069,21 @@
     }
    },
    "source": [
-    "The infinite recursions could easily be avoided by replacing `n == 0` with `n <= 0` in both functions and thereby **generalizing** them. But even then, calling either `countdown()` or `factorial()` with a non-integer number is *semantically* wrong and therefore we better leave the base cases unchanged.\n",
+    "The infinite recursions could easily be avoided by replacing `n == 0` with `n <= 0` in both functions and thereby **generalizing** them. But even then, calling either `countdown()` or `factorial()` with a non-integer number is *semantically* wrong, and, therefore, we better leave the base cases unchanged.\n",
     "\n",
-    "Errors as above are a symptom of missing **type checking**: By design, Python allows us to pass in not only integers but objects of any type as arguments to the `countdown()` and `factorial()` functions. As long as the arguments \"behave\" like integers, we will not encounter any *runtime* errors. This is the case here as the two example functions only use the `-` and `*` operators internally and, in the context of arithmetic, a `float` object behaves exactly like an `int` object. So, the functions keep calling themselves until Python decides with a built-in heuristic that the recursion is likely not going to end and aborts the computations with a `RecursionError`. Stricly speaking, a `RecursionError` is of course a *runtime* error as well.\n",
+    "Errors as above are a symptom of missing **type checking**: By design, Python allows us to pass in not only integers but objects of any type as arguments to the `countdown()` and `factorial()` functions. As long as the arguments \"behave\" like integers, we do not encounter any *runtime* errors. This is the case here as the two example functions only use the `-` and `*` operators internally, and, in the context of arithmetic, a `float` object behaves like an `int` object. So, the functions keep calling themselves until Python decides with a built-in heuristic that the recursion is likely not going to end and aborts the computations with a `RecursionError`. Strictly speaking, a `RecursionError` is, of course, a *runtime* error as well.\n",
     "\n",
     "The missing type checking is *100% intentional* and considered a **[feature of rather than a bug](https://www.urbandictionary.com/define.php?term=It%27s%20not%20a%20bug%2C%20it%27s%20a%20feature)** in Python!\n",
     "\n",
-    "Pythonistas often use the colloquial term **[duck typing](https://en.wikipedia.org/wiki/Duck_typing)** when refering to the same idea and the saying goes \"If it walks like a duck and it quacks like a duck, it must be a duck\". For example, we could call `factorial()` with the `float` object `3.0` and the recursion works out fine. So, as long as the `3.0` \"walks\" like a `3` and \"quacks\" like a `3`, it \"must be\" a `3`.\n",
+    "Pythonistas often use the colloquial term **[duck typing](https://en.wikipedia.org/wiki/Duck_typing)** when referring to the same idea, and the saying goes, \"If it walks like a duck and it quacks like a duck, it must be a duck.\" For example, we could call `factorial()` with the `float` object `3.0`, and the recursion works out fine. So, as long as the `3.0` \"walks\" like a `3` and \"quacks\" like a `3`, it \"must be\" a `3`.\n",
     "\n",
-    "We see a similar behavior when we mix objects of types `int` and `float` with arithmetic operators. For example, `1 + 2.0` works because Python implicitly views the `1` as a `1.0` at runtime and then knows how to do floating-point arithmetic: Here, the `int` \"walks\" and \"quacks\" like a `float`. Strictly speaking, this is yet another example of operator overloading whereas duck typing refers to the same behavior when passing arguments to function calls.\n",
+    "We see similar behavior when we mix objects of types `int` and `float` with arithmetic operators. For example, `1 + 2.0` works because Python implicitly views the `1` as a `1.0` at runtime and then knows how to do floating-point arithmetic: Here, the `int` \"walks\" and \"quacks\" like a `float`. Strictly speaking, this is yet another example of operator overloading, whereas duck typing refers to the same behavior when passing arguments to function calls.\n",
     "\n",
-    "The important lesson is that we must expect our functions be called with objects of *any* type at runtime, as opposed to the one type we had in mind when we defined the function.\n",
+    "The important lesson is that we must expect our functions to be called with objects of *any* type at runtime, as opposed to the one type we had in mind when we defined the function.\n",
     "\n",
-    "Duck typing is possible because Python is a dynamically typed language. On the contrary, in statically typed languages like C we *must* declare (i.e., \"specify\") the data type of every parameter in a function definition. Then, a `RecursionError` as for `countdown(3.1)` or `factorial(3.1)` above could not occur. For example, if we declared the `countdown()` and `factorial()` functions to only accept `int` objects, calling the functions with a `float` argument would immediately fail *syntactically*. As a downside, we would then lose the ability to call `factorial()` with `3.0`, which is *semantically* correct nevertheless.\n",
+    "Duck typing is possible because Python is a dynamically typed language. On the contrary, in statically typed languages like C, we *must* declare (i.e., \"specify\") the data type of every parameter in a function definition. Then, a `RecursionError` as for `countdown(3.1)` or `factorial(3.1)` above could not occur. For example, if we declared the `countdown()` and `factorial()` functions to only accept `int` objects, calling the functions with a `float` argument would immediately fail *syntactically*. As a downside, we would then lose the ability to call `factorial()` with `3.0`, which is *semantically* correct nevertheless.\n",
     "\n",
-    "So, there is no black or white answer as to which of the two language designs is better. Yet, most professional programmers have very strong opinions with respect to duck typing reaching from \"love\" to \"hate\". This is another example as to how programming is a subjective art rather than an \"objective\" science. Python's design is probably more appealing to beginners who intuitively regard `3` and `3.0` as interchangeable."
+    "So, there is no black or white answer as to which of the two language designs is better. Yet, most professional programmers have strong opinions concerning duck typing, reaching from \"love\" to \"hate.\" This is another example of how programming is a subjective art rather than \"objective\" science. Python's design is probably more appealing to beginners who intuitively regard `3` and `3.0` as interchangeable."
    ]
   },
   {
@@ -4109,11 +4109,11 @@
     "\n",
     "Meanwhile, we also see how we manually raise exceptions with the `raise` [statement](https://docs.python.org/3/reference/simple_stmts.html#the-raise-statement), another way of controlling the flow of execution.\n",
     "\n",
-    "The first two branches in the revised `factorial()` function act as **guardians** ensuring that the code does not produce *unexpected* runtime errors: Errors may certainly be expected when mentioned in the docstring.\n",
+    "The first two branches in the revised `factorial()` function act as **guardians** ensuring that the code does not produce *unexpected* runtime errors: Errors may be expected when mentioned in the docstring.\n",
     "\n",
-    "Forcing `n` to be an `int` is a very puritan way of handling the issues discussed above. A more relaxed approach could be to also accept a `float` and use its [is_integer()](https://docs.python.org/3/library/stdtypes.html#float.is_integer) method to check if `n` could be casted as an `int`. After all, being too puritan we cannot take advantage of duck typing.\n",
+    "Forcing `n` to be an `int` is a very puritan way of handling the issues discussed above. A more relaxed approach could be to also accept a `float` and use its [is_integer()](https://docs.python.org/3/library/stdtypes.html#float.is_integer) method to check if `n` could be cast as an `int`. After all, being too puritan, we cannot take advantage of duck typing.\n",
     "\n",
-    "So in essence, we are doing *two* things here: Besides checking the type, we also enforce **domain-specific** (i.e., mathematical here) rules with respect to the non-negativity of `n`."
+    "So, in essence, we are doing *two* things here: Besides checking the type, we also enforce **domain-specific** (i.e., mathematical here) rules concerning the non-negativity of `n`."
    ]
   },
   {
@@ -4215,7 +4215,7 @@
     }
    },
    "source": [
-    "Instead of running into an infinite recursion, we now receive very specifc error messages."
+    "Instead of running into a situation of infinite recursion, we now receive specific error messages."
    ]
   },
   {
@@ -4289,7 +4289,7 @@
     }
    },
    "source": [
-    "With everything *officially* introduced so far (i.e., without the introductary example in [Chapter 1](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/01_elements.ipynb) that only served as an overview), Python would be what is called **[Turing complete](https://en.wikipedia.org/wiki/Turing_completeness)**. That means that anything that could be formulated as an algorithm could be expressed with all the language features we have seen. Note that, in particular, we have *not* yet formally *introduced* the `for` and `while` statements!"
+    "With everything *officially* introduced so far (i.e., without the introductory example in [Chapter 1](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/01_elements.ipynb) that only served as an overview), Python would be what is called **[Turing complete](https://en.wikipedia.org/wiki/Turing_completeness)**. That means that anything that could be formulated as an algorithm could be expressed with all the language features we have seen. Note that, in particular, we have *not* yet formally *introduced* the `for` and `while` statements!"
    ]
   },
   {
@@ -4322,11 +4322,11 @@
     }
    },
    "source": [
-    "Whereas functions combined with `if` statements suffice to model any type of iteration with a recursion, Python comes with a `while` [statement](https://docs.python.org/3/reference/compound_stmts.html#the-while-statement) that often makes it easier to implement iterative ideas.\n",
+    "Whereas functions combined with `if` statements suffice to model any repetitive logic, Python comes with a `while` [statement](https://docs.python.org/3/reference/compound_stmts.html#the-while-statement) that often makes it easier to implement iterative ideas.\n",
     "\n",
-    "It consists of a header line with a boolean expression followed by an indented code block. Before the first and after every execution of the code block, the boolean expression is evaluated and if it is (still) equal to `True`, the code block runs (again). Eventually, some variable referenced in the boolean expression is changed in the code block such that the condition becomes `False`.\n",
+    "It consists of a header line with a boolean expression followed by an indented code block. Before the first and after every execution of the code block, the boolean expression is evaluated, and if it is (still) equal to `True`, the code block runs (again). Eventually, some variable referenced in the boolean expression is changed in the code block such that the condition becomes `False`.\n",
     "\n",
-    "If the condition is `False` before the first iteration, the entire code block is *never* executed. As the flow of control keeps **looping** (or **iterating**) back to the beginning of the code block, this concept is also called a `while`-loop and each pass through the loop an **iteration**."
+    "If the condition is `False` before the first iteration, the entire code block is *never* executed. As the flow of control keeps \"**looping**\" (i.e., more formally, **iterating**) back to the beginning of the code block, this concept is also called a `while`-loop and each pass through the loop an **iteration**."
    ]
   },
   {
@@ -4348,7 +4348,7 @@
     }
    },
    "source": [
-    "Let's rewrite the `countdown()` example in an iterative style. We also build in **input validation** by allowing the function to only be called with strictly positive integers. As any positive integer will hit $0$ at some point in time when iteratively decremented by $1$, `countdown()` is guaranteed to **terminate**. Note also that the base case is now handled at the end of the function, which commonly happens with iterative solutions to problems."
+    "Let's rewrite the `countdown()` example in an iterative style. We also build in **input validation** by allowing the function only to be called with strictly positive integers. As any positive integer hits $0$ at some point when iteratively decremented by $1$, `countdown()` is guaranteed to **terminate**. Also, the base case is now handled at the end of the function, which commonly happens with iterative solutions to problems."
    ]
   },
   {
@@ -4416,7 +4416,7 @@
     }
    },
    "source": [
-    "As [PythonTutor](http://pythontutor.com/visualize.html#code=def%20countdown%28n%29%3A%0A%20%20%20%20if%20not%20isinstance%28n,%20int%29%3A%0A%20%20%20%20%20%20%20%20raise%20TypeError%28%22...%22%29%0A%20%20%20%20elif%20n%20%3C%3D%200%3A%0A%20%20%20%20%20%20%20%20raise%20ValueError%28%22...%22%29%0A%0A%20%20%20%20while%20n%20!%3D%200%3A%0A%20%20%20%20%20%20%20%20print%28n%29%0A%20%20%20%20%20%20%20%20n%20-%3D%201%0A%0A%20%20%20%20print%28%22Happy%20new%20Year!%22%29%0A%0Acountdown%283%29&cumulative=false&curInstr=0&heapPrimitives=nevernest&mode=display&origin=opt-frontend.js&py=3&rawInputLstJSON=%5B%5D&textReferences=false) shows, there is a subtle but important difference in the way a `while` statement is treated in memory: In short, `while` statements can *not* run into a `RecursionError` as only *one* frame is needed to manage the names. After all, there is only *one* function call to be made. For common day-to-day applications this difference is, however, not important."
+    "As [PythonTutor](http://pythontutor.com/visualize.html#code=def%20countdown%28n%29%3A%0A%20%20%20%20if%20not%20isinstance%28n,%20int%29%3A%0A%20%20%20%20%20%20%20%20raise%20TypeError%28%22...%22%29%0A%20%20%20%20elif%20n%20%3C%3D%200%3A%0A%20%20%20%20%20%20%20%20raise%20ValueError%28%22...%22%29%0A%0A%20%20%20%20while%20n%20!%3D%200%3A%0A%20%20%20%20%20%20%20%20print%28n%29%0A%20%20%20%20%20%20%20%20n%20-%3D%201%0A%0A%20%20%20%20print%28%22Happy%20new%20Year!%22%29%0A%0Acountdown%283%29&cumulative=false&curInstr=0&heapPrimitives=nevernest&mode=display&origin=opt-frontend.js&py=3&rawInputLstJSON=%5B%5D&textReferences=false) shows, there is a subtle but essential difference in the way a `while` statement is treated in memory: In short, `while` statements can *not* run into a `RecursionError` as only *one* frame is needed to manage the names. After all, there is only *one* function call to be made. For typical day-to-day applications, this difference is, however, not so important *unless* a problem instance becomes so big that a large (i.e., $> 3.000$) number of recursive calls must be made."
    ]
   },
   {
@@ -4438,9 +4438,9 @@
     }
    },
    "source": [
-    "Finding the greatest common divisor of two numbers is still not so obvious when using a `while`-loop instead of a recursion.\n",
+    "Finding the greatest common divisor of two numbers is still not so obvious when using a `while`-loop instead of a recursive formulation.\n",
     "\n",
-    "The iterative implementation of `gcd()` below accepts any two strictly positive integers. As in any iteration through the loop the smaller number is subtracted from the larger one, the two decremented values of `a` and `b` will eventually be equal. Thus, this algorithm is also guaranteed to terminate. If one of the two numbers were negative or $0$ to begin with, `gcd()` would run forever and not even Python could detect this. Try this out by removing the input validation and running the function with negative arguments!"
+    "The iterative implementation of `gcd()` below accepts any two strictly positive integers. As in any iteration through the loop, the smaller number is subtracted from the larger one, the two decremented values of `a` and `b` eventually become equal. Thus, this algorithm is also guaranteed to terminate. If one of the two numbers were negative or $0$ in the first place, `gcd()` would run forever, and not even Python could detect this. Try this out by removing the input validation and running the function with negative arguments!"
    ]
   },
   {
@@ -4565,7 +4565,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "4.76 s ± 0 ns per loop (mean ± std. dev. of 1 run, 1 loop each)\n"
+      "4.69 s ± 0 ns per loop (mean ± std. dev. of 1 run, 1 loop each)\n"
      ]
     }
    ],
@@ -4593,7 +4593,7 @@
     }
    },
    "source": [
-    "As with recursion, we must ensure that the iteration ends. For the above `countdown()` and `gcd()` examples we could \"prove\" (i.e., at least argue in favor) that some pre-defined **termination criterion** will be reached eventually. However, this cannot be done in all cases as the following example shows."
+    "As with recursion, we must ensure that the iteration ends. For the above `countdown()` and `gcd()` examples, we could \"prove\" (i.e., at least argue in favor) that some pre-defined **termination criterion** is reached eventually. However, this cannot be done in all cases, as the following example shows."
    ]
   },
   {
@@ -4616,10 +4616,10 @@
    },
    "source": [
     "Let's play the following game:\n",
-    "- think of any positive integer $n$\n",
-    "- if $n$ is even, the next $n$ is half the old $n$\n",
-    "- if $n$ is odd, multiply the old $n$ by $3$ and add $1$ to obtain the next $n$\n",
-    "- repeat these steps until you reach $1$\n",
+    "- Think of any positive integer $n$.\n",
+    "- If $n$ is even, the next $n$ is half the old $n$.\n",
+    "- If $n$ is odd, multiply the old $n$ by $3$ and add $1$ to obtain the next $n$.\n",
+    "- Repeat these steps until you reach $1$.\n",
     "\n",
     "**Do we always reach the final $1$?**"
    ]
@@ -4632,7 +4632,7 @@
     }
    },
    "source": [
-    "The function below implements this game. Does it always reach $1$? No one has proven it so far! We include some input validation as before because `collatz()` would definitely not terminate if we called it with a negative number. Further, the Collatz sequence also works for real numbers but then we would have to study fractals (cf., [this](https://en.wikipedia.org/wiki/Collatz_conjecture#Iterating_on_real_or_complex_numbers)). So we restrict our example to integers only."
+    "The function below implements this game. Does it always reach $1$? No one has proven it so far! We include some input validation as before because `collatz()` would for sure not terminate if we called it with a negative number. Further, the Collatz sequence also works for real numbers, but then we would have to study fractals (cf., [this](https://en.wikipedia.org/wiki/Collatz_conjecture#Iterating_on_real_or_complex_numbers)). So we restrict our example to integers only."
    ]
   },
   {
@@ -4790,9 +4790,9 @@
     }
    },
    "source": [
-    "Recursion and the `while` statement are two sides of the same coin. Disregarding that in the case of recursion Python internally faces some additional burden for managing the stack of frames in memory, both approaches lead to the *same* computational steps in memory. More importantly, we can re-formulate a recursive implementation in an iterative way and vice verca despite one of the two ways often \"feeling\" a lot more natural given a particular problem.\n",
+    "Recursion and the `while` statement are two sides of the same coin. Disregarding that in the case of recursion Python internally faces some additional burden for managing the stack of frames in memory, both approaches lead to the *same* computational steps in memory. More importantly, we can re-formulate a recursive implementation in an iterative way and vice versa despite one of the two ways often \"feeling\" a lot more natural given a particular problem.\n",
     "\n",
-    "So how does the `for` [statement](https://docs.python.org/3/reference/compound_stmts.html#the-for-statement) we saw in the very first example in this book fit into this picture? It is really a *redundant* language construct to provide a *shorter* and more *convenient* syntax for common applications of the `while` statement. In programming, such additions to a language are called **syntactic sugar**. Sugar makes a cup of tea taste better but we may drink tea without sugar too.\n",
+    "So how does the `for` [statement](https://docs.python.org/3/reference/compound_stmts.html#the-for-statement) we saw in the very first example in this book fit into this picture? It is a *redundant* language construct to provide a *shorter* and more *convenient* syntax for common applications of the `while` statement. In programming, such additions to a language are called **syntactic sugar**. A cup of tea tastes better with sugar, but we may drink tea without sugar too.\n",
     "\n",
     "Consider the following `numbers` list. Without the `for` statement, we have to manage a temporary **index variable** `i` to loop over all the elements and also obtain the individual elements with the `[]` operator in each iteration of the loop."
    ]
@@ -4844,7 +4844,7 @@
     }
    },
    "source": [
-    "The `for` statement, on the contrary, makes the actual business logic more apparent by stripping all the **boilerplate code** away."
+    "The `for` statement, on the contrary, makes the actual business logic more apparent by stripping all the **[boilerplate code](https://en.wikipedia.org/wiki/Boilerplate_code)** away."
    ]
   },
   {
@@ -4877,7 +4877,7 @@
     }
    },
    "source": [
-    "For sequences of integers the [range()](https://docs.python.org/3/library/functions.html#func-range) built-in makes the `for` statement even more convenient: It creates a list-like object of type `range` that generates integers \"on the fly\" and we will look closely at the underlying effects in memory in Chapter 7."
+    "For sequences of integers, the [range()](https://docs.python.org/3/library/functions.html#func-range) built-in makes the `for` statement even more convenient: It creates a list-like object of type `range` that generates integers \"on the fly,\" and we look closely at the underlying effects in memory in Chapter 7."
    ]
   },
   {
@@ -5000,7 +5000,7 @@
     }
    },
    "source": [
-    "The important difference between the above `list` objects, `[0, 1, 2, 3, 4]` and `[1, 3, 5, 7, 9]`, and the `range` objects, `range(5)` and `range(1, 10, 2)`, is that in the former case *six* objects are created in memory *before* the `for` statement starts running, *one* `list` holding pointers to *five* `int` objects, whereas in the latter case only *one* `range` object is created that **generates** `int` objects one at a time *while* the `for`-loop runs.\n",
+    "The essential difference between the above `list` objects, `[0, 1, 2, 3, 4]` and `[1, 3, 5, 7, 9]`, and the `range` objects, `range(5)` and `range(1, 10, 2)`, is that in the former case *six* objects are created in memory *before* the `for` statement starts running, *one* `list` holding pointers to *five* `int` objects, whereas in the latter case only *one* `range` object is created that **generates** `int` objects one at a time *while* the `for`-loop runs.\n",
     "\n",
     "However, we can loop over both of them. So a natural question to ask is why Python treats objects of *different*  types in the *same* way when used with a `for` statement.\n",
     "\n",
@@ -5008,13 +5008,13 @@
     "\n",
     "Now, just as we classify objects by their types, we also classify these **concrete data types** (e.g., `int`, `float`, `str`, or `list`) into **abstract concepts**.\n",
     "\n",
-    "We actually did this already in [Chapter 1](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/01_elements.ipynb) when we described a `list` object as \"some sort of container that holds [...] pointers to other objects\". So, abstractly speaking, **containers** are any objects that are \"composed\" of other objects and also \"manage\" how these objects are organized. `list` objects, for example, have the property that they model an internal order associated with its elements. There exist, however, other container types, many of which do *not* come with an order. So, containers primarily \"contain\" other objects and have *nothing* to do with looping.\n",
+    "We did this already in [Chapter 1](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/01_elements.ipynb) when we described a `list` object as \"some sort of container that holds [...] pointers to other objects\". So, abstractly speaking, **containers** are any objects that are \"composed\" of other objects and also \"manage\" how these objects are organized. `list` objects, for example, have the property that they model an internal order associated with its elements. There exist, however, other container types, many of which do *not* come with an order. So, containers primarily \"contain\" other objects and have *nothing* to do with looping.\n",
     "\n",
-    "On the contrary, the abstract concept of **iterables** is all about looping: Any object that we can loop over is by definition an iterable. So, `range` objects, for example, are iterables that do *not* contain other objects. Moreover, looping does *not* have to occur in a *predictable* order although this is the case for both `list` and `range` objects.\n",
+    "On the contrary, the abstract concept of **iterables** is all about looping: Any object that we can loop over is, by definition, an iterable. So, `range` objects, for example, are iterables that do *not* contain other objects. Moreover, looping does *not* have to occur in a *predictable* order, although this is the case for both `list` and `range` objects.\n",
     "\n",
-    "Typically, containers are iterable and iterables are containers. Yet, only because these two concepts coincide often, we must not think of them as the same. Chapter 10 will finally give an explanation as to how abstract concepts are implemented and play together.\n",
+    "Typically, containers are iterables, and iterables are containers. Yet, only because these two concepts coincide often, we must not think of them as the same. Chapter 10 finally gives an explanation as to how abstract concepts are implemented and play together.\n",
     "\n",
-    "So, `list` objects like `first_names` below are iterable containers. They actually implement even more abstract concepts as we will see in Chapter 7."
+    "So, `list` objects like `first_names` below are iterable containers. They implement even more abstract concepts, as Chapter 7 reveals."
    ]
   },
   {
@@ -5165,7 +5165,7 @@
     }
    },
    "source": [
-    "If we must have an index variable in the loop's body, we use the [enumerate()](https://docs.python.org/3/library/functions.html#enumerate) built-in that takes an *iterable* as its argument and then generates a \"stream\" of \"pairs\" consisting of an index variable and an object provided by the iterable. There is *no* need to ever revert back to the `while` statement to loop over an iterable object."
+    "If we must have an index variable in the loop's body, we use the [enumerate()](https://docs.python.org/3/library/functions.html#enumerate) built-in that takes an *iterable* as its argument and then generates a \"stream\" of \"pairs\" consisting of an index variable and an object provided by the iterable. There is *no* need to ever revert to the `while` statement to loop over an iterable object."
    ]
   },
   {
@@ -5290,9 +5290,9 @@
    "source": [
     "In contrast to its recursive counterpart, the iterative `fibonacci()` function below is somewhat harder to read. For example, it is not so obvious as to how many iterations through the `for`-loop we need to make when implementing it. There is an increased risk of making an *off-by-one* error. Moreover, we need to track a `temp` variable along, at least until we have worked through Chapter 7. Do you understand what `temp` does?\n",
     "\n",
-    "However, one advantage of calculating Fibonacci numbers in a **forwards** fashion with a `for` statement is that we could list the entire sequence in ascending order as we calculate the desired number. To show this, we added `print()` statements in `fibonacci()` below.\n",
+    "However, one advantage of calculating Fibonacci numbers in a **forward** fashion with a `for` statement is that we could list the entire sequence in ascending order as we calculate the desired number. To show this, we added `print()` statements in `fibonacci()` below.\n",
     "\n",
-    "We do *not* need to store the index variable in the `for`-loop's header line: That is what the underscore variable `_` indicates; we \"throw it away\"."
+    "We do *not* need to store the index variable in the `for`-loop's header line: That is what the underscore variable `_` indicates; we \"throw it away.\""
    ]
   },
   {
@@ -5439,7 +5439,7 @@
     }
    },
    "source": [
-    "The iterative `factorial()` implementation is comparable to its recursive counterpart when it comes to readability. One advantage of calculating the factorial in a forwards fashion is that we could track the intermediate `product` as it grows."
+    "The iterative `factorial()` implementation is comparable to its recursive counterpart when it comes to readability. One advantage of calculating the factorial in a forward fashion is that we could track the intermediate `product` as it grows."
    ]
   },
   {
@@ -5528,7 +5528,7 @@
     }
    },
    "source": [
-    "The remainder of this chapter introduces more syntactic sugar. None of the language constructs introduced below are  actually needed but contribute to making Python the expressive and easy to read language that it is."
+    "The remainder of this chapter introduces more syntactic sugar: None of the language constructs introduced below are needed but contribute to making Python the expressive and easy to read language that it is."
    ]
   },
   {
@@ -5629,11 +5629,11 @@
     }
    },
    "source": [
-    "This implementation is *inefficient* as even if the *first* element in `numbers` has a square greater than `100`, we loop until the last element. This could take a long time for a big list.\n",
+    "This implementation is *inefficient* as even if the *first* element in `numbers` has a square greater than `100`, we loop until the last element: This could take a long time for a big list.\n",
     "\n",
     "Moreover, we must initialize `is_above` *before* the `for`-loop and write an `if`-`else`-logic *after* it to check for the result. The actual business logic is *not* clear right away.\n",
     "\n",
-    "Luckily, Python provides a `break` [statement](https://docs.python.org/3/reference/simple_stmts.html#the-break-statement) that let's us stop the `for`-loop in any iteration of the loop. Conceptually, it is yet another means of controlling the flow of execution."
+    "Luckily, Python provides a `break` [statement](https://docs.python.org/3/reference/simple_stmts.html#the-break-statement) that lets us stop the `for`-loop in any iteration of the loop. Conceptually, it is yet another means of controlling the flow of execution."
    ]
   },
   {
@@ -5676,7 +5676,7 @@
     }
    },
    "source": [
-    "This is a computational improvement. However, the code still consists *three* logical sections: some initialization *before* the `for`-loop, the loop itself, and some finalizing logic. We would prefer to convey the program's idea in *one* compound statement instead."
+    "This is a computational improvement. However, the code still consists of *three* logical sections: Some initialization *before* the `for`-loop, the loop itself, and some finalizing logic. We prefer to convey the program's idea in *one* compound statement instead."
    ]
   },
   {
@@ -5698,7 +5698,7 @@
     }
    },
    "source": [
-    "To express the logic in a prettier way, we add an `else`-clause at the end of the `for`-loop. The `else`-branch is executed *only if* the `for`-loop is *not* stopped with a `break` statement **prematurely** (i.e., *before* reaching the *last* iteration in the loop). The word \"else\" implies a rather unintuitive meaning and had better been named a `then`-clause. In most scenarios, however, the `else`-clause logically goes together with some `if` statement in the `for`-loop's body.\n",
+    "To express the logic in a prettier way, we add an `else`-clause at the end of the `for`-loop. The `else`-branch is executed *iff* the `for`-loop is *not* stopped with a `break` statement **prematurely** (i.e., *before* reaching the *last* iteration in the loop). The word \"else\" implies a somewhat unintuitive meaning and had better been named a `then`-clause. In most scenarios, however, the `else`-clause logically goes together with some `if` statement in the `for`-loop's body.\n",
     "\n",
     "Overall, the code's expressive power increases. Not many programming languages support a `for`-`else`-branching, which turns out to be very useful in practice."
    ]
@@ -5754,7 +5754,7 @@
     }
    },
    "source": [
-    "Lastly, we incorporate the finalizing `if`-`else` logic into the `for`-loop avoiding the `is_above` variable alltogether."
+    "Lastly, we incorporate the finalizing `if`-`else` logic into the `for`-loop, avoiding the `is_above` variable altogether."
    ]
   },
   {
@@ -5792,7 +5792,7 @@
     }
    },
    "source": [
-    "Of course, if we set the `threshold` an element's square has to pass higher, for example to `200`, we have to loop through the entire `numbers` list. There is *no way* to further optimize this **[linear search](https://en.wikipedia.org/wiki/Linear_search)**, at least as long as we model `numbers` as a `list` object. More advanced data types, however, exist that mitigate that downside."
+    "Of course, if we set the `threshold` an element's square has to pass higher, for example, to `200`, we have to loop through the entire `numbers` list. There is *no way* to optimize this **[linear search](https://en.wikipedia.org/wiki/Linear_search)** further, at least as long as we model `numbers` as a `list` object. More advanced data types, however, exist that mitigate that downside."
    ]
   },
   {
@@ -5843,7 +5843,7 @@
     }
    },
    "source": [
-    "Often times, we process some iterable with numeric data, for example, a list of numbers as in the introductory example in [Chapter 1](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/01_elements.ipynb) or, more realistically, data from a CSV file with many rows and columns.\n",
+    "Often, we process some iterable with numeric data, for example, a list of numbers as in the introductory example in [Chapter 1](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/01_elements.ipynb) or, more realistically, data from a CSV file with many rows and columns.\n",
     "\n",
     "Processing numeric data usually comes down to operations that may be grouped into one of the following three categories:\n",
     "\n",
@@ -5851,7 +5851,7 @@
     "- **filtering**: throw away individual samples (e.g., statistical outliers)\n",
     "- **reducing**: collect individual samples into summary statistics\n",
     "\n",
-    "We will study this **map-filter-reduce** paradigm extensively in Chapter 7 after introducing more advanced data types that are needed to work with \"big\" data.\n",
+    "We study this **map-filter-reduce** paradigm extensively in Chapter 7 after introducing more advanced data types that are needed to work with \"big\" data.\n",
     "\n",
     "In the remainder of this section, we focus on *filtering out* some samples within a `for`-loop."
    ]
@@ -5947,7 +5947,7 @@
     "\n",
     "In general, code gets harder to comprehend the more **horizontal space** it occupies. It is commonly considered good practice to grow a program **vertically** rather than horizontally. Code compliant with [PEP 8](https://www.python.org/dev/peps/pep-0008/#maximum-line-length) requires us to use *at most* 79 characters in a line!\n",
     "\n",
-    "Consider the next example, whose implementation in code already starts to look \"unbalanced\"."
+    "Consider the next example, whose implementation in code already starts to look unbalanced."
    ]
   },
   {
@@ -6050,11 +6050,11 @@
     }
    },
    "source": [
-    "With already three levels of indentation, less horizontal space is available for the actual code block. Of course, one could flatten the two `if` statements with the logical `and` operator as shown in [Chapter 3](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/03_conditionals.ipynb#The-if-Statement). Then, however, we trade off horizontal space against a more \"complex\" `if` logic and this is *not* a real improvement.\n",
+    "With already three levels of indentation, less horizontal space is available for the actual code block. Of course, one could flatten the two `if` statements with the logical `and` operator, as shown in [Chapter 3](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/03_conditionals.ipynb#The-if-Statement). Then, however, we trade off horizontal space against a more \"complex\" `if` logic, and this is *not* a real improvement.\n",
     "\n",
     "A Pythonista would instead make use of the `continue` [statement](https://docs.python.org/3/reference/simple_stmts.html#the-continue-statement) that causes a loop to jump into the next iteration skipping the rest of the code block.\n",
     "\n",
-    "The revised code fragement below occupies more vertical space and less horizontal space: A *good* trade-off."
+    "The revised code fragment below occupies more vertical space and less horizontal space: A *good* trade-off."
    ]
   },
   {
@@ -6119,11 +6119,11 @@
     }
    },
    "source": [
-    "This is yet another illustration of why programming is an art. The two preceding code cells do *exactly* the *same* with *identical* time complexity. However, the latter is arguably easier to read for a human, even more so when the business logic grows beyond two nested filters.\n",
+    "This is yet another illustration of why programming is an art. The two preceding code cells do the *same* with *identical* time complexity. However, the latter is arguably easier to read for a human, even more so when the business logic grows beyond two nested filters.\n",
     "\n",
     "The idea behind the `continue` statement is conceptually similar to the early exit pattern we saw above.\n",
     "\n",
-    "Both the `break` and `continue` statements as well as the optional `else`-clause are not only supported with `for`-loops but also `while`-loops."
+    "Both the `break` and `continue` statements, as well as the optional `else`-clause, are not only supported with `for`-loops but also `while`-loops."
    ]
   },
   {
@@ -6171,9 +6171,9 @@
     "\n",
     "Python provides the built-in [input()](https://docs.python.org/3/library/functions.html#input) function that prints a message to the user, called the **prompt**, and reads in what was typed in response as a `str` object. We use it to process a user's \"unreliable\" input to our program (i.e., a user might type in some invalid response). Further, we use the [random()](https://docs.python.org/3/library/random.html#random.random) function in the [random](https://docs.python.org/3/library/random.html) module to model the coin toss.\n",
     "\n",
-    "A popular pattern to approach such **indefinite loops** is to go with a `while True` statement which on its own would cause Python to enter into an infinite loop. Then, once a certain event occurs, we `break` out of the loop.\n",
+    "A popular pattern to approach such **indefinite loops** is to go with a `while True` statement, which on its own would cause Python to enter into an infinite loop. Then, once a particular event occurs, we `break` out of the loop.\n",
     "\n",
-    "Let's look at a first naive implementation."
+    "Let's look at a first and naive implementation."
    ]
   },
   {
@@ -6278,7 +6278,7 @@
     "\n",
     "First, we divide the business logic into two functions `get_guess()` and `toss_coin()` that are controlled from within a `while`-loop.\n",
     "\n",
-    "`get_guess()` not only reads in the user's input but also implements a simple input validation pattern in that the [strip()](https://docs.python.org/3/library/stdtypes.html?highlight=__contains__#str.strip) and [lower()](https://docs.python.org/3/library/stdtypes.html?highlight=__contains__#str.lower) methods remove preceeding and trailing whitespace and lower case the input ensuring that the user may spell the input in any possible way (e.g. all upper or lower case). Also, `get_guess()` checks if the user entered one of the two valid options. If so, it returns either `\"heads\"` or `\"tails\"`; if not, it returns `None`."
+    "`get_guess()` not only reads in the user's input but also implements a simple input validation pattern in that the [strip()](https://docs.python.org/3/library/stdtypes.html?highlight=__contains__#str.strip) and [lower()](https://docs.python.org/3/library/stdtypes.html?highlight=__contains__#str.lower) methods remove preceding and trailing whitespace and lower case the input ensuring that the user may spell the input in any possible way (e.g., all upper or lower case). Also, `get_guess()` checks if the user entered one of the two valid options. If so, it returns either `\"heads\"` or `\"tails\"`; if not, it returns `None`."
    ]
   },
   {
@@ -6351,7 +6351,7 @@
     }
    },
    "source": [
-    "Second, we rewrite the `if`-`else`-logic to explictly handle the case where `get_guess()` returns `None`: Whenever the user enters something invalid, a warning is shown, and another try is granted. We use the `is` operator and not the `==` operator as `None` is a singleton object.\n",
+    "Second, we rewrite the `if`-`else`-logic to handle the case where `get_guess()` returns `None` explicitly: Whenever the user enters something invalid, a warning is shown, and another try is granted. We use the `is` operator and not the `==` operator as `None` is a singleton object.\n",
     "\n",
     "The `while`-loop takes on the role of **glue code** that manages how other parts of the program interact with each other."
    ]
@@ -6411,7 +6411,7 @@
     }
    },
    "source": [
-    "Now, the program's business logic is expressed in a clearer way. More importantly, we can now change it more easily. For example, we could make the `toss_coin()` function base the tossing on a probability distribution other than the uniform (i.e., replace the [random.random()](https://docs.python.org/3/library/random.html#random.random) function with another one). In general, a modular architecture leads to improved software maintenance."
+    "Now, the program's business logic is expressed in a clearer way. More importantly, we can now change it more easily. For example, we could make the `toss_coin()` function base the tossing on a probability distribution other than the uniform (i.e., replace the [random.random()](https://docs.python.org/3/library/random.html#random.random) function with another one). In general, modular architecture leads to improved software maintenance."
    ]
   },
   {
@@ -6435,13 +6435,13 @@
    "source": [
     "**Iteration** is about **running blocks of code repeatedly**.\n",
     "\n",
-    "There are two redundant approaches of achieving that.\n",
+    "There are two redundant approaches to achieving that.\n",
     "\n",
-    "First, we combine functions that call themselves with conditional statements. This concept is known as **recursion** and suffices to control the flow of execution in *every* way we desire. For a beginner, this approach of **backwards** reasoning might not be intuitive but it turns out to be a very useful tool to have in one's toolbox.\n",
+    "First, we combine functions that call themselves with conditional statements. This concept is known as **recursion** and suffices to control the flow of execution in *every* way we desire. For a beginner, this approach of **backward** reasoning might not be intuitive, but it turns out to be a handy tool to have in one's toolbox.\n",
     "\n",
-    "Second, the `while` and `for` statements are alternative and potentially more intuitive ways to express iteration as they correspond to a **forwards** reasoning. The `for` statement is **syntactic sugar** that allows to rewrite common occurences of the `while` statement in a concise way. Python provides the `break` and `continue` statements as well as an optional `else`-clause that make working with the `for` and `while` statements even more convenient.\n",
+    "Second, the `while` and `for` statements are alternative and potentially more intuitive ways to express iteration as they correspond to a **forward** reasoning. The `for` statement is **syntactic sugar** that allows rewriting common occurrences of the `while` statement concisely. Python provides the `break` and `continue` statements as well as an optional `else`-clause that make working with the `for` and `while` statements even more convenient.\n",
     "\n",
-    "**Iterables** are any **concrete data types** that support being looped over, for example, with the `for` statement. The idea behind iterables is an **abstract concept** that may or may not be implemented by any given concrete data type. For example, both `list` and `range` objects can be looped over. The `list` type is also a **container** as any given `list` objects \"contains\" pointers to other objects in memory. On the contrary, the `range` type does not point to any other objects but rather creates *new* `int` objects \"on the fly\" (i.e., when being looped over)."
+    "**Iterables** are any **concrete data types** that support being looped over, for example, with the `for` statement. The idea behind iterables is an **abstract concept** that may or may not be implemented by any given concrete data type. For example, both `list` and `range` objects can be looped over. The `list` type is also a **container** as any given `list` objects \"contains\" pointers to other objects in memory. On the contrary, the `range` type does not point to any other objects but instead creates *new* `int` objects \"on the fly\" (i.e., when being looped over)."
    ]
   }
  ],
diff --git a/04_iteration_review_and_exercises.ipynb b/04_iteration_review_and_exercises.ipynb
index 8dd9025..a953796 100644
--- a/04_iteration_review_and_exercises.ipynb
+++ b/04_iteration_review_and_exercises.ipynb
@@ -54,7 +54,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "**Q2**: Solving a problem with a **recursion** is not only popular in computer science and math. Name some examples from the fields of business or economics where problems are also solved in a **backwards** fashion!"
+    "**Q2**: Solving a problem by **recursion** is not only popular in computer science and math. Name some examples from the fields of business or economics where problems are also solved in a **backward** fashion!"
    ]
   },
   {
@@ -68,7 +68,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "**Q3**: Explain what **duck typing** means! Why can it cause problems? Why it is [not a bug but a feature](https://www.urbandictionary.com/define.php?term=It%27s%20not%20a%20bug%2C%20it%27s%20a%20feature)?"
+    "**Q3**: Explain what **duck typing** means! Why can it cause problems? Why is it [not a bug but a feature](https://www.urbandictionary.com/define.php?term=It%27s%20not%20a%20bug%2C%20it%27s%20a%20feature)?"
    ]
   },
   {
@@ -208,7 +208,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "**Q12**: Whereas a **recursion** may accidently result in a **never ending** program, `while`-loops and `for`-loops are guaranteed to **terminate**."
+    "**Q12**: Whereas a **recursion** may accidentally result in a **never-ending** program, `while`-loops and `for`-loops are guaranteed to **terminate**."
    ]
   },
   {
@@ -275,7 +275,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "A popular example for a problem that is solved with a recursion art the **[Towers of Hanoi](https://en.wikipedia.org/wiki/Tower_of_Hanoi)**.\n",
+    "A popular example of a problem that is solved by recursion art the **[Towers of Hanoi](https://en.wikipedia.org/wiki/Tower_of_Hanoi)**.\n",
     "\n",
     "In its basic version, a tower consisting of, for example, four disks with increasing radii, is placed on the left-most of **three** adjacent spots. In the following, we refer to the number of disks as $n$, so here $n = 4$.\n",
     "\n",
@@ -304,9 +304,9 @@
    "source": [
     "Watch the following video by [MIT](https://www.mit.edu/)'s professor [Richard Larson](https://idss.mit.edu/staff/richard-larson/) for a comprehensive introduction.\n",
     "\n",
-    "The [MIT Blossoms Initative](https://blossoms.mit.edu/) is primarily aimed at high school students and does not have any prerequisites.\n",
+    "The [MIT Blossoms Initiative](https://blossoms.mit.edu/) is primarily aimed at high school students and does not have any prerequisites.\n",
     "\n",
-    "The video consists of three segments the last of which is *not* necessary to have watched in order to solve the tasks below. So, watch the video until 37:55."
+    "The video consists of three segments, the last of which is *not* necessary to have watched to solve the tasks below. So, watch the video until 37:55."
    ]
   },
   {
@@ -393,7 +393,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "As most likely the first couple of tries will result in *semantic* errors, it is advisable to have some sort of **visualization tool** for the progam's output: For example, an online version of the game can be found **[here](https://www.mathsisfun.com/games/towerofhanoi.html)**."
+    "As most likely the first couple of tries will result in *semantic* errors, it is advisable to have some sort of **visualization tool** for the program's output: For example, an online version of the game can be found **[here](https://www.mathsisfun.com/games/towerofhanoi.html)**."
    ]
   },
   {
@@ -402,7 +402,7 @@
    "source": [
     "Let's first **generalize** the mathematical relationship from above.\n",
     "\n",
-    "While the first number of $Sol(\\cdot)$ is the number of `disks` $n$, the second and third \"numbers\" are actually the **labels** for the three spots. Instead of spots `1`, `2`, and `3` we could also call them `\"left\"`, `\"center\"`, and `\"right\"` in our Python implementation. When \"passed\" to the $Sol(\\cdot)$ \"function\" they take on the role of an `origin` (= $o$) and `destination` (= $d$) pair.\n",
+    "While the first number of $Sol(\\cdot)$ is the number of `disks` $n$, the second and third \"numbers\" are the **labels** for the three spots. Instead of spots `1`, `2`, and `3`, we could also call them `\"left\"`, `\"center\"`, and `\"right\"` in our Python implementation. When \"passed\" to the $Sol(\\cdot)$ \"function\" they take on the role of an `origin` (= $o$) and `destination` (= $d$) pair.\n",
     "\n",
     "So, the expression $Sol(4, 1, 3)$ is the same as $Sol(4, \\text{\"left\"}, \\text{\"right\"})$ and describes the problem of moving a tower consisting of $n = 4$ disks from `origin` `1` / `\"left\"` to `destination` `3` / `right`. As we have seen in the video, we need some `intermediate` (= $i$) spot."
    ]
@@ -420,9 +420,9 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "In words, this means that in order to move a tower consisting of $n$ disks from an `origin` $o$ to a `destination` $d$, three steps must be executed:\n",
+    "In words, this means that to move a tower consisting of $n$ disks from an `origin` $o$ to a `destination` $d$, three steps must be executed:\n",
     "\n",
-    "1. Move the top most $n - 1$ disks of the tower temporarily from $o$ to $i$ (= sub-problem 1)\n",
+    "1. Move the topmost $n - 1$ disks of the tower temporarily from $o$ to $i$ (= sub-problem 1)\n",
     "2. Move the remaining and largest disk from $o$ to $d$\n",
     "3. Move the the $n - 1$ disks from the temporary spot $i$ to $d$ (= sub-problem 2)\n",
     "\n",
@@ -435,9 +435,9 @@
    "source": [
     "$Sol(\\cdot)$ can be written in Python as a function `sol()` that takes three arguments `disks`, `origin`, and `destination` that mirror $n$, $o$, and $d$.\n",
     "\n",
-    "Assume that all arguments to `sol()` will be `int` objects!\n",
+    "Assume that all arguments to `sol()` are `int` objects!\n",
     "\n",
-    "Once completed, `sol()` should print out all the moves in the correct order. With **printing a move**, we simply mean a line like \"1 -> 3\", short for \"Move the top-most disk from spot 1 to spot 3\".\n",
+    "Once completed, `sol()` should print out all the moves in the correct order. With **printing a move**, we mean a line like \"1 -> 3\", short for \"Move the top-most disk from spot 1 to spot 3\".\n",
     "\n",
     "Write your answers to **Q15.5** to **Q15.7** into the subsequent code cell and finalize `sol()`! No need to write a docstring or validate the input here."
    ]
@@ -550,9 +550,9 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "The previous `sol()` implementation does the job but the conditional statement needed in unnecessarily tedious. \n",
+    "The previous `sol()` implementation does the job, but the conditional statement needed in unnecessarily tedious. \n",
     "\n",
-    "Let's create a more concise `hanoi()` function that in addition to a positional `disks` argument takes three keyword-only arguments `origin`, `intermediate`, and `destination` with default values `\"left\"`, `\"center\"`, and `\"right\"`.\n",
+    "Let's create a more concise `hanoi()` function that, in addition to a positional `disks` argument, takes three keyword-only arguments `origin`, `intermediate`, and `destination` with default values `\"left\"`, `\"center\"`, and `\"right\"`.\n",
     "\n",
     "Write your answers to **Q15.9** and **Q15.10** into the subsequent code cell and finalize `hanoi()`! No need to write a docstring or validate the input here."
    ]
@@ -588,7 +588,7 @@
     "\n",
     "Figure out how the arguments are passed on in the two recursive `hanoi()` calls!\n",
     "\n",
-    "Also, write a `print()` statement analogous to the one in `sol()` in between the two recursive function calls. Is it ok to just copy and paste it?"
+    "Also, write a `print()` statement analogous to the one in `sol()` in between the two recursive function calls. Is it ok to copy and paste it?"
    ]
   },
   {
@@ -638,7 +638,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "We could of course also use **numeric labels** for the three steps like so."
+    "We could, of course, also use **numeric labels** for the three steps like so."
    ]
   },
   {
@@ -661,13 +661,13 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Let's say, we did not know about the **analytical formula** for the number of **minimal moves** given $n$.\n",
+    "Let's say we did not know about the **analytical formula** for the number of **minimal moves** given $n$.\n",
     "\n",
     "In such cases, we could modify a recursive function to return a count value to be passed up the recursion tree.\n",
     "\n",
-    "In fact, this is similar to what we do in the recursive versions of `factorial()` and `fibonacci()` in [Chapter 4](https://github.com/webartifex/intro-to-python/blob/master/04_iteration.ipynb) where we pass up an intermediate result.\n",
+    "This is similar to what we do in the recursive versions of `factorial()` and `fibonacci()` in [Chapter 4](https://github.com/webartifex/intro-to-python/blob/master/04_iteration.ipynb), where we pass up an intermediate result.\n",
     "\n",
-    "Let's create a `hanoi_moves()` function that follows the same internal logic as `hanoi()` but instead of printing out the moves returns the number of steps done so far in the recursion.\n",
+    "Let's create a `hanoi_moves()` function that follows the same internal logic as `hanoi()`, but, instead of printing out the moves, returns the number of steps done so far in the recursion.\n",
     "\n",
     "Write your answers to **Q15.12** to **Q15.14** into the subsequent code cell and finalize `hanoi_moves()`! No need to write a docstring or validate the input here."
    ]
@@ -742,7 +742,7 @@
    "source": [
     "Observe how quickly the `hanoi_moves()` function slows down for increasing `disks` arguments.\n",
     "\n",
-    "With `disks` in the range from `24` through `26` the computation time roughly doubles for each increase of `disks` by 1.\n",
+    "With `disks` in the range from `24` through `26`, the computation time roughly doubles for each increase of `disks` by 1.\n",
     "\n",
     "**Q15.16**: Execute the code cells below and see for yourself!"
    ]
@@ -788,7 +788,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "The above `hanoi()` prints the optimal solution's moves in the correct order but fails to label each move with an order number. This can be build in by passing on one more argument `offset` down the recursion tree. As the logic gets a bit \"involved\", `hanoi_ordered()` below is almost finished.\n",
+    "The above `hanoi()` prints the optimal solution's moves in the correct order but fails to label each move with an order number. This can be built in by passing on one more argument `offset` down the recursion tree. As the logic gets a bit \"involved,\" `hanoi_ordered()` below is almost finished.\n",
     "\n",
     "Write your answers to **Q15.17** and **Q15.18** into the subsequent code cell and finalize `hanoi_ordered()`! No need to write a docstring or validate the input here."
    ]
@@ -879,7 +879,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Lastly, it is to be mentioned that for problem instances with a small `disks` argument it is easier to collect all the moves first in a list and then add the order number with the [enumerate()](https://docs.python.org/3/library/functions.html#enumerate) built-in."
+    "Lastly, it is to be mentioned that for problem instances with a small `disks` argument, it is easier to collect all the moves first in a list and then add the order number with the [enumerate()](https://docs.python.org/3/library/functions.html#enumerate) built-in."
    ]
   },
   {
diff --git a/05_numbers.ipynb b/05_numbers.ipynb
index 2c06dc3..ccb1412 100644
--- a/05_numbers.ipynb
+++ b/05_numbers.ipynb
@@ -21,15 +21,15 @@
    "source": [
     "After learning about the basic building blocks of expressing and structuring the business logic in programs, we focus our attention on the **data types** Python offers us, both built-in and available via the [standard library](https://docs.python.org/3/library/index.html) or third-party packages.\n",
     "\n",
-    "We start with the \"simple\" ones: Numeric types in this chapter and textual data in Chapter 6. An important fact that holds true for all objects of these types is that they are **immutable**. To re-use the bag analogy from [Chapter 1](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/01_elements.ipynb#Objects-vs.-Types-vs.-Values), this means that the $0$s and $1$s making up an object's *value* cannot be changed once the bag is created in memory implying that any operation with or method on the object creates a *new* object in a *different* memory location.\n",
+    "We start with the \"simple\" ones: Numeric types in this chapter and textual data in Chapter 6. An important fact that holds for all objects of these types is that they are **immutable**. To re-use the bag analogy from [Chapter 1](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/01_elements.ipynb#Objects-vs.-Types-vs.-Values), this means that the $0$s and $1$s making up an object's *value* cannot be changed once the bag is created in memory, implying that any operation with or method on the object creates a *new* object in a *different* memory location.\n",
     "\n",
-    "Chapters 7, 8, and 9 then cover the more \"complex\" data types including, for example, the `list` type. Finally, Chapter 10 completes the picture by introducing language constructs to create custom types.\n",
+    "Chapters 7, 8, and 9 then cover the more \"complex\" data types, including, for example, the `list` type. Finally, Chapter 10 completes the picture by introducing language constructs to create custom types.\n",
     "\n",
     "We have already seen many hints indicating that numbers are not as trivial to work with as it seems at first sight:\n",
     "\n",
-    "- [Chapter 1](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/01_elements.ipynb#%28Data%29-Type-%2F-%22Behavior%22) revealed that numbers may come in *different* data types (i.e., `int` vs. `float` so far),\n",
-    "- [Chapter 3](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/03_conditionals.ipynb#Boolean-Expressions) raised questions regarding the **limited precision** of `float` numbers (e.g., `42 == 42.000000000000001` evaluates to `True`), and\n",
-    "- [Chapter 4](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/04_iteration.ipynb#Infinite-Recursion) showed that sometimes a `float` \"walks\" and \"quacks\" like an `int` whereas the reverse is true in other cases.\n",
+    "- [Chapter 1](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/01_elements.ipynb#%28Data%29-Type-%2F-%22Behavior%22) reveals that numbers may come in *different* data types (i.e., `int` vs. `float` so far),\n",
+    "- [Chapter 3](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/03_conditionals.ipynb#Boolean-Expressions) raises questions regarding the **limited precision** of `float` numbers (e.g., `42 == 42.000000000000001` evaluates to `True`), and\n",
+    "- [Chapter 4](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/04_iteration.ipynb#Infinite-Recursion) shows that sometimes a `float` \"walks\" and \"quacks\" like an `int`, whereas the reverse is true in other cases.\n",
     "\n",
     "This chapter introduces all the [built-in numeric types](https://docs.python.org/3/library/stdtypes.html#numeric-types-int-float-complex): `int`, `float`, and `complex`. To mitigate the limited precision of floating-point numbers, we also look at two replacements for the `float` type in the [standard library](https://docs.python.org/3/library/index.html), namely the `Decimal` type in the [decimals](https://docs.python.org/3/library/decimal.html#decimal.Decimal) and the `Fraction` type in the [fractions](https://docs.python.org/3/library/fractions.html#fractions.Fraction) module."
    ]
@@ -53,7 +53,7 @@
     }
    },
    "source": [
-    "The simplest numeric type is the `int` type. Python's integers behave like [integers in ordinary math](https://en.wikipedia.org/wiki/Integer) (i.e., the set $\\mathbb{Z}$) and support operators in the way we saw in the section on arithmetic operators in [Chapter 1](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/01_elements.ipynb#%28Arithmetic%29-Operators)."
+    "The simplest numeric type is the `int` type: It behaves like an [integer in ordinary math](https://en.wikipedia.org/wiki/Integer) (i.e., the set $\\mathbb{Z}$) and supports operators in the way we saw in the section on arithmetic operators in [Chapter 1](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/01_elements.ipynb#%28Arithmetic%29-Operators)."
    ]
   },
   {
@@ -66,7 +66,7 @@
    },
    "outputs": [],
    "source": [
-    "a = 789"
+    "i = 789"
    ]
   },
   {
@@ -92,7 +92,7 @@
     {
      "data": {
       "text/plain": [
-       "140526570988784"
+       "139927641322704"
       ]
      },
      "execution_count": 2,
@@ -101,7 +101,7 @@
     }
    ],
    "source": [
-    "id(a)"
+    "id(i)"
    ]
   },
   {
@@ -125,7 +125,7 @@
     }
    ],
    "source": [
-    "type(a)"
+    "type(i)"
    ]
   },
   {
@@ -149,7 +149,7 @@
     }
    ],
    "source": [
-    "a"
+    "i"
    ]
   },
   {
@@ -160,7 +160,7 @@
     }
    },
    "source": [
-    "A nice feature is using underscores `_` as (thousands) seperators in numeric literals. For example, `1_000_000` evaluates to just `1000000` in memory."
+    "A nice feature is using underscores `_` as (thousands) separator in numeric literals. For example, `1_000_000` evaluates to `1000000` in memory."
    ]
   },
   {
@@ -195,7 +195,7 @@
     }
    },
    "source": [
-    "Whereas mathematicians argue what the term $0^0$ means (cf., this [article](https://en.wikipedia.org/wiki/Zero_to_the_power_of_zero)), programmers are pragmatic about this and just define $0^0 = 1$."
+    "Whereas mathematicians argue what the term $0^0$ means (cf., this [article](https://en.wikipedia.org/wiki/Zero_to_the_power_of_zero)), programmers are pragmatic about this and define $0^0 = 1$."
    ]
   },
   {
@@ -241,7 +241,7 @@
     }
    },
    "source": [
-    "As computers can only store $0$s and $1$s, integers are nothing but that in memory as well. Consequently, computer scientists and engineers developed conventions as to how $0$s and $1$s are \"translated\" into integers and one such convention is the **[binary representation](https://en.wikipedia.org/wiki/Binary_number)** of **non-negative integers**. Consider the integers from $0$ through $255$ that are encoded into $0$s and $1$s with the help of this table:"
+    "As computers can only store $0$s and $1$s, `int` objects are nothing but that in memory as well. Consequently, computer scientists and engineers developed conventions as to how $0$s and $1$s are \"translated\" into integers, and one such convention is the **[binary representation](https://en.wikipedia.org/wiki/Binary_number)** of **non-negative integers**. Consider the integers from $0$ through $255$ that are encoded into $0$s and $1$s with the help of this table:"
    ]
   },
   {
@@ -266,13 +266,13 @@
     }
    },
    "source": [
-    "A number consists of exactly eight $0$s and $1$s that are read from right to left and referred to as the **bits** of the number. Each bit represents a distinct number, the **digit**, as indicated in the table. For sure, we start counting at $0$ again.\n",
+    "A number consists of exactly eight $0$s and $1$s that are read from right to left and referred to as the **bits** of the number. Each bit represents a distinct multiple of $2$, the **digit**. For sure, we start counting at $0$ again.\n",
     "\n",
-    "To encode the integer $3$, for example, we need to find a combination of $0$s and $1$s such that the sum of digits marked with a $1$ is equal to the number we want to encode. In the example, we set all bits to $0$ except for the first ($i=0$) and second ($i=1$) because $1 + 2 = 3$. So the binary representation of $3$ is $00~00~00~11$. To be precise, the $3$ is a linear combination of the digits where the coefficients are either $0$ or $1$: $3 = 0*128 + 0*64 + 0*32 + 0*16 + 0*8 + 0*4 + 1*2 + 1*1$. It is guaranteed that there is exactly one such combination for each number between $0$ and $255$.\n",
+    "To encode the integer $3$, for example, we need to find a combination of $0$s and $1$s such that the sum of digits marked with a $1$ is equal to the number we want to encode. In the example, we set all bits to $0$ except for the first ($i=0$) and second ($i=1$) as $2^0 + 2^1 = 1 + 2 = 3$. So the binary representation of $3$ is $00~00~00~11$. To borrow some terminology from linear algebra, the $3$ is a linear combination of the digits where the coefficients are either $0$ or $1$: $3 = 0*128 + 0*64 + 0*32 + 0*16 + 0*8 + 0*4 + 1*2 + 1*1$. It is *guaranteed* that there is exactly *one* such combination for each number between $0$ and $255$.\n",
     "\n",
-    "As each digit in the binary representation is one of two values, we say that this representation has a base of $2$. Often times, the base is indicated with a subscript to avoid confusion. For example, we write $3_{10} = 00000011_2$ or $3_{10} = 11_2$ for short omitting leading $0$s. A subscript of $10$ implies a decimal number as we know it from grade school.\n",
+    "As each bit in the binary representation is one of two values, we say that this representation has a base of $2$. Often, the base is indicated with a subscript to avoid confusion. For example, we write $3_{10} = 00000011_2$ or $3_{10} = 11_2$ for short omitting leading $0$s. A subscript of $10$ implies a decimal number as we know it from grade school.\n",
     "\n",
-    "In Python, we use the built-in [bin()](https://docs.python.org/3/library/functions.html#bin) function to obtain an integer's binary representation. It returns a `str` object starting with `\"0b\"` indicating the binary format and as many $0$s and $1$s as are necessary to encode the integer omitting leading $0$s."
+    "We use the built-in [bin()](https://docs.python.org/3/library/functions.html#bin) function to obtain an `int` object's binary representation: It returns a `str` object starting with `\"0b\"` indicating the binary format and as many $0$s and $1$s as are necessary to encode the integer omitting leading $0$s."
    ]
   },
   {
@@ -307,7 +307,7 @@
     }
    },
    "source": [
-    "We may pass such a `str` object as the argument to the [int()](https://docs.python.org/3/library/functions.html#int) built-in, together with `base=2`, to (re-)create an `int` object, for example, with the value of `3`."
+    "We may pass a `str` object formatted this way as the argument to the [int()](https://docs.python.org/3/library/functions.html#int) built-in, together with `base=2`, to (re-)create an `int` object, for example, with the value of `3`."
    ]
   },
   {
@@ -342,7 +342,7 @@
     }
    },
    "source": [
-    "Moreover, we may also use the contents of the returned `str` object as a **literal** instead: Just like we type, for example, `3` without quotes (i.e., \"literally\") into a code cell to create the `int` object `3`, we may type `0b11` and obtain an `int` object with the same value."
+    "Moreover, we may also use the contents of the returned `str` object as a **literal** instead: Just like we type, for example, `3` without quotes (i.e., \"literally\") into a code cell to create the `int` object `3`, we may type `0b11` to obtain an `int` object with the same value."
    ]
   },
   {
@@ -377,7 +377,7 @@
     }
    },
    "source": [
-    "Another example is the integer `177` that is the sum of $128 + 32 + 16 + 1$: Its binary representation is the sequence of bits $10~11~00~01$. To use our new notation, $177_{10} = 10110001_2$."
+    "Another example is the integer `177` that is the sum of $128 + 32 + 16 + 1$: Thus, its binary representation is the sequence of bits $10~11~00~01$, or to use our new notation, $177_{10} = 10110001_2$."
    ]
   },
   {
@@ -412,7 +412,7 @@
     }
    },
    "source": [
-    "Analogous to typing `177` into a code cell, we may type `0b10110001`, or `0b_1011_0001` to make it easier to read, to create the `int` object `177` in memory."
+    "Analogous to typing `177` into a code cell, we may write `0b10110001`, or `0b_1011_0001` to make use of the underscores, and create an `int` object with the value `177`."
    ]
   },
   {
@@ -554,9 +554,9 @@
     }
    },
    "source": [
-    "Groups of eight bits are also called a **byte**. As a byte can only represent non-negative integers up to $255$, the table above is extended conceptually with greater digits to the left to express integers beyond $255$. The memory management needed to implement this is built into Python and we do not need to worry about anything.\n",
+    "Groups of eight bits are also called a **byte**. As a byte can only represent non-negative integers up to $255$, the table above is extended conceptually with greater digits to the left to model integers beyond $255$. The memory management needed to implement this is built into Python, and we do not need to worry about it.\n",
     "\n",
-    "For example, the `789` from above is encoded with $10$ bits and $789_{10} = 1100010101_2$."
+    "For example, the `789` from above is encoded with ten bits and $789_{10} = 1100010101_2$."
    ]
   },
   {
@@ -580,7 +580,7 @@
     }
    ],
    "source": [
-    "bin(a)  # = bin(789)"
+    "bin(i)  # = bin(789)"
    ]
   },
   {
@@ -598,7 +598,7 @@
     "| Digit |$10^3$|$10^2$|$10^1$|$10^0$|\n",
     "|  $=$  |$1000$| $100$| $10$ |  $1$ |\n",
     "\n",
-    "Now, an integer is a linear combination of the digits where the coefficents are one of *ten* values. For example, the number $123$ can be expressed as $0*1000 + 1*100 + 2*10 + 3*1$. So, the binary representation follows the same logic as the decimal system taught in grade school. The decimal system is intuitive to us humans mostly as we learn to count with our *ten* fingers."
+    "Now, an integer is a linear combination of the digits where the coefficients are one of *ten* values, and the base is now $10$. For example, the number $123$ can be expressed as $0*1000 + 1*100 + 2*10 + 3*1$. So, the binary representation follows the same logic as the decimal system taught in grade school. The decimal system is intuitive to us humans, mostly as we learn to count with our *ten* fingers. The $0$s and $1$s in a computer's memory are therefore no rocket science; they only feel unintuitive for a beginner."
    ]
   },
   {
@@ -669,9 +669,9 @@
     "\n",
     "$46_{10} = 2\\text{e}_{16} = 101110_2$\n",
     "\n",
-    "The built-in [hex()](https://docs.python.org/3/library/functions.html#hex) function creates a `str` object starting with `\"0x\"` representing an integer's hexadecimal representation. The length depends on how many groups of four bits are implied by the corresponding binary representation.\n",
+    "The built-in [hex()](https://docs.python.org/3/library/functions.html#hex) function creates a `str` object starting with `\"0x\"` representing an `int` object's hexadecimal representation. The length depends on how many groups of four bits are implied by the corresponding binary representation.\n",
     "\n",
-    "For `0` and `1` the hexadecimal representation is similar to the binary one."
+    "For `0` and `1`, the hexadecimal representation is similar to the binary one."
    ]
   },
   {
@@ -765,7 +765,7 @@
     }
    },
    "source": [
-    "For `10` and `15` we see the letter digits for the first time."
+    "For `10` and `15`, we see the letter digits for the first time."
    ]
   },
   {
@@ -824,7 +824,7 @@
     }
    },
    "source": [
-    "For `177` the binary representation, `\"0b10110001\"`, can be read as *two* groups of four bits $1011$ and $0001$ that are encoded as $\\text{b}$ and $1$ in accordance with the first three columns of the table above."
+    "The binary representation of `177`, `\"0b10110001\"`, can be viewed as *two* groups of four bits, $1011$ and $0001$, that are encoded as $\\text{b}$ and $1$ in hexadecimal (cf., table above)."
    ]
   },
   {
@@ -883,7 +883,7 @@
     }
    },
    "source": [
-    "To obtain the original integer back, we call the [int()](https://docs.python.org/3/library/functions.html#int) built-in with a `base=16` argument."
+    "To (re-)create an `int` object with the value `177`, we call the [int()](https://docs.python.org/3/library/functions.html#int) built-in with a properly formatted `str` object and `base=16` as arguments."
    ]
   },
   {
@@ -953,7 +953,7 @@
     }
    },
    "source": [
-    "Hexadecimal values between $00_{16}$ and $\\text{ff}_{16}$ (i.e., $0_{10}$ and $255_{10}$) are commonly used to describe colors, for example, in web development but also in graphics editors. See this [online tool](https://www.w3schools.com/colors/colors_hexadecimal.asp) for some more background."
+    "Hexadecimals between $00_{16}$ and $\\text{ff}_{16}$ (i.e., $0_{10}$ and $255_{10}$) are commonly used to describe colors, for example, in web development but also graphics editors. See this [online tool](https://www.w3schools.com/colors/colors_hexadecimal.asp) for some more background."
    ]
   },
   {
@@ -988,7 +988,7 @@
     }
    },
    "source": [
-    "Just like binary representations, the hexadecimals extend to the left for larger values like `789`."
+    "Just like binary representations, the hexadecimals extend to the left for larger numbers like `789`."
    ]
   },
   {
@@ -1012,7 +1012,7 @@
     }
    ],
    "source": [
-    "hex(a)  # = hex(789)"
+    "hex(i)  # = hex(789)"
    ]
   },
   {
@@ -1023,7 +1023,7 @@
     }
    },
    "source": [
-    "For completeness sake, we mention that there is also the built-in [oct()](https://docs.python.org/3/library/functions.html#oct) function to obtain an integer's **octal representation**. The logic is the same as for the hexadecimal representation where we just use *eight* instead of *sixteen* digits. That is the equivalent of viewing the binary representations in groups of three bits."
+    "For completeness sake, we mention that there is also the [oct()](https://docs.python.org/3/library/functions.html#oct) built-in to obtain an integer's **octal representation**. The logic is the same as for the hexadecimal representation, and we use *eight* instead of *sixteen* digits. That is the equivalent of viewing the binary representations in groups of three bits. As of today, octal representations have become less important, and the data science practitioner may probably live without them quite well."
    ]
   },
   {
@@ -1045,7 +1045,7 @@
     }
    },
    "source": [
-    "While there are conventions that model negative integers with the $0$s and $1$s in memory (cf., [Two's Complement](https://en.wikipedia.org/wiki/Two%27s_complement)), Python manages that for us and we do not look into this here for brevity.\n",
+    "While there are conventions that model negative integers with $0$s and $1$s in memory (cf., [Two's Complement](https://en.wikipedia.org/wiki/Two%27s_complement)), Python manages that for us, and we do not look into the theory here for brevity. We have learned all we need to know about how integers are modeled in a computer.\n",
     "\n",
     "The binary and hexadecimal representations of negative integers are identical to their positive counterparts except that they start with a minus sign `-`."
    ]
@@ -1165,7 +1165,7 @@
     }
    },
    "source": [
-    "Whereas the boolean objects `True` and `False` are commonly *not* regarded as numeric types, they behave like `1` and `0` in an arithmetic context."
+    "Whereas the boolean literals `True` and `False` are commonly *not* regarded as numeric types, they behave like `1` and `0` in an arithmetic context."
    ]
   },
   {
@@ -1176,9 +1176,20 @@
      "slide_type": "slide"
     }
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "1"
+      ]
+     },
+     "execution_count": 32,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "b = True"
+    "True + False"
    ]
   },
   {
@@ -1186,14 +1197,14 @@
    "execution_count": 33,
    "metadata": {
     "slideshow": {
-     "slide_type": "fragment"
+     "slide_type": "-"
     }
    },
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "94554490840032"
+       "42"
       ]
      },
      "execution_count": 33,
@@ -1202,7 +1213,7 @@
     }
    ],
    "source": [
-    "id(b)"
+    "41 + True"
    ]
   },
   {
@@ -1217,7 +1228,7 @@
     {
      "data": {
       "text/plain": [
-       "bool"
+       "0.0"
       ]
      },
      "execution_count": 34,
@@ -1226,199 +1237,7 @@
     }
    ],
    "source": [
-    "type(b)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 35,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "-"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "True"
-      ]
-     },
-     "execution_count": 35,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "b"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 36,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "slide"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "c = False"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 37,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "fragment"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "94554490840000"
-      ]
-     },
-     "execution_count": 37,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "id(c)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 38,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "-"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "bool"
-      ]
-     },
-     "execution_count": 38,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "type(c)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 39,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "-"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "False"
-      ]
-     },
-     "execution_count": 39,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "c"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "source": [
-    "By including booleans as operands in arithmetic expressions, Python implicitly casts them as `int` objects."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 40,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "slide"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "1"
-      ]
-     },
-     "execution_count": 40,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "b + c  # = True + False"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 41,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "-"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "790"
-      ]
-     },
-     "execution_count": 41,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "a + b  # = 789 + True"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 42,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "-"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "0"
-      ]
-     },
-     "execution_count": 42,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "a * c  # = 789 * False"
+    "42.0 * False"
    ]
   },
   {
@@ -1434,7 +1253,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 43,
+   "execution_count": 35,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -1447,7 +1266,7 @@
        "1"
       ]
      },
-     "execution_count": 43,
+     "execution_count": 35,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1458,7 +1277,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 44,
+   "execution_count": 36,
    "metadata": {
     "slideshow": {
      "slide_type": "-"
@@ -1471,7 +1290,7 @@
        "0"
       ]
      },
-     "execution_count": 44,
+     "execution_count": 36,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1493,10 +1312,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 45,
+   "execution_count": 37,
    "metadata": {
     "slideshow": {
-     "slide_type": "skip"
+     "slide_type": "slide"
     }
    },
    "outputs": [
@@ -1506,7 +1325,7 @@
        "'0b1'"
       ]
      },
-     "execution_count": 45,
+     "execution_count": 37,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1517,10 +1336,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 46,
+   "execution_count": 38,
    "metadata": {
     "slideshow": {
-     "slide_type": "skip"
+     "slide_type": "-"
     }
    },
    "outputs": [
@@ -1530,7 +1349,7 @@
        "'0b0'"
       ]
      },
-     "execution_count": 46,
+     "execution_count": 38,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1547,15 +1366,15 @@
     }
    },
    "source": [
-    "Their hexadecimal representations occupy *four* bits in memory while only *one* bit is actually needed. This is because \"1\" and \"0\" are just two of the sixteen possible digits."
+    "Their hexadecimal representations occupy *four* bits in memory while only *one* bit is needed. This is because \"1\" and \"0\" are just two of the sixteen possible digits."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 47,
+   "execution_count": 39,
    "metadata": {
     "slideshow": {
-     "slide_type": "skip"
+     "slide_type": "slide"
     }
    },
    "outputs": [
@@ -1565,7 +1384,7 @@
        "'0x1'"
       ]
      },
-     "execution_count": 47,
+     "execution_count": 39,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1576,10 +1395,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 48,
+   "execution_count": 40,
    "metadata": {
     "slideshow": {
-     "slide_type": "skip"
+     "slide_type": "-"
     }
    },
    "outputs": [
@@ -1589,7 +1408,7 @@
        "'0x0'"
       ]
      },
-     "execution_count": 48,
+     "execution_count": 40,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1606,12 +1425,12 @@
     }
    },
    "source": [
-    "As a reminder, `None` is a type on its own, namely the `NoneType`, and different from `False`. It *cannot* be casted as an integer as the `TypeError` indicates."
+    "As a reminder, the `None` literal is a type on its own, namely the `NoneType`, and different from `False`. It *cannot* be cast as an integer as the `TypeError` indicates."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 49,
+   "execution_count": 41,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -1625,7 +1444,7 @@
      "traceback": [
       "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
       "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-49-af2123a46eb2>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;32m<ipython-input-41-af2123a46eb2>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
       "\u001b[0;31mTypeError\u001b[0m: int() argument must be a string, a bytes-like object or a number, not 'NoneType'"
      ]
     }
@@ -1653,16 +1472,16 @@
     }
    },
    "source": [
-    "Now that we know how integers are represented with $0$s and $1$s in memory, we look at ways of working with these individual bits, in particular with the so-called **[bitwise operators](https://wiki.python.org/moin/BitwiseOperators)**: As the name suggests, the operators perform some operation on a bit by bit basis. They only work with `int` objects.\n",
+    "Now that we know how integers are represented with $0$s and $1$s, we look at ways of working with the individual bits, in particular with the so-called **[bitwise operators](https://wiki.python.org/moin/BitwiseOperators)**: As the name suggests, the operators perform some operation on a bit by bit basis. They only work with and always return `int` objects.\n",
     "\n",
-    "We keep this overview rather short as such \"low-level\" operations are not needed by the data science practicioner on a regular basis. Yet, it is worthwhile to have heard about them as they form the basis of all of arithmetic in computers.\n",
+    "We keep this overview rather short as such \"low-level\" operations are not needed by the data science practitioner regularly. Yet, it is worthwhile to have heard about them as they form the basis of all of arithmetic in computers.\n",
     "\n",
-    "The first operator is the **bitwise AND** `&` operator: This operator looks at the bits of its two operands, `11` and `13` in the example, in a pairwise fashion and *only if* both operands have a $1$ in the same position will the resulting integer have a $1$ in this position. The binary representations of `11` and `13` have $1$s in their respective first and fourth bits, which is why `bin(11 & 13)` evaluates to `\"Ob1001\"`."
+    "The first operator is the **bitwise AND** operator `&`: It looks at the bits of its two operands, `11` and `13` in the example, in a pairwise fashion and *iff* both operands have a $1$ in the same position, will the resulting integer have a $1$ in this position as well. The binary representations of `11` and `13` have $1$s in their respective first and fourth bits, which is why `bin(11 & 13)` evaluates to `\"Ob1001\"`."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 50,
+   "execution_count": 42,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -1675,7 +1494,7 @@
        "'0b1011 & 0b1101'"
       ]
      },
-     "execution_count": 50,
+     "execution_count": 42,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1686,7 +1505,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 51,
+   "execution_count": 43,
    "metadata": {
     "slideshow": {
      "slide_type": "-"
@@ -1699,7 +1518,7 @@
        "'0b1001'"
       ]
      },
-     "execution_count": 51,
+     "execution_count": 43,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1716,15 +1535,15 @@
     }
    },
    "source": [
-    "Of course, `0b1001` is the binary representation of `9`."
+    "`0b1001` is the binary representation of `9`."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 52,
+   "execution_count": 44,
    "metadata": {
     "slideshow": {
-     "slide_type": "-"
+     "slide_type": "fragment"
     }
    },
    "outputs": [
@@ -1734,7 +1553,7 @@
        "9"
       ]
      },
-     "execution_count": 52,
+     "execution_count": 44,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1745,7 +1564,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 53,
+   "execution_count": 45,
    "metadata": {
     "slideshow": {
      "slide_type": "-"
@@ -1758,7 +1577,7 @@
        "9"
       ]
      },
-     "execution_count": 53,
+     "execution_count": 45,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1775,12 +1594,12 @@
     }
    },
    "source": [
-    "The **bitwise OR** `|` operator evaluates to an `int` object whose bits are set to $1$ if the corresponding bits of either *one* or *both* operands are $1$. So in the example `9 | 13` only the second bit is $0$ for both operands, which is why the expression evaluates to `\"0b1101\"`."
+    "The **bitwise OR** operator `|` evaluates to an `int` object whose bits are set to $1$ if the corresponding bits of either *one* or *both* operands are $1$. So in the example `9 | 13` only the second bit is $0$ for both operands, which is why the expression evaluates to `\"0b1101\"`."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 54,
+   "execution_count": 46,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -1793,7 +1612,7 @@
        "'0b1001 | 0b1101'"
       ]
      },
-     "execution_count": 54,
+     "execution_count": 46,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1804,7 +1623,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 55,
+   "execution_count": 47,
    "metadata": {
     "slideshow": {
      "slide_type": "-"
@@ -1817,7 +1636,7 @@
        "'0b1101'"
       ]
      },
-     "execution_count": 55,
+     "execution_count": 47,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1839,10 +1658,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 56,
+   "execution_count": 48,
    "metadata": {
     "slideshow": {
-     "slide_type": "-"
+     "slide_type": "fragment"
     }
    },
    "outputs": [
@@ -1852,7 +1671,7 @@
        "13"
       ]
      },
-     "execution_count": 56,
+     "execution_count": 48,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1863,7 +1682,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 57,
+   "execution_count": 49,
    "metadata": {
     "slideshow": {
      "slide_type": "-"
@@ -1876,7 +1695,7 @@
        "13"
       ]
      },
-     "execution_count": 57,
+     "execution_count": 49,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1893,12 +1712,12 @@
     }
    },
    "source": [
-    "The **bitwise XOR** `^` operator is a special case of the `|` operator in that it evaluates to an `int` object whose bits are set to $1$ if the corresponding bit of exactly *one* of the two operands is $1$. Colloquially, the \"X\" stands for \"exclusive\". The `^` operator must *not* be confused with the `**` operator that does exponentiation! In the example `9 ^ 13` only the third bit differs between the two operands, which is why it evaluates to `\"0b100\"` omitting the fourth bit."
+    "The **bitwise XOR** operator `^` is a special case of the `|` operator in that it evaluates to an `int` object whose bits are set to $1$ if the corresponding bit of *exactly one* of the two operands is $1$. Colloquially, the \"X\" stands for \"exclusive.\" The `^` operator must *not* be confused with the exponentiation operator `**`! In the example, `9 ^ 13`, only the third bit differs between the two operands, which is why it evaluates to `\"0b100\"` omitting the fourth bit."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 58,
+   "execution_count": 50,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -1911,7 +1730,7 @@
        "'0b1001 ^ 0b1101'"
       ]
      },
-     "execution_count": 58,
+     "execution_count": 50,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1922,7 +1741,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 59,
+   "execution_count": 51,
    "metadata": {
     "slideshow": {
      "slide_type": "-"
@@ -1935,7 +1754,7 @@
        "'0b100'"
       ]
      },
-     "execution_count": 59,
+     "execution_count": 51,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1957,10 +1776,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 60,
+   "execution_count": 52,
    "metadata": {
     "slideshow": {
-     "slide_type": "-"
+     "slide_type": "fragment"
     }
    },
    "outputs": [
@@ -1970,7 +1789,7 @@
        "4"
       ]
      },
-     "execution_count": 60,
+     "execution_count": 52,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1981,7 +1800,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 61,
+   "execution_count": 53,
    "metadata": {
     "slideshow": {
      "slide_type": "-"
@@ -1994,7 +1813,7 @@
        "4"
       ]
      },
-     "execution_count": 61,
+     "execution_count": 53,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2011,14 +1830,14 @@
     }
    },
    "source": [
-    "The **bitwise NOT** `~` operator, sometimes also called **inversion** operator, is said to simply \"flips\" the $0$s into $1$s and the $1$s into $0$s. However, it is based on the aforementioned [Two's Complement](https://en.wikipedia.org/wiki/Two%27s_complement) convention and `~x` is defined to be `~x = -(x + 1)` (cf., the [reference](https://docs.python.org/3/reference/expressions.html#unary-arithmetic-and-bitwise-operations)). The full logic behind this is considered out of scope in this book.\n",
+    "The **bitwise NOT** operator `~`, sometimes also called **inversion** operator, is said to \"flip\" the $0$s into $1$s and the $1$s into $0$s. However, it is based on the aforementioned [Two's Complement](https://en.wikipedia.org/wiki/Two%27s_complement) convention and `~x = -(x + 1)` by definition (cf., the [reference](https://docs.python.org/3/reference/expressions.html#unary-arithmetic-and-bitwise-operations)). The full logic behind this is considered out of scope in this book.\n",
     "\n",
     "We can at least verify the definition by comparing the binary representations of `8` and `-9`: They are indeed the same."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 62,
+   "execution_count": 54,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -2031,7 +1850,7 @@
        "'-0b1001'"
       ]
      },
-     "execution_count": 62,
+     "execution_count": 54,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2042,7 +1861,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 63,
+   "execution_count": 55,
    "metadata": {
     "slideshow": {
      "slide_type": "-"
@@ -2055,7 +1874,7 @@
        "'-0b1001'"
       ]
      },
-     "execution_count": 63,
+     "execution_count": 55,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2077,10 +1896,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 64,
+   "execution_count": 56,
    "metadata": {
     "slideshow": {
-     "slide_type": "-"
+     "slide_type": "fragment"
     }
    },
    "outputs": [
@@ -2090,7 +1909,7 @@
        "-1"
       ]
      },
-     "execution_count": 64,
+     "execution_count": 56,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2107,14 +1926,14 @@
     }
    },
    "source": [
-    "Lastly, the **bitwise left and right shift** operators, `<<` and `>>`, simply shift all the bits either to the left or to the right. This corresponds to multiplying or dividing the integer by powers of $2$.\n",
+    "Lastly, the **bitwise left and right shift** operators, `<<` and `>>`, shift all the bits either to the left or to the right. This corresponds to multiplying or dividing an integer by powers of $2$.\n",
     "\n",
     "When shifting left, $0$s are filled in."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 65,
+   "execution_count": 57,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -2127,7 +1946,7 @@
        "'0b1001'"
       ]
      },
-     "execution_count": 65,
+     "execution_count": 57,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2138,7 +1957,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 66,
+   "execution_count": 58,
    "metadata": {
     "slideshow": {
      "slide_type": "-"
@@ -2151,7 +1970,7 @@
        "'0b100100'"
       ]
      },
-     "execution_count": 66,
+     "execution_count": 58,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2162,7 +1981,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 67,
+   "execution_count": 59,
    "metadata": {
     "slideshow": {
      "slide_type": "-"
@@ -2175,7 +1994,7 @@
        "36"
       ]
      },
-     "execution_count": 67,
+     "execution_count": 59,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2197,7 +2016,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 68,
+   "execution_count": 60,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -2210,7 +2029,7 @@
        "'0b10'"
       ]
      },
-     "execution_count": 68,
+     "execution_count": 60,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2221,7 +2040,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 69,
+   "execution_count": 61,
    "metadata": {
     "slideshow": {
      "slide_type": "-"
@@ -2234,7 +2053,7 @@
        "2"
       ]
      },
-     "execution_count": 69,
+     "execution_count": 61,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2262,18 +2081,18 @@
     }
    },
    "source": [
-    "As we have seen above, some assumptions need to be made as to how the $0$s and $1$s in a computer's memory should be translated into numbers. This process becomes a lot more involved when we go beyond integers and model [real numbers](https://en.wikipedia.org/wiki/Real_number) (i.e., the set $\\mathbb{R}$) with possibly infinitely many digits to the right of the period like $1.23$.\n",
+    "As we have seen above, some assumptions need to be made as to how the $0$s and $1$s in a computer's memory are to be translated into numbers. This process becomes a lot more involved when we go beyond integers and model [real numbers](https://en.wikipedia.org/wiki/Real_number) (i.e., the set $\\mathbb{R}$) with possibly infinitely many digits to the right of the period like $1.23$.\n",
     "\n",
-    "The **[Institute of Electrical and Electronics Engineers](https://en.wikipedia.org/wiki/Institute_of_Electrical_and_Electronics_Engineers)** (IEEE, pronounced \"eye-tripple-E\") is one of the most important professional associations when it comes to standardizing all kinds of aspects regarding the implementation of soft- and hardware.\n",
+    "The **[Institute of Electrical and Electronics Engineers](https://en.wikipedia.org/wiki/Institute_of_Electrical_and_Electronics_Engineers)** (IEEE, pronounced \"eye-triple-E\") is one of the important professional associations when it comes to standardizing all kinds of aspects regarding the implementation of soft- and hardware.\n",
     "\n",
-    "The **[IEEE 754](https://en.wikipedia.org/wiki/IEEE_754)** standard defines the so-called **floating-point arithmetic** that is commonly used today by all major programming languages. The standard not only defines how the $0$s and $1$s are organized in memory but also, for example, how values are to be rounded, what happens in exceptional cases like divisions by zero, or what is a zero value to begin with.\n",
+    "The **[IEEE 754](https://en.wikipedia.org/wiki/IEEE_754)** standard defines the so-called **floating-point arithmetic** that is commonly used today by all major programming languages. The standard not only defines how the $0$s and $1$s are organized in memory but also, for example, how values are to be rounded, what happens in exceptional cases like divisions by zero, or what is a zero value in the first place.\n",
     "\n",
-    "In Python, the simplest way to create a `float` object is to just use a literal notation with a dot `.` in it."
+    "In Python, the simplest way to create a `float` object is to use a literal notation with a dot `.` in it."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 70,
+   "execution_count": 62,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -2281,12 +2100,12 @@
    },
    "outputs": [],
    "source": [
-    "d = 1.23"
+    "f = 1.23"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 71,
+   "execution_count": 63,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -2296,21 +2115,21 @@
     {
      "data": {
       "text/plain": [
-       "140526571166576"
+       "139927641500360"
       ]
      },
-     "execution_count": 71,
+     "execution_count": 63,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "id(d)"
+    "id(f)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 72,
+   "execution_count": 64,
    "metadata": {
     "slideshow": {
      "slide_type": "-"
@@ -2323,18 +2142,18 @@
        "float"
       ]
      },
-     "execution_count": 72,
+     "execution_count": 64,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "type(d)"
+    "type(f)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 73,
+   "execution_count": 65,
    "metadata": {
     "slideshow": {
      "slide_type": "-"
@@ -2347,13 +2166,13 @@
        "1.23"
       ]
      },
-     "execution_count": 73,
+     "execution_count": 65,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "d"
+    "f"
    ]
   },
   {
@@ -2369,7 +2188,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 74,
+   "execution_count": 66,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -2379,16 +2198,16 @@
     {
      "data": {
       "text/plain": [
-       "0.123456"
+       "0.123456789"
       ]
      },
-     "execution_count": 74,
+     "execution_count": 66,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "0.123_456"
+    "0.123_456_789"
    ]
   },
   {
@@ -2399,12 +2218,12 @@
     }
    },
    "source": [
-    "In cases where the dot `.` is unnecessary from a mathematical point of view, we either need to still end the number with it or use the [float()](https://docs.python.org/3/library/functions.html#float) built-in to cast the number as a float explicitly. [float()](https://docs.python.org/3/library/functions.html#float) can also `str` objects."
+    "In cases where the dot `.` is unnecessary from a mathematical point of view, we either need to end the number with it nevertheless or use the [float()](https://docs.python.org/3/library/functions.html#float) built-in to cast the number explicitly. [float()](https://docs.python.org/3/library/functions.html#float) can process any numeric object or a properly formatted `str` object."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 75,
+   "execution_count": 67,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -2417,18 +2236,18 @@
        "1.0"
       ]
      },
-     "execution_count": 75,
+     "execution_count": 67,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "1.  # on the contrary, 1 creates an integer object"
+    "1.  # on the contrary, 1 creates an int object"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 76,
+   "execution_count": 68,
    "metadata": {
     "slideshow": {
      "slide_type": "-"
@@ -2441,7 +2260,7 @@
        "1.0"
       ]
      },
-     "execution_count": 76,
+     "execution_count": 68,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2452,7 +2271,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 77,
+   "execution_count": 69,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -2465,7 +2284,7 @@
        "1.0"
       ]
      },
-     "execution_count": 77,
+     "execution_count": 69,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2482,15 +2301,15 @@
     }
    },
    "source": [
-    "Floats are implicitly created as a result of dividing an integer by another with the division operator `/`."
+    "`float` objects are implicitly created as the result of dividing an `int` object by another with the division operator `/`."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 78,
+   "execution_count": 70,
    "metadata": {
     "slideshow": {
-     "slide_type": "-"
+     "slide_type": "slide"
     }
    },
    "outputs": [
@@ -2500,7 +2319,7 @@
        "0.3333333333333333"
       ]
      },
-     "execution_count": 78,
+     "execution_count": 70,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2517,36 +2336,36 @@
     }
    },
    "source": [
-    "In general, if we combine `float` and `int` objects in arithmetic operations, we always end up with a `float` type."
+    "In general, if we combine `float` and `int` objects in arithmetic operations, we always end up with a `float` type: Python uses the \"broader\" representation."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 79,
+   "execution_count": 71,
    "metadata": {
     "slideshow": {
-     "slide_type": "slide"
+     "slide_type": "fragment"
     }
    },
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "3.0"
+       "42.0"
       ]
      },
-     "execution_count": 79,
+     "execution_count": 71,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "1.0 + 2"
+    "40.0 + 2"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 80,
+   "execution_count": 72,
    "metadata": {
     "slideshow": {
      "slide_type": "-"
@@ -2559,7 +2378,7 @@
        "42.0"
       ]
      },
-     "execution_count": 80,
+     "execution_count": 72,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2587,12 +2406,12 @@
     }
    },
    "source": [
-    "`float` objects may also be created with the **scientifc literal notation**: We use the symbol `e` to indicate powers of $10$, so $1.23 * 10^0$ translates into `1.23e0`."
+    "`float` objects may also be created with the **scientific literal notation**: We use the symbol `e` to indicate powers of $10$, so $1.23 * 10^0$ translates into `1.23e0`."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 81,
+   "execution_count": 73,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -2605,7 +2424,7 @@
        "1.23"
       ]
      },
-     "execution_count": 81,
+     "execution_count": 73,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2622,12 +2441,12 @@
     }
    },
    "source": [
-    "Syntactically, `e` needs a `float` object in its literal notation to its left and an `int` object to its right, both without a space. Otherwise, we get a `SyntaxError`."
+    "Syntactically, `e` needs a `float` or `int` object in its literal notation on its left and an `int` object on its right, both without a space. Otherwise, we get a `SyntaxError`."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 82,
+   "execution_count": 74,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -2636,10 +2455,10 @@
    "outputs": [
     {
      "ename": "SyntaxError",
-     "evalue": "invalid syntax (<ipython-input-82-1b5daaac0077>, line 1)",
+     "evalue": "invalid syntax (<ipython-input-74-1b5daaac0077>, line 1)",
      "output_type": "error",
      "traceback": [
-      "\u001b[0;36m  File \u001b[0;32m\"<ipython-input-82-1b5daaac0077>\"\u001b[0;36m, line \u001b[0;32m1\u001b[0m\n\u001b[0;31m    1.23 e0\u001b[0m\n\u001b[0m          ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m invalid syntax\n"
+      "\u001b[0;36m  File \u001b[0;32m\"<ipython-input-74-1b5daaac0077>\"\u001b[0;36m, line \u001b[0;32m1\u001b[0m\n\u001b[0;31m    1.23 e0\u001b[0m\n\u001b[0m          ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m invalid syntax\n"
      ]
     }
    ],
@@ -2649,7 +2468,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 83,
+   "execution_count": 75,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -2658,10 +2477,10 @@
    "outputs": [
     {
      "ename": "SyntaxError",
-     "evalue": "invalid syntax (<ipython-input-83-a236c4a30231>, line 1)",
+     "evalue": "invalid syntax (<ipython-input-75-a236c4a30231>, line 1)",
      "output_type": "error",
      "traceback": [
-      "\u001b[0;36m  File \u001b[0;32m\"<ipython-input-83-a236c4a30231>\"\u001b[0;36m, line \u001b[0;32m1\u001b[0m\n\u001b[0;31m    1.23e 0\u001b[0m\n\u001b[0m        ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m invalid syntax\n"
+      "\u001b[0;36m  File \u001b[0;32m\"<ipython-input-75-a236c4a30231>\"\u001b[0;36m, line \u001b[0;32m1\u001b[0m\n\u001b[0;31m    1.23e 0\u001b[0m\n\u001b[0m        ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m invalid syntax\n"
      ]
     }
    ],
@@ -2671,7 +2490,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 84,
+   "execution_count": 76,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -2680,10 +2499,10 @@
    "outputs": [
     {
      "ename": "SyntaxError",
-     "evalue": "invalid syntax (<ipython-input-84-05b9072d76be>, line 1)",
+     "evalue": "invalid syntax (<ipython-input-76-05b9072d76be>, line 1)",
      "output_type": "error",
      "traceback": [
-      "\u001b[0;36m  File \u001b[0;32m\"<ipython-input-84-05b9072d76be>\"\u001b[0;36m, line \u001b[0;32m1\u001b[0m\n\u001b[0;31m    1.23e0.0\u001b[0m\n\u001b[0m           ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m invalid syntax\n"
+      "\u001b[0;36m  File \u001b[0;32m\"<ipython-input-76-05b9072d76be>\"\u001b[0;36m, line \u001b[0;32m1\u001b[0m\n\u001b[0;31m    1.23e0.0\u001b[0m\n\u001b[0m           ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m invalid syntax\n"
      ]
     }
    ],
@@ -2699,12 +2518,12 @@
     }
    },
    "source": [
-    "If we leave out the number to the left, Python raises a `NameError` as it actually tries to look up a variable named `e1`."
+    "If we leave out the number to the left, Python raises a `NameError` as it unsuccessfully tries to look up a variable named `e1`."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 85,
+   "execution_count": 77,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -2718,7 +2537,7 @@
      "traceback": [
       "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
       "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-85-eff9d613b2d7>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0me1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;32m<ipython-input-77-eff9d613b2d7>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0me1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
       "\u001b[0;31mNameError\u001b[0m: name 'e1' is not defined"
      ]
     }
@@ -2735,12 +2554,12 @@
     }
    },
    "source": [
-    "So, in order to write $10^1$ in Python, we need to think of it as $1*10^1$ and write `1e1`."
+    "So, to write $10^1$ in Python, we need to think of it as $1*10^1$ and write `1e1`."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 86,
+   "execution_count": 78,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -2753,7 +2572,7 @@
        "10.0"
       ]
      },
-     "execution_count": 86,
+     "execution_count": 78,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2781,12 +2600,12 @@
     }
    },
    "source": [
-    "There are also three special values representing **\"not a number\"**, called `nan`, and positive or negative **infinity**, called `inf`, that are created by passing in the corresponding abbreviation as a `str` object to the [float()](https://docs.python.org/3/library/functions.html#float) built-in. These values could be used, for example, as the result of a mathematically undefined operation like division by zero or to model the value of a mathematical function as it goes to infinity."
+    "There are also three special values representing \"**not a number,**\" called `nan`, and positive or negative **infinity**, called `inf` or `-inf`, that are created by passing in the corresponding abbreviation as a `str` object to the [float()](https://docs.python.org/3/library/functions.html#float) built-in. These values could be used, for example, as the result of a mathematically undefined operation like division by zero or to model the value of a mathematical function as it goes to infinity."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 87,
+   "execution_count": 79,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -2799,7 +2618,7 @@
        "nan"
       ]
      },
-     "execution_count": 87,
+     "execution_count": 79,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2810,7 +2629,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 88,
+   "execution_count": 80,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -2823,7 +2642,7 @@
        "inf"
       ]
      },
-     "execution_count": 88,
+     "execution_count": 80,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2834,7 +2653,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 89,
+   "execution_count": 81,
    "metadata": {
     "slideshow": {
      "slide_type": "-"
@@ -2847,18 +2666,18 @@
        "inf"
       ]
      },
-     "execution_count": 89,
+     "execution_count": 81,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "float(\"inf\")  # by omitting the plus sign, we mean positive infinity"
+    "float(\"inf\")  # by omitting the plus sign we mean positive infinity"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 90,
+   "execution_count": 82,
    "metadata": {
     "slideshow": {
      "slide_type": "-"
@@ -2871,7 +2690,7 @@
        "-inf"
       ]
      },
-     "execution_count": 90,
+     "execution_count": 82,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2888,12 +2707,12 @@
     }
    },
    "source": [
-    "`nan` objects *never* compare equal to *anything*, not even to themselves. This happens in accordance with the IEEE 754 standard."
+    "`nan` objects *never* compare equal to *anything*, not even to themselves. This happens in accordance with the [IEEE 754](https://en.wikipedia.org/wiki/IEEE_754) standard."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 91,
+   "execution_count": 83,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -2906,7 +2725,7 @@
        "False"
       ]
      },
-     "execution_count": 91,
+     "execution_count": 83,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2923,7 +2742,243 @@
     }
    },
    "source": [
-    "On the contrary, as two values go to infinity, there is no such concept as difference any more and *everything* compares equal."
+    "On the contrary, as two values go to infinity, there is no such concept as difference and *everything* compares equal."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 84,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 84,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "float(\"inf\") == float(\"inf\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Adding `42` to `inf` makes no difference."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 85,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "inf"
+      ]
+     },
+     "execution_count": 85,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "float(\"inf\") + 42"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 86,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 86,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "float(\"inf\") + 42 == float(\"inf\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "We observe the same for multiplication ..."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 87,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "inf"
+      ]
+     },
+     "execution_count": 87,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "42 * float(\"inf\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 88,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 88,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "42 * float(\"inf\") == float(\"inf\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "... and even exponentiation!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 89,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "inf"
+      ]
+     },
+     "execution_count": 89,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "float(\"inf\") ** 42"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 90,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 90,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "float(\"inf\") ** 42 == float(\"inf\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Although absolute differences become unmeaningful as we approach infinity, signs are still respected."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 91,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "inf"
+      ]
+     },
+     "execution_count": 91,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "-42 * float(\"-inf\")"
    ]
   },
   {
@@ -2946,242 +3001,6 @@
      "output_type": "execute_result"
     }
    ],
-   "source": [
-    "float(\"inf\") == float(\"inf\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "source": [
-    "Adding `42` to `inf` makes no difference."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 93,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "fragment"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "inf"
-      ]
-     },
-     "execution_count": 93,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "float(\"inf\") + 42"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 94,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "fragment"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "True"
-      ]
-     },
-     "execution_count": 94,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "float(\"inf\") + 42 == float(\"inf\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "source": [
-    "We observe the same for multiplication ..."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 95,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "slide"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "inf"
-      ]
-     },
-     "execution_count": 95,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "42 * float(\"inf\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 96,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "fragment"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "True"
-      ]
-     },
-     "execution_count": 96,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "42 * float(\"inf\") == float(\"inf\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "source": [
-    "... and even exponentiation!"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 97,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "inf"
-      ]
-     },
-     "execution_count": 97,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "float(\"inf\") ** 42"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 98,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "True"
-      ]
-     },
-     "execution_count": 98,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "float(\"inf\") ** 42 == float(\"inf\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "source": [
-    "Although differences become unmeaningful as we approach infinity, signs are still respected."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 99,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "fragment"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "inf"
-      ]
-     },
-     "execution_count": 99,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "-42 * float(\"-inf\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 100,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "fragment"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "True"
-      ]
-     },
-     "execution_count": 100,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
    "source": [
     "-42 * float(\"-inf\") == float(\"inf\")"
    ]
@@ -3194,12 +3013,12 @@
     }
    },
    "source": [
-    "As a caveat, adding infinities of different signs is an *undefined operation* in math and results in a `nan` object."
+    "As a caveat, adding infinities of different signs is an *undefined operation* in math and results in a `nan` object. So, if we (accidentally or unknowingly) do this on a real dataset, we do *not* see any error messages, and our program may continue to run with non-meaningful results! This is an example of a piece of code **failing silently**."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 101,
+   "execution_count": 93,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -3212,7 +3031,7 @@
        "nan"
       ]
      },
-     "execution_count": 101,
+     "execution_count": 93,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3223,10 +3042,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 102,
+   "execution_count": 94,
    "metadata": {
     "slideshow": {
-     "slide_type": "fragment"
+     "slide_type": "-"
     }
    },
    "outputs": [
@@ -3236,7 +3055,7 @@
        "nan"
       ]
      },
-     "execution_count": 102,
+     "execution_count": 94,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3264,14 +3083,14 @@
     }
    },
    "source": [
-    "`float` objects are *inherently* imprecise and there is *nothing* we can do about it! In particular, arithmetic operations with two `float` objects may result in \"weird\" rounding \"errors\" if their exponents are \"too far apart\". These \"errors\" are actually stictly deterministic and occur as a consequence of the IEEE 754 standard.\n",
+    "`float` objects are *inherently* imprecise, and there is *nothing* we can do about it! In particular, arithmetic operations with two `float` objects may result in \"weird\" rounding \"errors\" that are strictly deterministic and occur in accordance with the [IEEE 754](https://en.wikipedia.org/wiki/IEEE_754) standard.\n",
     "\n",
-    "For example, let's add `1e0` to `1e15` and `1e16`, respectively. We see that in the second case, the `1e0` somehow gets \"lost\"."
+    "For example, let's add `1e0` to `1e15` and `1e16`, respectively. In the latter case, the `1e0` somehow gets \"lost\"."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 103,
+   "execution_count": 95,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -3284,7 +3103,7 @@
        "1000000000000001.0"
       ]
      },
-     "execution_count": 103,
+     "execution_count": 95,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3295,7 +3114,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 104,
+   "execution_count": 96,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -3308,7 +3127,7 @@
        "1e+16"
       ]
      },
-     "execution_count": 104,
+     "execution_count": 96,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3325,12 +3144,12 @@
     }
    },
    "source": [
-    "Of course, we may also add the integer `1` to the `float` objects in the literal `e` notation with the same outcome."
+    "Of course, we may also add the `int` object `1` to the `float` objects in the literal `e` notation with the same outcome."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 105,
+   "execution_count": 97,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -3343,7 +3162,7 @@
        "1000000000000001.0"
       ]
      },
-     "execution_count": 105,
+     "execution_count": 97,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3354,7 +3173,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 106,
+   "execution_count": 98,
    "metadata": {
     "slideshow": {
      "slide_type": "-"
@@ -3367,7 +3186,7 @@
        "1e+16"
       ]
      },
-     "execution_count": 106,
+     "execution_count": 98,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3384,12 +3203,12 @@
     }
    },
    "source": [
-    "Interactions between very large and very small `float` objects are not the only source of imprecision."
+    "Interactions between sufficiently large and small `float` objects are not the only source of imprecision."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 107,
+   "execution_count": 99,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -3402,7 +3221,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 108,
+   "execution_count": 100,
    "metadata": {
     "slideshow": {
      "slide_type": "-"
@@ -3415,7 +3234,7 @@
        "2.0000000000000004"
       ]
      },
-     "execution_count": 108,
+     "execution_count": 100,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3426,7 +3245,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 109,
+   "execution_count": 101,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -3439,7 +3258,7 @@
        "0.30000000000000004"
       ]
      },
-     "execution_count": 109,
+     "execution_count": 101,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3461,7 +3280,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 110,
+   "execution_count": 102,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -3474,7 +3293,7 @@
        "False"
       ]
      },
-     "execution_count": 110,
+     "execution_count": 102,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3485,7 +3304,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 111,
+   "execution_count": 103,
    "metadata": {
     "slideshow": {
      "slide_type": "-"
@@ -3498,7 +3317,7 @@
        "False"
       ]
      },
-     "execution_count": 111,
+     "execution_count": 103,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3515,12 +3334,12 @@
     }
    },
    "source": [
-    "A popular workaround is to check if the difference between the two numbers is smaller than a pre-defined `threshold` *sufficiently* close to `0`, for example, `1e-15`."
+    "A popular workaround is to benchmark the difference between the two numbers to be checked for equality against a pre-defined `threshold` *sufficiently* close to `0`, for example, `1e-15`."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 112,
+   "execution_count": 104,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -3533,7 +3352,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 113,
+   "execution_count": 105,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -3546,7 +3365,7 @@
        "True"
       ]
      },
-     "execution_count": 113,
+     "execution_count": 105,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3557,7 +3376,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 114,
+   "execution_count": 106,
    "metadata": {
     "slideshow": {
      "slide_type": "-"
@@ -3570,7 +3389,7 @@
        "True"
       ]
      },
-     "execution_count": 114,
+     "execution_count": 106,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3587,14 +3406,14 @@
     }
    },
    "source": [
-    "The built-in [format()](https://docs.python.org/3/library/functions.html#format) function allows us to show the **significant digits** of a `float` number, as they exist in memory, to an arbitrary precision. To exemplify it, let's view a couple of `float` objects with `50` digits. This analysis reveals that almost no `float` number is precise. After $14$ or $15$ digits \"weird\" things happen. As we will see below, the \"random\" digits do *not* actually exist in memory, which is why Python shows them to us in this \"weird\" way.\n",
+    "The built-in [format()](https://docs.python.org/3/library/functions.html#format) function allows us to show the **significant digits** of a `float` number as they exist in memory to arbitrary precision. To exemplify it, let's view a couple of `float` objects with `50` digits. This analysis reveals that almost no `float` number is precise! After $14$ or $15$ digits \"weird\" things happen. As we see further below, the \"random\" digits ending the `float` numbers do *not* \"physically\" exist in memory.\n",
     "\n",
-    "Note that the [format()](https://docs.python.org/3/library/functions.html#format) function is different from the [format()](https://docs.python.org/3/library/stdtypes.html#str.format) method on `str` objects introduced in the next chapter and that both work with the so-called [format specification mini-language](https://docs.python.org/3/library/string.html#format-specification-mini-language): `\".50f\"` is the instruction to show `50` digits of a `float` number. But let's not worry too much about these details for now."
+    "The [format()](https://docs.python.org/3/library/functions.html#format) function is different from the [format()](https://docs.python.org/3/library/stdtypes.html#str.format) method on `str` objects introduced in the next chapter and both work with the so-called [format specification mini-language](https://docs.python.org/3/library/string.html#format-specification-mini-language): `\".50f\"` is the instruction to show `50` digits of a `float` number. But let's not worry too much about these details for now."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 115,
+   "execution_count": 107,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -3607,7 +3426,7 @@
        "'0.10000000000000000555111512312578270211815834045410'"
       ]
      },
-     "execution_count": 115,
+     "execution_count": 107,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3618,7 +3437,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 116,
+   "execution_count": 108,
    "metadata": {
     "slideshow": {
      "slide_type": "-"
@@ -3631,7 +3450,7 @@
        "'0.20000000000000001110223024625156540423631668090820'"
       ]
      },
-     "execution_count": 116,
+     "execution_count": 108,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3642,7 +3461,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 117,
+   "execution_count": 109,
    "metadata": {
     "slideshow": {
      "slide_type": "-"
@@ -3655,7 +3474,7 @@
        "'0.29999999999999998889776975374843459576368331909180'"
       ]
      },
-     "execution_count": 117,
+     "execution_count": 109,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3666,7 +3485,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 118,
+   "execution_count": 110,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -3679,7 +3498,7 @@
        "'0.33333333333333331482961625624739099293947219848633'"
       ]
      },
-     "execution_count": 118,
+     "execution_count": 110,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3696,16 +3515,16 @@
     }
    },
    "source": [
-    "The [format()](https://docs.python.org/3/library/functions.html#format) function does *not* round a number in the mathematical sense! It just allows us to show an arbitrary number of the digits as stored in memory and it also does *not* change these.\n",
+    "The [format()](https://docs.python.org/3/library/functions.html#format) function does *not* round a `float` object in the mathematical sense! It just allows us to show an arbitrary number of the digits as stored in memory, and it also does *not* change these.\n",
     "\n",
-    "On the contrary, the built-in [round()](https://docs.python.org/3/library/functions.html#round) function creates a *new* numeric object that is a rounded version according to the common rules from math.\n",
+    "On the contrary, the built-in [round()](https://docs.python.org/3/library/functions.html#round) function creates a *new* numeric object that is a rounded version of the one passed in as the argument. It adheres to the common rules of math.\n",
     "\n",
-    "For example, let's \"round\" `1 / 3` to five digits. We see that the obtained value for `roughly_a_third` is also *imprecise* but different from the \"exact\" represenation of `1 / 3` above."
+    "For example, let's round `1 / 3` to five decimals. The obtained value for `roughly_a_third` is also *imprecise* but different from the \"exact\" representation of `1 / 3` above."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 119,
+   "execution_count": 111,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -3718,7 +3537,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 120,
+   "execution_count": 112,
    "metadata": {
     "slideshow": {
      "slide_type": "-"
@@ -3731,7 +3550,7 @@
        "0.33333"
       ]
      },
-     "execution_count": 120,
+     "execution_count": 112,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3742,7 +3561,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 121,
+   "execution_count": 113,
    "metadata": {
     "slideshow": {
      "slide_type": "-"
@@ -3755,7 +3574,7 @@
        "'0.33333000000000001517008740847813896834850311279297'"
       ]
      },
-     "execution_count": 121,
+     "execution_count": 113,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3772,12 +3591,12 @@
     }
    },
    "source": [
-    "Surprisingly, `0.125` and `0.25` appear to be *precise* and equality comparison works without the `threshold` workaround."
+    "Surprisingly, `0.125` and `0.25` appear to be *precise*, and equality comparison works without the `threshold` workaround: Both are powers of $2$ in disguise."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 122,
+   "execution_count": 114,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -3790,7 +3609,7 @@
        "'0.12500000000000000000000000000000000000000000000000'"
       ]
      },
-     "execution_count": 122,
+     "execution_count": 114,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3801,7 +3620,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 123,
+   "execution_count": 115,
    "metadata": {
     "slideshow": {
      "slide_type": "-"
@@ -3814,7 +3633,7 @@
        "'0.25000000000000000000000000000000000000000000000000'"
       ]
      },
-     "execution_count": 123,
+     "execution_count": 115,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3825,7 +3644,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 124,
+   "execution_count": 116,
    "metadata": {
     "slideshow": {
      "slide_type": "-"
@@ -3838,7 +3657,7 @@
        "True"
       ]
      },
-     "execution_count": 124,
+     "execution_count": 116,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3866,13 +3685,13 @@
     }
    },
    "source": [
-    "In order to understand these subtleties, we need to look at the **[binary representation of floats](https://en.wikipedia.org/wiki/Double-precision_floating-point_format)** and review the basics of the **[IEEE 754](https://en.wikipedia.org/wiki/IEEE_754)** standard. On modern machines, floats are modelled in so-called double precision with $64$ bits that are grouped as in the picture below. The first bit determines the sign ($0$ for plus, $1$ for minus), the next $11$ bits represent an $exponent$ term, and the last $52$ bits resemble the actual significant digits, the so-called $fraction$ part. The three groups are put together like so:\n",
+    "To understand these subtleties, we need to look at the **[binary representation of floats](https://en.wikipedia.org/wiki/Double-precision_floating-point_format)** and review the basics of the **[IEEE 754](https://en.wikipedia.org/wiki/IEEE_754)** standard. On modern machines, floats are modeled in so-called double precision with $64$ bits that are grouped as in the figure below. The first bit determines the sign ($0$ for plus, $1$ for minus), the next $11$ bits represent an $exponent$ term, and the last $52$ bits resemble the actual significant digits, the so-called $fraction$ part. The three groups are put together like so:\n",
     "\n",
     "$$float = (-1)^{sign} * 1.fraction * 2^{exponent-1023}$$\n",
     "\n",
-    "Observe that a $1.$ is implicitly prepended as the first digit and that both $fraction$ and $exponent$ are stored in base $2$ representation (i.e., they both are interpreted just as the integers above). As $exponent$ is consequently non-negative, between $0_{10}$ and $2047_{10}$ to be precise, the $-1023$ centers the entire $2^{exponent-1023}$ term around $1$ and allows for the period within the $1.fraction$ part to be shifted into either direction by the same amount. Floating-point numbers received their name as the period, formally called the **[radix point](https://en.wikipedia.org/wiki/Radix_point)**,\"floats\" along the significant digits. As an aside, an $exponent$ of all $0$s or all $1$s is used to model the special values `nan` or `inf`.\n",
+    "A $1.$ is implicitly prepended as the first digit, and both, $fraction$ and $exponent$, are stored in base $2$ representation (i.e., they both are interpreted like integers above). As $exponent$ is consequently non-negative, between $0_{10}$ and $2047_{10}$ to be precise, the $-1023$ centers the entire $2^{exponent-1023}$ term around $1$ and allows the period within the $1.fraction$ part be shifted into either direction by the same amount. Floating-point numbers received their name as the period, formally called the **[radix point](https://en.wikipedia.org/wiki/Radix_point)**, \"floats\" along the significant digits. As an aside, an $exponent$ of all $0$s or all $1$s is used to model the special values `nan` or `inf`.\n",
     "\n",
-    "As the standard defines the exponent part to come as a power of $2$, we now see why `0.125` is a *precise* float. It simply can be represented as a power of $2$, i.e., $0.125 = (-1)^0 * 1.0 * 2^{1020-1023} = 2^{-3} = \\frac{1}{8}$. In other words, the floating-point representation of $0.125_{10}$ is $0_2$, $1111111100_2 = 1020_{10}$, and $0_2$ for the three groups, respectively."
+    "As the standard defines the exponent part to come as a power of $2$, we now see why `0.125` is a *precise* float: It can be represented as a power of $2$, i.e., $0.125 = (-1)^0 * 1.0 * 2^{1020-1023} = 2^{-3} = \\frac{1}{8}$. In other words, the floating-point representation of $0.125_{10}$ is $0_2$, $1111111100_2 = 1020_{10}$, and $0_2$ for the three groups, respectively."
    ]
   },
   {
@@ -3894,22 +3713,20 @@
     }
    },
    "source": [
-    "The important fact for data science practitioners to understand is that mapping the *infinite* set of the real numbers $\\mathbb{R}$ to a *finite* set of bits leads to the imprecisions shown above!\n",
+    "The crucial fact for the data science practitioner to understand is that mapping the *infinite* set of the real numbers $\\mathbb{R}$ to a *finite* set of bits leads to the imprecisions shown above!\n",
     "\n",
-    "So, floats are usually good approximations of real numbers only with their first $14$ or $15$ significant digits. If more precision is required, we need to revert to other data types such as a `Decimal` or a `Fraction` as shown in the next two sections.\n",
+    "So, floats are usually good approximations of real numbers only with their first $14$ or $15$ digits. If more precision is required, we need to revert to other data types such as a `Decimal` or a `Fraction`, as shown in the next two sections.\n",
     "\n",
     "This [blog post](http://fabiensanglard.net/floating_point_visually_explained/) gives another neat and *visual* way as to how to think of floats. It also explains why floats become worse approximations of the reals as their absolute values increase.\n",
     "\n",
     "The Python [documentation](https://docs.python.org/3/tutorial/floatingpoint.html) provides another good discussion of floats and the goodness of their approximations.\n",
     "\n",
-    "If we are interested in the exact bits behind a `float` object, we use the [hex()](https://docs.python.org/3/library/stdtypes.html#float.hex) method that returns a `str` object beginning with `\"0x1.\"` followed by the $fraction$ in hexadecimal notation and the $exponent$ as an integer after subtraction of the $1023$ seperated by a `\"p\"`.\n",
-    "\n",
-    "Also, the [as_integer_ratio()](https://docs.python.org/3/library/stdtypes.html#float.as_integer_ratio) method returns the two smallest integers whose ratio best approximates a `float` object."
+    "If we are interested in the exact bits behind a `float` object, we use the [hex()](https://docs.python.org/3/library/stdtypes.html#float.hex) method that returns a `str` object beginning with `\"0x1.\"` followed by the $fraction$ in hexadecimal notation and the $exponent$ as an integer after subtraction of $1023$ and separated by a `\"p\"`."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 125,
+   "execution_count": 117,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -3922,7 +3739,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 126,
+   "execution_count": 118,
    "metadata": {
     "slideshow": {
      "slide_type": "-"
@@ -3935,18 +3752,29 @@
        "'0x1.0000000000000p-3'"
       ]
      },
-     "execution_count": 126,
+     "execution_count": 118,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "one_eighth.hex()  # the result basically says 2 ** (-3)"
+    "one_eighth.hex()  # this basically says 2 ** (-3)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Also, the [as_integer_ratio()](https://docs.python.org/3/library/stdtypes.html#float.as_integer_ratio) method returns the two smallest integers whose ratio best approximates a `float` object."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 127,
+   "execution_count": 119,
    "metadata": {
     "slideshow": {
      "slide_type": "-"
@@ -3959,7 +3787,7 @@
        "(1, 8)"
       ]
      },
-     "execution_count": 127,
+     "execution_count": 119,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3970,7 +3798,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 128,
+   "execution_count": 120,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -3983,7 +3811,7 @@
        "'0x1.555475a31a4bep-2'"
       ]
      },
-     "execution_count": 128,
+     "execution_count": 120,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3994,7 +3822,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 129,
+   "execution_count": 121,
    "metadata": {
     "slideshow": {
      "slide_type": "-"
@@ -4007,7 +3835,7 @@
        "(3002369727582815, 9007199254740992)"
       ]
      },
-     "execution_count": 129,
+     "execution_count": 121,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -4024,12 +3852,12 @@
     }
    },
    "source": [
-    "`0.0` is also a *precise* `float` number."
+    "`0.0` is also a power of $2$ and thus a *precise* `float` number."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 130,
+   "execution_count": 122,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -4042,7 +3870,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 131,
+   "execution_count": 123,
    "metadata": {
     "slideshow": {
      "slide_type": "-"
@@ -4055,7 +3883,7 @@
        "'0x0.0p+0'"
       ]
      },
-     "execution_count": 131,
+     "execution_count": 123,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -4066,7 +3894,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 132,
+   "execution_count": 124,
    "metadata": {
     "slideshow": {
      "slide_type": "-"
@@ -4079,7 +3907,7 @@
        "(0, 1)"
       ]
      },
-     "execution_count": 132,
+     "execution_count": 124,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -4096,12 +3924,12 @@
     }
    },
    "source": [
-    "As seen in [Chapter 1](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/01_elements.ipynb#%28Data%29-Type-%2F-%22Behavior%22), the [is_integer()](https://docs.python.org/3/library/stdtypes.html#float.is_integer) method tells us if a `float` can be converted into an `int` object without any loss in precision."
+    "As seen in [Chapter 1](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/01_elements.ipynb#%28Data%29-Type-%2F-%22Behavior%22), the [is_integer()](https://docs.python.org/3/library/stdtypes.html#float.is_integer) method tells us if a `float` can be casted as an `int` object without any loss in precision."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 133,
+   "execution_count": 125,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -4114,7 +3942,7 @@
        "False"
       ]
      },
-     "execution_count": 133,
+     "execution_count": 125,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -4125,7 +3953,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 134,
+   "execution_count": 126,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -4138,7 +3966,7 @@
        "True"
       ]
      },
-     "execution_count": 134,
+     "execution_count": 126,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -4157,12 +3985,12 @@
     }
    },
    "source": [
-    "As the exact implementation of floats may vary and be dependend on a particular Python installation, we can always use the [sys.float_info](https://docs.python.org/3/library/sys.html#sys.float_info) attribute in the [sys](https://docs.python.org/3/library/sys.html) module in the [standard library](https://docs.python.org/3/library/) to check the details. Usually, this is not necessary."
+    "As the exact implementation of floats may vary and be dependent on a particular Python installation, we look up the [float_info](https://docs.python.org/3/library/sys.html#sys.float_info) attribute in the [sys](https://docs.python.org/3/library/sys.html) module in the [standard library](https://docs.python.org/3/library/) to check the details. Usually, this is not necessary."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 135,
+   "execution_count": 127,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -4175,7 +4003,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 136,
+   "execution_count": 128,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -4188,7 +4016,7 @@
        "sys.float_info(max=1.7976931348623157e+308, max_exp=1024, max_10_exp=308, min=2.2250738585072014e-308, min_exp=-1021, min_10_exp=-307, dig=15, mant_dig=53, epsilon=2.220446049250313e-16, radix=2, rounds=1)"
       ]
      },
-     "execution_count": 136,
+     "execution_count": 128,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -4216,14 +4044,14 @@
     }
    },
    "source": [
-    "The [decimal](https://docs.python.org/3/library/decimal.html) module in the standard library provides a [Decimal](https://docs.python.org/3/library/decimal.html#decimal.Decimal) type that is used to represent any real number to a user-defined level of precision: \"User-defined\" does *not* mean an infinite or \"exact\" precision! The `Decimal` type merely allows us to work with a number of bits *different* from the $64$ as specified for the `float` type and also to customize the rounding rules and some other settings.\n",
+    "The [decimal](https://docs.python.org/3/library/decimal.html) module in the standard library provides a [Decimal](https://docs.python.org/3/library/decimal.html#decimal.Decimal) type that may be used to represent any real number to a user-defined level of precision: \"User-defined\" does *not* mean an infinite or \"exact\" precision! The `Decimal` type merely allows us to work with a number of bits *different* from the $64$ as specified for the `float` type and also to customize the rounding rules and some other settings.\n",
     "\n",
-    "Let's first import the `Decimal` type and also the [getcontext()](https://docs.python.org/3/library/decimal.html#decimal.getcontext) function from the [decimal](https://docs.python.org/3/library/decimal.html) module."
+    "We import the `Decimal` type and also the [getcontext()](https://docs.python.org/3/library/decimal.html#decimal.getcontext) function from the [decimal](https://docs.python.org/3/library/decimal.html) module."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 137,
+   "execution_count": 129,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -4247,7 +4075,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 138,
+   "execution_count": 130,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -4260,7 +4088,7 @@
        "Context(prec=28, rounding=ROUND_HALF_EVEN, Emin=-999999, Emax=999999, capitals=1, clamp=0, flags=[], traps=[InvalidOperation, DivisionByZero, Overflow])"
       ]
      },
-     "execution_count": 138,
+     "execution_count": 130,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -4277,12 +4105,12 @@
     }
    },
    "source": [
-    "The two simplest ways to create a `Decimal` object is to either **instantiate** it with an `int` object or a `str` object consisting of all the significant digits. In the latter case, a scientific notation is also possible."
+    "The two simplest ways to create a `Decimal` object is to either **instantiate** it with an `int` or a `str` object consisting of all the significant digits. In the latter case, the scientific notation is also possible."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 139,
+   "execution_count": 131,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -4295,7 +4123,7 @@
        "Decimal('42')"
       ]
      },
-     "execution_count": 139,
+     "execution_count": 131,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -4306,7 +4134,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 140,
+   "execution_count": 132,
    "metadata": {
     "slideshow": {
      "slide_type": "-"
@@ -4319,7 +4147,7 @@
        "Decimal('0.1')"
       ]
      },
-     "execution_count": 140,
+     "execution_count": 132,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -4330,7 +4158,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 141,
+   "execution_count": 133,
    "metadata": {
     "slideshow": {
      "slide_type": "-"
@@ -4343,7 +4171,7 @@
        "Decimal('1E+5')"
       ]
      },
-     "execution_count": 141,
+     "execution_count": 133,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -4360,12 +4188,12 @@
     }
    },
    "source": [
-    "Is is *not* a good idea to create a `Decimal` object from a `float` object. If we did so, we would create a `Decimal` object that internally used extra bits to store the \"random\" digits that are not stored for a `float` but \"seen\" by Python nevertheless."
+    "It is *not* a good idea to create a `Decimal` from a `float` object. If we did so, we would create a `Decimal` object that internally used extra bits to store the \"random\" digits that are not stored in the `float` object in the first place."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 142,
+   "execution_count": 134,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -4378,7 +4206,7 @@
        "Decimal('0.1000000000000000055511151231257827021181583404541015625')"
       ]
      },
-     "execution_count": 142,
+     "execution_count": 134,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -4395,12 +4223,12 @@
     }
    },
    "source": [
-    "With the `Decimal` type, the imprecisions in the arithmetic and equality comparisons from above are gone."
+    "With the `Decimal` type, the imprecisions in the arithmetic and equality comparisons from above go away."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 143,
+   "execution_count": 135,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -4413,7 +4241,7 @@
        "Decimal('0.3')"
       ]
      },
-     "execution_count": 143,
+     "execution_count": 135,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -4424,7 +4252,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 144,
+   "execution_count": 136,
    "metadata": {
     "slideshow": {
      "slide_type": "-"
@@ -4437,7 +4265,7 @@
        "True"
       ]
      },
-     "execution_count": 144,
+     "execution_count": 136,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -4459,7 +4287,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 145,
+   "execution_count": 137,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -4472,7 +4300,7 @@
        "Decimal('42')"
       ]
      },
-     "execution_count": 145,
+     "execution_count": 137,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -4483,7 +4311,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 146,
+   "execution_count": 138,
    "metadata": {
     "slideshow": {
      "slide_type": "-"
@@ -4493,40 +4321,40 @@
     {
      "data": {
       "text/plain": [
-       "Decimal('420')"
+       "Decimal('42')"
       ]
      },
-     "execution_count": 146,
+     "execution_count": 138,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "10 * Decimal(42)"
+    "10 * Decimal(42) / 10"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 147,
+   "execution_count": 139,
    "metadata": {
     "slideshow": {
-     "slide_type": "-"
+     "slide_type": "slide"
     }
    },
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "Decimal('1.05')"
+       "Decimal('0.1')"
       ]
      },
-     "execution_count": 147,
+     "execution_count": 139,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "Decimal(42) / 40"
+    "Decimal(1) / 10"
    ]
   },
   {
@@ -4542,7 +4370,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 148,
+   "execution_count": 140,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -4552,21 +4380,21 @@
     {
      "data": {
       "text/plain": [
-       "'1.05000000000000000000000000000000000000000000000000'"
+       "'0.10000000000000000000000000000000000000000000000000'"
       ]
      },
-     "execution_count": 148,
+     "execution_count": 140,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "format(Decimal(42) / 40, \".50f\")"
+    "format(Decimal(1) / 10, \".50f\")"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 149,
+   "execution_count": 141,
    "metadata": {
     "slideshow": {
      "slide_type": "-"
@@ -4576,16 +4404,16 @@
     {
      "data": {
       "text/plain": [
-       "'1.05000000000000004440892098500626161694526672363281'"
+       "'0.10000000000000000555111512312578270211815834045410'"
       ]
      },
-     "execution_count": 149,
+     "execution_count": 141,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "format(42 / 40, \".50f\")"
+    "format(1 / 10, \".50f\")"
    ]
   },
   {
@@ -4596,12 +4424,12 @@
     }
    },
    "source": [
-    "However, mixing `Decimal` and `float` objects raises a `TypeError`: So, Python prevents us from potentially introducing imprecisions via innocent looking arithmetic."
+    "However, mixing `Decimal` and `float` objects raises a `TypeError`: So, Python prevents us from potentially introducing imprecisions via innocent-looking arithmetic by **failing loudly**."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 150,
+   "execution_count": 142,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -4615,7 +4443,7 @@
      "traceback": [
       "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
       "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-150-a3cea145e6d1>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0;36m1.0\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0mDecimal\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m42\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;32m<ipython-input-142-a3cea145e6d1>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0;36m1.0\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0mDecimal\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m42\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
       "\u001b[0;31mTypeError\u001b[0m: unsupported operand type(s) for *: 'float' and 'decimal.Decimal'"
      ]
     }
@@ -4632,12 +4460,12 @@
     }
    },
    "source": [
-    "In order to preserve the precision even for more advanced mathematical functions, `Decimal` objects come with many methods bound on them. For example, [ln()](https://docs.python.org/3/library/decimal.html#decimal.Decimal.ln) and [log10()](https://docs.python.org/3/library/decimal.html#decimal.Decimal.log10) take the logarithm while [sqrt()](https://docs.python.org/3/library/decimal.html#decimal.Decimal.sqrt) calculates the square root. In general, the functions in the [math](https://docs.python.org/3/library/math.html) module in the [standard library](https://docs.python.org/3/library/) should only be used with `float` objects."
+    "To preserve the precision for more advanced mathematical functions, `Decimal` objects come with many methods bound on them. For example, [ln()](https://docs.python.org/3/library/decimal.html#decimal.Decimal.ln) and [log10()](https://docs.python.org/3/library/decimal.html#decimal.Decimal.log10) take the logarithm while [sqrt()](https://docs.python.org/3/library/decimal.html#decimal.Decimal.sqrt) calculates the square root. In general, the functions in the [math](https://docs.python.org/3/library/math.html) module in the [standard library](https://docs.python.org/3/library/) should only be used with `float` objects as they do *not* preserve precision."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 151,
+   "execution_count": 143,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -4650,7 +4478,7 @@
        "Decimal('2')"
       ]
      },
-     "execution_count": 151,
+     "execution_count": 143,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -4661,7 +4489,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 152,
+   "execution_count": 144,
    "metadata": {
     "slideshow": {
      "slide_type": "-"
@@ -4674,7 +4502,7 @@
        "Decimal('1.414213562373095048801688724')"
       ]
      },
-     "execution_count": 152,
+     "execution_count": 144,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -4691,14 +4519,14 @@
     }
    },
    "source": [
-    "The [sqrt()](https://docs.python.org/3/library/decimal.html#decimal.Decimal.sqrt) method is still limited with respect to the precision of the returned value as, for example, $\\sqrt{2}$ is an **[irrational number](https://en.wikipedia.org/wiki/Irrational_number)** that *cannot* be expressed with absolute precision using *any* number of bits even in theory.\n",
+    "The object returned by the [sqrt()](https://docs.python.org/3/library/decimal.html#decimal.Decimal.sqrt) method is still limited in precision: This must be so as, for example, $\\sqrt{2}$ is an **[irrational number](https://en.wikipedia.org/wiki/Irrational_number)** that *cannot* be expressed with absolute precision using *any* number of bits, even in theory.\n",
     "\n",
     "We see this as raising $\\sqrt{2}$ to the power of $2$ results in an imprecise value as before!"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 153,
+   "execution_count": 145,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -4711,7 +4539,7 @@
        "Decimal('1.999999999999999999999999999')"
       ]
      },
-     "execution_count": 153,
+     "execution_count": 145,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -4732,12 +4560,12 @@
    "source": [
     "The [quantize()](https://docs.python.org/3/library/decimal.html#decimal.Decimal.quantize) method allows us to cut off a `Decimal` number at any digit that is smaller than the set precision while adhering to the rounding rules from math.\n",
     "\n",
-    "For example, as the overall imprecise value of `two` still has a precision of $28$, we can correctly round it with respect to the first $20$ digits."
+    "For example, as the overall imprecise value of `two` still has a precision of $28$ digits, we can correctly round it to the first $20$ digits."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 154,
+   "execution_count": 146,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -4750,7 +4578,7 @@
        "Decimal('2')"
       ]
      },
-     "execution_count": 154,
+     "execution_count": 146,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -4772,10 +4600,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 155,
+   "execution_count": 147,
    "metadata": {
     "slideshow": {
-     "slide_type": "-"
+     "slide_type": "skip"
     }
    },
    "outputs": [
@@ -4785,7 +4613,7 @@
        "True"
       ]
      },
-     "execution_count": 155,
+     "execution_count": 147,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -4807,10 +4635,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 156,
+   "execution_count": 148,
    "metadata": {
     "slideshow": {
-     "slide_type": "skip"
+     "slide_type": "fragment"
     }
    },
    "outputs": [
@@ -4820,7 +4648,7 @@
        "True"
       ]
      },
-     "execution_count": 156,
+     "execution_count": 148,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -4837,12 +4665,12 @@
     }
    },
    "source": [
-    "`nan` and positive and negative `inf` exist as well and the same remarks from above apply."
+    "`nan` and positive and negative `inf` exist as well, and the same remarks from above apply."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 157,
+   "execution_count": 149,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -4855,7 +4683,7 @@
        "Decimal('NaN')"
       ]
      },
-     "execution_count": 157,
+     "execution_count": 149,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -4866,7 +4694,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 158,
+   "execution_count": 150,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -4879,7 +4707,7 @@
        "False"
       ]
      },
-     "execution_count": 158,
+     "execution_count": 150,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -4890,7 +4718,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 159,
+   "execution_count": 151,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -4903,7 +4731,7 @@
        "Decimal('Infinity')"
       ]
      },
-     "execution_count": 159,
+     "execution_count": 151,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -4914,7 +4742,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 160,
+   "execution_count": 152,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -4927,7 +4755,7 @@
        "Decimal('-Infinity')"
       ]
      },
-     "execution_count": 160,
+     "execution_count": 152,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -4938,7 +4766,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 161,
+   "execution_count": 153,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -4951,7 +4779,7 @@
        "Decimal('Infinity')"
       ]
      },
-     "execution_count": 161,
+     "execution_count": 153,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -4962,7 +4790,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 162,
+   "execution_count": 154,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -4975,7 +4803,7 @@
        "True"
       ]
      },
-     "execution_count": 162,
+     "execution_count": 154,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -4992,12 +4820,12 @@
     }
    },
    "source": [
-    "As with `float` objects, we cannot add infinities of different signs: Now get a module-specific `InvalidOperation` exception instead of a `nan` value."
+    "As with `float` objects, we cannot add infinities of different signs: Now get a module-specific `InvalidOperation` exception instead of a `nan` value. Here, **failing loudly** is a good thing as it prevents us from working with invalid results."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 163,
+   "execution_count": 155,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -5011,7 +4839,7 @@
      "traceback": [
       "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
       "\u001b[0;31mInvalidOperation\u001b[0m                          Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-163-87950ebbd6ef>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mDecimal\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"inf\"\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mDecimal\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"-inf\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;32m<ipython-input-155-87950ebbd6ef>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mDecimal\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"inf\"\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mDecimal\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"-inf\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
       "\u001b[0;31mInvalidOperation\u001b[0m: [<class 'decimal.InvalidOperation'>]"
      ]
     }
@@ -5022,7 +4850,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 164,
+   "execution_count": 156,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -5036,7 +4864,7 @@
      "traceback": [
       "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
       "\u001b[0;31mInvalidOperation\u001b[0m                          Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-164-996083f200c6>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mDecimal\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"inf\"\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0mDecimal\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"inf\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;32m<ipython-input-156-996083f200c6>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mDecimal\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"inf\"\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0mDecimal\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"inf\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
       "\u001b[0;31mInvalidOperation\u001b[0m: [<class 'decimal.InvalidOperation'>]"
      ]
     }
@@ -5053,7 +4881,7 @@
     }
    },
    "source": [
-    "For more information on decimals, see the tutorial at [PYMOTW](https://pymotw.com/3/decimal/index.html) or the official [documentation](https://docs.python.org/3/library/decimal.html)."
+    "For more information on the `Decimal` type, see the tutorial at [PYMOTW](https://pymotw.com/3/decimal/index.html) or the official [documentation](https://docs.python.org/3/library/decimal.html)."
    ]
   },
   {
@@ -5077,12 +4905,12 @@
    "source": [
     "If the numbers in an application can be expressed as [rational numbers](https://en.wikipedia.org/wiki/Rational_number) (i.e., the set $\\mathbb{Q}$), we may model them as a [Fraction](https://docs.python.org/3/library/fractions.html#fractions.Fraction) type from the [fractions](https://docs.python.org/3/library/fractions.html) module in the [standard library](https://docs.python.org/3/library/). As any fraction can always be formulated as the division of one integer by another, `Fraction` objects are inherently precise, just as `int` objects on their own. Further, we maintain the precision as long as we do not use them in a mathematical operation that could result in an irrational number (e.g., taking the square root).\n",
     "\n",
-    "Let's first import the `Fraction` type from the [fractions](https://docs.python.org/3/library/fractions.html) module."
+    "We import the `Fraction` type from the [fractions](https://docs.python.org/3/library/fractions.html) module."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 165,
+   "execution_count": 157,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -5101,12 +4929,12 @@
     }
    },
    "source": [
-    "Among others, there are two simple ways to create a `Fraction` object: We either instantiate one with two `int` objects representing the numerator and denominator or with a `str` object. In the latter case, we have again two options and use either the format \"numerator/denominator\" (i.e., *without* any spaces) or the same format as for `float` and `Decimal` objects above."
+    "Among others, there are two simple ways to create a `Fraction` object: We either instantiate one with two `int` objects representing the numerator and denominator or with a `str` object. In the latter case, we have two options again and use either the format \"numerator/denominator\" (i.e., *without* any spaces) or the same format as for `float` and `Decimal` objects above."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 166,
+   "execution_count": 158,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -5119,7 +4947,7 @@
        "Fraction(1, 3)"
       ]
      },
-     "execution_count": 166,
+     "execution_count": 158,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -5130,7 +4958,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 167,
+   "execution_count": 159,
    "metadata": {
     "slideshow": {
      "slide_type": "-"
@@ -5143,7 +4971,7 @@
        "Fraction(1, 3)"
       ]
      },
-     "execution_count": 167,
+     "execution_count": 159,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -5154,7 +4982,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 168,
+   "execution_count": 160,
    "metadata": {
     "slideshow": {
      "slide_type": "-"
@@ -5167,7 +4995,7 @@
        "Fraction(3333333333, 10000000000)"
       ]
      },
-     "execution_count": 168,
+     "execution_count": 160,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -5178,7 +5006,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 169,
+   "execution_count": 161,
    "metadata": {
     "slideshow": {
      "slide_type": "-"
@@ -5191,7 +5019,7 @@
        "Fraction(3333333333, 10000000000)"
       ]
      },
-     "execution_count": 169,
+     "execution_count": 161,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -5208,12 +5036,12 @@
     }
    },
    "source": [
-    "Only the lowest common denominator version is maintained after creation: For example, $\\frac{3}{2}$ and $\\frac{6}{4}$ are the same and both become `Fraction(3, 2)`."
+    "Only the lowest common denominator version is maintained after creation: For example, $\\frac{3}{2}$ and $\\frac{6}{4}$ are the same, and both become `Fraction(3, 2)`."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 170,
+   "execution_count": 162,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -5226,7 +5054,7 @@
        "Fraction(3, 2)"
       ]
      },
-     "execution_count": 170,
+     "execution_count": 162,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -5237,7 +5065,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 171,
+   "execution_count": 163,
    "metadata": {
     "slideshow": {
      "slide_type": "-"
@@ -5250,7 +5078,7 @@
        "Fraction(3, 2)"
       ]
      },
-     "execution_count": 171,
+     "execution_count": 163,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -5272,7 +5100,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 172,
+   "execution_count": 164,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -5285,7 +5113,7 @@
        "Fraction(1, 10)"
       ]
      },
-     "execution_count": 172,
+     "execution_count": 164,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -5302,12 +5130,12 @@
     }
    },
    "source": [
-    "`float` objects may *syntactically* be casted as `Fraction` objects as well. However, then we create a `Fraction` object that precisely remembers the `float` object's imprecision: A *bad* idea!"
+    "`float` objects may *syntactically* be cast as `Fraction` objects as well. However, then we create a `Fraction` object that precisely remembers the `float` object's imprecision: A *bad* idea!"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 173,
+   "execution_count": 165,
    "metadata": {
     "slideshow": {
      "slide_type": "-"
@@ -5320,7 +5148,7 @@
        "Fraction(3602879701896397, 36028797018963968)"
       ]
      },
-     "execution_count": 173,
+     "execution_count": 165,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -5337,12 +5165,12 @@
     }
    },
    "source": [
-    "`Fraction` objects follow the arithmetic rules from middle school and may be mixed with `int` objects *without* any loss of precision."
+    "`Fraction` objects follow the arithmetic rules from middle school and may be mixed with `int` objects *without* any loss of precision. The result is always a *new* `Fraction` object."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 174,
+   "execution_count": 166,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -5355,7 +5183,7 @@
        "Fraction(7, 4)"
       ]
      },
-     "execution_count": 174,
+     "execution_count": 166,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -5366,7 +5194,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 175,
+   "execution_count": 167,
    "metadata": {
     "slideshow": {
      "slide_type": "-"
@@ -5379,7 +5207,7 @@
        "Fraction(1, 2)"
       ]
      },
-     "execution_count": 175,
+     "execution_count": 167,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -5390,7 +5218,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 176,
+   "execution_count": 168,
    "metadata": {
     "slideshow": {
      "slide_type": "-"
@@ -5403,7 +5231,7 @@
        "Fraction(1, 1)"
       ]
      },
-     "execution_count": 176,
+     "execution_count": 168,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -5414,7 +5242,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 177,
+   "execution_count": 169,
    "metadata": {
     "slideshow": {
      "slide_type": "-"
@@ -5427,7 +5255,7 @@
        "Fraction(1, 1)"
       ]
      },
-     "execution_count": 177,
+     "execution_count": 169,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -5444,12 +5272,12 @@
     }
    },
    "source": [
-    "`Fraction` and `float` objects may also be mixed syntactically. However, then the results may exhibit imprecision again, even if we do not see them at first sight!"
+    "`Fraction` and `float` objects may also be mixed *syntactically*. However, then the results may exhibit imprecision again, even if we do not see them at first sight! This is another example of code **failing silently**."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 178,
+   "execution_count": 170,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -5462,7 +5290,7 @@
        "0.1"
       ]
      },
-     "execution_count": 178,
+     "execution_count": 170,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -5473,10 +5301,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 179,
+   "execution_count": 171,
    "metadata": {
     "slideshow": {
-     "slide_type": "-"
+     "slide_type": "fragment"
     }
    },
    "outputs": [
@@ -5486,7 +5314,7 @@
        "'0.10000000000000000555111512312578270211815834045410'"
       ]
      },
-     "execution_count": 179,
+     "execution_count": 171,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -5525,13 +5353,13 @@
     }
    },
    "source": [
-    "Some mathematical equations cannot be solved if the solution has to be in the set of the real numbers $\\mathbb{R}$: For example: $x^2 = -1 \\implies x = \\sqrt{-1}$ and the square root is not defined for negative numbers. To mitigate this, mathematicians introduced the concept of an [imaginary number](https://en.wikipedia.org/wiki/Imaginary_number) $\\textbf{i}$ that is *defined* as $\\textbf{i} = \\sqrt{-1}$ or often times as the solution to the equation $\\textbf{i}^2 = -1$. So, the solution to $x = \\sqrt{-1}$ then becomes $x = \\textbf{i}$.\n",
+    "Some mathematical equations cannot be solved if the solution has to be in the set of the real numbers $\\mathbb{R}$. For example, $x^2 = -1$ can be rearranged into $x = \\sqrt{-1}$, but the square root is not defined for negative numbers. To mitigate this, mathematicians introduced the concept of an [imaginary number](https://en.wikipedia.org/wiki/Imaginary_number) $\\textbf{i}$ that is *defined* as $\\textbf{i} = \\sqrt{-1}$ or often as the solution to the equation $\\textbf{i}^2 = -1$. So, the solution to $x = \\sqrt{-1}$ then becomes $x = \\textbf{i}$.\n",
     "\n",
-    "If we generalize the example equation into $(mx-n)^2 = -1 \\implies x = \\frac{1}{m}(\\sqrt{-1} + n)$ where $m$ and $n$ are constants chosen from the reals $\\mathbb{R}$, then the solution to the equation comes in the form $x = a + b\\textbf{i}$, the sum of a real number and an imaginary number, with $a=\\frac{n}{m}$ and $b = \\frac{1}{m}$ also from the reals $\\mathbb{R}$.\n",
+    "If we generalize the example equation into $(mx-n)^2 = -1 \\implies x = \\frac{1}{m}(\\sqrt{-1} + n)$ where $m$ and $n$ are constants chosen from the reals $\\mathbb{R}$, then the solution to the equation comes in the form $x = a + b\\textbf{i}$, the sum of a real number and an imaginary number, with $a=\\frac{n}{m}$ and $b = \\frac{1}{m}$.\n",
     "\n",
-    "Such compound numbers are called a **[complex numbers](https://en.wikipedia.org/wiki/Complex_number)** and the set of all such numbers is commonly denoted by $\\mathbb{C}$. The reals $\\mathbb{R}$ are a strict subset of $\\mathbb{C}$ with $b=0$. $a$ is referred to as the **real part** and $b$ as the **imaginary part** of the complex number.\n",
+    "Such \"compound\" numbers are called **[complex numbers](https://en.wikipedia.org/wiki/Complex_number)**, and the set of all such numbers is commonly denoted by $\\mathbb{C}$. The reals $\\mathbb{R}$ are a strict subset of $\\mathbb{C}$ with $b=0$. Further, $a$ is referred to as the **real part** and $b$ as the **imaginary part** of the complex number.\n",
     "\n",
-    "Complex numbers are often visualized in a plane like below where the real part is depicted on the x-axis and the imaginary part is depicted on the y-axis."
+    "Complex numbers are often visualized in a plane like below, where the real part is depicted on the x-axis and the imaginary part on the y-axis."
    ]
   },
   {
@@ -5553,14 +5381,14 @@
     }
    },
    "source": [
-    "Complex numbers are part of core Python. The simplest way to create one is to write an arithmetic expression with the literal `j` notation for an imaginary number. A `j` notation is commonly used in many engineering disciplines instead of the symbol $\\textbf{i}$ from math as $I$ in engineering more often than not means [electric current](https://en.wikipedia.org/wiki/Electric_current).\n",
+    "`complex` numbers are part of core Python. The simplest way to create one is to write an arithmetic expression with the literal `j` notation for $\\textbf{i}$. The `j` is commonly used in many engineering disciplines instead of the symbol $\\textbf{i}$ from math as $I$ in engineering more often than not means [electric current](https://en.wikipedia.org/wiki/Electric_current).\n",
     "\n",
-    "For example, the answer to $x^2 = -1$ can be written in Python as `1j` like below. This creates a `complex` object with value `1j`. The same syntactic rules apply as with the above `e` notation: No spaces are allowed between the number and the `j` and the number may be any `float` literal."
+    "For example, the answer to $x^2 = -1$ can be written in Python as `1j` like below. This creates a `complex` object with value `1j`. The same syntactic rules apply as with the above `e` notation: No spaces are allowed between the number and the `j`, and the number may be any `int` or `float` literal."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 180,
+   "execution_count": 172,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -5568,12 +5396,12 @@
    },
    "outputs": [],
    "source": [
-    "sqrt_of_neg_one = 1j"
+    "x = 1j"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 181,
+   "execution_count": 173,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -5583,21 +5411,21 @@
     {
      "data": {
       "text/plain": [
-       "140526569943600"
+       "139927632267248"
       ]
      },
-     "execution_count": 181,
+     "execution_count": 173,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "id(sqrt_of_neg_one)"
+    "id(x)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 182,
+   "execution_count": 174,
    "metadata": {
     "slideshow": {
      "slide_type": "-"
@@ -5610,18 +5438,18 @@
        "complex"
       ]
      },
-     "execution_count": 182,
+     "execution_count": 174,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "type(sqrt_of_neg_one)"
+    "type(x)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 183,
+   "execution_count": 175,
    "metadata": {
     "slideshow": {
      "slide_type": "-"
@@ -5634,13 +5462,13 @@
        "1j"
       ]
      },
-     "execution_count": 183,
+     "execution_count": 175,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "sqrt_of_neg_one"
+    "x"
    ]
   },
   {
@@ -5651,12 +5479,12 @@
     }
    },
    "source": [
-    "To verify that it solves the equation, let's just raise it to the power of $2$: the answer is $-1 + 0\\textbf{i} = -1$."
+    "To verify that it solves the equation, let's raise it to the power of $2$."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 184,
+   "execution_count": 176,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -5669,13 +5497,13 @@
        "True"
       ]
      },
-     "execution_count": 184,
+     "execution_count": 176,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "sqrt_of_neg_one ** 2 == -1"
+    "x ** 2 == -1"
    ]
   },
   {
@@ -5686,12 +5514,12 @@
     }
    },
    "source": [
-    "Often, we will write an expression of the form $a + b\\textbf{i}$."
+    "Often, we write an expression of the form $a + b\\textbf{i}$."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 185,
+   "execution_count": 177,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -5704,7 +5532,7 @@
        "(2+0.5j)"
       ]
      },
-     "execution_count": 185,
+     "execution_count": 177,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -5726,7 +5554,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 186,
+   "execution_count": 178,
    "metadata": {
     "slideshow": {
      "slide_type": "-"
@@ -5739,7 +5567,7 @@
        "(2+0.5j)"
       ]
      },
-     "execution_count": 186,
+     "execution_count": 178,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -5750,7 +5578,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 187,
+   "execution_count": 179,
    "metadata": {
     "slideshow": {
      "slide_type": "-"
@@ -5763,7 +5591,7 @@
        "(2+0j)"
       ]
      },
-     "execution_count": 187,
+     "execution_count": 179,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -5774,7 +5602,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 188,
+   "execution_count": 180,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -5787,7 +5615,7 @@
        "(2+0.5j)"
       ]
      },
-     "execution_count": 188,
+     "execution_count": 180,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -5798,7 +5626,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 189,
+   "execution_count": 181,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -5811,7 +5639,7 @@
        "(2+0.5j)"
       ]
      },
-     "execution_count": 189,
+     "execution_count": 181,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -5828,48 +5656,12 @@
     }
    },
    "source": [
-    "Spaces in the text string version lead to a `ValueError`."
+    "Arithmetic expressions work with `complex` numbers. They may be mixed with the other numeric types, and the result is always a `complex` number."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 190,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "outputs": [
-    {
-     "ename": "ValueError",
-     "evalue": "complex() arg is a malformed string",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-190-e3de6c558ade>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mcomplex\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"2 + 0.5j\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
-      "\u001b[0;31mValueError\u001b[0m: complex() arg is a malformed string"
-     ]
-    }
-   ],
-   "source": [
-    "complex(\"2 + 0.5j\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "source": [
-    "The basic arithmetic rules work for `complex` numbers. They may be mixed with the other numeric types and the result is always a `complex` number again."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 191,
+   "execution_count": 182,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -5877,13 +5669,13 @@
    },
    "outputs": [],
    "source": [
-    "e = 1 + 2j\n",
-    "f = 3 + 4j"
+    "c1 = 1 + 2j\n",
+    "c2 = 3 + 4j"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 192,
+   "execution_count": 183,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -5896,18 +5688,18 @@
        "(4+6j)"
       ]
      },
-     "execution_count": 192,
+     "execution_count": 183,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "e + f"
+    "c1 + c2"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 193,
+   "execution_count": 184,
    "metadata": {
     "slideshow": {
      "slide_type": "-"
@@ -5917,24 +5709,24 @@
     {
      "data": {
       "text/plain": [
-       "(2+2j)"
+       "(-2-2j)"
       ]
      },
-     "execution_count": 193,
+     "execution_count": 184,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "f - e"
+    "c1 - c2"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 194,
+   "execution_count": 185,
    "metadata": {
     "slideshow": {
-     "slide_type": "-"
+     "slide_type": "skip"
     }
    },
    "outputs": [
@@ -5944,21 +5736,21 @@
        "(40+2j)"
       ]
      },
-     "execution_count": 194,
+     "execution_count": 185,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "39 + e"
+    "c1 + 39"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 195,
+   "execution_count": 186,
    "metadata": {
     "slideshow": {
-     "slide_type": "-"
+     "slide_type": "skip"
     }
    },
    "outputs": [
@@ -5968,21 +5760,21 @@
        "-4j"
       ]
      },
-     "execution_count": 195,
+     "execution_count": 186,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "3.0 - f"
+    "3.0 - c2"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 196,
+   "execution_count": 187,
    "metadata": {
     "slideshow": {
-     "slide_type": "slide"
+     "slide_type": "skip"
     }
    },
    "outputs": [
@@ -5992,21 +5784,21 @@
        "(5+10j)"
       ]
      },
-     "execution_count": 196,
+     "execution_count": 187,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "5 * e"
+    "5 * c1"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 197,
+   "execution_count": 188,
    "metadata": {
     "slideshow": {
-     "slide_type": "-"
+     "slide_type": "skip"
     }
    },
    "outputs": [
@@ -6016,21 +5808,21 @@
        "(0.5+0.6666666666666666j)"
       ]
      },
-     "execution_count": 197,
+     "execution_count": 188,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "f / 6"
+    "c2 / 6"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 198,
+   "execution_count": 189,
    "metadata": {
     "slideshow": {
-     "slide_type": "-"
+     "slide_type": "fragment"
     }
    },
    "outputs": [
@@ -6040,18 +5832,18 @@
        "(-5+10j)"
       ]
      },
-     "execution_count": 198,
+     "execution_count": 189,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "e * f"
+    "c1 * c2"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 199,
+   "execution_count": 190,
    "metadata": {
     "slideshow": {
      "slide_type": "-"
@@ -6061,16 +5853,16 @@
     {
      "data": {
       "text/plain": [
-       "(2.2-0.4j)"
+       "(0.44+0.08j)"
       ]
      },
-     "execution_count": 199,
+     "execution_count": 190,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "f / e"
+    "c1 / c2"
    ]
   },
   {
@@ -6081,12 +5873,12 @@
     }
    },
    "source": [
-    "The absolute value of a `complex` number is commonly defined as the Pythagoram Theorem where $\\lVert x \\rVert = \\sqrt{a^2 + b^2}$ and the [abs()](https://docs.python.org/3/library/functions.html#abs) built-in function works implements that in Python."
+    "The absolute value of a `complex` number $x$ is defined with the Pythagorean Theorem where $\\lVert x \\rVert = \\sqrt{a^2 + b^2}$ and $a$ and $b$ are the real and imaginary parts. The [abs()](https://docs.python.org/3/library/functions.html#abs) built-in function implements that in Python."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 200,
+   "execution_count": 191,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -6099,7 +5891,7 @@
        "5.0"
       ]
      },
-     "execution_count": 200,
+     "execution_count": 191,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -6121,7 +5913,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 201,
+   "execution_count": 192,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -6134,18 +5926,18 @@
        "1.0"
       ]
      },
-     "execution_count": 201,
+     "execution_count": 192,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "e.real"
+    "c1.real"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 202,
+   "execution_count": 193,
    "metadata": {
     "slideshow": {
      "slide_type": "-"
@@ -6158,13 +5950,13 @@
        "2.0"
       ]
      },
-     "execution_count": 202,
+     "execution_count": 193,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "e.imag"
+    "c1.imag"
    ]
   },
   {
@@ -6180,10 +5972,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 203,
+   "execution_count": 194,
    "metadata": {
     "slideshow": {
-     "slide_type": "-"
+     "slide_type": "fragment"
     }
    },
    "outputs": [
@@ -6193,13 +5985,13 @@
        "(1-2j)"
       ]
      },
-     "execution_count": 203,
+     "execution_count": 194,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "e.conjugate()"
+    "c1.conjugate()"
    ]
   },
   {
@@ -6232,14 +6024,19 @@
     }
    },
    "source": [
-    "There are three numeric types that are part of core Python:\n",
-    "- `int`: perfect model for whole numbers (i.e., the set $\\mathbb{Z}$); inherently precise\n",
-    "- `float`: standard to approximate real numbers (i.e., the set $\\mathbb{R}$); inherently imprecise\n",
-    "- `complex`: layer on top of the `float` type\n",
+    "There exist three numeric types in core Python:\n",
+    "- `int`: a perfect model for whole numbers (i.e., the set $\\mathbb{Z}$); inherently precise\n",
+    "- `float`: the \"gold\" standard to approximate real numbers (i.e., the set $\\mathbb{R}$); inherently imprecise\n",
+    "- `complex`: layer on top of the `float` type; therefore inherently imprecise\n",
     "\n",
     "Furthermore, the [standard library](https://docs.python.org/3/library/) adds two more types that can be used as substitutes for `float` objects:\n",
     "- `Decimal`: similar to `float` but allows customizing the precision; still inherently imprecise\n",
-    "- `Fraction`: perfect model for rational numbers (i.e., the set $\\mathbb{Q}$)"
+    "- `Fraction`: a perfect model for rational numbers (i.e., the set $\\mathbb{Q}$); built on top of the `int` type and therefore inherently precise\n",
+    "\n",
+    "The important takeaways for the data science practicioner are:\n",
+    "\n",
+    "1. **Do not mix** precise and imprecise data types, and\n",
+    "2. actively expect `nan` results when working with `float` numbers as there are no **loud failures**."
    ]
   }
  ],

From 76350cf895cfe572329ec0a2bf68ea643f46ba5a Mon Sep 17 00:00:00 2001
From: Alexander Hess <alexander@webartifex.biz>
Date: Mon, 14 Oct 2019 18:27:42 +0200
Subject: [PATCH 032/242] Add section on the numeric tower

---
 05_numbers.ipynb   | 913 ++++++++++++++++++++++++++++++++++++++++++++-
 static/numbers.png | Bin 0 -> 90958 bytes
 2 files changed, 903 insertions(+), 10 deletions(-)
 create mode 100644 static/numbers.png

diff --git a/05_numbers.ipynb b/05_numbers.ipynb
index ccb1412..df7598c 100644
--- a/05_numbers.ipynb
+++ b/05_numbers.ipynb
@@ -92,7 +92,7 @@
     {
      "data": {
       "text/plain": [
-       "139927641322704"
+       "140218620396752"
       ]
      },
      "execution_count": 2,
@@ -2115,7 +2115,7 @@
     {
      "data": {
       "text/plain": [
-       "139927641500360"
+       "140218620574432"
       ]
      },
      "execution_count": 63,
@@ -3985,7 +3985,7 @@
     }
    },
    "source": [
-    "As the exact implementation of floats may vary and be dependent on a particular Python installation, we look up the [float_info](https://docs.python.org/3/library/sys.html#sys.float_info) attribute in the [sys](https://docs.python.org/3/library/sys.html) module in the [standard library](https://docs.python.org/3/library/) to check the details. Usually, this is not necessary."
+    "As the exact implementation of floats may vary and be dependent on a particular Python installation, we look up the [float_info](https://docs.python.org/3/library/sys.html#sys.float_info) attribute in the [sys](https://docs.python.org/3/library/sys.html) module in the [standard library](https://docs.python.org/3/library/index.html) to check the details. Usually, this is not necessary."
    ]
   },
   {
@@ -4044,7 +4044,7 @@
     }
    },
    "source": [
-    "The [decimal](https://docs.python.org/3/library/decimal.html) module in the standard library provides a [Decimal](https://docs.python.org/3/library/decimal.html#decimal.Decimal) type that may be used to represent any real number to a user-defined level of precision: \"User-defined\" does *not* mean an infinite or \"exact\" precision! The `Decimal` type merely allows us to work with a number of bits *different* from the $64$ as specified for the `float` type and also to customize the rounding rules and some other settings.\n",
+    "The [decimal](https://docs.python.org/3/library/decimal.html) module in the [standard library](https://docs.python.org/3/library/index.html) provides a [Decimal](https://docs.python.org/3/library/decimal.html#decimal.Decimal) type that may be used to represent any real number to a user-defined level of precision: \"User-defined\" does *not* mean an infinite or \"exact\" precision! The `Decimal` type merely allows us to work with a number of bits *different* from the $64$ as specified for the `float` type and also to customize the rounding rules and some other settings.\n",
     "\n",
     "We import the `Decimal` type and also the [getcontext()](https://docs.python.org/3/library/decimal.html#decimal.getcontext) function from the [decimal](https://docs.python.org/3/library/decimal.html) module."
    ]
@@ -4460,7 +4460,7 @@
     }
    },
    "source": [
-    "To preserve the precision for more advanced mathematical functions, `Decimal` objects come with many methods bound on them. For example, [ln()](https://docs.python.org/3/library/decimal.html#decimal.Decimal.ln) and [log10()](https://docs.python.org/3/library/decimal.html#decimal.Decimal.log10) take the logarithm while [sqrt()](https://docs.python.org/3/library/decimal.html#decimal.Decimal.sqrt) calculates the square root. In general, the functions in the [math](https://docs.python.org/3/library/math.html) module in the [standard library](https://docs.python.org/3/library/) should only be used with `float` objects as they do *not* preserve precision."
+    "To preserve the precision for more advanced mathematical functions, `Decimal` objects come with many methods bound on them. For example, [ln()](https://docs.python.org/3/library/decimal.html#decimal.Decimal.ln) and [log10()](https://docs.python.org/3/library/decimal.html#decimal.Decimal.log10) take the logarithm while [sqrt()](https://docs.python.org/3/library/decimal.html#decimal.Decimal.sqrt) calculates the square root. In general, the functions in the [math](https://docs.python.org/3/library/math.html) module in the [standard library](https://docs.python.org/3/library/index.html) should only be used with `float` objects as they do *not* preserve precision."
    ]
   },
   {
@@ -4903,7 +4903,7 @@
     }
    },
    "source": [
-    "If the numbers in an application can be expressed as [rational numbers](https://en.wikipedia.org/wiki/Rational_number) (i.e., the set $\\mathbb{Q}$), we may model them as a [Fraction](https://docs.python.org/3/library/fractions.html#fractions.Fraction) type from the [fractions](https://docs.python.org/3/library/fractions.html) module in the [standard library](https://docs.python.org/3/library/). As any fraction can always be formulated as the division of one integer by another, `Fraction` objects are inherently precise, just as `int` objects on their own. Further, we maintain the precision as long as we do not use them in a mathematical operation that could result in an irrational number (e.g., taking the square root).\n",
+    "If the numbers in an application can be expressed as [rational numbers](https://en.wikipedia.org/wiki/Rational_number) (i.e., the set $\\mathbb{Q}$), we may model them as a [Fraction](https://docs.python.org/3/library/fractions.html#fractions.Fraction) type from the [fractions](https://docs.python.org/3/library/fractions.html) module in the [standard library](https://docs.python.org/3/library/index.html). As any fraction can always be formulated as the division of one integer by another, `Fraction` objects are inherently precise, just as `int` objects on their own. Further, we maintain the precision as long as we do not use them in a mathematical operation that could result in an irrational number (e.g., taking the square root).\n",
     "\n",
     "We import the `Fraction` type from the [fractions](https://docs.python.org/3/library/fractions.html) module."
    ]
@@ -5411,7 +5411,7 @@
     {
      "data": {
       "text/plain": [
-       "139927632267248"
+       "140218611341648"
       ]
      },
      "execution_count": 173,
@@ -6002,7 +6002,898 @@
     }
    },
    "source": [
-    "The [cmath](https://docs.python.org/3/library/cmath.html) module in the [standard library](https://docs.python.org/3/library/) implements many of the functions from the [math](https://docs.python.org/3/library/math.html) module such that they work with complex numbers."
+    "The [cmath](https://docs.python.org/3/library/cmath.html) module in the [standard library](https://docs.python.org/3/library/index.html) implements many of the functions from the [math](https://docs.python.org/3/library/math.html) module such that they work with complex numbers."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "## The Numeric Tower"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Analogous to the discussion of containers and iterables in [Chapter 4](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/04_iteration.ipynb#Containers-vs.-Iterables), we contrast the *concrete* numeric data types in this chapter with the *abstract* ideas behind [numbers in mathematics](https://en.wikipedia.org/wiki/Number).\n",
+    "\n",
+    "The figure below summarizes five *major* sets of [numbers in mathematics](https://en.wikipedia.org/wiki/Number) as we know them from high school:\n",
+    "\n",
+    "- $\\mathbb{N}$: [Natural numbers](https://en.wikipedia.org/wiki/Natural_number) are all non-negative count numbers, e.g., $0, 1, 2, ...$\n",
+    "- $\\mathbb{Z}$: [Integers](https://en.wikipedia.org/wiki/Integer) are all numbers *without* a fractional component, e.g., $-1, 0, 1, ...$\n",
+    "- $\\mathbb{Q}$: [Rational numbers](https://en.wikipedia.org/wiki/Rational_number) are all numbers that can be expressed as a quotient of two integers, e.g., $-\\frac{1}{2}, 0, \\frac{1}{2}, ...$\n",
+    "- $\\mathbb{R}$: [Real numbers](https://en.wikipedia.org/wiki/Real_number) are all numbers that can be represented as a distance along a line (negative means \"reversed\"), e.g., $\\sqrt{2}, \\pi, \\text{e}, ...$\n",
+    "- $\\mathbb{C}$: [Complex numbers](https://en.wikipedia.org/wiki/Complex_number) are all numbers of the form $a + b\\textbf{i}$ where $a$ and $b$ are real numbers and $\\textbf{i}$ is the [imaginary number](https://en.wikipedia.org/wiki/Imaginary_number), e.g., $0, \\textbf{i}, 1 + \\textbf{i}, ...$\n",
+    "\n",
+    "In the listed order, the five sets are perfect subsets and $\\mathbb{C}$ is the largest set (to be precise, all sets are infinite, but they still have a different number of elements)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "<img src=\"static/numbers.png\" width=\"75%\" align=\"center\">"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "The *concrete* data types introduced in this chapter are all *imperfect* models of *abstract* mathematical ideas.\n",
+    "\n",
+    "The `int` and `Fraction` types are the models \"closest\" to the idea they implement: Whereas $\\mathbb{Z}$ and $\\mathbb{Q}$ are, by definition, infinite, every computer runs out of bits when representing sufficiently large integers or fractions with a sufficiently large number of decimals. However, within a system-dependent minimum and maximum integer range, we can model an integer or fraction without any loss in precision.\n",
+    "\n",
+    "For the other types, in particular, the `float` type, the implications of their imprecision are discussed in detail above.\n",
+    "\n",
+    "The abstract concepts behind the four outer-most mathematical sets are part of Python since [PEP 3141](https://www.python.org/dev/peps/pep-3141/) in 2007. The [numbers](https://docs.python.org/3/library/numbers.html) module in the [standard library](https://docs.python.org/3/library/index.html) defines what Pythonistas call the **numeric tower**, a collection of five **[abstract data types](https://en.wikipedia.org/wiki/Abstract_data_type)**, or **abstract base classes** as they are called in Python slang:\n",
+    "\n",
+    "- `numbers.Number`: \"any number\" (cf., [documentation](https://docs.python.org/3/library/numbers.html#numbers.Number))\n",
+    "- `numbers.Complex`: \"all complex numbers\" (cf., [documentation](https://docs.python.org/3/library/numbers.html#numbers.Complex))\n",
+    "- `numbers.Real`: \"all real numbers\" (cf., [documentation](https://docs.python.org/3/library/numbers.html#numbers.Real))\n",
+    "- `numbers.Rational`: \"all rational numbers\" (cf., [documentation](https://docs.python.org/3/library/numbers.html#numbers.Rational))\n",
+    "- `numbers.Integral`: \"all integers\" (cf., [documentation](https://docs.python.org/3/library/numbers.html#numbers.Integral))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 195,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "import numbers"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 196,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['ABCMeta',\n",
+       " 'Complex',\n",
+       " 'Integral',\n",
+       " 'Number',\n",
+       " 'Rational',\n",
+       " 'Real',\n",
+       " '__all__',\n",
+       " '__builtins__',\n",
+       " '__cached__',\n",
+       " '__doc__',\n",
+       " '__file__',\n",
+       " '__loader__',\n",
+       " '__name__',\n",
+       " '__package__',\n",
+       " '__spec__',\n",
+       " 'abstractmethod']"
+      ]
+     },
+     "execution_count": 196,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "dir(numbers)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "As a reminder, the built-in [help()](https://docs.python.org/3/library/functions.html#help) function is always our friend.\n",
+    "\n",
+    "The abstract types' docstrings are unsurprisingly similar to the corresponding concrete types' docstrings (for now, let's not worry about the dunder-style names in the docstrings).\n",
+    "\n",
+    "For example, both `numbers.Complex` and `complex` list the `imag` and `real` attributes."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 197,
+   "metadata": {
+    "scrolled": true,
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Help on class Complex in module numbers:\n",
+      "\n",
+      "class Complex(Number)\n",
+      " |  Complex defines the operations that work on the builtin complex type.\n",
+      " |  \n",
+      " |  In short, those are: a conversion to complex, .real, .imag, +, -,\n",
+      " |  *, /, abs(), .conjugate, ==, and !=.\n",
+      " |  \n",
+      " |  If it is given heterogeneous arguments, and doesn't have special\n",
+      " |  knowledge about them, it should fall back to the builtin complex\n",
+      " |  type as described below.\n",
+      " |  \n",
+      " |  Method resolution order:\n",
+      " |      Complex\n",
+      " |      Number\n",
+      " |      builtins.object\n",
+      " |  \n",
+      " |  Methods defined here:\n",
+      " |  \n",
+      " |  __abs__(self)\n",
+      " |      Returns the Real distance from 0. Called for abs(self).\n",
+      " |  \n",
+      " |  __add__(self, other)\n",
+      " |      self + other\n",
+      " |  \n",
+      " |  __bool__(self)\n",
+      " |      True if self != 0. Called for bool(self).\n",
+      " |  \n",
+      " |  __complex__(self)\n",
+      " |      Return a builtin complex instance. Called for complex(self).\n",
+      " |  \n",
+      " |  __eq__(self, other)\n",
+      " |      self == other\n",
+      " |  \n",
+      " |  __mul__(self, other)\n",
+      " |      self * other\n",
+      " |  \n",
+      " |  __neg__(self)\n",
+      " |      -self\n",
+      " |  \n",
+      " |  __pos__(self)\n",
+      " |      +self\n",
+      " |  \n",
+      " |  __pow__(self, exponent)\n",
+      " |      self**exponent; should promote to float or complex when necessary.\n",
+      " |  \n",
+      " |  __radd__(self, other)\n",
+      " |      other + self\n",
+      " |  \n",
+      " |  __rmul__(self, other)\n",
+      " |      other * self\n",
+      " |  \n",
+      " |  __rpow__(self, base)\n",
+      " |      base ** self\n",
+      " |  \n",
+      " |  __rsub__(self, other)\n",
+      " |      other - self\n",
+      " |  \n",
+      " |  __rtruediv__(self, other)\n",
+      " |      other / self\n",
+      " |  \n",
+      " |  __sub__(self, other)\n",
+      " |      self - other\n",
+      " |  \n",
+      " |  __truediv__(self, other)\n",
+      " |      self / other: Should promote to float when necessary.\n",
+      " |  \n",
+      " |  conjugate(self)\n",
+      " |      (x+y*i).conjugate() returns (x-y*i).\n",
+      " |  \n",
+      " |  ----------------------------------------------------------------------\n",
+      " |  Data descriptors defined here:\n",
+      " |  \n",
+      " |  imag\n",
+      " |      Retrieve the imaginary component of this number.\n",
+      " |      \n",
+      " |      This should subclass Real.\n",
+      " |  \n",
+      " |  real\n",
+      " |      Retrieve the real component of this number.\n",
+      " |      \n",
+      " |      This should subclass Real.\n",
+      " |  \n",
+      " |  ----------------------------------------------------------------------\n",
+      " |  Data and other attributes defined here:\n",
+      " |  \n",
+      " |  __abstractmethods__ = frozenset({'__abs__', '__add__', '__complex__', ...\n",
+      " |  \n",
+      " |  __hash__ = None\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "help(numbers.Complex)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "For sure, Python understands the built-in types as literals."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 198,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Help on class complex in module builtins:\n",
+      "\n",
+      "class complex(object)\n",
+      " |  complex(real=0, imag=0)\n",
+      " |  \n",
+      " |  Create a complex number from a real part and an optional imaginary part.\n",
+      " |  \n",
+      " |  This is equivalent to (real + imag*1j) where imag defaults to 0.\n",
+      " |  \n",
+      " |  Methods defined here:\n",
+      " |  \n",
+      " |  __abs__(self, /)\n",
+      " |      abs(self)\n",
+      " |  \n",
+      " |  __add__(self, value, /)\n",
+      " |      Return self+value.\n",
+      " |  \n",
+      " |  __bool__(self, /)\n",
+      " |      self != 0\n",
+      " |  \n",
+      " |  __divmod__(self, value, /)\n",
+      " |      Return divmod(self, value).\n",
+      " |  \n",
+      " |  __eq__(self, value, /)\n",
+      " |      Return self==value.\n",
+      " |  \n",
+      " |  __float__(self, /)\n",
+      " |      float(self)\n",
+      " |  \n",
+      " |  __floordiv__(self, value, /)\n",
+      " |      Return self//value.\n",
+      " |  \n",
+      " |  __format__(...)\n",
+      " |      complex.__format__() -> str\n",
+      " |      \n",
+      " |      Convert to a string according to format_spec.\n",
+      " |  \n",
+      " |  __ge__(self, value, /)\n",
+      " |      Return self>=value.\n",
+      " |  \n",
+      " |  __getattribute__(self, name, /)\n",
+      " |      Return getattr(self, name).\n",
+      " |  \n",
+      " |  __getnewargs__(...)\n",
+      " |  \n",
+      " |  __gt__(self, value, /)\n",
+      " |      Return self>value.\n",
+      " |  \n",
+      " |  __hash__(self, /)\n",
+      " |      Return hash(self).\n",
+      " |  \n",
+      " |  __int__(self, /)\n",
+      " |      int(self)\n",
+      " |  \n",
+      " |  __le__(self, value, /)\n",
+      " |      Return self<=value.\n",
+      " |  \n",
+      " |  __lt__(self, value, /)\n",
+      " |      Return self<value.\n",
+      " |  \n",
+      " |  __mod__(self, value, /)\n",
+      " |      Return self%value.\n",
+      " |  \n",
+      " |  __mul__(self, value, /)\n",
+      " |      Return self*value.\n",
+      " |  \n",
+      " |  __ne__(self, value, /)\n",
+      " |      Return self!=value.\n",
+      " |  \n",
+      " |  __neg__(self, /)\n",
+      " |      -self\n",
+      " |  \n",
+      " |  __pos__(self, /)\n",
+      " |      +self\n",
+      " |  \n",
+      " |  __pow__(self, value, mod=None, /)\n",
+      " |      Return pow(self, value, mod).\n",
+      " |  \n",
+      " |  __radd__(self, value, /)\n",
+      " |      Return value+self.\n",
+      " |  \n",
+      " |  __rdivmod__(self, value, /)\n",
+      " |      Return divmod(value, self).\n",
+      " |  \n",
+      " |  __repr__(self, /)\n",
+      " |      Return repr(self).\n",
+      " |  \n",
+      " |  __rfloordiv__(self, value, /)\n",
+      " |      Return value//self.\n",
+      " |  \n",
+      " |  __rmod__(self, value, /)\n",
+      " |      Return value%self.\n",
+      " |  \n",
+      " |  __rmul__(self, value, /)\n",
+      " |      Return value*self.\n",
+      " |  \n",
+      " |  __rpow__(self, value, mod=None, /)\n",
+      " |      Return pow(value, self, mod).\n",
+      " |  \n",
+      " |  __rsub__(self, value, /)\n",
+      " |      Return value-self.\n",
+      " |  \n",
+      " |  __rtruediv__(self, value, /)\n",
+      " |      Return value/self.\n",
+      " |  \n",
+      " |  __str__(self, /)\n",
+      " |      Return str(self).\n",
+      " |  \n",
+      " |  __sub__(self, value, /)\n",
+      " |      Return self-value.\n",
+      " |  \n",
+      " |  __truediv__(self, value, /)\n",
+      " |      Return self/value.\n",
+      " |  \n",
+      " |  conjugate(...)\n",
+      " |      complex.conjugate() -> complex\n",
+      " |      \n",
+      " |      Return the complex conjugate of its argument. (3-4j).conjugate() == 3+4j.\n",
+      " |  \n",
+      " |  ----------------------------------------------------------------------\n",
+      " |  Static methods defined here:\n",
+      " |  \n",
+      " |  __new__(*args, **kwargs) from builtins.type\n",
+      " |      Create and return a new object.  See help(type) for accurate signature.\n",
+      " |  \n",
+      " |  ----------------------------------------------------------------------\n",
+      " |  Data descriptors defined here:\n",
+      " |  \n",
+      " |  imag\n",
+      " |      the imaginary part of a complex number\n",
+      " |  \n",
+      " |  real\n",
+      " |      the real part of a complex number\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "help(complex)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "One way to use *abstract* data types is to use them in place of a *concrete* data type.\n",
+    "\n",
+    "For example, we may pass them as arguments to the built-in [isinstance()](https://docs.python.org/3/library/functions.html#isinstance) function and check in which of the five mathematical sets the object `1 / 10` is."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 199,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 199,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "isinstance(1 / 10, float)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 200,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 200,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "isinstance(1 / 10, numbers.Number)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 201,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 201,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "isinstance(1 / 10, numbers.Complex)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 202,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 202,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "isinstance(1 / 10, numbers.Real)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Due to the `float` type's inherent imprecision, `1 / 10` is *not* a rational number."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 203,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "False"
+      ]
+     },
+     "execution_count": 203,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "isinstance(1 / 10, numbers.Rational)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "The `Fraction` type qualifies as a rational number; however, the `Decimal` type does not."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 204,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 204,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "isinstance(Fraction(1, 10), numbers.Rational)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 205,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "False"
+      ]
+     },
+     "execution_count": 205,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "isinstance(Decimal(\"0.1\"), numbers.Rational)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "#### Example: [Factorial](https://en.wikipedia.org/wiki/Factorial) (revisited)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Replacing *concrete* data types with *abstract* ones is particularly valuable in the context of input validation: The revised version of the `factorial()` function below allows its user to *take advantage of duck typing*. If a real but non-integer argument `n` is passed in, `factorial()` tries to cast `n` as an `int` object with the [trunc()](https://docs.python.org/3/library/math.html#math.trunc) function from the [math](https://docs.python.org/3/library/math.html) module in the [standard library](https://docs.python.org/3/library/index.html). [trunc()](https://docs.python.org/3/library/math.html#math.trunc) cuts off all decimals and any *concrete* numeric type implementing the *abstract* `numbers.Real` type supports it (cf., [documentation](https://docs.python.org/3/library/numbers.html#numbers.Real))."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 206,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "import math"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 207,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0"
+      ]
+     },
+     "execution_count": 207,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "math.trunc(1 / 10)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 208,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "def factorial(n, *, strict=True):\n",
+    "    \"\"\"Calculate the factorial of a number.\n",
+    "\n",
+    "    Args:\n",
+    "        n (int): number to calculate the factorial for; must be positive\n",
+    "        strict (bool): if n must not contain decimals; defaults to True\n",
+    "\n",
+    "    Returns:\n",
+    "        factorial (int)\n",
+    "\n",
+    "    Raises:\n",
+    "        TypeError: if n is not an integer or cannot be cast as such\n",
+    "        ValueError: if n is negative\n",
+    "    \"\"\"\n",
+    "    if not isinstance(n, numbers.Integral):\n",
+    "        if isinstance(n, numbers.Real):\n",
+    "            if n != math.trunc(n) and strict:\n",
+    "                raise ValueError(\"n is not an integer-like value; it has decimals\")\n",
+    "            n = math.trunc(n)\n",
+    "        else:\n",
+    "            raise TypeError(\"Factorial is only defined for integers\")\n",
+    "\n",
+    "    if n < 0:\n",
+    "        raise ValueError(\"Factorial is not defined for negative integers\")\n",
+    "    elif n == 0:\n",
+    "        return 1\n",
+    "    return n * factorial(n - 1)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "`factorial()` works as before, but now also accepts, for example, `float` numbers."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 209,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "1"
+      ]
+     },
+     "execution_count": 209,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "factorial(0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 210,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "6"
+      ]
+     },
+     "execution_count": 210,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "factorial(3)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 211,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "6"
+      ]
+     },
+     "execution_count": 211,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "factorial(3.0)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "With the keyword-only argument `strict`, we can control whether or not a passed in `float` object may be rounded. By default, this is not allowed."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 212,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "ename": "ValueError",
+     "evalue": "n is not an integer-like value; it has decimals",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-212-188b816be8b6>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mfactorial\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m3.1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;32m<ipython-input-208-65a2caa81b4a>\u001b[0m in \u001b[0;36mfactorial\u001b[0;34m(n, strict)\u001b[0m\n\u001b[1;32m     16\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mn\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnumbers\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mReal\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     17\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mn\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0mmath\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtrunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mn\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mstrict\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 18\u001b[0;31m                 \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"n is not an integer-like value; it has decimals\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     19\u001b[0m             \u001b[0mn\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmath\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtrunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mn\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     20\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;31mValueError\u001b[0m: n is not an integer-like value; it has decimals"
+     ]
+    }
+   ],
+   "source": [
+    "factorial(3.1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 213,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "6"
+      ]
+     },
+     "execution_count": 213,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "factorial(3.1, strict=False)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "For `complex` numbers, `factorial()` still raises a `TypeError`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 214,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "ename": "TypeError",
+     "evalue": "Factorial is only defined for integers",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-214-7296a74f5dcf>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mfactorial\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m1\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;36m2j\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;32m<ipython-input-208-65a2caa81b4a>\u001b[0m in \u001b[0;36mfactorial\u001b[0;34m(n, strict)\u001b[0m\n\u001b[1;32m     19\u001b[0m             \u001b[0mn\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmath\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtrunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mn\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     20\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 21\u001b[0;31m             \u001b[0;32mraise\u001b[0m \u001b[0mTypeError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Factorial is only defined for integers\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     22\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     23\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mn\u001b[0m \u001b[0;34m<\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;31mTypeError\u001b[0m: Factorial is only defined for integers"
+     ]
+    }
+   ],
+   "source": [
+    "factorial(1 + 2j)"
    ]
   },
   {
@@ -6029,14 +6920,16 @@
     "- `float`: the \"gold\" standard to approximate real numbers (i.e., the set $\\mathbb{R}$); inherently imprecise\n",
     "- `complex`: layer on top of the `float` type; therefore inherently imprecise\n",
     "\n",
-    "Furthermore, the [standard library](https://docs.python.org/3/library/) adds two more types that can be used as substitutes for `float` objects:\n",
+    "Furthermore, the [standard library](https://docs.python.org/3/library/index.html) adds two more types that can be used as substitutes for `float` objects:\n",
     "- `Decimal`: similar to `float` but allows customizing the precision; still inherently imprecise\n",
     "- `Fraction`: a perfect model for rational numbers (i.e., the set $\\mathbb{Q}$); built on top of the `int` type and therefore inherently precise\n",
     "\n",
     "The important takeaways for the data science practicioner are:\n",
     "\n",
     "1. **Do not mix** precise and imprecise data types, and\n",
-    "2. actively expect `nan` results when working with `float` numbers as there are no **loud failures**."
+    "2. actively expect `nan` results when working with `float` numbers as there are no **loud failures**.\n",
+    "\n",
+    "The **numeric tower** is Python's way of implementing the various **abstract** ideas of what a number is in mathematics."
    ]
   }
  ],
diff --git a/static/numbers.png b/static/numbers.png
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literal 0
HcmV?d00001


From 61f6981ffd5ec7a9d2ea1a6f9c14f5e094954bef Mon Sep 17 00:00:00 2001
From: Alexander Hess <alexander@webartifex.biz>
Date: Tue, 15 Oct 2019 13:08:05 +0200
Subject: [PATCH 033/242] Add review and exercises for notebook 05

---
 05_numbers.ipynb                      | 685 +++++++++++++-----------
 05_numbers_review_and_exercises.ipynb | 722 ++++++++++++++++++++++++++
 2 files changed, 1108 insertions(+), 299 deletions(-)
 create mode 100644 05_numbers_review_and_exercises.ipynb

diff --git a/05_numbers.ipynb b/05_numbers.ipynb
index df7598c..d6409ba 100644
--- a/05_numbers.ipynb
+++ b/05_numbers.ipynb
@@ -92,7 +92,7 @@
     {
      "data": {
       "text/plain": [
-       "140218620396752"
+       "139928698315984"
       ]
      },
      "execution_count": 2,
@@ -2115,7 +2115,7 @@
     {
      "data": {
       "text/plain": [
-       "140218620574432"
+       "139928698489496"
       ]
      },
      "execution_count": 63,
@@ -4070,7 +4070,7 @@
     }
    },
    "source": [
-    "[getcontext()](https://docs.python.org/3/library/decimal.html#decimal.getcontext) shows us how the [decimal](https://docs.python.org/3/library/decimal.html) module is set up. By default, the precision is set to $28$ significant digits, which is roughly twice as many as with `float` objects."
+    "[getcontext()](https://docs.python.org/3/library/decimal.html#decimal.getcontext) shows us how the [decimal](https://docs.python.org/3/library/decimal.html) module is set up. By default, the precision is set to `28` significant digits, which is roughly twice as many as with `float` objects."
    ]
   },
   {
@@ -4282,7 +4282,7 @@
     }
    },
    "source": [
-    "Arithmetic operations between `Decimal` and `int` objects work as the latter are inherently precise: The results are *new* `Decimal` objects."
+    "`Decimal` numbers *preserve* the **significant digits**, even in cases where this is not needed."
    ]
   },
   {
@@ -4290,14 +4290,14 @@
    "execution_count": 137,
    "metadata": {
     "slideshow": {
-     "slide_type": "slide"
+     "slide_type": "fragment"
     }
    },
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "Decimal('42')"
+       "Decimal('0.30000')"
       ]
      },
      "execution_count": 137,
@@ -4306,7 +4306,7 @@
     }
    ],
    "source": [
-    "42 + Decimal(42) - 42"
+    "Decimal(\"0.10000\") + Decimal(\"0.20000\")"
    ]
   },
   {
@@ -4321,7 +4321,7 @@
     {
      "data": {
       "text/plain": [
-       "Decimal('42')"
+       "True"
       ]
      },
      "execution_count": 138,
@@ -4330,7 +4330,18 @@
     }
    ],
    "source": [
-    "10 * Decimal(42) / 10"
+    "Decimal(\"0.10000\") + Decimal(\"0.20000\") == Decimal(\"0.3\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Arithmetic operations between `Decimal` and `int` objects work as the latter are inherently precise: The results are *new* `Decimal` objects."
    ]
   },
   {
@@ -4345,7 +4356,7 @@
     {
      "data": {
       "text/plain": [
-       "Decimal('0.1')"
+       "Decimal('42')"
       ]
      },
      "execution_count": 139,
@@ -4353,6 +4364,54 @@
      "output_type": "execute_result"
     }
    ],
+   "source": [
+    "42 + Decimal(42) - 42"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 140,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Decimal('42')"
+      ]
+     },
+     "execution_count": 140,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "10 * Decimal(42) / 10"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 141,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Decimal('0.1')"
+      ]
+     },
+     "execution_count": 141,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "Decimal(1) / 10"
    ]
@@ -4370,7 +4429,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 140,
+   "execution_count": 142,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -4383,7 +4442,7 @@
        "'0.10000000000000000000000000000000000000000000000000'"
       ]
      },
-     "execution_count": 140,
+     "execution_count": 142,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -4394,7 +4453,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 141,
+   "execution_count": 143,
    "metadata": {
     "slideshow": {
      "slide_type": "-"
@@ -4407,7 +4466,7 @@
        "'0.10000000000000000555111512312578270211815834045410'"
       ]
      },
-     "execution_count": 141,
+     "execution_count": 143,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -4429,7 +4488,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 142,
+   "execution_count": 144,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -4443,7 +4502,7 @@
      "traceback": [
       "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
       "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-142-a3cea145e6d1>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0;36m1.0\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0mDecimal\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m42\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;32m<ipython-input-144-a3cea145e6d1>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0;36m1.0\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0mDecimal\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m42\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
       "\u001b[0;31mTypeError\u001b[0m: unsupported operand type(s) for *: 'float' and 'decimal.Decimal'"
      ]
     }
@@ -4465,7 +4524,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 143,
+   "execution_count": 145,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -4478,7 +4537,7 @@
        "Decimal('2')"
       ]
      },
-     "execution_count": 143,
+     "execution_count": 145,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -4489,7 +4548,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 144,
+   "execution_count": 146,
    "metadata": {
     "slideshow": {
      "slide_type": "-"
@@ -4502,7 +4561,7 @@
        "Decimal('1.414213562373095048801688724')"
       ]
      },
-     "execution_count": 144,
+     "execution_count": 146,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -4526,7 +4585,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 145,
+   "execution_count": 147,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -4539,7 +4598,7 @@
        "Decimal('1.999999999999999999999999999')"
       ]
      },
-     "execution_count": 145,
+     "execution_count": 147,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -4558,14 +4617,14 @@
     }
    },
    "source": [
-    "The [quantize()](https://docs.python.org/3/library/decimal.html#decimal.Decimal.quantize) method allows us to cut off a `Decimal` number at any digit that is smaller than the set precision while adhering to the rounding rules from math.\n",
+    "The [quantize()](https://docs.python.org/3/library/decimal.html#decimal.Decimal.quantize) method allows us to [quantize](https://www.dictionary.com/browse/quantize) (i.e., \"round\") a `Decimal` number at any precision that is *smaller* than the set precision. It looks at the number of decimals (i.e., to the right of the period) of the numeric argument we pass in.\n",
     "\n",
-    "For example, as the overall imprecise value of `two` still has a precision of $28$ digits, we can correctly round it to the first $20$ digits."
+    "For example, as the overall imprecise value of `two` still has an internal precision of `28` digits, we can correctly round it to *four* decimals (i.e., `Decimal(\"0.0001\")` has four decimals)."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 146,
+   "execution_count": 148,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -4575,16 +4634,44 @@
     {
      "data": {
       "text/plain": [
-       "Decimal('2')"
+       "Decimal('2.0000')"
       ]
      },
-     "execution_count": 146,
+     "execution_count": 148,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "two.quantize(20)"
+    "two.quantize(Decimal(\"0.0001\"))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can never round a `Decimal` number and obtain a greater precision than before: The `InvalidOperation` exception tells us that *loudly*."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 149,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "InvalidOperation",
+     "evalue": "[<class 'decimal.InvalidOperation'>]",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mInvalidOperation\u001b[0m                          Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-149-11848d4ba22a>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mtwo\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mquantize\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mDecimal\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"0.1\"\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m**\u001b[0m \u001b[0;36m28\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;31mInvalidOperation\u001b[0m: [<class 'decimal.InvalidOperation'>]"
+     ]
+    }
+   ],
+   "source": [
+    "two.quantize(Decimal(\"0.1\") ** 28)"
    ]
   },
   {
@@ -4595,12 +4682,12 @@
     }
    },
    "source": [
-    "Consequently, with this little workaround $\\sqrt{2}^2 = 2$ works even in Python."
+    "Consequently, with this little workaround $\\sqrt{2}^2 = 2$ works, even in Python."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 147,
+   "execution_count": 150,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -4613,13 +4700,13 @@
        "True"
       ]
      },
-     "execution_count": 147,
+     "execution_count": 150,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "two.quantize(20) == 2"
+    "two.quantize(Decimal(\"0.0001\")) == 2"
    ]
   },
   {
@@ -4635,7 +4722,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 148,
+   "execution_count": 151,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -4648,13 +4735,13 @@
        "True"
       ]
      },
-     "execution_count": 148,
+     "execution_count": 151,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "(Decimal(2).sqrt() ** 2).quantize(20) == 2"
+    "(Decimal(2).sqrt() ** 2).quantize(Decimal(\"0.0001\")) == 2"
    ]
   },
   {
@@ -4670,7 +4757,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 149,
+   "execution_count": 152,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -4683,85 +4770,13 @@
        "Decimal('NaN')"
       ]
      },
-     "execution_count": 149,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "Decimal(\"nan\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 150,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "False"
-      ]
-     },
-     "execution_count": 150,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "Decimal(\"nan\") == Decimal(\"nan\")  # nan's never compare equal to anything, not even to themselves"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 151,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Decimal('Infinity')"
-      ]
-     },
-     "execution_count": 151,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "Decimal(\"inf\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 152,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Decimal('-Infinity')"
-      ]
-     },
      "execution_count": 152,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "Decimal(\"-inf\")"
+    "Decimal(\"nan\")"
    ]
   },
   {
@@ -4776,7 +4791,7 @@
     {
      "data": {
       "text/plain": [
-       "Decimal('Infinity')"
+       "False"
       ]
      },
      "execution_count": 153,
@@ -4785,7 +4800,7 @@
     }
    ],
    "source": [
-    "Decimal(\"inf\") + 42  # Infinity is infinity, concrete numbers loose its meaning"
+    "Decimal(\"nan\") == Decimal(\"nan\")  # nan's never compare equal to anything, not even to themselves"
    ]
   },
   {
@@ -4800,7 +4815,7 @@
     {
      "data": {
       "text/plain": [
-       "True"
+       "Decimal('Infinity')"
       ]
      },
      "execution_count": 154,
@@ -4808,6 +4823,78 @@
      "output_type": "execute_result"
     }
    ],
+   "source": [
+    "Decimal(\"inf\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 155,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Decimal('-Infinity')"
+      ]
+     },
+     "execution_count": 155,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "Decimal(\"-inf\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 156,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Decimal('Infinity')"
+      ]
+     },
+     "execution_count": 156,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "Decimal(\"inf\") + 42  # Infinity is infinity, concrete numbers loose its meaning"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 157,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 157,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "Decimal(\"inf\") + 42 == Decimal(\"inf\")"
    ]
@@ -4825,7 +4912,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 155,
+   "execution_count": 158,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -4839,7 +4926,7 @@
      "traceback": [
       "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
       "\u001b[0;31mInvalidOperation\u001b[0m                          Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-155-87950ebbd6ef>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mDecimal\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"inf\"\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mDecimal\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"-inf\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;32m<ipython-input-158-87950ebbd6ef>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mDecimal\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"inf\"\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mDecimal\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"-inf\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
       "\u001b[0;31mInvalidOperation\u001b[0m: [<class 'decimal.InvalidOperation'>]"
      ]
     }
@@ -4850,7 +4937,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 156,
+   "execution_count": 159,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -4864,7 +4951,7 @@
      "traceback": [
       "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
       "\u001b[0;31mInvalidOperation\u001b[0m                          Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-156-996083f200c6>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mDecimal\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"inf\"\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0mDecimal\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"inf\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;32m<ipython-input-159-996083f200c6>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mDecimal\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"inf\"\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0mDecimal\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"inf\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
       "\u001b[0;31mInvalidOperation\u001b[0m: [<class 'decimal.InvalidOperation'>]"
      ]
     }
@@ -4910,7 +4997,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 157,
+   "execution_count": 160,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -4934,7 +5021,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 158,
+   "execution_count": 161,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -4947,7 +5034,7 @@
        "Fraction(1, 3)"
       ]
      },
-     "execution_count": 158,
+     "execution_count": 161,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -4958,7 +5045,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 159,
+   "execution_count": 162,
    "metadata": {
     "slideshow": {
      "slide_type": "-"
@@ -4971,7 +5058,7 @@
        "Fraction(1, 3)"
       ]
      },
-     "execution_count": 159,
+     "execution_count": 162,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -4982,7 +5069,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 160,
+   "execution_count": 163,
    "metadata": {
     "slideshow": {
      "slide_type": "-"
@@ -4995,7 +5082,7 @@
        "Fraction(3333333333, 10000000000)"
       ]
      },
-     "execution_count": 160,
+     "execution_count": 163,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -5006,7 +5093,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 161,
+   "execution_count": 164,
    "metadata": {
     "slideshow": {
      "slide_type": "-"
@@ -5019,7 +5106,7 @@
        "Fraction(3333333333, 10000000000)"
       ]
      },
-     "execution_count": 161,
+     "execution_count": 164,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -5041,7 +5128,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 162,
+   "execution_count": 165,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -5054,7 +5141,7 @@
        "Fraction(3, 2)"
       ]
      },
-     "execution_count": 162,
+     "execution_count": 165,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -5065,7 +5152,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 163,
+   "execution_count": 166,
    "metadata": {
     "slideshow": {
      "slide_type": "-"
@@ -5078,7 +5165,7 @@
        "Fraction(3, 2)"
       ]
      },
-     "execution_count": 163,
+     "execution_count": 166,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -5100,7 +5187,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 164,
+   "execution_count": 167,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -5113,7 +5200,7 @@
        "Fraction(1, 10)"
       ]
      },
-     "execution_count": 164,
+     "execution_count": 167,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -5135,7 +5222,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 165,
+   "execution_count": 168,
    "metadata": {
     "slideshow": {
      "slide_type": "-"
@@ -5148,7 +5235,7 @@
        "Fraction(3602879701896397, 36028797018963968)"
       ]
      },
-     "execution_count": 165,
+     "execution_count": 168,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -5170,7 +5257,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 166,
+   "execution_count": 169,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -5183,7 +5270,7 @@
        "Fraction(7, 4)"
       ]
      },
-     "execution_count": 166,
+     "execution_count": 169,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -5194,7 +5281,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 167,
+   "execution_count": 170,
    "metadata": {
     "slideshow": {
      "slide_type": "-"
@@ -5207,7 +5294,7 @@
        "Fraction(1, 2)"
       ]
      },
-     "execution_count": 167,
+     "execution_count": 170,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -5218,7 +5305,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 168,
+   "execution_count": 171,
    "metadata": {
     "slideshow": {
      "slide_type": "-"
@@ -5231,7 +5318,7 @@
        "Fraction(1, 1)"
       ]
      },
-     "execution_count": 168,
+     "execution_count": 171,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -5242,7 +5329,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 169,
+   "execution_count": 172,
    "metadata": {
     "slideshow": {
      "slide_type": "-"
@@ -5255,7 +5342,7 @@
        "Fraction(1, 1)"
       ]
      },
-     "execution_count": 169,
+     "execution_count": 172,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -5277,7 +5364,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 170,
+   "execution_count": 173,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -5290,7 +5377,7 @@
        "0.1"
       ]
      },
-     "execution_count": 170,
+     "execution_count": 173,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -5301,7 +5388,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 171,
+   "execution_count": 174,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -5314,7 +5401,7 @@
        "'0.10000000000000000555111512312578270211815834045410'"
       ]
      },
-     "execution_count": 171,
+     "execution_count": 174,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -5383,12 +5470,12 @@
    "source": [
     "`complex` numbers are part of core Python. The simplest way to create one is to write an arithmetic expression with the literal `j` notation for $\\textbf{i}$. The `j` is commonly used in many engineering disciplines instead of the symbol $\\textbf{i}$ from math as $I$ in engineering more often than not means [electric current](https://en.wikipedia.org/wiki/Electric_current).\n",
     "\n",
-    "For example, the answer to $x^2 = -1$ can be written in Python as `1j` like below. This creates a `complex` object with value `1j`. The same syntactic rules apply as with the above `e` notation: No spaces are allowed between the number and the `j`, and the number may be any `int` or `float` literal."
+    "For example, the answer to $x^2 = -1$ can be written in Python as `1j` like below. This creates a `complex` object with value `1j`. The same syntactic rules apply as with the above `e` notation: No spaces are allowed between the number and the `j`. The number may be any `int` or `float` literal; however, it is stored as a `float` internally. So, `complex` numbers suffer from the same imprecision as `float` numbers."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 172,
+   "execution_count": 175,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -5401,7 +5488,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 173,
+   "execution_count": 176,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -5411,10 +5498,10 @@
     {
      "data": {
       "text/plain": [
-       "140218611341648"
+       "139928697740272"
       ]
      },
-     "execution_count": 173,
+     "execution_count": 176,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -5425,7 +5512,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 174,
+   "execution_count": 177,
    "metadata": {
     "slideshow": {
      "slide_type": "-"
@@ -5438,7 +5525,7 @@
        "complex"
       ]
      },
-     "execution_count": 174,
+     "execution_count": 177,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -5449,7 +5536,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 175,
+   "execution_count": 178,
    "metadata": {
     "slideshow": {
      "slide_type": "-"
@@ -5462,7 +5549,7 @@
        "1j"
       ]
      },
-     "execution_count": 175,
+     "execution_count": 178,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -5484,7 +5571,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 176,
+   "execution_count": 179,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -5497,7 +5584,7 @@
        "True"
       ]
      },
-     "execution_count": 176,
+     "execution_count": 179,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -5519,7 +5606,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 177,
+   "execution_count": 180,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -5532,7 +5619,7 @@
        "(2+0.5j)"
       ]
      },
-     "execution_count": 177,
+     "execution_count": 180,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -5554,7 +5641,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 178,
+   "execution_count": 181,
    "metadata": {
     "slideshow": {
      "slide_type": "-"
@@ -5567,7 +5654,7 @@
        "(2+0.5j)"
       ]
      },
-     "execution_count": 178,
+     "execution_count": 181,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -5578,7 +5665,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 179,
+   "execution_count": 182,
    "metadata": {
     "slideshow": {
      "slide_type": "-"
@@ -5591,7 +5678,7 @@
        "(2+0j)"
       ]
      },
-     "execution_count": 179,
+     "execution_count": 182,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -5602,7 +5689,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 180,
+   "execution_count": 183,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -5615,7 +5702,7 @@
        "(2+0.5j)"
       ]
      },
-     "execution_count": 180,
+     "execution_count": 183,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -5626,7 +5713,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 181,
+   "execution_count": 184,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -5639,7 +5726,7 @@
        "(2+0.5j)"
       ]
      },
-     "execution_count": 181,
+     "execution_count": 184,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -5661,7 +5748,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 182,
+   "execution_count": 185,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -5675,7 +5762,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 183,
+   "execution_count": 186,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -5688,7 +5775,7 @@
        "(4+6j)"
       ]
      },
-     "execution_count": 183,
+     "execution_count": 186,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -5699,7 +5786,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 184,
+   "execution_count": 187,
    "metadata": {
     "slideshow": {
      "slide_type": "-"
@@ -5712,85 +5799,13 @@
        "(-2-2j)"
       ]
      },
-     "execution_count": 184,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "c1 - c2"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 185,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(40+2j)"
-      ]
-     },
-     "execution_count": 185,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "c1 + 39"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 186,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "-4j"
-      ]
-     },
-     "execution_count": 186,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "3.0 - c2"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 187,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(5+10j)"
-      ]
-     },
      "execution_count": 187,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "5 * c1"
+    "c1 - c2"
    ]
   },
   {
@@ -5805,7 +5820,7 @@
     {
      "data": {
       "text/plain": [
-       "(0.5+0.6666666666666666j)"
+       "(40+2j)"
       ]
      },
      "execution_count": 188,
@@ -5813,13 +5828,85 @@
      "output_type": "execute_result"
     }
    ],
+   "source": [
+    "c1 + 39"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 189,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "-4j"
+      ]
+     },
+     "execution_count": 189,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "3.0 - c2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 190,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(5+10j)"
+      ]
+     },
+     "execution_count": 190,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "5 * c1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 191,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(0.5+0.6666666666666666j)"
+      ]
+     },
+     "execution_count": 191,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "c2 / 6"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 189,
+   "execution_count": 192,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -5832,7 +5919,7 @@
        "(-5+10j)"
       ]
      },
-     "execution_count": 189,
+     "execution_count": 192,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -5843,7 +5930,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 190,
+   "execution_count": 193,
    "metadata": {
     "slideshow": {
      "slide_type": "-"
@@ -5856,7 +5943,7 @@
        "(0.44+0.08j)"
       ]
      },
-     "execution_count": 190,
+     "execution_count": 193,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -5878,7 +5965,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 191,
+   "execution_count": 194,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -5891,7 +5978,7 @@
        "5.0"
       ]
      },
-     "execution_count": 191,
+     "execution_count": 194,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -5913,7 +6000,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 192,
+   "execution_count": 195,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -5926,7 +6013,7 @@
        "1.0"
       ]
      },
-     "execution_count": 192,
+     "execution_count": 195,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -5937,7 +6024,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 193,
+   "execution_count": 196,
    "metadata": {
     "slideshow": {
      "slide_type": "-"
@@ -5950,7 +6037,7 @@
        "2.0"
       ]
      },
-     "execution_count": 193,
+     "execution_count": 196,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -5972,7 +6059,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 194,
+   "execution_count": 197,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -5985,7 +6072,7 @@
        "(1-2j)"
       ]
      },
-     "execution_count": 194,
+     "execution_count": 197,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -6062,7 +6149,7 @@
     "\n",
     "For the other types, in particular, the `float` type, the implications of their imprecision are discussed in detail above.\n",
     "\n",
-    "The abstract concepts behind the four outer-most mathematical sets are part of Python since [PEP 3141](https://www.python.org/dev/peps/pep-3141/) in 2007. The [numbers](https://docs.python.org/3/library/numbers.html) module in the [standard library](https://docs.python.org/3/library/index.html) defines what Pythonistas call the **numeric tower**, a collection of five **[abstract data types](https://en.wikipedia.org/wiki/Abstract_data_type)**, or **abstract base classes** as they are called in Python slang:\n",
+    "The abstract concepts behind the four outer-most mathematical sets are part of Python since [PEP 3141](https://www.python.org/dev/peps/pep-3141/) in 2007. The [numbers](https://docs.python.org/3/library/numbers.html) module in the [standard library](https://docs.python.org/3/library/index.html) defines what Pythonistas call the **numeric tower**, a collection of five **[abstract data types](https://en.wikipedia.org/wiki/Abstract_data_type)**, or **abstract base classes** as they are called in Python jargon:\n",
     "\n",
     "- `numbers.Number`: \"any number\" (cf., [documentation](https://docs.python.org/3/library/numbers.html#numbers.Number))\n",
     "- `numbers.Complex`: \"all complex numbers\" (cf., [documentation](https://docs.python.org/3/library/numbers.html#numbers.Complex))\n",
@@ -6073,7 +6160,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 195,
+   "execution_count": 198,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -6086,7 +6173,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 196,
+   "execution_count": 199,
    "metadata": {
     "slideshow": {
      "slide_type": "-"
@@ -6114,7 +6201,7 @@
        " 'abstractmethod']"
       ]
      },
-     "execution_count": 196,
+     "execution_count": 199,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -6140,7 +6227,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 197,
+   "execution_count": 200,
    "metadata": {
     "scrolled": true,
     "slideshow": {
@@ -6262,7 +6349,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 198,
+   "execution_count": 201,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -6429,7 +6516,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 199,
+   "execution_count": 202,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -6442,7 +6529,7 @@
        "True"
       ]
      },
-     "execution_count": 199,
+     "execution_count": 202,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -6453,7 +6540,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 200,
+   "execution_count": 203,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -6466,7 +6553,7 @@
        "True"
       ]
      },
-     "execution_count": 200,
+     "execution_count": 203,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -6477,7 +6564,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 201,
+   "execution_count": 204,
    "metadata": {
     "slideshow": {
      "slide_type": "-"
@@ -6490,7 +6577,7 @@
        "True"
       ]
      },
-     "execution_count": 201,
+     "execution_count": 204,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -6501,7 +6588,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 202,
+   "execution_count": 205,
    "metadata": {
     "slideshow": {
      "slide_type": "-"
@@ -6514,7 +6601,7 @@
        "True"
       ]
      },
-     "execution_count": 202,
+     "execution_count": 205,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -6536,7 +6623,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 203,
+   "execution_count": 206,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -6549,7 +6636,7 @@
        "False"
       ]
      },
-     "execution_count": 203,
+     "execution_count": 206,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -6571,7 +6658,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 204,
+   "execution_count": 207,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -6584,7 +6671,7 @@
        "True"
       ]
      },
-     "execution_count": 204,
+     "execution_count": 207,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -6595,7 +6682,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 205,
+   "execution_count": 208,
    "metadata": {
     "slideshow": {
      "slide_type": "-"
@@ -6608,7 +6695,7 @@
        "False"
       ]
      },
-     "execution_count": 205,
+     "execution_count": 208,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -6641,7 +6728,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 206,
+   "execution_count": 209,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -6654,7 +6741,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 207,
+   "execution_count": 210,
    "metadata": {
     "slideshow": {
      "slide_type": "-"
@@ -6667,7 +6754,7 @@
        "0"
       ]
      },
-     "execution_count": 207,
+     "execution_count": 210,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -6678,7 +6765,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 208,
+   "execution_count": 211,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -6703,7 +6790,7 @@
     "    if not isinstance(n, numbers.Integral):\n",
     "        if isinstance(n, numbers.Real):\n",
     "            if n != math.trunc(n) and strict:\n",
-    "                raise ValueError(\"n is not an integer-like value; it has decimals\")\n",
+    "                raise TypeError(\"n is not an integer-like value; it has decimals\")\n",
     "            n = math.trunc(n)\n",
     "        else:\n",
     "            raise TypeError(\"Factorial is only defined for integers\")\n",
@@ -6728,7 +6815,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 209,
+   "execution_count": 212,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -6741,7 +6828,7 @@
        "1"
       ]
      },
-     "execution_count": 209,
+     "execution_count": 212,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -6752,7 +6839,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 210,
+   "execution_count": 213,
    "metadata": {
     "slideshow": {
      "slide_type": "-"
@@ -6765,7 +6852,7 @@
        "6"
       ]
      },
-     "execution_count": 210,
+     "execution_count": 213,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -6776,7 +6863,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 211,
+   "execution_count": 214,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -6789,7 +6876,7 @@
        "6"
       ]
      },
-     "execution_count": 211,
+     "execution_count": 214,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -6811,7 +6898,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 212,
+   "execution_count": 215,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -6819,15 +6906,15 @@
    },
    "outputs": [
     {
-     "ename": "ValueError",
+     "ename": "TypeError",
      "evalue": "n is not an integer-like value; it has decimals",
      "output_type": "error",
      "traceback": [
       "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-212-188b816be8b6>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mfactorial\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m3.1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
-      "\u001b[0;32m<ipython-input-208-65a2caa81b4a>\u001b[0m in \u001b[0;36mfactorial\u001b[0;34m(n, strict)\u001b[0m\n\u001b[1;32m     16\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mn\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnumbers\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mReal\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     17\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mn\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0mmath\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtrunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mn\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mstrict\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 18\u001b[0;31m                 \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"n is not an integer-like value; it has decimals\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     19\u001b[0m             \u001b[0mn\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmath\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtrunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mn\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     20\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;31mValueError\u001b[0m: n is not an integer-like value; it has decimals"
+      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-215-188b816be8b6>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mfactorial\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m3.1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;32m<ipython-input-211-60ca579e64ac>\u001b[0m in \u001b[0;36mfactorial\u001b[0;34m(n, strict)\u001b[0m\n\u001b[1;32m     16\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mn\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnumbers\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mReal\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     17\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mn\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0mmath\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtrunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mn\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mstrict\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 18\u001b[0;31m                 \u001b[0;32mraise\u001b[0m \u001b[0mTypeError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"n is not an integer-like value; it has decimals\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     19\u001b[0m             \u001b[0mn\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmath\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtrunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mn\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     20\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;31mTypeError\u001b[0m: n is not an integer-like value; it has decimals"
      ]
     }
    ],
@@ -6837,7 +6924,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 213,
+   "execution_count": 216,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -6850,7 +6937,7 @@
        "6"
       ]
      },
-     "execution_count": 213,
+     "execution_count": 216,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -6872,7 +6959,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 214,
+   "execution_count": 217,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -6886,8 +6973,8 @@
      "traceback": [
       "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
       "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-214-7296a74f5dcf>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mfactorial\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m1\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;36m2j\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
-      "\u001b[0;32m<ipython-input-208-65a2caa81b4a>\u001b[0m in \u001b[0;36mfactorial\u001b[0;34m(n, strict)\u001b[0m\n\u001b[1;32m     19\u001b[0m             \u001b[0mn\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmath\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtrunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mn\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     20\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 21\u001b[0;31m             \u001b[0;32mraise\u001b[0m \u001b[0mTypeError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Factorial is only defined for integers\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     22\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     23\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mn\u001b[0m \u001b[0;34m<\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m<ipython-input-217-7296a74f5dcf>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mfactorial\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m1\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;36m2j\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;32m<ipython-input-211-60ca579e64ac>\u001b[0m in \u001b[0;36mfactorial\u001b[0;34m(n, strict)\u001b[0m\n\u001b[1;32m     19\u001b[0m             \u001b[0mn\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmath\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtrunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mn\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     20\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 21\u001b[0;31m             \u001b[0;32mraise\u001b[0m \u001b[0mTypeError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Factorial is only defined for integers\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     22\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     23\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mn\u001b[0m \u001b[0;34m<\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
       "\u001b[0;31mTypeError\u001b[0m: Factorial is only defined for integers"
      ]
     }
@@ -6924,7 +7011,7 @@
     "- `Decimal`: similar to `float` but allows customizing the precision; still inherently imprecise\n",
     "- `Fraction`: a perfect model for rational numbers (i.e., the set $\\mathbb{Q}$); built on top of the `int` type and therefore inherently precise\n",
     "\n",
-    "The important takeaways for the data science practicioner are:\n",
+    "The *important* takeaways for the data science practitioner are:\n",
     "\n",
     "1. **Do not mix** precise and imprecise data types, and\n",
     "2. actively expect `nan` results when working with `float` numbers as there are no **loud failures**.\n",
diff --git a/05_numbers_review_and_exercises.ipynb b/05_numbers_review_and_exercises.ipynb
new file mode 100644
index 0000000..4246965
--- /dev/null
+++ b/05_numbers_review_and_exercises.ipynb
@@ -0,0 +1,722 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n",
+    "# Chapter 5: Numbers"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Content Review"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Read [Chapter 5](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/05_numbers.ipynb) of the book. Then work through the eighteen review questions."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Essay Questions "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Answer the following questions briefly with *at most* 300 characters per question!"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q1**: In what way is the **binary representation** of `int` objects *similar* to the **decimal system** taught in elementary school?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q2**: Why may objects of type `bool` be regarded a **numeric type** as well?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q3**: Why is it *inefficient* to store `bool` objects in bits resembling a **hexadecimal representation**?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q4**: Colors are commonly expressed in the **hexadecimal system** in websites (cf., the [HTML](https://en.wikipedia.org/wiki/HTML) and [CSS](https://en.wikipedia.org/wiki/Cascading_Style_Sheets) formats).\n",
+    "\n",
+    "For example, $#000000$, $#ff9900$, and $#ffffff$ turn out to be black, orange, and white. The six digits are read in *pairs of two* from left to right, and the *three pairs* correspond to the proportions of red, green, and blue mixed together. They reach from $0_{16} = 0_{10}$ for $0$% to $\\text{ff}_{16} = 255_{10}$ for $100$% (cf., this [article](https://en.wikipedia.org/wiki/RGB_color_model) for an in-depth discussion).\n",
+    "\n",
+    "In percent, what are the proportions of red, green, and blue that make up orange? Calculate the three percentages separately! How many **bytes** are needed to encode a color? How many **bits** are that?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q5**: What does it mean for a code fragment to **fail silently**?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q6**: Explain why the mathematical set of all real numbers $\\mathbb{R}$ can only be **approximated** by floating-point numbers on a computer!"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q7**: How do we deal with a `float` object's imprecision if we need to **check for equality**?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q8**: What data type, built-in or from the [standard library](https://docs.python.org/3/library/index.html), is best suited to represent the [transcendental numbers](https://en.wikipedia.org/wiki/Transcendental_number) $\\pi$ and $\\text{e}$?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q9**: How can **abstract data types**, for example, as defined in the **numeric tower**, be helpful in enabling **duck typing**?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### True / False Questions"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Motivate your answer with *one short* sentence!"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q10**: The precision of `int` objects depends on how we choose to represent them in memory. For example, using a **hexadecimal representation** gives us $16^8$ digits whereas with a **binary representation** an `int` object can have *at most* $2^8$ digits."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q11**: With the built-in [round()](https://docs.python.org/3/library/functions.html#round) function, we obtain a *precise* representation for any `float` object if we can live with *less than* $15$ digits of precision."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q12**: As most currencies operate with $2$ or $3$ decimals (e.g., EUR $9.99$), the `float` type's limitation of *at most* $15$ digits is *not* a problem in practice."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q13**: The [IEEE 754](https://en.wikipedia.org/wiki/IEEE_754) standard's **special values** provide no benefit in practice as we could always use a **[sentinel](https://en.wikipedia.org/wiki/Sentinel_value)** value (i.e., a \"dummy\"). For example, instead of `nan`, we can always use `0` to indicate a *missing* value."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q14**: The following code fragment raises an `InvalidOperation` exception. That is an example of code **failing loudly**.\n",
+    "```python\n",
+    "float(\"inf\") + float(\"-inf\")\n",
+    "```"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q15**: Python provides a `scientific` type (e.g., `1.23e4`) that is useful mainly to model problems in the domains of physics or astrophysics."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q16**: From a practitioner's point of view, the built-in [format()](https://docs.python.org/3/library/functions.html#format) function does the *same* as the built-in [round()](https://docs.python.org/3/library/functions.html#round) function."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q17**: The `Decimal` type from the [decimal](https://docs.python.org/3/library/decimal.html) module in the [standard library](https://docs.python.org/3/library/index.html) allows us to model the set of the real numbers $\\mathbb{R}$ *precisely*."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q18**: The `Fraction` type from the [fractions](https://docs.python.org/3/library/fractions.html) module in the [standard library](https://docs.python.org/3/library/index.html) allows us to model the set of the rational numbers $\\mathbb{Q}$ *precisely*."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Coding Exercises"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Discounting Customer Orders (revisited)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q11** in [Chapter 2's Review & Exercises](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/02_functions_review_and_exercises.ipynb#Volume-of-a-Sphere) section already revealed that we must consider the effects of the `float` type's imprecision.\n",
+    "\n",
+    "This becomes even more important when we deal with numeric data modeling accounting or finance data (cf., [this comment](https://stackoverflow.com/a/24976426) on \"falsehoods programmers believe about money\").\n",
+    "\n",
+    "In addition to the *inherent imprecision* of numbers in general, the topic of **[rounding numbers](https://en.wikipedia.org/wiki/Rounding)** is also not as trivial as we might expect! [This article](https://realpython.com/python-rounding/) summarizes everything the data science practitioner needs to know.\n",
+    "\n",
+    "In this exercise, we revisit **Q9** from [Chapter 3's Review & Exercises](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/03_conditionals_review_and_exercises.ipynb#Discounting-Customer-Orders) section, and make the `discounted_price()` function work *correctly* for real-life sales data."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q19.1**: Execute the code cells below! What results would you have *expected*, and why?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "round(1.5)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "round(2.5)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "round(2.675, 2)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q19.2**: The built-in [round()](https://docs.python.org/3/library/functions.html#round) function implements the \"**[round half to even](https://en.wikipedia.org/wiki/Rounding#Round_half_to_even)**\" strategy. Describe in one or two sentences what that means!"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q19.3**: For the revised `discounted_price()` function, we have to tackle *two* issues: First, we have to replace the built-in `float` type with a data type that allows us to control the precision. Second, the discounted price should be rounded according to a more human-friendly rounding strategy, namely \"**[round half away from zero](https://en.wikipedia.org/wiki/Rounding#Round_half_away_from_zero)**.\"\n",
+    "\n",
+    "Describe in one or two sentences how \"**[round half away from zero](https://en.wikipedia.org/wiki/Rounding#Round_half_away_from_zero)**\" is more in line with how humans think of rounding!"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q19.4**: We use the `Decimal` type from the [decimal](https://docs.python.org/3/library/decimal.html) module in the [standard library](https://docs.python.org/3/library/index.html) to tackle *both* issues simultaneously.\n",
+    "\n",
+    "Assign `euro` a numeric object such that both `Decimal(\"1.5\")` and `Decimal(\"2.5\")` are rounded to `Decimal(\"2\")` (i.e., no decimal) with the [quantize()](https://docs.python.org/3/library/decimal.html#decimal.Decimal.quantize) method!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from decimal import Decimal"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "euro = ..."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "Decimal(\"1.5\").quantize(...)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "Decimal(\"2.5\").quantize(...)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q19.5**: Obviously, the two preceding code cells still [round half to even](https://en.wikipedia.org/wiki/Rounding#Round_half_to_even).\n",
+    "\n",
+    "The [decimal](https://docs.python.org/3/library/decimal.html) module defines a `ROUND_HALF_UP` flag that we can pass as the second argument to the [quantize()](https://docs.python.org/3/library/decimal.html#decimal.Decimal.quantize) method. Then, it [rounds half away from zero](https://en.wikipedia.org/wiki/Rounding#Round_half_away_from_zero).\n",
+    "\n",
+    "Add `ROUND_HALF_UP` to the code cells! `Decimal(\"2.5\")` should now be rounded to `Decimal(\"3\")`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from decimal import ROUND_HALF_UP"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "Decimal(\"1.5\").quantize(...)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "Decimal(\"2.5\").quantize(...)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q19.6**: Instead of `euro`, define `cents` such that rounding occurs to *two* decimals! `Decimal(\"2.675\")` should now be rounded to `Decimal(\"2.68\")`. Do *not* forget to include the `ROUND_HALF_UP` flag!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "cents = ..."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "Decimal(\"2.675\").quantize(...)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q19.7**: Re-write the function `discounted_price()` from [Chapter 3's Review & Exercises](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/03_conditionals_review_and_exercises.ipynb#Discounting-Customer-Orders) section!\n",
+    "\n",
+    "It takes the *positional* arguments `unit_price` and `quantity` and implements a discount scheme for a line item in a customer order as follows:\n",
+    "\n",
+    "- if the unit price is over 100 dollars, grant 10% relative discount\n",
+    "- if a customer orders more than 10 items, one in every five items is for free\n",
+    "\n",
+    "Only one of the two discounts is granted, whichever is better for the customer.\n",
+    "\n",
+    "The function then returns the overall price for the line item as a `Decimal` number with a precision of *two* decimals.\n",
+    "\n",
+    "Enable **duck typing** by allowing the function to be called with various numeric types as the arguments, in particular, `quantity` may be a non-integer as well: Use an appropriate **abstract data type** from the [numbers](https://docs.python.org/3/library/numbers.html) module in the [standard library](https://docs.python.org/3/library/index.html) to verify the arguments' types and also that they are both positive!\n",
+    "\n",
+    "It is considered a *best practice* to only round towards the *end* of the calculations."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numbers"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def discounted_price(...):\n",
+    "    ...\n",
+    "    ...\n",
+    "    ...\n",
+    "    ...\n",
+    "    ...\n",
+    "    ...\n",
+    "    ...\n",
+    "    ...\n",
+    "    ...\n",
+    "    ...\n",
+    "    ...\n",
+    "    ...\n",
+    "    ...\n",
+    "    ...\n",
+    "    ...\n",
+    "    ...\n",
+    "    ..."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q19.8**: Execute the code cells below and verify the final price for the following four test cases:\n",
+    "\n",
+    "- $7$ smartphones @ $99.00$ USD\n",
+    "- $3$ workstations @ $999.00$ USD\n",
+    "- $19$ GPUs @ $879.95$ USD\n",
+    "- $14$ Raspberry Pis @ $35.00$ USD\n",
+    "\n",
+    "The output should now *always* be a `Decimal` number with *two* decimals!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "discounted_price(99, 7)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "discounted_price(999, 3)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "discounted_price(879.95, 19)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "discounted_price(35, 14)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This also works if `quantity` is passed in as a `float` type."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "discounted_price(99, 7.0)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Decimals beyond the first two are gracefully discarded (i.e., *without* rounding errors accumulating)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "discounted_price(99.0001, 7)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The basic input validation ensures that the user of `discounted_price()` does not pass in invalid data. Here, the `\"abc\"` creates a `TypeError`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "discounted_price(\"abc\", 7)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "A `-1` passed in as `unit_price` results in a `ValueError`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "discounted_price(-1, 7)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.3"
+  },
+  "toc": {
+   "base_numbering": 1,
+   "nav_menu": {},
+   "number_sections": false,
+   "sideBar": true,
+   "skip_h1_title": true,
+   "title_cell": "Table of Contents",
+   "title_sidebar": "Contents",
+   "toc_cell": false,
+   "toc_position": {},
+   "toc_section_display": false,
+   "toc_window_display": false
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

From 4838c077f056c053b824161adad859b2cc509fcd Mon Sep 17 00:00:00 2001
From: Alexander Hess <alexander@webartifex.biz>
Date: Tue, 15 Oct 2019 19:36:16 +0200
Subject: [PATCH 034/242] Streamline previous content

---
 02_functions.ipynb | 2 +-
 05_numbers.ipynb   | 4 ++--
 2 files changed, 3 insertions(+), 3 deletions(-)

diff --git a/02_functions.ipynb b/02_functions.ipynb
index 6ab7c32..941c71b 100644
--- a/02_functions.ipynb
+++ b/02_functions.ipynb
@@ -57,7 +57,7 @@
     "\n",
     "A function may come with an *explicit* **[return value](https://docs.python.org/3/reference/simple_stmts.html#the-return-statement)** (i.e., \"result\" or \"output\") specified with the `return` statement: Functions that have one are considered **fruitful**; otherwise, they are **void**. Functions of the latter kind are still useful because of their **side effects** (e.g., the [print()](https://docs.python.org/3/library/functions.html#print) built-in). Strictly speaking, they also have an *implicit* return value of `None` that is different from the `False` we saw in Chapter 1.\n",
     "\n",
-    "A function should define a **docstring** that describes what it does in a short subject line, what parameters it expects (i.e., their types), and what it returns (if anything). A docstring is a syntactically valid multi-line string (i.e., type `str`) defined within **triple-double quotes** `\"\"\"`. Strings are covered in depth in Chapter 6. Widely adopted standards as to how to format a docstring are [PEP 257](https://www.python.org/dev/peps/pep-0257/) and section 3.8 in [Google's Python Style Guide](https://github.com/google/styleguide/blob/gh-pages/pyguide.md)."
+    "A function should define a **docstring** that describes what it does in a short subject line, what parameters it expects (i.e., their types), and what it returns (if anything). A docstring is a syntactically valid multi-line string (i.e., type `str`) defined within **triple-double quotes** `\"\"\"`. Strings are covered in depth in [Chapter 6](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/06_text.ipynb). Widely adopted standards as to how to format a docstring are [PEP 257](https://www.python.org/dev/peps/pep-0257/) and section 3.8 in [Google's Python Style Guide](https://github.com/google/styleguide/blob/gh-pages/pyguide.md)."
    ]
   },
   {
diff --git a/05_numbers.ipynb b/05_numbers.ipynb
index d6409ba..73daebb 100644
--- a/05_numbers.ipynb
+++ b/05_numbers.ipynb
@@ -21,7 +21,7 @@
    "source": [
     "After learning about the basic building blocks of expressing and structuring the business logic in programs, we focus our attention on the **data types** Python offers us, both built-in and available via the [standard library](https://docs.python.org/3/library/index.html) or third-party packages.\n",
     "\n",
-    "We start with the \"simple\" ones: Numeric types in this chapter and textual data in Chapter 6. An important fact that holds for all objects of these types is that they are **immutable**. To re-use the bag analogy from [Chapter 1](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/01_elements.ipynb#Objects-vs.-Types-vs.-Values), this means that the $0$s and $1$s making up an object's *value* cannot be changed once the bag is created in memory, implying that any operation with or method on the object creates a *new* object in a *different* memory location.\n",
+    "We start with the \"simple\" ones: Numeric types in this chapter and textual data in [Chapter 6](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/06_text.ipynb). An important fact that holds for all objects of these types is that they are **immutable**. To re-use the bag analogy from [Chapter 1](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/01_elements.ipynb#Objects-vs.-Types-vs.-Values), this means that the $0$s and $1$s making up an object's *value* cannot be changed once the bag is created in memory, implying that any operation with or method on the object creates a *new* object in a *different* memory location.\n",
     "\n",
     "Chapters 7, 8, and 9 then cover the more \"complex\" data types, including, for example, the `list` type. Finally, Chapter 10 completes the picture by introducing language constructs to create custom types.\n",
     "\n",
@@ -4519,7 +4519,7 @@
     }
    },
    "source": [
-    "To preserve the precision for more advanced mathematical functions, `Decimal` objects come with many methods bound on them. For example, [ln()](https://docs.python.org/3/library/decimal.html#decimal.Decimal.ln) and [log10()](https://docs.python.org/3/library/decimal.html#decimal.Decimal.log10) take the logarithm while [sqrt()](https://docs.python.org/3/library/decimal.html#decimal.Decimal.sqrt) calculates the square root. In general, the functions in the [math](https://docs.python.org/3/library/math.html) module in the [standard library](https://docs.python.org/3/library/index.html) should only be used with `float` objects as they do *not* preserve precision."
+    "To preserve the precision for more advanced mathematical functions, `Decimal` objects come with many **methods bound** on them. For example, [ln()](https://docs.python.org/3/library/decimal.html#decimal.Decimal.ln) and [log10()](https://docs.python.org/3/library/decimal.html#decimal.Decimal.log10) take the logarithm while [sqrt()](https://docs.python.org/3/library/decimal.html#decimal.Decimal.sqrt) calculates the square root. In general, the functions in the [math](https://docs.python.org/3/library/math.html) module in the [standard library](https://docs.python.org/3/library/index.html) should only be used with `float` objects as they do *not* preserve precision."
    ]
   },
   {

From e309f19d6ca24ea15b4a849ba3b5a2b1dde8a36b Mon Sep 17 00:00:00 2001
From: Alexander Hess <alexander@webartifex.biz>
Date: Tue, 15 Oct 2019 19:36:37 +0200
Subject: [PATCH 035/242] Add initial version of notebook 06

---
 06_text.ipynb | 2787 +++++++++++++++++++++++++++++++++++++++++++++++++
 1 file changed, 2787 insertions(+)
 create mode 100644 06_text.ipynb

diff --git a/06_text.ipynb b/06_text.ipynb
new file mode 100644
index 0000000..4cdd4ab
--- /dev/null
+++ b/06_text.ipynb
@@ -0,0 +1,2787 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "# Chapter 6: Text"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "In this chapter, we continue the study of the built-in data types. Building on our knowledge of numbers, the next layer consists of textual data that are modeled primarily with the `str` type in Python. `str` objects are naturally more \"complex\" than numeric objects as any text consists of an arbitrary and possibly large number of individual characters that may be chosen from any alphabet in the history of humankind. Luckily, Python abstracts away most of this complexity."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "## The `str` Type"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "The `str` type is the default way of modeling **textual data**. To create a `str` object, we use a **literal notation** and type the text between enclosing **double quotes** `\"`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "school = \"WHU - Otto Beisheim School of Management\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Like everything in Python, `school` is an object."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "140148947850512"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "id(school)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "str"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "type(school)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "A `str` object evaluates to itself in a literal notation with enclosing **single quotes** `'` by default. In [Chapter 1](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/01_elements.ipynb), we already specified the double quotes `\"` convention we stick to in this book. Yet, single quotes `'` and double quotes `\"` are *perfect* substitutes for all `str` objects that do *not* contain any of the two symbols in it. We could have used the reverse convention, as well.\n",
+    "\n",
+    "As [this discussion](https://stackoverflow.com/questions/56011/single-quotes-vs-double-quotes-in-python) shows, many programmers have *strong* opinions about that and make up *new* conventions for their projects. Consequently, the discussion was \"closed as not constructive\" by the moderators."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'WHU - Otto Beisheim School of Management'"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "school"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "As the single quote `'` is often used in the English language as a shortener, we could make an argument in favor of using the double quotes `\"`: There are possibly fewer situations like in the two code cells below, in which we must revert to using a `\\` to **escape** a single quote `'` in a text (cf., the section on special characters further below). However, double quotes `\"` are often used as well. So, this argument is somewhat not convincing.\n",
+    "\n",
+    "Many proponents of the single quote `'` usage claim that double quotes `\"` make more **visual noise** on the screen. This argument is also not convincing. On the contrary, one could claim that *two* single quotes `''` look so similar to *one* double quote `\"` that it might not be apparent right away what we are looking at. By sticking to double quotes `\"`, we avoid such danger of confusion.\n",
+    "\n",
+    "This discussion is an exellent example of a [flame war](https://en.wikipedia.org/wiki/Flaming_%28Internet%29#Flame_war) in the programming world that leads to *no* result.\n",
+    "\n",
+    "An *important* fact to know is that enclosing quotes of either kind are *not* part of the `str` object's *value*! They are merely *syntax* to make the text in a code cell a *literal* that Python converts into a `str` object upon reading."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "\"It's cool that strings are so versatile in Python!\""
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "\"It's cool that strings are so versatile in Python!\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "\"It's cool that strings are so versatile in Python!\""
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "'It\\'s cool that strings are so versatile in Python!'"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Alternatively, we can use the [str()](https://docs.python.org/3/library/stdtypes.html#str) built-in to **cast** non-`str` objects as a `str`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'123'"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "str(123)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "## A \"String\" of Characters"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "The idea of a **sequence** is yet another *abstract* concept.\n",
+    "\n",
+    "It unifies *four* orthogonal *abstract* concepts into one: Any *concrete* data type, such as `str` in this chapter, is considered a sequence if it simultaneously\n",
+    "\n",
+    "1. behaves like a **container** and\n",
+    "2. an **iterable**, and \n",
+    "3. comes with a predictable **order** of its\n",
+    "4. **finite** number of elements.\n",
+    "\n",
+    "Chapter 7 discusses sequences in a generic sense and in greater detail. Here, we keep our focus on the `str` type that historically received its name as it models a \"**[string of characters](https://en.wikipedia.org/wiki/String_%28computer_science%29)**,\" and a \"string\" is more formally called a sequence in the computer science literature.\n",
+    "\n",
+    "Behaving like a sequence, `str` objects may be treated like `list` objects in many cases. For example, the built-in [len()](https://docs.python.org/3/library/functions.html#len) function tells us how many elements (i.e., characters) make up `school`. [len()](https://docs.python.org/3/library/functions.html#len) would not work on an \"*infinite*\" object: As anything modeled in a program must fit into a computer's finite memory at runtime, there cannot exist objects containing a truly infinite number of elements; however, Chapter 7 introduces a *concrete* iterable data type that can be used to model an *infinite* series of elements and that, consequently, has no concept of \"length.\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "40"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "len(school)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Being iterable, we can iterate over a `str` object, for example, with a `for`-loop, and do something with the individual characters, for example, print them out with extra space in between them.\n",
+    "\n",
+    "[Chapter 4](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/04_iteration.ipynb#Containers-vs.-Iterables) already shows that we can loop over *different* concrete types: The example there first loops over the `list` object `[0, 1, 2, 3, 4]` and then the `range` object `range(5)`, with the same outcome. Now, we add the `str` type to the list of *concrete* types we can loop over. *Abstractly* speaking, all three are *iterable*, and there are many more iterable types in Python."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "W H U   -   O t t o   B e i s h e i m   S c h o o l   o f   M a n a g e m e n t "
+     ]
+    }
+   ],
+   "source": [
+    "for letter in school:\n",
+    "    print(letter, end=\" \")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Being a container, we can check if a given object is a member of a sequence with the `in` operator. In the context of strings, the `in` operator has *two* usages: First, it checks if a *single* character is contained in a `str` object. Second, it may also check if a shorter `str` object, then called a **substring**, is contained in a longer one."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "\"O\" in school"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "\"WHU\" in school"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "False"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "\"EBS\" in school"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "## Indexing"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "As `str` objects have the additional property of being *ordered*, we may **index** into them to obtain individual characters with the **indexing operator** `[]`. This is analogous to how we obtained individual elements of a `list` object in [Chapter 1](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/01_elements.ipynb#Who-am-I?-And-how-many?)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'W'"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "school[0]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'H'"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "school[1]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "The index must be of type `int`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "ename": "TypeError",
+     "evalue": "string indices must be integers",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-15-c89ad7e1a4ab>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mschool\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1.0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;31mTypeError\u001b[0m: string indices must be integers"
+     ]
+    }
+   ],
+   "source": [
+    "school[1.0]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "The last index is one less than the above \"length\" of the `str` object as we start counting at 0."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'t'"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "school[39]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "An `IndexError` is raised whenever the index is too large."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [
+    {
+     "ename": "IndexError",
+     "evalue": "string index out of range",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mIndexError\u001b[0m                                Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-17-fffdc8fb2058>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mschool\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m40\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;31mIndexError\u001b[0m: string index out of range"
+     ]
+    }
+   ],
+   "source": [
+    "school[40]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "We may use *negative* indexes to start counting from the end of the `str` object."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'t'"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "school[-1]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "One reason why programmers like to start counting at 0 is that a positive index and its *corresponding* negative index always add up to the length of the sequence."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'O'"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "school[6]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'O'"
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "school[-34]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "## Slicing"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "A **slice** is a substring of a `str` object.\n",
+    "\n",
+    "The **slicing operator** is a generalization of the indexing operator: We can put one, two, or three integers within the brackets, separated by colons `:`. The three integers are then referred to as the *start*, *end*, and *step* values."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'WHU'"
+      ]
+     },
+     "execution_count": 21,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "school[0:3]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Whereas the *start* is always included in the result, the *end* is not. Counter-intuitive at first, this makes working with individual slices easier as they add up to the original `str` object again. As the *end* is not included, we must end the second slice with `len(school)` or `40` below."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'WHU - Otto Beisheim School of Management'"
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "school[0:3] + school[3:40]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "For convenience, the indexes do not need to lie in the range from 0 to the string's \"length\" when slicing. This is *not* the case for indexing as the `IndexError` above shows."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'WHU - Otto Beisheim School of Management'"
+      ]
+     },
+     "execution_count": 23,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "school[0:999]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Commonly, we leave out the `0` for the *start* and the *end* if it is equal to the \"length.\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'WHU - Otto Beisheim School of Management'"
+      ]
+     },
+     "execution_count": 24,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "school[:3] + school[3:]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Slicing makes it easy to obtain shorter versions of the original `str` object."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'WHU Otto Beisheim School'"
+      ]
+     },
+     "execution_count": 25,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "school[:3] + school[5:26]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "A *step* value of $i$ can be used to obtain only every $i$th letter."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'WU-Ot esemSho fMngmn'"
+      ]
+     },
+     "execution_count": 26,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "school[::2]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "A negative *step* size reverses the order of the characters."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'tnemeganaM fo loohcS miehsieB ottO - UHW'"
+      ]
+     },
+     "execution_count": 27,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "school[::-1]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "## Immutability"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Whereas elements of a `list` object *may* be *re-assigned*, as shortly hinted at in [Chapter 1](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/01_elements.ipynb#Who-am-I?-And-how-many?), this is *not* allowed for `str` objects. Once created, they *cannot* be *changed*. Formally, we say that they are **immutable**. In that regard, `str` objects and all the numeric types in [Chapter 5](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/05_numbers.ipynb) are alike.\n",
+    "\n",
+    "On the contrary, objects that may be changed after creation, are called **mutable**. We already saw in [Chapter 1](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/01_elements.ipynb#Who-am-I?-And-how-many?) how mutable objects are more difficult to reason about for a beginner, in particular, if more than *one* variable point to one. Yet, mutability does have its place in a programmer's toolbox, and we revisit this idea in the next chapters.\n",
+    "\n",
+    "`str` objects are *immutable* as the `TypeError` indicates: Assignment to an index is *not* supported by this type."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "ename": "TypeError",
+     "evalue": "'str' object does not support item assignment",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-28-0d94e9e44adb>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mschool\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m\"E\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;31mTypeError\u001b[0m: 'str' object does not support item assignment"
+     ]
+    }
+   ],
+   "source": [
+    "school[0] = \"E\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "The only thing we can do is to create a *new* `str` object in memory."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "new_school = \"EBS\" + school[3:]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'EBS - Otto Beisheim School of Management'"
+      ]
+     },
+     "execution_count": 30,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "new_school"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "140148946876144"
+      ]
+     },
+     "execution_count": 31,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "id(new_school)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "140148947850512"
+      ]
+     },
+     "execution_count": 32,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "id(school)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "## String Operations"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "As mentioned before, the `+` and `*` operators are *overloaded* and used for **string concatenation**."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "greeting = \"Hello \""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Hello WHU'"
+      ]
+     },
+     "execution_count": 34,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "greeting + school[:3]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'WHU WHU WHU WHU WHU WHU WHU WHU WHU WHU '"
+      ]
+     },
+     "execution_count": 35,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "10 * school[:4]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "## String Methods"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Objects of type `str` come with many **methods** bound on them (cf., the [documentation](https://docs.python.org/3/library/stdtypes.html#string-methods) for a full list). As seen before, they work like *normal* functions and are accessed via the **dot operator** `.`. Calling a method is also referred to as **method invocation**.\n",
+    "\n",
+    "The [find()](https://docs.python.org/3/library/stdtypes.html#str.find) method returns the index of the first occurrence of a character or a substring. If no match is found, it returns `-1`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "6"
+      ]
+     },
+     "execution_count": 36,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "school.find(\"O\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "-1"
+      ]
+     },
+     "execution_count": 37,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "school.find(\"Z\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "11"
+      ]
+     },
+     "execution_count": 38,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "school.find(\"Beisheim\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "[find()](https://docs.python.org/3/library/stdtypes.html#str.find) takes optional *start* and *end* arguments that allow us to find occurrences other than the first one."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "12"
+      ]
+     },
+     "execution_count": 39,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "school.find(\"e\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "16"
+      ]
+     },
+     "execution_count": 40,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "school.find(\"e\", 13)  # 13 not 12 as otherwise the same character is found"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "-1"
+      ]
+     },
+     "execution_count": 41,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "school.find(\"e\", 13, 15)  # \"e\" does not occur in the specified slice"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "[count()](https://docs.python.org/3/library/stdtypes.html#str.count) does what we expect."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "4"
+      ]
+     },
+     "execution_count": 42,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "school.count(\"o\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "As [count()](https://docs.python.org/3/library/stdtypes.html#str.count) is *case-sensitive*, we must **chain** it with the [lower()](https://docs.python.org/3/library/stdtypes.html#str.lower) method to get the count of all \"O\"s and \"o\"s."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "5"
+      ]
+     },
+     "execution_count": 43,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "school.lower().count(\"o\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Alternatively, we may use the [upper()](https://docs.python.org/3/library/stdtypes.html#str.upper) method and search for \"O\"s."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "5"
+      ]
+     },
+     "execution_count": 44,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "school.upper().count(\"O\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Because `str` objects are *immutable*, the methods always return *new* objects, even if a method does *not* change the value of the `str` object at all."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "example = \"test\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "140149036799680"
+      ]
+     },
+     "execution_count": 46,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "id(example)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "lower = example.lower()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 48,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "140148946626016"
+      ]
+     },
+     "execution_count": 48,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "id(lower)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "`example` and `lower` are *different* objects with the *same* value."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 49,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "False"
+      ]
+     },
+     "execution_count": 49,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "example is lower"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 50,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 50,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "example == lower"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Another popular string method is [split()](https://docs.python.org/3/library/stdtypes.html#str.split): It separates a longer `str` object into a list of smaller ones. By default, groups of whitespace are used as the *separator*."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 51,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "WHU\n",
+      "-\n",
+      "Otto\n",
+      "Beisheim\n",
+      "School\n",
+      "of\n",
+      "Management\n"
+     ]
+    }
+   ],
+   "source": [
+    "for word in school.split():\n",
+    "    print(word)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "The opposite of splitting is done with the [join()](https://docs.python.org/3/library/stdtypes.html#str.join) method. It is typically invoked on a `str` object that represents a separator (e.g., `\" \"` or `\", \"`) and connects the elements of an *iterable* argument passed in (e.g., `words` below) into one *new* `str` object."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 52,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "words = [\"This\", \"will\", \"become\", \"a\", \"sentence\"]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 53,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "sentence = \" \".join(words)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 54,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'This will become a sentence'"
+      ]
+     },
+     "execution_count": 54,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "sentence"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "As the `str` object `\"abcde\"` below is an *iterable* itself, its elements (i.e., characters) are joined together with a space `\" \"` in between."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 55,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'a b c d e'"
+      ]
+     },
+     "execution_count": 55,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "\" \".join(\"abcde\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "The [replace()](https://docs.python.org/3/library/stdtypes.html#str.replace) method creates a *new* `str` object with parts of the text replaced."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 56,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'This is a sentence'"
+      ]
+     },
+     "execution_count": 56,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "sentence.replace(\"will become\", \"is\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "## String Comparison"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "The *relational* operators also work with `str` objects, another example of operator overloading. Comparison is done one character at a time until the first pair differs or one operand ends. However, `str` objects are sorted in a \"weird\" way. The reason for this is that computers store characters internally as numbers (i.e., $0$s and $1$s). Depending on the character encoding, these numbers vary. Commonly, characters and symbols used in the American language are encoded with the numbers 0 through 127, the so-called [ASCII standard](https://en.wikipedia.org/wiki/ASCII). However, Python works with the more general [Unicode/UTF-8 standard](https://en.wikipedia.org/wiki/UTF-8) that understands every language ever used by humans, even emojis."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 57,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "A = \"Apple\"  # ignore snake_case for variable names in this example\n",
+    "a = \"apple\"\n",
+    "B = \"Banana\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 58,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 58,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "A < B"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 59,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "False"
+      ]
+     },
+     "execution_count": 59,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "a < B"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "One way to fix this is to only compare lower-case strings."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 60,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 60,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "a < B.lower()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "To provide a simple intuition for the \"weird\" sorting above, let's think of the American alphabet as being represented by the numbers as listed below. Then `\"Banana\"` is clearly \"smaller\" than `\"apple\"`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 61,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "A -> 65 \t a -> 97\n",
+      "B -> 66 \t b -> 98\n",
+      "C -> 67 \t c -> 99\n",
+      "D -> 68 \t d -> 100\n",
+      "E -> 69 \t e -> 101\n",
+      "F -> 70 \t f -> 102\n",
+      "G -> 71 \t g -> 103\n",
+      "H -> 72 \t h -> 104\n",
+      "I -> 73 \t i -> 105\n",
+      "J -> 74 \t j -> 106\n",
+      "K -> 75 \t k -> 107\n",
+      "L -> 76 \t l -> 108\n",
+      "M -> 77 \t m -> 109\n",
+      "N -> 78 \t n -> 110\n",
+      "O -> 79 \t o -> 111\n",
+      "P -> 80 \t p -> 112\n",
+      "Q -> 81 \t q -> 113\n",
+      "R -> 82 \t r -> 114\n",
+      "S -> 83 \t s -> 115\n",
+      "T -> 84 \t t -> 116\n",
+      "U -> 85 \t u -> 117\n",
+      "V -> 86 \t v -> 118\n",
+      "W -> 87 \t w -> 119\n",
+      "X -> 88 \t x -> 120\n",
+      "Y -> 89 \t y -> 121\n",
+      "Z -> 90 \t z -> 122\n"
+     ]
+    }
+   ],
+   "source": [
+    "for lower_i in range(65, 91):\n",
+    "    upper_i = lower_i + 32  # all the upper case characters are offset by 32\n",
+    "    lower_char = chr(lower_i)  # from their lower case counterpart\n",
+    "    upper_char = chr(upper_i)\n",
+    "    print(f\"{lower_char} -> {lower_i} \\t {upper_char} -> {upper_i}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "## String Interpolation"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "The previous code cell shows an example of a so-called **f-string**, as introduced by [PEP 498](https://www.python.org/dev/peps/pep-0498/) only in 2016.\n",
+    "\n",
+    "So far, we have used the built-in [print()](https://docs.python.org/3/library/functions.html#print) function only with \"plain\" `str` objects (e.g., `\"example\"`) or variables. Sometimes, it is more convenient to fill a value determined at runtime in a \"draft\" `str` object. This is called **string interpolation**. There are three ways to achieve that in Python, but only two are commonly used."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "### f-strings"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "f-strings are the new and most readable way. Prepend the literal notation with an `f`, and put variables/expressions within curly braces. The latter are then filled in when the [print()](https://docs.python.org/3/library/functions.html#print) function is executed."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 62,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "name = \"Alexander\"\n",
+    "time_of_day = \"morning\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 63,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Hello Alexander! Good morning.\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(f\"Hello {name}! Good {time_of_day}.\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Separated by a colon `:`, various formatting options are available. In the beginning, only the ability to round is useful, and this can be achieved by adding `:.2f` to the variable name to cast the number as a float and round it to two digits."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 64,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "pi = 3.141592653"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 65,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Pi is 3.14\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(f\"Pi is {pi:.2f}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 66,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Pi is 3.142\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(f\"Pi is {pi:.3f}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "### format() Method"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "`str` objects also provide a [format()](https://docs.python.org/3/library/stdtypes.html#str.format) method that accepts an arbitrary number of *positional* arguments that are inserted into the `str` object in the same order replacing curly brackets. See the official [documentation](https://docs.python.org/3/library/string.html#formatspec) for a full reference. This is the more traditional way of string interpolation and many code examples on the internet use it. f-strings are the officially recommended way going forward, but the usage of the [format()](https://docs.python.org/3/library/stdtypes.html#str.format) method is most likely not going down any time soon."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 67,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Hello Alexander! Good morning.\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"Hello {}! Good {}.\".format(name, time_of_day))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Use index numbers if the order is different in the `str` object."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 68,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Good morning, Alexander\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"Good {1}, {0}\".format(name, time_of_day))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "The [format()](https://docs.python.org/3/library/stdtypes.html#str.format) method may alternatively be used with *keyword* arguments as well. Then, we must put the keyword names within the curly brackets."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 69,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Hello Alexander! Good morning.\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"Hello {name}! Good {time}.\".format(name=name, time=time_of_day))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Numbers are treated as in the f-strings case."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 70,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Pi is 3.14\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"Pi is {:.2f}\".format(pi))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "## Special Characters"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Some symbols have a special meaning within `str` objects. Popular examples are the newline `\\n` and tab `\\t` \"characters.\" The backslash symbol `\\` is also referred to as an **escape character** in this context, indicating that the following character has a meaning other than its literal meaning."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 71,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "This is a sentence\n",
+      "that is printed\n",
+      "on three lines.\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"This is a sentence\\nthat is printed\\non three lines.\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 72,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Words\taligned\twith\ttabs.\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"Words\\taligned\\twith\\ttabs.\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "As emojis are important as well, they can be inserted with the corresponding **unicode code point** number starting with `\\U`. See this [list](https://en.wikipedia.org/wiki/List_of_Unicode_characters) of unicode characters for an overview."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 73,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "�\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"\\U0001f604\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "## Raw Strings"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Sometimes we do *not* want the backslash `\\` and its following character be interpreted as special characters.\n",
+    "\n",
+    "For example, let's print a typical installation path on a Windows systems. Obviously, the newline character `\\n` does *not* makes sense here."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 74,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "C:\\Programs\n",
+      "ew_application\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"C:\\Programs\\new_application\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Some strings even produce a `SyntaxError` because the `\\U` *cannot* be interpreted as a unicode code point."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 75,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "ename": "SyntaxError",
+     "evalue": "(unicode error) 'unicodeescape' codec can't decode bytes in position 2-3: truncated \\UXXXXXXXX escape (<ipython-input-75-0aad8a365b02>, line 1)",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;36m  File \u001b[0;32m\"<ipython-input-75-0aad8a365b02>\"\u001b[0;36m, line \u001b[0;32m1\u001b[0m\n\u001b[0;31m    print(\"C:\\Users\\Administrator\\Desktop\\Project_Folder\")\u001b[0m\n\u001b[0m         ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m (unicode error) 'unicodeescape' codec can't decode bytes in position 2-3: truncated \\UXXXXXXXX escape\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"C:\\Users\\Administrator\\Desktop\\Project_Folder\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "A simple solution would be to escape the escape character with a *second* backslash `\\`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 76,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "C:\\Programs\\new_application\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"C:\\\\Programs\\\\new_application\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 77,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\Administrator\\Desktop\\Project_Folder\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"C:\\\\Users\\\\Administrator\\\\Desktop\\\\Project_Folder\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "However, this is tedious to remember and type. Luckily, Python allows treating any string literal as a \"raw\" string, and this is indicated in the string literal by a `r` prefix."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 78,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "C:\\Programs\\new_application\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(r\"C:\\Programs\\new_application\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 79,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\Administrator\\Desktop\\Project_Folder\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(r\"C:\\Users\\Administrator\\Desktop\\Project_Folder\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "## Multi-line Strings"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Sometimes, it is convenient to split text across multiple lines in source code. For example, to make lines fit into the 79 characters requirement of [PEP 8](https://www.python.org/dev/peps/pep-0008/) or because the text naturally contains many newlines. Using double quotes `\"` around multiple lines results in a `SyntaxError`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 80,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "ename": "SyntaxError",
+     "evalue": "EOL while scanning string literal (<ipython-input-80-4cef690f1f4a>, line 1)",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;36m  File \u001b[0;32m\"<ipython-input-80-4cef690f1f4a>\"\u001b[0;36m, line \u001b[0;32m1\u001b[0m\n\u001b[0;31m    \"\u001b[0m\n\u001b[0m     ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m EOL while scanning string literal\n"
+     ]
+    }
+   ],
+   "source": [
+    "\"\n",
+    "Do not break the lines like this\n",
+    "\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "However, by enclosing a string literal with either **triple-double** quotes `\"\"\"` or **triple-single** quotes `'''`, Python creates a \"plain\" `str` object. Docstrings are precisely that, and, by convention, always written in triple-double quotes `\"\"\"`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 81,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "multi_line = \"\"\"\n",
+    "I am a multi-line string\n",
+    "consisting of 4 lines.\n",
+    "\"\"\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Linebreaks are kept and implicitly converted into `\\n` characters."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 82,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'\\nI am a multi-line string\\nconsisting of 4 lines.\\n'"
+      ]
+     },
+     "execution_count": 82,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "multi_line"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "The built-in [print()](https://docs.python.org/3/library/functions.html#print) function correctly prints out the `\\n` characters."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 83,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "I am a multi-line string\n",
+      "consisting of 4 lines.\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(multi_line)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Using the [split()](https://docs.python.org/3/library/stdtypes.html#str.split) method with the optional *sep* argument, we confirm that `multi_line` consists of *four* lines with the first and last linebreaks being the first and last characters in the `str` object."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 84,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0 \n",
+      "1 I am a multi-line string\n",
+      "2 consisting of 4 lines.\n",
+      "3 \n"
+     ]
+    }
+   ],
+   "source": [
+    "for i, line in enumerate(multi_line.split(\"\\n\")):\n",
+    "    print(i, line)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "## TL;DR"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Textual data is modeled with the **immutable** `str` type.\n",
+    "\n",
+    "The `str` type supports *four* orthogonal **abstract concepts** that together make up the idea of a **sequence**: Every `str` object is an iterable container of a finite number of ordered characters."
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.3"
+  },
+  "livereveal": {
+   "auto_select": "code",
+   "auto_select_fragment": true,
+   "scroll": true,
+   "theme": "serif"
+  },
+  "toc": {
+   "base_numbering": 1,
+   "nav_menu": {},
+   "number_sections": false,
+   "sideBar": true,
+   "skip_h1_title": true,
+   "title_cell": "Table of Contents",
+   "title_sidebar": "Contents",
+   "toc_cell": false,
+   "toc_position": {
+    "height": "calc(100% - 180px)",
+    "left": "10px",
+    "top": "150px",
+    "width": "384px"
+   },
+   "toc_section_display": false,
+   "toc_window_display": false
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

From ffd407b6a8104bb6c4cfee5cb106c42dacbb20ed Mon Sep 17 00:00:00 2001
From: Alexander Hess <alexander@webartifex.biz>
Date: Tue, 15 Oct 2019 20:07:57 +0200
Subject: [PATCH 036/242] Add review and exercises for notebook 06

---
 06_text_review_and_exercises.ipynb | 205 +++++++++++++++++++++++++++++
 1 file changed, 205 insertions(+)
 create mode 100644 06_text_review_and_exercises.ipynb

diff --git a/06_text_review_and_exercises.ipynb b/06_text_review_and_exercises.ipynb
new file mode 100644
index 0000000..304a91d
--- /dev/null
+++ b/06_text_review_and_exercises.ipynb
@@ -0,0 +1,205 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n",
+    "# Chapter 6: Text"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Content Review"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Read [Chapter 6](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/06_text.ipynb) of the book. Then work through the eight review questions."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Essay Questions "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Answer the following questions briefly with *at most* 300 characters per question!"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q1**: In what sense is a **\"string\" of characters** a **sequence**? What is a **sequence** after all? A *concrete* **data type**, or something else?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q2**: What is a direct consequence of the `str` type's property of being **ordered**? What operations could we *not* do with it if it were *unordered*?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q3**: What does it mean for an object to be **immutable**? Discuss how we can verify the `str` type's immutability by comparing the two variables `example` and `full_slice` below. Are they pointing to the *same* object in memory?\n",
+    "```python\n",
+    "example = \"text\"\n",
+    "full_slice = example[:]\n",
+    "```\n",
+    "Hint: Find out what `[:]` does first!"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q4**: Describe in your own words what we mean with **string interpolation**!"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### True / False Questions"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Motivate your answer with *one short* sentence!"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q5**: **Triple-double** quotes `\"\"\"` and **triple-single** quotes `'''` create a *new* object of type `text` that model so-called **multi-line strings**."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q6**: A **substring** is a string that *subsumes* another string."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q7**: Indexing into a `str` object with a *negative* index **fails silently**: It does *not* raise an error but also does *not* do anything useful."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q8**: We *cannot* assign a *different* character to an index or slice of a `str` object because it is **immutable**."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " "
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.3"
+  },
+  "toc": {
+   "base_numbering": 1,
+   "nav_menu": {},
+   "number_sections": false,
+   "sideBar": true,
+   "skip_h1_title": true,
+   "title_cell": "Table of Contents",
+   "title_sidebar": "Contents",
+   "toc_cell": false,
+   "toc_position": {},
+   "toc_section_display": false,
+   "toc_window_display": false
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

From f38a3d1ea29b76e5a6df80188ad2c7a65a4b0d9e Mon Sep 17 00:00:00 2001
From: Alexander Hess <alexander@webartifex.biz>
Date: Tue, 15 Oct 2019 20:09:02 +0200
Subject: [PATCH 037/242] Adjust content overview in README.md

---
 README.md | 1 +
 1 file changed, 1 insertion(+)

diff --git a/README.md b/README.md
index 57c513a..daa2e93 100644
--- a/README.md
+++ b/README.md
@@ -20,6 +20,7 @@ As such they can be viewed in a plain web browser:
 - [03 - Conditionals & Exceptions](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/03_conditionals.ipynb)
 - [04 - Recursion & Looping](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/04_iteration.ipynb)
 - [05 - Numbers](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/05_numbers.ipynb)
+- [06 - Text](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/06_text.ipynb)
 
 However, it is recommended that students **install Python and Jupyter
 locally** and run the code in the notebooks on their own.

From 08e0ec7b7379415b83140952535cba5a087b0534 Mon Sep 17 00:00:00 2001
From: Alexander Hess <alexander@webartifex.biz>
Date: Tue, 15 Oct 2019 20:12:11 +0200
Subject: [PATCH 038/242] Bump version number

---
 pyproject.toml | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/pyproject.toml b/pyproject.toml
index 1e69fc5..c2a9bdc 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -1,6 +1,6 @@
 [tool.poetry]
 name = "intro-to-python"
-version = "0.1.0"
+version = "0.4.0"
 description = "An introduction to Python and programming for wanna-be data scientists"
 authors = ["Alexander Hess <alexander@webartifex.biz>"]
 license = "MIT"

From 04d53956a3644375996a69f48847cbabcbd3d603 Mon Sep 17 00:00:00 2001
From: Alexander Hess <alexander@webartifex.biz>
Date: Wed, 30 Oct 2019 11:04:59 +0100
Subject: [PATCH 039/242] Streamline previous content

---
 00_start_up.ipynb                       |    9 +-
 01_elements.ipynb                       |   32 +-
 02_functions.ipynb                      |   32 +-
 03_conditionals.ipynb                   |  225 +++--
 04_iteration.ipynb                      |   32 +-
 04_iteration_review_and_exercises.ipynb |    2 +-
 05_numbers.ipynb                        |   67 +-
 05_numbers_review_and_exercises.ipynb   |    4 +-
 06_text.ipynb                           | 1088 ++++++++++++++++++-----
 06_text_review_and_exercises.ipynb      |   10 +-
 lorem_ipsum.txt                         |    6 +
 11 files changed, 1097 insertions(+), 410 deletions(-)
 create mode 100644 lorem_ipsum.txt

diff --git a/00_start_up.ipynb b/00_start_up.ipynb
index 9ace23c..97fe84b 100644
--- a/00_start_up.ipynb
+++ b/00_start_up.ipynb
@@ -792,13 +792,12 @@
     "**Part 2: Managing Data and Memory**\n",
     "\n",
     "- How is data stored in memory?\n",
-    "  5. [Numbers](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/05_numbers.ipynb)\n",
-    "  6. Text\n",
-    "  7. Sequences\n",
+    "  5. [Bits & Numbers](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/05_numbers.ipynb)\n",
+    "  6. [Bytes & Text](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/06_text.ipynb)\n",
+    "  7. [Sequential Data](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/07_sequences.ipynb)\n",
     "  8. Mappings & Sets\n",
-    "  9. Arrays\n",
     "- How can we create custom data types?\n",
-    "  10. Object-Orientation"
+    "  9. Object-Orientation"
    ]
   },
   {
diff --git a/01_elements.ipynb b/01_elements.ipynb
index c941e43..f9c6131 100644
--- a/01_elements.ipynb
+++ b/01_elements.ipynb
@@ -46,11 +46,11 @@
     }
    },
    "source": [
-    "As our introductory example, we want to calculate the *average* of all *even* numbers from *one* through *ten*.\n",
+    "As our introductory example, we want to calculate the *average* of all *even* numbers from `1` through `10`.\n",
     "\n",
     "While we could come up with an [analytical solution](https://math.stackexchange.com/questions/935405/what-s-the-difference-between-analytical-and-numerical-approaches-to-problems/935446#935446) (i.e., derive some equation with \"pen and paper\" from, e.g., one of [Faulhaber's formulas](https://en.wikipedia.org/wiki/Faulhaber%27s_formula)), we instead solve the task programmatically.\n",
     "\n",
-    "We start by creating a **list** called `numbers` that holds all the individual numbers."
+    "We start by creating a **list** called `numbers` that holds all the individual numbers between **brackets** `[` and `]`."
    ]
   },
   {
@@ -988,7 +988,7 @@
     {
      "data": {
       "text/plain": [
-       "139829040614096"
+       "139878568414128"
       ]
      },
      "execution_count": 28,
@@ -1012,7 +1012,7 @@
     {
      "data": {
       "text/plain": [
-       "139829040789352"
+       "139878568593256"
       ]
      },
      "execution_count": 29,
@@ -1036,7 +1036,7 @@
     {
      "data": {
       "text/plain": [
-       "139829040474736"
+       "139878567760368"
       ]
      },
      "execution_count": 30,
@@ -1080,7 +1080,7 @@
     }
    },
    "source": [
-    "`a` and `d` indeed have the same value as is checked with the **equality operator** `==`. The resulting `True` (and the `False` further below) is yet another data type, a so-called **boolean**. We look into them closely in [Chapter 3](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/03_conditionals.ipynb)."
+    "`a` and `d` indeed have the same value as is checked with the **equality operator** `==`. The resulting `True` (and the `False` further below) is yet another data type, a so-called **boolean**. We look into them closely in [Chapter 3](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/03_conditionals.ipynb#Boolean-Expressions)."
    ]
   },
   {
@@ -1222,7 +1222,7 @@
    "source": [
     "Different types imply different behaviors for the objects. The `b` object, for example, can be \"asked\" if it could also be interpreted as an `int` with the [is_integer()](https://docs.python.org/3/library/stdtypes.html#float.is_integer) \"functionality\" that comes with every `float` object.\n",
     "\n",
-    "Formally, we call such type-specific functionalities **methods** (to differentiate them from functions) and we formally introduce them in Chapter 10. For now, it suffices to know that we access them using the **dot operator** `.`. Of course, `b` could be converted into an `int`, which the boolean value `True` tells us."
+    "Formally, we call such type-specific functionalities **methods** (to differentiate them from functions) and we formally introduce them in Chapter 9. For now, it suffices to know that we access them using the **dot operator** `.`. Of course, `b` could be converted into an `int`, which the boolean value `True` tells us."
    ]
   },
   {
@@ -1817,7 +1817,7 @@
        "        "
       ],
       "text/plain": [
-       "<IPython.lib.display.YouTubeVideo at 0x7f2c7c4206d8>"
+       "<IPython.lib.display.YouTubeVideo at 0x7f3804586400>"
       ]
      },
      "execution_count": 51,
@@ -1838,7 +1838,7 @@
     }
    },
    "source": [
-    "For example, while the above code to calculate the average of the even numbers from 1 through 10 is correct, a Pythonista would re-write it in a more \"Pythonic\" way and use the [sum()](https://docs.python.org/3/library/functions.html#sum) and [len()](https://docs.python.org/3/library/functions.html#len) (= \"length\") built-in functions (cf., [Chapter 2](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/02_functions.ipynb)) as well as a so-called **list comprehension** (cf., Chapter 7). Pythonic code runs faster in many cases and is less error-prone."
+    "For example, while the above code to calculate the average of the even numbers from 1 through 10 is correct, a Pythonista would re-write it in a more \"Pythonic\" way and use the [sum()](https://docs.python.org/3/library/functions.html#sum) and [len()](https://docs.python.org/3/library/functions.html#len) (= \"length\") [built-in functions](https://docs.python.org/3/library/functions.html) (cf., [Chapter 2](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/02_functions.ipynb#Built-in-Functions)) as well as a so-called **list comprehension** (cf., [Chapter 7](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/07_sequences.ipynb#List-Comprehensions)). Pythonic code runs faster in many cases and is less error-prone."
    ]
   },
   {
@@ -2040,7 +2040,7 @@
     "\n",
     "At the same time, for a beginner's course, it is often easier to code linearly.\n",
     "\n",
-    "In real data science projects, one would probably employ a mixed approach and put re-usable code into so-called Python modules (i.e., *.py* files; cf., [Chapter 2](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/02_functions.ipynb)) and then use Jupyter notebooks to build up a linear report or storyline for a business argument to be made."
+    "In real data science projects, one would probably employ a mixed approach and put re-usable code into so-called Python modules (i.e., *.py* files; cf., [Chapter 2](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/02_functions.ipynb#Local-Modules-and-Packages)) and then use Jupyter notebooks to build up a linear report or storyline for a business argument to be made."
    ]
   },
   {
@@ -2330,7 +2330,7 @@
     }
    },
    "source": [
-    "Some variables magically exist when we start a Python process or are added by Jupyter. We may safely ignore the former until Chapter 10 and the latter for good."
+    "Some variables magically exist when we start a Python process or are added by Jupyter. We may safely ignore the former until Chapter 9 and the latter for good."
    ]
   },
   {
@@ -2779,7 +2779,7 @@
    "source": [
     "Let's change the first element of `x`.\n",
     "\n",
-    "Chapter 7 discusses lists in more depth. For now, let's view a `list` object as some sort of **container** that holds an arbitrary number of pointers to other objects and treat the brackets `[]` attached to it as just another operator, called the **indexing operator**. `x[0]` instructs Python to first follow the pointer from the global list of all names to the `x` object. Then, it follows the first pointer it finds there to the `1` object. The indexing operator must be an operator as we merely read the first element and do not change anything in memory.\n",
+    "[Chapter 7](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/07_sequences.ipynb#The-list-Type) discusses lists in more depth. For now, let's view a `list` object as some sort of **container** that holds an arbitrary number of pointers to other objects and treat the brackets `[]` attached to it as just another operator, called the **indexing operator**. `x[0]` instructs Python to first follow the pointer from the global list of all names to the `x` object. Then, it follows the first pointer it finds there to the `1` object. The indexing operator must be an operator as we merely read the first element and do not change anything in memory.\n",
     "\n",
     "Note how Python **begins counting at 0**. This is not the case for many other languages, for example, [MATLAB](https://en.wikipedia.org/wiki/MATLAB), [R](https://en.wikipedia.org/wiki/R_%28programming_language%29), or [Stata](https://en.wikipedia.org/wiki/Stata). To understand why this makes sense, see this short [note](https://www.cs.utexas.edu/users/EWD/transcriptions/EWD08xx/EWD831.html) by one of the all-time greats in computer science, the late [Edsger Dijkstra](https://en.wikipedia.org/wiki/Edsger_W._Dijkstra)."
    ]
@@ -3189,7 +3189,7 @@
        "        "
       ],
       "text/plain": [
-       "<IPython.lib.display.YouTubeVideo at 0x7f2c7c41bc50>"
+       "<IPython.lib.display.YouTubeVideo at 0x7f3804634748>"
       ]
      },
      "execution_count": 94,
@@ -3579,12 +3579,12 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "I change the display of the computer\n"
+      "I change the state of the computer's display\n"
      ]
     }
    ],
    "source": [
-    "print(\"I change the display of the computer\")"
+    "print(\"I change the state of the computer's display\")"
    ]
   },
   {
@@ -3686,7 +3686,7 @@
     }
    },
    "source": [
-    "We end each chapter with a summary of the main points. The essence in this first chapter is that just like a sentence in a real language like English may be decomposed into its parts (e.g., subject, predicate, and objects), the same may be done with programming languages."
+    "We end each chapter with a summary of the main points (i.e., **TL;DR** = \"too long; didn't read\"). The essence in this first chapter is that just as a sentence in a real language like English may be decomposed into its parts (e.g., subject, predicate, and objects), the same may be done with programming languages."
    ]
   },
   {
diff --git a/02_functions.ipynb b/02_functions.ipynb
index 941c71b..809982f 100644
--- a/02_functions.ipynb
+++ b/02_functions.ipynb
@@ -45,7 +45,7 @@
     }
    },
    "source": [
-    "So-called **[user-defined functions](https://docs.python.org/3/reference/compound_stmts.html#function-definitions)** may be created with the `def` statement. To extend an already familiar example, we re-use the introductory example from [Chapter 1](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/01_elements.ipynb) in its final Pythonic version and transform it into the function `average_evens()` below. \n",
+    "So-called **[user-defined functions](https://docs.python.org/3/reference/compound_stmts.html#function-definitions)** may be created with the `def` statement. To extend an already familiar example, we re-use the introductory example from [Chapter 1](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/01_elements.ipynb#Best-Practices) in its final Pythonic version and transform it into the function `average_evens()` below. \n",
     "\n",
     "A function's **name** must be chosen according to the same naming rules as ordinary variables as Python manages function names like variables. In this book, we further adopt the convention of ending function names with parentheses `()` in text cells for faster comprehension when reading (i.e., `average_evens()` vs. `average_evens`). These are *not* part of the name but must always be written out in the `def` statement for syntactic reasons.\n",
     "\n",
@@ -55,9 +55,9 @@
     "\n",
     "Together, the name and the list of parameters are also referred to as the function's **[signature](https://en.wikipedia.org/wiki/Type_signature)** (i.e., `average_evens(numbers)` below).\n",
     "\n",
-    "A function may come with an *explicit* **[return value](https://docs.python.org/3/reference/simple_stmts.html#the-return-statement)** (i.e., \"result\" or \"output\") specified with the `return` statement: Functions that have one are considered **fruitful**; otherwise, they are **void**. Functions of the latter kind are still useful because of their **side effects** (e.g., the [print()](https://docs.python.org/3/library/functions.html#print) built-in). Strictly speaking, they also have an *implicit* return value of `None` that is different from the `False` we saw in Chapter 1.\n",
+    "A function may come with an *explicit* **[return value](https://docs.python.org/3/reference/simple_stmts.html#the-return-statement)** (i.e., \"result\" or \"output\") specified with the `return` statement: Functions that have one are considered **fruitful**; otherwise, they are **void**. Functions of the latter kind are still useful because of their **side effects** (e.g., the [print()](https://docs.python.org/3/library/functions.html#print) built-in). Strictly speaking, they also have an *implicit* return value of `None` that is different from the `False` we saw in [Chapter 1](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/01_elements.ipynb#Identity-/-\"Memory-Location\").\n",
     "\n",
-    "A function should define a **docstring** that describes what it does in a short subject line, what parameters it expects (i.e., their types), and what it returns (if anything). A docstring is a syntactically valid multi-line string (i.e., type `str`) defined within **triple-double quotes** `\"\"\"`. Strings are covered in depth in [Chapter 6](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/06_text.ipynb). Widely adopted standards as to how to format a docstring are [PEP 257](https://www.python.org/dev/peps/pep-0257/) and section 3.8 in [Google's Python Style Guide](https://github.com/google/styleguide/blob/gh-pages/pyguide.md)."
+    "A function should define a **docstring** that describes what it does in a short subject line, what parameters it expects (i.e., their types), and what it returns (if anything). A docstring is a syntactically valid multi-line string (i.e., type `str`) defined within **triple-double quotes** `\"\"\"`. Strings are covered in depth in [Chapter 6](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/06_text.ipynb#The-str-Type). Widely adopted standards as to how to format a docstring are [PEP 257](https://www.python.org/dev/peps/pep-0257/) and section 3.8 in [Google's Python Style Guide](https://github.com/google/styleguide/blob/gh-pages/pyguide.md)."
    ]
   },
   {
@@ -142,7 +142,7 @@
     {
      "data": {
       "text/plain": [
-       "140519730762208"
+       "139829511291360"
       ]
      },
      "execution_count": 3,
@@ -657,7 +657,7 @@
    "source": [
     "[PythonTutor](http://pythontutor.com/visualize.html#code=nums%20%3D%20%5B1,%202,%203,%204,%205,%206,%207,%208,%209,%2010%5D%0A%0Adef%20average_wrong%28numbers%29%3A%0A%20%20%20%20evens%20%3D%20%5Bn%20for%20n%20in%20nums%20if%20n%20%25%202%20%3D%3D%200%5D%0A%20%20%20%20average%20%3D%20sum%28evens%29%20/%20len%28evens%29%0A%20%20%20%20return%20average%0A%0Arv%20%3D%20average_wrong%28%5B123,%20456,%20789%5D%29&cumulative=false&curInstr=0&heapPrimitives=nevernest&mode=display&origin=opt-frontend.js&py=3&rawInputLstJSON=%5B%5D&textReferences=false) is again helpful at visualizing the error interactively: Creating the `list` object `evens` eventually points to takes *12* computational steps, namely one for setting up an empty `list` object, *ten* for filling it with elements derived from `nums` in the global scope, and one to make `evens` point at it (cf., steps 6-18).\n",
     "\n",
-    "The frames logic shown by PythonTutor is the mechanism by which Python not only manages the names inside *one* function call but also for *many* potentially simultaneous* calls, as revealed in [Chapter 4](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/04_iteration.ipynb). It is the reason why we may re-use the same names for the parameters and variables inside both `average_evens()` and `average_wrong()` without Python mixing them up. So, as we already read in the [Zen of Python](https://www.python.org/dev/peps/pep-0020/), \"namespaces are one honking great idea\" (cf., `import this`), and a frame is just a special kind of namespace."
+    "The frames logic shown by PythonTutor is the mechanism by which Python not only manages the names inside *one* function call but also for *many* potentially *simultaneous* calls, as revealed in [Chapter 4](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/04_iteration.ipynb#Trivial-Example:-Countdown). It is the reason why we may re-use the same names for the parameters and variables inside both `average_evens()` and `average_wrong()` without Python mixing them up. So, as we already read in the [Zen of Python](https://www.python.org/dev/peps/pep-0020/), \"namespaces are one honking great idea\" (cf., `import this`), and a frame is just a special kind of namespace."
    ]
   },
   {
@@ -1170,7 +1170,7 @@
     }
    },
    "source": [
-    "So far, we have only specified one parameter in each of our user-defined functions. In [Chapter 1](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/01_elements.ipynb), however, we saw the built-in function [divmod()](https://docs.python.org/3/library/functions.html#divmod) take two arguments. And, the order of the numbers passed in mattered! Whenever we call a function and list its arguments in a comma separated manner, we say that we pass in the arguments by position or refer to them as **positional arguments**."
+    "So far, we have only specified one parameter in each of our user-defined functions. In [Chapter 1](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/01_elements.ipynb#%28Arithmetic%29-Operators), however, we saw the built-in function [divmod()](https://docs.python.org/3/library/functions.html#divmod) take two arguments. And, the order of the numbers passed in mattered! Whenever we call a function and list its arguments in a comma separated manner, we say that we pass in the arguments by position or refer to them as **positional arguments**."
    ]
   },
   {
@@ -1941,7 +1941,7 @@
    "source": [
     "The main point of having functions without a name is to use them in a situation where we know ahead of time that we use the function *once* only.\n",
     "\n",
-    "Popular applications of lambda expressions are with the **map-filter-reduce** paradigm in Chapter 7 or when we do \"number crunching\" with **arrays** and **data frames** in Chapter 9."
+    "Popular applications of lambda expressions occur in combination with the **map-filter-reduce** paradigm or when we do \"number crunching\" with **arrays** and **data frames**. We look at both in detail in [Chapter 7](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/07_sequences.ipynb)."
    ]
   },
   {
@@ -2084,7 +2084,7 @@
     {
      "data": {
       "text/plain": [
-       "140519828111832"
+       "139829616976344"
       ]
      },
      "execution_count": 58,
@@ -2356,7 +2356,7 @@
    "source": [
     "Observe how the arguments passed to functions do not need to be just variables or simple literals. Instead, we may pass in any *expression* that evaluates to a *new* object of the type the function expects.\n",
     "\n",
-    "So just as a reminder from the expression vs. statement discussion in [Chapter 1](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/01_elements.ipynb): An expression is *any* syntactically correct combination of variables and literals with operators. And the call operator `()` is yet another operator. So both of the next two code cells are just expressions! They have no permanent side effects in memory. We may execute them as often as we want *without* changing the state of the program (i.e., this Jupyter notebook).\n",
+    "So just as a reminder from the expression vs. statement discussion in [Chapter 1](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/01_elements.ipynb#Expressions): An expression is *any* syntactically correct combination of variables and literals with operators. And the call operator `()` is yet another operator. So both of the next two code cells are just expressions! They have no permanent side effects in memory. We may execute them as often as we want *without* changing the state of the program (i.e., this Jupyter notebook).\n",
     "\n",
     "So, regarding the very next cell in particular: Although the `2 ** 2` creates a *new* object `4` in memory that is then immediately passed into the [math.sqrt()](https://docs.python.org/3/library/math.html#math.sqrt) function, once that function call returns, \"all is lost\" and the newly created `4` object is forgotten again, as well as the return value of [math.sqrt()](https://docs.python.org/3/library/math.html#math.sqrt)."
    ]
@@ -2537,7 +2537,7 @@
     }
    },
    "source": [
-    "Besides the usual dunder-style attributes, the built-in [dir()](https://docs.python.org/3/library/functions.html#dir) function lists some attributes in an upper case naming convention and many others starting with a *single* underscore `_`. To understand the former, we must wait until Chapter 10, while the latter is explained further below."
+    "Besides the usual dunder-style attributes, the built-in [dir()](https://docs.python.org/3/library/functions.html#dir) function lists some attributes in an upper case naming convention and many others starting with a *single* underscore `_`. To understand the former, we must wait until Chapter 9, while the latter is explained further below."
    ]
   },
   {
@@ -2697,7 +2697,7 @@
     {
      "data": {
       "text/plain": [
-       "0.5291407120147841"
+       "0.4165845384207939"
       ]
      },
      "execution_count": 75,
@@ -2732,7 +2732,7 @@
     {
      "data": {
       "text/plain": [
-       "<bound method Random.choice of <random.Random object at 0x5609be5deba8>>"
+       "<bound method Random.choice of <random.Random object at 0x56279067aaf8>>"
       ]
      },
      "execution_count": 76,
@@ -2781,7 +2781,7 @@
     {
      "data": {
       "text/plain": [
-       "4"
+       "2"
       ]
      },
      "execution_count": 78,
@@ -2927,9 +2927,9 @@
     }
    },
    "source": [
-    "[numpy](http://www.numpy.org/) is the de-facto standard in the Python world for handling **array-like** data. That is a fancy word for data that can be put into a matrix or vector format. We look at it in depth in Chapter 9.\n",
+    "[numpy](http://www.numpy.org/) is the de-facto standard in the Python world for handling **array-like** data. That is a fancy word for data that can be put into a matrix or vector format. We look at it in depth in [Chapter 7](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/07_sequences.ipynb).\n",
     "\n",
-    "As [numpy](http://www.numpy.org/) is *not* in the [standard library](https://docs.python.org/3/library/index.html), it must be *manually* installed, for example, with the [pip](https://pip.pypa.io/en/stable/) tool. As mentioned in [Chapter 0](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/00_start_up.ipynb), to execute terminal commands from within a Jupyter notebook, we start a code cell with an exclamation mark.\n",
+    "As [numpy](http://www.numpy.org/) is *not* in the [standard library](https://docs.python.org/3/library/index.html), it must be *manually* installed, for example, with the [pip](https://pip.pypa.io/en/stable/) tool. As mentioned in [Chapter 0](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/00_start_up.ipynb#Markdown-vs.-Code-Cells), to execute terminal commands from within a Jupyter notebook, we start a code cell with an exclamation mark.\n",
     "\n",
     "If you are running this notebook with an installation of the [Anaconda Distribution](https://www.anaconda.com/distribution/), then [numpy](http://www.numpy.org/) is probably already installed. Running the cell below confirms that."
    ]
@@ -3433,7 +3433,7 @@
     }
    },
    "source": [
-    "Packages are a generalization of modules, and we look at one in detail in Chapter 10. You may, however, already look at a [sample package](https://github.com/webartifex/intro-to-python/tree/master/sample_package) in the repository, which is nothing but a folder with *.py* files in it.\n",
+    "Packages are a generalization of modules, and we look at one in detail in Chapter 9. You may, however, already look at a [sample package](https://github.com/webartifex/intro-to-python/tree/master/sample_package) in the repository, which is nothing but a folder with *.py* files in it.\n",
     "\n",
     "As a further reading on modules and packages, we refer to the [official tutorial](https://docs.python.org/3/tutorial/modules.html)."
    ]
diff --git a/03_conditionals.ipynb b/03_conditionals.ipynb
index d0ed61d..90da64f 100644
--- a/03_conditionals.ipynb
+++ b/03_conditionals.ipynb
@@ -139,7 +139,7 @@
     }
    },
    "source": [
-    "There are, however, cases where even well-behaved Python does not make us happy. [Chapter 5](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/05_numbers.ipynb) provides more insights into this \"bug.\""
+    "There are, however, cases where even well-behaved Python does not make us happy. [Chapter 5](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/05_numbers.ipynb#Imprecision) provides more insights into this \"bug.\""
    ]
   },
   {
@@ -189,7 +189,7 @@
     {
      "data": {
       "text/plain": [
-       "94906834637792"
+       "94731133531104"
       ]
      },
      "execution_count": 5,
@@ -213,7 +213,7 @@
     {
      "data": {
       "text/plain": [
-       "94906834637760"
+       "94731133531072"
       ]
      },
      "execution_count": 6,
@@ -281,7 +281,7 @@
     }
    },
    "source": [
-    "Let's not confuse the boolean `False` with `None`, another built-in object! We saw the latter before in [Chapter 2](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/02_functions.ipynb) as the *implicit* return value of a function without a `return` statement.\n",
+    "Let's not confuse the boolean `False` with `None`, another built-in object! We saw the latter before in [Chapter 2](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/02_functions.ipynb#Function-Definition) as the *implicit* return value of a function without a `return` statement.\n",
     "\n",
     "We might think of `None` in a boolean context indicating a \"maybe\" or even an \"unknown\" answer; however, for Python, there are no \"maybe\" or \"unknown\" objects, as we see further below!\n",
     "\n",
@@ -313,7 +313,7 @@
     {
      "data": {
       "text/plain": [
-       "94906834624752"
+       "94731133518064"
       ]
      },
      "execution_count": 10,
@@ -357,7 +357,7 @@
     }
    },
    "source": [
-    "`True`, `False`, and `None` have the property that they each exist in memory only *once*. Objects designed this way are so-called **singletons**. This **[design pattern](https://en.wikipedia.org/wiki/Design_Patterns)** was originally developed to keep a program's memory usage at a minimum. It may only be employed in situations where we know that an object does *not* mutate its value (i.e., to re-use the bag analogy from [Chapter 1](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/01_elements.ipynb), no flipping of $0$s and $1$s in the bag is allowed). In languages \"closer\" to the memory like C, we would have to code this singleton logic ourselves, but Python has this built in for *some* types.\n",
+    "`True`, `False`, and `None` have the property that they each exist in memory only *once*. Objects designed this way are so-called **singletons**. This **[design pattern](https://en.wikipedia.org/wiki/Design_Patterns)** was originally developed to keep a program's memory usage at a minimum. It may only be employed in situations where we know that an object does *not* mutate its value (i.e., to re-use the bag analogy from [Chapter 1](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/01_elements.ipynb#Objects-vs.-Types-vs.-Values), no flipping of $0$s and $1$s in the bag is allowed). In languages \"closer\" to the memory like C, we would have to code this singleton logic ourselves, but Python has this built in for *some* types.\n",
     "\n",
     "We verify this with either the `is` operator or by comparing memory addresses."
    ]
@@ -897,9 +897,11 @@
     }
    },
    "source": [
-    "The operands of the logical operators do not need to be *boolean* expressions as defined above but may be *any* expression. If a sub-expression does *not* evaluate to an object of type `bool`, Python automatically casts it as such.\n",
+    "The operands of the logical operators do not need to be *boolean* expressions but may be *any* expression. If a sub-expression does *not* evaluate to an object of type `bool`, Python automatically casts it as such.\n",
     "\n",
-    "For example, any non-zero numeric object becomes `True`. While this behavior allows writing more concise and thus more \"beautiful\" code, it is also a common source of confusion. `(x - 9)` is cast as `True` and then the overall expression evaluates to `True` as well."
+    "For example, any non-zero numeric object becomes `True`. While this behavior allows writing more concise and thus more \"beautiful\" code, it is also a common source of confusion.\n",
+    "\n",
+    "So, `(x - 9)` is cast as `True` and then the overall expression evaluates to `True` as well."
    ]
   },
   {
@@ -1122,7 +1124,7 @@
     }
    },
    "source": [
-    "Pythonistas often use the terms **truthy** or **falsy** to describe a non-boolean expression's behavior when used in place of a boolean one."
+    "Pythonistas use the terms **truthy** or **falsy** to describe a non-boolean expression's behavior when evaluated in a boolean context."
    ]
   },
   {
@@ -1144,14 +1146,18 @@
     }
    },
    "source": [
-    "When evaluating boolean expressions with logical operators in it, Python follows the **[short-circuiting](https://en.wikipedia.org/wiki/Short-circuit_evaluation)** strategy: First, the inner-most sub-expressions are evaluated. Second, with identical **[operator precedence](https://docs.python.org/3/reference/expressions.html#operator-precedence)**, evaluation goes from left to right. Once it is clear what the overall truth value is, no more sub-expressions are evaluated, and the result is *immediately* returned.\n",
+    "When evaluating expressions involving the `and` and `or` operators, Python follows the **[short-circuiting](https://en.wikipedia.org/wiki/Short-circuit_evaluation)** strategy: Once it is clear what the overall truth value is, no more sub-expressions are evaluated, and the result is *immediately* returned.\n",
     "\n",
-    "In summary, data science practitioners must know *how* the following two generic expressions are evaluated:\n",
+    "Also, if such expressions are evaluated in a non-boolean context, the result is returned as is and *not* cast as a `bool` type.\n",
     "\n",
-    "- `x or y`: The `y` expression is evaluated *only if* `x` evaluates to `False`, in which case `y` is returned; otherwise, `x` is returned *without* even looking at `y`.\n",
-    "- `x and y`: The `y` expression is evaluated *only if* `x` evaluates to `True`. Then, if `y` also evaluates to `True`, it is returned; otherwise, `x` is returned.\n",
+    "The two rules can be summarized as:\n",
     "\n",
-    "Let's look at a couple of examples."
+    "- `x or y`: If `x` is truthy, it is returned *without* evaluating `y`. Otherwise, `y` is evaluated *and* returned.\n",
+    "- `x and y`: If `x` is falsy, it is returned *without* evaluating `y`. Otherwise, `y` is evaluated *and* returned.\n",
+    "\n",
+    "The rules may also be chained or combined.\n",
+    "\n",
+    "Let's look at a couple of examples below. To visualize which sub-expressions are evaluated, we define a helper function `expr()` that prints out the only argument it is passed before returning it."
    ]
   },
   {
@@ -1163,32 +1169,6 @@
     }
    },
    "outputs": [],
-   "source": [
-    "x = 0\n",
-    "y = 1\n",
-    "z = 2"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "source": [
-    "We define a helper function `expr()` that prints out the only argument it is passed before returning it. With `expr()`, we can see if a sub-expression is evaluated or not."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 37,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "fragment"
-    }
-   },
-   "outputs": [],
    "source": [
     "def expr(arg):\n",
     "    \"\"\"Print and return the only argument.\"\"\"\n",
@@ -1204,12 +1184,12 @@
     }
    },
    "source": [
-    "With the `or` operator, the first sub-expression that evaluates to `True` is returned."
+    "With the `or` operator, the first truthy sub-expression is returned."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 38,
+   "execution_count": 37,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -1230,18 +1210,18 @@
        "1"
       ]
      },
-     "execution_count": 38,
+     "execution_count": 37,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "expr(x) or expr(y)"
+    "expr(0) or expr(1)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 39,
+   "execution_count": 38,
    "metadata": {
     "slideshow": {
      "slide_type": "-"
@@ -1261,18 +1241,18 @@
        "1"
       ]
      },
-     "execution_count": 39,
+     "execution_count": 38,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "expr(y) or expr(z)"
+    "expr(1) or expr(2)  # 2 is not evaluated due to short-circuiting"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 40,
+   "execution_count": 39,
    "metadata": {
     "slideshow": {
      "slide_type": "-"
@@ -1293,13 +1273,13 @@
        "1"
       ]
      },
-     "execution_count": 40,
+     "execution_count": 39,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "expr(x) or expr(y) or expr(z)"
+    "expr(0) or expr(1) or expr(2)  # 2 is not evaluated due to short-circuiting"
    ]
   },
   {
@@ -1310,12 +1290,12 @@
     }
    },
    "source": [
-    "If all sub-expressions evaluate to `False`, the last one is the result."
+    "If all sub-expressions are falsy, the last one is returned."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 41,
+   "execution_count": 40,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -1337,13 +1317,13 @@
        "0"
       ]
      },
-     "execution_count": 41,
+     "execution_count": 40,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "expr(False) or expr([]) or expr(x)"
+    "expr(False) or expr([]) or expr(0)"
    ]
   },
   {
@@ -1354,12 +1334,12 @@
     }
    },
    "source": [
-    "With the `and` operator, the first sub-expression that evaluates to `False` is returned."
+    "With the `and` operator, the first falsy sub-expression is returned."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 42,
+   "execution_count": 41,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -1373,6 +1353,38 @@
       "Arg: 0\n"
      ]
     },
+    {
+     "data": {
+      "text/plain": [
+       "0"
+      ]
+     },
+     "execution_count": 41,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "expr(0) and expr(1)  # 1 is not evaluated due to short-circuiting"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Arg: 1\n",
+      "Arg: 0\n"
+     ]
+    },
     {
      "data": {
       "text/plain": [
@@ -1385,7 +1397,7 @@
     }
    ],
    "source": [
-    "expr(x) and expr(y)"
+    "expr(1) and expr(0)"
    ]
   },
   {
@@ -1417,40 +1429,7 @@
     }
    ],
    "source": [
-    "expr(y) and expr(x)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 44,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "-"
-    }
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Arg: 2\n",
-      "Arg: 1\n",
-      "Arg: 0\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "0"
-      ]
-     },
-     "execution_count": 44,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "expr(z) and expr(y) and expr(x)"
+    "expr(1) and expr(0) and expr(2)  # 2 is not evaluated due to short-circuiting"
    ]
   },
   {
@@ -1461,12 +1440,12 @@
     }
    },
    "source": [
-    "If all sub-expressions evaluate to `True`, the last one is returned."
+    "If all sub-expressions are truthy, the last one is returned."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 45,
+   "execution_count": 44,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -1487,13 +1466,13 @@
        "2"
       ]
      },
-     "execution_count": 45,
+     "execution_count": 44,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "expr(y) and expr(z)"
+    "expr(1) and expr(2)"
    ]
   },
   {
@@ -1543,7 +1522,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 46,
+   "execution_count": 45,
    "metadata": {
     "code_folding": [],
     "slideshow": {
@@ -1557,7 +1536,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 47,
+   "execution_count": 46,
    "metadata": {
     "code_folding": [],
     "slideshow": {
@@ -1599,7 +1578,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 48,
+   "execution_count": 47,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -1612,13 +1591,21 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 49,
+   "execution_count": 48,
    "metadata": {
     "slideshow": {
      "slide_type": "-"
     }
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "You read this just as often as you see heads when tossing a coin\n"
+     ]
+    }
+   ],
    "source": [
     "if random.random() > 0.5:\n",
     "    print(\"You read this just as often as you see heads when tossing a coin\")"
@@ -1637,7 +1624,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 50,
+   "execution_count": 49,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -1674,7 +1661,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 51,
+   "execution_count": 50,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -1717,7 +1704,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 52,
+   "execution_count": 51,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -1790,7 +1777,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 53,
+   "execution_count": 52,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -1814,7 +1801,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 54,
+   "execution_count": 53,
    "metadata": {
     "slideshow": {
      "slide_type": "-"
@@ -1830,7 +1817,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 55,
+   "execution_count": 54,
    "metadata": {
     "slideshow": {
      "slide_type": "-"
@@ -1843,7 +1830,7 @@
        "9"
       ]
      },
-     "execution_count": 55,
+     "execution_count": 54,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1865,7 +1852,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 56,
+   "execution_count": 55,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -1878,7 +1865,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 57,
+   "execution_count": 56,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -1891,7 +1878,7 @@
        "9"
       ]
      },
-     "execution_count": 57,
+     "execution_count": 56,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1913,7 +1900,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 58,
+   "execution_count": 57,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -1926,7 +1913,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 59,
+   "execution_count": 58,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -1939,7 +1926,7 @@
        "9"
       ]
      },
-     "execution_count": 59,
+     "execution_count": 58,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1956,7 +1943,7 @@
     }
    },
    "source": [
-    "Conditional expressions may not only be used in the way described in this section. We already saw them as part of a *list comprehension* in [Chapter 1](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/01_elements.ipynb) and [Chapter 2](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/02_functions.ipynb) and revisit this in Chapter 7 in greater detail."
+    "Conditional expressions may not only be used in the way described in this section. We already saw them as part of a *list comprehension* in [Chapter 1](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/01_elements.ipynb) and [Chapter 2](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/02_functions.ipynb) and revisit this construct in greater detail in [Chapter 7](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/07_sequences.ipynb#List-Comprehensions)."
    ]
   },
   {
@@ -1987,7 +1974,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 60,
+   "execution_count": 59,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -2000,7 +1987,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 61,
+   "execution_count": 60,
    "metadata": {
     "slideshow": {
      "slide_type": "-"
@@ -2039,7 +2026,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 62,
+   "execution_count": 61,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -2078,7 +2065,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 63,
+   "execution_count": 62,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -2089,7 +2076,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Yes, division worked smoothly.\n",
+      "Oops. Division by 0. How does that work?\n",
       "I am always printed\n"
      ]
     }
diff --git a/04_iteration.ipynb b/04_iteration.ipynb
index b760a25..3a2e42d 100644
--- a/04_iteration.ipynb
+++ b/04_iteration.ipynb
@@ -8,7 +8,7 @@
     }
    },
    "source": [
-    "# Chapter 4: Iteration"
+    "# Chapter 4: Recursion & Looping"
    ]
   },
   {
@@ -859,7 +859,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "39.9 µs ± 5.5 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n"
+      "69.6 µs ± 25.8 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n"
      ]
     }
    ],
@@ -881,7 +881,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "1.61 ms ± 8.84 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n"
+      "1.55 ms ± 11.9 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n"
      ]
     }
    ],
@@ -903,7 +903,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "199 ms ± 0 ns per loop (mean ± std. dev. of 1 run, 1 loop each)\n"
+      "189 ms ± 0 ns per loop (mean ± std. dev. of 1 run, 1 loop each)\n"
      ]
     }
    ],
@@ -925,7 +925,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "2.21 s ± 0 ns per loop (mean ± std. dev. of 1 run, 1 loop each)\n"
+      "2.07 s ± 0 ns per loop (mean ± std. dev. of 1 run, 1 loop each)\n"
      ]
     }
    ],
@@ -947,7 +947,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "5.8 s ± 0 ns per loop (mean ± std. dev. of 1 run, 1 loop each)\n"
+      "5.41 s ± 0 ns per loop (mean ± std. dev. of 1 run, 1 loop each)\n"
      ]
     }
    ],
@@ -4289,7 +4289,7 @@
     }
    },
    "source": [
-    "With everything *officially* introduced so far (i.e., without the introductory example in [Chapter 1](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/01_elements.ipynb) that only served as an overview), Python would be what is called **[Turing complete](https://en.wikipedia.org/wiki/Turing_completeness)**. That means that anything that could be formulated as an algorithm could be expressed with all the language features we have seen. Note that, in particular, we have *not* yet formally *introduced* the `for` and `while` statements!"
+    "With everything *officially* introduced so far, Python would be what is called **[Turing complete](https://en.wikipedia.org/wiki/Turing_completeness)**. That means that anything that could be formulated as an algorithm could be expressed with all the language features we have seen. Note that, in particular, we have *not* yet formally *introduced* the `for` and `while` statements!"
    ]
   },
   {
@@ -4565,7 +4565,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "4.69 s ± 0 ns per loop (mean ± std. dev. of 1 run, 1 loop each)\n"
+      "4.9 s ± 0 ns per loop (mean ± std. dev. of 1 run, 1 loop each)\n"
      ]
     }
    ],
@@ -4877,7 +4877,7 @@
     }
    },
    "source": [
-    "For sequences of integers, the [range()](https://docs.python.org/3/library/functions.html#func-range) built-in makes the `for` statement even more convenient: It creates a list-like object of type `range` that generates integers \"on the fly,\" and we look closely at the underlying effects in memory in Chapter 7."
+    "For sequences of integers, the [range()](https://docs.python.org/3/library/functions.html#func-range) built-in makes the `for` statement even more convenient: It creates a list-like object of type `range` that generates integers \"on the fly,\" and we look closely at the underlying effects in memory in [Chapter 7](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/07_sequences.ipynb#Mapping)."
    ]
   },
   {
@@ -5008,13 +5008,13 @@
     "\n",
     "Now, just as we classify objects by their types, we also classify these **concrete data types** (e.g., `int`, `float`, `str`, or `list`) into **abstract concepts**.\n",
     "\n",
-    "We did this already in [Chapter 1](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/01_elements.ipynb) when we described a `list` object as \"some sort of container that holds [...] pointers to other objects\". So, abstractly speaking, **containers** are any objects that are \"composed\" of other objects and also \"manage\" how these objects are organized. `list` objects, for example, have the property that they model an internal order associated with its elements. There exist, however, other container types, many of which do *not* come with an order. So, containers primarily \"contain\" other objects and have *nothing* to do with looping.\n",
+    "We did this already in [Chapter 1](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/01_elements.ipynb#Who-am-I?-And-how-many?) when we described a `list` object as \"some sort of container that holds [...] pointers to other objects\". So, abstractly speaking, **containers** are any objects that are \"composed\" of other objects and also \"manage\" how these objects are organized. `list` objects, for example, have the property that they model an internal order associated with its elements. There exist, however, other container types, many of which do *not* come with an order. So, containers primarily \"contain\" other objects and have *nothing* to do with looping.\n",
     "\n",
     "On the contrary, the abstract concept of **iterables** is all about looping: Any object that we can loop over is, by definition, an iterable. So, `range` objects, for example, are iterables that do *not* contain other objects. Moreover, looping does *not* have to occur in a *predictable* order, although this is the case for both `list` and `range` objects.\n",
     "\n",
-    "Typically, containers are iterables, and iterables are containers. Yet, only because these two concepts coincide often, we must not think of them as the same. Chapter 10 finally gives an explanation as to how abstract concepts are implemented and play together.\n",
+    "Typically, containers are iterables, and iterables are containers. Yet, only because these two concepts coincide often, we must not think of them as the same. Chapter 9 finally gives an explanation as to how abstract concepts are implemented and play together.\n",
     "\n",
-    "So, `list` objects like `first_names` below are iterable containers. They implement even more abstract concepts, as Chapter 7 reveals."
+    "So, `list` objects like `first_names` below are iterable containers. They implement even more abstract concepts, as [Chapter 7](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/07_sequences.ipynb#Collections-vs.-Sequences) reveals."
    ]
   },
   {
@@ -5165,7 +5165,7 @@
     }
    },
    "source": [
-    "If we must have an index variable in the loop's body, we use the [enumerate()](https://docs.python.org/3/library/functions.html#enumerate) built-in that takes an *iterable* as its argument and then generates a \"stream\" of \"pairs\" consisting of an index variable and an object provided by the iterable. There is *no* need to ever revert to the `while` statement to loop over an iterable object."
+    "If we must have an index variable in the loop's body, we use the [enumerate()](https://docs.python.org/3/library/functions.html#enumerate) built-in that takes an *iterable* as its argument and then generates a \"stream\" of \"pairs\" of an index variable, `i` below, and an object provided by the iterable, `name`, separated by a `,`. There is *no* need to ever revert to the `while` statement with an explicitly managed index variable to loop over an iterable object."
    ]
   },
   {
@@ -5288,7 +5288,7 @@
     }
    },
    "source": [
-    "In contrast to its recursive counterpart, the iterative `fibonacci()` function below is somewhat harder to read. For example, it is not so obvious as to how many iterations through the `for`-loop we need to make when implementing it. There is an increased risk of making an *off-by-one* error. Moreover, we need to track a `temp` variable along, at least until we have worked through Chapter 7. Do you understand what `temp` does?\n",
+    "In contrast to its recursive counterpart, the iterative `fibonacci()` function below is somewhat harder to read. For example, it is not so obvious as to how many iterations through the `for`-loop we need to make when implementing it. There is an increased risk of making an *off-by-one* error. Moreover, we need to track a `temp` variable along.\n",
     "\n",
     "However, one advantage of calculating Fibonacci numbers in a **forward** fashion with a `for` statement is that we could list the entire sequence in ascending order as we calculate the desired number. To show this, we added `print()` statements in `fibonacci()` below.\n",
     "\n",
@@ -5843,7 +5843,7 @@
     }
    },
    "source": [
-    "Often, we process some iterable with numeric data, for example, a list of numbers as in the introductory example in [Chapter 1](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/01_elements.ipynb) or, more realistically, data from a CSV file with many rows and columns.\n",
+    "Often, we process some iterable with numeric data, for example, a list of numbers as in the introductory example in [Chapter 1](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/01_elements.ipynb#Example:-Average-of-a-Subset-of-Numbers) or, more realistically, data from a CSV file with many rows and columns.\n",
     "\n",
     "Processing numeric data usually comes down to operations that may be grouped into one of the following three categories:\n",
     "\n",
@@ -5851,7 +5851,7 @@
     "- **filtering**: throw away individual samples (e.g., statistical outliers)\n",
     "- **reducing**: collect individual samples into summary statistics\n",
     "\n",
-    "We study this **map-filter-reduce** paradigm extensively in Chapter 7 after introducing more advanced data types that are needed to work with \"big\" data.\n",
+    "We study this **map-filter-reduce** paradigm extensively in [Chapter 7](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/07_sequences.ipynb#The-Map-Filter-Reduce-Paradigm) after introducing more advanced data types that are needed to work with \"big\" data.\n",
     "\n",
     "In the remainder of this section, we focus on *filtering out* some samples within a `for`-loop."
    ]
diff --git a/04_iteration_review_and_exercises.ipynb b/04_iteration_review_and_exercises.ipynb
index a953796..64d6903 100644
--- a/04_iteration_review_and_exercises.ipynb
+++ b/04_iteration_review_and_exercises.ipynb
@@ -5,7 +5,7 @@
    "metadata": {},
    "source": [
     "\n",
-    "# Chapter 4: Iteration"
+    "# Chapter 4: Recursion & Looping"
    ]
   },
   {
diff --git a/05_numbers.ipynb b/05_numbers.ipynb
index 73daebb..c545cde 100644
--- a/05_numbers.ipynb
+++ b/05_numbers.ipynb
@@ -8,7 +8,7 @@
     }
    },
    "source": [
-    "# Chapter 5: Numbers"
+    "# Chapter 5: Bits & Numbers"
    ]
   },
   {
@@ -21,9 +21,9 @@
    "source": [
     "After learning about the basic building blocks of expressing and structuring the business logic in programs, we focus our attention on the **data types** Python offers us, both built-in and available via the [standard library](https://docs.python.org/3/library/index.html) or third-party packages.\n",
     "\n",
-    "We start with the \"simple\" ones: Numeric types in this chapter and textual data in [Chapter 6](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/06_text.ipynb). An important fact that holds for all objects of these types is that they are **immutable**. To re-use the bag analogy from [Chapter 1](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/01_elements.ipynb#Objects-vs.-Types-vs.-Values), this means that the $0$s and $1$s making up an object's *value* cannot be changed once the bag is created in memory, implying that any operation with or method on the object creates a *new* object in a *different* memory location.\n",
+    "We start with the \"simple\" ones: Numerical types in this chapter and textual data in [Chapter 6](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/06_text.ipynb). An important fact that holds for all objects of these types is that they are **immutable**. To re-use the bag analogy from [Chapter 1](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/01_elements.ipynb#Objects-vs.-Types-vs.-Values), this means that the $0$s and $1$s making up an object's *value* cannot be changed once the bag is created in memory, implying that any operation with or method on the object creates a *new* object in a *different* memory location.\n",
     "\n",
-    "Chapters 7, 8, and 9 then cover the more \"complex\" data types, including, for example, the `list` type. Finally, Chapter 10 completes the picture by introducing language constructs to create custom types.\n",
+    "[Chapter 7](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/07_sequences.ipynb) and Chapter 8 then cover the more \"complex\" data types, including, for example, the `list` type. Finally, Chapter 9 completes the picture by introducing language constructs to create custom types.\n",
     "\n",
     "We have already seen many hints indicating that numbers are not as trivial to work with as it seems at first sight:\n",
     "\n",
@@ -31,7 +31,7 @@
     "- [Chapter 3](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/03_conditionals.ipynb#Boolean-Expressions) raises questions regarding the **limited precision** of `float` numbers (e.g., `42 == 42.000000000000001` evaluates to `True`), and\n",
     "- [Chapter 4](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/04_iteration.ipynb#Infinite-Recursion) shows that sometimes a `float` \"walks\" and \"quacks\" like an `int`, whereas the reverse is true in other cases.\n",
     "\n",
-    "This chapter introduces all the [built-in numeric types](https://docs.python.org/3/library/stdtypes.html#numeric-types-int-float-complex): `int`, `float`, and `complex`. To mitigate the limited precision of floating-point numbers, we also look at two replacements for the `float` type in the [standard library](https://docs.python.org/3/library/index.html), namely the `Decimal` type in the [decimals](https://docs.python.org/3/library/decimal.html#decimal.Decimal) and the `Fraction` type in the [fractions](https://docs.python.org/3/library/fractions.html#fractions.Fraction) module."
+    "This chapter introduces all the [built-in numerical types](https://docs.python.org/3/library/stdtypes.html#numeric-types-int-float-complex): `int`, `float`, and `complex`. To mitigate the limited precision of floating-point numbers, we also look at two replacements for the `float` type in the [standard library](https://docs.python.org/3/library/index.html), namely the `Decimal` type in the [decimals](https://docs.python.org/3/library/decimal.html#decimal.Decimal) and the `Fraction` type in the [fractions](https://docs.python.org/3/library/fractions.html#fractions.Fraction) module."
    ]
   },
   {
@@ -53,7 +53,7 @@
     }
    },
    "source": [
-    "The simplest numeric type is the `int` type: It behaves like an [integer in ordinary math](https://en.wikipedia.org/wiki/Integer) (i.e., the set $\\mathbb{Z}$) and supports operators in the way we saw in the section on arithmetic operators in [Chapter 1](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/01_elements.ipynb#%28Arithmetic%29-Operators)."
+    "The simplest numerical type is the `int` type: It behaves like an [integer in ordinary math](https://en.wikipedia.org/wiki/Integer) (i.e., the set $\\mathbb{Z}$) and supports operators in the way we saw in the section on arithmetic operators in [Chapter 1](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/01_elements.ipynb#%28Arithmetic%29-Operators)."
    ]
   },
   {
@@ -92,7 +92,7 @@
     {
      "data": {
       "text/plain": [
-       "139928698315984"
+       "139630197982416"
       ]
      },
      "execution_count": 2,
@@ -160,7 +160,7 @@
     }
    },
    "source": [
-    "A nice feature is using underscores `_` as (thousands) separator in numeric literals. For example, `1_000_000` evaluates to `1000000` in memory."
+    "A nice feature is using underscores `_` as (thousands) separator in numerical literals. For example, `1_000_000` evaluates to `1000000` in memory."
    ]
   },
   {
@@ -1165,7 +1165,7 @@
     }
    },
    "source": [
-    "Whereas the boolean literals `True` and `False` are commonly *not* regarded as numeric types, they behave like `1` and `0` in an arithmetic context."
+    "Whereas the boolean literals `True` and `False` are commonly *not* regarded as numerical types, they behave like `1` and `0` in an arithmetic context."
    ]
   },
   {
@@ -2115,7 +2115,7 @@
     {
      "data": {
       "text/plain": [
-       "139928698489496"
+       "139630198164096"
       ]
      },
      "execution_count": 63,
@@ -2218,7 +2218,7 @@
     }
    },
    "source": [
-    "In cases where the dot `.` is unnecessary from a mathematical point of view, we either need to end the number with it nevertheless or use the [float()](https://docs.python.org/3/library/functions.html#float) built-in to cast the number explicitly. [float()](https://docs.python.org/3/library/functions.html#float) can process any numeric object or a properly formatted `str` object."
+    "In cases where the dot `.` is unnecessary from a mathematical point of view, we either need to end the number with it nevertheless or use the [float()](https://docs.python.org/3/library/functions.html#float) built-in to cast the number explicitly. [float()](https://docs.python.org/3/library/functions.html#float) can process any numerical object or a properly formatted `str` object."
    ]
   },
   {
@@ -2242,7 +2242,7 @@
     }
    ],
    "source": [
-    "1.  # on the contrary, 1 creates an int object"
+    "1.  # 1 without a dot creates an int object"
    ]
   },
   {
@@ -3517,7 +3517,7 @@
    "source": [
     "The [format()](https://docs.python.org/3/library/functions.html#format) function does *not* round a `float` object in the mathematical sense! It just allows us to show an arbitrary number of the digits as stored in memory, and it also does *not* change these.\n",
     "\n",
-    "On the contrary, the built-in [round()](https://docs.python.org/3/library/functions.html#round) function creates a *new* numeric object that is a rounded version of the one passed in as the argument. It adheres to the common rules of math.\n",
+    "On the contrary, the built-in [round()](https://docs.python.org/3/library/functions.html#round) function creates a *new* numerical object that is a rounded version of the one passed in as the argument. It adheres to the common rules of math.\n",
     "\n",
     "For example, let's round `1 / 3` to five decimals. The obtained value for `roughly_a_third` is also *imprecise* but different from the \"exact\" representation of `1 / 3` above."
    ]
@@ -4617,7 +4617,7 @@
     }
    },
    "source": [
-    "The [quantize()](https://docs.python.org/3/library/decimal.html#decimal.Decimal.quantize) method allows us to [quantize](https://www.dictionary.com/browse/quantize) (i.e., \"round\") a `Decimal` number at any precision that is *smaller* than the set precision. It looks at the number of decimals (i.e., to the right of the period) of the numeric argument we pass in.\n",
+    "The [quantize()](https://docs.python.org/3/library/decimal.html#decimal.Decimal.quantize) method allows us to [quantize](https://www.dictionary.com/browse/quantize) (i.e., \"round\") a `Decimal` number at any precision that is *smaller* than the set precision. It looks at the number of decimals (i.e., to the right of the period) of the numerical argument we pass in.\n",
     "\n",
     "For example, as the overall imprecise value of `two` still has an internal precision of `28` digits, we can correctly round it to *four* decimals (i.e., `Decimal(\"0.0001\")` has four decimals)."
    ]
@@ -5498,7 +5498,7 @@
     {
      "data": {
       "text/plain": [
-       "139928697740272"
+       "139630197402224"
       ]
      },
      "execution_count": 176,
@@ -5636,7 +5636,7 @@
     }
    },
    "source": [
-    "Alternatively, we may use the [complex()](https://docs.python.org/3/library/functions.html#complex) built-in: This takes two parameters where the second is optional and defaults to `0`. We may either call it with one or two arguments of any numeric type or a `str` object in the format of the previous code cell without any spaces."
+    "Alternatively, we may use the [complex()](https://docs.python.org/3/library/functions.html#complex) built-in: This takes two parameters where the second is optional and defaults to `0`. We may either call it with one or two arguments of any numerical type or a `str` object in the format of the previous code cell without any spaces."
    ]
   },
   {
@@ -5708,7 +5708,7 @@
     }
    ],
    "source": [
-    "complex(Decimal(\"2.0\"), Fraction(1, 2))  # the arguments may be any numeric type"
+    "complex(Decimal(\"2.0\"), Fraction(1, 2))  # the arguments may be any numerical type"
    ]
   },
   {
@@ -5743,7 +5743,7 @@
     }
    },
    "source": [
-    "Arithmetic expressions work with `complex` numbers. They may be mixed with the other numeric types, and the result is always a `complex` number."
+    "Arithmetic expressions work with `complex` numbers. They may be mixed with the other numerical types, and the result is always a `complex` number."
    ]
   },
   {
@@ -6100,7 +6100,7 @@
     }
    },
    "source": [
-    "## The Numeric Tower"
+    "## The Numerical Tower"
    ]
   },
   {
@@ -6111,7 +6111,7 @@
     }
    },
    "source": [
-    "Analogous to the discussion of containers and iterables in [Chapter 4](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/04_iteration.ipynb#Containers-vs.-Iterables), we contrast the *concrete* numeric data types in this chapter with the *abstract* ideas behind [numbers in mathematics](https://en.wikipedia.org/wiki/Number).\n",
+    "Analogous to the discussion of containers and iterables in [Chapter 4](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/04_iteration.ipynb#Containers-vs.-Iterables), we contrast the *concrete* numerical data types in this chapter with the *abstract* ideas behind [numbers in mathematics](https://en.wikipedia.org/wiki/Number).\n",
     "\n",
     "The figure below summarizes five *major* sets of [numbers in mathematics](https://en.wikipedia.org/wiki/Number) as we know them from high school:\n",
     "\n",
@@ -6149,7 +6149,7 @@
     "\n",
     "For the other types, in particular, the `float` type, the implications of their imprecision are discussed in detail above.\n",
     "\n",
-    "The abstract concepts behind the four outer-most mathematical sets are part of Python since [PEP 3141](https://www.python.org/dev/peps/pep-3141/) in 2007. The [numbers](https://docs.python.org/3/library/numbers.html) module in the [standard library](https://docs.python.org/3/library/index.html) defines what Pythonistas call the **numeric tower**, a collection of five **[abstract data types](https://en.wikipedia.org/wiki/Abstract_data_type)**, or **abstract base classes** as they are called in Python jargon:\n",
+    "The abstract concepts behind the four outer-most mathematical sets are part of Python since [PEP 3141](https://www.python.org/dev/peps/pep-3141/) in 2007. The [numbers](https://docs.python.org/3/library/numbers.html) module in the [standard library](https://docs.python.org/3/library/index.html) defines what programmers call the **[numerical tower](https://en.wikipedia.org/wiki/Numerical_tower)**, a collection of five **[abstract data types](https://en.wikipedia.org/wiki/Abstract_data_type)**, or **abstract base classes** as they are called in Python jargon:\n",
     "\n",
     "- `numbers.Number`: \"any number\" (cf., [documentation](https://docs.python.org/3/library/numbers.html#numbers.Number))\n",
     "- `numbers.Complex`: \"all complex numbers\" (cf., [documentation](https://docs.python.org/3/library/numbers.html#numbers.Complex))\n",
@@ -6712,7 +6712,7 @@
     }
    },
    "source": [
-    "#### Example: [Factorial](https://en.wikipedia.org/wiki/Factorial) (revisited)"
+    "### Goose Typing"
    ]
   },
   {
@@ -6723,7 +6723,9 @@
     }
    },
    "source": [
-    "Replacing *concrete* data types with *abstract* ones is particularly valuable in the context of input validation: The revised version of the `factorial()` function below allows its user to *take advantage of duck typing*. If a real but non-integer argument `n` is passed in, `factorial()` tries to cast `n` as an `int` object with the [trunc()](https://docs.python.org/3/library/math.html#math.trunc) function from the [math](https://docs.python.org/3/library/math.html) module in the [standard library](https://docs.python.org/3/library/index.html). [trunc()](https://docs.python.org/3/library/math.html#math.trunc) cuts off all decimals and any *concrete* numeric type implementing the *abstract* `numbers.Real` type supports it (cf., [documentation](https://docs.python.org/3/library/numbers.html#numbers.Real))."
+    "Replacing *concrete* data types with *abstract* ones is particularly valuable in the context of input validation: The revised version of the `factorial()` function below allows its user to take advantage of *duck typing*: If a real but non-integer argument `n` is passed in, `factorial()` tries to cast `n` as an `int` object with the [trunc()](https://docs.python.org/3/library/math.html#math.trunc) function from the [math](https://docs.python.org/3/library/math.html) module in the [standard library](https://docs.python.org/3/library/index.html). [trunc()](https://docs.python.org/3/library/math.html#math.trunc) cuts off all decimals and any *concrete* numerical type implementing the *abstract* `numbers.Real` type supports it (cf., [documentation](https://docs.python.org/3/library/numbers.html#numbers.Real)).\n",
+    "\n",
+    "Two popular and distinguished Pythonistas, [Luciano Ramalho](https://github.com/ramalho) and [Alex Martelli](https://en.wikipedia.org/wiki/Alex_Martelli), coin the term **goose typing** to specifically mean using the built-in [isinstance()](https://docs.python.org/3/library/functions.html#isinstance) function with an *abstract base class* (cf., Chapter 11 in this [book](https://www.amazon.com/Fluent-Python-Concise-Effective-Programming/dp/1491946008) or this [summary](https://dgkim5360.github.io/blog/python/2017/07/duck-typing-vs-goose-typing-pythonic-interfaces/) thereof)."
    ]
   },
   {
@@ -6763,6 +6765,17 @@
     "math.trunc(1 / 10)"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "#### Example: [Factorial](https://en.wikipedia.org/wiki/Factorial) (revisited)"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 211,
@@ -6893,7 +6906,7 @@
     }
    },
    "source": [
-    "With the keyword-only argument `strict`, we can control whether or not a passed in `float` object may be rounded. By default, this is not allowed."
+    "With the keyword-only argument `strict`, we can control whether or not a passed in `float` object may be rounded. By default, this is not allowed and results in a `TypeError`."
    ]
   },
   {
@@ -7002,21 +7015,21 @@
     }
    },
    "source": [
-    "There exist three numeric types in core Python:\n",
-    "- `int`: a perfect model for whole numbers (i.e., the set $\\mathbb{Z}$); inherently precise\n",
+    "There exist three numerical types in core Python:\n",
+    "- `int`: a near-perfect model for whole numbers (i.e., the set $\\mathbb{Z}$); inherently precise\n",
     "- `float`: the \"gold\" standard to approximate real numbers (i.e., the set $\\mathbb{R}$); inherently imprecise\n",
     "- `complex`: layer on top of the `float` type; therefore inherently imprecise\n",
     "\n",
     "Furthermore, the [standard library](https://docs.python.org/3/library/index.html) adds two more types that can be used as substitutes for `float` objects:\n",
     "- `Decimal`: similar to `float` but allows customizing the precision; still inherently imprecise\n",
-    "- `Fraction`: a perfect model for rational numbers (i.e., the set $\\mathbb{Q}$); built on top of the `int` type and therefore inherently precise\n",
+    "- `Fraction`: a near-perfect model for rational numbers (i.e., the set $\\mathbb{Q}$); built on top of the `int` type and therefore inherently precise\n",
     "\n",
     "The *important* takeaways for the data science practitioner are:\n",
     "\n",
     "1. **Do not mix** precise and imprecise data types, and\n",
     "2. actively expect `nan` results when working with `float` numbers as there are no **loud failures**.\n",
     "\n",
-    "The **numeric tower** is Python's way of implementing the various **abstract** ideas of what a number is in mathematics."
+    "The **numerical tower** is Python's way of implementing the various **abstract** ideas of what numbers are in mathematics."
    ]
   }
  ],
diff --git a/05_numbers_review_and_exercises.ipynb b/05_numbers_review_and_exercises.ipynb
index 4246965..90983d4 100644
--- a/05_numbers_review_and_exercises.ipynb
+++ b/05_numbers_review_and_exercises.ipynb
@@ -5,7 +5,7 @@
    "metadata": {},
    "source": [
     "\n",
-    "# Chapter 5: Numbers"
+    "# Chapter 5: Bits & Numbers"
    ]
   },
   {
@@ -156,7 +156,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "**Q9**: How can **abstract data types**, for example, as defined in the **numeric tower**, be helpful in enabling **duck typing**?"
+    "**Q9**: How can **abstract data types**, for example, as defined in the **numerical tower**, be helpful in enabling **duck typing**?"
    ]
   },
   {
diff --git a/06_text.ipynb b/06_text.ipynb
index 4cdd4ab..9092188 100644
--- a/06_text.ipynb
+++ b/06_text.ipynb
@@ -8,7 +8,7 @@
     }
    },
    "source": [
-    "# Chapter 6: Text"
+    "# Chapter 6: Bytes & Text"
    ]
   },
   {
@@ -19,7 +19,7 @@
     }
    },
    "source": [
-    "In this chapter, we continue the study of the built-in data types. Building on our knowledge of numbers, the next layer consists of textual data that are modeled primarily with the `str` type in Python. `str` objects are naturally more \"complex\" than numeric objects as any text consists of an arbitrary and possibly large number of individual characters that may be chosen from any alphabet in the history of humankind. Luckily, Python abstracts away most of this complexity."
+    "In this chapter, we continue the study of the built-in data types. Building on our knowledge of numbers, the next layer consists of textual data that are modeled primarily with the `str` type in Python. `str` objects are naturally more \"complex\" than numerical objects as any text consists of an arbitrary and possibly large number of individual characters that may be chosen from any alphabet in the history of humankind. Luckily, Python abstracts away most of this complexity."
    ]
   },
   {
@@ -80,7 +80,7 @@
     {
      "data": {
       "text/plain": [
-       "140148947850512"
+       "140133793916176"
       ]
      },
      "execution_count": 2,
@@ -124,7 +124,7 @@
     }
    },
    "source": [
-    "A `str` object evaluates to itself in a literal notation with enclosing **single quotes** `'` by default. In [Chapter 1](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/01_elements.ipynb), we already specified the double quotes `\"` convention we stick to in this book. Yet, single quotes `'` and double quotes `\"` are *perfect* substitutes for all `str` objects that do *not* contain any of the two symbols in it. We could have used the reverse convention, as well.\n",
+    "A `str` object evaluates to itself in a literal notation with enclosing **single quotes** `'` by default. In [Chapter 1](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/01_elements.ipynb#Value), we already specified the double quotes `\"` convention we stick to in this book. Yet, single quotes `'` and double quotes `\"` are *perfect* substitutes for all `str` objects that do *not* contain any of the two symbols in it. We could use the reverse convention, as well.\n",
     "\n",
     "As [this discussion](https://stackoverflow.com/questions/56011/single-quotes-vs-double-quotes-in-python) shows, many programmers have *strong* opinions about that and make up *new* conventions for their projects. Consequently, the discussion was \"closed as not constructive\" by the moderators."
    ]
@@ -161,7 +161,7 @@
     }
    },
    "source": [
-    "As the single quote `'` is often used in the English language as a shortener, we could make an argument in favor of using the double quotes `\"`: There are possibly fewer situations like in the two code cells below, in which we must revert to using a `\\` to **escape** a single quote `'` in a text (cf., the section on special characters further below). However, double quotes `\"` are often used as well. So, this argument is somewhat not convincing.\n",
+    "As the single quote `'` is often used in the English language as a shortener, we could make an argument in favor of using the double quotes `\"`: There are possibly fewer situations like in the two code cells below, in which we must revert to using a `\\` to **escape** a single quote `'` in a text (cf., the [Special Characters](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/06_text.ipynb#Special-Characters) section further below). However, double quotes `\"` are often used as well. So, this argument is somewhat not convincing.\n",
     "\n",
     "Many proponents of the single quote `'` usage claim that double quotes `\"` make more **visual noise** on the screen. This argument is also not convincing. On the contrary, one could claim that *two* single quotes `''` look so similar to *one* double quote `\"` that it might not be apparent right away what we are looking at. By sticking to double quotes `\"`, we avoid such danger of confusion.\n",
     "\n",
@@ -226,7 +226,7 @@
     }
    },
    "source": [
-    "Alternatively, we can use the [str()](https://docs.python.org/3/library/stdtypes.html#str) built-in to **cast** non-`str` objects as a `str`."
+    "We can always use the [str()](https://docs.python.org/3/library/stdtypes.html#str) built-in to cast non-`str` objects as a `str`."
    ]
   },
   {
@@ -253,6 +253,471 @@
     "str(123)"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Another common situation where we obtain `str` objects is when reading the contents of a file with the [open()](https://docs.python.org/3/library/functions.html#open) built-in. In its simplest form, to open a [text file](https://en.wikipedia.org/wiki/Text_file) file in read-only mode, we pass in its path (i.e., \"filename\") as a `str` object."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "file = open(\"lorem_ipsum.txt\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "[open()](https://docs.python.org/3/library/functions.html#open) returns a **[proxy](https://en.wikipedia.org/wiki/Proxy_pattern)** object of type `TextIOWrapper` that allows us to interact with the file on disk."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<_io.TextIOWrapper name='lorem_ipsum.txt' mode='r' encoding='UTF-8'>"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "file"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "_io.TextIOWrapper"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "type(file)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "While `file` provides, for example, the [read()](https://docs.python.org/3/library/io.html#io.TextIOBase.read), [readline()](https://docs.python.org/3/library/io.html#io.TextIOBase.readline), and [readlines()](https://docs.python.org/3/library/io.html#io.IOBase.readlines) methods to access its contents, it is also *iterable*, and we may loop over the individual lines with a `for` statement."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Lorem Ipsum is simply dummy text of the printing and typesetting industry.\n",
+      "\n",
+      "Lorem Ipsum has been the industry's standard dummy text ever since the 1500s\n",
+      "\n",
+      "when an unknown printer took a galley of type and scrambled it to make a type\n",
+      "\n",
+      "specimen book. It has survived not only five centuries but also the leap into\n",
+      "\n",
+      "electronic typesetting, remaining essentially unchanged. It was popularised in\n",
+      "\n",
+      "the 1960s with the release of Letraset sheets.\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "for line in file:\n",
+    "    print(line)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Once we looped over `file` the first time, it is **exhausted**: That means we do not see any output if we loop over it another time."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "for line in file:\n",
+    "    print(line)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "After the `for`-loop, `line` is still set to the last line in the file, and we verify that it is indeed a `str` object."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'the 1960s with the release of Letraset sheets.\\n'"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "line"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "str"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "type(line)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "An important fact is that `file` is still associated with an *open* **[file descriptor](https://en.wikipedia.org/wiki/File_descriptor)**. Without going into any technical details, we note that an operating system can only handle a limited number of \"open files\" at the same time, and, therefore, we should always *close* the file once we are done processing it.\n",
+    "\n",
+    "`file` has a `closed` attribute on it that shows us if a file descriptor is open or closed, and with the [close()](https://docs.python.org/3/library/io.html#io.IOBase.close) method, we can \"manually\" close it."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "False"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "file.closed"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "file.close()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "file.closed"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "The more Pythonic way is to open a file with the `with` statement (cf., [reference](https://docs.python.org/3/reference/compound_stmts.html#the-with-statement)): The indented code block is said to be executed in the **context** of the header line that acts as a **[context manager](https://docs.python.org/3/reference/datamodel.html?highlight=context%20manager#with-statement-context-managers)**. Such objects may have many different purposes. Here, the context manager created with `with open(...) as file:` mainly ensures that the file descriptor gets automatically closed after the last line in the code block is executed."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Lorem Ipsum is simply dummy text of the printing and typesetting industry.\n",
+      "\n",
+      "Lorem Ipsum has been the industry's standard dummy text ever since the 1500s\n",
+      "\n",
+      "when an unknown printer took a galley of type and scrambled it to make a type\n",
+      "\n",
+      "specimen book. It has survived not only five centuries but also the leap into\n",
+      "\n",
+      "electronic typesetting, remaining essentially unchanged. It was popularised in\n",
+      "\n",
+      "the 1960s with the release of Letraset sheets.\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "with open(\"lorem_ipsum.txt\") as file:\n",
+    "    for line in file:\n",
+    "        print(line)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "file.closed"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "To use constructs familiar from [Chapter 3](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/03_conditionals.ipynb#The-try-Statement) to explain what `with open(...) as file:` does, below is a formulation with a `try` statement *equivalent* to the `with` statement above. The `finally`-branch is always executed, even if an exception is raised in the `for`-loop. So, `file` is sure to be closed too, with a somewhat less expressive formulation."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Lorem Ipsum is simply dummy text of the printing and typesetting industry.\n",
+      "\n",
+      "Lorem Ipsum has been the industry's standard dummy text ever since the 1500s\n",
+      "\n",
+      "when an unknown printer took a galley of type and scrambled it to make a type\n",
+      "\n",
+      "specimen book. It has survived not only five centuries but also the leap into\n",
+      "\n",
+      "electronic typesetting, remaining essentially unchanged. It was popularised in\n",
+      "\n",
+      "the 1960s with the release of Letraset sheets.\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "try:\n",
+    "    file = open(\"lorem_ipsum.txt\")\n",
+    "    for line in file:\n",
+    "        print(line)\n",
+    "finally:\n",
+    "    file.close()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 21,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "file.closed"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "A subtlety to notice is that there is an empty line printed between each `line`. That is because each `line` ends with a `\"\\n\"` that results in a line break and that is explained further below. To print the text without empty lines in between, we pass a `end=\"\"` argument to the [print()](https://docs.python.org/3/library/functions.html#print) function."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Lorem Ipsum is simply dummy text of the printing and typesetting industry.\n",
+      "Lorem Ipsum has been the industry's standard dummy text ever since the 1500s\n",
+      "when an unknown printer took a galley of type and scrambled it to make a type\n",
+      "specimen book. It has survived not only five centuries but also the leap into\n",
+      "electronic typesetting, remaining essentially unchanged. It was popularised in\n",
+      "the 1960s with the release of Letraset sheets.\n"
+     ]
+    }
+   ],
+   "source": [
+    "with open(\"lorem_ipsum.txt\") as file:\n",
+    "    for line in file:\n",
+    "        print(line, end=\"\")"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {
@@ -274,21 +739,21 @@
    "source": [
     "The idea of a **sequence** is yet another *abstract* concept.\n",
     "\n",
-    "It unifies *four* orthogonal *abstract* concepts into one: Any *concrete* data type, such as `str` in this chapter, is considered a sequence if it simultaneously\n",
+    "It unifies *four* [orthogonal](https://en.wikipedia.org/wiki/Orthogonality) (i.e., \"independent\") *abstract* concepts into one: Any *concrete* data type, such as `str`, is considered a sequence if it simultaneously\n",
     "\n",
-    "1. behaves like a **container** and\n",
-    "2. an **iterable**, and \n",
-    "3. comes with a predictable **order** of its\n",
+    "1. **contains** other \"things,\"\n",
+    "2. is **iterable**, and \n",
+    "3. comes with a *predictable* **order** of its\n",
     "4. **finite** number of elements.\n",
     "\n",
-    "Chapter 7 discusses sequences in a generic sense and in greater detail. Here, we keep our focus on the `str` type that historically received its name as it models a \"**[string of characters](https://en.wikipedia.org/wiki/String_%28computer_science%29)**,\" and a \"string\" is more formally called a sequence in the computer science literature.\n",
+    "[Chapter 7](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/07_sequences.ipynb#Collections-vs.-Sequences) formalizes sequences in great detail. Here, we keep our focus on the `str` type that historically received its name as it models a \"**[string of characters](https://en.wikipedia.org/wiki/String_%28computer_science%29)**,\" and a \"string\" is more formally called a sequence in the computer science literature.\n",
     "\n",
-    "Behaving like a sequence, `str` objects may be treated like `list` objects in many cases. For example, the built-in [len()](https://docs.python.org/3/library/functions.html#len) function tells us how many elements (i.e., characters) make up `school`. [len()](https://docs.python.org/3/library/functions.html#len) would not work on an \"*infinite*\" object: As anything modeled in a program must fit into a computer's finite memory at runtime, there cannot exist objects containing a truly infinite number of elements; however, Chapter 7 introduces a *concrete* iterable data type that can be used to model an *infinite* series of elements and that, consequently, has no concept of \"length.\""
+    "Behaving like a sequence, `str` objects may be treated like `list` objects in many cases. For example, the built-in [len()](https://docs.python.org/3/library/functions.html#len) function tells us how many elements (i.e., characters) make up `school`. [len()](https://docs.python.org/3/library/functions.html#len) would not work on an \"*infinite*\" object: As anything modeled in a program must fit into a computer's finite memory at runtime, there cannot exist objects containing a truly infinite number of elements; however, [Chapter 7](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/07_sequences.ipynb#Collections-vs.-Sequences#Mapping) introduces *concrete* iterable data types that can be used to model an *infinite* series of elements and that, consequently, have no concept of \"length.\""
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 23,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -301,7 +766,7 @@
        "40"
       ]
      },
-     "execution_count": 8,
+     "execution_count": 23,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -325,7 +790,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 24,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -353,12 +818,12 @@
     }
    },
    "source": [
-    "Being a container, we can check if a given object is a member of a sequence with the `in` operator. In the context of strings, the `in` operator has *two* usages: First, it checks if a *single* character is contained in a `str` object. Second, it may also check if a shorter `str` object, then called a **substring**, is contained in a longer one."
+    "Being a container, we can check if a given object is a member of a sequence with the `in` operator. In the context of `str` objects, the `in` operator has *two* usages: First, it checks if a *single* character is contained in a `str` object. Second, it may also check if a shorter `str` object, then called a **substring**, is contained in a longer one."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 25,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -371,7 +836,7 @@
        "True"
       ]
      },
-     "execution_count": 10,
+     "execution_count": 25,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -382,7 +847,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 26,
    "metadata": {
     "slideshow": {
      "slide_type": "-"
@@ -395,7 +860,7 @@
        "True"
       ]
      },
-     "execution_count": 11,
+     "execution_count": 26,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -406,7 +871,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 27,
    "metadata": {
     "slideshow": {
      "slide_type": "-"
@@ -419,7 +884,7 @@
        "False"
       ]
      },
-     "execution_count": 12,
+     "execution_count": 27,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -452,7 +917,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 28,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -465,7 +930,7 @@
        "'W'"
       ]
      },
-     "execution_count": 13,
+     "execution_count": 28,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -476,7 +941,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 29,
    "metadata": {
     "slideshow": {
      "slide_type": "-"
@@ -489,7 +954,7 @@
        "'H'"
       ]
      },
-     "execution_count": 14,
+     "execution_count": 29,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -511,7 +976,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 30,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -525,7 +990,7 @@
      "traceback": [
       "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
       "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-15-c89ad7e1a4ab>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mschool\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1.0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;32m<ipython-input-30-c89ad7e1a4ab>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mschool\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1.0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
       "\u001b[0;31mTypeError\u001b[0m: string indices must be integers"
      ]
     }
@@ -547,7 +1012,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 31,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -560,7 +1025,7 @@
        "'t'"
       ]
      },
-     "execution_count": 16,
+     "execution_count": 31,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -582,7 +1047,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 32,
    "metadata": {
     "slideshow": {
      "slide_type": "-"
@@ -596,7 +1061,7 @@
      "traceback": [
       "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
       "\u001b[0;31mIndexError\u001b[0m                                Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-17-fffdc8fb2058>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mschool\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m40\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;32m<ipython-input-32-fffdc8fb2058>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mschool\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m40\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
       "\u001b[0;31mIndexError\u001b[0m: string index out of range"
      ]
     }
@@ -618,7 +1083,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 33,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -631,7 +1096,7 @@
        "'t'"
       ]
      },
-     "execution_count": 18,
+     "execution_count": 33,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -653,7 +1118,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 34,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -666,7 +1131,7 @@
        "'O'"
       ]
      },
-     "execution_count": 19,
+     "execution_count": 34,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -677,7 +1142,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 35,
    "metadata": {
     "slideshow": {
      "slide_type": "-"
@@ -690,7 +1155,7 @@
        "'O'"
       ]
      },
-     "execution_count": 20,
+     "execution_count": 35,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -725,7 +1190,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 36,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -738,7 +1203,7 @@
        "'WHU'"
       ]
      },
-     "execution_count": 21,
+     "execution_count": 36,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -760,7 +1225,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 37,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -773,7 +1238,7 @@
        "'WHU - Otto Beisheim School of Management'"
       ]
      },
-     "execution_count": 22,
+     "execution_count": 37,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -790,12 +1255,12 @@
     }
    },
    "source": [
-    "For convenience, the indexes do not need to lie in the range from 0 to the string's \"length\" when slicing. This is *not* the case for indexing as the `IndexError` above shows."
+    "For convenience, the indexes do not need to lie in the range from 0 to the `str` object's \"length\" when slicing. This is *not* the case for indexing as the `IndexError` above shows."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 38,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -808,7 +1273,7 @@
        "'WHU - Otto Beisheim School of Management'"
       ]
      },
-     "execution_count": 23,
+     "execution_count": 38,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -830,7 +1295,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 39,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -843,7 +1308,7 @@
        "'WHU - Otto Beisheim School of Management'"
       ]
      },
-     "execution_count": 24,
+     "execution_count": 39,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -865,7 +1330,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 40,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -878,7 +1343,7 @@
        "'WHU Otto Beisheim School'"
       ]
      },
-     "execution_count": 25,
+     "execution_count": 40,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -900,7 +1365,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": 41,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -913,7 +1378,7 @@
        "'WU-Ot esemSho fMngmn'"
       ]
      },
-     "execution_count": 26,
+     "execution_count": 41,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -935,7 +1400,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": 42,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -948,7 +1413,7 @@
        "'tnemeganaM fo loohcS miehsieB ottO - UHW'"
       ]
      },
-     "execution_count": 27,
+     "execution_count": 42,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -976,7 +1441,7 @@
     }
    },
    "source": [
-    "Whereas elements of a `list` object *may* be *re-assigned*, as shortly hinted at in [Chapter 1](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/01_elements.ipynb#Who-am-I?-And-how-many?), this is *not* allowed for `str` objects. Once created, they *cannot* be *changed*. Formally, we say that they are **immutable**. In that regard, `str` objects and all the numeric types in [Chapter 5](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/05_numbers.ipynb) are alike.\n",
+    "Whereas elements of a `list` object *may* be *re-assigned*, as shortly hinted at in [Chapter 1](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/01_elements.ipynb#Who-am-I?-And-how-many?), this is *not* allowed for `str` objects. Once created, they *cannot* be *changed*. Formally, we say that they are **immutable**. In that regard, `str` objects and all the numerical types in [Chapter 5](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/05_numbers.ipynb) are alike.\n",
     "\n",
     "On the contrary, objects that may be changed after creation, are called **mutable**. We already saw in [Chapter 1](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/01_elements.ipynb#Who-am-I?-And-how-many?) how mutable objects are more difficult to reason about for a beginner, in particular, if more than *one* variable point to one. Yet, mutability does have its place in a programmer's toolbox, and we revisit this idea in the next chapters.\n",
     "\n",
@@ -985,7 +1450,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": 43,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -999,7 +1464,7 @@
      "traceback": [
       "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
       "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-28-0d94e9e44adb>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mschool\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m\"E\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;32m<ipython-input-43-0d94e9e44adb>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mschool\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m\"E\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
       "\u001b[0;31mTypeError\u001b[0m: 'str' object does not support item assignment"
      ]
     }
@@ -1021,7 +1486,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
+   "execution_count": 44,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -1034,7 +1499,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 30,
+   "execution_count": 45,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -1047,7 +1512,7 @@
        "'EBS - Otto Beisheim School of Management'"
       ]
      },
-     "execution_count": 30,
+     "execution_count": 45,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1058,7 +1523,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 31,
+   "execution_count": 46,
    "metadata": {
     "slideshow": {
      "slide_type": "-"
@@ -1068,10 +1533,10 @@
     {
      "data": {
       "text/plain": [
-       "140148946876144"
+       "140133784610832"
       ]
      },
-     "execution_count": 31,
+     "execution_count": 46,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1082,7 +1547,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 32,
+   "execution_count": 47,
    "metadata": {
     "slideshow": {
      "slide_type": "-"
@@ -1092,10 +1557,10 @@
     {
      "data": {
       "text/plain": [
-       "140148947850512"
+       "140133793916176"
       ]
      },
-     "execution_count": 32,
+     "execution_count": 47,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1128,7 +1593,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 33,
+   "execution_count": 48,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -1141,7 +1606,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 34,
+   "execution_count": 49,
    "metadata": {
     "slideshow": {
      "slide_type": "-"
@@ -1154,7 +1619,7 @@
        "'Hello WHU'"
       ]
      },
-     "execution_count": 34,
+     "execution_count": 49,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1165,7 +1630,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 35,
+   "execution_count": 50,
    "metadata": {
     "slideshow": {
      "slide_type": "-"
@@ -1178,7 +1643,7 @@
        "'WHU WHU WHU WHU WHU WHU WHU WHU WHU WHU '"
       ]
      },
-     "execution_count": 35,
+     "execution_count": 50,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1213,7 +1678,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 36,
+   "execution_count": 51,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -1226,7 +1691,7 @@
        "6"
       ]
      },
-     "execution_count": 36,
+     "execution_count": 51,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1237,7 +1702,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 37,
+   "execution_count": 52,
    "metadata": {
     "slideshow": {
      "slide_type": "-"
@@ -1250,7 +1715,7 @@
        "-1"
       ]
      },
-     "execution_count": 37,
+     "execution_count": 52,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1261,7 +1726,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 38,
+   "execution_count": 53,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -1274,7 +1739,7 @@
        "11"
       ]
      },
-     "execution_count": 38,
+     "execution_count": 53,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1296,7 +1761,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 39,
+   "execution_count": 54,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -1309,7 +1774,7 @@
        "12"
       ]
      },
-     "execution_count": 39,
+     "execution_count": 54,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1320,7 +1785,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 40,
+   "execution_count": 55,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -1333,7 +1798,7 @@
        "16"
       ]
      },
-     "execution_count": 40,
+     "execution_count": 55,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1344,7 +1809,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 41,
+   "execution_count": 56,
    "metadata": {
     "slideshow": {
      "slide_type": "-"
@@ -1357,7 +1822,7 @@
        "-1"
       ]
      },
-     "execution_count": 41,
+     "execution_count": 56,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1379,7 +1844,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 42,
+   "execution_count": 57,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -1392,7 +1857,7 @@
        "4"
       ]
      },
-     "execution_count": 42,
+     "execution_count": 57,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1414,7 +1879,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 43,
+   "execution_count": 58,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -1427,7 +1892,7 @@
        "5"
       ]
      },
-     "execution_count": 43,
+     "execution_count": 58,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1449,7 +1914,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 44,
+   "execution_count": 59,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -1462,7 +1927,7 @@
        "5"
       ]
      },
-     "execution_count": 44,
+     "execution_count": 59,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1484,7 +1949,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 45,
+   "execution_count": 60,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -1497,7 +1962,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 46,
+   "execution_count": 61,
    "metadata": {
     "slideshow": {
      "slide_type": "-"
@@ -1507,10 +1972,10 @@
     {
      "data": {
       "text/plain": [
-       "140149036799680"
+       "140133891261864"
       ]
      },
-     "execution_count": 46,
+     "execution_count": 61,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1521,7 +1986,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 47,
+   "execution_count": 62,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -1534,7 +1999,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 48,
+   "execution_count": 63,
    "metadata": {
     "slideshow": {
      "slide_type": "-"
@@ -1544,10 +2009,10 @@
     {
      "data": {
       "text/plain": [
-       "140148946626016"
+       "140133784910848"
       ]
      },
-     "execution_count": 48,
+     "execution_count": 63,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1569,7 +2034,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 49,
+   "execution_count": 64,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -1582,7 +2047,7 @@
        "False"
       ]
      },
-     "execution_count": 49,
+     "execution_count": 64,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1593,7 +2058,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 50,
+   "execution_count": 65,
    "metadata": {
     "slideshow": {
      "slide_type": "-"
@@ -1606,7 +2071,7 @@
        "True"
       ]
      },
-     "execution_count": 50,
+     "execution_count": 65,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1628,7 +2093,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 51,
+   "execution_count": 66,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -1667,7 +2132,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 52,
+   "execution_count": 67,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -1680,7 +2145,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 53,
+   "execution_count": 68,
    "metadata": {
     "slideshow": {
      "slide_type": "-"
@@ -1693,7 +2158,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 54,
+   "execution_count": 69,
    "metadata": {
     "slideshow": {
      "slide_type": "-"
@@ -1706,7 +2171,7 @@
        "'This will become a sentence'"
       ]
      },
-     "execution_count": 54,
+     "execution_count": 69,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1728,7 +2193,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 55,
+   "execution_count": 70,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -1741,7 +2206,7 @@
        "'a b c d e'"
       ]
      },
-     "execution_count": 55,
+     "execution_count": 70,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1763,7 +2228,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 56,
+   "execution_count": 71,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -1776,7 +2241,7 @@
        "'This is a sentence'"
       ]
      },
-     "execution_count": 56,
+     "execution_count": 71,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1809,7 +2274,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 57,
+   "execution_count": 72,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -1824,7 +2289,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 58,
+   "execution_count": 73,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -1837,7 +2302,7 @@
        "True"
       ]
      },
-     "execution_count": 58,
+     "execution_count": 73,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1848,7 +2313,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 59,
+   "execution_count": 74,
    "metadata": {
     "slideshow": {
      "slide_type": "-"
@@ -1861,7 +2326,7 @@
        "False"
       ]
      },
-     "execution_count": 59,
+     "execution_count": 74,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1878,12 +2343,12 @@
     }
    },
    "source": [
-    "One way to fix this is to only compare lower-case strings."
+    "One way to fix this is to only compare lower-cased `str` objects."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 60,
+   "execution_count": 75,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -1896,7 +2361,7 @@
        "True"
       ]
      },
-     "execution_count": 60,
+     "execution_count": 75,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1918,7 +2383,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 61,
+   "execution_count": 76,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -1985,9 +2450,9 @@
     }
    },
    "source": [
-    "The previous code cell shows an example of a so-called **f-string**, as introduced by [PEP 498](https://www.python.org/dev/peps/pep-0498/) only in 2016.\n",
+    "The previous code cell shows an example of a so-called **f-string**, as introduced by [PEP 498](https://www.python.org/dev/peps/pep-0498/) only in 2016, that is passed as the argument to the [print()](https://docs.python.org/3/library/functions.html#print) function.\n",
     "\n",
-    "So far, we have used the built-in [print()](https://docs.python.org/3/library/functions.html#print) function only with \"plain\" `str` objects (e.g., `\"example\"`) or variables. Sometimes, it is more convenient to fill a value determined at runtime in a \"draft\" `str` object. This is called **string interpolation**. There are three ways to achieve that in Python, but only two are commonly used."
+    "The \"f\" stands for \"formatted\", and we can think of the `str` object as a text \"draft\" that is filled in with values determined at runtime. This concept is formally called **string interpolation**, and there are three ways to achieve that in Python."
    ]
   },
   {
@@ -2009,12 +2474,12 @@
     }
    },
    "source": [
-    "f-strings are the new and most readable way. Prepend the literal notation with an `f`, and put variables/expressions within curly braces. The latter are then filled in when the [print()](https://docs.python.org/3/library/functions.html#print) function is executed."
+    "f-strings, formally called **[formatted string literals](https://docs.python.org/3/reference/lexical_analysis.html#formatted-string-literals)**, are the least recently added and most readable way: We prepend a `str` in literal notation with an `f`, and put variables, or more generally, expressions, within curly braces. These are then filled in when a `str` object is evaluated."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 62,
+   "execution_count": 77,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -2028,7 +2493,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 63,
+   "execution_count": 78,
    "metadata": {
     "slideshow": {
      "slide_type": "-"
@@ -2036,15 +2501,18 @@
    },
    "outputs": [
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Hello Alexander! Good morning.\n"
-     ]
+     "data": {
+      "text/plain": [
+       "'Hello Alexander! Good morning.'"
+      ]
+     },
+     "execution_count": 78,
+     "metadata": {},
+     "output_type": "execute_result"
     }
    ],
    "source": [
-    "print(f\"Hello {name}! Good {time_of_day}.\")"
+    "f\"Hello {name}! Good {time_of_day}.\""
    ]
   },
   {
@@ -2055,12 +2523,12 @@
     }
    },
    "source": [
-    "Separated by a colon `:`, various formatting options are available. In the beginning, only the ability to round is useful, and this can be achieved by adding `:.2f` to the variable name to cast the number as a float and round it to two digits."
+    "Separated by a colon `:`, various formatting options are available. In the beginning, the ability to round may be particularly useful: This can be achieved by adding `:.2f` to the variable name inside the curly braces, which casts the number as a `float` and rounds it to two digits. The `:.2f` is a so-called format specifier, and there exists a whole **[format specification mini-language](https://docs.python.org/3/library/string.html#formatspec)** to govern how specifiers work."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 64,
+   "execution_count": 79,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -2073,7 +2541,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 65,
+   "execution_count": 80,
    "metadata": {
     "slideshow": {
      "slide_type": "-"
@@ -2081,20 +2549,23 @@
    },
    "outputs": [
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Pi is 3.14\n"
-     ]
+     "data": {
+      "text/plain": [
+       "'Pi is 3.14'"
+      ]
+     },
+     "execution_count": 80,
+     "metadata": {},
+     "output_type": "execute_result"
     }
    ],
    "source": [
-    "print(f\"Pi is {pi:.2f}\")"
+    "f\"Pi is {pi:.2f}\""
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 66,
+   "execution_count": 81,
    "metadata": {
     "slideshow": {
      "slide_type": "-"
@@ -2102,15 +2573,18 @@
    },
    "outputs": [
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Pi is 3.142\n"
-     ]
+     "data": {
+      "text/plain": [
+       "'Pi is 3.142'"
+      ]
+     },
+     "execution_count": 81,
+     "metadata": {},
+     "output_type": "execute_result"
     }
    ],
    "source": [
-    "print(f\"Pi is {pi:.3f}\")"
+    "f\"Pi is {pi:.3f}\""
    ]
   },
   {
@@ -2121,7 +2595,7 @@
     }
    },
    "source": [
-    "### format() Method"
+    "### [format()](https://docs.python.org/3/library/stdtypes.html#str.format) Method"
    ]
   },
   {
@@ -2132,12 +2606,12 @@
     }
    },
    "source": [
-    "`str` objects also provide a [format()](https://docs.python.org/3/library/stdtypes.html#str.format) method that accepts an arbitrary number of *positional* arguments that are inserted into the `str` object in the same order replacing curly brackets. See the official [documentation](https://docs.python.org/3/library/string.html#formatspec) for a full reference. This is the more traditional way of string interpolation and many code examples on the internet use it. f-strings are the officially recommended way going forward, but the usage of the [format()](https://docs.python.org/3/library/stdtypes.html#str.format) method is most likely not going down any time soon."
+    "`str` objects also provide a [format()](https://docs.python.org/3/library/stdtypes.html#str.format) method that accepts an arbitrary number of *positional* arguments that are inserted into the `str` object in the same order replacing empty curly brackets. String interpolation with the [format()](https://docs.python.org/3/library/stdtypes.html#str.format) method is a more traditional and probably the most common way one as of today. While f-strings are the recommended way going forward, usage of the [format()](https://docs.python.org/3/library/stdtypes.html#str.format) method is likely not declining any time soon."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 67,
+   "execution_count": 82,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -2145,15 +2619,18 @@
    },
    "outputs": [
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Hello Alexander! Good morning.\n"
-     ]
+     "data": {
+      "text/plain": [
+       "'Hello Alexander! Good morning.'"
+      ]
+     },
+     "execution_count": 82,
+     "metadata": {},
+     "output_type": "execute_result"
     }
    ],
    "source": [
-    "print(\"Hello {}! Good {}.\".format(name, time_of_day))"
+    "\"Hello {}! Good {}.\".format(name, time_of_day)"
    ]
   },
   {
@@ -2164,12 +2641,12 @@
     }
    },
    "source": [
-    "Use index numbers if the order is different in the `str` object."
+    "We may use index numbers inside the curly braces if the order is different in the `str` object."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 68,
+   "execution_count": 83,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -2177,15 +2654,18 @@
    },
    "outputs": [
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Good morning, Alexander\n"
-     ]
+     "data": {
+      "text/plain": [
+       "'Good morning, Alexander'"
+      ]
+     },
+     "execution_count": 83,
+     "metadata": {},
+     "output_type": "execute_result"
     }
    ],
    "source": [
-    "print(\"Good {1}, {0}\".format(name, time_of_day))"
+    "\"Good {1}, {0}\".format(name, time_of_day)"
    ]
   },
   {
@@ -2196,12 +2676,12 @@
     }
    },
    "source": [
-    "The [format()](https://docs.python.org/3/library/stdtypes.html#str.format) method may alternatively be used with *keyword* arguments as well. Then, we must put the keyword names within the curly brackets."
+    "The [format()](https://docs.python.org/3/library/stdtypes.html#str.format) method may alternatively be used with *keyword* arguments as well. Then, we must put the keywords' names within the curly brackets."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 69,
+   "execution_count": 84,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -2209,15 +2689,18 @@
    },
    "outputs": [
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Hello Alexander! Good morning.\n"
-     ]
+     "data": {
+      "text/plain": [
+       "'Hello Alexander! Good morning.'"
+      ]
+     },
+     "execution_count": 84,
+     "metadata": {},
+     "output_type": "execute_result"
     }
    ],
    "source": [
-    "print(\"Hello {name}! Good {time}.\".format(name=name, time=time_of_day))"
+    "\"Hello {name}! Good {time}.\".format(name=name, time=time_of_day)"
    ]
   },
   {
@@ -2228,12 +2711,60 @@
     }
    },
    "source": [
-    "Numbers are treated as in the f-strings case."
+    "Format specifiers work as in the f-string case."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 70,
+   "execution_count": 85,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Pi is 3.14'"
+      ]
+     },
+     "execution_count": 85,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "\"Pi is {:.2f}\".format(pi)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "### `%` Operator"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "The `%` operator that we saw in the context of modulo division in [Chapter 1](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/01_elements.ipynb#%28Arithmetic%29-Operators) is overloaded with string interpolation when its first operand is a `str` object. The second operand consists of all expressions to be filled in. Format specifiers work with a `%` instead of curly braces and according to a different set of rules referred to as **[printf-style string formatting](https://docs.python.org/3/library/stdtypes.html#printf-style-string-formatting)**. So, `{:.2f}` becomes `%.2f`.\n",
+    "\n",
+    "This way of string interpolation is the oldest and originates from the [C language](https://en.wikipedia.org/wiki/C_%28programming_language%29). It is still widely spread, but we should use one of the other two ways instead. We show it here mainly for completeness sake."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 86,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -2241,15 +2772,53 @@
    },
    "outputs": [
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Pi is 3.14\n"
-     ]
+     "data": {
+      "text/plain": [
+       "'Pi is 3.14'"
+      ]
+     },
+     "execution_count": 86,
+     "metadata": {},
+     "output_type": "execute_result"
     }
    ],
    "source": [
-    "print(\"Pi is {:.2f}\".format(pi))"
+    "\"Pi is %.2f\" % pi"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "To insert more than one expression, we must list them in order and between parenthesis `(` and `)`. As [Chapter 7](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/07_sequences.ipynb#The-tuple-Type) reveals, this literal syntax creates an object of type `tuple`. Also, to format an expression as text, we use the format specifier `%s`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 87,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Hello Alexander! Good morning.'"
+      ]
+     },
+     "execution_count": 87,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "\"Hello %s! Good %s.\" % (name, time_of_day)"
    ]
   },
   {
@@ -2271,12 +2840,14 @@
     }
    },
    "source": [
-    "Some symbols have a special meaning within `str` objects. Popular examples are the newline `\\n` and tab `\\t` \"characters.\" The backslash symbol `\\` is also referred to as an **escape character** in this context, indicating that the following character has a meaning other than its literal meaning."
+    "Some symbols have a special meaning within `str` objects. Popular examples are the newline `\\n` and tab `\\t` \"characters.\" The backslash symbol `\\` is also referred to as an **escape character** in this context, indicating that the following character has a meaning other than its literal meaning.\n",
+    "\n",
+    "The built-in [print()](https://docs.python.org/3/library/functions.html#print) function then \"prints\" out these special characters accordingly."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 71,
+   "execution_count": 88,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -2299,7 +2870,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 72,
+   "execution_count": 89,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -2331,7 +2902,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 73,
+   "execution_count": 90,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -2350,6 +2921,41 @@
     "print(\"\\U0001f604\")"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Outside the [print()](https://docs.python.org/3/library/functions.html#print) function, the special characters are not treated any different from non-special ones."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 91,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'This is a sentence\\nthat is printed\\non three lines.'"
+      ]
+     },
+     "execution_count": 91,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "\"This is a sentence\\nthat is printed\\non three lines.\""
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {
@@ -2376,7 +2982,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 74,
+   "execution_count": 92,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -2404,12 +3010,12 @@
     }
    },
    "source": [
-    "Some strings even produce a `SyntaxError` because the `\\U` *cannot* be interpreted as a unicode code point."
+    "Some `str` objects even produce a `SyntaxError` because the `\\U` *cannot* be interpreted as a unicode code point."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 75,
+   "execution_count": 93,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -2418,10 +3024,10 @@
    "outputs": [
     {
      "ename": "SyntaxError",
-     "evalue": "(unicode error) 'unicodeescape' codec can't decode bytes in position 2-3: truncated \\UXXXXXXXX escape (<ipython-input-75-0aad8a365b02>, line 1)",
+     "evalue": "(unicode error) 'unicodeescape' codec can't decode bytes in position 2-3: truncated \\UXXXXXXXX escape (<ipython-input-93-0aad8a365b02>, line 1)",
      "output_type": "error",
      "traceback": [
-      "\u001b[0;36m  File \u001b[0;32m\"<ipython-input-75-0aad8a365b02>\"\u001b[0;36m, line \u001b[0;32m1\u001b[0m\n\u001b[0;31m    print(\"C:\\Users\\Administrator\\Desktop\\Project_Folder\")\u001b[0m\n\u001b[0m         ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m (unicode error) 'unicodeescape' codec can't decode bytes in position 2-3: truncated \\UXXXXXXXX escape\n"
+      "\u001b[0;36m  File \u001b[0;32m\"<ipython-input-93-0aad8a365b02>\"\u001b[0;36m, line \u001b[0;32m1\u001b[0m\n\u001b[0;31m    print(\"C:\\Users\\Administrator\\Desktop\\Project_Folder\")\u001b[0m\n\u001b[0m         ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m (unicode error) 'unicodeescape' codec can't decode bytes in position 2-3: truncated \\UXXXXXXXX escape\n"
      ]
     }
    ],
@@ -2442,7 +3048,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 76,
+   "execution_count": 94,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -2463,7 +3069,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 77,
+   "execution_count": 95,
    "metadata": {
     "slideshow": {
      "slide_type": "-"
@@ -2490,12 +3096,12 @@
     }
    },
    "source": [
-    "However, this is tedious to remember and type. Luckily, Python allows treating any string literal as a \"raw\" string, and this is indicated in the string literal by a `r` prefix."
+    "However, this is tedious to remember and type. Luckily, Python allows treating any string literal as \"raw,\" and this is indicated in the string literal by the prefix `r`."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 78,
+   "execution_count": 96,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -2516,7 +3122,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 79,
+   "execution_count": 97,
    "metadata": {
     "slideshow": {
      "slide_type": "-"
@@ -2559,7 +3165,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 80,
+   "execution_count": 98,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -2568,10 +3174,10 @@
    "outputs": [
     {
      "ename": "SyntaxError",
-     "evalue": "EOL while scanning string literal (<ipython-input-80-4cef690f1f4a>, line 1)",
+     "evalue": "EOL while scanning string literal (<ipython-input-98-4cef690f1f4a>, line 1)",
      "output_type": "error",
      "traceback": [
-      "\u001b[0;36m  File \u001b[0;32m\"<ipython-input-80-4cef690f1f4a>\"\u001b[0;36m, line \u001b[0;32m1\u001b[0m\n\u001b[0;31m    \"\u001b[0m\n\u001b[0m     ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m EOL while scanning string literal\n"
+      "\u001b[0;36m  File \u001b[0;32m\"<ipython-input-98-4cef690f1f4a>\"\u001b[0;36m, line \u001b[0;32m1\u001b[0m\n\u001b[0;31m    \"\u001b[0m\n\u001b[0m     ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m EOL while scanning string literal\n"
      ]
     }
    ],
@@ -2594,7 +3200,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 81,
+   "execution_count": 99,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -2616,12 +3222,12 @@
     }
    },
    "source": [
-    "Linebreaks are kept and implicitly converted into `\\n` characters."
+    "Line breaks are kept and implicitly converted into `\\n` characters."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 82,
+   "execution_count": 100,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -2634,7 +3240,7 @@
        "'\\nI am a multi-line string\\nconsisting of 4 lines.\\n'"
       ]
      },
-     "execution_count": 82,
+     "execution_count": 100,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2656,7 +3262,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 83,
+   "execution_count": 101,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -2686,12 +3292,12 @@
     }
    },
    "source": [
-    "Using the [split()](https://docs.python.org/3/library/stdtypes.html#str.split) method with the optional *sep* argument, we confirm that `multi_line` consists of *four* lines with the first and last linebreaks being the first and last characters in the `str` object."
+    "Using the [split()](https://docs.python.org/3/library/stdtypes.html#str.split) method with the optional *sep* argument, we confirm that `multi_line` consists of *four* lines with the first and last line breaks being the first and last characters in the `str` object."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 84,
+   "execution_count": 102,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -2714,6 +3320,82 @@
     "    print(i, line)"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "The next code cell puts several constructs from this chapter together to create a multi-line `str` object `content`: The `with` statement provides a context that ensures `file` is not left open. Then, the [readlines()](https://docs.python.org/3/library/io.html#io.IOBase.readlines) method returns the contents of `file` as a `list` object holding as many `str` objects as there are lines in the file on disk. Lastly, we concatenate these together with the [join()](https://docs.python.org/3/library/stdtypes.html#str.join) method to obtain `content`. We do so on an empty `str` object `\"\"` as each line already ends with a `\"\\n\"`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 103,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "with open(\"lorem_ipsum.txt\") as file:\n",
+    "    content = \"\".join(file.readlines())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 104,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "\"Lorem Ipsum is simply dummy text of the printing and typesetting industry.\\nLorem Ipsum has been the industry's standard dummy text ever since the 1500s\\nwhen an unknown printer took a galley of type and scrambled it to make a type\\nspecimen book. It has survived not only five centuries but also the leap into\\nelectronic typesetting, remaining essentially unchanged. It was popularised in\\nthe 1960s with the release of Letraset sheets.\\n\""
+      ]
+     },
+     "execution_count": 104,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "content"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 105,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Lorem Ipsum is simply dummy text of the printing and typesetting industry.\n",
+      "Lorem Ipsum has been the industry's standard dummy text ever since the 1500s\n",
+      "when an unknown printer took a galley of type and scrambled it to make a type\n",
+      "specimen book. It has survived not only five centuries but also the leap into\n",
+      "electronic typesetting, remaining essentially unchanged. It was popularised in\n",
+      "the 1960s with the release of Letraset sheets.\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(content)"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {
@@ -2735,7 +3417,7 @@
    "source": [
     "Textual data is modeled with the **immutable** `str` type.\n",
     "\n",
-    "The `str` type supports *four* orthogonal **abstract concepts** that together make up the idea of a **sequence**: Every `str` object is an iterable container of a finite number of ordered characters."
+    "The `str` type supports *four* orthogonal **abstract concepts** that together constitute the idea of a **sequence**: Every `str` object is an iterable container of a finite number of ordered characters."
    ]
   }
  ],
diff --git a/06_text_review_and_exercises.ipynb b/06_text_review_and_exercises.ipynb
index 304a91d..40ac8c4 100644
--- a/06_text_review_and_exercises.ipynb
+++ b/06_text_review_and_exercises.ipynb
@@ -5,7 +5,7 @@
    "metadata": {},
    "source": [
     "\n",
-    "# Chapter 6: Text"
+    "# Chapter 6: Bytes & Text"
    ]
   },
   {
@@ -54,7 +54,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "**Q2**: What is a direct consequence of the `str` type's property of being **ordered**? What operations could we *not* do with it if it were *unordered*?"
+    "**Q2**: What is a direct consequence of the `str` type's property of being an **ordered** sequence? What operations could we *not* do with it if it were *unordered*?"
    ]
   },
   {
@@ -87,7 +87,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "**Q4**: Describe in your own words what we mean with **string interpolation**!"
+    "**Q4**: Describe in your own words what we mean by **string interpolation**!"
    ]
   },
   {
@@ -115,7 +115,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "**Q5**: **Triple-double** quotes `\"\"\"` and **triple-single** quotes `'''` create a *new* object of type `text` that model so-called **multi-line strings**."
+    "**Q5**: **Triple-double** quotes `\"\"\"` and **triple-single** quotes `'''` create a *new* object of type `text` that models so-called **multi-line strings**."
    ]
   },
   {
@@ -143,7 +143,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "**Q7**: Indexing into a `str` object with a *negative* index **fails silently**: It does *not* raise an error but also does *not* do anything useful."
+    "**Q7**: Indexing into a `str` object with a *negative* index **fails silently**: It does *neither* raise an error *nor* do anything useful."
    ]
   },
   {
diff --git a/lorem_ipsum.txt b/lorem_ipsum.txt
new file mode 100644
index 0000000..c0e21ab
--- /dev/null
+++ b/lorem_ipsum.txt
@@ -0,0 +1,6 @@
+Lorem Ipsum is simply dummy text of the printing and typesetting industry.
+Lorem Ipsum has been the industry's standard dummy text ever since the 1500s
+when an unknown printer took a galley of type and scrambled it to make a type
+specimen book. It has survived not only five centuries but also the leap into
+electronic typesetting, remaining essentially unchanged. It was popularised in
+the 1960s with the release of Letraset sheets.

From 00e37555633ac7d9155efa56015ed9c85a3be16d Mon Sep 17 00:00:00 2001
From: Alexander Hess <alexander@webartifex.biz>
Date: Wed, 30 Oct 2019 11:05:37 +0100
Subject: [PATCH 040/242] Adjust content overview in README.md

---
 README.md | 5 +++--
 1 file changed, 3 insertions(+), 2 deletions(-)

diff --git a/README.md b/README.md
index daa2e93..3837323 100644
--- a/README.md
+++ b/README.md
@@ -19,8 +19,9 @@ As such they can be viewed in a plain web browser:
 - [02 - Functions & Modularization](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/02_functions.ipynb)
 - [03 - Conditionals & Exceptions](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/03_conditionals.ipynb)
 - [04 - Recursion & Looping](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/04_iteration.ipynb)
-- [05 - Numbers](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/05_numbers.ipynb)
-- [06 - Text](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/06_text.ipynb)
+- [05 - Bits & Numbers](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/05_numbers.ipynb)
+- [06 - Bytes & Text](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/06_text.ipynb)
+- [07 - Sequential Data](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/07_sequences.ipynb)
 
 However, it is recommended that students **install Python and Jupyter
 locally** and run the code in the notebooks on their own.

From 974362288ae7d7ccf75439c39aff5bfbdad988aa Mon Sep 17 00:00:00 2001
From: Alexander Hess <alexander@webartifex.biz>
Date: Wed, 30 Oct 2019 11:06:05 +0100
Subject: [PATCH 041/242] Add initial version of notebook 07

---
 07_sequences.ipynb | 10742 +++++++++++++++++++++++++++++++++++++++++++
 1 file changed, 10742 insertions(+)
 create mode 100644 07_sequences.ipynb

diff --git a/07_sequences.ipynb b/07_sequences.ipynb
new file mode 100644
index 0000000..11add0a
--- /dev/null
+++ b/07_sequences.ipynb
@@ -0,0 +1,10742 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "# Chapter 7: Sequential Data"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "We studied numbers (cf., [Chapter 5](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/05_numbers.ipynb)) and textual data (cf., [Chapter 6](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/06_text.ipynb)) first, mainly because objects of the presented data types are \"simple,\" for two reasons: First, they are *immutable*, and, as we saw in the \"*Who am I? And how many?*\" section in [Chapter 1](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/01_elements.ipynb#Who-am-I?-And-how-many?), mutable objects can quickly become hard to reason about. Second, they are \"flat\" in the sense that they are *not* composed of other objects.\n",
+    "\n",
+    "The `str` type is a bit of a corner case in this regard. While one could argue that a longer `str` object, for example, `\"text\"`, is composed of individual characters, this is *not* the case in memory as the literal `\"text\"` only creates *one* object (i.e., one \"bag\" of $0$s and $1$s modeling all characters).\n",
+    "\n",
+    "This chapter and Chapter 8 introduce various \"complex\" data types. While some are mutable and others are not, they all share that they are primarily used to \"manage,\" or structure, the memory in a program. Unsurprisingly, computer scientists refer to the ideas and theories behind these data types as **[data structures](https://en.wikipedia.org/wiki/Data_structure)**.\n",
+    "\n",
+    "In this chapter, we focus on data types that model all kinds of sequential data. Examples of such data are [spreadsheets](https://en.wikipedia.org/wiki/Spreadsheet) or [matrices](https://en.wikipedia.org/wiki/Matrix_%28mathematics%29)/[vectors](https://en.wikipedia.org/wiki/Vector_%28mathematics_and_physics%29). Such formats share the property that they are composed of smaller units that come in a sequence of, for example, rows/columns/cells or elements/entries."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "## Collections vs. Sequences"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "[Chapter 6](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/06_text.ipynb#A-\"String\"-of-Characters) already describes the *sequence* properties of `str` objects. Here, we take a step back and study these properties on their own before looking at bigger ideas.\n",
+    "\n",
+    "The [collections.abc](https://docs.python.org/3/library/collections.abc.html) module in the [standard library](https://docs.python.org/3/library/index.html) defines a variety of **abstract base classes** (ABCs). We saw ABCs already in [Chapter 5](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/05_numbers.ipynb#The-Numerical-Tower), where we use the ones from the [numbers](https://docs.python.org/3/library/numbers.html) module in the [standard library](https://docs.python.org/3/library/index.html) to classify Python's numeric data types according to mathematical ideas. Now, we take the ABCs from the [collections.abc](https://docs.python.org/3/library/collections.abc.html) module to classify the data types in this chapter according to their behavior in various contexts.\n",
+    "\n",
+    "As an illustration, consider `numbers` and `word` below, two objects of *different* types."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "numbers = [7, 11, 8, 5, 3, 12, 2, 6, 9, 10, 1, 4]\n",
+    "word = \"random\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "They have in common that we may loop over them with the `for` statement. So, in the context of iteration, both exhibit the *same* behavior."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "7 11 8 5 3 12 2 6 9 10 1 4 "
+     ]
+    }
+   ],
+   "source": [
+    "for number in numbers:\n",
+    "    print(number, end=\" \")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "r a n d o m "
+     ]
+    }
+   ],
+   "source": [
+    "for character in word:\n",
+    "    print(character, end=\" \")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "In [Chapter 4](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/04_iteration.ipynb#Containers-vs.-Iterables), we referred to such types as *iterables*. That is *not* a proper [English](https://dictionary.cambridge.org/spellcheck/english-german/?q=iterable) word, even if it may sound like one at first sight. Yet, it is an official term in the Python world formalized with the `Iterable` ABC in the [collections.abc](https://docs.python.org/3/library/collections.abc.html) module.\n",
+    "\n",
+    "For the data science practitioner, it is worthwhile to know such terms as, for example, the documentation on the [built-ins](https://docs.python.org/3/library/functions.html) uses them extensively: In simple words, any built-in that takes an argument called \"*iterable*\" may be called with *any* object that supports being looped over. Already familiar [built-ins](https://docs.python.org/3/library/functions.html) include, among others, [enumerate()](https://docs.python.org/3/library/functions.html#enumerate), [sum()](https://docs.python.org/3/library/functions.html#sum), or [zip()](https://docs.python.org/3/library/functions.html#zip). So, they do *not* require the argument to be of a certain concrete data type (e.g., `list`); instead, any *iterable* type works."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "import collections.abc as abc"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "collections.abc.Iterable"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "abc.Iterable"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "As in the context of *goose typing* in [Chapter 5](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/05_numbers.ipynb#Goose-Typing), we can use ABCs with the built-in [isinstance()](https://docs.python.org/3/library/functions.html#isinstance) function to check if an object supports a behavior.\n",
+    "\n",
+    "So, let's \"ask\" Python if it can loop over `numbers` or `word`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "isinstance(numbers, abc.Iterable)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "isinstance(word, abc.Iterable)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Contrary to `list` or `str` objects, numeric objects are *not* iterable."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "False"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "isinstance(999, abc.Iterable)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Instead of asking, we could try to loop over `999`, but this results in a `TypeError`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "ename": "TypeError",
+     "evalue": "'int' object is not iterable",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-9-8249bf25b5b7>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0;32mfor\u001b[0m \u001b[0mdigit\u001b[0m \u001b[0;32min\u001b[0m \u001b[0;36m999\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m     \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdigit\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;31mTypeError\u001b[0m: 'int' object is not iterable"
+     ]
+    }
+   ],
+   "source": [
+    "for digit in 999:\n",
+    "    print(digit)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Most of the data types in this and the next chapter exhibit three [orthogonal](https://en.wikipedia.org/wiki/Orthogonality) (i.e., \"independent\") behaviors, formalized by ABCs in the [collections.abc](https://docs.python.org/3/library/collections.abc.html) module as:\n",
+    "- `Iterable`: An object supports being looped over.\n",
+    "- `Container`: An object \"contains\" references to other objects; a \"whole\" is composed of many \"parts.\"\n",
+    "- `Sized`: The number of references to other objects, the \"parts,\" is *finite*.\n",
+    "\n",
+    "The characteristical operation supported by `Container` types is the `in` operator for membership testing."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "False"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "0 in numbers"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "\"r\" in word"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Alternatively, we could also check if `numbers` and `word` are `Container` types with the [isinstance()](https://docs.python.org/3/library/functions.html#isinstance) function."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "isinstance(numbers, abc.Container)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "isinstance(word, abc.Container)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Numeric objects do *not* \"contain\" references to other objects, and that is why they are considered \"flat\" data types. The `in` operator raises a `TypeError`. Conceptually speaking, Python views numeric types as \"wholes\" without any \"parts.\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "False"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "isinstance(999, abc.Container)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "ename": "TypeError",
+     "evalue": "argument of type 'int' is not iterable",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-15-b3e0b3c32aec>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0;36m9\u001b[0m \u001b[0;32min\u001b[0m \u001b[0;36m999\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;31mTypeError\u001b[0m: argument of type 'int' is not iterable"
+     ]
+    }
+   ],
+   "source": [
+    "9 in 999"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Analogously, being `Sized` types, we can pass `numbers` and `word` as the argument to the built-in [len()](https://docs.python.org/3/library/functions.html#len) function and obtain \"meaningful\" results. The exact meaning depends on the *concrete* data type: For `numbers`, [len()](https://docs.python.org/3/library/functions.html#len) tells us how many elements are in the `list` object; for `word`, it tells us how many [Unicode characters](https://en.wikipedia.org/wiki/Unicode) make up the `str` object. But, *abstractly* speaking, both data types exhibit the *same* behavior of *finiteness*."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "12"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "len(numbers)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "6"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "len(word)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "isinstance(numbers, abc.Sized)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "isinstance(word, abc.Sized)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "On the contrary, even though `999` consists of three digits for humans, numeric objects in Python have no concept of a \"size\" or \"length,\" and the [len()](https://docs.python.org/3/library/functions.html#len) function raises a `TypeError`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "False"
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "isinstance(999, abc.Sized)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "ename": "TypeError",
+     "evalue": "object of type 'int' has no len()",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-21-ae09c1aa5305>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m999\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;31mTypeError\u001b[0m: object of type 'int' has no len()"
+     ]
+    }
+   ],
+   "source": [
+    "len(999)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "These three behaviors are so essential that whenever they coincide for a data type, it is called a **collection**, formalized with the `Collection` ABC. That is where the [collections.abc](https://docs.python.org/3/library/collections.abc.html) module got its name from: It summarizes all ABCs related to collections; in particular, it defines a hierarchy of specialized kinds of collections.\n",
+    "\n",
+    "So, both `numbers` and `word` are collections."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "isinstance(numbers, abc.Collection)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 23,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "isinstance(word, abc.Collection)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "They share one more common behavior: When looping over them, we can *predict* the *order* of the elements or characters. The ABC in the [collections.abc](https://docs.python.org/3/library/collections.abc.html) module corresponding to this behavior is `Reversible`. While sounding unintuitive at first, it is evident that if something is reversible, it must have a forward order, to begin with.\n",
+    "\n",
+    "We add the [reversed()](https://docs.python.org/3/library/functions.html#reversed) built-in to the `for`-loop from above to iterate over the elements or characters in reverse order."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "4 1 10 9 6 2 12 3 5 8 11 7 "
+     ]
+    }
+   ],
+   "source": [
+    "for number in reversed(numbers):\n",
+    "    print(number, end=\" \")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "m o d n a r "
+     ]
+    }
+   ],
+   "source": [
+    "for character in reversed(word):\n",
+    "    print(character, end=\" \")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 26,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "isinstance(numbers, abc.Reversible)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 27,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "isinstance(word, abc.Reversible)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Collections that exhibit this fourth behavior are referred to as **sequences**, formalized with the `Sequence` ABC in the [collections.abc](https://docs.python.org/3/library/collections.abc.html) module."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 28,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "isinstance(numbers, abc.Sequence)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 29,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "isinstance(word, abc.Sequence)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Most of the data types introduced in the remainder of this chapter are sequences. Nevertheless, we also look at some data types that are neither collections nor sequences but still useful to model sequential data in practice.\n",
+    "\n",
+    "In Python-related documentations, the terms collection and sequence are heavily used, and the data science practitioner should always think of them in terms of the three or four behaviors they exhibit.\n",
+    "\n",
+    "Data types that are collections but not sequences are covered in Chapter 8."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "## The `list` Type"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "As already seen multiple times, to create a `list` object, we use the *literal notation* and list all elements within brackets `[` and `]`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "empty = []"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "simple = [40, 50]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "The elements do *not* need to be of the *same* type, and `list` objects may also be **nested**."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "nested = [empty, 10, 20.0, \"Thirty\", simple]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "[PythonTutor](http://www.pythontutor.com/visualize.html#code=empty%20%3D%20%5B%5D%0Asimple%20%3D%20%5B40,%2050%5D%0Anested%20%3D%20%5Bempty,%2010,%2020.0,%20%22Thirty%22,%20simple%5D&cumulative=false&curInstr=0&heapPrimitives=nevernest&mode=display&origin=opt-frontend.js&py=3&rawInputLstJSON=%5B%5D&textReferences=false) shows how `nested` holds references to the `empty` and `simple` objects. Technically, it holds three more references pointing to the `10`, `20.0`, and `\"Thirty\"` objects as well. However, to simplify the visualization, these three objects are shown right inside the `nested` object as they are immutable and of \"flat\" data types. In general, the $0$s and $1$s inside a `list` object in memory always constitute pointers to other objects only."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[[], 10, 20.0, 'Thirty', [40, 50]]"
+      ]
+     },
+     "execution_count": 33,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "nested"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Let's not forget that `nested` is an object on its own with an *identity* and *data type*."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "140322034424136"
+      ]
+     },
+     "execution_count": 34,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "id(nested)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "list"
+      ]
+     },
+     "execution_count": 35,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "type(nested)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Alternatively, we may use the [list()](https://docs.python.org/3/library/functions.html#func-list) built-in to create a `list` object out of an iterable we pass to it as the argument.\n",
+    "\n",
+    "For example, we can wrap the [range()](https://docs.python.org/3/library/functions.html#func-range) built-in with [list()](https://docs.python.org/3/library/functions.html#func-list): As described in [Chapter 4](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/04_iteration.ipynb#Containers-vs.-Iterables), `range` objects, like `range(1, 13)` below, are iterable and generate `int` objects \"on the fly\" (i.e., one by one). The [list()](https://docs.python.org/3/library/functions.html#func-list) around it acts like a `for`-loop and **materializes** twelve `int` objects in memory that then become the elements of the newly created `list` object. [PythonTutor](http://www.pythontutor.com/visualize.html#code=r%20%3D%20range%281,%2013%29%0Al%20%3D%20list%28range%281,%2013%29%29&cumulative=false&curInstr=0&heapPrimitives=nevernest&mode=display&origin=opt-frontend.js&py=3&rawInputLstJSON=%5B%5D&textReferences=false) shows this difference visually."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12]"
+      ]
+     },
+     "execution_count": 36,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "list(range(1, 13))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 37,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "isinstance(range(1, 13), abc.Iterable)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Beware of passing a `range` object over a \"big\" horizon as the argument to [list()](https://docs.python.org/3/library/functions.html#func-list) as that may lead to a `MemoryError` and the computer crashing."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "ename": "MemoryError",
+     "evalue": "",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mMemoryError\u001b[0m                               Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-38-dbb397cbb3b9>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mlist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m999_999_999_999\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;31mMemoryError\u001b[0m: "
+     ]
+    }
+   ],
+   "source": [
+    "list(range(999_999_999_999))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "As another example, we may also create a `list` object from a `str` object as the latter is iterable, as well. Then, the individual characters become the elements of the new `list` object!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['W', 'H', 'U']"
+      ]
+     },
+     "execution_count": 39,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "list(\"WHU\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "### Sequence Behaviors"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "`list` objects are *sequences*. To reiterate that concept from above *without* the formal ABCs from the [collections.abc](https://docs.python.org/3/library/collections.abc.html) module, we briefly summarize the *four* behaviors of a sequence and provide some more `list`-specific details below:"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "- **Container**:\n",
+    " - holds references to other objects in memory (with their own *identity* and *type*)\n",
+    " - implements membership testing via the `in` operator\n",
+    "- **Iterable**:\n",
+    " - supports being looped over\n",
+    " - works with the `for` or `while` statements\n",
+    "- **Reversible**:\n",
+    " - the elements come in a *predictable* order that we may traverse in a forward or backward fashion\n",
+    " - works with the [reversed()](https://docs.python.org/3/library/functions.html#reversed) built-in\n",
+    "- **Sized**:\n",
+    " - the number of elements is finite *and* known in advance\n",
+    " - works with the built-in [len()](https://docs.python.org/3/library/functions.html#len) function"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "The \"length\" of `nested` is *five* because `simple` counts as only *one* element. In other words, `nested` holds five references to other objects."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "5"
+      ]
+     },
+     "execution_count": 40,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "len(nested)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "With a `for`-loop, we can traverse all elements in a *predictable* order, forward or backward. As `list` objects only hold references to other objects, these have a *indentity* and may be of *different* types; however, this is rarely, if ever, useful in practice."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[]      \t140322025703432      \t<class 'list'>\n",
+      "10      \t94360180081984      \t<class 'int'>\n",
+      "20.0      \t140322034534104      \t<class 'float'>\n",
+      "Thirty      \t140322025251816      \t<class 'str'>\n",
+      "[40, 50]      \t140322034424072      \t<class 'list'>\n"
+     ]
+    }
+   ],
+   "source": [
+    "for element in nested:\n",
+    "    print(element, id(element), type(element), sep=\"      \\t\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[40, 50]     Thirty     20.0     10     []     "
+     ]
+    }
+   ],
+   "source": [
+    "for element in reversed(nested):\n",
+    "    print(element, end=\"     \")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "The `in` operator checks if a given object is \"contained\" in a `list` object. It uses the `==` operator behind the scenes (i.e., *not* the `is` operator) conducting a so-called **[linear search](https://en.wikipedia.org/wiki/Linear_search)**: So, Python implicitly loops over *all* elements and only stops prematurely if an element evaluates equal to the given object. A linear search may, therefore, be relatively *slow* for big `list` objects."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 43,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "10 in nested"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "`20` compares equal to the `20.0` in `nested`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 44,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "20 in nested"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "False"
+      ]
+     },
+     "execution_count": 45,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "30 in nested"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "### Indexing"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Because of the *predictable* order and the *finiteness*, each element in a sequence can be labeled with a unique *index* (i.e., an `int` object in the range $0 \\leq \\text{index} < \\lvert \\text{sequence} \\rvert$).\n",
+    "\n",
+    "Brackets, `[` and `]`, are the literal syntax for accessing individual elements of any sequence type. In this book, we also call them the *indexing operator*."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "10"
+      ]
+     },
+     "execution_count": 46,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "nested[1]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "The last index is one less than `len(nested)` above, and Python raises an `IndexError` if we look up an index that is not in the implied range."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [
+    {
+     "ename": "IndexError",
+     "evalue": "list index out of range",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mIndexError\u001b[0m                                Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-47-cb216f4a067e>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mnested\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m5\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;31mIndexError\u001b[0m: list index out of range"
+     ]
+    }
+   ],
+   "source": [
+    "nested[5]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Negative indices are used to count in reverse order from the end of a sequence, and brackets may be chained to access nested objects. So, to access the `50` inside `simple` via the `nested` object, we write `nested[-1][1]`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 48,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "50"
+      ]
+     },
+     "execution_count": 48,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "nested[-1][1]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "### Slicing"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Slicing `list` objects works analogously to slicing `str` objects: We use the literal syntax with either one or two colons `:` inside the brackets `[]` to separate the *start*, *stop*, and *step* values. Slicing creates a *new* `list` object with the elements chosen from the original one.\n",
+    "\n",
+    "For example, to obtain the three elements in the \"middle\" of `nested`, we slice from `1` (including) to `4` (excluding)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 49,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[10, 20.0, 'Thirty']"
+      ]
+     },
+     "execution_count": 49,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "nested[1:4]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "To obtain \"every other\" element, we slice from beginning to end, defaulting to `0` and `len(nested)`, in steps of `2`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 50,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[[], 20.0, [40, 50]]"
+      ]
+     },
+     "execution_count": 50,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "nested[::2]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "The literal notation with the colons `:` is *syntactic sugar*, and Python provides the [slice()](https://docs.python.org/3/library/functions.html#slice) built-in to slice with `slice` objects. [slice()](https://docs.python.org/3/library/functions.html#slice) takes *start*, *stop*, and *step* arguments in the same way as the already familiar [range()](https://docs.python.org/3/library/functions.html#func-range) built-in."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 51,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "middle = slice(1, 4)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 52,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "slice"
+      ]
+     },
+     "execution_count": 52,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "type(middle)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "In most cases, the literal notation is more convenient to use; however, with `slice` objects, we may give names to slices and re-use them across several sequences."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 53,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[10, 20.0, 'Thirty']"
+      ]
+     },
+     "execution_count": 53,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "nested[middle]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 54,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[11, 8, 5]"
+      ]
+     },
+     "execution_count": 54,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "numbers[middle]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 55,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'and'"
+      ]
+     },
+     "execution_count": 55,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "word[middle]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "`slice` objects come with three read-only attributes `start`, `stop`, and `step` on them."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 56,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "1"
+      ]
+     },
+     "execution_count": 56,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "middle.start"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 57,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "4"
+      ]
+     },
+     "execution_count": 57,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "middle.stop"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "If not passed to [slice()](https://docs.python.org/3/library/functions.html#slice), these attributes default to `None`. That is why the cell below has no output."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 58,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "middle.step"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "A good trick to know is taking a \"full\" slice: This copies *all* elements of a `list` object into a *new* `list` object."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 59,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "nested_copy = nested[:]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 60,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[[], 10, 20.0, 'Thirty', [40, 50]]"
+      ]
+     },
+     "execution_count": 60,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "nested_copy"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "At first glance, `nested` and `nested_copy` seem to cause no pain. For `list` objects, the comparison operator `==` goes over the elements in both operands in a pairwise fashion and checks if they all evaluate equal.\n",
+    "\n",
+    "We confirm that `nested` and `nested_copy` compare equal as expected but also that they are *different* objects."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 61,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 61,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "nested == nested_copy"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 62,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "False"
+      ]
+     },
+     "execution_count": 62,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "nested is nested_copy"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "However, as [PythonTutor](http://pythontutor.com/visualize.html#code=nested%20%3D%20%5B%5B%5D,%2010,%2020.0,%20%22Thirty%22,%20%5B40,%2050%5D%5D%0Anested_copy%20%3D%20nested%5B%3A%5D&cumulative=false&curInstr=0&heapPrimitives=nevernest&mode=display&origin=opt-frontend.js&py=3&rawInputLstJSON=%5B%5D&textReferences=false) reveals, only the *pointers* to the elements are copied! That concept is called a **[shallow copy](https://en.wikipedia.org/wiki/Object_copying#Shallow_copy)**.\n",
+    "\n",
+    "We could also see this with the [id()](https://docs.python.org/3/library/functions.html#id) function: The respective first elements in both `nested` and `nested_copy` are the *same* `empty` object."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 63,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 63,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "nested[0] is nested_copy[0]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 64,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[]"
+      ]
+     },
+     "execution_count": 64,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "nested[0]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Knowing this becomes critical if the elements in a `list` object are mutable objects. Then, because of the original `list` object and its copy both pointing at the *same* objects in memory, if some of them are mutated, these changes are visible to both! We already saw a similar kind of confusion in [Chapter 1](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/01_elements.ipynb#Who-am-I?-And-how-many?) in a \"simpler\" setting and look into this in detail in the next section.\n",
+    "\n",
+    "Instead of a shallow copy, we could also create a so-called **[deep copy](https://en.wikipedia.org/wiki/Object_copying#Deep_copy)** of `nested`: That concept recursively follows every pointer in a possible nested data structure and creates copies of *every* involved object.\n",
+    "\n",
+    "To explicitly create shallow or deep copies, the [copy](https://docs.python.org/3/library/copy.html) module in the [standard library](https://docs.python.org/3/library/index.html) provides two functions, [copy()](https://docs.python.org/3/library/copy.html#copy.copy) and [deepcopy()](https://docs.python.org/3/library/copy.html#copy.deepcopy). We must always remember that slicing creates *shallow* copies only."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 65,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "import copy"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 66,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "nested_deep_copy = copy.deepcopy(nested)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 67,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 67,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "nested == nested_deep_copy"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Now, the first elements of `nested` and `nested_deep_copy` are *different* objects, and [PythonTutor](http://pythontutor.com/visualize.html#code=import%20copy%0Anested%20%3D%20%5B%5B%5D,%2010,%2020.0,%20%22Thirty%22,%20%5B40,%2050%5D%5D%0Anested_deep_copy%20%3D%20copy.deepcopy%28nested%29&cumulative=false&curInstr=0&heapPrimitives=nevernest&mode=display&origin=opt-frontend.js&py=3&rawInputLstJSON=%5B%5D&textReferences=false) shows that there are *six* `list` objects in memory."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 68,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "False"
+      ]
+     },
+     "execution_count": 68,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "nested[0] is nested_deep_copy[0]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "As this [StackOverflow question](https://stackoverflow.com/questions/184710/what-is-the-difference-between-a-deep-copy-and-a-shallow-copy) shows, understanding shallow and deep copies is a common source of confusion independent of the programming language."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "### Mutability"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "In contrast to `str` objects, `list` objects are *mutable*: We may assign new elements to indices or slices and also remove elements. That changes *parts* of a `list` object in memory."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 69,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "nested[0] = 0"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 70,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[0, 10, 20.0, 'Thirty', [40, 50]]"
+      ]
+     },
+     "execution_count": 70,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "nested"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "When we re-assign a slice, we can even change the size of the `list` object."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 71,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "nested[:4] = [100, 100, 100]  # assign three elements where there were four before"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 72,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[100, 100, 100, [40, 50]]"
+      ]
+     },
+     "execution_count": 72,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "nested"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 73,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "4"
+      ]
+     },
+     "execution_count": 73,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "len(nested)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "The `list` object's identity does *not* change. That is the whole point behind mutable objects."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 74,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "140322034424136"
+      ]
+     },
+     "execution_count": 74,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "id(nested)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "`nested_copy` is still unchanged!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 75,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[[], 10, 20.0, 'Thirty', [40, 50]]"
+      ]
+     },
+     "execution_count": 75,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "nested_copy"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Let's change the nested `[40, 50]` via `nested_copy` into `[1, 2, 3]` by replacing all its elements."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 76,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "nested_copy[-1][:] = [1, 2, 3]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 77,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[[], 10, 20.0, 'Thirty', [1, 2, 3]]"
+      ]
+     },
+     "execution_count": 77,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "nested_copy"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "This has a surprising side effect on `nested`!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 78,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[100, 100, 100, [1, 2, 3]]"
+      ]
+     },
+     "execution_count": 78,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "nested"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "That is because `nested_copy` is a shallow copy of `nested`. [PythonTutor](http://pythontutor.com/visualize.html#code=nested%20%3D%20%5B%5B%5D,%2010,%2020.0,%20%22Thirty%22,%20%5B40,%2050%5D%5D%0Anested_copy%20%3D%20nested%5B%3A%5D%0Anested%5B%3A4%5D%20%3D%20%5B100,%20100,%20100%5D%0Anested_copy%5B-1%5D%5B%3A%5D%20%3D%20%5B1,%202,%203%5D&cumulative=false&curInstr=0&heapPrimitives=nevernest&mode=display&origin=opt-frontend.js&py=3&rawInputLstJSON=%5B%5D&textReferences=false) shows how both point to the *same* nested `list` object.\n",
+    "\n",
+    "Lastly, we use the `del` statement to remove an element."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 79,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "del nested[-1]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 80,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[100, 100, 100]"
+      ]
+     },
+     "execution_count": 80,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "nested"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "The `del` statement, of course, also works for slices."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 81,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "del nested[:2]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 82,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[100]"
+      ]
+     },
+     "execution_count": 82,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "nested"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "### List Operations"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "As with `str` objects, the `+` and `*` operators are overloaded for concatenation and always return *new* `list` objects."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 83,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "first = [10, 20, 30]\n",
+    "second = [40, 50, 60]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 84,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[10, 20, 30, 40, 50, 60]"
+      ]
+     },
+     "execution_count": 84,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "first + second"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 85,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[10, 20, 30, 10, 20, 30]"
+      ]
+     },
+     "execution_count": 85,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "2 * first"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 86,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[40, 50, 60, 40, 50, 60, 40, 50, 60]"
+      ]
+     },
+     "execution_count": 86,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "second * 3"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Besides being an operator, the `*` symbol has a second syntactical use, as explained in [PEP 3132](https://www.python.org/dev/peps/pep-3132/) and [PEP 448](https://www.python.org/dev/peps/pep-0448/): It implements what is called **iterable unpacking**. It is *not* an operator syntactically but a notation that Python processes as a literal.\n",
+    "\n",
+    "In the example, Python interprets the expression as if the elements of the iterable `second` were placed between `30` and `70` one by one. So, we do not obtain a nested but a *flat* list."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 87,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[30, 40, 50, 60, 70]"
+      ]
+     },
+     "execution_count": 87,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "[30, *second, 70]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "### List Methods"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "The `list` type is an essential data structure in any real-world Python application, and many typical `list` related algorithms from computer science theory are already built into it at the C level (cf., the [documentation](https://docs.python.org/3/tutorial/datastructures.html#more-on-lists) for a full overview). So, understanding and applying the built-in methods of the `list` type not only speeds up the development process but also makes programs significantly faster.\n",
+    "\n",
+    "In contrast to the `str` type's methods, the `list` type's methods *always* mutate (i.e., \"change\") an object *in place*. They do *not* create a *new* `list` object and return `None` to indicate that. So, we must *never* assign the return value of `list` methods to the variable holding the list!\n",
+    "\n",
+    "Let's look at the following `names` example."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 88,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "names = [\"Carl\", \"Berthold\", \"Achim\", \"Xavier\", \"Peter\"]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "To add an object to the end of `names`, we use the append() method. The code cell shows no output indicating that `None` must have been returned."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 89,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "names.append(\"Eckardt\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 90,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['Carl', 'Berthold', 'Achim', 'Xavier', 'Peter', 'Eckardt']"
+      ]
+     },
+     "execution_count": 90,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "names"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "With the extend() method, we may also append multiple elements provided by an iterable at once. Here, the iterable is a `list` object itself holding two `str` objects."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 91,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "names.extend([\"Karl\", \"Oliver\"])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 92,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['Carl', 'Berthold', 'Achim', 'Xavier', 'Peter', 'Eckardt', 'Karl', 'Oliver']"
+      ]
+     },
+     "execution_count": 92,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "names"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "`list` objects may be sorted *in place* with the [sort()](https://docs.python.org/3/library/stdtypes.html#list.sort) method. That is different from the built-in [sorted()](https://docs.python.org/3/library/functions.html#sorted) function that takes any *finite* and *iterable* object and returns a *new* `list` object with the iterable's elements sorted."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 93,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['Achim', 'Berthold', 'Carl', 'Eckardt', 'Karl', 'Oliver', 'Peter', 'Xavier']"
+      ]
+     },
+     "execution_count": 93,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "sorted(names)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 94,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['Carl', 'Berthold', 'Achim', 'Xavier', 'Peter', 'Eckardt', 'Karl', 'Oliver']"
+      ]
+     },
+     "execution_count": 94,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "names"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 95,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "names.sort()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 96,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['Achim', 'Berthold', 'Carl', 'Eckardt', 'Karl', 'Oliver', 'Peter', 'Xavier']"
+      ]
+     },
+     "execution_count": 96,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "names"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "To sort in reverse order, we pass a keyword-only `reverse=True` argument to either the [sort()](https://docs.python.org/3/library/stdtypes.html#list.sort) method or the [sorted()](https://docs.python.org/3/library/functions.html#sorted) function. In the latter case, we could also use the [reversed()](https://docs.python.org/3/library/functions.html#reversed) built-in instead; however, that *neither* returns a new `list` object *nor* changes the existing one in place. We revisit it at the end of this chapter."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 97,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "names.sort(reverse=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 98,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['Xavier', 'Peter', 'Oliver', 'Karl', 'Eckardt', 'Carl', 'Berthold', 'Achim']"
+      ]
+     },
+     "execution_count": 98,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "names"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Both, the [sort()](https://docs.python.org/3/library/stdtypes.html#list.sort) method and the [sorted()](https://docs.python.org/3/library/functions.html#sorted) function, also accept a keyword-only `key` argument that must be a reference to a `function` object accepting one positional argument. Then, the elements in the `list` object are passed to that on a one-by-one basis, and the return values are used as the **sort keys**.\n",
+    "\n",
+    "For example, to sort `names` not by alphabet but by the names' lengths, we pass in a reference to the built-in [len()](https://docs.python.org/3/library/functions.html#len) function as `key=len`. Note that there are *no* parentheses after `len`!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 99,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "names.sort(key=len)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "If two names have the same length, their relative order is kept as is. A [sorting algorithm](https://en.wikipedia.org/wiki/Sorting_algorithm) is called **[stable](https://en.wikipedia.org/wiki/Sorting_algorithm#Stability)** if it has that property. That is why `\"Karl\"` comes before `\"Carl\" ` below.\n",
+    "\n",
+    "Sorting is an important topic in programming and we refer to the official [HOWTO](https://docs.python.org/3/howto/sorting.html) for a more comprehensive introduction."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 100,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['Karl', 'Carl', 'Peter', 'Achim', 'Xavier', 'Oliver', 'Eckardt', 'Berthold']"
+      ]
+     },
+     "execution_count": 100,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "names"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "The pop() method removes the last element from a `list` object *and* returns it."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 101,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Berthold'"
+      ]
+     },
+     "execution_count": 101,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "names.pop()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 102,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['Karl', 'Carl', 'Peter', 'Achim', 'Xavier', 'Oliver', 'Eckardt']"
+      ]
+     },
+     "execution_count": 102,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "names"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "It takes an optional index argument and removes that instead."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 103,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Carl'"
+      ]
+     },
+     "execution_count": 103,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "names.pop(1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 104,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['Karl', 'Peter', 'Achim', 'Xavier', 'Oliver', 'Eckardt']"
+      ]
+     },
+     "execution_count": 104,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "names"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Instead of removing an element by its index, we can remove it by its value with the remove() method. Behind the scenes, Python then compares the object passed as its argument, `\"Peter\"` in the example, sequentially to each element with the `==` operator and removes the first one that evaluates equal."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 105,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "names.remove(\"Peter\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 106,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['Karl', 'Achim', 'Xavier', 'Oliver', 'Eckardt']"
+      ]
+     },
+     "execution_count": 106,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "names"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "remove() raises a `ValueError` if the value is not found."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 107,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "ename": "ValueError",
+     "evalue": "list.remove(x): x not in list",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-107-6bb75d50cf06>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mnames\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mremove\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Peter\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;31mValueError\u001b[0m: list.remove(x): x not in list"
+     ]
+    }
+   ],
+   "source": [
+    "names.remove(\"Peter\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "`list` objects implement an index() method that returns the position of the first occurrence of an element. It fails loudly with a `ValueError` if the element cannot be found by value."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 108,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['Karl', 'Achim', 'Xavier', 'Oliver', 'Eckardt']"
+      ]
+     },
+     "execution_count": 108,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "names"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 109,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "3"
+      ]
+     },
+     "execution_count": 109,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "names.index(\"Oliver\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 110,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [
+    {
+     "ename": "ValueError",
+     "evalue": "'Carl' is not in list",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-110-c10e7aae815d>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mnames\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Carl\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;31mValueError\u001b[0m: 'Carl' is not in list"
+     ]
+    }
+   ],
+   "source": [
+    "names.index(\"Carl\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "The count() method returns the number of occurrences of a value."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 111,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "1"
+      ]
+     },
+     "execution_count": 111,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "names.count(\"Xavier\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 112,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0"
+      ]
+     },
+     "execution_count": 112,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "names.count(\"Yves\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "### List Comparison"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "The relational operators also work with `list` objects; yet another example of operator overloading.\n",
+    "\n",
+    "Comparison is made in a pairwise fashion until the first pair of elements does not evaluate equal or one of the `list` objects ends. The exact comparison rules depend on the elements and not the `list` object. We say that comparison is **[delegated](https://en.wikipedia.org/wiki/Delegation_(object-oriented_programming))** to the objects to be compared. Usually, all elements are of the *same* type. Then, the comparison is straightforward and conceptually the same as for string comparison in [Chapter 6](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/06_text.ipynb#String-Comparison)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 113,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['Karl', 'Achim', 'Xavier', 'Oliver', 'Eckardt']"
+      ]
+     },
+     "execution_count": 113,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "names"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 114,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "False"
+      ]
+     },
+     "execution_count": 114,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "names < [\"Karl\", \"Achim\", \"Oliver\", \"Xavier\", \"Eckardt\"]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 115,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 115,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "names < [\"Karl\", \"Xavier\", \"Achim\", \"Oliver\", \"Eckardt\"]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "The shorter `list` object is considered \"smaller,\" and vice versa."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 116,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "False"
+      ]
+     },
+     "execution_count": 116,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "names < [\"Karl\", \"Achim\", \"Xavier\", \"Oliver\"]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 117,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 117,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "names < [\"Karl\", \"Achim\", \"Xavier\", \"Oliver\", \"Eckardt\", \"Peter\"]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "### Modifiers vs. Pure Functions"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "As `list` objects are mutable, the caller of a function can see the changes made to a `list` object passed to the function as an argument. That is often a surprising *side effect* and should be avoided.\n",
+    "\n",
+    "As an example, consider the `add_xyz()` function."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 118,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "letters = [\"a\", \"b\", \"c\"]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 119,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "def add_xyz(arg):\n",
+    "    \"\"\"Append letters to a list.\"\"\"\n",
+    "    arg.extend([\"x\", \"y\", \"z\"])\n",
+    "    return arg"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "While this function is being executed, two variables, namely `letters` in the global scope and `arg` inside the function's local scope, point to the *same* `list` object in memory. Furthermore, the passed in `arg` is also the return value.\n",
+    "\n",
+    "So, after the function call, `letters_with_xyz` and `letters` are **aliases** as well, pointing to the *same* object."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 120,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "letters_with_xyz = add_xyz(letters)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 121,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['a', 'b', 'c', 'x', 'y', 'z']"
+      ]
+     },
+     "execution_count": 121,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "letters_with_xyz"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 122,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['a', 'b', 'c', 'x', 'y', 'z']"
+      ]
+     },
+     "execution_count": 122,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "letters"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "A better practice is to first create a copy of `arg` within the function that is then modified and returned. If we are sure that `arg` contains immutable elements only, we get away with a shallow copy. The downside of this approach is the higher amount of memory necessary.\n",
+    "\n",
+    "The revised `add_xyz()` function below is more natural to reason about as it does *not* modify the passed in `arg` internally. This approach is following the **[functional programming](https://en.wikipedia.org/wiki/Functional_programming)** paradigm that is going through a \"renaissance\" currently. Two essential characteristics of functional programming are that a function *never* changes its inputs and *always* returns the same output given the same inputs.\n",
+    "\n",
+    "For a beginner, it is probably better to stick to this idea and not change any arguments as the original `add_xyz()` above. However, functions that modify and return the argument passed in are an important aspect of object-oriented programming, as explained in Chapter 9."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 123,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "letters = [\"a\", \"b\", \"c\"]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 124,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "def add_xyz(arg):\n",
+    "    \"\"\"Create a new list from an existing one.\"\"\"\n",
+    "    new_arg = arg[:]  # a shallow copy is good enough here\n",
+    "    new_arg.extend([\"x\", \"y\", \"z\"])\n",
+    "    return new_arg"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 125,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "letters_with_xyz = add_xyz(letters)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 126,
+   "metadata": {
+    "scrolled": true,
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['a', 'b', 'c', 'x', 'y', 'z']"
+      ]
+     },
+     "execution_count": 126,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "letters_with_xyz"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 127,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['a', 'b', 'c']"
+      ]
+     },
+     "execution_count": 127,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "letters"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "If we want to modify the argument passed in, it is best to return `None` and not `arg`, as does the final version of `add_xyz()` below. Then, the user of our function cannot accidentally create two aliases to the same object. That is also why the list methods above all return `None`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 128,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "letters = [\"a\", \"b\", \"c\"]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 129,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "def add_xyz(arg):\n",
+    "    \"\"\"Append letters to a list.\"\"\"\n",
+    "    arg.extend([\"x\", \"y\", \"z\"])\n",
+    "    return  # = None"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 130,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "add_xyz(letters)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 131,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['a', 'b', 'c', 'x', 'y', 'z']"
+      ]
+     },
+     "execution_count": 131,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "letters"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "If we call `add_xyz()` with `letters` as the argument again, we end up with an even longer `list` object."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 132,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "add_xyz(letters)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 133,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['a', 'b', 'c', 'x', 'y', 'z', 'x', 'y', 'z']"
+      ]
+     },
+     "execution_count": 133,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "letters"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Functions that only work on the argument passed in are called **modifiers**. Their primary purpose is to change the **state** of the argument. On the contrary, functions that have *no* side effects on the arguments are said to be **pure**."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "## The `tuple` Type"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "To create a `tuple` object, we can use the same literal notation as for `list` objects *without* the brackets and list all elements."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 134,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "numbers = 7, 11, 8, 5, 3, 12, 2, 6, 9, 10, 1, 4"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 135,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(7, 11, 8, 5, 3, 12, 2, 6, 9, 10, 1, 4)"
+      ]
+     },
+     "execution_count": 135,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "numbers"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "However, to be clearer, many Pythonistas write out the optional parentheses `(` and `)`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 136,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "numbers = (7, 11, 8, 5, 3, 12, 2, 6, 9, 10, 1, 4)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 137,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(7, 11, 8, 5, 3, 12, 2, 6, 9, 10, 1, 4)"
+      ]
+     },
+     "execution_count": 137,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "numbers"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "As before, `numbers` is an object on its own."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 138,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "140322025210648"
+      ]
+     },
+     "execution_count": 138,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "id(numbers)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 139,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "tuple"
+      ]
+     },
+     "execution_count": 139,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "type(numbers)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "While we could use empty parentheses `()` to create an empty `tuple` object ..."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 140,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "empty_tuple = ()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 141,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "()"
+      ]
+     },
+     "execution_count": 141,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "empty_tuple"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 142,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "tuple"
+      ]
+     },
+     "execution_count": 142,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "type(empty_tuple)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "... we must use a *trailing comma* to create a `tuple` object holding one element. If we forget the comma, the parentheses are interpreted as the grouping operator and effectively useless!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 143,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "one_tuple = (1,)  # we could ommit the parentheses but not the comma"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 144,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(1,)"
+      ]
+     },
+     "execution_count": 144,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "one_tuple"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 145,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "tuple"
+      ]
+     },
+     "execution_count": 145,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "type(one_tuple)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 146,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "no_tuple = (1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 147,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "1"
+      ]
+     },
+     "execution_count": 147,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "no_tuple"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 148,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "int"
+      ]
+     },
+     "execution_count": 148,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "type(no_tuple)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Alternatively, we may use the [tuple()](https://docs.python.org/3/library/functions.html#func-tuple) built-in that takes any iterable as its argument and creates a new `tuple` from its elements."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 149,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(1,)"
+      ]
+     },
+     "execution_count": 149,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "tuple([1])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "### Tuples are like \"Immutable Lists\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Most operations involving `tuple` objects work in the same way as with `list` objects. The main difference is that `tuple` objects are *immutable*. So, if our program does not depend on mutability, we may and should use `tuple` and not `list` objects to model sequential data. That way, we avoid the pitfalls seen above.\n",
+    "\n",
+    "`tuple` objects are *sequences* exhibiting the familiar *four* behaviors."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 150,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 150,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "isinstance(numbers, abc.Sequence)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    " So, `numbers` holds a *finite* number of elements ..."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 151,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "12"
+      ]
+     },
+     "execution_count": 151,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "len(numbers)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "... that we can obtain individually by looping over it in a predictable *forward* or *reverse* order."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 152,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "7   11   8   5   3   12   2   6   9   10   1   4   "
+     ]
+    }
+   ],
+   "source": [
+    "for number in numbers:\n",
+    "    print(number, end=\"   \")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 153,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "4   1   10   9   6   2   12   3   5   8   11   7   "
+     ]
+    }
+   ],
+   "source": [
+    "for number in reversed(numbers):\n",
+    "    print(number, end=\"   \")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "To check if a given object is *contained* in `numbers`, we use the `in` operator and conduct a linear search."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 154,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "False"
+      ]
+     },
+     "execution_count": 154,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "0 in numbers"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 155,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 155,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "1 in numbers"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 156,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 156,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "1.0 in numbers  # in relies on == behind the scenes"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "We may index and slice with the `[]` operator. The latter returns *new* `tuple` objects."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 157,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "7"
+      ]
+     },
+     "execution_count": 157,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "numbers[0]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 158,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "4"
+      ]
+     },
+     "execution_count": 158,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "numbers[-1]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 159,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(2, 6, 9, 10, 1, 4)"
+      ]
+     },
+     "execution_count": 159,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "numbers[6:]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Index assignment does *not* work as tuples are *immutable* and results in a `TypeError`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 160,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "ename": "TypeError",
+     "evalue": "'tuple' object does not support item assignment",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-160-80fddf1304f0>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mnumbers\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m99\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;31mTypeError\u001b[0m: 'tuple' object does not support item assignment"
+     ]
+    }
+   ],
+   "source": [
+    "numbers[-1] = 99"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "If we need to \"modify\" the `tuple` object, we must create a *new* `tuple` object, for example, like so: We take a slice of the elements we want to keep and use the overloaded `+` operator to concatenate the slice with another `tuple` object."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 161,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "new_numbers = numbers[:-1] + (99,)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 162,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(7, 11, 8, 5, 3, 12, 2, 6, 9, 10, 1, 99)"
+      ]
+     },
+     "execution_count": 162,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "new_numbers"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "The `*` operator works as well."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 163,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(7, 11, 8, 5, 3, 12, 2, 6, 9, 10, 1, 4, 7, 11, 8, 5, 3, 12, 2, 6, 9, 10, 1, 4)"
+      ]
+     },
+     "execution_count": 163,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "2 * numbers"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Being immutable, `tuple` objects only provide the count() and index() methods."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 164,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0"
+      ]
+     },
+     "execution_count": 164,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "numbers.count(0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 165,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "10"
+      ]
+     },
+     "execution_count": 165,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "numbers.index(1)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "The relational operators compare the elements of two `tuple` objects in a pairwise fashion as above."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 166,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 166,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "numbers < new_numbers"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "While `tuple` objects are immutable, this only relates to the references they hold. If a `tuple` object contains mutable objects, the entire nested structure is *not* immutable as a whole.\n",
+    "\n",
+    "Consider the following stylized example `not_immutable`: It contains *three* elements, `1`, `[2, ..., 11]`, and `12`, and the elements of the nested `list` object may be changed. While it is not practical to mix data types in a `tuple` object that is used as an \"immutable list,\" we want to make the point that the mere usage of the `tuple` type does *not* guarantee a nested object to be immutable."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 167,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "not_immutable = (1, [2, 3, 4, 5, 6, 7, 8, 9, 10, 11], 12)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 168,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "not_immutable[1][:] = [99, 99, 99]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 169,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(1, [99, 99, 99], 12)"
+      ]
+     },
+     "execution_count": 169,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "not_immutable"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "### Packing & Unpacking"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "In the \"*List Operations*\" section above, the `*` symbol **unpacks** the elements of a `list` object into another one. This idea of *iterable unpacking* is built into Python at various places, even *without* the `*` symbol.\n",
+    "\n",
+    "For example, we may write variables on the left-hand side of a `=` statement in a `tuple` style. Then,  any *finite* iterable on the right-hand side is unpacked. So, `numbers` is unpacked into *twelve* variables below."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 170,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "n1, n2, n3, n4, n5, n6, n7, n8, n9, n10, n11, n12 = numbers"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 171,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "7"
+      ]
+     },
+     "execution_count": 171,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "n1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 172,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "11"
+      ]
+     },
+     "execution_count": 172,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "n2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 173,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "8"
+      ]
+     },
+     "execution_count": 173,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "n3"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Having to type twelve variables on the left is already tedious. Furthermore, if the iterable on the right yields a number of elements *different* from the number of variables, we get a `ValueError`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 174,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "ename": "ValueError",
+     "evalue": "too many values to unpack (expected 11)",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-174-c5e79943681f>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mn1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn3\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn4\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn5\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn6\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn7\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn8\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn9\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn10\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn11\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnumbers\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;31mValueError\u001b[0m: too many values to unpack (expected 11)"
+     ]
+    }
+   ],
+   "source": [
+    "n1, n2, n3, n4, n5, n6, n7, n8, n9, n10, n11 = numbers"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 175,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "ename": "ValueError",
+     "evalue": "not enough values to unpack (expected 13, got 12)",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-175-214f2de70baf>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mn1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn3\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn4\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn5\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn6\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn7\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn8\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn9\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn10\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn11\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn12\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn13\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnumbers\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;31mValueError\u001b[0m: not enough values to unpack (expected 13, got 12)"
+     ]
+    }
+   ],
+   "source": [
+    "n1, n2, n3, n4, n5, n6, n7, n8, n9, n10, n11, n12, n13 = numbers"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "So, to make iterable unpacking useful, we prepend the `*` symbol to *one* of the variables on the left: That variable then becomes a `list` object holding the elements not captured by the other variables. We say that the excess elements from the iterable are **packed** into this variable.\n",
+    "\n",
+    "For example, let's get the `first` and `last` element of `numbers` and collect the rest in `middle`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 176,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "first, *middle, last = numbers"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 177,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "7"
+      ]
+     },
+     "execution_count": 177,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "first"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 178,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[11, 8, 5, 3, 12, 2, 6, 9, 10, 1]"
+      ]
+     },
+     "execution_count": 178,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "middle"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 179,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "4"
+      ]
+     },
+     "execution_count": 179,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "last"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "If we do not need the `middle` elements, we go with the underscore `_` convention and \"throw\" them away."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 180,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "first, *_, last = numbers"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 181,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "7"
+      ]
+     },
+     "execution_count": 181,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "first"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 182,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "4"
+      ]
+     },
+     "execution_count": 182,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "last"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "We already used unpacking before this section without knowing it. Whenever we write a `for`-loop over the [zip()](https://docs.python.org/3/library/functions.html#zip) built-in, that generates a new `tuple` object in each iteration, which we unpack by listing several loop variables.\n",
+    "\n",
+    "So, the `name, position` acts like a left-hand side of an `=` statement and unpacks the `tuple` objects generated from \"zipping\" the `names` list and the `positions` tuple together."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 183,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "positions = (\"goalkeeper\", \"defender\", \"midfielder\", \"striker\", \"coach\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 184,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Karl is a goalkeeper\n",
+      "Achim is a defender\n",
+      "Xavier is a midfielder\n",
+      "Oliver is a striker\n",
+      "Eckardt is a coach\n"
+     ]
+    }
+   ],
+   "source": [
+    "for name, position in zip(names, positions):\n",
+    "    print(name, \"is a\", position)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Without unpacking, [zip()](https://docs.python.org/3/library/functions.html#zip) generates a series of `tuple` objects."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 185,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'tuple'>   ('Karl', 'goalkeeper')\n",
+      "<class 'tuple'>   ('Achim', 'defender')\n",
+      "<class 'tuple'>   ('Xavier', 'midfielder')\n",
+      "<class 'tuple'>   ('Oliver', 'striker')\n",
+      "<class 'tuple'>   ('Eckardt', 'coach')\n"
+     ]
+    }
+   ],
+   "source": [
+    "for pair in zip(names, positions):\n",
+    "    print(type(pair), pair, sep=\"   \")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Unpacking also works for nested objects. Below, we wrap [zip()](https://docs.python.org/3/library/functions.html#zip) with the [enumerate()](https://docs.python.org/3/library/functions.html#enumerate) built-in to have an index variable `i` inside the `for`-loop. In each iteration, a `tuple` object consisting of `i` and another `tuple` object is created. The inner one then holds the `name` and `position`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 186,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0 -> Karl is a goalkeeper\n",
+      "1 -> Achim is a defender\n",
+      "2 -> Xavier is a midfielder\n",
+      "3 -> Oliver is a striker\n",
+      "4 -> Eckardt is a coach\n"
+     ]
+    }
+   ],
+   "source": [
+    "for i, (name, position) in enumerate(zip(names, positions)):\n",
+    "    print(i, \"->\", name, \"is a\", position)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "#### Swapping Variables"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "A popular use case of unpacking is **swapping** two variables.\n",
+    "\n",
+    "Consider `a` and `b` below."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 187,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "a = 0\n",
+    "b = 1"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Without unpacking, we must use a temporary variable `temp` to swap `a` and `b`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 188,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "temp = a\n",
+    "a = b\n",
+    "b = temp"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 189,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(1, 0)"
+      ]
+     },
+     "execution_count": 189,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "a, b"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "With unpacking, the solution is more elegant, and also a bit faster as well. *All* expressions on the right-hand side are evaluated *before* any assignment takes place."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 190,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "a = 0\n",
+    "b = 1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 191,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "a, b = b, a"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 192,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(1, 0)"
+      ]
+     },
+     "execution_count": 192,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "a, b"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "#### Example: [Fibonacci Numbers](https://en.wikipedia.org/wiki/Fibonacci_number) (revisited)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Unpacking allows us to rewrite the iterative `fibonacci()` function from [Chapter 4](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/04_iteration.ipynb#\"Hard-at-first-Glance\"-Example:-Fibonacci-Numbers-%28revisited%29) in a concise way, now also supporting *goose typing* with the [numbers](https://docs.python.org/3/library/numbers.html) module from the [standard library](https://docs.python.org/3/library/index.html)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 193,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "import numbers"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 194,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "def fibonacci(i):\n",
+    "    \"\"\"Calculate the ith Fibonacci number.\n",
+    "\n",
+    "    Args:\n",
+    "        i (int): index of the Fibonacci number to calculate\n",
+    "\n",
+    "    Returns:\n",
+    "        ith_fibonacci (int)\n",
+    "\n",
+    "    Raises:\n",
+    "        TypeError: if i is not an integer\n",
+    "        ValueError: if i is not positive\n",
+    "    \"\"\"\n",
+    "    if not isinstance(i, numbers.Integral):\n",
+    "        raise TypeError(\"i must be an integer\")\n",
+    "    elif i < 0:\n",
+    "        raise ValueError(\"i must be non-negative\")\n",
+    "\n",
+    "    a, b = 0, 1\n",
+    "\n",
+    "    for _ in range(i - 1):\n",
+    "        a, b = b, a + b\n",
+    "\n",
+    "    return b"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 195,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "144"
+      ]
+     },
+     "execution_count": 195,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "fibonacci(12)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "#### Function Definitions & Calls"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "The concepts of packing and unpacking are also helpful when writing and using functions.\n",
+    "\n",
+    "For example, let's look at the `product()` function below. Its implementation suggests that `args` must be a sequence type. Otherwise, it would not make sense to index into it with `[0]` or take a slice with `[1:]`. In line with the function's name, the `for`-loop multiplies all elements of the `args` sequence. So, what does the `*` do in the header line, and what is the exact data type of `args`?\n",
+    "\n",
+    "The `*` is again *not* an operator in this context but a special syntax that makes Python *pack* all *positional* arguments passed to `product()` into a single `tuple` object called `args`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 196,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "def product(*args):\n",
+    "    \"\"\"Multiply all arguments.\"\"\"\n",
+    "    result = args[0]\n",
+    "\n",
+    "    for arg in args[1:]:\n",
+    "        result *= arg\n",
+    "\n",
+    "    return result"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "So, we can pass an *arbitrary* (i.e., also none) number of *positional* arguments to `product()`.\n",
+    "\n",
+    "The product of just one number is the number itself."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 197,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "42"
+      ]
+     },
+     "execution_count": 197,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "product(42)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Passing in several numbers works as expected."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 198,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "100"
+      ]
+     },
+     "execution_count": 198,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "product(2, 5, 10)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "However, this implementation of `product()` needs *at least* one argument passed in due to the expression `args[0]` used internally. Otherwise, we see a *runtime* error, namely an `IndexError`. We emphasize that this error is *not* caused in the header line."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 199,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "ename": "IndexError",
+     "evalue": "tuple index out of range",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mIndexError\u001b[0m                                Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-199-640e0c632b8d>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mproduct\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;32m<ipython-input-196-02b0db9fbeab>\u001b[0m in \u001b[0;36mproduct\u001b[0;34m(*args)\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mproduct\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m     \u001b[0;34m\"\"\"Multiply all arguments.\"\"\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m     \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      4\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m     \u001b[0;32mfor\u001b[0m \u001b[0marg\u001b[0m \u001b[0;32min\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;31mIndexError\u001b[0m: tuple index out of range"
+     ]
+    }
+   ],
+   "source": [
+    "product()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Another downside of this implementation is that we can easily generate *semantic* errors: For example, if we pass in an iterable object like the `one_hundred` list, *no* exception is raised. However, the return value is also not a numeric object as we expect. The reason for this is that during the function call, `args` becomes a `tuple` object holding *one* element, which is `one_hundred`, a `list` object. So, we created a nested structure by accident."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 200,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "one_hundred = [2, 5, 10]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 201,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[2, 5, 10]"
+      ]
+     },
+     "execution_count": 201,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "product(one_hundred)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "This error does not occur if we unpack `one_hundred` upon passing it as the argument."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 202,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "100"
+      ]
+     },
+     "execution_count": 202,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "product(*one_hundred)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "That is the equivalent of writing out the following tedious expression. Yet, that does *not* scale for iterables with many elements in them."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 203,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "100"
+      ]
+     },
+     "execution_count": 203,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "product(one_hundred[0], one_hundred[1], one_hundred[2])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "While we needed to unpack `one_hundred` above to avoid the semantic error, unpacking an argument in a function call may also be a convenience in general.\n",
+    "\n",
+    "For example, to print the elements of `one_hundred` in one line, we need to use a `for` statement, until now. With unpacking, we get away *without* a loop."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 204,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[2, 5, 10]\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(one_hundred)  # prints the tuple; we do not want that"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 205,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "2 5 10 "
+     ]
+    }
+   ],
+   "source": [
+    "for number in one_hundred:\n",
+    "    print(number, end=\" \")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 206,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "2 5 10\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(*one_hundred)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "## The `namedtuple` Type"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Above, we proposed the idea that `tuple` objects are like \"immutable lists.\" Often, however, we use `tuple` objects to represent a **record** of related **fields**. Then, each element has a *semantic* meaning (i.e., a descriptive name).\n",
+    "\n",
+    "As an example, think of a spreadsheet with information on students in a course. Each row represents a record and holds all the data associated with an individual student. The columns (e.g., matriculation number, first name, last name) are the fields that may come as *different* data types (e.g., `int` for the matriculation number, `str` for the names).\n",
+    "\n",
+    "A simple way of modeling a single student is as a `tuple` object, for example, `(123456, \"John\", \"Doe\")`. A disadvantage of this approach is that we must remember the order and meaning of the elements/fields in the `tuple` object.\n",
+    "\n",
+    "An example from a different domain is the representation of $(x, y)$-points in the $x$-$y$-plane. Again, we could use a `tuple` object like `current_position` below to model the point $(4, 2)$."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 207,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "current_position = (4, 2)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "We implicitly assume that the first element represents the $x$ and the second the $y$ coordinate. While that follows intuitively from convention in math, we should at least add comments somewhere in the code to document this assumption.\n",
+    "\n",
+    "A better way is to create a *custom* data type. While that is covered in depth in Chapter 9, the [collections](https://docs.python.org/3/library/collections.html) module in the [standard library](https://docs.python.org/3/library/index.html) provides a [namedtuple()](https://docs.python.org/3/library/collections.html#collections.namedtuple) **factory function** that creates \"simple\" custom data types on top of the standard `tuple` type."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 208,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "from collections import namedtuple"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "[namedtuple()](https://docs.python.org/3/library/collections.html#collections.namedtuple) takes two arguments. The first argument is the name of the data type. That could be different from the variable `Point` we use to refer to the new type, but in most cases it is best to keep them in sync. The second argument is a sequence with the field names as `str` objects. The names' order corresponds to the one assumed in `current_position`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 209,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "Point = namedtuple(\"Point\", [\"x\", \"y\"])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "The `Point` object is a so-called **class**. That is what it means if an object is of type `type`. It can be used as a **factory** to create *new* `tuple`-like objects of type `Point`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 210,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "140321614839000"
+      ]
+     },
+     "execution_count": 210,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "id(Point)  # classes are objects as well"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 211,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "type"
+      ]
+     },
+     "execution_count": 211,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "type(Point)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "To create a `Point` object, we use the same *literal syntax* as for `current_position` above and prepend it with `Point`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 212,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "current_position = Point(4, 2)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Now, `current_position` has a somewhat nicer representation. In particular, the coordinates are named `x` and `y`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 213,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Point(x=4, y=2)"
+      ]
+     },
+     "execution_count": 213,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "current_position"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "It is *not* a `tuple` any more but an object of type `Point`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 214,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "140322025282656"
+      ]
+     },
+     "execution_count": 214,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "id(current_position)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 215,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "__main__.Point"
+      ]
+     },
+     "execution_count": 215,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "type(current_position)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "We use the dot operator `.` to access the defined attributes."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 216,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "4"
+      ]
+     },
+     "execution_count": 216,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "current_position.x"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 217,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "2"
+      ]
+     },
+     "execution_count": 217,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "current_position.y"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "As before, we get an `AttributeError` if we try to access an undefined attribute."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 218,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "ename": "AttributeError",
+     "evalue": "'Point' object has no attribute 'z'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-218-b30d0b930bac>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mcurrent_position\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mz\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;31mAttributeError\u001b[0m: 'Point' object has no attribute 'z'"
+     ]
+    }
+   ],
+   "source": [
+    "current_position.z"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "`current_position` continues to work like a `tuple` object! That is why we can use `namedtuple` as a replacement for `tuple`. The underlying implementations exhibit the *same* computational efficiencies and memory usages.\n",
+    "\n",
+    "For example, we can index into or loop over `current_position` as it is still a sequence."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 219,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 219,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "isinstance(current_position, abc.Sequence)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 220,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "4"
+      ]
+     },
+     "execution_count": 220,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "current_position[0]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 221,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "2"
+      ]
+     },
+     "execution_count": 221,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "current_position[1]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 222,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "4\n",
+      "2\n"
+     ]
+    }
+   ],
+   "source": [
+    "for number in current_position:\n",
+    "    print(number)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 223,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "2\n",
+      "4\n"
+     ]
+    }
+   ],
+   "source": [
+    "for number in reversed(current_position):\n",
+    "    print(number)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "## The Map-Filter-Reduce Paradigm"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Whenever we process sequential data, most tasks can be classified into one of the three categories **map**, **filter**, or **reduce**. This paradigm has caught attention in recent years as it enables **[parallel computing](https://en.wikipedia.org/wiki/Parallel_computing)**, and this gets important when dealing with big amounts of data.\n",
+    "\n",
+    "Let's look at a simple example."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 224,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "numbers = [7, 11, 8, 5, 3, 12, 2, 6, 9, 10, 1, 4]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "### Mapping"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "**Mapping** refers to the idea of applying a transformation to every element in a sequence.\n",
+    "\n",
+    "For example, let's square each element in `numbers` and add `1` to it. In essence, we apply the transformation $y := x^2 + 1$ expressed as the `transform()` function below."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 225,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "def transform(element):\n",
+    "    \"\"\"Map elements to their squares plus 1.\"\"\"\n",
+    "    return (element ** 2) + 1"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "With the syntax we know so far, we revert to a `for`-loop that iteratively appends the transformed elements to the initially empty `transformed_numbers` list."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 226,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "transformed_numbers = []\n",
+    "\n",
+    "for old in numbers:\n",
+    "    new = transform(old)\n",
+    "    transformed_numbers.append(new)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 227,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[50, 122, 65, 26, 10, 145, 5, 37, 82, 101, 2, 17]"
+      ]
+     },
+     "execution_count": 227,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "transformed_numbers"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "As this kind of data processing is so common, Python provides the [map()](https://docs.python.org/3/library/functions.html#map) built-in. In its simplest usage form, it takes two arguments: A transformation function that takes one positional argument and an iterable.\n",
+    "\n",
+    "We call [map()](https://docs.python.org/3/library/functions.html#map) with the `transform()` function and the `numbers` list as the arguments and store the result in the variable `transformer` to inspect it."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 228,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "transformer = map(transform, numbers)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "We might expect to get back a materialized sequence (i.e., all elements exist in memory), and a `list` object would feel the most natural because of the type of the `numbers` argument. However, `transformer` is an object of type `map`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 229,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<map at 0x7f9f44745ac8>"
+      ]
+     },
+     "execution_count": 229,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "transformer"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 230,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "map"
+      ]
+     },
+     "execution_count": 230,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "type(transformer)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Like `range` objects, `map` objects generate a series of objects \"on the fly\" (i.e., one by one), and we use the built-in [next()](https://docs.python.org/3/library/functions.html#next) function to obtain the next object in line. So, we should think of a `map` object as a \"rule\" stored in memory that only knows how to calculate the next object of possibly *infinitely* many.\n",
+    "\n",
+    "It is essential to understand that by creating a `map` object with the [map()](https://docs.python.org/3/library/functions.html#map) built-in, nothing happens in memory except the creation of the `map` object. In particular, no second `list` object derived from `numbers` is created. Also, we may view `range` objects as a special case of `map` objects: They are constrained to generating `int` objects only, and the *iterable* argument is replaced with *start*, *stop*, and *step* arguments."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 231,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "50"
+      ]
+     },
+     "execution_count": 231,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "next(transformer)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 232,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "122"
+      ]
+     },
+     "execution_count": 232,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "next(transformer)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 233,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "65"
+      ]
+     },
+     "execution_count": 233,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "next(transformer)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "If we are sure that a `map` object generates a *finite* number of elements, we may materialize them into a `list` object with the [list()](https://docs.python.org/3/library/functions.html#func-list) built-in. In the example, this is the case as `transformer` is derived from a *finite* `list` object.\n",
+    "\n",
+    "In summary, instead of creating an empty list first and appending it in a `for`-loop as above, we write the following one-liner and obtain an equal `transformed_numbers` list."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 234,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "transformed_numbers = list(map(transform, numbers))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 235,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[50, 122, 65, 26, 10, 145, 5, 37, 82, 101, 2, 17]"
+      ]
+     },
+     "execution_count": 235,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "transformed_numbers"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "### Filtering"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "**Filtering** refers to the idea of creating a subset of a sequence with a **boolean filter** function that indicates if an element should be kept or not.\n",
+    "\n",
+    "In the example, let's only keep the even elements in `numbers`. The `is_even()` function implements that as a filter."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 236,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "def is_even(element):\n",
+    "    \"\"\"Filter out odd numbers.\"\"\"\n",
+    "    if element % 2 == 0:\n",
+    "        return True\n",
+    "    return False"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "As before, we must revert to a `for`-loop that appends the elements to be kept iteratively to an initially empty `even_numbers` list."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 237,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "even_numbers = []\n",
+    "\n",
+    "for number in transformed_numbers:\n",
+    "    if is_even(number):\n",
+    "        even_numbers.append(number)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 238,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[50, 122, 26, 10, 82, 2]"
+      ]
+     },
+     "execution_count": 238,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "even_numbers"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "As filtering is also a common task, we use the [filter()](https://docs.python.org/3/library/functions.html#filter) built-in that returns an object of type `filter` stored in the `evens` variable."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 239,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "evens = filter(is_even, transformed_numbers)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 240,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<filter at 0x7f9f447271d0>"
+      ]
+     },
+     "execution_count": 240,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "evens"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 241,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "filter"
+      ]
+     },
+     "execution_count": 241,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "type(evens)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "`evens` works like `transformer` above, and we use the built-in [next()](https://docs.python.org/3/library/functions.html#next) function to obtain the even numbers one by one. So, the \"next\" element in line is simply the next even `int` object the `filter` object encounters."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 242,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[50, 122, 65, 26, 10, 145, 5, 37, 82, 101, 2, 17]"
+      ]
+     },
+     "execution_count": 242,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "transformed_numbers  # for quick reference"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 243,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "50"
+      ]
+     },
+     "execution_count": 243,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "next(evens)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 244,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "122"
+      ]
+     },
+     "execution_count": 244,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "next(evens)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 245,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "26"
+      ]
+     },
+     "execution_count": 245,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "next(evens)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "As above, we must explicitly create a materialized `list` object with the [list()](https://docs.python.org/3/library/functions.html#func-list) built-in."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 246,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[50, 122, 26, 10, 82, 2]"
+      ]
+     },
+     "execution_count": 246,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "list(filter(is_even, transformed_numbers))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "We may also chain mappings and filters based on the original `numbers` list."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 247,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[50, 122, 26, 10, 82, 2]"
+      ]
+     },
+     "execution_count": 247,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "list(filter(is_even, map(transform, numbers)))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Using the [map()](https://docs.python.org/3/library/functions.html#map) and [filter()](https://docs.python.org/3/library/functions.html#filter) built-ins, we can quickly switch the order: Filter first and then transform the remaining elements. This variant equals the \"*A simple Filter*\" example from [Chapter 4](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/04_iteration.ipynb#Example:-A-simple-Filter). On the contrary, code with `for`-loops and `if` statements is more tedious to adapt. Additionally, `map` and `filter` objects are optimized at the C level and, therefore, a lot faster as well."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 248,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[65, 145, 5, 37, 101, 17]"
+      ]
+     },
+     "execution_count": 248,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "list(map(transform, filter(is_even, numbers)))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "### Reducing"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Lastly, **reducing** sequential data means to summarize the elements into a single statistic.\n",
+    "\n",
+    "A simple example is the built-in [sum()](https://docs.python.org/3/library/functions.html#sum) function."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 249,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "370"
+      ]
+     },
+     "execution_count": 249,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "sum(map(transform, filter(is_even, numbers)))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Other straightforward examples are the built-in [min()](https://docs.python.org/3/library/functions.html#min) or [max()](https://docs.python.org/3/library/functions.html#max) functions."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 250,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "5"
+      ]
+     },
+     "execution_count": 250,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "min(map(transform, filter(is_even, numbers)))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 251,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "145"
+      ]
+     },
+     "execution_count": 251,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "max(map(transform, filter(is_even, numbers)))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "[sum()](https://docs.python.org/3/library/functions.html#sum), [min()](https://docs.python.org/3/library/functions.html#min), and [max()](https://docs.python.org/3/library/functions.html#max) can be regarded as special cases.\n",
+    "\n",
+    "The generic way of reducing a sequence is to apply a function of *two* arguments on a rolling horizon: Its first argument is the reduction of the elements processed so far, and the second the next element to be reduced.\n",
+    "\n",
+    "For illustration, let's replicate [sum()](https://docs.python.org/3/library/functions.html#sum) as such a function, called `add()`. Its implementation only adds two numbers."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 252,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "def add(sum_so_far, next_number):\n",
+    "    \"\"\"Reduce a sequence by addition.\"\"\"\n",
+    "    return sum_so_far + next_number"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Further, we create a *new* `map` object derived from `numbers` ..."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 253,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "evens_transformed = map(transform, filter(is_even, numbers))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "... and loop over all *but* the first element it generates. That we capture separately as the initial `result` with the [next()](https://docs.python.org/3/library/functions.html#next) function. So, `map` objects must be *iterable* as we may loop over them.\n",
+    "\n",
+    "We know that `evens_transformed` generates *six* elements. That is why we see *five* growing `result` values resembling a [cumulative sum](http://mathworld.wolfram.com/CumulativeSum.html)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 254,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "65 210 215 252 353 "
+     ]
+    }
+   ],
+   "source": [
+    "result = next(evens_transformed)  # first element is the initial value\n",
+    "\n",
+    "for number in evens_transformed:  # iterate over the remaining elements\n",
+    "    print(result, end=\" \")  # line added for didactical purposes\n",
+    "    result = add(result, number)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "The final `result` is the same `370` as above."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 255,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "370"
+      ]
+     },
+     "execution_count": 255,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "result"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "The [reduce()](https://docs.python.org/3/library/functools.html#functools.reduce) function in the [functools](https://docs.python.org/3/library/functools.html) module in the [standard library](https://docs.python.org/3/library/index.html) provides more convenience replacing the `for`-loop. It takes two arguments in the same way as the [map()](https://docs.python.org/3/library/functions.html#map) and [filter()](https://docs.python.org/3/library/functions.html#filter) built-ins.\n",
+    "\n",
+    "[reduce()](https://docs.python.org/3/library/functools.html#functools.reduce) is **[eager](https://en.wikipedia.org/wiki/Eager_evaluation)** meaning that all computations implied by the contained `map` and `filter` \"rules\" are executed right away, and the code cell returns `370`. On the contrary, [map()](https://docs.python.org/3/library/functions.html#map) and [filter()](https://docs.python.org/3/library/functions.html#filter) create **[lazy](https://en.wikipedia.org/wiki/Lazy_evaluation)** `map` and `filter` objects, and we have to use the [next()](https://docs.python.org/3/library/functions.html#next) function to obtain the elements."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 256,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "from functools import reduce"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 257,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "370"
+      ]
+     },
+     "execution_count": 257,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "reduce(add, map(transform, filter(is_even, numbers)))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "### Lambda Expressions"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "[map()](https://docs.python.org/3/library/functions.html#map), [filter()](https://docs.python.org/3/library/functions.html#filter), and [reduce()](https://docs.python.org/3/library/functools.html#functools.reduce) take a `function` object as their first argument, and we defined `transform()`, `is_even()`, and `add()` to be used precisely for that.\n",
+    "\n",
+    "Often, such functions are used *only once* in a program. However, the primary purpose of functions is to *re-use* them. In such cases, it makes more sense to define them \"anonymously\" right at the position where the first argument goes.\n",
+    "\n",
+    "As mentioned in [Chapter 2](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/02_functions.ipynb#Anonymous-Functions), Python provides `lambda` expressions to create `function` objects *without* a variable pointing to them.\n",
+    "\n",
+    "So, the above `add()` function could be rewritten as a `lambda` expression like so ..."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 258,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<function __main__.<lambda>(sum_so_far, next_number)>"
+      ]
+     },
+     "execution_count": 258,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "lambda sum_so_far, next_number: sum_so_far + next_number"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "... or even shorter."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 259,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<function __main__.<lambda>(x, y)>"
+      ]
+     },
+     "execution_count": 259,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "lambda x, y: x + y"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "With the new concepts in this section, we can rewrite the entire example in just a few lines of code *without* any `for`, `if`, and `def` statements. The resulting code is concise, easy to read, quick to modify, and even faster in execution. Most importantly, it is optimized to handle big amounts of data as *no* temporary `list` objects are materialized in memory."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 260,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "370"
+      ]
+     },
+     "execution_count": 260,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "numbers = [7, 11, 8, 5, 3, 12, 2, 6, 9, 10, 1, 4]\n",
+    "evens = filter(lambda x: x % 2 == 0, numbers)\n",
+    "transformed = map(lambda x: (x ** 2) + 1, evens)\n",
+    "sum(transformed)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "If `numbers` comes as a sorted sequence of whole numbers, we may use the [range()](https://docs.python.org/3/library/functions.html#func-range) built-in and get away *without any* `list` object in memory at all!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 261,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "370"
+      ]
+     },
+     "execution_count": 261,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "numbers = range(1, 13)\n",
+    "evens = filter(lambda x: x % 2 == 0, numbers)\n",
+    "transformed = map(lambda x: (x ** 2) + 1, evens)\n",
+    "sum(transformed)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "To additionally save the temporary variables, `numbers`, `evens`, and `transformed`, we write the entire computation as *one* expression."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 262,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "370"
+      ]
+     },
+     "execution_count": 262,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "sum(map(lambda x: (x ** 2) + 1, filter(lambda x: x % 2 == 0, range(1, 13))))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "PythonTutor visualizes the differences in the number of computational steps and memory usage:\n",
+    "- [Version 1](http://pythontutor.com/visualize.html#code=def%20is_even%28element%29%3A%0A%20%20%20%20if%20element%20%25%202%20%3D%3D%200%3A%0A%20%20%20%20%20%20%20%20return%20True%0A%20%20%20%20return%20False%0A%0Adef%20transform%28element%29%3A%0A%20%20%20%20return%20%28element%20**%202%29%20%2B%201%0A%0Anumbers%20%3D%20list%28range%281,%2013%29%29%0A%0Aevens%20%3D%20%5B%5D%0Afor%20number%20in%20numbers%3A%0A%20%20%20%20if%20is_even%28number%29%3A%0A%20%20%20%20%20%20%20%20evens.append%28number%29%0A%0Atransformed%20%3D%20%5B%5D%0Afor%20number%20in%20evens%3A%0A%20%20%20%20transformed.append%28transform%28number%29%29%0A%0Aresult%20%3D%20sum%28transformed%29&cumulative=false&curInstr=0&heapPrimitives=nevernest&mode=display&origin=opt-frontend.js&py=3&rawInputLstJSON=%5B%5D&textReferences=false): With `for`-loops, `if` statements, and named functions -> **116** steps\n",
+    "- [Version 2](http://pythontutor.com/visualize.html#code=numbers%20%3D%20range%281,%2013%29%0Aevens%20%3D%20filter%28lambda%20x%3A%20x%20%25%202%20%3D%3D%200,%20numbers%29%0Atransformed%20%3D%20map%28lambda%20x%3A%20%28x%20**%202%29%20%2B%201,%20evens%29%0Aresult%20%3D%20sum%28transformed%29&cumulative=false&curInstr=0&heapPrimitives=nevernest&mode=display&origin=opt-frontend.js&py=3&rawInputLstJSON=%5B%5D&textReferences=false): With named `map` and `filter` objects -> **58** steps\n",
+    "- [Version 3](http://pythontutor.com/visualize.html#code=result%20%3D%20sum%28map%28lambda%20x%3A%20%28x%20**%202%29%20%2B%201,%20filter%28lambda%20x%3A%20x%20%25%202%20%3D%3D%200,%20range%281,%2013%29%29%29%29&cumulative=false&curInstr=0&heapPrimitives=nevernest&mode=display&origin=opt-frontend.js&py=3&rawInputLstJSON=%5B%5D&textReferences=false): Everything in *one* expression -> **55** steps\n",
+    "\n",
+    "Versions 2 and 3 are the same, except for the three additional steps required to create the temporary variables. The *major* downside of Version 1 is that, in the worst case, it may need *three times* the memory as compared to the other two versions!\n",
+    "\n",
+    "An experienced Pythonista would probably go with Version 2 in a production system to keep the code readable and maintainable."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "### List Comprehensions"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "For [map()](https://docs.python.org/3/library/functions.html#map) and [filter()](https://docs.python.org/3/library/functions.html#filter), Python provides a nice syntax appealing to people who like mathematics.\n",
+    "\n",
+    "Consider again the \"*A simple Filter*\" example from [Chapter 4](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/04_iteration.ipynb#Example:-A-simple-Filter), written with combined `for` and `if` statements. So, the mapping and filtering steps happen simultaneously."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 263,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "numbers = [7, 11, 8, 5, 3, 12, 2, 6, 9, 10, 1, 4]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 264,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "evens_transformed = []\n",
+    "\n",
+    "for number in numbers:\n",
+    "    if number % 2 == 0:\n",
+    "        evens_transformed.append((number ** 2) + 1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 265,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[65, 145, 5, 37, 101, 17]"
+      ]
+     },
+     "execution_count": 265,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "evens_transformed"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "**List comprehensions**, or **list-comps** for short, are *expressions* to derive *new* `list` objects out of *existing* ones (cf., [reference](https://docs.python.org/3/reference/expressions.html#displays-for-lists-sets-and-dictionaries)). A single *expression* like below can replace the compound `for` *statement* from above."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 266,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[65, 145, 5, 37, 101, 17]"
+      ]
+     },
+     "execution_count": 266,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "[(n ** 2) + 1 for n in numbers if n % 2 == 0]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "A list comprehension may be used in place of any `list` object.\n",
+    "\n",
+    "For example, let's add up all the elements with [sum()](https://docs.python.org/3/library/functions.html#sum). The code below *materializes* all elements in memory *before* summing them up. So,  this code might cause a `MemoryError` when executed with a bigger `numbers` list. [PythonTutor](http://pythontutor.com/visualize.html#code=numbers%20%3D%20range%281,%2013%29%0Aresult%20%3D%20sum%28%5B%28n%20**%202%29%20%2B%201%20for%20n%20in%20numbers%20if%20n%20%25%202%20%3D%3D%200%5D%29&cumulative=false&curInstr=0&heapPrimitives=nevernest&mode=display&origin=opt-frontend.js&py=3&rawInputLstJSON=%5B%5D&textReferences=false) shows how a `list` object exists in memory at step 17 and then \"gets lost\" right after. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 267,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "370"
+      ]
+     },
+     "execution_count": 267,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "sum([(n ** 2) + 1 for n in numbers if n % 2 == 0])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "#### Example: Nested Lists"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "List comprehensions may come with several `for`'s and `if`'s.\n",
+    "\n",
+    "The cell below creates a `list` object that contains other `list` objects with numbers in them. The starting number in each inner `list` object is offset by `1`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 268,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "nested_numbers = [list(range(x, y + 1)) for x, y in zip([1, 2, 3], [7, 8, 9])]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 269,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[[1, 2, 3, 4, 5, 6, 7], [2, 3, 4, 5, 6, 7, 8], [3, 4, 5, 6, 7, 8, 9]]"
+      ]
+     },
+     "execution_count": 269,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "nested_numbers"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "To do something meaningful with the numbers, we have to get rid of the inner layer of `list` objects and flatten the data.\n",
+    "\n",
+    "Without list comprehensions, we would probably write two nested `for`-loops."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 270,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "flat_numbers = []\n",
+    "\n",
+    "for inner_numbers in nested_numbers:\n",
+    "    for number in inner_numbers:\n",
+    "        flat_numbers.append(number)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 271,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[1, 2, 3, 4, 5, 6, 7, 2, 3, 4, 5, 6, 7, 8, 3, 4, 5, 6, 7, 8, 9]"
+      ]
+     },
+     "execution_count": 271,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "flat_numbers"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "That translates into a list comprehension like below. The order of the `for`'s may be confusing at first but is the *same* as writing out the nested `for`-loops."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 272,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[1, 2, 3, 4, 5, 6, 7, 2, 3, 4, 5, 6, 7, 8, 3, 4, 5, 6, 7, 8, 9]"
+      ]
+     },
+     "execution_count": 272,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "[number for inner_numbers in nested_numbers for number in inner_numbers]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Now, we may use the `list` object resulting from the list comprehension in any context we want.\n",
+    "\n",
+    "As an example, we add up the flattened numbers with [sum()](https://docs.python.org/3/library/functions.html#sum). The same caveat holds as before: The `list` object passed into [sum()](https://docs.python.org/3/library/functions.html#sum) is *materialized* before the sum is calculated!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 273,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "105"
+      ]
+     },
+     "execution_count": 273,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "sum([number for inner_numbers in nested_numbers for number in inner_numbers])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "In this particular example, however, we can exploit the fact that any sum of numbers can be expressed as the sum of sums of mutually exclusive and collectively exhaustive subsets of these numbers and get away with just *one* `for` in the list comprehension."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 274,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "105"
+      ]
+     },
+     "execution_count": 274,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "sum([sum(inner_numbers) for inner_numbers in nested_numbers])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "#### Example: Cartesian Products"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "A popular use case of nested list comprehensions is applying a transformation to each $2$-tuple of the [Cartesian product](https://en.wikipedia.org/wiki/Cartesian_product) of two iterables.\n",
+    "\n",
+    "For example, let's add `1` to each quotient obtained by taking the numerator from `[10, 20, 30]` and the denominator from `[40, 50, 60]`, and then find the product of all quotients. The table below visualizes the calculations: The result is the product of *nine* entries.\n",
+    "\n",
+    "||**10**|**20**|**30**|\n",
+    "|-|-|-|-|\n",
+    "|**40**|1.25|1.50|1.75|\n",
+    "|**50**|1.20|1.40|1.60|\n",
+    "|**60**|1.17|1.33|1.50|\n",
+    "\n",
+    "To express that in Python, we start by creating two `list` objects, `first` and `second`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 275,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "first = [10, 20, 30]\n",
+    "second = [40, 50, 60]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "For a Cartesian product, we loop over *all* possible $2$-tuples where one element is drawn from `first` and the other from `second`. That is equivalent to two nested `for`-loops."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 276,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[1.25, 1.2, 1.1666666666666667, 1.5, 1.4, 1.3333333333333333, 1.75, 1.6, 1.5]"
+      ]
+     },
+     "execution_count": 276,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "cartesian_product = []\n",
+    "\n",
+    "for numerator in first:\n",
+    "    for denominator in second:\n",
+    "        quotient = numerator / denominator\n",
+    "        cartesian_product.append(quotient + 1)\n",
+    "\n",
+    "cartesian_product"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "We translate the two `for`-loops into one list comprehensions with two `for`'s in it and use `x` and `y` as shorter variable names."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 277,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[1.25, 1.2, 1.1666666666666667, 1.5, 1.4, 1.3333333333333333, 1.75, 1.6, 1.5]"
+      ]
+     },
+     "execution_count": 277,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "[(x / y) + 1 for x in first for y in second]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "The order of the `for`'s is *important*: The list comprehension above divides numbers from `first` by numbers from `second`, whereas the list comprehension below does the opposite."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 278,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[5.0, 3.0, 2.333333333333333, 6.0, 3.5, 2.666666666666667, 7.0, 4.0, 3.0]"
+      ]
+     },
+     "execution_count": 278,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "[(x / y) + 1 for x in second for y in first]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "To find the overall product, we *unpack* the first list comprehension right into the `product()` function from the \"*Packing & Unpacking*\" section above."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 279,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "20.58"
+      ]
+     },
+     "execution_count": 279,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "product(*[(x / y) + 1 for x in first for y in second])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Alternatively, we use a `lambda` expression with the [reduce()](https://docs.python.org/3/library/functools.html#functools.reduce) function from the [functools](https://docs.python.org/3/library/functools.html) module."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 280,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "20.58"
+      ]
+     },
+     "execution_count": 280,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "reduce(lambda x, y: x * y, [(x / y) + 1 for x in first for y in second])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "While this example is stylized, Cartesian products are hidden in many applications, and it shows how the various language features introduced in this chapter can be seamlessly combined to process sequential data."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "### Generator Expressions"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Pythonistas would forgo materialized `list` objects, and, thus, also list comprehensions, all together, and use a more memory-efficient approach with **[generator expressions](https://docs.python.org/3/reference/expressions.html#generator-expressions)**, or **genexps** for short. Syntactically, they work like list comprehensions except that parentheses replace the brackets.\n",
+    "\n",
+    "Let's go back to the original example in this section and find the transformation $y := x^2 + 1$ of all even elements in `numbers`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 281,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "numbers = [7, 11, 8, 5, 3, 12, 2, 6, 9, 10, 1, 4]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "To filter and transform `numbers`, we wrote this list comprehension above ..."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 282,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[65, 145, 5, 37, 101, 17]"
+      ]
+     },
+     "execution_count": 282,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "[(n ** 2) + 1 for n in numbers if n % 2 == 0]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "... that now becomes a generator expression."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 283,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<generator object <genexpr> at 0x7f9f447618b8>"
+      ]
+     },
+     "execution_count": 283,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "((n ** 2) + 1 for n in numbers if n % 2 == 0)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "We can think of it as yet another \"rule\" in memory that knows how to generate the individual objects in a series one by one. Whereas a list comprehension materializes its elements in memory *when* it is evaluated, the opposite holds for generator expressions, and *no* object is created in memory except the \"rule\" itself. Because of this behavior, we describe generator expressions as *lazy* and list comprehensions as *eager*.\n",
+    "\n",
+    "So, to materialize all elements specified by a generator expression, we revert to the [list()](https://docs.python.org/3/library/functions.html#func-list) built-in."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 284,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[65, 145, 5, 37, 101, 17]"
+      ]
+     },
+     "execution_count": 284,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "list(((n ** 2) + 1 for n in numbers if n % 2 == 0))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Whenever a generator expression is the only argument to a function, we may leave out the parentheses."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 285,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[65, 145, 5, 37, 101, 17]"
+      ]
+     },
+     "execution_count": 285,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "list((n ** 2) + 1 for n in numbers if n % 2 == 0)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "A common use case is to reduce the elements into a single object instead, for example, by adding them up with [sum()](https://docs.python.org/3/library/functions.html#sum). [PythonTutor](http://pythontutor.com/visualize.html#code=numbers%20%3D%20range%281,%2013%29%0Asum_with_list%20%3D%20sum%28%5B%28n%20**%202%29%20%2B%201%20for%20n%20in%20numbers%20if%20n%20%25%202%20%3D%3D%200%5D%29%0Asum_with_gen%20%3D%20sum%28%28n%20**%202%29%20%2B%201%20for%20n%20in%20numbers%20if%20n%20%25%202%20%3D%3D%200%29&cumulative=false&curInstr=0&heapPrimitives=nevernest&mode=display&origin=opt-frontend.js&py=3&rawInputLstJSON=%5B%5D&textReferences=false) shows how the code cell below does *not* create a temporary `list` object in memory, whereas a list comprehension would (cf., step 17)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 286,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "370"
+      ]
+     },
+     "execution_count": 286,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "sum((n ** 2) + 1 for n in numbers if n % 2 == 0)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Let's assign the object returned from a generator expression to a variable and inspect it."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 287,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "gen = ((n ** 2) + 1 for n in numbers if n % 2 == 0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 288,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<generator object <genexpr> at 0x7f9f44761d68>"
+      ]
+     },
+     "execution_count": 288,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "gen"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Unsurprisingly, generator expressions create objects of type `generator`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 289,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "generator"
+      ]
+     },
+     "execution_count": 289,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "type(gen)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "With the [next()](https://docs.python.org/3/library/functions.html#next) function, we can retrieve the generated elements one by one."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 290,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "65"
+      ]
+     },
+     "execution_count": 290,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "next(gen)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 291,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "145"
+      ]
+     },
+     "execution_count": 291,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "next(gen)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 292,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "5"
+      ]
+     },
+     "execution_count": 292,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "next(gen)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 293,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "37"
+      ]
+     },
+     "execution_count": 293,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "next(gen)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 294,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "101"
+      ]
+     },
+     "execution_count": 294,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "next(gen)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 295,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "17"
+      ]
+     },
+     "execution_count": 295,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "next(gen)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Once a `generator` object runs out of elements, it raises a `StopIteration` exception. We say that the `generator` object is **exhausted**, and to loop over its elements again, we must create a *new* one."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 296,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "ename": "StopIteration",
+     "evalue": "",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mStopIteration\u001b[0m                             Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-296-6e72e47198db>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mnext\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mgen\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;31mStopIteration\u001b[0m: "
+     ]
+    }
+   ],
+   "source": [
+    "next(gen)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 297,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "ename": "StopIteration",
+     "evalue": "",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mStopIteration\u001b[0m                             Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-297-6e72e47198db>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mnext\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mgen\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;31mStopIteration\u001b[0m: "
+     ]
+    }
+   ],
+   "source": [
+    "next(gen)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Calling the [next()](https://docs.python.org/3/library/functions.html#next) function repeatedly with the *same* `generator` object as the argument is essentially what a `for`-loop automates for us. So, `generator` objects are *iterable*."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 298,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "65 145 5 37 101 17 "
+     ]
+    }
+   ],
+   "source": [
+    "for number in ((n ** 2) + 1 for n in numbers if n % 2 == 0):\n",
+    "    print(number, end=\" \")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "#### Example: Nested Lists (revisited)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "If we are only interested in a *reduction* of `nested_numbers` into a single statistic, as the overall sum in the \"*Nested Lists*\" example, we should replace lists or list comprehensions with generator expressions wherever possible.\n",
+    "\n",
+    "The result is the *same*, but no intermediate lists are materialized! That makes our code scale to larger amounts of data and uses the available hardware more efficiently.\n",
+    "\n",
+    "Let's adapt the example but keep `nested_numbers` unchanged for now."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 299,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "nested_numbers = [list(range(x, y + 1)) for x, y in zip([1, 2, 3], [7, 8, 9])]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 300,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[[1, 2, 3, 4, 5, 6, 7], [2, 3, 4, 5, 6, 7, 8], [3, 4, 5, 6, 7, 8, 9]]"
+      ]
+     },
+     "execution_count": 300,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "nested_numbers"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "We leave out the brackets and keep everything else as-is: The argument to [sum()](https://docs.python.org/3/library/functions.html#sum), a list comprehension in the initial implementation above, becomes a generator expression."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 301,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "105"
+      ]
+     },
+     "execution_count": 301,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "sum(number for inner_numbers in nested_numbers for number in inner_numbers)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "That also holds for the alternative formulation as a sum of sums."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 302,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "105"
+      ]
+     },
+     "execution_count": 302,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "sum(sum(inner_numbers) for inner_numbers in nested_numbers)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Because `nested_numbers` has an internal structure, we can make it **memoryless** by expressing it as a generator expression derived from `range` objects. [PythonTutor](http://pythontutor.com/visualize.html#code=nested_numbers%20%3D%20%28%28range%28x,%20y%20%2B%201%29%29%20for%20x,%20y%20in%20zip%28range%281,%204%29,%20range%287,%2010%29%29%29%0Aresult%20%3D%20sum%28number%20for%20inner_numbers%20in%20nested_numbers%20for%20number%20in%20inner_numbers%29&cumulative=false&curInstr=0&heapPrimitives=nevernest&mode=display&origin=opt-frontend.js&py=3&rawInputLstJSON=%5B%5D&textReferences=false) confirms that no `list` object materializes at any point in time."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 303,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "nested_numbers = ((range(x, y + 1)) for x, y in zip(range(1, 4), range(7, 10)))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 304,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<generator object <genexpr> at 0x7f9f44761e58>"
+      ]
+     },
+     "execution_count": 304,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "nested_numbers"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 305,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "105"
+      ]
+     },
+     "execution_count": 305,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "sum(number for inner_numbers in nested_numbers for number in inner_numbers)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "We must be careful when assigning a `generator` object to a variable: If we use `nested_numbers` again, for example, in the alternative formulation below, [sum()](https://docs.python.org/3/library/functions.html#sum) returns `0` because `nested_numbers` is exhausted after executing the previous code cell. [PythonTutor](http://pythontutor.com/visualize.html#code=nested_numbers%20%3D%20%28%28range%28x,%20y%20%2B%201%29%29%20for%20x,%20y%20in%20zip%28range%281,%204%29,%20range%287,%2010%29%29%29%0Aresult%20%3D%20sum%28number%20for%20inner_numbers%20in%20nested_numbers%20for%20number%20in%20inner_numbers%29%0Ano_result%20%3D%20sum%28sum%28inner_numbers%29%20for%20inner_numbers%20in%20nested_numbers%29&cumulative=false&curInstr=0&heapPrimitives=nevernest&mode=display&origin=opt-frontend.js&py=3&rawInputLstJSON=%5B%5D&textReferences=false) also shows that."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 306,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0"
+      ]
+     },
+     "execution_count": 306,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "sum(sum(inner_numbers) for inner_numbers in nested_numbers)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "#### Example: Cartesian Products (revisited)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Let's also rewrite the \"*Cartesian Products*\" example from above with generator expressions.\n",
+    "\n",
+    "As a first optimization, we replace the materialized `list` objects, `first` and `second`, with memoryless `range` objects."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 307,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "first = range(10, 31, 10)  # = [10, 20, 30]\n",
+    "second = range(40, 61, 10)  # = [40, 50, 60]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Now, the first of the two alternatives may be more appealing to many readers. In general, many practitioners seem to dislike `lambda` expressions.\n",
+    "\n",
+    "The code cell below *unpacks* the elements produced by `((x / y) + 1 for x in first for y in second)` into the `product()` function from the \"*Packing & Unpacking*\" section above. However, inside `product()`, the elements are *packed* into `args`, a materialized `tuple` object. So, all the memory efficiency gained with the generator expression is voided! [PythonTutor](http://pythontutor.com/visualize.html#code=def%20product%28*args%29%3A%0A%20%20%20%20result%20%3D%20args%5B0%5D%0A%20%20%20%20for%20arg%20in%20args%5B1%3A%5D%3A%0A%20%20%20%20%20%20%20%20result%20*%3D%20arg%0A%20%20%20%20return%20result%0A%0Afirst%20%3D%20range%2810,%2031,%2010%29%0Asecond%20%3D%20range%2840,%2061,%2010%29%0A%0Aresult%20%3D%20product%28*%28%28x%20/%20y%29%20%2B%201%20for%20x%20in%20first%20for%20y%20in%20second%29%29&cumulative=false&curInstr=0&heapPrimitives=nevernest&mode=display&origin=opt-frontend.js&py=3&rawInputLstJSON=%5B%5D&textReferences=false) shows how a `tuple` object exists in steps 38-58."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 308,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "20.58"
+      ]
+     },
+     "execution_count": 308,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "product(*((x / y) + 1 for x in first for y in second))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "On the contrary, the solution with the [reduce()](https://docs.python.org/3/library/functools.html#functools.reduce) function from the [functools](https://docs.python.org/3/library/functools.html) module and the `lambda` expression works *without* all elements materialized at the same time, and [PythonTutor](http://pythontutor.com/visualize.html#code=from%20functools%20import%20reduce%0A%0Afirst%20%3D%20range%2810,%2031,%2010%29%0Asecond%20%3D%20range%2840,%2061,%2010%29%0A%0Aresult%20%3D%20reduce%28%0A%20%20%20%20lambda%20x,%20y%3A%20x%20*%20y,%0A%20%20%20%20%28%28x%20/%20y%29%20%2B%201%20for%20x%20in%20first%20for%20y%20in%20second%29%0A%29&cumulative=false&curInstr=0&heapPrimitives=nevernest&mode=display&origin=opt-frontend.js&py=3&rawInputLstJSON=%5B%5D&textReferences=false) confirms that. So, only the second alternative is truly memory-efficient."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 309,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "20.58"
+      ]
+     },
+     "execution_count": 309,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "reduce(lambda x, y: x * y, ((x / y) + 1 for x in first for y in second))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "In summary, we learn from this example that unpacking generator expressions may be a *bad* idea."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "### Tuple Comprehensions"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "There is no syntax to derive *new* `tuple` objects out of existing ones. However, we can mimic such a construct by combining the [tuple()](https://docs.python.org/3/library/functions.html#func-tuple) built-in with a generator expression.\n",
+    "\n",
+    "So, to convert the list comprehension `[(n ** 2) + 1 for n in numbers if n % 2 == 0]` from above into a \"tuple comprehension,\" we write the following."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 310,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(65, 145, 5, 37, 101, 17)"
+      ]
+     },
+     "execution_count": 310,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "tuple((n ** 2) + 1 for n in numbers if n % 2 == 0)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "### Boolean Reducers"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Besides [min()](https://docs.python.org/3/library/functions.html#min), [max()](https://docs.python.org/3/library/functions.html#max), and [sum()](https://docs.python.org/3/library/functions.html#sum), Python provides two boolean reduce functions: [all()](https://docs.python.org/3/library/functions.html#all) and [any()](https://docs.python.org/3/library/functions.html#any).\n",
+    "\n",
+    "Let's look at straightforward examples involving `numbers` again."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 311,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "numbers = [7, 11, 8, 5, 3, 12, 2, 6, 9, 10, 1, 4]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "[all()](https://docs.python.org/3/library/functions.html#all) takes an *iterable* argument and returns `True` if *all* elements are *truthy*.\n",
+    "\n",
+    "For example, let's check if the square of each element in `numbers` is below `100` or `150`, respectively. We express the computation with a generator expression passed as the only argument to [all()](https://docs.python.org/3/library/functions.html#all)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 312,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "False"
+      ]
+     },
+     "execution_count": 312,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "all(x ** 2 < 100 for x in numbers)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 313,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 313,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "all(x ** 2 < 150 for x in numbers)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "[all()](https://docs.python.org/3/library/functions.html#all) can be viewed as syntactic sugar replacing a `for`-loop: Internally, [all()](https://docs.python.org/3/library/functions.html#all) implements the *short-circuiting* strategy from [Chapter 3](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/03_conditionals.ipynb#Short-Circuiting), and we mimic that by testing for the *opposite* condition in the `if` statement and leaving the `for`-loop early with the `break` statement. In the worst case, if `threshold` were, for example, `150`, we would loop over *all* elements in the *iterable*, which must be *finite* for the code to work. So, [all()](https://docs.python.org/3/library/functions.html#all) is a *linear search* in disguise."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 314,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "False"
+      ]
+     },
+     "execution_count": 314,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "threshold = 100\n",
+    "\n",
+    "for number in numbers:\n",
+    "    if number ** 2 >= threshold:  # = the opposite of what we are checking for\n",
+    "        all_below_threshold = False\n",
+    "        break\n",
+    "else:\n",
+    "    all_below_threshold = True\n",
+    "\n",
+    "all_below_threshold"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "The documentation of [all()](https://docs.python.org/3/library/functions.html#all) shows in another way what it does with code: By placing a `return` statement inside the `for`-loop of a function's body, iteration is stopped prematurely once an element does *not* meet the condition. That is the familiar *early exit* pattern at work."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 315,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "def all_alt(iterable):\n",
+    "    \"\"\"Alternative implementation of the built-in all() function.\"\"\"\n",
+    "    for element in iterable:\n",
+    "        if not element:  # = the opposite of what we are checking for\n",
+    "            return False\n",
+    "    return True"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 316,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "False"
+      ]
+     },
+     "execution_count": 316,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "all_alt(x ** 2 < 100 for x in numbers)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 317,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 317,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "all_alt(x ** 2 < 150 for x in numbers)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Similarly, [any()](https://docs.python.org/3/library/functions.html#any) checks if *at least* one element in the *iterable* argument is *truthy*.\n",
+    "\n",
+    "To continue the example, let's check if the square of *any* element in `numbers` is above `100` or `150`, respectively."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 318,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 318,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "any(x ** 2 > 100 for x in numbers)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 319,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "False"
+      ]
+     },
+     "execution_count": 319,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "any(x ** 2 > 150 for x in numbers)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Expressed with a `for`-loop, the implementation below reveals that [any()](https://docs.python.org/3/library/functions.html#any) follows the *short-circuiting* strategy as well. Here, we do *not* need to check for the opposite condition."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 320,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 320,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "threshold = 100\n",
+    "\n",
+    "for number in numbers:\n",
+    "    if number ** 2 > threshold:\n",
+    "        any_above_threshold = True\n",
+    "        break\n",
+    "else:\n",
+    "    any_above_threshold = False\n",
+    "\n",
+    "any_above_threshold"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "The alternative formulation in the documentation of [any()](https://docs.python.org/3/library/functions.html#any) is straightforward."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 321,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "def any_alt(iterable):\n",
+    "    \"\"\"Alternative implementation of the built-in any() function.\"\"\"\n",
+    "    for element in iterable:\n",
+    "        if element:\n",
+    "            return True\n",
+    "    return False"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 322,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 322,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "any_alt(x ** 2 > 100 for x in numbers)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 323,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "False"
+      ]
+     },
+     "execution_count": 323,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "any_alt(x ** 2 > 150 for x in numbers)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "### Example: Averaging Even Numbers (revisited)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "With the new concepts in this chapter, let's rewrite the book's introductory \"*Averaging Even Numbers*\" example in [Chapter 1](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/01_elements.ipynb#Example:-Averaging-Even-Numbers) such that it efficiently handles a large sequence of numbers.\n",
+    "\n",
+    "We assume the `average_evens()` function below is called with a *finite* and *iterable* object, which generates a \"stream\" of numeric objects that can be cast as `int` objects because the idea of even and odd numbers only makes sense in the context of whole numbers.\n",
+    "\n",
+    "The generator expression `(int(n) for n in numbers)` implements the type casting, and when it is evaluated, *nothing* happens except that a `generator` object is stored in `integers`. Then, with the [reduce()](https://docs.python.org/3/library/functools.html#functools.reduce) function from the [functools](https://docs.python.org/3/library/functools.html) module, we *simultaneously* add up *and* count the even numbers produced by the inner generator expression `((n, 1) for n in integers if n % 2 == 0)`. That results in a `generator` object producing `tuple` objects consisting of the next *even* number in line and `1`. Two such `tuple` objects are then iteratively passed to the `lambda` expression as the `x` and `y` arguments. `x` represents the total and the count of the even numbers processed so far, while `y`'s first element, `y[0]`, is the next even number to be added to the running total. The result of the [reduce()](https://docs.python.org/3/library/functools.html#functools.reduce) function is again a `tuple` object, namely the final `total` and `count`. Lastly, we calculate the simple average.\n",
+    "\n",
+    "In summary, the implementation of `average_evens()` does *not* keep materialized `list` objects internally like its predecessors from [Chapter 2](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/02_functions.ipynb), but processes the elements of the `numbers` argument on a one-by-one basis."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 324,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "def average_evens(numbers):\n",
+    "    \"\"\"Calculate the average of all even integers.\n",
+    "\n",
+    "    Args:\n",
+    "        numbers (iterable): a finite stream of numbers;\n",
+    "            may be integers or floats; floats are truncated\n",
+    "\n",
+    "    Returns:\n",
+    "        float: average\n",
+    "    \"\"\"\n",
+    "    integers = (int(n) for n in numbers)\n",
+    "    total, count = reduce(\n",
+    "        lambda x, y: (x[0] + y[0], x[1] + y[1]),\n",
+    "        ((n, 1) for n in integers if n % 2 == 0)\n",
+    "    )\n",
+    "    return total / count"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 325,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "7.0"
+      ]
+     },
+     "execution_count": 325,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "average_evens([7, 11, 8, 5, 3, 12, 2, 6, 9, 10, 1, 4])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "An argument generating `float` objects works as well."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 326,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "7.0"
+      ]
+     },
+     "execution_count": 326,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "average_evens([7., 11., 8., 5., 3., 12., 2., 6., 9., 10., 1., 4.])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "To show that `average_evens()` can process a **stream** of data, we simulate `10_000_000` randomly drawn integers between `0` and `100` with the [randint()](https://docs.python.org/3/library/random.html#random.randint) function from the [random](https://docs.python.org/3/library/random.html) module. We use a generator expression derived from a `range` object as the `numbers` argument. So, at *no* point in time is there a materialized `list` or `tuple` object in memory. The result approaching `50` tells us that [randint()](https://docs.python.org/3/library/random.html#random.randint) must be based on a uniform distribution."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 327,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "import random"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 328,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "random.seed(42)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 329,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "49.994081434519636"
+      ]
+     },
+     "execution_count": 329,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "average_evens(random.randint(0, 100) for _ in range(10_000_000))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "To show that `average_evens()` filters out odd numbers, we simulate another stream of `10_000_000` randomly drawn odd integers between `1` and `99`. As no function in the [random](https://docs.python.org/3/library/random.html) module does that \"out of the box,\" we must be creative: Doubling a number drawn from `random.randint(0, 49)` results in an even number between `0` and `98`, and adding `1` makes it odd. Then, `average_evens()` raises a `TypeError`, essentially because `(int(n) for n in numbers)` does not generate any element."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 330,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "ename": "TypeError",
+     "evalue": "reduce() of empty sequence with no initial value",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-330-73a295f34553>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0maverage_evens\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m2\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0mrandom\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrandint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m49\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;36m1\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0m_\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m10_000_000\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;32m<ipython-input-324-491a5ff07381>\u001b[0m in \u001b[0;36maverage_evens\u001b[0;34m(numbers)\u001b[0m\n\u001b[1;32m     12\u001b[0m     total, count = reduce(\n\u001b[1;32m     13\u001b[0m         \u001b[0;32mlambda\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 14\u001b[0;31m         \u001b[0;34m(\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mn\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mn\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mintegers\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mn\u001b[0m \u001b[0;34m%\u001b[0m \u001b[0;36m2\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     15\u001b[0m     )\n\u001b[1;32m     16\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mtotal\u001b[0m \u001b[0;34m/\u001b[0m \u001b[0mcount\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;31mTypeError\u001b[0m: reduce() of empty sequence with no initial value"
+     ]
+    }
+   ],
+   "source": [
+    "average_evens(2 * random.randint(0, 49) + 1 for _ in range(10_000_000))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "## Iterators vs. Iterables"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "In the \"*Collections vs. Sequences*\" section above, we studied the three and four *behaviors* of collections and sequences. The latter two are *abstract* ideas, and we mainly use them to classify *concrete* data types.\n",
+    "\n",
+    "Similarly, we have introduced data types in this chapter that all share the \"behavior\" of modeling some \"rule\" in memory to generate objects \"on the fly:\" They are the `map`, `filter`, and `generator` types. Their main commonality is supporting the built-in [next()](https://docs.python.org/3/library/functions.html#next) function. In computer science terminology, such data types are called **[iterators](https://en.wikipedia.org/wiki/Iterator)**, and the [collections.abc](https://docs.python.org/3/library/collections.abc.html) module formalizes them with the `Iterator` ABC.\n",
+    "\n",
+    "So, an example of an iterator is `evens_transformed` below, an object of type `map`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 331,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "numbers = [7, 11, 8, 5, 3, 12, 2, 6, 9, 10, 1, 4]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 332,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "evens_transformed = map(lambda x: (x ** 2) + 1, filter(lambda x: x % 2 == 0, numbers))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Let's first confirm that `evens_transformed` is indeed an `Iterator`, \"abstractly speaking.\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 333,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 333,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "isinstance(evens_transformed, abc.Iterator)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "In Python, iterators are *always* also iterables. The reverse does *not* hold! To be precise, iterators are *specializations* of iterables. That is what the \"Inherits from\" columns means in the [collections.abc](https://docs.python.org/3/library/collections.abc.html) module's documentation."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 334,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 334,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "isinstance(evens_transformed, abc.Iterable)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Furthermore, we revise our definition of *iterables* from above: Just as we defined an *iterator* to be an object that supports the [next()](https://docs.python.org/3/library/functions.html#next) function, we define an *iterable* to be an object that supports the built-in [iter()](https://docs.python.org/3/library/functions.html#iter) function.\n",
+    "\n",
+    "The confused reader may now be wondering how the two concepts relate to each other.\n",
+    "\n",
+    "In short, the [iter()](https://docs.python.org/3/library/functions.html#iter) function is the general way to create an *iterator* object out of a given *iterable* object. In real-world code, we hardly ever see [iter()](https://docs.python.org/3/library/functions.html#iter) as Python calls it for us in the background. Then, the *iterator* object manages the iteration over the *iterable* object.\n",
+    "\n",
+    "For illustration, let's do that ourselves and create *two* iterators out of the iterable `numbers` and see what we can do with them."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 335,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "iterator1 = iter(numbers)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 336,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "iterator2 = iter(numbers)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "`iterator1` and `iterator2` are of type `list_iterator`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 337,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "list_iterator"
+      ]
+     },
+     "execution_count": 337,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "type(iterator1)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "*Iterators* are useful for only *one* operation: Get the next object from the associated *iterable*.\n",
+    "\n",
+    "By calling [next()](https://docs.python.org/3/library/functions.html#next) three times with `iterator1` as the argument, we obtain the first three elements of `numbers`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 338,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(7, 11, 8)"
+      ]
+     },
+     "execution_count": 338,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "next(iterator1), next(iterator1), next(iterator1)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "`iterator1` and `iterator2` keep their *states* separate. So, we could \"manually\" loop over an *iterable* in parallel."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 339,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(5, 7)"
+      ]
+     },
+     "execution_count": 339,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "next(iterator1), next(iterator2)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "We can also play a \"trick\" and exchange some elements in `numbers`. `iterator1` and `iterator2` do *not* see these changes and present us with the new elements. So, *iterators* not only have state on their own but also keep this separate from the underlying *iterable*."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 340,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "numbers[1], numbers[4] = 99, 99"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 341,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(99, 99)"
+      ]
+     },
+     "execution_count": 341,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "next(iterator1), next(iterator2)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Let's re-assign the elements in `numbers` so that they are in order. Now, the numbers returned from [next()](https://docs.python.org/3/library/functions.html#next) also tell us how often [next()](https://docs.python.org/3/library/functions.html#next) was called with `iterator1` or `iterator2`. We conclude that `list_iterator` objects must be keeping track of the *last* index obtained from the underlying *iterable*."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 342,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "numbers[:] = list(range(1, 13))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 343,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12]"
+      ]
+     },
+     "execution_count": 343,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "numbers"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 344,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(6, 3)"
+      ]
+     },
+     "execution_count": 344,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "next(iterator1), next(iterator2)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "With the concepts introduced in this section, we can now understand the first sentence in the documentation on the [zip()](https://docs.python.org/3/library/functions.html#zip) built-in better: \"Make an *iterator* that aggregates elements from each of the *iterables*.\"\n",
+    "\n",
+    "Because *iterators* are always also *iterables*, we pass `iterator1` and `iterator2` as arguments to [zip()](https://docs.python.org/3/library/functions.html#zip).\n",
+    "\n",
+    "The returned `zipper` object is of type `zip` and, \"abstractly speaking,\" an `Iterator` as well."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 345,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "zipper = zip(iterator1, iterator2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 346,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<zip at 0x7f9f44726a48>"
+      ]
+     },
+     "execution_count": 346,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "zipper"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 347,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "zip"
+      ]
+     },
+     "execution_count": 347,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "type(zipper)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 348,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 348,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "isinstance(zipper, abc.Iterator)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "So far, we have always used [zip()](https://docs.python.org/3/library/functions.html#zip) with a `for` statement and looped over it. That was our earlier definition of an *iterable*. Our revised definition in this section states that an *iterable* is an object that supports the [iter()](https://docs.python.org/3/library/functions.html#iter) function. So, let's see what happens if we pass `zipper` to [iter()](https://docs.python.org/3/library/functions.html#iter)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 349,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "zipper_iterator = iter(zipper)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 350,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<zip at 0x7f9f44726a48>"
+      ]
+     },
+     "execution_count": 350,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "zipper_iterator"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "`zipper_iterator` points to the *same* object as `zipper`! That holds for *iterators* in general: Any *iterator* created from an existing *iterator* with [iter()](https://docs.python.org/3/library/functions.html#iter) is the *iterator* itself."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 351,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 351,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "zipper is zipper_iterator"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "The Python core developers made that design decision so that *iterators* may also be looped over.\n",
+    "\n",
+    "The `for`-loop below prints out *six* more `tuple` objects derived from the re-ordered `numbers` because the `iterator1` object hidden inside `zipper` already returned the first *six* elements. So, the respective first elements of the `tuple` objects printed range from `7` to `12`. Similarly, as `iterator2` already provided *three* elements from `numbers`, we see the respective second elements in the range from `4` to `9`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 352,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "7 > 4   8 > 5   9 > 6   10 > 7   11 > 8   12 > 9   "
+     ]
+    }
+   ],
+   "source": [
+    "for x, y in zipper:\n",
+    "    print(x, \">\", y, end=\"   \")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "`zipper` is now *exhausted*. So, the `for`-loop below does *not* make any iteration at all."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 353,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "for x, y in zipper:\n",
+    "    print(x, \">\", y, end=\"   \")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "We verify that `iterator1` is exhausted by passing it to [next()](https://docs.python.org/3/library/functions.html#next) again, which raises a `StopIteration` exception."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 354,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "ename": "StopIteration",
+     "evalue": "",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mStopIteration\u001b[0m                             Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-354-0680f5ec8ce3>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mnext\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0miterator1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;31mStopIteration\u001b[0m: "
+     ]
+    }
+   ],
+   "source": [
+    "next(iterator1)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "On the contrary, `iterator2` is *not* yet exhausted."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 355,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "10"
+      ]
+     },
+     "execution_count": 355,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "next(iterator2)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "### The `for` Statement (revisited)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "In [Chapter 4](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/04_iteration.ipynb#The-for-Statement), we argue that the `for` statement is syntactic sugar, replacing the `while` statement in many scenarios. In particular, a `for`-loop saves us two tasks: Managing an index variable *and* obtaining the individual elements by indexing. In this sub-section, we look at a more realistic picture, using the new terminology as well.\n",
+    "\n",
+    "Let's print out the elements of a `list` object as the *iterable* to be looped over."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 356,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "iterable = [0, 1, 2, 3, 4]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 357,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0 1 2 3 4 "
+     ]
+    }
+   ],
+   "source": [
+    "for element in iterable:\n",
+    "    print(element, end=\" \")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Our previous and equivalent formulation with a `while` statement is like so."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 358,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0 1 2 3 4 "
+     ]
+    }
+   ],
+   "source": [
+    "index = 0\n",
+    "while index < len(iterable):\n",
+    "    element = iterable[index]\n",
+    "    print(element, end=\" \")\n",
+    "    index += 1\n",
+    "del index"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "What happens behind the scenes in the Python interpreter is shown below.\n",
+    "\n",
+    "First, Python calls [iter()](https://docs.python.org/3/library/functions.html#iter) with the `iterable` to be looped over, and obtains an `iterator`. That contains the entire logic of how the `iterable` is looped over. In particular, the `iterator` may or may not pick the `iterable`'s elements in a predictable order. That is up to the \"rule\" it models.\n",
+    "\n",
+    "Second, Python enters an *indefinite* `while`-loop. It tries to obtain the next element with [next()](https://docs.python.org/3/library/functions.html#next). If that succeeds, the `for`-loop's code block is executed. Below, that code is placed within the `else`-clause that runs only if *no* exception is raised in the `try`-clause. Then, Python jumps into the next iteration and tries to obtain the next element from the `iterator`, and so on. Once the `iterator` is exhausted, it raises a `StopIteration` exception, and Python leaves the `while`-loop with the `break` statement."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 359,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0 1 2 3 4 "
+     ]
+    }
+   ],
+   "source": [
+    "iterator = iter(iterable)\n",
+    "\n",
+    "while True:\n",
+    "    try:\n",
+    "        element = next(iterator)\n",
+    "    except StopIteration:\n",
+    "        break\n",
+    "    else:\n",
+    "        print(element, end=\" \")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Understanding *iterators* and *iterables* is helpful for any data science practitioner that deals with large amounts of data. Even without that, these two terms occur everywhere in Python-related texts and documentation."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "## TL;DR"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "**Sequences** are an abstract idea that summarizes *four* behaviors an object may or may not exhibit: We describe them as **finite** and **ordered** **containers** that we may **loop over**. Examples are objects of type `list`, `tuple`, but also `str`. Objects that exhibit all behaviors *except* being ordered are referred to as **collections**. The objects inside a sequence are labeled with a unique *index*, an `int` object in the range $0 \\leq \\text{index} < \\lvert \\text{sequence} \\rvert$, and called its **elements**.\n",
+    "\n",
+    "`list` objects are **mutable**. That means we can change the references to other objects it contains, and, in particular, re-assign them. On the contrary, `tuple` objects are like **immutable** lists: We can use them in place of any `list` object as long as we do *not* mutate it.\n",
+    "\n",
+    "Often, the work we do with sequences follows the **map-filter-reduce paradigm**: We apply the same transformation to all elements, filter some of them out, and calculate summary statistics from the remaining ones.\n",
+    "\n",
+    "An essential idea in this chapter is that, in many situations, we need *not* work with all the data **materialized** in memory. Instead, **iterators** allow us to process sequential data on a one-by-one basis."
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.3"
+  },
+  "livereveal": {
+   "auto_select": "code",
+   "auto_select_fragment": true,
+   "scroll": true,
+   "theme": "serif"
+  },
+  "toc": {
+   "base_numbering": 1,
+   "nav_menu": {},
+   "number_sections": false,
+   "sideBar": true,
+   "skip_h1_title": true,
+   "title_cell": "Table of Contents",
+   "title_sidebar": "Contents",
+   "toc_cell": false,
+   "toc_position": {
+    "height": "calc(100% - 180px)",
+    "left": "10px",
+    "top": "150px",
+    "width": "384px"
+   },
+   "toc_section_display": false,
+   "toc_window_display": false
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

From 8fd14a614de167abda7d92d5e450c258d5a220a0 Mon Sep 17 00:00:00 2001
From: Alexander Hess <alexander@webartifex.biz>
Date: Wed, 6 Nov 2019 11:10:29 +0100
Subject: [PATCH 042/242] Streamline previous content

---
 01_elements.ipynb     | 402 ++++++++++++++++++++++--------------------
 02_functions.ipynb    | 118 ++++++-------
 03_conditionals.ipynb |  59 +++----
 04_iteration.ipynb    | 184 +++++++++----------
 05_numbers.ipynb      |  34 ++--
 06_text.ipynb         |  20 +--
 6 files changed, 413 insertions(+), 404 deletions(-)

diff --git a/01_elements.ipynb b/01_elements.ipynb
index f9c6131..fed6458 100644
--- a/01_elements.ipynb
+++ b/01_elements.ipynb
@@ -35,7 +35,7 @@
     }
    },
    "source": [
-    "## Example: Average of a Subset of Numbers"
+    "## Example: Averaging Even Numbers"
    ]
   },
   {
@@ -46,11 +46,11 @@
     }
    },
    "source": [
-    "As our introductory example, we want to calculate the *average* of all *even* numbers from `1` through `10`.\n",
+    "As our introductory example, we want to calculate the *average* of all *evens* in a **list** of numbers: `[7, 11, 8, 5, 3, 12, 2, 6, 9, 10, 1, 4]`.\n",
     "\n",
-    "While we could come up with an [analytical solution](https://math.stackexchange.com/questions/935405/what-s-the-difference-between-analytical-and-numerical-approaches-to-problems/935446#935446) (i.e., derive some equation with \"pen and paper\" from, e.g., one of [Faulhaber's formulas](https://en.wikipedia.org/wiki/Faulhaber%27s_formula)), we instead solve the task programmatically.\n",
+    "While we could try to develop an [analytical solution](https://math.stackexchange.com/questions/935405/what-s-the-difference-between-analytical-and-numerical-approaches-to-problems/935446#935446) (i.e., derive some equation with \"pen and paper\"), we solve the task programmatically instead.\n",
     "\n",
-    "We start by creating a **list** called `numbers` that holds all the individual numbers between **brackets** `[` and `]`."
+    "We start by creating a list called `numbers` that holds all the individual numbers between **brackets** `[` and `]`."
    ]
   },
   {
@@ -64,7 +64,7 @@
    },
    "outputs": [],
    "source": [
-    "numbers = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]"
+    "numbers = [7, 11, 8, 5, 3, 12, 2, 6, 9, 10, 1, 4]"
    ]
   },
   {
@@ -90,7 +90,7 @@
     {
      "data": {
       "text/plain": [
-       "[1, 2, 3, 4, 5, 6, 7, 8, 9, 10]"
+       "[7, 11, 8, 5, 3, 12, 2, 6, 9, 10, 1, 4]"
       ]
      },
      "execution_count": 2,
@@ -116,7 +116,7 @@
     "\n",
     "The `if number % 2 == 0` may look disturbing at first sight. Both the `%` and `==` must have an unintuitive meaning here. Luckily, the **comment** in the same line after the `#` symbol has the answer: The program only does something if the current `number` is even.\n",
     "\n",
-    "In particular, it increases `count` by $1$ and adds the current `number` onto the [running](https://en.wikipedia.org/wiki/Running_total) `total`. Both `count` and `number` are initially set to $0$ and the single `=` symbol reads as \"... is *set* equal to ...\". It could not indicate a mathematical equation as, for example, `count` is generally not equal to `count + 1`.\n",
+    "In particular, it increases `count` by `1` and adds the current `number` onto the [running](https://en.wikipedia.org/wiki/Running_total) `total`. Both `count` and `number` are initially set to `0` and the single `=` symbol reads as \"... is *set* equal to ...\". It could not indicate a mathematical equation as, for example, `count` is generally not equal to `count + 1`.\n",
     "\n",
     "Lastly, the `average` is calculated as the ratio of the final **values** of `total` and `count`. Overall, we divide the sum of all even numbers by their count: This is nothing but the definition of an average.\n",
     "\n",
@@ -167,7 +167,7 @@
     {
      "data": {
       "text/plain": [
-       "6.0"
+       "7.0"
       ]
      },
      "execution_count": 4,
@@ -427,7 +427,7 @@
     }
    },
    "source": [
-    "When we compare the output of the `*` and `/` operators for multiplication and division, we note the subtle *difference* between the $42$ and the $42.0$: They are the *same* number represented as a *different* **data type**."
+    "When we compare the output of the `*` and `/` operators for multiplication and division, we note the subtle *difference* between the `42` and the `42.0`: They are the *same* number represented as a *different* **data type**."
    ]
   },
   {
@@ -615,9 +615,9 @@
     }
    },
    "source": [
-    "The remainder is $0$ *only if* a number is *divisible* by another.\n",
+    "The remainder is `0` *only if* a number is *divisible* by another.\n",
     "\n",
-    "A popular convention in both, computer science and mathematics, is to abbreviate \"only if\" as **iff**, which is short for \"**[if and only if](https://en.wikipedia.org/wiki/If_and_only_if)**.\" The iff means that a remainder of $0$ implies that a number is divisible by another but also that a number divisible by another implies a remainder of $0$. The implication goes in *both* directions!"
+    "A popular convention in both, computer science and mathematics, is to abbreviate \"only if\" as **iff**, which is short for \"**[if and only if](https://en.wikipedia.org/wiki/If_and_only_if)**.\" The iff means that a remainder of `0` implies that a number is divisible by another but also that a number divisible by another implies a remainder of `0`. The implication goes in *both* directions!"
    ]
   },
   {
@@ -988,7 +988,7 @@
     {
      "data": {
       "text/plain": [
-       "139878568414128"
+       "140706351197104"
       ]
      },
      "execution_count": 28,
@@ -1012,7 +1012,7 @@
     {
      "data": {
       "text/plain": [
-       "139878568593256"
+       "140706351380256"
       ]
      },
      "execution_count": 29,
@@ -1036,7 +1036,7 @@
     {
      "data": {
       "text/plain": [
-       "139878567760368"
+       "140706351003760"
       ]
      },
      "execution_count": 30,
@@ -1142,6 +1142,41 @@
     "a is d"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "If we want to check the opposite case, we use the negated version of the `is` operator."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 34,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "a is not d"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {
@@ -1166,7 +1201,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 34,
+   "execution_count": 35,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -1179,7 +1214,7 @@
        "int"
       ]
      },
-     "execution_count": 34,
+     "execution_count": 35,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1190,7 +1225,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 35,
+   "execution_count": 36,
    "metadata": {
     "slideshow": {
      "slide_type": "-"
@@ -1203,7 +1238,7 @@
        "float"
       ]
      },
-     "execution_count": 35,
+     "execution_count": 36,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1227,7 +1262,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 36,
+   "execution_count": 37,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -1240,7 +1275,7 @@
        "True"
       ]
      },
-     "execution_count": 36,
+     "execution_count": 37,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1262,7 +1297,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 37,
+   "execution_count": 38,
    "metadata": {
     "slideshow": {
      "slide_type": "-"
@@ -1276,7 +1311,7 @@
      "traceback": [
       "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
       "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-37-7db0a38aefcc>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0ma\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mis_integer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;32m<ipython-input-38-7db0a38aefcc>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0ma\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mis_integer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
       "\u001b[0;31mAttributeError\u001b[0m: 'int' object has no attribute 'is_integer'"
      ]
     }
@@ -1298,7 +1333,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 38,
+   "execution_count": 39,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -1311,7 +1346,7 @@
        "str"
       ]
      },
-     "execution_count": 38,
+     "execution_count": 39,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1322,7 +1357,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 39,
+   "execution_count": 40,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -1335,7 +1370,7 @@
        "'python rocks'"
       ]
      },
-     "execution_count": 39,
+     "execution_count": 40,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1346,7 +1381,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 40,
+   "execution_count": 41,
    "metadata": {
     "slideshow": {
      "slide_type": "-"
@@ -1359,7 +1394,7 @@
        "'PYTHON ROCKS'"
       ]
      },
-     "execution_count": 40,
+     "execution_count": 41,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1370,7 +1405,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 41,
+   "execution_count": 42,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -1383,7 +1418,7 @@
        "'Python Rocks'"
       ]
      },
-     "execution_count": 41,
+     "execution_count": 42,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1418,7 +1453,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 42,
+   "execution_count": 43,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -1431,7 +1466,7 @@
        "789"
       ]
      },
-     "execution_count": 42,
+     "execution_count": 43,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1442,7 +1477,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 43,
+   "execution_count": 44,
    "metadata": {
     "slideshow": {
      "slide_type": "-"
@@ -1455,7 +1490,7 @@
        "42.0"
       ]
      },
-     "execution_count": 43,
+     "execution_count": 44,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1477,7 +1512,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 44,
+   "execution_count": 45,
    "metadata": {
     "slideshow": {
      "slide_type": "-"
@@ -1490,7 +1525,7 @@
        "'Python rocks'"
       ]
      },
-     "execution_count": 44,
+     "execution_count": 45,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1549,7 +1584,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 45,
+   "execution_count": 46,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -1558,10 +1593,10 @@
    "outputs": [
     {
      "ename": "SyntaxError",
-     "evalue": "invalid syntax (<ipython-input-45-cafa82e54b9c>, line 1)",
+     "evalue": "invalid syntax (<ipython-input-46-cafa82e54b9c>, line 1)",
      "output_type": "error",
      "traceback": [
-      "\u001b[0;36m  File \u001b[0;32m\"<ipython-input-45-cafa82e54b9c>\"\u001b[0;36m, line \u001b[0;32m1\u001b[0m\n\u001b[0;31m    3.99 $ + 10.40 $\u001b[0m\n\u001b[0m         ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m invalid syntax\n"
+      "\u001b[0;36m  File \u001b[0;32m\"<ipython-input-46-cafa82e54b9c>\"\u001b[0;36m, line \u001b[0;32m1\u001b[0m\n\u001b[0;31m    3.99 $ + 10.40 $\u001b[0m\n\u001b[0m         ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m invalid syntax\n"
      ]
     }
    ],
@@ -1582,7 +1617,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 46,
+   "execution_count": 47,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -1591,10 +1626,10 @@
    "outputs": [
     {
      "ename": "SyntaxError",
-     "evalue": "invalid syntax (<ipython-input-46-499e4d0d0cbb>, line 1)",
+     "evalue": "invalid syntax (<ipython-input-47-499e4d0d0cbb>, line 1)",
      "output_type": "error",
      "traceback": [
-      "\u001b[0;36m  File \u001b[0;32m\"<ipython-input-46-499e4d0d0cbb>\"\u001b[0;36m, line \u001b[0;32m1\u001b[0m\n\u001b[0;31m    for number in numbers\u001b[0m\n\u001b[0m                         ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m invalid syntax\n"
+      "\u001b[0;36m  File \u001b[0;32m\"<ipython-input-47-499e4d0d0cbb>\"\u001b[0;36m, line \u001b[0;32m1\u001b[0m\n\u001b[0;31m    for number in numbers\u001b[0m\n\u001b[0m                         ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m invalid syntax\n"
      ]
     }
    ],
@@ -1616,7 +1651,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 47,
+   "execution_count": 48,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -1625,10 +1660,10 @@
    "outputs": [
     {
      "ename": "IndentationError",
-     "evalue": "expected an indented block (<ipython-input-47-19398c5f89de>, line 2)",
+     "evalue": "expected an indented block (<ipython-input-48-19398c5f89de>, line 2)",
      "output_type": "error",
      "traceback": [
-      "\u001b[0;36m  File \u001b[0;32m\"<ipython-input-47-19398c5f89de>\"\u001b[0;36m, line \u001b[0;32m2\u001b[0m\n\u001b[0;31m    print(number)\u001b[0m\n\u001b[0m        ^\u001b[0m\n\u001b[0;31mIndentationError\u001b[0m\u001b[0;31m:\u001b[0m expected an indented block\n"
+      "\u001b[0;36m  File \u001b[0;32m\"<ipython-input-48-19398c5f89de>\"\u001b[0;36m, line \u001b[0;32m2\u001b[0m\n\u001b[0;31m    print(number)\u001b[0m\n\u001b[0m        ^\u001b[0m\n\u001b[0;31mIndentationError\u001b[0m\u001b[0;31m:\u001b[0m expected an indented block\n"
      ]
     }
    ],
@@ -1660,12 +1695,12 @@
     "\n",
     "However, there are also so-called **runtime errors**, often called **exceptions**, that occur whenever otherwise (i.e., syntactically) correct code does not run because of invalid input.\n",
     "\n",
-    "This example does not work because just like in the \"real\" world, Python does not know how to divide by $0$. The syntactically correct code leads to a `ZeroDivisionError`."
+    "This example does not work because just like in the \"real\" world, Python does not know how to divide by `0`. The syntactically correct code leads to a `ZeroDivisionError`."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 48,
+   "execution_count": 49,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -1679,7 +1714,7 @@
      "traceback": [
       "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
       "\u001b[0;31mZeroDivisionError\u001b[0m                         Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-48-bc757c3fda29>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0;36m1\u001b[0m \u001b[0;34m/\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;32m<ipython-input-49-bc757c3fda29>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0;36m1\u001b[0m \u001b[0;34m/\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
       "\u001b[0;31mZeroDivisionError\u001b[0m: division by zero"
      ]
     }
@@ -1714,7 +1749,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 49,
+   "execution_count": 50,
    "metadata": {
     "code_folding": [],
     "slideshow": {
@@ -1736,7 +1771,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 50,
+   "execution_count": 51,
    "metadata": {
     "slideshow": {
      "slide_type": "-"
@@ -1746,10 +1781,10 @@
     {
      "data": {
       "text/plain": [
-       "3.0"
+       "3.5"
       ]
      },
-     "execution_count": 50,
+     "execution_count": 51,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1795,7 +1830,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 51,
+   "execution_count": 52,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -1817,10 +1852,10 @@
        "        "
       ],
       "text/plain": [
-       "<IPython.lib.display.YouTubeVideo at 0x7f3804586400>"
+       "<IPython.lib.display.YouTubeVideo at 0x7ff8c00b5358>"
       ]
      },
-     "execution_count": 51,
+     "execution_count": 52,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1838,12 +1873,12 @@
     }
    },
    "source": [
-    "For example, while the above code to calculate the average of the even numbers from 1 through 10 is correct, a Pythonista would re-write it in a more \"Pythonic\" way and use the [sum()](https://docs.python.org/3/library/functions.html#sum) and [len()](https://docs.python.org/3/library/functions.html#len) (= \"length\") [built-in functions](https://docs.python.org/3/library/functions.html) (cf., [Chapter 2](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/02_functions.ipynb#Built-in-Functions)) as well as a so-called **list comprehension** (cf., [Chapter 7](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/07_sequences.ipynb#List-Comprehensions)). Pythonic code runs faster in many cases and is less error-prone."
+    "For example, while the above code to calculate the average of the even numbers from `1` through `12` is correct, a Pythonista would re-write it in a more \"Pythonic\" way and use the [sum()](https://docs.python.org/3/library/functions.html#sum) and [len()](https://docs.python.org/3/library/functions.html#len) (= \"length\") [built-in functions](https://docs.python.org/3/library/functions.html) (cf., [Chapter 2](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/02_functions.ipynb#Built-in-Functions)) as well as a so-called **list comprehension** (cf., [Chapter 7](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/07_sequences.ipynb#List-Comprehensions)). Pythonic code runs faster in many cases and is less error-prone."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 52,
+   "execution_count": 53,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -1851,12 +1886,12 @@
    },
    "outputs": [],
    "source": [
-    "numbers = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]"
+    "numbers = [7, 11, 8, 5, 3, 12, 2, 6, 9, 10, 1, 4]"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 53,
+   "execution_count": 54,
    "metadata": {
     "slideshow": {
      "slide_type": "-"
@@ -1869,7 +1904,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 54,
+   "execution_count": 55,
    "metadata": {
     "slideshow": {
      "slide_type": "-"
@@ -1879,10 +1914,10 @@
     {
      "data": {
       "text/plain": [
-       "[2, 4, 6, 8, 10]"
+       "[8, 12, 2, 6, 10, 4]"
       ]
      },
-     "execution_count": 54,
+     "execution_count": 55,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1893,7 +1928,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 55,
+   "execution_count": 56,
    "metadata": {
     "slideshow": {
      "slide_type": "-"
@@ -1906,7 +1941,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 56,
+   "execution_count": 57,
    "metadata": {
     "slideshow": {
      "slide_type": "-"
@@ -1916,10 +1951,10 @@
     {
      "data": {
       "text/plain": [
-       "6.0"
+       "7.0"
       ]
      },
-     "execution_count": 56,
+     "execution_count": 57,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1941,7 +1976,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 57,
+   "execution_count": 58,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -2069,7 +2104,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 58,
+   "execution_count": 59,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -2094,7 +2129,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 59,
+   "execution_count": 60,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -2107,7 +2142,7 @@
        "20.0"
       ]
      },
-     "execution_count": 59,
+     "execution_count": 60,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2129,7 +2164,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 60,
+   "execution_count": 61,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -2142,7 +2177,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 61,
+   "execution_count": 62,
    "metadata": {
     "slideshow": {
      "slide_type": "-"
@@ -2155,7 +2190,7 @@
        "20"
       ]
      },
-     "execution_count": 61,
+     "execution_count": 62,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2177,7 +2212,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 62,
+   "execution_count": 63,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -2190,7 +2225,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 63,
+   "execution_count": 64,
    "metadata": {
     "slideshow": {
      "slide_type": "-"
@@ -2203,7 +2238,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 64,
+   "execution_count": 65,
    "metadata": {
     "slideshow": {
      "slide_type": "-"
@@ -2216,7 +2251,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 65,
+   "execution_count": 66,
    "metadata": {
     "slideshow": {
      "slide_type": "-"
@@ -2229,7 +2264,7 @@
        "42"
       ]
      },
-     "execution_count": 65,
+     "execution_count": 66,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2251,7 +2286,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 66,
+   "execution_count": 67,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -2264,7 +2299,7 @@
        "789"
       ]
      },
-     "execution_count": 66,
+     "execution_count": 67,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2275,7 +2310,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 67,
+   "execution_count": 68,
    "metadata": {
     "slideshow": {
      "slide_type": "-"
@@ -2299,7 +2334,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 68,
+   "execution_count": 69,
    "metadata": {
     "slideshow": {
      "slide_type": "-"
@@ -2313,7 +2348,7 @@
      "traceback": [
       "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
       "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-68-89e6c98d9288>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mb\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;32m<ipython-input-69-89e6c98d9288>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mb\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
       "\u001b[0;31mNameError\u001b[0m: name 'b' is not defined"
      ]
     }
@@ -2335,7 +2370,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 69,
+   "execution_count": 70,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -2348,7 +2383,7 @@
        "'__main__'"
       ]
      },
-     "execution_count": 69,
+     "execution_count": 70,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2370,7 +2405,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 70,
+   "execution_count": 71,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -2410,7 +2445,7 @@
        " '_34',\n",
        " '_35',\n",
        " '_36',\n",
-       " '_38',\n",
+       " '_37',\n",
        " '_39',\n",
        " '_4',\n",
        " '_40',\n",
@@ -2418,16 +2453,17 @@
        " '_42',\n",
        " '_43',\n",
        " '_44',\n",
+       " '_45',\n",
        " '_5',\n",
-       " '_50',\n",
        " '_51',\n",
-       " '_54',\n",
-       " '_56',\n",
-       " '_59',\n",
-       " '_61',\n",
-       " '_65',\n",
+       " '_52',\n",
+       " '_55',\n",
+       " '_57',\n",
+       " '_60',\n",
+       " '_62',\n",
        " '_66',\n",
-       " '_69',\n",
+       " '_67',\n",
+       " '_70',\n",
        " '_9',\n",
        " '__',\n",
        " '___',\n",
@@ -2508,6 +2544,7 @@
        " '_i69',\n",
        " '_i7',\n",
        " '_i70',\n",
+       " '_i71',\n",
        " '_i8',\n",
        " '_i9',\n",
        " '_ih',\n",
@@ -2529,7 +2566,7 @@
        " 'total']"
       ]
      },
-     "execution_count": 70,
+     "execution_count": 71,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2564,7 +2601,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 71,
+   "execution_count": 72,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -2575,30 +2612,6 @@
     "b = a"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": 72,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "-"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "42"
-      ]
-     },
-     "execution_count": 72,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "a"
-   ]
-  },
   {
    "cell_type": "code",
    "execution_count": 73,
@@ -2619,6 +2632,30 @@
      "output_type": "execute_result"
     }
    ],
+   "source": [
+    "a"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 74,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "42"
+      ]
+     },
+     "execution_count": 74,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "b"
    ]
@@ -2638,7 +2675,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 74,
+   "execution_count": 75,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -2651,7 +2688,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 75,
+   "execution_count": 76,
    "metadata": {
     "slideshow": {
      "slide_type": "-"
@@ -2664,7 +2701,7 @@
        "123"
       ]
      },
-     "execution_count": 75,
+     "execution_count": 76,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2686,7 +2723,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 76,
+   "execution_count": 77,
    "metadata": {
     "slideshow": {
      "slide_type": "-"
@@ -2699,7 +2736,7 @@
        "42"
       ]
      },
-     "execution_count": 76,
+     "execution_count": 77,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2721,7 +2758,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 77,
+   "execution_count": 78,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -2734,7 +2771,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 78,
+   "execution_count": 79,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -2747,7 +2784,7 @@
        "list"
       ]
      },
-     "execution_count": 78,
+     "execution_count": 79,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2758,7 +2795,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 79,
+   "execution_count": 80,
    "metadata": {
     "slideshow": {
      "slide_type": "-"
@@ -2786,7 +2823,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 80,
+   "execution_count": 81,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -2799,7 +2836,7 @@
        "1"
       ]
      },
-     "execution_count": 80,
+     "execution_count": 81,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2821,7 +2858,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 81,
+   "execution_count": 82,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -2834,7 +2871,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 82,
+   "execution_count": 83,
    "metadata": {
     "slideshow": {
      "slide_type": "-"
@@ -2847,7 +2884,7 @@
        "[99, 2, 3]"
       ]
      },
-     "execution_count": 82,
+     "execution_count": 83,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2869,7 +2906,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 83,
+   "execution_count": 84,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -2882,7 +2919,7 @@
        "[99, 2, 3]"
       ]
      },
-     "execution_count": 83,
+     "execution_count": 84,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2962,7 +2999,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 84,
+   "execution_count": 85,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -2975,7 +3012,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 85,
+   "execution_count": 86,
    "metadata": {
     "slideshow": {
      "slide_type": "-"
@@ -2988,7 +3025,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 86,
+   "execution_count": 87,
    "metadata": {
     "slideshow": {
      "slide_type": "-"
@@ -3001,7 +3038,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 87,
+   "execution_count": 88,
    "metadata": {
     "slideshow": {
      "slide_type": "-"
@@ -3025,7 +3062,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 88,
+   "execution_count": 89,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -3038,7 +3075,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 89,
+   "execution_count": 90,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -3051,7 +3088,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 90,
+   "execution_count": 91,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -3064,7 +3101,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 91,
+   "execution_count": 92,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -3073,10 +3110,10 @@
    "outputs": [
     {
      "ename": "SyntaxError",
-     "evalue": "can't assign to operator (<ipython-input-91-dfc191b6a91a>, line 1)",
+     "evalue": "can't assign to operator (<ipython-input-92-dfc191b6a91a>, line 1)",
      "output_type": "error",
      "traceback": [
-      "\u001b[0;36m  File \u001b[0;32m\"<ipython-input-91-dfc191b6a91a>\"\u001b[0;36m, line \u001b[0;32m1\u001b[0m\n\u001b[0;31m    address@work = \"Burgplatz 2, Vallendar\"\u001b[0m\n\u001b[0m                                           ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m can't assign to operator\n"
+      "\u001b[0;36m  File \u001b[0;32m\"<ipython-input-92-dfc191b6a91a>\"\u001b[0;36m, line \u001b[0;32m1\u001b[0m\n\u001b[0;31m    address@work = \"Burgplatz 2, Vallendar\"\u001b[0m\n\u001b[0m                                           ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m can't assign to operator\n"
      ]
     }
    ],
@@ -3097,7 +3134,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 92,
+   "execution_count": 93,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -3121,7 +3158,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 93,
+   "execution_count": 94,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -3134,7 +3171,7 @@
        "'__main__'"
       ]
      },
-     "execution_count": 93,
+     "execution_count": 94,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3167,7 +3204,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 94,
+   "execution_count": 95,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -3189,10 +3226,10 @@
        "        "
       ],
       "text/plain": [
-       "<IPython.lib.display.YouTubeVideo at 0x7f3804634748>"
+       "<IPython.lib.display.YouTubeVideo at 0x7ff8c011af28>"
       ]
      },
-     "execution_count": 94,
+     "execution_count": 95,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3232,7 +3269,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 95,
+   "execution_count": 96,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -3245,7 +3282,7 @@
        "123"
       ]
      },
-     "execution_count": 95,
+     "execution_count": 96,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3256,7 +3293,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 96,
+   "execution_count": 97,
    "metadata": {},
    "outputs": [
     {
@@ -3265,7 +3302,7 @@
        "42"
       ]
      },
-     "execution_count": 96,
+     "execution_count": 97,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3283,7 +3320,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 97,
+   "execution_count": 98,
    "metadata": {
     "slideshow": {
      "slide_type": "-"
@@ -3296,7 +3333,7 @@
        "165"
       ]
      },
-     "execution_count": 97,
+     "execution_count": 98,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3318,7 +3355,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 98,
+   "execution_count": 99,
    "metadata": {
     "slideshow": {
      "slide_type": "-"
@@ -3331,7 +3368,7 @@
        "4492125"
       ]
      },
-     "execution_count": 98,
+     "execution_count": 99,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3353,7 +3390,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 99,
+   "execution_count": 100,
    "metadata": {
     "slideshow": {
      "slide_type": "-"
@@ -3366,7 +3403,7 @@
        "3"
       ]
      },
-     "execution_count": 99,
+     "execution_count": 100,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3388,7 +3425,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 100,
+   "execution_count": 101,
    "metadata": {
     "slideshow": {
      "slide_type": "-"
@@ -3401,7 +3438,7 @@
        "104"
       ]
      },
-     "execution_count": 100,
+     "execution_count": 101,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3434,7 +3471,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 101,
+   "execution_count": 102,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -3448,7 +3485,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 102,
+   "execution_count": 103,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -3461,7 +3498,7 @@
        "'Hi class'"
       ]
      },
-     "execution_count": 102,
+     "execution_count": 103,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3483,7 +3520,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 103,
+   "execution_count": 104,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -3496,7 +3533,7 @@
        "'Hi Hi Hi Hi Hi Hi Hi Hi Hi Hi Hi Hi Hi Hi Hi Hi Hi Hi Hi Hi Hi Hi Hi Hi Hi Hi Hi Hi Hi Hi Hi Hi Hi Hi Hi Hi Hi Hi Hi Hi Hi Hi '"
       ]
      },
-     "execution_count": 103,
+     "execution_count": 104,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3531,7 +3568,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 104,
+   "execution_count": 105,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -3544,7 +3581,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 105,
+   "execution_count": 106,
    "metadata": {
     "slideshow": {
      "slide_type": "-"
@@ -3568,7 +3605,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 106,
+   "execution_count": 107,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -3615,7 +3652,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 107,
+   "execution_count": 108,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -3643,7 +3680,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 108,
+   "execution_count": 109,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -3656,7 +3693,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 109,
+   "execution_count": 110,
    "metadata": {
     "slideshow": {
      "slide_type": "-"
@@ -3686,17 +3723,8 @@
     }
    },
    "source": [
-    "We end each chapter with a summary of the main points (i.e., **TL;DR** = \"too long; didn't read\"). The essence in this first chapter is that just as a sentence in a real language like English may be decomposed into its parts (e.g., subject, predicate, and objects), the same may be done with programming languages."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "source": [
+    "We end each chapter with a summary of the main points (i.e., **TL;DR** = \"too long; didn't read\"). The essence in this first chapter is that just as a sentence in a real language like English may be decomposed into its parts (e.g., subject, predicate, and objects), the same may be done with programming languages.\n",
+    "\n",
     "- program\n",
     "  - **sequence** of **instructions** that specify how to perform a computation (= a \"recipe\")\n",
     "  - a \"black box\" that processes **inputs** and transforms them into meaningful **outputs** in a *deterministic* way\n",
@@ -3764,7 +3792,7 @@
     "\n",
     "- flow control (cf., [Chapter 3](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/03_conditionals.ipynb))\n",
     "  - expression of **business logic** or an **algorithm**\n",
-    "  - conditional execution of a small **branch** within a program (e.g., `if` statements)\n",
+    "  - conditional execution of parts of a program (e.g., `if` statements)\n",
     "  - repetitive execution of parts of a program (e.g., `for`-loops)"
    ]
   }
diff --git a/02_functions.ipynb b/02_functions.ipynb
index 809982f..897f8a7 100644
--- a/02_functions.ipynb
+++ b/02_functions.ipynb
@@ -19,7 +19,7 @@
     }
    },
    "source": [
-    "In [Chapter 1](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/01_elements.ipynb), we typed the **business logic** of our little program to calculate the mean of a subset of a list of numbers right into the code cells. Then, we executed them one after another. We had no way of **re-using** the code except for either re-executing the cells or copying and pasting their contents into other cells. And whenever we find ourselves doing repetitive manual work, we can be sure that there must be a way of automating what we are doing.\n",
+    "In [Chapter 1](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/01_elements.ipynb#Example:-Averaging-Even-Numbers), we typed the code to calculate the average of the even numbers in a list into several code cells. Then, we executed them one after another. We had no way of **re-using** the code except for either re-executing the cells or copying and pasting their contents into other cells. And, whenever we find ourselves doing repetitive manual work, we can be sure that there must be a way of automating what we are doing.\n",
     "\n",
     "At the same time, we executed built-in functions (e.g., [print()](https://docs.python.org/3/library/functions.html#print), [sum()](https://docs.python.org/3/library/functions.html#sum), [len()](https://docs.python.org/3/library/functions.html#len), [id()](https://docs.python.org/3/library/functions.html#id), or [type()](https://docs.python.org/3/library/functions.html#type)) that obviously must be re-using the same parts inside core Python every time we use them.\n",
     "\n",
@@ -55,7 +55,7 @@
     "\n",
     "Together, the name and the list of parameters are also referred to as the function's **[signature](https://en.wikipedia.org/wiki/Type_signature)** (i.e., `average_evens(numbers)` below).\n",
     "\n",
-    "A function may come with an *explicit* **[return value](https://docs.python.org/3/reference/simple_stmts.html#the-return-statement)** (i.e., \"result\" or \"output\") specified with the `return` statement: Functions that have one are considered **fruitful**; otherwise, they are **void**. Functions of the latter kind are still useful because of their **side effects** (e.g., the [print()](https://docs.python.org/3/library/functions.html#print) built-in). Strictly speaking, they also have an *implicit* return value of `None` that is different from the `False` we saw in [Chapter 1](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/01_elements.ipynb#Identity-/-\"Memory-Location\").\n",
+    "A function may come with an *explicit* **return value** (i.e., \"result\" or \"output\") specified with the `return` statement (cf., [reference](https://docs.python.org/3/reference/simple_stmts.html#the-return-statement)): Functions that have one are considered **fruitful**; otherwise, they are **void**. Functions of the latter kind are still useful because of their **side effects** as, for example, the built-in [print()](https://docs.python.org/3/library/functions.html#print) function. Strictly speaking, they also have an *implicit* return value of `None`.\n",
     "\n",
     "A function should define a **docstring** that describes what it does in a short subject line, what parameters it expects (i.e., their types), and what it returns (if anything). A docstring is a syntactically valid multi-line string (i.e., type `str`) defined within **triple-double quotes** `\"\"\"`. Strings are covered in depth in [Chapter 6](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/06_text.ipynb#The-str-Type). Widely adopted standards as to how to format a docstring are [PEP 257](https://www.python.org/dev/peps/pep-0257/) and section 3.8 in [Google's Python Style Guide](https://github.com/google/styleguide/blob/gh-pages/pyguide.md)."
    ]
@@ -142,7 +142,7 @@
     {
      "data": {
       "text/plain": [
-       "139829511291360"
+       "139646582981088"
       ]
      },
      "execution_count": 3,
@@ -302,7 +302,7 @@
    },
    "outputs": [],
    "source": [
-    "nums = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]"
+    "nums = [7, 11, 8, 5, 3, 12, 2, 6, 9, 10, 1, 4]"
    ]
   },
   {
@@ -317,7 +317,7 @@
     {
      "data": {
       "text/plain": [
-       "6.0"
+       "7.0"
       ]
      },
      "execution_count": 9,
@@ -337,7 +337,7 @@
     }
    },
    "source": [
-    "The return value is commonly assigned to a new variable for later reference. Otherwise, we would lose access to it in memory right away."
+    "The return value is commonly assigned to a variable for later reference. Otherwise, we would lose access to it in memory right away."
    ]
   },
   {
@@ -365,7 +365,7 @@
     {
      "data": {
       "text/plain": [
-       "6.0"
+       "7.0"
       ]
      },
      "execution_count": 11,
@@ -493,7 +493,7 @@
     }
    },
    "source": [
-    "[PythonTutor](http://pythontutor.com/visualize.html#code=nums%20%3D%20%5B1,%202,%203,%204,%205,%206,%207,%208,%209,%2010%5D%0A%0Adef%20average_evens%28numbers%29%3A%0A%20%20%20%20evens%20%3D%20%5Bn%20for%20n%20in%20numbers%20if%20n%20%25%202%20%3D%3D%200%5D%0A%20%20%20%20average%20%3D%20sum%28evens%29%20/%20len%28evens%29%0A%20%20%20%20return%20average%0A%0Arv%20%3D%20average_evens%28nums%29&cumulative=false&curInstr=0&heapPrimitives=nevernest&mode=display&origin=opt-frontend.js&py=3&rawInputLstJSON=%5B%5D&textReferences=false) visualizes what happens in memory: To be precise, in the exact moment when the function call is initiated and `nums` passed in as the `numbers` argument, there are *two* pointers to the *same* `list` object (cf., steps 4-5 in the visualization). We also see how Python creates a *new* **frame** that holds the function's local scope (i.e., \"internal names\") in addition to the **global** frame. Frames are nothing but [namespaces](https://en.wikipedia.org/wiki/Namespace) to *isolate* the names of different **scopes** from each other. The list comprehension `[n for n in numbers if n % 2 == 0]` constitutes yet another frame that is in scope as the `list` object assigned to `evens` is *being* created (cf., steps 6-18). When the function returns, only the global frame is left (cf., steps 21-22)."
+    "[PythonTutor](http://pythontutor.com/visualize.html#code=nums%20%3D%20%5B1,%202,%203,%204,%205,%206,%207,%208,%209,%2010,%2011,%2012%5D%0A%0Adef%20average_evens%28numbers%29%3A%0A%20%20%20%20evens%20%3D%20%5Bn%20for%20n%20in%20numbers%20if%20n%20%25%202%20%3D%3D%200%5D%0A%20%20%20%20average%20%3D%20sum%28evens%29%20/%20len%28evens%29%0A%20%20%20%20return%20average%0A%0Arv%20%3D%20average_evens%28nums%29&cumulative=false&curInstr=0&heapPrimitives=nevernest&mode=display&origin=opt-frontend.js&py=3&rawInputLstJSON=%5B%5D&textReferences=false) visualizes what happens in memory: To be precise, in the exact moment when the function call is initiated and `nums` passed in as the `numbers` argument, there are *two* pointers to the *same* `list` object (cf., steps 4-5 in the visualization). We also see how Python creates a *new* **frame** that holds the function's local scope (i.e., \"internal names\") in addition to the **global** frame. Frames are nothing but [namespaces](https://en.wikipedia.org/wiki/Namespace) to *isolate* the names of different **scopes** from each other. The list comprehension `[n for n in numbers if n % 2 == 0]` constitutes yet another frame that is in scope as the `list` object assigned to `evens` is *being* created (cf., steps 6-20). When the function returns, only the global frame is left (cf., last step)."
    ]
   },
   {
@@ -565,7 +565,7 @@
     {
      "data": {
       "text/plain": [
-       "[1, 2, 3, 4, 5, 6, 7, 8, 9, 10]"
+       "[7, 11, 8, 5, 3, 12, 2, 6, 9, 10, 1, 4]"
       ]
      },
      "execution_count": 16,
@@ -600,7 +600,7 @@
     {
      "data": {
       "text/plain": [
-       "6.0"
+       "7.0"
       ]
      },
      "execution_count": 17,
@@ -635,7 +635,7 @@
     {
      "data": {
       "text/plain": [
-       "6.0"
+       "7.0"
       ]
      },
      "execution_count": 18,
@@ -655,7 +655,7 @@
     }
    },
    "source": [
-    "[PythonTutor](http://pythontutor.com/visualize.html#code=nums%20%3D%20%5B1,%202,%203,%204,%205,%206,%207,%208,%209,%2010%5D%0A%0Adef%20average_wrong%28numbers%29%3A%0A%20%20%20%20evens%20%3D%20%5Bn%20for%20n%20in%20nums%20if%20n%20%25%202%20%3D%3D%200%5D%0A%20%20%20%20average%20%3D%20sum%28evens%29%20/%20len%28evens%29%0A%20%20%20%20return%20average%0A%0Arv%20%3D%20average_wrong%28%5B123,%20456,%20789%5D%29&cumulative=false&curInstr=0&heapPrimitives=nevernest&mode=display&origin=opt-frontend.js&py=3&rawInputLstJSON=%5B%5D&textReferences=false) is again helpful at visualizing the error interactively: Creating the `list` object `evens` eventually points to takes *12* computational steps, namely one for setting up an empty `list` object, *ten* for filling it with elements derived from `nums` in the global scope, and one to make `evens` point at it (cf., steps 6-18).\n",
+    "[PythonTutor](http://pythontutor.com/visualize.html#code=nums%20%3D%20%5B1,%202,%203,%204,%205,%206,%207,%208,%209,%2010,%2011,%2012%5D%0A%0Adef%20average_wrong%28numbers%29%3A%0A%20%20%20%20evens%20%3D%20%5Bn%20for%20n%20in%20nums%20if%20n%20%25%202%20%3D%3D%200%5D%0A%20%20%20%20average%20%3D%20sum%28evens%29%20/%20len%28evens%29%0A%20%20%20%20return%20average%0A%0Arv%20%3D%20average_wrong%28%5B123,%20456,%20789%5D%29&cumulative=false&curInstr=0&heapPrimitives=nevernest&mode=display&origin=opt-frontend.js&py=3&rawInputLstJSON=%5B%5D&textReferences=false) is again helpful at visualizing the error interactively: Creating the `list` object `evens` eventually points to takes *16* computational steps, namely two for managing the list comprehension, one for setting up an empty `list` object, *twelve* for filling it with elements derived from `nums` in the global scope (i.e., that is the error), and one to make `evens` point at it (cf., steps 6-21).\n",
     "\n",
     "The frames logic shown by PythonTutor is the mechanism by which Python not only manages the names inside *one* function call but also for *many* potentially *simultaneous* calls, as revealed in [Chapter 4](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/04_iteration.ipynb#Trivial-Example:-Countdown). It is the reason why we may re-use the same names for the parameters and variables inside both `average_evens()` and `average_wrong()` without Python mixing them up. So, as we already read in the [Zen of Python](https://www.python.org/dev/peps/pep-0020/), \"namespaces are one honking great idea\" (cf., `import this`), and a frame is just a special kind of namespace."
    ]
@@ -732,7 +732,7 @@
     {
      "data": {
       "text/plain": [
-       "[1, 2, 3, 4, 5, 6, 7, 8, 9, 10]"
+       "[7, 11, 8, 5, 3, 12, 2, 6, 9, 10, 1, 4]"
       ]
      },
      "execution_count": 20,
@@ -802,7 +802,7 @@
     {
      "data": {
       "text/plain": [
-       "5.0"
+       "6.0"
       ]
      },
      "execution_count": 22,
@@ -837,7 +837,7 @@
     {
      "data": {
       "text/plain": [
-       "[1, 2, 3, 4, 5, 6, 7, 8, 9, 10]"
+       "[7, 11, 8, 5, 3, 12, 2, 6, 9, 10, 1, 4]"
       ]
      },
      "execution_count": 23,
@@ -859,7 +859,7 @@
    "source": [
     "The reason why everything works is that *every time* we (re-)assign an object to a variable *inside* a function with the `=` statement, this is done in the *local* scope by default. There are ways to change variables existing in an outer scope from within a function, but this is a rather advanced topic on its own.\n",
     "\n",
-    "[PythonTutor](http://pythontutor.com/visualize.html#code=nums%20%3D%20%5B1,%202,%203,%204,%205,%206,%207,%208,%209,%2010%5D%0A%0Adef%20average_odds%28numbers%29%3A%0A%20%20%20%20nums%20%3D%20%5Bint%28n%29%20for%20n%20in%20numbers%5D%0A%20%20%20%20odds%20%3D%20%5Bn%20for%20n%20in%20nums%20if%20n%20%25%202%20!%3D%200%5D%0A%20%20%20%20average%20%3D%20sum%28odds%29%20/%20len%28odds%29%0A%20%20%20%20return%20average%0A%0Arv%20%3D%20average_odds%28%5B1.0,%2010.0,%203.0,%2010.0,%205.0%5D%29&cumulative=false&curInstr=0&heapPrimitives=nevernest&mode=display&origin=opt-frontend.js&py=3&rawInputLstJSON=%5B%5D&textReferences=false) shows how *two* `nums` variables exist in *different* scopes pointing to *different* objects (cf., steps 14-25) when we execute `average_odds([1.0, 10.0, 3.0, 10.0, 5.0])`.\n",
+    "[PythonTutor](http://pythontutor.com/visualize.html#code=nums%20%3D%20%5B1,%202,%203,%204,%205,%206,%207,%208,%209,%2010,%2011,%2012%5D%0A%0Adef%20average_odds%28numbers%29%3A%0A%20%20%20%20nums%20%3D%20%5Bint%28n%29%20for%20n%20in%20numbers%5D%0A%20%20%20%20odds%20%3D%20%5Bn%20for%20n%20in%20nums%20if%20n%20%25%202%20!%3D%200%5D%0A%20%20%20%20average%20%3D%20sum%28odds%29%20/%20len%28odds%29%0A%20%20%20%20return%20average%0A%0Arv%20%3D%20average_odds%28%5B1.0,%2010.0,%203.0,%2010.0,%205.0%5D%29&cumulative=false&curInstr=0&heapPrimitives=nevernest&mode=display&origin=opt-frontend.js&py=3&rawInputLstJSON=%5B%5D&textReferences=false) shows how *two* `nums` variables exist in *different* scopes pointing to *different* objects (cf., steps 14-25) when we execute `average_odds([1.0, 10.0, 3.0, 10.0, 5.0])`.\n",
     "\n",
     "Variables whose names collide with the ones of variables in enclosing scopes - and the global scope is just the most enclosing scope - are said to **shadow** them.\n",
     "\n",
@@ -902,7 +902,7 @@
     {
      "data": {
       "text/plain": [
-       "10"
+       "12"
       ]
      },
      "execution_count": 24,
@@ -926,7 +926,7 @@
     {
      "data": {
       "text/plain": [
-       "55"
+       "78"
       ]
      },
      "execution_count": 25,
@@ -946,7 +946,7 @@
     }
    },
    "source": [
-    "We may cast objects as a different type. For example, to \"convert\" a float or a text into an integer, we use the [int()](https://docs.python.org/3/library/functions.html#int) built-in. It creates a *new* object of type `int` from the provided `avg` or `\"6\"` objects that continue to exist in memory unchanged."
+    "We may cast objects as a different type. For example, to \"convert\" a float or a text into an integer, we use the [int()](https://docs.python.org/3/library/functions.html#int) built-in. It creates a *new* object of type `int` from the provided `avg` or `\"7\"` objects that continue to exist in memory unchanged."
    ]
   },
   {
@@ -961,7 +961,7 @@
     {
      "data": {
       "text/plain": [
-       "6.0"
+       "7.0"
       ]
      },
      "execution_count": 26,
@@ -985,7 +985,7 @@
     {
      "data": {
       "text/plain": [
-       "6"
+       "7"
       ]
      },
      "execution_count": 27,
@@ -1009,7 +1009,7 @@
     {
      "data": {
       "text/plain": [
-       "6"
+       "7"
       ]
      },
      "execution_count": 28,
@@ -1018,7 +1018,7 @@
     }
    ],
    "source": [
-    "int(\"6\")"
+    "int(\"7\")"
    ]
   },
   {
@@ -1029,7 +1029,7 @@
     }
    },
    "source": [
-    "Observe that casting as an integer is different from rounding with the [round()](https://docs.python.org/3/library/functions.html#round) built-in function."
+    "Casting as an `int` object is different from rounding with the built-in [round()](https://docs.python.org/3/library/functions.html#round) function!"
    ]
   },
   {
@@ -1102,18 +1102,18 @@
    "outputs": [
     {
      "ename": "ValueError",
-     "evalue": "invalid literal for int() with base 10: 'six'",
+     "evalue": "invalid literal for int() with base 10: 'seven'",
      "output_type": "error",
      "traceback": [
       "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
       "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-31-28b6aa9c0167>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"six\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
-      "\u001b[0;31mValueError\u001b[0m: invalid literal for int() with base 10: 'six'"
+      "\u001b[0;32m<ipython-input-31-af421b358f21>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"seven\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;31mValueError\u001b[0m: invalid literal for int() with base 10: 'seven'"
      ]
     }
    ],
    "source": [
-    "int(\"six\")"
+    "int(\"seven\")"
    ]
   },
   {
@@ -1281,7 +1281,7 @@
     {
      "data": {
       "text/plain": [
-       "12.0"
+       "14.0"
       ]
      },
      "execution_count": 36,
@@ -1318,7 +1318,7 @@
     {
      "data": {
       "text/plain": [
-       "12.0"
+       "14.0"
       ]
      },
      "execution_count": 37,
@@ -1342,7 +1342,7 @@
     {
      "data": {
       "text/plain": [
-       "12.0"
+       "14.0"
       ]
      },
      "execution_count": 38,
@@ -1366,7 +1366,7 @@
     {
      "data": {
       "text/plain": [
-       "12.0"
+       "14.0"
       ]
      },
      "execution_count": 39,
@@ -1483,7 +1483,7 @@
     }
    },
    "source": [
-    "The outcome of `average_evens(nums)` is, of course, still `6.0`."
+    "The outcome of `average_evens(nums)` is, of course, still `7.0`."
    ]
   },
   {
@@ -1498,7 +1498,7 @@
     {
      "data": {
       "text/plain": [
-       "6.0"
+       "7.0"
       ]
      },
      "execution_count": 43,
@@ -1555,7 +1555,7 @@
     }
    },
    "source": [
-    "Now we call the function either with or without the `scalar` argument.\n",
+    "Now, we call the function either with or without the `scalar` argument.\n",
     "\n",
     "If `scalar` is to be passed in, this may be done as either a positional or a keyword argument. Which of the two versions where `scalar` is `2` is more \"natural\" and faster to comprehend in a larger program?"
    ]
@@ -1572,7 +1572,7 @@
     {
      "data": {
       "text/plain": [
-       "6.0"
+       "7.0"
       ]
      },
      "execution_count": 45,
@@ -1596,7 +1596,7 @@
     {
      "data": {
       "text/plain": [
-       "12.0"
+       "14.0"
       ]
      },
      "execution_count": 46,
@@ -1620,7 +1620,7 @@
     {
      "data": {
       "text/plain": [
-       "12.0"
+       "14.0"
       ]
      },
      "execution_count": 47,
@@ -1705,7 +1705,7 @@
     {
      "data": {
       "text/plain": [
-       "6.0"
+       "7.0"
       ]
      },
      "execution_count": 49,
@@ -1729,7 +1729,7 @@
     {
      "data": {
       "text/plain": [
-       "12.0"
+       "14.0"
       ]
      },
      "execution_count": 50,
@@ -1749,7 +1749,7 @@
     }
    },
    "source": [
-    "If we pass in `scalar` as a positional argument instead, we obtain a `TypeError`."
+    "If we pass in `scalar` as a positional argument instead, we get a `TypeError`."
    ]
   },
   {
@@ -1841,11 +1841,11 @@
     }
    },
    "source": [
-    "If you think this is a rather pointless thing to do, you are absolutely correct!\n",
+    "If you think this is rather pointless to do, you are absolutely correct!\n",
     "\n",
     "We created a `function` object, dit *not* call it, and Python immediately forgot about it. So what's the point?\n",
     "\n",
-    "To prove that a `lambda` expression creates a callable `function` object, we use the simple `=` statement to assign it to the variable `add_three`, which is really `add_three()` as per our convention from above."
+    "To show that a `lambda` expression creates a callable `function` object, we use the simple `=` statement to assign it to the variable `add_three`, which is really `add_three()` as per our convention from above."
    ]
   },
   {
@@ -1928,7 +1928,7 @@
     }
    ],
    "source": [
-    "(lambda x: x + 3)(39)  # this looks weird but will become very useful"
+    "(lambda x: x + 3)(39)"
    ]
   },
   {
@@ -1939,7 +1939,7 @@
     }
    },
    "source": [
-    "The main point of having functions without a name is to use them in a situation where we know ahead of time that we use the function *once* only.\n",
+    "The main point of having functions without a name is to use them in a situation where we know ahead of time that we use the function only *once*.\n",
     "\n",
     "Popular applications of lambda expressions occur in combination with the **map-filter-reduce** paradigm or when we do \"number crunching\" with **arrays** and **data frames**. We look at both in detail in [Chapter 7](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/07_sequences.ipynb)."
    ]
@@ -2021,7 +2021,7 @@
    "source": [
     "The [math](https://docs.python.org/3/library/math.html) module provides non-trivial mathematical functions like $sin(x)$ and constants like $\\pi$ or $\\text{e}$.\n",
     "\n",
-    "To make functions and variables defined \"somewhere else\" available in our current program, we must first **[import](https://docs.python.org/3/reference/simple_stmts.html#import)** them with the `import` statement.  "
+    "To make functions and variables defined \"somewhere else\" available in our current program, we must first **import** them with the `import` statement (cf., [reference](https://docs.python.org/3/reference/simple_stmts.html#import)).  "
    ]
   },
   {
@@ -2084,7 +2084,7 @@
     {
      "data": {
       "text/plain": [
-       "139829616976344"
+       "139646755983752"
       ]
      },
      "execution_count": 58,
@@ -2489,7 +2489,7 @@
     }
    },
    "source": [
-    "Often, we need a random variable, for example, when we want to build a simulation program. The [random](https://docs.python.org/3/library/random.html) module in the [standard library](https://docs.python.org/3/library/index.html) often suffices for that."
+    "Often, we need a random variable, for example, when we want to build a simulation. The [random](https://docs.python.org/3/library/random.html) module in the [standard library](https://docs.python.org/3/library/index.html) often suffices for that."
    ]
   },
   {
@@ -2697,7 +2697,7 @@
     {
      "data": {
       "text/plain": [
-       "0.4165845384207939"
+       "0.21932911318720072"
       ]
      },
      "execution_count": 75,
@@ -2732,7 +2732,7 @@
     {
      "data": {
       "text/plain": [
-       "<bound method Random.choice of <random.Random object at 0x56279067aaf8>>"
+       "<bound method Random.choice of <random.Random object at 0x55a6c6c03af8>>"
       ]
      },
      "execution_count": 76,
@@ -2781,7 +2781,7 @@
     {
      "data": {
       "text/plain": [
-       "2"
+       "7"
       ]
      },
      "execution_count": 78,
@@ -2801,7 +2801,7 @@
     }
    },
    "source": [
-    "To reproduce the same random numbers in a simulation each time we run it, we set the **[random seed](https://en.wikipedia.org/wiki/Random_seed)**. It is good practice to do this at the beginning of a program or notebook. Then every time we re-start the program, we get the *same* random numbers again. This becomes very important, for example, when we employ machine learning algorithms that rely on randomization, like the infamous [Random Forest](https://en.wikipedia.org/wiki/Random_forest), and want to obtain **reproducable** results.\n",
+    "To reproduce the *same* random numbers in a simulation each time we run it, we set the **[random seed](https://en.wikipedia.org/wiki/Random_seed)**. It is good practice to do that at the beginning of a program or notebook. It becomes essential when we employ randomized machine learning algorithms, like the [Random Forest](https://en.wikipedia.org/wiki/Random_forest), and want to obtain **reproducible** results for publication in academic journals.\n",
     "\n",
     "The [random](https://docs.python.org/3/library/random.html) module provides the [random.seed()](https://docs.python.org/3/library/random.html#random.seed) function to do that."
    ]
@@ -3050,7 +3050,7 @@
     {
      "data": {
       "text/plain": [
-       "array([ 1,  2,  3,  4,  5,  6,  7,  8,  9, 10])"
+       "array([ 7, 11,  8,  5,  3, 12,  2,  6,  9, 10,  1,  4])"
       ]
      },
      "execution_count": 87,
@@ -3111,7 +3111,7 @@
     {
      "data": {
       "text/plain": [
-       "array([ 2,  4,  6,  8, 10, 12, 14, 16, 18, 20])"
+       "array([14, 22, 16, 10,  6, 24,  4, 12, 18, 20,  2,  8])"
       ]
      },
      "execution_count": 89,
@@ -3146,7 +3146,7 @@
     {
      "data": {
       "text/plain": [
-       "[1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10]"
+       "[7, 11, 8, 5, 3, 12, 2, 6, 9, 10, 1, 4, 7, 11, 8, 5, 3, 12, 2, 6, 9, 10, 1, 4]"
       ]
      },
      "execution_count": 90,
@@ -3181,7 +3181,7 @@
     {
      "data": {
       "text/plain": [
-       "55"
+       "78"
       ]
      },
      "execution_count": 91,
@@ -3205,7 +3205,7 @@
     {
      "data": {
       "text/plain": [
-       "1"
+       "7"
       ]
      },
      "execution_count": 92,
@@ -3389,7 +3389,7 @@
     {
      "data": {
       "text/plain": [
-       "6.0"
+       "7.0"
       ]
      },
      "execution_count": 97,
@@ -3413,7 +3413,7 @@
     {
      "data": {
       "text/plain": [
-       "12.0"
+       "14.0"
       ]
      },
      "execution_count": 98,
diff --git a/03_conditionals.ipynb b/03_conditionals.ipynb
index 90da64f..072448a 100644
--- a/03_conditionals.ipynb
+++ b/03_conditionals.ipynb
@@ -189,7 +189,7 @@
     {
      "data": {
       "text/plain": [
-       "94731133531104"
+       "94711290794976"
       ]
      },
      "execution_count": 5,
@@ -213,7 +213,7 @@
     {
      "data": {
       "text/plain": [
-       "94731133531072"
+       "94711290794944"
       ]
      },
      "execution_count": 6,
@@ -313,7 +313,7 @@
     {
      "data": {
       "text/plain": [
-       "94731133518064"
+       "94711290781936"
       ]
      },
      "execution_count": 10,
@@ -1505,19 +1505,19 @@
     }
    },
    "source": [
-    "To write useful programs, we need to control the flow of execution, for example, to react to user input. The logic by which a program does that is referred to as **business logic**.\n",
+    "To write useful programs, we need to control the flow of execution, for example, to react to user input. The logic by which a program follows the rules from the \"real world\" is referred to as **[business logic](https://en.wikipedia.org/wiki/Business_logic)**, even if \"real world\" refers to the domain of mathematics and not a business application.\n",
     "\n",
-    "One language construct to do so is the **[conditional statement](https://docs.python.org/3/reference/compound_stmts.html#the-if-statement)**, or `if` statement for short. It consists of:\n",
+    "One language feature to do so is the `if` statement (cf., [reference](https://docs.python.org/3/reference/compound_stmts.html#the-if-statement)). It consists of:\n",
     "\n",
     "- *one* mandatory `if`-clause,\n",
-    "- an *arbitrary* number of `elif`-clauses (i.e. \"else if\"), and\n",
+    "- an *arbitrary* number of `elif`-clauses (i.e., \"else if\"), and\n",
     "- an *optional* `else`-clause.\n",
     "\n",
-    "The `if`- and `elif`-clauses each specify one *boolean* expression, also called **condition**, while the `else`-clause serves as the \"catch everything else\" case.\n",
+    "The `if`- and `elif`-clauses each specify one *boolean* expression, also called **condition** in this context, while the `else`-clause serves as the \"catch everything else\" case.\n",
     "\n",
-    "In terms of syntax, the header lines end with a colon, and the code blocks are indented.\n",
+    "In contrast to our intuitive interpretation in natural languages, only the code in *one* of the alternatives, also called **branches**, is executed. To be precise, it is always the code in the first clause whose condition evaluates to `True`.\n",
     "\n",
-    "In contrast to our intuitive interpretation in natural languages, only the code in *one* of the alternatives, also called **branches**, is executed. To be precise, it is always the code in the first branch whose condition evaluates to `True`."
+    "In terms of syntax, the header lines end with a colon, and the code blocks are indented. Formally, any statement that is written across several lines is a **[compound statement](https://docs.python.org/3/reference/compound_stmts.html#compound-statements)**, the code blocks are called **suites** and belong to one header line, and the term **clause** refers to a header line and its suite as a whole. So far, we have seen three compound statements: `for`, `if`, and `def`. On the contrary, **[simple statements](https://docs.python.org/3/reference/simple_stmts.html#simple-statements)**, for example, `=`, `del`, or `return`, are written on *one* line."
    ]
   },
   {
@@ -1597,15 +1597,7 @@
      "slide_type": "-"
     }
    },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "You read this just as often as you see heads when tossing a coin\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "if random.random() > 0.5:\n",
     "    print(\"You read this just as often as you see heads when tossing a coin\")"
@@ -1715,7 +1707,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "z is odd\n"
+      "z is positive\n"
      ]
     }
    ],
@@ -1753,7 +1745,7 @@
     }
    },
    "source": [
-    "When all we do with an `if` statement is to assign an object to a variable according to a single true-or-false condition (i.e., a binary choice), there is a shortcut: We assign the variable the result of a so-called **conditional expression**, or `if` expression for short, instead.\n",
+    "When an `if` statement assigns an object to a variable according to a true-or-false condition (i.e., a binary choice), there is a shortcut: We assign the variable the result of a so-called **[conditional expression](https://docs.python.org/3/reference/expressions.html#conditional-expressions)**, or `if` expression for short, instead.\n",
     "\n",
     "Think of a situation where we evaluate a piece-wise functional relationship $y = f(x)$ at a given $x$, for example:"
    ]
@@ -1847,7 +1839,7 @@
     }
    },
    "source": [
-    "On the contrary, the `if` expression fits into one line. The main downside here is a potential loss in readability, in particular, if the functional relationship is not that simple."
+    "On the contrary, the `if` expression fits into one line. The main downside is a potential loss in readability, in particular, if the functional relationship is not that simple. Also, some practitioners do *not* like that the condition is in the middle of the expression."
    ]
   },
   {
@@ -1895,7 +1887,7 @@
     }
    },
    "source": [
-    "In this example, however, the most elegant solution would be to use the built-in [max()](https://docs.python.org/3/library/functions.html#max) function."
+    "In this example, however, the most elegant solution is to use the built-in [max()](https://docs.python.org/3/library/functions.html#max) function."
    ]
   },
   {
@@ -1935,17 +1927,6 @@
     "y"
    ]
   },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "source": [
-    "Conditional expressions may not only be used in the way described in this section. We already saw them as part of a *list comprehension* in [Chapter 1](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/01_elements.ipynb) and [Chapter 2](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/02_functions.ipynb) and revisit this construct in greater detail in [Chapter 7](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/07_sequences.ipynb#List-Comprehensions)."
-   ]
-  },
   {
    "cell_type": "markdown",
    "metadata": {
@@ -1967,9 +1948,9 @@
    "source": [
     "In the previous two chapters, we encountered a couple of *runtime* errors. A natural urge we might have after reading about conditional statements is to write code that somehow reacts to the occurrence of such exceptions. All we need is a way to formulate a condition for that.\n",
     "\n",
-    "For sure, this is such a common thing to do that Python provides a language construct for it, namely the `try` [statement](https://docs.python.org/3/reference/compound_stmts.html#the-try-statement).\n",
+    "For sure, this is such a common thing to do that Python provides a language construct for it, namely the compound `try` statement (cf., [reference](https://docs.python.org/3/reference/compound_stmts.html#the-try-statement)).\n",
     "\n",
-    "In its simplest form, it comes with just two branches: `try` and `except`. The following tells Python to execute the code in the `try`-branch, and if *anything* goes wrong, continue in the `except`-branch instead of **raising** an error to us. Of course, if nothing goes wrong, the `except`-branch is *not* executed."
+    "In its simplest form, it comes with just two clauses: `try` and `except`. The following tells Python to execute the code in the `try`-clause, and if *anything* goes wrong, continue in the `except`-clause instead of **raising** an error to us. Of course, if nothing goes wrong, the `except`-clause is *not* executed."
    ]
   },
   {
@@ -2017,9 +1998,9 @@
     }
    },
    "source": [
-    "However, it is good practice *not* to **handle** *any* possible exception but only the ones we may *expect* from the code in the `try`-branch. The reasoning why this is done is a bit involved. We only remark that the codebase becomes easier to understand as we communicate to any human reader what could go wrong during execution in an *explicit* way. Python comes with a lot of [built-in exceptions](https://docs.python.org/3/library/exceptions.html#concrete-exceptions) that we should familiarize ourselves with.\n",
+    "However, it is good practice *not* to **handle** *any* possible exception but only the ones we may *expect* from the code in the `try`-clause. The reasoning why this is done is a bit involved. We only remark that the codebase becomes easier to understand as we communicate to any human reader what could go wrong during execution in an *explicit* way. Python comes with a lot of [built-in exceptions](https://docs.python.org/3/library/exceptions.html#concrete-exceptions) that we should familiarize ourselves with.\n",
     "\n",
-    "Another good practice is to always keep the code in the `try`-branch short to not *accidentally* handle an exception we do *not* want to handle.\n",
+    "Another good practice is to always keep the code in the `try`-clause short to not *accidentally* handle an exception we do *not* want to handle.\n",
     "\n",
     "In the example, we are dividing numbers and may expect a `ZeroDivisionError`."
    ]
@@ -2056,9 +2037,9 @@
     }
    },
    "source": [
-    "Often, we may have to run some code *independent* of an exception occurring, for example, to close a connection to a database. To achieve that, we add a `finally`-branch to the `try` statement.\n",
+    "Often, we may have to run some code *independent* of an exception occurring, for example, to close a connection to a database. To achieve that, we add a `finally`-clause to the `try` statement.\n",
     "\n",
-    "Similarly, we may have to run some code *only if* no exception occurs, but we do not want to put it in the `try`-branch as per the good practice mentioned above. To achieve that, we add an `else`-branch to the `try` statement.\n",
+    "Similarly, we may have to run some code *only if* no exception occurs, but we do not want to put it in the `try`-clause as per the good practice mentioned above. To achieve that, we add an `else`-clause to the `try` statement.\n",
     "\n",
     "To showcase everything together, we look at one last example. To spice it up a bit, we randomize the input. So run the cell several times and see for yourself."
    ]
diff --git a/04_iteration.ipynb b/04_iteration.ipynb
index 3a2e42d..2246da5 100644
--- a/04_iteration.ipynb
+++ b/04_iteration.ipynb
@@ -859,7 +859,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "69.6 µs ± 25.8 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n"
+      "36.5 µs ± 2.47 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n"
      ]
     }
    ],
@@ -881,7 +881,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "1.55 ms ± 11.9 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n"
+      "1.58 ms ± 20.7 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n"
      ]
     }
    ],
@@ -903,7 +903,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "189 ms ± 0 ns per loop (mean ± std. dev. of 1 run, 1 loop each)\n"
+      "194 ms ± 0 ns per loop (mean ± std. dev. of 1 run, 1 loop each)\n"
      ]
     }
    ],
@@ -925,7 +925,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "2.07 s ± 0 ns per loop (mean ± std. dev. of 1 run, 1 loop each)\n"
+      "2.15 s ± 0 ns per loop (mean ± std. dev. of 1 run, 1 loop each)\n"
      ]
     }
    ],
@@ -947,7 +947,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "5.41 s ± 0 ns per loop (mean ± std. dev. of 1 run, 1 loop each)\n"
+      "5.59 s ± 0 ns per loop (mean ± std. dev. of 1 run, 1 loop each)\n"
      ]
     }
    ],
@@ -4107,7 +4107,7 @@
    "source": [
     "We use the built-in [isinstance()](https://docs.python.org/3/library/functions.html#isinstance) function to make sure `factorial()` is called with an `int` object as the argument. We further **validate the input** by verifying that the integer is non-negative.\n",
     "\n",
-    "Meanwhile, we also see how we manually raise exceptions with the `raise` [statement](https://docs.python.org/3/reference/simple_stmts.html#the-raise-statement), another way of controlling the flow of execution.\n",
+    "Meanwhile, we also see how we manually raise exceptions with the `raise` statement (cf., [reference](https://docs.python.org/3/reference/simple_stmts.html#the-raise-statement)), another way of controlling the flow of execution.\n",
     "\n",
     "The first two branches in the revised `factorial()` function act as **guardians** ensuring that the code does not produce *unexpected* runtime errors: Errors may be expected when mentioned in the docstring.\n",
     "\n",
@@ -4322,7 +4322,7 @@
     }
    },
    "source": [
-    "Whereas functions combined with `if` statements suffice to model any repetitive logic, Python comes with a `while` [statement](https://docs.python.org/3/reference/compound_stmts.html#the-while-statement) that often makes it easier to implement iterative ideas.\n",
+    "Whereas functions combined with `if` statements suffice to model any repetitive logic, Python comes with a compound `while` statement (cf., [reference](https://docs.python.org/3/reference/compound_stmts.html#the-while-statement)) that often makes it easier to implement iterative ideas.\n",
     "\n",
     "It consists of a header line with a boolean expression followed by an indented code block. Before the first and after every execution of the code block, the boolean expression is evaluated, and if it is (still) equal to `True`, the code block runs (again). Eventually, some variable referenced in the boolean expression is changed in the code block such that the condition becomes `False`.\n",
     "\n",
@@ -4565,7 +4565,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "4.9 s ± 0 ns per loop (mean ± std. dev. of 1 run, 1 loop each)\n"
+      "4.69 s ± 0 ns per loop (mean ± std. dev. of 1 run, 1 loop each)\n"
      ]
     }
    ],
@@ -4792,9 +4792,9 @@
    "source": [
     "Recursion and the `while` statement are two sides of the same coin. Disregarding that in the case of recursion Python internally faces some additional burden for managing the stack of frames in memory, both approaches lead to the *same* computational steps in memory. More importantly, we can re-formulate a recursive implementation in an iterative way and vice versa despite one of the two ways often \"feeling\" a lot more natural given a particular problem.\n",
     "\n",
-    "So how does the `for` [statement](https://docs.python.org/3/reference/compound_stmts.html#the-for-statement) we saw in the very first example in this book fit into this picture? It is a *redundant* language construct to provide a *shorter* and more *convenient* syntax for common applications of the `while` statement. In programming, such additions to a language are called **syntactic sugar**. A cup of tea tastes better with sugar, but we may drink tea without sugar too.\n",
+    "So how does the compound `for` statement (cf., [reference](https://docs.python.org/3/reference/compound_stmts.html#the-for-statement)) in this book's very first example fit into this picture? It is a *redundant* language construct to provide a *shorter* and more *convenient* syntax for common applications of the `while` statement. In programming, such additions to a language are called **syntactic sugar**. A cup of tea tastes better with sugar, but we may drink tea without sugar too.\n",
     "\n",
-    "Consider the following `numbers` list. Without the `for` statement, we have to manage a temporary **index variable** `i` to loop over all the elements and also obtain the individual elements with the `[]` operator in each iteration of the loop."
+    "Consider `elements` below. Without the `for` statement, we have to manage a temporary **index variable**, `index`, to loop over all the elements and also obtain the individual elements with the `[]` operator in each iteration of the loop."
    ]
   },
   {
@@ -4807,7 +4807,7 @@
    },
    "outputs": [],
    "source": [
-    "numbers = [0, 1, 2, 3, 4]"
+    "elements = [0, 1, 2, 3, 4]"
    ]
   },
   {
@@ -4828,12 +4828,12 @@
     }
    ],
    "source": [
-    "i = 0\n",
-    "while i < len(numbers):\n",
-    "    number = numbers[i]\n",
-    "    print(number, end=\" \")\n",
-    "    i += 1\n",
-    "del i"
+    "index = 0\n",
+    "while index < len(elements):\n",
+    "    element = elements[index]\n",
+    "    print(element, end=\" \")\n",
+    "    index += 1\n",
+    "del index"
    ]
   },
   {
@@ -4865,8 +4865,8 @@
     }
    ],
    "source": [
-    "for number in numbers:\n",
-    "    print(number, end=\" \")"
+    "for element in elements:\n",
+    "    print(element, end=\" \")"
    ]
   },
   {
@@ -4877,7 +4877,7 @@
     }
    },
    "source": [
-    "For sequences of integers, the [range()](https://docs.python.org/3/library/functions.html#func-range) built-in makes the `for` statement even more convenient: It creates a list-like object of type `range` that generates integers \"on the fly,\" and we look closely at the underlying effects in memory in [Chapter 7](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/07_sequences.ipynb#Mapping)."
+    "For sequences of integers, the [range()](https://docs.python.org/3/library/functions.html#func-range) built-in makes the `for` statement even more convenient: It creates a `list`-like object of type `range` that generates integers \"on the fly,\" and we look closely at the underlying effects in memory in [Chapter 7](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/07_sequences.ipynb#Mapping)."
    ]
   },
   {
@@ -4898,8 +4898,8 @@
     }
    ],
    "source": [
-    "for number in range(5):\n",
-    "    print(number, end=\" \")"
+    "for element in range(5):\n",
+    "    print(element, end=\" \")"
    ]
   },
   {
@@ -4955,8 +4955,8 @@
     }
    ],
    "source": [
-    "for number in [1, 3, 5, 7, 9]:\n",
-    "    print(number, end=\" \")"
+    "for element in [1, 3, 5, 7, 9]:\n",
+    "    print(element, end=\" \")"
    ]
   },
   {
@@ -4977,8 +4977,8 @@
     }
    ],
    "source": [
-    "for number in range(1, 10, 2):\n",
-    "    print(number, end=\" \")"
+    "for element in range(1, 10, 2):\n",
+    "    print(element, end=\" \")"
    ]
   },
   {
@@ -5097,7 +5097,7 @@
     }
    },
    "source": [
-    "The cell below shows the *exact* workings of the `in` operator: Although `3.0` is *not* contained in `numbers`, it evaluates equal to the `3` that is, which is why the following expression evaluates to `True`. So, while we could colloquially say that `numbers` \"contains\" `3.0`, it actually does not."
+    "The cell below shows the *exact* workings of the `in` operator: Although `3.0` is *not* contained in `elements`, it evaluates equal to the `3` that is, which is why the following expression evaluates to `True`. So, while we could colloquially say that `elements` \"contains\" `3.0`, it actually does not."
    ]
   },
   {
@@ -5121,7 +5121,7 @@
     }
    ],
    "source": [
-    "3.0 in numbers"
+    "3.0 in elements"
    ]
   },
   {
@@ -5550,7 +5550,7 @@
     }
    },
    "source": [
-    "Let's say we have a `numbers` list and want to check if the square of at least one of its elements is above a `threshold` of `100`."
+    "Let's say we have a `samples` list and want to check if the square of at least one of its elements is above a `threshold` of `100`."
    ]
   },
   {
@@ -5563,7 +5563,7 @@
    },
    "outputs": [],
    "source": [
-    "numbers = [3, 7, 2, 9, 11, 4, 7, 9, 4, 5]"
+    "samples = [3, 7, 2, 9, 11, 4, 7, 9, 4, 5]"
    ]
   },
   {
@@ -5587,7 +5587,7 @@
     }
    },
    "source": [
-    "A first naive implementation could look like this: We loop over *every* element in `numbers` and set an **indicator variable** `is_above`, initialized as `False`, to `True` once we encounter an element satisfying the search condition."
+    "A first naive implementation could look like this: We loop over *every* element in `samples` and set an **indicator variable** `is_above`, initialized as `False`, to `True` once we encounter an element satisfying the search condition."
    ]
   },
   {
@@ -5603,22 +5603,22 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "3   7   2   9   11   4   7   9   4   5   =>   at least one number's square is above 100\n"
+      "3   7   2   9   11   4   7   9   4   5   =>   at least one sample's square is above 100\n"
      ]
     }
    ],
    "source": [
     "is_above = False\n",
     "\n",
-    "for number in numbers:\n",
-    "    print(number, end=\"   \")  # added for didactical purposes\n",
-    "    if number ** 2 > threshold:\n",
+    "for sample in samples:\n",
+    "    print(sample, end=\"   \")  # added for didactical purposes\n",
+    "    if sample ** 2 > threshold:\n",
     "        is_above = True\n",
     "\n",
     "if is_above:\n",
-    "    print(\"=>   at least one number's square is above\", threshold)\n",
+    "    print(\"=>   at least one sample's square is above\", threshold)\n",
     "else:\n",
-    "    print(\"=>   no number's square is above\", threshold)"
+    "    print(\"=>   no sample's square is above\", threshold)"
    ]
   },
   {
@@ -5629,11 +5629,11 @@
     }
    },
    "source": [
-    "This implementation is *inefficient* as even if the *first* element in `numbers` has a square greater than `100`, we loop until the last element: This could take a long time for a big list.\n",
+    "This implementation is *inefficient* as even if the *first* element in `samples` has a square greater than `100`, we loop until the last element: This could take a long time for a big list.\n",
     "\n",
     "Moreover, we must initialize `is_above` *before* the `for`-loop and write an `if`-`else`-logic *after* it to check for the result. The actual business logic is *not* clear right away.\n",
     "\n",
-    "Luckily, Python provides a `break` [statement](https://docs.python.org/3/reference/simple_stmts.html#the-break-statement) that lets us stop the `for`-loop in any iteration of the loop. Conceptually, it is yet another means of controlling the flow of execution."
+    "Luckily, Python provides the `break` statement (cf., [reference](https://docs.python.org/3/reference/simple_stmts.html#the-break-statement)) that lets us stop the `for`-loop in any iteration of the loop. Conceptually, it is yet another means of controlling the flow of execution."
    ]
   },
   {
@@ -5649,23 +5649,23 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "3   7   2   9   11   =>   at least one number's square is above 100\n"
+      "3   7   2   9   11   =>   at least one sample's square is above 100\n"
      ]
     }
    ],
    "source": [
     "is_above = False\n",
     "\n",
-    "for number in numbers:\n",
-    "    print(number, end=\"   \")  # added for didactical purposes\n",
-    "    if number ** 2 > threshold:\n",
+    "for sample in samples:\n",
+    "    print(sample, end=\"   \")  # added for didactical purposes\n",
+    "    if sample ** 2 > threshold:\n",
     "        is_above = True\n",
     "        break\n",
     "\n",
     "if is_above:\n",
-    "    print(\"=>   at least one number's square is above\", threshold)\n",
+    "    print(\"=>   at least one sample's square is above\", threshold)\n",
     "else:\n",
-    "    print(\"=>   no number's square is above\", threshold)"
+    "    print(\"=>   no sample's square is above\", threshold)"
    ]
   },
   {
@@ -5687,7 +5687,7 @@
     }
    },
    "source": [
-    "### The `for`-`else` Clause"
+    "### The `for`-`else`-Clause"
    ]
   },
   {
@@ -5698,7 +5698,7 @@
     }
    },
    "source": [
-    "To express the logic in a prettier way, we add an `else`-clause at the end of the `for`-loop. The `else`-branch is executed *iff* the `for`-loop is *not* stopped with a `break` statement **prematurely** (i.e., *before* reaching the *last* iteration in the loop). The word \"else\" implies a somewhat unintuitive meaning and had better been named a `then`-clause. In most scenarios, however, the `else`-clause logically goes together with some `if` statement in the `for`-loop's body.\n",
+    "To express the logic in a prettier way, we add an `else`-clause at the end of the `for`-loop (cf., [reference](https://docs.python.org/3/reference/compound_stmts.html#the-for-statement)). The `else`-clause is executed *iff* the `for`-loop is *not* stopped with a `break` statement **prematurely** (i.e., *before* reaching the *last* iteration in the loop). The word \"else\" implies a somewhat unintuitive meaning and had better been named a `then`-clause. In most scenarios, however, the `else`-clause logically goes together with some `if` statement in the `for`-loop's body.\n",
     "\n",
     "Overall, the code's expressive power increases. Not many programming languages support a `for`-`else`-branching, which turns out to be very useful in practice."
    ]
@@ -5727,23 +5727,23 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "3   7   2   9   11   =>   at least one number's square is above 100\n"
+      "3   7   2   9   11   =>   at least one sample's square is above 100\n"
      ]
     }
    ],
    "source": [
-    "for number in numbers:\n",
-    "    print(number, end=\"   \")  # added for didactical purposes\n",
-    "    if number ** 2 > threshold:\n",
+    "for sample in samples:\n",
+    "    print(sample, end=\"   \")  # added for didactical purposes\n",
+    "    if sample ** 2 > threshold:\n",
     "        is_above = True\n",
     "        break\n",
     "else:\n",
     "    is_above = False\n",
     "\n",
     "if is_above:\n",
-    "    print(\"=>   at least one number's square is above\", threshold)\n",
+    "    print(\"=>   at least one sample's square is above\", threshold)\n",
     "else:\n",
-    "    print(\"=>   no number's square is above\", threshold)"
+    "    print(\"=>   no sample's square is above\", threshold)"
    ]
   },
   {
@@ -5770,18 +5770,18 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "3   7   2   9   11   =>   at least one number's square is above 100\n"
+      "3   7   2   9   11   =>   at least one sample's square is above 100\n"
      ]
     }
    ],
    "source": [
-    "for number in numbers:\n",
-    "    print(number, end=\"   \")  # added for didactical purposes\n",
-    "    if number ** 2 > threshold:\n",
-    "        print(\"=>   at least one number's square is above\", threshold)\n",
+    "for sample in samples:\n",
+    "    print(sample, end=\"   \")  # added for didactical purposes\n",
+    "    if sample ** 2 > threshold:\n",
+    "        print(\"=>   at least one sample's square is above\", threshold)\n",
     "        break\n",
     "else:\n",
-    "    print(\"=>   no number's square is above\", threshold)"
+    "    print(\"=>   no sample's square is above\", threshold)"
    ]
   },
   {
@@ -5792,7 +5792,7 @@
     }
    },
    "source": [
-    "Of course, if we set the `threshold` an element's square has to pass higher, for example, to `200`, we have to loop through the entire `numbers` list. There is *no way* to optimize this **[linear search](https://en.wikipedia.org/wiki/Linear_search)** further, at least as long as we model `numbers` as a `list` object. More advanced data types, however, exist that mitigate that downside."
+    "Of course, if we set the `threshold` an element's square has to pass higher, for example, to `200`, we have to loop over all `samples`. There is *no way* to optimize this **[linear search](https://en.wikipedia.org/wiki/Linear_search)** further, at least as long as we model `samples` as a `list` object. More advanced data types, however, exist that mitigate that downside."
    ]
   },
   {
@@ -5808,20 +5808,20 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "3   7   2   9   11   4   7   9   4   5   =>   no number's square is above 200\n"
+      "3   7   2   9   11   4   7   9   4   5   =>   no sample's square is above 200\n"
      ]
     }
    ],
    "source": [
     "threshold = 200\n",
     "\n",
-    "for number in numbers:\n",
-    "    print(number, end=\"   \")  # added for didactical purposes\n",
-    "    if number ** 2 > threshold:\n",
-    "        print(\"=>   at least one number's square is above\", threshold)\n",
+    "for sample in samples:\n",
+    "    print(sample, end=\"   \")  # added for didactical purposes\n",
+    "    if sample ** 2 > threshold:\n",
+    "        print(\"=>   at least one sample's square is above\", threshold)\n",
     "        break\n",
     "else:\n",
-    "    print(\"=>   no number's square is above\", threshold)"
+    "    print(\"=>   no sample's square is above\", threshold)"
    ]
   },
   {
@@ -5843,7 +5843,7 @@
     }
    },
    "source": [
-    "Often, we process some iterable with numeric data, for example, a list of numbers as in the introductory example in [Chapter 1](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/01_elements.ipynb#Example:-Average-of-a-Subset-of-Numbers) or, more realistically, data from a CSV file with many rows and columns.\n",
+    "Often, we process some iterable with numeric data, for example, a list of `numbers` as in this book's introductory example in [Chapter 1](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/01_elements.ipynb#Example:-Averaging-Even-Numbers) or, more realistically, data from a CSV file with many rows and columns.\n",
     "\n",
     "Processing numeric data usually comes down to operations that may be grouped into one of the following three categories:\n",
     "\n",
@@ -5875,7 +5875,7 @@
     }
    },
    "source": [
-    "Calculate the sum of all even numbers from $1$ through $12$ after squaring them and adding $1$ to the squares:\n",
+    "Calculate the sum of all even numbers in `[7, 11, 8, 5, 3, 12, 2, 6, 9, 10, 1, 4]` after squaring them and adding `1` to the squares:\n",
     "\n",
     "- **\"all\"** => loop over an iterable\n",
     "- **\"even\"** => *filter* out the odd numbers\n",
@@ -5893,7 +5893,7 @@
    },
    "outputs": [],
    "source": [
-    "numbers = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12]"
+    "numbers = [7, 11, 8, 5, 3, 12, 2, 6, 9, 10, 1, 4]"
    ]
   },
   {
@@ -5909,7 +5909,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "2 > 5   4 > 17   6 > 37   8 > 65   10 > 101   12 > 145   "
+      "8 > 65   12 > 145   2 > 5   6 > 37   10 > 101   4 > 17   "
      ]
     },
     {
@@ -5926,11 +5926,11 @@
    "source": [
     "total = 0\n",
     "\n",
-    "for x in numbers:\n",
-    "    if x % 2 == 0:  # only keep even numbers\n",
-    "        y = (x ** 2) + 1\n",
-    "        print(x, y, sep=\" > \", end=\"   \")  # added for didactical purposes\n",
-    "        total += y\n",
+    "for number in numbers:\n",
+    "    if number % 2 == 0:  # only keep even numbers\n",
+    "        square = (number ** 2) + 1\n",
+    "        print(number, square, sep=\" > \", end=\"   \")  # added for didactical purposes\n",
+    "        total += square\n",
     "\n",
     "total"
    ]
@@ -5969,7 +5969,7 @@
     }
    },
    "source": [
-    "Calculate the sum of every third and even number from $1$ through $12$ after squaring them and adding $1$ to the squares:\n",
+    "Calculate the sum of every third and even number in `[7, 11, 8, 5, 3, 12, 2, 6, 9, 10, 1, 4]` after squaring them and adding `1` to the squares:\n",
     "\n",
     "- **\"every\"** => loop over an iterable\n",
     "- **\"third\"** => *filter* out all numbers except every third\n",
@@ -5990,7 +5990,7 @@
     {
      "data": {
       "text/plain": [
-       "[1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12]"
+       "[7, 11, 8, 5, 3, 12, 2, 6, 9, 10, 1, 4]"
       ]
      },
      "execution_count": 78,
@@ -6015,13 +6015,13 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "6 > 37   12 > 145   "
+      "8 > 65   12 > 145   4 > 17   "
      ]
     },
     {
      "data": {
       "text/plain": [
-       "182"
+       "227"
       ]
      },
      "execution_count": 79,
@@ -6032,12 +6032,12 @@
    "source": [
     "total = 0\n",
     "\n",
-    "for i, x in enumerate(numbers, start=1):\n",
+    "for i, number in enumerate(numbers, start=1):\n",
     "    if i % 3 == 0:  # only keep every third number\n",
-    "        if x % 2 == 0:  # only keep even numbers\n",
-    "            y = (x ** 2) + 1\n",
-    "            print(x, y, sep=\" > \", end=\"   \")  # added for didactical purposes \n",
-    "            total += y\n",
+    "        if number % 2 == 0:  # only keep even numbers\n",
+    "            square = (number ** 2) + 1\n",
+    "            print(number, square, sep=\" > \", end=\"   \")  # added for didactical purposes \n",
+    "            total += square\n",
     "\n",
     "total"
    ]
@@ -6052,7 +6052,7 @@
    "source": [
     "With already three levels of indentation, less horizontal space is available for the actual code block. Of course, one could flatten the two `if` statements with the logical `and` operator, as shown in [Chapter 3](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/03_conditionals.ipynb#The-if-Statement). Then, however, we trade off horizontal space against a more \"complex\" `if` logic, and this is *not* a real improvement.\n",
     "\n",
-    "A Pythonista would instead make use of the `continue` [statement](https://docs.python.org/3/reference/simple_stmts.html#the-continue-statement) that causes a loop to jump into the next iteration skipping the rest of the code block.\n",
+    "A Pythonista would instead make use of the `continue` statement (cf., [reference](https://docs.python.org/3/reference/simple_stmts.html#the-continue-statement)) that causes a loop to jump into the next iteration skipping the rest of the code block.\n",
     "\n",
     "The revised code fragment below occupies more vertical space and less horizontal space: A *good* trade-off."
    ]
@@ -6081,13 +6081,13 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "6 > 37   12 > 145   "
+      "8 > 65   12 > 145   4 > 17   "
      ]
     },
     {
      "data": {
       "text/plain": [
-       "182"
+       "227"
       ]
      },
      "execution_count": 80,
@@ -6098,15 +6098,15 @@
    "source": [
     "total = 0\n",
     "\n",
-    "for i, x in enumerate(numbers, start=1):\n",
+    "for i, number in enumerate(numbers, start=1):\n",
     "    if i % 3 != 0:  # only keep every third number\n",
     "        continue\n",
-    "    elif x % 2 != 0:  # only keep even numbers\n",
+    "    elif number % 2 != 0:  # only keep even numbers\n",
     "        continue\n",
     "\n",
-    "    y = (x ** 2) + 1\n",
-    "    print(x, y, sep=\" > \", end=\"   \")  # added for didactical purposes \n",
-    "    total += y\n",
+    "    square = (number ** 2) + 1\n",
+    "    print(number, square, sep=\" > \", end=\"   \")  # added for didactical purposes \n",
+    "    total += square\n",
     "\n",
     "total"
    ]
diff --git a/05_numbers.ipynb b/05_numbers.ipynb
index c545cde..a671e68 100644
--- a/05_numbers.ipynb
+++ b/05_numbers.ipynb
@@ -21,7 +21,7 @@
    "source": [
     "After learning about the basic building blocks of expressing and structuring the business logic in programs, we focus our attention on the **data types** Python offers us, both built-in and available via the [standard library](https://docs.python.org/3/library/index.html) or third-party packages.\n",
     "\n",
-    "We start with the \"simple\" ones: Numerical types in this chapter and textual data in [Chapter 6](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/06_text.ipynb). An important fact that holds for all objects of these types is that they are **immutable**. To re-use the bag analogy from [Chapter 1](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/01_elements.ipynb#Objects-vs.-Types-vs.-Values), this means that the $0$s and $1$s making up an object's *value* cannot be changed once the bag is created in memory, implying that any operation with or method on the object creates a *new* object in a *different* memory location.\n",
+    "We start with the \"simple\" ones: Numeric types in this chapter and textual data in [Chapter 6](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/06_text.ipynb). An important fact that holds for all objects of these types is that they are **immutable**. To re-use the bag analogy from [Chapter 1](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/01_elements.ipynb#Objects-vs.-Types-vs.-Values), this means that the $0$s and $1$s making up an object's *value* cannot be changed once the bag is created in memory, implying that any operation with or method on the object creates a *new* object in a *different* memory location.\n",
     "\n",
     "[Chapter 7](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/07_sequences.ipynb) and Chapter 8 then cover the more \"complex\" data types, including, for example, the `list` type. Finally, Chapter 9 completes the picture by introducing language constructs to create custom types.\n",
     "\n",
@@ -31,7 +31,7 @@
     "- [Chapter 3](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/03_conditionals.ipynb#Boolean-Expressions) raises questions regarding the **limited precision** of `float` numbers (e.g., `42 == 42.000000000000001` evaluates to `True`), and\n",
     "- [Chapter 4](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/04_iteration.ipynb#Infinite-Recursion) shows that sometimes a `float` \"walks\" and \"quacks\" like an `int`, whereas the reverse is true in other cases.\n",
     "\n",
-    "This chapter introduces all the [built-in numerical types](https://docs.python.org/3/library/stdtypes.html#numeric-types-int-float-complex): `int`, `float`, and `complex`. To mitigate the limited precision of floating-point numbers, we also look at two replacements for the `float` type in the [standard library](https://docs.python.org/3/library/index.html), namely the `Decimal` type in the [decimals](https://docs.python.org/3/library/decimal.html#decimal.Decimal) and the `Fraction` type in the [fractions](https://docs.python.org/3/library/fractions.html#fractions.Fraction) module."
+    "This chapter introduces all the [built-in numeric types](https://docs.python.org/3/library/stdtypes.html#numeric-types-int-float-complex): `int`, `float`, and `complex`. To mitigate the limited precision of floating-point numbers, we also look at two replacements for the `float` type in the [standard library](https://docs.python.org/3/library/index.html), namely the `Decimal` type in the [decimals](https://docs.python.org/3/library/decimal.html#decimal.Decimal) and the `Fraction` type in the [fractions](https://docs.python.org/3/library/fractions.html#fractions.Fraction) module."
    ]
   },
   {
@@ -53,7 +53,7 @@
     }
    },
    "source": [
-    "The simplest numerical type is the `int` type: It behaves like an [integer in ordinary math](https://en.wikipedia.org/wiki/Integer) (i.e., the set $\\mathbb{Z}$) and supports operators in the way we saw in the section on arithmetic operators in [Chapter 1](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/01_elements.ipynb#%28Arithmetic%29-Operators)."
+    "The simplest numeric type is the `int` type: It behaves like an [integer in ordinary math](https://en.wikipedia.org/wiki/Integer) (i.e., the set $\\mathbb{Z}$) and supports operators in the way we saw in the section on arithmetic operators in [Chapter 1](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/01_elements.ipynb#%28Arithmetic%29-Operators)."
    ]
   },
   {
@@ -92,7 +92,7 @@
     {
      "data": {
       "text/plain": [
-       "139630197982416"
+       "140087541220560"
       ]
      },
      "execution_count": 2,
@@ -160,7 +160,7 @@
     }
    },
    "source": [
-    "A nice feature is using underscores `_` as (thousands) separator in numerical literals. For example, `1_000_000` evaluates to `1000000` in memory."
+    "A nice feature is using underscores `_` as (thousands) separator in numeric literals. For example, `1_000_000` evaluates to `1000000` in memory."
    ]
   },
   {
@@ -1165,7 +1165,7 @@
     }
    },
    "source": [
-    "Whereas the boolean literals `True` and `False` are commonly *not* regarded as numerical types, they behave like `1` and `0` in an arithmetic context."
+    "Whereas the boolean literals `True` and `False` are commonly *not* regarded as numeric types, they behave like `1` and `0` in an arithmetic context."
    ]
   },
   {
@@ -2115,7 +2115,7 @@
     {
      "data": {
       "text/plain": [
-       "139630198164096"
+       "140087541402072"
       ]
      },
      "execution_count": 63,
@@ -2218,7 +2218,7 @@
     }
    },
    "source": [
-    "In cases where the dot `.` is unnecessary from a mathematical point of view, we either need to end the number with it nevertheless or use the [float()](https://docs.python.org/3/library/functions.html#float) built-in to cast the number explicitly. [float()](https://docs.python.org/3/library/functions.html#float) can process any numerical object or a properly formatted `str` object."
+    "In cases where the dot `.` is unnecessary from a mathematical point of view, we either need to end the number with it nevertheless or use the [float()](https://docs.python.org/3/library/functions.html#float) built-in to cast the number explicitly. [float()](https://docs.python.org/3/library/functions.html#float) can process any numeric object or a properly formatted `str` object."
    ]
   },
   {
@@ -3517,7 +3517,7 @@
    "source": [
     "The [format()](https://docs.python.org/3/library/functions.html#format) function does *not* round a `float` object in the mathematical sense! It just allows us to show an arbitrary number of the digits as stored in memory, and it also does *not* change these.\n",
     "\n",
-    "On the contrary, the built-in [round()](https://docs.python.org/3/library/functions.html#round) function creates a *new* numerical object that is a rounded version of the one passed in as the argument. It adheres to the common rules of math.\n",
+    "On the contrary, the built-in [round()](https://docs.python.org/3/library/functions.html#round) function creates a *new* numeric object that is a rounded version of the one passed in as the argument. It adheres to the common rules of math.\n",
     "\n",
     "For example, let's round `1 / 3` to five decimals. The obtained value for `roughly_a_third` is also *imprecise* but different from the \"exact\" representation of `1 / 3` above."
    ]
@@ -4617,7 +4617,7 @@
     }
    },
    "source": [
-    "The [quantize()](https://docs.python.org/3/library/decimal.html#decimal.Decimal.quantize) method allows us to [quantize](https://www.dictionary.com/browse/quantize) (i.e., \"round\") a `Decimal` number at any precision that is *smaller* than the set precision. It looks at the number of decimals (i.e., to the right of the period) of the numerical argument we pass in.\n",
+    "The [quantize()](https://docs.python.org/3/library/decimal.html#decimal.Decimal.quantize) method allows us to [quantize](https://www.dictionary.com/browse/quantize) (i.e., \"round\") a `Decimal` number at any precision that is *smaller* than the set precision. It looks at the number of decimals (i.e., to the right of the period) of the numeric argument we pass in.\n",
     "\n",
     "For example, as the overall imprecise value of `two` still has an internal precision of `28` digits, we can correctly round it to *four* decimals (i.e., `Decimal(\"0.0001\")` has four decimals)."
    ]
@@ -5498,7 +5498,7 @@
     {
      "data": {
       "text/plain": [
-       "139630197402224"
+       "140087540555856"
       ]
      },
      "execution_count": 176,
@@ -5636,7 +5636,7 @@
     }
    },
    "source": [
-    "Alternatively, we may use the [complex()](https://docs.python.org/3/library/functions.html#complex) built-in: This takes two parameters where the second is optional and defaults to `0`. We may either call it with one or two arguments of any numerical type or a `str` object in the format of the previous code cell without any spaces."
+    "Alternatively, we may use the [complex()](https://docs.python.org/3/library/functions.html#complex) built-in: This takes two parameters where the second is optional and defaults to `0`. We may either call it with one or two arguments of any numeric type or a `str` object in the format of the previous code cell without any spaces."
    ]
   },
   {
@@ -5708,7 +5708,7 @@
     }
    ],
    "source": [
-    "complex(Decimal(\"2.0\"), Fraction(1, 2))  # the arguments may be any numerical type"
+    "complex(Decimal(\"2.0\"), Fraction(1, 2))  # the arguments may be any numeric type"
    ]
   },
   {
@@ -5743,7 +5743,7 @@
     }
    },
    "source": [
-    "Arithmetic expressions work with `complex` numbers. They may be mixed with the other numerical types, and the result is always a `complex` number."
+    "Arithmetic expressions work with `complex` numbers. They may be mixed with the other numeric types, and the result is always a `complex` number."
    ]
   },
   {
@@ -6111,7 +6111,7 @@
     }
    },
    "source": [
-    "Analogous to the discussion of containers and iterables in [Chapter 4](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/04_iteration.ipynb#Containers-vs.-Iterables), we contrast the *concrete* numerical data types in this chapter with the *abstract* ideas behind [numbers in mathematics](https://en.wikipedia.org/wiki/Number).\n",
+    "Analogous to the discussion of containers and iterables in [Chapter 4](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/04_iteration.ipynb#Containers-vs.-Iterables), we contrast the *concrete* numeric data types in this chapter with the *abstract* ideas behind [numbers in mathematics](https://en.wikipedia.org/wiki/Number).\n",
     "\n",
     "The figure below summarizes five *major* sets of [numbers in mathematics](https://en.wikipedia.org/wiki/Number) as we know them from high school:\n",
     "\n",
@@ -6723,7 +6723,7 @@
     }
    },
    "source": [
-    "Replacing *concrete* data types with *abstract* ones is particularly valuable in the context of input validation: The revised version of the `factorial()` function below allows its user to take advantage of *duck typing*: If a real but non-integer argument `n` is passed in, `factorial()` tries to cast `n` as an `int` object with the [trunc()](https://docs.python.org/3/library/math.html#math.trunc) function from the [math](https://docs.python.org/3/library/math.html) module in the [standard library](https://docs.python.org/3/library/index.html). [trunc()](https://docs.python.org/3/library/math.html#math.trunc) cuts off all decimals and any *concrete* numerical type implementing the *abstract* `numbers.Real` type supports it (cf., [documentation](https://docs.python.org/3/library/numbers.html#numbers.Real)).\n",
+    "Replacing *concrete* data types with *abstract* ones is particularly valuable in the context of input validation: The revised version of the `factorial()` function below allows its user to take advantage of *duck typing*: If a real but non-integer argument `n` is passed in, `factorial()` tries to cast `n` as an `int` object with the [trunc()](https://docs.python.org/3/library/math.html#math.trunc) function from the [math](https://docs.python.org/3/library/math.html) module in the [standard library](https://docs.python.org/3/library/index.html). [trunc()](https://docs.python.org/3/library/math.html#math.trunc) cuts off all decimals and any *concrete* numeric type implementing the *abstract* `numbers.Real` type supports it (cf., [documentation](https://docs.python.org/3/library/numbers.html#numbers.Real)).\n",
     "\n",
     "Two popular and distinguished Pythonistas, [Luciano Ramalho](https://github.com/ramalho) and [Alex Martelli](https://en.wikipedia.org/wiki/Alex_Martelli), coin the term **goose typing** to specifically mean using the built-in [isinstance()](https://docs.python.org/3/library/functions.html#isinstance) function with an *abstract base class* (cf., Chapter 11 in this [book](https://www.amazon.com/Fluent-Python-Concise-Effective-Programming/dp/1491946008) or this [summary](https://dgkim5360.github.io/blog/python/2017/07/duck-typing-vs-goose-typing-pythonic-interfaces/) thereof)."
    ]
@@ -7015,7 +7015,7 @@
     }
    },
    "source": [
-    "There exist three numerical types in core Python:\n",
+    "There exist three numeric types in core Python:\n",
     "- `int`: a near-perfect model for whole numbers (i.e., the set $\\mathbb{Z}$); inherently precise\n",
     "- `float`: the \"gold\" standard to approximate real numbers (i.e., the set $\\mathbb{R}$); inherently imprecise\n",
     "- `complex`: layer on top of the `float` type; therefore inherently imprecise\n",
diff --git a/06_text.ipynb b/06_text.ipynb
index 9092188..44eaa3c 100644
--- a/06_text.ipynb
+++ b/06_text.ipynb
@@ -19,7 +19,7 @@
     }
    },
    "source": [
-    "In this chapter, we continue the study of the built-in data types. Building on our knowledge of numbers, the next layer consists of textual data that are modeled primarily with the `str` type in Python. `str` objects are naturally more \"complex\" than numerical objects as any text consists of an arbitrary and possibly large number of individual characters that may be chosen from any alphabet in the history of humankind. Luckily, Python abstracts away most of this complexity."
+    "In this chapter, we continue the study of the built-in data types. Building on our knowledge of numbers, the next layer consists of textual data that are modeled primarily with the `str` type in Python. `str` objects are naturally more \"complex\" than numeric objects as any text consists of an arbitrary and possibly large number of individual characters that may be chosen from any alphabet in the history of humankind. Luckily, Python abstracts away most of this complexity."
    ]
   },
   {
@@ -80,7 +80,7 @@
     {
      "data": {
       "text/plain": [
-       "140133793916176"
+       "140488988107952"
       ]
      },
      "execution_count": 2,
@@ -737,14 +737,14 @@
     }
    },
    "source": [
-    "The idea of a **sequence** is yet another *abstract* concept.\n",
+    "A **sequence** is yet another *abstract* concept (cf., *containers* and *iterables* in [Chapter 4](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/04_iteration.ipynb#Containers-vs.-Iterables)).\n",
     "\n",
-    "It unifies *four* [orthogonal](https://en.wikipedia.org/wiki/Orthogonality) (i.e., \"independent\") *abstract* concepts into one: Any *concrete* data type, such as `str`, is considered a sequence if it simultaneously\n",
+    "It unifies *four* [orthogonal](https://en.wikipedia.org/wiki/Orthogonality) (i.e., \"independent\") behaviors into one idea: Any data type, such as `str`, is considered a sequence if it simultaneously\n",
     "\n",
     "1. **contains** other \"things,\"\n",
     "2. is **iterable**, and \n",
     "3. comes with a *predictable* **order** of its\n",
-    "4. **finite** number of elements.\n",
+    "4. **finite** number of \"things.\"\n",
     "\n",
     "[Chapter 7](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/07_sequences.ipynb#Collections-vs.-Sequences) formalizes sequences in great detail. Here, we keep our focus on the `str` type that historically received its name as it models a \"**[string of characters](https://en.wikipedia.org/wiki/String_%28computer_science%29)**,\" and a \"string\" is more formally called a sequence in the computer science literature.\n",
     "\n",
@@ -1441,7 +1441,7 @@
     }
    },
    "source": [
-    "Whereas elements of a `list` object *may* be *re-assigned*, as shortly hinted at in [Chapter 1](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/01_elements.ipynb#Who-am-I?-And-how-many?), this is *not* allowed for `str` objects. Once created, they *cannot* be *changed*. Formally, we say that they are **immutable**. In that regard, `str` objects and all the numerical types in [Chapter 5](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/05_numbers.ipynb) are alike.\n",
+    "Whereas elements of a `list` object *may* be *re-assigned*, as shortly hinted at in [Chapter 1](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/01_elements.ipynb#Who-am-I?-And-how-many?), this is *not* allowed for `str` objects. Once created, they *cannot* be *changed*. Formally, we say that they are **immutable**. In that regard, `str` objects and all the numeric types in [Chapter 5](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/05_numbers.ipynb) are alike.\n",
     "\n",
     "On the contrary, objects that may be changed after creation, are called **mutable**. We already saw in [Chapter 1](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/01_elements.ipynb#Who-am-I?-And-how-many?) how mutable objects are more difficult to reason about for a beginner, in particular, if more than *one* variable point to one. Yet, mutability does have its place in a programmer's toolbox, and we revisit this idea in the next chapters.\n",
     "\n",
@@ -1533,7 +1533,7 @@
     {
      "data": {
       "text/plain": [
-       "140133784610832"
+       "140488987187120"
       ]
      },
      "execution_count": 46,
@@ -1557,7 +1557,7 @@
     {
      "data": {
       "text/plain": [
-       "140133793916176"
+       "140488988107952"
       ]
      },
      "execution_count": 47,
@@ -1972,7 +1972,7 @@
     {
      "data": {
       "text/plain": [
-       "140133891261864"
+       "140489068668384"
       ]
      },
      "execution_count": 61,
@@ -2009,7 +2009,7 @@
     {
      "data": {
       "text/plain": [
-       "140133784910848"
+       "140488987340848"
       ]
      },
      "execution_count": 63,

From 3cab7a329d379cac354abe901de011f018f4c722 Mon Sep 17 00:00:00 2001
From: Alexander Hess <alexander@webartifex.biz>
Date: Wed, 6 Nov 2019 11:10:46 +0100
Subject: [PATCH 043/242] Add note why to use python -m pip ...

---
 README.md | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/README.md b/README.md
index 3837323..d2f8d4b 100644
--- a/README.md
+++ b/README.md
@@ -126,8 +126,8 @@ are popular tools to automate the described management of virtual environments.
 After activation for the first time, you must install the project's
 **dependencies** (= the third-party packages needed to run the code), most
 notably [Jupyter](https://pypi.org/project/jupyter/) in this project (the
-"python -m" is often left out; if you have poetry installed, you may just
-type `poetry install` instead).
+"python -m" is often left out [but should not be](https://snarky.ca/why-you-should-use-python-m-pip/);
+if you have poetry installed, you may just type `poetry install` instead).
 
 - `python -m pip install -r requirements.txt`
 

From 8c9af64b4e5fbfb787edf756bbc7494e1ed680ec Mon Sep 17 00:00:00 2001
From: Alexander Hess <alexander@webartifex.biz>
Date: Wed, 6 Nov 2019 16:19:16 +0100
Subject: [PATCH 044/242] Add review and exercises for notebook 07

---
 07_sequences_review_and_exercises.ipynb | 389 ++++++++++++++++++++++++
 1 file changed, 389 insertions(+)
 create mode 100644 07_sequences_review_and_exercises.ipynb

diff --git a/07_sequences_review_and_exercises.ipynb b/07_sequences_review_and_exercises.ipynb
new file mode 100644
index 0000000..17e1ffc
--- /dev/null
+++ b/07_sequences_review_and_exercises.ipynb
@@ -0,0 +1,389 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n",
+    "# Chapter 7: Sequential Data"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Content Review"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Read [Chapter 7](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/07_sequences.ipynb) of the book. Then work through the ten review questions."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Essay Questions "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Answer the following questions briefly with *at most* 300 characters per question!"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q1**: We have seen **containers** and **iterables** before in [Chapter 4](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/04_iteration.ipynb#Containers-vs.-Iterables). How do they relate to **sequences**? "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q2**: Explain the difference between a **mutable** and an **immutable** object in Python with an example!"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q3**: What is the difference between a **shallow** and a **deep** copy of an object? How can one of them become a problem?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q4.1**: `tuple` objects have *two* primary usages. First, they can be used in place of `list` objects where **mutability** is *not* required. Second, we use them to model **records**.\n",
+    "\n",
+    "Describe why `tuple` objects are a suitable replacement for `list` objects in general!"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q4.2**: What do we mean by a **record**? How are `tuple` objects suitable to model records? How can we integrate a **semantic** meaning when working with records?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q5**: With the [map()](https://docs.python.org/3/library/functions.html#map) and [filter()](https://docs.python.org/3/library/functions.html#filter) built-ins and the [reduce()](https://docs.python.org/3/library/functools.html#functools.reduce) function from the [functools](https://docs.python.org/3/library/functools.html) module in the [standard library](https://docs.python.org/3/library/index.html), we can replace many tedious `for`-loops and `if` statements. What are some advantages of doing so?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### True / False Questions"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Motivate your answer with *one short* sentence!"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q6**: `sequence` objects are *not* part of core Python but may be imported from the [standard library](https://docs.python.org/3/library/index.html)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q7**: Passing **mutable** objects as arguments to functions is not problematic because functions operate in a **local** scope without affecting the **global** scope."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q8**: The **map-filter-reduce** paradigm is an excellent mental concept to organize one's code with. Then, there is a good chance that a program can be **parallelized** if the data input grows."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q9**: `lambda` expressions are useful in the context of the **map-filter-reduce** paradigm, where we often do *not* re-use a `function` object more than once."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Coding Exercises"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Working with Lists"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q10.1**: Write a function `nested_sum()` that takes a `list` object as its argument, which contains other `list` objects with numbers, and adds up the numbers! Use `nested_numbers` below to test your function!\n",
+    "\n",
+    "Hint: You need at least one `for`-loop."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "nested_numbers = [[1, 2, 3], [4], [5], [6, 7], [8], [9]]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def nested_sum():\n",
+    "    ..."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "nested_sum(nested_numbers)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q10.2**: Provide a one-line expression to obtain the *same* result as `nested_sum()`!\n",
+    "\n",
+    "Hints: Use a *list comprehension*. You may want to use the built-in [sum()](https://docs.python.org/3/library/functions.html#sum) function several times."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q10.3**: Generalize `nested_sum()` into a function `mixed_sum()` that can process a \"mixed\" `list` object, which contains numbers and other `list` objects with numbers! Use `mixed_numbers` below for testing!\n",
+    "\n",
+    "Hints: Use the built-in [isinstance()](https://docs.python.org/3/library/functions.html#isinstance) function to check how an element is to be processed. Get extra credit for adhering to *goose typing*, as explained in [Chapter 5](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/05_numbers.ipynb#Goose-Typing)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "mixed_numbers = [[1, 2, 3], 4, 5, [6, 7], 8, [9]]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def mixed_sum():\n",
+    "    ..."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "mixed_sum(nums)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q10.4.1**: Write a function `cum_sum()` that takes a `list` object with numbers as its argument and returns a *new* `list` object with the **cumulative sums** of these numbers! So, `sum_up` below, `[1, 2, 3, 4, 5]`, should return `[1, 3, 6, 10, 15]`.\n",
+    "\n",
+    "Hint: The idea behind is similar to the [cumulative distribution function](https://en.wikipedia.org/wiki/Cumulative_distribution_function) from statistics."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sum_up = [1, 2, 3, 4, 5]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def cum_sum():\n",
+    "    ..."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "cum_sum(sum_up)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q10.4.2**: We should always make sure that our functions also work in corner cases. What happens if your implementation of `cum_sum()` is called with an empty list `[]`? Make sure it handles that case *without* crashing! What would be a good return value in this corner case? Describe everything in the docstring.\n",
+    "\n",
+    "Hint: It is possible to write this without any extra input validation."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "cum_sum([])"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.3"
+  },
+  "toc": {
+   "base_numbering": 1,
+   "nav_menu": {},
+   "number_sections": false,
+   "sideBar": true,
+   "skip_h1_title": true,
+   "title_cell": "Table of Contents",
+   "title_sidebar": "Contents",
+   "toc_cell": false,
+   "toc_position": {},
+   "toc_section_display": false,
+   "toc_window_display": false
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

From 16194990b94935ed7024207dfc66e42eafa39d8d Mon Sep 17 00:00:00 2001
From: Alexander Hess <alexander@webartifex.biz>
Date: Wed, 6 Nov 2019 16:27:13 +0100
Subject: [PATCH 045/242] Bump version number

---
 pyproject.toml | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/pyproject.toml b/pyproject.toml
index c2a9bdc..333c176 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -1,6 +1,6 @@
 [tool.poetry]
 name = "intro-to-python"
-version = "0.4.0"
+version = "0.5.0"
 description = "An introduction to Python and programming for wanna-be data scientists"
 authors = ["Alexander Hess <alexander@webartifex.biz>"]
 license = "MIT"

From 675dbc43b2fad74a6019493e65c5ba28680c0033 Mon Sep 17 00:00:00 2001
From: Alexander Hess <alexander@webartifex.biz>
Date: Wed, 20 Nov 2019 10:58:01 +0100
Subject: [PATCH 046/242] Adjust content overview in README.md

---
 README.md | 1 +
 1 file changed, 1 insertion(+)

diff --git a/README.md b/README.md
index d2f8d4b..4da6b2f 100644
--- a/README.md
+++ b/README.md
@@ -22,6 +22,7 @@ As such they can be viewed in a plain web browser:
 - [05 - Bits & Numbers](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/05_numbers.ipynb)
 - [06 - Bytes & Text](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/06_text.ipynb)
 - [07 - Sequential Data](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/07_sequences.ipynb)
+- [08 - Mappings & Sets](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/08_mappings.ipynb)
 
 However, it is recommended that students **install Python and Jupyter
 locally** and run the code in the notebooks on their own.

From fe138176058c9fbcb6fe85040421f46ceb267867 Mon Sep 17 00:00:00 2001
From: Alexander Hess <alexander@webartifex.biz>
Date: Wed, 20 Nov 2019 10:58:54 +0100
Subject: [PATCH 047/242] Add note for lecture in spring term 2020

---
 README.md | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/README.md b/README.md
index 4da6b2f..97c9120 100644
--- a/README.md
+++ b/README.md
@@ -146,6 +146,7 @@ This opens a new tab in your web browser just as above.
 Alexander Hess is a PhD student at the Chair of Logistics Management at the
 [WHU - Otto Beisheim School of Management](https://www.whu.edu) where he
 conducts research on urban delivery platforms and teaches an introductory
-course on Python (cf., [course listing](https://vlv.whu.edu/campus/all/event.asp?objgguid=0xE57C2715B01B441AAFD3E79AA05CACCF&from=vvz&gguid=0x6A2B0ED5B2B949E69957A2099E7DE2F1&mode=own&tguid=0x3980A9BBC3BF4A638E977F2DC163F44B&lang=en)).
+course on Python (cf., [Fall Term 2019](https://vlv.whu.edu/campus/all/event.asp?objgguid=0xE57C2715B01B441AAFD3E79AA05CACCF&from=vvz&gguid=0x6A2B0ED5B2B949E69957A2099E7DE2F1&mode=own&tguid=0x3980A9BBC3BF4A638E977F2DC163F44B&lang=en),
+[Spring Term 2020](https://vlv.whu.edu/campus/all/event.asp?objgguid=0x3354F4C108FF4E959CDD692A325D9AFE&from=vvz&gguid=0x262E29795DD742CFBDE72B12B69CEFD6&mode=own&lang=en&tguid=0x2E4A7D1FF3C34AD08FF07685461781C9)).
 
 Connect him on [LinkedIn](https://www.linkedin.com/in/webartifex).

From ca8fefdce19c731cc25e6fe498394a7991076a81 Mon Sep 17 00:00:00 2001
From: Alexander Hess <alexander@webartifex.biz>
Date: Wed, 20 Nov 2019 11:00:24 +0100
Subject: [PATCH 048/242] Streamline previous content

---
 00_start_up.ipynb                          |    8 +-
 01_elements.ipynb                          |   97 +-
 01_elements_review_and_exercises.ipynb     |    4 +-
 02_functions.ipynb                         |   58 +-
 02_functions_review_and_exercises.ipynb    |   58 +-
 03_conditionals.ipynb                      |   12 +-
 03_conditionals_review_and_exercises.ipynb |   30 +-
 04_iteration.ipynb                         |  160 +-
 04_iteration_review_and_exercises.ipynb    |   23 +-
 05_numbers.ipynb                           |  456 ++-
 05_numbers_review_and_exercises.ipynb      |   22 +-
 06_text.ipynb                              |  660 ++--
 07_sequences.ipynb                         | 3920 ++++++++++++++------
 13 files changed, 3595 insertions(+), 1913 deletions(-)

diff --git a/00_start_up.ipynb b/00_start_up.ipynb
index 97fe84b..9aed12d 100644
--- a/00_start_up.ipynb
+++ b/00_start_up.ipynb
@@ -585,7 +585,7 @@
     "  - replaced the infamous course on the [Scheme](https://groups.csail.mit.edu/mac/projects/scheme/) language (cf., [source](https://news.ycombinator.com/item?id=602307))\n",
     "- **[Google](https://www.google.com/)**\n",
     "  - used the strategy **\"Python where we can, C++ where we must\"** from its early days on to stay flexible in a rapidly changing environment (cf., [source](https://stackoverflow.com/questions/2560310/heavy-usage-of-python-at-google))\n",
-    "  - the very first web-crawler was written in **Java and so difficult to maintain** that it was **re-written in Python** right away (cf., [source](https://www.amazon.com/Plex-Google-Thinks-Works-Shapes/dp/1416596585/ref=sr_1_1?ie=UTF8&qid=1539101827&sr=8-1&keywords=in+the+plex))\n",
+    "  - the very first web-crawler was written in **Java and so difficult to maintain** that it was **rewritten in Python** right away (cf., [source](https://www.amazon.com/Plex-Google-Thinks-Works-Shapes/dp/1416596585/ref=sr_1_1?ie=UTF8&qid=1539101827&sr=8-1&keywords=in+the+plex))\n",
     "  - Python and C++, Java, and Go are the only four server-side languages to be deployed to production\n",
     "  - Guido van Rossom was hired by Google from 2005 to 2012 to advance the language there\n",
     "- **[NASA](https://www.nasa.gov/)** open-sources many of its projects, often written in Python and regarding analyses with big data (cf., [source](https://code.nasa.gov/language/python/))\n",
@@ -605,7 +605,7 @@
     }
    },
    "source": [
-    "As images tell more than words, here are two plots of popular languages' \"market shares\" based on the number of questions asked on [Stack Overflow](https://stackoverflow.blog/2017/09/06/incredible-growth-python/), the most relevant platform for answering programming related questions: As of late 2017, Python surpassed [Java](https://www.java.com/en/), heavily used in big corporates, and [JavaScript](https://developer.mozilla.org/en-US/docs/Web/JavaScript), the \"language of the internet\" that does everything in web browsers, in popularity. Two blog posts from \"technical\" people explain this in more depth to the layman: [Stack Overflow](https://stackoverflow.blog/2017/09/14/python-growing-quickly/) and [DataCamp](https://www.datacamp.com/community/blog/python-scientific-computing-case)."
+    "As images tell more than words, here are two plots of popular languages' \"market shares\" based on the number of questions asked on [Stack Overflow](https://stackoverflow.blog/2017/09/06/incredible-growth-python/), the most relevant platform for answering programming-related questions: As of late 2017, Python surpassed [Java](https://www.java.com/en/), heavily used in big corporates, and [JavaScript](https://developer.mozilla.org/en-US/docs/Web/JavaScript), the \"language of the internet\" that does everything in web browsers, in popularity. Two blog posts from \"technical\" people explain this in more depth to the layman: [Stack Overflow](https://stackoverflow.blog/2017/09/14/python-growing-quickly/) and [DataCamp](https://www.datacamp.com/community/blog/python-scientific-computing-case)."
    ]
   },
   {
@@ -715,7 +715,7 @@
     "- **Managers**: People that need to organize things and command others (like a \"boss\"). Their schedule is usually organized by the hour or even 30-minute intervals.\n",
     "- **Makers**: People that create things (like programmers, artists, or writers). Such people think in half days or full days.\n",
     "\n",
-    "Have you ever wondered why so many tech people work during nights and sleep at \"weird\" times? The reason is that many programming related tasks require a \"flow\" state in one's mind that is hard to achieve when one can get interrupted, even if it is only for one short question. Graham describes that only knowing that one has an appointment in three hours can cause a programmer to not get into a flow state.\n",
+    "Have you ever wondered why so many tech people work during nights and sleep at \"weird\" times? The reason is that many programming-related tasks require a \"flow\" state in one's mind that is hard to achieve when one can get interrupted, even if it is only for one short question. Graham describes that only knowing that one has an appointment in three hours can cause a programmer to not get into a flow state.\n",
     "\n",
     "As a result, do not set aside a certain amount of time for learning something but rather plan in an **entire evening** or a **rainy Sunday** where you can work on a problem in an **open end** setting. And do not be surprised anymore to hear \"I looked at it over the weekend\" from a programmer."
    ]
@@ -795,7 +795,7 @@
     "  5. [Bits & Numbers](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/05_numbers.ipynb)\n",
     "  6. [Bytes & Text](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/06_text.ipynb)\n",
     "  7. [Sequential Data](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/07_sequences.ipynb)\n",
-    "  8. Mappings & Sets\n",
+    "  8. [Mappings & Sets](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/08_mappings.ipynb)\n",
     "- How can we create custom data types?\n",
     "  9. Object-Orientation"
    ]
diff --git a/01_elements.ipynb b/01_elements.ipynb
index fed6458..07d6774 100644
--- a/01_elements.ipynb
+++ b/01_elements.ipynb
@@ -48,7 +48,7 @@
    "source": [
     "As our introductory example, we want to calculate the *average* of all *evens* in a **list** of numbers: `[7, 11, 8, 5, 3, 12, 2, 6, 9, 10, 1, 4]`.\n",
     "\n",
-    "While we could try to develop an [analytical solution](https://math.stackexchange.com/questions/935405/what-s-the-difference-between-analytical-and-numerical-approaches-to-problems/935446#935446) (i.e., derive some equation with \"pen and paper\"), we solve the task programmatically instead.\n",
+    "While we are used to finding an [analytical solution](https://math.stackexchange.com/questions/935405/what-s-the-difference-between-analytical-and-numerical-approaches-to-problems/935446#935446) in math (i.e., derive some equation with \"pen and paper\"), we solve this task *programmatically* instead.\n",
     "\n",
     "We start by creating a list called `numbers` that holds all the individual numbers between **brackets** `[` and `]`."
    ]
@@ -114,7 +114,7 @@
     "\n",
     "Intuitively, the line `for number in numbers` describes a \"loop\" over all the numbers in the `numbers` list, one at a time.\n",
     "\n",
-    "The `if number % 2 == 0` may look disturbing at first sight. Both the `%` and `==` must have an unintuitive meaning here. Luckily, the **comment** in the same line after the `#` symbol has the answer: The program only does something if the current `number` is even.\n",
+    "The `if number % 2 == 0` may look confusing at first sight. Both `%` and `==` must have an unintuitive meaning here. Luckily, the **comment** in the same line after the `#` symbol has the answer: The program does something only for an even `number`.\n",
     "\n",
     "In particular, it increases `count` by `1` and adds the current `number` onto the [running](https://en.wikipedia.org/wiki/Running_total) `total`. Both `count` and `number` are initially set to `0` and the single `=` symbol reads as \"... is *set* equal to ...\". It could not indicate a mathematical equation as, for example, `count` is generally not equal to `count + 1`.\n",
     "\n",
@@ -200,7 +200,7 @@
    "source": [
     "Note how only two of the previous four code cells generate an **output** while two remained \"silent\" (i.e., there is no \"**Out[...]**\" after running the cell).\n",
     "\n",
-    "By default, Jupyter notebooks show the value of a cell's last **expression**. This output can be suppressed by ending the last line with a semicolon `;`."
+    "By default, Jupyter notebooks show the value of the **expression** in the last line of a code cell only. This output can be suppressed by ending the last line with a semicolon `;`."
    ]
   },
   {
@@ -249,7 +249,7 @@
     }
    },
    "source": [
-    "To visualize something before the end of the cell, we use the built-in [print()](https://docs.python.org/3/library/functions.html#print) **function**. Here, the parentheses `()` indicate that we execute code defined somewhere else."
+    "To visualize something before the end of the cell, we use the built-in [print()](https://docs.python.org/3/library/functions.html#print) **function**. Here, the parentheses `()` indicate that we execute code written somewhere else."
    ]
   },
   {
@@ -331,9 +331,9 @@
     "\n",
     "The arithmetic operators either \"operate\" with the number immediately following them (= **unary** operators; e.g., negation) or \"process\" the two numbers \"around\" them (= **binary** operators; e.g., addition).\n",
     "\n",
-    "By definition, operators have **no** permanent **side effects** in the computer's memory. Although the code cells in this section do indeed create *new* numbers in memory (e.g., `77 + 13` creates `90`), they are immediately \"forgotten\" as they are not stored in a **variable** like `numbers` or `average` above. We continue this thought further below when we compare **expressions** with **statements**.\n",
+    "By definition, operators have **no** permanent **side effects** in the computer's memory. Although the code cells in this section do indeed create *new* numbers in memory (e.g., `77 + 13` creates `90`), they are immediately \"forgotten\" as they are not stored in a **variable** like `numbers` or `average` above. We develop this thought further at the end of this chapter when we compare **expressions** with **statements**.\n",
     "\n",
-    "Let's see some examples of operators. We start with the binary `+` and the `-` operators for addition and subtraction. Binary operators are designed to resemble what mathematicians call [infix notation](https://en.wikipedia.org/wiki/Infix_notation) and have the expected meaning."
+    "Let's see some examples of operators. We start with the binary `+` and the `-` operators for addition and subtraction. Binary operators mimic what mathematicians call [infix notation](https://en.wikipedia.org/wiki/Infix_notation) and have the expected meaning."
    ]
   },
   {
@@ -392,7 +392,7 @@
     }
    },
    "source": [
-    "The `-` operator is used as a unary operator as well. Then it just flips the sign of a number."
+    "The `-` operator may be used as a unary operator as well. Then it just flips the sign of a number."
    ]
   },
   {
@@ -617,7 +617,9 @@
    "source": [
     "The remainder is `0` *only if* a number is *divisible* by another.\n",
     "\n",
-    "A popular convention in both, computer science and mathematics, is to abbreviate \"only if\" as **iff**, which is short for \"**[if and only if](https://en.wikipedia.org/wiki/If_and_only_if)**.\" The iff means that a remainder of `0` implies that a number is divisible by another but also that a number divisible by another implies a remainder of `0`. The implication goes in *both* directions!"
+    "A popular convention in both, computer science and mathematics, is to abbreviate \"only if\" as **iff**, which is short for \"**[if and only if](https://en.wikipedia.org/wiki/If_and_only_if)**.\" The iff means that a remainder of `0` implies that a number is divisible by another but also that a number divisible by another implies a remainder of `0`. The implication goes in *both* directions!\n",
+    "\n",
+    "So, `49` is divisible by `7`."
    ]
   },
   {
@@ -652,7 +654,7 @@
     }
    },
    "source": [
-    "Modulo division is also useful if we, for example, need to get the last couple of digits of a large integer."
+    "Modulo division is also useful if we want to extract the last couple of digits in a large integer."
    ]
   },
   {
@@ -711,7 +713,7 @@
     }
    },
    "source": [
-    "The [divmod()](https://docs.python.org/3/library/functions.html#divmod) built-in function combines the integer and modulo divisions into one operation. However, this is not an operator but a function. Also, [divmod()](https://docs.python.org/3/library/functions.html#divmod) returns a \"pair\" of integers and not just one object."
+    "The built-in [divmod()](https://docs.python.org/3/library/functions.html#divmod) function combines the integer and modulo divisions into one operation. However, this is not an operator but a function. Also, [divmod()](https://docs.python.org/3/library/functions.html#divmod) returns a \"pair\" of integers and not just one."
    ]
   },
   {
@@ -781,7 +783,7 @@
     }
    },
    "source": [
-    "The standard [order of precedence](https://docs.python.org/3/reference/expressions.html#operator-precedence) from mathematics applies (i.e., \"PEMDAS\" rule)."
+    "The standard [order of precedence](https://docs.python.org/3/reference/expressions.html#operator-precedence) from mathematics applies (i.e., [PEMDAS](http://mathworld.wolfram.com/PEMDAS.html) rule) when several operators are combined."
    ]
   },
   {
@@ -988,7 +990,7 @@
     {
      "data": {
       "text/plain": [
-       "140706351197104"
+       "140398796190992"
       ]
      },
      "execution_count": 28,
@@ -1012,7 +1014,7 @@
     {
      "data": {
       "text/plain": [
-       "140706351380256"
+       "140398796375888"
       ]
      },
      "execution_count": 29,
@@ -1036,7 +1038,7 @@
     {
      "data": {
       "text/plain": [
-       "140706351003760"
+       "140398796056176"
       ]
      },
      "execution_count": 30,
@@ -1056,7 +1058,7 @@
     }
    },
    "source": [
-    "These addresses are *not* meaningful for anything other than checking if two variables **point** at the same object. Let's create a second variable `d` and also set it to `789`."
+    "These addresses are *not* meaningful for anything other than checking if two variables reference the *same* object. Let's create a second variable `d` and also set it to `789`."
    ]
   },
   {
@@ -1507,7 +1509,7 @@
     }
    },
    "source": [
-    "In this book, we follow the convention of creating strings with **double quotes** `\"` instead of the **single quotes** `'` to which Python defaults in its literal output for `str` objects. Both types of quotes may be used interchangeably."
+    "In this book, we follow the convention of creating strings with **double quotes** `\"` instead of the **single quotes** `'` to which Python defaults in its *literal* notation for `str` objects. Both types of quotes may be used interchangeably."
    ]
   },
   {
@@ -1825,7 +1827,7 @@
    "source": [
     "Thus, adhering to just syntax rules is *never* enough. Over time, **best practices** and **style guides** were created to make it less likely for a developer to mess up a program and also to allow \"onboarding\" him as a contributor to an established code base, often called **legacy code**, faster. These rules are not enforced by Python itself: Badly styled code still runs. At the very least, Python programs should be styled according to [PEP 8](https://www.python.org/dev/peps/pep-0008/) and documented \"inline\" (i.e., in the code itself) according to [PEP 257](https://www.python.org/dev/peps/pep-0257/).\n",
     "\n",
-    "An easier to read version of PEP 8 is [here](https://pep8.org/). The video below features a well known \"[Pythonista](https://en.wiktionary.org/wiki/Pythonista)\" talking about the importance of code style."
+    "An easier to read version of PEP 8 is [here](https://pep8.org/). The video below features a well known **[Pythonista](https://en.wiktionary.org/wiki/Pythonista)** talking about the importance of code style."
    ]
   },
   {
@@ -1852,7 +1854,7 @@
        "        "
       ],
       "text/plain": [
-       "<IPython.lib.display.YouTubeVideo at 0x7ff8c00b5358>"
+       "<IPython.lib.display.YouTubeVideo at 0x7fb124565080>"
       ]
      },
      "execution_count": 52,
@@ -1873,7 +1875,7 @@
     }
    },
    "source": [
-    "For example, while the above code to calculate the average of the even numbers from `1` through `12` is correct, a Pythonista would re-write it in a more \"Pythonic\" way and use the [sum()](https://docs.python.org/3/library/functions.html#sum) and [len()](https://docs.python.org/3/library/functions.html#len) (= \"length\") [built-in functions](https://docs.python.org/3/library/functions.html) (cf., [Chapter 2](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/02_functions.ipynb#Built-in-Functions)) as well as a so-called **list comprehension** (cf., [Chapter 7](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/07_sequences.ipynb#List-Comprehensions)). Pythonic code runs faster in many cases and is less error-prone."
+    "For example, while the above code to calculate the average of the even numbers in `[7, 11, 8, 5, 3, 12, 2, 6, 9, 10, 1, 4]` is correct, a Pythonista would rewrite it in a more \"Pythonic\" way and use the [sum()](https://docs.python.org/3/library/functions.html#sum) and [len()](https://docs.python.org/3/library/functions.html#len) (= \"length\") [built-in functions](https://docs.python.org/3/library/functions.html) (cf., [Chapter 2](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/02_functions.ipynb#Built-in-Functions)) as well as a so-called **list comprehension** (cf., [Chapter 7](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/07_sequences.ipynb#List-Comprehensions)). Pythonic code runs faster in many cases and is less error-prone."
    ]
   },
   {
@@ -2075,7 +2077,7 @@
     "\n",
     "At the same time, for a beginner's course, it is often easier to code linearly.\n",
     "\n",
-    "In real data science projects, one would probably employ a mixed approach and put re-usable code into so-called Python modules (i.e., *.py* files; cf., [Chapter 2](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/02_functions.ipynb#Local-Modules-and-Packages)) and then use Jupyter notebooks to build up a linear report or storyline for a business argument to be made."
+    "In real data science projects, one would probably employ a mixed approach and put reusable code into so-called Python modules (i.e., *.py* files; cf., [Chapter 2](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/02_functions.ipynb#Local-Modules-and-Packages)) and then use Jupyter notebooks to build up a linear report or storyline for a business argument to be made."
    ]
   },
   {
@@ -2097,9 +2099,9 @@
     }
    },
    "source": [
-    "**Variables** are created with the **[assignment statement](https://docs.python.org/3/reference/simple_stmts.html#assignment-statements)** `=`, which is *not* an operator, mainly because of its side effect of making a **[name](https://docs.python.org/3/reference/lexical_analysis.html#identifiers)** point to an object in memory.\n",
+    "**Variables** are created with the **[assignment statement](https://docs.python.org/3/reference/simple_stmts.html#assignment-statements)** `=`, which is *not* an operator, mainly because of its side effect of making a **[name](https://docs.python.org/3/reference/lexical_analysis.html#identifiers)** reference an object in memory.\n",
     "\n",
-    "We read the terms **variable**, **name**, and **identifier** used interchangebly in many Python-related texts. In this book, we adopt the following convention: First, we treat *name* and *identifier* as perfect synonyms but only use the term *name* in the text for clarity. Second, whereas *name* only refers to a string of letters, numbers, and some other symbols, a *variable* refers to the combination of a *name* and a *pointer* to some object in memory."
+    "We read the terms **variable**, **name**, and **identifier** used interchangebly in many Python-related texts. In this book, we adopt the following convention: First, we treat *name* and *identifier* as perfect synonyms but only use the term *name* in the text for clarity. Second, whereas *name* only refers to a string of letters, numbers, and some other symbols, a *variable* means the combination of a *name* and a *reference* to an object in memory."
    ]
   },
   {
@@ -2124,7 +2126,7 @@
     }
    },
    "source": [
-    "When referenced, a variable evaluates to the value of the object it points to. Colloquially, we could say that `a` evaluates to `20.0`, but this would not be an accurate description of what is going on in memory. We see some more colloquialisms in this section but should always relate this to what Python actually does in memory."
+    "When used as a *literal*, a variable evaluates to the value of the object it references. Colloquially, we could say that `a` evaluates to `20.0`, but this would not be an accurate description of what is going on in memory. We see some more colloquialisms in this section but should always relate this to what Python actually does in memory."
    ]
   },
   {
@@ -2172,7 +2174,7 @@
    },
    "outputs": [],
    "source": [
-    "a = 20  # this makes a point to an object of a different type"
+    "a = 20  # this makes a reference an object of a different type"
    ]
   },
   {
@@ -2281,7 +2283,7 @@
     }
    },
    "source": [
-    "Variables are **[de-referenced](https://docs.python.org/3/reference/simple_stmts.html#the-del-statement)** (i.e., \"deleted\") with the `del` statement. It does *not* delete the object to which a variable points to but merely removes the variable's name from the \"global list of all names.\""
+    "Variables are **[dereferenced](https://docs.python.org/3/reference/simple_stmts.html#the-del-statement)** (i.e., \"deleted\") with the `del` statement. It does *not* delete the object a variable references but merely removes the variable's name from the \"global list of all names.\""
    ]
   },
   {
@@ -2594,9 +2596,9 @@
     }
    },
    "source": [
-    "It is *crucial* to understand that *several* variables may point to the *same* object in memory. Not having this in mind may lead to many hard to track down bugs.\n",
+    "It is *crucial* to understand that *several* variables may reference the *same* object in memory. Not having this in mind may lead to many hard to track down bugs.\n",
     "\n",
-    "Make `b` point to whatever object `a` is pointing to."
+    "Make `b` reference whatever object `a` is referencing."
    ]
   },
   {
@@ -2670,7 +2672,7 @@
    "source": [
     "For \"simple\" types like `int` or `float` this never causes troubles.\n",
     "\n",
-    "Let's \"change the value\" of `a`. To be precise, let's create a *new* `123` object and make `a` point to it."
+    "Let's \"change the value\" of `a`. To be precise, let's create a *new* `123` object and make `a` reference it."
    ]
   },
   {
@@ -2718,7 +2720,7 @@
     }
    },
    "source": [
-    "`b` \"is still the same\" as before. To be precise, `b` still points to the *same object* as before."
+    "`b` \"is still the same\" as before. To be precise, `b` still references the *same object* as before."
    ]
   },
   {
@@ -2753,7 +2755,7 @@
     }
    },
    "source": [
-    "However, if a variable points to an object of a more \"complex\" type (e.g., `list`), \"weird\" things happen."
+    "However, if a variable references an object of a more \"complex\" type (e.g., `list`), \"weird\" things happen."
    ]
   },
   {
@@ -2816,7 +2818,7 @@
    "source": [
     "Let's change the first element of `x`.\n",
     "\n",
-    "[Chapter 7](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/07_sequences.ipynb#The-list-Type) discusses lists in more depth. For now, let's view a `list` object as some sort of **container** that holds an arbitrary number of pointers to other objects and treat the brackets `[]` attached to it as just another operator, called the **indexing operator**. `x[0]` instructs Python to first follow the pointer from the global list of all names to the `x` object. Then, it follows the first pointer it finds there to the `1` object. The indexing operator must be an operator as we merely read the first element and do not change anything in memory.\n",
+    "[Chapter 7](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/07_sequences.ipynb#The-list-Type) discusses lists in more depth. For now, let's view a `list` object as some sort of **container** that holds an arbitrary number of references to other objects and treat the brackets `[]` attached to it as just another operator, called the **indexing operator**. `x[0]` instructs Python to first follow the reference from the global list of all names to the `x` object. Then, it follows the first reference it finds there to the `1` object. The indexing operator must be an operator as we merely read the first element and do not change anything in memory.\n",
     "\n",
     "Note how Python **begins counting at 0**. This is not the case for many other languages, for example, [MATLAB](https://en.wikipedia.org/wiki/MATLAB), [R](https://en.wikipedia.org/wiki/R_%28programming_language%29), or [Stata](https://en.wikipedia.org/wiki/Stata). To understand why this makes sense, see this short [note](https://www.cs.utexas.edu/users/EWD/transcriptions/EWD08xx/EWD831.html) by one of the all-time greats in computer science, the late [Edsger Dijkstra](https://en.wikipedia.org/wiki/Edsger_W._Dijkstra)."
    ]
@@ -2853,7 +2855,7 @@
     }
    },
    "source": [
-    "To change the first entry in the list, we use the assignment statement `=` again. Here, this does *not* create a *new* variable (or overwrite an existing one) but only changes the object to which the first pointer in the `x` list points to. As we only change parts of the `x` object, we say that we **mutate** (i.e., \"change\") its **state**. To use the bag analogy from above, we keep the same bag but \"flip\" some of the $0$s into $1$s and some of the $1$s into $0$s."
+    "To change the first entry in the list, we use the assignment statement `=` again. Here, this does *not* create a *new* variable, or overwrite an existing one, but only changes the object which the first element in `x` referenced. As we only change parts of the `x` object, we say that we **mutate** (i.e., \"change\") its **state**. To use the bag analogy from above, we keep the same bag but \"flip\" some of the $0$s into $1$s and some of the $1$s into $0$s."
    ]
   },
   {
@@ -2901,7 +2903,7 @@
     }
    },
    "source": [
-    "The changes made to the object `x` is pointing to are also seen through the `y` variable!"
+    "The changes made to the object `x` is referencing can also be seen through the `y` variable!"
    ]
   },
   {
@@ -2938,11 +2940,11 @@
    "source": [
     "The illustrated difference in behavior has to do with the fact that integers and floats are **immutable** types while lists are **mutable**.\n",
     "\n",
-    "In the first case, an object cannot be changed \"in place\" once it is created in memory. When we assigned `123` to the already existing `a`, we did not change the $0$s and $1$s in the object `a` pointed to before the assignment but created a new integer object and made `a` point to it while the `b` variable is *not* affected.\n",
+    "In the first case, an object cannot be changed \"in place\" once it is created in memory. When we assigned `123` to the already existing `a`, we did not change the $0$s and $1$s in the object `a` referenced before the assignment but created a new integer object and made `a` reference it while the `b` variable is *not* affected.\n",
     "\n",
-    "In the second case, `x[0] = 99` creates a *new* integer object `99` and merely changes the first pointer in the `x` list.\n",
+    "In the second case, `x[0] = 99` creates a *new* integer object `99` and merely changes the first reference in the `x` list.\n",
     "\n",
-    "In general, the assignment statement creates a new name and makes it point to whatever object is on the right-hand side *iff* the left-hand side is a *pure* name (i.e., it contains no operators like the indexing operator in the example). Otherwise, it *mutates* an already existing object. And, we always must expect that the latter might have more than one variable pointing to it.\n",
+    "In general, the assignment statement creates a new name and makes it reference whatever object is on the right-hand side *iff* the left-hand side is a *pure* name (i.e., it contains no operators like the indexing operator in the example). Otherwise, it *mutates* an already existing object. And, we always must expect that the latter might have more than one variable referencing it.\n",
     "\n",
     "Visualizing what is going on in the memory with a tool like [PythonTutor](http://pythontutor.com/visualize.html#code=x%20%3D%20%5B1,%202,%203%5D%0Ay%20%3D%20x%0Ax%5B0%5D%20%3D%2099%0Aprint%28y%5B0%5D%29&cumulative=false&curInstr=0&heapPrimitives=nevernest&mode=display&origin=opt-frontend.js&py=3&rawInputLstJSON=%5B%5D&textReferences=false) might be helpful for a beginner."
    ]
@@ -3226,7 +3228,7 @@
        "        "
       ],
       "text/plain": [
-       "<IPython.lib.display.YouTubeVideo at 0x7ff8c011af28>"
+       "<IPython.lib.display.YouTubeVideo at 0x7fb1245d4320>"
       ]
      },
      "execution_count": 95,
@@ -3258,7 +3260,7 @@
     }
    },
    "source": [
-    "An **[expression](https://docs.python.org/3/reference/expressions.html)** is any syntactically correct **combination** of **variables** and **literals** with **operators**.\n",
+    "An **[expression](https://docs.python.org/3/reference/expressions.html)** is any syntactically correct *combination* of *variables* and *literals* with *operators*.\n",
     "\n",
     "In simple words, anything that may be used on the right-hand side of an assignment statement without creating a `SyntaxError` is an expression.\n",
     "\n",
@@ -3645,7 +3647,7 @@
    "source": [
     "We use the `#` symbol to write comments in plain English right into the code.\n",
     "\n",
-    "As a good practice, comments should not describe *what* happens (this should be evident by reading the code; otherwise, it is most likely badly written code) but *why* something happens.\n",
+    "As a good practice, comments should *not* describe *what* happens (this should be evident by reading the code; otherwise, it is most likely badly written code) but *why* something happens.\n",
     "\n",
     "Comments may be added either at the end of a line of code, by convention separated with two spaces, or on a line on their own."
    ]
@@ -3732,9 +3734,9 @@
     "\n",
     "\n",
     "- input (examples)\n",
-    "  - (numeric) data from a CSV file\n",
+    "  - data from a CSV file\n",
     "  - text entered on a command line\n",
-    "  - (relational) data obtained from a database\n",
+    "  - data obtained from a database\n",
     "  - etc.\n",
     "\n",
     "\n",
@@ -3749,20 +3751,20 @@
     "  - distinct and well-contained areas/parts of the memory that hold the actual data\n",
     "  - the concept by which Python manages the memory for us\n",
     "  - can be classified into objects of the same **type** (i.e., same abstract \"structure\" but different concrete data)\n",
-    "  - built-in objects (incl. **literals**) vs. user-defined objects (cf., Chapter 8)\n",
+    "  - built-in objects (incl. **literals**) vs. user-defined objects (cf., Chapter 9)\n",
     "  - e.g., `1`, `1.0`, and `\"one\"` are three different objects of distinct types that are also literals (i.e., by the way we type them into the command line Python knows what the value and type are)\n",
     "\n",
     "\n",
     "- variables\n",
     "  - storage of intermediate **state**\n",
-    "  - **names** that point to **objects** in **memory**\n",
-    "  - e.g., `x = 1` creates the variable `x` that points to the object `1`\n",
+    "  - are **names** referencing **objects** in **memory**\n",
+    "  - e.g., `x = 1` creates the variable `x` that references the object `1`\n",
     "\n",
     "\n",
     "- operators\n",
     "  - special built-in symbols that perform operations with objects in memory\n",
     "  - usually, operate with one or two objects\n",
-    "  - e.g., addition `+`, subtraction `-`, multiplication `*`, and division `/` all take two objects whereas the negation `-` only takes one\n",
+    "  - e.g., addition `+`, subtraction `-`, multiplication `*`, and division `/` all take two objects, whereas the negation `-` only takes one\n",
     "\n",
     "\n",
     "- expressions\n",
@@ -3774,9 +3776,9 @@
     "\n",
     "- statements\n",
     "  - instructions that **\"do\" something** and **have side effects** in memory\n",
-    "  - re-map names to different objects and *change* the state of the program\n",
+    "  - (re-)assign names to (different) objects, *change* an existing object *in place*, or, more conceptually, *change* the state of the program\n",
     "  - usually, work with expressions\n",
-    "  - e.g., the assignment statement `=` makes a name point to an object\n",
+    "  - e.g., the assignment statement `=` makes a name reference an object\n",
     "\n",
     "\n",
     "- comments\n",
@@ -3788,6 +3790,7 @@
     "  - named sequences of instructions\n",
     "  - the smaller parts in a larger program\n",
     "  - make a program more modular and thus easier to understand\n",
+    "  - include [built-in functions](https://docs.python.org/3/library/functions.html) like [print()](https://docs.python.org/3/library/functions.html#print), [sum()](https://docs.python.org/3/library/functions.html#sum), or [len()](https://docs.python.org/3/library/functions.html#len)\n",
     "\n",
     "\n",
     "- flow control (cf., [Chapter 3](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/03_conditionals.ipynb))\n",
diff --git a/01_elements_review_and_exercises.ipynb b/01_elements_review_and_exercises.ipynb
index 965b8be..21ba4c5 100644
--- a/01_elements_review_and_exercises.ipynb
+++ b/01_elements_review_and_exercises.ipynb
@@ -138,7 +138,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "**Q7**: \"**dunder**\" refers to a group of Australian (\"down under\") geeks that work on core Python."
+    "**Q7**: \"**dunder**\" refers to a group of Australian (i.e., \"down under\") geeks that work on core Python."
    ]
   },
   {
@@ -166,7 +166,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "**Q9**: When NASA famously converted some measurements to the wrong unit and lost a Mars satellite in 1999 ([source](https://www.wired.com/2010/11/1110mars-climate-observer-report/)), this is an example of a so-called **runtime error**."
+    "**Q9**: When NASA famously converted some measurements to the wrong unit and lost a Mars satellite in 1999 (cf., [source](https://www.wired.com/2010/11/1110mars-climate-observer-report/)), this is an example of a so-called **runtime error**."
    ]
   },
   {
diff --git a/02_functions.ipynb b/02_functions.ipynb
index 897f8a7..39d7084 100644
--- a/02_functions.ipynb
+++ b/02_functions.ipynb
@@ -19,9 +19,9 @@
     }
    },
    "source": [
-    "In [Chapter 1](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/01_elements.ipynb#Example:-Averaging-Even-Numbers), we typed the code to calculate the average of the even numbers in a list into several code cells. Then, we executed them one after another. We had no way of **re-using** the code except for either re-executing the cells or copying and pasting their contents into other cells. And, whenever we find ourselves doing repetitive manual work, we can be sure that there must be a way of automating what we are doing.\n",
+    "In [Chapter 1](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/01_elements.ipynb#Example:-Averaging-Even-Numbers), we typed the code to calculate the average of the even numbers in a list into several code cells. Then, we executed them one after another. We had no way of **reusing** the code except for either executing cells multiple times or copying and pasting their contents into other cells. And, whenever we find ourselves doing repetitive manual work, we can be sure that there must be a way of automating what we are doing.\n",
     "\n",
-    "At the same time, we executed built-in functions (e.g., [print()](https://docs.python.org/3/library/functions.html#print), [sum()](https://docs.python.org/3/library/functions.html#sum), [len()](https://docs.python.org/3/library/functions.html#len), [id()](https://docs.python.org/3/library/functions.html#id), or [type()](https://docs.python.org/3/library/functions.html#type)) that obviously must be re-using the same parts inside core Python every time we use them.\n",
+    "At the same time, we executed built-in functions (e.g., [print()](https://docs.python.org/3/library/functions.html#print), [sum()](https://docs.python.org/3/library/functions.html#sum), [len()](https://docs.python.org/3/library/functions.html#len), [id()](https://docs.python.org/3/library/functions.html#id), or [type()](https://docs.python.org/3/library/functions.html#type)) that obviously must be reusing the same parts inside core Python every time we use them.\n",
     "\n",
     "This chapter shows how Python offers language constructs that let us **define** functions ourselves that we may then **call** just like the built-in ones."
    ]
@@ -45,7 +45,7 @@
     }
    },
    "source": [
-    "So-called **[user-defined functions](https://docs.python.org/3/reference/compound_stmts.html#function-definitions)** may be created with the `def` statement. To extend an already familiar example, we re-use the introductory example from [Chapter 1](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/01_elements.ipynb#Best-Practices) in its final Pythonic version and transform it into the function `average_evens()` below. \n",
+    "So-called **[user-defined functions](https://docs.python.org/3/reference/compound_stmts.html#function-definitions)** may be created with the `def` statement. To extend an already familiar example, we reuse the introductory example from [Chapter 1](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/01_elements.ipynb#Best-Practices) in its final Pythonic version and transform it into the function `average_evens()` below. \n",
     "\n",
     "A function's **name** must be chosen according to the same naming rules as ordinary variables as Python manages function names like variables. In this book, we further adopt the convention of ending function names with parentheses `()` in text cells for faster comprehension when reading (i.e., `average_evens()` vs. `average_evens`). These are *not* part of the name but must always be written out in the `def` statement for syntactic reasons.\n",
     "\n",
@@ -142,7 +142,7 @@
     {
      "data": {
       "text/plain": [
-       "139646582981088"
+       "140384699277792"
       ]
      },
      "execution_count": 3,
@@ -407,7 +407,7 @@
     }
    },
    "source": [
-    "Notice how the parameters listed in a function's definition (i.e., `numbers`) and variables created inside it during execution (i.e., `evens` and `average`) are **local** to that function. That means they only point to an object in memory *while* the function is being executed and de-referenced immediately when the function returns. We say they **go out of scope** once the function terminates."
+    "Notice how the parameters listed in a function's definition (i.e., `numbers`) and variables created inside it during execution (i.e., `evens` and `average`) are **local** to that function. That means they only reference an object in memory *while* the function is being executed and de-referenced immediately when the function returns. We say they **go out of scope** once the function terminates."
    ]
   },
   {
@@ -493,7 +493,7 @@
     }
    },
    "source": [
-    "[PythonTutor](http://pythontutor.com/visualize.html#code=nums%20%3D%20%5B1,%202,%203,%204,%205,%206,%207,%208,%209,%2010,%2011,%2012%5D%0A%0Adef%20average_evens%28numbers%29%3A%0A%20%20%20%20evens%20%3D%20%5Bn%20for%20n%20in%20numbers%20if%20n%20%25%202%20%3D%3D%200%5D%0A%20%20%20%20average%20%3D%20sum%28evens%29%20/%20len%28evens%29%0A%20%20%20%20return%20average%0A%0Arv%20%3D%20average_evens%28nums%29&cumulative=false&curInstr=0&heapPrimitives=nevernest&mode=display&origin=opt-frontend.js&py=3&rawInputLstJSON=%5B%5D&textReferences=false) visualizes what happens in memory: To be precise, in the exact moment when the function call is initiated and `nums` passed in as the `numbers` argument, there are *two* pointers to the *same* `list` object (cf., steps 4-5 in the visualization). We also see how Python creates a *new* **frame** that holds the function's local scope (i.e., \"internal names\") in addition to the **global** frame. Frames are nothing but [namespaces](https://en.wikipedia.org/wiki/Namespace) to *isolate* the names of different **scopes** from each other. The list comprehension `[n for n in numbers if n % 2 == 0]` constitutes yet another frame that is in scope as the `list` object assigned to `evens` is *being* created (cf., steps 6-20). When the function returns, only the global frame is left (cf., last step)."
+    "[PythonTutor](http://pythontutor.com/visualize.html#code=nums%20%3D%20%5B1,%202,%203,%204,%205,%206,%207,%208,%209,%2010,%2011,%2012%5D%0A%0Adef%20average_evens%28numbers%29%3A%0A%20%20%20%20evens%20%3D%20%5Bn%20for%20n%20in%20numbers%20if%20n%20%25%202%20%3D%3D%200%5D%0A%20%20%20%20average%20%3D%20sum%28evens%29%20/%20len%28evens%29%0A%20%20%20%20return%20average%0A%0Arv%20%3D%20average_evens%28nums%29&cumulative=false&curInstr=0&heapPrimitives=nevernest&mode=display&origin=opt-frontend.js&py=3&rawInputLstJSON=%5B%5D&textReferences=false) visualizes what happens in memory: To be precise, in the exact moment when the function call is initiated and `nums` passed in as the `numbers` argument, there are *two* references to the *same* `list` object (cf., steps 4-5 in the visualization). We also see how Python creates a *new* **frame** that holds the function's local scope (i.e., \"internal names\") in addition to the **global** frame. Frames are nothing but [namespaces](https://en.wikipedia.org/wiki/Namespace) to *isolate* the names of different **scopes** from each other. The list comprehension `[n for n in numbers if n % 2 == 0]` constitutes yet another frame that is in scope as the `list` object assigned to `evens` is *being* created (cf., steps 6-20). When the function returns, only the global frame is left (cf., last step)."
    ]
   },
   {
@@ -655,9 +655,9 @@
     }
    },
    "source": [
-    "[PythonTutor](http://pythontutor.com/visualize.html#code=nums%20%3D%20%5B1,%202,%203,%204,%205,%206,%207,%208,%209,%2010,%2011,%2012%5D%0A%0Adef%20average_wrong%28numbers%29%3A%0A%20%20%20%20evens%20%3D%20%5Bn%20for%20n%20in%20nums%20if%20n%20%25%202%20%3D%3D%200%5D%0A%20%20%20%20average%20%3D%20sum%28evens%29%20/%20len%28evens%29%0A%20%20%20%20return%20average%0A%0Arv%20%3D%20average_wrong%28%5B123,%20456,%20789%5D%29&cumulative=false&curInstr=0&heapPrimitives=nevernest&mode=display&origin=opt-frontend.js&py=3&rawInputLstJSON=%5B%5D&textReferences=false) is again helpful at visualizing the error interactively: Creating the `list` object `evens` eventually points to takes *16* computational steps, namely two for managing the list comprehension, one for setting up an empty `list` object, *twelve* for filling it with elements derived from `nums` in the global scope (i.e., that is the error), and one to make `evens` point at it (cf., steps 6-21).\n",
+    "[PythonTutor](http://pythontutor.com/visualize.html#code=nums%20%3D%20%5B1,%202,%203,%204,%205,%206,%207,%208,%209,%2010,%2011,%2012%5D%0A%0Adef%20average_wrong%28numbers%29%3A%0A%20%20%20%20evens%20%3D%20%5Bn%20for%20n%20in%20nums%20if%20n%20%25%202%20%3D%3D%200%5D%0A%20%20%20%20average%20%3D%20sum%28evens%29%20/%20len%28evens%29%0A%20%20%20%20return%20average%0A%0Arv%20%3D%20average_wrong%28%5B123,%20456,%20789%5D%29&cumulative=false&curInstr=0&heapPrimitives=nevernest&mode=display&origin=opt-frontend.js&py=3&rawInputLstJSON=%5B%5D&textReferences=false) is again helpful at visualizing the error interactively: Creating the `list` object `evens` eventually references takes *16* computational steps, namely two for managing the list comprehension, one for setting up an empty `list` object, *twelve* for filling it with elements derived from `nums` in the global scope (i.e., that is the error), and one to make `evens` reference it (cf., steps 6-21).\n",
     "\n",
-    "The frames logic shown by PythonTutor is the mechanism by which Python not only manages the names inside *one* function call but also for *many* potentially *simultaneous* calls, as revealed in [Chapter 4](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/04_iteration.ipynb#Trivial-Example:-Countdown). It is the reason why we may re-use the same names for the parameters and variables inside both `average_evens()` and `average_wrong()` without Python mixing them up. So, as we already read in the [Zen of Python](https://www.python.org/dev/peps/pep-0020/), \"namespaces are one honking great idea\" (cf., `import this`), and a frame is just a special kind of namespace."
+    "The frames logic shown by PythonTutor is the mechanism by which Python not only manages the names inside *one* function call but also for *many* potentially *simultaneous* calls, as revealed in [Chapter 4](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/04_iteration.ipynb#Trivial-Example:-Countdown). It is the reason why we may reuse the same names for the parameters and variables inside both `average_evens()` and `average_wrong()` without Python mixing them up. So, as we already read in the [Zen of Python](https://www.python.org/dev/peps/pep-0020/), \"namespaces are one honking great idea\" (cf., `import this`), and a frame is just a special kind of namespace."
    ]
   },
   {
@@ -822,7 +822,7 @@
     }
    },
    "source": [
-    "Python, however, is again smart enough to keep all the involved `nums` variables apart. So the global `nums` is still pointing to the very same `list` object as before."
+    "Python, however, is again smart enough to keep all the involved `nums` variables apart. So, the global `nums` is still referencing the *same* `list` object as before."
    ]
   },
   {
@@ -859,7 +859,7 @@
    "source": [
     "The reason why everything works is that *every time* we (re-)assign an object to a variable *inside* a function with the `=` statement, this is done in the *local* scope by default. There are ways to change variables existing in an outer scope from within a function, but this is a rather advanced topic on its own.\n",
     "\n",
-    "[PythonTutor](http://pythontutor.com/visualize.html#code=nums%20%3D%20%5B1,%202,%203,%204,%205,%206,%207,%208,%209,%2010,%2011,%2012%5D%0A%0Adef%20average_odds%28numbers%29%3A%0A%20%20%20%20nums%20%3D%20%5Bint%28n%29%20for%20n%20in%20numbers%5D%0A%20%20%20%20odds%20%3D%20%5Bn%20for%20n%20in%20nums%20if%20n%20%25%202%20!%3D%200%5D%0A%20%20%20%20average%20%3D%20sum%28odds%29%20/%20len%28odds%29%0A%20%20%20%20return%20average%0A%0Arv%20%3D%20average_odds%28%5B1.0,%2010.0,%203.0,%2010.0,%205.0%5D%29&cumulative=false&curInstr=0&heapPrimitives=nevernest&mode=display&origin=opt-frontend.js&py=3&rawInputLstJSON=%5B%5D&textReferences=false) shows how *two* `nums` variables exist in *different* scopes pointing to *different* objects (cf., steps 14-25) when we execute `average_odds([1.0, 10.0, 3.0, 10.0, 5.0])`.\n",
+    "[PythonTutor](http://pythontutor.com/visualize.html#code=nums%20%3D%20%5B1,%202,%203,%204,%205,%206,%207,%208,%209,%2010,%2011,%2012%5D%0A%0Adef%20average_odds%28numbers%29%3A%0A%20%20%20%20nums%20%3D%20%5Bint%28n%29%20for%20n%20in%20numbers%5D%0A%20%20%20%20odds%20%3D%20%5Bn%20for%20n%20in%20nums%20if%20n%20%25%202%20!%3D%200%5D%0A%20%20%20%20average%20%3D%20sum%28odds%29%20/%20len%28odds%29%0A%20%20%20%20return%20average%0A%0Arv%20%3D%20average_odds%28%5B1.0,%2010.0,%203.0,%2010.0,%205.0%5D%29&cumulative=false&curInstr=0&heapPrimitives=nevernest&mode=display&origin=opt-frontend.js&py=3&rawInputLstJSON=%5B%5D&textReferences=false) shows how *two* `nums` variables exist in *different* scopes referencing *different* objects (cf., steps 14-25) when we execute `average_odds([1.0, 10.0, 3.0, 10.0, 5.0])`.\n",
     "\n",
     "Variables whose names collide with the ones of variables in enclosing scopes - and the global scope is just the most enclosing scope - are said to **shadow** them.\n",
     "\n",
@@ -1124,7 +1124,7 @@
     }
    },
    "source": [
-    "We may also go in the other direction with the [float()](https://docs.python.org/3/library/functions.html#float) built-in function."
+    "We may also go in the other direction with the [float()](https://docs.python.org/3/library/functions.html#float) built-in."
    ]
   },
   {
@@ -1432,9 +1432,9 @@
    "source": [
     "Defining both `average_evens()` and `scaled_average_evens()` is also kind of repetitive as most of their code is the same. Such a redundancy makes a codebase harder to maintain in the long run as whenever we change the logic in one function, we must *not* forget to do so for the other function as well.\n",
     "\n",
-    "A better way is to design related functions in a **modular** fashion such that they re-use each other's logic.\n",
+    "A better way is to design related functions in a **modular** fashion such that they reuse each other's logic.\n",
     "\n",
-    "For example, as not scaling an average is just a special case of scaling it with `1`, we could re-define the two functions like below: In this setting, the function resembling the *special* case (i.e., `average_evens()`) **forwards** the call to the more *general* function (i.e., `scaled_average_evens()`) using a `scalar=1` argument."
+    "For example, as not scaling an average is just a special case of scaling it with `1`, we could redefine the two functions like below: In this setting, the function resembling the *special* case (i.e., `average_evens()`) **forwards** the call to the more *general* function (i.e., `scaled_average_evens()`) using a `scalar=1` argument."
    ]
   },
   {
@@ -1796,17 +1796,17 @@
     }
    },
    "source": [
-    "The `def` statement is a statement because of its side effect of creating a *new* name that points to a *new* `function` object in memory.\n",
+    "The `def` statement is a statement because of its side effect of creating a *new* name that references a *new* `function` object in memory.\n",
     "\n",
-    "We can thus think of it as doing *two* things at once (i.e., either both of them happen or none). First, a `function` object is created that contains the concrete $0$s and $1$s that resemble the instructions we put into the function's body. In the context of a function, these $0$s and $1$s are also called **[byte code](https://en.wikipedia.org/wiki/Bytecode)**. Then, a name pointing at the new `function` object is created.\n",
+    "We can thus think of it as doing *two* things at once (i.e., either both of them happen or none). First, a `function` object is created that contains the concrete $0$s and $1$s that resemble the instructions we put into the function's body. In the context of a function, these $0$s and $1$s are also called **[byte code](https://en.wikipedia.org/wiki/Bytecode)**. Then, a name referencing the new `function` object is created.\n",
     "\n",
     "Only this second aspect makes `def` a statement: Merely creating a new object in memory without making it accessible for later reference does *not* constitute a side effect because the state the program is *not* changed. After all, if we cannot reference an object, how do we know it exists in the first place?\n",
     "\n",
-    "Python provides a so-called **[lambda expression](https://docs.python.org/3/reference/expressions.html#lambda)** syntax that allows us to *only* create a `function` object in memory *without* making a name point to it.\n",
+    "Python provides a so-called **[lambda expression](https://docs.python.org/3/reference/expressions.html#lambda)** syntax that allows us to *only* create a `function` object in memory *without* making a name reference it.\n",
     "\n",
     "It starts with the keyword `lambda` followed by an optional comma separated enumeration of parameters, a mandatory colon, and *one* expression that also is the resulting `function` object's return value.\n",
     "\n",
-    "Because it does not create a name pointing to the object, we effectively create \"anonymous\" functions with it. In the example, we create a `function` object that adds `3` to the only argument passed in as the parameter `x` and returns that sum."
+    "Because it does not create a name referencing the object, we effectively create \"anonymous\" functions with it. In the example, we create a `function` object that adds `3` to the only argument passed in as the parameter `x` and returns that sum."
    ]
   },
   {
@@ -1965,9 +1965,9 @@
    "source": [
     "So far, we have only used what we refer to as **core** Python in this book. By this, we mean all the syntactical rules as specified in the [language reference](https://docs.python.org/3/reference/) and a minimal set of about 50 built-in [functions](https://docs.python.org/3/library/functions.html). With this, we could already implement any algorithm or business logic we can think of!\n",
     "\n",
-    "However, after our first couple of programs, we would already start seeing recurring patterns in the code we write. In other words, we would constantly be \"re-inventing the wheel\" in each new project.\n",
+    "However, after our first couple of programs, we would already start seeing recurring patterns in the code we write. In other words, we would constantly be \"reinventing the wheel\" in each new project.\n",
     "\n",
-    "Would it not be smarter to pull out the re-usable components from our programs and put them into some project independent **library** of generically useful functionalities? Then we would only need a way of including these **utilities** in our projects.\n",
+    "Would it not be smarter to pull out the reusable components from our programs and put them into some project independent **library** of generically useful functionalities? Then we would only need a way of including these **utilities** in our projects.\n",
     "\n",
     "As all programmers across all languages face this very same issue, most programming languages come with a so-called **[standard library](https://en.wikipedia.org/wiki/Standard_library)** that provides utilities to accomplish everyday tasks without much code. Examples are making an HTTP request to some website, open and read popular file types (e.g., CSV or Excel files), do something on a computer's file system, and many more."
    ]
@@ -1993,7 +1993,7 @@
    "source": [
     "Python also comes with a [standard library](https://docs.python.org/3/library/index.html) that is structured into coherent modules and packages for given topics: A **module** is just a plain text file with the file extension *.py* that contains Python code while a **package** is a folder that groups several related modules.\n",
     "\n",
-    "The code in the [standard library](https://docs.python.org/3/library/index.html) is contributed and maintained by many volunteers around the world. In contrast to so-called \"third-party\" packages (cf., the next section below), the Python core development team closely monitors and tests the code in the [standard library](https://docs.python.org/3/library/index.html). Consequently, we can be reasonably sure that anything provided by it works correctly independent of our computer's operating system and will most likely also be there in the next Python versions. Parts in the [standard library](https://docs.python.org/3/library/index.html) that are computationally expensive are often re-written in C and, therefore, much faster than anything we could write in Python ourselves. So, whenever we can solve a problem with the help of the [standard library](https://docs.python.org/3/library/index.html), it is almost always the best way to do so as well.\n",
+    "The code in the [standard library](https://docs.python.org/3/library/index.html) is contributed and maintained by many volunteers around the world. In contrast to so-called \"third-party\" packages (cf., the next section below), the Python core development team closely monitors and tests the code in the [standard library](https://docs.python.org/3/library/index.html). Consequently, we can be reasonably sure that anything provided by it works correctly independent of our computer's operating system and will most likely also be there in the next Python versions. Parts in the [standard library](https://docs.python.org/3/library/index.html) that are computationally expensive are often rewritten in C and, therefore, much faster than anything we could write in Python ourselves. So, whenever we can solve a problem with the help of the [standard library](https://docs.python.org/3/library/index.html), it is almost always the best way to do so as well.\n",
     "\n",
     "The [standard library](https://docs.python.org/3/library/index.html) has grown very big over the years, and we refer to the website [PYMOTW](https://pymotw.com/3/index.html) (i.e., \"Python Module of the Week\") that features well written introductory tutorials and how-to guides to most parts of the library. The same author also published a [book](https://www.amazon.com/Python-Standard-Library-Example-Developers/dp/0134291050/ref=as_li_ss_tl?ie=UTF8&qid=1493563121&sr=8-1&keywords=python+3+standard+library+by+example) that many Pythonistas keep on their shelf for reference. Knowing what is in the [standard library](https://docs.python.org/3/library/index.html) is quite valuable for solving real-world tasks quickly.\n",
     "\n",
@@ -2045,7 +2045,7 @@
     }
    },
    "source": [
-    "This creates the variable `math` that points to a **[module object](https://docs.python.org/3/glossary.html#term-module)** (i.e., type `module`) in memory."
+    "This creates the variable `math` that references a **[module object](https://docs.python.org/3/glossary.html#term-module)** (i.e., type `module`) in memory."
    ]
   },
   {
@@ -2084,7 +2084,7 @@
     {
      "data": {
       "text/plain": [
-       "139646755983752"
+       "140384754610488"
       ]
      },
      "execution_count": 58,
@@ -2430,7 +2430,7 @@
    "source": [
     "If we only need one particular function from a module, we may also use the alternative `from ... import ...` syntax.\n",
     "\n",
-    "This does *not* create a module object but only makes a variable in our current location point to an object defined inside a module directly."
+    "This does *not* create a module object but only makes a variable in our current location reference an object defined inside a module directly."
    ]
   },
   {
@@ -2697,7 +2697,7 @@
     {
      "data": {
       "text/plain": [
-       "0.21932911318720072"
+       "0.17184403163109208"
       ]
      },
      "execution_count": 75,
@@ -2732,7 +2732,7 @@
     {
      "data": {
       "text/plain": [
-       "<bound method Random.choice of <random.Random object at 0x55a6c6c03af8>>"
+       "<bound method Random.choice of <random.Random object at 0x55bb3d27faf8>>"
       ]
      },
      "execution_count": 76,
@@ -2781,7 +2781,7 @@
     {
      "data": {
       "text/plain": [
-       "7"
+       "2"
       ]
      },
      "execution_count": 78,
@@ -3242,7 +3242,7 @@
     "\n",
     "The *name* to be imported is the file's name except for the *.py* part. For this to work, the file's name *must* adhere to the *same* rules as hold for [variable names](https://docs.python.org/3/reference/lexical_analysis.html#identifiers) in general.\n",
     "\n",
-    "What happens during an import is as follows. When Python sees the `import sample_module` part, it first creates a *new* object of type `module` in memory. This is effectively an *empty* namespace. Then, it executes the imported file's code from top to bottom. Whatever variables are still defined at the end of this, are put into the module's namespace. Only if the file's code does *not* raise an error, will Python make a variable in our current location (i.e., `mod` here) point to the created `module` object. Otherwise, it is discarded. In essence, it is as if we copied and pasted the file's code in place of the import statement. If we import an already imported module again, Python is smart enough to avoid doing all this work all over and does nothing."
+    "What happens during an import is as follows. When Python sees the `import sample_module` part, it first creates a *new* object of type `module` in memory. This is effectively an *empty* namespace. Then, it executes the imported file's code from top to bottom. Whatever variables are still defined at the end of this, are put into the module's namespace. Only if the file's code does *not* raise an error, will Python make a variable in our current location (i.e., `mod` here) reference the created `module` object. Otherwise, it is discarded. In essence, it is as if we copied and pasted the file's code in place of the import statement. If we import an already imported module again, Python is smart enough to avoid doing all this work all over and does nothing."
    ]
   },
   {
@@ -3341,7 +3341,7 @@
     }
    },
    "source": [
-    "We use the imported `mod.average_evens()` just like `average_evens()` defined above. The advantage we get from **modularization** with *.py* files is that we can now easily re-use functions across different Jupyter notebooks without re-defining them again and again. Also, we can \"source out\" code that distracts from the storyline told in a notebook."
+    "We use the imported `mod.average_evens()` just like `average_evens()` defined above. The advantage we get from **modularization** with *.py* files is that we can now easily reuse functions across different Jupyter notebooks without redefining them again and again. Also, we can \"source out\" code that distracts from the storyline told in a notebook."
    ]
   },
   {
@@ -3462,7 +3462,7 @@
     "Functions provide benefits as they:\n",
     "\n",
     "- make programs easier to comprehend and debug for humans as they give names to the smaller parts of a larger program (i.e., they **modularize** a codebase), and\n",
-    "- eliminate redundancies by allowing **re-use of code**.\n",
+    "- eliminate redundancies by allowing **reuse of code**.\n",
     "\n",
     "Functions are **defined** once with the `def` statement. Then, they may be **called** many times with the call operator `()`.\n",
     "\n",
diff --git a/02_functions_review_and_exercises.ipynb b/02_functions_review_and_exercises.ipynb
index d0935c6..ca631a7 100644
--- a/02_functions_review_and_exercises.ipynb
+++ b/02_functions_review_and_exercises.ipynb
@@ -215,21 +215,27 @@
    "execution_count": null,
    "metadata": {},
    "outputs": [],
-   "source": []
+   "source": [
+    "import ..."
+   ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
    "metadata": {},
    "outputs": [],
-   "source": []
+   "source": [
+    "r = ..."
+   ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
    "metadata": {},
    "outputs": [],
-   "source": []
+   "source": [
+    "..."
+   ]
   },
   {
    "cell_type": "markdown",
@@ -244,17 +250,8 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "\n",
-    "\n",
-    "\n",
-    "\n",
-    "\n",
-    "\n",
-    "\n",
-    "\n",
-    "\n",
-    "\n",
-    "\n"
+    "def sphere_volume(...):\n",
+    "    ..."
    ]
   },
   {
@@ -269,42 +266,54 @@
    "execution_count": null,
    "metadata": {},
    "outputs": [],
-   "source": []
+   "source": [
+    "radius = ..."
+   ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
    "metadata": {},
    "outputs": [],
-   "source": []
+   "source": [
+    "sphere_volume(...)"
+   ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
    "metadata": {},
    "outputs": [],
-   "source": []
+   "source": [
+    "sphere_volume(...)"
+   ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
    "metadata": {},
    "outputs": [],
-   "source": []
+   "source": [
+    "sphere_volume(...)"
+   ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
    "metadata": {},
    "outputs": [],
-   "source": []
+   "source": [
+    "sphere_volume(...)"
+   ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
    "metadata": {},
    "outputs": [],
-   "source": []
+   "source": [
+    "sphere_volume(...)"
+   ]
   },
   {
    "cell_type": "markdown",
@@ -334,7 +343,9 @@
    "execution_count": null,
    "metadata": {},
    "outputs": [],
-   "source": []
+   "source": [
+    "radius = ..."
+   ]
   },
   {
    "cell_type": "code",
@@ -342,14 +353,15 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "\n"
+    "for ... in ...:\n",
+    "    ..."
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "**Q11.6**: What lesson did you learn about the `float` type?"
+    "**Q11.6**: What lesson do you learn about the `float` type?"
    ]
   },
   {
diff --git a/03_conditionals.ipynb b/03_conditionals.ipynb
index 072448a..9ffe09e 100644
--- a/03_conditionals.ipynb
+++ b/03_conditionals.ipynb
@@ -19,7 +19,7 @@
     }
    },
    "source": [
-    "We analyzed every aspect of the `average_evens()` function in [Chapter 2](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/02_functions.ipynb) except for the `if` related parts. While it seems to do what we expect it to, there is a whole lot more we learn from taking it apart. In particular, the `if` may occur within both a **statement** or an **expression**, analogous as to how a noun in a natural language is *either* the subject of *or* an object in a sentence. What is common to both usages is that it leads to code being executed for *parts* of the input only. It is our first way of **controlling** the **flow of execution** in a program.\n",
+    "We analyzed every aspect of the `average_evens()` function in [Chapter 2](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/02_functions.ipynb) except for the `if`-related parts. While it seems to do what we expect it to, there is a whole lot more we learn from taking it apart. In particular, the `if` may occur within both a **statement** or an **expression**, analogous as to how a noun in a natural language is *either* the subject of *or* an object in a sentence. What is common to both usages is that it leads to code being executed for *parts* of the input only. It is our first way of **controlling** the **flow of execution** in a program.\n",
     "\n",
     "After deconstructing `if` in the first part of this chapter, we take a close look at a similar concept, namely handling **exceptions**."
    ]
@@ -189,7 +189,7 @@
     {
      "data": {
       "text/plain": [
-       "94711290794976"
+       "93918328484832"
       ]
      },
      "execution_count": 5,
@@ -213,7 +213,7 @@
     {
      "data": {
       "text/plain": [
-       "94711290794944"
+       "93918328484800"
       ]
      },
      "execution_count": 6,
@@ -313,7 +313,7 @@
     {
      "data": {
       "text/plain": [
-       "94711290781936"
+       "93918328471792"
       ]
      },
      "execution_count": 10,
@@ -357,7 +357,7 @@
     }
    },
    "source": [
-    "`True`, `False`, and `None` have the property that they each exist in memory only *once*. Objects designed this way are so-called **singletons**. This **[design pattern](https://en.wikipedia.org/wiki/Design_Patterns)** was originally developed to keep a program's memory usage at a minimum. It may only be employed in situations where we know that an object does *not* mutate its value (i.e., to re-use the bag analogy from [Chapter 1](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/01_elements.ipynb#Objects-vs.-Types-vs.-Values), no flipping of $0$s and $1$s in the bag is allowed). In languages \"closer\" to the memory like C, we would have to code this singleton logic ourselves, but Python has this built in for *some* types.\n",
+    "`True`, `False`, and `None` have the property that they each exist in memory only *once*. Objects designed this way are so-called **singletons**. This **[design pattern](https://en.wikipedia.org/wiki/Design_Patterns)** was originally developed to keep a program's memory usage at a minimum. It may only be employed in situations where we know that an object does *not* mutate its value (i.e., to reuse the bag analogy from [Chapter 1](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/01_elements.ipynb#Objects-vs.-Types-vs.-Values), no flipping of $0$s and $1$s in the bag is allowed). In languages \"closer\" to the memory like C, we would have to code this singleton logic ourselves, but Python has this built in for *some* types.\n",
     "\n",
     "We verify this with either the `is` operator or by comparing memory addresses."
    ]
@@ -418,7 +418,7 @@
     }
    },
    "source": [
-    "So the following expression regards *four* objects in memory: *One* `list` object holding ten pointers to *three* other objects."
+    "So the following expression regards *four* objects in memory: *One* `list` object holding ten references to *three* other objects."
    ]
   },
   {
diff --git a/03_conditionals_review_and_exercises.ipynb b/03_conditionals_review_and_exercises.ipynb
index 89d7009..90b4ff9 100644
--- a/03_conditionals_review_and_exercises.ipynb
+++ b/03_conditionals_review_and_exercises.ipynb
@@ -110,7 +110,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "**Q6**: What does the `finally`-branch enforce in this code snippet? How can a `try` statement be useful *without* an `except`-branch?"
+    "**Q6**: What does the `finally`-clause enforce in this code snippet? How can a `try` statement be useful *without* an `except`-clause?"
    ]
   },
   {
@@ -210,22 +210,8 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "\n",
-    "\n",
-    "\n",
-    "\n",
-    "\n",
-    "\n",
-    "\n",
-    "\n",
-    "\n",
-    "\n",
-    "\n",
-    "\n",
-    "\n",
-    "\n",
-    "\n",
-    "\n"
+    "def discounted_price(...):\n",
+    "    ..."
    ]
   },
   {
@@ -279,7 +265,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "**Q9.3**: Re-calculate the last two line items with order quantities of $20$ and $15$. What do you observe?"
+    "**Q9.3**: Calculate the last two line items with order quantities of $20$ and $15$. What do you observe?"
    ]
   },
   {
@@ -304,7 +290,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    " (your observation)"
+    " "
    ]
   },
   {
@@ -373,12 +359,6 @@
    "outputs": [],
    "source": [
     "for number in numbers:\n",
-    "    ...\n",
-    "    ...\n",
-    "    ...\n",
-    "    ...\n",
-    "    ...\n",
-    "    ...\n",
     "    ..."
    ]
   },
diff --git a/04_iteration.ipynb b/04_iteration.ipynb
index 2246da5..aabaaef 100644
--- a/04_iteration.ipynb
+++ b/04_iteration.ipynb
@@ -91,7 +91,7 @@
     "    Args:\n",
     "        n (int): seconds until the party begins\n",
     "    \"\"\"\n",
-    "    if n == 0:  # base case\n",
+    "    if n == 0:  # = base case\n",
     "        print(\"Happy New Year!\")\n",
     "    else:\n",
     "        print(n)\n",
@@ -222,7 +222,7 @@
     "    Returns:\n",
     "        factorial (int)\n",
     "    \"\"\"\n",
-    "    if n == 0:\n",
+    "    if n == 0:  # = base case\n",
     "        return 1\n",
     "    else:\n",
     "        recurse = factorial(n - 1)\n",
@@ -323,7 +323,7 @@
     "    Returns:\n",
     "        factorial (int)\n",
     "    \"\"\"\n",
-    "    if n == 0:\n",
+    "    if n == 0:  # = base case\n",
     "        return 1\n",
     "    return n * factorial(n - 1)"
    ]
@@ -517,7 +517,7 @@
     "    Returns:\n",
     "        gcd (int)\n",
     "    \"\"\"\n",
-    "    if b == 0:\n",
+    "    if b == 0:  # = base case\n",
     "        return a \n",
     "    return gcd(b, a % b)"
    ]
@@ -787,9 +787,9 @@
     "    Returns:\n",
     "        ith_fibonacci (int)\n",
     "    \"\"\"\n",
-    "    if i == 0:\n",
+    "    if i == 0:  # = first base case\n",
     "        return 0\n",
-    "    elif i == 1:\n",
+    "    elif i == 1:  # = second base case\n",
     "        return 1\n",
     "    return fibonacci(i - 1) + fibonacci(i - 2)"
    ]
@@ -841,7 +841,7 @@
     "\n",
     "To understand this in detail, we would have to study algorithms and data structures (e.g., with [this book](https://www.amazon.de/Introduction-Algorithms-Press-Thomas-Cormen/dp/0262033844/ref=sr_1_1?__mk_de_DE=%C3%85M%C3%85%C5%BD%C3%95%C3%91&crid=1JNE8U0VZGU0O&qid=1569837169&s=gateway&sprefix=algorithms+an%2Caps%2C180&sr=8-1)), a discipline within computer science, and dive into the analysis of **[time complexity of algorithms](https://en.wikipedia.org/wiki/Time_complexity)**.\n",
     "\n",
-    "Luckily, in the Fibonacci case, the inefficiency can be resolved with a **caching** (i.e., \"re-use\") strategy from the field of **[dynamic programming](https://en.wikipedia.org/wiki/Dynamic_programming)**, namely **[memoization](https://en.wikipedia.org/wiki/Memoization)**. We do so in Chapter 8, after introducing the [dictionaries](https://docs.python.org/3/library/stdtypes.html#dict) data type.\n",
+    "Luckily, in the Fibonacci case, the inefficiency can be resolved with a **caching** (i.e., \"reuse\") strategy from the field of **[dynamic programming](https://en.wikipedia.org/wiki/Dynamic_programming)**, namely **[memoization](https://en.wikipedia.org/wiki/Memoization)**. We do so in [Chapter 8](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/08_mappings.ipynb#Memoization), after introducing the [dictionaries](https://docs.python.org/3/library/stdtypes.html#dict) data type.\n",
     "\n",
     "Let's measure the average run times for `fibonacci()` and varying `i` arguments with the `%%timeit` [cell magic](https://ipython.readthedocs.io/en/stable/interactive/magics.html#magic-timeit) that comes with Jupyter."
    ]
@@ -859,7 +859,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "36.5 µs ± 2.47 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n"
+      "37.5 µs ± 4.26 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n"
      ]
     }
    ],
@@ -881,7 +881,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "1.58 ms ± 20.7 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n"
+      "1.49 ms ± 22.4 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n"
      ]
     }
    ],
@@ -903,7 +903,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "194 ms ± 0 ns per loop (mean ± std. dev. of 1 run, 1 loop each)\n"
+      "199 ms ± 0 ns per loop (mean ± std. dev. of 1 run, 1 loop each)\n"
      ]
     }
    ],
@@ -925,7 +925,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "2.15 s ± 0 ns per loop (mean ± std. dev. of 1 run, 1 loop each)\n"
+      "2.04 s ± 0 ns per loop (mean ± std. dev. of 1 run, 1 loop each)\n"
      ]
     }
    ],
@@ -947,7 +947,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "5.59 s ± 0 ns per loop (mean ± std. dev. of 1 run, 1 loop each)\n"
+      "5.33 s ± 0 ns per loop (mean ± std. dev. of 1 run, 1 loop each)\n"
      ]
     }
    ],
@@ -4011,9 +4011,9 @@
       "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
       "\u001b[0;31mRecursionError\u001b[0m                            Traceback (most recent call last)",
       "\u001b[0;32m<ipython-input-30-43e3ad331761>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mcountdown\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m3.1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
-      "\u001b[0;32m<ipython-input-1-6001bec3d961>\u001b[0m in \u001b[0;36mcountdown\u001b[0;34m(n)\u001b[0m\n\u001b[1;32m      9\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     10\u001b[0m         \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mn\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 11\u001b[0;31m         \u001b[0mcountdown\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mn\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;32m<ipython-input-1-d7441c038fc8>\u001b[0m in \u001b[0;36mcountdown\u001b[0;34m(n)\u001b[0m\n\u001b[1;32m      9\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     10\u001b[0m         \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mn\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 11\u001b[0;31m         \u001b[0mcountdown\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mn\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
       "... last 1 frames repeated, from the frame below ...\n",
-      "\u001b[0;32m<ipython-input-1-6001bec3d961>\u001b[0m in \u001b[0;36mcountdown\u001b[0;34m(n)\u001b[0m\n\u001b[1;32m      9\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     10\u001b[0m         \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mn\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 11\u001b[0;31m         \u001b[0mcountdown\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mn\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;32m<ipython-input-1-d7441c038fc8>\u001b[0m in \u001b[0;36mcountdown\u001b[0;34m(n)\u001b[0m\n\u001b[1;32m      9\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     10\u001b[0m         \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mn\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 11\u001b[0;31m         \u001b[0mcountdown\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mn\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
       "\u001b[0;31mRecursionError\u001b[0m: maximum recursion depth exceeded while calling a Python object"
      ]
     }
@@ -4050,9 +4050,9 @@
       "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
       "\u001b[0;31mRecursionError\u001b[0m                            Traceback (most recent call last)",
       "\u001b[0;32m<ipython-input-31-188b816be8b6>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mfactorial\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m3.1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
-      "\u001b[0;32m<ipython-input-6-88d8d933f651>\u001b[0m in \u001b[0;36mfactorial\u001b[0;34m(n)\u001b[0m\n\u001b[1;32m     10\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mn\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     11\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 12\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0mn\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0mfactorial\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mn\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;32m<ipython-input-6-d41f5be5adaa>\u001b[0m in \u001b[0;36mfactorial\u001b[0;34m(n)\u001b[0m\n\u001b[1;32m     10\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mn\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m  \u001b[0;31m# = base case\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     11\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 12\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0mn\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0mfactorial\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mn\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
       "... last 1 frames repeated, from the frame below ...\n",
-      "\u001b[0;32m<ipython-input-6-88d8d933f651>\u001b[0m in \u001b[0;36mfactorial\u001b[0;34m(n)\u001b[0m\n\u001b[1;32m     10\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mn\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     11\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 12\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0mn\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0mfactorial\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mn\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;32m<ipython-input-6-d41f5be5adaa>\u001b[0m in \u001b[0;36mfactorial\u001b[0;34m(n)\u001b[0m\n\u001b[1;32m     10\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mn\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m  \u001b[0;31m# = base case\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     11\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 12\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0mn\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0mfactorial\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mn\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
       "\u001b[0;31mRecursionError\u001b[0m: maximum recursion depth exceeded in comparison"
      ]
     }
@@ -4143,7 +4143,7 @@
     "        raise TypeError(\"Factorial is only defined for integers\")\n",
     "    elif n < 0:\n",
     "        raise ValueError(\"Factorial is not defined for negative integers\")\n",
-    "    elif n == 0:\n",
+    "    elif n == 0:  # = base case\n",
     "        return 1\n",
     "    return n * factorial(n - 1)"
    ]
@@ -4235,7 +4235,7 @@
       "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
       "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
       "\u001b[0;32m<ipython-input-35-188b816be8b6>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mfactorial\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m3.1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
-      "\u001b[0;32m<ipython-input-32-54b57bc2107d>\u001b[0m in \u001b[0;36mfactorial\u001b[0;34m(n)\u001b[0m\n\u001b[1;32m     13\u001b[0m     \"\"\"\n\u001b[1;32m     14\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mn\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mint\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 15\u001b[0;31m         \u001b[0;32mraise\u001b[0m \u001b[0mTypeError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Factorial is only defined for integers\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     16\u001b[0m     \u001b[0;32melif\u001b[0m \u001b[0mn\u001b[0m \u001b[0;34m<\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     17\u001b[0m         \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Factorial is not defined for negative integers\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m<ipython-input-32-ff0b507f76b8>\u001b[0m in \u001b[0;36mfactorial\u001b[0;34m(n)\u001b[0m\n\u001b[1;32m     13\u001b[0m     \"\"\"\n\u001b[1;32m     14\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mn\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mint\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 15\u001b[0;31m         \u001b[0;32mraise\u001b[0m \u001b[0mTypeError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Factorial is only defined for integers\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     16\u001b[0m     \u001b[0;32melif\u001b[0m \u001b[0mn\u001b[0m \u001b[0;34m<\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     17\u001b[0m         \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Factorial is not defined for negative integers\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
       "\u001b[0;31mTypeError\u001b[0m: Factorial is only defined for integers"
      ]
     }
@@ -4261,7 +4261,7 @@
       "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
       "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
       "\u001b[0;32m<ipython-input-36-b06b2cada645>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mfactorial\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m42\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
-      "\u001b[0;32m<ipython-input-32-54b57bc2107d>\u001b[0m in \u001b[0;36mfactorial\u001b[0;34m(n)\u001b[0m\n\u001b[1;32m     15\u001b[0m         \u001b[0;32mraise\u001b[0m \u001b[0mTypeError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Factorial is only defined for integers\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     16\u001b[0m     \u001b[0;32melif\u001b[0m \u001b[0mn\u001b[0m \u001b[0;34m<\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 17\u001b[0;31m         \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Factorial is not defined for negative integers\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     18\u001b[0m     \u001b[0;32melif\u001b[0m \u001b[0mn\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     19\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m<ipython-input-32-ff0b507f76b8>\u001b[0m in \u001b[0;36mfactorial\u001b[0;34m(n)\u001b[0m\n\u001b[1;32m     15\u001b[0m         \u001b[0;32mraise\u001b[0m \u001b[0mTypeError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Factorial is only defined for integers\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     16\u001b[0m     \u001b[0;32melif\u001b[0m \u001b[0mn\u001b[0m \u001b[0;34m<\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 17\u001b[0;31m         \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Factorial is not defined for negative integers\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     18\u001b[0m     \u001b[0;32melif\u001b[0m \u001b[0mn\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m  \u001b[0;31m# = base case\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     19\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
       "\u001b[0;31mValueError\u001b[0m: Factorial is not defined for negative integers"
      ]
     }
@@ -4565,7 +4565,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "4.69 s ± 0 ns per loop (mean ± std. dev. of 1 run, 1 loop each)\n"
+      "4.6 s ± 0 ns per loop (mean ± std. dev. of 1 run, 1 loop each)\n"
      ]
     }
    ],
@@ -4790,11 +4790,11 @@
     }
    },
    "source": [
-    "Recursion and the `while` statement are two sides of the same coin. Disregarding that in the case of recursion Python internally faces some additional burden for managing the stack of frames in memory, both approaches lead to the *same* computational steps in memory. More importantly, we can re-formulate a recursive implementation in an iterative way and vice versa despite one of the two ways often \"feeling\" a lot more natural given a particular problem.\n",
+    "Recursion and the `while` statement are two sides of the same coin. Disregarding that in the case of recursion Python internally faces some additional burden for managing the stack of frames in memory, both approaches lead to the *same* computational steps in memory. More importantly, we can formulate any recursive implementation in an iterative way and vice versa despite one of the two ways often \"feeling\" a lot more natural given a particular problem.\n",
     "\n",
     "So how does the compound `for` statement (cf., [reference](https://docs.python.org/3/reference/compound_stmts.html#the-for-statement)) in this book's very first example fit into this picture? It is a *redundant* language construct to provide a *shorter* and more *convenient* syntax for common applications of the `while` statement. In programming, such additions to a language are called **syntactic sugar**. A cup of tea tastes better with sugar, but we may drink tea without sugar too.\n",
     "\n",
-    "Consider `elements` below. Without the `for` statement, we have to manage a temporary **index variable**, `index`, to loop over all the elements and also obtain the individual elements with the `[]` operator in each iteration of the loop."
+    "Consider `elements` below. Without the `for` statement, we must manage a temporary **index variable**, `index`, to loop over all the elements and also obtain the individual elements with the `[]` operator in each iteration of the loop."
    ]
   },
   {
@@ -5000,21 +5000,21 @@
     }
    },
    "source": [
-    "The essential difference between the above `list` objects, `[0, 1, 2, 3, 4]` and `[1, 3, 5, 7, 9]`, and the `range` objects, `range(5)` and `range(1, 10, 2)`, is that in the former case *six* objects are created in memory *before* the `for` statement starts running, *one* `list` holding pointers to *five* `int` objects, whereas in the latter case only *one* `range` object is created that **generates** `int` objects one at a time *while* the `for`-loop runs.\n",
+    "The essential difference between the above `list` objects, `[0, 1, 2, 3, 4]` and `[1, 3, 5, 7, 9]`, and the `range` objects, `range(5)` and `range(1, 10, 2)`, is that in the former case *six* objects are created in memory *before* the `for` statement starts running, *one* `list` holding references to *five* `int` objects, whereas in the latter case only *one* `range` object is created that **generates** `int` objects one at a time *while* the `for`-loop runs.\n",
     "\n",
     "However, we can loop over both of them. So a natural question to ask is why Python treats objects of *different*  types in the *same* way when used with a `for` statement.\n",
     "\n",
-    "So far, the overarching storyline in this book goes like this: In Python, *everything* is an object. Besides its *identity* and *value*, every object is characterized by belonging to *one data type* that determines how the object behaves and what we may do with it.\n",
+    "So far, the overarching storyline in this book goes like this: In Python, *everything* is an object. Besides its *identity* and *value*, every object is characterized by \"belonging\" to *one* data type that determines how the object behaves and what we may do with it.\n",
     "\n",
-    "Now, just as we classify objects by their types, we also classify these **concrete data types** (e.g., `int`, `float`, `str`, or `list`) into **abstract concepts**.\n",
+    "Now, just as we classify objects by data type, we also classify these data types (e.g., `int`, `float`, `str`, or `list`) into **abstract concepts**.\n",
     "\n",
-    "We did this already in [Chapter 1](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/01_elements.ipynb#Who-am-I?-And-how-many?) when we described a `list` object as \"some sort of container that holds [...] pointers to other objects\". So, abstractly speaking, **containers** are any objects that are \"composed\" of other objects and also \"manage\" how these objects are organized. `list` objects, for example, have the property that they model an internal order associated with its elements. There exist, however, other container types, many of which do *not* come with an order. So, containers primarily \"contain\" other objects and have *nothing* to do with looping.\n",
+    "We did this already in [Chapter 1](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/01_elements.ipynb#Who-am-I?-And-how-many?) when we described a `list` object as \"some sort of container that holds [...] references to other objects\". So, abstractly speaking, **containers** are any objects that are \"composed\" of other objects and also \"manage\" how these objects are organized. `list` objects, for example, have the property that they model an order associated with their elements. There exist, however, other container types, many of which do *not* come with an order. So, containers primarily \"contain\" other objects and have *nothing* to do with looping.\n",
     "\n",
-    "On the contrary, the abstract concept of **iterables** is all about looping: Any object that we can loop over is, by definition, an iterable. So, `range` objects, for example, are iterables that do *not* contain other objects. Moreover, looping does *not* have to occur in a *predictable* order, although this is the case for both `list` and `range` objects.\n",
+    "On the contrary, the abstract concept of **iterables** is all about looping: Any object that we can loop over is, by definition, an iterable. So, `range` objects, for example, are iterables, even though they hold no references to other objects. Moreover, looping does *not* have to occur in a *predictable* order, although this is the case for both `list` and `range` objects.\n",
     "\n",
-    "Typically, containers are iterables, and iterables are containers. Yet, only because these two concepts coincide often, we must not think of them as the same. Chapter 9 finally gives an explanation as to how abstract concepts are implemented and play together.\n",
+    "Typically, containers are iterables, and iterables are containers. Yet, only because these two concepts coincide often, we must not think of them as the same. In [Chapter 7](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/07_sequences.ipynb#Collections-vs.-Sequences), we formalize these two concepts and introduce many more. Finally, Chapter 9 gives an explanation how abstract concepts are implemented and play together.\n",
     "\n",
-    "So, `list` objects like `first_names` below are iterable containers. They implement even more abstract concepts, as [Chapter 7](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/07_sequences.ipynb#Collections-vs.-Sequences) reveals."
+    "Let's continue with `first_names` below as an example an illustrate what iterable containers are."
    ]
   },
   {
@@ -5038,7 +5038,7 @@
     }
    },
    "source": [
-    "The characteristic operator associated with a container type is the `in` operator: It checks if a given object evaluates equal to at least one of the objects in the container. Colloquially, it checks if an object is \"contained\" in the container. Formally, this operation is called **membership testing**."
+    "The characteristic operator associated with container types is the `in` operator: It checks if a given object evaluates equal to at least one of the objects in the container. Colloquially, it checks if an object is \"contained\" in the container. Formally, this operation is called **membership testing**."
    ]
   },
   {
@@ -5112,7 +5112,7 @@
     {
      "data": {
       "text/plain": [
-       "True"
+       "[0, 1, 2, 3, 4]"
       ]
      },
      "execution_count": 58,
@@ -5120,6 +5120,30 @@
      "output_type": "execute_result"
     }
    ],
+   "source": [
+    "elements"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 59,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 59,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "3.0 in elements"
    ]
@@ -5137,7 +5161,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 59,
+   "execution_count": 60,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -5170,7 +5194,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 60,
+   "execution_count": 61,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -5203,7 +5227,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 61,
+   "execution_count": 62,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -5236,7 +5260,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 62,
+   "execution_count": 63,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -5249,7 +5273,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 63,
+   "execution_count": 64,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -5297,7 +5321,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 64,
+   "execution_count": 65,
    "metadata": {
     "code_folding": [],
     "slideshow": {
@@ -5338,7 +5362,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 65,
+   "execution_count": 66,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -5358,7 +5382,7 @@
        "144"
       ]
      },
-     "execution_count": 65,
+     "execution_count": 66,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -5391,7 +5415,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 66,
+   "execution_count": 67,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -5411,7 +5435,7 @@
        "218922995834555169026"
       ]
      },
-     "execution_count": 66,
+     "execution_count": 67,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -5444,7 +5468,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 67,
+   "execution_count": 68,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -5480,7 +5504,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 68,
+   "execution_count": 69,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -5500,7 +5524,7 @@
        "3628800"
       ]
      },
-     "execution_count": 68,
+     "execution_count": 69,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -5555,7 +5579,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 69,
+   "execution_count": 70,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -5568,7 +5592,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 70,
+   "execution_count": 71,
    "metadata": {
     "slideshow": {
      "slide_type": "-"
@@ -5592,7 +5616,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 71,
+   "execution_count": 72,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -5638,7 +5662,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 72,
+   "execution_count": 73,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -5716,7 +5740,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 73,
+   "execution_count": 74,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -5759,7 +5783,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 74,
+   "execution_count": 75,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -5797,7 +5821,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 75,
+   "execution_count": 76,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -5885,7 +5909,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 76,
+   "execution_count": 77,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -5898,7 +5922,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 77,
+   "execution_count": 78,
    "metadata": {
     "slideshow": {
      "slide_type": "-"
@@ -5918,7 +5942,7 @@
        "370"
       ]
      },
-     "execution_count": 77,
+     "execution_count": 78,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -5980,7 +6004,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 78,
+   "execution_count": 79,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -5993,7 +6017,7 @@
        "[7, 11, 8, 5, 3, 12, 2, 6, 9, 10, 1, 4]"
       ]
      },
-     "execution_count": 78,
+     "execution_count": 79,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -6004,7 +6028,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 79,
+   "execution_count": 80,
    "metadata": {
     "slideshow": {
      "slide_type": "-"
@@ -6024,7 +6048,7 @@
        "227"
       ]
      },
-     "execution_count": 79,
+     "execution_count": 80,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -6070,7 +6094,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 80,
+   "execution_count": 81,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -6090,7 +6114,7 @@
        "227"
       ]
      },
-     "execution_count": 80,
+     "execution_count": 81,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -6178,7 +6202,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 81,
+   "execution_count": 82,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -6191,7 +6215,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 82,
+   "execution_count": 83,
    "metadata": {
     "slideshow": {
      "slide_type": "-"
@@ -6204,7 +6228,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 83,
+   "execution_count": 84,
    "metadata": {
     "code_folding": [],
     "slideshow": {
@@ -6283,7 +6307,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 84,
+   "execution_count": 85,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -6320,7 +6344,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 85,
+   "execution_count": 86,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -6358,7 +6382,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 86,
+   "execution_count": 87,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -6371,7 +6395,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 87,
+   "execution_count": 88,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -6441,7 +6465,7 @@
     "\n",
     "Second, the `while` and `for` statements are alternative and potentially more intuitive ways to express iteration as they correspond to a **forward** reasoning. The `for` statement is **syntactic sugar** that allows rewriting common occurrences of the `while` statement concisely. Python provides the `break` and `continue` statements as well as an optional `else`-clause that make working with the `for` and `while` statements even more convenient.\n",
     "\n",
-    "**Iterables** are any **concrete data types** that support being looped over, for example, with the `for` statement. The idea behind iterables is an **abstract concept** that may or may not be implemented by any given concrete data type. For example, both `list` and `range` objects can be looped over. The `list` type is also a **container** as any given `list` objects \"contains\" pointers to other objects in memory. On the contrary, the `range` type does not point to any other objects but instead creates *new* `int` objects \"on the fly\" (i.e., when being looped over)."
+    "**Iterables** are any **concrete data types** that support being looped over, for example, with the `for` statement. The idea behind iterables is an **abstract concept** that may or may not be implemented by any given concrete data type. For example, both `list` and `range` objects can be looped over. The `list` type is also a **container** as any given `list` objects \"contains\" references to other objects in memory. On the contrary, the `range` type does not reference any other object but instead creates *new* `int` objects \"on the fly\" (i.e., when being looped over)."
    ]
   }
  ],
diff --git a/04_iteration_review_and_exercises.ipynb b/04_iteration_review_and_exercises.ipynb
index 64d6903..9e2b28f 100644
--- a/04_iteration_review_and_exercises.ipynb
+++ b/04_iteration_review_and_exercises.ipynb
@@ -455,19 +455,8 @@
     "\n",
     "    # answer to Q15.6\n",
     "    # ...\n",
-    "    # ...\n",
-    "    # ...\n",
-    "    # ...\n",
-    "    # ...\n",
-    "    # ...\n",
-    "    # ...\n",
-    "    # ...\n",
-    "    # ...\n",
-    "    # ...\n",
-    "    # ...\n",
     "\n",
     "    # answer to Q15.7\n",
-    "    # ...\n",
     "    # ..."
    ]
   },
@@ -543,7 +532,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "#### Pythonic Re-Factoring"
+    "#### Pythonic Refactoring"
    ]
   },
   {
@@ -569,7 +558,6 @@
     "    # ...\n",
     "\n",
     "    # answer to Q15.10\n",
-    "    # ...\n",
     "    # ..."
    ]
   },
@@ -726,7 +714,8 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "\n"
+    "for ... in ...:\n",
+    "    ..."
    ]
   },
   {
@@ -811,9 +800,9 @@
     "    if offset is not None:\n",
     "        count += offset\n",
     "\n",
-    "    hanoi_ordered(..., offset=offset)  # <- answer to Q15.18 between the ()\n",
-    "    # answer to Q15.18\n",
-    "    hanoi_ordered(..., offset=count)  # <- answer to Q15.18 between the ()"
+    "    hanoi_ordered(..., offset=offset)  # <- answer to Q15.18\n",
+    "    # ... <- answer to Q15.18\n",
+    "    hanoi_ordered(..., offset=count)  # <- answer to Q15.18"
    ]
   },
   {
diff --git a/05_numbers.ipynb b/05_numbers.ipynb
index a671e68..48d8623 100644
--- a/05_numbers.ipynb
+++ b/05_numbers.ipynb
@@ -21,9 +21,9 @@
    "source": [
     "After learning about the basic building blocks of expressing and structuring the business logic in programs, we focus our attention on the **data types** Python offers us, both built-in and available via the [standard library](https://docs.python.org/3/library/index.html) or third-party packages.\n",
     "\n",
-    "We start with the \"simple\" ones: Numeric types in this chapter and textual data in [Chapter 6](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/06_text.ipynb). An important fact that holds for all objects of these types is that they are **immutable**. To re-use the bag analogy from [Chapter 1](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/01_elements.ipynb#Objects-vs.-Types-vs.-Values), this means that the $0$s and $1$s making up an object's *value* cannot be changed once the bag is created in memory, implying that any operation with or method on the object creates a *new* object in a *different* memory location.\n",
+    "We start with the \"simple\" ones: Numeric types in this chapter and textual data in [Chapter 6](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/06_text.ipynb). An important fact that holds for all objects of these types is that they are **immutable**. To reuse the bag analogy from [Chapter 1](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/01_elements.ipynb#Objects-vs.-Types-vs.-Values), this means that the $0$s and $1$s making up an object's *value* cannot be changed once the bag is created in memory, implying that any operation with or method on the object creates a *new* object in a *different* memory location.\n",
     "\n",
-    "[Chapter 7](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/07_sequences.ipynb) and Chapter 8 then cover the more \"complex\" data types, including, for example, the `list` type. Finally, Chapter 9 completes the picture by introducing language constructs to create custom types.\n",
+    "[Chapter 7](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/07_sequences.ipynb) and [Chapter 8](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/08_mappings.ipynb) then cover the more \"complex\" data types, including, for example, the `list` type. Finally, Chapter 9 completes the picture by introducing language constructs to create custom types.\n",
     "\n",
     "We have already seen many hints indicating that numbers are not as trivial to work with as it seems at first sight:\n",
     "\n",
@@ -92,7 +92,7 @@
     {
      "data": {
       "text/plain": [
-       "140087541220560"
+       "140673805309168"
       ]
      },
      "execution_count": 2,
@@ -307,7 +307,7 @@
     }
    },
    "source": [
-    "We may pass a `str` object formatted this way as the argument to the [int()](https://docs.python.org/3/library/functions.html#int) built-in, together with `base=2`, to (re-)create an `int` object, for example, with the value of `3`."
+    "We may pass a `str` object formatted this way as the argument to the [int()](https://docs.python.org/3/library/functions.html#int) built-in, together with `base=2`, to create an `int` object, for example, with the value of `3`."
    ]
   },
   {
@@ -883,7 +883,7 @@
     }
    },
    "source": [
-    "To (re-)create an `int` object with the value `177`, we call the [int()](https://docs.python.org/3/library/functions.html#int) built-in with a properly formatted `str` object and `base=16` as arguments."
+    "To obtain a *new* `int` object with the value `177`, we call the [int()](https://docs.python.org/3/library/functions.html#int) built-in with a properly formatted `str` object and `base=16` as arguments."
    ]
   },
   {
@@ -2115,7 +2115,7 @@
     {
      "data": {
       "text/plain": [
-       "140087541402072"
+       "140673805486768"
       ]
      },
      "execution_count": 63,
@@ -5498,7 +5498,7 @@
     {
      "data": {
       "text/plain": [
-       "140087540555856"
+       "140673804641712"
       ]
      },
      "execution_count": 176,
@@ -6054,7 +6054,7 @@
     }
    },
    "source": [
-    "Also, a conjugate() method is bound to every `complex` object. The [complex conjugate](https://en.wikipedia.org/wiki/Complex_conjugate) is defined to be the complex number with identical real part but an imaginary part reversed in sign."
+    "Also, a `conjugate()` method is bound to every `complex` object. The [complex conjugate](https://en.wikipedia.org/wiki/Complex_conjugate) is defined to be the complex number with identical real part but an imaginary part reversed in sign."
    ]
   },
   {
@@ -6111,17 +6111,17 @@
     }
    },
    "source": [
-    "Analogous to the discussion of containers and iterables in [Chapter 4](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/04_iteration.ipynb#Containers-vs.-Iterables), we contrast the *concrete* numeric data types in this chapter with the *abstract* ideas behind [numbers in mathematics](https://en.wikipedia.org/wiki/Number).\n",
+    "Analogous to the discussion of *containers* and *iterables* in [Chapter 4](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/04_iteration.ipynb#Containers-vs.-Iterables), we contrast the *concrete* numeric data types in this chapter with the *abstract* ideas behind [numbers in mathematics](https://en.wikipedia.org/wiki/Number).\n",
     "\n",
     "The figure below summarizes five *major* sets of [numbers in mathematics](https://en.wikipedia.org/wiki/Number) as we know them from high school:\n",
     "\n",
     "- $\\mathbb{N}$: [Natural numbers](https://en.wikipedia.org/wiki/Natural_number) are all non-negative count numbers, e.g., $0, 1, 2, ...$\n",
     "- $\\mathbb{Z}$: [Integers](https://en.wikipedia.org/wiki/Integer) are all numbers *without* a fractional component, e.g., $-1, 0, 1, ...$\n",
     "- $\\mathbb{Q}$: [Rational numbers](https://en.wikipedia.org/wiki/Rational_number) are all numbers that can be expressed as a quotient of two integers, e.g., $-\\frac{1}{2}, 0, \\frac{1}{2}, ...$\n",
-    "- $\\mathbb{R}$: [Real numbers](https://en.wikipedia.org/wiki/Real_number) are all numbers that can be represented as a distance along a line (negative means \"reversed\"), e.g., $\\sqrt{2}, \\pi, \\text{e}, ...$\n",
+    "- $\\mathbb{R}$: [Real numbers](https://en.wikipedia.org/wiki/Real_number) are all numbers that can be represented as a distance along a line, and negative means \"reversed,\" e.g., $\\sqrt{2}, \\pi, \\text{e}, ...$\n",
     "- $\\mathbb{C}$: [Complex numbers](https://en.wikipedia.org/wiki/Complex_number) are all numbers of the form $a + b\\textbf{i}$ where $a$ and $b$ are real numbers and $\\textbf{i}$ is the [imaginary number](https://en.wikipedia.org/wiki/Imaginary_number), e.g., $0, \\textbf{i}, 1 + \\textbf{i}, ...$\n",
     "\n",
-    "In the listed order, the five sets are perfect subsets and $\\mathbb{C}$ is the largest set (to be precise, all sets are infinite, but they still have a different number of elements)."
+    "In the listed order, the five sets are perfect subsets of the respective following sets, and $\\mathbb{C}$ is the largest set (cf., the figure below illustrates that observation as well). To be precise, all sets are infinite, but they still have a different number of elements."
    ]
   },
   {
@@ -6143,13 +6143,13 @@
     }
    },
    "source": [
-    "The *concrete* data types introduced in this chapter are all *imperfect* models of *abstract* mathematical ideas.\n",
+    "The data types introduced in this chapter are all *imperfect* models of *abstract* mathematical ideas.\n",
     "\n",
-    "The `int` and `Fraction` types are the models \"closest\" to the idea they implement: Whereas $\\mathbb{Z}$ and $\\mathbb{Q}$ are, by definition, infinite, every computer runs out of bits when representing sufficiently large integers or fractions with a sufficiently large number of decimals. However, within a system-dependent minimum and maximum integer range, we can model an integer or fraction without any loss in precision.\n",
+    "The `int` and `Fraction` types are the models \"closest\" to the idea they implement: Whereas $\\mathbb{Z}$ and $\\mathbb{Q}$ are, by definition, infinite, every computer runs out of bits when representing sufficiently large integers or fractions with a sufficiently large number of decimals. However, within a system-dependent range, we can model an integer or fraction without any loss in precision.\n",
     "\n",
     "For the other types, in particular, the `float` type, the implications of their imprecision are discussed in detail above.\n",
     "\n",
-    "The abstract concepts behind the four outer-most mathematical sets are part of Python since [PEP 3141](https://www.python.org/dev/peps/pep-3141/) in 2007. The [numbers](https://docs.python.org/3/library/numbers.html) module in the [standard library](https://docs.python.org/3/library/index.html) defines what programmers call the **[numerical tower](https://en.wikipedia.org/wiki/Numerical_tower)**, a collection of five **[abstract data types](https://en.wikipedia.org/wiki/Abstract_data_type)**, or **abstract base classes** as they are called in Python jargon:\n",
+    "The abstract concepts behind the four outer-most mathematical sets are formalized in Python since [PEP 3141](https://www.python.org/dev/peps/pep-3141/) in 2007. The [numbers](https://docs.python.org/3/library/numbers.html) module in the [standard library](https://docs.python.org/3/library/index.html) defines what programmers call the **[numerical tower](https://en.wikipedia.org/wiki/Numerical_tower)**, a collection of five **[abstract data types](https://en.wikipedia.org/wiki/Abstract_data_type)**, or **abstract base classes** (ABCs) as they are called in Python jargon:\n",
     "\n",
     "- `numbers.Number`: \"any number\" (cf., [documentation](https://docs.python.org/3/library/numbers.html#numbers.Number))\n",
     "- `numbers.Complex`: \"all complex numbers\" (cf., [documentation](https://docs.python.org/3/library/numbers.html#numbers.Complex))\n",
@@ -6220,9 +6220,9 @@
    "source": [
     "As a reminder, the built-in [help()](https://docs.python.org/3/library/functions.html#help) function is always our friend.\n",
     "\n",
-    "The abstract types' docstrings are unsurprisingly similar to the corresponding concrete types' docstrings (for now, let's not worry about the dunder-style names in the docstrings).\n",
+    "The ABCs' docstrings are unsurprisingly similar to the corresponding data types' docstrings. For now, let's not worry about the dunder-style names in the docstrings.\n",
     "\n",
-    "For example, both `numbers.Complex` and `complex` list the `imag` and `real` attributes."
+    "For example, both `numbers.Complex` and `complex` list the `imag` and `real` attributes shown above."
    ]
   },
   {
@@ -6231,7 +6231,7 @@
    "metadata": {
     "scrolled": true,
     "slideshow": {
-     "slide_type": "slide"
+     "slide_type": "skip"
     }
    },
    "outputs": [
@@ -6336,17 +6336,6 @@
     "help(numbers.Complex)"
    ]
   },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "source": [
-    "For sure, Python understands the built-in types as literals."
-   ]
-  },
   {
    "cell_type": "code",
    "execution_count": 201,
@@ -6509,9 +6498,9 @@
     }
    },
    "source": [
-    "One way to use *abstract* data types is to use them in place of a *concrete* data type.\n",
+    "The primary purpose of ABCs is to classify the *concrete* data types and standardize how they behave.\n",
     "\n",
-    "For example, we may pass them as arguments to the built-in [isinstance()](https://docs.python.org/3/library/functions.html#isinstance) function and check in which of the five mathematical sets the object `1 / 10` is."
+    "For, example, as all numeric data types are `Complex` numbers in the abstract sense, they all work with the built-in [abs()](https://docs.python.org/3/library/functions.html#abs) function (cf., [documentation](https://docs.python.org/3/library/numbers.html#numbers.Complex)). While it is intuitively clear what the [absolute value](https://en.wikipedia.org/wiki/Absolute_value) (i.e., \"distance\" to $0$) of an integer, a fraction, or any real number is, [abs()](https://docs.python.org/3/library/functions.html#abs) calculates the equivalent of that for complex numbers. That concept is called the [magnitude](https://en.wikipedia.org/wiki/Magnitude_%28mathematics%29) of a number, and is really a *generalization* of the absolute value."
    ]
   },
   {
@@ -6526,7 +6515,7 @@
     {
      "data": {
       "text/plain": [
-       "True"
+       "42"
       ]
      },
      "execution_count": 202,
@@ -6535,7 +6524,7 @@
     }
    ],
    "source": [
-    "isinstance(1 / 10, float)"
+    "abs(-42)"
    ]
   },
   {
@@ -6543,14 +6532,14 @@
    "execution_count": 203,
    "metadata": {
     "slideshow": {
-     "slide_type": "fragment"
+     "slide_type": "-"
     }
    },
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "True"
+       "Decimal('0.1')"
       ]
      },
      "execution_count": 203,
@@ -6559,7 +6548,7 @@
     }
    ],
    "source": [
-    "isinstance(1 / 10, numbers.Number)"
+    "abs(Decimal(\"-0.1\"))"
    ]
   },
   {
@@ -6574,7 +6563,7 @@
     {
      "data": {
       "text/plain": [
-       "True"
+       "5.0"
       ]
      },
      "execution_count": 204,
@@ -6583,12 +6572,263 @@
     }
    ],
    "source": [
-    "isinstance(1 / 10, numbers.Complex)"
+    "abs(4 - 3j)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "On the contrary, only `Real` numbers in the abstract sense may be rounded with the built-in [round()](https://docs.python.org/3/library/functions.html#round) function."
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 205,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "42"
+      ]
+     },
+     "execution_count": 205,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "round(42.1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 206,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0"
+      ]
+     },
+     "execution_count": 206,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "round(Decimal(\"0.1\"))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "`Complex` numbers are two-dimensional. So, rounding makes no sense and raises a `TypeError`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 207,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [
+    {
+     "ename": "TypeError",
+     "evalue": "type complex doesn't define __round__ method",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-207-44b2943ede89>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mround\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m4\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;36m3j\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;31mTypeError\u001b[0m: type complex doesn't define __round__ method"
+     ]
+    }
+   ],
+   "source": [
+    "round(4 + 3j)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Knowing what ABCs a numeric type adheres to, is not only important in the context of built-ins. The [trunc()](https://docs.python.org/3/library/math.html#math.trunc) function from the [math](https://docs.python.org/3/library/math.html) module in the [standard library](https://docs.python.org/3/library/index.html), for example, only works with `Real` types (cf., [documentation](https://docs.python.org/3/library/numbers.html#numbers.Real))."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 208,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "import math"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "[trunc()](https://docs.python.org/3/library/math.html#math.trunc) cuts off a number's decimals."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 209,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0"
+      ]
+     },
+     "execution_count": 209,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "math.trunc(9 / 10)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "A `Complex` number leads to a `TypeError`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 210,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "ename": "TypeError",
+     "evalue": "type complex doesn't define __trunc__ method",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-210-7121e57d9639>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mmath\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtrunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m0.9\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;36m1j\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;31mTypeError\u001b[0m: type complex doesn't define __trunc__ method"
+     ]
+    }
+   ],
+   "source": [
+    "math.trunc(0.9 + 1j)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Another way to use ABCs is in place of a *concrete* data type.\n",
+    "\n",
+    "For example, we may pass them as arguments to the built-in [isinstance()](https://docs.python.org/3/library/functions.html#isinstance) function and check in which of the five mathematical sets the object `1 / 10` is."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 211,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 211,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "isinstance(1 / 10, float)  # we know that from before"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 212,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 212,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "isinstance(1 / 10, numbers.Number)  # a float object is a Number in the abstract sense"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 213,
    "metadata": {
     "slideshow": {
      "slide_type": "-"
@@ -6601,13 +6841,37 @@
        "True"
       ]
      },
-     "execution_count": 205,
+     "execution_count": 213,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "isinstance(1 / 10, numbers.Real)"
+    "isinstance(1 / 10, numbers.Complex)  # float objects are always also Complex numbers"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 214,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 214,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "isinstance(1 / 10, numbers.Real)  # a float object's purpose is to model a Real number"
    ]
   },
   {
@@ -6623,7 +6887,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 206,
+   "execution_count": 215,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -6636,13 +6900,13 @@
        "False"
       ]
      },
-     "execution_count": 206,
+     "execution_count": 215,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "isinstance(1 / 10, numbers.Rational)"
+    "isinstance(1 / 10, numbers.Rational)  # the type of `1 / 10` is what is important, not its value"
    ]
   },
   {
@@ -6658,7 +6922,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 207,
+   "execution_count": 216,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -6671,7 +6935,7 @@
        "True"
       ]
      },
-     "execution_count": 207,
+     "execution_count": 216,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -6682,7 +6946,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 208,
+   "execution_count": 217,
    "metadata": {
     "slideshow": {
      "slide_type": "-"
@@ -6695,7 +6959,7 @@
        "False"
       ]
      },
-     "execution_count": 208,
+     "execution_count": 217,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -6723,46 +6987,9 @@
     }
    },
    "source": [
-    "Replacing *concrete* data types with *abstract* ones is particularly valuable in the context of input validation: The revised version of the `factorial()` function below allows its user to take advantage of *duck typing*: If a real but non-integer argument `n` is passed in, `factorial()` tries to cast `n` as an `int` object with the [trunc()](https://docs.python.org/3/library/math.html#math.trunc) function from the [math](https://docs.python.org/3/library/math.html) module in the [standard library](https://docs.python.org/3/library/index.html). [trunc()](https://docs.python.org/3/library/math.html#math.trunc) cuts off all decimals and any *concrete* numeric type implementing the *abstract* `numbers.Real` type supports it (cf., [documentation](https://docs.python.org/3/library/numbers.html#numbers.Real)).\n",
+    "Replacing *concrete* data types with ABCs is particularly valuable in the context of input validation: The revised version of the `factorial()` function below allows its user to take advantage of *duck typing*: If a real but non-integer argument `n` is passed in, `factorial()` tries to cast `n` as an `int` object with [math.trunc()](https://docs.python.org/3/library/math.html#math.trunc).\n",
     "\n",
-    "Two popular and distinguished Pythonistas, [Luciano Ramalho](https://github.com/ramalho) and [Alex Martelli](https://en.wikipedia.org/wiki/Alex_Martelli), coin the term **goose typing** to specifically mean using the built-in [isinstance()](https://docs.python.org/3/library/functions.html#isinstance) function with an *abstract base class* (cf., Chapter 11 in this [book](https://www.amazon.com/Fluent-Python-Concise-Effective-Programming/dp/1491946008) or this [summary](https://dgkim5360.github.io/blog/python/2017/07/duck-typing-vs-goose-typing-pythonic-interfaces/) thereof)."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 209,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "slide"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "import math"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 210,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "-"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "0"
-      ]
-     },
-     "execution_count": 210,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "math.trunc(1 / 10)"
+    "Two popular and distinguished Pythonistas, [Luciano Ramalho](https://github.com/ramalho) and [Alex Martelli](https://en.wikipedia.org/wiki/Alex_Martelli), coin the term **goose typing** to specifically mean using the built-in [isinstance()](https://docs.python.org/3/library/functions.html#isinstance) function with an ABC (cf., Chapter 11 in this [book](https://www.amazon.com/Fluent-Python-Concise-Effective-Programming/dp/1491946008) or this [summary](https://dgkim5360.github.io/blog/python/2017/07/duck-typing-vs-goose-typing-pythonic-interfaces/) thereof)."
    ]
   },
   {
@@ -6778,7 +7005,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 211,
+   "execution_count": 218,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -6810,7 +7037,7 @@
     "\n",
     "    if n < 0:\n",
     "        raise ValueError(\"Factorial is not defined for negative integers\")\n",
-    "    elif n == 0:\n",
+    "    elif n == 0:  # = base case\n",
     "        return 1\n",
     "    return n * factorial(n - 1)"
    ]
@@ -6828,7 +7055,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 212,
+   "execution_count": 219,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -6841,7 +7068,7 @@
        "1"
       ]
      },
-     "execution_count": 212,
+     "execution_count": 219,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -6852,7 +7079,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 213,
+   "execution_count": 220,
    "metadata": {
     "slideshow": {
      "slide_type": "-"
@@ -6865,7 +7092,7 @@
        "6"
       ]
      },
-     "execution_count": 213,
+     "execution_count": 220,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -6876,7 +7103,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 214,
+   "execution_count": 221,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -6889,7 +7116,7 @@
        "6"
       ]
      },
-     "execution_count": 214,
+     "execution_count": 221,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -6906,12 +7133,12 @@
     }
    },
    "source": [
-    "With the keyword-only argument `strict`, we can control whether or not a passed in `float` object may be rounded. By default, this is not allowed and results in a `TypeError`."
+    "With the keyword-only argument `strict`, we can control whether or not a passed in `float` object may come with decimals that are then truncated. By default, this is not allowed and results in a `TypeError`."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 215,
+   "execution_count": 222,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -6925,8 +7152,8 @@
      "traceback": [
       "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
       "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-215-188b816be8b6>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mfactorial\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m3.1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
-      "\u001b[0;32m<ipython-input-211-60ca579e64ac>\u001b[0m in \u001b[0;36mfactorial\u001b[0;34m(n, strict)\u001b[0m\n\u001b[1;32m     16\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mn\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnumbers\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mReal\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     17\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mn\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0mmath\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtrunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mn\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mstrict\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 18\u001b[0;31m                 \u001b[0;32mraise\u001b[0m \u001b[0mTypeError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"n is not an integer-like value; it has decimals\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     19\u001b[0m             \u001b[0mn\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmath\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtrunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mn\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     20\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m<ipython-input-222-188b816be8b6>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mfactorial\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m3.1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;32m<ipython-input-218-d2a63febf49e>\u001b[0m in \u001b[0;36mfactorial\u001b[0;34m(n, strict)\u001b[0m\n\u001b[1;32m     16\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mn\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnumbers\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mReal\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     17\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mn\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0mmath\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtrunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mn\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mstrict\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 18\u001b[0;31m                 \u001b[0;32mraise\u001b[0m \u001b[0mTypeError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"n is not an integer-like value; it has decimals\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     19\u001b[0m             \u001b[0mn\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmath\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtrunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mn\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     20\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
       "\u001b[0;31mTypeError\u001b[0m: n is not an integer-like value; it has decimals"
      ]
     }
@@ -6935,9 +7162,20 @@
     "factorial(3.1)"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "In non-strict mode, the passed in `3.1` is truncated into `3` resulting in a factorial of `6`."
+   ]
+  },
   {
    "cell_type": "code",
-   "execution_count": 216,
+   "execution_count": 223,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -6950,7 +7188,7 @@
        "6"
       ]
      },
-     "execution_count": 216,
+     "execution_count": 223,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -6967,12 +7205,12 @@
     }
    },
    "source": [
-    "For `complex` numbers, `factorial()` still raises a `TypeError`."
+    "For `complex` numbers, `factorial()` still raises a `TypeError` because they are neither an `Integral` nor a `Real` number."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 217,
+   "execution_count": 224,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -6986,8 +7224,8 @@
      "traceback": [
       "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
       "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-217-7296a74f5dcf>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mfactorial\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m1\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;36m2j\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
-      "\u001b[0;32m<ipython-input-211-60ca579e64ac>\u001b[0m in \u001b[0;36mfactorial\u001b[0;34m(n, strict)\u001b[0m\n\u001b[1;32m     19\u001b[0m             \u001b[0mn\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmath\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtrunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mn\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     20\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 21\u001b[0;31m             \u001b[0;32mraise\u001b[0m \u001b[0mTypeError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Factorial is only defined for integers\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     22\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     23\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mn\u001b[0m \u001b[0;34m<\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m<ipython-input-224-7296a74f5dcf>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mfactorial\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m1\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;36m2j\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;32m<ipython-input-218-d2a63febf49e>\u001b[0m in \u001b[0;36mfactorial\u001b[0;34m(n, strict)\u001b[0m\n\u001b[1;32m     19\u001b[0m             \u001b[0mn\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmath\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtrunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mn\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     20\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 21\u001b[0;31m             \u001b[0;32mraise\u001b[0m \u001b[0mTypeError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Factorial is only defined for integers\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     22\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     23\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mn\u001b[0m \u001b[0;34m<\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
       "\u001b[0;31mTypeError\u001b[0m: Factorial is only defined for integers"
      ]
     }
@@ -7016,20 +7254,20 @@
    },
    "source": [
     "There exist three numeric types in core Python:\n",
-    "- `int`: a near-perfect model for whole numbers (i.e., the set $\\mathbb{Z}$); inherently precise\n",
-    "- `float`: the \"gold\" standard to approximate real numbers (i.e., the set $\\mathbb{R}$); inherently imprecise\n",
-    "- `complex`: layer on top of the `float` type; therefore inherently imprecise\n",
+    "- `int`: a near-perfect model for whole numbers (i.e., $\\mathbb{Z}$); inherently precise\n",
+    "- `float`: the \"gold\" standard to approximate real numbers (i.e., $\\mathbb{R}$); inherently imprecise\n",
+    "- `complex`: layer on top of the `float` type to approximate complex numbers (i.e., $\\mathbb{C}$); inherently imprecise\n",
     "\n",
-    "Furthermore, the [standard library](https://docs.python.org/3/library/index.html) adds two more types that can be used as substitutes for `float` objects:\n",
+    "Furthermore, the [standard library](https://docs.python.org/3/library/index.html) provides two more types that can be used as substitutes for the `float` type:\n",
     "- `Decimal`: similar to `float` but allows customizing the precision; still inherently imprecise\n",
-    "- `Fraction`: a near-perfect model for rational numbers (i.e., the set $\\mathbb{Q}$); built on top of the `int` type and therefore inherently precise\n",
+    "- `Fraction`: a near-perfect model for rational numbers (i.e., $\\mathbb{Q}$); built on top of the `int` type and therefore inherently precise\n",
     "\n",
     "The *important* takeaways for the data science practitioner are:\n",
     "\n",
     "1. **Do not mix** precise and imprecise data types, and\n",
     "2. actively expect `nan` results when working with `float` numbers as there are no **loud failures**.\n",
     "\n",
-    "The **numerical tower** is Python's way of implementing the various **abstract** ideas of what numbers are in mathematics."
+    "The **numerical tower** is Python's way of implementing various **abstract** ideas of what numbers are in mathematics."
    ]
   }
  ],
diff --git a/05_numbers_review_and_exercises.ipynb b/05_numbers_review_and_exercises.ipynb
index 90983d4..2ae8301 100644
--- a/05_numbers_review_and_exercises.ipynb
+++ b/05_numbers_review_and_exercises.ipynb
@@ -156,7 +156,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "**Q9**: How can **abstract data types**, for example, as defined in the **numerical tower**, be helpful in enabling **duck typing**?"
+    "**Q9**: How can **abstract base classes**, for example, as defined in the **numerical tower**, be helpful in enabling **duck typing**?"
    ]
   },
   {
@@ -519,7 +519,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "**Q19.7**: Re-write the function `discounted_price()` from [Chapter 3's Review & Exercises](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/03_conditionals_review_and_exercises.ipynb#Discounting-Customer-Orders) section!\n",
+    "**Q19.7**: Rewrite the function `discounted_price()` from [Chapter 3's Review & Exercises](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/03_conditionals_review_and_exercises.ipynb#Discounting-Customer-Orders) section!\n",
     "\n",
     "It takes the *positional* arguments `unit_price` and `quantity` and implements a discount scheme for a line item in a customer order as follows:\n",
     "\n",
@@ -530,7 +530,7 @@
     "\n",
     "The function then returns the overall price for the line item as a `Decimal` number with a precision of *two* decimals.\n",
     "\n",
-    "Enable **duck typing** by allowing the function to be called with various numeric types as the arguments, in particular, `quantity` may be a non-integer as well: Use an appropriate **abstract data type** from the [numbers](https://docs.python.org/3/library/numbers.html) module in the [standard library](https://docs.python.org/3/library/index.html) to verify the arguments' types and also that they are both positive!\n",
+    "Enable **duck typing** by allowing the function to be called with various numeric types as the arguments, in particular, `quantity` may be a non-integer as well: Use an appropriate **abstract base class** from the [numbers](https://docs.python.org/3/library/numbers.html) module in the [standard library](https://docs.python.org/3/library/index.html) to verify the arguments' types and also that they are both positive!\n",
     "\n",
     "It is considered a *best practice* to only round towards the *end* of the calculations."
    ]
@@ -551,22 +551,6 @@
    "outputs": [],
    "source": [
     "def discounted_price(...):\n",
-    "    ...\n",
-    "    ...\n",
-    "    ...\n",
-    "    ...\n",
-    "    ...\n",
-    "    ...\n",
-    "    ...\n",
-    "    ...\n",
-    "    ...\n",
-    "    ...\n",
-    "    ...\n",
-    "    ...\n",
-    "    ...\n",
-    "    ...\n",
-    "    ...\n",
-    "    ...\n",
     "    ..."
    ]
   },
diff --git a/06_text.ipynb b/06_text.ipynb
index 44eaa3c..3fbbcb5 100644
--- a/06_text.ipynb
+++ b/06_text.ipynb
@@ -54,7 +54,7 @@
    },
    "outputs": [],
    "source": [
-    "school = \"WHU - Otto Beisheim School of Management\""
+    "text = \"Lorem ipsum dolor sit amet, consectetur ...\""
    ]
   },
   {
@@ -65,7 +65,7 @@
     }
    },
    "source": [
-    "Like everything in Python, `school` is an object."
+    "Like everything in Python, `text` is an object."
    ]
   },
   {
@@ -80,7 +80,7 @@
     {
      "data": {
       "text/plain": [
-       "140488988107952"
+       "139674889049360"
       ]
      },
      "execution_count": 2,
@@ -89,7 +89,7 @@
     }
    ],
    "source": [
-    "id(school)"
+    "id(text)"
    ]
   },
   {
@@ -113,7 +113,7 @@
     }
    ],
    "source": [
-    "type(school)"
+    "type(text)"
    ]
   },
   {
@@ -141,7 +141,7 @@
     {
      "data": {
       "text/plain": [
-       "'WHU - Otto Beisheim School of Management'"
+       "'Lorem ipsum dolor sit amet, consectetur ...'"
       ]
      },
      "execution_count": 4,
@@ -150,7 +150,7 @@
     }
    ],
    "source": [
-    "school"
+    "text"
    ]
   },
   {
@@ -182,7 +182,7 @@
     {
      "data": {
       "text/plain": [
-       "\"It's cool that strings are so versatile in Python!\""
+       "\"It's cool that strings are so versatile!\""
       ]
      },
      "execution_count": 5,
@@ -191,7 +191,7 @@
     }
    ],
    "source": [
-    "\"It's cool that strings are so versatile in Python!\""
+    "\"It's cool that strings are so versatile!\""
    ]
   },
   {
@@ -206,7 +206,7 @@
     {
      "data": {
       "text/plain": [
-       "\"It's cool that strings are so versatile in Python!\""
+       "\"It's cool that strings are so versatile!\""
       ]
      },
      "execution_count": 6,
@@ -215,7 +215,7 @@
     }
    ],
    "source": [
-    "'It\\'s cool that strings are so versatile in Python!'"
+    "'It\\'s cool that strings are so versatile!'"
    ]
   },
   {
@@ -623,7 +623,7 @@
    "execution_count": 20,
    "metadata": {
     "slideshow": {
-     "slide_type": "slide"
+     "slide_type": "skip"
     }
    },
    "outputs": [
@@ -660,7 +660,7 @@
    "execution_count": 21,
    "metadata": {
     "slideshow": {
-     "slide_type": "fragment"
+     "slide_type": "skip"
     }
    },
    "outputs": [
@@ -737,7 +737,7 @@
     }
    },
    "source": [
-    "A **sequence** is yet another *abstract* concept (cf., *containers* and *iterables* in [Chapter 4](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/04_iteration.ipynb#Containers-vs.-Iterables)).\n",
+    "A **sequence** is yet another *abstract* concept (cf., the \"*Containers vs. Iterables*\" section in [Chapter 4](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/04_iteration.ipynb#Containers-vs.-Iterables)).\n",
     "\n",
     "It unifies *four* [orthogonal](https://en.wikipedia.org/wiki/Orthogonality) (i.e., \"independent\") behaviors into one idea: Any data type, such as `str`, is considered a sequence if it simultaneously\n",
     "\n",
@@ -748,7 +748,7 @@
     "\n",
     "[Chapter 7](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/07_sequences.ipynb#Collections-vs.-Sequences) formalizes sequences in great detail. Here, we keep our focus on the `str` type that historically received its name as it models a \"**[string of characters](https://en.wikipedia.org/wiki/String_%28computer_science%29)**,\" and a \"string\" is more formally called a sequence in the computer science literature.\n",
     "\n",
-    "Behaving like a sequence, `str` objects may be treated like `list` objects in many cases. For example, the built-in [len()](https://docs.python.org/3/library/functions.html#len) function tells us how many elements (i.e., characters) make up `school`. [len()](https://docs.python.org/3/library/functions.html#len) would not work on an \"*infinite*\" object: As anything modeled in a program must fit into a computer's finite memory at runtime, there cannot exist objects containing a truly infinite number of elements; however, [Chapter 7](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/07_sequences.ipynb#Collections-vs.-Sequences#Mapping) introduces *concrete* iterable data types that can be used to model an *infinite* series of elements and that, consequently, have no concept of \"length.\""
+    "Behaving like a sequence, `str` objects may be treated like `list` objects in many cases. For example, the built-in [len()](https://docs.python.org/3/library/functions.html#len) function tells us how many elements (i.e., characters) make up `text`. [len()](https://docs.python.org/3/library/functions.html#len) would not work with an *infinite* object: As anything modeled in a program must fit into a computer's finite memory at runtime, there cannot exist objects containing a truly infinite number of elements; however, [Chapter 7](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/07_sequences.ipynb#Iterators-vs.-Iterables) introduces iterable data types that can be used to model an *infinite* series of elements and that, consequently, have no concept of \"length.\""
    ]
   },
   {
@@ -763,7 +763,7 @@
     {
      "data": {
       "text/plain": [
-       "40"
+       "43"
       ]
      },
      "execution_count": 23,
@@ -772,7 +772,7 @@
     }
    ],
    "source": [
-    "len(school)"
+    "len(text)"
    ]
   },
   {
@@ -783,9 +783,7 @@
     }
    },
    "source": [
-    "Being iterable, we can iterate over a `str` object, for example, with a `for`-loop, and do something with the individual characters, for example, print them out with extra space in between them.\n",
-    "\n",
-    "[Chapter 4](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/04_iteration.ipynb#Containers-vs.-Iterables) already shows that we can loop over *different* concrete types: The example there first loops over the `list` object `[0, 1, 2, 3, 4]` and then the `range` object `range(5)`, with the same outcome. Now, we add the `str` type to the list of *concrete* types we can loop over. *Abstractly* speaking, all three are *iterable*, and there are many more iterable types in Python."
+    "Being iterable, we may loop over `text` and do something with the individual characters, for example, print them out with extra space in between them."
    ]
   },
   {
@@ -801,12 +799,12 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "W H U   -   O t t o   B e i s h e i m   S c h o o l   o f   M a n a g e m e n t "
+      "L o r e m   i p s u m   d o l o r   s i t   a m e t ,   c o n s e c t e t u r   . . . "
      ]
     }
    ],
    "source": [
-    "for letter in school:\n",
+    "for letter in text:\n",
     "    print(letter, end=\" \")"
    ]
   },
@@ -818,7 +816,9 @@
     }
    },
    "source": [
-    "Being a container, we can check if a given object is a member of a sequence with the `in` operator. In the context of `str` objects, the `in` operator has *two* usages: First, it checks if a *single* character is contained in a `str` object. Second, it may also check if a shorter `str` object, then called a **substring**, is contained in a longer one."
+    "Being a container, we may check if a given `str` object is contained in `text` with the `in` operator.\n",
+    "\n",
+    "The `in` operator has *two* usages: First, it checks if a *single* character is contained in a `str` object. Second, it may also check if a shorter `str` object, then called a **substring**, is contained in a longer one."
    ]
   },
   {
@@ -842,7 +842,7 @@
     }
    ],
    "source": [
-    "\"O\" in school"
+    "\"L\" in text"
    ]
   },
   {
@@ -866,7 +866,7 @@
     }
    ],
    "source": [
-    "\"WHU\" in school"
+    "\"ipsum\" in text"
    ]
   },
   {
@@ -890,7 +890,7 @@
     }
    ],
    "source": [
-    "\"EBS\" in school"
+    "\"veni, vidi, vici\" in text"
    ]
   },
   {
@@ -927,7 +927,7 @@
     {
      "data": {
       "text/plain": [
-       "'W'"
+       "'L'"
       ]
      },
      "execution_count": 28,
@@ -936,7 +936,7 @@
     }
    ],
    "source": [
-    "school[0]"
+    "text[0]"
    ]
   },
   {
@@ -951,7 +951,7 @@
     {
      "data": {
       "text/plain": [
-       "'H'"
+       "'o'"
       ]
      },
      "execution_count": 29,
@@ -960,7 +960,7 @@
     }
    ],
    "source": [
-    "school[1]"
+    "text[1]"
    ]
   },
   {
@@ -971,7 +971,7 @@
     }
    },
    "source": [
-    "The index must be of type `int`."
+    "The index must be of type `int` or we get a `TypeError`."
    ]
   },
   {
@@ -979,7 +979,7 @@
    "execution_count": 30,
    "metadata": {
     "slideshow": {
-     "slide_type": "slide"
+     "slide_type": "skip"
     }
    },
    "outputs": [
@@ -990,13 +990,13 @@
      "traceback": [
       "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
       "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-30-c89ad7e1a4ab>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mschool\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1.0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;32m<ipython-input-30-31fb90bbe515>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mtext\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1.0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
       "\u001b[0;31mTypeError\u001b[0m: string indices must be integers"
      ]
     }
    ],
    "source": [
-    "school[1.0]"
+    "text[1.0]"
    ]
   },
   {
@@ -1015,14 +1015,14 @@
    "execution_count": 31,
    "metadata": {
     "slideshow": {
-     "slide_type": "slide"
+     "slide_type": "fragment"
     }
    },
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "'t'"
+       "'.'"
       ]
      },
      "execution_count": 31,
@@ -1031,7 +1031,7 @@
     }
    ],
    "source": [
-    "school[39]"
+    "text[42]  # = len(text) - 1"
    ]
   },
   {
@@ -1061,13 +1061,13 @@
      "traceback": [
       "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
       "\u001b[0;31mIndexError\u001b[0m                                Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-32-fffdc8fb2058>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mschool\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m40\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;32m<ipython-input-32-5386eb8e1e94>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mtext\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m43\u001b[0m\u001b[0;34m]\u001b[0m  \u001b[0;31m# = len(text)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
       "\u001b[0;31mIndexError\u001b[0m: string index out of range"
      ]
     }
    ],
    "source": [
-    "school[40]"
+    "text[43]  # = len(text)"
    ]
   },
   {
@@ -1078,7 +1078,7 @@
     }
    },
    "source": [
-    "We may use *negative* indexes to start counting from the end of the `str` object."
+    "We may use *negative* indexes to start counting from the end of the `str` object. That only works because sequences are *finite*."
    ]
   },
   {
@@ -1093,7 +1093,7 @@
     {
      "data": {
       "text/plain": [
-       "'t'"
+       "'.'"
       ]
      },
      "execution_count": 33,
@@ -1102,7 +1102,7 @@
     }
    ],
    "source": [
-    "school[-1]"
+    "text[-1]"
    ]
   },
   {
@@ -1128,7 +1128,7 @@
     {
      "data": {
       "text/plain": [
-       "'O'"
+       "'i'"
       ]
      },
      "execution_count": 34,
@@ -1137,7 +1137,7 @@
     }
    ],
    "source": [
-    "school[6]"
+    "text[6]"
    ]
   },
   {
@@ -1152,7 +1152,7 @@
     {
      "data": {
       "text/plain": [
-       "'O'"
+       "'i'"
       ]
      },
      "execution_count": 35,
@@ -1161,7 +1161,7 @@
     }
    ],
    "source": [
-    "school[-34]"
+    "text[-37]"
    ]
   },
   {
@@ -1185,7 +1185,9 @@
    "source": [
     "A **slice** is a substring of a `str` object.\n",
     "\n",
-    "The **slicing operator** is a generalization of the indexing operator: We can put one, two, or three integers within the brackets, separated by colons `:`. The three integers are then referred to as the *start*, *end*, and *step* values."
+    "The **slicing operator** is a generalization of the indexing operator: We can put one, two, or three integers within the brackets, separated by colons `:`. The three integers are then referred to as the *start*, *end*, and *step* values.\n",
+    "\n",
+    "Let's start with two integers, *start* and *end*."
    ]
   },
   {
@@ -1200,7 +1202,7 @@
     {
      "data": {
       "text/plain": [
-       "'WHU'"
+       "'Lorem'"
       ]
      },
      "execution_count": 36,
@@ -1209,7 +1211,7 @@
     }
    ],
    "source": [
-    "school[0:3]"
+    "text[0:5]"
    ]
   },
   {
@@ -1220,7 +1222,9 @@
     }
    },
    "source": [
-    "Whereas the *start* is always included in the result, the *end* is not. Counter-intuitive at first, this makes working with individual slices easier as they add up to the original `str` object again. As the *end* is not included, we must end the second slice with `len(school)` or `40` below."
+    "Whereas the *start* is always included in the result, the *end* is not. Counter-intuitive at first, this makes working with individual slices easier as they add up to the original `str` object again.\n",
+    "\n",
+    "So, as the *end* is *not* included, we must end the second slice with `len(text)` or `43` below."
    ]
   },
   {
@@ -1235,7 +1239,7 @@
     {
      "data": {
       "text/plain": [
-       "'WHU - Otto Beisheim School of Management'"
+       "'Lorem ipsum dolor sit amet, consectetur ...'"
       ]
      },
      "execution_count": 37,
@@ -1244,7 +1248,7 @@
     }
    ],
    "source": [
-    "school[0:3] + school[3:40]"
+    "text[0:5] + text[5:len(text)]"
    ]
   },
   {
@@ -1255,7 +1259,7 @@
     }
    },
    "source": [
-    "For convenience, the indexes do not need to lie in the range from 0 to the `str` object's \"length\" when slicing. This is *not* the case for indexing as the `IndexError` above shows."
+    "For convenience, the indexes do not need to lie within the range from `0` to `len(text)` when slicing."
    ]
   },
   {
@@ -1270,7 +1274,7 @@
     {
      "data": {
       "text/plain": [
-       "'WHU - Otto Beisheim School of Management'"
+       "'Lorem ipsum dolor sit amet, consectetur ...'"
       ]
      },
      "execution_count": 38,
@@ -1279,7 +1283,7 @@
     }
    ],
    "source": [
-    "school[0:999]"
+    "text[0:999]"
    ]
   },
   {
@@ -1305,7 +1309,7 @@
     {
      "data": {
       "text/plain": [
-       "'WHU - Otto Beisheim School of Management'"
+       "'Lorem ipsum dolor sit amet, consectetur ...'"
       ]
      },
      "execution_count": 39,
@@ -1314,7 +1318,7 @@
     }
    ],
    "source": [
-    "school[:3] + school[3:]"
+    "text[:5] + text[5:]"
    ]
   },
   {
@@ -1340,7 +1344,7 @@
     {
      "data": {
       "text/plain": [
-       "'WHU Otto Beisheim School'"
+       "'Lorem ipsum dolor sit amet.'"
       ]
      },
      "execution_count": 40,
@@ -1349,7 +1353,7 @@
     }
    ],
    "source": [
-    "school[:3] + school[5:26]"
+    "text[:26] + text[42]"
    ]
   },
   {
@@ -1375,7 +1379,7 @@
     {
      "data": {
       "text/plain": [
-       "'WU-Ot esemSho fMngmn'"
+       "'Lrmismdlrstae,cnettr..'"
       ]
      },
      "execution_count": 41,
@@ -1384,7 +1388,7 @@
     }
    ],
    "source": [
-    "school[::2]"
+    "text[::2]"
    ]
   },
   {
@@ -1410,7 +1414,7 @@
     {
      "data": {
       "text/plain": [
-       "'tnemeganaM fo loohcS miehsieB ottO - UHW'"
+       "'... rutetcesnoc ,tema tis rolod muspi meroL'"
       ]
      },
      "execution_count": 42,
@@ -1419,7 +1423,7 @@
     }
    ],
    "source": [
-    "school[::-1]"
+    "text[::-1]"
    ]
   },
   {
@@ -1443,9 +1447,9 @@
    "source": [
     "Whereas elements of a `list` object *may* be *re-assigned*, as shortly hinted at in [Chapter 1](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/01_elements.ipynb#Who-am-I?-And-how-many?), this is *not* allowed for `str` objects. Once created, they *cannot* be *changed*. Formally, we say that they are **immutable**. In that regard, `str` objects and all the numeric types in [Chapter 5](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/05_numbers.ipynb) are alike.\n",
     "\n",
-    "On the contrary, objects that may be changed after creation, are called **mutable**. We already saw in [Chapter 1](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/01_elements.ipynb#Who-am-I?-And-how-many?) how mutable objects are more difficult to reason about for a beginner, in particular, if more than *one* variable point to one. Yet, mutability does have its place in a programmer's toolbox, and we revisit this idea in the next chapters.\n",
+    "On the contrary, objects that may be changed after creation, are called **mutable**. We already saw in [Chapter 1](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/01_elements.ipynb#Who-am-I?-And-how-many?) how mutable objects are more difficult to reason about for a beginner, in particular, if more than *one* variable references it. Yet, mutability does have its place in a programmer's toolbox, and we revisit this idea in the next chapters.\n",
     "\n",
-    "`str` objects are *immutable* as the `TypeError` indicates: Assignment to an index is *not* supported by this type."
+    "The `TypeError` indicates that `str` objects are *immutable*: Assignment to an index or a slice are *not* supported."
    ]
   },
   {
@@ -1464,66 +1468,18 @@
      "traceback": [
       "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
       "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-43-0d94e9e44adb>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mschool\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m\"E\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;32m<ipython-input-43-a627edbd2382>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mtext\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m\"Z\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
       "\u001b[0;31mTypeError\u001b[0m: 'str' object does not support item assignment"
      ]
     }
    ],
    "source": [
-    "school[0] = \"E\""
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "source": [
-    "The only thing we can do is to create a *new* `str` object in memory."
+    "text[0] = \"Z\""
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 44,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "slide"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "new_school = \"EBS\" + school[3:]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 45,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "fragment"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'EBS - Otto Beisheim School of Management'"
-      ]
-     },
-     "execution_count": 45,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "new_school"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 46,
    "metadata": {
     "slideshow": {
      "slide_type": "-"
@@ -1531,125 +1487,19 @@
    },
    "outputs": [
     {
-     "data": {
-      "text/plain": [
-       "140488987187120"
-      ]
-     },
-     "execution_count": 46,
-     "metadata": {},
-     "output_type": "execute_result"
+     "ename": "TypeError",
+     "evalue": "'str' object does not support item assignment",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-44-def135e84998>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mtext\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;36m5\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m\"random\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;31mTypeError\u001b[0m: 'str' object does not support item assignment"
+     ]
     }
    ],
    "source": [
-    "id(new_school)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 47,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "-"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "140488988107952"
-      ]
-     },
-     "execution_count": 47,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "id(school)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "slide"
-    }
-   },
-   "source": [
-    "## String Operations"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "source": [
-    "As mentioned before, the `+` and `*` operators are *overloaded* and used for **string concatenation**."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 48,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "slide"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "greeting = \"Hello \""
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 49,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "-"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'Hello WHU'"
-      ]
-     },
-     "execution_count": 49,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "greeting + school[:3]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 50,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "-"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'WHU WHU WHU WHU WHU WHU WHU WHU WHU WHU '"
-      ]
-     },
-     "execution_count": 50,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "10 * school[:4]"
+    "text[:5] = \"random\""
    ]
   },
   {
@@ -1678,7 +1528,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 51,
+   "execution_count": 45,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -1688,21 +1538,45 @@
     {
      "data": {
       "text/plain": [
-       "6"
+       "'Lorem ipsum dolor sit amet, consectetur ...'"
       ]
      },
-     "execution_count": 51,
+     "execution_count": 45,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "school.find(\"O\")"
+    "text"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 52,
+   "execution_count": 46,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "22"
+      ]
+     },
+     "execution_count": 46,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "text.find(\"a\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
    "metadata": {
     "slideshow": {
      "slide_type": "-"
@@ -1715,18 +1589,18 @@
        "-1"
       ]
      },
-     "execution_count": 52,
+     "execution_count": 47,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "school.find(\"Z\")"
+    "text.find(\"z\")"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 53,
+   "execution_count": 48,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -1736,16 +1610,16 @@
     {
      "data": {
       "text/plain": [
-       "11"
+       "12"
       ]
      },
-     "execution_count": 53,
+     "execution_count": 48,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "school.find(\"Beisheim\")"
+    "text.find(\"dolor\")"
    ]
   },
   {
@@ -1761,7 +1635,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 54,
+   "execution_count": 49,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -1771,21 +1645,21 @@
     {
      "data": {
       "text/plain": [
-       "12"
+       "1"
       ]
      },
-     "execution_count": 54,
+     "execution_count": 49,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "school.find(\"e\")"
+    "text.find(\"o\")"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 55,
+   "execution_count": 50,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -1795,24 +1669,24 @@
     {
      "data": {
       "text/plain": [
-       "16"
+       "13"
       ]
      },
-     "execution_count": 55,
+     "execution_count": 50,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "school.find(\"e\", 13)  # 13 not 12 as otherwise the same character is found"
+    "text.find(\"o\", 2)  # 2 not 1 as otherwise the same \"o\" is found again"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 56,
+   "execution_count": 51,
    "metadata": {
     "slideshow": {
-     "slide_type": "-"
+     "slide_type": "skip"
     }
    },
    "outputs": [
@@ -1822,13 +1696,13 @@
        "-1"
       ]
      },
-     "execution_count": 56,
+     "execution_count": 51,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "school.find(\"e\", 13, 15)  # \"e\" does not occur in the specified slice"
+    "text.find(\"o\", 2, 12)  # \"o\" does not occur in the specified slice"
    ]
   },
   {
@@ -1844,7 +1718,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 57,
+   "execution_count": 52,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -1854,16 +1728,16 @@
     {
      "data": {
       "text/plain": [
-       "4"
+       "1"
       ]
      },
-     "execution_count": 57,
+     "execution_count": 52,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "school.count(\"o\")"
+    "text.count(\"l\")"
    ]
   },
   {
@@ -1874,12 +1748,12 @@
     }
    },
    "source": [
-    "As [count()](https://docs.python.org/3/library/stdtypes.html#str.count) is *case-sensitive*, we must **chain** it with the [lower()](https://docs.python.org/3/library/stdtypes.html#str.lower) method to get the count of all \"O\"s and \"o\"s."
+    "As [count()](https://docs.python.org/3/library/stdtypes.html#str.count) is *case-sensitive*, we must **chain** it with the [lower()](https://docs.python.org/3/library/stdtypes.html#str.lower) method to get the count of all `\"L\"`s and `\"l\"`s."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 58,
+   "execution_count": 53,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -1889,16 +1763,16 @@
     {
      "data": {
       "text/plain": [
-       "5"
+       "2"
       ]
      },
-     "execution_count": 58,
+     "execution_count": 53,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "school.lower().count(\"o\")"
+    "text.lower().count(\"l\")"
    ]
   },
   {
@@ -1909,12 +1783,12 @@
     }
    },
    "source": [
-    "Alternatively, we may use the [upper()](https://docs.python.org/3/library/stdtypes.html#str.upper) method and search for \"O\"s."
+    "Alternatively, we may use the [upper()](https://docs.python.org/3/library/stdtypes.html#str.upper) method and search for `\"O\"`s."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 59,
+   "execution_count": 54,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -1924,16 +1798,16 @@
     {
      "data": {
       "text/plain": [
-       "5"
+       "2"
       ]
      },
-     "execution_count": 59,
+     "execution_count": 54,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "school.upper().count(\"O\")"
+    "text.upper().count(\"L\")"
    ]
   },
   {
@@ -1944,12 +1818,12 @@
     }
    },
    "source": [
-    "Because `str` objects are *immutable*, the methods always return *new* objects, even if a method does *not* change the value of the `str` object at all."
+    "Because `str` objects are *immutable*, [upper()](https://docs.python.org/3/library/stdtypes.html#str.upper) and [lower()](https://docs.python.org/3/library/stdtypes.html#str.lower) return *new* `str` objects, even if they do *not* change the value of the original `str` object."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 60,
+   "execution_count": 55,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -1957,12 +1831,12 @@
    },
    "outputs": [],
    "source": [
-    "example = \"test\""
+    "example = \"random\""
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 61,
+   "execution_count": 56,
    "metadata": {
     "slideshow": {
      "slide_type": "-"
@@ -1972,10 +1846,10 @@
     {
      "data": {
       "text/plain": [
-       "140489068668384"
+       "139674947440512"
       ]
      },
-     "execution_count": 61,
+     "execution_count": 56,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1986,7 +1860,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 62,
+   "execution_count": 57,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -1999,7 +1873,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 63,
+   "execution_count": 58,
    "metadata": {
     "slideshow": {
      "slide_type": "-"
@@ -2009,10 +1883,10 @@
     {
      "data": {
       "text/plain": [
-       "140488987340848"
+       "139674887826488"
       ]
      },
-     "execution_count": 63,
+     "execution_count": 58,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2034,7 +1908,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 64,
+   "execution_count": 59,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -2047,7 +1921,7 @@
        "False"
       ]
      },
-     "execution_count": 64,
+     "execution_count": 59,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2058,7 +1932,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 65,
+   "execution_count": 60,
    "metadata": {
     "slideshow": {
      "slide_type": "-"
@@ -2071,7 +1945,7 @@
        "True"
       ]
      },
-     "execution_count": 65,
+     "execution_count": 60,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2088,12 +1962,14 @@
     }
    },
    "source": [
-    "Another popular string method is [split()](https://docs.python.org/3/library/stdtypes.html#str.split): It separates a longer `str` object into a list of smaller ones. By default, groups of whitespace are used as the *separator*."
+    "Another popular string method is [split()](https://docs.python.org/3/library/stdtypes.html#str.split): It separates a longer `str` object into smaller ones contained in a `list` object. By default, groups of contiguous whitespace are used as the *separator*.\n",
+    "\n",
+    "As an example, we use [split()](https://docs.python.org/3/library/stdtypes.html#str.split) to print out the individual words in `text` on separate lines."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 66,
+   "execution_count": 61,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -2104,18 +1980,18 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "WHU\n",
-      "-\n",
-      "Otto\n",
-      "Beisheim\n",
-      "School\n",
-      "of\n",
-      "Management\n"
+      "Lorem\n",
+      "ipsum\n",
+      "dolor\n",
+      "sit\n",
+      "amet,\n",
+      "consectetur\n",
+      "...\n"
      ]
     }
    ],
    "source": [
-    "for word in school.split():\n",
+    "for word in text.split():\n",
     "    print(word)"
    ]
   },
@@ -2132,7 +2008,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 67,
+   "execution_count": 62,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -2145,7 +2021,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 68,
+   "execution_count": 63,
    "metadata": {
     "slideshow": {
      "slide_type": "-"
@@ -2158,7 +2034,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 69,
+   "execution_count": 64,
    "metadata": {
     "slideshow": {
      "slide_type": "-"
@@ -2171,7 +2047,7 @@
        "'This will become a sentence'"
       ]
      },
-     "execution_count": 69,
+     "execution_count": 64,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2193,7 +2069,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 70,
+   "execution_count": 65,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -2206,7 +2082,7 @@
        "'a b c d e'"
       ]
      },
-     "execution_count": 70,
+     "execution_count": 65,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2223,12 +2099,12 @@
     }
    },
    "source": [
-    "The [replace()](https://docs.python.org/3/library/stdtypes.html#str.replace) method creates a *new* `str` object with parts of the text replaced."
+    "The [replace()](https://docs.python.org/3/library/stdtypes.html#str.replace) method creates a *new* `str` object with parts of the original `str` object potentially replaced."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 71,
+   "execution_count": 66,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -2241,7 +2117,7 @@
        "'This is a sentence'"
       ]
      },
-     "execution_count": 71,
+     "execution_count": 66,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2258,7 +2134,7 @@
     }
    },
    "source": [
-    "## String Comparison"
+    "## String Operations"
    ]
   },
   {
@@ -2269,12 +2145,82 @@
     }
    },
    "source": [
-    "The *relational* operators also work with `str` objects, another example of operator overloading. Comparison is done one character at a time until the first pair differs or one operand ends. However, `str` objects are sorted in a \"weird\" way. The reason for this is that computers store characters internally as numbers (i.e., $0$s and $1$s). Depending on the character encoding, these numbers vary. Commonly, characters and symbols used in the American language are encoded with the numbers 0 through 127, the so-called [ASCII standard](https://en.wikipedia.org/wiki/ASCII). However, Python works with the more general [Unicode/UTF-8 standard](https://en.wikipedia.org/wiki/UTF-8) that understands every language ever used by humans, even emojis."
+    "As mentioned in [Chapter 1](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/01_elements.ipynb#Operator-Overloading), the `+` and `*` operators are *overloaded* and used for **string concatenation**. They always create *new* `str` objects. That has nothing to do with the `str` type's immutability, but is the default behavior of operators."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 72,
+   "execution_count": 67,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Hello Lore'"
+      ]
+     },
+     "execution_count": 67,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "\"Hello \" + text[:4]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 68,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Lorem ipsum Lorem ipsum Lorem ipsum Lorem ipsum Lorem ipsum '"
+      ]
+     },
+     "execution_count": 68,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "5 * text[:12]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "### String Comparison"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "The *relational* operators also work with `str` objects, another example of operator overloading. Comparison is done one character at a time until the first pair differs or one operand ends. However, `str` objects are sorted in a \"weird\" way. The reason for this is that computers store characters internally as numbers (i.e., $0$s and $1$s). Depending on the character encoding, these numbers vary. Commonly, characters and symbols used in American English are encoded with the numbers 0 through 127, the so-called [ASCII standard](https://en.wikipedia.org/wiki/ASCII). However, Python works with the more general [Unicode/UTF-8 standard](https://en.wikipedia.org/wiki/UTF-8) that understands every language ever used by humans, even emojis."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 69,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -2289,7 +2235,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 73,
+   "execution_count": 70,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -2302,7 +2248,7 @@
        "True"
       ]
      },
-     "execution_count": 73,
+     "execution_count": 70,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2313,7 +2259,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 74,
+   "execution_count": 71,
    "metadata": {
     "slideshow": {
      "slide_type": "-"
@@ -2326,7 +2272,7 @@
        "False"
       ]
      },
-     "execution_count": 74,
+     "execution_count": 71,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2348,7 +2294,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 75,
+   "execution_count": 72,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -2361,7 +2307,7 @@
        "True"
       ]
      },
-     "execution_count": 75,
+     "execution_count": 72,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2378,12 +2324,12 @@
     }
    },
    "source": [
-    "To provide a simple intuition for the \"weird\" sorting above, let's think of the American alphabet as being represented by the numbers as listed below. Then `\"Banana\"` is clearly \"smaller\" than `\"apple\"`."
+    "To provide a simple intuition for the \"weird\" sorting above, let's think of the alphabet as being represented by the numbers as listed below. Then `\"Banana\"` is clearly \"smaller\" than `\"apple\"`. In general, all the upper case letters are \"smaller\" than all the lower case letters."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 76,
+   "execution_count": 73,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -2479,7 +2425,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 77,
+   "execution_count": 74,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -2493,7 +2439,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 78,
+   "execution_count": 75,
    "metadata": {
     "slideshow": {
      "slide_type": "-"
@@ -2506,7 +2452,7 @@
        "'Hello Alexander! Good morning.'"
       ]
      },
-     "execution_count": 78,
+     "execution_count": 75,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2528,7 +2474,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 79,
+   "execution_count": 76,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -2541,7 +2487,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 80,
+   "execution_count": 77,
    "metadata": {
     "slideshow": {
      "slide_type": "-"
@@ -2554,7 +2500,7 @@
        "'Pi is 3.14'"
       ]
      },
-     "execution_count": 80,
+     "execution_count": 77,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2565,7 +2511,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 81,
+   "execution_count": 78,
    "metadata": {
     "slideshow": {
      "slide_type": "-"
@@ -2578,7 +2524,7 @@
        "'Pi is 3.142'"
       ]
      },
-     "execution_count": 81,
+     "execution_count": 78,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2611,7 +2557,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 82,
+   "execution_count": 79,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -2624,7 +2570,7 @@
        "'Hello Alexander! Good morning.'"
       ]
      },
-     "execution_count": 82,
+     "execution_count": 79,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2646,7 +2592,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 83,
+   "execution_count": 80,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -2659,7 +2605,7 @@
        "'Good morning, Alexander'"
       ]
      },
-     "execution_count": 83,
+     "execution_count": 80,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2681,7 +2627,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 84,
+   "execution_count": 81,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -2694,7 +2640,7 @@
        "'Hello Alexander! Good morning.'"
       ]
      },
-     "execution_count": 84,
+     "execution_count": 81,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2716,7 +2662,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 85,
+   "execution_count": 82,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -2729,7 +2675,7 @@
        "'Pi is 3.14'"
       ]
      },
-     "execution_count": 85,
+     "execution_count": 82,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2764,7 +2710,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 86,
+   "execution_count": 83,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -2777,7 +2723,7 @@
        "'Pi is 3.14'"
       ]
      },
-     "execution_count": 86,
+     "execution_count": 83,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2799,7 +2745,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 87,
+   "execution_count": 84,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -2812,7 +2758,7 @@
        "'Hello Alexander! Good morning.'"
       ]
      },
-     "execution_count": 87,
+     "execution_count": 84,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2847,7 +2793,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 88,
+   "execution_count": 85,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -2870,7 +2816,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 89,
+   "execution_count": 86,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -2902,7 +2848,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 90,
+   "execution_count": 87,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -2934,7 +2880,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 91,
+   "execution_count": 88,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -2947,7 +2893,7 @@
        "'This is a sentence\\nthat is printed\\non three lines.'"
       ]
      },
-     "execution_count": 91,
+     "execution_count": 88,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2982,7 +2928,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 92,
+   "execution_count": 89,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -3015,7 +2961,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 93,
+   "execution_count": 90,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -3024,10 +2970,10 @@
    "outputs": [
     {
      "ename": "SyntaxError",
-     "evalue": "(unicode error) 'unicodeescape' codec can't decode bytes in position 2-3: truncated \\UXXXXXXXX escape (<ipython-input-93-0aad8a365b02>, line 1)",
+     "evalue": "(unicode error) 'unicodeescape' codec can't decode bytes in position 2-3: truncated \\UXXXXXXXX escape (<ipython-input-90-0aad8a365b02>, line 1)",
      "output_type": "error",
      "traceback": [
-      "\u001b[0;36m  File \u001b[0;32m\"<ipython-input-93-0aad8a365b02>\"\u001b[0;36m, line \u001b[0;32m1\u001b[0m\n\u001b[0;31m    print(\"C:\\Users\\Administrator\\Desktop\\Project_Folder\")\u001b[0m\n\u001b[0m         ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m (unicode error) 'unicodeescape' codec can't decode bytes in position 2-3: truncated \\UXXXXXXXX escape\n"
+      "\u001b[0;36m  File \u001b[0;32m\"<ipython-input-90-0aad8a365b02>\"\u001b[0;36m, line \u001b[0;32m1\u001b[0m\n\u001b[0;31m    print(\"C:\\Users\\Administrator\\Desktop\\Project_Folder\")\u001b[0m\n\u001b[0m         ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m (unicode error) 'unicodeescape' codec can't decode bytes in position 2-3: truncated \\UXXXXXXXX escape\n"
      ]
     }
    ],
@@ -3048,7 +2994,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 94,
+   "execution_count": 91,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -3069,7 +3015,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 95,
+   "execution_count": 92,
    "metadata": {
     "slideshow": {
      "slide_type": "-"
@@ -3101,7 +3047,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 96,
+   "execution_count": 93,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -3122,7 +3068,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 97,
+   "execution_count": 94,
    "metadata": {
     "slideshow": {
      "slide_type": "-"
@@ -3165,7 +3111,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 98,
+   "execution_count": 95,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -3174,10 +3120,10 @@
    "outputs": [
     {
      "ename": "SyntaxError",
-     "evalue": "EOL while scanning string literal (<ipython-input-98-4cef690f1f4a>, line 1)",
+     "evalue": "EOL while scanning string literal (<ipython-input-95-4cef690f1f4a>, line 1)",
      "output_type": "error",
      "traceback": [
-      "\u001b[0;36m  File \u001b[0;32m\"<ipython-input-98-4cef690f1f4a>\"\u001b[0;36m, line \u001b[0;32m1\u001b[0m\n\u001b[0;31m    \"\u001b[0m\n\u001b[0m     ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m EOL while scanning string literal\n"
+      "\u001b[0;36m  File \u001b[0;32m\"<ipython-input-95-4cef690f1f4a>\"\u001b[0;36m, line \u001b[0;32m1\u001b[0m\n\u001b[0;31m    \"\u001b[0m\n\u001b[0m     ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m EOL while scanning string literal\n"
      ]
     }
    ],
@@ -3200,7 +3146,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 99,
+   "execution_count": 96,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -3227,7 +3173,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 100,
+   "execution_count": 97,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -3240,7 +3186,7 @@
        "'\\nI am a multi-line string\\nconsisting of 4 lines.\\n'"
       ]
      },
-     "execution_count": 100,
+     "execution_count": 97,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3262,7 +3208,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 101,
+   "execution_count": 98,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -3297,7 +3243,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 102,
+   "execution_count": 99,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -3333,7 +3279,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 103,
+   "execution_count": 100,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -3347,7 +3293,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 104,
+   "execution_count": 101,
    "metadata": {
     "slideshow": {
      "slide_type": "-"
@@ -3360,7 +3306,7 @@
        "\"Lorem Ipsum is simply dummy text of the printing and typesetting industry.\\nLorem Ipsum has been the industry's standard dummy text ever since the 1500s\\nwhen an unknown printer took a galley of type and scrambled it to make a type\\nspecimen book. It has survived not only five centuries but also the leap into\\nelectronic typesetting, remaining essentially unchanged. It was popularised in\\nthe 1960s with the release of Letraset sheets.\\n\""
       ]
      },
-     "execution_count": 104,
+     "execution_count": 101,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3371,7 +3317,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 105,
+   "execution_count": 102,
    "metadata": {
     "slideshow": {
      "slide_type": "-"
diff --git a/07_sequences.ipynb b/07_sequences.ipynb
index 11add0a..f20b670 100644
--- a/07_sequences.ipynb
+++ b/07_sequences.ipynb
@@ -23,7 +23,7 @@
     "\n",
     "The `str` type is a bit of a corner case in this regard. While one could argue that a longer `str` object, for example, `\"text\"`, is composed of individual characters, this is *not* the case in memory as the literal `\"text\"` only creates *one* object (i.e., one \"bag\" of $0$s and $1$s modeling all characters).\n",
     "\n",
-    "This chapter and Chapter 8 introduce various \"complex\" data types. While some are mutable and others are not, they all share that they are primarily used to \"manage,\" or structure, the memory in a program. Unsurprisingly, computer scientists refer to the ideas and theories behind these data types as **[data structures](https://en.wikipedia.org/wiki/Data_structure)**.\n",
+    "This chapter and [Chapter 8](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/08_mappings.ipynb) introduce various \"complex\" data types. While some are mutable and others are not, they all share that they are primarily used to \"manage,\" or structure, the memory in a program. Unsurprisingly, computer scientists refer to the ideas and theories behind these data types as **[data structures](https://en.wikipedia.org/wiki/Data_structure)**.\n",
     "\n",
     "In this chapter, we focus on data types that model all kinds of sequential data. Examples of such data are [spreadsheets](https://en.wikipedia.org/wiki/Spreadsheet) or [matrices](https://en.wikipedia.org/wiki/Matrix_%28mathematics%29)/[vectors](https://en.wikipedia.org/wiki/Vector_%28mathematics_and_physics%29). Such formats share the property that they are composed of smaller units that come in a sequence of, for example, rows/columns/cells or elements/entries."
    ]
@@ -49,9 +49,9 @@
    "source": [
     "[Chapter 6](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/06_text.ipynb#A-\"String\"-of-Characters) already describes the *sequence* properties of `str` objects. Here, we take a step back and study these properties on their own before looking at bigger ideas.\n",
     "\n",
-    "The [collections.abc](https://docs.python.org/3/library/collections.abc.html) module in the [standard library](https://docs.python.org/3/library/index.html) defines a variety of **abstract base classes** (ABCs). We saw ABCs already in [Chapter 5](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/05_numbers.ipynb#The-Numerical-Tower), where we use the ones from the [numbers](https://docs.python.org/3/library/numbers.html) module in the [standard library](https://docs.python.org/3/library/index.html) to classify Python's numeric data types according to mathematical ideas. Now, we take the ABCs from the [collections.abc](https://docs.python.org/3/library/collections.abc.html) module to classify the data types in this chapter according to their behavior in various contexts.\n",
+    "The [collections.abc](https://docs.python.org/3/library/collections.abc.html) module in the [standard library](https://docs.python.org/3/library/index.html) defines a variety of **abstract base classes** (ABCs). We saw ABCs already in [Chapter 5](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/05_numbers.ipynb#The-Numerical-Tower), where we use the ones from the [numbers](https://docs.python.org/3/library/numbers.html) module in the [standard library](https://docs.python.org/3/library/index.html) to classify Python's numeric data types according to mathematical ideas. Now, we take the ABCs from the [collections.abc](https://docs.python.org/3/library/collections.abc.html) module to classify the data types in this chapter and [Chapter 8](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/08_mappings.ipynb) according to their behavior in various contexts.\n",
     "\n",
-    "As an illustration, consider `numbers` and `word` below, two objects of *different* types."
+    "As an illustration, consider `numbers` and `text` below, two objects of *different* types."
    ]
   },
   {
@@ -65,7 +65,7 @@
    "outputs": [],
    "source": [
     "numbers = [7, 11, 8, 5, 3, 12, 2, 6, 9, 10, 1, 4]\n",
-    "word = \"random\""
+    "text = \"Lorem ipsum dolor sit amet, consectetur ...\""
    ]
   },
   {
@@ -76,7 +76,7 @@
     }
    },
    "source": [
-    "They have in common that we may loop over them with the `for` statement. So, in the context of iteration, both exhibit the *same* behavior."
+    "Among others, one commonality between the two is that we may loop over them with the `for` statement. So, in the context of iteration, both exhibit the *same* behavior."
    ]
   },
   {
@@ -114,12 +114,12 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "r a n d o m "
+      "L o r e m   i p s u m   d o l o r   s i t   a m e t ,   c o n s e c t e t u r   . . . "
      ]
     }
    ],
    "source": [
-    "for character in word:\n",
+    "for character in text:\n",
     "    print(character, end=\" \")"
    ]
   },
@@ -133,7 +133,7 @@
    "source": [
     "In [Chapter 4](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/04_iteration.ipynb#Containers-vs.-Iterables), we referred to such types as *iterables*. That is *not* a proper [English](https://dictionary.cambridge.org/spellcheck/english-german/?q=iterable) word, even if it may sound like one at first sight. Yet, it is an official term in the Python world formalized with the `Iterable` ABC in the [collections.abc](https://docs.python.org/3/library/collections.abc.html) module.\n",
     "\n",
-    "For the data science practitioner, it is worthwhile to know such terms as, for example, the documentation on the [built-ins](https://docs.python.org/3/library/functions.html) uses them extensively: In simple words, any built-in that takes an argument called \"*iterable*\" may be called with *any* object that supports being looped over. Already familiar [built-ins](https://docs.python.org/3/library/functions.html) include, among others, [enumerate()](https://docs.python.org/3/library/functions.html#enumerate), [sum()](https://docs.python.org/3/library/functions.html#sum), or [zip()](https://docs.python.org/3/library/functions.html#zip). So, they do *not* require the argument to be of a certain concrete data type (e.g., `list`); instead, any *iterable* type works."
+    "For the data science practitioner, it is worthwhile to know such terms as, for example, the documentation on the [built-ins](https://docs.python.org/3/library/functions.html) uses them extensively: In simple words, any built-in that takes an argument called \"*iterable*\" may be called with *any* object that supports being looped over. Already familiar [built-ins](https://docs.python.org/3/library/functions.html) include [enumerate()](https://docs.python.org/3/library/functions.html#enumerate), [sum()](https://docs.python.org/3/library/functions.html#sum), or [zip()](https://docs.python.org/3/library/functions.html#zip). So, they do *not* require the argument to be of a certain data type (e.g., `list`); instead, any *iterable* type works."
    ]
   },
   {
@@ -183,7 +183,7 @@
    "source": [
     "As in the context of *goose typing* in [Chapter 5](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/05_numbers.ipynb#Goose-Typing), we can use ABCs with the built-in [isinstance()](https://docs.python.org/3/library/functions.html#isinstance) function to check if an object supports a behavior.\n",
     "\n",
-    "So, let's \"ask\" Python if it can loop over `numbers` or `word`."
+    "So, let's \"ask\" Python if it can loop over `numbers` or `text`."
    ]
   },
   {
@@ -231,7 +231,7 @@
     }
    ],
    "source": [
-    "isinstance(word, abc.Iterable)"
+    "isinstance(text, abc.Iterable)"
    ]
   },
   {
@@ -315,7 +315,7 @@
    },
    "source": [
     "Most of the data types in this and the next chapter exhibit three [orthogonal](https://en.wikipedia.org/wiki/Orthogonality) (i.e., \"independent\") behaviors, formalized by ABCs in the [collections.abc](https://docs.python.org/3/library/collections.abc.html) module as:\n",
-    "- `Iterable`: An object supports being looped over.\n",
+    "- `Iterable`: An object may be looped over.\n",
     "- `Container`: An object \"contains\" references to other objects; a \"whole\" is composed of many \"parts.\"\n",
     "- `Sized`: The number of references to other objects, the \"parts,\" is *finite*.\n",
     "\n",
@@ -367,7 +367,7 @@
     }
    ],
    "source": [
-    "\"r\" in word"
+    "\"l\" in text"
    ]
   },
   {
@@ -378,7 +378,7 @@
     }
    },
    "source": [
-    "Alternatively, we could also check if `numbers` and `word` are `Container` types with the [isinstance()](https://docs.python.org/3/library/functions.html#isinstance) function."
+    "Alternatively, we could also check if `numbers` and `text` are `Container` types with [isinstance()](https://docs.python.org/3/library/functions.html#isinstance)."
    ]
   },
   {
@@ -426,7 +426,7 @@
     }
    ],
    "source": [
-    "isinstance(word, abc.Container)"
+    "isinstance(text, abc.Container)"
    ]
   },
   {
@@ -497,7 +497,7 @@
     }
    },
    "source": [
-    "Analogously, being `Sized` types, we can pass `numbers` and `word` as the argument to the built-in [len()](https://docs.python.org/3/library/functions.html#len) function and obtain \"meaningful\" results. The exact meaning depends on the *concrete* data type: For `numbers`, [len()](https://docs.python.org/3/library/functions.html#len) tells us how many elements are in the `list` object; for `word`, it tells us how many [Unicode characters](https://en.wikipedia.org/wiki/Unicode) make up the `str` object. But, *abstractly* speaking, both data types exhibit the *same* behavior of *finiteness*."
+    "Analogously, being `Sized` types, we can pass `numbers` and `text` as the argument to the built-in [len()](https://docs.python.org/3/library/functions.html#len) function and obtain \"meaningful\" results. The exact meaning depends on the data type: For `numbers`, [len()](https://docs.python.org/3/library/functions.html#len) tells us how many elements are in the `list` object; for `text`, it tells us how many [Unicode characters](https://en.wikipedia.org/wiki/Unicode) make up the `str` object. *Abstractly* speaking, both data types exhibit the *same* behavior of *finiteness*."
    ]
   },
   {
@@ -536,7 +536,7 @@
     {
      "data": {
       "text/plain": [
-       "6"
+       "43"
       ]
      },
      "execution_count": 17,
@@ -545,7 +545,7 @@
     }
    ],
    "source": [
-    "len(word)"
+    "len(text)"
    ]
   },
   {
@@ -593,7 +593,7 @@
     }
    ],
    "source": [
-    "isinstance(word, abc.Sized)"
+    "isinstance(text, abc.Sized)"
    ]
   },
   {
@@ -666,7 +666,9 @@
    "source": [
     "These three behaviors are so essential that whenever they coincide for a data type, it is called a **collection**, formalized with the `Collection` ABC. That is where the [collections.abc](https://docs.python.org/3/library/collections.abc.html) module got its name from: It summarizes all ABCs related to collections; in particular, it defines a hierarchy of specialized kinds of collections.\n",
     "\n",
-    "So, both `numbers` and `word` are collections."
+    "Without going into too much detail, one way to read the summary table at the beginning of the [collections.abc](https://docs.python.org/3/library/collections.abc.html) module's documention is as follows: The first column, titled \"ABC\", lists all collection-related ABCs in Python. The second column, titled \"Inherits from,\" indicates if the idea behind the ABC is *original* (e.g., the first row with the `Container` ABC has an empty \"Inherits from\" column) or a *combination* (e.g., the row with the `Collection` ABC has `Sized`, `Iterable`, and `Container` in the \"Inherits from\" column). The third and fourth columns list the methods that come with a data type following an ABC. We keep ignoring the methods named in the dunder style for now.\n",
+    "\n",
+    "So, let's confirm that both `numbers` and `text` are collections."
    ]
   },
   {
@@ -714,7 +716,7 @@
     }
    ],
    "source": [
-    "isinstance(word, abc.Collection)"
+    "isinstance(text, abc.Collection)"
    ]
   },
   {
@@ -727,7 +729,7 @@
    "source": [
     "They share one more common behavior: When looping over them, we can *predict* the *order* of the elements or characters. The ABC in the [collections.abc](https://docs.python.org/3/library/collections.abc.html) module corresponding to this behavior is `Reversible`. While sounding unintuitive at first, it is evident that if something is reversible, it must have a forward order, to begin with.\n",
     "\n",
-    "We add the [reversed()](https://docs.python.org/3/library/functions.html#reversed) built-in to the `for`-loop from above to iterate over the elements or characters in reverse order."
+    "The [reversed()](https://docs.python.org/3/library/functions.html#reversed) built-in allows us to loop over the elements or characters in reverse order."
    ]
   },
   {
@@ -765,12 +767,12 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "m o d n a r "
+      ". . .   r u t e t c e s n o c   , t e m a   t i s   r o l o d   m u s p i   m e r o L "
      ]
     }
    ],
    "source": [
-    "for character in reversed(word):\n",
+    "for character in reversed(text):\n",
     "    print(character, end=\" \")"
    ]
   },
@@ -819,7 +821,7 @@
     }
    ],
    "source": [
-    "isinstance(word, abc.Reversible)"
+    "isinstance(text, abc.Reversible)"
    ]
   },
   {
@@ -878,7 +880,7 @@
     }
    ],
    "source": [
-    "isinstance(word, abc.Sequence)"
+    "isinstance(text, abc.Sequence)"
    ]
   },
   {
@@ -976,7 +978,7 @@
     }
    },
    "source": [
-    "[PythonTutor](http://www.pythontutor.com/visualize.html#code=empty%20%3D%20%5B%5D%0Asimple%20%3D%20%5B40,%2050%5D%0Anested%20%3D%20%5Bempty,%2010,%2020.0,%20%22Thirty%22,%20simple%5D&cumulative=false&curInstr=0&heapPrimitives=nevernest&mode=display&origin=opt-frontend.js&py=3&rawInputLstJSON=%5B%5D&textReferences=false) shows how `nested` holds references to the `empty` and `simple` objects. Technically, it holds three more references pointing to the `10`, `20.0`, and `\"Thirty\"` objects as well. However, to simplify the visualization, these three objects are shown right inside the `nested` object as they are immutable and of \"flat\" data types. In general, the $0$s and $1$s inside a `list` object in memory always constitute pointers to other objects only."
+    "[PythonTutor](http://www.pythontutor.com/visualize.html#code=empty%20%3D%20%5B%5D%0Asimple%20%3D%20%5B40,%2050%5D%0Anested%20%3D%20%5Bempty,%2010,%2020.0,%20%22Thirty%22,%20simple%5D&cumulative=false&curInstr=0&heapPrimitives=nevernest&mode=display&origin=opt-frontend.js&py=3&rawInputLstJSON=%5B%5D&textReferences=false) shows how `nested` holds references to the `empty` and `simple` objects. Technically, it holds three more references to the `10`, `20.0`, and `\"Thirty\"` objects as well. However, to simplify the visualization, these three objects are shown right inside the `nested` object. That may be done because they are immutable and \"flat\" data types. In general, the $0$s and $1$s inside a `list` object in memory always constitute references to other objects only."
    ]
   },
   {
@@ -1026,7 +1028,7 @@
     {
      "data": {
       "text/plain": [
-       "140322034424136"
+       "140157873498184"
       ]
      },
      "execution_count": 34,
@@ -1070,7 +1072,7 @@
     }
    },
    "source": [
-    "Alternatively, we may use the [list()](https://docs.python.org/3/library/functions.html#func-list) built-in to create a `list` object out of an iterable we pass to it as the argument.\n",
+    "Alternatively, we use the [list()](https://docs.python.org/3/library/functions.html#func-list) built-in to create a `list` object out of an iterable we pass to it as the argument.\n",
     "\n",
     "For example, we can wrap the [range()](https://docs.python.org/3/library/functions.html#func-range) built-in with [list()](https://docs.python.org/3/library/functions.html#func-list): As described in [Chapter 4](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/04_iteration.ipynb#Containers-vs.-Iterables), `range` objects, like `range(1, 13)` below, are iterable and generate `int` objects \"on the fly\" (i.e., one by one). The [list()](https://docs.python.org/3/library/functions.html#func-list) around it acts like a `for`-loop and **materializes** twelve `int` objects in memory that then become the elements of the newly created `list` object. [PythonTutor](http://www.pythontutor.com/visualize.html#code=r%20%3D%20range%281,%2013%29%0Al%20%3D%20list%28range%281,%2013%29%29&cumulative=false&curInstr=0&heapPrimitives=nevernest&mode=display&origin=opt-frontend.js&py=3&rawInputLstJSON=%5B%5D&textReferences=false) shows this difference visually."
    ]
@@ -1167,7 +1169,7 @@
     }
    },
    "source": [
-    "As another example, we may also create a `list` object from a `str` object as the latter is iterable, as well. Then, the individual characters become the elements of the new `list` object!"
+    "As another example, we create a `list` object from a `str` object, which is iterable, as well. Then, the individual characters become the elements of the new `list` object!"
    ]
   },
   {
@@ -1182,7 +1184,7 @@
     {
      "data": {
       "text/plain": [
-       "['W', 'H', 'U']"
+       "['i', 't', 'e', 'r', 'a', 'b', 'l', 'e']"
       ]
      },
      "execution_count": 39,
@@ -1191,7 +1193,7 @@
     }
    ],
    "source": [
-    "list(\"WHU\")"
+    "list(\"iterable\")"
    ]
   },
   {
@@ -1213,7 +1215,7 @@
     }
    },
    "source": [
-    "`list` objects are *sequences*. To reiterate that concept from above *without* the formal ABCs from the [collections.abc](https://docs.python.org/3/library/collections.abc.html) module, we briefly summarize the *four* behaviors of a sequence and provide some more `list`-specific details below:"
+    "`list` objects are *sequences*. To reiterate that, we briefly summarize the *four* behaviors of a sequence and provide some more `list`-specific details below:"
    ]
   },
   {
@@ -1224,16 +1226,16 @@
     }
    },
    "source": [
-    "- **Container**:\n",
+    "- `Container`:\n",
     " - holds references to other objects in memory (with their own *identity* and *type*)\n",
     " - implements membership testing via the `in` operator\n",
-    "- **Iterable**:\n",
+    "- `Iterable`:\n",
     " - supports being looped over\n",
     " - works with the `for` or `while` statements\n",
-    "- **Reversible**:\n",
+    "- `Reversible`:\n",
     " - the elements come in a *predictable* order that we may traverse in a forward or backward fashion\n",
     " - works with the [reversed()](https://docs.python.org/3/library/functions.html#reversed) built-in\n",
-    "- **Sized**:\n",
+    "- `Sized`:\n",
     " - the number of elements is finite *and* known in advance\n",
     " - works with the built-in [len()](https://docs.python.org/3/library/functions.html#len) function"
    ]
@@ -1246,7 +1248,7 @@
     }
    },
    "source": [
-    "The \"length\" of `nested` is *five* because `simple` counts as only *one* element. In other words, `nested` holds five references to other objects."
+    "The \"length\" of `nested` is *five* because `empty` and `simple` count as *one* element each. In other words, `nested` holds five references to other objects, two of which are `list` objects."
    ]
   },
   {
@@ -1281,7 +1283,7 @@
     }
    },
    "source": [
-    "With a `for`-loop, we can traverse all elements in a *predictable* order, forward or backward. As `list` objects only hold references to other objects, these have a *indentity* and may be of *different* types; however, this is rarely, if ever, useful in practice."
+    "With a `for`-loop, we can iterate over all elements in a *predictable* order, forward or backward. As `list` objects hold *references* to other *objects*, these have an *indentity* and may even be of *different* types; however, the latter observation is rarely, if ever, useful in practice."
    ]
   },
   {
@@ -1297,11 +1299,11 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[]      \t140322025703432      \t<class 'list'>\n",
-      "10      \t94360180081984      \t<class 'int'>\n",
-      "20.0      \t140322034534104      \t<class 'float'>\n",
-      "Thirty      \t140322025251816      \t<class 'str'>\n",
-      "[40, 50]      \t140322034424072      \t<class 'list'>\n"
+      "[]      \t140157882223304      \t<class 'list'>\n",
+      "10      \t93987402609984      \t<class 'int'>\n",
+      "20.0      \t140157882328912      \t<class 'float'>\n",
+      "Thirty      \t140157873509520      \t<class 'str'>\n",
+      "[40, 50]      \t140157873031176      \t<class 'list'>\n"
      ]
     }
    ],
@@ -1340,7 +1342,7 @@
     }
    },
    "source": [
-    "The `in` operator checks if a given object is \"contained\" in a `list` object. It uses the `==` operator behind the scenes (i.e., *not* the `is` operator) conducting a so-called **[linear search](https://en.wikipedia.org/wiki/Linear_search)**: So, Python implicitly loops over *all* elements and only stops prematurely if an element evaluates equal to the given object. A linear search may, therefore, be relatively *slow* for big `list` objects."
+    "The `in` operator checks if a given object is \"contained\" in a `list` object. It uses the `==` operator behind the scenes (i.e., *not* the `is` operator) conducting a **[linear search](https://en.wikipedia.org/wiki/Linear_search)**: So, Python implicitly loops over *all* elements and only stops prematurely if an element evaluates equal to the searched object. A linear search may, therefore, be relatively *slow* for big `list` objects."
    ]
   },
   {
@@ -1445,9 +1447,9 @@
     }
    },
    "source": [
-    "Because of the *predictable* order and the *finiteness*, each element in a sequence can be labeled with a unique *index* (i.e., an `int` object in the range $0 \\leq \\text{index} < \\lvert \\text{sequence} \\rvert$).\n",
+    "Because of the *predictable* order and the *finiteness*, each element in a sequence can be labeled with a unique *index*, an `int` object in the range $0 \\leq \\text{index} < \\lvert \\text{sequence} \\rvert$.\n",
     "\n",
-    "Brackets, `[` and `]`, are the literal syntax for accessing individual elements of any sequence type. In this book, we also call them the *indexing operator*."
+    "Brackets, `[` and `]`, are the literal syntax for accessing individual elements of any sequence type. In this book, we also call them the *indexing operator* in this context."
    ]
   },
   {
@@ -1482,7 +1484,7 @@
     }
    },
    "source": [
-    "The last index is one less than `len(nested)` above, and Python raises an `IndexError` if we look up an index that is not in the implied range."
+    "The last index is one less than `len(nested)`, and Python raises an `IndexError` if we look up an index that is not in the range."
    ]
   },
   {
@@ -1533,7 +1535,7 @@
     {
      "data": {
       "text/plain": [
-       "50"
+       "[40, 50]"
       ]
      },
      "execution_count": 48,
@@ -1541,6 +1543,30 @@
      "output_type": "execute_result"
     }
    ],
+   "source": [
+    "nested[-1]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 49,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "50"
+      ]
+     },
+     "execution_count": 49,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "nested[-1][1]"
    ]
@@ -1571,7 +1597,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 49,
+   "execution_count": 50,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -1584,7 +1610,7 @@
        "[10, 20.0, 'Thirty']"
       ]
      },
-     "execution_count": 49,
+     "execution_count": 50,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1601,12 +1627,12 @@
     }
    },
    "source": [
-    "To obtain \"every other\" element, we slice from beginning to end, defaulting to `0` and `len(nested)`, in steps of `2`."
+    "To obtain \"every other\" element, we slice from beginning to end, defaulting to `0` and `len(nested)` when omitted, in steps of `2`."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 50,
+   "execution_count": 51,
    "metadata": {
     "slideshow": {
      "slide_type": "-"
@@ -1619,7 +1645,7 @@
        "[[], 20.0, [40, 50]]"
       ]
      },
-     "execution_count": 50,
+     "execution_count": 51,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1636,20 +1662,9 @@
     }
    },
    "source": [
-    "The literal notation with the colons `:` is *syntactic sugar*, and Python provides the [slice()](https://docs.python.org/3/library/functions.html#slice) built-in to slice with `slice` objects. [slice()](https://docs.python.org/3/library/functions.html#slice) takes *start*, *stop*, and *step* arguments in the same way as the already familiar [range()](https://docs.python.org/3/library/functions.html#func-range) built-in."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 51,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "middle = slice(1, 4)"
+    "The literal notation with the colons `:` is *syntactic sugar*. It saves us from using the [slice()](https://docs.python.org/3/library/functions.html#slice) built-in to create `slice` objects. [slice()](https://docs.python.org/3/library/functions.html#slice) takes *start*, *stop*, and *step* arguments in the same way as the familiar [range()](https://docs.python.org/3/library/functions.html#func-range), and the `slice` objects it creates are used just as *indexes* above.\n",
+    "\n",
+    "In most cases, the literal notation is more convenient to use; however, with `slice` objects, we can give names to slices and reuse them across several sequences."
    ]
   },
   {
@@ -1660,31 +1675,9 @@
      "slide_type": "skip"
     }
    },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "slice"
-      ]
-     },
-     "execution_count": 52,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
-    "type(middle)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "source": [
-    "In most cases, the literal notation is more convenient to use; however, with `slice` objects, we may give names to slices and re-use them across several sequences."
+    "middle = slice(1, 4)"
    ]
   },
   {
@@ -1699,7 +1692,7 @@
     {
      "data": {
       "text/plain": [
-       "[10, 20.0, 'Thirty']"
+       "slice"
       ]
      },
      "execution_count": 53,
@@ -1708,7 +1701,7 @@
     }
    ],
    "source": [
-    "nested[middle]"
+    "type(middle)"
    ]
   },
   {
@@ -1723,7 +1716,7 @@
     {
      "data": {
       "text/plain": [
-       "[11, 8, 5]"
+       "[10, 20.0, 'Thirty']"
       ]
      },
      "execution_count": 54,
@@ -1732,7 +1725,7 @@
     }
    ],
    "source": [
-    "numbers[middle]"
+    "nested[middle]"
    ]
   },
   {
@@ -1747,7 +1740,7 @@
     {
      "data": {
       "text/plain": [
-       "'and'"
+       "[11, 8, 5]"
       ]
      },
      "execution_count": 55,
@@ -1756,7 +1749,31 @@
     }
    ],
    "source": [
-    "word[middle]"
+    "numbers[middle]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 56,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'ore'"
+      ]
+     },
+     "execution_count": 56,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "text[middle]"
    ]
   },
   {
@@ -1772,7 +1789,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 56,
+   "execution_count": 57,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -1785,7 +1802,7 @@
        "1"
       ]
      },
-     "execution_count": 56,
+     "execution_count": 57,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1796,7 +1813,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 57,
+   "execution_count": 58,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -1809,7 +1826,7 @@
        "4"
       ]
      },
-     "execution_count": 57,
+     "execution_count": 58,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1831,7 +1848,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 58,
+   "execution_count": 59,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -1855,7 +1872,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 59,
+   "execution_count": 60,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -1868,7 +1885,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 60,
+   "execution_count": 61,
    "metadata": {
     "slideshow": {
      "slide_type": "-"
@@ -1881,7 +1898,7 @@
        "[[], 10, 20.0, 'Thirty', [40, 50]]"
       ]
      },
-     "execution_count": 60,
+     "execution_count": 61,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1898,14 +1915,14 @@
     }
    },
    "source": [
-    "At first glance, `nested` and `nested_copy` seem to cause no pain. For `list` objects, the comparison operator `==` goes over the elements in both operands in a pairwise fashion and checks if they all evaluate equal.\n",
+    "At first glance, `nested` and `nested_copy` seem to cause no pain. For `list` objects, the comparison operator `==` goes over the elements in both operands in a pairwise fashion and checks if they all evaluate equal (cf., the \"*List Comparison*\" section below for more details).\n",
     "\n",
-    "We confirm that `nested` and `nested_copy` compare equal as expected but also that they are *different* objects."
+    "We confirm that `nested` and `nested_copy` compare equal as could be expected but also that they are *different* objects."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 61,
+   "execution_count": 62,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -1918,7 +1935,7 @@
        "True"
       ]
      },
-     "execution_count": 61,
+     "execution_count": 62,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1929,7 +1946,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 62,
+   "execution_count": 63,
    "metadata": {
     "slideshow": {
      "slide_type": "-"
@@ -1942,7 +1959,7 @@
        "False"
       ]
      },
-     "execution_count": 62,
+     "execution_count": 63,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1959,14 +1976,14 @@
     }
    },
    "source": [
-    "However, as [PythonTutor](http://pythontutor.com/visualize.html#code=nested%20%3D%20%5B%5B%5D,%2010,%2020.0,%20%22Thirty%22,%20%5B40,%2050%5D%5D%0Anested_copy%20%3D%20nested%5B%3A%5D&cumulative=false&curInstr=0&heapPrimitives=nevernest&mode=display&origin=opt-frontend.js&py=3&rawInputLstJSON=%5B%5D&textReferences=false) reveals, only the *pointers* to the elements are copied! That concept is called a **[shallow copy](https://en.wikipedia.org/wiki/Object_copying#Shallow_copy)**.\n",
+    "However, as [PythonTutor](http://pythontutor.com/visualize.html#code=nested%20%3D%20%5B%5B%5D,%2010,%2020.0,%20%22Thirty%22,%20%5B40,%2050%5D%5D%0Anested_copy%20%3D%20nested%5B%3A%5D&cumulative=false&curInstr=0&heapPrimitives=nevernest&mode=display&origin=opt-frontend.js&py=3&rawInputLstJSON=%5B%5D&textReferences=false) reveals, only the *references* to the elements are copied, and not the objects in `nested` themselves! Because of that, `nested_copy` is a so-called **[shallow copy](https://en.wikipedia.org/wiki/Object_copying#Shallow_copy)** of `nested`.\n",
     "\n",
-    "We could also see this with the [id()](https://docs.python.org/3/library/functions.html#id) function: The respective first elements in both `nested` and `nested_copy` are the *same* `empty` object."
+    "We could also see this with the [id()](https://docs.python.org/3/library/functions.html#id) function: The respective first elements in both `nested` and `nested_copy` are the *same* object, namely `empty`.  So, we have three ways of accessing the *same* address in memory. Also, we say that `nested` and `nested_copy` partially share the *same* state."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 63,
+   "execution_count": 64,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -1979,7 +1996,7 @@
        "True"
       ]
      },
-     "execution_count": 63,
+     "execution_count": 64,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1990,7 +2007,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 64,
+   "execution_count": 65,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -2000,44 +2017,16 @@
     {
      "data": {
       "text/plain": [
-       "[]"
+       "140157882223304"
       ]
      },
-     "execution_count": 64,
+     "execution_count": 65,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "nested[0]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "source": [
-    "Knowing this becomes critical if the elements in a `list` object are mutable objects. Then, because of the original `list` object and its copy both pointing at the *same* objects in memory, if some of them are mutated, these changes are visible to both! We already saw a similar kind of confusion in [Chapter 1](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/01_elements.ipynb#Who-am-I?-And-how-many?) in a \"simpler\" setting and look into this in detail in the next section.\n",
-    "\n",
-    "Instead of a shallow copy, we could also create a so-called **[deep copy](https://en.wikipedia.org/wiki/Object_copying#Deep_copy)** of `nested`: That concept recursively follows every pointer in a possible nested data structure and creates copies of *every* involved object.\n",
-    "\n",
-    "To explicitly create shallow or deep copies, the [copy](https://docs.python.org/3/library/copy.html) module in the [standard library](https://docs.python.org/3/library/index.html) provides two functions, [copy()](https://docs.python.org/3/library/copy.html#copy.copy) and [deepcopy()](https://docs.python.org/3/library/copy.html#copy.deepcopy). We must always remember that slicing creates *shallow* copies only."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 65,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "import copy"
+    "id(nested[0])"
    ]
   },
   {
@@ -2048,9 +2037,20 @@
      "slide_type": "skip"
     }
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "140157882223304"
+      ]
+     },
+     "execution_count": 66,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "nested_deep_copy = copy.deepcopy(nested)"
+    "id(nested_copy[0])"
    ]
   },
   {
@@ -2065,7 +2065,7 @@
     {
      "data": {
       "text/plain": [
-       "True"
+       "140157882223304"
       ]
      },
      "execution_count": 67,
@@ -2073,6 +2073,73 @@
      "output_type": "execute_result"
     }
    ],
+   "source": [
+    "id(empty)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Knowing this becomes critical if the elements in a `list` object are mutable objects (i.e., we can change them *in place*), and this is the case with `nested` and `nested_copy`, as we see in the next section on \"*Mutability*\".\n",
+    "\n",
+    "As both the original `nested` object and its copy reference the *same* `list` objects in memory, any changes made to them are visible to both! Because of that, working with shallow copies can easily become confusing.\n",
+    "\n",
+    "Instead of a shallow copy, we could also create a so-called **[deep copy](https://en.wikipedia.org/wiki/Object_copying#Deep_copy)** of `nested`: Then, the copying process recursively follows every reference in a nested data structure and creates copies of *every* object found.\n",
+    "\n",
+    "To explicitly create shallow or deep copies, the [copy](https://docs.python.org/3/library/copy.html) module in the [standard library](https://docs.python.org/3/library/index.html) provides two functions, [copy()](https://docs.python.org/3/library/copy.html#copy.copy) and [deepcopy()](https://docs.python.org/3/library/copy.html#copy.deepcopy). We must always remember that slicing creates *shallow* copies only."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 68,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "import copy"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 69,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "nested_deep_copy = copy.deepcopy(nested)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 70,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 70,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "nested == nested_deep_copy"
    ]
@@ -2090,7 +2157,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 68,
+   "execution_count": 71,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -2103,7 +2170,7 @@
        "False"
       ]
      },
-     "execution_count": 68,
+     "execution_count": 71,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2112,6 +2179,54 @@
     "nested[0] is nested_deep_copy[0]"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 72,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "140157882223304"
+      ]
+     },
+     "execution_count": 72,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "id(nested[0])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 73,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "140157873029448"
+      ]
+     },
+     "execution_count": 73,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "id(nested_deep_copy[0])"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {
@@ -2142,12 +2257,12 @@
     }
    },
    "source": [
-    "In contrast to `str` objects, `list` objects are *mutable*: We may assign new elements to indices or slices and also remove elements. That changes *parts* of a `list` object in memory."
+    "In contrast to `str` objects, `list` objects are *mutable*: We may assign new elements to indices or slices and also remove elements. That changes the *references* in a `list` object. In general, if an object is *mutable*, we say that it may be changed *in place*."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 69,
+   "execution_count": 74,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -2160,7 +2275,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 70,
+   "execution_count": 75,
    "metadata": {
     "slideshow": {
      "slide_type": "-"
@@ -2173,7 +2288,7 @@
        "[0, 10, 20.0, 'Thirty', [40, 50]]"
       ]
      },
-     "execution_count": 70,
+     "execution_count": 75,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2195,7 +2310,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 71,
+   "execution_count": 76,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -2208,7 +2323,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 72,
+   "execution_count": 77,
    "metadata": {
     "slideshow": {
      "slide_type": "-"
@@ -2221,7 +2336,7 @@
        "[100, 100, 100, [40, 50]]"
       ]
      },
-     "execution_count": 72,
+     "execution_count": 77,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2232,7 +2347,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 73,
+   "execution_count": 78,
    "metadata": {
     "slideshow": {
      "slide_type": "-"
@@ -2245,7 +2360,7 @@
        "4"
       ]
      },
-     "execution_count": 73,
+     "execution_count": 78,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2262,12 +2377,12 @@
     }
    },
    "source": [
-    "The `list` object's identity does *not* change. That is the whole point behind mutable objects."
+    "The `list` object's identity does *not* change. That is the main point behind mutable objects."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 74,
+   "execution_count": 79,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -2277,10 +2392,10 @@
     {
      "data": {
       "text/plain": [
-       "140322034424136"
+       "140157873498184"
       ]
      },
-     "execution_count": 74,
+     "execution_count": 79,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2297,12 +2412,12 @@
     }
    },
    "source": [
-    "`nested_copy` is still unchanged!"
+    "`nested_copy` is unchanged!"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 75,
+   "execution_count": 80,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -2315,7 +2430,7 @@
        "[[], 10, 20.0, 'Thirty', [40, 50]]"
       ]
      },
-     "execution_count": 75,
+     "execution_count": 80,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2337,7 +2452,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 76,
+   "execution_count": 81,
    "metadata": {
     "slideshow": {
      "slide_type": "-"
@@ -2350,7 +2465,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 77,
+   "execution_count": 82,
    "metadata": {
     "slideshow": {
      "slide_type": "-"
@@ -2363,7 +2478,7 @@
        "[[], 10, 20.0, 'Thirty', [1, 2, 3]]"
       ]
      },
-     "execution_count": 77,
+     "execution_count": 82,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2380,12 +2495,12 @@
     }
    },
    "source": [
-    "This has a surprising side effect on `nested`!"
+    "That has a surprising side effect on `nested`."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 78,
+   "execution_count": 83,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -2398,7 +2513,7 @@
        "[100, 100, 100, [1, 2, 3]]"
       ]
      },
-     "execution_count": 78,
+     "execution_count": 83,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2415,14 +2530,14 @@
     }
    },
    "source": [
-    "That is because `nested_copy` is a shallow copy of `nested`. [PythonTutor](http://pythontutor.com/visualize.html#code=nested%20%3D%20%5B%5B%5D,%2010,%2020.0,%20%22Thirty%22,%20%5B40,%2050%5D%5D%0Anested_copy%20%3D%20nested%5B%3A%5D%0Anested%5B%3A4%5D%20%3D%20%5B100,%20100,%20100%5D%0Anested_copy%5B-1%5D%5B%3A%5D%20%3D%20%5B1,%202,%203%5D&cumulative=false&curInstr=0&heapPrimitives=nevernest&mode=display&origin=opt-frontend.js&py=3&rawInputLstJSON=%5B%5D&textReferences=false) shows how both point to the *same* nested `list` object.\n",
+    "That is precisely the confusion we talked about above when we said that `nested_copy` is a *shallow* copy of `nested`. [PythonTutor](http://pythontutor.com/visualize.html#code=nested%20%3D%20%5B%5B%5D,%2010,%2020.0,%20%22Thirty%22,%20%5B40,%2050%5D%5D%0Anested_copy%20%3D%20nested%5B%3A%5D%0Anested%5B%3A4%5D%20%3D%20%5B100,%20100,%20100%5D%0Anested_copy%5B-1%5D%5B%3A%5D%20%3D%20%5B1,%202,%203%5D&cumulative=false&curInstr=0&heapPrimitives=nevernest&mode=display&origin=opt-frontend.js&py=3&rawInputLstJSON=%5B%5D&textReferences=false) shows how both reference the *same* nested `list` object that is changed *in place* from `[40, 50]` into `[1, 2, 3]`.\n",
     "\n",
     "Lastly, we use the `del` statement to remove an element."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 79,
+   "execution_count": 84,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -2433,138 +2548,6 @@
     "del nested[-1]"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": 80,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "-"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[100, 100, 100]"
-      ]
-     },
-     "execution_count": 80,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "nested"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "source": [
-    "The `del` statement, of course, also works for slices."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 81,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "fragment"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "del nested[:2]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 82,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "-"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[100]"
-      ]
-     },
-     "execution_count": 82,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "nested"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "slide"
-    }
-   },
-   "source": [
-    "### List Operations"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "source": [
-    "As with `str` objects, the `+` and `*` operators are overloaded for concatenation and always return *new* `list` objects."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 83,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "slide"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "first = [10, 20, 30]\n",
-    "second = [40, 50, 60]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 84,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "-"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[10, 20, 30, 40, 50, 60]"
-      ]
-     },
-     "execution_count": 84,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "first + second"
-   ]
-  },
   {
    "cell_type": "code",
    "execution_count": 85,
@@ -2577,7 +2560,7 @@
     {
      "data": {
       "text/plain": [
-       "[10, 20, 30, 10, 20, 30]"
+       "[100, 100, 100]"
       ]
      },
      "execution_count": 85,
@@ -2586,31 +2569,7 @@
     }
    ],
    "source": [
-    "2 * first"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 86,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[40, 50, 60, 40, 50, 60, 40, 50, 60]"
-      ]
-     },
-     "execution_count": 86,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "second * 3"
+    "nested"
    ]
   },
   {
@@ -2621,9 +2580,20 @@
     }
    },
    "source": [
-    "Besides being an operator, the `*` symbol has a second syntactical use, as explained in [PEP 3132](https://www.python.org/dev/peps/pep-3132/) and [PEP 448](https://www.python.org/dev/peps/pep-0448/): It implements what is called **iterable unpacking**. It is *not* an operator syntactically but a notation that Python processes as a literal.\n",
-    "\n",
-    "In the example, Python interprets the expression as if the elements of the iterable `second` were placed between `30` and `70` one by one. So, we do not obtain a nested but a *flat* list."
+    "The `del` statement also works for slices. Here, we remove all references `nested` holds."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 86,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "del nested[:]"
    ]
   },
   {
@@ -2631,14 +2601,14 @@
    "execution_count": 87,
    "metadata": {
     "slideshow": {
-     "slide_type": "fragment"
+     "slide_type": "-"
     }
    },
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "[30, 40, 50, 60, 70]"
+       "[]"
       ]
      },
      "execution_count": 87,
@@ -2647,7 +2617,44 @@
     }
    ],
    "source": [
-    "[30, *second, 70]"
+    "nested"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Mutability for sequences is formalized by the `MutableSequence` ABC in the [collections.abc](https://docs.python.org/3/library/collections.abc.html) module.\n",
+    "\n",
+    "So, we can als \"ask\" Python if `nested` is mutable."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 88,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 88,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "isinstance(nested, abc.MutableSequence)"
    ]
   },
   {
@@ -2669,16 +2676,16 @@
     }
    },
    "source": [
-    "The `list` type is an essential data structure in any real-world Python application, and many typical `list` related algorithms from computer science theory are already built into it at the C level (cf., the [documentation](https://docs.python.org/3/tutorial/datastructures.html#more-on-lists) for a full overview). So, understanding and applying the built-in methods of the `list` type not only speeds up the development process but also makes programs significantly faster.\n",
+    "The `list` type is an essential data structure in any real-world Python application, and many typical `list`-related algorithms from computer science theory are already built into it at the C level (cf., the [documentation](https://docs.python.org/3/library/stdtypes.html#mutable-sequence-types) or the [tutorial](https://docs.python.org/3/tutorial/datastructures.html#more-on-lists) for a full overview; unfortunately, not all methods have direct links). So, understanding and applying the built-in methods of the `list` type not only speeds up the development process but also makes programs significantly faster.\n",
     "\n",
-    "In contrast to the `str` type's methods, the `list` type's methods *always* mutate (i.e., \"change\") an object *in place*. They do *not* create a *new* `list` object and return `None` to indicate that. So, we must *never* assign the return value of `list` methods to the variable holding the list!\n",
+    "In contrast to the `str` type's methods in [Chapter 6](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/06_text.ipynb#String-Methods) (e.g., [upper()](https://docs.python.org/3/library/stdtypes.html#str.upper) or [lower()](https://docs.python.org/3/library/stdtypes.html#str.lower)), the `list` type's methods that mutate an object do so *in place*. That means they *never* create *new* `list` objects and return `None` to indicate that. So, we must *never* assign the return value of `list` methods to the variable holding the list!\n",
     "\n",
     "Let's look at the following `names` example."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 88,
+   "execution_count": 89,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -2686,7 +2693,7 @@
    },
    "outputs": [],
    "source": [
-    "names = [\"Carl\", \"Berthold\", \"Achim\", \"Xavier\", \"Peter\"]"
+    "names = [\"Carl\", \"Peter\"]"
    ]
   },
   {
@@ -2697,12 +2704,12 @@
     }
    },
    "source": [
-    "To add an object to the end of `names`, we use the append() method. The code cell shows no output indicating that `None` must have been returned."
+    "To add an object to the end of `names`, we use the `append()` method. The code cell shows no output indicating that `None` must be the return value."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 89,
+   "execution_count": 90,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -2715,7 +2722,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 90,
+   "execution_count": 91,
    "metadata": {
     "slideshow": {
      "slide_type": "-"
@@ -2725,10 +2732,10 @@
     {
      "data": {
       "text/plain": [
-       "['Carl', 'Berthold', 'Achim', 'Xavier', 'Peter', 'Eckardt']"
+       "['Carl', 'Peter', 'Eckardt']"
       ]
      },
-     "execution_count": 90,
+     "execution_count": 91,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2745,12 +2752,12 @@
     }
    },
    "source": [
-    "With the extend() method, we may also append multiple elements provided by an iterable at once. Here, the iterable is a `list` object itself holding two `str` objects."
+    "With the `extend()` method, we may also append multiple elements provided by an iterable. Here, the iterable is a `list` object itself holding two `str` objects."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 91,
+   "execution_count": 92,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -2763,7 +2770,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 92,
+   "execution_count": 93,
    "metadata": {
     "slideshow": {
      "slide_type": "-"
@@ -2773,10 +2780,10 @@
     {
      "data": {
       "text/plain": [
-       "['Carl', 'Berthold', 'Achim', 'Xavier', 'Peter', 'Eckardt', 'Karl', 'Oliver']"
+       "['Carl', 'Peter', 'Eckardt', 'Karl', 'Oliver']"
       ]
      },
-     "execution_count": 92,
+     "execution_count": 93,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2793,36 +2800,25 @@
     }
    },
    "source": [
-    "`list` objects may be sorted *in place* with the [sort()](https://docs.python.org/3/library/stdtypes.html#list.sort) method. That is different from the built-in [sorted()](https://docs.python.org/3/library/functions.html#sorted) function that takes any *finite* and *iterable* object and returns a *new* `list` object with the iterable's elements sorted."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 93,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "slide"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "['Achim', 'Berthold', 'Carl', 'Eckardt', 'Karl', 'Oliver', 'Peter', 'Xavier']"
-      ]
-     },
-     "execution_count": 93,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "sorted(names)"
+    "Similar to `append()`, we may add a new element at an arbitrary position with the `insert()` method. `insert()` takes two arguments, an *index* and the element to be inserted."
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 94,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "names.insert(1, \"Berthold\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 95,
    "metadata": {
     "slideshow": {
      "slide_type": "-"
@@ -2832,10 +2828,10 @@
     {
      "data": {
       "text/plain": [
-       "['Carl', 'Berthold', 'Achim', 'Xavier', 'Peter', 'Eckardt', 'Karl', 'Oliver']"
+       "['Carl', 'Berthold', 'Peter', 'Eckardt', 'Karl', 'Oliver']"
       ]
      },
-     "execution_count": 94,
+     "execution_count": 95,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2844,9 +2840,90 @@
     "names"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "`list` objects may be sorted *in place* with the [sort()](https://docs.python.org/3/library/stdtypes.html#list.sort) method. That is different from the built-in [sorted()](https://docs.python.org/3/library/functions.html#sorted) function that takes any *finite* and *iterable* object and returns a *new* `list` object with the iterable's elements sorted!"
+   ]
+  },
   {
    "cell_type": "code",
-   "execution_count": 95,
+   "execution_count": 96,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['Berthold', 'Carl', 'Eckardt', 'Karl', 'Oliver', 'Peter']"
+      ]
+     },
+     "execution_count": 96,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "sorted(names)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "As the previous code cell created a *new* `list` object, `names` is still unsorted."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 97,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['Carl', 'Berthold', 'Peter', 'Eckardt', 'Karl', 'Oliver']"
+      ]
+     },
+     "execution_count": 97,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "names"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Let's sort the elements in `names` instead."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 98,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -2859,7 +2936,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 96,
+   "execution_count": 99,
    "metadata": {
     "slideshow": {
      "slide_type": "-"
@@ -2869,10 +2946,10 @@
     {
      "data": {
       "text/plain": [
-       "['Achim', 'Berthold', 'Carl', 'Eckardt', 'Karl', 'Oliver', 'Peter', 'Xavier']"
+       "['Berthold', 'Carl', 'Eckardt', 'Karl', 'Oliver', 'Peter']"
       ]
      },
-     "execution_count": 96,
+     "execution_count": 99,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2889,12 +2966,12 @@
     }
    },
    "source": [
-    "To sort in reverse order, we pass a keyword-only `reverse=True` argument to either the [sort()](https://docs.python.org/3/library/stdtypes.html#list.sort) method or the [sorted()](https://docs.python.org/3/library/functions.html#sorted) function. In the latter case, we could also use the [reversed()](https://docs.python.org/3/library/functions.html#reversed) built-in instead; however, that *neither* returns a new `list` object *nor* changes the existing one in place. We revisit it at the end of this chapter."
+    "To sort in reverse order, we pass a keyword-only `reverse=True` argument to either the [sort()](https://docs.python.org/3/library/stdtypes.html#list.sort) method or the [sorted()](https://docs.python.org/3/library/functions.html#sorted) function."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 97,
+   "execution_count": 100,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -2907,7 +2984,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 98,
+   "execution_count": 101,
    "metadata": {
     "slideshow": {
      "slide_type": "-"
@@ -2917,10 +2994,10 @@
     {
      "data": {
       "text/plain": [
-       "['Xavier', 'Peter', 'Oliver', 'Karl', 'Eckardt', 'Carl', 'Berthold', 'Achim']"
+       "['Peter', 'Oliver', 'Karl', 'Eckardt', 'Carl', 'Berthold']"
       ]
      },
-     "execution_count": 98,
+     "execution_count": 101,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2937,14 +3014,18 @@
     }
    },
    "source": [
-    "Both, the [sort()](https://docs.python.org/3/library/stdtypes.html#list.sort) method and the [sorted()](https://docs.python.org/3/library/functions.html#sorted) function, also accept a keyword-only `key` argument that must be a reference to a `function` object accepting one positional argument. Then, the elements in the `list` object are passed to that on a one-by-one basis, and the return values are used as the **sort keys**.\n",
+    "The [sort()](https://docs.python.org/3/library/stdtypes.html#list.sort) method and the [sorted()](https://docs.python.org/3/library/functions.html#sorted) function sort the elements in `names` in alphabetical order, forward or backward. However, that does *not* hold in general.\n",
+    "\n",
+    "We mention above that `list` objects may contain objects of *any* type and even of *mixed* types. Because of that, the sorting is **[delegated](https://en.wikipedia.org/wiki/Delegation_(object-oriented_programming))** to the elements in a `list` object. In a way, Python \"asks\" the elements in a `list` object to sort themselves. As `names` contains only `str` objects, they are sorted according the the comparison rules explained in [Chapter 6](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/06_text.ipynb#String-Comparison).\n",
+    "\n",
+    "To customize the sorting, we pass a keyword-only `key` argument to [sort()](https://docs.python.org/3/library/stdtypes.html#list.sort) or [sorted()](https://docs.python.org/3/library/functions.html#sorted), which must be a `function` object accepting *one* positional argument. Then, the elements in the `list` object are passed to that one by one, and the return values are used as the **sort keys**. The `key` argument is also a popular use case for `lambda` expressions.\n",
     "\n",
     "For example, to sort `names` not by alphabet but by the names' lengths, we pass in a reference to the built-in [len()](https://docs.python.org/3/library/functions.html#len) function as `key=len`. Note that there are *no* parentheses after `len`!"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 99,
+   "execution_count": 102,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -2963,103 +3044,9 @@
     }
    },
    "source": [
-    "If two names have the same length, their relative order is kept as is. A [sorting algorithm](https://en.wikipedia.org/wiki/Sorting_algorithm) is called **[stable](https://en.wikipedia.org/wiki/Sorting_algorithm#Stability)** if it has that property. That is why `\"Karl\"` comes before `\"Carl\" ` below.\n",
+    "If two names have the same length, their relative order is kept as is. That is why `\"Karl\"` comes before `\"Carl\" ` below. A [sorting algorithm](https://en.wikipedia.org/wiki/Sorting_algorithm) with that property is called **[stable](https://en.wikipedia.org/wiki/Sorting_algorithm#Stability)**.\n",
     "\n",
-    "Sorting is an important topic in programming and we refer to the official [HOWTO](https://docs.python.org/3/howto/sorting.html) for a more comprehensive introduction."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 100,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "-"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "['Karl', 'Carl', 'Peter', 'Achim', 'Xavier', 'Oliver', 'Eckardt', 'Berthold']"
-      ]
-     },
-     "execution_count": 100,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "names"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "source": [
-    "The pop() method removes the last element from a `list` object *and* returns it."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 101,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "slide"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'Berthold'"
-      ]
-     },
-     "execution_count": 101,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "names.pop()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 102,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "-"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "['Karl', 'Carl', 'Peter', 'Achim', 'Xavier', 'Oliver', 'Eckardt']"
-      ]
-     },
-     "execution_count": 102,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "names"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "source": [
-    "It takes an optional index argument and removes that instead."
+    "Sorting is an important topic in programming, and we refer to the official [HOWTO](https://docs.python.org/3/howto/sorting.html) for a more comprehensive introduction."
    ]
   },
   {
@@ -3067,14 +3054,14 @@
    "execution_count": 103,
    "metadata": {
     "slideshow": {
-     "slide_type": "fragment"
+     "slide_type": "-"
     }
    },
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "'Carl'"
+       "['Karl', 'Carl', 'Peter', 'Oliver', 'Eckardt', 'Berthold']"
       ]
      },
      "execution_count": 103,
@@ -3083,12 +3070,36 @@
     }
    ],
    "source": [
-    "names.pop(1)"
+    "names"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "`sort(reverse=True)` is different from the `reverse()` method. Whereas the former applies some sorting rule in reverse order, the latter simply reverses the elements in a `list` object."
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 104,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "names.reverse()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 105,
    "metadata": {
     "slideshow": {
      "slide_type": "-"
@@ -3098,10 +3109,10 @@
     {
      "data": {
       "text/plain": [
-       "['Karl', 'Peter', 'Achim', 'Xavier', 'Oliver', 'Eckardt']"
+       "['Berthold', 'Eckardt', 'Oliver', 'Peter', 'Carl', 'Karl']"
       ]
      },
-     "execution_count": 104,
+     "execution_count": 105,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3118,12 +3129,158 @@
     }
    },
    "source": [
-    "Instead of removing an element by its index, we can remove it by its value with the remove() method. Behind the scenes, Python then compares the object passed as its argument, `\"Peter\"` in the example, sequentially to each element with the `==` operator and removes the first one that evaluates equal."
+    "The `pop()` method removes the *last* element from a `list` object *and* returns it. Below we **capture** the `removed` element to show that the return value is not `None` as with all the methods introduced so far."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 105,
+   "execution_count": 106,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "removed = names.pop()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 107,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Karl'"
+      ]
+     },
+     "execution_count": 107,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "removed"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 108,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['Berthold', 'Eckardt', 'Oliver', 'Peter', 'Carl']"
+      ]
+     },
+     "execution_count": 108,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "names"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "`pop()` takes an optional *index* argument and removes that instead.\n",
+    "\n",
+    "So, to remove the second element, `\"Eckhardt\"`, from `names`, we write this."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 109,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "removed = names.pop(1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 110,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Eckardt'"
+      ]
+     },
+     "execution_count": 110,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "removed"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 111,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['Berthold', 'Oliver', 'Peter', 'Carl']"
+      ]
+     },
+     "execution_count": 111,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "names"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Instead of removing an element by its index, we can also remove it by its value with the `remove()` method. Behind the scenes, Python then compares the object to be removed, `\"Peter\"` in the example, sequentially to each element with the `==` operator and removes the *first* one that evaluates equal to it. `remove()` does *not* return the removed element."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 112,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -3136,7 +3293,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 106,
+   "execution_count": 113,
    "metadata": {
     "slideshow": {
      "slide_type": "-"
@@ -3146,10 +3303,10 @@
     {
      "data": {
       "text/plain": [
-       "['Karl', 'Achim', 'Xavier', 'Oliver', 'Eckardt']"
+       "['Berthold', 'Oliver', 'Carl']"
       ]
      },
-     "execution_count": 106,
+     "execution_count": 113,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3166,12 +3323,12 @@
     }
    },
    "source": [
-    "remove() raises a `ValueError` if the value is not found."
+    "Also, `remove()` raises a `ValueError` if the value is not found."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 107,
+   "execution_count": 114,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -3185,7 +3342,7 @@
      "traceback": [
       "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
       "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-107-6bb75d50cf06>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mnames\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mremove\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Peter\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;32m<ipython-input-114-6bb75d50cf06>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mnames\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mremove\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Peter\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
       "\u001b[0;31mValueError\u001b[0m: list.remove(x): x not in list"
      ]
     }
@@ -3202,211 +3359,7 @@
     }
    },
    "source": [
-    "`list` objects implement an index() method that returns the position of the first occurrence of an element. It fails loudly with a `ValueError` if the element cannot be found by value."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 108,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "slide"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "['Karl', 'Achim', 'Xavier', 'Oliver', 'Eckardt']"
-      ]
-     },
-     "execution_count": 108,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "names"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 109,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "-"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "3"
-      ]
-     },
-     "execution_count": 109,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "names.index(\"Oliver\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 110,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "-"
-    }
-   },
-   "outputs": [
-    {
-     "ename": "ValueError",
-     "evalue": "'Carl' is not in list",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-110-c10e7aae815d>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mnames\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Carl\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
-      "\u001b[0;31mValueError\u001b[0m: 'Carl' is not in list"
-     ]
-    }
-   ],
-   "source": [
-    "names.index(\"Carl\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "source": [
-    "The count() method returns the number of occurrences of a value."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 111,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "fragment"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "1"
-      ]
-     },
-     "execution_count": 111,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "names.count(\"Xavier\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 112,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "-"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "0"
-      ]
-     },
-     "execution_count": 112,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "names.count(\"Yves\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "slide"
-    }
-   },
-   "source": [
-    "### List Comparison"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "source": [
-    "The relational operators also work with `list` objects; yet another example of operator overloading.\n",
-    "\n",
-    "Comparison is made in a pairwise fashion until the first pair of elements does not evaluate equal or one of the `list` objects ends. The exact comparison rules depend on the elements and not the `list` object. We say that comparison is **[delegated](https://en.wikipedia.org/wiki/Delegation_(object-oriented_programming))** to the objects to be compared. Usually, all elements are of the *same* type. Then, the comparison is straightforward and conceptually the same as for string comparison in [Chapter 6](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/06_text.ipynb#String-Comparison)."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 113,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "slide"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "['Karl', 'Achim', 'Xavier', 'Oliver', 'Eckardt']"
-      ]
-     },
-     "execution_count": 113,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "names"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 114,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "-"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "False"
-      ]
-     },
-     "execution_count": 114,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "names < [\"Karl\", \"Achim\", \"Oliver\", \"Xavier\", \"Eckardt\"]"
+    "`list` objects implement an `index()` method that returns the position of the first element that compares equal to its argument. It fails *loudly* with a `ValueError` if no element compares equal."
    ]
   },
   {
@@ -3414,14 +3367,14 @@
    "execution_count": 115,
    "metadata": {
     "slideshow": {
-     "slide_type": "-"
+     "slide_type": "slide"
     }
    },
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "True"
+       "['Berthold', 'Oliver', 'Carl']"
       ]
      },
      "execution_count": 115,
@@ -3430,18 +3383,7 @@
     }
    ],
    "source": [
-    "names < [\"Karl\", \"Xavier\", \"Achim\", \"Oliver\", \"Eckardt\"]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "source": [
-    "The shorter `list` object is considered \"smaller,\" and vice versa."
+    "names"
    ]
   },
   {
@@ -3456,7 +3398,7 @@
     {
      "data": {
       "text/plain": [
-       "False"
+       "1"
       ]
      },
      "execution_count": 116,
@@ -3465,7 +3407,7 @@
     }
    ],
    "source": [
-    "names < [\"Karl\", \"Achim\", \"Xavier\", \"Oliver\"]"
+    "names.index(\"Oliver\")"
    ]
   },
   {
@@ -3476,20 +3418,597 @@
      "slide_type": "-"
     }
    },
+   "outputs": [
+    {
+     "ename": "ValueError",
+     "evalue": "'Karl' is not in list",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-117-8c741a6680be>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mnames\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Karl\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;31mValueError\u001b[0m: 'Karl' is not in list"
+     ]
+    }
+   ],
+   "source": [
+    "names.index(\"Karl\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "The `count()` method returns the number of elements that compare equal to its argument."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 118,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "True"
+       "1"
       ]
      },
-     "execution_count": 117,
+     "execution_count": 118,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "names < [\"Karl\", \"Achim\", \"Xavier\", \"Oliver\", \"Eckardt\", \"Peter\"]"
+    "names.count(\"Carl\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 119,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0"
+      ]
+     },
+     "execution_count": 119,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "names.count(\"Karl\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Two more methods, `copy()` and `clear()`, are *syntactic sugar* and replace working with slices.\n",
+    "\n",
+    "`copy()` creates a *shallow* copy. So, `names.copy()` below does the same as taking a full slice with `names[:]`, and the caveats from above apply, too."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 120,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "names_copy = names.copy()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 121,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['Berthold', 'Oliver', 'Carl']"
+      ]
+     },
+     "execution_count": 121,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "names_copy"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "`clear()` removes all references from a `list` object. So, `names_copy.clear()` is the same as `del names_copy[:]`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 122,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "names_copy.clear()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 123,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[]"
+      ]
+     },
+     "execution_count": 123,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "names_copy"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Many methods introduced in this section are mentioned in the [collections.abc](https://docs.python.org/3/library/collections.abc.html) module's documentation as well: While the `index()` and `count()` methods come with any data type that is a `Sequence`, the `append()`, `extend()`, `insert()`, `reverse()`, `pop()`, and `remove()` methods are part of any `MutableSequence` type. The `sort()`, `copy()`, and `clear()` methods are `list`-specific.\n",
+    "\n",
+    "So, being a sequence does not only imply the four *behaviors* specified above, but also means that a data type comes with certain standardized methods."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "### List Operations"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "As with `str` objects, the `+` and `*` operators are overloaded for concatenation and always return a *new* `list` object. The references in this newly created `list` object reference the *same* objects as the two original `list` objects. So, the same caveat as with *shallow* copies from above applies!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 124,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['Berthold', 'Oliver', 'Carl']"
+      ]
+     },
+     "execution_count": 124,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "names"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 125,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "more_names = [\"Diedrich\", \"Yves\"]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 126,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['Berthold', 'Oliver', 'Carl', 'Diedrich', 'Yves']"
+      ]
+     },
+     "execution_count": 126,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "names + more_names"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 127,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['Berthold', 'Oliver', 'Carl', 'Berthold', 'Oliver', 'Carl']"
+      ]
+     },
+     "execution_count": 127,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "2 * names"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 128,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['Diedrich', 'Yves', 'Diedrich', 'Yves', 'Diedrich', 'Yves']"
+      ]
+     },
+     "execution_count": 128,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "more_names * 3"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Besides being an operator, the `*` symbol has a second syntactical use, as explained in [PEP 3132](https://www.python.org/dev/peps/pep-3132/) and [PEP 448](https://www.python.org/dev/peps/pep-0448/): It implements what is called **iterable unpacking**. It is *not* an operator syntactically but a notation that Python reads as a literal.\n",
+    "\n",
+    "In the example, Python interprets the expression as if the elements of the iterable `names` were placed between `\"Achim\"` and `\"Xavier\"` one by one. So, we do not obtain a nested but a *flat* list."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 129,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['Achim', 'Berthold', 'Oliver', 'Carl', 'Xavier']"
+      ]
+     },
+     "execution_count": 129,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "[\"Achim\", *names, \"Xavier\"]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Effectively, Python reads that as if we wrote the following."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 130,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['Achim', 'Berthold', 'Oliver', 'Carl', 'Xavier']"
+      ]
+     },
+     "execution_count": 130,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "[\"Achim\", names[0], names[1], names[2], \"Xavier\"]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "#### List Comparison"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "The relational operators also work with `list` objects; yet another example of operator overloading.\n",
+    "\n",
+    "Comparison is made in a pairwise fashion until the first pair of elements does not evaluate equal or one of the `list` objects ends. The exact comparison rules depend on the elements and not the `list` objects. As with [sort()](https://docs.python.org/3/library/stdtypes.html#list.sort) or [sorted()](https://docs.python.org/3/library/functions.html#sorted) above, comparison is *delegated* to the objects to be compared, and Python \"asks\" the elements in the two `list` objects to compare themselves. Usually, all elements are of the *same* type, and comparison is straightforward."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 131,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['Berthold', 'Oliver', 'Carl']"
+      ]
+     },
+     "execution_count": 131,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "names"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 132,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 132,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "names == [\"Berthold\", \"Oliver\", \"Carl\"]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 133,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 133,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "names != [\"Berthold\", \"Oliver\", \"Karl\"]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 134,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 134,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "names < [\"Berthold\", \"Oliver\", \"Karl\"]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 135,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 135,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "[\"Achim\", \"Oliver\", \"Carl\"] < names"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "If two `list` objects have a different number of elements and all overlapping elements compare equal, the shorter `list` object is considered \"smaller.\" That rule is a common cause for *semantic* errors in a program."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 136,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 136,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "[\"Berthold\", \"Oliver\"] < names"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 137,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 137,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "names < [\"Berthold\", \"Oliver\", \"Carl\", \"Xavier\"]"
    ]
   },
   {
@@ -3518,7 +4037,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 118,
+   "execution_count": 138,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -3531,7 +4050,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 119,
+   "execution_count": 139,
    "metadata": {
     "slideshow": {
      "slide_type": "-"
@@ -3553,14 +4072,14 @@
     }
    },
    "source": [
-    "While this function is being executed, two variables, namely `letters` in the global scope and `arg` inside the function's local scope, point to the *same* `list` object in memory. Furthermore, the passed in `arg` is also the return value.\n",
+    "While this function is being executed, two variables, namely `letters` in the global scope and `arg` inside the function's local scope, reference the *same* `list` object in memory. Furthermore, the passed in `arg` is also the return value.\n",
     "\n",
-    "So, after the function call, `letters_with_xyz` and `letters` are **aliases** as well, pointing to the *same* object."
+    "So, after the function call, `letters_with_xyz` and `letters` are **aliases** as well, referencing the *same* object."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 120,
+   "execution_count": 140,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -3573,7 +4092,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 121,
+   "execution_count": 141,
    "metadata": {
     "slideshow": {
      "slide_type": "-"
@@ -3586,7 +4105,7 @@
        "['a', 'b', 'c', 'x', 'y', 'z']"
       ]
      },
-     "execution_count": 121,
+     "execution_count": 141,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3597,7 +4116,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 122,
+   "execution_count": 142,
    "metadata": {
     "slideshow": {
      "slide_type": "-"
@@ -3610,7 +4129,7 @@
        "['a', 'b', 'c', 'x', 'y', 'z']"
       ]
      },
-     "execution_count": 122,
+     "execution_count": 142,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3636,7 +4155,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 123,
+   "execution_count": 143,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -3649,7 +4168,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 124,
+   "execution_count": 144,
    "metadata": {
     "slideshow": {
      "slide_type": "-"
@@ -3666,7 +4185,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 125,
+   "execution_count": 145,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -3679,7 +4198,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 126,
+   "execution_count": 146,
    "metadata": {
     "scrolled": true,
     "slideshow": {
@@ -3693,7 +4212,7 @@
        "['a', 'b', 'c', 'x', 'y', 'z']"
       ]
      },
-     "execution_count": 126,
+     "execution_count": 146,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3704,7 +4223,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 127,
+   "execution_count": 147,
    "metadata": {
     "slideshow": {
      "slide_type": "-"
@@ -3717,7 +4236,7 @@
        "['a', 'b', 'c']"
       ]
      },
-     "execution_count": 127,
+     "execution_count": 147,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3739,7 +4258,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 128,
+   "execution_count": 148,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -3752,7 +4271,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 129,
+   "execution_count": 149,
    "metadata": {
     "slideshow": {
      "slide_type": "-"
@@ -3768,7 +4287,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 130,
+   "execution_count": 150,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -3781,7 +4300,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 131,
+   "execution_count": 151,
    "metadata": {
     "slideshow": {
      "slide_type": "-"
@@ -3794,7 +4313,7 @@
        "['a', 'b', 'c', 'x', 'y', 'z']"
       ]
      },
-     "execution_count": 131,
+     "execution_count": 151,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3816,7 +4335,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 132,
+   "execution_count": 152,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -3829,7 +4348,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 133,
+   "execution_count": 153,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -3842,7 +4361,7 @@
        "['a', 'b', 'c', 'x', 'y', 'z', 'x', 'y', 'z']"
       ]
      },
-     "execution_count": 133,
+     "execution_count": 153,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3886,7 +4405,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 134,
+   "execution_count": 154,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -3899,7 +4418,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 135,
+   "execution_count": 155,
    "metadata": {
     "slideshow": {
      "slide_type": "-"
@@ -3912,7 +4431,7 @@
        "(7, 11, 8, 5, 3, 12, 2, 6, 9, 10, 1, 4)"
       ]
      },
-     "execution_count": 135,
+     "execution_count": 155,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3934,7 +4453,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 136,
+   "execution_count": 156,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -3947,7 +4466,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 137,
+   "execution_count": 157,
    "metadata": {
     "slideshow": {
      "slide_type": "-"
@@ -3960,7 +4479,7 @@
        "(7, 11, 8, 5, 3, 12, 2, 6, 9, 10, 1, 4)"
       ]
      },
-     "execution_count": 137,
+     "execution_count": 157,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3982,20 +4501,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 138,
+   "execution_count": 158,
    "metadata": {
     "slideshow": {
-     "slide_type": "fragment"
+     "slide_type": "skip"
     }
    },
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "140322025210648"
+       "140157873009144"
       ]
      },
-     "execution_count": 138,
+     "execution_count": 158,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -4006,10 +4525,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 139,
+   "execution_count": 159,
    "metadata": {
     "slideshow": {
-     "slide_type": "-"
+     "slide_type": "skip"
     }
    },
    "outputs": [
@@ -4019,7 +4538,7 @@
        "tuple"
       ]
      },
-     "execution_count": 139,
+     "execution_count": 159,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -4041,10 +4560,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 140,
+   "execution_count": 160,
    "metadata": {
     "slideshow": {
-     "slide_type": "slide"
+     "slide_type": "skip"
     }
    },
    "outputs": [],
@@ -4054,10 +4573,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 141,
+   "execution_count": 161,
    "metadata": {
     "slideshow": {
-     "slide_type": "-"
+     "slide_type": "skip"
     }
    },
    "outputs": [
@@ -4067,7 +4586,7 @@
        "()"
       ]
      },
-     "execution_count": 141,
+     "execution_count": 161,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -4078,10 +4597,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 142,
+   "execution_count": 162,
    "metadata": {
     "slideshow": {
-     "slide_type": "-"
+     "slide_type": "skip"
     }
    },
    "outputs": [
@@ -4091,7 +4610,7 @@
        "tuple"
       ]
      },
-     "execution_count": 142,
+     "execution_count": 162,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -4113,10 +4632,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 143,
+   "execution_count": 163,
    "metadata": {
     "slideshow": {
-     "slide_type": "slide"
+     "slide_type": "skip"
     }
    },
    "outputs": [],
@@ -4126,10 +4645,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 144,
+   "execution_count": 164,
    "metadata": {
     "slideshow": {
-     "slide_type": "-"
+     "slide_type": "skip"
     }
    },
    "outputs": [
@@ -4139,7 +4658,7 @@
        "(1,)"
       ]
      },
-     "execution_count": 144,
+     "execution_count": 164,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -4150,10 +4669,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 145,
+   "execution_count": 165,
    "metadata": {
     "slideshow": {
-     "slide_type": "-"
+     "slide_type": "skip"
     }
    },
    "outputs": [
@@ -4163,7 +4682,7 @@
        "tuple"
       ]
      },
-     "execution_count": 145,
+     "execution_count": 165,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -4174,7 +4693,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 146,
+   "execution_count": 166,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -4187,7 +4706,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 147,
+   "execution_count": 167,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -4200,7 +4719,7 @@
        "1"
       ]
      },
-     "execution_count": 147,
+     "execution_count": 167,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -4211,7 +4730,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 148,
+   "execution_count": 168,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -4224,7 +4743,7 @@
        "int"
       ]
      },
-     "execution_count": 148,
+     "execution_count": 168,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -4246,10 +4765,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 149,
+   "execution_count": 169,
    "metadata": {
     "slideshow": {
-     "slide_type": "fragment"
+     "slide_type": "skip"
     }
    },
    "outputs": [
@@ -4259,7 +4778,7 @@
        "(1,)"
       ]
      },
-     "execution_count": 149,
+     "execution_count": 169,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -4268,6 +4787,30 @@
     "tuple([1])"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 170,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "('i', 't', 'e', 'r', 'a', 'b', 'l', 'e')"
+      ]
+     },
+     "execution_count": 170,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "tuple(\"iterable\")"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {
@@ -4294,10 +4837,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 150,
+   "execution_count": 171,
    "metadata": {
     "slideshow": {
-     "slide_type": "slide"
+     "slide_type": "skip"
     }
    },
    "outputs": [
@@ -4307,7 +4850,7 @@
        "True"
       ]
      },
-     "execution_count": 150,
+     "execution_count": 171,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -4329,10 +4872,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 151,
+   "execution_count": 172,
    "metadata": {
     "slideshow": {
-     "slide_type": "-"
+     "slide_type": "slide"
     }
    },
    "outputs": [
@@ -4342,7 +4885,7 @@
        "12"
       ]
      },
-     "execution_count": 151,
+     "execution_count": 172,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -4364,7 +4907,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 152,
+   "execution_count": 173,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -4386,7 +4929,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 153,
+   "execution_count": 174,
    "metadata": {
     "slideshow": {
      "slide_type": "-"
@@ -4419,7 +4962,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 154,
+   "execution_count": 175,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -4432,7 +4975,7 @@
        "False"
       ]
      },
-     "execution_count": 154,
+     "execution_count": 175,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -4443,7 +4986,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 155,
+   "execution_count": 176,
    "metadata": {
     "slideshow": {
      "slide_type": "-"
@@ -4456,7 +4999,7 @@
        "True"
       ]
      },
-     "execution_count": 155,
+     "execution_count": 176,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -4467,7 +5010,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 156,
+   "execution_count": 177,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -4480,7 +5023,7 @@
        "True"
       ]
      },
-     "execution_count": 156,
+     "execution_count": 177,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -4502,7 +5045,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 157,
+   "execution_count": 178,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -4515,7 +5058,7 @@
        "7"
       ]
      },
-     "execution_count": 157,
+     "execution_count": 178,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -4526,7 +5069,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 158,
+   "execution_count": 179,
    "metadata": {
     "slideshow": {
      "slide_type": "-"
@@ -4539,7 +5082,7 @@
        "4"
       ]
      },
-     "execution_count": 158,
+     "execution_count": 179,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -4550,7 +5093,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 159,
+   "execution_count": 180,
    "metadata": {
     "slideshow": {
      "slide_type": "-"
@@ -4563,7 +5106,7 @@
        "(2, 6, 9, 10, 1, 4)"
       ]
      },
-     "execution_count": 159,
+     "execution_count": 180,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -4585,7 +5128,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 160,
+   "execution_count": 181,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -4599,7 +5142,7 @@
      "traceback": [
       "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
       "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-160-80fddf1304f0>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mnumbers\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m99\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;32m<ipython-input-181-80fddf1304f0>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mnumbers\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m99\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
       "\u001b[0;31mTypeError\u001b[0m: 'tuple' object does not support item assignment"
      ]
     }
@@ -4616,44 +5159,31 @@
     }
    },
    "source": [
-    "If we need to \"modify\" the `tuple` object, we must create a *new* `tuple` object, for example, like so: We take a slice of the elements we want to keep and use the overloaded `+` operator to concatenate the slice with another `tuple` object."
+    "We can verify the immutability with the `MutableSequence` ABC from the [collections.abc](https://docs.python.org/3/library/collections.abc.html) module: [isinstance()](https://docs.python.org/3/library/functions.html#isinstance) returns `False`. So, a data type that is a `Sequence` may be mutable or not. If it is a `MutableSequence`, it is mutable. If it is *not* a `MutableSequence`, it is *immutable*. There is *no* `ImmutableSequence` ABC."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 161,
+   "execution_count": 182,
    "metadata": {
     "slideshow": {
-     "slide_type": "fragment"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "new_numbers = numbers[:-1] + (99,)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 162,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "-"
+     "slide_type": "skip"
     }
    },
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "(7, 11, 8, 5, 3, 12, 2, 6, 9, 10, 1, 99)"
+       "False"
       ]
      },
-     "execution_count": 162,
+     "execution_count": 182,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "new_numbers"
+    "isinstance(numbers, abc.MutableSequence)"
    ]
   },
   {
@@ -4664,17 +5194,44 @@
     }
    },
    "source": [
-    "The `*` operator works as well."
+    "The `+` and `*` operators work with `tuple` objects as well. However, we should *not* do that as the whole point of immutability is to *not* mutate an object.\n",
+    "\n",
+    "So, instead of writing something like below, we should use a `list` object and call its `append()` method."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 163,
+   "execution_count": 183,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
     }
    },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(7, 11, 8, 5, 3, 12, 2, 6, 9, 10, 1, 4, 99)"
+      ]
+     },
+     "execution_count": 183,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "numbers + (99,) "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 184,
+   "metadata": {
+    "scrolled": false,
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
    "outputs": [
     {
      "data": {
@@ -4682,7 +5239,7 @@
        "(7, 11, 8, 5, 3, 12, 2, 6, 9, 10, 1, 4, 7, 11, 8, 5, 3, 12, 2, 6, 9, 10, 1, 4)"
       ]
      },
-     "execution_count": 163,
+     "execution_count": 184,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -4699,12 +5256,12 @@
     }
    },
    "source": [
-    "Being immutable, `tuple` objects only provide the count() and index() methods."
+    "Being immutable, `tuple` objects only provide the `count()` and `index()` methods of `Sequence` types. The `append()`, `extend()`, `insert()`, `reverse()`, `pop()`, and `remove()` methods of `MutableSequence` types are *not* available. The same holds for the `list`-specific methods `sort()`, `copy()`, and `clear()`."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 164,
+   "execution_count": 185,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -4717,7 +5274,7 @@
        "0"
       ]
      },
-     "execution_count": 164,
+     "execution_count": 185,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -4728,7 +5285,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 165,
+   "execution_count": 186,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -4741,7 +5298,7 @@
        "10"
       ]
      },
-     "execution_count": 165,
+     "execution_count": 186,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -4758,15 +5315,39 @@
     }
    },
    "source": [
-    "The relational operators compare the elements of two `tuple` objects in a pairwise fashion as above."
+    "The relational operators work in the *same* way as for `list` objects."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 166,
+   "execution_count": 187,
    "metadata": {
     "slideshow": {
-     "slide_type": "slide"
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(7, 11, 8, 5, 3, 12, 2, 6, 9, 10, 1, 4)"
+      ]
+     },
+     "execution_count": 187,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "numbers"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 188,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
     }
    },
    "outputs": [
@@ -4776,13 +5357,85 @@
        "True"
       ]
      },
-     "execution_count": 166,
+     "execution_count": 188,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "numbers < new_numbers"
+    "numbers == (7, 11, 8, 5, 3, 12, 2, 6, 9, 10, 1, 4)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 189,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 189,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "numbers != (99, 11, 8, 5, 3, 12, 2, 6, 9, 10, 1, 4)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 190,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 190,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "numbers < (99, 11, 8, 5, 3, 12, 2, 6, 9, 10, 1, 4)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 191,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 191,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "(0, 11, 8, 5, 3, 12, 2, 6, 9, 10, 1, 4) < numbers"
    ]
   },
   {
@@ -4795,15 +5448,15 @@
    "source": [
     "While `tuple` objects are immutable, this only relates to the references they hold. If a `tuple` object contains mutable objects, the entire nested structure is *not* immutable as a whole.\n",
     "\n",
-    "Consider the following stylized example `not_immutable`: It contains *three* elements, `1`, `[2, ..., 11]`, and `12`, and the elements of the nested `list` object may be changed. While it is not practical to mix data types in a `tuple` object that is used as an \"immutable list,\" we want to make the point that the mere usage of the `tuple` type does *not* guarantee a nested object to be immutable."
+    "Consider the following stylized example `not_immutable`: It contains *three* elements, `1`, `[2, ..., 11]`, and `12`, and the elements of the nested `list` object may be changed. While it is not practical to mix data types in a `tuple` object that is used as an \"immutable list,\" we want to make the point that the mere usage of the `tuple` type does *not* guarantee a nested object to be immutable as a whole."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 167,
+   "execution_count": 192,
    "metadata": {
     "slideshow": {
-     "slide_type": "skip"
+     "slide_type": "slide"
     }
    },
    "outputs": [],
@@ -4813,10 +5466,34 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 168,
+   "execution_count": 193,
    "metadata": {
     "slideshow": {
-     "slide_type": "skip"
+     "slide_type": "-"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(1, [2, 3, 4, 5, 6, 7, 8, 9, 10, 11], 12)"
+      ]
+     },
+     "execution_count": 193,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "not_immutable"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 194,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
     }
    },
    "outputs": [],
@@ -4826,10 +5503,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 169,
+   "execution_count": 195,
    "metadata": {
     "slideshow": {
-     "slide_type": "skip"
+     "slide_type": "-"
     }
    },
    "outputs": [
@@ -4839,7 +5516,7 @@
        "(1, [99, 99, 99], 12)"
       ]
      },
-     "execution_count": 169,
+     "execution_count": 195,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -4874,7 +5551,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 170,
+   "execution_count": 196,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -4887,7 +5564,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 171,
+   "execution_count": 197,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -4900,7 +5577,7 @@
        "7"
       ]
      },
-     "execution_count": 171,
+     "execution_count": 197,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -4911,7 +5588,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 172,
+   "execution_count": 198,
    "metadata": {
     "slideshow": {
      "slide_type": "-"
@@ -4924,7 +5601,7 @@
        "11"
       ]
      },
-     "execution_count": 172,
+     "execution_count": 198,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -4935,7 +5612,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 173,
+   "execution_count": 199,
    "metadata": {
     "slideshow": {
      "slide_type": "-"
@@ -4948,7 +5625,7 @@
        "8"
       ]
      },
-     "execution_count": 173,
+     "execution_count": 199,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -4970,7 +5647,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 174,
+   "execution_count": 200,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -4984,7 +5661,7 @@
      "traceback": [
       "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
       "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-174-c5e79943681f>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mn1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn3\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn4\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn5\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn6\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn7\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn8\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn9\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn10\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn11\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnumbers\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;32m<ipython-input-200-c5e79943681f>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mn1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn3\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn4\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn5\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn6\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn7\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn8\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn9\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn10\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn11\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnumbers\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
       "\u001b[0;31mValueError\u001b[0m: too many values to unpack (expected 11)"
      ]
     }
@@ -4995,7 +5672,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 175,
+   "execution_count": 201,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -5009,7 +5686,7 @@
      "traceback": [
       "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
       "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-175-214f2de70baf>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mn1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn3\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn4\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn5\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn6\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn7\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn8\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn9\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn10\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn11\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn12\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn13\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnumbers\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;32m<ipython-input-201-214f2de70baf>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mn1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn3\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn4\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn5\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn6\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn7\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn8\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn9\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn10\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn11\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn12\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn13\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnumbers\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
       "\u001b[0;31mValueError\u001b[0m: not enough values to unpack (expected 13, got 12)"
      ]
     }
@@ -5033,7 +5710,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 176,
+   "execution_count": 202,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -5046,7 +5723,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 177,
+   "execution_count": 203,
    "metadata": {
     "slideshow": {
      "slide_type": "-"
@@ -5059,7 +5736,7 @@
        "7"
       ]
      },
-     "execution_count": 177,
+     "execution_count": 203,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -5070,7 +5747,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 178,
+   "execution_count": 204,
    "metadata": {
     "slideshow": {
      "slide_type": "-"
@@ -5083,7 +5760,7 @@
        "[11, 8, 5, 3, 12, 2, 6, 9, 10, 1]"
       ]
      },
-     "execution_count": 178,
+     "execution_count": 204,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -5094,7 +5771,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 179,
+   "execution_count": 205,
    "metadata": {
     "slideshow": {
      "slide_type": "-"
@@ -5107,7 +5784,7 @@
        "4"
       ]
      },
-     "execution_count": 179,
+     "execution_count": 205,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -5129,7 +5806,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 180,
+   "execution_count": 206,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -5142,7 +5819,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 181,
+   "execution_count": 207,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -5155,7 +5832,7 @@
        "7"
       ]
      },
-     "execution_count": 181,
+     "execution_count": 207,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -5166,7 +5843,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 182,
+   "execution_count": 208,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -5179,7 +5856,7 @@
        "4"
       ]
      },
-     "execution_count": 182,
+     "execution_count": 208,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -5203,7 +5880,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 183,
+   "execution_count": 209,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -5216,7 +5893,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 184,
+   "execution_count": 210,
    "metadata": {
     "slideshow": {
      "slide_type": "-"
@@ -5227,11 +5904,9 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Karl is a goalkeeper\n",
-      "Achim is a defender\n",
-      "Xavier is a midfielder\n",
-      "Oliver is a striker\n",
-      "Eckardt is a coach\n"
+      "Berthold is a goalkeeper\n",
+      "Oliver is a defender\n",
+      "Carl is a midfielder\n"
      ]
     }
    ],
@@ -5253,7 +5928,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 185,
+   "execution_count": 211,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -5264,11 +5939,9 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "<class 'tuple'>   ('Karl', 'goalkeeper')\n",
-      "<class 'tuple'>   ('Achim', 'defender')\n",
-      "<class 'tuple'>   ('Xavier', 'midfielder')\n",
-      "<class 'tuple'>   ('Oliver', 'striker')\n",
-      "<class 'tuple'>   ('Eckardt', 'coach')\n"
+      "<class 'tuple'>   ('Berthold', 'goalkeeper')\n",
+      "<class 'tuple'>   ('Oliver', 'defender')\n",
+      "<class 'tuple'>   ('Carl', 'midfielder')\n"
      ]
     }
    ],
@@ -5290,7 +5963,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 186,
+   "execution_count": 212,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -5301,11 +5974,9 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "0 -> Karl is a goalkeeper\n",
-      "1 -> Achim is a defender\n",
-      "2 -> Xavier is a midfielder\n",
-      "3 -> Oliver is a striker\n",
-      "4 -> Eckardt is a coach\n"
+      "0 -> Berthold is a goalkeeper\n",
+      "1 -> Oliver is a defender\n",
+      "2 -> Carl is a midfielder\n"
      ]
     }
    ],
@@ -5340,7 +6011,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 187,
+   "execution_count": 213,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -5365,7 +6036,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 188,
+   "execution_count": 214,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -5380,7 +6051,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 189,
+   "execution_count": 215,
    "metadata": {
     "slideshow": {
      "slide_type": "-"
@@ -5393,7 +6064,7 @@
        "(1, 0)"
       ]
      },
-     "execution_count": 189,
+     "execution_count": 215,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -5415,7 +6086,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 190,
+   "execution_count": 216,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -5429,7 +6100,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 191,
+   "execution_count": 217,
    "metadata": {
     "slideshow": {
      "slide_type": "-"
@@ -5442,7 +6113,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 192,
+   "execution_count": 218,
    "metadata": {
     "slideshow": {
      "slide_type": "-"
@@ -5455,7 +6126,7 @@
        "(1, 0)"
       ]
      },
-     "execution_count": 192,
+     "execution_count": 218,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -5488,7 +6159,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 193,
+   "execution_count": 219,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -5501,7 +6172,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 194,
+   "execution_count": 220,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -5519,12 +6190,17 @@
     "        ith_fibonacci (int)\n",
     "\n",
     "    Raises:\n",
-    "        TypeError: if i is not an integer\n",
+    "        TypeError: if i is not an integer or not integer-like\n",
     "        ValueError: if i is not positive\n",
     "    \"\"\"\n",
     "    if not isinstance(i, numbers.Integral):\n",
-    "        raise TypeError(\"i must be an integer\")\n",
-    "    elif i < 0:\n",
+    "        if isinstance(i, numbers.Real):\n",
+    "            if i != int(i):\n",
+    "                raise TypeError(\"i is not an integer-like value; it has decimals\")\n",
+    "            i = int(i)\n",
+    "        else:\n",
+    "            raise TypeError(\"i must be an integer\")\n",
+    "    if i < 0:\n",
     "        raise ValueError(\"i must be non-negative\")\n",
     "\n",
     "    a, b = 0, 1\n",
@@ -5537,7 +6213,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 195,
+   "execution_count": 221,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -5550,7 +6226,7 @@
        "144"
       ]
      },
-     "execution_count": 195,
+     "execution_count": 221,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -5559,6 +6235,67 @@
     "fibonacci(12)"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Because of the *goose typing*, we may pass `float` objects to `fibonacci()` as long as they contain no decimals."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 222,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "144"
+      ]
+     },
+     "execution_count": 222,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "fibonacci(12.0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 223,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "ename": "TypeError",
+     "evalue": "i is not an integer-like value; it has decimals",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-223-c98882eeae5f>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mfibonacci\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m12.3\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;32m<ipython-input-220-b3cde1c6219f>\u001b[0m in \u001b[0;36mfibonacci\u001b[0;34m(i)\u001b[0m\n\u001b[1;32m     15\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnumbers\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mReal\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     16\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mi\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0mint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 17\u001b[0;31m                 \u001b[0;32mraise\u001b[0m \u001b[0mTypeError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"i is not an integer-like value; it has decimals\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     18\u001b[0m             \u001b[0mi\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     19\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;31mTypeError\u001b[0m: i is not an integer-like value; it has decimals"
+     ]
+    }
+   ],
+   "source": [
+    "fibonacci(12.3)"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {
@@ -5587,7 +6324,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 196,
+   "execution_count": 224,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -5620,7 +6357,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 197,
+   "execution_count": 225,
    "metadata": {
     "slideshow": {
      "slide_type": "-"
@@ -5633,7 +6370,7 @@
        "42"
       ]
      },
-     "execution_count": 197,
+     "execution_count": 225,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -5655,7 +6392,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 198,
+   "execution_count": 226,
    "metadata": {
     "slideshow": {
      "slide_type": "-"
@@ -5668,7 +6405,7 @@
        "100"
       ]
      },
-     "execution_count": 198,
+     "execution_count": 226,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -5690,7 +6427,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 199,
+   "execution_count": 227,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -5704,8 +6441,8 @@
      "traceback": [
       "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
       "\u001b[0;31mIndexError\u001b[0m                                Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-199-640e0c632b8d>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mproduct\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
-      "\u001b[0;32m<ipython-input-196-02b0db9fbeab>\u001b[0m in \u001b[0;36mproduct\u001b[0;34m(*args)\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mproduct\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m     \u001b[0;34m\"\"\"Multiply all arguments.\"\"\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m     \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      4\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m     \u001b[0;32mfor\u001b[0m \u001b[0marg\u001b[0m \u001b[0;32min\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m<ipython-input-227-640e0c632b8d>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mproduct\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;32m<ipython-input-224-02b0db9fbeab>\u001b[0m in \u001b[0;36mproduct\u001b[0;34m(*args)\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mproduct\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m     \u001b[0;34m\"\"\"Multiply all arguments.\"\"\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m     \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      4\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m     \u001b[0;32mfor\u001b[0m \u001b[0marg\u001b[0m \u001b[0;32min\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
       "\u001b[0;31mIndexError\u001b[0m: tuple index out of range"
      ]
     }
@@ -5727,7 +6464,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 200,
+   "execution_count": 228,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -5740,7 +6477,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 201,
+   "execution_count": 229,
    "metadata": {
     "slideshow": {
      "slide_type": "-"
@@ -5753,7 +6490,7 @@
        "[2, 5, 10]"
       ]
      },
-     "execution_count": 201,
+     "execution_count": 229,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -5775,7 +6512,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 202,
+   "execution_count": 230,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -5788,7 +6525,7 @@
        "100"
       ]
      },
-     "execution_count": 202,
+     "execution_count": 230,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -5810,7 +6547,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 203,
+   "execution_count": 231,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -5823,7 +6560,7 @@
        "100"
       ]
      },
-     "execution_count": 203,
+     "execution_count": 231,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -5840,6 +6577,8 @@
     }
    },
    "source": [
+    "In the \"*Packing & Unpacking with Functions*\" [exercise](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/07_sequences_review_and_exercises.ipynb#Packing-&-Unpacking-with-Functions) at the end of this chapter, we look at `product()` in more detail.\n",
+    "\n",
     "While we needed to unpack `one_hundred` above to avoid the semantic error, unpacking an argument in a function call may also be a convenience in general.\n",
     "\n",
     "For example, to print the elements of `one_hundred` in one line, we need to use a `for` statement, until now. With unpacking, we get away *without* a loop."
@@ -5847,7 +6586,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 204,
+   "execution_count": 232,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -5868,7 +6607,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 205,
+   "execution_count": 233,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -5890,7 +6629,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 206,
+   "execution_count": 234,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -5906,7 +6645,7 @@
     }
    ],
    "source": [
-    "print(*one_hundred)"
+    "print(*one_hundred)  # replaces the for-loop"
    ]
   },
   {
@@ -5917,7 +6656,7 @@
     }
    },
    "source": [
-    "## The `namedtuple` Type"
+    "### The `namedtuple` Type"
    ]
   },
   {
@@ -5939,7 +6678,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 207,
+   "execution_count": 235,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -5965,7 +6704,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 208,
+   "execution_count": 236,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -5989,7 +6728,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 209,
+   "execution_count": 237,
    "metadata": {
     "slideshow": {
      "slide_type": "-"
@@ -6013,7 +6752,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 210,
+   "execution_count": 238,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -6023,10 +6762,10 @@
     {
      "data": {
       "text/plain": [
-       "140321614839000"
+       "140157466523464"
       ]
      },
-     "execution_count": 210,
+     "execution_count": 238,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -6037,7 +6776,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 211,
+   "execution_count": 239,
    "metadata": {
     "slideshow": {
      "slide_type": "-"
@@ -6050,7 +6789,7 @@
        "type"
       ]
      },
-     "execution_count": 211,
+     "execution_count": 239,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -6067,12 +6806,47 @@
     }
    },
    "source": [
-    "To create a `Point` object, we use the same *literal syntax* as for `current_position` above and prepend it with `Point`."
+    "The value of `Point` is just itself in a *literal notation*."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 212,
+   "execution_count": 240,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "__main__.Point"
+      ]
+     },
+     "execution_count": 240,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "Point"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "We write `Point(4, 2)` to create a *new* object of type `Point`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 241,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -6096,7 +6870,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 213,
+   "execution_count": 242,
    "metadata": {
     "slideshow": {
      "slide_type": "-"
@@ -6109,7 +6883,7 @@
        "Point(x=4, y=2)"
       ]
      },
-     "execution_count": 213,
+     "execution_count": 242,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -6131,7 +6905,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 214,
+   "execution_count": 243,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -6141,10 +6915,10 @@
     {
      "data": {
       "text/plain": [
-       "140322025282656"
+       "140157872590424"
       ]
      },
-     "execution_count": 214,
+     "execution_count": 243,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -6155,7 +6929,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 215,
+   "execution_count": 244,
    "metadata": {
     "slideshow": {
      "slide_type": "-"
@@ -6168,7 +6942,7 @@
        "__main__.Point"
       ]
      },
-     "execution_count": 215,
+     "execution_count": 244,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -6190,7 +6964,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 216,
+   "execution_count": 245,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -6203,7 +6977,7 @@
        "4"
       ]
      },
-     "execution_count": 216,
+     "execution_count": 245,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -6214,7 +6988,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 217,
+   "execution_count": 246,
    "metadata": {
     "slideshow": {
      "slide_type": "-"
@@ -6227,7 +7001,7 @@
        "2"
       ]
      },
-     "execution_count": 217,
+     "execution_count": 246,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -6249,7 +7023,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 218,
+   "execution_count": 247,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -6263,7 +7037,7 @@
      "traceback": [
       "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
       "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-218-b30d0b930bac>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mcurrent_position\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mz\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;32m<ipython-input-247-b30d0b930bac>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mcurrent_position\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mz\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
       "\u001b[0;31mAttributeError\u001b[0m: 'Point' object has no attribute 'z'"
      ]
     }
@@ -6287,7 +7061,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 219,
+   "execution_count": 248,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -6300,7 +7074,7 @@
        "True"
       ]
      },
-     "execution_count": 219,
+     "execution_count": 248,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -6311,7 +7085,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 220,
+   "execution_count": 249,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -6324,7 +7098,7 @@
        "4"
       ]
      },
-     "execution_count": 220,
+     "execution_count": 249,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -6335,7 +7109,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 221,
+   "execution_count": 250,
    "metadata": {
     "slideshow": {
      "slide_type": "-"
@@ -6348,7 +7122,7 @@
        "2"
       ]
      },
-     "execution_count": 221,
+     "execution_count": 250,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -6359,7 +7133,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 222,
+   "execution_count": 251,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -6382,7 +7156,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 223,
+   "execution_count": 252,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -6429,7 +7203,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 224,
+   "execution_count": 253,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -6466,7 +7240,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 225,
+   "execution_count": 254,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -6492,7 +7266,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 226,
+   "execution_count": 255,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -6509,7 +7283,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 227,
+   "execution_count": 256,
    "metadata": {
     "slideshow": {
      "slide_type": "-"
@@ -6522,7 +7296,7 @@
        "[50, 122, 65, 26, 10, 145, 5, 37, 82, 101, 2, 17]"
       ]
      },
-     "execution_count": 227,
+     "execution_count": 256,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -6546,7 +7320,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 228,
+   "execution_count": 257,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -6570,7 +7344,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 229,
+   "execution_count": 258,
    "metadata": {
     "slideshow": {
      "slide_type": "-"
@@ -6580,10 +7354,10 @@
     {
      "data": {
       "text/plain": [
-       "<map at 0x7f9f44745ac8>"
+       "<map at 0x7f790c37f470>"
       ]
      },
-     "execution_count": 229,
+     "execution_count": 258,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -6594,7 +7368,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 230,
+   "execution_count": 259,
    "metadata": {
     "slideshow": {
      "slide_type": "-"
@@ -6607,7 +7381,7 @@
        "map"
       ]
      },
-     "execution_count": 230,
+     "execution_count": 259,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -6631,7 +7405,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 231,
+   "execution_count": 260,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -6644,7 +7418,7 @@
        "50"
       ]
      },
-     "execution_count": 231,
+     "execution_count": 260,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -6655,7 +7429,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 232,
+   "execution_count": 261,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -6668,7 +7442,7 @@
        "122"
       ]
      },
-     "execution_count": 232,
+     "execution_count": 261,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -6679,7 +7453,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 233,
+   "execution_count": 262,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -6692,7 +7466,7 @@
        "65"
       ]
      },
-     "execution_count": 233,
+     "execution_count": 262,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -6716,7 +7490,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 234,
+   "execution_count": 263,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -6729,7 +7503,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 235,
+   "execution_count": 264,
    "metadata": {
     "slideshow": {
      "slide_type": "-"
@@ -6742,7 +7516,7 @@
        "[50, 122, 65, 26, 10, 145, 5, 37, 82, 101, 2, 17]"
       ]
      },
-     "execution_count": 235,
+     "execution_count": 264,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -6777,7 +7551,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 236,
+   "execution_count": 265,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -6805,7 +7579,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 237,
+   "execution_count": 266,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -6822,7 +7596,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 238,
+   "execution_count": 267,
    "metadata": {
     "slideshow": {
      "slide_type": "-"
@@ -6835,7 +7609,7 @@
        "[50, 122, 26, 10, 82, 2]"
       ]
      },
-     "execution_count": 238,
+     "execution_count": 267,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -6857,7 +7631,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 239,
+   "execution_count": 268,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -6870,7 +7644,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 240,
+   "execution_count": 269,
    "metadata": {
     "slideshow": {
      "slide_type": "-"
@@ -6880,10 +7654,10 @@
     {
      "data": {
       "text/plain": [
-       "<filter at 0x7f9f447271d0>"
+       "<filter at 0x7f790c387c88>"
       ]
      },
-     "execution_count": 240,
+     "execution_count": 269,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -6894,7 +7668,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 241,
+   "execution_count": 270,
    "metadata": {
     "slideshow": {
      "slide_type": "-"
@@ -6907,7 +7681,7 @@
        "filter"
       ]
      },
-     "execution_count": 241,
+     "execution_count": 270,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -6929,7 +7703,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 242,
+   "execution_count": 271,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -6942,7 +7716,7 @@
        "[50, 122, 65, 26, 10, 145, 5, 37, 82, 101, 2, 17]"
       ]
      },
-     "execution_count": 242,
+     "execution_count": 271,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -6953,7 +7727,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 243,
+   "execution_count": 272,
    "metadata": {
     "slideshow": {
      "slide_type": "-"
@@ -6966,7 +7740,7 @@
        "50"
       ]
      },
-     "execution_count": 243,
+     "execution_count": 272,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -6977,7 +7751,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 244,
+   "execution_count": 273,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -6990,7 +7764,7 @@
        "122"
       ]
      },
-     "execution_count": 244,
+     "execution_count": 273,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -7001,7 +7775,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 245,
+   "execution_count": 274,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -7014,7 +7788,7 @@
        "26"
       ]
      },
-     "execution_count": 245,
+     "execution_count": 274,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -7036,7 +7810,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 246,
+   "execution_count": 275,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -7049,7 +7823,7 @@
        "[50, 122, 26, 10, 82, 2]"
       ]
      },
-     "execution_count": 246,
+     "execution_count": 275,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -7071,7 +7845,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 247,
+   "execution_count": 276,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -7084,7 +7858,7 @@
        "[50, 122, 26, 10, 82, 2]"
       ]
      },
-     "execution_count": 247,
+     "execution_count": 276,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -7106,7 +7880,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 248,
+   "execution_count": 277,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -7119,7 +7893,7 @@
        "[65, 145, 5, 37, 101, 17]"
       ]
      },
-     "execution_count": 248,
+     "execution_count": 277,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -7154,7 +7928,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 249,
+   "execution_count": 278,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -7167,7 +7941,7 @@
        "370"
       ]
      },
-     "execution_count": 249,
+     "execution_count": 278,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -7189,7 +7963,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 250,
+   "execution_count": 279,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -7202,7 +7976,7 @@
        "5"
       ]
      },
-     "execution_count": 250,
+     "execution_count": 279,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -7213,7 +7987,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 251,
+   "execution_count": 280,
    "metadata": {
     "slideshow": {
      "slide_type": "-"
@@ -7226,7 +8000,7 @@
        "145"
       ]
      },
-     "execution_count": 251,
+     "execution_count": 280,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -7252,7 +8026,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 252,
+   "execution_count": 281,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -7278,7 +8052,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 253,
+   "execution_count": 282,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -7304,7 +8078,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 254,
+   "execution_count": 283,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -7340,7 +8114,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 255,
+   "execution_count": 284,
    "metadata": {
     "slideshow": {
      "slide_type": "-"
@@ -7353,7 +8127,7 @@
        "370"
       ]
      },
-     "execution_count": 255,
+     "execution_count": 284,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -7377,7 +8151,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 256,
+   "execution_count": 285,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -7390,7 +8164,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 257,
+   "execution_count": 286,
    "metadata": {
     "slideshow": {
      "slide_type": "-"
@@ -7403,7 +8177,7 @@
        "370"
       ]
      },
-     "execution_count": 257,
+     "execution_count": 286,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -7433,16 +8207,16 @@
    "source": [
     "[map()](https://docs.python.org/3/library/functions.html#map), [filter()](https://docs.python.org/3/library/functions.html#filter), and [reduce()](https://docs.python.org/3/library/functools.html#functools.reduce) take a `function` object as their first argument, and we defined `transform()`, `is_even()`, and `add()` to be used precisely for that.\n",
     "\n",
-    "Often, such functions are used *only once* in a program. However, the primary purpose of functions is to *re-use* them. In such cases, it makes more sense to define them \"anonymously\" right at the position where the first argument goes.\n",
+    "Often, such functions are used *only once* in a program. However, the primary purpose of functions is to *reuse* them. In such cases, it makes more sense to define them \"anonymously\" right at the position where the first argument goes.\n",
     "\n",
-    "As mentioned in [Chapter 2](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/02_functions.ipynb#Anonymous-Functions), Python provides `lambda` expressions to create `function` objects *without* a variable pointing to them.\n",
+    "As mentioned in [Chapter 2](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/02_functions.ipynb#Anonymous-Functions), Python provides `lambda` expressions to create `function` objects *without* a name referencing them.\n",
     "\n",
     "So, the above `add()` function could be rewritten as a `lambda` expression like so ..."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 258,
+   "execution_count": 287,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -7455,7 +8229,7 @@
        "<function __main__.<lambda>(sum_so_far, next_number)>"
       ]
      },
-     "execution_count": 258,
+     "execution_count": 287,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -7477,7 +8251,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 259,
+   "execution_count": 288,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -7490,7 +8264,7 @@
        "<function __main__.<lambda>(x, y)>"
       ]
      },
-     "execution_count": 259,
+     "execution_count": 288,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -7512,7 +8286,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 260,
+   "execution_count": 289,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -7525,7 +8299,7 @@
        "370"
       ]
      },
-     "execution_count": 260,
+     "execution_count": 289,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -7550,7 +8324,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 261,
+   "execution_count": 290,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -7563,7 +8337,7 @@
        "370"
       ]
      },
-     "execution_count": 261,
+     "execution_count": 290,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -7588,7 +8362,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 262,
+   "execution_count": 291,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -7601,7 +8375,7 @@
        "370"
       ]
      },
-     "execution_count": 262,
+     "execution_count": 291,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -7654,7 +8428,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 263,
+   "execution_count": 292,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -7667,7 +8441,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 264,
+   "execution_count": 293,
    "metadata": {
     "slideshow": {
      "slide_type": "-"
@@ -7684,7 +8458,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 265,
+   "execution_count": 294,
    "metadata": {
     "slideshow": {
      "slide_type": "-"
@@ -7697,7 +8471,7 @@
        "[65, 145, 5, 37, 101, 17]"
       ]
      },
-     "execution_count": 265,
+     "execution_count": 294,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -7719,7 +8493,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 266,
+   "execution_count": 295,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -7732,7 +8506,7 @@
        "[65, 145, 5, 37, 101, 17]"
       ]
      },
-     "execution_count": 266,
+     "execution_count": 295,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -7756,7 +8530,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 267,
+   "execution_count": 296,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -7769,7 +8543,7 @@
        "370"
       ]
      },
-     "execution_count": 267,
+     "execution_count": 296,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -7804,7 +8578,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 268,
+   "execution_count": 297,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -7817,7 +8591,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 269,
+   "execution_count": 298,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -7830,7 +8604,7 @@
        "[[1, 2, 3, 4, 5, 6, 7], [2, 3, 4, 5, 6, 7, 8], [3, 4, 5, 6, 7, 8, 9]]"
       ]
      },
-     "execution_count": 269,
+     "execution_count": 298,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -7854,7 +8628,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 270,
+   "execution_count": 299,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -7871,7 +8645,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 271,
+   "execution_count": 300,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -7884,7 +8658,7 @@
        "[1, 2, 3, 4, 5, 6, 7, 2, 3, 4, 5, 6, 7, 8, 3, 4, 5, 6, 7, 8, 9]"
       ]
      },
-     "execution_count": 271,
+     "execution_count": 300,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -7906,7 +8680,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 272,
+   "execution_count": 301,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -7919,7 +8693,7 @@
        "[1, 2, 3, 4, 5, 6, 7, 2, 3, 4, 5, 6, 7, 8, 3, 4, 5, 6, 7, 8, 9]"
       ]
      },
-     "execution_count": 272,
+     "execution_count": 301,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -7943,7 +8717,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 273,
+   "execution_count": 302,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -7956,7 +8730,7 @@
        "105"
       ]
      },
-     "execution_count": 273,
+     "execution_count": 302,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -7978,7 +8752,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 274,
+   "execution_count": 303,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -7991,7 +8765,7 @@
        "105"
       ]
      },
-     "execution_count": 274,
+     "execution_count": 303,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -8034,7 +8808,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 275,
+   "execution_count": 304,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -8059,7 +8833,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 276,
+   "execution_count": 305,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -8072,7 +8846,7 @@
        "[1.25, 1.2, 1.1666666666666667, 1.5, 1.4, 1.3333333333333333, 1.75, 1.6, 1.5]"
       ]
      },
-     "execution_count": 276,
+     "execution_count": 305,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -8101,7 +8875,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 277,
+   "execution_count": 306,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -8114,7 +8888,7 @@
        "[1.25, 1.2, 1.1666666666666667, 1.5, 1.4, 1.3333333333333333, 1.75, 1.6, 1.5]"
       ]
      },
-     "execution_count": 277,
+     "execution_count": 306,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -8136,7 +8910,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 278,
+   "execution_count": 307,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -8149,7 +8923,7 @@
        "[5.0, 3.0, 2.333333333333333, 6.0, 3.5, 2.666666666666667, 7.0, 4.0, 3.0]"
       ]
      },
-     "execution_count": 278,
+     "execution_count": 307,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -8171,7 +8945,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 279,
+   "execution_count": 308,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -8184,7 +8958,7 @@
        "20.58"
       ]
      },
-     "execution_count": 279,
+     "execution_count": 308,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -8206,7 +8980,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 280,
+   "execution_count": 309,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -8219,7 +8993,7 @@
        "20.58"
       ]
      },
-     "execution_count": 280,
+     "execution_count": 309,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -8265,7 +9039,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 281,
+   "execution_count": 310,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -8289,7 +9063,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 282,
+   "execution_count": 311,
    "metadata": {
     "slideshow": {
      "slide_type": "-"
@@ -8302,7 +9076,7 @@
        "[65, 145, 5, 37, 101, 17]"
       ]
      },
-     "execution_count": 282,
+     "execution_count": 311,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -8324,7 +9098,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 283,
+   "execution_count": 312,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -8334,10 +9108,10 @@
     {
      "data": {
       "text/plain": [
-       "<generator object <genexpr> at 0x7f9f447618b8>"
+       "<generator object <genexpr> at 0x7f790c3a55e8>"
       ]
      },
-     "execution_count": 283,
+     "execution_count": 312,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -8361,7 +9135,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 284,
+   "execution_count": 313,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -8374,7 +9148,7 @@
        "[65, 145, 5, 37, 101, 17]"
       ]
      },
-     "execution_count": 284,
+     "execution_count": 313,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -8396,7 +9170,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 285,
+   "execution_count": 314,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -8409,7 +9183,7 @@
        "[65, 145, 5, 37, 101, 17]"
       ]
      },
-     "execution_count": 285,
+     "execution_count": 314,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -8431,7 +9205,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 286,
+   "execution_count": 315,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -8444,7 +9218,7 @@
        "370"
       ]
      },
-     "execution_count": 286,
+     "execution_count": 315,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -8466,7 +9240,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 287,
+   "execution_count": 316,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -8479,7 +9253,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 288,
+   "execution_count": 317,
    "metadata": {
     "slideshow": {
      "slide_type": "-"
@@ -8489,10 +9263,10 @@
     {
      "data": {
       "text/plain": [
-       "<generator object <genexpr> at 0x7f9f44761d68>"
+       "<generator object <genexpr> at 0x7f790c3a5a20>"
       ]
      },
-     "execution_count": 288,
+     "execution_count": 317,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -8514,7 +9288,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 289,
+   "execution_count": 318,
    "metadata": {
     "slideshow": {
      "slide_type": "-"
@@ -8527,7 +9301,7 @@
        "generator"
       ]
      },
-     "execution_count": 289,
+     "execution_count": 318,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -8549,7 +9323,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 290,
+   "execution_count": 319,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -8562,7 +9336,7 @@
        "65"
       ]
      },
-     "execution_count": 290,
+     "execution_count": 319,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -8573,7 +9347,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 291,
+   "execution_count": 320,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -8586,7 +9360,7 @@
        "145"
       ]
      },
-     "execution_count": 291,
+     "execution_count": 320,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -8597,7 +9371,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 292,
+   "execution_count": 321,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -8610,7 +9384,7 @@
        "5"
       ]
      },
-     "execution_count": 292,
+     "execution_count": 321,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -8621,7 +9395,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 293,
+   "execution_count": 322,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -8634,7 +9408,7 @@
        "37"
       ]
      },
-     "execution_count": 293,
+     "execution_count": 322,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -8645,7 +9419,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 294,
+   "execution_count": 323,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -8658,7 +9432,7 @@
        "101"
       ]
      },
-     "execution_count": 294,
+     "execution_count": 323,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -8669,7 +9443,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 295,
+   "execution_count": 324,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -8682,7 +9456,7 @@
        "17"
       ]
      },
-     "execution_count": 295,
+     "execution_count": 324,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -8704,7 +9478,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 296,
+   "execution_count": 325,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -8718,7 +9492,7 @@
      "traceback": [
       "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
       "\u001b[0;31mStopIteration\u001b[0m                             Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-296-6e72e47198db>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mnext\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mgen\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;32m<ipython-input-325-6e72e47198db>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mnext\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mgen\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
       "\u001b[0;31mStopIteration\u001b[0m: "
      ]
     }
@@ -8729,7 +9503,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 297,
+   "execution_count": 326,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -8743,7 +9517,7 @@
      "traceback": [
       "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
       "\u001b[0;31mStopIteration\u001b[0m                             Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-297-6e72e47198db>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mnext\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mgen\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;32m<ipython-input-326-6e72e47198db>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mnext\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mgen\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
       "\u001b[0;31mStopIteration\u001b[0m: "
      ]
     }
@@ -8765,7 +9539,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 298,
+   "execution_count": 327,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -8813,7 +9587,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 299,
+   "execution_count": 328,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -8826,7 +9600,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 300,
+   "execution_count": 329,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -8839,7 +9613,7 @@
        "[[1, 2, 3, 4, 5, 6, 7], [2, 3, 4, 5, 6, 7, 8], [3, 4, 5, 6, 7, 8, 9]]"
       ]
      },
-     "execution_count": 300,
+     "execution_count": 329,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -8861,7 +9635,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 301,
+   "execution_count": 330,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -8874,7 +9648,7 @@
        "105"
       ]
      },
-     "execution_count": 301,
+     "execution_count": 330,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -8896,7 +9670,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 302,
+   "execution_count": 331,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -8909,7 +9683,7 @@
        "105"
       ]
      },
-     "execution_count": 302,
+     "execution_count": 331,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -8931,7 +9705,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 303,
+   "execution_count": 332,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -8944,7 +9718,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 304,
+   "execution_count": 333,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -8954,10 +9728,10 @@
     {
      "data": {
       "text/plain": [
-       "<generator object <genexpr> at 0x7f9f44761e58>"
+       "<generator object <genexpr> at 0x7f790c3a5b10>"
       ]
      },
-     "execution_count": 304,
+     "execution_count": 333,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -8968,7 +9742,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 305,
+   "execution_count": 334,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -8981,7 +9755,7 @@
        "105"
       ]
      },
-     "execution_count": 305,
+     "execution_count": 334,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -9003,7 +9777,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 306,
+   "execution_count": 335,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -9016,7 +9790,7 @@
        "0"
       ]
      },
-     "execution_count": 306,
+     "execution_count": 335,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -9051,7 +9825,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 307,
+   "execution_count": 336,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -9078,7 +9852,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 308,
+   "execution_count": 337,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -9091,7 +9865,7 @@
        "20.58"
       ]
      },
-     "execution_count": 308,
+     "execution_count": 337,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -9113,7 +9887,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 309,
+   "execution_count": 338,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -9126,7 +9900,7 @@
        "20.58"
       ]
      },
-     "execution_count": 309,
+     "execution_count": 338,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -9143,7 +9917,7 @@
     }
    },
    "source": [
-    "In summary, we learn from this example that unpacking generator expressions may be a *bad* idea."
+    "In summary, we learn from this example that unpacking generator expressions *may* be a *bad* idea."
    ]
   },
   {
@@ -9172,7 +9946,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 310,
+   "execution_count": 339,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -9185,7 +9959,7 @@
        "(65, 145, 5, 37, 101, 17)"
       ]
      },
-     "execution_count": 310,
+     "execution_count": 339,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -9220,7 +9994,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 311,
+   "execution_count": 340,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -9246,7 +10020,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 312,
+   "execution_count": 341,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -9259,7 +10033,7 @@
        "False"
       ]
      },
-     "execution_count": 312,
+     "execution_count": 341,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -9270,7 +10044,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 313,
+   "execution_count": 342,
    "metadata": {
     "slideshow": {
      "slide_type": "-"
@@ -9283,7 +10057,7 @@
        "True"
       ]
      },
-     "execution_count": 313,
+     "execution_count": 342,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -9305,7 +10079,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 314,
+   "execution_count": 343,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -9318,7 +10092,7 @@
        "False"
       ]
      },
-     "execution_count": 314,
+     "execution_count": 343,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -9349,7 +10123,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 315,
+   "execution_count": 344,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -9367,7 +10141,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 316,
+   "execution_count": 345,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -9380,7 +10154,7 @@
        "False"
       ]
      },
-     "execution_count": 316,
+     "execution_count": 345,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -9391,7 +10165,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 317,
+   "execution_count": 346,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -9404,7 +10178,7 @@
        "True"
       ]
      },
-     "execution_count": 317,
+     "execution_count": 346,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -9428,7 +10202,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 318,
+   "execution_count": 347,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -9441,7 +10215,7 @@
        "True"
       ]
      },
-     "execution_count": 318,
+     "execution_count": 347,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -9452,7 +10226,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 319,
+   "execution_count": 348,
    "metadata": {
     "slideshow": {
      "slide_type": "-"
@@ -9465,7 +10239,7 @@
        "False"
       ]
      },
-     "execution_count": 319,
+     "execution_count": 348,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -9487,7 +10261,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 320,
+   "execution_count": 349,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -9500,7 +10274,7 @@
        "True"
       ]
      },
-     "execution_count": 320,
+     "execution_count": 349,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -9531,7 +10305,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 321,
+   "execution_count": 350,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -9549,7 +10323,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 322,
+   "execution_count": 351,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -9562,7 +10336,7 @@
        "True"
       ]
      },
-     "execution_count": 322,
+     "execution_count": 351,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -9573,7 +10347,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 323,
+   "execution_count": 352,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -9586,7 +10360,7 @@
        "False"
       ]
      },
-     "execution_count": 323,
+     "execution_count": 352,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -9625,7 +10399,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 324,
+   "execution_count": 353,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -9653,7 +10427,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 325,
+   "execution_count": 354,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -9666,7 +10440,7 @@
        "7.0"
       ]
      },
-     "execution_count": 325,
+     "execution_count": 354,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -9688,7 +10462,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 326,
+   "execution_count": 355,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -9701,7 +10475,7 @@
        "7.0"
       ]
      },
-     "execution_count": 326,
+     "execution_count": 355,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -9723,7 +10497,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 327,
+   "execution_count": 356,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -9736,7 +10510,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 328,
+   "execution_count": 357,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -9749,7 +10523,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 329,
+   "execution_count": 358,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -9762,7 +10536,7 @@
        "49.994081434519636"
       ]
      },
-     "execution_count": 329,
+     "execution_count": 358,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -9784,7 +10558,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 330,
+   "execution_count": 359,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -9798,8 +10572,8 @@
      "traceback": [
       "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
       "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-330-73a295f34553>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0maverage_evens\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m2\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0mrandom\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrandint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m49\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;36m1\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0m_\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m10_000_000\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
-      "\u001b[0;32m<ipython-input-324-491a5ff07381>\u001b[0m in \u001b[0;36maverage_evens\u001b[0;34m(numbers)\u001b[0m\n\u001b[1;32m     12\u001b[0m     total, count = reduce(\n\u001b[1;32m     13\u001b[0m         \u001b[0;32mlambda\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 14\u001b[0;31m         \u001b[0;34m(\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mn\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mn\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mintegers\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mn\u001b[0m \u001b[0;34m%\u001b[0m \u001b[0;36m2\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     15\u001b[0m     )\n\u001b[1;32m     16\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mtotal\u001b[0m \u001b[0;34m/\u001b[0m \u001b[0mcount\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m<ipython-input-359-73a295f34553>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0maverage_evens\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m2\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0mrandom\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrandint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m49\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;36m1\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0m_\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m10_000_000\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;32m<ipython-input-353-491a5ff07381>\u001b[0m in \u001b[0;36maverage_evens\u001b[0;34m(numbers)\u001b[0m\n\u001b[1;32m     12\u001b[0m     total, count = reduce(\n\u001b[1;32m     13\u001b[0m         \u001b[0;32mlambda\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 14\u001b[0;31m         \u001b[0;34m(\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mn\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mn\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mintegers\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mn\u001b[0m \u001b[0;34m%\u001b[0m \u001b[0;36m2\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     15\u001b[0m     )\n\u001b[1;32m     16\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mtotal\u001b[0m \u001b[0;34m/\u001b[0m \u001b[0mcount\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
       "\u001b[0;31mTypeError\u001b[0m: reduce() of empty sequence with no initial value"
      ]
     }
@@ -9831,12 +10605,12 @@
     "\n",
     "Similarly, we have introduced data types in this chapter that all share the \"behavior\" of modeling some \"rule\" in memory to generate objects \"on the fly:\" They are the `map`, `filter`, and `generator` types. Their main commonality is supporting the built-in [next()](https://docs.python.org/3/library/functions.html#next) function. In computer science terminology, such data types are called **[iterators](https://en.wikipedia.org/wiki/Iterator)**, and the [collections.abc](https://docs.python.org/3/library/collections.abc.html) module formalizes them with the `Iterator` ABC.\n",
     "\n",
-    "So, an example of an iterator is `evens_transformed` below, an object of type `map`."
+    "So, an example of an iterator is `evens_transformed` below, an object of type `generator`."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 331,
+   "execution_count": 360,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -9849,7 +10623,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 332,
+   "execution_count": 361,
    "metadata": {
     "slideshow": {
      "slide_type": "-"
@@ -9857,7 +10631,7 @@
    },
    "outputs": [],
    "source": [
-    "evens_transformed = map(lambda x: (x ** 2) + 1, filter(lambda x: x % 2 == 0, numbers))"
+    "evens_transformed = ((x ** 2) + 1 for x in numbers if x % 2 == 0)"
    ]
   },
   {
@@ -9873,7 +10647,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 333,
+   "execution_count": 362,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -9886,7 +10660,7 @@
        "True"
       ]
      },
-     "execution_count": 333,
+     "execution_count": 362,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -9908,7 +10682,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 334,
+   "execution_count": 363,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -9921,7 +10695,7 @@
        "True"
       ]
      },
-     "execution_count": 334,
+     "execution_count": 363,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -9949,7 +10723,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 335,
+   "execution_count": 364,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -9962,7 +10736,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 336,
+   "execution_count": 365,
    "metadata": {
     "slideshow": {
      "slide_type": "-"
@@ -9986,7 +10760,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 337,
+   "execution_count": 366,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -9999,7 +10773,7 @@
        "list_iterator"
       ]
      },
-     "execution_count": 337,
+     "execution_count": 366,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -10023,7 +10797,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 338,
+   "execution_count": 367,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -10036,7 +10810,7 @@
        "(7, 11, 8)"
       ]
      },
-     "execution_count": 338,
+     "execution_count": 367,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -10058,7 +10832,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 339,
+   "execution_count": 368,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -10071,7 +10845,7 @@
        "(5, 7)"
       ]
      },
-     "execution_count": 339,
+     "execution_count": 368,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -10093,7 +10867,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 340,
+   "execution_count": 369,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -10106,7 +10880,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 341,
+   "execution_count": 370,
    "metadata": {
     "slideshow": {
      "slide_type": "-"
@@ -10119,7 +10893,7 @@
        "(99, 99)"
       ]
      },
-     "execution_count": 341,
+     "execution_count": 370,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -10141,7 +10915,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 342,
+   "execution_count": 371,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -10154,7 +10928,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 343,
+   "execution_count": 372,
    "metadata": {
     "slideshow": {
      "slide_type": "-"
@@ -10167,7 +10941,7 @@
        "[1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12]"
       ]
      },
-     "execution_count": 343,
+     "execution_count": 372,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -10178,7 +10952,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 344,
+   "execution_count": 373,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -10191,7 +10965,7 @@
        "(6, 3)"
       ]
      },
-     "execution_count": 344,
+     "execution_count": 373,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -10217,7 +10991,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 345,
+   "execution_count": 374,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -10230,7 +11004,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 346,
+   "execution_count": 375,
    "metadata": {
     "slideshow": {
      "slide_type": "-"
@@ -10240,10 +11014,10 @@
     {
      "data": {
       "text/plain": [
-       "<zip at 0x7f9f44726a48>"
+       "<zip at 0x7f790c318208>"
       ]
      },
-     "execution_count": 346,
+     "execution_count": 375,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -10254,7 +11028,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 347,
+   "execution_count": 376,
    "metadata": {
     "slideshow": {
      "slide_type": "-"
@@ -10267,7 +11041,7 @@
        "zip"
       ]
      },
-     "execution_count": 347,
+     "execution_count": 376,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -10278,7 +11052,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 348,
+   "execution_count": 377,
    "metadata": {
     "slideshow": {
      "slide_type": "-"
@@ -10291,7 +11065,7 @@
        "True"
       ]
      },
-     "execution_count": 348,
+     "execution_count": 377,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -10313,7 +11087,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 349,
+   "execution_count": 378,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -10326,7 +11100,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 350,
+   "execution_count": 379,
    "metadata": {
     "slideshow": {
      "slide_type": "-"
@@ -10336,10 +11110,10 @@
     {
      "data": {
       "text/plain": [
-       "<zip at 0x7f9f44726a48>"
+       "<zip at 0x7f790c318208>"
       ]
      },
-     "execution_count": 350,
+     "execution_count": 379,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -10356,12 +11130,12 @@
     }
    },
    "source": [
-    "`zipper_iterator` points to the *same* object as `zipper`! That holds for *iterators* in general: Any *iterator* created from an existing *iterator* with [iter()](https://docs.python.org/3/library/functions.html#iter) is the *iterator* itself."
+    "`zipper_iterator` references the *same* object as `zipper`! That holds for *iterators* in general: Any *iterator* created from an existing *iterator* with [iter()](https://docs.python.org/3/library/functions.html#iter) is the *iterator* itself."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 351,
+   "execution_count": 380,
    "metadata": {
     "slideshow": {
      "slide_type": "-"
@@ -10374,7 +11148,7 @@
        "True"
       ]
      },
-     "execution_count": 351,
+     "execution_count": 380,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -10393,12 +11167,12 @@
    "source": [
     "The Python core developers made that design decision so that *iterators* may also be looped over.\n",
     "\n",
-    "The `for`-loop below prints out *six* more `tuple` objects derived from the re-ordered `numbers` because the `iterator1` object hidden inside `zipper` already returned the first *six* elements. So, the respective first elements of the `tuple` objects printed range from `7` to `12`. Similarly, as `iterator2` already provided *three* elements from `numbers`, we see the respective second elements in the range from `4` to `9`."
+    "The `for`-loop below prints out *six* more `tuple` objects derived from the now ordered `numbers` because the `iterator1` object hidden inside `zipper` already returned the first *six* elements. So, the respective first elements of the `tuple` objects printed range from `7` to `12`. Similarly, as `iterator2` already provided *three* elements from `numbers`, we see the respective second elements in the range from `4` to `9`."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 352,
+   "execution_count": 381,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -10431,7 +11205,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 353,
+   "execution_count": 382,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -10456,7 +11230,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 354,
+   "execution_count": 383,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -10470,7 +11244,7 @@
      "traceback": [
       "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
       "\u001b[0;31mStopIteration\u001b[0m                             Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-354-0680f5ec8ce3>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mnext\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0miterator1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;32m<ipython-input-383-0680f5ec8ce3>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mnext\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0miterator1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
       "\u001b[0;31mStopIteration\u001b[0m: "
      ]
     }
@@ -10492,7 +11266,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 355,
+   "execution_count": 384,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -10505,7 +11279,7 @@
        "10"
       ]
      },
-     "execution_count": 355,
+     "execution_count": 384,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -10514,6 +11288,17 @@
     "next(iterator2)"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Understanding *iterators* and *iterables* is helpful for any data science practitioner that deals with large amounts of data. Even without that, these two terms occur everywhere in Python-related texts and documentation."
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {
@@ -10540,7 +11325,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 356,
+   "execution_count": 385,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -10553,7 +11338,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 357,
+   "execution_count": 386,
    "metadata": {
     "slideshow": {
      "slide_type": "-"
@@ -10586,10 +11371,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 358,
+   "execution_count": 387,
    "metadata": {
     "slideshow": {
-     "slide_type": "fragment"
+     "slide_type": "skip"
     }
    },
    "outputs": [
@@ -10627,10 +11412,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 359,
+   "execution_count": 388,
    "metadata": {
     "slideshow": {
-     "slide_type": "slide"
+     "slide_type": "fragment"
     }
    },
    "outputs": [
@@ -10654,6 +11439,17 @@
     "        print(element, end=\" \")"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "### sorted() vs. reversed()"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {
@@ -10662,7 +11458,703 @@
     }
    },
    "source": [
-    "Understanding *iterators* and *iterables* is helpful for any data science practitioner that deals with large amounts of data. Even without that, these two terms occur everywhere in Python-related texts and documentation."
+    "Now that we know the concept of an *iterator*, let's compare some of the built-ins introduced in this chapter in detail and make sure we understand what is going on in memory. This sub-section is thus a great summary of this chapter as well.\n",
+    "\n",
+    "We use two simple examples, `numbers` and `memoryless`, to guide us through the discussion. `numbers` creates *thirteen* objects in memory and `memoryless` only *one* (cf., [PythonTutor](http://www.pythontutor.com/visualize.html#code=numbers%20%3D%20%5B7,%2011,%208,%205,%203,%2012,%202,%206,%209,%2010,%201,%204%5D%0Amemoryless%20%3D%20range%281,%2013%29&cumulative=false&curInstr=2&heapPrimitives=nevernest&mode=display&origin=opt-frontend.js&py=3&rawInputLstJSON=%5B%5D&textReferences=false))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 389,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "numbers = [7, 11, 8, 5, 3, 12, 2, 6, 9, 10, 1, 4]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 390,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "memoryless = range(1, 13)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "The [sorted()](https://docs.python.org/3/library/functions.html#sorted) function takes a *finite* and *iterable* object as its argument and *materializes* its elements into a *new* `list` object that is returned.\n",
+    "\n",
+    "The argument may already be materialized, as is the case with `numbers`, but could also be an *iterator* that generates *new* objects, such as `memoryless`. In both cases, we end up with materialized `list` objects with the elements sorted in *forward* order (cf., [PythonTutor](http://www.pythontutor.com/visualize.html#code=numbers%20%3D%20%5B7,%2011,%208,%205,%203,%2012,%202,%206,%209,%2010,%201,%204%5D%0Amemoryless%20%3D%20range%281,%2013%29%0Aresult1%20%3D%20sorted%28numbers%29%0Aresult2%20%3D%20sorted%28memoryless%29&cumulative=false&curInstr=4&heapPrimitives=nevernest&mode=display&origin=opt-frontend.js&py=3&rawInputLstJSON=%5B%5D&textReferences=false))."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 391,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12]"
+      ]
+     },
+     "execution_count": 391,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "sorted(numbers)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 392,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12]"
+      ]
+     },
+     "execution_count": 392,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "sorted(memoryless)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "By adding a keyword-only argument `reverse=True`, the materialized `list` objects are sorted in *reverse* order (cf., [PythonTutor](http://www.pythontutor.com/visualize.html#code=numbers%20%3D%20%5B7,%2011,%208,%205,%203,%2012,%202,%206,%209,%2010,%201,%204%5D%0Amemoryless%20%3D%20range%281,%2013%29%0Aresult1%20%3D%20sorted%28numbers,%20reverse%3DTrue%29%0Aresult2%20%3D%20sorted%28memoryless,%20reverse%3DTrue%29&cumulative=false&curInstr=4&heapPrimitives=nevernest&mode=display&origin=opt-frontend.js&py=3&rawInputLstJSON=%5B%5D&textReferences=false))."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 393,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[12, 11, 10, 9, 8, 7, 6, 5, 4, 3, 2, 1]"
+      ]
+     },
+     "execution_count": 393,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "sorted(numbers, reverse=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 394,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[12, 11, 10, 9, 8, 7, 6, 5, 4, 3, 2, 1]"
+      ]
+     },
+     "execution_count": 394,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "sorted(memoryless, reverse=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "The order in `numbers` remains *unchanged*, and `memoryless` is still *not* materialized."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 395,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[7, 11, 8, 5, 3, 12, 2, 6, 9, 10, 1, 4]"
+      ]
+     },
+     "execution_count": 395,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "numbers"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 396,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "range(1, 13)"
+      ]
+     },
+     "execution_count": 396,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "memoryless"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "The [reversed()](https://docs.python.org/3/library/functions.html#reversed) built-in takes a *sequence* object as its argument and returns an *iterator*. The argument must be *finite* and *reversible* (i.e., *iterable* in *reverse* order) as otherwise [reversed()](https://docs.python.org/3/library/functions.html#reversed) could neither determine the last element that becomes the first nor loop in a *predictable* backward fashion. [PythonTutor](http://www.pythontutor.com/visualize.html#code=numbers%20%3D%20%5B7,%2011,%208,%205,%203,%2012,%202,%206,%209,%2010,%201,%204%5D%0Amemoryless%20%3D%20range%281,%2013%29%0Aiterator1%20%3D%20reversed%28numbers%29%0Aiterator2%20%3D%20reversed%28memoryless%29&cumulative=false&curInstr=4&heapPrimitives=nevernest&mode=display&origin=opt-frontend.js&py=3&rawInputLstJSON=%5B%5D&textReferences=false) confirms that [reversed()](https://docs.python.org/3/library/functions.html#reversed) does *not* materialize any elements but only returns an *iterator*.\n",
+    "\n",
+    "**Side Note**: Even though `range` objects, like `memoryless` here, do *not* \"contain\" references to other objects, they count as *sequence* types, and as such, they are also *container* types. The `in` operator works with `range` objects because we can always cast the object to be checked as an `int` and check if that lies within the `range` object's *start* and *stop* values, taking a potential *step* value into account (cf., this [blog post](https://treyhunner.com/2018/02/python-range-is-not-an-iterator/) for more details on the [range()](https://docs.python.org/3/library/functions.html#func-range) built-in)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 397,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<list_reverseiterator at 0x7f790c3339e8>"
+      ]
+     },
+     "execution_count": 397,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "reversed(numbers)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 398,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<range_iterator at 0x7f790c380780>"
+      ]
+     },
+     "execution_count": 398,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "reversed(memoryless)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "To materialize the elements, we can pass the returned *iterators* to, for example, the [list()](https://docs.python.org/3/library/functions.html#func-list) or [tuple()](https://docs.python.org/3/library/functions.html#func-tuple) built-ins. That creates *new* `list` and `tuple` objects (cf., [PythonTutor](http://www.pythontutor.com/visualize.html#code=numbers%20%3D%20%5B7,%2011,%208,%205,%203,%2012,%202,%206,%209,%2010,%201,%204%5D%0Amemoryless%20%3D%20range%281,%2013%29%0Aresult1%20%3D%20list%28reversed%28numbers%29%29%0Aresult2%20%3D%20tuple%28reversed%28memoryless%29%29&cumulative=false&curInstr=4&heapPrimitives=nevernest&mode=display&origin=opt-frontend.js&py=3&rawInputLstJSON=%5B%5D&textReferences=false)).\n",
+    "\n",
+    "To reiterate some more new terminology from this chapter, we describe [reversed()](https://docs.python.org/3/library/functions.html#reversed) as *lazy*, whereas [list()](https://docs.python.org/3/library/functions.html#func-list) and [tuple()](https://docs.python.org/3/library/functions.html#func-tuple) are *eager*. The former has no significant side effect in memory, while the latter may require a lot of memory."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 399,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[4, 1, 10, 9, 6, 2, 12, 3, 5, 8, 11, 7]"
+      ]
+     },
+     "execution_count": 399,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "list(reversed(numbers))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 400,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(12, 11, 10, 9, 8, 7, 6, 5, 4, 3, 2, 1)"
+      ]
+     },
+     "execution_count": 400,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "tuple(reversed(memoryless))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Of course, we can also loop over the returned *iterators* instead.\n",
+    "\n",
+    "That works because *iterators* are always *iterables*; in particular, as the previous \"*The for Statement (revisited)*\" sub-section explains, the `for`-loops below call `iter(reversed(numbers))` and `iter(reversed(memoryless))` behind the scenes. However, the *iterators* returned by [iter()](https://docs.python.org/3/library/functions.html#iter) are the *same* as the `reversed(numbers)` and `reversed(memoryless)` iterators passed in! In summary, the `for`-loops below involve many subtleties that together make Python the expressive language it is."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 401,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "4 1 10 9 6 2 12 3 5 8 11 7 "
+     ]
+    }
+   ],
+   "source": [
+    "for number in reversed(numbers):\n",
+    "    print(number, end=\" \")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 402,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "12 11 10 9 8 7 6 5 4 3 2 1 "
+     ]
+    }
+   ],
+   "source": [
+    "for element in reversed(memoryless):\n",
+    "    print(element, end=\" \")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "As with [sorted()](https://docs.python.org/3/library/functions.html#sorted), the [reversed()](https://docs.python.org/3/library/functions.html#reversed) built-in does *not* mutate its argument."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 403,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[7, 11, 8, 5, 3, 12, 2, 6, 9, 10, 1, 4]"
+      ]
+     },
+     "execution_count": 403,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "numbers"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 404,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "range(1, 13)"
+      ]
+     },
+     "execution_count": 404,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "memoryless"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "To point out the potentially obvious, we compare the results of *sorting* `numbers` in *reverse* order with *reversing* it: These are *different* concepts!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 405,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[7, 11, 8, 5, 3, 12, 2, 6, 9, 10, 1, 4]"
+      ]
+     },
+     "execution_count": 405,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "numbers"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 406,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[12, 11, 10, 9, 8, 7, 6, 5, 4, 3, 2, 1]"
+      ]
+     },
+     "execution_count": 406,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "sorted(numbers, reverse=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 407,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[4, 1, 10, 9, 6, 2, 12, 3, 5, 8, 11, 7]"
+      ]
+     },
+     "execution_count": 407,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "list(reversed(numbers))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Whereas both [sorted()](https://docs.python.org/3/library/functions.html#sorted) and [reversed()](https://docs.python.org/3/library/functions.html#reversed) do *not* mutate their arguments, the *mutable* `list` type comes with two methods, [sort()](https://docs.python.org/3/library/stdtypes.html#list.sort) and `reverse()`, that implement the same logic but mutate an object, like `numbers` below, *in place*. To indicate that all changes occur *in place*, the [sort()](https://docs.python.org/3/library/stdtypes.html#list.sort) and `reverse()` methods always return `None`, which is not shown."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 408,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[7, 11, 8, 5, 3, 12, 2, 6, 9, 10, 1, 4]"
+      ]
+     },
+     "execution_count": 408,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "numbers"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "The `reverse()` method is *eager*, as opposed to the *lazy* [reversed()](https://docs.python.org/3/library/functions.html#reversed) built-in. That means the mutations causes by the `reverse()` method are written into memory right away."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 409,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "numbers.reverse()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 410,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[4, 1, 10, 9, 6, 2, 12, 3, 5, 8, 11, 7]"
+      ]
+     },
+     "execution_count": 410,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "numbers"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "*Sorting* `numbers` in *reverse* order below is of course still *different* from simply *reversing* it above."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 411,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "numbers.sort(reverse=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 412,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[12, 11, 10, 9, 8, 7, 6, 5, 4, 3, 2, 1]"
+      ]
+     },
+     "execution_count": 412,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "numbers"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 413,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "numbers.sort()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 414,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12]"
+      ]
+     },
+     "execution_count": 414,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "numbers"
    ]
   },
   {
@@ -10684,13 +12176,27 @@
     }
    },
    "source": [
-    "**Sequences** are an abstract idea that summarizes *four* behaviors an object may or may not exhibit: We describe them as **finite** and **ordered** **containers** that we may **loop over**. Examples are objects of type `list`, `tuple`, but also `str`. Objects that exhibit all behaviors *except* being ordered are referred to as **collections**. The objects inside a sequence are labeled with a unique *index*, an `int` object in the range $0 \\leq \\text{index} < \\lvert \\text{sequence} \\rvert$, and called its **elements**.\n",
+    "**Sequences** are an *abstract* concept that summarizes *four* behaviors an object may or may not exhibit. Sequences are\n",
+    "- **finite** and\n",
+    "- **ordered**\n",
+    "- **containers** that we may\n",
+    "- **loop over**.\n",
     "\n",
-    "`list` objects are **mutable**. That means we can change the references to other objects it contains, and, in particular, re-assign them. On the contrary, `tuple` objects are like **immutable** lists: We can use them in place of any `list` object as long as we do *not* mutate it.\n",
+    "Examples are the `list`, `tuple`, but also the `str` types.\n",
     "\n",
-    "Often, the work we do with sequences follows the **map-filter-reduce paradigm**: We apply the same transformation to all elements, filter some of them out, and calculate summary statistics from the remaining ones.\n",
+    "Objects that exhibit all behaviors *except* being ordered are referred to as **collections**.\n",
     "\n",
-    "An essential idea in this chapter is that, in many situations, we need *not* work with all the data **materialized** in memory. Instead, **iterators** allow us to process sequential data on a one-by-one basis."
+    "The objects inside a sequence are called its **elements** and may be labeled with a unique **index**, an `int` object in the range $0 \\leq \\text{index} < \\lvert \\text{sequence} \\rvert$.\n",
+    "\n",
+    "`list` objects are **mutable**. That means we can change the references to the other objects it contains, and, in particular, re-assign them.\n",
+    "\n",
+    "On the contrary, `tuple` objects are like **immutable** lists: We can use them in place of any `list` object as long as we do *not* need to mutate it. Often, `tuple` objects are also used to model **records** of related **fields**.\n",
+    "\n",
+    "The tasks we do with sequential data follow the **map-filter-reduce paradigm**: We apply the same transformation to all elements, filter some of them out, and calculate summary statistics from the remaining ones.\n",
+    "\n",
+    "An essential idea in this chapter is that, in many situations, we need *not* have all the data **materialized** in memory. Instead, **iterators** allow us to process sequential data on a one-by-one basis.\n",
+    "\n",
+    "Examples for iterators are the `map`, `filter`, and `generator` types."
    ]
   }
  ],

From c69687f463c3ead4d373545aee87fca281fcae04 Mon Sep 17 00:00:00 2001
From: Alexander Hess <alexander@webartifex.biz>
Date: Wed, 20 Nov 2019 11:01:19 +0100
Subject: [PATCH 049/242] Add exercise on packing/unpacking with functions

---
 07_sequences_review_and_exercises.ipynb | 1810 ++++++++++++++++++++++-
 stream.py                               |   51 +
 2 files changed, 1840 insertions(+), 21 deletions(-)
 create mode 100644 stream.py

diff --git a/07_sequences_review_and_exercises.ipynb b/07_sequences_review_and_exercises.ipynb
index 17e1ffc..68582b2 100644
--- a/07_sequences_review_and_exercises.ipynb
+++ b/07_sequences_review_and_exercises.ipynb
@@ -19,7 +19,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Read [Chapter 7](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/07_sequences.ipynb) of the book. Then work through the ten review questions."
+    "Read [Chapter 7](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/07_sequences.ipynb) of the book. Then work through the twenty review questions."
    ]
   },
   {
@@ -54,7 +54,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "**Q2**: Explain the difference between a **mutable** and an **immutable** object in Python with an example!"
+    "**Q2**: What are **abstract base classes**? How can we make use of the ones from the [collections.abc](https://docs.python.org/3/library/collections.abc.html) module in the [standard library](https://docs.python.org/3/library/index.html)?"
    ]
   },
   {
@@ -68,7 +68,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "**Q3**: What is the difference between a **shallow** and a **deep** copy of an object? How can one of them become a problem?"
+    "**Q3**: How are the *abstract behaviors* of **reversibility** and **finiteness** essential for *indexing* and *slicing* sequences?"
    ]
   },
   {
@@ -82,7 +82,49 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "**Q4.1**: `tuple` objects have *two* primary usages. First, they can be used in place of `list` objects where **mutability** is *not* required. Second, we use them to model **records**.\n",
+    "**Q4**: Explain the difference between **mutable** and **immutable** objects in Python with the examples of the `list` and `tuple` types!"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q5**: What is the difference between a **shallow** and a **deep** copy of an object? How can one of them become a \"problem?\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q6**: Many **list methods** change `list` objects \"**in place**.\" What do we mean by that?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q7.1**: `tuple` objects have *two* primary usages. First, they can be used in place of `list` objects where **mutability** is *not* required. Second, we use them to model data **records**.\n",
     "\n",
     "Describe why `tuple` objects are a suitable replacement for `list` objects in general!"
    ]
@@ -98,7 +140,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "**Q4.2**: What do we mean by a **record**? How are `tuple` objects suitable to model records? How can we integrate a **semantic** meaning when working with records?"
+    "**Q7.2**: What do we mean by a **record**? How are `tuple` objects suitable to model records? How can we integrate a **semantic meaning** when working with records into our code?"
    ]
   },
   {
@@ -112,7 +154,104 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "**Q5**: With the [map()](https://docs.python.org/3/library/functions.html#map) and [filter()](https://docs.python.org/3/library/functions.html#filter) built-ins and the [reduce()](https://docs.python.org/3/library/functools.html#functools.reduce) function from the [functools](https://docs.python.org/3/library/functools.html) module in the [standard library](https://docs.python.org/3/library/index.html), we can replace many tedious `for`-loops and `if` statements. What are some advantages of doing so?"
+    "**Q8**: How is (iterable) **packing** and **unpacking** useful in the context of **function definitions** and **calls**?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q9**: With the [map()](https://docs.python.org/3/library/functions.html#map) and [filter()](https://docs.python.org/3/library/functions.html#filter) built-ins and the [reduce()](https://docs.python.org/3/library/functools.html#functools.reduce) function from the [functools](https://docs.python.org/3/library/functools.html) module in the [standard library](https://docs.python.org/3/library/index.html), we can replace many tedious `for`-loops and `if` statements. What are some advantages of doing so?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q10**: Looking at the `lambda` expression inside [reduce()](https://docs.python.org/3/library/functools.html#functools.reduce) below, what \"simple\" [built-in function](https://docs.python.org/3/library/functions.html) is mimicked here?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "```python\n",
+    "from functools import reduce\n",
+    "\n",
+    "numbers = [7, 11, 8, 5, 3, 12, 2, 6, 9, 10, 1, 4]\n",
+    "\n",
+    "reduce(lambda x, y: x if x > y else y, numbers)\n",
+    "```"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q11**: What is the primary use case of **list comprehensions**? Why do we describe them as **eager**?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q12**: **Generator expressions** may replace `list` objects and list comprehensions in many scenarios. When evaluated, they create a **lazy** `generator` object that does *not* **materialize** its elements right away. What do we mean by that? What does it mean for a `generator` object to be **exhausted**?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q13**: What does it mean for the **boolean reducers**, the built-in [all()](https://docs.python.org/3/library/functions.html#all) and [any()](https://docs.python.org/3/library/functions.html#any) functions, to follow the **short-circuiting** strategy?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q14**: What is an **iterator**? How does it relate to an **iterable**?"
    ]
   },
   {
@@ -140,7 +279,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "**Q6**: `sequence` objects are *not* part of core Python but may be imported from the [standard library](https://docs.python.org/3/library/index.html)."
+    "**Q15**: `sequence` objects are *not* part of core Python but may be imported from the [standard library](https://docs.python.org/3/library/index.html)."
    ]
   },
   {
@@ -154,7 +293,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "**Q7**: Passing **mutable** objects as arguments to functions is not problematic because functions operate in a **local** scope without affecting the **global** scope."
+    "**Q16**: The built-in [sort()](https://docs.python.org/3/library/stdtypes.html#list.sort) function takes a *finite* **iterable** as its argument an returns a *new* `list` object. On the contrary, the [sorted()](https://docs.python.org/3/library/functions.html#sorted) method on `list` objects *mutates* them *in place*."
    ]
   },
   {
@@ -168,7 +307,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "**Q8**: The **map-filter-reduce** paradigm is an excellent mental concept to organize one's code with. Then, there is a good chance that a program can be **parallelized** if the data input grows."
+    "**Q17**: Passing **mutable** objects as arguments to functions is not problematic because functions operate in a **local** scope without affecting the **global** scope."
    ]
   },
   {
@@ -182,7 +321,35 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "**Q9**: `lambda` expressions are useful in the context of the **map-filter-reduce** paradigm, where we often do *not* re-use a `function` object more than once."
+    "**Q18**: `lambda` expressions are useful in the context of the **map-filter-reduce** paradigm, where we often do *not* re-use a `function` object more than once."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q19**: Using **generator expressions** in place of **list comprehensions** wherever possible is a good practice as it makes our programs use memory more efficiently."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q20**: Just as **list comprehensions** create `list` objects, **tuple comprehensions** create `tuple` objects."
    ]
   },
   {
@@ -210,7 +377,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "**Q10.1**: Write a function `nested_sum()` that takes a `list` object as its argument, which contains other `list` objects with numbers, and adds up the numbers! Use `nested_numbers` below to test your function!\n",
+    "**Q21.1**: Write a function `nested_sum()` that takes a `list` object as its argument, which contains other `list` objects with numbers, and adds up the numbers! Use `nested_numbers` below to test your function!\n",
     "\n",
     "Hint: You need at least one `for`-loop."
    ]
@@ -230,7 +397,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "def nested_sum():\n",
+    "def nested_sum(list_of_lists):\n",
     "    ..."
    ]
   },
@@ -247,9 +414,9 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "**Q10.2**: Provide a one-line expression to obtain the *same* result as `nested_sum()`!\n",
+    "**Q21.2**: Provide a one-line expression to obtain the *same* result as `nested_sum()`!\n",
     "\n",
-    "Hints: Use a *list comprehension*. You may want to use the built-in [sum()](https://docs.python.org/3/library/functions.html#sum) function several times."
+    "Hints: Use a *list comprehension*, or maybe even a *generator expression*. You may want to use the built-in [sum()](https://docs.python.org/3/library/functions.html#sum) function several times."
    ]
   },
   {
@@ -257,13 +424,15 @@
    "execution_count": null,
    "metadata": {},
    "outputs": [],
-   "source": []
+   "source": [
+    "..."
+   ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "**Q10.3**: Generalize `nested_sum()` into a function `mixed_sum()` that can process a \"mixed\" `list` object, which contains numbers and other `list` objects with numbers! Use `mixed_numbers` below for testing!\n",
+    "**Q21.3**: Generalize `nested_sum()` into a function `mixed_sum()` that can process a \"mixed\" `list` object, which contains numbers and other `list` objects with numbers! Use `mixed_numbers` below for testing!\n",
     "\n",
     "Hints: Use the built-in [isinstance()](https://docs.python.org/3/library/functions.html#isinstance) function to check how an element is to be processed. Get extra credit for adhering to *goose typing*, as explained in [Chapter 5](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/05_numbers.ipynb#Goose-Typing)."
    ]
@@ -283,7 +452,16 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "def mixed_sum():\n",
+    "import collections.abc as abc"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def mixed_sum(list_of_lists):\n",
     "    ..."
    ]
   },
@@ -293,14 +471,14 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "mixed_sum(nums)"
+    "mixed_sum(mixed_numbers)"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "**Q10.4.1**: Write a function `cum_sum()` that takes a `list` object with numbers as its argument and returns a *new* `list` object with the **cumulative sums** of these numbers! So, `sum_up` below, `[1, 2, 3, 4, 5]`, should return `[1, 3, 6, 10, 15]`.\n",
+    "**Q21.4.1**: Write a function `cum_sum()` that takes a `list` object with numbers as its argument and returns a *new* `list` object with the **cumulative sums** of these numbers! So, `sum_up` below, `[1, 2, 3, 4, 5]`, should return `[1, 3, 6, 10, 15]`.\n",
     "\n",
     "Hint: The idea behind is similar to the [cumulative distribution function](https://en.wikipedia.org/wiki/Cumulative_distribution_function) from statistics."
    ]
@@ -320,7 +498,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "def cum_sum():\n",
+    "def cum_sum(numbers):\n",
     "    ..."
    ]
   },
@@ -337,7 +515,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "**Q10.4.2**: We should always make sure that our functions also work in corner cases. What happens if your implementation of `cum_sum()` is called with an empty list `[]`? Make sure it handles that case *without* crashing! What would be a good return value in this corner case? Describe everything in the docstring.\n",
+    "**Q21.4.2**: We should always make sure that our functions also work in corner cases. What happens if your implementation of `cum_sum()` is called with an empty list `[]`? Make sure it handles that case *without* crashing! What would be a good return value in this corner case? Describe everything in the docstring.\n",
     "\n",
     "Hint: It is possible to write this without any extra input validation."
    ]
@@ -350,6 +528,1596 @@
    "source": [
     "cum_sum([])"
    ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Packing & Unpacking with Functions"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In the \"*Function Definitions & Calls*\" section in [Chapter 7](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/07_sequences.ipynb#Function-Definitions-&-Calls), we define the following function `product()`. In this exercise, you will improve it by making it more \"user-friendly.\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def product(*args):\n",
+    "    \"\"\"Multiply all arguments.\"\"\"\n",
+    "    result = args[0]\n",
+    "\n",
+    "    for arg in args[1:]:\n",
+    "        result *= arg\n",
+    "\n",
+    "    return result"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The `*` in the function's header line *packs* all *positional* arguments passed to `product()` into one *iterable* called `args`.\n",
+    "\n",
+    "**Q22.1**: What is the data type of `args` within the function's body?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Because of the packing, we may call `product()` with an abitrary number of *positional* arguments: The product of just `42` remains `42`, while `2`, `5`, and `10` multiplied together result in `100`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "product(42)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "product(2, 5, 10)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "However, \"abitrary\" does not mean that we can pass *no* argument. If we do so, we get an `IndexError`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "product()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q22.2**: What line in the body of `product()` causes this exception? What is the exact problem?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In [Chapter 7](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/07_sequences.ipynb#Function-Definitions-&-Calls), we also pass a `list` object, like `one_hundred`, to `product()`, and *no* exception is raised."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "one_hundred = [2, 5, 10]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "product(one_hundred)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q22.3**: What is wrong with that? What *kind* of error (cf., [Chapter 1](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/01_elements.ipynb#Formal-vs.-Natural-Languages)) is that conceptually? Describe precisely what happens to the passed in `one_hundred` in every line within `product()`!"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Of course, one solution is to *unpack* `one_hundred` with the `*` symbol. We look at another solution further below."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "product(*one_hundred)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Let's continue with the issue when calling `product()` *without* any argument.\n",
+    "\n",
+    "This revised version of `product()` avoids the `IndexError` from before."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def product(*args):\n",
+    "    \"\"\"Multiply all arguments.\"\"\"\n",
+    "    result = None\n",
+    "\n",
+    "    for arg in args:\n",
+    "        result *= arg\n",
+    "\n",
+    "    return result"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "product()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q22.4**: Describe why no error occurs by going over every line in `product()`!"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Unfortunately, the new version cannot process any arguments we pass in any more."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "product(42)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "product(2, 5, 10)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q22.5**: What line causes troubles now? What is the exact problem?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q22.6**: Replace the `None` in `product()` above with something reasonable that does *not* cause exceptions! Ensure that `product(42)` and `product(2, 5, 10)` return a correct result.\n",
+    "\n",
+    "Hints: It is ok if `product()` returns a result *different* from the `None` above. Look at the documentation of the built-in [sum()](https://docs.python.org/3/library/functions.html#sum) function for some inspiration."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def product(*args):\n",
+    "    \"\"\"Multiply all arguments.\"\"\"\n",
+    "    result = ...\n",
+    "\n",
+    "    for arg in args:\n",
+    "        result *= arg\n",
+    "\n",
+    "    return result"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "product(42)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "product(2, 5, 10)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now, calling `product()` without any arguments returns what we would best describe as a *default* or *start* value. To be \"philosophical,\" what is the product of *no* numbers? We know that the product of *one* number is just the number itself, but what could be a reasonable result when multiplying *no* numbers? The answer is what you use as the initial value of `result` above, and there is only *one* way to make `product(42)` and `product(2, 5, 10)` work."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "product()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q22.7**: Rewrite `product()` so that it takes a *keyword-only* argument `start`, defaulting to the above *default* or *start* value, and use `start` internally instead of `result`!\n",
+    "\n",
+    "Hint: Remember that a *keyword-only* argument is any parameter specified in a function's header line after the first (and only) `*` (cf., [Chapter 2](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/02_functions.ipynb#Keyword-only-Arguments))."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def product(*args, ...):\n",
+    "    \"\"\"Multiply all arguments.\"\"\"\n",
+    "    ..."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now, we can call `product()` with a truly arbitrary number of *positional* arguments."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "product(42)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "product(2, 5, 10)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "product()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Without any *positional* arguments but only the *keyword* argument `start`, for example, `start=0`, we can adjust the answer to the \"philosophical\" problem of multiplying *no* numbers. Because of the *keyword-only* syntax, there is *no* way to pass in a `start` number *without* naming it."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "product(start=0)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We could use `start` to inject a multiplier, for example, to double the outcomes."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "product(42, start=2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "product(2, 5, 10, start=2)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "There is still one issue left: Because of the function's name, a user of `product()` may assume that it is ok to pass a *collection* of numbers, like `one_hundred`, which are then multiplied."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "product(one_hundred)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q22.8**: What is a **collection**? How is that different from a **sequence**?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q22.9**: Rewrite the latest version of `product()` to check if the *only* positional argument is a *collection* type! If so, its elements are multiplied together. Otherwise, the logic remains the same.\n",
+    "\n",
+    "Hints: Use the built-in [len()](https://docs.python.org/3/library/functions.html#len) and [isinstance()](https://docs.python.org/3/library/functions.html#isinstance) functions to check if there is only *one* positional argument and if it is a *collection* type. Use the *abstract base class* `Collection` from the [collections.abc](https://docs.python.org/3/library/collections.abc.html) module in the [standard library](https://docs.python.org/3/library/index.html). You may want to *re-assign* `args` inside the body."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import collections.abc as abc"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def product(*args, ...):\n",
+    "    \"\"\"Multiply all arguments.\"\"\"\n",
+    "    ..."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "All *five* code cells below now return correct results. We may unpack `one_hundred` or not."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "product(42)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "product(2, 5, 10)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "product()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "product(one_hundred)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "product(*one_hundred)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Side Note**: Above, we make `product()` work with a single *collection* type argument instead of a *sequence* type to keep it more generic: For example, we can pass in a `set` object, like `{2, 5, 10}` below, and `product()` continues to work correctly. The `set` type is introducted in [Chapter 8](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/08_mappings.ipynb#The-set-Type), and one essential difference to the `list` type is that objects of type `set` have *no* order regarding their elements. So, even though `[2, 5, 10]` and `{2, 5, 10}` look almost the same, the order implied in the literal notation gets lost in memory!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "product([2, 5, 10])  # the argument is a special collection type, namely a sequence"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "product({2, 5, 10})  # the argument is a collection that is NOT a sequence"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "isinstance({2, 5, 10}, abc.Sequence)  # sets are NO sequences"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Let's continue to improve `product()` and make it more Pythonic. It is always a good idea to mimic the behavior of built-ins when writing our own functions. And, [sum()](https://docs.python.org/3/library/functions.html#sum), for example, raises a `TypeError` if called *without* any arguments. It does *not* return the \"philosophical\" answer to adding *no* numbers, which would be `0`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sum()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q22.10**: Adapt the latest version of `product()` to also raise a `TypeError` if called *without* any *positional* arguments!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def product(*args, ...):\n",
+    "    \"\"\"Multiply all arguments.\"\"\"\n",
+    "    ..."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "product()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now, we have an implementation of `product()` that is convenient to use for the caller of our function. In particular, we can pass it a *collection* with or without *unpacking* it.\n",
+    "\n",
+    "However, this version of `product()` suffers from one more flaw: We cannot pass it a *stream* of data, as modeled, for example, with an *iterator* object that produces elements on a one-by-one basis.\n",
+    "\n",
+    "Let's look at an example. The [*stream.py*](https://github.com/webartifex/intro-to-python/blob/master/stream.py) module in the repository provides a `make_finite_stream()` function. It is a *factory* function creating objects of type `generator` that we use to model *streaming* data."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from stream import make_finite_stream"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "stream = make_finite_stream()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "stream"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "type(stream)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Being a `generator`, `stream` is also an `Iterator` in the abstract sense."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "isinstance(stream, abc.Iterator)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "*Iterators* are good for only *one* thing: Giving us the \"next\" element in a line of many."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "next(stream)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "They themselves have *no* idea of how many elements they produce eventually: The built-in [len()](https://docs.python.org/3/library/functions.html#len) function raises a `TypeError`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "len(stream)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can use the [list()](https://docs.python.org/3/library/functions.html#func-list) built-in to *materialize* the elements. However, in a real-world scenario, these may *not* fit into our machine's memory!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "list(stream)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "To be more realistic, `make_finite_stream()` creates `generator` objects producing a varying number of elements."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "list(make_finite_stream())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "list(make_finite_stream())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "list(make_finite_stream())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Let's see what happens if we pass an *iterator*, as created by `make_finite_stream()`, instead of a materialized *collection*, like `one_hundred`, to `product()`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "product(make_finite_stream())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q22.11**: What line causes the `TypeError`? What line is really the problem in the latest implementation of `product()`? Describe what happens on each line in the function's body until the exception is raised!"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q22.12**: Adapt `product()` one last time to make it work with *iterators* as well!\n",
+    "\n",
+    "Hints: This task is as easy as replacing `Collection` with something else. Which of the three behaviors of *collections* do *iterators* also exhibit? You may want to look at the documentations on the built-in [max()](https://docs.python.org/3/library/functions.html#max), [min()](https://docs.python.org/3/library/functions.html#min), and [sum()](https://docs.python.org/3/library/functions.html#sum) functions: What kind of argument do they take?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def product(*args, ...):\n",
+    "    \"\"\"Multiply all arguments.\"\"\"\n",
+    "    ..."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The final version of `product()` behaves like built-ins in edge cases, ..."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "product()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "... works with the arguments passed either as independent *positional* arguments, *packed* into a single *collection* argument, or *unpacked*, ..."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "product(42)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "product(2, 5, 10)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "product([2, 5, 10])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "product(*[2, 5, 10])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "... and can handle *streaming* data with *indefinite* \"length.\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "product(make_finite_stream())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In real-world projects, the data science practitioner must decide if it is worthwhile to make a function usable in various different forms as we did in this exercise, or if that is over-engineered.\n",
+    "\n",
+    "Yet, two lessons are important to take away:\n",
+    "- It is always a good idea to *mimic* the behavior of *built-ins* when in doubt.\n",
+    "- Make functions capable of working with *streaming* data."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Removing Outliers in Streaming Data"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Let's say we are given a `list` object with random integers like `sample` below, and we want to calculate some basic statistics on them."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sample = [\n",
+    "    45, 46, 40, 49, 36, 53, 49, 42, 25, 40, 39, 36, 38, 40, 40, 52, 36, 52, 40, 41,\n",
+    "    35, 29, 48, 43, 42, 30, 29, 33, 55, 33, 38, 50, 39, 56, 52, 28, 37, 56, 45, 37,\n",
+    "    41, 41, 37, 30, 51, 32, 23, 40, 53, 40, 45, 39, 99, 42, 34, 42, 34, 39, 39, 53,\n",
+    "    43, 37, 46, 36, 45, 42, 32, 38, 57, 34, 36, 44, 47, 51, 46, 39, 28, 40, 35, 46,\n",
+    "    41, 51, 41, 23, 46, 40, 40, 51, 50, 32, 47, 36, 38, 29, 32, 53, 34, 43, 39, 41,\n",
+    "    40, 34, 44, 40, 41, 43, 47, 57, 50, 42, 38, 25, 45, 41, 58, 37, 45, 55, 44, 53,\n",
+    "    82, 31, 45, 33, 32, 39, 46, 48, 42, 47, 40, 45, 51, 35, 31, 46, 40, 44, 61, 57,\n",
+    "    40, 36, 35, 55, 40, 56, 36, 35, 86, 36, 51, 40, 54, 50, 49, 36, 41, 37, 48, 41,\n",
+    "    42, 44, 40, 43, 51, 47, 46, 50, 40, 23, 40, 39, 28, 38, 42, 46, 46, 42, 46, 31,\n",
+    "    32, 40, 48, 27, 40, 40, 30, 32, 25, 31, 30, 43, 44, 29, 45, 41, 63, 32, 33, 58,\n",
+    "]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "len(sample)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q23.1**: `list` objects are **sequences**. What *four* behaviors do they always come with?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q23.2**: Write a function `mean()` that calculates the simple arithmetic mean of a given `sequence` with numbers!\n",
+    "\n",
+    "Hints: You can solve this task with [built-in functions](https://docs.python.org/3/library/functions.html) only. A `for`-loop is *not* needed."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def mean(sequence):\n",
+    "    ..."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sample_mean = mean(sample)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sample_mean"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q23.3**: Write a function `std()` that calculates the [standard deviation](https://en.wikipedia.org/wiki/Standard_deviation) of a `sequence` of numbers! Integrate your `mean()` version from before and the [sqrt()](https://docs.python.org/3/library/math.html#math.sqrt) function from the [math](https://docs.python.org/3/library/math.html) module in the [standard library](https://docs.python.org/3/library/index.html) provided to you below. Make sure `std()` calls `mean()` only *once* internally! Repeated calls to `mean()` would be a waste of computational resources.\n",
+    "\n",
+    "Hints: Parts of the code are probably too long to fit within the suggested 79 characters per line. So, use *temporary variables* inside your function. Instead of a `for`-loop, you may want to use a *list comprehension* or, even better, a memoryless *generator expression*."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from math import sqrt"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def std(sequence):\n",
+    "    ..."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sample_std = std(sample)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sample_std"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q23.4**: Complete `standardize()` below that takes a `sequence` of numbers and returns a `list` object with the **[z-scores](https://en.wikipedia.org/wiki/Standard_score)** of these numbers! A z-score is calculated by subtracting the mean and dividing by the standard deviation. Re-use `mean()` and `std()` from before. Again, ensure that `standardize()` calls `mean()` and `std()` only *once*! Further, round all z-scores with the built-in [round()](https://docs.python.org/3/library/functions.html#round) function and pass on the keyword-only argument `digits` to it.\n",
+    "\n",
+    "Hint: You may want to use a *list comprehension* instead of a `for`-loop."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def standardize(sequence, *, digits=3):\n",
+    "    ..."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "z_scores = standardize(sample)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The [pprint()](https://docs.python.org/3/library/pprint.html#pprint.pprint) function from the [pprint](https://docs.python.org/3/library/pprint.html) module in the [standard library](https://docs.python.org/3/library/index.html) allows us to \"pretty print\" long `list` objects compactly."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from pprint import pprint"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "pprint(z_scores, compact=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We know that `standardize()` works correctly if the resulting z-scores' mean and standard deviation approach `0` and `1` for a long enough `sequence`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "mean(z_scores), std(z_scores)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Even though `standardize()` calls `mean()` and `std()` only once each, `mean()` is called *twice*! That is so because `std()` internally also re-uses `mean()`!\n",
+    "\n",
+    "**Q23.5.1**: Rewrite `std()` to take an optional keyword-only argument `seq_mean`, defaulting to `None`. If provided, `seq_mean` is used instead of the result of calling `mean()`. Otherwise, the latter is called.\n",
+    "\n",
+    "Hint: You must check if `seq_mean` is still the default value."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def std(sequence, *, seq_mean=None):\n",
+    "    ..."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "`std()` continues to work as before."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sample_std = std(sample)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sample_std"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q23.5.2**: Now, rewrite `standardize()` to pass on the return value of `mean()` to `std()`! In summary, `standardize()` calculates the z-scores for the numbers in the `sequence` with as few computational steps as possible."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def standardize(sequence, *, digits=3):\n",
+    "    ..."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "z_scores = standardize(sample)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "mean(z_scores), std(z_scores)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q23.6**: With both `sample` and `z_scores` being materialized `list` objects, we can loop over pairs consisting of a number from `sample` and its corresponding z-score. Write a `for`-loop that prints out all the \"outliers,\" as which we define numbers with an absolute z-score above `1.96`. There are *four* of them in the `sample`.\n",
+    "\n",
+    "Hint: Use the [abs()](https://docs.python.org/3/library/functions.html#abs) and [zip()](https://docs.python.org/3/library/functions.html#zip) built-ins."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "..."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We provide a `stream` module with a `data` object that models an *infinite* **stream** of data (cf., the [*stream.py*](https://github.com/webartifex/intro-to-python/blob/master/stream.py) file in the repository)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from stream import data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "`data` is of type `generator` and has *no* length."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "type(data)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "len(data)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Being a `generator`, it is an `Iterator` in the abstract sense ..."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import collections.abc as abc"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "isinstance(data, abc.Iterator)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "... and so the only thing we can do with it is to pass it to the built-in [next()](https://docs.python.org/3/library/functions.html#next) function and go over the numbers it streams one by one."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "next(data)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q23.7**: What happens if you call `mean()` with `data` as the argument? What is the problem?\n",
+    "\n",
+    "Hints: If you try it out, you may have to press the \"Stop\" button in the toolbar at the top. Your computer should *not* crash, but you will *have to* restart this Jupyter notebook with \"Kernel\" > \"Restart\" and import `data` again."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "mean(data)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q23.8**: Write a function `take_sample()` that takes an `iterator` as its argument, like `data`, and creates a *materialized* `list` object out of its first `n` elements, defaulting to `1_000`!\n",
+    "\n",
+    "Hints: [next()](https://docs.python.org/3/library/functions.html#next) and the [range()](https://docs.python.org/3/library/functions.html#func-range) built-in may be helpful. You may want to use a *list comprehension* instead of a `for`-loop and write a one-liner. Audacious students may want to look at [isclice()](https://docs.python.org/3/library/itertools.html#itertools.islice) in the [itertools](https://docs.python.org/3/library/itertools.html) module in the [standard library](https://docs.python.org/3/library/index.html)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def take_sample(iterator, *, n=1_000):\n",
+    "    ..."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We take a `new_sample` from the stream of `data`, and its statistics are similar to the initial `sample`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "new_sample = take_sample(data)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "len(new_sample)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "mean(new_sample)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "std(new_sample)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q23.9**: Convert `standardize()` into a *new* function `standardized()` that implements the *same* logic but works on a possibly *infinite* stream of data, provided as an `iterable`, instead of a *finite* `sequence`.\n",
+    "\n",
+    "To calculate a z-score, we need the stream's overall mean and standard deviation, and that is *impossible* to calculate if we do not know how long the stream is, and, in particular, if it is *infinite*. So, `standardized()` first takes a sample from the `iterable` internally, and uses the sample's mean and standard deviation to calculate the z-scores.\n",
+    "\n",
+    "Hint: `standardized()` *must* return a `generator` object. So, use a *generator expression* as the return value; unless you know about the `yield` statement already (cf., [reference](https://docs.python.org/3/reference/simple_stmts.html#the-yield-statement))."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def standardized(iterable, *, digits=3):\n",
+    "    ..."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "`standardized()` works almost like `standardize()` except that we use it with [next()](https://docs.python.org/3/library/functions.html#next) to obtain the z-scores one by one."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "z_scores = standardized(data)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "z_scores"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "type(z_scores)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "next(z_scores)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q23.10.1**: `standardized()` allows us to go over an *infinite* stream of z-scores. What we want to do instead is to loop over the stream's raw numbers and skip the outliers. In the remainder of this exercise, you look at the parts that make up the `skip_outliers()` function below to achieve precisely that.\n",
+    "\n",
+    "The first steps in `skip_outliers()` are the same as in `standardized()`: We take a `sample` from the stream of `data` and calculate its statistics."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sample = ...\n",
+    "seq_mean = ...\n",
+    "seq_std = ..."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q23.10.2**: Just as in `standardized()`, write a *generator expression* that produces z-scores one by one! However, instead of just generating a z-score, the resulting `generator` object should produce `tuple` objects consisting of a \"raw\" number from `data` and its z-score.\n",
+    "\n",
+    "Hint: Look at the revisited \"*Averaging Even Numbers*\" example in [Chapter 7](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/07_sequences.ipynb#Example:-Averaging-Even-Numbers-%28revisited%29) for some inspiration, which also contains a generator expression producing `tuple` objects."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "standardizer = (... for ... in data)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "`standardizer` should produce `tuple` objects."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "next(standardizer)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q23.10.3**: Write another generator expression that loops over `standardizer`. It contains an `if`-clause that keeps only numbers with an absolute z-score below the `threshold_z`. If you fancy, use *tuple unpacking*."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "threshold_z = 1.96"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "no_outliers = (... for ... in standardizer if ...)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "`no_outliers` should produce `int` objects."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "next(no_outliers)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q23.10.4**: Lastly, put everything together in the `skip_outliers()` function! Make sure you refer to `iterable` inside the function and not the global `data`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def skip_outliers(iterable, *, threshold_z=1.96):\n",
+    "    sample = ...\n",
+    "    seq_mean = ...\n",
+    "    seq_std = ...\n",
+    "    standardizer = ...\n",
+    "    no_outliers = ...\n",
+    "    return no_outliers"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now, we can create a `generator` object and loop over the `data` in the stream with outliers skipped. Instead of the default `1.96`, we use a `threshold_z` of only `0.05`: That filters out all numbers except `42`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "skipper = skip_outliers(data, threshold_z=0.05)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "skipper"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "type(skipper)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "next(skipper)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q23.11**: You implemented the functions `mean()`, `std()`, `standardize()`, `standardized()`, and `skip_outliers()`. Which of them are **eager**, and which are **lazy**? How do these two concepts relate to **finite** and **infinite** data?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " "
+   ]
   }
  ],
  "metadata": {
diff --git a/stream.py b/stream.py
new file mode 100644
index 0000000..6aa6cc7
--- /dev/null
+++ b/stream.py
@@ -0,0 +1,51 @@
+"""Simulation of random streams of data.
+
+This module defines:
+- a generator object `data` modeling an infinite stream of integers
+- a function `make_finite_stream()` that creates finite streams of data
+
+The probability distribution underlying the integers is Gaussian-like with a
+mean of 42 and a standard deviation of 8. The left tail of the distribution is
+cut off meaning that the streams only produce non-negative numbers. Further,
+one in a hundred random numbers has an increased chance to be an outlier.
+"""
+
+import itertools as _itertools
+import random as _random
+
+
+_random.seed(87)
+
+
+def _infinite_stream():
+    """Internal generator function to simulate an infinite stream of data."""
+    while True:
+        number = max(0, int(_random.gauss(42, 8)))
+        if _random.randint(1, 100) == 1:
+            number *= 2
+        yield number
+
+
+def make_finite_stream(min_=5, max_=15):
+    """Simulate a finite stream of data.
+
+    The returned stream is finite, but the number of elements to be produced
+    by it is still random. This default behavior may be turned off by passing
+    in `min_` and `max_` arguments with `min_ == max_`.
+
+    Args:
+        min_ (optional, int): minimum numbers in the stream; defaults to 5
+        max_ (optional, int): maximum numbers in the stream; defaults to 15
+
+    Returns:
+        finite_stream (generator)
+
+    Raises:
+        ValueError: if max_ < min_
+    """
+    stream = _infinite_stream()
+    n = _random.randint(min_, max_)
+    yield from _itertools.islice(stream, n)
+
+
+data = _infinite_stream()

From b7ad171473887c7be0b738775d27670a2115ad8d Mon Sep 17 00:00:00 2001
From: Alexander Hess <alexander@webartifex.biz>
Date: Wed, 20 Nov 2019 11:01:43 +0100
Subject: [PATCH 050/242] Add initial version of notebook 08

---
 08_mappings.ipynb               | 8202 +++++++++++++++++++++++++++++++
 static/fibonacci_call_graph.png |  Bin 0 -> 32706 bytes
 2 files changed, 8202 insertions(+)
 create mode 100644 08_mappings.ipynb
 create mode 100644 static/fibonacci_call_graph.png

diff --git a/08_mappings.ipynb b/08_mappings.ipynb
new file mode 100644
index 0000000..d42237b
--- /dev/null
+++ b/08_mappings.ipynb
@@ -0,0 +1,8202 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "# Chapter 8: Mappings & Sets"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "While [Chapter 7](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/07_sequences.ipynb) focuses on one special kind of *collection* types, namely *sequences*, this chapter introduces two more: **Mappings** and **sets**. We present the data types belonging to these two groups in one chapter as they share the *same* underlying implementation at the C Level, known as **[hash tables](https://en.wikipedia.org/wiki/Hash_table)**.\n",
+    "\n",
+    "The most important mapping type in this chapter is the `dict` type that we have not yet seen before (cf, [documentation](https://docs.python.org/3/library/stdtypes.html#dict)). It is an essential part in a data science practitioner's toolbox for two reasons: First, Python employs `dict` objects basically \"everywhere\" internally. So, we must understand how they work to become better at Python in general. Second, after the many concepts related to *sequential* data, the ideas behind *mappings* enhance our general problem solving skills. As a concrete example, we look at the concept of **memoization** to complete our picture of *recursion*, as depicted in [Chapter 4](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/04_iteration.ipynb#Recursion). We end this chapter with a discussion of *set* types."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "## The `dict` Type"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "A *mapping* is a one-to-one correspondence from a set of **keys** to a set of **values**. In other words, a *mapping* is a *collection* of **key-value pairs**, also called **items** for short.\n",
+    "\n",
+    "In the context of mappings, the term *value* has a meaning different from the general *value* that *every* object has: In the \"bag\" analogy from [Chapter 1](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/01_elements.ipynb#Value), we descibe an object's value to be the concrete $0$s and $1$s it contains. Here, the terms *key* and *value* mean the *role* an object takes within a mapping. Both, *keys* and *values*, are real *objects* with a distinct *value*. So, the student should always remember the double meaning of the term *value* in this chapter!\n",
+    "\n",
+    "Let's continue with an example. To create a `dict` object, we commonly use the literal notation, `{..: .., ..: .., ...}`, and list all the items. `to_words` below maps the `int` objects `0`, `1`, and `2` to their English word equivalents, `\"zero\"`, `\"one\"`, and `\"two\"`, and `from_words` does the opposite. A stylistic side note: Pythonistas often expand `dict` or `list` definitions by writing each item or element on a line on their own. The commas `,` after the *last* items are *not* a mistake, as well, although they *may* be left out. Besides easier reading, such style has actual technical advantages (cf., [source](https://www.python.org/dev/peps/pep-0008/#when-to-use-trailing-commas)) that we do not go into detail about here."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "to_words = {\n",
+    "    0: \"zero\",\n",
+    "    1: \"one\",\n",
+    "    2: \"two\",\n",
+    "}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "from_words = {\n",
+    "    \"zero\": 0,\n",
+    "    \"one\": 1,\n",
+    "    \"two\": 2,\n",
+    "}"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "As before, `dict` objects are objects on their own: They have an identity, a type, and a value. The latter is a *literal* that creates a *new* `dict` object with the *same* value when evaluated."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "139752734794304"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "id(from_words)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "dict"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "type(from_words)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'zero': 0, 'one': 1, 'two': 2}"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from_words"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "The [dict()](https://docs.python.org/3/library/functions.html#func-dict) built-in gives us an alternative way to create a `dict` object. It is versatile and can be used in different ways.\n",
+    "\n",
+    "First, we may pass it any *mapping* type, for example, a `dict` object, to obtain a *new* `dict` object. That is the easiest way to convert a more specialized mapping type, such as the `OrderedDict`, `defaultdict`, and `Counter` types introduced further below, into an \"ordinary\" `dict` object."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'zero': 0, 'one': 1, 'two': 2}"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "dict(from_words)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Second, we may pass it an *iterable* of *iterables* with *two* elements each. So, both of the following two code cells work: A `list` of `tuple` objects, or a `tuple` of `list` objects. More importantly, we could use an *iterator*, for example, a `generator` object, that produces the inner iterables \"on the fly.\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'zero': 0, 'one': 1, 'two': 2}"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "dict([(\"zero\", 0), (\"one\", 1), (\"two\", 2)])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'zero': 0, 'one': 1, 'two': 2}"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "dict(([\"zero\", 0], [\"one\", 1], [\"two\", 2]))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Lastly, [dict()](https://docs.python.org/3/library/functions.html#func-dict) may also be called with *keyword* arguments: The keywords become the keys and the arguments the values."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'zero': 0, 'one': 1, 'two': 2}"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "dict(zero=0, one=1, two=2)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Keyword arguments may always be added in the first two cases as well. That is sometimes useful to take data as is and ensure that certain keys are *existent* and have a pre-defined value."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'zero': 0, 'one': 1, 'two': 2, 'three': 3}"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "dict(from_words, three=3)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "### Nested Data"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Often, `dict` objects occur in a nested form and combined with other collection types, such as `list` or `tuple` objects, to model more complex \"objects\" from the real world.\n",
+    "\n",
+    "The reason for this popularity is that many modern [ReST APIs](https://en.wikipedia.org/wiki/Representational_state_transfer#Applied_to_Web_services) on the internet (e.g., [Google Maps API](https://cloud.google.com/maps-platform/), [Yelp API](https://www.yelp.com/developers/documentation/v3), [Twilio API](https://www.twilio.com/docs/usage/api)) provide their data in the popular [JSON](https://en.wikipedia.org/wiki/JSON) format, which looks almost like a combination of `dict` and `list` objects in Python. \n",
+    "\n",
+    "The `people` example below models three groups of people: Mathematicians, physicists, and programmers. Each person may have an arbitrary number of email addresses (e.g., [Leonhard Euler](https://en.wikipedia.org/wiki/Leonhard_Euler) has not lived long enough to get one, whereas [Guido](https://en.wikipedia.org/wiki/Guido_van_Rossum) has more than one for sure).\n",
+    "\n",
+    "`people` has many (implicit) structural assumptions built in. For example, there are a [one-to-many](https://en.wikipedia.org/wiki/One-to-many_%28data_model%29) relationship between people and their email addresses and a [one-to-one](https://en.wikipedia.org/wiki/One-to-one_%28data_model%29) relationship between each person and their name. It is important to understand that we determine this structure by choosing the data types involved in `people` and that it is impossible to model nested data without any (implicit) assumption about the structure. So, the data science practitioner should have a basic understanding of [database normalization](https://en.wikipedia.org/wiki/Database_normalization)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "people = {\n",
+    "    \"mathematicians\": [\n",
+    "        {\n",
+    "            \"name\": \"Gilbert Strang\",\n",
+    "            \"emails\": [\"gilbert@mit.edu\"],\n",
+    "        },\n",
+    "        {\n",
+    "            \"name\": \"Leonhard Euler\",\n",
+    "            \"emails\": [],\n",
+    "        },\n",
+    "    ],\n",
+    "    \"physicists\": [],\n",
+    "    \"programmers\": [\n",
+    "        {\n",
+    "            \"name\": \"Guido\",\n",
+    "            \"emails\": [\"guido@python.org\", \"guido@dropbox.com\"],\n",
+    "        },\n",
+    "    ],\n",
+    "}"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "The literal notation of such a nested `dict` object may be hard to read."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'mathematicians': [{'name': 'Gilbert Strang', 'emails': ['gilbert@mit.edu']},\n",
+       "  {'name': 'Leonhard Euler', 'emails': []}],\n",
+       " 'physicists': [],\n",
+       " 'programmers': [{'name': 'Guido',\n",
+       "   'emails': ['guido@python.org', 'guido@dropbox.com']}]}"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "people"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Luckily, the [pprint](https://docs.python.org/3/library/pprint.html) module in the [standard library](https://docs.python.org/3/library/index.html) provides a [pprint()](https://docs.python.org/3/library/pprint.html#pprint.pprint) function for \"pretty printing.\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "from pprint import pprint"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{'mathematicians': [{'emails': ['gilbert@mit.edu'],\n",
+      "                     'name': 'Gilbert Strang'},\n",
+      "                    {'emails': [],\n",
+      "                     'name': 'Leonhard Euler'}],\n",
+      " 'physicists': [],\n",
+      " 'programmers': [{'emails': ['guido@python.org',\n",
+      "                             'guido@dropbox.com'],\n",
+      "                  'name': 'Guido'}]}\n"
+     ]
+    }
+   ],
+   "source": [
+    "pprint(people, indent=1, width=60)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "### Hash Tables & Key Hashability"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "In [Chapter 0](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/00_start_up.ipynb#Isn't-C-a-lot-faster?), we argue that a major advantage of using Python is that it takes care of the memory managment for us. In line with that, we have never talked about the C level implementation thus far in the book. However, the `dict` type, among others, exhibits some behaviors that may seem \"weird\" for a beginner. To built a solid intuition that enables the student to better \"predict\" how `dict` objects behave, we describe the underlying implementation details on a conceptual level (i.e., without C code).\n",
+    "\n",
+    "The first unintuitive behavior is that we may *not* use a *mutable* object as a key. That results in a `TypeError`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "ename": "TypeError",
+     "evalue": "unhashable type: 'list'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-15-de7a9b0b64a4>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m {\n\u001b[0;32m----> 2\u001b[0;31m     \u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m\"zero\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"one\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      3\u001b[0m }\n",
+      "\u001b[0;31mTypeError\u001b[0m: unhashable type: 'list'"
+     ]
+    }
+   ],
+   "source": [
+    "{\n",
+    "    [0, 1]: [\"zero\", \"one\"],\n",
+    "}"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Similarly surprising is that items with the *same* key get \"merged\" together. The resulting `dict` object seems to keep the position of the *first* mention of a key, while at the same time only the *last* mention of a value survives."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'zero': 3, 'one': 1, 'two': 2}"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "{\n",
+    "    \"zero\": 0,\n",
+    "    \"one\": 1,\n",
+    "    \"two\": 2,\n",
+    "    \"zero\": 3,\n",
+    "}"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "The reason for that is that the main building block behind the `dict` type is a [data structure](https://en.wikipedia.org/wiki/Data_structure) called a [hash table](https://en.wikipedia.org/wiki/Hash_table).\n",
+    "\n",
+    "Conceptually, when we create a *new* `dict` object, Python creates a \"bag\" in memory that takes significantly more space (i.e., $0$s and $1$s) than needed to store the references to all the key and value objects. This bag is a **contiguous** chunk of memory (i.e., all the $0$s and $1$s lie right next to each other) and divided into equally sized **buckets** that have just enough space to store *two* references each. These references go to an item's key and value objects. The buckets are labeled with *index* numbers, or \"integer addresses.\" Because Python knows how wide each bucket is and where the bag begins, it can jump directly into *any* bucket by calculating its **offset** from the start. It does not have to follow a reference to some \"random\" memory location once it has followed the reference to the `dict` object's \"start\" in memory.\n",
+    "\n",
+    "The figure below visualizes how we should think of hash tables. An empty `dict` object, created with the literal `{}`, still takes a lot of memory: It is essentially one big, contiguous, and empty table."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "| Bucket |  0  |  1  |  2  |  3  |  4  |  5  |  6  |  7  |\n",
+    "|  :---: |:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|\n",
+    "| Key    |*...*|*...*|*...*|*...*|*...*|*...*|*...*|*...*|\n",
+    "| Value  |*...*|*...*|*...*|*...*|*...*|*...*|*...*|*...*|"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "To insert a key-value pair, the key must be translated into a bucket's address. As the first step to do so, the built-in [hash()](https://docs.python.org/3/library/functions.html#hash) function maps any **hashable** object to its **hash value**, a long `int` number, similar to the ones returned by the built-in [id()](https://docs.python.org/3/library/functions.html#id) function. This hash value is a summary of all the $0$s and $1$s that make up an object's value, and, according to the official [glossary](https://docs.python.org/3/glossary.html#term-hashable), an object is hashable *only if* \"it has a hash value which *never* changes during its *lifetime*.\" So, hashability implies immutability! This formal requirement is also absolutely useful to have: Without it, an object may have *several* addresses and, thus, we could not sort it into a *predictable* bucket. The exact logic behind [hash()](https://docs.python.org/3/library/functions.html#hash) is beyond the scope of this book.\n",
+    "\n",
+    "Let's calculate the hash value of `\"zero\"`. Because `str` objects are immutable, that works. Hash values have *no* semantic meaning. Also, everytime we re-start Python, we see *different* hash values for the *same* objects. That is a security measure, and we do not go into the technicalities here (cf. [source](https://docs.python.org/3/using/cmdline.html#envvar-PYTHONHASHSEED))."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "-2242252796476499076"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "hash(\"zero\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "For numeric objects, we can sometimes predict the hash values. However, we must *never* interpret any meaning into them."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "hash(0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "230584300921369408"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "hash(0.1)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "The [glossary](https://docs.python.org/3/glossary.html#term-hashable) states a second requirement for hashability, namely that \"objects which *compare equal* must have the *same* hash value.\" The purpose of this is to ensure that if we put, for example, `1` as a key in a `dict` object, we can look it up later with `1.0`. In other words, we can look up keys by their object's value (i.e., in the meaning of [Chapter 1](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/01_elements.ipynb#Value)). The converse statement does *not* hold: Two objects *may* (accidentally) have the *same* hash value and *not* compare equal."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "1 == 1.0"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 21,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "hash(1) == hash(1.0)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Because `list` objects are not immutable, they are *never* hashable, as indicated by the `TypeError`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "ename": "TypeError",
+     "evalue": "unhashable type: 'list'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-22-1ddb6259478e>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mhash\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;31mTypeError\u001b[0m: unhashable type: 'list'"
+     ]
+    }
+   ],
+   "source": [
+    "hash([0, 1])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "If we need keys composed of several objects, we can use `tuple` objects instead. In general, the key object must be hashable as a whole. So, we must *never* put a *mutable* object in a `tuple` object used as a key, as well."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "3713080549409410656"
+      ]
+     },
+     "execution_count": 23,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "hash((0, 1))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "There is no such restiction on objects inserted into `dict` objects as *values*."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{(0, 1): ['zero', 'one']}"
+      ]
+     },
+     "execution_count": 24,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "{\n",
+    "    (0, 1): [\"zero\", \"one\"],\n",
+    "}"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "A natural question to ask is how does Python know how much memory it should reserve for a `dict` object's hash table. And, the answer is: Python allocates the memory according to some internal heuristics, and whenever a hash table is roughly 2/3 full, it creates a *new* one with twice the space, and re-inserts all items, one by one, from the *old* one. So, during its lifetime, a `dict` object may have several hash tables.\n",
+    "\n",
+    "Let's see how Python translates the keys' hash values into buckets and what happens if a hash table gets too crowded. Assume the now bigger `from_words` example ..."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "from_words = {\n",
+    "    \"zero\": 0,\n",
+    "    \"one\": 1,\n",
+    "    \"two\": 2,\n",
+    "    \"three\": 3,\n",
+    "    \"four\": 4,\n",
+    "    \"five\": 5,\n",
+    "}"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "... is to be stored in a hash table with eight buckets. Thus, to label the buckets, we need three bits (i.e., $2^3 = 8$).\n",
+    "\n",
+    "Once Python has obtained an object's hash value with [hash()](https://docs.python.org/3/library/functions.html#hash), that number's *least* significant bits in *binary* representation are used as the address. So, here, we cut off the last three digits. These digits could be converted back into an integer for nicer presentation, but Python itself does not need that.\n",
+    "\n",
+    "In summary, with a potentially infinite number of possible keys being mapped on a limited number of buckets, there is a chance that two or more keys end up in the *same* bucket. That is called a **hash collision** and sounds worse than it is. In such cases, Python uses a perturbation rule to rearrange the bits, and if the corresponding next bucket is empty, places an item there. The main disadvantage of that is that the nice offsetting logic from above breaks down, and Python needs more time on average to place items into a hash table. The remedy is to just use a bigger hash table as then the chance of colliding hashes decreases. Luckily, Python does all that for us in the background. So, the main cost we pay for that convenience is the *high* memory usage of `dict` objects.\n",
+    "\n",
+    "Another effect of this hashing logic is that items that have keys with the *same* value (i.e., $0$s and $1$s) end up in the *same* bucket, as well. The item that gets inserted last *overwrites* all previously inserted items. That is why the two `\"zero\"` keys get \"merged\" above.\n",
+    "\n",
+    "The code below shows how the key objects are transformed into their hash values in integer and binary representation, the least significant bits of the latter are cut off, and the buckets are obtained. Because we use `str` objects as the keys, their buckets are *unpredictable*. So, taking into account the randomization for security purposes from above, we may or may not see redundant buckets in the output. The chance is rather high."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "def buckets(mapping, *, bits):\n",
+    "    \"\"\"Calculate the bucket indices for a mapping's keys.\"\"\"\n",
+    "    for key in mapping:  # cf., next sub-section for details on looping\n",
+    "        hash_value = hash(key)\n",
+    "        binary = bin(hash_value)\n",
+    "        address = binary[-bits:]\n",
+    "        bucket = int(\"0b\" + address, base=2)\n",
+    "        print(key, hash_value, \"0b...\" + binary[-12:], address, bucket, sep=\"\\t\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "zero\t-2242252796476499076\t0b...010010000100\t100\t4\n",
+      "one\t6742734203392562231\t0b...010000110111\t111\t7\n",
+      "two\t8400825950927958036\t0b...000000010100\t100\t4\n",
+      "three\t-7473701886709619306\t0b...101001101010\t010\t2\n",
+      "four\t5854090567539607813\t0b...100100000101\t101\t5\n",
+      "five\t5010767421024509913\t0b...111111011001\t001\t1\n"
+     ]
+    }
+   ],
+   "source": [
+    "buckets(from_words, bits=3)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "With five of the six keys already inserted, the next insertion has a chance of more than 50% to be a hash collision."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "| Bucket |   0   |  1  |   2   |   3    |  4  |    5    |  6  |    7   |\n",
+    "|  :---: | :---: |:---:| :---: | :---:  |:---:|  :---:  |:---:|  :---: |\n",
+    "| Key    |`\"one\"`|*...*|`\"two\"`|`\"four\"`|*...*|`\"three\"`|*...*|`\"zero\"`|\n",
+    "| Value  |  `1`  |*...*|  `2`  |  `4`   |*...*|   `3`   |*...*|   `0`  |"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Luckily, in the given case, Python allocates sixteen buckets trading off memory against insertion speed."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "source": [
+    "| Bucket |   0   |  1  |  2  |  3  |  4  |  5  |  6  |    7   |  8  |  9  |  10   |   11   |  12 |    13   |  14 |   15   |\n",
+    "|  :---: | :---: |:---:|:---:|:---:|:---:|:---:|:---:|  :---: |:---:|:---:| :---: |  :---: |:---:|  :---:  |:---:|  :---: |\n",
+    "| Key    |`\"one\"`|*...*|*...*|*...*|*...*|*...*|*...*|`\"five\"`|*...*|*...*|`\"two\"`|`\"four\"`|*...*|`\"three\"`|*...*|`\"zero\"`|\n",
+    "| Value  |  `1`  |*...*|*...*|*...*|*...*|*...*|*...*|   `5`  |*...*|*...*|  `2`  |   `4`  |*...*|   `3`   |*...*|   `0`  |"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Sixteen buckets imply four bits (i.e., $2^4$) be cut off from the hash value's binary representation and used as the buckets' indices. It is unlikely we see redundant buckets in the code cell below."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "zero\t-2242252796476499076\t0b...010010000100\t0100\t4\n",
+      "one\t6742734203392562231\t0b...010000110111\t0111\t7\n",
+      "two\t8400825950927958036\t0b...000000010100\t0100\t4\n",
+      "three\t-7473701886709619306\t0b...101001101010\t1010\t10\n",
+      "four\t5854090567539607813\t0b...100100000101\t0101\t5\n",
+      "five\t5010767421024509913\t0b...111111011001\t1001\t9\n"
+     ]
+    }
+   ],
+   "source": [
+    "buckets(from_words, bits=4)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Although hash tables seem quite complex at first sight, they help us to make certain operations very fast as we see further below."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "### Mappings are Collections without \"Predictable Order\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "In [Chapter 7](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/07_sequences.ipynb#Collections-vs.-Sequences), we show how *sequences* are a special kind of *collections*. The latter can be described as iterable containers with a finite number of elements.\n",
+    "\n",
+    "The `dict` type is a special kind of a *collection*, as well, as revealed with the `Collection` ABC from the [collections.abc](https://docs.python.org/3/library/collections.abc.html) module in the [standard library](https://docs.python.org/3/library/index.html)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "import collections.abc as abc"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 30,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "isinstance(to_words, abc.Collection)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 31,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "isinstance(from_words, abc.Collection)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "So, we may pass `to_words` or `from_words` to the built-in [len()](https://docs.python.org/3/library/functions.html#len) function to obtain the number of *items* they contain."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "3"
+      ]
+     },
+     "execution_count": 32,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "len(to_words)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "6"
+      ]
+     },
+     "execution_count": 33,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "len(from_words)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "In the terminology of the [collections.abc](https://docs.python.org/3/library/collections.abc.html) module, both are `Sized` objects."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 34,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "isinstance(to_words, abc.Sized)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 35,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "isinstance(from_words, abc.Sized)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "`dict` objects may be looped over, for example, with the `for` statement. For technical reasons, we could *not* rely on the iteration order to be *predictable* in any form until Python 3.7 in 2018. Looping over the *same* `dict` objects multiple times during its lifetime could result in *different* iteration orders every time. That behavior is intentional as `dict` objects are optimized for use cases where order does not matter. Starting with Python 3.7, `dict` objects remember the order in that items are *inserted* (cf., [Python 3.7 release notes](https://www.python.org/downloads/release/python-370/)). A lot of research went into this preservation of order (cf., this [PyCon 2017 talk](https://www.youtube.com/watch?v=npw4s1QTmPg) by core developer [Raymond Hettinger](https://github.com/rhettinger)).\n",
+    "\n",
+    "Because of that, the order in the two `for`-loops below is the *same* as in the *source code* that defines `to_words` and `from_words` above. In that sense, it is \"*predictable*.\" However, if we fill `dict` objects with data from real-world sources, that kind of predictability is not really helpful as such data are not written as source code, and, thus, we consider the order of items in `dict` objects to be *unpredictable*. Further, when an \"insertion\" accidentally *updates* an item, the ordering remains unchanged."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Python 3.7.3\r\n"
+     ]
+    }
+   ],
+   "source": [
+    "!python --version  # the order in the for-loops is predictable for Python 3.7 or higher only"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "By convention, iteration goes over the *keys* in the `dict` object only. The \"*Dictionary Methods*\" sub-section below shows how to loop over the *items* or the *values* instead."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0\n",
+      "1\n",
+      "2\n"
+     ]
+    }
+   ],
+   "source": [
+    "for number in to_words:\n",
+    "    print(number)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "zero\n",
+      "one\n",
+      "two\n",
+      "three\n",
+      "four\n",
+      "five\n"
+     ]
+    }
+   ],
+   "source": [
+    "for word in from_words:\n",
+    "    print(word)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Without a predictable *forward* order, `dict` objects are not *reversible* either.\n",
+    "\n",
+    "So, passing a `dict` object to the [reversed()](https://docs.python.org/3/library/functions.html#reversed) built-in raises a `TypeError` ..."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "ename": "TypeError",
+     "evalue": "'dict' object is not reversible",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-39-2d09207399cf>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0;32mfor\u001b[0m \u001b[0mnumber\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mreversed\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mto_words\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m     \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnumber\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;31mTypeError\u001b[0m: 'dict' object is not reversible"
+     ]
+    }
+   ],
+   "source": [
+    "for number in reversed(to_words):\n",
+    "    print(number)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "ename": "TypeError",
+     "evalue": "'dict' object is not reversible",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-40-c723940a69be>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0;32mfor\u001b[0m \u001b[0mword\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mreversed\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfrom_words\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m     \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mword\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;31mTypeError\u001b[0m: 'dict' object is not reversible"
+     ]
+    }
+   ],
+   "source": [
+    "for word in reversed(from_words):\n",
+    "    print(word)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "... and also `Reversible` ABC in the [collections.abc](https://docs.python.org/3/library/collections.abc.html) module confirms that."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "False"
+      ]
+     },
+     "execution_count": 41,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "isinstance(to_words, abc.Reversible)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "False"
+      ]
+     },
+     "execution_count": 42,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "isinstance(from_words, abc.Reversible)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Of course, we could use the built-in [sorted()](https://docs.python.org/3/library/functions.html#sorted) function to loop over, for example, `to_words` in a *predictable* order. However, that *materializes* a temporary `list` object in memory containing references to all the key objects *and* creates a *new* order according to some sorting criterion that has *nothing* to do with how the items are ordered inside the hash table."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "five\n",
+      "four\n",
+      "one\n",
+      "three\n",
+      "two\n",
+      "zero\n"
+     ]
+    }
+   ],
+   "source": [
+    "for word in sorted(from_words):\n",
+    "    print(word)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "That approach may be combined with [reversed()](https://docs.python.org/3/library/functions.html#reversed)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "zero\n",
+      "two\n",
+      "three\n",
+      "one\n",
+      "four\n",
+      "five\n"
+     ]
+    }
+   ],
+   "source": [
+    "for word in reversed(sorted(from_words)):\n",
+    "    print(word)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "To show the third behavior of *collection* types, we use the boolean `in` operator to check if a given and immutable object evaluates equal to a *key* in `to_words` or `from_words`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 45,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "1 in to_words"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 46,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "1.0 in to_words  # 1.0 is not contained but compares equal to a key that is"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "False"
+      ]
+     },
+     "execution_count": 47,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "10 in to_words"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 48,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 48,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "\"one\" in from_words"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 49,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "False"
+      ]
+     },
+     "execution_count": 49,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "\"ten\" in from_words"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "The `Container` ABC in the [collections.abc](https://docs.python.org/3/library/collections.abc.html) module may be used to \"ask\" Python to confirm that `to_words` and `from_words` are indeed *container* types."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 50,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 50,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "isinstance(to_words, abc.Container)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 51,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 51,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "isinstance(from_words, abc.Container)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "#### Membership Testing: Lists vs. Dictionaries"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Because of the [hash table](https://en.wikipedia.org/wiki/Hash_table) implementation, the `in` operator is *extremely* fast: With the hashing/offsetting described above, Python does *not* need to initiate a [linear search](https://en.wikipedia.org/wiki/Linear_search) as in the `list` case but immediately knows the only places in memory where the searched object must be located if present in the hash table at all. Conceptually, that is like comparing the searched object against all key objects with the `==` operator *without* actually doing it.\n",
+    "\n",
+    "To show the difference, we run a little experiment. For that, we create a `haystack`, a `list` object, with `10_000_001` elements in it, *one* of which is the `needle`, namely `42`. Once again, the [randint()](https://docs.python.org/3/library/random.html#random.randint) function in the [random](https://docs.python.org/3/library/random.html) module is helpful."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 52,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "import random"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 53,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "random.seed(87)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 54,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "haystack = [random.randint(99, 9999) for _ in range(10_000_000)] + [42]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 55,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "needle = 42"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "We put the elements in `haystack` in a *random* order with the [shuffle()](https://docs.python.org/3/library/random.html#random.shuffle) function in the [random](https://docs.python.org/3/library/random.html) module."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 56,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "random.shuffle(haystack)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 57,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[8126, 7370, 3735, 213, 7922, 1434, 8557, 9609, 9704, 9564, 9601, 3444]"
+      ]
+     },
+     "execution_count": 57,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "haystack[:12]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 58,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[5842, 1894, 7237, 886, 5945, 4014, 4998, 2055, 3531, 6919, 7875, 1944]"
+      ]
+     },
+     "execution_count": 58,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "haystack[-12:]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "As modern computers are generally fast, we search the `needle` a total of `10` times."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 59,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "4.37 s ± 0 ns per loop (mean ± std. dev. of 1 run, 1 loop each)\n"
+     ]
+    }
+   ],
+   "source": [
+    "%%timeit -n 1 -r 1\n",
+    "for _ in range(10):\n",
+    "    needle in haystack"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Now, we convert the elements of `haystack` into the keys of `magic_haystack`, a `dict` object. We use `None` as a dummy value for all items."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 60,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "magic_haystack = dict((x, None) for x in haystack)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Because the hash table implementation is *extremely* fast, we search the `needle` not `10` but `10_000` times. The code cell still runs in only a fraction of the time its counterpart does above."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 61,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "426 ms ± 0 ns per loop (mean ± std. dev. of 1 run, 1 loop each)\n"
+     ]
+    }
+   ],
+   "source": [
+    "%%timeit -n 1 -r 1\n",
+    "for _ in range(10_000_000):\n",
+    "    needle in magic_haystack"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "However, there is no fast way to look up the values the keys are mapped to. To achieve that, we have to loop over *all* items and check for each value object if it evaluates equal to searched object. That is, by definition, a linear search, as well, and rather slow. In the context of `dict` objects, we call that a **reverse look-up**."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "### \"Indexing\" -> Key Look-up"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "The same efficient key look-up executed in the background with the `in` operator is also behind the indexing operator `[]`. Instead of returning either `True` or `False`, it returns the value object the looked up key maps to.\n",
+    "\n",
+    "To show the similarity to indexing into `list` objects, we provide another example with `to_words_list` below."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 62,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "to_words_list = [\"zero\", \"one\", \"two\"]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Without the above definitions, we could not tell the difference between `to_words` and `to_words_list`: The usage of the `[]` is the same."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 63,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'zero'"
+      ]
+     },
+     "execution_count": 63,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "to_words[0]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 64,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'one'"
+      ]
+     },
+     "execution_count": 64,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "to_words_list[1]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Because key objects can be of any immutable type and are, in particular, not constrained to just the `int` type, the word \"*indexing*\" is an understatement here. Therefore, in the context of `dict` objects, we view the `[]` operator as a generalization of the indexing operator and refer to it as the **(key) look-up** operator. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 65,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "2"
+      ]
+     },
+     "execution_count": 65,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from_words[\"two\"]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "If a key is not in a `dict` object, Python raises a `KeyError`. A sequence type would raise an `IndexError` in this situation."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 66,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "ename": "KeyError",
+     "evalue": "'drei'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-66-8602ef11ff73>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mfrom_words\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"drei\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;31mKeyError\u001b[0m: 'drei'"
+     ]
+    }
+   ],
+   "source": [
+    "from_words[\"drei\"]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "While `dict` objects support the `[]` operator to look up a *single* key, the more general concept of *slicing* is *not* available. That is in line with the idea that there is *no* predictable *order* associated with a `dict` object's keys, and slicing requires an order conceptually.\n",
+    "\n",
+    "To access \"lower\" levels in nested data, like `people`, we *chain* the look-up operator `[]`. For example, let's view all the mathematicians in `people`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 67,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[{'name': 'Gilbert Strang', 'emails': ['gilbert@mit.edu']},\n",
+       " {'name': 'Leonhard Euler', 'emails': []}]"
+      ]
+     },
+     "execution_count": 67,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "people[\"mathematicians\"]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Let's take the first mathematician on the list, ..."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 68,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'name': 'Gilbert Strang', 'emails': ['gilbert@mit.edu']}"
+      ]
+     },
+     "execution_count": 68,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "people[\"mathematicians\"][0]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "... and output his name ..."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 69,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Gilbert Strang'"
+      ]
+     },
+     "execution_count": 69,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "people[\"mathematicians\"][0][\"name\"]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "... or all his emails."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 70,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['gilbert@mit.edu']"
+      ]
+     },
+     "execution_count": 70,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "people[\"mathematicians\"][0][\"emails\"]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "### Mutability"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Analogous to `list` objects, we may mutate `dict` objects *in place*.\n",
+    "\n",
+    "For example, let's translate the English words in `to_words` to their German counterparts. Behind the scenes, Python determines the bucket of the objects passed to the `[]` operator, looks them up in the hash table, and, if present, *updates* their references to the mapped value objects."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 71,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "to_words[0] = \"null\"\n",
+    "to_words[1] = \"eins\"\n",
+    "to_words[2] = \"zwei\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 72,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{0: 'null', 1: 'eins', 2: 'zwei'}"
+      ]
+     },
+     "execution_count": 72,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "to_words"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Let's add two more items. Again, Python determines their buckets, but this time finds them to be empty, and *inserts* the references to their key and value objects."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 73,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "to_words[3] = \"drei\"\n",
+    "to_words[4] = \"vier\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 74,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{0: 'null', 1: 'eins', 2: 'zwei', 3: 'drei', 4: 'vier'}"
+      ]
+     },
+     "execution_count": 74,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "to_words"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "None of these operations change the identity of the `to_words` object."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 75,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "139752734711240"
+      ]
+     },
+     "execution_count": 75,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "id(to_words)  # same memory location as before"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "The `del` statement removes individual items. Python just removes the *two* references to the key and value objects in the corresponding bucket."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 76,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "del to_words[0]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 77,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{1: 'eins', 2: 'zwei', 3: 'drei', 4: 'vier'}"
+      ]
+     },
+     "execution_count": 77,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "to_words"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "We may also change parts of nested data, such as `people`.\n",
+    "\n",
+    "For example, let's add [Albert Einstein](https://en.wikipedia.org/wiki/Albert_Einstein) to the list of physicists, ..."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 78,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "people[\"physicists\"].append({\"name\": \"Albert Einstein\"})"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "... complete Guido's name, ..."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 79,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "people[\"programmers\"][0][\"name\"] = \"Guido van Rossum\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "... and remove his work email because he retired."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 80,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "del people[\"programmers\"][0][\"emails\"][1]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Now, `people` looks like this."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 81,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{'mathematicians': [{'emails': ['gilbert@mit.edu'],\n",
+      "                     'name': 'Gilbert Strang'},\n",
+      "                    {'emails': [],\n",
+      "                     'name': 'Leonhard Euler'}],\n",
+      " 'physicists': [{'name': 'Albert Einstein'}],\n",
+      " 'programmers': [{'emails': ['guido@python.org'],\n",
+      "                  'name': 'Guido van Rossum'}]}\n"
+     ]
+    }
+   ],
+   "source": [
+    "pprint(people, indent=1, width=60)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "### Dictionary Methods"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "`dict` objects come with many methods bound on them, many of which are standardized by the `Mapping` and `MutableMapping` ABCs from the [collections.abc](https://docs.python.org/3/library/collections.abc.html) module. While the former requires a data type adhering to it to implement the [keys()](https://docs.python.org/3/library/stdtypes.html#dict.keys), [values()](https://docs.python.org/3/library/stdtypes.html#dict.values), [items()](https://docs.python.org/3/library/stdtypes.html#dict.items), and [get()](https://docs.python.org/3/library/stdtypes.html#dict.get) methods, which *never* mutate an object, the latter formalizes the [update()](https://docs.python.org/3/library/stdtypes.html#dict.update), [pop()](https://docs.python.org/3/library/stdtypes.html#dict.pop), [popitem()](https://docs.python.org/3/library/stdtypes.html#dict.popitem), [clear()](https://docs.python.org/3/library/stdtypes.html#dict.clear), and [setdefault()](https://docs.python.org/3/library/stdtypes.html#dict.setdefault) methods, which *may* do so."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 82,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 82,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "isinstance(to_words, abc.Mapping)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 83,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 83,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "isinstance(from_words, abc.Mapping)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "While iteration over a mapping type goes over its keys, we may emphasize this explicitly by adding the [keys()](https://docs.python.org/3/library/stdtypes.html#dict.keys) method in the `for`-loop. Again, the iteration order is equivalent to the insertion order but still considered *unpredictable*."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 84,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "zero\n",
+      "one\n",
+      "two\n",
+      "three\n",
+      "four\n",
+      "five\n"
+     ]
+    }
+   ],
+   "source": [
+    "for word in from_words.keys():\n",
+    "    print(word)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "[keys()](https://docs.python.org/3/library/stdtypes.html#dict.keys) returns an object of type `dict_keys`. That is a dynamic **view** inside the `from_words`'s hash table, which means it does *not* copy the references to the keys, and changes to `from_words` can be seen through it. View objects behave much like `dict` objects themselves."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 85,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "dict_keys(['zero', 'one', 'two', 'three', 'four', 'five'])"
+      ]
+     },
+     "execution_count": 85,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from_words.keys()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "iews can be materialized with the [list()](https://docs.python.org/3/library/functions.html#func-list) built-in. However, that may introduce *semantic* errors into a program an the newly created `list` object has a \"*predictable*\" order (i.e., indexes `0`, `1`, ...) created from an *unpredictable* one."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 86,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['zero', 'one', 'two', 'three', 'four', 'five']"
+      ]
+     },
+     "execution_count": 86,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "list(from_words.keys())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "To loop over the value objects instead, we use the [values()](https://docs.python.org/3/library/stdtypes.html#dict.values) method. That returns a *view* on the value objects inside `from_words` without copying the references to them."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 87,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0\n",
+      "1\n",
+      "2\n",
+      "3\n",
+      "4\n",
+      "5\n"
+     ]
+    }
+   ],
+   "source": [
+    "for number in from_words.values():\n",
+    "    print(number)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 88,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "dict_values([0, 1, 2, 3, 4, 5])"
+      ]
+     },
+     "execution_count": 88,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from_words.values()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "To loop over key-value pairs, we invoke the [items()](https://docs.python.org/3/library/stdtypes.html#dict.items) method. That returns a view on the key-value pairs as `tuple` objects, where the first element is the key and the second the value. Because of that, we use tuple unpacking in the `for`-loop."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 89,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "zero -> 0\n",
+      "one -> 1\n",
+      "two -> 2\n",
+      "three -> 3\n",
+      "four -> 4\n",
+      "five -> 5\n"
+     ]
+    }
+   ],
+   "source": [
+    "for word, number in from_words.items():\n",
+    "    print(f\"{word} -> {number}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 90,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "dict_items([('zero', 0), ('one', 1), ('two', 2), ('three', 3), ('four', 4), ('five', 5)])"
+      ]
+     },
+     "execution_count": 90,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from_words.items()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Above, we see how the look-up operator fails *loudly* if a key is not in a `dict` object. For example, `to_words` does *not* have a key `0` any more."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 91,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{1: 'eins', 2: 'zwei', 3: 'drei', 4: 'vier'}"
+      ]
+     },
+     "execution_count": 91,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "to_words"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 92,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "ename": "KeyError",
+     "evalue": "0",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-92-30b825de37f0>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mto_words\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;31mKeyError\u001b[0m: 0"
+     ]
+    }
+   ],
+   "source": [
+    "to_words[0]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "That may be mitigated with the [get()](https://docs.python.org/3/library/stdtypes.html#dict.get) method that takes two arguments: `key` and `default`. It returns the value object `key` maps to if it is in the `dict` object; otherwise, `default` is returned. If not provided, `default` is `None`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 93,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'n/a'"
+      ]
+     },
+     "execution_count": 93,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "to_words.get(0, \"n/a\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 94,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'eins'"
+      ]
+     },
+     "execution_count": 94,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "to_words.get(1, \"n/a\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "`dict` objects are *mutable* as can be formally verified with the `MutableMapping` ABC from the [collections.abc](https://docs.python.org/3/library/collections.abc.html) module."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 95,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 95,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "isinstance(to_words, abc.MutableMapping)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 96,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 96,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "isinstance(from_words, abc.MutableMapping)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "The [update()](https://docs.python.org/3/library/stdtypes.html#dict.update) method takes the items of another mapping and either inserts them or overwrites the ones with matching keys already in the `dict` objects. It may be used in the other two ways as the [dict()](https://docs.python.org/3/library/functions.html#func-dict) built-in allows, as well."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 97,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{1: 'eins', 2: 'zwei', 3: 'drei', 4: 'vier'}"
+      ]
+     },
+     "execution_count": 97,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "to_words"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 98,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "to_spanish = {\n",
+    "    1: \"uno\",\n",
+    "    2: \"dos\",\n",
+    "    3: \"tres\",\n",
+    "    4: \"cuatro\",\n",
+    "    5: \"cinco\",  \n",
+    "}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 99,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "to_words.update(to_spanish)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 100,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{1: 'uno', 2: 'dos', 3: 'tres', 4: 'cuatro', 5: 'cinco'}"
+      ]
+     },
+     "execution_count": 100,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "to_words"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "In contrast to the `pop()` method of the `list` type, the [pop()](https://docs.python.org/3/library/stdtypes.html#dict.pop) method of the `dict` type *requires* a `key` argument to be passed. Then, it removes the corresponding key-value pair *and* returns the value object. If the `key` is not in the `dict` object, a `KeyError` is raised. With an optional `default` argument, that loud error may be suppressed and the `default` returned instead, just as with the [get()](https://docs.python.org/3/library/stdtypes.html#dict.get) method above."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 101,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'zero': 0, 'one': 1, 'two': 2, 'three': 3, 'four': 4, 'five': 5}"
+      ]
+     },
+     "execution_count": 101,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from_words"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 102,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "number = from_words.pop(\"zero\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 103,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0"
+      ]
+     },
+     "execution_count": 103,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "number"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 104,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'one': 1, 'two': 2, 'three': 3, 'four': 4, 'five': 5}"
+      ]
+     },
+     "execution_count": 104,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from_words"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 105,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "ename": "KeyError",
+     "evalue": "'zero'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-105-b91e52fa79fd>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mfrom_words\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpop\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"zero\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;31mKeyError\u001b[0m: 'zero'"
+     ]
+    }
+   ],
+   "source": [
+    "from_words.pop(\"zero\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 106,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0"
+      ]
+     },
+     "execution_count": 106,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from_words.pop(\"zero\", 0)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Similar to the `pop()` method of the `list` type, the [popitem()](https://docs.python.org/3/library/stdtypes.html#dict.popitem) method of the `dict` type removes *and* returns an \"arbitrary\" key-value pair as a `tuple` object from a `dict` object. With the preservation of the insertion order in Python 3.7 and higher, this effectively becomes a \"last in, first out\" rule, just as with the `list` type. Once a `dict` object is empty, [popitem()](https://docs.python.org/3/library/stdtypes.html#dict.popitem) raises a `KeyError`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 107,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'one': 1, 'two': 2, 'three': 3, 'four': 4, 'five': 5}"
+      ]
+     },
+     "execution_count": 107,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from_words"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 108,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "word, number = from_words.popitem()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 109,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "('five', 5)"
+      ]
+     },
+     "execution_count": 109,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "word, number"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 110,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'one': 1, 'two': 2, 'three': 3, 'four': 4}"
+      ]
+     },
+     "execution_count": 110,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from_words"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "The [clear()](https://docs.python.org/3/library/stdtypes.html#dict.clear) method removes all items but keeps the `dict` object alive in memory."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 111,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "to_words.clear()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 112,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{}"
+      ]
+     },
+     "execution_count": 112,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "to_words"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 113,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "from_words.clear()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 114,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{}"
+      ]
+     },
+     "execution_count": 114,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from_words"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "The [setdefault()](https://docs.python.org/3/library/stdtypes.html#dict.setdefault) method may have a bit of an unfortunate name but is useful, in particular, with nested `list` objects. It takes two arguments, `key` and `default`, and returns the value mapped to `key` if `key` is in the `dict` object; otherwise, it inserts the `key`-`default` pair *and* returns a reference to the newly created value object. So, it is similar to the [get()](https://docs.python.org/3/library/stdtypes.html#dict.get) method above, but *mutates* the `dict` object.\n",
+    "\n",
+    "Consider the `people` example again and note hwo the `dict` object modeling \"Albert Einstein\" has *no* `\"emails\"` key in it."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 115,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{'mathematicians': [{'emails': ['gilbert@mit.edu'],\n",
+      "                     'name': 'Gilbert Strang'},\n",
+      "                    {'emails': [],\n",
+      "                     'name': 'Leonhard Euler'}],\n",
+      " 'physicists': [{'name': 'Albert Einstein'}],\n",
+      " 'programmers': [{'emails': ['guido@python.org'],\n",
+      "                  'name': 'Guido van Rossum'}]}\n"
+     ]
+    }
+   ],
+   "source": [
+    "pprint(people, indent=1, width=60)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Let's say we want to append the imaginary emails `\"leonhard@math.org\"` and `\"albert@physics.org\"`. With the current \"messy\" structure, we cannot be sure if a `dict` object modeling a person has already a `\"emails\"` key or not. We could first use the `in` operator to check for that and create a new `list` object in a second step if one is missing. Third, we would finally append the new email.\n",
+    "\n",
+    "[setdefault()](https://docs.python.org/3/library/stdtypes.html#dict.setdefault) allows us to do all of the three steps at once. More importantly, behind the scenes Python only needs to make *one* key look-up instead of potentially three. For large nested data that could speed up the computations significantly.\n",
+    "\n",
+    "So, the first code cell below adds the email to the already existing empty `list` object, while the second one creates a new one."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 116,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "people[\"mathematicians\"][1].setdefault(\"emails\", []).append(\"leonhard@math.org\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 117,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "people[\"physicists\"][0].setdefault(\"emails\", []).append(\"albert@physics.org\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 118,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{'mathematicians': [{'emails': ['gilbert@mit.edu'],\n",
+      "                     'name': 'Gilbert Strang'},\n",
+      "                    {'emails': ['leonhard@math.org'],\n",
+      "                     'name': 'Leonhard Euler'}],\n",
+      " 'physicists': [{'emails': ['albert@physics.org'],\n",
+      "                 'name': 'Albert Einstein'}],\n",
+      " 'programmers': [{'emails': ['guido@python.org'],\n",
+      "                  'name': 'Guido van Rossum'}]}\n"
+     ]
+    }
+   ],
+   "source": [
+    "pprint(people, indent=1, width=60)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "In addition to the standardized methods, `dict` objects come with a [copy()](https://docs.python.org/3/library/stdtypes.html#dict.copy) method on them that creates *shallow* copies."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 119,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "guido = people[\"programmers\"][0].copy()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 120,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'name': 'Guido van Rossum', 'emails': ['guido@python.org']}"
+      ]
+     },
+     "execution_count": 120,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "guido"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "If we mutate `guido`, for example, remove all his emails with the `clear()` method on the `list` type, these changes are also visible through `people`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 121,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "guido[\"emails\"].clear()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 122,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'name': 'Guido van Rossum', 'emails': []}"
+      ]
+     },
+     "execution_count": 122,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "guido"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 123,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{'mathematicians': [{'emails': ['gilbert@mit.edu'],\n",
+      "                     'name': 'Gilbert Strang'},\n",
+      "                    {'emails': ['leonhard@math.org'],\n",
+      "                     'name': 'Leonhard Euler'}],\n",
+      " 'physicists': [{'emails': ['albert@physics.org'],\n",
+      "                 'name': 'Albert Einstein'}],\n",
+      " 'programmers': [{'emails': [],\n",
+      "                  'name': 'Guido van Rossum'}]}\n"
+     ]
+    }
+   ],
+   "source": [
+    "pprint(people, indent=1, width=60)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "### Packing & Unpacking (continued)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Just as a single `*` symbol is used for packing and unpacking iterables in [Chapter 7](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/07_sequences.ipynb#Packing-&-Unpacking), a double `**` symbol implements packing and unpacking for mappings.\n",
+    "\n",
+    "Let's say we have `to_words` and `more_words` as below and want to merge the items together into a *new* `dict` object."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 124,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "to_words = {\n",
+    "    0: \"zero\",\n",
+    "    1: \"one\",\n",
+    "    2: \"two\",\n",
+    "}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 125,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "more_words = {\n",
+    "    2: \"TWO\",  # upper case to illustrate a point\n",
+    "    3: \"three\",\n",
+    "    4: \"four\",\n",
+    "}"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "By *unpacking* the items with `**`, the newly created `dict` objects is first filled with the items from `to_words` and then from `more_words`. The item with the key `2` from `more_words` overwrites its counterpart from `to_words` as it is mentioned last."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 126,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{0: 'zero', 1: 'one', 2: 'TWO', 3: 'three', 4: 'four'}"
+      ]
+     },
+     "execution_count": 126,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "{**to_words, **more_words}"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "With the [update()](https://docs.python.org/3/library/stdtypes.html#dict.update) method from above, we can only *mutate* one of the two `dict` objects in place. So, unpacking is *no* syntactic sugar in this context."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 127,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "to_words.update(more_words)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 128,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{0: 'zero', 1: 'one', 2: 'TWO', 3: 'three', 4: 'four'}"
+      ]
+     },
+     "execution_count": 128,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "to_words  # we do not want to change an existing object"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "#### Function Definitions & Calls (continued)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Both, `*` and `**` may be used within the header line of a function definition, for example, as in `print_args1()` below. Here, *positional* arguments not captured by positional parameters are *packed* into the `tuple` object `args`, and *keyword* arguments not captured by keyword parameters are *packed* into the `dict` object `kwargs`.\n",
+    "\n",
+    "For `print_args1()`, all arguments are optional, and ..."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 129,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "def print_args1(*args, **kwargs):\n",
+    "    \"\"\"Print out all arguments passed in.\"\"\"\n",
+    "    for i, arg in enumerate(args):\n",
+    "        print(\"position\", i, arg)\n",
+    "    for key, value in kwargs.items():\n",
+    "        print(\"keyword\", key, value)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "... we may pass whatever we want to it, or nothing at all."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 130,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "print_args1()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 131,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "position 0 a\n",
+      "position 1 b\n",
+      "position 2 c\n"
+     ]
+    }
+   ],
+   "source": [
+    "print_args1(\"a\", \"b\", \"c\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 132,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "keyword first 1\n",
+      "keyword second 2\n",
+      "keyword third 3\n"
+     ]
+    }
+   ],
+   "source": [
+    "print_args1(first=1, second=2, third=3)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 133,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "position 0 x\n",
+      "position 1 y\n",
+      "keyword flag True\n"
+     ]
+    }
+   ],
+   "source": [
+    "print_args1(\"x\", \"y\", flag=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "The next example, `print_args2()`, requires the caller to pass one positional argument, captured in the `pos` parameter, and one keyword argument, captured in `key`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 134,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "def print_args2(pos, *args, key, **kwargs):\n",
+    "    \"\"\"Print out all arguments passed in.\"\"\"\n",
+    "    print(\"required position\", pos)\n",
+    "    for i, arg in enumerate(args):\n",
+    "        print(\"optional position\", i, arg)\n",
+    "    print(\"required keyword\", key)\n",
+    "    for key, value in kwargs.items():\n",
+    "        print(\"optional keyword\", key, value)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "If the caller does not respect that, a `TypeError` is raised."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 135,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "ename": "TypeError",
+     "evalue": "print_args2() missing 1 required positional argument: 'pos'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-135-21a06a55e707>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mprint_args2\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;31mTypeError\u001b[0m: print_args2() missing 1 required positional argument: 'pos'"
+     ]
+    }
+   ],
+   "source": [
+    "print_args2()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 136,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "ename": "TypeError",
+     "evalue": "print_args2() missing 1 required keyword-only argument: 'key'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-136-06c908f24fbb>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mprint_args2\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"p\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;31mTypeError\u001b[0m: print_args2() missing 1 required keyword-only argument: 'key'"
+     ]
+    }
+   ],
+   "source": [
+    "print_args2(\"p\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 137,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "required position p\n",
+      "required keyword k\n"
+     ]
+    }
+   ],
+   "source": [
+    "print_args2(\"p\", key=\"k\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 138,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "required position p\n",
+      "optional position 0 x\n",
+      "optional position 1 y\n",
+      "required keyword k\n",
+      "optional keyword flag True\n"
+     ]
+    }
+   ],
+   "source": [
+    "print_args2(\"p\", \"x\", \"y\", key=\"k\", flag=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Just as above when we merge `to_words` and `more_words`, we may use the `**` symbol to unpack the items of a mapping in a function call."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "### Dictionary Comprehensions"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Analogous to list comprehensions in [Chapter 7](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/07_sequences.ipynb#List-Comprehensions), **dictionary comprehensions**, or **dictcomps** for short, are a concise literal notation to derive new `dict` objects out of existing ones.\n",
+    "\n",
+    "For example, let's derive `from_words` from `to_words` below by swapping the keys and values."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 139,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "to_words = {\n",
+    "    0: \"zero\",\n",
+    "    1: \"one\",\n",
+    "    2: \"two\",\n",
+    "}"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Without a dictionary comprehension, we would have to initialize an empty `dict` object, loop over the items of the original one, and insert the key-value pairs one by one in a \"reversed\" fashion as \"value-key\" pairs. That assumes that the values are unique as otherwise some would be merged."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 140,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'zero': 0, 'one': 1, 'two': 2}"
+      ]
+     },
+     "execution_count": 140,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from_words = {}\n",
+    "\n",
+    "for number, word in to_words.items():\n",
+    "    from_words[word] = number\n",
+    "\n",
+    "from_words"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "While that code is correct, it is also unnecessarily verbose. The dictionary comprehension below works in the same way as list comprehensions except that entire expression is written within curly braces `{}` instead of brackets `[]`, and a colon `:` added to separate the keys from the values."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 141,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'zero': 0, 'one': 1, 'two': 2}"
+      ]
+     },
+     "execution_count": 141,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "{v: k for k, v in to_words.items()}"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "We may filter out items with an `if`-clause and transform the remaining key and value objects.\n",
+    "\n",
+    "For no good reason, let's filter out all words starting with a `\"z\"` and upper case the remainin words."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 142,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'ONE': 1, 'TWO': 2}"
+      ]
+     },
+     "execution_count": 142,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "{v.upper(): k for k, v in to_words.items() if not v.startswith(\"z\")}"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Multiple `for`- and/or `if`-clauses are allowed.\n",
+    "\n",
+    "For example, let's find all pairs of two numbers from `1` through `10` whose product is \"close\" to `50` (e.g., within a delta of `5`). The resulting `dict` object maps `tuple` to `int` objects."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 143,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{(5, 9): 45,\n",
+       " (5, 10): 50,\n",
+       " (6, 8): 48,\n",
+       " (6, 9): 54,\n",
+       " (7, 7): 49,\n",
+       " (8, 6): 48,\n",
+       " (9, 5): 45,\n",
+       " (9, 6): 54,\n",
+       " (10, 5): 50}"
+      ]
+     },
+     "execution_count": 143,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "one_to_ten = range(1, 11)\n",
+    "\n",
+    "{\n",
+    "    (x, y): x * y\n",
+    "    for x in one_to_ten for y in one_to_ten\n",
+    "    if abs(x * y - 50) <= 5\n",
+    "}"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "### Memoization"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "#### \"Easy at second Glance\" Example: [Fibonacci Numbers](https://en.wikipedia.org/wiki/Fibonacci_number) (revisited)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "The *recursive* implementation of the [Fibonacci numbers](https://en.wikipedia.org/wiki/Fibonacci_number) in [Chapter 4](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/04_iteration.ipynb#\"Easy-at-first-Glance\"-Example:-Fibonacci-Numbers) takes long to compute for large Fibonacci numbers as the number of function calls grows exponentially.\n",
+    "\n",
+    "The graph below visualizes what the problem is and also suggests a solution: Instead of calculating the return value of the `fibonacci()` function for the *same* argument over and over again, it makes sense to **cache** the result and reuse it. This concept is called **[memoization](https://en.wikipedia.org/wiki/Memoization)** in the computer science literature."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "<img src=\"static/fibonacci_call_graph.png\" width=\"50%\" align=\"left\">"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Below is revision of the recursive `fibonacci()` implementation that uses a **globally** defined `dict` object `memo` to store intermediate results and look them up.\n",
+    "\n",
+    "To be precise, called with a valid `i`, the the revised `fibonacci()` function first checks if the `i`th Fibonacci number has already been calculated before. If yes, it is in the `memo` dictionary. That number is then returned immediately *without* any more calculations. If no, there is no corresponding entry in `memo` and a recursive function call must be made. The number obtained by recursion is then put into `memo`.\n",
+    "\n",
+    "When we follow the flow of execution closely, we realize that the intermediate results represented by the left-most path in the graph above are calculated first. `fibonacci(1)` (i.e., the left-most leaf node) is the first base case reached, followed immediately by `fibonacci(0)`. From that moment onwards, the flow of execution moves back up the left-most path while adding together the two corresponding child nodes.\n",
+    "\n",
+    "Effectively, this mirrors the *iterative* implementation in that the order of all computational steps are *identical*.\n",
+    "\n",
+    "We added a keyword-only argument `debug` that allows the caller to print out a message every time a `i` was *not* in the `memo`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 144,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "memo = {\n",
+    "    0: 0,\n",
+    "    1: 1,\n",
+    "}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 145,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "import numbers  # for the goose typing"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 146,
+   "metadata": {
+    "code_folding": [],
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "def fibonacci(i, *, debug=False):\n",
+    "    \"\"\"Calculate the ith Fibonacci number.\n",
+    "\n",
+    "    Args:\n",
+    "        i (int): index of the Fibonacci number to calculate\n",
+    "        debug (bool): show non-cached calls; defaults to False\n",
+    "\n",
+    "    Returns:\n",
+    "        ith_fibonacci (int)\n",
+    "\n",
+    "    Raises:\n",
+    "        TypeError: if i is not an integer\n",
+    "        ValueError: if i is not positive\n",
+    "    \"\"\"\n",
+    "    if not isinstance(i, numbers.Integral):\n",
+    "        raise TypeError(\"i must be an integer\")\n",
+    "    elif i < 0:\n",
+    "        raise ValueError(\"i must be non-negative\")\n",
+    "\n",
+    "    if i in memo:\n",
+    "        return memo[i]\n",
+    "\n",
+    "    if debug:  # added for didactical purposes\n",
+    "        print(f\"fibonacci({i}) is calculated\")\n",
+    "\n",
+    "    recurse = fibonacci(i - 1, debug=debug) + fibonacci(i - 2, debug=debug)\n",
+    "    memo[i] = recurse\n",
+    "    return recurse"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 147,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "fibonacci(12) is calculated\n",
+      "fibonacci(11) is calculated\n",
+      "fibonacci(10) is calculated\n",
+      "fibonacci(9) is calculated\n",
+      "fibonacci(8) is calculated\n",
+      "fibonacci(7) is calculated\n",
+      "fibonacci(6) is calculated\n",
+      "fibonacci(5) is calculated\n",
+      "fibonacci(4) is calculated\n",
+      "fibonacci(3) is calculated\n",
+      "fibonacci(2) is calculated\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "144"
+      ]
+     },
+     "execution_count": 147,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "fibonacci(12, debug=True)  # = 13th number, 11 recursive calls necessary"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Now, calling `fibonacci()` has the *side effect* of growing the `memo` in the *global scope*. So, subsequent calls to `fibonacci()` need not calculate any Fibonacci number with an index `i` smaller than the maximum `i` used so far."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 148,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "144"
+      ]
+     },
+     "execution_count": 148,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "fibonacci(12, debug=True)  # = 13th number, no recursive calls needed"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 149,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{0: 0,\n",
+       " 1: 1,\n",
+       " 2: 1,\n",
+       " 3: 2,\n",
+       " 4: 3,\n",
+       " 5: 5,\n",
+       " 6: 8,\n",
+       " 7: 13,\n",
+       " 8: 21,\n",
+       " 9: 34,\n",
+       " 10: 55,\n",
+       " 11: 89,\n",
+       " 12: 144}"
+      ]
+     },
+     "execution_count": 149,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "memo"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "##### Efficiency of Algorithms (continued)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "With memoization, the recursive `fibonacci()` implementation is as fast as its iterative counterpart, even for large numbers.\n",
+    "\n",
+    "The `%%timeit` magic, by default, runs a code cell seven times. Whereas in the first run, *new* Fibonacci numbers (i.e., intermediate results) are added to the `memo`, `fibonacci()` has no work to do in the subsequent six runs. `%%timeit` realizes this and tells us that \"an intermediate result is being cached.\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 150,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The slowest run took 195.30 times longer than the fastest. This could mean that an intermediate result is being cached.\n",
+      "18.9 µs ± 44.4 µs per loop (mean ± std. dev. of 7 runs, 1 loop each)\n"
+     ]
+    }
+   ],
+   "source": [
+    "%%timeit -n 1\n",
+    "fibonacci(99)  # = 100th number"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 151,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The slowest run took 1919.60 times longer than the fastest. This could mean that an intermediate result is being cached.\n",
+      "346 µs ± 844 µs per loop (mean ± std. dev. of 7 runs, 1 loop each)\n"
+     ]
+    }
+   ],
+   "source": [
+    "%%timeit -n 1\n",
+    "fibonacci(999)  # = 1,000th number"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "The iterative implementation still has an advantage as the `RecursionError` shows for even larger numbers.\n",
+    "\n",
+    "This exception occurs as Python must keep track of *every* function call *until* it has returned, and with large enough `i`, the recursion tree above grows too big. By default, Python has a limit of up to 3000 *simultaneous* function calls. So, theoretically this exception is not a bug in the narrow sense but the result of a \"security\" measure that is supposed to keep a computer from crashing. However, practically most high-level languages like Python incur such an overhead cost: It results from the fact that someone (i.e., Python) needs to manage each function call's *local scope*. With the `for`-loop in the iterative version, we do this managing ourselves."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 152,
+   "metadata": {
+    "scrolled": true,
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "ename": "RecursionError",
+     "evalue": "maximum recursion depth exceeded in comparison",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mRecursionError\u001b[0m                            Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-152-cc76051ea9de>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mget_ipython\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrun_cell_magic\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'timeit'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'-n 1'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'fibonacci(9999)  # = 10,000th number\\n'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;32m~/.pyenv/versions/anaconda3-2019.07/lib/python3.7/site-packages/IPython/core/interactiveshell.py\u001b[0m in \u001b[0;36mrun_cell_magic\u001b[0;34m(self, magic_name, line, cell)\u001b[0m\n\u001b[1;32m   2356\u001b[0m             \u001b[0;32mwith\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbuiltin_trap\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2357\u001b[0m                 \u001b[0margs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mmagic_arg_s\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcell\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2358\u001b[0;31m                 \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2359\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mresult\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2360\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m</home/webartifex/.pyenv/versions/anaconda3-2019.07/lib/python3.7/site-packages/decorator.py:decorator-gen-60>\u001b[0m in \u001b[0;36mtimeit\u001b[0;34m(self, line, cell, local_ns)\u001b[0m\n",
+      "\u001b[0;32m~/.pyenv/versions/anaconda3-2019.07/lib/python3.7/site-packages/IPython/core/magic.py\u001b[0m in \u001b[0;36m<lambda>\u001b[0;34m(f, *a, **k)\u001b[0m\n\u001b[1;32m    185\u001b[0m     \u001b[0;31m# but it's overkill for just that one bit of state.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    186\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mmagic_deco\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marg\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 187\u001b[0;31m         \u001b[0mcall\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mlambda\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mk\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mk\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    188\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    189\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mcallable\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marg\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/.pyenv/versions/anaconda3-2019.07/lib/python3.7/site-packages/IPython/core/magics/execution.py\u001b[0m in \u001b[0;36mtimeit\u001b[0;34m(self, line, cell, local_ns)\u001b[0m\n\u001b[1;32m   1160\u001b[0m                     \u001b[0;32mbreak\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1161\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1162\u001b[0;31m         \u001b[0mall_runs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtimer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrepeat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrepeat\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnumber\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1163\u001b[0m         \u001b[0mbest\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mall_runs\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m/\u001b[0m \u001b[0mnumber\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1164\u001b[0m         \u001b[0mworst\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmax\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mall_runs\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m/\u001b[0m \u001b[0mnumber\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/.pyenv/versions/anaconda3-2019.07/lib/python3.7/timeit.py\u001b[0m in \u001b[0;36mrepeat\u001b[0;34m(self, repeat, number)\u001b[0m\n\u001b[1;32m    202\u001b[0m         \u001b[0mr\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    203\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrepeat\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 204\u001b[0;31m             \u001b[0mt\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtimeit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnumber\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    205\u001b[0m             \u001b[0mr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mt\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    206\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mr\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/.pyenv/versions/anaconda3-2019.07/lib/python3.7/site-packages/IPython/core/magics/execution.py\u001b[0m in \u001b[0;36mtimeit\u001b[0;34m(self, number)\u001b[0m\n\u001b[1;32m    167\u001b[0m         \u001b[0mgc\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdisable\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    168\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 169\u001b[0;31m             \u001b[0mtiming\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0minner\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mit\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtimer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    170\u001b[0m         \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    171\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mgcold\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m<magic-timeit>\u001b[0m in \u001b[0;36minner\u001b[0;34m(_it, _timer)\u001b[0m\n",
+      "\u001b[0;32m<ipython-input-146-223333299fa0>\u001b[0m in \u001b[0;36mfibonacci\u001b[0;34m(i, debug)\u001b[0m\n\u001b[1;32m     24\u001b[0m         \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34mf\"fibonacci({i}) is calculated\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     25\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 26\u001b[0;31m     \u001b[0mrecurse\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfibonacci\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mi\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdebug\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdebug\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mfibonacci\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mi\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0;36m2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdebug\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdebug\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     27\u001b[0m     \u001b[0mmemo\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mrecurse\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     28\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mrecurse\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "... last 1 frames repeated, from the frame below ...\n",
+      "\u001b[0;32m<ipython-input-146-223333299fa0>\u001b[0m in \u001b[0;36mfibonacci\u001b[0;34m(i, debug)\u001b[0m\n\u001b[1;32m     24\u001b[0m         \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34mf\"fibonacci({i}) is calculated\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     25\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 26\u001b[0;31m     \u001b[0mrecurse\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfibonacci\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mi\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdebug\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdebug\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mfibonacci\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mi\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0;36m2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdebug\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdebug\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     27\u001b[0m     \u001b[0mmemo\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mrecurse\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     28\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mrecurse\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;31mRecursionError\u001b[0m: maximum recursion depth exceeded in comparison"
+     ]
+    }
+   ],
+   "source": [
+    "%%timeit -n 1\n",
+    "fibonacci(9999)  # = 10,000th number"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "We could \"hack\" a bit with Python's default configuration using the [sys](https://docs.python.org/3/library/sys.html) module in the [standard library](https://docs.python.org/3/library/index.html) and make it work anyhow. As we are good citizens, we reset everything to the defaults after our hack is completed."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 153,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "import sys"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 154,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "old_recursion_limit = sys.getrecursionlimit()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 155,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "3000"
+      ]
+     },
+     "execution_count": 155,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "old_recursion_limit"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 156,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "sys.setrecursionlimit(99999)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Computational speed is *not* the problem here."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 157,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The slowest run took 25077.61 times longer than the fastest. This could mean that an intermediate result is being cached.\n",
+      "2.36 ms ± 5.77 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n"
+     ]
+    }
+   ],
+   "source": [
+    "%%timeit -n 1\n",
+    "fibonacci(9999)  # = 10,000th number"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 158,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "sys.setrecursionlimit(old_recursion_limit)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "#### \"Easy at third Glance\" Example: [Fibonacci Numbers](https://en.wikipedia.org/wiki/Fibonacci_number) (revisited)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "It is considered *bad practice* to make a function and thereby its correctness dependent on a program's *global state*: For example, `memo` above could be \"manipulated.\"\n",
+    "\n",
+    "More often than not, such things happen by accident: Imagine we wrote two independent recursive functions with memoization built-in to solve different problems, and, unintentionally, we made both work with the *same* global `memo`. As a result, we would observe \"random\" bugs depending on the order in which we executed these functions. Such bugs are hard to track down in practice.\n",
+    "\n",
+    "A common pattern is to avoid global state and pass intermediate results \"down\" the recursion tree in a \"hidden\" argument. By convention, we prefix variable and argument names with a single leading underscore `_`, such as with `_memo` below, to indicate that a user of our code *must not* use it. Also, we make `_memo` a *keyword-only* argument to force ourselves to always explicitly name it in a function call. Because it is an **implementation detail**, the `_memo` argument is *not* even mentioned in the docstring.\n",
+    "\n",
+    "When initially called, `fibonacci()` creates a new `dict` object named `_memo` in its *local scope*. That is then shared \"internally\" between successive function calls by passing it on as an argument. Once the first call to `fibonacci()` returns, `_memo` is \"forgotten.\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 159,
+   "metadata": {
+    "code_folding": [],
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "def fibonacci(i, *, debug=False, _memo=None):\n",
+    "    \"\"\"Calculate the ith Fibonacci number.\n",
+    "\n",
+    "    Args:\n",
+    "        i (int): index of the Fibonacci number to calculate\n",
+    "        debug (bool): show non-cached calls; defaults to False\n",
+    "\n",
+    "    Returns:\n",
+    "        ith_fibonacci (int)\n",
+    "\n",
+    "    Raises:\n",
+    "        TypeError: if i is not an integer\n",
+    "        ValueError: if i is not positive\n",
+    "    \"\"\"\n",
+    "    if not isinstance(i, numbers.Integral):\n",
+    "        raise TypeError(\"i must be an integer\")\n",
+    "    elif i < 0:\n",
+    "        raise ValueError(\"i must be non-negative\")\n",
+    "\n",
+    "    if _memo is None:\n",
+    "        _memo = {\n",
+    "            0: 0,\n",
+    "            1: 1,\n",
+    "        }\n",
+    "\n",
+    "    if i in _memo:\n",
+    "        return _memo[i]\n",
+    "\n",
+    "    if debug:  # added for didactical purposes\n",
+    "        print(f\"fibonacci({i}) is calculated\")\n",
+    "\n",
+    "    recurse = (\n",
+    "        fibonacci(i - 1, debug=debug, _memo=_memo)\n",
+    "        + fibonacci(i - 2, debug=debug, _memo=_memo)\n",
+    "    )\n",
+    "    _memo[i] = recurse\n",
+    "    return recurse"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 160,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "fibonacci(12) is calculated\n",
+      "fibonacci(11) is calculated\n",
+      "fibonacci(10) is calculated\n",
+      "fibonacci(9) is calculated\n",
+      "fibonacci(8) is calculated\n",
+      "fibonacci(7) is calculated\n",
+      "fibonacci(6) is calculated\n",
+      "fibonacci(5) is calculated\n",
+      "fibonacci(4) is calculated\n",
+      "fibonacci(3) is calculated\n",
+      "fibonacci(2) is calculated\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "144"
+      ]
+     },
+     "execution_count": 160,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "fibonacci(12, debug=True)  # = 13th number, 11 recursive calls necessary"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Because `fibonacci()` is now independent from *global state*, the same eleven recursive function calls are made each time."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 161,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "fibonacci(12) is calculated\n",
+      "fibonacci(11) is calculated\n",
+      "fibonacci(10) is calculated\n",
+      "fibonacci(9) is calculated\n",
+      "fibonacci(8) is calculated\n",
+      "fibonacci(7) is calculated\n",
+      "fibonacci(6) is calculated\n",
+      "fibonacci(5) is calculated\n",
+      "fibonacci(4) is calculated\n",
+      "fibonacci(3) is calculated\n",
+      "fibonacci(2) is calculated\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "144"
+      ]
+     },
+     "execution_count": 161,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "fibonacci(12, debug=True)  # = 13th number, still 11 recursive calls necessary"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "##### Efficiency of Algorithms (continued)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Also, the runtime is now stable (i.e., no message that \"an intermediate result is being cached\"). The limitation with respect to the maximum number of simultaneous function calls still applies."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 162,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "330 µs ± 18.7 µs per loop (mean ± std. dev. of 7 runs, 1 loop each)\n"
+     ]
+    }
+   ],
+   "source": [
+    "%%timeit -n 1\n",
+    "fibonacci(99)  # = 100th number"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 163,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "3.53 ms ± 75.2 µs per loop (mean ± std. dev. of 7 runs, 1 loop each)\n"
+     ]
+    }
+   ],
+   "source": [
+    "%%timeit -n 1\n",
+    "fibonacci(999)  # = 1,000th number"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 164,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "sys.setrecursionlimit(99999)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 165,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "15.5 ms ± 572 µs per loop (mean ± std. dev. of 7 runs, 1 loop each)\n"
+     ]
+    }
+   ],
+   "source": [
+    "%%timeit -n 1\n",
+    "fibonacci(9999)  # = 10,000th number"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 166,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "sys.setrecursionlimit(old_recursion_limit)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "### Specialized Mappings"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "The [collections](https://docs.python.org/3/library/collections.html) module in the [standard library](https://docs.python.org/3/library/index.html) provides specialized mapping types for common enough use cases."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "#### The `OrderedDict` Type"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "The [OrderedDict](https://docs.python.org/3/library/collections.html#collections.OrderedDict) type may be used to create a `dict`-like object with the added feature that *explicitly* remembers the *insertion* order of its items.\n",
+    "\n",
+    "Let's look at a quick example: We create an `OrderedDict` object by passing an iterable of $2$-element iterables, one of the three ways to use [dict()](https://docs.python.org/3/library/functions.html#func-dict)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 167,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "from collections import OrderedDict"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 168,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "ordered = OrderedDict([(\"first\", 1), (\"second\", 2), (\"third\", 3)])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 169,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "OrderedDict([('first', 1), ('second', 2), ('third', 3)])"
+      ]
+     },
+     "execution_count": 169,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ordered"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 170,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "collections.OrderedDict"
+      ]
+     },
+     "execution_count": 170,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "type(ordered)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "The iteration order is the insertion order, and ..."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 171,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "first 1\n",
+      "second 2\n",
+      "third 3\n"
+     ]
+    }
+   ],
+   "source": [
+    "for key, value in ordered.items():\n",
+    "    print(key, value)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "... we may also loop over `ordered` in *reverse* order."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 172,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "third 3\n",
+      "second 2\n",
+      "first 1\n"
+     ]
+    }
+   ],
+   "source": [
+    "for key, value in reversed(ordered.items()):\n",
+    "    print(key, value)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "For `OrderedDict` objects, the [popitem()](https://docs.python.org/3/library/collections.html#collections.OrderedDict.popitem) method takes an optional and boolean argument `last`. That allows us to remove either the *first* or *last* item inserted.\n",
+    "\n",
+    "Further, the [move_to_end()](https://docs.python.org/3/library/collections.html#collections.OrderedDict.move_to_end) method allows us to move any item by its key to the end of the order. It also takes an optional argument `last`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 173,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "ordered.move_to_end(\"first\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 174,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "second 2\n",
+      "third 3\n",
+      "first 1\n"
+     ]
+    }
+   ],
+   "source": [
+    "for key, value in ordered.items():\n",
+    "    print(key, value)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 175,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "ordered.move_to_end(\"first\", last=False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 176,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "first 1\n",
+      "second 2\n",
+      "third 3\n"
+     ]
+    }
+   ],
+   "source": [
+    "for key, value in ordered.items():\n",
+    "    print(key, value)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Otherwise, an `OrderedDict` object is no different than a normal `dict` one, starting with Python 3.7."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "#### The `defaultdict` Type"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "A more useful mapping is the [defaultdict](https://docs.python.org/3/library/collections.html#collections.defaultdict) type, which allows us to define a factory function. That creates default values whenever we look up a key that does not yet exist. Ordinary `dict` objects would throw a `KeyError` exception in such situations.\n",
+    "\n",
+    "Let's say we have a `list` with *records* of goals scored during a soccer game. The records consist of the fields \"Country,\" \"Player,\" and the \"Time\" when a goal was scored. Our task is to group the goals by player and/or country."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 177,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "goals = [\n",
+    "    (\"Germany\", \"MĂĽller\", 11), (\"Germany\", \"Klose\", 23),\n",
+    "    (\"Germany\", \"Kroos\", 24), (\"Germany\", \"Kroos\", 26),\n",
+    "    (\"Germany\", \"Khedira\", 29), (\"Germany\", \"SchĂĽrrle\", 69),\n",
+    "    (\"Germany\", \"SchĂĽrrle\", 79), (\"Brazil\", \"Oscar\", 90),\n",
+    "]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Using a normal `dict` object, we have to tediously check if a player has already scored a goal before. If not, we must create a *new* `list` object with the first time the player scored. Otherwise, we append the goal to an already existing `list` object."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 178,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'MĂĽller': [11],\n",
+       " 'Klose': [23],\n",
+       " 'Kroos': [24, 26],\n",
+       " 'Khedira': [29],\n",
+       " 'SchĂĽrrle': [69, 79],\n",
+       " 'Oscar': [90]}"
+      ]
+     },
+     "execution_count": 178,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "goals_by_player = {}\n",
+    "\n",
+    "for _, player, minute in goals:\n",
+    "    if player not in goals_by_player:\n",
+    "        goals_by_player[player] = [minute]\n",
+    "    else:\n",
+    "        goals_by_player[player].append(minute)\n",
+    "\n",
+    "goals_by_player"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "We could replace the `if`-`else`-logic with the [setdefault()](https://docs.python.org/3/library/stdtypes.html#dict.setdefault) method mentioned above."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 179,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'MĂĽller': [11],\n",
+       " 'Klose': [23],\n",
+       " 'Kroos': [24, 26],\n",
+       " 'Khedira': [29],\n",
+       " 'SchĂĽrrle': [69, 79],\n",
+       " 'Oscar': [90]}"
+      ]
+     },
+     "execution_count": 179,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "goals_by_player = {}\n",
+    "\n",
+    "for _, player, minute in goals:\n",
+    "    goals_by_player.setdefault(player, []).append(minute)\n",
+    "\n",
+    "goals_by_player"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Instead, with a `defaultdict` object, we can portray the code fragment's intent in a concise form. We pass a reference to the [list()](https://docs.python.org/3/library/functions.html#func-list) built-in to `defaultdict`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 180,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "from collections import defaultdict"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 181,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "defaultdict(list,\n",
+       "            {'MĂĽller': [11],\n",
+       "             'Klose': [23],\n",
+       "             'Kroos': [24, 26],\n",
+       "             'Khedira': [29],\n",
+       "             'SchĂĽrrle': [69, 79],\n",
+       "             'Oscar': [90]})"
+      ]
+     },
+     "execution_count": 181,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "goals_by_player = defaultdict(list)\n",
+    "\n",
+    "for _, player, minute in goals:\n",
+    "    goals_by_player[player].append(minute)\n",
+    "\n",
+    "goals_by_player"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 182,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "collections.defaultdict"
+      ]
+     },
+     "execution_count": 182,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "type(goals_by_player)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "The factory function is stored in the `default_factory` attribute."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 183,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "list"
+      ]
+     },
+     "execution_count": 183,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "goals_by_player.default_factory"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "If we want this code to produce a normal `dict` object, we pass `goals_by_player` to the [dict()](https://docs.python.org/3/library/functions.html#func-dict) built-in."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 184,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'MĂĽller': [11],\n",
+       " 'Klose': [23],\n",
+       " 'Kroos': [24, 26],\n",
+       " 'Khedira': [29],\n",
+       " 'SchĂĽrrle': [69, 79],\n",
+       " 'Oscar': [90]}"
+      ]
+     },
+     "execution_count": 184,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "dict(goals_by_player)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Being creative, we use a factory function that returns another [defaultdict](https://docs.python.org/3/library/collections.html#collections.defaultdict) with [list()](https://docs.python.org/3/library/functions.html#func-list) as its factory to group on the country and the player level simultaneously."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 185,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "defaultdict(<function __main__.<lambda>()>,\n",
+       "            {'Germany': defaultdict(list,\n",
+       "                         {'MĂĽller': [11],\n",
+       "                          'Klose': [23],\n",
+       "                          'Kroos': [24, 26],\n",
+       "                          'Khedira': [29],\n",
+       "                          'SchĂĽrrle': [69, 79]}),\n",
+       "             'Brazil': defaultdict(list, {'Oscar': [90]})})"
+      ]
+     },
+     "execution_count": 185,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "goals_by_country_and_player = defaultdict(lambda: defaultdict(list))\n",
+    "\n",
+    "for country, player, minute in goals:\n",
+    "    goals_by_country_and_player[country][player].append(minute)\n",
+    "\n",
+    "goals_by_country_and_player"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Conversion into a normal and nested `dict` object is now a bit tricky but can be achieved in one line with a comprehension."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 186,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{'Brazil': {'Oscar': [90]},\n",
+      " 'Germany': {'Khedira': [29],\n",
+      "             'Klose': [23],\n",
+      "             'Kroos': [24, 26],\n",
+      "             'MĂĽller': [11],\n",
+      "             'SchĂĽrrle': [69, 79]}}\n"
+     ]
+    }
+   ],
+   "source": [
+    "pprint({country: dict(by_player) for country, by_player in goals_by_country_and_player.items()})"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "#### The `Counter` Type"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "A common task is to count the number of occurrences of elements in an iterable.\n",
+    "\n",
+    "The [Counter](https://docs.python.org/3/library/collections.html#collections.Counter) type provides an easy-to-use interface that can be called with any iterable and returns a `dict`-like object of type `Counter` that maps each unique elements to the number of times it occurs.\n",
+    "\n",
+    "To continue the previous example, let's create an overview that shows how many goals a player scorred. We use a generator expression as the argument to `Counter`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 187,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[('Germany', 'MĂĽller', 11),\n",
+       " ('Germany', 'Klose', 23),\n",
+       " ('Germany', 'Kroos', 24),\n",
+       " ('Germany', 'Kroos', 26),\n",
+       " ('Germany', 'Khedira', 29),\n",
+       " ('Germany', 'SchĂĽrrle', 69),\n",
+       " ('Germany', 'SchĂĽrrle', 79),\n",
+       " ('Brazil', 'Oscar', 90)]"
+      ]
+     },
+     "execution_count": 187,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "goals"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 188,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "from collections import Counter"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 189,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "scorers = Counter(x[1] for x in goals)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 190,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Counter({'MĂĽller': 1,\n",
+       "         'Klose': 1,\n",
+       "         'Kroos': 2,\n",
+       "         'Khedira': 1,\n",
+       "         'SchĂĽrrle': 2,\n",
+       "         'Oscar': 1})"
+      ]
+     },
+     "execution_count": 190,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "scorers"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 191,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "collections.Counter"
+      ]
+     },
+     "execution_count": 191,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "type(scorers)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Now we can look up individual players. `scores` behaves like a normal dictionary with regard to key look-ups."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 192,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "1"
+      ]
+     },
+     "execution_count": 192,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "scorers[\"MĂĽller\"]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "By default, it returns `0` if a key is not found. So, we do not have to handle a `KeyError`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 193,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0"
+      ]
+     },
+     "execution_count": 193,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "scorers[\"Lahm\"]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "`Counter` objects have a [most_common()](https://docs.python.org/3/library/collections.html#collections.Counter.most_common) method that returns a `list` object containing $2$-element `tuple` objects, where the first element is the element from the original iterable and the second the number of occurrences. The `list` object is sorted in descending order of occurrences."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 194,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[('Kroos', 2), ('SchĂĽrrle', 2)]"
+      ]
+     },
+     "execution_count": 194,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "scorers.most_common(2)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "We can increase the count of individual entries with the [update()](https://docs.python.org/3/library/collections.html#collections.Counter.update) method: That takes an *iterable* of the elements we want to count.\n",
+    "\n",
+    "Imagine if [Philipp Lahm](https://en.wikipedia.org/wiki/Philipp_Lahm) had also scored against Brazil."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 195,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "scorers.update([\"Lahm\"])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 196,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Counter({'MĂĽller': 1,\n",
+       "         'Klose': 1,\n",
+       "         'Kroos': 2,\n",
+       "         'Khedira': 1,\n",
+       "         'SchĂĽrrle': 2,\n",
+       "         'Oscar': 1,\n",
+       "         'Lahm': 1})"
+      ]
+     },
+     "execution_count": 196,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "scorers"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "If we use a `str` object as the argument instead, each individual character is treated as an element to be updated. That is most likely not what we want."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 197,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "scorers.update(\"Lahm\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 198,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Counter({'MĂĽller': 1,\n",
+       "         'Klose': 1,\n",
+       "         'Kroos': 2,\n",
+       "         'Khedira': 1,\n",
+       "         'SchĂĽrrle': 2,\n",
+       "         'Oscar': 1,\n",
+       "         'Lahm': 1,\n",
+       "         'L': 1,\n",
+       "         'a': 1,\n",
+       "         'h': 1,\n",
+       "         'm': 1})"
+      ]
+     },
+     "execution_count": 198,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "scorers"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "#### The `ChainMap` Type"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Consider `to_words`, `more_words`, and `even_more_words` below. Instead of merging the items of the three `dict` objects together into a *new* one, we want to create an object that behaves as if it contained all the unified items in it without materializing them in memory a second time."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 199,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "to_words = {\n",
+    "    0: \"zero\",\n",
+    "    1: \"one\",\n",
+    "    2: \"two\",\n",
+    "}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 200,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "more_words = {\n",
+    "    2: \"TWO\",  # upper case to illustrate a point\n",
+    "    3: \"three\",\n",
+    "    4: \"four\",\n",
+    "}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 201,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "even_more_words = {\n",
+    "    4: \"FOUR\",  # upper case to illustrate a point\n",
+    "    5: \"five\",\n",
+    "    6: \"six\",\n",
+    "}"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "The [ChainMap](https://docs.python.org/3/library/collections.html#collections.ChainMap) type allows us to do precisely that."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 202,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "from collections import ChainMap"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "We simply pass all mappings as positional arguments to `ChainMap` and obtain a **proxy** object that occupies almost no memory but gives us access to the union of all the items."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 203,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "chain = ChainMap(to_words, more_words, even_more_words)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Let's loop over the items in `chain` and see what is \"in\" it. The order is obviously *unpredictable* but all seven items we expected are there. Keys of later mappings do *not* overwrite earlier keys."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 204,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "4 four\n",
+      "5 five\n",
+      "6 six\n",
+      "2 two\n",
+      "3 three\n",
+      "0 zero\n",
+      "1 one\n"
+     ]
+    }
+   ],
+   "source": [
+    "for number, word in chain.items():\n",
+    "    print(number, word)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "When looking up a non-existent key, `ChainMap` objects raise a `KeyError` just like normal `dict` objects would."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 205,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "ename": "KeyError",
+     "evalue": "10",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-205-25fb5b5b2e5f>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mchain\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m10\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;32m~/.pyenv/versions/anaconda3-2019.07/lib/python3.7/collections/__init__.py\u001b[0m in \u001b[0;36m__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m    912\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    913\u001b[0m                 \u001b[0;32mpass\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 914\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__missing__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m            \u001b[0;31m# support subclasses that define __missing__\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    915\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    916\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdefault\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/.pyenv/versions/anaconda3-2019.07/lib/python3.7/collections/__init__.py\u001b[0m in \u001b[0;36m__missing__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m    904\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    905\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__missing__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 906\u001b[0;31m         \u001b[0;32mraise\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    907\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    908\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__getitem__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;31mKeyError\u001b[0m: 10"
+     ]
+    }
+   ],
+   "source": [
+    "chain[10]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "## The `set` Type"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Python's provides a built-in `set` type that resembles [mathematical sets](https://en.wikipedia.org/wiki/Set_%28mathematics%29): Each element may only be a member of a set once, and there is no *predictable* order regarding the elements (cf., [documentation](https://docs.python.org/3/library/stdtypes.html#set)).\n",
+    "\n",
+    "To create a set, we use the literal notation, `{..., ...}`, and list all the elements. The redundant `7` and `4` are discarded."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 206,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "numbers = {7, 7, 11, 8, 5, 3, 12, 2, 6, 9, 10, 1, 4, 4}"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "`set` objects are objects on their own."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 207,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "139751998783784"
+      ]
+     },
+     "execution_count": 207,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "id(numbers)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 208,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "set"
+      ]
+     },
+     "execution_count": 208,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "type(numbers)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 209,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12}"
+      ]
+     },
+     "execution_count": 209,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "numbers"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "To create an empty set, we must use the [set()](https://docs.python.org/3/library/functions.html#func-set) built-in as empty curly brackets, `{}`, already creates an empty `dict` object."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 210,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "empty_dict = {}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 211,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "dict"
+      ]
+     },
+     "execution_count": 211,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "type(empty_dict)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 212,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "empty_set = set()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 213,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "set()"
+      ]
+     },
+     "execution_count": 213,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "empty_set"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "The [set()](https://docs.python.org/3/library/functions.html#func-set) built-in takes any iterable and only keeps unique elements.\n",
+    "\n",
+    "For example, we obtain all unique letters of a long word like so."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 214,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'a', 'b', 'c', 'd', 'r'}"
+      ]
+     },
+     "execution_count": 214,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "set(\"abracadabra\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "### Sets are like \"Dictionaries without Values\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "The curly brackets notation can be viewed as a hint that `dict` objects are conceptually generalizations of `set`  objects, and we think of `set` objects as a collection consisting of a `dict` object's keys with all the mapped values removed.\n",
+    "\n",
+    "Like `dict` objects, `set` objects are built on top of [hash tables](https://en.wikipedia.org/wiki/Hash_table), and, thus, each element must be a *hashable* (i.e., immutable at the very least) object and can only be contained in a set once due to the buckets logic."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 215,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "ename": "TypeError",
+     "evalue": "unhashable type: 'list'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-215-4ac30fae8196>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0;34m{\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m2\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m3\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;31mTypeError\u001b[0m: unhashable type: 'list'"
+     ]
+    }
+   ],
+   "source": [
+    "{0, [1, 2], 3}"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "[len()](https://docs.python.org/3/library/functions.html#len) tells us the number of elements in a `set` object, which is always `Sized`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 216,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "12"
+      ]
+     },
+     "execution_count": 216,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "len(numbers)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 217,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 217,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "isinstance(numbers, abc.Sized)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "We may loop over the elements in a `set` object, but we must keep in mind that there is no *predictable* order. In contrast to `dict` objects, the iteration order is also *not* guaranteed to be the insertion order. Because of the special hash values for `int` objects, `numbers` seems to be \"magically\" sorted, which is not the case."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 218,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "1 2 3 4 5 6 7 8 9 10 11 12 "
+     ]
+    }
+   ],
+   "source": [
+    "for number in numbers:\n",
+    "    print(number, end=\" \")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "`set` objects are `Iterable`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 219,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 219,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "isinstance(numbers, abc.Iterable)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "`set` objects are not `Reversible`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 220,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "ename": "TypeError",
+     "evalue": "'set' object is not reversible",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-220-1095d1ef6c80>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0;32mfor\u001b[0m \u001b[0mnumber\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mreversed\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnumbers\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m     \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnumber\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mend\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\" \"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;31mTypeError\u001b[0m: 'set' object is not reversible"
+     ]
+    }
+   ],
+   "source": [
+    "for number in reversed(numbers):\n",
+    "    print(number, end=\" \")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 221,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "False"
+      ]
+     },
+     "execution_count": 221,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "isinstance(numbers, abc.Reversible)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "The boolean `in` operator checks if a given and immutable object evaluates equal to an element in a `set` object. As with `dict` objects, membership testing is an *extremely* fast operation. Conceptually, it has the same result as conducting a linear search with the `==` operator behind the scenes."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 222,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "False"
+      ]
+     },
+     "execution_count": 222,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "0 in numbers"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 223,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 223,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "1 in numbers"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 224,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 224,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "1.0 in numbers"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "So, a `set` object is a `Container`. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 225,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 225,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "isinstance(numbers, abc.Container)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "There is a `Set` ABC in the [collections.abc](https://docs.python.org/3/library/collections.abc.html) module that formalizes, in particular, the operators supported by `set` objects. Furthermore, the ordinary `set` type is *mutable*, as expressed with the `MutableSet` ABC. The latter formalizes all the methods that mutate a `set` object."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 226,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 226,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "isinstance(numbers, abc.Set)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 227,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 227,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "isinstance(numbers, abc.MutableSet)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "### No Indexing / Key Look-up / Slicing"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "As `set` objects come without a *predictable* order, indexing and slicing is not supported and results in a `TypeError`. In particular, as there are no values to be looked up, these operations are not *semantically* meaningful. Instead, we check membership via the `in` operator, as shown in the previous sub-section."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 228,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "ename": "TypeError",
+     "evalue": "'set' object is not subscriptable",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-228-7486ea9d37bc>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mnumbers\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;31mTypeError\u001b[0m: 'set' object is not subscriptable"
+     ]
+    }
+   ],
+   "source": [
+    "numbers[0]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 229,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [
+    {
+     "ename": "TypeError",
+     "evalue": "'set' object is not subscriptable",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-229-4de4c0f84e99>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mnumbers\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;31mTypeError\u001b[0m: 'set' object is not subscriptable"
+     ]
+    }
+   ],
+   "source": [
+    "numbers[:]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "### Mutability & Set Methods"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Because the `[]` operator does not work for `set` objects, they are mutated maily via methods (cf., [documentation](https://docs.python.org/3/library/stdtypes.html#set)).\n",
+    "\n",
+    "We may add new elements to an existing `set` object with the [add()](https://docs.python.org/3/library/stdtypes.html#frozenset.add) method."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 230,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "numbers.add(99)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 231,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 99}"
+      ]
+     },
+     "execution_count": 231,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "numbers"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "The [update()](https://docs.python.org/3/library/stdtypes.html#frozenset.update) method takes an iterable and adds all its elements to a `set` object if they are not already contained in it."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 232,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "numbers.update(range(5))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 233,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 99}"
+      ]
+     },
+     "execution_count": 233,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "numbers"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "To remove an element by value, we use the [remove()](https://docs.python.org/3/library/stdtypes.html#frozenset.remove) or [discard()](https://docs.python.org/3/library/stdtypes.html#frozenset.discard) methods. If the element to be removed is not in the `set` object, [remove()](https://docs.python.org/3/library/stdtypes.html#frozenset.remove) raises a loud `KeyError` while [discard()](https://docs.python.org/3/library/stdtypes.html#frozenset.discard) stays *silent*."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 234,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "numbers.remove(99)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 235,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "ename": "KeyError",
+     "evalue": "99",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-235-b23ddd7c18b2>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mnumbers\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mremove\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m99\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;31mKeyError\u001b[0m: 99"
+     ]
+    }
+   ],
+   "source": [
+    "numbers.remove(99)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 236,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "numbers.discard(0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 237,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "numbers.discard(0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 238,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12}"
+      ]
+     },
+     "execution_count": 238,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "numbers"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "The [pop()](https://docs.python.org/3/library/stdtypes.html#frozenset.pop) method removes an *arbitrary* element. As not even the insertion order is tracked, that removes a \"random\" element in theory."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 239,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "1"
+      ]
+     },
+     "execution_count": 239,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "numbers.pop()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 240,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12}"
+      ]
+     },
+     "execution_count": 240,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "numbers"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "The [clear()](https://docs.python.org/3/library/stdtypes.html#frozenset.clear) method discards all elements but keeps the `set` object alive."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 241,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "numbers.clear()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 242,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "set()"
+      ]
+     },
+     "execution_count": 242,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "numbers"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 243,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "139751998783784"
+      ]
+     },
+     "execution_count": 243,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "id(numbers)  # same memory location as before"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "### Set Operations"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "The arithmetic and relational operators are overloaded with typical set operations from math. The operators have methods that do the equivalent. We omit them for brevity in this sub-section and only show them as comments in the code cells. Both the operators and the methods return *new* `set` objects without modifying the operands.\n",
+    "\n",
+    "We showcase the set operations with easy math examples."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 244,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "numbers = set(range(1, 13))\n",
+    "zero = {0}\n",
+    "evens = set(range(2, 13, 2))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 245,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12}"
+      ]
+     },
+     "execution_count": 245,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "numbers"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 246,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{0}"
+      ]
+     },
+     "execution_count": 246,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "zero"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 247,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{2, 4, 6, 8, 10, 12}"
+      ]
+     },
+     "execution_count": 247,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "evens"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "The bitwise OR operator `|` returns the union of two `set` objects."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 248,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12}"
+      ]
+     },
+     "execution_count": 248,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "zero | numbers  # zero.union(numbers) or reversed order"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Of course, the operators may be *chained*."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 249,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12}"
+      ]
+     },
+     "execution_count": 249,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "zero | numbers | evens  # zero.union(numbers).union(evens) or any possible order"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "To obtain an intersection of two or more `set` objects, we use the bitwise AND operator `&`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 250,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "set()"
+      ]
+     },
+     "execution_count": 250,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "zero & numbers  #  zero.intersection(numbers) or reversed order"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 251,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{2, 4, 6, 8, 10, 12}"
+      ]
+     },
+     "execution_count": 251,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "numbers & evens  # numbers.intersection(evens) or reversed order"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "To calculate a `set` object containing all elements that are in one but not the other `set` object, we use the minus operator `-`. This operation is *not* symmetric."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 252,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{1, 3, 5, 7, 9, 11}"
+      ]
+     },
+     "execution_count": 252,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "numbers - evens  # numbers.difference(evens)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 253,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "set()"
+      ]
+     },
+     "execution_count": 253,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "evens - numbers  # evens.difference(numbers)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "The *symmetric* difference is defined as the `set` object containing all elements that are in one but not both `set` objects. It is calculated with the bitwise XOR operator `^`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 254,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{1, 3, 5, 7, 9, 11}"
+      ]
+     },
+     "execution_count": 254,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "numbers ^ evens  # numbers.symmetric_difference(evens)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 255,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{1, 3, 5, 7, 9, 11}"
+      ]
+     },
+     "execution_count": 255,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "evens ^ numbers  # evens.symmetric_difference(numbers)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "The augmented versions of the four operators (e.g., `|` becomes `|=`) are also defined: They mutate the left operand *in place*."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "### Set Comprehensions"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Python provides a literal notation for **set comprehensions**, or **setcomps** for short, that works exactly like the one for dictionary comprehensions described above except that they use a single loop variable instead of a key-value pair.\n",
+    "\n",
+    "For example, let's create a new `set` object that consists of the squares of all the elements of `numbers`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 256,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{1, 4, 9, 16, 25, 36, 49, 64, 81, 100, 121, 144}"
+      ]
+     },
+     "execution_count": 256,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "{x ** 2 for x in numbers}"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "As before, we may have multiple `for`- and/or `if`-clauses.\n",
+    "\n",
+    "For example, let's only keep the squares if they turn out to be an even number, or ..."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 257,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{4, 16, 36, 64, 100, 144}"
+      ]
+     },
+     "execution_count": 257,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "{x ** 2 for x in numbers if (x ** 2) % 2 == 0}"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "... create a `set` object with all products obtained from the Cartesian product of `numbers` with itself as long as the products are greater than `80`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 258,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{81, 84, 88, 90, 96, 99, 100, 108, 110, 120, 121, 132, 144}"
+      ]
+     },
+     "execution_count": 258,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "{x * y for x in numbers for y in numbers if x * y > 80}"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "### The `frozenset` Type"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "As `set` objects are mutable, they may *not* be used, for example, as keys in a `dict` object. Similar to how we replace `list` with `tuple` objects, we may often use a `frozenset` object instead of an ordinary one. The `frozenset` type is a built-in, and as `frozenset` objects are immutable, the only limitation is that we must specify *all* elements *upon* creation (cf., [documentation](https://docs.python.org/3/library/stdtypes.html#frozenset)).\n",
+    "\n",
+    "`frozenset` objects are created by passing an iterable to the [frozenset()](https://docs.python.org/3/library/functions.html#func-frozenset) built-in. Note that even though `frozenset` objects are hashable, their elements do still *not* come in a predictable order."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 259,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "frozenset({1, 2, 3})"
+      ]
+     },
+     "execution_count": 259,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "frozenset([1, 1, 2, 2, 3, 3])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 260,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "frozenset({1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12})"
+      ]
+     },
+     "execution_count": 260,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "frozenset(numbers)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "## TL;DR"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "`dict` objects are **mutable** one-to-one **mappings** from a set of **key** objects to a set of **value** objects. The association between a key-value pair is also called **item**.\n",
+    "\n",
+    "The items contained in a `dict` object have **no order** that is *predictable*.\n",
+    "\n",
+    "The underlying **data structure** of the `dict` type are **hash tables**. They make key look-ups *extremely* fast by converting the items' keys into *deterministic* hash values specifiying *precisely* one of a fixed number of equally \"wide\" buckets in which an item's references are stored. A limitation is that objects used as keys must be *immutable* (for technical reasons) and *unique* (for practical reasons).\n",
+    "\n",
+    "A `set` object is a **mutable** and **unordered collection** of **immutable** objects. The `set` type mimics sets we know from math."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "## Further Resources"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "This section provides some conference talks that go into great detail regarding the workings of the `dict` type."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 261,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/jpeg": 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+      ],
+      "text/plain": [
+       "<IPython.lib.display.YouTubeVideo at 0x7f1a8c3e9d68>"
+      ]
+     },
+     "execution_count": 261,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from IPython.display import YouTubeVideo\n",
+    "YouTubeVideo(\"C4Kc8xzcA68\", width=\"60%\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 262,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/jpeg": 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\n",
+      "text/html": [
+       "\n",
+       "        <iframe\n",
+       "            width=\"60%\"\n",
+       "            height=\"300\"\n",
+       "            src=\"https://www.youtube.com/embed/66P5FMkWoVU\"\n",
+       "            frameborder=\"0\"\n",
+       "            allowfullscreen\n",
+       "        ></iframe>\n",
+       "        "
+      ],
+      "text/plain": [
+       "<IPython.lib.display.YouTubeVideo at 0x7f1a8c2a9630>"
+      ]
+     },
+     "execution_count": 262,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "YouTubeVideo(\"66P5FMkWoVU\", width=\"60%\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 263,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/jpeg": 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\n",
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+     },
+     "execution_count": 263,
+     "metadata": {},
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+    }
+   ],
+   "source": [
+    "YouTubeVideo(\"npw4s1QTmPg\", width=\"60%\")"
+   ]
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+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
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+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
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+   "nbconvert_exporter": "python",
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+   "sideBar": true,
+   "skip_h1_title": true,
+   "title_cell": "Table of Contents",
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+   "toc_position": {
+    "height": "calc(100% - 180px)",
+    "left": "10px",
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+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/static/fibonacci_call_graph.png b/static/fibonacci_call_graph.png
new file mode 100644
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HcmV?d00001


From 7fee5f553000aa46edc8f4874e5ffdce88a507b5 Mon Sep 17 00:00:00 2001
From: Alexander Hess <alexander@webartifex.biz>
Date: Wed, 20 Nov 2019 11:02:48 +0100
Subject: [PATCH 051/242] Add review and exercises for notebook 08

---
 08_mappings_review_and_exercises.ipynb | 596 +++++++++++++++++++++++++
 1 file changed, 596 insertions(+)
 create mode 100644 08_mappings_review_and_exercises.ipynb

diff --git a/08_mappings_review_and_exercises.ipynb b/08_mappings_review_and_exercises.ipynb
new file mode 100644
index 0000000..c05004e
--- /dev/null
+++ b/08_mappings_review_and_exercises.ipynb
@@ -0,0 +1,596 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n",
+    "# Chapter 8: Mappings & Sets"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Content Review"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Read [Chapter 8](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/08_mappings.ipynb) of the book. Then work through the twelve review questions."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Essay Questions "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Answer the following questions briefly with *at most* 300 characters per question!"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q1**: `dict` objects are well-suited **to model** discrete mathematical **functions** and to approximate continuous ones. What property of dictionaries is the basis for that claim, and how does it relate to functions in the mathematical sense?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q2**: Explain why **hash tables** are  a **trade-off** between **computational speed** and **memory** usage!"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q3:** The `dict` type is an **iterable** that **contains** a **finite** number of key-value pairs. Despite that, why is it *not* considered a **sequence**?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q4**: Whereas *key* **look-ups** in a `dict` object run in so-called **[constant time](https://en.wikipedia.org/wiki/Time_complexity#Constant_time)** (i.e., *extremely* fast), that does not hold for *reverse* look-ups. Why is that?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q5**: Why is it conceptually correct that the Python core developers do not implement **slicing** with the `[]` operator for `dict` objects?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q6**: **Memoization** is an essential concept to know to solve problems in the real world. Together with the idea of **recursion**, it enables us to solve problems in a \"backwards\" fashion *effectively*.\n",
+    "\n",
+    "\n",
+    "Compare the **recursive** formulation of `fibonacci()` in [Chapter 8](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/08_mappings.ipynb#\"Easy-at-third-Glance\"-Example:-Fibonacci-Numbers-%28revisited%29), the \"*Easy at third Glance*\" example, with the **iterative** version in [Chapter 4](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/04_iteration.ipynb#\"Hard-at-first-Glance\"-Example:-Fibonacci-Numbers-%28revisited%29), the \"*Hard at first Glance*\" example!\n",
+    "\n",
+    "How are they similar and how do they differ?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q7**: How are the `set` and the `dict` type related? How could we use the latter to mimic the former?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### True / False Questions"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Motivate your answer with *one short* sentence!"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q8**: We may *not* put `dict` objects inside other `dict` objects because they are **mutable**."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q9**: **Mutable** objects (e.g., `list`) may generally *not* be used as keys in a `dict` object. However, if we collect, for example, `list` objects in a `tuple` object, the composite object becomes **hashable**."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q10**: **Mutability** of a `dict` object works until the underlying hash table becomes too crowded. Then, we cannot insert any items any more making the `dict` object effectively **immutable**. Luckily, that almost never happens in practice."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q11**: A `dict` object's [update()](https://docs.python.org/3/library/stdtypes.html#dict.update) method only inserts key-value pairs whose key is *not* yet in the `dict` object. So, it does *not* overwrite anything."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q12**: The `set` type is both a mapping and a sequence."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Coding Exercises"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Working with Nested Data"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Let's write some code to analyze the historic soccer game [Brazil vs. Germany](https://en.wikipedia.org/wiki/Brazil_v_Germany_%282014_FIFA_World_Cup%29) during the 2014 World Cup.\n",
+    "\n",
+    "Below, `players` consists of two nested `dict` objects, one for each team, that hold `tuple` objects (i.e., records) with information on the players. Besides the jersey number, name, and position, each `tuple` objects contains a `list` object with the times when the player scored."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "players = {\n",
+    "    \"Brazil\": [\n",
+    "        (12, \"JĂşlio CĂ©sar\", \"Goalkeeper\", []),\n",
+    "        (4, \"David Luiz\", \"Defender\", []),\n",
+    "        (6, \"Marcelo\", \"Defender\", []),\n",
+    "        (13, \"Dante\", \"Defender\", []),\n",
+    "        (23, \"Maicon\", \"Defender\", []),\n",
+    "        (5, \"Fernandinho\", \"Midfielder\", []),\n",
+    "        (7, \"Hulk\", \"Midfielder\", []),\n",
+    "        (8, \"Paulinho\", \"Midfielder\", []),\n",
+    "        (11, \"Oscar\", \"Midfielder\", [90]),\n",
+    "        (16, \"Ramires\", \"Midfielder\", []),\n",
+    "        (17, \"Luiz Gustavo\", \"Midfielder\", []),\n",
+    "        (19, \"Willian\", \"Midfielder\", []),\n",
+    "        (9, \"Fred\", \"Striker\", []),\n",
+    "    ],\n",
+    "    \"Germany\": [\n",
+    "        (1, \"Manuel Neuer\", \"Goalkeeper\", []),\n",
+    "        (4, \"Benedikt Höwedes\", \"Defender\", []),\n",
+    "        (5, \"Mats Hummels\", \"Defender\", []),\n",
+    "        (16, \"Philipp Lahm\", \"Defender\", []),\n",
+    "        (17, \"Per Mertesacker\", \"Defender\", []),\n",
+    "        (20, \"JĂ©rĂ´me Boateng\", \"Defender\", []),\n",
+    "        (6, \"Sami Khedira\", \"Midfielder\", [29]),\n",
+    "        (7, \"Bastian Schweinsteiger\", \"Midfielder\", []),\n",
+    "        (8, \"Mesut Ă–zil\", \"Midfielder\", []),\n",
+    "        (13, \"Thomas MĂĽller\", \"Midfielder\", [11]),\n",
+    "        (14, \"Julian Draxler\", \"Midfielder\", []),\n",
+    "        (18, \"Toni Kroos\", \"Midfielder\", [24, 26]),\n",
+    "        (9, \"André Schürrle\", \"Striker\", [69, 79]),\n",
+    "        (11, \"Miroslav Klose\", \"Striker\", [23]),\n",
+    "    ],\n",
+    "}"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q13.1**: Write a dictionary comprehension to derive a new `dict` object, called `brazilian_players`, that maps a Brazilian player's name to his position!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "brazilian_players = {...}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "brazilian_players"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q13.2**: Generalize the code fragment into a `get_players()` function: Passed a `team` name, it returns a `dict` object like `brazilian_players`. Verify that the function works for the German team!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def get_players(team):\n",
+    "    ..."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "get_players(\"Germany\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Often, we are given a `dict` object like the one returned from `get_players()`: It is characterized by the observation that a large set of unique keys (i.e., the players' names) is mapped onto a smaller set of non-unique values (i.e., the positions).\n",
+    "\n",
+    "**Q13.3**: Create a generic `invert()` function that swaps the keys and values of a `mapping` argument passed to it and returns them in a *new* `dict` object! Ensure that *no* key gets lost. Verify your implementation with the `brazilian_players` dictionary!\n",
+    "\n",
+    "Hints: Think of this as a grouping operation. The *new* values are `list` or `tuple` objects that hold the original keys. You may want to use either the the [defaultdict](https://docs.python.org/3/library/collections.html#collections.defaultdict) type from the [collections](https://docs.python.org/3/library/collections.html) module in the [standard library](https://docs.python.org/3/library/index.html) or the [setdefault()](https://docs.python.org/3/library/stdtypes.html#dict.setdefault) method of the ordinary `dict` type."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def invert(mapping):\n",
+    "    ..."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "invert(brazilian_players)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q13.4**: Write a `score_at_minute()` function: It takes two arguments, `team` and `minute`, and returns the number of goals the `team` has scored up until this time in the game.\n",
+    "\n",
+    "Hints: The function may reference the global `players` for simplicity. Earn bonus points if you can write this in a one-line expression using some *reduction* function and a *generator expression*."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def score_at_minute(team, minute):\n",
+    "    ..."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The score at half time was:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "score_at_minute(\"Brazil\", 45)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "score_at_minute(\"Germany\", 45)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The final score was:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "score_at_minute(\"Brazil\", 90)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "score_at_minute(\"Germany\", 90)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q13.5**: Write a `goals_by_player()` function: It takes an argument like the global `players`, and returns a `dict` object mapping the players to the number of goals they scored.\n",
+    "\n",
+    "Hints: Do *not* \"hard code\" the names of the teams! Earn bonus points if you can solve it in a single dictionary comprehension."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def goals_by_player(players):\n",
+    "    ..."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "goals_by_player(players)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q13.6**: Write a *dictionary comprehension* to filter out the players who did *not* score from the preceding result. Then, write a *set comprehension* that does the same but discards the number of goals scored.\n",
+    "\n",
+    "Hints: Reference the `goals_by_player()` function from before."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "{...}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "{...}"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q13.7**: Write a `all_goals()` function: It takes one argument like the global `players` and returns a `list` object containing $2$-element `tuple` objects, where the first element is the minute a player scored and the second his name. The list should be sorted by the time.\n",
+    "\n",
+    "Hints: You may want to use either the built-in [sorted()](https://docs.python.org/3/library/functions.html#sorted) function or the `list` type's [sort()](https://docs.python.org/3/library/stdtypes.html#list.sort) method. Earn bonus points if you can write a one-line expression with a *generator expression*."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def all_goals(players):\n",
+    "    ..."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "all_goals(players)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q13.8**: Lastly, write a `summary()` function: It takes one argument like the global `players` and prints out a concise report of the goals, the score at the half, and the final result.\n",
+    "\n",
+    "Hints: Use the `all_goals()` and `score_at_minute()` functions from before.\n",
+    "\n",
+    "The output should look similar to this:\n",
+    "```\n",
+    "12' Gerd MĂĽller scores\n",
+    "...\n",
+    "HALFTIME: TeamA 1 TeamB 2\n",
+    "77' Ronaldo scores\n",
+    "...\n",
+    "FINAL: TeamA 1 TeamB 3\n",
+    "```"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def summary(players):\n",
+    "    ..."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "summary(players)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.3"
+  },
+  "toc": {
+   "base_numbering": 1,
+   "nav_menu": {},
+   "number_sections": false,
+   "sideBar": true,
+   "skip_h1_title": true,
+   "title_cell": "Table of Contents",
+   "title_sidebar": "Contents",
+   "toc_cell": false,
+   "toc_position": {},
+   "toc_section_display": false,
+   "toc_window_display": false
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

From 615c3f1cc34c62a39e65e5b796c59d4864312244 Mon Sep 17 00:00:00 2001
From: Alexander Hess <alexander@webartifex.biz>
Date: Wed, 20 Nov 2019 11:05:04 +0100
Subject: [PATCH 052/242] Bump version number

---
 pyproject.toml | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/pyproject.toml b/pyproject.toml
index 333c176..f25a893 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -1,6 +1,6 @@
 [tool.poetry]
 name = "intro-to-python"
-version = "0.5.0"
+version = "0.6.0"
 description = "An introduction to Python and programming for wanna-be data scientists"
 authors = ["Alexander Hess <alexander@webartifex.biz>"]
 license = "MIT"

From 8af0280271220c2b384b9596a82883c98b416003 Mon Sep 17 00:00:00 2001
From: Alexander Hess <alexander@webartifex.biz>
Date: Tue, 28 Jan 2020 11:07:14 +0100
Subject: [PATCH 053/242] Update the Python version in README.md

---
 README.md | 9 +++++----
 1 file changed, 5 insertions(+), 4 deletions(-)

diff --git a/README.md b/README.md
index 97c9120..a227cb2 100644
--- a/README.md
+++ b/README.md
@@ -1,4 +1,5 @@
-**Important**: The notebooks are being added throughout the fall semester of 2019!
+**Important**: The notebooks are being updated and amended throughout the
+spring semester of 2020!
 
 # An Introduction to Python and Programming
 
@@ -50,7 +51,7 @@ It is only expected that the student has:
 
 ## Installation
 
-To follow this course, a working installation of **Python 3.6** or higher is
+To follow this course, a working installation of **Python 3.7** or higher is
 expected.
 
 A popular and beginner friendly way is to install the [Anaconda Distribution](
@@ -58,7 +59,7 @@ https://www.anaconda.com/distribution/) that not only ships Python but comes
 pre-packaged with a lot of third-party libraries from the so-called
 "scientific stack".
 Just go to the [download](https://www.anaconda.com/distribution/#download-section)
-section and install the latest version (i.e., *2019-07* with Python 3.7 at the
+section and install the latest version (i.e., *2019-10* with Python 3.7 at the
 time of this writing) for your operating system.
 
 Then, among others, you will find an entry "Jupyter Notebook" in your start
@@ -84,7 +85,7 @@ used without a mouse by typing commands into it.
 
 <img src="static/terminal.png" width="50%" align="center">
 
-Assuming that you already have a working version of Python 3.6 or higher
+Assuming that you already have a working version of Python 3.7 or higher
 installed (cf., the official [download page](https://www.python.org/downloads/)),
 the following summarizes the commands to be typed into a terminal emulator to
 get the course materials up and running on a local machine without Anaconda.

From e24cf31104dda53acf752414987183517b40f695 Mon Sep 17 00:00:00 2001
From: Alexander Hess <alexander@webartifex.biz>
Date: Tue, 28 Jan 2020 10:48:37 +0100
Subject: [PATCH 054/242] Restructure *.ipynb files

- split review and exercises into files on their own
- update the contents overviews to include links to
  reviews and exercises
---
 ...t_up.ipynb => 00_start_up_00_content.ipynb |   35 +-
 ...cises.ipynb => 00_start_up_10_review.ipynb |   81 +-
 00_start_up_20_exercises.ipynb                |  115 ++
 ...ents.ipynb => 01_elements_00_content.ipynb |  199 ++--
 ...cises.ipynb => 01_elements_10_review.ipynb |   96 +-
 01_elements_20_exercises.ipynb                |  139 +++
 ...ons.ipynb => 02_functions_00_content.ipynb |  564 +++++----
 ...ises.ipynb => 02_functions_10_review.ipynb |  192 +--
 02_functions_20_exercises.ipynb               |  234 ++++
 ....ipynb => 03_conditionals_00_content.ipynb |  357 +++---
 03_conditionals_10_review.ipynb               |  214 ++++
 ...pynb => 03_conditionals_20_exercises.ipynb |  195 +---
 ...ion.ipynb => 04_iteration_00_content.ipynb |  162 ++-
 04_iteration_10_review.ipynb                  |  281 +++++
 ...s.ipynb => 04_iteration_20_exercises.ipynb |  310 +----
 ...mbers.ipynb => 05_numbers_00_content.ipynb | 1032 ++++++++---------
 05_numbers_10_review.ipynb                    |  347 ++++++
 05_numbers_20_exercises.ipynb                 |  412 +++++++
 05_numbers_review_and_exercises.ipynb         |  706 -----------
 06_text.ipynb => 06_text_00_content.ipynb     |  586 ++++++----
 ...exercises.ipynb => 06_text_10_review.ipynb |    8 +-
 ...ces.ipynb => 07_sequences_00_content.ipynb |  109 +-
 07_sequences_10_review.ipynb                  |  397 +++++++
 ...s.ipynb => 07_sequences_20_exercises.ipynb |  438 +------
 ...ings.ipynb => 08_mappings_00_content.ipynb |  175 +--
 08_mappings_10_review.ipynb                   |  261 +++++
 ...es.ipynb => 08_mappings_20_exercises.ipynb |  242 +---
 README.md                                     |   51 +-
 28 files changed, 4335 insertions(+), 3603 deletions(-)
 rename 00_start_up.ipynb => 00_start_up_00_content.ipynb (94%)
 rename 00_start_up_review_and_exercises.ipynb => 00_start_up_10_review.ipynb (60%)
 create mode 100644 00_start_up_20_exercises.ipynb
 rename 01_elements.ipynb => 01_elements_00_content.ipynb (97%)
 rename 01_elements_review_and_exercises.ipynb => 01_elements_10_review.ipynb (64%)
 create mode 100644 01_elements_20_exercises.ipynb
 rename 02_functions.ipynb => 02_functions_00_content.ipynb (91%)
 rename 02_functions_review_and_exercises.ipynb => 02_functions_10_review.ipynb (50%)
 create mode 100644 02_functions_20_exercises.ipynb
 rename 03_conditionals.ipynb => 03_conditionals_00_content.ipynb (95%)
 create mode 100644 03_conditionals_10_review.ipynb
 rename 03_conditionals_review_and_exercises.ipynb => 03_conditionals_20_exercises.ipynb (53%)
 rename 04_iteration.ipynb => 04_iteration_00_content.ipynb (96%)
 create mode 100644 04_iteration_10_review.ipynb
 rename 04_iteration_review_and_exercises.ipynb => 04_iteration_20_exercises.ipynb (64%)
 rename 05_numbers.ipynb => 05_numbers_00_content.ipynb (96%)
 create mode 100644 05_numbers_10_review.ipynb
 create mode 100644 05_numbers_20_exercises.ipynb
 delete mode 100644 05_numbers_review_and_exercises.ipynb
 rename 06_text.ipynb => 06_text_00_content.ipynb (90%)
 rename 06_text_review_and_exercises.ipynb => 06_text_10_review.ipynb (94%)
 rename 07_sequences.ipynb => 07_sequences_00_content.ipynb (97%)
 create mode 100644 07_sequences_10_review.ipynb
 rename 07_sequences_review_and_exercises.ipynb => 07_sequences_20_exercises.ipynb (70%)
 rename 08_mappings.ipynb => 08_mappings_00_content.ipynb (96%)
 create mode 100644 08_mappings_10_review.ipynb
 rename 08_mappings_review_and_exercises.ipynb => 08_mappings_20_exercises.ipynb (56%)

diff --git a/00_start_up.ipynb b/00_start_up_00_content.ipynb
similarity index 94%
rename from 00_start_up.ipynb
rename to 00_start_up_00_content.ipynb
index 9aed12d..6e3d84a 100644
--- a/00_start_up.ipynb
+++ b/00_start_up_00_content.ipynb
@@ -152,9 +152,9 @@
     }
    },
    "source": [
-    "To \"read\" this book in the most meaningful way, a working installation of **Python 3.6** or higher is needed.\n",
+    "To \"read\" this book in the most meaningful way, a working installation of **Python 3.7** or higher is expected.\n",
     "\n",
-    "A popular and beginner-friendly way is to install the [Anaconda Distribution](https://www.anaconda.com/distribution/) that not only ships Python and the standard library but comes pre-packaged with a lot of third-party libraries from the so-called \"scientific stack.\" Just go to the [download](https://www.anaconda.com/download/) page and install the latest version (i.e., *2019-07* with Python 3.7 at the time of this writing) for your operating system.\n",
+    "A popular and beginner-friendly way is to install the [Anaconda Distribution](https://www.anaconda.com/distribution/) that not only ships Python and the standard library but comes pre-packaged with a lot of third-party libraries from the so-called \"scientific stack.\" Just go to the [download](https://www.anaconda.com/download/) page and install the latest version (i.e., *2019-10* with Python 3.7 at the time of this writing) for your operating system.\n",
     "\n",
     "Then, among others, you find an entry \"Jupyter Notebook\" in your start menu like below. Click on it to open a new tab in your web browser where you can switch between folders as you could in your computer's default file browser."
    ]
@@ -330,7 +330,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Python 3.7.3\r\n"
+      "Python 3.7.4\n"
      ]
     }
    ],
@@ -649,7 +649,7 @@
     }
    },
    "source": [
-    "Even popular news and media outlets notice the recent popularity of Python: [Economist](https://www.economist.com/graphic-detail/2018/07/26/python-is-becoming-the-worlds-most-popular-coding-language), [Huffington Post](https://www.huffingtonpost.com/entry/why-python-is-the-best-programming-language-with-which_us_59ef8f62e4b04809c05011b9), [TechRepublic](https://www.techrepublic.com/article/why-python-is-so-popular-with-developers-3-reasons-the-language-has-exploded/), and [QZ](https://qz.com/1408660/the-rise-of-python-as-seen-through-a-decade-of-stack-overflow/)."
+    "[IEEE Sprectrum](https://spectrum.ieee.org/computing/software/the-top-programming-languages-2019) provides a more recent comparison of programming language's popularity. Even news and media outlets notice the recent popularity of Python: [Economist](https://www.economist.com/graphic-detail/2018/07/26/python-is-becoming-the-worlds-most-popular-coding-language), [Huffington Post](https://www.huffingtonpost.com/entry/why-python-is-the-best-programming-language-with-which_us_59ef8f62e4b04809c05011b9), [TechRepublic](https://www.techrepublic.com/article/why-python-is-so-popular-with-developers-3-reasons-the-language-has-exploded/), and [QZ](https://qz.com/1408660/the-rise-of-python-as-seen-through-a-decade-of-stack-overflow/)."
    ]
   },
   {
@@ -771,14 +771,14 @@
     }
    },
    "source": [
-    "**Part 1: Expressing Logic**\n",
+    "**Part A: Expressing Logic**\n",
     "\n",
     "- What is a programming language? What kind of words exist?\n",
-    "  1. [Elements of a Program](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/01_elements.ipynb)\n",
-    "  2. [Functions & Modularization](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/02_functions.ipynb)\n",
+    "  - *Chapter 1*: [Elements of a Program](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/01_elements_00_content.ipynb)\n",
+    "  - *Chapter 2*: [Functions & Modularization](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/02_functions_00_content.ipynb)\n",
     "- What is the flow of execution? How can we form sentences from words?\n",
-    "  3. [Conditionals & Exceptions](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/03_conditionals.ipynb)\n",
-    "  4. [Recursion & Looping](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/04_iteration.ipynb)"
+    "  - *Chapter 3*: [Conditionals & Exceptions](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/03_conditionals_00_content.ipynb)\n",
+    "  - *Chapter 4*: [Recursion & Looping](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/04_iteration_00_content.ipynb)"
    ]
   },
   {
@@ -789,15 +789,16 @@
     }
    },
    "source": [
-    "**Part 2: Managing Data and Memory**\n",
+    "**Part B: Managing Data and Memory**\n",
     "\n",
     "- How is data stored in memory?\n",
-    "  5. [Bits & Numbers](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/05_numbers.ipynb)\n",
-    "  6. [Bytes & Text](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/06_text.ipynb)\n",
-    "  7. [Sequential Data](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/07_sequences.ipynb)\n",
-    "  8. [Mappings & Sets](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/08_mappings.ipynb)\n",
+    "  - *Chapter 5*: [Bits & Numbers](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/05_numbers_00_content.ipynb)\n",
+    "  - *Chapter 6*: [Bytes & Text](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/06_text_00_content.ipynb)\n",
+    "  - *Chapter 7*: [Sequential Data](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/07_sequences_00_content.ipynb)\n",
+    "  - *Chapter 8*: [Mappings & Sets](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/08_mappings_00_content.ipynb)\n",
+    "  - *Chapter 9*: [Arrays & Dataframes](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/09_arrays_00_content.ipynb)\n",
     "- How can we create custom data types?\n",
-    "  9. Object-Orientation"
+    "  - *Chapter 10*: Object-Orientation"
    ]
   },
   {
@@ -863,7 +864,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.3"
+   "version": "3.7.4"
   },
   "livereveal": {
    "auto_select": "code",
@@ -891,5 +892,5 @@
   }
  },
  "nbformat": 4,
- "nbformat_minor": 2
+ "nbformat_minor": 4
 }
diff --git a/00_start_up_review_and_exercises.ipynb b/00_start_up_10_review.ipynb
similarity index 60%
rename from 00_start_up_review_and_exercises.ipynb
rename to 00_start_up_10_review.ipynb
index 109a2fc..e8cb215 100644
--- a/00_start_up_review_and_exercises.ipynb
+++ b/00_start_up_10_review.ipynb
@@ -11,14 +11,14 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Content Review"
+    "## Review"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Read [Chapter 0](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/00_start_up.ipynb) of the book. Then work through the ten review questions."
+    "Read [Chapter 0](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/00_start_up_00_content.ipynb) of the book. Then, work through the questions below."
    ]
   },
   {
@@ -32,14 +32,14 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Answer the following questions briefly with *at most* 300 characters per question!"
+    "Answer the following questions *briefly*!"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "**Q1**: Describe the difference between the terms *programming* and *computer science*!"
+    "**Q1**: Describe the difference between the terms **programming** and **computer science**!"
    ]
   },
   {
@@ -53,7 +53,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "**Q2**: Explain what is a *pull request* and elaborate on how this concept fits a *distributed* organization of work!"
+    "**Q2**: Explain what is a **pull request** and elaborate on how this concept fits a *distributed* organization of work!"
    ]
   },
   {
@@ -67,7 +67,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "**Q3**: In what sense are open-source communities democracies? How are they near-\"perfect\" [meritocracies](https://en.wikipedia.org/wiki/Meritocracy)? How is open-source software development similar to academia?"
+    "**Q3**: In what sense are **open-source** communities democracies? How are they near-perfect [meritocracies](https://en.wikipedia.org/wiki/Meritocracy)? How is open-source software development similar to academia?"
    ]
   },
   {
@@ -81,7 +81,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "**Q4**: What is a significant advantage of a \"slow\" programming language like Python over a faster one like C?"
+    "**Q4**: What is a *significant* advantage of a \"slow\" programming language like Python over a faster one like C?"
    ]
   },
   {
@@ -182,69 +182,6 @@
     "**Q10**: The primary purpose of PEPs is to regulate how code should be documented and styled."
    ]
   },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    " "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## \"Coding\" Exercises"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Mastering Markdown"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Briefly review GitHub's guide on [Mastering Markdown](https://guides.github.com/features/mastering-markdown/) and create nicely formatted \"text\" cells below!"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**Q11**: Check the latest [Bundesliga standings](https://www.bundesliga.com/en/bundesliga/table) and provide a table of the top three teams with the following four columns: rank, team name, games played, and points scored. Render the rank in **bold**, make the team name a clickable link (to the team's website), and put both the games played and points scored in *italics*. The header row should be visually different from the three rows with the teams' information."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    " "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**Q12**: The quote \"Education is what remains after one has forgotten what one has learned in school\" is attributed to Albert Einstein. Display the author and his quote appropriately!"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    " "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**Q13**: Integrate this image (https://i.ytimg.com/vi/-BoSRlzy9c4/maxresdefault.jpg) of the delicious dessert **milk rice** into this notebook."
-   ]
-  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -269,7 +206,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.3"
+   "version": "3.7.4"
   },
   "toc": {
    "base_numbering": 1,
@@ -286,5 +223,5 @@
   }
  },
  "nbformat": 4,
- "nbformat_minor": 2
+ "nbformat_minor": 4
 }
diff --git a/00_start_up_20_exercises.ipynb b/00_start_up_20_exercises.ipynb
new file mode 100644
index 0000000..afd7f26
--- /dev/null
+++ b/00_start_up_20_exercises.ipynb
@@ -0,0 +1,115 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Chapter 0: Start up"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## \"Coding\" Exercises"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Read [Chapter 0](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/00_start_up_00_content.ipynb) of the book. Then, work through the exercises below."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Mastering Markdown"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Briefly review GitHub's guide on [Mastering Markdown](https://guides.github.com/features/mastering-markdown/) and create nicely formatted \"text\" cells below!"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q1**: Check the latest [Bundesliga standings](https://www.bundesliga.com/en/bundesliga/table) and provide a table of the top three teams with the following four columns: rank, team name, games played, and points scored. Render the rank in **bold**, make the team name a clickable link (to the team's website), and put both the games played and points scored in *italics*. The header row should be visually different from the three rows with the teams' information."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q2**: The quote \"Education is what remains after one has forgotten what one has learned in school\" is attributed to Albert Einstein. Display the author and his quote appropriately!"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q3**: Integrate this image (https://i.ytimg.com/vi/-BoSRlzy9c4/maxresdefault.jpg) of the delicious dessert **milk rice** into this notebook."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " "
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.4"
+  },
+  "toc": {
+   "base_numbering": 1,
+   "nav_menu": {},
+   "number_sections": false,
+   "sideBar": true,
+   "skip_h1_title": true,
+   "title_cell": "Table of Contents",
+   "title_sidebar": "Contents",
+   "toc_cell": false,
+   "toc_position": {},
+   "toc_section_display": false,
+   "toc_window_display": false
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/01_elements.ipynb b/01_elements_00_content.ipynb
similarity index 97%
rename from 01_elements.ipynb
rename to 01_elements_00_content.ipynb
index 07d6774..38b63b1 100644
--- a/01_elements.ipynb
+++ b/01_elements_00_content.ipynb
@@ -936,9 +936,9 @@
    "source": [
     "Python is a so-called **object-oriented** language, which is a paradigm of organizing a program's memory.\n",
     "\n",
-    "An **object** may be viewed as a \"bag\" of $0$s and $1$s in a distinct memory location. The concrete $0$s and $1$s in a bag portray the idea of the object's **value**, and there exist different **types** of bags that each come with associated rules as to how the $0$s and $1$s are interpreted and may be worked with.\n",
+    "An **object** may be viewed as a \"bag\" of $0$s and $1$s in a given memory location. The $0$s and $1$s in a bag make up the object's **value**. There exist different **types** of bags: Each type comes with distinct rules how the $0$s and $1$s are interpreted and may be worked with.\n",
     "\n",
-    "So, an object *always* has **three** main characteristics. Let's look at the following examples and work them out."
+    "So, an object *always* has *three* main characteristics. Let's look at the following examples and work them out."
    ]
   },
   {
@@ -990,7 +990,7 @@
     {
      "data": {
       "text/plain": [
-       "140398796190992"
+       "140173037405648"
       ]
      },
      "execution_count": 28,
@@ -1014,7 +1014,7 @@
     {
      "data": {
       "text/plain": [
-       "140398796375888"
+       "140173037405584"
       ]
      },
      "execution_count": 29,
@@ -1038,7 +1038,7 @@
     {
      "data": {
       "text/plain": [
-       "140398796056176"
+       "140173037234160"
       ]
      },
      "execution_count": 30,
@@ -1082,7 +1082,7 @@
     }
    },
    "source": [
-    "`a` and `d` indeed have the same value as is checked with the **equality operator** `==`. The resulting `True` (and the `False` further below) is yet another data type, a so-called **boolean**. We look into them closely in [Chapter 3](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/03_conditionals.ipynb#Boolean-Expressions)."
+    "`a` and `d` indeed have the same value as is checked with the **equality operator** `==`. The resulting `True` (and the `False` further below) is yet another data type, a so-called **boolean**. We look into them closely in [Chapter 3](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/03_conditionals_00_content.ipynb#Boolean-Expressions)."
    ]
   },
   {
@@ -1259,7 +1259,7 @@
    "source": [
     "Different types imply different behaviors for the objects. The `b` object, for example, can be \"asked\" if it could also be interpreted as an `int` with the [is_integer()](https://docs.python.org/3/library/stdtypes.html#float.is_integer) \"functionality\" that comes with every `float` object.\n",
     "\n",
-    "Formally, we call such type-specific functionalities **methods** (to differentiate them from functions) and we formally introduce them in Chapter 9. For now, it suffices to know that we access them using the **dot operator** `.`. Of course, `b` could be converted into an `int`, which the boolean value `True` tells us."
+    "Formally, we call such type-specific functionalities **methods** (to differentiate them from functions) and we formally introduce them in Chapter 10. For now, it suffices to know that we access them using the **dot operator** `.`. Of course, `b` could be converted into an `int`, which the boolean value `True` tells us."
    ]
   },
   {
@@ -1448,7 +1448,7 @@
     }
    },
    "source": [
-    "Almost trivially, every object also has a value to which it \"evaluates\" when referenced. We think of the value as the **conceptual idea** of what the $0$s and $1$s in memory mean to humans as machines cannot see beyond $0$s and $1$s.\n",
+    "Almost trivially, every object also has a value to which it \"evaluates\" when referenced. We think of the value as the **conceptual idea** of what the $0$s and $1$s in the bag mean to *humans* as machines cannot see beyond $0$s and $1$s.\n",
     "\n",
     "For built-in data types, Python prints out the object's value as a so-called **[literal](https://docs.python.org/3/reference/lexical_analysis.html#literals)**: This means that we can copy and paste the output back into a code cell to create a *new* object with the *same* value."
    ]
@@ -1854,7 +1854,7 @@
        "        "
       ],
       "text/plain": [
-       "<IPython.lib.display.YouTubeVideo at 0x7fb124565080>"
+       "<IPython.lib.display.YouTubeVideo at 0x7f7c94098310>"
       ]
      },
      "execution_count": 52,
@@ -1875,7 +1875,7 @@
     }
    },
    "source": [
-    "For example, while the above code to calculate the average of the even numbers in `[7, 11, 8, 5, 3, 12, 2, 6, 9, 10, 1, 4]` is correct, a Pythonista would rewrite it in a more \"Pythonic\" way and use the [sum()](https://docs.python.org/3/library/functions.html#sum) and [len()](https://docs.python.org/3/library/functions.html#len) (= \"length\") [built-in functions](https://docs.python.org/3/library/functions.html) (cf., [Chapter 2](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/02_functions.ipynb#Built-in-Functions)) as well as a so-called **list comprehension** (cf., [Chapter 7](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/07_sequences.ipynb#List-Comprehensions)). Pythonic code runs faster in many cases and is less error-prone."
+    "For example, while the above code to calculate the average of the even numbers in `[7, 11, 8, 5, 3, 12, 2, 6, 9, 10, 1, 4]` is correct, a Pythonista would rewrite it in a more \"Pythonic\" way and use the [sum()](https://docs.python.org/3/library/functions.html#sum) and [len()](https://docs.python.org/3/library/functions.html#len) (= \"length\") [built-in functions](https://docs.python.org/3/library/functions.html) (cf., [Chapter 2](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/02_functions_00_content.ipynb#Built-in-Functions)) as well as a so-called **list comprehension** (cf., [Chapter 7](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/07_sequences_00_content.ipynb#List-Comprehensions)). Pythonic code runs faster in many cases and is less error-prone."
    ]
   },
   {
@@ -2077,7 +2077,7 @@
     "\n",
     "At the same time, for a beginner's course, it is often easier to code linearly.\n",
     "\n",
-    "In real data science projects, one would probably employ a mixed approach and put reusable code into so-called Python modules (i.e., *.py* files; cf., [Chapter 2](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/02_functions.ipynb#Local-Modules-and-Packages)) and then use Jupyter notebooks to build up a linear report or storyline for a business argument to be made."
+    "In real data science projects, one would probably employ a mixed approach and put reusable code into so-called Python modules (i.e., *.py* files; cf., [Chapter 2](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/02_functions_00_content.ipynb#Local-Modules-and-Packages)) and then use Jupyter notebooks to build up a linear report or storyline for a business argument to be made."
    ]
   },
   {
@@ -2367,7 +2367,7 @@
     }
    },
    "source": [
-    "Some variables magically exist when we start a Python process or are added by Jupyter. We may safely ignore the former until Chapter 9 and the latter for good."
+    "Some variables magically exist when we start a Python process or are added by Jupyter. We may safely ignore the former until Chapter 10 and the latter for good."
    ]
   },
   {
@@ -2818,7 +2818,7 @@
    "source": [
     "Let's change the first element of `x`.\n",
     "\n",
-    "[Chapter 7](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/07_sequences.ipynb#The-list-Type) discusses lists in more depth. For now, let's view a `list` object as some sort of **container** that holds an arbitrary number of references to other objects and treat the brackets `[]` attached to it as just another operator, called the **indexing operator**. `x[0]` instructs Python to first follow the reference from the global list of all names to the `x` object. Then, it follows the first reference it finds there to the `1` object. The indexing operator must be an operator as we merely read the first element and do not change anything in memory.\n",
+    "[Chapter 7](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/07_sequences_00_content.ipynb#The-list-Type) discusses lists in more depth. For now, let's view a `list` object as some sort of **container** that holds an arbitrary number of references to other objects and treat the brackets `[]` attached to it as just another operator, called the **indexing operator**. `x[0]` instructs Python to first follow the reference from the global list of all names to the `x` object. Then, it follows the first reference it finds there to the `1` object. The indexing operator must be an operator as we merely read the first element and do not change anything in memory.\n",
     "\n",
     "Note how Python **begins counting at 0**. This is not the case for many other languages, for example, [MATLAB](https://en.wikipedia.org/wiki/MATLAB), [R](https://en.wikipedia.org/wiki/R_%28programming_language%29), or [Stata](https://en.wikipedia.org/wiki/Stata). To understand why this makes sense, see this short [note](https://www.cs.utexas.edu/users/EWD/transcriptions/EWD08xx/EWD831.html) by one of the all-time greats in computer science, the late [Edsger Dijkstra](https://en.wikipedia.org/wiki/Edsger_W._Dijkstra)."
    ]
@@ -3182,65 +3182,6 @@
     "__name__"
    ]
   },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "source": [
-    "### The big Picture"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "source": [
-    "This PyCon talk by [Ned Batchelder](https://nedbatchelder.com/), a well-known Pythonista and the organizer of the [Python User Group](https://www.meetup.com/bostonpython/) in Boston, summarizes all situations where some sort of assignment is done in Python. The content is intermediate, and, thus, it might be worthwhile to come back to this talk at a later point in time. However, the contents should be known by everyone claiming to be proficient in Python."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 95,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "outputs": [
-    {
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-     },
-     "execution_count": 95,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from IPython.display import YouTubeVideo\n",
-    "YouTubeVideo(\"_AEJHKGk9ns\", width=\"60%\")"
-   ]
-  },
   {
    "cell_type": "markdown",
    "metadata": {
@@ -3271,7 +3212,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 96,
+   "execution_count": 95,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -3284,7 +3225,7 @@
        "123"
       ]
      },
-     "execution_count": 96,
+     "execution_count": 95,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3295,7 +3236,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 97,
+   "execution_count": 96,
    "metadata": {},
    "outputs": [
     {
@@ -3304,7 +3245,7 @@
        "42"
       ]
      },
-     "execution_count": 97,
+     "execution_count": 96,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3322,7 +3263,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 98,
+   "execution_count": 97,
    "metadata": {
     "slideshow": {
      "slide_type": "-"
@@ -3335,7 +3276,7 @@
        "165"
       ]
      },
-     "execution_count": 98,
+     "execution_count": 97,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3357,7 +3298,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 99,
+   "execution_count": 98,
    "metadata": {
     "slideshow": {
      "slide_type": "-"
@@ -3370,7 +3311,7 @@
        "4492125"
       ]
      },
-     "execution_count": 99,
+     "execution_count": 98,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3392,7 +3333,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 100,
+   "execution_count": 99,
    "metadata": {
     "slideshow": {
      "slide_type": "-"
@@ -3405,7 +3346,7 @@
        "3"
       ]
      },
-     "execution_count": 100,
+     "execution_count": 99,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3427,7 +3368,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 101,
+   "execution_count": 100,
    "metadata": {
     "slideshow": {
      "slide_type": "-"
@@ -3440,7 +3381,7 @@
        "104"
       ]
      },
-     "execution_count": 101,
+     "execution_count": 100,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3473,7 +3414,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 102,
+   "execution_count": 101,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -3487,7 +3428,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 103,
+   "execution_count": 102,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -3500,7 +3441,7 @@
        "'Hi class'"
       ]
      },
-     "execution_count": 103,
+     "execution_count": 102,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3522,7 +3463,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 104,
+   "execution_count": 103,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -3535,7 +3476,7 @@
        "'Hi Hi Hi Hi Hi Hi Hi Hi Hi Hi Hi Hi Hi Hi Hi Hi Hi Hi Hi Hi Hi Hi Hi Hi Hi Hi Hi Hi Hi Hi Hi Hi Hi Hi Hi Hi Hi Hi Hi Hi Hi Hi '"
       ]
      },
-     "execution_count": 104,
+     "execution_count": 103,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3570,7 +3511,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 105,
+   "execution_count": 104,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -3583,7 +3524,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 106,
+   "execution_count": 105,
    "metadata": {
     "slideshow": {
      "slide_type": "-"
@@ -3607,7 +3548,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 107,
+   "execution_count": 106,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -3654,7 +3595,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 108,
+   "execution_count": 107,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -3682,7 +3623,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 109,
+   "execution_count": 108,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -3695,7 +3636,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 110,
+   "execution_count": 109,
    "metadata": {
     "slideshow": {
      "slide_type": "-"
@@ -3751,7 +3692,7 @@
     "  - distinct and well-contained areas/parts of the memory that hold the actual data\n",
     "  - the concept by which Python manages the memory for us\n",
     "  - can be classified into objects of the same **type** (i.e., same abstract \"structure\" but different concrete data)\n",
-    "  - built-in objects (incl. **literals**) vs. user-defined objects (cf., Chapter 9)\n",
+    "  - built-in objects (incl. **literals**) vs. user-defined objects (cf., Chapter 10)\n",
     "  - e.g., `1`, `1.0`, and `\"one\"` are three different objects of distinct types that are also literals (i.e., by the way we type them into the command line Python knows what the value and type are)\n",
     "\n",
     "\n",
@@ -3786,18 +3727,76 @@
     "  - ignored by Python\n",
     "\n",
     "\n",
-    "- functions (cf., [Chapter 2](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/02_functions.ipynb))\n",
+    "- functions (cf., [Chapter 2](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/02_functions_00_content.ipynb))\n",
     "  - named sequences of instructions\n",
     "  - the smaller parts in a larger program\n",
     "  - make a program more modular and thus easier to understand\n",
     "  - include [built-in functions](https://docs.python.org/3/library/functions.html) like [print()](https://docs.python.org/3/library/functions.html#print), [sum()](https://docs.python.org/3/library/functions.html#sum), or [len()](https://docs.python.org/3/library/functions.html#len)\n",
     "\n",
     "\n",
-    "- flow control (cf., [Chapter 3](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/03_conditionals.ipynb))\n",
+    "- flow control (cf., [Chapter 3](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/03_conditionals_00_content.ipynb))\n",
     "  - expression of **business logic** or an **algorithm**\n",
     "  - conditional execution of parts of a program (e.g., `if` statements)\n",
     "  - repetitive execution of parts of a program (e.g., `for`-loops)"
    ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "## Further Resources"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "This PyCon 2015 talk by [Ned Batchelder](https://nedbatchelder.com/), a well-known Pythonista and the organizer of the [Python User Group](https://www.meetup.com/bostonpython/) in Boston, summarizes all situations where some sort of assignment is done in Python. The content is intermediate, and, thus, it might be worthwhile to come back to this talk at a later point in time. However, the contents should be known by everyone claiming to be proficient in Python."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 110,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
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+      "text/html": [
+       "\n",
+       "        <iframe\n",
+       "            width=\"60%\"\n",
+       "            height=\"300\"\n",
+       "            src=\"https://www.youtube.com/embed/_AEJHKGk9ns\"\n",
+       "            frameborder=\"0\"\n",
+       "            allowfullscreen\n",
+       "        ></iframe>\n",
+       "        "
+      ],
+      "text/plain": [
+       "<IPython.lib.display.YouTubeVideo at 0x7f7c940bc050>"
+      ]
+     },
+     "execution_count": 110,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "YouTubeVideo(\"_AEJHKGk9ns\", width=\"60%\")"
+   ]
   }
  ],
  "metadata": {
@@ -3816,7 +3815,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.3"
+   "version": "3.7.4"
   },
   "livereveal": {
    "auto_select": "code",
@@ -3844,5 +3843,5 @@
   }
  },
  "nbformat": 4,
- "nbformat_minor": 2
+ "nbformat_minor": 4
 }
diff --git a/01_elements_review_and_exercises.ipynb b/01_elements_10_review.ipynb
similarity index 64%
rename from 01_elements_review_and_exercises.ipynb
rename to 01_elements_10_review.ipynb
index 21ba4c5..1c2ee76 100644
--- a/01_elements_review_and_exercises.ipynb
+++ b/01_elements_10_review.ipynb
@@ -19,7 +19,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Read [Chapter 1](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/01_elements.ipynb) of the book. Then work through the ten review questions."
+    "Read [Chapter 1](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/01_elements_00_content.ipynb) of the book. Then, work through the questions below."
    ]
   },
   {
@@ -33,7 +33,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Answer the following questions briefly with *at most* 300 characters per question!"
+    "Answer the following questions *briefly*!"
    ]
   },
   {
@@ -152,7 +152,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "**Q8**: The **Zen of Python** has a high opinion on Indian genius programmers."
+    "**Q8**: The **Zen of Python** talks about Indian genius programmers."
    ]
   },
   {
@@ -189,92 +189,6 @@
    "source": [
     " "
    ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Coding Exercises"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Printing Output"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**Q11.1**: Read about the [print()](https://docs.python.org/3/library/functions.html#print) built-in. How can you use it to print both `greeting` and `audience` *without* concatenating the two strings with the `+` operator?\n",
-    "\n",
-    "Hint: The `*objects` in the documentation implies that we can insert several comma-seperated variables."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "greeting = \"Hello\"\n",
-    "audience = \"World\""
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "print(...)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**Q11.2**: What does the `sep=\" \"` mean in the documentation? Use it to print out the three names in `first`, `second`, and `third` on one line seperated by commas with one [print()](https://docs.python.org/3/library/functions.html#print) statement."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "first = \"Anthony\"\n",
-    "second = \"Berta\"\n",
-    "third = \"Christian\""
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "print(...)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**Q11.3**: Lastly, what does the `end=\"\\n\"` mean in the documentation? Use it in the `for`-loop to print the numbers 1 through 10 in just one line."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "for number in [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]:\n",
-    "    print(...)"
-   ]
   }
  ],
  "metadata": {
@@ -293,7 +207,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.3"
+   "version": "3.7.4"
   },
   "toc": {
    "base_numbering": 1,
@@ -310,5 +224,5 @@
   }
  },
  "nbformat": 4,
- "nbformat_minor": 2
+ "nbformat_minor": 4
 }
diff --git a/01_elements_20_exercises.ipynb b/01_elements_20_exercises.ipynb
new file mode 100644
index 0000000..e34097c
--- /dev/null
+++ b/01_elements_20_exercises.ipynb
@@ -0,0 +1,139 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n",
+    "# Chapter 1: Elements of a Program"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Coding Exercises"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Read [Chapter 1](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/01_elements_00_content.ipynb) of the book. Then, work through the exercises below."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Printing Output"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q1.1**: Read about the [print()](https://docs.python.org/3/library/functions.html#print) built-in. How can you use it to print both `greeting` and `audience` *without* concatenating the two strings with the `+` operator?\n",
+    "\n",
+    "Hint: The `*objects` in the documentation implies that we can insert several comma-seperated variables."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "greeting = \"Hello\"\n",
+    "audience = \"World\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "print(...)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q1.2**: What does the `sep=\" \"` mean in the documentation? Use it to print out the three names in `first`, `second`, and `third` on one line seperated by commas with one [print()](https://docs.python.org/3/library/functions.html#print) statement."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "first = \"Anthony\"\n",
+    "second = \"Berta\"\n",
+    "third = \"Christian\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "print(...)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q1.3**: Lastly, what does the `end=\"\\n\"` mean in the documentation? Use it in the `for`-loop to print the numbers 1 through 10 in just one line."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "for number in [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]:\n",
+    "    print(...)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.4"
+  },
+  "toc": {
+   "base_numbering": 1,
+   "nav_menu": {},
+   "number_sections": false,
+   "sideBar": true,
+   "skip_h1_title": true,
+   "title_cell": "Table of Contents",
+   "title_sidebar": "Contents",
+   "toc_cell": false,
+   "toc_position": {},
+   "toc_section_display": false,
+   "toc_window_display": false
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/02_functions.ipynb b/02_functions_00_content.ipynb
similarity index 91%
rename from 02_functions.ipynb
rename to 02_functions_00_content.ipynb
index 39d7084..d0000d8 100644
--- a/02_functions.ipynb
+++ b/02_functions_00_content.ipynb
@@ -19,7 +19,7 @@
     }
    },
    "source": [
-    "In [Chapter 1](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/01_elements.ipynb#Example:-Averaging-Even-Numbers), we typed the code to calculate the average of the even numbers in a list into several code cells. Then, we executed them one after another. We had no way of **reusing** the code except for either executing cells multiple times or copying and pasting their contents into other cells. And, whenever we find ourselves doing repetitive manual work, we can be sure that there must be a way of automating what we are doing.\n",
+    "In [Chapter 1](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/01_elements_00_content.ipynb#Example:-Averaging-Even-Numbers), we typed the code to calculate the average of the even numbers in a list into several code cells. Then, we executed them one after another. We had no way of **reusing** the code except for either executing cells multiple times or copying and pasting their contents into other cells. And, whenever we find ourselves doing repetitive manual work, we can be sure that there must be a way of automating what we are doing.\n",
     "\n",
     "At the same time, we executed built-in functions (e.g., [print()](https://docs.python.org/3/library/functions.html#print), [sum()](https://docs.python.org/3/library/functions.html#sum), [len()](https://docs.python.org/3/library/functions.html#len), [id()](https://docs.python.org/3/library/functions.html#id), or [type()](https://docs.python.org/3/library/functions.html#type)) that obviously must be reusing the same parts inside core Python every time we use them.\n",
     "\n",
@@ -45,7 +45,7 @@
     }
    },
    "source": [
-    "So-called **[user-defined functions](https://docs.python.org/3/reference/compound_stmts.html#function-definitions)** may be created with the `def` statement. To extend an already familiar example, we reuse the introductory example from [Chapter 1](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/01_elements.ipynb#Best-Practices) in its final Pythonic version and transform it into the function `average_evens()` below. \n",
+    "So-called **[user-defined functions](https://docs.python.org/3/reference/compound_stmts.html#function-definitions)** may be created with the `def` statement. To extend an already familiar example, we reuse the introductory example from [Chapter 1](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/01_elements_00_content.ipynb#Best-Practices) in its final Pythonic version and transform it into the function `average_evens()` below. \n",
     "\n",
     "A function's **name** must be chosen according to the same naming rules as ordinary variables as Python manages function names like variables. In this book, we further adopt the convention of ending function names with parentheses `()` in text cells for faster comprehension when reading (i.e., `average_evens()` vs. `average_evens`). These are *not* part of the name but must always be written out in the `def` statement for syntactic reasons.\n",
     "\n",
@@ -57,7 +57,7 @@
     "\n",
     "A function may come with an *explicit* **return value** (i.e., \"result\" or \"output\") specified with the `return` statement (cf., [reference](https://docs.python.org/3/reference/simple_stmts.html#the-return-statement)): Functions that have one are considered **fruitful**; otherwise, they are **void**. Functions of the latter kind are still useful because of their **side effects** as, for example, the built-in [print()](https://docs.python.org/3/library/functions.html#print) function. Strictly speaking, they also have an *implicit* return value of `None`.\n",
     "\n",
-    "A function should define a **docstring** that describes what it does in a short subject line, what parameters it expects (i.e., their types), and what it returns (if anything). A docstring is a syntactically valid multi-line string (i.e., type `str`) defined within **triple-double quotes** `\"\"\"`. Strings are covered in depth in [Chapter 6](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/06_text.ipynb#The-str-Type). Widely adopted standards as to how to format a docstring are [PEP 257](https://www.python.org/dev/peps/pep-0257/) and section 3.8 in [Google's Python Style Guide](https://github.com/google/styleguide/blob/gh-pages/pyguide.md)."
+    "A function should define a **docstring** that describes what it does in a short subject line, what parameters it expects (i.e., their types), and what it returns (if anything). A docstring is a syntactically valid multi-line string (i.e., type `str`) defined within **triple-double quotes** `\"\"\"`. Strings are covered in depth in [Chapter 6](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/06_text_00_content.ipynb#The-str-Type). Widely adopted standards as to how to format a docstring are [PEP 257](https://www.python.org/dev/peps/pep-0257/) and section 3.8 in [Google's Python Style Guide](https://github.com/google/styleguide/blob/gh-pages/pyguide.md)."
    ]
   },
   {
@@ -142,7 +142,7 @@
     {
      "data": {
       "text/plain": [
-       "140384699277792"
+       "140371816700960"
       ]
      },
      "execution_count": 3,
@@ -241,7 +241,27 @@
      "slide_type": "skip"
     }
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "\u001b[0;31mSignature:\u001b[0m \u001b[0maverage_evens\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnumbers\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+       "\u001b[0;31mDocstring:\u001b[0m\n",
+       "Calculate the average of all even numbers in a list.\n",
+       "\n",
+       "Args:\n",
+       "    numbers (list): a list of numbers; may be integers or floats\n",
+       "\n",
+       "Returns:\n",
+       "    float: average\n",
+       "\u001b[0;31mFile:\u001b[0m      ~/repos/intro-to-python/<ipython-input-1-46ef912ad233>\n",
+       "\u001b[0;31mType:\u001b[0m      function\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "average_evens?"
    ]
@@ -265,7 +285,32 @@
      "slide_type": "skip"
     }
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "\u001b[0;31mSignature:\u001b[0m \u001b[0maverage_evens\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnumbers\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+       "\u001b[0;31mSource:\u001b[0m   \n",
+       "\u001b[0;32mdef\u001b[0m \u001b[0maverage_evens\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnumbers\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n",
+       "\u001b[0;34m\u001b[0m    \u001b[0;34m\"\"\"Calculate the average of all even numbers in a list.\u001b[0m\n",
+       "\u001b[0;34m\u001b[0m\n",
+       "\u001b[0;34m    Args:\u001b[0m\n",
+       "\u001b[0;34m        numbers (list): a list of numbers; may be integers or floats\u001b[0m\n",
+       "\u001b[0;34m\u001b[0m\n",
+       "\u001b[0;34m    Returns:\u001b[0m\n",
+       "\u001b[0;34m        float: average\u001b[0m\n",
+       "\u001b[0;34m    \"\"\"\u001b[0m\u001b[0;34m\u001b[0m\n",
+       "\u001b[0;34m\u001b[0m    \u001b[0mevens\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mn\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mn\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mnumbers\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mn\u001b[0m \u001b[0;34m%\u001b[0m \u001b[0;36m2\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\n",
+       "\u001b[0;34m\u001b[0m    \u001b[0maverage\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msum\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mevens\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m/\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mevens\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n",
+       "\u001b[0;34m\u001b[0m    \u001b[0;32mreturn\u001b[0m \u001b[0maverage\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+       "\u001b[0;31mFile:\u001b[0m      ~/repos/intro-to-python/<ipython-input-1-46ef912ad233>\n",
+       "\u001b[0;31mType:\u001b[0m      function\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "average_evens??"
    ]
@@ -337,12 +382,71 @@
     }
    },
    "source": [
-    "The return value is commonly assigned to a variable for later reference. Otherwise, we would lose access to it in memory right away."
+    "If we are ever unsure if a variable references a `function` object that may be called, we can verify that with the built-in [callable()](https://docs.python.org/3/library/functions.html#callable) function that takes any object as its only argument and tells us `True` if that is **callable** and `False` otherwise."
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 10,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "callable(average_evens)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "False"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "callable(nums)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "The return value is commonly assigned to a variable for later reference. Otherwise, we would lose access to it in memory right away."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -355,7 +459,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 13,
    "metadata": {
     "slideshow": {
      "slide_type": "-"
@@ -368,7 +472,7 @@
        "7.0"
       ]
      },
-     "execution_count": 11,
+     "execution_count": 13,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -412,7 +516,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 14,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -426,7 +530,7 @@
      "traceback": [
       "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
       "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-12-6a54518a0c2c>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mnumbers\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;32m<ipython-input-14-6a54518a0c2c>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mnumbers\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
       "\u001b[0;31mNameError\u001b[0m: name 'numbers' is not defined"
      ]
     }
@@ -437,7 +541,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 15,
    "metadata": {
     "slideshow": {
      "slide_type": "-"
@@ -451,7 +555,7 @@
      "traceback": [
       "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
       "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-13-df246468d241>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mevens\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;32m<ipython-input-15-df246468d241>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mevens\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
       "\u001b[0;31mNameError\u001b[0m: name 'evens' is not defined"
      ]
     }
@@ -462,7 +566,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 16,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -476,7 +580,7 @@
      "traceback": [
       "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
       "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-14-c3fe9b4213f6>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0maverage\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;32m<ipython-input-16-c3fe9b4213f6>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0maverage\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
       "\u001b[0;31mNameError\u001b[0m: name 'average' is not defined"
      ]
     }
@@ -520,7 +624,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 17,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -555,7 +659,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 18,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -568,7 +672,7 @@
        "[7, 11, 8, 5, 3, 12, 2, 6, 9, 10, 1, 4]"
       ]
      },
-     "execution_count": 16,
+     "execution_count": 18,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -590,7 +694,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 19,
    "metadata": {
     "slideshow": {
      "slide_type": "-"
@@ -603,7 +707,7 @@
        "7.0"
       ]
      },
-     "execution_count": 17,
+     "execution_count": 19,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -625,7 +729,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 20,
    "metadata": {
     "slideshow": {
      "slide_type": "-"
@@ -638,7 +742,7 @@
        "7.0"
       ]
      },
-     "execution_count": 18,
+     "execution_count": 20,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -657,7 +761,7 @@
    "source": [
     "[PythonTutor](http://pythontutor.com/visualize.html#code=nums%20%3D%20%5B1,%202,%203,%204,%205,%206,%207,%208,%209,%2010,%2011,%2012%5D%0A%0Adef%20average_wrong%28numbers%29%3A%0A%20%20%20%20evens%20%3D%20%5Bn%20for%20n%20in%20nums%20if%20n%20%25%202%20%3D%3D%200%5D%0A%20%20%20%20average%20%3D%20sum%28evens%29%20/%20len%28evens%29%0A%20%20%20%20return%20average%0A%0Arv%20%3D%20average_wrong%28%5B123,%20456,%20789%5D%29&cumulative=false&curInstr=0&heapPrimitives=nevernest&mode=display&origin=opt-frontend.js&py=3&rawInputLstJSON=%5B%5D&textReferences=false) is again helpful at visualizing the error interactively: Creating the `list` object `evens` eventually references takes *16* computational steps, namely two for managing the list comprehension, one for setting up an empty `list` object, *twelve* for filling it with elements derived from `nums` in the global scope (i.e., that is the error), and one to make `evens` reference it (cf., steps 6-21).\n",
     "\n",
-    "The frames logic shown by PythonTutor is the mechanism by which Python not only manages the names inside *one* function call but also for *many* potentially *simultaneous* calls, as revealed in [Chapter 4](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/04_iteration.ipynb#Trivial-Example:-Countdown). It is the reason why we may reuse the same names for the parameters and variables inside both `average_evens()` and `average_wrong()` without Python mixing them up. So, as we already read in the [Zen of Python](https://www.python.org/dev/peps/pep-0020/), \"namespaces are one honking great idea\" (cf., `import this`), and a frame is just a special kind of namespace."
+    "The frames logic shown by PythonTutor is the mechanism by which Python not only manages the names inside *one* function call but also for *many* potentially *simultaneous* calls, as revealed in [Chapter 4](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/04_iteration_00_content.ipynb#Trivial-Example:-Countdown). It is the reason why we may reuse the same names for the parameters and variables inside both `average_evens()` and `average_wrong()` without Python mixing them up. So, as we already read in the [Zen of Python](https://www.python.org/dev/peps/pep-0020/), \"namespaces are one honking great idea\" (cf., `import this`), and a frame is just a special kind of namespace."
    ]
   },
   {
@@ -686,7 +790,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 21,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -722,7 +826,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 22,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -735,7 +839,7 @@
        "[7, 11, 8, 5, 3, 12, 2, 6, 9, 10, 1, 4]"
       ]
      },
-     "execution_count": 20,
+     "execution_count": 22,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -757,7 +861,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 23,
    "metadata": {
     "slideshow": {
      "slide_type": "-"
@@ -770,7 +874,7 @@
        "3.0"
       ]
      },
-     "execution_count": 21,
+     "execution_count": 23,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -792,7 +896,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 24,
    "metadata": {
     "slideshow": {
      "slide_type": "-"
@@ -805,7 +909,7 @@
        "6.0"
       ]
      },
-     "execution_count": 22,
+     "execution_count": 24,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -827,7 +931,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 25,
    "metadata": {
     "slideshow": {
      "slide_type": "-"
@@ -840,7 +944,7 @@
        "[7, 11, 8, 5, 3, 12, 2, 6, 9, 10, 1, 4]"
       ]
      },
-     "execution_count": 23,
+     "execution_count": 25,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -892,7 +996,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 26,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -905,7 +1009,7 @@
        "12"
       ]
      },
-     "execution_count": 24,
+     "execution_count": 26,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -916,7 +1020,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 27,
    "metadata": {
     "slideshow": {
      "slide_type": "-"
@@ -929,7 +1033,7 @@
        "78"
       ]
      },
-     "execution_count": 25,
+     "execution_count": 27,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -951,7 +1055,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": 28,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -964,7 +1068,7 @@
        "7.0"
       ]
      },
-     "execution_count": 26,
+     "execution_count": 28,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -975,7 +1079,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": 29,
    "metadata": {
     "slideshow": {
      "slide_type": "-"
@@ -988,7 +1092,7 @@
        "7"
       ]
      },
-     "execution_count": 27,
+     "execution_count": 29,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -999,7 +1103,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": 30,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -1012,7 +1116,7 @@
        "7"
       ]
      },
-     "execution_count": 28,
+     "execution_count": 30,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1034,7 +1138,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
+   "execution_count": 31,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -1047,7 +1151,7 @@
        "7"
       ]
      },
-     "execution_count": 29,
+     "execution_count": 31,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1058,7 +1162,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 30,
+   "execution_count": 32,
    "metadata": {
     "slideshow": {
      "slide_type": "-"
@@ -1071,7 +1175,7 @@
        "8"
       ]
      },
-     "execution_count": 30,
+     "execution_count": 32,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1093,7 +1197,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 31,
+   "execution_count": 33,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -1107,7 +1211,7 @@
      "traceback": [
       "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
       "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-31-af421b358f21>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"seven\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;32m<ipython-input-33-af421b358f21>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"seven\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
       "\u001b[0;31mValueError\u001b[0m: invalid literal for int() with base 10: 'seven'"
      ]
     }
@@ -1129,7 +1233,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 32,
+   "execution_count": 34,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -1142,7 +1246,7 @@
        "42.0"
       ]
      },
-     "execution_count": 32,
+     "execution_count": 34,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1170,12 +1274,12 @@
     }
    },
    "source": [
-    "So far, we have only specified one parameter in each of our user-defined functions. In [Chapter 1](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/01_elements.ipynb#%28Arithmetic%29-Operators), however, we saw the built-in function [divmod()](https://docs.python.org/3/library/functions.html#divmod) take two arguments. And, the order of the numbers passed in mattered! Whenever we call a function and list its arguments in a comma separated manner, we say that we pass in the arguments by position or refer to them as **positional arguments**."
+    "So far, we have only specified one parameter in each of our user-defined functions. In [Chapter 1](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/01_elements_00_content.ipynb#%28Arithmetic%29-Operators), however, we saw the built-in function [divmod()](https://docs.python.org/3/library/functions.html#divmod) take two arguments. And, the order of the numbers passed in mattered! Whenever we call a function and list its arguments in a comma separated manner, we say that we pass in the arguments by position or refer to them as **positional arguments**."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 33,
+   "execution_count": 35,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -1188,7 +1292,7 @@
        "(4, 2)"
       ]
      },
-     "execution_count": 33,
+     "execution_count": 35,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1199,7 +1303,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 34,
+   "execution_count": 36,
    "metadata": {
     "slideshow": {
      "slide_type": "-"
@@ -1212,7 +1316,7 @@
        "(0, 10)"
       ]
      },
-     "execution_count": 34,
+     "execution_count": 36,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1234,7 +1338,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 35,
+   "execution_count": 37,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -1271,7 +1375,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 36,
+   "execution_count": 38,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -1284,7 +1388,7 @@
        "14.0"
       ]
      },
-     "execution_count": 36,
+     "execution_count": 38,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1306,54 +1410,6 @@
     "Luckily, we may also reference the formal parameter names as **keyword arguments**. We can even combine positional and keyword arguments in the same function call. Each of the following does the *same*."
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": 37,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "fragment"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "14.0"
-      ]
-     },
-     "execution_count": 37,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "scaled_average_evens(nums, scalar=2)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 38,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "fragment"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "14.0"
-      ]
-     },
-     "execution_count": 38,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "scaled_average_evens(numbers=nums, scalar=2)"
-   ]
-  },
   {
    "cell_type": "code",
    "execution_count": 39,
@@ -1374,6 +1430,54 @@
      "output_type": "execute_result"
     }
    ],
+   "source": [
+    "scaled_average_evens(nums, scalar=2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "14.0"
+      ]
+     },
+     "execution_count": 40,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "scaled_average_evens(numbers=nums, scalar=2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "14.0"
+      ]
+     },
+     "execution_count": 41,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "scaled_average_evens(scalar=2, numbers=nums)"
    ]
@@ -1391,7 +1495,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 40,
+   "execution_count": 42,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -1400,10 +1504,10 @@
    "outputs": [
     {
      "ename": "SyntaxError",
-     "evalue": "positional argument follows keyword argument (<ipython-input-40-a6b957a168d8>, line 1)",
+     "evalue": "positional argument follows keyword argument (<ipython-input-42-a6b957a168d8>, line 1)",
      "output_type": "error",
      "traceback": [
-      "\u001b[0;36m  File \u001b[0;32m\"<ipython-input-40-a6b957a168d8>\"\u001b[0;36m, line \u001b[0;32m1\u001b[0m\n\u001b[0;31m    scaled_average_evens(numbers=nums, 2)\u001b[0m\n\u001b[0m                                      ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m positional argument follows keyword argument\n"
+      "\u001b[0;36m  File \u001b[0;32m\"<ipython-input-42-a6b957a168d8>\"\u001b[0;36m, line \u001b[0;32m1\u001b[0m\n\u001b[0;31m    scaled_average_evens(numbers=nums, 2)\u001b[0m\n\u001b[0m                                      ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m positional argument follows keyword argument\n"
      ]
     }
    ],
@@ -1439,7 +1543,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 41,
+   "execution_count": 43,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -1450,7 +1554,13 @@
     "def scaled_average_evens(numbers, scalar):\n",
     "    \"\"\"Calculate a scaled average of all even numbers in a list.\n",
     "\n",
-    "    ...\n",
+    "    Args:\n",
+    "        numbers (list): a list of numbers; may be integers or floats\n",
+    "        scalar (float): the scalar that multiplies the average\n",
+    "            of the even numbers\n",
+    "\n",
+    "    Returns:\n",
+    "        float: scaled average\n",
     "    \"\"\"\n",
     "    evens = [n for n in numbers if n % 2 == 0]\n",
     "    average = sum(evens) / len(evens)\n",
@@ -1459,7 +1569,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 42,
+   "execution_count": 44,
    "metadata": {
     "slideshow": {
      "slide_type": "-"
@@ -1470,7 +1580,11 @@
     "def average_evens(numbers):\n",
     "    \"\"\"Calculate the average of all even numbers in a list.\n",
     "\n",
-    "    ...\n",
+    "    Args:\n",
+    "        numbers (list): a list of numbers; may be integers or floats\n",
+    "\n",
+    "    Returns:\n",
+    "        float: average\n",
     "    \"\"\"\n",
     "    return scaled_average_evens(numbers, scalar=1)  # refactored to use the logic in scaled_average_evens()"
    ]
@@ -1488,7 +1602,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 43,
+   "execution_count": 45,
    "metadata": {
     "slideshow": {
      "slide_type": "-"
@@ -1501,7 +1615,7 @@
        "7.0"
       ]
      },
-     "execution_count": 43,
+     "execution_count": 45,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1523,7 +1637,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 44,
+   "execution_count": 46,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -1562,7 +1676,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 45,
+   "execution_count": 47,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -1575,7 +1689,7 @@
        "7.0"
       ]
      },
-     "execution_count": 45,
+     "execution_count": 47,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1586,7 +1700,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 46,
+   "execution_count": 48,
    "metadata": {
     "slideshow": {
      "slide_type": "-"
@@ -1599,7 +1713,7 @@
        "14.0"
       ]
      },
-     "execution_count": 46,
+     "execution_count": 48,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1610,7 +1724,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 47,
+   "execution_count": 49,
    "metadata": {
     "slideshow": {
      "slide_type": "-"
@@ -1623,7 +1737,7 @@
        "14.0"
       ]
      },
-     "execution_count": 47,
+     "execution_count": 49,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1658,7 +1772,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 48,
+   "execution_count": 50,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -1695,7 +1809,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 49,
+   "execution_count": 51,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -1708,7 +1822,7 @@
        "7.0"
       ]
      },
-     "execution_count": 49,
+     "execution_count": 51,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1719,7 +1833,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 50,
+   "execution_count": 52,
    "metadata": {
     "slideshow": {
      "slide_type": "-"
@@ -1732,7 +1846,7 @@
        "14.0"
       ]
      },
-     "execution_count": 50,
+     "execution_count": 52,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1754,7 +1868,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 51,
+   "execution_count": 53,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -1768,7 +1882,7 @@
      "traceback": [
       "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
       "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-51-b36617fef43e>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0maverage_evens\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnums\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m2\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;32m<ipython-input-53-b36617fef43e>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0maverage_evens\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnums\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m2\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
       "\u001b[0;31mTypeError\u001b[0m: average_evens() takes 1 positional argument but 2 were given"
      ]
     }
@@ -1811,7 +1925,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 52,
+   "execution_count": 54,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -1824,7 +1938,7 @@
        "<function __main__.<lambda>(x)>"
       ]
      },
-     "execution_count": 52,
+     "execution_count": 54,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1850,7 +1964,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 53,
+   "execution_count": 55,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -1874,7 +1988,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 54,
+   "execution_count": 56,
    "metadata": {
     "slideshow": {
      "slide_type": "-"
@@ -1887,7 +2001,7 @@
        "13"
       ]
      },
-     "execution_count": 54,
+     "execution_count": 56,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1909,7 +2023,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 55,
+   "execution_count": 57,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -1922,7 +2036,7 @@
        "42"
       ]
      },
-     "execution_count": 55,
+     "execution_count": 57,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1941,7 +2055,7 @@
    "source": [
     "The main point of having functions without a name is to use them in a situation where we know ahead of time that we use the function only *once*.\n",
     "\n",
-    "Popular applications of lambda expressions occur in combination with the **map-filter-reduce** paradigm or when we do \"number crunching\" with **arrays** and **data frames**. We look at both in detail in [Chapter 7](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/07_sequences.ipynb)."
+    "Popular applications of lambda expressions occur in combination with the **map-filter-reduce** paradigm or when we do \"number crunching\" with **arrays** and **data frames**. We look at both in detail in [Chapter 7](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/07_sequences_00_content.ipynb)."
    ]
   },
   {
@@ -2026,7 +2140,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 56,
+   "execution_count": 58,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -2050,7 +2164,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 57,
+   "execution_count": 59,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -2060,10 +2174,10 @@
     {
      "data": {
       "text/plain": [
-       "<module 'math' from '/home/webartifex/.pyenv/versions/anaconda3-2019.07/lib/python3.7/lib-dynload/math.cpython-37m-x86_64-linux-gnu.so'>"
+       "<module 'math' from '/home/webartifex/.pyenv/versions/anaconda3-2019.10/lib/python3.7/lib-dynload/math.cpython-37m-x86_64-linux-gnu.so'>"
       ]
      },
-     "execution_count": 57,
+     "execution_count": 59,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2074,7 +2188,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 58,
+   "execution_count": 60,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -2084,10 +2198,10 @@
     {
      "data": {
       "text/plain": [
-       "140384754610488"
+       "140371899337520"
       ]
      },
-     "execution_count": 58,
+     "execution_count": 60,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2098,7 +2212,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 59,
+   "execution_count": 61,
    "metadata": {
     "slideshow": {
      "slide_type": "-"
@@ -2111,7 +2225,7 @@
        "module"
       ]
      },
-     "execution_count": 59,
+     "execution_count": 61,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2137,7 +2251,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 60,
+   "execution_count": 62,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -2205,7 +2319,7 @@
        " 'trunc']"
       ]
      },
-     "execution_count": 60,
+     "execution_count": 62,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2227,7 +2341,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 61,
+   "execution_count": 63,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -2240,7 +2354,7 @@
        "3.141592653589793"
       ]
      },
-     "execution_count": 61,
+     "execution_count": 63,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2251,7 +2365,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 62,
+   "execution_count": 64,
    "metadata": {
     "slideshow": {
      "slide_type": "-"
@@ -2264,7 +2378,7 @@
        "2.718281828459045"
       ]
      },
-     "execution_count": 62,
+     "execution_count": 64,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2275,7 +2389,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 63,
+   "execution_count": 65,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -2288,7 +2402,7 @@
        "<function math.sqrt(x, /)>"
       ]
      },
-     "execution_count": 63,
+     "execution_count": 65,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2299,7 +2413,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 64,
+   "execution_count": 66,
    "metadata": {
     "slideshow": {
      "slide_type": "-"
@@ -2324,7 +2438,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 65,
+   "execution_count": 67,
    "metadata": {
     "slideshow": {
      "slide_type": "-"
@@ -2337,7 +2451,7 @@
        "1.4142135623730951"
       ]
      },
-     "execution_count": 65,
+     "execution_count": 67,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2356,14 +2470,14 @@
    "source": [
     "Observe how the arguments passed to functions do not need to be just variables or simple literals. Instead, we may pass in any *expression* that evaluates to a *new* object of the type the function expects.\n",
     "\n",
-    "So just as a reminder from the expression vs. statement discussion in [Chapter 1](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/01_elements.ipynb#Expressions): An expression is *any* syntactically correct combination of variables and literals with operators. And the call operator `()` is yet another operator. So both of the next two code cells are just expressions! They have no permanent side effects in memory. We may execute them as often as we want *without* changing the state of the program (i.e., this Jupyter notebook).\n",
+    "So just as a reminder from the expression vs. statement discussion in [Chapter 1](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/01_elements_00_content.ipynb#Expressions): An expression is *any* syntactically correct combination of variables and literals with operators. And the call operator `()` is yet another operator. So both of the next two code cells are just expressions! They have no permanent side effects in memory. We may execute them as often as we want *without* changing the state of the program (i.e., this Jupyter notebook).\n",
     "\n",
     "So, regarding the very next cell in particular: Although the `2 ** 2` creates a *new* object `4` in memory that is then immediately passed into the [math.sqrt()](https://docs.python.org/3/library/math.html#math.sqrt) function, once that function call returns, \"all is lost\" and the newly created `4` object is forgotten again, as well as the return value of [math.sqrt()](https://docs.python.org/3/library/math.html#math.sqrt)."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 66,
+   "execution_count": 68,
    "metadata": {
     "slideshow": {
      "slide_type": "-"
@@ -2376,7 +2490,7 @@
        "2.0"
       ]
      },
-     "execution_count": 66,
+     "execution_count": 68,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2398,7 +2512,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 67,
+   "execution_count": 69,
    "metadata": {
     "slideshow": {
      "slide_type": "-"
@@ -2411,7 +2525,7 @@
        "10.0"
       ]
      },
-     "execution_count": 67,
+     "execution_count": 69,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2435,7 +2549,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 68,
+   "execution_count": 70,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -2448,7 +2562,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 69,
+   "execution_count": 71,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -2461,7 +2575,7 @@
        "4.0"
       ]
      },
-     "execution_count": 69,
+     "execution_count": 71,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2494,7 +2608,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 70,
+   "execution_count": 72,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -2507,7 +2621,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 71,
+   "execution_count": 73,
    "metadata": {
     "slideshow": {
      "slide_type": "-"
@@ -2517,10 +2631,10 @@
     {
      "data": {
       "text/plain": [
-       "<module 'random' from '/home/webartifex/.pyenv/versions/anaconda3-2019.07/lib/python3.7/random.py'>"
+       "<module 'random' from '/home/webartifex/.pyenv/versions/anaconda3-2019.10/lib/python3.7/random.py'>"
       ]
      },
-     "execution_count": 71,
+     "execution_count": 73,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2537,12 +2651,12 @@
     }
    },
    "source": [
-    "Besides the usual dunder-style attributes, the built-in [dir()](https://docs.python.org/3/library/functions.html#dir) function lists some attributes in an upper case naming convention and many others starting with a *single* underscore `_`. To understand the former, we must wait until Chapter 9, while the latter is explained further below."
+    "Besides the usual dunder-style attributes, the built-in [dir()](https://docs.python.org/3/library/functions.html#dir) function lists some attributes in an upper case naming convention and many others starting with a *single* underscore `_`. To understand the former, we must wait until Chapter 10, while the latter is explained further below."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 72,
+   "execution_count": 74,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -2616,7 +2730,7 @@
        " 'weibullvariate']"
       ]
      },
-     "execution_count": 72,
+     "execution_count": 74,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2638,7 +2752,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 73,
+   "execution_count": 75,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -2651,7 +2765,7 @@
        "<function Random.random>"
       ]
      },
-     "execution_count": 73,
+     "execution_count": 75,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2662,7 +2776,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 74,
+   "execution_count": 76,
    "metadata": {
     "slideshow": {
      "slide_type": "-"
@@ -2687,7 +2801,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 75,
+   "execution_count": 77,
    "metadata": {
     "slideshow": {
      "slide_type": "-"
@@ -2697,10 +2811,10 @@
     {
      "data": {
       "text/plain": [
-       "0.17184403163109208"
+       "0.64575874518665"
       ]
      },
-     "execution_count": 75,
+     "execution_count": 77,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2722,7 +2836,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 76,
+   "execution_count": 78,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -2732,10 +2846,10 @@
     {
      "data": {
       "text/plain": [
-       "<bound method Random.choice of <random.Random object at 0x55bb3d27faf8>>"
+       "<bound method Random.choice of <random.Random object at 0x563af2c67aa0>>"
       ]
      },
-     "execution_count": 76,
+     "execution_count": 78,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2746,7 +2860,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 77,
+   "execution_count": 79,
    "metadata": {
     "slideshow": {
      "slide_type": "-"
@@ -2771,7 +2885,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 78,
+   "execution_count": 80,
    "metadata": {
     "slideshow": {
      "slide_type": "-"
@@ -2781,10 +2895,10 @@
     {
      "data": {
       "text/plain": [
-       "2"
+       "12"
       ]
      },
-     "execution_count": 78,
+     "execution_count": 80,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2808,7 +2922,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 79,
+   "execution_count": 81,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -2821,7 +2935,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 80,
+   "execution_count": 82,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -2834,7 +2948,7 @@
        "0.6394267984578837"
       ]
      },
-     "execution_count": 80,
+     "execution_count": 82,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2845,7 +2959,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 81,
+   "execution_count": 83,
    "metadata": {
     "slideshow": {
      "slide_type": "-"
@@ -2858,7 +2972,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 82,
+   "execution_count": 84,
    "metadata": {
     "slideshow": {
      "slide_type": "-"
@@ -2871,7 +2985,7 @@
        "0.6394267984578837"
       ]
      },
-     "execution_count": 82,
+     "execution_count": 84,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2927,16 +3041,16 @@
     }
    },
    "source": [
-    "[numpy](http://www.numpy.org/) is the de-facto standard in the Python world for handling **array-like** data. That is a fancy word for data that can be put into a matrix or vector format. We look at it in depth in [Chapter 7](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/07_sequences.ipynb).\n",
+    "[numpy](http://www.numpy.org/) is the de-facto standard in the Python world for handling **array-like** data. That is a fancy word for data that can be put into a matrix or vector format. We look at it in depth in [Chapter 9](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/09_arrays_00_content.ipynb).\n",
     "\n",
-    "As [numpy](http://www.numpy.org/) is *not* in the [standard library](https://docs.python.org/3/library/index.html), it must be *manually* installed, for example, with the [pip](https://pip.pypa.io/en/stable/) tool. As mentioned in [Chapter 0](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/00_start_up.ipynb#Markdown-vs.-Code-Cells), to execute terminal commands from within a Jupyter notebook, we start a code cell with an exclamation mark.\n",
+    "As [numpy](http://www.numpy.org/) is *not* in the [standard library](https://docs.python.org/3/library/index.html), it must be *manually* installed, for example, with the [pip](https://pip.pypa.io/en/stable/) tool. As mentioned in [Chapter 0](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/00_start_up_00_content.ipynb#Markdown-vs.-Code-Cells), to execute terminal commands from within a Jupyter notebook, we start a code cell with an exclamation mark.\n",
     "\n",
     "If you are running this notebook with an installation of the [Anaconda Distribution](https://www.anaconda.com/distribution/), then [numpy](http://www.numpy.org/) is probably already installed. Running the cell below confirms that."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 83,
+   "execution_count": 85,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -2947,7 +3061,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Requirement already satisfied: numpy in /home/webartifex/.pyenv/versions/anaconda3-2019.07/lib/python3.7/site-packages (1.16.4)\r\n"
+      "Requirement already satisfied: numpy in /home/webartifex/.pyenv/versions/anaconda3-2019.10/lib/python3.7/site-packages (1.17.2)\n"
      ]
     }
    ],
@@ -2968,7 +3082,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 84,
+   "execution_count": 86,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -2992,7 +3106,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 85,
+   "execution_count": 87,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -3002,10 +3116,10 @@
     {
      "data": {
       "text/plain": [
-       "<module 'numpy' from '/home/webartifex/.pyenv/versions/anaconda3-2019.07/lib/python3.7/site-packages/numpy/__init__.py'>"
+       "<module 'numpy' from '/home/webartifex/.pyenv/versions/anaconda3-2019.10/lib/python3.7/site-packages/numpy/__init__.py'>"
       ]
      },
-     "execution_count": 85,
+     "execution_count": 87,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3027,7 +3141,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 86,
+   "execution_count": 88,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -3040,7 +3154,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 87,
+   "execution_count": 89,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -3053,7 +3167,7 @@
        "array([ 7, 11,  8,  5,  3, 12,  2,  6,  9, 10,  1,  4])"
       ]
      },
-     "execution_count": 87,
+     "execution_count": 89,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3064,7 +3178,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 88,
+   "execution_count": 90,
    "metadata": {
     "slideshow": {
      "slide_type": "-"
@@ -3077,7 +3191,7 @@
        "numpy.ndarray"
       ]
      },
-     "execution_count": 88,
+     "execution_count": 90,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3101,7 +3215,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 89,
+   "execution_count": 91,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -3114,7 +3228,7 @@
        "array([14, 22, 16, 10,  6, 24,  4, 12, 18, 20,  2,  8])"
       ]
      },
-     "execution_count": 89,
+     "execution_count": 91,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3136,7 +3250,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 90,
+   "execution_count": 92,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -3149,7 +3263,7 @@
        "[7, 11, 8, 5, 3, 12, 2, 6, 9, 10, 1, 4, 7, 11, 8, 5, 3, 12, 2, 6, 9, 10, 1, 4]"
       ]
      },
-     "execution_count": 90,
+     "execution_count": 92,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3171,7 +3285,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 91,
+   "execution_count": 93,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -3184,7 +3298,7 @@
        "78"
       ]
      },
-     "execution_count": 91,
+     "execution_count": 93,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3195,7 +3309,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 92,
+   "execution_count": 94,
    "metadata": {
     "slideshow": {
      "slide_type": "-"
@@ -3208,7 +3322,7 @@
        "7"
       ]
      },
-     "execution_count": 92,
+     "execution_count": 94,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3247,7 +3361,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 93,
+   "execution_count": 95,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -3260,7 +3374,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 94,
+   "execution_count": 96,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -3273,7 +3387,7 @@
        "<module 'sample_module' from '/home/webartifex/repos/intro-to-python/sample_module.py'>"
       ]
      },
-     "execution_count": 94,
+     "execution_count": 96,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3299,7 +3413,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 95,
+   "execution_count": 97,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -3324,7 +3438,7 @@
        " 'average_odds']"
       ]
      },
-     "execution_count": 95,
+     "execution_count": 97,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3346,7 +3460,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 96,
+   "execution_count": 98,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -3379,7 +3493,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 97,
+   "execution_count": 99,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -3392,7 +3506,7 @@
        "7.0"
       ]
      },
-     "execution_count": 97,
+     "execution_count": 99,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3403,7 +3517,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 98,
+   "execution_count": 100,
    "metadata": {
     "slideshow": {
      "slide_type": "-"
@@ -3416,7 +3530,7 @@
        "14.0"
       ]
      },
-     "execution_count": 98,
+     "execution_count": 100,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3433,7 +3547,7 @@
     }
    },
    "source": [
-    "Packages are a generalization of modules, and we look at one in detail in Chapter 9. You may, however, already look at a [sample package](https://github.com/webartifex/intro-to-python/tree/master/sample_package) in the repository, which is nothing but a folder with *.py* files in it.\n",
+    "Packages are a generalization of modules, and we look at one in detail in Chapter 10. You may, however, already look at a [sample package](https://github.com/webartifex/intro-to-python/tree/master/sample_package) in the repository, which is nothing but a folder with *.py* files in it.\n",
     "\n",
     "As a further reading on modules and packages, we refer to the [official tutorial](https://docs.python.org/3/tutorial/modules.html)."
    ]
@@ -3494,7 +3608,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.3"
+   "version": "3.7.4"
   },
   "livereveal": {
    "auto_select": "code",
@@ -3522,5 +3636,5 @@
   }
  },
  "nbformat": 4,
- "nbformat_minor": 2
+ "nbformat_minor": 4
 }
diff --git a/02_functions_review_and_exercises.ipynb b/02_functions_10_review.ipynb
similarity index 50%
rename from 02_functions_review_and_exercises.ipynb
rename to 02_functions_10_review.ipynb
index ca631a7..a320a3d 100644
--- a/02_functions_review_and_exercises.ipynb
+++ b/02_functions_10_review.ipynb
@@ -18,7 +18,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Read [Chapter 2](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/02_functions.ipynb) of the book. Then work through the ten review questions."
+    "Read [Chapter 2](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/02_functions_00_content.ipynb) of the book. Then, work through the questions below."
    ]
   },
   {
@@ -32,14 +32,14 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Answer the following questions briefly with *at most* 300 characters per question!"
+    "Answer the following questions *briefly*!"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "**Q1**: What property of the `def` statement makes it a **statement**? Is there a way to use an **expression** to create a function?"
+    "**Q1**: What behavior of the `def` statement makes it a **statement**? Is there a way to use an **expression** to create a function?"
    ]
   },
   {
@@ -182,188 +182,6 @@
     "**Q10**: The [standard library](https://docs.python.org/3/library/index.html) is a collection of numerical tools often used in scientific computing, for example, advanced mathematical functions or utilities for simulation."
    ]
   },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    " "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Coding Exercises"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Volume of a Sphere"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**Q11.1**: The [volume of a sphere](https://en.wikipedia.org/wiki/Sphere) is defined as $\\frac{4}{3} * \\pi * r^3$. Calculate this value for $r=10.0$ and round it to 10 digits after the comma. Use the [standard library](https://docs.python.org/3/library/index.html) to obtain a good approximation of $\\pi$."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import ..."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "r = ..."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "..."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**Q11.2**: Encapsulate the logic into a function `sphere_volume()` that takes one *positional* argument `radius` and one *keyword-only* argument `digits` defaulting to `5`. The volume should be returned as a `float` object under *all* circumstances. Document your work appropriately in a docstring according to [Google's Python Style Guide](https://github.com/google/styleguide/blob/gh-pages/pyguide.md)."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def sphere_volume(...):\n",
-    "    ..."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**Q11.3**: Evaluate the function with `radius = 100.0` and 1, 5, 10, 15, and 20 digits respectively."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "radius = ..."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "sphere_volume(...)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "sphere_volume(...)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "sphere_volume(...)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "sphere_volume(...)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "sphere_volume(...)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**Q11.4**: What observation do you make?"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    " "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**Q11.5**: Using the [range()](https://docs.python.org/3/library/functions.html#func-range) built-in, write a `for`-loop and calculate the volume of a sphere with `radius = 42.0` for all `digits` from `1` through `20`. Print out each volume on a separate line.\n",
-    "\n",
-    "Note: This is the first task where you need to use the built-in [print()](https://docs.python.org/3/library/functions.html#print) function."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "radius = ..."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "for ... in ...:\n",
-    "    ..."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**Q11.6**: What lesson do you learn about the `float` type?"
-   ]
-  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -388,7 +206,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.3"
+   "version": "3.7.4"
   },
   "toc": {
    "base_numbering": 1,
@@ -405,5 +223,5 @@
   }
  },
  "nbformat": 4,
- "nbformat_minor": 2
+ "nbformat_minor": 4
 }
diff --git a/02_functions_20_exercises.ipynb b/02_functions_20_exercises.ipynb
new file mode 100644
index 0000000..0f449e4
--- /dev/null
+++ b/02_functions_20_exercises.ipynb
@@ -0,0 +1,234 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Chapter 2: Functions & Modularization"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Coding Exercises"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Read [Chapter 2](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/02_functions_00_content.ipynb) of the book. Then, work through the exercises below."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Volume of a Sphere"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q1.1**: The [volume of a sphere](https://en.wikipedia.org/wiki/Sphere) is defined as $\\frac{4}{3} * \\pi * r^3$. Calculate this value for $r=10.0$ and round it to 10 digits after the comma. Use the [standard library](https://docs.python.org/3/library/index.html) to obtain a good approximation of $\\pi$."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import ..."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "r = ..."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "..."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q1.2**: Encapsulate the logic into a function `sphere_volume()` that takes one *positional* argument `radius` and one *keyword-only* argument `digits` defaulting to `5`. The volume should be returned as a `float` object under *all* circumstances. Document your work appropriately in a docstring according to [Google's Python Style Guide](https://github.com/google/styleguide/blob/gh-pages/pyguide.md)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def sphere_volume(...):\n",
+    "    ..."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q1.3**: Evaluate the function with `radius = 100.0` and 1, 5, 10, 15, and 20 digits respectively."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "radius = ..."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sphere_volume(...)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sphere_volume(...)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sphere_volume(...)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sphere_volume(...)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sphere_volume(...)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q1.4**: What observation do you make?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q1.5**: Using the [range()](https://docs.python.org/3/library/functions.html#func-range) built-in, write a `for`-loop and calculate the volume of a sphere with `radius = 42.0` for all `digits` from `1` through `20`. Print out each volume on a separate line.\n",
+    "\n",
+    "Note: This is the first task where you need to use the built-in [print()](https://docs.python.org/3/library/functions.html#print) function."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "radius = ..."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "for ... in ...:\n",
+    "    ..."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q1.6**: What lesson do you learn about the `float` type?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " "
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.4"
+  },
+  "toc": {
+   "base_numbering": 1,
+   "nav_menu": {},
+   "number_sections": false,
+   "sideBar": true,
+   "skip_h1_title": true,
+   "title_cell": "Table of Contents",
+   "title_sidebar": "Contents",
+   "toc_cell": false,
+   "toc_position": {},
+   "toc_section_display": false,
+   "toc_window_display": false
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/03_conditionals.ipynb b/03_conditionals_00_content.ipynb
similarity index 95%
rename from 03_conditionals.ipynb
rename to 03_conditionals_00_content.ipynb
index 9ffe09e..a9113a3 100644
--- a/03_conditionals.ipynb
+++ b/03_conditionals_00_content.ipynb
@@ -19,7 +19,7 @@
     }
    },
    "source": [
-    "We analyzed every aspect of the `average_evens()` function in [Chapter 2](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/02_functions.ipynb) except for the `if`-related parts. While it seems to do what we expect it to, there is a whole lot more we learn from taking it apart. In particular, the `if` may occur within both a **statement** or an **expression**, analogous as to how a noun in a natural language is *either* the subject of *or* an object in a sentence. What is common to both usages is that it leads to code being executed for *parts* of the input only. It is our first way of **controlling** the **flow of execution** in a program.\n",
+    "We analyzed every aspect of the `average_evens()` function in [Chapter 2](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/02_functions_00_content.ipynb) except for the `if`-related parts. While it seems to do what we expect it to, there is a whole lot more we learn from taking it apart. In particular, the `if` may occur within both a **statement** or an **expression**, analogous as to how a noun in a natural language is *either* the subject of *or* an object in a sentence. What is common to both usages is that it leads to code being executed for *parts* of the input only. It is our first way of **controlling** the **flow of execution** in a program.\n",
     "\n",
     "After deconstructing `if` in the first part of this chapter, we take a close look at a similar concept, namely handling **exceptions**."
    ]
@@ -139,7 +139,7 @@
     }
    },
    "source": [
-    "There are, however, cases where even well-behaved Python does not make us happy. [Chapter 5](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/05_numbers.ipynb#Imprecision) provides more insights into this \"bug.\""
+    "There are, however, cases where even well-behaved Python does not make us happy. [Chapter 5](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/05_numbers_00_content.ipynb#Imprecision) provides more insights into this \"bug.\""
    ]
   },
   {
@@ -189,7 +189,7 @@
     {
      "data": {
       "text/plain": [
-       "93918328484832"
+       "94426021241472"
       ]
      },
      "execution_count": 5,
@@ -213,7 +213,7 @@
     {
      "data": {
       "text/plain": [
-       "93918328484800"
+       "94426021241440"
       ]
      },
      "execution_count": 6,
@@ -281,7 +281,7 @@
     }
    },
    "source": [
-    "Let's not confuse the boolean `False` with `None`, another built-in object! We saw the latter before in [Chapter 2](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/02_functions.ipynb#Function-Definition) as the *implicit* return value of a function without a `return` statement.\n",
+    "Let's not confuse the boolean `False` with `None`, another built-in object! We saw the latter before in [Chapter 2](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/02_functions_00_content.ipynb#Function-Definition) as the *implicit* return value of a function without a `return` statement.\n",
     "\n",
     "We might think of `None` in a boolean context indicating a \"maybe\" or even an \"unknown\" answer; however, for Python, there are no \"maybe\" or \"unknown\" objects, as we see further below!\n",
     "\n",
@@ -313,7 +313,7 @@
     {
      "data": {
       "text/plain": [
-       "93918328471792"
+       "94426021228432"
       ]
      },
      "execution_count": 10,
@@ -357,7 +357,7 @@
     }
    },
    "source": [
-    "`True`, `False`, and `None` have the property that they each exist in memory only *once*. Objects designed this way are so-called **singletons**. This **[design pattern](https://en.wikipedia.org/wiki/Design_Patterns)** was originally developed to keep a program's memory usage at a minimum. It may only be employed in situations where we know that an object does *not* mutate its value (i.e., to reuse the bag analogy from [Chapter 1](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/01_elements.ipynb#Objects-vs.-Types-vs.-Values), no flipping of $0$s and $1$s in the bag is allowed). In languages \"closer\" to the memory like C, we would have to code this singleton logic ourselves, but Python has this built in for *some* types.\n",
+    "`True`, `False`, and `None` have the property that they each exist in memory only *once*. Objects designed this way are so-called **singletons**. This **[design pattern](https://en.wikipedia.org/wiki/Design_Patterns)** was originally developed to keep a program's memory usage at a minimum. It may only be employed in situations where we know that an object does *not* mutate its value (i.e., to reuse the bag analogy from [Chapter 1](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/01_elements_00_content.ipynb#Objects-vs.-Types-vs.-Values), no flipping of $0$s and $1$s in the bag is allowed). In languages \"closer\" to the memory like C, we would have to code this singleton logic ourselves, but Python has this built in for *some* types.\n",
     "\n",
     "We verify this with either the `is` operator or by comparing memory addresses."
    ]
@@ -1116,6 +1116,65 @@
     "bool([False])"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "With `str` objects, the empty `\"\"` evaluates to `False`, and any other to `True`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "False"
+      ]
+     },
+     "execution_count": 36,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "bool(\"\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 37,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "bool(\"Lorem ipsum dolor sit amet, ...\")"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {
@@ -1162,7 +1221,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 36,
+   "execution_count": 38,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -1187,75 +1246,12 @@
     "With the `or` operator, the first truthy sub-expression is returned."
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": 37,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "slide"
-    }
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Arg: 0\n",
-      "Arg: 1\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "1"
-      ]
-     },
-     "execution_count": 37,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "expr(0) or expr(1)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 38,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "-"
-    }
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Arg: 1\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "1"
-      ]
-     },
-     "execution_count": 38,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "expr(1) or expr(2)  # 2 is not evaluated due to short-circuiting"
-   ]
-  },
   {
    "cell_type": "code",
    "execution_count": 39,
    "metadata": {
     "slideshow": {
-     "slide_type": "-"
+     "slide_type": "slide"
     }
    },
    "outputs": [
@@ -1278,6 +1274,69 @@
      "output_type": "execute_result"
     }
    ],
+   "source": [
+    "expr(0) or expr(1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Arg: 1\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "1"
+      ]
+     },
+     "execution_count": 40,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "expr(1) or expr(2)  # 2 is not evaluated due to short-circuiting"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Arg: 0\n",
+      "Arg: 1\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "1"
+      ]
+     },
+     "execution_count": 41,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "expr(0) or expr(1) or expr(2)  # 2 is not evaluated due to short-circuiting"
    ]
@@ -1295,7 +1354,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 40,
+   "execution_count": 42,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -1317,7 +1376,7 @@
        "0"
       ]
      },
-     "execution_count": 40,
+     "execution_count": 42,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1339,7 +1398,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 41,
+   "execution_count": 43,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -1359,7 +1418,7 @@
        "0"
       ]
      },
-     "execution_count": 41,
+     "execution_count": 43,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1370,7 +1429,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 42,
+   "execution_count": 44,
    "metadata": {
     "slideshow": {
      "slide_type": "-"
@@ -1391,7 +1450,7 @@
        "0"
       ]
      },
-     "execution_count": 42,
+     "execution_count": 44,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1402,7 +1461,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 43,
+   "execution_count": 45,
    "metadata": {
     "slideshow": {
      "slide_type": "-"
@@ -1423,7 +1482,7 @@
        "0"
       ]
      },
-     "execution_count": 43,
+     "execution_count": 45,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1445,7 +1504,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 44,
+   "execution_count": 46,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -1466,7 +1525,7 @@
        "2"
       ]
      },
-     "execution_count": 44,
+     "execution_count": 46,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1522,7 +1581,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 45,
+   "execution_count": 47,
    "metadata": {
     "code_folding": [],
     "slideshow": {
@@ -1536,7 +1595,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 46,
+   "execution_count": 48,
    "metadata": {
     "code_folding": [],
     "slideshow": {
@@ -1578,7 +1637,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 47,
+   "execution_count": 49,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -1591,7 +1650,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 48,
+   "execution_count": 50,
    "metadata": {
     "slideshow": {
      "slide_type": "-"
@@ -1616,7 +1675,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 49,
+   "execution_count": 51,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -1653,7 +1712,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 50,
+   "execution_count": 52,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -1696,7 +1755,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 51,
+   "execution_count": 53,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -1769,7 +1828,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 52,
+   "execution_count": 54,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -1793,7 +1852,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 53,
+   "execution_count": 55,
    "metadata": {
     "slideshow": {
      "slide_type": "-"
@@ -1807,60 +1866,12 @@
     "    y = x ** 2"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": 54,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "-"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "9"
-      ]
-     },
-     "execution_count": 54,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "y"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "source": [
-    "On the contrary, the `if` expression fits into one line. The main downside is a potential loss in readability, in particular, if the functional relationship is not that simple. Also, some practitioners do *not* like that the condition is in the middle of the expression."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 55,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "fragment"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "y = 0 if x <= 0 else x ** 2"
-   ]
-  },
   {
    "cell_type": "code",
    "execution_count": 56,
    "metadata": {
     "slideshow": {
-     "slide_type": "skip"
+     "slide_type": "-"
     }
    },
    "outputs": [
@@ -1887,7 +1898,7 @@
     }
    },
    "source": [
-    "In this example, however, the most elegant solution is to use the built-in [max()](https://docs.python.org/3/library/functions.html#max) function."
+    "On the contrary, the `if` expression fits into one line. The main downside is a potential loss in readability, in particular, if the functional relationship is not that simple. Also, some practitioners do *not* like that the condition is in the middle of the expression."
    ]
   },
   {
@@ -1900,7 +1911,7 @@
    },
    "outputs": [],
    "source": [
-    "y = max(0, x) ** 2"
+    "y = 0 if x <= 0 else x ** 2"
    ]
   },
   {
@@ -1927,6 +1938,54 @@
     "y"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "In this example, however, the most elegant solution is to use the built-in [max()](https://docs.python.org/3/library/functions.html#max) function."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 59,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "y = max(0, x) ** 2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 60,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "9"
+      ]
+     },
+     "execution_count": 60,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "y"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {
@@ -1955,7 +2014,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 59,
+   "execution_count": 61,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -1968,7 +2027,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 60,
+   "execution_count": 62,
    "metadata": {
     "slideshow": {
      "slide_type": "-"
@@ -2007,7 +2066,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 61,
+   "execution_count": 63,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -2046,7 +2105,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 62,
+   "execution_count": 64,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -2057,7 +2116,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Oops. Division by 0. How does that work?\n",
+      "Yes, division worked smoothly.\n",
       "I am always printed\n"
      ]
     }
@@ -2119,7 +2178,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.3"
+   "version": "3.7.4"
   },
   "livereveal": {
    "auto_select": "code",
@@ -2147,5 +2206,5 @@
   }
  },
  "nbformat": 4,
- "nbformat_minor": 2
+ "nbformat_minor": 4
 }
diff --git a/03_conditionals_10_review.ipynb b/03_conditionals_10_review.ipynb
new file mode 100644
index 0000000..de1a419
--- /dev/null
+++ b/03_conditionals_10_review.ipynb
@@ -0,0 +1,214 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n",
+    "# Chapter 3: Conditionals & Exceptions"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Content Review"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Read [Chapter 3](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/03_conditionals_00_content.ipynb) of the book. Then, work through the questions below."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Essay Questions "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Answer the following questions *briefly*!"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q1**: What is the **singleton** design pattern? How many objects does the expression `[True, False, True, False]` generate in memory?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q2**: What do we mean when we talk about **truthy** and **falsy** expressions?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q3**: Describe in your own words the concept of **short-circuiting**! What does it imply for an individual sub-expression in a boolean expression?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q4**: Explain how the conceptual difference between a **statement** and an **expression** relates to the difference between a **conditional statement** and a **conditional expression**."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q5**: Why is the use of **temporary variables** encouraged in Python?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q6**: What does the `finally`-clause enforce in this code snippet? How can a `try` statement be useful *without* an `except`-clause?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "```python\n",
+    "try:\n",
+    "    print(\"Make a request to a service on the internet\")\n",
+    "finally:\n",
+    "    print(\"This could be clean-up code\")\n",
+    "```"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### True / False Questions"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Motivate your answer with *one short* sentence!"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q7**: The objects `True`, `False`, and `None` represent the idea of *yes*, *no*, and *maybe* answers in a natural language.\n",
+    "\n",
+    "Hint: you also respond with a code cell."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q8**: The `try` statement is useful for handling **syntax** errors."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " "
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.4"
+  },
+  "toc": {
+   "base_numbering": 1,
+   "nav_menu": {},
+   "number_sections": false,
+   "sideBar": true,
+   "skip_h1_title": true,
+   "title_cell": "Table of Contents",
+   "title_sidebar": "Contents",
+   "toc_cell": false,
+   "toc_position": {},
+   "toc_section_display": false,
+   "toc_window_display": false
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/03_conditionals_review_and_exercises.ipynb b/03_conditionals_20_exercises.ipynb
similarity index 53%
rename from 03_conditionals_review_and_exercises.ipynb
rename to 03_conditionals_20_exercises.ipynb
index 90b4ff9..23692a3 100644
--- a/03_conditionals_review_and_exercises.ipynb
+++ b/03_conditionals_20_exercises.ipynb
@@ -8,174 +8,6 @@
     "# Chapter 3: Conditionals & Exceptions"
    ]
   },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Content Review"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Read [Chapter 3](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/03_conditionals.ipynb) of the book. Then work through the eight review questions."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Essay Questions "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Answer the following questions briefly with *at most* 300 characters per question!"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**Q1**: What is the **singleton** design pattern? How many objects does the expression `[True, False, True, False]` generate in memory?"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    " "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**Q2**: What do we mean when we talk about **truthy** and **falsy** expressions?"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    " "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**Q3**: Describe in your own words the concept of **short-circuiting**! What does it imply for an individual sub-expression in a boolean expression?"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    " "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**Q4**: Explain how the conceptual difference between a **statement** and an **expression** relates to the difference between a **conditional statement** and a **conditional expression**."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    " "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**Q5**: Why is the use of **temporary variables** encouraged in Python?"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    " "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**Q6**: What does the `finally`-clause enforce in this code snippet? How can a `try` statement be useful *without* an `except`-clause?"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "```python\n",
-    "try:\n",
-    "    print(\"Make a request to a service on the internet\")\n",
-    "finally:\n",
-    "    print(\"This could be clean-up code\")\n",
-    "```"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    " "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### True / False Questions"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Motivate your answer with *one short* sentence!"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**Q7**: The objects `True`, `False`, and `None` represent the idea of *yes*, *no*, and *maybe* answers in a natural language.\n",
-    "\n",
-    "Hint: you also respond with a code cell."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    " "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**Q8**: The `try` statement is useful for handling **syntax** errors."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    " "
-   ]
-  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -183,6 +15,13 @@
     "## Coding Exercises"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Read [Chapter 3](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/03_conditionals_00_content.ipynb) of the book. Then, work through the exercises below."
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -194,7 +33,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "**Q9.1**: Write a function `discounted_price()` that takes the positional arguments `unit_price` (of type `float`) and `quantity` (of type `int`) and implements a discount scheme for a line item in a customer order as follows:\n",
+    "**Q1.1**: Write a function `discounted_price()` that takes the positional arguments `unit_price` (of type `float`) and `quantity` (of type `int`) and implements a discount scheme for a line item in a customer order as follows:\n",
     "\n",
     "- if the unit price is over 100 dollars, grant 10% relative discount\n",
     "- if a customer orders more than 10 items, one in every five items is for free\n",
@@ -218,7 +57,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "**Q9.2**: Calculate the final price for the following line items of an order:\n",
+    "**Q1.2**: Calculate the final price for the following line items of an order:\n",
     "- $7$ smartphones @ $99.00$ USD\n",
     "- $3$ workstations @ $999.00$ USD\n",
     "- $19$ GPUs @ $879.95$ USD\n",
@@ -265,7 +104,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "**Q9.3**: Calculate the last two line items with order quantities of $20$ and $15$. What do you observe?"
+    "**Q1.3**: Calculate the last two line items with order quantities of $20$ and $15$. What do you observe?"
    ]
   },
   {
@@ -297,7 +136,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "**Q9.4**: Looking at the `if`-`else`-logic in the function, why do you think the four example line items in **Q9.2** were chosen as they were?"
+    "**Q1.4**: Looking at the `if`-`else`-logic in the function, why do you think the four example line items in **Q9.2** were chosen as they were?"
    ]
   },
   {
@@ -327,7 +166,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "**Q10.1**: First, create a list `numbers` with the numbers from 1 through 100. You could type all numbers manually, but there is, of course, a smarter way. The built-in [range()](https://docs.python.org/3/library/functions.html#func-range) may be useful here. Read how it works in the documentation. To make the output of [range()](https://docs.python.org/3/library/functions.html#func-range) a `list` object, you have to wrap it with the [list()](https://docs.python.org/3/library/functions.html#func-list) built-in (i.e., `list(range(...))`)."
+    "**Q2.1**: First, create a list `numbers` with the numbers from 1 through 100. You could type all numbers manually, but there is, of course, a smarter way. The built-in [range()](https://docs.python.org/3/library/functions.html#func-range) may be useful here. Read how it works in the documentation. To make the output of [range()](https://docs.python.org/3/library/functions.html#func-range) a `list` object, you have to wrap it with the [list()](https://docs.python.org/3/library/functions.html#func-list) built-in (i.e., `list(range(...))`)."
    ]
   },
   {
@@ -343,9 +182,9 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "**Q10.2**: Loop over the `numbers` list and replace numbers for which one of the two (or both) conditions apply with text strings `\"Fizz\"`, `\"Buzz\"`, or `\"FizzBuzz\"` using the indexing operator `[]` and the assignment statement `=`.\n",
+    "**Q2.2**: Loop over the `numbers` list and replace numbers for which one of the two (or both) conditions apply with text strings `\"Fizz\"`, `\"Buzz\"`, or `\"FizzBuzz\"` using the indexing operator `[]` and the assignment statement `=`.\n",
     "\n",
-    "In [Chapter 1](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/01_elements.ipynb), we saw that Python starts indexing with `0` as the first element. Keep that in mind.\n",
+    "In [Chapter 1](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/01_elements_00_content.ipynb), we saw that Python starts indexing with `0` as the first element. Keep that in mind.\n",
     "\n",
     "So in each iteration of the `for`-loop, you have to determine an `index` variable as well as check the actual `number` for its divisors.\n",
     "\n",
@@ -366,7 +205,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "**Q10.3**: Create a loop that prints out either the number or any of the Fizz Buzz substitutes. Do it in such a way that we do not end up with 100 lines of output here."
+    "**Q2.3**: Create a loop that prints out either the number or any of the Fizz Buzz substitutes. Do it in such a way that we do not end up with 100 lines of output here."
    ]
   },
   {
@@ -396,7 +235,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.3"
+   "version": "3.7.4"
   },
   "toc": {
    "base_numbering": 1,
@@ -413,5 +252,5 @@
   }
  },
  "nbformat": 4,
- "nbformat_minor": 2
+ "nbformat_minor": 4
 }
diff --git a/04_iteration.ipynb b/04_iteration_00_content.ipynb
similarity index 96%
rename from 04_iteration.ipynb
rename to 04_iteration_00_content.ipynb
index aabaaef..ba6467d 100644
--- a/04_iteration.ipynb
+++ b/04_iteration_00_content.ipynb
@@ -839,9 +839,9 @@
    "source": [
     "This implementation is *highly* **inefficient** as small Fibonacci numbers already take a very long time to compute. The reason for this is **exponential growth** in the number of function calls. As [PythonTutor](http://pythontutor.com/visualize.html#code=def%20fibonacci%28i%29%3A%0A%20%20%20%20if%20i%20%3D%3D%200%3A%0A%20%20%20%20%20%20%20%20return%200%0A%20%20%20%20elif%20i%20%3D%3D%201%3A%0A%20%20%20%20%20%20%20%20return%201%0A%20%20%20%20return%20fibonacci%28i%20-%201%29%20%2B%20fibonacci%28i%20-%202%29%0A%0Arv%20%3D%20fibonacci%285%29&cumulative=false&curInstr=0&heapPrimitives=nevernest&mode=display&origin=opt-frontend.js&py=3&rawInputLstJSON=%5B%5D&textReferences=false) shows, `fibonacci()` is called again and again with the same `i` arguments.\n",
     "\n",
-    "To understand this in detail, we would have to study algorithms and data structures (e.g., with [this book](https://www.amazon.de/Introduction-Algorithms-Press-Thomas-Cormen/dp/0262033844/ref=sr_1_1?__mk_de_DE=%C3%85M%C3%85%C5%BD%C3%95%C3%91&crid=1JNE8U0VZGU0O&qid=1569837169&s=gateway&sprefix=algorithms+an%2Caps%2C180&sr=8-1)), a discipline within computer science, and dive into the analysis of **[time complexity of algorithms](https://en.wikipedia.org/wiki/Time_complexity)**.\n",
+    "To understand this in detail, we have to study algorithms and data structures (e.g., with [this book](https://www.amazon.de/Introduction-Algorithms-Press-Thomas-Cormen/dp/0262033844/ref=sr_1_1?__mk_de_DE=%C3%85M%C3%85%C5%BD%C3%95%C3%91&crid=1JNE8U0VZGU0O&qid=1569837169&s=gateway&sprefix=algorithms+an%2Caps%2C180&sr=8-1)), a discipline within computer science, and dive into the analysis of **[time complexity of algorithms](https://en.wikipedia.org/wiki/Time_complexity)**.\n",
     "\n",
-    "Luckily, in the Fibonacci case, the inefficiency can be resolved with a **caching** (i.e., \"reuse\") strategy from the field of **[dynamic programming](https://en.wikipedia.org/wiki/Dynamic_programming)**, namely **[memoization](https://en.wikipedia.org/wiki/Memoization)**. We do so in [Chapter 8](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/08_mappings.ipynb#Memoization), after introducing the [dictionaries](https://docs.python.org/3/library/stdtypes.html#dict) data type.\n",
+    "Luckily, in the Fibonacci case, the inefficiency can be resolved with a **caching** (i.e., \"reuse\") strategy from the field of **[dynamic programming](https://en.wikipedia.org/wiki/Dynamic_programming)**, namely **[memoization](https://en.wikipedia.org/wiki/Memoization)**. We do so in [Chapter 8](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/08_mappings_00_content.ipynb#Memoization), after introducing the `dict` data type.\n",
     "\n",
     "Let's measure the average run times for `fibonacci()` and varying `i` arguments with the `%%timeit` [cell magic](https://ipython.readthedocs.io/en/stable/interactive/magics.html#magic-timeit) that comes with Jupyter."
    ]
@@ -859,7 +859,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "37.5 µs ± 4.26 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n"
+      "49.8 µs ± 20.8 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n"
      ]
     }
    ],
@@ -881,7 +881,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "1.49 ms ± 22.4 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n"
+      "1.63 ms ± 14.8 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n"
      ]
     }
    ],
@@ -903,7 +903,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "199 ms ± 0 ns per loop (mean ± std. dev. of 1 run, 1 loop each)\n"
+      "202 ms ± 0 ns per loop (mean ± std. dev. of 1 run, 1 loop each)\n"
      ]
     }
    ],
@@ -925,7 +925,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "2.04 s ± 0 ns per loop (mean ± std. dev. of 1 run, 1 loop each)\n"
+      "2.25 s ± 0 ns per loop (mean ± std. dev. of 1 run, 1 loop each)\n"
      ]
     }
    ],
@@ -947,7 +947,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "5.33 s ± 0 ns per loop (mean ± std. dev. of 1 run, 1 loop each)\n"
+      "5.79 s ± 0 ns per loop (mean ± std. dev. of 1 run, 1 loop each)\n"
      ]
     }
    ],
@@ -4538,7 +4538,7 @@
     }
    },
    "source": [
-    "##### Efficiency of Algorithms (revisited)"
+    "##### Efficiency of Algorithms (continued)"
    ]
   },
   {
@@ -4565,7 +4565,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "4.6 s ± 0 ns per loop (mean ± std. dev. of 1 run, 1 loop each)\n"
+      "5.14 s ± 0 ns per loop (mean ± std. dev. of 1 run, 1 loop each)\n"
      ]
     }
    ],
@@ -4877,7 +4877,7 @@
     }
    },
    "source": [
-    "For sequences of integers, the [range()](https://docs.python.org/3/library/functions.html#func-range) built-in makes the `for` statement even more convenient: It creates a `list`-like object of type `range` that generates integers \"on the fly,\" and we look closely at the underlying effects in memory in [Chapter 7](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/07_sequences.ipynb#Mapping)."
+    "For sequences of integers, the [range()](https://docs.python.org/3/library/functions.html#func-range) built-in makes the `for` statement even more convenient: It creates a `list`-like object of type `range` that generates integers \"on the fly,\" and we look closely at the underlying effects in memory in [Chapter 7](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/07_sequences_00_content.ipynb#Mapping)."
    ]
   },
   {
@@ -5008,11 +5008,11 @@
     "\n",
     "Now, just as we classify objects by data type, we also classify these data types (e.g., `int`, `float`, `str`, or `list`) into **abstract concepts**.\n",
     "\n",
-    "We did this already in [Chapter 1](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/01_elements.ipynb#Who-am-I?-And-how-many?) when we described a `list` object as \"some sort of container that holds [...] references to other objects\". So, abstractly speaking, **containers** are any objects that are \"composed\" of other objects and also \"manage\" how these objects are organized. `list` objects, for example, have the property that they model an order associated with their elements. There exist, however, other container types, many of which do *not* come with an order. So, containers primarily \"contain\" other objects and have *nothing* to do with looping.\n",
+    "We did this already in [Chapter 1](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/01_elements_00_content.ipynb#Who-am-I?-And-how-many?) when we described a `list` object as \"some sort of container that holds [...] references to other objects\". So, abstractly speaking, **containers** are any objects that are \"composed\" of other objects and also \"manage\" how these objects are organized. `list` objects, for example, have the property that they model an order associated with their elements. There exist, however, other container types, many of which do *not* come with an order. So, containers primarily \"contain\" other objects and have *nothing* to do with looping.\n",
     "\n",
     "On the contrary, the abstract concept of **iterables** is all about looping: Any object that we can loop over is, by definition, an iterable. So, `range` objects, for example, are iterables, even though they hold no references to other objects. Moreover, looping does *not* have to occur in a *predictable* order, although this is the case for both `list` and `range` objects.\n",
     "\n",
-    "Typically, containers are iterables, and iterables are containers. Yet, only because these two concepts coincide often, we must not think of them as the same. In [Chapter 7](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/07_sequences.ipynb#Collections-vs.-Sequences), we formalize these two concepts and introduce many more. Finally, Chapter 9 gives an explanation how abstract concepts are implemented and play together.\n",
+    "Typically, containers are iterables, and iterables are containers. Yet, only because these two concepts coincide often, we must not think of them as the same. In [Chapter 7](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/07_sequences_00_content.ipynb#Collections-vs.-Sequences), we formalize these two concepts and introduce many more. Finally, Chapter 10 gives an explanation how abstract concepts are implemented and play together.\n",
     "\n",
     "Let's continue with `first_names` below as an example an illustrate what iterable containers are."
    ]
@@ -5399,7 +5399,7 @@
     }
    },
    "source": [
-    "##### Efficiency of Algorithms (revisited)"
+    "##### Efficiency of Algorithms (continued)"
    ]
   },
   {
@@ -5574,7 +5574,7 @@
     }
    },
    "source": [
-    "Let's say we have a `samples` list and want to check if the square of at least one of its elements is above a `threshold` of `100`."
+    "Let's say we have a list of `numbers` and want to check if the square of at least one of its elements is above a `threshold` of `100`."
    ]
   },
   {
@@ -5587,7 +5587,7 @@
    },
    "outputs": [],
    "source": [
-    "samples = [3, 7, 2, 9, 11, 4, 7, 9, 4, 5]"
+    "numbers = [7, 11, 8, 5, 3, 12, 2, 6, 9, 10, 1, 4]"
    ]
   },
   {
@@ -5611,7 +5611,7 @@
     }
    },
    "source": [
-    "A first naive implementation could look like this: We loop over *every* element in `samples` and set an **indicator variable** `is_above`, initialized as `False`, to `True` once we encounter an element satisfying the search condition."
+    "A first naive implementation could look like this: We loop over *every* element in `numbers` and set an **indicator variable** `is_above`, initialized as `False`, to `True` once we encounter an element satisfying the search condition."
    ]
   },
   {
@@ -5627,22 +5627,22 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "3   7   2   9   11   4   7   9   4   5   =>   at least one sample's square is above 100\n"
+      "7   11   8   5   3   12   2   6   9   10   1   4   =>   at least one number's square is above 100\n"
      ]
     }
    ],
    "source": [
     "is_above = False\n",
     "\n",
-    "for sample in samples:\n",
-    "    print(sample, end=\"   \")  # added for didactical purposes\n",
-    "    if sample ** 2 > threshold:\n",
+    "for number in numbers:\n",
+    "    print(number, end=\"   \")  # added for didactical purposes\n",
+    "    if number ** 2 > threshold:\n",
     "        is_above = True\n",
     "\n",
     "if is_above:\n",
-    "    print(\"=>   at least one sample's square is above\", threshold)\n",
+    "    print(\"=>   at least one number's square is above\", threshold)\n",
     "else:\n",
-    "    print(\"=>   no sample's square is above\", threshold)"
+    "    print(\"=>   no number's square is above\", threshold)"
    ]
   },
   {
@@ -5653,7 +5653,7 @@
     }
    },
    "source": [
-    "This implementation is *inefficient* as even if the *first* element in `samples` has a square greater than `100`, we loop until the last element: This could take a long time for a big list.\n",
+    "This implementation is *inefficient* as even if the *first* element in `numbers` has a square greater than `100`, we loop until the last element: This could take a long time for a big list.\n",
     "\n",
     "Moreover, we must initialize `is_above` *before* the `for`-loop and write an `if`-`else`-logic *after* it to check for the result. The actual business logic is *not* clear right away.\n",
     "\n",
@@ -5673,23 +5673,23 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "3   7   2   9   11   =>   at least one sample's square is above 100\n"
+      "7   11   =>   at least one number's square is above 100\n"
      ]
     }
    ],
    "source": [
     "is_above = False\n",
     "\n",
-    "for sample in samples:\n",
-    "    print(sample, end=\"   \")  # added for didactical purposes\n",
-    "    if sample ** 2 > threshold:\n",
+    "for number in numbers:\n",
+    "    print(number, end=\"   \")  # added for didactical purposes\n",
+    "    if number ** 2 > threshold:\n",
     "        is_above = True\n",
     "        break\n",
     "\n",
     "if is_above:\n",
-    "    print(\"=>   at least one sample's square is above\", threshold)\n",
+    "    print(\"=>   at least one number's square is above\", threshold)\n",
     "else:\n",
-    "    print(\"=>   no sample's square is above\", threshold)"
+    "    print(\"=>   no number's square is above\", threshold)"
    ]
   },
   {
@@ -5751,23 +5751,23 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "3   7   2   9   11   =>   at least one sample's square is above 100\n"
+      "7   11   =>   at least one number's square is above 100\n"
      ]
     }
    ],
    "source": [
-    "for sample in samples:\n",
-    "    print(sample, end=\"   \")  # added for didactical purposes\n",
-    "    if sample ** 2 > threshold:\n",
+    "for number in numbers:\n",
+    "    print(number, end=\"   \")  # added for didactical purposes\n",
+    "    if number ** 2 > threshold:\n",
     "        is_above = True\n",
     "        break\n",
     "else:\n",
     "    is_above = False\n",
     "\n",
     "if is_above:\n",
-    "    print(\"=>   at least one sample's square is above\", threshold)\n",
+    "    print(\"=>   at least one number's square is above\", threshold)\n",
     "else:\n",
-    "    print(\"=>   no sample's square is above\", threshold)"
+    "    print(\"=>   no number's square is above\", threshold)"
    ]
   },
   {
@@ -5794,18 +5794,18 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "3   7   2   9   11   =>   at least one sample's square is above 100\n"
+      "7   11   =>   at least one number's square is above 100\n"
      ]
     }
    ],
    "source": [
-    "for sample in samples:\n",
-    "    print(sample, end=\"   \")  # added for didactical purposes\n",
-    "    if sample ** 2 > threshold:\n",
-    "        print(\"=>   at least one sample's square is above\", threshold)\n",
+    "for number in numbers:\n",
+    "    print(number, end=\"   \")  # added for didactical purposes\n",
+    "    if number ** 2 > threshold:\n",
+    "        print(\"=>   at least one number's square is above\", threshold)\n",
     "        break\n",
     "else:\n",
-    "    print(\"=>   no sample's square is above\", threshold)"
+    "    print(\"=>   no number's square is above\", threshold)"
    ]
   },
   {
@@ -5816,7 +5816,7 @@
     }
    },
    "source": [
-    "Of course, if we set the `threshold` an element's square has to pass higher, for example, to `200`, we have to loop over all `samples`. There is *no way* to optimize this **[linear search](https://en.wikipedia.org/wiki/Linear_search)** further, at least as long as we model `samples` as a `list` object. More advanced data types, however, exist that mitigate that downside."
+    "Of course, if we set the `threshold` an element's square has to pass higher, for example, to `200`, we have to loop over all `numbers`. There is *no way* to optimize this **[linear search](https://en.wikipedia.org/wiki/Linear_search)** further, at least as long as we model `numbers` as a `list` object. More advanced data types, however, exist that mitigate that downside."
    ]
   },
   {
@@ -5832,20 +5832,20 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "3   7   2   9   11   4   7   9   4   5   =>   no sample's square is above 200\n"
+      "7   11   8   5   3   12   2   6   9   10   1   4   =>   no number's square is above 200\n"
      ]
     }
    ],
    "source": [
     "threshold = 200\n",
     "\n",
-    "for sample in samples:\n",
-    "    print(sample, end=\"   \")  # added for didactical purposes\n",
-    "    if sample ** 2 > threshold:\n",
-    "        print(\"=>   at least one sample's square is above\", threshold)\n",
+    "for number in numbers:\n",
+    "    print(number, end=\"   \")  # added for didactical purposes\n",
+    "    if number ** 2 > threshold:\n",
+    "        print(\"=>   at least one number's square is above\", threshold)\n",
     "        break\n",
     "else:\n",
-    "    print(\"=>   no sample's square is above\", threshold)"
+    "    print(\"=>   no number's square is above\", threshold)"
    ]
   },
   {
@@ -5867,17 +5867,17 @@
     }
    },
    "source": [
-    "Often, we process some iterable with numeric data, for example, a list of `numbers` as in this book's introductory example in [Chapter 1](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/01_elements.ipynb#Example:-Averaging-Even-Numbers) or, more realistically, data from a CSV file with many rows and columns.\n",
+    "Often, we process some iterable with numeric data, for example, a list of `numbers` as in this book's introductory example in [Chapter 1](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/01_elements_00_content.ipynb#Example:-Averaging-Even-Numbers) or, more realistically, data from a CSV file with many rows and columns.\n",
     "\n",
     "Processing numeric data usually comes down to operations that may be grouped into one of the following three categories:\n",
     "\n",
-    "- **mapping**: transform a sample according to some functional relationship $y = f(x)$\n",
-    "- **filtering**: throw away individual samples (e.g., statistical outliers)\n",
-    "- **reducing**: collect individual samples into summary statistics\n",
+    "- **mapping**: transform a number according to some functional relationship $y = f(x)$\n",
+    "- **filtering**: throw away individual numbers (e.g., statistical outliers in a sample)\n",
+    "- **reducing**: collect individual numbers into summary statistics\n",
     "\n",
-    "We study this **map-filter-reduce** paradigm extensively in [Chapter 7](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/07_sequences.ipynb#The-Map-Filter-Reduce-Paradigm) after introducing more advanced data types that are needed to work with \"big\" data.\n",
+    "We study this **map-filter-reduce** paradigm extensively in [Chapter 7](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/07_sequences_00_content.ipynb#The-Map-Filter-Reduce-Paradigm) after introducing more advanced data types that are needed to work with \"big\" data.\n",
     "\n",
-    "In the remainder of this section, we focus on *filtering out* some samples within a `for`-loop."
+    "Here, we focus on *filtering out* some numbers with a `for`-loop."
    ]
   },
   {
@@ -6074,7 +6074,7 @@
     }
    },
    "source": [
-    "With already three levels of indentation, less horizontal space is available for the actual code block. Of course, one could flatten the two `if` statements with the logical `and` operator, as shown in [Chapter 3](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/03_conditionals.ipynb#The-if-Statement). Then, however, we trade off horizontal space against a more \"complex\" `if` logic, and this is *not* a real improvement.\n",
+    "With already three levels of indentation, less horizontal space is available for the actual code block. Of course, one could flatten the two `if` statements with the logical `and` operator, as shown in [Chapter 3](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/03_conditionals_00_content.ipynb#The-if-Statement). Then, however, we trade off horizontal space against a more \"complex\" `if` logic, and this is *not* a real improvement.\n",
     "\n",
     "A Pythonista would instead make use of the `continue` statement (cf., [reference](https://docs.python.org/3/reference/simple_stmts.html#the-continue-statement)) that causes a loop to jump into the next iteration skipping the rest of the code block.\n",
     "\n",
@@ -6236,13 +6236,31 @@
     }
    },
    "outputs": [
+    {
+     "name": "stdin",
+     "output_type": "stream",
+     "text": [
+      "Guess if the coin comes up as heads or tails:  Tails\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Ooops, it was tails\n"
+     ]
+    },
+    {
+     "name": "stdin",
+     "output_type": "stream",
+     "text": [
+      "Guess if the coin comes up as heads or tails:  heads\n"
+     ]
+    },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Guess if the coin comes up as heads or tails: Tails\n",
-      "Ooops, it was tails\n",
-      "Guess if the coin comes up as heads or tails: heads\n",
       "Yes, it was heads\n"
      ]
     }
@@ -6402,13 +6420,31 @@
     }
    },
    "outputs": [
+    {
+     "name": "stdin",
+     "output_type": "stream",
+     "text": [
+      "Guess if the coin comes up as heads or tails:  invalid\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Make sure to enter your guess correctly!\n"
+     ]
+    },
+    {
+     "name": "stdin",
+     "output_type": "stream",
+     "text": [
+      "Guess if the coin comes up as heads or tails:  Heads\n"
+     ]
+    },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Guess if the coin comes up as heads or tails: invalid\n",
-      "Make sure to enter your guess correctly!\n",
-      "Guess if the coin comes up as heads or tails: Heads\n",
       "Yes, it was heads\n"
      ]
     }
@@ -6485,7 +6521,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.3"
+   "version": "3.7.4"
   },
   "livereveal": {
    "auto_select": "code",
@@ -6513,5 +6549,5 @@
   }
  },
  "nbformat": 4,
- "nbformat_minor": 2
+ "nbformat_minor": 4
 }
diff --git a/04_iteration_10_review.ipynb b/04_iteration_10_review.ipynb
new file mode 100644
index 0000000..808e309
--- /dev/null
+++ b/04_iteration_10_review.ipynb
@@ -0,0 +1,281 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n",
+    "# Chapter 4: Recursion & Looping"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Content Review"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Read [Chapter 4](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/04_iteration_00_content.ipynb) of the book. Then, work through the questions below."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Essay Questions "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Answer the following questions *briefly*!"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q1**: Solving a problem by **recursion** is not only popular in computer science and math. Name some examples from the fields of business or economics where problems are also solved in a **backward** fashion!"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q2**: Explain what **duck typing** means! Why can it cause problems? Why is it [not a bug but a feature](https://www.urbandictionary.com/define.php?term=It%27s%20not%20a%20bug%2C%20it%27s%20a%20feature)?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q3**: What is **syntactic sugar**?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q4**: Describe in your own words why the **recursive** version of `fibonacci()`, the \"Easy at first Glance\" example in the chapter, is computationally **inefficient**! Why does the **iterative** version of `fibonacci()`, the \"Hard at first Glance\" example, run so much faster?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q5**: What is the conceptual difference between a **container** and a **list**?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q6**: What is a good use case for the `for`-loop's optional `else`-clause?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### True / False Questions"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Motivate your answer with *one short* sentence!"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q7**: When a **recursion** does **not** reach the base case, this is an example of the **early exit** strategy."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q8**: Any programming language **without** looping constructs like the `for` or `while` statements is **not** Turing complete."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q9**: A **recursive** formulation is the same as a **circular** one: The terms are **synonyms**."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q10**: Formulating a computational problem as a **recursion** results in an **efficient** implementation."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q11**: Whereas a **recursion** may accidentally result in a **never-ending** program, `while`-loops and `for`-loops are guaranteed to **terminate**."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q12**: Before writing **any** kind of **loop**, we **always** need to think about a **stopping criterion** ahead of time."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q13**: **Container** types such as `list` objects are characterized by their **support** for **being looped over**, for example as in:"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "```python\n",
+    "for element in container:\n",
+    "    # do something for every element\n",
+    "    ...\n",
+    "```"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " "
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.4"
+  },
+  "toc": {
+   "base_numbering": 1,
+   "nav_menu": {},
+   "number_sections": false,
+   "sideBar": true,
+   "skip_h1_title": true,
+   "title_cell": "Table of Contents",
+   "title_sidebar": "Contents",
+   "toc_cell": false,
+   "toc_position": {},
+   "toc_section_display": false,
+   "toc_window_display": false
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/04_iteration_review_and_exercises.ipynb b/04_iteration_20_exercises.ipynb
similarity index 64%
rename from 04_iteration_review_and_exercises.ipynb
rename to 04_iteration_20_exercises.ipynb
index 9e2b28f..81f6928 100644
--- a/04_iteration_review_and_exercises.ipynb
+++ b/04_iteration_20_exercises.ipynb
@@ -8,255 +8,6 @@
     "# Chapter 4: Recursion & Looping"
    ]
   },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Content Review"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Read [Chapter 4](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/04_iteration.ipynb) of the book. Then work through the fourteen review questions."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Essay Questions "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Answer the following questions briefly with *at most* 300 characters per question!"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**Q1**: What is so \"special\" about the number **7919**?"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    " "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**Q2**: Solving a problem by **recursion** is not only popular in computer science and math. Name some examples from the fields of business or economics where problems are also solved in a **backward** fashion!"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    " "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**Q3**: Explain what **duck typing** means! Why can it cause problems? Why is it [not a bug but a feature](https://www.urbandictionary.com/define.php?term=It%27s%20not%20a%20bug%2C%20it%27s%20a%20feature)?"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    " "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**Q4**: What is **syntactic sugar**?"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    " "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**Q5**: Describe in your own words why the **recursive** version of `fibonacci()`, the \"Easy at first Glance\" example in the chapter, is computationally **inefficient**! Why does the **iterative** version of `fibonacci()`, the \"Hard at first Glance\" example, run so much faster?"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    " "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**Q6**: What is the conceptual difference between a **container** and a **list**?"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    " "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**Q7**: What is a good use case for the `for`-loop's optional `else`-clause?"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    " "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### True / False Questions"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Motivate your answer with *one short* sentence!"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**Q8**: When a **recursion** does **not** reach the base case, this is an example of the **early exit** strategy."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    " "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**Q9**: Any programming language **without** looping constructs like the `for` or `while` statements is **not** Turing complete."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    " "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**Q10**: A **recursive** formulation is the same as a **circular** one: The terms are **synonyms**."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    " "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**Q11**: Formulating a computational problem as a **recursion** results in an **efficient** implementation."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    " "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**Q12**: Whereas a **recursion** may accidentally result in a **never-ending** program, `while`-loops and `for`-loops are guaranteed to **terminate**."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    " "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**Q13**: Before writing **any** kind of **loop**, we **always** need to think about a **stopping criterion** ahead of time."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    " "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**Q14**: **Container** types such as `list` objects are characterized by their **support** for **being looped over**, for example as in:"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "```python\n",
-    "for element in container:\n",
-    "    # do something for every element\n",
-    "    ...\n",
-    "```"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    " "
-   ]
-  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -264,6 +15,13 @@
     "## Coding Exercises"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Read [Chapter 4](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/04_iteration_00_content.ipynb) of the book. Then, work through the exercises below."
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -330,7 +88,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "**Q15.1**: Explain for the $n = 3$ case why it can be solved as a **recursion**!"
+    "**Q1.1**: Explain for the $n = 3$ case why it can be solved as a **recursion**!"
    ]
   },
   {
@@ -344,7 +102,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "**Q15.2**: How does the number of minimal moves needed to solve a problem with three spots and $n$ disks grow as a function of $n$? How does this relate to the answer to **Q15.1**?"
+    "**Q1.2**: How does the number of minimal moves needed to solve a problem with three spots and $n$ disks grow as a function of $n$? How does this relate to the answer to **Q1.1**?"
    ]
   },
   {
@@ -358,7 +116,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "**Q15.3**: The **[Towers of Hanoi](https://en.wikipedia.org/wiki/Tower_of_Hanoi)** problem is of **exponential growth**. What does that mean? What does that imply for large $n$?"
+    "**Q1.3**: The **[Towers of Hanoi](https://en.wikipedia.org/wiki/Tower_of_Hanoi)** problem is of **exponential growth**. What does that mean? What does that imply for large $n$?"
    ]
   },
   {
@@ -372,7 +130,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "**Q15.4**: The video introduces the recursive relationship $Sol(4, 1, 3) = Sol(3, 1, 2) ~ \\bigoplus ~ Sol(1, 1, 3) ~ \\bigoplus ~ Sol(3, 2, 3)$. The $\\bigoplus$ is to be interpreted as some sort of \"plus\" operation. How does this \"plus\" operation work? How does this way of expressing the problem relate to the answer to **Q15.1**?"
+    "**Q1.4**: The video introduces the recursive relationship $Sol(4, 1, 3) = Sol(3, 1, 2) ~ \\bigoplus ~ Sol(1, 1, 3) ~ \\bigoplus ~ Sol(3, 2, 3)$. The $\\bigoplus$ is to be interpreted as some sort of \"plus\" operation. How does this \"plus\" operation work? How does this way of expressing the problem relate to the answer to **Q1.1**?"
    ]
   },
   {
@@ -439,7 +197,7 @@
     "\n",
     "Once completed, `sol()` should print out all the moves in the correct order. With **printing a move**, we mean a line like \"1 -> 3\", short for \"Move the top-most disk from spot 1 to spot 3\".\n",
     "\n",
-    "Write your answers to **Q15.5** to **Q15.7** into the subsequent code cell and finalize `sol()`! No need to write a docstring or validate the input here."
+    "Write your answers to **Q1.5** to **Q1.7** into the subsequent code cell and finalize `sol()`! No need to write a docstring or validate the input here."
    ]
   },
   {
@@ -464,14 +222,14 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "**Q15.5**: What is the `disks` argument when the function reaches its **base case**? Check for the base case with a simple `if` statement and return from the function using the **early exit** pattern!"
+    "**Q1.5**: What is the `disks` argument when the function reaches its **base case**? Check for the base case with a simple `if` statement and return from the function using the **early exit** pattern!"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "**Q15.6**: If not in the base case, `sol()` needs to determine the `intermediate` spot given concrete `origin` and `destination` arguments. For example, if called with `origin=1` and `destination=2`, `intermediate` must be `3`.\n",
+    "**Q1.6**: If not in the base case, `sol()` needs to determine the `intermediate` spot given concrete `origin` and `destination` arguments. For example, if called with `origin=1` and `destination=2`, `intermediate` must be `3`.\n",
     "\n",
     "Add **one** compound `if` statement to `sol()` that has a branch for **every** possible `origin`-`destination` pair that sets a variable `intermediate` to the correct temporary spot. **How many** branches will there be?"
    ]
@@ -480,7 +238,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "**Q15.7**: `sol()` needs to call itself **two more times** with the correct 2-pairs chosen from the three available spots `origin`, `intermediate`, and `destination`.\n",
+    "**Q1.7**: `sol()` needs to call itself **two more times** with the correct 2-pairs chosen from the three available spots `origin`, `intermediate`, and `destination`.\n",
     "\n",
     "In between the two recursive function calls, write a `print()` statement that prints out from where to where the \"remaining and largest\" disk has to be moved!"
    ]
@@ -489,7 +247,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "**Q15.8**: Execute the code cells below and confirm that the printed moves are correct!"
+    "**Q1.8**: Execute the code cells below and confirm that the printed moves are correct!"
    ]
   },
   {
@@ -543,7 +301,7 @@
     "\n",
     "Let's create a more concise `hanoi()` function that, in addition to a positional `disks` argument, takes three keyword-only arguments `origin`, `intermediate`, and `destination` with default values `\"left\"`, `\"center\"`, and `\"right\"`.\n",
     "\n",
-    "Write your answers to **Q15.9** and **Q15.10** into the subsequent code cell and finalize `hanoi()`! No need to write a docstring or validate the input here."
+    "Write your answers to **Q1.9** and **Q1.10** into the subsequent code cell and finalize `hanoi()`! No need to write a docstring or validate the input here."
    ]
   },
   {
@@ -565,14 +323,14 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "**Q15.9**: Copy the base case from `sol()`!"
+    "**Q1.9**: Copy the base case from `sol()`!"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "**Q15.10**: Instead of conditional logic, `hanoi()` calls itself **two times** with the **three** arguments `origin`, `intermediate`, and `destination` passed on in a **different** order.\n",
+    "**Q1.10**: Instead of conditional logic, `hanoi()` calls itself **two times** with the **three** arguments `origin`, `intermediate`, and `destination` passed on in a **different** order.\n",
     "\n",
     "Figure out how the arguments are passed on in the two recursive `hanoi()` calls!\n",
     "\n",
@@ -583,7 +341,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "**Q15.11**: Execute the code cells below and confirm that the printed moves are correct!"
+    "**Q1.11**: Execute the code cells below and confirm that the printed moves are correct!"
    ]
   },
   {
@@ -653,11 +411,11 @@
     "\n",
     "In such cases, we could modify a recursive function to return a count value to be passed up the recursion tree.\n",
     "\n",
-    "This is similar to what we do in the recursive versions of `factorial()` and `fibonacci()` in [Chapter 4](https://github.com/webartifex/intro-to-python/blob/master/04_iteration.ipynb), where we pass up an intermediate result.\n",
+    "This is similar to what we do in the recursive versions of `factorial()` and `fibonacci()` in [Chapter 4](https://github.com/webartifex/intro-to-python/blob/master/04_iteration_00_content.ipynb), where we pass up an intermediate result.\n",
     "\n",
     "Let's create a `hanoi_moves()` function that follows the same internal logic as `hanoi()`, but, instead of printing out the moves, returns the number of steps done so far in the recursion.\n",
     "\n",
-    "Write your answers to **Q15.12** to **Q15.14** into the subsequent code cell and finalize `hanoi_moves()`! No need to write a docstring or validate the input here."
+    "Write your answers to **Q1.12** to **Q1.14** into the subsequent code cell and finalize `hanoi_moves()`! No need to write a docstring or validate the input here."
    ]
   },
   {
@@ -682,21 +440,21 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "**Q15.12**: Copy the base case from `hanoi()`! What count should be returned when it is reached?"
+    "**Q1.12**: Copy the base case from `hanoi()`! What count should be returned when it is reached?"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "**Q15.13**: Initialize the variable `moves` with an appropriate count! This is the number of moves that corresponds to **one** recursive function call."
+    "**Q1.13**: Initialize the variable `moves` with an appropriate count! This is the number of moves that corresponds to **one** recursive function call."
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "**Q15.14**: `moves` is updated with the counts passed up from the two recursive calls.\n",
+    "**Q1.14**: `moves` is updated with the counts passed up from the two recursive calls.\n",
     "\n",
     "Complete the two recursive function calls with the same arguments as in `hanoi()`!"
    ]
@@ -705,7 +463,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "**Q15.15**: Write a `for`-loop that prints out the **minimum number** of moves needed to solve Towers of Hanoi for any number of `disks` from `1` through `20` to confirm your answer to **Q15.2**."
+    "**Q1.15**: Write a `for`-loop that prints out the **minimum number** of moves needed to solve Towers of Hanoi for any number of `disks` from `1` through `20` to confirm your answer to **Q1.2**."
    ]
   },
   {
@@ -733,7 +491,7 @@
     "\n",
     "With `disks` in the range from `24` through `26`, the computation time roughly doubles for each increase of `disks` by 1.\n",
     "\n",
-    "**Q15.16**: Execute the code cells below and see for yourself!"
+    "**Q1.16**: Execute the code cells below and see for yourself!"
    ]
   },
   {
@@ -779,7 +537,7 @@
    "source": [
     "The above `hanoi()` prints the optimal solution's moves in the correct order but fails to label each move with an order number. This can be built in by passing on one more argument `offset` down the recursion tree. As the logic gets a bit \"involved,\" `hanoi_ordered()` below is almost finished.\n",
     "\n",
-    "Write your answers to **Q15.17** and **Q15.18** into the subsequent code cell and finalize `hanoi_ordered()`! No need to write a docstring or validate the input here."
+    "Write your answers to **Q1.17** and **Q1.18** into the subsequent code cell and finalize `hanoi_ordered()`! No need to write a docstring or validate the input here."
    ]
   },
   {
@@ -809,14 +567,14 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "**Q15.17**: Copy the base case from the original `hanoi()`!"
+    "**Q1.17**: Copy the base case from the original `hanoi()`!"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "**Q15.18**: Complete the two recursive function calls with the same arguments as in `hanoi()` or `hanoi_moves()`! Do not change the already filled in `offset` arguments!\n",
+    "**Q1.18**: Complete the two recursive function calls with the same arguments as in `hanoi()` or `hanoi_moves()`! Do not change the already filled in `offset` arguments!\n",
     "\n",
     "Then, copy the `print()` statement from `hanoi()` and adjust it to print out `count` as well!"
    ]
@@ -825,7 +583,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "**Q15.19**: Execute the code cells below and confirm that the order numbers are correct!"
+    "**Q1.19**: Execute the code cells below and confirm that the order numbers are correct!"
    ]
   },
   {
@@ -882,7 +640,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "**Q15.20**: Conducting your own research on the internet (max. 15 minutes), what can you say about generalizing the **[Towers of Hanoi](https://en.wikipedia.org/wiki/Tower_of_Hanoi)** problem to a setting with **more than three** landing spots?"
+    "**Q1.20**: Conducting your own research on the internet (max. 15 minutes), what can you say about generalizing the **[Towers of Hanoi](https://en.wikipedia.org/wiki/Tower_of_Hanoi)** problem to a setting with **more than three** landing spots?"
    ]
   },
   {
@@ -909,7 +667,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.3"
+   "version": "3.7.4"
   },
   "toc": {
    "base_numbering": 1,
@@ -926,5 +684,5 @@
   }
  },
  "nbformat": 4,
- "nbformat_minor": 2
+ "nbformat_minor": 4
 }
diff --git a/05_numbers.ipynb b/05_numbers_00_content.ipynb
similarity index 96%
rename from 05_numbers.ipynb
rename to 05_numbers_00_content.ipynb
index 48d8623..1f7ebc2 100644
--- a/05_numbers.ipynb
+++ b/05_numbers_00_content.ipynb
@@ -21,15 +21,15 @@
    "source": [
     "After learning about the basic building blocks of expressing and structuring the business logic in programs, we focus our attention on the **data types** Python offers us, both built-in and available via the [standard library](https://docs.python.org/3/library/index.html) or third-party packages.\n",
     "\n",
-    "We start with the \"simple\" ones: Numeric types in this chapter and textual data in [Chapter 6](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/06_text.ipynb). An important fact that holds for all objects of these types is that they are **immutable**. To reuse the bag analogy from [Chapter 1](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/01_elements.ipynb#Objects-vs.-Types-vs.-Values), this means that the $0$s and $1$s making up an object's *value* cannot be changed once the bag is created in memory, implying that any operation with or method on the object creates a *new* object in a *different* memory location.\n",
+    "We start with the \"simple\" ones: Numeric types in this chapter and textual data in [Chapter 6](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/06_text_00_content.ipynb). An important fact that holds for all objects of these types is that they are **immutable**. To reuse the bag analogy from [Chapter 1](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/01_elements_00_content.ipynb#Objects-vs.-Types-vs.-Values), this means that the $0$s and $1$s making up an object's *value* cannot be changed once the bag is created in memory, implying that any operation with or method on the object creates a *new* object in a *different* memory location.\n",
     "\n",
-    "[Chapter 7](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/07_sequences.ipynb) and [Chapter 8](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/08_mappings.ipynb) then cover the more \"complex\" data types, including, for example, the `list` type. Finally, Chapter 9 completes the picture by introducing language constructs to create custom types.\n",
+    "[Chapter 7](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/07_sequences_00_content.ipynb), [Chapter 8](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/08_mappings_00_content.ipynb), and [Chapter 9](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/09_arrays_00_content.ipynb) then cover the more \"complex\" data types, including, for example, the `list` type. Finally, Chapter 10 completes the picture by introducing language constructs to create custom types.\n",
     "\n",
     "We have already seen many hints indicating that numbers are not as trivial to work with as it seems at first sight:\n",
     "\n",
-    "- [Chapter 1](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/01_elements.ipynb#%28Data%29-Type-%2F-%22Behavior%22) reveals that numbers may come in *different* data types (i.e., `int` vs. `float` so far),\n",
-    "- [Chapter 3](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/03_conditionals.ipynb#Boolean-Expressions) raises questions regarding the **limited precision** of `float` numbers (e.g., `42 == 42.000000000000001` evaluates to `True`), and\n",
-    "- [Chapter 4](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/04_iteration.ipynb#Infinite-Recursion) shows that sometimes a `float` \"walks\" and \"quacks\" like an `int`, whereas the reverse is true in other cases.\n",
+    "- [Chapter 1](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/01_elements_00_content.ipynb#%28Data%29-Type-%2F-%22Behavior%22) reveals that numbers may come in *different* data types (i.e., `int` vs. `float` so far),\n",
+    "- [Chapter 3](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/03_conditionals_00_content.ipynb#Boolean-Expressions) raises questions regarding the **limited precision** of `float` numbers (e.g., `42 == 42.000000000000001` evaluates to `True`), and\n",
+    "- [Chapter 4](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/04_iteration_00_content.ipynb#Infinite-Recursion) shows that sometimes a `float` \"walks\" and \"quacks\" like an `int`, whereas the reverse is true in other cases.\n",
     "\n",
     "This chapter introduces all the [built-in numeric types](https://docs.python.org/3/library/stdtypes.html#numeric-types-int-float-complex): `int`, `float`, and `complex`. To mitigate the limited precision of floating-point numbers, we also look at two replacements for the `float` type in the [standard library](https://docs.python.org/3/library/index.html), namely the `Decimal` type in the [decimals](https://docs.python.org/3/library/decimal.html#decimal.Decimal) and the `Fraction` type in the [fractions](https://docs.python.org/3/library/fractions.html#fractions.Fraction) module."
    ]
@@ -53,7 +53,7 @@
     }
    },
    "source": [
-    "The simplest numeric type is the `int` type: It behaves like an [integer in ordinary math](https://en.wikipedia.org/wiki/Integer) (i.e., the set $\\mathbb{Z}$) and supports operators in the way we saw in the section on arithmetic operators in [Chapter 1](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/01_elements.ipynb#%28Arithmetic%29-Operators)."
+    "The simplest numeric type is the `int` type: It behaves like an [integer in ordinary math](https://en.wikipedia.org/wiki/Integer) (i.e., the set $\\mathbb{Z}$) and supports operators in the way we saw in the section on arithmetic operators in [Chapter 1](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/01_elements_00_content.ipynb#%28Arithmetic%29-Operators)."
    ]
   },
   {
@@ -92,7 +92,7 @@
     {
      "data": {
       "text/plain": [
-       "140673805309168"
+       "140166838695792"
       ]
      },
      "execution_count": 2,
@@ -1481,7 +1481,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 42,
+   "execution_count": 47,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -1494,7 +1494,7 @@
        "'0b1011 & 0b1101'"
       ]
      },
-     "execution_count": 42,
+     "execution_count": 47,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1505,7 +1505,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 43,
+   "execution_count": 48,
    "metadata": {
     "slideshow": {
      "slide_type": "-"
@@ -1518,7 +1518,7 @@
        "'0b1001'"
       ]
      },
-     "execution_count": 43,
+     "execution_count": 48,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1540,7 +1540,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 44,
+   "execution_count": 49,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -1553,7 +1553,7 @@
        "9"
       ]
      },
-     "execution_count": 44,
+     "execution_count": 49,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1564,7 +1564,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 45,
+   "execution_count": 50,
    "metadata": {
     "slideshow": {
      "slide_type": "-"
@@ -1577,7 +1577,7 @@
        "9"
       ]
      },
-     "execution_count": 45,
+     "execution_count": 50,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1599,7 +1599,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 46,
+   "execution_count": 51,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -1612,7 +1612,7 @@
        "'0b1001 | 0b1101'"
       ]
      },
-     "execution_count": 46,
+     "execution_count": 51,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1623,7 +1623,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 47,
+   "execution_count": 52,
    "metadata": {
     "slideshow": {
      "slide_type": "-"
@@ -1636,7 +1636,7 @@
        "'0b1101'"
       ]
      },
-     "execution_count": 47,
+     "execution_count": 52,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1658,7 +1658,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 48,
+   "execution_count": 53,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -1671,7 +1671,7 @@
        "13"
       ]
      },
-     "execution_count": 48,
+     "execution_count": 53,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1682,7 +1682,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 49,
+   "execution_count": 54,
    "metadata": {
     "slideshow": {
      "slide_type": "-"
@@ -1695,7 +1695,7 @@
        "13"
       ]
      },
-     "execution_count": 49,
+     "execution_count": 54,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1717,7 +1717,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 50,
+   "execution_count": 55,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -1730,7 +1730,7 @@
        "'0b1001 ^ 0b1101'"
       ]
      },
-     "execution_count": 50,
+     "execution_count": 55,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1741,7 +1741,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 51,
+   "execution_count": 56,
    "metadata": {
     "slideshow": {
      "slide_type": "-"
@@ -1754,7 +1754,7 @@
        "'0b100'"
       ]
      },
-     "execution_count": 51,
+     "execution_count": 56,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1776,7 +1776,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 52,
+   "execution_count": 57,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -1789,7 +1789,7 @@
        "4"
       ]
      },
-     "execution_count": 52,
+     "execution_count": 57,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1800,7 +1800,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 53,
+   "execution_count": 58,
    "metadata": {
     "slideshow": {
      "slide_type": "-"
@@ -1813,7 +1813,7 @@
        "4"
       ]
      },
-     "execution_count": 53,
+     "execution_count": 58,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1837,7 +1837,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 54,
+   "execution_count": 59,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -1850,7 +1850,7 @@
        "'-0b1001'"
       ]
      },
-     "execution_count": 54,
+     "execution_count": 59,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1861,7 +1861,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 55,
+   "execution_count": 60,
    "metadata": {
     "slideshow": {
      "slide_type": "-"
@@ -1874,7 +1874,7 @@
        "'-0b1001'"
       ]
      },
-     "execution_count": 55,
+     "execution_count": 60,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1896,7 +1896,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 56,
+   "execution_count": 61,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -1909,7 +1909,7 @@
        "-1"
       ]
      },
-     "execution_count": 56,
+     "execution_count": 61,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1933,7 +1933,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 57,
+   "execution_count": 62,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -1946,7 +1946,7 @@
        "'0b1001'"
       ]
      },
-     "execution_count": 57,
+     "execution_count": 62,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1957,7 +1957,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 58,
+   "execution_count": 63,
    "metadata": {
     "slideshow": {
      "slide_type": "-"
@@ -1970,7 +1970,7 @@
        "'0b100100'"
       ]
      },
-     "execution_count": 58,
+     "execution_count": 63,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1981,7 +1981,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 59,
+   "execution_count": 64,
    "metadata": {
     "slideshow": {
      "slide_type": "-"
@@ -1994,7 +1994,7 @@
        "36"
       ]
      },
-     "execution_count": 59,
+     "execution_count": 64,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2016,7 +2016,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 60,
+   "execution_count": 65,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -2029,7 +2029,7 @@
        "'0b10'"
       ]
      },
-     "execution_count": 60,
+     "execution_count": 65,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2040,7 +2040,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 61,
+   "execution_count": 66,
    "metadata": {
     "slideshow": {
      "slide_type": "-"
@@ -2053,7 +2053,7 @@
        "2"
       ]
      },
-     "execution_count": 61,
+     "execution_count": 66,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2092,7 +2092,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 62,
+   "execution_count": 67,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -2105,7 +2105,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 63,
+   "execution_count": 68,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -2115,10 +2115,10 @@
     {
      "data": {
       "text/plain": [
-       "140673805486768"
+       "140166567715664"
       ]
      },
-     "execution_count": 63,
+     "execution_count": 68,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2129,7 +2129,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 64,
+   "execution_count": 69,
    "metadata": {
     "slideshow": {
      "slide_type": "-"
@@ -2142,7 +2142,7 @@
        "float"
       ]
      },
-     "execution_count": 64,
+     "execution_count": 69,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2153,7 +2153,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 65,
+   "execution_count": 70,
    "metadata": {
     "slideshow": {
      "slide_type": "-"
@@ -2166,7 +2166,7 @@
        "1.23"
       ]
      },
-     "execution_count": 65,
+     "execution_count": 70,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2188,7 +2188,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 66,
+   "execution_count": 71,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -2201,7 +2201,7 @@
        "0.123456789"
       ]
      },
-     "execution_count": 66,
+     "execution_count": 71,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2223,7 +2223,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 67,
+   "execution_count": 72,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -2236,7 +2236,7 @@
        "1.0"
       ]
      },
-     "execution_count": 67,
+     "execution_count": 72,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2247,7 +2247,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 68,
+   "execution_count": 73,
    "metadata": {
     "slideshow": {
      "slide_type": "-"
@@ -2260,7 +2260,7 @@
        "1.0"
       ]
      },
-     "execution_count": 68,
+     "execution_count": 73,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2271,7 +2271,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 69,
+   "execution_count": 74,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -2284,7 +2284,7 @@
        "1.0"
       ]
      },
-     "execution_count": 69,
+     "execution_count": 74,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2306,7 +2306,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 70,
+   "execution_count": 75,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -2319,7 +2319,7 @@
        "0.3333333333333333"
       ]
      },
-     "execution_count": 70,
+     "execution_count": 75,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2341,7 +2341,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 71,
+   "execution_count": 76,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -2354,7 +2354,7 @@
        "42.0"
       ]
      },
-     "execution_count": 71,
+     "execution_count": 76,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2365,7 +2365,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 72,
+   "execution_count": 77,
    "metadata": {
     "slideshow": {
      "slide_type": "-"
@@ -2378,7 +2378,7 @@
        "42.0"
       ]
      },
-     "execution_count": 72,
+     "execution_count": 77,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2411,7 +2411,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 73,
+   "execution_count": 78,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -2424,7 +2424,7 @@
        "1.23"
       ]
      },
-     "execution_count": 73,
+     "execution_count": 78,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2446,7 +2446,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 74,
+   "execution_count": 79,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -2455,10 +2455,10 @@
    "outputs": [
     {
      "ename": "SyntaxError",
-     "evalue": "invalid syntax (<ipython-input-74-1b5daaac0077>, line 1)",
+     "evalue": "invalid syntax (<ipython-input-79-1b5daaac0077>, line 1)",
      "output_type": "error",
      "traceback": [
-      "\u001b[0;36m  File \u001b[0;32m\"<ipython-input-74-1b5daaac0077>\"\u001b[0;36m, line \u001b[0;32m1\u001b[0m\n\u001b[0;31m    1.23 e0\u001b[0m\n\u001b[0m          ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m invalid syntax\n"
+      "\u001b[0;36m  File \u001b[0;32m\"<ipython-input-79-1b5daaac0077>\"\u001b[0;36m, line \u001b[0;32m1\u001b[0m\n\u001b[0;31m    1.23 e0\u001b[0m\n\u001b[0m          ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m invalid syntax\n"
      ]
     }
    ],
@@ -2468,7 +2468,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 75,
+   "execution_count": 80,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -2477,10 +2477,10 @@
    "outputs": [
     {
      "ename": "SyntaxError",
-     "evalue": "invalid syntax (<ipython-input-75-a236c4a30231>, line 1)",
+     "evalue": "invalid syntax (<ipython-input-80-a236c4a30231>, line 1)",
      "output_type": "error",
      "traceback": [
-      "\u001b[0;36m  File \u001b[0;32m\"<ipython-input-75-a236c4a30231>\"\u001b[0;36m, line \u001b[0;32m1\u001b[0m\n\u001b[0;31m    1.23e 0\u001b[0m\n\u001b[0m        ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m invalid syntax\n"
+      "\u001b[0;36m  File \u001b[0;32m\"<ipython-input-80-a236c4a30231>\"\u001b[0;36m, line \u001b[0;32m1\u001b[0m\n\u001b[0;31m    1.23e 0\u001b[0m\n\u001b[0m        ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m invalid syntax\n"
      ]
     }
    ],
@@ -2490,7 +2490,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 76,
+   "execution_count": 81,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -2499,10 +2499,10 @@
    "outputs": [
     {
      "ename": "SyntaxError",
-     "evalue": "invalid syntax (<ipython-input-76-05b9072d76be>, line 1)",
+     "evalue": "invalid syntax (<ipython-input-81-05b9072d76be>, line 1)",
      "output_type": "error",
      "traceback": [
-      "\u001b[0;36m  File \u001b[0;32m\"<ipython-input-76-05b9072d76be>\"\u001b[0;36m, line \u001b[0;32m1\u001b[0m\n\u001b[0;31m    1.23e0.0\u001b[0m\n\u001b[0m           ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m invalid syntax\n"
+      "\u001b[0;36m  File \u001b[0;32m\"<ipython-input-81-05b9072d76be>\"\u001b[0;36m, line \u001b[0;32m1\u001b[0m\n\u001b[0;31m    1.23e0.0\u001b[0m\n\u001b[0m           ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m invalid syntax\n"
      ]
     }
    ],
@@ -2523,7 +2523,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 77,
+   "execution_count": 82,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -2537,7 +2537,7 @@
      "traceback": [
       "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
       "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-77-eff9d613b2d7>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0me1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;32m<ipython-input-82-eff9d613b2d7>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0me1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
       "\u001b[0;31mNameError\u001b[0m: name 'e1' is not defined"
      ]
     }
@@ -2559,7 +2559,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 78,
+   "execution_count": 83,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -2572,7 +2572,7 @@
        "10.0"
       ]
      },
-     "execution_count": 78,
+     "execution_count": 83,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2605,7 +2605,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 79,
+   "execution_count": 84,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -2618,166 +2618,13 @@
        "nan"
       ]
      },
-     "execution_count": 79,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "float(\"nan\")  # also works as float(\"NaN\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 80,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "inf"
-      ]
-     },
-     "execution_count": 80,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "float(\"+inf\")  # also works as float(\"+infinity\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 81,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "-"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "inf"
-      ]
-     },
-     "execution_count": 81,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "float(\"inf\")  # by omitting the plus sign we mean positive infinity"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 82,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "-"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "-inf"
-      ]
-     },
-     "execution_count": 82,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "float(\"-inf\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "source": [
-    "`nan` objects *never* compare equal to *anything*, not even to themselves. This happens in accordance with the [IEEE 754](https://en.wikipedia.org/wiki/IEEE_754) standard."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 83,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "slide"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "False"
-      ]
-     },
-     "execution_count": 83,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "float(\"nan\") == float(\"nan\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "source": [
-    "On the contrary, as two values go to infinity, there is no such concept as difference and *everything* compares equal."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 84,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "fragment"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "True"
-      ]
-     },
      "execution_count": 84,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "float(\"inf\") == float(\"inf\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "source": [
-    "Adding `42` to `inf` makes no difference."
+    "float(\"nan\")  # also works as float(\"NaN\")"
    ]
   },
   {
@@ -2801,12 +2648,106 @@
     }
    ],
    "source": [
-    "float(\"inf\") + 42"
+    "float(\"+inf\")  # also works as float(\"+infinity\")"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 86,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "inf"
+      ]
+     },
+     "execution_count": 86,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "float(\"inf\")  # by omitting the plus sign we mean positive infinity"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 87,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "-inf"
+      ]
+     },
+     "execution_count": 87,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "float(\"-inf\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "`nan` objects *never* compare equal to *anything*, not even to themselves. This happens in accordance with the [IEEE 754](https://en.wikipedia.org/wiki/IEEE_754) standard."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 88,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "False"
+      ]
+     },
+     "execution_count": 88,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "float(\"nan\") == float(\"nan\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "On the contrary, as two values go to infinity, there is no such concept as difference and *everything* compares equal."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 89,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -2819,7 +2760,66 @@
        "True"
       ]
      },
-     "execution_count": 86,
+     "execution_count": 89,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "float(\"inf\") == float(\"inf\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Adding `42` to `inf` makes no difference."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 90,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "inf"
+      ]
+     },
+     "execution_count": 90,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "float(\"inf\") + 42"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 91,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 91,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2841,7 +2841,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 87,
+   "execution_count": 92,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -2854,7 +2854,7 @@
        "inf"
       ]
      },
-     "execution_count": 87,
+     "execution_count": 92,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2865,7 +2865,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 88,
+   "execution_count": 93,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -2878,7 +2878,7 @@
        "True"
       ]
      },
-     "execution_count": 88,
+     "execution_count": 93,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2900,7 +2900,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 89,
+   "execution_count": 94,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -2913,7 +2913,7 @@
        "inf"
       ]
      },
-     "execution_count": 89,
+     "execution_count": 94,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2924,7 +2924,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 90,
+   "execution_count": 95,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -2937,7 +2937,7 @@
        "True"
       ]
      },
-     "execution_count": 90,
+     "execution_count": 95,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2959,7 +2959,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 91,
+   "execution_count": 96,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -2972,7 +2972,7 @@
        "inf"
       ]
      },
-     "execution_count": 91,
+     "execution_count": 96,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2983,7 +2983,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 92,
+   "execution_count": 97,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -2996,7 +2996,7 @@
        "True"
       ]
      },
-     "execution_count": 92,
+     "execution_count": 97,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3018,7 +3018,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 93,
+   "execution_count": 98,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -3031,7 +3031,7 @@
        "nan"
       ]
      },
-     "execution_count": 93,
+     "execution_count": 98,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3042,7 +3042,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 94,
+   "execution_count": 99,
    "metadata": {
     "slideshow": {
      "slide_type": "-"
@@ -3055,7 +3055,7 @@
        "nan"
       ]
      },
-     "execution_count": 94,
+     "execution_count": 99,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3085,12 +3085,12 @@
    "source": [
     "`float` objects are *inherently* imprecise, and there is *nothing* we can do about it! In particular, arithmetic operations with two `float` objects may result in \"weird\" rounding \"errors\" that are strictly deterministic and occur in accordance with the [IEEE 754](https://en.wikipedia.org/wiki/IEEE_754) standard.\n",
     "\n",
-    "For example, let's add `1e0` to `1e15` and `1e16`, respectively. In the latter case, the `1e0` somehow gets \"lost\"."
+    "For example, let's add `1e0` to `1e15` and `1e16`, respectively. In the latter case, the `1e0` somehow gets \"lost.\""
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 95,
+   "execution_count": 100,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -3103,7 +3103,7 @@
        "1000000000000001.0"
       ]
      },
-     "execution_count": 95,
+     "execution_count": 100,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3114,7 +3114,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 96,
+   "execution_count": 101,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -3127,7 +3127,7 @@
        "1e+16"
       ]
      },
-     "execution_count": 96,
+     "execution_count": 101,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3149,7 +3149,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 97,
+   "execution_count": 102,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -3162,7 +3162,7 @@
        "1000000000000001.0"
       ]
      },
-     "execution_count": 97,
+     "execution_count": 102,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3173,7 +3173,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 98,
+   "execution_count": 103,
    "metadata": {
     "slideshow": {
      "slide_type": "-"
@@ -3186,7 +3186,7 @@
        "1e+16"
       ]
      },
-     "execution_count": 98,
+     "execution_count": 103,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3208,7 +3208,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 99,
+   "execution_count": 104,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -3221,7 +3221,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 100,
+   "execution_count": 105,
    "metadata": {
     "slideshow": {
      "slide_type": "-"
@@ -3234,7 +3234,7 @@
        "2.0000000000000004"
       ]
      },
-     "execution_count": 100,
+     "execution_count": 105,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3245,7 +3245,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 101,
+   "execution_count": 106,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -3258,7 +3258,7 @@
        "0.30000000000000004"
       ]
      },
-     "execution_count": 101,
+     "execution_count": 106,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3280,7 +3280,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 102,
+   "execution_count": 107,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -3293,7 +3293,7 @@
        "False"
       ]
      },
-     "execution_count": 102,
+     "execution_count": 107,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3304,7 +3304,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 103,
+   "execution_count": 108,
    "metadata": {
     "slideshow": {
      "slide_type": "-"
@@ -3317,7 +3317,7 @@
        "False"
       ]
      },
-     "execution_count": 103,
+     "execution_count": 108,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3339,7 +3339,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 104,
+   "execution_count": 109,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -3352,7 +3352,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 105,
+   "execution_count": 110,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -3365,7 +3365,7 @@
        "True"
       ]
      },
-     "execution_count": 105,
+     "execution_count": 110,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3376,7 +3376,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 106,
+   "execution_count": 111,
    "metadata": {
     "slideshow": {
      "slide_type": "-"
@@ -3389,7 +3389,7 @@
        "True"
       ]
      },
-     "execution_count": 106,
+     "execution_count": 111,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3413,7 +3413,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 107,
+   "execution_count": 112,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -3426,7 +3426,7 @@
        "'0.10000000000000000555111512312578270211815834045410'"
       ]
      },
-     "execution_count": 107,
+     "execution_count": 112,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3437,7 +3437,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 108,
+   "execution_count": 113,
    "metadata": {
     "slideshow": {
      "slide_type": "-"
@@ -3450,7 +3450,7 @@
        "'0.20000000000000001110223024625156540423631668090820'"
       ]
      },
-     "execution_count": 108,
+     "execution_count": 113,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3461,7 +3461,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 109,
+   "execution_count": 114,
    "metadata": {
     "slideshow": {
      "slide_type": "-"
@@ -3474,7 +3474,7 @@
        "'0.29999999999999998889776975374843459576368331909180'"
       ]
      },
-     "execution_count": 109,
+     "execution_count": 114,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3485,7 +3485,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 110,
+   "execution_count": 115,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -3498,7 +3498,7 @@
        "'0.33333333333333331482961625624739099293947219848633'"
       ]
      },
-     "execution_count": 110,
+     "execution_count": 115,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3524,7 +3524,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 111,
+   "execution_count": 116,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -3537,7 +3537,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 112,
+   "execution_count": 117,
    "metadata": {
     "slideshow": {
      "slide_type": "-"
@@ -3550,7 +3550,7 @@
        "0.33333"
       ]
      },
-     "execution_count": 112,
+     "execution_count": 117,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3561,7 +3561,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 113,
+   "execution_count": 118,
    "metadata": {
     "slideshow": {
      "slide_type": "-"
@@ -3574,7 +3574,7 @@
        "'0.33333000000000001517008740847813896834850311279297'"
       ]
      },
-     "execution_count": 113,
+     "execution_count": 118,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3596,7 +3596,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 114,
+   "execution_count": 119,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -3609,7 +3609,7 @@
        "'0.12500000000000000000000000000000000000000000000000'"
       ]
      },
-     "execution_count": 114,
+     "execution_count": 119,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3620,7 +3620,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 115,
+   "execution_count": 120,
    "metadata": {
     "slideshow": {
      "slide_type": "-"
@@ -3633,7 +3633,7 @@
        "'0.25000000000000000000000000000000000000000000000000'"
       ]
      },
-     "execution_count": 115,
+     "execution_count": 120,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3644,7 +3644,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 116,
+   "execution_count": 121,
    "metadata": {
     "slideshow": {
      "slide_type": "-"
@@ -3657,7 +3657,7 @@
        "True"
       ]
      },
-     "execution_count": 116,
+     "execution_count": 121,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3689,7 +3689,7 @@
     "\n",
     "$$float = (-1)^{sign} * 1.fraction * 2^{exponent-1023}$$\n",
     "\n",
-    "A $1.$ is implicitly prepended as the first digit, and both, $fraction$ and $exponent$, are stored in base $2$ representation (i.e., they both are interpreted like integers above). As $exponent$ is consequently non-negative, between $0_{10}$ and $2047_{10}$ to be precise, the $-1023$ centers the entire $2^{exponent-1023}$ term around $1$ and allows the period within the $1.fraction$ part be shifted into either direction by the same amount. Floating-point numbers received their name as the period, formally called the **[radix point](https://en.wikipedia.org/wiki/Radix_point)**, \"floats\" along the significant digits. As an aside, an $exponent$ of all $0$s or all $1$s is used to model the special values `nan` or `inf`.\n",
+    "A $1.$ is implicitly prepended as the first digit, and both, $fraction$ and $exponent$, are stored in base $2$ representation (i.e., they both are interpreted like integers above). As $exponent$ is consequently non-negative, between $0_{10}$ and $2047_{10}$ to be precise, the $-1023$, called the exponent bias, centers the entire $2^{exponent-1023}$ term around $1$ and allows the period within the $1.fraction$ part be shifted into either direction by the same amount. Floating-point numbers received their name as the period, formally called the **[radix point](https://en.wikipedia.org/wiki/Radix_point)**, \"floats\" along the significant digits. As an aside, an $exponent$ of all $0$s or all $1$s is used to model the special values `nan` or `inf`.\n",
     "\n",
     "As the standard defines the exponent part to come as a power of $2$, we now see why `0.125` is a *precise* float: It can be represented as a power of $2$, i.e., $0.125 = (-1)^0 * 1.0 * 2^{1020-1023} = 2^{-3} = \\frac{1}{8}$. In other words, the floating-point representation of $0.125_{10}$ is $0_2$, $1111111100_2 = 1020_{10}$, and $0_2$ for the three groups, respectively."
    ]
@@ -3726,7 +3726,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 117,
+   "execution_count": 122,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -3739,7 +3739,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 118,
+   "execution_count": 123,
    "metadata": {
     "slideshow": {
      "slide_type": "-"
@@ -3752,7 +3752,7 @@
        "'0x1.0000000000000p-3'"
       ]
      },
-     "execution_count": 118,
+     "execution_count": 123,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3774,7 +3774,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 119,
+   "execution_count": 124,
    "metadata": {
     "slideshow": {
      "slide_type": "-"
@@ -3787,7 +3787,7 @@
        "(1, 8)"
       ]
      },
-     "execution_count": 119,
+     "execution_count": 124,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3798,7 +3798,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 120,
+   "execution_count": 125,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -3811,7 +3811,7 @@
        "'0x1.555475a31a4bep-2'"
       ]
      },
-     "execution_count": 120,
+     "execution_count": 125,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3822,7 +3822,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 121,
+   "execution_count": 126,
    "metadata": {
     "slideshow": {
      "slide_type": "-"
@@ -3835,7 +3835,7 @@
        "(3002369727582815, 9007199254740992)"
       ]
      },
-     "execution_count": 121,
+     "execution_count": 126,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3857,7 +3857,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 122,
+   "execution_count": 127,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -3870,7 +3870,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 123,
+   "execution_count": 128,
    "metadata": {
     "slideshow": {
      "slide_type": "-"
@@ -3883,7 +3883,7 @@
        "'0x0.0p+0'"
       ]
      },
-     "execution_count": 123,
+     "execution_count": 128,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3894,7 +3894,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 124,
+   "execution_count": 129,
    "metadata": {
     "slideshow": {
      "slide_type": "-"
@@ -3907,7 +3907,7 @@
        "(0, 1)"
       ]
      },
-     "execution_count": 124,
+     "execution_count": 129,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3924,12 +3924,12 @@
     }
    },
    "source": [
-    "As seen in [Chapter 1](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/01_elements.ipynb#%28Data%29-Type-%2F-%22Behavior%22), the [is_integer()](https://docs.python.org/3/library/stdtypes.html#float.is_integer) method tells us if a `float` can be casted as an `int` object without any loss in precision."
+    "As seen in [Chapter 1](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/01_elements_00_content.ipynb#%28Data%29-Type-%2F-%22Behavior%22), the [is_integer()](https://docs.python.org/3/library/stdtypes.html#float.is_integer) method tells us if a `float` can be casted as an `int` object without any loss in precision."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 125,
+   "execution_count": 130,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -3942,7 +3942,7 @@
        "False"
       ]
      },
-     "execution_count": 125,
+     "execution_count": 130,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3953,7 +3953,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 126,
+   "execution_count": 131,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -3966,7 +3966,7 @@
        "True"
       ]
      },
-     "execution_count": 126,
+     "execution_count": 131,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3990,7 +3990,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 127,
+   "execution_count": 132,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -4003,7 +4003,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 128,
+   "execution_count": 133,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -4016,7 +4016,7 @@
        "sys.float_info(max=1.7976931348623157e+308, max_exp=1024, max_10_exp=308, min=2.2250738585072014e-308, min_exp=-1021, min_10_exp=-307, dig=15, mant_dig=53, epsilon=2.220446049250313e-16, radix=2, rounds=1)"
       ]
      },
-     "execution_count": 128,
+     "execution_count": 133,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -4051,7 +4051,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 129,
+   "execution_count": 134,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -4075,7 +4075,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 130,
+   "execution_count": 135,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -4088,7 +4088,7 @@
        "Context(prec=28, rounding=ROUND_HALF_EVEN, Emin=-999999, Emax=999999, capitals=1, clamp=0, flags=[], traps=[InvalidOperation, DivisionByZero, Overflow])"
       ]
      },
-     "execution_count": 130,
+     "execution_count": 135,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -4110,7 +4110,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 131,
+   "execution_count": 136,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -4123,7 +4123,7 @@
        "Decimal('42')"
       ]
      },
-     "execution_count": 131,
+     "execution_count": 136,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -4134,7 +4134,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 132,
+   "execution_count": 137,
    "metadata": {
     "slideshow": {
      "slide_type": "-"
@@ -4147,7 +4147,7 @@
        "Decimal('0.1')"
       ]
      },
-     "execution_count": 132,
+     "execution_count": 137,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -4158,7 +4158,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 133,
+   "execution_count": 138,
    "metadata": {
     "slideshow": {
      "slide_type": "-"
@@ -4171,7 +4171,7 @@
        "Decimal('1E+5')"
       ]
      },
-     "execution_count": 133,
+     "execution_count": 138,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -4193,7 +4193,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 134,
+   "execution_count": 139,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -4206,7 +4206,7 @@
        "Decimal('0.1000000000000000055511151231257827021181583404541015625')"
       ]
      },
-     "execution_count": 134,
+     "execution_count": 139,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -4228,7 +4228,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 135,
+   "execution_count": 140,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -4241,7 +4241,7 @@
        "Decimal('0.3')"
       ]
      },
-     "execution_count": 135,
+     "execution_count": 140,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -4252,7 +4252,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 136,
+   "execution_count": 141,
    "metadata": {
     "slideshow": {
      "slide_type": "-"
@@ -4265,7 +4265,7 @@
        "True"
       ]
      },
-     "execution_count": 136,
+     "execution_count": 141,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -4287,7 +4287,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 137,
+   "execution_count": 142,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -4300,7 +4300,7 @@
        "Decimal('0.30000')"
       ]
      },
-     "execution_count": 137,
+     "execution_count": 142,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -4311,7 +4311,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 138,
+   "execution_count": 143,
    "metadata": {
     "slideshow": {
      "slide_type": "-"
@@ -4324,7 +4324,7 @@
        "True"
       ]
      },
-     "execution_count": 138,
+     "execution_count": 143,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -4346,7 +4346,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 139,
+   "execution_count": 144,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -4359,7 +4359,7 @@
        "Decimal('42')"
       ]
      },
-     "execution_count": 139,
+     "execution_count": 144,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -4370,7 +4370,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 140,
+   "execution_count": 145,
    "metadata": {
     "slideshow": {
      "slide_type": "-"
@@ -4383,7 +4383,7 @@
        "Decimal('42')"
       ]
      },
-     "execution_count": 140,
+     "execution_count": 145,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -4394,7 +4394,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 141,
+   "execution_count": 146,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -4407,7 +4407,7 @@
        "Decimal('0.1')"
       ]
      },
-     "execution_count": 141,
+     "execution_count": 146,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -4429,7 +4429,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 142,
+   "execution_count": 147,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -4442,7 +4442,7 @@
        "'0.10000000000000000000000000000000000000000000000000'"
       ]
      },
-     "execution_count": 142,
+     "execution_count": 147,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -4453,7 +4453,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 143,
+   "execution_count": 148,
    "metadata": {
     "slideshow": {
      "slide_type": "-"
@@ -4466,7 +4466,7 @@
        "'0.10000000000000000555111512312578270211815834045410'"
       ]
      },
-     "execution_count": 143,
+     "execution_count": 148,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -4488,7 +4488,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 144,
+   "execution_count": 149,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -4502,7 +4502,7 @@
      "traceback": [
       "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
       "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-144-a3cea145e6d1>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0;36m1.0\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0mDecimal\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m42\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;32m<ipython-input-149-a3cea145e6d1>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0;36m1.0\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0mDecimal\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m42\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
       "\u001b[0;31mTypeError\u001b[0m: unsupported operand type(s) for *: 'float' and 'decimal.Decimal'"
      ]
     }
@@ -4524,7 +4524,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 145,
+   "execution_count": 150,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -4537,7 +4537,7 @@
        "Decimal('2')"
       ]
      },
-     "execution_count": 145,
+     "execution_count": 150,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -4548,7 +4548,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 146,
+   "execution_count": 151,
    "metadata": {
     "slideshow": {
      "slide_type": "-"
@@ -4561,7 +4561,7 @@
        "Decimal('1.414213562373095048801688724')"
       ]
      },
-     "execution_count": 146,
+     "execution_count": 151,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -4585,7 +4585,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 147,
+   "execution_count": 152,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -4598,7 +4598,7 @@
        "Decimal('1.999999999999999999999999999')"
       ]
      },
-     "execution_count": 147,
+     "execution_count": 152,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -4624,7 +4624,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 148,
+   "execution_count": 153,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -4637,7 +4637,7 @@
        "Decimal('2.0000')"
       ]
      },
-     "execution_count": 148,
+     "execution_count": 153,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -4655,7 +4655,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 149,
+   "execution_count": 154,
    "metadata": {},
    "outputs": [
     {
@@ -4665,7 +4665,7 @@
      "traceback": [
       "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
       "\u001b[0;31mInvalidOperation\u001b[0m                          Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-149-11848d4ba22a>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mtwo\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mquantize\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mDecimal\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"0.1\"\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m**\u001b[0m \u001b[0;36m28\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;32m<ipython-input-154-11848d4ba22a>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mtwo\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mquantize\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mDecimal\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"0.1\"\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m**\u001b[0m \u001b[0;36m28\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
       "\u001b[0;31mInvalidOperation\u001b[0m: [<class 'decimal.InvalidOperation'>]"
      ]
     }
@@ -4687,7 +4687,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 150,
+   "execution_count": 155,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -4700,7 +4700,7 @@
        "True"
       ]
      },
-     "execution_count": 150,
+     "execution_count": 155,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -4722,7 +4722,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 151,
+   "execution_count": 156,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -4735,7 +4735,7 @@
        "True"
       ]
      },
-     "execution_count": 151,
+     "execution_count": 156,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -4757,7 +4757,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 152,
+   "execution_count": 157,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -4770,7 +4770,7 @@
        "Decimal('NaN')"
       ]
      },
-     "execution_count": 152,
+     "execution_count": 157,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -4781,7 +4781,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 153,
+   "execution_count": 158,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -4794,7 +4794,7 @@
        "False"
       ]
      },
-     "execution_count": 153,
+     "execution_count": 158,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -4805,7 +4805,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 154,
+   "execution_count": 159,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -4818,7 +4818,7 @@
        "Decimal('Infinity')"
       ]
      },
-     "execution_count": 154,
+     "execution_count": 159,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -4829,7 +4829,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 155,
+   "execution_count": 160,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -4842,7 +4842,7 @@
        "Decimal('-Infinity')"
       ]
      },
-     "execution_count": 155,
+     "execution_count": 160,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -4853,7 +4853,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 156,
+   "execution_count": 161,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -4866,7 +4866,7 @@
        "Decimal('Infinity')"
       ]
      },
-     "execution_count": 156,
+     "execution_count": 161,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -4877,7 +4877,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 157,
+   "execution_count": 162,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -4890,7 +4890,7 @@
        "True"
       ]
      },
-     "execution_count": 157,
+     "execution_count": 162,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -4912,7 +4912,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 158,
+   "execution_count": 163,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -4926,7 +4926,7 @@
      "traceback": [
       "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
       "\u001b[0;31mInvalidOperation\u001b[0m                          Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-158-87950ebbd6ef>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mDecimal\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"inf\"\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mDecimal\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"-inf\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;32m<ipython-input-163-87950ebbd6ef>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mDecimal\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"inf\"\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mDecimal\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"-inf\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
       "\u001b[0;31mInvalidOperation\u001b[0m: [<class 'decimal.InvalidOperation'>]"
      ]
     }
@@ -4937,7 +4937,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 159,
+   "execution_count": 164,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -4951,7 +4951,7 @@
      "traceback": [
       "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
       "\u001b[0;31mInvalidOperation\u001b[0m                          Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-159-996083f200c6>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mDecimal\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"inf\"\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0mDecimal\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"inf\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;32m<ipython-input-164-996083f200c6>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mDecimal\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"inf\"\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0mDecimal\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"inf\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
       "\u001b[0;31mInvalidOperation\u001b[0m: [<class 'decimal.InvalidOperation'>]"
      ]
     }
@@ -4997,7 +4997,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 160,
+   "execution_count": 165,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -5021,7 +5021,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 161,
+   "execution_count": 166,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -5034,7 +5034,7 @@
        "Fraction(1, 3)"
       ]
      },
-     "execution_count": 161,
+     "execution_count": 166,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -5045,7 +5045,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 162,
+   "execution_count": 167,
    "metadata": {
     "slideshow": {
      "slide_type": "-"
@@ -5058,7 +5058,7 @@
        "Fraction(1, 3)"
       ]
      },
-     "execution_count": 162,
+     "execution_count": 167,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -5069,7 +5069,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 163,
+   "execution_count": 168,
    "metadata": {
     "slideshow": {
      "slide_type": "-"
@@ -5082,7 +5082,7 @@
        "Fraction(3333333333, 10000000000)"
       ]
      },
-     "execution_count": 163,
+     "execution_count": 168,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -5093,7 +5093,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 164,
+   "execution_count": 169,
    "metadata": {
     "slideshow": {
      "slide_type": "-"
@@ -5106,7 +5106,7 @@
        "Fraction(3333333333, 10000000000)"
       ]
      },
-     "execution_count": 164,
+     "execution_count": 169,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -5128,7 +5128,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 165,
+   "execution_count": 170,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -5141,7 +5141,7 @@
        "Fraction(3, 2)"
       ]
      },
-     "execution_count": 165,
+     "execution_count": 170,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -5152,7 +5152,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 166,
+   "execution_count": 171,
    "metadata": {
     "slideshow": {
      "slide_type": "-"
@@ -5165,7 +5165,7 @@
        "Fraction(3, 2)"
       ]
      },
-     "execution_count": 166,
+     "execution_count": 171,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -5187,7 +5187,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 167,
+   "execution_count": 172,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -5200,7 +5200,7 @@
        "Fraction(1, 10)"
       ]
      },
-     "execution_count": 167,
+     "execution_count": 172,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -5222,7 +5222,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 168,
+   "execution_count": 173,
    "metadata": {
     "slideshow": {
      "slide_type": "-"
@@ -5235,7 +5235,7 @@
        "Fraction(3602879701896397, 36028797018963968)"
       ]
      },
-     "execution_count": 168,
+     "execution_count": 173,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -5257,7 +5257,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 169,
+   "execution_count": 174,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -5270,7 +5270,7 @@
        "Fraction(7, 4)"
       ]
      },
-     "execution_count": 169,
+     "execution_count": 174,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -5281,7 +5281,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 170,
+   "execution_count": 175,
    "metadata": {
     "slideshow": {
      "slide_type": "-"
@@ -5294,7 +5294,7 @@
        "Fraction(1, 2)"
       ]
      },
-     "execution_count": 170,
+     "execution_count": 175,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -5305,7 +5305,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 171,
+   "execution_count": 176,
    "metadata": {
     "slideshow": {
      "slide_type": "-"
@@ -5318,7 +5318,7 @@
        "Fraction(1, 1)"
       ]
      },
-     "execution_count": 171,
+     "execution_count": 176,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -5329,7 +5329,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 172,
+   "execution_count": 177,
    "metadata": {
     "slideshow": {
      "slide_type": "-"
@@ -5342,7 +5342,7 @@
        "Fraction(1, 1)"
       ]
      },
-     "execution_count": 172,
+     "execution_count": 177,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -5364,7 +5364,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 173,
+   "execution_count": 178,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -5377,7 +5377,7 @@
        "0.1"
       ]
      },
-     "execution_count": 173,
+     "execution_count": 178,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -5388,7 +5388,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 174,
+   "execution_count": 179,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -5401,7 +5401,7 @@
        "'0.10000000000000000555111512312578270211815834045410'"
       ]
      },
-     "execution_count": 174,
+     "execution_count": 179,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -5475,7 +5475,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 175,
+   "execution_count": 180,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -5488,7 +5488,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 176,
+   "execution_count": 181,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -5498,10 +5498,10 @@
     {
      "data": {
       "text/plain": [
-       "140673804641712"
+       "140166567772848"
       ]
      },
-     "execution_count": 176,
+     "execution_count": 181,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -5512,7 +5512,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 177,
+   "execution_count": 182,
    "metadata": {
     "slideshow": {
      "slide_type": "-"
@@ -5525,7 +5525,7 @@
        "complex"
       ]
      },
-     "execution_count": 177,
+     "execution_count": 182,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -5536,7 +5536,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 178,
+   "execution_count": 183,
    "metadata": {
     "slideshow": {
      "slide_type": "-"
@@ -5549,7 +5549,7 @@
        "1j"
       ]
      },
-     "execution_count": 178,
+     "execution_count": 183,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -5571,7 +5571,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 179,
+   "execution_count": 184,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -5584,7 +5584,7 @@
        "True"
       ]
      },
-     "execution_count": 179,
+     "execution_count": 184,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -5606,7 +5606,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 180,
+   "execution_count": 185,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -5619,7 +5619,7 @@
        "(2+0.5j)"
       ]
      },
-     "execution_count": 180,
+     "execution_count": 185,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -5641,7 +5641,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 181,
+   "execution_count": 186,
    "metadata": {
     "slideshow": {
      "slide_type": "-"
@@ -5654,7 +5654,7 @@
        "(2+0.5j)"
       ]
      },
-     "execution_count": 181,
+     "execution_count": 186,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -5665,7 +5665,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 182,
+   "execution_count": 187,
    "metadata": {
     "slideshow": {
      "slide_type": "-"
@@ -5678,7 +5678,7 @@
        "(2+0j)"
       ]
      },
-     "execution_count": 182,
+     "execution_count": 187,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -5689,7 +5689,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 183,
+   "execution_count": 188,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -5702,7 +5702,7 @@
        "(2+0.5j)"
       ]
      },
-     "execution_count": 183,
+     "execution_count": 188,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -5713,7 +5713,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 184,
+   "execution_count": 189,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -5726,7 +5726,7 @@
        "(2+0.5j)"
       ]
      },
-     "execution_count": 184,
+     "execution_count": 189,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -5748,7 +5748,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 185,
+   "execution_count": 190,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -5762,7 +5762,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 186,
+   "execution_count": 191,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -5775,7 +5775,7 @@
        "(4+6j)"
       ]
      },
-     "execution_count": 186,
+     "execution_count": 191,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -5786,7 +5786,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 187,
+   "execution_count": 192,
    "metadata": {
     "slideshow": {
      "slide_type": "-"
@@ -5799,7 +5799,7 @@
        "(-2-2j)"
       ]
      },
-     "execution_count": 187,
+     "execution_count": 192,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -5810,7 +5810,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 188,
+   "execution_count": 193,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -5823,7 +5823,7 @@
        "(40+2j)"
       ]
      },
-     "execution_count": 188,
+     "execution_count": 193,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -5834,7 +5834,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 189,
+   "execution_count": 194,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -5847,7 +5847,7 @@
        "-4j"
       ]
      },
-     "execution_count": 189,
+     "execution_count": 194,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -5858,7 +5858,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 190,
+   "execution_count": 195,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -5871,7 +5871,7 @@
        "(5+10j)"
       ]
      },
-     "execution_count": 190,
+     "execution_count": 195,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -5882,7 +5882,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 191,
+   "execution_count": 196,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -5895,7 +5895,7 @@
        "(0.5+0.6666666666666666j)"
       ]
      },
-     "execution_count": 191,
+     "execution_count": 196,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -5906,7 +5906,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 192,
+   "execution_count": 197,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -5919,7 +5919,7 @@
        "(-5+10j)"
       ]
      },
-     "execution_count": 192,
+     "execution_count": 197,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -5930,7 +5930,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 193,
+   "execution_count": 198,
    "metadata": {
     "slideshow": {
      "slide_type": "-"
@@ -5943,7 +5943,7 @@
        "(0.44+0.08j)"
       ]
      },
-     "execution_count": 193,
+     "execution_count": 198,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -5965,7 +5965,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 194,
+   "execution_count": 199,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -5978,7 +5978,7 @@
        "5.0"
       ]
      },
-     "execution_count": 194,
+     "execution_count": 199,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -6000,7 +6000,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 195,
+   "execution_count": 200,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -6013,7 +6013,7 @@
        "1.0"
       ]
      },
-     "execution_count": 195,
+     "execution_count": 200,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -6024,7 +6024,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 196,
+   "execution_count": 201,
    "metadata": {
     "slideshow": {
      "slide_type": "-"
@@ -6037,7 +6037,7 @@
        "2.0"
       ]
      },
-     "execution_count": 196,
+     "execution_count": 201,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -6059,7 +6059,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 197,
+   "execution_count": 202,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -6072,7 +6072,7 @@
        "(1-2j)"
       ]
      },
-     "execution_count": 197,
+     "execution_count": 202,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -6111,7 +6111,7 @@
     }
    },
    "source": [
-    "Analogous to the discussion of *containers* and *iterables* in [Chapter 4](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/04_iteration.ipynb#Containers-vs.-Iterables), we contrast the *concrete* numeric data types in this chapter with the *abstract* ideas behind [numbers in mathematics](https://en.wikipedia.org/wiki/Number).\n",
+    "Analogous to the discussion of *containers* and *iterables* in [Chapter 4](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/04_iteration_00_content.ipynb#Containers-vs.-Iterables), we contrast the *concrete* numeric data types in this chapter with the *abstract* ideas behind [numbers in mathematics](https://en.wikipedia.org/wiki/Number).\n",
     "\n",
     "The figure below summarizes five *major* sets of [numbers in mathematics](https://en.wikipedia.org/wiki/Number) as we know them from high school:\n",
     "\n",
@@ -6151,16 +6151,16 @@
     "\n",
     "The abstract concepts behind the four outer-most mathematical sets are formalized in Python since [PEP 3141](https://www.python.org/dev/peps/pep-3141/) in 2007. The [numbers](https://docs.python.org/3/library/numbers.html) module in the [standard library](https://docs.python.org/3/library/index.html) defines what programmers call the **[numerical tower](https://en.wikipedia.org/wiki/Numerical_tower)**, a collection of five **[abstract data types](https://en.wikipedia.org/wiki/Abstract_data_type)**, or **abstract base classes** (ABCs) as they are called in Python jargon:\n",
     "\n",
-    "- `numbers.Number`: \"any number\" (cf., [documentation](https://docs.python.org/3/library/numbers.html#numbers.Number))\n",
-    "- `numbers.Complex`: \"all complex numbers\" (cf., [documentation](https://docs.python.org/3/library/numbers.html#numbers.Complex))\n",
-    "- `numbers.Real`: \"all real numbers\" (cf., [documentation](https://docs.python.org/3/library/numbers.html#numbers.Real))\n",
-    "- `numbers.Rational`: \"all rational numbers\" (cf., [documentation](https://docs.python.org/3/library/numbers.html#numbers.Rational))\n",
-    "- `numbers.Integral`: \"all integers\" (cf., [documentation](https://docs.python.org/3/library/numbers.html#numbers.Integral))"
+    "- `Number`: \"any number\" (cf., [documentation](https://docs.python.org/3/library/numbers.html#numbers.Number))\n",
+    "- `Complex`: \"all complex numbers\" (cf., [documentation](https://docs.python.org/3/library/numbers.html#numbers.Complex))\n",
+    "- `Real`: \"all real numbers\" (cf., [documentation](https://docs.python.org/3/library/numbers.html#numbers.Real))\n",
+    "- `Rational`: \"all rational numbers\" (cf., [documentation](https://docs.python.org/3/library/numbers.html#numbers.Rational))\n",
+    "- `Integral`: \"all integers\" (cf., [documentation](https://docs.python.org/3/library/numbers.html#numbers.Integral))"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 198,
+   "execution_count": 203,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -6173,7 +6173,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 199,
+   "execution_count": 204,
    "metadata": {
     "slideshow": {
      "slide_type": "-"
@@ -6201,7 +6201,7 @@
        " 'abstractmethod']"
       ]
      },
-     "execution_count": 199,
+     "execution_count": 204,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -6227,7 +6227,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 200,
+   "execution_count": 205,
    "metadata": {
     "scrolled": true,
     "slideshow": {
@@ -6338,7 +6338,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 201,
+   "execution_count": 206,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -6505,7 +6505,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 202,
+   "execution_count": 207,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -6518,7 +6518,7 @@
        "42"
       ]
      },
-     "execution_count": 202,
+     "execution_count": 207,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -6529,7 +6529,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 203,
+   "execution_count": 208,
    "metadata": {
     "slideshow": {
      "slide_type": "-"
@@ -6542,7 +6542,7 @@
        "Decimal('0.1')"
       ]
      },
-     "execution_count": 203,
+     "execution_count": 208,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -6553,7 +6553,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 204,
+   "execution_count": 209,
    "metadata": {
     "slideshow": {
      "slide_type": "-"
@@ -6566,7 +6566,7 @@
        "5.0"
       ]
      },
-     "execution_count": 204,
+     "execution_count": 209,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -6588,7 +6588,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 205,
+   "execution_count": 210,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -6601,7 +6601,7 @@
        "42"
       ]
      },
-     "execution_count": 205,
+     "execution_count": 210,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -6612,7 +6612,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 206,
+   "execution_count": 211,
    "metadata": {
     "slideshow": {
      "slide_type": "-"
@@ -6625,7 +6625,7 @@
        "0"
       ]
      },
-     "execution_count": 206,
+     "execution_count": 211,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -6647,7 +6647,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 207,
+   "execution_count": 212,
    "metadata": {
     "slideshow": {
      "slide_type": "-"
@@ -6661,7 +6661,7 @@
      "traceback": [
       "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
       "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-207-44b2943ede89>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mround\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m4\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;36m3j\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;32m<ipython-input-212-44b2943ede89>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mround\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m4\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;36m3j\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
       "\u001b[0;31mTypeError\u001b[0m: type complex doesn't define __round__ method"
      ]
     }
@@ -6683,7 +6683,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 208,
+   "execution_count": 213,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -6707,7 +6707,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 209,
+   "execution_count": 214,
    "metadata": {
     "slideshow": {
      "slide_type": "-"
@@ -6720,7 +6720,7 @@
        "0"
       ]
      },
-     "execution_count": 209,
+     "execution_count": 214,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -6742,7 +6742,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 210,
+   "execution_count": 215,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -6756,7 +6756,7 @@
      "traceback": [
       "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
       "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-210-7121e57d9639>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mmath\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtrunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m0.9\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;36m1j\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;32m<ipython-input-215-7121e57d9639>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mmath\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtrunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m0.9\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;36m1j\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
       "\u001b[0;31mTypeError\u001b[0m: type complex doesn't define __trunc__ method"
      ]
     }
@@ -6780,7 +6780,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 211,
+   "execution_count": 216,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -6793,7 +6793,7 @@
        "True"
       ]
      },
-     "execution_count": 211,
+     "execution_count": 216,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -6804,7 +6804,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 212,
+   "execution_count": 217,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -6817,7 +6817,7 @@
        "True"
       ]
      },
-     "execution_count": 212,
+     "execution_count": 217,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -6828,7 +6828,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 213,
+   "execution_count": 218,
    "metadata": {
     "slideshow": {
      "slide_type": "-"
@@ -6841,7 +6841,7 @@
        "True"
       ]
      },
-     "execution_count": 213,
+     "execution_count": 218,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -6852,7 +6852,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 214,
+   "execution_count": 219,
    "metadata": {
     "slideshow": {
      "slide_type": "-"
@@ -6865,7 +6865,7 @@
        "True"
       ]
      },
-     "execution_count": 214,
+     "execution_count": 219,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -6887,7 +6887,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 215,
+   "execution_count": 220,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -6900,7 +6900,7 @@
        "False"
       ]
      },
-     "execution_count": 215,
+     "execution_count": 220,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -6922,7 +6922,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 216,
+   "execution_count": 221,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -6935,7 +6935,7 @@
        "True"
       ]
      },
-     "execution_count": 216,
+     "execution_count": 221,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -6946,7 +6946,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 217,
+   "execution_count": 222,
    "metadata": {
     "slideshow": {
      "slide_type": "-"
@@ -6959,7 +6959,7 @@
        "False"
       ]
      },
-     "execution_count": 217,
+     "execution_count": 222,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -7005,7 +7005,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 218,
+   "execution_count": 223,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -7055,7 +7055,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 219,
+   "execution_count": 224,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -7068,7 +7068,7 @@
        "1"
       ]
      },
-     "execution_count": 219,
+     "execution_count": 224,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -7079,7 +7079,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 220,
+   "execution_count": 225,
    "metadata": {
     "slideshow": {
      "slide_type": "-"
@@ -7092,7 +7092,7 @@
        "6"
       ]
      },
-     "execution_count": 220,
+     "execution_count": 225,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -7103,7 +7103,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 221,
+   "execution_count": 226,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -7116,7 +7116,7 @@
        "6"
       ]
      },
-     "execution_count": 221,
+     "execution_count": 226,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -7138,7 +7138,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 222,
+   "execution_count": 227,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -7152,8 +7152,8 @@
      "traceback": [
       "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
       "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-222-188b816be8b6>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mfactorial\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m3.1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
-      "\u001b[0;32m<ipython-input-218-d2a63febf49e>\u001b[0m in \u001b[0;36mfactorial\u001b[0;34m(n, strict)\u001b[0m\n\u001b[1;32m     16\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mn\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnumbers\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mReal\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     17\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mn\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0mmath\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtrunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mn\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mstrict\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 18\u001b[0;31m                 \u001b[0;32mraise\u001b[0m \u001b[0mTypeError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"n is not an integer-like value; it has decimals\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     19\u001b[0m             \u001b[0mn\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmath\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtrunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mn\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     20\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m<ipython-input-227-188b816be8b6>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mfactorial\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m3.1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;32m<ipython-input-223-d2a63febf49e>\u001b[0m in \u001b[0;36mfactorial\u001b[0;34m(n, strict)\u001b[0m\n\u001b[1;32m     16\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mn\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnumbers\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mReal\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     17\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mn\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0mmath\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtrunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mn\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mstrict\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 18\u001b[0;31m                 \u001b[0;32mraise\u001b[0m \u001b[0mTypeError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"n is not an integer-like value; it has decimals\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     19\u001b[0m             \u001b[0mn\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmath\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtrunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mn\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     20\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
       "\u001b[0;31mTypeError\u001b[0m: n is not an integer-like value; it has decimals"
      ]
     }
@@ -7175,7 +7175,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 223,
+   "execution_count": 228,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -7188,7 +7188,7 @@
        "6"
       ]
      },
-     "execution_count": 223,
+     "execution_count": 228,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -7210,7 +7210,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 224,
+   "execution_count": 229,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -7224,8 +7224,8 @@
      "traceback": [
       "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
       "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-224-7296a74f5dcf>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mfactorial\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m1\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;36m2j\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
-      "\u001b[0;32m<ipython-input-218-d2a63febf49e>\u001b[0m in \u001b[0;36mfactorial\u001b[0;34m(n, strict)\u001b[0m\n\u001b[1;32m     19\u001b[0m             \u001b[0mn\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmath\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtrunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mn\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     20\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 21\u001b[0;31m             \u001b[0;32mraise\u001b[0m \u001b[0mTypeError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Factorial is only defined for integers\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     22\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     23\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mn\u001b[0m \u001b[0;34m<\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m<ipython-input-229-7296a74f5dcf>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mfactorial\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m1\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;36m2j\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;32m<ipython-input-223-d2a63febf49e>\u001b[0m in \u001b[0;36mfactorial\u001b[0;34m(n, strict)\u001b[0m\n\u001b[1;32m     19\u001b[0m             \u001b[0mn\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmath\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtrunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mn\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     20\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 21\u001b[0;31m             \u001b[0;32mraise\u001b[0m \u001b[0mTypeError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Factorial is only defined for integers\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     22\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     23\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mn\u001b[0m \u001b[0;34m<\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
       "\u001b[0;31mTypeError\u001b[0m: Factorial is only defined for integers"
      ]
     }
@@ -7287,7 +7287,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.3"
+   "version": "3.7.4"
   },
   "livereveal": {
    "auto_select": "code",
@@ -7315,5 +7315,5 @@
   }
  },
  "nbformat": 4,
- "nbformat_minor": 2
+ "nbformat_minor": 4
 }
diff --git a/05_numbers_10_review.ipynb b/05_numbers_10_review.ipynb
new file mode 100644
index 0000000..819d668
--- /dev/null
+++ b/05_numbers_10_review.ipynb
@@ -0,0 +1,347 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n",
+    "# Chapter 5: Bits & Numbers"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Content Review"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Read [Chapter 5](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/05_numbers_00_content.ipynb) of the book. Then, work through the questions below."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Essay Questions "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Answer the following questions briefly with *at most* 300 characters per question!"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q1**: In what way is the **binary representation** of `int` objects *similar* to the **decimal system** taught in elementary school?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q2**: Why may objects of type `bool` be regarded a **numeric type** as well?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q3**: Why is it *inefficient* to store `bool` objects in bits resembling a **hexadecimal representation**?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q4**: Colors are commonly expressed in the **hexadecimal system** in websites (cf., the [HTML](https://en.wikipedia.org/wiki/HTML) and [CSS](https://en.wikipedia.org/wiki/Cascading_Style_Sheets) formats).\n",
+    "\n",
+    "For example, $#000000$, $#ff9900$, and $#ffffff$ turn out to be black, orange, and white. The six digits are read in *pairs of two* from left to right, and the *three pairs* correspond to the proportions of red, green, and blue mixed together. They reach from $0_{16} = 0_{10}$ for $0$% to $\\text{ff}_{16} = 255_{10}$ for $100$% (cf., this [article](https://en.wikipedia.org/wiki/RGB_color_model) for an in-depth discussion).\n",
+    "\n",
+    "In percent, what are the proportions of red, green, and blue that make up orange? Calculate the three percentages separately! How many **bytes** are needed to encode a color? How many **bits** are that?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q5**: What does it mean for a code fragment to **fail silently**?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q6**: Explain why the mathematical set of all real numbers $\\mathbb{R}$ can only be **approximated** by floating-point numbers on a computer!"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q7**: How do we deal with a `float` object's imprecision if we need to **check for equality**?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q8**: What data type, built-in or from the [standard library](https://docs.python.org/3/library/index.html), is best suited to represent the [transcendental numbers](https://en.wikipedia.org/wiki/Transcendental_number) $\\pi$ and $\\text{e}$?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q9**: How can **abstract base classes**, for example, as defined in the **numerical tower**, be helpful in enabling **duck typing**?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### True / False Questions"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Motivate your answer with *one short* sentence!"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q10**: The precision of `int` objects depends on how we choose to represent them in memory. For example, using a **hexadecimal representation** gives us $16^8$ digits whereas with a **binary representation** an `int` object can have *at most* $2^8$ digits."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q11**: With the built-in [round()](https://docs.python.org/3/library/functions.html#round) function, we obtain a *precise* representation for any `float` object if we can live with *less than* $15$ digits of precision."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q12**: As most currencies operate with $2$ or $3$ decimals (e.g., EUR $9.99$), the `float` type's limitation of *at most* $15$ digits is *not* a problem in practice."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q13**: The [IEEE 754](https://en.wikipedia.org/wiki/IEEE_754) standard's **special values** provide no benefit in practice as we could always use a **[sentinel](https://en.wikipedia.org/wiki/Sentinel_value)** value (i.e., a \"dummy\"). For example, instead of `nan`, we can always use `0` to indicate a *missing* value."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q14**: The following code fragment raises an `InvalidOperation` exception. That is an example of code **failing loudly**.\n",
+    "```python\n",
+    "float(\"inf\") + float(\"-inf\")\n",
+    "```"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q15**: Python provides a `scientific` type (e.g., `1.23e4`) that is useful mainly to model problems in the domains of physics or astrophysics."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q16**: From a practitioner's point of view, the built-in [format()](https://docs.python.org/3/library/functions.html#format) function does the *same* as the built-in [round()](https://docs.python.org/3/library/functions.html#round) function."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q17**: The `Decimal` type from the [decimal](https://docs.python.org/3/library/decimal.html) module in the [standard library](https://docs.python.org/3/library/index.html) allows us to model the set of the real numbers $\\mathbb{R}$ *precisely*."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q18**: The `Fraction` type from the [fractions](https://docs.python.org/3/library/fractions.html) module in the [standard library](https://docs.python.org/3/library/index.html) allows us to model the set of the rational numbers $\\mathbb{Q}$ *precisely*."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " "
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.4"
+  },
+  "toc": {
+   "base_numbering": 1,
+   "nav_menu": {},
+   "number_sections": false,
+   "sideBar": true,
+   "skip_h1_title": true,
+   "title_cell": "Table of Contents",
+   "title_sidebar": "Contents",
+   "toc_cell": false,
+   "toc_position": {},
+   "toc_section_display": false,
+   "toc_window_display": false
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/05_numbers_20_exercises.ipynb b/05_numbers_20_exercises.ipynb
new file mode 100644
index 0000000..d00ef2e
--- /dev/null
+++ b/05_numbers_20_exercises.ipynb
@@ -0,0 +1,412 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n",
+    "# Chapter 5: Bits & Numbers"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Coding Exercises"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Read [Chapter 5](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/05_numbers_00_content.ipynb) of the book. Then, work through the exercises below."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Discounting Customer Orders (revisited)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q1** in [Chapter 2's Exercises](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/02_functions_20_exercises.ipynb#Volume-of-a-Sphere) section already revealed that we must consider the effects of the `float` type's imprecision.\n",
+    "\n",
+    "This becomes even more important when we deal with numeric data modeling accounting or finance data (cf., [this comment](https://stackoverflow.com/a/24976426) on \"falsehoods programmers believe about money\").\n",
+    "\n",
+    "In addition to the *inherent imprecision* of numbers in general, the topic of **[rounding numbers](https://en.wikipedia.org/wiki/Rounding)** is also not as trivial as we might expect! [This article](https://realpython.com/python-rounding/) summarizes everything the data science practitioner needs to know.\n",
+    "\n",
+    "In this exercise, we revisit **Q1** from [Chapter 3's Exercises](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/03_conditionals_20_exercises.ipynb#Discounting-Customer-Orders) section, and make the `discounted_price()` function work *correctly* for real-life sales data."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q1.1**: Execute the code cells below! What results would you have *expected*, and why?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "round(1.5)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "round(2.5)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "round(2.675, 2)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q1.2**: The built-in [round()](https://docs.python.org/3/library/functions.html#round) function implements the \"**[round half to even](https://en.wikipedia.org/wiki/Rounding#Round_half_to_even)**\" strategy. Describe in one or two sentences what that means!"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q1.3**: For the revised `discounted_price()` function, we have to tackle *two* issues: First, we have to replace the built-in `float` type with a data type that allows us to control the precision. Second, the discounted price should be rounded according to a more human-friendly rounding strategy, namely \"**[round half away from zero](https://en.wikipedia.org/wiki/Rounding#Round_half_away_from_zero)**.\"\n",
+    "\n",
+    "Describe in one or two sentences how \"**[round half away from zero](https://en.wikipedia.org/wiki/Rounding#Round_half_away_from_zero)**\" is more in line with how humans think of rounding!"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q1.4**: We use the `Decimal` type from the [decimal](https://docs.python.org/3/library/decimal.html) module in the [standard library](https://docs.python.org/3/library/index.html) to tackle *both* issues simultaneously.\n",
+    "\n",
+    "Assign `euro` a numeric object such that both `Decimal(\"1.5\")` and `Decimal(\"2.5\")` are rounded to `Decimal(\"2\")` (i.e., no decimal) with the [quantize()](https://docs.python.org/3/library/decimal.html#decimal.Decimal.quantize) method!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from decimal import Decimal"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "euro = ..."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "Decimal(\"1.5\").quantize(...)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "Decimal(\"2.5\").quantize(...)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q1.5**: Obviously, the two preceding code cells still [round half to even](https://en.wikipedia.org/wiki/Rounding#Round_half_to_even).\n",
+    "\n",
+    "The [decimal](https://docs.python.org/3/library/decimal.html) module defines a `ROUND_HALF_UP` flag that we can pass as the second argument to the [quantize()](https://docs.python.org/3/library/decimal.html#decimal.Decimal.quantize) method. Then, it [rounds half away from zero](https://en.wikipedia.org/wiki/Rounding#Round_half_away_from_zero).\n",
+    "\n",
+    "Add `ROUND_HALF_UP` to the code cells! `Decimal(\"2.5\")` should now be rounded to `Decimal(\"3\")`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from decimal import ROUND_HALF_UP"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "Decimal(\"1.5\").quantize(...)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "Decimal(\"2.5\").quantize(...)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q1.6**: Instead of `euro`, define `cents` such that rounding occurs to *two* decimals! `Decimal(\"2.675\")` should now be rounded to `Decimal(\"2.68\")`. Do *not* forget to include the `ROUND_HALF_UP` flag!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "cents = ..."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "Decimal(\"2.675\").quantize(...)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q1.7**: Rewrite the function `discounted_price()` from [Chapter 3's Exercises](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/03_conditionals_20_exercises.ipynb#Discounting-Customer-Orders) section!\n",
+    "\n",
+    "It takes the *positional* arguments `unit_price` and `quantity` and implements a discount scheme for a line item in a customer order as follows:\n",
+    "\n",
+    "- if the unit price is over 100 dollars, grant 10% relative discount\n",
+    "- if a customer orders more than 10 items, one in every five items is for free\n",
+    "\n",
+    "Only one of the two discounts is granted, whichever is better for the customer.\n",
+    "\n",
+    "The function then returns the overall price for the line item as a `Decimal` number with a precision of *two* decimals.\n",
+    "\n",
+    "Enable **duck typing** by allowing the function to be called with various numeric types as the arguments, in particular, `quantity` may be a non-integer as well: Use an appropriate **abstract base class** from the [numbers](https://docs.python.org/3/library/numbers.html) module in the [standard library](https://docs.python.org/3/library/index.html) to verify the arguments' types and also that they are both positive!\n",
+    "\n",
+    "It is considered a *best practice* to only round towards the *end* of the calculations."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numbers"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def discounted_price(...):\n",
+    "    ..."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q1.8**: Execute the code cells below and verify the final price for the following four test cases:\n",
+    "\n",
+    "- $7$ smartphones @ $99.00$ USD\n",
+    "- $3$ workstations @ $999.00$ USD\n",
+    "- $19$ GPUs @ $879.95$ USD\n",
+    "- $14$ Raspberry Pis @ $35.00$ USD\n",
+    "\n",
+    "The output should now *always* be a `Decimal` number with *two* decimals!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "discounted_price(99, 7)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "discounted_price(999, 3)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "discounted_price(879.95, 19)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "discounted_price(35, 14)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This also works if `quantity` is passed in as a `float` type."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "discounted_price(99, 7.0)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Decimals beyond the first two are gracefully discarded (i.e., *without* rounding errors accumulating)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "discounted_price(99.0001, 7)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The basic input validation ensures that the user of `discounted_price()` does not pass in invalid data. Here, the `\"abc\"` creates a `TypeError`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "discounted_price(\"abc\", 7)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "A `-1` passed in as `unit_price` results in a `ValueError`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "discounted_price(-1, 7)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.4"
+  },
+  "toc": {
+   "base_numbering": 1,
+   "nav_menu": {},
+   "number_sections": false,
+   "sideBar": true,
+   "skip_h1_title": true,
+   "title_cell": "Table of Contents",
+   "title_sidebar": "Contents",
+   "toc_cell": false,
+   "toc_position": {},
+   "toc_section_display": false,
+   "toc_window_display": false
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/05_numbers_review_and_exercises.ipynb b/05_numbers_review_and_exercises.ipynb
deleted file mode 100644
index 2ae8301..0000000
--- a/05_numbers_review_and_exercises.ipynb
+++ /dev/null
@@ -1,706 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "\n",
-    "# Chapter 5: Bits & Numbers"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Content Review"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Read [Chapter 5](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/05_numbers.ipynb) of the book. Then work through the eighteen review questions."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Essay Questions "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Answer the following questions briefly with *at most* 300 characters per question!"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**Q1**: In what way is the **binary representation** of `int` objects *similar* to the **decimal system** taught in elementary school?"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    " "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**Q2**: Why may objects of type `bool` be regarded a **numeric type** as well?"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    " "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**Q3**: Why is it *inefficient* to store `bool` objects in bits resembling a **hexadecimal representation**?"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    " "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**Q4**: Colors are commonly expressed in the **hexadecimal system** in websites (cf., the [HTML](https://en.wikipedia.org/wiki/HTML) and [CSS](https://en.wikipedia.org/wiki/Cascading_Style_Sheets) formats).\n",
-    "\n",
-    "For example, $#000000$, $#ff9900$, and $#ffffff$ turn out to be black, orange, and white. The six digits are read in *pairs of two* from left to right, and the *three pairs* correspond to the proportions of red, green, and blue mixed together. They reach from $0_{16} = 0_{10}$ for $0$% to $\\text{ff}_{16} = 255_{10}$ for $100$% (cf., this [article](https://en.wikipedia.org/wiki/RGB_color_model) for an in-depth discussion).\n",
-    "\n",
-    "In percent, what are the proportions of red, green, and blue that make up orange? Calculate the three percentages separately! How many **bytes** are needed to encode a color? How many **bits** are that?"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    " "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**Q5**: What does it mean for a code fragment to **fail silently**?"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    " "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**Q6**: Explain why the mathematical set of all real numbers $\\mathbb{R}$ can only be **approximated** by floating-point numbers on a computer!"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    " "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**Q7**: How do we deal with a `float` object's imprecision if we need to **check for equality**?"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    " "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**Q8**: What data type, built-in or from the [standard library](https://docs.python.org/3/library/index.html), is best suited to represent the [transcendental numbers](https://en.wikipedia.org/wiki/Transcendental_number) $\\pi$ and $\\text{e}$?"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    " "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**Q9**: How can **abstract base classes**, for example, as defined in the **numerical tower**, be helpful in enabling **duck typing**?"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    " "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### True / False Questions"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Motivate your answer with *one short* sentence!"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**Q10**: The precision of `int` objects depends on how we choose to represent them in memory. For example, using a **hexadecimal representation** gives us $16^8$ digits whereas with a **binary representation** an `int` object can have *at most* $2^8$ digits."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    " "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**Q11**: With the built-in [round()](https://docs.python.org/3/library/functions.html#round) function, we obtain a *precise* representation for any `float` object if we can live with *less than* $15$ digits of precision."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    " "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**Q12**: As most currencies operate with $2$ or $3$ decimals (e.g., EUR $9.99$), the `float` type's limitation of *at most* $15$ digits is *not* a problem in practice."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    " "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**Q13**: The [IEEE 754](https://en.wikipedia.org/wiki/IEEE_754) standard's **special values** provide no benefit in practice as we could always use a **[sentinel](https://en.wikipedia.org/wiki/Sentinel_value)** value (i.e., a \"dummy\"). For example, instead of `nan`, we can always use `0` to indicate a *missing* value."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    " "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**Q14**: The following code fragment raises an `InvalidOperation` exception. That is an example of code **failing loudly**.\n",
-    "```python\n",
-    "float(\"inf\") + float(\"-inf\")\n",
-    "```"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    " "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**Q15**: Python provides a `scientific` type (e.g., `1.23e4`) that is useful mainly to model problems in the domains of physics or astrophysics."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    " "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**Q16**: From a practitioner's point of view, the built-in [format()](https://docs.python.org/3/library/functions.html#format) function does the *same* as the built-in [round()](https://docs.python.org/3/library/functions.html#round) function."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    " "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**Q17**: The `Decimal` type from the [decimal](https://docs.python.org/3/library/decimal.html) module in the [standard library](https://docs.python.org/3/library/index.html) allows us to model the set of the real numbers $\\mathbb{R}$ *precisely*."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    " "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**Q18**: The `Fraction` type from the [fractions](https://docs.python.org/3/library/fractions.html) module in the [standard library](https://docs.python.org/3/library/index.html) allows us to model the set of the rational numbers $\\mathbb{Q}$ *precisely*."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    " "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Coding Exercises"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Discounting Customer Orders (revisited)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**Q11** in [Chapter 2's Review & Exercises](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/02_functions_review_and_exercises.ipynb#Volume-of-a-Sphere) section already revealed that we must consider the effects of the `float` type's imprecision.\n",
-    "\n",
-    "This becomes even more important when we deal with numeric data modeling accounting or finance data (cf., [this comment](https://stackoverflow.com/a/24976426) on \"falsehoods programmers believe about money\").\n",
-    "\n",
-    "In addition to the *inherent imprecision* of numbers in general, the topic of **[rounding numbers](https://en.wikipedia.org/wiki/Rounding)** is also not as trivial as we might expect! [This article](https://realpython.com/python-rounding/) summarizes everything the data science practitioner needs to know.\n",
-    "\n",
-    "In this exercise, we revisit **Q9** from [Chapter 3's Review & Exercises](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/03_conditionals_review_and_exercises.ipynb#Discounting-Customer-Orders) section, and make the `discounted_price()` function work *correctly* for real-life sales data."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**Q19.1**: Execute the code cells below! What results would you have *expected*, and why?"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "round(1.5)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "round(2.5)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "round(2.675, 2)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    " "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**Q19.2**: The built-in [round()](https://docs.python.org/3/library/functions.html#round) function implements the \"**[round half to even](https://en.wikipedia.org/wiki/Rounding#Round_half_to_even)**\" strategy. Describe in one or two sentences what that means!"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    " "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**Q19.3**: For the revised `discounted_price()` function, we have to tackle *two* issues: First, we have to replace the built-in `float` type with a data type that allows us to control the precision. Second, the discounted price should be rounded according to a more human-friendly rounding strategy, namely \"**[round half away from zero](https://en.wikipedia.org/wiki/Rounding#Round_half_away_from_zero)**.\"\n",
-    "\n",
-    "Describe in one or two sentences how \"**[round half away from zero](https://en.wikipedia.org/wiki/Rounding#Round_half_away_from_zero)**\" is more in line with how humans think of rounding!"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    " "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**Q19.4**: We use the `Decimal` type from the [decimal](https://docs.python.org/3/library/decimal.html) module in the [standard library](https://docs.python.org/3/library/index.html) to tackle *both* issues simultaneously.\n",
-    "\n",
-    "Assign `euro` a numeric object such that both `Decimal(\"1.5\")` and `Decimal(\"2.5\")` are rounded to `Decimal(\"2\")` (i.e., no decimal) with the [quantize()](https://docs.python.org/3/library/decimal.html#decimal.Decimal.quantize) method!"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from decimal import Decimal"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "euro = ..."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "Decimal(\"1.5\").quantize(...)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "Decimal(\"2.5\").quantize(...)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**Q19.5**: Obviously, the two preceding code cells still [round half to even](https://en.wikipedia.org/wiki/Rounding#Round_half_to_even).\n",
-    "\n",
-    "The [decimal](https://docs.python.org/3/library/decimal.html) module defines a `ROUND_HALF_UP` flag that we can pass as the second argument to the [quantize()](https://docs.python.org/3/library/decimal.html#decimal.Decimal.quantize) method. Then, it [rounds half away from zero](https://en.wikipedia.org/wiki/Rounding#Round_half_away_from_zero).\n",
-    "\n",
-    "Add `ROUND_HALF_UP` to the code cells! `Decimal(\"2.5\")` should now be rounded to `Decimal(\"3\")`."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from decimal import ROUND_HALF_UP"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "Decimal(\"1.5\").quantize(...)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "Decimal(\"2.5\").quantize(...)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**Q19.6**: Instead of `euro`, define `cents` such that rounding occurs to *two* decimals! `Decimal(\"2.675\")` should now be rounded to `Decimal(\"2.68\")`. Do *not* forget to include the `ROUND_HALF_UP` flag!"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "cents = ..."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "Decimal(\"2.675\").quantize(...)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**Q19.7**: Rewrite the function `discounted_price()` from [Chapter 3's Review & Exercises](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/03_conditionals_review_and_exercises.ipynb#Discounting-Customer-Orders) section!\n",
-    "\n",
-    "It takes the *positional* arguments `unit_price` and `quantity` and implements a discount scheme for a line item in a customer order as follows:\n",
-    "\n",
-    "- if the unit price is over 100 dollars, grant 10% relative discount\n",
-    "- if a customer orders more than 10 items, one in every five items is for free\n",
-    "\n",
-    "Only one of the two discounts is granted, whichever is better for the customer.\n",
-    "\n",
-    "The function then returns the overall price for the line item as a `Decimal` number with a precision of *two* decimals.\n",
-    "\n",
-    "Enable **duck typing** by allowing the function to be called with various numeric types as the arguments, in particular, `quantity` may be a non-integer as well: Use an appropriate **abstract base class** from the [numbers](https://docs.python.org/3/library/numbers.html) module in the [standard library](https://docs.python.org/3/library/index.html) to verify the arguments' types and also that they are both positive!\n",
-    "\n",
-    "It is considered a *best practice* to only round towards the *end* of the calculations."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import numbers"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def discounted_price(...):\n",
-    "    ..."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**Q19.8**: Execute the code cells below and verify the final price for the following four test cases:\n",
-    "\n",
-    "- $7$ smartphones @ $99.00$ USD\n",
-    "- $3$ workstations @ $999.00$ USD\n",
-    "- $19$ GPUs @ $879.95$ USD\n",
-    "- $14$ Raspberry Pis @ $35.00$ USD\n",
-    "\n",
-    "The output should now *always* be a `Decimal` number with *two* decimals!"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "discounted_price(99, 7)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "discounted_price(999, 3)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "discounted_price(879.95, 19)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "discounted_price(35, 14)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "This also works if `quantity` is passed in as a `float` type."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "discounted_price(99, 7.0)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Decimals beyond the first two are gracefully discarded (i.e., *without* rounding errors accumulating)."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "discounted_price(99.0001, 7)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The basic input validation ensures that the user of `discounted_price()` does not pass in invalid data. Here, the `\"abc\"` creates a `TypeError`."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "discounted_price(\"abc\", 7)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "A `-1` passed in as `unit_price` results in a `ValueError`."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "discounted_price(-1, 7)"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.7.3"
-  },
-  "toc": {
-   "base_numbering": 1,
-   "nav_menu": {},
-   "number_sections": false,
-   "sideBar": true,
-   "skip_h1_title": true,
-   "title_cell": "Table of Contents",
-   "title_sidebar": "Contents",
-   "toc_cell": false,
-   "toc_position": {},
-   "toc_section_display": false,
-   "toc_window_display": false
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/06_text.ipynb b/06_text_00_content.ipynb
similarity index 90%
rename from 06_text.ipynb
rename to 06_text_00_content.ipynb
index 3fbbcb5..912142e 100644
--- a/06_text.ipynb
+++ b/06_text_00_content.ipynb
@@ -54,7 +54,7 @@
    },
    "outputs": [],
    "source": [
-    "text = \"Lorem ipsum dolor sit amet, consectetur ...\""
+    "text = \"Lorem ipsum dolor sit amet, ...\""
    ]
   },
   {
@@ -80,7 +80,7 @@
     {
      "data": {
       "text/plain": [
-       "139674889049360"
+       "140483431254256"
       ]
      },
      "execution_count": 2,
@@ -124,7 +124,7 @@
     }
    },
    "source": [
-    "A `str` object evaluates to itself in a literal notation with enclosing **single quotes** `'` by default. In [Chapter 1](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/01_elements.ipynb#Value), we already specified the double quotes `\"` convention we stick to in this book. Yet, single quotes `'` and double quotes `\"` are *perfect* substitutes for all `str` objects that do *not* contain any of the two symbols in it. We could use the reverse convention, as well.\n",
+    "A `str` object evaluates to itself in a literal notation with enclosing **single quotes** `'` by default. In [Chapter 1](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/01_elements_00_content.ipynb#Value), we already specified the double quotes `\"` convention we stick to in this book. Yet, single quotes `'` and double quotes `\"` are *perfect* substitutes for all `str` objects that do *not* contain any of the two symbols in it. We could use the reverse convention, as well.\n",
     "\n",
     "As [this discussion](https://stackoverflow.com/questions/56011/single-quotes-vs-double-quotes-in-python) shows, many programmers have *strong* opinions about that and make up *new* conventions for their projects. Consequently, the discussion was \"closed as not constructive\" by the moderators."
    ]
@@ -141,7 +141,7 @@
     {
      "data": {
       "text/plain": [
-       "'Lorem ipsum dolor sit amet, consectetur ...'"
+       "'Lorem ipsum dolor sit amet, ...'"
       ]
      },
      "execution_count": 4,
@@ -161,7 +161,7 @@
     }
    },
    "source": [
-    "As the single quote `'` is often used in the English language as a shortener, we could make an argument in favor of using the double quotes `\"`: There are possibly fewer situations like in the two code cells below, in which we must revert to using a `\\` to **escape** a single quote `'` in a text (cf., the [Special Characters](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/06_text.ipynb#Special-Characters) section further below). However, double quotes `\"` are often used as well. So, this argument is somewhat not convincing.\n",
+    "As the single quote `'` is often used in the English language as a shortener, we could make an argument in favor of using the double quotes `\"`: There are possibly fewer situations like in the two code cells below, in which we must revert to using a `\\` to **escape** a single quote `'` in a text (cf., the [Special Characters](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/06_text_00_content.ipynb#Special-Characters) section further below). However, double quotes `\"` are often used as well. So, this argument is somewhat not convincing.\n",
     "\n",
     "Many proponents of the single quote `'` usage claim that double quotes `\"` make more **visual noise** on the screen. This argument is also not convincing. On the contrary, one could claim that *two* single quotes `''` look so similar to *one* double quote `\"` that it might not be apparent right away what we are looking at. By sticking to double quotes `\"`, we avoid such danger of confusion.\n",
     "\n",
@@ -182,7 +182,7 @@
     {
      "data": {
       "text/plain": [
-       "\"It's cool that strings are so versatile!\""
+       "'It\\'s cool that \"strings\" are versatile'"
       ]
      },
      "execution_count": 5,
@@ -191,7 +191,7 @@
     }
    ],
    "source": [
-    "\"It's cool that strings are so versatile!\""
+    "\"It's cool that \\\"strings\\\" are versatile\""
    ]
   },
   {
@@ -206,7 +206,7 @@
     {
      "data": {
       "text/plain": [
-       "\"It's cool that strings are so versatile!\""
+       "'It\\'s cool that \"strings\" are versatile'"
       ]
      },
      "execution_count": 6,
@@ -215,7 +215,7 @@
     }
    ],
    "source": [
-    "'It\\'s cool that strings are so versatile!'"
+    "'It\\'s cool that \"strings\" are versatile'"
    ]
   },
   {
@@ -615,7 +615,7 @@
     }
    },
    "source": [
-    "To use constructs familiar from [Chapter 3](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/03_conditionals.ipynb#The-try-Statement) to explain what `with open(...) as file:` does, below is a formulation with a `try` statement *equivalent* to the `with` statement above. The `finally`-branch is always executed, even if an exception is raised in the `for`-loop. So, `file` is sure to be closed too, with a somewhat less expressive formulation."
+    "To use constructs familiar from [Chapter 3](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/03_conditionals_00_content.ipynb#The-try-Statement) to explain what `with open(...) as file:` does, below is a formulation with a `try` statement *equivalent* to the `with` statement above. The `finally`-branch is *always* executed, even if an exception is raised in the `for`-loop. So, `file` is sure to be closed too, with a somewhat less expressive formulation."
    ]
   },
   {
@@ -737,7 +737,7 @@
     }
    },
    "source": [
-    "A **sequence** is yet another *abstract* concept (cf., the \"*Containers vs. Iterables*\" section in [Chapter 4](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/04_iteration.ipynb#Containers-vs.-Iterables)).\n",
+    "A **sequence** is yet another *abstract* concept (cf., the \"*Containers vs. Iterables*\" section in [Chapter 4](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/04_iteration_00_content.ipynb#Containers-vs.-Iterables)).\n",
     "\n",
     "It unifies *four* [orthogonal](https://en.wikipedia.org/wiki/Orthogonality) (i.e., \"independent\") behaviors into one idea: Any data type, such as `str`, is considered a sequence if it simultaneously\n",
     "\n",
@@ -746,9 +746,9 @@
     "3. comes with a *predictable* **order** of its\n",
     "4. **finite** number of \"things.\"\n",
     "\n",
-    "[Chapter 7](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/07_sequences.ipynb#Collections-vs.-Sequences) formalizes sequences in great detail. Here, we keep our focus on the `str` type that historically received its name as it models a \"**[string of characters](https://en.wikipedia.org/wiki/String_%28computer_science%29)**,\" and a \"string\" is more formally called a sequence in the computer science literature.\n",
+    "[Chapter 7](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/07_sequences_00_content.ipynb#Collections-vs.-Sequences) formalizes sequences in great detail. Here, we keep our focus on the `str` type that historically received its name as it models a \"**[string of characters](https://en.wikipedia.org/wiki/String_%28computer_science%29)**,\" and a \"string\" is more formally called a sequence in the computer science literature.\n",
     "\n",
-    "Behaving like a sequence, `str` objects may be treated like `list` objects in many cases. For example, the built-in [len()](https://docs.python.org/3/library/functions.html#len) function tells us how many elements (i.e., characters) make up `text`. [len()](https://docs.python.org/3/library/functions.html#len) would not work with an *infinite* object: As anything modeled in a program must fit into a computer's finite memory at runtime, there cannot exist objects containing a truly infinite number of elements; however, [Chapter 7](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/07_sequences.ipynb#Iterators-vs.-Iterables) introduces iterable data types that can be used to model an *infinite* series of elements and that, consequently, have no concept of \"length.\""
+    "Behaving like a sequence, `str` objects may be treated like `list` objects in many cases. For example, the built-in [len()](https://docs.python.org/3/library/functions.html#len) function tells us how many elements (i.e., characters) make up `text`. [len()](https://docs.python.org/3/library/functions.html#len) would not work with an *infinite* object: As anything modeled in a program must fit into a computer's finite memory at runtime, there cannot exist objects containing a truly infinite number of elements; however, [Chapter 7](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/07_sequences_00_content.ipynb#Iterators-vs.-Iterables) introduces iterable data types that can be used to model an *infinite* series of elements and that, consequently, have no concept of \"length.\""
    ]
   },
   {
@@ -763,7 +763,7 @@
     {
      "data": {
       "text/plain": [
-       "43"
+       "31"
       ]
      },
      "execution_count": 23,
@@ -799,7 +799,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "L o r e m   i p s u m   d o l o r   s i t   a m e t ,   c o n s e c t e t u r   . . . "
+      "L o r e m   i p s u m   d o l o r   s i t   a m e t ,   . . . "
      ]
     }
    ],
@@ -912,7 +912,7 @@
     }
    },
    "source": [
-    "As `str` objects have the additional property of being *ordered*, we may **index** into them to obtain individual characters with the **indexing operator** `[]`. This is analogous to how we obtained individual elements of a `list` object in [Chapter 1](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/01_elements.ipynb#Who-am-I?-And-how-many?)."
+    "As `str` objects have the additional property of being *ordered*, we may **index** into them to obtain individual characters with the **indexing operator** `[]`. This is analogous to how we obtained individual elements of a `list` object in [Chapter 1](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/01_elements_00_content.ipynb#Who-am-I?-And-how-many?)."
    ]
   },
   {
@@ -1031,7 +1031,7 @@
     }
    ],
    "source": [
-    "text[42]  # = len(text) - 1"
+    "text[30]  # = len(text) - 1"
    ]
   },
   {
@@ -1061,13 +1061,13 @@
      "traceback": [
       "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
       "\u001b[0;31mIndexError\u001b[0m                                Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-32-5386eb8e1e94>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mtext\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m43\u001b[0m\u001b[0;34m]\u001b[0m  \u001b[0;31m# = len(text)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;32m<ipython-input-32-a8c62c212778>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mtext\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m31\u001b[0m\u001b[0;34m]\u001b[0m  \u001b[0;31m# = len(text)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
       "\u001b[0;31mIndexError\u001b[0m: string index out of range"
      ]
     }
    ],
    "source": [
-    "text[43]  # = len(text)"
+    "text[31]  # = len(text)"
    ]
   },
   {
@@ -1078,7 +1078,21 @@
     }
    },
    "source": [
-    "We may use *negative* indexes to start counting from the end of the `str` object. That only works because sequences are *finite*."
+    "We may use *negative* indexes to start counting from the end of the `str` object, as shown in the figure below. That only works because sequences are *finite*."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "|   Slot    | 0 | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10| 11| 12| 13| 14| 15| 16| 17| 18| 19| 20| 21| 22| 23| 24| 25| 26| 27| 28| 29| 30|\n",
+    "|:---------:|:-:|:-:|:-:|:-:|:-:|:-:|:-:|:-:|:-:|:-:|:-:|:-:|:-:|:-:|:-:|:-:|:-:|:-:|:-:|:-:|:-:|:-:|:-:|:-:|:-:|:-:|:-:|:-:|:-:|:-:|:-:|\n",
+    "|**Reverse**|-31|-30|-29|-28|-27|-26|-25|-24|-23|-22|-21|-20|-19|-18|-17|-16|-15|-14|-13|-12|-11|-10|-9 |-8 |-7 |-6 |-5 |-4 |-3 |-2 |-1 |\n",
+    "| **Char**  |`L`|`o`|`r`|`e`|`m`|` `|`i`|`p`|`s`|`u`|`m`|` `|`d`|`o`|`l`|`o`|`r`|` `|`s`|`i`|`t`|` `|`a`|`m`|`e`|`t`|`,`|` `|`.`|`.`|`.`|"
    ]
   },
   {
@@ -1086,7 +1100,7 @@
    "execution_count": 33,
    "metadata": {
     "slideshow": {
-     "slide_type": "slide"
+     "slide_type": "-"
     }
    },
    "outputs": [
@@ -1105,17 +1119,6 @@
     "text[-1]"
    ]
   },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "source": [
-    "One reason why programmers like to start counting at 0 is that a positive index and its *corresponding* negative index always add up to the length of the sequence."
-   ]
-  },
   {
    "cell_type": "code",
    "execution_count": 34,
@@ -1128,7 +1131,7 @@
     {
      "data": {
       "text/plain": [
-       "'i'"
+       "'L'"
       ]
      },
      "execution_count": 34,
@@ -1137,7 +1140,18 @@
     }
    ],
    "source": [
-    "text[6]"
+    "text[-31]  # = -len(text)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "One reason why programmers like to start counting at 0 is that a positive index and its *corresponding* negative index always add up to the length of the sequence. Here, `6` and `25` add to `31`."
    ]
   },
   {
@@ -1145,7 +1159,7 @@
    "execution_count": 35,
    "metadata": {
     "slideshow": {
-     "slide_type": "-"
+     "slide_type": "fragment"
     }
    },
    "outputs": [
@@ -1161,7 +1175,31 @@
     }
    ],
    "source": [
-    "text[-37]"
+    "text[6]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'i'"
+      ]
+     },
+     "execution_count": 36,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "text[-25]"
    ]
   },
   {
@@ -1192,7 +1230,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 36,
+   "execution_count": 37,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -1205,7 +1243,7 @@
        "'Lorem'"
       ]
      },
-     "execution_count": 36,
+     "execution_count": 37,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1222,14 +1260,14 @@
     }
    },
    "source": [
-    "Whereas the *start* is always included in the result, the *end* is not. Counter-intuitive at first, this makes working with individual slices easier as they add up to the original `str` object again.\n",
+    "Whereas the *start* is always included in the result, the *end* is not. Counter-intuitive at first, this makes working with individual slices easier as they \"add\" up to the original `str` object again (cf., the \"*String Operations*\" sub-section below regarding the overloaded `+` operator). Because the *end* is *not* included, we end the second slice below with `len(text)` or `31` below.\n",
     "\n",
-    "So, as the *end* is *not* included, we must end the second slice with `len(text)` or `43` below."
+    "Not including the *end* has another advantage: The difference \"*end* minus *start*\" tells us how many elements the resulting slice has. Above, for example, `5 - 0` implies that `\"Lorem\"` consists of `5` characters. So, colloquially, `0:5` means \"taking the first five characters.\" That rule only works if both *start* and *end* are *positive*."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 37,
+   "execution_count": 38,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -1239,10 +1277,10 @@
     {
      "data": {
       "text/plain": [
-       "'Lorem ipsum dolor sit amet, consectetur ...'"
+       "'Lorem ipsum dolor sit amet, ...'"
       ]
      },
-     "execution_count": 37,
+     "execution_count": 38,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1259,12 +1297,12 @@
     }
    },
    "source": [
-    "For convenience, the indexes do not need to lie within the range from `0` to `len(text)` when slicing."
+    "By combining a *positive* start with a *negative* end index, we specify both ends of the slice *relative* to the ends of the entire `str` object. So, colloquially, `6:-5` below means \"drop the first six and last five characters.\" The length of the resulting slice can *not* be calculated from the indexes and depends only on the length of the original `str` object."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 38,
+   "execution_count": 39,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -1274,10 +1312,45 @@
     {
      "data": {
       "text/plain": [
-       "'Lorem ipsum dolor sit amet, consectetur ...'"
+       "'ipsum dolor sit amet'"
       ]
      },
-     "execution_count": 38,
+     "execution_count": 39,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "text[6:-5]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "For convenience, the indexes do not need to lie within the range from `0` to `len(text)` when slicing. So, no `IndexError` is raised here."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Lorem ipsum dolor sit amet, ...'"
+      ]
+     },
+     "execution_count": 40,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1294,12 +1367,12 @@
     }
    },
    "source": [
-    "Commonly, we leave out the `0` for the *start* and the *end* if it is equal to the \"length.\""
+    "If left out, *start* defaults to `0` and *end* to the \"length\" of the `str` object. Here, we take a \"full\" slice that is essentially a copy of the original `str` object."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 39,
+   "execution_count": 41,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -1309,16 +1382,16 @@
     {
      "data": {
       "text/plain": [
-       "'Lorem ipsum dolor sit amet, consectetur ...'"
+       "'Lorem ipsum dolor sit amet, ...'"
       ]
      },
-     "execution_count": 39,
+     "execution_count": 41,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "text[:5] + text[5:]"
+    "text[:]"
    ]
   },
   {
@@ -1329,12 +1402,12 @@
     }
    },
    "source": [
-    "Slicing makes it easy to obtain shorter versions of the original `str` object."
+    "Slicing (and indexing) makes it easy to obtain shorter versions of the original `str` object."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 40,
+   "execution_count": 42,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -1347,13 +1420,13 @@
        "'Lorem ipsum dolor sit amet.'"
       ]
      },
-     "execution_count": 40,
+     "execution_count": 42,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "text[:26] + text[42]"
+    "text[:26] + text[30]"
    ]
   },
   {
@@ -1369,7 +1442,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 41,
+   "execution_count": 43,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -1379,10 +1452,10 @@
     {
      "data": {
       "text/plain": [
-       "'Lrmismdlrstae,cnettr..'"
+       "'Lrmismdlrstae,..'"
       ]
      },
-     "execution_count": 41,
+     "execution_count": 43,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1404,7 +1477,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 42,
+   "execution_count": 44,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -1414,10 +1487,10 @@
     {
      "data": {
       "text/plain": [
-       "'... rutetcesnoc ,tema tis rolod muspi meroL'"
+       "'... ,tema tis rolod muspi meroL'"
       ]
      },
-     "execution_count": 42,
+     "execution_count": 44,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1445,16 +1518,16 @@
     }
    },
    "source": [
-    "Whereas elements of a `list` object *may* be *re-assigned*, as shortly hinted at in [Chapter 1](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/01_elements.ipynb#Who-am-I?-And-how-many?), this is *not* allowed for `str` objects. Once created, they *cannot* be *changed*. Formally, we say that they are **immutable**. In that regard, `str` objects and all the numeric types in [Chapter 5](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/05_numbers.ipynb) are alike.\n",
+    "Whereas elements of a `list` object *may* be *re-assigned*, as shortly hinted at in [Chapter 1](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/01_elements_00_content.ipynb#Who-am-I?-And-how-many?), this is *not* allowed for `str` objects. Once created, they *cannot* be *changed*. Formally, we say that they are **immutable**. In that regard, `str` objects and all the numeric types in [Chapter 5](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/05_numbers_00_content.ipynb) are alike.\n",
     "\n",
-    "On the contrary, objects that may be changed after creation, are called **mutable**. We already saw in [Chapter 1](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/01_elements.ipynb#Who-am-I?-And-how-many?) how mutable objects are more difficult to reason about for a beginner, in particular, if more than *one* variable references it. Yet, mutability does have its place in a programmer's toolbox, and we revisit this idea in the next chapters.\n",
+    "On the contrary, objects that may be changed after creation, are called **mutable**. We already saw in [Chapter 1](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/01_elements_00_content.ipynb#Who-am-I?-And-how-many?) how mutable objects are more difficult to reason about for a beginner, in particular, if more than *one* variable references it. Yet, mutability does have its place in a programmer's toolbox, and we revisit this idea in the next chapters.\n",
     "\n",
     "The `TypeError` indicates that `str` objects are *immutable*: Assignment to an index or a slice are *not* supported."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 43,
+   "execution_count": 45,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -1468,7 +1541,7 @@
      "traceback": [
       "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
       "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-43-a627edbd2382>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mtext\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m\"Z\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;32m<ipython-input-45-a627edbd2382>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mtext\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m\"Z\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
       "\u001b[0;31mTypeError\u001b[0m: 'str' object does not support item assignment"
      ]
     }
@@ -1479,7 +1552,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 44,
+   "execution_count": 46,
    "metadata": {
     "slideshow": {
      "slide_type": "-"
@@ -1493,7 +1566,7 @@
      "traceback": [
       "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
       "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-44-def135e84998>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mtext\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;36m5\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m\"random\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;32m<ipython-input-46-def135e84998>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mtext\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;36m5\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m\"random\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
       "\u001b[0;31mTypeError\u001b[0m: 'str' object does not support item assignment"
      ]
     }
@@ -1528,7 +1601,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 45,
+   "execution_count": 47,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -1538,10 +1611,10 @@
     {
      "data": {
       "text/plain": [
-       "'Lorem ipsum dolor sit amet, consectetur ...'"
+       "'Lorem ipsum dolor sit amet, ...'"
       ]
      },
-     "execution_count": 45,
+     "execution_count": 47,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1552,7 +1625,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 46,
+   "execution_count": 48,
    "metadata": {
     "slideshow": {
      "slide_type": "-"
@@ -1565,7 +1638,7 @@
        "22"
       ]
      },
-     "execution_count": 46,
+     "execution_count": 48,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1576,7 +1649,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 47,
+   "execution_count": 49,
    "metadata": {
     "slideshow": {
      "slide_type": "-"
@@ -1589,7 +1662,7 @@
        "-1"
       ]
      },
-     "execution_count": 47,
+     "execution_count": 49,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1600,7 +1673,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 48,
+   "execution_count": 50,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -1613,7 +1686,7 @@
        "12"
       ]
      },
-     "execution_count": 48,
+     "execution_count": 50,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1635,7 +1708,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 49,
+   "execution_count": 51,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -1648,7 +1721,7 @@
        "1"
       ]
      },
-     "execution_count": 49,
+     "execution_count": 51,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1659,7 +1732,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 50,
+   "execution_count": 52,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -1672,7 +1745,7 @@
        "13"
       ]
      },
-     "execution_count": 50,
+     "execution_count": 52,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1683,7 +1756,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 51,
+   "execution_count": 53,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -1696,7 +1769,7 @@
        "-1"
       ]
      },
-     "execution_count": 51,
+     "execution_count": 53,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1718,7 +1791,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 52,
+   "execution_count": 54,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -1731,7 +1804,7 @@
        "1"
       ]
      },
-     "execution_count": 52,
+     "execution_count": 54,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1753,7 +1826,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 53,
+   "execution_count": 55,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -1766,7 +1839,7 @@
        "2"
       ]
      },
-     "execution_count": 53,
+     "execution_count": 55,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1788,7 +1861,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 54,
+   "execution_count": 56,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -1801,7 +1874,7 @@
        "2"
       ]
      },
-     "execution_count": 54,
+     "execution_count": 56,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1823,7 +1896,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 55,
+   "execution_count": 57,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -1834,43 +1907,6 @@
     "example = \"random\""
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": 56,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "-"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "139674947440512"
-      ]
-     },
-     "execution_count": 56,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "id(example)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 57,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "fragment"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "lower = example.lower()"
-   ]
-  },
   {
    "cell_type": "code",
    "execution_count": 58,
@@ -1883,7 +1919,7 @@
     {
      "data": {
       "text/plain": [
-       "139674887826488"
+       "140483525349552"
       ]
      },
      "execution_count": 58,
@@ -1891,6 +1927,43 @@
      "output_type": "execute_result"
     }
    ],
+   "source": [
+    "id(example)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 59,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "lower = example.lower()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 60,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "140483422704176"
+      ]
+     },
+     "execution_count": 60,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "id(lower)"
    ]
@@ -1908,7 +1981,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 59,
+   "execution_count": 61,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -1921,7 +1994,7 @@
        "False"
       ]
      },
-     "execution_count": 59,
+     "execution_count": 61,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1932,7 +2005,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 60,
+   "execution_count": 62,
    "metadata": {
     "slideshow": {
      "slide_type": "-"
@@ -1945,7 +2018,7 @@
        "True"
       ]
      },
-     "execution_count": 60,
+     "execution_count": 62,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1969,7 +2042,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 61,
+   "execution_count": 63,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -1985,7 +2058,6 @@
       "dolor\n",
       "sit\n",
       "amet,\n",
-      "consectetur\n",
       "...\n"
      ]
     }
@@ -2008,7 +2080,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 62,
+   "execution_count": 64,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -2021,7 +2093,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 63,
+   "execution_count": 65,
    "metadata": {
     "slideshow": {
      "slide_type": "-"
@@ -2034,7 +2106,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 64,
+   "execution_count": 66,
    "metadata": {
     "slideshow": {
      "slide_type": "-"
@@ -2047,7 +2119,7 @@
        "'This will become a sentence'"
       ]
      },
-     "execution_count": 64,
+     "execution_count": 66,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2069,7 +2141,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 65,
+   "execution_count": 67,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -2082,7 +2154,7 @@
        "'a b c d e'"
       ]
      },
-     "execution_count": 65,
+     "execution_count": 67,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2104,7 +2176,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 66,
+   "execution_count": 68,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -2117,7 +2189,7 @@
        "'This is a sentence'"
       ]
      },
-     "execution_count": 66,
+     "execution_count": 68,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2145,12 +2217,12 @@
     }
    },
    "source": [
-    "As mentioned in [Chapter 1](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/01_elements.ipynb#Operator-Overloading), the `+` and `*` operators are *overloaded* and used for **string concatenation**. They always create *new* `str` objects. That has nothing to do with the `str` type's immutability, but is the default behavior of operators."
+    "As mentioned in [Chapter 1](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/01_elements_00_content.ipynb#Operator-Overloading), the `+` and `*` operators are *overloaded* and used for **string concatenation**. They always create *new* `str` objects. That has nothing to do with the `str` type's immutability, but is the default behavior of operators."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 67,
+   "execution_count": 69,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -2163,7 +2235,7 @@
        "'Hello Lore'"
       ]
      },
-     "execution_count": 67,
+     "execution_count": 69,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2174,7 +2246,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 68,
+   "execution_count": 70,
    "metadata": {
     "slideshow": {
      "slide_type": "-"
@@ -2187,7 +2259,7 @@
        "'Lorem ipsum Lorem ipsum Lorem ipsum Lorem ipsum Lorem ipsum '"
       ]
      },
-     "execution_count": 68,
+     "execution_count": 70,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2220,7 +2292,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 69,
+   "execution_count": 71,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -2233,65 +2305,6 @@
     "B = \"Banana\""
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": 70,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "fragment"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "True"
-      ]
-     },
-     "execution_count": 70,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "A < B"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 71,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "-"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "False"
-      ]
-     },
-     "execution_count": 71,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "a < B"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "source": [
-    "One way to fix this is to only compare lower-cased `str` objects."
-   ]
-  },
   {
    "cell_type": "code",
    "execution_count": 72,
@@ -2312,6 +2325,65 @@
      "output_type": "execute_result"
     }
    ],
+   "source": [
+    "A < B"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 73,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "False"
+      ]
+     },
+     "execution_count": 73,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "a < B"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "One way to fix this is to only compare lower-cased `str` objects."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 74,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 74,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "a < B.lower()"
    ]
@@ -2329,7 +2401,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 73,
+   "execution_count": 75,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -2425,7 +2497,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 74,
+   "execution_count": 76,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -2439,7 +2511,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 75,
+   "execution_count": 77,
    "metadata": {
     "slideshow": {
      "slide_type": "-"
@@ -2452,7 +2524,7 @@
        "'Hello Alexander! Good morning.'"
       ]
      },
-     "execution_count": 75,
+     "execution_count": 77,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2474,7 +2546,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 76,
+   "execution_count": 78,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -2487,7 +2559,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 77,
+   "execution_count": 79,
    "metadata": {
     "slideshow": {
      "slide_type": "-"
@@ -2500,7 +2572,7 @@
        "'Pi is 3.14'"
       ]
      },
-     "execution_count": 77,
+     "execution_count": 79,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2511,7 +2583,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 78,
+   "execution_count": 80,
    "metadata": {
     "slideshow": {
      "slide_type": "-"
@@ -2524,7 +2596,7 @@
        "'Pi is 3.142'"
       ]
      },
-     "execution_count": 78,
+     "execution_count": 80,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2557,7 +2629,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 79,
+   "execution_count": 81,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -2570,7 +2642,7 @@
        "'Hello Alexander! Good morning.'"
       ]
      },
-     "execution_count": 79,
+     "execution_count": 81,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2592,7 +2664,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 80,
+   "execution_count": 82,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -2605,7 +2677,7 @@
        "'Good morning, Alexander'"
       ]
      },
-     "execution_count": 80,
+     "execution_count": 82,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2627,7 +2699,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 81,
+   "execution_count": 83,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -2640,7 +2712,7 @@
        "'Hello Alexander! Good morning.'"
       ]
      },
-     "execution_count": 81,
+     "execution_count": 83,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2662,7 +2734,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 82,
+   "execution_count": 84,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -2675,7 +2747,7 @@
        "'Pi is 3.14'"
       ]
      },
-     "execution_count": 82,
+     "execution_count": 84,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2703,14 +2775,14 @@
     }
    },
    "source": [
-    "The `%` operator that we saw in the context of modulo division in [Chapter 1](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/01_elements.ipynb#%28Arithmetic%29-Operators) is overloaded with string interpolation when its first operand is a `str` object. The second operand consists of all expressions to be filled in. Format specifiers work with a `%` instead of curly braces and according to a different set of rules referred to as **[printf-style string formatting](https://docs.python.org/3/library/stdtypes.html#printf-style-string-formatting)**. So, `{:.2f}` becomes `%.2f`.\n",
+    "The `%` operator that we saw in the context of modulo division in [Chapter 1](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/01_elements_00_content.ipynb#%28Arithmetic%29-Operators) is overloaded with string interpolation when its first operand is a `str` object. The second operand consists of all expressions to be filled in. Format specifiers work with a `%` instead of curly braces and according to a different set of rules referred to as **[printf-style string formatting](https://docs.python.org/3/library/stdtypes.html#printf-style-string-formatting)**. So, `{:.2f}` becomes `%.2f`.\n",
     "\n",
     "This way of string interpolation is the oldest and originates from the [C language](https://en.wikipedia.org/wiki/C_%28programming_language%29). It is still widely spread, but we should use one of the other two ways instead. We show it here mainly for completeness sake."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 83,
+   "execution_count": 85,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -2723,7 +2795,7 @@
        "'Pi is 3.14'"
       ]
      },
-     "execution_count": 83,
+     "execution_count": 85,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2740,12 +2812,12 @@
     }
    },
    "source": [
-    "To insert more than one expression, we must list them in order and between parenthesis `(` and `)`. As [Chapter 7](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/07_sequences.ipynb#The-tuple-Type) reveals, this literal syntax creates an object of type `tuple`. Also, to format an expression as text, we use the format specifier `%s`."
+    "To insert more than one expression, we must list them in order and between parenthesis `(` and `)`. As [Chapter 7](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/07_sequences_00_content.ipynb#The-tuple-Type) reveals, this literal syntax creates an object of type `tuple`. Also, to format an expression as text, we use the format specifier `%s`."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 84,
+   "execution_count": 86,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -2758,7 +2830,7 @@
        "'Hello Alexander! Good morning.'"
       ]
      },
-     "execution_count": 84,
+     "execution_count": 86,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2793,7 +2865,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 85,
+   "execution_count": 87,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -2816,7 +2888,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 86,
+   "execution_count": 88,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -2848,7 +2920,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 87,
+   "execution_count": 89,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -2880,7 +2952,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 88,
+   "execution_count": 90,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -2893,7 +2965,7 @@
        "'This is a sentence\\nthat is printed\\non three lines.'"
       ]
      },
-     "execution_count": 88,
+     "execution_count": 90,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2928,7 +3000,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 89,
+   "execution_count": 91,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -2961,7 +3033,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 90,
+   "execution_count": 92,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -2970,10 +3042,10 @@
    "outputs": [
     {
      "ename": "SyntaxError",
-     "evalue": "(unicode error) 'unicodeescape' codec can't decode bytes in position 2-3: truncated \\UXXXXXXXX escape (<ipython-input-90-0aad8a365b02>, line 1)",
+     "evalue": "(unicode error) 'unicodeescape' codec can't decode bytes in position 2-3: truncated \\UXXXXXXXX escape (<ipython-input-92-0aad8a365b02>, line 1)",
      "output_type": "error",
      "traceback": [
-      "\u001b[0;36m  File \u001b[0;32m\"<ipython-input-90-0aad8a365b02>\"\u001b[0;36m, line \u001b[0;32m1\u001b[0m\n\u001b[0;31m    print(\"C:\\Users\\Administrator\\Desktop\\Project_Folder\")\u001b[0m\n\u001b[0m         ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m (unicode error) 'unicodeescape' codec can't decode bytes in position 2-3: truncated \\UXXXXXXXX escape\n"
+      "\u001b[0;36m  File \u001b[0;32m\"<ipython-input-92-0aad8a365b02>\"\u001b[0;36m, line \u001b[0;32m1\u001b[0m\n\u001b[0;31m    print(\"C:\\Users\\Administrator\\Desktop\\Project_Folder\")\u001b[0m\n\u001b[0m         ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m (unicode error) 'unicodeescape' codec can't decode bytes in position 2-3: truncated \\UXXXXXXXX escape\n"
      ]
     }
    ],
@@ -2994,7 +3066,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 91,
+   "execution_count": 93,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -3015,7 +3087,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 92,
+   "execution_count": 94,
    "metadata": {
     "slideshow": {
      "slide_type": "-"
@@ -3047,7 +3119,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 93,
+   "execution_count": 95,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -3068,7 +3140,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 94,
+   "execution_count": 96,
    "metadata": {
     "slideshow": {
      "slide_type": "-"
@@ -3111,7 +3183,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 95,
+   "execution_count": 97,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -3120,10 +3192,10 @@
    "outputs": [
     {
      "ename": "SyntaxError",
-     "evalue": "EOL while scanning string literal (<ipython-input-95-4cef690f1f4a>, line 1)",
+     "evalue": "EOL while scanning string literal (<ipython-input-97-4cef690f1f4a>, line 1)",
      "output_type": "error",
      "traceback": [
-      "\u001b[0;36m  File \u001b[0;32m\"<ipython-input-95-4cef690f1f4a>\"\u001b[0;36m, line \u001b[0;32m1\u001b[0m\n\u001b[0;31m    \"\u001b[0m\n\u001b[0m     ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m EOL while scanning string literal\n"
+      "\u001b[0;36m  File \u001b[0;32m\"<ipython-input-97-4cef690f1f4a>\"\u001b[0;36m, line \u001b[0;32m1\u001b[0m\n\u001b[0;31m    \"\u001b[0m\n\u001b[0m     ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m EOL while scanning string literal\n"
      ]
     }
    ],
@@ -3146,7 +3218,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 96,
+   "execution_count": 98,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -3173,7 +3245,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 97,
+   "execution_count": 99,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -3186,7 +3258,7 @@
        "'\\nI am a multi-line string\\nconsisting of 4 lines.\\n'"
       ]
      },
-     "execution_count": 97,
+     "execution_count": 99,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3208,7 +3280,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 98,
+   "execution_count": 100,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -3243,7 +3315,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 99,
+   "execution_count": 101,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -3279,7 +3351,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 100,
+   "execution_count": 102,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -3293,7 +3365,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 101,
+   "execution_count": 103,
    "metadata": {
     "slideshow": {
      "slide_type": "-"
@@ -3306,7 +3378,7 @@
        "\"Lorem Ipsum is simply dummy text of the printing and typesetting industry.\\nLorem Ipsum has been the industry's standard dummy text ever since the 1500s\\nwhen an unknown printer took a galley of type and scrambled it to make a type\\nspecimen book. It has survived not only five centuries but also the leap into\\nelectronic typesetting, remaining essentially unchanged. It was popularised in\\nthe 1960s with the release of Letraset sheets.\\n\""
       ]
      },
-     "execution_count": 101,
+     "execution_count": 103,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3317,7 +3389,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 102,
+   "execution_count": 104,
    "metadata": {
     "slideshow": {
      "slide_type": "-"
@@ -3383,7 +3455,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.3"
+   "version": "3.7.4"
   },
   "livereveal": {
    "auto_select": "code",
@@ -3411,5 +3483,5 @@
   }
  },
  "nbformat": 4,
- "nbformat_minor": 2
+ "nbformat_minor": 4
 }
diff --git a/06_text_review_and_exercises.ipynb b/06_text_10_review.ipynb
similarity index 94%
rename from 06_text_review_and_exercises.ipynb
rename to 06_text_10_review.ipynb
index 40ac8c4..fe9588e 100644
--- a/06_text_review_and_exercises.ipynb
+++ b/06_text_10_review.ipynb
@@ -19,7 +19,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Read [Chapter 6](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/06_text.ipynb) of the book. Then work through the eight review questions."
+    "Read [Chapter 6](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/06_text_00_content.ipynb) of the book. Then, work through the questions below."
    ]
   },
   {
@@ -33,7 +33,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Answer the following questions briefly with *at most* 300 characters per question!"
+    "Answer the following questions *briefly*!"
    ]
   },
   {
@@ -184,7 +184,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.3"
+   "version": "3.7.4"
   },
   "toc": {
    "base_numbering": 1,
@@ -201,5 +201,5 @@
   }
  },
  "nbformat": 4,
- "nbformat_minor": 2
+ "nbformat_minor": 4
 }
diff --git a/07_sequences.ipynb b/07_sequences_00_content.ipynb
similarity index 97%
rename from 07_sequences.ipynb
rename to 07_sequences_00_content.ipynb
index f20b670..924467a 100644
--- a/07_sequences.ipynb
+++ b/07_sequences_00_content.ipynb
@@ -19,11 +19,11 @@
     }
    },
    "source": [
-    "We studied numbers (cf., [Chapter 5](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/05_numbers.ipynb)) and textual data (cf., [Chapter 6](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/06_text.ipynb)) first, mainly because objects of the presented data types are \"simple,\" for two reasons: First, they are *immutable*, and, as we saw in the \"*Who am I? And how many?*\" section in [Chapter 1](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/01_elements.ipynb#Who-am-I?-And-how-many?), mutable objects can quickly become hard to reason about. Second, they are \"flat\" in the sense that they are *not* composed of other objects.\n",
+    "We studied numbers (cf., [Chapter 5](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/05_numbers_00_content.ipynb)) and textual data (cf., [Chapter 6](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/06_text_00_content.ipynb)) first, mainly because objects of the presented data types are \"simple,\" for two reasons: First, they are *immutable*, and, as we saw in the \"*Who am I? And how many?*\" section in [Chapter 1](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/01_elements_00_content.ipynb#Who-am-I?-And-how-many?), mutable objects can quickly become hard to reason about. Second, they are \"flat\" in the sense that they are *not* composed of other objects.\n",
     "\n",
     "The `str` type is a bit of a corner case in this regard. While one could argue that a longer `str` object, for example, `\"text\"`, is composed of individual characters, this is *not* the case in memory as the literal `\"text\"` only creates *one* object (i.e., one \"bag\" of $0$s and $1$s modeling all characters).\n",
     "\n",
-    "This chapter and [Chapter 8](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/08_mappings.ipynb) introduce various \"complex\" data types. While some are mutable and others are not, they all share that they are primarily used to \"manage,\" or structure, the memory in a program. Unsurprisingly, computer scientists refer to the ideas and theories behind these data types as **[data structures](https://en.wikipedia.org/wiki/Data_structure)**.\n",
+    "This chapter, [Chapter 8](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/08_mappings_00_content.ipynb), and [Chapter 9](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/09_arrays_00_content.ipynb) introduce various \"complex\" data types. While some are mutable and others are not, they all share that they are primarily used to \"manage,\" or structure, the memory in a program. Unsurprisingly, computer scientists refer to the ideas and theories behind these data types as **[data structures](https://en.wikipedia.org/wiki/Data_structure)**.\n",
     "\n",
     "In this chapter, we focus on data types that model all kinds of sequential data. Examples of such data are [spreadsheets](https://en.wikipedia.org/wiki/Spreadsheet) or [matrices](https://en.wikipedia.org/wiki/Matrix_%28mathematics%29)/[vectors](https://en.wikipedia.org/wiki/Vector_%28mathematics_and_physics%29). Such formats share the property that they are composed of smaller units that come in a sequence of, for example, rows/columns/cells or elements/entries."
    ]
@@ -47,9 +47,9 @@
     }
    },
    "source": [
-    "[Chapter 6](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/06_text.ipynb#A-\"String\"-of-Characters) already describes the *sequence* properties of `str` objects. Here, we take a step back and study these properties on their own before looking at bigger ideas.\n",
+    "[Chapter 6](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/06_text_00_content.ipynb#A-\"String\"-of-Characters) already describes the *sequence* properties of `str` objects. Here, we take a step back and study these properties on their own before looking at bigger ideas.\n",
     "\n",
-    "The [collections.abc](https://docs.python.org/3/library/collections.abc.html) module in the [standard library](https://docs.python.org/3/library/index.html) defines a variety of **abstract base classes** (ABCs). We saw ABCs already in [Chapter 5](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/05_numbers.ipynb#The-Numerical-Tower), where we use the ones from the [numbers](https://docs.python.org/3/library/numbers.html) module in the [standard library](https://docs.python.org/3/library/index.html) to classify Python's numeric data types according to mathematical ideas. Now, we take the ABCs from the [collections.abc](https://docs.python.org/3/library/collections.abc.html) module to classify the data types in this chapter and [Chapter 8](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/08_mappings.ipynb) according to their behavior in various contexts.\n",
+    "The [collections.abc](https://docs.python.org/3/library/collections.abc.html) module in the [standard library](https://docs.python.org/3/library/index.html) defines a variety of **abstract base classes** (ABCs). We saw ABCs already in [Chapter 5](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/05_numbers_00_content.ipynb#The-Numerical-Tower), where we use the ones from the [numbers](https://docs.python.org/3/library/numbers.html) module in the [standard library](https://docs.python.org/3/library/index.html) to classify Python's numeric data types according to mathematical ideas. Now, we take the ABCs from the [collections.abc](https://docs.python.org/3/library/collections.abc.html) module to classify the data types in this chapter and [Chapter 8](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/08_mappings_00_content.ipynb) according to their behavior in various contexts.\n",
     "\n",
     "As an illustration, consider `numbers` and `text` below, two objects of *different* types."
    ]
@@ -65,7 +65,7 @@
    "outputs": [],
    "source": [
     "numbers = [7, 11, 8, 5, 3, 12, 2, 6, 9, 10, 1, 4]\n",
-    "text = \"Lorem ipsum dolor sit amet, consectetur ...\""
+    "text = \"Lorem ipsum dolor sit amet, ...\""
    ]
   },
   {
@@ -114,7 +114,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "L o r e m   i p s u m   d o l o r   s i t   a m e t ,   c o n s e c t e t u r   . . . "
+      "L o r e m   i p s u m   d o l o r   s i t   a m e t ,   . . . "
      ]
     }
    ],
@@ -131,7 +131,7 @@
     }
    },
    "source": [
-    "In [Chapter 4](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/04_iteration.ipynb#Containers-vs.-Iterables), we referred to such types as *iterables*. That is *not* a proper [English](https://dictionary.cambridge.org/spellcheck/english-german/?q=iterable) word, even if it may sound like one at first sight. Yet, it is an official term in the Python world formalized with the `Iterable` ABC in the [collections.abc](https://docs.python.org/3/library/collections.abc.html) module.\n",
+    "In [Chapter 4](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/04_iteration_00_content.ipynb#Containers-vs.-Iterables), we referred to such types as *iterables*. That is *not* a proper [English](https://dictionary.cambridge.org/spellcheck/english-german/?q=iterable) word, even if it may sound like one at first sight. Yet, it is an official term in the Python world formalized with the `Iterable` ABC in the [collections.abc](https://docs.python.org/3/library/collections.abc.html) module.\n",
     "\n",
     "For the data science practitioner, it is worthwhile to know such terms as, for example, the documentation on the [built-ins](https://docs.python.org/3/library/functions.html) uses them extensively: In simple words, any built-in that takes an argument called \"*iterable*\" may be called with *any* object that supports being looped over. Already familiar [built-ins](https://docs.python.org/3/library/functions.html) include [enumerate()](https://docs.python.org/3/library/functions.html#enumerate), [sum()](https://docs.python.org/3/library/functions.html#sum), or [zip()](https://docs.python.org/3/library/functions.html#zip). So, they do *not* require the argument to be of a certain data type (e.g., `list`); instead, any *iterable* type works."
    ]
@@ -181,7 +181,7 @@
     }
    },
    "source": [
-    "As in the context of *goose typing* in [Chapter 5](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/05_numbers.ipynb#Goose-Typing), we can use ABCs with the built-in [isinstance()](https://docs.python.org/3/library/functions.html#isinstance) function to check if an object supports a behavior.\n",
+    "As in the context of *goose typing* in [Chapter 5](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/05_numbers_00_content.ipynb#Goose-Typing), we can use ABCs with the built-in [isinstance()](https://docs.python.org/3/library/functions.html#isinstance) function to check if an object supports a behavior.\n",
     "\n",
     "So, let's \"ask\" Python if it can loop over `numbers` or `text`."
    ]
@@ -536,7 +536,7 @@
     {
      "data": {
       "text/plain": [
-       "43"
+       "31"
       ]
      },
      "execution_count": 17,
@@ -767,7 +767,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      ". . .   r u t e t c e s n o c   , t e m a   t i s   r o l o d   m u s p i   m e r o L "
+      ". . .   , t e m a   t i s   r o l o d   m u s p i   m e r o L "
      ]
     }
    ],
@@ -1028,7 +1028,7 @@
     {
      "data": {
       "text/plain": [
-       "140157873498184"
+       "140417317434464"
       ]
      },
      "execution_count": 34,
@@ -1074,7 +1074,7 @@
    "source": [
     "Alternatively, we use the [list()](https://docs.python.org/3/library/functions.html#func-list) built-in to create a `list` object out of an iterable we pass to it as the argument.\n",
     "\n",
-    "For example, we can wrap the [range()](https://docs.python.org/3/library/functions.html#func-range) built-in with [list()](https://docs.python.org/3/library/functions.html#func-list): As described in [Chapter 4](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/04_iteration.ipynb#Containers-vs.-Iterables), `range` objects, like `range(1, 13)` below, are iterable and generate `int` objects \"on the fly\" (i.e., one by one). The [list()](https://docs.python.org/3/library/functions.html#func-list) around it acts like a `for`-loop and **materializes** twelve `int` objects in memory that then become the elements of the newly created `list` object. [PythonTutor](http://www.pythontutor.com/visualize.html#code=r%20%3D%20range%281,%2013%29%0Al%20%3D%20list%28range%281,%2013%29%29&cumulative=false&curInstr=0&heapPrimitives=nevernest&mode=display&origin=opt-frontend.js&py=3&rawInputLstJSON=%5B%5D&textReferences=false) shows this difference visually."
+    "For example, we can wrap the [range()](https://docs.python.org/3/library/functions.html#func-range) built-in with [list()](https://docs.python.org/3/library/functions.html#func-list): As described in [Chapter 4](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/04_iteration_00_content.ipynb#Containers-vs.-Iterables), `range` objects, like `range(1, 13)` below, are iterable and generate `int` objects \"on the fly\" (i.e., one by one). The [list()](https://docs.python.org/3/library/functions.html#func-list) around it acts like a `for`-loop and **materializes** twelve `int` objects in memory that then become the elements of the newly created `list` object. [PythonTutor](http://www.pythontutor.com/visualize.html#code=r%20%3D%20range%281,%2013%29%0Al%20%3D%20list%28range%281,%2013%29%29&cumulative=false&curInstr=0&heapPrimitives=nevernest&mode=display&origin=opt-frontend.js&py=3&rawInputLstJSON=%5B%5D&textReferences=false) shows this difference visually."
    ]
   },
   {
@@ -1299,11 +1299,11 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[]      \t140157882223304      \t<class 'list'>\n",
-      "10      \t93987402609984      \t<class 'int'>\n",
-      "20.0      \t140157882328912      \t<class 'float'>\n",
-      "Thirty      \t140157873509520      \t<class 'str'>\n",
-      "[40, 50]      \t140157873031176      \t<class 'list'>\n"
+      "[]      \t140417315676720      \t<class 'list'>\n",
+      "10      \t94860849595456      \t<class 'int'>\n",
+      "20.0      \t140417316476784      \t<class 'float'>\n",
+      "Thirty      \t140417315739568      \t<class 'str'>\n",
+      "[40, 50]      \t140417315720800      \t<class 'list'>\n"
      ]
     }
    ],
@@ -2017,7 +2017,7 @@
     {
      "data": {
       "text/plain": [
-       "140157882223304"
+       "140417315676720"
       ]
      },
      "execution_count": 65,
@@ -2041,7 +2041,7 @@
     {
      "data": {
       "text/plain": [
-       "140157882223304"
+       "140417315676720"
       ]
      },
      "execution_count": 66,
@@ -2065,7 +2065,7 @@
     {
      "data": {
       "text/plain": [
-       "140157882223304"
+       "140417315676720"
       ]
      },
      "execution_count": 67,
@@ -2191,7 +2191,7 @@
     {
      "data": {
       "text/plain": [
-       "140157882223304"
+       "140417315676720"
       ]
      },
      "execution_count": 72,
@@ -2215,7 +2215,7 @@
     {
      "data": {
       "text/plain": [
-       "140157873029448"
+       "140417376809600"
       ]
      },
      "execution_count": 73,
@@ -2392,7 +2392,7 @@
     {
      "data": {
       "text/plain": [
-       "140157873498184"
+       "140417317434464"
       ]
      },
      "execution_count": 79,
@@ -2401,7 +2401,7 @@
     }
    ],
    "source": [
-    "id(nested)"
+    "id(nested)  # same memory location as before"
    ]
   },
   {
@@ -2678,7 +2678,7 @@
    "source": [
     "The `list` type is an essential data structure in any real-world Python application, and many typical `list`-related algorithms from computer science theory are already built into it at the C level (cf., the [documentation](https://docs.python.org/3/library/stdtypes.html#mutable-sequence-types) or the [tutorial](https://docs.python.org/3/tutorial/datastructures.html#more-on-lists) for a full overview; unfortunately, not all methods have direct links). So, understanding and applying the built-in methods of the `list` type not only speeds up the development process but also makes programs significantly faster.\n",
     "\n",
-    "In contrast to the `str` type's methods in [Chapter 6](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/06_text.ipynb#String-Methods) (e.g., [upper()](https://docs.python.org/3/library/stdtypes.html#str.upper) or [lower()](https://docs.python.org/3/library/stdtypes.html#str.lower)), the `list` type's methods that mutate an object do so *in place*. That means they *never* create *new* `list` objects and return `None` to indicate that. So, we must *never* assign the return value of `list` methods to the variable holding the list!\n",
+    "In contrast to the `str` type's methods in [Chapter 6](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/06_text_00_content.ipynb#String-Methods) (e.g., [upper()](https://docs.python.org/3/library/stdtypes.html#str.upper) or [lower()](https://docs.python.org/3/library/stdtypes.html#str.lower)), the `list` type's methods that mutate an object do so *in place*. That means they *never* create *new* `list` objects and return `None` to indicate that. So, we must *never* assign the return value of `list` methods to the variable holding the list!\n",
     "\n",
     "Let's look at the following `names` example."
    ]
@@ -3016,7 +3016,7 @@
    "source": [
     "The [sort()](https://docs.python.org/3/library/stdtypes.html#list.sort) method and the [sorted()](https://docs.python.org/3/library/functions.html#sorted) function sort the elements in `names` in alphabetical order, forward or backward. However, that does *not* hold in general.\n",
     "\n",
-    "We mention above that `list` objects may contain objects of *any* type and even of *mixed* types. Because of that, the sorting is **[delegated](https://en.wikipedia.org/wiki/Delegation_(object-oriented_programming))** to the elements in a `list` object. In a way, Python \"asks\" the elements in a `list` object to sort themselves. As `names` contains only `str` objects, they are sorted according the the comparison rules explained in [Chapter 6](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/06_text.ipynb#String-Comparison).\n",
+    "We mention above that `list` objects may contain objects of *any* type and even of *mixed* types. Because of that, the sorting is **[delegated](https://en.wikipedia.org/wiki/Delegation_(object-oriented_programming))** to the elements in a `list` object. In a way, Python \"asks\" the elements in a `list` object to sort themselves. As `names` contains only `str` objects, they are sorted according the the comparison rules explained in [Chapter 6](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/06_text_00_content.ipynb#String-Comparison).\n",
     "\n",
     "To customize the sorting, we pass a keyword-only `key` argument to [sort()](https://docs.python.org/3/library/stdtypes.html#list.sort) or [sorted()](https://docs.python.org/3/library/functions.html#sorted), which must be a `function` object accepting *one* positional argument. Then, the elements in the `list` object are passed to that one by one, and the return values are used as the **sort keys**. The `key` argument is also a popular use case for `lambda` expressions.\n",
     "\n",
@@ -4150,7 +4150,7 @@
     "\n",
     "The revised `add_xyz()` function below is more natural to reason about as it does *not* modify the passed in `arg` internally. This approach is following the **[functional programming](https://en.wikipedia.org/wiki/Functional_programming)** paradigm that is going through a \"renaissance\" currently. Two essential characteristics of functional programming are that a function *never* changes its inputs and *always* returns the same output given the same inputs.\n",
     "\n",
-    "For a beginner, it is probably better to stick to this idea and not change any arguments as the original `add_xyz()` above. However, functions that modify and return the argument passed in are an important aspect of object-oriented programming, as explained in Chapter 9."
+    "For a beginner, it is probably better to stick to this idea and not change any arguments as the original `add_xyz()` above. However, functions that modify and return the argument passed in are an important aspect of object-oriented programming, as explained in Chapter 10."
    ]
   },
   {
@@ -4511,7 +4511,7 @@
     {
      "data": {
       "text/plain": [
-       "140157873009144"
+       "140417315626832"
       ]
      },
      "execution_count": 158,
@@ -5227,7 +5227,6 @@
    "cell_type": "code",
    "execution_count": 184,
    "metadata": {
-    "scrolled": false,
     "slideshow": {
      "slide_type": "skip"
     }
@@ -6154,7 +6153,7 @@
     }
    },
    "source": [
-    "Unpacking allows us to rewrite the iterative `fibonacci()` function from [Chapter 4](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/04_iteration.ipynb#\"Hard-at-first-Glance\"-Example:-Fibonacci-Numbers-%28revisited%29) in a concise way, now also supporting *goose typing* with the [numbers](https://docs.python.org/3/library/numbers.html) module from the [standard library](https://docs.python.org/3/library/index.html)."
+    "Unpacking allows us to rewrite the iterative `fibonacci()` function from [Chapter 4](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/04_iteration_00_content.ipynb#\"Hard-at-first-Glance\"-Example:-Fibonacci-Numbers-%28revisited%29) in a concise way, now also supporting *goose typing* with the [numbers](https://docs.python.org/3/library/numbers.html) module from the [standard library](https://docs.python.org/3/library/index.html)."
    ]
   },
   {
@@ -6577,7 +6576,7 @@
     }
    },
    "source": [
-    "In the \"*Packing & Unpacking with Functions*\" [exercise](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/07_sequences_review_and_exercises.ipynb#Packing-&-Unpacking-with-Functions) at the end of this chapter, we look at `product()` in more detail.\n",
+    "In the \"*Packing & Unpacking with Functions*\" [exercise](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/07_sequences_review_and_exercises_00_content.ipynb#Packing-&-Unpacking-with-Functions) at the end of this chapter, we look at `product()` in more detail.\n",
     "\n",
     "While we needed to unpack `one_hundred` above to avoid the semantic error, unpacking an argument in a function call may also be a convenience in general.\n",
     "\n",
@@ -6699,7 +6698,7 @@
    "source": [
     "We implicitly assume that the first element represents the $x$ and the second the $y$ coordinate. While that follows intuitively from convention in math, we should at least add comments somewhere in the code to document this assumption.\n",
     "\n",
-    "A better way is to create a *custom* data type. While that is covered in depth in Chapter 9, the [collections](https://docs.python.org/3/library/collections.html) module in the [standard library](https://docs.python.org/3/library/index.html) provides a [namedtuple()](https://docs.python.org/3/library/collections.html#collections.namedtuple) **factory function** that creates \"simple\" custom data types on top of the standard `tuple` type."
+    "A better way is to create a *custom* data type. While that is covered in depth in Chapter 10, the [collections](https://docs.python.org/3/library/collections.html) module in the [standard library](https://docs.python.org/3/library/index.html) provides a [namedtuple()](https://docs.python.org/3/library/collections.html#collections.namedtuple) **factory function** that creates \"simple\" custom data types on top of the standard `tuple` type."
    ]
   },
   {
@@ -6762,7 +6761,7 @@
     {
      "data": {
       "text/plain": [
-       "140157466523464"
+       "140416708056240"
       ]
      },
      "execution_count": 238,
@@ -6915,7 +6914,7 @@
     {
      "data": {
       "text/plain": [
-       "140157872590424"
+       "140417315422528"
       ]
      },
      "execution_count": 243,
@@ -7354,7 +7353,7 @@
     {
      "data": {
       "text/plain": [
-       "<map at 0x7f790c37f470>"
+       "<map at 0x7fb5743a9ad0>"
       ]
      },
      "execution_count": 258,
@@ -7654,7 +7653,7 @@
     {
      "data": {
       "text/plain": [
-       "<filter at 0x7f790c387c88>"
+       "<filter at 0x7fb57435cc10>"
       ]
      },
      "execution_count": 269,
@@ -7875,7 +7874,7 @@
     }
    },
    "source": [
-    "Using the [map()](https://docs.python.org/3/library/functions.html#map) and [filter()](https://docs.python.org/3/library/functions.html#filter) built-ins, we can quickly switch the order: Filter first and then transform the remaining elements. This variant equals the \"*A simple Filter*\" example from [Chapter 4](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/04_iteration.ipynb#Example:-A-simple-Filter). On the contrary, code with `for`-loops and `if` statements is more tedious to adapt. Additionally, `map` and `filter` objects are optimized at the C level and, therefore, a lot faster as well."
+    "Using the [map()](https://docs.python.org/3/library/functions.html#map) and [filter()](https://docs.python.org/3/library/functions.html#filter) built-ins, we can quickly switch the order: Filter first and then transform the remaining elements. This variant equals the \"*A simple Filter*\" example from [Chapter 4](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/04_iteration_00_content.ipynb#Example:-A-simple-Filter). On the contrary, code with `for`-loops and `if` statements is more tedious to adapt. Additionally, `map` and `filter` objects are optimized at the C level and, therefore, a lot faster as well."
    ]
   },
   {
@@ -8209,7 +8208,7 @@
     "\n",
     "Often, such functions are used *only once* in a program. However, the primary purpose of functions is to *reuse* them. In such cases, it makes more sense to define them \"anonymously\" right at the position where the first argument goes.\n",
     "\n",
-    "As mentioned in [Chapter 2](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/02_functions.ipynb#Anonymous-Functions), Python provides `lambda` expressions to create `function` objects *without* a name referencing them.\n",
+    "As mentioned in [Chapter 2](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/02_functions_00_content.ipynb#Anonymous-Functions), Python provides `lambda` expressions to create `function` objects *without* a name referencing them.\n",
     "\n",
     "So, the above `add()` function could be rewritten as a `lambda` expression like so ..."
    ]
@@ -8423,7 +8422,7 @@
    "source": [
     "For [map()](https://docs.python.org/3/library/functions.html#map) and [filter()](https://docs.python.org/3/library/functions.html#filter), Python provides a nice syntax appealing to people who like mathematics.\n",
     "\n",
-    "Consider again the \"*A simple Filter*\" example from [Chapter 4](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/04_iteration.ipynb#Example:-A-simple-Filter), written with combined `for` and `if` statements. So, the mapping and filtering steps happen simultaneously."
+    "Consider again the \"*A simple Filter*\" example from [Chapter 4](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/04_iteration_00_content.ipynb#Example:-A-simple-Filter), written with combined `for` and `if` statements. So, the mapping and filtering steps happen simultaneously."
    ]
   },
   {
@@ -8488,7 +8487,7 @@
     }
    },
    "source": [
-    "**List comprehensions**, or **list-comps** for short, are *expressions* to derive *new* `list` objects out of *existing* ones (cf., [reference](https://docs.python.org/3/reference/expressions.html#displays-for-lists-sets-and-dictionaries)). A single *expression* like below can replace the compound `for` *statement* from above."
+    "**List comprehensions**, or **listcomps** for short, are *expressions* to derive *new* `list` objects out of *existing* ones (cf., [reference](https://docs.python.org/3/reference/expressions.html#displays-for-lists-sets-and-dictionaries)). A single *expression* like below can replace the compound `for` *statement* from above."
    ]
   },
   {
@@ -9108,7 +9107,7 @@
     {
      "data": {
       "text/plain": [
-       "<generator object <genexpr> at 0x7f790c3a55e8>"
+       "<generator object <genexpr> at 0x7fb57436f3d0>"
       ]
      },
      "execution_count": 312,
@@ -9263,7 +9262,7 @@
     {
      "data": {
       "text/plain": [
-       "<generator object <genexpr> at 0x7f790c3a5a20>"
+       "<generator object <genexpr> at 0x7fb57436f650>"
       ]
      },
      "execution_count": 317,
@@ -9728,7 +9727,7 @@
     {
      "data": {
       "text/plain": [
-       "<generator object <genexpr> at 0x7f790c3a5b10>"
+       "<generator object <genexpr> at 0x7fb57436fad0>"
       ]
      },
      "execution_count": 333,
@@ -10074,7 +10073,7 @@
     }
    },
    "source": [
-    "[all()](https://docs.python.org/3/library/functions.html#all) can be viewed as syntactic sugar replacing a `for`-loop: Internally, [all()](https://docs.python.org/3/library/functions.html#all) implements the *short-circuiting* strategy from [Chapter 3](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/03_conditionals.ipynb#Short-Circuiting), and we mimic that by testing for the *opposite* condition in the `if` statement and leaving the `for`-loop early with the `break` statement. In the worst case, if `threshold` were, for example, `150`, we would loop over *all* elements in the *iterable*, which must be *finite* for the code to work. So, [all()](https://docs.python.org/3/library/functions.html#all) is a *linear search* in disguise."
+    "[all()](https://docs.python.org/3/library/functions.html#all) can be viewed as syntactic sugar replacing a `for`-loop: Internally, [all()](https://docs.python.org/3/library/functions.html#all) implements the *short-circuiting* strategy from [Chapter 3](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/03_conditionals_00_content.ipynb#Short-Circuiting), and we mimic that by testing for the *opposite* condition in the `if` statement and leaving the `for`-loop early with the `break` statement. In the worst case, if `threshold` were, for example, `150`, we would loop over *all* elements in the *iterable*, which must be *finite* for the code to work. So, [all()](https://docs.python.org/3/library/functions.html#all) is a *linear search* in disguise."
    ]
   },
   {
@@ -10388,13 +10387,13 @@
     }
    },
    "source": [
-    "With the new concepts in this chapter, let's rewrite the book's introductory \"*Averaging Even Numbers*\" example in [Chapter 1](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/01_elements.ipynb#Example:-Averaging-Even-Numbers) such that it efficiently handles a large sequence of numbers.\n",
+    "With the new concepts in this chapter, let's rewrite the book's introductory \"*Averaging Even Numbers*\" example in [Chapter 1](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/01_elements_00_content.ipynb#Example:-Averaging-Even-Numbers) such that it efficiently handles a large sequence of numbers.\n",
     "\n",
     "We assume the `average_evens()` function below is called with a *finite* and *iterable* object, which generates a \"stream\" of numeric objects that can be cast as `int` objects because the idea of even and odd numbers only makes sense in the context of whole numbers.\n",
     "\n",
     "The generator expression `(int(n) for n in numbers)` implements the type casting, and when it is evaluated, *nothing* happens except that a `generator` object is stored in `integers`. Then, with the [reduce()](https://docs.python.org/3/library/functools.html#functools.reduce) function from the [functools](https://docs.python.org/3/library/functools.html) module, we *simultaneously* add up *and* count the even numbers produced by the inner generator expression `((n, 1) for n in integers if n % 2 == 0)`. That results in a `generator` object producing `tuple` objects consisting of the next *even* number in line and `1`. Two such `tuple` objects are then iteratively passed to the `lambda` expression as the `x` and `y` arguments. `x` represents the total and the count of the even numbers processed so far, while `y`'s first element, `y[0]`, is the next even number to be added to the running total. The result of the [reduce()](https://docs.python.org/3/library/functools.html#functools.reduce) function is again a `tuple` object, namely the final `total` and `count`. Lastly, we calculate the simple average.\n",
     "\n",
-    "In summary, the implementation of `average_evens()` does *not* keep materialized `list` objects internally like its predecessors from [Chapter 2](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/02_functions.ipynb), but processes the elements of the `numbers` argument on a one-by-one basis."
+    "In summary, the implementation of `average_evens()` does *not* keep materialized `list` objects internally like its predecessors from [Chapter 2](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/02_functions_00_content.ipynb), but processes the elements of the `numbers` argument on a one-by-one basis."
    ]
   },
   {
@@ -11014,7 +11013,7 @@
     {
      "data": {
       "text/plain": [
-       "<zip at 0x7f790c318208>"
+       "<zip at 0x7fb57438a500>"
       ]
      },
      "execution_count": 375,
@@ -11110,7 +11109,7 @@
     {
      "data": {
       "text/plain": [
-       "<zip at 0x7f790c318208>"
+       "<zip at 0x7fb57438a500>"
       ]
      },
      "execution_count": 379,
@@ -11318,7 +11317,7 @@
     }
    },
    "source": [
-    "In [Chapter 4](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/04_iteration.ipynb#The-for-Statement), we argue that the `for` statement is syntactic sugar, replacing the `while` statement in many scenarios. In particular, a `for`-loop saves us two tasks: Managing an index variable *and* obtaining the individual elements by indexing. In this sub-section, we look at a more realistic picture, using the new terminology as well.\n",
+    "In [Chapter 4](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/04_iteration_00_content.ipynb#The-for-Statement), we argue that the `for` statement is syntactic sugar, replacing the `while` statement in many scenarios. In particular, a `for`-loop saves us two tasks: Managing an index variable *and* obtaining the individual elements by indexing. In this sub-section, we look at a more realistic picture, using the new terminology as well.\n",
     "\n",
     "Let's print out the elements of a `list` object as the *iterable* to be looped over."
    ]
@@ -11460,7 +11459,7 @@
    "source": [
     "Now that we know the concept of an *iterator*, let's compare some of the built-ins introduced in this chapter in detail and make sure we understand what is going on in memory. This sub-section is thus a great summary of this chapter as well.\n",
     "\n",
-    "We use two simple examples, `numbers` and `memoryless`, to guide us through the discussion. `numbers` creates *thirteen* objects in memory and `memoryless` only *one* (cf., [PythonTutor](http://www.pythontutor.com/visualize.html#code=numbers%20%3D%20%5B7,%2011,%208,%205,%203,%2012,%202,%206,%209,%2010,%201,%204%5D%0Amemoryless%20%3D%20range%281,%2013%29&cumulative=false&curInstr=2&heapPrimitives=nevernest&mode=display&origin=opt-frontend.js&py=3&rawInputLstJSON=%5B%5D&textReferences=false))"
+    "We use two simple examples, `numbers` and `memoryless`, to guide us through the discussion. `numbers` creates *thirteen* objects in memory and `memoryless` only *one* (cf., [PythonTutor](http://www.pythontutor.com/visualize.html#code=numbers%20%3D%20%5B7,%2011,%208,%205,%203,%2012,%202,%206,%209,%2010,%201,%204%5D%0Amemoryless%20%3D%20range%281,%2013%29&cumulative=false&curInstr=2&heapPrimitives=nevernest&mode=display&origin=opt-frontend.js&py=3&rawInputLstJSON=%5B%5D&textReferences=false))."
    ]
   },
   {
@@ -11499,7 +11498,7 @@
    "source": [
     "The [sorted()](https://docs.python.org/3/library/functions.html#sorted) function takes a *finite* and *iterable* object as its argument and *materializes* its elements into a *new* `list` object that is returned.\n",
     "\n",
-    "The argument may already be materialized, as is the case with `numbers`, but could also be an *iterator* that generates *new* objects, such as `memoryless`. In both cases, we end up with materialized `list` objects with the elements sorted in *forward* order (cf., [PythonTutor](http://www.pythontutor.com/visualize.html#code=numbers%20%3D%20%5B7,%2011,%208,%205,%203,%2012,%202,%206,%209,%2010,%201,%204%5D%0Amemoryless%20%3D%20range%281,%2013%29%0Aresult1%20%3D%20sorted%28numbers%29%0Aresult2%20%3D%20sorted%28memoryless%29&cumulative=false&curInstr=4&heapPrimitives=nevernest&mode=display&origin=opt-frontend.js&py=3&rawInputLstJSON=%5B%5D&textReferences=false))."
+    "The argument may already be materialized, as is the case with `numbers`, but may also be an *iterable* without any objects in it, such as `memoryless`. In both cases, we end up with materialized `list` objects with the elements sorted in *forward* order (cf., [PythonTutor](http://www.pythontutor.com/visualize.html#code=numbers%20%3D%20%5B7,%2011,%208,%205,%203,%2012,%202,%206,%209,%2010,%201,%204%5D%0Amemoryless%20%3D%20range%281,%2013%29%0Aresult1%20%3D%20sorted%28numbers%29%0Aresult2%20%3D%20sorted%28memoryless%29&cumulative=false&curInstr=4&heapPrimitives=nevernest&mode=display&origin=opt-frontend.js&py=3&rawInputLstJSON=%5B%5D&textReferences=false))."
    ]
   },
   {
@@ -11693,7 +11692,7 @@
     {
      "data": {
       "text/plain": [
-       "<list_reverseiterator at 0x7f790c3339e8>"
+       "<list_reverseiterator at 0x7fb574372e90>"
       ]
      },
      "execution_count": 397,
@@ -11717,7 +11716,7 @@
     {
      "data": {
       "text/plain": [
-       "<range_iterator at 0x7f790c380780>"
+       "<range_iterator at 0x7fb574347540>"
       ]
      },
      "execution_count": 398,
@@ -12216,7 +12215,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.3"
+   "version": "3.7.4"
   },
   "livereveal": {
    "auto_select": "code",
@@ -12244,5 +12243,5 @@
   }
  },
  "nbformat": 4,
- "nbformat_minor": 2
+ "nbformat_minor": 4
 }
diff --git a/07_sequences_10_review.ipynb b/07_sequences_10_review.ipynb
new file mode 100644
index 0000000..a2d2f5c
--- /dev/null
+++ b/07_sequences_10_review.ipynb
@@ -0,0 +1,397 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n",
+    "# Chapter 7: Sequential Data"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Content Review"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Read [Chapter 7](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/07_sequences_00_content.ipynb) of the book. Then, work through the questions below."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Essay Questions "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Answer the following questions *briefly*!"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q1**: We have seen **containers** and **iterables** before in [Chapter 4](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/04_iteration_00_content.ipynb#Containers-vs.-Iterables). How do they relate to **sequences**? "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q2**: What are **abstract base classes**? How can we make use of the ones from the [collections.abc](https://docs.python.org/3/library/collections.abc.html) module in the [standard library](https://docs.python.org/3/library/index.html)?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q3**: How are the *abstract behaviors* of **reversibility** and **finiteness** essential for *indexing* and *slicing* sequences?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q4**: Explain the difference between **mutable** and **immutable** objects in Python with the examples of the `list` and `tuple` types!"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q5**: What is the difference between a **shallow** and a **deep** copy of an object? How can one of them become a \"problem?\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q6**: Many **list methods** change `list` objects \"**in place**.\" What do we mean by that?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q7.1**: `tuple` objects have *two* primary usages. First, they can be used in place of `list` objects where **mutability** is *not* required. Second, we use them to model data **records**.\n",
+    "\n",
+    "Describe why `tuple` objects are a suitable replacement for `list` objects in general!"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q7.2**: What do we mean by a **record**? How are `tuple` objects suitable to model records? How can we integrate a **semantic meaning** when working with records into our code?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q8**: How is (iterable) **packing** and **unpacking** useful in the context of **function definitions** and **calls**?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q9**: With the [map()](https://docs.python.org/3/library/functions.html#map) and [filter()](https://docs.python.org/3/library/functions.html#filter) built-ins and the [reduce()](https://docs.python.org/3/library/functools.html#functools.reduce) function from the [functools](https://docs.python.org/3/library/functools.html) module in the [standard library](https://docs.python.org/3/library/index.html), we can replace many tedious `for`-loops and `if` statements. What are some advantages of doing so?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q10**: Looking at the `lambda` expression inside [reduce()](https://docs.python.org/3/library/functools.html#functools.reduce) below, what \"simple\" [built-in function](https://docs.python.org/3/library/functions.html) is mimicked here?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "```python\n",
+    "from functools import reduce\n",
+    "\n",
+    "numbers = [7, 11, 8, 5, 3, 12, 2, 6, 9, 10, 1, 4]\n",
+    "\n",
+    "reduce(lambda x, y: x if x > y else y, numbers)\n",
+    "```"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q11**: What is the primary use case of **list comprehensions**? Why do we describe them as **eager**?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q12**: **Generator expressions** may replace `list` objects and list comprehensions in many scenarios. When evaluated, they create a **lazy** `generator` object that does *not* **materialize** its elements right away. What do we mean by that? What does it mean for a `generator` object to be **exhausted**?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q13**: What does it mean for the **boolean reducers**, the built-in [all()](https://docs.python.org/3/library/functions.html#all) and [any()](https://docs.python.org/3/library/functions.html#any) functions, to follow the **short-circuiting** strategy?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q14**: What is an **iterator**? How does it relate to an **iterable**?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### True / False Questions"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Motivate your answer with *one short* sentence!"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q15**: `sequence` objects are *not* part of core Python but may be imported from the [standard library](https://docs.python.org/3/library/index.html)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q16**: The built-in [sort()](https://docs.python.org/3/library/stdtypes.html#list.sort) function takes a *finite* **iterable** as its argument an returns a *new* `list` object. On the contrary, the [sorted()](https://docs.python.org/3/library/functions.html#sorted) method on `list` objects *mutates* them *in place*."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q17**: Passing **mutable** objects as arguments to functions is not problematic because functions operate in a **local** scope without affecting the **global** scope."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q18**: `lambda` expressions are useful in the context of the **map-filter-reduce** paradigm, where we often do *not* re-use a `function` object more than once."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q19**: Using **generator expressions** in place of **list comprehensions** wherever possible is a good practice as it makes our programs use memory more efficiently."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q20**: Just as **list comprehensions** create `list` objects, **tuple comprehensions** create `tuple` objects."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " "
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.4"
+  },
+  "toc": {
+   "base_numbering": 1,
+   "nav_menu": {},
+   "number_sections": false,
+   "sideBar": true,
+   "skip_h1_title": true,
+   "title_cell": "Table of Contents",
+   "title_sidebar": "Contents",
+   "toc_cell": false,
+   "toc_position": {},
+   "toc_section_display": false,
+   "toc_window_display": false
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/07_sequences_review_and_exercises.ipynb b/07_sequences_20_exercises.ipynb
similarity index 70%
rename from 07_sequences_review_and_exercises.ipynb
rename to 07_sequences_20_exercises.ipynb
index 68582b2..b2a8169 100644
--- a/07_sequences_review_and_exercises.ipynb
+++ b/07_sequences_20_exercises.ipynb
@@ -8,357 +8,6 @@
     "# Chapter 7: Sequential Data"
    ]
   },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Content Review"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Read [Chapter 7](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/07_sequences.ipynb) of the book. Then work through the twenty review questions."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Essay Questions "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Answer the following questions briefly with *at most* 300 characters per question!"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**Q1**: We have seen **containers** and **iterables** before in [Chapter 4](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/04_iteration.ipynb#Containers-vs.-Iterables). How do they relate to **sequences**? "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    " "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**Q2**: What are **abstract base classes**? How can we make use of the ones from the [collections.abc](https://docs.python.org/3/library/collections.abc.html) module in the [standard library](https://docs.python.org/3/library/index.html)?"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    " "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**Q3**: How are the *abstract behaviors* of **reversibility** and **finiteness** essential for *indexing* and *slicing* sequences?"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    " "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**Q4**: Explain the difference between **mutable** and **immutable** objects in Python with the examples of the `list` and `tuple` types!"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    " "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**Q5**: What is the difference between a **shallow** and a **deep** copy of an object? How can one of them become a \"problem?\""
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    " "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**Q6**: Many **list methods** change `list` objects \"**in place**.\" What do we mean by that?"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    " "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**Q7.1**: `tuple` objects have *two* primary usages. First, they can be used in place of `list` objects where **mutability** is *not* required. Second, we use them to model data **records**.\n",
-    "\n",
-    "Describe why `tuple` objects are a suitable replacement for `list` objects in general!"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    " "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**Q7.2**: What do we mean by a **record**? How are `tuple` objects suitable to model records? How can we integrate a **semantic meaning** when working with records into our code?"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    " "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**Q8**: How is (iterable) **packing** and **unpacking** useful in the context of **function definitions** and **calls**?"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    " "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**Q9**: With the [map()](https://docs.python.org/3/library/functions.html#map) and [filter()](https://docs.python.org/3/library/functions.html#filter) built-ins and the [reduce()](https://docs.python.org/3/library/functools.html#functools.reduce) function from the [functools](https://docs.python.org/3/library/functools.html) module in the [standard library](https://docs.python.org/3/library/index.html), we can replace many tedious `for`-loops and `if` statements. What are some advantages of doing so?"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    " "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**Q10**: Looking at the `lambda` expression inside [reduce()](https://docs.python.org/3/library/functools.html#functools.reduce) below, what \"simple\" [built-in function](https://docs.python.org/3/library/functions.html) is mimicked here?"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "```python\n",
-    "from functools import reduce\n",
-    "\n",
-    "numbers = [7, 11, 8, 5, 3, 12, 2, 6, 9, 10, 1, 4]\n",
-    "\n",
-    "reduce(lambda x, y: x if x > y else y, numbers)\n",
-    "```"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    " "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**Q11**: What is the primary use case of **list comprehensions**? Why do we describe them as **eager**?"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    " "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**Q12**: **Generator expressions** may replace `list` objects and list comprehensions in many scenarios. When evaluated, they create a **lazy** `generator` object that does *not* **materialize** its elements right away. What do we mean by that? What does it mean for a `generator` object to be **exhausted**?"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    " "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**Q13**: What does it mean for the **boolean reducers**, the built-in [all()](https://docs.python.org/3/library/functions.html#all) and [any()](https://docs.python.org/3/library/functions.html#any) functions, to follow the **short-circuiting** strategy?"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    " "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**Q14**: What is an **iterator**? How does it relate to an **iterable**?"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    " "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### True / False Questions"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Motivate your answer with *one short* sentence!"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**Q15**: `sequence` objects are *not* part of core Python but may be imported from the [standard library](https://docs.python.org/3/library/index.html)."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    " "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**Q16**: The built-in [sort()](https://docs.python.org/3/library/stdtypes.html#list.sort) function takes a *finite* **iterable** as its argument an returns a *new* `list` object. On the contrary, the [sorted()](https://docs.python.org/3/library/functions.html#sorted) method on `list` objects *mutates* them *in place*."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    " "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**Q17**: Passing **mutable** objects as arguments to functions is not problematic because functions operate in a **local** scope without affecting the **global** scope."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    " "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**Q18**: `lambda` expressions are useful in the context of the **map-filter-reduce** paradigm, where we often do *not* re-use a `function` object more than once."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    " "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**Q19**: Using **generator expressions** in place of **list comprehensions** wherever possible is a good practice as it makes our programs use memory more efficiently."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    " "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**Q20**: Just as **list comprehensions** create `list` objects, **tuple comprehensions** create `tuple` objects."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    " "
-   ]
-  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -366,6 +15,13 @@
     "## Coding Exercises"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Read [Chapter 7](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/07_sequences_00_content.ipynb) of the book. Then, work through the exercises below."
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -377,7 +33,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "**Q21.1**: Write a function `nested_sum()` that takes a `list` object as its argument, which contains other `list` objects with numbers, and adds up the numbers! Use `nested_numbers` below to test your function!\n",
+    "**Q1.1**: Write a function `nested_sum()` that takes a `list` object as its argument, which contains other `list` objects with numbers, and adds up the numbers! Use `nested_numbers` below to test your function!\n",
     "\n",
     "Hint: You need at least one `for`-loop."
    ]
@@ -414,7 +70,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "**Q21.2**: Provide a one-line expression to obtain the *same* result as `nested_sum()`!\n",
+    "**Q1.2**: Provide a one-line expression to obtain the *same* result as `nested_sum()`!\n",
     "\n",
     "Hints: Use a *list comprehension*, or maybe even a *generator expression*. You may want to use the built-in [sum()](https://docs.python.org/3/library/functions.html#sum) function several times."
    ]
@@ -432,9 +88,9 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "**Q21.3**: Generalize `nested_sum()` into a function `mixed_sum()` that can process a \"mixed\" `list` object, which contains numbers and other `list` objects with numbers! Use `mixed_numbers` below for testing!\n",
+    "**Q1.3**: Generalize `nested_sum()` into a function `mixed_sum()` that can process a \"mixed\" `list` object, which contains numbers and other `list` objects with numbers! Use `mixed_numbers` below for testing!\n",
     "\n",
-    "Hints: Use the built-in [isinstance()](https://docs.python.org/3/library/functions.html#isinstance) function to check how an element is to be processed. Get extra credit for adhering to *goose typing*, as explained in [Chapter 5](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/05_numbers.ipynb#Goose-Typing)."
+    "Hints: Use the built-in [isinstance()](https://docs.python.org/3/library/functions.html#isinstance) function to check how an element is to be processed. Get extra credit for adhering to *goose typing*, as explained in [Chapter 5](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/05_numbers_00_content.ipynb#Goose-Typing)."
    ]
   },
   {
@@ -478,7 +134,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "**Q21.4.1**: Write a function `cum_sum()` that takes a `list` object with numbers as its argument and returns a *new* `list` object with the **cumulative sums** of these numbers! So, `sum_up` below, `[1, 2, 3, 4, 5]`, should return `[1, 3, 6, 10, 15]`.\n",
+    "**Q1.4.1**: Write a function `cum_sum()` that takes a `list` object with numbers as its argument and returns a *new* `list` object with the **cumulative sums** of these numbers! So, `sum_up` below, `[1, 2, 3, 4, 5]`, should return `[1, 3, 6, 10, 15]`.\n",
     "\n",
     "Hint: The idea behind is similar to the [cumulative distribution function](https://en.wikipedia.org/wiki/Cumulative_distribution_function) from statistics."
    ]
@@ -515,7 +171,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "**Q21.4.2**: We should always make sure that our functions also work in corner cases. What happens if your implementation of `cum_sum()` is called with an empty list `[]`? Make sure it handles that case *without* crashing! What would be a good return value in this corner case? Describe everything in the docstring.\n",
+    "**Q1.4.2**: We should always make sure that our functions also work in corner cases. What happens if your implementation of `cum_sum()` is called with an empty list `[]`? Make sure it handles that case *without* crashing! What would be a good return value in this corner case? Describe everything in the docstring.\n",
     "\n",
     "Hint: It is possible to write this without any extra input validation."
    ]
@@ -540,7 +196,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "In the \"*Function Definitions & Calls*\" section in [Chapter 7](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/07_sequences.ipynb#Function-Definitions-&-Calls), we define the following function `product()`. In this exercise, you will improve it by making it more \"user-friendly.\""
+    "In the \"*Function Definitions & Calls*\" section in [Chapter 7](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/07_sequences_00_content.ipynb#Function-Definitions-&-Calls), we define the following function `product()`. In this exercise, you will improve it by making it more \"user-friendly.\""
    ]
   },
   {
@@ -565,7 +221,7 @@
    "source": [
     "The `*` in the function's header line *packs* all *positional* arguments passed to `product()` into one *iterable* called `args`.\n",
     "\n",
-    "**Q22.1**: What is the data type of `args` within the function's body?"
+    "**Q2.1**: What is the data type of `args` within the function's body?"
    ]
   },
   {
@@ -620,7 +276,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "**Q22.2**: What line in the body of `product()` causes this exception? What is the exact problem?"
+    "**Q2.2**: What line in the body of `product()` causes this exception? What is the exact problem?"
    ]
   },
   {
@@ -634,7 +290,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "In [Chapter 7](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/07_sequences.ipynb#Function-Definitions-&-Calls), we also pass a `list` object, like `one_hundred`, to `product()`, and *no* exception is raised."
+    "In [Chapter 7](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/07_sequences_00_content.ipynb#Function-Definitions-&-Calls), we also pass a `list` object, like `one_hundred`, to `product()`, and *no* exception is raised."
    ]
   },
   {
@@ -659,7 +315,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "**Q22.3**: What is wrong with that? What *kind* of error (cf., [Chapter 1](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/01_elements.ipynb#Formal-vs.-Natural-Languages)) is that conceptually? Describe precisely what happens to the passed in `one_hundred` in every line within `product()`!"
+    "**Q2.3**: What is wrong with that? What *kind* of error (cf., [Chapter 1](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/01_elements_00_content.ipynb#Formal-vs.-Natural-Languages)) is that conceptually? Describe precisely what happens to the passed in `one_hundred` in every line within `product()`!"
    ]
   },
   {
@@ -723,7 +379,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "**Q22.4**: Describe why no error occurs by going over every line in `product()`!"
+    "**Q2.4**: Describe why no error occurs by going over every line in `product()`!"
    ]
   },
   {
@@ -762,7 +418,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "**Q22.5**: What line causes troubles now? What is the exact problem?"
+    "**Q2.5**: What line causes troubles now? What is the exact problem?"
    ]
   },
   {
@@ -776,7 +432,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "**Q22.6**: Replace the `None` in `product()` above with something reasonable that does *not* cause exceptions! Ensure that `product(42)` and `product(2, 5, 10)` return a correct result.\n",
+    "**Q2.6**: Replace the `None` in `product()` above with something reasonable that does *not* cause exceptions! Ensure that `product(42)` and `product(2, 5, 10)` return a correct result.\n",
     "\n",
     "Hints: It is ok if `product()` returns a result *different* from the `None` above. Look at the documentation of the built-in [sum()](https://docs.python.org/3/library/functions.html#sum) function for some inspiration."
    ]
@@ -835,9 +491,9 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "**Q22.7**: Rewrite `product()` so that it takes a *keyword-only* argument `start`, defaulting to the above *default* or *start* value, and use `start` internally instead of `result`!\n",
+    "**Q2.7**: Rewrite `product()` so that it takes a *keyword-only* argument `start`, defaulting to the above *default* or *start* value, and use `start` internally instead of `result`!\n",
     "\n",
-    "Hint: Remember that a *keyword-only* argument is any parameter specified in a function's header line after the first (and only) `*` (cf., [Chapter 2](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/02_functions.ipynb#Keyword-only-Arguments))."
+    "Hint: Remember that a *keyword-only* argument is any parameter specified in a function's header line after the first (and only) `*` (cf., [Chapter 2](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/02_functions_00_content.ipynb#Keyword-only-Arguments))."
    ]
   },
   {
@@ -946,7 +602,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "**Q22.8**: What is a **collection**? How is that different from a **sequence**?"
+    "**Q2.8**: What is a **collection**? How is that different from a **sequence**?"
    ]
   },
   {
@@ -960,7 +616,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "**Q22.9**: Rewrite the latest version of `product()` to check if the *only* positional argument is a *collection* type! If so, its elements are multiplied together. Otherwise, the logic remains the same.\n",
+    "**Q2.9**: Rewrite the latest version of `product()` to check if the *only* positional argument is a *collection* type! If so, its elements are multiplied together. Otherwise, the logic remains the same.\n",
     "\n",
     "Hints: Use the built-in [len()](https://docs.python.org/3/library/functions.html#len) and [isinstance()](https://docs.python.org/3/library/functions.html#isinstance) functions to check if there is only *one* positional argument and if it is a *collection* type. Use the *abstract base class* `Collection` from the [collections.abc](https://docs.python.org/3/library/collections.abc.html) module in the [standard library](https://docs.python.org/3/library/index.html). You may want to *re-assign* `args` inside the body."
    ]
@@ -1041,7 +697,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "**Side Note**: Above, we make `product()` work with a single *collection* type argument instead of a *sequence* type to keep it more generic: For example, we can pass in a `set` object, like `{2, 5, 10}` below, and `product()` continues to work correctly. The `set` type is introducted in [Chapter 8](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/08_mappings.ipynb#The-set-Type), and one essential difference to the `list` type is that objects of type `set` have *no* order regarding their elements. So, even though `[2, 5, 10]` and `{2, 5, 10}` look almost the same, the order implied in the literal notation gets lost in memory!"
+    "**Side Note**: Above, we make `product()` work with a single *collection* type argument instead of a *sequence* type to keep it more generic: For example, we can pass in a `set` object, like `{2, 5, 10}` below, and `product()` continues to work correctly. The `set` type is introducted in [Chapter 8](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/08_mappings_00_content.ipynb#The-set-Type), and one essential difference to the `list` type is that objects of type `set` have *no* order regarding their elements. So, even though `[2, 5, 10]` and `{2, 5, 10}` look almost the same, the order implied in the literal notation gets lost in memory!"
    ]
   },
   {
@@ -1091,7 +747,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "**Q22.10**: Adapt the latest version of `product()` to also raise a `TypeError` if called *without* any *positional* arguments!"
+    "**Q2.10**: Adapt the latest version of `product()` to also raise a `TypeError` if called *without* any *positional* arguments!"
    ]
   },
   {
@@ -1279,7 +935,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "**Q22.11**: What line causes the `TypeError`? What line is really the problem in the latest implementation of `product()`? Describe what happens on each line in the function's body until the exception is raised!"
+    "**Q2.11**: What line causes the `TypeError`? What line is really the problem in the latest implementation of `product()`? Describe what happens on each line in the function's body until the exception is raised!"
    ]
   },
   {
@@ -1293,7 +949,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "**Q22.12**: Adapt `product()` one last time to make it work with *iterators* as well!\n",
+    "**Q2.12**: Adapt `product()` one last time to make it work with *iterators* as well!\n",
     "\n",
     "Hints: This task is as easy as replacing `Collection` with something else. Which of the three behaviors of *collections* do *iterators* also exhibit? You may want to look at the documentations on the built-in [max()](https://docs.python.org/3/library/functions.html#max), [min()](https://docs.python.org/3/library/functions.html#min), and [sum()](https://docs.python.org/3/library/functions.html#sum) functions: What kind of argument do they take?"
    ]
@@ -1442,7 +1098,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "**Q23.1**: `list` objects are **sequences**. What *four* behaviors do they always come with?"
+    "**Q3.1**: `list` objects are **sequences**. What *four* behaviors do they always come with?"
    ]
   },
   {
@@ -1456,7 +1112,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "**Q23.2**: Write a function `mean()` that calculates the simple arithmetic mean of a given `sequence` with numbers!\n",
+    "**Q3.2**: Write a function `mean()` that calculates the simple arithmetic mean of a given `sequence` with numbers!\n",
     "\n",
     "Hints: You can solve this task with [built-in functions](https://docs.python.org/3/library/functions.html) only. A `for`-loop is *not* needed."
    ]
@@ -1493,7 +1149,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "**Q23.3**: Write a function `std()` that calculates the [standard deviation](https://en.wikipedia.org/wiki/Standard_deviation) of a `sequence` of numbers! Integrate your `mean()` version from before and the [sqrt()](https://docs.python.org/3/library/math.html#math.sqrt) function from the [math](https://docs.python.org/3/library/math.html) module in the [standard library](https://docs.python.org/3/library/index.html) provided to you below. Make sure `std()` calls `mean()` only *once* internally! Repeated calls to `mean()` would be a waste of computational resources.\n",
+    "**Q3.3**: Write a function `std()` that calculates the [standard deviation](https://en.wikipedia.org/wiki/Standard_deviation) of a `sequence` of numbers! Integrate your `mean()` version from before and the [sqrt()](https://docs.python.org/3/library/math.html#math.sqrt) function from the [math](https://docs.python.org/3/library/math.html) module in the [standard library](https://docs.python.org/3/library/index.html) provided to you below. Make sure `std()` calls `mean()` only *once* internally! Repeated calls to `mean()` would be a waste of computational resources.\n",
     "\n",
     "Hints: Parts of the code are probably too long to fit within the suggested 79 characters per line. So, use *temporary variables* inside your function. Instead of a `for`-loop, you may want to use a *list comprehension* or, even better, a memoryless *generator expression*."
    ]
@@ -1539,7 +1195,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "**Q23.4**: Complete `standardize()` below that takes a `sequence` of numbers and returns a `list` object with the **[z-scores](https://en.wikipedia.org/wiki/Standard_score)** of these numbers! A z-score is calculated by subtracting the mean and dividing by the standard deviation. Re-use `mean()` and `std()` from before. Again, ensure that `standardize()` calls `mean()` and `std()` only *once*! Further, round all z-scores with the built-in [round()](https://docs.python.org/3/library/functions.html#round) function and pass on the keyword-only argument `digits` to it.\n",
+    "**Q3.4**: Complete `standardize()` below that takes a `sequence` of numbers and returns a `list` object with the **[z-scores](https://en.wikipedia.org/wiki/Standard_score)** of these numbers! A z-score is calculated by subtracting the mean and dividing by the standard deviation. Re-use `mean()` and `std()` from before. Again, ensure that `standardize()` calls `mean()` and `std()` only *once*! Further, round all z-scores with the built-in [round()](https://docs.python.org/3/library/functions.html#round) function and pass on the keyword-only argument `digits` to it.\n",
     "\n",
     "Hint: You may want to use a *list comprehension* instead of a `for`-loop."
    ]
@@ -1610,7 +1266,7 @@
    "source": [
     "Even though `standardize()` calls `mean()` and `std()` only once each, `mean()` is called *twice*! That is so because `std()` internally also re-uses `mean()`!\n",
     "\n",
-    "**Q23.5.1**: Rewrite `std()` to take an optional keyword-only argument `seq_mean`, defaulting to `None`. If provided, `seq_mean` is used instead of the result of calling `mean()`. Otherwise, the latter is called.\n",
+    "**Q3.5.1**: Rewrite `std()` to take an optional keyword-only argument `seq_mean`, defaulting to `None`. If provided, `seq_mean` is used instead of the result of calling `mean()`. Otherwise, the latter is called.\n",
     "\n",
     "Hint: You must check if `seq_mean` is still the default value."
    ]
@@ -1654,7 +1310,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "**Q23.5.2**: Now, rewrite `standardize()` to pass on the return value of `mean()` to `std()`! In summary, `standardize()` calculates the z-scores for the numbers in the `sequence` with as few computational steps as possible."
+    "**Q3.5.2**: Now, rewrite `standardize()` to pass on the return value of `mean()` to `std()`! In summary, `standardize()` calculates the z-scores for the numbers in the `sequence` with as few computational steps as possible."
    ]
   },
   {
@@ -1689,7 +1345,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "**Q23.6**: With both `sample` and `z_scores` being materialized `list` objects, we can loop over pairs consisting of a number from `sample` and its corresponding z-score. Write a `for`-loop that prints out all the \"outliers,\" as which we define numbers with an absolute z-score above `1.96`. There are *four* of them in the `sample`.\n",
+    "**Q3.6**: With both `sample` and `z_scores` being materialized `list` objects, we can loop over pairs consisting of a number from `sample` and its corresponding z-score. Write a `for`-loop that prints out all the \"outliers,\" as which we define numbers with an absolute z-score above `1.96`. There are *four* of them in the `sample`.\n",
     "\n",
     "Hint: Use the [abs()](https://docs.python.org/3/library/functions.html#abs) and [zip()](https://docs.python.org/3/library/functions.html#zip) built-ins."
    ]
@@ -1798,7 +1454,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "**Q23.7**: What happens if you call `mean()` with `data` as the argument? What is the problem?\n",
+    "**Q3.7**: What happens if you call `mean()` with `data` as the argument? What is the problem?\n",
     "\n",
     "Hints: If you try it out, you may have to press the \"Stop\" button in the toolbar at the top. Your computer should *not* crash, but you will *have to* restart this Jupyter notebook with \"Kernel\" > \"Restart\" and import `data` again."
    ]
@@ -1823,7 +1479,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "**Q23.8**: Write a function `take_sample()` that takes an `iterator` as its argument, like `data`, and creates a *materialized* `list` object out of its first `n` elements, defaulting to `1_000`!\n",
+    "**Q3.8**: Write a function `take_sample()` that takes an `iterator` as its argument, like `data`, and creates a *materialized* `list` object out of its first `n` elements, defaulting to `1_000`!\n",
     "\n",
     "Hints: [next()](https://docs.python.org/3/library/functions.html#next) and the [range()](https://docs.python.org/3/library/functions.html#func-range) built-in may be helpful. You may want to use a *list comprehension* instead of a `for`-loop and write a one-liner. Audacious students may want to look at [isclice()](https://docs.python.org/3/library/itertools.html#itertools.islice) in the [itertools](https://docs.python.org/3/library/itertools.html) module in the [standard library](https://docs.python.org/3/library/index.html)."
    ]
@@ -1885,7 +1541,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "**Q23.9**: Convert `standardize()` into a *new* function `standardized()` that implements the *same* logic but works on a possibly *infinite* stream of data, provided as an `iterable`, instead of a *finite* `sequence`.\n",
+    "**Q3.9**: Convert `standardize()` into a *new* function `standardized()` that implements the *same* logic but works on a possibly *infinite* stream of data, provided as an `iterable`, instead of a *finite* `sequence`.\n",
     "\n",
     "To calculate a z-score, we need the stream's overall mean and standard deviation, and that is *impossible* to calculate if we do not know how long the stream is, and, in particular, if it is *infinite*. So, `standardized()` first takes a sample from the `iterable` internally, and uses the sample's mean and standard deviation to calculate the z-scores.\n",
     "\n",
@@ -1949,7 +1605,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "**Q23.10.1**: `standardized()` allows us to go over an *infinite* stream of z-scores. What we want to do instead is to loop over the stream's raw numbers and skip the outliers. In the remainder of this exercise, you look at the parts that make up the `skip_outliers()` function below to achieve precisely that.\n",
+    "**Q3.10.1**: `standardized()` allows us to go over an *infinite* stream of z-scores. What we want to do instead is to loop over the stream's raw numbers and skip the outliers. In the remainder of this exercise, you look at the parts that make up the `skip_outliers()` function below to achieve precisely that.\n",
     "\n",
     "The first steps in `skip_outliers()` are the same as in `standardized()`: We take a `sample` from the stream of `data` and calculate its statistics."
    ]
@@ -1969,9 +1625,9 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "**Q23.10.2**: Just as in `standardized()`, write a *generator expression* that produces z-scores one by one! However, instead of just generating a z-score, the resulting `generator` object should produce `tuple` objects consisting of a \"raw\" number from `data` and its z-score.\n",
+    "**Q3.10.2**: Just as in `standardized()`, write a *generator expression* that produces z-scores one by one! However, instead of just generating a z-score, the resulting `generator` object should produce `tuple` objects consisting of a \"raw\" number from `data` and its z-score.\n",
     "\n",
-    "Hint: Look at the revisited \"*Averaging Even Numbers*\" example in [Chapter 7](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/07_sequences.ipynb#Example:-Averaging-Even-Numbers-%28revisited%29) for some inspiration, which also contains a generator expression producing `tuple` objects."
+    "Hint: Look at the revisited \"*Averaging Even Numbers*\" example in [Chapter 7](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/07_sequences_00_content.ipynb#Example:-Averaging-Even-Numbers-%28revisited%29) for some inspiration, which also contains a generator expression producing `tuple` objects."
    ]
   },
   {
@@ -2003,7 +1659,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "**Q23.10.3**: Write another generator expression that loops over `standardizer`. It contains an `if`-clause that keeps only numbers with an absolute z-score below the `threshold_z`. If you fancy, use *tuple unpacking*."
+    "**Q3.10.3**: Write another generator expression that loops over `standardizer`. It contains an `if`-clause that keeps only numbers with an absolute z-score below the `threshold_z`. If you fancy, use *tuple unpacking*."
    ]
   },
   {
@@ -2044,7 +1700,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "**Q23.10.4**: Lastly, put everything together in the `skip_outliers()` function! Make sure you refer to `iterable` inside the function and not the global `data`."
+    "**Q3.10.4**: Lastly, put everything together in the `skip_outliers()` function! Make sure you refer to `iterable` inside the function and not the global `data`."
    ]
   },
   {
@@ -2109,7 +1765,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "**Q23.11**: You implemented the functions `mean()`, `std()`, `standardize()`, `standardized()`, and `skip_outliers()`. Which of them are **eager**, and which are **lazy**? How do these two concepts relate to **finite** and **infinite** data?"
+    "**Q3.11**: You implemented the functions `mean()`, `std()`, `standardize()`, `standardized()`, and `skip_outliers()`. Which of them are **eager**, and which are **lazy**? How do these two concepts relate to **finite** and **infinite** data?"
    ]
   },
   {
@@ -2136,7 +1792,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.3"
+   "version": "3.7.4"
   },
   "toc": {
    "base_numbering": 1,
@@ -2153,5 +1809,5 @@
   }
  },
  "nbformat": 4,
- "nbformat_minor": 2
+ "nbformat_minor": 4
 }
diff --git a/08_mappings.ipynb b/08_mappings_00_content.ipynb
similarity index 96%
rename from 08_mappings.ipynb
rename to 08_mappings_00_content.ipynb
index d42237b..2e19dbe 100644
--- a/08_mappings.ipynb
+++ b/08_mappings_00_content.ipynb
@@ -19,9 +19,9 @@
     }
    },
    "source": [
-    "While [Chapter 7](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/07_sequences.ipynb) focuses on one special kind of *collection* types, namely *sequences*, this chapter introduces two more: **Mappings** and **sets**. We present the data types belonging to these two groups in one chapter as they share the *same* underlying implementation at the C Level, known as **[hash tables](https://en.wikipedia.org/wiki/Hash_table)**.\n",
+    "While [Chapter 7](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/07_sequences_00_content.ipynb) focuses on one special kind of *collection* types, namely *sequences*, this chapter introduces two more kinds: **Mappings** and **sets**. We present the data types belonging to these two groups in one chapter as they share the *same* underlying implementation at the C Level, known as **[hash tables](https://en.wikipedia.org/wiki/Hash_table)**.\n",
     "\n",
-    "The most important mapping type in this chapter is the `dict` type that we have not yet seen before (cf, [documentation](https://docs.python.org/3/library/stdtypes.html#dict)). It is an essential part in a data science practitioner's toolbox for two reasons: First, Python employs `dict` objects basically \"everywhere\" internally. So, we must understand how they work to become better at Python in general. Second, after the many concepts related to *sequential* data, the ideas behind *mappings* enhance our general problem solving skills. As a concrete example, we look at the concept of **memoization** to complete our picture of *recursion*, as depicted in [Chapter 4](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/04_iteration.ipynb#Recursion). We end this chapter with a discussion of *set* types."
+    "The most important mapping type in this chapter is the `dict` type that we have not yet seen before (cf, [documentation](https://docs.python.org/3/library/stdtypes.html#dict)). It is an essential part in a data science practitioner's toolbox for two reasons: First, Python employs `dict` objects basically \"everywhere\" internally. So, we must understand how they work to become better at Python in general. Second, after the many concepts related to *sequential* data, the ideas behind *mappings* enhance our general problem solving skills. As a concrete example, we look at the concept of **memoization** to complete our picture of *recursion*, as depicted in [Chapter 4](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/04_iteration_00_content.ipynb#Recursion). We end this chapter with a discussion of *set* types."
    ]
   },
   {
@@ -45,7 +45,7 @@
    "source": [
     "A *mapping* is a one-to-one correspondence from a set of **keys** to a set of **values**. In other words, a *mapping* is a *collection* of **key-value pairs**, also called **items** for short.\n",
     "\n",
-    "In the context of mappings, the term *value* has a meaning different from the general *value* that *every* object has: In the \"bag\" analogy from [Chapter 1](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/01_elements.ipynb#Value), we descibe an object's value to be the concrete $0$s and $1$s it contains. Here, the terms *key* and *value* mean the *role* an object takes within a mapping. Both, *keys* and *values*, are real *objects* with a distinct *value*. So, the student should always remember the double meaning of the term *value* in this chapter!\n",
+    "In the context of mappings, the term *value* has a meaning different from the general *value* that *every* object has: In the \"bag\" analogy from [Chapter 1](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/01_elements_00_content.ipynb#Value), we descibe an object's value to be the concrete $0$s and $1$s it contains. Here, the terms *key* and *value* mean the *role* an object takes within a mapping. Both, *keys* and *values*, are real *objects* with a distinct *value*. So, the student should always remember the double meaning of the term *value* in this chapter!\n",
     "\n",
     "Let's continue with an example. To create a `dict` object, we commonly use the literal notation, `{..: .., ..: .., ...}`, and list all the items. `to_words` below maps the `int` objects `0`, `1`, and `2` to their English word equivalents, `\"zero\"`, `\"one\"`, and `\"two\"`, and `from_words` does the opposite. A stylistic side note: Pythonistas often expand `dict` or `list` definitions by writing each item or element on a line on their own. The commas `,` after the *last* items are *not* a mistake, as well, although they *may* be left out. Besides easier reading, such style has actual technical advantages (cf., [source](https://www.python.org/dev/peps/pep-0008/#when-to-use-trailing-commas)) that we do not go into detail about here."
    ]
@@ -107,7 +107,7 @@
     {
      "data": {
       "text/plain": [
-       "139752734794304"
+       "139953979690352"
       ]
      },
      "execution_count": 3,
@@ -502,7 +502,7 @@
     }
    },
    "source": [
-    "In [Chapter 0](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/00_start_up.ipynb#Isn't-C-a-lot-faster?), we argue that a major advantage of using Python is that it takes care of the memory managment for us. In line with that, we have never talked about the C level implementation thus far in the book. However, the `dict` type, among others, exhibits some behaviors that may seem \"weird\" for a beginner. To built a solid intuition that enables the student to better \"predict\" how `dict` objects behave, we describe the underlying implementation details on a conceptual level (i.e., without C code).\n",
+    "In [Chapter 0](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/00_start_up_00_content.ipynb#Isn't-C-a-lot-faster?), we argue that a major advantage of using Python is that it takes care of the memory managment for us. In line with that, we have never talked about the C level implementation thus far in the book. However, the `dict` type, among others, exhibits some behaviors that may seem \"weird\" for a beginner. To built a solid intuition that enables the student to better \"predict\" how `dict` objects behave, we describe the underlying implementation details on a conceptual level (i.e., without C code).\n",
     "\n",
     "The first unintuitive behavior is that we may *not* use a *mutable* object as a key. That results in a `TypeError`."
    ]
@@ -597,10 +597,10 @@
     }
    },
    "source": [
-    "| Bucket |  0  |  1  |  2  |  3  |  4  |  5  |  6  |  7  |\n",
-    "|  :---: |:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|\n",
-    "| Key    |*...*|*...*|*...*|*...*|*...*|*...*|*...*|*...*|\n",
-    "| Value  |*...*|*...*|*...*|*...*|*...*|*...*|*...*|*...*|"
+    "|  Bucket |  0  |  1  |  2  |  3  |  4  |  5  |  6  |  7  |\n",
+    "|  :---:  |:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|\n",
+    "| **Key** |*...*|*...*|*...*|*...*|*...*|*...*|*...*|*...*|\n",
+    "|**Value**|*...*|*...*|*...*|*...*|*...*|*...*|*...*|*...*|"
    ]
   },
   {
@@ -628,7 +628,7 @@
     {
      "data": {
       "text/plain": [
-       "-2242252796476499076"
+       "-1033934764475250610"
       ]
      },
      "execution_count": 17,
@@ -707,7 +707,7 @@
     }
    },
    "source": [
-    "The [glossary](https://docs.python.org/3/glossary.html#term-hashable) states a second requirement for hashability, namely that \"objects which *compare equal* must have the *same* hash value.\" The purpose of this is to ensure that if we put, for example, `1` as a key in a `dict` object, we can look it up later with `1.0`. In other words, we can look up keys by their object's value (i.e., in the meaning of [Chapter 1](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/01_elements.ipynb#Value)). The converse statement does *not* hold: Two objects *may* (accidentally) have the *same* hash value and *not* compare equal."
+    "The [glossary](https://docs.python.org/3/glossary.html#term-hashable) states a second requirement for hashability, namely that \"objects which *compare equal* must have the *same* hash value.\" The purpose of this is to ensure that if we put, for example, `1` as a key in a `dict` object, we can look it up later with `1.0`. In other words, we can look up keys by their object's value (i.e., in the meaning of [Chapter 1](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/01_elements_00_content.ipynb#Value)). The converse statement does *not* hold: Two objects *may* (accidentally) have the *same* hash value and *not* compare equal."
    ]
   },
   {
@@ -951,12 +951,12 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "zero\t-2242252796476499076\t0b...010010000100\t100\t4\n",
-      "one\t6742734203392562231\t0b...010000110111\t111\t7\n",
-      "two\t8400825950927958036\t0b...000000010100\t100\t4\n",
-      "three\t-7473701886709619306\t0b...101001101010\t010\t2\n",
-      "four\t5854090567539607813\t0b...100100000101\t101\t5\n",
-      "five\t5010767421024509913\t0b...111111011001\t001\t1\n"
+      "zero\t-1033934764475250610\t0b...011110110010\t010\t2\n",
+      "one\t-3496683303036773129\t0b...001100001001\t001\t1\n",
+      "two\t-4468022785493412346\t0b...100111111010\t010\t2\n",
+      "three\t1491334499222274950\t0b...001110000110\t110\t6\n",
+      "four\t490235692124377396\t0b...000100110100\t100\t4\n",
+      "five\t-550474145318920636\t0b...000110111100\t100\t4\n"
      ]
     }
    ],
@@ -983,10 +983,10 @@
     }
    },
    "source": [
-    "| Bucket |   0   |  1  |   2   |   3    |  4  |    5    |  6  |    7   |\n",
-    "|  :---: | :---: |:---:| :---: | :---:  |:---:|  :---:  |:---:|  :---: |\n",
-    "| Key    |`\"one\"`|*...*|`\"two\"`|`\"four\"`|*...*|`\"three\"`|*...*|`\"zero\"`|\n",
-    "| Value  |  `1`  |*...*|  `2`  |  `4`   |*...*|   `3`   |*...*|   `0`  |"
+    "|  Bucket |   0   |  1  |   2   |   3    |  4  |    5    |  6  |    7   |\n",
+    "|  :---:  | :---: |:---:| :---: | :---:  |:---:|  :---:  |:---:|  :---: |\n",
+    "| **Key** |`\"one\"`|*...*|`\"two\"`|`\"four\"`|*...*|`\"three\"`|*...*|`\"zero\"`|\n",
+    "|**Value**|  `1`  |*...*|  `2`  |  `4`   |*...*|   `3`   |*...*|   `0`  |"
    ]
   },
   {
@@ -1008,10 +1008,10 @@
     }
    },
    "source": [
-    "| Bucket |   0   |  1  |  2  |  3  |  4  |  5  |  6  |    7   |  8  |  9  |  10   |   11   |  12 |    13   |  14 |   15   |\n",
-    "|  :---: | :---: |:---:|:---:|:---:|:---:|:---:|:---:|  :---: |:---:|:---:| :---: |  :---: |:---:|  :---:  |:---:|  :---: |\n",
-    "| Key    |`\"one\"`|*...*|*...*|*...*|*...*|*...*|*...*|`\"five\"`|*...*|*...*|`\"two\"`|`\"four\"`|*...*|`\"three\"`|*...*|`\"zero\"`|\n",
-    "| Value  |  `1`  |*...*|*...*|*...*|*...*|*...*|*...*|   `5`  |*...*|*...*|  `2`  |   `4`  |*...*|   `3`   |*...*|   `0`  |"
+    "|  Bucket |   0   |  1  |  2  |  3  |  4  |  5  |  6  |    7   |  8  |  9  |  10   |   11   |  12 |    13   |  14 |   15   |\n",
+    "|  :---:  | :---: |:---:|:---:|:---:|:---:|:---:|:---:|  :---: |:---:|:---:| :---: |  :---: |:---:|  :---:  |:---:|  :---: |\n",
+    "| **Key** |`\"one\"`|*...*|*...*|*...*|*...*|*...*|*...*|`\"five\"`|*...*|*...*|`\"two\"`|`\"four\"`|*...*|`\"three\"`|*...*|`\"zero\"`|\n",
+    "|**Value**|  `1`  |*...*|*...*|*...*|*...*|*...*|*...*|   `5`  |*...*|*...*|  `2`  |   `4`  |*...*|   `3`   |*...*|   `0`  |"
    ]
   },
   {
@@ -1038,12 +1038,12 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "zero\t-2242252796476499076\t0b...010010000100\t0100\t4\n",
-      "one\t6742734203392562231\t0b...010000110111\t0111\t7\n",
-      "two\t8400825950927958036\t0b...000000010100\t0100\t4\n",
-      "three\t-7473701886709619306\t0b...101001101010\t1010\t10\n",
-      "four\t5854090567539607813\t0b...100100000101\t0101\t5\n",
-      "five\t5010767421024509913\t0b...111111011001\t1001\t9\n"
+      "zero\t-1033934764475250610\t0b...011110110010\t0010\t2\n",
+      "one\t-3496683303036773129\t0b...001100001001\t1001\t9\n",
+      "two\t-4468022785493412346\t0b...100111111010\t1010\t10\n",
+      "three\t1491334499222274950\t0b...001110000110\t0110\t6\n",
+      "four\t490235692124377396\t0b...000100110100\t0100\t4\n",
+      "five\t-550474145318920636\t0b...000110111100\t1100\t12\n"
      ]
     }
    ],
@@ -1070,7 +1070,7 @@
     }
    },
    "source": [
-    "### Mappings are Collections without \"Predictable Order\""
+    "### Mappings are Collections without \"Predictable\" Order"
    ]
   },
   {
@@ -1081,7 +1081,7 @@
     }
    },
    "source": [
-    "In [Chapter 7](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/07_sequences.ipynb#Collections-vs.-Sequences), we show how *sequences* are a special kind of *collections*. The latter can be described as iterable containers with a finite number of elements.\n",
+    "In [Chapter 7](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/07_sequences_00_content.ipynb#Collections-vs.-Sequences), we show how *sequences* are a special kind of *collections*. The latter can be described as iterable containers with a finite number of elements.\n",
     "\n",
     "The `dict` type is a special kind of a *collection*, as well, as revealed with the `Collection` ABC from the [collections.abc](https://docs.python.org/3/library/collections.abc.html) module in the [standard library](https://docs.python.org/3/library/index.html)."
    ]
@@ -1291,12 +1291,12 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Python 3.7.3\r\n"
+      "Python 3.7.4\n"
      ]
     }
    ],
    "source": [
-    "!python --version  # the order in the for-loops is predictable for Python 3.7 or higher only"
+    "!python --version  # the order in the for-loops is predictable only for Python 3.7 or higher"
    ]
   },
   {
@@ -1811,7 +1811,7 @@
    },
    "outputs": [],
    "source": [
-    "haystack = [random.randint(99, 9999) for _ in range(10_000_000)] + [42]"
+    "needle = 42"
    ]
   },
   {
@@ -1824,7 +1824,8 @@
    },
    "outputs": [],
    "source": [
-    "needle = 42"
+    "haystack = [random.randint(99, 9999) for _ in range(10_000_000)]\n",
+    "haystack.append(needle)"
    ]
   },
   {
@@ -1863,7 +1864,7 @@
     {
      "data": {
       "text/plain": [
-       "[8126, 7370, 3735, 213, 7922, 1434, 8557, 9609, 9704, 9564, 9601, 3444]"
+       "[8126, 7370, 3735, 213, 7922, 1434, 8557, 9609, 9704, 9564]"
       ]
      },
      "execution_count": 57,
@@ -1872,7 +1873,7 @@
     }
    ],
    "source": [
-    "haystack[:12]"
+    "haystack[:10]"
    ]
   },
   {
@@ -1887,7 +1888,7 @@
     {
      "data": {
       "text/plain": [
-       "[5842, 1894, 7237, 886, 5945, 4014, 4998, 2055, 3531, 6919, 7875, 1944]"
+       "[7237, 886, 5945, 4014, 4998, 2055, 3531, 6919, 7875, 1944]"
       ]
      },
      "execution_count": 58,
@@ -1896,7 +1897,7 @@
     }
    ],
    "source": [
-    "haystack[-12:]"
+    "haystack[-10:]"
    ]
   },
   {
@@ -1923,7 +1924,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "4.37 s ± 0 ns per loop (mean ± std. dev. of 1 run, 1 loop each)\n"
+      "4.32 s ± 0 ns per loop (mean ± std. dev. of 1 run, 1 loop each)\n"
      ]
     }
    ],
@@ -1965,7 +1966,7 @@
     }
    },
    "source": [
-    "Because the hash table implementation is *extremely* fast, we search the `needle` not `10` but `10_000` times. The code cell still runs in only a fraction of the time its counterpart does above."
+    "To show the *massive* effect of the hash table implementation, we search the `needle` not `10` but `10_000_000` times. The code cell still runs in only a fraction of the time its counterpart does above."
    ]
   },
   {
@@ -1981,7 +1982,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "426 ms ± 0 ns per loop (mean ± std. dev. of 1 run, 1 loop each)\n"
+      "419 ms ± 0 ns per loop (mean ± std. dev. of 1 run, 1 loop each)\n"
      ]
     }
    ],
@@ -2447,7 +2448,7 @@
     {
      "data": {
       "text/plain": [
-       "139752734711240"
+       "139953979638320"
       ]
      },
      "execution_count": 75,
@@ -2638,7 +2639,7 @@
     }
    },
    "source": [
-    "`dict` objects come with many methods bound on them, many of which are standardized by the `Mapping` and `MutableMapping` ABCs from the [collections.abc](https://docs.python.org/3/library/collections.abc.html) module. While the former requires a data type adhering to it to implement the [keys()](https://docs.python.org/3/library/stdtypes.html#dict.keys), [values()](https://docs.python.org/3/library/stdtypes.html#dict.values), [items()](https://docs.python.org/3/library/stdtypes.html#dict.items), and [get()](https://docs.python.org/3/library/stdtypes.html#dict.get) methods, which *never* mutate an object, the latter formalizes the [update()](https://docs.python.org/3/library/stdtypes.html#dict.update), [pop()](https://docs.python.org/3/library/stdtypes.html#dict.pop), [popitem()](https://docs.python.org/3/library/stdtypes.html#dict.popitem), [clear()](https://docs.python.org/3/library/stdtypes.html#dict.clear), and [setdefault()](https://docs.python.org/3/library/stdtypes.html#dict.setdefault) methods, which *may* do so."
+    "`dict` objects come with many methods bound on them (cf., [documentation](https://docs.python.org/3/library/stdtypes.html#dict)), many of which are standardized by the `Mapping` and `MutableMapping` ABCs from the [collections.abc](https://docs.python.org/3/library/collections.abc.html) module. While the former requires a data type adhering to it to implement the [keys()](https://docs.python.org/3/library/stdtypes.html#dict.keys), [values()](https://docs.python.org/3/library/stdtypes.html#dict.values), [items()](https://docs.python.org/3/library/stdtypes.html#dict.items), and [get()](https://docs.python.org/3/library/stdtypes.html#dict.get) methods, which *never* mutate an object, the latter formalizes the [update()](https://docs.python.org/3/library/stdtypes.html#dict.update), [pop()](https://docs.python.org/3/library/stdtypes.html#dict.pop), [popitem()](https://docs.python.org/3/library/stdtypes.html#dict.popitem), [clear()](https://docs.python.org/3/library/stdtypes.html#dict.clear), and [setdefault()](https://docs.python.org/3/library/stdtypes.html#dict.setdefault) methods, which *may* do so."
    ]
   },
   {
@@ -2929,7 +2930,7 @@
     }
    },
    "source": [
-    "Above, we see how the look-up operator fails *loudly* if a key is not in a `dict` object. For example, `to_words` does *not* have a key `0` any more."
+    "Above, we see how the look-up operator fails *loudly* with a `KeyError` if a key is *not* in a `dict` object. For example, `to_words` does *not* have a key `0` any more."
    ]
   },
   {
@@ -3768,7 +3769,7 @@
     }
    },
    "source": [
-    "Just as a single `*` symbol is used for packing and unpacking iterables in [Chapter 7](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/07_sequences.ipynb#Packing-&-Unpacking), a double `**` symbol implements packing and unpacking for mappings.\n",
+    "Just as a single `*` symbol is used for packing and unpacking iterables in [Chapter 7](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/07_sequences_00_content.ipynb#Packing-&-Unpacking), a double `**` symbol implements packing and unpacking for mappings.\n",
     "\n",
     "Let's say we have `to_words` and `more_words` as below and want to merge the items together into a *new* `dict` object."
    ]
@@ -3815,7 +3816,7 @@
     }
    },
    "source": [
-    "By *unpacking* the items with `**`, the newly created `dict` objects is first filled with the items from `to_words` and then from `more_words`. The item with the key `2` from `more_words` overwrites its counterpart from `to_words` as it is mentioned last."
+    "By *unpacking* the items with `**`, the newly created `dict` object is first filled with the items from `to_words` and then from `more_words`. The item with the key `2` from `more_words` overwrites its counterpart from `to_words` as it is mentioned last."
    ]
   },
   {
@@ -4194,7 +4195,7 @@
     }
    },
    "source": [
-    "Analogous to list comprehensions in [Chapter 7](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/07_sequences.ipynb#List-Comprehensions), **dictionary comprehensions**, or **dictcomps** for short, are a concise literal notation to derive new `dict` objects out of existing ones.\n",
+    "Analogous to list comprehensions in [Chapter 7](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/07_sequences_00_content.ipynb#List-Comprehensions), **dictionary comprehensions**, or **dictcomps** for short, are a concise literal notation to derive new `dict` objects out of existing ones.\n",
     "\n",
     "For example, let's derive `from_words` from `to_words` below by swapping the keys and values."
    ]
@@ -4409,7 +4410,7 @@
     }
    },
    "source": [
-    "The *recursive* implementation of the [Fibonacci numbers](https://en.wikipedia.org/wiki/Fibonacci_number) in [Chapter 4](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/04_iteration.ipynb#\"Easy-at-first-Glance\"-Example:-Fibonacci-Numbers) takes long to compute for large Fibonacci numbers as the number of function calls grows exponentially.\n",
+    "The *recursive* implementation of the [Fibonacci numbers](https://en.wikipedia.org/wiki/Fibonacci_number) in [Chapter 4](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/04_iteration_00_content.ipynb#\"Easy-at-first-Glance\"-Example:-Fibonacci-Numbers) takes long to compute for large Fibonacci numbers as the number of function calls grows exponentially.\n",
     "\n",
     "The graph below visualizes what the problem is and also suggests a solution: Instead of calculating the return value of the `fibonacci()` function for the *same* argument over and over again, it makes sense to **cache** the result and reuse it. This concept is called **[memoization](https://en.wikipedia.org/wiki/Memoization)** in the computer science literature."
    ]
@@ -4563,7 +4564,7 @@
     }
    },
    "source": [
-    "Now, calling `fibonacci()` has the *side effect* of growing the `memo` in the *global scope*. So, subsequent calls to `fibonacci()` need not calculate any Fibonacci number with an index `i` smaller than the maximum `i` used so far."
+    "Now, calling `fibonacci()` has the *side effect* of growing the `memo` in the *global scope*. So, subsequent calls to `fibonacci()` need not calculate any Fibonacci number with an index `i` smaller than the maximum `i` used so far. Because of that, this `fibonacci()` is *not* a *pure* function."
    ]
   },
   {
@@ -4663,8 +4664,8 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "The slowest run took 195.30 times longer than the fastest. This could mean that an intermediate result is being cached.\n",
-      "18.9 µs ± 44.4 µs per loop (mean ± std. dev. of 7 runs, 1 loop each)\n"
+      "The slowest run took 183.70 times longer than the fastest. This could mean that an intermediate result is being cached.\n",
+      "21.4 µs ± 50.4 µs per loop (mean ± std. dev. of 7 runs, 1 loop each)\n"
      ]
     }
    ],
@@ -4686,8 +4687,8 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "The slowest run took 1919.60 times longer than the fastest. This could mean that an intermediate result is being cached.\n",
-      "346 µs ± 844 µs per loop (mean ± std. dev. of 7 runs, 1 loop each)\n"
+      "The slowest run took 1974.48 times longer than the fastest. This could mean that an intermediate result is being cached.\n",
+      "580 µs ± 1.41 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n"
      ]
     }
    ],
@@ -4727,12 +4728,12 @@
       "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
       "\u001b[0;31mRecursionError\u001b[0m                            Traceback (most recent call last)",
       "\u001b[0;32m<ipython-input-152-cc76051ea9de>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mget_ipython\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrun_cell_magic\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'timeit'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'-n 1'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'fibonacci(9999)  # = 10,000th number\\n'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
-      "\u001b[0;32m~/.pyenv/versions/anaconda3-2019.07/lib/python3.7/site-packages/IPython/core/interactiveshell.py\u001b[0m in \u001b[0;36mrun_cell_magic\u001b[0;34m(self, magic_name, line, cell)\u001b[0m\n\u001b[1;32m   2356\u001b[0m             \u001b[0;32mwith\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbuiltin_trap\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2357\u001b[0m                 \u001b[0margs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mmagic_arg_s\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcell\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2358\u001b[0;31m                 \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2359\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mresult\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2360\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m</home/webartifex/.pyenv/versions/anaconda3-2019.07/lib/python3.7/site-packages/decorator.py:decorator-gen-60>\u001b[0m in \u001b[0;36mtimeit\u001b[0;34m(self, line, cell, local_ns)\u001b[0m\n",
-      "\u001b[0;32m~/.pyenv/versions/anaconda3-2019.07/lib/python3.7/site-packages/IPython/core/magic.py\u001b[0m in \u001b[0;36m<lambda>\u001b[0;34m(f, *a, **k)\u001b[0m\n\u001b[1;32m    185\u001b[0m     \u001b[0;31m# but it's overkill for just that one bit of state.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    186\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mmagic_deco\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marg\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 187\u001b[0;31m         \u001b[0mcall\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mlambda\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mk\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mk\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    188\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    189\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mcallable\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marg\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/.pyenv/versions/anaconda3-2019.07/lib/python3.7/site-packages/IPython/core/magics/execution.py\u001b[0m in \u001b[0;36mtimeit\u001b[0;34m(self, line, cell, local_ns)\u001b[0m\n\u001b[1;32m   1160\u001b[0m                     \u001b[0;32mbreak\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1161\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1162\u001b[0;31m         \u001b[0mall_runs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtimer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrepeat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrepeat\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnumber\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1163\u001b[0m         \u001b[0mbest\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mall_runs\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m/\u001b[0m \u001b[0mnumber\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1164\u001b[0m         \u001b[0mworst\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmax\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mall_runs\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m/\u001b[0m \u001b[0mnumber\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/.pyenv/versions/anaconda3-2019.07/lib/python3.7/timeit.py\u001b[0m in \u001b[0;36mrepeat\u001b[0;34m(self, repeat, number)\u001b[0m\n\u001b[1;32m    202\u001b[0m         \u001b[0mr\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    203\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrepeat\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 204\u001b[0;31m             \u001b[0mt\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtimeit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnumber\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    205\u001b[0m             \u001b[0mr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mt\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    206\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mr\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/.pyenv/versions/anaconda3-2019.07/lib/python3.7/site-packages/IPython/core/magics/execution.py\u001b[0m in \u001b[0;36mtimeit\u001b[0;34m(self, number)\u001b[0m\n\u001b[1;32m    167\u001b[0m         \u001b[0mgc\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdisable\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    168\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 169\u001b[0;31m             \u001b[0mtiming\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0minner\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mit\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtimer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    170\u001b[0m         \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    171\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mgcold\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/.pyenv/versions/anaconda3-2019.10/lib/python3.7/site-packages/IPython/core/interactiveshell.py\u001b[0m in \u001b[0;36mrun_cell_magic\u001b[0;34m(self, magic_name, line, cell)\u001b[0m\n\u001b[1;32m   2357\u001b[0m             \u001b[0;32mwith\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbuiltin_trap\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2358\u001b[0m                 \u001b[0margs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mmagic_arg_s\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcell\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2359\u001b[0;31m                 \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2360\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mresult\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2361\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m</home/webartifex/.pyenv/versions/anaconda3-2019.10/lib/python3.7/site-packages/decorator.py:decorator-gen-60>\u001b[0m in \u001b[0;36mtimeit\u001b[0;34m(self, line, cell, local_ns)\u001b[0m\n",
+      "\u001b[0;32m~/.pyenv/versions/anaconda3-2019.10/lib/python3.7/site-packages/IPython/core/magic.py\u001b[0m in \u001b[0;36m<lambda>\u001b[0;34m(f, *a, **k)\u001b[0m\n\u001b[1;32m    185\u001b[0m     \u001b[0;31m# but it's overkill for just that one bit of state.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    186\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mmagic_deco\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marg\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 187\u001b[0;31m         \u001b[0mcall\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mlambda\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mk\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mk\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    188\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    189\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mcallable\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marg\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/.pyenv/versions/anaconda3-2019.10/lib/python3.7/site-packages/IPython/core/magics/execution.py\u001b[0m in \u001b[0;36mtimeit\u001b[0;34m(self, line, cell, local_ns)\u001b[0m\n\u001b[1;32m   1160\u001b[0m                     \u001b[0;32mbreak\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1161\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1162\u001b[0;31m         \u001b[0mall_runs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtimer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrepeat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrepeat\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnumber\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1163\u001b[0m         \u001b[0mbest\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mall_runs\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m/\u001b[0m \u001b[0mnumber\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1164\u001b[0m         \u001b[0mworst\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmax\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mall_runs\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m/\u001b[0m \u001b[0mnumber\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/.pyenv/versions/anaconda3-2019.10/lib/python3.7/timeit.py\u001b[0m in \u001b[0;36mrepeat\u001b[0;34m(self, repeat, number)\u001b[0m\n\u001b[1;32m    202\u001b[0m         \u001b[0mr\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    203\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrepeat\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 204\u001b[0;31m             \u001b[0mt\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtimeit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnumber\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    205\u001b[0m             \u001b[0mr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mt\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    206\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mr\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/.pyenv/versions/anaconda3-2019.10/lib/python3.7/site-packages/IPython/core/magics/execution.py\u001b[0m in \u001b[0;36mtimeit\u001b[0;34m(self, number)\u001b[0m\n\u001b[1;32m    167\u001b[0m         \u001b[0mgc\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdisable\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    168\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 169\u001b[0;31m             \u001b[0mtiming\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0minner\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mit\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtimer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    170\u001b[0m         \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    171\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mgcold\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
       "\u001b[0;32m<magic-timeit>\u001b[0m in \u001b[0;36minner\u001b[0;34m(_it, _timer)\u001b[0m\n",
       "\u001b[0;32m<ipython-input-146-223333299fa0>\u001b[0m in \u001b[0;36mfibonacci\u001b[0;34m(i, debug)\u001b[0m\n\u001b[1;32m     24\u001b[0m         \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34mf\"fibonacci({i}) is calculated\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     25\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 26\u001b[0;31m     \u001b[0mrecurse\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfibonacci\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mi\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdebug\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdebug\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mfibonacci\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mi\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0;36m2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdebug\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdebug\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     27\u001b[0m     \u001b[0mmemo\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mrecurse\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     28\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mrecurse\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
       "... last 1 frames repeated, from the frame below ...\n",
@@ -4844,8 +4845,8 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "The slowest run took 25077.61 times longer than the fastest. This could mean that an intermediate result is being cached.\n",
-      "2.36 ms ± 5.77 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n"
+      "The slowest run took 27724.52 times longer than the fastest. This could mean that an intermediate result is being cached.\n",
+      "3.11 ms ± 7.61 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n"
      ]
     }
    ],
@@ -4994,7 +4995,7 @@
     }
    },
    "source": [
-    "Because `fibonacci()` is now independent from *global state*, the same eleven recursive function calls are made each time."
+    "Because `fibonacci()` is now independent from *global state*, the same eleven recursive function calls are made each time. So, this `fibonacci()` is a *pure* function."
    ]
   },
   {
@@ -5073,7 +5074,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "330 µs ± 18.7 µs per loop (mean ± std. dev. of 7 runs, 1 loop each)\n"
+      "138 µs ± 5.01 µs per loop (mean ± std. dev. of 7 runs, 1 loop each)\n"
      ]
     }
    ],
@@ -5095,7 +5096,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "3.53 ms ± 75.2 µs per loop (mean ± std. dev. of 7 runs, 1 loop each)\n"
+      "1.55 ms ± 56.7 µs per loop (mean ± std. dev. of 7 runs, 1 loop each)\n"
      ]
     }
    ],
@@ -5130,7 +5131,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "15.5 ms ± 572 µs per loop (mean ± std. dev. of 7 runs, 1 loop each)\n"
+      "20.8 ms ± 8.09 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n"
      ]
     }
    ],
@@ -5193,7 +5194,7 @@
     }
    },
    "source": [
-    "The [OrderedDict](https://docs.python.org/3/library/collections.html#collections.OrderedDict) type may be used to create a `dict`-like object with the added feature that *explicitly* remembers the *insertion* order of its items.\n",
+    "The [OrderedDict](https://docs.python.org/3/library/collections.html#collections.OrderedDict) type may be used to create a `dict`-like object with the added feature of *explicitly* remembering the *insertion* order of its items.\n",
     "\n",
     "Let's look at a quick example: We create an `OrderedDict` object by passing an iterable of $2$-element iterables, one of the three ways to use [dict()](https://docs.python.org/3/library/functions.html#func-dict)."
    ]
@@ -6360,8 +6361,8 @@
       "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
       "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
       "\u001b[0;32m<ipython-input-205-25fb5b5b2e5f>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mchain\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m10\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
-      "\u001b[0;32m~/.pyenv/versions/anaconda3-2019.07/lib/python3.7/collections/__init__.py\u001b[0m in \u001b[0;36m__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m    912\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    913\u001b[0m                 \u001b[0;32mpass\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 914\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__missing__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m            \u001b[0;31m# support subclasses that define __missing__\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    915\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    916\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdefault\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/.pyenv/versions/anaconda3-2019.07/lib/python3.7/collections/__init__.py\u001b[0m in \u001b[0;36m__missing__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m    904\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    905\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__missing__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 906\u001b[0;31m         \u001b[0;32mraise\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    907\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    908\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__getitem__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/.pyenv/versions/anaconda3-2019.10/lib/python3.7/collections/__init__.py\u001b[0m in \u001b[0;36m__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m    914\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    915\u001b[0m                 \u001b[0;32mpass\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 916\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__missing__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m            \u001b[0;31m# support subclasses that define __missing__\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    917\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    918\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdefault\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/.pyenv/versions/anaconda3-2019.10/lib/python3.7/collections/__init__.py\u001b[0m in \u001b[0;36m__missing__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m    906\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    907\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__missing__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 908\u001b[0;31m         \u001b[0;32mraise\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    909\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    910\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__getitem__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
       "\u001b[0;31mKeyError\u001b[0m: 10"
      ]
     }
@@ -6430,7 +6431,7 @@
     {
      "data": {
       "text/plain": [
-       "139751998783784"
+       "139953088728672"
       ]
      },
      "execution_count": 207,
@@ -6929,7 +6930,7 @@
     }
    ],
    "source": [
-    "1.0 in numbers"
+    "2.0 in numbers"
    ]
   },
   {
@@ -7421,7 +7422,7 @@
     {
      "data": {
       "text/plain": [
-       "139751998783784"
+       "139953088728672"
       ]
      },
      "execution_count": 243,
@@ -8041,7 +8042,7 @@
     }
    },
    "source": [
-    "This section provides some conference talks that go into great detail regarding the workings of the `dict` type."
+    "Next, we list some conference talks that go into great detail regarding the workings of the `dict` type."
    ]
   },
   {
@@ -8068,7 +8069,7 @@
        "        "
       ],
       "text/plain": [
-       "<IPython.lib.display.YouTubeVideo at 0x7f1a8c3e9d68>"
+       "<IPython.lib.display.YouTubeVideo at 0x7f495e8601d0>"
       ]
      },
      "execution_count": 261,
@@ -8105,7 +8106,7 @@
        "        "
       ],
       "text/plain": [
-       "<IPython.lib.display.YouTubeVideo at 0x7f1a8c2a9630>"
+       "<IPython.lib.display.YouTubeVideo at 0x7f495e2b4810>"
       ]
      },
      "execution_count": 262,
@@ -8141,7 +8142,7 @@
        "        "
       ],
       "text/plain": [
-       "<IPython.lib.display.YouTubeVideo at 0x7f1a8c214358>"
+       "<IPython.lib.display.YouTubeVideo at 0x7f495e249810>"
       ]
      },
      "execution_count": 263,
@@ -8152,6 +8153,20 @@
    "source": [
     "YouTubeVideo(\"npw4s1QTmPg\", width=\"60%\")"
    ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "The `dict` type's order has been worked on in many PEPs in recent years:\n",
+    "- [PEP 412](https://www.python.org/dev/peps/pep-0412/): Key-Sharing Dictionary\n",
+    "- [PEP 468](https://www.python.org/dev/peps/pep-0468/): Preserving the order of \\*\\*kwargs in a function\n",
+    "- [PEP 520](https://www.python.org/dev/peps/pep-0520/): Preserving Class Attribute Definition Order"
+   ]
   }
  ],
  "metadata": {
@@ -8170,7 +8185,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.3"
+   "version": "3.7.4"
   },
   "livereveal": {
    "auto_select": "code",
@@ -8198,5 +8213,5 @@
   }
  },
  "nbformat": 4,
- "nbformat_minor": 2
+ "nbformat_minor": 4
 }
diff --git a/08_mappings_10_review.ipynb b/08_mappings_10_review.ipynb
new file mode 100644
index 0000000..471bd41
--- /dev/null
+++ b/08_mappings_10_review.ipynb
@@ -0,0 +1,261 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n",
+    "# Chapter 8: Mappings & Sets"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Content Review"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Read [Chapter 8](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/08_mappings_00_content.ipynb) of the book. Then, work through the questions below."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Essay Questions "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Answer the following questions *briefly*!"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q1**: `dict` objects are well-suited **to model** discrete mathematical **functions** and to approximate continuous ones. What property of dictionaries is the basis for that claim, and how does it relate to functions in the mathematical sense?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q2**: Explain why **hash tables** are  a **trade-off** between **computational speed** and **memory** usage!"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q3:** The `dict` type is an **iterable** that **contains** a **finite** number of key-value pairs. Despite that, why is it *not* considered a **sequence**?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q4**: Whereas *key* **look-ups** in a `dict` object run in so-called **[constant time](https://en.wikipedia.org/wiki/Time_complexity#Constant_time)** (i.e., *extremely* fast), that does not hold for *reverse* look-ups. Why is that?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q5**: Why is it conceptually correct that the Python core developers do not implement **slicing** with the `[]` operator for `dict` objects?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q6**: **Memoization** is an essential concept to know to solve problems in the real world. Together with the idea of **recursion**, it enables us to solve problems in a \"backwards\" fashion *effectively*.\n",
+    "\n",
+    "\n",
+    "Compare the **recursive** formulation of `fibonacci()` in [Chapter 8](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/08_mappings_00_content.ipynb#\"Easy-at-third-Glance\"-Example:-Fibonacci-Numbers-%28revisited%29), the \"*Easy at third Glance*\" example, with the **iterative** version in [Chapter 4](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/04_iteration_00_content.ipynb#\"Hard-at-first-Glance\"-Example:-Fibonacci-Numbers-%28revisited%29), the \"*Hard at first Glance*\" example!\n",
+    "\n",
+    "How are they similar and how do they differ?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q7**: How are the `set` and the `dict` type related? How could we use the latter to mimic the former?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### True / False Questions"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Motivate your answer with *one short* sentence!"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q8**: We may *not* put `dict` objects inside other `dict` objects because they are **mutable**."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q9**: **Mutable** objects (e.g., `list`) may generally *not* be used as keys in a `dict` object. However, if we collect, for example, `list` objects in a `tuple` object, the composite object becomes **hashable**."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q10**: **Mutability** of a `dict` object works until the underlying hash table becomes too crowded. Then, we cannot insert any items any more making the `dict` object effectively **immutable**. Luckily, that almost never happens in practice."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q11**: A `dict` object's [update()](https://docs.python.org/3/library/stdtypes.html#dict.update) method only inserts key-value pairs whose key is *not* yet in the `dict` object. So, it does *not* overwrite anything."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q12**: The `set` type is both a mapping and a sequence."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " "
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.4"
+  },
+  "toc": {
+   "base_numbering": 1,
+   "nav_menu": {},
+   "number_sections": false,
+   "sideBar": true,
+   "skip_h1_title": true,
+   "title_cell": "Table of Contents",
+   "title_sidebar": "Contents",
+   "toc_cell": false,
+   "toc_position": {},
+   "toc_section_display": false,
+   "toc_window_display": false
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/08_mappings_review_and_exercises.ipynb b/08_mappings_20_exercises.ipynb
similarity index 56%
rename from 08_mappings_review_and_exercises.ipynb
rename to 08_mappings_20_exercises.ipynb
index c05004e..0f3f1a5 100644
--- a/08_mappings_review_and_exercises.ipynb
+++ b/08_mappings_20_exercises.ipynb
@@ -8,221 +8,6 @@
     "# Chapter 8: Mappings & Sets"
    ]
   },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Content Review"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Read [Chapter 8](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/08_mappings.ipynb) of the book. Then work through the twelve review questions."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Essay Questions "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Answer the following questions briefly with *at most* 300 characters per question!"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**Q1**: `dict` objects are well-suited **to model** discrete mathematical **functions** and to approximate continuous ones. What property of dictionaries is the basis for that claim, and how does it relate to functions in the mathematical sense?"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    " "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**Q2**: Explain why **hash tables** are  a **trade-off** between **computational speed** and **memory** usage!"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    " "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**Q3:** The `dict` type is an **iterable** that **contains** a **finite** number of key-value pairs. Despite that, why is it *not* considered a **sequence**?"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    " "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**Q4**: Whereas *key* **look-ups** in a `dict` object run in so-called **[constant time](https://en.wikipedia.org/wiki/Time_complexity#Constant_time)** (i.e., *extremely* fast), that does not hold for *reverse* look-ups. Why is that?"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    " "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**Q5**: Why is it conceptually correct that the Python core developers do not implement **slicing** with the `[]` operator for `dict` objects?"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    " "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**Q6**: **Memoization** is an essential concept to know to solve problems in the real world. Together with the idea of **recursion**, it enables us to solve problems in a \"backwards\" fashion *effectively*.\n",
-    "\n",
-    "\n",
-    "Compare the **recursive** formulation of `fibonacci()` in [Chapter 8](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/08_mappings.ipynb#\"Easy-at-third-Glance\"-Example:-Fibonacci-Numbers-%28revisited%29), the \"*Easy at third Glance*\" example, with the **iterative** version in [Chapter 4](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/04_iteration.ipynb#\"Hard-at-first-Glance\"-Example:-Fibonacci-Numbers-%28revisited%29), the \"*Hard at first Glance*\" example!\n",
-    "\n",
-    "How are they similar and how do they differ?"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    " "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**Q7**: How are the `set` and the `dict` type related? How could we use the latter to mimic the former?"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    " "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### True / False Questions"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Motivate your answer with *one short* sentence!"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**Q8**: We may *not* put `dict` objects inside other `dict` objects because they are **mutable**."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    " "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**Q9**: **Mutable** objects (e.g., `list`) may generally *not* be used as keys in a `dict` object. However, if we collect, for example, `list` objects in a `tuple` object, the composite object becomes **hashable**."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    " "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**Q10**: **Mutability** of a `dict` object works until the underlying hash table becomes too crowded. Then, we cannot insert any items any more making the `dict` object effectively **immutable**. Luckily, that almost never happens in practice."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    " "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**Q11**: A `dict` object's [update()](https://docs.python.org/3/library/stdtypes.html#dict.update) method only inserts key-value pairs whose key is *not* yet in the `dict` object. So, it does *not* overwrite anything."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    " "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**Q12**: The `set` type is both a mapping and a sequence."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    " "
-   ]
-  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -230,6 +15,13 @@
     "## Coding Exercises"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Read [Chapter 8](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/08_mappings_00_content.ipynb) of the book. Then, work through the exercises below."
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -291,7 +83,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "**Q13.1**: Write a dictionary comprehension to derive a new `dict` object, called `brazilian_players`, that maps a Brazilian player's name to his position!"
+    "**Q1.1**: Write a dictionary comprehension to derive a new `dict` object, called `brazilian_players`, that maps a Brazilian player's name to his position!"
    ]
   },
   {
@@ -316,7 +108,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "**Q13.2**: Generalize the code fragment into a `get_players()` function: Passed a `team` name, it returns a `dict` object like `brazilian_players`. Verify that the function works for the German team!"
+    "**Q1.2**: Generalize the code fragment into a `get_players()` function: Passed a `team` name, it returns a `dict` object like `brazilian_players`. Verify that the function works for the German team!"
    ]
   },
   {
@@ -344,7 +136,7 @@
    "source": [
     "Often, we are given a `dict` object like the one returned from `get_players()`: It is characterized by the observation that a large set of unique keys (i.e., the players' names) is mapped onto a smaller set of non-unique values (i.e., the positions).\n",
     "\n",
-    "**Q13.3**: Create a generic `invert()` function that swaps the keys and values of a `mapping` argument passed to it and returns them in a *new* `dict` object! Ensure that *no* key gets lost. Verify your implementation with the `brazilian_players` dictionary!\n",
+    "**Q1.3**: Create a generic `invert()` function that swaps the keys and values of a `mapping` argument passed to it and returns them in a *new* `dict` object! Ensure that *no* key gets lost. Verify your implementation with the `brazilian_players` dictionary!\n",
     "\n",
     "Hints: Think of this as a grouping operation. The *new* values are `list` or `tuple` objects that hold the original keys. You may want to use either the the [defaultdict](https://docs.python.org/3/library/collections.html#collections.defaultdict) type from the [collections](https://docs.python.org/3/library/collections.html) module in the [standard library](https://docs.python.org/3/library/index.html) or the [setdefault()](https://docs.python.org/3/library/stdtypes.html#dict.setdefault) method of the ordinary `dict` type."
    ]
@@ -372,7 +164,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "**Q13.4**: Write a `score_at_minute()` function: It takes two arguments, `team` and `minute`, and returns the number of goals the `team` has scored up until this time in the game.\n",
+    "**Q1.4**: Write a `score_at_minute()` function: It takes two arguments, `team` and `minute`, and returns the number of goals the `team` has scored up until this time in the game.\n",
     "\n",
     "Hints: The function may reference the global `players` for simplicity. Earn bonus points if you can write this in a one-line expression using some *reduction* function and a *generator expression*."
    ]
@@ -441,7 +233,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "**Q13.5**: Write a `goals_by_player()` function: It takes an argument like the global `players`, and returns a `dict` object mapping the players to the number of goals they scored.\n",
+    "**Q1.5**: Write a `goals_by_player()` function: It takes an argument like the global `players`, and returns a `dict` object mapping the players to the number of goals they scored.\n",
     "\n",
     "Hints: Do *not* \"hard code\" the names of the teams! Earn bonus points if you can solve it in a single dictionary comprehension."
    ]
@@ -469,7 +261,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "**Q13.6**: Write a *dictionary comprehension* to filter out the players who did *not* score from the preceding result. Then, write a *set comprehension* that does the same but discards the number of goals scored.\n",
+    "**Q1.6**: Write a *dictionary comprehension* to filter out the players who did *not* score from the preceding result. Then, write a *set comprehension* that does the same but discards the number of goals scored.\n",
     "\n",
     "Hints: Reference the `goals_by_player()` function from before."
    ]
@@ -496,7 +288,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "**Q13.7**: Write a `all_goals()` function: It takes one argument like the global `players` and returns a `list` object containing $2$-element `tuple` objects, where the first element is the minute a player scored and the second his name. The list should be sorted by the time.\n",
+    "**Q1.7**: Write a `all_goals()` function: It takes one argument like the global `players` and returns a `list` object containing $2$-element `tuple` objects, where the first element is the minute a player scored and the second his name. The list should be sorted by the time.\n",
     "\n",
     "Hints: You may want to use either the built-in [sorted()](https://docs.python.org/3/library/functions.html#sorted) function or the `list` type's [sort()](https://docs.python.org/3/library/stdtypes.html#list.sort) method. Earn bonus points if you can write a one-line expression with a *generator expression*."
    ]
@@ -524,7 +316,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "**Q13.8**: Lastly, write a `summary()` function: It takes one argument like the global `players` and prints out a concise report of the goals, the score at the half, and the final result.\n",
+    "**Q1.8**: Lastly, write a `summary()` function: It takes one argument like the global `players` and prints out a concise report of the goals, the score at the half, and the final result.\n",
     "\n",
     "Hints: Use the `all_goals()` and `score_at_minute()` functions from before.\n",
     "\n",
@@ -575,7 +367,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.3"
+   "version": "3.7.4"
   },
   "toc": {
    "base_numbering": 1,
@@ -592,5 +384,5 @@
   }
  },
  "nbformat": 4,
- "nbformat_minor": 2
+ "nbformat_minor": 4
 }
diff --git a/README.md b/README.md
index a227cb2..0da3f5e 100644
--- a/README.md
+++ b/README.md
@@ -13,23 +13,52 @@ studies** in the "field" of **data science**.
 The "chapters" are written in [Jupyter notebooks](https://jupyter-notebook.readthedocs.io/en/stable/)
 which are a de-facto standard for exchanging code and results among data
 science professionals and researchers.
-As such they can be viewed in a plain web browser:
+They can be viewed in a plain web browser with the help of
+[nbviewer](https://nbviewer.jupyter.org/):
 
-- [00 - Start up](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/00_start_up.ipynb)
-- [01 - Elements of a Program](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/01_elements.ipynb)
-- [02 - Functions & Modularization](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/02_functions.ipynb)
-- [03 - Conditionals & Exceptions](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/03_conditionals.ipynb)
-- [04 - Recursion & Looping](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/04_iteration.ipynb)
-- [05 - Bits & Numbers](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/05_numbers.ipynb)
-- [06 - Bytes & Text](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/06_text.ipynb)
-- [07 - Sequential Data](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/07_sequences.ipynb)
-- [08 - Mappings & Sets](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/08_mappings.ipynb)
+- *Introduction*: Start up
+  ([content](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/00_start_up_00_content.ipynb)
+  | [review](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/00_start_up_10_review.ipynb)
+  | [exercises](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/00_start_up_20_exercises.ipynb))
+- **Part A: Expressing Logic**
+  - *Chapter 1*: Elements of a Program
+    ([content](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/01_elements_00_content.ipynb)
+    | [review](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/01_elements_10_review.ipynb)
+    | [exercises](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/01_elements_20_exercises.ipynb))
+  - *Chapter 2*: Functions & Modularization
+    ([content](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/02_functions_00_content.ipynb)
+    | [review](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/02_functions_10_review.ipynb)
+    | [exercises](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/02_functions_20_exercises.ipynb))
+  - *Chapter 3*: Conditionals & Exceptions
+    ([content](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/03_conditionals_00_content.ipynb)
+    | [review](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/03_conditionals_10_review.ipynb)
+    | [exercises](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/03_conditionals_20_exercises.ipynb))
+  - *Chapter 4*: Recursion & Looping
+    ([content](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/04_iteration_00_content.ipynb)
+    | [review](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/04_iteration_10_review.ipynb)
+    | [exercises](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/04_iteration_20_exercises.ipynb))
+- **Part B: Managing Data and Memory**
+  - *Chapter 5*: Bits & Numbers
+    ([content](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/05_numbers_00_content.ipynb)
+    | [review](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/05_numbers_10_review.ipynb)
+    | [exercises](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/05_numbers_20_exercises.ipynb))
+  - *Chapter 6*: Bytes & Text
+    ([content](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/06_text_00_content.ipynb)
+    | [review](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/06_text_10_review.ipynb))
+  - *Chapter 7*: Sequential Data
+    ([content](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/07_sequences_00_content.ipynb)
+    | [review](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/07_sequences_10_review.ipynb)
+    | [exercises](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/07_sequences_20_exercises.ipynb))
+  - *Chapter 8*: Mappings & Sets
+    ([content](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/08_mappings_00_content.ipynb)
+    | [review](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/08_mappings_10_review.ipynb)
+    | [exercises](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/08_mappings_20_exercises.ipynb))
 
 However, it is recommended that students **install Python and Jupyter
 locally** and run the code in the notebooks on their own.
 This way, the student can play with the code and learn more efficiently.
 Precise **installation instructions** are either in the [00th notebook](
-https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/00_start_up.ipynb)
+https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/00_start_up_00_content.ipynb)
 or further below.
 
 Feedback is encouraged and will be incorporated.

From 759271b6a144d4823c37b1060f6772020d60f39c Mon Sep 17 00:00:00 2001
From: Alexander Hess <alexander@webartifex.biz>
Date: Tue, 28 Jan 2020 13:07:53 +0100
Subject: [PATCH 055/242] Update pyproject.toml, poetry.lock, requirements.txt

- use JupyterLab instead of just Jupyter
- update dependencies
---
 poetry.lock      | 814 ++++++++++++++++++++++++++++++++---------------
 pyproject.toml   |  13 +-
 requirements.txt |  74 ++---
 3 files changed, 595 insertions(+), 306 deletions(-)

diff --git a/poetry.lock b/poetry.lock
index ad1610a..07e1126 100644
--- a/poetry.lock
+++ b/poetry.lock
@@ -9,7 +9,7 @@ version = "1.4.3"
 [[package]]
 category = "main"
 description = "Disable App Nap on OS X 10.9"
-marker = "python_version >= \"3.3\" and sys_platform == \"darwin\" or sys_platform == \"darwin\""
+marker = "sys_platform == \"darwin\" or platform_system == \"Darwin\""
 name = "appnope"
 optional = false
 python-versions = "*"
@@ -21,7 +21,13 @@ description = "Classes Without Boilerplate"
 name = "attrs"
 optional = false
 python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*"
-version = "19.1.0"
+version = "19.3.0"
+
+[package.extras]
+azure-pipelines = ["coverage", "hypothesis", "pympler", "pytest (>=4.3.0)", "six", "zope.interface", "pytest-azurepipelines"]
+dev = ["coverage", "hypothesis", "pympler", "pytest (>=4.3.0)", "six", "zope.interface", "sphinx", "pre-commit"]
+docs = ["sphinx", "zope.interface"]
+tests = ["coverage", "hypothesis", "pympler", "pytest (>=4.3.0)", "six", "zope.interface"]
 
 [[package]]
 category = "main"
@@ -37,13 +43,19 @@ description = "The uncompromising code formatter."
 name = "black"
 optional = false
 python-versions = ">=3.6"
-version = "18.9b0"
+version = "19.10b0"
 
 [package.dependencies]
 appdirs = "*"
-attrs = ">=17.4.0"
+attrs = ">=18.1.0"
 click = ">=6.5"
+pathspec = ">=0.6,<1"
+regex = "*"
 toml = ">=0.9.4"
+typed-ast = ">=1.4.0"
+
+[package.extras]
+d = ["aiohttp (>=3.3.2)", "aiohttp-cors"]
 
 [[package]]
 category = "dev"
@@ -80,19 +92,19 @@ version = "7.0"
 [[package]]
 category = "main"
 description = "Cross-platform colored terminal text."
-marker = "python_version >= \"3.3\" and sys_platform == \"win32\" or sys_platform == \"win32\""
+marker = "sys_platform == \"win32\""
 name = "colorama"
 optional = false
-python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*"
-version = "0.4.1"
+python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*, !=3.4.*"
+version = "0.4.3"
 
 [[package]]
 category = "main"
-description = "Better living through Python with decorators"
+description = "Decorators for Humans"
 name = "decorator"
 optional = false
 python-versions = ">=2.6, !=3.0.*, !=3.1.*"
-version = "4.4.0"
+version = "4.4.1"
 
 [[package]]
 category = "main"
@@ -110,27 +122,47 @@ optional = false
 python-versions = ">=2.7"
 version = "0.3"
 
+[[package]]
+category = "main"
+description = "Read metadata from Python packages"
+marker = "python_version < \"3.8\""
+name = "importlib-metadata"
+optional = false
+python-versions = "!=3.0.*,!=3.1.*,!=3.2.*,!=3.3.*,!=3.4.*,>=2.7"
+version = "1.4.0"
+
+[package.dependencies]
+zipp = ">=0.5"
+
+[package.extras]
+docs = ["sphinx", "rst.linker"]
+testing = ["packaging", "importlib-resources"]
+
 [[package]]
 category = "main"
 description = "IPython Kernel for Jupyter"
 name = "ipykernel"
 optional = false
 python-versions = ">=3.4"
-version = "5.1.2"
+version = "5.1.4"
 
 [package.dependencies]
+appnope = "*"
 ipython = ">=5.0.0"
 jupyter-client = "*"
 tornado = ">=4.2"
 traitlets = ">=4.1.0"
 
+[package.extras]
+test = ["pytest", "pytest-cov", "flaky", "nose"]
+
 [[package]]
 category = "main"
 description = "IPython: Productive Interactive Computing"
 name = "ipython"
 optional = false
-python-versions = ">=3.5"
-version = "7.8.0"
+python-versions = ">=3.6"
+version = "7.11.1"
 
 [package.dependencies]
 appnope = "*"
@@ -140,11 +172,22 @@ decorator = "*"
 jedi = ">=0.10"
 pexpect = "*"
 pickleshare = "*"
-prompt-toolkit = ">=2.0.0,<2.1.0"
+prompt-toolkit = ">=2.0.0,<3.0.0 || >3.0.0,<3.0.1 || >3.0.1,<3.1.0"
 pygments = "*"
 setuptools = ">=18.5"
 traitlets = ">=4.2"
 
+[package.extras]
+all = ["ipywidgets", "ipyparallel", "qtconsole", "ipykernel", "nbconvert", "notebook", "nbformat", "testpath", "pygments", "requests", "numpy (>=1.14)", "nose (>=0.10.1)", "Sphinx (>=1.3)"]
+doc = ["Sphinx (>=1.3)"]
+kernel = ["ipykernel"]
+nbconvert = ["nbconvert"]
+nbformat = ["nbformat"]
+notebook = ["notebook", "ipywidgets"]
+parallel = ["ipyparallel"]
+qtconsole = ["qtconsole"]
+test = ["nose (>=0.10.1)", "requests", "testpath", "pygments", "nbformat", "ipykernel", "numpy (>=1.14)"]
+
 [[package]]
 category = "main"
 description = "Vestigial utilities from IPython"
@@ -153,53 +196,50 @@ optional = false
 python-versions = "*"
 version = "0.2.0"
 
-[[package]]
-category = "main"
-description = "IPython HTML widgets for Jupyter"
-name = "ipywidgets"
-optional = false
-python-versions = "*"
-version = "7.5.1"
-
-[package.dependencies]
-ipykernel = ">=4.5.1"
-nbformat = ">=4.2.0"
-traitlets = ">=4.3.1"
-widgetsnbextension = ">=3.5.0,<3.6.0"
-
-[package.dependencies.ipython]
-python = ">=3.3"
-version = ">=4.0.0"
-
 [[package]]
 category = "main"
 description = "An autocompletion tool for Python that can be used for text editors."
 name = "jedi"
 optional = false
 python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*"
-version = "0.15.1"
+version = "0.16.0"
 
 [package.dependencies]
-parso = ">=0.5.0"
+parso = ">=0.5.2"
+
+[package.extras]
+qa = ["flake8 (3.7.9)"]
+testing = ["colorama (0.4.1)", "docopt", "pytest (>=3.9.0,<5.0.0)"]
 
 [[package]]
 category = "main"
-description = "A small but fast and easy to use stand-alone template engine written in pure python."
+description = "A very fast and expressive template engine."
 name = "jinja2"
 optional = false
-python-versions = "*"
-version = "2.10.1"
+python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*, !=3.4.*"
+version = "2.11.0"
 
 [package.dependencies]
 MarkupSafe = ">=0.23"
 
+[package.extras]
+i18n = ["Babel (>=0.8)"]
+
+[[package]]
+category = "main"
+description = "A Python implementation of the JSON5 data format."
+name = "json5"
+optional = false
+python-versions = "*"
+version = "0.8.5"
+
 [[package]]
 category = "main"
 description = "An implementation of JSON Schema validation for Python"
 name = "jsonschema"
 optional = false
 python-versions = "*"
-version = "3.0.2"
+version = "3.2.0"
 
 [package.dependencies]
 attrs = ">=17.4.0"
@@ -207,21 +247,13 @@ pyrsistent = ">=0.14.0"
 setuptools = "*"
 six = ">=1.11.0"
 
-[[package]]
-category = "main"
-description = "Jupyter metapackage. Install all the Jupyter components in one go."
-name = "jupyter"
-optional = false
-python-versions = "*"
-version = "1.0.0"
+[package.dependencies.importlib-metadata]
+python = "<3.8"
+version = "*"
 
-[package.dependencies]
-ipykernel = "*"
-ipywidgets = "*"
-jupyter-console = "*"
-nbconvert = "*"
-notebook = "*"
-qtconsole = "*"
+[package.extras]
+format = ["idna", "jsonpointer (>1.13)", "rfc3987", "strict-rfc3339", "webcolors"]
+format_nongpl = ["idna", "jsonpointer (>1.13)", "webcolors", "rfc3986-validator (>0.1.0)", "rfc3339-validator"]
 
 [[package]]
 category = "main"
@@ -229,67 +261,18 @@ description = "Jupyter protocol implementation and client libraries"
 name = "jupyter-client"
 optional = false
 python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*, !=3.4.*"
-version = "5.3.2"
+version = "5.3.4"
 
 [package.dependencies]
-jupyter-core = "*"
+jupyter-core = ">=4.6.0"
 python-dateutil = ">=2.1"
 pywin32 = ">=1.0"
 pyzmq = ">=13"
 tornado = ">=4.1"
 traitlets = "*"
 
-[[package]]
-category = "main"
-description = "Jupyter terminal console"
-name = "jupyter-console"
-optional = false
-python-versions = ">=3.5"
-version = "6.0.0"
-
-[package.dependencies]
-ipykernel = "*"
-ipython = "*"
-jupyter-client = "*"
-prompt-toolkit = ">=2.0.0,<2.1.0"
-pygments = "*"
-
-[[package]]
-category = "dev"
-description = "Common utilities for jupyter-contrib projects."
-name = "jupyter-contrib-core"
-optional = false
-python-versions = "*"
-version = "0.3.3"
-
-[package.dependencies]
-jupyter-core = "*"
-notebook = ">=4.0"
-setuptools = "*"
-tornado = "*"
-traitlets = "*"
-
-[[package]]
-category = "dev"
-description = "A collection of Jupyter nbextensions."
-name = "jupyter-contrib-nbextensions"
-optional = false
-python-versions = "*"
-version = "0.5.1"
-
-[package.dependencies]
-ipython-genutils = "*"
-jupyter-contrib-core = ">=0.3.3"
-jupyter-core = "*"
-jupyter-highlight-selected-word = ">=0.1.1"
-jupyter-latex-envs = ">=1.3.8"
-jupyter-nbextensions-configurator = ">=0.4.0"
-lxml = "*"
-nbconvert = ">=4.2"
-notebook = ">=4.0"
-pyyaml = "*"
-tornado = "*"
-traitlets = ">=4.1"
+[package.extras]
+test = ["ipykernel", "ipython", "mock", "pytest"]
 
 [[package]]
 category = "main"
@@ -297,57 +280,46 @@ description = "Jupyter core package. A base package on which Jupyter projects re
 name = "jupyter-core"
 optional = false
 python-versions = ">=2.7, !=3.0, !=3.1, !=3.2"
-version = "4.5.0"
+version = "4.6.1"
 
 [package.dependencies]
+pywin32 = ">=1.0"
 traitlets = "*"
 
 [[package]]
-category = "dev"
-description = "Jupyter notebook extension that enables highlighting every instance of the current word in the notebook."
-name = "jupyter-highlight-selected-word"
+category = "main"
+description = "The JupyterLab notebook server extension."
+name = "jupyterlab"
 optional = false
-python-versions = "*"
-version = "0.2.0"
-
-[[package]]
-category = "dev"
-description = "Jupyter notebook extension which supports (some) LaTeX environments within markdown cells. Also provides support for labels and crossreferences, document wide numbering, bibliography, and more..."
-name = "jupyter-latex-envs"
-optional = false
-python-versions = "*"
-version = "1.4.6"
+python-versions = ">=3.5"
+version = "1.2.6"
 
 [package.dependencies]
-ipython = "*"
-jupyter_core = "*"
-nbconvert = "*"
-notebook = ">=4.0"
-traitlets = ">=4.1"
+jinja2 = ">=2.10"
+jupyterlab-server = ">=1.0.0,<1.1.0"
+notebook = ">=4.3.1"
+tornado = "<6.0.0 || >6.0.0,<6.0.1 || >6.0.1,<6.0.2 || >6.0.2"
+
+[package.extras]
+docs = ["sphinx", "recommonmark", "sphinx-rtd-theme", "sphinx-copybutton"]
+test = ["pytest", "pytest-check-links", "requests"]
 
 [[package]]
-category = "dev"
-description = "jupyter serverextension providing configuration interfaces for nbextensions."
-name = "jupyter-nbextensions-configurator"
+category = "main"
+description = "JupyterLab Server"
+name = "jupyterlab-server"
 optional = false
-python-versions = "*"
-version = "0.4.1"
+python-versions = ">=3.5"
+version = "1.0.6"
 
 [package.dependencies]
-jupyter_contrib_core = ">=0.3.3"
-jupyter_core = "*"
-notebook = ">=4.0"
-pyyaml = "*"
-tornado = "*"
-traitlets = "*"
+jinja2 = ">=2.10"
+json5 = "*"
+jsonschema = ">=3.0.1"
+notebook = ">=4.2.0"
 
-[[package]]
-category = "dev"
-description = "Powerful and Pythonic XML processing library combining libxml2/libxslt with the ElementTree API."
-name = "lxml"
-optional = false
-python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*, != 3.4.*"
-version = "4.4.1"
+[package.extras]
+test = ["pytest", "requests"]
 
 [[package]]
 category = "main"
@@ -371,7 +343,7 @@ description = "Converting Jupyter Notebooks"
 name = "nbconvert"
 optional = false
 python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*, !=3.4.*"
-version = "5.6.0"
+version = "5.6.1"
 
 [package.dependencies]
 bleach = "*"
@@ -386,13 +358,20 @@ pygments = "*"
 testpath = "*"
 traitlets = ">=4.2"
 
+[package.extras]
+all = ["pytest", "pytest-cov", "ipykernel", "jupyter-client (>=5.3.1)", "ipywidgets (>=7)", "pebble", "tornado (>=4.0)", "sphinx (>=1.5.1)", "sphinx-rtd-theme", "nbsphinx (>=0.2.12)", "sphinxcontrib-github-alt", "ipython", "mock"]
+docs = ["sphinx (>=1.5.1)", "sphinx-rtd-theme", "nbsphinx (>=0.2.12)", "sphinxcontrib-github-alt", "ipython", "jupyter-client (>=5.3.1)"]
+execute = ["jupyter-client (>=5.3.1)"]
+serve = ["tornado (>=4.0)"]
+test = ["pytest", "pytest-cov", "ipykernel", "jupyter-client (>=5.3.1)", "ipywidgets (>=7)", "pebble", "mock"]
+
 [[package]]
 category = "main"
 description = "The Jupyter Notebook format"
 name = "nbformat"
 optional = false
-python-versions = "*"
-version = "4.4.0"
+python-versions = ">=3.5"
+version = "5.0.4"
 
 [package.dependencies]
 ipython-genutils = "*"
@@ -400,21 +379,24 @@ jsonschema = ">=2.4,<2.5.0 || >2.5.0"
 jupyter-core = "*"
 traitlets = ">=4.1"
 
+[package.extras]
+test = ["testpath", "pytest", "pytest-cov"]
+
 [[package]]
 category = "main"
 description = "A web-based notebook environment for interactive computing"
 name = "notebook"
 optional = false
 python-versions = ">=3.5"
-version = "6.0.1"
+version = "6.0.3"
 
 [package.dependencies]
 Send2Trash = "*"
 ipykernel = "*"
 ipython-genutils = "*"
 jinja2 = "*"
-jupyter-client = ">=5.3.1"
-jupyter-core = ">=4.4.0"
+jupyter-client = ">=5.3.4"
+jupyter-core = ">=4.6.1"
 nbconvert = "*"
 nbformat = "*"
 prometheus-client = "*"
@@ -423,13 +405,16 @@ terminado = ">=0.8.1"
 tornado = ">=5.0"
 traitlets = ">=4.2.1"
 
+[package.extras]
+test = ["nose", "coverage", "requests", "nose-warnings-filters", "nbval", "nose-exclude", "selenium", "pytest", "pytest-cov", "nose-exclude"]
+
 [[package]]
 category = "main"
 description = "NumPy is the fundamental package for array computing with Python."
 name = "numpy"
 optional = false
 python-versions = ">=3.5"
-version = "1.17.2"
+version = "1.18.1"
 
 [[package]]
 category = "main"
@@ -444,17 +429,28 @@ category = "main"
 description = "A Python Parser"
 name = "parso"
 optional = false
-python-versions = "*"
-version = "0.5.1"
+python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*"
+version = "0.6.0"
+
+[package.extras]
+testing = ["docopt", "pytest (>=3.0.7)"]
+
+[[package]]
+category = "dev"
+description = "Utility library for gitignore style pattern matching of file paths."
+name = "pathspec"
+optional = false
+python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*, !=3.4.*"
+version = "0.7.0"
 
 [[package]]
 category = "main"
 description = "Pexpect allows easy control of interactive console applications."
-marker = "python_version >= \"3.3\" and sys_platform != \"win32\" or sys_platform != \"win32\""
+marker = "sys_platform != \"win32\""
 name = "pexpect"
 optional = false
 python-versions = "*"
-version = "4.7.0"
+version = "4.8.0"
 
 [package.dependencies]
 ptyprocess = ">=0.5"
@@ -475,22 +471,24 @@ optional = false
 python-versions = "*"
 version = "0.7.1"
 
+[package.extras]
+twisted = ["twisted"]
+
 [[package]]
 category = "main"
 description = "Library for building powerful interactive command lines in Python"
 name = "prompt-toolkit"
 optional = false
-python-versions = "*"
-version = "2.0.9"
+python-versions = ">=3.6"
+version = "3.0.3"
 
 [package.dependencies]
-six = ">=1.9.0"
 wcwidth = "*"
 
 [[package]]
 category = "main"
 description = "Run a subprocess in a pseudo terminal"
-marker = "sys_platform != \"win32\" or os_name != \"nt\" or python_version >= \"3.3\" and sys_platform != \"win32\""
+marker = "sys_platform != \"win32\" or os_name != \"nt\""
 name = "ptyprocess"
 optional = false
 python-versions = "*"
@@ -502,7 +500,7 @@ description = "Pygments is a syntax highlighting package written in Python."
 name = "pygments"
 optional = false
 python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*, !=3.4.*"
-version = "2.4.2"
+version = "2.5.2"
 
 [[package]]
 category = "main"
@@ -510,7 +508,7 @@ description = "Persistent/Functional/Immutable data structures"
 name = "pyrsistent"
 optional = false
 python-versions = "*"
-version = "0.15.4"
+version = "0.15.7"
 
 [package.dependencies]
 six = "*"
@@ -520,8 +518,8 @@ category = "main"
 description = "Extensions to the standard Python datetime module"
 name = "python-dateutil"
 optional = false
-python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*"
-version = "2.8.0"
+python-versions = "!=3.0.*,!=3.1.*,!=3.2.*,>=2.7"
+version = "2.8.1"
 
 [package.dependencies]
 six = ">=1.5"
@@ -533,7 +531,7 @@ marker = "sys_platform == \"win32\""
 name = "pywin32"
 optional = false
 python-versions = "*"
-version = "225"
+version = "227"
 
 [[package]]
 category = "main"
@@ -542,15 +540,7 @@ marker = "os_name == \"nt\""
 name = "pywinpty"
 optional = false
 python-versions = "*"
-version = "0.5.5"
-
-[[package]]
-category = "dev"
-description = "YAML parser and emitter for Python"
-name = "pyyaml"
-optional = false
-python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*"
-version = "5.1.2"
+version = "0.5.7"
 
 [[package]]
 category = "main"
@@ -558,23 +548,15 @@ description = "Python bindings for 0MQ"
 name = "pyzmq"
 optional = false
 python-versions = ">=2.7,!=3.0.*,!=3.1.*,!=3.2.*"
-version = "18.1.0"
+version = "18.1.1"
 
 [[package]]
-category = "main"
-description = "Jupyter Qt console"
-name = "qtconsole"
+category = "dev"
+description = "Alternative regular expression module, to replace re."
+name = "regex"
 optional = false
 python-versions = "*"
-version = "4.5.5"
-
-[package.dependencies]
-ipykernel = ">=4.1"
-ipython-genutils = "*"
-jupyter-client = ">=4.1"
-jupyter-core = "*"
-pygments = "*"
-traitlets = "*"
+version = "2020.1.8"
 
 [[package]]
 category = "dev"
@@ -582,7 +564,7 @@ description = "Reveal.js - Jupyter/IPython Slideshow Extension"
 name = "rise"
 optional = false
 python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*, <4"
-version = "5.5.1"
+version = "5.6.0"
 
 [package.dependencies]
 notebook = ">=5.5.0"
@@ -600,8 +582,8 @@ category = "main"
 description = "Python 2 and 3 compatibility utilities"
 name = "six"
 optional = false
-python-versions = ">=2.6, !=3.0.*, !=3.1.*"
-version = "1.12.0"
+python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*"
+version = "1.14.0"
 
 [[package]]
 category = "main"
@@ -609,7 +591,7 @@ description = "Terminals served to xterm.js using Tornado websockets"
 name = "terminado"
 optional = false
 python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*"
-version = "0.8.2"
+version = "0.8.3"
 
 [package.dependencies]
 ptyprocess = "*"
@@ -622,7 +604,10 @@ description = "Test utilities for code working with files and commands"
 name = "testpath"
 optional = false
 python-versions = "*"
-version = "0.4.2"
+version = "0.4.4"
+
+[package.extras]
+test = ["pathlib2"]
 
 [[package]]
 category = "dev"
@@ -646,20 +631,31 @@ description = "Traitlets Python config system"
 name = "traitlets"
 optional = false
 python-versions = "*"
-version = "4.3.2"
+version = "4.3.3"
 
 [package.dependencies]
 decorator = "*"
 ipython-genutils = "*"
 six = "*"
 
+[package.extras]
+test = ["pytest", "mock"]
+
+[[package]]
+category = "dev"
+description = "a fork of Python 2 and 3 ast modules with type comment support"
+name = "typed-ast"
+optional = false
+python-versions = "*"
+version = "1.4.1"
+
 [[package]]
 category = "main"
 description = "Measures number of Terminal column cells of wide-character codes"
 name = "wcwidth"
 optional = false
 python-versions = "*"
-version = "0.1.7"
+version = "0.1.8"
 
 [[package]]
 category = "main"
@@ -671,78 +667,376 @@ version = "0.5.1"
 
 [[package]]
 category = "main"
-description = "IPython HTML widgets for Jupyter"
-name = "widgetsnbextension"
+description = "Backport of pathlib-compatible object wrapper for zip files"
+marker = "python_version < \"3.8\""
+name = "zipp"
 optional = false
-python-versions = "*"
-version = "3.5.1"
+python-versions = ">=3.6"
+version = "2.1.0"
 
-[package.dependencies]
-notebook = ">=4.4.1"
+[package.extras]
+docs = ["sphinx", "jaraco.packaging (>=3.2)", "rst.linker (>=1.9)"]
+testing = ["jaraco.itertools"]
 
 [metadata]
-content-hash = "7c3d541c65a27324b49fc7ed2b84067e223eb4e20c20310ff9e485b016f65f91"
-python-versions = "^3.6"
+content-hash = "c7cc19bb83c0f0869c6fbe7c1a65a57cf18f5d46d115e007080e497af615c1a3"
+python-versions = "^3.7"
 
-[metadata.hashes]
-appdirs = ["9e5896d1372858f8dd3344faf4e5014d21849c756c8d5701f78f8a103b372d92", "d8b24664561d0d34ddfaec54636d502d7cea6e29c3eaf68f3df6180863e2166e"]
-appnope = ["5b26757dc6f79a3b7dc9fab95359328d5747fcb2409d331ea66d0272b90ab2a0", "8b995ffe925347a2138d7ac0fe77155e4311a0ea6d6da4f5128fe4b3cbe5ed71"]
-attrs = ["69c0dbf2ed392de1cb5ec704444b08a5ef81680a61cb899dc08127123af36a79", "f0b870f674851ecbfbbbd364d6b5cbdff9dcedbc7f3f5e18a6891057f21fe399"]
-backcall = ["38ecd85be2c1e78f77fd91700c76e14667dc21e2713b63876c0eb901196e01e4", "bbbf4b1e5cd2bdb08f915895b51081c041bac22394fdfcfdfbe9f14b77c08bf2"]
-black = ["817243426042db1d36617910df579a54f1afd659adb96fc5032fcf4b36209739", "e030a9a28f542debc08acceb273f228ac422798e5215ba2a791a6ddeaaca22a5"]
-blackcellmagic = ["089b98d28aa647f2d86c3161b60cbf09793384ea94ceb1227f4f1d425db4002c"]
-bleach = ["213336e49e102af26d9cde77dd2d0397afabc5a6bf2fed985dc35b5d1e285a16", "3fdf7f77adcf649c9911387df51254b813185e32b2c6619f690b593a617e19fa"]
-click = ["2335065e6395b9e67ca716de5f7526736bfa6ceead690adf616d925bdc622b13", "5b94b49521f6456670fdb30cd82a4eca9412788a93fa6dd6df72c94d5a8ff2d7"]
-colorama = ["05eed71e2e327246ad6b38c540c4a3117230b19679b875190486ddd2d721422d", "f8ac84de7840f5b9c4e3347b3c1eaa50f7e49c2b07596221daec5edaabbd7c48"]
-decorator = ["86156361c50488b84a3f148056ea716ca587df2f0de1d34750d35c21312725de", "f069f3a01830ca754ba5258fde2278454a0b5b79e0d7f5c13b3b97e57d4acff6"]
-defusedxml = ["6687150770438374ab581bb7a1b327a847dd9c5749e396102de3fad4e8a3ef93", "f684034d135af4c6cbb949b8a4d2ed61634515257a67299e5f940fbaa34377f5"]
-entrypoints = ["589f874b313739ad35be6e0cd7efde2a4e9b6fea91edcc34e58ecbb8dbe56d19", "c70dd71abe5a8c85e55e12c19bd91ccfeec11a6e99044204511f9ed547d48451"]
-ipykernel = ["167c3ef08450f5e060b76c749905acb0e0fbef9365899377a4a1eae728864383", "b503913e0b4cce7ed2de965457dfb2edd633e8234161a60e23f2fe2161345d12"]
-ipython = ["c4ab005921641e40a68e405e286e7a1fcc464497e14d81b6914b4fd95e5dee9b", "dd76831f065f17bddd7eaa5c781f5ea32de5ef217592cf019e34043b56895aa1"]
-ipython-genutils = ["72dd37233799e619666c9f639a9da83c34013a73e8bbc79a7a6348d93c61fab8", "eb2e116e75ecef9d4d228fdc66af54269afa26ab4463042e33785b887c628ba8"]
-ipywidgets = ["13ffeca438e0c0f91ae583dc22f50379b9d6b28390ac7be8b757140e9a771516", "e945f6e02854a74994c596d9db83444a1850c01648f1574adf144fbbabe05c97"]
-jedi = ["786b6c3d80e2f06fd77162a07fed81b8baa22dde5d62896a790a331d6ac21a27", "ba859c74fa3c966a22f2aeebe1b74ee27e2a462f56d3f5f7ca4a59af61bfe42e"]
-jinja2 = ["065c4f02ebe7f7cf559e49ee5a95fb800a9e4528727aec6f24402a5374c65013", "14dd6caf1527abb21f08f86c784eac40853ba93edb79552aa1e4b8aef1b61c7b"]
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+    {file = "six-1.14.0-py2.py3-none-any.whl", hash = "sha256:8f3cd2e254d8f793e7f3d6d9df77b92252b52637291d0f0da013c76ea2724b6c"},
+    {file = "six-1.14.0.tar.gz", hash = "sha256:236bdbdce46e6e6a3d61a337c0f8b763ca1e8717c03b369e87a7ec7ce1319c0a"},
+]
+terminado = [
+    {file = "terminado-0.8.3-py2.py3-none-any.whl", hash = "sha256:a43dcb3e353bc680dd0783b1d9c3fc28d529f190bc54ba9a229f72fe6e7a54d7"},
+    {file = "terminado-0.8.3.tar.gz", hash = "sha256:4804a774f802306a7d9af7322193c5390f1da0abb429e082a10ef1d46e6fb2c2"},
+]
+testpath = [
+    {file = "testpath-0.4.4-py2.py3-none-any.whl", hash = "sha256:bfcf9411ef4bf3db7579063e0546938b1edda3d69f4e1fb8756991f5951f85d4"},
+    {file = "testpath-0.4.4.tar.gz", hash = "sha256:60e0a3261c149755f4399a1fff7d37523179a70fdc3abdf78de9fc2604aeec7e"},
+]
+toml = [
+    {file = "toml-0.10.0-py2.7.egg", hash = "sha256:f1db651f9657708513243e61e6cc67d101a39bad662eaa9b5546f789338e07a3"},
+    {file = "toml-0.10.0-py2.py3-none-any.whl", hash = "sha256:235682dd292d5899d361a811df37e04a8828a5b1da3115886b73cf81ebc9100e"},
+    {file = "toml-0.10.0.tar.gz", hash = "sha256:229f81c57791a41d65e399fc06bf0848bab550a9dfd5ed66df18ce5f05e73d5c"},
+]
+tornado = [
+    {file = "tornado-6.0.3-cp35-cp35m-win32.whl", hash = "sha256:c9399267c926a4e7c418baa5cbe91c7d1cf362d505a1ef898fde44a07c9dd8a5"},
+    {file = "tornado-6.0.3-cp35-cp35m-win_amd64.whl", hash = "sha256:398e0d35e086ba38a0427c3b37f4337327231942e731edaa6e9fd1865bbd6f60"},
+    {file = "tornado-6.0.3-cp36-cp36m-win32.whl", hash = "sha256:4e73ef678b1a859f0cb29e1d895526a20ea64b5ffd510a2307b5998c7df24281"},
+    {file = "tornado-6.0.3-cp36-cp36m-win_amd64.whl", hash = "sha256:349884248c36801afa19e342a77cc4458caca694b0eda633f5878e458a44cb2c"},
+    {file = "tornado-6.0.3-cp37-cp37m-win32.whl", hash = "sha256:559bce3d31484b665259f50cd94c5c28b961b09315ccd838f284687245f416e5"},
+    {file = "tornado-6.0.3-cp37-cp37m-win_amd64.whl", hash = "sha256:abbe53a39734ef4aba061fca54e30c6b4639d3e1f59653f0da37a0003de148c7"},
+    {file = "tornado-6.0.3.tar.gz", hash = "sha256:c845db36ba616912074c5b1ee897f8e0124df269468f25e4fe21fe72f6edd7a9"},
+]
+traitlets = [
+    {file = "traitlets-4.3.3-py2.py3-none-any.whl", hash = "sha256:70b4c6a1d9019d7b4f6846832288f86998aa3b9207c6821f3578a6a6a467fe44"},
+    {file = "traitlets-4.3.3.tar.gz", hash = "sha256:d023ee369ddd2763310e4c3eae1ff649689440d4ae59d7485eb4cfbbe3e359f7"},
+]
+typed-ast = [
+    {file = "typed_ast-1.4.1-cp35-cp35m-manylinux1_i686.whl", hash = "sha256:73d785a950fc82dd2a25897d525d003f6378d1cb23ab305578394694202a58c3"},
+    {file = "typed_ast-1.4.1-cp35-cp35m-manylinux1_x86_64.whl", hash = "sha256:aaee9905aee35ba5905cfb3c62f3e83b3bec7b39413f0a7f19be4e547ea01ebb"},
+    {file = "typed_ast-1.4.1-cp35-cp35m-win32.whl", hash = "sha256:0c2c07682d61a629b68433afb159376e24e5b2fd4641d35424e462169c0a7919"},
+    {file = "typed_ast-1.4.1-cp35-cp35m-win_amd64.whl", hash = "sha256:4083861b0aa07990b619bd7ddc365eb7fa4b817e99cf5f8d9cf21a42780f6e01"},
+    {file = "typed_ast-1.4.1-cp36-cp36m-macosx_10_9_x86_64.whl", hash = "sha256:269151951236b0f9a6f04015a9004084a5ab0d5f19b57de779f908621e7d8b75"},
+    {file = "typed_ast-1.4.1-cp36-cp36m-manylinux1_i686.whl", hash = "sha256:24995c843eb0ad11a4527b026b4dde3da70e1f2d8806c99b7b4a7cf491612652"},
+    {file = "typed_ast-1.4.1-cp36-cp36m-manylinux1_x86_64.whl", hash = "sha256:fe460b922ec15dd205595c9b5b99e2f056fd98ae8f9f56b888e7a17dc2b757e7"},
+    {file = "typed_ast-1.4.1-cp36-cp36m-win32.whl", hash = "sha256:4e3e5da80ccbebfff202a67bf900d081906c358ccc3d5e3c8aea42fdfdfd51c1"},
+    {file = "typed_ast-1.4.1-cp36-cp36m-win_amd64.whl", hash = "sha256:249862707802d40f7f29f6e1aad8d84b5aa9e44552d2cc17384b209f091276aa"},
+    {file = "typed_ast-1.4.1-cp37-cp37m-macosx_10_9_x86_64.whl", hash = "sha256:8ce678dbaf790dbdb3eba24056d5364fb45944f33553dd5869b7580cdbb83614"},
+    {file = "typed_ast-1.4.1-cp37-cp37m-manylinux1_i686.whl", hash = "sha256:c9e348e02e4d2b4a8b2eedb48210430658df6951fa484e59de33ff773fbd4b41"},
+    {file = "typed_ast-1.4.1-cp37-cp37m-manylinux1_x86_64.whl", hash = "sha256:bcd3b13b56ea479b3650b82cabd6b5343a625b0ced5429e4ccad28a8973f301b"},
+    {file = "typed_ast-1.4.1-cp37-cp37m-win32.whl", hash = "sha256:d5d33e9e7af3b34a40dc05f498939f0ebf187f07c385fd58d591c533ad8562fe"},
+    {file = "typed_ast-1.4.1-cp37-cp37m-win_amd64.whl", hash = "sha256:0666aa36131496aed8f7be0410ff974562ab7eeac11ef351def9ea6fa28f6355"},
+    {file = "typed_ast-1.4.1-cp38-cp38-macosx_10_15_x86_64.whl", hash = "sha256:d205b1b46085271b4e15f670058ce182bd1199e56b317bf2ec004b6a44f911f6"},
+    {file = "typed_ast-1.4.1-cp38-cp38-manylinux1_i686.whl", hash = "sha256:6daac9731f172c2a22ade6ed0c00197ee7cc1221aa84cfdf9c31defeb059a907"},
+    {file = "typed_ast-1.4.1-cp38-cp38-manylinux1_x86_64.whl", hash = "sha256:498b0f36cc7054c1fead3d7fc59d2150f4d5c6c56ba7fb150c013fbc683a8d2d"},
+    {file = "typed_ast-1.4.1-cp38-cp38-win32.whl", hash = "sha256:715ff2f2df46121071622063fc7543d9b1fd19ebfc4f5c8895af64a77a8c852c"},
+    {file = "typed_ast-1.4.1-cp38-cp38-win_amd64.whl", hash = "sha256:fc0fea399acb12edbf8a628ba8d2312f583bdbdb3335635db062fa98cf71fca4"},
+    {file = "typed_ast-1.4.1-cp39-cp39-macosx_10_15_x86_64.whl", hash = "sha256:d43943ef777f9a1c42bf4e552ba23ac77a6351de620aa9acf64ad54933ad4d34"},
+    {file = "typed_ast-1.4.1.tar.gz", hash = "sha256:8c8aaad94455178e3187ab22c8b01a3837f8ee50e09cf31f1ba129eb293ec30b"},
+]
+wcwidth = [
+    {file = "wcwidth-0.1.8-py2.py3-none-any.whl", hash = "sha256:8fd29383f539be45b20bd4df0dc29c20ba48654a41e661925e612311e9f3c603"},
+    {file = "wcwidth-0.1.8.tar.gz", hash = "sha256:f28b3e8a6483e5d49e7f8949ac1a78314e740333ae305b4ba5defd3e74fb37a8"},
+]
+webencodings = [
+    {file = "webencodings-0.5.1-py2.py3-none-any.whl", hash = "sha256:a0af1213f3c2226497a97e2b3aa01a7e4bee4f403f95be16fc9acd2947514a78"},
+    {file = "webencodings-0.5.1.tar.gz", hash = "sha256:b36a1c245f2d304965eb4e0a82848379241dc04b865afcc4aab16748587e1923"},
+]
+zipp = [
+    {file = "zipp-2.1.0-py3-none-any.whl", hash = "sha256:ccc94ed0909b58ffe34430ea5451f07bc0c76467d7081619a454bf5c98b89e28"},
+    {file = "zipp-2.1.0.tar.gz", hash = "sha256:feae2f18633c32fc71f2de629bfb3bd3c9325cd4419642b1f1da42ee488d9b98"},
+]
diff --git a/pyproject.toml b/pyproject.toml
index f25a893..c30f700 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -1,20 +1,19 @@
 [tool.poetry]
 name = "intro-to-python"
-version = "0.6.0"
+version = "0.6.1"
 description = "An introduction to Python and programming for wanna-be data scientists"
 authors = ["Alexander Hess <alexander@webartifex.biz>"]
 license = "MIT"
 
 [tool.poetry.dependencies]
-python = "^3.6"
-jupyter = "^1.0"
-numpy = "^1.17"
+python = "^3.7"
+jupyterlab = "^1.2.6"
+numpy = "^1.18.1"
 
 [tool.poetry.dev-dependencies]
-black = {version = "^18.3-alpha.0", allows-prereleases = true}
+black = "^19.10b0"
 blackcellmagic = "^0.0.2"
-jupyter-contrib-nbextensions = "^0.5"
-rise = "^5.5"
+RISE = "^5.6.0"
 
 [build-system]
 requires = ["poetry>=0.12"]
diff --git a/requirements.txt b/requirements.txt
index 28405c8..f9ab033 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -1,57 +1,53 @@
 appdirs==1.4.3
-attrs==19.1.0
+attrs==19.3.0
 backcall==0.1.0
-black==18.9b0
+black==19.10b0
 blackcellmagic==0.0.2
 bleach==3.1.0
 Click==7.0
-decorator==4.4.0
+decorator==4.4.1
 defusedxml==0.6.0
 entrypoints==0.3
-ipykernel==5.1.2
-ipython==7.8.0
+importlib-metadata==1.4.0
+ipykernel==5.1.4
+ipython==7.11.1
 ipython-genutils==0.2.0
-ipywidgets==7.5.1
-jedi==0.15.1
-Jinja2==2.10.1
-jsonschema==3.0.2
-jupyter==1.0.0
-jupyter-client==5.3.2
-jupyter-console==6.0.0
-jupyter-contrib-core==0.3.3
-jupyter-contrib-nbextensions==0.5.1
-jupyter-core==4.5.0
-jupyter-highlight-selected-word==0.2.0
-jupyter-latex-envs==1.4.6
-jupyter-nbextensions-configurator==0.4.1
-lxml==4.4.1
+jedi==0.16.0
+Jinja2==2.11.0
+json5==0.8.5
+jsonschema==3.2.0
+jupyter-client==5.3.4
+jupyter-core==4.6.1
+jupyterlab==1.2.6
+jupyterlab-server==1.0.6
 MarkupSafe==1.1.1
 mistune==0.8.4
-nbconvert==5.6.0
-nbformat==4.4.0
-notebook==6.0.1
-numpy==1.17.2
+nbconvert==5.6.1
+nbformat==5.0.4
+notebook==6.0.3
+numpy==1.18.1
 pandocfilters==1.4.2
-parso==0.5.1
-pexpect==4.7.0
+parso==0.6.0
+pathspec==0.7.0
+pexpect==4.8.0
 pickleshare==0.7.5
 prometheus-client==0.7.1
-prompt-toolkit==2.0.9
+prompt-toolkit==3.0.3
 ptyprocess==0.6.0
-Pygments==2.4.2
-pyrsistent==0.15.4
-python-dateutil==2.8.0
-PyYAML==5.1.2
-pyzmq==18.1.0
-qtconsole==4.5.5
-rise==5.5.1
+Pygments==2.5.2
+pyrsistent==0.15.7
+python-dateutil==2.8.1
+pyzmq==18.1.1
+regex==2020.1.8
+rise==5.6.0
 Send2Trash==1.5.0
-six==1.12.0
-terminado==0.8.2
-testpath==0.4.2
+six==1.14.0
+terminado==0.8.3
+testpath==0.4.4
 toml==0.10.0
 tornado==6.0.3
-traitlets==4.3.2
-wcwidth==0.1.7
+traitlets==4.3.3
+typed-ast==1.4.1
+wcwidth==0.1.8
 webencodings==0.5.1
-widgetsnbextension==3.5.1
+zipp==2.1.0

From e8cd8a7e6303fbbb759d1563f0af6d8a5a369738 Mon Sep 17 00:00:00 2001
From: Alexander Hess <alexander@webartifex.biz>
Date: Tue, 28 Jan 2020 18:23:55 +0100
Subject: [PATCH 056/242] Update the installation notes to JupyterLab

---
 00_start_up_00_content.ipynb   |  14 ++++----
 README.md                      |  59 +++++++++++++++++++++++----------
 static/anaconda.png            | Bin 142513 -> 0 bytes
 static/anaconda_navigator.png  | Bin 0 -> 317986 bytes
 static/anaconda_start_menu.png | Bin 0 -> 187769 bytes
 static/jupyter_lab.png         | Bin 0 -> 129745 bytes
 6 files changed, 49 insertions(+), 24 deletions(-)
 delete mode 100644 static/anaconda.png
 create mode 100644 static/anaconda_navigator.png
 create mode 100644 static/anaconda_start_menu.png
 create mode 100644 static/jupyter_lab.png

diff --git a/00_start_up_00_content.ipynb b/00_start_up_00_content.ipynb
index 6e3d84a..3ca98d4 100644
--- a/00_start_up_00_content.ipynb
+++ b/00_start_up_00_content.ipynb
@@ -156,7 +156,7 @@
     "\n",
     "A popular and beginner-friendly way is to install the [Anaconda Distribution](https://www.anaconda.com/distribution/) that not only ships Python and the standard library but comes pre-packaged with a lot of third-party libraries from the so-called \"scientific stack.\" Just go to the [download](https://www.anaconda.com/download/) page and install the latest version (i.e., *2019-10* with Python 3.7 at the time of this writing) for your operating system.\n",
     "\n",
-    "Then, among others, you find an entry \"Jupyter Notebook\" in your start menu like below. Click on it to open a new tab in your web browser where you can switch between folders as you could in your computer's default file browser."
+    "Then, among others, you will find an entry \"Anaconda Navigator\" in your start menu like below. Click on it."
    ]
   },
   {
@@ -167,7 +167,7 @@
     }
    },
    "source": [
-    "<img src=\"static/anaconda.png\" width=\"40%\">"
+    "<img src=\"static/anaconda_start_menu.png\" width=\"30%\">"
    ]
   },
   {
@@ -178,7 +178,7 @@
     }
    },
    "source": [
-    "To download the materials accompanying this book as a ZIP file, open this [GitHub repository](https://github.com/webartifex/intro-to-python) in a web browser and click on the green \"Clone or download\" button on the right. Then, unpack the ZIP file into a folder of your choosing (ideally somewhere within your user folder so that the files show up right away)."
+    "A window opens showing you several applications that come with the Anaconda Distribution. Now, click on \"JupyterLab.\""
    ]
   },
   {
@@ -189,7 +189,7 @@
     }
    },
    "source": [
-    "### Alternative Installation"
+    "<img src=\"static/anaconda_navigator.png\" width=\"50%\">"
    ]
   },
   {
@@ -200,7 +200,7 @@
     }
    },
    "source": [
-    "Python can also be installed in a \"pure\" way as obtained from its core development team. However, this is somewhat too \"advanced\" for a beginner who would then also be responsible for setting up all the third-party libraries needed to view this document. Plus, many of the involved steps are typed in a [terminal](https://en.wikipedia.org/wiki/Terminal_emulator) window, which tends to be a bit intimidating for most beginners."
+    "A new tab in your web browser opens with the website being \"localhost\" and some number (e.g., 8888). This is the [JupyterLab](https://jupyterlab.readthedocs.io/en/stable/) application that is used to display and run the Jupyter notebooks mentioned above. On the left, you see the files and folders in your local user folder. This file browser works like any other. In the center, you have several options to launch a new notebook file."
    ]
   },
   {
@@ -211,7 +211,7 @@
     }
    },
    "source": [
-    "<img src=\"static/terminal.png\" width=\"50%\" align=\"center\">"
+    "<img src=\"static/jupyter_lab.png\" width=\"50%\">"
    ]
   },
   {
@@ -222,7 +222,7 @@
     }
    },
    "source": [
-    "For more \"courageous\" beginners wanting to learn how to accomplish this, here is a rough sketch of the aspects to know. First, all Python releases are available for free on the official [download](https://www.python.org/downloads/) page for any supported operating system. Choose the latest one applicable, then download and install it (cf., the [instruction notes](https://wiki.python.org/moin/BeginnersGuide/Download)). As this only includes core Python and the standard library, the beginner then needs to learn about the [pip](https://pip.pypa.io/en/stable/) module. With the command `python -m pip install jupyter`, all necessary third-party libraries are installed (cf., more background [here](https://jupyter.readthedocs.io/en/latest/install.html)). However, this would be done in a *system-wide* fashion and is not recommended. Instead, the best practice is to create a so-called **virtual environment** with the [venv](https://docs.python.org/3/library/venv.html) module with which the installed third-party packages are *isolated* on a per-project basis (the command `python -m venv env-name` creates a virtual environment called \"env-name\"). This tactic is employed to avoid a situation known as **[dependency hell](https://en.wikipedia.org/wiki/Dependency_hell)**. Once created, the virtual environment must then be activated each time before resuming work in each terminal (with the command `source env-name/bin/activate`). While there exist convenience tools that automate parts of this (e.g., [poetry](https://poetry.eustace.io/docs/) or [virtualenvwrapper](https://virtualenvwrapper.readthedocs.io/en/latest/)), it only distracts a beginner from studying the Python language. Yet, it is still worthwhile to have heard about these terms and concepts as many online resources often implicitly assume the user to know about them."
+    "Next, to download the materials accompanying this book as a ZIP file, open this [GitHub repository](https://github.com/webartifex/intro-to-python) in a web browser, and click on the green \"Clone or download\" button on the top right. Then, unpack the ZIP file into a folder of your choosing, ideally somewhere within your personal user folder so that the files show up right away in JupyterLab."
    ]
   },
   {
diff --git a/README.md b/README.md
index 0da3f5e..8c1cba2 100644
--- a/README.md
+++ b/README.md
@@ -91,33 +91,52 @@ Just go to the [download](https://www.anaconda.com/distribution/#download-sectio
 section and install the latest version (i.e., *2019-10* with Python 3.7 at the
 time of this writing) for your operating system.
 
-Then, among others, you will find an entry "Jupyter Notebook" in your start
+Then, among others, you will find an entry "Anaconda Navigator" in your start
 menu like below.
-Click on it and a new tab in your web browser will open where you can switch
-between folders as you could in your computer's default file browser.
+Click on it.
 
-<img src="static/anaconda.png" width="50%">
+<img src="static/anaconda_start_menu.png" width="30%">
 
-To download the course's materials as a ZIP file, click on the green "Clone or
-download" button on the top right on this website.
-Then, unpack the ZIP file into a folder of your choosing (ideally somewhere
-within your personal user folder so that the files show up right away).
+A window opens showing you several applications that come with the Anaconda
+Distribution.
+Now, click on "JupyterLab."
+
+<img src="static/anaconda_navigator.png" width="50%">
+
+A new tab in your web browser opens with the website being "localhost" and some
+number (e.g., 8888).
+This is the [JupyterLab](https://jupyterlab.readthedocs.io/en/stable/)
+application that is used to display and run the Jupyter notebooks mentioned
+above.
+On the left, you see the files and folders in your local user folder.
+This file browser works like any other.
+In the center, you have several options to launch a new notebook file.
+
+<img src="static/jupyter_lab.png" width="50%">
+
+Next, to download the course's materials as a ZIP file, click on the green
+"Clone or download" button on the top right on this website.
+Then, unpack the ZIP file into a folder of your choosing, ideally somewhere
+within your personal user folder so that the files show up right away in
+JupyterLab.
 
 
-### Alternative Installation
+### Alternative Installation (for Instructors)
 
 Python can also be installed in a "pure" way as obtained from its core
 development team (i.e., without any third-party packages installed).
-However, this is somewhat too "advanced" for a beginner as it involves working
-with a terminal emulator, which looks similar to the picture below and is
-used without a mouse by typing commands into it.
+However, this may be too "advanced" for a beginner as it involves working
+with a [terminal emulator](https://en.wikipedia.org/wiki/Terminal_emulator),
+which looks like the one in the picture below and is used *without* a mouse by
+typing commands into it.
 
 <img src="static/terminal.png" width="50%" align="center">
 
 Assuming that you already have a working version of Python 3.7 or higher
 installed (cf., the official [download page](https://www.python.org/downloads/)),
 the following summarizes the commands to be typed into a terminal emulator to
-get the course materials up and running on a local machine without Anaconda.
+get the course materials up and running on a local machine without the
+Anaconda Distribution.
 You are then responsible for understanding the concepts behind them.
 
 First, the [git](https://git-scm.com/) command line tool is a more professional
@@ -156,17 +175,23 @@ are popular tools to automate the described management of virtual environments.
 
 After activation for the first time, you must install the project's
 **dependencies** (= the third-party packages needed to run the code), most
-notably [Jupyter](https://pypi.org/project/jupyter/) in this project (the
-"python -m" is often left out [but should not be](https://snarky.ca/why-you-should-use-python-m-pip/);
+notably [JupyterLab](https://pypi.org/project/jupyterlab/) in this project
+(the "python -m" is often left out [but should not be](https://snarky.ca/why-you-should-use-python-m-pip/);
 if you have poetry installed, you may just type `poetry install` instead).
 
 - `python -m pip install -r requirements.txt`
 
+The *requirements.txt* file also installs the [black](https://github.com/psf/black)
+tool (incl. the [blackcellmagic](https://github.com/csurfer/blackcellmagic)
+extension) and the [RISE](https://github.com/damianavila/RISE) extension.
+With them, the instructor can easily re-format code in a class session and
+execute code in presentation mode (currently RISE only works with the
+older `jupyter notebook` command).
 
 With everything installed, you can now do the equivalent of clicking the
-"Jupyter Notebook" entry in your start menu.
+"JupyterLab" entry in the Anaconda Navigator.
 
-- `jupyter notebook`
+- `jupyter lab`
 
 This opens a new tab in your web browser just as above.
 
diff --git a/static/anaconda.png b/static/anaconda.png
deleted file mode 100644
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HcmV?d00001


From b27409efcc744cd1b3cf63d79180d246bd70f657 Mon Sep 17 00:00:00 2001
From: Alexander Hess <alexander@webartifex.biz>
Date: Sun, 2 Feb 2020 13:46:48 +0100
Subject: [PATCH 057/242] Update dependencies

- add pandas 1.0 (for future chapter 09)
- add jupyter nbextensions (so that RISE can be configured)
---
 poetry.lock      | 239 ++++++++++++++++++++++++++++++++++++++++++++---
 pyproject.toml   |   2 +
 requirements.txt |  17 +++-
 3 files changed, 240 insertions(+), 18 deletions(-)

diff --git a/poetry.lock b/poetry.lock
index 07e1126..4fd7c13 100644
--- a/poetry.lock
+++ b/poetry.lock
@@ -129,7 +129,7 @@ marker = "python_version < \"3.8\""
 name = "importlib-metadata"
 optional = false
 python-versions = "!=3.0.*,!=3.1.*,!=3.2.*,!=3.3.*,!=3.4.*,>=2.7"
-version = "1.4.0"
+version = "1.5.0"
 
 [package.dependencies]
 zipp = ">=0.5"
@@ -162,7 +162,7 @@ description = "IPython: Productive Interactive Computing"
 name = "ipython"
 optional = false
 python-versions = ">=3.6"
-version = "7.11.1"
+version = "7.12.0"
 
 [package.dependencies]
 appnope = "*"
@@ -178,7 +178,7 @@ setuptools = ">=18.5"
 traitlets = ">=4.2"
 
 [package.extras]
-all = ["ipywidgets", "ipyparallel", "qtconsole", "ipykernel", "nbconvert", "notebook", "nbformat", "testpath", "pygments", "requests", "numpy (>=1.14)", "nose (>=0.10.1)", "Sphinx (>=1.3)"]
+all = ["ipyparallel", "requests", "notebook", "qtconsole", "ipywidgets", "pygments", "nbconvert", "testpath", "Sphinx (>=1.3)", "nbformat", "numpy (>=1.14)", "ipykernel", "nose (>=0.10.1)"]
 doc = ["Sphinx (>=1.3)"]
 kernel = ["ipykernel"]
 nbconvert = ["nbconvert"]
@@ -217,7 +217,7 @@ description = "A very fast and expressive template engine."
 name = "jinja2"
 optional = false
 python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*, !=3.4.*"
-version = "2.11.0"
+version = "2.11.1"
 
 [package.dependencies]
 MarkupSafe = ">=0.23"
@@ -231,7 +231,7 @@ description = "A Python implementation of the JSON5 data format."
 name = "json5"
 optional = false
 python-versions = "*"
-version = "0.8.5"
+version = "0.9.0"
 
 [[package]]
 category = "main"
@@ -274,6 +274,49 @@ traitlets = "*"
 [package.extras]
 test = ["ipykernel", "ipython", "mock", "pytest"]
 
+[[package]]
+category = "dev"
+description = "Common utilities for jupyter-contrib projects."
+name = "jupyter-contrib-core"
+optional = false
+python-versions = "*"
+version = "0.3.3"
+
+[package.dependencies]
+jupyter-core = "*"
+notebook = ">=4.0"
+setuptools = "*"
+tornado = "*"
+traitlets = "*"
+
+[package.extras]
+testing_utils = ["nose", "mock"]
+
+[[package]]
+category = "dev"
+description = "A collection of Jupyter nbextensions."
+name = "jupyter-contrib-nbextensions"
+optional = false
+python-versions = "*"
+version = "0.5.1"
+
+[package.dependencies]
+ipython-genutils = "*"
+jupyter-contrib-core = ">=0.3.3"
+jupyter-core = "*"
+jupyter-highlight-selected-word = ">=0.1.1"
+jupyter-latex-envs = ">=1.3.8"
+jupyter-nbextensions-configurator = ">=0.4.0"
+lxml = "*"
+nbconvert = ">=4.2"
+notebook = ">=4.0"
+pyyaml = "*"
+tornado = "*"
+traitlets = ">=4.1"
+
+[package.extras]
+test = ["nbformat", "nose", "pip", "requests", "mock"]
+
 [[package]]
 category = "main"
 description = "Jupyter core package. A base package on which Jupyter projects rely."
@@ -286,6 +329,48 @@ version = "4.6.1"
 pywin32 = ">=1.0"
 traitlets = "*"
 
+[[package]]
+category = "dev"
+description = "Jupyter notebook extension that enables highlighting every instance of the current word in the notebook."
+name = "jupyter-highlight-selected-word"
+optional = false
+python-versions = "*"
+version = "0.2.0"
+
+[[package]]
+category = "dev"
+description = "Jupyter notebook extension which supports (some) LaTeX environments within markdown cells. Also provides support for labels and crossreferences, document wide numbering, bibliography, and more..."
+name = "jupyter-latex-envs"
+optional = false
+python-versions = "*"
+version = "1.4.6"
+
+[package.dependencies]
+ipython = "*"
+jupyter_core = "*"
+nbconvert = "*"
+notebook = ">=4.0"
+traitlets = ">=4.1"
+
+[[package]]
+category = "dev"
+description = "jupyter serverextension providing configuration interfaces for nbextensions."
+name = "jupyter-nbextensions-configurator"
+optional = false
+python-versions = "*"
+version = "0.4.1"
+
+[package.dependencies]
+jupyter_contrib_core = ">=0.3.3"
+jupyter_core = "*"
+notebook = ">=4.0"
+pyyaml = "*"
+tornado = "*"
+traitlets = "*"
+
+[package.extras]
+test = ["jupyter-contrib-core", "nose", "requests", "selenium", "mock"]
+
 [[package]]
 category = "main"
 description = "The JupyterLab notebook server extension."
@@ -321,6 +406,20 @@ notebook = ">=4.2.0"
 [package.extras]
 test = ["pytest", "requests"]
 
+[[package]]
+category = "dev"
+description = "Powerful and Pythonic XML processing library combining libxml2/libxslt with the ElementTree API."
+name = "lxml"
+optional = false
+python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*, != 3.4.*"
+version = "4.5.0"
+
+[package.extras]
+cssselect = ["cssselect (>=0.7)"]
+html5 = ["html5lib"]
+htmlsoup = ["beautifulsoup4"]
+source = ["Cython (>=0.29.7)"]
+
 [[package]]
 category = "main"
 description = "Safely add untrusted strings to HTML/XML markup."
@@ -416,6 +515,22 @@ optional = false
 python-versions = ">=3.5"
 version = "1.18.1"
 
+[[package]]
+category = "main"
+description = "Powerful data structures for data analysis, time series, and statistics"
+name = "pandas"
+optional = false
+python-versions = ">=3.6.1"
+version = "1.0.0"
+
+[package.dependencies]
+numpy = ">=1.13.3"
+python-dateutil = ">=2.6.1"
+pytz = ">=2017.2"
+
+[package.extras]
+test = ["pytest (>=4.0.2)", "pytest-xdist", "hypothesis (>=3.58)"]
+
 [[package]]
 category = "main"
 description = "Utilities for writing pandoc filters in python"
@@ -524,6 +639,14 @@ version = "2.8.1"
 [package.dependencies]
 six = ">=1.5"
 
+[[package]]
+category = "main"
+description = "World timezone definitions, modern and historical"
+name = "pytz"
+optional = false
+python-versions = "*"
+version = "2019.3"
+
 [[package]]
 category = "main"
 description = "Python for Window Extensions"
@@ -542,6 +665,14 @@ optional = false
 python-versions = "*"
 version = "0.5.7"
 
+[[package]]
+category = "dev"
+description = "YAML parser and emitter for Python"
+name = "pyyaml"
+optional = false
+python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*, !=3.4.*"
+version = "5.3"
+
 [[package]]
 category = "main"
 description = "Python bindings for 0MQ"
@@ -679,7 +810,7 @@ docs = ["sphinx", "jaraco.packaging (>=3.2)", "rst.linker (>=1.9)"]
 testing = ["jaraco.itertools"]
 
 [metadata]
-content-hash = "c7cc19bb83c0f0869c6fbe7c1a65a57cf18f5d46d115e007080e497af615c1a3"
+content-hash = "57a0723ba6d72fe21cc17ebfd12347a16a17d9cfefb90f7b3f5eb76da5d07d97"
 python-versions = "^3.7"
 
 [metadata.files]
@@ -731,16 +862,16 @@ entrypoints = [
     {file = "entrypoints-0.3.tar.gz", hash = "sha256:c70dd71abe5a8c85e55e12c19bd91ccfeec11a6e99044204511f9ed547d48451"},
 ]
 importlib-metadata = [
-    {file = "importlib_metadata-1.4.0-py2.py3-none-any.whl", hash = "sha256:bdd9b7c397c273bcc9a11d6629a38487cd07154fa255a467bf704cd2c258e359"},
-    {file = "importlib_metadata-1.4.0.tar.gz", hash = "sha256:f17c015735e1a88296994c0697ecea7e11db24290941983b08c9feb30921e6d8"},
+    {file = "importlib_metadata-1.5.0-py2.py3-none-any.whl", hash = "sha256:b97607a1a18a5100839aec1dc26a1ea17ee0d93b20b0f008d80a5a050afb200b"},
+    {file = "importlib_metadata-1.5.0.tar.gz", hash = "sha256:06f5b3a99029c7134207dd882428a66992a9de2bef7c2b699b5641f9886c3302"},
 ]
 ipykernel = [
     {file = "ipykernel-5.1.4-py3-none-any.whl", hash = "sha256:ba8c9e5561f3223fb47ce06ad7925cb9444337ac367341c0c520ffb68ea6d120"},
     {file = "ipykernel-5.1.4.tar.gz", hash = "sha256:7f1f01df22f1229c8879501057877ccaf92a3b01c1d00db708aad5003e5f9238"},
 ]
 ipython = [
-    {file = "ipython-7.11.1-py3-none-any.whl", hash = "sha256:387686dd7fc9caf29d2fddcf3116c4b07a11d9025701d220c589a430b0171d8a"},
-    {file = "ipython-7.11.1.tar.gz", hash = "sha256:0f4bcf18293fb666df8511feec0403bdb7e061a5842ea6e88a3177b0ceb34ead"},
+    {file = "ipython-7.12.0-py3-none-any.whl", hash = "sha256:f6689108b1734501d3b59c84427259fd5ac5141afe2e846cfa8598eb811886c9"},
+    {file = "ipython-7.12.0.tar.gz", hash = "sha256:d9459e7237e2e5858738ff9c3e26504b79899b58a6d49e574d352493d80684c6"},
 ]
 ipython-genutils = [
     {file = "ipython_genutils-0.2.0-py2.py3-none-any.whl", hash = "sha256:72dd37233799e619666c9f639a9da83c34013a73e8bbc79a7a6348d93c61fab8"},
@@ -751,12 +882,12 @@ jedi = [
     {file = "jedi-0.16.0.tar.gz", hash = "sha256:d5c871cb9360b414f981e7072c52c33258d598305280fef91c6cae34739d65d5"},
 ]
 jinja2 = [
-    {file = "Jinja2-2.11.0-py2.py3-none-any.whl", hash = "sha256:6e7a3c2934694d59ad334c93dd1b6c96699cf24c53fdb8ec848ac6b23e685734"},
-    {file = "Jinja2-2.11.0.tar.gz", hash = "sha256:d6609ae5ec3d56212ca7d802eda654eaf2310000816ce815361041465b108be4"},
+    {file = "Jinja2-2.11.1-py2.py3-none-any.whl", hash = "sha256:b0eaf100007721b5c16c1fc1eecb87409464edc10469ddc9a22a27a99123be49"},
+    {file = "Jinja2-2.11.1.tar.gz", hash = "sha256:93187ffbc7808079673ef52771baa950426fd664d3aad1d0fa3e95644360e250"},
 ]
 json5 = [
-    {file = "json5-0.8.5-py2.py3-none-any.whl", hash = "sha256:32bd17e0553bf53927f6c29b6089f3a320c12897120a4bcfea76ea81c10b2d9c"},
-    {file = "json5-0.8.5.tar.gz", hash = "sha256:124b0f0da1ed2ff3bfe3a3e9b8630abd3c650852465cb52c15ef60b8e82a73b0"},
+    {file = "json5-0.9.0-py2.py3-none-any.whl", hash = "sha256:f2dd81b3de344143144532804171ff8e7da93cd57af6b56d4d4afa859f8c77ff"},
+    {file = "json5-0.9.0.tar.gz", hash = "sha256:62a7a36f5f9806108d39bfc59d4458e2a083762a2fc15fd3c7828c72502fd702"},
 ]
 jsonschema = [
     {file = "jsonschema-3.2.0-py2.py3-none-any.whl", hash = "sha256:4e5b3cf8216f577bee9ce139cbe72eca3ea4f292ec60928ff24758ce626cd163"},
@@ -766,10 +897,28 @@ jupyter-client = [
     {file = "jupyter_client-5.3.4-py2.py3-none-any.whl", hash = "sha256:d0c077c9aaa4432ad485e7733e4d91e48f87b4f4bab7d283d42bb24cbbba0a0f"},
     {file = "jupyter_client-5.3.4.tar.gz", hash = "sha256:60e6faec1031d63df57f1cc671ed673dced0ed420f4377ea33db37b1c188b910"},
 ]
+jupyter-contrib-core = [
+    {file = "jupyter_contrib_core-0.3.3-py2.py3-none-any.whl", hash = "sha256:1ec81e275a8f5858d56b0c4c6cd85335aa8e915001b8657fe51c620c3cdde50f"},
+    {file = "jupyter_contrib_core-0.3.3.tar.gz", hash = "sha256:e65bc0e932ff31801003cef160a4665f2812efe26a53801925a634735e9a5794"},
+]
+jupyter-contrib-nbextensions = [
+    {file = "jupyter_contrib_nbextensions-0.5.1-py2.py3-none-any.whl", hash = "sha256:2c071f0aa208c569666f656bdc0f66906ca493cf9f06f46db6350db11030ff40"},
+    {file = "jupyter_contrib_nbextensions-0.5.1.tar.gz", hash = "sha256:eecd28ecc2fc410226c0a3d4932ed2fac4860ccf8d9e9b1b29548835a35b22ab"},
+]
 jupyter-core = [
     {file = "jupyter_core-4.6.1-py2.py3-none-any.whl", hash = "sha256:464769f7387d7a62a2403d067f1ddc616655b7f77f5d810c0dd62cb54bfd0fb9"},
     {file = "jupyter_core-4.6.1.tar.gz", hash = "sha256:a183e0ec2e8f6adddf62b0a3fc6a2237e3e0056d381e536d3e7c7ecc3067e244"},
 ]
+jupyter-highlight-selected-word = [
+    {file = "jupyter_highlight_selected_word-0.2.0-py2.py3-none-any.whl", hash = "sha256:9545dfa9cb057eebe3a5795604dcd3a5294ea18637e553f61a0b67c1b5903c58"},
+    {file = "jupyter_highlight_selected_word-0.2.0.tar.gz", hash = "sha256:9fa740424859a807950ca08d2bfd28a35154cd32dd6d50ac4e0950022adc0e7b"},
+]
+jupyter-latex-envs = [
+    {file = "jupyter_latex_envs-1.4.6.tar.gz", hash = "sha256:070a31eb2dc488bba983915879a7c2939247bf5c3b669b398bdb36a9b5343872"},
+]
+jupyter-nbextensions-configurator = [
+    {file = "jupyter_nbextensions_configurator-0.4.1.tar.gz", hash = "sha256:e5e86b5d9d898e1ffb30ebb08e4ad8696999f798fef3ff3262d7b999076e4e83"},
+]
 jupyterlab = [
     {file = "jupyterlab-1.2.6-py2.py3-none-any.whl", hash = "sha256:56c108e28934ac463754b7656441c0d92e76a81ad5dad446fe1071c6fd86245c"},
     {file = "jupyterlab-1.2.6.tar.gz", hash = "sha256:42134b13fb0c410a9f55e8492a31ba5a1a346430a22690a512b8307764b68355"},
@@ -778,6 +927,35 @@ jupyterlab-server = [
     {file = "jupyterlab_server-1.0.6-py3-none-any.whl", hash = "sha256:d9c3bcf097f7ad8d8fd2f8d0c1e8a1b833671c02808e5f807088975495364447"},
     {file = "jupyterlab_server-1.0.6.tar.gz", hash = "sha256:d0977527bfce6f47c782cb6bf79d2c949ebe3f22ac695fa000b730c671445dad"},
 ]
+lxml = [
+    {file = "lxml-4.5.0-cp27-cp27m-macosx_10_9_x86_64.whl", hash = "sha256:0701f7965903a1c3f6f09328c1278ac0eee8f56f244e66af79cb224b7ef3801c"},
+    {file = "lxml-4.5.0-cp27-cp27m-manylinux1_i686.whl", hash = "sha256:06d4e0bbb1d62e38ae6118406d7cdb4693a3fa34ee3762238bcb96c9e36a93cd"},
+    {file = "lxml-4.5.0-cp27-cp27m-manylinux1_x86_64.whl", hash = "sha256:5828c7f3e615f3975d48f40d4fe66e8a7b25f16b5e5705ffe1d22e43fb1f6261"},
+    {file = "lxml-4.5.0-cp27-cp27m-win32.whl", hash = "sha256:afdb34b715daf814d1abea0317b6d672476b498472f1e5aacbadc34ebbc26e89"},
+    {file = "lxml-4.5.0-cp27-cp27m-win_amd64.whl", hash = "sha256:585c0869f75577ac7a8ff38d08f7aac9033da2c41c11352ebf86a04652758b7a"},
+    {file = "lxml-4.5.0-cp27-cp27mu-manylinux1_i686.whl", hash = "sha256:8a0ebda56ebca1a83eb2d1ac266649b80af8dd4b4a3502b2c1e09ac2f88fe128"},
+    {file = "lxml-4.5.0-cp27-cp27mu-manylinux1_x86_64.whl", hash = "sha256:fe976a0f1ef09b3638778024ab9fb8cde3118f203364212c198f71341c0715ca"},
+    {file = "lxml-4.5.0-cp35-cp35m-manylinux1_i686.whl", hash = "sha256:7bc1b221e7867f2e7ff1933165c0cec7153dce93d0cdba6554b42a8beb687bdb"},
+    {file = "lxml-4.5.0-cp35-cp35m-manylinux1_x86_64.whl", hash = "sha256:d068f55bda3c2c3fcaec24bd083d9e2eede32c583faf084d6e4b9daaea77dde8"},
+    {file = "lxml-4.5.0-cp35-cp35m-win32.whl", hash = "sha256:e4aa948eb15018a657702fee0b9db47e908491c64d36b4a90f59a64741516e77"},
+    {file = "lxml-4.5.0-cp35-cp35m-win_amd64.whl", hash = "sha256:1f2c4ec372bf1c4a2c7e4bb20845e8bcf8050365189d86806bad1e3ae473d081"},
+    {file = "lxml-4.5.0-cp36-cp36m-macosx_10_9_x86_64.whl", hash = "sha256:5d467ce9c5d35b3bcc7172c06320dddb275fea6ac2037f72f0a4d7472035cea9"},
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+    {file = "lxml-4.5.0-cp37-cp37m-manylinux1_i686.whl", hash = "sha256:edc15fcfd77395e24543be48871c251f38132bb834d9fdfdad756adb6ea37679"},
+    {file = "lxml-4.5.0-cp37-cp37m-manylinux1_x86_64.whl", hash = "sha256:fd52e796fee7171c4361d441796b64df1acfceb51f29e545e812f16d023c4bbc"},
+    {file = "lxml-4.5.0-cp37-cp37m-win32.whl", hash = "sha256:90ed0e36455a81b25b7034038e40880189169c308a3df360861ad74da7b68c1a"},
+    {file = "lxml-4.5.0-cp37-cp37m-win_amd64.whl", hash = "sha256:df533af6f88080419c5a604d0d63b2c33b1c0c4409aba7d0cb6de305147ea8c8"},
+    {file = "lxml-4.5.0-cp38-cp38-macosx_10_9_x86_64.whl", hash = "sha256:b4b2c63cc7963aedd08a5f5a454c9f67251b1ac9e22fd9d72836206c42dc2a72"},
+    {file = "lxml-4.5.0-cp38-cp38-manylinux1_i686.whl", hash = "sha256:e5d842c73e4ef6ed8c1bd77806bf84a7cb535f9c0cf9b2c74d02ebda310070e1"},
+    {file = "lxml-4.5.0-cp38-cp38-manylinux1_x86_64.whl", hash = "sha256:63dbc21efd7e822c11d5ddbedbbb08cd11a41e0032e382a0fd59b0b08e405a3a"},
+    {file = "lxml-4.5.0-cp38-cp38-win32.whl", hash = "sha256:4235bc124fdcf611d02047d7034164897ade13046bda967768836629bc62784f"},
+    {file = "lxml-4.5.0-cp38-cp38-win_amd64.whl", hash = "sha256:d5b3c4b7edd2e770375a01139be11307f04341ec709cf724e0f26ebb1eef12c3"},
+    {file = "lxml-4.5.0.tar.gz", hash = "sha256:8620ce80f50d023d414183bf90cc2576c2837b88e00bea3f33ad2630133bbb60"},
+]
 markupsafe = [
     {file = "MarkupSafe-1.1.1-cp27-cp27m-macosx_10_6_intel.whl", hash = "sha256:09027a7803a62ca78792ad89403b1b7a73a01c8cb65909cd876f7fcebd79b161"},
     {file = "MarkupSafe-1.1.1-cp27-cp27m-manylinux1_i686.whl", hash = "sha256:e249096428b3ae81b08327a63a485ad0878de3fb939049038579ac0ef61e17e7"},
@@ -847,6 +1025,22 @@ numpy = [
     {file = "numpy-1.18.1-cp38-cp38-win_amd64.whl", hash = "sha256:39d2c685af15d3ce682c99ce5925cc66efc824652e10990d2462dfe9b8918c6a"},
     {file = "numpy-1.18.1.zip", hash = "sha256:b6ff59cee96b454516e47e7721098e6ceebef435e3e21ac2d6c3b8b02628eb77"},
 ]
+pandas = [
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+    {file = "pandas-1.0.0-cp36-cp36m-manylinux1_i686.whl", hash = "sha256:e8be4f6da608930c0d565240bfbe04fc6f5764d6a9214b02c6231cd5e223591d"},
+    {file = "pandas-1.0.0-cp36-cp36m-manylinux1_x86_64.whl", hash = "sha256:d76a8ec22adf0323d362dac8c900b2c66e06eab984ecf04ef072866d8ab6c538"},
+    {file = "pandas-1.0.0-cp36-cp36m-win32.whl", hash = "sha256:bad77cf498362590ef3a30bc9e769f4fe4399d853861a1ddbefeea8cbf39906c"},
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+    {file = "pandas-1.0.0-cp37-cp37m-macosx_10_9_x86_64.whl", hash = "sha256:35d07389efaf3c478d93725a226941c7fc14714814ba77d6d43b2c9e63ef4af5"},
+    {file = "pandas-1.0.0-cp37-cp37m-manylinux1_x86_64.whl", hash = "sha256:46b0a146e4ba744e350847244767ef297950e9ce02424734b2dd0befd77d9aff"},
+    {file = "pandas-1.0.0-cp37-cp37m-win32.whl", hash = "sha256:6d5c2d2a3e42100700bac7fe762c17ba0a04d0355feac04bce74a1aa6c8be164"},
+    {file = "pandas-1.0.0-cp37-cp37m-win_amd64.whl", hash = "sha256:f66c63f357ac31c913f4917f55348ce99c639031567c3284f01dff605da58264"},
+    {file = "pandas-1.0.0-cp38-cp38-macosx_10_9_x86_64.whl", hash = "sha256:c36e4d44d34eaa503776a8fb57ba1305e680e178458c050c2fd8de67604fa209"},
+    {file = "pandas-1.0.0-cp38-cp38-manylinux1_x86_64.whl", hash = "sha256:66c1a49b47c0953dbc6864a6d2578c4c24610f6bb8e4ab165d49b8371aa7745f"},
+    {file = "pandas-1.0.0-cp38-cp38-win32.whl", hash = "sha256:18bbce2e69855d42397486ee0bb79cb0e4c94af6679fd9392e32ffdb7fcfade0"},
+    {file = "pandas-1.0.0-cp38-cp38-win_amd64.whl", hash = "sha256:ab1aa2c50b7c6ba0eccebb146b4d80ed7f5804897b8d54ccddbe49f28c881a94"},
+    {file = "pandas-1.0.0.tar.gz", hash = "sha256:3ea6cc86931f57f18b1240572216f09922d91b19ab8a01cf24734394a3db3bec"},
+]
 pandocfilters = [
     {file = "pandocfilters-1.4.2.tar.gz", hash = "sha256:b3dd70e169bb5449e6bc6ff96aea89c5eea8c5f6ab5e207fc2f521a2cf4a0da9"},
 ]
@@ -888,6 +1082,10 @@ python-dateutil = [
     {file = "python-dateutil-2.8.1.tar.gz", hash = "sha256:73ebfe9dbf22e832286dafa60473e4cd239f8592f699aa5adaf10050e6e1823c"},
     {file = "python_dateutil-2.8.1-py2.py3-none-any.whl", hash = "sha256:75bb3f31ea686f1197762692a9ee6a7550b59fc6ca3a1f4b5d7e32fb98e2da2a"},
 ]
+pytz = [
+    {file = "pytz-2019.3-py2.py3-none-any.whl", hash = "sha256:1c557d7d0e871de1f5ccd5833f60fb2550652da6be2693c1e02300743d21500d"},
+    {file = "pytz-2019.3.tar.gz", hash = "sha256:b02c06db6cf09c12dd25137e563b31700d3b80fcc4ad23abb7a315f2789819be"},
+]
 pywin32 = [
     {file = "pywin32-227-cp27-cp27m-win32.whl", hash = "sha256:371fcc39416d736401f0274dd64c2302728c9e034808e37381b5e1b22be4a6b0"},
     {file = "pywin32-227-cp27-cp27m-win_amd64.whl", hash = "sha256:4cdad3e84191194ea6d0dd1b1b9bdda574ff563177d2adf2b4efec2a244fa116"},
@@ -914,6 +1112,19 @@ pywinpty = [
     {file = "pywinpty-0.5.7-cp38-cp38-win_amd64.whl", hash = "sha256:8fc5019ff3efb4f13708bd3b5ad327589c1a554cb516d792527361525a7cb78c"},
     {file = "pywinpty-0.5.7.tar.gz", hash = "sha256:2d7e9c881638a72ffdca3f5417dd1563b60f603e1b43e5895674c2a1b01f95a0"},
 ]
+pyyaml = [
+    {file = "PyYAML-5.3-cp27-cp27m-win32.whl", hash = "sha256:940532b111b1952befd7db542c370887a8611660d2b9becff75d39355303d82d"},
+    {file = "PyYAML-5.3-cp27-cp27m-win_amd64.whl", hash = "sha256:059b2ee3194d718896c0ad077dd8c043e5e909d9180f387ce42012662a4946d6"},
+    {file = "PyYAML-5.3-cp35-cp35m-win32.whl", hash = "sha256:4fee71aa5bc6ed9d5f116327c04273e25ae31a3020386916905767ec4fc5317e"},
+    {file = "PyYAML-5.3-cp35-cp35m-win_amd64.whl", hash = "sha256:dbbb2379c19ed6042e8f11f2a2c66d39cceb8aeace421bfc29d085d93eda3689"},
+    {file = "PyYAML-5.3-cp36-cp36m-win32.whl", hash = "sha256:e3a057b7a64f1222b56e47bcff5e4b94c4f61faac04c7c4ecb1985e18caa3994"},
+    {file = "PyYAML-5.3-cp36-cp36m-win_amd64.whl", hash = "sha256:74782fbd4d4f87ff04159e986886931456a1894c61229be9eaf4de6f6e44b99e"},
+    {file = "PyYAML-5.3-cp37-cp37m-win32.whl", hash = "sha256:24521fa2890642614558b492b473bee0ac1f8057a7263156b02e8b14c88ce6f5"},
+    {file = "PyYAML-5.3-cp37-cp37m-win_amd64.whl", hash = "sha256:1cf708e2ac57f3aabc87405f04b86354f66799c8e62c28c5fc5f88b5521b2dbf"},
+    {file = "PyYAML-5.3-cp38-cp38-win32.whl", hash = "sha256:70024e02197337533eef7b85b068212420f950319cc8c580261963aefc75f811"},
+    {file = "PyYAML-5.3-cp38-cp38-win_amd64.whl", hash = "sha256:cb1f2f5e426dc9f07a7681419fe39cee823bb74f723f36f70399123f439e9b20"},
+    {file = "PyYAML-5.3.tar.gz", hash = "sha256:e9f45bd5b92c7974e59bcd2dcc8631a6b6cc380a904725fce7bc08872e691615"},
+]
 pyzmq = [
     {file = "pyzmq-18.1.1-cp27-cp27m-macosx_10_6_intel.whl", hash = "sha256:0573b9790aa26faff33fba40f25763657271d26f64bffb55a957a3d4165d6098"},
     {file = "pyzmq-18.1.1-cp27-cp27m-win32.whl", hash = "sha256:972d723a36ab6a60b7806faa5c18aa3c080b7d046c407e816a1d8673989e2485"},
diff --git a/pyproject.toml b/pyproject.toml
index c30f700..106e21f 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -9,10 +9,12 @@ license = "MIT"
 python = "^3.7"
 jupyterlab = "^1.2.6"
 numpy = "^1.18.1"
+pandas = "^1.0.0"
 
 [tool.poetry.dev-dependencies]
 black = "^19.10b0"
 blackcellmagic = "^0.0.2"
+jupyter_contrib_nbextensions = "^0.5.1"
 RISE = "^5.6.0"
 
 [build-system]
diff --git a/requirements.txt b/requirements.txt
index f9ab033..fe89b09 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -8,24 +8,31 @@ Click==7.0
 decorator==4.4.1
 defusedxml==0.6.0
 entrypoints==0.3
-importlib-metadata==1.4.0
+importlib-metadata==1.5.0
 ipykernel==5.1.4
-ipython==7.11.1
+ipython==7.12.0
 ipython-genutils==0.2.0
 jedi==0.16.0
-Jinja2==2.11.0
-json5==0.8.5
+Jinja2==2.11.1
+json5==0.9.0
 jsonschema==3.2.0
 jupyter-client==5.3.4
+jupyter-contrib-core==0.3.3
+jupyter-contrib-nbextensions==0.5.1
 jupyter-core==4.6.1
+jupyter-highlight-selected-word==0.2.0
+jupyter-latex-envs==1.4.6
+jupyter-nbextensions-configurator==0.4.1
 jupyterlab==1.2.6
 jupyterlab-server==1.0.6
+lxml==4.5.0
 MarkupSafe==1.1.1
 mistune==0.8.4
 nbconvert==5.6.1
 nbformat==5.0.4
 notebook==6.0.3
 numpy==1.18.1
+pandas==1.0.0
 pandocfilters==1.4.2
 parso==0.6.0
 pathspec==0.7.0
@@ -37,6 +44,8 @@ ptyprocess==0.6.0
 Pygments==2.5.2
 pyrsistent==0.15.7
 python-dateutil==2.8.1
+pytz==2019.3
+PyYAML==5.3
 pyzmq==18.1.1
 regex==2020.1.8
 rise==5.6.0

From 86e59b847afa92230f5be8fdf972a01dec891610 Mon Sep 17 00:00:00 2001
From: Alexander Hess <alexander@webartifex.biz>
Date: Sun, 2 Feb 2020 16:31:31 +0100
Subject: [PATCH 058/242] Restructure *.ipynb files

- rename *_content.ipynb into *_lecture.ipynb
- adjust links in README.md
---
 ...content.ipynb => 00_intro_00_lecture.ipynb | 18 +++----
 ...0_review.ipynb => 00_intro_01_review.ipynb |  2 +-
 ...cises.ipynb => 00_intro_02_exercises.ipynb |  2 +-
 ...tent.ipynb => 01_elements_00_lecture.ipynb | 12 ++---
 ...eview.ipynb => 01_elements_01_review.ipynb |  2 +-
 ...es.ipynb => 01_elements_02_exercises.ipynb |  2 +-
 ...ent.ipynb => 02_functions_00_lecture.ipynb | 18 +++----
 ...view.ipynb => 02_functions_01_review.ipynb |  2 +-
 ...s.ipynb => 02_functions_02_exercises.ipynb |  2 +-
 ....ipynb => 03_conditionals_00_lecture.ipynb |  8 +--
 ...w.ipynb => 03_conditionals_01_review.ipynb |  2 +-
 ...pynb => 03_conditionals_02_exercises.ipynb |  4 +-
 ...ent.ipynb => 04_iteration_00_lecture.ipynb | 14 ++---
 ...view.ipynb => 04_iteration_01_review.ipynb |  2 +-
 ...s.ipynb => 04_iteration_02_exercises.ipynb |  4 +-
 ...ntent.ipynb => 05_numbers_00_lecture.ipynb | 16 +++---
 ...review.ipynb => 05_numbers_01_review.ipynb |  2 +-
 ...ses.ipynb => 05_numbers_02_exercises.ipynb |  8 +--
 ..._content.ipynb => 06_text_00_lecture.ipynb | 24 ++++-----
 ...10_review.ipynb => 06_text_01_review.ipynb |  2 +-
 ...ent.ipynb => 07_sequences_00_lecture.ipynb | 36 ++++++-------
 ...view.ipynb => 07_sequences_01_review.ipynb |  4 +-
 ...s.ipynb => 07_sequences_02_exercises.ipynb | 16 +++---
 ...tent.ipynb => 08_mappings_00_lecture.ipynb | 18 +++----
 ...eview.ipynb => 08_mappings_01_review.ipynb |  4 +-
 ...es.ipynb => 08_mappings_02_exercises.ipynb |  2 +-
 README.md                                     | 54 +++++++++----------
 27 files changed, 140 insertions(+), 140 deletions(-)
 rename 00_start_up_00_content.ipynb => 00_intro_00_lecture.ipynb (98%)
 rename 00_start_up_10_review.ipynb => 00_intro_01_review.ipynb (97%)
 rename 00_start_up_20_exercises.ipynb => 00_intro_02_exercises.ipynb (95%)
 rename 01_elements_00_content.ipynb => 01_elements_00_lecture.ipynb (99%)
 rename 01_elements_10_review.ipynb => 01_elements_01_review.ipynb (98%)
 rename 01_elements_20_exercises.ipynb => 01_elements_02_exercises.ipynb (97%)
 rename 02_functions_00_content.ipynb => 02_functions_00_lecture.ipynb (99%)
 rename 02_functions_10_review.ipynb => 02_functions_01_review.ipynb (98%)
 rename 02_functions_20_exercises.ipynb => 02_functions_02_exercises.ipynb (98%)
 rename 03_conditionals_00_content.ipynb => 03_conditionals_00_lecture.ipynb (99%)
 rename 03_conditionals_10_review.ipynb => 03_conditionals_01_review.ipynb (98%)
 rename 03_conditionals_20_exercises.ipynb => 03_conditionals_02_exercises.ipynb (98%)
 rename 04_iteration_00_content.ipynb => 04_iteration_00_lecture.ipynb (99%)
 rename 04_iteration_10_review.ipynb => 04_iteration_01_review.ipynb (98%)
 rename 04_iteration_20_exercises.ipynb => 04_iteration_02_exercises.ipynb (99%)
 rename 05_numbers_00_content.ipynb => 05_numbers_00_lecture.ipynb (99%)
 rename 05_numbers_10_review.ipynb => 05_numbers_01_review.ipynb (99%)
 rename 05_numbers_20_exercises.ipynb => 05_numbers_02_exercises.ipynb (97%)
 rename 06_text_00_content.ipynb => 06_text_00_lecture.ipynb (98%)
 rename 06_text_10_review.ipynb => 06_text_01_review.ipynb (98%)
 rename 07_sequences_00_content.ipynb => 07_sequences_00_lecture.ipynb (99%)
 rename 07_sequences_10_review.ipynb => 07_sequences_01_review.ipynb (98%)
 rename 07_sequences_20_exercises.ipynb => 07_sequences_02_exercises.ipynb (98%)
 rename 08_mappings_00_content.ipynb => 08_mappings_00_lecture.ipynb (99%)
 rename 08_mappings_10_review.ipynb => 08_mappings_01_review.ipynb (96%)
 rename 08_mappings_20_exercises.ipynb => 08_mappings_02_exercises.ipynb (99%)

diff --git a/00_start_up_00_content.ipynb b/00_intro_00_lecture.ipynb
similarity index 98%
rename from 00_start_up_00_content.ipynb
rename to 00_intro_00_lecture.ipynb
index 3ca98d4..b812c61 100644
--- a/00_start_up_00_content.ipynb
+++ b/00_intro_00_lecture.ipynb
@@ -774,11 +774,11 @@
     "**Part A: Expressing Logic**\n",
     "\n",
     "- What is a programming language? What kind of words exist?\n",
-    "  - *Chapter 1*: [Elements of a Program](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/01_elements_00_content.ipynb)\n",
-    "  - *Chapter 2*: [Functions & Modularization](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/02_functions_00_content.ipynb)\n",
+    "  - *Chapter 1*: [Elements of a Program](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/01_elements_00_lecture.ipynb)\n",
+    "  - *Chapter 2*: [Functions & Modularization](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/02_functions_00_lecture.ipynb)\n",
     "- What is the flow of execution? How can we form sentences from words?\n",
-    "  - *Chapter 3*: [Conditionals & Exceptions](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/03_conditionals_00_content.ipynb)\n",
-    "  - *Chapter 4*: [Recursion & Looping](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/04_iteration_00_content.ipynb)"
+    "  - *Chapter 3*: [Conditionals & Exceptions](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/03_conditionals_00_lecture.ipynb)\n",
+    "  - *Chapter 4*: [Recursion & Looping](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/04_iteration_00_lecture.ipynb)"
    ]
   },
   {
@@ -792,11 +792,11 @@
     "**Part B: Managing Data and Memory**\n",
     "\n",
     "- How is data stored in memory?\n",
-    "  - *Chapter 5*: [Bits & Numbers](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/05_numbers_00_content.ipynb)\n",
-    "  - *Chapter 6*: [Bytes & Text](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/06_text_00_content.ipynb)\n",
-    "  - *Chapter 7*: [Sequential Data](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/07_sequences_00_content.ipynb)\n",
-    "  - *Chapter 8*: [Mappings & Sets](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/08_mappings_00_content.ipynb)\n",
-    "  - *Chapter 9*: [Arrays & Dataframes](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/09_arrays_00_content.ipynb)\n",
+    "  - *Chapter 5*: [Bits & Numbers](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/05_numbers_00_lecture.ipynb)\n",
+    "  - *Chapter 6*: [Bytes & Text](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/06_text_00_lecture.ipynb)\n",
+    "  - *Chapter 7*: [Sequential Data](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/07_sequences_00_lecture.ipynb)\n",
+    "  - *Chapter 8*: [Mappings & Sets](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/08_mappings_00_lecture.ipynb)\n",
+    "  - *Chapter 9*: Arrays & Dataframes\n",
     "- How can we create custom data types?\n",
     "  - *Chapter 10*: Object-Orientation"
    ]
diff --git a/00_start_up_10_review.ipynb b/00_intro_01_review.ipynb
similarity index 97%
rename from 00_start_up_10_review.ipynb
rename to 00_intro_01_review.ipynb
index e8cb215..f08c6ca 100644
--- a/00_start_up_10_review.ipynb
+++ b/00_intro_01_review.ipynb
@@ -18,7 +18,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Read [Chapter 0](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/00_start_up_00_content.ipynb) of the book. Then, work through the questions below."
+    "Read [Chapter 0](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/00_intro_00_lecture.ipynb) of the book. Then, work through the questions below."
    ]
   },
   {
diff --git a/00_start_up_20_exercises.ipynb b/00_intro_02_exercises.ipynb
similarity index 95%
rename from 00_start_up_20_exercises.ipynb
rename to 00_intro_02_exercises.ipynb
index afd7f26..3b394db 100644
--- a/00_start_up_20_exercises.ipynb
+++ b/00_intro_02_exercises.ipynb
@@ -18,7 +18,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Read [Chapter 0](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/00_start_up_00_content.ipynb) of the book. Then, work through the exercises below."
+    "Read [Chapter 0](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/00_intro_00_lecture.ipynb) of the book. Then, work through the exercises below."
    ]
   },
   {
diff --git a/01_elements_00_content.ipynb b/01_elements_00_lecture.ipynb
similarity index 99%
rename from 01_elements_00_content.ipynb
rename to 01_elements_00_lecture.ipynb
index 38b63b1..c627331 100644
--- a/01_elements_00_content.ipynb
+++ b/01_elements_00_lecture.ipynb
@@ -1082,7 +1082,7 @@
     }
    },
    "source": [
-    "`a` and `d` indeed have the same value as is checked with the **equality operator** `==`. The resulting `True` (and the `False` further below) is yet another data type, a so-called **boolean**. We look into them closely in [Chapter 3](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/03_conditionals_00_content.ipynb#Boolean-Expressions)."
+    "`a` and `d` indeed have the same value as is checked with the **equality operator** `==`. The resulting `True` (and the `False` further below) is yet another data type, a so-called **boolean**. We look into them closely in [Chapter 3](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/03_conditionals_00_lecture.ipynb#Boolean-Expressions)."
    ]
   },
   {
@@ -1875,7 +1875,7 @@
     }
    },
    "source": [
-    "For example, while the above code to calculate the average of the even numbers in `[7, 11, 8, 5, 3, 12, 2, 6, 9, 10, 1, 4]` is correct, a Pythonista would rewrite it in a more \"Pythonic\" way and use the [sum()](https://docs.python.org/3/library/functions.html#sum) and [len()](https://docs.python.org/3/library/functions.html#len) (= \"length\") [built-in functions](https://docs.python.org/3/library/functions.html) (cf., [Chapter 2](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/02_functions_00_content.ipynb#Built-in-Functions)) as well as a so-called **list comprehension** (cf., [Chapter 7](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/07_sequences_00_content.ipynb#List-Comprehensions)). Pythonic code runs faster in many cases and is less error-prone."
+    "For example, while the above code to calculate the average of the even numbers in `[7, 11, 8, 5, 3, 12, 2, 6, 9, 10, 1, 4]` is correct, a Pythonista would rewrite it in a more \"Pythonic\" way and use the [sum()](https://docs.python.org/3/library/functions.html#sum) and [len()](https://docs.python.org/3/library/functions.html#len) (= \"length\") [built-in functions](https://docs.python.org/3/library/functions.html) (cf., [Chapter 2](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/02_functions_00_lecture.ipynb#Built-in-Functions)) as well as a so-called **list comprehension** (cf., [Chapter 7](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/07_sequences_00_lecture.ipynb#List-Comprehensions)). Pythonic code runs faster in many cases and is less error-prone."
    ]
   },
   {
@@ -2077,7 +2077,7 @@
     "\n",
     "At the same time, for a beginner's course, it is often easier to code linearly.\n",
     "\n",
-    "In real data science projects, one would probably employ a mixed approach and put reusable code into so-called Python modules (i.e., *.py* files; cf., [Chapter 2](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/02_functions_00_content.ipynb#Local-Modules-and-Packages)) and then use Jupyter notebooks to build up a linear report or storyline for a business argument to be made."
+    "In real data science projects, one would probably employ a mixed approach and put reusable code into so-called Python modules (i.e., *.py* files; cf., [Chapter 2](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/02_functions_00_lecture.ipynb#Local-Modules-and-Packages)) and then use Jupyter notebooks to build up a linear report or storyline for a business argument to be made."
    ]
   },
   {
@@ -2818,7 +2818,7 @@
    "source": [
     "Let's change the first element of `x`.\n",
     "\n",
-    "[Chapter 7](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/07_sequences_00_content.ipynb#The-list-Type) discusses lists in more depth. For now, let's view a `list` object as some sort of **container** that holds an arbitrary number of references to other objects and treat the brackets `[]` attached to it as just another operator, called the **indexing operator**. `x[0]` instructs Python to first follow the reference from the global list of all names to the `x` object. Then, it follows the first reference it finds there to the `1` object. The indexing operator must be an operator as we merely read the first element and do not change anything in memory.\n",
+    "[Chapter 7](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/07_sequences_00_lecture.ipynb#The-list-Type) discusses lists in more depth. For now, let's view a `list` object as some sort of **container** that holds an arbitrary number of references to other objects and treat the brackets `[]` attached to it as just another operator, called the **indexing operator**. `x[0]` instructs Python to first follow the reference from the global list of all names to the `x` object. Then, it follows the first reference it finds there to the `1` object. The indexing operator must be an operator as we merely read the first element and do not change anything in memory.\n",
     "\n",
     "Note how Python **begins counting at 0**. This is not the case for many other languages, for example, [MATLAB](https://en.wikipedia.org/wiki/MATLAB), [R](https://en.wikipedia.org/wiki/R_%28programming_language%29), or [Stata](https://en.wikipedia.org/wiki/Stata). To understand why this makes sense, see this short [note](https://www.cs.utexas.edu/users/EWD/transcriptions/EWD08xx/EWD831.html) by one of the all-time greats in computer science, the late [Edsger Dijkstra](https://en.wikipedia.org/wiki/Edsger_W._Dijkstra)."
    ]
@@ -3727,14 +3727,14 @@
     "  - ignored by Python\n",
     "\n",
     "\n",
-    "- functions (cf., [Chapter 2](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/02_functions_00_content.ipynb))\n",
+    "- functions (cf., [Chapter 2](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/02_functions_00_lecture.ipynb))\n",
     "  - named sequences of instructions\n",
     "  - the smaller parts in a larger program\n",
     "  - make a program more modular and thus easier to understand\n",
     "  - include [built-in functions](https://docs.python.org/3/library/functions.html) like [print()](https://docs.python.org/3/library/functions.html#print), [sum()](https://docs.python.org/3/library/functions.html#sum), or [len()](https://docs.python.org/3/library/functions.html#len)\n",
     "\n",
     "\n",
-    "- flow control (cf., [Chapter 3](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/03_conditionals_00_content.ipynb))\n",
+    "- flow control (cf., [Chapter 3](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/03_conditionals_00_lecture.ipynb))\n",
     "  - expression of **business logic** or an **algorithm**\n",
     "  - conditional execution of parts of a program (e.g., `if` statements)\n",
     "  - repetitive execution of parts of a program (e.g., `for`-loops)"
diff --git a/01_elements_10_review.ipynb b/01_elements_01_review.ipynb
similarity index 98%
rename from 01_elements_10_review.ipynb
rename to 01_elements_01_review.ipynb
index 1c2ee76..0ed455c 100644
--- a/01_elements_10_review.ipynb
+++ b/01_elements_01_review.ipynb
@@ -19,7 +19,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Read [Chapter 1](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/01_elements_00_content.ipynb) of the book. Then, work through the questions below."
+    "Read [Chapter 1](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/01_elements_00_lecture.ipynb) of the book. Then, work through the questions below."
    ]
   },
   {
diff --git a/01_elements_20_exercises.ipynb b/01_elements_02_exercises.ipynb
similarity index 97%
rename from 01_elements_20_exercises.ipynb
rename to 01_elements_02_exercises.ipynb
index e34097c..c7fffb6 100644
--- a/01_elements_20_exercises.ipynb
+++ b/01_elements_02_exercises.ipynb
@@ -19,7 +19,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Read [Chapter 1](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/01_elements_00_content.ipynb) of the book. Then, work through the exercises below."
+    "Read [Chapter 1](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/01_elements_00_lecture.ipynb) of the book. Then, work through the exercises below."
    ]
   },
   {
diff --git a/02_functions_00_content.ipynb b/02_functions_00_lecture.ipynb
similarity index 99%
rename from 02_functions_00_content.ipynb
rename to 02_functions_00_lecture.ipynb
index d0000d8..6dcccec 100644
--- a/02_functions_00_content.ipynb
+++ b/02_functions_00_lecture.ipynb
@@ -19,7 +19,7 @@
     }
    },
    "source": [
-    "In [Chapter 1](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/01_elements_00_content.ipynb#Example:-Averaging-Even-Numbers), we typed the code to calculate the average of the even numbers in a list into several code cells. Then, we executed them one after another. We had no way of **reusing** the code except for either executing cells multiple times or copying and pasting their contents into other cells. And, whenever we find ourselves doing repetitive manual work, we can be sure that there must be a way of automating what we are doing.\n",
+    "In [Chapter 1](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/01_elements_00_lecture.ipynb#Example:-Averaging-Even-Numbers), we typed the code to calculate the average of the even numbers in a list into several code cells. Then, we executed them one after another. We had no way of **reusing** the code except for either executing cells multiple times or copying and pasting their contents into other cells. And, whenever we find ourselves doing repetitive manual work, we can be sure that there must be a way of automating what we are doing.\n",
     "\n",
     "At the same time, we executed built-in functions (e.g., [print()](https://docs.python.org/3/library/functions.html#print), [sum()](https://docs.python.org/3/library/functions.html#sum), [len()](https://docs.python.org/3/library/functions.html#len), [id()](https://docs.python.org/3/library/functions.html#id), or [type()](https://docs.python.org/3/library/functions.html#type)) that obviously must be reusing the same parts inside core Python every time we use them.\n",
     "\n",
@@ -45,7 +45,7 @@
     }
    },
    "source": [
-    "So-called **[user-defined functions](https://docs.python.org/3/reference/compound_stmts.html#function-definitions)** may be created with the `def` statement. To extend an already familiar example, we reuse the introductory example from [Chapter 1](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/01_elements_00_content.ipynb#Best-Practices) in its final Pythonic version and transform it into the function `average_evens()` below. \n",
+    "So-called **[user-defined functions](https://docs.python.org/3/reference/compound_stmts.html#function-definitions)** may be created with the `def` statement. To extend an already familiar example, we reuse the introductory example from [Chapter 1](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/01_elements_00_lecture.ipynb#Best-Practices) in its final Pythonic version and transform it into the function `average_evens()` below. \n",
     "\n",
     "A function's **name** must be chosen according to the same naming rules as ordinary variables as Python manages function names like variables. In this book, we further adopt the convention of ending function names with parentheses `()` in text cells for faster comprehension when reading (i.e., `average_evens()` vs. `average_evens`). These are *not* part of the name but must always be written out in the `def` statement for syntactic reasons.\n",
     "\n",
@@ -57,7 +57,7 @@
     "\n",
     "A function may come with an *explicit* **return value** (i.e., \"result\" or \"output\") specified with the `return` statement (cf., [reference](https://docs.python.org/3/reference/simple_stmts.html#the-return-statement)): Functions that have one are considered **fruitful**; otherwise, they are **void**. Functions of the latter kind are still useful because of their **side effects** as, for example, the built-in [print()](https://docs.python.org/3/library/functions.html#print) function. Strictly speaking, they also have an *implicit* return value of `None`.\n",
     "\n",
-    "A function should define a **docstring** that describes what it does in a short subject line, what parameters it expects (i.e., their types), and what it returns (if anything). A docstring is a syntactically valid multi-line string (i.e., type `str`) defined within **triple-double quotes** `\"\"\"`. Strings are covered in depth in [Chapter 6](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/06_text_00_content.ipynb#The-str-Type). Widely adopted standards as to how to format a docstring are [PEP 257](https://www.python.org/dev/peps/pep-0257/) and section 3.8 in [Google's Python Style Guide](https://github.com/google/styleguide/blob/gh-pages/pyguide.md)."
+    "A function should define a **docstring** that describes what it does in a short subject line, what parameters it expects (i.e., their types), and what it returns (if anything). A docstring is a syntactically valid multi-line string (i.e., type `str`) defined within **triple-double quotes** `\"\"\"`. Strings are covered in depth in [Chapter 6](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/06_text_00_lecture.ipynb#The-str-Type). Widely adopted standards as to how to format a docstring are [PEP 257](https://www.python.org/dev/peps/pep-0257/) and section 3.8 in [Google's Python Style Guide](https://github.com/google/styleguide/blob/gh-pages/pyguide.md)."
    ]
   },
   {
@@ -761,7 +761,7 @@
    "source": [
     "[PythonTutor](http://pythontutor.com/visualize.html#code=nums%20%3D%20%5B1,%202,%203,%204,%205,%206,%207,%208,%209,%2010,%2011,%2012%5D%0A%0Adef%20average_wrong%28numbers%29%3A%0A%20%20%20%20evens%20%3D%20%5Bn%20for%20n%20in%20nums%20if%20n%20%25%202%20%3D%3D%200%5D%0A%20%20%20%20average%20%3D%20sum%28evens%29%20/%20len%28evens%29%0A%20%20%20%20return%20average%0A%0Arv%20%3D%20average_wrong%28%5B123,%20456,%20789%5D%29&cumulative=false&curInstr=0&heapPrimitives=nevernest&mode=display&origin=opt-frontend.js&py=3&rawInputLstJSON=%5B%5D&textReferences=false) is again helpful at visualizing the error interactively: Creating the `list` object `evens` eventually references takes *16* computational steps, namely two for managing the list comprehension, one for setting up an empty `list` object, *twelve* for filling it with elements derived from `nums` in the global scope (i.e., that is the error), and one to make `evens` reference it (cf., steps 6-21).\n",
     "\n",
-    "The frames logic shown by PythonTutor is the mechanism by which Python not only manages the names inside *one* function call but also for *many* potentially *simultaneous* calls, as revealed in [Chapter 4](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/04_iteration_00_content.ipynb#Trivial-Example:-Countdown). It is the reason why we may reuse the same names for the parameters and variables inside both `average_evens()` and `average_wrong()` without Python mixing them up. So, as we already read in the [Zen of Python](https://www.python.org/dev/peps/pep-0020/), \"namespaces are one honking great idea\" (cf., `import this`), and a frame is just a special kind of namespace."
+    "The frames logic shown by PythonTutor is the mechanism by which Python not only manages the names inside *one* function call but also for *many* potentially *simultaneous* calls, as revealed in [Chapter 4](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/04_iteration_00_lecture.ipynb#Trivial-Example:-Countdown). It is the reason why we may reuse the same names for the parameters and variables inside both `average_evens()` and `average_wrong()` without Python mixing them up. So, as we already read in the [Zen of Python](https://www.python.org/dev/peps/pep-0020/), \"namespaces are one honking great idea\" (cf., `import this`), and a frame is just a special kind of namespace."
    ]
   },
   {
@@ -1274,7 +1274,7 @@
     }
    },
    "source": [
-    "So far, we have only specified one parameter in each of our user-defined functions. In [Chapter 1](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/01_elements_00_content.ipynb#%28Arithmetic%29-Operators), however, we saw the built-in function [divmod()](https://docs.python.org/3/library/functions.html#divmod) take two arguments. And, the order of the numbers passed in mattered! Whenever we call a function and list its arguments in a comma separated manner, we say that we pass in the arguments by position or refer to them as **positional arguments**."
+    "So far, we have only specified one parameter in each of our user-defined functions. In [Chapter 1](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/01_elements_00_lecture.ipynb#%28Arithmetic%29-Operators), however, we saw the built-in function [divmod()](https://docs.python.org/3/library/functions.html#divmod) take two arguments. And, the order of the numbers passed in mattered! Whenever we call a function and list its arguments in a comma separated manner, we say that we pass in the arguments by position or refer to them as **positional arguments**."
    ]
   },
   {
@@ -2055,7 +2055,7 @@
    "source": [
     "The main point of having functions without a name is to use them in a situation where we know ahead of time that we use the function only *once*.\n",
     "\n",
-    "Popular applications of lambda expressions occur in combination with the **map-filter-reduce** paradigm or when we do \"number crunching\" with **arrays** and **data frames**. We look at both in detail in [Chapter 7](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/07_sequences_00_content.ipynb)."
+    "Popular applications of lambda expressions occur in combination with the **map-filter-reduce** paradigm or when we do \"number crunching\" with **arrays** and **data frames**. We look at both in detail in [Chapter 7](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/07_sequences_00_lecture.ipynb)."
    ]
   },
   {
@@ -2470,7 +2470,7 @@
    "source": [
     "Observe how the arguments passed to functions do not need to be just variables or simple literals. Instead, we may pass in any *expression* that evaluates to a *new* object of the type the function expects.\n",
     "\n",
-    "So just as a reminder from the expression vs. statement discussion in [Chapter 1](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/01_elements_00_content.ipynb#Expressions): An expression is *any* syntactically correct combination of variables and literals with operators. And the call operator `()` is yet another operator. So both of the next two code cells are just expressions! They have no permanent side effects in memory. We may execute them as often as we want *without* changing the state of the program (i.e., this Jupyter notebook).\n",
+    "So just as a reminder from the expression vs. statement discussion in [Chapter 1](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/01_elements_00_lecture.ipynb#Expressions): An expression is *any* syntactically correct combination of variables and literals with operators. And the call operator `()` is yet another operator. So both of the next two code cells are just expressions! They have no permanent side effects in memory. We may execute them as often as we want *without* changing the state of the program (i.e., this Jupyter notebook).\n",
     "\n",
     "So, regarding the very next cell in particular: Although the `2 ** 2` creates a *new* object `4` in memory that is then immediately passed into the [math.sqrt()](https://docs.python.org/3/library/math.html#math.sqrt) function, once that function call returns, \"all is lost\" and the newly created `4` object is forgotten again, as well as the return value of [math.sqrt()](https://docs.python.org/3/library/math.html#math.sqrt)."
    ]
@@ -3041,9 +3041,9 @@
     }
    },
    "source": [
-    "[numpy](http://www.numpy.org/) is the de-facto standard in the Python world for handling **array-like** data. That is a fancy word for data that can be put into a matrix or vector format. We look at it in depth in [Chapter 9](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/09_arrays_00_content.ipynb).\n",
+    "[numpy](http://www.numpy.org/) is the de-facto standard in the Python world for handling **array-like** data. That is a fancy word for data that can be put into a matrix or vector format. We look at it in depth in [Chapter 9](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/09_arrays_00_lecture.ipynb).\n",
     "\n",
-    "As [numpy](http://www.numpy.org/) is *not* in the [standard library](https://docs.python.org/3/library/index.html), it must be *manually* installed, for example, with the [pip](https://pip.pypa.io/en/stable/) tool. As mentioned in [Chapter 0](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/00_start_up_00_content.ipynb#Markdown-vs.-Code-Cells), to execute terminal commands from within a Jupyter notebook, we start a code cell with an exclamation mark.\n",
+    "As [numpy](http://www.numpy.org/) is *not* in the [standard library](https://docs.python.org/3/library/index.html), it must be *manually* installed, for example, with the [pip](https://pip.pypa.io/en/stable/) tool. As mentioned in [Chapter 0](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/00_intro_00_lecture.ipynb#Markdown-vs.-Code-Cells), to execute terminal commands from within a Jupyter notebook, we start a code cell with an exclamation mark.\n",
     "\n",
     "If you are running this notebook with an installation of the [Anaconda Distribution](https://www.anaconda.com/distribution/), then [numpy](http://www.numpy.org/) is probably already installed. Running the cell below confirms that."
    ]
diff --git a/02_functions_10_review.ipynb b/02_functions_01_review.ipynb
similarity index 98%
rename from 02_functions_10_review.ipynb
rename to 02_functions_01_review.ipynb
index a320a3d..f0e4772 100644
--- a/02_functions_10_review.ipynb
+++ b/02_functions_01_review.ipynb
@@ -18,7 +18,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Read [Chapter 2](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/02_functions_00_content.ipynb) of the book. Then, work through the questions below."
+    "Read [Chapter 2](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/02_functions_00_lecture.ipynb) of the book. Then, work through the questions below."
    ]
   },
   {
diff --git a/02_functions_20_exercises.ipynb b/02_functions_02_exercises.ipynb
similarity index 98%
rename from 02_functions_20_exercises.ipynb
rename to 02_functions_02_exercises.ipynb
index 0f449e4..a17b45b 100644
--- a/02_functions_20_exercises.ipynb
+++ b/02_functions_02_exercises.ipynb
@@ -18,7 +18,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Read [Chapter 2](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/02_functions_00_content.ipynb) of the book. Then, work through the exercises below."
+    "Read [Chapter 2](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/02_functions_00_lecture.ipynb) of the book. Then, work through the exercises below."
    ]
   },
   {
diff --git a/03_conditionals_00_content.ipynb b/03_conditionals_00_lecture.ipynb
similarity index 99%
rename from 03_conditionals_00_content.ipynb
rename to 03_conditionals_00_lecture.ipynb
index a9113a3..a6902df 100644
--- a/03_conditionals_00_content.ipynb
+++ b/03_conditionals_00_lecture.ipynb
@@ -19,7 +19,7 @@
     }
    },
    "source": [
-    "We analyzed every aspect of the `average_evens()` function in [Chapter 2](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/02_functions_00_content.ipynb) except for the `if`-related parts. While it seems to do what we expect it to, there is a whole lot more we learn from taking it apart. In particular, the `if` may occur within both a **statement** or an **expression**, analogous as to how a noun in a natural language is *either* the subject of *or* an object in a sentence. What is common to both usages is that it leads to code being executed for *parts* of the input only. It is our first way of **controlling** the **flow of execution** in a program.\n",
+    "We analyzed every aspect of the `average_evens()` function in [Chapter 2](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/02_functions_00_lecture.ipynb) except for the `if`-related parts. While it seems to do what we expect it to, there is a whole lot more we learn from taking it apart. In particular, the `if` may occur within both a **statement** or an **expression**, analogous as to how a noun in a natural language is *either* the subject of *or* an object in a sentence. What is common to both usages is that it leads to code being executed for *parts* of the input only. It is our first way of **controlling** the **flow of execution** in a program.\n",
     "\n",
     "After deconstructing `if` in the first part of this chapter, we take a close look at a similar concept, namely handling **exceptions**."
    ]
@@ -139,7 +139,7 @@
     }
    },
    "source": [
-    "There are, however, cases where even well-behaved Python does not make us happy. [Chapter 5](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/05_numbers_00_content.ipynb#Imprecision) provides more insights into this \"bug.\""
+    "There are, however, cases where even well-behaved Python does not make us happy. [Chapter 5](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/05_numbers_00_lecture.ipynb#Imprecision) provides more insights into this \"bug.\""
    ]
   },
   {
@@ -281,7 +281,7 @@
     }
    },
    "source": [
-    "Let's not confuse the boolean `False` with `None`, another built-in object! We saw the latter before in [Chapter 2](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/02_functions_00_content.ipynb#Function-Definition) as the *implicit* return value of a function without a `return` statement.\n",
+    "Let's not confuse the boolean `False` with `None`, another built-in object! We saw the latter before in [Chapter 2](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/02_functions_00_lecture.ipynb#Function-Definition) as the *implicit* return value of a function without a `return` statement.\n",
     "\n",
     "We might think of `None` in a boolean context indicating a \"maybe\" or even an \"unknown\" answer; however, for Python, there are no \"maybe\" or \"unknown\" objects, as we see further below!\n",
     "\n",
@@ -357,7 +357,7 @@
     }
    },
    "source": [
-    "`True`, `False`, and `None` have the property that they each exist in memory only *once*. Objects designed this way are so-called **singletons**. This **[design pattern](https://en.wikipedia.org/wiki/Design_Patterns)** was originally developed to keep a program's memory usage at a minimum. It may only be employed in situations where we know that an object does *not* mutate its value (i.e., to reuse the bag analogy from [Chapter 1](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/01_elements_00_content.ipynb#Objects-vs.-Types-vs.-Values), no flipping of $0$s and $1$s in the bag is allowed). In languages \"closer\" to the memory like C, we would have to code this singleton logic ourselves, but Python has this built in for *some* types.\n",
+    "`True`, `False`, and `None` have the property that they each exist in memory only *once*. Objects designed this way are so-called **singletons**. This **[design pattern](https://en.wikipedia.org/wiki/Design_Patterns)** was originally developed to keep a program's memory usage at a minimum. It may only be employed in situations where we know that an object does *not* mutate its value (i.e., to reuse the bag analogy from [Chapter 1](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/01_elements_00_lecture.ipynb#Objects-vs.-Types-vs.-Values), no flipping of $0$s and $1$s in the bag is allowed). In languages \"closer\" to the memory like C, we would have to code this singleton logic ourselves, but Python has this built in for *some* types.\n",
     "\n",
     "We verify this with either the `is` operator or by comparing memory addresses."
    ]
diff --git a/03_conditionals_10_review.ipynb b/03_conditionals_01_review.ipynb
similarity index 98%
rename from 03_conditionals_10_review.ipynb
rename to 03_conditionals_01_review.ipynb
index de1a419..cd56223 100644
--- a/03_conditionals_10_review.ipynb
+++ b/03_conditionals_01_review.ipynb
@@ -19,7 +19,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Read [Chapter 3](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/03_conditionals_00_content.ipynb) of the book. Then, work through the questions below."
+    "Read [Chapter 3](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/03_conditionals_00_lecture.ipynb) of the book. Then, work through the questions below."
    ]
   },
   {
diff --git a/03_conditionals_20_exercises.ipynb b/03_conditionals_02_exercises.ipynb
similarity index 98%
rename from 03_conditionals_20_exercises.ipynb
rename to 03_conditionals_02_exercises.ipynb
index 23692a3..650b063 100644
--- a/03_conditionals_20_exercises.ipynb
+++ b/03_conditionals_02_exercises.ipynb
@@ -19,7 +19,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Read [Chapter 3](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/03_conditionals_00_content.ipynb) of the book. Then, work through the exercises below."
+    "Read [Chapter 3](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/03_conditionals_00_lecture.ipynb) of the book. Then, work through the exercises below."
    ]
   },
   {
@@ -184,7 +184,7 @@
    "source": [
     "**Q2.2**: Loop over the `numbers` list and replace numbers for which one of the two (or both) conditions apply with text strings `\"Fizz\"`, `\"Buzz\"`, or `\"FizzBuzz\"` using the indexing operator `[]` and the assignment statement `=`.\n",
     "\n",
-    "In [Chapter 1](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/01_elements_00_content.ipynb), we saw that Python starts indexing with `0` as the first element. Keep that in mind.\n",
+    "In [Chapter 1](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/01_elements_00_lecture.ipynb), we saw that Python starts indexing with `0` as the first element. Keep that in mind.\n",
     "\n",
     "So in each iteration of the `for`-loop, you have to determine an `index` variable as well as check the actual `number` for its divisors.\n",
     "\n",
diff --git a/04_iteration_00_content.ipynb b/04_iteration_00_lecture.ipynb
similarity index 99%
rename from 04_iteration_00_content.ipynb
rename to 04_iteration_00_lecture.ipynb
index ba6467d..0c95c8a 100644
--- a/04_iteration_00_content.ipynb
+++ b/04_iteration_00_lecture.ipynb
@@ -841,7 +841,7 @@
     "\n",
     "To understand this in detail, we have to study algorithms and data structures (e.g., with [this book](https://www.amazon.de/Introduction-Algorithms-Press-Thomas-Cormen/dp/0262033844/ref=sr_1_1?__mk_de_DE=%C3%85M%C3%85%C5%BD%C3%95%C3%91&crid=1JNE8U0VZGU0O&qid=1569837169&s=gateway&sprefix=algorithms+an%2Caps%2C180&sr=8-1)), a discipline within computer science, and dive into the analysis of **[time complexity of algorithms](https://en.wikipedia.org/wiki/Time_complexity)**.\n",
     "\n",
-    "Luckily, in the Fibonacci case, the inefficiency can be resolved with a **caching** (i.e., \"reuse\") strategy from the field of **[dynamic programming](https://en.wikipedia.org/wiki/Dynamic_programming)**, namely **[memoization](https://en.wikipedia.org/wiki/Memoization)**. We do so in [Chapter 8](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/08_mappings_00_content.ipynb#Memoization), after introducing the `dict` data type.\n",
+    "Luckily, in the Fibonacci case, the inefficiency can be resolved with a **caching** (i.e., \"reuse\") strategy from the field of **[dynamic programming](https://en.wikipedia.org/wiki/Dynamic_programming)**, namely **[memoization](https://en.wikipedia.org/wiki/Memoization)**. We do so in [Chapter 8](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/08_mappings_00_lecture.ipynb#Memoization), after introducing the `dict` data type.\n",
     "\n",
     "Let's measure the average run times for `fibonacci()` and varying `i` arguments with the `%%timeit` [cell magic](https://ipython.readthedocs.io/en/stable/interactive/magics.html#magic-timeit) that comes with Jupyter."
    ]
@@ -4877,7 +4877,7 @@
     }
    },
    "source": [
-    "For sequences of integers, the [range()](https://docs.python.org/3/library/functions.html#func-range) built-in makes the `for` statement even more convenient: It creates a `list`-like object of type `range` that generates integers \"on the fly,\" and we look closely at the underlying effects in memory in [Chapter 7](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/07_sequences_00_content.ipynb#Mapping)."
+    "For sequences of integers, the [range()](https://docs.python.org/3/library/functions.html#func-range) built-in makes the `for` statement even more convenient: It creates a `list`-like object of type `range` that generates integers \"on the fly,\" and we look closely at the underlying effects in memory in [Chapter 7](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/07_sequences_00_lecture.ipynb#Mapping)."
    ]
   },
   {
@@ -5008,11 +5008,11 @@
     "\n",
     "Now, just as we classify objects by data type, we also classify these data types (e.g., `int`, `float`, `str`, or `list`) into **abstract concepts**.\n",
     "\n",
-    "We did this already in [Chapter 1](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/01_elements_00_content.ipynb#Who-am-I?-And-how-many?) when we described a `list` object as \"some sort of container that holds [...] references to other objects\". So, abstractly speaking, **containers** are any objects that are \"composed\" of other objects and also \"manage\" how these objects are organized. `list` objects, for example, have the property that they model an order associated with their elements. There exist, however, other container types, many of which do *not* come with an order. So, containers primarily \"contain\" other objects and have *nothing* to do with looping.\n",
+    "We did this already in [Chapter 1](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/01_elements_00_lecture.ipynb#Who-am-I?-And-how-many?) when we described a `list` object as \"some sort of container that holds [...] references to other objects\". So, abstractly speaking, **containers** are any objects that are \"composed\" of other objects and also \"manage\" how these objects are organized. `list` objects, for example, have the property that they model an order associated with their elements. There exist, however, other container types, many of which do *not* come with an order. So, containers primarily \"contain\" other objects and have *nothing* to do with looping.\n",
     "\n",
     "On the contrary, the abstract concept of **iterables** is all about looping: Any object that we can loop over is, by definition, an iterable. So, `range` objects, for example, are iterables, even though they hold no references to other objects. Moreover, looping does *not* have to occur in a *predictable* order, although this is the case for both `list` and `range` objects.\n",
     "\n",
-    "Typically, containers are iterables, and iterables are containers. Yet, only because these two concepts coincide often, we must not think of them as the same. In [Chapter 7](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/07_sequences_00_content.ipynb#Collections-vs.-Sequences), we formalize these two concepts and introduce many more. Finally, Chapter 10 gives an explanation how abstract concepts are implemented and play together.\n",
+    "Typically, containers are iterables, and iterables are containers. Yet, only because these two concepts coincide often, we must not think of them as the same. In [Chapter 7](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/07_sequences_00_lecture.ipynb#Collections-vs.-Sequences), we formalize these two concepts and introduce many more. Finally, Chapter 10 gives an explanation how abstract concepts are implemented and play together.\n",
     "\n",
     "Let's continue with `first_names` below as an example an illustrate what iterable containers are."
    ]
@@ -5867,7 +5867,7 @@
     }
    },
    "source": [
-    "Often, we process some iterable with numeric data, for example, a list of `numbers` as in this book's introductory example in [Chapter 1](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/01_elements_00_content.ipynb#Example:-Averaging-Even-Numbers) or, more realistically, data from a CSV file with many rows and columns.\n",
+    "Often, we process some iterable with numeric data, for example, a list of `numbers` as in this book's introductory example in [Chapter 1](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/01_elements_00_lecture.ipynb#Example:-Averaging-Even-Numbers) or, more realistically, data from a CSV file with many rows and columns.\n",
     "\n",
     "Processing numeric data usually comes down to operations that may be grouped into one of the following three categories:\n",
     "\n",
@@ -5875,7 +5875,7 @@
     "- **filtering**: throw away individual numbers (e.g., statistical outliers in a sample)\n",
     "- **reducing**: collect individual numbers into summary statistics\n",
     "\n",
-    "We study this **map-filter-reduce** paradigm extensively in [Chapter 7](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/07_sequences_00_content.ipynb#The-Map-Filter-Reduce-Paradigm) after introducing more advanced data types that are needed to work with \"big\" data.\n",
+    "We study this **map-filter-reduce** paradigm extensively in [Chapter 7](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/07_sequences_00_lecture.ipynb#The-Map-Filter-Reduce-Paradigm) after introducing more advanced data types that are needed to work with \"big\" data.\n",
     "\n",
     "Here, we focus on *filtering out* some numbers with a `for`-loop."
    ]
@@ -6074,7 +6074,7 @@
     }
    },
    "source": [
-    "With already three levels of indentation, less horizontal space is available for the actual code block. Of course, one could flatten the two `if` statements with the logical `and` operator, as shown in [Chapter 3](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/03_conditionals_00_content.ipynb#The-if-Statement). Then, however, we trade off horizontal space against a more \"complex\" `if` logic, and this is *not* a real improvement.\n",
+    "With already three levels of indentation, less horizontal space is available for the actual code block. Of course, one could flatten the two `if` statements with the logical `and` operator, as shown in [Chapter 3](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/03_conditionals_00_lecture.ipynb#The-if-Statement). Then, however, we trade off horizontal space against a more \"complex\" `if` logic, and this is *not* a real improvement.\n",
     "\n",
     "A Pythonista would instead make use of the `continue` statement (cf., [reference](https://docs.python.org/3/reference/simple_stmts.html#the-continue-statement)) that causes a loop to jump into the next iteration skipping the rest of the code block.\n",
     "\n",
diff --git a/04_iteration_10_review.ipynb b/04_iteration_01_review.ipynb
similarity index 98%
rename from 04_iteration_10_review.ipynb
rename to 04_iteration_01_review.ipynb
index 808e309..29c1f2f 100644
--- a/04_iteration_10_review.ipynb
+++ b/04_iteration_01_review.ipynb
@@ -19,7 +19,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Read [Chapter 4](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/04_iteration_00_content.ipynb) of the book. Then, work through the questions below."
+    "Read [Chapter 4](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/04_iteration_00_lecture.ipynb) of the book. Then, work through the questions below."
    ]
   },
   {
diff --git a/04_iteration_20_exercises.ipynb b/04_iteration_02_exercises.ipynb
similarity index 99%
rename from 04_iteration_20_exercises.ipynb
rename to 04_iteration_02_exercises.ipynb
index 81f6928..5858e2d 100644
--- a/04_iteration_20_exercises.ipynb
+++ b/04_iteration_02_exercises.ipynb
@@ -19,7 +19,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Read [Chapter 4](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/04_iteration_00_content.ipynb) of the book. Then, work through the exercises below."
+    "Read [Chapter 4](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/04_iteration_00_lecture.ipynb) of the book. Then, work through the exercises below."
    ]
   },
   {
@@ -411,7 +411,7 @@
     "\n",
     "In such cases, we could modify a recursive function to return a count value to be passed up the recursion tree.\n",
     "\n",
-    "This is similar to what we do in the recursive versions of `factorial()` and `fibonacci()` in [Chapter 4](https://github.com/webartifex/intro-to-python/blob/master/04_iteration_00_content.ipynb), where we pass up an intermediate result.\n",
+    "This is similar to what we do in the recursive versions of `factorial()` and `fibonacci()` in [Chapter 4](https://github.com/webartifex/intro-to-python/blob/master/04_iteration_00_lecture.ipynb), where we pass up an intermediate result.\n",
     "\n",
     "Let's create a `hanoi_moves()` function that follows the same internal logic as `hanoi()`, but, instead of printing out the moves, returns the number of steps done so far in the recursion.\n",
     "\n",
diff --git a/05_numbers_00_content.ipynb b/05_numbers_00_lecture.ipynb
similarity index 99%
rename from 05_numbers_00_content.ipynb
rename to 05_numbers_00_lecture.ipynb
index 1f7ebc2..531de04 100644
--- a/05_numbers_00_content.ipynb
+++ b/05_numbers_00_lecture.ipynb
@@ -21,15 +21,15 @@
    "source": [
     "After learning about the basic building blocks of expressing and structuring the business logic in programs, we focus our attention on the **data types** Python offers us, both built-in and available via the [standard library](https://docs.python.org/3/library/index.html) or third-party packages.\n",
     "\n",
-    "We start with the \"simple\" ones: Numeric types in this chapter and textual data in [Chapter 6](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/06_text_00_content.ipynb). An important fact that holds for all objects of these types is that they are **immutable**. To reuse the bag analogy from [Chapter 1](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/01_elements_00_content.ipynb#Objects-vs.-Types-vs.-Values), this means that the $0$s and $1$s making up an object's *value* cannot be changed once the bag is created in memory, implying that any operation with or method on the object creates a *new* object in a *different* memory location.\n",
+    "We start with the \"simple\" ones: Numeric types in this chapter and textual data in [Chapter 6](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/06_text_00_lecture.ipynb). An important fact that holds for all objects of these types is that they are **immutable**. To reuse the bag analogy from [Chapter 1](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/01_elements_00_lecture.ipynb#Objects-vs.-Types-vs.-Values), this means that the $0$s and $1$s making up an object's *value* cannot be changed once the bag is created in memory, implying that any operation with or method on the object creates a *new* object in a *different* memory location.\n",
     "\n",
-    "[Chapter 7](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/07_sequences_00_content.ipynb), [Chapter 8](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/08_mappings_00_content.ipynb), and [Chapter 9](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/09_arrays_00_content.ipynb) then cover the more \"complex\" data types, including, for example, the `list` type. Finally, Chapter 10 completes the picture by introducing language constructs to create custom types.\n",
+    "[Chapter 7](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/07_sequences_00_lecture.ipynb), [Chapter 8](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/08_mappings_00_lecture.ipynb), and [Chapter 9](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/09_arrays_00_lecture.ipynb) then cover the more \"complex\" data types, including, for example, the `list` type. Finally, Chapter 10 completes the picture by introducing language constructs to create custom types.\n",
     "\n",
     "We have already seen many hints indicating that numbers are not as trivial to work with as it seems at first sight:\n",
     "\n",
-    "- [Chapter 1](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/01_elements_00_content.ipynb#%28Data%29-Type-%2F-%22Behavior%22) reveals that numbers may come in *different* data types (i.e., `int` vs. `float` so far),\n",
-    "- [Chapter 3](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/03_conditionals_00_content.ipynb#Boolean-Expressions) raises questions regarding the **limited precision** of `float` numbers (e.g., `42 == 42.000000000000001` evaluates to `True`), and\n",
-    "- [Chapter 4](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/04_iteration_00_content.ipynb#Infinite-Recursion) shows that sometimes a `float` \"walks\" and \"quacks\" like an `int`, whereas the reverse is true in other cases.\n",
+    "- [Chapter 1](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/01_elements_00_lecture.ipynb#%28Data%29-Type-%2F-%22Behavior%22) reveals that numbers may come in *different* data types (i.e., `int` vs. `float` so far),\n",
+    "- [Chapter 3](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/03_conditionals_00_lecture.ipynb#Boolean-Expressions) raises questions regarding the **limited precision** of `float` numbers (e.g., `42 == 42.000000000000001` evaluates to `True`), and\n",
+    "- [Chapter 4](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/04_iteration_00_lecture.ipynb#Infinite-Recursion) shows that sometimes a `float` \"walks\" and \"quacks\" like an `int`, whereas the reverse is true in other cases.\n",
     "\n",
     "This chapter introduces all the [built-in numeric types](https://docs.python.org/3/library/stdtypes.html#numeric-types-int-float-complex): `int`, `float`, and `complex`. To mitigate the limited precision of floating-point numbers, we also look at two replacements for the `float` type in the [standard library](https://docs.python.org/3/library/index.html), namely the `Decimal` type in the [decimals](https://docs.python.org/3/library/decimal.html#decimal.Decimal) and the `Fraction` type in the [fractions](https://docs.python.org/3/library/fractions.html#fractions.Fraction) module."
    ]
@@ -53,7 +53,7 @@
     }
    },
    "source": [
-    "The simplest numeric type is the `int` type: It behaves like an [integer in ordinary math](https://en.wikipedia.org/wiki/Integer) (i.e., the set $\\mathbb{Z}$) and supports operators in the way we saw in the section on arithmetic operators in [Chapter 1](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/01_elements_00_content.ipynb#%28Arithmetic%29-Operators)."
+    "The simplest numeric type is the `int` type: It behaves like an [integer in ordinary math](https://en.wikipedia.org/wiki/Integer) (i.e., the set $\\mathbb{Z}$) and supports operators in the way we saw in the section on arithmetic operators in [Chapter 1](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/01_elements_00_lecture.ipynb#%28Arithmetic%29-Operators)."
    ]
   },
   {
@@ -3924,7 +3924,7 @@
     }
    },
    "source": [
-    "As seen in [Chapter 1](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/01_elements_00_content.ipynb#%28Data%29-Type-%2F-%22Behavior%22), the [is_integer()](https://docs.python.org/3/library/stdtypes.html#float.is_integer) method tells us if a `float` can be casted as an `int` object without any loss in precision."
+    "As seen in [Chapter 1](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/01_elements_00_lecture.ipynb#%28Data%29-Type-%2F-%22Behavior%22), the [is_integer()](https://docs.python.org/3/library/stdtypes.html#float.is_integer) method tells us if a `float` can be casted as an `int` object without any loss in precision."
    ]
   },
   {
@@ -6111,7 +6111,7 @@
     }
    },
    "source": [
-    "Analogous to the discussion of *containers* and *iterables* in [Chapter 4](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/04_iteration_00_content.ipynb#Containers-vs.-Iterables), we contrast the *concrete* numeric data types in this chapter with the *abstract* ideas behind [numbers in mathematics](https://en.wikipedia.org/wiki/Number).\n",
+    "Analogous to the discussion of *containers* and *iterables* in [Chapter 4](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/04_iteration_00_lecture.ipynb#Containers-vs.-Iterables), we contrast the *concrete* numeric data types in this chapter with the *abstract* ideas behind [numbers in mathematics](https://en.wikipedia.org/wiki/Number).\n",
     "\n",
     "The figure below summarizes five *major* sets of [numbers in mathematics](https://en.wikipedia.org/wiki/Number) as we know them from high school:\n",
     "\n",
diff --git a/05_numbers_10_review.ipynb b/05_numbers_01_review.ipynb
similarity index 99%
rename from 05_numbers_10_review.ipynb
rename to 05_numbers_01_review.ipynb
index 819d668..01d88fe 100644
--- a/05_numbers_10_review.ipynb
+++ b/05_numbers_01_review.ipynb
@@ -19,7 +19,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Read [Chapter 5](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/05_numbers_00_content.ipynb) of the book. Then, work through the questions below."
+    "Read [Chapter 5](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/05_numbers_00_lecture.ipynb) of the book. Then, work through the questions below."
    ]
   },
   {
diff --git a/05_numbers_20_exercises.ipynb b/05_numbers_02_exercises.ipynb
similarity index 97%
rename from 05_numbers_20_exercises.ipynb
rename to 05_numbers_02_exercises.ipynb
index d00ef2e..ab2499d 100644
--- a/05_numbers_20_exercises.ipynb
+++ b/05_numbers_02_exercises.ipynb
@@ -19,7 +19,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Read [Chapter 5](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/05_numbers_00_content.ipynb) of the book. Then, work through the exercises below."
+    "Read [Chapter 5](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/05_numbers_00_lecture.ipynb) of the book. Then, work through the exercises below."
    ]
   },
   {
@@ -33,13 +33,13 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "**Q1** in [Chapter 2's Exercises](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/02_functions_20_exercises.ipynb#Volume-of-a-Sphere) section already revealed that we must consider the effects of the `float` type's imprecision.\n",
+    "**Q1** in [Chapter 2's Exercises](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/02_functions_02_exercises.ipynb#Volume-of-a-Sphere) section already revealed that we must consider the effects of the `float` type's imprecision.\n",
     "\n",
     "This becomes even more important when we deal with numeric data modeling accounting or finance data (cf., [this comment](https://stackoverflow.com/a/24976426) on \"falsehoods programmers believe about money\").\n",
     "\n",
     "In addition to the *inherent imprecision* of numbers in general, the topic of **[rounding numbers](https://en.wikipedia.org/wiki/Rounding)** is also not as trivial as we might expect! [This article](https://realpython.com/python-rounding/) summarizes everything the data science practitioner needs to know.\n",
     "\n",
-    "In this exercise, we revisit **Q1** from [Chapter 3's Exercises](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/03_conditionals_20_exercises.ipynb#Discounting-Customer-Orders) section, and make the `discounted_price()` function work *correctly* for real-life sales data."
+    "In this exercise, we revisit **Q1** from [Chapter 3's Exercises](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/03_conditionals_02_exercises.ipynb#Discounting-Customer-Orders) section, and make the `discounted_price()` function work *correctly* for real-life sales data."
    ]
   },
   {
@@ -225,7 +225,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "**Q1.7**: Rewrite the function `discounted_price()` from [Chapter 3's Exercises](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/03_conditionals_20_exercises.ipynb#Discounting-Customer-Orders) section!\n",
+    "**Q1.7**: Rewrite the function `discounted_price()` from [Chapter 3's Exercises](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/03_conditionals_02_exercises.ipynb#Discounting-Customer-Orders) section!\n",
     "\n",
     "It takes the *positional* arguments `unit_price` and `quantity` and implements a discount scheme for a line item in a customer order as follows:\n",
     "\n",
diff --git a/06_text_00_content.ipynb b/06_text_00_lecture.ipynb
similarity index 98%
rename from 06_text_00_content.ipynb
rename to 06_text_00_lecture.ipynb
index 912142e..d1bbc91 100644
--- a/06_text_00_content.ipynb
+++ b/06_text_00_lecture.ipynb
@@ -124,7 +124,7 @@
     }
    },
    "source": [
-    "A `str` object evaluates to itself in a literal notation with enclosing **single quotes** `'` by default. In [Chapter 1](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/01_elements_00_content.ipynb#Value), we already specified the double quotes `\"` convention we stick to in this book. Yet, single quotes `'` and double quotes `\"` are *perfect* substitutes for all `str` objects that do *not* contain any of the two symbols in it. We could use the reverse convention, as well.\n",
+    "A `str` object evaluates to itself in a literal notation with enclosing **single quotes** `'` by default. In [Chapter 1](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/01_elements_00_lecture.ipynb#Value), we already specified the double quotes `\"` convention we stick to in this book. Yet, single quotes `'` and double quotes `\"` are *perfect* substitutes for all `str` objects that do *not* contain any of the two symbols in it. We could use the reverse convention, as well.\n",
     "\n",
     "As [this discussion](https://stackoverflow.com/questions/56011/single-quotes-vs-double-quotes-in-python) shows, many programmers have *strong* opinions about that and make up *new* conventions for their projects. Consequently, the discussion was \"closed as not constructive\" by the moderators."
    ]
@@ -161,7 +161,7 @@
     }
    },
    "source": [
-    "As the single quote `'` is often used in the English language as a shortener, we could make an argument in favor of using the double quotes `\"`: There are possibly fewer situations like in the two code cells below, in which we must revert to using a `\\` to **escape** a single quote `'` in a text (cf., the [Special Characters](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/06_text_00_content.ipynb#Special-Characters) section further below). However, double quotes `\"` are often used as well. So, this argument is somewhat not convincing.\n",
+    "As the single quote `'` is often used in the English language as a shortener, we could make an argument in favor of using the double quotes `\"`: There are possibly fewer situations like in the two code cells below, in which we must revert to using a `\\` to **escape** a single quote `'` in a text (cf., the [Special Characters](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/06_text_00_lecture.ipynb#Special-Characters) section further below). However, double quotes `\"` are often used as well. So, this argument is somewhat not convincing.\n",
     "\n",
     "Many proponents of the single quote `'` usage claim that double quotes `\"` make more **visual noise** on the screen. This argument is also not convincing. On the contrary, one could claim that *two* single quotes `''` look so similar to *one* double quote `\"` that it might not be apparent right away what we are looking at. By sticking to double quotes `\"`, we avoid such danger of confusion.\n",
     "\n",
@@ -615,7 +615,7 @@
     }
    },
    "source": [
-    "To use constructs familiar from [Chapter 3](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/03_conditionals_00_content.ipynb#The-try-Statement) to explain what `with open(...) as file:` does, below is a formulation with a `try` statement *equivalent* to the `with` statement above. The `finally`-branch is *always* executed, even if an exception is raised in the `for`-loop. So, `file` is sure to be closed too, with a somewhat less expressive formulation."
+    "To use constructs familiar from [Chapter 3](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/03_conditionals_00_lecture.ipynb#The-try-Statement) to explain what `with open(...) as file:` does, below is a formulation with a `try` statement *equivalent* to the `with` statement above. The `finally`-branch is *always* executed, even if an exception is raised in the `for`-loop. So, `file` is sure to be closed too, with a somewhat less expressive formulation."
    ]
   },
   {
@@ -737,7 +737,7 @@
     }
    },
    "source": [
-    "A **sequence** is yet another *abstract* concept (cf., the \"*Containers vs. Iterables*\" section in [Chapter 4](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/04_iteration_00_content.ipynb#Containers-vs.-Iterables)).\n",
+    "A **sequence** is yet another *abstract* concept (cf., the \"*Containers vs. Iterables*\" section in [Chapter 4](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/04_iteration_00_lecture.ipynb#Containers-vs.-Iterables)).\n",
     "\n",
     "It unifies *four* [orthogonal](https://en.wikipedia.org/wiki/Orthogonality) (i.e., \"independent\") behaviors into one idea: Any data type, such as `str`, is considered a sequence if it simultaneously\n",
     "\n",
@@ -746,9 +746,9 @@
     "3. comes with a *predictable* **order** of its\n",
     "4. **finite** number of \"things.\"\n",
     "\n",
-    "[Chapter 7](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/07_sequences_00_content.ipynb#Collections-vs.-Sequences) formalizes sequences in great detail. Here, we keep our focus on the `str` type that historically received its name as it models a \"**[string of characters](https://en.wikipedia.org/wiki/String_%28computer_science%29)**,\" and a \"string\" is more formally called a sequence in the computer science literature.\n",
+    "[Chapter 7](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/07_sequences_00_lecture.ipynb#Collections-vs.-Sequences) formalizes sequences in great detail. Here, we keep our focus on the `str` type that historically received its name as it models a \"**[string of characters](https://en.wikipedia.org/wiki/String_%28computer_science%29)**,\" and a \"string\" is more formally called a sequence in the computer science literature.\n",
     "\n",
-    "Behaving like a sequence, `str` objects may be treated like `list` objects in many cases. For example, the built-in [len()](https://docs.python.org/3/library/functions.html#len) function tells us how many elements (i.e., characters) make up `text`. [len()](https://docs.python.org/3/library/functions.html#len) would not work with an *infinite* object: As anything modeled in a program must fit into a computer's finite memory at runtime, there cannot exist objects containing a truly infinite number of elements; however, [Chapter 7](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/07_sequences_00_content.ipynb#Iterators-vs.-Iterables) introduces iterable data types that can be used to model an *infinite* series of elements and that, consequently, have no concept of \"length.\""
+    "Behaving like a sequence, `str` objects may be treated like `list` objects in many cases. For example, the built-in [len()](https://docs.python.org/3/library/functions.html#len) function tells us how many elements (i.e., characters) make up `text`. [len()](https://docs.python.org/3/library/functions.html#len) would not work with an *infinite* object: As anything modeled in a program must fit into a computer's finite memory at runtime, there cannot exist objects containing a truly infinite number of elements; however, [Chapter 7](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/07_sequences_00_lecture.ipynb#Iterators-vs.-Iterables) introduces iterable data types that can be used to model an *infinite* series of elements and that, consequently, have no concept of \"length.\""
    ]
   },
   {
@@ -912,7 +912,7 @@
     }
    },
    "source": [
-    "As `str` objects have the additional property of being *ordered*, we may **index** into them to obtain individual characters with the **indexing operator** `[]`. This is analogous to how we obtained individual elements of a `list` object in [Chapter 1](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/01_elements_00_content.ipynb#Who-am-I?-And-how-many?)."
+    "As `str` objects have the additional property of being *ordered*, we may **index** into them to obtain individual characters with the **indexing operator** `[]`. This is analogous to how we obtained individual elements of a `list` object in [Chapter 1](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/01_elements_00_lecture.ipynb#Who-am-I?-And-how-many?)."
    ]
   },
   {
@@ -1518,9 +1518,9 @@
     }
    },
    "source": [
-    "Whereas elements of a `list` object *may* be *re-assigned*, as shortly hinted at in [Chapter 1](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/01_elements_00_content.ipynb#Who-am-I?-And-how-many?), this is *not* allowed for `str` objects. Once created, they *cannot* be *changed*. Formally, we say that they are **immutable**. In that regard, `str` objects and all the numeric types in [Chapter 5](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/05_numbers_00_content.ipynb) are alike.\n",
+    "Whereas elements of a `list` object *may* be *re-assigned*, as shortly hinted at in [Chapter 1](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/01_elements_00_lecture.ipynb#Who-am-I?-And-how-many?), this is *not* allowed for `str` objects. Once created, they *cannot* be *changed*. Formally, we say that they are **immutable**. In that regard, `str` objects and all the numeric types in [Chapter 5](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/05_numbers_00_lecture.ipynb) are alike.\n",
     "\n",
-    "On the contrary, objects that may be changed after creation, are called **mutable**. We already saw in [Chapter 1](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/01_elements_00_content.ipynb#Who-am-I?-And-how-many?) how mutable objects are more difficult to reason about for a beginner, in particular, if more than *one* variable references it. Yet, mutability does have its place in a programmer's toolbox, and we revisit this idea in the next chapters.\n",
+    "On the contrary, objects that may be changed after creation, are called **mutable**. We already saw in [Chapter 1](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/01_elements_00_lecture.ipynb#Who-am-I?-And-how-many?) how mutable objects are more difficult to reason about for a beginner, in particular, if more than *one* variable references it. Yet, mutability does have its place in a programmer's toolbox, and we revisit this idea in the next chapters.\n",
     "\n",
     "The `TypeError` indicates that `str` objects are *immutable*: Assignment to an index or a slice are *not* supported."
    ]
@@ -2217,7 +2217,7 @@
     }
    },
    "source": [
-    "As mentioned in [Chapter 1](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/01_elements_00_content.ipynb#Operator-Overloading), the `+` and `*` operators are *overloaded* and used for **string concatenation**. They always create *new* `str` objects. That has nothing to do with the `str` type's immutability, but is the default behavior of operators."
+    "As mentioned in [Chapter 1](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/01_elements_00_lecture.ipynb#Operator-Overloading), the `+` and `*` operators are *overloaded* and used for **string concatenation**. They always create *new* `str` objects. That has nothing to do with the `str` type's immutability, but is the default behavior of operators."
    ]
   },
   {
@@ -2775,7 +2775,7 @@
     }
    },
    "source": [
-    "The `%` operator that we saw in the context of modulo division in [Chapter 1](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/01_elements_00_content.ipynb#%28Arithmetic%29-Operators) is overloaded with string interpolation when its first operand is a `str` object. The second operand consists of all expressions to be filled in. Format specifiers work with a `%` instead of curly braces and according to a different set of rules referred to as **[printf-style string formatting](https://docs.python.org/3/library/stdtypes.html#printf-style-string-formatting)**. So, `{:.2f}` becomes `%.2f`.\n",
+    "The `%` operator that we saw in the context of modulo division in [Chapter 1](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/01_elements_00_lecture.ipynb#%28Arithmetic%29-Operators) is overloaded with string interpolation when its first operand is a `str` object. The second operand consists of all expressions to be filled in. Format specifiers work with a `%` instead of curly braces and according to a different set of rules referred to as **[printf-style string formatting](https://docs.python.org/3/library/stdtypes.html#printf-style-string-formatting)**. So, `{:.2f}` becomes `%.2f`.\n",
     "\n",
     "This way of string interpolation is the oldest and originates from the [C language](https://en.wikipedia.org/wiki/C_%28programming_language%29). It is still widely spread, but we should use one of the other two ways instead. We show it here mainly for completeness sake."
    ]
@@ -2812,7 +2812,7 @@
     }
    },
    "source": [
-    "To insert more than one expression, we must list them in order and between parenthesis `(` and `)`. As [Chapter 7](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/07_sequences_00_content.ipynb#The-tuple-Type) reveals, this literal syntax creates an object of type `tuple`. Also, to format an expression as text, we use the format specifier `%s`."
+    "To insert more than one expression, we must list them in order and between parenthesis `(` and `)`. As [Chapter 7](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/07_sequences_00_lecture.ipynb#The-tuple-Type) reveals, this literal syntax creates an object of type `tuple`. Also, to format an expression as text, we use the format specifier `%s`."
    ]
   },
   {
diff --git a/06_text_10_review.ipynb b/06_text_01_review.ipynb
similarity index 98%
rename from 06_text_10_review.ipynb
rename to 06_text_01_review.ipynb
index fe9588e..d92bd12 100644
--- a/06_text_10_review.ipynb
+++ b/06_text_01_review.ipynb
@@ -19,7 +19,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Read [Chapter 6](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/06_text_00_content.ipynb) of the book. Then, work through the questions below."
+    "Read [Chapter 6](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/06_text_00_lecture.ipynb) of the book. Then, work through the questions below."
    ]
   },
   {
diff --git a/07_sequences_00_content.ipynb b/07_sequences_00_lecture.ipynb
similarity index 99%
rename from 07_sequences_00_content.ipynb
rename to 07_sequences_00_lecture.ipynb
index 924467a..e3887a4 100644
--- a/07_sequences_00_content.ipynb
+++ b/07_sequences_00_lecture.ipynb
@@ -19,11 +19,11 @@
     }
    },
    "source": [
-    "We studied numbers (cf., [Chapter 5](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/05_numbers_00_content.ipynb)) and textual data (cf., [Chapter 6](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/06_text_00_content.ipynb)) first, mainly because objects of the presented data types are \"simple,\" for two reasons: First, they are *immutable*, and, as we saw in the \"*Who am I? And how many?*\" section in [Chapter 1](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/01_elements_00_content.ipynb#Who-am-I?-And-how-many?), mutable objects can quickly become hard to reason about. Second, they are \"flat\" in the sense that they are *not* composed of other objects.\n",
+    "We studied numbers (cf., [Chapter 5](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/05_numbers_00_lecture.ipynb)) and textual data (cf., [Chapter 6](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/06_text_00_lecture.ipynb)) first, mainly because objects of the presented data types are \"simple,\" for two reasons: First, they are *immutable*, and, as we saw in the \"*Who am I? And how many?*\" section in [Chapter 1](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/01_elements_00_lecture.ipynb#Who-am-I?-And-how-many?), mutable objects can quickly become hard to reason about. Second, they are \"flat\" in the sense that they are *not* composed of other objects.\n",
     "\n",
     "The `str` type is a bit of a corner case in this regard. While one could argue that a longer `str` object, for example, `\"text\"`, is composed of individual characters, this is *not* the case in memory as the literal `\"text\"` only creates *one* object (i.e., one \"bag\" of $0$s and $1$s modeling all characters).\n",
     "\n",
-    "This chapter, [Chapter 8](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/08_mappings_00_content.ipynb), and [Chapter 9](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/09_arrays_00_content.ipynb) introduce various \"complex\" data types. While some are mutable and others are not, they all share that they are primarily used to \"manage,\" or structure, the memory in a program. Unsurprisingly, computer scientists refer to the ideas and theories behind these data types as **[data structures](https://en.wikipedia.org/wiki/Data_structure)**.\n",
+    "This chapter, [Chapter 8](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/08_mappings_00_lecture.ipynb), and [Chapter 9](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/09_arrays_00_lecture.ipynb) introduce various \"complex\" data types. While some are mutable and others are not, they all share that they are primarily used to \"manage,\" or structure, the memory in a program. Unsurprisingly, computer scientists refer to the ideas and theories behind these data types as **[data structures](https://en.wikipedia.org/wiki/Data_structure)**.\n",
     "\n",
     "In this chapter, we focus on data types that model all kinds of sequential data. Examples of such data are [spreadsheets](https://en.wikipedia.org/wiki/Spreadsheet) or [matrices](https://en.wikipedia.org/wiki/Matrix_%28mathematics%29)/[vectors](https://en.wikipedia.org/wiki/Vector_%28mathematics_and_physics%29). Such formats share the property that they are composed of smaller units that come in a sequence of, for example, rows/columns/cells or elements/entries."
    ]
@@ -47,9 +47,9 @@
     }
    },
    "source": [
-    "[Chapter 6](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/06_text_00_content.ipynb#A-\"String\"-of-Characters) already describes the *sequence* properties of `str` objects. Here, we take a step back and study these properties on their own before looking at bigger ideas.\n",
+    "[Chapter 6](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/06_text_00_lecture.ipynb#A-\"String\"-of-Characters) already describes the *sequence* properties of `str` objects. Here, we take a step back and study these properties on their own before looking at bigger ideas.\n",
     "\n",
-    "The [collections.abc](https://docs.python.org/3/library/collections.abc.html) module in the [standard library](https://docs.python.org/3/library/index.html) defines a variety of **abstract base classes** (ABCs). We saw ABCs already in [Chapter 5](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/05_numbers_00_content.ipynb#The-Numerical-Tower), where we use the ones from the [numbers](https://docs.python.org/3/library/numbers.html) module in the [standard library](https://docs.python.org/3/library/index.html) to classify Python's numeric data types according to mathematical ideas. Now, we take the ABCs from the [collections.abc](https://docs.python.org/3/library/collections.abc.html) module to classify the data types in this chapter and [Chapter 8](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/08_mappings_00_content.ipynb) according to their behavior in various contexts.\n",
+    "The [collections.abc](https://docs.python.org/3/library/collections.abc.html) module in the [standard library](https://docs.python.org/3/library/index.html) defines a variety of **abstract base classes** (ABCs). We saw ABCs already in [Chapter 5](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/05_numbers_00_lecture.ipynb#The-Numerical-Tower), where we use the ones from the [numbers](https://docs.python.org/3/library/numbers.html) module in the [standard library](https://docs.python.org/3/library/index.html) to classify Python's numeric data types according to mathematical ideas. Now, we take the ABCs from the [collections.abc](https://docs.python.org/3/library/collections.abc.html) module to classify the data types in this chapter and [Chapter 8](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/08_mappings_00_lecture.ipynb) according to their behavior in various contexts.\n",
     "\n",
     "As an illustration, consider `numbers` and `text` below, two objects of *different* types."
    ]
@@ -131,7 +131,7 @@
     }
    },
    "source": [
-    "In [Chapter 4](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/04_iteration_00_content.ipynb#Containers-vs.-Iterables), we referred to such types as *iterables*. That is *not* a proper [English](https://dictionary.cambridge.org/spellcheck/english-german/?q=iterable) word, even if it may sound like one at first sight. Yet, it is an official term in the Python world formalized with the `Iterable` ABC in the [collections.abc](https://docs.python.org/3/library/collections.abc.html) module.\n",
+    "In [Chapter 4](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/04_iteration_00_lecture.ipynb#Containers-vs.-Iterables), we referred to such types as *iterables*. That is *not* a proper [English](https://dictionary.cambridge.org/spellcheck/english-german/?q=iterable) word, even if it may sound like one at first sight. Yet, it is an official term in the Python world formalized with the `Iterable` ABC in the [collections.abc](https://docs.python.org/3/library/collections.abc.html) module.\n",
     "\n",
     "For the data science practitioner, it is worthwhile to know such terms as, for example, the documentation on the [built-ins](https://docs.python.org/3/library/functions.html) uses them extensively: In simple words, any built-in that takes an argument called \"*iterable*\" may be called with *any* object that supports being looped over. Already familiar [built-ins](https://docs.python.org/3/library/functions.html) include [enumerate()](https://docs.python.org/3/library/functions.html#enumerate), [sum()](https://docs.python.org/3/library/functions.html#sum), or [zip()](https://docs.python.org/3/library/functions.html#zip). So, they do *not* require the argument to be of a certain data type (e.g., `list`); instead, any *iterable* type works."
    ]
@@ -181,7 +181,7 @@
     }
    },
    "source": [
-    "As in the context of *goose typing* in [Chapter 5](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/05_numbers_00_content.ipynb#Goose-Typing), we can use ABCs with the built-in [isinstance()](https://docs.python.org/3/library/functions.html#isinstance) function to check if an object supports a behavior.\n",
+    "As in the context of *goose typing* in [Chapter 5](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/05_numbers_00_lecture.ipynb#Goose-Typing), we can use ABCs with the built-in [isinstance()](https://docs.python.org/3/library/functions.html#isinstance) function to check if an object supports a behavior.\n",
     "\n",
     "So, let's \"ask\" Python if it can loop over `numbers` or `text`."
    ]
@@ -1074,7 +1074,7 @@
    "source": [
     "Alternatively, we use the [list()](https://docs.python.org/3/library/functions.html#func-list) built-in to create a `list` object out of an iterable we pass to it as the argument.\n",
     "\n",
-    "For example, we can wrap the [range()](https://docs.python.org/3/library/functions.html#func-range) built-in with [list()](https://docs.python.org/3/library/functions.html#func-list): As described in [Chapter 4](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/04_iteration_00_content.ipynb#Containers-vs.-Iterables), `range` objects, like `range(1, 13)` below, are iterable and generate `int` objects \"on the fly\" (i.e., one by one). The [list()](https://docs.python.org/3/library/functions.html#func-list) around it acts like a `for`-loop and **materializes** twelve `int` objects in memory that then become the elements of the newly created `list` object. [PythonTutor](http://www.pythontutor.com/visualize.html#code=r%20%3D%20range%281,%2013%29%0Al%20%3D%20list%28range%281,%2013%29%29&cumulative=false&curInstr=0&heapPrimitives=nevernest&mode=display&origin=opt-frontend.js&py=3&rawInputLstJSON=%5B%5D&textReferences=false) shows this difference visually."
+    "For example, we can wrap the [range()](https://docs.python.org/3/library/functions.html#func-range) built-in with [list()](https://docs.python.org/3/library/functions.html#func-list): As described in [Chapter 4](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/04_iteration_00_lecture.ipynb#Containers-vs.-Iterables), `range` objects, like `range(1, 13)` below, are iterable and generate `int` objects \"on the fly\" (i.e., one by one). The [list()](https://docs.python.org/3/library/functions.html#func-list) around it acts like a `for`-loop and **materializes** twelve `int` objects in memory that then become the elements of the newly created `list` object. [PythonTutor](http://www.pythontutor.com/visualize.html#code=r%20%3D%20range%281,%2013%29%0Al%20%3D%20list%28range%281,%2013%29%29&cumulative=false&curInstr=0&heapPrimitives=nevernest&mode=display&origin=opt-frontend.js&py=3&rawInputLstJSON=%5B%5D&textReferences=false) shows this difference visually."
    ]
   },
   {
@@ -2678,7 +2678,7 @@
    "source": [
     "The `list` type is an essential data structure in any real-world Python application, and many typical `list`-related algorithms from computer science theory are already built into it at the C level (cf., the [documentation](https://docs.python.org/3/library/stdtypes.html#mutable-sequence-types) or the [tutorial](https://docs.python.org/3/tutorial/datastructures.html#more-on-lists) for a full overview; unfortunately, not all methods have direct links). So, understanding and applying the built-in methods of the `list` type not only speeds up the development process but also makes programs significantly faster.\n",
     "\n",
-    "In contrast to the `str` type's methods in [Chapter 6](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/06_text_00_content.ipynb#String-Methods) (e.g., [upper()](https://docs.python.org/3/library/stdtypes.html#str.upper) or [lower()](https://docs.python.org/3/library/stdtypes.html#str.lower)), the `list` type's methods that mutate an object do so *in place*. That means they *never* create *new* `list` objects and return `None` to indicate that. So, we must *never* assign the return value of `list` methods to the variable holding the list!\n",
+    "In contrast to the `str` type's methods in [Chapter 6](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/06_text_00_lecture.ipynb#String-Methods) (e.g., [upper()](https://docs.python.org/3/library/stdtypes.html#str.upper) or [lower()](https://docs.python.org/3/library/stdtypes.html#str.lower)), the `list` type's methods that mutate an object do so *in place*. That means they *never* create *new* `list` objects and return `None` to indicate that. So, we must *never* assign the return value of `list` methods to the variable holding the list!\n",
     "\n",
     "Let's look at the following `names` example."
    ]
@@ -3016,7 +3016,7 @@
    "source": [
     "The [sort()](https://docs.python.org/3/library/stdtypes.html#list.sort) method and the [sorted()](https://docs.python.org/3/library/functions.html#sorted) function sort the elements in `names` in alphabetical order, forward or backward. However, that does *not* hold in general.\n",
     "\n",
-    "We mention above that `list` objects may contain objects of *any* type and even of *mixed* types. Because of that, the sorting is **[delegated](https://en.wikipedia.org/wiki/Delegation_(object-oriented_programming))** to the elements in a `list` object. In a way, Python \"asks\" the elements in a `list` object to sort themselves. As `names` contains only `str` objects, they are sorted according the the comparison rules explained in [Chapter 6](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/06_text_00_content.ipynb#String-Comparison).\n",
+    "We mention above that `list` objects may contain objects of *any* type and even of *mixed* types. Because of that, the sorting is **[delegated](https://en.wikipedia.org/wiki/Delegation_(object-oriented_programming))** to the elements in a `list` object. In a way, Python \"asks\" the elements in a `list` object to sort themselves. As `names` contains only `str` objects, they are sorted according the the comparison rules explained in [Chapter 6](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/06_text_00_lecture.ipynb#String-Comparison).\n",
     "\n",
     "To customize the sorting, we pass a keyword-only `key` argument to [sort()](https://docs.python.org/3/library/stdtypes.html#list.sort) or [sorted()](https://docs.python.org/3/library/functions.html#sorted), which must be a `function` object accepting *one* positional argument. Then, the elements in the `list` object are passed to that one by one, and the return values are used as the **sort keys**. The `key` argument is also a popular use case for `lambda` expressions.\n",
     "\n",
@@ -6153,7 +6153,7 @@
     }
    },
    "source": [
-    "Unpacking allows us to rewrite the iterative `fibonacci()` function from [Chapter 4](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/04_iteration_00_content.ipynb#\"Hard-at-first-Glance\"-Example:-Fibonacci-Numbers-%28revisited%29) in a concise way, now also supporting *goose typing* with the [numbers](https://docs.python.org/3/library/numbers.html) module from the [standard library](https://docs.python.org/3/library/index.html)."
+    "Unpacking allows us to rewrite the iterative `fibonacci()` function from [Chapter 4](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/04_iteration_00_lecture.ipynb#\"Hard-at-first-Glance\"-Example:-Fibonacci-Numbers-%28revisited%29) in a concise way, now also supporting *goose typing* with the [numbers](https://docs.python.org/3/library/numbers.html) module from the [standard library](https://docs.python.org/3/library/index.html)."
    ]
   },
   {
@@ -6576,7 +6576,7 @@
     }
    },
    "source": [
-    "In the \"*Packing & Unpacking with Functions*\" [exercise](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/07_sequences_review_and_exercises_00_content.ipynb#Packing-&-Unpacking-with-Functions) at the end of this chapter, we look at `product()` in more detail.\n",
+    "In the \"*Packing & Unpacking with Functions*\" [exercise](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/07_sequences_02_exercises.ipynb#Packing-&-Unpacking-with-Functions) at the end of this chapter, we look at `product()` in more detail.\n",
     "\n",
     "While we needed to unpack `one_hundred` above to avoid the semantic error, unpacking an argument in a function call may also be a convenience in general.\n",
     "\n",
@@ -7874,7 +7874,7 @@
     }
    },
    "source": [
-    "Using the [map()](https://docs.python.org/3/library/functions.html#map) and [filter()](https://docs.python.org/3/library/functions.html#filter) built-ins, we can quickly switch the order: Filter first and then transform the remaining elements. This variant equals the \"*A simple Filter*\" example from [Chapter 4](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/04_iteration_00_content.ipynb#Example:-A-simple-Filter). On the contrary, code with `for`-loops and `if` statements is more tedious to adapt. Additionally, `map` and `filter` objects are optimized at the C level and, therefore, a lot faster as well."
+    "Using the [map()](https://docs.python.org/3/library/functions.html#map) and [filter()](https://docs.python.org/3/library/functions.html#filter) built-ins, we can quickly switch the order: Filter first and then transform the remaining elements. This variant equals the \"*A simple Filter*\" example from [Chapter 4](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/04_iteration_00_lecture.ipynb#Example:-A-simple-Filter). On the contrary, code with `for`-loops and `if` statements is more tedious to adapt. Additionally, `map` and `filter` objects are optimized at the C level and, therefore, a lot faster as well."
    ]
   },
   {
@@ -8208,7 +8208,7 @@
     "\n",
     "Often, such functions are used *only once* in a program. However, the primary purpose of functions is to *reuse* them. In such cases, it makes more sense to define them \"anonymously\" right at the position where the first argument goes.\n",
     "\n",
-    "As mentioned in [Chapter 2](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/02_functions_00_content.ipynb#Anonymous-Functions), Python provides `lambda` expressions to create `function` objects *without* a name referencing them.\n",
+    "As mentioned in [Chapter 2](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/02_functions_00_lecture.ipynb#Anonymous-Functions), Python provides `lambda` expressions to create `function` objects *without* a name referencing them.\n",
     "\n",
     "So, the above `add()` function could be rewritten as a `lambda` expression like so ..."
    ]
@@ -8422,7 +8422,7 @@
    "source": [
     "For [map()](https://docs.python.org/3/library/functions.html#map) and [filter()](https://docs.python.org/3/library/functions.html#filter), Python provides a nice syntax appealing to people who like mathematics.\n",
     "\n",
-    "Consider again the \"*A simple Filter*\" example from [Chapter 4](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/04_iteration_00_content.ipynb#Example:-A-simple-Filter), written with combined `for` and `if` statements. So, the mapping and filtering steps happen simultaneously."
+    "Consider again the \"*A simple Filter*\" example from [Chapter 4](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/04_iteration_00_lecture.ipynb#Example:-A-simple-Filter), written with combined `for` and `if` statements. So, the mapping and filtering steps happen simultaneously."
    ]
   },
   {
@@ -10073,7 +10073,7 @@
     }
    },
    "source": [
-    "[all()](https://docs.python.org/3/library/functions.html#all) can be viewed as syntactic sugar replacing a `for`-loop: Internally, [all()](https://docs.python.org/3/library/functions.html#all) implements the *short-circuiting* strategy from [Chapter 3](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/03_conditionals_00_content.ipynb#Short-Circuiting), and we mimic that by testing for the *opposite* condition in the `if` statement and leaving the `for`-loop early with the `break` statement. In the worst case, if `threshold` were, for example, `150`, we would loop over *all* elements in the *iterable*, which must be *finite* for the code to work. So, [all()](https://docs.python.org/3/library/functions.html#all) is a *linear search* in disguise."
+    "[all()](https://docs.python.org/3/library/functions.html#all) can be viewed as syntactic sugar replacing a `for`-loop: Internally, [all()](https://docs.python.org/3/library/functions.html#all) implements the *short-circuiting* strategy from [Chapter 3](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/03_conditionals_00_lecture.ipynb#Short-Circuiting), and we mimic that by testing for the *opposite* condition in the `if` statement and leaving the `for`-loop early with the `break` statement. In the worst case, if `threshold` were, for example, `150`, we would loop over *all* elements in the *iterable*, which must be *finite* for the code to work. So, [all()](https://docs.python.org/3/library/functions.html#all) is a *linear search* in disguise."
    ]
   },
   {
@@ -10387,13 +10387,13 @@
     }
    },
    "source": [
-    "With the new concepts in this chapter, let's rewrite the book's introductory \"*Averaging Even Numbers*\" example in [Chapter 1](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/01_elements_00_content.ipynb#Example:-Averaging-Even-Numbers) such that it efficiently handles a large sequence of numbers.\n",
+    "With the new concepts in this chapter, let's rewrite the book's introductory \"*Averaging Even Numbers*\" example in [Chapter 1](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/01_elements_00_lecture.ipynb#Example:-Averaging-Even-Numbers) such that it efficiently handles a large sequence of numbers.\n",
     "\n",
     "We assume the `average_evens()` function below is called with a *finite* and *iterable* object, which generates a \"stream\" of numeric objects that can be cast as `int` objects because the idea of even and odd numbers only makes sense in the context of whole numbers.\n",
     "\n",
     "The generator expression `(int(n) for n in numbers)` implements the type casting, and when it is evaluated, *nothing* happens except that a `generator` object is stored in `integers`. Then, with the [reduce()](https://docs.python.org/3/library/functools.html#functools.reduce) function from the [functools](https://docs.python.org/3/library/functools.html) module, we *simultaneously* add up *and* count the even numbers produced by the inner generator expression `((n, 1) for n in integers if n % 2 == 0)`. That results in a `generator` object producing `tuple` objects consisting of the next *even* number in line and `1`. Two such `tuple` objects are then iteratively passed to the `lambda` expression as the `x` and `y` arguments. `x` represents the total and the count of the even numbers processed so far, while `y`'s first element, `y[0]`, is the next even number to be added to the running total. The result of the [reduce()](https://docs.python.org/3/library/functools.html#functools.reduce) function is again a `tuple` object, namely the final `total` and `count`. Lastly, we calculate the simple average.\n",
     "\n",
-    "In summary, the implementation of `average_evens()` does *not* keep materialized `list` objects internally like its predecessors from [Chapter 2](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/02_functions_00_content.ipynb), but processes the elements of the `numbers` argument on a one-by-one basis."
+    "In summary, the implementation of `average_evens()` does *not* keep materialized `list` objects internally like its predecessors from [Chapter 2](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/02_functions_00_lecture.ipynb), but processes the elements of the `numbers` argument on a one-by-one basis."
    ]
   },
   {
@@ -11317,7 +11317,7 @@
     }
    },
    "source": [
-    "In [Chapter 4](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/04_iteration_00_content.ipynb#The-for-Statement), we argue that the `for` statement is syntactic sugar, replacing the `while` statement in many scenarios. In particular, a `for`-loop saves us two tasks: Managing an index variable *and* obtaining the individual elements by indexing. In this sub-section, we look at a more realistic picture, using the new terminology as well.\n",
+    "In [Chapter 4](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/04_iteration_00_lecture.ipynb#The-for-Statement), we argue that the `for` statement is syntactic sugar, replacing the `while` statement in many scenarios. In particular, a `for`-loop saves us two tasks: Managing an index variable *and* obtaining the individual elements by indexing. In this sub-section, we look at a more realistic picture, using the new terminology as well.\n",
     "\n",
     "Let's print out the elements of a `list` object as the *iterable* to be looped over."
    ]
diff --git a/07_sequences_10_review.ipynb b/07_sequences_01_review.ipynb
similarity index 98%
rename from 07_sequences_10_review.ipynb
rename to 07_sequences_01_review.ipynb
index a2d2f5c..d374b3c 100644
--- a/07_sequences_10_review.ipynb
+++ b/07_sequences_01_review.ipynb
@@ -19,7 +19,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Read [Chapter 7](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/07_sequences_00_content.ipynb) of the book. Then, work through the questions below."
+    "Read [Chapter 7](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/07_sequences_00_lecture.ipynb) of the book. Then, work through the questions below."
    ]
   },
   {
@@ -40,7 +40,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "**Q1**: We have seen **containers** and **iterables** before in [Chapter 4](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/04_iteration_00_content.ipynb#Containers-vs.-Iterables). How do they relate to **sequences**? "
+    "**Q1**: We have seen **containers** and **iterables** before in [Chapter 4](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/04_iteration_00_lecture.ipynb#Containers-vs.-Iterables). How do they relate to **sequences**? "
    ]
   },
   {
diff --git a/07_sequences_20_exercises.ipynb b/07_sequences_02_exercises.ipynb
similarity index 98%
rename from 07_sequences_20_exercises.ipynb
rename to 07_sequences_02_exercises.ipynb
index b2a8169..daad5e0 100644
--- a/07_sequences_20_exercises.ipynb
+++ b/07_sequences_02_exercises.ipynb
@@ -19,7 +19,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Read [Chapter 7](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/07_sequences_00_content.ipynb) of the book. Then, work through the exercises below."
+    "Read [Chapter 7](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/07_sequences_00_lecture.ipynb) of the book. Then, work through the exercises below."
    ]
   },
   {
@@ -90,7 +90,7 @@
    "source": [
     "**Q1.3**: Generalize `nested_sum()` into a function `mixed_sum()` that can process a \"mixed\" `list` object, which contains numbers and other `list` objects with numbers! Use `mixed_numbers` below for testing!\n",
     "\n",
-    "Hints: Use the built-in [isinstance()](https://docs.python.org/3/library/functions.html#isinstance) function to check how an element is to be processed. Get extra credit for adhering to *goose typing*, as explained in [Chapter 5](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/05_numbers_00_content.ipynb#Goose-Typing)."
+    "Hints: Use the built-in [isinstance()](https://docs.python.org/3/library/functions.html#isinstance) function to check how an element is to be processed. Get extra credit for adhering to *goose typing*, as explained in [Chapter 5](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/05_numbers_00_lecture.ipynb#Goose-Typing)."
    ]
   },
   {
@@ -196,7 +196,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "In the \"*Function Definitions & Calls*\" section in [Chapter 7](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/07_sequences_00_content.ipynb#Function-Definitions-&-Calls), we define the following function `product()`. In this exercise, you will improve it by making it more \"user-friendly.\""
+    "In the \"*Function Definitions & Calls*\" section in [Chapter 7](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/07_sequences_00_lecture.ipynb#Function-Definitions-&-Calls), we define the following function `product()`. In this exercise, you will improve it by making it more \"user-friendly.\""
    ]
   },
   {
@@ -290,7 +290,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "In [Chapter 7](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/07_sequences_00_content.ipynb#Function-Definitions-&-Calls), we also pass a `list` object, like `one_hundred`, to `product()`, and *no* exception is raised."
+    "In [Chapter 7](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/07_sequences_00_lecture.ipynb#Function-Definitions-&-Calls), we also pass a `list` object, like `one_hundred`, to `product()`, and *no* exception is raised."
    ]
   },
   {
@@ -315,7 +315,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "**Q2.3**: What is wrong with that? What *kind* of error (cf., [Chapter 1](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/01_elements_00_content.ipynb#Formal-vs.-Natural-Languages)) is that conceptually? Describe precisely what happens to the passed in `one_hundred` in every line within `product()`!"
+    "**Q2.3**: What is wrong with that? What *kind* of error (cf., [Chapter 1](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/01_elements_00_lecture.ipynb#Formal-vs.-Natural-Languages)) is that conceptually? Describe precisely what happens to the passed in `one_hundred` in every line within `product()`!"
    ]
   },
   {
@@ -493,7 +493,7 @@
    "source": [
     "**Q2.7**: Rewrite `product()` so that it takes a *keyword-only* argument `start`, defaulting to the above *default* or *start* value, and use `start` internally instead of `result`!\n",
     "\n",
-    "Hint: Remember that a *keyword-only* argument is any parameter specified in a function's header line after the first (and only) `*` (cf., [Chapter 2](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/02_functions_00_content.ipynb#Keyword-only-Arguments))."
+    "Hint: Remember that a *keyword-only* argument is any parameter specified in a function's header line after the first (and only) `*` (cf., [Chapter 2](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/02_functions_00_lecture.ipynb#Keyword-only-Arguments))."
    ]
   },
   {
@@ -697,7 +697,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "**Side Note**: Above, we make `product()` work with a single *collection* type argument instead of a *sequence* type to keep it more generic: For example, we can pass in a `set` object, like `{2, 5, 10}` below, and `product()` continues to work correctly. The `set` type is introducted in [Chapter 8](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/08_mappings_00_content.ipynb#The-set-Type), and one essential difference to the `list` type is that objects of type `set` have *no* order regarding their elements. So, even though `[2, 5, 10]` and `{2, 5, 10}` look almost the same, the order implied in the literal notation gets lost in memory!"
+    "**Side Note**: Above, we make `product()` work with a single *collection* type argument instead of a *sequence* type to keep it more generic: For example, we can pass in a `set` object, like `{2, 5, 10}` below, and `product()` continues to work correctly. The `set` type is introducted in [Chapter 8](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/08_mappings_00_lecture.ipynb#The-set-Type), and one essential difference to the `list` type is that objects of type `set` have *no* order regarding their elements. So, even though `[2, 5, 10]` and `{2, 5, 10}` look almost the same, the order implied in the literal notation gets lost in memory!"
    ]
   },
   {
@@ -1627,7 +1627,7 @@
    "source": [
     "**Q3.10.2**: Just as in `standardized()`, write a *generator expression* that produces z-scores one by one! However, instead of just generating a z-score, the resulting `generator` object should produce `tuple` objects consisting of a \"raw\" number from `data` and its z-score.\n",
     "\n",
-    "Hint: Look at the revisited \"*Averaging Even Numbers*\" example in [Chapter 7](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/07_sequences_00_content.ipynb#Example:-Averaging-Even-Numbers-%28revisited%29) for some inspiration, which also contains a generator expression producing `tuple` objects."
+    "Hint: Look at the revisited \"*Averaging Even Numbers*\" example in [Chapter 7](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/07_sequences_00_lecture.ipynb#Example:-Averaging-Even-Numbers-%28revisited%29) for some inspiration, which also contains a generator expression producing `tuple` objects."
    ]
   },
   {
diff --git a/08_mappings_00_content.ipynb b/08_mappings_00_lecture.ipynb
similarity index 99%
rename from 08_mappings_00_content.ipynb
rename to 08_mappings_00_lecture.ipynb
index 2e19dbe..79483b5 100644
--- a/08_mappings_00_content.ipynb
+++ b/08_mappings_00_lecture.ipynb
@@ -19,9 +19,9 @@
     }
    },
    "source": [
-    "While [Chapter 7](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/07_sequences_00_content.ipynb) focuses on one special kind of *collection* types, namely *sequences*, this chapter introduces two more kinds: **Mappings** and **sets**. We present the data types belonging to these two groups in one chapter as they share the *same* underlying implementation at the C Level, known as **[hash tables](https://en.wikipedia.org/wiki/Hash_table)**.\n",
+    "While [Chapter 7](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/07_sequences_00_lecture.ipynb) focuses on one special kind of *collection* types, namely *sequences*, this chapter introduces two more kinds: **Mappings** and **sets**. We present the data types belonging to these two groups in one chapter as they share the *same* underlying implementation at the C Level, known as **[hash tables](https://en.wikipedia.org/wiki/Hash_table)**.\n",
     "\n",
-    "The most important mapping type in this chapter is the `dict` type that we have not yet seen before (cf, [documentation](https://docs.python.org/3/library/stdtypes.html#dict)). It is an essential part in a data science practitioner's toolbox for two reasons: First, Python employs `dict` objects basically \"everywhere\" internally. So, we must understand how they work to become better at Python in general. Second, after the many concepts related to *sequential* data, the ideas behind *mappings* enhance our general problem solving skills. As a concrete example, we look at the concept of **memoization** to complete our picture of *recursion*, as depicted in [Chapter 4](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/04_iteration_00_content.ipynb#Recursion). We end this chapter with a discussion of *set* types."
+    "The most important mapping type in this chapter is the `dict` type that we have not yet seen before (cf, [documentation](https://docs.python.org/3/library/stdtypes.html#dict)). It is an essential part in a data science practitioner's toolbox for two reasons: First, Python employs `dict` objects basically \"everywhere\" internally. So, we must understand how they work to become better at Python in general. Second, after the many concepts related to *sequential* data, the ideas behind *mappings* enhance our general problem solving skills. As a concrete example, we look at the concept of **memoization** to complete our picture of *recursion*, as depicted in [Chapter 4](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/04_iteration_00_lecture.ipynb#Recursion). We end this chapter with a discussion of *set* types."
    ]
   },
   {
@@ -45,7 +45,7 @@
    "source": [
     "A *mapping* is a one-to-one correspondence from a set of **keys** to a set of **values**. In other words, a *mapping* is a *collection* of **key-value pairs**, also called **items** for short.\n",
     "\n",
-    "In the context of mappings, the term *value* has a meaning different from the general *value* that *every* object has: In the \"bag\" analogy from [Chapter 1](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/01_elements_00_content.ipynb#Value), we descibe an object's value to be the concrete $0$s and $1$s it contains. Here, the terms *key* and *value* mean the *role* an object takes within a mapping. Both, *keys* and *values*, are real *objects* with a distinct *value*. So, the student should always remember the double meaning of the term *value* in this chapter!\n",
+    "In the context of mappings, the term *value* has a meaning different from the general *value* that *every* object has: In the \"bag\" analogy from [Chapter 1](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/01_elements_00_lecture.ipynb#Value), we descibe an object's value to be the concrete $0$s and $1$s it contains. Here, the terms *key* and *value* mean the *role* an object takes within a mapping. Both, *keys* and *values*, are real *objects* with a distinct *value*. So, the student should always remember the double meaning of the term *value* in this chapter!\n",
     "\n",
     "Let's continue with an example. To create a `dict` object, we commonly use the literal notation, `{..: .., ..: .., ...}`, and list all the items. `to_words` below maps the `int` objects `0`, `1`, and `2` to their English word equivalents, `\"zero\"`, `\"one\"`, and `\"two\"`, and `from_words` does the opposite. A stylistic side note: Pythonistas often expand `dict` or `list` definitions by writing each item or element on a line on their own. The commas `,` after the *last* items are *not* a mistake, as well, although they *may* be left out. Besides easier reading, such style has actual technical advantages (cf., [source](https://www.python.org/dev/peps/pep-0008/#when-to-use-trailing-commas)) that we do not go into detail about here."
    ]
@@ -502,7 +502,7 @@
     }
    },
    "source": [
-    "In [Chapter 0](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/00_start_up_00_content.ipynb#Isn't-C-a-lot-faster?), we argue that a major advantage of using Python is that it takes care of the memory managment for us. In line with that, we have never talked about the C level implementation thus far in the book. However, the `dict` type, among others, exhibits some behaviors that may seem \"weird\" for a beginner. To built a solid intuition that enables the student to better \"predict\" how `dict` objects behave, we describe the underlying implementation details on a conceptual level (i.e., without C code).\n",
+    "In [Chapter 0](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/00_intro_00_lecture.ipynb#Isn't-C-a-lot-faster?), we argue that a major advantage of using Python is that it takes care of the memory managment for us. In line with that, we have never talked about the C level implementation thus far in the book. However, the `dict` type, among others, exhibits some behaviors that may seem \"weird\" for a beginner. To built a solid intuition that enables the student to better \"predict\" how `dict` objects behave, we describe the underlying implementation details on a conceptual level (i.e., without C code).\n",
     "\n",
     "The first unintuitive behavior is that we may *not* use a *mutable* object as a key. That results in a `TypeError`."
    ]
@@ -707,7 +707,7 @@
     }
    },
    "source": [
-    "The [glossary](https://docs.python.org/3/glossary.html#term-hashable) states a second requirement for hashability, namely that \"objects which *compare equal* must have the *same* hash value.\" The purpose of this is to ensure that if we put, for example, `1` as a key in a `dict` object, we can look it up later with `1.0`. In other words, we can look up keys by their object's value (i.e., in the meaning of [Chapter 1](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/01_elements_00_content.ipynb#Value)). The converse statement does *not* hold: Two objects *may* (accidentally) have the *same* hash value and *not* compare equal."
+    "The [glossary](https://docs.python.org/3/glossary.html#term-hashable) states a second requirement for hashability, namely that \"objects which *compare equal* must have the *same* hash value.\" The purpose of this is to ensure that if we put, for example, `1` as a key in a `dict` object, we can look it up later with `1.0`. In other words, we can look up keys by their object's value (i.e., in the meaning of [Chapter 1](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/01_elements_00_lecture.ipynb#Value)). The converse statement does *not* hold: Two objects *may* (accidentally) have the *same* hash value and *not* compare equal."
    ]
   },
   {
@@ -1081,7 +1081,7 @@
     }
    },
    "source": [
-    "In [Chapter 7](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/07_sequences_00_content.ipynb#Collections-vs.-Sequences), we show how *sequences* are a special kind of *collections*. The latter can be described as iterable containers with a finite number of elements.\n",
+    "In [Chapter 7](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/07_sequences_00_lecture.ipynb#Collections-vs.-Sequences), we show how *sequences* are a special kind of *collections*. The latter can be described as iterable containers with a finite number of elements.\n",
     "\n",
     "The `dict` type is a special kind of a *collection*, as well, as revealed with the `Collection` ABC from the [collections.abc](https://docs.python.org/3/library/collections.abc.html) module in the [standard library](https://docs.python.org/3/library/index.html)."
    ]
@@ -3769,7 +3769,7 @@
     }
    },
    "source": [
-    "Just as a single `*` symbol is used for packing and unpacking iterables in [Chapter 7](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/07_sequences_00_content.ipynb#Packing-&-Unpacking), a double `**` symbol implements packing and unpacking for mappings.\n",
+    "Just as a single `*` symbol is used for packing and unpacking iterables in [Chapter 7](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/07_sequences_00_lecture.ipynb#Packing-&-Unpacking), a double `**` symbol implements packing and unpacking for mappings.\n",
     "\n",
     "Let's say we have `to_words` and `more_words` as below and want to merge the items together into a *new* `dict` object."
    ]
@@ -4195,7 +4195,7 @@
     }
    },
    "source": [
-    "Analogous to list comprehensions in [Chapter 7](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/07_sequences_00_content.ipynb#List-Comprehensions), **dictionary comprehensions**, or **dictcomps** for short, are a concise literal notation to derive new `dict` objects out of existing ones.\n",
+    "Analogous to list comprehensions in [Chapter 7](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/07_sequences_00_lecture.ipynb#List-Comprehensions), **dictionary comprehensions**, or **dictcomps** for short, are a concise literal notation to derive new `dict` objects out of existing ones.\n",
     "\n",
     "For example, let's derive `from_words` from `to_words` below by swapping the keys and values."
    ]
@@ -4410,7 +4410,7 @@
     }
    },
    "source": [
-    "The *recursive* implementation of the [Fibonacci numbers](https://en.wikipedia.org/wiki/Fibonacci_number) in [Chapter 4](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/04_iteration_00_content.ipynb#\"Easy-at-first-Glance\"-Example:-Fibonacci-Numbers) takes long to compute for large Fibonacci numbers as the number of function calls grows exponentially.\n",
+    "The *recursive* implementation of the [Fibonacci numbers](https://en.wikipedia.org/wiki/Fibonacci_number) in [Chapter 4](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/04_iteration_00_lecture.ipynb#\"Easy-at-first-Glance\"-Example:-Fibonacci-Numbers) takes long to compute for large Fibonacci numbers as the number of function calls grows exponentially.\n",
     "\n",
     "The graph below visualizes what the problem is and also suggests a solution: Instead of calculating the return value of the `fibonacci()` function for the *same* argument over and over again, it makes sense to **cache** the result and reuse it. This concept is called **[memoization](https://en.wikipedia.org/wiki/Memoization)** in the computer science literature."
    ]
diff --git a/08_mappings_10_review.ipynb b/08_mappings_01_review.ipynb
similarity index 96%
rename from 08_mappings_10_review.ipynb
rename to 08_mappings_01_review.ipynb
index 471bd41..0ff62f9 100644
--- a/08_mappings_10_review.ipynb
+++ b/08_mappings_01_review.ipynb
@@ -19,7 +19,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Read [Chapter 8](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/08_mappings_00_content.ipynb) of the book. Then, work through the questions below."
+    "Read [Chapter 8](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/08_mappings_00_lecture.ipynb) of the book. Then, work through the questions below."
    ]
   },
   {
@@ -113,7 +113,7 @@
     "**Q6**: **Memoization** is an essential concept to know to solve problems in the real world. Together with the idea of **recursion**, it enables us to solve problems in a \"backwards\" fashion *effectively*.\n",
     "\n",
     "\n",
-    "Compare the **recursive** formulation of `fibonacci()` in [Chapter 8](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/08_mappings_00_content.ipynb#\"Easy-at-third-Glance\"-Example:-Fibonacci-Numbers-%28revisited%29), the \"*Easy at third Glance*\" example, with the **iterative** version in [Chapter 4](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/04_iteration_00_content.ipynb#\"Hard-at-first-Glance\"-Example:-Fibonacci-Numbers-%28revisited%29), the \"*Hard at first Glance*\" example!\n",
+    "Compare the **recursive** formulation of `fibonacci()` in [Chapter 8](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/08_mappings_00_lecture.ipynb#\"Easy-at-third-Glance\"-Example:-Fibonacci-Numbers-%28revisited%29), the \"*Easy at third Glance*\" example, with the **iterative** version in [Chapter 4](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/04_iteration_00_lecture.ipynb#\"Hard-at-first-Glance\"-Example:-Fibonacci-Numbers-%28revisited%29), the \"*Hard at first Glance*\" example!\n",
     "\n",
     "How are they similar and how do they differ?"
    ]
diff --git a/08_mappings_20_exercises.ipynb b/08_mappings_02_exercises.ipynb
similarity index 99%
rename from 08_mappings_20_exercises.ipynb
rename to 08_mappings_02_exercises.ipynb
index 0f3f1a5..4db33ee 100644
--- a/08_mappings_20_exercises.ipynb
+++ b/08_mappings_02_exercises.ipynb
@@ -19,7 +19,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Read [Chapter 8](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/08_mappings_00_content.ipynb) of the book. Then, work through the exercises below."
+    "Read [Chapter 8](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/08_mappings_00_lecture.ipynb) of the book. Then, work through the exercises below."
    ]
   },
   {
diff --git a/README.md b/README.md
index 8c1cba2..c9b4933 100644
--- a/README.md
+++ b/README.md
@@ -17,48 +17,48 @@ They can be viewed in a plain web browser with the help of
 [nbviewer](https://nbviewer.jupyter.org/):
 
 - *Introduction*: Start up
-  ([content](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/00_start_up_00_content.ipynb)
-  | [review](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/00_start_up_10_review.ipynb)
-  | [exercises](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/00_start_up_20_exercises.ipynb))
+  ([lecture](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/00_intro_00_lecture.ipynb)
+  | [review](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/00_intro_01_review.ipynb)
+  | [exercises](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/00_intro_02_exercises.ipynb))
 - **Part A: Expressing Logic**
   - *Chapter 1*: Elements of a Program
-    ([content](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/01_elements_00_content.ipynb)
-    | [review](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/01_elements_10_review.ipynb)
-    | [exercises](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/01_elements_20_exercises.ipynb))
+    ([lecture](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/01_elements_00_lecture.ipynb)
+    | [review](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/01_elements_01_review.ipynb)
+    | [exercises](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/01_elements_02_exercises.ipynb))
   - *Chapter 2*: Functions & Modularization
-    ([content](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/02_functions_00_content.ipynb)
-    | [review](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/02_functions_10_review.ipynb)
-    | [exercises](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/02_functions_20_exercises.ipynb))
+    ([lecture](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/02_functions_00_lecture.ipynb)
+    | [review](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/02_functions_01_review.ipynb)
+    | [exercises](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/02_functions_02_exercises.ipynb))
   - *Chapter 3*: Conditionals & Exceptions
-    ([content](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/03_conditionals_00_content.ipynb)
-    | [review](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/03_conditionals_10_review.ipynb)
-    | [exercises](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/03_conditionals_20_exercises.ipynb))
+    ([lecture](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/03_conditionals_00_lecture.ipynb)
+    | [review](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/03_conditionals_01_review.ipynb)
+    | [exercises](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/03_conditionals_02_exercises.ipynb))
   - *Chapter 4*: Recursion & Looping
-    ([content](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/04_iteration_00_content.ipynb)
-    | [review](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/04_iteration_10_review.ipynb)
-    | [exercises](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/04_iteration_20_exercises.ipynb))
+    ([lecture](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/04_iteration_00_lecture.ipynb)
+    | [review](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/04_iteration_01_review.ipynb)
+    | [exercises](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/04_iteration_02_exercises.ipynb))
 - **Part B: Managing Data and Memory**
   - *Chapter 5*: Bits & Numbers
-    ([content](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/05_numbers_00_content.ipynb)
-    | [review](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/05_numbers_10_review.ipynb)
-    | [exercises](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/05_numbers_20_exercises.ipynb))
+    ([lecture](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/05_numbers_00_lecture.ipynb)
+    | [review](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/05_numbers_01_review.ipynb)
+    | [exercises](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/05_numbers_02_exercises.ipynb))
   - *Chapter 6*: Bytes & Text
-    ([content](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/06_text_00_content.ipynb)
-    | [review](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/06_text_10_review.ipynb))
+    ([lecture](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/06_text_00_lecture.ipynb)
+    | [review](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/06_text_01_review.ipynb))
   - *Chapter 7*: Sequential Data
-    ([content](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/07_sequences_00_content.ipynb)
-    | [review](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/07_sequences_10_review.ipynb)
-    | [exercises](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/07_sequences_20_exercises.ipynb))
+    ([lecture](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/07_sequences_00_lecture.ipynb)
+    | [review](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/07_sequences_01_review.ipynb)
+    | [exercises](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/07_sequences_02_exercises.ipynb))
   - *Chapter 8*: Mappings & Sets
-    ([content](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/08_mappings_00_content.ipynb)
-    | [review](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/08_mappings_10_review.ipynb)
-    | [exercises](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/08_mappings_20_exercises.ipynb))
+    ([lecture](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/08_mappings_00_lecture.ipynb)
+    | [review](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/08_mappings_01_review.ipynb)
+    | [exercises](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/08_mappings_02_exercises.ipynb))
 
 However, it is recommended that students **install Python and Jupyter
 locally** and run the code in the notebooks on their own.
 This way, the student can play with the code and learn more efficiently.
 Precise **installation instructions** are either in the [00th notebook](
-https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/00_start_up_00_content.ipynb)
+https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/00_intro_00_lecture.ipynb)
 or further below.
 
 Feedback is encouraged and will be incorporated.

From d372b4d58992ebb19a80911e49dc0636e8ac4a42 Mon Sep 17 00:00:00 2001
From: Alexander Hess <alexander@webartifex.biz>
Date: Sun, 2 Feb 2020 17:23:36 +0100
Subject: [PATCH 059/242] Adjust update notification in README.md

---
 README.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/README.md b/README.md
index c9b4933..b6b1b9d 100644
--- a/README.md
+++ b/README.md
@@ -1,4 +1,4 @@
-**Important**: The notebooks are being updated and amended throughout the
+**Important**: The content is being updated and amended throughout the
 spring semester of 2020!
 
 # An Introduction to Python and Programming

From 2f5ee9a8da6ac636ce8be79dbf0d044b58df2cb6 Mon Sep 17 00:00:00 2001
From: Alexander Hess <alexander@webartifex.biz>
Date: Sun, 2 Feb 2020 17:24:06 +0100
Subject: [PATCH 060/242] Add notes for instructors

- use jupyter notebook and not jupyter lab
- do not use RISE with Collabsible Headings extension
---
 README.md | 34 +++++++++++++++++++++++++++-------
 1 file changed, 27 insertions(+), 7 deletions(-)

diff --git a/README.md b/README.md
index b6b1b9d..e64527c 100644
--- a/README.md
+++ b/README.md
@@ -181,13 +181,6 @@ if you have poetry installed, you may just type `poetry install` instead).
 
 - `python -m pip install -r requirements.txt`
 
-The *requirements.txt* file also installs the [black](https://github.com/psf/black)
-tool (incl. the [blackcellmagic](https://github.com/csurfer/blackcellmagic)
-extension) and the [RISE](https://github.com/damianavila/RISE) extension.
-With them, the instructor can easily re-format code in a class session and
-execute code in presentation mode (currently RISE only works with the
-older `jupyter notebook` command).
-
 With everything installed, you can now do the equivalent of clicking the
 "JupyterLab" entry in the Anaconda Navigator.
 
@@ -196,6 +189,33 @@ With everything installed, you can now do the equivalent of clicking the
 This opens a new tab in your web browser just as above.
 
 
+#### Interactive Presentation Mode & Live Coding
+
+The *requirements.txt* file also installs the [nbextensions](https://github.com/ipython-contrib/jupyter_contrib_nbextensions)
+for Jupyter notebooks, the [black](https://github.com/psf/black) code
+formatting tool (incl. the [blackcellmagic](https://github.com/csurfer/blackcellmagic)
+Jupyter extension) and the [RISE](https://github.com/damianavila/RISE) Jupyter
+extension.
+With them, the instructor can easily re-format code in a class session and
+execute code in presentation mode.
+
+**Note**: Currently, the RISE extension *only* works with the older
+notebook command.
+
+- `jupyter notebook` (so, `jupyter lab` may *not* be used).
+
+After installing the dependencies, the instructor must copy the extensions'
+JavaScript and CSS files into Jupyter's search directory.
+
+- `jupyter contrib nbextension install --user`
+
+Now, the instructor can enable/disable the various Jupyter notebook
+extensions.
+
+**Note**: The extension "Collapsible Headings" may interfere with the
+RISE presentation if hotkeys are enabled.
+
+
 ## About the Author
 
 Alexander Hess is a PhD student at the Chair of Logistics Management at the

From 40b6c9c5a60ba4208b229701e16c6c5a03f84a82 Mon Sep 17 00:00:00 2001
From: Alexander Hess <alexander@webartifex.biz>
Date: Sun, 2 Feb 2020 17:25:17 +0100
Subject: [PATCH 061/242] Bump version number

---
 pyproject.toml | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/pyproject.toml b/pyproject.toml
index 106e21f..b77fd3a 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -1,6 +1,6 @@
 [tool.poetry]
 name = "intro-to-python"
-version = "0.6.1"
+version = "0.6.2"
 description = "An introduction to Python and programming for wanna-be data scientists"
 authors = ["Alexander Hess <alexander@webartifex.biz>"]
 license = "MIT"

From 945debb25e527b7539fd9a6e8cb7d4f3d43b9ee9 Mon Sep 17 00:00:00 2001
From: Alexander Hess <alexander@webartifex.biz>
Date: Sun, 2 Feb 2020 18:47:08 +0100
Subject: [PATCH 062/242] Streamline text

---
 00_intro_00_lecture.ipynb | 139 ++++++++++++++++++++++++--------------
 README.md                 |   2 +-
 2 files changed, 90 insertions(+), 51 deletions(-)

diff --git a/00_intro_00_lecture.ipynb b/00_intro_00_lecture.ipynb
index b812c61..a8d2237 100644
--- a/00_intro_00_lecture.ipynb
+++ b/00_intro_00_lecture.ipynb
@@ -128,9 +128,9 @@
     }
    },
    "source": [
-    "The term **[data science](https://en.wikipedia.org/wiki/Data_science)** is rather vague and does *not* refer to an academic discipline. Instead, the term was popularized by the tech industry, who also coined non-meaningful job titles such as \"[rockstar](https://www.quora.com/Why-are-engineers-called-rockstars-and-ninjas)\" or \"[ninja developers](https://www.quora.com/Why-are-engineers-called-rockstars-and-ninjas).\" Most *serious* definitions describe the field as being **multi-disciplinary** *integrating* scientific methods, algorithms, and systems thinking to extract knowledge from (structured and unstructured) data *and* also emphasize the importance of **[domain knowledge](https://en.wikipedia.org/wiki/Domain_knowledge)**.\n",
+    "The term **[data science](https://en.wikipedia.org/wiki/Data_science)** is rather vague and does *not* refer to an academic discipline. Instead, the term was popularized by the tech industry, who also coined non-meaningful job titles such as \"[rockstar](https://www.quora.com/Why-are-engineers-called-rockstars-and-ninjas)\" or \"[ninja developers](https://www.quora.com/Why-are-engineers-called-rockstars-and-ninjas).\" Most *serious* definitions describe the field as being **multi-disciplinary** *integrating* scientific methods, algorithms, and systems thinking to extract knowledge from structured and unstructured data, *and* also emphasize the importance of **[domain knowledge](https://en.wikipedia.org/wiki/Domain_knowledge)**.\n",
     "\n",
-    "Recently, this integration aspect feeds back into the academic world. The [MIT](https://www.mit.edu/), for example, created the new [Stephen A. Schwarzman College of Computing](http://computing.mit.edu) for [artificial intelligence](https://en.wikipedia.org/wiki/Artificial_intelligence) (with a 1 billion dollar initial investment) where students undergo a \"bilingual\" curriculum with half the classes in quantitative and method-centric fields (like the ones mentioned above) and the other half in domains such as biology, business, chemistry, politics, (art) history, or linguistics (cf., the [official Q&As](http://computing.mit.edu/faq/) or this [NYT article](https://www.nytimes.com/2018/10/15/technology/mit-college-artificial-intelligence.html)). Their strategists see a future where programming skills are just as naturally embedded into every students' curricula as are nowadays subjects like calculus, statistics, or academic writing. Then, programming literacy is not just another \"nice to have\" skill but a prerequisite, or an enabler, to understanding more advanced topics in the actual domains studied. Top-notch researchers who use programming in their day-to-day lives could then teach students more efficiently in their \"language.\""
+    "Recently, this integration aspect feeds back into the academic world. The [MIT](https://www.mit.edu/), for example, created the new [Stephen A. Schwarzman College of Computing](http://computing.mit.edu) for [artificial intelligence](https://en.wikipedia.org/wiki/Artificial_intelligence) with a 1 billion dollar initial investment where students undergo a \"bilingual\" curriculum with half the classes in quantitative and method-centric fields - like the ones mentioned above - and the other half in domains such as biology, business, chemistry, politics, (art) history, or linguistics (cf., the [official Q&As](http://computing.mit.edu/faq/) or this [NYT article](https://www.nytimes.com/2018/10/15/technology/mit-college-artificial-intelligence.html)). Their strategists see a future where programming skills are just as naturally embedded into students' curricula as are nowadays subjects like calculus, statistics, or academic writing. Then, programming literacy is not just another \"nice to have\" skill but a prerequisite, or an enabler, to understanding more advanced topics in the actual domains studied. Top-notch researchers who use programming in their day-to-day lives could then teach students more efficiently in their \"language.\""
    ]
   },
   {
@@ -200,7 +200,7 @@
     }
    },
    "source": [
-    "A new tab in your web browser opens with the website being \"localhost\" and some number (e.g., 8888). This is the [JupyterLab](https://jupyterlab.readthedocs.io/en/stable/) application that is used to display and run the Jupyter notebooks mentioned above. On the left, you see the files and folders in your local user folder. This file browser works like any other. In the center, you have several options to launch a new notebook file."
+    "A new tab in your web browser opens with the website being \"localhost\" and some number (e.g., 8888). This is the [JupyterLab](https://jupyterlab.readthedocs.io/en/stable/) application that is used to display and run Jupyter notebooks as described below. On the left, you see the files and folders in your local user folder. This file browser works like any other. In the center, you have several options to launch (i.e., \"create\") new files."
    ]
   },
   {
@@ -248,9 +248,29 @@
     "\n",
     "\"Jupyter\" is an [acronym](https://en.wikipedia.org/wiki/Acronym) derived from the names of the three major programming languages **[Julia](https://julialang.org/)**, **[Python](https://www.python.org)**, and **[R](https://www.r-project.org/)**, all of which play significant roles in the world of data science. The Jupyter Project's idea is to serve as an integrating platform such that different programming languages and software packages can be used together within the same project easily.\n",
     "\n",
-    "Furthermore, Jupyter notebooks have become a de-facto standard for communicating and exchanging results in the data science community (both in academia and business) and often provide a more intuitive alternative to terminal-based ways of running Python (e.g., the default [Python interpreter](https://docs.python.org/3/tutorial/interpreter.html) as shown above or a more advanced interactive version like [IPython](https://ipython.org/)) or even a full-fledged [Integrated Development Environment](https://en.wikipedia.org/wiki/Integrated_development_environment) (e.g., the commercial [PyCharm](https://www.jetbrains.com/pycharm/) or the free [Spyder](https://github.com/spyder-ide/spyder)).\n",
-    "\n",
-    "In particular, they allow mixing plain English text with Python code cells. The plain text can be formatted using the [Markdown](https://guides.github.com/features/mastering-markdown/) language, and mathematical expressions can be typeset with [LaTeX](https://www.overleaf.com/learn/latex/Free_online_introduction_to_LaTeX_%28part_1%29). Lastly, we can include pictures, plots, and even videos. Because of these features, the notebooks developed for this book come in a self-contained \"tutorial\" style that enables students to learn and review the material on their own."
+    "Furthermore, Jupyter notebooks have become a de-facto standard for communicating and exchanging results in the data science community - both in academia and business - and provide an alternative to terminal-based ways of running Python (e.g., the default [Python interpreter](https://docs.python.org/3/tutorial/interpreter.html) as shown below or a more advanced interactive version like [IPython](https://ipython.org/)) or a full-fledged [Integrated Development Environment](https://en.wikipedia.org/wiki/Integrated_development_environment) (e.g., the commercial [PyCharm](https://www.jetbrains.com/pycharm/) or the free [Spyder](https://github.com/spyder-ide/spyder) that comes with the Anaconda Distribution)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "<img src=\"static/terminal.png\" width=\"50%\">"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Jupyter notebooks allow mixing formatted English with Python code in the same document. Text is formatted with the [Markdown](https://guides.github.com/features/mastering-markdown/) language and mathematical formulas are typeset with [LaTeX](https://www.overleaf.com/learn/latex/Free_online_introduction_to_LaTeX_%28part_1%29). Moreover, we may include pictures, plots, and even videos. Because of these features, the notebooks developed for this book come in a self-contained \"tutorial\" style enabling students to learn and review the material on their own."
    ]
   },
   {
@@ -274,15 +294,15 @@
    "source": [
     "A Jupyter notebook consists of cells that have a type associated with them. So far, only cells of type \"Markdown\" have been used, which is the default way to present formatted text.\n",
     "\n",
-    "The next cell below is an example of a \"Code\" cell containing a line of actual Python code: it merely outputs the text \"Hello world\" when executed. To edit an existing code cell, enter into it with a mouse click. You know that you are \"in\" a code cell when you see the frame of the code cell turn green.\n",
+    "The cell below is an example of a \"Code\" cell containing a line of actual Python code: It merely outputs the text \"Hello world\" when executed. To edit an existing code cell, enter into it with a mouse click. You know that you are \"in\" a code cell when its frame is highlighted in blue.\n",
     "\n",
-    "Besides this **edit mode**, there is also a so-called **command mode** that you can reach by hitting the \"Escape\" key after entering a code cell, which turns the frame's color blue. Using the \"Enter\" and \"Escape\" keys, you can now switch between the two modes.\n",
+    "Besides this **edit mode**, there is also a so-called **command mode** that you reach by hitting the \"Escape\" key *after* entering a code cell, which un-highlights the frame. Using the \"Enter\" and \"Escape\" keys, you can now switch between the two modes.\n",
     "\n",
-    "To *execute*, or \"*run*,\" a code cell, hold the \"Control\" key and press \"Enter.\" Note how you do not go to the subsequent cell. Alternatively, you can hold the \"Shift\" key and press \"Enter,\" which executes the cell and places your focus on the next cell right after.\n",
+    "To *execute*, or \"*run*,\" a code cell, hold the \"Control\" key and press \"Enter.\" Note that you do *not* go to the subsequent cell. Alternatively, you can hold the \"Shift\" key and press \"Enter,\" which executes the cell *and* places your focus on the subsequent cell.\n",
     "\n",
-    "Similarly, a Markdown cell is also in either edit or command mode. For example, double-click on the text you are just reading, which takes you into edit mode. Now you could change the formatting (e.g., make a word printed in *italics* or **bold** with single or double asterisks) and then \"execute\" the cell to render the text as specified.\n",
+    "Similarly, a Markdown cell is also in either edit or command mode. For example, double-click on the text you are reading: This puts you into edit mode. Now, you could change the formatting (e.g., make a word printed in *italics* or **bold** with single or double asterisks) and \"execute\" the cell to render the text as specified.\n",
     "\n",
-    "To change a cell's type, choose either \"Code\" or \"Markdown\" in the navigation bar at the top."
+    "To change a cell's type, choose either \"Code\" or \"Markdown\" in the navigation bar at the top. Alternatively, you can hit either the \"Y\" or \"M\" key on your keyboard when in command mode to make the focused cell a code or markdown cell."
    ]
   },
   {
@@ -314,7 +334,7 @@
     }
    },
    "source": [
-    "Sometimes a code cell starts with an exclamation mark `!`. Then, the Jupyter notebook behaves as if the following command were typed directly into a terminal. The cell below asks `python` to show its version number and is *not* Python code but a command in the [Shell](https://en.wikipedia.org/wiki/Shell_script) language. The `!` is useful to execute short commands without leaving a Jupyter notebook."
+    "Sometimes, a code cell starts with an exclamation mark `!`. Then, the Jupyter notebook behaves as if the following command were typed directly into a terminal. The cell below asks `python` to show its version number and is *not* Python code but a command in the [Shell](https://en.wikipedia.org/wiki/Shell_script) language. The `!` is useful to execute short commands without leaving a Jupyter notebook."
    ]
   },
   {
@@ -322,7 +342,7 @@
    "execution_count": 2,
    "metadata": {
     "slideshow": {
-     "slide_type": "-"
+     "slide_type": "skip"
     }
    },
    "outputs": [
@@ -342,7 +362,7 @@
    "cell_type": "markdown",
    "metadata": {
     "slideshow": {
-     "slide_type": "skip"
+     "slide_type": "slide"
     }
    },
    "source": [
@@ -357,15 +377,15 @@
     }
    },
    "source": [
-    "In this book, *programming* is \"defined\" as:\n",
+    "In this book, **programming** is \"defined\" as\n",
     "\n",
-    "- a **structured** way of **problem-solving**\n",
-    "- by **expressing** the steps of a **computation/process**\n",
-    "- and thereby **documenting** the process in a formal way\n",
+    "- a *structured* way of *problem-solving*\n",
+    "- by *expressing* the steps of a *computation/process*\n",
+    "- and thereby *documenting* the process in a formal way.\n",
     "\n",
-    "Programming is always **concrete** and based on a **particular case**.\n",
+    "Programming is always *concrete* and based on a *particular case*.\n",
     "\n",
-    "It exhibits elements of a form of **art** or a **craft** as we hear programmers call code \"beautiful\" or \"ugly\" or talk about the \"expressive\" power of an application."
+    "It exhibits elements of a form of *art* or a *craft* as we hear programmers call code \"beautiful\" or \"ugly\" or talk about the \"expressive\" power of an application."
    ]
   },
   {
@@ -376,14 +396,14 @@
     }
    },
    "source": [
-    "That is different from *computer science*, which is:\n",
+    "That is different from **computer science**, which is\n",
     "\n",
-    "- a field of study comparable to (applied) **mathematics** that\n",
-    "- asks **abstract** questions (\"Is something computable at all?\"),\n",
-    "- develops and analyses **algorithms** and **data structures**,\n",
-    "- and **proves** the **correctness** of a program\n",
+    "- a field of study comparable to (applied) *mathematics* that\n",
+    "- asks *abstract* questions (e.g., \"Is something computable at all?\"),\n",
+    "- develops and analyses *algorithms* and *data structures*,\n",
+    "- and *proves* the *correctness* of a program.\n",
     "\n",
-    "In a sense, a computer scientist does not need to know a programming language to work, and many computer scientists only know how to produce \"ugly\"-looking code in the eyes of professional programmers."
+    "In a sense, a computer scientist does not need to know a programming language to work, and many computer scientists only know how to produce \"ugly\" looking code in the eyes of professional programmers."
    ]
   },
   {
@@ -394,7 +414,7 @@
     }
    },
    "source": [
-    "*IT* or *information technology* is a term that has many meanings to many people. Often, it has something to do with hardware or physical devices, both of which are out of scope for programmers and computer scientists. Many computer scientists and programmers are more than happy if their printer and internet connection work as they often do not know a lot more about that than non-technical people."
+    "**IT** or **information technology** is a term that has many meanings to different people. Often, it has something to do with hardware or physical devices, both of which are out of scope for programmers and computer scientists. Many computer scientists and programmers are more than happy if their printer and internet connection work as they often do not know a lot more about that than \"non-technical\" people."
    ]
   },
   {
@@ -430,10 +450,10 @@
     "Here is a brief history of and some background on Python (cf., also this [TechRepublic article](https://www.techrepublic.com/article/python-is-eating-the-world-how-one-developers-side-project-became-the-hottest-programming-language-on-the-planet/) for a more elaborate story):\n",
     "\n",
     "- [Guido van Rossum](https://en.wikipedia.org/wiki/Guido_van_Rossum) (Python’s **[Benevolent Dictator for Life](https://en.wikipedia.org/wiki/Benevolent_dictator_for_life)**) was bored during a week around Christmas 1989 and started Python as a hobby project \"that would keep \\[him\\] occupied\" for some days\n",
-    "- the idea was to create a **general-purpose scripting** language that would allow fast **prototyping** and would **run on every operating system**\n",
-    "- Python grew through the 90s as van Rossum promoted it via his \"Computer Programming for Everybody\" initiative that had the **goal to encourage a basic level of coding literacy** as an equal knowledge alongside English literacy and math skills\n",
-    "- to become more independent from its creator the next major version **Python 2** (released in 2000; still in heavy use as of today) was **open-sourced** from the get-go which attracted a **large and global community of programmers** that **contributed** their expertise and best practices in their free time to make Python even better\n",
-    "- **Python 3** resulted from a significant overhaul of the language in 2008 taking into account the **learnings from almost two decades**, streamlining the language, and getting ready for the age of **big data**\n",
+    "- the idea was to create a **general-purpose** scripting **language** that would allow fast *prototyping* and would *run on every operating system*\n",
+    "- Python grew through the 90s as van Rossum promoted it via his \"Computer Programming for Everybody\" initiative that had the *goal to encourage a basic level of coding literacy* as an equal knowledge alongside English literacy and math skills\n",
+    "- to become more independent from its creator, the next major version **Python 2** - released in 2000 and still in heavy use as of today - was **open-source** from the get-go which attracted a *large and global community of programmers* that *contributed* their expertise and best practices in their free time to make Python even better\n",
+    "- **Python 3** resulted from a significant overhaul of the language in 2008 taking into account the *learnings from almost two decades*, streamlining the language, and getting ready for the age of **big data**\n",
     "- the language is named after the sketch comedy group [Monty Python](https://en.wikipedia.org/wiki/Monty_Python)"
    ]
   },
@@ -456,7 +476,7 @@
     }
    },
    "source": [
-    "Python is a **general-purpose** programming language that allows for **fast development**, is **easy to read**, **open-source**, long-established, unifies the knowledge of **hundreds of thousands of experts** around the world, runs on basically every machine, and can handle the complexities of applications involving **big data**."
+    "Python is a **general-purpose** programming **language** that allows for *fast development*, is *easy to read*, **open-source**, long-established, unifies the knowledge of *hundreds of thousands of experts* around the world, runs on basically every machine, and can handle the complexities of applications involving **big data**."
    ]
   },
   {
@@ -478,9 +498,9 @@
     }
    },
    "source": [
-    "Couldn't a company like Google, Facebook, or Microsoft come up with a better programming language? The following argument provides hints on why this cannot be the case.\n",
+    "Couldn't a company like Google, Facebook, or Microsoft come up with a better programming language? The following is an argument on why this can likely not be the case.\n",
     "\n",
-    "Wouldn't it be weird if professors and scholars of English literature and language studies dictated how we'd have to speak in day-to-day casual conversations or how authors of poesy and novels should use language constructs to achieve a particular type of mood? If you agree with that premise, it makes sense to assume that even programming languages should evolve in a \"natural\" way as users *use* the language over time and in new and unpredictable contexts creating new conventions."
+    "Wouldn't it be weird if professors and scholars of English literature and language studies dictated how we'd have to speak in day-to-day casual conversations or how authors of poesy and novels should use language constructs to achieve a particular type of mood? If you agree with that premise, it makes sense to assume that even programming languages should evolve in a \"natural\" way as users *use* the language over time and in *new* and *unpredictable* contexts creating new conventions."
    ]
   },
   {
@@ -491,7 +511,7 @@
     }
    },
    "source": [
-    "Loose *communities* are the primary building block around which open-source software projects are built. Someone, like Guido, starts a project and makes it free to use for anybody (e.g., on a code-sharing platform like [GitHub](https://github.com/)). People find it useful enough to solve one of their daily problems and start using it. They see how a project could be improved and provide new use cases (via the popularized concept of a \"[pull request](https://help.github.com/articles/about-pull-requests/)\"). The project grows both in lines of code and people using it. After a while, people start local user groups to share their same interests and meet regularly (e.g., this is a big market for companies like [Meetup](https://www.meetup.com/) or non-profits like [PyData](https://pydata.org/)). Out of these local and usually monthly meetups grow yearly conferences on the country or even continental level (e.g., the original [PyCon](https://us.pycon.org/) in the US, [EuroPython](https://europython.eu/), or [PyCon.DE](https://de.pycon.org/)). The content presented at these conferences is made publicly available via GitHub and YouTube (e.g., [PyCon 2019](https://www.youtube.com/channel/UCxs2IIVXaEHHA4BtTiWZ2mQ) or [EuroPython](http://europython.tv/)) and serves as references on what people are working on and introductions to the endless number of specialized fields."
+    "Loose *communities* are the primary building block around which open-source software projects are built. Someone - like Guido - starts a project and makes it free to use for anybody (e.g., on a code-sharing platform like [GitHub](https://github.com/)). People find it useful enough to solve one of their daily problems and start using it. They see how a project could be improved and provide new use cases (e.g., via the popularized concept of a [pull request](https://help.github.com/articles/about-pull-requests/)). The project grows both in lines of code and people using it. After a while, people start local user groups to share their same interests and meet regularly (e.g., this is a big market for companies like [Meetup](https://www.meetup.com/) or non-profits like [PyData](https://pydata.org/)). Out of these local and usually monthly meetups grow yearly conferences on the country or even continental level (e.g., the original [PyCon](https://us.pycon.org/) in the US, [EuroPython](https://europython.eu/), or [PyCon.DE](https://de.pycon.org/)). The content presented at these conferences is made publicly available via GitHub and YouTube (e.g., [PyCon 2019](https://www.youtube.com/channel/UCxs2IIVXaEHHA4BtTiWZ2mQ) or [EuroPython](http://europython.tv/)) and serves as references on what people are working on and introductions to the endless number of specialized fields."
    ]
   },
   {
@@ -504,7 +524,7 @@
    "source": [
     "While these communities are somewhat loose and continuously changing, smaller in-groups, often democratically organized and elected (e.g., the [Python Software Foundation](https://www.python.org/psf/)), take care of, for example, the development of the \"core\" Python language itself.\n",
     "\n",
-    "Interestingly, Python is just a specification (i.e., a set of rules) as to what is allowed and what not. The current version of Python can always be looked up in the [Python Language Reference](https://docs.python.org/3/reference/index.html). To make changes to that, anyone can make a so-called **[Python Enhancement Proposal](https://www.python.org/dev/peps/)**, or **PEP** for short, where it needs to be specified what exact changes are to be made and argued why that is a good thing to do. These PEPs are reviewed by the [core developers](https://devguide.python.org/coredev/) and interested people and are then either accepted, modified, or rejected if, for example, the change introduces internal inconsistencies. This process is similar to the **double-blind peer review** established in academia. Many of the contributors held or hold positions in academia, one more indicator of the high quality standards in the Python community. To learn more about PEPs, check out [PEP 1](https://www.python.org/dev/peps/pep-0001/) that describes the entire process.\n",
+    "Python itself is just a specification (i.e., a set of rules) as to what is allowed and what not: It must first be implemented (c.f., next section below). The current version of Python can always be looked up in the [Python Language Reference](https://docs.python.org/3/reference/index.html). To make changes to that, anyone can make a so-called **[Python Enhancement Proposal](https://www.python.org/dev/peps/)**, or **PEP** for short, where it needs to be specified what exact changes are to be made and argued why that is a good thing to do. These PEPs are reviewed by the [core developers](https://devguide.python.org/coredev/) and interested people and are then either accepted, modified, or rejected if, for example, the change introduces internal inconsistencies. This process is similar to the **double-blind peer review** established in academia, just a lot more transparent. Many of the contributors even held or hold positions in academia, one more indicator of the high quality standards in the Python community. To learn more about PEPs, check out [PEP 1](https://www.python.org/dev/peps/pep-0001/) that describes the entire process.\n",
     "\n",
     "In total, no one single entity can control how the language evolves, and the users' needs and ideas always feed back to the language specification via a quality controlled and \"democratic\" process."
    ]
@@ -517,7 +537,7 @@
     }
    },
    "source": [
-    "Besides being free as in **\"free beer**,\" a major benefit of open-source is that one can always **look up how something works in detail**: That is the literal meaning of *open* source and different as compared to commercial languages (e.g., MATLAB) since a programmer can always continue to **study best practices** or find out how things are implemented. Along this way, many **errors are uncovered** as well. Furthermore, if one runs an open-source application, one can be reasonably sure that no bad people built in a \"backdoor.\" [Free software](https://en.wikipedia.org/wiki/Free_software) is consequently free of charge but brings many other freedoms with it, most notably the freedom to change the code."
+    "Besides being **free** as in \"free beer,\" a major benefit of open-source is that one can always *look up how something works in detail*: That is the literal meaning of *open* source and a difference to commercial languages (e.g., MATLAB) as a programmer can always continue to *study best practices* or find out how things are implemented. Along this way, many *errors are uncovered*, as well. Furthermore, if one runs an open-source application, one can be reasonably sure that no bad people built in a \"backdoor.\" [Free software](https://en.wikipedia.org/wiki/Free_software) is consequently free of charge but brings *many other freedoms* with it, most notably the freedom to change the code."
    ]
   },
   {
@@ -541,11 +561,31 @@
    "source": [
     "The \"weird\" thing is that the default Python implementation is written in the C language.\n",
     "\n",
-    "[C](https://en.wikipedia.org/wiki/C_%28programming_language%29) and [C++](https://en.wikipedia.org/wiki/C%2B%2B) (cf., this [introduction](https://www.learncpp.com/)) are wide-spread and long-established (i.e., since the 1970s) programming languages employed in many mission-critical software systems (e.g., operating systems themselves, low latency databases and web servers, nuclear reactor control systems, airplanes, ...). They are fast, mainly because the programmer not only needs to come up with the **business logic** but also manage the computer's memory \"manually\" (and the knowledge necessary to do that is not easy to learn).\n",
+    "[C](https://en.wikipedia.org/wiki/C_%28programming_language%29) and [C++](https://en.wikipedia.org/wiki/C%2B%2B) (cf., this [introduction](https://www.learncpp.com/)) are wide-spread and long-established (i.e., since the 1970s) programming languages employed in many mission-critical software systems (e.g., operating systems themselves, low latency databases and web servers, nuclear reactor control systems, airplanes, ...). They are fast, mainly because the programmer not only needs to come up with the **business logic** but also manage the computer's memory \"manually\" - and the knowledge necessary to do that is not easy to obtain.\n",
     "\n",
-    "In contrast, Python automatically manages the memory for the programmer. So, speed here is a trade-off between application run time and engineering/development time. Often, the program's run time is not that important: For example, what if C needs 0.001 seconds in a case where Python needs 0.1 seconds to train a linear regression model? When the requirements change and computing speed becomes an issue, the Python community offers many third-party libraries (usually also written in C) where specific problems can be solved in near-C time.\n",
-    "\n",
-    "**In a nutshell**: While it is, of course, true that C is a lot faster than Python when it comes to **pure computation time**, often, this does not matter as the **significantly shorter development cycles** are the more significant cost factor in a rapidly changing business world."
+    "In contrast, Python automatically manages the memory for the programmer. So, speed here is a trade-off between application run time and engineering/development time. Often, the program's run time is not that important: For example, what if C needs 0.001 seconds in a case where Python needs 0.1 seconds to do the same thing? When the requirements change and computing speed becomes an issue, the Python community offers many third-party libraries - usually also written in C - where specific problems can be solved in near-C time."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "#### Summary"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "While it is true that a language like C is a lot faster than Python when it comes to *pure* **computation time**, this does not matter in many cases as the *significantly shorter* **development cycles** are the more significant cost factor in a rapidly changing world."
    ]
   },
   {
@@ -578,15 +618,14 @@
     }
    },
    "source": [
-    "While it is usually not the best argument to quote authoritative figures like the pope, we briefly look at who uses Python here and leave it up to the reader to decide if this is convincing or not:\n",
+    "While ad-hominem arguments are usually not the best kind of reasoning, we briefly look at some examples of who uses Python and leave it up to the reader to decide if this is convincing or not:\n",
     "\n",
     "- **[Massachusetts Institute of Technology](https://www.mit.edu/)**\n",
     "  - teaches Python in its [introductory course](https://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-0001-introduction-to-computer-science-and-programming-in-python-fall-2016/) to computer science independent of the student's major\n",
     "  - replaced the infamous course on the [Scheme](https://groups.csail.mit.edu/mac/projects/scheme/) language (cf., [source](https://news.ycombinator.com/item?id=602307))\n",
     "- **[Google](https://www.google.com/)**\n",
-    "  - used the strategy **\"Python where we can, C++ where we must\"** from its early days on to stay flexible in a rapidly changing environment (cf., [source](https://stackoverflow.com/questions/2560310/heavy-usage-of-python-at-google))\n",
-    "  - the very first web-crawler was written in **Java and so difficult to maintain** that it was **rewritten in Python** right away (cf., [source](https://www.amazon.com/Plex-Google-Thinks-Works-Shapes/dp/1416596585/ref=sr_1_1?ie=UTF8&qid=1539101827&sr=8-1&keywords=in+the+plex))\n",
-    "  - Python and C++, Java, and Go are the only four server-side languages to be deployed to production\n",
+    "  - used the strategy \"Python where we can, C++ where we must\" from its early days on to stay flexible in a rapidly changing environment (cf., [source](https://stackoverflow.com/questions/2560310/heavy-usage-of-python-at-google))\n",
+    "  - the very first web-crawler was written in Java and so difficult to maintain that it was rewritten in Python right away (cf., [source](https://www.amazon.com/Plex-Google-Thinks-Works-Shapes/dp/1416596585/ref=sr_1_1?ie=UTF8&qid=1539101827&sr=8-1&keywords=in+the+plex))\n",
     "  - Guido van Rossom was hired by Google from 2005 to 2012 to advance the language there\n",
     "- **[NASA](https://www.nasa.gov/)** open-sources many of its projects, often written in Python and regarding analyses with big data (cf., [source](https://code.nasa.gov/language/python/))\n",
     "- **[Facebook](https://facebook.com/)** uses Python besides C++ and its legacy PHP (a language for building websites; the \"cool kid\" from the early 2000s)\n",
@@ -684,11 +723,11 @@
    "source": [
     "**A**lways **b**e **c**oding.\n",
     "\n",
-    "Programming is more than just writing code into a text file. It means reading through parts of the [documentation](https://docs.python.org/), blogs with best practices, and tutorials, or researching problems on Stack Overflow while trying to implement features in the application at hand. Also, it means using command-line tools to automate some part of the work or manage different versions of a program, for example, with **[git](https://git-scm.com/)**. In short, programming involves a lot of \"muscle memory,\" which can only be built and kept up through near-daily usage.\n",
+    "Programming is more than just writing code into a text file. It means reading through parts of the [documentation](https://docs.python.org/), blogs with best practices, and tutorials, or researching problems on [Stack Overflow](https://stackoverflow.com/) while trying to implement features in the application at hand. Also, it means using command-line tools to automate some part of the work or manage different versions of a program, for example, with **[git](https://git-scm.com/)**. In short, programming involves a lot of \"muscle memory,\" which can only be built and kept up through near-daily usage.\n",
     "\n",
     "Further, many aspects of software architecture and best practices can only be understood after having implemented some requirements for the very first time. Coding also means \"breaking\" things to find out what makes them work in the first place.\n",
     "\n",
-    "Coding is learned best by just doing it for some time on a daily or at least a regular basis and not right before some task is due, just like learning a \"real\" language."
+    "Therefore, coding is learned best by just doing it for some time on a daily or at least a regular basis and not right before some task is due, just like learning a \"real\" language."
    ]
   },
   {
@@ -712,12 +751,12 @@
    "source": [
     "[Y Combinator](https://www.ycombinator.com/)'s co-founder [Paul Graham](https://en.wikipedia.org/wiki/Paul_Graham_%28programmer%29) wrote a very popular and often cited [article](http://www.paulgraham.com/makersschedule.html) where he divides every person into belonging to one of two groups:\n",
     "\n",
-    "- **Managers**: People that need to organize things and command others (like a \"boss\"). Their schedule is usually organized by the hour or even 30-minute intervals.\n",
-    "- **Makers**: People that create things (like programmers, artists, or writers). Such people think in half days or full days.\n",
+    "- **Managers**: People that need to organize things and command others (e.g., a \"boss\" or manager). Their schedule is usually organized by the hour or even 30-minute intervals.\n",
+    "- **Makers**: People that create things (e.g., programmers, artists, or writers). Such people think in half days or full days.\n",
     "\n",
     "Have you ever wondered why so many tech people work during nights and sleep at \"weird\" times? The reason is that many programming-related tasks require a \"flow\" state in one's mind that is hard to achieve when one can get interrupted, even if it is only for one short question. Graham describes that only knowing that one has an appointment in three hours can cause a programmer to not get into a flow state.\n",
     "\n",
-    "As a result, do not set aside a certain amount of time for learning something but rather plan in an **entire evening** or a **rainy Sunday** where you can work on a problem in an **open end** setting. And do not be surprised anymore to hear \"I looked at it over the weekend\" from a programmer."
+    "As a result, do not set aside a certain amount of time for learning something but rather plan in an *entire evening* or a *rainy Sunday* where you can work on a problem in an *open end* setting. And do not be surprised anymore to hear \"I looked at it over the weekend\" from a programmer."
    ]
   },
   {
diff --git a/README.md b/README.md
index e64527c..3df2247 100644
--- a/README.md
+++ b/README.md
@@ -110,7 +110,7 @@ application that is used to display and run the Jupyter notebooks mentioned
 above.
 On the left, you see the files and folders in your local user folder.
 This file browser works like any other.
-In the center, you have several options to launch a new notebook file.
+In the center, you have several options to launch (i.e., "create") new files.
 
 <img src="static/jupyter_lab.png" width="50%">
 

From 18a0ee7c4c7eea0f5f75c1175b63937dca7e74eb Mon Sep 17 00:00:00 2001
From: Alexander Hess <alexander@webartifex.biz>
Date: Sun, 2 Feb 2020 20:00:22 +0100
Subject: [PATCH 063/242] Add pdf files for live presentations

- warning that a class room session is recorded
- template for memory diagrams
---
 static/in_class_memory_diagram.pdf             | Bin 0 -> 163306 bytes
 .../in_class_recording_in_progress_warning.pdf | Bin 0 -> 14433 bytes
 2 files changed, 0 insertions(+), 0 deletions(-)
 create mode 100644 static/in_class_memory_diagram.pdf
 create mode 100644 static/in_class_recording_in_progress_warning.pdf

diff --git a/static/in_class_memory_diagram.pdf b/static/in_class_memory_diagram.pdf
new file mode 100644
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From 522eeb1b433bb5dd98a3a0b7dfdf740388755a8a Mon Sep 17 00:00:00 2001
From: Alexander Hess <alexander@webartifex.biz>
Date: Sun, 2 Feb 2020 22:27:49 +0100
Subject: [PATCH 064/242] Streamline text

---
 01_elements_00_lecture.ipynb   | 146 ++++++++++++++++-----------------
 01_elements_02_exercises.ipynb |   8 +-
 2 files changed, 77 insertions(+), 77 deletions(-)

diff --git a/01_elements_00_lecture.ipynb b/01_elements_00_lecture.ipynb
index c627331..b37b958 100644
--- a/01_elements_00_lecture.ipynb
+++ b/01_elements_00_lecture.ipynb
@@ -20,9 +20,9 @@
     }
    },
    "source": [
-    "Do you remember how you first learned to speak in your mother tongue? Probably not. No one's memory goes back that far. Your earliest memory as a child should probably be around the age of three or four years old when you could already say simple things and interact with your environment. Although you did not know any grammar rules yet, other people just understood what you said. Well, most of the time.\n",
+    "Do you remember how you first learned to speak in your mother tongue? Probably not. No one's memory goes back that far. Your earliest memory as a child should probably be around the age of three or four years old when you could already say simple things and interact with your environment. Although you did not know any grammar rules yet, other people just understood what you said. At least most of the time.\n",
     "\n",
-    "It is intuitively best to take the very mindset of a small child when learning a foreign language. This first chapter introduces simplistic examples, and we accept them as they are *without* knowing any of the \"grammar\" rules yet. Then, we analyze them in parts and slowly build up our understanding.\n",
+    "It is intuitively best to take the very mindset of a small child when learning a new language. And a programming language is no different from that. This first chapter introduces simplistic examples and we accept them as they are *without* knowing any of the \"grammar\" rules yet. Then, we analyze them in parts and slowly build up our understanding.\n",
     "\n",
     "Consequently, if parts of this chapter do not make sense right away, let's not worry too much. Besides introducing the basic elements, it also serves as an outlook for what is to come. So, many terms and concepts used here are deconstructed in great detail in the following chapters."
    ]
@@ -46,7 +46,7 @@
     }
    },
    "source": [
-    "As our introductory example, we want to calculate the *average* of all *evens* in a **list** of numbers: `[7, 11, 8, 5, 3, 12, 2, 6, 9, 10, 1, 4]`.\n",
+    "As our introductory example, we want to calculate the *average* of all *evens* in a **list** of whole numbers: `[7, 11, 8, 5, 3, 12, 2, 6, 9, 10, 1, 4]`.\n",
     "\n",
     "While we are used to finding an [analytical solution](https://math.stackexchange.com/questions/935405/what-s-the-difference-between-analytical-and-numerical-approaches-to-problems/935446#935446) in math (i.e., derive some equation with \"pen and paper\"), we solve this task *programmatically* instead.\n",
     "\n",
@@ -110,17 +110,17 @@
     }
    },
    "source": [
-    "So far, so good. Let's see how the desired **computation** could be expressed as a **sequence of instructions** in Python.\n",
+    "So far, so good. Let's see how the desired **computation** could be expressed as a **sequence of instructions** in the next code cell.\n",
     "\n",
     "Intuitively, the line `for number in numbers` describes a \"loop\" over all the numbers in the `numbers` list, one at a time.\n",
     "\n",
     "The `if number % 2 == 0` may look confusing at first sight. Both `%` and `==` must have an unintuitive meaning here. Luckily, the **comment** in the same line after the `#` symbol has the answer: The program does something only for an even `number`.\n",
     "\n",
-    "In particular, it increases `count` by `1` and adds the current `number` onto the [running](https://en.wikipedia.org/wiki/Running_total) `total`. Both `count` and `number` are initially set to `0` and the single `=` symbol reads as \"... is *set* equal to ...\". It could not indicate a mathematical equation as, for example, `count` is generally not equal to `count + 1`.\n",
+    "In particular, it increases `count` by `1` and adds the current `number` onto the [running](https://en.wikipedia.org/wiki/Running_total) `total`. Both `count` and `number` are **initialized** to `0` and the single `=` symbol reads as \"... is *set* equal to ...\". It cannot indicate a mathematical equation as, for example, `count` is generally *not* equal to `count + 1`.\n",
     "\n",
     "Lastly, the `average` is calculated as the ratio of the final **values** of `total` and `count`. Overall, we divide the sum of all even numbers by their count: This is nothing but the definition of an average.\n",
     "\n",
-    "The lines of code \"within\" the `for` and `if` **statements** are **indented** and **aligned** with multiples of **four spaces**: This shows immediately how the lines relate to each other."
+    "The lines of code \"within\" the `for` and `if` **statements** are **indented** and aligned with multiples of *four spaces*: This shows immediately how the lines relate to each other."
    ]
   },
   {
@@ -187,7 +187,7 @@
     }
    },
    "source": [
-    "## Generating Cell Output in a Jupyter Notebook"
+    "## Output in a Jupyter Notebook"
    ]
   },
   {
@@ -198,9 +198,9 @@
     }
    },
    "source": [
-    "Note how only two of the previous four code cells generate an **output** while two remained \"silent\" (i.e., there is no \"**Out[...]**\" after running the cell).\n",
+    "Only two of the previous four code cells generate an **output** while two remained \"silent\" (i.e., nothing appears below the cell after running it).\n",
     "\n",
-    "By default, Jupyter notebooks show the value of the **expression** in the last line of a code cell only. This output can be suppressed by ending the last line with a semicolon `;`."
+    "By default, Jupyter notebooks only show the value of the **expression** in the last line of a code cell. And, this output may also be suppressed by ending the last line with a semicolon `;`."
    ]
   },
   {
@@ -249,7 +249,7 @@
     }
    },
    "source": [
-    "To visualize something before the end of the cell, we use the built-in [print()](https://docs.python.org/3/library/functions.html#print) **function**. Here, the parentheses `()` indicate that we execute code written somewhere else."
+    "To see any output other than that, we use the built-in [print()](https://docs.python.org/3/library/functions.html#print) **function**. Here, the parentheses `()` indicate that we **call** (i.e., \"execute\") code written somewhere else."
    ]
   },
   {
@@ -329,9 +329,9 @@
    "source": [
     "Python comes with many built-in **[operators](https://docs.python.org/3/reference/lexical_analysis.html#operators)**: They are **tokens** (i.e., \"symbols\") that have a special meaning to the Python interpreter.\n",
     "\n",
-    "The arithmetic operators either \"operate\" with the number immediately following them (= **unary** operators; e.g., negation) or \"process\" the two numbers \"around\" them (= **binary** operators; e.g., addition).\n",
+    "The arithmetic operators either \"operate\" with the number immediately following them, so-called **unary** operators (e.g., negation), or \"process\" the two numbers \"around\" them, so-called **binary** operators (e.g., addition).\n",
     "\n",
-    "By definition, operators have **no** permanent **side effects** in the computer's memory. Although the code cells in this section do indeed create *new* numbers in memory (e.g., `77 + 13` creates `90`), they are immediately \"forgotten\" as they are not stored in a **variable** like `numbers` or `average` above. We develop this thought further at the end of this chapter when we compare **expressions** with **statements**.\n",
+    "By definition, operators on their own have *no* permanent **side effects** in the computer's memory. Although the code cells in this section do indeed create *new* numbers in memory (e.g., `77 + 13` creates `90`), they are immediately \"forgotten\" as they are not stored in a **variable** like `numbers` or `average` above. We develop this thought further at the end of this chapter when we compare **expressions** with **statements**.\n",
     "\n",
     "Let's see some examples of operators. We start with the binary `+` and the `-` operators for addition and subtraction. Binary operators mimic what mathematicians call [infix notation](https://en.wikipedia.org/wiki/Infix_notation) and have the expected meaning."
    ]
@@ -392,7 +392,7 @@
     }
    },
    "source": [
-    "The `-` operator may be used as a unary operator as well. Then it just flips the sign of a number."
+    "The `-` operator may be used as a unary operator as well. Then, it unsurprisingly flips the sign of a number."
    ]
   },
   {
@@ -545,7 +545,7 @@
     }
    },
    "source": [
-    "Even though it appears that the `//` operator **truncates** (i.e., \"cuts off\") the decimals so as to effectively \"rounding\" down (i.e., the `42.5` became `42` in the previous code cell), this is *not* the case: The result is always \"rounded\" towards minus infinity!"
+    "Even though it appears that the `//` operator **truncates** (i.e., \"cuts off\") the decimals so as to effectively \"round down\" (i.e., the `42.5` became `42` in the previous code cell), this is *not* the case: The result is always \"rounded\" towards minus infinity!"
    ]
   },
   {
@@ -617,7 +617,7 @@
    "source": [
     "The remainder is `0` *only if* a number is *divisible* by another.\n",
     "\n",
-    "A popular convention in both, computer science and mathematics, is to abbreviate \"only if\" as **iff**, which is short for \"**[if and only if](https://en.wikipedia.org/wiki/If_and_only_if)**.\" The iff means that a remainder of `0` implies that a number is divisible by another but also that a number divisible by another implies a remainder of `0`. The implication goes in *both* directions!\n",
+    "A popular convention in both computer science and mathematics is to abbreviate \"only if\" as \"iff\", which is short for \"**[if and only if](https://en.wikipedia.org/wiki/If_and_only_if)**.\" The iff means that a remainder of `0` implies that a number is divisible by another but also that a number's being divisible by another implies a remainder of `0`. The implication goes in *both* directions!\n",
     "\n",
     "So, `49` is divisible by `7`."
    ]
@@ -713,7 +713,7 @@
     }
    },
    "source": [
-    "The built-in [divmod()](https://docs.python.org/3/library/functions.html#divmod) function combines the integer and modulo divisions into one operation. However, this is not an operator but a function. Also, [divmod()](https://docs.python.org/3/library/functions.html#divmod) returns a \"pair\" of integers and not just one."
+    "The built-in [divmod()](https://docs.python.org/3/library/functions.html#divmod) function combines the integer and modulo divisions into one operation. However, grammatically this is *not* an operator but a function. Also, [divmod()](https://docs.python.org/3/library/functions.html#divmod) returns a \"pair\" of integers and not just one."
    ]
   },
   {
@@ -877,7 +877,7 @@
     }
    },
    "source": [
-    "Some programmers also use \"style\" conventions. For example, we might play with the **whitespace**, which is an umbrella term that refers to any non-printable sign like spaces, tabs, or the like. However, parentheses convey a much clearer picture."
+    "Some programmers also use \"style\" conventions. For example, we might play with the **whitespace**, which is an umbrella term that refers to any non-printable sign like spaces, tabs, or the like. However, this is *not* a good practice and parentheses convey a much clearer picture."
    ]
   },
   {
@@ -912,7 +912,7 @@
     }
    },
    "source": [
-    "There exist many non-mathematical operators that are introduced throughout this book, together with the concepts they implement. They often come in a form different from the unary and binary mentioned above."
+    "There exist many non-mathematical operators that are introduced throughout this book, together with the concepts they implement. They often come in a form different from the unary and binary ones mentioned above."
    ]
   },
   {
@@ -936,7 +936,7 @@
    "source": [
     "Python is a so-called **object-oriented** language, which is a paradigm of organizing a program's memory.\n",
     "\n",
-    "An **object** may be viewed as a \"bag\" of $0$s and $1$s in a given memory location. The $0$s and $1$s in a bag make up the object's **value**. There exist different **types** of bags: Each type comes with distinct rules how the $0$s and $1$s are interpreted and may be worked with.\n",
+    "An **object** may be viewed as a \"bag\" of $0$s and $1$s in a given memory location. The $0$s and $1$s in a bag make up the object's **value**. There exist different **types** of bags, and each type comes with its own rules how the $0$s and $1$s are interpreted and may be worked with.\n",
     "\n",
     "So, an object *always* has *three* main characteristics. Let's look at the following examples and work them out."
    ]
@@ -975,7 +975,7 @@
     }
    },
    "source": [
-    "The [id()](https://docs.python.org/3/library/functions.html#id) built-in function shows an object's \"address\" in memory."
+    "The built-in [id()](https://docs.python.org/3/library/functions.html#id) function shows an object's \"address\" in memory."
    ]
   },
   {
@@ -990,7 +990,7 @@
     {
      "data": {
       "text/plain": [
-       "140173037405648"
+       "139627575613392"
       ]
      },
      "execution_count": 28,
@@ -1014,7 +1014,7 @@
     {
      "data": {
       "text/plain": [
-       "140173037405584"
+       "139627575613200"
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      "execution_count": 29,
@@ -1038,7 +1038,7 @@
     {
      "data": {
       "text/plain": [
-       "140173037234160"
+       "139627575358192"
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      },
      "execution_count": 30,
@@ -1058,7 +1058,7 @@
     }
    },
    "source": [
-    "These addresses are *not* meaningful for anything other than checking if two variables reference the *same* object. Let's create a second variable `d` and also set it to `789`."
+    "These addresses are *not* meaningful for anything other than checking if two variables reference the *same* object. Let's create a new variable `d` and also set it to `789`."
    ]
   },
   {
@@ -1082,7 +1082,7 @@
     }
    },
    "source": [
-    "`a` and `d` indeed have the same value as is checked with the **equality operator** `==`. The resulting `True` (and the `False` further below) is yet another data type, a so-called **boolean**. We look into them closely in [Chapter 3](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/03_conditionals_00_lecture.ipynb#Boolean-Expressions)."
+    "`a` and `d` indeed have the *same value* as is checked with the **equality operator** `==`: We say `a` and `d` \"evaluate equal.\" The resulting `True` - and the `False` further below - is yet another data type, a so-called **boolean**. We look into them in [Chapter 3](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/03_conditionals_00_lecture.ipynb#Boolean-Expressions)."
    ]
   },
   {
@@ -1117,7 +1117,7 @@
     }
    },
    "source": [
-    "On the contrary, `a` and `d` are different objects as the **identity operator** `is` shows: they are stored at separate addresses in the memory."
+    "On the contrary, `a` and `d` are *different objects* as the **identity operator** `is` shows: They are stored at *different* addresses in the memory."
    ]
   },
   {
@@ -1152,7 +1152,7 @@
     }
    },
    "source": [
-    "If we want to check the opposite case, we use the negated version of the `is` operator."
+    "If we want to check the opposite case, we use the negated version of the `is` operator, namely `is not`."
    ]
   },
   {
@@ -1198,7 +1198,7 @@
     }
    },
    "source": [
-    "The [type()](https://docs.python.org/3/library/functions.html#type) built-in function shows an object's type. For example, `a` is an integer (i.e., `int`) while `b` is a so-called [floating-point number](https://en.wikipedia.org/wiki/Floating-point_arithmetic) (i.e., `float`)."
+    "The [type()](https://docs.python.org/3/library/functions.html#type) built-in shows an object's type. For example, `a` is an integer (i.e., `int`) while `b` is a so-called [floating-point number](https://en.wikipedia.org/wiki/Floating-point_arithmetic) (i.e., `float`)."
    ]
   },
   {
@@ -1257,9 +1257,9 @@
     }
    },
    "source": [
-    "Different types imply different behaviors for the objects. The `b` object, for example, can be \"asked\" if it could also be interpreted as an `int` with the [is_integer()](https://docs.python.org/3/library/stdtypes.html#float.is_integer) \"functionality\" that comes with every `float` object.\n",
+    "Different types imply different behaviors for the objects. The `b` object, for example, may be \"asked\" if it is a whole number with the [is_integer()](https://docs.python.org/3/library/stdtypes.html#float.is_integer) \"functionality\" that comes with every `float` object.\n",
     "\n",
-    "Formally, we call such type-specific functionalities **methods** (to differentiate them from functions) and we formally introduce them in Chapter 10. For now, it suffices to know that we access them using the **dot operator** `.`. Of course, `b` could be converted into an `int`, which the boolean value `True` tells us."
+    "Formally, we call such type-specific functionalities **methods** (i.e., as opposed to functions) and we formally introduce them in Chapter 10. For now, it suffices to know that we access them with the **dot operator** `.` on the object. Of course, `b` is a whole number, which the boolean object `True` tells us."
    ]
   },
   {
@@ -1330,7 +1330,7 @@
     }
    },
    "source": [
-    "The `c` object is a so-called **string** type (i.e., `str`), which is Python's way of representing \"text.\" Strings also come with peculiar behaviors, for example, to convert a text to lower or upper case."
+    "The `c` object is a so-called **string** type (i.e., `str`), which is Python's way of representing \"text.\" Strings also come with peculiar behaviors, for example, to convert a text to lower, upper, or title case."
    ]
   },
   {
@@ -1448,9 +1448,9 @@
     }
    },
    "source": [
-    "Almost trivially, every object also has a value to which it \"evaluates\" when referenced. We think of the value as the **conceptual idea** of what the $0$s and $1$s in the bag mean to *humans* as machines cannot see beyond $0$s and $1$s.\n",
+    "Almost trivially, every object also has a value to which it **evaluates** when referenced. We think of the value as the **conceptual idea** of what the $0$s and $1$s in the bag mean to *humans*.\n",
     "\n",
-    "For built-in data types, Python prints out the object's value as a so-called **[literal](https://docs.python.org/3/reference/lexical_analysis.html#literals)**: This means that we can copy and paste the output back into a code cell to create a *new* object with the *same* value."
+    "For built-in data types, Python prints out an object's value as a so-called **[literal](https://docs.python.org/3/reference/lexical_analysis.html#literals)**: This means that we may copy and paste the value back into a code cell and create a *new* object with the *same* value."
    ]
   },
   {
@@ -1509,7 +1509,7 @@
     }
    },
    "source": [
-    "In this book, we follow the convention of creating strings with **double quotes** `\"` instead of the **single quotes** `'` to which Python defaults in its *literal* notation for `str` objects. Both types of quotes may be used interchangeably."
+    "In this book, we follow the convention of creating strings with **double quotes** `\"` instead of the **single quotes** `'` to which Python defaults in its *literal* notation for `str` objects. Both types of quotes may be used interchangeably. So, the `\"Python rocks\"` from above and `'Python rocks'` create two objects that evaluate equal (i.e., `\"Python rocks\" == 'Python rocks'`)."
    ]
   },
   {
@@ -1533,7 +1533,7 @@
     }
    ],
    "source": [
-    "c  # we defined c = \"Python rocks\" with double quotes \" above"
+    "c"
    ]
   },
   {
@@ -1581,7 +1581,7 @@
    "source": [
     "If we do not follow the rules, the code cannot be **parsed** correctly, i.e., the program does not even start to run but **raises** a **syntax error** indicated as `SyntaxError` in the output. Computers are very dumb in the sense that the slightest syntax error leads to the machine not understanding our code.\n",
     "\n",
-    "For example, if we wanted to write an accounting program that adds up currencies, we would have to model dollar prices as `float` objects as the dollar symbol cannot be understood by Python."
+    "If we were to write an accounting program that adds up currencies, we would, for example, have to model dollar prices as `float` objects as the dollar symbol cannot be understood by Python."
    ]
   },
   {
@@ -1693,9 +1693,9 @@
     }
    },
    "source": [
-    "Syntax errors are easy to find as the code does not even run in the first place.\n",
+    "Syntax errors are easy to find as the code does *not* even run in the first place.\n",
     "\n",
-    "However, there are also so-called **runtime errors**, often called **exceptions**, that occur whenever otherwise (i.e., syntactically) correct code does not run because of invalid input.\n",
+    "However, there are also so-called **runtime errors** that occur whenever otherwise (i.e., syntactically) correct code does not run because of invalid input. Runtime errors are also often referred to as **exceptions**.\n",
     "\n",
     "This example does not work because just like in the \"real\" world, Python does not know how to divide by `0`. The syntactically correct code leads to a `ZeroDivisionError`."
    ]
@@ -1825,7 +1825,7 @@
     }
    },
    "source": [
-    "Thus, adhering to just syntax rules is *never* enough. Over time, **best practices** and **style guides** were created to make it less likely for a developer to mess up a program and also to allow \"onboarding\" him as a contributor to an established code base, often called **legacy code**, faster. These rules are not enforced by Python itself: Badly styled code still runs. At the very least, Python programs should be styled according to [PEP 8](https://www.python.org/dev/peps/pep-0008/) and documented \"inline\" (i.e., in the code itself) according to [PEP 257](https://www.python.org/dev/peps/pep-0257/).\n",
+    "Thus, adhering to just syntax rules is *never* enough. Over time, **best practices** and **style guides** were created to make it less likely for a developer to mess up a program and also to allow \"onboarding\" him as a contributor to an established code base, often called **legacy code**, faster. These rules are *not* enforced by Python itself: Badly styled code still runs. At the very least, Python programs should be styled according to [PEP 8](https://www.python.org/dev/peps/pep-0008/) and documented \"inline\" (i.e., in the code itself) according to [PEP 257](https://www.python.org/dev/peps/pep-0257/).\n",
     "\n",
     "An easier to read version of PEP 8 is [here](https://pep8.org/). The video below features a well known **[Pythonista](https://en.wiktionary.org/wiki/Pythonista)** talking about the importance of code style."
    ]
@@ -1854,7 +1854,7 @@
        "        "
       ],
       "text/plain": [
-       "<IPython.lib.display.YouTubeVideo at 0x7f7c94098310>"
+       "<IPython.lib.display.YouTubeVideo at 0x7efd940bd210>"
       ]
      },
      "execution_count": 52,
@@ -1875,7 +1875,7 @@
     }
    },
    "source": [
-    "For example, while the above code to calculate the average of the even numbers in `[7, 11, 8, 5, 3, 12, 2, 6, 9, 10, 1, 4]` is correct, a Pythonista would rewrite it in a more \"Pythonic\" way and use the [sum()](https://docs.python.org/3/library/functions.html#sum) and [len()](https://docs.python.org/3/library/functions.html#len) (= \"length\") [built-in functions](https://docs.python.org/3/library/functions.html) (cf., [Chapter 2](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/02_functions_00_lecture.ipynb#Built-in-Functions)) as well as a so-called **list comprehension** (cf., [Chapter 7](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/07_sequences_00_lecture.ipynb#List-Comprehensions)). Pythonic code runs faster in many cases and is less error-prone."
+    "For example, while the above code to calculate the average of the even numbers in `[7, 11, 8, 5, 3, 12, 2, 6, 9, 10, 1, 4]` is correct, a Pythonista would rewrite it in a more \"Pythonic\" way and use the built-in [sum()](https://docs.python.org/3/library/functions.html#sum) and [len()](https://docs.python.org/3/library/functions.html#len) functions (cf., [Chapter 2](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/02_functions_00_lecture.ipynb#Built-in-Functions)) as well as a so-called **list comprehension** (cf., [Chapter 7](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/07_sequences_00_lecture.ipynb#List-Comprehensions)). Pythonic code runs faster in many cases and is less error-prone."
    ]
   },
   {
@@ -1973,7 +1973,7 @@
     }
    },
    "source": [
-    "To get a rough overview of the mindsets of a typical Python programmer, see these rules by an early Python core developer deemed so important that they are included in every Python installation."
+    "To get a rough overview of the mindset of a typical Python programmer, look at these rules, also known as the **Zen of Python**, that are deemed so important that they are included in every Python installation."
    ]
   },
   {
@@ -2025,7 +2025,7 @@
     }
    },
    "source": [
-    "### Jupyter Notebook Aspects to keep in Mind"
+    "### Jupyter Notebook Aspects"
    ]
   },
   {
@@ -2047,11 +2047,11 @@
     }
    },
    "source": [
-    "Observe that you can run the code cells in a Jupyter notebook in any arbitrary order.\n",
+    "We can run the code cells in a Jupyter notebook in *any* arbitrary order.\n",
     "\n",
     "That means, for example, that a variable defined towards the bottom could accidentally be referenced at the top of the notebook. This happens quickly when we iteratively built a program and go back and forth between cells.\n",
     "\n",
-    "As a good practice, it is recommended to click on \"Kernel\" > \"Restart & Run All\" in the navigation bar once a notebook is finished. That restarts the Python process forgetting any **state** (i.e., all variables) and ensures that the notebook runs top to bottom without any errors the next time it is opened."
+    "As a good practice, it is recommended to click on \"Kernel\" > \"Restart Kernel and Run All Cells\" in the navigation bar once a notebook is finished. That restarts the Python process forgetting all **state** (i.e., all variables) and ensures that the notebook runs top to bottom without any errors the next time it is opened."
    ]
   },
   {
@@ -2073,11 +2073,11 @@
     }
    },
    "source": [
-    "While this book is built with Jupyter notebooks, it is crucial to understand that \"real\" programs are almost always just \"linear\" (= top to bottom) sequences of instructions but instead may take many different **flows of execution**.\n",
+    "While this book is built with Jupyter notebooks, it is crucial to understand that \"real\" programs are almost never \"linear\" (i.e., top to bottom) sequences of instructions but instead may take many different **flows of execution**.\n",
     "\n",
     "At the same time, for a beginner's course, it is often easier to code linearly.\n",
     "\n",
-    "In real data science projects, one would probably employ a mixed approach and put reusable code into so-called Python modules (i.e., *.py* files; cf., [Chapter 2](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/02_functions_00_lecture.ipynb#Local-Modules-and-Packages)) and then use Jupyter notebooks to build up a linear report or storyline for a business argument to be made."
+    "In real data science projects, one would probably employ a mixed approach and put reusable code into so-called Python modules (i.e., *.py* files; cf., [Chapter 2](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/02_functions_00_lecture.ipynb#Local-Modules-and-Packages)) and then use Jupyter notebooks to build up a linear report or storyline for an analysis."
    ]
   },
   {
@@ -2088,7 +2088,7 @@
     }
    },
    "source": [
-    "## Variables vs. Names vs. Identifiers"
+    "## Variables vs. Names vs. Identifiers vs. References"
    ]
   },
   {
@@ -2099,7 +2099,7 @@
     }
    },
    "source": [
-    "**Variables** are created with the **[assignment statement](https://docs.python.org/3/reference/simple_stmts.html#assignment-statements)** `=`, which is *not* an operator, mainly because of its side effect of making a **[name](https://docs.python.org/3/reference/lexical_analysis.html#identifiers)** reference an object in memory.\n",
+    "**Variables** are created with the **[assignment statement](https://docs.python.org/3/reference/simple_stmts.html#assignment-statements)** `=`, which is *not* an operator because of its *side effect* of making a **[name](https://docs.python.org/3/reference/lexical_analysis.html#identifiers)** reference an object in memory.\n",
     "\n",
     "We read the terms **variable**, **name**, and **identifier** used interchangebly in many Python-related texts. In this book, we adopt the following convention: First, we treat *name* and *identifier* as perfect synonyms but only use the term *name* in the text for clarity. Second, whereas *name* only refers to a string of letters, numbers, and some other symbols, a *variable* means the combination of a *name* and a *reference* to an object in memory."
    ]
@@ -2161,7 +2161,7 @@
     }
    },
    "source": [
-    "A variable may be **re-assigned** as often as we wish. Thereby, we could also assign an object of a *different* type. Because this is allowed, Python is said to be a **dynamically typed** language. On the contrary, a **statically typed** language like C also allows re-assignment but only with objects of the *same* type. This subtle distinction is one reason why Python is slower at execution than C: As it runs a program, it needs to figure out an object's type each time it is referenced. But as mentioned before, this is mitigated with third-party libraries."
+    "A variable may be **re-assigned** as often as we wish. Thereby, we could also assign an object of a *different* type. Because this is allowed, Python is said to be a **dynamically typed** language. On the contrary, a **statically typed** language like C also allows re-assignment but only with objects of the *same* type. This subtle distinction is one reason why Python is slower at execution than C: As it runs a program, it needs to figure out an object's type each time it is referenced."
    ]
   },
   {
@@ -2209,7 +2209,7 @@
     }
    },
    "source": [
-    "If we want to re-assign a variable while referencing its \"old\" (i.e., current) object, we may also **update** it using a so-called **[augmented assignment statement](https://docs.python.org/3/reference/simple_stmts.html#augmented-assignment-statements)** (*not* operator), introduced with [PEP 203](https://www.python.org/dev/peps/pep-0203/): The currently mapped object is implicitly inserted as the first operand on the right-hand side."
+    "If we want to re-assign a variable while referencing its \"old\" (i.e., current) object, we may also **update** it using a so-called **[augmented assignment statement](https://docs.python.org/3/reference/simple_stmts.html#augmented-assignment-statements)** (i.e., *not* operator), as introduced with [PEP 203](https://www.python.org/dev/peps/pep-0203/): The currently mapped object is implicitly inserted as the first operand on the right-hand side."
    ]
   },
   {
@@ -2235,7 +2235,7 @@
    },
    "outputs": [],
    "source": [
-    "a //= 2  # same as a = a // 2, \"//\" to retain the integer type"
+    "a //= 2  # same as a = a // 2; \"//\" to retain the integer type"
    ]
   },
   {
@@ -2283,7 +2283,7 @@
     }
    },
    "source": [
-    "Variables are **[dereferenced](https://docs.python.org/3/reference/simple_stmts.html#the-del-statement)** (i.e., \"deleted\") with the `del` statement. It does *not* delete the object a variable references but merely removes the variable's name from the \"global list of all names.\""
+    "Variables are **[dereferenced](https://docs.python.org/3/reference/simple_stmts.html#the-del-statement)** (i.e., \"deleted\") with the `del` statement. This does *not* delete the object a variable references but merely removes the variable's name from the \"global list of all names.\""
    ]
   },
   {
@@ -2331,7 +2331,7 @@
     }
    },
    "source": [
-    "If we refer to an unknown name, a *runtime* error occurs, namely a `NameError`. The `Name` in `NameError` gives a hint as to why we prefer the term *name* over *identifier*: Python uses it more often in its error messages."
+    "If we refer to an unknown name, a *runtime* error occurs, namely a `NameError`. The `Name` in `NameError` gives a hint why we choose the term *name* over *identifier* above: Python uses it more often in its error messages."
    ]
   },
   {
@@ -2367,7 +2367,7 @@
     }
    },
    "source": [
-    "Some variables magically exist when we start a Python process or are added by Jupyter. We may safely ignore the former until Chapter 10 and the latter for good."
+    "Some variables magically exist when a Python process is started or are added by Jupyter. We may safely ignore the former until Chapter 10 and the latter for good."
    ]
   },
   {
@@ -2598,7 +2598,7 @@
    "source": [
     "It is *crucial* to understand that *several* variables may reference the *same* object in memory. Not having this in mind may lead to many hard to track down bugs.\n",
     "\n",
-    "Make `b` reference whatever object `a` is referencing."
+    "Let's make `b` reference whatever object `a` is referencing."
    ]
   },
   {
@@ -2755,7 +2755,7 @@
     }
    },
    "source": [
-    "However, if a variable references an object of a more \"complex\" type (e.g., `list`), \"weird\" things happen."
+    "However, if a variable references an object of a more \"complex\" type (e.g., `list`), predicting the outcome of a code snippet may be unintuitive for a beginner."
    ]
   },
   {
@@ -2818,9 +2818,9 @@
    "source": [
     "Let's change the first element of `x`.\n",
     "\n",
-    "[Chapter 7](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/07_sequences_00_lecture.ipynb#The-list-Type) discusses lists in more depth. For now, let's view a `list` object as some sort of **container** that holds an arbitrary number of references to other objects and treat the brackets `[]` attached to it as just another operator, called the **indexing operator**. `x[0]` instructs Python to first follow the reference from the global list of all names to the `x` object. Then, it follows the first reference it finds there to the `1` object. The indexing operator must be an operator as we merely read the first element and do not change anything in memory.\n",
+    "[Chapter 7](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/07_sequences_00_lecture.ipynb#The-list-Type) discusses lists in more depth. For now, let's view a `list` object as some sort of **container** that holds an arbitrary number of references to other objects and treat the brackets `[]` attached to it as yet another operator, namely the **indexing operator**. So, `x[0]` instructs Python to first follow the reference from the global list of all names to the `x` object. Then, it follows the first reference it finds there to the `1` object we put in the list. The indexing operator must be an operator as we merely *read* the first element and do not change anything in memory permanently.\n",
     "\n",
-    "Note how Python **begins counting at 0**. This is not the case for many other languages, for example, [MATLAB](https://en.wikipedia.org/wiki/MATLAB), [R](https://en.wikipedia.org/wiki/R_%28programming_language%29), or [Stata](https://en.wikipedia.org/wiki/Stata). To understand why this makes sense, see this short [note](https://www.cs.utexas.edu/users/EWD/transcriptions/EWD08xx/EWD831.html) by one of the all-time greats in computer science, the late [Edsger Dijkstra](https://en.wikipedia.org/wiki/Edsger_W._Dijkstra)."
+    "Python **begins counting at 0**. This is not the case for many other languages, for example, [MATLAB](https://en.wikipedia.org/wiki/MATLAB), [R](https://en.wikipedia.org/wiki/R_%28programming_language%29), or [Stata](https://en.wikipedia.org/wiki/Stata). To understand why this makes sense, see this short [note](https://www.cs.utexas.edu/users/EWD/transcriptions/EWD08xx/EWD831.html) by one of the all-time greats in computer science, the late [Edsger Dijkstra](https://en.wikipedia.org/wiki/Edsger_W._Dijkstra)."
    ]
   },
   {
@@ -2855,7 +2855,7 @@
     }
    },
    "source": [
-    "To change the first entry in the list, we use the assignment statement `=` again. Here, this does *not* create a *new* variable, or overwrite an existing one, but only changes the object which the first element in `x` referenced. As we only change parts of the `x` object, we say that we **mutate** (i.e., \"change\") its **state**. To use the bag analogy from above, we keep the same bag but \"flip\" some of the $0$s into $1$s and some of the $1$s into $0$s."
+    "To change the first entry in the list, we use the assignment statement `=` again. Here, this does *not* create a *new* variable, nor overwrite an existing one, but only changes the object referenced as the first element in `x`. As we only change parts of the `x` object, we say that we **mutate** its **state**. To use the bag analogy from above, we keep the same bag but \"flip\" some of the $0$s into $1$s and some of the $1$s into $0$s."
    ]
   },
   {
@@ -2938,15 +2938,15 @@
     }
    },
    "source": [
-    "The illustrated difference in behavior has to do with the fact that integers and floats are **immutable** types while lists are **mutable**.\n",
+    "The difference in behavior illustrated in this sub-section has to do with the fact that `int` and `float` objects are **immutable** types while `list` objects are **mutable**.\n",
     "\n",
-    "In the first case, an object cannot be changed \"in place\" once it is created in memory. When we assigned `123` to the already existing `a`, we did not change the $0$s and $1$s in the object `a` referenced before the assignment but created a new integer object and made `a` reference it while the `b` variable is *not* affected.\n",
+    "In the first case, an object cannot be changed \"in place\" once it is created in memory. When we assigned `123` to the already existing `a`, we did *not* change the $0$s and $1$s in the object `a` referenced before the assignment but created a new `int` object and made `a` reference it while the `b` variable is *not* affected.\n",
     "\n",
-    "In the second case, `x[0] = 99` creates a *new* integer object `99` and merely changes the first reference in the `x` list.\n",
+    "In the second case, `x[0] = 99` creates a *new* `int` object `99` and merely changes the first reference in the `x` list.\n",
     "\n",
-    "In general, the assignment statement creates a new name and makes it reference whatever object is on the right-hand side *iff* the left-hand side is a *pure* name (i.e., it contains no operators like the indexing operator in the example). Otherwise, it *mutates* an already existing object. And, we always must expect that the latter might have more than one variable referencing it.\n",
+    "In general, the assignment statement creates a new name and makes it reference whatever object is on the right-hand side *iff* the left-hand side is a *pure* name (i.e., it contains no operators like the indexing operator in the example). Otherwise, it *mutates* an already existing object. And, we must always expect that the latter may have more than one variable referencing it.\n",
     "\n",
-    "Visualizing what is going on in the memory with a tool like [PythonTutor](http://pythontutor.com/visualize.html#code=x%20%3D%20%5B1,%202,%203%5D%0Ay%20%3D%20x%0Ax%5B0%5D%20%3D%2099%0Aprint%28y%5B0%5D%29&cumulative=false&curInstr=0&heapPrimitives=nevernest&mode=display&origin=opt-frontend.js&py=3&rawInputLstJSON=%5B%5D&textReferences=false) might be helpful for a beginner."
+    "Visualizing what is going on in memory with a tool like [PythonTutor](http://pythontutor.com/visualize.html#code=x%20%3D%20%5B1,%202,%203%5D%0Ay%20%3D%20x%0Ax%5B0%5D%20%3D%2099%0Aprint%28y%5B0%5D%29&cumulative=false&curInstr=0&heapPrimitives=nevernest&mode=display&origin=opt-frontend.js&py=3&rawInputLstJSON=%5B%5D&textReferences=false) may be helpful for a beginner."
    ]
   },
   {
@@ -3155,7 +3155,7 @@
     }
    },
    "source": [
-    "Variables with leading and trailing double underscores, referred to as **dunder** in Python jargon, are used for built-in functionalities. Do *not* use this style for custom variables unless you exactly know what you are doing!"
+    "Variables with leading and trailing double underscores, referred to as **dunder** in Python jargon, are used for built-in functionalities and to implement object-oriented features as we see in Chapter 10. We must *not* use this style for variables!"
    ]
   },
   {
@@ -3205,7 +3205,7 @@
     "\n",
     "In simple words, anything that may be used on the right-hand side of an assignment statement without creating a `SyntaxError` is an expression.\n",
     "\n",
-    "What we said about individual operators above, namely that they have *no* side effects, should have been put here, to begin with. The code cells in the section on operators above are all expressions!\n",
+    "What we say about *individual* operators above, namely that they have *no* permanent side effects in memory, should be put here, to begin with: The absence of any permanent side effects is the characteristic property of expressions, and all the code cells in the \"*(Arithmetic) Operators*\" section above contain only expressions!\n",
     "\n",
     "The simplest possible expressions contain only one variable or literal."
    ]
@@ -3293,7 +3293,7 @@
     }
    },
    "source": [
-    "The definition of an expression is **recursive**. So, the sub-expression `a + b` is combined with the literal `3` by the operator `**` to form the full expression."
+    "The definition of an expression is **recursive**. So, the sub-expression `a + b` is combined with the literal `3` by the operator `**` to form the full expression `(a + b) ** 3`."
    ]
   },
   {
@@ -3363,7 +3363,7 @@
     }
    },
    "source": [
-    "When not used as a delimiter, parentheses also constitute an operator, namely the **call operator** `()`. We saw this syntax above when we \"called\" (i.e., executed) built-in functions and methods."
+    "When not used as a delimiter, parentheses also constitute an operator, namely the **call operator** `()`. We saw this syntax above when we called built-in functions and methods."
    ]
   },
   {
@@ -3504,7 +3504,7 @@
     }
    },
    "source": [
-    "A **[statement](https://docs.python.org/3/reference/simple_stmts.html)** is anything that *changes* the *state of a program* or has another *side effect*. Statements, unlike expressions, do not just evaluate to a value; instead, they create or change values.\n",
+    "A **[statement](https://docs.python.org/3/reference/simple_stmts.html)** is anything that *changes* the *state of a program* or has another permanent *side effect*. Statements, unlike expressions, do not evaluate to a value; instead, they create or change values.\n",
     "\n",
     "Most notably, of course, are the `=` and `del` statements."
    ]
@@ -3543,7 +3543,7 @@
     }
    },
    "source": [
-    "The built-in [print()](https://docs.python.org/3/library/functions.html#print) function is regarded as a \"statement\" as well. It used to be an actual statement in Python 2 and has all the necessary properties. It is a bit of a corner case as expressions are also \"printed\" in a Jupyter notebook when evaluated last in a code cell."
+    "The built-in [print()](https://docs.python.org/3/library/functions.html#print) function is sometimes regarded as a \"statement\" as well. It used to be an actual statement in Python 2 and has all the necessary properties. It is a bit of a corner case but we can think of it as changing the state of the screen."
    ]
   },
   {
@@ -3588,7 +3588,7 @@
    "source": [
     "We use the `#` symbol to write comments in plain English right into the code.\n",
     "\n",
-    "As a good practice, comments should *not* describe *what* happens (this should be evident by reading the code; otherwise, it is most likely badly written code) but *why* something happens.\n",
+    "As a good practice, comments should *not* describe *what* happens. This should be evident by reading the code. Otherwise, it is most likely badly written code. Rather, comments should describe *why* something happens.\n",
     "\n",
     "Comments may be added either at the end of a line of code, by convention separated with two spaces, or on a line on their own."
    ]
@@ -3786,7 +3786,7 @@
        "        "
       ],
       "text/plain": [
-       "<IPython.lib.display.YouTubeVideo at 0x7f7c940bc050>"
+       "<IPython.lib.display.YouTubeVideo at 0x7efd86fd2bd0>"
       ]
      },
      "execution_count": 110,
diff --git a/01_elements_02_exercises.ipynb b/01_elements_02_exercises.ipynb
index c7fffb6..56bf985 100644
--- a/01_elements_02_exercises.ipynb
+++ b/01_elements_02_exercises.ipynb
@@ -33,9 +33,9 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "**Q1.1**: Read about the [print()](https://docs.python.org/3/library/functions.html#print) built-in. How can you use it to print both `greeting` and `audience` *without* concatenating the two strings with the `+` operator?\n",
+    "**Q1**: Read the documentation on the built-in [print()](https://docs.python.org/3/library/functions.html#print) function! How can you use it to print both `greeting` and `audience` *without* concatenating the two strings with the `+` operator?\n",
     "\n",
-    "Hint: The `*objects` in the documentation implies that we can insert several comma-seperated variables."
+    "Hint: The `*objects` in the documentation implies that we can insert several *comma-seperated* variables."
    ]
   },
   {
@@ -61,7 +61,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "**Q1.2**: What does the `sep=\" \"` mean in the documentation? Use it to print out the three names in `first`, `second`, and `third` on one line seperated by commas with one [print()](https://docs.python.org/3/library/functions.html#print) statement."
+    "**Q2**: What does the `sep=\" \"` mean in the documentation? Use it and print out the three names in `first`, `second`, and `third` on one line seperated by *commas* with only *one* call of the [print()](https://docs.python.org/3/library/functions.html#print) function!"
    ]
   },
   {
@@ -88,7 +88,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "**Q1.3**: Lastly, what does the `end=\"\\n\"` mean in the documentation? Use it in the `for`-loop to print the numbers 1 through 10 in just one line."
+    "**Q3**: Lastly, what does the `end=\"\\n\"` mean in the documentation? Use it in the `for`-loop and print the numbers 1 through 10 in just *one* line!"
    ]
   },
   {

From 3bbcc3a04dd89c8efa8391a2755bd5ca6b845a8e Mon Sep 17 00:00:00 2001
From: Alexander Hess <alexander@webartifex.biz>
Date: Wed, 5 Feb 2020 18:25:39 +0100
Subject: [PATCH 065/242] Streamline text

---
 01_elements_00_lecture.ipynb   | 8 ++++----
 01_elements_02_exercises.ipynb | 2 +-
 06_text_00_lecture.ipynb       | 2 +-
 08_mappings_00_lecture.ipynb   | 4 ++--
 4 files changed, 8 insertions(+), 8 deletions(-)

diff --git a/01_elements_00_lecture.ipynb b/01_elements_00_lecture.ipynb
index b37b958..ba54aa3 100644
--- a/01_elements_00_lecture.ipynb
+++ b/01_elements_00_lecture.ipynb
@@ -1437,7 +1437,7 @@
     }
    },
    "source": [
-    "### Value"
+    "### Value / \"Meaning\""
    ]
   },
   {
@@ -1448,7 +1448,7 @@
     }
    },
    "source": [
-    "Almost trivially, every object also has a value to which it **evaluates** when referenced. We think of the value as the **conceptual idea** of what the $0$s and $1$s in the bag mean to *humans*.\n",
+    "Almost trivially, every object also has a value to which it **evaluates** when referenced. We think of the value as the **conceptual idea** of what the $0$s and $1$s in the bag mean to *humans*. In other words, an object's value regards its *semantic* meaning.\n",
     "\n",
     "For built-in data types, Python prints out an object's value as a so-called **[literal](https://docs.python.org/3/reference/lexical_analysis.html#literals)**: This means that we may copy and paste the value back into a code cell and create a *new* object with the *same* value."
    ]
@@ -2981,7 +2981,7 @@
     }
    },
    "source": [
-    "Variable names may contain upper and lower case letters, numbers, and underscores (i.e., `_`) and be as long as we want them to be. However, they must not begin with a number. Also, they must not be any of Python's built-in **[keywords](https://docs.python.org/3/reference/lexical_analysis.html#keywords)**.\n",
+    "Variable names may contain upper and lower case letters, numbers, and underscores (i.e., `_`) and be as long as we want them to be. However, they must not begin with a number. Also, they must not be any of Python's built-in **[keywords](https://docs.python.org/3/reference/lexical_analysis.html#keywords)** like `for` or `if`.\n",
     "\n",
     "Variable names should be chosen such that they do not need any more documentation and are self-explanatory. A widespread convention is to use so-called **[snake\\_case](https://en.wikipedia.org/wiki/Snake_case)**: Keep everything lowercase and use underscores to separate words.\n",
     "\n",
@@ -3734,7 +3734,7 @@
     "  - include [built-in functions](https://docs.python.org/3/library/functions.html) like [print()](https://docs.python.org/3/library/functions.html#print), [sum()](https://docs.python.org/3/library/functions.html#sum), or [len()](https://docs.python.org/3/library/functions.html#len)\n",
     "\n",
     "\n",
-    "- flow control (cf., [Chapter 3](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/03_conditionals_00_lecture.ipynb))\n",
+    "- flow control (cf., [Chapter 3](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/03_conditionals_00_lecture.ipynb) and [Chapter 4](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/04_iteration_00_lecture.ipynb))\n",
     "  - expression of **business logic** or an **algorithm**\n",
     "  - conditional execution of parts of a program (e.g., `if` statements)\n",
     "  - repetitive execution of parts of a program (e.g., `for`-loops)"
diff --git a/01_elements_02_exercises.ipynb b/01_elements_02_exercises.ipynb
index 56bf985..64fc68f 100644
--- a/01_elements_02_exercises.ipynb
+++ b/01_elements_02_exercises.ipynb
@@ -88,7 +88,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "**Q3**: Lastly, what does the `end=\"\\n\"` mean in the documentation? Use it in the `for`-loop and print the numbers 1 through 10 in just *one* line!"
+    "**Q3**: Lastly, what does the `end=\"\\n\"` mean in the documentation? Use it in the `for`-loop and print the numbers 1 through 10 in just *one* line of output!"
    ]
   },
   {
diff --git a/06_text_00_lecture.ipynb b/06_text_00_lecture.ipynb
index d1bbc91..fc0eeb2 100644
--- a/06_text_00_lecture.ipynb
+++ b/06_text_00_lecture.ipynb
@@ -124,7 +124,7 @@
     }
    },
    "source": [
-    "A `str` object evaluates to itself in a literal notation with enclosing **single quotes** `'` by default. In [Chapter 1](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/01_elements_00_lecture.ipynb#Value), we already specified the double quotes `\"` convention we stick to in this book. Yet, single quotes `'` and double quotes `\"` are *perfect* substitutes for all `str` objects that do *not* contain any of the two symbols in it. We could use the reverse convention, as well.\n",
+    "A `str` object evaluates to itself in a literal notation with enclosing **single quotes** `'` by default. In [Chapter 1](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/01_elements_00_lecture.ipynb#Value-/-\"Meaning\"), we already specified the double quotes `\"` convention we stick to in this book. Yet, single quotes `'` and double quotes `\"` are *perfect* substitutes for all `str` objects that do *not* contain any of the two symbols in it. We could use the reverse convention, as well.\n",
     "\n",
     "As [this discussion](https://stackoverflow.com/questions/56011/single-quotes-vs-double-quotes-in-python) shows, many programmers have *strong* opinions about that and make up *new* conventions for their projects. Consequently, the discussion was \"closed as not constructive\" by the moderators."
    ]
diff --git a/08_mappings_00_lecture.ipynb b/08_mappings_00_lecture.ipynb
index 79483b5..69ff961 100644
--- a/08_mappings_00_lecture.ipynb
+++ b/08_mappings_00_lecture.ipynb
@@ -45,7 +45,7 @@
    "source": [
     "A *mapping* is a one-to-one correspondence from a set of **keys** to a set of **values**. In other words, a *mapping* is a *collection* of **key-value pairs**, also called **items** for short.\n",
     "\n",
-    "In the context of mappings, the term *value* has a meaning different from the general *value* that *every* object has: In the \"bag\" analogy from [Chapter 1](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/01_elements_00_lecture.ipynb#Value), we descibe an object's value to be the concrete $0$s and $1$s it contains. Here, the terms *key* and *value* mean the *role* an object takes within a mapping. Both, *keys* and *values*, are real *objects* with a distinct *value*. So, the student should always remember the double meaning of the term *value* in this chapter!\n",
+    "In the context of mappings, the term *value* has a meaning different from the general *value* that *every* object has: In the \"bag\" analogy from [Chapter 1](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/01_elements_00_lecture.ipynb#Value-/-\"Meaning\"), we descibe an object's value to be the concrete $0$s and $1$s it contains. Here, the terms *key* and *value* mean the *role* an object takes within a mapping. Both, *keys* and *values*, are real *objects* with a distinct *value*. So, the student should always remember the double meaning of the term *value* in this chapter!\n",
     "\n",
     "Let's continue with an example. To create a `dict` object, we commonly use the literal notation, `{..: .., ..: .., ...}`, and list all the items. `to_words` below maps the `int` objects `0`, `1`, and `2` to their English word equivalents, `\"zero\"`, `\"one\"`, and `\"two\"`, and `from_words` does the opposite. A stylistic side note: Pythonistas often expand `dict` or `list` definitions by writing each item or element on a line on their own. The commas `,` after the *last* items are *not* a mistake, as well, although they *may* be left out. Besides easier reading, such style has actual technical advantages (cf., [source](https://www.python.org/dev/peps/pep-0008/#when-to-use-trailing-commas)) that we do not go into detail about here."
    ]
@@ -707,7 +707,7 @@
     }
    },
    "source": [
-    "The [glossary](https://docs.python.org/3/glossary.html#term-hashable) states a second requirement for hashability, namely that \"objects which *compare equal* must have the *same* hash value.\" The purpose of this is to ensure that if we put, for example, `1` as a key in a `dict` object, we can look it up later with `1.0`. In other words, we can look up keys by their object's value (i.e., in the meaning of [Chapter 1](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/01_elements_00_lecture.ipynb#Value)). The converse statement does *not* hold: Two objects *may* (accidentally) have the *same* hash value and *not* compare equal."
+    "The [glossary](https://docs.python.org/3/glossary.html#term-hashable) states a second requirement for hashability, namely that \"objects which *compare equal* must have the *same* hash value.\" The purpose of this is to ensure that if we put, for example, `1` as a key in a `dict` object, we can look it up later with `1.0`. In other words, we can look up keys by their object's value (i.e., in the meaning of [Chapter 1](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/01_elements_00_lecture.ipynb#Value-/-\"Meaning\")). The converse statement does *not* hold: Two objects *may* (accidentally) have the *same* hash value and *not* compare equal."
    ]
   },
   {

From 3754297c9333df3dd3776e6a66946d92a14ad2cc Mon Sep 17 00:00:00 2001
From: Alexander Hess <alexander@webartifex.biz>
Date: Wed, 5 Feb 2020 18:31:52 +0100
Subject: [PATCH 066/242] Refurbish chapter 02

- streamline text
- streamline example functions
- move section on built-ins to beginning
- add notes on the concept of a callable
---
 02_functions_00_lecture.ipynb   | 2275 +++++++++++++++++++++----------
 02_functions_01_review.ipynb    |   26 +-
 02_functions_02_exercises.ipynb |   12 +-
 sample_module.py                |   41 +-
 4 files changed, 1639 insertions(+), 715 deletions(-)

diff --git a/02_functions_00_lecture.ipynb b/02_functions_00_lecture.ipynb
index 6dcccec..69170d8 100644
--- a/02_functions_00_lecture.ipynb
+++ b/02_functions_00_lecture.ipynb
@@ -19,11 +19,9 @@
     }
    },
    "source": [
-    "In [Chapter 1](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/01_elements_00_lecture.ipynb#Example:-Averaging-Even-Numbers), we typed the code to calculate the average of the even numbers in a list into several code cells. Then, we executed them one after another. We had no way of **reusing** the code except for either executing cells multiple times or copying and pasting their contents into other cells. And, whenever we find ourselves doing repetitive manual work, we can be sure that there must be a way of automating what we are doing.\n",
+    "In [Chapter 1](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/01_elements_00_lecture.ipynb#Example:-Averaging-Even-Numbers), we simply typed the code to calculate the average of the even numbers in a list of whole numbers into several code cells. Then, we executed them one after another. We had no way of *reusing* the code except for either executing cells multiple times. And, whenever we find ourselves doing repetitive manual work, we can be sure that there must be a way of automating what we are doing.\n",
     "\n",
-    "At the same time, we executed built-in functions (e.g., [print()](https://docs.python.org/3/library/functions.html#print), [sum()](https://docs.python.org/3/library/functions.html#sum), [len()](https://docs.python.org/3/library/functions.html#len), [id()](https://docs.python.org/3/library/functions.html#id), or [type()](https://docs.python.org/3/library/functions.html#type)) that obviously must be reusing the same parts inside core Python every time we use them.\n",
-    "\n",
-    "This chapter shows how Python offers language constructs that let us **define** functions ourselves that we may then **call** just like the built-in ones."
+    "This chapter shows how Python offers language constructs that let us **define** functions ourselves that we may then **call** just like the built-in ones. Also, we look at how we can extend our Python installation with functionalities written by other people."
    ]
   },
   {
@@ -34,7 +32,7 @@
     }
    },
    "source": [
-    "##  Function Definition"
+    "## [Built-in Functions](https://docs.python.org/3/library/functions.html)"
    ]
   },
   {
@@ -45,19 +43,9 @@
     }
    },
    "source": [
-    "So-called **[user-defined functions](https://docs.python.org/3/reference/compound_stmts.html#function-definitions)** may be created with the `def` statement. To extend an already familiar example, we reuse the introductory example from [Chapter 1](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/01_elements_00_lecture.ipynb#Best-Practices) in its final Pythonic version and transform it into the function `average_evens()` below. \n",
+    "Python comes with plenty of useful functions built in, some of which we have already seen before (e.g., [print()](https://docs.python.org/3/library/functions.html#print), [sum()](https://docs.python.org/3/library/functions.html#sum), [len()](https://docs.python.org/3/library/functions.html#len), or [id()](https://docs.python.org/3/library/functions.html#id)). The [documentation](https://docs.python.org/3/library/functions.html) has the full list. Just as core Python itself, they are mostly implemented in C and thus very fast.\n",
     "\n",
-    "A function's **name** must be chosen according to the same naming rules as ordinary variables as Python manages function names like variables. In this book, we further adopt the convention of ending function names with parentheses `()` in text cells for faster comprehension when reading (i.e., `average_evens()` vs. `average_evens`). These are *not* part of the name but must always be written out in the `def` statement for syntactic reasons.\n",
-    "\n",
-    "Functions may define an arbitrary number of **parameters** as inputs that can then be referenced within the indented **code block**: They are listed within the parentheses in the `def` statement (i.e., `numbers` below). \n",
-    "\n",
-    "The code block is also called a function's **body**, while the first line starting with `def` and ending with a colon is the **header**.\n",
-    "\n",
-    "Together, the name and the list of parameters are also referred to as the function's **[signature](https://en.wikipedia.org/wiki/Type_signature)** (i.e., `average_evens(numbers)` below).\n",
-    "\n",
-    "A function may come with an *explicit* **return value** (i.e., \"result\" or \"output\") specified with the `return` statement (cf., [reference](https://docs.python.org/3/reference/simple_stmts.html#the-return-statement)): Functions that have one are considered **fruitful**; otherwise, they are **void**. Functions of the latter kind are still useful because of their **side effects** as, for example, the built-in [print()](https://docs.python.org/3/library/functions.html#print) function. Strictly speaking, they also have an *implicit* return value of `None`.\n",
-    "\n",
-    "A function should define a **docstring** that describes what it does in a short subject line, what parameters it expects (i.e., their types), and what it returns (if anything). A docstring is a syntactically valid multi-line string (i.e., type `str`) defined within **triple-double quotes** `\"\"\"`. Strings are covered in depth in [Chapter 6](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/06_text_00_lecture.ipynb#The-str-Type). Widely adopted standards as to how to format a docstring are [PEP 257](https://www.python.org/dev/peps/pep-0257/) and section 3.8 in [Google's Python Style Guide](https://github.com/google/styleguide/blob/gh-pages/pyguide.md)."
+    "Below, [sum()](https://docs.python.org/3/library/functions.html#sum) adds up all the elements in the `numbers` list while [len()](https://docs.python.org/3/library/functions.html#len) counts the number of elements in it."
    ]
   },
   {
@@ -70,16 +58,841 @@
    },
    "outputs": [],
    "source": [
-    "def average_evens(numbers):\n",
+    "numbers = [7, 11, 8, 5, 3, 12, 2, 6, 9, 10, 1, 4]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "78"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "sum(numbers)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "12"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "len(numbers)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "`sum` and `len` are *no* [keywords](https://docs.python.org/3/reference/lexical_analysis.html#keywords) like `for` or `if` but variables that reference *objects* in memory. Often, we hear people say that \"everything is an object in Python\" (e.g., this [question](https://stackoverflow.com/questions/40478536/in-python-what-does-it-mean-by-everything-is-an-object)). While this phrase may sound abstract in the beginning, it simply means that the entire memory is organized with \"bags\" of $0$s and $1$s, and there are even bags for the built-in functions. That is *not* true for many other languages (e.g., C or Java) and often a source of confusion for people coming to Python from another language.\n",
+    "\n",
+    "The built-in [id()](https://docs.python.org/3/library/functions.html#id) function tells us where in memory a particular built-in function is stored."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "140413081843264"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "id(sum)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "140413081842224"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "id(len)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "[type()](https://docs.python.org/3/library/functions.html#type) reveals that built-in functions like [sum()](https://docs.python.org/3/library/functions.html#sum) or [len()](https://docs.python.org/3/library/functions.html#len) are objects of type `builtin_function_or_method`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "builtin_function_or_method"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "type(sum)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "builtin_function_or_method"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "type(len)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Python's object-oriented nature allows us to have functions work with themselves. While seemingly not useful from a beginner's point of view, that enables a lot of powerful programming styles later on."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "140413081841824"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "id(id)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "builtin_function_or_method"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "type(id)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "To execute a function, we **call** it with the **call operator** `()` as shown many times in [Chapter 1](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/01_elements_00_lecture.ipynb) and above.\n",
+    "\n",
+    "If we are unsure whether a variable references a function or not, we can verify that with the built-in [callable()](https://docs.python.org/3/library/functions.html#callable) function.\n",
+    "\n",
+    "Abstractly speaking, *any* object that can be called with the call operator `()` is a so-called **callable**. And, objects of type `builtin_function_or_method` are just one kind of examples thereof. We will see another one already in the next sub-section."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "callable(sum)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "callable(len)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "`list` objects, for example, are *not* callable."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "False"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "callable(numbers)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "### Constructors"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "The list of [built-in functions](https://docs.python.org/3/library/functions.html) in the documentation should really be named a list of built-in *callables*.\n",
+    "\n",
+    "Besides the built-in functions, the list also features **constructors** for the built-in types. They may be used to **[cast](https://en.wikipedia.org/wiki/Type_conversion)** (i.e., \"convert\") any object as an object of a given type.\n",
+    "\n",
+    "For example, to \"convert\" a `float` or a `str` into an `int` object, we use the [int()](https://docs.python.org/3/library/functions.html#int) built-in. Below, *new* `int` objects are created from the `7.0` and `\"7\"` objects that are *newly* created themselves before being processed by [int()](https://docs.python.org/3/library/functions.html#int) right away *without* ever being referenced by a variable."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "7"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "int(7.0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "7"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "int(\"7\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Casting an object as an `int` is different from rounding with the built-in [round()](https://docs.python.org/3/library/functions.html#round) function!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "7"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "int(7.99)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "8"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "round(7.99)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Notice the subtle difference compared to the behavior of the `//` operator in [Chapter 1](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/01_elements_00_lecture.ipynb##%28Arithmetic#%29-Operators) that \"rounds\" towards minus infinity: [int()](https://docs.python.org/3/library/functions.html#int) always \"rounds\" towards `0`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "-7"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "int(-7.99)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Not all conversions are valid and *runtime* errors may occur as the `ValueError` shows."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "ename": "ValueError",
+     "evalue": "invalid literal for int() with base 10: 'seven'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-18-af421b358f21>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"seven\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;31mValueError\u001b[0m: invalid literal for int() with base 10: 'seven'"
+     ]
+    }
+   ],
+   "source": [
+    "int(\"seven\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "We may also cast in the other direction with the [float()](https://docs.python.org/3/library/functions.html#float)  or [str()](https://docs.python.org/3/library/functions.html#func-str) built-ins."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "7.0"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "float(7)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'7'"
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "str(7)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Constructors are full-fledged objects as well."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "94916229764288"
+      ]
+     },
+     "execution_count": 21,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "id(int)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "94916229768192"
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "id(float)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "They are of type `type`, which is different from `builtin_function_or_method` above."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "type"
+      ]
+     },
+     "execution_count": 23,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "type(int)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "type"
+      ]
+     },
+     "execution_count": 24,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "type(float)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "As already noted, constructors are *callables*. In that regard, they behave the same as built-in functions. We may call them with the call operator `()`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 25,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "callable(int)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 26,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "callable(float)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "The attentive student may already have discovered that we refer to `builtin_function_or_method` objects as \"built-in functions\" and `type` objects as just \"built-ins.\" For a beginner, that difference is not so important. But, the ambitious student should already be aware that such subtleties exist.\n",
+    "\n",
+    "Next, let's look at a third kind of callables."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "##  Function Definitions"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "We may create so-called *user-defined* **functions** with the `def` statement (cf., [reference](https://docs.python.org/3/reference/compound_stmts.html#function-definitions)). To extend an already familiar example, we reuse the introductory example from [Chapter 1](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/01_elements_00_lecture.ipynb#Best-Practices) in its final Pythonic version and transform it into the function `average_evens()` below. We replace the variable name `numbers` with `integers` for didactical purposes in the first couple of examples.\n",
+    "\n",
+    "A function's **name** must be chosen according to the same naming rules as ordinary variables since Python manages function names like variables. In this book, we further adopt the convention of ending function names with parentheses `()` in text cells for faster comprehension when reading (i.e., `average_evens()` vs. `average_evens`). These are *not* part of the name but must always be written out in the `def` statement for syntactic reasons.\n",
+    "\n",
+    "Functions may define an arbitrary number of **parameters** as inputs that can then be referenced within the indented **code block**: They are listed within the parentheses in the `def` statement (i.e., `integers` below). \n",
+    "\n",
+    "The code block is also called a function's **body**, while the first line starting with `def` and ending with a colon is the **header**.\n",
+    "\n",
+    "Together, the name and the list of parameters are also referred to as the function's **[signature](https://en.wikipedia.org/wiki/Type_signature)** (i.e., `average_evens(integers)` below).\n",
+    "\n",
+    "A function may specify an *explicit* **return value** (i.e., \"result\" or \"output\") with the `return` statement (cf., [reference](https://docs.python.org/3/reference/simple_stmts.html#the-return-statement)): Functions that have one are considered **fruitful**; otherwise, they are **void**. Functions of the latter kind are still useful because of their **side effects**. For example, the built-in [print()](https://docs.python.org/3/library/functions.html#print) function changes what we see on the screen. Strictly speaking, [print()](https://docs.python.org/3/library/functions.html#print) and other void functions also have an *implicit* return value, namely the `None` object.\n",
+    "\n",
+    "A function should define a **docstring** that describes what it does in a short subject line, what parameters it expects (i.e., their types), and what it returns, if anything. A docstring is a syntactically valid multi-line string (i.e., type `str`) defined within **triple-double quotes** `\"\"\"`. Strings are covered in depth in [Chapter 6](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/06_text_00_lecture.ipynb#The-str-Type). Widely adopted standards for docstrings are [PEP 257](https://www.python.org/dev/peps/pep-0257/) and section 3.8 of [Google's Python Style Guide](https://github.com/google/styleguide/blob/gh-pages/pyguide.md)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "def average_evens(integers):\n",
     "    \"\"\"Calculate the average of all even numbers in a list.\n",
     "\n",
     "    Args:\n",
-    "        numbers (list): a list of numbers; may be integers or floats\n",
+    "        integers (list of int's): whole numbers to be averaged\n",
     "\n",
     "    Returns:\n",
     "        float: average\n",
     "    \"\"\"\n",
-    "    evens = [n for n in numbers if n % 2 == 0]\n",
+    "    evens = [n for n in integers if n % 2 == 0]\n",
     "    average = sum(evens) / len(evens)\n",
     "    return average"
    ]
@@ -92,12 +905,12 @@
     }
    },
    "source": [
-    "Once defined, a function may be referenced just like any other variable by its name (i.e., *without* the parenthesis). Its value might seem awkward at first: It consists of the location where we defined the function (i.e., `__main__`, which is Python's way of saying \"in this notebook\") and the signature."
+    "Once defined, a function may be referenced just like any other variable by its name (i.e., *without* the parentheses)."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 28,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -107,10 +920,10 @@
     {
      "data": {
       "text/plain": [
-       "<function __main__.average_evens(numbers)>"
+       "<function __main__.average_evens(integers)>"
       ]
      },
-     "execution_count": 2,
+     "execution_count": 28,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -127,25 +940,25 @@
     }
    },
    "source": [
-    "A function is an **object** on its own with an **identity** (i.e., memory location) and a **type**, namely `function`."
+    "This works as functions are full-fledged *objects*. So, `average_evens` is just a name referencing an object in memory with an **identity**, a **type**, namely `function`, and a **value**. In that regard, `average_evens` is *no* different from the variable `numbers` or the built-ins' names."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 29,
    "metadata": {
     "slideshow": {
-     "slide_type": "-"
+     "slide_type": "fragment"
     }
    },
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "140371816700960"
+       "140413021591744"
       ]
      },
-     "execution_count": 3,
+     "execution_count": 29,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -156,7 +969,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 30,
    "metadata": {
     "slideshow": {
      "slide_type": "-"
@@ -169,7 +982,7 @@
        "function"
       ]
      },
-     "execution_count": 4,
+     "execution_count": 30,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -178,6 +991,76 @@
     "type(average_evens)"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Its value may seem awkward at first: It consists of a location showing where the function is defined (i.e., `__main__` here, which is Python's way of saying \"in this notebook\") and the signature wrapped inside angle brackets `<` and `>`.\n",
+    " \n",
+    "The angle brackets are a convention to indicate that the value may *not* be used as a *literal* (i.e., typed back into another code cell). Chapter 10 introduces the concept of a **text representation** of an object, which is related to the *semantic* meaning of an object's value as discussed in [Chapter 1](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/01_elements_00_lecture.ipynb#Value-/-\"Meaning\"), and the angle brackets convention is one such way to represent an object as text. When executed, the angle brackets cause a `SyntaxError` because Python expects the `<` operator to come with an operand on both sides (cf., [Chapter 3](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/03_conditionals_00_lecture.ipynb#Relational-Operators))."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "ename": "SyntaxError",
+     "evalue": "invalid syntax (<ipython-input-31-7f49dff38622>, line 1)",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;36m  File \u001b[0;32m\"<ipython-input-31-7f49dff38622>\"\u001b[0;36m, line \u001b[0;32m1\u001b[0m\n\u001b[0;31m    <function __main__.average_evens(numbers)>\u001b[0m\n\u001b[0m    ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m invalid syntax\n"
+     ]
+    }
+   ],
+   "source": [
+    "<function __main__.average_evens(numbers)>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "`average_evens` is, of course, callable. So, the `function` type is the third kind of callable in this chapter."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 32,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "callable(average_evens)"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {
@@ -193,10 +1076,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 33,
    "metadata": {
     "slideshow": {
-     "slide_type": "slide"
+     "slide_type": "skip"
     }
    },
    "outputs": [
@@ -206,11 +1089,11 @@
      "text": [
       "Help on function average_evens in module __main__:\n",
       "\n",
-      "average_evens(numbers)\n",
+      "average_evens(integers)\n",
       "    Calculate the average of all even numbers in a list.\n",
       "    \n",
       "    Args:\n",
-      "        numbers (list): a list of numbers; may be integers or floats\n",
+      "        integers (list of int's): whole numbers to be averaged\n",
       "    \n",
       "    Returns:\n",
       "        float: average\n",
@@ -230,12 +1113,12 @@
     }
    },
    "source": [
-    "In a Jupyter notebook, we can just as well add a question mark to a function's name to achieve the same. Then, a small tab opens in our browser."
+    "In a Jupyter notebook, we can just as well add a question mark to a function's name to achieve the same."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 34,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -245,16 +1128,16 @@
     {
      "data": {
       "text/plain": [
-       "\u001b[0;31mSignature:\u001b[0m \u001b[0maverage_evens\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnumbers\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+       "\u001b[0;31mSignature:\u001b[0m \u001b[0maverage_evens\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mintegers\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
        "\u001b[0;31mDocstring:\u001b[0m\n",
        "Calculate the average of all even numbers in a list.\n",
        "\n",
        "Args:\n",
-       "    numbers (list): a list of numbers; may be integers or floats\n",
+       "    integers (list of int's): whole numbers to be averaged\n",
        "\n",
        "Returns:\n",
        "    float: average\n",
-       "\u001b[0;31mFile:\u001b[0m      ~/repos/intro-to-python/<ipython-input-1-46ef912ad233>\n",
+       "\u001b[0;31mFile:\u001b[0m      ~/repos/intro-to-python/<ipython-input-27-37752489e27e>\n",
        "\u001b[0;31mType:\u001b[0m      function\n"
       ]
      },
@@ -274,12 +1157,12 @@
     }
    },
    "source": [
-    "Two question marks even show a function's source code."
+    "Two question marks show a function's source code."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 35,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -289,21 +1172,21 @@
     {
      "data": {
       "text/plain": [
-       "\u001b[0;31mSignature:\u001b[0m \u001b[0maverage_evens\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnumbers\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+       "\u001b[0;31mSignature:\u001b[0m \u001b[0maverage_evens\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mintegers\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
        "\u001b[0;31mSource:\u001b[0m   \n",
-       "\u001b[0;32mdef\u001b[0m \u001b[0maverage_evens\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnumbers\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n",
+       "\u001b[0;32mdef\u001b[0m \u001b[0maverage_evens\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mintegers\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n",
        "\u001b[0;34m\u001b[0m    \u001b[0;34m\"\"\"Calculate the average of all even numbers in a list.\u001b[0m\n",
        "\u001b[0;34m\u001b[0m\n",
        "\u001b[0;34m    Args:\u001b[0m\n",
-       "\u001b[0;34m        numbers (list): a list of numbers; may be integers or floats\u001b[0m\n",
+       "\u001b[0;34m        integers (list of int's): whole numbers to be averaged\u001b[0m\n",
        "\u001b[0;34m\u001b[0m\n",
        "\u001b[0;34m    Returns:\u001b[0m\n",
        "\u001b[0;34m        float: average\u001b[0m\n",
        "\u001b[0;34m    \"\"\"\u001b[0m\u001b[0;34m\u001b[0m\n",
-       "\u001b[0;34m\u001b[0m    \u001b[0mevens\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mn\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mn\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mnumbers\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mn\u001b[0m \u001b[0;34m%\u001b[0m \u001b[0;36m2\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\n",
+       "\u001b[0;34m\u001b[0m    \u001b[0mevens\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mn\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mn\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mintegers\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mn\u001b[0m \u001b[0;34m%\u001b[0m \u001b[0;36m2\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\n",
        "\u001b[0;34m\u001b[0m    \u001b[0maverage\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msum\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mevens\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m/\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mevens\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n",
        "\u001b[0;34m\u001b[0m    \u001b[0;32mreturn\u001b[0m \u001b[0maverage\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-       "\u001b[0;31mFile:\u001b[0m      ~/repos/intro-to-python/<ipython-input-1-46ef912ad233>\n",
+       "\u001b[0;31mFile:\u001b[0m      ~/repos/intro-to-python/<ipython-input-27-37752489e27e>\n",
        "\u001b[0;31mType:\u001b[0m      function\n"
       ]
      },
@@ -315,6 +1198,46 @@
     "average_evens??"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "[help()](https://docs.python.org/3/library/functions.html#help) and the `?`s also work for built-ins."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Help on built-in function sum in module builtins:\n",
+      "\n",
+      "sum(iterable, start=0, /)\n",
+      "    Return the sum of a 'start' value (default: 0) plus an iterable of numbers\n",
+      "    \n",
+      "    When the iterable is empty, return the start value.\n",
+      "    This function is intended specifically for use with numeric values and may\n",
+      "    reject non-numeric types.\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "help(sum)"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {
@@ -334,30 +1257,17 @@
     }
    },
    "source": [
-    "We **call** (i.e., \"execute\") a function with the **call operator** `()` as often as we wish. The formal parameters are filled in by passing variables or expressions as **arguments** to the function within the parentheses."
+    "Once defined, we may call a function with the call operator `()` as often as we wish. The formal parameters are then filled in by **passing** *expressions* (e.g., literals or variables) as **arguments** to the function within the parentheses."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 37,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
     }
    },
-   "outputs": [],
-   "source": [
-    "nums = [7, 11, 8, 5, 3, 12, 2, 6, 9, 10, 1, 4]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "-"
-    }
-   },
    "outputs": [
     {
      "data": {
@@ -365,13 +1275,37 @@
        "7.0"
       ]
      },
-     "execution_count": 9,
+     "execution_count": 37,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "average_evens(nums)"
+    "average_evens([7, 11, 8, 5, 3, 12, 2, 6, 9, 10, 1, 4])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "7.0"
+      ]
+     },
+     "execution_count": 38,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "average_evens(numbers)"
    ]
   },
   {
@@ -382,71 +1316,12 @@
     }
    },
    "source": [
-    "If we are ever unsure if a variable references a `function` object that may be called, we can verify that with the built-in [callable()](https://docs.python.org/3/library/functions.html#callable) function that takes any object as its only argument and tells us `True` if that is **callable** and `False` otherwise."
+    "A function call's return value is commonly assigned to a variable for subsequent use. Otherwise, we lose access to the returned object right away."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "True"
-      ]
-     },
-     "execution_count": 10,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "callable(average_evens)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "False"
-      ]
-     },
-     "execution_count": 11,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "callable(nums)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "source": [
-    "The return value is commonly assigned to a variable for later reference. Otherwise, we would lose access to it in memory right away."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 39,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -454,12 +1329,12 @@
    },
    "outputs": [],
    "source": [
-    "avg = average_evens(nums)"
+    "result = average_evens(numbers)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 40,
    "metadata": {
     "slideshow": {
      "slide_type": "-"
@@ -472,13 +1347,13 @@
        "7.0"
       ]
      },
-     "execution_count": 13,
+     "execution_count": 40,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "avg"
+    "result"
    ]
   },
   {
@@ -511,12 +1386,12 @@
     }
    },
    "source": [
-    "Notice how the parameters listed in a function's definition (i.e., `numbers`) and variables created inside it during execution (i.e., `evens` and `average`) are **local** to that function. That means they only reference an object in memory *while* the function is being executed and de-referenced immediately when the function returns. We say they **go out of scope** once the function terminates."
+    "The parameters listed in a function's definition (i.e., `integers` in the example) and variables created *inside* it during execution (i.e., `evens` and `average`) are **local** to that function. That means they only reference an object in memory *while* the function is being executed and are dereferenced immediately when the function call returns. We say they go out of **scope**. That is why we see the `NameError`s below."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 41,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -525,23 +1400,23 @@
    "outputs": [
     {
      "ename": "NameError",
-     "evalue": "name 'numbers' is not defined",
+     "evalue": "name 'integers' is not defined",
      "output_type": "error",
      "traceback": [
       "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
       "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-14-6a54518a0c2c>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mnumbers\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
-      "\u001b[0;31mNameError\u001b[0m: name 'numbers' is not defined"
+      "\u001b[0;32m<ipython-input-41-756dfa02d4a8>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mintegers\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;31mNameError\u001b[0m: name 'integers' is not defined"
      ]
     }
    ],
    "source": [
-    "numbers"
+    "integers"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 42,
    "metadata": {
     "slideshow": {
      "slide_type": "-"
@@ -555,7 +1430,7 @@
      "traceback": [
       "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
       "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-15-df246468d241>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mevens\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;32m<ipython-input-42-df246468d241>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mevens\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
       "\u001b[0;31mNameError\u001b[0m: name 'evens' is not defined"
      ]
     }
@@ -566,7 +1441,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 43,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -580,7 +1455,7 @@
      "traceback": [
       "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
       "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-16-c3fe9b4213f6>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0maverage\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;32m<ipython-input-43-c3fe9b4213f6>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0maverage\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
       "\u001b[0;31mNameError\u001b[0m: name 'average' is not defined"
      ]
     }
@@ -597,7 +1472,7 @@
     }
    },
    "source": [
-    "[PythonTutor](http://pythontutor.com/visualize.html#code=nums%20%3D%20%5B1,%202,%203,%204,%205,%206,%207,%208,%209,%2010,%2011,%2012%5D%0A%0Adef%20average_evens%28numbers%29%3A%0A%20%20%20%20evens%20%3D%20%5Bn%20for%20n%20in%20numbers%20if%20n%20%25%202%20%3D%3D%200%5D%0A%20%20%20%20average%20%3D%20sum%28evens%29%20/%20len%28evens%29%0A%20%20%20%20return%20average%0A%0Arv%20%3D%20average_evens%28nums%29&cumulative=false&curInstr=0&heapPrimitives=nevernest&mode=display&origin=opt-frontend.js&py=3&rawInputLstJSON=%5B%5D&textReferences=false) visualizes what happens in memory: To be precise, in the exact moment when the function call is initiated and `nums` passed in as the `numbers` argument, there are *two* references to the *same* `list` object (cf., steps 4-5 in the visualization). We also see how Python creates a *new* **frame** that holds the function's local scope (i.e., \"internal names\") in addition to the **global** frame. Frames are nothing but [namespaces](https://en.wikipedia.org/wiki/Namespace) to *isolate* the names of different **scopes** from each other. The list comprehension `[n for n in numbers if n % 2 == 0]` constitutes yet another frame that is in scope as the `list` object assigned to `evens` is *being* created (cf., steps 6-20). When the function returns, only the global frame is left (cf., last step)."
+    "[PythonTutor](http://pythontutor.com/visualize.html#code=numbers%20%3D%20%5B7,%2011,%208,%205,%203,%2012,%202,%206,%209,%2010,%201,%204%5D%0A%0Adef%20average_evens%28integers%29%3A%0A%20%20%20%20evens%20%3D%20%5Bn%20for%20n%20in%20integers%20if%20n%20%25%202%20%3D%3D%200%5D%0A%20%20%20%20average%20%3D%20sum%28evens%29%20/%20len%28evens%29%0A%20%20%20%20return%20average%0A%0Aresult%20%3D%20average_evens%28numbers%29&cumulative=false&curInstr=0&heapPrimitives=nevernest&mode=display&origin=opt-frontend.js&py=3&rawInputLstJSON=%5B%5D&textReferences=false) visualizes what happens in memory: To be precise, in the exact moment when the function call is initiated and `numbers` passed in as the `integers` argument, there are *two* references to the *same* `list` object (cf., steps 4-5 in the visualization). We also see how Python creates a *new* **frame** that holds the function's local scope (i.e., \"internal names\") in addition to the **global** frame. Frames are nothing but [namespaces](https://en.wikipedia.org/wiki/Namespace) to *isolate* the names of different **scopes** from each other. The list comprehension `[n for n in integers if n % 2 == 0]` constitutes yet another frame that is in scope as the `list` object assigned to `evens` is *being* created (cf., steps 6-20). When the function returns, only the global frame is left (cf., last step)."
    ]
   },
   {
@@ -619,12 +1494,12 @@
     }
    },
    "source": [
-    "On the contrary, while a function is *being* executed, it can reference the variables of **enclosing scopes** (i.e., \"outside\" of it). This is a common source of *semantic* errors. Consider the following stylized (and incorrect) example `average_wrong()`. The error is hard to spot with eyes: The function never references the `numbers` parameter but the `nums` variable in the **global scope** instead."
+    "On the contrary, while a function is *being* executed, it may reference the variables of **enclosing scopes** (i.e., \"outside\" of it). This is a common source of *semantic* errors. Consider the following stylized and incorrect example `average_wrong()`. The error is hard to spot with eyes: The function never references the `integers` parameter but the `numbers` variable in the **global scope** instead."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 44,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -632,16 +1507,16 @@
    },
    "outputs": [],
    "source": [
-    "def average_wrong(numbers):\n",
+    "def average_wrong(integers):\n",
     "    \"\"\"Calculate the average of all even numbers in a list.\n",
     "\n",
     "    Args:\n",
-    "        numbers (list): a list of numbers; may be integers or floats\n",
+    "        integers (list of int's): whole numbers to be averaged\n",
     "\n",
     "    Returns:\n",
     "        float: average\n",
     "    \"\"\"\n",
-    "    evens = [n for n in nums if n % 2 == 0]  # should reference numbers not nums\n",
+    "    evens = [n for n in numbers if n % 2 == 0]\n",
     "    average = sum(evens) / len(evens)\n",
     "    return average"
    ]
@@ -654,12 +1529,12 @@
     }
    },
    "source": [
-    "`nums` in the global scope is, of course, unchanged."
+    "`numbers` in the global scope is, of course, *not* changed by merely defining `average_wrong()`."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 45,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -672,13 +1547,13 @@
        "[7, 11, 8, 5, 3, 12, 2, 6, 9, 10, 1, 4]"
       ]
      },
-     "execution_count": 18,
+     "execution_count": 45,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "nums"
+    "numbers"
    ]
   },
   {
@@ -689,15 +1564,15 @@
     }
    },
    "source": [
-    "Sometimes a function might return a correct solution for *some* inputs ..."
+    "Sometimes a function may return a correct solution for *some* inputs ..."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 46,
    "metadata": {
     "slideshow": {
-     "slide_type": "-"
+     "slide_type": "fragment"
     }
    },
    "outputs": [
@@ -707,13 +1582,13 @@
        "7.0"
       ]
      },
-     "execution_count": 19,
+     "execution_count": 46,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "average_wrong(nums)  # this is correct by accident"
+    "average_wrong(numbers)  # correct by accident"
    ]
   },
   {
@@ -729,10 +1604,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 47,
    "metadata": {
     "slideshow": {
-     "slide_type": "-"
+     "slide_type": "fragment"
     }
    },
    "outputs": [
@@ -742,7 +1617,7 @@
        "7.0"
       ]
      },
-     "execution_count": 20,
+     "execution_count": 47,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -759,9 +1634,9 @@
     }
    },
    "source": [
-    "[PythonTutor](http://pythontutor.com/visualize.html#code=nums%20%3D%20%5B1,%202,%203,%204,%205,%206,%207,%208,%209,%2010,%2011,%2012%5D%0A%0Adef%20average_wrong%28numbers%29%3A%0A%20%20%20%20evens%20%3D%20%5Bn%20for%20n%20in%20nums%20if%20n%20%25%202%20%3D%3D%200%5D%0A%20%20%20%20average%20%3D%20sum%28evens%29%20/%20len%28evens%29%0A%20%20%20%20return%20average%0A%0Arv%20%3D%20average_wrong%28%5B123,%20456,%20789%5D%29&cumulative=false&curInstr=0&heapPrimitives=nevernest&mode=display&origin=opt-frontend.js&py=3&rawInputLstJSON=%5B%5D&textReferences=false) is again helpful at visualizing the error interactively: Creating the `list` object `evens` eventually references takes *16* computational steps, namely two for managing the list comprehension, one for setting up an empty `list` object, *twelve* for filling it with elements derived from `nums` in the global scope (i.e., that is the error), and one to make `evens` reference it (cf., steps 6-21).\n",
+    "[PythonTutor](http://pythontutor.com/visualize.html#code=numbers%20%3D%20%5B7,%2011,%208,%205,%203,%2012,%202,%206,%209,%2010,%201,%204%5D%0A%0Adef%20average_wrong%28integers%29%3A%0A%20%20%20%20evens%20%3D%20%5Bn%20for%20n%20in%20numbers%20if%20n%20%25%202%20%3D%3D%200%5D%0A%20%20%20%20average%20%3D%20sum%28evens%29%20/%20len%28evens%29%0A%20%20%20%20return%20average%0A%0Aresult%20%3D%20average_wrong%28%5B123,%20456,%20789%5D%29&cumulative=false&curInstr=0&heapPrimitives=nevernest&mode=display&origin=opt-frontend.js&py=3&rawInputLstJSON=%5B%5D&textReferences=false) is again helpful at visualizing the error interactively: Creating the `list` object `evens` eventually references takes *16* computational steps, namely two for managing the list comprehension, one for setting up an empty `list` object, *twelve* for filling it with elements derived from `numbers` in the global scope (i.e., that is the error), and one to make `evens` reference it (cf., steps 6-21).\n",
     "\n",
-    "The frames logic shown by PythonTutor is the mechanism by which Python not only manages the names inside *one* function call but also for *many* potentially *simultaneous* calls, as revealed in [Chapter 4](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/04_iteration_00_lecture.ipynb#Trivial-Example:-Countdown). It is the reason why we may reuse the same names for the parameters and variables inside both `average_evens()` and `average_wrong()` without Python mixing them up. So, as we already read in the [Zen of Python](https://www.python.org/dev/peps/pep-0020/), \"namespaces are one honking great idea\" (cf., `import this`), and a frame is just a special kind of namespace."
+    "The frames logic shown by PythonTutor is the mechanism with which Python not only manages the names inside *one* function call but also for *many* potentially *simultaneous* calls, as revealed in [Chapter 4](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/04_iteration_00_lecture.ipynb#Trivial-Example:-Countdown). It is the reason why we may reuse the same names for the parameters and variables inside both `average_evens()` and `average_wrong()` without Python mixing them up. So, as we already read in the [Zen of Python](https://www.python.org/dev/peps/pep-0020/), \"namespaces are one honking great idea\" (cf., `import this`), and a frame is just a special kind of namespace."
    ]
   },
   {
@@ -783,14 +1658,14 @@
     }
    },
    "source": [
-    "Code gets even more confusing when variables by the *same* name from *different* scopes collide. In particular, what should we expect to happen if a function \"changes\" a globally defined variable internally?\n",
+    "Code gets even more confusing when variables by the *same* name from *different* scopes collide. In particular, what should we expect to happen if a function \"changes\" a globally defined variable in its body?\n",
     "\n",
-    "`average_odds()` below works like `average_evens()` above except that it **[casts](https://en.wikipedia.org/wiki/Type_conversion)** (i.e., \"converts\") the elements of `numbers` as objects of type `int` with the [int()](https://docs.python.org/3/library/functions.html#int) built-in first before filtering and averaging them. In doing so, it introduces an *internal* variable `nums` whose name collides with the one in the global scope. To filter for odd numbers, we use the **inequality operator** `!=` that is just the **reversed** version of `==`."
+    "`average_evens()` below works like `average_evens()` above except that it rounds the numbers in `integers` with the built-in [round()](https://docs.python.org/3/library/functions.html#round) function before filtering and averaging them. [round()](https://docs.python.org/3/library/functions.html#round) returns `int` objects independent of its argument being an `int` or a `float` object. On the first line in its body, `average_evens()` introduces a *local* variable `numbers` whose name collides with the one defined in the global scope."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 48,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -798,18 +1673,19 @@
    },
    "outputs": [],
    "source": [
-    "def average_odds(numbers):\n",
-    "    \"\"\"Calculate the average of all odd numbers in a list.\n",
+    "def average_evens(integers):\n",
+    "    \"\"\"Calculate the average of all even numbers in a list.\n",
     "\n",
     "    Args:\n",
-    "        numbers (list): a list of numbers; must be integer-like\n",
+    "        integers (list of int's/float's): numbers to be averaged;\n",
+    "            if non-whole numbers are provided, they are rounded\n",
     "\n",
     "    Returns:\n",
     "        float: average\n",
     "    \"\"\"\n",
-    "    nums = [int(n) for n in numbers]  # cast all numbers as integers first\n",
-    "    odds = [n for n in nums if n % 2 != 0]  # before filtering for odd numbers\n",
-    "    average = sum(odds) / len(odds)\n",
+    "    numbers = [round(n) for n in integers]\n",
+    "    evens = [n for n in numbers if n % 2 == 0]\n",
+    "    average = sum(evens) / len(evens)\n",
     "    return average"
    ]
   },
@@ -821,12 +1697,12 @@
     }
    },
    "source": [
-    "`nums` in the global scope is still unchanged."
+    "As a good practice, let's first \"verify\" that `average_evens()` is \"correct\" by calling it with inputs for which we can calculate the answer in our heads. Treating a function as a \"black box\" (i.e., input-output specification) when testing is also called [unit testing](https://en.wikipedia.org/wiki/Unit_testing) and plays an important role in modern software engineering."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 49,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -836,16 +1712,16 @@
     {
      "data": {
       "text/plain": [
-       "[7, 11, 8, 5, 3, 12, 2, 6, 9, 10, 1, 4]"
+       "42.0"
       ]
      },
-     "execution_count": 22,
+     "execution_count": 49,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "nums"
+    "average_evens([40.0, 41.1, 42.2, 43.3, 44.4])  # inputs with predictable result"
    ]
   },
   {
@@ -856,31 +1732,56 @@
     }
    },
    "source": [
-    "As good practice, let's first use inputs for which we can calculate the answer in our heads to \"verify\" that `average_odds()` is \"correct.\""
+    "Such tests are often and conveniently expressed with the `assert` statement (cf., [reference](https://docs.python.org/3/reference/simple_stmts.html#the-assert-statement)): If the expression following `assert` evaluates to `True`, nothing happens."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 50,
    "metadata": {
     "slideshow": {
-     "slide_type": "-"
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "assert average_evens([40.0, 41.1, 42.2, 43.3, 44.4]) == 42.0"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "However, if the expression evaluates to `False`, an `AssertionError` is raised."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 51,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
     }
    },
    "outputs": [
     {
-     "data": {
-      "text/plain": [
-       "3.0"
-      ]
-     },
-     "execution_count": 23,
-     "metadata": {},
-     "output_type": "execute_result"
+     "ename": "AssertionError",
+     "evalue": "",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mAssertionError\u001b[0m                            Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-51-f47586a118b9>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0;32massert\u001b[0m \u001b[0maverage_evens\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m40.0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m41.1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m42.2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m43.3\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m44.4\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m87.0\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;31mAssertionError\u001b[0m: "
+     ]
     }
    ],
    "source": [
-    "average_odds([1.0, 10.0, 3.0, 10.0, 5.0])  # verify correctness with predictable inputs"
+    "assert average_evens([40.0, 41.1, 42.2, 43.3, 44.4]) == 87.0"
    ]
   },
   {
@@ -891,47 +1792,12 @@
     }
    },
    "source": [
-    "To add to the confusion, let's also pass the global `nums` as an argument to `average_odds()`."
+    "Calling `average_evens()` leaves `numbers` in the global scope unchanged."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "-"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "6.0"
-      ]
-     },
-     "execution_count": 24,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "average_odds(nums)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "source": [
-    "Python, however, is again smart enough to keep all the involved `nums` variables apart. So, the global `nums` is still referencing the *same* `list` object as before."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 52,
    "metadata": {
     "slideshow": {
      "slide_type": "-"
@@ -944,13 +1810,13 @@
        "[7, 11, 8, 5, 3, 12, 2, 6, 9, 10, 1, 4]"
       ]
      },
-     "execution_count": 25,
+     "execution_count": 52,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "nums"
+    "numbers"
    ]
   },
   {
@@ -961,101 +1827,114 @@
     }
    },
    "source": [
-    "The reason why everything works is that *every time* we (re-)assign an object to a variable *inside* a function with the `=` statement, this is done in the *local* scope by default. There are ways to change variables existing in an outer scope from within a function, but this is a rather advanced topic on its own.\n",
+    "To add to the confusion, let's also pass the global `numbers` list as an argument to `average_evens()`. The return value is the same as before."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 53,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "7.0"
+      ]
+     },
+     "execution_count": 53,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "average_evens(numbers)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "In summary, Python is smart enough to keep all the involved `numbers` variables apart. So, the global `numbers` variable is still referencing the *same* `list` object as before."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 54,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[7, 11, 8, 5, 3, 12, 2, 6, 9, 10, 1, 4]"
+      ]
+     },
+     "execution_count": 54,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "numbers"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "The reason why everything works is that *every* time we (re-)assign an object to a variable *inside* a function's body with the `=` statement, this is done in the *local* scope by default. There are ways to change variables existing in an outer scope from within a function, but this is a rather advanced topic.\n",
     "\n",
-    "[PythonTutor](http://pythontutor.com/visualize.html#code=nums%20%3D%20%5B1,%202,%203,%204,%205,%206,%207,%208,%209,%2010,%2011,%2012%5D%0A%0Adef%20average_odds%28numbers%29%3A%0A%20%20%20%20nums%20%3D%20%5Bint%28n%29%20for%20n%20in%20numbers%5D%0A%20%20%20%20odds%20%3D%20%5Bn%20for%20n%20in%20nums%20if%20n%20%25%202%20!%3D%200%5D%0A%20%20%20%20average%20%3D%20sum%28odds%29%20/%20len%28odds%29%0A%20%20%20%20return%20average%0A%0Arv%20%3D%20average_odds%28%5B1.0,%2010.0,%203.0,%2010.0,%205.0%5D%29&cumulative=false&curInstr=0&heapPrimitives=nevernest&mode=display&origin=opt-frontend.js&py=3&rawInputLstJSON=%5B%5D&textReferences=false) shows how *two* `nums` variables exist in *different* scopes referencing *different* objects (cf., steps 14-25) when we execute `average_odds([1.0, 10.0, 3.0, 10.0, 5.0])`.\n",
+    "[PythonTutor](http://pythontutor.com/visualize.html#code=numbers%20%3D%20%5B7,%2011,%208,%205,%203,%2012,%202,%206,%209,%2010,%201,%204%5D%0A%0Adef%20average_evens%28integers%29%3A%0A%20%20%20%20numbers%20%3D%20%5Bround%28n%29%20for%20n%20in%20integers%5D%0A%20%20%20%20evens%20%3D%20%5Bn%20for%20n%20in%20numbers%20if%20n%20%25%202%20%3D%3D%200%5D%0A%20%20%20%20average%20%3D%20sum%28evens%29%20/%20len%28evens%29%0A%20%20%20%20return%20average%0A%0Aresult%20%3D%20average_evens%28%5B40.0,%2041.1,%2042.2,%2043.3,%2044.4%5D%29&cumulative=false&curInstr=0&heapPrimitives=nevernest&mode=display&origin=opt-frontend.js&py=3&rawInputLstJSON=%5B%5D&textReferences=false) shows how *two* `numbers` variables exist in *different* scopes referencing *different* objects (cf., steps 14-25) when we execute `average_evens([40.0, 41.1, 42.2, 43.3, 44.4])`.\n",
     "\n",
     "Variables whose names collide with the ones of variables in enclosing scopes - and the global scope is just the most enclosing scope - are said to **shadow** them.\n",
     "\n",
-    "While this is not a problem for Python, it may lead to less readable code for us humans and should be avoided if possible. But, as we have also heard, \"[naming things](https://skeptics.stackexchange.com/questions/19836/has-phil-karlton-ever-said-there-are-only-two-hard-things-in-computer-science)\" is often considered hard as well, and we have to be prepared to encounter shadowing variables."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "slide"
-    }
-   },
-   "source": [
-    "## Built-in Functions"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "source": [
-    "Python comes with plenty of useful functions built in, some of which we have already seen before. The [documentation](https://docs.python.org/3/library/functions.html) has the full list. Just as core Python itself, they are implemented in C and thus very fast.\n",
+    "While this is not a problem for Python, it may lead to less readable code for humans and should be avoided if possible. But, as the software engineering wisdom goes, \"[naming things](https://skeptics.stackexchange.com/questions/19836/has-phil-karlton-ever-said-there-are-only-two-hard-things-in-computer-science)\" is often considered a hard problem as well, and we have to be prepared to encounter shadowing variables.\n",
     "\n",
-    "[len()](https://docs.python.org/3/library/functions.html#len) counts the number of elements in a container object while [sum()](https://docs.python.org/3/library/functions.html#sum) adds up all the elements."
+    "Shadowing also occurs if a parameter in the function definition goes by the same name as a variable in an outer scope. Below, `average_evens()` is identical to the first version in this chapter except that the parameter `integers` is now called `numbers` as well."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": 55,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
     }
    },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "12"
-      ]
-     },
-     "execution_count": 26,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
-    "len(nums)"
+    "def average_evens(numbers):\n",
+    "    \"\"\"Calculate the average of all even numbers in a list.\n",
+    "\n",
+    "    Args:\n",
+    "        numbers (list of int's): whole numbers to be averaged\n",
+    "\n",
+    "    Returns:\n",
+    "        float: average\n",
+    "    \"\"\"\n",
+    "    evens = [n for n in numbers if n % 2 == 0]\n",
+    "    average = sum(evens) / len(evens)\n",
+    "    return average"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "-"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "78"
-      ]
-     },
-     "execution_count": 27,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "sum(nums)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "source": [
-    "We may cast objects as a different type. For example, to \"convert\" a float or a text into an integer, we use the [int()](https://docs.python.org/3/library/functions.html#int) built-in. It creates a *new* object of type `int` from the provided `avg` or `\"7\"` objects that continue to exist in memory unchanged."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": 56,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -1068,18 +1947,18 @@
        "7.0"
       ]
      },
-     "execution_count": 28,
+     "execution_count": 56,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "avg"
+    "average_evens(numbers)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
+   "execution_count": 57,
    "metadata": {
     "slideshow": {
      "slide_type": "-"
@@ -1089,40 +1968,16 @@
     {
      "data": {
       "text/plain": [
-       "7"
+       "[7, 11, 8, 5, 3, 12, 2, 6, 9, 10, 1, 4]"
       ]
      },
-     "execution_count": 29,
+     "execution_count": 57,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "int(avg)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 30,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "fragment"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "7"
-      ]
-     },
-     "execution_count": 30,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "int(\"7\")"
+    "numbers"
    ]
   },
   {
@@ -1133,126 +1988,7 @@
     }
    },
    "source": [
-    "Casting as an `int` object is different from rounding with the built-in [round()](https://docs.python.org/3/library/functions.html#round) function!"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 31,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "slide"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "7"
-      ]
-     },
-     "execution_count": 31,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "int(7.99)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 32,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "-"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "8"
-      ]
-     },
-     "execution_count": 32,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "round(7.99)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "source": [
-    "Not all conversions are valid and *runtime* errors may occur as the `ValueError` shows."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 33,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "slide"
-    }
-   },
-   "outputs": [
-    {
-     "ename": "ValueError",
-     "evalue": "invalid literal for int() with base 10: 'seven'",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-33-af421b358f21>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"seven\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
-      "\u001b[0;31mValueError\u001b[0m: invalid literal for int() with base 10: 'seven'"
-     ]
-    }
-   ],
-   "source": [
-    "int(\"seven\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "source": [
-    "We may also go in the other direction with the [float()](https://docs.python.org/3/library/functions.html#float) built-in."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 34,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "42.0"
-      ]
-     },
-     "execution_count": 34,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "float(42)"
+    "[PythonTutor](http://pythontutor.com/visualize.html#code=numbers%20%3D%20%5B7,%2011,%208,%205,%203,%2012,%202,%206,%209,%2010,%201,%204%5D%0A%0Adef%20average_evens%28numbers%29%3A%0A%20%20%20%20evens%20%3D%20%5Bn%20for%20n%20in%20numbers%20if%20n%20%25%202%20%3D%3D%200%5D%0A%20%20%20%20average%20%3D%20sum%28evens%29%20/%20len%28evens%29%0A%20%20%20%20return%20average%0A%0Aresult%20%3D%20average_evens%28numbers%29&cumulative=false&curInstr=0&heapPrimitives=nevernest&mode=display&origin=opt-frontend.js&py=3&rawInputLstJSON=%5B%5D&textReferences=false) reveals that in this example there are *two* `numbers` variables in *different* scope referencing the *same* `list` object in memory (cf., steps 4-23)."
    ]
   },
   {
@@ -1274,12 +2010,12 @@
     }
    },
    "source": [
-    "So far, we have only specified one parameter in each of our user-defined functions. In [Chapter 1](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/01_elements_00_lecture.ipynb#%28Arithmetic%29-Operators), however, we saw the built-in function [divmod()](https://docs.python.org/3/library/functions.html#divmod) take two arguments. And, the order of the numbers passed in mattered! Whenever we call a function and list its arguments in a comma separated manner, we say that we pass in the arguments by position or refer to them as **positional arguments**."
+    "So far, we have specified only one parameter in each of our user-defined functions. In [Chapter 1](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/01_elements_00_lecture.ipynb#%28Arithmetic%29-Operators), however, we saw the built-in [divmod()](https://docs.python.org/3/library/functions.html#divmod) function take two arguments. And, the order in which they are passed in matters! Whenever we call a function and list its arguments in a comma separated manner, we say that we pass in the arguments *by position* or refer to them as **positional arguments**."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 35,
+   "execution_count": 58,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -1292,7 +2028,7 @@
        "(4, 2)"
       ]
      },
-     "execution_count": 35,
+     "execution_count": 58,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1303,7 +2039,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 36,
+   "execution_count": 59,
    "metadata": {
     "slideshow": {
      "slide_type": "-"
@@ -1316,7 +2052,7 @@
        "(0, 10)"
       ]
      },
-     "execution_count": 36,
+     "execution_count": 59,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1333,12 +2069,12 @@
     }
    },
    "source": [
-    "For many functions, there is a natural order to the arguments. But what if this is not the case? For example, let's create a close relative of the above `average_evens()` function that also scales the resulting average by a factor. What is more natural? Passing in `numbers` first? Or `scalar`? There is no obvious way and we continue with the first alternative for no concrete reason."
+    "For many functions, there is a natural order to the arguments: For example, for any kind of division passing the dividend first and the divisor second seems intuitive. But what if that is not the case in another setting? For example, let's create a close relative of the above `average_evens()` function that also scales the resulting average by a factor. What is more natural? Passing in `numbers` first? Or `scalar`? There is no obvious way and we continue with the first alternative for no concrete reason."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 37,
+   "execution_count": 60,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -1347,16 +2083,17 @@
    "outputs": [],
    "source": [
     "def scaled_average_evens(numbers, scalar):\n",
-    "    \"\"\"Calculate a scaled average of all even numbers in a list.\n",
+    "    \"\"\"Calculate the scaled average of all even numbers in a list.\n",
     "\n",
     "    Args:\n",
-    "        numbers (list): a list of numbers; may be integers or floats\n",
-    "        scalar (float): the scalar that multiplies the average\n",
-    "            of the even numbers\n",
+    "        numbers (list of int's/float's): numbers to be averaged;\n",
+    "            if non-whole numbers are provided, they are rounded\n",
+    "        scalar (float): multiplies the average\n",
     "\n",
     "    Returns:\n",
     "        float: scaled average\n",
     "    \"\"\"\n",
+    "    numbers = [round(n) for n in numbers]\n",
     "    evens = [n for n in numbers if n % 2 == 0]\n",
     "    average = sum(evens) / len(evens)\n",
     "    return scalar * average"
@@ -1370,12 +2107,12 @@
     }
    },
    "source": [
-    "As with [divmod()](https://docs.python.org/3/library/functions.html#divmod), we pass in the arguments by position."
+    "As with [divmod()](https://docs.python.org/3/library/functions.html#divmod), we may pass in the arguments by position."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 38,
+   "execution_count": 61,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -1388,13 +2125,13 @@
        "14.0"
       ]
      },
-     "execution_count": 38,
+     "execution_count": 61,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "scaled_average_evens(nums, 2)"
+    "scaled_average_evens(numbers, 2)"
    ]
   },
   {
@@ -1405,14 +2142,14 @@
     }
    },
    "source": [
-    "However, now the function call is a bit harder to comprehend as we always need to remember what the `2` means. This becomes harder the more parameters we specify.\n",
+    "Now, this function call is a bit harder to understand as we always need to remember what the `2` means. This becomes even harder with more parameters.\n",
     "\n",
-    "Luckily, we may also reference the formal parameter names as **keyword arguments**. We can even combine positional and keyword arguments in the same function call. Each of the following does the *same*."
+    "Luckily, we may also pass in arguments *by name*. Then, we refer to them as **keyword arguments**."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 39,
+   "execution_count": 62,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -1425,18 +2162,29 @@
        "14.0"
       ]
      },
-     "execution_count": 39,
+     "execution_count": 62,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "scaled_average_evens(nums, scalar=2)"
+    "scaled_average_evens(numbers=numbers, scalar=2)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "When passing all arguments by name, we may do so in any order."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 40,
+   "execution_count": 63,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -1449,18 +2197,29 @@
        "14.0"
       ]
      },
-     "execution_count": 40,
+     "execution_count": 63,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "scaled_average_evens(numbers=nums, scalar=2)"
+    "scaled_average_evens(scalar=2, numbers=numbers)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "We may even combine positional and keyword arguments in the same function call."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 41,
+   "execution_count": 64,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -1473,13 +2232,13 @@
        "14.0"
       ]
      },
-     "execution_count": 41,
+     "execution_count": 64,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "scaled_average_evens(scalar=2, numbers=nums)"
+    "scaled_average_evens(numbers, scalar=2)"
    ]
   },
   {
@@ -1495,35 +2254,24 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 42,
+   "execution_count": 65,
    "metadata": {
     "slideshow": {
-     "slide_type": "slide"
+     "slide_type": "skip"
     }
    },
    "outputs": [
     {
      "ename": "SyntaxError",
-     "evalue": "positional argument follows keyword argument (<ipython-input-42-a6b957a168d8>, line 1)",
+     "evalue": "positional argument follows keyword argument (<ipython-input-65-b00eb6f72e84>, line 1)",
      "output_type": "error",
      "traceback": [
-      "\u001b[0;36m  File \u001b[0;32m\"<ipython-input-42-a6b957a168d8>\"\u001b[0;36m, line \u001b[0;32m1\u001b[0m\n\u001b[0;31m    scaled_average_evens(numbers=nums, 2)\u001b[0m\n\u001b[0m                                      ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m positional argument follows keyword argument\n"
+      "\u001b[0;36m  File \u001b[0;32m\"<ipython-input-65-b00eb6f72e84>\"\u001b[0;36m, line \u001b[0;32m1\u001b[0m\n\u001b[0;31m    scaled_average_evens(numbers=numbers, 2)\u001b[0m\n\u001b[0m                                         ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m positional argument follows keyword argument\n"
      ]
     }
    ],
    "source": [
-    "scaled_average_evens(numbers=nums, 2)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "slide"
-    }
-   },
-   "source": [
-    "### Default Argument Values"
+    "scaled_average_evens(numbers=numbers, 2)"
    ]
   },
   {
@@ -1534,16 +2282,86 @@
     }
    },
    "source": [
-    "Defining both `average_evens()` and `scaled_average_evens()` is also kind of repetitive as most of their code is the same. Such a redundancy makes a codebase harder to maintain in the long run as whenever we change the logic in one function, we must *not* forget to do so for the other function as well.\n",
-    "\n",
-    "A better way is to design related functions in a **modular** fashion such that they reuse each other's logic.\n",
-    "\n",
-    "For example, as not scaling an average is just a special case of scaling it with `1`, we could redefine the two functions like below: In this setting, the function resembling the *special* case (i.e., `average_evens()`) **forwards** the call to the more *general* function (i.e., `scaled_average_evens()`) using a `scalar=1` argument."
+    "Similarly, we must always pass in the right number of arguments. Otherwise, a `TypeError` is raised."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 43,
+   "execution_count": 66,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "ename": "TypeError",
+     "evalue": "scaled_average_evens() missing 1 required positional argument: 'scalar'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-66-d910518345ec>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mscaled_average_evens\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnumbers\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;31mTypeError\u001b[0m: scaled_average_evens() missing 1 required positional argument: 'scalar'"
+     ]
+    }
+   ],
+   "source": [
+    "scaled_average_evens(numbers)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 67,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "ename": "TypeError",
+     "evalue": "scaled_average_evens() takes 2 positional arguments but 3 were given",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-67-2e7ca1d2a555>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mscaled_average_evens\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnumbers\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m3\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;31mTypeError\u001b[0m: scaled_average_evens() takes 2 positional arguments but 3 were given"
+     ]
+    }
+   ],
+   "source": [
+    "scaled_average_evens(numbers, 2, 3)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "### Default Arguments"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Defining `average_evens()` and `scaled_average_evens()` as above leads to a repetition of most of their code. That is *not* good as such a redundancy makes a code base hard to maintain in the long run: Whenever we change the logic in one function, we must *not* forget to do so for the other function as well. And, most likely, we forget about such issues in larger projects.\n",
+    "\n",
+    "Below, three of four lines in the functions' bodies are identical!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 68,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -1551,42 +2369,56 @@
    },
    "outputs": [],
    "source": [
+    "def average_evens(numbers):\n",
+    "    \"\"\" ... ... ... \"\"\"\n",
+    "    numbers = [round(n) for n in numbers]\n",
+    "    evens = [n for n in numbers if n % 2 == 0]\n",
+    "    average = sum(evens) / len(evens)\n",
+    "    return average\n",
+    "\n",
+    "\n",
     "def scaled_average_evens(numbers, scalar):\n",
-    "    \"\"\"Calculate a scaled average of all even numbers in a list.\n",
-    "\n",
-    "    Args:\n",
-    "        numbers (list): a list of numbers; may be integers or floats\n",
-    "        scalar (float): the scalar that multiplies the average\n",
-    "            of the even numbers\n",
-    "\n",
-    "    Returns:\n",
-    "        float: scaled average\n",
-    "    \"\"\"\n",
+    "    \"\"\" ... ... ... \"\"\"\n",
+    "    numbers = [round(n) for n in numbers]\n",
     "    evens = [n for n in numbers if n % 2 == 0]\n",
     "    average = sum(evens) / len(evens)\n",
     "    return scalar * average"
    ]
   },
   {
-   "cell_type": "code",
-   "execution_count": 44,
+   "cell_type": "markdown",
    "metadata": {
     "slideshow": {
-     "slide_type": "-"
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "A better way is to design related functions in a **modular** fashion such that they reuse each other's code.\n",
+    "\n",
+    "For example, as not scaling an average is just a special case of scaling it with `1`, we could redefine the two functions like below: In this version, the function resembling the *special* case, `average_evens()`, **forwards** the call to the more *general* function, `scaled_average_evens()`, passing a `scalar` argument of `1`. As the name `scaled_average_evens` within the body of `average_evens()` is looked up each time the function is *being* executed, we may define `average_evens()` before `scaled_average_evens()`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 69,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
     }
    },
    "outputs": [],
    "source": [
     "def average_evens(numbers):\n",
-    "    \"\"\"Calculate the average of all even numbers in a list.\n",
+    "    \"\"\" ... ... ... \"\"\"\n",
+    "    return scaled_average_evens(numbers, scalar=1)\n",
     "\n",
-    "    Args:\n",
-    "        numbers (list): a list of numbers; may be integers or floats\n",
     "\n",
-    "    Returns:\n",
-    "        float: average\n",
-    "    \"\"\"\n",
-    "    return scaled_average_evens(numbers, scalar=1)  # refactored to use the logic in scaled_average_evens()"
+    "def scaled_average_evens(numbers, scalar):\n",
+    "    \"\"\" ... ... ... \"\"\"\n",
+    "    numbers = [round(n) for n in numbers]\n",
+    "    evens = [n for n in numbers if n % 2 == 0]\n",
+    "    average = sum(evens) / len(evens)\n",
+    "    return scalar * average"
    ]
   },
   {
@@ -1597,31 +2429,33 @@
     }
    },
    "source": [
-    "The outcome of `average_evens(nums)` is, of course, still `7.0`."
+    "After **refactoring** the functions, it is a good idea to test them again."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 45,
+   "execution_count": 70,
    "metadata": {
     "slideshow": {
-     "slide_type": "-"
+     "slide_type": "skip"
     }
    },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "7.0"
-      ]
-     },
-     "execution_count": 45,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
-    "average_evens(nums)"
+    "assert average_evens(numbers) == 7.0"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 71,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "assert scaled_average_evens(numbers, 2) == 14.0"
    ]
   },
   {
@@ -1632,12 +2466,12 @@
     }
    },
    "source": [
-    "If we *assume* that scaling the average occurs rarely, we could also handle both cases in *one* function definition by providing a **default value** for the `scalar` parameter."
+    "*Assuming* that scaling the average occurs rarely, it may be a good idea to handle both cases in *one* function definition by providing a **default argument** of `1` for the `scalar` parameter."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 46,
+   "execution_count": 72,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -1649,13 +2483,14 @@
     "    \"\"\"Calculate the average of all even numbers in a list.\n",
     "\n",
     "    Args:\n",
-    "        numbers (list): list of numbers; may be integers or floats\n",
-    "        scalar (float, optional): the scalar that multiplies the\n",
-    "            average of the even numbers\n",
+    "        numbers (list of int's/float's): numbers to be averaged;\n",
+    "            if non-whole numbers are provided, they are rounded\n",
+    "        scalar (float, optional): multiplies the average; defaults to 1\n",
     "\n",
     "    Returns:\n",
     "        float: (scaled) average\n",
     "    \"\"\"\n",
+    "    numbers = [round(n) for n in numbers]\n",
     "    evens = [n for n in numbers if n % 2 == 0]\n",
     "    average = sum(evens) / len(evens)\n",
     "    return scalar * average"
@@ -1669,14 +2504,16 @@
     }
    },
    "source": [
-    "Now, we call the function either with or without the `scalar` argument.\n",
+    "Now, we call the function with or without passing a `scalar` argument.\n",
     "\n",
-    "If `scalar` is to be passed in, this may be done as either a positional or a keyword argument. Which of the two versions where `scalar` is `2` is more \"natural\" and faster to comprehend in a larger program?"
+    "If `scalar` is *not* passed in, it automatically takes the value `1`.\n",
+    "\n",
+    "If `scalar` is passed in, this may be done as either a positional or a keyword argument. Which of the two calls where `scalar` is `2` is faster to understand in a larger program?"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 47,
+   "execution_count": 73,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -1689,18 +2526,18 @@
        "7.0"
       ]
      },
-     "execution_count": 47,
+     "execution_count": 73,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "average_evens(nums)"
+    "average_evens(numbers)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 48,
+   "execution_count": 74,
    "metadata": {
     "slideshow": {
      "slide_type": "-"
@@ -1713,18 +2550,18 @@
        "14.0"
       ]
      },
-     "execution_count": 48,
+     "execution_count": 74,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "average_evens(nums, 2)"
+    "average_evens(numbers, 2)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 49,
+   "execution_count": 75,
    "metadata": {
     "slideshow": {
      "slide_type": "-"
@@ -1737,13 +2574,13 @@
        "14.0"
       ]
      },
-     "execution_count": 49,
+     "execution_count": 75,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "average_evens(nums, scalar=2)"
+    "average_evens(numbers, scalar=2)"
    ]
   },
   {
@@ -1765,14 +2602,14 @@
     }
    },
    "source": [
-    "Since we *assumed* that scaling occurs *rarely*, we'd prefer that our new version of `average_evens()` be called with a *keyword argument* whenever `scalar` is to be passed in explicitly. Then, the second argument is never ambiguous as we could always read its name.\n",
+    "Because we *assumed* that scaling occurs rarely, we would prefer that our new version of `average_evens()` be called with a *keyword argument* whenever `scalar` is passed in. Then, a function call is never ambiguous when reading the source code.\n",
     "\n",
-    "Luckily, Python offers a **keyword-only** syntax, where all we need to do is to place the arguments for which we require *explicit* keyword use after an asterisk `*`."
+    "Python offers a **keyword-only** syntax when defining a function that *forces* a caller to pass the `scalar` argument *by name* if it is passed in at all: To do so, we place an asterisk `*` before the arguments that may only be passed in by name. Note that the keyword-only syntax also works *without* a default argument."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 50,
+   "execution_count": 76,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -1784,13 +2621,14 @@
     "    \"\"\"Calculate the average of all even numbers in a list.\n",
     "\n",
     "    Args:\n",
-    "        numbers (list): list of numbers; may be integers or floats\n",
-    "        scalar (float, optional): the scalar that multiplies the\n",
-    "            average of the even numbers\n",
+    "        numbers (list of int's/float's): numbers to be averaged;\n",
+    "            if non-whole numbers are provided, they are rounded\n",
+    "        scalar (float, optional): multiplies the average; defaults to 1\n",
     "\n",
     "    Returns:\n",
     "        float: (scaled) average\n",
     "    \"\"\"\n",
+    "    numbers = [round(n) for n in numbers]\n",
     "    evens = [n for n in numbers if n % 2 == 0]\n",
     "    average = sum(evens) / len(evens)\n",
     "    return scalar * average"
@@ -1804,12 +2642,12 @@
     }
    },
    "source": [
-    "If we now call the function with a `scalar` argument passed in, we *must* use keyword notation."
+    "As before, we may call `average_evens()` without passing in an argument for the `scalar` parameter."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 51,
+   "execution_count": 77,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -1822,18 +2660,29 @@
        "7.0"
       ]
      },
-     "execution_count": 51,
+     "execution_count": 77,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "average_evens(nums)"
+    "average_evens(numbers)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "If we call `average_evens()` with a `scalar` argument, we *must* use keyword notation."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 52,
+   "execution_count": 78,
    "metadata": {
     "slideshow": {
      "slide_type": "-"
@@ -1846,13 +2695,13 @@
        "14.0"
       ]
      },
-     "execution_count": 52,
+     "execution_count": 78,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "average_evens(nums, scalar=2)"
+    "average_evens(numbers, scalar=2)"
    ]
   },
   {
@@ -1863,12 +2712,12 @@
     }
    },
    "source": [
-    "If we pass in `scalar` as a positional argument instead, we get a `TypeError`."
+    "If instead we pass in `scalar` as a positional argument, we get a `TypeError`."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 53,
+   "execution_count": 79,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -1882,13 +2731,13 @@
      "traceback": [
       "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
       "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-53-b36617fef43e>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0maverage_evens\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnums\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m2\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;32m<ipython-input-79-12eb635366db>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0maverage_evens\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnumbers\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m2\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
       "\u001b[0;31mTypeError\u001b[0m: average_evens() takes 1 positional argument but 2 were given"
      ]
     }
    ],
    "source": [
-    "average_evens(nums, 2)"
+    "average_evens(numbers, 2)"
    ]
   },
   {
@@ -1910,22 +2759,20 @@
     }
    },
    "source": [
-    "The `def` statement is a statement because of its side effect of creating a *new* name that references a *new* `function` object in memory.\n",
+    "The `def` statement is a statement because of its *side effect* of creating a *new* name that references a *new* `function` object in memory.\n",
     "\n",
-    "We can thus think of it as doing *two* things at once (i.e., either both of them happen or none). First, a `function` object is created that contains the concrete $0$s and $1$s that resemble the instructions we put into the function's body. In the context of a function, these $0$s and $1$s are also called **[byte code](https://en.wikipedia.org/wiki/Bytecode)**. Then, a name referencing the new `function` object is created.\n",
+    "We can thus think of it as doing *two* things **atomically** (i.e., either both of them happen or none). First, a `function` object is created that contains the concrete $0$s and $1$s that resemble the instructions we put into the function's body. In the context of a function, these $0$s and $1$s are also called **[byte code](https://en.wikipedia.org/wiki/Bytecode)**. Then, a name referencing the new `function` object is created.\n",
     "\n",
     "Only this second aspect makes `def` a statement: Merely creating a new object in memory without making it accessible for later reference does *not* constitute a side effect because the state the program is *not* changed. After all, if we cannot reference an object, how do we know it exists in the first place?\n",
     "\n",
-    "Python provides a so-called **[lambda expression](https://docs.python.org/3/reference/expressions.html#lambda)** syntax that allows us to *only* create a `function` object in memory *without* making a name reference it.\n",
+    "Python provides a `lambda` expression syntax that allows us to *only* create a `function` object in memory *without* making a name reference it (cf., [reference](https://docs.python.org/3/reference/expressions.html#lambda)). It starts with the keyword `lambda` followed by an optional listing of comma separated parameters, a mandatory colon, and *one* expression that serves as the return value of the resulting `function` object. Because it does *not* create a name referencing the object, we effectively create \"anonymous\" functions with it.\n",
     "\n",
-    "It starts with the keyword `lambda` followed by an optional comma separated enumeration of parameters, a mandatory colon, and *one* expression that also is the resulting `function` object's return value.\n",
-    "\n",
-    "Because it does not create a name referencing the object, we effectively create \"anonymous\" functions with it. In the example, we create a `function` object that adds `3` to the only argument passed in as the parameter `x` and returns that sum."
+    "In the example, we create a `function` object that adds `3` to the only argument passed in as the parameter `x` and returns that sum."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 54,
+   "execution_count": 80,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -1938,7 +2785,7 @@
        "<function __main__.<lambda>(x)>"
       ]
      },
-     "execution_count": 54,
+     "execution_count": 80,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1959,12 +2806,12 @@
     "\n",
     "We created a `function` object, dit *not* call it, and Python immediately forgot about it. So what's the point?\n",
     "\n",
-    "To show that a `lambda` expression creates a callable `function` object, we use the simple `=` statement to assign it to the variable `add_three`, which is really `add_three()` as per our convention from above."
+    "To inspect the object created by a `lambda` expression, we use the simple `=` statement and assign it to the variable `add_three`, which is really `add_three()` as per our convention from above."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 55,
+   "execution_count": 81,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -1983,12 +2830,36 @@
     }
    },
    "source": [
-    "Now we call `add_three()` as if we defined it with the `def` statement in the first place."
+    "[type()](https://docs.python.org/3/library/functions.html#type) and [callable()](https://docs.python.org/3/library/functions.html#callable) confirm that `add_three` is indeed a callable `function` object."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 56,
+   "execution_count": 82,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "function"
+      ]
+     },
+     "execution_count": 82,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "type(add_three)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 83,
    "metadata": {
     "slideshow": {
      "slide_type": "-"
@@ -1998,16 +2869,16 @@
     {
      "data": {
       "text/plain": [
-       "13"
+       "True"
       ]
      },
-     "execution_count": 56,
+     "execution_count": 83,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "add_three(10)"
+    "callable(add_three)"
    ]
   },
   {
@@ -2018,12 +2889,12 @@
     }
    },
    "source": [
-    "Alternatively, we could call an anonymous `function` object created with a `lambda` expression right away (i.e., without assigning it to a variable), which looks quite weird for now as we need *two* pairs of parentheses: The first is just a delimiter whereas the second the call operator."
+    "Now we may call `add_three()` as if we defined it with the `def` statement."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 57,
+   "execution_count": 84,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -2036,7 +2907,42 @@
        "42"
       ]
      },
-     "execution_count": 57,
+     "execution_count": 84,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "add_three(39)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Alternatively, we could call an `function` object created with a `lambda` expression right away (i.e., without assigning it to a variable), which looks quite weird for now as we need *two* pairs of parentheses: The first one serves as a delimiter whereas the second represents the call operator."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 85,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "42"
+      ]
+     },
+     "execution_count": 85,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2053,9 +2959,9 @@
     }
    },
    "source": [
-    "The main point of having functions without a name is to use them in a situation where we know ahead of time that we use the function only *once*.\n",
+    "The main point of having functions without a reference to them is to use them in a situation where we know ahead of time that we use the function only *once*.\n",
     "\n",
-    "Popular applications of lambda expressions occur in combination with the **map-filter-reduce** paradigm or when we do \"number crunching\" with **arrays** and **data frames**. We look at both in detail in [Chapter 7](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/07_sequences_00_lecture.ipynb)."
+    "Popular applications of lambda expressions occur in combination with the **map-filter-reduce** paradigm (cf., [Chapter 7](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/07_sequences_00_lecture.ipynb#Lambda-Expressions)) or when we do \"number crunching\" with **arrays** and **data frames** (cf., Chapter 9)."
    ]
   },
   {
@@ -2094,7 +3000,7 @@
     }
    },
    "source": [
-    "### Standard Library"
+    "### The Standard Library"
    ]
   },
   {
@@ -2122,7 +3028,7 @@
     }
    },
    "source": [
-    "#### The [math](https://docs.python.org/3/library/math.html) Module"
+    "#### [math](https://docs.python.org/3/library/math.html) Module"
    ]
   },
   {
@@ -2140,7 +3046,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 58,
+   "execution_count": 86,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -2164,7 +3070,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 59,
+   "execution_count": 87,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -2177,7 +3083,7 @@
        "<module 'math' from '/home/webartifex/.pyenv/versions/anaconda3-2019.10/lib/python3.7/lib-dynload/math.cpython-37m-x86_64-linux-gnu.so'>"
       ]
      },
-     "execution_count": 59,
+     "execution_count": 87,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2188,7 +3094,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 60,
+   "execution_count": 88,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -2198,10 +3104,10 @@
     {
      "data": {
       "text/plain": [
-       "140371899337520"
+       "140413079419024"
       ]
      },
-     "execution_count": 60,
+     "execution_count": 88,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2212,7 +3118,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 61,
+   "execution_count": 89,
    "metadata": {
     "slideshow": {
      "slide_type": "-"
@@ -2225,7 +3131,7 @@
        "module"
       ]
      },
-     "execution_count": 61,
+     "execution_count": 89,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2251,7 +3157,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 62,
+   "execution_count": 90,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -2319,7 +3225,7 @@
        " 'trunc']"
       ]
      },
-     "execution_count": 62,
+     "execution_count": 90,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2341,7 +3247,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 63,
+   "execution_count": 91,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -2354,7 +3260,7 @@
        "3.141592653589793"
       ]
      },
-     "execution_count": 63,
+     "execution_count": 91,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2365,7 +3271,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 64,
+   "execution_count": 92,
    "metadata": {
     "slideshow": {
      "slide_type": "-"
@@ -2378,7 +3284,7 @@
        "2.718281828459045"
       ]
      },
-     "execution_count": 64,
+     "execution_count": 92,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2389,7 +3295,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 65,
+   "execution_count": 93,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -2402,7 +3308,7 @@
        "<function math.sqrt(x, /)>"
       ]
      },
-     "execution_count": 65,
+     "execution_count": 93,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2413,7 +3319,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 66,
+   "execution_count": 94,
    "metadata": {
     "slideshow": {
      "slide_type": "-"
@@ -2438,7 +3344,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 67,
+   "execution_count": 95,
    "metadata": {
     "slideshow": {
      "slide_type": "-"
@@ -2451,7 +3357,7 @@
        "1.4142135623730951"
       ]
      },
-     "execution_count": 67,
+     "execution_count": 95,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2477,7 +3383,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 68,
+   "execution_count": 96,
    "metadata": {
     "slideshow": {
      "slide_type": "-"
@@ -2490,7 +3396,7 @@
        "2.0"
       ]
      },
-     "execution_count": 68,
+     "execution_count": 96,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2512,7 +3418,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 69,
+   "execution_count": 97,
    "metadata": {
     "slideshow": {
      "slide_type": "-"
@@ -2525,7 +3431,7 @@
        "10.0"
       ]
      },
-     "execution_count": 69,
+     "execution_count": 97,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2549,7 +3455,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 70,
+   "execution_count": 98,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -2562,7 +3468,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 71,
+   "execution_count": 99,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -2575,7 +3481,7 @@
        "4.0"
       ]
      },
-     "execution_count": 71,
+     "execution_count": 99,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2592,7 +3498,7 @@
     }
    },
    "source": [
-    "#### The [random](https://docs.python.org/3/library/random.html) Module"
+    "#### [random](https://docs.python.org/3/library/random.html) Module"
    ]
   },
   {
@@ -2608,7 +3514,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 72,
+   "execution_count": 100,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -2621,7 +3527,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 73,
+   "execution_count": 101,
    "metadata": {
     "slideshow": {
      "slide_type": "-"
@@ -2634,7 +3540,7 @@
        "<module 'random' from '/home/webartifex/.pyenv/versions/anaconda3-2019.10/lib/python3.7/random.py'>"
       ]
      },
-     "execution_count": 73,
+     "execution_count": 101,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2656,7 +3562,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 74,
+   "execution_count": 102,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -2730,7 +3636,7 @@
        " 'weibullvariate']"
       ]
      },
-     "execution_count": 74,
+     "execution_count": 102,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2752,7 +3658,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 75,
+   "execution_count": 103,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -2765,7 +3671,7 @@
        "<function Random.random>"
       ]
      },
-     "execution_count": 75,
+     "execution_count": 103,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2776,7 +3682,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 76,
+   "execution_count": 104,
    "metadata": {
     "slideshow": {
      "slide_type": "-"
@@ -2801,7 +3707,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 77,
+   "execution_count": 105,
    "metadata": {
     "slideshow": {
      "slide_type": "-"
@@ -2811,10 +3717,10 @@
     {
      "data": {
       "text/plain": [
-       "0.64575874518665"
+       "0.8837282454584094"
       ]
      },
-     "execution_count": 77,
+     "execution_count": 105,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2831,12 +3737,12 @@
     }
    },
    "source": [
-    "While we could build some conditional logic with an `if` statement to map the number generated by [random.random()](https://docs.python.org/3/library/random.html#random.random) to a finite set of elements manually, the [random.choice()](https://docs.python.org/3/library/random.html#random.choice) function provides a lot more **convenience** for us. We call it with, for example, the `nums` list and it draws one element out of it with equal chance."
+    "While we could build some conditional logic with an `if` statement to map the number generated by [random.random()](https://docs.python.org/3/library/random.html#random.random) to a finite set of elements manually, the [random.choice()](https://docs.python.org/3/library/random.html#random.choice) function provides a lot more **convenience** for us. We call it with, for example, the `numbers` list and it draws one element out of it with equal chance."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 78,
+   "execution_count": 106,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -2846,10 +3752,10 @@
     {
      "data": {
       "text/plain": [
-       "<bound method Random.choice of <random.Random object at 0x563af2c67aa0>>"
+       "<bound method Random.choice of <random.Random object at 0x5653695270d0>>"
       ]
      },
-     "execution_count": 78,
+     "execution_count": 106,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2860,7 +3766,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 79,
+   "execution_count": 107,
    "metadata": {
     "slideshow": {
      "slide_type": "-"
@@ -2885,7 +3791,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 80,
+   "execution_count": 108,
    "metadata": {
     "slideshow": {
      "slide_type": "-"
@@ -2895,16 +3801,16 @@
     {
      "data": {
       "text/plain": [
-       "12"
+       "9"
       ]
      },
-     "execution_count": 80,
+     "execution_count": 108,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "random.choice(nums)"
+    "random.choice(numbers)"
    ]
   },
   {
@@ -2922,7 +3828,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 81,
+   "execution_count": 109,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -2935,7 +3841,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 82,
+   "execution_count": 110,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -2948,7 +3854,7 @@
        "0.6394267984578837"
       ]
      },
-     "execution_count": 82,
+     "execution_count": 110,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2959,7 +3865,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 83,
+   "execution_count": 111,
    "metadata": {
     "slideshow": {
      "slide_type": "-"
@@ -2972,7 +3878,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 84,
+   "execution_count": 112,
    "metadata": {
     "slideshow": {
      "slide_type": "-"
@@ -2985,7 +3891,7 @@
        "0.6394267984578837"
       ]
      },
-     "execution_count": 84,
+     "execution_count": 112,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3030,7 +3936,7 @@
     }
    },
    "source": [
-    "#### The [numpy](http://www.numpy.org/) Library"
+    "#### [numpy](http://www.numpy.org/) Library"
    ]
   },
   {
@@ -3050,7 +3956,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 85,
+   "execution_count": 113,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -3082,7 +3988,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 86,
+   "execution_count": 114,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -3106,7 +4012,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 87,
+   "execution_count": 115,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -3119,7 +4025,7 @@
        "<module 'numpy' from '/home/webartifex/.pyenv/versions/anaconda3-2019.10/lib/python3.7/site-packages/numpy/__init__.py'>"
       ]
      },
-     "execution_count": 87,
+     "execution_count": 115,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3136,12 +4042,12 @@
     }
    },
    "source": [
-    "Let's convert the above `nums` list into a vector-like object of type `numpy.ndarray`."
+    "Let's convert the above `numbers` list into a vector-like object of type `numpy.ndarray`."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 88,
+   "execution_count": 116,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -3149,12 +4055,12 @@
    },
    "outputs": [],
    "source": [
-    "vec = np.array(nums)"
+    "vec = np.array(numbers)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 89,
+   "execution_count": 117,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -3167,7 +4073,7 @@
        "array([ 7, 11,  8,  5,  3, 12,  2,  6,  9, 10,  1,  4])"
       ]
      },
-     "execution_count": 89,
+     "execution_count": 117,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3178,7 +4084,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 90,
+   "execution_count": 118,
    "metadata": {
     "slideshow": {
      "slide_type": "-"
@@ -3191,7 +4097,7 @@
        "numpy.ndarray"
       ]
      },
-     "execution_count": 90,
+     "execution_count": 118,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3215,7 +4121,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 91,
+   "execution_count": 119,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -3228,7 +4134,7 @@
        "array([14, 22, 16, 10,  6, 24,  4, 12, 18, 20,  2,  8])"
       ]
      },
-     "execution_count": 91,
+     "execution_count": 119,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3245,12 +4151,12 @@
     }
    },
    "source": [
-    "This scalar multiplication would \"fail\" if we used a plain `list` object like `nums` instead of an `numpy.ndarray` object like `vec`. The two types exhibit different **behavior** when used with the same operator, another example of **operator overloading**."
+    "This scalar multiplication would \"fail\" if we used a plain `list` object like `numbers` instead of an `numpy.ndarray` object like `vec`. The two types exhibit different **behavior** when used with the same operator, another example of **operator overloading**."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 92,
+   "execution_count": 120,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -3263,13 +4169,13 @@
        "[7, 11, 8, 5, 3, 12, 2, 6, 9, 10, 1, 4, 7, 11, 8, 5, 3, 12, 2, 6, 9, 10, 1, 4]"
       ]
      },
-     "execution_count": 92,
+     "execution_count": 120,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "2 * nums  # surprise, surprise"
+    "2 * numbers  # surprise, surprise"
    ]
   },
   {
@@ -3285,7 +4191,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 93,
+   "execution_count": 121,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -3298,7 +4204,7 @@
        "78"
       ]
      },
-     "execution_count": 93,
+     "execution_count": 121,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3309,7 +4215,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 94,
+   "execution_count": 122,
    "metadata": {
     "slideshow": {
      "slide_type": "-"
@@ -3322,7 +4228,7 @@
        "7"
       ]
      },
-     "execution_count": 94,
+     "execution_count": 122,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3361,7 +4267,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 95,
+   "execution_count": 123,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -3374,7 +4280,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 96,
+   "execution_count": 124,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -3387,7 +4293,7 @@
        "<module 'sample_module' from '/home/webartifex/repos/intro-to-python/sample_module.py'>"
       ]
      },
-     "execution_count": 96,
+     "execution_count": 124,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3413,7 +4319,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 97,
+   "execution_count": 125,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -3431,14 +4337,14 @@
        " '__name__',\n",
        " '__package__',\n",
        " '__spec__',\n",
-       " '_default_scalar',\n",
+       " '_round_all',\n",
        " '_scaled_average',\n",
        " 'average',\n",
        " 'average_evens',\n",
        " 'average_odds']"
       ]
      },
-     "execution_count": 97,
+     "execution_count": 125,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3460,7 +4366,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 98,
+   "execution_count": 126,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -3477,9 +4383,9 @@
       "    Calculate the average of all even numbers in a list.\n",
       "    \n",
       "    Args:\n",
-      "        numbers (list): list of numbers; may be integers or floats\n",
-      "        scalar (float, optional): the scalar that multiplies the\n",
-      "            average of the even numbers\n",
+      "        numbers (list of int's/float's): numbers to be averaged;\n",
+      "            if non-whole numbers are provided, they are rounded\n",
+      "        scalar (float, optional): multiplies the average; defaults to 1\n",
       "    \n",
       "    Returns:\n",
       "        float: (scaled) average\n",
@@ -3493,7 +4399,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 99,
+   "execution_count": 127,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -3506,18 +4412,18 @@
        "7.0"
       ]
      },
-     "execution_count": 99,
+     "execution_count": 127,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "mod.average_evens(nums)"
+    "mod.average_evens(numbers)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 100,
+   "execution_count": 128,
    "metadata": {
     "slideshow": {
      "slide_type": "-"
@@ -3530,13 +4436,13 @@
        "14.0"
       ]
      },
-     "execution_count": 100,
+     "execution_count": 128,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "mod.average_evens(nums, scalar=2)"
+    "mod.average_evens(numbers, scalar=2)"
    ]
   },
   {
@@ -3571,11 +4477,11 @@
     }
    },
    "source": [
-    "A **function** is a **named sequence** of statements that perform a computation.\n",
+    "A user-defined **function** is a **named sequence** of statements that perform a computation.\n",
     "\n",
-    "Functions provide benefits as they:\n",
+    "Functions provide benefits as they\n",
     "\n",
-    "- make programs easier to comprehend and debug for humans as they give names to the smaller parts of a larger program (i.e., they **modularize** a codebase), and\n",
+    "- make programs easier to comprehend and debug for humans as they give names to the smaller parts of a larger program (i.e., they **modularize** a code base), and\n",
     "- eliminate redundancies by allowing **reuse of code**.\n",
     "\n",
     "Functions are **defined** once with the `def` statement. Then, they may be **called** many times with the call operator `()`.\n",
@@ -3586,6 +4492,8 @@
     "\n",
     "**Lambda expressions** create anonymous functions.\n",
     "\n",
+    "Functions are a special kind of **callables**. Any object that may be **called** with the call operator `()` is a callable. Built-in functions and **constructors** are other kinds of callables.\n",
+    "\n",
     "Core Python can be extended with code from either the **standard library** or **third-party** libraries.\n",
     "\n",
     "Outside Jupyter notebooks, Python code is put into **modules** that are grouped in **packages**."
@@ -3593,6 +4501,7 @@
   }
  ],
  "metadata": {
+  "celltoolbar": "Slideshow",
   "kernelspec": {
    "display_name": "Python 3",
    "language": "python",
diff --git a/02_functions_01_review.ipynb b/02_functions_01_review.ipynb
index f0e4772..f081ec6 100644
--- a/02_functions_01_review.ipynb
+++ b/02_functions_01_review.ipynb
@@ -67,7 +67,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "**Q3**: What does it mean for a variable to **go out of scope**?"
+    "**Q3**: What does it mean for a variable to go out of **scope**?"
    ]
   },
   {
@@ -95,7 +95,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "**Q5**: Explain the concept of **forwarding** a function **call**."
+    "**Q5**: Explain the concept of **forwarding** a **function call**."
    ]
   },
   {
@@ -119,6 +119,20 @@
     " "
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q7**: What are **callables**? How do they relate to `function` objects?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " "
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -137,7 +151,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "**Q7**: A mere function **call** is just an **expression**."
+    "**Q8**: A mere function **call** is just an **expression**."
    ]
   },
   {
@@ -151,7 +165,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "**Q8**: When using the `import` statement, we need to ensure that the imported attributes do **not** overwrite any already defined variables and functions."
+    "**Q9**: When using the `import` statement, we need to ensure that the imported attributes do *not* overwrite any already defined variables and functions."
    ]
   },
   {
@@ -165,7 +179,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "**Q9:** Functions always have a name by which we can call them."
+    "**Q10:** Functions always have a name by which we can call them."
    ]
   },
   {
@@ -179,7 +193,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "**Q10**: The [standard library](https://docs.python.org/3/library/index.html) is a collection of numerical tools often used in scientific computing, for example, advanced mathematical functions or utilities for simulation."
+    "**Q11**: The [standard library](https://docs.python.org/3/library/index.html) is a collection of numerical tools often used in scientific computing, for example, advanced mathematical functions or utilities for simulation."
    ]
   },
   {
diff --git a/02_functions_02_exercises.ipynb b/02_functions_02_exercises.ipynb
index a17b45b..00cd198 100644
--- a/02_functions_02_exercises.ipynb
+++ b/02_functions_02_exercises.ipynb
@@ -32,7 +32,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "**Q1.1**: The [volume of a sphere](https://en.wikipedia.org/wiki/Sphere) is defined as $\\frac{4}{3} * \\pi * r^3$. Calculate this value for $r=10.0$ and round it to 10 digits after the comma. Use the [standard library](https://docs.python.org/3/library/index.html) to obtain a good approximation of $\\pi$."
+    "**Q1**: The [volume of a sphere](https://en.wikipedia.org/wiki/Sphere) is defined as $\\frac{4}{3} * \\pi * r^3$. Calculate this value for $r=10.0$ and round it to 10 digits after the comma. Use the [standard library](https://docs.python.org/3/library/index.html) to obtain a good approximation of $\\pi$."
    ]
   },
   {
@@ -66,7 +66,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "**Q1.2**: Encapsulate the logic into a function `sphere_volume()` that takes one *positional* argument `radius` and one *keyword-only* argument `digits` defaulting to `5`. The volume should be returned as a `float` object under *all* circumstances. Document your work appropriately in a docstring according to [Google's Python Style Guide](https://github.com/google/styleguide/blob/gh-pages/pyguide.md)."
+    "**Q2**: Encapsulate the logic into a function `sphere_volume()` that takes one *positional* argument `radius` and one *keyword-only* argument `digits` defaulting to `5`. The volume should be returned as a `float` object under *all* circumstances. Document your work appropriately in a docstring according to [Google's Python Style Guide](https://github.com/google/styleguide/blob/gh-pages/pyguide.md)."
    ]
   },
   {
@@ -83,7 +83,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "**Q1.3**: Evaluate the function with `radius = 100.0` and 1, 5, 10, 15, and 20 digits respectively."
+    "**Q3**: Evaluate the function with `radius = 100.0` and 1, 5, 10, 15, and 20 digits respectively."
    ]
   },
   {
@@ -144,7 +144,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "**Q1.4**: What observation do you make?"
+    "**Q4**: What observation do you make?"
    ]
   },
   {
@@ -158,7 +158,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "**Q1.5**: Using the [range()](https://docs.python.org/3/library/functions.html#func-range) built-in, write a `for`-loop and calculate the volume of a sphere with `radius = 42.0` for all `digits` from `1` through `20`. Print out each volume on a separate line.\n",
+    "**Q5**: Using the [range()](https://docs.python.org/3/library/functions.html#func-range) built-in, write a `for`-loop and calculate the volume of a sphere with `radius = 42.0` for all `digits` from `1` through `20`. Print out each volume on a separate line.\n",
     "\n",
     "Note: This is the first task where you need to use the built-in [print()](https://docs.python.org/3/library/functions.html#print) function."
    ]
@@ -186,7 +186,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "**Q1.6**: What lesson do you learn about the `float` type?"
+    "**Q6**: What lesson do you learn about the `float` type?"
    ]
   },
   {
diff --git a/sample_module.py b/sample_module.py
index 2efcda4..b16e7b2 100644
--- a/sample_module.py
+++ b/sample_module.py
@@ -15,16 +15,17 @@ be used outside the module with a single underscore "_". This way, we can
 design the code within a module in a modular fashion and only "export" what we
 want.
 
-Here, all three functions internally forward the computation to an internal
-utility function _scaled_average() that contains all the logic common to the
-three functions. Also, we define one _default_scalar variable that is used as
-the default for the scalar parameter in each of the functions.
+Here, all three functions internally forward parts of their computations
+to the utility functions _round_all() and _scaled_average() that contain all
+the logic common to the three functions.
 
 While this example is stylized, it shows how Python modules are often
 designed.
 """
 
-_default_scalar = 1
+def _round_all(numbers):
+    """Internal utility function to round all numbers in a list."""
+    return [round(n) for n in numbers]
 
 
 def _scaled_average(numbers, scalar):
@@ -33,43 +34,43 @@ def _scaled_average(numbers, scalar):
     return scalar * average
 
 
-def average(numbers, *, scalar=_default_scalar):
+def average(numbers, *, scalar=1):
     """Calculate the average of all numbers in a list.
 
     Args:
-        numbers (list): list of numbers; may be integers or floats
-        scalar (float, optional): the scalar that multiplies the
-            average of the even numbers
+        numbers (list of int's/float's): numbers to be averaged;
+            if non-whole numbers are provided, they are rounded
+        scalar (float, optional): multiplies the average; defaults to 1
 
     Returns:
         float: (scaled) average
     """
-    return _scaled_average(numbers, scalar)
+    return _scaled_average(_round_all(numbers), scalar)
 
 
-def average_evens(numbers, *, scalar=_default_scalar):
+def average_evens(numbers, *, scalar=1):
     """Calculate the average of all even numbers in a list.
 
     Args:
-        numbers (list): list of numbers; may be integers or floats
-        scalar (float, optional): the scalar that multiplies the
-            average of the even numbers
+        numbers (list of int's/float's): numbers to be averaged;
+            if non-whole numbers are provided, they are rounded
+        scalar (float, optional): multiplies the average; defaults to 1
 
     Returns:
         float: (scaled) average
     """
-    return _scaled_average([n for n in numbers if n % 2 == 0], scalar)
+    return _scaled_average([n for n in _round_all(numbers) if n % 2 == 0], scalar)
 
 
-def average_odds(numbers, *, scalar=_default_scalar):
+def average_odds(numbers, *, scalar=1):
     """Calculate the average of all odd numbers in a list.
 
     Args:
-        numbers (list): list of numbers; may be integers or floats
-        scalar (float, optional): the scalar that multiplies the
-            average of the even numbers
+        numbers (list of int's/float's): numbers to be averaged;
+            if non-whole numbers are provided, they are rounded
+        scalar (float, optional): multiplies the average; defaults to 1
 
     Returns:
         float: (scaled) average
     """
-    return _scaled_average([n for n in numbers if n % 2 != 0], scalar)
+    return _scaled_average([n for n in _round_all(numbers) if n % 2 != 0], scalar)

From 431d2aa31a10b30a556fa4214a448a62dadc8111 Mon Sep 17 00:00:00 2001
From: Alexander Hess <alexander@webartifex.biz>
Date: Wed, 5 Feb 2020 18:33:32 +0100
Subject: [PATCH 067/242] Bump version number

---
 pyproject.toml | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/pyproject.toml b/pyproject.toml
index b77fd3a..9cc1c12 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -1,6 +1,6 @@
 [tool.poetry]
 name = "intro-to-python"
-version = "0.6.2"
+version = "0.6.3"
 description = "An introduction to Python and programming for wanna-be data scientists"
 authors = ["Alexander Hess <alexander@webartifex.biz>"]
 license = "MIT"

From 51670fcdd407e7b7c51cb0737e12d5faf5276c74 Mon Sep 17 00:00:00 2001
From: Alexander Hess <alexander@webartifex.biz>
Date: Sun, 9 Feb 2020 22:54:28 +0100
Subject: [PATCH 068/242] Streamline text

---
 01_elements_00_lecture.ipynb | 20 ++++++++++++++------
 1 file changed, 14 insertions(+), 6 deletions(-)

diff --git a/01_elements_00_lecture.ipynb b/01_elements_00_lecture.ipynb
index ba54aa3..ba6e476 100644
--- a/01_elements_00_lecture.ipynb
+++ b/01_elements_00_lecture.ipynb
@@ -3237,7 +3237,11 @@
   {
    "cell_type": "code",
    "execution_count": 96,
-   "metadata": {},
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
    "outputs": [
     {
      "data": {
@@ -3256,7 +3260,11 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
    "source": [
     "For sure, we need to include operators to achieve something useful."
    ]
@@ -3266,7 +3274,7 @@
    "execution_count": 97,
    "metadata": {
     "slideshow": {
-     "slide_type": "-"
+     "slide_type": "fragment"
     }
    },
    "outputs": [
@@ -3301,7 +3309,7 @@
    "execution_count": 98,
    "metadata": {
     "slideshow": {
-     "slide_type": "-"
+     "slide_type": "fragment"
     }
    },
    "outputs": [
@@ -3336,7 +3344,7 @@
    "execution_count": 99,
    "metadata": {
     "slideshow": {
-     "slide_type": "-"
+     "slide_type": "fragment"
     }
    },
    "outputs": [
@@ -3371,7 +3379,7 @@
    "execution_count": 100,
    "metadata": {
     "slideshow": {
-     "slide_type": "-"
+     "slide_type": "fragment"
     }
    },
    "outputs": [

From 690b96d44d7e65bd02a08af5d8f3b07c65674669 Mon Sep 17 00:00:00 2001
From: Alexander Hess <alexander@webartifex.biz>
Date: Sun, 9 Feb 2020 23:02:23 +0100
Subject: [PATCH 069/242] Refurbish chapter 03

- streamline text
- streamline examples for if statement
---
 03_conditionals_00_lecture.ipynb | 655 +++++++++++++++++++++++--------
 1 file changed, 481 insertions(+), 174 deletions(-)

diff --git a/03_conditionals_00_lecture.ipynb b/03_conditionals_00_lecture.ipynb
index a6902df..7b769c4 100644
--- a/03_conditionals_00_lecture.ipynb
+++ b/03_conditionals_00_lecture.ipynb
@@ -19,7 +19,7 @@
     }
    },
    "source": [
-    "We analyzed every aspect of the `average_evens()` function in [Chapter 2](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/02_functions_00_lecture.ipynb) except for the `if`-related parts. While it seems to do what we expect it to, there is a whole lot more we learn from taking it apart. In particular, the `if` may occur within both a **statement** or an **expression**, analogous as to how a noun in a natural language is *either* the subject of *or* an object in a sentence. What is common to both usages is that it leads to code being executed for *parts* of the input only. It is our first way of **controlling** the **flow of execution** in a program.\n",
+    "We analyzed every aspect of the `average_evens()` function in [Chapter 2](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/02_functions_00_lecture.ipynb) except for the `if`-related parts. While it does what we expect it to, there is a whole lot more to learn by taking it apart. In particular, the `if` may occur within both a **statement** or an **expression**, analogous as to how a noun in a natural language can be the subject of *or* an object in a sentence. What is common to both usages is that it leads to code being executed for *parts* of the input only. It is a way of controlling the **flow of execution** in a program.\n",
     "\n",
     "After deconstructing `if` in the first part of this chapter, we take a close look at a similar concept, namely handling **exceptions**."
    ]
@@ -43,7 +43,7 @@
     }
    },
    "source": [
-    "Any expression that is either true or not is called a **boolean expression**. It is such simple true-or-false \"statements\" about the world on which mathematicians, and originally philosophers, base their rules of reasoning: They are studied formally in the field of [propositional logic](https://en.wikipedia.org/wiki/Propositional_calculus).\n",
+    "Any expression that is either true or not is called a **boolean expression**. It is such simple true-or-false observations about the world on which mathematicians, and originally philosophers, base their rules of reasoning: They are studied formally in the field of [propositional logic](https://en.wikipedia.org/wiki/Propositional_calculus).\n",
     "\n",
     "A trivial example involves the equality operator `==` that evaluates to either `True` or `False` depending on its operands \"comparing equal\" or not."
    ]
@@ -77,7 +77,7 @@
    "execution_count": 2,
    "metadata": {
     "slideshow": {
-     "slide_type": "-"
+     "slide_type": "fragment"
     }
    },
    "outputs": [
@@ -104,7 +104,7 @@
     }
    },
    "source": [
-    "The `==` operator handles objects of *different* types: It implements a notion of equality in line with how humans think of things being equal or not. After all, `42` and `42.0` are different $0$s and $1$s for a computer and other programming languages might say `False` here! Technically, this is yet another example of operator overloading."
+    "The `==` operator handles objects of *different* types: Because of that, it implements a notion of equality in line with how humans think of things being equal or not. After all, `42` and `42.0` are different $0$s and $1$s for a computer and other programming languages may say `False` here! Technically, this is yet another example of operator overloading."
    ]
   },
   {
@@ -139,7 +139,7 @@
     }
    },
    "source": [
-    "There are, however, cases where even well-behaved Python does not make us happy. [Chapter 5](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/05_numbers_00_lecture.ipynb#Imprecision) provides more insights into this \"bug.\""
+    "There are, however, cases where the `==` operator seems to not work intuitively. [Chapter 5](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/05_numbers_00_lecture.ipynb#Imprecision) provides more insights into this \"bug.\""
    ]
   },
   {
@@ -166,6 +166,17 @@
     "42 == 42.000000000000001"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "## The `bool` Type"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {
@@ -182,7 +193,7 @@
    "execution_count": 5,
    "metadata": {
     "slideshow": {
-     "slide_type": "skip"
+     "slide_type": "slide"
     }
    },
    "outputs": [
@@ -230,7 +241,7 @@
    "execution_count": 7,
    "metadata": {
     "slideshow": {
-     "slide_type": "slide"
+     "slide_type": "fragment"
     }
    },
    "outputs": [
@@ -281,11 +292,11 @@
     }
    },
    "source": [
-    "Let's not confuse the boolean `False` with `None`, another built-in object! We saw the latter before in [Chapter 2](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/02_functions_00_lecture.ipynb#Function-Definition) as the *implicit* return value of a function without a `return` statement.\n",
+    "Let's not confuse the boolean `False` with `None`, another built-in object! We saw the latter before in [Chapter 2](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/02_functions_00_lecture.ipynb#Function-Definitions) as the *implicit* return value of a function without a `return` statement.\n",
     "\n",
-    "We might think of `None` in a boolean context indicating a \"maybe\" or even an \"unknown\" answer; however, for Python, there are no \"maybe\" or \"unknown\" objects, as we see further below!\n",
+    "We might think of `None` indicating a \"maybe\" or even an \"unknown\" answer; however, for Python, there are no \"maybe\" or \"unknown\" objects, as we see further below!\n",
     "\n",
-    "Whereas `False` is of type `bool`, `None` is of type `NoneType`. So, they are unrelated. On the contrary, as both `True` and `False` are of the same type, we could call them \"siblings.\""
+    "Whereas `False` is of type `bool`, `None` is of type `NoneType`. So, they are unrelated! On the contrary, as both `True` and `False` are of the same type, we could call them \"siblings.\""
    ]
   },
   {
@@ -293,7 +304,7 @@
    "execution_count": 9,
    "metadata": {
     "slideshow": {
-     "slide_type": "fragment"
+     "slide_type": "slide"
     }
    },
    "outputs": [],
@@ -306,7 +317,7 @@
    "execution_count": 10,
    "metadata": {
     "slideshow": {
-     "slide_type": "skip"
+     "slide_type": "fragment"
     }
    },
    "outputs": [
@@ -349,6 +360,17 @@
     "type(None)"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "### Singletons"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {
@@ -367,7 +389,7 @@
    "execution_count": 12,
    "metadata": {
     "slideshow": {
-     "slide_type": "skip"
+     "slide_type": "slide"
     }
    },
    "outputs": [
@@ -383,7 +405,21 @@
     }
    ],
    "source": [
-    "True is True"
+    "a = True\n",
+    "b = True\n",
+    "\n",
+    "a is b"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "To contrast this, we create *two* `789` objects."
    ]
   },
   {
@@ -391,14 +427,14 @@
    "execution_count": 13,
    "metadata": {
     "slideshow": {
-     "slide_type": "skip"
+     "slide_type": "fragment"
     }
    },
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "True"
+       "False"
       ]
      },
      "execution_count": 13,
@@ -407,7 +443,10 @@
     }
    ],
    "source": [
-    "id(True) == id(True)"
+    "a = 789\n",
+    "b = 789\n",
+    "\n",
+    "a is b"
    ]
   },
   {
@@ -426,7 +465,7 @@
    "execution_count": 14,
    "metadata": {
     "slideshow": {
-     "slide_type": "skip"
+     "slide_type": "slide"
     }
    },
    "outputs": [
@@ -464,7 +503,7 @@
     }
    },
    "source": [
-    "The equality operator is only one of several **relational (i.e., \"comparison\") operators** who all evaluate to a boolean object."
+    "The equality operator is only one of several **relational** (i.e., \"comparison\") **operators** who all evaluate to a `bool` object."
    ]
   },
   {
@@ -512,7 +551,7 @@
     }
    ],
    "source": [
-    "42 != 123  # = \"not equal to\"; other languages may use \"<>\""
+    "42 != 123  # \"not equal to\"; other languages may use \"<>\""
    ]
   },
   {
@@ -523,7 +562,7 @@
     }
    },
    "source": [
-    "The \"less than\" `<` or \"greater than\" `>` operators mean \"strictly less than\" or \"strictly greater than\" but may be combined with the equality operator into just `<=` and `>=`. This is a shortcut for using the logical `or` operator as described in the next section."
+    "The \"less than\" `<` or \"greater than\" `>` operators mean \"*strictly* less than\" or \"*strictly* greater than\" but may be combined with the equality operator into just `<=` and `>=`. This is a shortcut for using the logical `or` operator as described in the next section."
    ]
   },
   {
@@ -571,7 +610,7 @@
     }
    ],
    "source": [
-    "42 <= 123  # same as 42 < 123 or 42 == 123; cf., next section"
+    "42 <= 123"
    ]
   },
   {
@@ -619,7 +658,7 @@
     }
    ],
    "source": [
-    "42 >= 123  # same as 42 > 123 or 42 == 123; cf., next section"
+    "42 >= 123"
    ]
   },
   {
@@ -641,13 +680,13 @@
     }
    },
    "source": [
-    "Boolean expressions may be combined or negated with the **logical operators** `and`, `or`, and `not` to form new boolean expressions. Of course, this may be done *recursively* as well to obtain boolean expressions of arbitrary complexity.\n",
+    "Boolean expressions may be combined or negated with the **logical operators** `and`, `or`, and `not` to form new boolean expressions. This may be done repeatedly to obtain boolean expressions of arbitrary complexity.\n",
     "\n",
     "Their usage is similar to how the equivalent words are used in everyday English:\n",
     "\n",
-    "- `and` evaluates to `True` if *both* sub-expressions evaluate to `True` and `False` otherwise,\n",
-    "- `or` evaluates to `True` if either one *or* both sub-expressions evaluate to `True` and `False` otherwise, and\n",
-    "- `not` evaluates to `True` if its *only* sub-expression evaluates to `False` and vice versa."
+    "- `and` evaluates to `True` if *both* operands evaluate to `True` and `False` otherwise,\n",
+    "- `or` evaluates to `True` if either one *or* both operands evaluate to `True` and `False` otherwise, and\n",
+    "- `not` evaluates to `True` if its *only* operand evaluates to `False` and vice versa."
    ]
   },
   {
@@ -660,8 +699,8 @@
    },
    "outputs": [],
    "source": [
-    "x = 42\n",
-    "y = 87"
+    "a = 42\n",
+    "b = 87"
    ]
   },
   {
@@ -696,7 +735,7 @@
     }
    ],
    "source": [
-    "x > 5 and y <= 100"
+    "a > 5 and b <= 100"
    ]
   },
   {
@@ -707,7 +746,7 @@
     }
    },
    "source": [
-    "However, sometimes, it is good to use *parentheses* around each sub-expression for clarity."
+    "However, sometimes, it is good to use *parentheses* around each operand for clarity."
    ]
   },
   {
@@ -731,7 +770,7 @@
     }
    ],
    "source": [
-    "(x > 5) and (y <= 100)"
+    "(a > 5) and (b <= 100)"
    ]
   },
   {
@@ -742,7 +781,7 @@
     }
    },
    "source": [
-    "This is especially the case when several logical operators are combined."
+    "This is especially so when several logical operators are combined."
    ]
   },
   {
@@ -766,7 +805,7 @@
     }
    ],
    "source": [
-    "x <= 5 or not y > 100"
+    "a <= 5 or not b > 100"
    ]
   },
   {
@@ -774,7 +813,7 @@
    "execution_count": 25,
    "metadata": {
     "slideshow": {
-     "slide_type": "-"
+     "slide_type": "fragment"
     }
    },
    "outputs": [
@@ -790,7 +829,7 @@
     }
    ],
    "source": [
-    "(x <= 5) or not (y > 100)"
+    "(a <= 5) or not (b > 100)"
    ]
   },
   {
@@ -814,7 +853,7 @@
     }
    ],
    "source": [
-    "(x <= 5) or (not (y > 100))  # but no need to \"over do\" it"
+    "(a <= 5) or (not (b > 100))  # no need to \"over do\" it"
    ]
   },
   {
@@ -827,7 +866,7 @@
    "source": [
     "For even better readability, some practitioners suggest to *never* use the `>` and `>=` operators (cf., [source](https://llewellynfalco.blogspot.com/2016/02/dont-use-greater-than-sign-in.html); note that the included example is written in [Java](https://en.wikipedia.org/wiki/Java_%28programming_language%29) where `&&` means `and` and `||` means `or`).\n",
     "\n",
-    "Python allows **chaining** relational operators that are combined with the `and` operator. For example, the following two cells implement the same logic, where the second is a lot easier to read."
+    "We may **chain** operators if the expressions that contain them are combined with the `and` operator. For example, the following two cells implement the same logic, where the second is a lot easier to read."
    ]
   },
   {
@@ -842,7 +881,7 @@
     {
      "data": {
       "text/plain": [
-       "False"
+       "True"
       ]
      },
      "execution_count": 27,
@@ -851,7 +890,7 @@
     }
    ],
    "source": [
-    "(5 < x) and (x < 21)"
+    "5 < a and a < 87"
    ]
   },
   {
@@ -859,14 +898,14 @@
    "execution_count": 28,
    "metadata": {
     "slideshow": {
-     "slide_type": "-"
+     "slide_type": "fragment"
     }
    },
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "False"
+       "True"
       ]
      },
      "execution_count": 28,
@@ -875,7 +914,7 @@
     }
    ],
    "source": [
-    "5 < x < 21"
+    "5 < a < 87"
    ]
   },
   {
@@ -897,11 +936,11 @@
     }
    },
    "source": [
-    "The operands of the logical operators do not need to be *boolean* expressions but may be *any* expression. If a sub-expression does *not* evaluate to an object of type `bool`, Python automatically casts it as such.\n",
+    "The operands of a logical operator do not need to be *boolean* expressions but may be *any* expression. If an operand does *not* evaluate to an object of type `bool`, Python automatically casts it as such. Then, Pythonistas say that the expression is evaluated in a boolean context.\n",
     "\n",
-    "For example, any non-zero numeric object becomes `True`. While this behavior allows writing more concise and thus more \"beautiful\" code, it is also a common source of confusion.\n",
+    "For example, any non-zero numeric object is cast as `True`. While this behavior allows writing more concise and thus more \"beautiful\" code, it may also be a source of confusion.\n",
     "\n",
-    "So, `(x - 9)` is cast as `True` and then the overall expression evaluates to `True` as well."
+    "So, `(a - 21)` is cast as `True` and then the overall expression evaluates to `True` as well."
    ]
   },
   {
@@ -925,7 +964,7 @@
     }
    ],
    "source": [
-    "(x - 9) and (y < 100)  # = 33 and (y < 100) = 33 and True"
+    "(a - 21) and (b < 100)  # => 21 and (b < 100) => 21 and True"
    ]
   },
   {
@@ -936,7 +975,7 @@
     }
    },
    "source": [
-    "Whenever we are unsure as to how Python evaluates a non-boolean expression in a boolean context, the [bool()](https://docs.python.org/3/library/functions.html#bool) built-in allows us to check it ourselves."
+    "Whenever we are unsure how Python evaluates a non-boolean expression in a boolean context, the [bool()](https://docs.python.org/3/library/functions.html#bool) built-in allows us to do it ourselves. [bool()](https://docs.python.org/3/library/functions.html#bool), like [int()](https://docs.python.org/3/library/functions.html#int), is yet another *constructor*."
    ]
   },
   {
@@ -960,7 +999,7 @@
     }
    ],
    "source": [
-    "bool(x - 9)  # = bool(33)"
+    "bool(a - 21)  # = bool(21)"
    ]
   },
   {
@@ -968,7 +1007,7 @@
    "execution_count": 31,
    "metadata": {
     "slideshow": {
-     "slide_type": "-"
+     "slide_type": "fragment"
     }
    },
    "outputs": [
@@ -984,7 +1023,7 @@
     }
    ],
    "source": [
-    "bool(x - 42)  # = bool(0)"
+    "bool(a - 42)  # = bool(0)"
    ]
   },
   {
@@ -995,7 +1034,7 @@
     }
    },
    "source": [
-    "Keep in mind that negative numbers also evaluate to `True`."
+    "Let's keep in mind that negative numbers also evaluate to `True`!"
    ]
   },
   {
@@ -1003,7 +1042,7 @@
    "execution_count": 32,
    "metadata": {
     "slideshow": {
-     "slide_type": "-"
+     "slide_type": "fragment"
     }
    },
    "outputs": [
@@ -1019,7 +1058,7 @@
     }
    ],
    "source": [
-    "bool(x - 99)  # = bool(-57)"
+    "bool(a - 43)  # = bool(-1)"
    ]
   },
   {
@@ -1038,7 +1077,7 @@
    "execution_count": 33,
    "metadata": {
     "slideshow": {
-     "slide_type": "fragment"
+     "slide_type": "slide"
     }
    },
    "outputs": [
@@ -1065,7 +1104,7 @@
     }
    },
    "source": [
-    "Another good rule to know is that container types (e.g., `list`) evaluate to `True` whenever they are *not* empty and `False` if they are."
+    "Another good rule to know is that container types (e.g., `list`) evaluate to `False` whenever they are empty and `True` if they hold at least one element."
    ]
   },
   {
@@ -1097,7 +1136,7 @@
    "execution_count": 35,
    "metadata": {
     "slideshow": {
-     "slide_type": "-"
+     "slide_type": "fragment"
     }
    },
    "outputs": [
@@ -1205,18 +1244,18 @@
     }
    },
    "source": [
-    "When evaluating expressions involving the `and` and `or` operators, Python follows the **[short-circuiting](https://en.wikipedia.org/wiki/Short-circuit_evaluation)** strategy: Once it is clear what the overall truth value is, no more sub-expressions are evaluated, and the result is *immediately* returned.\n",
+    "When evaluating expressions involving the `and` and `or` operators, Python follows the **[short-circuiting](https://en.wikipedia.org/wiki/Short-circuit_evaluation)** strategy: Once it is clear what the overall truth value is, no more operands are evaluated, and the result is *immediately* returned.\n",
     "\n",
     "Also, if such expressions are evaluated in a non-boolean context, the result is returned as is and *not* cast as a `bool` type.\n",
     "\n",
     "The two rules can be summarized as:\n",
     "\n",
-    "- `x or y`: If `x` is truthy, it is returned *without* evaluating `y`. Otherwise, `y` is evaluated *and* returned.\n",
-    "- `x and y`: If `x` is falsy, it is returned *without* evaluating `y`. Otherwise, `y` is evaluated *and* returned.\n",
+    "- `a or b`: If `a` is truthy, it is returned *without* evaluating `b`. Otherwise, `b` is evaluated *and* returned.\n",
+    "- `a and b`: If `a` is falsy, it is returned *without* evaluating `b`. Otherwise, `b` is evaluated *and* returned.\n",
     "\n",
     "The rules may also be chained or combined.\n",
     "\n",
-    "Let's look at a couple of examples below. To visualize which sub-expressions are evaluated, we define a helper function `expr()` that prints out the only argument it is passed before returning it."
+    "Let's look at a couple of examples below. To visualize which operands are evaluated, we define a helper function `expr()` that prints out the only argument it is passed before returning it."
    ]
   },
   {
@@ -1243,7 +1282,7 @@
     }
    },
    "source": [
-    "With the `or` operator, the first truthy sub-expression is returned."
+    "With the `or` operator, the first truthy operand is returned."
    ]
   },
   {
@@ -1283,7 +1322,7 @@
    "execution_count": 40,
    "metadata": {
     "slideshow": {
-     "slide_type": "-"
+     "slide_type": "fragment"
     }
    },
    "outputs": [
@@ -1306,7 +1345,7 @@
     }
    ],
    "source": [
-    "expr(1) or expr(2)  # 2 is not evaluated due to short-circuiting"
+    "expr(1) or expr(2)  # 2 is not evaluated"
    ]
   },
   {
@@ -1314,7 +1353,7 @@
    "execution_count": 41,
    "metadata": {
     "slideshow": {
-     "slide_type": "-"
+     "slide_type": "slide"
     }
    },
    "outputs": [
@@ -1338,7 +1377,7 @@
     }
    ],
    "source": [
-    "expr(0) or expr(1) or expr(2)  # 2 is not evaluated due to short-circuiting"
+    "expr(0) or expr(1) or expr(2)  # 2 is not evaluated"
    ]
   },
   {
@@ -1349,7 +1388,7 @@
     }
    },
    "source": [
-    "If all sub-expressions are falsy, the last one is returned."
+    "If all operands are falsy, the last one is returned."
    ]
   },
   {
@@ -1393,7 +1432,7 @@
     }
    },
    "source": [
-    "With the `and` operator, the first falsy sub-expression is returned."
+    "With the `and` operator, the first falsy operand is returned."
    ]
   },
   {
@@ -1424,7 +1463,7 @@
     }
    ],
    "source": [
-    "expr(0) and expr(1)  # 1 is not evaluated due to short-circuiting"
+    "expr(0) and expr(1)  # 1 is not evaluated"
    ]
   },
   {
@@ -1432,7 +1471,7 @@
    "execution_count": 44,
    "metadata": {
     "slideshow": {
-     "slide_type": "-"
+     "slide_type": "fragment"
     }
    },
    "outputs": [
@@ -1464,7 +1503,7 @@
    "execution_count": 45,
    "metadata": {
     "slideshow": {
-     "slide_type": "-"
+     "slide_type": "slide"
     }
    },
    "outputs": [
@@ -1488,7 +1527,7 @@
     }
    ],
    "source": [
-    "expr(1) and expr(0) and expr(2)  # 2 is not evaluated due to short-circuiting"
+    "expr(1) and expr(0) and expr(2)  # 2 is not evaluated"
    ]
   },
   {
@@ -1499,7 +1538,7 @@
     }
    },
    "source": [
-    "If all sub-expressions are truthy, the last one is returned."
+    "If all operands are truthy, the last one is returned."
    ]
   },
   {
@@ -1542,7 +1581,7 @@
     }
    },
    "source": [
-    "The crucial takeaway is that Python does *not* necessarily evaluate *all* sub-expressions and, therefore, our code should never rely on that assumption."
+    "The crucial takeaway is that Python does *not* necessarily evaluate *all* operands and, therefore, our code should never rely on that assumption."
    ]
   },
   {
@@ -1564,7 +1603,7 @@
     }
    },
    "source": [
-    "To write useful programs, we need to control the flow of execution, for example, to react to user input. The logic by which a program follows the rules from the \"real world\" is referred to as **[business logic](https://en.wikipedia.org/wiki/Business_logic)**, even if \"real world\" refers to the domain of mathematics and not a business application.\n",
+    "To write useful programs, we need to control the flow of execution, for example, to react to user input. The logic by which a program follows the rules from the \"real world\" is referred to as **[business logic](https://en.wikipedia.org/wiki/Business_logic)**.\n",
     "\n",
     "One language feature to do so is the `if` statement (cf., [reference](https://docs.python.org/3/reference/compound_stmts.html#the-if-statement)). It consists of:\n",
     "\n",
@@ -1572,75 +1611,34 @@
     "- an *arbitrary* number of `elif`-clauses (i.e., \"else if\"), and\n",
     "- an *optional* `else`-clause.\n",
     "\n",
-    "The `if`- and `elif`-clauses each specify one *boolean* expression, also called **condition** in this context, while the `else`-clause serves as the \"catch everything else\" case.\n",
+    "The `if`- and `elif`-clauses each specify one *boolean* expression, also called **condition** here, while the `else`-clause serves as the \"catch everything else\" case.\n",
     "\n",
     "In contrast to our intuitive interpretation in natural languages, only the code in *one* of the alternatives, also called **branches**, is executed. To be precise, it is always the code in the first clause whose condition evaluates to `True`.\n",
     "\n",
-    "In terms of syntax, the header lines end with a colon, and the code blocks are indented. Formally, any statement that is written across several lines is a **[compound statement](https://docs.python.org/3/reference/compound_stmts.html#compound-statements)**, the code blocks are called **suites** and belong to one header line, and the term **clause** refers to a header line and its suite as a whole. So far, we have seen three compound statements: `for`, `if`, and `def`. On the contrary, **[simple statements](https://docs.python.org/3/reference/simple_stmts.html#simple-statements)**, for example, `=`, `del`, or `return`, are written on *one* line."
+    "In terms of syntax, the header lines end with a colon, and the code blocks are indented. Formally, any statement that is written across several lines is called a **[compound statement](https://docs.python.org/3/reference/compound_stmts.html#compound-statements)**, the code blocks are called **suites** and belong to one header line, and the term **clause** refers to a header line and its suite as a whole. So far, we have seen three compound statements: `for`, `if`, and `def`. On the contrary, **[simple statements](https://docs.python.org/3/reference/simple_stmts.html#simple-statements)**, for example, `=`, `del`, or `return`, are written on *one* line.\n",
+    "\n",
+    "As an example, let's write code that checks if a randomly drawn number is divisible by `2`, `3`, both, or none. The code should print out a customized message for each of the *four* cases."
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 47,
    "metadata": {
-    "code_folding": [],
     "slideshow": {
      "slide_type": "slide"
     }
    },
    "outputs": [],
    "source": [
-    "z = 101"
+    "numbers = [7, 11, 8, 5, 3, 12, 2, 6, 9, 10, 1, 4]"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 48,
    "metadata": {
-    "code_folding": [],
     "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "z is positive but odd\n"
-     ]
-    }
-   ],
-   "source": [
-    "if (z % 2 == 0) and (z > 0):\n",
-    "    print(\"z is even and positive\")\n",
-    "elif z % 2 == 0:\n",
-    "    print(\"z is even but negative\")\n",
-    "elif z > 0:\n",
-    "    print(\"z is positive but odd\")\n",
-    "else:\n",
-    "    print(\"z is neither even nor positive\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "source": [
-    "In many situations, we only need a reduced form of the `if` statement.\n",
-    "\n",
-    "We could **inject** code only at random, for example, to implement an [A/B testing](https://en.wikipedia.org/wiki/A/B_testing) strategy."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 49,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "slide"
+     "slide_type": "-"
     }
    },
    "outputs": [],
@@ -1650,7 +1648,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 50,
+   "execution_count": 49,
    "metadata": {
     "slideshow": {
      "slide_type": "-"
@@ -1658,8 +1656,7 @@
    },
    "outputs": [],
    "source": [
-    "if random.random() > 0.5:\n",
-    "    print(\"You read this just as often as you see heads when tossing a coin\")"
+    "random.seed(789)"
    ]
   },
   {
@@ -1670,7 +1667,48 @@
     }
    },
    "source": [
-    "More often than not, we model a **binary choice**."
+    "It turns out that translating this task into code is not so trivial. Whereas the code below looks right, it is *incorrect*. The reason is that the order of the `if`-, `elif`-, and `else`-clauses matters."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 50,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "6 is divisible by 2 only\n"
+     ]
+    }
+   ],
+   "source": [
+    "number = random.choice(numbers)\n",
+    "\n",
+    "if number % 2 == 0:\n",
+    "    print(number, \"is divisible by 2 only\")\n",
+    "elif number % 3 == 0:\n",
+    "    print(number, \"is divisible by 3 only\")\n",
+    "elif number % 2 == 0 and number % 3 == 0:\n",
+    "    print(number, \"is divisible by 2 and 3\")\n",
+    "else:\n",
+    "    print(number, \"is divisible by neither 2 nor 3\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "As a number divisible by both `2` and `3` is always a special (i.e., narrower) case of a number being divisible by either `2` or `3` on their own, we must check for that condition first. The order of the two latter cases is not important as they are mutually exclusive. Below is a correct implementation of the program."
    ]
   },
   {
@@ -1681,20 +1719,229 @@
      "slide_type": "skip"
     }
    },
+   "outputs": [],
+   "source": [
+    "random.seed(789)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 52,
+   "metadata": {
+    "code_folding": [],
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "z is positive\n"
+      "6 is divisible by 2 and 3\n"
      ]
     }
    ],
    "source": [
-    "if z > 0:\n",
-    "    print(\"z is positive\")\n",
+    "number = random.choice(numbers)\n",
+    "\n",
+    "if number % 3 == 0 and number % 2 == 0:\n",
+    "    print(number, \"is divisible by 2 and 3\")\n",
+    "elif number % 3 == 0:\n",
+    "    print(number, \"is divisible by 3 only\")\n",
+    "elif number % 2 == 0:\n",
+    "    print(number, \"is divisible by 2 only\")\n",
     "else:\n",
-    "    print(\"z is negative\")"
+    "    print(number, \"is divisible by neither 2 nor 3\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "A minor improvement could be to replace `number % 3 == 0 and number % 2 == 0` with the conciser `number % 6 == 0`. However, this has no effect on the order that is still essential for the code's correctness."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 53,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "random.seed(789)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 54,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "6 is divisible by 2 and 3\n"
+     ]
+    }
+   ],
+   "source": [
+    "number = random.choice(numbers)\n",
+    "\n",
+    "if number % 6 == 0:\n",
+    "    print(number, \"is divisible by 2 and 3\")\n",
+    "elif number % 3 == 0:\n",
+    "    print(number, \"is divisible by 3 only\")\n",
+    "elif number % 2 == 0:\n",
+    "    print(number, \"is divisible by 2 only\")\n",
+    "else:\n",
+    "    print(number, \"is divisible by neither 2 nor 3\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Often, we only need a reduced form of the `if` statement.\n",
+    "\n",
+    "For example, below we **inject** code to print a message at random."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 55,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "You read this as often as you see heads when tossing a coin\n"
+     ]
+    }
+   ],
+   "source": [
+    "if random.random() > 0.5:\n",
+    "    print(\"You read this as often as you see heads when tossing a coin\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "More often than not, we model a **binary choice**. Then, we only need to write an `if`- and an `else`-clause."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 56,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "random.seed(789)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 57,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "6 is even\n"
+     ]
+    }
+   ],
+   "source": [
+    "number = random.choice(numbers)\n",
+    "\n",
+    "if number % 2 == 0:\n",
+    "    print(number, \"is even\")\n",
+    "else:\n",
+    "    print(number, \"is odd\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "To write the condition even conciser, we may take advantage of Python's implicit casting and leave out the `== 0`. However, then we *must* exchange the two suits! The `if`-clause below means \"If the `number` is odd\" in plain English. That is the opposite of the `if`-clause above."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 58,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "random.seed(789)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 59,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "6 is even\n"
+     ]
+    }
+   ],
+   "source": [
+    "number = random.choice(numbers)\n",
+    "\n",
+    "if number % 2:  # Beware of the opposite meaning!\n",
+    "    print(number, \"is odd\")\n",
+    "else:\n",
+    "    print(number, \"is even\")"
    ]
   },
   {
@@ -1707,12 +1954,25 @@
    "source": [
     "We may **nest** `if` statements to control the flow of execution in a more granular way. Every additional layer, however, makes the code *less* readable, in particular, if we have more than one line per code block.\n",
     "\n",
-    "The code cell below *either* checks if a number is even or odd *or* if it is positive or negative."
+    "As another example, the code cell below first tosses a coin and then *either* checks if `signed_number` is even or odd *or* if it is positive or negative."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 52,
+   "execution_count": 60,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "random.seed(789)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 61,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -1723,21 +1983,23 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "z is positive\n"
+      "4 is even\n"
      ]
     }
    ],
    "source": [
+    "signed_number = random.choice([-1, +1]) * random.choice(numbers)\n",
+    "\n",
     "if random.random() > 0.5:\n",
-    "    if z % 2:  # no need to write out the \"== 0\"\n",
-    "        print(\"z is odd\")\n",
+    "    if signed_number % 2:\n",
+    "        print(signed_number, \"is odd\")\n",
     "    else:\n",
-    "        print(\"z is even\")\n",
+    "        print(signed_number, \"is even\")\n",
     "else:\n",
-    "    if z > 0:\n",
-    "        print(\"z is positive\")\n",
+    "    if signed_number > 0:\n",
+    "        print(signed_number, \"is positive\")\n",
     "    else:\n",
-    "        print(\"z is negative\")"
+    "        print(signed_number, \"is negative\")"
    ]
   },
   {
@@ -1755,7 +2017,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 53,
+   "execution_count": 62,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "random.seed(789)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 63,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -1766,23 +2041,25 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "z is positive\n"
+      "4 is even\n"
      ]
     }
    ],
    "source": [
-    "check_oddness = (random.random() > 0.5)\n",
-    "is_odd = (z % 2)\n",
-    "is_positive = (z > 0)\n",
+    "signed_number = random.choice([-1, +1]) * random.choice(numbers)\n",
+    "\n",
+    "check_oddness = random.random() > 0.5\n",
+    "is_odd = signed_number % 2\n",
+    "is_positive = signed_number > 0\n",
     "\n",
     "if check_oddness and is_odd:\n",
-    "    print(\"z is odd\")\n",
+    "    print(signed_number, \"is odd\")\n",
     "elif check_oddness and not is_odd:\n",
-    "    print(\"z is even\")\n",
+    "    print(signed_number, \"is even\")\n",
     "elif not check_oddness and is_positive:\n",
-    "    print(\"z is positive\")\n",
+    "    print(signed_number, \"is positive\")\n",
     "else:\n",
-    "    print(\"z is negative\")"
+    "    print(signed_number, \"is negative\")"
    ]
   },
   {
@@ -1828,7 +2105,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 54,
+   "execution_count": 64,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -1852,10 +2129,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 55,
+   "execution_count": 65,
    "metadata": {
     "slideshow": {
-     "slide_type": "-"
+     "slide_type": "fragment"
     }
    },
    "outputs": [],
@@ -1868,7 +2145,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 56,
+   "execution_count": 66,
    "metadata": {
     "slideshow": {
      "slide_type": "-"
@@ -1881,7 +2158,7 @@
        "9"
       ]
      },
-     "execution_count": 56,
+     "execution_count": 66,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1903,10 +2180,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 57,
+   "execution_count": 67,
    "metadata": {
     "slideshow": {
-     "slide_type": "fragment"
+     "slide_type": "slide"
     }
    },
    "outputs": [],
@@ -1916,10 +2193,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 58,
+   "execution_count": 68,
    "metadata": {
     "slideshow": {
-     "slide_type": "skip"
+     "slide_type": "-"
     }
    },
    "outputs": [
@@ -1929,7 +2206,7 @@
        "9"
       ]
      },
-     "execution_count": 58,
+     "execution_count": 68,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1951,7 +2228,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 59,
+   "execution_count": 69,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -1964,10 +2241,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 60,
+   "execution_count": 70,
    "metadata": {
     "slideshow": {
-     "slide_type": "skip"
+     "slide_type": "-"
     }
    },
    "outputs": [
@@ -1977,7 +2254,7 @@
        "9"
       ]
      },
-     "execution_count": 60,
+     "execution_count": 70,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2014,23 +2291,23 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 61,
+   "execution_count": 71,
    "metadata": {
     "slideshow": {
-     "slide_type": "slide"
+     "slide_type": "skip"
     }
    },
    "outputs": [],
    "source": [
-    "user_input = 0"
+    "random.seed(123)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 62,
+   "execution_count": 72,
    "metadata": {
     "slideshow": {
-     "slide_type": "-"
+     "slide_type": "slide"
     }
    },
    "outputs": [
@@ -2043,6 +2320,8 @@
     }
    ],
    "source": [
+    "user_input = random.choice([0, 1])\n",
+    "\n",
     "try:\n",
     "    1 / user_input\n",
     "except:\n",
@@ -2057,7 +2336,7 @@
     }
    },
    "source": [
-    "However, it is good practice *not* to **handle** *any* possible exception but only the ones we may *expect* from the code in the `try`-clause. The reasoning why this is done is a bit involved. We only remark that the codebase becomes easier to understand as we communicate to any human reader what could go wrong during execution in an *explicit* way. Python comes with a lot of [built-in exceptions](https://docs.python.org/3/library/exceptions.html#concrete-exceptions) that we should familiarize ourselves with.\n",
+    "However, it is good practice *not* to **handle** *any* possible exception but only the ones we may *expect* from the code in the `try`-clause. The reason for that is that we do not want to risk *suppressing* an exception that we do *not* expect. Also, the code base becomes easier to understand as we communicate what could go wrong during execution in an *explicit* way to the (human) reader. Python comes with a lot of [built-in exceptions](https://docs.python.org/3/library/exceptions.html#concrete-exceptions) that we should familiarize ourselves with.\n",
     "\n",
     "Another good practice is to always keep the code in the `try`-clause short to not *accidentally* handle an exception we do *not* want to handle.\n",
     "\n",
@@ -2066,7 +2345,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 63,
+   "execution_count": 73,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "random.seed(123)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 74,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -2082,6 +2374,8 @@
     }
    ],
    "source": [
+    "user_input = random.choice([0, 1])\n",
+    "\n",
     "try:\n",
     "    1 / user_input\n",
     "except ZeroDivisionError:\n",
@@ -2100,12 +2394,25 @@
     "\n",
     "Similarly, we may have to run some code *only if* no exception occurs, but we do not want to put it in the `try`-clause as per the good practice mentioned above. To achieve that, we add an `else`-clause to the `try` statement.\n",
     "\n",
-    "To showcase everything together, we look at one last example. To spice it up a bit, we randomize the input. So run the cell several times and see for yourself."
+    "To showcase everything together, we look at one last example. It is randomized: So, run the cell several times and see for yourself."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 64,
+   "execution_count": 75,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "random.seed(123)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 76,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -2116,16 +2423,16 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Yes, division worked smoothly.\n",
+      "Oops. Division by 0. How does that work?\n",
       "I am always printed\n"
      ]
     }
    ],
    "source": [
-    "divisor = random.choice([0, 1])\n",
+    "user_input = random.choice([0, 1])\n",
     "\n",
     "try:\n",
-    "    1 / divisor\n",
+    "    1 / user_input\n",
     "except ZeroDivisionError:\n",
     "    print(\"Oops. Division by 0. How does that work?\")\n",
     "else:\n",

From 0519b499892270f8f14d7481ad5cb9eda022b804 Mon Sep 17 00:00:00 2001
From: Alexander Hess <alexander@webartifex.biz>
Date: Sun, 9 Feb 2020 23:03:31 +0100
Subject: [PATCH 070/242] Bump version number

---
 pyproject.toml | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/pyproject.toml b/pyproject.toml
index 9cc1c12..679d3d7 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -1,6 +1,6 @@
 [tool.poetry]
 name = "intro-to-python"
-version = "0.6.3"
+version = "0.6.4"
 description = "An introduction to Python and programming for wanna-be data scientists"
 authors = ["Alexander Hess <alexander@webartifex.biz>"]
 license = "MIT"

From cf0a8acd1752704956f22026980317bded3ea21d Mon Sep 17 00:00:00 2001
From: Alexander Hess <alexander@webartifex.biz>
Date: Sat, 7 Mar 2020 16:40:11 +0100
Subject: [PATCH 071/242] Streamline presentation

---
 00_intro_00_lecture.ipynb   | 10 +++++-----
 00_intro_01_review.ipynb    |  2 +-
 00_intro_02_exercises.ipynb |  2 +-
 3 files changed, 7 insertions(+), 7 deletions(-)

diff --git a/00_intro_00_lecture.ipynb b/00_intro_00_lecture.ipynb
index a8d2237..e53b008 100644
--- a/00_intro_00_lecture.ipynb
+++ b/00_intro_00_lecture.ipynb
@@ -77,7 +77,7 @@
    "cell_type": "markdown",
    "metadata": {
     "slideshow": {
-     "slide_type": "slide"
+     "slide_type": "skip"
     }
    },
    "source": [
@@ -350,7 +350,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Python 3.7.4\n"
+      "Python 3.7.6\n"
      ]
     }
    ],
@@ -472,7 +472,7 @@
    "cell_type": "markdown",
    "metadata": {
     "slideshow": {
-     "slide_type": "slide"
+     "slide_type": "skip"
     }
    },
    "source": [
@@ -673,7 +673,7 @@
    "cell_type": "markdown",
    "metadata": {
     "slideshow": {
-     "slide_type": "slide"
+     "slide_type": "skip"
     }
    },
    "source": [
@@ -903,7 +903,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.4"
+   "version": "3.7.6"
   },
   "livereveal": {
    "auto_select": "code",
diff --git a/00_intro_01_review.ipynb b/00_intro_01_review.ipynb
index f08c6ca..325fc74 100644
--- a/00_intro_01_review.ipynb
+++ b/00_intro_01_review.ipynb
@@ -206,7 +206,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.4"
+   "version": "3.7.6"
   },
   "toc": {
    "base_numbering": 1,
diff --git a/00_intro_02_exercises.ipynb b/00_intro_02_exercises.ipynb
index 3b394db..b0f53db 100644
--- a/00_intro_02_exercises.ipynb
+++ b/00_intro_02_exercises.ipynb
@@ -94,7 +94,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.4"
+   "version": "3.7.6"
   },
   "toc": {
    "base_numbering": 1,

From d0a20dfdee35376d869f9257b5e1c29c925d7f24 Mon Sep 17 00:00:00 2001
From: Alexander Hess <alexander@webartifex.biz>
Date: Sat, 7 Mar 2020 18:37:33 +0100
Subject: [PATCH 072/242] Streamline presentation

---
 01_elements_00_lecture.ipynb | 766 ++++++++++++++++++-----------------
 1 file changed, 404 insertions(+), 362 deletions(-)

diff --git a/01_elements_00_lecture.ipynb b/01_elements_00_lecture.ipynb
index ba6e476..f7583ba 100644
--- a/01_elements_00_lecture.ipynb
+++ b/01_elements_00_lecture.ipynb
@@ -35,7 +35,7 @@
     }
    },
    "source": [
-    "## Example: Averaging Even Numbers"
+    "## Example: Averaging all even Numbers in a List"
    ]
   },
   {
@@ -137,7 +137,7 @@
     "total = 0  # running total and the count of even numbers\n",
     "\n",
     "for number in numbers:\n",
-    "    if number % 2 == 0:  # only look at even numbers\n",
+    "    if number % 2 == 0:  # only work with even numbers\n",
     "        count = count + 1\n",
     "        total = total + number\n",
     "\n",
@@ -233,7 +233,7 @@
    "execution_count": 6,
    "metadata": {
     "slideshow": {
-     "slide_type": "fragment"
+     "slide_type": "skip"
     }
    },
    "outputs": [],
@@ -257,7 +257,7 @@
    "execution_count": 7,
    "metadata": {
     "slideshow": {
-     "slide_type": "slide"
+     "slide_type": "fragment"
     }
    },
    "outputs": [
@@ -365,7 +365,7 @@
    "execution_count": 10,
    "metadata": {
     "slideshow": {
-     "slide_type": "-"
+     "slide_type": "fragment"
     }
    },
    "outputs": [
@@ -435,7 +435,7 @@
    "execution_count": 12,
    "metadata": {
     "slideshow": {
-     "slide_type": "slide"
+     "slide_type": "fragment"
     }
    },
    "outputs": [
@@ -459,7 +459,7 @@
    "execution_count": 13,
    "metadata": {
     "slideshow": {
-     "slide_type": "-"
+     "slide_type": "slide"
     }
    },
    "outputs": [
@@ -494,7 +494,7 @@
    "execution_count": 14,
    "metadata": {
     "slideshow": {
-     "slide_type": "slide"
+     "slide_type": "fragment"
     }
    },
    "outputs": [
@@ -518,7 +518,7 @@
    "execution_count": 15,
    "metadata": {
     "slideshow": {
-     "slide_type": "-"
+     "slide_type": "fragment"
     }
    },
    "outputs": [
@@ -669,7 +669,7 @@
     {
      "data": {
       "text/plain": [
-       "3"
+       "9"
       ]
      },
      "execution_count": 19,
@@ -678,7 +678,7 @@
     }
    ],
    "source": [
-    "123 % 10"
+    "789 % 10"
    ]
   },
   {
@@ -686,14 +686,14 @@
    "execution_count": 20,
    "metadata": {
     "slideshow": {
-     "slide_type": "-"
+     "slide_type": "fragment"
     }
    },
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "23"
+       "89"
       ]
      },
      "execution_count": 20,
@@ -702,7 +702,7 @@
     }
    ],
    "source": [
-    "123 % 100"
+    "789 % 100"
    ]
   },
   {
@@ -713,7 +713,7 @@
     }
    },
    "source": [
-    "The built-in [divmod()](https://docs.python.org/3/library/functions.html#divmod) function combines the integer and modulo divisions into one operation. However, grammatically this is *not* an operator but a function. Also, [divmod()](https://docs.python.org/3/library/functions.html#divmod) returns a \"pair\" of integers and not just one."
+    "The built-in [divmod()](https://docs.python.org/3/library/functions.html#divmod) function combines the integer and modulo divisions into one step. However, grammatically this is *not* an operator but a function. Also, [divmod()](https://docs.python.org/3/library/functions.html#divmod) returns a \"pair\" of integers and not a single one."
    ]
   },
   {
@@ -721,7 +721,7 @@
    "execution_count": 21,
    "metadata": {
     "slideshow": {
-     "slide_type": "fragment"
+     "slide_type": "skip"
     }
    },
    "outputs": [
@@ -826,7 +826,7 @@
    "execution_count": 24,
    "metadata": {
     "slideshow": {
-     "slide_type": "-"
+     "slide_type": "fragment"
     }
    },
    "outputs": [
@@ -842,7 +842,7 @@
     }
    ],
    "source": [
-    "(3 ** 2) * 2  # same result as before but much clearer code"
+    "(3 ** 2) * 2"
    ]
   },
   {
@@ -850,7 +850,7 @@
    "execution_count": 25,
    "metadata": {
     "slideshow": {
-     "slide_type": "-"
+     "slide_type": "fragment"
     }
    },
    "outputs": [
@@ -866,7 +866,7 @@
     }
    ],
    "source": [
-    "3 ** (2 * 2)  # different result"
+    "3 ** (2 * 2)"
    ]
   },
   {
@@ -951,7 +951,7 @@
    },
    "outputs": [],
    "source": [
-    "a = 789\n",
+    "a = 42\n",
     "b = 42.0\n",
     "c = \"Python rocks\""
    ]
@@ -1007,14 +1007,14 @@
    "execution_count": 29,
    "metadata": {
     "slideshow": {
-     "slide_type": "-"
+     "slide_type": "fragment"
     }
    },
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "139627575613200"
+       "140361812063056"
       ]
      },
      "execution_count": 29,
@@ -1058,7 +1058,9 @@
     }
    },
    "source": [
-    "These addresses are *not* meaningful for anything other than checking if two variables reference the *same* object. Let's create a new variable `d` and also set it to `789`."
+    "These addresses are *not* meaningful for anything other than checking if two variables reference the *same* object.\n",
+    "\n",
+    "Obviously, `a` and `b`  have the same *value* as revealed by the **equality operator** `==`: We say `a` and `b` \"evaluate equal.\" The resulting `True` - and the `False` further below - is yet another data type, a so-called **boolean**. We look into them in [Chapter 3](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/03_conditionals_00_lecture.ipynb#Boolean-Expressions)."
    ]
   },
   {
@@ -1069,30 +1071,6 @@
      "slide_type": "slide"
     }
    },
-   "outputs": [],
-   "source": [
-    "d = 789"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "source": [
-    "`a` and `d` indeed have the *same value* as is checked with the **equality operator** `==`: We say `a` and `d` \"evaluate equal.\" The resulting `True` - and the `False` further below - is yet another data type, a so-called **boolean**. We look into them in [Chapter 3](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/03_conditionals_00_lecture.ipynb#Boolean-Expressions)."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 32,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "-"
-    }
-   },
    "outputs": [
     {
      "data": {
@@ -1100,13 +1078,13 @@
        "True"
       ]
      },
-     "execution_count": 32,
+     "execution_count": 31,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "a == d"
+    "a == b"
    ]
   },
   {
@@ -1117,15 +1095,15 @@
     }
    },
    "source": [
-    "On the contrary, `a` and `d` are *different objects* as the **identity operator** `is` shows: They are stored at *different* addresses in the memory."
+    "On the contrary, `a` and `b` are *different* objects as the **identity operator** `is` shows: They are stored at *different* addresses in the memory."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 33,
+   "execution_count": 32,
    "metadata": {
     "slideshow": {
-     "slide_type": "-"
+     "slide_type": "fragment"
     }
    },
    "outputs": [
@@ -1135,13 +1113,13 @@
        "False"
       ]
      },
-     "execution_count": 33,
+     "execution_count": 32,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "a is d"
+    "a is b"
    ]
   },
   {
@@ -1157,7 +1135,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 34,
+   "execution_count": 33,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -1170,13 +1148,13 @@
        "True"
       ]
      },
-     "execution_count": 34,
+     "execution_count": 33,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "a is not d"
+    "a is not b"
    ]
   },
   {
@@ -1203,7 +1181,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 35,
+   "execution_count": 34,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -1216,7 +1194,7 @@
        "int"
       ]
      },
-     "execution_count": 35,
+     "execution_count": 34,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1227,10 +1205,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 36,
+   "execution_count": 35,
    "metadata": {
     "slideshow": {
-     "slide_type": "-"
+     "slide_type": "fragment"
     }
    },
    "outputs": [
@@ -1240,7 +1218,7 @@
        "float"
       ]
      },
-     "execution_count": 36,
+     "execution_count": 35,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1257,14 +1235,14 @@
     }
    },
    "source": [
-    "Different types imply different behaviors for the objects. The `b` object, for example, may be \"asked\" if it is a whole number with the [is_integer()](https://docs.python.org/3/library/stdtypes.html#float.is_integer) \"functionality\" that comes with every `float` object.\n",
+    "Different types imply different behaviors for the objects. The `b` object, for example, may be \"asked\" if it is a whole number with the [is_integer()](https://docs.python.org/3/library/stdtypes.html#float.is_integer) \"functionality\" that comes with *every* `float` object.\n",
     "\n",
-    "Formally, we call such type-specific functionalities **methods** (i.e., as opposed to functions) and we formally introduce them in Chapter 10. For now, it suffices to know that we access them with the **dot operator** `.` on the object. Of course, `b` is a whole number, which the boolean object `True` tells us."
+    "Formally, we call such type-specific functionalities **methods** (i.e., as opposed to functions) and we look at them in detail in Chapter 10. For now, it suffices to know that we access them with the **dot operator** `.` on the object. Of course, `b` is a whole number, which the boolean object `True` tells us."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 37,
+   "execution_count": 36,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -1277,7 +1255,7 @@
        "True"
       ]
      },
-     "execution_count": 37,
+     "execution_count": 36,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1299,10 +1277,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 38,
+   "execution_count": 37,
    "metadata": {
     "slideshow": {
-     "slide_type": "-"
+     "slide_type": "fragment"
     }
    },
    "outputs": [
@@ -1313,7 +1291,7 @@
      "traceback": [
       "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
       "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-38-7db0a38aefcc>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0ma\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mis_integer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;32m<ipython-input-37-7db0a38aefcc>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0ma\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mis_integer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
       "\u001b[0;31mAttributeError\u001b[0m: 'int' object has no attribute 'is_integer'"
      ]
     }
@@ -1335,7 +1313,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 39,
+   "execution_count": 38,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -1348,7 +1326,7 @@
        "str"
       ]
      },
-     "execution_count": 39,
+     "execution_count": 38,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1357,6 +1335,30 @@
     "type(c)"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'python rocks'"
+      ]
+     },
+     "execution_count": 39,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "c.lower()"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 40,
@@ -1365,30 +1367,6 @@
      "slide_type": "fragment"
     }
    },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'python rocks'"
-      ]
-     },
-     "execution_count": 40,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "c.lower()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 41,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "-"
-    }
-   },
    "outputs": [
     {
      "data": {
@@ -1396,7 +1374,7 @@
        "'PYTHON ROCKS'"
       ]
      },
-     "execution_count": 41,
+     "execution_count": 40,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1407,7 +1385,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 42,
+   "execution_count": 41,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -1420,7 +1398,7 @@
        "'Python Rocks'"
       ]
      },
-     "execution_count": 42,
+     "execution_count": 41,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1437,7 +1415,7 @@
     }
    },
    "source": [
-    "### Value / \"Meaning\""
+    "### Value / (Semantic) \"Meaning\""
    ]
   },
   {
@@ -1455,7 +1433,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 43,
+   "execution_count": 42,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -1465,10 +1443,10 @@
     {
      "data": {
       "text/plain": [
-       "789"
+       "42"
       ]
      },
-     "execution_count": 43,
+     "execution_count": 42,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1479,10 +1457,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 44,
+   "execution_count": 43,
    "metadata": {
     "slideshow": {
-     "slide_type": "-"
+     "slide_type": "fragment"
     }
    },
    "outputs": [
@@ -1492,7 +1470,7 @@
        "42.0"
       ]
      },
-     "execution_count": 44,
+     "execution_count": 43,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1509,12 +1487,12 @@
     }
    },
    "source": [
-    "In this book, we follow the convention of creating strings with **double quotes** `\"` instead of the **single quotes** `'` to which Python defaults in its *literal* notation for `str` objects. Both types of quotes may be used interchangeably. So, the `\"Python rocks\"` from above and `'Python rocks'` create two objects that evaluate equal (i.e., `\"Python rocks\" == 'Python rocks'`)."
+    "In this book, we follow the convention of creating strings with **double quotes** `\"` instead of the **single quotes** `'` to which Python defaults in its *literal* notation for `str` objects. Both types of quotes may be used interchangeably. So, the `\"Python rocks\"` from above and `'Python rocks'` below create two objects that evaluate equal (i.e., `\"Python rocks\" == 'Python rocks'`)."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 45,
+   "execution_count": 44,
    "metadata": {
     "slideshow": {
      "slide_type": "-"
@@ -1527,7 +1505,7 @@
        "'Python rocks'"
       ]
      },
-     "execution_count": 45,
+     "execution_count": 44,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1586,7 +1564,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 46,
+   "execution_count": 45,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -1595,10 +1573,10 @@
    "outputs": [
     {
      "ename": "SyntaxError",
-     "evalue": "invalid syntax (<ipython-input-46-cafa82e54b9c>, line 1)",
+     "evalue": "invalid syntax (<ipython-input-45-cafa82e54b9c>, line 1)",
      "output_type": "error",
      "traceback": [
-      "\u001b[0;36m  File \u001b[0;32m\"<ipython-input-46-cafa82e54b9c>\"\u001b[0;36m, line \u001b[0;32m1\u001b[0m\n\u001b[0;31m    3.99 $ + 10.40 $\u001b[0m\n\u001b[0m         ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m invalid syntax\n"
+      "\u001b[0;36m  File \u001b[0;32m\"<ipython-input-45-cafa82e54b9c>\"\u001b[0;36m, line \u001b[0;32m1\u001b[0m\n\u001b[0;31m    3.99 $ + 10.40 $\u001b[0m\n\u001b[0m         ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m invalid syntax\n"
      ]
     }
    ],
@@ -1619,7 +1597,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 47,
+   "execution_count": 46,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -1628,10 +1606,10 @@
    "outputs": [
     {
      "ename": "SyntaxError",
-     "evalue": "invalid syntax (<ipython-input-47-499e4d0d0cbb>, line 1)",
+     "evalue": "invalid syntax (<ipython-input-46-499e4d0d0cbb>, line 1)",
      "output_type": "error",
      "traceback": [
-      "\u001b[0;36m  File \u001b[0;32m\"<ipython-input-47-499e4d0d0cbb>\"\u001b[0;36m, line \u001b[0;32m1\u001b[0m\n\u001b[0;31m    for number in numbers\u001b[0m\n\u001b[0m                         ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m invalid syntax\n"
+      "\u001b[0;36m  File \u001b[0;32m\"<ipython-input-46-499e4d0d0cbb>\"\u001b[0;36m, line \u001b[0;32m1\u001b[0m\n\u001b[0;31m    for number in numbers\u001b[0m\n\u001b[0m                         ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m invalid syntax\n"
      ]
     }
    ],
@@ -1653,7 +1631,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 48,
+   "execution_count": 47,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -1662,10 +1640,10 @@
    "outputs": [
     {
      "ename": "IndentationError",
-     "evalue": "expected an indented block (<ipython-input-48-19398c5f89de>, line 2)",
+     "evalue": "expected an indented block (<ipython-input-47-19398c5f89de>, line 2)",
      "output_type": "error",
      "traceback": [
-      "\u001b[0;36m  File \u001b[0;32m\"<ipython-input-48-19398c5f89de>\"\u001b[0;36m, line \u001b[0;32m2\u001b[0m\n\u001b[0;31m    print(number)\u001b[0m\n\u001b[0m        ^\u001b[0m\n\u001b[0;31mIndentationError\u001b[0m\u001b[0;31m:\u001b[0m expected an indented block\n"
+      "\u001b[0;36m  File \u001b[0;32m\"<ipython-input-47-19398c5f89de>\"\u001b[0;36m, line \u001b[0;32m2\u001b[0m\n\u001b[0;31m    print(number)\u001b[0m\n\u001b[0m        ^\u001b[0m\n\u001b[0;31mIndentationError\u001b[0m\u001b[0;31m:\u001b[0m expected an indented block\n"
      ]
     }
    ],
@@ -1702,7 +1680,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 49,
+   "execution_count": 48,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -1716,7 +1694,7 @@
      "traceback": [
       "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
       "\u001b[0;31mZeroDivisionError\u001b[0m                         Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-49-bc757c3fda29>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0;36m1\u001b[0m \u001b[0;34m/\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;32m<ipython-input-48-bc757c3fda29>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0;36m1\u001b[0m \u001b[0;34m/\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
       "\u001b[0;31mZeroDivisionError\u001b[0m: division by zero"
      ]
     }
@@ -1751,7 +1729,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 50,
+   "execution_count": 49,
    "metadata": {
     "code_folding": [],
     "slideshow": {
@@ -1773,7 +1751,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 51,
+   "execution_count": 50,
    "metadata": {
     "slideshow": {
      "slide_type": "-"
@@ -1786,7 +1764,7 @@
        "3.5"
       ]
      },
-     "execution_count": 51,
+     "execution_count": 50,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1832,7 +1810,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 52,
+   "execution_count": 51,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -1854,10 +1832,10 @@
        "        "
       ],
       "text/plain": [
-       "<IPython.lib.display.YouTubeVideo at 0x7efd940bd210>"
+       "<IPython.lib.display.YouTubeVideo at 0x7fa88451b510>"
       ]
      },
-     "execution_count": 52,
+     "execution_count": 51,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1880,7 +1858,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 53,
+   "execution_count": 52,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -1893,7 +1871,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 54,
+   "execution_count": 53,
    "metadata": {
     "slideshow": {
      "slide_type": "-"
@@ -1901,12 +1879,12 @@
    },
    "outputs": [],
    "source": [
-    "evens = [n for n in numbers if n % 2 == 0]  # example of a so-called list comprehension"
+    "evens = [n for n in numbers if n % 2 == 0]  # use of a list comprehension"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 55,
+   "execution_count": 54,
    "metadata": {
     "slideshow": {
      "slide_type": "-"
@@ -1919,7 +1897,7 @@
        "[8, 12, 2, 6, 10, 4]"
       ]
      },
-     "execution_count": 55,
+     "execution_count": 54,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1930,7 +1908,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 56,
+   "execution_count": 55,
    "metadata": {
     "slideshow": {
      "slide_type": "-"
@@ -1938,12 +1916,12 @@
    },
    "outputs": [],
    "source": [
-    "average = sum(evens) / len(evens)  # built-in functions are much faster than a for-loop"
+    "average = sum(evens) / len(evens)  # use built-in functions"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 57,
+   "execution_count": 56,
    "metadata": {
     "slideshow": {
      "slide_type": "-"
@@ -1956,7 +1934,7 @@
        "7.0"
       ]
      },
-     "execution_count": 57,
+     "execution_count": 56,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1978,7 +1956,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 58,
+   "execution_count": 57,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -2106,7 +2084,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 59,
+   "execution_count": 58,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -2114,8 +2092,7 @@
    },
    "outputs": [],
    "source": [
-    "a = 20.0\n",
-    "b = 789"
+    "variable = 20.0"
    ]
   },
   {
@@ -2126,12 +2103,12 @@
     }
    },
    "source": [
-    "When used as a *literal*, a variable evaluates to the value of the object it references. Colloquially, we could say that `a` evaluates to `20.0`, but this would not be an accurate description of what is going on in memory. We see some more colloquialisms in this section but should always relate this to what Python actually does in memory."
+    "When used as a *literal*, a variable evaluates to the value of the object it references. Colloquially, we could say that `variable` evaluates to `20.0`, but this would not be an accurate description of what is going on in memory. We see some more colloquialisms in this section but should always relate this to what Python actually does in memory."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 60,
+   "execution_count": 59,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -2144,13 +2121,13 @@
        "20.0"
       ]
      },
-     "execution_count": 60,
+     "execution_count": 59,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "a"
+    "variable"
    ]
   },
   {
@@ -2166,7 +2143,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 61,
+   "execution_count": 60,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -2174,15 +2151,15 @@
    },
    "outputs": [],
    "source": [
-    "a = 20  # this makes a reference an object of a different type"
+    "variable = 20"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 62,
+   "execution_count": 61,
    "metadata": {
     "slideshow": {
-     "slide_type": "-"
+     "slide_type": "fragment"
     }
    },
    "outputs": [
@@ -2192,13 +2169,13 @@
        "20"
       ]
      },
-     "execution_count": 62,
+     "execution_count": 61,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "a"
+    "variable"
    ]
   },
   {
@@ -2214,7 +2191,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 63,
+   "execution_count": 62,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -2222,7 +2199,31 @@
    },
    "outputs": [],
    "source": [
-    "a *= 4  # same as a = a * 4"
+    "variable *= 4  # same as variable = variable * 4"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 63,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "80"
+      ]
+     },
+     "execution_count": 63,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "variable"
    ]
   },
   {
@@ -2230,12 +2231,12 @@
    "execution_count": 64,
    "metadata": {
     "slideshow": {
-     "slide_type": "-"
+     "slide_type": "fragment"
     }
    },
    "outputs": [],
    "source": [
-    "a //= 2  # same as a = a // 2; \"//\" to retain the integer type"
+    "variable //= 2  # same as variable = variable // 2; \"//\" to retain the integer type"
    ]
   },
   {
@@ -2246,14 +2247,38 @@
      "slide_type": "-"
     }
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "40"
+      ]
+     },
+     "execution_count": 65,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "a += 2  # same as a = a + 2"
+    "variable"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 66,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "variable += 2  # same as variable = variable + 2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 67,
    "metadata": {
     "slideshow": {
      "slide_type": "-"
@@ -2266,13 +2291,13 @@
        "42"
       ]
      },
-     "execution_count": 66,
+     "execution_count": 67,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "a"
+    "variable"
    ]
   },
   {
@@ -2288,7 +2313,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 67,
+   "execution_count": 68,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -2298,29 +2323,29 @@
     {
      "data": {
       "text/plain": [
-       "789"
+       "42"
       ]
      },
-     "execution_count": 67,
+     "execution_count": 68,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "b"
+    "variable"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 68,
+   "execution_count": 69,
    "metadata": {
     "slideshow": {
-     "slide_type": "-"
+     "slide_type": "fragment"
     }
    },
    "outputs": [],
    "source": [
-    "del b"
+    "del variable"
    ]
   },
   {
@@ -2336,27 +2361,27 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 69,
+   "execution_count": 70,
    "metadata": {
     "slideshow": {
-     "slide_type": "-"
+     "slide_type": "fragment"
     }
    },
    "outputs": [
     {
      "ename": "NameError",
-     "evalue": "name 'b' is not defined",
+     "evalue": "name 'variable' is not defined",
      "output_type": "error",
      "traceback": [
       "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
       "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-69-89e6c98d9288>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mb\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
-      "\u001b[0;31mNameError\u001b[0m: name 'b' is not defined"
+      "\u001b[0;32m<ipython-input-70-1748287bc46a>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mvariable\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;31mNameError\u001b[0m: name 'variable' is not defined"
      ]
     }
    ],
    "source": [
-    "b"
+    "variable"
    ]
   },
   {
@@ -2372,7 +2397,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 70,
+   "execution_count": 71,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -2385,7 +2410,7 @@
        "'__main__'"
       ]
      },
-     "execution_count": 70,
+     "execution_count": 71,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2407,10 +2432,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 71,
+   "execution_count": 72,
    "metadata": {
     "slideshow": {
-     "slide_type": "slide"
+     "slide_type": "skip"
     }
    },
    "outputs": [
@@ -2442,12 +2467,13 @@
        " '_28',\n",
        " '_29',\n",
        " '_30',\n",
+       " '_31',\n",
        " '_32',\n",
        " '_33',\n",
        " '_34',\n",
        " '_35',\n",
        " '_36',\n",
-       " '_37',\n",
+       " '_38',\n",
        " '_39',\n",
        " '_4',\n",
        " '_40',\n",
@@ -2455,17 +2481,18 @@
        " '_42',\n",
        " '_43',\n",
        " '_44',\n",
-       " '_45',\n",
        " '_5',\n",
+       " '_50',\n",
        " '_51',\n",
-       " '_52',\n",
-       " '_55',\n",
-       " '_57',\n",
-       " '_60',\n",
-       " '_62',\n",
-       " '_66',\n",
+       " '_54',\n",
+       " '_56',\n",
+       " '_59',\n",
+       " '_61',\n",
+       " '_63',\n",
+       " '_65',\n",
        " '_67',\n",
-       " '_70',\n",
+       " '_68',\n",
+       " '_71',\n",
        " '_9',\n",
        " '__',\n",
        " '___',\n",
@@ -2547,6 +2574,7 @@
        " '_i7',\n",
        " '_i70',\n",
        " '_i71',\n",
+       " '_i72',\n",
        " '_i8',\n",
        " '_i9',\n",
        " '_ih',\n",
@@ -2555,9 +2583,9 @@
        " '_oh',\n",
        " 'a',\n",
        " 'average',\n",
+       " 'b',\n",
        " 'c',\n",
        " 'count',\n",
-       " 'd',\n",
        " 'evens',\n",
        " 'exit',\n",
        " 'get_ipython',\n",
@@ -2568,7 +2596,7 @@
        " 'total']"
       ]
      },
-     "execution_count": 71,
+     "execution_count": 72,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2603,23 +2631,36 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 72,
+   "execution_count": 73,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
     }
    },
    "outputs": [],
+   "source": [
+    "a = 42"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 74,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [],
    "source": [
     "b = a"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 73,
+   "execution_count": 75,
    "metadata": {
     "slideshow": {
-     "slide_type": "-"
+     "slide_type": "fragment"
     }
    },
    "outputs": [
@@ -2629,31 +2670,7 @@
        "42"
       ]
      },
-     "execution_count": 73,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "a"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 74,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "-"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "42"
-      ]
-     },
-     "execution_count": 74,
+     "execution_count": 75,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2672,20 +2689,7 @@
    "source": [
     "For \"simple\" types like `int` or `float` this never causes troubles.\n",
     "\n",
-    "Let's \"change the value\" of `a`. To be precise, let's create a *new* `123` object and make `a` reference it."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 75,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "fragment"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "a = 123"
+    "Let's \"change the value\" of `a`. To be precise, let's create a *new* `87` object and make `a` reference it."
    ]
   },
   {
@@ -2693,17 +2697,30 @@
    "execution_count": 76,
    "metadata": {
     "slideshow": {
-     "slide_type": "-"
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "a = 87"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 77,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
     }
    },
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "123"
+       "87"
       ]
      },
-     "execution_count": 76,
+     "execution_count": 77,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2725,7 +2742,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 77,
+   "execution_count": 78,
    "metadata": {
     "slideshow": {
      "slide_type": "-"
@@ -2738,7 +2755,7 @@
        "42"
       ]
      },
-     "execution_count": 77,
+     "execution_count": 78,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2760,7 +2777,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 78,
+   "execution_count": 79,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -2773,7 +2790,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 79,
+   "execution_count": 80,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -2786,7 +2803,7 @@
        "list"
       ]
      },
-     "execution_count": 79,
+     "execution_count": 80,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2797,10 +2814,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 80,
+   "execution_count": 81,
    "metadata": {
     "slideshow": {
-     "slide_type": "-"
+     "slide_type": "fragment"
     }
    },
    "outputs": [],
@@ -2808,6 +2825,30 @@
     "y = x"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 82,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[1, 2, 3]"
+      ]
+     },
+     "execution_count": 82,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "y"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {
@@ -2825,10 +2866,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 81,
+   "execution_count": 83,
    "metadata": {
     "slideshow": {
-     "slide_type": "fragment"
+     "slide_type": "slide"
     }
    },
    "outputs": [
@@ -2838,7 +2879,7 @@
        "1"
       ]
      },
-     "execution_count": 81,
+     "execution_count": 83,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2860,10 +2901,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 82,
+   "execution_count": 84,
    "metadata": {
     "slideshow": {
-     "slide_type": "slide"
+     "slide_type": "fragment"
     }
    },
    "outputs": [],
@@ -2873,10 +2914,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 83,
+   "execution_count": 85,
    "metadata": {
     "slideshow": {
-     "slide_type": "-"
+     "slide_type": "fragment"
     }
    },
    "outputs": [
@@ -2886,7 +2927,7 @@
        "[99, 2, 3]"
       ]
      },
-     "execution_count": 83,
+     "execution_count": 85,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2908,7 +2949,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 84,
+   "execution_count": 86,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -2921,7 +2962,7 @@
        "[99, 2, 3]"
       ]
      },
-     "execution_count": 84,
+     "execution_count": 86,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2940,7 +2981,7 @@
    "source": [
     "The difference in behavior illustrated in this sub-section has to do with the fact that `int` and `float` objects are **immutable** types while `list` objects are **mutable**.\n",
     "\n",
-    "In the first case, an object cannot be changed \"in place\" once it is created in memory. When we assigned `123` to the already existing `a`, we did *not* change the $0$s and $1$s in the object `a` referenced before the assignment but created a new `int` object and made `a` reference it while the `b` variable is *not* affected.\n",
+    "In the first case, an object cannot be changed \"in place\" once it is created in memory. When we assigned `87` to the already existing `a`, we did *not* change the $0$s and $1$s in the object `a` referenced before the assignment but created a new `int` object and made `a` reference it while the `b` variable is *not* affected.\n",
     "\n",
     "In the second case, `x[0] = 99` creates a *new* `int` object `99` and merely changes the first reference in the `x` list.\n",
     "\n",
@@ -3001,7 +3042,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 85,
+   "execution_count": 87,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -3014,7 +3055,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 86,
+   "execution_count": 88,
    "metadata": {
     "slideshow": {
      "slide_type": "-"
@@ -3027,7 +3068,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 87,
+   "execution_count": 89,
    "metadata": {
     "slideshow": {
      "slide_type": "-"
@@ -3040,7 +3081,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 88,
+   "execution_count": 90,
    "metadata": {
     "slideshow": {
      "slide_type": "-"
@@ -3048,7 +3089,7 @@
    },
    "outputs": [],
    "source": [
-    "work_address = \"Burgplatz 2, Vallendar\""
+    "work_address = \"WHU, Burgplatz 2, Vallendar\""
    ]
   },
   {
@@ -3064,7 +3105,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 89,
+   "execution_count": 91,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -3077,7 +3118,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 90,
+   "execution_count": 92,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -3090,7 +3131,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 91,
+   "execution_count": 93,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -3103,7 +3144,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 92,
+   "execution_count": 94,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -3112,15 +3153,15 @@
    "outputs": [
     {
      "ename": "SyntaxError",
-     "evalue": "can't assign to operator (<ipython-input-92-dfc191b6a91a>, line 1)",
+     "evalue": "can't assign to operator (<ipython-input-94-ec51dae29567>, line 1)",
      "output_type": "error",
      "traceback": [
-      "\u001b[0;36m  File \u001b[0;32m\"<ipython-input-92-dfc191b6a91a>\"\u001b[0;36m, line \u001b[0;32m1\u001b[0m\n\u001b[0;31m    address@work = \"Burgplatz 2, Vallendar\"\u001b[0m\n\u001b[0m                                           ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m can't assign to operator\n"
+      "\u001b[0;36m  File \u001b[0;32m\"<ipython-input-94-ec51dae29567>\"\u001b[0;36m, line \u001b[0;32m1\u001b[0m\n\u001b[0;31m    address@work = \"WHU, Burgplatz 2, Vallendar\"\u001b[0m\n\u001b[0m                                                ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m can't assign to operator\n"
      ]
     }
    ],
    "source": [
-    "address@work = \"Burgplatz 2, Vallendar\""
+    "address@work = \"WHU, Burgplatz 2, Vallendar\""
    ]
   },
   {
@@ -3136,7 +3177,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 93,
+   "execution_count": 95,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -3160,7 +3201,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 94,
+   "execution_count": 96,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -3173,7 +3214,7 @@
        "'__main__'"
       ]
      },
-     "execution_count": 94,
+     "execution_count": 96,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3212,7 +3253,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 95,
+   "execution_count": 97,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -3222,10 +3263,10 @@
     {
      "data": {
       "text/plain": [
-       "123"
+       "87"
       ]
      },
-     "execution_count": 95,
+     "execution_count": 97,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3236,7 +3277,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 96,
+   "execution_count": 98,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -3249,7 +3290,7 @@
        "42"
       ]
      },
-     "execution_count": 96,
+     "execution_count": 98,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3269,76 +3310,6 @@
     "For sure, we need to include operators to achieve something useful."
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": 97,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "fragment"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "165"
-      ]
-     },
-     "execution_count": 97,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "a + b"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "source": [
-    "The definition of an expression is **recursive**. So, the sub-expression `a + b` is combined with the literal `3` by the operator `**` to form the full expression `(a + b) ** 3`."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 98,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "fragment"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "4492125"
-      ]
-     },
-     "execution_count": 98,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "(a + b) ** 3"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "source": [
-    "Here, the variable `y` is combined with the literal `2` by the indexing operator `[]`. The resulting expression evaluates to the third element in the `y` list."
-   ]
-  },
   {
    "cell_type": "code",
    "execution_count": 99,
@@ -3351,7 +3322,7 @@
     {
      "data": {
       "text/plain": [
-       "3"
+       "45"
       ]
      },
      "execution_count": 99,
@@ -3360,7 +3331,77 @@
     }
    ],
    "source": [
-    "y[2]"
+    "a - 42"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "The definition of an expression is **recursive**. So, the sub-expression `a - 42` is combined with the literal `9` by the operator `//` to form the full expression `(a - 42) // 9`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 100,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "5"
+      ]
+     },
+     "execution_count": 100,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "(a - 42) // 9"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Here, the variable `x` is combined with the literal `2` by the indexing operator `[]`. The resulting expression evaluates to the third element in the `x` list."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 101,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "3"
+      ]
+     },
+     "execution_count": 101,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "x[2]"
    ]
   },
   {
@@ -3376,10 +3417,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 100,
+   "execution_count": 102,
    "metadata": {
     "slideshow": {
-     "slide_type": "fragment"
+     "slide_type": "skip"
     }
    },
    "outputs": [
@@ -3389,7 +3430,7 @@
        "104"
       ]
      },
-     "execution_count": 100,
+     "execution_count": 102,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3422,7 +3463,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 101,
+   "execution_count": 103,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -3436,7 +3477,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 102,
+   "execution_count": 104,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -3449,7 +3490,7 @@
        "'Hi class'"
       ]
      },
-     "execution_count": 102,
+     "execution_count": 104,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3471,7 +3512,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 103,
+   "execution_count": 105,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -3481,16 +3522,16 @@
     {
      "data": {
       "text/plain": [
-       "'Hi Hi Hi Hi Hi Hi Hi Hi Hi Hi Hi Hi Hi Hi Hi Hi Hi Hi Hi Hi Hi Hi Hi Hi Hi Hi Hi Hi Hi Hi Hi Hi Hi Hi Hi Hi Hi Hi Hi Hi Hi Hi '"
+       "'Hi Hi Hi Hi Hi Hi Hi Hi Hi Hi '"
       ]
      },
-     "execution_count": 103,
+     "execution_count": 105,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "b * greeting"
+    "10 * greeting"
    ]
   },
   {
@@ -3519,7 +3560,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 104,
+   "execution_count": 106,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -3527,12 +3568,12 @@
    },
    "outputs": [],
    "source": [
-    "a = 123"
+    "a = 42"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 105,
+   "execution_count": 107,
    "metadata": {
     "slideshow": {
      "slide_type": "-"
@@ -3556,7 +3597,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 106,
+   "execution_count": 108,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -3603,7 +3644,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 107,
+   "execution_count": 109,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -3613,6 +3654,7 @@
    "source": [
     "distance = 891  # in meters\n",
     "elapsed_time = 93  # in seconds\n",
+    "\n",
     "# Calculate the speed in km/h.\n",
     "speed = 3.6 * distance / elapsed_time"
    ]
@@ -3631,7 +3673,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 108,
+   "execution_count": 110,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -3644,10 +3686,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 109,
+   "execution_count": 111,
    "metadata": {
     "slideshow": {
-     "slide_type": "-"
+     "slide_type": "fragment"
     }
    },
    "outputs": [],
@@ -3772,7 +3814,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 110,
+   "execution_count": 112,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -3794,10 +3836,10 @@
        "        "
       ],
       "text/plain": [
-       "<IPython.lib.display.YouTubeVideo at 0x7efd86fd2bd0>"
+       "<IPython.lib.display.YouTubeVideo at 0x7fa8840ad310>"
       ]
      },
-     "execution_count": 110,
+     "execution_count": 112,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3823,7 +3865,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.4"
+   "version": "3.7.6"
   },
   "livereveal": {
    "auto_select": "code",

From ed3da36fcf616193b9f4825162baea717393836a Mon Sep 17 00:00:00 2001
From: Alexander Hess <alexander@webartifex.biz>
Date: Sun, 8 Mar 2020 17:10:41 +0100
Subject: [PATCH 073/242] Add more exercises to chapter 01

---
 01_elements_02_exercises.ipynb | 338 ++++++++++++++++++++++++++++++++-
 1 file changed, 332 insertions(+), 6 deletions(-)

diff --git a/01_elements_02_exercises.ipynb b/01_elements_02_exercises.ipynb
index 64fc68f..3f5026a 100644
--- a/01_elements_02_exercises.ipynb
+++ b/01_elements_02_exercises.ipynb
@@ -19,7 +19,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Read [Chapter 1](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/01_elements_00_lecture.ipynb) of the book. Then, work through the exercises below."
+    "Read [Chapter 1](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/01_elements_00_content.ipynb) of the book. Then, work through the exercises below. The `...` indicate where you need to fill in your answers. You should not need to create any additional code cells."
    ]
   },
   {
@@ -33,9 +33,9 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "**Q1**: Read the documentation on the built-in [print()](https://docs.python.org/3/library/functions.html#print) function! How can you use it to print both `greeting` and `audience` *without* concatenating the two strings with the `+` operator?\n",
+    "**Q1.1**: *Concatenate* `greeting` and `audience` below with the `+` operator and print out the resulting message `\"Hello World\"` with only *one* call of the built-in [print()](https://docs.python.org/3/library/functions.html#print) function!\n",
     "\n",
-    "Hint: The `*objects` in the documentation implies that we can insert several *comma-seperated* variables."
+    "Hint: You may have to \"add\" a space character in between `greeting` and `audience`."
    ]
   },
   {
@@ -61,7 +61,53 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "**Q2**: What does the `sep=\" \"` mean in the documentation? Use it and print out the three names in `first`, `second`, and `third` on one line seperated by *commas* with only *one* call of the [print()](https://docs.python.org/3/library/functions.html#print) function!"
+    "**Q1.2**: How is your answer to **Q1.1** an example of the concept of **operator overloading**?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " ..."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q1.3**: Read the documentation on the built-in [print()](https://docs.python.org/3/library/functions.html#print) function! How can you print the above message *without* concatenating `greeting` and `audience` first in *one* call of [print()](https://docs.python.org/3/library/functions.html#print)?\n",
+    "\n",
+    "Hint: The `*objects` in the documentation implies that we can put several *expressions* (i.e., variables) separated by commas within the same call of the [print()](https://docs.python.org/3/library/functions.html#print) function."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "print(...)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q1.4**: When using a function, we may only put **expressions** between the parentheses `(` and `)`. What are expressions syntactically?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " ..."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q1.5**: What does the `sep=\" \"` mean in the documentation on the built-in [print()](https://docs.python.org/3/library/functions.html#print) function? Adjust and use it to print out the three names referenced by `first`, `second`, and `third` on *one* line separated by *commas* with only *one* call of the [print()](https://docs.python.org/3/library/functions.html#print) function!"
    ]
   },
   {
@@ -88,7 +134,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "**Q3**: Lastly, what does the `end=\"\\n\"` mean in the documentation? Use it in the `for`-loop and print the numbers 1 through 10 in just *one* line of output!"
+    "**Q1.6**: Lastly, what does the `end=\"\\n\"` mean in the documentation? Adjust and use it within the `for`-loop to print the numbers `1` through `10` on *one* line with only *one* call of the [print()](https://docs.python.org/3/library/functions.html#print) function!"
    ]
   },
   {
@@ -100,6 +146,286 @@
     "for number in [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]:\n",
     "    print(...)"
    ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Python as a Calculator"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The [volume of a sphere](https://en.wikipedia.org/wiki/Sphere) is defined as $\\frac{4}{3} * \\pi * r^3$.\n",
+    "\n",
+    "**Q2.1**: Calculate it for `r = 2.88` and approximate $\\pi$ with `pi = 3.14`!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "pi = 3.14\n",
+    "r = 2.88"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "..."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "While Python may be used as a calculator, it behaves a bit differently compared to calculator apps that phones or computers come with and that we are accustomed to.\n",
+    "\n",
+    "A major difference is that Python \"forgets\" intermediate results that are not assigned to variables. On the contrary, the calculators we work with outside of programming always keep the last result and allow us to use it as the first input for the next calculation.\n",
+    "\n",
+    "One way to keep on working with intermediate results in Python is to write the entire calculation as just *one* big expression that is composed of many sub-expressions representing the individual steps in our overall calculation.\n",
+    "\n",
+    "**Q2.2.1**: Given `a` and `b` like below, subtract the smaller `a` from the larger `b`, divide the difference by `9`, and raise the result to the power of `2`! Use operators that preserve the `int` type of the final result! The entire calculations *must* be placed within *one* code cell.\n",
+    "\n",
+    "Hint: You may need to group sub-expressions with parentheses `(` and `)`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "a = 42\n",
+    "b = 87"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "..."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The code cell below contains nothing but a single underscore `_`. In both, a Python command-line prompt and Jupyter notebooks, the variable `_` is automatically updated and always references the object to which the *last* expression executed evaluated to.\n",
+    "\n",
+    "**Q2.2.2**: Execute the code cell below! It should evaluate to the *same* result as the previous code cell (i.e., your answer to **Q2.2.1** assuming you go through this notebook in order)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "_"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q2.2.3**: Implement the same overall calculation as in your answer to **Q2.2.1** in several independent steps (i.e., code cells)! Use only *one* operator per code cell!\n",
+    "\n",
+    "Hint: You should need *two* more code cells after the `b - a` one immediately below. If you *need* to use parentheses, you must be doing something wrong."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "b - a"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "_ ..."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "_ ..."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Simple `for`-loops"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "`for`-loops are extremely versatile in Python. That is different from many other programming languages.\n",
+    "\n",
+    "As shown in the first example in [Chapter 1](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/01_elements_00_content.ipynb), we can create a `list` like `numbers` and loop over the numbers in it on a one-by-one basis."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "numbers = [7, 11, 8, 5, 3, 12, 2, 6, 9, 10, 1, 4]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q3.1**: Fill in the *condition* of the `if` statement such that only numbers divisible by `3` are printed! Adjust the call of the [print()](https://docs.python.org/3/library/functions.html#print) function such that the `for`-loop prints out all the numbers on *one* line of output!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "for number in numbers:\n",
+    "    if ...:\n",
+    "        print(...)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Instead of looping over an *existing* object referenced by a variable like `numbers`, we may also create a *new* object within the `for` statement and loop over it directly. For example, below we write out the `list` object as a *literal*.\n",
+    "\n",
+    "Generically, the objects contained in a `list` objects are referred to as its **elements**. We reflect that in the name of the *target* variable `element` that is assigned a different number in every iteration of the `for`-loop. While we could use *any* syntactically valid name, it is best to choose one that makes sense in the context (e.g., `number` in `numbers`).\n",
+    "\n",
+    "**Q3.2**: Fill in the condition of the `if` statement such that only numbers consisting of *one* digit are printed out! As before, print out all the numbers on *one* line of output!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "for element in [7, 11, 8, 5, 3, 12, 2, 6, 9, 10, 1, 4]:\n",
+    "    if ...:\n",
+    "        print(...)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "An easy way to loop over a `list` object in a sorted manner, is to wrap it with the built-in [sorted()](https://docs.python.org/3/library/functions.html#sorted) function.\n",
+    "\n",
+    "**Q3.3**: Fill in the condition of the `if` statement such that only odd numbers are printed out! Put all the numbers on *one* line of output!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "for number in sorted(numbers):\n",
+    "    if ...:\n",
+    "        print(...)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Whenever we want to loop over numbers representing a [series](https://en.wikipedia.org/wiki/Series_%28mathematics%29) in the mathematical sense (i.e., a rule to calculate the next number from its predecessor), we may be able to use the [range()](https://docs.python.org/3/library/functions.html#func-range) built-in.\n",
+    "\n",
+    "For example, to loop over the whole numbers from `0` to `9` (both including) in order, we could write them out in a `list` like below.\n",
+    "\n",
+    "**Q3.4**: Fill in the call to the [print()](https://docs.python.org/3/library/functions.html#print) function such that all the numbers are printed on *one* line ouf output!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "for number in [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]:\n",
+    "    print(...)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q3.5**: Read the documentation on the [range()](https://docs.python.org/3/library/functions.html#func-range) built-in! It may be used with either one, two, or three expressions \"passed\" in. What do `start`, `stop`, and `step` mean? Fill in the calls to [range()](https://docs.python.org/3/library/functions.html#func-range) and [print()](https://docs.python.org/3/library/functions.html#print) to mimic the output of **Q3.4**!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "for number in range(...):\n",
+    "    print(...)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q3.6**: Fill in the calls to [range()](https://docs.python.org/3/library/functions.html#func-range) and [print()](https://docs.python.org/3/library/functions.html#print) to print out *all* numbers from `1` to `10` (both including)!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "for number in range(...):\n",
+    "    print(...)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q3.7**: Fill in the calls to [range()](https://docs.python.org/3/library/functions.html#func-range) and [print()](https://docs.python.org/3/library/functions.html#print) to print out the *even* numbers from `1` to `10` (both including)! Do *not* use an `if` statement to accomplish this!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "for number in range(...):\n",
+    "    print(...)"
+   ]
   }
  ],
  "metadata": {
@@ -118,7 +444,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.4"
+   "version": "3.7.6"
   },
   "toc": {
    "base_numbering": 1,

From 4b8aae1f28e9948848e81f7fb6b8744938282951 Mon Sep 17 00:00:00 2001
From: Alexander Hess <alexander@webartifex.biz>
Date: Sun, 8 Mar 2020 17:46:19 +0100
Subject: [PATCH 074/242] Streamline presentation

---
 01_elements_01_review.ipynb     |   2 +-
 02_functions_00_lecture.ipynb   | 360 ++++++++++++++++++--------------
 02_functions_01_review.ipynb    |   2 +-
 02_functions_02_exercises.ipynb |   4 +-
 4 files changed, 211 insertions(+), 157 deletions(-)

diff --git a/01_elements_01_review.ipynb b/01_elements_01_review.ipynb
index 0ed455c..c17b487 100644
--- a/01_elements_01_review.ipynb
+++ b/01_elements_01_review.ipynb
@@ -207,7 +207,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.4"
+   "version": "3.7.6"
   },
   "toc": {
    "base_numbering": 1,
diff --git a/02_functions_00_lecture.ipynb b/02_functions_00_lecture.ipynb
index 69170d8..c25b194 100644
--- a/02_functions_00_lecture.ipynb
+++ b/02_functions_00_lecture.ipynb
@@ -134,7 +134,7 @@
     {
      "data": {
       "text/plain": [
-       "140413081843264"
+       "140451963172416"
       ]
      },
      "execution_count": 4,
@@ -158,7 +158,7 @@
     {
      "data": {
       "text/plain": [
-       "140413081842224"
+       "140451963171376"
       ]
      },
      "execution_count": 5,
@@ -252,7 +252,7 @@
     {
      "data": {
       "text/plain": [
-       "140413081841824"
+       "140451963170976"
       ]
      },
      "execution_count": 8,
@@ -630,7 +630,7 @@
    "execution_count": 20,
    "metadata": {
     "slideshow": {
-     "slide_type": "-"
+     "slide_type": "fragment"
     }
    },
    "outputs": [
@@ -672,7 +672,7 @@
     {
      "data": {
       "text/plain": [
-       "94916229764288"
+       "93979750270848"
       ]
      },
      "execution_count": 21,
@@ -696,7 +696,7 @@
     {
      "data": {
       "text/plain": [
-       "94916229768192"
+       "93979750256352"
       ]
      },
      "execution_count": 22,
@@ -955,7 +955,7 @@
     {
      "data": {
       "text/plain": [
-       "140413021591744"
+       "140451741882272"
       ]
      },
      "execution_count": 29,
@@ -972,7 +972,7 @@
    "execution_count": 30,
    "metadata": {
     "slideshow": {
-     "slide_type": "-"
+     "slide_type": "fragment"
     }
    },
    "outputs": [
@@ -1721,7 +1721,7 @@
     }
    ],
    "source": [
-    "average_evens([40.0, 41.1, 42.2, 43.3, 44.4])  # inputs with predictable result"
+    "average_evens([40.0, 41.1, 42.2, 43.3, 44.4])"
    ]
   },
   {
@@ -1800,7 +1800,7 @@
    "execution_count": 52,
    "metadata": {
     "slideshow": {
-     "slide_type": "-"
+     "slide_type": "fragment"
     }
    },
    "outputs": [
@@ -1961,7 +1961,7 @@
    "execution_count": 57,
    "metadata": {
     "slideshow": {
-     "slide_type": "-"
+     "slide_type": "fragment"
     }
    },
    "outputs": [
@@ -2042,7 +2042,7 @@
    "execution_count": 59,
    "metadata": {
     "slideshow": {
-     "slide_type": "-"
+     "slide_type": "fragment"
     }
    },
    "outputs": [
@@ -2343,7 +2343,7 @@
     }
    },
    "source": [
-    "### Default Arguments"
+    "### Modularization"
    ]
   },
   {
@@ -2374,9 +2374,19 @@
     "    numbers = [round(n) for n in numbers]\n",
     "    evens = [n for n in numbers if n % 2 == 0]\n",
     "    average = sum(evens) / len(evens)\n",
-    "    return average\n",
-    "\n",
-    "\n",
+    "    return average"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 69,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [],
+   "source": [
     "def scaled_average_evens(numbers, scalar):\n",
     "    \"\"\" ... ... ... \"\"\"\n",
     "    numbers = [round(n) for n in numbers]\n",
@@ -2400,7 +2410,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 69,
+   "execution_count": 70,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -2410,9 +2420,19 @@
    "source": [
     "def average_evens(numbers):\n",
     "    \"\"\" ... ... ... \"\"\"\n",
-    "    return scaled_average_evens(numbers, scalar=1)\n",
-    "\n",
-    "\n",
+    "    return scaled_average_evens(numbers, scalar=1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 71,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [],
+   "source": [
     "def scaled_average_evens(numbers, scalar):\n",
     "    \"\"\" ... ... ... \"\"\"\n",
     "    numbers = [round(n) for n in numbers]\n",
@@ -2434,7 +2454,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 70,
+   "execution_count": 72,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -2447,7 +2467,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 71,
+   "execution_count": 73,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -2458,6 +2478,17 @@
     "assert scaled_average_evens(numbers, 2) == 14.0"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "### Default Arguments"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {
@@ -2471,7 +2502,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 72,
+   "execution_count": 74,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -2513,7 +2544,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 73,
+   "execution_count": 75,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -2526,7 +2557,7 @@
        "7.0"
       ]
      },
-     "execution_count": 73,
+     "execution_count": 75,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2537,10 +2568,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 74,
+   "execution_count": 76,
    "metadata": {
     "slideshow": {
-     "slide_type": "-"
+     "slide_type": "fragment"
     }
    },
    "outputs": [
@@ -2550,7 +2581,7 @@
        "14.0"
       ]
      },
-     "execution_count": 74,
+     "execution_count": 76,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2561,10 +2592,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 75,
+   "execution_count": 77,
    "metadata": {
     "slideshow": {
-     "slide_type": "-"
+     "slide_type": "fragment"
     }
    },
    "outputs": [
@@ -2574,7 +2605,7 @@
        "14.0"
       ]
      },
-     "execution_count": 75,
+     "execution_count": 77,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2609,7 +2640,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 76,
+   "execution_count": 78,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -2647,7 +2678,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 77,
+   "execution_count": 79,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -2660,7 +2691,7 @@
        "7.0"
       ]
      },
-     "execution_count": 77,
+     "execution_count": 79,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2682,10 +2713,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 78,
+   "execution_count": 80,
    "metadata": {
     "slideshow": {
-     "slide_type": "-"
+     "slide_type": "fragment"
     }
    },
    "outputs": [
@@ -2695,7 +2726,7 @@
        "14.0"
       ]
      },
-     "execution_count": 78,
+     "execution_count": 80,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2717,7 +2748,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 79,
+   "execution_count": 81,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -2731,7 +2762,7 @@
      "traceback": [
       "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
       "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-79-12eb635366db>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0maverage_evens\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnumbers\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m2\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;32m<ipython-input-81-12eb635366db>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0maverage_evens\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnumbers\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m2\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
       "\u001b[0;31mTypeError\u001b[0m: average_evens() takes 1 positional argument but 2 were given"
      ]
     }
@@ -2772,7 +2803,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 80,
+   "execution_count": 82,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -2785,7 +2816,7 @@
        "<function __main__.<lambda>(x)>"
       ]
      },
-     "execution_count": 80,
+     "execution_count": 82,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2811,7 +2842,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 81,
+   "execution_count": 83,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -2835,7 +2866,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 82,
+   "execution_count": 84,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -2848,7 +2879,7 @@
        "function"
       ]
      },
-     "execution_count": 82,
+     "execution_count": 84,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2859,10 +2890,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 83,
+   "execution_count": 85,
    "metadata": {
     "slideshow": {
-     "slide_type": "-"
+     "slide_type": "fragment"
     }
    },
    "outputs": [
@@ -2872,7 +2903,7 @@
        "True"
       ]
      },
-     "execution_count": 83,
+     "execution_count": 85,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2894,7 +2925,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 84,
+   "execution_count": 86,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -2907,7 +2938,7 @@
        "42"
       ]
      },
-     "execution_count": 84,
+     "execution_count": 86,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2929,7 +2960,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 85,
+   "execution_count": 87,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -2942,7 +2973,7 @@
        "42"
       ]
      },
-     "execution_count": 85,
+     "execution_count": 87,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3046,7 +3077,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 86,
+   "execution_count": 88,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -3070,7 +3101,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 87,
+   "execution_count": 89,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -3080,10 +3111,10 @@
     {
      "data": {
       "text/plain": [
-       "<module 'math' from '/home/webartifex/.pyenv/versions/anaconda3-2019.10/lib/python3.7/lib-dynload/math.cpython-37m-x86_64-linux-gnu.so'>"
+       "<module 'math' from '/home/webartifex/.pyenv/versions/3.7.6/lib/python3.7/lib-dynload/math.cpython-37m-x86_64-linux-gnu.so'>"
       ]
      },
-     "execution_count": 87,
+     "execution_count": 89,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3094,7 +3125,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 88,
+   "execution_count": 90,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -3104,10 +3135,10 @@
     {
      "data": {
       "text/plain": [
-       "140413079419024"
+       "140451956463472"
       ]
      },
-     "execution_count": 88,
+     "execution_count": 90,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3118,10 +3149,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 89,
+   "execution_count": 91,
    "metadata": {
     "slideshow": {
-     "slide_type": "-"
+     "slide_type": "fragment"
     }
    },
    "outputs": [
@@ -3131,7 +3162,7 @@
        "module"
       ]
      },
-     "execution_count": 89,
+     "execution_count": 91,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3157,10 +3188,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 90,
+   "execution_count": 92,
    "metadata": {
     "slideshow": {
-     "slide_type": "slide"
+     "slide_type": "skip"
     }
    },
    "outputs": [
@@ -3225,7 +3256,7 @@
        " 'trunc']"
       ]
      },
-     "execution_count": 90,
+     "execution_count": 92,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3247,7 +3278,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 91,
+   "execution_count": 93,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -3260,7 +3291,7 @@
        "3.141592653589793"
       ]
      },
-     "execution_count": 91,
+     "execution_count": 93,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3271,10 +3302,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 92,
+   "execution_count": 94,
    "metadata": {
     "slideshow": {
-     "slide_type": "-"
+     "slide_type": "fragment"
     }
    },
    "outputs": [
@@ -3284,7 +3315,7 @@
        "2.718281828459045"
       ]
      },
-     "execution_count": 92,
+     "execution_count": 94,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3295,7 +3326,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 93,
+   "execution_count": 95,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -3308,7 +3339,7 @@
        "<function math.sqrt(x, /)>"
       ]
      },
-     "execution_count": 93,
+     "execution_count": 95,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3319,10 +3350,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 94,
+   "execution_count": 96,
    "metadata": {
     "slideshow": {
-     "slide_type": "-"
+     "slide_type": "fragment"
     }
    },
    "outputs": [
@@ -3344,10 +3375,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 95,
+   "execution_count": 97,
    "metadata": {
     "slideshow": {
-     "slide_type": "-"
+     "slide_type": "slide"
     }
    },
    "outputs": [
@@ -3357,7 +3388,7 @@
        "1.4142135623730951"
       ]
      },
-     "execution_count": 95,
+     "execution_count": 97,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3383,10 +3414,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 96,
+   "execution_count": 98,
    "metadata": {
     "slideshow": {
-     "slide_type": "-"
+     "slide_type": "fragment"
     }
    },
    "outputs": [
@@ -3396,7 +3427,7 @@
        "2.0"
       ]
      },
-     "execution_count": 96,
+     "execution_count": 98,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3418,10 +3449,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 97,
+   "execution_count": 99,
    "metadata": {
     "slideshow": {
-     "slide_type": "-"
+     "slide_type": "fragment"
     }
    },
    "outputs": [
@@ -3431,7 +3462,7 @@
        "10.0"
       ]
      },
-     "execution_count": 97,
+     "execution_count": 99,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3455,7 +3486,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 98,
+   "execution_count": 100,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -3468,7 +3499,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 99,
+   "execution_count": 101,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -3481,7 +3512,7 @@
        "4.0"
       ]
      },
-     "execution_count": 99,
+     "execution_count": 101,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3514,7 +3545,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 100,
+   "execution_count": 102,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -3527,20 +3558,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 101,
+   "execution_count": 103,
    "metadata": {
     "slideshow": {
-     "slide_type": "-"
+     "slide_type": "fragment"
     }
    },
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "<module 'random' from '/home/webartifex/.pyenv/versions/anaconda3-2019.10/lib/python3.7/random.py'>"
+       "<module 'random' from '/home/webartifex/.pyenv/versions/3.7.6/lib/python3.7/random.py'>"
       ]
      },
-     "execution_count": 101,
+     "execution_count": 103,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3562,10 +3593,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 102,
+   "execution_count": 104,
    "metadata": {
     "slideshow": {
-     "slide_type": "slide"
+     "slide_type": "skip"
     }
    },
    "outputs": [
@@ -3636,7 +3667,7 @@
        " 'weibullvariate']"
       ]
      },
-     "execution_count": 102,
+     "execution_count": 104,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3658,7 +3689,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 103,
+   "execution_count": 105,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -3671,7 +3702,7 @@
        "<function Random.random>"
       ]
      },
-     "execution_count": 103,
+     "execution_count": 105,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3682,10 +3713,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 104,
+   "execution_count": 106,
    "metadata": {
     "slideshow": {
-     "slide_type": "-"
+     "slide_type": "fragment"
     }
    },
    "outputs": [
@@ -3707,20 +3738,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 105,
+   "execution_count": 107,
    "metadata": {
     "slideshow": {
-     "slide_type": "-"
+     "slide_type": "fragment"
     }
    },
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "0.8837282454584094"
+       "0.09696128509713109"
       ]
      },
-     "execution_count": 105,
+     "execution_count": 107,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3742,7 +3773,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 106,
+   "execution_count": 108,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -3752,10 +3783,10 @@
     {
      "data": {
       "text/plain": [
-       "<bound method Random.choice of <random.Random object at 0x5653695270d0>>"
+       "<bound method Random.choice of <random.Random object at 0x55795e713bf0>>"
       ]
      },
-     "execution_count": 106,
+     "execution_count": 108,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3766,10 +3797,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 107,
+   "execution_count": 109,
    "metadata": {
     "slideshow": {
-     "slide_type": "-"
+     "slide_type": "fragment"
     }
    },
    "outputs": [
@@ -3791,20 +3822,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 108,
+   "execution_count": 110,
    "metadata": {
     "slideshow": {
-     "slide_type": "-"
+     "slide_type": "fragment"
     }
    },
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "9"
+       "3"
       ]
      },
-     "execution_count": 108,
+     "execution_count": 110,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3828,7 +3859,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 109,
+   "execution_count": 111,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -3841,7 +3872,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 110,
+   "execution_count": 112,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -3854,7 +3885,7 @@
        "0.6394267984578837"
       ]
      },
-     "execution_count": 110,
+     "execution_count": 112,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3865,10 +3896,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 111,
+   "execution_count": 113,
    "metadata": {
     "slideshow": {
-     "slide_type": "-"
+     "slide_type": "fragment"
     }
    },
    "outputs": [],
@@ -3878,10 +3909,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 112,
+   "execution_count": 114,
    "metadata": {
     "slideshow": {
-     "slide_type": "-"
+     "slide_type": "fragment"
     }
    },
    "outputs": [
@@ -3891,7 +3922,7 @@
        "0.6394267984578837"
       ]
      },
-     "execution_count": 112,
+     "execution_count": 114,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3956,7 +3987,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 113,
+   "execution_count": 115,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -3967,7 +3998,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Requirement already satisfied: numpy in /home/webartifex/.pyenv/versions/anaconda3-2019.10/lib/python3.7/site-packages (1.17.2)\n"
+      "Requirement already satisfied: numpy in /home/webartifex/.pyenv/versions/3.7.6/envs/v-ipp/lib/python3.7/site-packages (1.18.1)\n"
      ]
     }
    ],
@@ -3988,7 +4019,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 114,
+   "execution_count": 116,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -4012,7 +4043,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 115,
+   "execution_count": 117,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -4022,10 +4053,10 @@
     {
      "data": {
       "text/plain": [
-       "<module 'numpy' from '/home/webartifex/.pyenv/versions/anaconda3-2019.10/lib/python3.7/site-packages/numpy/__init__.py'>"
+       "<module 'numpy' from '/home/webartifex/.pyenv/versions/3.7.6/envs/v-ipp/lib/python3.7/site-packages/numpy/__init__.py'>"
       ]
      },
-     "execution_count": 115,
+     "execution_count": 117,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -4047,7 +4078,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 116,
+   "execution_count": 118,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -4060,7 +4091,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 117,
+   "execution_count": 119,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -4073,7 +4104,7 @@
        "array([ 7, 11,  8,  5,  3, 12,  2,  6,  9, 10,  1,  4])"
       ]
      },
-     "execution_count": 117,
+     "execution_count": 119,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -4084,10 +4115,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 118,
+   "execution_count": 120,
    "metadata": {
     "slideshow": {
-     "slide_type": "-"
+     "slide_type": "fragment"
     }
    },
    "outputs": [
@@ -4097,7 +4128,7 @@
        "numpy.ndarray"
       ]
      },
-     "execution_count": 118,
+     "execution_count": 120,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -4121,7 +4152,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 119,
+   "execution_count": 121,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -4134,7 +4165,7 @@
        "array([14, 22, 16, 10,  6, 24,  4, 12, 18, 20,  2,  8])"
       ]
      },
-     "execution_count": 119,
+     "execution_count": 121,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -4156,7 +4187,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 120,
+   "execution_count": 122,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -4169,7 +4200,7 @@
        "[7, 11, 8, 5, 3, 12, 2, 6, 9, 10, 1, 4, 7, 11, 8, 5, 3, 12, 2, 6, 9, 10, 1, 4]"
       ]
      },
-     "execution_count": 120,
+     "execution_count": 122,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -4191,7 +4222,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 121,
+   "execution_count": 123,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -4204,7 +4235,7 @@
        "78"
       ]
      },
-     "execution_count": 121,
+     "execution_count": 123,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -4215,10 +4246,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 122,
+   "execution_count": 124,
    "metadata": {
     "slideshow": {
-     "slide_type": "-"
+     "slide_type": "fragment"
     }
    },
    "outputs": [
@@ -4228,7 +4259,7 @@
        "7"
       ]
      },
-     "execution_count": 122,
+     "execution_count": 124,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -4267,7 +4298,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 123,
+   "execution_count": 125,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -4280,7 +4311,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 124,
+   "execution_count": 126,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -4293,7 +4324,7 @@
        "<module 'sample_module' from '/home/webartifex/repos/intro-to-python/sample_module.py'>"
       ]
      },
-     "execution_count": 124,
+     "execution_count": 126,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -4319,10 +4350,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 125,
+   "execution_count": 127,
    "metadata": {
     "slideshow": {
-     "slide_type": "fragment"
+     "slide_type": "skip"
     }
    },
    "outputs": [
@@ -4344,7 +4375,7 @@
        " 'average_odds']"
       ]
      },
-     "execution_count": 125,
+     "execution_count": 127,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -4366,12 +4397,36 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 126,
+   "execution_count": 128,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
     }
    },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<function sample_module.average_evens(numbers, *, scalar=1)>"
+      ]
+     },
+     "execution_count": 128,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "mod.average_evens"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 129,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
    "outputs": [
     {
      "name": "stdout",
@@ -4399,7 +4454,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 127,
+   "execution_count": 130,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -4412,7 +4467,7 @@
        "7.0"
       ]
      },
-     "execution_count": 127,
+     "execution_count": 130,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -4423,10 +4478,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 128,
+   "execution_count": 131,
    "metadata": {
     "slideshow": {
-     "slide_type": "-"
+     "slide_type": "fragment"
     }
    },
    "outputs": [
@@ -4436,7 +4491,7 @@
        "14.0"
       ]
      },
-     "execution_count": 128,
+     "execution_count": 131,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -4501,7 +4556,6 @@
   }
  ],
  "metadata": {
-  "celltoolbar": "Slideshow",
   "kernelspec": {
    "display_name": "Python 3",
    "language": "python",
@@ -4517,7 +4571,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.4"
+   "version": "3.7.6"
   },
   "livereveal": {
    "auto_select": "code",
diff --git a/02_functions_01_review.ipynb b/02_functions_01_review.ipynb
index f081ec6..3b58b22 100644
--- a/02_functions_01_review.ipynb
+++ b/02_functions_01_review.ipynb
@@ -220,7 +220,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.4"
+   "version": "3.7.6"
   },
   "toc": {
    "base_numbering": 1,
diff --git a/02_functions_02_exercises.ipynb b/02_functions_02_exercises.ipynb
index 00cd198..cc0accd 100644
--- a/02_functions_02_exercises.ipynb
+++ b/02_functions_02_exercises.ipynb
@@ -18,7 +18,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Read [Chapter 2](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/02_functions_00_lecture.ipynb) of the book. Then, work through the exercises below."
+    "Read [Chapter 2](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/02_functions_00_lecture.ipynb) of the book. Then, work through the exercises below. The `...` indicate where you need to fill in your answers. You should not need to create any additional code cells."
    ]
   },
   {
@@ -213,7 +213,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.4"
+   "version": "3.7.6"
   },
   "toc": {
    "base_numbering": 1,

From 8dea642ce98ca70814f5e80830b10f3439b2ae06 Mon Sep 17 00:00:00 2001
From: Alexander Hess <alexander@webartifex.biz>
Date: Tue, 10 Mar 2020 15:27:49 +0100
Subject: [PATCH 075/242] Streamline presentation

---
 04_iteration_00_lecture.ipynb   | 694 ++++++++++++++++----------------
 04_iteration_02_exercises.ipynb |  76 ++--
 2 files changed, 388 insertions(+), 382 deletions(-)

diff --git a/04_iteration_00_lecture.ipynb b/04_iteration_00_lecture.ipynb
index 0c95c8a..1a72c7b 100644
--- a/04_iteration_00_lecture.ipynb
+++ b/04_iteration_00_lecture.ipynb
@@ -562,7 +562,7 @@
    "execution_count": 15,
    "metadata": {
     "slideshow": {
-     "slide_type": "-"
+     "slide_type": "fragment"
     }
    },
    "outputs": [
@@ -597,7 +597,7 @@
    "execution_count": 16,
    "metadata": {
     "slideshow": {
-     "slide_type": "-"
+     "slide_type": "fragment"
     }
    },
    "outputs": [
@@ -613,7 +613,7 @@
     }
    ],
    "source": [
-    "gcd(2, 7919)"
+    "gcd(7, 7919)"
    ]
   },
   {
@@ -721,7 +721,7 @@
     }
    ],
    "source": [
-    "math.gcd(2, 7919)"
+    "math.gcd(7, 7919)"
    ]
   },
   {
@@ -859,7 +859,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "49.8 µs ± 20.8 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n"
+      "37.3 µs ± 1.25 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n"
      ]
     }
    ],
@@ -873,7 +873,7 @@
    "execution_count": 24,
    "metadata": {
     "slideshow": {
-     "slide_type": "-"
+     "slide_type": "fragment"
     }
    },
    "outputs": [
@@ -881,13 +881,13 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "1.63 ms ± 14.8 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n"
+      "11.4 ms ± 144 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n"
      ]
     }
    ],
    "source": [
     "%%timeit -n 100\n",
-    "fibonacci(20)"
+    "fibonacci(24)"
    ]
   },
   {
@@ -895,7 +895,7 @@
    "execution_count": 25,
    "metadata": {
     "slideshow": {
-     "slide_type": "-"
+     "slide_type": "fragment"
     }
    },
    "outputs": [
@@ -903,40 +903,18 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "202 ms ± 0 ns per loop (mean ± std. dev. of 1 run, 1 loop each)\n"
+      "3.63 s ± 0 ns per loop (mean ± std. dev. of 1 run, 1 loop each)\n"
      ]
     }
    ],
    "source": [
     "%%timeit -n 1 -r 1\n",
-    "fibonacci(30)"
+    "fibonacci(36)"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 26,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "-"
-    }
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "2.25 s ± 0 ns per loop (mean ± std. dev. of 1 run, 1 loop each)\n"
-     ]
-    }
-   ],
-   "source": [
-    "%%timeit -n 1 -r 1\n",
-    "fibonacci(35)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 27,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -947,7 +925,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "5.79 s ± 0 ns per loop (mean ± std. dev. of 1 run, 1 loop each)\n"
+      "5.93 s ± 0 ns per loop (mean ± std. dev. of 1 run, 1 loop each)\n"
      ]
     }
    ],
@@ -982,7 +960,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": 27,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -997,7 +975,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
+   "execution_count": 28,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -1011,10 +989,10 @@
      "traceback": [
       "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
       "\u001b[0;31mRecursionError\u001b[0m                            Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-29-62f686a9f8a1>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mrun_forever\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
-      "\u001b[0;32m<ipython-input-28-80575699c62c>\u001b[0m in \u001b[0;36mrun_forever\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mrun_forever\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m     \u001b[0;34m\"\"\"Also a pointless function should have a docstring.\"\"\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m     \u001b[0mrun_forever\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;32m<ipython-input-28-62f686a9f8a1>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mrun_forever\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;32m<ipython-input-27-80575699c62c>\u001b[0m in \u001b[0;36mrun_forever\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mrun_forever\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m     \u001b[0;34m\"\"\"Also a pointless function should have a docstring.\"\"\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m     \u001b[0mrun_forever\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
       "... last 1 frames repeated, from the frame below ...\n",
-      "\u001b[0;32m<ipython-input-28-80575699c62c>\u001b[0m in \u001b[0;36mrun_forever\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mrun_forever\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m     \u001b[0;34m\"\"\"Also a pointless function should have a docstring.\"\"\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m     \u001b[0mrun_forever\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;32m<ipython-input-27-80575699c62c>\u001b[0m in \u001b[0;36mrun_forever\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mrun_forever\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m     \u001b[0;34m\"\"\"Also a pointless function should have a docstring.\"\"\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m     \u001b[0mrun_forever\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
       "\u001b[0;31mRecursionError\u001b[0m: maximum recursion depth exceeded"
      ]
     }
@@ -1036,7 +1014,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 30,
+   "execution_count": 29,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -4010,7 +3988,7 @@
      "traceback": [
       "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
       "\u001b[0;31mRecursionError\u001b[0m                            Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-30-43e3ad331761>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mcountdown\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m3.1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;32m<ipython-input-29-43e3ad331761>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mcountdown\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m3.1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
       "\u001b[0;32m<ipython-input-1-d7441c038fc8>\u001b[0m in \u001b[0;36mcountdown\u001b[0;34m(n)\u001b[0m\n\u001b[1;32m      9\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     10\u001b[0m         \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mn\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 11\u001b[0;31m         \u001b[0mcountdown\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mn\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
       "... last 1 frames repeated, from the frame below ...\n",
       "\u001b[0;32m<ipython-input-1-d7441c038fc8>\u001b[0m in \u001b[0;36mcountdown\u001b[0;34m(n)\u001b[0m\n\u001b[1;32m      9\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     10\u001b[0m         \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mn\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 11\u001b[0;31m         \u001b[0mcountdown\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mn\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
@@ -4035,7 +4013,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 31,
+   "execution_count": 30,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -4049,7 +4027,7 @@
      "traceback": [
       "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
       "\u001b[0;31mRecursionError\u001b[0m                            Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-31-188b816be8b6>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mfactorial\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m3.1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;32m<ipython-input-30-188b816be8b6>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mfactorial\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m3.1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
       "\u001b[0;32m<ipython-input-6-d41f5be5adaa>\u001b[0m in \u001b[0;36mfactorial\u001b[0;34m(n)\u001b[0m\n\u001b[1;32m     10\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mn\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m  \u001b[0;31m# = base case\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     11\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 12\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0mn\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0mfactorial\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mn\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
       "... last 1 frames repeated, from the frame below ...\n",
       "\u001b[0;32m<ipython-input-6-d41f5be5adaa>\u001b[0m in \u001b[0;36mfactorial\u001b[0;34m(n)\u001b[0m\n\u001b[1;32m     10\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mn\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m  \u001b[0;31m# = base case\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     11\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 12\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0mn\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0mfactorial\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mn\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
@@ -4118,7 +4096,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 32,
+   "execution_count": 31,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -4161,7 +4139,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 33,
+   "execution_count": 32,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -4174,7 +4152,7 @@
        "1"
       ]
      },
-     "execution_count": 33,
+     "execution_count": 32,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -4185,26 +4163,26 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 34,
+   "execution_count": 33,
    "metadata": {
     "slideshow": {
-     "slide_type": "-"
+     "slide_type": "fragment"
     }
    },
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "3628800"
+       "6"
       ]
      },
-     "execution_count": 34,
+     "execution_count": 33,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "factorial(10)"
+    "factorial(3)"
    ]
   },
   {
@@ -4220,7 +4198,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 35,
+   "execution_count": 34,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -4234,8 +4212,8 @@
      "traceback": [
       "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
       "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-35-188b816be8b6>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mfactorial\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m3.1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
-      "\u001b[0;32m<ipython-input-32-ff0b507f76b8>\u001b[0m in \u001b[0;36mfactorial\u001b[0;34m(n)\u001b[0m\n\u001b[1;32m     13\u001b[0m     \"\"\"\n\u001b[1;32m     14\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mn\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mint\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 15\u001b[0;31m         \u001b[0;32mraise\u001b[0m \u001b[0mTypeError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Factorial is only defined for integers\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     16\u001b[0m     \u001b[0;32melif\u001b[0m \u001b[0mn\u001b[0m \u001b[0;34m<\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     17\u001b[0m         \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Factorial is not defined for negative integers\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m<ipython-input-34-188b816be8b6>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mfactorial\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m3.1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;32m<ipython-input-31-ff0b507f76b8>\u001b[0m in \u001b[0;36mfactorial\u001b[0;34m(n)\u001b[0m\n\u001b[1;32m     13\u001b[0m     \"\"\"\n\u001b[1;32m     14\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mn\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mint\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 15\u001b[0;31m         \u001b[0;32mraise\u001b[0m \u001b[0mTypeError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Factorial is only defined for integers\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     16\u001b[0m     \u001b[0;32melif\u001b[0m \u001b[0mn\u001b[0m \u001b[0;34m<\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     17\u001b[0m         \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Factorial is not defined for negative integers\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
       "\u001b[0;31mTypeError\u001b[0m: Factorial is only defined for integers"
      ]
     }
@@ -4246,7 +4224,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 36,
+   "execution_count": 35,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -4260,8 +4238,8 @@
      "traceback": [
       "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
       "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-36-b06b2cada645>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mfactorial\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m42\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
-      "\u001b[0;32m<ipython-input-32-ff0b507f76b8>\u001b[0m in \u001b[0;36mfactorial\u001b[0;34m(n)\u001b[0m\n\u001b[1;32m     15\u001b[0m         \u001b[0;32mraise\u001b[0m \u001b[0mTypeError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Factorial is only defined for integers\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     16\u001b[0m     \u001b[0;32melif\u001b[0m \u001b[0mn\u001b[0m \u001b[0;34m<\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 17\u001b[0;31m         \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Factorial is not defined for negative integers\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     18\u001b[0m     \u001b[0;32melif\u001b[0m \u001b[0mn\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m  \u001b[0;31m# = base case\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     19\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m<ipython-input-35-b06b2cada645>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mfactorial\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m42\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;32m<ipython-input-31-ff0b507f76b8>\u001b[0m in \u001b[0;36mfactorial\u001b[0;34m(n)\u001b[0m\n\u001b[1;32m     15\u001b[0m         \u001b[0;32mraise\u001b[0m \u001b[0mTypeError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Factorial is only defined for integers\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     16\u001b[0m     \u001b[0;32melif\u001b[0m \u001b[0mn\u001b[0m \u001b[0;34m<\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 17\u001b[0;31m         \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Factorial is not defined for negative integers\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     18\u001b[0m     \u001b[0;32melif\u001b[0m \u001b[0mn\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m  \u001b[0;31m# = base case\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     19\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
       "\u001b[0;31mValueError\u001b[0m: Factorial is not defined for negative integers"
      ]
     }
@@ -4353,7 +4331,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 37,
+   "execution_count": 36,
    "metadata": {
     "code_folding": [],
     "slideshow": {
@@ -4381,12 +4359,12 @@
     "        print(n)\n",
     "        n -= 1\n",
     "\n",
-    "    print(\"Happy new Year!\")  # = base case"
+    "    print(\"Happy new Year!\")"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 38,
+   "execution_count": 37,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -4445,7 +4423,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 39,
+   "execution_count": 38,
    "metadata": {
     "code_folding": [],
     "slideshow": {
@@ -4484,7 +4462,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 40,
+   "execution_count": 39,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -4497,7 +4475,7 @@
        "4"
       ]
      },
-     "execution_count": 40,
+     "execution_count": 39,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -4508,10 +4486,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 41,
+   "execution_count": 40,
    "metadata": {
     "slideshow": {
-     "slide_type": "-"
+     "slide_type": "fragment"
     }
    },
    "outputs": [
@@ -4521,13 +4499,13 @@
        "1"
       ]
      },
-     "execution_count": 41,
+     "execution_count": 40,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "gcd(2, 7919)"
+    "gcd(7, 7919)"
    ]
   },
   {
@@ -4554,7 +4532,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 42,
+   "execution_count": 41,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -4565,7 +4543,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "5.14 s ± 0 ns per loop (mean ± std. dev. of 1 run, 1 loop each)\n"
+      "5.24 s ± 0 ns per loop (mean ± std. dev. of 1 run, 1 loop each)\n"
      ]
     }
    ],
@@ -4637,7 +4615,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 43,
+   "execution_count": 42,
    "metadata": {
     "code_folding": [],
     "slideshow": {
@@ -4669,11 +4647,11 @@
     "    while n != 1:\n",
     "        print(n, end=\" \")\n",
     "        if n % 2 == 0:\n",
-    "            n //= 2  # //= instead of /= so that n remains an int\n",
+    "            n //= 2  # //= to preserve the int type\n",
     "        else:\n",
     "            n = 3 * n + 1\n",
     "\n",
-    "    print(n)  # = base case"
+    "    print(1)"
    ]
   },
   {
@@ -4689,7 +4667,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 44,
+   "execution_count": 43,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -4710,10 +4688,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 45,
+   "execution_count": 44,
    "metadata": {
     "slideshow": {
-     "slide_type": "-"
+     "slide_type": "fragment"
     }
    },
    "outputs": [
@@ -4731,10 +4709,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 46,
+   "execution_count": 45,
    "metadata": {
     "slideshow": {
-     "slide_type": "-"
+     "slide_type": "fragment"
     }
    },
    "outputs": [
@@ -4752,7 +4730,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 47,
+   "execution_count": 46,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -4799,7 +4777,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 48,
+   "execution_count": 47,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -4812,10 +4790,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 49,
+   "execution_count": 48,
    "metadata": {
     "slideshow": {
-     "slide_type": "-"
+     "slide_type": "fragment"
     }
    },
    "outputs": [
@@ -4829,10 +4807,12 @@
    ],
    "source": [
     "index = 0\n",
+    "\n",
     "while index < len(elements):\n",
     "    element = elements[index]\n",
     "    print(element, end=\" \")\n",
     "    index += 1\n",
+    "\n",
     "del index"
    ]
   },
@@ -4849,7 +4829,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 50,
+   "execution_count": 49,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -4882,10 +4862,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 51,
+   "execution_count": 50,
    "metadata": {
     "slideshow": {
-     "slide_type": "fragment"
+     "slide_type": "slide"
     }
    },
    "outputs": [
@@ -4904,7 +4884,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 52,
+   "execution_count": 51,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -4917,7 +4897,7 @@
        "range"
       ]
      },
-     "execution_count": 52,
+     "execution_count": 51,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -4939,7 +4919,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 53,
+   "execution_count": 52,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -4961,7 +4941,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 54,
+   "execution_count": 53,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -5019,7 +4999,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 55,
+   "execution_count": 54,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -5043,7 +5023,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 56,
+   "execution_count": 55,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -5056,7 +5036,7 @@
        "True"
       ]
      },
-     "execution_count": 56,
+     "execution_count": 55,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -5067,10 +5047,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 57,
+   "execution_count": 56,
    "metadata": {
     "slideshow": {
-     "slide_type": "-"
+     "slide_type": "fragment"
     }
    },
    "outputs": [
@@ -5080,7 +5060,7 @@
        "False"
       ]
      },
-     "execution_count": 57,
+     "execution_count": 56,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -5102,7 +5082,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 58,
+   "execution_count": 57,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -5115,7 +5095,7 @@
        "[0, 1, 2, 3, 4]"
       ]
      },
-     "execution_count": 58,
+     "execution_count": 57,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -5126,7 +5106,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 59,
+   "execution_count": 58,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -5139,7 +5119,7 @@
        "True"
       ]
      },
-     "execution_count": 59,
+     "execution_count": 58,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -5161,7 +5141,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 60,
+   "execution_count": 59,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -5194,7 +5174,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 61,
+   "execution_count": 60,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -5205,13 +5185,13 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "0 > Achim   1 > Berthold   2 > Carl   3 > Diedrich   4 > Eckardt   "
+      "1 > Achim   2 > Berthold   3 > Carl   4 > Diedrich   5 > Eckardt   "
      ]
     }
    ],
    "source": [
-    "for i, name in enumerate(first_names):\n",
-    "    print(i, name, sep=\" > \", end=\"   \")"
+    "for i, name in enumerate(first_names, start=1):\n",
+    "    print(i, \">\", name, end=\"   \")"
    ]
   },
   {
@@ -5227,7 +5207,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 62,
+   "execution_count": 61,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -5244,7 +5224,7 @@
    ],
    "source": [
     "for i, name in enumerate(first_names, start=1):\n",
-    "    print(i, name, sep=\" > \", end=\"   \")"
+    "    print(i, \">\", name, end=\"   \")"
    ]
   },
   {
@@ -5260,7 +5240,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 63,
+   "execution_count": 62,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -5273,7 +5253,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 64,
+   "execution_count": 63,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -5321,7 +5301,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 65,
+   "execution_count": 64,
    "metadata": {
     "code_folding": [],
     "slideshow": {
@@ -5351,7 +5331,7 @@
     "    a = 0\n",
     "    b = 1\n",
     "    print(a, b, sep=\" \", end=\" \")  # added for didactical purposes\n",
-    "    for _ in range(i - 1):  # the index variable is not needed\n",
+    "    for _ in range(i - 1):\n",
     "        temp = a + b\n",
     "        a = b\n",
     "        b = temp\n",
@@ -5362,7 +5342,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 66,
+   "execution_count": 65,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -5382,7 +5362,7 @@
        "144"
       ]
      },
-     "execution_count": 66,
+     "execution_count": 65,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -5415,7 +5395,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 67,
+   "execution_count": 66,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -5435,7 +5415,7 @@
        "218922995834555169026"
       ]
      },
-     "execution_count": 67,
+     "execution_count": 66,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -5468,7 +5448,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 68,
+   "execution_count": 67,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -5495,7 +5475,7 @@
     "        raise ValueError(\"Factorial is not defined for negative integers\")\n",
     "\n",
     "    product = 1  # because 0! = 1\n",
-    "    for i in range(1, n + 1):  # loop starts at 1 as 0! is already covered\n",
+    "    for i in range(1, n + 1):\n",
     "        product *= i\n",
     "        print(product, end=\" \")  # added for didactical purposes\n",
     "\n",
@@ -5504,12 +5484,43 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 69,
+   "execution_count": 68,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
     }
    },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "1 2 6 "
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "6"
+      ]
+     },
+     "execution_count": 68,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "factorial(3)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 69,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
    "outputs": [
     {
      "name": "stdout",
@@ -5541,7 +5552,7 @@
     }
    },
    "source": [
-    "### The `break` Statement"
+    "### Customizing `for`- and `while`-loops"
    ]
   },
   {
@@ -5552,7 +5563,7 @@
     }
    },
    "source": [
-    "The remainder of this chapter introduces more syntactic sugar: None of the language constructs introduced below are needed but contribute to making Python the expressive and easy to read language that it is."
+    "This section introduces additional syntax to customize `for` and `while` statements in our code even further. They are mostly syntactic sugar in that they do not change how a program runs but make its code more readable. We illustrate them for the `for` statement only. However, everything presented in this section also works for the `while` statement."
    ]
   },
   {
@@ -5563,7 +5574,7 @@
     }
    },
    "source": [
-    "#### Example: Searching an Iterable"
+    "#### Example: Is the square of a number in `[7, 11, 8, 5, 3, 12, 2, 6, 9, 10, 1, 4]` greater than `100`?"
    ]
   },
   {
@@ -5574,7 +5585,7 @@
     }
    },
    "source": [
-    "Let's say we have a list of `numbers` and want to check if the square of at least one of its elements is above a `threshold` of `100`."
+    "Let's say we have a list of `numbers` and want to check if the square of at least one of its elements is greater than `100`. So, conceptually, we are asking the question if a list of numbers as a whole satisfies a certain condition."
    ]
   },
   {
@@ -5590,19 +5601,6 @@
     "numbers = [7, 11, 8, 5, 3, 12, 2, 6, 9, 10, 1, 4]"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": 71,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "-"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "threshold = 100  # is the square of an element in numbers greater than this?"
-   ]
-  },
   {
    "cell_type": "markdown",
    "metadata": {
@@ -5611,12 +5609,16 @@
     }
    },
    "source": [
-    "A first naive implementation could look like this: We loop over *every* element in `numbers` and set an **indicator variable** `is_above`, initialized as `False`, to `True` once we encounter an element satisfying the search condition."
+    "A first naive implementation could look like this: We loop over *every* element in `numbers` and set an **indicator variable** `is_above`, initialized as `False`, to `True` once we encounter an element satisfying the condition.\n",
+    "\n",
+    "This implementation is *inefficient* as even if the *first* element in `numbers` has a square greater than `100`, we loop until the last element: This could take a long time for a big list.\n",
+    "\n",
+    "Moreover, we must initialize `is_above` *before* the `for`-loop and write an `if`-`else`-logic *after* it to check for the result. The actual business logic is *not* conveyed in a clear way."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 72,
+   "execution_count": 71,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -5627,7 +5629,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "7   11   8   5   3   12   2   6   9   10   1   4   =>   at least one number's square is above 100\n"
+      "7   11   8   5   3   12   2   6   9   10   1   4   =>   at least one number satisfies the condition\n"
      ]
     }
    ],
@@ -5636,13 +5638,24 @@
     "\n",
     "for number in numbers:\n",
     "    print(number, end=\"   \")  # added for didactical purposes\n",
-    "    if number ** 2 > threshold:\n",
+    "    if number ** 2 > 100:\n",
     "        is_above = True\n",
     "\n",
     "if is_above:\n",
-    "    print(\"=>   at least one number's square is above\", threshold)\n",
+    "    print(\"=>   at least one number satisfies the condition\")\n",
     "else:\n",
-    "    print(\"=>   no number's square is above\", threshold)"
+    "    print(\"=>   no number satisfies the condition\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "#### The `break` Statement"
    ]
   },
   {
@@ -5653,11 +5666,74 @@
     }
    },
    "source": [
-    "This implementation is *inefficient* as even if the *first* element in `numbers` has a square greater than `100`, we loop until the last element: This could take a long time for a big list.\n",
+    "Python provides the `break` statement (cf., [reference](https://docs.python.org/3/reference/simple_stmts.html#the-break-statement)) that lets us stop a loop prematurely at any iteration. It is yet another means of controlling the flow of execution, and we say that we \"break out of a loop.\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 72,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "7   11   =>   at least one number satisfies the condition\n"
+     ]
+    }
+   ],
+   "source": [
+    "is_above = False\n",
     "\n",
-    "Moreover, we must initialize `is_above` *before* the `for`-loop and write an `if`-`else`-logic *after* it to check for the result. The actual business logic is *not* clear right away.\n",
+    "for number in numbers:\n",
+    "    print(number, end=\"   \")  # added for didactical purposes\n",
+    "    if number ** 2 > 100:\n",
+    "        is_above = True\n",
+    "        break\n",
     "\n",
-    "Luckily, Python provides the `break` statement (cf., [reference](https://docs.python.org/3/reference/simple_stmts.html#the-break-statement)) that lets us stop the `for`-loop in any iteration of the loop. Conceptually, it is yet another means of controlling the flow of execution."
+    "if is_above:\n",
+    "    print(\"=>   at least one number satisfies the condition\")\n",
+    "else:\n",
+    "    print(\"=>   no number satisfies the condition\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "This is a computational improvement. However, the code still consists of *three* sections: Some initialization *before* the `for`-loop, the loop itself, and some finalizing logic. We prefer to convey the program's idea in *one* compound statement instead."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "#### The `else`-clause"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "To express the logic in a prettier way, we add an `else`-clause at the end of the `for`-loop (cf., [reference](https://docs.python.org/3/reference/compound_stmts.html#the-for-statement)). The `else`-clause is executed *only if* the `for`-loop is *not* stopped with a `break` statement *prematurely* (i.e., *before* reaching the *last* iteration in the loop). The word \"else\" implies a somewhat unintuitive meaning and may have better been named a `then`-clause. In most use cases, however, the `else`-clause logically goes together with some `if` statement in the loop's body.\n",
+    "\n",
+    "Overall, the code's expressive power increases. Not many programming languages support an optional `else`-branching for the `for` and `while` statements, which turns out to be very useful in practice."
    ]
   },
   {
@@ -5673,101 +5749,23 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "7   11   =>   at least one number's square is above 100\n"
-     ]
-    }
-   ],
-   "source": [
-    "is_above = False\n",
-    "\n",
-    "for number in numbers:\n",
-    "    print(number, end=\"   \")  # added for didactical purposes\n",
-    "    if number ** 2 > threshold:\n",
-    "        is_above = True\n",
-    "        break\n",
-    "\n",
-    "if is_above:\n",
-    "    print(\"=>   at least one number's square is above\", threshold)\n",
-    "else:\n",
-    "    print(\"=>   no number's square is above\", threshold)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "source": [
-    "This is a computational improvement. However, the code still consists of *three* logical sections: Some initialization *before* the `for`-loop, the loop itself, and some finalizing logic. We prefer to convey the program's idea in *one* compound statement instead."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "slide"
-    }
-   },
-   "source": [
-    "### The `for`-`else`-Clause"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "source": [
-    "To express the logic in a prettier way, we add an `else`-clause at the end of the `for`-loop (cf., [reference](https://docs.python.org/3/reference/compound_stmts.html#the-for-statement)). The `else`-clause is executed *iff* the `for`-loop is *not* stopped with a `break` statement **prematurely** (i.e., *before* reaching the *last* iteration in the loop). The word \"else\" implies a somewhat unintuitive meaning and had better been named a `then`-clause. In most scenarios, however, the `else`-clause logically goes together with some `if` statement in the `for`-loop's body.\n",
-    "\n",
-    "Overall, the code's expressive power increases. Not many programming languages support a `for`-`else`-branching, which turns out to be very useful in practice."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "source": [
-    "#### Example: Searching an Iterable (revisited)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 74,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "slide"
-    }
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "7   11   =>   at least one number's square is above 100\n"
+      "7   11   =>   at least one number satisfies the condition\n"
      ]
     }
    ],
    "source": [
     "for number in numbers:\n",
     "    print(number, end=\"   \")  # added for didactical purposes\n",
-    "    if number ** 2 > threshold:\n",
+    "    if number ** 2 > 100:\n",
     "        is_above = True\n",
     "        break\n",
     "else:\n",
     "    is_above = False\n",
     "\n",
     "if is_above:\n",
-    "    print(\"=>   at least one number's square is above\", threshold)\n",
+    "    print(\"=>   at least one number satisfies the condition\")\n",
     "else:\n",
-    "    print(\"=>   no number's square is above\", threshold)"
+    "    print(\"=>   no number satisfies the condition\")"
    ]
   },
   {
@@ -5783,7 +5781,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 75,
+   "execution_count": 74,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -5794,18 +5792,18 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "7   11   =>   at least one number's square is above 100\n"
+      "7   11   =>   at least one number satisfies the condition\n"
      ]
     }
    ],
    "source": [
     "for number in numbers:\n",
     "    print(number, end=\"   \")  # added for didactical purposes\n",
-    "    if number ** 2 > threshold:\n",
-    "        print(\"=>   at least one number's square is above\", threshold)\n",
+    "    if number ** 2 > 100:\n",
+    "        print(\"=>   at least one number satisfies the condition\")\n",
     "        break\n",
     "else:\n",
-    "    print(\"=>   no number's square is above\", threshold)"
+    "    print(\"=>   no number satisfies the condition\")"
    ]
   },
   {
@@ -5816,12 +5814,12 @@
     }
    },
    "source": [
-    "Of course, if we set the `threshold` an element's square has to pass higher, for example, to `200`, we have to loop over all `numbers`. There is *no way* to optimize this **[linear search](https://en.wikipedia.org/wiki/Linear_search)** further, at least as long as we model `numbers` as a `list` object. More advanced data types, however, exist that mitigate that downside."
+    "Of course, if we choose the number an element's square has to pass to be larger, for example, to `200`, we have to loop over all `numbers`. There is *no way* to optimize this **[linear search](https://en.wikipedia.org/wiki/Linear_search)** further."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 76,
+   "execution_count": 75,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -5832,31 +5830,29 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "7   11   8   5   3   12   2   6   9   10   1   4   =>   no number's square is above 200\n"
+      "7   11   8   5   3   12   2   6   9   10   1   4   =>   no number satisfies the condition\n"
      ]
     }
    ],
    "source": [
-    "threshold = 200\n",
-    "\n",
     "for number in numbers:\n",
     "    print(number, end=\"   \")  # added for didactical purposes\n",
-    "    if number ** 2 > threshold:\n",
-    "        print(\"=>   at least one number's square is above\", threshold)\n",
+    "    if number ** 2 > 200:\n",
+    "        print(\"=>   at least one number satisfies the condition\")\n",
     "        break\n",
     "else:\n",
-    "    print(\"=>   no number's square is above\", threshold)"
+    "    print(\"=>   no number satisfies the condition\")"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {
     "slideshow": {
-     "slide_type": "slide"
+     "slide_type": "skip"
     }
    },
    "source": [
-    "### The `continue` Statement"
+    "#### A first Glance at the Map-Filter-Reduce Paradigm"
    ]
   },
   {
@@ -5877,7 +5873,7 @@
     "\n",
     "We study this **map-filter-reduce** paradigm extensively in [Chapter 7](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/07_sequences_00_lecture.ipynb#The-Map-Filter-Reduce-Paradigm) after introducing more advanced data types that are needed to work with \"big\" data.\n",
     "\n",
-    "Here, we focus on *filtering out* some numbers with a `for`-loop."
+    "Here, we focus on *filtering out* some numbers in a `for`-loop."
    ]
   },
   {
@@ -5909,7 +5905,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 77,
+   "execution_count": 76,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -5922,10 +5918,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 78,
+   "execution_count": 77,
    "metadata": {
     "slideshow": {
-     "slide_type": "-"
+     "slide_type": "fragment"
     }
    },
    "outputs": [
@@ -5933,7 +5929,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "8 > 65   12 > 145   2 > 5   6 > 37   10 > 101   4 > 17   "
+      "8 -> 65   12 -> 145   2 -> 5   6 -> 37   10 -> 101   4 -> 17   "
      ]
     },
     {
@@ -5942,7 +5938,7 @@
        "370"
       ]
      },
-     "execution_count": 78,
+     "execution_count": 77,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -5953,7 +5949,7 @@
     "for number in numbers:\n",
     "    if number % 2 == 0:  # only keep even numbers\n",
     "        square = (number ** 2) + 1\n",
-    "        print(number, square, sep=\" > \", end=\"   \")  # added for didactical purposes\n",
+    "        print(number, \"->\", square, end=\"   \")  # added for didactical purposes\n",
     "        total += square\n",
     "\n",
     "total"
@@ -5982,7 +5978,7 @@
     }
    },
    "source": [
-    "#### Example: Nested Filters"
+    "#### Example: Several Filters"
    ]
   },
   {
@@ -6004,31 +6000,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 79,
+   "execution_count": 78,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
     }
    },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[7, 11, 8, 5, 3, 12, 2, 6, 9, 10, 1, 4]"
-      ]
-     },
-     "execution_count": 79,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
-    "numbers"
+    "numbers = [7, 11, 8, 5, 3, 12, 2, 6, 9, 10, 1, 4]"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 80,
+   "execution_count": 79,
    "metadata": {
     "slideshow": {
      "slide_type": "-"
@@ -6039,7 +6024,84 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "8 > 65   12 > 145   4 > 17   "
+      "8 -> 65   12 -> 145   4 -> 17   "
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "227"
+      ]
+     },
+     "execution_count": 79,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "total = 0\n",
+    "\n",
+    "for i, number in enumerate(numbers, start=1):\n",
+    "    if i % 3 == 0:  # only keep every third number\n",
+    "        if number % 2 == 0:  # only keep even numbers\n",
+    "            square = (number ** 2) + 1\n",
+    "            print(number, \"->\", square, end=\"   \")  # added for didactical purposes \n",
+    "            total += square\n",
+    "\n",
+    "total"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "With already three levels of indentation, less horizontal space is available for the actual code block. Of course, one could flatten the two `if` statements with the logical `and` operator, as shown in [Chapter 3](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/03_conditionals_00_lecture.ipynb#The-if-Statement). Then, however, we trade off horizontal space against a more \"complex\" `if` logic, and this is *not* a real improvement."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "#### The `continue` Statement"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "A Pythonista would instead make use of the `continue` statement (cf., [reference](https://docs.python.org/3/reference/simple_stmts.html#the-continue-statement)) that causes a loop to jump into the next iteration skipping the rest of the code block.\n",
+    "\n",
+    "The revised code fragment below occupies more vertical space and less horizontal space: A *good* trade-off.\n",
+    "\n",
+    "One caveat is that we need to negate the conditions in the `if` statements. Conceptually, we are now filtering \"out\" and not \"in.\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 80,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "8 -> 65   12 -> 145   4 -> 17   "
      ]
     },
     {
@@ -6053,72 +6115,6 @@
      "output_type": "execute_result"
     }
    ],
-   "source": [
-    "total = 0\n",
-    "\n",
-    "for i, number in enumerate(numbers, start=1):\n",
-    "    if i % 3 == 0:  # only keep every third number\n",
-    "        if number % 2 == 0:  # only keep even numbers\n",
-    "            square = (number ** 2) + 1\n",
-    "            print(number, square, sep=\" > \", end=\"   \")  # added for didactical purposes \n",
-    "            total += square\n",
-    "\n",
-    "total"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "source": [
-    "With already three levels of indentation, less horizontal space is available for the actual code block. Of course, one could flatten the two `if` statements with the logical `and` operator, as shown in [Chapter 3](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/03_conditionals_00_lecture.ipynb#The-if-Statement). Then, however, we trade off horizontal space against a more \"complex\" `if` logic, and this is *not* a real improvement.\n",
-    "\n",
-    "A Pythonista would instead make use of the `continue` statement (cf., [reference](https://docs.python.org/3/reference/simple_stmts.html#the-continue-statement)) that causes a loop to jump into the next iteration skipping the rest of the code block.\n",
-    "\n",
-    "The revised code fragment below occupies more vertical space and less horizontal space: A *good* trade-off."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "source": [
-    "#### Example: Nested Filters (revisited)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 81,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "slide"
-    }
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "8 > 65   12 > 145   4 > 17   "
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "227"
-      ]
-     },
-     "execution_count": 81,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
    "source": [
     "total = 0\n",
     "\n",
@@ -6129,7 +6125,7 @@
     "        continue\n",
     "\n",
     "    square = (number ** 2) + 1\n",
-    "    print(number, square, sep=\" > \", end=\"   \")  # added for didactical purposes \n",
+    "    print(number, \"->\", square, end=\"   \")  # added for didactical purposes \n",
     "    total += square\n",
     "\n",
     "total"
@@ -6143,11 +6139,7 @@
     }
    },
    "source": [
-    "This is yet another illustration of why programming is an art. The two preceding code cells do the *same* with *identical* time complexity. However, the latter is arguably easier to read for a human, even more so when the business logic grows beyond two nested filters.\n",
-    "\n",
-    "The idea behind the `continue` statement is conceptually similar to the early exit pattern we saw above.\n",
-    "\n",
-    "Both the `break` and `continue` statements, as well as the optional `else`-clause, are not only supported with `for`-loops but also `while`-loops."
+    "This is yet another illustration of why programming is an art. The two preceding code cells do the *same* with *identical* time complexity. However, the latter is arguably easier to read for a human, even more so when the business logic grows beyond two filters."
    ]
   },
   {
@@ -6202,7 +6194,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 82,
+   "execution_count": 81,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -6215,10 +6207,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 83,
+   "execution_count": 82,
    "metadata": {
     "slideshow": {
-     "slide_type": "-"
+     "slide_type": "fragment"
     }
    },
    "outputs": [],
@@ -6228,7 +6220,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 84,
+   "execution_count": 83,
    "metadata": {
     "code_folding": [],
     "slideshow": {
@@ -6240,7 +6232,7 @@
      "name": "stdin",
      "output_type": "stream",
      "text": [
-      "Guess if the coin comes up as heads or tails:  Tails\n"
+      "Guess if the coin comes up as heads or tails:  Heads\n"
      ]
     },
     {
@@ -6250,6 +6242,20 @@
       "Ooops, it was tails\n"
      ]
     },
+    {
+     "name": "stdin",
+     "output_type": "stream",
+     "text": [
+      "Guess if the coin comes up as heads or tails:  Heads\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Ooops, it was heads\n"
+     ]
+    },
     {
      "name": "stdin",
      "output_type": "stream",
@@ -6325,7 +6331,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 85,
+   "execution_count": 84,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -6362,7 +6368,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 86,
+   "execution_count": 85,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -6400,7 +6406,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 87,
+   "execution_count": 86,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -6413,7 +6419,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 88,
+   "execution_count": 87,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
diff --git a/04_iteration_02_exercises.ipynb b/04_iteration_02_exercises.ipynb
index 5858e2d..ab80178 100644
--- a/04_iteration_02_exercises.ipynb
+++ b/04_iteration_02_exercises.ipynb
@@ -19,7 +19,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Read [Chapter 4](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/04_iteration_00_lecture.ipynb) of the book. Then, work through the exercises below."
+    "Read [Chapter 4](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/04_iteration_00_lecture.ipynb) of the book. Then, work through the exercises below. The `...` indicate where you need to fill in your answers. You should not need to create any additional code cells. Occasionally, the `...` mean that you have to write more than one line of code."
    ]
   },
   {
@@ -88,7 +88,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "**Q1.1**: Explain for the $n = 3$ case why it can be solved as a **recursion**!"
+    "**Q1**: Explain for the $n = 3$ case why it can be solved as a **recursion**!"
    ]
   },
   {
@@ -102,7 +102,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "**Q1.2**: How does the number of minimal moves needed to solve a problem with three spots and $n$ disks grow as a function of $n$? How does this relate to the answer to **Q1.1**?"
+    "**Q2**: How does the number of minimal moves needed to solve a problem with three spots and $n$ disks grow as a function of $n$? How does this relate to the answer to **Q1**?"
    ]
   },
   {
@@ -116,7 +116,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "**Q1.3**: The **[Towers of Hanoi](https://en.wikipedia.org/wiki/Tower_of_Hanoi)** problem is of **exponential growth**. What does that mean? What does that imply for large $n$?"
+    "**Q3**: The **[Towers of Hanoi](https://en.wikipedia.org/wiki/Tower_of_Hanoi)** problem is of **exponential growth**. What does that mean? What does that imply for large $n$?"
    ]
   },
   {
@@ -130,7 +130,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "**Q1.4**: The video introduces the recursive relationship $Sol(4, 1, 3) = Sol(3, 1, 2) ~ \\bigoplus ~ Sol(1, 1, 3) ~ \\bigoplus ~ Sol(3, 2, 3)$. The $\\bigoplus$ is to be interpreted as some sort of \"plus\" operation. How does this \"plus\" operation work? How does this way of expressing the problem relate to the answer to **Q1.1**?"
+    "**Q4**: The video introduces the recursive relationship $Sol(4, 1, 3) = Sol(3, 1, 2) ~ \\bigoplus ~ Sol(1, 1, 3) ~ \\bigoplus ~ Sol(3, 2, 3)$. The $\\bigoplus$ is to be interpreted as some sort of \"plus\" operation. How does this \"plus\" operation work? How does this way of expressing the problem relate to the answer to **Q1**?"
    ]
   },
   {
@@ -197,7 +197,7 @@
     "\n",
     "Once completed, `sol()` should print out all the moves in the correct order. With **printing a move**, we mean a line like \"1 -> 3\", short for \"Move the top-most disk from spot 1 to spot 3\".\n",
     "\n",
-    "Write your answers to **Q1.5** to **Q1.7** into the subsequent code cell and finalize `sol()`! No need to write a docstring or validate the input here."
+    "Write your answers to **Q5** to **Q7** into the subsequent code cell and finalize `sol()`! No need to write a docstring or validate the input here."
    ]
   },
   {
@@ -208,13 +208,13 @@
    "source": [
     "def sol(disks, origin, destination):\n",
     "\n",
-    "    # answer to Q15.5\n",
+    "    # answer to Q5\n",
     "    # ...\n",
     "\n",
-    "    # answer to Q15.6\n",
+    "    # answer to Q6\n",
     "    # ...\n",
     "\n",
-    "    # answer to Q15.7\n",
+    "    # answer to Q7\n",
     "    # ..."
    ]
   },
@@ -222,14 +222,14 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "**Q1.5**: What is the `disks` argument when the function reaches its **base case**? Check for the base case with a simple `if` statement and return from the function using the **early exit** pattern!"
+    "**Q5**: What is the `disks` argument when the function reaches its **base case**? Check for the base case with a simple `if` statement and return from the function using the **early exit** pattern!"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "**Q1.6**: If not in the base case, `sol()` needs to determine the `intermediate` spot given concrete `origin` and `destination` arguments. For example, if called with `origin=1` and `destination=2`, `intermediate` must be `3`.\n",
+    "**Q6**: If not in the base case, `sol()` needs to determine the `intermediate` spot given concrete `origin` and `destination` arguments. For example, if called with `origin=1` and `destination=2`, `intermediate` must be `3`.\n",
     "\n",
     "Add **one** compound `if` statement to `sol()` that has a branch for **every** possible `origin`-`destination` pair that sets a variable `intermediate` to the correct temporary spot. **How many** branches will there be?"
    ]
@@ -238,7 +238,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "**Q1.7**: `sol()` needs to call itself **two more times** with the correct 2-pairs chosen from the three available spots `origin`, `intermediate`, and `destination`.\n",
+    "**Q7**: `sol()` needs to call itself **two more times** with the correct 2-pairs chosen from the three available spots `origin`, `intermediate`, and `destination`.\n",
     "\n",
     "In between the two recursive function calls, write a `print()` statement that prints out from where to where the \"remaining and largest\" disk has to be moved!"
    ]
@@ -247,7 +247,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "**Q1.8**: Execute the code cells below and confirm that the printed moves are correct!"
+    "**Q8**: Execute the code cells below and confirm that the printed moves are correct!"
    ]
   },
   {
@@ -301,7 +301,7 @@
     "\n",
     "Let's create a more concise `hanoi()` function that, in addition to a positional `disks` argument, takes three keyword-only arguments `origin`, `intermediate`, and `destination` with default values `\"left\"`, `\"center\"`, and `\"right\"`.\n",
     "\n",
-    "Write your answers to **Q1.9** and **Q1.10** into the subsequent code cell and finalize `hanoi()`! No need to write a docstring or validate the input here."
+    "Write your answers to **Q9** and **Q10** into the subsequent code cell and finalize `hanoi()`! No need to write a docstring or validate the input here."
    ]
   },
   {
@@ -312,10 +312,10 @@
    "source": [
     "def hanoi(disks, *, origin=\"left\", intermediate=\"center\", destination=\"right\"):\n",
     "\n",
-    "    # answer to Q15.9\n",
+    "    # answer to Q9\n",
     "    # ...\n",
     "\n",
-    "    # answer to Q15.10\n",
+    "    # answer to Q10\n",
     "    # ..."
    ]
   },
@@ -323,14 +323,14 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "**Q1.9**: Copy the base case from `sol()`!"
+    "**Q9**: Copy the base case from `sol()`!"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "**Q1.10**: Instead of conditional logic, `hanoi()` calls itself **two times** with the **three** arguments `origin`, `intermediate`, and `destination` passed on in a **different** order.\n",
+    "**Q10**: Instead of conditional logic, `hanoi()` calls itself **two times** with the **three** arguments `origin`, `intermediate`, and `destination` passed on in a **different** order.\n",
     "\n",
     "Figure out how the arguments are passed on in the two recursive `hanoi()` calls!\n",
     "\n",
@@ -341,7 +341,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "**Q1.11**: Execute the code cells below and confirm that the printed moves are correct!"
+    "**Q11**: Execute the code cells below and confirm that the printed moves are correct!"
    ]
   },
   {
@@ -415,7 +415,7 @@
     "\n",
     "Let's create a `hanoi_moves()` function that follows the same internal logic as `hanoi()`, but, instead of printing out the moves, returns the number of steps done so far in the recursion.\n",
     "\n",
-    "Write your answers to **Q1.12** to **Q1.14** into the subsequent code cell and finalize `hanoi_moves()`! No need to write a docstring or validate the input here."
+    "Write your answers to **Q12** to **Q14** into the subsequent code cell and finalize `hanoi_moves()`! No need to write a docstring or validate the input here."
    ]
   },
   {
@@ -426,12 +426,12 @@
    "source": [
     "def hanoi_moves(disks, *, origin=\"left\", intermediate=\"center\", destination=\"right\"):\n",
     "\n",
-    "    # answer to Q15.12\n",
+    "    # answer to Q12\n",
     "    # ...\n",
     "\n",
-    "    moves = ...  # <- answer to Q15.13\n",
-    "    moves += hanoi_moves(...)  # <- answer to Q15.14 between the ()\n",
-    "    moves += hanoi_moves(...)  # <- answer to Q15.14 between the ()\n",
+    "    moves = ...  # <- answer to Q13\n",
+    "    moves += hanoi_moves(...)  # <- answer to Q14 between the ()\n",
+    "    moves += hanoi_moves(...)  # <- answer to Q14 between the ()\n",
     "\n",
     "    return moves"
    ]
@@ -440,21 +440,21 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "**Q1.12**: Copy the base case from `hanoi()`! What count should be returned when it is reached?"
+    "**Q12**: Copy the base case from `hanoi()`! What count should be returned when it is reached?"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "**Q1.13**: Initialize the variable `moves` with an appropriate count! This is the number of moves that corresponds to **one** recursive function call."
+    "**Q13**: Initialize the variable `moves` with an appropriate count! This is the number of moves that corresponds to **one** recursive function call."
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "**Q1.14**: `moves` is updated with the counts passed up from the two recursive calls.\n",
+    "**Q14**: `moves` is updated with the counts passed up from the two recursive calls.\n",
     "\n",
     "Complete the two recursive function calls with the same arguments as in `hanoi()`!"
    ]
@@ -463,7 +463,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "**Q1.15**: Write a `for`-loop that prints out the **minimum number** of moves needed to solve Towers of Hanoi for any number of `disks` from `1` through `20` to confirm your answer to **Q1.2**."
+    "**Q15**: Write a `for`-loop that prints out the **minimum number** of moves needed to solve Towers of Hanoi for any number of `disks` from `1` through `20` to confirm your answer to **Q2**."
    ]
   },
   {
@@ -491,7 +491,7 @@
     "\n",
     "With `disks` in the range from `24` through `26`, the computation time roughly doubles for each increase of `disks` by 1.\n",
     "\n",
-    "**Q1.16**: Execute the code cells below and see for yourself!"
+    "**Q16**: Execute the code cells below and see for yourself!"
    ]
   },
   {
@@ -537,7 +537,7 @@
    "source": [
     "The above `hanoi()` prints the optimal solution's moves in the correct order but fails to label each move with an order number. This can be built in by passing on one more argument `offset` down the recursion tree. As the logic gets a bit \"involved,\" `hanoi_ordered()` below is almost finished.\n",
     "\n",
-    "Write your answers to **Q1.17** and **Q1.18** into the subsequent code cell and finalize `hanoi_ordered()`! No need to write a docstring or validate the input here."
+    "Write your answers to **Q17** and **Q18** into the subsequent code cell and finalize `hanoi_ordered()`! No need to write a docstring or validate the input here."
    ]
   },
   {
@@ -548,7 +548,7 @@
    "source": [
     "def hanoi_ordered(disks, *, origin=\"left\", intermediate=\"center\", destination=\"right\", offset=None):\n",
     "\n",
-    "    # answer to Q15.17\n",
+    "    # answer to Q17\n",
     "    # ...\n",
     "\n",
     "    total = (2 ** disks - 1)\n",
@@ -558,23 +558,23 @@
     "    if offset is not None:\n",
     "        count += offset\n",
     "\n",
-    "    hanoi_ordered(..., offset=offset)  # <- answer to Q15.18\n",
-    "    # ... <- answer to Q15.18\n",
-    "    hanoi_ordered(..., offset=count)  # <- answer to Q15.18"
+    "    hanoi_ordered(..., offset=offset)  # <- answer to Q18\n",
+    "    # ... <- answer to Q18\n",
+    "    hanoi_ordered(..., offset=count)  # <- answer to Q18"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "**Q1.17**: Copy the base case from the original `hanoi()`!"
+    "**Q17**: Copy the base case from the original `hanoi()`!"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "**Q1.18**: Complete the two recursive function calls with the same arguments as in `hanoi()` or `hanoi_moves()`! Do not change the already filled in `offset` arguments!\n",
+    "**Q18**: Complete the two recursive function calls with the same arguments as in `hanoi()` or `hanoi_moves()`! Do not change the already filled in `offset` arguments!\n",
     "\n",
     "Then, copy the `print()` statement from `hanoi()` and adjust it to print out `count` as well!"
    ]
@@ -583,7 +583,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "**Q1.19**: Execute the code cells below and confirm that the order numbers are correct!"
+    "**Q19**: Execute the code cells below and confirm that the order numbers are correct!"
    ]
   },
   {
@@ -640,7 +640,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "**Q1.20**: Conducting your own research on the internet (max. 15 minutes), what can you say about generalizing the **[Towers of Hanoi](https://en.wikipedia.org/wiki/Tower_of_Hanoi)** problem to a setting with **more than three** landing spots?"
+    "**Q20**: Conducting your own research on the internet (max. 15 minutes), what can you say about generalizing the **[Towers of Hanoi](https://en.wikipedia.org/wiki/Tower_of_Hanoi)** problem to a setting with **more than three** landing spots?"
    ]
   },
   {

From c1ad5d8b961f9231c12125a317c4b9ad984f5b8a Mon Sep 17 00:00:00 2001
From: Alexander Hess <alexander@webartifex.biz>
Date: Thu, 12 Mar 2020 00:53:56 +0100
Subject: [PATCH 076/242] Add new exercises set for chapter 04

---
 04_iteration_00_lecture.ipynb   |  34 ++-
 04_iteration_03_exercises.ipynb | 402 ++++++++++++++++++++++++++++++++
 2 files changed, 430 insertions(+), 6 deletions(-)
 create mode 100644 04_iteration_03_exercises.ipynb

diff --git a/04_iteration_00_lecture.ipynb b/04_iteration_00_lecture.ipynb
index 1a72c7b..fb89130 100644
--- a/04_iteration_00_lecture.ipynb
+++ b/04_iteration_00_lecture.ipynb
@@ -37,6 +37,28 @@
     "## Recursion"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "A popular joke among programmers by an unknown author goes like this (cf., [discussion](https://www.quora.com/What-does-the-phrase-in-order-to-understand-recursion-you-must-first-understand-recursion-mean-to-you)):"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "source": [
+    "> \"In order to understand **recursion**, you must first understand **recursion**.\""
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {
@@ -859,7 +881,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "37.3 µs ± 1.25 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n"
+      "35.2 µs ± 971 ns per loop (mean ± std. dev. of 7 runs, 100 loops each)\n"
      ]
     }
    ],
@@ -881,7 +903,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "11.4 ms ± 144 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n"
+      "11.2 ms ± 81.1 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n"
      ]
     }
    ],
@@ -903,7 +925,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "3.63 s ± 0 ns per loop (mean ± std. dev. of 1 run, 1 loop each)\n"
+      "3.67 s ± 0 ns per loop (mean ± std. dev. of 1 run, 1 loop each)\n"
      ]
     }
    ],
@@ -925,7 +947,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "5.93 s ± 0 ns per loop (mean ± std. dev. of 1 run, 1 loop each)\n"
+      "5.94 s ± 0 ns per loop (mean ± std. dev. of 1 run, 1 loop each)\n"
      ]
     }
    ],
@@ -4543,7 +4565,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "5.24 s ± 0 ns per loop (mean ± std. dev. of 1 run, 1 loop each)\n"
+      "5.22 s ± 0 ns per loop (mean ± std. dev. of 1 run, 1 loop each)\n"
      ]
     }
    ],
@@ -4824,7 +4846,7 @@
     }
    },
    "source": [
-    "The `for` statement, on the contrary, makes the actual business logic more apparent by stripping all the **[boilerplate code](https://en.wikipedia.org/wiki/Boilerplate_code)** away."
+    "The `for` statement, on the contrary, makes the actual business logic more apparent by stripping all the **[boilerplate code](https://en.wikipedia.org/wiki/Boilerplate_code)** away. The variable that is automatically set by Python in each iteration of the loop (i.e., `element` in the example) is called the **target variable**."
    ]
   },
   {
diff --git a/04_iteration_03_exercises.ipynb b/04_iteration_03_exercises.ipynb
new file mode 100644
index 0000000..da32407
--- /dev/null
+++ b/04_iteration_03_exercises.ipynb
@@ -0,0 +1,402 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n",
+    "# Chapter 4: Recursion & Looping"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Coding Exercises"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Read [Chapter 4](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/04_iteration_00_lecture.ipynb) of the book. Then, work through the exercises below. The `...` indicate where you need to fill in your answers. You should not need to create any additional code cells."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Throwing the Dice"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In this exercise, you will model the throwing of dice within the context of a guessing game similar to the one shown in the \"*Example: Guessing a Coin Toss*\" section in [Chapter 4](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/04_iteration_00_lecture.ipynb#Example:-Guessing-a-Coin-Toss).\n",
+    "\n",
+    "As the game involves randomness, we import the [random](https://docs.python.org/3/library/random.html) module from the [standard library](https://docs.python.org/3/library/index.html). To follow best practices, we set the random seed as well."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import random"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "random.seed(42)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "A die has six sides that we labeled with integers `1` to `6` in this exercise. For a fair die, the probability for each side is the same.\n",
+    "\n",
+    "**Q1**: Model a `fair_die` as a `list` object!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "fair_die = ..."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q2**: What function from the [random](https://docs.python.org/3/library/random.html) module that we have seen already is useful for modeling a single throw of the `fair_die`? Write a simple expression (i.e., one function call) that draws one of the equally likely sides! Execute the cell a couple of times to \"see\" the probability distribution!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "..."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Let's check if the `fair_die` is indeed fair. To do so, we create a little numerical experiment and throw the `fair_die` `100000` times. We track the six different outcomes in a `list` object called `throws` that we initialize with all `0`s for each outcome.\n",
+    "\n",
+    "**Q3**: Complete the `for`-loop below such that it runs `100000` times! In the body, use your answer to **Q2** to simulate a single throw of the `fair_die` and update the corresponding count in `throws`!\n",
+    "\n",
+    "Hints: You need to use the indexing operator `[]` and calculate an `index` in each iteration of the loop. Do do not actually need the target variable provided by the `for`-loop and may want to indicate that with an underscore `_`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "throws = [0, 0, 0, 0, 0, 0]\n",
+    "\n",
+    "for ... in ...:\n",
+    "    ...\n",
+    "    ...\n",
+    "    ...\n",
+    "\n",
+    "throws"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "`throws` contains the simulation results as absolute counts.\n",
+    "\n",
+    "**Q4**: Complete the `for`-loop below to convert the counts in `throws` to relative frequencies stored in a `list` called `frequencies`! Round the frequencies to three decimals with the built-in [round()](https://docs.python.org/3/library/functions.html#round) function!\n",
+    "\n",
+    "Hints: Initialize `frequencies` just as `throws` above. How many iterations does the `for`-loop have? `6` or `100000`? You may want to obtain an `index` variable with the [enumerate()](https://docs.python.org/3/library/functions.html#enumerate) built-in."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "frequencies = [0, 0, 0, 0, 0, 0]\n",
+    "\n",
+    "for ... in ...:\n",
+    "    ...\n",
+    "\n",
+    "frequencies"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q5**: How could we adapt the `list` object used above to model an `unfair_die` where `1` is as likely as `2`, `2` is twice as likely as `3`, and `3` is twice as likely as `4`, `5`, or `6`, who are all equally likely?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "unfair_die = ..."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q6**: Copy your solution to **Q2** for the `unfair_die`! Execute the cell a couple of times to \"see\" the probability distribution!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "..."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q7**: Copy and adapt your solutions to **Q3** and **Q4** to calculate the `frequencies` for the `unfair_die`!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "throws = [0, 0, 0, 0, 0, 0]\n",
+    "frequencies = [0, 0, 0, 0, 0, 0]\n",
+    "\n",
+    "for ... in ...:\n",
+    "    ...\n",
+    "    ...\n",
+    "    ...\n",
+    "\n",
+    "for ... in ...:\n",
+    "    ...\n",
+    "\n",
+    "frequencies"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q8**: The built-in [input()](https://docs.python.org/3/library/functions.html#input) allows us to ask the user to enter a `guess`. What is the data type of the object returned by [input()](https://docs.python.org/3/library/functions.html#input)? Assume the user enters the `guess` as a number (i.e., \"1\", \"2\", ...) and not as a text (e.g., \"one\")."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "guess = input(\"Guess the side of the die: \")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "guess"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "..."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q9**: Use a built-in constructor to cast `guess` as an `int` object!\n",
+    "\n",
+    "Hint: Simply wrap `guess` or `input(\"Guess the side of the die: \")` with the constructor you choose."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "..."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q10**: What type of error is raised if `guess` cannot be cast as an `int` object?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " ..."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q11**: Write a `try` statement that catches the type of error (i.e., your answer to **Q10**) raised if the user's input cannot be cast as an `int` object! Print out some nice error message notifying the user of the bad input!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "try:\n",
+    "    ...\n",
+    "except ...:\n",
+    "    ..."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q12**: Write a function `get_guess()` that takes a user's input and checks if it is a valid side of the die! The function should *return* either an `int` object between `1` and `6` or `None` if the user enters something invalid.\n",
+    "\n",
+    "Hints: You may want to re-use the `try` statement from **Q11**. Instead of printing out an error message, you can also `return` directly from the `except`-clause (i.e., early exit) with `None`. So, the user can make *two* kinds of input errors and maybe you want to model that with two *distinct* `return None` statements. Also, you may want to allow the user to enter leading and trailing whitespace that gets removed without an error message."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def get_guess():\n",
+    "    \"\"\"Process the user's input.\n",
+    "    \n",
+    "    Returns:\n",
+    "        guess (int / NoneType): either 1, 2, 3, 4, 5 or 6\n",
+    "            if the input can be parsed and None otherwise\n",
+    "    \"\"\"\n",
+    "    ...\n",
+    "\n",
+    "    # Check if the user entered an integer.\n",
+    "    ...\n",
+    "    ...\n",
+    "    ...\n",
+    "    ...\n",
+    "\n",
+    "    # Check if the user entered a valid side.\n",
+    "    ...\n",
+    "    ...\n",
+    "    ..."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q13** Test your function for all *three* cases!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "get_guess()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q14**: Write an *indefinite* loop where in each iteration a `fair_die` is thrown and the user makes a guess! Print out an error message if the user does not enter something that can be understood as a number between `1` and `6`! The game should continue until the user makes a correct guess."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "...\n",
+    "...\n",
+    "...\n",
+    "\n",
+    "...\n",
+    "...\n",
+    "...\n",
+    "...\n",
+    "...\n",
+    "...\n",
+    "..."
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.4"
+  },
+  "toc": {
+   "base_numbering": 1,
+   "nav_menu": {},
+   "number_sections": false,
+   "sideBar": true,
+   "skip_h1_title": true,
+   "title_cell": "Table of Contents",
+   "title_sidebar": "Contents",
+   "toc_cell": false,
+   "toc_position": {},
+   "toc_section_display": false,
+   "toc_window_display": false
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}

From 5108f8446f964359d6d11e33dad77d57ba0fcc8c Mon Sep 17 00:00:00 2001
From: Alexander Hess <alexander@webartifex.biz>
Date: Thu, 12 Mar 2020 01:00:16 +0100
Subject: [PATCH 077/242] Bump version number

---
 pyproject.toml | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/pyproject.toml b/pyproject.toml
index 679d3d7..630bac7 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -1,6 +1,6 @@
 [tool.poetry]
 name = "intro-to-python"
-version = "0.6.4"
+version = "0.6.5"
 description = "An introduction to Python and programming for wanna-be data scientists"
 authors = ["Alexander Hess <alexander@webartifex.biz>"]
 license = "MIT"

From d9c11c5796c06feadf7cad1e3c3f02a8b552f398 Mon Sep 17 00:00:00 2001
From: Alexander Hess <alexander@webartifex.biz>
Date: Thu, 12 Mar 2020 19:42:10 +0100
Subject: [PATCH 078/242] Streamline presentation

---
 03_conditionals_02_exercises.ipynb |    4 +-
 05_numbers_00_lecture.ipynb        | 2891 +++++++++++++++++-----------
 05_numbers_02_exercises.ipynb      |   22 +-
 3 files changed, 1818 insertions(+), 1099 deletions(-)

diff --git a/03_conditionals_02_exercises.ipynb b/03_conditionals_02_exercises.ipynb
index 650b063..ac4acd1 100644
--- a/03_conditionals_02_exercises.ipynb
+++ b/03_conditionals_02_exercises.ipynb
@@ -19,7 +19,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Read [Chapter 3](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/03_conditionals_00_lecture.ipynb) of the book. Then, work through the exercises below."
+    "Read [Chapter 3](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/03_conditionals_00_lecture.ipynb) of the book. Then, work through the exercises below. The `...` indicate where you need to fill in your answers. You should not need to create any additional code cells."
    ]
   },
   {
@@ -136,7 +136,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "**Q1.4**: Looking at the `if`-`else`-logic in the function, why do you think the four example line items in **Q9.2** were chosen as they were?"
+    "**Q1.4**: Looking at the `if`-`else`-logic in the function, why do you think the four example line items in **Q1.2** were chosen as they were?"
    ]
   },
   {
diff --git a/05_numbers_00_lecture.ipynb b/05_numbers_00_lecture.ipynb
index 531de04..e4bed12 100644
--- a/05_numbers_00_lecture.ipynb
+++ b/05_numbers_00_lecture.ipynb
@@ -66,7 +66,7 @@
    },
    "outputs": [],
    "source": [
-    "i = 789"
+    "a = 42"
    ]
   },
   {
@@ -77,7 +77,7 @@
     }
    },
    "source": [
-    "Just like any other object, `789` has an identity, a type, and a value."
+    "Just like any other object, the `42` has an identity, a type, and a value."
    ]
   },
   {
@@ -92,7 +92,7 @@
     {
      "data": {
       "text/plain": [
-       "140166838695792"
+       "94315929536576"
       ]
      },
      "execution_count": 2,
@@ -101,7 +101,7 @@
     }
    ],
    "source": [
-    "id(i)"
+    "id(a)"
    ]
   },
   {
@@ -109,7 +109,7 @@
    "execution_count": 3,
    "metadata": {
     "slideshow": {
-     "slide_type": "-"
+     "slide_type": "fragment"
     }
    },
    "outputs": [
@@ -125,7 +125,7 @@
     }
    ],
    "source": [
-    "type(i)"
+    "type(a)"
    ]
   },
   {
@@ -133,14 +133,14 @@
    "execution_count": 4,
    "metadata": {
     "slideshow": {
-     "slide_type": "-"
+     "slide_type": "fragment"
     }
    },
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "789"
+       "42"
       ]
      },
      "execution_count": 4,
@@ -149,7 +149,7 @@
     }
    ],
    "source": [
-    "i"
+    "a"
    ]
   },
   {
@@ -160,7 +160,7 @@
     }
    },
    "source": [
-    "A nice feature is using underscores `_` as (thousands) separator in numeric literals. For example, `1_000_000` evaluates to `1000000` in memory."
+    "A nice feature in newer Python versions is using underscores `_` as (thousands) separators in numeric literals. For example, `1_000_000` evaluates to `1000000` in memory; the `_` is simply ignored by the interpreter."
    ]
   },
   {
@@ -195,7 +195,7 @@
     }
    },
    "source": [
-    "Whereas mathematicians argue what the term $0^0$ means (cf., this [article](https://en.wikipedia.org/wiki/Zero_to_the_power_of_zero)), programmers are pragmatic about this and define $0^0 = 1$."
+    "The `int` type follows all rules we know from math, apart from one exception: Whereas mathematicians argue what the term $0^0$ means (cf., this [article](https://en.wikipedia.org/wiki/Zero_to_the_power_of_zero)), programmers are pragmatic about this and define $0^0 = 1$."
    ]
   },
   {
@@ -270,7 +270,7 @@
     "\n",
     "To encode the integer $3$, for example, we need to find a combination of $0$s and $1$s such that the sum of digits marked with a $1$ is equal to the number we want to encode. In the example, we set all bits to $0$ except for the first ($i=0$) and second ($i=1$) as $2^0 + 2^1 = 1 + 2 = 3$. So the binary representation of $3$ is $00~00~00~11$. To borrow some terminology from linear algebra, the $3$ is a linear combination of the digits where the coefficients are either $0$ or $1$: $3 = 0*128 + 0*64 + 0*32 + 0*16 + 0*8 + 0*4 + 1*2 + 1*1$. It is *guaranteed* that there is exactly *one* such combination for each number between $0$ and $255$.\n",
     "\n",
-    "As each bit in the binary representation is one of two values, we say that this representation has a base of $2$. Often, the base is indicated with a subscript to avoid confusion. For example, we write $3_{10} = 00000011_2$ or $3_{10} = 11_2$ for short omitting leading $0$s. A subscript of $10$ implies a decimal number as we know it from grade school.\n",
+    "As each bit in the binary representation is one of two values, we say that this representation has a base of $2$. Often, the base is indicated with a subscript to avoid confusion. For example, we write $3_{10} = 00000011_2$ or $3_{10} = 11_2$ for short omitting leading $0$s. A subscript of $10$ implies a decimal number as we know it from elementary school.\n",
     "\n",
     "We use the built-in [bin()](https://docs.python.org/3/library/functions.html#bin) function to obtain an `int` object's binary representation: It returns a `str` object starting with `\"0b\"` indicating the binary format and as many $0$s and $1$s as are necessary to encode the integer omitting leading $0$s."
    ]
@@ -280,7 +280,7 @@
    "execution_count": 7,
    "metadata": {
     "slideshow": {
-     "slide_type": "fragment"
+     "slide_type": "slide"
     }
    },
    "outputs": [
@@ -377,7 +377,7 @@
     }
    },
    "source": [
-    "Another example is the integer `177` that is the sum of $128 + 32 + 16 + 1$: Thus, its binary representation is the sequence of bits $10~11~00~01$, or to use our new notation, $177_{10} = 10110001_2$."
+    "It is convenient to use the underscore `_` to separate the `\"0b\"` prefix from the bits."
    ]
   },
   {
@@ -385,14 +385,14 @@
    "execution_count": 10,
    "metadata": {
     "slideshow": {
-     "slide_type": "slide"
+     "slide_type": "fragment"
     }
    },
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "'0b10110001'"
+       "3"
       ]
      },
      "execution_count": 10,
@@ -401,7 +401,7 @@
     }
    ],
    "source": [
-    "bin(177)"
+    "0b_11"
    ]
   },
   {
@@ -412,7 +412,7 @@
     }
    },
    "source": [
-    "Analogous to typing `177` into a code cell, we may write `0b10110001`, or `0b_1011_0001` to make use of the underscores, and create an `int` object with the value `177`."
+    "Another example is the integer `123` that is the sum of $64 + 32 + 16 + 8 + 2 + 1$: Thus, its binary representation is the sequence of bits $01~11~10~11$, or to use our new notation, $123_{10} = 1111011_2$."
    ]
   },
   {
@@ -420,14 +420,14 @@
    "execution_count": 11,
    "metadata": {
     "slideshow": {
-     "slide_type": "-"
+     "slide_type": "skip"
     }
    },
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "177"
+       "'0b1111011'"
       ]
      },
      "execution_count": 11,
@@ -436,7 +436,42 @@
     }
    ],
    "source": [
-    "0b_1011_0001"
+    "bin(123)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Analogous to typing `123` into a code cell, we may write `0b1111011`, or `0b_111_1011` to make use of the underscores, and create an `int` object with the value `123`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "123"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "0b_111_1011"
    ]
   },
   {
@@ -452,7 +487,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 13,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -465,7 +500,7 @@
        "'0b0'"
       ]
      },
-     "execution_count": 12,
+     "execution_count": 13,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -476,10 +511,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 14,
    "metadata": {
     "slideshow": {
-     "slide_type": "-"
+     "slide_type": "skip"
     }
    },
    "outputs": [
@@ -489,7 +524,7 @@
        "'0b1'"
       ]
      },
-     "execution_count": 13,
+     "execution_count": 14,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -500,10 +535,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 15,
    "metadata": {
     "slideshow": {
-     "slide_type": "-"
+     "slide_type": "skip"
     }
    },
    "outputs": [
@@ -513,7 +548,7 @@
        "'0b10'"
       ]
      },
-     "execution_count": 14,
+     "execution_count": 15,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -524,10 +559,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 16,
    "metadata": {
     "slideshow": {
-     "slide_type": "-"
+     "slide_type": "fragment"
     }
    },
    "outputs": [
@@ -537,7 +572,7 @@
        "'0b11111111'"
       ]
      },
-     "execution_count": 15,
+     "execution_count": 16,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -556,12 +591,12 @@
    "source": [
     "Groups of eight bits are also called a **byte**. As a byte can only represent non-negative integers up to $255$, the table above is extended conceptually with greater digits to the left to model integers beyond $255$. The memory management needed to implement this is built into Python, and we do not need to worry about it.\n",
     "\n",
-    "For example, the `789` from above is encoded with ten bits and $789_{10} = 1100010101_2$."
+    "For example, `789` is encoded with ten bits and $789_{10} = 1100010101_2$."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 17,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -574,13 +609,13 @@
        "'0b1100010101'"
       ]
      },
-     "execution_count": 16,
+     "execution_count": 17,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "bin(i)  # = bin(789)"
+    "bin(789)"
    ]
   },
   {
@@ -598,7 +633,254 @@
     "| Digit |$10^3$|$10^2$|$10^1$|$10^0$|\n",
     "|  $=$  |$1000$| $100$| $10$ |  $1$ |\n",
     "\n",
-    "Now, an integer is a linear combination of the digits where the coefficients are one of *ten* values, and the base is now $10$. For example, the number $123$ can be expressed as $0*1000 + 1*100 + 2*10 + 3*1$. So, the binary representation follows the same logic as the decimal system taught in grade school. The decimal system is intuitive to us humans, mostly as we learn to count with our *ten* fingers. The $0$s and $1$s in a computer's memory are therefore no rocket science; they only feel unintuitive for a beginner."
+    "Now, an integer is a linear combination of the digits where the coefficients are one of *ten* values, and the base is now $10$. For example, the number $123$ can be expressed as $0*1000 + 1*100 + 2*10 + 3*1$. So, the binary representation follows the same logic as the decimal system taught in elementary school. The decimal system is intuitive to us humans, mostly as we learn to count with our *ten* fingers. The $0$s and $1$s in a computer's memory are therefore no rocket science; they only feel unintuitive for a beginner."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "#### Arithmetic with Bits"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Adding two numbers in their binary representations is straightforward and works just like we all learned addition in elementary school. Going from right to left, we add the individual digits, and ..."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "3"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "1 + 2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'0b1 + 0b10 = 0b11'"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "bin(1) + \" + \" + bin(2) + \" = \" + bin(3)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "... if any two digits add up to $2$, the resulting digit is $0$ and a $1$ carries over."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "4"
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "1 + 3"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'0b1 + 0b11 = 0b100'"
+      ]
+     },
+     "execution_count": 21,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "bin(1) + \" + \" + bin(3) + \" = \" + bin(4)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Multiplication is also quite easy. All we need to do is to multiply the left operand by all digits of the right operand separately and then add up the individual products, just like in elementary school."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "12"
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "4 * 3"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'0b100 * 0b11 = 0b1100'"
+      ]
+     },
+     "execution_count": 23,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "bin(4) + \" * \" + bin(3) + \" = \" + bin(12)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'0b100 * 0b1 = 0b100'"
+      ]
+     },
+     "execution_count": 24,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "bin(4) + \" * \" + bin(1) + \" = \" + bin(4)  # multiply with first digit"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'0b100 * 0b10 = 0b1000'"
+      ]
+     },
+     "execution_count": 25,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "bin(4) + \" * \" + bin(2) + \" = \" + bin(8)  # multiply with second digit"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "The \"*Further Resources*\" section at the end of this chapter provides video tutorials on addition and multiplication in binary. Subtraction and division are a bit more involved but essentially also easy to understand."
    ]
   },
   {
@@ -676,7 +958,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 26,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -689,7 +971,7 @@
        "'0x0'"
       ]
      },
-     "execution_count": 17,
+     "execution_count": 26,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -700,10 +982,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 27,
    "metadata": {
     "slideshow": {
-     "slide_type": "-"
+     "slide_type": "fragment"
     }
    },
    "outputs": [
@@ -713,7 +995,7 @@
        "'0x1'"
       ]
      },
-     "execution_count": 18,
+     "execution_count": 27,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -735,10 +1017,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 28,
    "metadata": {
     "slideshow": {
-     "slide_type": "fragment"
+     "slide_type": "skip"
     }
    },
    "outputs": [
@@ -748,7 +1030,7 @@
        "'0x3'"
       ]
      },
-     "execution_count": 19,
+     "execution_count": 28,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -770,7 +1052,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 29,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -783,7 +1065,7 @@
        "'0xa'"
       ]
      },
-     "execution_count": 20,
+     "execution_count": 29,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -794,10 +1076,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 30,
    "metadata": {
     "slideshow": {
-     "slide_type": "-"
+     "slide_type": "fragment"
     }
    },
    "outputs": [
@@ -807,7 +1089,7 @@
        "'0xf'"
       ]
      },
-     "execution_count": 21,
+     "execution_count": 30,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -824,12 +1106,12 @@
     }
    },
    "source": [
-    "The binary representation of `177`, `\"0b10110001\"`, can be viewed as *two* groups of four bits, $1011$ and $0001$, that are encoded as $\\text{b}$ and $1$ in hexadecimal (cf., table above)."
+    "The binary representation of `123`, `0b_111_1011`, can be viewed as *two* groups of four bits, $0111$ and $1011$, that are encoded as $7$ and $\\text{b}$ in hexadecimal (cf., table above)."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 31,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -839,40 +1121,40 @@
     {
      "data": {
       "text/plain": [
-       "'0b10110001'"
+       "'0b1111011'"
       ]
      },
-     "execution_count": 22,
+     "execution_count": 31,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "bin(177)"
+    "bin(123)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 32,
    "metadata": {
     "slideshow": {
-     "slide_type": "-"
+     "slide_type": "fragment"
     }
    },
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "'0xb1'"
+       "'0x7b'"
       ]
      },
-     "execution_count": 23,
+     "execution_count": 32,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "hex(177)"
+    "hex(123)"
    ]
   },
   {
@@ -883,12 +1165,12 @@
     }
    },
    "source": [
-    "To obtain a *new* `int` object with the value `177`, we call the [int()](https://docs.python.org/3/library/functions.html#int) built-in with a properly formatted `str` object and `base=16` as arguments."
+    "To obtain a *new* `int` object with the value `123`, we call the [int()](https://docs.python.org/3/library/functions.html#int) built-in with a properly formatted `str` object and `base=16` as arguments."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 33,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -898,16 +1180,16 @@
     {
      "data": {
       "text/plain": [
-       "177"
+       "123"
       ]
      },
-     "execution_count": 24,
+     "execution_count": 33,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "int(\"0xb1\", base=16)"
+    "int(\"0x7b\", base=16)"
    ]
   },
   {
@@ -923,7 +1205,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 34,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -933,16 +1215,16 @@
     {
      "data": {
       "text/plain": [
-       "177"
+       "123"
       ]
      },
-     "execution_count": 25,
+     "execution_count": 34,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "0xb1"
+    "0x_7b"
    ]
   },
   {
@@ -958,7 +1240,31 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": 35,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'0x0'"
+      ]
+     },
+     "execution_count": 35,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "hex(0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -971,7 +1277,7 @@
        "'0xff'"
       ]
      },
-     "execution_count": 26,
+     "execution_count": 36,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -993,7 +1299,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": 37,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -1006,13 +1312,13 @@
        "'0x315'"
       ]
      },
-     "execution_count": 27,
+     "execution_count": 37,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "hex(i)  # = hex(789)"
+    "hex(789)"
    ]
   },
   {
@@ -1045,14 +1351,14 @@
     }
    },
    "source": [
-    "While there are conventions that model negative integers with $0$s and $1$s in memory (cf., [Two's Complement](https://en.wikipedia.org/wiki/Two%27s_complement)), Python manages that for us, and we do not look into the theory here for brevity. We have learned all we need to know about how integers are modeled in a computer.\n",
+    "While there are conventions that model negative integers with $0$s and $1$s in memory (cf., [Two's Complement](https://en.wikipedia.org/wiki/Two%27s_complement)), Python manages that for us, and we do not look into the theory here for brevity. We have learned all that a practitioner needs to know about how integers are modeled in a computer. The \"*Further Resources*\" section at the end of this chapter provides a video tutorial on how the [Two's Complement](https://en.wikipedia.org/wiki/Two%27s_complement) idea works.\n",
     "\n",
-    "The binary and hexadecimal representations of negative integers are identical to their positive counterparts except that they start with a minus sign `-`."
+    "The binary and hexadecimal representations of negative integers are identical to their positive counterparts except that they start with a minus sign `-`. However, as the video tutorial at the end of the chapter reveals, that is *not* how the bits are organized in memory."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": 38,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -1065,7 +1371,7 @@
        "'-0b11'"
       ]
      },
-     "execution_count": 28,
+     "execution_count": 38,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1076,7 +1382,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
+   "execution_count": 39,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -1089,7 +1395,7 @@
        "'-0x3'"
       ]
      },
-     "execution_count": 29,
+     "execution_count": 39,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1100,7 +1406,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 30,
+   "execution_count": 40,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -1113,7 +1419,7 @@
        "'-0b11111111'"
       ]
      },
-     "execution_count": 30,
+     "execution_count": 40,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1124,7 +1430,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 31,
+   "execution_count": 41,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -1137,7 +1443,7 @@
        "'-0xff'"
       ]
      },
-     "execution_count": 31,
+     "execution_count": 41,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1170,7 +1476,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 32,
+   "execution_count": 42,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -1183,7 +1489,7 @@
        "1"
       ]
      },
-     "execution_count": 32,
+     "execution_count": 42,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1194,10 +1500,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 33,
+   "execution_count": 43,
    "metadata": {
     "slideshow": {
-     "slide_type": "-"
+     "slide_type": "fragment"
     }
    },
    "outputs": [
@@ -1207,7 +1513,7 @@
        "42"
       ]
      },
-     "execution_count": 33,
+     "execution_count": 43,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1218,10 +1524,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 34,
+   "execution_count": 44,
    "metadata": {
     "slideshow": {
-     "slide_type": "-"
+     "slide_type": "fragment"
     }
    },
    "outputs": [
@@ -1231,13 +1537,13 @@
        "0.0"
       ]
      },
-     "execution_count": 34,
+     "execution_count": 44,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "42.0 * False"
+    "42.87 * False"
    ]
   },
   {
@@ -1253,7 +1559,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 35,
+   "execution_count": 45,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -1266,7 +1572,7 @@
        "1"
       ]
      },
-     "execution_count": 35,
+     "execution_count": 45,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1277,10 +1583,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 36,
+   "execution_count": 46,
    "metadata": {
     "slideshow": {
-     "slide_type": "-"
+     "slide_type": "fragment"
     }
    },
    "outputs": [
@@ -1290,7 +1596,7 @@
        "0"
       ]
      },
-     "execution_count": 36,
+     "execution_count": 46,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1312,7 +1618,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 37,
+   "execution_count": 47,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -1325,7 +1631,7 @@
        "'0b1'"
       ]
      },
-     "execution_count": 37,
+     "execution_count": 47,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1336,10 +1642,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 38,
+   "execution_count": 48,
    "metadata": {
     "slideshow": {
-     "slide_type": "-"
+     "slide_type": "fragment"
     }
    },
    "outputs": [
@@ -1349,7 +1655,7 @@
        "'0b0'"
       ]
      },
-     "execution_count": 38,
+     "execution_count": 48,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1371,10 +1677,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 39,
+   "execution_count": 49,
    "metadata": {
     "slideshow": {
-     "slide_type": "slide"
+     "slide_type": "skip"
     }
    },
    "outputs": [
@@ -1384,7 +1690,7 @@
        "'0x1'"
       ]
      },
-     "execution_count": 39,
+     "execution_count": 49,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1395,10 +1701,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 40,
+   "execution_count": 50,
    "metadata": {
     "slideshow": {
-     "slide_type": "-"
+     "slide_type": "skip"
     }
    },
    "outputs": [
@@ -1408,7 +1714,7 @@
        "'0x0'"
       ]
      },
-     "execution_count": 40,
+     "execution_count": 50,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1425,12 +1731,12 @@
     }
    },
    "source": [
-    "As a reminder, the `None` literal is a type on its own, namely the `NoneType`, and different from `False`. It *cannot* be cast as an integer as the `TypeError` indicates."
+    "As a reminder, the `None` object is a type on its own, namely the `NoneType`, and different from `False`. It *cannot* be cast as an integer as the `TypeError` indicates."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 41,
+   "execution_count": 51,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -1444,7 +1750,7 @@
      "traceback": [
       "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
       "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-41-af2123a46eb2>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;32m<ipython-input-51-af2123a46eb2>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
       "\u001b[0;31mTypeError\u001b[0m: int() argument must be a string, a bytes-like object or a number, not 'NoneType'"
      ]
     }
@@ -1476,12 +1782,12 @@
     "\n",
     "We keep this overview rather short as such \"low-level\" operations are not needed by the data science practitioner regularly. Yet, it is worthwhile to have heard about them as they form the basis of all of arithmetic in computers.\n",
     "\n",
-    "The first operator is the **bitwise AND** operator `&`: It looks at the bits of its two operands, `11` and `13` in the example, in a pairwise fashion and *iff* both operands have a $1$ in the same position, will the resulting integer have a $1$ in this position as well. The binary representations of `11` and `13` have $1$s in their respective first and fourth bits, which is why `bin(11 & 13)` evaluates to `\"Ob1001\"`."
+    "The first operator is the **bitwise AND** operator `&`: It looks at the bits of its two operands, `11` and `13` in the example, in a pairwise fashion and if *both* operands have a $1$ in the *same* position, the resulting integer will have a $1$ in this position as well. Otherwise, the resulting integer will have a $0$ in this position. The binary representations of `11` and `13` both have $1$s in their respective first and fourth bits, which is why `bin(11 & 13)` evaluates to `Ob_1001` or `9`."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 47,
+   "execution_count": 52,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -1491,24 +1797,48 @@
     {
      "data": {
       "text/plain": [
-       "'0b1011 & 0b1101'"
+       "9"
       ]
      },
-     "execution_count": 47,
+     "execution_count": 52,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "bin(11) + \" & \" + bin(13)  # to show the operands' binary representations"
+    "11 & 13"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 48,
+   "execution_count": 53,
    "metadata": {
     "slideshow": {
-     "slide_type": "-"
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'0b1011 & 0b1101'"
+      ]
+     },
+     "execution_count": 53,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "bin(11) + \" & \" + bin(13)  # to show the operands' bits"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 54,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
     }
    },
    "outputs": [
@@ -1518,7 +1848,7 @@
        "'0b1001'"
       ]
      },
-     "execution_count": 48,
+     "execution_count": 54,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1535,12 +1865,12 @@
     }
    },
    "source": [
-    "`0b1001` is the binary representation of `9`."
+    "`0b_1001` is the binary representation of `9`."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 49,
+   "execution_count": 55,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -1553,37 +1883,13 @@
        "9"
       ]
      },
-     "execution_count": 49,
+     "execution_count": 55,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "0b1001"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 50,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "-"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "9"
-      ]
-     },
-     "execution_count": 50,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "11 & 13"
+    "0b_1001"
    ]
   },
   {
@@ -1594,12 +1900,12 @@
     }
    },
    "source": [
-    "The **bitwise OR** operator `|` evaluates to an `int` object whose bits are set to $1$ if the corresponding bits of either *one* or *both* operands are $1$. So in the example `9 | 13` only the second bit is $0$ for both operands, which is why the expression evaluates to `\"0b1101\"`."
+    "The **bitwise OR** operator `|` evaluates to an `int` object whose bits are set to $1$ if the corresponding bits of either *one* or *both* operands are $1$. So in the example `9 | 13` only the second bit is $0$ for both operands, which is why the expression evaluates to `0b_1101` or `13`."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 51,
+   "execution_count": 56,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -1609,24 +1915,48 @@
     {
      "data": {
       "text/plain": [
-       "'0b1001 | 0b1101'"
+       "13"
       ]
      },
-     "execution_count": 51,
+     "execution_count": 56,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "bin(9) + \" | \" + bin(13)  # to show the operands' binary representations"
+    "9 | 13"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 52,
+   "execution_count": 57,
    "metadata": {
     "slideshow": {
-     "slide_type": "-"
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'0b1001 | 0b1101'"
+      ]
+     },
+     "execution_count": 57,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "bin(9) + \" | \" + bin(13)  # to show the operands' bits"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 58,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
     }
    },
    "outputs": [
@@ -1636,7 +1966,7 @@
        "'0b1101'"
       ]
      },
-     "execution_count": 52,
+     "execution_count": 58,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1653,12 +1983,12 @@
     }
    },
    "source": [
-    "`0b1101` evaluates to the `int` object `13`."
+    "`0b_1101` evaluates to an `int` object with the value `13`."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 53,
+   "execution_count": 59,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -1671,37 +2001,13 @@
        "13"
       ]
      },
-     "execution_count": 53,
+     "execution_count": 59,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "0b1101"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 54,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "-"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "13"
-      ]
-     },
-     "execution_count": 54,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "9 | 13"
+    "0b_1101"
    ]
   },
   {
@@ -1712,12 +2018,12 @@
     }
    },
    "source": [
-    "The **bitwise XOR** operator `^` is a special case of the `|` operator in that it evaluates to an `int` object whose bits are set to $1$ if the corresponding bit of *exactly one* of the two operands is $1$. Colloquially, the \"X\" stands for \"exclusive.\" The `^` operator must *not* be confused with the exponentiation operator `**`! In the example, `9 ^ 13`, only the third bit differs between the two operands, which is why it evaluates to `\"0b100\"` omitting the fourth bit."
+    "The **bitwise XOR** operator `^` is a special case of the `|` operator in that it evaluates to an `int` object whose bits are set to $1$ if the corresponding bit of *exactly one* of the two operands is $1$. Colloquially, the \"X\" stands for \"exclusive.\" The `^` operator must *not* be confused with the exponentiation operator `**`! In the example, `9 ^ 13`, only the third bit differs between the two operands, which is why it evaluates to `0b_100` omitting the leading $0$."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 55,
+   "execution_count": 60,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -1727,24 +2033,48 @@
     {
      "data": {
       "text/plain": [
-       "'0b1001 ^ 0b1101'"
+       "4"
       ]
      },
-     "execution_count": 55,
+     "execution_count": 60,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "bin(9) + \" ^ \" + bin(13)  # to show the operands' binary representations"
+    "9 ^ 13"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 56,
+   "execution_count": 61,
    "metadata": {
     "slideshow": {
-     "slide_type": "-"
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'0b1001 ^ 0b1101'"
+      ]
+     },
+     "execution_count": 61,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "bin(9) + \" ^ \" + bin(13)  # to show the operands' bits"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 62,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
     }
    },
    "outputs": [
@@ -1754,7 +2084,7 @@
        "'0b100'"
       ]
      },
-     "execution_count": 56,
+     "execution_count": 62,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1771,12 +2101,12 @@
     }
    },
    "source": [
-    "`0b100` evaluates to the `int` object `4`."
+    "`0b_100` evaluates to an `int` object with the value `4`."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 57,
+   "execution_count": 63,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -1789,37 +2119,13 @@
        "4"
       ]
      },
-     "execution_count": 57,
+     "execution_count": 63,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "0b100"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 58,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "-"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "4"
-      ]
-     },
-     "execution_count": 58,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "9 ^ 13"
+    "0b_100"
    ]
   },
   {
@@ -1830,14 +2136,14 @@
     }
    },
    "source": [
-    "The **bitwise NOT** operator `~`, sometimes also called **inversion** operator, is said to \"flip\" the $0$s into $1$s and the $1$s into $0$s. However, it is based on the aforementioned [Two's Complement](https://en.wikipedia.org/wiki/Two%27s_complement) convention and `~x = -(x + 1)` by definition (cf., the [reference](https://docs.python.org/3/reference/expressions.html#unary-arithmetic-and-bitwise-operations)). The full logic behind this is considered out of scope in this book.\n",
+    "The **bitwise NOT** operator `~`, sometimes also called **inversion** operator, is said to \"flip\" the $0$s into $1$s and the $1$s into $0$s. However, it is based on the aforementioned [Two's Complement](https://en.wikipedia.org/wiki/Two%27s_complement) convention and `~x = -(x + 1)` by definition (cf., the [reference](https://docs.python.org/3/reference/expressions.html#unary-arithmetic-and-bitwise-operations)). The full logic behind this, while actually quite simple, is considered out of scope in this book.\n",
     "\n",
-    "We can at least verify the definition by comparing the binary representations of `8` and `-9`: They are indeed the same."
+    "We can at least verify the definition by comparing the binary representations of `7` and `-8`: They are indeed the same."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 59,
+   "execution_count": 64,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -1847,40 +2153,88 @@
     {
      "data": {
       "text/plain": [
-       "'-0b1001'"
+       "-8"
       ]
      },
-     "execution_count": 59,
+     "execution_count": 64,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "bin(~8)"
+    "~7"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 60,
+   "execution_count": 65,
    "metadata": {
     "slideshow": {
-     "slide_type": "-"
+     "slide_type": "fragment"
     }
    },
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "'-0b1001'"
+       "True"
       ]
      },
-     "execution_count": 60,
+     "execution_count": 65,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "bin(-(8 + 1))"
+    "~7 == -(7 + 1)  # = Two's Complement"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 66,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'-0b1000'"
+      ]
+     },
+     "execution_count": 66,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "bin(~7)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 67,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'-0b1000'"
+      ]
+     },
+     "execution_count": 67,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "bin(-(7 + 1))"
    ]
   },
   {
@@ -1896,10 +2250,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 61,
+   "execution_count": 68,
    "metadata": {
     "slideshow": {
-     "slide_type": "fragment"
+     "slide_type": "skip"
     }
    },
    "outputs": [
@@ -1909,13 +2263,13 @@
        "-1"
       ]
      },
-     "execution_count": 61,
+     "execution_count": 68,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "~8 + 8"
+    "~7 + 7"
    ]
   },
   {
@@ -1926,14 +2280,14 @@
     }
    },
    "source": [
-    "Lastly, the **bitwise left and right shift** operators, `<<` and `>>`, shift all the bits either to the left or to the right. This corresponds to multiplying or dividing an integer by powers of $2$.\n",
+    "Lastly, the **bitwise left and right shift** operators, `<<` and `>>`, shift all the bits either to the left or to the right. This corresponds to multiplying or dividing an integer by powers of `2`.\n",
     "\n",
     "When shifting left, $0$s are filled in."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 62,
+   "execution_count": 69,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -1943,64 +2297,88 @@
     {
      "data": {
       "text/plain": [
-       "'0b1001'"
+       "28"
       ]
      },
-     "execution_count": 62,
+     "execution_count": 69,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "bin(9)"
+    "7 << 2"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 63,
+   "execution_count": 70,
    "metadata": {
     "slideshow": {
-     "slide_type": "-"
+     "slide_type": "fragment"
     }
    },
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "'0b100100'"
+       "'0b111'"
       ]
      },
-     "execution_count": 63,
+     "execution_count": 70,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "bin(9 << 2)"
+    "bin(7)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 64,
+   "execution_count": 71,
    "metadata": {
     "slideshow": {
-     "slide_type": "-"
+     "slide_type": "fragment"
     }
    },
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "36"
+       "'0b11100'"
       ]
      },
-     "execution_count": 64,
+     "execution_count": 71,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "9 << 2"
+    "bin(7 << 2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 72,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "28"
+      ]
+     },
+     "execution_count": 72,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "0b_1_1100"
    ]
   },
   {
@@ -2016,7 +2394,31 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 65,
+   "execution_count": 73,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "3"
+      ]
+     },
+     "execution_count": 73,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "7 >> 1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 74,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -2026,40 +2428,64 @@
     {
      "data": {
       "text/plain": [
-       "'0b10'"
+       "'0b111'"
       ]
      },
-     "execution_count": 65,
+     "execution_count": 74,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "bin(9 >> 2)"
+    "bin(7)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 66,
+   "execution_count": 75,
    "metadata": {
     "slideshow": {
-     "slide_type": "-"
+     "slide_type": "fragment"
     }
    },
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "2"
+       "'0b11'"
       ]
      },
-     "execution_count": 66,
+     "execution_count": 75,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "9 >> 2"
+    "bin(7 >> 1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 76,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "3"
+      ]
+     },
+     "execution_count": 76,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "0b_11"
    ]
   },
   {
@@ -2092,7 +2518,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 67,
+   "execution_count": 77,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -2100,12 +2526,12 @@
    },
    "outputs": [],
    "source": [
-    "f = 1.23"
+    "b = 42.0"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 68,
+   "execution_count": 78,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -2115,24 +2541,24 @@
     {
      "data": {
       "text/plain": [
-       "140166567715664"
+       "139674754277776"
       ]
      },
-     "execution_count": 68,
+     "execution_count": 78,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "id(f)"
+    "id(b)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 69,
+   "execution_count": 79,
    "metadata": {
     "slideshow": {
-     "slide_type": "-"
+     "slide_type": "fragment"
     }
    },
    "outputs": [
@@ -2142,37 +2568,37 @@
        "float"
       ]
      },
-     "execution_count": 69,
+     "execution_count": 79,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "type(f)"
+    "type(b)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 70,
+   "execution_count": 80,
    "metadata": {
     "slideshow": {
-     "slide_type": "-"
+     "slide_type": "fragment"
     }
    },
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "1.23"
+       "42.0"
       ]
      },
-     "execution_count": 70,
+     "execution_count": 80,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "f"
+    "b"
    ]
   },
   {
@@ -2188,7 +2614,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 71,
+   "execution_count": 81,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -2201,7 +2627,7 @@
        "0.123456789"
       ]
      },
-     "execution_count": 71,
+     "execution_count": 81,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2223,7 +2649,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 72,
+   "execution_count": 82,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -2233,45 +2659,80 @@
     {
      "data": {
       "text/plain": [
-       "1.0"
+       "42.0"
       ]
      },
-     "execution_count": 72,
+     "execution_count": 82,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "1.  # 1 without a dot creates an int object"
+    "42."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 73,
+   "execution_count": 83,
    "metadata": {
     "slideshow": {
-     "slide_type": "-"
+     "slide_type": "fragment"
     }
    },
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "1.0"
+       "42.0"
       ]
      },
-     "execution_count": 73,
+     "execution_count": 83,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "float(1)"
+    "float(42)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 74,
+   "execution_count": 84,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "42.0"
+      ]
+     },
+     "execution_count": 84,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "float(\"42\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Leading and trailing whitespace is ignored ..."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 85,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -2281,16 +2742,52 @@
     {
      "data": {
       "text/plain": [
-       "1.0"
+       "42.87"
       ]
      },
-     "execution_count": 74,
+     "execution_count": 85,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "float(\"1.000\")"
+    "float(\" 42.87 \")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "... but not whitespace in between."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 86,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "ename": "ValueError",
+     "evalue": "could not convert string to float: '42. 87'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-86-c7953b8b7956>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mfloat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"42. 87\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;31mValueError\u001b[0m: could not convert string to float: '42. 87'"
+     ]
+    }
+   ],
+   "source": [
+    "float(\"42. 87\")"
    ]
   },
   {
@@ -2306,7 +2803,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 75,
+   "execution_count": 87,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -2319,7 +2816,7 @@
        "0.3333333333333333"
       ]
      },
-     "execution_count": 75,
+     "execution_count": 87,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2341,7 +2838,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 76,
+   "execution_count": 88,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -2354,7 +2851,7 @@
        "42.0"
       ]
      },
-     "execution_count": 76,
+     "execution_count": 88,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2365,10 +2862,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 77,
+   "execution_count": 89,
    "metadata": {
     "slideshow": {
-     "slide_type": "-"
+     "slide_type": "fragment"
     }
    },
    "outputs": [
@@ -2378,7 +2875,7 @@
        "42.0"
       ]
      },
-     "execution_count": 77,
+     "execution_count": 89,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2411,7 +2908,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 78,
+   "execution_count": 90,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -2424,7 +2921,7 @@
        "1.23"
       ]
      },
-     "execution_count": 78,
+     "execution_count": 90,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2446,7 +2943,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 79,
+   "execution_count": 91,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -2455,10 +2952,10 @@
    "outputs": [
     {
      "ename": "SyntaxError",
-     "evalue": "invalid syntax (<ipython-input-79-1b5daaac0077>, line 1)",
+     "evalue": "invalid syntax (<ipython-input-91-1b5daaac0077>, line 1)",
      "output_type": "error",
      "traceback": [
-      "\u001b[0;36m  File \u001b[0;32m\"<ipython-input-79-1b5daaac0077>\"\u001b[0;36m, line \u001b[0;32m1\u001b[0m\n\u001b[0;31m    1.23 e0\u001b[0m\n\u001b[0m          ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m invalid syntax\n"
+      "\u001b[0;36m  File \u001b[0;32m\"<ipython-input-91-1b5daaac0077>\"\u001b[0;36m, line \u001b[0;32m1\u001b[0m\n\u001b[0;31m    1.23 e0\u001b[0m\n\u001b[0m          ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m invalid syntax\n"
      ]
     }
    ],
@@ -2468,7 +2965,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 80,
+   "execution_count": 92,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -2477,10 +2974,10 @@
    "outputs": [
     {
      "ename": "SyntaxError",
-     "evalue": "invalid syntax (<ipython-input-80-a236c4a30231>, line 1)",
+     "evalue": "invalid syntax (<ipython-input-92-a236c4a30231>, line 1)",
      "output_type": "error",
      "traceback": [
-      "\u001b[0;36m  File \u001b[0;32m\"<ipython-input-80-a236c4a30231>\"\u001b[0;36m, line \u001b[0;32m1\u001b[0m\n\u001b[0;31m    1.23e 0\u001b[0m\n\u001b[0m        ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m invalid syntax\n"
+      "\u001b[0;36m  File \u001b[0;32m\"<ipython-input-92-a236c4a30231>\"\u001b[0;36m, line \u001b[0;32m1\u001b[0m\n\u001b[0;31m    1.23e 0\u001b[0m\n\u001b[0m        ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m invalid syntax\n"
      ]
     }
    ],
@@ -2490,7 +2987,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 81,
+   "execution_count": 93,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -2499,10 +2996,10 @@
    "outputs": [
     {
      "ename": "SyntaxError",
-     "evalue": "invalid syntax (<ipython-input-81-05b9072d76be>, line 1)",
+     "evalue": "invalid syntax (<ipython-input-93-05b9072d76be>, line 1)",
      "output_type": "error",
      "traceback": [
-      "\u001b[0;36m  File \u001b[0;32m\"<ipython-input-81-05b9072d76be>\"\u001b[0;36m, line \u001b[0;32m1\u001b[0m\n\u001b[0;31m    1.23e0.0\u001b[0m\n\u001b[0m           ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m invalid syntax\n"
+      "\u001b[0;36m  File \u001b[0;32m\"<ipython-input-93-05b9072d76be>\"\u001b[0;36m, line \u001b[0;32m1\u001b[0m\n\u001b[0;31m    1.23e0.0\u001b[0m\n\u001b[0m           ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m invalid syntax\n"
      ]
     }
    ],
@@ -2518,32 +3015,32 @@
     }
    },
    "source": [
-    "If we leave out the number to the left, Python raises a `NameError` as it unsuccessfully tries to look up a variable named `e1`."
+    "If we leave out the number to the left, Python raises a `NameError` as it unsuccessfully tries to look up a variable named `e0`."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 82,
+   "execution_count": 94,
    "metadata": {
     "slideshow": {
-     "slide_type": "fragment"
+     "slide_type": "skip"
     }
    },
    "outputs": [
     {
      "ename": "NameError",
-     "evalue": "name 'e1' is not defined",
+     "evalue": "name 'e0' is not defined",
      "output_type": "error",
      "traceback": [
       "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
       "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-82-eff9d613b2d7>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0me1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
-      "\u001b[0;31mNameError\u001b[0m: name 'e1' is not defined"
+      "\u001b[0;32m<ipython-input-94-abc4de6ccb5e>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0me0\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;31mNameError\u001b[0m: name 'e0' is not defined"
      ]
     }
    ],
    "source": [
-    "e1"
+    "e0"
    ]
   },
   {
@@ -2554,12 +3051,12 @@
     }
    },
    "source": [
-    "So, to write $10^1$ in Python, we need to think of it as $1*10^1$ and write `1e1`."
+    "So, to write $10^0$ in Python, we need to think of it as $1*10^0$ and write `1e0`."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 83,
+   "execution_count": 95,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -2569,16 +3066,86 @@
     {
      "data": {
       "text/plain": [
-       "10.0"
+       "1.0"
       ]
      },
-     "execution_count": 83,
+     "execution_count": 95,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "1e1"
+    "1e0"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "To express thousands of something (i.e., $10^3$), we write `1e3`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 96,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "1000.0"
+      ]
+     },
+     "execution_count": 96,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "1e3  # = thousands"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Similarly, to express, for example, milliseconds (i.e., $10^{-3} s$), we write `1e-3`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 97,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.001"
+      ]
+     },
+     "execution_count": 97,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "1e-3  # = milli"
    ]
   },
   {
@@ -2605,7 +3172,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 84,
+   "execution_count": 98,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -2618,18 +3185,18 @@
        "nan"
       ]
      },
-     "execution_count": 84,
+     "execution_count": 98,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "float(\"nan\")  # also works as float(\"NaN\")"
+    "float(\"nan\")  # also float(\"NaN\")"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 85,
+   "execution_count": 99,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -2642,21 +3209,21 @@
        "inf"
       ]
      },
-     "execution_count": 85,
+     "execution_count": 99,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "float(\"+inf\")  # also works as float(\"+infinity\")"
+    "float(\"+inf\")  # also float(\"+infinity\") or float(\"infinity\")"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 86,
+   "execution_count": 100,
    "metadata": {
     "slideshow": {
-     "slide_type": "-"
+     "slide_type": "fragment"
     }
    },
    "outputs": [
@@ -2666,21 +3233,21 @@
        "inf"
       ]
      },
-     "execution_count": 86,
+     "execution_count": 100,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "float(\"inf\")  # by omitting the plus sign we mean positive infinity"
+    "float(\"inf\")  # same as float(\"+inf\")"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 87,
+   "execution_count": 101,
    "metadata": {
     "slideshow": {
-     "slide_type": "-"
+     "slide_type": "fragment"
     }
    },
    "outputs": [
@@ -2690,7 +3257,7 @@
        "-inf"
       ]
      },
-     "execution_count": 87,
+     "execution_count": 101,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2712,7 +3279,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 88,
+   "execution_count": 102,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -2725,7 +3292,7 @@
        "False"
       ]
      },
-     "execution_count": 88,
+     "execution_count": 102,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2742,17 +3309,52 @@
     }
    },
    "source": [
-    "On the contrary, as two values go to infinity, there is no such concept as difference and *everything* compares equal."
+    "Another caveat is that any arithmetic involving a `nan` object results in `nan`. In other words, the addition below **fails silently** as no error is raised. As this also happens in accordance with the [IEEE 754](https://en.wikipedia.org/wiki/IEEE_754) standard, we *need* to be aware of that and check any data we work with for any `nan` occurrences *before* doing any calculations."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 89,
+   "execution_count": 103,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
     }
    },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "nan"
+      ]
+     },
+     "execution_count": 103,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "42 + float(\"nan\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "On the contrary, as two values go to infinity, there is no such concept as difference and *everything* compares equal."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 104,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
    "outputs": [
     {
      "data": {
@@ -2760,7 +3362,7 @@
        "True"
       ]
      },
-     "execution_count": 89,
+     "execution_count": 104,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2782,7 +3384,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 90,
+   "execution_count": 105,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -2795,7 +3397,7 @@
        "inf"
       ]
      },
-     "execution_count": 90,
+     "execution_count": 105,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2806,7 +3408,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 91,
+   "execution_count": 106,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -2819,7 +3421,7 @@
        "True"
       ]
      },
-     "execution_count": 91,
+     "execution_count": 106,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2841,7 +3443,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 92,
+   "execution_count": 107,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -2854,7 +3456,7 @@
        "inf"
       ]
      },
-     "execution_count": 92,
+     "execution_count": 107,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2865,7 +3467,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 93,
+   "execution_count": 108,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -2878,7 +3480,7 @@
        "True"
       ]
      },
-     "execution_count": 93,
+     "execution_count": 108,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2900,7 +3502,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 94,
+   "execution_count": 109,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -2913,7 +3515,7 @@
        "inf"
       ]
      },
-     "execution_count": 94,
+     "execution_count": 109,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2924,7 +3526,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 95,
+   "execution_count": 110,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -2937,7 +3539,7 @@
        "True"
       ]
      },
-     "execution_count": 95,
+     "execution_count": 110,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2959,7 +3561,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 96,
+   "execution_count": 111,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -2972,7 +3574,7 @@
        "inf"
       ]
      },
-     "execution_count": 96,
+     "execution_count": 111,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2983,7 +3585,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 97,
+   "execution_count": 112,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -2996,7 +3598,7 @@
        "True"
       ]
      },
-     "execution_count": 97,
+     "execution_count": 112,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3013,12 +3615,12 @@
     }
    },
    "source": [
-    "As a caveat, adding infinities of different signs is an *undefined operation* in math and results in a `nan` object. So, if we (accidentally or unknowingly) do this on a real dataset, we do *not* see any error messages, and our program may continue to run with non-meaningful results! This is an example of a piece of code **failing silently**."
+    "As a caveat, adding infinities of different signs is an *undefined operation* in math and results in a `nan` object. So, if we (accidentally or unknowingly) do this on a real dataset, we do *not* see any error messages, and our program may continue to run with non-meaningful results! This is another example of a piece of code **failing silently**."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 98,
+   "execution_count": 113,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -3031,7 +3633,7 @@
        "nan"
       ]
      },
-     "execution_count": 98,
+     "execution_count": 113,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3042,10 +3644,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 99,
+   "execution_count": 114,
    "metadata": {
     "slideshow": {
-     "slide_type": "-"
+     "slide_type": "fragment"
     }
    },
    "outputs": [
@@ -3055,7 +3657,7 @@
        "nan"
       ]
      },
-     "execution_count": 99,
+     "execution_count": 114,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3085,71 +3687,12 @@
    "source": [
     "`float` objects are *inherently* imprecise, and there is *nothing* we can do about it! In particular, arithmetic operations with two `float` objects may result in \"weird\" rounding \"errors\" that are strictly deterministic and occur in accordance with the [IEEE 754](https://en.wikipedia.org/wiki/IEEE_754) standard.\n",
     "\n",
-    "For example, let's add `1e0` to `1e15` and `1e16`, respectively. In the latter case, the `1e0` somehow gets \"lost.\""
+    "For example, let's add `1` to `1e15` and `1e16`, respectively. In the latter case, the `1` somehow gets \"lost.\""
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 100,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "1000000000000001.0"
-      ]
-     },
-     "execution_count": 100,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "1e15 + 1e0"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 101,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "1e+16"
-      ]
-     },
-     "execution_count": 101,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "1e16 + 1e0"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "source": [
-    "Of course, we may also add the `int` object `1` to the `float` objects in the literal `e` notation with the same outcome."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 102,
+   "execution_count": 115,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -3162,7 +3705,7 @@
        "1000000000000001.0"
       ]
      },
-     "execution_count": 102,
+     "execution_count": 115,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3173,10 +3716,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 103,
+   "execution_count": 116,
    "metadata": {
     "slideshow": {
-     "slide_type": "-"
+     "slide_type": "fragment"
     }
    },
    "outputs": [
@@ -3186,7 +3729,7 @@
        "1e+16"
       ]
      },
-     "execution_count": 103,
+     "execution_count": 116,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3208,7 +3751,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 104,
+   "execution_count": 117,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -3221,10 +3764,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 105,
+   "execution_count": 118,
    "metadata": {
     "slideshow": {
-     "slide_type": "-"
+     "slide_type": "fragment"
     }
    },
    "outputs": [
@@ -3234,7 +3777,7 @@
        "2.0000000000000004"
       ]
      },
-     "execution_count": 105,
+     "execution_count": 118,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3245,7 +3788,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 106,
+   "execution_count": 119,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -3258,7 +3801,7 @@
        "0.30000000000000004"
       ]
      },
-     "execution_count": 106,
+     "execution_count": 119,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3280,7 +3823,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 107,
+   "execution_count": 120,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -3293,7 +3836,7 @@
        "False"
       ]
      },
-     "execution_count": 107,
+     "execution_count": 120,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3304,10 +3847,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 108,
+   "execution_count": 121,
    "metadata": {
     "slideshow": {
-     "slide_type": "-"
+     "slide_type": "fragment"
     }
    },
    "outputs": [
@@ -3317,7 +3860,7 @@
        "False"
       ]
      },
-     "execution_count": 108,
+     "execution_count": 121,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3339,7 +3882,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 109,
+   "execution_count": 122,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -3352,7 +3895,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 110,
+   "execution_count": 123,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -3365,7 +3908,7 @@
        "True"
       ]
      },
-     "execution_count": 110,
+     "execution_count": 123,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3376,10 +3919,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 111,
+   "execution_count": 124,
    "metadata": {
     "slideshow": {
-     "slide_type": "-"
+     "slide_type": "fragment"
     }
    },
    "outputs": [
@@ -3389,7 +3932,7 @@
        "True"
       ]
      },
-     "execution_count": 111,
+     "execution_count": 124,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3413,7 +3956,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 112,
+   "execution_count": 125,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -3426,7 +3969,7 @@
        "'0.10000000000000000555111512312578270211815834045410'"
       ]
      },
-     "execution_count": 112,
+     "execution_count": 125,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3437,10 +3980,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 113,
+   "execution_count": 126,
    "metadata": {
     "slideshow": {
-     "slide_type": "-"
+     "slide_type": "fragment"
     }
    },
    "outputs": [
@@ -3450,7 +3993,7 @@
        "'0.20000000000000001110223024625156540423631668090820'"
       ]
      },
-     "execution_count": 113,
+     "execution_count": 126,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3461,10 +4004,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 114,
+   "execution_count": 127,
    "metadata": {
     "slideshow": {
-     "slide_type": "-"
+     "slide_type": "fragment"
     }
    },
    "outputs": [
@@ -3474,7 +4017,7 @@
        "'0.29999999999999998889776975374843459576368331909180'"
       ]
      },
-     "execution_count": 114,
+     "execution_count": 127,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3485,7 +4028,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 115,
+   "execution_count": 128,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -3498,7 +4041,7 @@
        "'0.33333333333333331482961625624739099293947219848633'"
       ]
      },
-     "execution_count": 115,
+     "execution_count": 128,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3524,7 +4067,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 116,
+   "execution_count": 129,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -3537,10 +4080,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 117,
+   "execution_count": 130,
    "metadata": {
     "slideshow": {
-     "slide_type": "-"
+     "slide_type": "fragment"
     }
    },
    "outputs": [
@@ -3550,7 +4093,7 @@
        "0.33333"
       ]
      },
-     "execution_count": 117,
+     "execution_count": 130,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3561,10 +4104,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 118,
+   "execution_count": 131,
    "metadata": {
     "slideshow": {
-     "slide_type": "-"
+     "slide_type": "fragment"
     }
    },
    "outputs": [
@@ -3574,7 +4117,7 @@
        "'0.33333000000000001517008740847813896834850311279297'"
       ]
      },
-     "execution_count": 118,
+     "execution_count": 131,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3596,7 +4139,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 119,
+   "execution_count": 132,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -3609,7 +4152,7 @@
        "'0.12500000000000000000000000000000000000000000000000'"
       ]
      },
-     "execution_count": 119,
+     "execution_count": 132,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3620,10 +4163,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 120,
+   "execution_count": 133,
    "metadata": {
     "slideshow": {
-     "slide_type": "-"
+     "slide_type": "fragment"
     }
    },
    "outputs": [
@@ -3633,7 +4176,7 @@
        "'0.25000000000000000000000000000000000000000000000000'"
       ]
      },
-     "execution_count": 120,
+     "execution_count": 133,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3644,10 +4187,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 121,
+   "execution_count": 134,
    "metadata": {
     "slideshow": {
-     "slide_type": "-"
+     "slide_type": "fragment"
     }
    },
    "outputs": [
@@ -3657,7 +4200,7 @@
        "True"
       ]
      },
-     "execution_count": 121,
+     "execution_count": 134,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3685,10 +4228,28 @@
     }
    },
    "source": [
-    "To understand these subtleties, we need to look at the **[binary representation of floats](https://en.wikipedia.org/wiki/Double-precision_floating-point_format)** and review the basics of the **[IEEE 754](https://en.wikipedia.org/wiki/IEEE_754)** standard. On modern machines, floats are modeled in so-called double precision with $64$ bits that are grouped as in the figure below. The first bit determines the sign ($0$ for plus, $1$ for minus), the next $11$ bits represent an $exponent$ term, and the last $52$ bits resemble the actual significant digits, the so-called $fraction$ part. The three groups are put together like so:\n",
-    "\n",
-    "$$float = (-1)^{sign} * 1.fraction * 2^{exponent-1023}$$\n",
-    "\n",
+    "To understand these subtleties, we need to look at the **[binary representation of floats](https://en.wikipedia.org/wiki/Double-precision_floating-point_format)** and review the basics of the **[IEEE 754](https://en.wikipedia.org/wiki/IEEE_754)** standard. On modern machines, floats are modeled in so-called double precision with $64$ bits that are grouped as in the figure below. The first bit determines the sign ($0$ for plus, $1$ for minus), the next $11$ bits represent an $exponent$ term, and the last $52$ bits resemble the actual significant digits, the so-called $fraction$ part. The three groups are put together like so:"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "$$float = (-1)^{sign} * 1.fraction * 2^{exponent-1023}$$"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
     "A $1.$ is implicitly prepended as the first digit, and both, $fraction$ and $exponent$, are stored in base $2$ representation (i.e., they both are interpreted like integers above). As $exponent$ is consequently non-negative, between $0_{10}$ and $2047_{10}$ to be precise, the $-1023$, called the exponent bias, centers the entire $2^{exponent-1023}$ term around $1$ and allows the period within the $1.fraction$ part be shifted into either direction by the same amount. Floating-point numbers received their name as the period, formally called the **[radix point](https://en.wikipedia.org/wiki/Radix_point)**, \"floats\" along the significant digits. As an aside, an $exponent$ of all $0$s or all $1$s is used to model the special values `nan` or `inf`.\n",
     "\n",
     "As the standard defines the exponent part to come as a power of $2$, we now see why `0.125` is a *precise* float: It can be represented as a power of $2$, i.e., $0.125 = (-1)^0 * 1.0 * 2^{1020-1023} = 2^{-3} = \\frac{1}{8}$. In other words, the floating-point representation of $0.125_{10}$ is $0_2$, $1111111100_2 = 1020_{10}$, and $0_2$ for the three groups, respectively."
@@ -3698,7 +4259,7 @@
    "cell_type": "markdown",
    "metadata": {
     "slideshow": {
-     "slide_type": "slide"
+     "slide_type": "-"
     }
    },
    "source": [
@@ -3726,7 +4287,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 122,
+   "execution_count": 135,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -3739,10 +4300,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 123,
+   "execution_count": 136,
    "metadata": {
     "slideshow": {
-     "slide_type": "-"
+     "slide_type": "fragment"
     }
    },
    "outputs": [
@@ -3752,13 +4313,13 @@
        "'0x1.0000000000000p-3'"
       ]
      },
-     "execution_count": 123,
+     "execution_count": 136,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "one_eighth.hex()  # this basically says 2 ** (-3)"
+    "one_eighth.hex()"
    ]
   },
   {
@@ -3774,10 +4335,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 124,
+   "execution_count": 137,
    "metadata": {
     "slideshow": {
-     "slide_type": "-"
+     "slide_type": "fragment"
     }
    },
    "outputs": [
@@ -3787,7 +4348,7 @@
        "(1, 8)"
       ]
      },
-     "execution_count": 124,
+     "execution_count": 137,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3798,10 +4359,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 125,
+   "execution_count": 138,
    "metadata": {
     "slideshow": {
-     "slide_type": "fragment"
+     "slide_type": "slide"
     }
    },
    "outputs": [
@@ -3811,7 +4372,7 @@
        "'0x1.555475a31a4bep-2'"
       ]
      },
-     "execution_count": 125,
+     "execution_count": 138,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3822,10 +4383,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 126,
+   "execution_count": 139,
    "metadata": {
     "slideshow": {
-     "slide_type": "-"
+     "slide_type": "fragment"
     }
    },
    "outputs": [
@@ -3835,7 +4396,7 @@
        "(3002369727582815, 9007199254740992)"
       ]
      },
-     "execution_count": 126,
+     "execution_count": 139,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3857,10 +4418,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 127,
+   "execution_count": 140,
    "metadata": {
     "slideshow": {
-     "slide_type": "slide"
+     "slide_type": "skip"
     }
    },
    "outputs": [],
@@ -3870,10 +4431,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 128,
+   "execution_count": 141,
    "metadata": {
     "slideshow": {
-     "slide_type": "-"
+     "slide_type": "skip"
     }
    },
    "outputs": [
@@ -3883,7 +4444,7 @@
        "'0x0.0p+0'"
       ]
      },
-     "execution_count": 128,
+     "execution_count": 141,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3894,10 +4455,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 129,
+   "execution_count": 142,
    "metadata": {
     "slideshow": {
-     "slide_type": "-"
+     "slide_type": "skip"
     }
    },
    "outputs": [
@@ -3907,7 +4468,7 @@
        "(0, 1)"
       ]
      },
-     "execution_count": 129,
+     "execution_count": 142,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3929,7 +4490,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 130,
+   "execution_count": 143,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -3942,7 +4503,7 @@
        "False"
       ]
      },
-     "execution_count": 130,
+     "execution_count": 143,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3953,7 +4514,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 131,
+   "execution_count": 144,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -3966,7 +4527,7 @@
        "True"
       ]
      },
-     "execution_count": 131,
+     "execution_count": 144,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3990,7 +4551,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 132,
+   "execution_count": 145,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -4003,7 +4564,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 133,
+   "execution_count": 146,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -4016,7 +4577,7 @@
        "sys.float_info(max=1.7976931348623157e+308, max_exp=1024, max_10_exp=308, min=2.2250738585072014e-308, min_exp=-1021, min_10_exp=-307, dig=15, mant_dig=53, epsilon=2.220446049250313e-16, radix=2, rounds=1)"
       ]
      },
-     "execution_count": 133,
+     "execution_count": 146,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -4044,14 +4605,14 @@
     }
    },
    "source": [
-    "The [decimal](https://docs.python.org/3/library/decimal.html) module in the [standard library](https://docs.python.org/3/library/index.html) provides a [Decimal](https://docs.python.org/3/library/decimal.html#decimal.Decimal) type that may be used to represent any real number to a user-defined level of precision: \"User-defined\" does *not* mean an infinite or \"exact\" precision! The `Decimal` type merely allows us to work with a number of bits *different* from the $64$ as specified for the `float` type and also to customize the rounding rules and some other settings.\n",
+    "The [decimal](https://docs.python.org/3/library/decimal.html) module in the [standard library](https://docs.python.org/3/library/index.html) provides a [Decimal](https://docs.python.org/3/library/decimal.html#decimal.Decimal) type that may be used to represent any real number to a user-defined level of precision: \"User-defined\" does *not* mean an infinite or exact precision! The `Decimal` type merely allows us to work with a number of bits *different* from the $64$ as specified for the `float` type and also to customize the rounding rules and some other settings.\n",
     "\n",
     "We import the `Decimal` type and also the [getcontext()](https://docs.python.org/3/library/decimal.html#decimal.getcontext) function from the [decimal](https://docs.python.org/3/library/decimal.html) module."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 134,
+   "execution_count": 147,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -4075,7 +4636,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 135,
+   "execution_count": 148,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -4088,7 +4649,7 @@
        "Context(prec=28, rounding=ROUND_HALF_EVEN, Emin=-999999, Emax=999999, capitals=1, clamp=0, flags=[], traps=[InvalidOperation, DivisionByZero, Overflow])"
       ]
      },
-     "execution_count": 135,
+     "execution_count": 148,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -4110,7 +4671,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 136,
+   "execution_count": 149,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -4123,7 +4684,7 @@
        "Decimal('42')"
       ]
      },
-     "execution_count": 136,
+     "execution_count": 149,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -4134,10 +4695,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 137,
+   "execution_count": 150,
    "metadata": {
     "slideshow": {
-     "slide_type": "-"
+     "slide_type": "fragment"
     }
    },
    "outputs": [
@@ -4147,7 +4708,7 @@
        "Decimal('0.1')"
       ]
      },
-     "execution_count": 137,
+     "execution_count": 150,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -4158,26 +4719,26 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 138,
+   "execution_count": 151,
    "metadata": {
     "slideshow": {
-     "slide_type": "-"
+     "slide_type": "fragment"
     }
    },
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "Decimal('1E+5')"
+       "Decimal('0.001')"
       ]
      },
-     "execution_count": 138,
+     "execution_count": 151,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "Decimal(\"1e5\")"
+    "Decimal(\"1e-3\")"
    ]
   },
   {
@@ -4193,7 +4754,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 139,
+   "execution_count": 152,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -4206,13 +4767,13 @@
        "Decimal('0.1000000000000000055511151231257827021181583404541015625')"
       ]
      },
-     "execution_count": 139,
+     "execution_count": 152,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "Decimal(0.1)  # do not create a Decimal from a float"
+    "Decimal(0.1)  # do not do this"
    ]
   },
   {
@@ -4228,7 +4789,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 140,
+   "execution_count": 153,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -4241,7 +4802,7 @@
        "Decimal('0.3')"
       ]
      },
-     "execution_count": 140,
+     "execution_count": 153,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -4252,10 +4813,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 141,
+   "execution_count": 154,
    "metadata": {
     "slideshow": {
-     "slide_type": "-"
+     "slide_type": "fragment"
     }
    },
    "outputs": [
@@ -4265,7 +4826,7 @@
        "True"
       ]
      },
-     "execution_count": 141,
+     "execution_count": 154,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -4287,7 +4848,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 142,
+   "execution_count": 155,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -4300,7 +4861,7 @@
        "Decimal('0.30000')"
       ]
      },
-     "execution_count": 142,
+     "execution_count": 155,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -4311,10 +4872,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 143,
+   "execution_count": 156,
    "metadata": {
     "slideshow": {
-     "slide_type": "-"
+     "slide_type": "skip"
     }
    },
    "outputs": [
@@ -4324,7 +4885,7 @@
        "True"
       ]
      },
-     "execution_count": 143,
+     "execution_count": 156,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -4346,7 +4907,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 144,
+   "execution_count": 157,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -4359,42 +4920,42 @@
        "Decimal('42')"
       ]
      },
-     "execution_count": 144,
+     "execution_count": 157,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "42 + Decimal(42) - 42"
+    "21 + Decimal(21)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 145,
+   "execution_count": 158,
    "metadata": {
     "slideshow": {
-     "slide_type": "-"
+     "slide_type": "fragment"
     }
    },
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "Decimal('42')"
+       "Decimal('42.0')"
       ]
      },
-     "execution_count": 145,
+     "execution_count": 158,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "10 * Decimal(42) / 10"
+    "10 * Decimal(\"4.2\")"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 146,
+   "execution_count": 159,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -4407,7 +4968,7 @@
        "Decimal('0.1')"
       ]
      },
-     "execution_count": 146,
+     "execution_count": 159,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -4429,7 +4990,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 147,
+   "execution_count": 160,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -4442,7 +5003,7 @@
        "'0.10000000000000000000000000000000000000000000000000'"
       ]
      },
-     "execution_count": 147,
+     "execution_count": 160,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -4453,10 +5014,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 148,
+   "execution_count": 161,
    "metadata": {
     "slideshow": {
-     "slide_type": "-"
+     "slide_type": "fragment"
     }
    },
    "outputs": [
@@ -4466,7 +5027,7 @@
        "'0.10000000000000000555111512312578270211815834045410'"
       ]
      },
-     "execution_count": 148,
+     "execution_count": 161,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -4488,7 +5049,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 149,
+   "execution_count": 162,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -4502,7 +5063,7 @@
      "traceback": [
       "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
       "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-149-a3cea145e6d1>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0;36m1.0\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0mDecimal\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m42\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;32m<ipython-input-162-a3cea145e6d1>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0;36m1.0\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0mDecimal\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m42\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
       "\u001b[0;31mTypeError\u001b[0m: unsupported operand type(s) for *: 'float' and 'decimal.Decimal'"
      ]
     }
@@ -4519,15 +5080,15 @@
     }
    },
    "source": [
-    "To preserve the precision for more advanced mathematical functions, `Decimal` objects come with many **methods bound** on them. For example, [ln()](https://docs.python.org/3/library/decimal.html#decimal.Decimal.ln) and [log10()](https://docs.python.org/3/library/decimal.html#decimal.Decimal.log10) take the logarithm while [sqrt()](https://docs.python.org/3/library/decimal.html#decimal.Decimal.sqrt) calculates the square root. In general, the functions in the [math](https://docs.python.org/3/library/math.html) module in the [standard library](https://docs.python.org/3/library/index.html) should only be used with `float` objects as they do *not* preserve precision."
+    "To preserve the precision for more advanced mathematical functions, `Decimal` objects come with many **methods bound** on them. For example, [ln()](https://docs.python.org/3/library/decimal.html#decimal.Decimal.ln) and [log10()](https://docs.python.org/3/library/decimal.html#decimal.Decimal.log10) take the logarithm while [sqrt()](https://docs.python.org/3/library/decimal.html#decimal.Decimal.sqrt) calculates the square root. The methods always return a *new* `Decimal` object. We must never use the functions in the [math](https://docs.python.org/3/library/math.html) module in the [standard library](https://docs.python.org/3/library/index.html) with `Decimal` objects as they do *not* preserve precision."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 150,
+   "execution_count": 163,
    "metadata": {
     "slideshow": {
-     "slide_type": "slide"
+     "slide_type": "skip"
     }
    },
    "outputs": [
@@ -4537,7 +5098,7 @@
        "Decimal('2')"
       ]
      },
-     "execution_count": 150,
+     "execution_count": 163,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -4548,10 +5109,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 151,
+   "execution_count": 164,
    "metadata": {
     "slideshow": {
-     "slide_type": "-"
+     "slide_type": "slide"
     }
    },
    "outputs": [
@@ -4561,7 +5122,7 @@
        "Decimal('1.414213562373095048801688724')"
       ]
      },
-     "execution_count": 151,
+     "execution_count": 164,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -4585,7 +5146,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 152,
+   "execution_count": 165,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -4598,7 +5159,7 @@
        "Decimal('1.999999999999999999999999999')"
       ]
      },
-     "execution_count": 152,
+     "execution_count": 165,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -4617,17 +5178,17 @@
     }
    },
    "source": [
-    "The [quantize()](https://docs.python.org/3/library/decimal.html#decimal.Decimal.quantize) method allows us to [quantize](https://www.dictionary.com/browse/quantize) (i.e., \"round\") a `Decimal` number at any precision that is *smaller* than the set precision. It looks at the number of decimals (i.e., to the right of the period) of the numeric argument we pass in.\n",
+    "However, the [quantize()](https://docs.python.org/3/library/decimal.html#decimal.Decimal.quantize) method allows us to [quantize](https://www.dictionary.com/browse/quantize) (i.e., \"round\") a `Decimal` number at any precision that is *smaller* than the set precision. It takes the number of decimals to the right of the period of the `Decimal` argument we pass in and rounds accordingly.\n",
     "\n",
-    "For example, as the overall imprecise value of `two` still has an internal precision of `28` digits, we can correctly round it to *four* decimals (i.e., `Decimal(\"0.0001\")` has four decimals)."
+    "For example, as the overall imprecise value of `two` still has an internal precision of `28` digits, we can correctly round it to *four* decimals (i.e., `Decimal(\"0.0000\")` has four decimals)."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 153,
+   "execution_count": 166,
    "metadata": {
     "slideshow": {
-     "slide_type": "fragment"
+     "slide_type": "slide"
     }
    },
    "outputs": [
@@ -4637,26 +5198,34 @@
        "Decimal('2.0000')"
       ]
      },
-     "execution_count": 153,
+     "execution_count": 166,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "two.quantize(Decimal(\"0.0001\"))"
+    "two.quantize(Decimal(\"0.0000\"))"
    ]
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
    "source": [
     "We can never round a `Decimal` number and obtain a greater precision than before: The `InvalidOperation` exception tells us that *loudly*."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 154,
-   "metadata": {},
+   "execution_count": 167,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
    "outputs": [
     {
      "ename": "InvalidOperation",
@@ -4665,13 +5234,13 @@
      "traceback": [
       "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
       "\u001b[0;31mInvalidOperation\u001b[0m                          Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-154-11848d4ba22a>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mtwo\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mquantize\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mDecimal\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"0.1\"\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m**\u001b[0m \u001b[0;36m28\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;32m<ipython-input-167-5b555b105c8f>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mtwo\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mquantize\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mDecimal\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"1e-28\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
       "\u001b[0;31mInvalidOperation\u001b[0m: [<class 'decimal.InvalidOperation'>]"
      ]
     }
    ],
    "source": [
-    "two.quantize(Decimal(\"0.1\") ** 28)"
+    "two.quantize(Decimal(\"1e-28\"))"
    ]
   },
   {
@@ -4687,7 +5256,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 155,
+   "execution_count": 168,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -4700,13 +5269,13 @@
        "True"
       ]
      },
-     "execution_count": 155,
+     "execution_count": 168,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "two.quantize(Decimal(\"0.0001\")) == 2"
+    "two.quantize(Decimal(\"0.0000\")) == 2"
    ]
   },
   {
@@ -4722,10 +5291,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 156,
+   "execution_count": 169,
    "metadata": {
     "slideshow": {
-     "slide_type": "fragment"
+     "slide_type": "skip"
     }
    },
    "outputs": [
@@ -4735,13 +5304,13 @@
        "True"
       ]
      },
-     "execution_count": 156,
+     "execution_count": 169,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "(Decimal(2).sqrt() ** 2).quantize(Decimal(\"0.0001\")) == 2"
+    "(Decimal(2).sqrt() ** 2).quantize(Decimal(\"0.0000\")) == 2"
    ]
   },
   {
@@ -4757,7 +5326,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 157,
+   "execution_count": 170,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -4770,7 +5339,7 @@
        "Decimal('NaN')"
       ]
      },
-     "execution_count": 157,
+     "execution_count": 170,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -4779,9 +5348,20 @@
     "Decimal(\"nan\")"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "`Decimal(\"nan\")`s never compare equal to anything, not even to themselves."
+   ]
+  },
   {
    "cell_type": "code",
-   "execution_count": 158,
+   "execution_count": 171,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -4794,18 +5374,29 @@
        "False"
       ]
      },
-     "execution_count": 158,
+     "execution_count": 171,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "Decimal(\"nan\") == Decimal(\"nan\")  # nan's never compare equal to anything, not even to themselves"
+    "Decimal(\"nan\") == Decimal(\"nan\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Infinity is larger than any concrete number."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 159,
+   "execution_count": 172,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -4818,7 +5409,7 @@
        "Decimal('Infinity')"
       ]
      },
-     "execution_count": 159,
+     "execution_count": 172,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -4829,7 +5420,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 160,
+   "execution_count": 173,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -4842,7 +5433,7 @@
        "Decimal('-Infinity')"
       ]
      },
-     "execution_count": 160,
+     "execution_count": 173,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -4853,7 +5444,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 161,
+   "execution_count": 174,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -4866,18 +5457,18 @@
        "Decimal('Infinity')"
       ]
      },
-     "execution_count": 161,
+     "execution_count": 174,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "Decimal(\"inf\") + 42  # Infinity is infinity, concrete numbers loose its meaning"
+    "Decimal(\"inf\") + 42"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 162,
+   "execution_count": 175,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -4890,7 +5481,7 @@
        "True"
       ]
      },
-     "execution_count": 162,
+     "execution_count": 175,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -4907,12 +5498,12 @@
     }
    },
    "source": [
-    "As with `float` objects, we cannot add infinities of different signs: Now get a module-specific `InvalidOperation` exception instead of a `nan` value. Here, **failing loudly** is a good thing as it prevents us from working with invalid results."
+    "As with `float` objects, we cannot add infinities of different signs: Now, get a module-specific `InvalidOperation` exception instead of a `nan` value. Here, **failing loudly** is a good thing as it prevents us from working with invalid results."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 163,
+   "execution_count": 176,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -4926,7 +5517,7 @@
      "traceback": [
       "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
       "\u001b[0;31mInvalidOperation\u001b[0m                          Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-163-87950ebbd6ef>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mDecimal\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"inf\"\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mDecimal\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"-inf\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;32m<ipython-input-176-87950ebbd6ef>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mDecimal\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"inf\"\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mDecimal\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"-inf\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
       "\u001b[0;31mInvalidOperation\u001b[0m: [<class 'decimal.InvalidOperation'>]"
      ]
     }
@@ -4937,7 +5528,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 164,
+   "execution_count": 177,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -4951,7 +5542,7 @@
      "traceback": [
       "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
       "\u001b[0;31mInvalidOperation\u001b[0m                          Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-164-996083f200c6>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mDecimal\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"inf\"\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0mDecimal\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"inf\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;32m<ipython-input-177-996083f200c6>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mDecimal\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"inf\"\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0mDecimal\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"inf\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
       "\u001b[0;31mInvalidOperation\u001b[0m: [<class 'decimal.InvalidOperation'>]"
      ]
     }
@@ -4997,7 +5588,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 165,
+   "execution_count": 178,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -5021,7 +5612,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 166,
+   "execution_count": 179,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -5034,21 +5625,21 @@
        "Fraction(1, 3)"
       ]
      },
-     "execution_count": 166,
+     "execution_count": 179,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "Fraction(1, 3)  # this is now 1/3 with \"full\" precision"
+    "Fraction(1, 3)  # 1/3 with \"full\" precision"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 167,
+   "execution_count": 180,
    "metadata": {
     "slideshow": {
-     "slide_type": "-"
+     "slide_type": "fragment"
     }
    },
    "outputs": [
@@ -5058,21 +5649,21 @@
        "Fraction(1, 3)"
       ]
      },
-     "execution_count": 167,
+     "execution_count": 180,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "Fraction(\"1/3\")  # this is 1/3 with \"full\" precision again"
+    "Fraction(\"1/3\")  # 1/3 with \"full\" precision"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 168,
+   "execution_count": 181,
    "metadata": {
     "slideshow": {
-     "slide_type": "-"
+     "slide_type": "fragment"
     }
    },
    "outputs": [
@@ -5082,21 +5673,21 @@
        "Fraction(3333333333, 10000000000)"
       ]
      },
-     "execution_count": 168,
+     "execution_count": 181,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "Fraction(\"0.3333333333\")  # this is 1/3 with a precision of 10 significant digits"
+    "Fraction(\"0.3333333333\")  # 1/3 with a precision of 10 significant digits"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 169,
+   "execution_count": 182,
    "metadata": {
     "slideshow": {
-     "slide_type": "-"
+     "slide_type": "skip"
     }
    },
    "outputs": [
@@ -5106,13 +5697,13 @@
        "Fraction(3333333333, 10000000000)"
       ]
      },
-     "execution_count": 169,
+     "execution_count": 182,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "Fraction(\"3333333333e-10\")  # the same in scientific notation"
+    "Fraction(\"3333333333e-10\")  # scientific notation is also allowed"
    ]
   },
   {
@@ -5128,7 +5719,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 170,
+   "execution_count": 183,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -5141,7 +5732,7 @@
        "Fraction(3, 2)"
       ]
      },
-     "execution_count": 170,
+     "execution_count": 183,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -5152,10 +5743,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 171,
+   "execution_count": 184,
    "metadata": {
     "slideshow": {
-     "slide_type": "-"
+     "slide_type": "fragment"
     }
    },
    "outputs": [
@@ -5165,7 +5756,7 @@
        "Fraction(3, 2)"
       ]
      },
-     "execution_count": 171,
+     "execution_count": 184,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -5187,7 +5778,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 172,
+   "execution_count": 185,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -5200,7 +5791,7 @@
        "Fraction(1, 10)"
       ]
      },
-     "execution_count": 172,
+     "execution_count": 185,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -5222,10 +5813,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 173,
+   "execution_count": 186,
    "metadata": {
     "slideshow": {
-     "slide_type": "-"
+     "slide_type": "fragment"
     }
    },
    "outputs": [
@@ -5235,7 +5826,7 @@
        "Fraction(3602879701896397, 36028797018963968)"
       ]
      },
-     "execution_count": 173,
+     "execution_count": 186,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -5257,7 +5848,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 174,
+   "execution_count": 187,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -5270,7 +5861,7 @@
        "Fraction(7, 4)"
       ]
      },
-     "execution_count": 174,
+     "execution_count": 187,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -5281,10 +5872,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 175,
+   "execution_count": 188,
    "metadata": {
     "slideshow": {
-     "slide_type": "-"
+     "slide_type": "fragment"
     }
    },
    "outputs": [
@@ -5294,7 +5885,7 @@
        "Fraction(1, 2)"
       ]
      },
-     "execution_count": 175,
+     "execution_count": 188,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -5305,10 +5896,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 176,
+   "execution_count": 189,
    "metadata": {
     "slideshow": {
-     "slide_type": "-"
+     "slide_type": "fragment"
     }
    },
    "outputs": [
@@ -5318,7 +5909,7 @@
        "Fraction(1, 1)"
       ]
      },
-     "execution_count": 176,
+     "execution_count": 189,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -5329,10 +5920,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 177,
+   "execution_count": 190,
    "metadata": {
     "slideshow": {
-     "slide_type": "-"
+     "slide_type": "fragment"
     }
    },
    "outputs": [
@@ -5342,7 +5933,7 @@
        "Fraction(1, 1)"
       ]
      },
-     "execution_count": 177,
+     "execution_count": 190,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -5364,7 +5955,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 178,
+   "execution_count": 191,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -5377,18 +5968,18 @@
        "0.1"
       ]
      },
-     "execution_count": 178,
+     "execution_count": 191,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "10.0 * Fraction(1, 100)"
+    "10.0 * Fraction(1, 100)  # do not do this"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 179,
+   "execution_count": 192,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -5401,7 +5992,7 @@
        "'0.10000000000000000555111512312578270211815834045410'"
       ]
      },
-     "execution_count": 179,
+     "execution_count": 192,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -5432,6 +6023,17 @@
     "## The `complex` Type"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "#### What is the solution to $x^2 = -1$ ?"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {
@@ -5453,7 +6055,7 @@
    "cell_type": "markdown",
    "metadata": {
     "slideshow": {
-     "slide_type": "slide"
+     "slide_type": "-"
     }
    },
    "source": [
@@ -5475,7 +6077,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 180,
+   "execution_count": 193,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -5488,7 +6090,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 181,
+   "execution_count": 194,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -5498,10 +6100,10 @@
     {
      "data": {
       "text/plain": [
-       "140166567772848"
+       "139674754426928"
       ]
      },
-     "execution_count": 181,
+     "execution_count": 194,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -5512,10 +6114,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 182,
+   "execution_count": 195,
    "metadata": {
     "slideshow": {
-     "slide_type": "-"
+     "slide_type": "fragment"
     }
    },
    "outputs": [
@@ -5525,7 +6127,7 @@
        "complex"
       ]
      },
-     "execution_count": 182,
+     "execution_count": 195,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -5536,10 +6138,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 183,
+   "execution_count": 196,
    "metadata": {
     "slideshow": {
-     "slide_type": "-"
+     "slide_type": "fragment"
     }
    },
    "outputs": [
@@ -5549,7 +6151,7 @@
        "1j"
       ]
      },
-     "execution_count": 183,
+     "execution_count": 196,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -5571,7 +6173,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 184,
+   "execution_count": 197,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -5584,7 +6186,7 @@
        "True"
       ]
      },
-     "execution_count": 184,
+     "execution_count": 197,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -5606,7 +6208,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 185,
+   "execution_count": 198,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -5619,7 +6221,7 @@
        "(2+0.5j)"
       ]
      },
-     "execution_count": 185,
+     "execution_count": 198,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -5641,10 +6243,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 186,
+   "execution_count": 199,
    "metadata": {
     "slideshow": {
-     "slide_type": "-"
+     "slide_type": "fragment"
     }
    },
    "outputs": [
@@ -5654,21 +6256,32 @@
        "(2+0.5j)"
       ]
      },
-     "execution_count": 186,
+     "execution_count": 199,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "complex(2, 0.5)  # an integer and a float work just fine as arguments"
+    "complex(2, 0.5)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "By omitting the second argument, we set the imaginary part to $0$."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 187,
+   "execution_count": 200,
    "metadata": {
     "slideshow": {
-     "slide_type": "-"
+     "slide_type": "skip"
     }
    },
    "outputs": [
@@ -5678,18 +6291,29 @@
        "(2+0j)"
       ]
      },
-     "execution_count": 187,
+     "execution_count": 200,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "complex(2)  # omitting the second argument sets the imaginary part to 0"
+    "complex(2)  "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    " The arguments to [complex()](https://docs.python.org/3/library/functions.html#complex) may be any numeric type."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 188,
+   "execution_count": 201,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -5702,18 +6326,18 @@
        "(2+0.5j)"
       ]
      },
-     "execution_count": 188,
+     "execution_count": 201,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "complex(Decimal(\"2.0\"), Fraction(1, 2))  # the arguments may be any numeric type"
+    "complex(Decimal(\"2.0\"), Fraction(1, 2))"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 189,
+   "execution_count": 202,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -5726,7 +6350,7 @@
        "(2+0.5j)"
       ]
      },
-     "execution_count": 189,
+     "execution_count": 202,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -5748,7 +6372,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 190,
+   "execution_count": 203,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -5762,7 +6386,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 191,
+   "execution_count": 204,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -5775,7 +6399,7 @@
        "(4+6j)"
       ]
      },
-     "execution_count": 191,
+     "execution_count": 204,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -5786,10 +6410,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 192,
+   "execution_count": 205,
    "metadata": {
     "slideshow": {
-     "slide_type": "-"
+     "slide_type": "fragment"
     }
    },
    "outputs": [
@@ -5799,7 +6423,7 @@
        "(-2-2j)"
       ]
      },
-     "execution_count": 192,
+     "execution_count": 205,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -5810,7 +6434,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 193,
+   "execution_count": 206,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -5820,21 +6444,21 @@
     {
      "data": {
       "text/plain": [
-       "(40+2j)"
+       "(2+2j)"
       ]
      },
-     "execution_count": 193,
+     "execution_count": 206,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "c1 + 39"
+    "c1 + 1"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 194,
+   "execution_count": 207,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -5844,21 +6468,21 @@
     {
      "data": {
       "text/plain": [
-       "-4j"
+       "(0.5-4j)"
       ]
      },
-     "execution_count": 194,
+     "execution_count": 207,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "3.0 - c2"
+    "3.5 - c2"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 195,
+   "execution_count": 208,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -5871,7 +6495,7 @@
        "(5+10j)"
       ]
      },
-     "execution_count": 195,
+     "execution_count": 208,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -5882,7 +6506,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 196,
+   "execution_count": 209,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -5895,7 +6519,7 @@
        "(0.5+0.6666666666666666j)"
       ]
      },
-     "execution_count": 196,
+     "execution_count": 209,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -5906,7 +6530,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 197,
+   "execution_count": 210,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -5919,7 +6543,7 @@
        "(-5+10j)"
       ]
      },
-     "execution_count": 197,
+     "execution_count": 210,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -5930,10 +6554,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 198,
+   "execution_count": 211,
    "metadata": {
     "slideshow": {
-     "slide_type": "-"
+     "slide_type": "fragment"
     }
    },
    "outputs": [
@@ -5943,7 +6567,7 @@
        "(0.44+0.08j)"
       ]
      },
-     "execution_count": 198,
+     "execution_count": 211,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -5965,7 +6589,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 199,
+   "execution_count": 212,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -5978,7 +6602,7 @@
        "5.0"
       ]
      },
-     "execution_count": 199,
+     "execution_count": 212,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -6000,7 +6624,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 200,
+   "execution_count": 213,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -6013,7 +6637,7 @@
        "1.0"
       ]
      },
-     "execution_count": 200,
+     "execution_count": 213,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -6024,10 +6648,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 201,
+   "execution_count": 214,
    "metadata": {
     "slideshow": {
-     "slide_type": "-"
+     "slide_type": "fragment"
     }
    },
    "outputs": [
@@ -6037,7 +6661,7 @@
        "2.0"
       ]
      },
-     "execution_count": 201,
+     "execution_count": 214,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -6059,7 +6683,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 202,
+   "execution_count": 215,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -6072,7 +6696,7 @@
        "(1-2j)"
       ]
      },
-     "execution_count": 202,
+     "execution_count": 215,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -6160,7 +6784,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 203,
+   "execution_count": 216,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -6173,10 +6797,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 204,
+   "execution_count": 217,
    "metadata": {
     "slideshow": {
-     "slide_type": "-"
+     "slide_type": "slide"
     }
    },
    "outputs": [
@@ -6201,7 +6825,7 @@
        " 'abstractmethod']"
       ]
      },
-     "execution_count": 204,
+     "execution_count": 217,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -6227,7 +6851,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 205,
+   "execution_count": 218,
    "metadata": {
     "scrolled": true,
     "slideshow": {
@@ -6338,7 +6962,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 206,
+   "execution_count": 219,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -6490,6 +7114,17 @@
     "help(complex)"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "### Duck Typing"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {
@@ -6498,14 +7133,16 @@
     }
    },
    "source": [
-    "The primary purpose of ABCs is to classify the *concrete* data types and standardize how they behave.\n",
+    "The primary purpose of ABCs is to classify the *concrete* data types and standardize how they behave. This guides us as the programmers in what kind of behavior we should expect from objects of a given data type. In this context, ABCs are not reflected in code but only in our heads.\n",
     "\n",
-    "For, example, as all numeric data types are `Complex` numbers in the abstract sense, they all work with the built-in [abs()](https://docs.python.org/3/library/functions.html#abs) function (cf., [documentation](https://docs.python.org/3/library/numbers.html#numbers.Complex)). While it is intuitively clear what the [absolute value](https://en.wikipedia.org/wiki/Absolute_value) (i.e., \"distance\" to $0$) of an integer, a fraction, or any real number is, [abs()](https://docs.python.org/3/library/functions.html#abs) calculates the equivalent of that for complex numbers. That concept is called the [magnitude](https://en.wikipedia.org/wiki/Magnitude_%28mathematics%29) of a number, and is really a *generalization* of the absolute value."
+    "For, example, as all numeric data types are `Complex` numbers in the abstract sense, they all work with the built-in [abs()](https://docs.python.org/3/library/functions.html#abs) function (cf., [documentation](https://docs.python.org/3/library/numbers.html#numbers.Complex)). While it is intuitively clear what the [absolute value](https://en.wikipedia.org/wiki/Absolute_value) (i.e., \"distance\" to $0$) of an integer, a fraction, or any real number is, [abs()](https://docs.python.org/3/library/functions.html#abs) calculates the equivalent of that for complex numbers. That concept is called the [magnitude](https://en.wikipedia.org/wiki/Magnitude_%28mathematics%29) of a number, and is really a *generalization* of the absolute value.\n",
+    "\n",
+    "Relating back to the concept of **duck typing** mentioned in [Chapter 4](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/04_iteration_00_lecture.ipynb#Type-Checking-&-Input-Validation), `int`, `float`, and `complex` objects \"walk\" and \"quack\" alike in context of the [abs()](https://docs.python.org/3/library/functions.html#abs) function."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 207,
+   "execution_count": 220,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -6515,48 +7152,48 @@
     {
      "data": {
       "text/plain": [
-       "42"
+       "1"
       ]
      },
-     "execution_count": 207,
+     "execution_count": 220,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "abs(-42)"
+    "abs(-1)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 208,
+   "execution_count": 221,
    "metadata": {
     "slideshow": {
-     "slide_type": "-"
+     "slide_type": "fragment"
     }
    },
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "Decimal('0.1')"
+       "42.87"
       ]
      },
-     "execution_count": 208,
+     "execution_count": 221,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "abs(Decimal(\"-0.1\"))"
+    "abs(-42.87)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 209,
+   "execution_count": 222,
    "metadata": {
     "slideshow": {
-     "slide_type": "-"
+     "slide_type": "fragment"
     }
    },
    "outputs": [
@@ -6566,7 +7203,7 @@
        "5.0"
       ]
      },
-     "execution_count": 209,
+     "execution_count": 222,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -6588,7 +7225,31 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 210,
+   "execution_count": 223,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "100"
+      ]
+     },
+     "execution_count": 223,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "round(123, -2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 224,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -6601,7 +7262,7 @@
        "42"
       ]
      },
-     "execution_count": 210,
+     "execution_count": 224,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -6610,30 +7271,6 @@
     "round(42.1)"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": 211,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "-"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "0"
-      ]
-     },
-     "execution_count": 211,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "round(Decimal(\"0.1\"))"
-   ]
-  },
   {
    "cell_type": "markdown",
    "metadata": {
@@ -6642,15 +7279,15 @@
     }
    },
    "source": [
-    "`Complex` numbers are two-dimensional. So, rounding makes no sense and raises a `TypeError`."
+    "`Complex` numbers are two-dimensional. So, rounding makes no sense here and leads to a `TypeError`. So, in the context of the [round()](https://docs.python.org/3/library/functions.html#round) function, `int` and `float` objects \"walk\" and \"quack\" alike whereas `complex` objects do not."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 212,
+   "execution_count": 225,
    "metadata": {
     "slideshow": {
-     "slide_type": "-"
+     "slide_type": "fragment"
     }
    },
    "outputs": [
@@ -6661,311 +7298,13 @@
      "traceback": [
       "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
       "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-212-44b2943ede89>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mround\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m4\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;36m3j\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;32m<ipython-input-225-e40cd01fb15a>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mround\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m1\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;36m2j\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
       "\u001b[0;31mTypeError\u001b[0m: type complex doesn't define __round__ method"
      ]
     }
    ],
    "source": [
-    "round(4 + 3j)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "source": [
-    "Knowing what ABCs a numeric type adheres to, is not only important in the context of built-ins. The [trunc()](https://docs.python.org/3/library/math.html#math.trunc) function from the [math](https://docs.python.org/3/library/math.html) module in the [standard library](https://docs.python.org/3/library/index.html), for example, only works with `Real` types (cf., [documentation](https://docs.python.org/3/library/numbers.html#numbers.Real))."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 213,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "slide"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "import math"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "source": [
-    "[trunc()](https://docs.python.org/3/library/math.html#math.trunc) cuts off a number's decimals."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 214,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "-"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "0"
-      ]
-     },
-     "execution_count": 214,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "math.trunc(9 / 10)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "source": [
-    "A `Complex` number leads to a `TypeError`."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 215,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "fragment"
-    }
-   },
-   "outputs": [
-    {
-     "ename": "TypeError",
-     "evalue": "type complex doesn't define __trunc__ method",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-215-7121e57d9639>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mmath\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtrunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m0.9\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;36m1j\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
-      "\u001b[0;31mTypeError\u001b[0m: type complex doesn't define __trunc__ method"
-     ]
-    }
-   ],
-   "source": [
-    "math.trunc(0.9 + 1j)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "source": [
-    "Another way to use ABCs is in place of a *concrete* data type.\n",
-    "\n",
-    "For example, we may pass them as arguments to the built-in [isinstance()](https://docs.python.org/3/library/functions.html#isinstance) function and check in which of the five mathematical sets the object `1 / 10` is."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 216,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "slide"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "True"
-      ]
-     },
-     "execution_count": 216,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "isinstance(1 / 10, float)  # we know that from before"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 217,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "fragment"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "True"
-      ]
-     },
-     "execution_count": 217,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "isinstance(1 / 10, numbers.Number)  # a float object is a Number in the abstract sense"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 218,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "-"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "True"
-      ]
-     },
-     "execution_count": 218,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "isinstance(1 / 10, numbers.Complex)  # float objects are always also Complex numbers"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 219,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "-"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "True"
-      ]
-     },
-     "execution_count": 219,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "isinstance(1 / 10, numbers.Real)  # a float object's purpose is to model a Real number"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "source": [
-    "Due to the `float` type's inherent imprecision, `1 / 10` is *not* a rational number."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 220,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "slide"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "False"
-      ]
-     },
-     "execution_count": 220,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "isinstance(1 / 10, numbers.Rational)  # the type of `1 / 10` is what is important, not its value"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "source": [
-    "The `Fraction` type qualifies as a rational number; however, the `Decimal` type does not."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 221,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "fragment"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "True"
-      ]
-     },
-     "execution_count": 221,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "isinstance(Fraction(1, 10), numbers.Rational)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 222,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "-"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "False"
-      ]
-     },
-     "execution_count": 222,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "isinstance(Decimal(\"0.1\"), numbers.Rational)"
+    "round(1 + 2j)"
    ]
   },
   {
@@ -6987,7 +7326,197 @@
     }
    },
    "source": [
-    "Replacing *concrete* data types with ABCs is particularly valuable in the context of input validation: The revised version of the `factorial()` function below allows its user to take advantage of *duck typing*: If a real but non-integer argument `n` is passed in, `factorial()` tries to cast `n` as an `int` object with [math.trunc()](https://docs.python.org/3/library/math.html#math.trunc).\n",
+    "Another way to use ABCs is in place of a *concrete* data type.\n",
+    "\n",
+    "For example, we may pass them as arguments to the built-in [isinstance()](https://docs.python.org/3/library/functions.html#isinstance) function and check in which of the five mathematical sets the object `1 / 10` is."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 226,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 226,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "isinstance(1 / 10, float)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "A `float` object is a generic `Number` in the abstract sense but may also be seen as a `Complex` or `Real` number."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 227,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 227,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "isinstance(1 / 10, numbers.Number)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 228,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 228,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "isinstance(1 / 10, numbers.Complex)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 229,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 229,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "isinstance(1 / 10, numbers.Real)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Due to the `float` type's inherent imprecision, `1 / 10` is *not* a `Rational` number."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 230,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "False"
+      ]
+     },
+     "execution_count": 230,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "isinstance(1 / 10, numbers.Rational)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "However, if we model `1 / 10` as a `Fraction`, it is recognized as a `Rational` number."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 231,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 231,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "isinstance(Fraction(\"1/10\"), numbers.Rational)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Replacing *concrete* data types with ABCs is particularly valuable in the context of input validation: The revised version of the `factorial()` function below allows its user to take advantage of *duck typing*: If a real but non-integer argument `n` is passed in, `factorial()` tries to cast `n` as an `int` object with the [int()](https://docs.python.org/3/library/functions.html#int) built-in.\n",
     "\n",
     "Two popular and distinguished Pythonistas, [Luciano Ramalho](https://github.com/ramalho) and [Alex Martelli](https://en.wikipedia.org/wiki/Alex_Martelli), coin the term **goose typing** to specifically mean using the built-in [isinstance()](https://docs.python.org/3/library/functions.html#isinstance) function with an ABC (cf., Chapter 11 in this [book](https://www.amazon.com/Fluent-Python-Concise-Effective-Programming/dp/1491946008) or this [summary](https://dgkim5360.github.io/blog/python/2017/07/duck-typing-vs-goose-typing-pythonic-interfaces/) thereof)."
    ]
@@ -7005,7 +7534,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 223,
+   "execution_count": 232,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -7018,7 +7547,8 @@
     "\n",
     "    Args:\n",
     "        n (int): number to calculate the factorial for; must be positive\n",
-    "        strict (bool): if n must not contain decimals; defaults to True\n",
+    "        strict (bool): if n must not contain decimals; defaults to True;\n",
+    "            if set to False, the decimals in n are ignored\n",
     "\n",
     "    Returns:\n",
     "        factorial (int)\n",
@@ -7029,15 +7559,15 @@
     "    \"\"\"\n",
     "    if not isinstance(n, numbers.Integral):\n",
     "        if isinstance(n, numbers.Real):\n",
-    "            if n != math.trunc(n) and strict:\n",
-    "                raise TypeError(\"n is not an integer-like value; it has decimals\")\n",
-    "            n = math.trunc(n)\n",
+    "            if n != int(n) and strict:\n",
+    "                raise TypeError(\"n is not integer-like; it has decimals\")\n",
+    "            n = int(n)\n",
     "        else:\n",
     "            raise TypeError(\"Factorial is only defined for integers\")\n",
     "\n",
     "    if n < 0:\n",
     "        raise ValueError(\"Factorial is not defined for negative integers\")\n",
-    "    elif n == 0:  # = base case\n",
+    "    elif n == 0:\n",
     "        return 1\n",
     "    return n * factorial(n - 1)"
    ]
@@ -7055,7 +7585,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 224,
+   "execution_count": 233,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -7068,7 +7598,7 @@
        "1"
       ]
      },
-     "execution_count": 224,
+     "execution_count": 233,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -7079,31 +7609,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 225,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "-"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "6"
-      ]
-     },
-     "execution_count": 225,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "factorial(3)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 226,
+   "execution_count": 234,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -7116,7 +7622,31 @@
        "6"
       ]
      },
-     "execution_count": 226,
+     "execution_count": 234,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "factorial(3)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 235,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "6"
+      ]
+     },
+     "execution_count": 235,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -7138,7 +7668,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 227,
+   "execution_count": 236,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -7147,14 +7677,14 @@
    "outputs": [
     {
      "ename": "TypeError",
-     "evalue": "n is not an integer-like value; it has decimals",
+     "evalue": "n is not integer-like; it has decimals",
      "output_type": "error",
      "traceback": [
       "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
       "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-227-188b816be8b6>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mfactorial\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m3.1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
-      "\u001b[0;32m<ipython-input-223-d2a63febf49e>\u001b[0m in \u001b[0;36mfactorial\u001b[0;34m(n, strict)\u001b[0m\n\u001b[1;32m     16\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mn\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnumbers\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mReal\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     17\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mn\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0mmath\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtrunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mn\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mstrict\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 18\u001b[0;31m                 \u001b[0;32mraise\u001b[0m \u001b[0mTypeError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"n is not an integer-like value; it has decimals\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     19\u001b[0m             \u001b[0mn\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmath\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtrunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mn\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     20\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;31mTypeError\u001b[0m: n is not an integer-like value; it has decimals"
+      "\u001b[0;32m<ipython-input-236-188b816be8b6>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mfactorial\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m3.1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;32m<ipython-input-232-0b8c729be194>\u001b[0m in \u001b[0;36mfactorial\u001b[0;34m(n, strict)\u001b[0m\n\u001b[1;32m     17\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mn\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnumbers\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mReal\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     18\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mn\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0mint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mn\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mstrict\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 19\u001b[0;31m                 \u001b[0;32mraise\u001b[0m \u001b[0mTypeError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"n is not integer-like; it has decimals\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     20\u001b[0m             \u001b[0mn\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mn\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     21\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;31mTypeError\u001b[0m: n is not integer-like; it has decimals"
      ]
     }
    ],
@@ -7175,7 +7705,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 228,
+   "execution_count": 237,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -7188,7 +7718,7 @@
        "6"
       ]
      },
-     "execution_count": 228,
+     "execution_count": 237,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -7210,7 +7740,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 229,
+   "execution_count": 238,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -7224,8 +7754,8 @@
      "traceback": [
       "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
       "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-229-7296a74f5dcf>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mfactorial\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m1\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;36m2j\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
-      "\u001b[0;32m<ipython-input-223-d2a63febf49e>\u001b[0m in \u001b[0;36mfactorial\u001b[0;34m(n, strict)\u001b[0m\n\u001b[1;32m     19\u001b[0m             \u001b[0mn\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmath\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtrunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mn\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     20\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 21\u001b[0;31m             \u001b[0;32mraise\u001b[0m \u001b[0mTypeError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Factorial is only defined for integers\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     22\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     23\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mn\u001b[0m \u001b[0;34m<\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m<ipython-input-238-7296a74f5dcf>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mfactorial\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m1\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;36m2j\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;32m<ipython-input-232-0b8c729be194>\u001b[0m in \u001b[0;36mfactorial\u001b[0;34m(n, strict)\u001b[0m\n\u001b[1;32m     20\u001b[0m             \u001b[0mn\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mn\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     21\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 22\u001b[0;31m             \u001b[0;32mraise\u001b[0m \u001b[0mTypeError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Factorial is only defined for integers\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     23\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     24\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mn\u001b[0m \u001b[0;34m<\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
       "\u001b[0;31mTypeError\u001b[0m: Factorial is only defined for integers"
      ]
     }
@@ -7269,6 +7799,195 @@
     "\n",
     "The **numerical tower** is Python's way of implementing various **abstract** ideas of what numbers are in mathematics."
    ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "## Further Resources"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "The two videos below show how addition and multiplication works with numbers in their binary representations. Subtraction is a bit more involved as we need to understand how negative numbers are represented in binary with the concept of [Two's Complement](https://en.wikipedia.org/wiki/Two%27s_complement) first. A video on that is shown further below. Division in binary is actually also quite simple."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 239,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/jpeg": 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\n",
+      "text/html": [
+       "\n",
+       "        <iframe\n",
+       "            width=\"60%\"\n",
+       "            height=\"300\"\n",
+       "            src=\"https://www.youtube.com/embed/RgklPQ8rbkg\"\n",
+       "            frameborder=\"0\"\n",
+       "            allowfullscreen\n",
+       "        ></iframe>\n",
+       "        "
+      ],
+      "text/plain": [
+       "<IPython.lib.display.YouTubeVideo at 0x7f089024e2d0>"
+      ]
+     },
+     "execution_count": 239,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from IPython.display import YouTubeVideo\n",
+    "YouTubeVideo(\"RgklPQ8rbkg\", width=\"60%\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 240,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/jpeg": 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+      "text/html": [
+       "\n",
+       "        <iframe\n",
+       "            width=\"60%\"\n",
+       "            height=\"300\"\n",
+       "            src=\"https://www.youtube.com/embed/xHWKYFhhtJQ\"\n",
+       "            frameborder=\"0\"\n",
+       "            allowfullscreen\n",
+       "        ></iframe>\n",
+       "        "
+      ],
+      "text/plain": [
+       "<IPython.lib.display.YouTubeVideo at 0x7f08901d2650>"
+      ]
+     },
+     "execution_count": 240,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "YouTubeVideo(\"xHWKYFhhtJQ\", width=\"60%\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "The video below explains the idea behind [Two's Complement](https://en.wikipedia.org/wiki/Two%27s_complement). This is how most modern programming languages implement negative integers. The video also shows how subtraction in binary works."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 241,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/jpeg": 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+      "text/html": [
+       "\n",
+       "        <iframe\n",
+       "            width=\"60%\"\n",
+       "            height=\"300\"\n",
+       "            src=\"https://www.youtube.com/embed/4qH4unVtJkE\"\n",
+       "            frameborder=\"0\"\n",
+       "            allowfullscreen\n",
+       "        ></iframe>\n",
+       "        "
+      ],
+      "text/plain": [
+       "<IPython.lib.display.YouTubeVideo at 0x7f08901dad50>"
+      ]
+     },
+     "execution_count": 241,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "YouTubeVideo(\"4qH4unVtJkE\", width=\"60%\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Below is a short introduction to [complex numbers](https://en.wikipedia.org/wiki/Complex_number) by [MIT](https://www.mit.edu) professor [Gilbert Strang](https://en.wikipedia.org/wiki/Gilbert_Strang) aimed at high school students."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 242,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/jpeg": 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\n",
+      "text/html": [
+       "\n",
+       "        <iframe\n",
+       "            width=\"60%\"\n",
+       "            height=\"300\"\n",
+       "            src=\"https://www.youtube.com/embed/Jkv-55ndVYY\"\n",
+       "            frameborder=\"0\"\n",
+       "            allowfullscreen\n",
+       "        ></iframe>\n",
+       "        "
+      ],
+      "text/plain": [
+       "<IPython.lib.display.YouTubeVideo at 0x7f08901e47d0>"
+      ]
+     },
+     "execution_count": 242,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "YouTubeVideo(\"Jkv-55ndVYY\", width=\"60%\")"
+   ]
   }
  ],
  "metadata": {
diff --git a/05_numbers_02_exercises.ipynb b/05_numbers_02_exercises.ipynb
index ab2499d..ff5e9a8 100644
--- a/05_numbers_02_exercises.ipynb
+++ b/05_numbers_02_exercises.ipynb
@@ -19,7 +19,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Read [Chapter 5](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/05_numbers_00_lecture.ipynb) of the book. Then, work through the exercises below."
+    "Read [Chapter 5](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/05_numbers_00_lecture.ipynb) of the book. Then, work through the exercises below. The `...` indicate where you need to fill in your answers. You should not need to create any additional code cells."
    ]
   },
   {
@@ -33,20 +33,20 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "**Q1** in [Chapter 2's Exercises](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/02_functions_02_exercises.ipynb#Volume-of-a-Sphere) section already revealed that we must consider the effects of the `float` type's imprecision.\n",
+    "The \"*Volume of a Sphere*\" problem in [Chapter 2's Exercises](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/02_functions_02_exercises.ipynb#Volume-of-a-Sphere) section revealed that we must consider the effects of the `float` type's imprecision.\n",
     "\n",
     "This becomes even more important when we deal with numeric data modeling accounting or finance data (cf., [this comment](https://stackoverflow.com/a/24976426) on \"falsehoods programmers believe about money\").\n",
     "\n",
     "In addition to the *inherent imprecision* of numbers in general, the topic of **[rounding numbers](https://en.wikipedia.org/wiki/Rounding)** is also not as trivial as we might expect! [This article](https://realpython.com/python-rounding/) summarizes everything the data science practitioner needs to know.\n",
     "\n",
-    "In this exercise, we revisit **Q1** from [Chapter 3's Exercises](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/03_conditionals_02_exercises.ipynb#Discounting-Customer-Orders) section, and make the `discounted_price()` function work *correctly* for real-life sales data."
+    "In this exercise, we revisit the \"*Discounting Customer Orders*\" problem from [Chapter 3's Exercises](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/03_conditionals_02_exercises.ipynb#Discounting-Customer-Orders) section and make the `discounted_price()` function work *correctly* for real-life sales data."
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "**Q1.1**: Execute the code cells below! What results would you have *expected*, and why?"
+    "**Q1**: Execute the code cells below! What results would you have *expected*, and why?"
    ]
   },
   {
@@ -87,7 +87,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "**Q1.2**: The built-in [round()](https://docs.python.org/3/library/functions.html#round) function implements the \"**[round half to even](https://en.wikipedia.org/wiki/Rounding#Round_half_to_even)**\" strategy. Describe in one or two sentences what that means!"
+    "**Q2**: The built-in [round()](https://docs.python.org/3/library/functions.html#round) function implements the \"**[round half to even](https://en.wikipedia.org/wiki/Rounding#Round_half_to_even)**\" strategy. Describe in one or two sentences what that means!"
    ]
   },
   {
@@ -101,7 +101,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "**Q1.3**: For the revised `discounted_price()` function, we have to tackle *two* issues: First, we have to replace the built-in `float` type with a data type that allows us to control the precision. Second, the discounted price should be rounded according to a more human-friendly rounding strategy, namely \"**[round half away from zero](https://en.wikipedia.org/wiki/Rounding#Round_half_away_from_zero)**.\"\n",
+    "**Q3**: For the revised `discounted_price()` function, we have to tackle *two* issues: First, we have to replace the built-in `float` type with a data type that allows us to control the precision. Second, the discounted price should be rounded according to a more human-friendly rounding strategy, namely \"**[round half away from zero](https://en.wikipedia.org/wiki/Rounding#Round_half_away_from_zero)**.\"\n",
     "\n",
     "Describe in one or two sentences how \"**[round half away from zero](https://en.wikipedia.org/wiki/Rounding#Round_half_away_from_zero)**\" is more in line with how humans think of rounding!"
    ]
@@ -117,7 +117,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "**Q1.4**: We use the `Decimal` type from the [decimal](https://docs.python.org/3/library/decimal.html) module in the [standard library](https://docs.python.org/3/library/index.html) to tackle *both* issues simultaneously.\n",
+    "**Q4**: We use the `Decimal` type from the [decimal](https://docs.python.org/3/library/decimal.html) module in the [standard library](https://docs.python.org/3/library/index.html) to tackle *both* issues simultaneously.\n",
     "\n",
     "Assign `euro` a numeric object such that both `Decimal(\"1.5\")` and `Decimal(\"2.5\")` are rounded to `Decimal(\"2\")` (i.e., no decimal) with the [quantize()](https://docs.python.org/3/library/decimal.html#decimal.Decimal.quantize) method!"
    ]
@@ -162,7 +162,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "**Q1.5**: Obviously, the two preceding code cells still [round half to even](https://en.wikipedia.org/wiki/Rounding#Round_half_to_even).\n",
+    "**Q5**: Obviously, the two preceding code cells still [round half to even](https://en.wikipedia.org/wiki/Rounding#Round_half_to_even).\n",
     "\n",
     "The [decimal](https://docs.python.org/3/library/decimal.html) module defines a `ROUND_HALF_UP` flag that we can pass as the second argument to the [quantize()](https://docs.python.org/3/library/decimal.html#decimal.Decimal.quantize) method. Then, it [rounds half away from zero](https://en.wikipedia.org/wiki/Rounding#Round_half_away_from_zero).\n",
     "\n",
@@ -200,7 +200,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "**Q1.6**: Instead of `euro`, define `cents` such that rounding occurs to *two* decimals! `Decimal(\"2.675\")` should now be rounded to `Decimal(\"2.68\")`. Do *not* forget to include the `ROUND_HALF_UP` flag!"
+    "**Q6**: Instead of `euro`, define `cents` such that rounding occurs to *two* decimals! `Decimal(\"2.675\")` should now be rounded to `Decimal(\"2.68\")`. Do *not* forget to include the `ROUND_HALF_UP` flag!"
    ]
   },
   {
@@ -225,7 +225,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "**Q1.7**: Rewrite the function `discounted_price()` from [Chapter 3's Exercises](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/03_conditionals_02_exercises.ipynb#Discounting-Customer-Orders) section!\n",
+    "**Q7**: Rewrite the function `discounted_price()` from [Chapter 3's Exercises](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/03_conditionals_02_exercises.ipynb#Discounting-Customer-Orders) section!\n",
     "\n",
     "It takes the *positional* arguments `unit_price` and `quantity` and implements a discount scheme for a line item in a customer order as follows:\n",
     "\n",
@@ -264,7 +264,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "**Q1.8**: Execute the code cells below and verify the final price for the following four test cases:\n",
+    "**Q8**: Execute the code cells below and verify the final price for the following four test cases:\n",
     "\n",
     "- $7$ smartphones @ $99.00$ USD\n",
     "- $3$ workstations @ $999.00$ USD\n",

From 3ca88a8db18215f4b70aa17570dfde0d8c6fb1c6 Mon Sep 17 00:00:00 2001
From: Alexander Hess <alexander@webartifex.biz>
Date: Mon, 16 Mar 2020 20:51:51 +0100
Subject: [PATCH 079/242] Streamline presentation

---
 01_elements_00_lecture.ipynb  |   26 +-
 04_iteration_00_lecture.ipynb |    2 +-
 05_numbers_00_lecture.ipynb   | 2627 ++++++++++++++++++---------------
 3 files changed, 1463 insertions(+), 1192 deletions(-)

diff --git a/01_elements_00_lecture.ipynb b/01_elements_00_lecture.ipynb
index f7583ba..89beede 100644
--- a/01_elements_00_lecture.ipynb
+++ b/01_elements_00_lecture.ipynb
@@ -1308,7 +1308,7 @@
     }
    },
    "source": [
-    "The `c` object is a so-called **string** type (i.e., `str`), which is Python's way of representing \"text.\" Strings also come with peculiar behaviors, for example, to convert a text to lower, upper, or title case."
+    "The `c` object is a so-called **string** type (i.e., `str`), which is Python's way of representing \"text.\" Strings also come with peculiar behaviors, for example, to make a text lower or upper case."
    ]
   },
   {
@@ -1383,30 +1383,6 @@
     "c.upper()"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": 41,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'Python Rocks'"
-      ]
-     },
-     "execution_count": 41,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "c.title()"
-   ]
-  },
   {
    "cell_type": "markdown",
    "metadata": {
diff --git a/04_iteration_00_lecture.ipynb b/04_iteration_00_lecture.ipynb
index fb89130..61858c5 100644
--- a/04_iteration_00_lecture.ipynb
+++ b/04_iteration_00_lecture.ipynb
@@ -52,7 +52,7 @@
    "cell_type": "markdown",
    "metadata": {
     "slideshow": {
-     "slide_type": "-"
+     "slide_type": "slide"
     }
    },
    "source": [
diff --git a/05_numbers_00_lecture.ipynb b/05_numbers_00_lecture.ipynb
index e4bed12..771a0ad 100644
--- a/05_numbers_00_lecture.ipynb
+++ b/05_numbers_00_lecture.ipynb
@@ -29,7 +29,7 @@
     "\n",
     "- [Chapter 1](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/01_elements_00_lecture.ipynb#%28Data%29-Type-%2F-%22Behavior%22) reveals that numbers may come in *different* data types (i.e., `int` vs. `float` so far),\n",
     "- [Chapter 3](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/03_conditionals_00_lecture.ipynb#Boolean-Expressions) raises questions regarding the **limited precision** of `float` numbers (e.g., `42 == 42.000000000000001` evaluates to `True`), and\n",
-    "- [Chapter 4](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/04_iteration_00_lecture.ipynb#Infinite-Recursion) shows that sometimes a `float` \"walks\" and \"quacks\" like an `int`, whereas the reverse is true in other cases.\n",
+    "- [Chapter 4](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/04_iteration_00_lecture.ipynb#Infinite-Recursion) shows that sometimes a `float` \"walks\" and \"quacks\" like an `int`, whereas the reverse is true.\n",
     "\n",
     "This chapter introduces all the [built-in numeric types](https://docs.python.org/3/library/stdtypes.html#numeric-types-int-float-complex): `int`, `float`, and `complex`. To mitigate the limited precision of floating-point numbers, we also look at two replacements for the `float` type in the [standard library](https://docs.python.org/3/library/index.html), namely the `Decimal` type in the [decimals](https://docs.python.org/3/library/decimal.html#decimal.Decimal) and the `Fraction` type in the [fractions](https://docs.python.org/3/library/fractions.html#fractions.Fraction) module."
    ]
@@ -53,7 +53,9 @@
     }
    },
    "source": [
-    "The simplest numeric type is the `int` type: It behaves like an [integer in ordinary math](https://en.wikipedia.org/wiki/Integer) (i.e., the set $\\mathbb{Z}$) and supports operators in the way we saw in the section on arithmetic operators in [Chapter 1](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/01_elements_00_lecture.ipynb#%28Arithmetic%29-Operators)."
+    "The simplest numeric type is the `int` type: It behaves like an [integer in ordinary math](https://en.wikipedia.org/wiki/Integer) (i.e., the set $\\mathbb{Z}$) and supports operators in the way we saw in the section on arithmetic operators in [Chapter 1](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/01_elements_00_lecture.ipynb#%28Arithmetic%29-Operators).\n",
+    "\n",
+    "One way to create `int` objects is by simply writing its value as a literal with the digits `0` to `9`."
    ]
   },
   {
@@ -92,7 +94,7 @@
     {
      "data": {
       "text/plain": [
-       "94315929536576"
+       "94784085682240"
       ]
      },
      "execution_count": 2,
@@ -160,7 +162,7 @@
     }
    },
    "source": [
-    "A nice feature in newer Python versions is using underscores `_` as (thousands) separators in numeric literals. For example, `1_000_000` evaluates to `1000000` in memory; the `_` is simply ignored by the interpreter."
+    "A nice feature in newer Python versions is using underscores `_` as (thousands) separators in numeric literals. For example, `1_000_000` evaluates to `1000000` in memory; the `_` is ignored by the interpreter."
    ]
   },
   {
@@ -195,7 +197,7 @@
     }
    },
    "source": [
-    "The `int` type follows all rules we know from math, apart from one exception: Whereas mathematicians argue what the term $0^0$ means (cf., this [article](https://en.wikipedia.org/wiki/Zero_to_the_power_of_zero)), programmers are pragmatic about this and define $0^0 = 1$."
+    "We may place the `_`s anywhere we want."
    ]
   },
   {
@@ -210,7 +212,7 @@
     {
      "data": {
       "text/plain": [
-       "1"
+       "123456789"
       ]
      },
      "execution_count": 6,
@@ -218,6 +220,252 @@
      "output_type": "execute_result"
     }
    ],
+   "source": [
+    "1_2_3_4_5_6_7_8_9"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "It is syntactically invalid to write out leading `0` in numeric literals. The reason for that will become apparent in the next section."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "ename": "SyntaxError",
+     "evalue": "invalid token (<ipython-input-7-9a7f0f99e867>, line 1)",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;36m  File \u001b[0;32m\"<ipython-input-7-9a7f0f99e867>\"\u001b[0;36m, line \u001b[0;32m1\u001b[0m\n\u001b[0;31m    042\u001b[0m\n\u001b[0m      ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m invalid token\n"
+     ]
+    }
+   ],
+   "source": [
+    "042"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Another way to create `int` objects is with the [int()](https://docs.python.org/3/library/functions.html#int) built-in that casts `float` or properly formatted `str` objects as integers. So, decimals are truncated (i.e., \"cut off\")."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "42"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "int(42.11)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "42"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "int(42.87)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Whereas the floor division operator `//` effectively rounds towards negative infinity (cf., the \"*(Arithmetic) Operators*\" section in [Chapter 1](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/01_elements_00_lecture.ipynb#%28Arithmetic%29-Operators)), the [int()](https://docs.python.org/3/library/functions.html#int) built-in effectively rounds towards `0`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "-42"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "int(-42.87)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "When casting `str` objects as `int`, the [int()](https://docs.python.org/3/library/functions.html#int) built-in is less forgiving. We must not include any decimals as shows by the `ValueError`. Yet, leading and trailing whitespace is gracefully ignored."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "42"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "int(\"42\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "ename": "ValueError",
+     "evalue": "invalid literal for int() with base 10: '42.0'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-12-faae6b054331>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"42.0\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;31mValueError\u001b[0m: invalid literal for int() with base 10: '42.0'"
+     ]
+    }
+   ],
+   "source": [
+    "int(\"42.0\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "42"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "int(\" 42 \")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "The `int` type follows all rules we know from math, apart from one exception: Whereas mathematicians to this day argue what the term $0^0$ means (cf., this [article](https://en.wikipedia.org/wiki/Zero_to_the_power_of_zero)), programmers are pragmatic about this and simply define $0^0 = 1$."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "1"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "0 ** 0"
    ]
@@ -277,7 +525,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 15,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -290,7 +538,7 @@
        "'0b11'"
       ]
      },
-     "execution_count": 7,
+     "execution_count": 15,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -312,7 +560,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 16,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -325,7 +573,7 @@
        "3"
       ]
      },
-     "execution_count": 8,
+     "execution_count": 16,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -347,7 +595,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 17,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -360,7 +608,7 @@
        "3"
       ]
      },
-     "execution_count": 9,
+     "execution_count": 17,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -382,7 +630,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 18,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -395,7 +643,7 @@
        "3"
       ]
      },
-     "execution_count": 10,
+     "execution_count": 18,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -417,7 +665,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 19,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -430,7 +678,7 @@
        "'0b1111011'"
       ]
      },
-     "execution_count": 11,
+     "execution_count": 19,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -452,7 +700,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 20,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -465,7 +713,7 @@
        "123"
       ]
      },
-     "execution_count": 12,
+     "execution_count": 20,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -487,7 +735,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 21,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -500,7 +748,7 @@
        "'0b0'"
       ]
      },
-     "execution_count": 13,
+     "execution_count": 21,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -511,7 +759,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 22,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -524,7 +772,7 @@
        "'0b1'"
       ]
      },
-     "execution_count": 14,
+     "execution_count": 22,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -535,7 +783,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 23,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -548,7 +796,7 @@
        "'0b10'"
       ]
      },
-     "execution_count": 15,
+     "execution_count": 23,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -559,7 +807,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 24,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -572,7 +820,7 @@
        "'0b11111111'"
       ]
      },
-     "execution_count": 16,
+     "execution_count": 24,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -596,7 +844,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 25,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -609,7 +857,7 @@
        "'0b1100010101'"
       ]
      },
-     "execution_count": 17,
+     "execution_count": 25,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -660,7 +908,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 26,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -673,7 +921,7 @@
        "3"
       ]
      },
-     "execution_count": 18,
+     "execution_count": 26,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -684,7 +932,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 27,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -697,7 +945,7 @@
        "'0b1 + 0b10 = 0b11'"
       ]
      },
-     "execution_count": 19,
+     "execution_count": 27,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -719,7 +967,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 28,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -732,7 +980,7 @@
        "4"
       ]
      },
-     "execution_count": 20,
+     "execution_count": 28,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -743,7 +991,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 29,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -756,7 +1004,7 @@
        "'0b1 + 0b11 = 0b100'"
       ]
      },
-     "execution_count": 21,
+     "execution_count": 29,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -778,7 +1026,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 30,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -791,7 +1039,7 @@
        "12"
       ]
      },
-     "execution_count": 22,
+     "execution_count": 30,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -802,7 +1050,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 31,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -815,7 +1063,7 @@
        "'0b100 * 0b11 = 0b1100'"
       ]
      },
-     "execution_count": 23,
+     "execution_count": 31,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -826,7 +1074,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 32,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -839,7 +1087,7 @@
        "'0b100 * 0b1 = 0b100'"
       ]
      },
-     "execution_count": 24,
+     "execution_count": 32,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -850,7 +1098,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 33,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -863,7 +1111,7 @@
        "'0b100 * 0b10 = 0b1000'"
       ]
      },
-     "execution_count": 25,
+     "execution_count": 33,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -958,7 +1206,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": 34,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -971,7 +1219,7 @@
        "'0x0'"
       ]
      },
-     "execution_count": 26,
+     "execution_count": 34,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -982,7 +1230,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": 35,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -995,7 +1243,7 @@
        "'0x1'"
       ]
      },
-     "execution_count": 27,
+     "execution_count": 35,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1017,7 +1265,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": 36,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -1030,7 +1278,7 @@
        "'0x3'"
       ]
      },
-     "execution_count": 28,
+     "execution_count": 36,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1052,7 +1300,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
+   "execution_count": 37,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -1065,7 +1313,7 @@
        "'0xa'"
       ]
      },
-     "execution_count": 29,
+     "execution_count": 37,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1076,7 +1324,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 30,
+   "execution_count": 38,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -1089,7 +1337,7 @@
        "'0xf'"
       ]
      },
-     "execution_count": 30,
+     "execution_count": 38,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1111,7 +1359,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 31,
+   "execution_count": 39,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -1124,7 +1372,7 @@
        "'0b1111011'"
       ]
      },
-     "execution_count": 31,
+     "execution_count": 39,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1135,7 +1383,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 32,
+   "execution_count": 40,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -1148,7 +1396,7 @@
        "'0x7b'"
       ]
      },
-     "execution_count": 32,
+     "execution_count": 40,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1170,7 +1418,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 33,
+   "execution_count": 41,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -1183,7 +1431,7 @@
        "123"
       ]
      },
-     "execution_count": 33,
+     "execution_count": 41,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1205,7 +1453,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 34,
+   "execution_count": 42,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -1218,7 +1466,7 @@
        "123"
       ]
      },
-     "execution_count": 34,
+     "execution_count": 42,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1240,7 +1488,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 35,
+   "execution_count": 43,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -1253,7 +1501,7 @@
        "'0x0'"
       ]
      },
-     "execution_count": 35,
+     "execution_count": 43,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1264,7 +1512,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 36,
+   "execution_count": 44,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -1277,7 +1525,7 @@
        "'0xff'"
       ]
      },
-     "execution_count": 36,
+     "execution_count": 44,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1299,7 +1547,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 37,
+   "execution_count": 45,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -1312,7 +1560,7 @@
        "'0x315'"
       ]
      },
-     "execution_count": 37,
+     "execution_count": 45,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1358,7 +1606,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 38,
+   "execution_count": 46,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -1371,7 +1619,7 @@
        "'-0b11'"
       ]
      },
-     "execution_count": 38,
+     "execution_count": 46,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1382,7 +1630,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 39,
+   "execution_count": 47,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -1395,7 +1643,7 @@
        "'-0x3'"
       ]
      },
-     "execution_count": 39,
+     "execution_count": 47,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1406,7 +1654,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 40,
+   "execution_count": 48,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -1419,7 +1667,7 @@
        "'-0b11111111'"
       ]
      },
-     "execution_count": 40,
+     "execution_count": 48,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1430,7 +1678,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 41,
+   "execution_count": 49,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -1443,7 +1691,7 @@
        "'-0xff'"
       ]
      },
-     "execution_count": 41,
+     "execution_count": 49,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1476,7 +1724,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 42,
+   "execution_count": 50,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -1489,7 +1737,7 @@
        "1"
       ]
      },
-     "execution_count": 42,
+     "execution_count": 50,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1500,7 +1748,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 43,
+   "execution_count": 51,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -1513,7 +1761,7 @@
        "42"
       ]
      },
-     "execution_count": 43,
+     "execution_count": 51,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1524,7 +1772,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 44,
+   "execution_count": 52,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -1537,7 +1785,7 @@
        "0.0"
       ]
      },
-     "execution_count": 44,
+     "execution_count": 52,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1559,7 +1807,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 45,
+   "execution_count": 53,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -1572,7 +1820,7 @@
        "1"
       ]
      },
-     "execution_count": 45,
+     "execution_count": 53,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1583,7 +1831,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 46,
+   "execution_count": 54,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -1596,7 +1844,7 @@
        "0"
       ]
      },
-     "execution_count": 46,
+     "execution_count": 54,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1618,7 +1866,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 47,
+   "execution_count": 55,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -1631,7 +1879,7 @@
        "'0b1'"
       ]
      },
-     "execution_count": 47,
+     "execution_count": 55,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1642,7 +1890,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 48,
+   "execution_count": 56,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -1655,7 +1903,7 @@
        "'0b0'"
       ]
      },
-     "execution_count": 48,
+     "execution_count": 56,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1677,7 +1925,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 49,
+   "execution_count": 57,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -1690,7 +1938,7 @@
        "'0x1'"
       ]
      },
-     "execution_count": 49,
+     "execution_count": 57,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1701,7 +1949,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 50,
+   "execution_count": 58,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -1714,7 +1962,7 @@
        "'0x0'"
       ]
      },
-     "execution_count": 50,
+     "execution_count": 58,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1736,7 +1984,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 51,
+   "execution_count": 59,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -1750,7 +1998,7 @@
      "traceback": [
       "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
       "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-51-af2123a46eb2>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;32m<ipython-input-59-af2123a46eb2>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
       "\u001b[0;31mTypeError\u001b[0m: int() argument must be a string, a bytes-like object or a number, not 'NoneType'"
      ]
     }
@@ -1787,7 +2035,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 52,
+   "execution_count": 60,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -1800,7 +2048,7 @@
        "9"
       ]
      },
-     "execution_count": 52,
+     "execution_count": 60,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1811,7 +2059,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 53,
+   "execution_count": 61,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -1824,7 +2072,7 @@
        "'0b1011 & 0b1101'"
       ]
      },
-     "execution_count": 53,
+     "execution_count": 61,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1835,7 +2083,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 54,
+   "execution_count": 62,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -1848,7 +2096,7 @@
        "'0b1001'"
       ]
      },
-     "execution_count": 54,
+     "execution_count": 62,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1870,7 +2118,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 55,
+   "execution_count": 63,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -1883,7 +2131,7 @@
        "9"
       ]
      },
-     "execution_count": 55,
+     "execution_count": 63,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1905,7 +2153,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 56,
+   "execution_count": 64,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -1918,7 +2166,7 @@
        "13"
       ]
      },
-     "execution_count": 56,
+     "execution_count": 64,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1929,7 +2177,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 57,
+   "execution_count": 65,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -1942,7 +2190,7 @@
        "'0b1001 | 0b1101'"
       ]
      },
-     "execution_count": 57,
+     "execution_count": 65,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1953,7 +2201,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 58,
+   "execution_count": 66,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -1966,7 +2214,7 @@
        "'0b1101'"
       ]
      },
-     "execution_count": 58,
+     "execution_count": 66,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1988,7 +2236,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 59,
+   "execution_count": 67,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -2001,7 +2249,7 @@
        "13"
       ]
      },
-     "execution_count": 59,
+     "execution_count": 67,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2023,7 +2271,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 60,
+   "execution_count": 68,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -2036,7 +2284,7 @@
        "4"
       ]
      },
-     "execution_count": 60,
+     "execution_count": 68,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2047,7 +2295,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 61,
+   "execution_count": 69,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -2060,7 +2308,7 @@
        "'0b1001 ^ 0b1101'"
       ]
      },
-     "execution_count": 61,
+     "execution_count": 69,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2071,7 +2319,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 62,
+   "execution_count": 70,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -2084,7 +2332,7 @@
        "'0b100'"
       ]
      },
-     "execution_count": 62,
+     "execution_count": 70,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2106,7 +2354,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 63,
+   "execution_count": 71,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -2119,7 +2367,7 @@
        "4"
       ]
      },
-     "execution_count": 63,
+     "execution_count": 71,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2143,7 +2391,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 64,
+   "execution_count": 72,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -2156,7 +2404,7 @@
        "-8"
       ]
      },
-     "execution_count": 64,
+     "execution_count": 72,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2167,7 +2415,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 65,
+   "execution_count": 73,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -2180,7 +2428,7 @@
        "True"
       ]
      },
-     "execution_count": 65,
+     "execution_count": 73,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2191,7 +2439,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 66,
+   "execution_count": 74,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -2204,7 +2452,7 @@
        "'-0b1000'"
       ]
      },
-     "execution_count": 66,
+     "execution_count": 74,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2215,7 +2463,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 67,
+   "execution_count": 75,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -2228,7 +2476,7 @@
        "'-0b1000'"
       ]
      },
-     "execution_count": 67,
+     "execution_count": 75,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2250,7 +2498,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 68,
+   "execution_count": 76,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -2263,7 +2511,7 @@
        "-1"
       ]
      },
-     "execution_count": 68,
+     "execution_count": 76,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2287,7 +2535,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 69,
+   "execution_count": 77,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -2300,7 +2548,7 @@
        "28"
       ]
      },
-     "execution_count": 69,
+     "execution_count": 77,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2311,7 +2559,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 70,
+   "execution_count": 78,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -2324,7 +2572,7 @@
        "'0b111'"
       ]
      },
-     "execution_count": 70,
+     "execution_count": 78,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2335,7 +2583,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 71,
+   "execution_count": 79,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -2348,7 +2596,7 @@
        "'0b11100'"
       ]
      },
-     "execution_count": 71,
+     "execution_count": 79,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2359,7 +2607,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 72,
+   "execution_count": 80,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -2372,7 +2620,7 @@
        "28"
       ]
      },
-     "execution_count": 72,
+     "execution_count": 80,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2394,7 +2642,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 73,
+   "execution_count": 81,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -2407,7 +2655,7 @@
        "3"
       ]
      },
-     "execution_count": 73,
+     "execution_count": 81,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2418,7 +2666,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 74,
+   "execution_count": 82,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -2431,7 +2679,7 @@
        "'0b111'"
       ]
      },
-     "execution_count": 74,
+     "execution_count": 82,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2442,7 +2690,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 75,
+   "execution_count": 83,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -2455,7 +2703,7 @@
        "'0b11'"
       ]
      },
-     "execution_count": 75,
+     "execution_count": 83,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2466,7 +2714,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 76,
+   "execution_count": 84,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -2479,7 +2727,7 @@
        "3"
       ]
      },
-     "execution_count": 76,
+     "execution_count": 84,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2518,7 +2766,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 77,
+   "execution_count": 85,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -2531,7 +2779,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 78,
+   "execution_count": 86,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -2541,10 +2789,10 @@
     {
      "data": {
       "text/plain": [
-       "139674754277776"
+       "139663468493296"
       ]
      },
-     "execution_count": 78,
+     "execution_count": 86,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2555,7 +2803,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 79,
+   "execution_count": 87,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -2568,272 +2816,13 @@
        "float"
       ]
      },
-     "execution_count": 79,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "type(b)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 80,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "fragment"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "42.0"
-      ]
-     },
-     "execution_count": 80,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "b"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "source": [
-    "As with integer literals above, we may use underscores `_` to make longer `float` objects easier to read."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 81,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "0.123456789"
-      ]
-     },
-     "execution_count": 81,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "0.123_456_789"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "source": [
-    "In cases where the dot `.` is unnecessary from a mathematical point of view, we either need to end the number with it nevertheless or use the [float()](https://docs.python.org/3/library/functions.html#float) built-in to cast the number explicitly. [float()](https://docs.python.org/3/library/functions.html#float) can process any numeric object or a properly formatted `str` object."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 82,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "slide"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "42.0"
-      ]
-     },
-     "execution_count": 82,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "42."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 83,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "fragment"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "42.0"
-      ]
-     },
-     "execution_count": 83,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "float(42)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 84,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "fragment"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "42.0"
-      ]
-     },
-     "execution_count": 84,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "float(\"42\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "source": [
-    "Leading and trailing whitespace is ignored ..."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 85,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "42.87"
-      ]
-     },
-     "execution_count": 85,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "float(\" 42.87 \")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "source": [
-    "... but not whitespace in between."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 86,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "outputs": [
-    {
-     "ename": "ValueError",
-     "evalue": "could not convert string to float: '42. 87'",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-86-c7953b8b7956>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mfloat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"42. 87\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
-      "\u001b[0;31mValueError\u001b[0m: could not convert string to float: '42. 87'"
-     ]
-    }
-   ],
-   "source": [
-    "float(\"42. 87\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "source": [
-    "`float` objects are implicitly created as the result of dividing an `int` object by another with the division operator `/`."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 87,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "slide"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "0.3333333333333333"
-      ]
-     },
      "execution_count": 87,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "1 / 3"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "source": [
-    "In general, if we combine `float` and `int` objects in arithmetic operations, we always end up with a `float` type: Python uses the \"broader\" representation."
+    "type(b)"
    ]
   },
   {
@@ -2857,12 +2846,82 @@
     }
    ],
    "source": [
-    "40.0 + 2"
+    "b"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "As with integer literals above, we may use underscores `_` to make longer `float` objects easier to read."
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 89,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.123456789"
+      ]
+     },
+     "execution_count": 89,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "0.123_456_789"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "In cases where the dot `.` is unnecessary from a mathematical point of view, we either need to end the number with it nevertheless or use the [float()](https://docs.python.org/3/library/functions.html#float) built-in to cast the number explicitly. [float()](https://docs.python.org/3/library/functions.html#float) can process any numeric object or a properly formatted `str` object."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 90,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "42.0"
+      ]
+     },
+     "execution_count": 90,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "42."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 91,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -2875,7 +2934,196 @@
        "42.0"
       ]
      },
-     "execution_count": 89,
+     "execution_count": 91,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "float(42)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 92,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "42.0"
+      ]
+     },
+     "execution_count": 92,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "float(\"42\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Leading and trailing whitespace is ignored ..."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 93,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "42.87"
+      ]
+     },
+     "execution_count": 93,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "float(\" 42.87 \")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "... but not whitespace in between."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 94,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "ename": "ValueError",
+     "evalue": "could not convert string to float: '42. 87'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-94-c7953b8b7956>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mfloat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"42. 87\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;31mValueError\u001b[0m: could not convert string to float: '42. 87'"
+     ]
+    }
+   ],
+   "source": [
+    "float(\"42. 87\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "`float` objects are implicitly created as the result of dividing an `int` object by another with the division operator `/`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 95,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.3333333333333333"
+      ]
+     },
+     "execution_count": 95,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "1 / 3"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "In general, if we combine `float` and `int` objects in arithmetic operations, we always end up with a `float` type: Python uses the \"broader\" representation."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 96,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "42.0"
+      ]
+     },
+     "execution_count": 96,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "40.0 + 2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 97,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "42.0"
+      ]
+     },
+     "execution_count": 97,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2908,7 +3156,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 90,
+   "execution_count": 98,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -2921,7 +3169,7 @@
        "1.23"
       ]
      },
-     "execution_count": 90,
+     "execution_count": 98,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2943,7 +3191,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 91,
+   "execution_count": 99,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -2952,10 +3200,10 @@
    "outputs": [
     {
      "ename": "SyntaxError",
-     "evalue": "invalid syntax (<ipython-input-91-1b5daaac0077>, line 1)",
+     "evalue": "invalid syntax (<ipython-input-99-1b5daaac0077>, line 1)",
      "output_type": "error",
      "traceback": [
-      "\u001b[0;36m  File \u001b[0;32m\"<ipython-input-91-1b5daaac0077>\"\u001b[0;36m, line \u001b[0;32m1\u001b[0m\n\u001b[0;31m    1.23 e0\u001b[0m\n\u001b[0m          ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m invalid syntax\n"
+      "\u001b[0;36m  File \u001b[0;32m\"<ipython-input-99-1b5daaac0077>\"\u001b[0;36m, line \u001b[0;32m1\u001b[0m\n\u001b[0;31m    1.23 e0\u001b[0m\n\u001b[0m          ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m invalid syntax\n"
      ]
     }
    ],
@@ -2965,7 +3213,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 92,
+   "execution_count": 100,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -2974,10 +3222,10 @@
    "outputs": [
     {
      "ename": "SyntaxError",
-     "evalue": "invalid syntax (<ipython-input-92-a236c4a30231>, line 1)",
+     "evalue": "invalid syntax (<ipython-input-100-a236c4a30231>, line 1)",
      "output_type": "error",
      "traceback": [
-      "\u001b[0;36m  File \u001b[0;32m\"<ipython-input-92-a236c4a30231>\"\u001b[0;36m, line \u001b[0;32m1\u001b[0m\n\u001b[0;31m    1.23e 0\u001b[0m\n\u001b[0m        ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m invalid syntax\n"
+      "\u001b[0;36m  File \u001b[0;32m\"<ipython-input-100-a236c4a30231>\"\u001b[0;36m, line \u001b[0;32m1\u001b[0m\n\u001b[0;31m    1.23e 0\u001b[0m\n\u001b[0m        ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m invalid syntax\n"
      ]
     }
    ],
@@ -2987,7 +3235,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 93,
+   "execution_count": 101,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -2996,10 +3244,10 @@
    "outputs": [
     {
      "ename": "SyntaxError",
-     "evalue": "invalid syntax (<ipython-input-93-05b9072d76be>, line 1)",
+     "evalue": "invalid syntax (<ipython-input-101-05b9072d76be>, line 1)",
      "output_type": "error",
      "traceback": [
-      "\u001b[0;36m  File \u001b[0;32m\"<ipython-input-93-05b9072d76be>\"\u001b[0;36m, line \u001b[0;32m1\u001b[0m\n\u001b[0;31m    1.23e0.0\u001b[0m\n\u001b[0m           ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m invalid syntax\n"
+      "\u001b[0;36m  File \u001b[0;32m\"<ipython-input-101-05b9072d76be>\"\u001b[0;36m, line \u001b[0;32m1\u001b[0m\n\u001b[0;31m    1.23e0.0\u001b[0m\n\u001b[0m           ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m invalid syntax\n"
      ]
     }
    ],
@@ -3020,7 +3268,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 94,
+   "execution_count": 102,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -3034,7 +3282,7 @@
      "traceback": [
       "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
       "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-94-abc4de6ccb5e>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0me0\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;32m<ipython-input-102-abc4de6ccb5e>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0me0\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
       "\u001b[0;31mNameError\u001b[0m: name 'e0' is not defined"
      ]
     }
@@ -3056,7 +3304,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 95,
+   "execution_count": 103,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -3069,7 +3317,7 @@
        "1.0"
       ]
      },
-     "execution_count": 95,
+     "execution_count": 103,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3091,7 +3339,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 96,
+   "execution_count": 104,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -3104,7 +3352,7 @@
        "1000.0"
       ]
      },
-     "execution_count": 96,
+     "execution_count": 104,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3126,7 +3374,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 97,
+   "execution_count": 105,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -3139,7 +3387,7 @@
        "0.001"
       ]
      },
-     "execution_count": 97,
+     "execution_count": 105,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3170,255 +3418,19 @@
     "There are also three special values representing \"**not a number,**\" called `nan`, and positive or negative **infinity**, called `inf` or `-inf`, that are created by passing in the corresponding abbreviation as a `str` object to the [float()](https://docs.python.org/3/library/functions.html#float) built-in. These values could be used, for example, as the result of a mathematically undefined operation like division by zero or to model the value of a mathematical function as it goes to infinity."
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": 98,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "slide"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "nan"
-      ]
-     },
-     "execution_count": 98,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "float(\"nan\")  # also float(\"NaN\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 99,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "inf"
-      ]
-     },
-     "execution_count": 99,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "float(\"+inf\")  # also float(\"+infinity\") or float(\"infinity\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 100,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "fragment"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "inf"
-      ]
-     },
-     "execution_count": 100,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "float(\"inf\")  # same as float(\"+inf\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 101,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "fragment"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "-inf"
-      ]
-     },
-     "execution_count": 101,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "float(\"-inf\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "source": [
-    "`nan` objects *never* compare equal to *anything*, not even to themselves. This happens in accordance with the [IEEE 754](https://en.wikipedia.org/wiki/IEEE_754) standard."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 102,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "slide"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "False"
-      ]
-     },
-     "execution_count": 102,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "float(\"nan\") == float(\"nan\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "source": [
-    "Another caveat is that any arithmetic involving a `nan` object results in `nan`. In other words, the addition below **fails silently** as no error is raised. As this also happens in accordance with the [IEEE 754](https://en.wikipedia.org/wiki/IEEE_754) standard, we *need* to be aware of that and check any data we work with for any `nan` occurrences *before* doing any calculations."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 103,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "fragment"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "nan"
-      ]
-     },
-     "execution_count": 103,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "42 + float(\"nan\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "source": [
-    "On the contrary, as two values go to infinity, there is no such concept as difference and *everything* compares equal."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 104,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "slide"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "True"
-      ]
-     },
-     "execution_count": 104,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "float(\"inf\") == float(\"inf\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "source": [
-    "Adding `42` to `inf` makes no difference."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 105,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "inf"
-      ]
-     },
-     "execution_count": 105,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "float(\"inf\") + 42"
-   ]
-  },
   {
    "cell_type": "code",
    "execution_count": 106,
    "metadata": {
     "slideshow": {
-     "slide_type": "fragment"
+     "slide_type": "slide"
     }
    },
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "True"
+       "nan"
       ]
      },
      "execution_count": 106,
@@ -3427,18 +3439,7 @@
     }
    ],
    "source": [
-    "float(\"inf\") + 42 == float(\"inf\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "source": [
-    "We observe the same for multiplication ..."
+    "float(\"nan\")  # also float(\"NaN\")"
    ]
   },
   {
@@ -3462,12 +3463,259 @@
     }
    ],
    "source": [
-    "42 * float(\"inf\")"
+    "float(\"+inf\")  # also float(\"+infinity\") or float(\"infinity\")"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 108,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "inf"
+      ]
+     },
+     "execution_count": 108,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "float(\"inf\")  # same as float(\"+inf\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 109,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "-inf"
+      ]
+     },
+     "execution_count": 109,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "float(\"-inf\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "`nan` objects *never* compare equal to *anything*, not even to themselves. This happens in accordance with the [IEEE 754](https://en.wikipedia.org/wiki/IEEE_754) standard."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 110,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "False"
+      ]
+     },
+     "execution_count": 110,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "float(\"nan\") == float(\"nan\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Another caveat is that any arithmetic involving a `nan` object results in `nan`. In other words, the addition below **fails silently** as no error is raised. As this also happens in accordance with the [IEEE 754](https://en.wikipedia.org/wiki/IEEE_754) standard, we *need* to be aware of that and check any data we work with for any `nan` occurrences *before* doing any calculations."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 111,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "nan"
+      ]
+     },
+     "execution_count": 111,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "42 + float(\"nan\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "On the contrary, as two values go to infinity, there is no such concept as difference and *everything* compares equal."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 112,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 112,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "float(\"inf\") == float(\"inf\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Adding `42` to `inf` makes no difference."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 113,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "inf"
+      ]
+     },
+     "execution_count": 113,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "float(\"inf\") + 42"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 114,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 114,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "float(\"inf\") + 42 == float(\"inf\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "We observe the same for multiplication ..."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 115,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "inf"
+      ]
+     },
+     "execution_count": 115,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "42 * float(\"inf\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 116,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -3480,7 +3728,7 @@
        "True"
       ]
      },
-     "execution_count": 108,
+     "execution_count": 116,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3502,7 +3750,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 109,
+   "execution_count": 117,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -3515,7 +3763,7 @@
        "inf"
       ]
      },
-     "execution_count": 109,
+     "execution_count": 117,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3526,7 +3774,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 110,
+   "execution_count": 118,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -3539,7 +3787,7 @@
        "True"
       ]
      },
-     "execution_count": 110,
+     "execution_count": 118,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3561,7 +3809,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 111,
+   "execution_count": 119,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -3574,7 +3822,7 @@
        "inf"
       ]
      },
-     "execution_count": 111,
+     "execution_count": 119,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3585,7 +3833,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 112,
+   "execution_count": 120,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -3598,7 +3846,7 @@
        "True"
       ]
      },
-     "execution_count": 112,
+     "execution_count": 120,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3620,7 +3868,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 113,
+   "execution_count": 121,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -3633,7 +3881,7 @@
        "nan"
       ]
      },
-     "execution_count": 113,
+     "execution_count": 121,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3644,7 +3892,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 114,
+   "execution_count": 122,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -3657,7 +3905,7 @@
        "nan"
       ]
      },
-     "execution_count": 114,
+     "execution_count": 122,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3692,7 +3940,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 115,
+   "execution_count": 123,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -3705,7 +3953,7 @@
        "1000000000000001.0"
       ]
      },
-     "execution_count": 115,
+     "execution_count": 123,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3716,7 +3964,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 116,
+   "execution_count": 124,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -3729,7 +3977,7 @@
        "1e+16"
       ]
      },
-     "execution_count": 116,
+     "execution_count": 124,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3751,7 +3999,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 117,
+   "execution_count": 125,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -3764,7 +4012,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 118,
+   "execution_count": 126,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -3777,7 +4025,7 @@
        "2.0000000000000004"
       ]
      },
-     "execution_count": 118,
+     "execution_count": 126,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3788,7 +4036,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 119,
+   "execution_count": 127,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -3801,7 +4049,7 @@
        "0.30000000000000004"
       ]
      },
-     "execution_count": 119,
+     "execution_count": 127,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3823,7 +4071,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 120,
+   "execution_count": 128,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -3836,7 +4084,7 @@
        "False"
       ]
      },
-     "execution_count": 120,
+     "execution_count": 128,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3847,7 +4095,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 121,
+   "execution_count": 129,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -3860,7 +4108,7 @@
        "False"
       ]
      },
-     "execution_count": 121,
+     "execution_count": 129,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3882,7 +4130,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 122,
+   "execution_count": 130,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -3895,7 +4143,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 123,
+   "execution_count": 131,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -3908,7 +4156,7 @@
        "True"
       ]
      },
-     "execution_count": 123,
+     "execution_count": 131,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3919,7 +4167,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 124,
+   "execution_count": 132,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -3932,7 +4180,7 @@
        "True"
       ]
      },
-     "execution_count": 124,
+     "execution_count": 132,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3949,14 +4197,14 @@
     }
    },
    "source": [
-    "The built-in [format()](https://docs.python.org/3/library/functions.html#format) function allows us to show the **significant digits** of a `float` number as they exist in memory to arbitrary precision. To exemplify it, let's view a couple of `float` objects with `50` digits. This analysis reveals that almost no `float` number is precise! After $14$ or $15$ digits \"weird\" things happen. As we see further below, the \"random\" digits ending the `float` numbers do *not* \"physically\" exist in memory.\n",
+    "The built-in [format()](https://docs.python.org/3/library/functions.html#format) function allows us to show the **significant digits** of a `float` number as they exist in memory to arbitrary precision. To exemplify it, let's view a couple of `float` objects with `50` digits. This analysis reveals that almost no `float` number is precise! After 14 or 15 digits \"weird\" things happen. As we see further below, the \"random\" digits ending the `float` numbers do *not* \"physically\" exist in memory! Rather, they are \"calculated\" by the [format()](https://docs.python.org/3/library/functions.html#format) function that is forced to show `50` digits.\n",
     "\n",
-    "The [format()](https://docs.python.org/3/library/functions.html#format) function is different from the [format()](https://docs.python.org/3/library/stdtypes.html#str.format) method on `str` objects introduced in the next chapter and both work with the so-called [format specification mini-language](https://docs.python.org/3/library/string.html#format-specification-mini-language): `\".50f\"` is the instruction to show `50` digits of a `float` number. But let's not worry too much about these details for now."
+    "The [format()](https://docs.python.org/3/library/functions.html#format) function is different from the [format()](https://docs.python.org/3/library/stdtypes.html#str.format) method on `str` objects introduced in the next chapter (cf., [Chapter 6](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/06_text_00_lecture.ipynb#format%28%29-Method)): Yet, both work with the so-called [format specification mini-language](https://docs.python.org/3/library/string.html#format-specification-mini-language): `\".50f\"` is the instruction to show `50` digits of a `float` number."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 125,
+   "execution_count": 133,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -3969,7 +4217,7 @@
        "'0.10000000000000000555111512312578270211815834045410'"
       ]
      },
-     "execution_count": 125,
+     "execution_count": 133,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3980,7 +4228,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 126,
+   "execution_count": 134,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -3993,7 +4241,7 @@
        "'0.20000000000000001110223024625156540423631668090820'"
       ]
      },
-     "execution_count": 126,
+     "execution_count": 134,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -4004,7 +4252,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 127,
+   "execution_count": 135,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -4017,7 +4265,7 @@
        "'0.29999999999999998889776975374843459576368331909180'"
       ]
      },
-     "execution_count": 127,
+     "execution_count": 135,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -4028,7 +4276,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 128,
+   "execution_count": 136,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -4041,7 +4289,7 @@
        "'0.33333333333333331482961625624739099293947219848633'"
       ]
      },
-     "execution_count": 128,
+     "execution_count": 136,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -4067,7 +4315,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 129,
+   "execution_count": 137,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -4080,7 +4328,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 130,
+   "execution_count": 138,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -4093,7 +4341,7 @@
        "0.33333"
       ]
      },
-     "execution_count": 130,
+     "execution_count": 138,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -4104,7 +4352,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 131,
+   "execution_count": 139,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -4117,7 +4365,7 @@
        "'0.33333000000000001517008740847813896834850311279297'"
       ]
      },
-     "execution_count": 131,
+     "execution_count": 139,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -4139,7 +4387,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 132,
+   "execution_count": 140,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -4152,7 +4400,7 @@
        "'0.12500000000000000000000000000000000000000000000000'"
       ]
      },
-     "execution_count": 132,
+     "execution_count": 140,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -4163,7 +4411,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 133,
+   "execution_count": 141,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -4176,7 +4424,7 @@
        "'0.25000000000000000000000000000000000000000000000000'"
       ]
      },
-     "execution_count": 133,
+     "execution_count": 141,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -4187,7 +4435,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 134,
+   "execution_count": 142,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -4200,7 +4448,7 @@
        "True"
       ]
      },
-     "execution_count": 134,
+     "execution_count": 142,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -4287,7 +4535,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 135,
+   "execution_count": 143,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -4300,7 +4548,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 136,
+   "execution_count": 144,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -4313,7 +4561,7 @@
        "'0x1.0000000000000p-3'"
       ]
      },
-     "execution_count": 136,
+     "execution_count": 144,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -4335,7 +4583,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 137,
+   "execution_count": 145,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -4348,7 +4596,7 @@
        "(1, 8)"
       ]
      },
-     "execution_count": 137,
+     "execution_count": 145,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -4359,7 +4607,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 138,
+   "execution_count": 146,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -4372,7 +4620,7 @@
        "'0x1.555475a31a4bep-2'"
       ]
      },
-     "execution_count": 138,
+     "execution_count": 146,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -4383,7 +4631,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 139,
+   "execution_count": 147,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -4396,7 +4644,7 @@
        "(3002369727582815, 9007199254740992)"
       ]
      },
-     "execution_count": 139,
+     "execution_count": 147,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -4418,7 +4666,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 140,
+   "execution_count": 148,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -4431,7 +4679,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 141,
+   "execution_count": 149,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -4444,7 +4692,7 @@
        "'0x0.0p+0'"
       ]
      },
-     "execution_count": 141,
+     "execution_count": 149,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -4455,7 +4703,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 142,
+   "execution_count": 150,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -4468,7 +4716,7 @@
        "(0, 1)"
       ]
      },
-     "execution_count": 142,
+     "execution_count": 150,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -4490,7 +4738,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 143,
+   "execution_count": 151,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -4503,7 +4751,7 @@
        "False"
       ]
      },
-     "execution_count": 143,
+     "execution_count": 151,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -4514,7 +4762,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 144,
+   "execution_count": 152,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -4527,7 +4775,7 @@
        "True"
       ]
      },
-     "execution_count": 144,
+     "execution_count": 152,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -4551,7 +4799,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 145,
+   "execution_count": 153,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -4564,7 +4812,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 146,
+   "execution_count": 154,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -4577,7 +4825,7 @@
        "sys.float_info(max=1.7976931348623157e+308, max_exp=1024, max_10_exp=308, min=2.2250738585072014e-308, min_exp=-1021, min_10_exp=-307, dig=15, mant_dig=53, epsilon=2.220446049250313e-16, radix=2, rounds=1)"
       ]
      },
-     "execution_count": 146,
+     "execution_count": 154,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -4612,7 +4860,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 147,
+   "execution_count": 155,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -4636,7 +4884,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 148,
+   "execution_count": 156,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -4649,7 +4897,7 @@
        "Context(prec=28, rounding=ROUND_HALF_EVEN, Emin=-999999, Emax=999999, capitals=1, clamp=0, flags=[], traps=[InvalidOperation, DivisionByZero, Overflow])"
       ]
      },
-     "execution_count": 148,
+     "execution_count": 156,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -4669,242 +4917,6 @@
     "The two simplest ways to create a `Decimal` object is to either **instantiate** it with an `int` or a `str` object consisting of all the significant digits. In the latter case, the scientific notation is also possible."
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": 149,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "slide"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Decimal('42')"
-      ]
-     },
-     "execution_count": 149,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "Decimal(42)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 150,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "fragment"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Decimal('0.1')"
-      ]
-     },
-     "execution_count": 150,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "Decimal(\"0.1\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 151,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "fragment"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Decimal('0.001')"
-      ]
-     },
-     "execution_count": 151,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "Decimal(\"1e-3\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "source": [
-    "It is *not* a good idea to create a `Decimal` from a `float` object. If we did so, we would create a `Decimal` object that internally used extra bits to store the \"random\" digits that are not stored in the `float` object in the first place."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 152,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "fragment"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Decimal('0.1000000000000000055511151231257827021181583404541015625')"
-      ]
-     },
-     "execution_count": 152,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "Decimal(0.1)  # do not do this"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "source": [
-    "With the `Decimal` type, the imprecisions in the arithmetic and equality comparisons from above go away."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 153,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "slide"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Decimal('0.3')"
-      ]
-     },
-     "execution_count": 153,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "Decimal(\"0.1\") + Decimal(\"0.2\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 154,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "fragment"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "True"
-      ]
-     },
-     "execution_count": 154,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "Decimal(\"0.1\") + Decimal(\"0.2\") == Decimal(\"0.3\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "source": [
-    "`Decimal` numbers *preserve* the **significant digits**, even in cases where this is not needed."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 155,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "fragment"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Decimal('0.30000')"
-      ]
-     },
-     "execution_count": 155,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "Decimal(\"0.10000\") + Decimal(\"0.20000\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 156,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "True"
-      ]
-     },
-     "execution_count": 156,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "Decimal(\"0.10000\") + Decimal(\"0.20000\") == Decimal(\"0.3\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "source": [
-    "Arithmetic operations between `Decimal` and `int` objects work as the latter are inherently precise: The results are *new* `Decimal` objects."
-   ]
-  },
   {
    "cell_type": "code",
    "execution_count": 157,
@@ -4926,7 +4938,7 @@
     }
    ],
    "source": [
-    "21 + Decimal(21)"
+    "Decimal(42)"
    ]
   },
   {
@@ -4941,7 +4953,7 @@
     {
      "data": {
       "text/plain": [
-       "Decimal('42.0')"
+       "Decimal('0.1')"
       ]
      },
      "execution_count": 158,
@@ -4949,13 +4961,249 @@
      "output_type": "execute_result"
     }
    ],
+   "source": [
+    "Decimal(\"0.1\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 159,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Decimal('0.001')"
+      ]
+     },
+     "execution_count": 159,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "Decimal(\"1e-3\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "It is *not* a good idea to create a `Decimal` from a `float` object. If we did so, we would create a `Decimal` object that internally used extra bits to store the \"random\" digits that are not stored in the `float` object in the first place."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 160,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Decimal('0.1000000000000000055511151231257827021181583404541015625')"
+      ]
+     },
+     "execution_count": 160,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "Decimal(0.1)  # do not do this"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "With the `Decimal` type, the imprecisions in the arithmetic and equality comparisons from above go away."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 161,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Decimal('0.3')"
+      ]
+     },
+     "execution_count": 161,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "Decimal(\"0.1\") + Decimal(\"0.2\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 162,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 162,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "Decimal(\"0.1\") + Decimal(\"0.2\") == Decimal(\"0.3\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "`Decimal` numbers *preserve* the **significant digits**, even in cases where this is not needed."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 163,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Decimal('0.30000')"
+      ]
+     },
+     "execution_count": 163,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "Decimal(\"0.10000\") + Decimal(\"0.20000\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 164,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 164,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "Decimal(\"0.10000\") + Decimal(\"0.20000\") == Decimal(\"0.3\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Arithmetic operations between `Decimal` and `int` objects work as the latter are inherently precise: The results are *new* `Decimal` objects."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 165,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Decimal('42')"
+      ]
+     },
+     "execution_count": 165,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "21 + Decimal(21)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 166,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Decimal('42.0')"
+      ]
+     },
+     "execution_count": 166,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "10 * Decimal(\"4.2\")"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 159,
+   "execution_count": 167,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -4968,7 +5216,7 @@
        "Decimal('0.1')"
       ]
      },
-     "execution_count": 159,
+     "execution_count": 167,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -4990,7 +5238,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 160,
+   "execution_count": 168,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -5003,7 +5251,7 @@
        "'0.10000000000000000000000000000000000000000000000000'"
       ]
      },
-     "execution_count": 160,
+     "execution_count": 168,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -5014,7 +5262,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 161,
+   "execution_count": 169,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -5027,7 +5275,7 @@
        "'0.10000000000000000555111512312578270211815834045410'"
       ]
      },
-     "execution_count": 161,
+     "execution_count": 169,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -5049,7 +5297,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 162,
+   "execution_count": 170,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -5063,7 +5311,7 @@
      "traceback": [
       "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
       "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-162-a3cea145e6d1>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0;36m1.0\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0mDecimal\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m42\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;32m<ipython-input-170-a3cea145e6d1>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0;36m1.0\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0mDecimal\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m42\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
       "\u001b[0;31mTypeError\u001b[0m: unsupported operand type(s) for *: 'float' and 'decimal.Decimal'"
      ]
     }
@@ -5085,7 +5333,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 163,
+   "execution_count": 171,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -5098,7 +5346,7 @@
        "Decimal('2')"
       ]
      },
-     "execution_count": 163,
+     "execution_count": 171,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -5109,7 +5357,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 164,
+   "execution_count": 172,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -5122,7 +5370,7 @@
        "Decimal('1.414213562373095048801688724')"
       ]
      },
-     "execution_count": 164,
+     "execution_count": 172,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -5146,7 +5394,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 165,
+   "execution_count": 173,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -5159,7 +5407,7 @@
        "Decimal('1.999999999999999999999999999')"
       ]
      },
-     "execution_count": 165,
+     "execution_count": 173,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -5185,7 +5433,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 166,
+   "execution_count": 174,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -5198,7 +5446,7 @@
        "Decimal('2.0000')"
       ]
      },
-     "execution_count": 166,
+     "execution_count": 174,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -5220,7 +5468,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 167,
+   "execution_count": 175,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -5234,7 +5482,7 @@
      "traceback": [
       "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
       "\u001b[0;31mInvalidOperation\u001b[0m                          Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-167-5b555b105c8f>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mtwo\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mquantize\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mDecimal\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"1e-28\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;32m<ipython-input-175-5b555b105c8f>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mtwo\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mquantize\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mDecimal\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"1e-28\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
       "\u001b[0;31mInvalidOperation\u001b[0m: [<class 'decimal.InvalidOperation'>]"
      ]
     }
@@ -5256,7 +5504,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 168,
+   "execution_count": 176,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -5269,7 +5517,7 @@
        "True"
       ]
      },
-     "execution_count": 168,
+     "execution_count": 176,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -5291,7 +5539,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 169,
+   "execution_count": 177,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -5304,7 +5552,7 @@
        "True"
       ]
      },
-     "execution_count": 169,
+     "execution_count": 177,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -5326,7 +5574,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 170,
+   "execution_count": 178,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -5339,7 +5587,7 @@
        "Decimal('NaN')"
       ]
      },
-     "execution_count": 170,
+     "execution_count": 178,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -5361,7 +5609,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 171,
+   "execution_count": 179,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -5374,7 +5622,7 @@
        "False"
       ]
      },
-     "execution_count": 171,
+     "execution_count": 179,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -5396,7 +5644,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 172,
+   "execution_count": 180,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -5409,7 +5657,7 @@
        "Decimal('Infinity')"
       ]
      },
-     "execution_count": 172,
+     "execution_count": 180,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -5420,7 +5668,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 173,
+   "execution_count": 181,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -5433,7 +5681,7 @@
        "Decimal('-Infinity')"
       ]
      },
-     "execution_count": 173,
+     "execution_count": 181,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -5444,7 +5692,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 174,
+   "execution_count": 182,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -5457,7 +5705,7 @@
        "Decimal('Infinity')"
       ]
      },
-     "execution_count": 174,
+     "execution_count": 182,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -5468,7 +5716,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 175,
+   "execution_count": 183,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -5481,7 +5729,7 @@
        "True"
       ]
      },
-     "execution_count": 175,
+     "execution_count": 183,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -5503,7 +5751,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 176,
+   "execution_count": 184,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -5517,7 +5765,7 @@
      "traceback": [
       "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
       "\u001b[0;31mInvalidOperation\u001b[0m                          Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-176-87950ebbd6ef>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mDecimal\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"inf\"\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mDecimal\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"-inf\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;32m<ipython-input-184-87950ebbd6ef>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mDecimal\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"inf\"\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mDecimal\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"-inf\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
       "\u001b[0;31mInvalidOperation\u001b[0m: [<class 'decimal.InvalidOperation'>]"
      ]
     }
@@ -5528,7 +5776,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 177,
+   "execution_count": 185,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -5542,7 +5790,7 @@
      "traceback": [
       "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
       "\u001b[0;31mInvalidOperation\u001b[0m                          Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-177-996083f200c6>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mDecimal\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"inf\"\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0mDecimal\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"inf\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;32m<ipython-input-185-996083f200c6>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mDecimal\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"inf\"\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0mDecimal\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"inf\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
       "\u001b[0;31mInvalidOperation\u001b[0m: [<class 'decimal.InvalidOperation'>]"
      ]
     }
@@ -5588,7 +5836,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 178,
+   "execution_count": 186,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -5612,7 +5860,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 179,
+   "execution_count": 187,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -5625,7 +5873,7 @@
        "Fraction(1, 3)"
       ]
      },
-     "execution_count": 179,
+     "execution_count": 187,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -5636,7 +5884,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 180,
+   "execution_count": 188,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -5649,7 +5897,7 @@
        "Fraction(1, 3)"
       ]
      },
-     "execution_count": 180,
+     "execution_count": 188,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -5660,7 +5908,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 181,
+   "execution_count": 189,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -5673,7 +5921,7 @@
        "Fraction(3333333333, 10000000000)"
       ]
      },
-     "execution_count": 181,
+     "execution_count": 189,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -5684,7 +5932,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 182,
+   "execution_count": 190,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -5697,7 +5945,7 @@
        "Fraction(3333333333, 10000000000)"
       ]
      },
-     "execution_count": 182,
+     "execution_count": 190,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -5719,7 +5967,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 183,
+   "execution_count": 191,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -5732,7 +5980,7 @@
        "Fraction(3, 2)"
       ]
      },
-     "execution_count": 183,
+     "execution_count": 191,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -5743,7 +5991,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 184,
+   "execution_count": 192,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -5756,7 +6004,7 @@
        "Fraction(3, 2)"
       ]
      },
-     "execution_count": 184,
+     "execution_count": 192,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -5778,7 +6026,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 185,
+   "execution_count": 193,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -5791,7 +6039,7 @@
        "Fraction(1, 10)"
       ]
      },
-     "execution_count": 185,
+     "execution_count": 193,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -5813,7 +6061,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 186,
+   "execution_count": 194,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -5826,7 +6074,7 @@
        "Fraction(3602879701896397, 36028797018963968)"
       ]
      },
-     "execution_count": 186,
+     "execution_count": 194,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -5848,7 +6096,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 187,
+   "execution_count": 195,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -5861,7 +6109,7 @@
        "Fraction(7, 4)"
       ]
      },
-     "execution_count": 187,
+     "execution_count": 195,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -5872,7 +6120,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 188,
+   "execution_count": 196,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -5885,7 +6133,7 @@
        "Fraction(1, 2)"
       ]
      },
-     "execution_count": 188,
+     "execution_count": 196,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -5896,7 +6144,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 189,
+   "execution_count": 197,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -5909,7 +6157,7 @@
        "Fraction(1, 1)"
       ]
      },
-     "execution_count": 189,
+     "execution_count": 197,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -5920,7 +6168,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 190,
+   "execution_count": 198,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -5933,7 +6181,7 @@
        "Fraction(1, 1)"
       ]
      },
-     "execution_count": 190,
+     "execution_count": 198,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -5955,7 +6203,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 191,
+   "execution_count": 199,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -5968,7 +6216,7 @@
        "0.1"
       ]
      },
-     "execution_count": 191,
+     "execution_count": 199,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -5979,7 +6227,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 192,
+   "execution_count": 200,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -5992,7 +6240,7 @@
        "'0.10000000000000000555111512312578270211815834045410'"
       ]
      },
-     "execution_count": 192,
+     "execution_count": 200,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -6031,7 +6279,7 @@
     }
    },
    "source": [
-    "#### What is the solution to $x^2 = -1$ ?"
+    "**What is the solution to $x^2 = -1$ ?**"
    ]
   },
   {
@@ -6077,7 +6325,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 193,
+   "execution_count": 201,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -6090,7 +6338,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 194,
+   "execution_count": 202,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -6100,10 +6348,10 @@
     {
      "data": {
       "text/plain": [
-       "139674754426928"
+       "139663415663408"
       ]
      },
-     "execution_count": 194,
+     "execution_count": 202,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -6114,7 +6362,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 195,
+   "execution_count": 203,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -6127,7 +6375,7 @@
        "complex"
       ]
      },
-     "execution_count": 195,
+     "execution_count": 203,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -6138,7 +6386,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 196,
+   "execution_count": 204,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -6151,7 +6399,7 @@
        "1j"
       ]
      },
-     "execution_count": 196,
+     "execution_count": 204,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -6173,7 +6421,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 197,
+   "execution_count": 205,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -6186,7 +6434,7 @@
        "True"
       ]
      },
-     "execution_count": 197,
+     "execution_count": 205,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -6208,7 +6456,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 198,
+   "execution_count": 206,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -6221,7 +6469,7 @@
        "(2+0.5j)"
       ]
      },
-     "execution_count": 198,
+     "execution_count": 206,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -6243,7 +6491,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 199,
+   "execution_count": 207,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -6256,7 +6504,7 @@
        "(2+0.5j)"
       ]
      },
-     "execution_count": 199,
+     "execution_count": 207,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -6278,7 +6526,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 200,
+   "execution_count": 208,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -6291,7 +6539,7 @@
        "(2+0j)"
       ]
      },
-     "execution_count": 200,
+     "execution_count": 208,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -6313,7 +6561,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 201,
+   "execution_count": 209,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -6326,7 +6574,7 @@
        "(2+0.5j)"
       ]
      },
-     "execution_count": 201,
+     "execution_count": 209,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -6337,7 +6585,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 202,
+   "execution_count": 210,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -6350,7 +6598,7 @@
        "(2+0.5j)"
       ]
      },
-     "execution_count": 202,
+     "execution_count": 210,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -6372,7 +6620,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 203,
+   "execution_count": 211,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -6386,7 +6634,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 204,
+   "execution_count": 212,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -6399,7 +6647,7 @@
        "(4+6j)"
       ]
      },
-     "execution_count": 204,
+     "execution_count": 212,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -6410,7 +6658,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 205,
+   "execution_count": 213,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -6423,7 +6671,7 @@
        "(-2-2j)"
       ]
      },
-     "execution_count": 205,
+     "execution_count": 213,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -6434,7 +6682,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 206,
+   "execution_count": 214,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -6447,7 +6695,7 @@
        "(2+2j)"
       ]
      },
-     "execution_count": 206,
+     "execution_count": 214,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -6458,7 +6706,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 207,
+   "execution_count": 215,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -6471,7 +6719,7 @@
        "(0.5-4j)"
       ]
      },
-     "execution_count": 207,
+     "execution_count": 215,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -6482,7 +6730,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 208,
+   "execution_count": 216,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -6495,7 +6743,7 @@
        "(5+10j)"
       ]
      },
-     "execution_count": 208,
+     "execution_count": 216,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -6506,7 +6754,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 209,
+   "execution_count": 217,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -6519,7 +6767,7 @@
        "(0.5+0.6666666666666666j)"
       ]
      },
-     "execution_count": 209,
+     "execution_count": 217,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -6530,7 +6778,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 210,
+   "execution_count": 218,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -6543,7 +6791,7 @@
        "(-5+10j)"
       ]
      },
-     "execution_count": 210,
+     "execution_count": 218,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -6554,7 +6802,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 211,
+   "execution_count": 219,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -6567,7 +6815,7 @@
        "(0.44+0.08j)"
       ]
      },
-     "execution_count": 211,
+     "execution_count": 219,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -6589,7 +6837,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 212,
+   "execution_count": 220,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -6602,7 +6850,7 @@
        "5.0"
       ]
      },
-     "execution_count": 212,
+     "execution_count": 220,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -6624,7 +6872,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 213,
+   "execution_count": 221,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -6637,7 +6885,7 @@
        "1.0"
       ]
      },
-     "execution_count": 213,
+     "execution_count": 221,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -6648,7 +6896,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 214,
+   "execution_count": 222,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -6661,7 +6909,7 @@
        "2.0"
       ]
      },
-     "execution_count": 214,
+     "execution_count": 222,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -6683,7 +6931,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 215,
+   "execution_count": 223,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -6696,7 +6944,7 @@
        "(1-2j)"
       ]
      },
-     "execution_count": 215,
+     "execution_count": 223,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -6784,7 +7032,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 216,
+   "execution_count": 224,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -6797,7 +7045,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 217,
+   "execution_count": 225,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -6825,7 +7073,7 @@
        " 'abstractmethod']"
       ]
      },
-     "execution_count": 217,
+     "execution_count": 225,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -6851,7 +7099,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 218,
+   "execution_count": 226,
    "metadata": {
     "scrolled": true,
     "slideshow": {
@@ -6962,7 +7210,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 219,
+   "execution_count": 227,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -7142,7 +7390,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 220,
+   "execution_count": 228,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -7155,7 +7403,7 @@
        "1"
       ]
      },
-     "execution_count": 220,
+     "execution_count": 228,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -7166,7 +7414,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 221,
+   "execution_count": 229,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -7179,7 +7427,7 @@
        "42.87"
       ]
      },
-     "execution_count": 221,
+     "execution_count": 229,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -7190,7 +7438,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 222,
+   "execution_count": 230,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -7203,7 +7451,7 @@
        "5.0"
       ]
      },
-     "execution_count": 222,
+     "execution_count": 230,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -7225,7 +7473,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 223,
+   "execution_count": 231,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -7238,7 +7486,7 @@
        "100"
       ]
      },
-     "execution_count": 223,
+     "execution_count": 231,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -7249,7 +7497,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 224,
+   "execution_count": 232,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -7262,7 +7510,7 @@
        "42"
       ]
      },
-     "execution_count": 224,
+     "execution_count": 232,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -7284,7 +7532,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 225,
+   "execution_count": 233,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -7298,7 +7546,7 @@
      "traceback": [
       "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
       "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-225-e40cd01fb15a>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mround\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m1\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;36m2j\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;32m<ipython-input-233-e40cd01fb15a>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mround\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m1\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;36m2j\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
       "\u001b[0;31mTypeError\u001b[0m: type complex doesn't define __round__ method"
      ]
     }
@@ -7333,7 +7581,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 226,
+   "execution_count": 234,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -7346,7 +7594,7 @@
        "True"
       ]
      },
-     "execution_count": 226,
+     "execution_count": 234,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -7368,7 +7616,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 227,
+   "execution_count": 235,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -7381,7 +7629,7 @@
        "True"
       ]
      },
-     "execution_count": 227,
+     "execution_count": 235,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -7392,7 +7640,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 228,
+   "execution_count": 236,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -7405,7 +7653,7 @@
        "True"
       ]
      },
-     "execution_count": 228,
+     "execution_count": 236,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -7416,7 +7664,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 229,
+   "execution_count": 237,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -7429,7 +7677,7 @@
        "True"
       ]
      },
-     "execution_count": 229,
+     "execution_count": 237,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -7451,7 +7699,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 230,
+   "execution_count": 238,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -7464,7 +7712,7 @@
        "False"
       ]
      },
-     "execution_count": 230,
+     "execution_count": 238,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -7486,7 +7734,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 231,
+   "execution_count": 239,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -7499,7 +7747,7 @@
        "True"
       ]
      },
-     "execution_count": 231,
+     "execution_count": 239,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -7534,7 +7782,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 232,
+   "execution_count": 240,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -7585,7 +7833,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 233,
+   "execution_count": 241,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -7598,7 +7846,7 @@
        "1"
       ]
      },
-     "execution_count": 233,
+     "execution_count": 241,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -7609,7 +7857,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 234,
+   "execution_count": 242,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -7622,7 +7870,7 @@
        "6"
       ]
      },
-     "execution_count": 234,
+     "execution_count": 242,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -7633,7 +7881,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 235,
+   "execution_count": 243,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -7646,7 +7894,7 @@
        "6"
       ]
      },
-     "execution_count": 235,
+     "execution_count": 243,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -7668,7 +7916,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 236,
+   "execution_count": 244,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -7682,8 +7930,8 @@
      "traceback": [
       "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
       "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-236-188b816be8b6>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mfactorial\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m3.1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
-      "\u001b[0;32m<ipython-input-232-0b8c729be194>\u001b[0m in \u001b[0;36mfactorial\u001b[0;34m(n, strict)\u001b[0m\n\u001b[1;32m     17\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mn\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnumbers\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mReal\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     18\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mn\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0mint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mn\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mstrict\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 19\u001b[0;31m                 \u001b[0;32mraise\u001b[0m \u001b[0mTypeError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"n is not integer-like; it has decimals\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     20\u001b[0m             \u001b[0mn\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mn\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     21\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m<ipython-input-244-188b816be8b6>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mfactorial\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m3.1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;32m<ipython-input-240-0b8c729be194>\u001b[0m in \u001b[0;36mfactorial\u001b[0;34m(n, strict)\u001b[0m\n\u001b[1;32m     17\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mn\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnumbers\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mReal\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     18\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mn\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0mint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mn\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mstrict\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 19\u001b[0;31m                 \u001b[0;32mraise\u001b[0m \u001b[0mTypeError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"n is not integer-like; it has decimals\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     20\u001b[0m             \u001b[0mn\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mn\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     21\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
       "\u001b[0;31mTypeError\u001b[0m: n is not integer-like; it has decimals"
      ]
     }
@@ -7705,7 +7953,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 237,
+   "execution_count": 245,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -7718,7 +7966,7 @@
        "6"
       ]
      },
-     "execution_count": 237,
+     "execution_count": 245,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -7740,7 +7988,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 238,
+   "execution_count": 246,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -7754,8 +8002,8 @@
      "traceback": [
       "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
       "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-238-7296a74f5dcf>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mfactorial\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m1\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;36m2j\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
-      "\u001b[0;32m<ipython-input-232-0b8c729be194>\u001b[0m in \u001b[0;36mfactorial\u001b[0;34m(n, strict)\u001b[0m\n\u001b[1;32m     20\u001b[0m             \u001b[0mn\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mn\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     21\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 22\u001b[0;31m             \u001b[0;32mraise\u001b[0m \u001b[0mTypeError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Factorial is only defined for integers\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     23\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     24\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mn\u001b[0m \u001b[0;34m<\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m<ipython-input-246-7296a74f5dcf>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mfactorial\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m1\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;36m2j\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;32m<ipython-input-240-0b8c729be194>\u001b[0m in \u001b[0;36mfactorial\u001b[0;34m(n, strict)\u001b[0m\n\u001b[1;32m     20\u001b[0m             \u001b[0mn\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mn\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     21\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 22\u001b[0;31m             \u001b[0;32mraise\u001b[0m \u001b[0mTypeError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Factorial is only defined for integers\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     23\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     24\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mn\u001b[0m \u001b[0;34m<\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
       "\u001b[0;31mTypeError\u001b[0m: Factorial is only defined for integers"
      ]
     }
@@ -7824,7 +8072,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 239,
+   "execution_count": 247,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -7846,10 +8094,10 @@
        "        "
       ],
       "text/plain": [
-       "<IPython.lib.display.YouTubeVideo at 0x7f089024e2d0>"
+       "<IPython.lib.display.YouTubeVideo at 0x7f05ec430a10>"
       ]
      },
-     "execution_count": 239,
+     "execution_count": 247,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -7861,7 +8109,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 240,
+   "execution_count": 248,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -7883,10 +8131,10 @@
        "        "
       ],
       "text/plain": [
-       "<IPython.lib.display.YouTubeVideo at 0x7f08901d2650>"
+       "<IPython.lib.display.YouTubeVideo at 0x7f05ec42ac90>"
       ]
      },
-     "execution_count": 240,
+     "execution_count": 248,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -7908,7 +8156,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 241,
+   "execution_count": 249,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -7930,10 +8178,10 @@
        "        "
       ],
       "text/plain": [
-       "<IPython.lib.display.YouTubeVideo at 0x7f08901dad50>"
+       "<IPython.lib.display.YouTubeVideo at 0x7f05ec428650>"
       ]
      },
-     "execution_count": 241,
+     "execution_count": 249,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -7942,6 +8190,53 @@
     "YouTubeVideo(\"4qH4unVtJkE\", width=\"60%\")"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "This video by the YouTube channel [Computerphile](https://www.youtube.com/channel/UC9-y-6csu5WGm29I7JiwpnA) explains floating point numbers in an intuitive way with some numeric examples."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 250,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/jpeg": 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\n",
+      "text/html": [
+       "\n",
+       "        <iframe\n",
+       "            width=\"60%\"\n",
+       "            height=\"300\"\n",
+       "            src=\"https://www.youtube.com/embed/PZRI1IfStY0\"\n",
+       "            frameborder=\"0\"\n",
+       "            allowfullscreen\n",
+       "        ></iframe>\n",
+       "        "
+      ],
+      "text/plain": [
+       "<IPython.lib.display.YouTubeVideo at 0x7f05ec426110>"
+      ]
+     },
+     "execution_count": 250,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "YouTubeVideo(\"PZRI1IfStY0\", width=\"60%\")"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {
@@ -7955,7 +8250,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 242,
+   "execution_count": 251,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -7977,10 +8272,10 @@
        "        "
       ],
       "text/plain": [
-       "<IPython.lib.display.YouTubeVideo at 0x7f08901e47d0>"
+       "<IPython.lib.display.YouTubeVideo at 0x7f05ec411c10>"
       ]
      },
-     "execution_count": 242,
+     "execution_count": 251,
      "metadata": {},
      "output_type": "execute_result"
     }

From 6d1d394d39b3527fb981c1702ad38a127d2945dc Mon Sep 17 00:00:00 2001
From: Alexander Hess <alexander@webartifex.biz>
Date: Mon, 16 Mar 2020 20:56:05 +0100
Subject: [PATCH 080/242] Refurbish chapter 06

- streamline old text and examples
- add new section on Unicode
- add new section on bytes
- add further resources
---
 06_text_00_lecture.ipynb | 3909 ++++++++++++++++++++++++++++++++------
 full_house.bin           |    1 +
 umlauts.txt              |   12 +
 3 files changed, 3357 insertions(+), 565 deletions(-)
 create mode 100644 full_house.bin
 create mode 100644 umlauts.txt

diff --git a/06_text_00_lecture.ipynb b/06_text_00_lecture.ipynb
index fc0eeb2..3c23901 100644
--- a/06_text_00_lecture.ipynb
+++ b/06_text_00_lecture.ipynb
@@ -19,7 +19,7 @@
     }
    },
    "source": [
-    "In this chapter, we continue the study of the built-in data types. Building on our knowledge of numbers, the next layer consists of textual data that are modeled primarily with the `str` type in Python. `str` objects are naturally more \"complex\" than numeric objects as any text consists of an arbitrary and possibly large number of individual characters that may be chosen from any alphabet in the history of humankind. Luckily, Python abstracts away most of this complexity."
+    "In this chapter, we continue the study of the built-in data types. The next layer on top of numbers consists of **textual data** that are modeled primarily with the `str` type in Python. `str` objects are more complex than the numeric objects in [Chapter 5](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/05_numbers_00_lecture.ipynb) as they *consist* of an *arbitrary* and possibly large number of *individual* characters that may be chosen from *any* alphabet in the history of humankind. Luckily, Python abstracts away most of this complexity from us. However, after looking at the `str` type in great detail, we briefly introduce the `bytes` type at the end of this chapter, and learn how characters are modeled in memory."
    ]
   },
   {
@@ -41,7 +41,7 @@
     }
    },
    "source": [
-    "The `str` type is the default way of modeling **textual data**. To create a `str` object, we use a **literal notation** and type the text between enclosing **double quotes** `\"`."
+    "To create a `str` object, we use the *literal* notation and type the text between enclosing **double quotes** `\"`."
    ]
   },
   {
@@ -54,7 +54,7 @@
    },
    "outputs": [],
    "source": [
-    "text = \"Lorem ipsum dolor sit amet, ...\""
+    "text = \"Lorem ipsum dolor sit amet.\""
    ]
   },
   {
@@ -65,7 +65,7 @@
     }
    },
    "source": [
-    "Like everything in Python, `text` is an object."
+    "Like everything in Python, `text` is an object with an *identity*, a *type*, and a *value*."
    ]
   },
   {
@@ -80,7 +80,7 @@
     {
      "data": {
       "text/plain": [
-       "140483431254256"
+       "140461336295424"
       ]
      },
      "execution_count": 2,
@@ -97,7 +97,7 @@
    "execution_count": 3,
    "metadata": {
     "slideshow": {
-     "slide_type": "-"
+     "slide_type": "fragment"
     }
    },
    "outputs": [
@@ -124,9 +124,9 @@
     }
    },
    "source": [
-    "A `str` object evaluates to itself in a literal notation with enclosing **single quotes** `'` by default. In [Chapter 1](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/01_elements_00_lecture.ipynb#Value-/-\"Meaning\"), we already specified the double quotes `\"` convention we stick to in this book. Yet, single quotes `'` and double quotes `\"` are *perfect* substitutes for all `str` objects that do *not* contain any of the two symbols in it. We could use the reverse convention, as well.\n",
+    "As seen before, a `str` object evaluates to itself in a literal notation with enclosing **single quotes** `'`.\n",
     "\n",
-    "As [this discussion](https://stackoverflow.com/questions/56011/single-quotes-vs-double-quotes-in-python) shows, many programmers have *strong* opinions about that and make up *new* conventions for their projects. Consequently, the discussion was \"closed as not constructive\" by the moderators."
+    "In [Chapter 1](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/01_elements_00_lecture.ipynb#Value-/-\"Meaning\"), we specify the double quotes `\"` convention this book follows. Yet, single quotes `'` and double quotes `\"` are *perfect* substitutes. We could use the reverse convention, as well. As [this discussion](https://stackoverflow.com/questions/56011/single-quotes-vs-double-quotes-in-python) shows, many programmers have *strong* opinions about such conventions. Consequently, the discussion was \"closed as not constructive\" by the moderators."
    ]
   },
   {
@@ -134,14 +134,14 @@
    "execution_count": 4,
    "metadata": {
     "slideshow": {
-     "slide_type": "-"
+     "slide_type": "fragment"
     }
    },
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "'Lorem ipsum dolor sit amet, ...'"
+       "'Lorem ipsum dolor sit amet.'"
       ]
      },
      "execution_count": 4,
@@ -161,13 +161,11 @@
     }
    },
    "source": [
-    "As the single quote `'` is often used in the English language as a shortener, we could make an argument in favor of using the double quotes `\"`: There are possibly fewer situations like in the two code cells below, in which we must revert to using a `\\` to **escape** a single quote `'` in a text (cf., the [Special Characters](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/06_text_00_lecture.ipynb#Special-Characters) section further below). However, double quotes `\"` are often used as well. So, this argument is somewhat not convincing.\n",
+    "As the single quote `'` is often used in the English language as a shortener, we could make an argument in favor of using the double quotes `\"`: There are possibly fewer situations like the two code cells below, where we must **escape** the kind of quote used as the `str` object's delimiter with a backslash `\"\\\"` inside the text (cf., also the \"*Unicode & (Special) Characters*\" section further below). However, double quotes `\"` are often used as well, for example, to indicate a quote like the one by [Albert Einstein](https://de.wikipedia.org/wiki/Albert_Einstein) below. So, such arguments are not convincing.\n",
     "\n",
-    "Many proponents of the single quote `'` usage claim that double quotes `\"` make more **visual noise** on the screen. This argument is also not convincing. On the contrary, one could claim that *two* single quotes `''` look so similar to *one* double quote `\"` that it might not be apparent right away what we are looking at. By sticking to double quotes `\"`, we avoid such danger of confusion.\n",
+    "Many proponents of the single quote `'` usage claim that double quotes `\"` cause more **visual noise** on the screen. However, this argument is also not convincing as, for example, one could claim that *two* single quotes `''` look so similar to *one* double quote `\"` that a reader may confuse an *empty* `str` object with a missing closing quote `\"`. With the double quotes `\"` convention we at least avoid such confusion (i.e., empty `str` objects are written as `\"\"`).\n",
     "\n",
-    "This discussion is an exellent example of a [flame war](https://en.wikipedia.org/wiki/Flaming_%28Internet%29#Flame_war) in the programming world that leads to *no* result.\n",
-    "\n",
-    "An *important* fact to know is that enclosing quotes of either kind are *not* part of the `str` object's *value*! They are merely *syntax* to make the text in a code cell a *literal* that Python converts into a `str` object upon reading."
+    "This discussion is an excellent example of a [flame war](https://en.wikipedia.org/wiki/Flaming_%28Internet%29#Flame_war) in the programming world: Everyone has an opinion and the discussion leads to *no* result."
    ]
   },
   {
@@ -182,7 +180,7 @@
     {
      "data": {
       "text/plain": [
-       "'It\\'s cool that \"strings\" are versatile'"
+       "'Einstein said, \"If you can\\'t explain it, you don\\'t understand it.\"'"
       ]
      },
      "execution_count": 5,
@@ -191,7 +189,7 @@
     }
    ],
    "source": [
-    "\"It's cool that \\\"strings\\\" are versatile\""
+    "\"Einstein said, \\\"If you can't explain it, you don't understand it.\\\"\""
    ]
   },
   {
@@ -199,14 +197,14 @@
    "execution_count": 6,
    "metadata": {
     "slideshow": {
-     "slide_type": "-"
+     "slide_type": "fragment"
     }
    },
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "'It\\'s cool that \"strings\" are versatile'"
+       "'Einstein said, \"If you can\\'t explain it, you don\\'t understand it.\"'"
       ]
      },
      "execution_count": 6,
@@ -215,7 +213,7 @@
     }
    ],
    "source": [
-    "'It\\'s cool that \"strings\" are versatile'"
+    "'Einstein said, \"If you can\\'t explain it, you don\\'t understand it.\"'"
    ]
   },
   {
@@ -226,12 +224,67 @@
     }
    },
    "source": [
-    "We can always use the [str()](https://docs.python.org/3/library/stdtypes.html#str) built-in to cast non-`str` objects as a `str`."
+    "An *important* fact to know is that enclosing quotes of either kind are *not* part of the `str` object's *value*! They are merely *syntax* indicating the literal notation.\n",
+    "\n",
+    "So, printing out the sentence with the built-in [print()](https://docs.python.org/3/library/functions.html#print) function does the same in both cases."
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 7,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Einstein said, \"If you can't explain it, you don't understand it.\"\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"Einstein said, \\\"If you can't explain it, you don't understand it.\\\"\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Einstein said, \"If you can't explain it, you don't understand it.\"\n"
+     ]
+    }
+   ],
+   "source": [
+    "print('Einstein said, \"If you can\\'t explain it, you don\\'t understand it.\"')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "As an alternative to the literal notation, we may use the built-in [str()](https://docs.python.org/3/library/stdtypes.html#str) constructor to cast non-`str` objects as `str` ones. As Chapter 10 reveals, basically any object in Python has a **text representation**. Because of that we may also pass `list` objects, the boolean `True` and `False`, or `None` to [str()](https://docs.python.org/3/library/stdtypes.html#str)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -241,16 +294,147 @@
     {
      "data": {
       "text/plain": [
-       "'123'"
+       "'42'"
       ]
      },
-     "execution_count": 7,
+     "execution_count": 9,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "str(123)"
+    "str(42)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'42.87'"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "str(42.87)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'[1, 2, 3]'"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "str([1, 2, 3])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'True'"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "str(True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'False'"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "str(False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'None'"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "str(None)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "#### User Input"
    ]
   },
   {
@@ -261,12 +445,103 @@
     }
    },
    "source": [
-    "Another common situation where we obtain `str` objects is when reading the contents of a file with the [open()](https://docs.python.org/3/library/functions.html#open) built-in. In its simplest form, to open a [text file](https://en.wikipedia.org/wiki/Text_file) file in read-only mode, we pass in its path (i.e., \"filename\") as a `str` object."
+    "As shown in the \"*Guessing a Coin Toss*\" example in [Chapter 4](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/04_iteration_00_lecture.ipynb#Example:-Guessing-a-Coin-Toss), the built-in [input()](https://docs.python.org/3/library/functions.html#input) function displays a prompt to the user and returns whatever is entered as a `str` object. [input()](https://docs.python.org/3/library/functions.html#input) is in particular valuable when writing command-line tools."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 15,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdin",
+     "output_type": "stream",
+     "text": [
+      "Whatever you enter is put in a new string:  I will be a string\n"
+     ]
+    }
+   ],
+   "source": [
+    "user_input = input(\"Whatever you enter is put in a new string: \")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "str"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "type(user_input)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'I will be a string'"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "user_input"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "#### Reading Files"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "A more common situation where we obtain `str` objects is when reading the contents of a file with the [open()](https://docs.python.org/3/library/functions.html#open) built-in. In its simplest usage form, to open a [text file](https://en.wikipedia.org/wiki/Text_file) file, we pass in its path (i.e., \"filename\") as a `str` object."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -285,12 +560,36 @@
     }
    },
    "source": [
-    "[open()](https://docs.python.org/3/library/functions.html#open) returns a **[proxy](https://en.wikipedia.org/wiki/Proxy_pattern)** object of type `TextIOWrapper` that allows us to interact with the file on disk."
+    "[open()](https://docs.python.org/3/library/functions.html#open) returns a **[proxy](https://en.wikipedia.org/wiki/Proxy_pattern)** object of type `TextIOWrapper` that allows us to interact with the file on disk. `mode='r'` shows that we opened the file in read-only mode and `encoding='UTF-8'` is explained in detail in the \"*The `bytes` Type*\" section at the end of this chapter."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 19,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "_io.TextIOWrapper"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "type(file)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -303,7 +602,7 @@
        "<_io.TextIOWrapper name='lorem_ipsum.txt' mode='r' encoding='UTF-8'>"
       ]
      },
-     "execution_count": 9,
+     "execution_count": 20,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -312,30 +611,6 @@
     "file"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "-"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "_io.TextIOWrapper"
-      ]
-     },
-     "execution_count": 10,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "type(file)"
-   ]
-  },
   {
    "cell_type": "markdown",
    "metadata": {
@@ -344,12 +619,121 @@
     }
    },
    "source": [
-    "While `file` provides, for example, the [read()](https://docs.python.org/3/library/io.html#io.TextIOBase.read), [readline()](https://docs.python.org/3/library/io.html#io.TextIOBase.readline), and [readlines()](https://docs.python.org/3/library/io.html#io.IOBase.readlines) methods to access its contents, it is also *iterable*, and we may loop over the individual lines with a `for` statement."
+    "`TextIOWrapper` objects come with plenty of type-specific methods and attributes."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 21,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 21,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "file.readable()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "False"
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "file.writable()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'lorem_ipsum.txt'"
+      ]
+     },
+     "execution_count": 23,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "file.name"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'UTF-8'"
+      ]
+     },
+     "execution_count": 24,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "file.encoding"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "So far, we have not yet read anything from the file (i.e., from disk)! That is intentional as, for example, the file could contain more data than could fit into our computer's memory. Therefore, we have to explicitly instruct the `file` object to read some of or all the data in the file.\n",
+    "\n",
+    "One way to do that, is to simply loop over the `file` object with the `for` statement as shown next: In each iteration, `line` is assigned the next line in the file. Because we may loop over `TextIOWrapper` objects, they are *iterables*."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -388,12 +772,12 @@
     }
    },
    "source": [
-    "Once we looped over `file` the first time, it is **exhausted**: That means we do not see any output if we loop over it another time."
+    "Once we looped over the `file` object, it is **exhausted**: We can *not* loop over it a second time. So, the built-in [print()](https://docs.python.org/3/library/functions.html#print) function is *never* called in the code cell below!"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 26,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -413,12 +797,12 @@
     }
    },
    "source": [
-    "After the `for`-loop, `line` is still set to the last line in the file, and we verify that it is indeed a `str` object."
+    "After the `for`-loop, the `line` variable is still set and references the *last* line in the file. We verify that it is indeed a `str` object."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 27,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -431,7 +815,7 @@
        "'the 1960s with the release of Letraset sheets.\\n'"
       ]
      },
-     "execution_count": 13,
+     "execution_count": 27,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -442,10 +826,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 28,
    "metadata": {
     "slideshow": {
-     "slide_type": "-"
+     "slide_type": "fragment"
     }
    },
    "outputs": [
@@ -455,7 +839,7 @@
        "str"
       ]
      },
-     "execution_count": 14,
+     "execution_count": 28,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -472,14 +856,14 @@
     }
    },
    "source": [
-    "An important fact is that `file` is still associated with an *open* **[file descriptor](https://en.wikipedia.org/wiki/File_descriptor)**. Without going into any technical details, we note that an operating system can only handle a limited number of \"open files\" at the same time, and, therefore, we should always *close* the file once we are done processing it.\n",
+    "An *important* observation is that the `file` object is still associated with an *open* **[file descriptor](https://en.wikipedia.org/wiki/File_descriptor)**. Without going into any technical details, we note that an operating system can only handle a *limited* number of \"open files\" at the same time, and, therefore, we should always *close* the file once we are done processing it.\n",
     "\n",
-    "`file` has a `closed` attribute on it that shows us if a file descriptor is open or closed, and with the [close()](https://docs.python.org/3/library/io.html#io.IOBase.close) method, we can \"manually\" close it."
+    "`TextIOWrapper` objects have a `closed` attribute on them that indicates if the associated file descriptor is still open or has been closed. We can \"manually\" close any `TextIOWrapper` object with the [close()](https://docs.python.org/3/library/io.html#io.IOBase.close) method."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 29,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -492,7 +876,7 @@
        "False"
       ]
      },
-     "execution_count": 15,
+     "execution_count": 29,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -503,7 +887,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 30,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -516,10 +900,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 31,
    "metadata": {
     "slideshow": {
-     "slide_type": "-"
+     "slide_type": "fragment"
     }
    },
    "outputs": [
@@ -529,7 +913,7 @@
        "True"
       ]
      },
-     "execution_count": 17,
+     "execution_count": 31,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -546,12 +930,12 @@
     }
    },
    "source": [
-    "The more Pythonic way is to open a file with the `with` statement (cf., [reference](https://docs.python.org/3/reference/compound_stmts.html#the-with-statement)): The indented code block is said to be executed in the **context** of the header line that acts as a **[context manager](https://docs.python.org/3/reference/datamodel.html?highlight=context%20manager#with-statement-context-managers)**. Such objects may have many different purposes. Here, the context manager created with `with open(...) as file:` mainly ensures that the file descriptor gets automatically closed after the last line in the code block is executed."
+    "The more Pythonic way is to use [open()](https://docs.python.org/3/library/functions.html#open) within the compound `with` statement (cf., [reference](https://docs.python.org/3/reference/compound_stmts.html#the-with-statement)): In the example below, the indented code block is said to be executed within the **context** of the `file` object that now plays the role of a **[context manager](https://docs.python.org/3/reference/datamodel.html#with-statement-context-managers)**. Many different kinds of context managers exist in Python with different applications and purposes. Context managers returned from [open()](https://docs.python.org/3/library/functions.html#open) mainly ensure that file descriptors get automatically closed after the last line in the code block is executed."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 32,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -585,7 +969,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 33,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -598,7 +982,7 @@
        "True"
       ]
      },
-     "execution_count": 19,
+     "execution_count": 33,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -615,12 +999,12 @@
     }
    },
    "source": [
-    "To use constructs familiar from [Chapter 3](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/03_conditionals_00_lecture.ipynb#The-try-Statement) to explain what `with open(...) as file:` does, below is a formulation with a `try` statement *equivalent* to the `with` statement above. The `finally`-branch is *always* executed, even if an exception is raised in the `for`-loop. So, `file` is sure to be closed too, with a somewhat less expressive formulation."
+    "Using syntax familiar from [Chapter 3](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/03_conditionals_00_lecture.ipynb#The-try-Statement) to explain what the `with open(...) as file:` does above, we provide an alternative formulation with a `try` statement below: The `finally`-branch is *always* executed, even if an exception is raised inside the `for`-loop. Therefore, `file` is sure to be closed too. However, this formulation is somewhat less expressive."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 34,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -657,7 +1041,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 35,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -670,7 +1054,7 @@
        "True"
       ]
      },
-     "execution_count": 21,
+     "execution_count": 35,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -687,17 +1071,258 @@
     }
    },
    "source": [
-    "A subtlety to notice is that there is an empty line printed between each `line`. That is because each `line` ends with a `\"\\n\"` that results in a line break and that is explained further below. To print the text without empty lines in between, we pass a `end=\"\"` argument to the [print()](https://docs.python.org/3/library/functions.html#print) function."
+    "As an alternative to reading the contents of a file by looping over a `TextIOWrapper` object, we may also call one of the methods they come with.\n",
+    "\n",
+    "For example, the [read()](https://docs.python.org/3/library/io.html#io.TextIOBase.read) method takes a single `size` argument of type `int` and returns a `str` object with the specified number of characters."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 36,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
     }
    },
+   "outputs": [],
+   "source": [
+    "file = open(\"lorem_ipsum.txt\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Lorem Ipsum'"
+      ]
+     },
+     "execution_count": 37,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "file.read(11)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "When we call [read()](https://docs.python.org/3/library/io.html#io.TextIOBase.read) again, the returned `str` object begins where the previous one left off. This is because `TextIOWrapper` objects like `file` simply store a position at which the associated file on disk is being read. In other words, `file` is like a **cursor** pointing into a file."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "' is simply '"
+      ]
+     },
+     "execution_count": 38,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "file.read(11)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "On the contrary, the [readline()](https://docs.python.org/3/library/io.html#io.TextIOBase.readline) method keeps reading until it hits a **newline character**. These are shown in `str` objects as `\"\\n\"`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'dummy text of the printing and typesetting industry.\\n'"
+      ]
+     },
+     "execution_count": 39,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "file.readline()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "When we call [readline()](https://docs.python.org/3/library/io.html#io.TextIOBase.readline) again, we obtain the next line."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "\"Lorem Ipsum has been the industry's standard dummy text ever since the 1500s\\n\""
+      ]
+     },
+     "execution_count": 40,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "file.readline()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Lastly, the [readlines()](https://docs.python.org/3/library/io.html#io.IOBase.readlines) method returns a `list` object that holds *all* lines in the `file` from the current position to the end of the file. The latter position is often abbreviated as **EOF** in the documentation. Let's always remember that [readlines()](https://docs.python.org/3/library/io.html#io.IOBase.readlines) has the potential to crash a computer with a `MemoryError`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['when an unknown printer took a galley of type and scrambled it to make a type\\n',\n",
+       " 'specimen book. It has survived not only five centuries but also the leap into\\n',\n",
+       " 'electronic typesetting, remaining essentially unchanged. It was popularised in\\n',\n",
+       " 'the 1960s with the release of Letraset sheets.\\n']"
+      ]
+     },
+     "execution_count": 41,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "file.readlines()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Calling [readlines()](https://docs.python.org/3/library/io.html#io.IOBase.readlines) a second time, is as pointless as looping over `file` a second time."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[]"
+      ]
+     },
+     "execution_count": 42,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "file.readlines()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "file.close()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Because every `str` object created by reading the contents of a file in any of the ways shown in this section ends with a `\"\\n\"`, we see empty lines printed between each `line` in the `for`-loops above. To print the entire text without empty lines in between, we pass a `end=\"\"` argument to the [print()](https://docs.python.org/3/library/functions.html#print) function."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
    "outputs": [
     {
      "name": "stdout",
@@ -726,7 +1351,7 @@
     }
    },
    "source": [
-    "## A \"String\" of Characters"
+    "### A String of Characters"
    ]
   },
   {
@@ -739,21 +1364,23 @@
    "source": [
     "A **sequence** is yet another *abstract* concept (cf., the \"*Containers vs. Iterables*\" section in [Chapter 4](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/04_iteration_00_lecture.ipynb#Containers-vs.-Iterables)).\n",
     "\n",
-    "It unifies *four* [orthogonal](https://en.wikipedia.org/wiki/Orthogonality) (i.e., \"independent\") behaviors into one idea: Any data type, such as `str`, is considered a sequence if it simultaneously\n",
+    "It unifies *four* [orthogonal](https://en.wikipedia.org/wiki/Orthogonality) (i.e., \"independent\") concepts into one bigger idea: Any data type, such as `str`, is considered a sequence if it\n",
     "\n",
-    "1. **contains** other \"things,\"\n",
-    "2. is **iterable**, and \n",
-    "3. comes with a *predictable* **order** of its\n",
-    "4. **finite** number of \"things.\"\n",
+    "1. **contains**\n",
+    "2. a **finite** number of other \"things\" that\n",
+    "3. can be **iterated** over\n",
+    "4. in a *predictable* **order**.\n",
     "\n",
-    "[Chapter 7](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/07_sequences_00_lecture.ipynb#Collections-vs.-Sequences) formalizes sequences in great detail. Here, we keep our focus on the `str` type that historically received its name as it models a \"**[string of characters](https://en.wikipedia.org/wiki/String_%28computer_science%29)**,\" and a \"string\" is more formally called a sequence in the computer science literature.\n",
+    "[Chapter 7](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/07_sequences_00_lecture.ipynb#Collections-vs.-Sequences) formalizes these concepts in great detail. Here, we keep our focus on the `str` type that historically received its name as it models a **[string of characters](https://en.wikipedia.org/wiki/String_%28computer_science%29)**, and *string* is simply another term for *sequence* in the computer science literature.\n",
     "\n",
-    "Behaving like a sequence, `str` objects may be treated like `list` objects in many cases. For example, the built-in [len()](https://docs.python.org/3/library/functions.html#len) function tells us how many elements (i.e., characters) make up `text`. [len()](https://docs.python.org/3/library/functions.html#len) would not work with an *infinite* object: As anything modeled in a program must fit into a computer's finite memory at runtime, there cannot exist objects containing a truly infinite number of elements; however, [Chapter 7](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/07_sequences_00_lecture.ipynb#Iterators-vs.-Iterables) introduces iterable data types that can be used to model an *infinite* series of elements and that, consequently, have no concept of \"length.\""
+    "Another example of a sequence is the `list` type. Because of that, `str` objects may be treated like `list` objects in many situations.\n",
+    "\n",
+    "Below, the built-in [len()](https://docs.python.org/3/library/functions.html#len) function tells us how many characters make up `text`. [len()](https://docs.python.org/3/library/functions.html#len) would not work with an \"infinite\" object. As anything modeled in a program must fit into a computer's finite memory, there cannot exist truly infinite objects; however, [Chapter 7](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/07_sequences_00_lecture.ipynb#Iterators-vs.-Iterables) introduces specialized iterable data types that can be used to model an *infinite* series of \"things\" and that, consequently, have no concept of \"length.\""
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 45,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -763,10 +1390,34 @@
     {
      "data": {
       "text/plain": [
-       "31"
+       "'Lorem ipsum dolor sit amet.'"
       ]
      },
-     "execution_count": 23,
+     "execution_count": 45,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "text"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "27"
+      ]
+     },
+     "execution_count": 46,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -783,12 +1434,45 @@
     }
    },
    "source": [
-    "Being iterable, we may loop over `text` and do something with the individual characters, for example, print them out with extra space in between them."
+    "Being iterable, we may loop over `text` and do something with the individual characters, for example, print them out with extra space in between them. If it were not for the appropriately chosen name of the `text` variable, we could not tell what *concrete* type of object the `for` statement is looping over."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 47,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "L  o  r  e  m     i  p  s  u  m     d  o  l  o  r     s  i  t     a  m  e  t  .  "
+     ]
+    }
+   ],
+   "source": [
+    "for character in text:\n",
+    "    print(character, end=\"  \")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "With the [reversed()](https://docs.python.org/3/library/functions.html#reversed) built-in, we may loop over `text` in reversed order. Reversing `text` only works as it has a forward order to begin with."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 48,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -799,13 +1483,13 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "L o r e m   i p s u m   d o l o r   s i t   a m e t ,   . . . "
+      ".  t  e  m  a     t  i  s     r  o  l  o  d     m  u  s  p  i     m  e  r  o  L  "
      ]
     }
    ],
    "source": [
-    "for letter in text:\n",
-    "    print(letter, end=\" \")"
+    "for character in reversed(text):\n",
+    "    print(character, end=\"  \")"
    ]
   },
   {
@@ -818,12 +1502,36 @@
    "source": [
     "Being a container, we may check if a given `str` object is contained in `text` with the `in` operator.\n",
     "\n",
-    "The `in` operator has *two* usages: First, it checks if a *single* character is contained in a `str` object. Second, it may also check if a shorter `str` object, then called a **substring**, is contained in a longer one."
+    "The `in` operator has *two* distinct usages: First, it checks if a *single* character is contained in a `str` object. Second, it may also check if a shorter `str` object, then called a **substring**, is contained in a longer one."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 49,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 49,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "\"L\" in text"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 50,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -836,31 +1544,7 @@
        "True"
       ]
      },
-     "execution_count": 25,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "\"L\" in text"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 26,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "-"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "True"
-      ]
-     },
-     "execution_count": 26,
+     "execution_count": 50,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -871,10 +1555,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": 51,
    "metadata": {
     "slideshow": {
-     "slide_type": "-"
+     "slide_type": "fragment"
     }
    },
    "outputs": [
@@ -884,7 +1568,7 @@
        "False"
       ]
      },
-     "execution_count": 27,
+     "execution_count": 51,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -901,7 +1585,7 @@
     }
    },
    "source": [
-    "## Indexing"
+    "### Indexing"
    ]
   },
   {
@@ -912,12 +1596,12 @@
     }
    },
    "source": [
-    "As `str` objects have the additional property of being *ordered*, we may **index** into them to obtain individual characters with the **indexing operator** `[]`. This is analogous to how we obtained individual elements of a `list` object in [Chapter 1](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/01_elements_00_lecture.ipynb#Who-am-I?-And-how-many?)."
+    "As `str` objects are *ordered* and *finite*, we may **index** into them to obtain individual characters with the **indexing operator** `[]`. This is analogous to how we obtained individual elements of a `list` object in [Chapter 1](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/01_elements_00_lecture.ipynb#Who-am-I?-And-how-many?)."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": 52,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -930,7 +1614,7 @@
        "'L'"
       ]
      },
-     "execution_count": 28,
+     "execution_count": 52,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -941,10 +1625,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
+   "execution_count": 53,
    "metadata": {
     "slideshow": {
-     "slide_type": "-"
+     "slide_type": "fragment"
     }
    },
    "outputs": [
@@ -954,7 +1638,7 @@
        "'o'"
       ]
      },
-     "execution_count": 29,
+     "execution_count": 53,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -971,12 +1655,12 @@
     }
    },
    "source": [
-    "The index must be of type `int` or we get a `TypeError`."
+    "The index must be of type `int`; othewise, we get a `TypeError`."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 30,
+   "execution_count": 54,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -990,7 +1674,7 @@
      "traceback": [
       "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
       "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-30-31fb90bbe515>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mtext\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1.0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;32m<ipython-input-54-31fb90bbe515>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mtext\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1.0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
       "\u001b[0;31mTypeError\u001b[0m: string indices must be integers"
      ]
     }
@@ -1007,12 +1691,12 @@
     }
    },
    "source": [
-    "The last index is one less than the above \"length\" of the `str` object as we start counting at 0."
+    "The last index is one less than the above \"length\" of the `str` object as we start counting at `0`."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 31,
+   "execution_count": 55,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -1025,13 +1709,13 @@
        "'.'"
       ]
      },
-     "execution_count": 31,
+     "execution_count": 55,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "text[30]  # = len(text) - 1"
+    "text[26]  # == text[len(text) - 1]"
    ]
   },
   {
@@ -1042,15 +1726,15 @@
     }
    },
    "source": [
-    "An `IndexError` is raised whenever the index is too large."
+    "An `IndexError` is raised whenever the index is out of range."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 32,
+   "execution_count": 56,
    "metadata": {
     "slideshow": {
-     "slide_type": "-"
+     "slide_type": "fragment"
     }
    },
    "outputs": [
@@ -1061,13 +1745,13 @@
      "traceback": [
       "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
       "\u001b[0;31mIndexError\u001b[0m                                Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-32-a8c62c212778>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mtext\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m31\u001b[0m\u001b[0;34m]\u001b[0m  \u001b[0;31m# = len(text)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;32m<ipython-input-56-ffa3e1bc2f86>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mtext\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m27\u001b[0m\u001b[0;34m]\u001b[0m  \u001b[0;31m# == text[len(text)]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
       "\u001b[0;31mIndexError\u001b[0m: string index out of range"
      ]
     }
    ],
    "source": [
-    "text[31]  # = len(text)"
+    "text[27]  # == text[len(text)]"
    ]
   },
   {
@@ -1078,7 +1762,7 @@
     }
    },
    "source": [
-    "We may use *negative* indexes to start counting from the end of the `str` object, as shown in the figure below. That only works because sequences are *finite*."
+    "We may use *negative* indexes to start counting from the end of the `str` object, as shown in the figure below. Note how this only works because sequences are *finite*."
    ]
   },
   {
@@ -1089,18 +1773,18 @@
     }
    },
    "source": [
-    "|   Slot    | 0 | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10| 11| 12| 13| 14| 15| 16| 17| 18| 19| 20| 21| 22| 23| 24| 25| 26| 27| 28| 29| 30|\n",
-    "|:---------:|:-:|:-:|:-:|:-:|:-:|:-:|:-:|:-:|:-:|:-:|:-:|:-:|:-:|:-:|:-:|:-:|:-:|:-:|:-:|:-:|:-:|:-:|:-:|:-:|:-:|:-:|:-:|:-:|:-:|:-:|:-:|\n",
-    "|**Reverse**|-31|-30|-29|-28|-27|-26|-25|-24|-23|-22|-21|-20|-19|-18|-17|-16|-15|-14|-13|-12|-11|-10|-9 |-8 |-7 |-6 |-5 |-4 |-3 |-2 |-1 |\n",
-    "| **Char**  |`L`|`o`|`r`|`e`|`m`|` `|`i`|`p`|`s`|`u`|`m`|` `|`d`|`o`|`l`|`o`|`r`|` `|`s`|`i`|`t`|` `|`a`|`m`|`e`|`t`|`,`|` `|`.`|`.`|`.`|"
+    "|   Index   | 0 | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10| 11| 12| 13| 14| 15| 16| 17| 18| 19| 20| 21| 22| 23| 24| 25| 26|\n",
+    "|:---------:|:-:|:-:|:-:|:-:|:-:|:-:|:-:|:-:|:-:|:-:|:-:|:-:|:-:|:-:|:-:|:-:|:-:|:-:|:-:|:-:|:-:|:-:|:-:|:-:|:-:|:-:|:-:|\n",
+    "|**Reverse**|-27|-26|-25|-24|-23|-22|-21|-20|-19|-18|-17|-16|-15|-14|-13|-12|-11|-10|-9 |-8 |-7 |-6 |-5 |-4 |-3 |-2 |-1 |\n",
+    "| **Character**  |`L`|`o`|`r`|`e`|`m`|` `|`i`|`p`|`s`|`u`|`m`|` `|`d`|`o`|`l`|`o`|`r`|` `|`s`|`i`|`t`|` `|`a`|`m`|`e`|`t`|`.`|"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 33,
+   "execution_count": 57,
    "metadata": {
     "slideshow": {
-     "slide_type": "-"
+     "slide_type": "fragment"
     }
    },
    "outputs": [
@@ -1110,7 +1794,7 @@
        "'.'"
       ]
      },
-     "execution_count": 33,
+     "execution_count": 57,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1121,7 +1805,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 34,
+   "execution_count": 58,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -1134,13 +1818,13 @@
        "'L'"
       ]
      },
-     "execution_count": 34,
+     "execution_count": 58,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "text[-31]  # = -len(text)"
+    "text[-27]  # == text[-len(text)]"
    ]
   },
   {
@@ -1151,15 +1835,15 @@
     }
    },
    "source": [
-    "One reason why programmers like to start counting at 0 is that a positive index and its *corresponding* negative index always add up to the length of the sequence. Here, `6` and `25` add to `31`."
+    "One reason why programmers like to start counting at `0` is that a positive index and its *corresponding* negative index always add up to the length of the sequence. Here, `6` and `21` add to `27`."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 35,
+   "execution_count": 59,
    "metadata": {
     "slideshow": {
-     "slide_type": "fragment"
+     "slide_type": "skip"
     }
    },
    "outputs": [
@@ -1169,7 +1853,7 @@
        "'i'"
       ]
      },
-     "execution_count": 35,
+     "execution_count": 59,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1180,10 +1864,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 36,
+   "execution_count": 60,
    "metadata": {
     "slideshow": {
-     "slide_type": "-"
+     "slide_type": "skip"
     }
    },
    "outputs": [
@@ -1193,13 +1877,13 @@
        "'i'"
       ]
      },
-     "execution_count": 36,
+     "execution_count": 60,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "text[-25]"
+    "text[-21]"
    ]
   },
   {
@@ -1210,7 +1894,7 @@
     }
    },
    "source": [
-    "## Slicing"
+    "### Slicing"
    ]
   },
   {
@@ -1223,14 +1907,14 @@
    "source": [
     "A **slice** is a substring of a `str` object.\n",
     "\n",
-    "The **slicing operator** is a generalization of the indexing operator: We can put one, two, or three integers within the brackets, separated by colons `:`. The three integers are then referred to as the *start*, *end*, and *step* values.\n",
+    "The **slicing operator** is a generalization of the indexing operator: We put one, two, or three integers within the brackets `[]`, separated by colons `:`. The three integers are then referred to as the *start*, *stop*, and *step* values.\n",
     "\n",
-    "Let's start with two integers, *start* and *end*."
+    "Let's start with two integers, *start* and *stop*. Whereas the character at the *start* position is included in the returned `str` object, the one at the *stop* position is not. If both *start* and *stop* are positive, the difference \"*stop* minus *start*\" tells us how many characters the resulting slice has. So, below, `5 - 0 == 5` implies that `\"Lorem\"` consists of `5` characters. So, colloquially speaking, `text[0:5]` means \"taking the first `5 - 0 == 5` characters of `text`.\""
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 37,
+   "execution_count": 61,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -1243,7 +1927,7 @@
        "'Lorem'"
       ]
      },
-     "execution_count": 37,
+     "execution_count": 61,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1252,22 +1936,9 @@
     "text[0:5]"
    ]
   },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "source": [
-    "Whereas the *start* is always included in the result, the *end* is not. Counter-intuitive at first, this makes working with individual slices easier as they \"add\" up to the original `str` object again (cf., the \"*String Operations*\" sub-section below regarding the overloaded `+` operator). Because the *end* is *not* included, we end the second slice below with `len(text)` or `31` below.\n",
-    "\n",
-    "Not including the *end* has another advantage: The difference \"*end* minus *start*\" tells us how many elements the resulting slice has. Above, for example, `5 - 0` implies that `\"Lorem\"` consists of `5` characters. So, colloquially, `0:5` means \"taking the first five characters.\" That rule only works if both *start* and *end* are *positive*."
-   ]
-  },
   {
    "cell_type": "code",
-   "execution_count": 38,
+   "execution_count": 62,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -1277,16 +1948,16 @@
     {
      "data": {
       "text/plain": [
-       "'Lorem ipsum dolor sit amet, ...'"
+       "'dolor sit amet.'"
       ]
      },
-     "execution_count": 38,
+     "execution_count": 62,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "text[0:5] + text[5:len(text)]"
+    "text[12:len(text)]"
    ]
   },
   {
@@ -1297,12 +1968,12 @@
     }
    },
    "source": [
-    "By combining a *positive* start with a *negative* end index, we specify both ends of the slice *relative* to the ends of the entire `str` object. So, colloquially, `6:-5` below means \"drop the first six and last five characters.\" The length of the resulting slice can *not* be calculated from the indexes and depends only on the length of the original `str` object."
+    "If left out, *start* defaults to `0` and *stop* to the length of the `str` object (i.e., the end)."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 39,
+   "execution_count": 63,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -1312,16 +1983,145 @@
     {
      "data": {
       "text/plain": [
-       "'ipsum dolor sit amet'"
+       "'Lorem'"
       ]
      },
-     "execution_count": 39,
+     "execution_count": 63,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "text[6:-5]"
+    "text[:5]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 64,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'dolor sit amet.'"
+      ]
+     },
+     "execution_count": 64,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "text[12:]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Not including the character at the *stop* position makes working with individual slices easier as they add up to the original `str` object again (cf., the \"*String Operations*\" section below regarding the overloaded `+` operator)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 65,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Lorem ipsum dolor sit amet.'"
+      ]
+     },
+     "execution_count": 65,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "text[:5] + text[5:]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Slicing and indexing makes it easy to obtain shorter versions of the original `str` object. A common application would be to **parse** out meaningful substrings from raw text data."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 66,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Lorem ipsum sit amet.'"
+      ]
+     },
+     "execution_count": 66,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "text[:11] + text[-10:]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "By combining a positive *start* with a negative *stop* index, we specify both ends of the slice *relative* to the ends of the entire `str` object. So, colloquially speaking, `6:-10` below means \"drop the first six and last ten characters.\" The length of the resulting slice can then *not* be calculated from the indexes and depends only on the length of the original `str` object!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 67,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'ipsum dolor'"
+      ]
+     },
+     "execution_count": 67,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "text[6:-10]"
    ]
   },
   {
@@ -1337,7 +2137,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 40,
+   "execution_count": 68,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -1347,16 +2147,16 @@
     {
      "data": {
       "text/plain": [
-       "'Lorem ipsum dolor sit amet, ...'"
+       "'Lorem ipsum dolor sit amet.'"
       ]
      },
-     "execution_count": 40,
+     "execution_count": 68,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "text[0:999]"
+    "text[-999:999]"
    ]
   },
   {
@@ -1367,12 +2167,12 @@
     }
    },
    "source": [
-    "If left out, *start* defaults to `0` and *end* to the \"length\" of the `str` object. Here, we take a \"full\" slice that is essentially a copy of the original `str` object."
+    "By leaving out both *start* and *stop*, we take a \"full\" slice that is essentially a *copy* of the original `str` object."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 41,
+   "execution_count": 69,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -1382,10 +2182,10 @@
     {
      "data": {
       "text/plain": [
-       "'Lorem ipsum dolor sit amet, ...'"
+       "'Lorem ipsum dolor sit amet.'"
       ]
      },
-     "execution_count": 41,
+     "execution_count": 69,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1402,47 +2202,12 @@
     }
    },
    "source": [
-    "Slicing (and indexing) makes it easy to obtain shorter versions of the original `str` object."
+    "A *step* value of `i` can be used to obtain only every `i`th character."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 42,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'Lorem ipsum dolor sit amet.'"
-      ]
-     },
-     "execution_count": 42,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "text[:26] + text[30]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "source": [
-    "A *step* value of $i$ can be used to obtain only every $i$th letter."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 43,
+   "execution_count": 70,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -1452,10 +2217,10 @@
     {
      "data": {
       "text/plain": [
-       "'Lrmismdlrstae,..'"
+       "'Lrmismdlrstae.'"
       ]
      },
-     "execution_count": 43,
+     "execution_count": 70,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1477,7 +2242,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 44,
+   "execution_count": 71,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -1487,10 +2252,10 @@
     {
      "data": {
       "text/plain": [
-       "'... ,tema tis rolod muspi meroL'"
+       "'.tema tis rolod muspi meroL'"
       ]
      },
-     "execution_count": 44,
+     "execution_count": 71,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1507,7 +2272,7 @@
     }
    },
    "source": [
-    "## Immutability"
+    "### Immutability"
    ]
   },
   {
@@ -1518,16 +2283,16 @@
     }
    },
    "source": [
-    "Whereas elements of a `list` object *may* be *re-assigned*, as shortly hinted at in [Chapter 1](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/01_elements_00_lecture.ipynb#Who-am-I?-And-how-many?), this is *not* allowed for `str` objects. Once created, they *cannot* be *changed*. Formally, we say that they are **immutable**. In that regard, `str` objects and all the numeric types in [Chapter 5](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/05_numbers_00_lecture.ipynb) are alike.\n",
+    "Whereas elements of a `list` object *may* be *re-assigned*, as shortly hinted at in [Chapter 1](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/01_elements_00_lecture.ipynb#Who-am-I?-And-how-many?), this is *not* allowed for the individual characters of `str` objects. Once created, they can *not* be changed. Formally, we say that `str` objects are **immutable**. In that regard, they are like the numeric types in [Chapter 5](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/05_numbers_00_lecture.ipynb).\n",
     "\n",
-    "On the contrary, objects that may be changed after creation, are called **mutable**. We already saw in [Chapter 1](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/01_elements_00_lecture.ipynb#Who-am-I?-And-how-many?) how mutable objects are more difficult to reason about for a beginner, in particular, if more than *one* variable references it. Yet, mutability does have its place in a programmer's toolbox, and we revisit this idea in the next chapters.\n",
+    "On the contrary, objects that may be changed after creation, are called **mutable**. We already saw in [Chapter 1](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/01_elements_00_lecture.ipynb#Who-am-I?-And-how-many?) how mutable objects are more difficult to reason about for a beginner, in particular, if more than one variable references it. Yet, mutability does have its place in a programmer's toolbox, and we revisit this idea in the next chapters.\n",
     "\n",
     "The `TypeError` indicates that `str` objects are *immutable*: Assignment to an index or a slice are *not* supported."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 45,
+   "execution_count": 72,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -1541,21 +2306,21 @@
      "traceback": [
       "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
       "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-45-a627edbd2382>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mtext\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m\"Z\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;32m<ipython-input-72-d8a1e6b0b786>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mtext\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m\"X\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
       "\u001b[0;31mTypeError\u001b[0m: 'str' object does not support item assignment"
      ]
     }
    ],
    "source": [
-    "text[0] = \"Z\""
+    "text[0] = \"X\""
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 46,
+   "execution_count": 73,
    "metadata": {
     "slideshow": {
-     "slide_type": "-"
+     "slide_type": "fragment"
     }
    },
    "outputs": [
@@ -1566,7 +2331,7 @@
      "traceback": [
       "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
       "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-46-def135e84998>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mtext\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;36m5\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m\"random\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;32m<ipython-input-73-def135e84998>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mtext\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;36m5\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m\"random\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
       "\u001b[0;31mTypeError\u001b[0m: 'str' object does not support item assignment"
      ]
     }
@@ -1583,7 +2348,7 @@
     }
    },
    "source": [
-    "## String Methods"
+    "### String Methods"
    ]
   },
   {
@@ -1596,12 +2361,12 @@
    "source": [
     "Objects of type `str` come with many **methods** bound on them (cf., the [documentation](https://docs.python.org/3/library/stdtypes.html#string-methods) for a full list). As seen before, they work like *normal* functions and are accessed via the **dot operator** `.`. Calling a method is also referred to as **method invocation**.\n",
     "\n",
-    "The [find()](https://docs.python.org/3/library/stdtypes.html#str.find) method returns the index of the first occurrence of a character or a substring. If no match is found, it returns `-1`."
+    "The [find()](https://docs.python.org/3/library/stdtypes.html#str.find) method returns the index of the first occurrence of a character or a substring. If no match is found, it returns `-1`. A mirrored version searching from the right called [rfind()](https://docs.python.org/3/library/stdtypes.html#str.rfind) exists as well. The [index()](https://docs.python.org/3/library/stdtypes.html#str.index) and [rindex()](https://docs.python.org/3/library/stdtypes.html#str.rindex) methods work in the same way but raise a `ValueError` if no match is found. So, we can control if a search fails *silently* or *loudly*."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 47,
+   "execution_count": 74,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -1611,10 +2376,10 @@
     {
      "data": {
       "text/plain": [
-       "'Lorem ipsum dolor sit amet, ...'"
+       "'Lorem ipsum dolor sit amet.'"
       ]
      },
-     "execution_count": 47,
+     "execution_count": 74,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1625,10 +2390,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 48,
+   "execution_count": 75,
    "metadata": {
     "slideshow": {
-     "slide_type": "-"
+     "slide_type": "fragment"
     }
    },
    "outputs": [
@@ -1638,7 +2403,7 @@
        "22"
       ]
      },
-     "execution_count": 48,
+     "execution_count": 75,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1649,10 +2414,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 49,
+   "execution_count": 76,
    "metadata": {
     "slideshow": {
-     "slide_type": "-"
+     "slide_type": "fragment"
     }
    },
    "outputs": [
@@ -1662,18 +2427,18 @@
        "-1"
       ]
      },
-     "execution_count": 49,
+     "execution_count": 76,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "text.find(\"z\")"
+    "text.find(\"b\")"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 50,
+   "execution_count": 77,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -1686,7 +2451,7 @@
        "12"
       ]
      },
-     "execution_count": 50,
+     "execution_count": 77,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1708,10 +2473,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 51,
+   "execution_count": 78,
    "metadata": {
     "slideshow": {
-     "slide_type": "slide"
+     "slide_type": "fragment"
     }
    },
    "outputs": [
@@ -1721,7 +2486,7 @@
        "1"
       ]
      },
-     "execution_count": 51,
+     "execution_count": 78,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1732,10 +2497,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 52,
+   "execution_count": 79,
    "metadata": {
     "slideshow": {
-     "slide_type": "fragment"
+     "slide_type": "skip"
     }
    },
    "outputs": [
@@ -1745,18 +2510,18 @@
        "13"
       ]
      },
-     "execution_count": 52,
+     "execution_count": 79,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "text.find(\"o\", 2)  # 2 not 1 as otherwise the same \"o\" is found again"
+    "text.find(\"o\", 2)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 53,
+   "execution_count": 80,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -1769,13 +2534,13 @@
        "-1"
       ]
      },
-     "execution_count": 53,
+     "execution_count": 80,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "text.find(\"o\", 2, 12)  # \"o\" does not occur in the specified slice"
+    "text.find(\"o\", 2, 12)"
    ]
   },
   {
@@ -1786,17 +2551,41 @@
     }
    },
    "source": [
-    "[count()](https://docs.python.org/3/library/stdtypes.html#str.count) does what we expect."
+    "The [count()](https://docs.python.org/3/library/stdtypes.html#str.count) method does what we expect."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 54,
+   "execution_count": 81,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
     }
    },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Lorem ipsum dolor sit amet.'"
+      ]
+     },
+     "execution_count": 81,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "text"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 82,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
    "outputs": [
     {
      "data": {
@@ -1804,7 +2593,7 @@
        "1"
       ]
      },
-     "execution_count": 54,
+     "execution_count": 82,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1826,7 +2615,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 55,
+   "execution_count": 83,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -1839,7 +2628,7 @@
        "2"
       ]
      },
-     "execution_count": 55,
+     "execution_count": 83,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1856,12 +2645,12 @@
     }
    },
    "source": [
-    "Alternatively, we may use the [upper()](https://docs.python.org/3/library/stdtypes.html#str.upper) method and search for `\"O\"`s."
+    "Alternatively, we can use the [upper()](https://docs.python.org/3/library/stdtypes.html#str.upper) method and search for `\"L\"`s."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 56,
+   "execution_count": 84,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -1874,7 +2663,7 @@
        "2"
       ]
      },
-     "execution_count": 56,
+     "execution_count": 84,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1896,7 +2685,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 57,
+   "execution_count": 85,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -1909,20 +2698,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 58,
+   "execution_count": 86,
    "metadata": {
     "slideshow": {
-     "slide_type": "-"
+     "slide_type": "skip"
     }
    },
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "140483525349552"
+       "140461430391152"
       ]
      },
-     "execution_count": 58,
+     "execution_count": 86,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1933,7 +2722,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 59,
+   "execution_count": 87,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -1946,20 +2735,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 60,
+   "execution_count": 88,
    "metadata": {
     "slideshow": {
-     "slide_type": "-"
+     "slide_type": "skip"
     }
    },
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "140483422704176"
+       "140461335949424"
       ]
      },
-     "execution_count": 60,
+     "execution_count": 88,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1981,7 +2770,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 61,
+   "execution_count": 89,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -1994,7 +2783,7 @@
        "False"
       ]
      },
-     "execution_count": 61,
+     "execution_count": 89,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2005,10 +2794,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 62,
+   "execution_count": 90,
    "metadata": {
     "slideshow": {
-     "slide_type": "-"
+     "slide_type": "fragment"
     }
    },
    "outputs": [
@@ -2018,7 +2807,7 @@
        "True"
       ]
      },
-     "execution_count": 62,
+     "execution_count": 90,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2035,36 +2824,103 @@
     }
    },
    "source": [
-    "Another popular string method is [split()](https://docs.python.org/3/library/stdtypes.html#str.split): It separates a longer `str` object into smaller ones contained in a `list` object. By default, groups of contiguous whitespace are used as the *separator*.\n",
-    "\n",
-    "As an example, we use [split()](https://docs.python.org/3/library/stdtypes.html#str.split) to print out the individual words in `text` on separate lines."
+    "Besides [upper()](https://docs.python.org/3/library/stdtypes.html#str.upper) and [lower()](https://docs.python.org/3/library/stdtypes.html#str.lower) there exist also [title()](https://docs.python.org/3/library/stdtypes.html#str.title) and [swapcase()](https://docs.python.org/3/library/stdtypes.html#str.swapcase) methods."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 63,
+   "execution_count": 91,
    "metadata": {
     "slideshow": {
-     "slide_type": "slide"
+     "slide_type": "skip"
     }
    },
    "outputs": [
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Lorem\n",
-      "ipsum\n",
-      "dolor\n",
-      "sit\n",
-      "amet,\n",
-      "...\n"
-     ]
+     "data": {
+      "text/plain": [
+       "'lorem ipsum dolor sit amet.'"
+      ]
+     },
+     "execution_count": 91,
+     "metadata": {},
+     "output_type": "execute_result"
     }
    ],
    "source": [
-    "for word in text.split():\n",
-    "    print(word)"
+    "text.lower()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 92,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'LOREM IPSUM DOLOR SIT AMET.'"
+      ]
+     },
+     "execution_count": 92,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "text.upper()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 93,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Lorem Ipsum Dolor Sit Amet.'"
+      ]
+     },
+     "execution_count": 93,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "text.title()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 94,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'lOREM IPSUM DOLOR SIT AMET.'"
+      ]
+     },
+     "execution_count": 94,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "text.swapcase()"
    ]
   },
   {
@@ -2075,12 +2931,71 @@
     }
    },
    "source": [
-    "The opposite of splitting is done with the [join()](https://docs.python.org/3/library/stdtypes.html#str.join) method. It is typically invoked on a `str` object that represents a separator (e.g., `\" \"` or `\", \"`) and connects the elements of an *iterable* argument passed in (e.g., `words` below) into one *new* `str` object."
+    "Another popular string method is [split()](https://docs.python.org/3/library/stdtypes.html#str.split): It separates a longer `str` object into smaller ones collected in a `list` object. By default, groups of contiguous whitespace characters are used as the *separator*.\n",
+    "\n",
+    "As an example, we use [split()](https://docs.python.org/3/library/stdtypes.html#str.split) to print out the individual words in `text` with more whitespace in between them."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 64,
+   "execution_count": 95,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['Lorem', 'ipsum', 'dolor', 'sit', 'amet.']"
+      ]
+     },
+     "execution_count": 95,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "text.split()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 96,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Lorem   ipsum   dolor   sit   amet.   "
+     ]
+    }
+   ],
+   "source": [
+    "for word in text.split():\n",
+    "    print(word, end=\"   \")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "The opposite of splitting is done with the [join()](https://docs.python.org/3/library/stdtypes.html#str.join) method. It is typically invoked on a `str` object that represents a separator (e.g., `\" \"` or `\", \"`) and connects the elements provided by an *iterable* argument (e.g., `words` below) into one *new* `str` object."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 97,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -2088,15 +3003,15 @@
    },
    "outputs": [],
    "source": [
-    "words = [\"This\", \"will\", \"become\", \"a\", \"sentence\"]"
+    "words = [\"This\", \"will\", \"become\", \"a\", \"sentence.\"]"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 65,
+   "execution_count": 98,
    "metadata": {
     "slideshow": {
-     "slide_type": "-"
+     "slide_type": "fragment"
     }
    },
    "outputs": [],
@@ -2106,20 +3021,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 66,
+   "execution_count": 99,
    "metadata": {
     "slideshow": {
-     "slide_type": "-"
+     "slide_type": "fragment"
     }
    },
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "'This will become a sentence'"
+       "'This will become a sentence.'"
       ]
      },
-     "execution_count": 66,
+     "execution_count": 99,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2136,12 +3051,12 @@
     }
    },
    "source": [
-    "As the `str` object `\"abcde\"` below is an *iterable* itself, its elements (i.e., characters) are joined together with a space `\" \"` in between."
+    "As the `str` object `\"abcde\"` below is an *iterable* itself, its characters (!) are joined together with a space `\" \"` in between."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 67,
+   "execution_count": 100,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -2154,7 +3069,7 @@
        "'a b c d e'"
       ]
      },
-     "execution_count": 67,
+     "execution_count": 100,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2176,7 +3091,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 68,
+   "execution_count": 101,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -2186,10 +3101,10 @@
     {
      "data": {
       "text/plain": [
-       "'This is a sentence'"
+       "'This is a sentence.'"
       ]
      },
-     "execution_count": 68,
+     "execution_count": 101,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2198,6 +3113,242 @@
     "sentence.replace(\"will become\", \"is\")"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Note how `sentence` itself remains unchanged. Bound to an immutable object, [replace()](https://docs.python.org/3/library/stdtypes.html#str.replace) must create *new* objects."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 102,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'This will become a sentence.'"
+      ]
+     },
+     "execution_count": 102,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "sentence"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "As seen previously, the [strip()](https://docs.python.org/3/library/stdtypes.html#str.strip) method is often helpful in cleaning text data from unreliable sources like user input from unnecessary leading and trailing whitespace. The [lstrip()](https://docs.python.org/3/library/stdtypes.html#str.lstrip) and [rstrip()](https://docs.python.org/3/library/stdtypes.html#str.rstrip) methods are specialized versions of it."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 103,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'text with whitespace'"
+      ]
+     },
+     "execution_count": 103,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "\"  text with whitespace  \".strip()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 104,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'text with whitespace  '"
+      ]
+     },
+     "execution_count": 104,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "\"  text with whitespace  \".lstrip()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 105,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'  text with whitespace'"
+      ]
+     },
+     "execution_count": 105,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "\"  text with whitespace  \".rstrip()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "When justifying a `str` object for output, the [ljust()](https://docs.python.org/3/library/stdtypes.html#str.ljust) and [rjust()](https://docs.python.org/3/library/stdtypes.html#str.rjust) methods may be helpful."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 106,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'This will become a sentence.            '"
+      ]
+     },
+     "execution_count": 106,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "sentence.ljust(40)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 107,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'            This will become a sentence.'"
+      ]
+     },
+     "execution_count": 107,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "sentence.rjust(40)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Similarly, the [zfill()](https://docs.python.org/3/library/stdtypes.html#str.zfill) method can be used to pad a `str` representation of a number with leading `0`s for justified output."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 108,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'0000042.87'"
+      ]
+     },
+     "execution_count": 108,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "\"42.87\".zfill(10)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 109,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'-000042.87'"
+      ]
+     },
+     "execution_count": 109,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "\"-42.87\".zfill(10)"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {
@@ -2206,7 +3357,7 @@
     }
    },
    "source": [
-    "## String Operations"
+    "### String Operations"
    ]
   },
   {
@@ -2222,7 +3373,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 69,
+   "execution_count": 110,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -2235,7 +3386,7 @@
        "'Hello Lore'"
       ]
      },
-     "execution_count": 69,
+     "execution_count": 110,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2246,26 +3397,26 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 70,
+   "execution_count": 111,
    "metadata": {
     "slideshow": {
-     "slide_type": "-"
+     "slide_type": "fragment"
     }
    },
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "'Lorem ipsum Lorem ipsum Lorem ipsum Lorem ipsum Lorem ipsum '"
+       "'Lorem ipsum Lorem ipsum Lorem ipsum Lorem ipsum Lorem ipsum ...'"
       ]
      },
-     "execution_count": 70,
+     "execution_count": 111,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "5 * text[:12]"
+    "5 * text[:12] + \"...\""
    ]
   },
   {
@@ -2276,7 +3427,7 @@
     }
    },
    "source": [
-    "### String Comparison"
+    "#### String Comparison"
    ]
   },
   {
@@ -2287,32 +3438,17 @@
     }
    },
    "source": [
-    "The *relational* operators also work with `str` objects, another example of operator overloading. Comparison is done one character at a time until the first pair differs or one operand ends. However, `str` objects are sorted in a \"weird\" way. The reason for this is that computers store characters internally as numbers (i.e., $0$s and $1$s). Depending on the character encoding, these numbers vary. Commonly, characters and symbols used in American English are encoded with the numbers 0 through 127, the so-called [ASCII standard](https://en.wikipedia.org/wiki/ASCII). However, Python works with the more general [Unicode/UTF-8 standard](https://en.wikipedia.org/wiki/UTF-8) that understands every language ever used by humans, even emojis."
+    "The *relational* operators also work with `str` objects, another example of operator overloading. Comparison is done one character at a time in a pairwise fashion until the first pair differs or one operand ends. However, `str` objects are sorted in a \"weird\" way. For example, all upper case characters come before all lower case characters. The reason for that is given in the \"*Characters are Numbers with a Convention*\" sub-section further below."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 71,
+   "execution_count": 112,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
     }
    },
-   "outputs": [],
-   "source": [
-    "A = \"Apple\"  # ignore snake_case for variable names in this example\n",
-    "a = \"apple\"\n",
-    "B = \"Banana\""
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 72,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "fragment"
-    }
-   },
    "outputs": [
     {
      "data": {
@@ -2320,21 +3456,21 @@
        "True"
       ]
      },
-     "execution_count": 72,
+     "execution_count": 112,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "A < B"
+    "\"Apple\" < \"Banana\""
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 73,
+   "execution_count": 113,
    "metadata": {
     "slideshow": {
-     "slide_type": "-"
+     "slide_type": "fragment"
     }
    },
    "outputs": [
@@ -2344,29 +3480,18 @@
        "False"
       ]
      },
-     "execution_count": 73,
+     "execution_count": 113,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "a < B"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "source": [
-    "One way to fix this is to only compare lower-cased `str` objects."
+    "\"apple\" < \"Banana\"  # upper case letter come before lower case ones"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 74,
+   "execution_count": 114,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -2379,13 +3504,13 @@
        "True"
       ]
      },
-     "execution_count": 74,
+     "execution_count": 114,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "a < B.lower()"
+    "\"apple\" < \"Banana\".lower()"
    ]
   },
   {
@@ -2396,57 +3521,31 @@
     }
    },
    "source": [
-    "To provide a simple intuition for the \"weird\" sorting above, let's think of the alphabet as being represented by the numbers as listed below. Then `\"Banana\"` is clearly \"smaller\" than `\"apple\"`. In general, all the upper case letters are \"smaller\" than all the lower case letters."
+    "Below is an example with typical German last names that shows how characters other than the first decide the ordering."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 75,
+   "execution_count": 115,
    "metadata": {
     "slideshow": {
-     "slide_type": "slide"
+     "slide_type": "fragment"
     }
    },
    "outputs": [
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "A -> 65 \t a -> 97\n",
-      "B -> 66 \t b -> 98\n",
-      "C -> 67 \t c -> 99\n",
-      "D -> 68 \t d -> 100\n",
-      "E -> 69 \t e -> 101\n",
-      "F -> 70 \t f -> 102\n",
-      "G -> 71 \t g -> 103\n",
-      "H -> 72 \t h -> 104\n",
-      "I -> 73 \t i -> 105\n",
-      "J -> 74 \t j -> 106\n",
-      "K -> 75 \t k -> 107\n",
-      "L -> 76 \t l -> 108\n",
-      "M -> 77 \t m -> 109\n",
-      "N -> 78 \t n -> 110\n",
-      "O -> 79 \t o -> 111\n",
-      "P -> 80 \t p -> 112\n",
-      "Q -> 81 \t q -> 113\n",
-      "R -> 82 \t r -> 114\n",
-      "S -> 83 \t s -> 115\n",
-      "T -> 84 \t t -> 116\n",
-      "U -> 85 \t u -> 117\n",
-      "V -> 86 \t v -> 118\n",
-      "W -> 87 \t w -> 119\n",
-      "X -> 88 \t x -> 120\n",
-      "Y -> 89 \t y -> 121\n",
-      "Z -> 90 \t z -> 122\n"
-     ]
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 115,
+     "metadata": {},
+     "output_type": "execute_result"
     }
    ],
    "source": [
-    "for lower_i in range(65, 91):\n",
-    "    upper_i = lower_i + 32  # all the upper case characters are offset by 32\n",
-    "    lower_char = chr(lower_i)  # from their lower case counterpart\n",
-    "    upper_char = chr(upper_i)\n",
-    "    print(f\"{lower_char} -> {lower_i} \\t {upper_char} -> {upper_i}\")"
+    "\"Mai\" < \"Maier\" < \"Mayer\" < \"Meier\" < \"Meyer\""
    ]
   },
   {
@@ -2457,7 +3556,7 @@
     }
    },
    "source": [
-    "## String Interpolation"
+    "### String Interpolation"
    ]
   },
   {
@@ -2468,9 +3567,7 @@
     }
    },
    "source": [
-    "The previous code cell shows an example of a so-called **f-string**, as introduced by [PEP 498](https://www.python.org/dev/peps/pep-0498/) only in 2016, that is passed as the argument to the [print()](https://docs.python.org/3/library/functions.html#print) function.\n",
-    "\n",
-    "The \"f\" stands for \"formatted\", and we can think of the `str` object as a text \"draft\" that is filled in with values determined at runtime. This concept is formally called **string interpolation**, and there are three ways to achieve that in Python."
+    "Often, we want to use `str` objects as drafts in the source code that are filled in with concrete text only at runtime. This approach is called **string interpolation**. There are three ways to do that in Python."
    ]
   },
   {
@@ -2481,7 +3578,7 @@
     }
    },
    "source": [
-    "### f-strings"
+    "#### f-strings"
    ]
   },
   {
@@ -2492,12 +3589,12 @@
     }
    },
    "source": [
-    "f-strings, formally called **[formatted string literals](https://docs.python.org/3/reference/lexical_analysis.html#formatted-string-literals)**, are the least recently added and most readable way: We prepend a `str` in literal notation with an `f`, and put variables, or more generally, expressions, within curly braces. These are then filled in when a `str` object is evaluated."
+    "**[Formatted string literals](https://docs.python.org/3/reference/lexical_analysis.html#formatted-string-literals)**, of **f-strings** for short, are the least recently added (cf., [PEP 498](https://www.python.org/dev/peps/pep-0498/) in 2016) and most readable way: We simply prepend a `str` in its literal notation with an `f`, and put variables, or more generally, expressions, within curly braces `{}`. These are then filled in when the string literal is evaluated."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 76,
+   "execution_count": 116,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -2511,10 +3608,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 77,
+   "execution_count": 117,
    "metadata": {
     "slideshow": {
-     "slide_type": "-"
+     "slide_type": "fragment"
     }
    },
    "outputs": [
@@ -2524,7 +3621,7 @@
        "'Hello Alexander! Good morning.'"
       ]
      },
-     "execution_count": 77,
+     "execution_count": 117,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2541,15 +3638,15 @@
     }
    },
    "source": [
-    "Separated by a colon `:`, various formatting options are available. In the beginning, the ability to round may be particularly useful: This can be achieved by adding `:.2f` to the variable name inside the curly braces, which casts the number as a `float` and rounds it to two digits. The `:.2f` is a so-called format specifier, and there exists a whole **[format specification mini-language](https://docs.python.org/3/library/string.html#formatspec)** to govern how specifiers work."
+    "Separated by a colon `:`, various formatting options are available. In the beginning, the ability to round numbers for output may be particularly useful: This can be achieved by adding `:.2f` to the variable name inside the curly braces, which casts the number as a `float` and rounds it to two digits. The `:.2f` is a so-called format specifier, and there exists a whole **[format specification mini-language](https://docs.python.org/3/library/string.html#formatspec)** to govern how specifiers work."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 78,
+   "execution_count": 118,
    "metadata": {
     "slideshow": {
-     "slide_type": "slide"
+     "slide_type": "fragment"
     }
    },
    "outputs": [],
@@ -2559,10 +3656,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 79,
+   "execution_count": 119,
    "metadata": {
     "slideshow": {
-     "slide_type": "-"
+     "slide_type": "fragment"
     }
    },
    "outputs": [
@@ -2572,7 +3669,7 @@
        "'Pi is 3.14'"
       ]
      },
-     "execution_count": 79,
+     "execution_count": 119,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2581,30 +3678,6 @@
     "f\"Pi is {pi:.2f}\""
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": 80,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "-"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'Pi is 3.142'"
-      ]
-     },
-     "execution_count": 80,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "f\"Pi is {pi:.3f}\""
-   ]
-  },
   {
    "cell_type": "markdown",
    "metadata": {
@@ -2613,7 +3686,7 @@
     }
    },
    "source": [
-    "### [format()](https://docs.python.org/3/library/stdtypes.html#str.format) Method"
+    "#### [format()](https://docs.python.org/3/library/stdtypes.html#str.format) Method"
    ]
   },
   {
@@ -2624,12 +3697,12 @@
     }
    },
    "source": [
-    "`str` objects also provide a [format()](https://docs.python.org/3/library/stdtypes.html#str.format) method that accepts an arbitrary number of *positional* arguments that are inserted into the `str` object in the same order replacing empty curly brackets. String interpolation with the [format()](https://docs.python.org/3/library/stdtypes.html#str.format) method is a more traditional and probably the most common way one as of today. While f-strings are the recommended way going forward, usage of the [format()](https://docs.python.org/3/library/stdtypes.html#str.format) method is likely not declining any time soon."
+    "`str` objects also provide a [format()](https://docs.python.org/3/library/stdtypes.html#str.format) method that accepts an arbitrary number of *positional* arguments that are inserted into the `str` object in the same order replacing empty curly brackets `{}`. String interpolation with the [format()](https://docs.python.org/3/library/stdtypes.html#str.format) method is a more traditional and probably the most common way as of today. While f-strings are the recommended way going forward, usage of the [format()](https://docs.python.org/3/library/stdtypes.html#str.format) method is likely not declining any time soon."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 81,
+   "execution_count": 120,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -2642,7 +3715,7 @@
        "'Hello Alexander! Good morning.'"
       ]
      },
-     "execution_count": 81,
+     "execution_count": 120,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2664,7 +3737,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 82,
+   "execution_count": 121,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -2677,7 +3750,7 @@
        "'Good morning, Alexander'"
       ]
      },
-     "execution_count": 82,
+     "execution_count": 121,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2699,7 +3772,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 83,
+   "execution_count": 122,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -2712,7 +3785,7 @@
        "'Hello Alexander! Good morning.'"
       ]
      },
-     "execution_count": 83,
+     "execution_count": 122,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2734,7 +3807,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 84,
+   "execution_count": 123,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -2747,7 +3820,7 @@
        "'Pi is 3.14'"
       ]
      },
-     "execution_count": 84,
+     "execution_count": 123,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2760,11 +3833,11 @@
    "cell_type": "markdown",
    "metadata": {
     "slideshow": {
-     "slide_type": "skip"
+     "slide_type": "slide"
     }
    },
    "source": [
-    "### `%` Operator"
+    "#### `%` Operator"
    ]
   },
   {
@@ -2782,10 +3855,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 85,
+   "execution_count": 124,
    "metadata": {
     "slideshow": {
-     "slide_type": "skip"
+     "slide_type": "slide"
     }
    },
    "outputs": [
@@ -2795,7 +3868,7 @@
        "'Pi is 3.14'"
       ]
      },
-     "execution_count": 85,
+     "execution_count": 124,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2817,10 +3890,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 86,
+   "execution_count": 125,
    "metadata": {
     "slideshow": {
-     "slide_type": "skip"
+     "slide_type": "fragment"
     }
    },
    "outputs": [
@@ -2830,7 +3903,7 @@
        "'Hello Alexander! Good morning.'"
       ]
      },
-     "execution_count": 86,
+     "execution_count": 125,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2847,7 +3920,7 @@
     }
    },
    "source": [
-    "## Special Characters"
+    "### Unicode & (Special) Characters"
    ]
   },
   {
@@ -2858,19 +3931,43 @@
     }
    },
    "source": [
-    "Some symbols have a special meaning within `str` objects. Popular examples are the newline `\\n` and tab `\\t` \"characters.\" The backslash symbol `\\` is also referred to as an **escape character** in this context, indicating that the following character has a meaning other than its literal meaning.\n",
+    "As previously seen, some characters have a special meaning when following the **escape character** `\"\\\"`. Besides escaping the kind of quote used as the `str` object's delimiter, `'` or `\"`, most of these **escape sequences** (i.e., `\"\\\"` with the subsequent character), act as a **control character** that moves the \"cursor\" in the output *without* generating any pixel on the screen. Because of that, we only see the effect of such escape sequences when used with the [print()](https://docs.python.org/3/library/functions.html#print) function. The [documentation](https://docs.python.org/3/reference/lexical_analysis.html#string-and-bytes-literals) lists all available escape sequences, of which we show the most important ones below.\n",
     "\n",
-    "The built-in [print()](https://docs.python.org/3/library/functions.html#print) function then \"prints\" out these special characters accordingly."
+    "The most common escape sequence is `\"\\n\"` that \"prints\" a [newline character](https://en.wikipedia.org/wiki/Newline) that is also called the line feed character or LF for short."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 87,
+   "execution_count": 126,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
     }
    },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'This is a sentence\\nthat is printed\\non three lines.'"
+      ]
+     },
+     "execution_count": 126,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "\"This is a sentence\\nthat is printed\\non three lines.\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 127,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
    "outputs": [
     {
      "name": "stdout",
@@ -2887,11 +3984,22 @@
    ]
   },
   {
-   "cell_type": "code",
-   "execution_count": 88,
+   "cell_type": "markdown",
    "metadata": {
     "slideshow": {
-     "slide_type": "fragment"
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "`\"\\b\"` is the [backspace character](https://en.wikipedia.org/wiki/Backspace), or BS for short, that moves the cursor back by one character."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 128,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
     }
    },
    "outputs": [
@@ -2899,12 +4007,33 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Words\taligned\twith\ttabs.\n"
+      "ABX\n"
      ]
     }
    ],
    "source": [
-    "print(\"Words\\taligned\\twith\\ttabs.\")"
+    "print(\"ABC\\bX\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 129,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "ABXY\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"ABC\\bXY\")"
    ]
   },
   {
@@ -2915,12 +4044,12 @@
     }
    },
    "source": [
-    "As emojis are important as well, they can be inserted with the corresponding **unicode code point** number starting with `\\U`. See this [list](https://en.wikipedia.org/wiki/List_of_Unicode_characters) of unicode characters for an overview."
+    "Similarly, `\"\\r\"` is the [carriage return character](https://en.wikipedia.org/wiki/Carriage_return), or CR for short, that moves the cursor back to the beginning of the line."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 89,
+   "execution_count": 130,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -2931,12 +4060,33 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "�\n"
+      "XBC\n"
      ]
     }
    ],
    "source": [
-    "print(\"\\U0001f604\")"
+    "print(\"ABC\\rX\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 131,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "XYC\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"ABC\\rXY\")"
    ]
   },
   {
@@ -2947,12 +4097,12 @@
     }
    },
    "source": [
-    "Outside the [print()](https://docs.python.org/3/library/functions.html#print) function, the special characters are not treated any different from non-special ones."
+    "While Linux and modern MacOS systems use solely `\"\\n\"` to express a new line, Windows systems default to using `\"\\r\\n\"`. This may lead to \"weird\" bugs on software projects where people using both kind of operating systems collaborate."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 90,
+   "execution_count": 132,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -2960,18 +4110,50 @@
    },
    "outputs": [
     {
-     "data": {
-      "text/plain": [
-       "'This is a sentence\\nthat is printed\\non three lines.'"
-      ]
-     },
-     "execution_count": 90,
-     "metadata": {},
-     "output_type": "execute_result"
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "This is a sentence\n",
+      "that is printed\n",
+      "on three lines.\n"
+     ]
     }
    ],
    "source": [
-    "\"This is a sentence\\nthat is printed\\non three lines.\""
+    "print(\"This is a sentence\\r\\nthat is printed\\r\\non three lines.\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "`\"\\t\"` makes the cursor \"jump\" in equidistant tab stops. That may be useful for formatting a program with lengthy and tabular results."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 133,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Jump\tfrom\ttab\tstop\tto\ttab\tstop.\n",
+      "The\tsecond\tline\tdoes\tso\ttoo.\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"Jump\\tfrom\\ttab\\tstop\\tto\\ttab\\tstop.\\nThe\\tsecond\\tline\\tdoes\\tso\\ttoo.\")"
    ]
   },
   {
@@ -2982,7 +4164,7 @@
     }
    },
    "source": [
-    "## Raw Strings"
+    "#### Raw Strings"
    ]
   },
   {
@@ -2993,14 +4175,12 @@
     }
    },
    "source": [
-    "Sometimes we do *not* want the backslash `\\` and its following character be interpreted as special characters.\n",
-    "\n",
-    "For example, let's print a typical installation path on a Windows systems. Obviously, the newline character `\\n` does *not* makes sense here."
+    "Sometimes we do *not* want the backslash `\"\\\"` and its subsequent character be interpreted as an escape sequence. For example, let's print a typical installation path on a Windows systems. Obviously, the newline character `\"\\n\"` does *not* makes sense here."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 91,
+   "execution_count": 134,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -3028,12 +4208,12 @@
     }
    },
    "source": [
-    "Some `str` objects even produce a `SyntaxError` because the `\\U` *cannot* be interpreted as a unicode code point."
+    "Some `str` objects even produce a `SyntaxError` because the `\"\\U\"` can *not* be interpreted as a Unicode code point (cf., next section)."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 92,
+   "execution_count": 135,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -3042,15 +4222,15 @@
    "outputs": [
     {
      "ename": "SyntaxError",
-     "evalue": "(unicode error) 'unicodeescape' codec can't decode bytes in position 2-3: truncated \\UXXXXXXXX escape (<ipython-input-92-0aad8a365b02>, line 1)",
+     "evalue": "(unicode error) 'unicodeescape' codec can't decode bytes in position 2-3: truncated \\UXXXXXXXX escape (<ipython-input-135-61308f43d404>, line 1)",
      "output_type": "error",
      "traceback": [
-      "\u001b[0;36m  File \u001b[0;32m\"<ipython-input-92-0aad8a365b02>\"\u001b[0;36m, line \u001b[0;32m1\u001b[0m\n\u001b[0;31m    print(\"C:\\Users\\Administrator\\Desktop\\Project_Folder\")\u001b[0m\n\u001b[0m         ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m (unicode error) 'unicodeescape' codec can't decode bytes in position 2-3: truncated \\UXXXXXXXX escape\n"
+      "\u001b[0;36m  File \u001b[0;32m\"<ipython-input-135-61308f43d404>\"\u001b[0;36m, line \u001b[0;32m1\u001b[0m\n\u001b[0;31m    print(\"C:\\Users\\Administrator\\Desktop\\Project\")\u001b[0m\n\u001b[0m         ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m (unicode error) 'unicodeescape' codec can't decode bytes in position 2-3: truncated \\UXXXXXXXX escape\n"
      ]
     }
    ],
    "source": [
-    "print(\"C:\\Users\\Administrator\\Desktop\\Project_Folder\")"
+    "print(\"C:\\Users\\Administrator\\Desktop\\Project\")"
    ]
   },
   {
@@ -3061,12 +4241,12 @@
     }
    },
    "source": [
-    "A simple solution would be to escape the escape character with a *second* backslash `\\`."
+    "A simple solution would be to escape the escape character with a *second* backslash `\"\\\"`."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 93,
+   "execution_count": 136,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -3087,10 +4267,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 94,
+   "execution_count": 137,
    "metadata": {
     "slideshow": {
-     "slide_type": "-"
+     "slide_type": "fragment"
     }
    },
    "outputs": [
@@ -3098,12 +4278,12 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "C:\\Users\\Administrator\\Desktop\\Project_Folder\n"
+      "C:\\Users\\Administrator\\Desktop\\Project\n"
      ]
     }
    ],
    "source": [
-    "print(\"C:\\\\Users\\\\Administrator\\\\Desktop\\\\Project_Folder\")"
+    "print(\"C:\\\\Users\\\\Administrator\\\\Desktop\\\\Project\")"
    ]
   },
   {
@@ -3114,12 +4294,12 @@
     }
    },
    "source": [
-    "However, this is tedious to remember and type. Luckily, Python allows treating any string literal as \"raw,\" and this is indicated in the string literal by the prefix `r`."
+    "However, this is tedious to remember and type. For such use cases, Python allows to prefix any string literal with a `r`. The literal is then interpreted in a \"raw\" way."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 95,
+   "execution_count": 138,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -3140,10 +4320,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 96,
+   "execution_count": 139,
    "metadata": {
     "slideshow": {
-     "slide_type": "-"
+     "slide_type": "fragment"
     }
    },
    "outputs": [
@@ -3151,12 +4331,12 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "C:\\Users\\Administrator\\Desktop\\Project_Folder\n"
+      "C:\\Users\\Administrator\\Desktop\\Project\n"
      ]
     }
    ],
    "source": [
-    "print(r\"C:\\Users\\Administrator\\Desktop\\Project_Folder\")"
+    "print(r\"C:\\Users\\Administrator\\Desktop\\Project\")"
    ]
   },
   {
@@ -3167,7 +4347,7 @@
     }
    },
    "source": [
-    "## Multi-line Strings"
+    "#### Characters are Numbers with a Convention"
    ]
   },
   {
@@ -3178,12 +4358,693 @@
     }
    },
    "source": [
-    "Sometimes, it is convenient to split text across multiple lines in source code. For example, to make lines fit into the 79 characters requirement of [PEP 8](https://www.python.org/dev/peps/pep-0008/) or because the text naturally contains many newlines. Using double quotes `\"` around multiple lines results in a `SyntaxError`."
+    "So far, we used the term **character** without any further consideration. In this section, we briefly look into what characters are and how they are modeled in software.\n",
+    "\n",
+    "[Chapter 5](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/05_numbers_00_lecture.ipynb) gives us an idea on how individual **bits** are used to express all types of numbers, from \"simple\" `int` objects to \"complex\" `float` ones. To model characters, another **layer of abstraction** is put on top of whole numbers. So, just as bits are used to express integers, they themselves are used to express characters."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "##### ASCII"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Many conventions have been developed as to what integer is associated with which character. The most basic one that was also adopted around the world is the the so-called [American Standard Code for Information Interchange](https://en.wikipedia.org/wiki/ASCII), or **ASCII** for short. It uses 7 bits of information to map the unprintable control characters as well as the printable letters of the alphabet, numbers, and common symbols to the numbers `0` through `127`.\n",
+    "\n",
+    "A mapping from characters to numbers is referred to by the technical term **encoding**. We may use the built-in [ord()](https://docs.python.org/3/library/functions.html#ord) function to **encode** any single character. The inverse to that is the built-in [chr()](https://docs.python.org/3/library/functions.html#chr) function, which **decodes** a number into a character."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 97,
+   "execution_count": 140,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "65"
+      ]
+     },
+     "execution_count": 140,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ord(\"A\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 141,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'A'"
+      ]
+     },
+     "execution_count": 141,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "chr(65)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Of course, unprintable escape sequences like `\"\\n\"` count as only *one* character."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 142,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "10"
+      ]
+     },
+     "execution_count": 142,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ord(\"\\n\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 143,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'\\n'"
+      ]
+     },
+     "execution_count": 143,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "chr(10)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "In ASCII, the numbers `0` through `31` (and `127`) are mapped to all kinds of unprintable control characters. The decimal digits are encoded with the numbers `48` through `57`, the upper case letters with `65` through `90`, and the lower case letters with `97` through `122`. While this seems random as first, there is of course a \"sophisticated\" system behind it. That can immediately be seen when looking at the encoded numbers in their *binary* representations.\n",
+    "\n",
+    "For example, the digit `5` is mapped to the number `53` in ASCII. The binary representation of `53` is `0b_11_0101` and the least significant four bits, `0101`, mean $5$. Similarly, the letter `\"E\"` is the fifth letter in the alphabet. It is encoded with the number `69` in ASCII, which is `0b_100_0101` in binary. And, the least significant bits, `0_0101`, mean $5$. Analogously, `\"e\"` is encoded with `101` in ASCII, which is `0b_110_0101` in binary. And, the least significant bits, `0_0101`, mean $5$ again. This encoding was chosen mainly because programmers \"in the old days\" needed to implement these encodings \"by hand.\" Python abstracts that logic away from its users.\n",
+    "\n",
+    "This encoding scheme is also the cause for the \"weird\" sorting in the \"*String Comparison*\" section above, where `\"apple\"` comes *after* `\"Banana\"`. As `\"a\"` is encoded with `97` and `\"B\"` with `66`, `\"Banana\"` must of course be \"smaller\" than `\"apple\"` when comparison is done in a pairwise fashion of the individual characters."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 144,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "48 0b110000 ->  0\n",
+      "49 0b110001 ->  1\n",
+      "50 0b110010 ->  2\n",
+      "51 0b110011 ->  3\n",
+      "52 0b110100 ->  4\n",
+      "53 0b110101 ->  5\n",
+      "54 0b110110 ->  6\n",
+      "55 0b110111 ->  7\n",
+      "56 0b111000 ->  8\n",
+      "57 0b111001 ->  9\n"
+     ]
+    }
+   ],
+   "source": [
+    "for number in range(48, 58):\n",
+    "    print(number, bin(number), \"-> \", chr(number))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 145,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "65 0b1000001 ->  A\t66 0b1000010 ->  B\t67 0b1000011 ->  C\n",
+      "68 0b1000100 ->  D\t69 0b1000101 ->  E\t70 0b1000110 ->  F\n",
+      "71 0b1000111 ->  G\t72 0b1001000 ->  H\t73 0b1001001 ->  I\n",
+      "74 0b1001010 ->  J\t75 0b1001011 ->  K\t76 0b1001100 ->  L\n",
+      "77 0b1001101 ->  M\t78 0b1001110 ->  N\t79 0b1001111 ->  O\n",
+      "80 0b1010000 ->  P\t81 0b1010001 ->  Q\t82 0b1010010 ->  R\n",
+      "83 0b1010011 ->  S\t84 0b1010100 ->  T\t85 0b1010101 ->  U\n",
+      "86 0b1010110 ->  V\t87 0b1010111 ->  W\t88 0b1011000 ->  X\n",
+      "89 0b1011001 ->  Y\t90 0b1011010 ->  Z\t"
+     ]
+    }
+   ],
+   "source": [
+    "for i, number in enumerate(range(65, 91), start=1):\n",
+    "    end = \"\\n\" if i % 3 == 0 else \"\\t\"\n",
+    "    print(number, bin(number), \"-> \", chr(number), end=end)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 146,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      " 97 0b1100001 ->  a\t 98 0b1100010 ->  b\t 99 0b1100011 ->  c\n",
+      "100 0b1100100 ->  d\t101 0b1100101 ->  e\t102 0b1100110 ->  f\n",
+      "103 0b1100111 ->  g\t104 0b1101000 ->  h\t105 0b1101001 ->  i\n",
+      "106 0b1101010 ->  j\t107 0b1101011 ->  k\t108 0b1101100 ->  l\n",
+      "109 0b1101101 ->  m\t110 0b1101110 ->  n\t111 0b1101111 ->  o\n",
+      "112 0b1110000 ->  p\t113 0b1110001 ->  q\t114 0b1110010 ->  r\n",
+      "115 0b1110011 ->  s\t116 0b1110100 ->  t\t117 0b1110101 ->  u\n",
+      "118 0b1110110 ->  v\t119 0b1110111 ->  w\t120 0b1111000 ->  x\n",
+      "121 0b1111001 ->  y\t122 0b1111010 ->  z\t"
+     ]
+    }
+   ],
+   "source": [
+    "for i, number in enumerate(range(97, 123), start=1):\n",
+    "    end = \"\\n\" if i % 3 == 0 else \"\\t\"\n",
+    "    print(str(number).rjust(3), bin(number), \"-> \", chr(number), end=end)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "The remaining `symbols` encoded in ASCII are encoded with the numbers still unused, which is why they are scattered."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 147,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "symbols = (\n",
+    "    list(range(32, 48))\n",
+    "    + list(range(58, 65))\n",
+    "    + list(range(91, 97))\n",
+    "    + list(range(123, 127))\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 148,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      " 32   0b100000 ->   \t 33   0b100001 ->  !\t 34   0b100010 ->  \"\n",
+      " 35   0b100011 ->  #\t 36   0b100100 ->  $\t 37   0b100101 ->  %\n",
+      " 38   0b100110 ->  &\t 39   0b100111 ->  '\t 40   0b101000 ->  (\n",
+      " 41   0b101001 ->  )\t 42   0b101010 ->  *\t 43   0b101011 ->  +\n",
+      " 44   0b101100 ->  ,\t 45   0b101101 ->  -\t 46   0b101110 ->  .\n",
+      " 47   0b101111 ->  /\t 58   0b111010 ->  :\t 59   0b111011 ->  ;\n",
+      " 60   0b111100 ->  <\t 61   0b111101 ->  =\t 62   0b111110 ->  >\n",
+      " 63   0b111111 ->  ?\t 64  0b1000000 ->  @\t 91  0b1011011 ->  [\n",
+      " 92  0b1011100 ->  \\\t 93  0b1011101 ->  ]\t 94  0b1011110 ->  ^\n",
+      " 95  0b1011111 ->  _\t 96  0b1100000 ->  `\t123  0b1111011 ->  {\n",
+      "124  0b1111100 ->  |\t125  0b1111101 ->  }\t126  0b1111110 ->  ~\n"
+     ]
+    }
+   ],
+   "source": [
+    "for i, number in enumerate(symbols, start=1):\n",
+    "    end = \"\\n\" if i % 3 == 0 else \"\\t\"\n",
+    "    print(str(number).rjust(3), bin(number).rjust(10), \"-> \", chr(number), end=end)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "As the ASCII character set does not work for many languages other than English, various encodings were developed. Popular examples are [ISO 8859-1](https://en.wikipedia.org/wiki/ISO/IEC_8859-1) for western European letters or [Windows 1250](https://en.wikipedia.org/wiki/Windows-1250) for Latin ones. Many of these encodings use 8-bit numbers (i.e., `0` through `255`) to map the multitude of non-English letters (e.g., the German [umlauts](https://en.wikipedia.org/wiki/Umlaut_%28linguistics%29) `\"ä\"`, `\"ö\"`, `\"ü\"`, or `\"ß\"`)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "##### Unicode"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "However, none of these specialized encodings can map *all* characters of *all* languages around the world from *all* times in human history. To achieve that, a truly global standard called **[Unicode](https://en.wikipedia.org/wiki/Unicode)** was developed and its first version released in 1991. Since then, Unicode has been amended with many other \"characters.\" The most popular among them being [emojis](https://en.wikipedia.org/wiki/Emoji) or the [Klingon](https://en.wikipedia.org/wiki/Klingon_scripts) language (from the science fiction series [Star Trek](https://en.wikipedia.org/wiki/Star_Trek)). In Unicode, every character is given an identity referred to as the **code point**. Code points are hexadecimal numbers from `0x0000` through `0x10ffff`, written as U+0000 and U+10FFFF outside of Python. Consequently, there exist at most $1,114,112$ code points, of which only about 10% are currently in use, allowing lots of room for new characters to be invented. The first `127` code points are identical to the ASCII encoding for reasons explained in the \"*The `bytes` Type*\" section further below. There exist plenty of lists of all Unicode characters on the web (e.g., [Wikipedia](https://en.wikipedia.org/wiki/List_of_Unicode_characters)).\n",
+    "\n",
+    "All we need to know to print a character is its code point. Python uses the escape sequence `\"\\U\"` that is followed by eight hexadecimal digits. Underscore separators are unfortunately *not* allowed here.\n",
+    "\n",
+    "So, to print a smiley, we just need to look up the corresponding number (e.g., [here](https://en.wikipedia.org/wiki/Emoji#Unicode_blocks))."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 149,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'�'"
+      ]
+     },
+     "execution_count": 149,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "\"\\U0001f604\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Every Unicode character also has a descriptive name that we can use with the escape sequence `\"\\N\"` and within curly braces `{}`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 150,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'�'"
+      ]
+     },
+     "execution_count": 150,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "\"\\N{FACE WITH TEARS OF JOY}\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Whenever the code point can be expressed with just four hexadecimal digits, we may use the escape sequence `\"\\u\"` for brevity."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 151,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'A'"
+      ]
+     },
+     "execution_count": 151,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "\"\\U00000041\"  # hex(65) == 0x41"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 152,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'A'"
+      ]
+     },
+     "execution_count": 152,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "\"\\u0041\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Analogously, if the code point can be expressed with two hexadecimal digits, we may use the escape sequence `\"\\x\"` for even conciser code."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 153,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'A'"
+      ]
+     },
+     "execution_count": 153,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "\"\\x41\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "As the `str` type is based on Unicode, a `str` object's behavior is more in line with how humans view text and not how it is expressed in source code.\n",
+    "\n",
+    "For example, while it is obvious that `len(\"A\")` evaluates to `1`, ..."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 154,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "1"
+      ]
+     },
+     "execution_count": 154,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "len(\"A\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "... what should `len(\"\\N{SNAKE}\")` evaluate to? As the idea of a snake is expressed as *one* \"character,\" [len()](https://docs.python.org/3/library/functions.html#len) also returns `1` here."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 155,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'�'"
+      ]
+     },
+     "execution_count": 155,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "\"\\N{SNAKE}\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 156,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "1"
+      ]
+     },
+     "execution_count": 156,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "len(\"\\N{SNAKE}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Many of the built-in `str` methods also consider Unicode. For example, in contrast to [lower()](https://docs.python.org/3/library/stdtypes.html#str.lower), the [casefold()](https://docs.python.org/3/library/stdtypes.html#str.casefold) method knows that the German `\"Ăź\"` is commonly converted to `\"ss\"`. So, when searching for exact matches, normalizing text with [casefold()](https://docs.python.org/3/library/stdtypes.html#str.casefold) may yield better results than with [lower()](https://docs.python.org/3/library/stdtypes.html#str.lower)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 157,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'straĂźe'"
+      ]
+     },
+     "execution_count": 157,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "\"StraĂźe\".lower()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 158,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'strasse'"
+      ]
+     },
+     "execution_count": 158,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "\"StraĂźe\".casefold()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Many other methods like [isdecimal()](https://docs.python.org/3/library/stdtypes.html#str.isdecimal), [isdigit()](https://docs.python.org/3/library/stdtypes.html#str.isdigit), [isnumeric()](https://docs.python.org/3/library/stdtypes.html#str.isnumeric), [isprintable()](https://docs.python.org/3/library/stdtypes.html#str.isprintable), [isidentifier()](https://docs.python.org/3/library/stdtypes.html#str.isidentifier), and many more may be worthwhile to know for the data science practitioner, especially when it comes to data cleaning."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "### Multi-line Strings"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Sometimes, it is convenient to split text across multiple lines in source code. For example, to make lines fit into the 79 characters requirement of [PEP 8](https://www.python.org/dev/peps/pep-0008/) or because the text consists of many lines and typing out `\"\\n\"` is tedious. However, using single double quotes `\"` around multiple lines results in a `SyntaxError`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 159,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -3192,10 +5053,10 @@
    "outputs": [
     {
      "ename": "SyntaxError",
-     "evalue": "EOL while scanning string literal (<ipython-input-97-4cef690f1f4a>, line 1)",
+     "evalue": "EOL while scanning string literal (<ipython-input-159-4cef690f1f4a>, line 1)",
      "output_type": "error",
      "traceback": [
-      "\u001b[0;36m  File \u001b[0;32m\"<ipython-input-97-4cef690f1f4a>\"\u001b[0;36m, line \u001b[0;32m1\u001b[0m\n\u001b[0;31m    \"\u001b[0m\n\u001b[0m     ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m EOL while scanning string literal\n"
+      "\u001b[0;36m  File \u001b[0;32m\"<ipython-input-159-4cef690f1f4a>\"\u001b[0;36m, line \u001b[0;32m1\u001b[0m\n\u001b[0;31m    \"\u001b[0m\n\u001b[0m     ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m EOL while scanning string literal\n"
      ]
     }
    ],
@@ -3213,22 +5074,22 @@
     }
    },
    "source": [
-    "However, by enclosing a string literal with either **triple-double** quotes `\"\"\"` or **triple-single** quotes `'''`, Python creates a \"plain\" `str` object. Docstrings are precisely that, and, by convention, always written in triple-double quotes `\"\"\"`."
+    "Instead, we may enclose a string literal with either **triple double** quotes `\"\"\"` or **triple single** quotes `'''`. Then, newline characters in the source code are converted into `\"\\n\"` characters in the resulting `str` object. Docstrings are precisely that, and, by convention, always written within triple double quotes `\"\"\"`."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 98,
+   "execution_count": 160,
    "metadata": {
     "slideshow": {
-     "slide_type": "fragment"
+     "slide_type": "slide"
     }
    },
    "outputs": [],
    "source": [
     "multi_line = \"\"\"\n",
     "I am a multi-line string\n",
-    "consisting of 4 lines.\n",
+    "consisting of four lines.\n",
     "\"\"\""
    ]
   },
@@ -3240,25 +5101,25 @@
     }
    },
    "source": [
-    "Line breaks are kept and implicitly converted into `\\n` characters."
+    "A caveat is that `\"\\n\"` characters are often inserted at the beginning or end of the text when we try to format the source code nicely."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 99,
+   "execution_count": 161,
    "metadata": {
     "slideshow": {
-     "slide_type": "fragment"
+     "slide_type": "skip"
     }
    },
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "'\\nI am a multi-line string\\nconsisting of 4 lines.\\n'"
+       "'\\nI am a multi-line string\\nconsisting of four lines.\\n'"
       ]
      },
-     "execution_count": 99,
+     "execution_count": 161,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3267,20 +5128,9 @@
     "multi_line"
    ]
   },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "source": [
-    "The built-in [print()](https://docs.python.org/3/library/functions.html#print) function correctly prints out the `\\n` characters."
-   ]
-  },
   {
    "cell_type": "code",
-   "execution_count": 100,
+   "execution_count": 162,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -3293,7 +5143,7 @@
      "text": [
       "\n",
       "I am a multi-line string\n",
-      "consisting of 4 lines.\n",
+      "consisting of four lines.\n",
       "\n"
      ]
     }
@@ -3310,15 +5160,15 @@
     }
    },
    "source": [
-    "Using the [split()](https://docs.python.org/3/library/stdtypes.html#str.split) method with the optional *sep* argument, we confirm that `multi_line` consists of *four* lines with the first and last line breaks being the first and last characters in the `str` object."
+    "Using the [split()](https://docs.python.org/3/library/stdtypes.html#str.split) method with the optional `sep` argument, we confirm that `multi_line` consists of *four* lines with the first and last line being empty."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 101,
+   "execution_count": 163,
    "metadata": {
     "slideshow": {
-     "slide_type": "slide"
+     "slide_type": "fragment"
     }
    },
    "outputs": [
@@ -3326,15 +5176,15 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "0 \n",
-      "1 I am a multi-line string\n",
-      "2 consisting of 4 lines.\n",
-      "3 \n"
+      "1 \n",
+      "2 I am a multi-line string\n",
+      "3 consisting of four lines.\n",
+      "4 \n"
      ]
     }
    ],
    "source": [
-    "for i, line in enumerate(multi_line.split(\"\\n\")):\n",
+    "for i, line in enumerate(multi_line.split(\"\\n\"), start=1):\n",
     "    print(i, line)"
    ]
   },
@@ -3346,12 +5196,75 @@
     }
    },
    "source": [
-    "The next code cell puts several constructs from this chapter together to create a multi-line `str` object `content`: The `with` statement provides a context that ensures `file` is not left open. Then, the [readlines()](https://docs.python.org/3/library/io.html#io.IOBase.readlines) method returns the contents of `file` as a `list` object holding as many `str` objects as there are lines in the file on disk. Lastly, we concatenate these together with the [join()](https://docs.python.org/3/library/stdtypes.html#str.join) method to obtain `content`. We do so on an empty `str` object `\"\"` as each line already ends with a `\"\\n\"`."
+    "To mitigate that, we often see the [strip()](https://docs.python.org/3/library/stdtypes.html#bytes.strip) method in source code."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 102,
+   "execution_count": 164,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "multi_line = \"\"\"\n",
+    "I am a multi-line string\n",
+    "consisting of two lines.\n",
+    "\"\"\".strip()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 165,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "1 I am a multi-line string\n",
+      "2 consisting of two lines.\n"
+     ]
+    }
+   ],
+   "source": [
+    "for i, line in enumerate(multi_line.split(\"\\n\"), start=1):\n",
+    "    print(i, line)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "## The `bytes` Type"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "To end this chapter, we want to briefly look at the `bytes` data type, which conceptually is a sequence of bytes. That data format is probably one of the most generic ways of exchanging data between any two programs or computers (e.g., a web browser obtains its data from a web server in this format).\n",
+    "\n",
+    "Let's open a binary file in read-only mode (i.e., `mode=\"rb\"`) and read in all of its contents."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 166,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -3359,40 +5272,619 @@
    },
    "outputs": [],
    "source": [
-    "with open(\"lorem_ipsum.txt\") as file:\n",
-    "    content = \"\".join(file.readlines())"
+    "with open(\"full_house.bin\", mode=\"rb\") as binary_file:\n",
+    "    data = binary_file.read()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "`data` is an object of type `bytes`."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 103,
+   "execution_count": 167,
    "metadata": {
     "slideshow": {
-     "slide_type": "-"
+     "slide_type": "fragment"
     }
    },
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "\"Lorem Ipsum is simply dummy text of the printing and typesetting industry.\\nLorem Ipsum has been the industry's standard dummy text ever since the 1500s\\nwhen an unknown printer took a galley of type and scrambled it to make a type\\nspecimen book. It has survived not only five centuries but also the leap into\\nelectronic typesetting, remaining essentially unchanged. It was popularised in\\nthe 1960s with the release of Letraset sheets.\\n\""
+       "140461335555696"
       ]
      },
-     "execution_count": 103,
+     "execution_count": 167,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "content"
+    "id(data)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 104,
+   "execution_count": 168,
    "metadata": {
     "slideshow": {
-     "slide_type": "-"
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "bytes"
+      ]
+     },
+     "execution_count": 168,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "type(data)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "It's value is given out in the literal bytes notation with a `b` prefix (cf., the [reference](https://docs.python.org/3/reference/lexical_analysis.html#string-and-bytes-literals)). Every byte is expressed in hexadecimal representation with the escape sequence `\"\\x\"`. This representation is commonly chosen as we can *not* tell what kind of information is hidden in the `data` by just looking at the bytes. Instead, we must be told by some other source how to **decode** the raw bytes into information we can interpret."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 169,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "b'\\xf0\\x9f\\x82\\xa7\\xf0\\x9f\\x82\\xb7\\xf0\\x9f\\x83\\x97\\xf0\\x9f\\x83\\x8e\\xf0\\x9f\\x83\\x9e'"
+      ]
+     },
+     "execution_count": 169,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "`bytes` objects work like `str` objects in many ways. In particular, they are *sequences* as well: The number of bytes is *finite* and we may *iterate* over them in *order*."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 170,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "20"
+      ]
+     },
+     "execution_count": 170,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "len(data)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Consisting of 8 bits, a single byte can always be interpreted as a whole number between `0` through `255`. That is exactly what we see when we loop over the `data` ..."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 171,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "240 159 130 167 240 159 130 183 240 159 131 151 240 159 131 142 240 159 131 158 "
+     ]
+    }
+   ],
+   "source": [
+    "for byte in data:\n",
+    "    print(byte, end=\" \")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "... or index into them."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 172,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "158"
+      ]
+     },
+     "execution_count": 172,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data[-1]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Slicing returns another `bytes` object."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 173,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "b'\\xf0\\x82\\xf0\\x82\\xf0\\x83\\xf0\\x83\\xf0\\x83'"
+      ]
+     },
+     "execution_count": 173,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data[::2]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "### Character Encodings"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Luckily, `data` consists of bytes encoded with the [UTF-8](https://en.wikipedia.org/wiki/UTF-8) encoding. That is the most common way of mapping a Unicode character's code point to a sequence of bytes.\n",
+    "\n",
+    "To obtain a `str` object out of a given `bytes` object, we decode it with the `bytes` type's [decode()](https://docs.python.org/3/library/stdtypes.html#bytes.decode) method."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 174,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "cards = data.decode()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 175,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "str"
+      ]
+     },
+     "execution_count": 175,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "type(cards)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "So, `data` consisted of a [full house](https://en.wikipedia.org/wiki/List_of_poker_hands#Full_house) hand in a poker game."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 176,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'🂧🂷���'"
+      ]
+     },
+     "execution_count": 176,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "cards"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "To go the opposite direction and encode a given `str` object, we use the `str` type's [encode()](https://docs.python.org/3/library/stdtypes.html#str.encode) method."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 177,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "place = \"Café Kastanientörtchen\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 178,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "b'Caf\\xc3\\xa9 Kastanient\\xc3\\xb6rtchen'"
+      ]
+     },
+     "execution_count": 178,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "place.encode()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "By default, [encode()](https://docs.python.org/3/library/stdtypes.html#str.encode) and [decode()](https://docs.python.org/3/library/stdtypes.html#bytes.decode) use an `encoding=\"utf-8\"` argument. We may use another encoding like, for example, `\"iso-8859-1\"`, which can deal with ASCII and western European letters."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 179,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "b'Caf\\xe9 Kastanient\\xf6rtchen'"
+      ]
+     },
+     "execution_count": 179,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "place.encode(\"iso-8859-1\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "However, we must use the *same* encoding for the decoding step as for the encoding step. Otherwise, a `UnicodeDecodeError` is raised."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 180,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "ename": "UnicodeDecodeError",
+     "evalue": "'utf-8' codec can't decode byte 0xe9 in position 3: invalid continuation byte",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mUnicodeDecodeError\u001b[0m                        Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-180-1630cf8a20cc>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mplace\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mencode\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"iso-8859-1\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdecode\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;31mUnicodeDecodeError\u001b[0m: 'utf-8' codec can't decode byte 0xe9 in position 3: invalid continuation byte"
+     ]
+    }
+   ],
+   "source": [
+    "place.encode(\"iso-8859-1\").decode()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Not all encodings map all Unicode code points. For example `\"iso-8859-1\"` does not know Czech letters. Below, [encode()](https://docs.python.org/3/library/stdtypes.html#str.encode) raises a `UnicodeEncodeError` because of that."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 181,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "ename": "UnicodeEncodeError",
+     "evalue": "'latin-1' codec can't encode character '\\u0159' in position 12: ordinal not in range(256)",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mUnicodeEncodeError\u001b[0m                        Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-181-8dd3b1bfcf5d>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0;34m\"Dobrý den, přátelé!\"\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mencode\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"iso-8859-1\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;31mUnicodeEncodeError\u001b[0m: 'latin-1' codec can't encode character '\\u0159' in position 12: ordinal not in range(256)"
+     ]
+    }
+   ],
+   "source": [
+    "\"Dobrý den, přátelé!\".encode(\"iso-8859-1\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "### Reading Files (continued)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "The [open()](https://docs.python.org/3/library/functions.html#open) function takes an optional `encoding` argument as well."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 182,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "ename": "UnicodeDecodeError",
+     "evalue": "'utf-8' codec can't decode byte 0xe4 in position 9: invalid continuation byte",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mUnicodeDecodeError\u001b[0m                        Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-182-83a7d1245cc7>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;32mwith\u001b[0m \u001b[0mopen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"umlauts.txt\"\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mfile\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m     \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"\"\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mjoin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfile\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreadlines\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;32m~/.pyenv/versions/anaconda3-2019.10/lib/python3.7/codecs.py\u001b[0m in \u001b[0;36mdecode\u001b[0;34m(self, input, final)\u001b[0m\n\u001b[1;32m    320\u001b[0m         \u001b[0;31m# decode input (taking the buffer into account)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    321\u001b[0m         \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbuffer\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0minput\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 322\u001b[0;31m         \u001b[0;34m(\u001b[0m\u001b[0mresult\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mconsumed\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_buffer_decode\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0merrors\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfinal\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    323\u001b[0m         \u001b[0;31m# keep undecoded input until the next call\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    324\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbuffer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mconsumed\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;31mUnicodeDecodeError\u001b[0m: 'utf-8' codec can't decode byte 0xe4 in position 9: invalid continuation byte"
+     ]
+    }
+   ],
+   "source": [
+    "with open(\"umlauts.txt\") as file:\n",
+    "    print(\"\".join(file.readlines()))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 183,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Lerchen-Lärchen-Ähnlichkeiten\n",
+      "fehlen. Dieses abzustreiten\n",
+      "mag im Klang der Worte liegen.\n",
+      "Merke, eine Lerch' kann fliegen,\n",
+      "Lärchen nicht, was kaum verwundert,\n",
+      "denn nicht eine unter hundert\n",
+      "ist geflĂĽgelt. Auch im Singen\n",
+      "sind die Bäume zu bezwingen.\n",
+      "Die Bätrachtung sollte reichen,\n",
+      "Rächtschreibfählern auszuweichen.\n",
+      "Leicht gälingt's, zu unterscheiden,\n",
+      "wär ist wär nun von dän beiden.\n"
+     ]
+    }
+   ],
+   "source": [
+    "with open(\"umlauts.txt\", encoding=\"iso-8859-1\") as file:\n",
+    "    print(\"\".join(file.readlines()))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "#### Best Practice: Use UTF-8 explicitly"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "A best practice is to *always* specify the `encoding`, especially on computers running on Windows (cf., the talk by �ukasz Langa in the \"*Further Resources*\" section below).\n",
+    "\n",
+    "Below is the first example involving [open()](https://docs.python.org/3/library/functions.html#open) one last time: It shows how *all* the contents of a text file should be read into one `str` object."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 184,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "with open(\"lorem_ipsum.txt\", encoding=\"utf-8\") as file:\n",
+    "    content = \"\".join(file.readlines())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 185,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
     }
    },
    "outputs": [
@@ -3435,7 +5927,294 @@
    "source": [
     "Textual data is modeled with the **immutable** `str` type.\n",
     "\n",
-    "The `str` type supports *four* orthogonal **abstract concepts** that together constitute the idea of a **sequence**: Every `str` object is an iterable container of a finite number of ordered characters."
+    "The `str` type supports *four* orthogonal **abstract concepts** that together constitute the idea of a **sequence**: Every `str` object is an *iterable container* of a *finite* number of *ordered* characters.\n",
+    "\n",
+    "A single **character** in a `str` object follows the idea of a **Unicode** character. It is mapped to a *unique* **code point** that is encoded into **bytes** with a dedicated character encoding, for example, **UTF-8**."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "## Further Resources"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "We refer to the official [Unicode HOWTO](https://docs.python.org/3/howto/unicode.html) in the Python documentation. Furthermore, the [unicodedata](https://docs.python.org/3/library/unicodedata.html) module in the [standard library](https://docs.python.org/3/library/index.html) provides a lot of utility functions around the Unicode standard.\n",
+    "\n",
+    "Next is a brief summary video by the YouTube channel [Computerphile](https://www.youtube.com/channel/UC9-y-6csu5WGm29I7JiwpnA) titled \"*Characters, Symbols and the Unicode Miracle*\"."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 186,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/jpeg": 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\n",
+      "text/html": [
+       "\n",
+       "        <iframe\n",
+       "            width=\"60%\"\n",
+       "            height=\"300\"\n",
+       "            src=\"https://www.youtube.com/embed/MijmeoH9LT4\"\n",
+       "            frameborder=\"0\"\n",
+       "            allowfullscreen\n",
+       "        ></iframe>\n",
+       "        "
+      ],
+      "text/plain": [
+       "<IPython.lib.display.YouTubeVideo at 0x7fbfb40ac810>"
+      ]
+     },
+     "execution_count": 186,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from IPython.display import YouTubeVideo\n",
+    "YouTubeVideo(\"MijmeoH9LT4\", width=\"60%\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "In his [PyCon Australia 2018](https://2018.pycon-au.org/) talk titled \"*Unicode and Python: The absolute minimum you need to know*\" [Raphaël Merx](https://www.linkedin.com/in/raphaelmerx/) explains some caveats and best practices regarding Unicode."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 187,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/jpeg": 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+      "text/html": [
+       "\n",
+       "        <iframe\n",
+       "            width=\"60%\"\n",
+       "            height=\"300\"\n",
+       "            src=\"https://www.youtube.com/embed/oXVmZGN6plY\"\n",
+       "            frameborder=\"0\"\n",
+       "            allowfullscreen\n",
+       "        ></iframe>\n",
+       "        "
+      ],
+      "text/plain": [
+       "<IPython.lib.display.YouTubeVideo at 0x7fbfb4149f90>"
+      ]
+     },
+     "execution_count": 187,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "YouTubeVideo(\"oXVmZGN6plY\", width=\"60%\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "In a similar talk at [PyCon 2017](https://us.pycon.org/2017/) titled \"*Unicode what is the big deal*\" [�ukasz Langa](https://www.linkedin.com/in/llanga/) provides further lessons learned regarding Unicode."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 188,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
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\n",
+      "text/html": [
+       "\n",
+       "        <iframe\n",
+       "            width=\"60%\"\n",
+       "            height=\"300\"\n",
+       "            src=\"https://www.youtube.com/embed/7m5JA3XaZ4k\"\n",
+       "            frameborder=\"0\"\n",
+       "            allowfullscreen\n",
+       "        ></iframe>\n",
+       "        "
+      ],
+      "text/plain": [
+       "<IPython.lib.display.YouTubeVideo at 0x7fbfb7a602d0>"
+      ]
+     },
+     "execution_count": 188,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "YouTubeVideo(\"7m5JA3XaZ4k\", width=\"60%\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "In a \"classic\" talk from PyCon 2012 titled \"*Pragmatic Unicode, or, How do I stop the pain?*\" [Ned Batchelder](https://nedbatchelder.com/) explains among others the concept of a \"Unicode Sandwich.\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 189,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/jpeg": 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\n",
+      "text/html": [
+       "\n",
+       "        <iframe\n",
+       "            width=\"60%\"\n",
+       "            height=\"300\"\n",
+       "            src=\"https://www.youtube.com/embed/sgHbC6udIqc\"\n",
+       "            frameborder=\"0\"\n",
+       "            allowfullscreen\n",
+       "        ></iframe>\n",
+       "        "
+      ],
+      "text/plain": [
+       "<IPython.lib.display.YouTubeVideo at 0x7fbfb403b710>"
+      ]
+     },
+     "execution_count": 189,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "YouTubeVideo(\"sgHbC6udIqc\", width=\"60%\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Lastly, in his entertaining talk at [PyCon.DE 2019](https://de.pycon.org/) titled \"*Your Name is Invalid!*\" [Miroslav Ĺ edivĂ˝](https://www.linkedin.com/in/%C5%A1ediv%C3%BD/) shows how hard it actually is to write software that can process any name a human can possibly have. Miroslav also gave a lightning talk where he shows how he uses only one keyboard for the 12 (!!!) languages he speaks."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 190,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/jpeg": 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+      "text/html": [
+       "\n",
+       "        <iframe\n",
+       "            width=\"60%\"\n",
+       "            height=\"300\"\n",
+       "            src=\"https://www.youtube.com/embed/pBuS7EUPnQA\"\n",
+       "            frameborder=\"0\"\n",
+       "            allowfullscreen\n",
+       "        ></iframe>\n",
+       "        "
+      ],
+      "text/plain": [
+       "<IPython.lib.display.YouTubeVideo at 0x7fbfb409be10>"
+      ]
+     },
+     "execution_count": 190,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "YouTubeVideo(\"pBuS7EUPnQA\", width=\"60%\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 191,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/jpeg": 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+      "text/html": [
+       "\n",
+       "        <iframe\n",
+       "            width=\"60%\"\n",
+       "            height=\"300\"\n",
+       "            src=\"https://www.youtube.com/embed/-4QjII981sM\"\n",
+       "            frameborder=\"0\"\n",
+       "            allowfullscreen\n",
+       "        ></iframe>\n",
+       "        "
+      ],
+      "text/plain": [
+       "<IPython.lib.display.YouTubeVideo at 0x7fbfb4049950>"
+      ]
+     },
+     "execution_count": 191,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "YouTubeVideo(\"-4QjII981sM\", width=\"60%\")"
    ]
   }
  ],
diff --git a/full_house.bin b/full_house.bin
new file mode 100644
index 0000000..4ad5e35
--- /dev/null
+++ b/full_house.bin
@@ -0,0 +1 @@
+🂧🂷���
\ No newline at end of file
diff --git a/umlauts.txt b/umlauts.txt
new file mode 100644
index 0000000..9c0f911
--- /dev/null
+++ b/umlauts.txt
@@ -0,0 +1,12 @@
+Lerchen-Lärchen-Ähnlichkeiten
+fehlen. Dieses abzustreiten
+mag im Klang der Worte liegen.
+Merke, eine Lerch' kann fliegen,
+Lärchen nicht, was kaum verwundert,
+denn nicht eine unter hundert
+ist geflügelt. Auch im Singen
+sind die Bäume zu bezwingen.
+Die Bätrachtung sollte reichen,
+Rächtschreibfählern auszuweichen.
+Leicht gälingt's, zu unterscheiden,
+wär ist wär nun von dän beiden.
\ No newline at end of file

From c97c6acfd216c58c20839ed899c5679751c1c3b2 Mon Sep 17 00:00:00 2001
From: Alexander Hess <alexander@webartifex.biz>
Date: Thu, 26 Mar 2020 02:51:27 +0100
Subject: [PATCH 081/242] Add exercises for chapter 6

Add palindromes exercises
---
 06_text_02_exercises.ipynb | 976 +++++++++++++++++++++++++++++++++++++
 README.md                  |   3 +-
 2 files changed, 978 insertions(+), 1 deletion(-)
 create mode 100644 06_text_02_exercises.ipynb

diff --git a/06_text_02_exercises.ipynb b/06_text_02_exercises.ipynb
new file mode 100644
index 0000000..d8fe96b
--- /dev/null
+++ b/06_text_02_exercises.ipynb
@@ -0,0 +1,976 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n",
+    "# Chapter 6: Text & Bytes"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Coding Exercises"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Read [Chapter 6](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/06_text_00_lecture.ipynb) of the book. Then, work through the exercises below. The `...` indicate where you need to fill in your answers. You should not need to create any additional code cells."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Detecting Palindromes"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "[Palindromes](https://en.wikipedia.org/wiki/Palindrome) are sequences of characters that read the same backward as forward. Examples are first names like \"Hannah\" or \"Otto,\" words like \"radar\" or \"level,\" or sentences like \"Was it a car or a cat I saw?\"\n",
+    "\n",
+    "In this exercise, you implement various functions that check if the given arguments are palindromes or not. We start with an iterative implementation and end with a recursive one.\n",
+    "\n",
+    "Conceptually, the first function, `unpythonic_palindrome()`, is similar to the \"*Is the square of a number in `[7, 11, 8, 5, 3, 12, 2, 6, 9, 10, 1, 4]` greater than `100`?*\" example in [Chapter 4](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/04_iteration_00_lecture.ipynb#Example:-Is-the-square-of-a-number-in-[7,-11,-8,-5,-3,-12,-2,-6,-9,-10,-1,-4]-greater-than-100?): It assumes that the `text` argument is a palindrome (i.e., it initializes `is_palindrom` to `True`) and then checks in a `for`-loop if a pair of corresponding characters, `forward` and `backward`, contradicts that.\n",
+    "\n",
+    "**Q1**: How many iterations are needed in the `for`-loop? Take into account that `text` may contain an even or odd number of characters! Inside `unpythonic_palindrome()` below, write an expression whose result is assigned to `chars_to_check`!"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " < your answer > "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q2**: `forward_index` is the index going from left to right. How can we calculate `backward_index`, the index going from right to left, for a given `forward_index`? Write an expression whose result is assigned to `backward_index`! Then, use the indexing operator `[]` to obtain the two characters, `forward` and `backward`, from `text`."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " < your answer >"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q3**: Finish `unpythonic_palindrome()` below! Add code that adjusts `text` such that the function is case insensitive if the `ignore_case` argument is `True`! Make sure that the function returns once the first pair of corresponding characters does not match!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def unpythonic_palindrome(text, *, ignore_case=True):\n",
+    "    \"\"\"Check if a text is a palindrome or not.\n",
+    "\n",
+    "    Args:\n",
+    "        text (str): Text to be checked; must be an individual word\n",
+    "        ignore_case (bool): If the check is case insensitive; defaults to True\n",
+    "\n",
+    "    Returns:\n",
+    "        is_palindrome (bool)\n",
+    "    \"\"\"\n",
+    "    is_palindrome = ...  # Q3\n",
+    "    if ignore_case:\n",
+    "        ...  # Q3\n",
+    "    chars_to_check = # Q1\n",
+    "\n",
+    "    for forward_index in range(chars_to_check):\n",
+    "        backward_index = ... # Q2\n",
+    "        forward = ...  # Q2\n",
+    "        backward = ...  # Q2\n",
+    "\n",
+    "        print(forward, \"and\", backward)  # added for didactical purposes\n",
+    "\n",
+    "        if ...:  # Q3\n",
+    "            is_palindrome = ...  # Q3\n",
+    "            ...  # Q3\n",
+    "\n",
+    "    return is_palindrome"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q4**: Ensure that `unpythonic_palindrome()` works for the provided test cases (i.e., the `assert` statements do *not* raise an `AssertionError`)! Also, for each of the test cases, provide a brief explanation of what makes them *unique*!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "assert unpythonic_palindrome(\"noon\") is True"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " < your explanation 1 >"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "assert unpythonic_palindrome(\"Hannah\") is True"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " < your explanation 2 >"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "assert unpythonic_palindrome(\"Hannah\", ignore_case=False) is False"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " < your explanation 3 >"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "assert unpythonic_palindrome(\"radar\") is True"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " < your explanation 4 >"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "assert unpythonic_palindrome(\"Hanna\") is False"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " < your explanation 5 >"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "assert unpythonic_palindrome(\"Warsaw\") is False"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " < your explanation 6 >"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "`unpythonic_palindrome()` is considered *not* Pythonic as it uses index variables to implement the looping logic. Instead, we should simply loop over an *iterable* object to work with its elements one by one.\n",
+    "\n",
+    "**Q5**: Copy your solutions to the previous questions into `almost_pythonic_palindrome()` below!\n",
+    "\n",
+    "**Q6**: The [reversed()](https://docs.python.org/3/library/functions.html#reversed) and [zip()](https://docs.python.org/3/library/functions.html#zip) built-ins allow us to loop over the same `text` argument *in parallel* in both forward *and* backward order. Finish the `for` statement's header line to do just that!\n",
+    "\n",
+    "Hint: You may need to slice the `text` argument."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def almost_pythonic_palindrome(text, *, ignore_case=True):\n",
+    "    \"\"\"Check if a text is a palindrome or not.\n",
+    "\n",
+    "    Args:\n",
+    "        text (str): Text to be checked; must be an individual word\n",
+    "        ignore_case (bool): If the check is case insensitive; defaults to True\n",
+    "\n",
+    "    Returns:\n",
+    "        is_palindrome (bool)\n",
+    "    \"\"\"\n",
+    "    is_palindrome = ...  # Q5\n",
+    "    if ignore_case:\n",
+    "        ...  # Q5\n",
+    "    chars_to_check = # Q5\n",
+    "\n",
+    "    for ... in ...:  #Q6\n",
+    "\n",
+    "        print(forward, \"and\", backward)  # added for didactical purposes\n",
+    "\n",
+    "        if ...:  # Q5\n",
+    "            is_palindrome = ...  # Q5\n",
+    "            ...  # Q5\n",
+    "\n",
+    "    return is_palindrome"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q7**: Verify that the test cases work as before!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "assert almost_pythonic_palindrome(\"noon\") is True"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "assert almost_pythonic_palindrome(\"Hannah\") is True"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "assert almost_pythonic_palindrome(\"Hannah\", ignore_case=False) is False"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "assert almost_pythonic_palindrome(\"radar\") is True"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "assert almost_pythonic_palindrome(\"Hanna\") is False"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "assert almost_pythonic_palindrome(\"Warsaw\") is False"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q8**: `almost_pythonic_palindrome()` above may be made more Pythonic by removing the variable `is_palindrome` with the *early exit* pattern. Make the corresponding changes!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def pythonic_palindrome(text, *, ignore_case=True):\n",
+    "    \"\"\"Check if a text is a palindrome or not.\n",
+    "\n",
+    "    Args:\n",
+    "        text (str): Text to be checked; must be an individual word\n",
+    "        ignore_case (bool): If the check is case insensitive; defaults to True\n",
+    "\n",
+    "    Returns:\n",
+    "        is_palindrome (bool)\n",
+    "    \"\"\"\n",
+    "    if ignore_case:\n",
+    "        ...\n",
+    "    chars_to_check = ...\n",
+    "\n",
+    "    for ... in ...:\n",
+    "\n",
+    "        print(forward, \"and\", backward)  # added for didactical purposes\n",
+    "\n",
+    "        if ...:\n",
+    "            ...  # Q8\n",
+    "\n",
+    "    ...  # Q8"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q9**: Verify that the test cases still work!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "assert pythonic_palindrome(\"noon\") is True"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": [
+    "assert pythonic_palindrome(\"Hannah\") is True"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "assert pythonic_palindrome(\"Hannah\", ignore_case=False) is False"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "assert pythonic_palindrome(\"radar\") is True"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "assert pythonic_palindrome(\"Hanna\") is False"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "assert pythonic_palindrome(\"Warsaw\") is False"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q10**: `pythonic_palindrome()` is *not* able to check numeric palindromes. In addition to the string method that implements the case insensitivity and that essentially causes the `AttributeError`, what *abstract behaviors* are numeric data types, such as the `int` type in the example below, missing that would also cause runtime errors? "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "pythonic_palindrome(12321)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " < your answer >"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q11**: Copy your code from `pythonic_palindrome()` above into `palindrome_ducks()` below and make the latter conform to *duck typing*!\n",
+    "\n",
+    "Hints: You may want to use the [str()](https://docs.python.org/3/library/functions.html#func-str) built-in. You only need to add *one* short line of code."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def palindrome_ducks(text, *, ignore_case=True):\n",
+    "    \"\"\"Check if a text is a palindrome or not.\n",
+    "\n",
+    "    Args:\n",
+    "        text (str): Text to be checked; must be an individual word\n",
+    "        ignore_case (bool): If the check is case insensitive; defaults to True\n",
+    "\n",
+    "    Returns:\n",
+    "        is_palindrome (bool)\n",
+    "    \"\"\"\n",
+    "    ...  # Q11\n",
+    "    if ignore_case:\n",
+    "        text = ...\n",
+    "    chars_to_check = ...\n",
+    "\n",
+    "    for ... in ...:\n",
+    "        if ...:\n",
+    "            ...\n",
+    "\n",
+    "    ..."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q12**: Verify that the two new test cases work as well!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "assert palindrome_ducks(12321) is True"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "assert palindrome_ducks(12345) is False"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "`palindrome_ducks()` can *not* process palindromes that consist of more than one word."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "palindrome_ducks(\"Never odd or even.\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "palindrome_ducks(\"Eva, can I stab bats in a cave?\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "palindrome_ducks(\"A man, a plan, a canal - Panama.\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "palindrome_ducks(\"A Santa lived as a devil at NASA!\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "palindrome_ducks(\"\"\"\n",
+    "    Dennis, Nell, Edna, Leon, Nedra, Anita, Rolf, Nora, Alice, Carol, Leo, Jane,\n",
+    "    Reed, Dena, Dale, Basil, Rae, Penny, Lana, Dave, Denny, Lena, Ida, Bernadette,\n",
+    "    Ben, Ray, Lila, Nina, Jo, Ira, Mara, Sara, Mario, Jan, Ina, Lily, Arne, Bette,\n",
+    "    Dan, Reba, Diane, Lynn, Ed, Eva, Dana, Lynne, Pearl, Isabel, Ada, Ned, Dee,\n",
+    "    Rena, Joel, Lora, Cecil, Aaron, Flora, Tina, Arden, Noel, and Ellen sinned.\n",
+    "\"\"\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q13**: Implement the final iterative version `is_a_palindrome()` below. Copy your solution from `palindrome_ducks()` above and add code that removes the \"special\" characters (and symbols) from the longer example palindromes above so that they are effectively ignored! Note that this processing should only be done if the `ignore_symbols` argument is set to `True`.\n",
+    "\n",
+    "Hints: Use the [replace()](https://docs.python.org/3/library/stdtypes.html#str.replace) method on the `str` type to achieve that. You may want to do so within another `for`-loop."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def is_a_palindrome(text, *, ignore_case=True, ignore_symbols=True):\n",
+    "    \"\"\"Check if a text is a palindrome or not.\n",
+    "\n",
+    "    Args:\n",
+    "        text (str): Text to be checked; may be multiple words\n",
+    "        ignore_case (bool): If the check is case insensitive; defaults to True\n",
+    "        ignore_symbols (bool): If special characters like \".\" or \"?\" and others\n",
+    "            are ignored; defaults to True\n",
+    "\n",
+    "    Returns:\n",
+    "        is_palindrome (bool)\n",
+    "    \"\"\"\n",
+    "    ...\n",
+    "    if ignore_case:\n",
+    "        ...\n",
+    "    if ignore_symbols:\n",
+    "        for ... in ...:  # Q13\n",
+    "            ...  # Q13\n",
+    "    chars_to_check = ...\n",
+    "\n",
+    "    for ... in ...:\n",
+    "        if ...:\n",
+    "            ...\n",
+    "\n",
+    "    ..."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q14**: Verify that all test cases below work!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "assert is_a_palindrome(\"noon\") is True"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": [
+    "assert is_a_palindrome(\"Hannah\") is True"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "assert is_a_palindrome(\"Hannah\", ignore_case=False) is False"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "assert is_a_palindrome(\"radar\") is True"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "assert is_a_palindrome(\"Hanna\") is False"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "assert is_a_palindrome(\"Warsaw\") is False"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "assert is_a_palindrome(12321) is True"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "assert is_a_palindrome(12345) is False"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "assert is_a_palindrome(\"Never odd or even.\") is True"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "assert is_a_palindrome(\"Never odd or even.\", ignore_symbols=False) is False"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "assert is_a_palindrome(\"Eva, can I stab bats in a cave?\") is True"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "assert is_a_palindrome(\"A man, a plan, a canal - Panama.\") is True"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "assert is_a_palindrome(\"A Santa lived as a devil at NASA!\") is True"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "assert is_a_palindrome(\"\"\"\n",
+    "    Dennis, Nell, Edna, Leon, Nedra, Anita, Rolf, Nora, Alice, Carol, Leo, Jane,\n",
+    "    Reed, Dena, Dale, Basil, Rae, Penny, Lana, Dave, Denny, Lena, Ida, Bernadette,\n",
+    "    Ben, Ray, Lila, Nina, Jo, Ira, Mara, Sara, Mario, Jan, Ina, Lily, Arne, Bette,\n",
+    "    Dan, Reba, Diane, Lynn, Ed, Eva, Dana, Lynne, Pearl, Isabel, Ada, Ned, Dee,\n",
+    "    Rena, Joel, Lora, Cecil, Aaron, Flora, Tina, Arden, Noel, and Ellen sinned.\n",
+    "\"\"\") is True"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now, let's look at a *recursive* formulation in `recursive_palindrome()` below.\n",
+    "\n",
+    "**Q15**: Copy the code from `is_a_palindrome()` that implements the duck typing, the case insensitivity, and the removal of special characters!\n",
+    "\n",
+    "The recursion becomes apparent if we remove the *first* and the *last* character from a given `text`: `text` can only be a palindrome if the two removed characters are the same *and* the remaining substring is a palindrome itself! So, the word `\"noon\"` has only *one* recursive call while `\"radar\"` has *two*.\n",
+    "\n",
+    "Further, `recursive_palindrome()` has *two* base cases of which only *one* is reached for a given `text`: First, if `recursive_palindrome()` is called with either an empty `\"\"` or a `text` argument with `len(text) == 1`, and, second, if the two removed characters are *not* the same.\n",
+    "\n",
+    "**Q16**: Implement the two base cases in `recursive_palindrome()`! Use the *early exit* pattern!\n",
+    "\n",
+    "**Q17**: Add the recursive call to `recursive_palindrome()` with a substring of `text`! Pass in the `ignore_case` and `ignore_symbols` arguments as `False`! This avoids unnecessary computations in the recursive calls. Why is that the case?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " < your answer >"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def recursive_palindrome(text, *, ignore_case=True, ignore_symbols=True):\n",
+    "    \"\"\"Check if a text is a palindrome or not.\n",
+    "\n",
+    "    Args:\n",
+    "        text (str): Text to be checked; may be multiple words\n",
+    "        ignore_case (bool): If the check is case insensitive; defaults to True\n",
+    "        ignore_symbols (bool): If special characters like \".\" or \"?\" and others\n",
+    "            are ignored; defaults to True\n",
+    "\n",
+    "    Returns:\n",
+    "        is_palindrome (bool)\n",
+    "    \"\"\"\n",
+    "    ...\n",
+    "    if ignore_case:\n",
+    "        ...\n",
+    "    if ignore_symbols:\n",
+    "        for ... in ...:\n",
+    "            ...\n",
+    "\n",
+    "    if ...:  # Q16\n",
+    "        ...  # Q16\n",
+    "    elif ...:  # Q16\n",
+    "        ...  # Q16\n",
+    "\n",
+    "    return ...  # Q17"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q18**: Lastly, verify that `recursive_palindrome()` passes all the test cases below!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "assert recursive_palindrome(\"noon\") is True"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": [
+    "assert recursive_palindrome(\"Hannah\") is True"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "assert recursive_palindrome(\"Hannah\", ignore_case=False) is False"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "assert recursive_palindrome(\"radar\") is True"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "assert recursive_palindrome(\"Hanna\") is False"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "assert recursive_palindrome(\"Warsaw\") is False"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "assert recursive_palindrome(12321) is True"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "assert recursive_palindrome(12345) is False"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "assert recursive_palindrome(\"Never odd or even.\") is True"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "assert recursive_palindrome(\"Never odd or even.\", ignore_symbols=False) is False"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "assert recursive_palindrome(\"Eva, can I stab bats in a cave?\") is True"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "assert recursive_palindrome(\"A man, a plan, a canal - Panama.\") is True"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "assert recursive_palindrome(\"A Santa lived as a devil at NASA!\") is True"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "assert recursive_palindrome(\"\"\"\n",
+    "    Dennis, Nell, Edna, Leon, Nedra, Anita, Rolf, Nora, Alice, Carol, Leo, Jane,\n",
+    "    Reed, Dena, Dale, Basil, Rae, Penny, Lana, Dave, Denny, Lena, Ida, Bernadette,\n",
+    "    Ben, Ray, Lila, Nina, Jo, Ira, Mara, Sara, Mario, Jan, Ina, Lily, Arne, Bette,\n",
+    "    Dan, Reba, Diane, Lynn, Ed, Eva, Dana, Lynne, Pearl, Isabel, Ada, Ned, Dee,\n",
+    "    Rena, Joel, Lora, Cecil, Aaron, Flora, Tina, Arden, Noel, and Ellen sinned.\n",
+    "\"\"\") is True"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.6"
+  },
+  "toc": {
+   "base_numbering": 1,
+   "nav_menu": {},
+   "number_sections": false,
+   "sideBar": true,
+   "skip_h1_title": true,
+   "title_cell": "Table of Contents",
+   "title_sidebar": "Contents",
+   "toc_cell": false,
+   "toc_position": {},
+   "toc_section_display": false,
+   "toc_window_display": false
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/README.md b/README.md
index 3df2247..5c2c6e3 100644
--- a/README.md
+++ b/README.md
@@ -44,7 +44,8 @@ They can be viewed in a plain web browser with the help of
     | [exercises](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/05_numbers_02_exercises.ipynb))
   - *Chapter 6*: Bytes & Text
     ([lecture](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/06_text_00_lecture.ipynb)
-    | [review](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/06_text_01_review.ipynb))
+    | [review](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/06_text_01_review.ipynb)
+    | [exercises](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/06_text_02_exercises.ipynb)))
   - *Chapter 7*: Sequential Data
     ([lecture](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/07_sequences_00_lecture.ipynb)
     | [review](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/07_sequences_01_review.ipynb)

From 3169b196b14ecb96861edfa56e8ecdab0d3229a7 Mon Sep 17 00:00:00 2001
From: Alexander Hess <alexander@webartifex.biz>
Date: Thu, 26 Mar 2020 02:52:16 +0100
Subject: [PATCH 082/242] Bump version number

---
 pyproject.toml | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/pyproject.toml b/pyproject.toml
index 630bac7..63ec6ae 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -1,6 +1,6 @@
 [tool.poetry]
 name = "intro-to-python"
-version = "0.6.5"
+version = "0.6.7"
 description = "An introduction to Python and programming for wanna-be data scientists"
 authors = ["Alexander Hess <alexander@webartifex.biz>"]
 license = "MIT"

From 5d7b2c47b49be0581f0c2d1f16308497dd2591a9 Mon Sep 17 00:00:00 2001
From: Alexander Hess <alexander@webartifex.biz>
Date: Mon, 30 Mar 2020 02:50:49 +0200
Subject: [PATCH 083/242] Add video links to the README.md

Also, split chapter 7 into two chapters.
---
 README.md | 51 ++++++++++++++++++++++++++++++++++-----------------
 1 file changed, 34 insertions(+), 17 deletions(-)

diff --git a/README.md b/README.md
index 5c2c6e3..b46a9ff 100644
--- a/README.md
+++ b/README.md
@@ -1,6 +1,3 @@
-**Important**: The content is being updated and amended throughout the
-spring semester of 2020!
-
 # An Introduction to Python and Programming
 
 The purpose of this repository is to serve as an interactive "book" for a
@@ -17,41 +14,56 @@ They can be viewed in a plain web browser with the help of
 [nbviewer](https://nbviewer.jupyter.org/):
 
 - *Introduction*: Start up
-  ([lecture](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/00_intro_00_lecture.ipynb)
+  ([video](https://www.youtube.com/watch?v=YTU8jaG27Xk)
+  | [lecture](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/00_intro_00_lecture.ipynb)
   | [review](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/00_intro_01_review.ipynb)
   | [exercises](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/00_intro_02_exercises.ipynb))
 - **Part A: Expressing Logic**
   - *Chapter 1*: Elements of a Program
-    ([lecture](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/01_elements_00_lecture.ipynb)
+    ([video](https://www.youtube.com/watch?v=v0lk1Qfaw8Y)
+    | [lecture](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/01_elements_00_lecture.ipynb)
     | [review](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/01_elements_01_review.ipynb)
     | [exercises](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/01_elements_02_exercises.ipynb))
   - *Chapter 2*: Functions & Modularization
-    ([lecture](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/02_functions_00_lecture.ipynb)
+    ([video](https://www.youtube.com/watch?v=j4Xn8QFysmc)
+    | [lecture](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/02_functions_00_lecture.ipynb)
     | [review](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/02_functions_01_review.ipynb)
     | [exercises](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/02_functions_02_exercises.ipynb))
   - *Chapter 3*: Conditionals & Exceptions
-    ([lecture](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/03_conditionals_00_lecture.ipynb)
+    ([video](https://www.youtube.com/watch?v=aDbblINzuGQ)
+    | [lecture](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/03_conditionals_00_lecture.ipynb)
     | [review](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/03_conditionals_01_review.ipynb)
     | [exercises](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/03_conditionals_02_exercises.ipynb))
   - *Chapter 4*: Recursion & Looping
-    ([lecture](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/04_iteration_00_lecture.ipynb)
+    ([video](https://www.youtube.com/watch?v=jT6hr4vOJks)
+    | [lecture](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/04_iteration_00_lecture.ipynb)
     | [review](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/04_iteration_01_review.ipynb)
-    | [exercises](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/04_iteration_02_exercises.ipynb))
+    | [exercises 1](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/04_iteration_02_exercises.ipynb)
+    | [exercises 2](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/04_iteration_03_exercises.ipynb))
 - **Part B: Managing Data and Memory**
-  - *Chapter 5*: Bits & Numbers
-    ([lecture](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/05_numbers_00_lecture.ipynb)
+  - *Chapter 5*: Numbers & Bits
+    ([video](https://www.youtube.com/watch?v=nB00WGCnVjg)
+    | [lecture](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/05_numbers_00_lecture.ipynb)
     | [review](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/05_numbers_01_review.ipynb)
     | [exercises](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/05_numbers_02_exercises.ipynb))
-  - *Chapter 6*: Bytes & Text
-    ([lecture](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/06_text_00_lecture.ipynb)
+  - *Chapter 6*: Text & Bytes
+    ([video](https://www.youtube.com/watch?v=3WTRlgN09sM)
+    | [lecture](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/06_text_00_lecture.ipynb)
     | [review](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/06_text_01_review.ipynb)
-    | [exercises](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/06_text_02_exercises.ipynb)))
+    | [exercises](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/06_text_02_exercises.ipynb))
   - *Chapter 7*: Sequential Data
-    ([lecture](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/07_sequences_00_lecture.ipynb)
+    ([video](https://www.youtube.com/watch?v=nx2sCDoeC3I)
+    | [lecture](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/07_sequences_00_lecture.ipynb)
     | [review](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/07_sequences_01_review.ipynb)
     | [exercises](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/07_sequences_02_exercises.ipynb))
-  - *Chapter 8*: Mappings & Sets
-    ([lecture](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/08_mappings_00_lecture.ipynb)
+  - *Chapter 8*: Map, Filter, & Reduce
+    ([video](https://www.youtube.com/watch?v=ePzzq2YBWjY)
+    | [lecture](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/08_mfr_00_lecture.ipynb)
+    | [review](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/08_mfr_01_review.ipynb)
+    | [exercises](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/08_mfr_02_exercises.ipynb))
+  - *Chapter 9*: Mappings & Sets
+    ([video](https://www.youtube.com/watch?v=vbp0svA35TE)
+    | [lecture](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/08_mappings_00_lecture.ipynb)
     | [review](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/08_mappings_01_review.ipynb)
     | [exercises](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/08_mappings_02_exercises.ipynb))
 
@@ -62,11 +74,16 @@ Precise **installation instructions** are either in the [00th notebook](
 https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/00_intro_00_lecture.ipynb)
 or further below.
 
+A playlist with all video presentations of the individiual chapters can be found [here](https://www.youtube.com/playlist?list=PL-2JV1G3J10lQ2xokyQowcRJI5jjNfW7f).
+
 Feedback is encouraged and will be incorporated.
 Open an issue in the [issues tracker](https://github.com/webartifex/intro-to-python/issues)
 or initiate a [pull request](https://help.github.com/en/articles/about-pull-requests)
 if you are familiar with the concept.
 
+A "Show HN" post about this introduction was made on [Hacker News](https://news.ycombinator.com/item?id=22669084)
+and some ideas for improvement were discussed there.
+
 
 ## Prerequisites
 

From 0f5b439256c0a575940f68419f7b17e58cfc808c Mon Sep 17 00:00:00 2001
From: Alexander Hess <alexander@webartifex.biz>
Date: Mon, 30 Mar 2020 02:51:38 +0200
Subject: [PATCH 084/242] Bump version number

---
 pyproject.toml | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/pyproject.toml b/pyproject.toml
index 63ec6ae..d4c3c99 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -1,6 +1,6 @@
 [tool.poetry]
 name = "intro-to-python"
-version = "0.6.7"
+version = "0.6.8"
 description = "An introduction to Python and programming for wanna-be data scientists"
 authors = ["Alexander Hess <alexander@webartifex.biz>"]
 license = "MIT"

From 4d336e46428d177f7f424f21feb753bf65783fb0 Mon Sep 17 00:00:00 2001
From: Alexander Hess <alexander@webartifex.biz>
Date: Mon, 30 Mar 2020 03:35:54 +0200
Subject: [PATCH 085/242] Add video and streamline content

---
 00_intro_00_lecture.ipynb   | 132 ++++++++++++++++++++++++++++++++----
 00_intro_01_review.ipynb    |  37 +++++-----
 00_intro_02_exercises.ipynb |   6 +-
 3 files changed, 138 insertions(+), 37 deletions(-)

diff --git a/00_intro_00_lecture.ipynb b/00_intro_00_lecture.ipynb
index e53b008..a0ea14b 100644
--- a/00_intro_00_lecture.ipynb
+++ b/00_intro_00_lecture.ipynb
@@ -1,5 +1,53 @@
 {
  "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "A **video presentation** of the contents in this chapter is shown below. A playlist with *all* chapters as videos is linked [here](https://www.youtube.com/playlist?list=PL-2JV1G3J10lQ2xokyQowcRJI5jjNfW7f)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/jpeg": 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8E7HRTObSfJPNNyngOEY5jG7sdpHxqELts2JJXbpZHyOwBukc57sDsG5xJx1K6lihSLMxcbkyY00A/YBSxeIbVytYIa1uUTc6lxnTeT0FkWR4ZH/PUv1ut/fjWOXZWmdG9kjDtfG5r2OGMtcwhzT16dCAtr25mkLRReGPa47iCs13RPuvqf6/a/vPWBXfftyTyyTzO3yyvdJI8gAue8lznENAAySewLoUaLCxjWncAFLE1BUqueNCSfUypnwS7vTT9T1RjWG1C+rVpyPY2QQPsOeZ5mseC3mctgDTjpud5iQeXFFg6hpFXU5gzvyO9Np88zY2Rmy3kMswvlEYDS9jSWZwMjtyvHwhdgfU1DTbE7awud7y1rEoeYY7FZ7jsm5bS5jJGPLd+Dt2g4K5cTWq9fT62l1547Tm2Zb1ueHf3vz3xtgiigdI1rpA2JuS7aBlwx58c4sPbzHvZheP6cvHSJm3G67jao/g4zDJ2ZBbI/7naGLazEGY+G0xIUUXtpavbgY6KG1ZhifnfHFPLHG/Iwd7GODXdOnULxIum5odYhfPse5hlpjoiL3aHpU92dteuzfK5r3AZwA2Nhe9znHo1oDT1PzDzrwoHCY3/f8AtCxwaHEWMiekT+49UWV1XWTPUoVOWGigyy0P3ZMvfE5nJLceJgnHacrFLOs4Wt94T6hJE+GvCYAwyxvYbHPdtBh3ABzQMEu7PGGMrXVNMFped9upt9Vvw7azg9tMGC33o/tEOvw+EHyhYJERblWRSTuZ6nBT1anZsv5cERmMj9j37d9aaNvixtLjlz2joPOo2vq11aYqMLDoQR6rdh67qFVtVurSCJ0sZXFo6D8i+oi2LSvq+IiIu2rA+V7Io2l8kj2xxsaMue97g1jWjzuJIH71xlYWuLXAhzSWuB7QQcEH58hTPuPasYNTqQtr1ZDPZjaZpYRJYia4FpEEhd9iyM9QPOVFdb/9VZ/WJv7jlXbWcaxpkWABnjJP+FdfhmNwzawdJLiCI0gA+eq8aIisKki4WPIf+a7+RXNcLHkP/Nd/IrDtCp0/iHVbsIiLyhfohERMoiIiZRERERERERERMplEREREREREREREREREREREREREREREREREWu3uj/uxF+z4P79pVmrM90f92Iv2fB/ftKs16Rsr8pT6Lw32i/mNb9RRERdBcZEWV4S0d2oXqtNrtvfErWF3aWMALpHAechjXkD4wFJWUtJ1Bl+GhVmrTU601utYdZfP37FVI5jZoXNAikfGd4DOgPTzeNWq4ptN2Ug7iSNwJgE/PSdCr2HwD6zMwIFyADMuIEkCARpGpAuBqoKiIrKooiIURERERERERT/uNa5aZeiotmIqzMuvkh2R4e5tKd4Jft39DGw4z5lAG9gWW4S1k6fbjtiMSmNszdhdsB50EsBO4A4wJM9nXCxIVanRy1nuAgEN8yC6f3Cv1sT2mFp0y4ktc+xmzSGRHKQ6w+qKa6Nenm0LWmyzSytik0hsbZJHyNjbzpxiMOJDBhreg+SPiUKWQp6tLFVtVGhnKuOrulJaS8Gs5749js4AzIc5Bz07FnEUu0aI1BafRwJ+QUMFiBReSTYteLc2OA+ZCx6EohCsKmrKuOo6ffraNJp1SxHtpxX7MrHm2+e3HG+WSvOH5gYwTM2tA+8PXrkQXiTTu87lqrku73sTQhxxlzY3uax5x0yWgH96nuqae3UtSrayyxVZSk7ynvPkswxvpSVo4o54ZIXOErpDyfF2tIcZG9cHKg3Fmoi5euWm5DZ7M0rARgiNz3GMEfK27crlYAnML3y+9+qfkdfKN0L6La7QGGwAzns4AvTjdGo+G/Gd8rFoi9ek2mQytkkrxWWtz9hmMgjcSCAXcp7XHB64zjouo4kCQJ5L59gBcATHPh6XXRNA9m3ex7N7BIze1zd7HeS9uR4zDg4I6dF1qZ91ycS2aEojjiEmjadIIohtiiD43uEcbfvWNzgD4gFDFqw9U1KYeRE7lvxtAUKzqYMgb+Ky3B+pspX6luRrnMrzMlc1mN7g09Q3cQM/lK8GoTCSaWQAgSSSPAPaA95cAcefquhfVPsxnz74hazWcaYpbgSfMgD6BfERFNakXCx5D/zXfyK5rhY8h/5rv5FYdoVOn8Q6rdhcZQdpx1ODgbi3Jx0G4Alv5QFyXVbic9ha2R8LjjEkYjL24IPQSsczrjHVp7T2dq8oX6IVVUKGo0RBFBWu12TPgieYBpLL9h0WnatJIbBdM6lLKJm1CbIZC6Q7Q7e1uBIdN0/WSa5szSCR8tsWpYu8hyoDacYIq/ibhCYY4sdDJ453EO7M3WaJXFsesTSOD5Yy1h0x7hJAQ2aMhtXIewuaHN7W5GcL1e9k3pG5/Bp/1NWX1idQPRV20gNJUU4L1LUn6hFXuyzGRlGR12L/AJA12ThmmcggVxzo5CZLflYY9xmLNzGs2+IaNqcE9mSnVfE+V87pbL+83WZGy6pXleyKVk7W3IjV745ZsRNkiaA0P3OcDOPeyb0jc/g0/wCprhHRkdu26nadscWO2t047XAAlrsVPFdgg4PxhY7a8gBZ7O15UXuU9fdAHMs2ROI3ANaNMj3EabYfG6RpD2Cbv4Vmu2v2ZDsYjLl0WWcRiKVwdYfLzGERRM0yMOcItQ5ghsTTPEdV0hobXPic9vLbujcHy7ZbZqPiAdJqlmNpfHGHPGmsBklkbFEwF1TBe+R7GAdpc9oHUhfYqUjy8M1O04xu2SBrdOcWP2tdsfip4rtr2nB64cPjQVbaD0Q0+Z9VHWVNZZZY1kk7a3f00rie852mGTUnSvY/fM2RkHeTmsj2hzmO5uW+LGD6LlW/7z045oJdRvllc2mSOqBvOcwunfYibLBBZgjkJxCx7Q4tjGQMvGe97JvSNz+DT/qa8WryR02tfb1qSqx7i1jrD9Kga5waXlrXS1QHODWudgeZpPmWO0JIgDyH2VnIBxUVj0S9CH4qXrRbBGzdPertmtV20NPgbTsSizhsvfEU8ryCWdLBa4mfD/Roeh3Y56jnQWBt5ThJJJXYymO+9Tmu1214rUojrPjnrxxxsdJhjawccwAtkVqSOKJ08utSRwMcxj5pH6UyJj5SxsTHSOq7Wuc6WIAE5JkZjtC5M2ugNpusTOqhjpDZDtLMAjZnfIZhV2bBtdl2cDBUjVcR/wAqIptB/wCFn0WCoMFhofBq88zCXgOi97HgmN2yQZbVPVrjgjzHoV3PpSNc1p1O0HPJDGlunBzy0Fzg0GplxABPTzBaMq3Zll0WJfp8zQXO1K2AASSWaeAAO0kmp0C5e9k/pG5/Bp/1NYjmszyWURYv3sn9I3P4NP8Aqae9k/pG5/Bp/wBTSOaTyWURYv3sn9I3P4NP+pp72T+kbn8Gn/U0jmk8llEWL97J/SNz+DT/AKmnvZP6Rufwaf8AU0jmk8llEWL97J/SNz+DT/qae9k/pG5/Bp/1NI5pPJZRFi/eyf0jc/g0/wCpp72T+kbn8Gn/AFNI5pPJZRFi/eyf0jc/g0/6mnvZP6Rufwaf9TSOaTyVFe6P+7EX7Pg/v2lWasL3Q1V7NWiDrM8p7wgO57awOOfa6fY4GjH7vOq55Tvwr/8ASL/GvR9lflafReI+0DQdoVr/ANR4rsRdfKd+Ff8A6Rf41wkDm7TzHHx2AgiPBDnAHsYD51flccMB3j5/4Us7l+ox1dYoTzODImzFj3k4awTRSQbnE+S0GUEnzAErPcLaFb0h+qWLsMkEVbT7laOWRpZFZsTgQ1467ndJg45dluQAOuOirxd0tmR7WMfI9zIxiNrnuc1g+JjScNH5FUr4U1HEg2IAPQEm3qQuhg9oNosALSS1xc0gxdwAvYyLNO7QjfbpREVxctFYOpa3PotbSoKIiY6zQi1G298MUptOtSSbIZTI0nksZHtDWkdHnz9VXym9kU9Vq6cZL9ejPRqto2W2RMd1aGR7oJqvKjcJpNkjgY8tOWjzEFUcY1pLM4lsmRE7jEgT/wAwutsx7mip2TofAymQD8QmCYvHPSd0rHd07Toq2pzsrsEcErYbMUY7I22IY5XMA7A0Pc8ADoBhRlZ7j7WI7+oT2IQ5sB5cUAd5XJgiZCwkeYuDN2PNuwsCt2FDhRYH6wJ9N/NVdommcTUNP4cxiNIndy4cl7tF0i1dl5NSCSxJguLY252tHQucexjckDJIGSB51w1bTLFSV0FmGSCVuCWSNLTg9jh5nNOD1GR0Kk1J5h4btPjO11rWIak5HQvgipyWGRnH3vMJOPypxKTLoeizPJMkcmoVWuJy4wMkjfEwk9drNzmgeYOwtIxL+0i2XNl5zlmeG6IjnO5WjgKfYEyc4YH7ssFwbHGYOaZ5RvUOWS4a0aW/ZjqwlrXP3Fz5DtjijjaXySyO+9Y1rSf9B2kLGqX9y4Zk1Rjeskuh6nHCB5TpTGwhrf8AuLWvW/FVDTpOc3WPv/Kq7PotrYhjHaE358vPReXWOGYWVX3KN+O/BBKyK1iCatJA6XIheY5uroXOaWh3TrgY7cRpS/g3A0niBzvI73os+bmvuDk/vy1x/cVEFDDOdL2uM5TE2/tBvEDfw0hTx1NgbTqMGXM2S0TAIc5tpJMGN5N53IiIrSoIvq+L26LpVi7M2vVidNM4EhjcDDWjLnOc4hrGD5TiB1Cw5waJJgKTGOe4NaJJ0A1K8SLKa/w/coGMWoTEJmudE8PiljkDSA7ZLC5zHEEjIzkZGe0LFrDHteMzTI4hZqUn03FrwQRuIg/NZ7jLVorbqRi34r6ZSqSb2hv2WvGWybcE5Zk9CsCiLFOmGNDQpV6zqry92pRS3gaKKGpqmpSQxWJKUdWKtFOwSQie5M6PnPjd0e6NkbiAcjLh8xESUr4JswyVdT02aeKsb0dV9eed2yBs9OZ0gjlf2RtkY943HoCB8wOjGT2XmJjhmE6cplW9mECuJiYdE/3ZHZdbfFETvXfxG5l7SYdTdDBDai1B+nzurxMhZZY+v3zDM+OMBglbtezIAyMZ82IapfxC+GppUGmNsV7ViS87ULD6sgmhhAg72hh5zfEkeQXuO3s6D4lEFjBiGGNJMdP8axy0ss7UM1RPxZW5o/ujfG/SeczeUXCx5D/zXfyK5rhY8h/5rv5FWnaFUafxDqt2Fwm3bXbA0v2naHEhpdjxQ4gEhucdgK5ovKF+iFWp7nt2MFsN4SB8cEszpBHBLNdEtY3S50VYsMFiKtG1xex56O3CQOIHvl4T1AMaGWWPAqwwOhmnncHPbLFJLOZWRhjpDE2SsN0JBaGOcHAvhM7RbziHlaexaoAeC7roWg2g2dkToWPbZs9IuTqUQYHRtj2bu+qpcWNbjkNI6xRkeetwRfbO+UTRwRyCQsggtSujqOc2wNkfMq75o382IOw6LAhZ0cGMa2x0T+Ics9i1YDWuHzNRhpxu2cuxp0pdzJdxbTvVrcuJQeZzXNhfhxOdzgSfOsCOCrbL752W5O93yxyMabUnNhMYHMJc+F8kxnAbE7bLFhkTOr+gbPUUW1nNELJpNJlQHSeE9RidSLpYcV5i5wNmWTbCTWMmQ2rGJ7D+XY8ePvZo5w3tny/fINQ0ua53hJK51V8W+SxHBO7c18tZ0boo52tG9rXvPjYbnaD07FnkWHVSTKCmBZQKxwNJCQaPIY2OWu6OB808cRhqWNGkrwucGP2BkemzNB2uwZyfv3k5kaHM6jbhkFcz25Zp3RNfOyux0rw7lRzxbJozho+ztDXCRzpA0HDFJEWTWcdUFJoUDh4X1Fs0doms6eN8u1j7UznNhdLpjm133RVEloFtSw4ySM3DmRM8cN3rHzcC6k5sQdYhMsTaxfYFmzzJxHBTimqOjlhkjjrONebxiH5Fh+WHfLzLMRSGIcFE0WlV/qHA1iWsYzM2SQxPiPPnme10TtMs1hA97YwHR99SwSF3LGRXY4t3NaBP2joOwfMOwfMF9Ra31C7VTawN0RERQU0RERERERERERERERERERFrt7o/7sRfs+D+/aVZqzPdH/diL9nwf37SrNekbK/KU+i8N9ov5jW/UUXVZ7G/pI/6wu1dVnsb+kj/AKwuguQz4gu1ERFFe7RNNNqXlCerX8UuMtudlaEAEDBkf9917Bk9D8SyPH2iR6dekqRSGVscVZ3MJB3ulrxSvc3aMbC55wPix1PasAexS/uwfdaX9Xo/7KuqrnOGIaJsWutzBb/ldBjKZwb3ZfeD2DNyIfb5BRBfV8XJjSSAASSQAACSSewADqT8ytLnriik2o8D34IpZHCu51dgltVorMMturGQDzLFdji5jcEZxnAOT0BIjK106zKglhB6LfXw1WgQKrSDz+/JSLhrW68dazQvRzSVLD4pg6u6Ns9exDkNliEg2PDmOLHB3mxghcOK9ahsMqVakUkVOiyVsImc188kk7xJPNM5gDQ5zg3xR0AaMfEMAih/DMz59+vKYiY4xZbDjqppdlaIiYExOaJ1ib/6ARerSr81WaOxXkdFNE7dHI3GWnBB6EEEEEgggggkEEFeVFuIBEHRVmuLSHNMEbws5rnFFm3EIHNrwQCTnOhqV4q0ck2NvOlEQHMkx0yVg0RRp020xDRAU61epWdmqEk80QFSLua6ZHc1ajXmAdE+Yukaeoe2GN85Y4edrhHg/MSpLoPEVnW/fKpcLZIH0bVqpFsY1tKes0SV+Q5jQWMDQWEffDt8+a1fFGm4gCQACTOgJItYzoTuV7CbPFZgJdBcS1oiZLQCZMiBdo0OvJVwpfwcdmk8QytOJRBp8AcO3k2boZYb+a5rWghRBZrhXXe8nTtkhFmtahdXtVy8x8yMkOa5kgB5crHgODsHz/HkbMUwvpw0TcGOMOBjziFp2fVbTrS8wCHCeGZpaDa9iQbX4XWWrnfw1MHHpBrELoc+Yy1XiRjc9jSAHYHnGVD1INf16GWrDQp1nVqkUzrLxLNz5rFlzOWJZXhjWt2x+IGtGMEk5z0j6xhmOAcSIlxMcPT181LH1GOcxrTOVoaSJgkTpMG0xpuREWT4Upss36NeQZjnuVoZBnGWSTMY8Z8x2kre9wa0uO5U6VM1Hhg1Jj1XjfUlEbZTFIInHDZSxwjcevRryNpPQ9h8y6Faui69ZvcQWNOnle/T7Ul2gaZJ73igijmbX5MXkRSMMMR3tAPQ/GqqHYq+HrueS14gwDYzZ09L2Ku43CU6TQ6m4kZnNuIu2J0JscwjeiIitLnouFjyH/mu/kVzXCx5D/zXfyKw7QqdP4h1W7CIi8oX6IREREREREREREREREREREREREREREREREREREREREREREREREREREWu3uj/ALsRfs+D+/aVZqzPdH/diL9nwf37SrNekbK/KU+i8N9ov5jW/UUXVZ7G/pI/6wu1dVnsb+kj/rC6C5DPiC7V6GUpTC+wGO5McjInyfeiSRr3MZ85LY3np2Y69oXnVgajq77fDb90VaFsOrwMYytAyuzrUlcXObGMOeT2u7eir16rmZYGpAPKVbweHZWD8xgtaSABrA+Sr9d1y1LM8yTSSSyENBfK90jyGgNaC55JIDQAPiAC6UW+BqqeYxCKQdzdjHavpof5Pftc9fO4SAsH8Yao+u2rO+KRksbiySN7ZI3jtY9jg5jhnzggH9yhWYXsc0bwR8ltw1UUqrXkSAQY6GVO+ApHP4kmEhOJ36s20D2OY6C294fnzb2t/eAq/b2D8imFzjZju+poNPgrXrsckVq5HLM7LZ8d8OgrvOyvJJjq4EkZdjGcqHqthWPDi5wizRFt03tPGBvsr2PrUyxtNjs3vPdNx8WWBeDPuyd0m0oi9ej6dNbnirQN3zTPDI29nU9pJ8zQAST5gCs3q/CLoa81mC5TvMqvYy2Kj5HOrmRxY15EkbeZCXjbvbnqfykb312McGuNz/x/ocSqlLB1ajDUa2QN/QSY3mBcxoLmyjKIi3KspRonBNyxSs6g9phqQVZbEczg1wnfGdoiY3eHAHD/AB8EDZ84UXUs7nQ8TWz5/eK5/eqqJqtRe81Hhx0IiBFo81fxVOkKNJ1MEEgzJmSDHAQOXzKy/Bus+99+rc2l4glDntHlOjcCyUNycbtjnYz58KSVJdN0xt+erebbfZqz1KMDIZ45IWWSGumsvlYGsfHGCA0F24nzDsgiJWwrajpJO4EDeAZg/PSNSmG2g6gzKADBJaTMtJEEiCBuGs3ARERWVQUrPBksel2NSsOYzYagrxRywyue2w/BfMI3O5Q2kYacOzuyBjrFFLeHPuHrv6bR/wC/ZUSVXDOeS8PMw7pbK08+Kv45lMNpOptiWSZMmc7xOg4Dci9Ol3HVp4LDMF8E0U7M9m+J7ZG5+bLQvMiskAiCqTXFpDhqFYcetaTWuz6zWnnfZk74lrae+sWd727THtc6azu5ckLDLIQGjJy3s25NeBEWihhxS3k6C/AaC0cTz4q1isa7EQCABJMNmJdEm5NzA5CLAIiIrCpouFjyH/mu/kVzXCx5D/zXfyKw7QqdP4h1W7CIi8oX6IUW7nfGLNYinkZA+AQSiIh7w8uJbuyNoGApTla78ITPj4Z158b3Me21Ww9jixw+y1wcOacjoSP3r2a9oNiHQaute+moOtiOo5recWwxxSljGMjaPGa9oc0l247iHEjLsru1tlM7UhrsozZQIJvAPpdfI4X2gqjDtc9mcin2jiCBbM4acbbtVcXEPE9ShLUhsue2S9IYq4axzw54dEzDiOjRmZnU/GVmsqi+6jWNqfhizJNOH6i2uJQyTayFxNEukrNx9hlJncS4Z6sZ8SyHHtW0Ne0fT6t6zBu09kHPdI978NFtj5ngECSwY2k7+h3YPmWnu1rmMh0EhxM6e6Tw6c+KsnblRlSrLJaHU2tg3OcA3m2+d3DmrkyipvierZit6PwxBetMhkZLYs2t+LEzZJrUpY6QHPRsUoA7CXNyDjC7e9ZuH9b0ytBdt2KWpExSQWpOaWvLgzeMANbh0kZBABw1wJIWvu4EWfcguAg3Am/ImCQFvO2iHHNTOUOaxzpHuudFo3gEgE+gKt/KjPBfFzNTm1CFsD4jQn5DnOeHCQ75mbmgAbR9i/8Asq/4c0yxxHd1O1Y1C5VjqWTBTiqTcrlbS/a4gggYa1hJGC4l3UYAWH4LM0Wm8W7pSZ43APmYdpdK19oPkaW+TlwJ6fGt7dmsDHtLpeMnH3cxHkbFVX7cquq03NYRTPaXJHvBjSerbi3EK/sr5lVRfty/8FCUSyCXkQnm8x4kz39GCeZndnHTtWO1rU7c1ThfS4rMsA1GCA2rDHuEzmbYm4Emc9jpCR98duemc6GbNc7+rRzmn/5Ek+m5W6u3GsA9wmabHgA6l7sob671dOVhdC4nqXbFytA57paMnKsBzHNDX75I8Ncejhuif1HxKL6DwRe06+01b8sulSwvjtQ27D32WyObIBJX2Q8sODuUd3inyx16KMdxnRmt1vWT3xaPeFp8bd0xIsh0tyDdcGPs7wBuB6eMSUbg6Jp1Hh85WgiBxMQR96ysVNp4kVqNM0suZzmuBM2DZlpGo36biOasjgjX7F9k7rGnzaeYpjExkxkJmZtB5reZDGdvXHQHs7VIcqh+F+JrdTQNZsslkdOL7YIZJHOkMQkETS5u8nBDS4jzZx2r063wpYoaOzWYtV1A6gyOtZlLrBdC/nujDmAOG5wbzB5ZcHbTkeN03VdmN7QjMGy7K0XMmAfIXHFVaG3X9iHBheQwvcZAgS4cgTY2EacVd2V9yqV411S1escLvisSVJL8LDI6FzgGPldX3uDM4ftLnbd3zLvq6fLo3Eun1YL1yxXvQvdMy1NzS4ls48bADSQ6Njg7GR1GSCc6hs33ZLveyuOWP7SZvpusrJ27/wBSG0yWBzGl0j+sAgxrvEq40RFy130RERERERERERERERFrt7o/7sRfs+D+/aVZqzPdH/diL9nwf37SrNekbK/KU+i8N9ov5jW/UUXVZ7G/pI/6wu1dVnsb+kj/AKwuguQz4gu1ZuLWWDSpdP2O3vvx2xJkbA1kD4iwjt3Zdn9ywiKD6YfE7jPop0qzqc5d4I8jqiIpn3J9J063ersuzPLzPiOmIN0c+1m8GWffhrMggs2nIHb1UK9YUaZeZtw+/wDS2YPCuxNVtJpAJMSTA+f7C53KGIucww5wHyj/ADK4LcFXIgwiLPXODtUhrd9y0pmVw1ry87dzGO7HviDuZGz53NAWBUGVWPu0g9DK2VaFSkQKjSJvcEW81Me4991GgfbDVvCH4+b3pNjb8+3euHc3+064T9q947QPyeaZa/Iz/wB2d2P3qNaXelrTRWIHmOaF7ZI3jGWub2dD0I8xB6EEg9qzOscXTWIJa7a1KpHYkZJZ7zgdC6y6M7mCYukd4jXEuDW7QD1wqVfDvc85dHZRPDKSfrbmupg8ZSZSbnJlheQI+LO0AdIIvy0uo6iIuguMvXp2pTVxOIX7BZgfWm8Vjt8Eha57PHaduSxvVuD07V5ERYDQDKkXuIAJsNOSLM6VwtqNuF1itSsTQtzmSOMkOLfKEY7ZSMEYaD16LDKcd0W7LUtaW2B7mChptB9baSAJHM5skoA6b3uPjH77ABVevUeHNYyJM66W6RxH+1dweHpOY+rVnK3KIbEy4njOgBOl7C2qhBC+KVd1usyLW9RZGAG88SYHZumijmf/APd7lFVso1O0ptfxAPqJVfFUDQrPpEzlcRPQwvTBemZFNAyRzYZzE6aMY2yGEudEXefxS5x/evNlCVaur69Ppmq1tHg2DT6/eNaxWMcTo7ffMUL7Us4c3L5H84jPm2jHnzpr1jTMMbJIJ1iwgcDe4AVrCYYV25qryGgtaLTdxJAiRAs4n9iSqqRZbjHT21NQu1mfa4LU0cYySRG2R3LBJ6khu3qsSrLHh7Q4aESqVWkaT3MdqCQfKyLIa1o89MwCwzY6xWjtMac7hFK6RrN4I8Vx5ZOPiIXTpWoTVZmWK8jopo92yRmNzdzXMdjPxtc4fvUq7rFmSaTSJpXl8suhUJJHu8p73vsuc4/OSStD6jxVa20GetvvirVGhTdhqlQk5mkW3QT6zyjzULREVlUUXCx5D/zXfyK5rhY8h/5rv5FYdoVOn8Q6rdhEReUL9EKsdJ7mc8Ok6lpptQufemikbKI3hsYjfG8hzc5JPLP+qzOt8GS2NBh0gTxtkjiqxmYtcWHvdzHEhuc9dn/ypqiuOx9Zzg4m+bNoNYA+gXLZsfCsYWBtizJqfhJJj1JvqoBxZwDPbraO2G0yG1pDYhHI6Mvie5jK4Ltucjx67CAc9pBXpt8HWZtV0vVJbMTnUqrIbDRG5pml2TiSSPrhjS6bIHmwpsq67vetW6NGtJUnfXe+4I3OjIBLDBM7acg9Mtaf3Ldha9es9tFpF8wBIH9Uzz4qtj8LhMLSfiXtJjK4gE3LIDTExIt13rKd0Hgt2oyVbVayad+m7MM4bvaW5Dtj25HY7JB6jxnghwPTwcP8C3HahFqer323Z67S2vHFEIooz42HHAAONzjgNHXBJOAFVuh6txZejM1Sa7PEHmMvYYsB7Q0lvjYOcOb/AKpa4y4n0qZnfcthpdktjtwxvimDcbgHbQSBludjgRkdRldZuzsSG9i2qwkAiP6gDqAYkL51+2sC54xNShVAJBzXyEjRxGbKSNxVk2+59qFe7as6RqYoxXnF9mJ8LZdrnOc5zog4EHxnvI8kt3EA4Xbwf3NjTratUntc6PUmhgka0iWNoEwD3lxw6T7I0/Flp+NSXgDiRmq0YrjG8tzi6OaLO7lzMOHtB87Tlrgfie3sWfXIrY3ENmk8wRANhPum0nUxHFfS4bZeCqZa9MEggke87KA8XgEwJkyICqQdzLV3UX6bJrDDSZgwQNgADnCUSjnPI5mwHc4M3OGdvZhZvXO526xQ0uFlrkXtKZEK9prCWFzBHnLMg43RMcD5iOw5IVgKKd1Hix+jU4rTIGzmSyyDY55jADoppN2Q05P2IDH/AHKVPG4mtUa1kTMiABJIgzoL75WqtsrAYWi99QHLlAJLnGGgyIuSIOkaLwcM8HX23majquo9+TQxmOCKFnJgZkObve1oa15w9/Tb2nOTgY+8OcF2qGr3L0NuM1L0sk1iu6I80udznsa2TOAGyyk5GMjphZLuacUO1ekbb4WwETyRbGvLxhgYd24tHU7vi8yqzuhcW6lBxDNVhuTR1xPSaImkbQ2SCs54GRnqXuP/ALirFGnia9WpRkAhpBECIBFhAjW4hVMVWwOEw9HEgOcC8FrpOaXA3JcQSCBBB3WhTjhzubNi07UNOtzNlZen5wfE1zHROAYY3Dd2ua+MH4j2FYt/c31eaCLTbOtNfpcRYBGyuGzOjiILIyT1AbgYDnvDSG9DtAFrIqY2lXBJkXM3AMGIkSLHoum7YeEc0NymAMtnOEtmcroIkTNioVr3A/OuaNPXkZDBpOxohLXOc6NjotrWuz0w2PGT8a7te4Sks6zp+qNmY1lOMsdEWuL3553Vrh0H20f6KXplaRjKoi+gI8nTP7lWDs2gZ93VzXG51bEekC2iIiKsr6IiIiIiIiIiIiIiIi1290f92Iv2fB/ftKs1Znuj/uxF+z4P79pVmvSNlflKfReG+0X8xrfqKLqs9jf0kf8AWF2rqs9jf0kf9YXQXIZ8QXaiIiiilPcmka3WtPc5wa0TOy5xAA+xSdpPQKLItdan2lNzOII9Qt+Fr9hWZVicrgY4wZXZP5TvznfzKyfBcDJNS06OQAskvVGPaRkOa6xG1zSPiIJH71iF21LD4pI5Y3bZInskjcO1r2ODmuGfOCAUqNJYWjhCxRqBtVrzoCD81YnCVqSbiyYSkvFq1qVaw1xJD65jsN5Ts9sbQyPA83Lb8SrZvYPyKcS8YUmzT6jWpTRapZZMC42GOp1prDCye1Xj5fMMrg6Qhr3EAyu7cYMHVXCMcHFxbHutEW1bM6Ta4HkujtKsxzAxr8xzvdInR2WNQDNiSN08ZRZLQNDt35RDUgkmduaHFjHFkQecB0zwNsTOh6ux2FY1ZbhKzJHdqCOR7A+1WDwx7mhw5zOjtp8YdT0Pxq1WLgwlusb1z8K1jqrW1Jgm8a/OV5+INONS1Zqlwea88sJeBtDjG8s3AEnAOOxeFZ7uifdfU/161/eesCsUHF1NrjqQP2WcWwMrPa3QOIHkVK9T4PNbSzflmhfK63DXZFXnhsNYx8MsjzM6IuAkJazADug3ZzkYiil9X/pux+2q/wDspVEFqwrnnNnMw4jhuCsbQZTb2ZptgFgMTN5PRFMoOI9NnZSk1Gtbks6fDFXZ3vLC2C3DXcXV2WeY0vjIB2FzMlw+LpiGotlWg2pEzbeDBWjDYt9CcsX1BEixka8Pu0r3a9qcl2zPamxzLEr5XAeS3cejG567WjDR8zQvCiLY1oaABoFoe9z3FzjJNyeZQqxLF3S7t2rq9i8IHsbVfepmCd88lioxjAKxa0xujlELBlzht3ElQGpWkme2OKN8sjs7WRtc97sAuOGtGTgAn8gK6VprUBVOpBAOkaHXWeHyVvC4t1AH3Q4Egw6YluhsRpJ8j0Xu1/UXW7Vm04bXWJ5Ztuc7OY8vDAfOGggZ+ZeFEW5rQ0ADQKo95e4udqTJ80WV4i1l13vPdG2PvOjXot2kne2uZCJDnsceYeg+JYpELASHHUKTarmtLQbGJ8tEREUlrRcLHkP/ADXfyK5rhY8h/wCa7+RWHaFTp/EOq3YRF0ajzuTL3vy+fy38jnbuTztp5XN2eNy9+3O3rjOF5SLr9DkwJXax4OcEHHbgg4/LhclSfcYu2a0uuWJu9m1IJJ5r5YJTMJouc/8A5YeSYcNl8rxvJ+dZCpxhxJcqTarUq0GUY+a5kEnNfYlihJEjmlrgJC0teOmzJYcA+fpVdlvbULQ4QIuTAkiY6/S64NDb9N9Jr3MdmOY5WiSA0wTutp52Eq3FVXumPubU/X2/7ewpzwLxCzVKMN1jDGZA5skZO7lyRuLHtDvvm5GQemQ4dB2KDe6Y+5tT9fb/ALews7MY5mNY12odB+abdqtq7KqVGGQWSDyMLh7n3VasGlSMns14Xm7M4NlmjjcWmKAA7XuBxkHr8y8PuhuIaFinWqwWIbFgWmzHkyMl5UbYZWO3uYSGkmRmGntxnzKIdz3uZyaxUdaZcZAGzvh2OhdISWMjdu3CQfhMYx5lLdM7hTRIDZ1AviB8ZkNflvd83MfI4M/hP7l2KowdHGGs+qcwM5QDr1XzGHdtPE7NbhaVAZC0DOXDTjCw/Cmn3W8J3LNaexWkjvvuRurzSQmSCKKGCxudGQSwBsrsfHAFJvc78TT2mXatqxNYljdHYjfPK+aQxvHLkYHSEkMa5jDjszKfjVm1NKrxVm044mtrNi5IiGdvLLS0tOersgnJPU5JPateu5+52i8TCrISG8+Wg9zu18cxArv6dgc8V3/kK006zcdSrti8528Y4fL5q3Vwz9k4nBuzHLHZu/tk7/nPRqkHd94ptx6hBTp2bMHKgDpBWnlidJLYd4rHiJw3kMZGRnP2047evg7sejarWqVTatmek3vSLZJM+WU3hWkM0zi9pO0uE+DvPRwH5PPwhF798VSWTh8MdiS4T16w1S2Op+XqKwI84ypz7pX7k1/2jF/trasMcMPWw+HAEx71t5+uvqqNZjsbhcZjHOOXNDACYhtukG3mFH+4BQ1QmGwyw0aU2aw2Wvvw50vJwHBnL6je6I+UPJ/1indgn5XElyXG7ly0pNucZ2VKrsZ82cdqtP3On3HP65P/AExKse6mAeKJwRkGxp4IPYQa1QEH5lPCVM+0asgWa4WtMOGvPmte0aHZ7Fw+Un3ntNzMEtOk6DkslxRR4rnhk1WeSxBEGmY14bToDXgALtwrRvG0Nb25y/A8bqCpT3A+NLV0z0bkrp3wxCeCZ5zKYw5scjJH9smHPjIccnxnZJ6YsriYf8lc/VbH9p6on3Nf3Wn/AGbN/uaapsrNxeCqlzGjLEZRELpVMM/Z21MOGVXu7SQ7MZn7meUKQd2XujWoLTtN055idGGixOwbpTLIA4QQ9CG4a5uXDxi52Bt2ndgLHCfFkNc3TatlzW810LdQnfaDQNxzHuw8gddgcT5sE9FjK20cWnvjs9/JfK+M238jt+93cvHzYWzKlia42eymymxplskkTKjgMI7bFWvVr1HDK4ta1pjLG+PTreVU/cT7oc9+R1C84PnbGZILGGtMrGY3xyBoAMgByHAdQ12eoy6L93bXr9fVnRV71yvH3tAdkNmeJgcd+XbI3gZPTqsN3NSz/ieDvfHK78umLbjbyeVZxtx97y//AIXf7of7sv8A1SD+T1cpYSkzaIytEOZmjcLrl19o4irsUl7yXNq5c03IAnXfqstqtHizVYzfaZ4IHN5lerDaMDjDjLS2JjgZHEdcv8Z2egwQF6+4TxzcluDTrc77Ec0cjq75nGSVkkbTIW8x3jPYWB5w4nGxuMDKu2sAGMHxNbj/AEC1r7kP/Utb9Nf/ANrbVPD1m4vDVmuY0BrZbA0sf8fuunjcK/Z2NwtRlV7jUflfmMgyWjSw3m260aLZhY7V9cpU9nfdqvWL87BPNHEX4xktDyCQMjJ82VkVQPdev162uTTSMrakJaBgdWkcSaMuwNY44BDXB2HgZz9lf5JLXHjbPwn8TUyX0Jtv5XsPNfUbZ2kcDQFURqBfQTvtc9B10BV9wSte1r2Oa9jgHNe0hzXNcMhzXDo5pHnC5qG9zFkNClS0uS3DNc73daDI37wYZpZJA+N3Y6IbsbvPgnsIUyVatTyPLRcTY8RxV7C1jVpNeRBgSJmDFx5IiItSsLXb3R/3Yi/Z8H9+0qzVme6P+7EX7Pg/v2lWa9I2V+Up9F4b7RfzGt+oouqz2N/SR/1hdq6rPY39JH/WF0FyGfEF2oiIooiIiIiIiIpIeDbjNOn1KeN9eGM1xC2RmHWRO/buZlwLGNBadxGHbxjz4jal/D0jjoeuAuJAl0fAJJA+zWOzPZ2D/RVsU97QC0/1NB6FwFvVXsBTpVHObUBPuPIgwAWsc6/HTiOciyiC7qdh0UkcrMb4pGSNyMjcxwc3I84yAulFYIkQVSaSDIXq1a8+1PNZlxzJ5XyybRhu+Rxc7A8wySvKiI0ACAsucXEuOpXobclEJriR/IdIJTFuPLMrWlgkLezeGkjK86IgAGiwXE6lFL+C+FIrcNmxNargQ0rs7KjJv+cdJBG8sc+Lb4kO4B2c5OGjHXIiCl/ct+3aj+xdT/shVsaXCkS0wV0NlNpvxLW1GyDu+9eiiCIitLnKf9xjVtl6OqKtRxmjuk2nxPdbYBTmfsil5m1jcxgeTnD3DPVQBvYFnOBNYioX4bUzZHRxsstc2INLyZq00DcB7mjAdICevYD29iwYVWnSy13uAsQ31l0/RdCviM+EpsJu1z7cAQyPmHIiIrS56IiIiIiIiLhY8h/5rv5Fc1wseQ/8138isO0KnT+IdVuwiIvKF+iFUPBWhW2za/plmpYij1I2jHd25rta7nNad46OJErXAfMQcLyaJf1zTdNfovvLYmnAsRV7cWX1tth8jy97w0s8V0jiMubkYB24KulF0ztIuJzsBBgxfVoideGoXBbsIMA7Oo5pAc2QGzlccxFxFjcHVRbuWcPSaZpkFWYjnZfLMGnLWvlcXbAR0O1u1pI6Eg46LAe6B0mzcoVY6sEth7bjXubCxz3NYIJ27iG9gy4D94VkIq9PGPbiO3NzM8rq7W2ZTqYP+DBIblDecBV/3BdMsVNMkitQS15DcleGSsLHFhigAcAfNlpH7irARFqxFc1qjqh1Jlb8FhW4Wgyi0yGiJOqKhPdIaLyrla/GMNsx8qQjpieDGxxPynRloH6BXvZeWsc4DJa1xA+MgZA6LXTifUdc4klr1zQfExjvFY2GZkLXuADpp5pejQBkebAJGCT16uwmuFftZAaPik7iF8/7WvY7CdgWkvcRkDRNwR6WMeam/ubtF5VKxecPGtS8uM//AIa+Wkj4syukB/RBSHu1cO2NS0zlVW75obEdhseQ0yBjJI3NaXEDdtlLup67cKT8N6UyjUrVI+ra8LIt2Mby0eO8j43O3OPzuKyCqVsc44o4husyJ4DT5Lo4XZLG7OGDfplgxxNyR56Kle4lJrdOwzTpqEsNB8s8ss01WZjmP5B2tZMSGbS+NnaD2nr1WK7ovDOozcRzWYqVmSA2KLhMyJ7oy1kFVryHAYIBa4H80q/0W8bXc2u6s1gBLYIvvMz1VR3s2x2EbhX1HENcHA2kQIDei8PEEbn1LTGguc6tO1rQMlznROAAHnJKpvuBcOX6epzS2qdivG6hLGHyxOY0vNio4NBcPKw1xx/2lXkiqUMa6lSfSAEO1XQxey2YjE0sQ4kGnMAaGeKpzux9zazZsu1HTmiSSQN74rhwZIXsAaJoS4hpJa0ZbkHLcjcXHEbn13i+aA0XVr+HDlOl7wkZM5hy0tdYMYaBjpvGCfj6krYdFbo7Xc1jWVGNfl0LhcLnYn2bY+q+rRqvp5/iDDY/d/Xqqs7i/c7m057r14NbZcwxwwNcH8hjsb3yOblplOA3DSQBnqS7DYt3cuGdQt6q6WtSszx97Qt3xRPe3c0PyMgYyMhX4ihT2tWbiDiDBJERuA5LbW9nMM/BDBtJDQZkak85XCAYa3PTxR/JUD3MOGNRg4gr2JqVmKBstwulfE9rAH1rLWEuIwAS5o/eFsCi0YXGuoMqMAHviDy109Vcx+ymYupRqOJHZuzCN9wb+iKjmV7Gly61TuaLZ1OHUpXyxz1o3v5wc97o2vlYxxYcuDunjMeCQDkFXiijhcV2MiJBjeRoZEEXU9obPGKykOylswYBEOEEEOkGypfuNaHe07UD74UbYM1NsVawSZoq0e8yugkLMtj3bR8W0txgb+l0IixjMUcTU7Rwg8tPms7M2e3A0exYSRJMmJvxiJ+wiIiqroLXb3R/3Yi/Z8H9+0qzVme6P+7EX7Pg/v2lWa9I2V+Up9F4b7RfzGt+oouqz2N/SR/1hdq6rPY39JH/AFhdBchnxBdqIiKKIiIiIiIiL21dTmjr2KrHAQ2nQOmbtBLjXc50WHHq3Be7s7V4kWHNDtfuLqTHuYZaY1HkRB9RIRERZUUREREREREXbXsSRlxje+Mua5jixzmFzHjDmOLT1YR2g9CupFgidVkEgyEREWVhERERERERERERERERFwseQ/8ANd/IrmuFjyH/AJrv5FYdoVOn8Q6rdhEReUL9EKqdb7s8FWzZrOoyuNaxNXLxOwBxhldGXAFvQEtz+9cNN7uNF7w2apYhYSAZGujmDM/fOaMO2j/tyfiBUU4N1GvV4r1GW1NHBELWqtMkrg1m51l+Bk9MlZbu98Q6TbqV2VZoLNptgOD4cOMcPLeJA6VoxtLjH4meuAfvV9T/AAGH7VlLsnHMAc4JtI9PmvPu+Mb/AA9XEfxDRkc4CmWtkgHjIN+iuqrYZKxksbg+ORjZI3tILXseA5rmkdrSCDn512KkeI9e1LSOH9C72nNeWVjuZmKGQmMt5kTSJ43bcNc3swpDwLrWvXrkNyzEYNHNZxaXd7N5mIsssSDPOy8+P4oDACMZHU8ipsxzWGpmbllwEm5ymLczuhfSUdu031W0Cx2eGEwJAzCZJ3AaEmPNWaipKHjHX9euTx6M6OpVg673tjzy3EiJ08kkbyJH7XEMY0YAIOcFx93CXHGq09UZpGubHule2OKw1rGuD5ekBBiAZLC84bnaHAnr2EKTtk1Wg3bmAksn3gP2+ahT9o8O9w91+RzsoqEe4TprM8phW+iqnunccX26hHo2jgC07YJZdrHv3yNEjYmCUGNjRHhznkHo773aSY7xjxXxRpEMVe3NGJJH8yK7FHXk3xta4SV3tdDsyC6J2drT0PV2eijsirUDbtBdcNJvHHomJ9o8PQNSWvLWGHOa2Wg/2zIvu4TvV8Iqx7p3FF+lommW61jl2LD6omk5UL94kpyyv8SSMsbl7WnoB2fF0Xn4E17iHULOn2ZInR6S2HbZlIrNdZkZVe187mdJdrrAyBE0NxjoepWtuzqhpdqXNAvqYuNw4k7lvdtuiMQMMGvLoabCQA7eYNgP6idOatZFTFfWuKtZsWDRxplaIgsZYhERLXF3LBfLC98kuGknbho6fGM5TuS8aajYv2tK1IslmrCYidjWNdvrzNiljdygGPb42QQB5BznPSVTZdRjC7M0loktBuAfl81ro7fo1KrWZHgOJDXuENcRwvPqFaaKiqHGXEVrVdQ0+nMyYiS5HCJYqzGVI4rIaJy5sQc8tYNgDtwJlBIcQuqpxxxJSvy6XOI7tyQtiha9sQEcsrWvjlY+FrQ+PY7JD8Aect2kLb3LV0zNmM0TeONwq/4pw1jkqZcxbmy2zDdYmSd0T+6vpFRdniziPRtQrR6rNHYhsFpcxrICx0bnhjzG+KNjmSMz2dnZ0Kzvdx4v1DTLFFtOcxMkjkfKzlQScwskYB40sbi3oSOnxqHdNU1GMa5pzAkEG1td30W78RUG0alV7Xt7MgOaQMwzab4jzVroqL4w4j4r00w3rL4YoJ5NrarWQSRROLTI2vL4vMyWtd1DyfFd4w6Lnr/EfFMlQ6zE6OnQIa+OBgryPbC5wYyV4mjLpGklvXI6HIaApjY9Qhpzsg2BzWnhpr0stTvaai0vaaVSWiSMt8v92th1g8tYtnjPWve6jZuiMS97sD+WX8sOy9rMb9p2+V8R7F5O51xMdWotuGEQbpJI+WJOaBy3Yzv2N7fyKM/8Z2rHC82qMLYLkbSwuY1r2CRlhkRe1koc3DmnOCDjcfiyuvhnie9Lwxa1CSfdcjZbcyblQN2mInZ9jbGIzj52/lUP4IiiQWjN2mWZPDSIiN8zPJbe9WuxLS1xyGiamXKIInWSc0xbLEc1ZqKjOEuIeKtXqPFSaMcqZ3MuSNrRueSxhbWjaItg2jxidufsrfGA6HPdxrja/ctWtN1LD7Fdj5GybGMe0wythmhlEWGOIL24IH3rsk9ErbJq02uOZpLdQDcc9P8AaYb2ioV302hjwH/C5zYaTw1N93BWqipfV+NNa1bU5aGhubBFXMgdMWxHeInbHTSSStcGxl+A1rBkggnPUN8N/jfiGtqVDT7cjYJBNWisbIqz2XI5bDQJ2uMZ2bmEtOwgZaejTkCTdj1TAzNmJyzcDmIUH+02GbJyPLc2UPDfdJmIBkfONDCvZERclfRKIcYdzzT9VsNs2jYEjYmwjlSNY3Yxz3joWHrmR3n+JYb4F9H+Vc+nZ/jVkIrbMfiGNDWvIA3SubV2Ngqry99JpJ1JFyq3+BfR/lXPp2f414dZ7jukMbEQbfjWazDmdvY+ZjT/AP1/EVayxvEXkQ/rlP8A3Ea2N2nip/7jvVajsLAC4ot9AoT8C+j/ACrn07P8afAvo/yrn07P8ashFjvPFeI71TuHZ/gs9Aq3+BfR/lXPp2f40+BfR/lXPp2f41ZCJ3nivEd6p3Ds/wAFnoFW/wAC+j/KufTs/wAafAvo/wAq59Oz/GrIRO88V4jvVO4dn+Cz0Crf4F9H+Vc+nZ/jT4F9H+Vc+nZ/jVkIneeK8R3qncOz/BZ6BVv8C+j/ACrn07P8afAvo/yrn07P8ashE7zxXiO9U7h2f4LPQKt/gX0f5Vz6dn+NPgX0f5Vz6dn+NWQid54rxHeqdw7P8FnoFW/wL6P8q59Oz/GnwL6P8q59Oz/GrIRO88V4jvVO4dn+Cz0Crf4F9H+Vc+nZ/jT4F9H+Vc+nZ/jVkIneeK8R3qncOz/BZ6BVv8C+j/KufTs/xp8C+j/KufTs/wAashE7zxXiO9U7h2f4LPQKt/gX0f5Vz6dn+NPgX0f5Vz6dn+NWQid54rxHeqdw7P8ABZ6BVv8AAvo/yrn07P8AGnwL6P8AKufTs/xqyETvPFeI71TuHZ/gs9Aq3+BfR/lXPp2f40+BfR/lXPp2f41ZCJ3nivEd6p3Ds/wWegVb/Avo/wAq59Oz/GnwL6P8q59Oz/GrIRO88V4jvVO4dn+Cz0Crf4F9H+Vc+nZ/jXn1HuM6O2GVwNzIikI+zs8zCfwatBebVftE/wChk/ocneeK8R3qsjYOzx/4W+gXpREVFdZa5cP6DX1LifUq1prnRG3qjyGPLDuZZk2+M3rjqrW03uVaJBI2UVTK5hy1s0skkeR53Rk7X/kcCPmWU0vgrT612TUYYnNtTOmfI8zSuaXWHF8p5bnbRkk9g6KRrr43aj6hApOcG5QCJi410K+b2XsClRa44hjHOL3OBibE21Cp/wB07/6bT/0839tqsapWMulxws6GTT2RNPZgvrBg/J2hfOLeFaWqMjZdjdI2JznMDZJI8FwAJJjcM9B51lqkDYmMjYMMjY1jRknDWANaMnqegCr1MU04enTGrSTyubK7Q2e9uNrV3Rle1oHGwIM/S6o/uAcQ1aBvU70kdSV0jHtdYc2JpdFvjlie9+Gse048UnJ3O+JefjS/Fq/FOnNoOEzYXVInSxncx3IsSWZpGOHR0bGOPUdCWHGeitLibue6VqMpnsVsTOxvlhkfE5+OmZAw7XuwAMkE4A6r2cKcHadpe4064ZI8bXyuc6SVzeh275CS1mQDtbgEgHC6DtpYftHYhodncIi2UEiJnWPJcVmw8Z2LMG9zOya4HMJzkAyBEQDzn131LqVxmmcZusW/Ehe/cJXAkNjsVDEyUf8Aa1+WE+YNf8S7/dCcUUbcNSrUnisvZM6eR8D2yxxtEbo2sMjCWlzi8nAJxs64yM5juta1p3fsVPWNLldB4pg1GOZzXCN7RzCxsbQXhjyQ6PcewOx1bmC8ajRpo6uncPQSWJ5bHMkl2TmV5Eb2MhBsAPx45ccAMG3J85HQwjRUfRrPY4ENAkRkgTcnd04rjbSe6hTxWGpVGEOeTlMirLiPdDYE7oIkRdS3u0f9N6N+ko/+PnVg8GTcrQqEobuMelV5A0dri2q1+0fOSP8A5XK/whVu0KdG8wysqsgxskkj+yxQGHcCwgkYc/p86zemUo60MNeIFsUEUcMbSS4tjjaGMBc7qTgDqVwa+KY6g2kNQ4nlBX12F2fVp4t+IJEOptaOII47vmqD4T1E62+zNrHEEtFrC3ZWjssqMe124kxNe7llrcBvRrndRk9mefcLEA4hsisXurivcEDpPLdCJ4RG5/QeOW7Seg6lWZL3K9EdOZzTxl28xNllbBuznpE12Gtz96MN82MdFkdN4H06tdN+vC6GwS7JjmlbFh7drmckO5ez/txgEAjsC6VbamHLHsYHAObAEABp8teq4mG9n8Y2rSqVSwlj5Lszi5w/+rDoNeIVZ9x3/qjWPzNS/wDJV191f/rmH8+D/wAeFZ+h8G0KVue9XicyzYEoleZZXhwmlbNJhjnFrcvY09B0X2fg6g/UBqjonG60tIk5soblkfKH2Pds8jp2LQ7aVI1nvgwaeTdrA56K0zYVcYanSlstr9obmMsk8Nb9OarD3Rv/AK7SvzH/AN6NPdG/+u0r8yT+9GrP4o4OoalJDLcidI+AERFssse0FwcchjgD1A7U4o4OoanJDLcidI+AERlssse0FwcejHAHqB2rGG2lSp9jIPuB4P8A9aRdSxuw69b+Jylv/UdTIkn+iJm3pEqG+6X+5dX9ox/7W2u7Xf8Aoxn7Ko/yrqa8V8N1NUhZBcjdJGyUTNDZHxkSBj2A5jIJG2R/T519scO1ZKI01zHGoIY4BHzHh3Ki27G8wHdkbG9c56LRTxrG0qTCDLX5j05K5W2XVfia9UEQ+nkHGYOttPVVLoX/AERc/Pk/3US9nB3/AEXe/RX/AOoqxK/B1COg/TGxOFOQkvj5shcSXiQ4kLt48Zo8650eE6UNCTTY43CpKJGvjMshcRL1f9kJ3DP5VuqbRpuDoBvVz+XrqqtHYtdhYSRbD9lqfi46afPkoh7m/wC5Ev6/N/ZrKNdyn/q3WPztV/8AIxq2+FuHaumQur02OjidI6UtdI+Q73Na0ndISQMMb0+ZeXR+DqFS5Pfgic21Y5xleZZXB3PlE0uGOdtbl4B6Doou2hTL67oPvi3rvv8A5U2bGrNp4RsiaRl2t7Ra37wqg7ieqwaTqWoVNQkZXe4crmzODIxLWleHMc93Ru7cXAnodnb1GePdJ16rf4i0w1HsmZBLShdMzqx7+/OYQx46PY0PHUdMlyyPFer6Ha1OaHW9Nm0+aPc022SyuMwYQ2Jz44IwZGOYPFkw7oGjOOzCsrUtQ1zTYNCruFOm6u+WbZINwjsGeaxI6Tx8bQ1gMmCSA0dNq7TA11U4h7XAllzbJ8MSCNZ4L5eoXsw4wVOoxzRVEAT2p9+YLSBEXJPktikRF8avTkREREWN4i8iH9cp/wC4jWSWN4i8iH9cp/7iNSbqsO0WSREUVlERERERERERERERERERERERERERERERERERERERERERERERERF5tV+0T/oZP6HL0rzar9on/Qyf0ORF6URERERERERERERERdVmtHK0sljZIw9rJGte0/la4YK6qOm14M8iCGHPbyomR5/LsAyvUizmMQo5GzMX4oiIsKSIiIiIiIiIiIiIiIiIiIiIiIi816hBOA2eGKYDsEsbJAPyB4OFzp1IoW7IYo4mdu2NjWNz+a0ALuRZzGIUcjZzRfiiIiwpIiIiIsbxF5EP65T/ANxGsksbxF5EP65T/wBxGpN1WHaLJIiKKyiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIi82q/aJ/0Mn9Dl6V5tV+0T/oZP6HIi9KIuq3YZFG+WRwZHGx0kj3HDWMY0ue5x8wABP7kRdqKkIe7RrFyGfUtK4VsXdEgdL/zsl6GtPPHBkTSw1HRmR4btd4rOZ1aQSHAgWZwlxpp+paVBrEUzYqU8ZeZLD2RCFzHuiljmcXbGvZK17DgkZb0JBBVmrhKtIS4b4sQYPAxMHkVpp12P0P3ynVSNF5NK1OtbjE1WxBZiJIEteWOaMkYyA+NxaT1Hn86hPdT7p1bR6T7NXvXUporlenPWjuxsfAZy8bpeW2R0bgWeS5oytVOi97sjRfRTfUa1uYmysFFEtN4msu1XVqlmCrX0/T4a8sN3v+s+SUSQRyzmxVEnMpsYXPG6QNDg0EdDlZqHiGg+WKBl6m+eZglhhbZgdLLGRkSRRh+6RmOu4AhYdTcPSbX1E7vsb1kPCyaLxXNXqQyxQTWa8U8/2iGSaKOWbHbyo3ODpP8A2grt069DZjEteaKeJ2Q2WGRksbi0lrsPYS04II/cowYlZkaL0Iq81PuklvElfh6tUjsF1cWbdt96KAVmGQxcuOAsJsTB2zLA5rvH6A4JExm1+iyw2o+7UZbdjbVdZhbYdnGNsJfvOcjsHnWx9F7YkaifLyUG1WmYOhjzWSReK5q1WF5jms14pBE6cxyTRxvELNxfNtc4HlDa7LuwbT8S6Nf1dtfT7V+MNnbBTntxhrwGTNigdM0NkaCA1waPGAPbnqoBpMKZcFlEVVdx/uxR67R1S5PUbQOlsbPLGLJnBrOglmbNvdDHtB5E47D5Gcr73Be62/ib3x5unt0/vBlJ+e+zY5gti07J3QR8trRXBz1yJPNjrYfgqzA8ub8MTcWnTr5LS3E03ZYOsx5K1EWN0jiChcc9lS7UtPj+2NrWYZ3R9ceO2J5LOvxrqn4n02MbpNQosbznV8vt12jvhuN0GS/7cNzcs7RuHRV8jpiFtzDWVl0Xh1fWKlNglt2q9WMnaJLM8UDC74g+VwBPzLur3oZIhPHLE+AtLxMyRjoiwZy8SNO0tGD1z5liDErMjRehFjKXEOnz8rkXqc3PdIyDlWYJOc+EAyti2PPMcwEZDc4yMrm7XKQsimblUWyMiqbEIskYzkQbt5GOvYs5HcEzDisgix97W6UDpGz26sLoYTYlbLYijdFACGmaQPcCyHJA3np1HVcdS4goVpI4rN2pXllAMUc9mGJ8oPQGNkjwXj8iBpO5Mw4rJIuE0rWNc9xw1jS5xPYGtGSfyYCpODuz6zZqTaxp/C8lnQ4TM4W36nBDZlgrue2edtTY542bH5aN3knr0ONtHDPqzli3Ega6C8XWupWazX5An9ld6LA6LxdRs6ZW1Yzsq07MMcwfbfHX5XMH2uVz3bGyNcHNIBIy04JHVZH33qd799981+9cB3fPOi732l2wHnbtmC4gZz2nC1FjgYI5eamHA6Fe1FDuPuL5KlYSaY2lfsCzVhmhk1GpVbFFZY6RsrpJpWt3FgDmszl4JIypDqmuUqr447VyrWfKcRMnsRQvlPZiNsjgXnPxLPZugHj99UziVkFjeIvIh/XKf+4jXo1PUa9WN01meGvC3ypZ5WQxtz2ZkkIaP9VjtQvwWa9eatNFYhdbqbZYJGSxu/5iPyXxktP7ijQdUcRos2iIoKSIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiLzar9on/Qyf0OXpXm1X7RP+hk/ociL0qP90rTJbujatTrjM9rTb1eEE4DpZq0kbGk+YFzgM/OpAikxxa4OG5YcJEKi+4p3UtCpcMVorlyvTtaZBNBaoTuEVwzQPkLhFWfiSZ7+hw0HDnlpwQQI/3RuIKmrzcEXdSqvocOXLN+Wevb2sr85rQ2hJaMZ5Yik6vaXHBillJ8Xcr11LgnRrNjvuxpOmz2sh3fE1KtJMXN8lxkfGXOcMDBJ6YWU1bS61uF1e1Xgs1343QWIo5oXbTkbo5GlpwR8S6IxdFtTtGtMkmb6SCPd6TIJ4KmcPULMhIgRFtYI1/Zazl0MWs8XDhIt7xHCsr5/ew7qrNTG3kmoYPEE4h5+wR/f8/HjA4h/GEPCzeFeHnac6n79Olpd9iCRhuFxjc6826wHeIBOGbN4x4sezxSVuHoOh0qEXJo1K1OHcXGKrBFXjLzgFxZE0AuOB17eixh4D0M80nRtKPPe2SfOn1DzZGuL2vkzF47w4l2T53E9pW+ntRrXAw60b7mBHvcfsc1qfgSREi87rCTNlSXEY//AJnuof8A69B/4NijXEPC+n0uF+DdTq1Y4dQm1bSHS3Wg98ScyKzMQ+UncWh8MJaOxvLaG4HRbQy8Oae99uV1Ck6TUIxDfkdVgL7sIjEIitvLM2YxGAza/I2jHYvljhrTpIIKsmn0n1ar45KtZ9WB0FaSIObE+CFzNkL2hzgC0AgOOO1QZtINywDbLPOGZf8Aam7B5pvx8pdP+lrzF7wSa7xm7iqSsy1HKxtHvyQRyRUWxSOgdpu4h3fXL73cOV4+Swt8p2bC9yNn/hLTs9vNv5/L3/YyufGnBGuWNSluVzw5eY5rBSk1fTR39pBbk5q2a8RdZAe5zwJC3B2fE4vlncn4OboGkVNLbMbHe4kL5i3ZzJJppJ5C1mTsYHSEAZJw0ZJOSmKxDH0MoNyWWmwytIMWEbvpa5xQoubVki3vX3mTPmqxGh1fhGs8upVMg4ddqEeYY8DUTciaLnk9LJDiOZ5WCeqq3SY+GncF6pLqjqv/ABMZrzpzZeBq41Hvk8gNa888RnxC/aNuefu6h2NvBo9QWjeFWsLph73NzkRd9GvuD+QbG3mGHc0O2ZxkA4Xis8IaTJaF6TTNPkuhzXC2+nXdZDm42uE7mb9wwMHOQsUtpBsAzYN0N/dm3Qz8lmpg5mIvm1HH6hUDJoA1biThOrrcckz5OEa8l+GV743TTxiy9zLWwhzjzg17m5ALmdemQb048rMh0HU4Y2hkcWkXY42DOGsZTlaxoz1wAAP3LLy6PUdaZddVrOuxxGGO26CI2o4SXExMsFvMbES952g48Y/GvVarxyxvilYySKRjo5I5Gh8ckbwWvY9jhhzC0kEHoQSqtfF9oWcG7t2s2/ZbqWHyB3E7/KFpRpdueho1OOADPFPD9jR4W4P2S/DxFLXJe4dje8r8g7O0NHnUorS09Nj7pDJ681ilBLolPvaCd1V8kZs3KsURnYCYojlgdgHLS5uDnC2YbwhpIbUYNL04MoSGWgwUq22lK6QTOlqN5eK0hla1+5mDuaD2hdzOGtOBuEafSB1DAvkVIAbwG/Hfh2f8z9sk+2bvtjvjKvv2qxxPum5k3/8AdpGnIR1VZmBc0D3tBGn/AKkfuZWsnATq7OMOFxV/4fiDq14SQ8PPkmayI6dZdGzUrTji1aO3d1aCNoJzlpXLhvhLTbeg8eX7NSKa5V1PXe9p5AXPr97wtsRmHJ+xu5jiSW43ANByAAtkaPBukQGuYdK06J1R0j6jo6VZjqz5dvNfXc2PML37W7nNwTtGc4Xpr8OadHDZrx0KTK918slyBlWBsNt87Qyd9mJrNs73tADi8EuA65UX7UEy0HQDW9nE/WFJuBt70b93EALWOxqVGY8K1bVfSpbcfC1WdtziW8+PR4YHmSLa2njbZtHkk5c5uREwZ8TLcFp9mX/gXWWwyHvH/i0R2nVWvZG3TpIqbnd7xvJdFA6YwYY4n7Zg5yc7Y2+DtImbVZLpenSMpNDabH0qz2VGjbhtZro8QNG1vRuB4rfiCx3FXCG6heg0YUdKtXZDNPMNOqTQ2pHn7MLsDo9tgyNLgXuy4E58bq0zZtKnYQfiBubCHE8yNdwtwKg7BOuZ3EWF9PJUjZi4dZxjwqOGnUXfYdQNhlGRslcP7wmFR0uxxaLLg2UPJ8chke771V5oWmMtaDLJbvcM0Lp1CSWxevG4ziiC+y5vOXRbpASW52sYQAXOOHtLhfvB3ctus1XTdRvs0OhFpLbZq0dAqywQz2LsIgms2XStbjxWtwxoONjBuwDmxn8I6U633+7TNPdeDg8XDTrm1vAwH88s37wOm7OVN20WUoDSTAF5kyHOMSbEXHGNLqIwbnyTAubaagD6KlNQ4Xravx0yrq0TbTBwrXnnjzLFFNOyzG0l7Btc6LdI54Y4Dq1hIy0KId0irSbq/Elpk3D2rNc4RX9O1vn6dqtPkQmER6PYs7GvO0ANmhccgQAA+KTtONHqC0bwq1u/TD3ubnIi76NfcH8g2NvM5O4B2zOMgHC8GucG6RelE93S9OuTABoms0q08u1vkt3ysLi0fF2KtS2llcJmA0CB1n6DQgrdUwcgxEzM/f1WF7kupwWOGtOsV6lhlfvDEVKWQ2ZhHCHxCBssu3ntIjwxztu5pZnGVRWhO0OPTp9Q4f4uv8Nub3zIND1K3WlEUsTnYibpz5C55kDG4cDM7xwDkgsG1UUbWNaxjQ1rQGta0BrWtaMBrQOgAAAwo/qPAmiWZzasaRpk9lzt7p5aNaSV7x2Oe98ZL3DA6nJ6LTQxbWOcSDBMwIPGxDgQdddfVbKuHLg2IkCOHpGnRa065r2o6zLwXZ1caW1tmjqMjBrccjNHsW4554RNZijIYXyVm05G9jC6duBh4aey3p/e/DHGrYb+lWqj7Gmyiro/fTqFG267F3wyB87Nha9og8WN7w0Rt8kFudpNa0Sleh73uVK1uuCCILMEU8Qc0Ya4RyNLQ4A9DjoukcMaaKZ0/wB76PeDsbqPekHejsPEgzW2cs+O1ruztaD2qyNqNAaA2ACDAiID81ue77haTgSZl0yDc63bH+/uVQHdV4Vo6bwlpLqkOyS5qWh2Lcznvklszuglc6aV0jjl5L3HpgDOAAAAMDxDT761/i9uoycNskEjI2niM2BNDQML+9pdKdG4ct3KdG4uj8YOdH8rrtFqOhUbMMdaxTq2K8Lo3QwTV4ZYYnRDbE6OJ7S1jmAkAgDHmXn17hTS78jJb2m0LksQxHJaqV7D2DO7a18rCWtz1x2ZUKW0so96ZvffctP0hSqYKdNLW3WBH1WtvE2mRv0fg2GxrmlTWa/vg/T2atWvnQ9VgbKxsPfE8kLWxCGu2OJvNADxMNp8YF0m7gGpVnzaxVh0+rSsQ6ho8lt2l3Tb0eeSSZ7WvpRNc6OqcMOWscchoBwY9ovTWdCpXYRXuU6tquC0iCzXiniaWjDS2ORpa0gdhA6LHSaJSoV4YKNStTh79qOMVWCKvGXmxGC4siaAXHA69qw/Hh9IsIMk+Ql2bdHoRrcRYLIwpa8OB3fSN8qRIiLlK8iIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIi82q/aJ/0Mn9Dl6V5tV+0T/oZP6HIi9KLQDw1eKvR/D/quo+0U8NXir0fw/wCq6j7RRFv+i0A8NXir0fw/6rqPtFPDV4q9H8P+q6j7RRFv+i0A8NXir0fw/wCq6j7RTw1eKvR/D/quo+0URb/otAPDV4q9H8P+q6j7RTw1eKvR/D/quo+0URb/AKLQDw1eKvR/D/quo+0U8NXir0fw/wCq6j7RRFv+i0A8NXir0fw/6rqPtFPDV4q9H8P+q6j7RRFv+i0A8NXir0fw/wCq6j7RTw1eKvR/D/quo+0URb/otAPDV4q9H8P+q6j7RTw1eKvR/D/quo+0URb/AKLQDw1eKvR/D/quo+0U8NXir0fw/wCq6j7RRFv+i0A8NXir0fw/6rqPtFPDV4q9H8P+q6j7RRFv+i0A8NXir0fw/wCq6j7RTw1eKvR/D/quo+0URb/otAPDV4q9H8P+q6j7RTw1eKvR/D/quo+0URb/AKLQDw1eKvR/D/quo+0U8NXir0fw/wCq6j7RRFv+i0A8NXir0fw/6rqPtFPDV4q9H8P+q6j7RRFv+i0A8NXir0fw/wCq6j7RTw1eKvR/D/quo+0URb/rG8ReRD+uU/8AcRrRLw1eKvR/D/quo+0V0XfdmcUShodQ0EBkkco21dQ8qJ4e0HOodmWhZBgrB0X6DItAPDV4q9H8P+q6j7RTw1eKvR/D/quo+0VhZW/6LQDw1eKvR/D/AKrqPtFPDV4q9H8P+q6j7RRFv+i0A8NXir0fw/6rqPtFPDV4q9H8P+q6j7RRFv8AotAPDV4q9H8P+q6j7RTw1eKvR/D/AKrqPtFEW/6LQDw1eKv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+       "            width=\"60%\"\n",
+       "            height=\"300\"\n",
+       "            src=\"https://www.youtube.com/embed/YTU8jaG27Xk\"\n",
+       "            frameborder=\"0\"\n",
+       "            allowfullscreen\n",
+       "        ></iframe>\n",
+       "        "
+      ],
+      "text/plain": [
+       "<IPython.lib.display.YouTubeVideo at 0x7f40ec50b590>"
+      ]
+     },
+     "execution_count": 1,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from IPython.display import YouTubeVideo\n",
+    "YouTubeVideo(\"YTU8jaG27Xk\", width=\"60%\")"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {
@@ -113,7 +161,7 @@
    "cell_type": "markdown",
    "metadata": {
     "slideshow": {
-     "slide_type": "skip"
+     "slide_type": "slide"
     }
    },
    "source": [
@@ -307,7 +355,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 2,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -339,7 +387,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 3,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -350,7 +398,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Python 3.7.6\n"
+      "Python 3.7.4\n"
      ]
     }
    ],
@@ -483,7 +531,7 @@
    "cell_type": "markdown",
    "metadata": {
     "slideshow": {
-     "slide_type": "skip"
+     "slide_type": "slide"
     }
    },
    "source": [
@@ -651,7 +699,7 @@
    "cell_type": "markdown",
    "metadata": {
     "slideshow": {
-     "slide_type": "slide"
+     "slide_type": "skip"
     }
    },
    "source": [
@@ -831,13 +879,13 @@
     "**Part B: Managing Data and Memory**\n",
     "\n",
     "- How is data stored in memory?\n",
-    "  - *Chapter 5*: [Bits & Numbers](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/05_numbers_00_lecture.ipynb)\n",
-    "  - *Chapter 6*: [Bytes & Text](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/06_text_00_lecture.ipynb)\n",
+    "  - *Chapter 5*: [Numbers & Bits](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/05_numbers_00_lecture.ipynb)\n",
+    "  - *Chapter 6*: [Text & Bytes](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/06_text_00_lecture.ipynb)\n",
     "  - *Chapter 7*: [Sequential Data](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/07_sequences_00_lecture.ipynb)\n",
-    "  - *Chapter 8*: [Mappings & Sets](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/08_mappings_00_lecture.ipynb)\n",
-    "  - *Chapter 9*: Arrays & Dataframes\n",
+    "  - *Chapter 8*: [Map, Filter, & Reduce](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/08_mfr_00_lecture.ipynb)\n",
+    "  - *Chapter 9*: [Mappings & Sets](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/08_mappings_00_lecture.ipynb)\n",
     "- How can we create custom data types?\n",
-    "  - *Chapter 10*: Object-Orientation"
+    "  - *Chapter 10*: [Classes & Instances](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/10_classes_00_lecture.ipynb)"
    ]
   },
   {
@@ -864,7 +912,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 4,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -885,6 +933,64 @@
    "source": [
     "<img src=\"static/xkcd.png\" width=\"30%\">"
    ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "## Further Resources"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "A video tutorial on how to use Jupyter Lab is shown below."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/jpeg": 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+       "            width=\"60%\"\n",
+       "            height=\"300\"\n",
+       "            src=\"https://www.youtube.com/embed/5GGyKq1F_5c\"\n",
+       "            frameborder=\"0\"\n",
+       "            allowfullscreen\n",
+       "        ></iframe>\n",
+       "        "
+      ],
+      "text/plain": [
+       "<IPython.lib.display.YouTubeVideo at 0x7f40e8368810>"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "YouTubeVideo(\"5GGyKq1F_5c\", width=\"60%\")"
+   ]
   }
  ],
  "metadata": {
@@ -903,7 +1009,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.6"
+   "version": "3.7.4"
   },
   "livereveal": {
    "auto_select": "code",
diff --git a/00_intro_01_review.ipynb b/00_intro_01_review.ipynb
index 325fc74..6542d71 100644
--- a/00_intro_01_review.ipynb
+++ b/00_intro_01_review.ipynb
@@ -4,7 +4,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# Chapter 0: Start up"
+    "# Chapter 0: Introduction"
    ]
   },
   {
@@ -18,7 +18,9 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Read [Chapter 0](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/00_intro_00_lecture.ipynb) of the book. Then, work through the questions below."
+    "The questions below assume that you have read [Chapter 0](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/00_intro_00_lecture.ipynb) in the book.\n",
+    "\n",
+    "Be concise in your answers! Most questions can be answered in *one* sentence."
    ]
   },
   {
@@ -28,13 +30,6 @@
     "### Essay Questions "
    ]
   },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Answer the following questions *briefly*!"
-   ]
-  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -46,7 +41,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    " "
+    " < your answer >"
    ]
   },
   {
@@ -60,7 +55,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    " "
+    " < your answer >"
    ]
   },
   {
@@ -74,7 +69,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    " "
+    " < your answer >"
    ]
   },
   {
@@ -88,7 +83,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    " "
+    " < your answer >"
    ]
   },
   {
@@ -116,7 +111,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    " "
+    " < your answer >"
    ]
   },
   {
@@ -130,7 +125,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    " "
+    " < your answer >"
    ]
   },
   {
@@ -144,7 +139,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    " "
+    " < your answer >"
    ]
   },
   {
@@ -158,21 +153,21 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    " "
+    " < your answer >"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "**Q9**: Python is *free software*. That means it does not cost anything."
+    "**Q9**: Python is **free software**. That means it does not cost anything."
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    " "
+    " < your answer >"
    ]
   },
   {
@@ -186,7 +181,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    " "
+    " < your answer >"
    ]
   }
  ],
@@ -206,7 +201,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.6"
+   "version": "3.7.4"
   },
   "toc": {
    "base_numbering": 1,
diff --git a/00_intro_02_exercises.ipynb b/00_intro_02_exercises.ipynb
index b0f53db..db84599 100644
--- a/00_intro_02_exercises.ipynb
+++ b/00_intro_02_exercises.ipynb
@@ -4,7 +4,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# Chapter 0: Start up"
+    "# Chapter 0: Introduction"
    ]
   },
   {
@@ -18,7 +18,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Read [Chapter 0](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/00_intro_00_lecture.ipynb) of the book. Then, work through the exercises below."
+    "The exercises below assume that you have read [Chapter 0](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/00_introduction_00_lecture.ipynb) in the book."
    ]
   },
   {
@@ -94,7 +94,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.6"
+   "version": "3.7.4"
   },
   "toc": {
    "base_numbering": 1,

From 4171517c7798f15a83ef8f507c0411af802bd5dc Mon Sep 17 00:00:00 2001
From: Alexander Hess <alexander@webartifex.biz>
Date: Mon, 30 Mar 2020 03:42:23 +0200
Subject: [PATCH 086/242] Add video and streamline content

---
 01_elements_00_lecture.ipynb   | 1494 +++++++++++++++++---------------
 01_elements_01_review.ipynb    |   33 +-
 01_elements_02_exercises.ipynb |   12 +-
 3 files changed, 795 insertions(+), 744 deletions(-)

diff --git a/01_elements_00_lecture.ipynb b/01_elements_00_lecture.ipynb
index 89beede..91c7411 100644
--- a/01_elements_00_lecture.ipynb
+++ b/01_elements_00_lecture.ipynb
@@ -1,5 +1,53 @@
 {
  "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "A **video presentation** of the contents in this chapter is shown below. A playlist with *all* chapters as videos is linked [here](https://www.youtube.com/playlist?list=PL-2JV1G3J10lQ2xokyQowcRJI5jjNfW7f)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/jpeg": 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+       "            width=\"60%\"\n",
+       "            height=\"300\"\n",
+       "            src=\"https://www.youtube.com/embed/v0lk1Qfaw8Y\"\n",
+       "            frameborder=\"0\"\n",
+       "            allowfullscreen\n",
+       "        ></iframe>\n",
+       "        "
+      ],
+      "text/plain": [
+       "<IPython.lib.display.YouTubeVideo at 0x7f24bd5c6310>"
+      ]
+     },
+     "execution_count": 1,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from IPython.display import YouTubeVideo\n",
+    "YouTubeVideo(\"v0lk1Qfaw8Y\", width=\"60%\")"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {
@@ -55,7 +103,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 2,
    "metadata": {
     "scrolled": true,
     "slideshow": {
@@ -80,7 +128,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 3,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -93,7 +141,7 @@
        "[7, 11, 8, 5, 3, 12, 2, 6, 9, 10, 1, 4]"
       ]
      },
-     "execution_count": 2,
+     "execution_count": 3,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -125,7 +173,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 4,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -157,7 +205,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 5,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -170,7 +218,7 @@
        "7.0"
       ]
      },
-     "execution_count": 4,
+     "execution_count": 5,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -205,7 +253,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 6,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -218,7 +266,7 @@
        "'I am feeling great :-)'"
       ]
      },
-     "execution_count": 5,
+     "execution_count": 6,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -230,7 +278,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 7,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -254,7 +302,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 8,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -288,7 +336,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 9,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -338,7 +386,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 10,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -351,7 +399,7 @@
        "90"
       ]
      },
-     "execution_count": 9,
+     "execution_count": 10,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -362,7 +410,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 11,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -375,7 +423,7 @@
        "8"
       ]
      },
-     "execution_count": 10,
+     "execution_count": 11,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -397,7 +445,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 12,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -410,7 +458,7 @@
        "-1"
       ]
      },
-     "execution_count": 11,
+     "execution_count": 12,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -432,7 +480,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 13,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -445,7 +493,7 @@
        "42"
       ]
      },
-     "execution_count": 12,
+     "execution_count": 13,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -456,7 +504,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 14,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -469,7 +517,7 @@
        "42.0"
       ]
      },
-     "execution_count": 13,
+     "execution_count": 14,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -489,30 +537,6 @@
     "The so-called **floor division operator** `//` always \"rounds\" to an integer and is thus also called **integer division operator**. It is an example of an arithmetic operator we commonly do not know from high school mathematics."
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "fragment"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "42"
-      ]
-     },
-     "execution_count": 14,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "84 // 2"
-   ]
-  },
   {
    "cell_type": "code",
    "execution_count": 15,
@@ -533,6 +557,30 @@
      "output_type": "execute_result"
     }
    ],
+   "source": [
+    "84 // 2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "42"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "85 // 2"
    ]
@@ -550,7 +598,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 17,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -563,7 +611,7 @@
        "-43"
       ]
      },
-     "execution_count": 16,
+     "execution_count": 17,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -585,7 +633,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 18,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -598,7 +646,7 @@
        "1"
       ]
      },
-     "execution_count": 17,
+     "execution_count": 18,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -624,7 +672,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 19,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -637,7 +685,7 @@
        "0"
       ]
      },
-     "execution_count": 18,
+     "execution_count": 19,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -659,7 +707,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 20,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -672,7 +720,7 @@
        "9"
       ]
      },
-     "execution_count": 19,
+     "execution_count": 20,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -683,7 +731,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 21,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -696,7 +744,7 @@
        "89"
       ]
      },
-     "execution_count": 20,
+     "execution_count": 21,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -718,7 +766,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 22,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -731,7 +779,7 @@
        "(4, 2)"
       ]
      },
-     "execution_count": 21,
+     "execution_count": 22,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -753,7 +801,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 23,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -766,7 +814,7 @@
        "8"
       ]
      },
-     "execution_count": 22,
+     "execution_count": 23,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -786,41 +834,6 @@
     "The standard [order of precedence](https://docs.python.org/3/reference/expressions.html#operator-precedence) from mathematics applies (i.e., [PEMDAS](http://mathworld.wolfram.com/PEMDAS.html) rule) when several operators are combined."
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": 23,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "fragment"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "18"
-      ]
-     },
-     "execution_count": 23,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "3 ** 2 * 2 "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "source": [
-    "Parentheses help avoid confusion and take the role of a **delimiter** here."
-   ]
-  },
   {
    "cell_type": "code",
    "execution_count": 24,
@@ -842,7 +855,18 @@
     }
    ],
    "source": [
-    "(3 ** 2) * 2"
+    "3 ** 2 * 2 "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Parentheses help avoid confusion and take the role of a **delimiter** here."
    ]
   },
   {
@@ -857,7 +881,7 @@
     {
      "data": {
       "text/plain": [
-       "81"
+       "18"
       ]
      },
      "execution_count": 25,
@@ -865,6 +889,30 @@
      "output_type": "execute_result"
     }
    ],
+   "source": [
+    "(3 ** 2) * 2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "81"
+      ]
+     },
+     "execution_count": 26,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "3 ** (2 * 2)"
    ]
@@ -882,7 +930,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": 27,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -895,7 +943,7 @@
        "18"
       ]
      },
-     "execution_count": 26,
+     "execution_count": 27,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -915,6 +963,101 @@
     "There exist many non-mathematical operators that are introduced throughout this book, together with the concepts they implement. They often come in a form different from the unary and binary ones mentioned above."
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "### Operator Overloading"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Python **overloads** certain operators. For example, you may not only \"add\" numbers but also strings: This is called **string concatenation**."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "greeting = \"Hi \"\n",
+    "audience = \"class\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Hi class'"
+      ]
+     },
+     "execution_count": 29,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "greeting + audience"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Duplicate strings using multiplication."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Hi Hi Hi Hi Hi Hi Hi Hi Hi Hi '"
+      ]
+     },
+     "execution_count": 30,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "10 * greeting"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {
@@ -943,7 +1086,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": 31,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -980,7 +1123,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": 32,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -990,10 +1133,10 @@
     {
      "data": {
       "text/plain": [
-       "139627575613392"
+       "94551559411776"
       ]
      },
-     "execution_count": 28,
+     "execution_count": 32,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1004,7 +1147,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
+   "execution_count": 33,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -1014,10 +1157,10 @@
     {
      "data": {
       "text/plain": [
-       "140361812063056"
+       "139795753996464"
       ]
      },
-     "execution_count": 29,
+     "execution_count": 33,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1028,20 +1171,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 30,
+   "execution_count": 34,
    "metadata": {
     "slideshow": {
-     "slide_type": "-"
+     "slide_type": "fragment"
     }
    },
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "139627575358192"
+       "139795753985072"
       ]
      },
-     "execution_count": 30,
+     "execution_count": 34,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1065,7 +1208,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 31,
+   "execution_count": 35,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -1078,7 +1221,7 @@
        "True"
       ]
      },
-     "execution_count": 31,
+     "execution_count": 35,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1100,7 +1243,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 32,
+   "execution_count": 36,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -1113,7 +1256,7 @@
        "False"
       ]
      },
-     "execution_count": 32,
+     "execution_count": 36,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1135,7 +1278,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 33,
+   "execution_count": 37,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -1148,7 +1291,7 @@
        "True"
       ]
      },
-     "execution_count": 33,
+     "execution_count": 37,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1181,7 +1324,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 34,
+   "execution_count": 38,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -1194,7 +1337,7 @@
        "int"
       ]
      },
-     "execution_count": 34,
+     "execution_count": 38,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1205,7 +1348,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 35,
+   "execution_count": 39,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -1218,7 +1361,7 @@
        "float"
       ]
      },
-     "execution_count": 35,
+     "execution_count": 39,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1242,7 +1385,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 36,
+   "execution_count": 40,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -1255,7 +1398,7 @@
        "True"
       ]
      },
-     "execution_count": 36,
+     "execution_count": 40,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1277,7 +1420,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 37,
+   "execution_count": 41,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -1291,7 +1434,7 @@
      "traceback": [
       "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
       "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-37-7db0a38aefcc>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0ma\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mis_integer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;32m<ipython-input-41-7db0a38aefcc>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0ma\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mis_integer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
       "\u001b[0;31mAttributeError\u001b[0m: 'int' object has no attribute 'is_integer'"
      ]
     }
@@ -1313,7 +1456,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 38,
+   "execution_count": 42,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -1326,7 +1469,7 @@
        "str"
       ]
      },
-     "execution_count": 38,
+     "execution_count": 42,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1337,7 +1480,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 39,
+   "execution_count": 43,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -1350,7 +1493,7 @@
        "'python rocks'"
       ]
      },
-     "execution_count": 39,
+     "execution_count": 43,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1361,7 +1504,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 40,
+   "execution_count": 44,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -1374,7 +1517,7 @@
        "'PYTHON ROCKS'"
       ]
      },
-     "execution_count": 40,
+     "execution_count": 44,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1409,7 +1552,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 42,
+   "execution_count": 45,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -1422,7 +1565,7 @@
        "42"
       ]
      },
-     "execution_count": 42,
+     "execution_count": 45,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1433,7 +1576,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 43,
+   "execution_count": 46,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -1446,7 +1589,7 @@
        "42.0"
       ]
      },
-     "execution_count": 43,
+     "execution_count": 46,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1468,10 +1611,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 44,
+   "execution_count": 47,
    "metadata": {
     "slideshow": {
-     "slide_type": "-"
+     "slide_type": "fragment"
     }
    },
    "outputs": [
@@ -1481,7 +1624,7 @@
        "'Python rocks'"
       ]
      },
-     "execution_count": 44,
+     "execution_count": 47,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1540,7 +1683,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 45,
+   "execution_count": 48,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -1549,10 +1692,10 @@
    "outputs": [
     {
      "ename": "SyntaxError",
-     "evalue": "invalid syntax (<ipython-input-45-cafa82e54b9c>, line 1)",
+     "evalue": "invalid syntax (<ipython-input-48-cafa82e54b9c>, line 1)",
      "output_type": "error",
      "traceback": [
-      "\u001b[0;36m  File \u001b[0;32m\"<ipython-input-45-cafa82e54b9c>\"\u001b[0;36m, line \u001b[0;32m1\u001b[0m\n\u001b[0;31m    3.99 $ + 10.40 $\u001b[0m\n\u001b[0m         ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m invalid syntax\n"
+      "\u001b[0;36m  File \u001b[0;32m\"<ipython-input-48-cafa82e54b9c>\"\u001b[0;36m, line \u001b[0;32m1\u001b[0m\n\u001b[0;31m    3.99 $ + 10.40 $\u001b[0m\n\u001b[0m         ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m invalid syntax\n"
      ]
     }
    ],
@@ -1573,7 +1716,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 46,
+   "execution_count": 49,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -1582,10 +1725,10 @@
    "outputs": [
     {
      "ename": "SyntaxError",
-     "evalue": "invalid syntax (<ipython-input-46-499e4d0d0cbb>, line 1)",
+     "evalue": "invalid syntax (<ipython-input-49-499e4d0d0cbb>, line 1)",
      "output_type": "error",
      "traceback": [
-      "\u001b[0;36m  File \u001b[0;32m\"<ipython-input-46-499e4d0d0cbb>\"\u001b[0;36m, line \u001b[0;32m1\u001b[0m\n\u001b[0;31m    for number in numbers\u001b[0m\n\u001b[0m                         ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m invalid syntax\n"
+      "\u001b[0;36m  File \u001b[0;32m\"<ipython-input-49-499e4d0d0cbb>\"\u001b[0;36m, line \u001b[0;32m1\u001b[0m\n\u001b[0;31m    for number in numbers\u001b[0m\n\u001b[0m                         ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m invalid syntax\n"
      ]
     }
    ],
@@ -1607,7 +1750,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 47,
+   "execution_count": 50,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -1616,10 +1759,10 @@
    "outputs": [
     {
      "ename": "IndentationError",
-     "evalue": "expected an indented block (<ipython-input-47-19398c5f89de>, line 2)",
+     "evalue": "expected an indented block (<ipython-input-50-19398c5f89de>, line 2)",
      "output_type": "error",
      "traceback": [
-      "\u001b[0;36m  File \u001b[0;32m\"<ipython-input-47-19398c5f89de>\"\u001b[0;36m, line \u001b[0;32m2\u001b[0m\n\u001b[0;31m    print(number)\u001b[0m\n\u001b[0m        ^\u001b[0m\n\u001b[0;31mIndentationError\u001b[0m\u001b[0;31m:\u001b[0m expected an indented block\n"
+      "\u001b[0;36m  File \u001b[0;32m\"<ipython-input-50-19398c5f89de>\"\u001b[0;36m, line \u001b[0;32m2\u001b[0m\n\u001b[0;31m    print(number)\u001b[0m\n\u001b[0m        ^\u001b[0m\n\u001b[0;31mIndentationError\u001b[0m\u001b[0;31m:\u001b[0m expected an indented block\n"
      ]
     }
    ],
@@ -1656,7 +1799,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 48,
+   "execution_count": 51,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -1670,7 +1813,7 @@
      "traceback": [
       "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
       "\u001b[0;31mZeroDivisionError\u001b[0m                         Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-48-bc757c3fda29>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0;36m1\u001b[0m \u001b[0;34m/\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;32m<ipython-input-51-bc757c3fda29>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0;36m1\u001b[0m \u001b[0;34m/\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
       "\u001b[0;31mZeroDivisionError\u001b[0m: division by zero"
      ]
     }
@@ -1705,7 +1848,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 49,
+   "execution_count": 52,
    "metadata": {
     "code_folding": [],
     "slideshow": {
@@ -1727,10 +1870,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 50,
+   "execution_count": 53,
    "metadata": {
     "slideshow": {
-     "slide_type": "-"
+     "slide_type": "fragment"
     }
    },
    "outputs": [
@@ -1740,7 +1883,7 @@
        "3.5"
       ]
      },
-     "execution_count": 50,
+     "execution_count": 53,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1786,7 +1929,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 51,
+   "execution_count": 54,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -1808,16 +1951,15 @@
        "        "
       ],
       "text/plain": [
-       "<IPython.lib.display.YouTubeVideo at 0x7fa88451b510>"
+       "<IPython.lib.display.YouTubeVideo at 0x7f24bc42bf50>"
       ]
      },
-     "execution_count": 51,
+     "execution_count": 54,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "from IPython.display import YouTubeVideo\n",
     "YouTubeVideo(\"Hwckt4J96dI\", width=\"60%\")"
    ]
   },
@@ -1829,12 +1971,12 @@
     }
    },
    "source": [
-    "For example, while the above code to calculate the average of the even numbers in `[7, 11, 8, 5, 3, 12, 2, 6, 9, 10, 1, 4]` is correct, a Pythonista would rewrite it in a more \"Pythonic\" way and use the built-in [sum()](https://docs.python.org/3/library/functions.html#sum) and [len()](https://docs.python.org/3/library/functions.html#len) functions (cf., [Chapter 2](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/02_functions_00_lecture.ipynb#Built-in-Functions)) as well as a so-called **list comprehension** (cf., [Chapter 7](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/07_sequences_00_lecture.ipynb#List-Comprehensions)). Pythonic code runs faster in many cases and is less error-prone."
+    "For example, while the above code to calculate the average of the even numbers in `[7, 11, 8, 5, 3, 12, 2, 6, 9, 10, 1, 4]` is correct, a Pythonista would rewrite it in a more \"Pythonic\" way and use the built-in [sum()](https://docs.python.org/3/library/functions.html#sum) and [len()](https://docs.python.org/3/library/functions.html#len) functions (cf., [Chapter 2](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/02_functions_00_lecture.ipynb#Built-in-Functions)) as well as a so-called **list comprehension** (cf., [Chapter 8](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/08_mfr_00_lecture.ipynb#List-Comprehensions)). Pythonic code runs faster in many cases and is less error-prone."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 52,
+   "execution_count": 55,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -1847,10 +1989,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 53,
+   "execution_count": 56,
    "metadata": {
     "slideshow": {
-     "slide_type": "-"
+     "slide_type": "fragment"
     }
    },
    "outputs": [],
@@ -1860,10 +2002,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 54,
+   "execution_count": 57,
    "metadata": {
     "slideshow": {
-     "slide_type": "-"
+     "slide_type": "fragment"
     }
    },
    "outputs": [
@@ -1873,7 +2015,7 @@
        "[8, 12, 2, 6, 10, 4]"
       ]
      },
-     "execution_count": 54,
+     "execution_count": 57,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1884,10 +2026,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 55,
+   "execution_count": 58,
    "metadata": {
     "slideshow": {
-     "slide_type": "-"
+     "slide_type": "fragment"
     }
    },
    "outputs": [],
@@ -1897,10 +2039,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 56,
+   "execution_count": 59,
    "metadata": {
     "slideshow": {
-     "slide_type": "-"
+     "slide_type": "fragment"
     }
    },
    "outputs": [
@@ -1910,7 +2052,7 @@
        "7.0"
       ]
      },
-     "execution_count": 56,
+     "execution_count": 59,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1932,10 +2074,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 57,
+   "execution_count": 60,
    "metadata": {
     "slideshow": {
-     "slide_type": "slide"
+     "slide_type": "skip"
     }
    },
    "outputs": [
@@ -2060,7 +2202,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 58,
+   "execution_count": 61,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -2084,7 +2226,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 59,
+   "execution_count": 62,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -2097,7 +2239,7 @@
        "20.0"
       ]
      },
-     "execution_count": 59,
+     "execution_count": 62,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2119,7 +2261,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 60,
+   "execution_count": 63,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -2132,7 +2274,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 61,
+   "execution_count": 64,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -2145,7 +2287,7 @@
        "20"
       ]
      },
-     "execution_count": 61,
+     "execution_count": 64,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2167,7 +2309,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 62,
+   "execution_count": 65,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -2180,10 +2322,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 63,
+   "execution_count": 66,
    "metadata": {
     "slideshow": {
-     "slide_type": "-"
+     "slide_type": "fragment"
     }
    },
    "outputs": [
@@ -2193,7 +2335,7 @@
        "80"
       ]
      },
-     "execution_count": 63,
+     "execution_count": 66,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2204,7 +2346,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 64,
+   "execution_count": 67,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -2217,10 +2359,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 65,
+   "execution_count": 68,
    "metadata": {
     "slideshow": {
-     "slide_type": "-"
+     "slide_type": "fragment"
     }
    },
    "outputs": [
@@ -2230,7 +2372,7 @@
        "40"
       ]
      },
-     "execution_count": 65,
+     "execution_count": 68,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2241,7 +2383,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 66,
+   "execution_count": 69,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -2254,10 +2396,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 67,
+   "execution_count": 70,
    "metadata": {
     "slideshow": {
-     "slide_type": "-"
+     "slide_type": "fragment"
     }
    },
    "outputs": [
@@ -2267,7 +2409,7 @@
        "42"
       ]
      },
-     "execution_count": 67,
+     "execution_count": 70,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2289,7 +2431,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 68,
+   "execution_count": 71,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -2302,7 +2444,7 @@
        "42"
       ]
      },
-     "execution_count": 68,
+     "execution_count": 71,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2313,7 +2455,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 69,
+   "execution_count": 72,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -2337,7 +2479,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 70,
+   "execution_count": 73,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -2351,7 +2493,7 @@
      "traceback": [
       "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
       "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-70-1748287bc46a>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mvariable\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;32m<ipython-input-73-1748287bc46a>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mvariable\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
       "\u001b[0;31mNameError\u001b[0m: name 'variable' is not defined"
      ]
     }
@@ -2373,7 +2515,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 71,
+   "execution_count": 74,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -2386,7 +2528,7 @@
        "'__main__'"
       ]
      },
-     "execution_count": 71,
+     "execution_count": 74,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2408,7 +2550,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 72,
+   "execution_count": 75,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -2422,6 +2564,7 @@
        " 'Out',\n",
        " 'YouTubeVideo',\n",
        " '_',\n",
+       " '_1',\n",
        " '_10',\n",
        " '_11',\n",
        " '_12',\n",
@@ -2432,7 +2575,6 @@
        " '_17',\n",
        " '_18',\n",
        " '_19',\n",
-       " '_2',\n",
        " '_20',\n",
        " '_21',\n",
        " '_22',\n",
@@ -2440,36 +2582,38 @@
        " '_24',\n",
        " '_25',\n",
        " '_26',\n",
-       " '_28',\n",
+       " '_27',\n",
        " '_29',\n",
+       " '_3',\n",
        " '_30',\n",
-       " '_31',\n",
        " '_32',\n",
        " '_33',\n",
        " '_34',\n",
        " '_35',\n",
        " '_36',\n",
+       " '_37',\n",
        " '_38',\n",
        " '_39',\n",
-       " '_4',\n",
        " '_40',\n",
-       " '_41',\n",
        " '_42',\n",
        " '_43',\n",
        " '_44',\n",
+       " '_45',\n",
+       " '_46',\n",
+       " '_47',\n",
        " '_5',\n",
-       " '_50',\n",
-       " '_51',\n",
+       " '_53',\n",
        " '_54',\n",
-       " '_56',\n",
+       " '_57',\n",
        " '_59',\n",
-       " '_61',\n",
-       " '_63',\n",
-       " '_65',\n",
-       " '_67',\n",
+       " '_6',\n",
+       " '_62',\n",
+       " '_64',\n",
+       " '_66',\n",
        " '_68',\n",
+       " '_70',\n",
        " '_71',\n",
-       " '_9',\n",
+       " '_74',\n",
        " '__',\n",
        " '___',\n",
        " '__builtin__',\n",
@@ -2551,6 +2695,9 @@
        " '_i70',\n",
        " '_i71',\n",
        " '_i72',\n",
+       " '_i73',\n",
+       " '_i74',\n",
+       " '_i75',\n",
        " '_i8',\n",
        " '_i9',\n",
        " '_ih',\n",
@@ -2558,6 +2705,7 @@
        " '_iii',\n",
        " '_oh',\n",
        " 'a',\n",
+       " 'audience',\n",
        " 'average',\n",
        " 'b',\n",
        " 'c',\n",
@@ -2565,6 +2713,7 @@
        " 'evens',\n",
        " 'exit',\n",
        " 'get_ipython',\n",
+       " 'greeting',\n",
        " 'number',\n",
        " 'numbers',\n",
        " 'quit',\n",
@@ -2572,398 +2721,13 @@
        " 'total']"
       ]
      },
-     "execution_count": 72,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "dir()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "slide"
-    }
-   },
-   "source": [
-    "### Who am I? And how many?"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "source": [
-    "It is *crucial* to understand that *several* variables may reference the *same* object in memory. Not having this in mind may lead to many hard to track down bugs.\n",
-    "\n",
-    "Let's make `b` reference whatever object `a` is referencing."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 73,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "slide"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "a = 42"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 74,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "fragment"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "b = a"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 75,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "fragment"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "42"
-      ]
-     },
      "execution_count": 75,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "b"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "source": [
-    "For \"simple\" types like `int` or `float` this never causes troubles.\n",
-    "\n",
-    "Let's \"change the value\" of `a`. To be precise, let's create a *new* `87` object and make `a` reference it."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 76,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "fragment"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "a = 87"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 77,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "fragment"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "87"
-      ]
-     },
-     "execution_count": 77,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "a"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "source": [
-    "`b` \"is still the same\" as before. To be precise, `b` still references the *same object* as before."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 78,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "-"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "42"
-      ]
-     },
-     "execution_count": 78,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "b"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "source": [
-    "However, if a variable references an object of a more \"complex\" type (e.g., `list`), predicting the outcome of a code snippet may be unintuitive for a beginner."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 79,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "slide"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "x = [1, 2, 3]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 80,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "list"
-      ]
-     },
-     "execution_count": 80,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "type(x)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 81,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "fragment"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "y = x"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 82,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "fragment"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[1, 2, 3]"
-      ]
-     },
-     "execution_count": 82,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "y"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "source": [
-    "Let's change the first element of `x`.\n",
-    "\n",
-    "[Chapter 7](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/07_sequences_00_lecture.ipynb#The-list-Type) discusses lists in more depth. For now, let's view a `list` object as some sort of **container** that holds an arbitrary number of references to other objects and treat the brackets `[]` attached to it as yet another operator, namely the **indexing operator**. So, `x[0]` instructs Python to first follow the reference from the global list of all names to the `x` object. Then, it follows the first reference it finds there to the `1` object we put in the list. The indexing operator must be an operator as we merely *read* the first element and do not change anything in memory permanently.\n",
-    "\n",
-    "Python **begins counting at 0**. This is not the case for many other languages, for example, [MATLAB](https://en.wikipedia.org/wiki/MATLAB), [R](https://en.wikipedia.org/wiki/R_%28programming_language%29), or [Stata](https://en.wikipedia.org/wiki/Stata). To understand why this makes sense, see this short [note](https://www.cs.utexas.edu/users/EWD/transcriptions/EWD08xx/EWD831.html) by one of the all-time greats in computer science, the late [Edsger Dijkstra](https://en.wikipedia.org/wiki/Edsger_W._Dijkstra)."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 83,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "slide"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "1"
-      ]
-     },
-     "execution_count": 83,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "x[0]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "source": [
-    "To change the first entry in the list, we use the assignment statement `=` again. Here, this does *not* create a *new* variable, nor overwrite an existing one, but only changes the object referenced as the first element in `x`. As we only change parts of the `x` object, we say that we **mutate** its **state**. To use the bag analogy from above, we keep the same bag but \"flip\" some of the $0$s into $1$s and some of the $1$s into $0$s."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 84,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "fragment"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "x[0] = 99"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 85,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "fragment"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[99, 2, 3]"
-      ]
-     },
-     "execution_count": 85,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "x"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "source": [
-    "The changes made to the object `x` is referencing can also be seen through the `y` variable!"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 86,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "fragment"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[99, 2, 3]"
-      ]
-     },
-     "execution_count": 86,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "y"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "source": [
-    "The difference in behavior illustrated in this sub-section has to do with the fact that `int` and `float` objects are **immutable** types while `list` objects are **mutable**.\n",
-    "\n",
-    "In the first case, an object cannot be changed \"in place\" once it is created in memory. When we assigned `87` to the already existing `a`, we did *not* change the $0$s and $1$s in the object `a` referenced before the assignment but created a new `int` object and made `a` reference it while the `b` variable is *not* affected.\n",
-    "\n",
-    "In the second case, `x[0] = 99` creates a *new* `int` object `99` and merely changes the first reference in the `x` list.\n",
-    "\n",
-    "In general, the assignment statement creates a new name and makes it reference whatever object is on the right-hand side *iff* the left-hand side is a *pure* name (i.e., it contains no operators like the indexing operator in the example). Otherwise, it *mutates* an already existing object. And, we must always expect that the latter may have more than one variable referencing it.\n",
-    "\n",
-    "Visualizing what is going on in memory with a tool like [PythonTutor](http://pythontutor.com/visualize.html#code=x%20%3D%20%5B1,%202,%203%5D%0Ay%20%3D%20x%0Ax%5B0%5D%20%3D%2099%0Aprint%28y%5B0%5D%29&cumulative=false&curInstr=0&heapPrimitives=nevernest&mode=display&origin=opt-frontend.js&py=3&rawInputLstJSON=%5B%5D&textReferences=false) may be helpful for a beginner."
+    "dir()"
    ]
   },
   {
@@ -3018,7 +2782,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 87,
+   "execution_count": 76,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -3031,10 +2795,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 88,
+   "execution_count": 77,
    "metadata": {
     "slideshow": {
-     "slide_type": "-"
+     "slide_type": "fragment"
     }
    },
    "outputs": [],
@@ -3044,10 +2808,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 89,
+   "execution_count": 78,
    "metadata": {
     "slideshow": {
-     "slide_type": "-"
+     "slide_type": "fragment"
     }
    },
    "outputs": [],
@@ -3057,10 +2821,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 90,
+   "execution_count": 79,
    "metadata": {
     "slideshow": {
-     "slide_type": "-"
+     "slide_type": "fragment"
     }
    },
    "outputs": [],
@@ -3081,7 +2845,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 91,
+   "execution_count": 80,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -3094,7 +2858,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 92,
+   "execution_count": 81,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -3107,7 +2871,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 93,
+   "execution_count": 82,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -3120,7 +2884,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 94,
+   "execution_count": 83,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -3129,10 +2893,10 @@
    "outputs": [
     {
      "ename": "SyntaxError",
-     "evalue": "can't assign to operator (<ipython-input-94-ec51dae29567>, line 1)",
+     "evalue": "can't assign to operator (<ipython-input-83-ec51dae29567>, line 1)",
      "output_type": "error",
      "traceback": [
-      "\u001b[0;36m  File \u001b[0;32m\"<ipython-input-94-ec51dae29567>\"\u001b[0;36m, line \u001b[0;32m1\u001b[0m\n\u001b[0;31m    address@work = \"WHU, Burgplatz 2, Vallendar\"\u001b[0m\n\u001b[0m                                                ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m can't assign to operator\n"
+      "\u001b[0;36m  File \u001b[0;32m\"<ipython-input-83-ec51dae29567>\"\u001b[0;36m, line \u001b[0;32m1\u001b[0m\n\u001b[0;31m    address@work = \"WHU, Burgplatz 2, Vallendar\"\u001b[0m\n\u001b[0m                                                ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m can't assign to operator\n"
      ]
     }
    ],
@@ -3153,7 +2917,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 95,
+   "execution_count": 84,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -3177,7 +2941,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 96,
+   "execution_count": 85,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -3190,7 +2954,7 @@
        "'__main__'"
       ]
      },
-     "execution_count": 96,
+     "execution_count": 85,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3199,6 +2963,391 @@
     "__name__"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "## Who am I? And how many?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "It is *crucial* to understand that *several* variables may reference the *same* object in memory. Not having this in mind may lead to many hard to track down bugs.\n",
+    "\n",
+    "Let's make `b` reference whatever object `a` is referencing."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 86,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "a = 42"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 87,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "b = a"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 88,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "42"
+      ]
+     },
+     "execution_count": 88,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "b"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "For \"simple\" types like `int` or `float` this never causes troubles.\n",
+    "\n",
+    "Let's \"change the value\" of `a`. To be precise, let's create a *new* `87` object and make `a` reference it."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 89,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "a = 87"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 90,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "87"
+      ]
+     },
+     "execution_count": 90,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "a"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "`b` \"is still the same\" as before. To be precise, `b` still references the *same object* as before."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 91,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "42"
+      ]
+     },
+     "execution_count": 91,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "b"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "However, if a variable references an object of a more \"complex\" type (e.g., `list`), predicting the outcome of a code snippet may be unintuitive for a beginner."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 92,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "x = [1, 2, 3]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 93,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "list"
+      ]
+     },
+     "execution_count": 93,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "type(x)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 94,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "y = x"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 95,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[1, 2, 3]"
+      ]
+     },
+     "execution_count": 95,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "y"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Let's change the first element of `x`.\n",
+    "\n",
+    "[Chapter 7](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/07_sequences_00_lecture.ipynb#The-list-Type) discusses lists in more depth. For now, let's view a `list` object as some sort of **container** that holds an arbitrary number of references to other objects and treat the brackets `[]` attached to it as yet another operator, namely the **indexing operator**. So, `x[0]` instructs Python to first follow the reference from the global list of all names to the `x` object. Then, it follows the first reference it finds there to the `1` object we put in the list. The indexing operator must be an operator as we merely *read* the first element and do not change anything in memory permanently.\n",
+    "\n",
+    "Python **begins counting at 0**. This is not the case for many other languages, for example, [MATLAB](https://en.wikipedia.org/wiki/MATLAB), [R](https://en.wikipedia.org/wiki/R_%28programming_language%29), or [Stata](https://en.wikipedia.org/wiki/Stata). To understand why this makes sense, see this short [note](https://www.cs.utexas.edu/users/EWD/transcriptions/EWD08xx/EWD831.html) by one of the all-time greats in computer science, the late [Edsger Dijkstra](https://en.wikipedia.org/wiki/Edsger_W._Dijkstra)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 96,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "1"
+      ]
+     },
+     "execution_count": 96,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "x[0]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "To change the first entry in the list, we use the assignment statement `=` again. Here, this does *not* create a *new* variable, nor overwrite an existing one, but only changes the object referenced as the first element in `x`. As we only change parts of the `x` object, we say that we **mutate** its **state**. To use the bag analogy from above, we keep the same bag but \"flip\" some of the $0$s into $1$s and some of the $1$s into $0$s."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 97,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "x[0] = 99"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 98,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[99, 2, 3]"
+      ]
+     },
+     "execution_count": 98,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "x"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "The changes made to the object `x` is referencing can also be seen through the `y` variable!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 99,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[99, 2, 3]"
+      ]
+     },
+     "execution_count": 99,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "y"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "The difference in behavior illustrated in this sub-section has to do with the fact that `int` and `float` objects are **immutable** types while `list` objects are **mutable**.\n",
+    "\n",
+    "In the first case, an object cannot be changed \"in place\" once it is created in memory. When we assigned `87` to the already existing `a`, we did *not* change the $0$s and $1$s in the object `a` referenced before the assignment but created a new `int` object and made `a` reference it while the `b` variable is *not* affected.\n",
+    "\n",
+    "In the second case, `x[0] = 99` creates a *new* `int` object `99` and merely changes the first reference in the `x` list.\n",
+    "\n",
+    "In general, the assignment statement creates a new name and makes it reference whatever object is on the right-hand side *iff* the left-hand side is a *pure* name (i.e., it contains no operators like the indexing operator in the example). Otherwise, it *mutates* an already existing object. And, we must always expect that the latter may have more than one variable referencing it.\n",
+    "\n",
+    "Visualizing what is going on in memory with a tool like [PythonTutor](http://pythontutor.com/visualize.html#code=x%20%3D%20%5B1,%202,%203%5D%0Ay%20%3D%20x%0Ax%5B0%5D%20%3D%2099%0Aprint%28y%5B0%5D%29&cumulative=false&curInstr=0&heapPrimitives=nevernest&mode=display&origin=opt-frontend.js&py=3&rawInputLstJSON=%5B%5D&textReferences=false) may be helpful for a beginner."
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {
@@ -3229,7 +3378,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 97,
+   "execution_count": 100,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -3242,7 +3391,7 @@
        "87"
       ]
      },
-     "execution_count": 97,
+     "execution_count": 100,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3253,7 +3402,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 98,
+   "execution_count": 101,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -3266,7 +3415,7 @@
        "42"
       ]
      },
-     "execution_count": 98,
+     "execution_count": 101,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3288,7 +3437,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 99,
+   "execution_count": 102,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -3301,7 +3450,7 @@
        "45"
       ]
      },
-     "execution_count": 99,
+     "execution_count": 102,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3323,7 +3472,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 100,
+   "execution_count": 103,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -3336,7 +3485,7 @@
        "5"
       ]
      },
-     "execution_count": 100,
+     "execution_count": 103,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3358,7 +3507,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 101,
+   "execution_count": 104,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -3371,7 +3520,7 @@
        "3"
       ]
      },
-     "execution_count": 101,
+     "execution_count": 104,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3393,7 +3542,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 102,
+   "execution_count": 105,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -3406,108 +3555,13 @@
        "104"
       ]
      },
-     "execution_count": 102,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "sum(x)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "slide"
-    }
-   },
-   "source": [
-    "### Operator Overloading"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "source": [
-    "Python **overloads** certain operators. For example, you may not only \"add\" numbers but also strings: This is called **string concatenation**."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 103,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "slide"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "greeting = \"Hi \"\n",
-    "audience = \"class\""
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 104,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "fragment"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'Hi class'"
-      ]
-     },
-     "execution_count": 104,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "greeting + audience"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "source": [
-    "Duplicate strings using multiplication."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 105,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "fragment"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'Hi Hi Hi Hi Hi Hi Hi Hi Hi Hi '"
-      ]
-     },
      "execution_count": 105,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "10 * greeting"
+    "sum(x)"
    ]
   },
   {
@@ -3552,7 +3606,7 @@
    "execution_count": 107,
    "metadata": {
     "slideshow": {
-     "slide_type": "-"
+     "slide_type": "fragment"
     }
    },
    "outputs": [],
@@ -3812,7 +3866,7 @@
        "        "
       ],
       "text/plain": [
-       "<IPython.lib.display.YouTubeVideo at 0x7fa8840ad310>"
+       "<IPython.lib.display.YouTubeVideo at 0x7f24bc3f5410>"
       ]
      },
      "execution_count": 112,
@@ -3841,7 +3895,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.6"
+   "version": "3.7.4"
   },
   "livereveal": {
    "auto_select": "code",
diff --git a/01_elements_01_review.ipynb b/01_elements_01_review.ipynb
index c17b487..68ae395 100644
--- a/01_elements_01_review.ipynb
+++ b/01_elements_01_review.ipynb
@@ -19,7 +19,9 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Read [Chapter 1](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/01_elements_00_lecture.ipynb) of the book. Then, work through the questions below."
+    "The questions below assume that you have read [Chapter 1](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/01_elements_00_lecture.ipynb) in the book.\n",
+    "\n",
+    "Be concise in your answers! Most questions can be answered in *one* sentence."
    ]
   },
   {
@@ -29,13 +31,6 @@
     "### Essay Questions "
    ]
   },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Answer the following questions *briefly*!"
-   ]
-  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -47,7 +42,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    " "
+    " < your answer >"
    ]
   },
   {
@@ -61,7 +56,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    " "
+    " < your answer >"
    ]
   },
   {
@@ -75,7 +70,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    " "
+    " < your answer >"
    ]
   },
   {
@@ -89,7 +84,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    " "
+    " < your answer >"
    ]
   },
   {
@@ -103,7 +98,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    " "
+    " < your answer >"
    ]
   },
   {
@@ -117,7 +112,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    " "
+    " < your answer >"
    ]
   },
   {
@@ -145,7 +140,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    " "
+    " < your answer >"
    ]
   },
   {
@@ -159,7 +154,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    " "
+    " < your answer >"
    ]
   },
   {
@@ -173,7 +168,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    " "
+    " < your answer >"
    ]
   },
   {
@@ -187,7 +182,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    " "
+    " < your answer >"
    ]
   }
  ],
@@ -207,7 +202,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.6"
+   "version": "3.7.4"
   },
   "toc": {
    "base_numbering": 1,
diff --git a/01_elements_02_exercises.ipynb b/01_elements_02_exercises.ipynb
index 3f5026a..7cdd98f 100644
--- a/01_elements_02_exercises.ipynb
+++ b/01_elements_02_exercises.ipynb
@@ -19,7 +19,9 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Read [Chapter 1](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/01_elements_00_content.ipynb) of the book. Then, work through the exercises below. The `...` indicate where you need to fill in your answers. You should not need to create any additional code cells."
+    "The exercises below assume that you have read [Chapter 1](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/01_elements_00_lecture.ipynb) in the book.\n",
+    "\n",
+    "The `...`'s in the code cells indicate where you need to fill in code snippets. The number of `...`'s within a code cell give you a rough idea of how many lines of code are needed to solve the task. You should not need to create any additional code cells for your final solution. However, you may want to use temporary code cells to try out some ideas."
    ]
   },
   {
@@ -68,7 +70,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    " ..."
+    " < your answer >"
    ]
   },
   {
@@ -100,7 +102,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    " ..."
+    " < your answer >"
    ]
   },
   {
@@ -283,7 +285,7 @@
    "source": [
     "`for`-loops are extremely versatile in Python. That is different from many other programming languages.\n",
     "\n",
-    "As shown in the first example in [Chapter 1](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/01_elements_00_content.ipynb), we can create a `list` like `numbers` and loop over the numbers in it on a one-by-one basis."
+    "As shown in the first example in [Chapter 1](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/01_elements_00_lecture.ipynb#Example:-Averaging-all-even-Numbers-in-a-List), we can create a `list` like `numbers` and loop over the numbers in it on a one-by-one basis."
    ]
   },
   {
@@ -444,7 +446,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.6"
+   "version": "3.7.4"
   },
   "toc": {
    "base_numbering": 1,

From c1268172d09e1e1e085d28a8f1b90d2d7e152c7c Mon Sep 17 00:00:00 2001
From: Alexander Hess <alexander@webartifex.biz>
Date: Mon, 30 Mar 2020 04:25:04 +0200
Subject: [PATCH 087/242] Add video and streamline content

---
 02_functions_00_lecture.ipynb   | 868 +++++++++++++++++---------------
 02_functions_01_review.ipynb    |  35 +-
 02_functions_02_exercises.ipynb |  24 +-
 sample_module.py                |   6 +-
 4 files changed, 494 insertions(+), 439 deletions(-)

diff --git a/02_functions_00_lecture.ipynb b/02_functions_00_lecture.ipynb
index c25b194..8950027 100644
--- a/02_functions_00_lecture.ipynb
+++ b/02_functions_00_lecture.ipynb
@@ -1,5 +1,53 @@
 {
  "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "A **video presentation** of the contents in this chapter is shown below. A playlist with *all* chapters as videos is linked [here](https://www.youtube.com/playlist?list=PL-2JV1G3J10lQ2xokyQowcRJI5jjNfW7f)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/jpeg": 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+       "            width=\"60%\"\n",
+       "            height=\"300\"\n",
+       "            src=\"https://www.youtube.com/embed/j4Xn8QFysmc\"\n",
+       "            frameborder=\"0\"\n",
+       "            allowfullscreen\n",
+       "        ></iframe>\n",
+       "        "
+      ],
+      "text/plain": [
+       "<IPython.lib.display.YouTubeVideo at 0x7f0b83dd0350>"
+      ]
+     },
+     "execution_count": 1,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from IPython.display import YouTubeVideo\n",
+    "YouTubeVideo(\"j4Xn8QFysmc\", width=\"60%\")"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {
@@ -50,7 +98,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 2,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -63,7 +111,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 3,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -76,7 +124,7 @@
        "78"
       ]
      },
-     "execution_count": 2,
+     "execution_count": 3,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -87,7 +135,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 4,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -100,7 +148,7 @@
        "12"
       ]
      },
-     "execution_count": 3,
+     "execution_count": 4,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -124,7 +172,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 5,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -134,10 +182,10 @@
     {
      "data": {
       "text/plain": [
-       "140451963172416"
+       "139687475209792"
       ]
      },
-     "execution_count": 4,
+     "execution_count": 5,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -148,7 +196,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 6,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -158,10 +206,10 @@
     {
      "data": {
       "text/plain": [
-       "140451963171376"
+       "139687475208752"
       ]
      },
-     "execution_count": 5,
+     "execution_count": 6,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -183,7 +231,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 7,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -196,7 +244,7 @@
        "builtin_function_or_method"
       ]
      },
-     "execution_count": 6,
+     "execution_count": 7,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -207,7 +255,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 8,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -220,7 +268,7 @@
        "builtin_function_or_method"
       ]
      },
-     "execution_count": 7,
+     "execution_count": 8,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -240,30 +288,6 @@
     "Python's object-oriented nature allows us to have functions work with themselves. While seemingly not useful from a beginner's point of view, that enables a lot of powerful programming styles later on."
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "slide"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "140451963170976"
-      ]
-     },
-     "execution_count": 8,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "id(id)"
-   ]
-  },
   {
    "cell_type": "code",
    "execution_count": 9,
@@ -276,7 +300,7 @@
     {
      "data": {
       "text/plain": [
-       "builtin_function_or_method"
+       "139687475208352"
       ]
      },
      "execution_count": 9,
@@ -285,7 +309,31 @@
     }
    ],
    "source": [
-    "type(id)"
+    "id(id)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "type"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "type(type)"
    ]
   },
   {
@@ -305,7 +353,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 11,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -318,7 +366,7 @@
        "True"
       ]
      },
-     "execution_count": 10,
+     "execution_count": 11,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -329,7 +377,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 12,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -342,7 +390,7 @@
        "True"
       ]
      },
-     "execution_count": 11,
+     "execution_count": 12,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -364,7 +412,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 13,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -377,7 +425,7 @@
        "False"
       ]
      },
-     "execution_count": 12,
+     "execution_count": 13,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -414,7 +462,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 14,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -427,7 +475,7 @@
        "7"
       ]
      },
-     "execution_count": 13,
+     "execution_count": 14,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -438,7 +486,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 15,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -451,7 +499,7 @@
        "7"
       ]
      },
-     "execution_count": 14,
+     "execution_count": 15,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -473,7 +521,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 16,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -486,7 +534,7 @@
        "7"
       ]
      },
-     "execution_count": 15,
+     "execution_count": 16,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -497,7 +545,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 17,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -510,7 +558,7 @@
        "8"
       ]
      },
-     "execution_count": 16,
+     "execution_count": 17,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -532,7 +580,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 18,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -545,7 +593,7 @@
        "-7"
       ]
      },
-     "execution_count": 17,
+     "execution_count": 18,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -567,7 +615,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 19,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -581,7 +629,7 @@
      "traceback": [
       "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
       "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-18-af421b358f21>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"seven\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;32m<ipython-input-19-af421b358f21>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"seven\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
       "\u001b[0;31mValueError\u001b[0m: invalid literal for int() with base 10: 'seven'"
      ]
     }
@@ -603,7 +651,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 20,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -616,7 +664,7 @@
        "7.0"
       ]
      },
-     "execution_count": 19,
+     "execution_count": 20,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -627,7 +675,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 21,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -640,7 +688,7 @@
        "'7'"
       ]
      },
-     "execution_count": 20,
+     "execution_count": 21,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -662,7 +710,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 22,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -672,10 +720,10 @@
     {
      "data": {
       "text/plain": [
-       "93979750270848"
+       "94868913087680"
       ]
      },
-     "execution_count": 21,
+     "execution_count": 22,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -686,7 +734,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 23,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -696,10 +744,10 @@
     {
      "data": {
       "text/plain": [
-       "93979750256352"
+       "94868913091584"
       ]
      },
-     "execution_count": 22,
+     "execution_count": 23,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -721,7 +769,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 24,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -734,7 +782,7 @@
        "type"
       ]
      },
-     "execution_count": 23,
+     "execution_count": 24,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -745,7 +793,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 25,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -758,7 +806,7 @@
        "type"
       ]
      },
-     "execution_count": 24,
+     "execution_count": 25,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -780,7 +828,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 26,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -793,7 +841,7 @@
        "True"
       ]
      },
-     "execution_count": 25,
+     "execution_count": 26,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -804,7 +852,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": 27,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -817,7 +865,7 @@
        "True"
       ]
      },
-     "execution_count": 26,
+     "execution_count": 27,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -875,7 +923,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": 28,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -890,7 +938,7 @@
     "        integers (list of int's): whole numbers to be averaged\n",
     "\n",
     "    Returns:\n",
-    "        float: average\n",
+    "        average (float)\n",
     "    \"\"\"\n",
     "    evens = [n for n in integers if n % 2 == 0]\n",
     "    average = sum(evens) / len(evens)\n",
@@ -910,7 +958,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": 29,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -923,7 +971,7 @@
        "<function __main__.average_evens(integers)>"
       ]
      },
-     "execution_count": 28,
+     "execution_count": 29,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -945,7 +993,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
+   "execution_count": 30,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -955,10 +1003,10 @@
     {
      "data": {
       "text/plain": [
-       "140451741882272"
+       "139687370296784"
       ]
      },
-     "execution_count": 29,
+     "execution_count": 30,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -969,7 +1017,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 30,
+   "execution_count": 31,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -982,7 +1030,7 @@
        "function"
       ]
      },
-     "execution_count": 30,
+     "execution_count": 31,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1006,7 +1054,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 31,
+   "execution_count": 32,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -1015,10 +1063,10 @@
    "outputs": [
     {
      "ename": "SyntaxError",
-     "evalue": "invalid syntax (<ipython-input-31-7f49dff38622>, line 1)",
+     "evalue": "invalid syntax (<ipython-input-32-7f49dff38622>, line 1)",
      "output_type": "error",
      "traceback": [
-      "\u001b[0;36m  File \u001b[0;32m\"<ipython-input-31-7f49dff38622>\"\u001b[0;36m, line \u001b[0;32m1\u001b[0m\n\u001b[0;31m    <function __main__.average_evens(numbers)>\u001b[0m\n\u001b[0m    ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m invalid syntax\n"
+      "\u001b[0;36m  File \u001b[0;32m\"<ipython-input-32-7f49dff38622>\"\u001b[0;36m, line \u001b[0;32m1\u001b[0m\n\u001b[0;31m    <function __main__.average_evens(numbers)>\u001b[0m\n\u001b[0m    ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m invalid syntax\n"
      ]
     }
    ],
@@ -1039,7 +1087,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 32,
+   "execution_count": 33,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -1052,7 +1100,7 @@
        "True"
       ]
      },
-     "execution_count": 32,
+     "execution_count": 33,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1076,7 +1124,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 33,
+   "execution_count": 34,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -1096,7 +1144,7 @@
       "        integers (list of int's): whole numbers to be averaged\n",
       "    \n",
       "    Returns:\n",
-      "        float: average\n",
+      "        average (float)\n",
       "\n"
      ]
     }
@@ -1118,7 +1166,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 34,
+   "execution_count": 35,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -1136,8 +1184,8 @@
        "    integers (list of int's): whole numbers to be averaged\n",
        "\n",
        "Returns:\n",
-       "    float: average\n",
-       "\u001b[0;31mFile:\u001b[0m      ~/repos/intro-to-python/<ipython-input-27-37752489e27e>\n",
+       "    average (float)\n",
+       "\u001b[0;31mFile:\u001b[0m      ~/repos/intro-to-python/<ipython-input-28-bcb78a3113d7>\n",
        "\u001b[0;31mType:\u001b[0m      function\n"
       ]
      },
@@ -1162,7 +1210,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 35,
+   "execution_count": 36,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -1181,12 +1229,12 @@
        "\u001b[0;34m        integers (list of int's): whole numbers to be averaged\u001b[0m\n",
        "\u001b[0;34m\u001b[0m\n",
        "\u001b[0;34m    Returns:\u001b[0m\n",
-       "\u001b[0;34m        float: average\u001b[0m\n",
+       "\u001b[0;34m        average (float)\u001b[0m\n",
        "\u001b[0;34m    \"\"\"\u001b[0m\u001b[0;34m\u001b[0m\n",
        "\u001b[0;34m\u001b[0m    \u001b[0mevens\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mn\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mn\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mintegers\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mn\u001b[0m \u001b[0;34m%\u001b[0m \u001b[0;36m2\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\n",
        "\u001b[0;34m\u001b[0m    \u001b[0maverage\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msum\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mevens\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m/\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mevens\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n",
        "\u001b[0;34m\u001b[0m    \u001b[0;32mreturn\u001b[0m \u001b[0maverage\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-       "\u001b[0;31mFile:\u001b[0m      ~/repos/intro-to-python/<ipython-input-27-37752489e27e>\n",
+       "\u001b[0;31mFile:\u001b[0m      ~/repos/intro-to-python/<ipython-input-28-bcb78a3113d7>\n",
        "\u001b[0;31mType:\u001b[0m      function\n"
       ]
      },
@@ -1211,7 +1259,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 36,
+   "execution_count": 37,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -1262,7 +1310,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 37,
+   "execution_count": 38,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -1275,7 +1323,7 @@
        "7.0"
       ]
      },
-     "execution_count": 37,
+     "execution_count": 38,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1286,7 +1334,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 38,
+   "execution_count": 39,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -1299,7 +1347,7 @@
        "7.0"
       ]
      },
-     "execution_count": 38,
+     "execution_count": 39,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1321,7 +1369,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 39,
+   "execution_count": 40,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -1334,10 +1382,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 40,
+   "execution_count": 41,
    "metadata": {
     "slideshow": {
-     "slide_type": "-"
+     "slide_type": "fragment"
     }
    },
    "outputs": [
@@ -1347,7 +1395,7 @@
        "7.0"
       ]
      },
-     "execution_count": 40,
+     "execution_count": 41,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1391,7 +1439,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 41,
+   "execution_count": 42,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -1405,7 +1453,7 @@
      "traceback": [
       "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
       "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-41-756dfa02d4a8>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mintegers\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;32m<ipython-input-42-756dfa02d4a8>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mintegers\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
       "\u001b[0;31mNameError\u001b[0m: name 'integers' is not defined"
      ]
     }
@@ -1416,10 +1464,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 42,
+   "execution_count": 43,
    "metadata": {
     "slideshow": {
-     "slide_type": "-"
+     "slide_type": "fragment"
     }
    },
    "outputs": [
@@ -1430,7 +1478,7 @@
      "traceback": [
       "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
       "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-42-df246468d241>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mevens\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;32m<ipython-input-43-df246468d241>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mevens\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
       "\u001b[0;31mNameError\u001b[0m: name 'evens' is not defined"
      ]
     }
@@ -1441,7 +1489,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 43,
+   "execution_count": 44,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -1455,7 +1503,7 @@
      "traceback": [
       "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
       "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-43-c3fe9b4213f6>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0maverage\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;32m<ipython-input-44-c3fe9b4213f6>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0maverage\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
       "\u001b[0;31mNameError\u001b[0m: name 'average' is not defined"
      ]
     }
@@ -1499,7 +1547,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 44,
+   "execution_count": 45,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -1514,7 +1562,7 @@
     "        integers (list of int's): whole numbers to be averaged\n",
     "\n",
     "    Returns:\n",
-    "        float: average\n",
+    "        average (float)\n",
     "    \"\"\"\n",
     "    evens = [n for n in numbers if n % 2 == 0]\n",
     "    average = sum(evens) / len(evens)\n",
@@ -1534,7 +1582,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 45,
+   "execution_count": 46,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -1547,7 +1595,7 @@
        "[7, 11, 8, 5, 3, 12, 2, 6, 9, 10, 1, 4]"
       ]
      },
-     "execution_count": 45,
+     "execution_count": 46,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1569,7 +1617,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 46,
+   "execution_count": 47,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -1582,7 +1630,7 @@
        "7.0"
       ]
      },
-     "execution_count": 46,
+     "execution_count": 47,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1604,7 +1652,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 47,
+   "execution_count": 48,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -1617,7 +1665,7 @@
        "7.0"
       ]
      },
-     "execution_count": 47,
+     "execution_count": 48,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1665,7 +1713,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 48,
+   "execution_count": 49,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -1681,7 +1729,7 @@
     "            if non-whole numbers are provided, they are rounded\n",
     "\n",
     "    Returns:\n",
-    "        float: average\n",
+    "        average (float)\n",
     "    \"\"\"\n",
     "    numbers = [round(n) for n in integers]\n",
     "    evens = [n for n in numbers if n % 2 == 0]\n",
@@ -1702,7 +1750,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 49,
+   "execution_count": 50,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -1715,7 +1763,7 @@
        "42.0"
       ]
      },
-     "execution_count": 49,
+     "execution_count": 50,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1737,7 +1785,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 50,
+   "execution_count": 51,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -1761,7 +1809,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 51,
+   "execution_count": 52,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -1775,7 +1823,7 @@
      "traceback": [
       "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
       "\u001b[0;31mAssertionError\u001b[0m                            Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-51-f47586a118b9>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0;32massert\u001b[0m \u001b[0maverage_evens\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m40.0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m41.1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m42.2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m43.3\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m44.4\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m87.0\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;32m<ipython-input-52-f47586a118b9>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0;32massert\u001b[0m \u001b[0maverage_evens\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m40.0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m41.1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m42.2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m43.3\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m44.4\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m87.0\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
       "\u001b[0;31mAssertionError\u001b[0m: "
      ]
     }
@@ -1797,7 +1845,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 52,
+   "execution_count": 53,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -1810,7 +1858,7 @@
        "[7, 11, 8, 5, 3, 12, 2, 6, 9, 10, 1, 4]"
       ]
      },
-     "execution_count": 52,
+     "execution_count": 53,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1832,7 +1880,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 53,
+   "execution_count": 54,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -1845,7 +1893,7 @@
        "7.0"
       ]
      },
-     "execution_count": 53,
+     "execution_count": 54,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1867,7 +1915,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 54,
+   "execution_count": 55,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -1880,7 +1928,7 @@
        "[7, 11, 8, 5, 3, 12, 2, 6, 9, 10, 1, 4]"
       ]
      },
-     "execution_count": 54,
+     "execution_count": 55,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1910,7 +1958,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 55,
+   "execution_count": 56,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -1925,7 +1973,7 @@
     "        numbers (list of int's): whole numbers to be averaged\n",
     "\n",
     "    Returns:\n",
-    "        float: average\n",
+    "        average (float)\n",
     "    \"\"\"\n",
     "    evens = [n for n in numbers if n % 2 == 0]\n",
     "    average = sum(evens) / len(evens)\n",
@@ -1934,7 +1982,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 56,
+   "execution_count": 57,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -1947,7 +1995,7 @@
        "7.0"
       ]
      },
-     "execution_count": 56,
+     "execution_count": 57,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1958,7 +2006,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 57,
+   "execution_count": 58,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -1971,7 +2019,7 @@
        "[7, 11, 8, 5, 3, 12, 2, 6, 9, 10, 1, 4]"
       ]
      },
-     "execution_count": 57,
+     "execution_count": 58,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2015,7 +2063,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 58,
+   "execution_count": 59,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -2028,7 +2076,7 @@
        "(4, 2)"
       ]
      },
-     "execution_count": 58,
+     "execution_count": 59,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2039,7 +2087,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 59,
+   "execution_count": 60,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -2052,7 +2100,7 @@
        "(0, 10)"
       ]
      },
-     "execution_count": 59,
+     "execution_count": 60,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2074,7 +2122,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 60,
+   "execution_count": 61,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -2091,7 +2139,7 @@
     "        scalar (float): multiplies the average\n",
     "\n",
     "    Returns:\n",
-    "        float: scaled average\n",
+    "        scaled_average (float)\n",
     "    \"\"\"\n",
     "    numbers = [round(n) for n in numbers]\n",
     "    evens = [n for n in numbers if n % 2 == 0]\n",
@@ -2112,7 +2160,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 61,
+   "execution_count": 62,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -2125,7 +2173,7 @@
        "14.0"
       ]
      },
-     "execution_count": 61,
+     "execution_count": 62,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2147,41 +2195,6 @@
     "Luckily, we may also pass in arguments *by name*. Then, we refer to them as **keyword arguments**."
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": 62,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "fragment"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "14.0"
-      ]
-     },
-     "execution_count": 62,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "scaled_average_evens(numbers=numbers, scalar=2)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "source": [
-    "When passing all arguments by name, we may do so in any order."
-   ]
-  },
   {
    "cell_type": "code",
    "execution_count": 63,
@@ -2203,7 +2216,7 @@
     }
    ],
    "source": [
-    "scaled_average_evens(scalar=2, numbers=numbers)"
+    "scaled_average_evens(numbers=numbers, scalar=2)"
    ]
   },
   {
@@ -2214,7 +2227,7 @@
     }
    },
    "source": [
-    "We may even combine positional and keyword arguments in the same function call."
+    "When passing all arguments by name, we may do so in any order."
    ]
   },
   {
@@ -2237,6 +2250,41 @@
      "output_type": "execute_result"
     }
    ],
+   "source": [
+    "scaled_average_evens(scalar=2, numbers=numbers)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "We may even combine positional and keyword arguments in the same function call."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 65,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "14.0"
+      ]
+     },
+     "execution_count": 65,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "scaled_average_evens(numbers, scalar=2)"
    ]
@@ -2254,7 +2302,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 65,
+   "execution_count": 66,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -2263,10 +2311,10 @@
    "outputs": [
     {
      "ename": "SyntaxError",
-     "evalue": "positional argument follows keyword argument (<ipython-input-65-b00eb6f72e84>, line 1)",
+     "evalue": "positional argument follows keyword argument (<ipython-input-66-b00eb6f72e84>, line 1)",
      "output_type": "error",
      "traceback": [
-      "\u001b[0;36m  File \u001b[0;32m\"<ipython-input-65-b00eb6f72e84>\"\u001b[0;36m, line \u001b[0;32m1\u001b[0m\n\u001b[0;31m    scaled_average_evens(numbers=numbers, 2)\u001b[0m\n\u001b[0m                                         ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m positional argument follows keyword argument\n"
+      "\u001b[0;36m  File \u001b[0;32m\"<ipython-input-66-b00eb6f72e84>\"\u001b[0;36m, line \u001b[0;32m1\u001b[0m\n\u001b[0;31m    scaled_average_evens(numbers=numbers, 2)\u001b[0m\n\u001b[0m                                         ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m positional argument follows keyword argument\n"
      ]
     }
    ],
@@ -2287,7 +2335,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 66,
+   "execution_count": 67,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -2301,7 +2349,7 @@
      "traceback": [
       "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
       "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-66-d910518345ec>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mscaled_average_evens\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnumbers\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;32m<ipython-input-67-d910518345ec>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mscaled_average_evens\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnumbers\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
       "\u001b[0;31mTypeError\u001b[0m: scaled_average_evens() missing 1 required positional argument: 'scalar'"
      ]
     }
@@ -2312,7 +2360,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 67,
+   "execution_count": 68,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -2326,7 +2374,7 @@
      "traceback": [
       "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
       "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-67-2e7ca1d2a555>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mscaled_average_evens\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnumbers\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m3\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;32m<ipython-input-68-2e7ca1d2a555>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mscaled_average_evens\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnumbers\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m3\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
       "\u001b[0;31mTypeError\u001b[0m: scaled_average_evens() takes 2 positional arguments but 3 were given"
      ]
     }
@@ -2361,7 +2409,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 68,
+   "execution_count": 69,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -2379,7 +2427,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 69,
+   "execution_count": 70,
    "metadata": {
     "slideshow": {
      "slide_type": "-"
@@ -2410,7 +2458,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 70,
+   "execution_count": 71,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -2425,7 +2473,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 71,
+   "execution_count": 72,
    "metadata": {
     "slideshow": {
      "slide_type": "-"
@@ -2454,7 +2502,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 72,
+   "execution_count": 73,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -2467,7 +2515,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 73,
+   "execution_count": 74,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -2502,7 +2550,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 74,
+   "execution_count": 75,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -2519,7 +2567,7 @@
     "        scalar (float, optional): multiplies the average; defaults to 1\n",
     "\n",
     "    Returns:\n",
-    "        float: (scaled) average\n",
+    "        scaled_average (float)\n",
     "    \"\"\"\n",
     "    numbers = [round(n) for n in numbers]\n",
     "    evens = [n for n in numbers if n % 2 == 0]\n",
@@ -2544,7 +2592,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 75,
+   "execution_count": 76,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -2557,37 +2605,13 @@
        "7.0"
       ]
      },
-     "execution_count": 75,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "average_evens(numbers)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 76,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "fragment"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "14.0"
-      ]
-     },
      "execution_count": 76,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "average_evens(numbers, 2)"
+    "average_evens(numbers)"
    ]
   },
   {
@@ -2610,6 +2634,30 @@
      "output_type": "execute_result"
     }
    ],
+   "source": [
+    "average_evens(numbers, 2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 78,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "14.0"
+      ]
+     },
+     "execution_count": 78,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "average_evens(numbers, scalar=2)"
    ]
@@ -2640,7 +2688,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 78,
+   "execution_count": 79,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -2657,7 +2705,7 @@
     "        scalar (float, optional): multiplies the average; defaults to 1\n",
     "\n",
     "    Returns:\n",
-    "        float: (scaled) average\n",
+    "        scaled_average (float)\n",
     "    \"\"\"\n",
     "    numbers = [round(n) for n in numbers]\n",
     "    evens = [n for n in numbers if n % 2 == 0]\n",
@@ -2678,7 +2726,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 79,
+   "execution_count": 80,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -2691,7 +2739,7 @@
        "7.0"
       ]
      },
-     "execution_count": 79,
+     "execution_count": 80,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2713,7 +2761,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 80,
+   "execution_count": 81,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -2726,7 +2774,7 @@
        "14.0"
       ]
      },
-     "execution_count": 80,
+     "execution_count": 81,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2748,7 +2796,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 81,
+   "execution_count": 82,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -2762,7 +2810,7 @@
      "traceback": [
       "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
       "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-81-12eb635366db>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0maverage_evens\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnumbers\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m2\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;32m<ipython-input-82-12eb635366db>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0maverage_evens\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnumbers\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m2\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
       "\u001b[0;31mTypeError\u001b[0m: average_evens() takes 1 positional argument but 2 were given"
      ]
     }
@@ -2775,7 +2823,7 @@
    "cell_type": "markdown",
    "metadata": {
     "slideshow": {
-     "slide_type": "slide"
+     "slide_type": "skip"
     }
    },
    "source": [
@@ -2803,10 +2851,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 82,
+   "execution_count": 83,
    "metadata": {
     "slideshow": {
-     "slide_type": "slide"
+     "slide_type": "skip"
     }
    },
    "outputs": [
@@ -2816,7 +2864,7 @@
        "<function __main__.<lambda>(x)>"
       ]
      },
-     "execution_count": 82,
+     "execution_count": 83,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2842,10 +2890,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 83,
+   "execution_count": 84,
    "metadata": {
     "slideshow": {
-     "slide_type": "fragment"
+     "slide_type": "skip"
     }
    },
    "outputs": [],
@@ -2866,10 +2914,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 84,
+   "execution_count": 85,
    "metadata": {
     "slideshow": {
-     "slide_type": "fragment"
+     "slide_type": "skip"
     }
    },
    "outputs": [
@@ -2879,7 +2927,7 @@
        "function"
       ]
      },
-     "execution_count": 84,
+     "execution_count": 85,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2890,10 +2938,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 85,
+   "execution_count": 86,
    "metadata": {
     "slideshow": {
-     "slide_type": "fragment"
+     "slide_type": "skip"
     }
    },
    "outputs": [
@@ -2903,7 +2951,7 @@
        "True"
       ]
      },
-     "execution_count": 85,
+     "execution_count": 86,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2923,41 +2971,6 @@
     "Now we may call `add_three()` as if we defined it with the `def` statement."
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": 86,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "fragment"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "42"
-      ]
-     },
-     "execution_count": 86,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "add_three(39)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "source": [
-    "Alternatively, we could call an `function` object created with a `lambda` expression right away (i.e., without assigning it to a variable), which looks quite weird for now as we need *two* pairs of parentheses: The first one serves as a delimiter whereas the second represents the call operator."
-   ]
-  },
   {
    "cell_type": "code",
    "execution_count": 87,
@@ -2978,6 +2991,41 @@
      "output_type": "execute_result"
     }
    ],
+   "source": [
+    "add_three(39)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Alternatively, we could call an `function` object created with a `lambda` expression right away (i.e., without assigning it to a variable), which looks quite weird for now as we need *two* pairs of parentheses: The first one serves as a delimiter whereas the second represents the call operator."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 88,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "42"
+      ]
+     },
+     "execution_count": 88,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "(lambda x: x + 3)(39)"
    ]
@@ -2992,7 +3040,7 @@
    "source": [
     "The main point of having functions without a reference to them is to use them in a situation where we know ahead of time that we use the function only *once*.\n",
     "\n",
-    "Popular applications of lambda expressions occur in combination with the **map-filter-reduce** paradigm (cf., [Chapter 7](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/07_sequences_00_lecture.ipynb#Lambda-Expressions)) or when we do \"number crunching\" with **arrays** and **data frames** (cf., Chapter 9)."
+    "Popular applications of lambda expressions occur in combination with the **map-filter-reduce** paradigm (cf., [Chapter 8](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/08_mfr_00_lecture.ipynb#Lambda-Expressions))."
    ]
   },
   {
@@ -3077,7 +3125,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 88,
+   "execution_count": 89,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -3099,30 +3147,6 @@
     "This creates the variable `math` that references a **[module object](https://docs.python.org/3/glossary.html#term-module)** (i.e., type `module`) in memory."
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": 89,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "fragment"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<module 'math' from '/home/webartifex/.pyenv/versions/3.7.6/lib/python3.7/lib-dynload/math.cpython-37m-x86_64-linux-gnu.so'>"
-      ]
-     },
-     "execution_count": 89,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "math"
-   ]
-  },
   {
    "cell_type": "code",
    "execution_count": 90,
@@ -3135,7 +3159,7 @@
     {
      "data": {
       "text/plain": [
-       "140451956463472"
+       "<module 'math' from '/home/webartifex/.pyenv/versions/anaconda3-2019.10/lib/python3.7/lib-dynload/math.cpython-37m-x86_64-linux-gnu.so'>"
       ]
      },
      "execution_count": 90,
@@ -3144,7 +3168,7 @@
     }
    ],
    "source": [
-    "id(math)"
+    "math"
    ]
   },
   {
@@ -3159,7 +3183,7 @@
     {
      "data": {
       "text/plain": [
-       "module"
+       "139687472778032"
       ]
      },
      "execution_count": 91,
@@ -3167,6 +3191,30 @@
      "output_type": "execute_result"
     }
    ],
+   "source": [
+    "id(math)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 92,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "module"
+      ]
+     },
+     "execution_count": 92,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "type(math)"
    ]
@@ -3188,7 +3236,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 92,
+   "execution_count": 93,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -3256,7 +3304,7 @@
        " 'trunc']"
       ]
      },
-     "execution_count": 92,
+     "execution_count": 93,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3278,7 +3326,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 93,
+   "execution_count": 94,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -3291,7 +3339,7 @@
        "3.141592653589793"
       ]
      },
-     "execution_count": 93,
+     "execution_count": 94,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3302,7 +3350,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 94,
+   "execution_count": 95,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -3315,7 +3363,7 @@
        "2.718281828459045"
       ]
      },
-     "execution_count": 94,
+     "execution_count": 95,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3326,7 +3374,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 95,
+   "execution_count": 96,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -3339,7 +3387,7 @@
        "<function math.sqrt(x, /)>"
       ]
      },
-     "execution_count": 95,
+     "execution_count": 96,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3350,7 +3398,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 96,
+   "execution_count": 97,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -3375,7 +3423,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 97,
+   "execution_count": 98,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -3388,7 +3436,7 @@
        "1.4142135623730951"
       ]
      },
-     "execution_count": 97,
+     "execution_count": 98,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3414,7 +3462,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 98,
+   "execution_count": 99,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -3427,7 +3475,7 @@
        "2.0"
       ]
      },
-     "execution_count": 98,
+     "execution_count": 99,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3449,7 +3497,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 99,
+   "execution_count": 100,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -3462,7 +3510,7 @@
        "10.0"
       ]
      },
-     "execution_count": 99,
+     "execution_count": 100,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3486,7 +3534,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 100,
+   "execution_count": 101,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -3499,7 +3547,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 101,
+   "execution_count": 102,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -3512,7 +3560,7 @@
        "4.0"
       ]
      },
-     "execution_count": 101,
+     "execution_count": 102,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3545,7 +3593,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 102,
+   "execution_count": 103,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -3558,7 +3606,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 103,
+   "execution_count": 104,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -3568,10 +3616,10 @@
     {
      "data": {
       "text/plain": [
-       "<module 'random' from '/home/webartifex/.pyenv/versions/3.7.6/lib/python3.7/random.py'>"
+       "<module 'random' from '/home/webartifex/.pyenv/versions/anaconda3-2019.10/lib/python3.7/random.py'>"
       ]
      },
-     "execution_count": 103,
+     "execution_count": 104,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3593,7 +3641,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 104,
+   "execution_count": 105,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -3667,7 +3715,7 @@
        " 'weibullvariate']"
       ]
      },
-     "execution_count": 104,
+     "execution_count": 105,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3689,7 +3737,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 105,
+   "execution_count": 106,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -3702,7 +3750,7 @@
        "<function Random.random>"
       ]
      },
-     "execution_count": 105,
+     "execution_count": 106,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3713,7 +3761,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 106,
+   "execution_count": 107,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -3738,7 +3786,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 107,
+   "execution_count": 108,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -3748,10 +3796,10 @@
     {
      "data": {
       "text/plain": [
-       "0.09696128509713109"
+       "0.5217159553088394"
       ]
      },
-     "execution_count": 107,
+     "execution_count": 108,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3773,7 +3821,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 108,
+   "execution_count": 109,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -3783,10 +3831,10 @@
     {
      "data": {
       "text/plain": [
-       "<bound method Random.choice of <random.Random object at 0x55795e713bf0>>"
+       "<bound method Random.choice of <random.Random object at 0x5648648822a0>>"
       ]
      },
-     "execution_count": 108,
+     "execution_count": 109,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3797,7 +3845,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 109,
+   "execution_count": 110,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -3822,7 +3870,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 110,
+   "execution_count": 111,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -3832,10 +3880,10 @@
     {
      "data": {
       "text/plain": [
-       "3"
+       "10"
       ]
      },
-     "execution_count": 110,
+     "execution_count": 111,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3859,7 +3907,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 111,
+   "execution_count": 112,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -3872,7 +3920,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 112,
+   "execution_count": 113,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -3885,7 +3933,7 @@
        "0.6394267984578837"
       ]
      },
-     "execution_count": 112,
+     "execution_count": 113,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3896,7 +3944,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 113,
+   "execution_count": 114,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -3909,7 +3957,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 114,
+   "execution_count": 115,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -3922,7 +3970,7 @@
        "0.6394267984578837"
       ]
      },
-     "execution_count": 114,
+     "execution_count": 115,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3978,7 +4026,7 @@
     }
    },
    "source": [
-    "[numpy](http://www.numpy.org/) is the de-facto standard in the Python world for handling **array-like** data. That is a fancy word for data that can be put into a matrix or vector format. We look at it in depth in [Chapter 9](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/09_arrays_00_lecture.ipynb).\n",
+    "[numpy](http://www.numpy.org/) is the de-facto standard in the Python world for handling **array-like** data. That is a fancy word for data that can be put into a matrix or vector format.\n",
     "\n",
     "As [numpy](http://www.numpy.org/) is *not* in the [standard library](https://docs.python.org/3/library/index.html), it must be *manually* installed, for example, with the [pip](https://pip.pypa.io/en/stable/) tool. As mentioned in [Chapter 0](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/00_intro_00_lecture.ipynb#Markdown-vs.-Code-Cells), to execute terminal commands from within a Jupyter notebook, we start a code cell with an exclamation mark.\n",
     "\n",
@@ -3987,7 +4035,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 115,
+   "execution_count": 116,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -3998,7 +4046,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Requirement already satisfied: numpy in /home/webartifex/.pyenv/versions/3.7.6/envs/v-ipp/lib/python3.7/site-packages (1.18.1)\n"
+      "Requirement already satisfied: numpy in /home/webartifex/.pyenv/versions/anaconda3-2019.10/lib/python3.7/site-packages (1.17.2)\n"
      ]
     }
    ],
@@ -4019,7 +4067,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 116,
+   "execution_count": 117,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -4043,7 +4091,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 117,
+   "execution_count": 118,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -4053,10 +4101,10 @@
     {
      "data": {
       "text/plain": [
-       "<module 'numpy' from '/home/webartifex/.pyenv/versions/3.7.6/envs/v-ipp/lib/python3.7/site-packages/numpy/__init__.py'>"
+       "<module 'numpy' from '/home/webartifex/.pyenv/versions/anaconda3-2019.10/lib/python3.7/site-packages/numpy/__init__.py'>"
       ]
      },
-     "execution_count": 117,
+     "execution_count": 118,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -4078,7 +4126,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 118,
+   "execution_count": 119,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -4089,30 +4137,6 @@
     "vec = np.array(numbers)"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": 119,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "fragment"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([ 7, 11,  8,  5,  3, 12,  2,  6,  9, 10,  1,  4])"
-      ]
-     },
-     "execution_count": 119,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "vec"
-   ]
-  },
   {
    "cell_type": "code",
    "execution_count": 120,
@@ -4125,7 +4149,7 @@
     {
      "data": {
       "text/plain": [
-       "numpy.ndarray"
+       "array([ 7, 11,  8,  5,  3, 12,  2,  6,  9, 10,  1,  4])"
       ]
      },
      "execution_count": 120,
@@ -4133,6 +4157,30 @@
      "output_type": "execute_result"
     }
    ],
+   "source": [
+    "vec"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 121,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "numpy.ndarray"
+      ]
+     },
+     "execution_count": 121,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "type(vec)"
    ]
@@ -4152,7 +4200,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 121,
+   "execution_count": 122,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -4165,7 +4213,7 @@
        "array([14, 22, 16, 10,  6, 24,  4, 12, 18, 20,  2,  8])"
       ]
      },
-     "execution_count": 121,
+     "execution_count": 122,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -4187,7 +4235,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 122,
+   "execution_count": 123,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -4200,7 +4248,7 @@
        "[7, 11, 8, 5, 3, 12, 2, 6, 9, 10, 1, 4, 7, 11, 8, 5, 3, 12, 2, 6, 9, 10, 1, 4]"
       ]
      },
-     "execution_count": 122,
+     "execution_count": 123,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -4222,7 +4270,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 123,
+   "execution_count": 124,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -4235,7 +4283,7 @@
        "78"
       ]
      },
-     "execution_count": 123,
+     "execution_count": 124,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -4246,7 +4294,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 124,
+   "execution_count": 125,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -4259,7 +4307,7 @@
        "7"
       ]
      },
-     "execution_count": 124,
+     "execution_count": 125,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -4298,7 +4346,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 125,
+   "execution_count": 126,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -4311,7 +4359,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 126,
+   "execution_count": 127,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -4324,7 +4372,7 @@
        "<module 'sample_module' from '/home/webartifex/repos/intro-to-python/sample_module.py'>"
       ]
      },
-     "execution_count": 126,
+     "execution_count": 127,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -4350,7 +4398,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 127,
+   "execution_count": 128,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -4375,7 +4423,7 @@
        " 'average_odds']"
       ]
      },
-     "execution_count": 127,
+     "execution_count": 128,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -4397,7 +4445,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 128,
+   "execution_count": 129,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -4410,7 +4458,7 @@
        "<function sample_module.average_evens(numbers, *, scalar=1)>"
       ]
      },
-     "execution_count": 128,
+     "execution_count": 129,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -4421,7 +4469,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 129,
+   "execution_count": 130,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -4443,7 +4491,7 @@
       "        scalar (float, optional): multiplies the average; defaults to 1\n",
       "    \n",
       "    Returns:\n",
-      "        float: (scaled) average\n",
+      "        scaled_average (float)\n",
       "\n"
      ]
     }
@@ -4454,7 +4502,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 130,
+   "execution_count": 131,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -4467,7 +4515,7 @@
        "7.0"
       ]
      },
-     "execution_count": 130,
+     "execution_count": 131,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -4478,7 +4526,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 131,
+   "execution_count": 132,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -4491,7 +4539,7 @@
        "14.0"
       ]
      },
-     "execution_count": 131,
+     "execution_count": 132,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -4571,7 +4619,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.6"
+   "version": "3.7.4"
   },
   "livereveal": {
    "auto_select": "code",
diff --git a/02_functions_01_review.ipynb b/02_functions_01_review.ipynb
index 3b58b22..36a95ea 100644
--- a/02_functions_01_review.ipynb
+++ b/02_functions_01_review.ipynb
@@ -18,7 +18,9 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Read [Chapter 2](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/02_functions_00_lecture.ipynb) of the book. Then, work through the questions below."
+    "The questions below assume that you have read [Chapter 2](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/02_functions_00_lecture.ipynb) in the book.\n",
+    "\n",
+    "Be concise in your answers! Most questions can be answered in *one* sentence."
    ]
   },
   {
@@ -28,13 +30,6 @@
     "### Essay Questions "
    ]
   },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Answer the following questions *briefly*!"
-   ]
-  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -46,7 +41,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    " "
+    " < your answer >"
    ]
   },
   {
@@ -60,7 +55,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    " "
+    " < your answer >"
    ]
   },
   {
@@ -74,7 +69,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    " "
+    " < your answer >"
    ]
   },
   {
@@ -88,7 +83,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    " "
+    " < your answer >"
    ]
   },
   {
@@ -102,7 +97,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    " "
+    " < your answer >"
    ]
   },
   {
@@ -116,7 +111,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    " "
+    " < your answer >"
    ]
   },
   {
@@ -130,7 +125,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    " "
+    " < your answer >"
    ]
   },
   {
@@ -158,7 +153,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    " "
+    " < your answer >"
    ]
   },
   {
@@ -172,7 +167,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    " "
+    " < your answer >"
    ]
   },
   {
@@ -186,7 +181,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    " "
+    " < your answer >"
    ]
   },
   {
@@ -200,7 +195,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    " "
+    " < your answer >"
    ]
   }
  ],
@@ -220,7 +215,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.6"
+   "version": "3.7.4"
   },
   "toc": {
    "base_numbering": 1,
diff --git a/02_functions_02_exercises.ipynb b/02_functions_02_exercises.ipynb
index cc0accd..e749839 100644
--- a/02_functions_02_exercises.ipynb
+++ b/02_functions_02_exercises.ipynb
@@ -18,7 +18,9 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Read [Chapter 2](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/02_functions_00_lecture.ipynb) of the book. Then, work through the exercises below. The `...` indicate where you need to fill in your answers. You should not need to create any additional code cells."
+    "The exercises below assume that you have read [Chapter 2](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/02_functions_00_lecture.ipynb) in the book.\n",
+    "\n",
+    "The `...`'s in the code cells indicate where you need to fill in code snippets. The number of `...`'s within a code cell give you a rough idea of how many lines of code are needed to solve the task. You should not need to create any additional code cells for your final solution. However, you may want to use temporary code cells to try out some ideas."
    ]
   },
   {
@@ -66,7 +68,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "**Q2**: Encapsulate the logic into a function `sphere_volume()` that takes one *positional* argument `radius` and one *keyword-only* argument `digits` defaulting to `5`. The volume should be returned as a `float` object under *all* circumstances. Document your work appropriately in a docstring according to [Google's Python Style Guide](https://github.com/google/styleguide/blob/gh-pages/pyguide.md)."
+    "**Q2**: Encapsulate the logic into a function `sphere_volume()` that takes one *positional* argument `radius` and one *keyword-only* argument `digits` defaulting to `5`. The volume should be returned as a `float` object under *all* circumstances."
    ]
   },
   {
@@ -76,7 +78,17 @@
    "outputs": [],
    "source": [
     "def sphere_volume(...):\n",
-    "    ..."
+    "    \"\"\"Calculate the volume of a sphere.\n",
+    "\n",
+    "    Args:\n",
+    "        radius (float): radius of the sphere\n",
+    "        digits (optional, int): number of digits\n",
+    "            for rounding the resulting volume\n",
+    "\n",
+    "    Returns:\n",
+    "        volume (float)\n",
+    "    \"\"\"\n",
+    "    return ..."
    ]
   },
   {
@@ -151,7 +163,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    " "
+    " < your answer >"
    ]
   },
   {
@@ -193,7 +205,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    " "
+    " < your answer >"
    ]
   }
  ],
@@ -213,7 +225,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.6"
+   "version": "3.7.4"
   },
   "toc": {
    "base_numbering": 1,
diff --git a/sample_module.py b/sample_module.py
index b16e7b2..3a097e1 100644
--- a/sample_module.py
+++ b/sample_module.py
@@ -43,7 +43,7 @@ def average(numbers, *, scalar=1):
         scalar (float, optional): multiplies the average; defaults to 1
 
     Returns:
-        float: (scaled) average
+        scaled_average (float)
     """
     return _scaled_average(_round_all(numbers), scalar)
 
@@ -57,7 +57,7 @@ def average_evens(numbers, *, scalar=1):
         scalar (float, optional): multiplies the average; defaults to 1
 
     Returns:
-        float: (scaled) average
+        scaled_average (float)
     """
     return _scaled_average([n for n in _round_all(numbers) if n % 2 == 0], scalar)
 
@@ -71,6 +71,6 @@ def average_odds(numbers, *, scalar=1):
         scalar (float, optional): multiplies the average; defaults to 1
 
     Returns:
-        float: (scaled) average
+        scaled_average (float)
     """
     return _scaled_average([n for n in _round_all(numbers) if n % 2 != 0], scalar)

From 0151478bae69fdfa5d9f2e4867b1d001f85c55ff Mon Sep 17 00:00:00 2001
From: Alexander Hess <alexander@webartifex.biz>
Date: Mon, 30 Mar 2020 05:30:09 +0200
Subject: [PATCH 088/242] Add video and streamline content

---
 03_conditionals_00_lecture.ipynb   | 1262 ++++++++++++++++++----------
 03_conditionals_01_review.ipynb    |   27 +-
 03_conditionals_02_exercises.ipynb |   41 +-
 3 files changed, 866 insertions(+), 464 deletions(-)

diff --git a/03_conditionals_00_lecture.ipynb b/03_conditionals_00_lecture.ipynb
index 7b769c4..61a1d3f 100644
--- a/03_conditionals_00_lecture.ipynb
+++ b/03_conditionals_00_lecture.ipynb
@@ -1,5 +1,53 @@
 {
  "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "A **video presentation** of the contents in this chapter is shown below. A playlist with *all* chapters as videos is linked [here](https://www.youtube.com/playlist?list=PL-2JV1G3J10lQ2xokyQowcRJI5jjNfW7f)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/jpeg": 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8E7HRTObSfJPNNyngOEY5jG7sdpHxqELts2JJXbpZHyOwBukc57sDsG5xJx1K6lihSLMxcbkyY00A/YBSxeIbVytYIa1uUTc6lxnTeT0FkWR4ZH/PUv1ut/fjWOXZWmdG9kjDtfG5r2OGMtcwhzT16dCAtr25mkLRReGPa47iCs13RPuvqf6/a/vPWBXfftyTyyTzO3yyvdJI8gAue8lznENAAySewLoUaLCxjWncAFLE1BUqueNCSfUypnwS7vTT9T1RjWG1C+rVpyPY2QQPsOeZ5mseC3mctgDTjpud5iQeXFFg6hpFXU5gzvyO9Np88zY2Rmy3kMswvlEYDS9jSWZwMjtyvHwhdgfU1DTbE7awud7y1rEoeYY7FZ7jsm5bS5jJGPLd+Dt2g4K5cTWq9fT62l1547Tm2Zb1ueHf3vz3xtgiigdI1rpA2JuS7aBlwx58c4sPbzHvZheP6cvHSJm3G67jao/g4zDJ2ZBbI/7naGLazEGY+G0xIUUXtpavbgY6KG1ZhifnfHFPLHG/Iwd7GODXdOnULxIum5odYhfPse5hlpjoiL3aHpU92dteuzfK5r3AZwA2Nhe9znHo1oDT1PzDzrwoHCY3/f8AtCxwaHEWMiekT+49UWV1XWTPUoVOWGigyy0P3ZMvfE5nJLceJgnHacrFLOs4Wt94T6hJE+GvCYAwyxvYbHPdtBh3ABzQMEu7PGGMrXVNMFped9upt9Vvw7azg9tMGC33o/tEOvw+EHyhYJERblWRSTuZ6nBT1anZsv5cERmMj9j37d9aaNvixtLjlz2joPOo2vq11aYqMLDoQR6rdh67qFVtVurSCJ0sZXFo6D8i+oi2LSvq+IiIu2rA+V7Io2l8kj2xxsaMue97g1jWjzuJIH71xlYWuLXAhzSWuB7QQcEH58hTPuPasYNTqQtr1ZDPZjaZpYRJYia4FpEEhd9iyM9QPOVFdb/9VZ/WJv7jlXbWcaxpkWABnjJP+FdfhmNwzawdJLiCI0gA+eq8aIisKki4WPIf+a7+RXNcLHkP/Nd/IrDtCp0/iHVbsIiLyhfohERMoiIiZRERERERERERMplEREREREREREREREREREREREREREREREREWu3uj/uxF+z4P79pVmrM90f92Iv2fB/ftKs16Rsr8pT6Lw32i/mNb9RRERdBcZEWV4S0d2oXqtNrtvfErWF3aWMALpHAechjXkD4wFJWUtJ1Bl+GhVmrTU601utYdZfP37FVI5jZoXNAikfGd4DOgPTzeNWq4ptN2Ug7iSNwJgE/PSdCr2HwD6zMwIFyADMuIEkCARpGpAuBqoKiIrKooiIURERERERERT/uNa5aZeiotmIqzMuvkh2R4e5tKd4Jft39DGw4z5lAG9gWW4S1k6fbjtiMSmNszdhdsB50EsBO4A4wJM9nXCxIVanRy1nuAgEN8yC6f3Cv1sT2mFp0y4ktc+xmzSGRHKQ6w+qKa6Nenm0LWmyzSytik0hsbZJHyNjbzpxiMOJDBhreg+SPiUKWQp6tLFVtVGhnKuOrulJaS8Gs5749js4AzIc5Bz07FnEUu0aI1BafRwJ+QUMFiBReSTYteLc2OA+ZCx6EohCsKmrKuOo6ffraNJp1SxHtpxX7MrHm2+e3HG+WSvOH5gYwTM2tA+8PXrkQXiTTu87lqrku73sTQhxxlzY3uax5x0yWgH96nuqae3UtSrayyxVZSk7ynvPkswxvpSVo4o54ZIXOErpDyfF2tIcZG9cHKg3Fmoi5euWm5DZ7M0rARgiNz3GMEfK27crlYAnML3y+9+qfkdfKN0L6La7QGGwAzns4AvTjdGo+G/Gd8rFoi9ek2mQytkkrxWWtz9hmMgjcSCAXcp7XHB64zjouo4kCQJ5L59gBcATHPh6XXRNA9m3ex7N7BIze1zd7HeS9uR4zDg4I6dF1qZ91ycS2aEojjiEmjadIIohtiiD43uEcbfvWNzgD4gFDFqw9U1KYeRE7lvxtAUKzqYMgb+Ky3B+pspX6luRrnMrzMlc1mN7g09Q3cQM/lK8GoTCSaWQAgSSSPAPaA95cAcefquhfVPsxnz74hazWcaYpbgSfMgD6BfERFNakXCx5D/zXfyK5rhY8h/5rv5FYdoVOn8Q6rdhcZQdpx1ODgbi3Jx0G4Alv5QFyXVbic9ha2R8LjjEkYjL24IPQSsczrjHVp7T2dq8oX6IVVUKGo0RBFBWu12TPgieYBpLL9h0WnatJIbBdM6lLKJm1CbIZC6Q7Q7e1uBIdN0/WSa5szSCR8tsWpYu8hyoDacYIq/ibhCYY4sdDJ453EO7M3WaJXFsesTSOD5Yy1h0x7hJAQ2aMhtXIewuaHN7W5GcL1e9k3pG5/Bp/1NWX1idQPRV20gNJUU4L1LUn6hFXuyzGRlGR12L/AJA12ThmmcggVxzo5CZLflYY9xmLNzGs2+IaNqcE9mSnVfE+V87pbL+83WZGy6pXleyKVk7W3IjV745ZsRNkiaA0P3OcDOPeyb0jc/g0/wCprhHRkdu26nadscWO2t047XAAlrsVPFdgg4PxhY7a8gBZ7O15UXuU9fdAHMs2ROI3ANaNMj3EabYfG6RpD2Cbv4Vmu2v2ZDsYjLl0WWcRiKVwdYfLzGERRM0yMOcItQ5ghsTTPEdV0hobXPic9vLbujcHy7ZbZqPiAdJqlmNpfHGHPGmsBklkbFEwF1TBe+R7GAdpc9oHUhfYqUjy8M1O04xu2SBrdOcWP2tdsfip4rtr2nB64cPjQVbaD0Q0+Z9VHWVNZZZY1kk7a3f00rie852mGTUnSvY/fM2RkHeTmsj2hzmO5uW+LGD6LlW/7z045oJdRvllc2mSOqBvOcwunfYibLBBZgjkJxCx7Q4tjGQMvGe97JvSNz+DT/qa8WryR02tfb1qSqx7i1jrD9Kga5waXlrXS1QHODWudgeZpPmWO0JIgDyH2VnIBxUVj0S9CH4qXrRbBGzdPertmtV20NPgbTsSizhsvfEU8ryCWdLBa4mfD/Roeh3Y56jnQWBt5ThJJJXYymO+9Tmu1214rUojrPjnrxxxsdJhjawccwAtkVqSOKJ08utSRwMcxj5pH6UyJj5SxsTHSOq7Wuc6WIAE5JkZjtC5M2ugNpusTOqhjpDZDtLMAjZnfIZhV2bBtdl2cDBUjVcR/wAqIptB/wCFn0WCoMFhofBq88zCXgOi97HgmN2yQZbVPVrjgjzHoV3PpSNc1p1O0HPJDGlunBzy0Fzg0GplxABPTzBaMq3Zll0WJfp8zQXO1K2AASSWaeAAO0kmp0C5e9k/pG5/Bp/1NYjmszyWURYv3sn9I3P4NP8Aqae9k/pG5/Bp/wBTSOaTyWURYv3sn9I3P4NP+pp72T+kbn8Gn/U0jmk8llEWL97J/SNz+DT/AKmnvZP6Rufwaf8AU0jmk8llEWL97J/SNz+DT/qae9k/pG5/Bp/1NI5pPJZRFi/eyf0jc/g0/wCpp72T+kbn8Gn/AFNI5pPJZRFi/eyf0jc/g0/6mnvZP6Rufwaf9TSOaTyVFe6P+7EX7Pg/v2lWasL3Q1V7NWiDrM8p7wgO57awOOfa6fY4GjH7vOq55Tvwr/8ASL/GvR9lflafReI+0DQdoVr/ANR4rsRdfKd+Ff8A6Rf41wkDm7TzHHx2AgiPBDnAHsYD51flccMB3j5/4Us7l+ox1dYoTzODImzFj3k4awTRSQbnE+S0GUEnzAErPcLaFb0h+qWLsMkEVbT7laOWRpZFZsTgQ1467ndJg45dluQAOuOirxd0tmR7WMfI9zIxiNrnuc1g+JjScNH5FUr4U1HEg2IAPQEm3qQuhg9oNosALSS1xc0gxdwAvYyLNO7QjfbpREVxctFYOpa3PotbSoKIiY6zQi1G298MUptOtSSbIZTI0nksZHtDWkdHnz9VXym9kU9Vq6cZL9ejPRqto2W2RMd1aGR7oJqvKjcJpNkjgY8tOWjzEFUcY1pLM4lsmRE7jEgT/wAwutsx7mip2TofAymQD8QmCYvHPSd0rHd07Toq2pzsrsEcErYbMUY7I22IY5XMA7A0Pc8ADoBhRlZ7j7WI7+oT2IQ5sB5cUAd5XJgiZCwkeYuDN2PNuwsCt2FDhRYH6wJ9N/NVdommcTUNP4cxiNIndy4cl7tF0i1dl5NSCSxJguLY252tHQucexjckDJIGSB51w1bTLFSV0FmGSCVuCWSNLTg9jh5nNOD1GR0Kk1J5h4btPjO11rWIak5HQvgipyWGRnH3vMJOPypxKTLoeizPJMkcmoVWuJy4wMkjfEwk9drNzmgeYOwtIxL+0i2XNl5zlmeG6IjnO5WjgKfYEyc4YH7ssFwbHGYOaZ5RvUOWS4a0aW/ZjqwlrXP3Fz5DtjijjaXySyO+9Y1rSf9B2kLGqX9y4Zk1Rjeskuh6nHCB5TpTGwhrf8AuLWvW/FVDTpOc3WPv/Kq7PotrYhjHaE358vPReXWOGYWVX3KN+O/BBKyK1iCatJA6XIheY5uroXOaWh3TrgY7cRpS/g3A0niBzvI73os+bmvuDk/vy1x/cVEFDDOdL2uM5TE2/tBvEDfw0hTx1NgbTqMGXM2S0TAIc5tpJMGN5N53IiIrSoIvq+L26LpVi7M2vVidNM4EhjcDDWjLnOc4hrGD5TiB1Cw5waJJgKTGOe4NaJJ0A1K8SLKa/w/coGMWoTEJmudE8PiljkDSA7ZLC5zHEEjIzkZGe0LFrDHteMzTI4hZqUn03FrwQRuIg/NZ7jLVorbqRi34r6ZSqSb2hv2WvGWybcE5Zk9CsCiLFOmGNDQpV6zqry92pRS3gaKKGpqmpSQxWJKUdWKtFOwSQie5M6PnPjd0e6NkbiAcjLh8xESUr4JswyVdT02aeKsb0dV9eed2yBs9OZ0gjlf2RtkY943HoCB8wOjGT2XmJjhmE6cplW9mECuJiYdE/3ZHZdbfFETvXfxG5l7SYdTdDBDai1B+nzurxMhZZY+v3zDM+OMBglbtezIAyMZ82IapfxC+GppUGmNsV7ViS87ULD6sgmhhAg72hh5zfEkeQXuO3s6D4lEFjBiGGNJMdP8axy0ss7UM1RPxZW5o/ujfG/SeczeUXCx5D/zXfyK5rhY8h/5rv5FWnaFUafxDqt2Fwm3bXbA0v2naHEhpdjxQ4gEhucdgK5ovKF+iFWp7nt2MFsN4SB8cEszpBHBLNdEtY3S50VYsMFiKtG1xex56O3CQOIHvl4T1AMaGWWPAqwwOhmnncHPbLFJLOZWRhjpDE2SsN0JBaGOcHAvhM7RbziHlaexaoAeC7roWg2g2dkToWPbZs9IuTqUQYHRtj2bu+qpcWNbjkNI6xRkeetwRfbO+UTRwRyCQsggtSujqOc2wNkfMq75o382IOw6LAhZ0cGMa2x0T+Ics9i1YDWuHzNRhpxu2cuxp0pdzJdxbTvVrcuJQeZzXNhfhxOdzgSfOsCOCrbL752W5O93yxyMabUnNhMYHMJc+F8kxnAbE7bLFhkTOr+gbPUUW1nNELJpNJlQHSeE9RidSLpYcV5i5wNmWTbCTWMmQ2rGJ7D+XY8ePvZo5w3tny/fINQ0ua53hJK51V8W+SxHBO7c18tZ0boo52tG9rXvPjYbnaD07FnkWHVSTKCmBZQKxwNJCQaPIY2OWu6OB808cRhqWNGkrwucGP2BkemzNB2uwZyfv3k5kaHM6jbhkFcz25Zp3RNfOyux0rw7lRzxbJozho+ztDXCRzpA0HDFJEWTWcdUFJoUDh4X1Fs0doms6eN8u1j7UznNhdLpjm133RVEloFtSw4ySM3DmRM8cN3rHzcC6k5sQdYhMsTaxfYFmzzJxHBTimqOjlhkjjrONebxiH5Fh+WHfLzLMRSGIcFE0WlV/qHA1iWsYzM2SQxPiPPnme10TtMs1hA97YwHR99SwSF3LGRXY4t3NaBP2joOwfMOwfMF9Ra31C7VTawN0RERQU0RERERERERERERERERERFrt7o/7sRfs+D+/aVZqzPdH/diL9nwf37SrNekbK/KU+i8N9ov5jW/UUXVZ7G/pI/6wu1dVnsb+kj/AKwuguQz4gu1ERFFe7RNNNqXlCerX8UuMtudlaEAEDBkf9917Bk9D8SyPH2iR6dekqRSGVscVZ3MJB3ulrxSvc3aMbC55wPix1PasAexS/uwfdaX9Xo/7KuqrnOGIaJsWutzBb/ldBjKZwb3ZfeD2DNyIfb5BRBfV8XJjSSAASSQAACSSewADqT8ytLnriik2o8D34IpZHCu51dgltVorMMturGQDzLFdji5jcEZxnAOT0BIjK106zKglhB6LfXw1WgQKrSDz+/JSLhrW68dazQvRzSVLD4pg6u6Ns9exDkNliEg2PDmOLHB3mxghcOK9ahsMqVakUkVOiyVsImc188kk7xJPNM5gDQ5zg3xR0AaMfEMAih/DMz59+vKYiY4xZbDjqppdlaIiYExOaJ1ib/6ARerSr81WaOxXkdFNE7dHI3GWnBB6EEEEEgggggkEEFeVFuIBEHRVmuLSHNMEbws5rnFFm3EIHNrwQCTnOhqV4q0ck2NvOlEQHMkx0yVg0RRp020xDRAU61epWdmqEk80QFSLua6ZHc1ajXmAdE+Yukaeoe2GN85Y4edrhHg/MSpLoPEVnW/fKpcLZIH0bVqpFsY1tKes0SV+Q5jQWMDQWEffDt8+a1fFGm4gCQACTOgJItYzoTuV7CbPFZgJdBcS1oiZLQCZMiBdo0OvJVwpfwcdmk8QytOJRBp8AcO3k2boZYb+a5rWghRBZrhXXe8nTtkhFmtahdXtVy8x8yMkOa5kgB5crHgODsHz/HkbMUwvpw0TcGOMOBjziFp2fVbTrS8wCHCeGZpaDa9iQbX4XWWrnfw1MHHpBrELoc+Yy1XiRjc9jSAHYHnGVD1INf16GWrDQp1nVqkUzrLxLNz5rFlzOWJZXhjWt2x+IGtGMEk5z0j6xhmOAcSIlxMcPT181LH1GOcxrTOVoaSJgkTpMG0xpuREWT4Upss36NeQZjnuVoZBnGWSTMY8Z8x2kre9wa0uO5U6VM1Hhg1Jj1XjfUlEbZTFIInHDZSxwjcevRryNpPQ9h8y6Faui69ZvcQWNOnle/T7Ul2gaZJ73igijmbX5MXkRSMMMR3tAPQ/GqqHYq+HrueS14gwDYzZ09L2Ku43CU6TQ6m4kZnNuIu2J0JscwjeiIitLnouFjyH/mu/kVzXCx5D/zXfyKw7QqdP4h1W7CIi8oX6IREREREREREREREREREREREREREREREREREREREREREREREREREREWu3uj/ALsRfs+D+/aVZqzPdH/diL9nwf37SrNekbK/KU+i8N9ov5jW/UUXVZ7G/pI/6wu1dVnsb+kj/rC6C5DPiC7V6GUpTC+wGO5McjInyfeiSRr3MZ85LY3np2Y69oXnVgajq77fDb90VaFsOrwMYytAyuzrUlcXObGMOeT2u7eir16rmZYGpAPKVbweHZWD8xgtaSABrA+Sr9d1y1LM8yTSSSyENBfK90jyGgNaC55JIDQAPiAC6UW+BqqeYxCKQdzdjHavpof5Pftc9fO4SAsH8Yao+u2rO+KRksbiySN7ZI3jtY9jg5jhnzggH9yhWYXsc0bwR8ltw1UUqrXkSAQY6GVO+ApHP4kmEhOJ36s20D2OY6C294fnzb2t/eAq/b2D8imFzjZju+poNPgrXrsckVq5HLM7LZ8d8OgrvOyvJJjq4EkZdjGcqHqthWPDi5wizRFt03tPGBvsr2PrUyxtNjs3vPdNx8WWBeDPuyd0m0oi9ej6dNbnirQN3zTPDI29nU9pJ8zQAST5gCs3q/CLoa81mC5TvMqvYy2Kj5HOrmRxY15EkbeZCXjbvbnqfykb312McGuNz/x/ocSqlLB1ajDUa2QN/QSY3mBcxoLmyjKIi3KspRonBNyxSs6g9phqQVZbEczg1wnfGdoiY3eHAHD/AB8EDZ84UXUs7nQ8TWz5/eK5/eqqJqtRe81Hhx0IiBFo81fxVOkKNJ1MEEgzJmSDHAQOXzKy/Bus+99+rc2l4glDntHlOjcCyUNycbtjnYz58KSVJdN0xt+erebbfZqz1KMDIZ45IWWSGumsvlYGsfHGCA0F24nzDsgiJWwrajpJO4EDeAZg/PSNSmG2g6gzKADBJaTMtJEEiCBuGs3ARERWVQUrPBksel2NSsOYzYagrxRywyue2w/BfMI3O5Q2kYacOzuyBjrFFLeHPuHrv6bR/wC/ZUSVXDOeS8PMw7pbK08+Kv45lMNpOptiWSZMmc7xOg4Dci9Ol3HVp4LDMF8E0U7M9m+J7ZG5+bLQvMiskAiCqTXFpDhqFYcetaTWuz6zWnnfZk74lrae+sWd727THtc6azu5ckLDLIQGjJy3s25NeBEWihhxS3k6C/AaC0cTz4q1isa7EQCABJMNmJdEm5NzA5CLAIiIrCpouFjyH/mu/kVzXCx5D/zXfyKw7QqdP4h1W7CIi8oX6IUW7nfGLNYinkZA+AQSiIh7w8uJbuyNoGApTla78ITPj4Z158b3Me21Ww9jixw+y1wcOacjoSP3r2a9oNiHQaute+moOtiOo5recWwxxSljGMjaPGa9oc0l247iHEjLsru1tlM7UhrsozZQIJvAPpdfI4X2gqjDtc9mcin2jiCBbM4acbbtVcXEPE9ShLUhsue2S9IYq4axzw54dEzDiOjRmZnU/GVmsqi+6jWNqfhizJNOH6i2uJQyTayFxNEukrNx9hlJncS4Z6sZ8SyHHtW0Ne0fT6t6zBu09kHPdI978NFtj5ngECSwY2k7+h3YPmWnu1rmMh0EhxM6e6Tw6c+KsnblRlSrLJaHU2tg3OcA3m2+d3DmrkyipvierZit6PwxBetMhkZLYs2t+LEzZJrUpY6QHPRsUoA7CXNyDjC7e9ZuH9b0ytBdt2KWpExSQWpOaWvLgzeMANbh0kZBABw1wJIWvu4EWfcguAg3Am/ImCQFvO2iHHNTOUOaxzpHuudFo3gEgE+gKt/KjPBfFzNTm1CFsD4jQn5DnOeHCQ75mbmgAbR9i/8Asq/4c0yxxHd1O1Y1C5VjqWTBTiqTcrlbS/a4gggYa1hJGC4l3UYAWH4LM0Wm8W7pSZ43APmYdpdK19oPkaW+TlwJ6fGt7dmsDHtLpeMnH3cxHkbFVX7cquq03NYRTPaXJHvBjSerbi3EK/sr5lVRfty/8FCUSyCXkQnm8x4kz39GCeZndnHTtWO1rU7c1ThfS4rMsA1GCA2rDHuEzmbYm4Emc9jpCR98duemc6GbNc7+rRzmn/5Ek+m5W6u3GsA9wmabHgA6l7sob671dOVhdC4nqXbFytA57paMnKsBzHNDX75I8Ncejhuif1HxKL6DwRe06+01b8sulSwvjtQ27D32WyObIBJX2Q8sODuUd3inyx16KMdxnRmt1vWT3xaPeFp8bd0xIsh0tyDdcGPs7wBuB6eMSUbg6Jp1Hh85WgiBxMQR96ysVNp4kVqNM0suZzmuBM2DZlpGo36biOasjgjX7F9k7rGnzaeYpjExkxkJmZtB5reZDGdvXHQHs7VIcqh+F+JrdTQNZsslkdOL7YIZJHOkMQkETS5u8nBDS4jzZx2r063wpYoaOzWYtV1A6gyOtZlLrBdC/nujDmAOG5wbzB5ZcHbTkeN03VdmN7QjMGy7K0XMmAfIXHFVaG3X9iHBheQwvcZAgS4cgTY2EacVd2V9yqV411S1escLvisSVJL8LDI6FzgGPldX3uDM4ftLnbd3zLvq6fLo3Eun1YL1yxXvQvdMy1NzS4ls48bADSQ6Njg7GR1GSCc6hs33ZLveyuOWP7SZvpusrJ27/wBSG0yWBzGl0j+sAgxrvEq40RFy130RERERERERERERERFrt7o/7sRfs+D+/aVZqzPdH/diL9nwf37SrNekbK/KU+i8N9ov5jW/UUXVZ7G/pI/6wu1dVnsb+kj/AKwuguQz4gu1ZuLWWDSpdP2O3vvx2xJkbA1kD4iwjt3Zdn9ywiKD6YfE7jPop0qzqc5d4I8jqiIpn3J9J063ersuzPLzPiOmIN0c+1m8GWffhrMggs2nIHb1UK9YUaZeZtw+/wDS2YPCuxNVtJpAJMSTA+f7C53KGIucww5wHyj/ADK4LcFXIgwiLPXODtUhrd9y0pmVw1ry87dzGO7HviDuZGz53NAWBUGVWPu0g9DK2VaFSkQKjSJvcEW81Me4991GgfbDVvCH4+b3pNjb8+3euHc3+064T9q947QPyeaZa/Iz/wB2d2P3qNaXelrTRWIHmOaF7ZI3jGWub2dD0I8xB6EEg9qzOscXTWIJa7a1KpHYkZJZ7zgdC6y6M7mCYukd4jXEuDW7QD1wqVfDvc85dHZRPDKSfrbmupg8ZSZSbnJlheQI+LO0AdIIvy0uo6iIuguMvXp2pTVxOIX7BZgfWm8Vjt8Eha57PHaduSxvVuD07V5ERYDQDKkXuIAJsNOSLM6VwtqNuF1itSsTQtzmSOMkOLfKEY7ZSMEYaD16LDKcd0W7LUtaW2B7mChptB9baSAJHM5skoA6b3uPjH77ABVevUeHNYyJM66W6RxH+1dweHpOY+rVnK3KIbEy4njOgBOl7C2qhBC+KVd1usyLW9RZGAG88SYHZumijmf/APd7lFVso1O0ptfxAPqJVfFUDQrPpEzlcRPQwvTBemZFNAyRzYZzE6aMY2yGEudEXefxS5x/evNlCVaur69Ppmq1tHg2DT6/eNaxWMcTo7ffMUL7Us4c3L5H84jPm2jHnzpr1jTMMbJIJ1iwgcDe4AVrCYYV25qryGgtaLTdxJAiRAs4n9iSqqRZbjHT21NQu1mfa4LU0cYySRG2R3LBJ6khu3qsSrLHh7Q4aESqVWkaT3MdqCQfKyLIa1o89MwCwzY6xWjtMac7hFK6RrN4I8Vx5ZOPiIXTpWoTVZmWK8jopo92yRmNzdzXMdjPxtc4fvUq7rFmSaTSJpXl8suhUJJHu8p73vsuc4/OSStD6jxVa20GetvvirVGhTdhqlQk5mkW3QT6zyjzULREVlUUXCx5D/zXfyK5rhY8h/5rv5FYdoVOn8Q6rdhEReUL9EKsdJ7mc8Ok6lpptQufemikbKI3hsYjfG8hzc5JPLP+qzOt8GS2NBh0gTxtkjiqxmYtcWHvdzHEhuc9dn/ypqiuOx9Zzg4m+bNoNYA+gXLZsfCsYWBtizJqfhJJj1JvqoBxZwDPbraO2G0yG1pDYhHI6Mvie5jK4Ltucjx67CAc9pBXpt8HWZtV0vVJbMTnUqrIbDRG5pml2TiSSPrhjS6bIHmwpsq67vetW6NGtJUnfXe+4I3OjIBLDBM7acg9Mtaf3Ldha9es9tFpF8wBIH9Uzz4qtj8LhMLSfiXtJjK4gE3LIDTExIt13rKd0Hgt2oyVbVayad+m7MM4bvaW5Dtj25HY7JB6jxnghwPTwcP8C3HahFqer323Z67S2vHFEIooz42HHAAONzjgNHXBJOAFVuh6txZejM1Sa7PEHmMvYYsB7Q0lvjYOcOb/AKpa4y4n0qZnfcthpdktjtwxvimDcbgHbQSBludjgRkdRldZuzsSG9i2qwkAiP6gDqAYkL51+2sC54xNShVAJBzXyEjRxGbKSNxVk2+59qFe7as6RqYoxXnF9mJ8LZdrnOc5zog4EHxnvI8kt3EA4Xbwf3NjTratUntc6PUmhgka0iWNoEwD3lxw6T7I0/Flp+NSXgDiRmq0YrjG8tzi6OaLO7lzMOHtB87Tlrgfie3sWfXIrY3ENmk8wRANhPum0nUxHFfS4bZeCqZa9MEggke87KA8XgEwJkyICqQdzLV3UX6bJrDDSZgwQNgADnCUSjnPI5mwHc4M3OGdvZhZvXO526xQ0uFlrkXtKZEK9prCWFzBHnLMg43RMcD5iOw5IVgKKd1Hix+jU4rTIGzmSyyDY55jADoppN2Q05P2IDH/AHKVPG4mtUa1kTMiABJIgzoL75WqtsrAYWi99QHLlAJLnGGgyIuSIOkaLwcM8HX23majquo9+TQxmOCKFnJgZkObve1oa15w9/Tb2nOTgY+8OcF2qGr3L0NuM1L0sk1iu6I80udznsa2TOAGyyk5GMjphZLuacUO1ekbb4WwETyRbGvLxhgYd24tHU7vi8yqzuhcW6lBxDNVhuTR1xPSaImkbQ2SCs54GRnqXuP/ALirFGnia9WpRkAhpBECIBFhAjW4hVMVWwOEw9HEgOcC8FrpOaXA3JcQSCBBB3WhTjhzubNi07UNOtzNlZen5wfE1zHROAYY3Dd2ua+MH4j2FYt/c31eaCLTbOtNfpcRYBGyuGzOjiILIyT1AbgYDnvDSG9DtAFrIqY2lXBJkXM3AMGIkSLHoum7YeEc0NymAMtnOEtmcroIkTNioVr3A/OuaNPXkZDBpOxohLXOc6NjotrWuz0w2PGT8a7te4Sks6zp+qNmY1lOMsdEWuL3553Vrh0H20f6KXplaRjKoi+gI8nTP7lWDs2gZ93VzXG51bEekC2iIiKsr6IiIiIiIiIiIiIiIi1290f92Iv2fB/ftKs1Znuj/uxF+z4P79pVmvSNlflKfReG+0X8xrfqKLqs9jf0kf8AWF2rqs9jf0kf9YXQXIZ8QXaiIiiilPcmka3WtPc5wa0TOy5xAA+xSdpPQKLItdan2lNzOII9Qt+Fr9hWZVicrgY4wZXZP5TvznfzKyfBcDJNS06OQAskvVGPaRkOa6xG1zSPiIJH71iF21LD4pI5Y3bZInskjcO1r2ODmuGfOCAUqNJYWjhCxRqBtVrzoCD81YnCVqSbiyYSkvFq1qVaw1xJD65jsN5Ts9sbQyPA83Lb8SrZvYPyKcS8YUmzT6jWpTRapZZMC42GOp1prDCye1Xj5fMMrg6Qhr3EAyu7cYMHVXCMcHFxbHutEW1bM6Ta4HkujtKsxzAxr8xzvdInR2WNQDNiSN08ZRZLQNDt35RDUgkmduaHFjHFkQecB0zwNsTOh6ux2FY1ZbhKzJHdqCOR7A+1WDwx7mhw5zOjtp8YdT0Pxq1WLgwlusb1z8K1jqrW1Jgm8a/OV5+INONS1Zqlwea88sJeBtDjG8s3AEnAOOxeFZ7uifdfU/161/eesCsUHF1NrjqQP2WcWwMrPa3QOIHkVK9T4PNbSzflmhfK63DXZFXnhsNYx8MsjzM6IuAkJazADug3ZzkYiil9X/pux+2q/wDspVEFqwrnnNnMw4jhuCsbQZTb2ZptgFgMTN5PRFMoOI9NnZSk1Gtbks6fDFXZ3vLC2C3DXcXV2WeY0vjIB2FzMlw+LpiGotlWg2pEzbeDBWjDYt9CcsX1BEixka8Pu0r3a9qcl2zPamxzLEr5XAeS3cejG567WjDR8zQvCiLY1oaABoFoe9z3FzjJNyeZQqxLF3S7t2rq9i8IHsbVfepmCd88lioxjAKxa0xujlELBlzht3ElQGpWkme2OKN8sjs7WRtc97sAuOGtGTgAn8gK6VprUBVOpBAOkaHXWeHyVvC4t1AH3Q4Egw6YluhsRpJ8j0Xu1/UXW7Vm04bXWJ5Ztuc7OY8vDAfOGggZ+ZeFEW5rQ0ADQKo95e4udqTJ80WV4i1l13vPdG2PvOjXot2kne2uZCJDnsceYeg+JYpELASHHUKTarmtLQbGJ8tEREUlrRcLHkP/ADXfyK5rhY8h/wCa7+RWHaFTp/EOq3YRF0ajzuTL3vy+fy38jnbuTztp5XN2eNy9+3O3rjOF5SLr9DkwJXax4OcEHHbgg4/LhclSfcYu2a0uuWJu9m1IJJ5r5YJTMJouc/8A5YeSYcNl8rxvJ+dZCpxhxJcqTarUq0GUY+a5kEnNfYlihJEjmlrgJC0teOmzJYcA+fpVdlvbULQ4QIuTAkiY6/S64NDb9N9Jr3MdmOY5WiSA0wTutp52Eq3FVXumPubU/X2/7ewpzwLxCzVKMN1jDGZA5skZO7lyRuLHtDvvm5GQemQ4dB2KDe6Y+5tT9fb/ALews7MY5mNY12odB+abdqtq7KqVGGQWSDyMLh7n3VasGlSMns14Xm7M4NlmjjcWmKAA7XuBxkHr8y8PuhuIaFinWqwWIbFgWmzHkyMl5UbYZWO3uYSGkmRmGntxnzKIdz3uZyaxUdaZcZAGzvh2OhdISWMjdu3CQfhMYx5lLdM7hTRIDZ1AviB8ZkNflvd83MfI4M/hP7l2KowdHGGs+qcwM5QDr1XzGHdtPE7NbhaVAZC0DOXDTjCw/Cmn3W8J3LNaexWkjvvuRurzSQmSCKKGCxudGQSwBsrsfHAFJvc78TT2mXatqxNYljdHYjfPK+aQxvHLkYHSEkMa5jDjszKfjVm1NKrxVm044mtrNi5IiGdvLLS0tOersgnJPU5JPateu5+52i8TCrISG8+Wg9zu18cxArv6dgc8V3/kK006zcdSrti8528Y4fL5q3Vwz9k4nBuzHLHZu/tk7/nPRqkHd94ptx6hBTp2bMHKgDpBWnlidJLYd4rHiJw3kMZGRnP2047evg7sejarWqVTatmek3vSLZJM+WU3hWkM0zi9pO0uE+DvPRwH5PPwhF798VSWTh8MdiS4T16w1S2Op+XqKwI84ypz7pX7k1/2jF/trasMcMPWw+HAEx71t5+uvqqNZjsbhcZjHOOXNDACYhtukG3mFH+4BQ1QmGwyw0aU2aw2Wvvw50vJwHBnL6je6I+UPJ/1indgn5XElyXG7ly0pNucZ2VKrsZ82cdqtP3On3HP65P/AExKse6mAeKJwRkGxp4IPYQa1QEH5lPCVM+0asgWa4WtMOGvPmte0aHZ7Fw+Un3ntNzMEtOk6DkslxRR4rnhk1WeSxBEGmY14bToDXgALtwrRvG0Nb25y/A8bqCpT3A+NLV0z0bkrp3wxCeCZ5zKYw5scjJH9smHPjIccnxnZJ6YsriYf8lc/VbH9p6on3Nf3Wn/AGbN/uaapsrNxeCqlzGjLEZRELpVMM/Z21MOGVXu7SQ7MZn7meUKQd2XujWoLTtN055idGGixOwbpTLIA4QQ9CG4a5uXDxi52Bt2ndgLHCfFkNc3TatlzW810LdQnfaDQNxzHuw8gddgcT5sE9FjK20cWnvjs9/JfK+M238jt+93cvHzYWzKlia42eymymxplskkTKjgMI7bFWvVr1HDK4ta1pjLG+PTreVU/cT7oc9+R1C84PnbGZILGGtMrGY3xyBoAMgByHAdQ12eoy6L93bXr9fVnRV71yvH3tAdkNmeJgcd+XbI3gZPTqsN3NSz/ieDvfHK78umLbjbyeVZxtx97y//AIXf7of7sv8A1SD+T1cpYSkzaIytEOZmjcLrl19o4irsUl7yXNq5c03IAnXfqstqtHizVYzfaZ4IHN5lerDaMDjDjLS2JjgZHEdcv8Z2egwQF6+4TxzcluDTrc77Ec0cjq75nGSVkkbTIW8x3jPYWB5w4nGxuMDKu2sAGMHxNbj/AEC1r7kP/Utb9Nf/ANrbVPD1m4vDVmuY0BrZbA0sf8fuunjcK/Z2NwtRlV7jUflfmMgyWjSw3m260aLZhY7V9cpU9nfdqvWL87BPNHEX4xktDyCQMjJ82VkVQPdev162uTTSMrakJaBgdWkcSaMuwNY44BDXB2HgZz9lf5JLXHjbPwn8TUyX0Jtv5XsPNfUbZ2kcDQFURqBfQTvtc9B10BV9wSte1r2Oa9jgHNe0hzXNcMhzXDo5pHnC5qG9zFkNClS0uS3DNc73daDI37wYZpZJA+N3Y6IbsbvPgnsIUyVatTyPLRcTY8RxV7C1jVpNeRBgSJmDFx5IiItSsLXb3R/3Yi/Z8H9+0qzVme6P+7EX7Pg/v2lWa9I2V+Up9F4b7RfzGt+oouqz2N/SR/1hdq6rPY39JH/WF0FyGfEF2oiIooiIiIiIiIpIeDbjNOn1KeN9eGM1xC2RmHWRO/buZlwLGNBadxGHbxjz4jal/D0jjoeuAuJAl0fAJJA+zWOzPZ2D/RVsU97QC0/1NB6FwFvVXsBTpVHObUBPuPIgwAWsc6/HTiOciyiC7qdh0UkcrMb4pGSNyMjcxwc3I84yAulFYIkQVSaSDIXq1a8+1PNZlxzJ5XyybRhu+Rxc7A8wySvKiI0ACAsucXEuOpXobclEJriR/IdIJTFuPLMrWlgkLezeGkjK86IgAGiwXE6lFL+C+FIrcNmxNargQ0rs7KjJv+cdJBG8sc+Lb4kO4B2c5OGjHXIiCl/ct+3aj+xdT/shVsaXCkS0wV0NlNpvxLW1GyDu+9eiiCIitLnKf9xjVtl6OqKtRxmjuk2nxPdbYBTmfsil5m1jcxgeTnD3DPVQBvYFnOBNYioX4bUzZHRxsstc2INLyZq00DcB7mjAdICevYD29iwYVWnSy13uAsQ31l0/RdCviM+EpsJu1z7cAQyPmHIiIrS56IiIiIiIiLhY8h/5rv5Fc1wseQ/8138isO0KnT+IdVuwiIvKF+iFUPBWhW2za/plmpYij1I2jHd25rta7nNad46OJErXAfMQcLyaJf1zTdNfovvLYmnAsRV7cWX1tth8jy97w0s8V0jiMubkYB24KulF0ztIuJzsBBgxfVoideGoXBbsIMA7Oo5pAc2QGzlccxFxFjcHVRbuWcPSaZpkFWYjnZfLMGnLWvlcXbAR0O1u1pI6Eg46LAe6B0mzcoVY6sEth7bjXubCxz3NYIJ27iG9gy4D94VkIq9PGPbiO3NzM8rq7W2ZTqYP+DBIblDecBV/3BdMsVNMkitQS15DcleGSsLHFhigAcAfNlpH7irARFqxFc1qjqh1Jlb8FhW4Wgyi0yGiJOqKhPdIaLyrla/GMNsx8qQjpieDGxxPynRloH6BXvZeWsc4DJa1xA+MgZA6LXTifUdc4klr1zQfExjvFY2GZkLXuADpp5pejQBkebAJGCT16uwmuFftZAaPik7iF8/7WvY7CdgWkvcRkDRNwR6WMeam/ubtF5VKxecPGtS8uM//AIa+Wkj4syukB/RBSHu1cO2NS0zlVW75obEdhseQ0yBjJI3NaXEDdtlLup67cKT8N6UyjUrVI+ra8LIt2Mby0eO8j43O3OPzuKyCqVsc44o4husyJ4DT5Lo4XZLG7OGDfplgxxNyR56Kle4lJrdOwzTpqEsNB8s8ss01WZjmP5B2tZMSGbS+NnaD2nr1WK7ovDOozcRzWYqVmSA2KLhMyJ7oy1kFVryHAYIBa4H80q/0W8bXc2u6s1gBLYIvvMz1VR3s2x2EbhX1HENcHA2kQIDei8PEEbn1LTGguc6tO1rQMlznROAAHnJKpvuBcOX6epzS2qdivG6hLGHyxOY0vNio4NBcPKw1xx/2lXkiqUMa6lSfSAEO1XQxey2YjE0sQ4kGnMAaGeKpzux9zazZsu1HTmiSSQN74rhwZIXsAaJoS4hpJa0ZbkHLcjcXHEbn13i+aA0XVr+HDlOl7wkZM5hy0tdYMYaBjpvGCfj6krYdFbo7Xc1jWVGNfl0LhcLnYn2bY+q+rRqvp5/iDDY/d/Xqqs7i/c7m057r14NbZcwxwwNcH8hjsb3yOblplOA3DSQBnqS7DYt3cuGdQt6q6WtSszx97Qt3xRPe3c0PyMgYyMhX4ihT2tWbiDiDBJERuA5LbW9nMM/BDBtJDQZkak85XCAYa3PTxR/JUD3MOGNRg4gr2JqVmKBstwulfE9rAH1rLWEuIwAS5o/eFsCi0YXGuoMqMAHviDy109Vcx+ymYupRqOJHZuzCN9wb+iKjmV7Gly61TuaLZ1OHUpXyxz1o3v5wc97o2vlYxxYcuDunjMeCQDkFXiijhcV2MiJBjeRoZEEXU9obPGKykOylswYBEOEEEOkGypfuNaHe07UD74UbYM1NsVawSZoq0e8yugkLMtj3bR8W0txgb+l0IixjMUcTU7Rwg8tPms7M2e3A0exYSRJMmJvxiJ+wiIiqroLXb3R/3Yi/Z8H9+0qzVme6P+7EX7Pg/v2lWa9I2V+Up9F4b7RfzGt+oouqz2N/SR/1hdq6rPY39JH/AFhdBchnxBdqIiKKIiIiIiIiL21dTmjr2KrHAQ2nQOmbtBLjXc50WHHq3Be7s7V4kWHNDtfuLqTHuYZaY1HkRB9RIRERZUUREREREREXbXsSRlxje+Mua5jixzmFzHjDmOLT1YR2g9CupFgidVkEgyEREWVhERERERERERERERERFwseQ/8ANd/IrmuFjyH/AJrv5FYdoVOn8Q6rdhEReUL9EKqdb7s8FWzZrOoyuNaxNXLxOwBxhldGXAFvQEtz+9cNN7uNF7w2apYhYSAZGujmDM/fOaMO2j/tyfiBUU4N1GvV4r1GW1NHBELWqtMkrg1m51l+Bk9MlZbu98Q6TbqV2VZoLNptgOD4cOMcPLeJA6VoxtLjH4meuAfvV9T/AAGH7VlLsnHMAc4JtI9PmvPu+Mb/AA9XEfxDRkc4CmWtkgHjIN+iuqrYZKxksbg+ORjZI3tILXseA5rmkdrSCDn512KkeI9e1LSOH9C72nNeWVjuZmKGQmMt5kTSJ43bcNc3swpDwLrWvXrkNyzEYNHNZxaXd7N5mIsssSDPOy8+P4oDACMZHU8ipsxzWGpmbllwEm5ymLczuhfSUdu031W0Cx2eGEwJAzCZJ3AaEmPNWaipKHjHX9euTx6M6OpVg673tjzy3EiJ08kkbyJH7XEMY0YAIOcFx93CXHGq09UZpGubHule2OKw1rGuD5ekBBiAZLC84bnaHAnr2EKTtk1Wg3bmAksn3gP2+ahT9o8O9w91+RzsoqEe4TprM8phW+iqnunccX26hHo2jgC07YJZdrHv3yNEjYmCUGNjRHhznkHo773aSY7xjxXxRpEMVe3NGJJH8yK7FHXk3xta4SV3tdDsyC6J2drT0PV2eijsirUDbtBdcNJvHHomJ9o8PQNSWvLWGHOa2Wg/2zIvu4TvV8Iqx7p3FF+lommW61jl2LD6omk5UL94kpyyv8SSMsbl7WnoB2fF0Xn4E17iHULOn2ZInR6S2HbZlIrNdZkZVe187mdJdrrAyBE0NxjoepWtuzqhpdqXNAvqYuNw4k7lvdtuiMQMMGvLoabCQA7eYNgP6idOatZFTFfWuKtZsWDRxplaIgsZYhERLXF3LBfLC98kuGknbho6fGM5TuS8aajYv2tK1IslmrCYidjWNdvrzNiljdygGPb42QQB5BznPSVTZdRjC7M0loktBuAfl81ro7fo1KrWZHgOJDXuENcRwvPqFaaKiqHGXEVrVdQ0+nMyYiS5HCJYqzGVI4rIaJy5sQc8tYNgDtwJlBIcQuqpxxxJSvy6XOI7tyQtiha9sQEcsrWvjlY+FrQ+PY7JD8Aect2kLb3LV0zNmM0TeONwq/4pw1jkqZcxbmy2zDdYmSd0T+6vpFRdniziPRtQrR6rNHYhsFpcxrICx0bnhjzG+KNjmSMz2dnZ0Kzvdx4v1DTLFFtOcxMkjkfKzlQScwskYB40sbi3oSOnxqHdNU1GMa5pzAkEG1td30W78RUG0alV7Xt7MgOaQMwzab4jzVroqL4w4j4r00w3rL4YoJ5NrarWQSRROLTI2vL4vMyWtd1DyfFd4w6Lnr/EfFMlQ6zE6OnQIa+OBgryPbC5wYyV4mjLpGklvXI6HIaApjY9Qhpzsg2BzWnhpr0stTvaai0vaaVSWiSMt8v92th1g8tYtnjPWve6jZuiMS97sD+WX8sOy9rMb9p2+V8R7F5O51xMdWotuGEQbpJI+WJOaBy3Yzv2N7fyKM/8Z2rHC82qMLYLkbSwuY1r2CRlhkRe1koc3DmnOCDjcfiyuvhnie9Lwxa1CSfdcjZbcyblQN2mInZ9jbGIzj52/lUP4IiiQWjN2mWZPDSIiN8zPJbe9WuxLS1xyGiamXKIInWSc0xbLEc1ZqKjOEuIeKtXqPFSaMcqZ3MuSNrRueSxhbWjaItg2jxidufsrfGA6HPdxrja/ctWtN1LD7Fdj5GybGMe0wythmhlEWGOIL24IH3rsk9ErbJq02uOZpLdQDcc9P8AaYb2ioV302hjwH/C5zYaTw1N93BWqipfV+NNa1bU5aGhubBFXMgdMWxHeInbHTSSStcGxl+A1rBkggnPUN8N/jfiGtqVDT7cjYJBNWisbIqz2XI5bDQJ2uMZ2bmEtOwgZaejTkCTdj1TAzNmJyzcDmIUH+02GbJyPLc2UPDfdJmIBkfONDCvZERclfRKIcYdzzT9VsNs2jYEjYmwjlSNY3Yxz3joWHrmR3n+JYb4F9H+Vc+nZ/jVkIrbMfiGNDWvIA3SubV2Ngqry99JpJ1JFyq3+BfR/lXPp2f414dZ7jukMbEQbfjWazDmdvY+ZjT/AP1/EVayxvEXkQ/rlP8A3Ea2N2nip/7jvVajsLAC4ot9AoT8C+j/ACrn07P8afAvo/yrn07P8ashFjvPFeI71TuHZ/gs9Aq3+BfR/lXPp2f40+BfR/lXPp2f41ZCJ3nivEd6p3Ds/wAFnoFW/wAC+j/KufTs/wAafAvo/wAq59Oz/GrIRO88V4jvVO4dn+Cz0Crf4F9H+Vc+nZ/jT4F9H+Vc+nZ/jVkIneeK8R3qncOz/BZ6BVv8C+j/ACrn07P8afAvo/yrn07P8ashE7zxXiO9U7h2f4LPQKt/gX0f5Vz6dn+NPgX0f5Vz6dn+NWQid54rxHeqdw7P8FnoFW/wL6P8q59Oz/GnwL6P8q59Oz/GrIRO88V4jvVO4dn+Cz0Crf4F9H+Vc+nZ/jT4F9H+Vc+nZ/jVkIneeK8R3qncOz/BZ6BVv8C+j/KufTs/xp8C+j/KufTs/wAashE7zxXiO9U7h2f4LPQKt/gX0f5Vz6dn+NPgX0f5Vz6dn+NWQid54rxHeqdw7P8ABZ6BVv8AAvo/yrn07P8AGnwL6P8AKufTs/xqyETvPFeI71TuHZ/gs9Aq3+BfR/lXPp2f40+BfR/lXPp2f41ZCJ3nivEd6p3Ds/wWegVb/Avo/wAq59Oz/GnwL6P8q59Oz/GrIRO88V4jvVO4dn+Cz0Crf4F9H+Vc+nZ/jXn1HuM6O2GVwNzIikI+zs8zCfwatBebVftE/wChk/ocneeK8R3qsjYOzx/4W+gXpREVFdZa5cP6DX1LifUq1prnRG3qjyGPLDuZZk2+M3rjqrW03uVaJBI2UVTK5hy1s0skkeR53Rk7X/kcCPmWU0vgrT612TUYYnNtTOmfI8zSuaXWHF8p5bnbRkk9g6KRrr43aj6hApOcG5QCJi410K+b2XsClRa44hjHOL3OBibE21Cp/wB07/6bT/0839tqsapWMulxws6GTT2RNPZgvrBg/J2hfOLeFaWqMjZdjdI2JznMDZJI8FwAJJjcM9B51lqkDYmMjYMMjY1jRknDWANaMnqegCr1MU04enTGrSTyubK7Q2e9uNrV3Rle1oHGwIM/S6o/uAcQ1aBvU70kdSV0jHtdYc2JpdFvjlie9+Gse048UnJ3O+JefjS/Fq/FOnNoOEzYXVInSxncx3IsSWZpGOHR0bGOPUdCWHGeitLibue6VqMpnsVsTOxvlhkfE5+OmZAw7XuwAMkE4A6r2cKcHadpe4064ZI8bXyuc6SVzeh275CS1mQDtbgEgHC6DtpYftHYhodncIi2UEiJnWPJcVmw8Z2LMG9zOya4HMJzkAyBEQDzn131LqVxmmcZusW/Ehe/cJXAkNjsVDEyUf8Aa1+WE+YNf8S7/dCcUUbcNSrUnisvZM6eR8D2yxxtEbo2sMjCWlzi8nAJxs64yM5juta1p3fsVPWNLldB4pg1GOZzXCN7RzCxsbQXhjyQ6PcewOx1bmC8ajRpo6uncPQSWJ5bHMkl2TmV5Eb2MhBsAPx45ccAMG3J85HQwjRUfRrPY4ENAkRkgTcnd04rjbSe6hTxWGpVGEOeTlMirLiPdDYE7oIkRdS3u0f9N6N+ko/+PnVg8GTcrQqEobuMelV5A0dri2q1+0fOSP8A5XK/whVu0KdG8wysqsgxskkj+yxQGHcCwgkYc/p86zemUo60MNeIFsUEUcMbSS4tjjaGMBc7qTgDqVwa+KY6g2kNQ4nlBX12F2fVp4t+IJEOptaOII47vmqD4T1E62+zNrHEEtFrC3ZWjssqMe124kxNe7llrcBvRrndRk9mefcLEA4hsisXurivcEDpPLdCJ4RG5/QeOW7Seg6lWZL3K9EdOZzTxl28xNllbBuznpE12Gtz96MN82MdFkdN4H06tdN+vC6GwS7JjmlbFh7drmckO5ez/txgEAjsC6VbamHLHsYHAObAEABp8teq4mG9n8Y2rSqVSwlj5Lszi5w/+rDoNeIVZ9x3/qjWPzNS/wDJV191f/rmH8+D/wAeFZ+h8G0KVue9XicyzYEoleZZXhwmlbNJhjnFrcvY09B0X2fg6g/UBqjonG60tIk5soblkfKH2Pds8jp2LQ7aVI1nvgwaeTdrA56K0zYVcYanSlstr9obmMsk8Nb9OarD3Rv/AK7SvzH/AN6NPdG/+u0r8yT+9GrP4o4OoalJDLcidI+AERFssse0FwcchjgD1A7U4o4OoanJDLcidI+AERlssse0FwcejHAHqB2rGG2lSp9jIPuB4P8A9aRdSxuw69b+Jylv/UdTIkn+iJm3pEqG+6X+5dX9ox/7W2u7Xf8Aoxn7Ko/yrqa8V8N1NUhZBcjdJGyUTNDZHxkSBj2A5jIJG2R/T519scO1ZKI01zHGoIY4BHzHh3Ki27G8wHdkbG9c56LRTxrG0qTCDLX5j05K5W2XVfia9UEQ+nkHGYOttPVVLoX/AERc/Pk/3US9nB3/AEXe/RX/AOoqxK/B1COg/TGxOFOQkvj5shcSXiQ4kLt48Zo8650eE6UNCTTY43CpKJGvjMshcRL1f9kJ3DP5VuqbRpuDoBvVz+XrqqtHYtdhYSRbD9lqfi46afPkoh7m/wC5Ev6/N/ZrKNdyn/q3WPztV/8AIxq2+FuHaumQur02OjidI6UtdI+Q73Na0ndISQMMb0+ZeXR+DqFS5Pfgic21Y5xleZZXB3PlE0uGOdtbl4B6Doou2hTL67oPvi3rvv8A5U2bGrNp4RsiaRl2t7Ra37wqg7ieqwaTqWoVNQkZXe4crmzODIxLWleHMc93Ru7cXAnodnb1GePdJ16rf4i0w1HsmZBLShdMzqx7+/OYQx46PY0PHUdMlyyPFer6Ha1OaHW9Nm0+aPc022SyuMwYQ2Jz44IwZGOYPFkw7oGjOOzCsrUtQ1zTYNCruFOm6u+WbZINwjsGeaxI6Tx8bQ1gMmCSA0dNq7TA11U4h7XAllzbJ8MSCNZ4L5eoXsw4wVOoxzRVEAT2p9+YLSBEXJPktikRF8avTkREREWN4i8iH9cp/wC4jWSWN4i8iH9cp/7iNSbqsO0WSREUVlERERERERERERERERERERERERERERERERERERERERERERERERF5tV+0T/oZP6HL0rzar9on/Qyf0ORF6URERERERERERERERdVmtHK0sljZIw9rJGte0/la4YK6qOm14M8iCGHPbyomR5/LsAyvUizmMQo5GzMX4oiIsKSIiIiIiIiIiIiIiIiIiIiIiIi816hBOA2eGKYDsEsbJAPyB4OFzp1IoW7IYo4mdu2NjWNz+a0ALuRZzGIUcjZzRfiiIiwpIiIiIsbxF5EP65T/ANxGsksbxF5EP65T/wBxGpN1WHaLJIiKKyiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIi82q/aJ/0Mn9Dl6V5tV+0T/oZP6HIi9KIuq3YZFG+WRwZHGx0kj3HDWMY0ue5x8wABP7kRdqKkIe7RrFyGfUtK4VsXdEgdL/zsl6GtPPHBkTSw1HRmR4btd4rOZ1aQSHAgWZwlxpp+paVBrEUzYqU8ZeZLD2RCFzHuiljmcXbGvZK17DgkZb0JBBVmrhKtIS4b4sQYPAxMHkVpp12P0P3ynVSNF5NK1OtbjE1WxBZiJIEteWOaMkYyA+NxaT1Hn86hPdT7p1bR6T7NXvXUporlenPWjuxsfAZy8bpeW2R0bgWeS5oytVOi97sjRfRTfUa1uYmysFFEtN4msu1XVqlmCrX0/T4a8sN3v+s+SUSQRyzmxVEnMpsYXPG6QNDg0EdDlZqHiGg+WKBl6m+eZglhhbZgdLLGRkSRRh+6RmOu4AhYdTcPSbX1E7vsb1kPCyaLxXNXqQyxQTWa8U8/2iGSaKOWbHbyo3ODpP8A2grt069DZjEteaKeJ2Q2WGRksbi0lrsPYS04II/cowYlZkaL0Iq81PuklvElfh6tUjsF1cWbdt96KAVmGQxcuOAsJsTB2zLA5rvH6A4JExm1+iyw2o+7UZbdjbVdZhbYdnGNsJfvOcjsHnWx9F7YkaifLyUG1WmYOhjzWSReK5q1WF5jms14pBE6cxyTRxvELNxfNtc4HlDa7LuwbT8S6Nf1dtfT7V+MNnbBTntxhrwGTNigdM0NkaCA1waPGAPbnqoBpMKZcFlEVVdx/uxR67R1S5PUbQOlsbPLGLJnBrOglmbNvdDHtB5E47D5Gcr73Be62/ib3x5unt0/vBlJ+e+zY5gti07J3QR8trRXBz1yJPNjrYfgqzA8ub8MTcWnTr5LS3E03ZYOsx5K1EWN0jiChcc9lS7UtPj+2NrWYZ3R9ceO2J5LOvxrqn4n02MbpNQosbznV8vt12jvhuN0GS/7cNzcs7RuHRV8jpiFtzDWVl0Xh1fWKlNglt2q9WMnaJLM8UDC74g+VwBPzLur3oZIhPHLE+AtLxMyRjoiwZy8SNO0tGD1z5liDErMjRehFjKXEOnz8rkXqc3PdIyDlWYJOc+EAyti2PPMcwEZDc4yMrm7XKQsimblUWyMiqbEIskYzkQbt5GOvYs5HcEzDisgix97W6UDpGz26sLoYTYlbLYijdFACGmaQPcCyHJA3np1HVcdS4goVpI4rN2pXllAMUc9mGJ8oPQGNkjwXj8iBpO5Mw4rJIuE0rWNc9xw1jS5xPYGtGSfyYCpODuz6zZqTaxp/C8lnQ4TM4W36nBDZlgrue2edtTY542bH5aN3knr0ONtHDPqzli3Ega6C8XWupWazX5An9ld6LA6LxdRs6ZW1Yzsq07MMcwfbfHX5XMH2uVz3bGyNcHNIBIy04JHVZH33qd799981+9cB3fPOi732l2wHnbtmC4gZz2nC1FjgYI5eamHA6Fe1FDuPuL5KlYSaY2lfsCzVhmhk1GpVbFFZY6RsrpJpWt3FgDmszl4JIypDqmuUqr447VyrWfKcRMnsRQvlPZiNsjgXnPxLPZugHj99UziVkFjeIvIh/XKf+4jXo1PUa9WN01meGvC3ypZ5WQxtz2ZkkIaP9VjtQvwWa9eatNFYhdbqbZYJGSxu/5iPyXxktP7ijQdUcRos2iIoKSIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiLzar9on/Qyf0OXpXm1X7RP+hk/ociL0qP90rTJbujatTrjM9rTb1eEE4DpZq0kbGk+YFzgM/OpAikxxa4OG5YcJEKi+4p3UtCpcMVorlyvTtaZBNBaoTuEVwzQPkLhFWfiSZ7+hw0HDnlpwQQI/3RuIKmrzcEXdSqvocOXLN+Wevb2sr85rQ2hJaMZ5Yik6vaXHBillJ8Xcr11LgnRrNjvuxpOmz2sh3fE1KtJMXN8lxkfGXOcMDBJ6YWU1bS61uF1e1Xgs1343QWIo5oXbTkbo5GlpwR8S6IxdFtTtGtMkmb6SCPd6TIJ4KmcPULMhIgRFtYI1/Zazl0MWs8XDhIt7xHCsr5/ew7qrNTG3kmoYPEE4h5+wR/f8/HjA4h/GEPCzeFeHnac6n79Olpd9iCRhuFxjc6826wHeIBOGbN4x4sezxSVuHoOh0qEXJo1K1OHcXGKrBFXjLzgFxZE0AuOB17eixh4D0M80nRtKPPe2SfOn1DzZGuL2vkzF47w4l2T53E9pW+ntRrXAw60b7mBHvcfsc1qfgSREi87rCTNlSXEY//AJnuof8A69B/4NijXEPC+n0uF+DdTq1Y4dQm1bSHS3Wg98ScyKzMQ+UncWh8MJaOxvLaG4HRbQy8Oae99uV1Ck6TUIxDfkdVgL7sIjEIitvLM2YxGAza/I2jHYvljhrTpIIKsmn0n1ar45KtZ9WB0FaSIObE+CFzNkL2hzgC0AgOOO1QZtINywDbLPOGZf8Aam7B5pvx8pdP+lrzF7wSa7xm7iqSsy1HKxtHvyQRyRUWxSOgdpu4h3fXL73cOV4+Swt8p2bC9yNn/hLTs9vNv5/L3/YyufGnBGuWNSluVzw5eY5rBSk1fTR39pBbk5q2a8RdZAe5zwJC3B2fE4vlncn4OboGkVNLbMbHe4kL5i3ZzJJppJ5C1mTsYHSEAZJw0ZJOSmKxDH0MoNyWWmwytIMWEbvpa5xQoubVki3vX3mTPmqxGh1fhGs8upVMg4ddqEeYY8DUTciaLnk9LJDiOZ5WCeqq3SY+GncF6pLqjqv/ABMZrzpzZeBq41Hvk8gNa888RnxC/aNuefu6h2NvBo9QWjeFWsLph73NzkRd9GvuD+QbG3mGHc0O2ZxkA4Xis8IaTJaF6TTNPkuhzXC2+nXdZDm42uE7mb9wwMHOQsUtpBsAzYN0N/dm3Qz8lmpg5mIvm1HH6hUDJoA1biThOrrcckz5OEa8l+GV743TTxiy9zLWwhzjzg17m5ALmdemQb048rMh0HU4Y2hkcWkXY42DOGsZTlaxoz1wAAP3LLy6PUdaZddVrOuxxGGO26CI2o4SXExMsFvMbES952g48Y/GvVarxyxvilYySKRjo5I5Gh8ckbwWvY9jhhzC0kEHoQSqtfF9oWcG7t2s2/ZbqWHyB3E7/KFpRpdueho1OOADPFPD9jR4W4P2S/DxFLXJe4dje8r8g7O0NHnUorS09Nj7pDJ681ilBLolPvaCd1V8kZs3KsURnYCYojlgdgHLS5uDnC2YbwhpIbUYNL04MoSGWgwUq22lK6QTOlqN5eK0hla1+5mDuaD2hdzOGtOBuEafSB1DAvkVIAbwG/Hfh2f8z9sk+2bvtjvjKvv2qxxPum5k3/8AdpGnIR1VZmBc0D3tBGn/AKkfuZWsnATq7OMOFxV/4fiDq14SQ8PPkmayI6dZdGzUrTji1aO3d1aCNoJzlpXLhvhLTbeg8eX7NSKa5V1PXe9p5AXPr97wtsRmHJ+xu5jiSW43ANByAAtkaPBukQGuYdK06J1R0j6jo6VZjqz5dvNfXc2PML37W7nNwTtGc4Xpr8OadHDZrx0KTK918slyBlWBsNt87Qyd9mJrNs73tADi8EuA65UX7UEy0HQDW9nE/WFJuBt70b93EALWOxqVGY8K1bVfSpbcfC1WdtziW8+PR4YHmSLa2njbZtHkk5c5uREwZ8TLcFp9mX/gXWWwyHvH/i0R2nVWvZG3TpIqbnd7xvJdFA6YwYY4n7Zg5yc7Y2+DtImbVZLpenSMpNDabH0qz2VGjbhtZro8QNG1vRuB4rfiCx3FXCG6heg0YUdKtXZDNPMNOqTQ2pHn7MLsDo9tgyNLgXuy4E58bq0zZtKnYQfiBubCHE8yNdwtwKg7BOuZ3EWF9PJUjZi4dZxjwqOGnUXfYdQNhlGRslcP7wmFR0uxxaLLg2UPJ8chke771V5oWmMtaDLJbvcM0Lp1CSWxevG4ziiC+y5vOXRbpASW52sYQAXOOHtLhfvB3ctus1XTdRvs0OhFpLbZq0dAqywQz2LsIgms2XStbjxWtwxoONjBuwDmxn8I6U633+7TNPdeDg8XDTrm1vAwH88s37wOm7OVN20WUoDSTAF5kyHOMSbEXHGNLqIwbnyTAubaagD6KlNQ4Xravx0yrq0TbTBwrXnnjzLFFNOyzG0l7Btc6LdI54Y4Dq1hIy0KId0irSbq/Elpk3D2rNc4RX9O1vn6dqtPkQmER6PYs7GvO0ANmhccgQAA+KTtONHqC0bwq1u/TD3ubnIi76NfcH8g2NvM5O4B2zOMgHC8GucG6RelE93S9OuTABoms0q08u1vkt3ysLi0fF2KtS2llcJmA0CB1n6DQgrdUwcgxEzM/f1WF7kupwWOGtOsV6lhlfvDEVKWQ2ZhHCHxCBssu3ntIjwxztu5pZnGVRWhO0OPTp9Q4f4uv8Nub3zIND1K3WlEUsTnYibpz5C55kDG4cDM7xwDkgsG1UUbWNaxjQ1rQGta0BrWtaMBrQOgAAAwo/qPAmiWZzasaRpk9lzt7p5aNaSV7x2Oe98ZL3DA6nJ6LTQxbWOcSDBMwIPGxDgQdddfVbKuHLg2IkCOHpGnRa065r2o6zLwXZ1caW1tmjqMjBrccjNHsW4554RNZijIYXyVm05G9jC6duBh4aey3p/e/DHGrYb+lWqj7Gmyiro/fTqFG267F3wyB87Nha9og8WN7w0Rt8kFudpNa0Sleh73uVK1uuCCILMEU8Qc0Ya4RyNLQ4A9DjoukcMaaKZ0/wB76PeDsbqPekHejsPEgzW2cs+O1ruztaD2qyNqNAaA2ACDAiID81ue77haTgSZl0yDc63bH+/uVQHdV4Vo6bwlpLqkOyS5qWh2Lcznvklszuglc6aV0jjl5L3HpgDOAAAAMDxDT761/i9uoycNskEjI2niM2BNDQML+9pdKdG4ct3KdG4uj8YOdH8rrtFqOhUbMMdaxTq2K8Lo3QwTV4ZYYnRDbE6OJ7S1jmAkAgDHmXn17hTS78jJb2m0LksQxHJaqV7D2DO7a18rCWtz1x2ZUKW0so96ZvffctP0hSqYKdNLW3WBH1WtvE2mRv0fg2GxrmlTWa/vg/T2atWvnQ9VgbKxsPfE8kLWxCGu2OJvNADxMNp8YF0m7gGpVnzaxVh0+rSsQ6ho8lt2l3Tb0eeSSZ7WvpRNc6OqcMOWscchoBwY9ovTWdCpXYRXuU6tquC0iCzXiniaWjDS2ORpa0gdhA6LHSaJSoV4YKNStTh79qOMVWCKvGXmxGC4siaAXHA69qw/Hh9IsIMk+Ql2bdHoRrcRYLIwpa8OB3fSN8qRIiLlK8iIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIi82q/aJ/0Mn9Dl6V5tV+0T/oZP6HIi9KLQDw1eKvR/D/quo+0U8NXir0fw/wCq6j7RRFv+i0A8NXir0fw/6rqPtFPDV4q9H8P+q6j7RRFv+i0A8NXir0fw/wCq6j7RTw1eKvR/D/quo+0URb/otAPDV4q9H8P+q6j7RTw1eKvR/D/quo+0URb/AKLQDw1eKvR/D/quo+0U8NXir0fw/wCq6j7RRFv+i0A8NXir0fw/6rqPtFPDV4q9H8P+q6j7RRFv+i0A8NXir0fw/wCq6j7RTw1eKvR/D/quo+0URb/otAPDV4q9H8P+q6j7RTw1eKvR/D/quo+0URb/AKLQDw1eKvR/D/quo+0U8NXir0fw/wCq6j7RRFv+i0A8NXir0fw/6rqPtFPDV4q9H8P+q6j7RRFv+i0A8NXir0fw/wCq6j7RTw1eKvR/D/quo+0URb/otAPDV4q9H8P+q6j7RTw1eKvR/D/quo+0URb/AKLQDw1eKvR/D/quo+0U8NXir0fw/wCq6j7RRFv+i0A8NXir0fw/6rqPtFPDV4q9H8P+q6j7RRFv+i0A8NXir0fw/wCq6j7RTw1eKvR/D/quo+0URb/rG8ReRD+uU/8AcRrRLw1eKvR/D/quo+0V0XfdmcUShodQ0EBkkco21dQ8qJ4e0HOodmWhZBgrB0X6DItAPDV4q9H8P+q6j7RTw1eKvR/D/quo+0VhZW/6LQDw1eKvR/D/AKrqPtFPDV4q9H8P+q6j7RRFv+i0A8NXir0fw/6rqPtFPDV4q9H8P+q6j7RRFv8AotAPDV4q9H8P+q6j7RTw1eKvR/D/AKrqPtFEW/6LQDw1eKv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+       "            width=\"60%\"\n",
+       "            height=\"300\"\n",
+       "            src=\"https://www.youtube.com/embed/aDbblINzuGQ\"\n",
+       "            frameborder=\"0\"\n",
+       "            allowfullscreen\n",
+       "        ></iframe>\n",
+       "        "
+      ],
+      "text/plain": [
+       "<IPython.lib.display.YouTubeVideo at 0x7ff5d516f050>"
+      ]
+     },
+     "execution_count": 1,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from IPython.display import YouTubeVideo\n",
+    "YouTubeVideo(\"aDbblINzuGQ\", width=\"60%\")"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {
@@ -50,7 +98,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 2,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -63,7 +111,7 @@
        "True"
       ]
      },
-     "execution_count": 1,
+     "execution_count": 2,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -74,7 +122,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 3,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -87,7 +135,7 @@
        "False"
       ]
      },
-     "execution_count": 2,
+     "execution_count": 3,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -109,7 +157,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 4,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -122,7 +170,7 @@
        "True"
       ]
      },
-     "execution_count": 3,
+     "execution_count": 4,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -144,7 +192,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 5,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -157,7 +205,7 @@
        "True"
       ]
      },
-     "execution_count": 4,
+     "execution_count": 5,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -190,7 +238,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 6,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -200,31 +248,7 @@
     {
      "data": {
       "text/plain": [
-       "94426021241472"
-      ]
-     },
-     "execution_count": 5,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "id(True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "94426021241440"
+       "True"
       ]
      },
      "execution_count": 6,
@@ -233,7 +257,7 @@
     }
    ],
    "source": [
-    "id(False)"
+    "True"
    ]
   },
   {
@@ -248,7 +272,7 @@
     {
      "data": {
       "text/plain": [
-       "bool"
+       "94082696684160"
       ]
      },
      "execution_count": 7,
@@ -257,7 +281,7 @@
     }
    ],
    "source": [
-    "type(True)"
+    "id(True)"
    ]
   },
   {
@@ -265,7 +289,7 @@
    "execution_count": 8,
    "metadata": {
     "slideshow": {
-     "slide_type": "-"
+     "slide_type": "fragment"
     }
    },
    "outputs": [
@@ -280,6 +304,78 @@
      "output_type": "execute_result"
     }
    ],
+   "source": [
+    "type(True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "False"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "False"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "94082696684128"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "id(False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "bool"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "type(False)"
    ]
@@ -301,7 +397,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 12,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -314,7 +410,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 13,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -324,10 +420,10 @@
     {
      "data": {
       "text/plain": [
-       "94426021228432"
+       "94082696671120"
       ]
      },
-     "execution_count": 10,
+     "execution_count": 13,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -338,10 +434,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 14,
    "metadata": {
     "slideshow": {
-     "slide_type": "-"
+     "slide_type": "fragment"
     }
    },
    "outputs": [
@@ -351,7 +447,7 @@
        "NoneType"
       ]
      },
-     "execution_count": 11,
+     "execution_count": 14,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -386,10 +482,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 15,
    "metadata": {
     "slideshow": {
-     "slide_type": "slide"
+     "slide_type": "skip"
     }
    },
    "outputs": [
@@ -399,7 +495,7 @@
        "True"
       ]
      },
-     "execution_count": 12,
+     "execution_count": 15,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -424,10 +520,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 16,
    "metadata": {
     "slideshow": {
-     "slide_type": "fragment"
+     "slide_type": "skip"
     }
    },
    "outputs": [
@@ -437,7 +533,7 @@
        "False"
       ]
      },
-     "execution_count": 13,
+     "execution_count": 16,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -457,12 +553,12 @@
     }
    },
    "source": [
-    "So the following expression regards *four* objects in memory: *One* `list` object holding ten references to *three* other objects."
+    "So the following expression regards *four* objects in memory: *One* `list` object holding six references to *three* other objects."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 17,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -472,16 +568,16 @@
     {
      "data": {
       "text/plain": [
-       "[True, False, None, None, None, True, False, None, None, None]"
+       "[True, False, None, True, False, None]"
       ]
      },
-     "execution_count": 14,
+     "execution_count": 17,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "[True, False, None, None, None, True, False, None, None, None]"
+    "[True, False, None, True, False, None]"
    ]
   },
   {
@@ -508,7 +604,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 18,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -521,7 +617,7 @@
        "False"
       ]
      },
-     "execution_count": 15,
+     "execution_count": 18,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -532,7 +628,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 19,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -545,13 +641,13 @@
        "True"
       ]
      },
-     "execution_count": 16,
+     "execution_count": 19,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "42 != 123  # \"not equal to\"; other languages may use \"<>\""
+    "42 != 123  # \"not equal to\""
    ]
   },
   {
@@ -567,7 +663,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 20,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -580,7 +676,7 @@
        "True"
       ]
      },
-     "execution_count": 17,
+     "execution_count": 20,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -591,10 +687,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 21,
    "metadata": {
     "slideshow": {
-     "slide_type": "-"
+     "slide_type": "fragment"
     }
    },
    "outputs": [
@@ -604,7 +700,7 @@
        "True"
       ]
      },
-     "execution_count": 18,
+     "execution_count": 21,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -615,7 +711,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 22,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -628,7 +724,7 @@
        "False"
       ]
      },
-     "execution_count": 19,
+     "execution_count": 22,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -639,10 +735,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 23,
    "metadata": {
     "slideshow": {
-     "slide_type": "-"
+     "slide_type": "fragment"
     }
    },
    "outputs": [
@@ -652,7 +748,7 @@
        "False"
       ]
      },
-     "execution_count": 20,
+     "execution_count": 23,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -691,7 +787,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 24,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -716,7 +812,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 25,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -729,7 +825,7 @@
        "True"
       ]
      },
-     "execution_count": 22,
+     "execution_count": 25,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -749,89 +845,6 @@
     "However, sometimes, it is good to use *parentheses* around each operand for clarity."
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": 23,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "fragment"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "True"
-      ]
-     },
-     "execution_count": 23,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "(a > 5) and (b <= 100)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "source": [
-    "This is especially so when several logical operators are combined."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 24,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "slide"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "True"
-      ]
-     },
-     "execution_count": 24,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "a <= 5 or not b > 100"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 25,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "fragment"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "True"
-      ]
-     },
-     "execution_count": 25,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "(a <= 5) or not (b > 100)"
-   ]
-  },
   {
    "cell_type": "code",
    "execution_count": 26,
@@ -853,7 +866,7 @@
     }
    ],
    "source": [
-    "(a <= 5) or (not (b > 100))  # no need to \"over do\" it"
+    "(a > 5) and (b <= 100)"
    ]
   },
   {
@@ -864,9 +877,7 @@
     }
    },
    "source": [
-    "For even better readability, some practitioners suggest to *never* use the `>` and `>=` operators (cf., [source](https://llewellynfalco.blogspot.com/2016/02/dont-use-greater-than-sign-in.html); note that the included example is written in [Java](https://en.wikipedia.org/wiki/Java_%28programming_language%29) where `&&` means `and` and `||` means `or`).\n",
-    "\n",
-    "We may **chain** operators if the expressions that contain them are combined with the `and` operator. For example, the following two cells implement the same logic, where the second is a lot easier to read."
+    "This is especially so when several logical operators are combined."
    ]
   },
   {
@@ -890,7 +901,7 @@
     }
    ],
    "source": [
-    "5 < a and a < 87"
+    "a <= 5 or not b > 100"
    ]
   },
   {
@@ -913,6 +924,91 @@
      "output_type": "execute_result"
     }
    ],
+   "source": [
+    "(a <= 5) or not (b > 100)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 29,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "(a <= 5) or (not (b > 100))  # no need to \"over do\" it"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "For even better readability, some practitioners suggest to *never* use the `>` and `>=` operators (cf., [source](https://llewellynfalco.blogspot.com/2016/02/dont-use-greater-than-sign-in.html); note that the included example is written in [Java](https://en.wikipedia.org/wiki/Java_%28programming_language%29) where `&&` means `and` and `||` means `or`).\n",
+    "\n",
+    "We may **chain** operators if the expressions that contain them are combined with the `and` operator. For example, the following two cells implement the same logic, where the second is a lot easier to read."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 30,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "5 < a and a < 87"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 31,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "5 < a < 87"
    ]
@@ -940,101 +1036,7 @@
     "\n",
     "For example, any non-zero numeric object is cast as `True`. While this behavior allows writing more concise and thus more \"beautiful\" code, it may also be a source of confusion.\n",
     "\n",
-    "So, `(a - 21)` is cast as `True` and then the overall expression evaluates to `True` as well."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 29,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "slide"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "True"
-      ]
-     },
-     "execution_count": 29,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "(a - 21) and (b < 100)  # => 21 and (b < 100) => 21 and True"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "source": [
-    "Whenever we are unsure how Python evaluates a non-boolean expression in a boolean context, the [bool()](https://docs.python.org/3/library/functions.html#bool) built-in allows us to do it ourselves. [bool()](https://docs.python.org/3/library/functions.html#bool), like [int()](https://docs.python.org/3/library/functions.html#int), is yet another *constructor*."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 30,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "fragment"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "True"
-      ]
-     },
-     "execution_count": 30,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "bool(a - 21)  # = bool(21)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 31,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "fragment"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "False"
-      ]
-     },
-     "execution_count": 31,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "bool(a - 42)  # = bool(0)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "source": [
-    "Let's keep in mind that negative numbers also evaluate to `True`!"
+    "So, `(a - 40)` is cast as `True` and then the overall expression evaluates to `True` as well."
    ]
   },
   {
@@ -1042,7 +1044,7 @@
    "execution_count": 32,
    "metadata": {
     "slideshow": {
-     "slide_type": "fragment"
+     "slide_type": "slide"
     }
    },
    "outputs": [
@@ -1058,7 +1060,7 @@
     }
    ],
    "source": [
-    "bool(a - 43)  # = bool(-1)"
+    "(a - 40) and (b < 100)"
    ]
   },
   {
@@ -1069,7 +1071,7 @@
     }
    },
    "source": [
-    "In a boolean context, `None` is cast as `False`! So, `None` is *not* a \"maybe\" answer but a \"no.\""
+    "Whenever we are unsure how Python evaluates a non-boolean expression in a boolean context, the [bool()](https://docs.python.org/3/library/functions.html#bool) built-in allows us to do it ourselves. [bool()](https://docs.python.org/3/library/functions.html#bool), like [int()](https://docs.python.org/3/library/functions.html#int), is yet another *constructor*."
    ]
   },
   {
@@ -1077,14 +1079,14 @@
    "execution_count": 33,
    "metadata": {
     "slideshow": {
-     "slide_type": "slide"
+     "slide_type": "fragment"
     }
    },
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "False"
+       "True"
       ]
      },
      "execution_count": 33,
@@ -1093,18 +1095,7 @@
     }
    ],
    "source": [
-    "bool(None)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "source": [
-    "Another good rule to know is that container types (e.g., `list`) evaluate to `False` whenever they are empty and `True` if they hold at least one element."
+    "bool(a - 40)"
    ]
   },
   {
@@ -1112,7 +1103,7 @@
    "execution_count": 34,
    "metadata": {
     "slideshow": {
-     "slide_type": "slide"
+     "slide_type": "fragment"
     }
    },
    "outputs": [
@@ -1128,7 +1119,18 @@
     }
    ],
    "source": [
-    "bool([])"
+    "bool(a - 42)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Let's keep in mind that negative numbers also evaluate to `True`!"
    ]
   },
   {
@@ -1151,6 +1153,100 @@
      "output_type": "execute_result"
     }
    ],
+   "source": [
+    "bool(a - 44)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "In a boolean context, `None` is cast as `False`! So, `None` is *not* a \"maybe\" answer but a \"no.\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "False"
+      ]
+     },
+     "execution_count": 36,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "bool(None)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Another good rule to know is that container types (e.g., `list`) evaluate to `False` whenever they are empty and `True` if they hold at least one element."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "False"
+      ]
+     },
+     "execution_count": 37,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "bool([])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 38,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "bool([False])"
    ]
@@ -1168,7 +1264,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 36,
+   "execution_count": 39,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -1181,7 +1277,7 @@
        "False"
       ]
      },
-     "execution_count": 36,
+     "execution_count": 39,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1192,10 +1288,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 37,
+   "execution_count": 40,
    "metadata": {
     "slideshow": {
-     "slide_type": "-"
+     "slide_type": "fragment"
     }
    },
    "outputs": [
@@ -1205,13 +1301,13 @@
        "True"
       ]
      },
-     "execution_count": 37,
+     "execution_count": 40,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "bool(\"Lorem ipsum dolor sit amet, ...\")"
+    "bool(\"Lorem ipsum dolor sit amet.\")"
    ]
   },
   {
@@ -1260,10 +1356,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 38,
+   "execution_count": 41,
    "metadata": {
     "slideshow": {
-     "slide_type": "slide"
+     "slide_type": "skip"
     }
    },
    "outputs": [],
@@ -1287,12 +1383,36 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 39,
+   "execution_count": 42,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
     }
    },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "1"
+      ]
+     },
+     "execution_count": 42,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "0 or 1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
    "outputs": [
     {
      "name": "stdout",
@@ -1308,23 +1428,47 @@
        "1"
       ]
      },
-     "execution_count": 39,
+     "execution_count": 43,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "expr(0) or expr(1)"
+    "expr(0) or expr(1)  # both operands are evaluated"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 40,
+   "execution_count": 44,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
     }
    },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "1"
+      ]
+     },
+     "execution_count": 44,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "1 or 2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
    "outputs": [
     {
      "name": "stdout",
@@ -1339,7 +1483,7 @@
        "1"
       ]
      },
-     "execution_count": 40,
+     "execution_count": 45,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1350,10 +1494,34 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 41,
+   "execution_count": 46,
    "metadata": {
     "slideshow": {
-     "slide_type": "slide"
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "1"
+      ]
+     },
+     "execution_count": 46,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "0 or 1 or 2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
     }
    },
    "outputs": [
@@ -1371,7 +1539,7 @@
        "1"
       ]
      },
-     "execution_count": 41,
+     "execution_count": 47,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1393,12 +1561,36 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 42,
+   "execution_count": 48,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
     }
    },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0"
+      ]
+     },
+     "execution_count": 48,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "False or [] or 0"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 49,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
    "outputs": [
     {
      "name": "stdout",
@@ -1415,13 +1607,13 @@
        "0"
       ]
      },
-     "execution_count": 42,
+     "execution_count": 49,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "expr(False) or expr([]) or expr(0)"
+    "expr(False) or expr([]) or expr(0)  # all operands are evaluated"
    ]
   },
   {
@@ -1437,12 +1629,36 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 43,
+   "execution_count": 50,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
     }
    },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0"
+      ]
+     },
+     "execution_count": 50,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "0 and 1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 51,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
    "outputs": [
     {
      "name": "stdout",
@@ -1457,7 +1673,7 @@
        "0"
       ]
      },
-     "execution_count": 43,
+     "execution_count": 51,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1468,42 +1684,34 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 44,
+   "execution_count": 52,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
     }
    },
    "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Arg: 1\n",
-      "Arg: 0\n"
-     ]
-    },
     {
      "data": {
       "text/plain": [
        "0"
       ]
      },
-     "execution_count": 44,
+     "execution_count": 52,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "expr(1) and expr(0)"
+    "1 and 0"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 45,
+   "execution_count": 53,
    "metadata": {
     "slideshow": {
-     "slide_type": "slide"
+     "slide_type": "skip"
     }
    },
    "outputs": [
@@ -1521,7 +1729,63 @@
        "0"
       ]
      },
-     "execution_count": 45,
+     "execution_count": 53,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "expr(1) and expr(0)  # both operands are evaluated"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 54,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0"
+      ]
+     },
+     "execution_count": 54,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "1 and 0 and 2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 55,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Arg: 1\n",
+      "Arg: 0\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "0"
+      ]
+     },
+     "execution_count": 55,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1543,34 +1807,59 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 46,
+   "execution_count": 56,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
     }
    },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "3"
+      ]
+     },
+     "execution_count": 56,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "1 and 2 and 3"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 57,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
       "Arg: 1\n",
-      "Arg: 2\n"
+      "Arg: 2\n",
+      "Arg: 3\n"
      ]
     },
     {
      "data": {
       "text/plain": [
-       "2"
+       "3"
       ]
      },
-     "execution_count": 46,
+     "execution_count": 57,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "expr(1) and expr(2)"
+    "expr(1) and expr(2) and expr(3)"
    ]
   },
   {
@@ -1622,7 +1911,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 47,
+   "execution_count": 58,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -1635,10 +1924,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 48,
+   "execution_count": 59,
    "metadata": {
     "slideshow": {
-     "slide_type": "-"
+     "slide_type": "fragment"
     }
    },
    "outputs": [],
@@ -1648,10 +1937,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 49,
+   "execution_count": 60,
    "metadata": {
     "slideshow": {
-     "slide_type": "-"
+     "slide_type": "skip"
     }
    },
    "outputs": [],
@@ -1659,6 +1948,17 @@
     "random.seed(789)"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "#### \"Wrong Logic\" Example: Is the number divisible by `2`, `3`, both, or none?"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {
@@ -1672,7 +1972,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 50,
+   "execution_count": 61,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -1700,6 +2000,17 @@
     "    print(number, \"is divisible by neither 2 nor 3\")"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "#### \"Correct Logic\" Example: Is the number divisible by `2`, `3`, both, or none?"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {
@@ -1713,7 +2024,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 51,
+   "execution_count": 62,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -1726,7 +2037,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 52,
+   "execution_count": 63,
    "metadata": {
     "code_folding": [],
     "slideshow": {
@@ -1755,6 +2066,17 @@
     "    print(number, \"is divisible by neither 2 nor 3\")"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "#### \"Concise Logic\" Example: Is the number divisible by `2`, `3`, both, or none?"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {
@@ -1768,7 +2090,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 53,
+   "execution_count": 64,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -1781,7 +2103,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 54,
+   "execution_count": 65,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -1809,6 +2131,17 @@
     "    print(number, \"is divisible by neither 2 nor 3\")"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "#### Only the `if`-clause is mandatory"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {
@@ -1824,7 +2157,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 55,
+   "execution_count": 66,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -1844,6 +2177,17 @@
     "    print(\"You read this as often as you see heads when tossing a coin\")"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "#### Common Use Case: A binary Choice"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {
@@ -1857,7 +2201,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 56,
+   "execution_count": 67,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -1870,7 +2214,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 57,
+   "execution_count": 68,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -1907,7 +2251,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 58,
+   "execution_count": 69,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -1920,7 +2264,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 59,
+   "execution_count": 70,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -1938,12 +2282,23 @@
    "source": [
     "number = random.choice(numbers)\n",
     "\n",
-    "if number % 2:  # Beware of the opposite meaning!\n",
+    "if number % 2:  # Note the opposite meaning!\n",
     "    print(number, \"is odd\")\n",
     "else:\n",
     "    print(number, \"is even\")"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "#### \"Hard to read\" Example: Nesting `if` Statements"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {
@@ -1954,12 +2309,12 @@
    "source": [
     "We may **nest** `if` statements to control the flow of execution in a more granular way. Every additional layer, however, makes the code *less* readable, in particular, if we have more than one line per code block.\n",
     "\n",
-    "As another example, the code cell below first tosses a coin and then *either* checks if `signed_number` is even or odd *or* if it is positive or negative."
+    "For example, the code cell below implements an [A/B Testing](https://en.wikipedia.org/wiki/A/B_testing) strategy where half the time a \"complex\" message is shown to a \"user\" while in the remaining times an \"easy\" message is shown. To do so, the code first \"tosses a coin\" and then checks a randomly drawn `number`."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 60,
+   "execution_count": 71,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -1972,7 +2327,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 61,
+   "execution_count": 72,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -1983,23 +2338,36 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "4 is even\n"
+      "6 can be divided by 2 without a rest\n"
      ]
     }
    ],
    "source": [
-    "signed_number = random.choice([-1, +1]) * random.choice(numbers)\n",
+    "number = random.choice(numbers)\n",
     "\n",
+    "# Coin is heads.\n",
     "if random.random() > 0.5:\n",
-    "    if signed_number % 2:\n",
-    "        print(signed_number, \"is odd\")\n",
+    "    if number % 2 == 0:\n",
+    "        print(number, \"can be divided by 2 without a rest\")\n",
     "    else:\n",
-    "        print(signed_number, \"is even\")\n",
+    "        print(number, \"divided by 2 results in a non-zero rest\")\n",
+    "# Coin is tails.\n",
     "else:\n",
-    "    if signed_number > 0:\n",
-    "        print(signed_number, \"is positive\")\n",
+    "    if number % 2 == 0:\n",
+    "        print(number, \"is even\")\n",
     "    else:\n",
-    "        print(signed_number, \"is negative\")"
+    "        print(number, \"is odd\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "#### \"Easy to read\" Example: Flattening nested `if` Statements"
    ]
   },
   {
@@ -2010,14 +2378,14 @@
     }
    },
    "source": [
-    "A way to make this code more readable is to introduce **temporary variables** *in combination* with the `and` operator to **flatten** the branching logic. The `if` statement then reads almost like plain English. In contrast to many other languages, creating variables is a computationally *cheap* operation in Python and also helps to document the code *inline* with meaningful variable names.\n",
+    "A way to make this code more readable is to introduce **temporary variables** *in combination* with the `and` operator to **flatten** the branching logic. The `if` statement then reads almost like plain English. In contrast to many other languages, creating variables is a computationally *cheap* operation in Python (i.e., only a reference is created) and also helps to document the code *inline* with meaningful variable names.\n",
     "\n",
     "Flattening the logic *without* temporary variables could lead to *more* sub-expressions in the conditions be evaluated than necessary. Do you see why?"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 62,
+   "execution_count": 73,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -2030,7 +2398,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 63,
+   "execution_count": 74,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -2041,25 +2409,24 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "4 is even\n"
+      "6 can be divided by 2 without a rest\n"
      ]
     }
    ],
    "source": [
-    "signed_number = random.choice([-1, +1]) * random.choice(numbers)\n",
+    "number = random.choice(numbers)\n",
     "\n",
-    "check_oddness = random.random() > 0.5\n",
-    "is_odd = signed_number % 2\n",
-    "is_positive = signed_number > 0\n",
+    "coin_is_heads = random.random() > 0.5\n",
+    "number_is_even = number % 2 == 0\n",
     "\n",
-    "if check_oddness and is_odd:\n",
-    "    print(signed_number, \"is odd\")\n",
-    "elif check_oddness and not is_odd:\n",
-    "    print(signed_number, \"is even\")\n",
-    "elif not check_oddness and is_positive:\n",
-    "    print(signed_number, \"is positive\")\n",
+    "if coin_is_heads and number_is_even:\n",
+    "    print(number, \"can be divided by 2 without a rest\")\n",
+    "elif coin_is_heads and not number_is_even:\n",
+    "    print(number, \"divided by 2 results in a non-zero rest\")\n",
+    "elif not coin_is_heads and number_is_even:\n",
+    "    print(number, \"is even\")\n",
     "else:\n",
-    "    print(signed_number, \"is negative\")"
+    "    print(number, \"is odd\")"
    ]
   },
   {
@@ -2098,24 +2465,11 @@
     "y = f(x) =\n",
     "\\begin{cases}\n",
     "0, \\text{ if } x \\le 0 \\\\\n",
-    "x^2, \\text{ otherwise}\n",
+    "x, \\text{ otherwise}\n",
     "\\end{cases}\n",
     "$"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": 64,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "slide"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "x = 3"
-   ]
-  },
   {
    "cell_type": "markdown",
    "metadata": {
@@ -2129,41 +2483,32 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 65,
+   "execution_count": 75,
    "metadata": {
     "slideshow": {
-     "slide_type": "fragment"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "if x <= 0:\n",
-    "    y = 0\n",
-    "else:\n",
-    "    y = x ** 2"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 66,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "-"
+     "slide_type": "slide"
     }
    },
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "9"
+       "3"
       ]
      },
-     "execution_count": 66,
+     "execution_count": 75,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
+    "x = 3\n",
+    "\n",
+    "if x <= 0:\n",
+    "    y = 0\n",
+    "else:\n",
+    "    y = x\n",
+    "\n",
     "y"
    ]
   },
@@ -2180,38 +2525,29 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 67,
+   "execution_count": 76,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
     }
    },
-   "outputs": [],
-   "source": [
-    "y = 0 if x <= 0 else x ** 2"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 68,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "-"
-    }
-   },
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "9"
+       "3"
       ]
      },
-     "execution_count": 68,
+     "execution_count": 76,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
+    "x = 3\n",
+    "\n",
+    "y = 0 if x <= 0 else x\n",
+    "\n",
     "y"
    ]
   },
@@ -2228,38 +2564,29 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 69,
+   "execution_count": 77,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
     }
    },
-   "outputs": [],
-   "source": [
-    "y = max(0, x) ** 2"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 70,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "-"
-    }
-   },
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "9"
+       "3"
       ]
      },
-     "execution_count": 70,
+     "execution_count": 77,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
+    "x = 3\n",
+    "\n",
+    "y = max(0, x)\n",
+    "\n",
     "y"
    ]
   },
@@ -2282,16 +2609,14 @@
     }
    },
    "source": [
-    "In the previous two chapters, we encountered a couple of *runtime* errors. A natural urge we might have after reading about conditional statements is to write code that somehow reacts to the occurrence of such exceptions. All we need is a way to formulate a condition for that.\n",
+    "In the previous two chapters, we encountered a couple of *runtime* errors. A natural urge we might have after reading about conditional statements is to write code that somehow reacts to the occurrence of such exceptions.\n",
     "\n",
-    "For sure, this is such a common thing to do that Python provides a language construct for it, namely the compound `try` statement (cf., [reference](https://docs.python.org/3/reference/compound_stmts.html#the-try-statement)).\n",
-    "\n",
-    "In its simplest form, it comes with just two clauses: `try` and `except`. The following tells Python to execute the code in the `try`-clause, and if *anything* goes wrong, continue in the `except`-clause instead of **raising** an error to us. Of course, if nothing goes wrong, the `except`-clause is *not* executed."
+    "Consider a situation where we are given some user input that may contain values that cause problems. To illustrate this, we draw a random integer between `0` and `5`, and then divide by this number. Naturally, we see a `ZeroDivisionError` in 16.6% of the cases."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 71,
+   "execution_count": 78,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -2304,7 +2629,60 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 72,
+   "execution_count": 79,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "ename": "ZeroDivisionError",
+     "evalue": "division by zero",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mZeroDivisionError\u001b[0m                         Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-79-7996bd5496ba>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0muser_input\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mrandom\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mchoice\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m3\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m4\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m5\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0;36m1\u001b[0m \u001b[0;34m/\u001b[0m \u001b[0muser_input\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;31mZeroDivisionError\u001b[0m: division by zero"
+     ]
+    }
+   ],
+   "source": [
+    "user_input = random.choice([0, 1, 2, 3, 4, 5])\n",
+    "\n",
+    "1 / user_input"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "With the compound `try` statement (cf., [reference](https://docs.python.org/3/reference/compound_stmts.html#the-try-statement)), we can **handle** any *runtime* error.\n",
+    "\n",
+    "In its simplest form, it comes with just two clauses: `try` and `except`. The following tells Python to execute the code in the `try`-clause, and if *anything* goes wrong, continue in the `except`-clause instead of **raising** an error to us. Of course, if nothing goes wrong, the `except`-clause is *not* executed."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 80,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "random.seed(123)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 81,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -2320,10 +2698,10 @@
     }
    ],
    "source": [
-    "user_input = random.choice([0, 1])\n",
+    "user_input = random.choice([0, 1, 2, 3, 4, 5])\n",
     "\n",
     "try:\n",
-    "    1 / user_input\n",
+    "    print(\"The result is\", 1 / user_input)\n",
     "except:\n",
     "    print(\"Something went wrong\")"
    ]
@@ -2336,7 +2714,7 @@
     }
    },
    "source": [
-    "However, it is good practice *not* to **handle** *any* possible exception but only the ones we may *expect* from the code in the `try`-clause. The reason for that is that we do not want to risk *suppressing* an exception that we do *not* expect. Also, the code base becomes easier to understand as we communicate what could go wrong during execution in an *explicit* way to the (human) reader. Python comes with a lot of [built-in exceptions](https://docs.python.org/3/library/exceptions.html#concrete-exceptions) that we should familiarize ourselves with.\n",
+    "However, it is good practice *not* to handle *any* possible exception but only the ones we may *expect* from the code in the `try`-clause. The reason for that is that we do not want to risk *suppressing* an exception that we do *not* expect. Also, the code base becomes easier to understand as we communicate what could go wrong during execution in an *explicit* way to the (human) reader. Python comes with a lot of [built-in exceptions](https://docs.python.org/3/library/exceptions.html#concrete-exceptions) that we should familiarize ourselves with.\n",
     "\n",
     "Another good practice is to always keep the code in the `try`-clause short to not *accidentally* handle an exception we do *not* want to handle.\n",
     "\n",
@@ -2345,7 +2723,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 73,
+   "execution_count": 82,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -2358,7 +2736,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 74,
+   "execution_count": 83,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -2374,10 +2752,10 @@
     }
    ],
    "source": [
-    "user_input = random.choice([0, 1])\n",
+    "user_input = random.choice([0, 1, 2, 3, 4, 5])\n",
     "\n",
     "try:\n",
-    "    1 / user_input\n",
+    "    print(\"The result is\", 1 / user_input)\n",
     "except ZeroDivisionError:\n",
     "    print(\"Something went wrong\")"
    ]
@@ -2399,7 +2777,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 75,
+   "execution_count": 84,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -2412,7 +2790,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 76,
+   "execution_count": 85,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -2429,14 +2807,14 @@
     }
    ],
    "source": [
-    "user_input = random.choice([0, 1])\n",
+    "user_input = random.choice([0, 1, 2, 3, 4, 5])\n",
     "\n",
     "try:\n",
-    "    1 / user_input\n",
+    "    result = 1 / user_input\n",
     "except ZeroDivisionError:\n",
     "    print(\"Oops. Division by 0. How does that work?\")\n",
     "else:\n",
-    "    print(\"Yes, division worked smoothly.\")\n",
+    "    print(\"The result is\", result)\n",
     "finally:\n",
     "    print(\"I am always printed\")"
    ]
diff --git a/03_conditionals_01_review.ipynb b/03_conditionals_01_review.ipynb
index cd56223..57a706e 100644
--- a/03_conditionals_01_review.ipynb
+++ b/03_conditionals_01_review.ipynb
@@ -19,7 +19,9 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Read [Chapter 3](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/03_conditionals_00_lecture.ipynb) of the book. Then, work through the questions below."
+    "The questions below assume that you have read [Chapter 3](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/03_conditionals_00_lecture.ipynb) in the book.\n",
+    "\n",
+    "Be concise in your answers! Most questions can be answered in *one* sentence."
    ]
   },
   {
@@ -29,13 +31,6 @@
     "### Essay Questions "
    ]
   },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Answer the following questions *briefly*!"
-   ]
-  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -47,7 +42,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    " "
+    " < your answer >"
    ]
   },
   {
@@ -61,7 +56,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    " "
+    " < your answer >"
    ]
   },
   {
@@ -75,7 +70,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    " "
+    " < your answer >"
    ]
   },
   {
@@ -89,7 +84,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    " "
+    " < your answer >"
    ]
   },
   {
@@ -103,7 +98,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    " "
+    " < your answer >"
    ]
   },
   {
@@ -129,7 +124,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    " "
+    " < your answer >"
    ]
   },
   {
@@ -159,7 +154,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    " "
+    " < your answer >"
    ]
   },
   {
@@ -173,7 +168,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    " "
+    " < your answer >"
    ]
   }
  ],
diff --git a/03_conditionals_02_exercises.ipynb b/03_conditionals_02_exercises.ipynb
index ac4acd1..c8870ce 100644
--- a/03_conditionals_02_exercises.ipynb
+++ b/03_conditionals_02_exercises.ipynb
@@ -19,7 +19,9 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Read [Chapter 3](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/03_conditionals_00_lecture.ipynb) of the book. Then, work through the exercises below. The `...` indicate where you need to fill in your answers. You should not need to create any additional code cells."
+    "The exercises below assume that you have read [Chapter 3](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/03_conditionals_00_lecture.ipynb) in the book.\n",
+    "\n",
+    "The `...`'s in the code cells indicate where you need to fill in code snippets. The number of `...`'s within a code cell give you a rough idea of how many lines of code are needed to solve the task. You should not need to create any additional code cells for your final solution. However, you may want to use temporary code cells to try out some ideas."
    ]
   },
   {
@@ -49,8 +51,28 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "def discounted_price(...):\n",
-    "    ..."
+    "def discounted_price(unit_price, quantity):\n",
+    "    \"\"\"Calculate the price of a line item in an order.\n",
+    "\n",
+    "    Args:\n",
+    "        unit_price (float): price of one ordered item\n",
+    "        quantity (int): number of items ordered\n",
+    "\n",
+    "    Returns:\n",
+    "        line_item_price (float)\n",
+    "    \"\"\"\n",
+    "    ...\n",
+    "    ...\n",
+    "    ...\n",
+    "    ...\n",
+    "\n",
+    "    ...\n",
+    "    ...\n",
+    "    ...\n",
+    "    ...\n",
+    "    ...\n",
+    "\n",
+    "    return ..."
    ]
   },
   {
@@ -143,7 +165,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    " "
+    " < your answer >"
    ]
   },
   {
@@ -182,9 +204,9 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "**Q2.2**: Loop over the `numbers` list and replace numbers for which one of the two (or both) conditions apply with text strings `\"Fizz\"`, `\"Buzz\"`, or `\"FizzBuzz\"` using the indexing operator `[]` and the assignment statement `=`.\n",
+    "**Q2.2**: Loop over the `numbers` list and *replace* numbers for which one of the two (or both) conditions apply with text strings `\"Fizz\"`, `\"Buzz\"`, or `\"FizzBuzz\"` using the indexing operator `[]` and the assignment statement `=`.\n",
     "\n",
-    "In [Chapter 1](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/01_elements_00_lecture.ipynb), we saw that Python starts indexing with `0` as the first element. Keep that in mind.\n",
+    "In [Chapter 1](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/01_elements_00_lecture.ipynb#Who-am-I?-And-how-many?), we saw that Python starts indexing with `0` as the first element. Keep that in mind.\n",
     "\n",
     "So in each iteration of the `for`-loop, you have to determine an `index` variable as well as check the actual `number` for its divisors.\n",
     "\n",
@@ -198,6 +220,13 @@
    "outputs": [],
    "source": [
     "for number in numbers:\n",
+    "    ...\n",
+    "\n",
+    "    ...\n",
+    "    ...\n",
+    "    ...\n",
+    "    ...\n",
+    "    ...\n",
     "    ..."
    ]
   },

From 6e681752520a69d47ce85eb29d79abe6dbc64ac9 Mon Sep 17 00:00:00 2001
From: Alexander Hess <alexander@webartifex.biz>
Date: Mon, 30 Mar 2020 08:32:16 +0200
Subject: [PATCH 089/242] Add video and streamline content

---
 04_iteration_00_lecture.ipynb   | 1220 ++++++++++++++++++++++---------
 04_iteration_01_review.ipynb    |   37 +-
 04_iteration_02_exercises.ipynb |  327 ++++-----
 04_iteration_03_exercises.ipynb |    6 +-
 4 files changed, 1032 insertions(+), 558 deletions(-)

diff --git a/04_iteration_00_lecture.ipynb b/04_iteration_00_lecture.ipynb
index 61858c5..cba0152 100644
--- a/04_iteration_00_lecture.ipynb
+++ b/04_iteration_00_lecture.ipynb
@@ -1,5 +1,53 @@
 {
  "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "A **video presentation** of the contents in this chapter is shown below. A playlist with *all* chapters as videos is linked [here](https://www.youtube.com/playlist?list=PL-2JV1G3J10lQ2xokyQowcRJI5jjNfW7f)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/jpeg": 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+       "            width=\"60%\"\n",
+       "            height=\"300\"\n",
+       "            src=\"https://www.youtube.com/embed/jT6hr4vOJks\"\n",
+       "            frameborder=\"0\"\n",
+       "            allowfullscreen\n",
+       "        ></iframe>\n",
+       "        "
+      ],
+      "text/plain": [
+       "<IPython.lib.display.YouTubeVideo at 0x7f432f422450>"
+      ]
+     },
+     "execution_count": 1,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from IPython.display import YouTubeVideo\n",
+    "YouTubeVideo(\"jT6hr4vOJks\", width=\"60%\")"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {
@@ -98,7 +146,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 2,
    "metadata": {
     "code_folding": [],
     "slideshow": {
@@ -113,7 +161,7 @@
     "    Args:\n",
     "        n (int): seconds until the party begins\n",
     "    \"\"\"\n",
-    "    if n == 0:  # = base case\n",
+    "    if n == 0:\n",
     "        print(\"Happy New Year!\")\n",
     "    else:\n",
     "        print(n)\n",
@@ -122,7 +170,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 3,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -226,7 +274,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 4,
    "metadata": {
     "code_folding": [],
     "slideshow": {
@@ -244,7 +292,7 @@
     "    Returns:\n",
     "        factorial (int)\n",
     "    \"\"\"\n",
-    "    if n == 0:  # = base case\n",
+    "    if n == 0:\n",
     "        return 1\n",
     "    else:\n",
     "        recurse = factorial(n - 1)\n",
@@ -267,7 +315,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 5,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -280,7 +328,7 @@
        "6"
       ]
      },
-     "execution_count": 4,
+     "execution_count": 5,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -291,7 +339,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 6,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -304,7 +352,7 @@
        "3628800"
       ]
      },
-     "execution_count": 5,
+     "execution_count": 6,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -328,7 +376,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 7,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -345,14 +393,14 @@
     "    Returns:\n",
     "        factorial (int)\n",
     "    \"\"\"\n",
-    "    if n == 0:  # = base case\n",
+    "    if n == 0:\n",
     "        return 1\n",
     "    return n * factorial(n - 1)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 8,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -365,7 +413,7 @@
        "6"
       ]
      },
-     "execution_count": 7,
+     "execution_count": 8,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -376,7 +424,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 9,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -389,7 +437,7 @@
        "3628800"
       ]
      },
-     "execution_count": 8,
+     "execution_count": 9,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -411,7 +459,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 10,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -424,7 +472,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 11,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -451,7 +499,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 12,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -464,7 +512,7 @@
        "6"
       ]
      },
-     "execution_count": 11,
+     "execution_count": 12,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -475,7 +523,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 13,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -488,7 +536,7 @@
        "3628800"
       ]
      },
-     "execution_count": 12,
+     "execution_count": 13,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -521,7 +569,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 14,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -539,14 +587,14 @@
     "    Returns:\n",
     "        gcd (int)\n",
     "    \"\"\"\n",
-    "    if b == 0:  # = base case\n",
+    "    if b == 0:\n",
     "        return a \n",
     "    return gcd(b, a % b)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 15,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -559,7 +607,7 @@
        "4"
       ]
      },
-     "execution_count": 14,
+     "execution_count": 15,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -581,7 +629,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 16,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -594,7 +642,7 @@
        "9"
       ]
      },
-     "execution_count": 15,
+     "execution_count": 16,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -616,7 +664,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 17,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -629,7 +677,7 @@
        "1"
       ]
      },
-     "execution_count": 16,
+     "execution_count": 17,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -651,7 +699,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 18,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -676,7 +724,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 19,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -689,37 +737,13 @@
        "4"
       ]
      },
-     "execution_count": 18,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "math.gcd(12, 4)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "9"
-      ]
-     },
      "execution_count": 19,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "math.gcd(112233445566778899, 987654321)"
+    "math.gcd(12, 4)"
    ]
   },
   {
@@ -734,7 +758,7 @@
     {
      "data": {
       "text/plain": [
-       "1"
+       "9"
       ]
      },
      "execution_count": 20,
@@ -742,6 +766,30 @@
      "output_type": "execute_result"
     }
    ],
+   "source": [
+    "math.gcd(112233445566778899, 987654321)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "1"
+      ]
+     },
+     "execution_count": 21,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "math.gcd(7, 7919)"
    ]
@@ -792,7 +840,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 22,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -809,16 +857,16 @@
     "    Returns:\n",
     "        ith_fibonacci (int)\n",
     "    \"\"\"\n",
-    "    if i == 0:  # = first base case\n",
+    "    if i == 0:\n",
     "        return 0\n",
-    "    elif i == 1:  # = second base case\n",
+    "    elif i == 1:\n",
     "        return 1\n",
     "    return fibonacci(i - 1) + fibonacci(i - 2)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 23,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -831,7 +879,7 @@
        "144"
       ]
      },
-     "execution_count": 22,
+     "execution_count": 23,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -863,14 +911,14 @@
     "\n",
     "To understand this in detail, we have to study algorithms and data structures (e.g., with [this book](https://www.amazon.de/Introduction-Algorithms-Press-Thomas-Cormen/dp/0262033844/ref=sr_1_1?__mk_de_DE=%C3%85M%C3%85%C5%BD%C3%95%C3%91&crid=1JNE8U0VZGU0O&qid=1569837169&s=gateway&sprefix=algorithms+an%2Caps%2C180&sr=8-1)), a discipline within computer science, and dive into the analysis of **[time complexity of algorithms](https://en.wikipedia.org/wiki/Time_complexity)**.\n",
     "\n",
-    "Luckily, in the Fibonacci case, the inefficiency can be resolved with a **caching** (i.e., \"reuse\") strategy from the field of **[dynamic programming](https://en.wikipedia.org/wiki/Dynamic_programming)**, namely **[memoization](https://en.wikipedia.org/wiki/Memoization)**. We do so in [Chapter 8](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/08_mappings_00_lecture.ipynb#Memoization), after introducing the `dict` data type.\n",
+    "Luckily, in the Fibonacci case, the inefficiency can be resolved with a **caching** (i.e., \"reuse\") strategy from the field of **[dynamic programming](https://en.wikipedia.org/wiki/Dynamic_programming)**, namely **[memoization](https://en.wikipedia.org/wiki/Memoization)**. We do so in [Chapter 9](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/09_mappings_00_lecture.ipynb#Memoization), after introducing the `dict` data type.\n",
     "\n",
     "Let's measure the average run times for `fibonacci()` and varying `i` arguments with the `%%timeit` [cell magic](https://ipython.readthedocs.io/en/stable/interactive/magics.html#magic-timeit) that comes with Jupyter."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 24,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -881,7 +929,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "35.2 µs ± 971 ns per loop (mean ± std. dev. of 7 runs, 100 loops each)\n"
+      "70.9 µs ± 22.5 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n"
      ]
     }
    ],
@@ -890,28 +938,6 @@
     "fibonacci(12)"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": 24,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "fragment"
-    }
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "11.2 ms ± 81.1 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n"
-     ]
-    }
-   ],
-   "source": [
-    "%%timeit -n 100\n",
-    "fibonacci(24)"
-   ]
-  },
   {
    "cell_type": "code",
    "execution_count": 25,
@@ -925,7 +951,29 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "3.67 s ± 0 ns per loop (mean ± std. dev. of 1 run, 1 loop each)\n"
+      "11.3 ms ± 31.9 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n"
+     ]
+    }
+   ],
+   "source": [
+    "%%timeit -n 100\n",
+    "fibonacci(24)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "3.65 s ± 0 ns per loop (mean ± std. dev. of 1 run, 1 loop each)\n"
      ]
     }
    ],
@@ -936,7 +984,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": 27,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -947,7 +995,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "5.94 s ± 0 ns per loop (mean ± std. dev. of 1 run, 1 loop each)\n"
+      "5.9 s ± 0 ns per loop (mean ± std. dev. of 1 run, 1 loop each)\n"
      ]
     }
    ],
@@ -982,7 +1030,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": 28,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -997,7 +1045,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": 29,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -1011,10 +1059,10 @@
      "traceback": [
       "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
       "\u001b[0;31mRecursionError\u001b[0m                            Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-28-62f686a9f8a1>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mrun_forever\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
-      "\u001b[0;32m<ipython-input-27-80575699c62c>\u001b[0m in \u001b[0;36mrun_forever\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mrun_forever\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m     \u001b[0;34m\"\"\"Also a pointless function should have a docstring.\"\"\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m     \u001b[0mrun_forever\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;32m<ipython-input-29-62f686a9f8a1>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mrun_forever\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;32m<ipython-input-28-80575699c62c>\u001b[0m in \u001b[0;36mrun_forever\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mrun_forever\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m     \u001b[0;34m\"\"\"Also a pointless function should have a docstring.\"\"\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m     \u001b[0mrun_forever\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
       "... last 1 frames repeated, from the frame below ...\n",
-      "\u001b[0;32m<ipython-input-27-80575699c62c>\u001b[0m in \u001b[0;36mrun_forever\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mrun_forever\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m     \u001b[0;34m\"\"\"Also a pointless function should have a docstring.\"\"\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m     \u001b[0mrun_forever\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;32m<ipython-input-28-80575699c62c>\u001b[0m in \u001b[0;36mrun_forever\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mrun_forever\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m     \u001b[0;34m\"\"\"Also a pointless function should have a docstring.\"\"\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m     \u001b[0mrun_forever\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
       "\u001b[0;31mRecursionError\u001b[0m: maximum recursion depth exceeded"
      ]
     }
@@ -1036,7 +1084,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
+   "execution_count": 30,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -4010,10 +4058,10 @@
      "traceback": [
       "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
       "\u001b[0;31mRecursionError\u001b[0m                            Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-29-43e3ad331761>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mcountdown\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m3.1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
-      "\u001b[0;32m<ipython-input-1-d7441c038fc8>\u001b[0m in \u001b[0;36mcountdown\u001b[0;34m(n)\u001b[0m\n\u001b[1;32m      9\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     10\u001b[0m         \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mn\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 11\u001b[0;31m         \u001b[0mcountdown\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mn\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;32m<ipython-input-30-43e3ad331761>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mcountdown\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m3.1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;32m<ipython-input-2-10499518927d>\u001b[0m in \u001b[0;36mcountdown\u001b[0;34m(n)\u001b[0m\n\u001b[1;32m      9\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     10\u001b[0m         \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mn\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 11\u001b[0;31m         \u001b[0mcountdown\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mn\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
       "... last 1 frames repeated, from the frame below ...\n",
-      "\u001b[0;32m<ipython-input-1-d7441c038fc8>\u001b[0m in \u001b[0;36mcountdown\u001b[0;34m(n)\u001b[0m\n\u001b[1;32m      9\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     10\u001b[0m         \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mn\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 11\u001b[0;31m         \u001b[0mcountdown\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mn\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;32m<ipython-input-2-10499518927d>\u001b[0m in \u001b[0;36mcountdown\u001b[0;34m(n)\u001b[0m\n\u001b[1;32m      9\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     10\u001b[0m         \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mn\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 11\u001b[0;31m         \u001b[0mcountdown\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mn\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
       "\u001b[0;31mRecursionError\u001b[0m: maximum recursion depth exceeded while calling a Python object"
      ]
     }
@@ -4035,7 +4083,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 30,
+   "execution_count": 31,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -4049,10 +4097,10 @@
      "traceback": [
       "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
       "\u001b[0;31mRecursionError\u001b[0m                            Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-30-188b816be8b6>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mfactorial\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m3.1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
-      "\u001b[0;32m<ipython-input-6-d41f5be5adaa>\u001b[0m in \u001b[0;36mfactorial\u001b[0;34m(n)\u001b[0m\n\u001b[1;32m     10\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mn\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m  \u001b[0;31m# = base case\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     11\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 12\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0mn\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0mfactorial\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mn\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;32m<ipython-input-31-188b816be8b6>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mfactorial\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m3.1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;32m<ipython-input-7-88d8d933f651>\u001b[0m in \u001b[0;36mfactorial\u001b[0;34m(n)\u001b[0m\n\u001b[1;32m     10\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mn\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     11\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 12\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0mn\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0mfactorial\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mn\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
       "... last 1 frames repeated, from the frame below ...\n",
-      "\u001b[0;32m<ipython-input-6-d41f5be5adaa>\u001b[0m in \u001b[0;36mfactorial\u001b[0;34m(n)\u001b[0m\n\u001b[1;32m     10\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mn\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m  \u001b[0;31m# = base case\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     11\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 12\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0mn\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0mfactorial\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mn\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;32m<ipython-input-7-88d8d933f651>\u001b[0m in \u001b[0;36mfactorial\u001b[0;34m(n)\u001b[0m\n\u001b[1;32m     10\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mn\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     11\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 12\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0mn\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0mfactorial\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mn\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
       "\u001b[0;31mRecursionError\u001b[0m: maximum recursion depth exceeded in comparison"
      ]
     }
@@ -4069,94 +4117,35 @@
     }
    },
    "source": [
-    "The infinite recursions could easily be avoided by replacing `n == 0` with `n <= 0` in both functions and thereby **generalizing** them. But even then, calling either `countdown()` or `factorial()` with a non-integer number is *semantically* wrong, and, therefore, we better leave the base cases unchanged.\n",
-    "\n",
-    "Errors as above are a symptom of missing **type checking**: By design, Python allows us to pass in not only integers but objects of any type as arguments to the `countdown()` and `factorial()` functions. As long as the arguments \"behave\" like integers, we do not encounter any *runtime* errors. This is the case here as the two example functions only use the `-` and `*` operators internally, and, in the context of arithmetic, a `float` object behaves like an `int` object. So, the functions keep calling themselves until Python decides with a built-in heuristic that the recursion is likely not going to end and aborts the computations with a `RecursionError`. Strictly speaking, a `RecursionError` is, of course, a *runtime* error as well.\n",
+    "The infinite recursions could easily be avoided by replacing `n == 0` with `n <= 0` in both functions and thereby **generalizing** them. However, even then, calling either `countdown()` or `factorial()` with a non-integer number is still *semantically* wrong.\n",
     "\n",
+    "Errors as above are a symptom of missing **type checking**: By design, Python allows us to pass in not only integers but objects of any type as arguments to the `countdown()` and `factorial()` functions. As long as the arguments \"behave\" like integers, we do not encounter any *runtime* errors. This is the case here as the two example functions only use the `-` and `*` operators internally, and, in the context of arithmetic, a `float` object behaves like an `int` object. So, the functions keep calling themselves until Python decides with a built-in heuristic that the recursion is likely not going to end and aborts the computations with a `RecursionError`. Strictly speaking, a `RecursionError` is, of course, a *runtime* error as well."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "### Duck Typing"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
     "The missing type checking is *100% intentional* and considered a **[feature of rather than a bug](https://www.urbandictionary.com/define.php?term=It%27s%20not%20a%20bug%2C%20it%27s%20a%20feature)** in Python!\n",
     "\n",
-    "Pythonistas often use the colloquial term **[duck typing](https://en.wikipedia.org/wiki/Duck_typing)** when referring to the same idea, and the saying goes, \"If it walks like a duck and it quacks like a duck, it must be a duck.\" For example, we could call `factorial()` with the `float` object `3.0`, and the recursion works out fine. So, as long as the `3.0` \"walks\" like a `3` and \"quacks\" like a `3`, it \"must be\" a `3`.\n",
+    "Pythonistas use the \"technical\" term **[duck typing](https://en.wikipedia.org/wiki/Duck_typing)** to express the idea of two objects of *different* types behaving in the *same* way in a given context. The colloquial saying goes, \"If it walks like a duck and it quacks like a duck, it must be a duck.\"\n",
     "\n",
-    "We see similar behavior when we mix objects of types `int` and `float` with arithmetic operators. For example, `1 + 2.0` works because Python implicitly views the `1` as a `1.0` at runtime and then knows how to do floating-point arithmetic: Here, the `int` \"walks\" and \"quacks\" like a `float`. Strictly speaking, this is yet another example of operator overloading, whereas duck typing refers to the same behavior when passing arguments to function calls.\n",
-    "\n",
-    "The important lesson is that we must expect our functions to be called with objects of *any* type at runtime, as opposed to the one type we had in mind when we defined the function.\n",
-    "\n",
-    "Duck typing is possible because Python is a dynamically typed language. On the contrary, in statically typed languages like C, we *must* declare (i.e., \"specify\") the data type of every parameter in a function definition. Then, a `RecursionError` as for `countdown(3.1)` or `factorial(3.1)` above could not occur. For example, if we declared the `countdown()` and `factorial()` functions to only accept `int` objects, calling the functions with a `float` argument would immediately fail *syntactically*. As a downside, we would then lose the ability to call `factorial()` with `3.0`, which is *semantically* correct nevertheless.\n",
-    "\n",
-    "So, there is no black or white answer as to which of the two language designs is better. Yet, most professional programmers have strong opinions concerning duck typing, reaching from \"love\" to \"hate.\" This is another example of how programming is a subjective art rather than \"objective\" science. Python's design is probably more appealing to beginners who intuitively regard `3` and `3.0` as interchangeable."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "slide"
-    }
-   },
-   "source": [
-    "### Type Checking & Input Validation"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "source": [
-    "We use the built-in [isinstance()](https://docs.python.org/3/library/functions.html#isinstance) function to make sure `factorial()` is called with an `int` object as the argument. We further **validate the input** by verifying that the integer is non-negative.\n",
-    "\n",
-    "Meanwhile, we also see how we manually raise exceptions with the `raise` statement (cf., [reference](https://docs.python.org/3/reference/simple_stmts.html#the-raise-statement)), another way of controlling the flow of execution.\n",
-    "\n",
-    "The first two branches in the revised `factorial()` function act as **guardians** ensuring that the code does not produce *unexpected* runtime errors: Errors may be expected when mentioned in the docstring.\n",
-    "\n",
-    "Forcing `n` to be an `int` is a very puritan way of handling the issues discussed above. A more relaxed approach could be to also accept a `float` and use its [is_integer()](https://docs.python.org/3/library/stdtypes.html#float.is_integer) method to check if `n` could be cast as an `int`. After all, being too puritan, we cannot take advantage of duck typing.\n",
-    "\n",
-    "So, in essence, we are doing *two* things here: Besides checking the type, we also enforce **domain-specific** (i.e., mathematical here) rules concerning the non-negativity of `n`."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 31,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "slide"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "def factorial(n):\n",
-    "    \"\"\"Calculate the factorial of a number.\n",
-    "\n",
-    "    Args:\n",
-    "        n (int): number to calculate the factorial for; must be positive\n",
-    "\n",
-    "    Returns:\n",
-    "        factorial (int)\n",
-    "\n",
-    "    Raises:\n",
-    "        TypeError: if n is not an integer\n",
-    "        ValueError: if n is negative\n",
-    "    \"\"\"\n",
-    "    if not isinstance(n, int):\n",
-    "        raise TypeError(\"Factorial is only defined for integers\")\n",
-    "    elif n < 0:\n",
-    "        raise ValueError(\"Factorial is not defined for negative integers\")\n",
-    "    elif n == 0:  # = base case\n",
-    "        return 1\n",
-    "    return n * factorial(n - 1)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "source": [
-    "The revised `factorial()` function works like the old one."
+    "For example, we could call `factorial()` with the `float` object `3.0`, and the recursion works out fine. So, because the `3.0` \"walks\" and \"quacks\" like a `3`, it \"must be\" a `3`."
    ]
   },
   {
@@ -4171,7 +4160,7 @@
     {
      "data": {
       "text/plain": [
-       "1"
+       "6.0"
       ]
      },
      "execution_count": 32,
@@ -4180,7 +4169,7 @@
     }
    ],
    "source": [
-    "factorial(0)"
+    "factorial(3.0)"
    ]
   },
   {
@@ -4215,12 +4204,205 @@
     }
    },
    "source": [
-    "Instead of running into a situation of infinite recursion, we now receive specific error messages."
+    "We see similar behavior when we mix objects of types `int` and `float` with arithmetic operators. For example, `1 + 2.0` works because Python implicitly views the `1` as a `1.0` at runtime and then knows how to do floating-point arithmetic: Here, the `int` \"walks\" and \"quacks\" like a `float`."
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 34,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "3.0"
+      ]
+     },
+     "execution_count": 34,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "1 + 2.0"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "3.0"
+      ]
+     },
+     "execution_count": 35,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "1.0 + 2.0"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "The important lesson is that we must expect our functions to be called with objects of *any* type at runtime, as opposed to the one type we had in mind when defining the function.\n",
+    "\n",
+    "Duck typing is possible because Python is a dynamically typed language. On the contrary, in statically typed languages like C, we *must* declare (i.e., \"specify\") the data type of every parameter in a function definition. Then, a `RecursionError` as for `countdown(3.1)` or `factorial(3.1)` above could not occur. For example, if we declared the `countdown()` and `factorial()` functions to only accept `int` objects, calling the functions with a `float` argument would immediately fail *syntactically*. As a downside, we would then lose the ability to call `factorial()` with `3.0`, which is *semantically* correct nevertheless.\n",
+    "\n",
+    "So, there is no black or white answer as to which of the two language designs is better. Yet, most professional programmers have strong opinions concerning duck typing, reaching from \"love\" to \"hate.\" This is another example of how programming is a subjective art rather than \"objective\" science. Python's design is probably more appealing to beginners who intuitively regard `3` and `3.0` as interchangeable."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "### Type Checking & Input Validation"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "We use the built-in [isinstance()](https://docs.python.org/3/library/functions.html#isinstance) function to make sure `factorial()` is called with an `int` object as the argument. We further **validate** the **input** by verifying that the integer is non-negative.\n",
+    "\n",
+    "Meanwhile, we also see how we manually raise exceptions with the `raise` statement (cf., [reference](https://docs.python.org/3/reference/simple_stmts.html#the-raise-statement)), another way of controlling the flow of execution.\n",
+    "\n",
+    "The first two branches in the revised `factorial()` function act as **guardians** ensuring that the code does not produce *unexpected* runtime errors: Errors may be expected when mentioned in the docstring.\n",
+    "\n",
+    "So, in essence, we are doing *two* things here: Besides checking the type, we also enforce **domain-specific** (i.e., mathematical here) rules concerning the non-negativity of `n`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "def factorial(n):\n",
+    "    \"\"\"Calculate the factorial of a number.\n",
+    "\n",
+    "    Args:\n",
+    "        n (int): number to calculate the factorial for; must be positive\n",
+    "\n",
+    "    Returns:\n",
+    "        factorial (int)\n",
+    "\n",
+    "    Raises:\n",
+    "        TypeError: if n is not an integer\n",
+    "        ValueError: if n is negative\n",
+    "    \"\"\"\n",
+    "    if not isinstance(n, int):\n",
+    "        raise TypeError(\"Factorial is only defined for integers\")\n",
+    "    elif n < 0:\n",
+    "        raise ValueError(\"Factorial is not defined for negative integers\")\n",
+    "    elif n == 0:\n",
+    "        return 1\n",
+    "    return n * factorial(n - 1)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "The revised `factorial()` function works like the old one."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "1"
+      ]
+     },
+     "execution_count": 37,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "factorial(0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "6"
+      ]
+     },
+     "execution_count": 38,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "factorial(3)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Instead of running into a situation of infinite recursion, we now receive specific error messages."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -4234,8 +4416,8 @@
      "traceback": [
       "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
       "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-34-188b816be8b6>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mfactorial\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m3.1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
-      "\u001b[0;32m<ipython-input-31-ff0b507f76b8>\u001b[0m in \u001b[0;36mfactorial\u001b[0;34m(n)\u001b[0m\n\u001b[1;32m     13\u001b[0m     \"\"\"\n\u001b[1;32m     14\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mn\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mint\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 15\u001b[0;31m         \u001b[0;32mraise\u001b[0m \u001b[0mTypeError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Factorial is only defined for integers\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     16\u001b[0m     \u001b[0;32melif\u001b[0m \u001b[0mn\u001b[0m \u001b[0;34m<\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     17\u001b[0m         \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Factorial is not defined for negative integers\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m<ipython-input-39-188b816be8b6>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mfactorial\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m3.1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;32m<ipython-input-36-54b57bc2107d>\u001b[0m in \u001b[0;36mfactorial\u001b[0;34m(n)\u001b[0m\n\u001b[1;32m     13\u001b[0m     \"\"\"\n\u001b[1;32m     14\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mn\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mint\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 15\u001b[0;31m         \u001b[0;32mraise\u001b[0m \u001b[0mTypeError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Factorial is only defined for integers\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     16\u001b[0m     \u001b[0;32melif\u001b[0m \u001b[0mn\u001b[0m \u001b[0;34m<\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     17\u001b[0m         \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Factorial is not defined for negative integers\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
       "\u001b[0;31mTypeError\u001b[0m: Factorial is only defined for integers"
      ]
     }
@@ -4246,7 +4428,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 35,
+   "execution_count": 40,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -4260,8 +4442,8 @@
      "traceback": [
       "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
       "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-35-b06b2cada645>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mfactorial\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m42\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
-      "\u001b[0;32m<ipython-input-31-ff0b507f76b8>\u001b[0m in \u001b[0;36mfactorial\u001b[0;34m(n)\u001b[0m\n\u001b[1;32m     15\u001b[0m         \u001b[0;32mraise\u001b[0m \u001b[0mTypeError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Factorial is only defined for integers\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     16\u001b[0m     \u001b[0;32melif\u001b[0m \u001b[0mn\u001b[0m \u001b[0;34m<\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 17\u001b[0;31m         \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Factorial is not defined for negative integers\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     18\u001b[0m     \u001b[0;32melif\u001b[0m \u001b[0mn\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m  \u001b[0;31m# = base case\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     19\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m<ipython-input-40-b06b2cada645>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mfactorial\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m42\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;32m<ipython-input-36-54b57bc2107d>\u001b[0m in \u001b[0;36mfactorial\u001b[0;34m(n)\u001b[0m\n\u001b[1;32m     15\u001b[0m         \u001b[0;32mraise\u001b[0m \u001b[0mTypeError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Factorial is only defined for integers\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     16\u001b[0m     \u001b[0;32melif\u001b[0m \u001b[0mn\u001b[0m \u001b[0;34m<\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 17\u001b[0;31m         \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Factorial is not defined for negative integers\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     18\u001b[0m     \u001b[0;32melif\u001b[0m \u001b[0mn\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     19\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
       "\u001b[0;31mValueError\u001b[0m: Factorial is not defined for negative integers"
      ]
     }
@@ -4270,6 +4452,436 @@
     "factorial(-42)"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Forcing `n` to be an `int` is a very puritan way of handling the issues discussed above. So, we can *not* call `factorial()` with, for example, `3.0`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "ename": "TypeError",
+     "evalue": "Factorial is only defined for integers",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-41-32f1a9e8cf49>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mfactorial\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m3.0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;32m<ipython-input-36-54b57bc2107d>\u001b[0m in \u001b[0;36mfactorial\u001b[0;34m(n)\u001b[0m\n\u001b[1;32m     13\u001b[0m     \"\"\"\n\u001b[1;32m     14\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mn\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mint\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 15\u001b[0;31m         \u001b[0;32mraise\u001b[0m \u001b[0mTypeError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Factorial is only defined for integers\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     16\u001b[0m     \u001b[0;32melif\u001b[0m \u001b[0mn\u001b[0m \u001b[0;34m<\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     17\u001b[0m         \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Factorial is not defined for negative integers\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;31mTypeError\u001b[0m: Factorial is only defined for integers"
+     ]
+    }
+   ],
+   "source": [
+    "factorial(3.0)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "### Type Casting"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "A similar way to prevent an infinite recursion is to **cast** the **type** of the `n` argument with the built-in [int()](https://docs.python.org/3/library/functions.html#int) constructor."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "def factorial(n):\n",
+    "    \"\"\"Calculate the factorial of a number.\n",
+    "\n",
+    "    Args:\n",
+    "        n (int): number to calculate the factorial for; must be positive\n",
+    "\n",
+    "    Returns:\n",
+    "        factorial (int)\n",
+    "\n",
+    "    Raises:\n",
+    "        TypeError: if n cannot be cast as an integer\n",
+    "        ValueError: if n is negative\n",
+    "    \"\"\"\n",
+    "    n = int(n)\n",
+    "    if n < 0:\n",
+    "        raise ValueError(\"Factorial is not defined for negative integers\")\n",
+    "    elif n == 0:\n",
+    "        return 1\n",
+    "    return n * factorial(n - 1)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "The not so strict type casting implements duck typing for `factorial()` as, for example, `3.0` \"walks\" and \"quacks\" like a `3`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "6"
+      ]
+     },
+     "execution_count": 43,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "factorial(3)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "6"
+      ]
+     },
+     "execution_count": 44,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "factorial(3.0)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "However, if we now call `factorial()` with a non-integer `float` object like `3.1`, *no* error is raised. This is a potential source for *semantic* errors as the function runs for invalid input."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "6"
+      ]
+     },
+     "execution_count": 45,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "factorial(3.1)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "We could adjust the type casting logic such that a `TypeError` is raised for `n` arguments with non-zero decimals. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "metadata": {
+    "code_folding": [],
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "def factorial(n):\n",
+    "    \"\"\"Calculate the factorial of a number.\n",
+    "\n",
+    "    Args:\n",
+    "        n (int): number to calculate the factorial for; must be positive\n",
+    "\n",
+    "    Returns:\n",
+    "        factorial (int)\n",
+    "\n",
+    "    Raises:\n",
+    "        TypeError: if n cannot be cast as an integer\n",
+    "        ValueError: if n is negative\n",
+    "    \"\"\"\n",
+    "    if n != int(n):\n",
+    "        raise TypeError(\"n is not integer-like; it has non-zero decimals\")\n",
+    "    n = int(n)\n",
+    "\n",
+    "    if n < 0:\n",
+    "        raise ValueError(\"Factorial is not defined for negative integers\")\n",
+    "    elif n == 0:\n",
+    "        return 1\n",
+    "    return n * factorial(n - 1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "6"
+      ]
+     },
+     "execution_count": 47,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "factorial(3.0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 48,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "ename": "TypeError",
+     "evalue": "n is not integer-like; it has non-zero decimals",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-48-188b816be8b6>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mfactorial\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m3.1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;32m<ipython-input-46-07dc981a3422>\u001b[0m in \u001b[0;36mfactorial\u001b[0;34m(n)\u001b[0m\n\u001b[1;32m     13\u001b[0m     \"\"\"\n\u001b[1;32m     14\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mn\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0mint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mn\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 15\u001b[0;31m         \u001b[0;32mraise\u001b[0m \u001b[0mTypeError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"n is not integer-like; it has non-zero decimals\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     16\u001b[0m     \u001b[0mn\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mn\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     17\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;31mTypeError\u001b[0m: n is not integer-like; it has non-zero decimals"
+     ]
+    }
+   ],
+   "source": [
+    "factorial(3.1)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "However, using built-in constructors for type casting leads to another subtle inconsistency. As constructors are designed to take *any* object as their argument, they do not raise a `TypeError` when called with invalid input but a `ValueError` instead. So, if we, for example, called `factorial()` with `\"text\"` as the `n` argument, we see the `ValueError` raised by [int()](https://docs.python.org/3/library/functions.html#int) in a situation where a `TypeError` would be more appropriate."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 49,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "ename": "ValueError",
+     "evalue": "invalid literal for int() with base 10: 'text'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-49-9a12eb120fc3>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mfactorial\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"text\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;32m<ipython-input-46-07dc981a3422>\u001b[0m in \u001b[0;36mfactorial\u001b[0;34m(n)\u001b[0m\n\u001b[1;32m     12\u001b[0m         \u001b[0mValueError\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mn\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0mnegative\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     13\u001b[0m     \"\"\"\n\u001b[0;32m---> 14\u001b[0;31m     \u001b[0;32mif\u001b[0m \u001b[0mn\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0mint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mn\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     15\u001b[0m         \u001b[0;32mraise\u001b[0m \u001b[0mTypeError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"n is not integer-like; it has non-zero decimals\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     16\u001b[0m     \u001b[0mn\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mn\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;31mValueError\u001b[0m: invalid literal for int() with base 10: 'text'"
+     ]
+    }
+   ],
+   "source": [
+    "factorial(\"text\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "We could, of course, use a `try` statement to suppress the exceptions raised by [int()](https://docs.python.org/3/library/functions.html#int) and replace them with a custom `TypeError`. However, now the implementation as a whole is more about type checking than about the actual logic solving the problem. We took this example to the extreme on purpose. In practice, we rarely see such code!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 50,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "def factorial(n):\n",
+    "    \"\"\"Calculate the factorial of a number.\n",
+    "\n",
+    "    Args:\n",
+    "        n (int): number to calculate the factorial for; must be positive\n",
+    "\n",
+    "    Returns:\n",
+    "        factorial (int)\n",
+    "\n",
+    "    Raises:\n",
+    "        TypeError: if n cannot be cast as an integer\n",
+    "        ValueError: if n is negative\n",
+    "    \"\"\"\n",
+    "    try:\n",
+    "        casted_n = int(n)\n",
+    "    except ValueError:\n",
+    "        raise TypeError(\"n cannot be casted as an integer\") from None\n",
+    "    else:\n",
+    "        if n != casted_n:\n",
+    "            raise TypeError(\"n is not integer-like; it has non-zero decimals\")\n",
+    "        n = casted_n\n",
+    "\n",
+    "    if n < 0:\n",
+    "        raise ValueError(\"Factorial is not defined for negative integers\")\n",
+    "    elif n == 0:\n",
+    "        return 1\n",
+    "    return n * factorial(n - 1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 51,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "ename": "TypeError",
+     "evalue": "n cannot be casted as an integer",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-51-9a12eb120fc3>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mfactorial\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"text\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;32m<ipython-input-50-9ed22b5a2981>\u001b[0m in \u001b[0;36mfactorial\u001b[0;34m(n)\u001b[0m\n\u001b[1;32m     15\u001b[0m         \u001b[0mcasted_n\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mn\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     16\u001b[0m     \u001b[0;32mexcept\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 17\u001b[0;31m         \u001b[0;32mraise\u001b[0m \u001b[0mTypeError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"n cannot be casted as an integer\"\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     18\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     19\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mn\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0mcasted_n\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;31mTypeError\u001b[0m: n cannot be casted as an integer"
+     ]
+    }
+   ],
+   "source": [
+    "factorial(\"text\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 52,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "ename": "TypeError",
+     "evalue": "n is not integer-like; it has non-zero decimals",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-52-188b816be8b6>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mfactorial\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m3.1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;32m<ipython-input-50-9ed22b5a2981>\u001b[0m in \u001b[0;36mfactorial\u001b[0;34m(n)\u001b[0m\n\u001b[1;32m     18\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     19\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mn\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0mcasted_n\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 20\u001b[0;31m             \u001b[0;32mraise\u001b[0m \u001b[0mTypeError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"n is not integer-like; it has non-zero decimals\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     21\u001b[0m         \u001b[0mn\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcasted_n\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     22\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;31mTypeError\u001b[0m: n is not integer-like; it has non-zero decimals"
+     ]
+    }
+   ],
+   "source": [
+    "factorial(3.1)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Which way we choose for **hardening** the `factorial()` function depends on the concrete circumstances. If we are the main caller of the function ourselves, we may choose to *not* do any of the approaches at all. After all, we should be able to call our own function in the correct way. The lesson is that just because Python has no static typing, this does *not* mean that we cannot do this \"manually.\" Yet, the idea behind a dynamically typed language is to *not* deal with the types too much at all."
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {
@@ -4353,7 +4965,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 36,
+   "execution_count": 53,
    "metadata": {
     "code_folding": [],
     "slideshow": {
@@ -4367,16 +4979,7 @@
     "\n",
     "    Args:\n",
     "        n (int): seconds until the party begins; must be positive\n",
-    "\n",
-    "    Raises:\n",
-    "        TypeError: if n is not an integer\n",
-    "        ValueError: if n is not positive\n",
     "    \"\"\"\n",
-    "    if not isinstance(n, int):\n",
-    "        raise TypeError(\"Can only count down with whole numbers\")\n",
-    "    elif n <= 0:\n",
-    "        raise ValueError(\"n must be stricly positive\")\n",
-    "\n",
     "    while n != 0:\n",
     "        print(n)\n",
     "        n -= 1\n",
@@ -4386,7 +4989,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 37,
+   "execution_count": 54,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -4416,7 +5019,7 @@
     }
    },
    "source": [
-    "As [PythonTutor](http://pythontutor.com/visualize.html#code=def%20countdown%28n%29%3A%0A%20%20%20%20if%20not%20isinstance%28n,%20int%29%3A%0A%20%20%20%20%20%20%20%20raise%20TypeError%28%22...%22%29%0A%20%20%20%20elif%20n%20%3C%3D%200%3A%0A%20%20%20%20%20%20%20%20raise%20ValueError%28%22...%22%29%0A%0A%20%20%20%20while%20n%20!%3D%200%3A%0A%20%20%20%20%20%20%20%20print%28n%29%0A%20%20%20%20%20%20%20%20n%20-%3D%201%0A%0A%20%20%20%20print%28%22Happy%20new%20Year!%22%29%0A%0Acountdown%283%29&cumulative=false&curInstr=0&heapPrimitives=nevernest&mode=display&origin=opt-frontend.js&py=3&rawInputLstJSON=%5B%5D&textReferences=false) shows, there is a subtle but essential difference in the way a `while` statement is treated in memory: In short, `while` statements can *not* run into a `RecursionError` as only *one* frame is needed to manage the names. After all, there is only *one* function call to be made. For typical day-to-day applications, this difference is, however, not so important *unless* a problem instance becomes so big that a large (i.e., $> 3.000$) number of recursive calls must be made."
+    "As [PythonTutor](http://pythontutor.com/visualize.html#code=def%20countdown%28n%29%3A%0A%20%20%20%20while%20n%20!%3D%200%3A%0A%20%20%20%20%20%20%20%20print%28n%29%0A%20%20%20%20%20%20%20%20n%20-%3D%201%0A%0A%20%20%20%20print%28%22Happy%20new%20Year!%22%29%0A%0Acountdown%283%29&cumulative=false&curInstr=0&heapPrimitives=nevernest&mode=display&origin=opt-frontend.js&py=3&rawInputLstJSON=%5B%5D&textReferences=false) shows, there is a subtle but essential difference in the way a `while` statement is treated in memory: In short, `while` statements can *not* run into a `RecursionError` as only *one* frame is needed to manage the names. After all, there is only *one* function call to be made. For typical day-to-day applications, this difference is, however, not so important *unless* a problem instance becomes so big that a large (i.e., $> 3.000$) number of recursive calls must be made."
    ]
   },
   {
@@ -4445,7 +5048,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 38,
+   "execution_count": 55,
    "metadata": {
     "code_folding": [],
     "slideshow": {
@@ -4463,16 +5066,7 @@
     "\n",
     "    Returns:\n",
     "        gcd (int)\n",
-    "\n",
-    "    Raises:\n",
-    "        TypeError: if a or b are not integers\n",
-    "        ValueError: if a or b are not positive\n",
     "    \"\"\"\n",
-    "    if not isinstance(a, int) or not isinstance(b, int):\n",
-    "        raise TypeError(\"Greatest common divisor is only defined for two integers\")\n",
-    "    elif a <= 0 or b <= 0:\n",
-    "        raise ValueError(\"a and b must be strictly positive\")\n",
-    "\n",
     "    while a != b:\n",
     "        if a > b:\n",
     "            a -= b\n",
@@ -4484,7 +5078,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 39,
+   "execution_count": 56,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -4497,7 +5091,7 @@
        "4"
       ]
      },
-     "execution_count": 39,
+     "execution_count": 56,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -4508,7 +5102,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 40,
+   "execution_count": 57,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -4521,7 +5115,7 @@
        "1"
       ]
      },
-     "execution_count": 40,
+     "execution_count": 57,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -4554,7 +5148,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 41,
+   "execution_count": 58,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -4565,7 +5159,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "5.22 s ± 0 ns per loop (mean ± std. dev. of 1 run, 1 loop each)\n"
+      "5.18 s ± 0 ns per loop (mean ± std. dev. of 1 run, 1 loop each)\n"
      ]
     }
    ],
@@ -4637,7 +5231,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 42,
+   "execution_count": 59,
    "metadata": {
     "code_folding": [],
     "slideshow": {
@@ -4656,16 +5250,7 @@
     "\n",
     "    Args:\n",
     "        n (int): a positive number to start the Collatz sequence at\n",
-    "\n",
-    "    Raises:\n",
-    "        TypeError: if n is not an integer\n",
-    "        ValueError: if n is not positive\n",
     "    \"\"\"\n",
-    "    if not isinstance(n, int):\n",
-    "        raise TypeError(\"non-integers require some advanced math\")\n",
-    "    elif n <= 0:\n",
-    "        raise ValueError(\"n must be stricly positive\")\n",
-    "\n",
     "    while n != 1:\n",
     "        print(n, end=\" \")\n",
     "        if n % 2 == 0:\n",
@@ -4689,7 +5274,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 43,
+   "execution_count": 60,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -4710,7 +5295,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 44,
+   "execution_count": 61,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -4731,7 +5316,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 45,
+   "execution_count": 62,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -4752,7 +5337,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 46,
+   "execution_count": 63,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -4799,7 +5384,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 47,
+   "execution_count": 64,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -4812,7 +5397,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 48,
+   "execution_count": 65,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -4851,7 +5436,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 49,
+   "execution_count": 66,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -4879,12 +5464,12 @@
     }
    },
    "source": [
-    "For sequences of integers, the [range()](https://docs.python.org/3/library/functions.html#func-range) built-in makes the `for` statement even more convenient: It creates a `list`-like object of type `range` that generates integers \"on the fly,\" and we look closely at the underlying effects in memory in [Chapter 7](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/07_sequences_00_lecture.ipynb#Mapping)."
+    "For sequences of integers, the [range()](https://docs.python.org/3/library/functions.html#func-range) built-in makes the `for` statement even more convenient: It creates a `list`-like object of type `range` that generates integers \"on the fly,\" and we look closely at the underlying effects in memory in [Chapter 8](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/08_mfr_00_lecture.ipynb#Mapping)."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 50,
+   "execution_count": 67,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -4906,7 +5491,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 51,
+   "execution_count": 68,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -4919,7 +5504,7 @@
        "range"
       ]
      },
-     "execution_count": 51,
+     "execution_count": 68,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -4941,7 +5526,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 52,
+   "execution_count": 69,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -4963,7 +5548,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 53,
+   "execution_count": 70,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -5021,7 +5606,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 54,
+   "execution_count": 71,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -5045,7 +5630,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 55,
+   "execution_count": 72,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -5058,7 +5643,7 @@
        "True"
       ]
      },
-     "execution_count": 55,
+     "execution_count": 72,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -5069,7 +5654,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 56,
+   "execution_count": 73,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -5082,7 +5667,7 @@
        "False"
       ]
      },
-     "execution_count": 56,
+     "execution_count": 73,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -5104,7 +5689,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 57,
+   "execution_count": 74,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -5117,7 +5702,7 @@
        "[0, 1, 2, 3, 4]"
       ]
      },
-     "execution_count": 57,
+     "execution_count": 74,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -5128,7 +5713,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 58,
+   "execution_count": 75,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -5141,7 +5726,7 @@
        "True"
       ]
      },
-     "execution_count": 58,
+     "execution_count": 75,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -5163,7 +5748,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 59,
+   "execution_count": 76,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -5196,7 +5781,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 60,
+   "execution_count": 77,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -5229,7 +5814,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 61,
+   "execution_count": 78,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -5262,7 +5847,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 62,
+   "execution_count": 79,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -5275,7 +5860,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 63,
+   "execution_count": 80,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -5323,7 +5908,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 64,
+   "execution_count": 81,
    "metadata": {
     "code_folding": [],
     "slideshow": {
@@ -5340,16 +5925,7 @@
     "\n",
     "    Returns:\n",
     "        ith_fibonacci (int)\n",
-    "\n",
-    "    Raises:\n",
-    "        TypeError: if i is not an integer\n",
-    "        ValueError: if i is not positive\n",
     "    \"\"\"\n",
-    "    if not isinstance(i, int):\n",
-    "        raise TypeError(\"i must be an integer\")\n",
-    "    elif i < 0:\n",
-    "        raise ValueError(\"i must be non-negative\")\n",
-    "\n",
     "    a = 0\n",
     "    b = 1\n",
     "    print(a, b, sep=\" \", end=\" \")  # added for didactical purposes\n",
@@ -5364,7 +5940,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 65,
+   "execution_count": 82,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -5384,7 +5960,7 @@
        "144"
       ]
      },
-     "execution_count": 65,
+     "execution_count": 82,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -5417,7 +5993,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 66,
+   "execution_count": 83,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -5437,7 +6013,7 @@
        "218922995834555169026"
       ]
      },
-     "execution_count": 66,
+     "execution_count": 83,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -5470,7 +6046,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 67,
+   "execution_count": 84,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -5486,16 +6062,7 @@
     "\n",
     "    Returns:\n",
     "        factorial (int)\n",
-    "\n",
-    "    Raises:\n",
-    "        TypeError: if n is not an integer\n",
-    "        ValueError: if n is negative\n",
     "    \"\"\"\n",
-    "    if not isinstance(n, int):\n",
-    "        raise TypeError(\"Factorial is only defined for integers\")\n",
-    "    elif n < 0:\n",
-    "        raise ValueError(\"Factorial is not defined for negative integers\")\n",
-    "\n",
     "    product = 1  # because 0! = 1\n",
     "    for i in range(1, n + 1):\n",
     "        product *= i\n",
@@ -5506,7 +6073,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 68,
+   "execution_count": 85,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -5526,7 +6093,7 @@
        "6"
       ]
      },
-     "execution_count": 68,
+     "execution_count": 85,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -5537,7 +6104,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 69,
+   "execution_count": 86,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -5557,7 +6124,7 @@
        "3628800"
       ]
      },
-     "execution_count": 69,
+     "execution_count": 86,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -5612,7 +6179,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 70,
+   "execution_count": 87,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -5640,7 +6207,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 71,
+   "execution_count": 88,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -5693,7 +6260,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 72,
+   "execution_count": 89,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -5760,7 +6327,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 73,
+   "execution_count": 90,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -5803,7 +6370,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 74,
+   "execution_count": 91,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -5841,7 +6408,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 75,
+   "execution_count": 92,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -5870,7 +6437,7 @@
    "cell_type": "markdown",
    "metadata": {
     "slideshow": {
-     "slide_type": "skip"
+     "slide_type": "slide"
     }
    },
    "source": [
@@ -5893,7 +6460,7 @@
     "- **filtering**: throw away individual numbers (e.g., statistical outliers in a sample)\n",
     "- **reducing**: collect individual numbers into summary statistics\n",
     "\n",
-    "We study this **map-filter-reduce** paradigm extensively in [Chapter 7](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/07_sequences_00_lecture.ipynb#The-Map-Filter-Reduce-Paradigm) after introducing more advanced data types that are needed to work with \"big\" data.\n",
+    "We study this **map-filter-reduce** paradigm extensively in [Chapter 8](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/08_mfr_00_lecture.ipynb) after introducing more advanced data types that are needed to work with \"big\" data.\n",
     "\n",
     "Here, we focus on *filtering out* some numbers in a `for`-loop."
    ]
@@ -5919,15 +6486,15 @@
    "source": [
     "Calculate the sum of all even numbers in `[7, 11, 8, 5, 3, 12, 2, 6, 9, 10, 1, 4]` after squaring them and adding `1` to the squares:\n",
     "\n",
-    "- **\"all\"** => loop over an iterable\n",
-    "- **\"even\"** => *filter* out the odd numbers\n",
-    "- **\"square and add $1$\"** => apply the *map* $y = f(x) = x^2 + 1$\n",
-    "- **\"sum\"** => *reduce* the remaining and mapped numbers to their sum"
+    "- \"*all*\" => **loop** over an iterable\n",
+    "- \"*even*\" => **filter** out the odd numbers\n",
+    "- \"*square and add $1$*\" => apply the **map** $y = f(x) = x^2 + 1$\n",
+    "- \"*sum*\" => **reduce** the remaining and mapped numbers to their sum"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 76,
+   "execution_count": 93,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -5940,7 +6507,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 77,
+   "execution_count": 94,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -5960,7 +6527,7 @@
        "370"
       ]
      },
-     "execution_count": 77,
+     "execution_count": 94,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -6013,34 +6580,21 @@
    "source": [
     "Calculate the sum of every third and even number in `[7, 11, 8, 5, 3, 12, 2, 6, 9, 10, 1, 4]` after squaring them and adding `1` to the squares:\n",
     "\n",
-    "- **\"every\"** => loop over an iterable\n",
-    "- **\"third\"** => *filter* out all numbers except every third\n",
-    "- **\"even\"** => *filter* out the odd numbers\n",
-    "- **\"square and add $1$\"** => apply the *map* $y = f(x) = x^2 + 1$\n",
-    "- **\"sum\"** => *reduce* the remaining and mapped numbers to their sum"
+    "- \"*every*\" => **loop** over an iterable\n",
+    "- \"*third*\" => **filter** out all numbers except every third\n",
+    "- \"*even*\" => **filter** out the odd numbers\n",
+    "- \"*square and add $1$*\" => apply the **map** $y = f(x) = x^2 + 1$\n",
+    "- \"*sum*\" => **reduce** the remaining and mapped numbers to their sum"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 78,
+   "execution_count": 95,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
     }
    },
-   "outputs": [],
-   "source": [
-    "numbers = [7, 11, 8, 5, 3, 12, 2, 6, 9, 10, 1, 4]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 79,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "-"
-    }
-   },
    "outputs": [
     {
      "name": "stdout",
@@ -6055,7 +6609,7 @@
        "227"
       ]
      },
-     "execution_count": 79,
+     "execution_count": 95,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -6112,7 +6666,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 80,
+   "execution_count": 96,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -6132,7 +6686,7 @@
        "227"
       ]
      },
-     "execution_count": 80,
+     "execution_count": 96,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -6216,7 +6770,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 81,
+   "execution_count": 97,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -6229,10 +6783,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 82,
+   "execution_count": 98,
    "metadata": {
     "slideshow": {
-     "slide_type": "fragment"
+     "slide_type": "skip"
     }
    },
    "outputs": [],
@@ -6242,7 +6796,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 83,
+   "execution_count": 99,
    "metadata": {
     "code_folding": [],
     "slideshow": {
@@ -6353,7 +6907,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 84,
+   "execution_count": 100,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -6390,7 +6944,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 85,
+   "execution_count": 101,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -6428,7 +6982,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 86,
+   "execution_count": 102,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -6441,7 +6995,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 87,
+   "execution_count": 103,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
diff --git a/04_iteration_01_review.ipynb b/04_iteration_01_review.ipynb
index 29c1f2f..de4a566 100644
--- a/04_iteration_01_review.ipynb
+++ b/04_iteration_01_review.ipynb
@@ -19,7 +19,9 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Read [Chapter 4](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/04_iteration_00_lecture.ipynb) of the book. Then, work through the questions below."
+    "The questions below assume that you have read [Chapter 4](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/04_iteration_00_lecture.ipynb) in the book.\n",
+    "\n",
+    "Be concise in your answers! Most questions can be answered in *one* sentence."
    ]
   },
   {
@@ -29,13 +31,6 @@
     "### Essay Questions "
    ]
   },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Answer the following questions *briefly*!"
-   ]
-  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -47,7 +42,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    " "
+    " < your answer >"
    ]
   },
   {
@@ -61,7 +56,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    " "
+    " < your answer >"
    ]
   },
   {
@@ -75,7 +70,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    " "
+    " < your answer >"
    ]
   },
   {
@@ -89,7 +84,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    " "
+    " < your answer >"
    ]
   },
   {
@@ -103,7 +98,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    " "
+    " < your answer >"
    ]
   },
   {
@@ -117,7 +112,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    " "
+    " < your answer >"
    ]
   },
   {
@@ -145,7 +140,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    " "
+    " < your answer >"
    ]
   },
   {
@@ -159,7 +154,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    " "
+    " < your answer >"
    ]
   },
   {
@@ -173,7 +168,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    " "
+    " < your answer >"
    ]
   },
   {
@@ -187,7 +182,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    " "
+    " < your answer >"
    ]
   },
   {
@@ -201,7 +196,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    " "
+    " < your answer >"
    ]
   },
   {
@@ -215,7 +210,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    " "
+    " < your answer >"
    ]
   },
   {
@@ -240,7 +235,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    " "
+    " < your answer >"
    ]
   }
  ],
diff --git a/04_iteration_02_exercises.ipynb b/04_iteration_02_exercises.ipynb
index ab80178..4f819ca 100644
--- a/04_iteration_02_exercises.ipynb
+++ b/04_iteration_02_exercises.ipynb
@@ -19,7 +19,9 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Read [Chapter 4](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/04_iteration_00_lecture.ipynb) of the book. Then, work through the exercises below. The `...` indicate where you need to fill in your answers. You should not need to create any additional code cells. Occasionally, the `...` mean that you have to write more than one line of code."
+    "The exercises below assume that you have read the \"*Recursion*\" part in [Chapter 4](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/04_iteration_00_lecture.ipynb) of the book.\n",
+    "\n",
+    "The `...`'s in the code cells indicate where you need to fill in code snippets. The number of `...`'s within a code cell give you a rough idea of how many lines of code are needed to solve the task. You should not need to create any additional code cells for your final solution. However, you may want to use temporary code cells to try out some ideas."
    ]
   },
   {
@@ -95,7 +97,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    " "
+    " < your answer >"
    ]
   },
   {
@@ -109,7 +111,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    " "
+    " < your answer >"
    ]
   },
   {
@@ -123,7 +125,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    " "
+    " < your answer >"
    ]
   },
   {
@@ -137,7 +139,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    " "
+    " < your answer >"
    ]
   },
   {
@@ -151,53 +153,52 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "As most likely the first couple of tries will result in *semantic* errors, it is advisable to have some sort of **visualization tool** for the program's output: For example, an online version of the game can be found **[here](https://www.mathsisfun.com/games/towerofhanoi.html)**."
+    "As most likely the first couple of tries will result in *semantic* errors, it is advisable to have some sort of **visualization tool** for the program's output: For example, an online version of the game can be found [here](https://www.mathsisfun.com/games/towerofhanoi.html)."
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Let's first **generalize** the mathematical relationship from above.\n",
+    "Let's first **generalize** the mathematical relationship from above and then introduce the variable names used in our `sol()` implementation below.\n",
     "\n",
-    "While the first number of $Sol(\\cdot)$ is the number of `disks` $n$, the second and third \"numbers\" are the **labels** for the three spots. Instead of spots `1`, `2`, and `3`, we could also call them `\"left\"`, `\"center\"`, and `\"right\"` in our Python implementation. When \"passed\" to the $Sol(\\cdot)$ \"function\" they take on the role of an `origin` (= $o$) and `destination` (= $d$) pair.\n",
+    "Unsurprisingly, the recursive relationship in the video may be generalized into:\n",
     "\n",
-    "So, the expression $Sol(4, 1, 3)$ is the same as $Sol(4, \\text{\"left\"}, \\text{\"right\"})$ and describes the problem of moving a tower consisting of $n = 4$ disks from `origin` `1` / `\"left\"` to `destination` `3` / `right`. As we have seen in the video, we need some `intermediate` (= $i$) spot."
+    "$Sol(n, o, d) = Sol(n-1, o, i) ~ \\bigoplus ~ Sol(1, o, d) ~ \\bigoplus ~ Sol(n-1, i, d)$\n",
+    "\n",
+    "$Sol(\\cdot)$ takes three \"arguments\" $n$, $o$, and $d$ and is defined with *three* references to itself that take modified versions of $n$, $o$, and $d$ in different orders. The middle reference, Sol(1, o, d), constitutes the \"end\" of the recursive definition: It is the problem of solving Towers of Hanoi for a \"tower\" of only one disk.\n",
+    "\n",
+    "While the first \"argument\" of $Sol(\\cdot)$ is a number that we refer to as `n_disks` below, the second and third \"arguments\" are merely **labels** for the spots, and we refer to the **roles** they take in a given problem as `origin` and `destination` below. Instead of labeling individual spots with the numbers `1`, `2`, and `3` as in the video, we may also call them `\"left\"`, `\"center\"`, and `\"right\"`. Both ways are equally correct! So, only the first \"argument\" of $Sol(\\cdot)$ is really a number!\n",
+    "\n",
+    "As an example, the notation $Sol(4, 1, 3)$ from above can then be \"translated\" into Python as either the function call `sol(4, 1, 3)` or `sol(4, \"left\", \"right\")`. This describes the problem of moving a tower consisting of `n_disks=4` disks from either the `origin=1` spot to the `destination=3` spot or from the `origin=\"left\"` spot to the `destination=\"right\"` spot.\n",
+    "\n",
+    "To adhere to the rules, an `intermediate` spot $i$ is needed. In `sol()` below, this is a temporary variable within a function call and *not* a parameter of the function itself."
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "In summary, the generalized functional relationship can be expressed as:\n",
+    "In summary, to move a tower consisting of `n_disks` (= $n$) disks from an `origin` (= $o$) to a `destination` (= $d$), three steps must be executed:\n",
     "\n",
-    "$Sol(n, o, d) = Sol(n-1, o, i) ~ \\bigoplus ~ Sol(1, o, d) ~ \\bigoplus ~ Sol(n-1, i, d)$"
+    "1. Move the tower's topmost `n_disks - 1` (= $n - 1$) disks from the `origin` (= $o$) to an `intermediate` (= $i$) spot (= **Sub-Problem 1**),\n",
+    "2. move the remaining and largest disk from the `origin` (= $o$) to the `destination` (= $d$), and\n",
+    "3. move the `n_disks - 1` (= $n - 1$) disks from the `intermediate` (= $i$) spot to the `destination` (= $d$) spot (= **Sub-Problem 2**).\n",
+    "\n",
+    "The two sub-problems themselves are solved via the same *recursive* logic."
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "In words, this means that to move a tower consisting of $n$ disks from an `origin` $o$ to a `destination` $d$, three steps must be executed:\n",
+    "Write your answers to **Q5** to **Q7** into the skeleton of `sol()` below.\n",
     "\n",
-    "1. Move the topmost $n - 1$ disks of the tower temporarily from $o$ to $i$ (= sub-problem 1)\n",
-    "2. Move the remaining and largest disk from $o$ to $d$\n",
-    "3. Move the the $n - 1$ disks from the temporary spot $i$ to $d$ (= sub-problem 2)\n",
+    "`sol()` takes three arguments `n_disks`, `origin`, and `destination` that mirror $n$, $o$, and $d$ above.\n",
     "\n",
-    "The two sub-problems can be solved via the same recursive logic."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "$Sol(\\cdot)$ can be written in Python as a function `sol()` that takes three arguments `disks`, `origin`, and `destination` that mirror $n$, $o$, and $d$.\n",
+    "For now, assume that all arguments to `sol()` are `int` objects! We generalize this into real labels further below in the `hanoi()` function.\n",
     "\n",
-    "Assume that all arguments to `sol()` are `int` objects!\n",
-    "\n",
-    "Once completed, `sol()` should print out all the moves in the correct order. With **printing a move**, we mean a line like \"1 -> 3\", short for \"Move the top-most disk from spot 1 to spot 3\".\n",
-    "\n",
-    "Write your answers to **Q5** to **Q7** into the subsequent code cell and finalize `sol()`! No need to write a docstring or validate the input here."
+    "Once completed, `sol()` should *print* out all the moves in the correct order. For example, *print* `\"1 -> 3\"` to mean \"Move the top-most `n_disks - 1` disks from spot `1` to spot `3`.\""
    ]
   },
   {
@@ -206,48 +207,74 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "def sol(disks, origin, destination):\n",
+    "def sol(n_disks, origin, destination):\n",
+    "    \"\"\"A naive implementation of Towers of Hanoi.\n",
     "\n",
+    "    This function prints out the moves to solve a Towers of Hanoi problem.\n",
+    "\n",
+    "    Args:\n",
+    "        n_disks (int): number of disks in the tower\n",
+    "        origin (int): spot of the tower at the start; 1, 2, or 3\n",
+    "        destination (int): spot of the tower at the end; 1, 2, or 3\n",
+    "    \"\"\"\n",
     "    # answer to Q5\n",
-    "    # ...\n",
+    "    ...\n",
+    "    ...\n",
     "\n",
     "    # answer to Q6\n",
-    "    # ...\n",
+    "    ...\n",
+    "    ...\n",
+    "    ...\n",
+    "    ...\n",
+    "    ...\n",
+    "    ...\n",
+    "    ...\n",
+    "    ...\n",
+    "    ...\n",
+    "    ...\n",
+    "    ...\n",
+    "    ...\n",
     "\n",
     "    # answer to Q7\n",
-    "    # ..."
+    "    ...\n",
+    "    ...\n",
+    "    ..."
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "**Q5**: What is the `disks` argument when the function reaches its **base case**? Check for the base case with a simple `if` statement and return from the function using the **early exit** pattern!"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**Q6**: If not in the base case, `sol()` needs to determine the `intermediate` spot given concrete `origin` and `destination` arguments. For example, if called with `origin=1` and `destination=2`, `intermediate` must be `3`.\n",
+    "**Q5**: What is the `n_disks` argument when the function reaches its **base case**? Check for the base case with a simple `if` statement and return from the function using the **early exit** pattern!\n",
     "\n",
-    "Add **one** compound `if` statement to `sol()` that has a branch for **every** possible `origin`-`destination` pair that sets a variable `intermediate` to the correct temporary spot. **How many** branches will there be?"
+    "Hint: The base case in the Python implementation may be slightly different than the one shown in the generalized mathematical relationship above!"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "**Q7**: `sol()` needs to call itself **two more times** with the correct 2-pairs chosen from the three available spots `origin`, `intermediate`, and `destination`.\n",
+    "**Q6**: If not in the base case, `sol()` determines the `intermediate` spot given concrete `origin` and `destination` arguments. For example, if called with `origin=1` and `destination=2`, `intermediate` must be `3`.\n",
     "\n",
-    "In between the two recursive function calls, write a `print()` statement that prints out from where to where the \"remaining and largest\" disk has to be moved!"
+    "Add *one* compound `if` statement to `sol()` that has a branch for *every* possible `origin`-`destination`-pair that assigns the correct temporary spot to a variable `intermediate`.\n",
+    "\n",
+    "Hint: How many 2-tuples of 3 elements can there be if the order matters?"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "**Q8**: Execute the code cells below and confirm that the printed moves are correct!"
+    "**Q7**: `sol()` calls itself *two* more times with the correct 2-tuples chosen from the three available spots `origin`, `intermediate`, and `destination`.\n",
+    "\n",
+    "*In between* the two recursive function calls, use [print()](https://docs.python.org/3/library/functions.html#print) to print out from where to where the \"remaining and largest\" disk has to be moved!"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q8**: Execute the code cells below and confirm that the moves are correct!"
    ]
   },
   {
@@ -297,11 +324,11 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "The previous `sol()` implementation does the job, but the conditional statement needed in unnecessarily tedious. \n",
+    "The previous `sol()` implementation does the job, but the conditional statement is unnecessarily tedious. \n",
     "\n",
-    "Let's create a more concise `hanoi()` function that, in addition to a positional `disks` argument, takes three keyword-only arguments `origin`, `intermediate`, and `destination` with default values `\"left\"`, `\"center\"`, and `\"right\"`.\n",
+    "Let's create a concise `hanoi()` function that, in addition to a positional `n_disks` argument, takes three keyword-only arguments `origin`, `intermediate`, and `destination` with default values `\"left\"`, `\"center\"`, and `\"right\"`.\n",
     "\n",
-    "Write your answers to **Q9** and **Q10** into the subsequent code cell and finalize `hanoi()`! No need to write a docstring or validate the input here."
+    "Write your answers to **Q9** and **Q10** into the subsequent code cell and finalize `hanoi()`!"
    ]
   },
   {
@@ -310,13 +337,25 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "def hanoi(disks, *, origin=\"left\", intermediate=\"center\", destination=\"right\"):\n",
+    "def hanoi(n_disks, *, origin=\"left\", intermediate=\"center\", destination=\"right\"):\n",
+    "    \"\"\"A Pythonic implementation of Towers of Hanoi.\n",
     "\n",
+    "    This function prints out the moves to solve a Towers of Hanoi problem.\n",
+    "\n",
+    "    Args:\n",
+    "        n_disks (int): number of disks in the tower\n",
+    "        origin (str, optional): label for the spot of the tower at the start\n",
+    "        intermediate (str, optional): label for the intermediate spot\n",
+    "        destination (str, optional): label for the spot of the tower at the end\n",
+    "    \"\"\"\n",
     "    # answer to Q9\n",
-    "    # ...\n",
+    "    ...\n",
+    "    ...\n",
     "\n",
     "    # answer to Q10\n",
-    "    # ..."
+    "    ...\n",
+    "    ...\n",
+    "    ..."
    ]
   },
   {
@@ -330,18 +369,18 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "**Q10**: Instead of conditional logic, `hanoi()` calls itself **two times** with the **three** arguments `origin`, `intermediate`, and `destination` passed on in a **different** order.\n",
+    "**Q10**: Instead of conditional logic, `hanoi()` calls itself *two* times with the *three* arguments `origin`, `intermediate`, and `destination` passed on in a *different* order.\n",
     "\n",
-    "Figure out how the arguments are passed on in the two recursive `hanoi()` calls!\n",
+    "Figure out how the arguments are passed on in the two recursive `hanoi()` calls and finish `hanoi()`.\n",
     "\n",
-    "Also, write a `print()` statement analogous to the one in `sol()` in between the two recursive function calls. Is it ok to copy and paste it?"
+    "Hint: Do not forget to use [print()](https://docs.python.org/3/library/functions.html#print) to print out the moves!"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "**Q11**: Execute the code cells below and confirm that the printed moves are correct!"
+    "**Q11**: Execute the code cells below and confirm that the moves are correct!"
    ]
   },
   {
@@ -384,7 +423,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "We could, of course, also use **numeric labels** for the three steps like so."
+    "We could, of course, also use *numeric* labels for the three steps like so."
    ]
   },
   {
@@ -400,22 +439,16 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "#### Passing a Value \"up\" the Recursion Tree"
+    "#### Passing a Value \"down\" the Recursion Tree"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Let's say we did not know about the **analytical formula** for the number of **minimal moves** given $n$.\n",
+    "The above `hanoi()` prints the optimal solution's moves in the correct order but fails to label each move with an order number. This is built in the `hanoi_ordered()` function below by passing on a \"private\" `_offset` argument \"down\" the recursion tree. The leading underscore `_` in the parameter name indicates that it is *not* to be used by the caller of the function. That is also why the parameter is *not* mentioned in the docstring.\n",
     "\n",
-    "In such cases, we could modify a recursive function to return a count value to be passed up the recursion tree.\n",
-    "\n",
-    "This is similar to what we do in the recursive versions of `factorial()` and `fibonacci()` in [Chapter 4](https://github.com/webartifex/intro-to-python/blob/master/04_iteration_00_lecture.ipynb), where we pass up an intermediate result.\n",
-    "\n",
-    "Let's create a `hanoi_moves()` function that follows the same internal logic as `hanoi()`, but, instead of printing out the moves, returns the number of steps done so far in the recursion.\n",
-    "\n",
-    "Write your answers to **Q12** to **Q14** into the subsequent code cell and finalize `hanoi_moves()`! No need to write a docstring or validate the input here."
+    "Write your answers to **Q12** and **Q13** into the subsequent code cell and finalize `hanoi_ordered()`! As the logic gets a bit \"involved,\" `hanoi_ordered()` below is almost finished."
    ]
   },
   {
@@ -424,166 +457,56 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "def hanoi_moves(disks, *, origin=\"left\", intermediate=\"center\", destination=\"right\"):\n",
+    "def hanoi_ordered(n_disks, *, origin=\"left\", intermediate=\"center\", destination=\"right\", _offset=None):\n",
+    "    \"\"\"A Pythonic implementation of Towers of Hanoi.\n",
     "\n",
+    "    This function prints out the moves to solve a Towers of Hanoi problem.\n",
+    "    Each move is labeled with an order number.\n",
+    "\n",
+    "    Args:\n",
+    "        n_disks (int): number of disks in the tower\n",
+    "        origin (str, optional): label for the spot of the tower at the start\n",
+    "        intermediate (str, optional): label for the intermediate spot\n",
+    "        destination (str, optional): label for the spot of the tower at the end\n",
+    "    \"\"\"\n",
     "    # answer to Q12\n",
-    "    # ...\n",
+    "    ...\n",
+    "    ...\n",
     "\n",
-    "    moves = ...  # <- answer to Q13\n",
-    "    moves += hanoi_moves(...)  # <- answer to Q14 between the ()\n",
-    "    moves += hanoi_moves(...)  # <- answer to Q14 between the ()\n",
-    "\n",
-    "    return moves"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**Q12**: Copy the base case from `hanoi()`! What count should be returned when it is reached?"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**Q13**: Initialize the variable `moves` with an appropriate count! This is the number of moves that corresponds to **one** recursive function call."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**Q14**: `moves` is updated with the counts passed up from the two recursive calls.\n",
-    "\n",
-    "Complete the two recursive function calls with the same arguments as in `hanoi()`!"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**Q15**: Write a `for`-loop that prints out the **minimum number** of moves needed to solve Towers of Hanoi for any number of `disks` from `1` through `20` to confirm your answer to **Q2**."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "for ... in ...:\n",
-    "    ..."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "##### Time Complexity"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Observe how quickly the `hanoi_moves()` function slows down for increasing `disks` arguments.\n",
-    "\n",
-    "With `disks` in the range from `24` through `26`, the computation time roughly doubles for each increase of `disks` by 1.\n",
-    "\n",
-    "**Q16**: Execute the code cells below and see for yourself!"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "%%timeit -n 1 -r 1\n",
-    "print(\"Number of moves:\", hanoi_moves(24))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "%%timeit -n 1 -r 1\n",
-    "print(\"Number of moves:\", hanoi_moves(25))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "%%timeit -n 1 -r 1\n",
-    "print(\"Number of moves:\", hanoi_moves(26))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### Passing a Value \"down\" the Recursion Tree (Advanced)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The above `hanoi()` prints the optimal solution's moves in the correct order but fails to label each move with an order number. This can be built in by passing on one more argument `offset` down the recursion tree. As the logic gets a bit \"involved,\" `hanoi_ordered()` below is almost finished.\n",
-    "\n",
-    "Write your answers to **Q17** and **Q18** into the subsequent code cell and finalize `hanoi_ordered()`! No need to write a docstring or validate the input here."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def hanoi_ordered(disks, *, origin=\"left\", intermediate=\"center\", destination=\"right\", offset=None):\n",
-    "\n",
-    "    # answer to Q17\n",
-    "    # ...\n",
-    "\n",
-    "    total = (2 ** disks - 1)\n",
-    "    half = (2 ** (disks - 1) - 1)\n",
+    "    total = (2 ** n_disks - 1)\n",
+    "    half = (2 ** (n_disks - 1) - 1)\n",
     "    count = total - half\n",
     "\n",
-    "    if offset is not None:\n",
-    "        count += offset\n",
+    "    if _offset is not None:\n",
+    "        count += _offset\n",
     "\n",
-    "    hanoi_ordered(..., offset=offset)  # <- answer to Q18\n",
-    "    # ... <- answer to Q18\n",
-    "    hanoi_ordered(..., offset=count)  # <- answer to Q18"
+    "    # answer to Q18\n",
+    "    hanoi_ordered(..., _offset=_offset)\n",
+    "    ...\n",
+    "    hanoi_ordered(..., _offset=count)"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "**Q17**: Copy the base case from the original `hanoi()`!"
+    "**Q12**: Copy the base case from the original `hanoi()`!"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "**Q18**: Complete the two recursive function calls with the same arguments as in `hanoi()` or `hanoi_moves()`! Do not change the already filled in `offset` arguments!\n",
+    "**Q13**: Complete the two recursive function calls with the same arguments as in `hanoi()`! Do *not* change the already filled in `offset` arguments!\n",
     "\n",
-    "Then, copy the `print()` statement from `hanoi()` and adjust it to print out `count` as well!"
+    "Then, adjust the use of [print()](https://docs.python.org/3/library/functions.html#print) from above to print out the moves with their order number!"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "**Q19**: Execute the code cells below and confirm that the order numbers are correct!"
+    "**Q14**: Execute the code cells below and confirm that the order numbers are correct!"
    ]
   },
   {
@@ -626,7 +549,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Lastly, it is to be mentioned that for problem instances with a small `disks` argument, it is easier to collect all the moves first in a list and then add the order number with the [enumerate()](https://docs.python.org/3/library/functions.html#enumerate) built-in."
+    "Lastly, it is to be mentioned that for problem instances with a small `n_disks` argument, it is easier to collect all the moves first in a `list` object and then add the order number with the [enumerate()](https://docs.python.org/3/library/functions.html#enumerate) built-in."
    ]
   },
   {
@@ -640,14 +563,14 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "**Q20**: Conducting your own research on the internet (max. 15 minutes), what can you say about generalizing the **[Towers of Hanoi](https://en.wikipedia.org/wiki/Tower_of_Hanoi)** problem to a setting with **more than three** landing spots?"
+    "**Q15**: Conducting your own research on the internet, what can you say about generalizing the **[Towers of Hanoi](https://en.wikipedia.org/wiki/Tower_of_Hanoi)** problem to a setting with *more than three* landing spots?"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    " "
+    " < your answer >"
    ]
   }
  ],
diff --git a/04_iteration_03_exercises.ipynb b/04_iteration_03_exercises.ipynb
index da32407..b44c488 100644
--- a/04_iteration_03_exercises.ipynb
+++ b/04_iteration_03_exercises.ipynb
@@ -19,7 +19,9 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Read [Chapter 4](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/04_iteration_00_lecture.ipynb) of the book. Then, work through the exercises below. The `...` indicate where you need to fill in your answers. You should not need to create any additional code cells."
+    "The exercises below assume that you have read the \"*Looping*\" part in [Chapter 4](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/04_iteration_00_lecture.ipynb) of the book.\n",
+    "\n",
+    "The `...`'s in the code cells indicate where you need to fill in code snippets. The number of `...`'s within a code cell give you a rough idea of how many lines of code are needed to solve the task. You should not need to create any additional code cells for your final solution. However, you may want to use temporary code cells to try out some ideas."
    ]
   },
   {
@@ -264,7 +266,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    " ..."
+    " < your answer >"
    ]
   },
   {

From 6d2c7aeaa177bb017577ff1257718795aa319e22 Mon Sep 17 00:00:00 2001
From: Alexander Hess <alexander@webartifex.biz>
Date: Mon, 30 Mar 2020 14:14:52 +0200
Subject: [PATCH 090/242] Add video and streamline content

---
 05_numbers_00_lecture.ipynb   | 968 ++++++++++++----------------------
 05_numbers_01_review.ipynb    |  84 ++-
 05_numbers_02_exercises.ipynb |  54 +-
 3 files changed, 419 insertions(+), 687 deletions(-)

diff --git a/05_numbers_00_lecture.ipynb b/05_numbers_00_lecture.ipynb
index 771a0ad..3f0bdb3 100644
--- a/05_numbers_00_lecture.ipynb
+++ b/05_numbers_00_lecture.ipynb
@@ -1,5 +1,53 @@
 {
  "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "A **video presentation** of the contents in this chapter is shown below. A playlist with *all* chapters as videos is linked [here](https://www.youtube.com/playlist?list=PL-2JV1G3J10lQ2xokyQowcRJI5jjNfW7f)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/jpeg": 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+       "            width=\"60%\"\n",
+       "            height=\"300\"\n",
+       "            src=\"https://www.youtube.com/embed/nB00WGCnVjg\"\n",
+       "            frameborder=\"0\"\n",
+       "            allowfullscreen\n",
+       "        ></iframe>\n",
+       "        "
+      ],
+      "text/plain": [
+       "<IPython.lib.display.YouTubeVideo at 0x7f4b21935410>"
+      ]
+     },
+     "execution_count": 1,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from IPython.display import YouTubeVideo\n",
+    "YouTubeVideo(\"nB00WGCnVjg\", width=\"60%\")"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {
@@ -8,7 +56,7 @@
     }
    },
    "source": [
-    "# Chapter 5: Bits & Numbers"
+    "# Chapter 5: Numbers & Bits"
    ]
   },
   {
@@ -23,7 +71,7 @@
     "\n",
     "We start with the \"simple\" ones: Numeric types in this chapter and textual data in [Chapter 6](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/06_text_00_lecture.ipynb). An important fact that holds for all objects of these types is that they are **immutable**. To reuse the bag analogy from [Chapter 1](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/01_elements_00_lecture.ipynb#Objects-vs.-Types-vs.-Values), this means that the $0$s and $1$s making up an object's *value* cannot be changed once the bag is created in memory, implying that any operation with or method on the object creates a *new* object in a *different* memory location.\n",
     "\n",
-    "[Chapter 7](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/07_sequences_00_lecture.ipynb), [Chapter 8](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/08_mappings_00_lecture.ipynb), and [Chapter 9](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/09_arrays_00_lecture.ipynb) then cover the more \"complex\" data types, including, for example, the `list` type. Finally, Chapter 10 completes the picture by introducing language constructs to create custom types.\n",
+    "[Chapter 7](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/07_sequences_00_lecture.ipynb), [Chapter 8](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/08_mfr_00_lecture.ipynb), and [Chapter 9](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/09_mappings_00_lecture.ipynb) then cover the more \"complex\" data types, including, for example, the `list` type. Finally, Chapter 10 completes the picture by introducing language constructs to create custom types.\n",
     "\n",
     "We have already seen many hints indicating that numbers are not as trivial to work with as it seems at first sight:\n",
     "\n",
@@ -60,7 +108,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 2,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -82,30 +130,6 @@
     "Just like any other object, the `42` has an identity, a type, and a value."
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "fragment"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "94784085682240"
-      ]
-     },
-     "execution_count": 2,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "id(a)"
-   ]
-  },
   {
    "cell_type": "code",
    "execution_count": 3,
@@ -118,7 +142,7 @@
     {
      "data": {
       "text/plain": [
-       "int"
+       "94381541328960"
       ]
      },
      "execution_count": 3,
@@ -127,7 +151,7 @@
     }
    ],
    "source": [
-    "type(a)"
+    "id(a)"
    ]
   },
   {
@@ -142,7 +166,7 @@
     {
      "data": {
       "text/plain": [
-       "42"
+       "int"
       ]
      },
      "execution_count": 4,
@@ -150,6 +174,30 @@
      "output_type": "execute_result"
     }
    ],
+   "source": [
+    "type(a)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "42"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "a"
    ]
@@ -167,7 +215,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 6,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -180,7 +228,7 @@
        "1000000"
       ]
      },
-     "execution_count": 5,
+     "execution_count": 6,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -202,7 +250,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 7,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -215,7 +263,7 @@
        "123456789"
       ]
      },
-     "execution_count": 6,
+     "execution_count": 7,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -237,7 +285,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 8,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -246,10 +294,10 @@
    "outputs": [
     {
      "ename": "SyntaxError",
-     "evalue": "invalid token (<ipython-input-7-9a7f0f99e867>, line 1)",
+     "evalue": "invalid token (<ipython-input-8-9a7f0f99e867>, line 1)",
      "output_type": "error",
      "traceback": [
-      "\u001b[0;36m  File \u001b[0;32m\"<ipython-input-7-9a7f0f99e867>\"\u001b[0;36m, line \u001b[0;32m1\u001b[0m\n\u001b[0;31m    042\u001b[0m\n\u001b[0m      ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m invalid token\n"
+      "\u001b[0;36m  File \u001b[0;32m\"<ipython-input-8-9a7f0f99e867>\"\u001b[0;36m, line \u001b[0;32m1\u001b[0m\n\u001b[0;31m    042\u001b[0m\n\u001b[0m      ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m invalid token\n"
      ]
     }
    ],
@@ -270,7 +318,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 9,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -283,7 +331,7 @@
        "42"
       ]
      },
-     "execution_count": 8,
+     "execution_count": 9,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -294,7 +342,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 10,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -307,7 +355,7 @@
        "42"
       ]
      },
-     "execution_count": 9,
+     "execution_count": 10,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -329,7 +377,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 11,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -342,7 +390,7 @@
        "-42"
       ]
      },
-     "execution_count": 10,
+     "execution_count": 11,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -364,10 +412,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 12,
    "metadata": {
     "slideshow": {
-     "slide_type": "slide"
+     "slide_type": "fragment"
     }
    },
    "outputs": [
@@ -377,7 +425,7 @@
        "42"
       ]
      },
-     "execution_count": 11,
+     "execution_count": 12,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -388,10 +436,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 13,
    "metadata": {
     "slideshow": {
-     "slide_type": "fragment"
+     "slide_type": "skip"
     }
    },
    "outputs": [
@@ -402,7 +450,7 @@
      "traceback": [
       "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
       "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-12-faae6b054331>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"42.0\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;32m<ipython-input-13-faae6b054331>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"42.0\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
       "\u001b[0;31mValueError\u001b[0m: invalid literal for int() with base 10: '42.0'"
      ]
     }
@@ -413,10 +461,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 14,
    "metadata": {
     "slideshow": {
-     "slide_type": "fragment"
+     "slide_type": "skip"
     }
    },
    "outputs": [
@@ -426,7 +474,7 @@
        "42"
       ]
      },
-     "execution_count": 13,
+     "execution_count": 14,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -448,7 +496,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 15,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -461,7 +509,7 @@
        "1"
       ]
      },
-     "execution_count": 14,
+     "execution_count": 15,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -525,7 +573,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 16,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -538,7 +586,7 @@
        "'0b11'"
       ]
      },
-     "execution_count": 15,
+     "execution_count": 16,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -558,41 +606,6 @@
     "We may pass a `str` object formatted this way as the argument to the [int()](https://docs.python.org/3/library/functions.html#int) built-in, together with `base=2`, to create an `int` object, for example, with the value of `3`."
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "fragment"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "3"
-      ]
-     },
-     "execution_count": 16,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "int(\"0b11\", base=2)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "source": [
-    "Moreover, we may also use the contents of the returned `str` object as a **literal** instead: Just like we type, for example, `3` without quotes (i.e., \"literally\") into a code cell to create the `int` object `3`, we may type `0b11` to obtain an `int` object with the same value."
-   ]
-  },
   {
    "cell_type": "code",
    "execution_count": 17,
@@ -614,7 +627,7 @@
     }
    ],
    "source": [
-    "0b11"
+    "int(\"0b11\", base=2)"
    ]
   },
   {
@@ -625,7 +638,7 @@
     }
    },
    "source": [
-    "It is convenient to use the underscore `_` to separate the `\"0b\"` prefix from the bits."
+    "Moreover, we may also use the contents of the returned `str` object as a **literal** instead: Just like we type, for example, `3` without quotes (i.e., \"literally\") into a code cell to create the `int` object `3`, we may type `0b11` to obtain an `int` object with the same value."
    ]
   },
   {
@@ -649,7 +662,7 @@
     }
    ],
    "source": [
-    "0b_11"
+    "0b11"
    ]
   },
   {
@@ -1472,7 +1485,7 @@
     }
    ],
    "source": [
-    "0x_7b"
+    "0x7b"
    ]
   },
   {
@@ -1491,7 +1504,7 @@
    "execution_count": 43,
    "metadata": {
     "slideshow": {
-     "slide_type": "skip"
+     "slide_type": "slide"
     }
    },
    "outputs": [
@@ -1515,7 +1528,7 @@
    "execution_count": 44,
    "metadata": {
     "slideshow": {
-     "slide_type": "skip"
+     "slide_type": "fragment"
     }
    },
    "outputs": [
@@ -1550,7 +1563,7 @@
    "execution_count": 45,
    "metadata": {
     "slideshow": {
-     "slide_type": "skip"
+     "slide_type": "fragment"
     }
    },
    "outputs": [
@@ -1869,7 +1882,7 @@
    "execution_count": 55,
    "metadata": {
     "slideshow": {
-     "slide_type": "slide"
+     "slide_type": "skip"
     }
    },
    "outputs": [
@@ -1893,7 +1906,7 @@
    "execution_count": 56,
    "metadata": {
     "slideshow": {
-     "slide_type": "fragment"
+     "slide_type": "skip"
     }
    },
    "outputs": [
@@ -1987,7 +2000,7 @@
    "execution_count": 59,
    "metadata": {
     "slideshow": {
-     "slide_type": "slide"
+     "slide_type": "fragment"
     }
    },
    "outputs": [
@@ -2062,7 +2075,7 @@
    "execution_count": 61,
    "metadata": {
     "slideshow": {
-     "slide_type": "fragment"
+     "slide_type": "skip"
     }
    },
    "outputs": [
@@ -2086,7 +2099,7 @@
    "execution_count": 62,
    "metadata": {
     "slideshow": {
-     "slide_type": "fragment"
+     "slide_type": "skip"
     }
    },
    "outputs": [
@@ -2121,7 +2134,7 @@
    "execution_count": 63,
    "metadata": {
     "slideshow": {
-     "slide_type": "fragment"
+     "slide_type": "skip"
     }
    },
    "outputs": [
@@ -2156,7 +2169,7 @@
    "execution_count": 64,
    "metadata": {
     "slideshow": {
-     "slide_type": "slide"
+     "slide_type": "fragment"
     }
    },
    "outputs": [
@@ -2180,7 +2193,7 @@
    "execution_count": 65,
    "metadata": {
     "slideshow": {
-     "slide_type": "fragment"
+     "slide_type": "skip"
     }
    },
    "outputs": [
@@ -2204,7 +2217,7 @@
    "execution_count": 66,
    "metadata": {
     "slideshow": {
-     "slide_type": "fragment"
+     "slide_type": "skip"
     }
    },
    "outputs": [
@@ -2239,7 +2252,7 @@
    "execution_count": 67,
    "metadata": {
     "slideshow": {
-     "slide_type": "fragment"
+     "slide_type": "skip"
     }
    },
    "outputs": [
@@ -2274,7 +2287,7 @@
    "execution_count": 68,
    "metadata": {
     "slideshow": {
-     "slide_type": "slide"
+     "slide_type": "skip"
     }
    },
    "outputs": [
@@ -2298,7 +2311,7 @@
    "execution_count": 69,
    "metadata": {
     "slideshow": {
-     "slide_type": "fragment"
+     "slide_type": "skip"
     }
    },
    "outputs": [
@@ -2322,7 +2335,7 @@
    "execution_count": 70,
    "metadata": {
     "slideshow": {
-     "slide_type": "fragment"
+     "slide_type": "skip"
     }
    },
    "outputs": [
@@ -2357,7 +2370,7 @@
    "execution_count": 71,
    "metadata": {
     "slideshow": {
-     "slide_type": "fragment"
+     "slide_type": "skip"
     }
    },
    "outputs": [
@@ -2394,7 +2407,7 @@
    "execution_count": 72,
    "metadata": {
     "slideshow": {
-     "slide_type": "slide"
+     "slide_type": "fragment"
     }
    },
    "outputs": [
@@ -2418,7 +2431,7 @@
    "execution_count": 73,
    "metadata": {
     "slideshow": {
-     "slide_type": "fragment"
+     "slide_type": "skip"
     }
    },
    "outputs": [
@@ -2442,7 +2455,7 @@
    "execution_count": 74,
    "metadata": {
     "slideshow": {
-     "slide_type": "fragment"
+     "slide_type": "skip"
     }
    },
    "outputs": [
@@ -2466,7 +2479,7 @@
    "execution_count": 75,
    "metadata": {
     "slideshow": {
-     "slide_type": "fragment"
+     "slide_type": "skip"
     }
    },
    "outputs": [
@@ -2538,7 +2551,7 @@
    "execution_count": 77,
    "metadata": {
     "slideshow": {
-     "slide_type": "slide"
+     "slide_type": "skip"
     }
    },
    "outputs": [
@@ -2562,7 +2575,7 @@
    "execution_count": 78,
    "metadata": {
     "slideshow": {
-     "slide_type": "fragment"
+     "slide_type": "skip"
     }
    },
    "outputs": [
@@ -2586,7 +2599,7 @@
    "execution_count": 79,
    "metadata": {
     "slideshow": {
-     "slide_type": "fragment"
+     "slide_type": "skip"
     }
    },
    "outputs": [
@@ -2610,7 +2623,7 @@
    "execution_count": 80,
    "metadata": {
     "slideshow": {
-     "slide_type": "fragment"
+     "slide_type": "skip"
     }
    },
    "outputs": [
@@ -2645,7 +2658,7 @@
    "execution_count": 81,
    "metadata": {
     "slideshow": {
-     "slide_type": "slide"
+     "slide_type": "skip"
     }
    },
    "outputs": [
@@ -2669,7 +2682,7 @@
    "execution_count": 82,
    "metadata": {
     "slideshow": {
-     "slide_type": "fragment"
+     "slide_type": "skip"
     }
    },
    "outputs": [
@@ -2693,7 +2706,7 @@
    "execution_count": 83,
    "metadata": {
     "slideshow": {
-     "slide_type": "fragment"
+     "slide_type": "skip"
     }
    },
    "outputs": [
@@ -2717,7 +2730,7 @@
    "execution_count": 84,
    "metadata": {
     "slideshow": {
-     "slide_type": "fragment"
+     "slide_type": "skip"
     }
    },
    "outputs": [
@@ -2789,7 +2802,7 @@
     {
      "data": {
       "text/plain": [
-       "139663468493296"
+       "139960518791696"
       ]
      },
      "execution_count": 86,
@@ -3113,7 +3126,7 @@
    "execution_count": 97,
    "metadata": {
     "slideshow": {
-     "slide_type": "fragment"
+     "slide_type": "skip"
     }
    },
    "outputs": [
@@ -3487,7 +3500,7 @@
     }
    ],
    "source": [
-    "float(\"inf\")  # same as float(\"+inf\")"
+    "float(\"inf\")  # also float(\"+inf\")"
    ]
   },
   {
@@ -3836,7 +3849,7 @@
    "execution_count": 120,
    "metadata": {
     "slideshow": {
-     "slide_type": "fragment"
+     "slide_type": "skip"
     }
    },
    "outputs": [
@@ -3871,7 +3884,7 @@
    "execution_count": 121,
    "metadata": {
     "slideshow": {
-     "slide_type": "slide"
+     "slide_type": "fragment"
     }
    },
    "outputs": [
@@ -4125,7 +4138,7 @@
     }
    },
    "source": [
-    "A popular workaround is to benchmark the difference between the two numbers to be checked for equality against a pre-defined `threshold` *sufficiently* close to `0`, for example, `1e-15`."
+    "A popular workaround is to benchmark the absolute value of the difference between the two numbers to be checked for equality against a pre-defined `threshold` *sufficiently* close to `0`, for example, `1e-15`."
    ]
   },
   {
@@ -4162,7 +4175,7 @@
     }
    ],
    "source": [
-    "(sqrt(2) ** 2) - 2 < threshold"
+    "abs((sqrt(2) ** 2) - 2) < threshold"
    ]
   },
   {
@@ -4186,7 +4199,7 @@
     }
    ],
    "source": [
-    "(0.1 + 0.2) - 0.3 < threshold"
+    "abs((0.1 + 0.2) - 0.3) < threshold"
    ]
   },
   {
@@ -5021,7 +5034,7 @@
     }
    ],
    "source": [
-    "Decimal(0.1)  # do not do this"
+    "Decimal(0.1)  # do not do this!"
    ]
   },
   {
@@ -5182,7 +5195,7 @@
    "execution_count": 166,
    "metadata": {
     "slideshow": {
-     "slide_type": "fragment"
+     "slide_type": "skip"
     }
    },
    "outputs": [
@@ -5206,7 +5219,7 @@
    "execution_count": 167,
    "metadata": {
     "slideshow": {
-     "slide_type": "slide"
+     "slide_type": "fragment"
     }
    },
    "outputs": [
@@ -5241,7 +5254,7 @@
    "execution_count": 168,
    "metadata": {
     "slideshow": {
-     "slide_type": "fragment"
+     "slide_type": "skip"
     }
    },
    "outputs": [
@@ -5265,7 +5278,7 @@
    "execution_count": 169,
    "metadata": {
     "slideshow": {
-     "slide_type": "fragment"
+     "slide_type": "skip"
     }
    },
    "outputs": [
@@ -5300,7 +5313,7 @@
    "execution_count": 170,
    "metadata": {
     "slideshow": {
-     "slide_type": "slide"
+     "slide_type": "fragment"
     }
    },
    "outputs": [
@@ -5436,7 +5449,7 @@
    "execution_count": 174,
    "metadata": {
     "slideshow": {
-     "slide_type": "slide"
+     "slide_type": "fragment"
     }
    },
    "outputs": [
@@ -5471,7 +5484,7 @@
    "execution_count": 175,
    "metadata": {
     "slideshow": {
-     "slide_type": "fragment"
+     "slide_type": "skip"
     }
    },
    "outputs": [
@@ -5879,7 +5892,7 @@
     }
    ],
    "source": [
-    "Fraction(1, 3)  # 1/3 with \"full\" precision"
+    "Fraction(1, 3)  # 1 / 3 with \"full\" precision"
    ]
   },
   {
@@ -5903,7 +5916,7 @@
     }
    ],
    "source": [
-    "Fraction(\"1/3\")  # 1/3 with \"full\" precision"
+    "Fraction(\"1/3\")  # 1 / 3 with \"full\" precision"
    ]
   },
   {
@@ -5927,7 +5940,7 @@
     }
    ],
    "source": [
-    "Fraction(\"0.3333333333\")  # 1/3 with a precision of 10 significant digits"
+    "Fraction(\"0.3333333333\")  # 1 / 3 with a precision of 10 significant digits"
    ]
   },
   {
@@ -5970,7 +5983,7 @@
    "execution_count": 191,
    "metadata": {
     "slideshow": {
-     "slide_type": "slide"
+     "slide_type": "skip"
     }
    },
    "outputs": [
@@ -5994,7 +6007,7 @@
    "execution_count": 192,
    "metadata": {
     "slideshow": {
-     "slide_type": "fragment"
+     "slide_type": "skip"
     }
    },
    "outputs": [
@@ -6222,7 +6235,7 @@
     }
    ],
    "source": [
-    "10.0 * Fraction(1, 100)  # do not do this"
+    "10.0 * Fraction(1, 100)  # do not do this!"
    ]
   },
   {
@@ -6348,7 +6361,7 @@
     {
      "data": {
       "text/plain": [
-       "139663415663408"
+       "139960519210800"
       ]
      },
      "execution_count": 202,
@@ -6556,7 +6569,7 @@
     }
    },
    "source": [
-    " The arguments to [complex()](https://docs.python.org/3/library/functions.html#complex) may be any numeric type."
+    "The arguments to [complex()](https://docs.python.org/3/library/functions.html#complex) may be any numeric type or properly formated `str` object."
    ]
   },
   {
@@ -6623,7 +6636,7 @@
    "execution_count": 211,
    "metadata": {
     "slideshow": {
-     "slide_type": "slide"
+     "slide_type": "skip"
     }
    },
    "outputs": [],
@@ -6637,7 +6650,7 @@
    "execution_count": 212,
    "metadata": {
     "slideshow": {
-     "slide_type": "fragment"
+     "slide_type": "skip"
     }
    },
    "outputs": [
@@ -6661,7 +6674,7 @@
    "execution_count": 213,
    "metadata": {
     "slideshow": {
-     "slide_type": "fragment"
+     "slide_type": "skip"
     }
    },
    "outputs": [
@@ -6781,7 +6794,7 @@
    "execution_count": 218,
    "metadata": {
     "slideshow": {
-     "slide_type": "fragment"
+     "slide_type": "skip"
     }
    },
    "outputs": [
@@ -6805,7 +6818,7 @@
    "execution_count": 219,
    "metadata": {
     "slideshow": {
-     "slide_type": "fragment"
+     "slide_type": "skip"
     }
    },
    "outputs": [
@@ -6832,7 +6845,7 @@
     }
    },
    "source": [
-    "The absolute value of a `complex` number $x$ is defined with the Pythagorean Theorem where $\\lVert x \\rVert = \\sqrt{a^2 + b^2}$ and $a$ and $b$ are the real and imaginary parts. The [abs()](https://docs.python.org/3/library/functions.html#abs) built-in function implements that in Python."
+    "A `complex` number comes with two **attributes** `real` and `imag` that return the two parts as `float` objects on their own."
    ]
   },
   {
@@ -6847,7 +6860,7 @@
     {
      "data": {
       "text/plain": [
-       "5.0"
+       "0.0"
       ]
      },
      "execution_count": 220,
@@ -6856,18 +6869,7 @@
     }
    ],
    "source": [
-    "abs(3 + 4j)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "source": [
-    "A `complex` number comes with two **attributes** `real` and `imag` that return the two parts as `float` objects on their own."
+    "x.real"
    ]
   },
   {
@@ -6891,31 +6893,7 @@
     }
    ],
    "source": [
-    "c1.real"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 222,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "fragment"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "2.0"
-      ]
-     },
-     "execution_count": 222,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "c1.imag"
+    "x.imag"
    ]
   },
   {
@@ -6931,7 +6909,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 223,
+   "execution_count": 222,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -6941,16 +6919,16 @@
     {
      "data": {
       "text/plain": [
-       "(1-2j)"
+       "-1j"
       ]
      },
-     "execution_count": 223,
+     "execution_count": 222,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "c1.conjugate()"
+    "x.conjugate()"
    ]
   },
   {
@@ -6993,7 +6971,7 @@
     "- $\\mathbb{R}$: [Real numbers](https://en.wikipedia.org/wiki/Real_number) are all numbers that can be represented as a distance along a line, and negative means \"reversed,\" e.g., $\\sqrt{2}, \\pi, \\text{e}, ...$\n",
     "- $\\mathbb{C}$: [Complex numbers](https://en.wikipedia.org/wiki/Complex_number) are all numbers of the form $a + b\\textbf{i}$ where $a$ and $b$ are real numbers and $\\textbf{i}$ is the [imaginary number](https://en.wikipedia.org/wiki/Imaginary_number), e.g., $0, \\textbf{i}, 1 + \\textbf{i}, ...$\n",
     "\n",
-    "In the listed order, the five sets are perfect subsets of the respective following sets, and $\\mathbb{C}$ is the largest set (cf., the figure below illustrates that observation as well). To be precise, all sets are infinite, but they still have a different number of elements."
+    "In the listed order, the five sets are perfect subsets of the respective following sets, and $\\mathbb{C}$ is the largest set. To be precise, all sets are infinite, but they still have a different number of elements."
    ]
   },
   {
@@ -7015,7 +6993,7 @@
     }
    },
    "source": [
-    "The data types introduced in this chapter are all *imperfect* models of *abstract* mathematical ideas.\n",
+    "The data types introduced in this chapter are all *(im)perfect* models of *abstract* mathematical ideas.\n",
     "\n",
     "The `int` and `Fraction` types are the models \"closest\" to the idea they implement: Whereas $\\mathbb{Z}$ and $\\mathbb{Q}$ are, by definition, infinite, every computer runs out of bits when representing sufficiently large integers or fractions with a sufficiently large number of decimals. However, within a system-dependent range, we can model an integer or fraction without any loss in precision.\n",
     "\n",
@@ -7032,7 +7010,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 224,
+   "execution_count": 223,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -7045,7 +7023,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 225,
+   "execution_count": 224,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -7073,7 +7051,7 @@
        " 'abstractmethod']"
       ]
      },
-     "execution_count": 225,
+     "execution_count": 224,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -7082,286 +7060,6 @@
     "dir(numbers)"
    ]
   },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "source": [
-    "As a reminder, the built-in [help()](https://docs.python.org/3/library/functions.html#help) function is always our friend.\n",
-    "\n",
-    "The ABCs' docstrings are unsurprisingly similar to the corresponding data types' docstrings. For now, let's not worry about the dunder-style names in the docstrings.\n",
-    "\n",
-    "For example, both `numbers.Complex` and `complex` list the `imag` and `real` attributes shown above."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 226,
-   "metadata": {
-    "scrolled": true,
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Help on class Complex in module numbers:\n",
-      "\n",
-      "class Complex(Number)\n",
-      " |  Complex defines the operations that work on the builtin complex type.\n",
-      " |  \n",
-      " |  In short, those are: a conversion to complex, .real, .imag, +, -,\n",
-      " |  *, /, abs(), .conjugate, ==, and !=.\n",
-      " |  \n",
-      " |  If it is given heterogeneous arguments, and doesn't have special\n",
-      " |  knowledge about them, it should fall back to the builtin complex\n",
-      " |  type as described below.\n",
-      " |  \n",
-      " |  Method resolution order:\n",
-      " |      Complex\n",
-      " |      Number\n",
-      " |      builtins.object\n",
-      " |  \n",
-      " |  Methods defined here:\n",
-      " |  \n",
-      " |  __abs__(self)\n",
-      " |      Returns the Real distance from 0. Called for abs(self).\n",
-      " |  \n",
-      " |  __add__(self, other)\n",
-      " |      self + other\n",
-      " |  \n",
-      " |  __bool__(self)\n",
-      " |      True if self != 0. Called for bool(self).\n",
-      " |  \n",
-      " |  __complex__(self)\n",
-      " |      Return a builtin complex instance. Called for complex(self).\n",
-      " |  \n",
-      " |  __eq__(self, other)\n",
-      " |      self == other\n",
-      " |  \n",
-      " |  __mul__(self, other)\n",
-      " |      self * other\n",
-      " |  \n",
-      " |  __neg__(self)\n",
-      " |      -self\n",
-      " |  \n",
-      " |  __pos__(self)\n",
-      " |      +self\n",
-      " |  \n",
-      " |  __pow__(self, exponent)\n",
-      " |      self**exponent; should promote to float or complex when necessary.\n",
-      " |  \n",
-      " |  __radd__(self, other)\n",
-      " |      other + self\n",
-      " |  \n",
-      " |  __rmul__(self, other)\n",
-      " |      other * self\n",
-      " |  \n",
-      " |  __rpow__(self, base)\n",
-      " |      base ** self\n",
-      " |  \n",
-      " |  __rsub__(self, other)\n",
-      " |      other - self\n",
-      " |  \n",
-      " |  __rtruediv__(self, other)\n",
-      " |      other / self\n",
-      " |  \n",
-      " |  __sub__(self, other)\n",
-      " |      self - other\n",
-      " |  \n",
-      " |  __truediv__(self, other)\n",
-      " |      self / other: Should promote to float when necessary.\n",
-      " |  \n",
-      " |  conjugate(self)\n",
-      " |      (x+y*i).conjugate() returns (x-y*i).\n",
-      " |  \n",
-      " |  ----------------------------------------------------------------------\n",
-      " |  Data descriptors defined here:\n",
-      " |  \n",
-      " |  imag\n",
-      " |      Retrieve the imaginary component of this number.\n",
-      " |      \n",
-      " |      This should subclass Real.\n",
-      " |  \n",
-      " |  real\n",
-      " |      Retrieve the real component of this number.\n",
-      " |      \n",
-      " |      This should subclass Real.\n",
-      " |  \n",
-      " |  ----------------------------------------------------------------------\n",
-      " |  Data and other attributes defined here:\n",
-      " |  \n",
-      " |  __abstractmethods__ = frozenset({'__abs__', '__add__', '__complex__', ...\n",
-      " |  \n",
-      " |  __hash__ = None\n",
-      "\n"
-     ]
-    }
-   ],
-   "source": [
-    "help(numbers.Complex)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 227,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Help on class complex in module builtins:\n",
-      "\n",
-      "class complex(object)\n",
-      " |  complex(real=0, imag=0)\n",
-      " |  \n",
-      " |  Create a complex number from a real part and an optional imaginary part.\n",
-      " |  \n",
-      " |  This is equivalent to (real + imag*1j) where imag defaults to 0.\n",
-      " |  \n",
-      " |  Methods defined here:\n",
-      " |  \n",
-      " |  __abs__(self, /)\n",
-      " |      abs(self)\n",
-      " |  \n",
-      " |  __add__(self, value, /)\n",
-      " |      Return self+value.\n",
-      " |  \n",
-      " |  __bool__(self, /)\n",
-      " |      self != 0\n",
-      " |  \n",
-      " |  __divmod__(self, value, /)\n",
-      " |      Return divmod(self, value).\n",
-      " |  \n",
-      " |  __eq__(self, value, /)\n",
-      " |      Return self==value.\n",
-      " |  \n",
-      " |  __float__(self, /)\n",
-      " |      float(self)\n",
-      " |  \n",
-      " |  __floordiv__(self, value, /)\n",
-      " |      Return self//value.\n",
-      " |  \n",
-      " |  __format__(...)\n",
-      " |      complex.__format__() -> str\n",
-      " |      \n",
-      " |      Convert to a string according to format_spec.\n",
-      " |  \n",
-      " |  __ge__(self, value, /)\n",
-      " |      Return self>=value.\n",
-      " |  \n",
-      " |  __getattribute__(self, name, /)\n",
-      " |      Return getattr(self, name).\n",
-      " |  \n",
-      " |  __getnewargs__(...)\n",
-      " |  \n",
-      " |  __gt__(self, value, /)\n",
-      " |      Return self>value.\n",
-      " |  \n",
-      " |  __hash__(self, /)\n",
-      " |      Return hash(self).\n",
-      " |  \n",
-      " |  __int__(self, /)\n",
-      " |      int(self)\n",
-      " |  \n",
-      " |  __le__(self, value, /)\n",
-      " |      Return self<=value.\n",
-      " |  \n",
-      " |  __lt__(self, value, /)\n",
-      " |      Return self<value.\n",
-      " |  \n",
-      " |  __mod__(self, value, /)\n",
-      " |      Return self%value.\n",
-      " |  \n",
-      " |  __mul__(self, value, /)\n",
-      " |      Return self*value.\n",
-      " |  \n",
-      " |  __ne__(self, value, /)\n",
-      " |      Return self!=value.\n",
-      " |  \n",
-      " |  __neg__(self, /)\n",
-      " |      -self\n",
-      " |  \n",
-      " |  __pos__(self, /)\n",
-      " |      +self\n",
-      " |  \n",
-      " |  __pow__(self, value, mod=None, /)\n",
-      " |      Return pow(self, value, mod).\n",
-      " |  \n",
-      " |  __radd__(self, value, /)\n",
-      " |      Return value+self.\n",
-      " |  \n",
-      " |  __rdivmod__(self, value, /)\n",
-      " |      Return divmod(value, self).\n",
-      " |  \n",
-      " |  __repr__(self, /)\n",
-      " |      Return repr(self).\n",
-      " |  \n",
-      " |  __rfloordiv__(self, value, /)\n",
-      " |      Return value//self.\n",
-      " |  \n",
-      " |  __rmod__(self, value, /)\n",
-      " |      Return value%self.\n",
-      " |  \n",
-      " |  __rmul__(self, value, /)\n",
-      " |      Return value*self.\n",
-      " |  \n",
-      " |  __rpow__(self, value, mod=None, /)\n",
-      " |      Return pow(value, self, mod).\n",
-      " |  \n",
-      " |  __rsub__(self, value, /)\n",
-      " |      Return value-self.\n",
-      " |  \n",
-      " |  __rtruediv__(self, value, /)\n",
-      " |      Return value/self.\n",
-      " |  \n",
-      " |  __str__(self, /)\n",
-      " |      Return str(self).\n",
-      " |  \n",
-      " |  __sub__(self, value, /)\n",
-      " |      Return self-value.\n",
-      " |  \n",
-      " |  __truediv__(self, value, /)\n",
-      " |      Return self/value.\n",
-      " |  \n",
-      " |  conjugate(...)\n",
-      " |      complex.conjugate() -> complex\n",
-      " |      \n",
-      " |      Return the complex conjugate of its argument. (3-4j).conjugate() == 3+4j.\n",
-      " |  \n",
-      " |  ----------------------------------------------------------------------\n",
-      " |  Static methods defined here:\n",
-      " |  \n",
-      " |  __new__(*args, **kwargs) from builtins.type\n",
-      " |      Create and return a new object.  See help(type) for accurate signature.\n",
-      " |  \n",
-      " |  ----------------------------------------------------------------------\n",
-      " |  Data descriptors defined here:\n",
-      " |  \n",
-      " |  imag\n",
-      " |      the imaginary part of a complex number\n",
-      " |  \n",
-      " |  real\n",
-      " |      the real part of a complex number\n",
-      "\n"
-     ]
-    }
-   ],
-   "source": [
-    "help(complex)"
-   ]
-  },
   {
    "cell_type": "markdown",
    "metadata": {
@@ -7370,7 +7068,7 @@
     }
    },
    "source": [
-    "### Duck Typing"
+    "### Duck Typing (continued)"
    ]
   },
   {
@@ -7383,14 +7081,14 @@
    "source": [
     "The primary purpose of ABCs is to classify the *concrete* data types and standardize how they behave. This guides us as the programmers in what kind of behavior we should expect from objects of a given data type. In this context, ABCs are not reflected in code but only in our heads.\n",
     "\n",
-    "For, example, as all numeric data types are `Complex` numbers in the abstract sense, they all work with the built-in [abs()](https://docs.python.org/3/library/functions.html#abs) function (cf., [documentation](https://docs.python.org/3/library/numbers.html#numbers.Complex)). While it is intuitively clear what the [absolute value](https://en.wikipedia.org/wiki/Absolute_value) (i.e., \"distance\" to $0$) of an integer, a fraction, or any real number is, [abs()](https://docs.python.org/3/library/functions.html#abs) calculates the equivalent of that for complex numbers. That concept is called the [magnitude](https://en.wikipedia.org/wiki/Magnitude_%28mathematics%29) of a number, and is really a *generalization* of the absolute value.\n",
+    "For, example, as all numeric data types are `Complex` numbers in the abstract sense, they all work with the built-in [abs()](https://docs.python.org/3/library/functions.html#abs) function (cf., [documentation](https://docs.python.org/3/library/numbers.html#numbers.Complex)). While it is intuitively clear what the [absolute value](https://en.wikipedia.org/wiki/Absolute_value) (i.e., \"distance\" from $0$) of an integer, a fraction, or any real number is, [abs()](https://docs.python.org/3/library/functions.html#abs) calculates the equivalent of that for complex numbers. That concept is called the [magnitude](https://en.wikipedia.org/wiki/Magnitude_%28mathematics%29) of a number, and is really a *generalization* of the absolute value.\n",
     "\n",
-    "Relating back to the concept of **duck typing** mentioned in [Chapter 4](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/04_iteration_00_lecture.ipynb#Type-Checking-&-Input-Validation), `int`, `float`, and `complex` objects \"walk\" and \"quack\" alike in context of the [abs()](https://docs.python.org/3/library/functions.html#abs) function."
+    "Relating back to the concept of **duck typing** mentioned in [Chapter 4](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/04_iteration_00_lecture.ipynb#Duck-Typing), `int`, `float`, and `complex` objects \"walk\" and \"quack\" alike in context of the [abs()](https://docs.python.org/3/library/functions.html#abs) function."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 228,
+   "execution_count": 225,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -7403,7 +7101,7 @@
        "1"
       ]
      },
-     "execution_count": 228,
+     "execution_count": 225,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -7414,7 +7112,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 229,
+   "execution_count": 226,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -7427,7 +7125,7 @@
        "42.87"
       ]
      },
-     "execution_count": 229,
+     "execution_count": 226,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -7436,9 +7134,20 @@
     "abs(-42.87)"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "The absolute value of a `complex` number $x$ is defined with the Pythagorean Theorem where $\\lVert x \\rVert = \\sqrt{a^2 + b^2}$ and $a$ and $b$ are the real and imaginary parts."
+   ]
+  },
   {
    "cell_type": "code",
-   "execution_count": 230,
+   "execution_count": 227,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -7451,13 +7160,13 @@
        "5.0"
       ]
      },
-     "execution_count": 230,
+     "execution_count": 227,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "abs(4 - 3j)"
+    "abs(3 + 4j)"
    ]
   },
   {
@@ -7473,7 +7182,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 231,
+   "execution_count": 228,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -7486,7 +7195,7 @@
        "100"
       ]
      },
-     "execution_count": 231,
+     "execution_count": 228,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -7497,7 +7206,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 232,
+   "execution_count": 229,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -7510,7 +7219,7 @@
        "42"
       ]
      },
-     "execution_count": 232,
+     "execution_count": 229,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -7532,7 +7241,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 233,
+   "execution_count": 230,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -7546,7 +7255,7 @@
      "traceback": [
       "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
       "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-233-e40cd01fb15a>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mround\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m1\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;36m2j\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;32m<ipython-input-230-e40cd01fb15a>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mround\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m1\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;36m2j\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
       "\u001b[0;31mTypeError\u001b[0m: type complex doesn't define __round__ method"
      ]
     }
@@ -7581,7 +7290,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 234,
+   "execution_count": 231,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -7594,7 +7303,7 @@
        "True"
       ]
      },
-     "execution_count": 234,
+     "execution_count": 231,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -7616,7 +7325,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 235,
+   "execution_count": 232,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -7629,7 +7338,7 @@
        "True"
       ]
      },
-     "execution_count": 235,
+     "execution_count": 232,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -7640,7 +7349,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 236,
+   "execution_count": 233,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -7653,7 +7362,7 @@
        "True"
       ]
      },
-     "execution_count": 236,
+     "execution_count": 233,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -7664,7 +7373,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 237,
+   "execution_count": 234,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -7677,7 +7386,7 @@
        "True"
       ]
      },
-     "execution_count": 237,
+     "execution_count": 234,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -7699,7 +7408,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 238,
+   "execution_count": 235,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -7712,7 +7421,7 @@
        "False"
       ]
      },
-     "execution_count": 238,
+     "execution_count": 235,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -7729,12 +7438,12 @@
     }
    },
    "source": [
-    "However, if we model `1 / 10` as a `Fraction`, it is recognized as a `Rational` number."
+    "However, if we model `1 / 10` as a `Fraction` object, it is recognized as a `Rational` number."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 239,
+   "execution_count": 236,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -7747,7 +7456,7 @@
        "True"
       ]
      },
-     "execution_count": 239,
+     "execution_count": 236,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -7764,7 +7473,7 @@
     }
    },
    "source": [
-    "Replacing *concrete* data types with ABCs is particularly valuable in the context of input validation: The revised version of the `factorial()` function below allows its user to take advantage of *duck typing*: If a real but non-integer argument `n` is passed in, `factorial()` tries to cast `n` as an `int` object with the [int()](https://docs.python.org/3/library/functions.html#int) built-in.\n",
+    "Replacing *concrete* data types with ABCs is particularly valuable in the context of \"type checking:\" The revised version of the `factorial()` function below allows its user to take advantage of *duck typing*: If a real but non-integer argument `n` is passed in, `factorial()` tries to cast `n` as an `int` object with the [int()](https://docs.python.org/3/library/functions.html#int) built-in.\n",
     "\n",
     "Two popular and distinguished Pythonistas, [Luciano Ramalho](https://github.com/ramalho) and [Alex Martelli](https://en.wikipedia.org/wiki/Alex_Martelli), coin the term **goose typing** to specifically mean using the built-in [isinstance()](https://docs.python.org/3/library/functions.html#isinstance) function with an ABC (cf., Chapter 11 in this [book](https://www.amazon.com/Fluent-Python-Concise-Effective-Programming/dp/1491946008) or this [summary](https://dgkim5360.github.io/blog/python/2017/07/duck-typing-vs-goose-typing-pythonic-interfaces/) thereof)."
    ]
@@ -7782,7 +7491,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 240,
+   "execution_count": 237,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -7808,7 +7517,7 @@
     "    if not isinstance(n, numbers.Integral):\n",
     "        if isinstance(n, numbers.Real):\n",
     "            if n != int(n) and strict:\n",
-    "                raise TypeError(\"n is not integer-like; it has decimals\")\n",
+    "                raise TypeError(\"n is not integer-like; it has non-zero decimals\")\n",
     "            n = int(n)\n",
     "        else:\n",
     "            raise TypeError(\"Factorial is only defined for integers\")\n",
@@ -7833,7 +7542,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 241,
+   "execution_count": 238,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -7846,7 +7555,7 @@
        "1"
       ]
      },
-     "execution_count": 241,
+     "execution_count": 238,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -7855,6 +7564,102 @@
     "factorial(0)"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 239,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "6"
+      ]
+     },
+     "execution_count": 239,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "factorial(3)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 240,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "6"
+      ]
+     },
+     "execution_count": 240,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "factorial(3.0)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "With the keyword-only argument `strict`, we can control whether or not a passed in `float` object may come with decimals that are then truncated. By default, this is not allowed and results in a `TypeError`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 241,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "ename": "TypeError",
+     "evalue": "n is not integer-like; it has non-zero decimals",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-241-188b816be8b6>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mfactorial\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m3.1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;32m<ipython-input-237-be6602f598f7>\u001b[0m in \u001b[0;36mfactorial\u001b[0;34m(n, strict)\u001b[0m\n\u001b[1;32m     17\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mn\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnumbers\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mReal\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     18\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mn\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0mint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mn\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mstrict\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 19\u001b[0;31m                 \u001b[0;32mraise\u001b[0m \u001b[0mTypeError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"n is not integer-like; it has non-zero decimals\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     20\u001b[0m             \u001b[0mn\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mn\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     21\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;31mTypeError\u001b[0m: n is not integer-like; it has non-zero decimals"
+     ]
+    }
+   ],
+   "source": [
+    "factorial(3.1)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "In non-strict mode, the passed in `3.1` is truncated into `3` resulting in a factorial of `6`."
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 242,
@@ -7875,102 +7680,6 @@
      "output_type": "execute_result"
     }
    ],
-   "source": [
-    "factorial(3)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 243,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "fragment"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "6"
-      ]
-     },
-     "execution_count": 243,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "factorial(3.0)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "source": [
-    "With the keyword-only argument `strict`, we can control whether or not a passed in `float` object may come with decimals that are then truncated. By default, this is not allowed and results in a `TypeError`."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 244,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "slide"
-    }
-   },
-   "outputs": [
-    {
-     "ename": "TypeError",
-     "evalue": "n is not integer-like; it has decimals",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-244-188b816be8b6>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mfactorial\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m3.1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
-      "\u001b[0;32m<ipython-input-240-0b8c729be194>\u001b[0m in \u001b[0;36mfactorial\u001b[0;34m(n, strict)\u001b[0m\n\u001b[1;32m     17\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mn\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnumbers\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mReal\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     18\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mn\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0mint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mn\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mstrict\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 19\u001b[0;31m                 \u001b[0;32mraise\u001b[0m \u001b[0mTypeError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"n is not integer-like; it has decimals\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     20\u001b[0m             \u001b[0mn\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mn\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     21\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;31mTypeError\u001b[0m: n is not integer-like; it has decimals"
-     ]
-    }
-   ],
-   "source": [
-    "factorial(3.1)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "source": [
-    "In non-strict mode, the passed in `3.1` is truncated into `3` resulting in a factorial of `6`."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 245,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "fragment"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "6"
-      ]
-     },
-     "execution_count": 245,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
    "source": [
     "factorial(3.1, strict=False)"
    ]
@@ -7988,7 +7697,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 246,
+   "execution_count": 243,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -8002,8 +7711,8 @@
      "traceback": [
       "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
       "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-246-7296a74f5dcf>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mfactorial\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m1\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;36m2j\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
-      "\u001b[0;32m<ipython-input-240-0b8c729be194>\u001b[0m in \u001b[0;36mfactorial\u001b[0;34m(n, strict)\u001b[0m\n\u001b[1;32m     20\u001b[0m             \u001b[0mn\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mn\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     21\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 22\u001b[0;31m             \u001b[0;32mraise\u001b[0m \u001b[0mTypeError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Factorial is only defined for integers\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     23\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     24\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mn\u001b[0m \u001b[0;34m<\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m<ipython-input-243-7296a74f5dcf>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mfactorial\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m1\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;36m2j\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;32m<ipython-input-237-be6602f598f7>\u001b[0m in \u001b[0;36mfactorial\u001b[0;34m(n, strict)\u001b[0m\n\u001b[1;32m     20\u001b[0m             \u001b[0mn\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mn\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     21\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 22\u001b[0;31m             \u001b[0;32mraise\u001b[0m \u001b[0mTypeError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Factorial is only defined for integers\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     23\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     24\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mn\u001b[0m \u001b[0;34m<\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
       "\u001b[0;31mTypeError\u001b[0m: Factorial is only defined for integers"
      ]
     }
@@ -8072,7 +7781,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 247,
+   "execution_count": 244,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -8094,22 +7803,21 @@
        "        "
       ],
       "text/plain": [
-       "<IPython.lib.display.YouTubeVideo at 0x7f05ec430a10>"
+       "<IPython.lib.display.YouTubeVideo at 0x7f4b19022490>"
       ]
      },
-     "execution_count": 247,
+     "execution_count": 244,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "from IPython.display import YouTubeVideo\n",
     "YouTubeVideo(\"RgklPQ8rbkg\", width=\"60%\")"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 248,
+   "execution_count": 245,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -8131,10 +7839,10 @@
        "        "
       ],
       "text/plain": [
-       "<IPython.lib.display.YouTubeVideo at 0x7f05ec42ac90>"
+       "<IPython.lib.display.YouTubeVideo at 0x7f4b1901cc50>"
       ]
      },
-     "execution_count": 248,
+     "execution_count": 245,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -8156,7 +7864,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 249,
+   "execution_count": 246,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -8178,10 +7886,10 @@
        "        "
       ],
       "text/plain": [
-       "<IPython.lib.display.YouTubeVideo at 0x7f05ec428650>"
+       "<IPython.lib.display.YouTubeVideo at 0x7f4b190196d0>"
       ]
      },
-     "execution_count": 249,
+     "execution_count": 246,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -8203,7 +7911,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 250,
+   "execution_count": 247,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -8225,10 +7933,10 @@
        "        "
       ],
       "text/plain": [
-       "<IPython.lib.display.YouTubeVideo at 0x7f05ec426110>"
+       "<IPython.lib.display.YouTubeVideo at 0x7f4b190160d0>"
       ]
      },
-     "execution_count": 250,
+     "execution_count": 247,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -8250,7 +7958,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 251,
+   "execution_count": 248,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -8272,10 +7980,10 @@
        "        "
       ],
       "text/plain": [
-       "<IPython.lib.display.YouTubeVideo at 0x7f05ec411c10>"
+       "<IPython.lib.display.YouTubeVideo at 0x7f4b1907e190>"
       ]
      },
-     "execution_count": 251,
+     "execution_count": 248,
      "metadata": {},
      "output_type": "execute_result"
     }
diff --git a/05_numbers_01_review.ipynb b/05_numbers_01_review.ipynb
index 01d88fe..cb297bb 100644
--- a/05_numbers_01_review.ipynb
+++ b/05_numbers_01_review.ipynb
@@ -5,7 +5,7 @@
    "metadata": {},
    "source": [
     "\n",
-    "# Chapter 5: Bits & Numbers"
+    "# Chapter 5: Numbers & Bits"
    ]
   },
   {
@@ -19,7 +19,9 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Read [Chapter 5](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/05_numbers_00_lecture.ipynb) of the book. Then, work through the questions below."
+    "The questions below assume that you have read [Chapter 5](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/05_numbers_00_lecture.ipynb) in the book.\n",
+    "\n",
+    "Be concise in your answers! Most questions can be answered in *one* sentence."
    ]
   },
   {
@@ -47,7 +49,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    " "
+    " < your answer >"
    ]
   },
   {
@@ -61,28 +63,14 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    " "
+    " < your answer >"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "**Q3**: Why is it *inefficient* to store `bool` objects in bits resembling a **hexadecimal representation**?"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    " "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**Q4**: Colors are commonly expressed in the **hexadecimal system** in websites (cf., the [HTML](https://en.wikipedia.org/wiki/HTML) and [CSS](https://en.wikipedia.org/wiki/Cascading_Style_Sheets) formats).\n",
+    "**Q3**: Colors are commonly expressed in the **hexadecimal system** in websites (cf., the [HTML](https://en.wikipedia.org/wiki/HTML) and [CSS](https://en.wikipedia.org/wiki/Cascading_Style_Sheets) formats).\n",
     "\n",
     "For example, $#000000$, $#ff9900$, and $#ffffff$ turn out to be black, orange, and white. The six digits are read in *pairs of two* from left to right, and the *three pairs* correspond to the proportions of red, green, and blue mixed together. They reach from $0_{16} = 0_{10}$ for $0$% to $\\text{ff}_{16} = 255_{10}$ for $100$% (cf., this [article](https://en.wikipedia.org/wiki/RGB_color_model) for an in-depth discussion).\n",
     "\n",
@@ -93,77 +81,77 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    " "
+    " < your answer >"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "**Q5**: What does it mean for a code fragment to **fail silently**?"
+    "**Q4**: What does it mean for a code fragment to **fail silently**?"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    " "
+    " < your answer >"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "**Q6**: Explain why the mathematical set of all real numbers $\\mathbb{R}$ can only be **approximated** by floating-point numbers on a computer!"
+    "**Q5**: Explain why the mathematical set of all real numbers $\\mathbb{R}$ can only be **approximated** by floating-point numbers on a computer!"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    " "
+    " < your answer >"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "**Q7**: How do we deal with a `float` object's imprecision if we need to **check for equality**?"
+    "**Q6**: How do we deal with a `float` object's imprecision if we need to **check for equality**?"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    " "
+    " < your answer >"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "**Q8**: What data type, built-in or from the [standard library](https://docs.python.org/3/library/index.html), is best suited to represent the [transcendental numbers](https://en.wikipedia.org/wiki/Transcendental_number) $\\pi$ and $\\text{e}$?"
+    "**Q7**: What data type, built-in or from the [standard library](https://docs.python.org/3/library/index.html), is best suited to represent the [transcendental numbers](https://en.wikipedia.org/wiki/Transcendental_number) $\\pi$ and $\\text{e}$?"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    " "
+    " < your answer >"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "**Q9**: How can **abstract base classes**, for example, as defined in the **numerical tower**, be helpful in enabling **duck typing**?"
+    "**Q8**: How can **abstract base classes**, for example, as defined in the **numerical tower**, be helpful in enabling **duck typing**?"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    " "
+    " < your answer >"
    ]
   },
   {
@@ -184,63 +172,63 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "**Q10**: The precision of `int` objects depends on how we choose to represent them in memory. For example, using a **hexadecimal representation** gives us $16^8$ digits whereas with a **binary representation** an `int` object can have *at most* $2^8$ digits."
+    "**Q9**: The precision of `int` objects depends on how we choose to represent them in memory. For example, using a **hexadecimal representation** gives us $16^8$ digits whereas with a **binary representation** an `int` object can have *at most* $2^8$ digits."
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    " "
+    " < your answer >"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "**Q11**: With the built-in [round()](https://docs.python.org/3/library/functions.html#round) function, we obtain a *precise* representation for any `float` object if we can live with *less than* $15$ digits of precision."
+    "**Q10**: With the built-in [round()](https://docs.python.org/3/library/functions.html#round) function, we obtain a *precise* representation for any `float` object if we can live with *less than* $15$ digits of precision."
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    " "
+    " < your answer >"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "**Q12**: As most currencies operate with $2$ or $3$ decimals (e.g., EUR $9.99$), the `float` type's limitation of *at most* $15$ digits is *not* a problem in practice."
+    "**Q11**: As most currencies operate with $2$ or $3$ decimals (e.g., EUR $9.99$), the `float` type's limitation of *at most* $15$ digits is *not* a problem in practice."
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    " "
+    " < your answer >"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "**Q13**: The [IEEE 754](https://en.wikipedia.org/wiki/IEEE_754) standard's **special values** provide no benefit in practice as we could always use a **[sentinel](https://en.wikipedia.org/wiki/Sentinel_value)** value (i.e., a \"dummy\"). For example, instead of `nan`, we can always use `0` to indicate a *missing* value."
+    "**Q12**: The [IEEE 754](https://en.wikipedia.org/wiki/IEEE_754) standard's **special values** provide no benefit in practice as we could always use a **[sentinel](https://en.wikipedia.org/wiki/Sentinel_value)** value (i.e., a \"dummy\"). For example, instead of `nan`, we can always use `0` to indicate a *missing* value."
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    " "
+    " < your answer >"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "**Q14**: The following code fragment raises an `InvalidOperation` exception. That is an example of code **failing loudly**.\n",
+    "**Q13**: The following code fragment raises an `InvalidOperation` exception. That is an example of code **failing loudly**.\n",
     "```python\n",
     "float(\"inf\") + float(\"-inf\")\n",
     "```"
@@ -250,63 +238,63 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    " "
+    " < your answer >"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "**Q15**: Python provides a `scientific` type (e.g., `1.23e4`) that is useful mainly to model problems in the domains of physics or astrophysics."
+    "**Q14**: Python provides a `scientific` type (e.g., `1.23e4`) that is useful mainly to model problems in the domains of physics or astrophysics."
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    " "
+    " < your answer >"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "**Q16**: From a practitioner's point of view, the built-in [format()](https://docs.python.org/3/library/functions.html#format) function does the *same* as the built-in [round()](https://docs.python.org/3/library/functions.html#round) function."
+    "**Q15**: From a practitioner's point of view, the built-in [format()](https://docs.python.org/3/library/functions.html#format) function does the *same* as the built-in [round()](https://docs.python.org/3/library/functions.html#round) function."
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    " "
+    " < your answer >"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "**Q17**: The `Decimal` type from the [decimal](https://docs.python.org/3/library/decimal.html) module in the [standard library](https://docs.python.org/3/library/index.html) allows us to model the set of the real numbers $\\mathbb{R}$ *precisely*."
+    "**Q16**: The `Decimal` type from the [decimal](https://docs.python.org/3/library/decimal.html) module in the [standard library](https://docs.python.org/3/library/index.html) allows us to model the set of the real numbers $\\mathbb{R}$ *precisely*."
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    " "
+    " < your answer >"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "**Q18**: The `Fraction` type from the [fractions](https://docs.python.org/3/library/fractions.html) module in the [standard library](https://docs.python.org/3/library/index.html) allows us to model the set of the rational numbers $\\mathbb{Q}$ *precisely*."
+    "**Q17**: The `Fraction` type from the [fractions](https://docs.python.org/3/library/fractions.html) module in the [standard library](https://docs.python.org/3/library/index.html) allows us to model the set of the rational numbers $\\mathbb{Q}$ *precisely*."
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    " "
+    " < your answer >"
    ]
   }
  ],
diff --git a/05_numbers_02_exercises.ipynb b/05_numbers_02_exercises.ipynb
index ff5e9a8..8eaeaac 100644
--- a/05_numbers_02_exercises.ipynb
+++ b/05_numbers_02_exercises.ipynb
@@ -4,8 +4,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "\n",
-    "# Chapter 5: Bits & Numbers"
+    "# Chapter 5: Numbers & Bits"
    ]
   },
   {
@@ -19,7 +18,9 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Read [Chapter 5](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/05_numbers_00_lecture.ipynb) of the book. Then, work through the exercises below. The `...` indicate where you need to fill in your answers. You should not need to create any additional code cells."
+    "The exercises below assume that you have read [Chapter 5](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/05_numbers_00_lecture.ipynb) in the book.\n",
+    "\n",
+    "The `...`'s in the code cells indicate where you need to fill in code snippets. The number of `...`'s within a code cell give you a rough idea of how many lines of code are needed to solve the task. You should not need to create any additional code cells for your final solution. However, you may want to use temporary code cells to try out some ideas."
    ]
   },
   {
@@ -80,7 +81,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    " "
+    " < your answer >"
    ]
   },
   {
@@ -94,7 +95,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    " "
+    " < your answer >"
    ]
   },
   {
@@ -110,7 +111,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    " "
+    " < your answer >"
    ]
   },
   {
@@ -238,7 +239,7 @@
     "\n",
     "Enable **duck typing** by allowing the function to be called with various numeric types as the arguments, in particular, `quantity` may be a non-integer as well: Use an appropriate **abstract base class** from the [numbers](https://docs.python.org/3/library/numbers.html) module in the [standard library](https://docs.python.org/3/library/index.html) to verify the arguments' types and also that they are both positive!\n",
     "\n",
-    "It is considered a *best practice* to only round towards the *end* of the calculations."
+    "Hint: It is considered a *best practice* to only round towards the *end* of the calculations."
    ]
   },
   {
@@ -256,8 +257,43 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "def discounted_price(...):\n",
-    "    ..."
+    "def discounted_price(unit_price, quantity):\n",
+    "    \"\"\"Calculate the price of a line item in an order.\n",
+    "\n",
+    "    Args:\n",
+    "        unit_price (numbers.Real): price of an ordered item; must be positive\n",
+    "        quantity (numbers.Real): number of items ordered; must be positive\n",
+    "\n",
+    "    Returns:\n",
+    "        line_item_price (decimal.Decimal): precision of 2 decimals\n",
+    "    \"\"\"\n",
+    "    # Basic input validation.\n",
+    "    ...\n",
+    "    ...\n",
+    "    ...\n",
+    "    ...\n",
+    "\n",
+    "    # Calculate the final price if only\n",
+    "    # the first discount scheme is applied.\n",
+    "    ...\n",
+    "    ...\n",
+    "    ...\n",
+    "    ...\n",
+    "\n",
+    "    # Calculate the final price if only\n",
+    "    # the second discount scheme is applied.\n",
+    "    # \"One in every five\" means we need to figure out\n",
+    "    # how many full groups of five are contained.\n",
+    "    ...\n",
+    "    ...\n",
+    "    ...\n",
+    "    ...\n",
+    "    ...\n",
+    "\n",
+    "    # Choose the better option for the customer.\n",
+    "    ...\n",
+    "    # Round correctly for pricing purposes.\n",
+    "    return ..."
    ]
   },
   {

From 26983f19fd8e2a173994d88bba28fd7fefcd0adb Mon Sep 17 00:00:00 2001
From: Alexander Hess <alexander@webartifex.biz>
Date: Mon, 30 Mar 2020 14:49:41 +0200
Subject: [PATCH 091/242] Add video and streamline content

---
 06_text_00_lecture.ipynb   | 1245 +++++++++++++++++++-----------------
 06_text_01_review.ipynb    |   22 +-
 06_text_02_exercises.ipynb |   80 ++-
 3 files changed, 720 insertions(+), 627 deletions(-)

diff --git a/06_text_00_lecture.ipynb b/06_text_00_lecture.ipynb
index 3c23901..6a676d4 100644
--- a/06_text_00_lecture.ipynb
+++ b/06_text_00_lecture.ipynb
@@ -1,5 +1,53 @@
 {
  "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "A **video presentation** of the contents in this chapter is shown below. A playlist with *all* chapters as videos is linked [here](https://www.youtube.com/playlist?list=PL-2JV1G3J10lQ2xokyQowcRJI5jjNfW7f)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/jpeg": 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+       "            width=\"60%\"\n",
+       "            height=\"300\"\n",
+       "            src=\"https://www.youtube.com/embed/3WTRlgN09sM\"\n",
+       "            frameborder=\"0\"\n",
+       "            allowfullscreen\n",
+       "        ></iframe>\n",
+       "        "
+      ],
+      "text/plain": [
+       "<IPython.lib.display.YouTubeVideo at 0x7faec8a5e410>"
+      ]
+     },
+     "execution_count": 1,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from IPython.display import YouTubeVideo\n",
+    "YouTubeVideo(\"3WTRlgN09sM\", width=\"60%\")"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {
@@ -8,7 +56,7 @@
     }
    },
    "source": [
-    "# Chapter 6: Bytes & Text"
+    "# Chapter 6: Text & Bytes"
    ]
   },
   {
@@ -46,7 +94,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 2,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -70,7 +118,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 3,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -80,10 +128,10 @@
     {
      "data": {
       "text/plain": [
-       "140461336295424"
+       "140388598371792"
       ]
      },
-     "execution_count": 2,
+     "execution_count": 3,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -94,7 +142,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 4,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -107,7 +155,7 @@
        "str"
       ]
      },
-     "execution_count": 3,
+     "execution_count": 4,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -131,7 +179,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 5,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -144,7 +192,7 @@
        "'Lorem ipsum dolor sit amet.'"
       ]
      },
-     "execution_count": 4,
+     "execution_count": 5,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -170,7 +218,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 6,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -183,7 +231,7 @@
        "'Einstein said, \"If you can\\'t explain it, you don\\'t understand it.\"'"
       ]
      },
-     "execution_count": 5,
+     "execution_count": 6,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -194,7 +242,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 7,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -207,7 +255,7 @@
        "'Einstein said, \"If you can\\'t explain it, you don\\'t understand it.\"'"
       ]
      },
-     "execution_count": 6,
+     "execution_count": 7,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -231,7 +279,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 8,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -252,7 +300,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 9,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -284,7 +332,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 10,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -297,37 +345,13 @@
        "'42'"
       ]
      },
-     "execution_count": 9,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "str(42)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "fragment"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'42.87'"
-      ]
-     },
      "execution_count": 10,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "str(42.87)"
+    "str(42)"
    ]
   },
   {
@@ -342,7 +366,7 @@
     {
      "data": {
       "text/plain": [
-       "'[1, 2, 3]'"
+       "'42.87'"
       ]
      },
      "execution_count": 11,
@@ -351,7 +375,7 @@
     }
    ],
    "source": [
-    "str([1, 2, 3])"
+    "str(42.87)"
    ]
   },
   {
@@ -366,7 +390,7 @@
     {
      "data": {
       "text/plain": [
-       "'True'"
+       "'[1, 2, 3]'"
       ]
      },
      "execution_count": 12,
@@ -375,7 +399,7 @@
     }
    ],
    "source": [
-    "str(True)"
+    "str([1, 2, 3])"
    ]
   },
   {
@@ -383,14 +407,14 @@
    "execution_count": 13,
    "metadata": {
     "slideshow": {
-     "slide_type": "skip"
+     "slide_type": "fragment"
     }
    },
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "'False'"
+       "'True'"
       ]
      },
      "execution_count": 13,
@@ -399,7 +423,7 @@
     }
    ],
    "source": [
-    "str(False)"
+    "str(True)"
    ]
   },
   {
@@ -414,7 +438,7 @@
     {
      "data": {
       "text/plain": [
-       "'None'"
+       "'False'"
       ]
      },
      "execution_count": 14,
@@ -422,6 +446,30 @@
      "output_type": "execute_result"
     }
    ],
+   "source": [
+    "str(False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'None'"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "str(None)"
    ]
@@ -450,7 +498,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 16,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -461,7 +509,7 @@
      "name": "stdin",
      "output_type": "stream",
      "text": [
-      "Whatever you enter is put in a new string:  I will be a string\n"
+      "Whatever you enter is put in a new string:  1\n"
      ]
     }
    ],
@@ -469,30 +517,6 @@
     "user_input = input(\"Whatever you enter is put in a new string: \")"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "fragment"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "str"
-      ]
-     },
-     "execution_count": 16,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "type(user_input)"
-   ]
-  },
   {
    "cell_type": "code",
    "execution_count": 17,
@@ -505,7 +529,7 @@
     {
      "data": {
       "text/plain": [
-       "'I will be a string'"
+       "str"
       ]
      },
      "execution_count": 17,
@@ -513,6 +537,30 @@
      "output_type": "execute_result"
     }
    ],
+   "source": [
+    "type(user_input)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'1'"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "user_input"
    ]
@@ -541,7 +589,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 19,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -565,7 +613,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 20,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -578,7 +626,7 @@
        "_io.TextIOWrapper"
       ]
      },
-     "execution_count": 19,
+     "execution_count": 20,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -589,7 +637,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 21,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -602,7 +650,7 @@
        "<_io.TextIOWrapper name='lorem_ipsum.txt' mode='r' encoding='UTF-8'>"
       ]
      },
-     "execution_count": 20,
+     "execution_count": 21,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -624,7 +672,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 22,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -637,37 +685,13 @@
        "True"
       ]
      },
-     "execution_count": 21,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "file.readable()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 22,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "fragment"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "False"
-      ]
-     },
      "execution_count": 22,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "file.writable()"
+    "file.readable()"
    ]
   },
   {
@@ -682,7 +706,7 @@
     {
      "data": {
       "text/plain": [
-       "'lorem_ipsum.txt'"
+       "False"
       ]
      },
      "execution_count": 23,
@@ -691,7 +715,7 @@
     }
    ],
    "source": [
-    "file.name"
+    "file.writable()"
    ]
   },
   {
@@ -706,7 +730,7 @@
     {
      "data": {
       "text/plain": [
-       "'UTF-8'"
+       "'lorem_ipsum.txt'"
       ]
      },
      "execution_count": 24,
@@ -714,6 +738,30 @@
      "output_type": "execute_result"
     }
    ],
+   "source": [
+    "file.name"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'UTF-8'"
+      ]
+     },
+     "execution_count": 25,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "file.encoding"
    ]
@@ -733,7 +781,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 26,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -777,7 +825,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": 27,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -802,7 +850,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": 28,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -815,7 +863,7 @@
        "'the 1960s with the release of Letraset sheets.\\n'"
       ]
      },
-     "execution_count": 27,
+     "execution_count": 28,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -826,7 +874,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": 29,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -839,7 +887,7 @@
        "str"
       ]
      },
-     "execution_count": 28,
+     "execution_count": 29,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -863,7 +911,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
+   "execution_count": 30,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -876,7 +924,7 @@
        "False"
       ]
      },
-     "execution_count": 29,
+     "execution_count": 30,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -887,7 +935,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 30,
+   "execution_count": 31,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -900,7 +948,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 31,
+   "execution_count": 32,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -913,7 +961,7 @@
        "True"
       ]
      },
-     "execution_count": 31,
+     "execution_count": 32,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -935,7 +983,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 32,
+   "execution_count": 33,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -969,7 +1017,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 33,
+   "execution_count": 34,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -982,7 +1030,7 @@
        "True"
       ]
      },
-     "execution_count": 33,
+     "execution_count": 34,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1004,7 +1052,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 34,
+   "execution_count": 35,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -1041,7 +1089,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 35,
+   "execution_count": 36,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -1054,7 +1102,7 @@
        "True"
       ]
      },
-     "execution_count": 35,
+     "execution_count": 36,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1078,7 +1126,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 36,
+   "execution_count": 37,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -1091,7 +1139,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 37,
+   "execution_count": 38,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -1104,7 +1152,7 @@
        "'Lorem Ipsum'"
       ]
      },
-     "execution_count": 37,
+     "execution_count": 38,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1126,7 +1174,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 38,
+   "execution_count": 39,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -1139,7 +1187,7 @@
        "' is simply '"
       ]
      },
-     "execution_count": 38,
+     "execution_count": 39,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1161,7 +1209,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 39,
+   "execution_count": 40,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -1174,7 +1222,7 @@
        "'dummy text of the printing and typesetting industry.\\n'"
       ]
      },
-     "execution_count": 39,
+     "execution_count": 40,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1196,7 +1244,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 40,
+   "execution_count": 41,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -1209,7 +1257,7 @@
        "\"Lorem Ipsum has been the industry's standard dummy text ever since the 1500s\\n\""
       ]
      },
-     "execution_count": 40,
+     "execution_count": 41,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1231,7 +1279,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 41,
+   "execution_count": 42,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -1247,7 +1295,7 @@
        " 'the 1960s with the release of Letraset sheets.\\n']"
       ]
      },
-     "execution_count": 41,
+     "execution_count": 42,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1269,7 +1317,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 42,
+   "execution_count": 43,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -1282,7 +1330,7 @@
        "[]"
       ]
      },
-     "execution_count": 42,
+     "execution_count": 43,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1293,7 +1341,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 43,
+   "execution_count": 44,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -1317,7 +1365,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 44,
+   "execution_count": 45,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -1380,7 +1428,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 45,
+   "execution_count": 46,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -1393,7 +1441,7 @@
        "'Lorem ipsum dolor sit amet.'"
       ]
      },
-     "execution_count": 45,
+     "execution_count": 46,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1404,7 +1452,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 46,
+   "execution_count": 47,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -1417,7 +1465,7 @@
        "27"
       ]
      },
-     "execution_count": 46,
+     "execution_count": 47,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1439,7 +1487,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 47,
+   "execution_count": 48,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -1472,7 +1520,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 48,
+   "execution_count": 49,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -1505,36 +1553,12 @@
     "The `in` operator has *two* distinct usages: First, it checks if a *single* character is contained in a `str` object. Second, it may also check if a shorter `str` object, then called a **substring**, is contained in a longer one."
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": 49,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "slide"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "True"
-      ]
-     },
-     "execution_count": 49,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "\"L\" in text"
-   ]
-  },
   {
    "cell_type": "code",
    "execution_count": 50,
    "metadata": {
     "slideshow": {
-     "slide_type": "fragment"
+     "slide_type": "slide"
     }
    },
    "outputs": [
@@ -1550,7 +1574,7 @@
     }
    ],
    "source": [
-    "\"ipsum\" in text"
+    "\"L\" in text"
    ]
   },
   {
@@ -1565,7 +1589,7 @@
     {
      "data": {
       "text/plain": [
-       "False"
+       "True"
       ]
      },
      "execution_count": 51,
@@ -1573,6 +1597,30 @@
      "output_type": "execute_result"
     }
    ],
+   "source": [
+    "\"ipsum\" in text"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 52,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "False"
+      ]
+     },
+     "execution_count": 52,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "\"veni, vidi, vici\" in text"
    ]
@@ -1601,7 +1649,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 52,
+   "execution_count": 53,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -1614,7 +1662,7 @@
        "'L'"
       ]
      },
-     "execution_count": 52,
+     "execution_count": 53,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1625,7 +1673,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 53,
+   "execution_count": 54,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -1638,7 +1686,7 @@
        "'o'"
       ]
      },
-     "execution_count": 53,
+     "execution_count": 54,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1660,7 +1708,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 54,
+   "execution_count": 55,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -1674,7 +1722,7 @@
      "traceback": [
       "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
       "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-54-31fb90bbe515>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mtext\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1.0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;32m<ipython-input-55-31fb90bbe515>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mtext\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1.0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
       "\u001b[0;31mTypeError\u001b[0m: string indices must be integers"
      ]
     }
@@ -1696,7 +1744,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 55,
+   "execution_count": 56,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -1709,7 +1757,7 @@
        "'.'"
       ]
      },
-     "execution_count": 55,
+     "execution_count": 56,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1731,7 +1779,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 56,
+   "execution_count": 57,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -1745,7 +1793,7 @@
      "traceback": [
       "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
       "\u001b[0;31mIndexError\u001b[0m                                Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-56-ffa3e1bc2f86>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mtext\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m27\u001b[0m\u001b[0;34m]\u001b[0m  \u001b[0;31m# == text[len(text)]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;32m<ipython-input-57-ffa3e1bc2f86>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mtext\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m27\u001b[0m\u001b[0;34m]\u001b[0m  \u001b[0;31m# == text[len(text)]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
       "\u001b[0;31mIndexError\u001b[0m: string index out of range"
      ]
     }
@@ -1781,7 +1829,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 57,
+   "execution_count": 58,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -1794,7 +1842,7 @@
        "'.'"
       ]
      },
-     "execution_count": 57,
+     "execution_count": 58,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1805,7 +1853,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 58,
+   "execution_count": 59,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -1818,7 +1866,7 @@
        "'L'"
       ]
      },
-     "execution_count": 58,
+     "execution_count": 59,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1838,30 +1886,6 @@
     "One reason why programmers like to start counting at `0` is that a positive index and its *corresponding* negative index always add up to the length of the sequence. Here, `6` and `21` add to `27`."
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": 59,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'i'"
-      ]
-     },
-     "execution_count": 59,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "text[6]"
-   ]
-  },
   {
    "cell_type": "code",
    "execution_count": 60,
@@ -1882,6 +1906,30 @@
      "output_type": "execute_result"
     }
    ],
+   "source": [
+    "text[6]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 61,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'i'"
+      ]
+     },
+     "execution_count": 61,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "text[-21]"
    ]
@@ -1914,7 +1962,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 61,
+   "execution_count": 62,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -1927,7 +1975,7 @@
        "'Lorem'"
       ]
      },
-     "execution_count": 61,
+     "execution_count": 62,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1938,7 +1986,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 62,
+   "execution_count": 63,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -1951,7 +1999,7 @@
        "'dolor sit amet.'"
       ]
      },
-     "execution_count": 62,
+     "execution_count": 63,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1973,7 +2021,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 63,
+   "execution_count": 64,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -1986,7 +2034,7 @@
        "'Lorem'"
       ]
      },
-     "execution_count": 63,
+     "execution_count": 64,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1997,7 +2045,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 64,
+   "execution_count": 65,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -2010,7 +2058,7 @@
        "'dolor sit amet.'"
       ]
      },
-     "execution_count": 64,
+     "execution_count": 65,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2032,7 +2080,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 65,
+   "execution_count": 66,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -2045,7 +2093,7 @@
        "'Lorem ipsum dolor sit amet.'"
       ]
      },
-     "execution_count": 65,
+     "execution_count": 66,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2067,7 +2115,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 66,
+   "execution_count": 67,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -2080,7 +2128,7 @@
        "'Lorem ipsum sit amet.'"
       ]
      },
-     "execution_count": 66,
+     "execution_count": 67,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2102,7 +2150,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 67,
+   "execution_count": 68,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -2115,7 +2163,7 @@
        "'ipsum dolor'"
       ]
      },
-     "execution_count": 67,
+     "execution_count": 68,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2137,7 +2185,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 68,
+   "execution_count": 69,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -2150,7 +2198,7 @@
        "'Lorem ipsum dolor sit amet.'"
       ]
      },
-     "execution_count": 68,
+     "execution_count": 69,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2172,7 +2220,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 69,
+   "execution_count": 70,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -2185,7 +2233,7 @@
        "'Lorem ipsum dolor sit amet.'"
       ]
      },
-     "execution_count": 69,
+     "execution_count": 70,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2207,7 +2255,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 70,
+   "execution_count": 71,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -2220,7 +2268,7 @@
        "'Lrmismdlrstae.'"
       ]
      },
-     "execution_count": 70,
+     "execution_count": 71,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2242,7 +2290,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 71,
+   "execution_count": 72,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -2255,7 +2303,7 @@
        "'.tema tis rolod muspi meroL'"
       ]
      },
-     "execution_count": 71,
+     "execution_count": 72,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2292,7 +2340,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 72,
+   "execution_count": 73,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -2306,7 +2354,7 @@
      "traceback": [
       "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
       "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-72-d8a1e6b0b786>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mtext\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m\"X\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;32m<ipython-input-73-d8a1e6b0b786>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mtext\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m\"X\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
       "\u001b[0;31mTypeError\u001b[0m: 'str' object does not support item assignment"
      ]
     }
@@ -2317,7 +2365,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 73,
+   "execution_count": 74,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -2331,7 +2379,7 @@
      "traceback": [
       "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
       "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-73-def135e84998>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mtext\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;36m5\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m\"random\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;32m<ipython-input-74-def135e84998>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mtext\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;36m5\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m\"random\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
       "\u001b[0;31mTypeError\u001b[0m: 'str' object does not support item assignment"
      ]
     }
@@ -2366,7 +2414,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 74,
+   "execution_count": 75,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -2379,37 +2427,13 @@
        "'Lorem ipsum dolor sit amet.'"
       ]
      },
-     "execution_count": 74,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "text"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 75,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "fragment"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "22"
-      ]
-     },
      "execution_count": 75,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "text.find(\"a\")"
+    "text"
    ]
   },
   {
@@ -2424,7 +2448,7 @@
     {
      "data": {
       "text/plain": [
-       "-1"
+       "22"
       ]
      },
      "execution_count": 76,
@@ -2433,7 +2457,7 @@
     }
    ],
    "source": [
-    "text.find(\"b\")"
+    "text.find(\"a\")"
    ]
   },
   {
@@ -2448,7 +2472,7 @@
     {
      "data": {
       "text/plain": [
-       "12"
+       "-1"
       ]
      },
      "execution_count": 77,
@@ -2456,6 +2480,30 @@
      "output_type": "execute_result"
     }
    ],
+   "source": [
+    "text.find(\"b\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 78,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "12"
+      ]
+     },
+     "execution_count": 78,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "text.find(\"dolor\")"
    ]
@@ -2471,30 +2519,6 @@
     "[find()](https://docs.python.org/3/library/stdtypes.html#str.find) takes optional *start* and *end* arguments that allow us to find occurrences other than the first one."
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": 78,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "fragment"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "1"
-      ]
-     },
-     "execution_count": 78,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "text.find(\"o\")"
-   ]
-  },
   {
    "cell_type": "code",
    "execution_count": 79,
@@ -2507,7 +2531,7 @@
     {
      "data": {
       "text/plain": [
-       "13"
+       "1"
       ]
      },
      "execution_count": 79,
@@ -2516,7 +2540,7 @@
     }
    ],
    "source": [
-    "text.find(\"o\", 2)"
+    "text.find(\"o\")"
    ]
   },
   {
@@ -2531,7 +2555,7 @@
     {
      "data": {
       "text/plain": [
-       "-1"
+       "13"
       ]
      },
      "execution_count": 80,
@@ -2539,6 +2563,30 @@
      "output_type": "execute_result"
     }
    ],
+   "source": [
+    "text.find(\"o\", 2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 81,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "-1"
+      ]
+     },
+     "execution_count": 81,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "text.find(\"o\", 2, 12)"
    ]
@@ -2556,7 +2604,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 81,
+   "execution_count": 82,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -2569,7 +2617,7 @@
        "'Lorem ipsum dolor sit amet.'"
       ]
      },
-     "execution_count": 81,
+     "execution_count": 82,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2580,7 +2628,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 82,
+   "execution_count": 83,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -2593,7 +2641,7 @@
        "1"
       ]
      },
-     "execution_count": 82,
+     "execution_count": 83,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2615,7 +2663,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 83,
+   "execution_count": 84,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -2628,7 +2676,7 @@
        "2"
       ]
      },
-     "execution_count": 83,
+     "execution_count": 84,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2650,7 +2698,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 84,
+   "execution_count": 85,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -2663,7 +2711,7 @@
        "2"
       ]
      },
-     "execution_count": 84,
+     "execution_count": 85,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2685,7 +2733,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 85,
+   "execution_count": 86,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -2698,7 +2746,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 86,
+   "execution_count": 87,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -2708,10 +2756,10 @@
     {
      "data": {
       "text/plain": [
-       "140461430391152"
+       "140388701131184"
       ]
      },
-     "execution_count": 86,
+     "execution_count": 87,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2722,7 +2770,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 87,
+   "execution_count": 88,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -2735,7 +2783,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 88,
+   "execution_count": 89,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -2745,10 +2793,10 @@
     {
      "data": {
       "text/plain": [
-       "140461335949424"
+       "140388590822320"
       ]
      },
-     "execution_count": 88,
+     "execution_count": 89,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2770,7 +2818,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 89,
+   "execution_count": 90,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -2783,7 +2831,7 @@
        "False"
       ]
      },
-     "execution_count": 89,
+     "execution_count": 90,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2794,7 +2842,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 90,
+   "execution_count": 91,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -2807,7 +2855,7 @@
        "True"
       ]
      },
-     "execution_count": 90,
+     "execution_count": 91,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2829,7 +2877,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 91,
+   "execution_count": 92,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -2842,37 +2890,13 @@
        "'lorem ipsum dolor sit amet.'"
       ]
      },
-     "execution_count": 91,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "text.lower()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 92,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'LOREM IPSUM DOLOR SIT AMET.'"
-      ]
-     },
      "execution_count": 92,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "text.upper()"
+    "text.lower()"
    ]
   },
   {
@@ -2887,7 +2911,7 @@
     {
      "data": {
       "text/plain": [
-       "'Lorem Ipsum Dolor Sit Amet.'"
+       "'LOREM IPSUM DOLOR SIT AMET.'"
       ]
      },
      "execution_count": 93,
@@ -2896,7 +2920,7 @@
     }
    ],
    "source": [
-    "text.title()"
+    "text.upper()"
    ]
   },
   {
@@ -2911,7 +2935,7 @@
     {
      "data": {
       "text/plain": [
-       "'lOREM IPSUM DOLOR SIT AMET.'"
+       "'Lorem Ipsum Dolor Sit Amet.'"
       ]
      },
      "execution_count": 94,
@@ -2919,6 +2943,30 @@
      "output_type": "execute_result"
     }
    ],
+   "source": [
+    "text.title()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 95,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'lOREM IPSUM DOLOR SIT AMET.'"
+      ]
+     },
+     "execution_count": 95,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "text.swapcase()"
    ]
@@ -2938,7 +2986,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 95,
+   "execution_count": 96,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -2951,7 +2999,7 @@
        "['Lorem', 'ipsum', 'dolor', 'sit', 'amet.']"
       ]
      },
-     "execution_count": 95,
+     "execution_count": 96,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2962,7 +3010,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 96,
+   "execution_count": 97,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -2995,7 +3043,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 97,
+   "execution_count": 98,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -3008,7 +3056,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 98,
+   "execution_count": 99,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -3021,7 +3069,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 99,
+   "execution_count": 100,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -3034,7 +3082,7 @@
        "'This will become a sentence.'"
       ]
      },
-     "execution_count": 99,
+     "execution_count": 100,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3056,7 +3104,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 100,
+   "execution_count": 101,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -3069,7 +3117,7 @@
        "'a b c d e'"
       ]
      },
-     "execution_count": 100,
+     "execution_count": 101,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3091,7 +3139,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 101,
+   "execution_count": 102,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -3104,7 +3152,7 @@
        "'This is a sentence.'"
       ]
      },
-     "execution_count": 101,
+     "execution_count": 102,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3126,7 +3174,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 102,
+   "execution_count": 103,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -3139,7 +3187,7 @@
        "'This will become a sentence.'"
       ]
      },
-     "execution_count": 102,
+     "execution_count": 103,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3161,7 +3209,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 103,
+   "execution_count": 104,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -3174,37 +3222,13 @@
        "'text with whitespace'"
       ]
      },
-     "execution_count": 103,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "\"  text with whitespace  \".strip()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 104,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "fragment"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'text with whitespace  '"
-      ]
-     },
      "execution_count": 104,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "\"  text with whitespace  \".lstrip()"
+    "\"  text with whitespace  \".strip()"
    ]
   },
   {
@@ -3219,7 +3243,7 @@
     {
      "data": {
       "text/plain": [
-       "'  text with whitespace'"
+       "'text with whitespace  '"
       ]
      },
      "execution_count": 105,
@@ -3227,6 +3251,30 @@
      "output_type": "execute_result"
     }
    ],
+   "source": [
+    "\"  text with whitespace  \".lstrip()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 106,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'  text with whitespace'"
+      ]
+     },
+     "execution_count": 106,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "\"  text with whitespace  \".rstrip()"
    ]
@@ -3244,7 +3292,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 106,
+   "execution_count": 107,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -3257,7 +3305,7 @@
        "'This will become a sentence.            '"
       ]
      },
-     "execution_count": 106,
+     "execution_count": 107,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3268,7 +3316,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 107,
+   "execution_count": 108,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -3281,7 +3329,7 @@
        "'            This will become a sentence.'"
       ]
      },
-     "execution_count": 107,
+     "execution_count": 108,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3303,7 +3351,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 108,
+   "execution_count": 109,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -3316,7 +3364,7 @@
        "'0000042.87'"
       ]
      },
-     "execution_count": 108,
+     "execution_count": 109,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3327,7 +3375,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 109,
+   "execution_count": 110,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -3340,7 +3388,7 @@
        "'-000042.87'"
       ]
      },
-     "execution_count": 109,
+     "execution_count": 110,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3373,7 +3421,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 110,
+   "execution_count": 111,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -3386,7 +3434,7 @@
        "'Hello Lore'"
       ]
      },
-     "execution_count": 110,
+     "execution_count": 111,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3397,7 +3445,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 111,
+   "execution_count": 112,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -3410,7 +3458,7 @@
        "'Lorem ipsum Lorem ipsum Lorem ipsum Lorem ipsum Lorem ipsum ...'"
       ]
      },
-     "execution_count": 111,
+     "execution_count": 112,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3443,7 +3491,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 112,
+   "execution_count": 113,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -3456,37 +3504,13 @@
        "True"
       ]
      },
-     "execution_count": 112,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "\"Apple\" < \"Banana\""
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 113,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "fragment"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "False"
-      ]
-     },
      "execution_count": 113,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "\"apple\" < \"Banana\"  # upper case letter come before lower case ones"
+    "\"Apple\" < \"Banana\""
    ]
   },
   {
@@ -3501,7 +3525,7 @@
     {
      "data": {
       "text/plain": [
-       "True"
+       "False"
       ]
      },
      "execution_count": 114,
@@ -3509,6 +3533,30 @@
      "output_type": "execute_result"
     }
    ],
+   "source": [
+    "\"apple\" < \"Banana\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 115,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 115,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "\"apple\" < \"Banana\".lower()"
    ]
@@ -3526,7 +3574,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 115,
+   "execution_count": 116,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -3539,7 +3587,7 @@
        "True"
       ]
      },
-     "execution_count": 115,
+     "execution_count": 116,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3594,7 +3642,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 116,
+   "execution_count": 117,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -3608,7 +3656,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 117,
+   "execution_count": 118,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -3621,7 +3669,7 @@
        "'Hello Alexander! Good morning.'"
       ]
      },
-     "execution_count": 117,
+     "execution_count": 118,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3643,7 +3691,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 118,
+   "execution_count": 119,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -3656,7 +3704,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 119,
+   "execution_count": 120,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -3669,7 +3717,7 @@
        "'Pi is 3.14'"
       ]
      },
-     "execution_count": 119,
+     "execution_count": 120,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3702,7 +3750,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 120,
+   "execution_count": 121,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -3715,7 +3763,7 @@
        "'Hello Alexander! Good morning.'"
       ]
      },
-     "execution_count": 120,
+     "execution_count": 121,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3737,7 +3785,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 121,
+   "execution_count": 122,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -3750,7 +3798,7 @@
        "'Good morning, Alexander'"
       ]
      },
-     "execution_count": 121,
+     "execution_count": 122,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3772,7 +3820,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 122,
+   "execution_count": 123,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -3785,7 +3833,7 @@
        "'Hello Alexander! Good morning.'"
       ]
      },
-     "execution_count": 122,
+     "execution_count": 123,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3807,7 +3855,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 123,
+   "execution_count": 124,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -3820,7 +3868,7 @@
        "'Pi is 3.14'"
       ]
      },
-     "execution_count": 123,
+     "execution_count": 124,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3855,7 +3903,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 124,
+   "execution_count": 125,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -3868,7 +3916,7 @@
        "'Pi is 3.14'"
       ]
      },
-     "execution_count": 124,
+     "execution_count": 125,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3890,7 +3938,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 125,
+   "execution_count": 126,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -3903,7 +3951,7 @@
        "'Hello Alexander! Good morning.'"
       ]
      },
-     "execution_count": 125,
+     "execution_count": 126,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3938,7 +3986,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 126,
+   "execution_count": 127,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -3951,7 +3999,7 @@
        "'This is a sentence\\nthat is printed\\non three lines.'"
       ]
      },
-     "execution_count": 126,
+     "execution_count": 127,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3962,7 +4010,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 127,
+   "execution_count": 128,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -3996,7 +4044,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 128,
+   "execution_count": 129,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -4017,7 +4065,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 129,
+   "execution_count": 130,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -4049,7 +4097,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 130,
+   "execution_count": 131,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -4070,7 +4118,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 131,
+   "execution_count": 132,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -4102,7 +4150,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 132,
+   "execution_count": 133,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -4136,7 +4184,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 133,
+   "execution_count": 134,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -4180,7 +4228,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 134,
+   "execution_count": 135,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -4213,7 +4261,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 135,
+   "execution_count": 136,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -4222,10 +4270,10 @@
    "outputs": [
     {
      "ename": "SyntaxError",
-     "evalue": "(unicode error) 'unicodeescape' codec can't decode bytes in position 2-3: truncated \\UXXXXXXXX escape (<ipython-input-135-61308f43d404>, line 1)",
+     "evalue": "(unicode error) 'unicodeescape' codec can't decode bytes in position 2-3: truncated \\UXXXXXXXX escape (<ipython-input-136-61308f43d404>, line 1)",
      "output_type": "error",
      "traceback": [
-      "\u001b[0;36m  File \u001b[0;32m\"<ipython-input-135-61308f43d404>\"\u001b[0;36m, line \u001b[0;32m1\u001b[0m\n\u001b[0;31m    print(\"C:\\Users\\Administrator\\Desktop\\Project\")\u001b[0m\n\u001b[0m         ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m (unicode error) 'unicodeescape' codec can't decode bytes in position 2-3: truncated \\UXXXXXXXX escape\n"
+      "\u001b[0;36m  File \u001b[0;32m\"<ipython-input-136-61308f43d404>\"\u001b[0;36m, line \u001b[0;32m1\u001b[0m\n\u001b[0;31m    print(\"C:\\Users\\Administrator\\Desktop\\Project\")\u001b[0m\n\u001b[0m         ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m (unicode error) 'unicodeescape' codec can't decode bytes in position 2-3: truncated \\UXXXXXXXX escape\n"
      ]
     }
    ],
@@ -4246,7 +4294,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 136,
+   "execution_count": 137,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -4267,7 +4315,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 137,
+   "execution_count": 138,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -4299,7 +4347,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 138,
+   "execution_count": 139,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -4320,7 +4368,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 139,
+   "execution_count": 140,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -4389,7 +4437,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 140,
+   "execution_count": 141,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -4402,7 +4450,7 @@
        "65"
       ]
      },
-     "execution_count": 140,
+     "execution_count": 141,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -4413,7 +4461,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 141,
+   "execution_count": 142,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -4426,7 +4474,7 @@
        "'A'"
       ]
      },
-     "execution_count": 141,
+     "execution_count": 142,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -4448,7 +4496,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 142,
+   "execution_count": 143,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -4461,7 +4509,7 @@
        "10"
       ]
      },
-     "execution_count": 142,
+     "execution_count": 143,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -4472,7 +4520,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 143,
+   "execution_count": 144,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -4485,7 +4533,7 @@
        "'\\n'"
       ]
      },
-     "execution_count": 143,
+     "execution_count": 144,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -4511,7 +4559,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 144,
+   "execution_count": 145,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -4542,7 +4590,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 145,
+   "execution_count": 146,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -4573,7 +4621,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 146,
+   "execution_count": 147,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -4615,7 +4663,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 147,
+   "execution_count": 148,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -4633,7 +4681,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 148,
+   "execution_count": 149,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -4703,7 +4751,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 149,
+   "execution_count": 150,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -4716,7 +4764,7 @@
        "'�'"
       ]
      },
-     "execution_count": 149,
+     "execution_count": 150,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -4738,7 +4786,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 150,
+   "execution_count": 151,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -4751,7 +4799,7 @@
        "'�'"
       ]
      },
-     "execution_count": 150,
+     "execution_count": 151,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -4771,30 +4819,6 @@
     "Whenever the code point can be expressed with just four hexadecimal digits, we may use the escape sequence `\"\\u\"` for brevity."
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": 151,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "fragment"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'A'"
-      ]
-     },
-     "execution_count": 151,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "\"\\U00000041\"  # hex(65) == 0x41"
-   ]
-  },
   {
    "cell_type": "code",
    "execution_count": 152,
@@ -4816,18 +4840,7 @@
     }
    ],
    "source": [
-    "\"\\u0041\""
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "source": [
-    "Analogously, if the code point can be expressed with two hexadecimal digits, we may use the escape sequence `\"\\x\"` for even conciser code."
+    "\"\\U00000041\"  # hex(65) == 0x41"
    ]
   },
   {
@@ -4850,6 +4863,41 @@
      "output_type": "execute_result"
     }
    ],
+   "source": [
+    "\"\\u0041\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Analogously, if the code point can be expressed with two hexadecimal digits, we may use the escape sequence `\"\\x\"` for even conciser code."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 154,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'A'"
+      ]
+     },
+     "execution_count": 154,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "\"\\x41\""
    ]
@@ -4869,7 +4917,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 154,
+   "execution_count": 155,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -4882,7 +4930,7 @@
        "1"
       ]
      },
-     "execution_count": 154,
+     "execution_count": 155,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -4904,7 +4952,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 155,
+   "execution_count": 156,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -4917,7 +4965,7 @@
        "'�'"
       ]
      },
-     "execution_count": 155,
+     "execution_count": 156,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -4928,7 +4976,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 156,
+   "execution_count": 157,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -4941,7 +4989,7 @@
        "1"
       ]
      },
-     "execution_count": 156,
+     "execution_count": 157,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -4963,7 +5011,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 157,
+   "execution_count": 158,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -4976,7 +5024,7 @@
        "'straĂźe'"
       ]
      },
-     "execution_count": 157,
+     "execution_count": 158,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -4987,7 +5035,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 158,
+   "execution_count": 159,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -5000,7 +5048,7 @@
        "'strasse'"
       ]
      },
-     "execution_count": 158,
+     "execution_count": 159,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -5044,19 +5092,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 159,
+   "execution_count": 160,
    "metadata": {
     "slideshow": {
-     "slide_type": "slide"
+     "slide_type": "skip"
     }
    },
    "outputs": [
     {
      "ename": "SyntaxError",
-     "evalue": "EOL while scanning string literal (<ipython-input-159-4cef690f1f4a>, line 1)",
+     "evalue": "EOL while scanning string literal (<ipython-input-160-4cef690f1f4a>, line 1)",
      "output_type": "error",
      "traceback": [
-      "\u001b[0;36m  File \u001b[0;32m\"<ipython-input-159-4cef690f1f4a>\"\u001b[0;36m, line \u001b[0;32m1\u001b[0m\n\u001b[0;31m    \"\u001b[0m\n\u001b[0m     ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m EOL while scanning string literal\n"
+      "\u001b[0;36m  File \u001b[0;32m\"<ipython-input-160-4cef690f1f4a>\"\u001b[0;36m, line \u001b[0;32m1\u001b[0m\n\u001b[0;31m    \"\u001b[0m\n\u001b[0m     ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m EOL while scanning string literal\n"
      ]
     }
    ],
@@ -5079,7 +5127,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 160,
+   "execution_count": 161,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -5106,7 +5154,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 161,
+   "execution_count": 162,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -5119,7 +5167,7 @@
        "'\\nI am a multi-line string\\nconsisting of four lines.\\n'"
       ]
      },
-     "execution_count": 161,
+     "execution_count": 162,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -5130,7 +5178,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 162,
+   "execution_count": 163,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -5165,7 +5213,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 163,
+   "execution_count": 164,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -5201,7 +5249,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 164,
+   "execution_count": 165,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -5217,7 +5265,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 165,
+   "execution_count": 166,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -5264,7 +5312,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 166,
+   "execution_count": 167,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -5289,7 +5337,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 167,
+   "execution_count": 168,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -5299,10 +5347,10 @@
     {
      "data": {
       "text/plain": [
-       "140461335555696"
+       "140388598520624"
       ]
      },
-     "execution_count": 167,
+     "execution_count": 168,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -5313,7 +5361,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 168,
+   "execution_count": 169,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -5326,7 +5374,7 @@
        "bytes"
       ]
      },
-     "execution_count": 168,
+     "execution_count": 169,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -5348,7 +5396,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 169,
+   "execution_count": 170,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -5361,7 +5409,7 @@
        "b'\\xf0\\x9f\\x82\\xa7\\xf0\\x9f\\x82\\xb7\\xf0\\x9f\\x83\\x97\\xf0\\x9f\\x83\\x8e\\xf0\\x9f\\x83\\x9e'"
       ]
      },
-     "execution_count": 169,
+     "execution_count": 170,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -5383,7 +5431,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 170,
+   "execution_count": 171,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -5396,7 +5444,7 @@
        "20"
       ]
      },
-     "execution_count": 170,
+     "execution_count": 171,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -5418,7 +5466,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 171,
+   "execution_count": 172,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -5451,7 +5499,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 172,
+   "execution_count": 173,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -5464,7 +5512,7 @@
        "158"
       ]
      },
-     "execution_count": 172,
+     "execution_count": 173,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -5486,7 +5534,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 173,
+   "execution_count": 174,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -5499,7 +5547,7 @@
        "b'\\xf0\\x82\\xf0\\x82\\xf0\\x83\\xf0\\x83\\xf0\\x83'"
       ]
      },
-     "execution_count": 173,
+     "execution_count": 174,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -5534,7 +5582,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 174,
+   "execution_count": 175,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -5547,7 +5595,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 175,
+   "execution_count": 176,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -5560,7 +5608,7 @@
        "str"
       ]
      },
-     "execution_count": 175,
+     "execution_count": 176,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -5582,7 +5630,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 176,
+   "execution_count": 177,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -5595,7 +5643,7 @@
        "'🂧🂷���'"
       ]
      },
-     "execution_count": 176,
+     "execution_count": 177,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -5617,7 +5665,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 177,
+   "execution_count": 178,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -5630,7 +5678,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 178,
+   "execution_count": 179,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -5643,7 +5691,7 @@
        "b'Caf\\xc3\\xa9 Kastanient\\xc3\\xb6rtchen'"
       ]
      },
-     "execution_count": 178,
+     "execution_count": 179,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -5665,7 +5713,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 179,
+   "execution_count": 180,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -5678,7 +5726,7 @@
        "b'Caf\\xe9 Kastanient\\xf6rtchen'"
       ]
      },
-     "execution_count": 179,
+     "execution_count": 180,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -5700,7 +5748,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 180,
+   "execution_count": 181,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -5714,7 +5762,7 @@
      "traceback": [
       "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
       "\u001b[0;31mUnicodeDecodeError\u001b[0m                        Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-180-1630cf8a20cc>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mplace\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mencode\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"iso-8859-1\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdecode\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;32m<ipython-input-181-1630cf8a20cc>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mplace\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mencode\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"iso-8859-1\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdecode\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
       "\u001b[0;31mUnicodeDecodeError\u001b[0m: 'utf-8' codec can't decode byte 0xe9 in position 3: invalid continuation byte"
      ]
     }
@@ -5736,7 +5784,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 181,
+   "execution_count": 182,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -5750,7 +5798,7 @@
      "traceback": [
       "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
       "\u001b[0;31mUnicodeEncodeError\u001b[0m                        Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-181-8dd3b1bfcf5d>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0;34m\"Dobrý den, přátelé!\"\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mencode\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"iso-8859-1\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;32m<ipython-input-182-8dd3b1bfcf5d>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0;34m\"Dobrý den, přátelé!\"\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mencode\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"iso-8859-1\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
       "\u001b[0;31mUnicodeEncodeError\u001b[0m: 'latin-1' codec can't encode character '\\u0159' in position 12: ordinal not in range(256)"
      ]
     }
@@ -5783,7 +5831,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 182,
+   "execution_count": 183,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -5797,7 +5845,7 @@
      "traceback": [
       "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
       "\u001b[0;31mUnicodeDecodeError\u001b[0m                        Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-182-83a7d1245cc7>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;32mwith\u001b[0m \u001b[0mopen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"umlauts.txt\"\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mfile\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m     \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"\"\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mjoin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfile\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreadlines\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;32m<ipython-input-183-83a7d1245cc7>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;32mwith\u001b[0m \u001b[0mopen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"umlauts.txt\"\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mfile\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m     \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"\"\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mjoin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfile\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreadlines\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
       "\u001b[0;32m~/.pyenv/versions/anaconda3-2019.10/lib/python3.7/codecs.py\u001b[0m in \u001b[0;36mdecode\u001b[0;34m(self, input, final)\u001b[0m\n\u001b[1;32m    320\u001b[0m         \u001b[0;31m# decode input (taking the buffer into account)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    321\u001b[0m         \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbuffer\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0minput\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 322\u001b[0;31m         \u001b[0;34m(\u001b[0m\u001b[0mresult\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mconsumed\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_buffer_decode\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0merrors\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfinal\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    323\u001b[0m         \u001b[0;31m# keep undecoded input until the next call\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    324\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbuffer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mconsumed\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
       "\u001b[0;31mUnicodeDecodeError\u001b[0m: 'utf-8' codec can't decode byte 0xe4 in position 9: invalid continuation byte"
      ]
@@ -5810,7 +5858,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 183,
+   "execution_count": 184,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -5867,7 +5915,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 184,
+   "execution_count": 185,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -5881,7 +5929,31 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 185,
+   "execution_count": 186,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "\"Lorem Ipsum is simply dummy text of the printing and typesetting industry.\\nLorem Ipsum has been the industry's standard dummy text ever since the 1500s\\nwhen an unknown printer took a galley of type and scrambled it to make a type\\nspecimen book. It has survived not only five centuries but also the leap into\\nelectronic typesetting, remaining essentially unchanged. It was popularised in\\nthe 1960s with the release of Letraset sheets.\\n\""
+      ]
+     },
+     "execution_count": 186,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "content"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 187,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -5958,7 +6030,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 186,
+   "execution_count": 188,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -5980,16 +6052,15 @@
        "        "
       ],
       "text/plain": [
-       "<IPython.lib.display.YouTubeVideo at 0x7fbfb40ac810>"
+       "<IPython.lib.display.YouTubeVideo at 0x7faec4157310>"
       ]
      },
-     "execution_count": 186,
+     "execution_count": 188,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "from IPython.display import YouTubeVideo\n",
     "YouTubeVideo(\"MijmeoH9LT4\", width=\"60%\")"
    ]
   },
@@ -6006,7 +6077,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 187,
+   "execution_count": 189,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -6028,10 +6099,10 @@
        "        "
       ],
       "text/plain": [
-       "<IPython.lib.display.YouTubeVideo at 0x7fbfb4149f90>"
+       "<IPython.lib.display.YouTubeVideo at 0x7faec41d5790>"
       ]
      },
-     "execution_count": 187,
+     "execution_count": 189,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -6053,7 +6124,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 188,
+   "execution_count": 190,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -6075,10 +6146,10 @@
        "        "
       ],
       "text/plain": [
-       "<IPython.lib.display.YouTubeVideo at 0x7fbfb7a602d0>"
+       "<IPython.lib.display.YouTubeVideo at 0x7faec4167e10>"
       ]
      },
-     "execution_count": 188,
+     "execution_count": 190,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -6100,7 +6171,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 189,
+   "execution_count": 191,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -6122,10 +6193,10 @@
        "        "
       ],
       "text/plain": [
-       "<IPython.lib.display.YouTubeVideo at 0x7fbfb403b710>"
+       "<IPython.lib.display.YouTubeVideo at 0x7faec416bc50>"
       ]
      },
-     "execution_count": 189,
+     "execution_count": 191,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -6147,7 +6218,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 190,
+   "execution_count": 192,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -6169,10 +6240,10 @@
        "        "
       ],
       "text/plain": [
-       "<IPython.lib.display.YouTubeVideo at 0x7fbfb409be10>"
+       "<IPython.lib.display.YouTubeVideo at 0x7faec4157e50>"
       ]
      },
-     "execution_count": 190,
+     "execution_count": 192,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -6183,7 +6254,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 191,
+   "execution_count": 193,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -6205,10 +6276,10 @@
        "        "
       ],
       "text/plain": [
-       "<IPython.lib.display.YouTubeVideo at 0x7fbfb4049950>"
+       "<IPython.lib.display.YouTubeVideo at 0x7faec416f6d0>"
       ]
      },
-     "execution_count": 191,
+     "execution_count": 193,
      "metadata": {},
      "output_type": "execute_result"
     }
diff --git a/06_text_01_review.ipynb b/06_text_01_review.ipynb
index d92bd12..cbe5d80 100644
--- a/06_text_01_review.ipynb
+++ b/06_text_01_review.ipynb
@@ -5,7 +5,7 @@
    "metadata": {},
    "source": [
     "\n",
-    "# Chapter 6: Bytes & Text"
+    "# Chapter 6: Text & Bytes"
    ]
   },
   {
@@ -19,7 +19,9 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Read [Chapter 6](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/06_text_00_lecture.ipynb) of the book. Then, work through the questions below."
+    "The questions below assume that you have read [Chapter 6](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/06_text_00_lecture.ipynb) in the book.\n",
+    "\n",
+    "Be concise in your answers! Most questions can be answered in *one* sentence."
    ]
   },
   {
@@ -47,7 +49,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    " "
+    " < your answer >"
    ]
   },
   {
@@ -61,7 +63,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    " "
+    " < your answer >"
    ]
   },
   {
@@ -80,7 +82,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    " "
+    " < your answer >"
    ]
   },
   {
@@ -94,7 +96,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    " "
+    " < your answer >"
    ]
   },
   {
@@ -122,7 +124,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    " "
+    " < your answer >"
    ]
   },
   {
@@ -136,7 +138,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    " "
+    " < your answer >"
    ]
   },
   {
@@ -150,7 +152,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    " "
+    " < your answer >"
    ]
   },
   {
@@ -164,7 +166,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    " "
+    " < your answer >"
    ]
   }
  ],
diff --git a/06_text_02_exercises.ipynb b/06_text_02_exercises.ipynb
index d8fe96b..92df25a 100644
--- a/06_text_02_exercises.ipynb
+++ b/06_text_02_exercises.ipynb
@@ -19,7 +19,9 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Read [Chapter 6](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/06_text_00_lecture.ipynb) of the book. Then, work through the exercises below. The `...` indicate where you need to fill in your answers. You should not need to create any additional code cells."
+    "The exercises below assume that you have read [Chapter 6](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/06_text_00_lecture.ipynb) in the book.\n",
+    "\n",
+    "The `...`'s in the code cells indicate where you need to fill in code snippets. The number of `...`'s within a code cell give you a rough idea of how many lines of code are needed to solve the task. You should not need to create any additional code cells for your final solution. However, you may want to use temporary code cells to try out some ideas."
    ]
   },
   {
@@ -86,21 +88,25 @@
     "    Returns:\n",
     "        is_palindrome (bool)\n",
     "    \"\"\"\n",
-    "    is_palindrome = ...  # Q3\n",
+    "    # answer to Q3\n",
+    "    is_palindrome = ...\n",
     "    if ignore_case:\n",
-    "        ...  # Q3\n",
-    "    chars_to_check = # Q1\n",
+    "        ...\n",
+    "    # answer to Q1\n",
+    "    chars_to_check = ...\n",
     "\n",
     "    for forward_index in range(chars_to_check):\n",
-    "        backward_index = ... # Q2\n",
-    "        forward = ...  # Q2\n",
-    "        backward = ...  # Q2\n",
+    "        # answer to Q2\n",
+    "        backward_index = ...\n",
+    "        forward = ...\n",
+    "        backward = ...\n",
     "\n",
     "        print(forward, \"and\", backward)  # added for didactical purposes\n",
     "\n",
-    "        if ...:  # Q3\n",
-    "            is_palindrome = ...  # Q3\n",
-    "            ...  # Q3\n",
+    "        # answer to Q3\n",
+    "        if ...:\n",
+    "            is_palindrome = ...\n",
+    "            ...\n",
     "\n",
     "    return is_palindrome"
    ]
@@ -237,18 +243,21 @@
     "    Returns:\n",
     "        is_palindrome (bool)\n",
     "    \"\"\"\n",
-    "    is_palindrome = ...  # Q5\n",
+    "    # answers from above\n",
+    "    is_palindrome = ...\n",
     "    if ignore_case:\n",
-    "        ...  # Q5\n",
-    "    chars_to_check = # Q5\n",
+    "        ...\n",
+    "    chars_to_check = ...\n",
     "\n",
-    "    for ... in ...:  #Q6\n",
+    "    # answer to Q6\n",
+    "    for ... in ...:\n",
     "\n",
     "        print(forward, \"and\", backward)  # added for didactical purposes\n",
     "\n",
-    "        if ...:  # Q5\n",
-    "            is_palindrome = ...  # Q5\n",
-    "            ...  # Q5\n",
+    "        # answers from above\n",
+    "        if ...:\n",
+    "            is_palindrome = ...\n",
+    "            ...\n",
     "\n",
     "    return is_palindrome"
    ]
@@ -337,6 +346,7 @@
     "    Returns:\n",
     "        is_palindrome (bool)\n",
     "    \"\"\"\n",
+    "    # answers from above\n",
     "    if ignore_case:\n",
     "        ...\n",
     "    chars_to_check = ...\n",
@@ -346,9 +356,11 @@
     "        print(forward, \"and\", backward)  # added for didactical purposes\n",
     "\n",
     "        if ...:\n",
-    "            ...  # Q8\n",
+    "            # answer to Q8\n",
+    "            ...\n",
     "\n",
-    "    ...  # Q8"
+    "    # answer to Q8\n",
+    "    return ..."
    ]
   },
   {
@@ -462,7 +474,9 @@
     "    Returns:\n",
     "        is_palindrome (bool)\n",
     "    \"\"\"\n",
-    "    ...  # Q11\n",
+    "    # answer to Q11\n",
+    "    ...\n",
+    "    # answers from above\n",
     "    if ignore_case:\n",
     "        text = ...\n",
     "    chars_to_check = ...\n",
@@ -471,7 +485,7 @@
     "        if ...:\n",
     "            ...\n",
     "\n",
-    "    ..."
+    "    return ..."
    ]
   },
   {
@@ -584,19 +598,22 @@
     "    Returns:\n",
     "        is_palindrome (bool)\n",
     "    \"\"\"\n",
+    "    # answers from above\n",
     "    ...\n",
     "    if ignore_case:\n",
     "        ...\n",
+    "    # answer to Q13\n",
     "    if ignore_symbols:\n",
-    "        for ... in ...:  # Q13\n",
-    "            ...  # Q13\n",
+    "        for ... in ...:\n",
+    "            ...\n",
+    "    # answers from above\n",
     "    chars_to_check = ...\n",
     "\n",
     "    for ... in ...:\n",
     "        if ...:\n",
     "            ...\n",
     "\n",
-    "    ..."
+    "    return ..."
    ]
   },
   {
@@ -782,6 +799,7 @@
     "    Returns:\n",
     "        is_palindrome (bool)\n",
     "    \"\"\"\n",
+    "    # answers from above\n",
     "    ...\n",
     "    if ignore_case:\n",
     "        ...\n",
@@ -789,12 +807,14 @@
     "        for ... in ...:\n",
     "            ...\n",
     "\n",
-    "    if ...:  # Q16\n",
-    "        ...  # Q16\n",
-    "    elif ...:  # Q16\n",
-    "        ...  # Q16\n",
+    "    # answer to Q16\n",
+    "    if ...:\n",
+    "        ...\n",
+    "    elif ...:\n",
+    "        ...\n",
     "\n",
-    "    return ...  # Q17"
+    "    # answer to Q17\n",
+    "    return ..."
    ]
   },
   {
@@ -955,7 +975,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.6"
+   "version": "3.7.4"
   },
   "toc": {
    "base_numbering": 1,

From a9756ea6296200e0246f25ae3883323ca8331826 Mon Sep 17 00:00:00 2001
From: Alexander Hess <alexander@webartifex.biz>
Date: Mon, 30 Mar 2020 17:23:01 +0200
Subject: [PATCH 092/242] Add video and streamline content

---
 07_sequences_00_lecture.ipynb   | 7107 ++++---------------------------
 07_sequences_01_review.ipynb    |  173 +-
 07_sequences_02_exercises.ipynb | 1131 +----
 3 files changed, 1001 insertions(+), 7410 deletions(-)

diff --git a/07_sequences_00_lecture.ipynb b/07_sequences_00_lecture.ipynb
index e3887a4..fc97607 100644
--- a/07_sequences_00_lecture.ipynb
+++ b/07_sequences_00_lecture.ipynb
@@ -1,5 +1,53 @@
 {
  "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "A **video presentation** of the contents in this chapter is shown below. A playlist with *all* chapters as videos is linked [here](https://www.youtube.com/playlist?list=PL-2JV1G3J10lQ2xokyQowcRJI5jjNfW7f)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/jpeg": 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+       "            width=\"60%\"\n",
+       "            height=\"300\"\n",
+       "            src=\"https://www.youtube.com/embed/nx2sCDoeC3I\"\n",
+       "            frameborder=\"0\"\n",
+       "            allowfullscreen\n",
+       "        ></iframe>\n",
+       "        "
+      ],
+      "text/plain": [
+       "<IPython.lib.display.YouTubeVideo at 0x7f46a9329390>"
+      ]
+     },
+     "execution_count": 1,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from IPython.display import YouTubeVideo\n",
+    "YouTubeVideo(\"nx2sCDoeC3I\", width=\"60%\")"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {
@@ -23,7 +71,7 @@
     "\n",
     "The `str` type is a bit of a corner case in this regard. While one could argue that a longer `str` object, for example, `\"text\"`, is composed of individual characters, this is *not* the case in memory as the literal `\"text\"` only creates *one* object (i.e., one \"bag\" of $0$s and $1$s modeling all characters).\n",
     "\n",
-    "This chapter, [Chapter 8](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/08_mappings_00_lecture.ipynb), and [Chapter 9](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/09_arrays_00_lecture.ipynb) introduce various \"complex\" data types. While some are mutable and others are not, they all share that they are primarily used to \"manage,\" or structure, the memory in a program. Unsurprisingly, computer scientists refer to the ideas and theories behind these data types as **[data structures](https://en.wikipedia.org/wiki/Data_structure)**.\n",
+    "This chapter, [Chapter 8](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/08_mfr_00_lecture.ipynb), and [Chapter 9](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/09_mappings_00_lecture.ipynb) introduce various \"complex\" data types. While some are mutable and others are not, they all share that they are primarily used to \"manage,\" or structure, the memory in a program. Unsurprisingly, computer scientists refer to the ideas and theories behind these data types as **[data structures](https://en.wikipedia.org/wiki/Data_structure)**.\n",
     "\n",
     "In this chapter, we focus on data types that model all kinds of sequential data. Examples of such data are [spreadsheets](https://en.wikipedia.org/wiki/Spreadsheet) or [matrices](https://en.wikipedia.org/wiki/Matrix_%28mathematics%29)/[vectors](https://en.wikipedia.org/wiki/Vector_%28mathematics_and_physics%29). Such formats share the property that they are composed of smaller units that come in a sequence of, for example, rows/columns/cells or elements/entries."
    ]
@@ -47,16 +95,16 @@
     }
    },
    "source": [
-    "[Chapter 6](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/06_text_00_lecture.ipynb#A-\"String\"-of-Characters) already describes the *sequence* properties of `str` objects. Here, we take a step back and study these properties on their own before looking at bigger ideas.\n",
+    "[Chapter 6](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/06_text_00_lecture.ipynb#A-\"String\"-of-Characters) already describes the **sequence** properties of `str` objects. In this section, we take a step back and study these properties one by one.\n",
     "\n",
-    "The [collections.abc](https://docs.python.org/3/library/collections.abc.html) module in the [standard library](https://docs.python.org/3/library/index.html) defines a variety of **abstract base classes** (ABCs). We saw ABCs already in [Chapter 5](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/05_numbers_00_lecture.ipynb#The-Numerical-Tower), where we use the ones from the [numbers](https://docs.python.org/3/library/numbers.html) module in the [standard library](https://docs.python.org/3/library/index.html) to classify Python's numeric data types according to mathematical ideas. Now, we take the ABCs from the [collections.abc](https://docs.python.org/3/library/collections.abc.html) module to classify the data types in this chapter and [Chapter 8](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/08_mappings_00_lecture.ipynb) according to their behavior in various contexts.\n",
+    "The [collections.abc](https://docs.python.org/3/library/collections.abc.html) module in the [standard library](https://docs.python.org/3/library/index.html) defines a variety of **abstract base classes** (ABCs). We saw ABCs already in [Chapter 5](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/05_numbers_00_lecture.ipynb#The-Numerical-Tower), where we use the ones from the [numbers](https://docs.python.org/3/library/numbers.html) module in the [standard library](https://docs.python.org/3/library/index.html) to classify Python's numeric data types according to mathematical ideas. Now, we take the ABCs from the [collections.abc](https://docs.python.org/3/library/collections.abc.html) module to classify the data types in this chapter according to their behavior in various contexts.\n",
     "\n",
     "As an illustration, consider `numbers` and `text` below, two objects of *different* types."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 2,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -65,7 +113,7 @@
    "outputs": [],
    "source": [
     "numbers = [7, 11, 8, 5, 3, 12, 2, 6, 9, 10, 1, 4]\n",
-    "text = \"Lorem ipsum dolor sit amet, ...\""
+    "text = \"Lorem ipsum dolor sit amet.\""
    ]
   },
   {
@@ -81,7 +129,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 3,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -103,10 +151,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 4,
    "metadata": {
     "slideshow": {
-     "slide_type": "-"
+     "slide_type": "fragment"
     }
    },
    "outputs": [
@@ -114,7 +162,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "L o r e m   i p s u m   d o l o r   s i t   a m e t ,   . . . "
+      "L o r e m   i p s u m   d o l o r   s i t   a m e t . "
      ]
     }
    ],
@@ -138,7 +186,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 5,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -151,7 +199,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 6,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -164,7 +212,7 @@
        "collections.abc.Iterable"
       ]
      },
-     "execution_count": 5,
+     "execution_count": 6,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -181,14 +229,14 @@
     }
    },
    "source": [
-    "As in the context of *goose typing* in [Chapter 5](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/05_numbers_00_lecture.ipynb#Goose-Typing), we can use ABCs with the built-in [isinstance()](https://docs.python.org/3/library/functions.html#isinstance) function to check if an object supports a behavior.\n",
+    "As seen in [Chapter 5](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/05_numbers_00_lecture.ipynb#Goose-Typing), we can use ABCs with the built-in [isinstance()](https://docs.python.org/3/library/functions.html#isinstance) function to check if an object supports a behavior.\n",
     "\n",
     "So, let's \"ask\" Python if it can loop over `numbers` or `text`."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 7,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -201,7 +249,7 @@
        "True"
       ]
      },
-     "execution_count": 6,
+     "execution_count": 7,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -212,10 +260,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 8,
    "metadata": {
     "slideshow": {
-     "slide_type": "-"
+     "slide_type": "fragment"
     }
    },
    "outputs": [
@@ -225,7 +273,7 @@
        "True"
       ]
      },
-     "execution_count": 7,
+     "execution_count": 8,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -247,10 +295,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 9,
    "metadata": {
     "slideshow": {
-     "slide_type": "fragment"
+     "slide_type": "skip"
     }
    },
    "outputs": [
@@ -260,7 +308,7 @@
        "False"
       ]
      },
-     "execution_count": 8,
+     "execution_count": 9,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -282,7 +330,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 10,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -296,7 +344,7 @@
      "traceback": [
       "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
       "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-9-8249bf25b5b7>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0;32mfor\u001b[0m \u001b[0mdigit\u001b[0m \u001b[0;32min\u001b[0m \u001b[0;36m999\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m     \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdigit\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m<ipython-input-10-8249bf25b5b7>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0;32mfor\u001b[0m \u001b[0mdigit\u001b[0m \u001b[0;32min\u001b[0m \u001b[0;36m999\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m     \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdigit\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
       "\u001b[0;31mTypeError\u001b[0m: 'int' object is not iterable"
      ]
     }
@@ -324,7 +372,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 11,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -337,48 +385,13 @@
        "False"
       ]
      },
-     "execution_count": 10,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "0 in numbers"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "fragment"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "True"
-      ]
-     },
      "execution_count": 11,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "\"l\" in text"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "source": [
-    "Alternatively, we could also check if `numbers` and `text` are `Container` types with [isinstance()](https://docs.python.org/3/library/functions.html#isinstance)."
+    "0 in numbers"
    ]
   },
   {
@@ -402,7 +415,18 @@
     }
    ],
    "source": [
-    "isinstance(numbers, abc.Container)"
+    "\"l\" in text"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Alternatively, we could also check if `numbers` and `text` are `Container` types with [isinstance()](https://docs.python.org/3/library/functions.html#isinstance)."
    ]
   },
   {
@@ -410,7 +434,7 @@
    "execution_count": 13,
    "metadata": {
     "slideshow": {
-     "slide_type": "-"
+     "slide_type": "fragment"
     }
    },
    "outputs": [
@@ -425,6 +449,30 @@
      "output_type": "execute_result"
     }
    ],
+   "source": [
+    "isinstance(numbers, abc.Container)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "isinstance(text, abc.Container)"
    ]
@@ -442,10 +490,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 15,
    "metadata": {
     "slideshow": {
-     "slide_type": "fragment"
+     "slide_type": "skip"
     }
    },
    "outputs": [
@@ -455,7 +503,7 @@
        "False"
       ]
      },
-     "execution_count": 14,
+     "execution_count": 15,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -466,7 +514,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 16,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -480,7 +528,7 @@
      "traceback": [
       "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
       "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-15-b3e0b3c32aec>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0;36m9\u001b[0m \u001b[0;32min\u001b[0m \u001b[0;36m999\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;32m<ipython-input-16-b3e0b3c32aec>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0;36m9\u001b[0m \u001b[0;32min\u001b[0m \u001b[0;36m999\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
       "\u001b[0;31mTypeError\u001b[0m: argument of type 'int' is not iterable"
      ]
     }
@@ -502,7 +550,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 17,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -515,37 +563,13 @@
        "12"
       ]
      },
-     "execution_count": 16,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "len(numbers)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "-"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "31"
-      ]
-     },
      "execution_count": 17,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "len(text)"
+    "len(numbers)"
    ]
   },
   {
@@ -560,7 +584,7 @@
     {
      "data": {
       "text/plain": [
-       "True"
+       "27"
       ]
      },
      "execution_count": 18,
@@ -569,7 +593,7 @@
     }
    ],
    "source": [
-    "isinstance(numbers, abc.Sized)"
+    "len(text)"
    ]
   },
   {
@@ -577,7 +601,7 @@
    "execution_count": 19,
    "metadata": {
     "slideshow": {
-     "slide_type": "-"
+     "slide_type": "fragment"
     }
    },
    "outputs": [
@@ -592,6 +616,30 @@
      "output_type": "execute_result"
     }
    ],
+   "source": [
+    "isinstance(numbers, abc.Sized)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "isinstance(text, abc.Sized)"
    ]
@@ -609,10 +657,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 21,
    "metadata": {
     "slideshow": {
-     "slide_type": "fragment"
+     "slide_type": "skip"
     }
    },
    "outputs": [
@@ -622,7 +670,7 @@
        "False"
       ]
      },
-     "execution_count": 20,
+     "execution_count": 21,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -633,7 +681,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 22,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -647,7 +695,7 @@
      "traceback": [
       "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
       "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-21-ae09c1aa5305>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m999\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;32m<ipython-input-22-ae09c1aa5305>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m999\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
       "\u001b[0;31mTypeError\u001b[0m: object of type 'int' has no len()"
      ]
     }
@@ -673,7 +721,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 23,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -686,7 +734,7 @@
        "True"
       ]
      },
-     "execution_count": 22,
+     "execution_count": 23,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -697,10 +745,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 24,
    "metadata": {
     "slideshow": {
-     "slide_type": "-"
+     "slide_type": "fragment"
     }
    },
    "outputs": [
@@ -710,7 +758,7 @@
        "True"
       ]
      },
-     "execution_count": 23,
+     "execution_count": 24,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -734,7 +782,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 25,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -756,10 +804,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 26,
    "metadata": {
     "slideshow": {
-     "slide_type": "-"
+     "slide_type": "fragment"
     }
    },
    "outputs": [
@@ -767,7 +815,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      ". . .   , t e m a   t i s   r o l o d   m u s p i   m e r o L "
+      ". t e m a   t i s   r o l o d   m u s p i   m e r o L "
      ]
     }
    ],
@@ -778,7 +826,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": 27,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -791,7 +839,7 @@
        "True"
       ]
      },
-     "execution_count": 26,
+     "execution_count": 27,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -802,10 +850,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": 28,
    "metadata": {
     "slideshow": {
-     "slide_type": "-"
+     "slide_type": "fragment"
     }
    },
    "outputs": [
@@ -815,7 +863,7 @@
        "True"
       ]
      },
-     "execution_count": 27,
+     "execution_count": 28,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -837,7 +885,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": 29,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -850,7 +898,7 @@
        "True"
       ]
      },
-     "execution_count": 28,
+     "execution_count": 29,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -861,10 +909,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
+   "execution_count": 30,
    "metadata": {
     "slideshow": {
-     "slide_type": "-"
+     "slide_type": "fragment"
     }
    },
    "outputs": [
@@ -874,7 +922,7 @@
        "True"
       ]
      },
-     "execution_count": 29,
+     "execution_count": 30,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -895,7 +943,7 @@
     "\n",
     "In Python-related documentations, the terms collection and sequence are heavily used, and the data science practitioner should always think of them in terms of the three or four behaviors they exhibit.\n",
     "\n",
-    "Data types that are collections but not sequences are covered in Chapter 8."
+    "Data types that are collections but not sequences are covered in [Chapter 9](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/09_mappings_00_lecture.ipynb)."
    ]
   },
   {
@@ -922,7 +970,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 30,
+   "execution_count": 31,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -935,10 +983,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 31,
+   "execution_count": 32,
    "metadata": {
     "slideshow": {
-     "slide_type": "-"
+     "slide_type": "fragment"
     }
    },
    "outputs": [],
@@ -959,7 +1007,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 32,
+   "execution_count": 33,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -983,10 +1031,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 33,
+   "execution_count": 34,
    "metadata": {
     "slideshow": {
-     "slide_type": "-"
+     "slide_type": "fragment"
     }
    },
    "outputs": [
@@ -996,7 +1044,7 @@
        "[[], 10, 20.0, 'Thirty', [40, 50]]"
       ]
      },
-     "execution_count": 33,
+     "execution_count": 34,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1018,20 +1066,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 34,
+   "execution_count": 35,
    "metadata": {
     "slideshow": {
-     "slide_type": "slide"
+     "slide_type": "skip"
     }
    },
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "140417317434464"
+       "139941444092304"
       ]
      },
-     "execution_count": 34,
+     "execution_count": 35,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1042,10 +1090,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 35,
+   "execution_count": 36,
    "metadata": {
     "slideshow": {
-     "slide_type": "-"
+     "slide_type": "skip"
     }
    },
    "outputs": [
@@ -1055,7 +1103,7 @@
        "list"
       ]
      },
-     "execution_count": 35,
+     "execution_count": 36,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1072,14 +1120,14 @@
     }
    },
    "source": [
-    "Alternatively, we use the [list()](https://docs.python.org/3/library/functions.html#func-list) built-in to create a `list` object out of an iterable we pass to it as the argument.\n",
+    "Alternatively, we use the built-in [list()](https://docs.python.org/3/library/functions.html#func-list) constructor to create a `list` object out of any (finite) *iterable* we pass to it as the argument.\n",
     "\n",
     "For example, we can wrap the [range()](https://docs.python.org/3/library/functions.html#func-range) built-in with [list()](https://docs.python.org/3/library/functions.html#func-list): As described in [Chapter 4](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/04_iteration_00_lecture.ipynb#Containers-vs.-Iterables), `range` objects, like `range(1, 13)` below, are iterable and generate `int` objects \"on the fly\" (i.e., one by one). The [list()](https://docs.python.org/3/library/functions.html#func-list) around it acts like a `for`-loop and **materializes** twelve `int` objects in memory that then become the elements of the newly created `list` object. [PythonTutor](http://www.pythontutor.com/visualize.html#code=r%20%3D%20range%281,%2013%29%0Al%20%3D%20list%28range%281,%2013%29%29&cumulative=false&curInstr=0&heapPrimitives=nevernest&mode=display&origin=opt-frontend.js&py=3&rawInputLstJSON=%5B%5D&textReferences=false) shows this difference visually."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 36,
+   "execution_count": 37,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -1092,37 +1140,13 @@
        "[1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12]"
       ]
      },
-     "execution_count": 36,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "list(range(1, 13))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 37,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "True"
-      ]
-     },
      "execution_count": 37,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "isinstance(range(1, 13), abc.Iterable)"
+    "list(range(1, 13))"
    ]
   },
   {
@@ -1233,7 +1257,7 @@
     " - supports being looped over\n",
     " - works with the `for` or `while` statements\n",
     "- `Reversible`:\n",
-    " - the elements come in a *predictable* order that we may traverse in a forward or backward fashion\n",
+    " - the elements come in a *predictable* order that we may loop over in a forward or backward fashion\n",
     " - works with the [reversed()](https://docs.python.org/3/library/functions.html#reversed) built-in\n",
     "- `Sized`:\n",
     " - the number of elements is finite *and* known in advance\n",
@@ -1291,7 +1315,7 @@
    "execution_count": 41,
    "metadata": {
     "slideshow": {
-     "slide_type": "fragment"
+     "slide_type": "slide"
     }
    },
    "outputs": [
@@ -1299,17 +1323,17 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[]      \t140417315676720      \t<class 'list'>\n",
-      "10      \t94860849595456      \t<class 'int'>\n",
-      "20.0      \t140417316476784      \t<class 'float'>\n",
-      "Thirty      \t140417315739568      \t<class 'str'>\n",
-      "[40, 50]      \t140417315720800      \t<class 'list'>\n"
+      "[]         139941463482272    <class 'list'>\n",
+      "10         94715524076608     <class 'int'>\n",
+      "20.0       139941444613712    <class 'float'>\n",
+      "Thirty     139941444470960    <class 'str'>\n",
+      "[40, 50]   139941444068128    <class 'list'>\n"
      ]
     }
    ],
    "source": [
     "for element in nested:\n",
-    "    print(element, id(element), type(element), sep=\"      \\t\")"
+    "    print(str(element).ljust(10), str(id(element)).ljust(18), type(element))"
    ]
   },
   {
@@ -1317,7 +1341,7 @@
    "execution_count": 42,
    "metadata": {
     "slideshow": {
-     "slide_type": "fragment"
+     "slide_type": "slide"
     }
    },
    "outputs": [
@@ -1409,7 +1433,7 @@
    "execution_count": 45,
    "metadata": {
     "slideshow": {
-     "slide_type": "-"
+     "slide_type": "fragment"
     }
    },
    "outputs": [
@@ -1464,7 +1488,7 @@
     {
      "data": {
       "text/plain": [
-       "10"
+       "[]"
       ]
      },
      "execution_count": 46,
@@ -1473,7 +1497,7 @@
     }
    ],
    "source": [
-    "nested[1]"
+    "nested[0]"
    ]
   },
   {
@@ -1492,7 +1516,7 @@
    "execution_count": 47,
    "metadata": {
     "slideshow": {
-     "slide_type": "-"
+     "slide_type": "skip"
     }
    },
    "outputs": [
@@ -1552,7 +1576,7 @@
    "execution_count": 49,
    "metadata": {
     "slideshow": {
-     "slide_type": "-"
+     "slide_type": "fragment"
     }
    },
    "outputs": [
@@ -1635,7 +1659,7 @@
    "execution_count": 51,
    "metadata": {
     "slideshow": {
-     "slide_type": "-"
+     "slide_type": "fragment"
     }
    },
    "outputs": [
@@ -1888,7 +1912,7 @@
    "execution_count": 61,
    "metadata": {
     "slideshow": {
-     "slide_type": "-"
+     "slide_type": "fragment"
     }
    },
    "outputs": [
@@ -1949,7 +1973,7 @@
    "execution_count": 63,
    "metadata": {
     "slideshow": {
-     "slide_type": "-"
+     "slide_type": "fragment"
     }
    },
    "outputs": [
@@ -1986,7 +2010,7 @@
    "execution_count": 64,
    "metadata": {
     "slideshow": {
-     "slide_type": "fragment"
+     "slide_type": "skip"
     }
    },
    "outputs": [
@@ -2017,7 +2041,7 @@
     {
      "data": {
       "text/plain": [
-       "140417315676720"
+       "139941463482272"
       ]
      },
      "execution_count": 65,
@@ -2041,7 +2065,7 @@
     {
      "data": {
       "text/plain": [
-       "140417315676720"
+       "139941463482272"
       ]
      },
      "execution_count": 66,
@@ -2053,30 +2077,6 @@
     "id(nested_copy[0])"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": 67,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "140417315676720"
-      ]
-     },
-     "execution_count": 67,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "id(empty)"
-   ]
-  },
   {
    "cell_type": "markdown",
    "metadata": {
@@ -2096,7 +2096,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 68,
+   "execution_count": 67,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -2109,7 +2109,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 69,
+   "execution_count": 68,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -2122,7 +2122,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 70,
+   "execution_count": 69,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -2135,7 +2135,7 @@
        "True"
       ]
      },
-     "execution_count": 70,
+     "execution_count": 69,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2157,7 +2157,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 71,
+   "execution_count": 70,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -2170,7 +2170,7 @@
        "False"
       ]
      },
-     "execution_count": 71,
+     "execution_count": 70,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2179,6 +2179,30 @@
     "nested[0] is nested_deep_copy[0]"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 71,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "139941463482272"
+      ]
+     },
+     "execution_count": 71,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "id(nested[0])"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 72,
@@ -2191,7 +2215,7 @@
     {
      "data": {
       "text/plain": [
-       "140417315676720"
+       "139941444435792"
       ]
      },
      "execution_count": 72,
@@ -2199,30 +2223,6 @@
      "output_type": "execute_result"
     }
    ],
-   "source": [
-    "id(nested[0])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 73,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "140417376809600"
-      ]
-     },
-     "execution_count": 73,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
    "source": [
     "id(nested_deep_copy[0])"
    ]
@@ -2262,7 +2262,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 74,
+   "execution_count": 73,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -2275,10 +2275,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 75,
+   "execution_count": 74,
    "metadata": {
     "slideshow": {
-     "slide_type": "-"
+     "slide_type": "fragment"
     }
    },
    "outputs": [
@@ -2288,7 +2288,7 @@
        "[0, 10, 20.0, 'Thirty', [40, 50]]"
       ]
      },
-     "execution_count": 75,
+     "execution_count": 74,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2310,7 +2310,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 76,
+   "execution_count": 75,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -2318,15 +2318,15 @@
    },
    "outputs": [],
    "source": [
-    "nested[:4] = [100, 100, 100]  # assign three elements where there were four before"
+    "nested[:4] = [100, 100, 100]"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 77,
+   "execution_count": 76,
    "metadata": {
     "slideshow": {
-     "slide_type": "-"
+     "slide_type": "fragment"
     }
    },
    "outputs": [
@@ -2336,7 +2336,7 @@
        "[100, 100, 100, [40, 50]]"
       ]
      },
-     "execution_count": 77,
+     "execution_count": 76,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2347,10 +2347,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 78,
+   "execution_count": 77,
    "metadata": {
     "slideshow": {
-     "slide_type": "-"
+     "slide_type": "fragment"
     }
    },
    "outputs": [
@@ -2360,7 +2360,7 @@
        "4"
       ]
      },
-     "execution_count": 78,
+     "execution_count": 77,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2382,7 +2382,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 79,
+   "execution_count": 78,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -2392,10 +2392,10 @@
     {
      "data": {
       "text/plain": [
-       "140417317434464"
+       "139941444092304"
       ]
      },
-     "execution_count": 79,
+     "execution_count": 78,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2417,7 +2417,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 80,
+   "execution_count": 79,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -2430,7 +2430,7 @@
        "[[], 10, 20.0, 'Thirty', [40, 50]]"
       ]
      },
-     "execution_count": 80,
+     "execution_count": 79,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2452,10 +2452,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 81,
+   "execution_count": 80,
    "metadata": {
     "slideshow": {
-     "slide_type": "-"
+     "slide_type": "fragment"
     }
    },
    "outputs": [],
@@ -2465,10 +2465,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 82,
+   "execution_count": 81,
    "metadata": {
     "slideshow": {
-     "slide_type": "-"
+     "slide_type": "fragment"
     }
    },
    "outputs": [
@@ -2478,7 +2478,7 @@
        "[[], 10, 20.0, 'Thirty', [1, 2, 3]]"
       ]
      },
-     "execution_count": 82,
+     "execution_count": 81,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2500,7 +2500,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 83,
+   "execution_count": 82,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -2513,7 +2513,7 @@
        "[100, 100, 100, [1, 2, 3]]"
       ]
      },
-     "execution_count": 83,
+     "execution_count": 82,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2537,7 +2537,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 84,
+   "execution_count": 83,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -2550,10 +2550,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 85,
+   "execution_count": 84,
    "metadata": {
     "slideshow": {
-     "slide_type": "-"
+     "slide_type": "fragment"
     }
    },
    "outputs": [
@@ -2563,7 +2563,7 @@
        "[100, 100, 100]"
       ]
      },
-     "execution_count": 85,
+     "execution_count": 84,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2585,7 +2585,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 86,
+   "execution_count": 85,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -2598,10 +2598,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 87,
+   "execution_count": 86,
    "metadata": {
     "slideshow": {
-     "slide_type": "-"
+     "slide_type": "fragment"
     }
    },
    "outputs": [
@@ -2611,7 +2611,7 @@
        "[]"
       ]
      },
-     "execution_count": 87,
+     "execution_count": 86,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2628,33 +2628,7 @@
     }
    },
    "source": [
-    "Mutability for sequences is formalized by the `MutableSequence` ABC in the [collections.abc](https://docs.python.org/3/library/collections.abc.html) module.\n",
-    "\n",
-    "So, we can als \"ask\" Python if `nested` is mutable."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 88,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "True"
-      ]
-     },
-     "execution_count": 88,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "isinstance(nested, abc.MutableSequence)"
+    "Mutability for sequences is formalized by the `MutableSequence` ABC in the [collections.abc](https://docs.python.org/3/library/collections.abc.html) module."
    ]
   },
   {
@@ -2685,7 +2659,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 89,
+   "execution_count": 87,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -2709,7 +2683,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 90,
+   "execution_count": 88,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -2722,10 +2696,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 91,
+   "execution_count": 89,
    "metadata": {
     "slideshow": {
-     "slide_type": "-"
+     "slide_type": "fragment"
     }
    },
    "outputs": [
@@ -2735,7 +2709,7 @@
        "['Carl', 'Peter', 'Eckardt']"
       ]
      },
-     "execution_count": 91,
+     "execution_count": 89,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2757,10 +2731,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 92,
+   "execution_count": 90,
    "metadata": {
     "slideshow": {
-     "slide_type": "fragment"
+     "slide_type": "slide"
     }
    },
    "outputs": [],
@@ -2770,10 +2744,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 93,
+   "execution_count": 91,
    "metadata": {
     "slideshow": {
-     "slide_type": "-"
+     "slide_type": "fragment"
     }
    },
    "outputs": [
@@ -2783,7 +2757,7 @@
        "['Carl', 'Peter', 'Eckardt', 'Karl', 'Oliver']"
       ]
      },
-     "execution_count": 93,
+     "execution_count": 91,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2805,10 +2779,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 94,
+   "execution_count": 92,
    "metadata": {
     "slideshow": {
-     "slide_type": "fragment"
+     "slide_type": "slide"
     }
    },
    "outputs": [],
@@ -2816,12 +2790,82 @@
     "names.insert(1, \"Berthold\")"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 93,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['Carl', 'Berthold', 'Peter', 'Eckardt', 'Karl', 'Oliver']"
+      ]
+     },
+     "execution_count": 93,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "names"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "`list` objects may be sorted *in place* with the [sort()](https://docs.python.org/3/library/stdtypes.html#list.sort) method. That is different from the built-in [sorted()](https://docs.python.org/3/library/functions.html#sorted) function that takes any *finite* and *iterable* object and returns a *new* `list` object with the iterable's elements sorted!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 94,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['Berthold', 'Carl', 'Eckardt', 'Karl', 'Oliver', 'Peter']"
+      ]
+     },
+     "execution_count": 94,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "sorted(names)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "As the previous code cell created a *new* `list` object, `names` is still unsorted."
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 95,
    "metadata": {
     "slideshow": {
-     "slide_type": "-"
+     "slide_type": "fragment"
     }
    },
    "outputs": [
@@ -2840,76 +2884,6 @@
     "names"
    ]
   },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "source": [
-    "`list` objects may be sorted *in place* with the [sort()](https://docs.python.org/3/library/stdtypes.html#list.sort) method. That is different from the built-in [sorted()](https://docs.python.org/3/library/functions.html#sorted) function that takes any *finite* and *iterable* object and returns a *new* `list` object with the iterable's elements sorted!"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 96,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "slide"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "['Berthold', 'Carl', 'Eckardt', 'Karl', 'Oliver', 'Peter']"
-      ]
-     },
-     "execution_count": 96,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "sorted(names)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "source": [
-    "As the previous code cell created a *new* `list` object, `names` is still unsorted."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 97,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "-"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "['Carl', 'Berthold', 'Peter', 'Eckardt', 'Karl', 'Oliver']"
-      ]
-     },
-     "execution_count": 97,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "names"
-   ]
-  },
   {
    "cell_type": "markdown",
    "metadata": {
@@ -2923,7 +2897,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 98,
+   "execution_count": 96,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -2936,10 +2910,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 99,
+   "execution_count": 97,
    "metadata": {
     "slideshow": {
-     "slide_type": "-"
+     "slide_type": "fragment"
     }
    },
    "outputs": [
@@ -2949,7 +2923,7 @@
        "['Berthold', 'Carl', 'Eckardt', 'Karl', 'Oliver', 'Peter']"
       ]
      },
-     "execution_count": 99,
+     "execution_count": 97,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2971,7 +2945,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 100,
+   "execution_count": 98,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -2984,10 +2958,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 101,
+   "execution_count": 99,
    "metadata": {
     "slideshow": {
-     "slide_type": "-"
+     "slide_type": "fragment"
     }
    },
    "outputs": [
@@ -2997,7 +2971,7 @@
        "['Peter', 'Oliver', 'Karl', 'Eckardt', 'Carl', 'Berthold']"
       ]
      },
-     "execution_count": 101,
+     "execution_count": 99,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3025,7 +2999,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 102,
+   "execution_count": 100,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -3051,10 +3025,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 103,
+   "execution_count": 101,
    "metadata": {
     "slideshow": {
-     "slide_type": "-"
+     "slide_type": "fragment"
     }
    },
    "outputs": [
@@ -3064,7 +3038,7 @@
        "['Karl', 'Carl', 'Peter', 'Oliver', 'Eckardt', 'Berthold']"
       ]
      },
-     "execution_count": 103,
+     "execution_count": 101,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3086,10 +3060,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 104,
+   "execution_count": 102,
    "metadata": {
     "slideshow": {
-     "slide_type": "fragment"
+     "slide_type": "slide"
     }
    },
    "outputs": [],
@@ -3099,10 +3073,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 105,
+   "execution_count": 103,
    "metadata": {
     "slideshow": {
-     "slide_type": "-"
+     "slide_type": "fragment"
     }
    },
    "outputs": [
@@ -3112,7 +3086,7 @@
        "['Berthold', 'Eckardt', 'Oliver', 'Peter', 'Carl', 'Karl']"
       ]
      },
-     "execution_count": 105,
+     "execution_count": 103,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3134,7 +3108,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 106,
+   "execution_count": 104,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -3147,10 +3121,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 107,
+   "execution_count": 105,
    "metadata": {
     "slideshow": {
-     "slide_type": "-"
+     "slide_type": "fragment"
     }
    },
    "outputs": [
@@ -3160,7 +3134,7 @@
        "'Karl'"
       ]
      },
-     "execution_count": 107,
+     "execution_count": 105,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3171,10 +3145,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 108,
+   "execution_count": 106,
    "metadata": {
     "slideshow": {
-     "slide_type": "-"
+     "slide_type": "fragment"
     }
    },
    "outputs": [
@@ -3184,7 +3158,7 @@
        "['Berthold', 'Eckardt', 'Oliver', 'Peter', 'Carl']"
       ]
      },
-     "execution_count": 108,
+     "execution_count": 106,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3208,7 +3182,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 109,
+   "execution_count": 107,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -3221,10 +3195,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 110,
+   "execution_count": 108,
    "metadata": {
     "slideshow": {
-     "slide_type": "-"
+     "slide_type": "fragment"
     }
    },
    "outputs": [
@@ -3234,7 +3208,7 @@
        "'Eckardt'"
       ]
      },
-     "execution_count": 110,
+     "execution_count": 108,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3245,10 +3219,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 111,
+   "execution_count": 109,
    "metadata": {
     "slideshow": {
-     "slide_type": "-"
+     "slide_type": "fragment"
     }
    },
    "outputs": [
@@ -3258,7 +3232,7 @@
        "['Berthold', 'Oliver', 'Peter', 'Carl']"
       ]
      },
-     "execution_count": 111,
+     "execution_count": 109,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3280,7 +3254,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 112,
+   "execution_count": 110,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -3293,10 +3267,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 113,
+   "execution_count": 111,
    "metadata": {
     "slideshow": {
-     "slide_type": "-"
+     "slide_type": "fragment"
     }
    },
    "outputs": [
@@ -3306,7 +3280,7 @@
        "['Berthold', 'Oliver', 'Carl']"
       ]
      },
-     "execution_count": 113,
+     "execution_count": 111,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3328,7 +3302,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 114,
+   "execution_count": 112,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -3342,7 +3316,7 @@
      "traceback": [
       "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
       "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-114-6bb75d50cf06>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mnames\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mremove\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Peter\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;32m<ipython-input-112-6bb75d50cf06>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mnames\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mremove\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Peter\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
       "\u001b[0;31mValueError\u001b[0m: list.remove(x): x not in list"
      ]
     }
@@ -3364,7 +3338,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 115,
+   "execution_count": 113,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -3377,7 +3351,7 @@
        "['Berthold', 'Oliver', 'Carl']"
       ]
      },
-     "execution_count": 115,
+     "execution_count": 113,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3388,10 +3362,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 116,
+   "execution_count": 114,
    "metadata": {
     "slideshow": {
-     "slide_type": "-"
+     "slide_type": "fragment"
     }
    },
    "outputs": [
@@ -3401,7 +3375,7 @@
        "1"
       ]
      },
-     "execution_count": 116,
+     "execution_count": 114,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3412,10 +3386,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 117,
+   "execution_count": 115,
    "metadata": {
     "slideshow": {
-     "slide_type": "-"
+     "slide_type": "fragment"
     }
    },
    "outputs": [
@@ -3426,7 +3400,7 @@
      "traceback": [
       "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
       "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-117-8c741a6680be>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mnames\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Karl\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;32m<ipython-input-115-8c741a6680be>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mnames\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Karl\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
       "\u001b[0;31mValueError\u001b[0m: 'Karl' is not in list"
      ]
     }
@@ -3448,7 +3422,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 118,
+   "execution_count": 116,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -3461,7 +3435,7 @@
        "1"
       ]
      },
-     "execution_count": 118,
+     "execution_count": 116,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3472,7 +3446,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 119,
+   "execution_count": 117,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -3485,7 +3459,7 @@
        "0"
       ]
      },
-     "execution_count": 119,
+     "execution_count": 117,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3509,10 +3483,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 120,
+   "execution_count": 118,
    "metadata": {
     "slideshow": {
-     "slide_type": "slide"
+     "slide_type": "skip"
     }
    },
    "outputs": [],
@@ -3522,10 +3496,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 121,
+   "execution_count": 119,
    "metadata": {
     "slideshow": {
-     "slide_type": "-"
+     "slide_type": "skip"
     }
    },
    "outputs": [
@@ -3535,7 +3509,7 @@
        "['Berthold', 'Oliver', 'Carl']"
       ]
      },
-     "execution_count": 121,
+     "execution_count": 119,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3557,10 +3531,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 122,
+   "execution_count": 120,
    "metadata": {
     "slideshow": {
-     "slide_type": "fragment"
+     "slide_type": "skip"
     }
    },
    "outputs": [],
@@ -3570,10 +3544,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 123,
+   "execution_count": 121,
    "metadata": {
     "slideshow": {
-     "slide_type": "-"
+     "slide_type": "skip"
     }
    },
    "outputs": [
@@ -3583,7 +3557,7 @@
        "[]"
       ]
      },
-     "execution_count": 123,
+     "execution_count": 121,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3629,7 +3603,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 124,
+   "execution_count": 122,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -3642,7 +3616,7 @@
        "['Berthold', 'Oliver', 'Carl']"
       ]
      },
-     "execution_count": 124,
+     "execution_count": 122,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3653,23 +3627,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 125,
+   "execution_count": 123,
    "metadata": {
     "slideshow": {
-     "slide_type": "-"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "more_names = [\"Diedrich\", \"Yves\"]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 126,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "-"
+     "slide_type": "fragment"
     }
    },
    "outputs": [
@@ -3679,21 +3640,21 @@
        "['Berthold', 'Oliver', 'Carl', 'Diedrich', 'Yves']"
       ]
      },
-     "execution_count": 126,
+     "execution_count": 123,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "names + more_names"
+    "names + [\"Diedrich\", \"Yves\"]"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 127,
+   "execution_count": 124,
    "metadata": {
     "slideshow": {
-     "slide_type": "-"
+     "slide_type": "fragment"
     }
    },
    "outputs": [
@@ -3703,7 +3664,7 @@
        "['Berthold', 'Oliver', 'Carl', 'Berthold', 'Oliver', 'Carl']"
       ]
      },
-     "execution_count": 127,
+     "execution_count": 124,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3712,30 +3673,6 @@
     "2 * names"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": 128,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "['Diedrich', 'Yves', 'Diedrich', 'Yves', 'Diedrich', 'Yves']"
-      ]
-     },
-     "execution_count": 128,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "more_names * 3"
-   ]
-  },
   {
    "cell_type": "markdown",
    "metadata": {
@@ -3751,7 +3688,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 129,
+   "execution_count": 125,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -3764,7 +3701,7 @@
        "['Achim', 'Berthold', 'Oliver', 'Carl', 'Xavier']"
       ]
      },
-     "execution_count": 129,
+     "execution_count": 125,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3786,7 +3723,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 130,
+   "execution_count": 126,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -3799,7 +3736,7 @@
        "['Achim', 'Berthold', 'Oliver', 'Carl', 'Xavier']"
       ]
      },
-     "execution_count": 130,
+     "execution_count": 126,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3834,7 +3771,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 131,
+   "execution_count": 127,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -3847,7 +3784,7 @@
        "['Berthold', 'Oliver', 'Carl']"
       ]
      },
-     "execution_count": 131,
+     "execution_count": 127,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3858,55 +3795,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 132,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "-"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "True"
-      ]
-     },
-     "execution_count": 132,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "names == [\"Berthold\", \"Oliver\", \"Carl\"]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 133,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "-"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "True"
-      ]
-     },
-     "execution_count": 133,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "names != [\"Berthold\", \"Oliver\", \"Karl\"]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 134,
+   "execution_count": 128,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -3919,21 +3808,21 @@
        "True"
       ]
      },
-     "execution_count": 134,
+     "execution_count": 128,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "names < [\"Berthold\", \"Oliver\", \"Karl\"]"
+    "names == [\"Berthold\", \"Oliver\", \"Carl\"]"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 135,
+   "execution_count": 129,
    "metadata": {
     "slideshow": {
-     "slide_type": "-"
+     "slide_type": "fragment"
     }
    },
    "outputs": [
@@ -3943,13 +3832,37 @@
        "True"
       ]
      },
-     "execution_count": 135,
+     "execution_count": 129,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "[\"Achim\", \"Oliver\", \"Carl\"] < names"
+    "names != [\"Berthold\", \"Oliver\", \"Karl\"]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 130,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 130,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "names < [\"Berthold\", \"Oliver\", \"Karl\"]"
    ]
   },
   {
@@ -3960,15 +3873,15 @@
     }
    },
    "source": [
-    "If two `list` objects have a different number of elements and all overlapping elements compare equal, the shorter `list` object is considered \"smaller.\" That rule is a common cause for *semantic* errors in a program."
+    "If two `list` objects have a different number of elements and all overlapping elements compare equal, the shorter `list` object is considered \"smaller.\""
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 136,
+   "execution_count": 131,
    "metadata": {
     "slideshow": {
-     "slide_type": "skip"
+     "slide_type": "fragment"
     }
    },
    "outputs": [
@@ -3978,37 +3891,13 @@
        "True"
       ]
      },
-     "execution_count": 136,
+     "execution_count": 131,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "[\"Berthold\", \"Oliver\"] < names"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 137,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "True"
-      ]
-     },
-     "execution_count": 137,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "names < [\"Berthold\", \"Oliver\", \"Carl\", \"Xavier\"]"
+    "[\"Berthold\", \"Oliver\"] < names < [\"Berthold\", \"Oliver\", \"Carl\", \"Xavier\"]"
    ]
   },
   {
@@ -4037,7 +3926,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 138,
+   "execution_count": 132,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -4050,10 +3939,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 139,
+   "execution_count": 133,
    "metadata": {
     "slideshow": {
-     "slide_type": "-"
+     "slide_type": "fragment"
     }
    },
    "outputs": [],
@@ -4074,12 +3963,12 @@
    "source": [
     "While this function is being executed, two variables, namely `letters` in the global scope and `arg` inside the function's local scope, reference the *same* `list` object in memory. Furthermore, the passed in `arg` is also the return value.\n",
     "\n",
-    "So, after the function call, `letters_with_xyz` and `letters` are **aliases** as well, referencing the *same* object."
+    "So, after the function call, `letters_with_xyz` and `letters` are **aliases** as well, referencing the *same* object. We can also visualize that with [PythonTutor](http://www.pythontutor.com/visualize.html#code=letters%20%3D%20%5B%22a%22,%20%22b%22,%20%22c%22%5D%0A%0Adef%20add_xyz%28arg%29%3A%0A%20%20%20%20arg.extend%28%5B%22x%22,%20%22y%22,%20%22z%22%5D%29%0A%20%20%20%20return%20arg%0A%0Aletters_with_xyz%20%3D%20add_xyz%28letters%29&cumulative=false&curInstr=0&heapPrimitives=nevernest&mode=display&origin=opt-frontend.js&py=3&rawInputLstJSON=%5B%5D&textReferences=false)."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 140,
+   "execution_count": 134,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -4092,10 +3981,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 141,
+   "execution_count": 135,
    "metadata": {
     "slideshow": {
-     "slide_type": "-"
+     "slide_type": "fragment"
     }
    },
    "outputs": [
@@ -4105,7 +3994,7 @@
        "['a', 'b', 'c', 'x', 'y', 'z']"
       ]
      },
-     "execution_count": 141,
+     "execution_count": 135,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -4116,10 +4005,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 142,
+   "execution_count": 136,
    "metadata": {
     "slideshow": {
-     "slide_type": "-"
+     "slide_type": "fragment"
     }
    },
    "outputs": [
@@ -4129,7 +4018,7 @@
        "['a', 'b', 'c', 'x', 'y', 'z']"
       ]
      },
-     "execution_count": 142,
+     "execution_count": 136,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -4148,14 +4037,14 @@
    "source": [
     "A better practice is to first create a copy of `arg` within the function that is then modified and returned. If we are sure that `arg` contains immutable elements only, we get away with a shallow copy. The downside of this approach is the higher amount of memory necessary.\n",
     "\n",
-    "The revised `add_xyz()` function below is more natural to reason about as it does *not* modify the passed in `arg` internally. This approach is following the **[functional programming](https://en.wikipedia.org/wiki/Functional_programming)** paradigm that is going through a \"renaissance\" currently. Two essential characteristics of functional programming are that a function *never* changes its inputs and *always* returns the same output given the same inputs.\n",
+    "The revised `add_xyz()` function below is more natural to reason about as it does *not* modify the passed in `arg` internally. [PythonTutor](http://www.pythontutor.com/visualize.html#code=letters%20%3D%20%5B%22a%22,%20%22b%22,%20%22c%22%5D%0A%0Adef%20add_xyz%28arg%29%3A%0A%20%20%20%20new_arg%20%3D%20arg%5B%3A%5D%0A%20%20%20%20new_arg.extend%28%5B%22x%22,%20%22y%22,%20%22z%22%5D%29%0A%20%20%20%20return%20new_arg%0A%0Aletters_with_xyz%20%3D%20add_xyz%28letters%29&cumulative=false&curInstr=0&heapPrimitives=nevernest&mode=display&origin=opt-frontend.js&py=3&rawInputLstJSON=%5B%5D&textReferences=false) shows that as well. This approach is following the **[functional programming](https://en.wikipedia.org/wiki/Functional_programming)** paradigm that is going through a \"renaissance\" currently. Two essential characteristics of functional programming are that a function *never* changes its inputs and *always* returns the same output given the same inputs.\n",
     "\n",
     "For a beginner, it is probably better to stick to this idea and not change any arguments as the original `add_xyz()` above. However, functions that modify and return the argument passed in are an important aspect of object-oriented programming, as explained in Chapter 10."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 143,
+   "execution_count": 137,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -4168,24 +4057,24 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 144,
+   "execution_count": 138,
    "metadata": {
     "slideshow": {
-     "slide_type": "-"
+     "slide_type": "fragment"
     }
    },
    "outputs": [],
    "source": [
     "def add_xyz(arg):\n",
     "    \"\"\"Create a new list from an existing one.\"\"\"\n",
-    "    new_arg = arg[:]  # a shallow copy is good enough here\n",
+    "    new_arg = arg[:]\n",
     "    new_arg.extend([\"x\", \"y\", \"z\"])\n",
     "    return new_arg"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 145,
+   "execution_count": 139,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -4198,11 +4087,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 146,
+   "execution_count": 140,
    "metadata": {
     "scrolled": true,
     "slideshow": {
-     "slide_type": "-"
+     "slide_type": "fragment"
     }
    },
    "outputs": [
@@ -4212,7 +4101,7 @@
        "['a', 'b', 'c', 'x', 'y', 'z']"
       ]
      },
-     "execution_count": 146,
+     "execution_count": 140,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -4223,10 +4112,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 147,
+   "execution_count": 141,
    "metadata": {
     "slideshow": {
-     "slide_type": "-"
+     "slide_type": "fragment"
     }
    },
    "outputs": [
@@ -4236,7 +4125,7 @@
        "['a', 'b', 'c']"
       ]
      },
-     "execution_count": 147,
+     "execution_count": 141,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -4253,12 +4142,12 @@
     }
    },
    "source": [
-    "If we want to modify the argument passed in, it is best to return `None` and not `arg`, as does the final version of `add_xyz()` below. Then, the user of our function cannot accidentally create two aliases to the same object. That is also why the list methods above all return `None`."
+    "If we want to modify the argument passed in, it is best to return `None` and not `arg`, as does the final version of `add_xyz()` below. Then, the user of our function cannot accidentally create two aliases to the same object. That is also why the list methods above all return `None`. [PythonTutor](http://www.pythontutor.com/visualize.html#code=letters%20%3D%20%5B%22a%22,%20%22b%22,%20%22c%22%5D%0A%0Adef%20add_xyz%28arg%29%3A%0A%20%20%20%20arg.extend%28%5B%22x%22,%20%22y%22,%20%22z%22%5D%29%0A%20%20%20%20return%0A%0Aadd_xyz%28letters%29&cumulative=false&curInstr=0&heapPrimitives=nevernest&mode=display&origin=opt-frontend.js&py=3&rawInputLstJSON=%5B%5D&textReferences=false) shows how there is only *one* reference to `letters` after the function call."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 148,
+   "execution_count": 142,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -4271,10 +4160,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 149,
+   "execution_count": 143,
    "metadata": {
     "slideshow": {
-     "slide_type": "-"
+     "slide_type": "fragment"
     }
    },
    "outputs": [],
@@ -4282,12 +4171,12 @@
     "def add_xyz(arg):\n",
     "    \"\"\"Append letters to a list.\"\"\"\n",
     "    arg.extend([\"x\", \"y\", \"z\"])\n",
-    "    return  # = None"
+    "    return  # None"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 150,
+   "execution_count": 144,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -4300,10 +4189,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 151,
+   "execution_count": 145,
    "metadata": {
     "slideshow": {
-     "slide_type": "-"
+     "slide_type": "fragment"
     }
    },
    "outputs": [
@@ -4313,7 +4202,7 @@
        "['a', 'b', 'c', 'x', 'y', 'z']"
       ]
      },
-     "execution_count": 151,
+     "execution_count": 145,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -4335,7 +4224,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 152,
+   "execution_count": 146,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -4348,7 +4237,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 153,
+   "execution_count": 147,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -4361,7 +4250,7 @@
        "['a', 'b', 'c', 'x', 'y', 'z', 'x', 'y', 'z']"
       ]
      },
-     "execution_count": 153,
+     "execution_count": 147,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -4405,7 +4294,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 154,
+   "execution_count": 148,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -4418,10 +4307,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 155,
+   "execution_count": 149,
    "metadata": {
     "slideshow": {
-     "slide_type": "-"
+     "slide_type": "fragment"
     }
    },
    "outputs": [
@@ -4431,7 +4320,7 @@
        "(7, 11, 8, 5, 3, 12, 2, 6, 9, 10, 1, 4)"
       ]
      },
-     "execution_count": 155,
+     "execution_count": 149,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -4453,10 +4342,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 156,
+   "execution_count": 150,
    "metadata": {
     "slideshow": {
-     "slide_type": "fragment"
+     "slide_type": "skip"
     }
    },
    "outputs": [],
@@ -4466,10 +4355,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 157,
+   "execution_count": 151,
    "metadata": {
     "slideshow": {
-     "slide_type": "-"
+     "slide_type": "skip"
     }
    },
    "outputs": [
@@ -4479,7 +4368,7 @@
        "(7, 11, 8, 5, 3, 12, 2, 6, 9, 10, 1, 4)"
       ]
      },
-     "execution_count": 157,
+     "execution_count": 151,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -4499,6 +4388,150 @@
     "As before, `numbers` is an object on its own."
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 152,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "139941463332656"
+      ]
+     },
+     "execution_count": 152,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "id(numbers)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 153,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "tuple"
+      ]
+     },
+     "execution_count": 153,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "type(numbers)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "While we could use empty parentheses `()` to create an empty `tuple` object ..."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 154,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "empty_tuple = ()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 155,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "()"
+      ]
+     },
+     "execution_count": 155,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "empty_tuple"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 156,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "tuple"
+      ]
+     },
+     "execution_count": 156,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "type(empty_tuple)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "... we must use a *trailing comma* to create a `tuple` object holding one element. If we forget the comma, the parentheses are interpreted as the grouping operator and effectively useless!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 157,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "one_tuple = (1,)  # we could ommit the parentheses but not the comma"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 158,
@@ -4511,7 +4544,7 @@
     {
      "data": {
       "text/plain": [
-       "140417315626832"
+       "(1,)"
       ]
      },
      "execution_count": 158,
@@ -4520,7 +4553,7 @@
     }
    ],
    "source": [
-    "id(numbers)"
+    "one_tuple"
    ]
   },
   {
@@ -4544,18 +4577,7 @@
     }
    ],
    "source": [
-    "type(numbers)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "source": [
-    "While we could use empty parentheses `()` to create an empty `tuple` object ..."
+    "type(one_tuple)"
    ]
   },
   {
@@ -4568,7 +4590,7 @@
    },
    "outputs": [],
    "source": [
-    "empty_tuple = ()"
+    "no_tuple = (1)"
    ]
   },
   {
@@ -4583,7 +4605,7 @@
     {
      "data": {
       "text/plain": [
-       "()"
+       "1"
       ]
      },
      "execution_count": 161,
@@ -4592,7 +4614,7 @@
     }
    ],
    "source": [
-    "empty_tuple"
+    "no_tuple"
    ]
   },
   {
@@ -4603,139 +4625,6 @@
      "slide_type": "skip"
     }
    },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "tuple"
-      ]
-     },
-     "execution_count": 162,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "type(empty_tuple)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "source": [
-    "... we must use a *trailing comma* to create a `tuple` object holding one element. If we forget the comma, the parentheses are interpreted as the grouping operator and effectively useless!"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 163,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "one_tuple = (1,)  # we could ommit the parentheses but not the comma"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 164,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(1,)"
-      ]
-     },
-     "execution_count": 164,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "one_tuple"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 165,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "tuple"
-      ]
-     },
-     "execution_count": 165,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "type(one_tuple)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 166,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "no_tuple = (1)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 167,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "1"
-      ]
-     },
-     "execution_count": 167,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "no_tuple"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 168,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
    "outputs": [
     {
      "data": {
@@ -4743,7 +4632,7 @@
        "int"
       ]
      },
-     "execution_count": 168,
+     "execution_count": 162,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -4765,10 +4654,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 169,
+   "execution_count": 163,
    "metadata": {
     "slideshow": {
-     "slide_type": "skip"
+     "slide_type": "slide"
     }
    },
    "outputs": [
@@ -4778,7 +4667,7 @@
        "(1,)"
       ]
      },
-     "execution_count": 169,
+     "execution_count": 163,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -4789,7 +4678,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 170,
+   "execution_count": 164,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -4802,7 +4691,7 @@
        "('i', 't', 'e', 'r', 'a', 'b', 'l', 'e')"
       ]
      },
-     "execution_count": 170,
+     "execution_count": 164,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -4832,47 +4721,12 @@
    "source": [
     "Most operations involving `tuple` objects work in the same way as with `list` objects. The main difference is that `tuple` objects are *immutable*. So, if our program does not depend on mutability, we may and should use `tuple` and not `list` objects to model sequential data. That way, we avoid the pitfalls seen above.\n",
     "\n",
-    "`tuple` objects are *sequences* exhibiting the familiar *four* behaviors."
+    "`tuple` objects are *sequences* exhibiting the familiar *four* behaviors. So, `numbers` holds a *finite* number of elements ..."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 171,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "True"
-      ]
-     },
-     "execution_count": 171,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "isinstance(numbers, abc.Sequence)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "source": [
-    " So, `numbers` holds a *finite* number of elements ..."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 172,
+   "execution_count": 165,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -4885,7 +4739,7 @@
        "12"
       ]
      },
-     "execution_count": 172,
+     "execution_count": 165,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -4907,7 +4761,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 173,
+   "execution_count": 166,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -4929,10 +4783,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 174,
+   "execution_count": 167,
    "metadata": {
     "slideshow": {
-     "slide_type": "-"
+     "slide_type": "fragment"
     }
    },
    "outputs": [
@@ -4962,7 +4816,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 175,
+   "execution_count": 168,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -4975,7 +4829,7 @@
        "False"
       ]
      },
-     "execution_count": 175,
+     "execution_count": 168,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -4986,31 +4840,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 176,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "-"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "True"
-      ]
-     },
-     "execution_count": 176,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "1 in numbers"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 177,
+   "execution_count": 169,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -5023,7 +4853,31 @@
        "True"
       ]
      },
-     "execution_count": 177,
+     "execution_count": 169,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "1 in numbers"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 170,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 170,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -5045,7 +4899,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 178,
+   "execution_count": 171,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -5058,7 +4912,7 @@
        "7"
       ]
      },
-     "execution_count": 178,
+     "execution_count": 171,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -5069,10 +4923,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 179,
+   "execution_count": 172,
    "metadata": {
     "slideshow": {
-     "slide_type": "-"
+     "slide_type": "skip"
     }
    },
    "outputs": [
@@ -5082,7 +4936,7 @@
        "4"
       ]
      },
-     "execution_count": 179,
+     "execution_count": 172,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -5093,10 +4947,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 180,
+   "execution_count": 173,
    "metadata": {
     "slideshow": {
-     "slide_type": "-"
+     "slide_type": "fragment"
     }
    },
    "outputs": [
@@ -5106,7 +4960,7 @@
        "(2, 6, 9, 10, 1, 4)"
       ]
      },
-     "execution_count": 180,
+     "execution_count": 173,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -5128,10 +4982,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 181,
+   "execution_count": 174,
    "metadata": {
     "slideshow": {
-     "slide_type": "slide"
+     "slide_type": "fragment"
     }
    },
    "outputs": [
@@ -5142,7 +4996,7 @@
      "traceback": [
       "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
       "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-181-80fddf1304f0>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mnumbers\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m99\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;32m<ipython-input-174-80fddf1304f0>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mnumbers\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m99\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
       "\u001b[0;31mTypeError\u001b[0m: 'tuple' object does not support item assignment"
      ]
     }
@@ -5159,49 +5013,12 @@
     }
    },
    "source": [
-    "We can verify the immutability with the `MutableSequence` ABC from the [collections.abc](https://docs.python.org/3/library/collections.abc.html) module: [isinstance()](https://docs.python.org/3/library/functions.html#isinstance) returns `False`. So, a data type that is a `Sequence` may be mutable or not. If it is a `MutableSequence`, it is mutable. If it is *not* a `MutableSequence`, it is *immutable*. There is *no* `ImmutableSequence` ABC."
+    "The `+` and `*` operators work with `tuple` objects as well: They always create *new* `tuple` objects."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 182,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "False"
-      ]
-     },
-     "execution_count": 182,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "isinstance(numbers, abc.MutableSequence)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "source": [
-    "The `+` and `*` operators work with `tuple` objects as well. However, we should *not* do that as the whole point of immutability is to *not* mutate an object.\n",
-    "\n",
-    "So, instead of writing something like below, we should use a `list` object and call its `append()` method."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 183,
+   "execution_count": 175,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -5214,7 +5031,7 @@
        "(7, 11, 8, 5, 3, 12, 2, 6, 9, 10, 1, 4, 99)"
       ]
      },
-     "execution_count": 183,
+     "execution_count": 175,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -5225,7 +5042,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 184,
+   "execution_count": 176,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -5238,7 +5055,7 @@
        "(7, 11, 8, 5, 3, 12, 2, 6, 9, 10, 1, 4, 7, 11, 8, 5, 3, 12, 2, 6, 9, 10, 1, 4)"
       ]
      },
-     "execution_count": 184,
+     "execution_count": 176,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -5260,7 +5077,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 185,
+   "execution_count": 177,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -5273,7 +5090,7 @@
        "0"
       ]
      },
-     "execution_count": 185,
+     "execution_count": 177,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -5284,7 +5101,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 186,
+   "execution_count": 178,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -5297,7 +5114,7 @@
        "10"
       ]
      },
-     "execution_count": 186,
+     "execution_count": 178,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -5319,7 +5136,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 187,
+   "execution_count": 179,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -5332,7 +5149,7 @@
        "(7, 11, 8, 5, 3, 12, 2, 6, 9, 10, 1, 4)"
       ]
      },
-     "execution_count": 187,
+     "execution_count": 179,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -5343,7 +5160,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 188,
+   "execution_count": 180,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -5356,7 +5173,7 @@
        "True"
       ]
      },
-     "execution_count": 188,
+     "execution_count": 180,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -5367,7 +5184,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 189,
+   "execution_count": 181,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -5380,7 +5197,7 @@
        "True"
       ]
      },
-     "execution_count": 189,
+     "execution_count": 181,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -5391,7 +5208,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 190,
+   "execution_count": 182,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -5404,7 +5221,7 @@
        "True"
       ]
      },
-     "execution_count": 190,
+     "execution_count": 182,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -5413,30 +5230,6 @@
     "numbers < (99, 11, 8, 5, 3, 12, 2, 6, 9, 10, 1, 4)"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": 191,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "True"
-      ]
-     },
-     "execution_count": 191,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "(0, 11, 8, 5, 3, 12, 2, 6, 9, 10, 1, 4) < numbers"
-   ]
-  },
   {
    "cell_type": "markdown",
    "metadata": {
@@ -5445,17 +5238,17 @@
     }
    },
    "source": [
-    "While `tuple` objects are immutable, this only relates to the references they hold. If a `tuple` object contains mutable objects, the entire nested structure is *not* immutable as a whole.\n",
+    "While `tuple` objects are immutable, this only relates to the references they hold. If a `tuple` object contains references to mutable objects, the entire nested structure is *not* immutable as a whole!\n",
     "\n",
     "Consider the following stylized example `not_immutable`: It contains *three* elements, `1`, `[2, ..., 11]`, and `12`, and the elements of the nested `list` object may be changed. While it is not practical to mix data types in a `tuple` object that is used as an \"immutable list,\" we want to make the point that the mere usage of the `tuple` type does *not* guarantee a nested object to be immutable as a whole."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 192,
+   "execution_count": 183,
    "metadata": {
     "slideshow": {
-     "slide_type": "slide"
+     "slide_type": "skip"
     }
    },
    "outputs": [],
@@ -5465,10 +5258,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 193,
+   "execution_count": 184,
    "metadata": {
     "slideshow": {
-     "slide_type": "-"
+     "slide_type": "skip"
     }
    },
    "outputs": [
@@ -5478,7 +5271,7 @@
        "(1, [2, 3, 4, 5, 6, 7, 8, 9, 10, 11], 12)"
       ]
      },
-     "execution_count": 193,
+     "execution_count": 184,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -5489,10 +5282,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 194,
+   "execution_count": 185,
    "metadata": {
     "slideshow": {
-     "slide_type": "fragment"
+     "slide_type": "skip"
     }
    },
    "outputs": [],
@@ -5502,10 +5295,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 195,
+   "execution_count": 186,
    "metadata": {
     "slideshow": {
-     "slide_type": "-"
+     "slide_type": "skip"
     }
    },
    "outputs": [
@@ -5515,7 +5308,7 @@
        "(1, [99, 99, 99], 12)"
       ]
      },
-     "execution_count": 195,
+     "execution_count": 186,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -5550,7 +5343,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 196,
+   "execution_count": 187,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -5563,7 +5356,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 197,
+   "execution_count": 188,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -5576,7 +5369,7 @@
        "7"
       ]
      },
-     "execution_count": 197,
+     "execution_count": 188,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -5587,10 +5380,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 198,
+   "execution_count": 189,
    "metadata": {
     "slideshow": {
-     "slide_type": "-"
+     "slide_type": "fragment"
     }
    },
    "outputs": [
@@ -5600,7 +5393,7 @@
        "11"
       ]
      },
-     "execution_count": 198,
+     "execution_count": 189,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -5611,10 +5404,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 199,
+   "execution_count": 190,
    "metadata": {
     "slideshow": {
-     "slide_type": "-"
+     "slide_type": "fragment"
     }
    },
    "outputs": [
@@ -5624,7 +5417,7 @@
        "8"
       ]
      },
-     "execution_count": 199,
+     "execution_count": 190,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -5646,7 +5439,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 200,
+   "execution_count": 191,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -5660,7 +5453,7 @@
      "traceback": [
       "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
       "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-200-c5e79943681f>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mn1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn3\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn4\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn5\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn6\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn7\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn8\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn9\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn10\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn11\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnumbers\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;32m<ipython-input-191-c5e79943681f>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mn1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn3\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn4\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn5\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn6\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn7\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn8\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn9\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn10\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn11\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnumbers\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
       "\u001b[0;31mValueError\u001b[0m: too many values to unpack (expected 11)"
      ]
     }
@@ -5671,7 +5464,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 201,
+   "execution_count": 192,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -5685,7 +5478,7 @@
      "traceback": [
       "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
       "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-201-214f2de70baf>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mn1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn3\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn4\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn5\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn6\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn7\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn8\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn9\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn10\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn11\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn12\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn13\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnumbers\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;32m<ipython-input-192-214f2de70baf>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mn1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn3\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn4\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn5\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn6\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn7\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn8\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn9\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn10\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn11\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn12\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn13\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnumbers\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
       "\u001b[0;31mValueError\u001b[0m: not enough values to unpack (expected 13, got 12)"
      ]
     }
@@ -5709,7 +5502,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 202,
+   "execution_count": 193,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -5722,10 +5515,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 203,
+   "execution_count": 194,
    "metadata": {
     "slideshow": {
-     "slide_type": "-"
+     "slide_type": "fragment"
     }
    },
    "outputs": [
@@ -5735,7 +5528,7 @@
        "7"
       ]
      },
-     "execution_count": 203,
+     "execution_count": 194,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -5746,10 +5539,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 204,
+   "execution_count": 195,
    "metadata": {
     "slideshow": {
-     "slide_type": "-"
+     "slide_type": "fragment"
     }
    },
    "outputs": [
@@ -5759,21 +5552,21 @@
        "[11, 8, 5, 3, 12, 2, 6, 9, 10, 1]"
       ]
      },
-     "execution_count": 204,
+     "execution_count": 195,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "middle"
+    "middle  # always a list!"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 205,
+   "execution_count": 196,
    "metadata": {
     "slideshow": {
-     "slide_type": "-"
+     "slide_type": "fragment"
     }
    },
    "outputs": [
@@ -5783,79 +5576,7 @@
        "4"
       ]
      },
-     "execution_count": 205,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "last"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "source": [
-    "If we do not need the `middle` elements, we go with the underscore `_` convention and \"throw\" them away."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 206,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "first, *_, last = numbers"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 207,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "7"
-      ]
-     },
-     "execution_count": 207,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "first"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 208,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "4"
-      ]
-     },
-     "execution_count": 208,
+     "execution_count": 196,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -5879,10 +5600,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 209,
+   "execution_count": 197,
    "metadata": {
     "slideshow": {
-     "slide_type": "slide"
+     "slide_type": "skip"
     }
    },
    "outputs": [],
@@ -5892,10 +5613,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 210,
+   "execution_count": 198,
    "metadata": {
     "slideshow": {
-     "slide_type": "-"
+     "slide_type": "skip"
     }
    },
    "outputs": [
@@ -5927,10 +5648,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 211,
+   "execution_count": 199,
    "metadata": {
     "slideshow": {
-     "slide_type": "fragment"
+     "slide_type": "skip"
     }
    },
    "outputs": [
@@ -5957,12 +5678,12 @@
     }
    },
    "source": [
-    "Unpacking also works for nested objects. Below, we wrap [zip()](https://docs.python.org/3/library/functions.html#zip) with the [enumerate()](https://docs.python.org/3/library/functions.html#enumerate) built-in to have an index variable `i` inside the `for`-loop. In each iteration, a `tuple` object consisting of `i` and another `tuple` object is created. The inner one then holds the `name` and `position`."
+    "Unpacking also works for nested objects. Below, we wrap [zip()](https://docs.python.org/3/library/functions.html#zip) with the [enumerate()](https://docs.python.org/3/library/functions.html#enumerate) built-in to have an index variable `number` inside the `for`-loop. In each iteration, a `tuple` object consisting of `number` and another `tuple` object is created. The inner one then holds the `name` and `position`."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 212,
+   "execution_count": 200,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -5973,15 +5694,15 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "0 -> Berthold is a goalkeeper\n",
-      "1 -> Oliver is a defender\n",
-      "2 -> Carl is a midfielder\n"
+      "Berthold (jersey #1) is a goalkeeper\n",
+      "Oliver (jersey #2) is a defender\n",
+      "Carl (jersey #3) is a midfielder\n"
      ]
     }
    ],
    "source": [
-    "for i, (name, position) in enumerate(zip(names, positions)):\n",
-    "    print(i, \"->\", name, \"is a\", position)"
+    "for number, (name, position) in enumerate(zip(names, positions), start=1):\n",
+    "    print(f\"{name} (jersey #{number}) is a {position}\")"
    ]
   },
   {
@@ -6010,7 +5731,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 213,
+   "execution_count": 201,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -6035,7 +5756,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 214,
+   "execution_count": 202,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -6045,31 +5766,57 @@
    "source": [
     "temp = a\n",
     "a = b\n",
-    "b = temp"
+    "b = temp\n",
+    "\n",
+    "del temp"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 215,
+   "execution_count": 203,
    "metadata": {
     "slideshow": {
-     "slide_type": "-"
+     "slide_type": "fragment"
     }
    },
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "(1, 0)"
+       "1"
       ]
      },
-     "execution_count": 215,
+     "execution_count": 203,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "a, b"
+    "a"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 204,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0"
+      ]
+     },
+     "execution_count": 204,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "b"
    ]
   },
   {
@@ -6085,7 +5832,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 216,
+   "execution_count": 205,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -6093,16 +5840,15 @@
    },
    "outputs": [],
    "source": [
-    "a = 0\n",
-    "b = 1"
+    "a, b = 0, 1"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 217,
+   "execution_count": 206,
    "metadata": {
     "slideshow": {
-     "slide_type": "-"
+     "slide_type": "fragment"
     }
    },
    "outputs": [],
@@ -6112,10 +5858,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 218,
+   "execution_count": 207,
    "metadata": {
     "slideshow": {
-     "slide_type": "-"
+     "slide_type": "fragment"
     }
    },
    "outputs": [
@@ -6125,7 +5871,7 @@
        "(1, 0)"
       ]
      },
-     "execution_count": 218,
+     "execution_count": 207,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -6138,11 +5884,11 @@
    "cell_type": "markdown",
    "metadata": {
     "slideshow": {
-     "slide_type": "skip"
+     "slide_type": "slide"
     }
    },
    "source": [
-    "#### Example: [Fibonacci Numbers](https://en.wikipedia.org/wiki/Fibonacci_number) (revisited)"
+    "##### Example: [Fibonacci Numbers](https://en.wikipedia.org/wiki/Fibonacci_number) (revisited)"
    ]
   },
   {
@@ -6153,28 +5899,15 @@
     }
    },
    "source": [
-    "Unpacking allows us to rewrite the iterative `fibonacci()` function from [Chapter 4](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/04_iteration_00_lecture.ipynb#\"Hard-at-first-Glance\"-Example:-Fibonacci-Numbers-%28revisited%29) in a concise way, now also supporting *goose typing* with the [numbers](https://docs.python.org/3/library/numbers.html) module from the [standard library](https://docs.python.org/3/library/index.html)."
+    "Unpacking allows us to rewrite the iterative `fibonacci()` function from [Chapter 4](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/04_iteration_00_lecture.ipynb#\"Hard-at-first-Glance\"-Example:-Fibonacci-Numbers-%28revisited%29) in a concise way."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 219,
+   "execution_count": 208,
    "metadata": {
     "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "import numbers"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 220,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
+     "slide_type": "slide"
     }
    },
    "outputs": [],
@@ -6187,21 +5920,7 @@
     "\n",
     "    Returns:\n",
     "        ith_fibonacci (int)\n",
-    "\n",
-    "    Raises:\n",
-    "        TypeError: if i is not an integer or not integer-like\n",
-    "        ValueError: if i is not positive\n",
     "    \"\"\"\n",
-    "    if not isinstance(i, numbers.Integral):\n",
-    "        if isinstance(i, numbers.Real):\n",
-    "            if i != int(i):\n",
-    "                raise TypeError(\"i is not an integer-like value; it has decimals\")\n",
-    "            i = int(i)\n",
-    "        else:\n",
-    "            raise TypeError(\"i must be an integer\")\n",
-    "    if i < 0:\n",
-    "        raise ValueError(\"i must be non-negative\")\n",
-    "\n",
     "    a, b = 0, 1\n",
     "\n",
     "    for _ in range(i - 1):\n",
@@ -6212,10 +5931,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 221,
+   "execution_count": 209,
    "metadata": {
     "slideshow": {
-     "slide_type": "skip"
+     "slide_type": "fragment"
     }
    },
    "outputs": [
@@ -6225,7 +5944,7 @@
        "144"
       ]
      },
-     "execution_count": 221,
+     "execution_count": 209,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -6234,67 +5953,6 @@
     "fibonacci(12)"
    ]
   },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "source": [
-    "Because of the *goose typing*, we may pass `float` objects to `fibonacci()` as long as they contain no decimals."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 222,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "144"
-      ]
-     },
-     "execution_count": 222,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "fibonacci(12.0)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 223,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "outputs": [
-    {
-     "ename": "TypeError",
-     "evalue": "i is not an integer-like value; it has decimals",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-223-c98882eeae5f>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mfibonacci\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m12.3\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
-      "\u001b[0;32m<ipython-input-220-b3cde1c6219f>\u001b[0m in \u001b[0;36mfibonacci\u001b[0;34m(i)\u001b[0m\n\u001b[1;32m     15\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnumbers\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mReal\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     16\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mi\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0mint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 17\u001b[0;31m                 \u001b[0;32mraise\u001b[0m \u001b[0mTypeError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"i is not an integer-like value; it has decimals\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     18\u001b[0m             \u001b[0mi\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     19\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;31mTypeError\u001b[0m: i is not an integer-like value; it has decimals"
-     ]
-    }
-   ],
-   "source": [
-    "fibonacci(12.3)"
-   ]
-  },
   {
    "cell_type": "markdown",
    "metadata": {
@@ -6323,7 +5981,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 224,
+   "execution_count": 210,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -6356,10 +6014,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 225,
+   "execution_count": 211,
    "metadata": {
     "slideshow": {
-     "slide_type": "-"
+     "slide_type": "fragment"
     }
    },
    "outputs": [
@@ -6369,7 +6027,7 @@
        "42"
       ]
      },
-     "execution_count": 225,
+     "execution_count": 211,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -6391,10 +6049,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 226,
+   "execution_count": 212,
    "metadata": {
     "slideshow": {
-     "slide_type": "-"
+     "slide_type": "fragment"
     }
    },
    "outputs": [
@@ -6404,7 +6062,7 @@
        "100"
       ]
      },
-     "execution_count": 226,
+     "execution_count": 212,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -6426,10 +6084,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 227,
+   "execution_count": 213,
    "metadata": {
     "slideshow": {
-     "slide_type": "slide"
+     "slide_type": "skip"
     }
    },
    "outputs": [
@@ -6440,8 +6098,8 @@
      "traceback": [
       "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
       "\u001b[0;31mIndexError\u001b[0m                                Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-227-640e0c632b8d>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mproduct\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
-      "\u001b[0;32m<ipython-input-224-02b0db9fbeab>\u001b[0m in \u001b[0;36mproduct\u001b[0;34m(*args)\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mproduct\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m     \u001b[0;34m\"\"\"Multiply all arguments.\"\"\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m     \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      4\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m     \u001b[0;32mfor\u001b[0m \u001b[0marg\u001b[0m \u001b[0;32min\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m<ipython-input-213-640e0c632b8d>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mproduct\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;32m<ipython-input-210-02b0db9fbeab>\u001b[0m in \u001b[0;36mproduct\u001b[0;34m(*args)\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mproduct\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m     \u001b[0;34m\"\"\"Multiply all arguments.\"\"\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m     \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      4\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m     \u001b[0;32mfor\u001b[0m \u001b[0marg\u001b[0m \u001b[0;32min\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
       "\u001b[0;31mIndexError\u001b[0m: tuple index out of range"
      ]
     }
@@ -6463,7 +6121,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 228,
+   "execution_count": 214,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -6476,10 +6134,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 229,
+   "execution_count": 215,
    "metadata": {
     "slideshow": {
-     "slide_type": "-"
+     "slide_type": "fragment"
     }
    },
    "outputs": [
@@ -6489,13 +6147,13 @@
        "[2, 5, 10]"
       ]
      },
-     "execution_count": 229,
+     "execution_count": 215,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "product(one_hundred)"
+    "product(one_hundred)  # a semantic error!"
    ]
   },
   {
@@ -6511,7 +6169,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 230,
+   "execution_count": 216,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -6524,7 +6182,7 @@
        "100"
       ]
      },
-     "execution_count": 230,
+     "execution_count": 216,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -6546,7 +6204,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 231,
+   "execution_count": 217,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -6559,7 +6217,7 @@
        "100"
       ]
      },
-     "execution_count": 231,
+     "execution_count": 217,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -6578,14 +6236,12 @@
    "source": [
     "In the \"*Packing & Unpacking with Functions*\" [exercise](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/07_sequences_02_exercises.ipynb#Packing-&-Unpacking-with-Functions) at the end of this chapter, we look at `product()` in more detail.\n",
     "\n",
-    "While we needed to unpack `one_hundred` above to avoid the semantic error, unpacking an argument in a function call may also be a convenience in general.\n",
-    "\n",
-    "For example, to print the elements of `one_hundred` in one line, we need to use a `for` statement, until now. With unpacking, we get away *without* a loop."
+    "While we needed to unpack `one_hundred` above to avoid the semantic error, unpacking an argument in a function call may also be a convenience in general. For example, to print the elements of `one_hundred` in one line, we need to use a `for` statement, until now. With unpacking, we get away *without* a loop."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 232,
+   "execution_count": 218,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -6606,7 +6262,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 233,
+   "execution_count": 219,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -6628,7 +6284,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 234,
+   "execution_count": 220,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -6677,7 +6333,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 235,
+   "execution_count": 221,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -6703,10 +6359,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 236,
+   "execution_count": 222,
    "metadata": {
     "slideshow": {
-     "slide_type": "fragment"
+     "slide_type": "slide"
     }
    },
    "outputs": [],
@@ -6727,10 +6383,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 237,
+   "execution_count": 223,
    "metadata": {
     "slideshow": {
-     "slide_type": "-"
+     "slide_type": "fragment"
     }
    },
    "outputs": [],
@@ -6746,12 +6402,12 @@
     }
    },
    "source": [
-    "The `Point` object is a so-called **class**. That is what it means if an object is of type `type`. It can be used as a **factory** to create *new* `tuple`-like objects of type `Point`."
+    "The `Point` object is a so-called **class**. That is what it means if an object is of type `type`. It can be used as a **factory** to create *new* `tuple`-like objects of type `Point`. In a way, [namedtuple()](https://docs.python.org/3/library/collections.html#collections.namedtuple) gives us a way to create our own custom **constructors**."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 238,
+   "execution_count": 224,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -6761,24 +6417,24 @@
     {
      "data": {
       "text/plain": [
-       "140416708056240"
+       "94715539542800"
       ]
      },
-     "execution_count": 238,
+     "execution_count": 224,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "id(Point)  # classes are objects as well"
+    "id(Point)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 239,
+   "execution_count": 225,
    "metadata": {
     "slideshow": {
-     "slide_type": "-"
+     "slide_type": "fragment"
     }
    },
    "outputs": [
@@ -6788,7 +6444,7 @@
        "type"
       ]
      },
-     "execution_count": 239,
+     "execution_count": 225,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -6810,10 +6466,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 240,
+   "execution_count": 226,
    "metadata": {
     "slideshow": {
-     "slide_type": "-"
+     "slide_type": "fragment"
     }
    },
    "outputs": [
@@ -6823,7 +6479,7 @@
        "__main__.Point"
       ]
      },
-     "execution_count": 240,
+     "execution_count": 226,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -6845,7 +6501,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 241,
+   "execution_count": 227,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -6869,10 +6525,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 242,
+   "execution_count": 228,
    "metadata": {
     "slideshow": {
-     "slide_type": "-"
+     "slide_type": "fragment"
     }
    },
    "outputs": [
@@ -6882,7 +6538,7 @@
        "Point(x=4, y=2)"
       ]
      },
-     "execution_count": 242,
+     "execution_count": 228,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -6904,7 +6560,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 243,
+   "execution_count": 229,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -6914,10 +6570,10 @@
     {
      "data": {
       "text/plain": [
-       "140417315422528"
+       "139941443365056"
       ]
      },
-     "execution_count": 243,
+     "execution_count": 229,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -6928,10 +6584,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 244,
+   "execution_count": 230,
    "metadata": {
     "slideshow": {
-     "slide_type": "-"
+     "slide_type": "fragment"
     }
    },
    "outputs": [
@@ -6941,7 +6597,7 @@
        "__main__.Point"
       ]
      },
-     "execution_count": 244,
+     "execution_count": 230,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -6963,7 +6619,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 245,
+   "execution_count": 231,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -6976,7 +6632,7 @@
        "4"
       ]
      },
-     "execution_count": 245,
+     "execution_count": 231,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -6987,10 +6643,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 246,
+   "execution_count": 232,
    "metadata": {
     "slideshow": {
-     "slide_type": "-"
+     "slide_type": "fragment"
     }
    },
    "outputs": [
@@ -7000,7 +6656,7 @@
        "2"
       ]
      },
-     "execution_count": 246,
+     "execution_count": 232,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -7022,7 +6678,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 247,
+   "execution_count": 233,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -7036,7 +6692,7 @@
      "traceback": [
       "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
       "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-247-b30d0b930bac>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mcurrent_position\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mz\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;32m<ipython-input-233-b30d0b930bac>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mcurrent_position\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mz\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
       "\u001b[0;31mAttributeError\u001b[0m: 'Point' object has no attribute 'z'"
      ]
     }
@@ -7055,41 +6711,17 @@
    "source": [
     "`current_position` continues to work like a `tuple` object! That is why we can use `namedtuple` as a replacement for `tuple`. The underlying implementations exhibit the *same* computational efficiencies and memory usages.\n",
     "\n",
-    "For example, we can index into or loop over `current_position` as it is still a sequence."
+    "For example, we can index into or loop over `current_position` as it is still a sequence with the familiar four properties."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 248,
+   "execution_count": 234,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
     }
    },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "True"
-      ]
-     },
-     "execution_count": 248,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "isinstance(current_position, abc.Sequence)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 249,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "fragment"
-    }
-   },
    "outputs": [
     {
      "data": {
@@ -7097,7 +6729,7 @@
        "4"
       ]
      },
-     "execution_count": 249,
+     "execution_count": 234,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -7108,10 +6740,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 250,
+   "execution_count": 235,
    "metadata": {
     "slideshow": {
-     "slide_type": "-"
+     "slide_type": "skip"
     }
    },
    "outputs": [
@@ -7121,7 +6753,7 @@
        "2"
       ]
      },
-     "execution_count": 250,
+     "execution_count": 235,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -7132,7 +6764,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 251,
+   "execution_count": 236,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -7155,10 +6787,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 252,
+   "execution_count": 237,
    "metadata": {
     "slideshow": {
-     "slide_type": "skip"
+     "slide_type": "fragment"
     }
    },
    "outputs": [
@@ -7176,17 +6808,6 @@
     "    print(number)"
    ]
   },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "slide"
-    }
-   },
-   "source": [
-    "## The Map-Filter-Reduce Paradigm"
-   ]
-  },
   {
    "cell_type": "markdown",
    "metadata": {
@@ -7195,216 +6816,12 @@
     }
    },
    "source": [
-    "Whenever we process sequential data, most tasks can be classified into one of the three categories **map**, **filter**, or **reduce**. This paradigm has caught attention in recent years as it enables **[parallel computing](https://en.wikipedia.org/wiki/Parallel_computing)**, and this gets important when dealing with big amounts of data.\n",
-    "\n",
-    "Let's look at a simple example."
+    "Because of that it has \"length.\""
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 253,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "slide"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "numbers = [7, 11, 8, 5, 3, 12, 2, 6, 9, 10, 1, 4]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "slide"
-    }
-   },
-   "source": [
-    "### Mapping"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "source": [
-    "**Mapping** refers to the idea of applying a transformation to every element in a sequence.\n",
-    "\n",
-    "For example, let's square each element in `numbers` and add `1` to it. In essence, we apply the transformation $y := x^2 + 1$ expressed as the `transform()` function below."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 254,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "slide"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "def transform(element):\n",
-    "    \"\"\"Map elements to their squares plus 1.\"\"\"\n",
-    "    return (element ** 2) + 1"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "source": [
-    "With the syntax we know so far, we revert to a `for`-loop that iteratively appends the transformed elements to the initially empty `transformed_numbers` list."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 255,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "fragment"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "transformed_numbers = []\n",
-    "\n",
-    "for old in numbers:\n",
-    "    new = transform(old)\n",
-    "    transformed_numbers.append(new)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 256,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "-"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[50, 122, 65, 26, 10, 145, 5, 37, 82, 101, 2, 17]"
-      ]
-     },
-     "execution_count": 256,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "transformed_numbers"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "source": [
-    "As this kind of data processing is so common, Python provides the [map()](https://docs.python.org/3/library/functions.html#map) built-in. In its simplest usage form, it takes two arguments: A transformation function that takes one positional argument and an iterable.\n",
-    "\n",
-    "We call [map()](https://docs.python.org/3/library/functions.html#map) with the `transform()` function and the `numbers` list as the arguments and store the result in the variable `transformer` to inspect it."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 257,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "slide"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "transformer = map(transform, numbers)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "source": [
-    "We might expect to get back a materialized sequence (i.e., all elements exist in memory), and a `list` object would feel the most natural because of the type of the `numbers` argument. However, `transformer` is an object of type `map`."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 258,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "-"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<map at 0x7fb5743a9ad0>"
-      ]
-     },
-     "execution_count": 258,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "transformer"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 259,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "-"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "map"
-      ]
-     },
-     "execution_count": 259,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "type(transformer)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "source": [
-    "Like `range` objects, `map` objects generate a series of objects \"on the fly\" (i.e., one by one), and we use the built-in [next()](https://docs.python.org/3/library/functions.html#next) function to obtain the next object in line. So, we should think of a `map` object as a \"rule\" stored in memory that only knows how to calculate the next object of possibly *infinitely* many.\n",
-    "\n",
-    "It is essential to understand that by creating a `map` object with the [map()](https://docs.python.org/3/library/functions.html#map) built-in, nothing happens in memory except the creation of the `map` object. In particular, no second `list` object derived from `numbers` is created. Also, we may view `range` objects as a special case of `map` objects: They are constrained to generating `int` objects only, and the *iterable* argument is replaced with *start*, *stop*, and *step* arguments."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 260,
+   "execution_count": 238,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -7414,4746 +6831,16 @@
     {
      "data": {
       "text/plain": [
-       "50"
+       "2"
       ]
      },
-     "execution_count": 260,
+     "execution_count": 238,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "next(transformer)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 261,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "122"
-      ]
-     },
-     "execution_count": 261,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "next(transformer)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 262,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "65"
-      ]
-     },
-     "execution_count": 262,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "next(transformer)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "source": [
-    "If we are sure that a `map` object generates a *finite* number of elements, we may materialize them into a `list` object with the [list()](https://docs.python.org/3/library/functions.html#func-list) built-in. In the example, this is the case as `transformer` is derived from a *finite* `list` object.\n",
-    "\n",
-    "In summary, instead of creating an empty list first and appending it in a `for`-loop as above, we write the following one-liner and obtain an equal `transformed_numbers` list."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 263,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "slide"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "transformed_numbers = list(map(transform, numbers))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 264,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "-"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[50, 122, 65, 26, 10, 145, 5, 37, 82, 101, 2, 17]"
-      ]
-     },
-     "execution_count": 264,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "transformed_numbers"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "slide"
-    }
-   },
-   "source": [
-    "### Filtering"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "source": [
-    "**Filtering** refers to the idea of creating a subset of a sequence with a **boolean filter** function that indicates if an element should be kept or not.\n",
-    "\n",
-    "In the example, let's only keep the even elements in `numbers`. The `is_even()` function implements that as a filter."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 265,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "slide"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "def is_even(element):\n",
-    "    \"\"\"Filter out odd numbers.\"\"\"\n",
-    "    if element % 2 == 0:\n",
-    "        return True\n",
-    "    return False"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "source": [
-    "As before, we must revert to a `for`-loop that appends the elements to be kept iteratively to an initially empty `even_numbers` list."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 266,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "fragment"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "even_numbers = []\n",
-    "\n",
-    "for number in transformed_numbers:\n",
-    "    if is_even(number):\n",
-    "        even_numbers.append(number)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 267,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "-"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[50, 122, 26, 10, 82, 2]"
-      ]
-     },
-     "execution_count": 267,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "even_numbers"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "source": [
-    "As filtering is also a common task, we use the [filter()](https://docs.python.org/3/library/functions.html#filter) built-in that returns an object of type `filter` stored in the `evens` variable."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 268,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "slide"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "evens = filter(is_even, transformed_numbers)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 269,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "-"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<filter at 0x7fb57435cc10>"
-      ]
-     },
-     "execution_count": 269,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "evens"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 270,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "-"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "filter"
-      ]
-     },
-     "execution_count": 270,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "type(evens)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "source": [
-    "`evens` works like `transformer` above, and we use the built-in [next()](https://docs.python.org/3/library/functions.html#next) function to obtain the even numbers one by one. So, the \"next\" element in line is simply the next even `int` object the `filter` object encounters."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 271,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "fragment"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[50, 122, 65, 26, 10, 145, 5, 37, 82, 101, 2, 17]"
-      ]
-     },
-     "execution_count": 271,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "transformed_numbers  # for quick reference"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 272,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "-"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "50"
-      ]
-     },
-     "execution_count": 272,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "next(evens)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 273,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "122"
-      ]
-     },
-     "execution_count": 273,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "next(evens)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 274,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "26"
-      ]
-     },
-     "execution_count": 274,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "next(evens)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "source": [
-    "As above, we must explicitly create a materialized `list` object with the [list()](https://docs.python.org/3/library/functions.html#func-list) built-in."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 275,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "slide"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[50, 122, 26, 10, 82, 2]"
-      ]
-     },
-     "execution_count": 275,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "list(filter(is_even, transformed_numbers))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "source": [
-    "We may also chain mappings and filters based on the original `numbers` list."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 276,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "fragment"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[50, 122, 26, 10, 82, 2]"
-      ]
-     },
-     "execution_count": 276,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "list(filter(is_even, map(transform, numbers)))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "source": [
-    "Using the [map()](https://docs.python.org/3/library/functions.html#map) and [filter()](https://docs.python.org/3/library/functions.html#filter) built-ins, we can quickly switch the order: Filter first and then transform the remaining elements. This variant equals the \"*A simple Filter*\" example from [Chapter 4](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/04_iteration_00_lecture.ipynb#Example:-A-simple-Filter). On the contrary, code with `for`-loops and `if` statements is more tedious to adapt. Additionally, `map` and `filter` objects are optimized at the C level and, therefore, a lot faster as well."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 277,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "fragment"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[65, 145, 5, 37, 101, 17]"
-      ]
-     },
-     "execution_count": 277,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "list(map(transform, filter(is_even, numbers)))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "slide"
-    }
-   },
-   "source": [
-    "### Reducing"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "source": [
-    "Lastly, **reducing** sequential data means to summarize the elements into a single statistic.\n",
-    "\n",
-    "A simple example is the built-in [sum()](https://docs.python.org/3/library/functions.html#sum) function."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 278,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "slide"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "370"
-      ]
-     },
-     "execution_count": 278,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "sum(map(transform, filter(is_even, numbers)))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "source": [
-    "Other straightforward examples are the built-in [min()](https://docs.python.org/3/library/functions.html#min) or [max()](https://docs.python.org/3/library/functions.html#max) functions."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 279,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "fragment"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "5"
-      ]
-     },
-     "execution_count": 279,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "min(map(transform, filter(is_even, numbers)))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 280,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "-"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "145"
-      ]
-     },
-     "execution_count": 280,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "max(map(transform, filter(is_even, numbers)))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "source": [
-    "[sum()](https://docs.python.org/3/library/functions.html#sum), [min()](https://docs.python.org/3/library/functions.html#min), and [max()](https://docs.python.org/3/library/functions.html#max) can be regarded as special cases.\n",
-    "\n",
-    "The generic way of reducing a sequence is to apply a function of *two* arguments on a rolling horizon: Its first argument is the reduction of the elements processed so far, and the second the next element to be reduced.\n",
-    "\n",
-    "For illustration, let's replicate [sum()](https://docs.python.org/3/library/functions.html#sum) as such a function, called `add()`. Its implementation only adds two numbers."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 281,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "slide"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "def add(sum_so_far, next_number):\n",
-    "    \"\"\"Reduce a sequence by addition.\"\"\"\n",
-    "    return sum_so_far + next_number"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "source": [
-    "Further, we create a *new* `map` object derived from `numbers` ..."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 282,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "fragment"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "evens_transformed = map(transform, filter(is_even, numbers))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "source": [
-    "... and loop over all *but* the first element it generates. That we capture separately as the initial `result` with the [next()](https://docs.python.org/3/library/functions.html#next) function. So, `map` objects must be *iterable* as we may loop over them.\n",
-    "\n",
-    "We know that `evens_transformed` generates *six* elements. That is why we see *five* growing `result` values resembling a [cumulative sum](http://mathworld.wolfram.com/CumulativeSum.html)."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 283,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "fragment"
-    }
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "65 210 215 252 353 "
-     ]
-    }
-   ],
-   "source": [
-    "result = next(evens_transformed)  # first element is the initial value\n",
-    "\n",
-    "for number in evens_transformed:  # iterate over the remaining elements\n",
-    "    print(result, end=\" \")  # line added for didactical purposes\n",
-    "    result = add(result, number)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "source": [
-    "The final `result` is the same `370` as above."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 284,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "-"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "370"
-      ]
-     },
-     "execution_count": 284,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "result"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "source": [
-    "The [reduce()](https://docs.python.org/3/library/functools.html#functools.reduce) function in the [functools](https://docs.python.org/3/library/functools.html) module in the [standard library](https://docs.python.org/3/library/index.html) provides more convenience replacing the `for`-loop. It takes two arguments in the same way as the [map()](https://docs.python.org/3/library/functions.html#map) and [filter()](https://docs.python.org/3/library/functions.html#filter) built-ins.\n",
-    "\n",
-    "[reduce()](https://docs.python.org/3/library/functools.html#functools.reduce) is **[eager](https://en.wikipedia.org/wiki/Eager_evaluation)** meaning that all computations implied by the contained `map` and `filter` \"rules\" are executed right away, and the code cell returns `370`. On the contrary, [map()](https://docs.python.org/3/library/functions.html#map) and [filter()](https://docs.python.org/3/library/functions.html#filter) create **[lazy](https://en.wikipedia.org/wiki/Lazy_evaluation)** `map` and `filter` objects, and we have to use the [next()](https://docs.python.org/3/library/functions.html#next) function to obtain the elements."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 285,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "slide"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "from functools import reduce"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 286,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "-"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "370"
-      ]
-     },
-     "execution_count": 286,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "reduce(add, map(transform, filter(is_even, numbers)))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "slide"
-    }
-   },
-   "source": [
-    "### Lambda Expressions"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "source": [
-    "[map()](https://docs.python.org/3/library/functions.html#map), [filter()](https://docs.python.org/3/library/functions.html#filter), and [reduce()](https://docs.python.org/3/library/functools.html#functools.reduce) take a `function` object as their first argument, and we defined `transform()`, `is_even()`, and `add()` to be used precisely for that.\n",
-    "\n",
-    "Often, such functions are used *only once* in a program. However, the primary purpose of functions is to *reuse* them. In such cases, it makes more sense to define them \"anonymously\" right at the position where the first argument goes.\n",
-    "\n",
-    "As mentioned in [Chapter 2](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/02_functions_00_lecture.ipynb#Anonymous-Functions), Python provides `lambda` expressions to create `function` objects *without* a name referencing them.\n",
-    "\n",
-    "So, the above `add()` function could be rewritten as a `lambda` expression like so ..."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 287,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "slide"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<function __main__.<lambda>(sum_so_far, next_number)>"
-      ]
-     },
-     "execution_count": 287,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "lambda sum_so_far, next_number: sum_so_far + next_number"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "source": [
-    "... or even shorter."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 288,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "fragment"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<function __main__.<lambda>(x, y)>"
-      ]
-     },
-     "execution_count": 288,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "lambda x, y: x + y"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "source": [
-    "With the new concepts in this section, we can rewrite the entire example in just a few lines of code *without* any `for`, `if`, and `def` statements. The resulting code is concise, easy to read, quick to modify, and even faster in execution. Most importantly, it is optimized to handle big amounts of data as *no* temporary `list` objects are materialized in memory."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 289,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "slide"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "370"
-      ]
-     },
-     "execution_count": 289,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "numbers = [7, 11, 8, 5, 3, 12, 2, 6, 9, 10, 1, 4]\n",
-    "evens = filter(lambda x: x % 2 == 0, numbers)\n",
-    "transformed = map(lambda x: (x ** 2) + 1, evens)\n",
-    "sum(transformed)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "source": [
-    "If `numbers` comes as a sorted sequence of whole numbers, we may use the [range()](https://docs.python.org/3/library/functions.html#func-range) built-in and get away *without any* `list` object in memory at all!"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 290,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "fragment"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "370"
-      ]
-     },
-     "execution_count": 290,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "numbers = range(1, 13)\n",
-    "evens = filter(lambda x: x % 2 == 0, numbers)\n",
-    "transformed = map(lambda x: (x ** 2) + 1, evens)\n",
-    "sum(transformed)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "source": [
-    "To additionally save the temporary variables, `numbers`, `evens`, and `transformed`, we write the entire computation as *one* expression."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 291,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "fragment"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "370"
-      ]
-     },
-     "execution_count": 291,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "sum(map(lambda x: (x ** 2) + 1, filter(lambda x: x % 2 == 0, range(1, 13))))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "source": [
-    "PythonTutor visualizes the differences in the number of computational steps and memory usage:\n",
-    "- [Version 1](http://pythontutor.com/visualize.html#code=def%20is_even%28element%29%3A%0A%20%20%20%20if%20element%20%25%202%20%3D%3D%200%3A%0A%20%20%20%20%20%20%20%20return%20True%0A%20%20%20%20return%20False%0A%0Adef%20transform%28element%29%3A%0A%20%20%20%20return%20%28element%20**%202%29%20%2B%201%0A%0Anumbers%20%3D%20list%28range%281,%2013%29%29%0A%0Aevens%20%3D%20%5B%5D%0Afor%20number%20in%20numbers%3A%0A%20%20%20%20if%20is_even%28number%29%3A%0A%20%20%20%20%20%20%20%20evens.append%28number%29%0A%0Atransformed%20%3D%20%5B%5D%0Afor%20number%20in%20evens%3A%0A%20%20%20%20transformed.append%28transform%28number%29%29%0A%0Aresult%20%3D%20sum%28transformed%29&cumulative=false&curInstr=0&heapPrimitives=nevernest&mode=display&origin=opt-frontend.js&py=3&rawInputLstJSON=%5B%5D&textReferences=false): With `for`-loops, `if` statements, and named functions -> **116** steps\n",
-    "- [Version 2](http://pythontutor.com/visualize.html#code=numbers%20%3D%20range%281,%2013%29%0Aevens%20%3D%20filter%28lambda%20x%3A%20x%20%25%202%20%3D%3D%200,%20numbers%29%0Atransformed%20%3D%20map%28lambda%20x%3A%20%28x%20**%202%29%20%2B%201,%20evens%29%0Aresult%20%3D%20sum%28transformed%29&cumulative=false&curInstr=0&heapPrimitives=nevernest&mode=display&origin=opt-frontend.js&py=3&rawInputLstJSON=%5B%5D&textReferences=false): With named `map` and `filter` objects -> **58** steps\n",
-    "- [Version 3](http://pythontutor.com/visualize.html#code=result%20%3D%20sum%28map%28lambda%20x%3A%20%28x%20**%202%29%20%2B%201,%20filter%28lambda%20x%3A%20x%20%25%202%20%3D%3D%200,%20range%281,%2013%29%29%29%29&cumulative=false&curInstr=0&heapPrimitives=nevernest&mode=display&origin=opt-frontend.js&py=3&rawInputLstJSON=%5B%5D&textReferences=false): Everything in *one* expression -> **55** steps\n",
-    "\n",
-    "Versions 2 and 3 are the same, except for the three additional steps required to create the temporary variables. The *major* downside of Version 1 is that, in the worst case, it may need *three times* the memory as compared to the other two versions!\n",
-    "\n",
-    "An experienced Pythonista would probably go with Version 2 in a production system to keep the code readable and maintainable."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "slide"
-    }
-   },
-   "source": [
-    "### List Comprehensions"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "source": [
-    "For [map()](https://docs.python.org/3/library/functions.html#map) and [filter()](https://docs.python.org/3/library/functions.html#filter), Python provides a nice syntax appealing to people who like mathematics.\n",
-    "\n",
-    "Consider again the \"*A simple Filter*\" example from [Chapter 4](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/04_iteration_00_lecture.ipynb#Example:-A-simple-Filter), written with combined `for` and `if` statements. So, the mapping and filtering steps happen simultaneously."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 292,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "slide"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "numbers = [7, 11, 8, 5, 3, 12, 2, 6, 9, 10, 1, 4]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 293,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "-"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "evens_transformed = []\n",
-    "\n",
-    "for number in numbers:\n",
-    "    if number % 2 == 0:\n",
-    "        evens_transformed.append((number ** 2) + 1)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 294,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "-"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[65, 145, 5, 37, 101, 17]"
-      ]
-     },
-     "execution_count": 294,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "evens_transformed"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "source": [
-    "**List comprehensions**, or **listcomps** for short, are *expressions* to derive *new* `list` objects out of *existing* ones (cf., [reference](https://docs.python.org/3/reference/expressions.html#displays-for-lists-sets-and-dictionaries)). A single *expression* like below can replace the compound `for` *statement* from above."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 295,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "slide"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[65, 145, 5, 37, 101, 17]"
-      ]
-     },
-     "execution_count": 295,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "[(n ** 2) + 1 for n in numbers if n % 2 == 0]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "source": [
-    "A list comprehension may be used in place of any `list` object.\n",
-    "\n",
-    "For example, let's add up all the elements with [sum()](https://docs.python.org/3/library/functions.html#sum). The code below *materializes* all elements in memory *before* summing them up. So,  this code might cause a `MemoryError` when executed with a bigger `numbers` list. [PythonTutor](http://pythontutor.com/visualize.html#code=numbers%20%3D%20range%281,%2013%29%0Aresult%20%3D%20sum%28%5B%28n%20**%202%29%20%2B%201%20for%20n%20in%20numbers%20if%20n%20%25%202%20%3D%3D%200%5D%29&cumulative=false&curInstr=0&heapPrimitives=nevernest&mode=display&origin=opt-frontend.js&py=3&rawInputLstJSON=%5B%5D&textReferences=false) shows how a `list` object exists in memory at step 17 and then \"gets lost\" right after. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 296,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "fragment"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "370"
-      ]
-     },
-     "execution_count": 296,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "sum([(n ** 2) + 1 for n in numbers if n % 2 == 0])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "source": [
-    "#### Example: Nested Lists"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "source": [
-    "List comprehensions may come with several `for`'s and `if`'s.\n",
-    "\n",
-    "The cell below creates a `list` object that contains other `list` objects with numbers in them. The starting number in each inner `list` object is offset by `1`."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 297,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "nested_numbers = [list(range(x, y + 1)) for x, y in zip([1, 2, 3], [7, 8, 9])]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 298,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[[1, 2, 3, 4, 5, 6, 7], [2, 3, 4, 5, 6, 7, 8], [3, 4, 5, 6, 7, 8, 9]]"
-      ]
-     },
-     "execution_count": 298,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "nested_numbers"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "source": [
-    "To do something meaningful with the numbers, we have to get rid of the inner layer of `list` objects and flatten the data.\n",
-    "\n",
-    "Without list comprehensions, we would probably write two nested `for`-loops."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 299,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "flat_numbers = []\n",
-    "\n",
-    "for inner_numbers in nested_numbers:\n",
-    "    for number in inner_numbers:\n",
-    "        flat_numbers.append(number)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 300,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[1, 2, 3, 4, 5, 6, 7, 2, 3, 4, 5, 6, 7, 8, 3, 4, 5, 6, 7, 8, 9]"
-      ]
-     },
-     "execution_count": 300,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "flat_numbers"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "source": [
-    "That translates into a list comprehension like below. The order of the `for`'s may be confusing at first but is the *same* as writing out the nested `for`-loops."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 301,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[1, 2, 3, 4, 5, 6, 7, 2, 3, 4, 5, 6, 7, 8, 3, 4, 5, 6, 7, 8, 9]"
-      ]
-     },
-     "execution_count": 301,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "[number for inner_numbers in nested_numbers for number in inner_numbers]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "source": [
-    "Now, we may use the `list` object resulting from the list comprehension in any context we want.\n",
-    "\n",
-    "As an example, we add up the flattened numbers with [sum()](https://docs.python.org/3/library/functions.html#sum). The same caveat holds as before: The `list` object passed into [sum()](https://docs.python.org/3/library/functions.html#sum) is *materialized* before the sum is calculated!"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 302,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "105"
-      ]
-     },
-     "execution_count": 302,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "sum([number for inner_numbers in nested_numbers for number in inner_numbers])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "source": [
-    "In this particular example, however, we can exploit the fact that any sum of numbers can be expressed as the sum of sums of mutually exclusive and collectively exhaustive subsets of these numbers and get away with just *one* `for` in the list comprehension."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 303,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "105"
-      ]
-     },
-     "execution_count": 303,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "sum([sum(inner_numbers) for inner_numbers in nested_numbers])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "source": [
-    "#### Example: Cartesian Products"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "source": [
-    "A popular use case of nested list comprehensions is applying a transformation to each $2$-tuple of the [Cartesian product](https://en.wikipedia.org/wiki/Cartesian_product) of two iterables.\n",
-    "\n",
-    "For example, let's add `1` to each quotient obtained by taking the numerator from `[10, 20, 30]` and the denominator from `[40, 50, 60]`, and then find the product of all quotients. The table below visualizes the calculations: The result is the product of *nine* entries.\n",
-    "\n",
-    "||**10**|**20**|**30**|\n",
-    "|-|-|-|-|\n",
-    "|**40**|1.25|1.50|1.75|\n",
-    "|**50**|1.20|1.40|1.60|\n",
-    "|**60**|1.17|1.33|1.50|\n",
-    "\n",
-    "To express that in Python, we start by creating two `list` objects, `first` and `second`."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 304,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "first = [10, 20, 30]\n",
-    "second = [40, 50, 60]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "source": [
-    "For a Cartesian product, we loop over *all* possible $2$-tuples where one element is drawn from `first` and the other from `second`. That is equivalent to two nested `for`-loops."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 305,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[1.25, 1.2, 1.1666666666666667, 1.5, 1.4, 1.3333333333333333, 1.75, 1.6, 1.5]"
-      ]
-     },
-     "execution_count": 305,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "cartesian_product = []\n",
-    "\n",
-    "for numerator in first:\n",
-    "    for denominator in second:\n",
-    "        quotient = numerator / denominator\n",
-    "        cartesian_product.append(quotient + 1)\n",
-    "\n",
-    "cartesian_product"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "source": [
-    "We translate the two `for`-loops into one list comprehensions with two `for`'s in it and use `x` and `y` as shorter variable names."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 306,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[1.25, 1.2, 1.1666666666666667, 1.5, 1.4, 1.3333333333333333, 1.75, 1.6, 1.5]"
-      ]
-     },
-     "execution_count": 306,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "[(x / y) + 1 for x in first for y in second]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "source": [
-    "The order of the `for`'s is *important*: The list comprehension above divides numbers from `first` by numbers from `second`, whereas the list comprehension below does the opposite."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 307,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[5.0, 3.0, 2.333333333333333, 6.0, 3.5, 2.666666666666667, 7.0, 4.0, 3.0]"
-      ]
-     },
-     "execution_count": 307,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "[(x / y) + 1 for x in second for y in first]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "source": [
-    "To find the overall product, we *unpack* the first list comprehension right into the `product()` function from the \"*Packing & Unpacking*\" section above."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 308,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "20.58"
-      ]
-     },
-     "execution_count": 308,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "product(*[(x / y) + 1 for x in first for y in second])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "source": [
-    "Alternatively, we use a `lambda` expression with the [reduce()](https://docs.python.org/3/library/functools.html#functools.reduce) function from the [functools](https://docs.python.org/3/library/functools.html) module."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 309,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "20.58"
-      ]
-     },
-     "execution_count": 309,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "reduce(lambda x, y: x * y, [(x / y) + 1 for x in first for y in second])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "source": [
-    "While this example is stylized, Cartesian products are hidden in many applications, and it shows how the various language features introduced in this chapter can be seamlessly combined to process sequential data."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "slide"
-    }
-   },
-   "source": [
-    "### Generator Expressions"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "source": [
-    "Pythonistas would forgo materialized `list` objects, and, thus, also list comprehensions, all together, and use a more memory-efficient approach with **[generator expressions](https://docs.python.org/3/reference/expressions.html#generator-expressions)**, or **genexps** for short. Syntactically, they work like list comprehensions except that parentheses replace the brackets.\n",
-    "\n",
-    "Let's go back to the original example in this section and find the transformation $y := x^2 + 1$ of all even elements in `numbers`."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 310,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "slide"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "numbers = [7, 11, 8, 5, 3, 12, 2, 6, 9, 10, 1, 4]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "source": [
-    "To filter and transform `numbers`, we wrote this list comprehension above ..."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 311,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "-"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[65, 145, 5, 37, 101, 17]"
-      ]
-     },
-     "execution_count": 311,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "[(n ** 2) + 1 for n in numbers if n % 2 == 0]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "source": [
-    "... that now becomes a generator expression."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 312,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "fragment"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<generator object <genexpr> at 0x7fb57436f3d0>"
-      ]
-     },
-     "execution_count": 312,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "((n ** 2) + 1 for n in numbers if n % 2 == 0)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "source": [
-    "We can think of it as yet another \"rule\" in memory that knows how to generate the individual objects in a series one by one. Whereas a list comprehension materializes its elements in memory *when* it is evaluated, the opposite holds for generator expressions, and *no* object is created in memory except the \"rule\" itself. Because of this behavior, we describe generator expressions as *lazy* and list comprehensions as *eager*.\n",
-    "\n",
-    "So, to materialize all elements specified by a generator expression, we revert to the [list()](https://docs.python.org/3/library/functions.html#func-list) built-in."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 313,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "fragment"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[65, 145, 5, 37, 101, 17]"
-      ]
-     },
-     "execution_count": 313,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "list(((n ** 2) + 1 for n in numbers if n % 2 == 0))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "source": [
-    "Whenever a generator expression is the only argument to a function, we may leave out the parentheses."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 314,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "fragment"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[65, 145, 5, 37, 101, 17]"
-      ]
-     },
-     "execution_count": 314,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "list((n ** 2) + 1 for n in numbers if n % 2 == 0)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "source": [
-    "A common use case is to reduce the elements into a single object instead, for example, by adding them up with [sum()](https://docs.python.org/3/library/functions.html#sum). [PythonTutor](http://pythontutor.com/visualize.html#code=numbers%20%3D%20range%281,%2013%29%0Asum_with_list%20%3D%20sum%28%5B%28n%20**%202%29%20%2B%201%20for%20n%20in%20numbers%20if%20n%20%25%202%20%3D%3D%200%5D%29%0Asum_with_gen%20%3D%20sum%28%28n%20**%202%29%20%2B%201%20for%20n%20in%20numbers%20if%20n%20%25%202%20%3D%3D%200%29&cumulative=false&curInstr=0&heapPrimitives=nevernest&mode=display&origin=opt-frontend.js&py=3&rawInputLstJSON=%5B%5D&textReferences=false) shows how the code cell below does *not* create a temporary `list` object in memory, whereas a list comprehension would (cf., step 17)."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 315,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "fragment"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "370"
-      ]
-     },
-     "execution_count": 315,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "sum((n ** 2) + 1 for n in numbers if n % 2 == 0)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "source": [
-    "Let's assign the object returned from a generator expression to a variable and inspect it."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 316,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "slide"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "gen = ((n ** 2) + 1 for n in numbers if n % 2 == 0)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 317,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "-"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<generator object <genexpr> at 0x7fb57436f650>"
-      ]
-     },
-     "execution_count": 317,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "gen"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "source": [
-    "Unsurprisingly, generator expressions create objects of type `generator`."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 318,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "-"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "generator"
-      ]
-     },
-     "execution_count": 318,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "type(gen)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "source": [
-    "With the [next()](https://docs.python.org/3/library/functions.html#next) function, we can retrieve the generated elements one by one."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 319,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "fragment"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "65"
-      ]
-     },
-     "execution_count": 319,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "next(gen)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 320,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "145"
-      ]
-     },
-     "execution_count": 320,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "next(gen)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 321,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "5"
-      ]
-     },
-     "execution_count": 321,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "next(gen)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 322,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "37"
-      ]
-     },
-     "execution_count": 322,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "next(gen)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 323,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "101"
-      ]
-     },
-     "execution_count": 323,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "next(gen)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 324,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "17"
-      ]
-     },
-     "execution_count": 324,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "next(gen)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "source": [
-    "Once a `generator` object runs out of elements, it raises a `StopIteration` exception. We say that the `generator` object is **exhausted**, and to loop over its elements again, we must create a *new* one."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 325,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "outputs": [
-    {
-     "ename": "StopIteration",
-     "evalue": "",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mStopIteration\u001b[0m                             Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-325-6e72e47198db>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mnext\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mgen\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
-      "\u001b[0;31mStopIteration\u001b[0m: "
-     ]
-    }
-   ],
-   "source": [
-    "next(gen)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 326,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "outputs": [
-    {
-     "ename": "StopIteration",
-     "evalue": "",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mStopIteration\u001b[0m                             Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-326-6e72e47198db>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mnext\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mgen\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
-      "\u001b[0;31mStopIteration\u001b[0m: "
-     ]
-    }
-   ],
-   "source": [
-    "next(gen)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "source": [
-    "Calling the [next()](https://docs.python.org/3/library/functions.html#next) function repeatedly with the *same* `generator` object as the argument is essentially what a `for`-loop automates for us. So, `generator` objects are *iterable*."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 327,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "65 145 5 37 101 17 "
-     ]
-    }
-   ],
-   "source": [
-    "for number in ((n ** 2) + 1 for n in numbers if n % 2 == 0):\n",
-    "    print(number, end=\" \")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "source": [
-    "#### Example: Nested Lists (revisited)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "source": [
-    "If we are only interested in a *reduction* of `nested_numbers` into a single statistic, as the overall sum in the \"*Nested Lists*\" example, we should replace lists or list comprehensions with generator expressions wherever possible.\n",
-    "\n",
-    "The result is the *same*, but no intermediate lists are materialized! That makes our code scale to larger amounts of data and uses the available hardware more efficiently.\n",
-    "\n",
-    "Let's adapt the example but keep `nested_numbers` unchanged for now."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 328,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "nested_numbers = [list(range(x, y + 1)) for x, y in zip([1, 2, 3], [7, 8, 9])]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 329,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[[1, 2, 3, 4, 5, 6, 7], [2, 3, 4, 5, 6, 7, 8], [3, 4, 5, 6, 7, 8, 9]]"
-      ]
-     },
-     "execution_count": 329,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "nested_numbers"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "source": [
-    "We leave out the brackets and keep everything else as-is: The argument to [sum()](https://docs.python.org/3/library/functions.html#sum), a list comprehension in the initial implementation above, becomes a generator expression."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 330,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "105"
-      ]
-     },
-     "execution_count": 330,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "sum(number for inner_numbers in nested_numbers for number in inner_numbers)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "source": [
-    "That also holds for the alternative formulation as a sum of sums."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 331,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "105"
-      ]
-     },
-     "execution_count": 331,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "sum(sum(inner_numbers) for inner_numbers in nested_numbers)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "source": [
-    "Because `nested_numbers` has an internal structure, we can make it **memoryless** by expressing it as a generator expression derived from `range` objects. [PythonTutor](http://pythontutor.com/visualize.html#code=nested_numbers%20%3D%20%28%28range%28x,%20y%20%2B%201%29%29%20for%20x,%20y%20in%20zip%28range%281,%204%29,%20range%287,%2010%29%29%29%0Aresult%20%3D%20sum%28number%20for%20inner_numbers%20in%20nested_numbers%20for%20number%20in%20inner_numbers%29&cumulative=false&curInstr=0&heapPrimitives=nevernest&mode=display&origin=opt-frontend.js&py=3&rawInputLstJSON=%5B%5D&textReferences=false) confirms that no `list` object materializes at any point in time."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 332,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "nested_numbers = ((range(x, y + 1)) for x, y in zip(range(1, 4), range(7, 10)))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 333,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<generator object <genexpr> at 0x7fb57436fad0>"
-      ]
-     },
-     "execution_count": 333,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "nested_numbers"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 334,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "105"
-      ]
-     },
-     "execution_count": 334,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "sum(number for inner_numbers in nested_numbers for number in inner_numbers)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "source": [
-    "We must be careful when assigning a `generator` object to a variable: If we use `nested_numbers` again, for example, in the alternative formulation below, [sum()](https://docs.python.org/3/library/functions.html#sum) returns `0` because `nested_numbers` is exhausted after executing the previous code cell. [PythonTutor](http://pythontutor.com/visualize.html#code=nested_numbers%20%3D%20%28%28range%28x,%20y%20%2B%201%29%29%20for%20x,%20y%20in%20zip%28range%281,%204%29,%20range%287,%2010%29%29%29%0Aresult%20%3D%20sum%28number%20for%20inner_numbers%20in%20nested_numbers%20for%20number%20in%20inner_numbers%29%0Ano_result%20%3D%20sum%28sum%28inner_numbers%29%20for%20inner_numbers%20in%20nested_numbers%29&cumulative=false&curInstr=0&heapPrimitives=nevernest&mode=display&origin=opt-frontend.js&py=3&rawInputLstJSON=%5B%5D&textReferences=false) also shows that."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 335,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "0"
-      ]
-     },
-     "execution_count": 335,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "sum(sum(inner_numbers) for inner_numbers in nested_numbers)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "source": [
-    "#### Example: Cartesian Products (revisited)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "source": [
-    "Let's also rewrite the \"*Cartesian Products*\" example from above with generator expressions.\n",
-    "\n",
-    "As a first optimization, we replace the materialized `list` objects, `first` and `second`, with memoryless `range` objects."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 336,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "first = range(10, 31, 10)  # = [10, 20, 30]\n",
-    "second = range(40, 61, 10)  # = [40, 50, 60]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "source": [
-    "Now, the first of the two alternatives may be more appealing to many readers. In general, many practitioners seem to dislike `lambda` expressions.\n",
-    "\n",
-    "The code cell below *unpacks* the elements produced by `((x / y) + 1 for x in first for y in second)` into the `product()` function from the \"*Packing & Unpacking*\" section above. However, inside `product()`, the elements are *packed* into `args`, a materialized `tuple` object. So, all the memory efficiency gained with the generator expression is voided! [PythonTutor](http://pythontutor.com/visualize.html#code=def%20product%28*args%29%3A%0A%20%20%20%20result%20%3D%20args%5B0%5D%0A%20%20%20%20for%20arg%20in%20args%5B1%3A%5D%3A%0A%20%20%20%20%20%20%20%20result%20*%3D%20arg%0A%20%20%20%20return%20result%0A%0Afirst%20%3D%20range%2810,%2031,%2010%29%0Asecond%20%3D%20range%2840,%2061,%2010%29%0A%0Aresult%20%3D%20product%28*%28%28x%20/%20y%29%20%2B%201%20for%20x%20in%20first%20for%20y%20in%20second%29%29&cumulative=false&curInstr=0&heapPrimitives=nevernest&mode=display&origin=opt-frontend.js&py=3&rawInputLstJSON=%5B%5D&textReferences=false) shows how a `tuple` object exists in steps 38-58."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 337,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "20.58"
-      ]
-     },
-     "execution_count": 337,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "product(*((x / y) + 1 for x in first for y in second))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "source": [
-    "On the contrary, the solution with the [reduce()](https://docs.python.org/3/library/functools.html#functools.reduce) function from the [functools](https://docs.python.org/3/library/functools.html) module and the `lambda` expression works *without* all elements materialized at the same time, and [PythonTutor](http://pythontutor.com/visualize.html#code=from%20functools%20import%20reduce%0A%0Afirst%20%3D%20range%2810,%2031,%2010%29%0Asecond%20%3D%20range%2840,%2061,%2010%29%0A%0Aresult%20%3D%20reduce%28%0A%20%20%20%20lambda%20x,%20y%3A%20x%20*%20y,%0A%20%20%20%20%28%28x%20/%20y%29%20%2B%201%20for%20x%20in%20first%20for%20y%20in%20second%29%0A%29&cumulative=false&curInstr=0&heapPrimitives=nevernest&mode=display&origin=opt-frontend.js&py=3&rawInputLstJSON=%5B%5D&textReferences=false) confirms that. So, only the second alternative is truly memory-efficient."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 338,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "20.58"
-      ]
-     },
-     "execution_count": 338,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "reduce(lambda x, y: x * y, ((x / y) + 1 for x in first for y in second))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "source": [
-    "In summary, we learn from this example that unpacking generator expressions *may* be a *bad* idea."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "source": [
-    "### Tuple Comprehensions"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "source": [
-    "There is no syntax to derive *new* `tuple` objects out of existing ones. However, we can mimic such a construct by combining the [tuple()](https://docs.python.org/3/library/functions.html#func-tuple) built-in with a generator expression.\n",
-    "\n",
-    "So, to convert the list comprehension `[(n ** 2) + 1 for n in numbers if n % 2 == 0]` from above into a \"tuple comprehension,\" we write the following."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 339,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(65, 145, 5, 37, 101, 17)"
-      ]
-     },
-     "execution_count": 339,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "tuple((n ** 2) + 1 for n in numbers if n % 2 == 0)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "slide"
-    }
-   },
-   "source": [
-    "### Boolean Reducers"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "source": [
-    "Besides [min()](https://docs.python.org/3/library/functions.html#min), [max()](https://docs.python.org/3/library/functions.html#max), and [sum()](https://docs.python.org/3/library/functions.html#sum), Python provides two boolean reduce functions: [all()](https://docs.python.org/3/library/functions.html#all) and [any()](https://docs.python.org/3/library/functions.html#any).\n",
-    "\n",
-    "Let's look at straightforward examples involving `numbers` again."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 340,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "slide"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "numbers = [7, 11, 8, 5, 3, 12, 2, 6, 9, 10, 1, 4]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "source": [
-    "[all()](https://docs.python.org/3/library/functions.html#all) takes an *iterable* argument and returns `True` if *all* elements are *truthy*.\n",
-    "\n",
-    "For example, let's check if the square of each element in `numbers` is below `100` or `150`, respectively. We express the computation with a generator expression passed as the only argument to [all()](https://docs.python.org/3/library/functions.html#all)."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 341,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "fragment"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "False"
-      ]
-     },
-     "execution_count": 341,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "all(x ** 2 < 100 for x in numbers)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 342,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "-"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "True"
-      ]
-     },
-     "execution_count": 342,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "all(x ** 2 < 150 for x in numbers)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "source": [
-    "[all()](https://docs.python.org/3/library/functions.html#all) can be viewed as syntactic sugar replacing a `for`-loop: Internally, [all()](https://docs.python.org/3/library/functions.html#all) implements the *short-circuiting* strategy from [Chapter 3](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/03_conditionals_00_lecture.ipynb#Short-Circuiting), and we mimic that by testing for the *opposite* condition in the `if` statement and leaving the `for`-loop early with the `break` statement. In the worst case, if `threshold` were, for example, `150`, we would loop over *all* elements in the *iterable*, which must be *finite* for the code to work. So, [all()](https://docs.python.org/3/library/functions.html#all) is a *linear search* in disguise."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 343,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "False"
-      ]
-     },
-     "execution_count": 343,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "threshold = 100\n",
-    "\n",
-    "for number in numbers:\n",
-    "    if number ** 2 >= threshold:  # = the opposite of what we are checking for\n",
-    "        all_below_threshold = False\n",
-    "        break\n",
-    "else:\n",
-    "    all_below_threshold = True\n",
-    "\n",
-    "all_below_threshold"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "source": [
-    "The documentation of [all()](https://docs.python.org/3/library/functions.html#all) shows in another way what it does with code: By placing a `return` statement inside the `for`-loop of a function's body, iteration is stopped prematurely once an element does *not* meet the condition. That is the familiar *early exit* pattern at work."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 344,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "def all_alt(iterable):\n",
-    "    \"\"\"Alternative implementation of the built-in all() function.\"\"\"\n",
-    "    for element in iterable:\n",
-    "        if not element:  # = the opposite of what we are checking for\n",
-    "            return False\n",
-    "    return True"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 345,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "False"
-      ]
-     },
-     "execution_count": 345,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "all_alt(x ** 2 < 100 for x in numbers)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 346,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "True"
-      ]
-     },
-     "execution_count": 346,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "all_alt(x ** 2 < 150 for x in numbers)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "source": [
-    "Similarly, [any()](https://docs.python.org/3/library/functions.html#any) checks if *at least* one element in the *iterable* argument is *truthy*.\n",
-    "\n",
-    "To continue the example, let's check if the square of *any* element in `numbers` is above `100` or `150`, respectively."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 347,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "fragment"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "True"
-      ]
-     },
-     "execution_count": 347,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "any(x ** 2 > 100 for x in numbers)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 348,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "-"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "False"
-      ]
-     },
-     "execution_count": 348,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "any(x ** 2 > 150 for x in numbers)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "source": [
-    "Expressed with a `for`-loop, the implementation below reveals that [any()](https://docs.python.org/3/library/functions.html#any) follows the *short-circuiting* strategy as well. Here, we do *not* need to check for the opposite condition."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 349,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "True"
-      ]
-     },
-     "execution_count": 349,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "threshold = 100\n",
-    "\n",
-    "for number in numbers:\n",
-    "    if number ** 2 > threshold:\n",
-    "        any_above_threshold = True\n",
-    "        break\n",
-    "else:\n",
-    "    any_above_threshold = False\n",
-    "\n",
-    "any_above_threshold"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "source": [
-    "The alternative formulation in the documentation of [any()](https://docs.python.org/3/library/functions.html#any) is straightforward."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 350,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "def any_alt(iterable):\n",
-    "    \"\"\"Alternative implementation of the built-in any() function.\"\"\"\n",
-    "    for element in iterable:\n",
-    "        if element:\n",
-    "            return True\n",
-    "    return False"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 351,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "True"
-      ]
-     },
-     "execution_count": 351,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "any_alt(x ** 2 > 100 for x in numbers)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 352,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "False"
-      ]
-     },
-     "execution_count": 352,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "any_alt(x ** 2 > 150 for x in numbers)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "source": [
-    "### Example: Averaging Even Numbers (revisited)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "source": [
-    "With the new concepts in this chapter, let's rewrite the book's introductory \"*Averaging Even Numbers*\" example in [Chapter 1](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/01_elements_00_lecture.ipynb#Example:-Averaging-Even-Numbers) such that it efficiently handles a large sequence of numbers.\n",
-    "\n",
-    "We assume the `average_evens()` function below is called with a *finite* and *iterable* object, which generates a \"stream\" of numeric objects that can be cast as `int` objects because the idea of even and odd numbers only makes sense in the context of whole numbers.\n",
-    "\n",
-    "The generator expression `(int(n) for n in numbers)` implements the type casting, and when it is evaluated, *nothing* happens except that a `generator` object is stored in `integers`. Then, with the [reduce()](https://docs.python.org/3/library/functools.html#functools.reduce) function from the [functools](https://docs.python.org/3/library/functools.html) module, we *simultaneously* add up *and* count the even numbers produced by the inner generator expression `((n, 1) for n in integers if n % 2 == 0)`. That results in a `generator` object producing `tuple` objects consisting of the next *even* number in line and `1`. Two such `tuple` objects are then iteratively passed to the `lambda` expression as the `x` and `y` arguments. `x` represents the total and the count of the even numbers processed so far, while `y`'s first element, `y[0]`, is the next even number to be added to the running total. The result of the [reduce()](https://docs.python.org/3/library/functools.html#functools.reduce) function is again a `tuple` object, namely the final `total` and `count`. Lastly, we calculate the simple average.\n",
-    "\n",
-    "In summary, the implementation of `average_evens()` does *not* keep materialized `list` objects internally like its predecessors from [Chapter 2](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/02_functions_00_lecture.ipynb), but processes the elements of the `numbers` argument on a one-by-one basis."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 353,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "def average_evens(numbers):\n",
-    "    \"\"\"Calculate the average of all even integers.\n",
-    "\n",
-    "    Args:\n",
-    "        numbers (iterable): a finite stream of numbers;\n",
-    "            may be integers or floats; floats are truncated\n",
-    "\n",
-    "    Returns:\n",
-    "        float: average\n",
-    "    \"\"\"\n",
-    "    integers = (int(n) for n in numbers)\n",
-    "    total, count = reduce(\n",
-    "        lambda x, y: (x[0] + y[0], x[1] + y[1]),\n",
-    "        ((n, 1) for n in integers if n % 2 == 0)\n",
-    "    )\n",
-    "    return total / count"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 354,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "7.0"
-      ]
-     },
-     "execution_count": 354,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "average_evens([7, 11, 8, 5, 3, 12, 2, 6, 9, 10, 1, 4])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "source": [
-    "An argument generating `float` objects works as well."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 355,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "7.0"
-      ]
-     },
-     "execution_count": 355,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "average_evens([7., 11., 8., 5., 3., 12., 2., 6., 9., 10., 1., 4.])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "source": [
-    "To show that `average_evens()` can process a **stream** of data, we simulate `10_000_000` randomly drawn integers between `0` and `100` with the [randint()](https://docs.python.org/3/library/random.html#random.randint) function from the [random](https://docs.python.org/3/library/random.html) module. We use a generator expression derived from a `range` object as the `numbers` argument. So, at *no* point in time is there a materialized `list` or `tuple` object in memory. The result approaching `50` tells us that [randint()](https://docs.python.org/3/library/random.html#random.randint) must be based on a uniform distribution."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 356,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "import random"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 357,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "random.seed(42)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 358,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "49.994081434519636"
-      ]
-     },
-     "execution_count": 358,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "average_evens(random.randint(0, 100) for _ in range(10_000_000))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "source": [
-    "To show that `average_evens()` filters out odd numbers, we simulate another stream of `10_000_000` randomly drawn odd integers between `1` and `99`. As no function in the [random](https://docs.python.org/3/library/random.html) module does that \"out of the box,\" we must be creative: Doubling a number drawn from `random.randint(0, 49)` results in an even number between `0` and `98`, and adding `1` makes it odd. Then, `average_evens()` raises a `TypeError`, essentially because `(int(n) for n in numbers)` does not generate any element."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 359,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "outputs": [
-    {
-     "ename": "TypeError",
-     "evalue": "reduce() of empty sequence with no initial value",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-359-73a295f34553>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0maverage_evens\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m2\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0mrandom\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrandint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m49\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;36m1\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0m_\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m10_000_000\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
-      "\u001b[0;32m<ipython-input-353-491a5ff07381>\u001b[0m in \u001b[0;36maverage_evens\u001b[0;34m(numbers)\u001b[0m\n\u001b[1;32m     12\u001b[0m     total, count = reduce(\n\u001b[1;32m     13\u001b[0m         \u001b[0;32mlambda\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 14\u001b[0;31m         \u001b[0;34m(\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mn\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mn\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mintegers\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mn\u001b[0m \u001b[0;34m%\u001b[0m \u001b[0;36m2\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     15\u001b[0m     )\n\u001b[1;32m     16\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mtotal\u001b[0m \u001b[0;34m/\u001b[0m \u001b[0mcount\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;31mTypeError\u001b[0m: reduce() of empty sequence with no initial value"
-     ]
-    }
-   ],
-   "source": [
-    "average_evens(2 * random.randint(0, 49) + 1 for _ in range(10_000_000))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "slide"
-    }
-   },
-   "source": [
-    "## Iterators vs. Iterables"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "source": [
-    "In the \"*Collections vs. Sequences*\" section above, we studied the three and four *behaviors* of collections and sequences. The latter two are *abstract* ideas, and we mainly use them to classify *concrete* data types.\n",
-    "\n",
-    "Similarly, we have introduced data types in this chapter that all share the \"behavior\" of modeling some \"rule\" in memory to generate objects \"on the fly:\" They are the `map`, `filter`, and `generator` types. Their main commonality is supporting the built-in [next()](https://docs.python.org/3/library/functions.html#next) function. In computer science terminology, such data types are called **[iterators](https://en.wikipedia.org/wiki/Iterator)**, and the [collections.abc](https://docs.python.org/3/library/collections.abc.html) module formalizes them with the `Iterator` ABC.\n",
-    "\n",
-    "So, an example of an iterator is `evens_transformed` below, an object of type `generator`."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 360,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "slide"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "numbers = [7, 11, 8, 5, 3, 12, 2, 6, 9, 10, 1, 4]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 361,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "-"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "evens_transformed = ((x ** 2) + 1 for x in numbers if x % 2 == 0)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "source": [
-    "Let's first confirm that `evens_transformed` is indeed an `Iterator`, \"abstractly speaking.\""
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 362,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "fragment"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "True"
-      ]
-     },
-     "execution_count": 362,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "isinstance(evens_transformed, abc.Iterator)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "source": [
-    "In Python, iterators are *always* also iterables. The reverse does *not* hold! To be precise, iterators are *specializations* of iterables. That is what the \"Inherits from\" columns means in the [collections.abc](https://docs.python.org/3/library/collections.abc.html) module's documentation."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 363,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "fragment"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "True"
-      ]
-     },
-     "execution_count": 363,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "isinstance(evens_transformed, abc.Iterable)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "source": [
-    "Furthermore, we revise our definition of *iterables* from above: Just as we defined an *iterator* to be an object that supports the [next()](https://docs.python.org/3/library/functions.html#next) function, we define an *iterable* to be an object that supports the built-in [iter()](https://docs.python.org/3/library/functions.html#iter) function.\n",
-    "\n",
-    "The confused reader may now be wondering how the two concepts relate to each other.\n",
-    "\n",
-    "In short, the [iter()](https://docs.python.org/3/library/functions.html#iter) function is the general way to create an *iterator* object out of a given *iterable* object. In real-world code, we hardly ever see [iter()](https://docs.python.org/3/library/functions.html#iter) as Python calls it for us in the background. Then, the *iterator* object manages the iteration over the *iterable* object.\n",
-    "\n",
-    "For illustration, let's do that ourselves and create *two* iterators out of the iterable `numbers` and see what we can do with them."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 364,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "slide"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "iterator1 = iter(numbers)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 365,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "-"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "iterator2 = iter(numbers)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "source": [
-    "`iterator1` and `iterator2` are of type `list_iterator`."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 366,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "fragment"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "list_iterator"
-      ]
-     },
-     "execution_count": 366,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "type(iterator1)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "source": [
-    "*Iterators* are useful for only *one* operation: Get the next object from the associated *iterable*.\n",
-    "\n",
-    "By calling [next()](https://docs.python.org/3/library/functions.html#next) three times with `iterator1` as the argument, we obtain the first three elements of `numbers`."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 367,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "fragment"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(7, 11, 8)"
-      ]
-     },
-     "execution_count": 367,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "next(iterator1), next(iterator1), next(iterator1)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "source": [
-    "`iterator1` and `iterator2` keep their *states* separate. So, we could \"manually\" loop over an *iterable* in parallel."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 368,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "fragment"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(5, 7)"
-      ]
-     },
-     "execution_count": 368,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "next(iterator1), next(iterator2)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "source": [
-    "We can also play a \"trick\" and exchange some elements in `numbers`. `iterator1` and `iterator2` do *not* see these changes and present us with the new elements. So, *iterators* not only have state on their own but also keep this separate from the underlying *iterable*."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 369,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "slide"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "numbers[1], numbers[4] = 99, 99"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 370,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "-"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(99, 99)"
-      ]
-     },
-     "execution_count": 370,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "next(iterator1), next(iterator2)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "source": [
-    "Let's re-assign the elements in `numbers` so that they are in order. Now, the numbers returned from [next()](https://docs.python.org/3/library/functions.html#next) also tell us how often [next()](https://docs.python.org/3/library/functions.html#next) was called with `iterator1` or `iterator2`. We conclude that `list_iterator` objects must be keeping track of the *last* index obtained from the underlying *iterable*."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 371,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "fragment"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "numbers[:] = list(range(1, 13))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 372,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "-"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12]"
-      ]
-     },
-     "execution_count": 372,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "numbers"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 373,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "fragment"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(6, 3)"
-      ]
-     },
-     "execution_count": 373,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "next(iterator1), next(iterator2)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "source": [
-    "With the concepts introduced in this section, we can now understand the first sentence in the documentation on the [zip()](https://docs.python.org/3/library/functions.html#zip) built-in better: \"Make an *iterator* that aggregates elements from each of the *iterables*.\"\n",
-    "\n",
-    "Because *iterators* are always also *iterables*, we pass `iterator1` and `iterator2` as arguments to [zip()](https://docs.python.org/3/library/functions.html#zip).\n",
-    "\n",
-    "The returned `zipper` object is of type `zip` and, \"abstractly speaking,\" an `Iterator` as well."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 374,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "slide"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "zipper = zip(iterator1, iterator2)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 375,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "-"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<zip at 0x7fb57438a500>"
-      ]
-     },
-     "execution_count": 375,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "zipper"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 376,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "-"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "zip"
-      ]
-     },
-     "execution_count": 376,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "type(zipper)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 377,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "-"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "True"
-      ]
-     },
-     "execution_count": 377,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "isinstance(zipper, abc.Iterator)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "source": [
-    "So far, we have always used [zip()](https://docs.python.org/3/library/functions.html#zip) with a `for` statement and looped over it. That was our earlier definition of an *iterable*. Our revised definition in this section states that an *iterable* is an object that supports the [iter()](https://docs.python.org/3/library/functions.html#iter) function. So, let's see what happens if we pass `zipper` to [iter()](https://docs.python.org/3/library/functions.html#iter)."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 378,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "slide"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "zipper_iterator = iter(zipper)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 379,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "-"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<zip at 0x7fb57438a500>"
-      ]
-     },
-     "execution_count": 379,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "zipper_iterator"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "source": [
-    "`zipper_iterator` references the *same* object as `zipper`! That holds for *iterators* in general: Any *iterator* created from an existing *iterator* with [iter()](https://docs.python.org/3/library/functions.html#iter) is the *iterator* itself."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 380,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "-"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "True"
-      ]
-     },
-     "execution_count": 380,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "zipper is zipper_iterator"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "source": [
-    "The Python core developers made that design decision so that *iterators* may also be looped over.\n",
-    "\n",
-    "The `for`-loop below prints out *six* more `tuple` objects derived from the now ordered `numbers` because the `iterator1` object hidden inside `zipper` already returned the first *six* elements. So, the respective first elements of the `tuple` objects printed range from `7` to `12`. Similarly, as `iterator2` already provided *three* elements from `numbers`, we see the respective second elements in the range from `4` to `9`."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 381,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "fragment"
-    }
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "7 > 4   8 > 5   9 > 6   10 > 7   11 > 8   12 > 9   "
-     ]
-    }
-   ],
-   "source": [
-    "for x, y in zipper:\n",
-    "    print(x, \">\", y, end=\"   \")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "source": [
-    "`zipper` is now *exhausted*. So, the `for`-loop below does *not* make any iteration at all."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 382,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "for x, y in zipper:\n",
-    "    print(x, \">\", y, end=\"   \")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "source": [
-    "We verify that `iterator1` is exhausted by passing it to [next()](https://docs.python.org/3/library/functions.html#next) again, which raises a `StopIteration` exception."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 383,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "fragment"
-    }
-   },
-   "outputs": [
-    {
-     "ename": "StopIteration",
-     "evalue": "",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mStopIteration\u001b[0m                             Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-383-0680f5ec8ce3>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mnext\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0miterator1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
-      "\u001b[0;31mStopIteration\u001b[0m: "
-     ]
-    }
-   ],
-   "source": [
-    "next(iterator1)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "source": [
-    "On the contrary, `iterator2` is *not* yet exhausted."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 384,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "10"
-      ]
-     },
-     "execution_count": 384,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "next(iterator2)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "source": [
-    "Understanding *iterators* and *iterables* is helpful for any data science practitioner that deals with large amounts of data. Even without that, these two terms occur everywhere in Python-related texts and documentation."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "slide"
-    }
-   },
-   "source": [
-    "### The `for` Statement (revisited)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "source": [
-    "In [Chapter 4](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/04_iteration_00_lecture.ipynb#The-for-Statement), we argue that the `for` statement is syntactic sugar, replacing the `while` statement in many scenarios. In particular, a `for`-loop saves us two tasks: Managing an index variable *and* obtaining the individual elements by indexing. In this sub-section, we look at a more realistic picture, using the new terminology as well.\n",
-    "\n",
-    "Let's print out the elements of a `list` object as the *iterable* to be looped over."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 385,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "slide"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "iterable = [0, 1, 2, 3, 4]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 386,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "-"
-    }
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "0 1 2 3 4 "
-     ]
-    }
-   ],
-   "source": [
-    "for element in iterable:\n",
-    "    print(element, end=\" \")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "source": [
-    "Our previous and equivalent formulation with a `while` statement is like so."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 387,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "0 1 2 3 4 "
-     ]
-    }
-   ],
-   "source": [
-    "index = 0\n",
-    "while index < len(iterable):\n",
-    "    element = iterable[index]\n",
-    "    print(element, end=\" \")\n",
-    "    index += 1\n",
-    "del index"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "source": [
-    "What happens behind the scenes in the Python interpreter is shown below.\n",
-    "\n",
-    "First, Python calls [iter()](https://docs.python.org/3/library/functions.html#iter) with the `iterable` to be looped over, and obtains an `iterator`. That contains the entire logic of how the `iterable` is looped over. In particular, the `iterator` may or may not pick the `iterable`'s elements in a predictable order. That is up to the \"rule\" it models.\n",
-    "\n",
-    "Second, Python enters an *indefinite* `while`-loop. It tries to obtain the next element with [next()](https://docs.python.org/3/library/functions.html#next). If that succeeds, the `for`-loop's code block is executed. Below, that code is placed within the `else`-clause that runs only if *no* exception is raised in the `try`-clause. Then, Python jumps into the next iteration and tries to obtain the next element from the `iterator`, and so on. Once the `iterator` is exhausted, it raises a `StopIteration` exception, and Python leaves the `while`-loop with the `break` statement."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 388,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "fragment"
-    }
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "0 1 2 3 4 "
-     ]
-    }
-   ],
-   "source": [
-    "iterator = iter(iterable)\n",
-    "\n",
-    "while True:\n",
-    "    try:\n",
-    "        element = next(iterator)\n",
-    "    except StopIteration:\n",
-    "        break\n",
-    "    else:\n",
-    "        print(element, end=\" \")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "slide"
-    }
-   },
-   "source": [
-    "### sorted() vs. reversed()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "source": [
-    "Now that we know the concept of an *iterator*, let's compare some of the built-ins introduced in this chapter in detail and make sure we understand what is going on in memory. This sub-section is thus a great summary of this chapter as well.\n",
-    "\n",
-    "We use two simple examples, `numbers` and `memoryless`, to guide us through the discussion. `numbers` creates *thirteen* objects in memory and `memoryless` only *one* (cf., [PythonTutor](http://www.pythontutor.com/visualize.html#code=numbers%20%3D%20%5B7,%2011,%208,%205,%203,%2012,%202,%206,%209,%2010,%201,%204%5D%0Amemoryless%20%3D%20range%281,%2013%29&cumulative=false&curInstr=2&heapPrimitives=nevernest&mode=display&origin=opt-frontend.js&py=3&rawInputLstJSON=%5B%5D&textReferences=false))."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 389,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "slide"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "numbers = [7, 11, 8, 5, 3, 12, 2, 6, 9, 10, 1, 4]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 390,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "memoryless = range(1, 13)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "source": [
-    "The [sorted()](https://docs.python.org/3/library/functions.html#sorted) function takes a *finite* and *iterable* object as its argument and *materializes* its elements into a *new* `list` object that is returned.\n",
-    "\n",
-    "The argument may already be materialized, as is the case with `numbers`, but may also be an *iterable* without any objects in it, such as `memoryless`. In both cases, we end up with materialized `list` objects with the elements sorted in *forward* order (cf., [PythonTutor](http://www.pythontutor.com/visualize.html#code=numbers%20%3D%20%5B7,%2011,%208,%205,%203,%2012,%202,%206,%209,%2010,%201,%204%5D%0Amemoryless%20%3D%20range%281,%2013%29%0Aresult1%20%3D%20sorted%28numbers%29%0Aresult2%20%3D%20sorted%28memoryless%29&cumulative=false&curInstr=4&heapPrimitives=nevernest&mode=display&origin=opt-frontend.js&py=3&rawInputLstJSON=%5B%5D&textReferences=false))."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 391,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "fragment"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12]"
-      ]
-     },
-     "execution_count": 391,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "sorted(numbers)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 392,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12]"
-      ]
-     },
-     "execution_count": 392,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "sorted(memoryless)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "source": [
-    "By adding a keyword-only argument `reverse=True`, the materialized `list` objects are sorted in *reverse* order (cf., [PythonTutor](http://www.pythontutor.com/visualize.html#code=numbers%20%3D%20%5B7,%2011,%208,%205,%203,%2012,%202,%206,%209,%2010,%201,%204%5D%0Amemoryless%20%3D%20range%281,%2013%29%0Aresult1%20%3D%20sorted%28numbers,%20reverse%3DTrue%29%0Aresult2%20%3D%20sorted%28memoryless,%20reverse%3DTrue%29&cumulative=false&curInstr=4&heapPrimitives=nevernest&mode=display&origin=opt-frontend.js&py=3&rawInputLstJSON=%5B%5D&textReferences=false))."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 393,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "fragment"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[12, 11, 10, 9, 8, 7, 6, 5, 4, 3, 2, 1]"
-      ]
-     },
-     "execution_count": 393,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "sorted(numbers, reverse=True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 394,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[12, 11, 10, 9, 8, 7, 6, 5, 4, 3, 2, 1]"
-      ]
-     },
-     "execution_count": 394,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "sorted(memoryless, reverse=True)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "source": [
-    "The order in `numbers` remains *unchanged*, and `memoryless` is still *not* materialized."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 395,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "fragment"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[7, 11, 8, 5, 3, 12, 2, 6, 9, 10, 1, 4]"
-      ]
-     },
-     "execution_count": 395,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "numbers"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 396,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "range(1, 13)"
-      ]
-     },
-     "execution_count": 396,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "memoryless"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "source": [
-    "The [reversed()](https://docs.python.org/3/library/functions.html#reversed) built-in takes a *sequence* object as its argument and returns an *iterator*. The argument must be *finite* and *reversible* (i.e., *iterable* in *reverse* order) as otherwise [reversed()](https://docs.python.org/3/library/functions.html#reversed) could neither determine the last element that becomes the first nor loop in a *predictable* backward fashion. [PythonTutor](http://www.pythontutor.com/visualize.html#code=numbers%20%3D%20%5B7,%2011,%208,%205,%203,%2012,%202,%206,%209,%2010,%201,%204%5D%0Amemoryless%20%3D%20range%281,%2013%29%0Aiterator1%20%3D%20reversed%28numbers%29%0Aiterator2%20%3D%20reversed%28memoryless%29&cumulative=false&curInstr=4&heapPrimitives=nevernest&mode=display&origin=opt-frontend.js&py=3&rawInputLstJSON=%5B%5D&textReferences=false) confirms that [reversed()](https://docs.python.org/3/library/functions.html#reversed) does *not* materialize any elements but only returns an *iterator*.\n",
-    "\n",
-    "**Side Note**: Even though `range` objects, like `memoryless` here, do *not* \"contain\" references to other objects, they count as *sequence* types, and as such, they are also *container* types. The `in` operator works with `range` objects because we can always cast the object to be checked as an `int` and check if that lies within the `range` object's *start* and *stop* values, taking a potential *step* value into account (cf., this [blog post](https://treyhunner.com/2018/02/python-range-is-not-an-iterator/) for more details on the [range()](https://docs.python.org/3/library/functions.html#func-range) built-in)."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 397,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "slide"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<list_reverseiterator at 0x7fb574372e90>"
-      ]
-     },
-     "execution_count": 397,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "reversed(numbers)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 398,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<range_iterator at 0x7fb574347540>"
-      ]
-     },
-     "execution_count": 398,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "reversed(memoryless)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "source": [
-    "To materialize the elements, we can pass the returned *iterators* to, for example, the [list()](https://docs.python.org/3/library/functions.html#func-list) or [tuple()](https://docs.python.org/3/library/functions.html#func-tuple) built-ins. That creates *new* `list` and `tuple` objects (cf., [PythonTutor](http://www.pythontutor.com/visualize.html#code=numbers%20%3D%20%5B7,%2011,%208,%205,%203,%2012,%202,%206,%209,%2010,%201,%204%5D%0Amemoryless%20%3D%20range%281,%2013%29%0Aresult1%20%3D%20list%28reversed%28numbers%29%29%0Aresult2%20%3D%20tuple%28reversed%28memoryless%29%29&cumulative=false&curInstr=4&heapPrimitives=nevernest&mode=display&origin=opt-frontend.js&py=3&rawInputLstJSON=%5B%5D&textReferences=false)).\n",
-    "\n",
-    "To reiterate some more new terminology from this chapter, we describe [reversed()](https://docs.python.org/3/library/functions.html#reversed) as *lazy*, whereas [list()](https://docs.python.org/3/library/functions.html#func-list) and [tuple()](https://docs.python.org/3/library/functions.html#func-tuple) are *eager*. The former has no significant side effect in memory, while the latter may require a lot of memory."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 399,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "fragment"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[4, 1, 10, 9, 6, 2, 12, 3, 5, 8, 11, 7]"
-      ]
-     },
-     "execution_count": 399,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "list(reversed(numbers))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 400,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(12, 11, 10, 9, 8, 7, 6, 5, 4, 3, 2, 1)"
-      ]
-     },
-     "execution_count": 400,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "tuple(reversed(memoryless))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "source": [
-    "Of course, we can also loop over the returned *iterators* instead.\n",
-    "\n",
-    "That works because *iterators* are always *iterables*; in particular, as the previous \"*The for Statement (revisited)*\" sub-section explains, the `for`-loops below call `iter(reversed(numbers))` and `iter(reversed(memoryless))` behind the scenes. However, the *iterators* returned by [iter()](https://docs.python.org/3/library/functions.html#iter) are the *same* as the `reversed(numbers)` and `reversed(memoryless)` iterators passed in! In summary, the `for`-loops below involve many subtleties that together make Python the expressive language it is."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 401,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "fragment"
-    }
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "4 1 10 9 6 2 12 3 5 8 11 7 "
-     ]
-    }
-   ],
-   "source": [
-    "for number in reversed(numbers):\n",
-    "    print(number, end=\" \")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 402,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "-"
-    }
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "12 11 10 9 8 7 6 5 4 3 2 1 "
-     ]
-    }
-   ],
-   "source": [
-    "for element in reversed(memoryless):\n",
-    "    print(element, end=\" \")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "source": [
-    "As with [sorted()](https://docs.python.org/3/library/functions.html#sorted), the [reversed()](https://docs.python.org/3/library/functions.html#reversed) built-in does *not* mutate its argument."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 403,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "fragment"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[7, 11, 8, 5, 3, 12, 2, 6, 9, 10, 1, 4]"
-      ]
-     },
-     "execution_count": 403,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "numbers"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 404,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "range(1, 13)"
-      ]
-     },
-     "execution_count": 404,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "memoryless"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "source": [
-    "To point out the potentially obvious, we compare the results of *sorting* `numbers` in *reverse* order with *reversing* it: These are *different* concepts!"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 405,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "slide"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[7, 11, 8, 5, 3, 12, 2, 6, 9, 10, 1, 4]"
-      ]
-     },
-     "execution_count": 405,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "numbers"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 406,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "-"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[12, 11, 10, 9, 8, 7, 6, 5, 4, 3, 2, 1]"
-      ]
-     },
-     "execution_count": 406,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "sorted(numbers, reverse=True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 407,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "-"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[4, 1, 10, 9, 6, 2, 12, 3, 5, 8, 11, 7]"
-      ]
-     },
-     "execution_count": 407,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "list(reversed(numbers))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "source": [
-    "Whereas both [sorted()](https://docs.python.org/3/library/functions.html#sorted) and [reversed()](https://docs.python.org/3/library/functions.html#reversed) do *not* mutate their arguments, the *mutable* `list` type comes with two methods, [sort()](https://docs.python.org/3/library/stdtypes.html#list.sort) and `reverse()`, that implement the same logic but mutate an object, like `numbers` below, *in place*. To indicate that all changes occur *in place*, the [sort()](https://docs.python.org/3/library/stdtypes.html#list.sort) and `reverse()` methods always return `None`, which is not shown."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 408,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "slide"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[7, 11, 8, 5, 3, 12, 2, 6, 9, 10, 1, 4]"
-      ]
-     },
-     "execution_count": 408,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "numbers"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "source": [
-    "The `reverse()` method is *eager*, as opposed to the *lazy* [reversed()](https://docs.python.org/3/library/functions.html#reversed) built-in. That means the mutations causes by the `reverse()` method are written into memory right away."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 409,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "-"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "numbers.reverse()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 410,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "-"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[4, 1, 10, 9, 6, 2, 12, 3, 5, 8, 11, 7]"
-      ]
-     },
-     "execution_count": 410,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "numbers"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "source": [
-    "*Sorting* `numbers` in *reverse* order below is of course still *different* from simply *reversing* it above."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 411,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "fragment"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "numbers.sort(reverse=True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 412,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "-"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[12, 11, 10, 9, 8, 7, 6, 5, 4, 3, 2, 1]"
-      ]
-     },
-     "execution_count": 412,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "numbers"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 413,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "numbers.sort()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 414,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12]"
-      ]
-     },
-     "execution_count": 414,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "numbers"
+    "len(current_position)"
    ]
   },
   {
@@ -12189,13 +6876,7 @@
     "\n",
     "`list` objects are **mutable**. That means we can change the references to the other objects it contains, and, in particular, re-assign them.\n",
     "\n",
-    "On the contrary, `tuple` objects are like **immutable** lists: We can use them in place of any `list` object as long as we do *not* need to mutate it. Often, `tuple` objects are also used to model **records** of related **fields**.\n",
-    "\n",
-    "The tasks we do with sequential data follow the **map-filter-reduce paradigm**: We apply the same transformation to all elements, filter some of them out, and calculate summary statistics from the remaining ones.\n",
-    "\n",
-    "An essential idea in this chapter is that, in many situations, we need *not* have all the data **materialized** in memory. Instead, **iterators** allow us to process sequential data on a one-by-one basis.\n",
-    "\n",
-    "Examples for iterators are the `map`, `filter`, and `generator` types."
+    "On the contrary, `tuple` objects are like **immutable** lists: We can use them in place of any `list` object as long as we do *not* need to mutate it. Often, `tuple` objects are also used to model **records** of related **fields**."
    ]
   }
  ],
diff --git a/07_sequences_01_review.ipynb b/07_sequences_01_review.ipynb
index d374b3c..de51edc 100644
--- a/07_sequences_01_review.ipynb
+++ b/07_sequences_01_review.ipynb
@@ -19,7 +19,9 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Read [Chapter 7](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/07_sequences_00_lecture.ipynb) of the book. Then, work through the questions below."
+    "The questions below assume that you have read [Chapter 7](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/07_sequences_00_lecture.ipynb) in the book.\n",
+    "\n",
+    "Be concise in your answers! Most questions can be answered in *one* sentence."
    ]
   },
   {
@@ -47,7 +49,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    " "
+    " < your answer >"
    ]
   },
   {
@@ -61,7 +63,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    " "
+    " < your answer >"
    ]
   },
   {
@@ -75,7 +77,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    " "
+    " < your answer >"
    ]
   },
   {
@@ -89,7 +91,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    " "
+    " < your answer >"
    ]
   },
   {
@@ -103,7 +105,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    " "
+    " < your answer >"
    ]
   },
   {
@@ -117,7 +119,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    " "
+    " < your answer >"
    ]
   },
   {
@@ -133,7 +135,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    " "
+    " < your answer >"
    ]
   },
   {
@@ -147,7 +149,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    " "
+    " < your answer >"
    ]
   },
   {
@@ -161,104 +163,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    " "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**Q9**: With the [map()](https://docs.python.org/3/library/functions.html#map) and [filter()](https://docs.python.org/3/library/functions.html#filter) built-ins and the [reduce()](https://docs.python.org/3/library/functools.html#functools.reduce) function from the [functools](https://docs.python.org/3/library/functools.html) module in the [standard library](https://docs.python.org/3/library/index.html), we can replace many tedious `for`-loops and `if` statements. What are some advantages of doing so?"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    " "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**Q10**: Looking at the `lambda` expression inside [reduce()](https://docs.python.org/3/library/functools.html#functools.reduce) below, what \"simple\" [built-in function](https://docs.python.org/3/library/functions.html) is mimicked here?"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "```python\n",
-    "from functools import reduce\n",
-    "\n",
-    "numbers = [7, 11, 8, 5, 3, 12, 2, 6, 9, 10, 1, 4]\n",
-    "\n",
-    "reduce(lambda x, y: x if x > y else y, numbers)\n",
-    "```"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    " "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**Q11**: What is the primary use case of **list comprehensions**? Why do we describe them as **eager**?"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    " "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**Q12**: **Generator expressions** may replace `list` objects and list comprehensions in many scenarios. When evaluated, they create a **lazy** `generator` object that does *not* **materialize** its elements right away. What do we mean by that? What does it mean for a `generator` object to be **exhausted**?"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    " "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**Q13**: What does it mean for the **boolean reducers**, the built-in [all()](https://docs.python.org/3/library/functions.html#all) and [any()](https://docs.python.org/3/library/functions.html#any) functions, to follow the **short-circuiting** strategy?"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    " "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**Q14**: What is an **iterator**? How does it relate to an **iterable**?"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    " "
+    " < your answer >"
    ]
   },
   {
@@ -279,84 +184,42 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "**Q15**: `sequence` objects are *not* part of core Python but may be imported from the [standard library](https://docs.python.org/3/library/index.html)."
+    "**Q9**: `sequence` objects are *not* part of core Python but may be imported from the [standard library](https://docs.python.org/3/library/index.html)."
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    " "
+    " < your answer >"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "**Q16**: The built-in [sort()](https://docs.python.org/3/library/stdtypes.html#list.sort) function takes a *finite* **iterable** as its argument an returns a *new* `list` object. On the contrary, the [sorted()](https://docs.python.org/3/library/functions.html#sorted) method on `list` objects *mutates* them *in place*."
+    "**Q10**: The built-in [sort()](https://docs.python.org/3/library/stdtypes.html#list.sort) function takes a *finite* **iterable** as its argument an returns a *new* `list` object. On the contrary, the [sorted()](https://docs.python.org/3/library/functions.html#sorted) method on `list` objects *mutates* them *in place*."
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    " "
+    " < your answer >"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "**Q17**: Passing **mutable** objects as arguments to functions is not problematic because functions operate in a **local** scope without affecting the **global** scope."
+    "**Q11**: Passing **mutable** objects as arguments to functions is not problematic because functions operate in a **local** scope without affecting the **global** scope."
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    " "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**Q18**: `lambda` expressions are useful in the context of the **map-filter-reduce** paradigm, where we often do *not* re-use a `function` object more than once."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    " "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**Q19**: Using **generator expressions** in place of **list comprehensions** wherever possible is a good practice as it makes our programs use memory more efficiently."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    " "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**Q20**: Just as **list comprehensions** create `list` objects, **tuple comprehensions** create `tuple` objects."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    " "
+    " < your answer >"
    ]
   }
  ],
diff --git a/07_sequences_02_exercises.ipynb b/07_sequences_02_exercises.ipynb
index daad5e0..575c9c9 100644
--- a/07_sequences_02_exercises.ipynb
+++ b/07_sequences_02_exercises.ipynb
@@ -19,7 +19,9 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Read [Chapter 7](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/07_sequences_00_lecture.ipynb) of the book. Then, work through the exercises below."
+    "The exercises below assume that you have read [Chapter 7](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/07_sequences_00_lecture.ipynb) in the book.\n",
+    "\n",
+    "The `...`'s in the code cells indicate where you need to fill in code snippets. The number of `...`'s within a code cell give you a rough idea of how many lines of code are needed to solve the task. You should not need to create any additional code cells for your final solution. However, you may want to use temporary code cells to try out some ideas."
    ]
   },
   {
@@ -54,7 +56,19 @@
    "outputs": [],
    "source": [
     "def nested_sum(list_of_lists):\n",
-    "    ..."
+    "    \"\"\"Add up numbers in nested lists.\n",
+    "    \n",
+    "    Args:\n",
+    "        list_of_lists (list): A list containing the lists with the numbers\n",
+    "    \n",
+    "    Returns:\n",
+    "        sum (int or float)\n",
+    "    \"\"\"\n",
+    "    ...\n",
+    "    ...\n",
+    "    ...\n",
+    "\n",
+    "    return ..."
    ]
   },
   {
@@ -70,27 +84,9 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "**Q1.2**: Provide a one-line expression to obtain the *same* result as `nested_sum()`!\n",
+    "**Q1.2**: Generalize `nested_sum()` into a function `mixed_sum()` that can process a \"mixed\" `list` object, which contains numbers and other `list` objects with numbers! Use `mixed_numbers` below for testing!\n",
     "\n",
-    "Hints: Use a *list comprehension*, or maybe even a *generator expression*. You may want to use the built-in [sum()](https://docs.python.org/3/library/functions.html#sum) function several times."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "..."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**Q1.3**: Generalize `nested_sum()` into a function `mixed_sum()` that can process a \"mixed\" `list` object, which contains numbers and other `list` objects with numbers! Use `mixed_numbers` below for testing!\n",
-    "\n",
-    "Hints: Use the built-in [isinstance()](https://docs.python.org/3/library/functions.html#isinstance) function to check how an element is to be processed. Get extra credit for adhering to *goose typing*, as explained in [Chapter 5](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/05_numbers_00_lecture.ipynb#Goose-Typing)."
+    "Hints: Use the built-in [isinstance()](https://docs.python.org/3/library/functions.html#isinstance) function to check how an element is to be processed. Get extra credit for adhering to *goose typing*, as explained in [Chapter 5](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/05_numbers_00_lecture.ipynb#Goose-Typing): Use some appropriate abstract base class (ABC) from the [collections.abc](https://docs.python.org/3/library/collections.abc.html) module in the [standard library](https://docs.python.org/3/library/index.html)."
    ]
   },
   {
@@ -117,8 +113,24 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "def mixed_sum(list_of_lists):\n",
-    "    ..."
+    "def mixed_sum(list_of_lists_or_numbers):\n",
+    "    \"\"\"Add up numbers in nested lists.\n",
+    "    \n",
+    "    Args:\n",
+    "        list_of_lists_or_numbers (list): A list containing both numbers and\n",
+    "            lists with numbers\n",
+    "    \n",
+    "    Returns:\n",
+    "        sum (int or float)\n",
+    "    \"\"\"\n",
+    "    ...\n",
+    "    ...\n",
+    "    ...\n",
+    "    ...\n",
+    "    ...\n",
+    "    ...\n",
+    "\n",
+    "    return ..."
    ]
   },
   {
@@ -134,7 +146,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "**Q1.4.1**: Write a function `cum_sum()` that takes a `list` object with numbers as its argument and returns a *new* `list` object with the **cumulative sums** of these numbers! So, `sum_up` below, `[1, 2, 3, 4, 5]`, should return `[1, 3, 6, 10, 15]`.\n",
+    "**Q1.3.1**: Write a function `cum_sum()` that takes a `list` object with numbers as its argument and returns a *new* `list` object with the **cumulative sums** of these numbers! So, `sum_up` below, `[1, 2, 3, 4, 5]`, should return `[1, 3, 6, 10, 15]`.\n",
     "\n",
     "Hint: The idea behind is similar to the [cumulative distribution function](https://en.wikipedia.org/wiki/Cumulative_distribution_function) from statistics."
    ]
@@ -155,7 +167,23 @@
    "outputs": [],
    "source": [
     "def cum_sum(numbers):\n",
-    "    ..."
+    "    \"\"\"Create the cumulative sums for some numbers.\n",
+    "\n",
+    "    Args:\n",
+    "        numbers (list): A list with numbers for that the cumulative sums\n",
+    "            are calculated\n",
+    "    \n",
+    "    Returns:\n",
+    "        cum_sums (list): A list with all the cumulative sums\n",
+    "    \"\"\"\n",
+    "    ...\n",
+    "    ...\n",
+    "\n",
+    "    ...\n",
+    "    ...\n",
+    "    ...\n",
+    "\n",
+    "    return ..."
    ]
   },
   {
@@ -171,7 +199,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "**Q1.4.2**: We should always make sure that our functions also work in corner cases. What happens if your implementation of `cum_sum()` is called with an empty list `[]`? Make sure it handles that case *without* crashing! What would be a good return value in this corner case? Describe everything in the docstring.\n",
+    "**Q1.3.2**: We should always make sure that our functions also work in corner cases. What happens if your implementation of `cum_sum()` is called with an empty list `[]`? Make sure it handles that case *without* crashing! What would be a good return value in this corner case?\n",
     "\n",
     "Hint: It is possible to write this without any extra input validation."
    ]
@@ -185,6 +213,13 @@
     "cum_sum([])"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " < your answer >"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -228,7 +263,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    " "
+    " < your answer >"
    ]
   },
   {
@@ -283,7 +318,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    " "
+    " < your answer >"
    ]
   },
   {
@@ -322,7 +357,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    " "
+    " < your answer >"
    ]
   },
   {
@@ -386,7 +421,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    " "
+    " < your answer >"
    ]
   },
   {
@@ -425,7 +460,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    " "
+    " < your answer >"
    ]
   },
   {
@@ -504,7 +539,10 @@
    "source": [
     "def product(*args, ...):\n",
     "    \"\"\"Multiply all arguments.\"\"\"\n",
-    "    ..."
+    "    ...\n",
+    "    ...\n",
+    "\n",
+    "    return ..."
    ]
   },
   {
@@ -609,7 +647,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    " "
+    " < your answer >"
    ]
   },
   {
@@ -638,7 +676,13 @@
    "source": [
     "def product(*args, ...):\n",
     "    \"\"\"Multiply all arguments.\"\"\"\n",
-    "    ..."
+    "    ...\n",
+    "    ...\n",
+    "\n",
+    "    ...\n",
+    "    ...\n",
+    "\n",
+    "    return ..."
    ]
   },
   {
@@ -706,7 +750,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "product([2, 5, 10])  # the argument is a special collection type, namely a sequence"
+    "product([2, 5, 10])  # the argument is a collection that is also a sequence"
    ]
   },
   {
@@ -758,7 +802,15 @@
    "source": [
     "def product(*args, ...):\n",
     "    \"\"\"Multiply all arguments.\"\"\"\n",
-    "    ..."
+    "    ...\n",
+    "    ...\n",
+    "    ...\n",
+    "    ...\n",
+    "\n",
+    "    ...\n",
+    "    ...\n",
+    "\n",
+    "    return ..."
    ]
   },
   {
@@ -769,1011 +821,6 @@
    "source": [
     "product()"
    ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Now, we have an implementation of `product()` that is convenient to use for the caller of our function. In particular, we can pass it a *collection* with or without *unpacking* it.\n",
-    "\n",
-    "However, this version of `product()` suffers from one more flaw: We cannot pass it a *stream* of data, as modeled, for example, with an *iterator* object that produces elements on a one-by-one basis.\n",
-    "\n",
-    "Let's look at an example. The [*stream.py*](https://github.com/webartifex/intro-to-python/blob/master/stream.py) module in the repository provides a `make_finite_stream()` function. It is a *factory* function creating objects of type `generator` that we use to model *streaming* data."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from stream import make_finite_stream"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "stream = make_finite_stream()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "stream"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "type(stream)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Being a `generator`, `stream` is also an `Iterator` in the abstract sense."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "isinstance(stream, abc.Iterator)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "*Iterators* are good for only *one* thing: Giving us the \"next\" element in a line of many."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "next(stream)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "They themselves have *no* idea of how many elements they produce eventually: The built-in [len()](https://docs.python.org/3/library/functions.html#len) function raises a `TypeError`."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "len(stream)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We can use the [list()](https://docs.python.org/3/library/functions.html#func-list) built-in to *materialize* the elements. However, in a real-world scenario, these may *not* fit into our machine's memory!"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "list(stream)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "To be more realistic, `make_finite_stream()` creates `generator` objects producing a varying number of elements."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "list(make_finite_stream())"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "list(make_finite_stream())"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "list(make_finite_stream())"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Let's see what happens if we pass an *iterator*, as created by `make_finite_stream()`, instead of a materialized *collection*, like `one_hundred`, to `product()`."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "product(make_finite_stream())"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**Q2.11**: What line causes the `TypeError`? What line is really the problem in the latest implementation of `product()`? Describe what happens on each line in the function's body until the exception is raised!"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    " "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**Q2.12**: Adapt `product()` one last time to make it work with *iterators* as well!\n",
-    "\n",
-    "Hints: This task is as easy as replacing `Collection` with something else. Which of the three behaviors of *collections* do *iterators* also exhibit? You may want to look at the documentations on the built-in [max()](https://docs.python.org/3/library/functions.html#max), [min()](https://docs.python.org/3/library/functions.html#min), and [sum()](https://docs.python.org/3/library/functions.html#sum) functions: What kind of argument do they take?"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def product(*args, ...):\n",
-    "    \"\"\"Multiply all arguments.\"\"\"\n",
-    "    ..."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The final version of `product()` behaves like built-ins in edge cases, ..."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "product()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "... works with the arguments passed either as independent *positional* arguments, *packed* into a single *collection* argument, or *unpacked*, ..."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "product(42)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "product(2, 5, 10)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "product([2, 5, 10])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "product(*[2, 5, 10])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "... and can handle *streaming* data with *indefinite* \"length.\""
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "product(make_finite_stream())"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "In real-world projects, the data science practitioner must decide if it is worthwhile to make a function usable in various different forms as we did in this exercise, or if that is over-engineered.\n",
-    "\n",
-    "Yet, two lessons are important to take away:\n",
-    "- It is always a good idea to *mimic* the behavior of *built-ins* when in doubt.\n",
-    "- Make functions capable of working with *streaming* data."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Removing Outliers in Streaming Data"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Let's say we are given a `list` object with random integers like `sample` below, and we want to calculate some basic statistics on them."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "sample = [\n",
-    "    45, 46, 40, 49, 36, 53, 49, 42, 25, 40, 39, 36, 38, 40, 40, 52, 36, 52, 40, 41,\n",
-    "    35, 29, 48, 43, 42, 30, 29, 33, 55, 33, 38, 50, 39, 56, 52, 28, 37, 56, 45, 37,\n",
-    "    41, 41, 37, 30, 51, 32, 23, 40, 53, 40, 45, 39, 99, 42, 34, 42, 34, 39, 39, 53,\n",
-    "    43, 37, 46, 36, 45, 42, 32, 38, 57, 34, 36, 44, 47, 51, 46, 39, 28, 40, 35, 46,\n",
-    "    41, 51, 41, 23, 46, 40, 40, 51, 50, 32, 47, 36, 38, 29, 32, 53, 34, 43, 39, 41,\n",
-    "    40, 34, 44, 40, 41, 43, 47, 57, 50, 42, 38, 25, 45, 41, 58, 37, 45, 55, 44, 53,\n",
-    "    82, 31, 45, 33, 32, 39, 46, 48, 42, 47, 40, 45, 51, 35, 31, 46, 40, 44, 61, 57,\n",
-    "    40, 36, 35, 55, 40, 56, 36, 35, 86, 36, 51, 40, 54, 50, 49, 36, 41, 37, 48, 41,\n",
-    "    42, 44, 40, 43, 51, 47, 46, 50, 40, 23, 40, 39, 28, 38, 42, 46, 46, 42, 46, 31,\n",
-    "    32, 40, 48, 27, 40, 40, 30, 32, 25, 31, 30, 43, 44, 29, 45, 41, 63, 32, 33, 58,\n",
-    "]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "len(sample)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**Q3.1**: `list` objects are **sequences**. What *four* behaviors do they always come with?"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    " "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**Q3.2**: Write a function `mean()` that calculates the simple arithmetic mean of a given `sequence` with numbers!\n",
-    "\n",
-    "Hints: You can solve this task with [built-in functions](https://docs.python.org/3/library/functions.html) only. A `for`-loop is *not* needed."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def mean(sequence):\n",
-    "    ..."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "sample_mean = mean(sample)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "sample_mean"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**Q3.3**: Write a function `std()` that calculates the [standard deviation](https://en.wikipedia.org/wiki/Standard_deviation) of a `sequence` of numbers! Integrate your `mean()` version from before and the [sqrt()](https://docs.python.org/3/library/math.html#math.sqrt) function from the [math](https://docs.python.org/3/library/math.html) module in the [standard library](https://docs.python.org/3/library/index.html) provided to you below. Make sure `std()` calls `mean()` only *once* internally! Repeated calls to `mean()` would be a waste of computational resources.\n",
-    "\n",
-    "Hints: Parts of the code are probably too long to fit within the suggested 79 characters per line. So, use *temporary variables* inside your function. Instead of a `for`-loop, you may want to use a *list comprehension* or, even better, a memoryless *generator expression*."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from math import sqrt"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def std(sequence):\n",
-    "    ..."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "sample_std = std(sample)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "sample_std"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**Q3.4**: Complete `standardize()` below that takes a `sequence` of numbers and returns a `list` object with the **[z-scores](https://en.wikipedia.org/wiki/Standard_score)** of these numbers! A z-score is calculated by subtracting the mean and dividing by the standard deviation. Re-use `mean()` and `std()` from before. Again, ensure that `standardize()` calls `mean()` and `std()` only *once*! Further, round all z-scores with the built-in [round()](https://docs.python.org/3/library/functions.html#round) function and pass on the keyword-only argument `digits` to it.\n",
-    "\n",
-    "Hint: You may want to use a *list comprehension* instead of a `for`-loop."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def standardize(sequence, *, digits=3):\n",
-    "    ..."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "z_scores = standardize(sample)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The [pprint()](https://docs.python.org/3/library/pprint.html#pprint.pprint) function from the [pprint](https://docs.python.org/3/library/pprint.html) module in the [standard library](https://docs.python.org/3/library/index.html) allows us to \"pretty print\" long `list` objects compactly."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from pprint import pprint"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "pprint(z_scores, compact=True)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We know that `standardize()` works correctly if the resulting z-scores' mean and standard deviation approach `0` and `1` for a long enough `sequence`."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "mean(z_scores), std(z_scores)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Even though `standardize()` calls `mean()` and `std()` only once each, `mean()` is called *twice*! That is so because `std()` internally also re-uses `mean()`!\n",
-    "\n",
-    "**Q3.5.1**: Rewrite `std()` to take an optional keyword-only argument `seq_mean`, defaulting to `None`. If provided, `seq_mean` is used instead of the result of calling `mean()`. Otherwise, the latter is called.\n",
-    "\n",
-    "Hint: You must check if `seq_mean` is still the default value."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def std(sequence, *, seq_mean=None):\n",
-    "    ..."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "`std()` continues to work as before."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "sample_std = std(sample)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "sample_std"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**Q3.5.2**: Now, rewrite `standardize()` to pass on the return value of `mean()` to `std()`! In summary, `standardize()` calculates the z-scores for the numbers in the `sequence` with as few computational steps as possible."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def standardize(sequence, *, digits=3):\n",
-    "    ..."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "z_scores = standardize(sample)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "mean(z_scores), std(z_scores)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**Q3.6**: With both `sample` and `z_scores` being materialized `list` objects, we can loop over pairs consisting of a number from `sample` and its corresponding z-score. Write a `for`-loop that prints out all the \"outliers,\" as which we define numbers with an absolute z-score above `1.96`. There are *four* of them in the `sample`.\n",
-    "\n",
-    "Hint: Use the [abs()](https://docs.python.org/3/library/functions.html#abs) and [zip()](https://docs.python.org/3/library/functions.html#zip) built-ins."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "..."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We provide a `stream` module with a `data` object that models an *infinite* **stream** of data (cf., the [*stream.py*](https://github.com/webartifex/intro-to-python/blob/master/stream.py) file in the repository)."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from stream import data"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "data"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "`data` is of type `generator` and has *no* length."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "type(data)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "len(data)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Being a `generator`, it is an `Iterator` in the abstract sense ..."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import collections.abc as abc"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "isinstance(data, abc.Iterator)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "... and so the only thing we can do with it is to pass it to the built-in [next()](https://docs.python.org/3/library/functions.html#next) function and go over the numbers it streams one by one."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "next(data)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**Q3.7**: What happens if you call `mean()` with `data` as the argument? What is the problem?\n",
-    "\n",
-    "Hints: If you try it out, you may have to press the \"Stop\" button in the toolbar at the top. Your computer should *not* crash, but you will *have to* restart this Jupyter notebook with \"Kernel\" > \"Restart\" and import `data` again."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    " "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "mean(data)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**Q3.8**: Write a function `take_sample()` that takes an `iterator` as its argument, like `data`, and creates a *materialized* `list` object out of its first `n` elements, defaulting to `1_000`!\n",
-    "\n",
-    "Hints: [next()](https://docs.python.org/3/library/functions.html#next) and the [range()](https://docs.python.org/3/library/functions.html#func-range) built-in may be helpful. You may want to use a *list comprehension* instead of a `for`-loop and write a one-liner. Audacious students may want to look at [isclice()](https://docs.python.org/3/library/itertools.html#itertools.islice) in the [itertools](https://docs.python.org/3/library/itertools.html) module in the [standard library](https://docs.python.org/3/library/index.html)."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def take_sample(iterator, *, n=1_000):\n",
-    "    ..."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We take a `new_sample` from the stream of `data`, and its statistics are similar to the initial `sample`."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "new_sample = take_sample(data)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "len(new_sample)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "mean(new_sample)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "std(new_sample)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**Q3.9**: Convert `standardize()` into a *new* function `standardized()` that implements the *same* logic but works on a possibly *infinite* stream of data, provided as an `iterable`, instead of a *finite* `sequence`.\n",
-    "\n",
-    "To calculate a z-score, we need the stream's overall mean and standard deviation, and that is *impossible* to calculate if we do not know how long the stream is, and, in particular, if it is *infinite*. So, `standardized()` first takes a sample from the `iterable` internally, and uses the sample's mean and standard deviation to calculate the z-scores.\n",
-    "\n",
-    "Hint: `standardized()` *must* return a `generator` object. So, use a *generator expression* as the return value; unless you know about the `yield` statement already (cf., [reference](https://docs.python.org/3/reference/simple_stmts.html#the-yield-statement))."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def standardized(iterable, *, digits=3):\n",
-    "    ..."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "`standardized()` works almost like `standardize()` except that we use it with [next()](https://docs.python.org/3/library/functions.html#next) to obtain the z-scores one by one."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "z_scores = standardized(data)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "z_scores"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "type(z_scores)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "next(z_scores)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**Q3.10.1**: `standardized()` allows us to go over an *infinite* stream of z-scores. What we want to do instead is to loop over the stream's raw numbers and skip the outliers. In the remainder of this exercise, you look at the parts that make up the `skip_outliers()` function below to achieve precisely that.\n",
-    "\n",
-    "The first steps in `skip_outliers()` are the same as in `standardized()`: We take a `sample` from the stream of `data` and calculate its statistics."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "sample = ...\n",
-    "seq_mean = ...\n",
-    "seq_std = ..."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**Q3.10.2**: Just as in `standardized()`, write a *generator expression* that produces z-scores one by one! However, instead of just generating a z-score, the resulting `generator` object should produce `tuple` objects consisting of a \"raw\" number from `data` and its z-score.\n",
-    "\n",
-    "Hint: Look at the revisited \"*Averaging Even Numbers*\" example in [Chapter 7](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/07_sequences_00_lecture.ipynb#Example:-Averaging-Even-Numbers-%28revisited%29) for some inspiration, which also contains a generator expression producing `tuple` objects."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "standardizer = (... for ... in data)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "`standardizer` should produce `tuple` objects."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "next(standardizer)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**Q3.10.3**: Write another generator expression that loops over `standardizer`. It contains an `if`-clause that keeps only numbers with an absolute z-score below the `threshold_z`. If you fancy, use *tuple unpacking*."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "threshold_z = 1.96"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "no_outliers = (... for ... in standardizer if ...)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "`no_outliers` should produce `int` objects."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "next(no_outliers)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**Q3.10.4**: Lastly, put everything together in the `skip_outliers()` function! Make sure you refer to `iterable` inside the function and not the global `data`."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def skip_outliers(iterable, *, threshold_z=1.96):\n",
-    "    sample = ...\n",
-    "    seq_mean = ...\n",
-    "    seq_std = ...\n",
-    "    standardizer = ...\n",
-    "    no_outliers = ...\n",
-    "    return no_outliers"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Now, we can create a `generator` object and loop over the `data` in the stream with outliers skipped. Instead of the default `1.96`, we use a `threshold_z` of only `0.05`: That filters out all numbers except `42`."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "skipper = skip_outliers(data, threshold_z=0.05)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "skipper"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "type(skipper)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "next(skipper)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**Q3.11**: You implemented the functions `mean()`, `std()`, `standardize()`, `standardized()`, and `skip_outliers()`. Which of them are **eager**, and which are **lazy**? How do these two concepts relate to **finite** and **infinite** data?"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    " "
-   ]
   }
  ],
  "metadata": {

From 5f633220cce26f83c3a66827b61cdc250122b5f6 Mon Sep 17 00:00:00 2001
From: Alexander Hess <alexander@webartifex.biz>
Date: Mon, 30 Mar 2020 17:44:16 +0200
Subject: [PATCH 093/242] Remove empty lines

---
 01_elements_01_review.ipynb        | 1 -
 01_elements_02_exercises.ipynb     | 1 -
 03_conditionals_01_review.ipynb    | 1 -
 03_conditionals_02_exercises.ipynb | 1 -
 04_iteration_01_review.ipynb       | 1 -
 04_iteration_02_exercises.ipynb    | 1 -
 04_iteration_03_exercises.ipynb    | 1 -
 05_numbers_01_review.ipynb         | 1 -
 06_text_01_review.ipynb            | 1 -
 06_text_02_exercises.ipynb         | 1 -
 07_sequences_01_review.ipynb       | 1 -
 07_sequences_02_exercises.ipynb    | 3 +--
 12 files changed, 1 insertion(+), 13 deletions(-)

diff --git a/01_elements_01_review.ipynb b/01_elements_01_review.ipynb
index 68ae395..9c398f7 100644
--- a/01_elements_01_review.ipynb
+++ b/01_elements_01_review.ipynb
@@ -4,7 +4,6 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "\n",
     "# Chapter 1: Elements of a Program"
    ]
   },
diff --git a/01_elements_02_exercises.ipynb b/01_elements_02_exercises.ipynb
index 7cdd98f..fb7bc75 100644
--- a/01_elements_02_exercises.ipynb
+++ b/01_elements_02_exercises.ipynb
@@ -4,7 +4,6 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "\n",
     "# Chapter 1: Elements of a Program"
    ]
   },
diff --git a/03_conditionals_01_review.ipynb b/03_conditionals_01_review.ipynb
index 57a706e..5a5da9d 100644
--- a/03_conditionals_01_review.ipynb
+++ b/03_conditionals_01_review.ipynb
@@ -4,7 +4,6 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "\n",
     "# Chapter 3: Conditionals & Exceptions"
    ]
   },
diff --git a/03_conditionals_02_exercises.ipynb b/03_conditionals_02_exercises.ipynb
index c8870ce..510b8cb 100644
--- a/03_conditionals_02_exercises.ipynb
+++ b/03_conditionals_02_exercises.ipynb
@@ -4,7 +4,6 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "\n",
     "# Chapter 3: Conditionals & Exceptions"
    ]
   },
diff --git a/04_iteration_01_review.ipynb b/04_iteration_01_review.ipynb
index de4a566..283a72a 100644
--- a/04_iteration_01_review.ipynb
+++ b/04_iteration_01_review.ipynb
@@ -4,7 +4,6 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "\n",
     "# Chapter 4: Recursion & Looping"
    ]
   },
diff --git a/04_iteration_02_exercises.ipynb b/04_iteration_02_exercises.ipynb
index 4f819ca..d795aa5 100644
--- a/04_iteration_02_exercises.ipynb
+++ b/04_iteration_02_exercises.ipynb
@@ -4,7 +4,6 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "\n",
     "# Chapter 4: Recursion & Looping"
    ]
   },
diff --git a/04_iteration_03_exercises.ipynb b/04_iteration_03_exercises.ipynb
index b44c488..7d69c33 100644
--- a/04_iteration_03_exercises.ipynb
+++ b/04_iteration_03_exercises.ipynb
@@ -4,7 +4,6 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "\n",
     "# Chapter 4: Recursion & Looping"
    ]
   },
diff --git a/05_numbers_01_review.ipynb b/05_numbers_01_review.ipynb
index cb297bb..7b3ac58 100644
--- a/05_numbers_01_review.ipynb
+++ b/05_numbers_01_review.ipynb
@@ -4,7 +4,6 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "\n",
     "# Chapter 5: Numbers & Bits"
    ]
   },
diff --git a/06_text_01_review.ipynb b/06_text_01_review.ipynb
index cbe5d80..8727868 100644
--- a/06_text_01_review.ipynb
+++ b/06_text_01_review.ipynb
@@ -4,7 +4,6 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "\n",
     "# Chapter 6: Text & Bytes"
    ]
   },
diff --git a/06_text_02_exercises.ipynb b/06_text_02_exercises.ipynb
index 92df25a..d488f52 100644
--- a/06_text_02_exercises.ipynb
+++ b/06_text_02_exercises.ipynb
@@ -4,7 +4,6 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "\n",
     "# Chapter 6: Text & Bytes"
    ]
   },
diff --git a/07_sequences_01_review.ipynb b/07_sequences_01_review.ipynb
index de51edc..7e9e5da 100644
--- a/07_sequences_01_review.ipynb
+++ b/07_sequences_01_review.ipynb
@@ -4,7 +4,6 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "\n",
     "# Chapter 7: Sequential Data"
    ]
   },
diff --git a/07_sequences_02_exercises.ipynb b/07_sequences_02_exercises.ipynb
index 575c9c9..b8d0a7b 100644
--- a/07_sequences_02_exercises.ipynb
+++ b/07_sequences_02_exercises.ipynb
@@ -4,7 +4,6 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "\n",
     "# Chapter 7: Sequential Data"
    ]
   },
@@ -741,7 +740,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "**Side Note**: Above, we make `product()` work with a single *collection* type argument instead of a *sequence* type to keep it more generic: For example, we can pass in a `set` object, like `{2, 5, 10}` below, and `product()` continues to work correctly. The `set` type is introducted in [Chapter 8](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/08_mappings_00_lecture.ipynb#The-set-Type), and one essential difference to the `list` type is that objects of type `set` have *no* order regarding their elements. So, even though `[2, 5, 10]` and `{2, 5, 10}` look almost the same, the order implied in the literal notation gets lost in memory!"
+    "**Side Note**: Above, we make `product()` work with a single *collection* type argument instead of a *sequence* type to keep it more generic: For example, we can pass in a `set` object, like `{2, 5, 10}` below, and `product()` continues to work correctly. The `set` type is introducted in [Chapter 9](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/09_mappings_00_lecture.ipynb#The-set-Type), and one essential difference to the `list` type is that objects of type `set` have *no* order regarding their elements. So, even though `[2, 5, 10]` and `{2, 5, 10}` look almost the same, the order implied in the literal notation gets lost in memory!"
    ]
   },
   {

From e1a0dd79247a4dbbc7f50decd46d59582274ae6b Mon Sep 17 00:00:00 2001
From: Alexander Hess <alexander@webartifex.biz>
Date: Thu, 2 Apr 2020 15:49:27 +0200
Subject: [PATCH 094/242] Make links look nice with images

- rename all *_00_lecture.ipynb files into *_00_content.ipynb
- add links to YouTube videos at the end of the chapters
- add links to mybinder for all chapters and exercises
- remove the old chapter 8 on mappings & sets
- update the README.md and make it look nicer
---
 ...lecture.ipynb => 00_intro_00_content.ipynb |  165 +-
 00_intro_01_review.ipynb                      |    4 +-
 00_intro_02_exercises.ipynb                   |   15 +-
 ...ture.ipynb => 01_elements_00_content.ipynb |  811 +-
 01_elements_01_review.ipynb                   |    8 +-
 01_elements_02_exercises.ipynb                |   37 +-
 ...ure.ipynb => 02_functions_00_content.ipynb | 1110 +--
 02_functions_01_review.ipynb                  |    4 +-
 02_functions_02_exercises.ipynb               |   15 +-
 ....ipynb => 03_conditionals_00_content.ipynb |  827 +-
 03_conditionals_01_review.ipynb               |    2 +-
 03_conditionals_02_exercises.ipynb            |   15 +-
 ...ure.ipynb => 04_iteration_00_content.ipynb |  713 +-
 04_iteration_01_review.ipynb                  |    2 +-
 04_iteration_02_exercises.ipynb               |   25 +-
 04_iteration_03_exercises.ipynb               |   21 +-
 ...cture.ipynb => 05_numbers_00_content.ipynb | 2887 +++---
 05_numbers_01_review.ipynb                    |   18 +-
 05_numbers_02_exercises.ipynb                 |   35 +-
 ..._lecture.ipynb => 06_text_00_content.ipynb | 1682 ++--
 06_text_01_review.ipynb                       |    2 +-
 06_text_02_exercises.ipynb                    |   19 +-
 ...ure.ipynb => 07_sequences_00_content.ipynb | 1985 ++--
 07_sequences_01_review.ipynb                  |   10 +-
 07_sequences_02_exercises.ipynb               |   29 +-
 08_mappings_00_lecture.ipynb                  | 8217 -----------------
 08_mappings_01_review.ipynb                   |  261 -
 08_mappings_02_exercises.ipynb                |  388 -
 README.md                                     |  201 +-
 static/link_to_gh.png                         |  Bin 0 -> 1236 bytes
 static/link_to_hn.png                         |  Bin 0 -> 632 bytes
 static/link_to_jp.png                         |  Bin 0 -> 526 bytes
 static/link_to_mb.png                         |  Bin 0 -> 1222 bytes
 static/link_to_nb.png                         |  Bin 0 -> 1138 bytes
 static/link_to_py.png                         |  Bin 0 -> 1308 bytes
 static/link_to_so.png                         |  Bin 0 -> 991 bytes
 static/link_to_wiki.png                       |  Bin 0 -> 503 bytes
 static/link_to_yt.png                         |  Bin 0 -> 912 bytes
 38 files changed, 5445 insertions(+), 14063 deletions(-)
 rename 00_intro_00_lecture.ipynb => 00_intro_00_content.ipynb (85%)
 rename 01_elements_00_lecture.ipynb => 01_elements_00_content.ipynb (91%)
 rename 02_functions_00_lecture.ipynb => 02_functions_00_content.ipynb (80%)
 rename 03_conditionals_00_lecture.ipynb => 03_conditionals_00_content.ipynb (92%)
 rename 04_iteration_00_lecture.ipynb => 04_iteration_00_content.ipynb (89%)
 rename 05_numbers_00_lecture.ipynb => 05_numbers_00_content.ipynb (89%)
 rename 06_text_00_lecture.ipynb => 06_text_00_content.ipynb (88%)
 rename 07_sequences_00_lecture.ipynb => 07_sequences_00_content.ipynb (83%)
 delete mode 100644 08_mappings_00_lecture.ipynb
 delete mode 100644 08_mappings_01_review.ipynb
 delete mode 100644 08_mappings_02_exercises.ipynb
 create mode 100644 static/link_to_gh.png
 create mode 100644 static/link_to_hn.png
 create mode 100644 static/link_to_jp.png
 create mode 100644 static/link_to_mb.png
 create mode 100644 static/link_to_nb.png
 create mode 100644 static/link_to_py.png
 create mode 100644 static/link_to_so.png
 create mode 100644 static/link_to_wiki.png
 create mode 100644 static/link_to_yt.png

diff --git a/00_intro_00_lecture.ipynb b/00_intro_00_content.ipynb
similarity index 85%
rename from 00_intro_00_lecture.ipynb
rename to 00_intro_00_content.ipynb
index a0ea14b..7942097 100644
--- a/00_intro_00_lecture.ipynb
+++ b/00_intro_00_content.ipynb
@@ -8,44 +8,7 @@
     }
    },
    "source": [
-    "A **video presentation** of the contents in this chapter is shown below. A playlist with *all* chapters as videos is linked [here](https://www.youtube.com/playlist?list=PL-2JV1G3J10lQ2xokyQowcRJI5jjNfW7f)."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/jpeg": 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-       "            width=\"60%\"\n",
-       "            height=\"300\"\n",
-       "            src=\"https://www.youtube.com/embed/YTU8jaG27Xk\"\n",
-       "            frameborder=\"0\"\n",
-       "            allowfullscreen\n",
-       "        ></iframe>\n",
-       "        "
-      ],
-      "text/plain": [
-       "<IPython.lib.display.YouTubeVideo at 0x7f40ec50b590>"
-      ]
-     },
-     "execution_count": 1,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from IPython.display import YouTubeVideo\n",
-    "YouTubeVideo(\"YTU8jaG27Xk\", width=\"60%\")"
+    "**Important**: Click on \"*Kernel*\" > \"*Restart Kernel and Clear All Outputs*\" *before* reading this chapter in [JupyterLab <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_jp.png\">](https://jupyterlab.readthedocs.io/en/stable/) (e.g., in the cloud on [MyBinder <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_mb.png\">](https://mybinder.org/v2/gh/webartifex/intro-to-python/master?urlpath=lab/tree/00_intro_00_content.ipynb))"
    ]
   },
   {
@@ -67,9 +30,9 @@
     }
    },
    "source": [
-    "This book is a *thorough* introduction to programming in [Python](https://www.python.org/).\n",
+    "This book is a *thorough* introduction to programming in [Python <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://www.python.org/).\n",
     "\n",
-    "It teaches the concepts behind and the syntax of the core Python language as defined by the [Python Software Foundation](https://www.python.org/psf/) in the official [language reference](https://docs.python.org/3/reference/index.html). Furthermore, it introduces commonly used functionalities from the [standard library](https://docs.python.org/3/library/index.html) and popular third-party libraries like [numpy](https://www.numpy.org/), [pandas](https://pandas.pydata.org/), [matplotlib](https://matplotlib.org/), and others."
+    "It teaches the concepts behind and the syntax of the core Python language as defined by the [Python Software Foundation <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://www.python.org/psf/) in the official [language reference <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/reference/index.html). Furthermore, it introduces commonly used functionalities from the [standard library <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/index.html) and popular third-party libraries like [numpy <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://www.numpy.org/), [pandas <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://pandas.pydata.org/), [matplotlib <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://matplotlib.org/), and others."
    ]
   },
   {
@@ -107,7 +70,7 @@
     "- a *solid* understanding of the **English language**,\n",
     "- knowledge of **basic mathematics** from high school,\n",
     "- the ability to **think conceptually** and **reason logically**, and\n",
-    "- the willingness to **invest 2-4 hours a day for a month**."
+    "- the willingness to **invest around 90 - 120 hours on this course**."
    ]
   },
   {
@@ -176,9 +139,9 @@
     }
    },
    "source": [
-    "The term **[data science](https://en.wikipedia.org/wiki/Data_science)** is rather vague and does *not* refer to an academic discipline. Instead, the term was popularized by the tech industry, who also coined non-meaningful job titles such as \"[rockstar](https://www.quora.com/Why-are-engineers-called-rockstars-and-ninjas)\" or \"[ninja developers](https://www.quora.com/Why-are-engineers-called-rockstars-and-ninjas).\" Most *serious* definitions describe the field as being **multi-disciplinary** *integrating* scientific methods, algorithms, and systems thinking to extract knowledge from structured and unstructured data, *and* also emphasize the importance of **[domain knowledge](https://en.wikipedia.org/wiki/Domain_knowledge)**.\n",
+    "The term **[data science <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_wiki.png\">](https://en.wikipedia.org/wiki/Data_science)** is rather vague and does *not* refer to an academic discipline. Instead, the term was popularized by the tech industry, who also coined non-meaningful job titles such as \"[rockstar](https://www.quora.com/Why-are-engineers-called-rockstars-and-ninjas)\" or \"[ninja developers](https://www.quora.com/Why-are-engineers-called-rockstars-and-ninjas).\" Most *serious* definitions describe the field as being **multi-disciplinary** *integrating* scientific methods, algorithms, and systems thinking to extract knowledge from structured and unstructured data, *and* also emphasize the importance of **[domain knowledge <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_wiki.png\">](https://en.wikipedia.org/wiki/Domain_knowledge)**.\n",
     "\n",
-    "Recently, this integration aspect feeds back into the academic world. The [MIT](https://www.mit.edu/), for example, created the new [Stephen A. Schwarzman College of Computing](http://computing.mit.edu) for [artificial intelligence](https://en.wikipedia.org/wiki/Artificial_intelligence) with a 1 billion dollar initial investment where students undergo a \"bilingual\" curriculum with half the classes in quantitative and method-centric fields - like the ones mentioned above - and the other half in domains such as biology, business, chemistry, politics, (art) history, or linguistics (cf., the [official Q&As](http://computing.mit.edu/faq/) or this [NYT article](https://www.nytimes.com/2018/10/15/technology/mit-college-artificial-intelligence.html)). Their strategists see a future where programming skills are just as naturally embedded into students' curricula as are nowadays subjects like calculus, statistics, or academic writing. Then, programming literacy is not just another \"nice to have\" skill but a prerequisite, or an enabler, to understanding more advanced topics in the actual domains studied. Top-notch researchers who use programming in their day-to-day lives could then teach students more efficiently in their \"language.\""
+    "Recently, this integration aspect feeds back into the academic world. The [MIT](https://www.mit.edu/), for example, created the new [Stephen A. Schwarzman College of Computing](http://computing.mit.edu) for [artificial intelligence <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_wiki.png\">](https://en.wikipedia.org/wiki/Artificial_intelligence) with a 1 billion dollar initial investment where students undergo a \"bilingual\" curriculum with half the classes in quantitative and method-centric fields - like the ones mentioned above - and the other half in domains such as biology, business, chemistry, politics, (art) history, or linguistics (cf., the [official Q&As](http://computing.mit.edu/faq/) or this [NYT article](https://www.nytimes.com/2018/10/15/technology/mit-college-artificial-intelligence.html)). Their strategists see a future where programming skills are just as naturally embedded into students' curricula as are nowadays subjects like calculus, statistics, or academic writing. Then, programming literacy is not just another \"nice to have\" skill but a prerequisite, or an enabler, to understanding more advanced topics in the actual domains studied. Top-notch researchers who use programming in their day-to-day lives could then teach students more efficiently in their \"language.\""
    ]
   },
   {
@@ -248,7 +211,7 @@
     }
    },
    "source": [
-    "A new tab in your web browser opens with the website being \"localhost\" and some number (e.g., 8888). This is the [JupyterLab](https://jupyterlab.readthedocs.io/en/stable/) application that is used to display and run Jupyter notebooks as described below. On the left, you see the files and folders in your local user folder. This file browser works like any other. In the center, you have several options to launch (i.e., \"create\") new files."
+    "A new tab in your web browser opens with the website being \"localhost\" and some number (e.g., 8888). This is the [JupyterLab <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_jp.png\">](https://jupyterlab.readthedocs.io/en/stable/) application that is used to display and run [Jupyter notebooks <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_jp.png\">](https://jupyter-notebook.readthedocs.io/en/stable/notebook.html) as described below. On the left, you see the files and folders in your local user folder. This file browser works like any other. In the center, you have several options to launch (i.e., \"create\") new files."
    ]
   },
   {
@@ -270,7 +233,7 @@
     }
    },
    "source": [
-    "Next, to download the materials accompanying this book as a ZIP file, open this [GitHub repository](https://github.com/webartifex/intro-to-python) in a web browser, and click on the green \"Clone or download\" button on the top right. Then, unpack the ZIP file into a folder of your choosing, ideally somewhere within your personal user folder so that the files show up right away in JupyterLab."
+    "Next, to download the materials accompanying this book as a ZIP file, open this [GitHub repository <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_gh.png\">](https://github.com/webartifex/intro-to-python) in a web browser, and click on the green \"Clone or download\" button on the top right. Then, unpack the ZIP file into a folder of your choosing, ideally somewhere within your personal user folder so that the files show up right away in JupyterLab."
    ]
   },
   {
@@ -292,11 +255,11 @@
     }
    },
    "source": [
-    "The document you are viewing is a so-called [Jupyter notebook](https://jupyter-notebook.readthedocs.io/en/stable/notebook.html), a file format introduced by the [Jupyter Project](https://jupyter.org/).\n",
+    "The document you are viewing is a so-called [Jupyter notebook <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_jp.png\">](https://jupyter-notebook.readthedocs.io/en/stable/notebook.html), a file format introduced by the [Jupyter Project <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_jp.png\">](https://jupyter.org/).\n",
     "\n",
-    "\"Jupyter\" is an [acronym](https://en.wikipedia.org/wiki/Acronym) derived from the names of the three major programming languages **[Julia](https://julialang.org/)**, **[Python](https://www.python.org)**, and **[R](https://www.r-project.org/)**, all of which play significant roles in the world of data science. The Jupyter Project's idea is to serve as an integrating platform such that different programming languages and software packages can be used together within the same project easily.\n",
+    "\"Jupyter\" is an [acronym <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_wiki.png\">](https://en.wikipedia.org/wiki/Acronym) derived from the names of the three major programming languages **[Julia](https://julialang.org/)**, **[Python <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://www.python.org)**, and **[R](https://www.r-project.org/)**, all of which play significant roles in the world of data science. The Jupyter Project's idea is to serve as an integrating platform such that different programming languages and software packages can be used together within the same project easily.\n",
     "\n",
-    "Furthermore, Jupyter notebooks have become a de-facto standard for communicating and exchanging results in the data science community - both in academia and business - and provide an alternative to terminal-based ways of running Python (e.g., the default [Python interpreter](https://docs.python.org/3/tutorial/interpreter.html) as shown below or a more advanced interactive version like [IPython](https://ipython.org/)) or a full-fledged [Integrated Development Environment](https://en.wikipedia.org/wiki/Integrated_development_environment) (e.g., the commercial [PyCharm](https://www.jetbrains.com/pycharm/) or the free [Spyder](https://github.com/spyder-ide/spyder) that comes with the Anaconda Distribution)."
+    "Furthermore, Jupyter notebooks have become a de-facto standard for communicating and exchanging results in the data science community - both in academia and business - and provide an alternative to terminal-based ways of running Python (e.g., the default [Python interpreter <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/tutorial/interpreter.html) as shown below or a more advanced interactive version like [IPython <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://ipython.org/)) or a full-fledged [Integrated Development Environment <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_wiki.png\">](https://en.wikipedia.org/wiki/Integrated_development_environment) (e.g., the commercial [PyCharm](https://www.jetbrains.com/pycharm/) or the free [Spyder <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_gh.png\">](https://github.com/spyder-ide/spyder) that comes with the Anaconda Distribution)."
    ]
   },
   {
@@ -318,7 +281,7 @@
     }
    },
    "source": [
-    "Jupyter notebooks allow mixing formatted English with Python code in the same document. Text is formatted with the [Markdown](https://guides.github.com/features/mastering-markdown/) language and mathematical formulas are typeset with [LaTeX](https://www.overleaf.com/learn/latex/Free_online_introduction_to_LaTeX_%28part_1%29). Moreover, we may include pictures, plots, and even videos. Because of these features, the notebooks developed for this book come in a self-contained \"tutorial\" style enabling students to learn and review the material on their own."
+    "Jupyter notebooks allow mixing formatted English with Python code in the same document. Text is formatted with the [Markdown <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_gh.png\">](https://guides.github.com/features/mastering-markdown/) language and mathematical formulas are typeset with [LaTeX](https://www.overleaf.com/learn/latex/Free_online_introduction_to_LaTeX_%28part_1%29). Moreover, we may include pictures, plots, and even videos. Because of these features, the notebooks developed for this book come in a self-contained \"tutorial\" style enabling students to learn and review the material on their own."
    ]
   },
   {
@@ -355,7 +318,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 1,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -382,12 +345,12 @@
     }
    },
    "source": [
-    "Sometimes, a code cell starts with an exclamation mark `!`. Then, the Jupyter notebook behaves as if the following command were typed directly into a terminal. The cell below asks `python` to show its version number and is *not* Python code but a command in the [Shell](https://en.wikipedia.org/wiki/Shell_script) language. The `!` is useful to execute short commands without leaving a Jupyter notebook."
+    "Sometimes, a code cell starts with an exclamation mark `!`. Then, the Jupyter notebook behaves as if the following command were typed directly into a terminal. The cell below asks `python` to show its version number and is *not* Python code but a command in the [Shell <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_wiki.png\">](https://en.wikipedia.org/wiki/Shell_%28computing%29) language. The `!` is useful to execute short commands without leaving a Jupyter notebook."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 2,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -497,12 +460,12 @@
    "source": [
     "Here is a brief history of and some background on Python (cf., also this [TechRepublic article](https://www.techrepublic.com/article/python-is-eating-the-world-how-one-developers-side-project-became-the-hottest-programming-language-on-the-planet/) for a more elaborate story):\n",
     "\n",
-    "- [Guido van Rossum](https://en.wikipedia.org/wiki/Guido_van_Rossum) (Python’s **[Benevolent Dictator for Life](https://en.wikipedia.org/wiki/Benevolent_dictator_for_life)**) was bored during a week around Christmas 1989 and started Python as a hobby project \"that would keep \\[him\\] occupied\" for some days\n",
+    "- [Guido van Rossum <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_wiki.png\">](https://en.wikipedia.org/wiki/Guido_van_Rossum) (Python’s **[Benevolent Dictator for Life <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_wiki.png\">](https://en.wikipedia.org/wiki/Benevolent_dictator_for_life)**) was bored during a week around Christmas 1989 and started Python as a hobby project \"that would keep \\[him\\] occupied\" for some days\n",
     "- the idea was to create a **general-purpose** scripting **language** that would allow fast *prototyping* and would *run on every operating system*\n",
     "- Python grew through the 90s as van Rossum promoted it via his \"Computer Programming for Everybody\" initiative that had the *goal to encourage a basic level of coding literacy* as an equal knowledge alongside English literacy and math skills\n",
     "- to become more independent from its creator, the next major version **Python 2** - released in 2000 and still in heavy use as of today - was **open-source** from the get-go which attracted a *large and global community of programmers* that *contributed* their expertise and best practices in their free time to make Python even better\n",
     "- **Python 3** resulted from a significant overhaul of the language in 2008 taking into account the *learnings from almost two decades*, streamlining the language, and getting ready for the age of **big data**\n",
-    "- the language is named after the sketch comedy group [Monty Python](https://en.wikipedia.org/wiki/Monty_Python)"
+    "- the language is named after the sketch comedy group [Monty Python <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_wiki.png\">](https://en.wikipedia.org/wiki/Monty_Python)"
    ]
   },
   {
@@ -559,7 +522,7 @@
     }
    },
    "source": [
-    "Loose *communities* are the primary building block around which open-source software projects are built. Someone - like Guido - starts a project and makes it free to use for anybody (e.g., on a code-sharing platform like [GitHub](https://github.com/)). People find it useful enough to solve one of their daily problems and start using it. They see how a project could be improved and provide new use cases (e.g., via the popularized concept of a [pull request](https://help.github.com/articles/about-pull-requests/)). The project grows both in lines of code and people using it. After a while, people start local user groups to share their same interests and meet regularly (e.g., this is a big market for companies like [Meetup](https://www.meetup.com/) or non-profits like [PyData](https://pydata.org/)). Out of these local and usually monthly meetups grow yearly conferences on the country or even continental level (e.g., the original [PyCon](https://us.pycon.org/) in the US, [EuroPython](https://europython.eu/), or [PyCon.DE](https://de.pycon.org/)). The content presented at these conferences is made publicly available via GitHub and YouTube (e.g., [PyCon 2019](https://www.youtube.com/channel/UCxs2IIVXaEHHA4BtTiWZ2mQ) or [EuroPython](http://europython.tv/)) and serves as references on what people are working on and introductions to the endless number of specialized fields."
+    "Loose *communities* are the primary building block around which open-source software projects are built. Someone - like Guido - starts a project and makes it free to use for anybody (e.g., on a code-sharing platform like [GitHub <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_gh.png\">](https://github.com/)). People find it useful enough to solve one of their daily problems and start using it. They see how a project could be improved and provide new use cases (e.g., via the popularized concept of a [pull request <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_gh.png\">](https://help.github.com/articles/about-pull-requests/)). The project grows both in lines of code and people using it. After a while, people start local user groups to share their same interests and meet regularly (e.g., this is a big market for companies like [Meetup](https://www.meetup.com/) or non-profits like [PyData <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://pydata.org/)). Out of these local and usually monthly meetups grow yearly conferences on the country or even continental level (e.g., the original [PyCon <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://us.pycon.org/) in the US, [EuroPython <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://europython.eu/), or [PyCon.DE <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://de.pycon.org/)). The content presented at these conferences is made publicly available via GitHub and YouTube (e.g., [PyCon 2019 <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://www.youtube.com/channel/UCxs2IIVXaEHHA4BtTiWZ2mQ) or [EuroPython <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](http://europython.tv/)) and serves as references on what people are working on and introductions to the endless number of specialized fields."
    ]
   },
   {
@@ -570,9 +533,9 @@
     }
    },
    "source": [
-    "While these communities are somewhat loose and continuously changing, smaller in-groups, often democratically organized and elected (e.g., the [Python Software Foundation](https://www.python.org/psf/)), take care of, for example, the development of the \"core\" Python language itself.\n",
+    "While these communities are somewhat loose and continuously changing, smaller in-groups, often democratically organized and elected (e.g., the [Python Software Foundation <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://www.python.org/psf/)), take care of, for example, the development of the \"core\" Python language itself.\n",
     "\n",
-    "Python itself is just a specification (i.e., a set of rules) as to what is allowed and what not: It must first be implemented (c.f., next section below). The current version of Python can always be looked up in the [Python Language Reference](https://docs.python.org/3/reference/index.html). To make changes to that, anyone can make a so-called **[Python Enhancement Proposal](https://www.python.org/dev/peps/)**, or **PEP** for short, where it needs to be specified what exact changes are to be made and argued why that is a good thing to do. These PEPs are reviewed by the [core developers](https://devguide.python.org/coredev/) and interested people and are then either accepted, modified, or rejected if, for example, the change introduces internal inconsistencies. This process is similar to the **double-blind peer review** established in academia, just a lot more transparent. Many of the contributors even held or hold positions in academia, one more indicator of the high quality standards in the Python community. To learn more about PEPs, check out [PEP 1](https://www.python.org/dev/peps/pep-0001/) that describes the entire process.\n",
+    "Python itself is just a specification (i.e., a set of rules) as to what is allowed and what not: It must first be implemented (c.f., next section below). The current version of Python can always be looked up in the [Python Language Reference <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/reference/index.html). To make changes to that, anyone can make a so-called **[Python Enhancement Proposal <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://www.python.org/dev/peps/)**, or **PEP** for short, where it needs to be specified what exact changes are to be made and argued why that is a good thing to do. These PEPs are reviewed by the [core developers <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://devguide.python.org/coredev/) and interested people and are then either accepted, modified, or rejected if, for example, the change introduces internal inconsistencies. This process is similar to the **double-blind peer review** established in academia, just a lot more transparent. Many of the contributors even held or hold positions in academia, one more indicator of the high quality standards in the Python community. To learn more about PEPs, check out [PEP 1 <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://www.python.org/dev/peps/pep-0001/) that describes the entire process.\n",
     "\n",
     "In total, no one single entity can control how the language evolves, and the users' needs and ideas always feed back to the language specification via a quality controlled and \"democratic\" process."
    ]
@@ -585,7 +548,7 @@
     }
    },
    "source": [
-    "Besides being **free** as in \"free beer,\" a major benefit of open-source is that one can always *look up how something works in detail*: That is the literal meaning of *open* source and a difference to commercial languages (e.g., MATLAB) as a programmer can always continue to *study best practices* or find out how things are implemented. Along this way, many *errors are uncovered*, as well. Furthermore, if one runs an open-source application, one can be reasonably sure that no bad people built in a \"backdoor.\" [Free software](https://en.wikipedia.org/wiki/Free_software) is consequently free of charge but brings *many other freedoms* with it, most notably the freedom to change the code."
+    "Besides being **free** as in \"free beer,\" a major benefit of open-source is that one can always *look up how something works in detail*: That is the literal meaning of *open* source and a difference to commercial languages (e.g., MATLAB) as a programmer can always continue to *study best practices* or find out how things are implemented. Along this way, many *errors are uncovered*, as well. Furthermore, if one runs an open-source application, one can be reasonably sure that no bad people built in a \"backdoor.\" [Free software <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_wiki.png\">](https://en.wikipedia.org/wiki/Free_software) is consequently free of charge but brings *many other freedoms* with it, most notably the freedom to change the code."
    ]
   },
   {
@@ -609,7 +572,7 @@
    "source": [
     "The \"weird\" thing is that the default Python implementation is written in the C language.\n",
     "\n",
-    "[C](https://en.wikipedia.org/wiki/C_%28programming_language%29) and [C++](https://en.wikipedia.org/wiki/C%2B%2B) (cf., this [introduction](https://www.learncpp.com/)) are wide-spread and long-established (i.e., since the 1970s) programming languages employed in many mission-critical software systems (e.g., operating systems themselves, low latency databases and web servers, nuclear reactor control systems, airplanes, ...). They are fast, mainly because the programmer not only needs to come up with the **business logic** but also manage the computer's memory \"manually\" - and the knowledge necessary to do that is not easy to obtain.\n",
+    "[C <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_wiki.png\">](https://en.wikipedia.org/wiki/C_%28programming_language%29) and [C++ <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_wiki.png\">](https://en.wikipedia.org/wiki/C%2B%2B) (cf., this [introduction](https://www.learncpp.com/)) are wide-spread and long-established (i.e., since the 1970s) programming languages employed in many mission-critical software systems (e.g., operating systems themselves, low latency databases and web servers, nuclear reactor control systems, airplanes, ...). They are fast, mainly because the programmer not only needs to come up with the **business logic** but also manage the computer's memory \"manually\" - and the knowledge necessary to do that is not easy to obtain.\n",
     "\n",
     "In contrast, Python automatically manages the memory for the programmer. So, speed here is a trade-off between application run time and engineering/development time. Often, the program's run time is not that important: For example, what if C needs 0.001 seconds in a case where Python needs 0.1 seconds to do the same thing? When the requirements change and computing speed becomes an issue, the Python community offers many third-party libraries - usually also written in C - where specific problems can be solved in near-C time."
    ]
@@ -670,9 +633,9 @@
     "\n",
     "- **[Massachusetts Institute of Technology](https://www.mit.edu/)**\n",
     "  - teaches Python in its [introductory course](https://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-0001-introduction-to-computer-science-and-programming-in-python-fall-2016/) to computer science independent of the student's major\n",
-    "  - replaced the infamous course on the [Scheme](https://groups.csail.mit.edu/mac/projects/scheme/) language (cf., [source](https://news.ycombinator.com/item?id=602307))\n",
+    "  - replaced the infamous course on the [Scheme](https://groups.csail.mit.edu/mac/projects/scheme/) language (cf., [source <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_hn.png\">](https://news.ycombinator.com/item?id=602307))\n",
     "- **[Google](https://www.google.com/)**\n",
-    "  - used the strategy \"Python where we can, C++ where we must\" from its early days on to stay flexible in a rapidly changing environment (cf., [source](https://stackoverflow.com/questions/2560310/heavy-usage-of-python-at-google))\n",
+    "  - used the strategy \"Python where we can, C++ where we must\" from its early days on to stay flexible in a rapidly changing environment (cf., [source <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_so.png\">](https://stackoverflow.com/questions/2560310/heavy-usage-of-python-at-google))\n",
     "  - the very first web-crawler was written in Java and so difficult to maintain that it was rewritten in Python right away (cf., [source](https://www.amazon.com/Plex-Google-Thinks-Works-Shapes/dp/1416596585/ref=sr_1_1?ie=UTF8&qid=1539101827&sr=8-1&keywords=in+the+plex))\n",
     "  - Guido van Rossom was hired by Google from 2005 to 2012 to advance the language there\n",
     "- **[NASA](https://www.nasa.gov/)** open-sources many of its projects, often written in Python and regarding analyses with big data (cf., [source](https://code.nasa.gov/language/python/))\n",
@@ -692,7 +655,7 @@
     }
    },
    "source": [
-    "As images tell more than words, here are two plots of popular languages' \"market shares\" based on the number of questions asked on [Stack Overflow](https://stackoverflow.blog/2017/09/06/incredible-growth-python/), the most relevant platform for answering programming-related questions: As of late 2017, Python surpassed [Java](https://www.java.com/en/), heavily used in big corporates, and [JavaScript](https://developer.mozilla.org/en-US/docs/Web/JavaScript), the \"language of the internet\" that does everything in web browsers, in popularity. Two blog posts from \"technical\" people explain this in more depth to the layman: [Stack Overflow](https://stackoverflow.blog/2017/09/14/python-growing-quickly/) and [DataCamp](https://www.datacamp.com/community/blog/python-scientific-computing-case)."
+    "As images tell more than words, here are two plots of popular languages' \"market shares\" based on the number of questions asked on [Stack Overflow <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_so.png\">](https://stackoverflow.blog/2017/09/06/incredible-growth-python/), the most relevant platform for answering programming-related questions: As of late 2017, Python surpassed [Java](https://www.java.com/en/), heavily used in big corporates, and [JavaScript](https://developer.mozilla.org/en-US/docs/Web/JavaScript), the \"language of the internet\" that does everything in web browsers, in popularity. Two blog posts from \"technical\" people explain this in more depth to the layman: [Stack Overflow <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_so.png\">](https://stackoverflow.blog/2017/09/14/python-growing-quickly/) and [DataCamp](https://www.datacamp.com/community/blog/python-scientific-computing-case)."
    ]
   },
   {
@@ -771,7 +734,7 @@
    "source": [
     "**A**lways **b**e **c**oding.\n",
     "\n",
-    "Programming is more than just writing code into a text file. It means reading through parts of the [documentation](https://docs.python.org/), blogs with best practices, and tutorials, or researching problems on [Stack Overflow](https://stackoverflow.com/) while trying to implement features in the application at hand. Also, it means using command-line tools to automate some part of the work or manage different versions of a program, for example, with **[git](https://git-scm.com/)**. In short, programming involves a lot of \"muscle memory,\" which can only be built and kept up through near-daily usage.\n",
+    "Programming is more than just writing code into a text file. It means reading through parts of the [documentation <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/), blogs with best practices, and tutorials, or researching problems on [Stack Overflow <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_so.png\">](https://stackoverflow.com/) while trying to implement features in the application at hand. Also, it means using command-line tools to automate some part of the work or manage different versions of a program, for example, with **[git](https://git-scm.com/)**. In short, programming involves a lot of \"muscle memory,\" which can only be built and kept up through near-daily usage.\n",
     "\n",
     "Further, many aspects of software architecture and best practices can only be understood after having implemented some requirements for the very first time. Coding also means \"breaking\" things to find out what makes them work in the first place.\n",
     "\n",
@@ -797,7 +760,7 @@
     }
    },
    "source": [
-    "[Y Combinator](https://www.ycombinator.com/)'s co-founder [Paul Graham](https://en.wikipedia.org/wiki/Paul_Graham_%28programmer%29) wrote a very popular and often cited [article](http://www.paulgraham.com/makersschedule.html) where he divides every person into belonging to one of two groups:\n",
+    "[Y Combinator <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_hn.png\">](https://www.ycombinator.com/) co-founder [Paul Graham <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_wiki.png\">](https://en.wikipedia.org/wiki/Paul_Graham_%28programmer%29) wrote a very popular and often cited [article](http://www.paulgraham.com/makersschedule.html) where he divides every person into belonging to one of two groups:\n",
     "\n",
     "- **Managers**: People that need to organize things and command others (e.g., a \"boss\" or manager). Their schedule is usually organized by the hour or even 30-minute intervals.\n",
     "- **Makers**: People that create things (e.g., programmers, artists, or writers). Such people think in half days or full days.\n",
@@ -861,11 +824,11 @@
     "**Part A: Expressing Logic**\n",
     "\n",
     "- What is a programming language? What kind of words exist?\n",
-    "  - *Chapter 1*: [Elements of a Program](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/01_elements_00_lecture.ipynb)\n",
-    "  - *Chapter 2*: [Functions & Modularization](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/02_functions_00_lecture.ipynb)\n",
+    "  - *Chapter 1*: [Elements of a Program <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/01_elements_00_content.ipynb)\n",
+    "  - *Chapter 2*: [Functions & Modularization <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/02_functions_00_content.ipynb)\n",
     "- What is the flow of execution? How can we form sentences from words?\n",
-    "  - *Chapter 3*: [Conditionals & Exceptions](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/03_conditionals_00_lecture.ipynb)\n",
-    "  - *Chapter 4*: [Recursion & Looping](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/04_iteration_00_lecture.ipynb)"
+    "  - *Chapter 3*: [Conditionals & Exceptions <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/03_conditionals_00_content.ipynb)\n",
+    "  - *Chapter 4*: [Recursion & Looping <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/04_iteration_00_content.ipynb)"
    ]
   },
   {
@@ -879,13 +842,13 @@
     "**Part B: Managing Data and Memory**\n",
     "\n",
     "- How is data stored in memory?\n",
-    "  - *Chapter 5*: [Numbers & Bits](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/05_numbers_00_lecture.ipynb)\n",
-    "  - *Chapter 6*: [Text & Bytes](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/06_text_00_lecture.ipynb)\n",
-    "  - *Chapter 7*: [Sequential Data](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/07_sequences_00_lecture.ipynb)\n",
-    "  - *Chapter 8*: [Map, Filter, & Reduce](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/08_mfr_00_lecture.ipynb)\n",
-    "  - *Chapter 9*: [Mappings & Sets](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/08_mappings_00_lecture.ipynb)\n",
+    "  - *Chapter 5*: [Numbers & Bits <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/05_numbers_00_content.ipynb)\n",
+    "  - *Chapter 6*: [Text & Bytes <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/06_text_00_content.ipynb)\n",
+    "  - *Chapter 7*: [Sequential Data <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/07_sequences_00_content.ipynb)\n",
+    "  - *Chapter 8*: [Map, Filter, & Reduce <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/08_mfr_00_content.ipynb)\n",
+    "  - *Chapter 9*: [Mappings & Sets <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/08_mappings_00_content.ipynb)\n",
     "- How can we create custom data types?\n",
-    "  - *Chapter 10*: [Classes & Instances](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/10_classes_00_lecture.ipynb)"
+    "  - *Chapter 10*: [Classes & Instances <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/10_classes_00_content.ipynb)"
    ]
   },
   {
@@ -912,7 +875,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 3,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -953,7 +916,55 @@
     }
    },
    "source": [
-    "A video tutorial on how to use Jupyter Lab is shown below."
+    "A lecture-style **video presentation** of this chapter is integrated below (cf., the [video <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_yt.png\">](https://www.youtube.com/watch?v=YTU8jaG27Xk&list=PL-2JV1G3J10lQ2xokyQowcRJI5jjNfW7f) or the entire [playlist <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_yt.png\">](https://www.youtube.com/playlist?list=PL-2JV1G3J10lQ2xokyQowcRJI5jjNfW7f))."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
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+       "            width=\"60%\"\n",
+       "            height=\"300\"\n",
+       "            src=\"https://www.youtube.com/embed/YTU8jaG27Xk\"\n",
+       "            frameborder=\"0\"\n",
+       "            allowfullscreen\n",
+       "        ></iframe>\n",
+       "        "
+      ],
+      "text/plain": [
+       "<IPython.lib.display.YouTubeVideo at 0x7f9386ce7390>"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from IPython.display import YouTubeVideo\n",
+    "YouTubeVideo(\"YTU8jaG27Xk\", width=\"60%\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Also, to see some common shortcuts in [JupyterLab <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_jp.png\">](https://jupyterlab.readthedocs.io/en/stable/), find a **video tutorial** below."
    ]
   },
   {
@@ -980,7 +991,7 @@
        "        "
       ],
       "text/plain": [
-       "<IPython.lib.display.YouTubeVideo at 0x7f40e8368810>"
+       "<IPython.lib.display.YouTubeVideo at 0x7f938410af90>"
       ]
      },
      "execution_count": 5,
diff --git a/00_intro_01_review.ipynb b/00_intro_01_review.ipynb
index 6542d71..33ff218 100644
--- a/00_intro_01_review.ipynb
+++ b/00_intro_01_review.ipynb
@@ -18,7 +18,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "The questions below assume that you have read [Chapter 0](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/00_intro_00_lecture.ipynb) in the book.\n",
+    "The questions below assume that you have read [Chapter 0 <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/00_intro_00_content.ipynb) in the book.\n",
     "\n",
     "Be concise in your answers! Most questions can be answered in *one* sentence."
    ]
@@ -62,7 +62,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "**Q3**: In what sense are **open-source** communities democracies? How are they near-perfect [meritocracies](https://en.wikipedia.org/wiki/Meritocracy)? How is open-source software development similar to academia?"
+    "**Q3**: In what sense are **open-source** communities democracies? How are they near-perfect [meritocracies <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_wiki.png\">](https://en.wikipedia.org/wiki/Meritocracy)? How is open-source software development similar to academia?"
    ]
   },
   {
diff --git a/00_intro_02_exercises.ipynb b/00_intro_02_exercises.ipynb
index db84599..cef189c 100644
--- a/00_intro_02_exercises.ipynb
+++ b/00_intro_02_exercises.ipynb
@@ -1,5 +1,12 @@
 {
  "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Important**: Click on \"*Kernel*\" > \"*Restart Kernel and Run All*\" *after* finishing the exercises in [JupyterLab <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_jp.png\">](https://jupyterlab.readthedocs.io/en/stable/) (e.g., in the cloud on [MyBinder <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_mb.png\">](https://mybinder.org/v2/gh/webartifex/intro-to-python/master?urlpath=lab/tree/00_intro_02_exercises.ipynb)) to ensure that your solution runs top to bottom *without* any errors"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -18,7 +25,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "The exercises below assume that you have read [Chapter 0](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/00_introduction_00_lecture.ipynb) in the book."
+    "The exercises below assume that you have read [Chapter 0 <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/00_introduction_00_content.ipynb) in the book."
    ]
   },
   {
@@ -46,7 +53,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    " "
+    " < your answer >"
    ]
   },
   {
@@ -60,7 +67,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    " "
+    " < your answer >"
    ]
   },
   {
@@ -74,7 +81,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    " "
+    " < your answer >"
    ]
   }
  ],
diff --git a/01_elements_00_lecture.ipynb b/01_elements_00_content.ipynb
similarity index 91%
rename from 01_elements_00_lecture.ipynb
rename to 01_elements_00_content.ipynb
index 91c7411..88d36b1 100644
--- a/01_elements_00_lecture.ipynb
+++ b/01_elements_00_content.ipynb
@@ -8,44 +8,7 @@
     }
    },
    "source": [
-    "A **video presentation** of the contents in this chapter is shown below. A playlist with *all* chapters as videos is linked [here](https://www.youtube.com/playlist?list=PL-2JV1G3J10lQ2xokyQowcRJI5jjNfW7f)."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/jpeg": 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-       "            width=\"60%\"\n",
-       "            height=\"300\"\n",
-       "            src=\"https://www.youtube.com/embed/v0lk1Qfaw8Y\"\n",
-       "            frameborder=\"0\"\n",
-       "            allowfullscreen\n",
-       "        ></iframe>\n",
-       "        "
-      ],
-      "text/plain": [
-       "<IPython.lib.display.YouTubeVideo at 0x7f24bd5c6310>"
-      ]
-     },
-     "execution_count": 1,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from IPython.display import YouTubeVideo\n",
-    "YouTubeVideo(\"v0lk1Qfaw8Y\", width=\"60%\")"
+    "**Important**: Click on \"*Kernel*\" > \"*Restart Kernel and Clear All Outputs*\" *before* reading this chapter in [JupyterLab <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_jp.png\">](https://jupyterlab.readthedocs.io/en/stable/) (e.g., in the cloud on [MyBinder <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_mb.png\">](https://mybinder.org/v2/gh/webartifex/intro-to-python/master?urlpath=lab/tree/01_elements_00_content.ipynb))"
    ]
   },
   {
@@ -103,7 +66,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 1,
    "metadata": {
     "scrolled": true,
     "slideshow": {
@@ -128,7 +91,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 2,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -141,7 +104,7 @@
        "[7, 11, 8, 5, 3, 12, 2, 6, 9, 10, 1, 4]"
       ]
      },
-     "execution_count": 3,
+     "execution_count": 2,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -164,7 +127,7 @@
     "\n",
     "The `if number % 2 == 0` may look confusing at first sight. Both `%` and `==` must have an unintuitive meaning here. Luckily, the **comment** in the same line after the `#` symbol has the answer: The program does something only for an even `number`.\n",
     "\n",
-    "In particular, it increases `count` by `1` and adds the current `number` onto the [running](https://en.wikipedia.org/wiki/Running_total) `total`. Both `count` and `number` are **initialized** to `0` and the single `=` symbol reads as \"... is *set* equal to ...\". It cannot indicate a mathematical equation as, for example, `count` is generally *not* equal to `count + 1`.\n",
+    "In particular, it increases `count` by `1` and adds the current `number` onto the [running <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_wiki.png\">](https://en.wikipedia.org/wiki/Running_total) `total`. Both `count` and `number` are **initialized** to `0` and the single `=` symbol reads as \"... is *set* equal to ...\". It cannot indicate a mathematical equation as, for example, `count` is generally *not* equal to `count + 1`.\n",
     "\n",
     "Lastly, the `average` is calculated as the ratio of the final **values** of `total` and `count`. Overall, we divide the sum of all even numbers by their count: This is nothing but the definition of an average.\n",
     "\n",
@@ -173,7 +136,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 3,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -205,7 +168,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 4,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -218,7 +181,7 @@
        "7.0"
       ]
      },
-     "execution_count": 5,
+     "execution_count": 4,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -253,7 +216,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 5,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -266,7 +229,7 @@
        "'I am feeling great :-)'"
       ]
      },
-     "execution_count": 6,
+     "execution_count": 5,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -278,7 +241,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 6,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -297,12 +260,12 @@
     }
    },
    "source": [
-    "To see any output other than that, we use the built-in [print()](https://docs.python.org/3/library/functions.html#print) **function**. Here, the parentheses `()` indicate that we **call** (i.e., \"execute\") code written somewhere else."
+    "To see any output other than that, we use the built-in [print() <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/functions.html#print) **function**. Here, the parentheses `()` indicate that we **call** (i.e., \"execute\") code written somewhere else."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 7,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -336,7 +299,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 8,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -375,18 +338,18 @@
     }
    },
    "source": [
-    "Python comes with many built-in **[operators](https://docs.python.org/3/reference/lexical_analysis.html#operators)**: They are **tokens** (i.e., \"symbols\") that have a special meaning to the Python interpreter.\n",
+    "Python comes with many built-in **[operators <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/reference/lexical_analysis.html#operators)**: They are **tokens** (i.e., \"symbols\") that have a special meaning to the Python interpreter.\n",
     "\n",
     "The arithmetic operators either \"operate\" with the number immediately following them, so-called **unary** operators (e.g., negation), or \"process\" the two numbers \"around\" them, so-called **binary** operators (e.g., addition).\n",
     "\n",
     "By definition, operators on their own have *no* permanent **side effects** in the computer's memory. Although the code cells in this section do indeed create *new* numbers in memory (e.g., `77 + 13` creates `90`), they are immediately \"forgotten\" as they are not stored in a **variable** like `numbers` or `average` above. We develop this thought further at the end of this chapter when we compare **expressions** with **statements**.\n",
     "\n",
-    "Let's see some examples of operators. We start with the binary `+` and the `-` operators for addition and subtraction. Binary operators mimic what mathematicians call [infix notation](https://en.wikipedia.org/wiki/Infix_notation) and have the expected meaning."
+    "Let's see some examples of operators. We start with the binary `+` and the `-` operators for addition and subtraction. Binary operators mimic what mathematicians call [infix notation <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_wiki.png\">](https://en.wikipedia.org/wiki/Infix_notation) and have the expected meaning."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 9,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -399,7 +362,7 @@
        "90"
       ]
      },
-     "execution_count": 10,
+     "execution_count": 9,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -410,7 +373,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 10,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -423,7 +386,7 @@
        "8"
       ]
      },
-     "execution_count": 11,
+     "execution_count": 10,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -445,7 +408,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 11,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -458,7 +421,7 @@
        "-1"
       ]
      },
-     "execution_count": 12,
+     "execution_count": 11,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -480,7 +443,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 12,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -493,7 +456,7 @@
        "42"
       ]
      },
-     "execution_count": 13,
+     "execution_count": 12,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -504,7 +467,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 13,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -517,7 +480,7 @@
        "42.0"
       ]
      },
-     "execution_count": 14,
+     "execution_count": 13,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -537,6 +500,30 @@
     "The so-called **floor division operator** `//` always \"rounds\" to an integer and is thus also called **integer division operator**. It is an example of an arithmetic operator we commonly do not know from high school mathematics."
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "42"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "84 // 2"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 15,
@@ -557,30 +544,6 @@
      "output_type": "execute_result"
     }
    ],
-   "source": [
-    "84 // 2"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "fragment"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "42"
-      ]
-     },
-     "execution_count": 16,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
    "source": [
     "85 // 2"
    ]
@@ -598,7 +561,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 16,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -611,7 +574,7 @@
        "-43"
       ]
      },
-     "execution_count": 17,
+     "execution_count": 16,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -633,7 +596,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 17,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -646,7 +609,7 @@
        "1"
       ]
      },
-     "execution_count": 18,
+     "execution_count": 17,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -665,14 +628,14 @@
    "source": [
     "The remainder is `0` *only if* a number is *divisible* by another.\n",
     "\n",
-    "A popular convention in both computer science and mathematics is to abbreviate \"only if\" as \"iff\", which is short for \"**[if and only if](https://en.wikipedia.org/wiki/If_and_only_if)**.\" The iff means that a remainder of `0` implies that a number is divisible by another but also that a number's being divisible by another implies a remainder of `0`. The implication goes in *both* directions!\n",
+    "A popular convention in both computer science and mathematics is to abbreviate \"only if\" as \"iff\", which is short for \"**[if and only if <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_wiki.png\">](https://en.wikipedia.org/wiki/If_and_only_if)**.\" The iff means that a remainder of `0` implies that a number is divisible by another but also that a number's being divisible by another implies a remainder of `0`. The implication goes in *both* directions!\n",
     "\n",
     "So, `49` is divisible by `7`."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 18,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -685,7 +648,7 @@
        "0"
       ]
      },
-     "execution_count": 19,
+     "execution_count": 18,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -707,7 +670,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 19,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -720,7 +683,7 @@
        "9"
       ]
      },
-     "execution_count": 20,
+     "execution_count": 19,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -731,7 +694,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 20,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -744,7 +707,7 @@
        "89"
       ]
      },
-     "execution_count": 21,
+     "execution_count": 20,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -761,12 +724,12 @@
     }
    },
    "source": [
-    "The built-in [divmod()](https://docs.python.org/3/library/functions.html#divmod) function combines the integer and modulo divisions into one step. However, grammatically this is *not* an operator but a function. Also, [divmod()](https://docs.python.org/3/library/functions.html#divmod) returns a \"pair\" of integers and not a single one."
+    "The built-in [divmod() <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/functions.html#divmod) function combines the integer and modulo divisions into one step. However, grammatically this is *not* an operator but a function. Also, [divmod() <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/functions.html#divmod) returns a \"pair\" of integers and not a single one."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 21,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -779,7 +742,7 @@
        "(4, 2)"
       ]
      },
-     "execution_count": 22,
+     "execution_count": 21,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -801,7 +764,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 22,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -814,7 +777,7 @@
        "8"
       ]
      },
-     "execution_count": 23,
+     "execution_count": 22,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -831,7 +794,42 @@
     }
    },
    "source": [
-    "The standard [order of precedence](https://docs.python.org/3/reference/expressions.html#operator-precedence) from mathematics applies (i.e., [PEMDAS](http://mathworld.wolfram.com/PEMDAS.html) rule) when several operators are combined."
+    "The standard [order of precedence <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/reference/expressions.html#operator-precedence) from mathematics applies (i.e., [PEMDAS](http://mathworld.wolfram.com/PEMDAS.html) rule) when several operators are combined."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "18"
+      ]
+     },
+     "execution_count": 23,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "3 ** 2 * 2 "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Parentheses help avoid confusion and take the role of a **delimiter** here."
    ]
   },
   {
@@ -855,18 +853,7 @@
     }
    ],
    "source": [
-    "3 ** 2 * 2 "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "source": [
-    "Parentheses help avoid confusion and take the role of a **delimiter** here."
+    "(3 ** 2) * 2"
    ]
   },
   {
@@ -877,30 +864,6 @@
      "slide_type": "fragment"
     }
    },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "18"
-      ]
-     },
-     "execution_count": 25,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "(3 ** 2) * 2"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 26,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "fragment"
-    }
-   },
    "outputs": [
     {
      "data": {
@@ -908,7 +871,7 @@
        "81"
       ]
      },
-     "execution_count": 26,
+     "execution_count": 25,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -930,7 +893,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": 26,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -943,7 +906,7 @@
        "18"
       ]
      },
-     "execution_count": 27,
+     "execution_count": 26,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -987,7 +950,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": 27,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -1001,7 +964,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
+   "execution_count": 28,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -1014,7 +977,7 @@
        "'Hi class'"
       ]
      },
-     "execution_count": 29,
+     "execution_count": 28,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1036,7 +999,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 30,
+   "execution_count": 29,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -1049,7 +1012,7 @@
        "'Hi Hi Hi Hi Hi Hi Hi Hi Hi Hi '"
       ]
      },
-     "execution_count": 30,
+     "execution_count": 29,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1086,7 +1049,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 31,
+   "execution_count": 30,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -1118,12 +1081,12 @@
     }
    },
    "source": [
-    "The built-in [id()](https://docs.python.org/3/library/functions.html#id) function shows an object's \"address\" in memory."
+    "The built-in [id() <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/functions.html#id) function shows an object's \"address\" in memory."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 32,
+   "execution_count": 31,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -1133,7 +1096,31 @@
     {
      "data": {
       "text/plain": [
-       "94551559411776"
+       "94194112985152"
+      ]
+     },
+     "execution_count": 31,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "id(a)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "140586628675056"
       ]
      },
      "execution_count": 32,
@@ -1142,7 +1129,7 @@
     }
    ],
    "source": [
-    "id(a)"
+    "id(b)"
    ]
   },
   {
@@ -1157,7 +1144,7 @@
     {
      "data": {
       "text/plain": [
-       "139795753996464"
+       "140586628487280"
       ]
      },
      "execution_count": 33,
@@ -1165,30 +1152,6 @@
      "output_type": "execute_result"
     }
    ],
-   "source": [
-    "id(b)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 34,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "fragment"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "139795753985072"
-      ]
-     },
-     "execution_count": 34,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
    "source": [
     "id(c)"
    ]
@@ -1203,12 +1166,12 @@
    "source": [
     "These addresses are *not* meaningful for anything other than checking if two variables reference the *same* object.\n",
     "\n",
-    "Obviously, `a` and `b`  have the same *value* as revealed by the **equality operator** `==`: We say `a` and `b` \"evaluate equal.\" The resulting `True` - and the `False` further below - is yet another data type, a so-called **boolean**. We look into them in [Chapter 3](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/03_conditionals_00_lecture.ipynb#Boolean-Expressions)."
+    "Obviously, `a` and `b`  have the same *value* as revealed by the **equality operator** `==`: We say `a` and `b` \"evaluate equal.\" The resulting `True` - and the `False` further below - is yet another data type, a so-called **boolean**. We look into them in [Chapter 3 <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/03_conditionals_00_content.ipynb#Boolean-Expressions)."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 35,
+   "execution_count": 34,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -1221,7 +1184,7 @@
        "True"
       ]
      },
-     "execution_count": 35,
+     "execution_count": 34,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1243,7 +1206,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 36,
+   "execution_count": 35,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -1256,7 +1219,7 @@
        "False"
       ]
      },
-     "execution_count": 36,
+     "execution_count": 35,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1278,7 +1241,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 37,
+   "execution_count": 36,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -1291,7 +1254,7 @@
        "True"
       ]
      },
-     "execution_count": 37,
+     "execution_count": 36,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1319,12 +1282,12 @@
     }
    },
    "source": [
-    "The [type()](https://docs.python.org/3/library/functions.html#type) built-in shows an object's type. For example, `a` is an integer (i.e., `int`) while `b` is a so-called [floating-point number](https://en.wikipedia.org/wiki/Floating-point_arithmetic) (i.e., `float`)."
+    "The [type() <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/functions.html#type) built-in shows an object's type. For example, `a` is an integer (i.e., `int`) while `b` is a so-called [floating-point number <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_wiki.png\">](https://en.wikipedia.org/wiki/Floating-point_arithmetic) (i.e., `float`)."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 38,
+   "execution_count": 37,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -1337,7 +1300,7 @@
        "int"
       ]
      },
-     "execution_count": 38,
+     "execution_count": 37,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1348,7 +1311,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 39,
+   "execution_count": 38,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -1361,7 +1324,7 @@
        "float"
       ]
      },
-     "execution_count": 39,
+     "execution_count": 38,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1378,14 +1341,14 @@
     }
    },
    "source": [
-    "Different types imply different behaviors for the objects. The `b` object, for example, may be \"asked\" if it is a whole number with the [is_integer()](https://docs.python.org/3/library/stdtypes.html#float.is_integer) \"functionality\" that comes with *every* `float` object.\n",
+    "Different types imply different behaviors for the objects. The `b` object, for example, may be \"asked\" if it is a whole number with the [is_integer() <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/stdtypes.html#float.is_integer) \"functionality\" that comes with *every* `float` object.\n",
     "\n",
     "Formally, we call such type-specific functionalities **methods** (i.e., as opposed to functions) and we look at them in detail in Chapter 10. For now, it suffices to know that we access them with the **dot operator** `.` on the object. Of course, `b` is a whole number, which the boolean object `True` tells us."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 40,
+   "execution_count": 39,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -1398,7 +1361,7 @@
        "True"
       ]
      },
-     "execution_count": 40,
+     "execution_count": 39,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1415,12 +1378,12 @@
     }
    },
    "source": [
-    "For an `int` object, this [is_integer()](https://docs.python.org/3/library/stdtypes.html#float.is_integer) check does *not* make sense as we already know it is an `int`: We see the `AttributeError` below as `a` does not even know what `is_integer()` means."
+    "For an `int` object, this [is_integer() <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/stdtypes.html#float.is_integer) check does *not* make sense as we already know it is an `int`: We see the `AttributeError` below as `a` does not even know what `is_integer()` means."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 41,
+   "execution_count": 40,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -1434,7 +1397,7 @@
      "traceback": [
       "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
       "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-41-7db0a38aefcc>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0ma\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mis_integer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;32m<ipython-input-40-7db0a38aefcc>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0ma\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mis_integer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
       "\u001b[0;31mAttributeError\u001b[0m: 'int' object has no attribute 'is_integer'"
      ]
     }
@@ -1456,7 +1419,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 42,
+   "execution_count": 41,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -1469,7 +1432,7 @@
        "str"
       ]
      },
-     "execution_count": 42,
+     "execution_count": 41,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1478,6 +1441,30 @@
     "type(c)"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'python rocks'"
+      ]
+     },
+     "execution_count": 42,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "c.lower()"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 43,
@@ -1486,30 +1473,6 @@
      "slide_type": "fragment"
     }
    },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'python rocks'"
-      ]
-     },
-     "execution_count": 43,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "c.lower()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 44,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "fragment"
-    }
-   },
    "outputs": [
     {
      "data": {
@@ -1517,7 +1480,7 @@
        "'PYTHON ROCKS'"
       ]
      },
-     "execution_count": 44,
+     "execution_count": 43,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1547,12 +1510,12 @@
    "source": [
     "Almost trivially, every object also has a value to which it **evaluates** when referenced. We think of the value as the **conceptual idea** of what the $0$s and $1$s in the bag mean to *humans*. In other words, an object's value regards its *semantic* meaning.\n",
     "\n",
-    "For built-in data types, Python prints out an object's value as a so-called **[literal](https://docs.python.org/3/reference/lexical_analysis.html#literals)**: This means that we may copy and paste the value back into a code cell and create a *new* object with the *same* value."
+    "For built-in data types, Python prints out an object's value as a so-called **[literal <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/reference/lexical_analysis.html#literals)**: This means that we may copy and paste the value back into a code cell and create a *new* object with the *same* value."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 45,
+   "execution_count": 44,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -1565,7 +1528,7 @@
        "42"
       ]
      },
-     "execution_count": 45,
+     "execution_count": 44,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1576,7 +1539,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 46,
+   "execution_count": 45,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -1589,7 +1552,7 @@
        "42.0"
       ]
      },
-     "execution_count": 46,
+     "execution_count": 45,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1611,7 +1574,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 47,
+   "execution_count": 46,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -1624,7 +1587,7 @@
        "'Python rocks'"
       ]
      },
-     "execution_count": 47,
+     "execution_count": 46,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1683,7 +1646,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 48,
+   "execution_count": 47,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -1692,10 +1655,10 @@
    "outputs": [
     {
      "ename": "SyntaxError",
-     "evalue": "invalid syntax (<ipython-input-48-cafa82e54b9c>, line 1)",
+     "evalue": "invalid syntax (<ipython-input-47-cafa82e54b9c>, line 1)",
      "output_type": "error",
      "traceback": [
-      "\u001b[0;36m  File \u001b[0;32m\"<ipython-input-48-cafa82e54b9c>\"\u001b[0;36m, line \u001b[0;32m1\u001b[0m\n\u001b[0;31m    3.99 $ + 10.40 $\u001b[0m\n\u001b[0m         ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m invalid syntax\n"
+      "\u001b[0;36m  File \u001b[0;32m\"<ipython-input-47-cafa82e54b9c>\"\u001b[0;36m, line \u001b[0;32m1\u001b[0m\n\u001b[0;31m    3.99 $ + 10.40 $\u001b[0m\n\u001b[0m         ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m invalid syntax\n"
      ]
     }
    ],
@@ -1716,7 +1679,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 49,
+   "execution_count": 48,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -1725,10 +1688,10 @@
    "outputs": [
     {
      "ename": "SyntaxError",
-     "evalue": "invalid syntax (<ipython-input-49-499e4d0d0cbb>, line 1)",
+     "evalue": "invalid syntax (<ipython-input-48-499e4d0d0cbb>, line 1)",
      "output_type": "error",
      "traceback": [
-      "\u001b[0;36m  File \u001b[0;32m\"<ipython-input-49-499e4d0d0cbb>\"\u001b[0;36m, line \u001b[0;32m1\u001b[0m\n\u001b[0;31m    for number in numbers\u001b[0m\n\u001b[0m                         ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m invalid syntax\n"
+      "\u001b[0;36m  File \u001b[0;32m\"<ipython-input-48-499e4d0d0cbb>\"\u001b[0;36m, line \u001b[0;32m1\u001b[0m\n\u001b[0;31m    for number in numbers\u001b[0m\n\u001b[0m                         ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m invalid syntax\n"
      ]
     }
    ],
@@ -1750,7 +1713,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 50,
+   "execution_count": 49,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -1759,10 +1722,10 @@
    "outputs": [
     {
      "ename": "IndentationError",
-     "evalue": "expected an indented block (<ipython-input-50-19398c5f89de>, line 2)",
+     "evalue": "expected an indented block (<ipython-input-49-19398c5f89de>, line 2)",
      "output_type": "error",
      "traceback": [
-      "\u001b[0;36m  File \u001b[0;32m\"<ipython-input-50-19398c5f89de>\"\u001b[0;36m, line \u001b[0;32m2\u001b[0m\n\u001b[0;31m    print(number)\u001b[0m\n\u001b[0m        ^\u001b[0m\n\u001b[0;31mIndentationError\u001b[0m\u001b[0;31m:\u001b[0m expected an indented block\n"
+      "\u001b[0;36m  File \u001b[0;32m\"<ipython-input-49-19398c5f89de>\"\u001b[0;36m, line \u001b[0;32m2\u001b[0m\n\u001b[0;31m    print(number)\u001b[0m\n\u001b[0m        ^\u001b[0m\n\u001b[0;31mIndentationError\u001b[0m\u001b[0;31m:\u001b[0m expected an indented block\n"
      ]
     }
    ],
@@ -1799,7 +1762,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 51,
+   "execution_count": 50,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -1813,7 +1776,7 @@
      "traceback": [
       "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
       "\u001b[0;31mZeroDivisionError\u001b[0m                         Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-51-bc757c3fda29>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0;36m1\u001b[0m \u001b[0;34m/\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;32m<ipython-input-50-bc757c3fda29>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0;36m1\u001b[0m \u001b[0;34m/\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
       "\u001b[0;31mZeroDivisionError\u001b[0m: division by zero"
      ]
     }
@@ -1848,7 +1811,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 52,
+   "execution_count": 51,
    "metadata": {
     "code_folding": [],
     "slideshow": {
@@ -1870,7 +1833,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 53,
+   "execution_count": 52,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -1883,7 +1846,7 @@
        "3.5"
       ]
      },
-     "execution_count": 53,
+     "execution_count": 52,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1900,7 +1863,7 @@
     }
    },
    "source": [
-    "Systematically finding errors is called **debugging**. For the history of the term, see this [article](https://en.wikipedia.org/wiki/Debugging)."
+    "Systematically finding errors is called **debugging**. For the history of the term, see this [article <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_wiki.png\">](https://en.wikipedia.org/wiki/Debugging)."
    ]
   },
   {
@@ -1922,14 +1885,14 @@
     }
    },
    "source": [
-    "Thus, adhering to just syntax rules is *never* enough. Over time, **best practices** and **style guides** were created to make it less likely for a developer to mess up a program and also to allow \"onboarding\" him as a contributor to an established code base, often called **legacy code**, faster. These rules are *not* enforced by Python itself: Badly styled code still runs. At the very least, Python programs should be styled according to [PEP 8](https://www.python.org/dev/peps/pep-0008/) and documented \"inline\" (i.e., in the code itself) according to [PEP 257](https://www.python.org/dev/peps/pep-0257/).\n",
+    "Thus, adhering to just syntax rules is *never* enough. Over time, **best practices** and **style guides** were created to make it less likely for a developer to mess up a program and also to allow \"onboarding\" him as a contributor to an established code base, often called **legacy code**, faster. These rules are *not* enforced by Python itself: Badly styled code still runs. At the very least, Python programs should be styled according to [PEP 8 <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://www.python.org/dev/peps/pep-0008/) and documented \"inline\" (i.e., in the code itself) according to [PEP 257 <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://www.python.org/dev/peps/pep-0257/).\n",
     "\n",
     "An easier to read version of PEP 8 is [here](https://pep8.org/). The video below features a well known **[Pythonista](https://en.wiktionary.org/wiki/Pythonista)** talking about the importance of code style."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 54,
+   "execution_count": 53,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -1951,15 +1914,16 @@
        "        "
       ],
       "text/plain": [
-       "<IPython.lib.display.YouTubeVideo at 0x7f24bc42bf50>"
+       "<IPython.lib.display.YouTubeVideo at 0x7fdce00c69d0>"
       ]
      },
-     "execution_count": 54,
+     "execution_count": 53,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
+    "from IPython.display import YouTubeVideo\n",
     "YouTubeVideo(\"Hwckt4J96dI\", width=\"60%\")"
    ]
   },
@@ -1971,12 +1935,12 @@
     }
    },
    "source": [
-    "For example, while the above code to calculate the average of the even numbers in `[7, 11, 8, 5, 3, 12, 2, 6, 9, 10, 1, 4]` is correct, a Pythonista would rewrite it in a more \"Pythonic\" way and use the built-in [sum()](https://docs.python.org/3/library/functions.html#sum) and [len()](https://docs.python.org/3/library/functions.html#len) functions (cf., [Chapter 2](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/02_functions_00_lecture.ipynb#Built-in-Functions)) as well as a so-called **list comprehension** (cf., [Chapter 8](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/08_mfr_00_lecture.ipynb#List-Comprehensions)). Pythonic code runs faster in many cases and is less error-prone."
+    "For example, while the above code to calculate the average of the even numbers in `[7, 11, 8, 5, 3, 12, 2, 6, 9, 10, 1, 4]` is correct, a Pythonista would rewrite it in a more \"Pythonic\" way and use the built-in [sum() <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/functions.html#sum) and [len() <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/functions.html#len) functions (cf., [Chapter 2 <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/02_functions_00_content.ipynb#Built-in-Functions)) as well as a so-called **list comprehension** (cf., [Chapter 8 <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/08_mfr_00_content.ipynb#List-Comprehensions)). Pythonic code runs faster in many cases and is less error-prone."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 55,
+   "execution_count": 54,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -1989,7 +1953,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 56,
+   "execution_count": 55,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -2002,7 +1966,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 57,
+   "execution_count": 56,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -2015,7 +1979,7 @@
        "[8, 12, 2, 6, 10, 4]"
       ]
      },
-     "execution_count": 57,
+     "execution_count": 56,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2026,7 +1990,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 58,
+   "execution_count": 57,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -2039,7 +2003,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 59,
+   "execution_count": 58,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -2052,7 +2016,7 @@
        "7.0"
       ]
      },
-     "execution_count": 59,
+     "execution_count": 58,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2074,7 +2038,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 60,
+   "execution_count": 59,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -2173,7 +2137,7 @@
     "\n",
     "At the same time, for a beginner's course, it is often easier to code linearly.\n",
     "\n",
-    "In real data science projects, one would probably employ a mixed approach and put reusable code into so-called Python modules (i.e., *.py* files; cf., [Chapter 2](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/02_functions_00_lecture.ipynb#Local-Modules-and-Packages)) and then use Jupyter notebooks to build up a linear report or storyline for an analysis."
+    "In real data science projects, one would probably employ a mixed approach and put reusable code into so-called Python modules (i.e., *.py* files; cf., [Chapter 2 <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/02_functions_00_content.ipynb#Local-Modules-and-Packages)) and then use Jupyter notebooks to build up a linear report or storyline for an analysis."
    ]
   },
   {
@@ -2195,14 +2159,14 @@
     }
    },
    "source": [
-    "**Variables** are created with the **[assignment statement](https://docs.python.org/3/reference/simple_stmts.html#assignment-statements)** `=`, which is *not* an operator because of its *side effect* of making a **[name](https://docs.python.org/3/reference/lexical_analysis.html#identifiers)** reference an object in memory.\n",
+    "**Variables** are created with the **[assignment statement <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/reference/simple_stmts.html#assignment-statements)** `=`, which is *not* an operator because of its *side effect* of making a **[name <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/reference/lexical_analysis.html#identifiers)** reference an object in memory.\n",
     "\n",
     "We read the terms **variable**, **name**, and **identifier** used interchangebly in many Python-related texts. In this book, we adopt the following convention: First, we treat *name* and *identifier* as perfect synonyms but only use the term *name* in the text for clarity. Second, whereas *name* only refers to a string of letters, numbers, and some other symbols, a *variable* means the combination of a *name* and a *reference* to an object in memory."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 61,
+   "execution_count": 60,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -2226,7 +2190,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 62,
+   "execution_count": 61,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -2239,7 +2203,7 @@
        "20.0"
       ]
      },
-     "execution_count": 62,
+     "execution_count": 61,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2261,7 +2225,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 63,
+   "execution_count": 62,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -2274,7 +2238,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 64,
+   "execution_count": 63,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -2287,7 +2251,7 @@
        "20"
       ]
      },
-     "execution_count": 64,
+     "execution_count": 63,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2304,12 +2268,12 @@
     }
    },
    "source": [
-    "If we want to re-assign a variable while referencing its \"old\" (i.e., current) object, we may also **update** it using a so-called **[augmented assignment statement](https://docs.python.org/3/reference/simple_stmts.html#augmented-assignment-statements)** (i.e., *not* operator), as introduced with [PEP 203](https://www.python.org/dev/peps/pep-0203/): The currently mapped object is implicitly inserted as the first operand on the right-hand side."
+    "If we want to re-assign a variable while referencing its \"old\" (i.e., current) object, we may also **update** it using a so-called **[augmented assignment statement <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/reference/simple_stmts.html#augmented-assignment-statements)** (i.e., *not* operator), as introduced with [PEP 203 <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://www.python.org/dev/peps/pep-0203/): The currently mapped object is implicitly inserted as the first operand on the right-hand side."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 65,
+   "execution_count": 64,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -2322,7 +2286,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 66,
+   "execution_count": 65,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -2335,7 +2299,7 @@
        "80"
       ]
      },
-     "execution_count": 66,
+     "execution_count": 65,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2346,7 +2310,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 67,
+   "execution_count": 66,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -2359,7 +2323,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 68,
+   "execution_count": 67,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -2372,7 +2336,7 @@
        "40"
       ]
      },
-     "execution_count": 68,
+     "execution_count": 67,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2383,7 +2347,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 69,
+   "execution_count": 68,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -2396,12 +2360,47 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 70,
+   "execution_count": 69,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
     }
    },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "42"
+      ]
+     },
+     "execution_count": 69,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "variable"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Variables are **[dereferenced <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/reference/simple_stmts.html#the-del-statement)** (i.e., \"deleted\") with the `del` statement. This does *not* delete the object a variable references but merely removes the variable's name from the \"global list of all names.\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 70,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
    "outputs": [
     {
      "data": {
@@ -2418,44 +2417,9 @@
     "variable"
    ]
   },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "source": [
-    "Variables are **[dereferenced](https://docs.python.org/3/reference/simple_stmts.html#the-del-statement)** (i.e., \"deleted\") with the `del` statement. This does *not* delete the object a variable references but merely removes the variable's name from the \"global list of all names.\""
-   ]
-  },
   {
    "cell_type": "code",
    "execution_count": 71,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "slide"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "42"
-      ]
-     },
-     "execution_count": 71,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "variable"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 72,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -2479,7 +2443,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 73,
+   "execution_count": 72,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -2493,7 +2457,7 @@
      "traceback": [
       "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
       "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-73-1748287bc46a>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mvariable\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;32m<ipython-input-72-1748287bc46a>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mvariable\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
       "\u001b[0;31mNameError\u001b[0m: name 'variable' is not defined"
      ]
     }
@@ -2515,7 +2479,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 74,
+   "execution_count": 73,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -2528,7 +2492,7 @@
        "'__main__'"
       ]
      },
-     "execution_count": 74,
+     "execution_count": 73,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2545,12 +2509,12 @@
     }
    },
    "source": [
-    "To see all defined names, the built-in function [dir()](https://docs.python.org/3/library/functions.html#dir) is helpful."
+    "To see all defined names, the built-in function [dir() <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/functions.html#dir) is helpful."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 75,
+   "execution_count": 74,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -2564,7 +2528,6 @@
        " 'Out',\n",
        " 'YouTubeVideo',\n",
        " '_',\n",
-       " '_1',\n",
        " '_10',\n",
        " '_11',\n",
        " '_12',\n",
@@ -2575,6 +2538,7 @@
        " '_17',\n",
        " '_18',\n",
        " '_19',\n",
+       " '_2',\n",
        " '_20',\n",
        " '_21',\n",
        " '_22',\n",
@@ -2582,10 +2546,9 @@
        " '_24',\n",
        " '_25',\n",
        " '_26',\n",
-       " '_27',\n",
+       " '_28',\n",
        " '_29',\n",
-       " '_3',\n",
-       " '_30',\n",
+       " '_31',\n",
        " '_32',\n",
        " '_33',\n",
        " '_34',\n",
@@ -2594,26 +2557,26 @@
        " '_37',\n",
        " '_38',\n",
        " '_39',\n",
-       " '_40',\n",
+       " '_4',\n",
+       " '_41',\n",
        " '_42',\n",
        " '_43',\n",
        " '_44',\n",
        " '_45',\n",
        " '_46',\n",
-       " '_47',\n",
        " '_5',\n",
+       " '_52',\n",
        " '_53',\n",
-       " '_54',\n",
-       " '_57',\n",
-       " '_59',\n",
-       " '_6',\n",
-       " '_62',\n",
-       " '_64',\n",
-       " '_66',\n",
-       " '_68',\n",
+       " '_56',\n",
+       " '_58',\n",
+       " '_61',\n",
+       " '_63',\n",
+       " '_65',\n",
+       " '_67',\n",
+       " '_69',\n",
        " '_70',\n",
-       " '_71',\n",
-       " '_74',\n",
+       " '_73',\n",
+       " '_9',\n",
        " '__',\n",
        " '___',\n",
        " '__builtin__',\n",
@@ -2697,7 +2660,6 @@
        " '_i72',\n",
        " '_i73',\n",
        " '_i74',\n",
-       " '_i75',\n",
        " '_i8',\n",
        " '_i9',\n",
        " '_ih',\n",
@@ -2721,7 +2683,7 @@
        " 'total']"
       ]
      },
-     "execution_count": 75,
+     "execution_count": 74,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2749,7 +2711,7 @@
     }
    },
    "source": [
-    "[Phil Karlton](https://skeptics.stackexchange.com/questions/19836/has-phil-karlton-ever-said-there-are-only-two-hard-things-in-computer-science) famously noted during his time at [Netscape](https://en.wikipedia.org/wiki/Netscape):\n",
+    "[Phil Karlton](https://skeptics.stackexchange.com/questions/19836/has-phil-karlton-ever-said-there-are-only-two-hard-things-in-computer-science) famously noted during his time at [Netscape <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_wiki.png\">](https://en.wikipedia.org/wiki/Netscape):\n",
     "\n",
     "> \"There are *two* hard problems in computer science: *naming things* and *cache invalidation* ... and *off-by-one* errors.\""
    ]
@@ -2762,11 +2724,11 @@
     }
    },
    "source": [
-    "Variable names may contain upper and lower case letters, numbers, and underscores (i.e., `_`) and be as long as we want them to be. However, they must not begin with a number. Also, they must not be any of Python's built-in **[keywords](https://docs.python.org/3/reference/lexical_analysis.html#keywords)** like `for` or `if`.\n",
+    "Variable names may contain upper and lower case letters, numbers, and underscores (i.e., `_`) and be as long as we want them to be. However, they must not begin with a number. Also, they must not be any of Python's built-in **[keywords <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/reference/lexical_analysis.html#keywords)** like `for` or `if`.\n",
     "\n",
-    "Variable names should be chosen such that they do not need any more documentation and are self-explanatory. A widespread convention is to use so-called **[snake\\_case](https://en.wikipedia.org/wiki/Snake_case)**: Keep everything lowercase and use underscores to separate words.\n",
+    "Variable names should be chosen such that they do not need any more documentation and are self-explanatory. A widespread convention is to use so-called **[snake\\_case <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_wiki.png\">](https://en.wikipedia.org/wiki/Snake_case)**: Keep everything lowercase and use underscores to separate words.\n",
     "\n",
-    "See this [link](https://en.wikipedia.org/wiki/Naming_convention_%28programming%29#Python_and_Ruby) for a comparison of different naming conventions."
+    "See this [link <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_wiki.png\">](https://en.wikipedia.org/wiki/Naming_convention_%28programming%29#Python_and_Ruby) for a comparison of different naming conventions."
    ]
   },
   {
@@ -2782,7 +2744,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 76,
+   "execution_count": 75,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -2795,7 +2757,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 77,
+   "execution_count": 76,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -2808,7 +2770,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 78,
+   "execution_count": 77,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -2821,7 +2783,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 79,
+   "execution_count": 78,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -2845,7 +2807,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 80,
+   "execution_count": 79,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -2858,7 +2820,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 81,
+   "execution_count": 80,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -2871,7 +2833,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 82,
+   "execution_count": 81,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -2884,7 +2846,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 83,
+   "execution_count": 82,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -2893,10 +2855,10 @@
    "outputs": [
     {
      "ename": "SyntaxError",
-     "evalue": "can't assign to operator (<ipython-input-83-ec51dae29567>, line 1)",
+     "evalue": "can't assign to operator (<ipython-input-82-ec51dae29567>, line 1)",
      "output_type": "error",
      "traceback": [
-      "\u001b[0;36m  File \u001b[0;32m\"<ipython-input-83-ec51dae29567>\"\u001b[0;36m, line \u001b[0;32m1\u001b[0m\n\u001b[0;31m    address@work = \"WHU, Burgplatz 2, Vallendar\"\u001b[0m\n\u001b[0m                                                ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m can't assign to operator\n"
+      "\u001b[0;36m  File \u001b[0;32m\"<ipython-input-82-ec51dae29567>\"\u001b[0;36m, line \u001b[0;32m1\u001b[0m\n\u001b[0;31m    address@work = \"WHU, Burgplatz 2, Vallendar\"\u001b[0m\n\u001b[0m                                                ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m can't assign to operator\n"
      ]
     }
    ],
@@ -2917,7 +2879,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 84,
+   "execution_count": 83,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -2941,7 +2903,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 85,
+   "execution_count": 84,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -2954,7 +2916,7 @@
        "'__main__'"
       ]
      },
-     "execution_count": 85,
+     "execution_count": 84,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2989,7 +2951,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 86,
+   "execution_count": 85,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -3002,7 +2964,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 87,
+   "execution_count": 86,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -3015,7 +2977,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 88,
+   "execution_count": 87,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -3028,7 +2990,7 @@
        "42"
       ]
      },
-     "execution_count": 88,
+     "execution_count": 87,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3052,7 +3014,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 89,
+   "execution_count": 88,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -3065,7 +3027,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 90,
+   "execution_count": 89,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -3078,7 +3040,7 @@
        "87"
       ]
      },
-     "execution_count": 90,
+     "execution_count": 89,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3100,7 +3062,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 91,
+   "execution_count": 90,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -3113,7 +3075,7 @@
        "42"
       ]
      },
-     "execution_count": 91,
+     "execution_count": 90,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3135,7 +3097,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 92,
+   "execution_count": 91,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -3148,7 +3110,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 93,
+   "execution_count": 92,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -3161,7 +3123,7 @@
        "list"
       ]
      },
-     "execution_count": 93,
+     "execution_count": 92,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3172,7 +3134,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 94,
+   "execution_count": 93,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -3185,7 +3147,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 95,
+   "execution_count": 94,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -3198,7 +3160,7 @@
        "[1, 2, 3]"
       ]
      },
-     "execution_count": 95,
+     "execution_count": 94,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3217,14 +3179,14 @@
    "source": [
     "Let's change the first element of `x`.\n",
     "\n",
-    "[Chapter 7](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/07_sequences_00_lecture.ipynb#The-list-Type) discusses lists in more depth. For now, let's view a `list` object as some sort of **container** that holds an arbitrary number of references to other objects and treat the brackets `[]` attached to it as yet another operator, namely the **indexing operator**. So, `x[0]` instructs Python to first follow the reference from the global list of all names to the `x` object. Then, it follows the first reference it finds there to the `1` object we put in the list. The indexing operator must be an operator as we merely *read* the first element and do not change anything in memory permanently.\n",
+    "[Chapter 7 <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/07_sequences_00_content.ipynb#The-list-Type) discusses lists in more depth. For now, let's view a `list` object as some sort of **container** that holds an arbitrary number of references to other objects and treat the brackets `[]` attached to it as yet another operator, namely the **indexing operator**. So, `x[0]` instructs Python to first follow the reference from the global list of all names to the `x` object. Then, it follows the first reference it finds there to the `1` object we put in the list. The indexing operator must be an operator as we merely *read* the first element and do not change anything in memory permanently.\n",
     "\n",
-    "Python **begins counting at 0**. This is not the case for many other languages, for example, [MATLAB](https://en.wikipedia.org/wiki/MATLAB), [R](https://en.wikipedia.org/wiki/R_%28programming_language%29), or [Stata](https://en.wikipedia.org/wiki/Stata). To understand why this makes sense, see this short [note](https://www.cs.utexas.edu/users/EWD/transcriptions/EWD08xx/EWD831.html) by one of the all-time greats in computer science, the late [Edsger Dijkstra](https://en.wikipedia.org/wiki/Edsger_W._Dijkstra)."
+    "Python **begins counting at 0**. This is not the case for many other languages, for example, [MATLAB <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_wiki.png\">](https://en.wikipedia.org/wiki/MATLAB), [R <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_wiki.png\">](https://en.wikipedia.org/wiki/R_%28programming_language%29), or [Stata <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_wiki.png\">](https://en.wikipedia.org/wiki/Stata). To understand why this makes sense, see this short [note](https://www.cs.utexas.edu/users/EWD/transcriptions/EWD08xx/EWD831.html) by one of the all-time greats in computer science, the late [Edsger Dijkstra <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_wiki.png\">](https://en.wikipedia.org/wiki/Edsger_W._Dijkstra)."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 96,
+   "execution_count": 95,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -3237,7 +3199,7 @@
        "1"
       ]
      },
-     "execution_count": 96,
+     "execution_count": 95,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3259,7 +3221,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 97,
+   "execution_count": 96,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -3272,7 +3234,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 98,
+   "execution_count": 97,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -3285,7 +3247,7 @@
        "[99, 2, 3]"
       ]
      },
-     "execution_count": 98,
+     "execution_count": 97,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3307,7 +3269,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 99,
+   "execution_count": 98,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -3320,7 +3282,7 @@
        "[99, 2, 3]"
       ]
      },
-     "execution_count": 99,
+     "execution_count": 98,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3345,7 +3307,7 @@
     "\n",
     "In general, the assignment statement creates a new name and makes it reference whatever object is on the right-hand side *iff* the left-hand side is a *pure* name (i.e., it contains no operators like the indexing operator in the example). Otherwise, it *mutates* an already existing object. And, we must always expect that the latter may have more than one variable referencing it.\n",
     "\n",
-    "Visualizing what is going on in memory with a tool like [PythonTutor](http://pythontutor.com/visualize.html#code=x%20%3D%20%5B1,%202,%203%5D%0Ay%20%3D%20x%0Ax%5B0%5D%20%3D%2099%0Aprint%28y%5B0%5D%29&cumulative=false&curInstr=0&heapPrimitives=nevernest&mode=display&origin=opt-frontend.js&py=3&rawInputLstJSON=%5B%5D&textReferences=false) may be helpful for a beginner."
+    "Visualizing what is going on in memory with a tool like [PythonTutor <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](http://pythontutor.com/visualize.html#code=x%20%3D%20%5B1,%202,%203%5D%0Ay%20%3D%20x%0Ax%5B0%5D%20%3D%2099%0Aprint%28y%5B0%5D%29&cumulative=false&curInstr=0&heapPrimitives=nevernest&mode=display&origin=opt-frontend.js&py=3&rawInputLstJSON=%5B%5D&textReferences=false) may be helpful for a beginner."
    ]
   },
   {
@@ -3367,7 +3329,7 @@
     }
    },
    "source": [
-    "An **[expression](https://docs.python.org/3/reference/expressions.html)** is any syntactically correct *combination* of *variables* and *literals* with *operators*.\n",
+    "An **[expression <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/reference/expressions.html)** is any syntactically correct *combination* of *variables* and *literals* with *operators*.\n",
     "\n",
     "In simple words, anything that may be used on the right-hand side of an assignment statement without creating a `SyntaxError` is an expression.\n",
     "\n",
@@ -3378,7 +3340,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 100,
+   "execution_count": 99,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -3391,7 +3353,7 @@
        "87"
       ]
      },
-     "execution_count": 100,
+     "execution_count": 99,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3402,7 +3364,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 101,
+   "execution_count": 100,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -3415,7 +3377,7 @@
        "42"
       ]
      },
-     "execution_count": 101,
+     "execution_count": 100,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3437,7 +3399,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 102,
+   "execution_count": 101,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -3450,7 +3412,7 @@
        "45"
       ]
      },
-     "execution_count": 102,
+     "execution_count": 101,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3472,7 +3434,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 103,
+   "execution_count": 102,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -3485,7 +3447,7 @@
        "5"
       ]
      },
-     "execution_count": 103,
+     "execution_count": 102,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3507,7 +3469,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 104,
+   "execution_count": 103,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -3520,7 +3482,7 @@
        "3"
       ]
      },
-     "execution_count": 104,
+     "execution_count": 103,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3542,7 +3504,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 105,
+   "execution_count": 104,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -3555,7 +3517,7 @@
        "104"
       ]
      },
-     "execution_count": 105,
+     "execution_count": 104,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3583,14 +3545,14 @@
     }
    },
    "source": [
-    "A **[statement](https://docs.python.org/3/reference/simple_stmts.html)** is anything that *changes* the *state of a program* or has another permanent *side effect*. Statements, unlike expressions, do not evaluate to a value; instead, they create or change values.\n",
+    "A **[statement <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/reference/simple_stmts.html)** is anything that *changes* the *state of a program* or has another permanent *side effect*. Statements, unlike expressions, do not evaluate to a value; instead, they create or change values.\n",
     "\n",
     "Most notably, of course, are the `=` and `del` statements."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 106,
+   "execution_count": 105,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -3603,7 +3565,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 107,
+   "execution_count": 106,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -3622,12 +3584,12 @@
     }
    },
    "source": [
-    "The built-in [print()](https://docs.python.org/3/library/functions.html#print) function is sometimes regarded as a \"statement\" as well. It used to be an actual statement in Python 2 and has all the necessary properties. It is a bit of a corner case but we can think of it as changing the state of the screen."
+    "The built-in [print() <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/functions.html#print) function is sometimes regarded as a \"statement\" as well. It used to be an actual statement in Python 2 and has all the necessary properties. It is a bit of a corner case but we can think of it as changing the state of the screen."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 108,
+   "execution_count": 107,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -3674,7 +3636,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 109,
+   "execution_count": 108,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -3703,7 +3665,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 110,
+   "execution_count": 109,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -3716,7 +3678,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 111,
+   "execution_count": 110,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -3807,14 +3769,14 @@
     "  - ignored by Python\n",
     "\n",
     "\n",
-    "- functions (cf., [Chapter 2](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/02_functions_00_lecture.ipynb))\n",
+    "- functions (cf., [Chapter 2 <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/02_functions_00_content.ipynb))\n",
     "  - named sequences of instructions\n",
     "  - the smaller parts in a larger program\n",
     "  - make a program more modular and thus easier to understand\n",
-    "  - include [built-in functions](https://docs.python.org/3/library/functions.html) like [print()](https://docs.python.org/3/library/functions.html#print), [sum()](https://docs.python.org/3/library/functions.html#sum), or [len()](https://docs.python.org/3/library/functions.html#len)\n",
+    "  - include [built-in functions <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/functions.html) like [print() <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/functions.html#print), [sum() <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/functions.html#sum), or [len() <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/functions.html#len)\n",
     "\n",
     "\n",
-    "- flow control (cf., [Chapter 3](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/03_conditionals_00_lecture.ipynb) and [Chapter 4](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/04_iteration_00_lecture.ipynb))\n",
+    "- flow control (cf., [Chapter 3 <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/03_conditionals_00_content.ipynb) and [Chapter 4 <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/04_iteration_00_content.ipynb))\n",
     "  - expression of **business logic** or an **algorithm**\n",
     "  - conditional execution of parts of a program (e.g., `if` statements)\n",
     "  - repetitive execution of parts of a program (e.g., `for`-loops)"
@@ -3831,6 +3793,53 @@
     "## Further Resources"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "A lecture-style **video presentation** of this chapter is integrated below (cf., the [video <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_yt.png\">](https://www.youtube.com/watch?v=v0lk1Qfaw8Y&list=PL-2JV1G3J10lQ2xokyQowcRJI5jjNfW7f) or the entire [playlist <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_yt.png\">](https://www.youtube.com/playlist?list=PL-2JV1G3J10lQ2xokyQowcRJI5jjNfW7f))."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 111,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/jpeg": 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+       "            width=\"60%\"\n",
+       "            height=\"300\"\n",
+       "            src=\"https://www.youtube.com/embed/v0lk1Qfaw8Y\"\n",
+       "            frameborder=\"0\"\n",
+       "            allowfullscreen\n",
+       "        ></iframe>\n",
+       "        "
+      ],
+      "text/plain": [
+       "<IPython.lib.display.YouTubeVideo at 0x7fdce0060310>"
+      ]
+     },
+     "execution_count": 111,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "YouTubeVideo(\"v0lk1Qfaw8Y\", width=\"60%\")"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {
@@ -3866,7 +3875,7 @@
        "        "
       ],
       "text/plain": [
-       "<IPython.lib.display.YouTubeVideo at 0x7f24bc3f5410>"
+       "<IPython.lib.display.YouTubeVideo at 0x7fdce00600d0>"
       ]
      },
      "execution_count": 112,
diff --git a/01_elements_01_review.ipynb b/01_elements_01_review.ipynb
index 9c398f7..a04fd77 100644
--- a/01_elements_01_review.ipynb
+++ b/01_elements_01_review.ipynb
@@ -18,7 +18,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "The questions below assume that you have read [Chapter 1](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/01_elements_00_lecture.ipynb) in the book.\n",
+    "The questions below assume that you have read [Chapter 1 <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/01_elements_00_content.ipynb) in the book.\n",
     "\n",
     "Be concise in your answers! Most questions can be answered in *one* sentence."
    ]
@@ -90,7 +90,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "**Q5**: What are the basic **naming conventions** for variables? What happens if a name collides with one of Python's [keywords](https://docs.python.org/3/reference/lexical_analysis.html#keywords)?"
+    "**Q5**: What are the basic **naming conventions** for variables? What happens if a name collides with one of Python's [keywords <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/reference/lexical_analysis.html#keywords)?"
    ]
   },
   {
@@ -104,7 +104,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "**Q6**: Advocates of the [functional programming](https://en.wikipedia.org/wiki/Functional_programming) paradigm suggest not to use **mutable** data types in a program. What are the advantages of that approach? What might be a downside?"
+    "**Q6**: Advocates of the [functional programming <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_wiki.png\">](https://en.wikipedia.org/wiki/Functional_programming) paradigm suggest not to use **mutable** data types in a program. What are the advantages of that approach? What might be a downside?"
    ]
   },
   {
@@ -174,7 +174,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "**Q10**: [PEP 8](https://www.python.org/dev/peps/pep-0008/) suggests that developers use **8 spaces** per level of indentation."
+    "**Q10**: [PEP 8 <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://www.python.org/dev/peps/pep-0008/) suggests that developers use **8 spaces** per level of indentation."
    ]
   },
   {
diff --git a/01_elements_02_exercises.ipynb b/01_elements_02_exercises.ipynb
index fb7bc75..2370b10 100644
--- a/01_elements_02_exercises.ipynb
+++ b/01_elements_02_exercises.ipynb
@@ -1,5 +1,12 @@
 {
  "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Important**: Click on \"*Kernel*\" > \"*Restart Kernel and Run All*\" *after* finishing the exercises in [JupyterLab <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_jp.png\">](https://jupyterlab.readthedocs.io/en/stable/) (e.g., in the cloud on [MyBinder <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_mb.png\">](https://mybinder.org/v2/gh/webartifex/intro-to-python/master?urlpath=lab/tree/01_elements_02_exercises.ipynb)) to ensure that your solution runs top to bottom *without* any errors"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -18,7 +25,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "The exercises below assume that you have read [Chapter 1](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/01_elements_00_lecture.ipynb) in the book.\n",
+    "The exercises below assume that you have read [Chapter 1 <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/01_elements_00_content.ipynb) in the book.\n",
     "\n",
     "The `...`'s in the code cells indicate where you need to fill in code snippets. The number of `...`'s within a code cell give you a rough idea of how many lines of code are needed to solve the task. You should not need to create any additional code cells for your final solution. However, you may want to use temporary code cells to try out some ideas."
    ]
@@ -34,7 +41,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "**Q1.1**: *Concatenate* `greeting` and `audience` below with the `+` operator and print out the resulting message `\"Hello World\"` with only *one* call of the built-in [print()](https://docs.python.org/3/library/functions.html#print) function!\n",
+    "**Q1.1**: *Concatenate* `greeting` and `audience` below with the `+` operator and print out the resulting message `\"Hello World\"` with only *one* call of the built-in [print() <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/functions.html#print) function!\n",
     "\n",
     "Hint: You may have to \"add\" a space character in between `greeting` and `audience`."
    ]
@@ -76,9 +83,9 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "**Q1.3**: Read the documentation on the built-in [print()](https://docs.python.org/3/library/functions.html#print) function! How can you print the above message *without* concatenating `greeting` and `audience` first in *one* call of [print()](https://docs.python.org/3/library/functions.html#print)?\n",
+    "**Q1.3**: Read the documentation on the built-in [print() <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/functions.html#print) function! How can you print the above message *without* concatenating `greeting` and `audience` first in *one* call of [print() <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/functions.html#print)?\n",
     "\n",
-    "Hint: The `*objects` in the documentation implies that we can put several *expressions* (i.e., variables) separated by commas within the same call of the [print()](https://docs.python.org/3/library/functions.html#print) function."
+    "Hint: The `*objects` in the documentation implies that we can put several *expressions* (i.e., variables) separated by commas within the same call of the [print() <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/functions.html#print) function."
    ]
   },
   {
@@ -108,7 +115,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "**Q1.5**: What does the `sep=\" \"` mean in the documentation on the built-in [print()](https://docs.python.org/3/library/functions.html#print) function? Adjust and use it to print out the three names referenced by `first`, `second`, and `third` on *one* line separated by *commas* with only *one* call of the [print()](https://docs.python.org/3/library/functions.html#print) function!"
+    "**Q1.5**: What does the `sep=\" \"` mean in the documentation on the built-in [print() <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/functions.html#print) function? Adjust and use it to print out the three names referenced by `first`, `second`, and `third` on *one* line separated by *commas* with only *one* call of the [print() <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/functions.html#print) function!"
    ]
   },
   {
@@ -135,7 +142,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "**Q1.6**: Lastly, what does the `end=\"\\n\"` mean in the documentation? Adjust and use it within the `for`-loop to print the numbers `1` through `10` on *one* line with only *one* call of the [print()](https://docs.python.org/3/library/functions.html#print) function!"
+    "**Q1.6**: Lastly, what does the `end=\"\\n\"` mean in the documentation? Adjust and use it within the `for`-loop to print the numbers `1` through `10` on *one* line with only *one* call of the [print() <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/functions.html#print) function!"
    ]
   },
   {
@@ -159,7 +166,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "The [volume of a sphere](https://en.wikipedia.org/wiki/Sphere) is defined as $\\frac{4}{3} * \\pi * r^3$.\n",
+    "The [volume of a sphere <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_wiki.png\">](https://en.wikipedia.org/wiki/Sphere) is defined as $\\frac{4}{3} * \\pi * r^3$.\n",
     "\n",
     "**Q2.1**: Calculate it for `r = 2.88` and approximate $\\pi$ with `pi = 3.14`!"
    ]
@@ -284,7 +291,7 @@
    "source": [
     "`for`-loops are extremely versatile in Python. That is different from many other programming languages.\n",
     "\n",
-    "As shown in the first example in [Chapter 1](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/01_elements_00_lecture.ipynb#Example:-Averaging-all-even-Numbers-in-a-List), we can create a `list` like `numbers` and loop over the numbers in it on a one-by-one basis."
+    "As shown in the first example in [Chapter 1 <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/01_elements_00_content.ipynb#Example:-Averaging-all-even-Numbers-in-a-List), we can create a `list` like `numbers` and loop over the numbers in it on a one-by-one basis."
    ]
   },
   {
@@ -300,7 +307,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "**Q3.1**: Fill in the *condition* of the `if` statement such that only numbers divisible by `3` are printed! Adjust the call of the [print()](https://docs.python.org/3/library/functions.html#print) function such that the `for`-loop prints out all the numbers on *one* line of output!"
+    "**Q3.1**: Fill in the *condition* of the `if` statement such that only numbers divisible by `3` are printed! Adjust the call of the [print() <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/functions.html#print) function such that the `for`-loop prints out all the numbers on *one* line of output!"
    ]
   },
   {
@@ -340,7 +347,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "An easy way to loop over a `list` object in a sorted manner, is to wrap it with the built-in [sorted()](https://docs.python.org/3/library/functions.html#sorted) function.\n",
+    "An easy way to loop over a `list` object in a sorted manner, is to wrap it with the built-in [sorted() <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/functions.html#sorted) function.\n",
     "\n",
     "**Q3.3**: Fill in the condition of the `if` statement such that only odd numbers are printed out! Put all the numbers on *one* line of output!"
    ]
@@ -360,11 +367,11 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Whenever we want to loop over numbers representing a [series](https://en.wikipedia.org/wiki/Series_%28mathematics%29) in the mathematical sense (i.e., a rule to calculate the next number from its predecessor), we may be able to use the [range()](https://docs.python.org/3/library/functions.html#func-range) built-in.\n",
+    "Whenever we want to loop over numbers representing a [series <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_wiki.png\">](https://en.wikipedia.org/wiki/Series_%28mathematics%29) in the mathematical sense (i.e., a rule to calculate the next number from its predecessor), we may be able to use the [range() <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/functions.html#func-range) built-in.\n",
     "\n",
     "For example, to loop over the whole numbers from `0` to `9` (both including) in order, we could write them out in a `list` like below.\n",
     "\n",
-    "**Q3.4**: Fill in the call to the [print()](https://docs.python.org/3/library/functions.html#print) function such that all the numbers are printed on *one* line ouf output!"
+    "**Q3.4**: Fill in the call to the [print() <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/functions.html#print) function such that all the numbers are printed on *one* line ouf output!"
    ]
   },
   {
@@ -381,7 +388,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "**Q3.5**: Read the documentation on the [range()](https://docs.python.org/3/library/functions.html#func-range) built-in! It may be used with either one, two, or three expressions \"passed\" in. What do `start`, `stop`, and `step` mean? Fill in the calls to [range()](https://docs.python.org/3/library/functions.html#func-range) and [print()](https://docs.python.org/3/library/functions.html#print) to mimic the output of **Q3.4**!"
+    "**Q3.5**: Read the documentation on the [range() <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/functions.html#func-range) built-in! It may be used with either one, two, or three expressions \"passed\" in. What do `start`, `stop`, and `step` mean? Fill in the calls to [range() <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/functions.html#func-range) and [print() <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/functions.html#print) to mimic the output of **Q3.4**!"
    ]
   },
   {
@@ -398,7 +405,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "**Q3.6**: Fill in the calls to [range()](https://docs.python.org/3/library/functions.html#func-range) and [print()](https://docs.python.org/3/library/functions.html#print) to print out *all* numbers from `1` to `10` (both including)!"
+    "**Q3.6**: Fill in the calls to [range() <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/functions.html#func-range) and [print() <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/functions.html#print) to print out *all* numbers from `1` to `10` (both including)!"
    ]
   },
   {
@@ -415,7 +422,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "**Q3.7**: Fill in the calls to [range()](https://docs.python.org/3/library/functions.html#func-range) and [print()](https://docs.python.org/3/library/functions.html#print) to print out the *even* numbers from `1` to `10` (both including)! Do *not* use an `if` statement to accomplish this!"
+    "**Q3.7**: Fill in the calls to [range() <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/functions.html#func-range) and [print() <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/functions.html#print) to print out the *even* numbers from `1` to `10` (both including)! Do *not* use an `if` statement to accomplish this!"
    ]
   },
   {
diff --git a/02_functions_00_lecture.ipynb b/02_functions_00_content.ipynb
similarity index 80%
rename from 02_functions_00_lecture.ipynb
rename to 02_functions_00_content.ipynb
index 8950027..e810c48 100644
--- a/02_functions_00_lecture.ipynb
+++ b/02_functions_00_content.ipynb
@@ -8,44 +8,7 @@
     }
    },
    "source": [
-    "A **video presentation** of the contents in this chapter is shown below. A playlist with *all* chapters as videos is linked [here](https://www.youtube.com/playlist?list=PL-2JV1G3J10lQ2xokyQowcRJI5jjNfW7f)."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/jpeg": 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8E7HRTObSfJPNNyngOEY5jG7sdpHxqELts2JJXbpZHyOwBukc57sDsG5xJx1K6lihSLMxcbkyY00A/YBSxeIbVytYIa1uUTc6lxnTeT0FkWR4ZH/PUv1ut/fjWOXZWmdG9kjDtfG5r2OGMtcwhzT16dCAtr25mkLRReGPa47iCs13RPuvqf6/a/vPWBXfftyTyyTzO3yyvdJI8gAue8lznENAAySewLoUaLCxjWncAFLE1BUqueNCSfUypnwS7vTT9T1RjWG1C+rVpyPY2QQPsOeZ5mseC3mctgDTjpud5iQeXFFg6hpFXU5gzvyO9Np88zY2Rmy3kMswvlEYDS9jSWZwMjtyvHwhdgfU1DTbE7awud7y1rEoeYY7FZ7jsm5bS5jJGPLd+Dt2g4K5cTWq9fT62l1547Tm2Zb1ueHf3vz3xtgiigdI1rpA2JuS7aBlwx58c4sPbzHvZheP6cvHSJm3G67jao/g4zDJ2ZBbI/7naGLazEGY+G0xIUUXtpavbgY6KG1ZhifnfHFPLHG/Iwd7GODXdOnULxIum5odYhfPse5hlpjoiL3aHpU92dteuzfK5r3AZwA2Nhe9znHo1oDT1PzDzrwoHCY3/f8AtCxwaHEWMiekT+49UWV1XWTPUoVOWGigyy0P3ZMvfE5nJLceJgnHacrFLOs4Wt94T6hJE+GvCYAwyxvYbHPdtBh3ABzQMEu7PGGMrXVNMFped9upt9Vvw7azg9tMGC33o/tEOvw+EHyhYJERblWRSTuZ6nBT1anZsv5cERmMj9j37d9aaNvixtLjlz2joPOo2vq11aYqMLDoQR6rdh67qFVtVurSCJ0sZXFo6D8i+oi2LSvq+IiIu2rA+V7Io2l8kj2xxsaMue97g1jWjzuJIH71xlYWuLXAhzSWuB7QQcEH58hTPuPasYNTqQtr1ZDPZjaZpYRJYia4FpEEhd9iyM9QPOVFdb/9VZ/WJv7jlXbWcaxpkWABnjJP+FdfhmNwzawdJLiCI0gA+eq8aIisKki4WPIf+a7+RXNcLHkP/Nd/IrDtCp0/iHVbsIiLyhfohERMoiIiZRERERERERERMplEREREREREREREREREREREREREREREREREWu3uj/uxF+z4P79pVmrM90f92Iv2fB/ftKs16Rsr8pT6Lw32i/mNb9RRERdBcZEWV4S0d2oXqtNrtvfErWF3aWMALpHAechjXkD4wFJWUtJ1Bl+GhVmrTU601utYdZfP37FVI5jZoXNAikfGd4DOgPTzeNWq4ptN2Ug7iSNwJgE/PSdCr2HwD6zMwIFyADMuIEkCARpGpAuBqoKiIrKooiIURERERERERT/uNa5aZeiotmIqzMuvkh2R4e5tKd4Jft39DGw4z5lAG9gWW4S1k6fbjtiMSmNszdhdsB50EsBO4A4wJM9nXCxIVanRy1nuAgEN8yC6f3Cv1sT2mFp0y4ktc+xmzSGRHKQ6w+qKa6Nenm0LWmyzSytik0hsbZJHyNjbzpxiMOJDBhreg+SPiUKWQp6tLFVtVGhnKuOrulJaS8Gs5749js4AzIc5Bz07FnEUu0aI1BafRwJ+QUMFiBReSTYteLc2OA+ZCx6EohCsKmrKuOo6ffraNJp1SxHtpxX7MrHm2+e3HG+WSvOH5gYwTM2tA+8PXrkQXiTTu87lqrku73sTQhxxlzY3uax5x0yWgH96nuqae3UtSrayyxVZSk7ynvPkswxvpSVo4o54ZIXOErpDyfF2tIcZG9cHKg3Fmoi5euWm5DZ7M0rARgiNz3GMEfK27crlYAnML3y+9+qfkdfKN0L6La7QGGwAzns4AvTjdGo+G/Gd8rFoi9ek2mQytkkrxWWtz9hmMgjcSCAXcp7XHB64zjouo4kCQJ5L59gBcATHPh6XXRNA9m3ex7N7BIze1zd7HeS9uR4zDg4I6dF1qZ91ycS2aEojjiEmjadIIohtiiD43uEcbfvWNzgD4gFDFqw9U1KYeRE7lvxtAUKzqYMgb+Ky3B+pspX6luRrnMrzMlc1mN7g09Q3cQM/lK8GoTCSaWQAgSSSPAPaA95cAcefquhfVPsxnz74hazWcaYpbgSfMgD6BfERFNakXCx5D/zXfyK5rhY8h/5rv5FYdoVOn8Q6rdhcZQdpx1ODgbi3Jx0G4Alv5QFyXVbic9ha2R8LjjEkYjL24IPQSsczrjHVp7T2dq8oX6IVVUKGo0RBFBWu12TPgieYBpLL9h0WnatJIbBdM6lLKJm1CbIZC6Q7Q7e1uBIdN0/WSa5szSCR8tsWpYu8hyoDacYIq/ibhCYY4sdDJ453EO7M3WaJXFsesTSOD5Yy1h0x7hJAQ2aMhtXIewuaHN7W5GcL1e9k3pG5/Bp/1NWX1idQPRV20gNJUU4L1LUn6hFXuyzGRlGR12L/AJA12ThmmcggVxzo5CZLflYY9xmLNzGs2+IaNqcE9mSnVfE+V87pbL+83WZGy6pXleyKVk7W3IjV745ZsRNkiaA0P3OcDOPeyb0jc/g0/wCprhHRkdu26nadscWO2t047XAAlrsVPFdgg4PxhY7a8gBZ7O15UXuU9fdAHMs2ROI3ANaNMj3EabYfG6RpD2Cbv4Vmu2v2ZDsYjLl0WWcRiKVwdYfLzGERRM0yMOcItQ5ghsTTPEdV0hobXPic9vLbujcHy7ZbZqPiAdJqlmNpfHGHPGmsBklkbFEwF1TBe+R7GAdpc9oHUhfYqUjy8M1O04xu2SBrdOcWP2tdsfip4rtr2nB64cPjQVbaD0Q0+Z9VHWVNZZZY1kk7a3f00rie852mGTUnSvY/fM2RkHeTmsj2hzmO5uW+LGD6LlW/7z045oJdRvllc2mSOqBvOcwunfYibLBBZgjkJxCx7Q4tjGQMvGe97JvSNz+DT/qa8WryR02tfb1qSqx7i1jrD9Kga5waXlrXS1QHODWudgeZpPmWO0JIgDyH2VnIBxUVj0S9CH4qXrRbBGzdPertmtV20NPgbTsSizhsvfEU8ryCWdLBa4mfD/Roeh3Y56jnQWBt5ThJJJXYymO+9Tmu1214rUojrPjnrxxxsdJhjawccwAtkVqSOKJ08utSRwMcxj5pH6UyJj5SxsTHSOq7Wuc6WIAE5JkZjtC5M2ugNpusTOqhjpDZDtLMAjZnfIZhV2bBtdl2cDBUjVcR/wAqIptB/wCFn0WCoMFhofBq88zCXgOi97HgmN2yQZbVPVrjgjzHoV3PpSNc1p1O0HPJDGlunBzy0Fzg0GplxABPTzBaMq3Zll0WJfp8zQXO1K2AASSWaeAAO0kmp0C5e9k/pG5/Bp/1NYjmszyWURYv3sn9I3P4NP8Aqae9k/pG5/Bp/wBTSOaTyWURYv3sn9I3P4NP+pp72T+kbn8Gn/U0jmk8llEWL97J/SNz+DT/AKmnvZP6Rufwaf8AU0jmk8llEWL97J/SNz+DT/qae9k/pG5/Bp/1NI5pPJZRFi/eyf0jc/g0/wCpp72T+kbn8Gn/AFNI5pPJZRFi/eyf0jc/g0/6mnvZP6Rufwaf9TSOaTyVFe6P+7EX7Pg/v2lWasL3Q1V7NWiDrM8p7wgO57awOOfa6fY4GjH7vOq55Tvwr/8ASL/GvR9lflafReI+0DQdoVr/ANR4rsRdfKd+Ff8A6Rf41wkDm7TzHHx2AgiPBDnAHsYD51flccMB3j5/4Us7l+ox1dYoTzODImzFj3k4awTRSQbnE+S0GUEnzAErPcLaFb0h+qWLsMkEVbT7laOWRpZFZsTgQ1467ndJg45dluQAOuOirxd0tmR7WMfI9zIxiNrnuc1g+JjScNH5FUr4U1HEg2IAPQEm3qQuhg9oNosALSS1xc0gxdwAvYyLNO7QjfbpREVxctFYOpa3PotbSoKIiY6zQi1G298MUptOtSSbIZTI0nksZHtDWkdHnz9VXym9kU9Vq6cZL9ejPRqto2W2RMd1aGR7oJqvKjcJpNkjgY8tOWjzEFUcY1pLM4lsmRE7jEgT/wAwutsx7mip2TofAymQD8QmCYvHPSd0rHd07Toq2pzsrsEcErYbMUY7I22IY5XMA7A0Pc8ADoBhRlZ7j7WI7+oT2IQ5sB5cUAd5XJgiZCwkeYuDN2PNuwsCt2FDhRYH6wJ9N/NVdommcTUNP4cxiNIndy4cl7tF0i1dl5NSCSxJguLY252tHQucexjckDJIGSB51w1bTLFSV0FmGSCVuCWSNLTg9jh5nNOD1GR0Kk1J5h4btPjO11rWIak5HQvgipyWGRnH3vMJOPypxKTLoeizPJMkcmoVWuJy4wMkjfEwk9drNzmgeYOwtIxL+0i2XNl5zlmeG6IjnO5WjgKfYEyc4YH7ssFwbHGYOaZ5RvUOWS4a0aW/ZjqwlrXP3Fz5DtjijjaXySyO+9Y1rSf9B2kLGqX9y4Zk1Rjeskuh6nHCB5TpTGwhrf8AuLWvW/FVDTpOc3WPv/Kq7PotrYhjHaE358vPReXWOGYWVX3KN+O/BBKyK1iCatJA6XIheY5uroXOaWh3TrgY7cRpS/g3A0niBzvI73os+bmvuDk/vy1x/cVEFDDOdL2uM5TE2/tBvEDfw0hTx1NgbTqMGXM2S0TAIc5tpJMGN5N53IiIrSoIvq+L26LpVi7M2vVidNM4EhjcDDWjLnOc4hrGD5TiB1Cw5waJJgKTGOe4NaJJ0A1K8SLKa/w/coGMWoTEJmudE8PiljkDSA7ZLC5zHEEjIzkZGe0LFrDHteMzTI4hZqUn03FrwQRuIg/NZ7jLVorbqRi34r6ZSqSb2hv2WvGWybcE5Zk9CsCiLFOmGNDQpV6zqry92pRS3gaKKGpqmpSQxWJKUdWKtFOwSQie5M6PnPjd0e6NkbiAcjLh8xESUr4JswyVdT02aeKsb0dV9eed2yBs9OZ0gjlf2RtkY943HoCB8wOjGT2XmJjhmE6cplW9mECuJiYdE/3ZHZdbfFETvXfxG5l7SYdTdDBDai1B+nzurxMhZZY+v3zDM+OMBglbtezIAyMZ82IapfxC+GppUGmNsV7ViS87ULD6sgmhhAg72hh5zfEkeQXuO3s6D4lEFjBiGGNJMdP8axy0ss7UM1RPxZW5o/ujfG/SeczeUXCx5D/zXfyK5rhY8h/5rv5FWnaFUafxDqt2Fwm3bXbA0v2naHEhpdjxQ4gEhucdgK5ovKF+iFWp7nt2MFsN4SB8cEszpBHBLNdEtY3S50VYsMFiKtG1xex56O3CQOIHvl4T1AMaGWWPAqwwOhmnncHPbLFJLOZWRhjpDE2SsN0JBaGOcHAvhM7RbziHlaexaoAeC7roWg2g2dkToWPbZs9IuTqUQYHRtj2bu+qpcWNbjkNI6xRkeetwRfbO+UTRwRyCQsggtSujqOc2wNkfMq75o382IOw6LAhZ0cGMa2x0T+Ics9i1YDWuHzNRhpxu2cuxp0pdzJdxbTvVrcuJQeZzXNhfhxOdzgSfOsCOCrbL752W5O93yxyMabUnNhMYHMJc+F8kxnAbE7bLFhkTOr+gbPUUW1nNELJpNJlQHSeE9RidSLpYcV5i5wNmWTbCTWMmQ2rGJ7D+XY8ePvZo5w3tny/fINQ0ua53hJK51V8W+SxHBO7c18tZ0boo52tG9rXvPjYbnaD07FnkWHVSTKCmBZQKxwNJCQaPIY2OWu6OB808cRhqWNGkrwucGP2BkemzNB2uwZyfv3k5kaHM6jbhkFcz25Zp3RNfOyux0rw7lRzxbJozho+ztDXCRzpA0HDFJEWTWcdUFJoUDh4X1Fs0doms6eN8u1j7UznNhdLpjm133RVEloFtSw4ySM3DmRM8cN3rHzcC6k5sQdYhMsTaxfYFmzzJxHBTimqOjlhkjjrONebxiH5Fh+WHfLzLMRSGIcFE0WlV/qHA1iWsYzM2SQxPiPPnme10TtMs1hA97YwHR99SwSF3LGRXY4t3NaBP2joOwfMOwfMF9Ra31C7VTawN0RERQU0RERERERERERERERERERFrt7o/7sRfs+D+/aVZqzPdH/diL9nwf37SrNekbK/KU+i8N9ov5jW/UUXVZ7G/pI/6wu1dVnsb+kj/AKwuguQz4gu1ERFFe7RNNNqXlCerX8UuMtudlaEAEDBkf9917Bk9D8SyPH2iR6dekqRSGVscVZ3MJB3ulrxSvc3aMbC55wPix1PasAexS/uwfdaX9Xo/7KuqrnOGIaJsWutzBb/ldBjKZwb3ZfeD2DNyIfb5BRBfV8XJjSSAASSQAACSSewADqT8ytLnriik2o8D34IpZHCu51dgltVorMMturGQDzLFdji5jcEZxnAOT0BIjK106zKglhB6LfXw1WgQKrSDz+/JSLhrW68dazQvRzSVLD4pg6u6Ns9exDkNliEg2PDmOLHB3mxghcOK9ahsMqVakUkVOiyVsImc188kk7xJPNM5gDQ5zg3xR0AaMfEMAih/DMz59+vKYiY4xZbDjqppdlaIiYExOaJ1ib/6ARerSr81WaOxXkdFNE7dHI3GWnBB6EEEEEgggggkEEFeVFuIBEHRVmuLSHNMEbws5rnFFm3EIHNrwQCTnOhqV4q0ck2NvOlEQHMkx0yVg0RRp020xDRAU61epWdmqEk80QFSLua6ZHc1ajXmAdE+Yukaeoe2GN85Y4edrhHg/MSpLoPEVnW/fKpcLZIH0bVqpFsY1tKes0SV+Q5jQWMDQWEffDt8+a1fFGm4gCQACTOgJItYzoTuV7CbPFZgJdBcS1oiZLQCZMiBdo0OvJVwpfwcdmk8QytOJRBp8AcO3k2boZYb+a5rWghRBZrhXXe8nTtkhFmtahdXtVy8x8yMkOa5kgB5crHgODsHz/HkbMUwvpw0TcGOMOBjziFp2fVbTrS8wCHCeGZpaDa9iQbX4XWWrnfw1MHHpBrELoc+Yy1XiRjc9jSAHYHnGVD1INf16GWrDQp1nVqkUzrLxLNz5rFlzOWJZXhjWt2x+IGtGMEk5z0j6xhmOAcSIlxMcPT181LH1GOcxrTOVoaSJgkTpMG0xpuREWT4Upss36NeQZjnuVoZBnGWSTMY8Z8x2kre9wa0uO5U6VM1Hhg1Jj1XjfUlEbZTFIInHDZSxwjcevRryNpPQ9h8y6Faui69ZvcQWNOnle/T7Ul2gaZJ73igijmbX5MXkRSMMMR3tAPQ/GqqHYq+HrueS14gwDYzZ09L2Ku43CU6TQ6m4kZnNuIu2J0JscwjeiIitLnouFjyH/mu/kVzXCx5D/zXfyKw7QqdP4h1W7CIi8oX6IREREREREREREREREREREREREREREREREREREREREREREREREREREWu3uj/ALsRfs+D+/aVZqzPdH/diL9nwf37SrNekbK/KU+i8N9ov5jW/UUXVZ7G/pI/6wu1dVnsb+kj/rC6C5DPiC7V6GUpTC+wGO5McjInyfeiSRr3MZ85LY3np2Y69oXnVgajq77fDb90VaFsOrwMYytAyuzrUlcXObGMOeT2u7eir16rmZYGpAPKVbweHZWD8xgtaSABrA+Sr9d1y1LM8yTSSSyENBfK90jyGgNaC55JIDQAPiAC6UW+BqqeYxCKQdzdjHavpof5Pftc9fO4SAsH8Yao+u2rO+KRksbiySN7ZI3jtY9jg5jhnzggH9yhWYXsc0bwR8ltw1UUqrXkSAQY6GVO+ApHP4kmEhOJ36s20D2OY6C294fnzb2t/eAq/b2D8imFzjZju+poNPgrXrsckVq5HLM7LZ8d8OgrvOyvJJjq4EkZdjGcqHqthWPDi5wizRFt03tPGBvsr2PrUyxtNjs3vPdNx8WWBeDPuyd0m0oi9ej6dNbnirQN3zTPDI29nU9pJ8zQAST5gCs3q/CLoa81mC5TvMqvYy2Kj5HOrmRxY15EkbeZCXjbvbnqfykb312McGuNz/x/ocSqlLB1ajDUa2QN/QSY3mBcxoLmyjKIi3KspRonBNyxSs6g9phqQVZbEczg1wnfGdoiY3eHAHD/AB8EDZ84UXUs7nQ8TWz5/eK5/eqqJqtRe81Hhx0IiBFo81fxVOkKNJ1MEEgzJmSDHAQOXzKy/Bus+99+rc2l4glDntHlOjcCyUNycbtjnYz58KSVJdN0xt+erebbfZqz1KMDIZ45IWWSGumsvlYGsfHGCA0F24nzDsgiJWwrajpJO4EDeAZg/PSNSmG2g6gzKADBJaTMtJEEiCBuGs3ARERWVQUrPBksel2NSsOYzYagrxRywyue2w/BfMI3O5Q2kYacOzuyBjrFFLeHPuHrv6bR/wC/ZUSVXDOeS8PMw7pbK08+Kv45lMNpOptiWSZMmc7xOg4Dci9Ol3HVp4LDMF8E0U7M9m+J7ZG5+bLQvMiskAiCqTXFpDhqFYcetaTWuz6zWnnfZk74lrae+sWd727THtc6azu5ckLDLIQGjJy3s25NeBEWihhxS3k6C/AaC0cTz4q1isa7EQCABJMNmJdEm5NzA5CLAIiIrCpouFjyH/mu/kVzXCx5D/zXfyKw7QqdP4h1W7CIi8oX6IUW7nfGLNYinkZA+AQSiIh7w8uJbuyNoGApTla78ITPj4Z158b3Me21Ww9jixw+y1wcOacjoSP3r2a9oNiHQaute+moOtiOo5recWwxxSljGMjaPGa9oc0l247iHEjLsru1tlM7UhrsozZQIJvAPpdfI4X2gqjDtc9mcin2jiCBbM4acbbtVcXEPE9ShLUhsue2S9IYq4axzw54dEzDiOjRmZnU/GVmsqi+6jWNqfhizJNOH6i2uJQyTayFxNEukrNx9hlJncS4Z6sZ8SyHHtW0Ne0fT6t6zBu09kHPdI978NFtj5ngECSwY2k7+h3YPmWnu1rmMh0EhxM6e6Tw6c+KsnblRlSrLJaHU2tg3OcA3m2+d3DmrkyipvierZit6PwxBetMhkZLYs2t+LEzZJrUpY6QHPRsUoA7CXNyDjC7e9ZuH9b0ytBdt2KWpExSQWpOaWvLgzeMANbh0kZBABw1wJIWvu4EWfcguAg3Am/ImCQFvO2iHHNTOUOaxzpHuudFo3gEgE+gKt/KjPBfFzNTm1CFsD4jQn5DnOeHCQ75mbmgAbR9i/8Asq/4c0yxxHd1O1Y1C5VjqWTBTiqTcrlbS/a4gggYa1hJGC4l3UYAWH4LM0Wm8W7pSZ43APmYdpdK19oPkaW+TlwJ6fGt7dmsDHtLpeMnH3cxHkbFVX7cquq03NYRTPaXJHvBjSerbi3EK/sr5lVRfty/8FCUSyCXkQnm8x4kz39GCeZndnHTtWO1rU7c1ThfS4rMsA1GCA2rDHuEzmbYm4Emc9jpCR98duemc6GbNc7+rRzmn/5Ek+m5W6u3GsA9wmabHgA6l7sob671dOVhdC4nqXbFytA57paMnKsBzHNDX75I8Ncejhuif1HxKL6DwRe06+01b8sulSwvjtQ27D32WyObIBJX2Q8sODuUd3inyx16KMdxnRmt1vWT3xaPeFp8bd0xIsh0tyDdcGPs7wBuB6eMSUbg6Jp1Hh85WgiBxMQR96ysVNp4kVqNM0suZzmuBM2DZlpGo36biOasjgjX7F9k7rGnzaeYpjExkxkJmZtB5reZDGdvXHQHs7VIcqh+F+JrdTQNZsslkdOL7YIZJHOkMQkETS5u8nBDS4jzZx2r063wpYoaOzWYtV1A6gyOtZlLrBdC/nujDmAOG5wbzB5ZcHbTkeN03VdmN7QjMGy7K0XMmAfIXHFVaG3X9iHBheQwvcZAgS4cgTY2EacVd2V9yqV411S1escLvisSVJL8LDI6FzgGPldX3uDM4ftLnbd3zLvq6fLo3Eun1YL1yxXvQvdMy1NzS4ls48bADSQ6Njg7GR1GSCc6hs33ZLveyuOWP7SZvpusrJ27/wBSG0yWBzGl0j+sAgxrvEq40RFy130RERERERERERERERFrt7o/7sRfs+D+/aVZqzPdH/diL9nwf37SrNekbK/KU+i8N9ov5jW/UUXVZ7G/pI/6wu1dVnsb+kj/AKwuguQz4gu1ZuLWWDSpdP2O3vvx2xJkbA1kD4iwjt3Zdn9ywiKD6YfE7jPop0qzqc5d4I8jqiIpn3J9J063ersuzPLzPiOmIN0c+1m8GWffhrMggs2nIHb1UK9YUaZeZtw+/wDS2YPCuxNVtJpAJMSTA+f7C53KGIucww5wHyj/ADK4LcFXIgwiLPXODtUhrd9y0pmVw1ry87dzGO7HviDuZGz53NAWBUGVWPu0g9DK2VaFSkQKjSJvcEW81Me4991GgfbDVvCH4+b3pNjb8+3euHc3+064T9q947QPyeaZa/Iz/wB2d2P3qNaXelrTRWIHmOaF7ZI3jGWub2dD0I8xB6EEg9qzOscXTWIJa7a1KpHYkZJZ7zgdC6y6M7mCYukd4jXEuDW7QD1wqVfDvc85dHZRPDKSfrbmupg8ZSZSbnJlheQI+LO0AdIIvy0uo6iIuguMvXp2pTVxOIX7BZgfWm8Vjt8Eha57PHaduSxvVuD07V5ERYDQDKkXuIAJsNOSLM6VwtqNuF1itSsTQtzmSOMkOLfKEY7ZSMEYaD16LDKcd0W7LUtaW2B7mChptB9baSAJHM5skoA6b3uPjH77ABVevUeHNYyJM66W6RxH+1dweHpOY+rVnK3KIbEy4njOgBOl7C2qhBC+KVd1usyLW9RZGAG88SYHZumijmf/APd7lFVso1O0ptfxAPqJVfFUDQrPpEzlcRPQwvTBemZFNAyRzYZzE6aMY2yGEudEXefxS5x/evNlCVaur69Ppmq1tHg2DT6/eNaxWMcTo7ffMUL7Us4c3L5H84jPm2jHnzpr1jTMMbJIJ1iwgcDe4AVrCYYV25qryGgtaLTdxJAiRAs4n9iSqqRZbjHT21NQu1mfa4LU0cYySRG2R3LBJ6khu3qsSrLHh7Q4aESqVWkaT3MdqCQfKyLIa1o89MwCwzY6xWjtMac7hFK6RrN4I8Vx5ZOPiIXTpWoTVZmWK8jopo92yRmNzdzXMdjPxtc4fvUq7rFmSaTSJpXl8suhUJJHu8p73vsuc4/OSStD6jxVa20GetvvirVGhTdhqlQk5mkW3QT6zyjzULREVlUUXCx5D/zXfyK5rhY8h/5rv5FYdoVOn8Q6rdhEReUL9EKsdJ7mc8Ok6lpptQufemikbKI3hsYjfG8hzc5JPLP+qzOt8GS2NBh0gTxtkjiqxmYtcWHvdzHEhuc9dn/ypqiuOx9Zzg4m+bNoNYA+gXLZsfCsYWBtizJqfhJJj1JvqoBxZwDPbraO2G0yG1pDYhHI6Mvie5jK4Ltucjx67CAc9pBXpt8HWZtV0vVJbMTnUqrIbDRG5pml2TiSSPrhjS6bIHmwpsq67vetW6NGtJUnfXe+4I3OjIBLDBM7acg9Mtaf3Ldha9es9tFpF8wBIH9Uzz4qtj8LhMLSfiXtJjK4gE3LIDTExIt13rKd0Hgt2oyVbVayad+m7MM4bvaW5Dtj25HY7JB6jxnghwPTwcP8C3HahFqer323Z67S2vHFEIooz42HHAAONzjgNHXBJOAFVuh6txZejM1Sa7PEHmMvYYsB7Q0lvjYOcOb/AKpa4y4n0qZnfcthpdktjtwxvimDcbgHbQSBludjgRkdRldZuzsSG9i2qwkAiP6gDqAYkL51+2sC54xNShVAJBzXyEjRxGbKSNxVk2+59qFe7as6RqYoxXnF9mJ8LZdrnOc5zog4EHxnvI8kt3EA4Xbwf3NjTratUntc6PUmhgka0iWNoEwD3lxw6T7I0/Flp+NSXgDiRmq0YrjG8tzi6OaLO7lzMOHtB87Tlrgfie3sWfXIrY3ENmk8wRANhPum0nUxHFfS4bZeCqZa9MEggke87KA8XgEwJkyICqQdzLV3UX6bJrDDSZgwQNgADnCUSjnPI5mwHc4M3OGdvZhZvXO526xQ0uFlrkXtKZEK9prCWFzBHnLMg43RMcD5iOw5IVgKKd1Hix+jU4rTIGzmSyyDY55jADoppN2Q05P2IDH/AHKVPG4mtUa1kTMiABJIgzoL75WqtsrAYWi99QHLlAJLnGGgyIuSIOkaLwcM8HX23majquo9+TQxmOCKFnJgZkObve1oa15w9/Tb2nOTgY+8OcF2qGr3L0NuM1L0sk1iu6I80udznsa2TOAGyyk5GMjphZLuacUO1ekbb4WwETyRbGvLxhgYd24tHU7vi8yqzuhcW6lBxDNVhuTR1xPSaImkbQ2SCs54GRnqXuP/ALirFGnia9WpRkAhpBECIBFhAjW4hVMVWwOEw9HEgOcC8FrpOaXA3JcQSCBBB3WhTjhzubNi07UNOtzNlZen5wfE1zHROAYY3Dd2ua+MH4j2FYt/c31eaCLTbOtNfpcRYBGyuGzOjiILIyT1AbgYDnvDSG9DtAFrIqY2lXBJkXM3AMGIkSLHoum7YeEc0NymAMtnOEtmcroIkTNioVr3A/OuaNPXkZDBpOxohLXOc6NjotrWuz0w2PGT8a7te4Sks6zp+qNmY1lOMsdEWuL3553Vrh0H20f6KXplaRjKoi+gI8nTP7lWDs2gZ93VzXG51bEekC2iIiKsr6IiIiIiIiIiIiIiIi1290f92Iv2fB/ftKs1Znuj/uxF+z4P79pVmvSNlflKfReG+0X8xrfqKLqs9jf0kf8AWF2rqs9jf0kf9YXQXIZ8QXaiIiiilPcmka3WtPc5wa0TOy5xAA+xSdpPQKLItdan2lNzOII9Qt+Fr9hWZVicrgY4wZXZP5TvznfzKyfBcDJNS06OQAskvVGPaRkOa6xG1zSPiIJH71iF21LD4pI5Y3bZInskjcO1r2ODmuGfOCAUqNJYWjhCxRqBtVrzoCD81YnCVqSbiyYSkvFq1qVaw1xJD65jsN5Ts9sbQyPA83Lb8SrZvYPyKcS8YUmzT6jWpTRapZZMC42GOp1prDCye1Xj5fMMrg6Qhr3EAyu7cYMHVXCMcHFxbHutEW1bM6Ta4HkujtKsxzAxr8xzvdInR2WNQDNiSN08ZRZLQNDt35RDUgkmduaHFjHFkQecB0zwNsTOh6ux2FY1ZbhKzJHdqCOR7A+1WDwx7mhw5zOjtp8YdT0Pxq1WLgwlusb1z8K1jqrW1Jgm8a/OV5+INONS1Zqlwea88sJeBtDjG8s3AEnAOOxeFZ7uifdfU/161/eesCsUHF1NrjqQP2WcWwMrPa3QOIHkVK9T4PNbSzflmhfK63DXZFXnhsNYx8MsjzM6IuAkJazADug3ZzkYiil9X/pux+2q/wDspVEFqwrnnNnMw4jhuCsbQZTb2ZptgFgMTN5PRFMoOI9NnZSk1Gtbks6fDFXZ3vLC2C3DXcXV2WeY0vjIB2FzMlw+LpiGotlWg2pEzbeDBWjDYt9CcsX1BEixka8Pu0r3a9qcl2zPamxzLEr5XAeS3cejG567WjDR8zQvCiLY1oaABoFoe9z3FzjJNyeZQqxLF3S7t2rq9i8IHsbVfepmCd88lioxjAKxa0xujlELBlzht3ElQGpWkme2OKN8sjs7WRtc97sAuOGtGTgAn8gK6VprUBVOpBAOkaHXWeHyVvC4t1AH3Q4Egw6YluhsRpJ8j0Xu1/UXW7Vm04bXWJ5Ztuc7OY8vDAfOGggZ+ZeFEW5rQ0ADQKo95e4udqTJ80WV4i1l13vPdG2PvOjXot2kne2uZCJDnsceYeg+JYpELASHHUKTarmtLQbGJ8tEREUlrRcLHkP/ADXfyK5rhY8h/wCa7+RWHaFTp/EOq3YRF0ajzuTL3vy+fy38jnbuTztp5XN2eNy9+3O3rjOF5SLr9DkwJXax4OcEHHbgg4/LhclSfcYu2a0uuWJu9m1IJJ5r5YJTMJouc/8A5YeSYcNl8rxvJ+dZCpxhxJcqTarUq0GUY+a5kEnNfYlihJEjmlrgJC0teOmzJYcA+fpVdlvbULQ4QIuTAkiY6/S64NDb9N9Jr3MdmOY5WiSA0wTutp52Eq3FVXumPubU/X2/7ewpzwLxCzVKMN1jDGZA5skZO7lyRuLHtDvvm5GQemQ4dB2KDe6Y+5tT9fb/ALews7MY5mNY12odB+abdqtq7KqVGGQWSDyMLh7n3VasGlSMns14Xm7M4NlmjjcWmKAA7XuBxkHr8y8PuhuIaFinWqwWIbFgWmzHkyMl5UbYZWO3uYSGkmRmGntxnzKIdz3uZyaxUdaZcZAGzvh2OhdISWMjdu3CQfhMYx5lLdM7hTRIDZ1AviB8ZkNflvd83MfI4M/hP7l2KowdHGGs+qcwM5QDr1XzGHdtPE7NbhaVAZC0DOXDTjCw/Cmn3W8J3LNaexWkjvvuRurzSQmSCKKGCxudGQSwBsrsfHAFJvc78TT2mXatqxNYljdHYjfPK+aQxvHLkYHSEkMa5jDjszKfjVm1NKrxVm044mtrNi5IiGdvLLS0tOersgnJPU5JPateu5+52i8TCrISG8+Wg9zu18cxArv6dgc8V3/kK006zcdSrti8528Y4fL5q3Vwz9k4nBuzHLHZu/tk7/nPRqkHd94ptx6hBTp2bMHKgDpBWnlidJLYd4rHiJw3kMZGRnP2047evg7sejarWqVTatmek3vSLZJM+WU3hWkM0zi9pO0uE+DvPRwH5PPwhF798VSWTh8MdiS4T16w1S2Op+XqKwI84ypz7pX7k1/2jF/trasMcMPWw+HAEx71t5+uvqqNZjsbhcZjHOOXNDACYhtukG3mFH+4BQ1QmGwyw0aU2aw2Wvvw50vJwHBnL6je6I+UPJ/1indgn5XElyXG7ly0pNucZ2VKrsZ82cdqtP3On3HP65P/AExKse6mAeKJwRkGxp4IPYQa1QEH5lPCVM+0asgWa4WtMOGvPmte0aHZ7Fw+Un3ntNzMEtOk6DkslxRR4rnhk1WeSxBEGmY14bToDXgALtwrRvG0Nb25y/A8bqCpT3A+NLV0z0bkrp3wxCeCZ5zKYw5scjJH9smHPjIccnxnZJ6YsriYf8lc/VbH9p6on3Nf3Wn/AGbN/uaapsrNxeCqlzGjLEZRELpVMM/Z21MOGVXu7SQ7MZn7meUKQd2XujWoLTtN055idGGixOwbpTLIA4QQ9CG4a5uXDxi52Bt2ndgLHCfFkNc3TatlzW810LdQnfaDQNxzHuw8gddgcT5sE9FjK20cWnvjs9/JfK+M238jt+93cvHzYWzKlia42eymymxplskkTKjgMI7bFWvVr1HDK4ta1pjLG+PTreVU/cT7oc9+R1C84PnbGZILGGtMrGY3xyBoAMgByHAdQ12eoy6L93bXr9fVnRV71yvH3tAdkNmeJgcd+XbI3gZPTqsN3NSz/ieDvfHK78umLbjbyeVZxtx97y//AIXf7of7sv8A1SD+T1cpYSkzaIytEOZmjcLrl19o4irsUl7yXNq5c03IAnXfqstqtHizVYzfaZ4IHN5lerDaMDjDjLS2JjgZHEdcv8Z2egwQF6+4TxzcluDTrc77Ec0cjq75nGSVkkbTIW8x3jPYWB5w4nGxuMDKu2sAGMHxNbj/AEC1r7kP/Utb9Nf/ANrbVPD1m4vDVmuY0BrZbA0sf8fuunjcK/Z2NwtRlV7jUflfmMgyWjSw3m260aLZhY7V9cpU9nfdqvWL87BPNHEX4xktDyCQMjJ82VkVQPdev162uTTSMrakJaBgdWkcSaMuwNY44BDXB2HgZz9lf5JLXHjbPwn8TUyX0Jtv5XsPNfUbZ2kcDQFURqBfQTvtc9B10BV9wSte1r2Oa9jgHNe0hzXNcMhzXDo5pHnC5qG9zFkNClS0uS3DNc73daDI37wYZpZJA+N3Y6IbsbvPgnsIUyVatTyPLRcTY8RxV7C1jVpNeRBgSJmDFx5IiItSsLXb3R/3Yi/Z8H9+0qzVme6P+7EX7Pg/v2lWa9I2V+Up9F4b7RfzGt+oouqz2N/SR/1hdq6rPY39JH/WF0FyGfEF2oiIooiIiIiIiIpIeDbjNOn1KeN9eGM1xC2RmHWRO/buZlwLGNBadxGHbxjz4jal/D0jjoeuAuJAl0fAJJA+zWOzPZ2D/RVsU97QC0/1NB6FwFvVXsBTpVHObUBPuPIgwAWsc6/HTiOciyiC7qdh0UkcrMb4pGSNyMjcxwc3I84yAulFYIkQVSaSDIXq1a8+1PNZlxzJ5XyybRhu+Rxc7A8wySvKiI0ACAsucXEuOpXobclEJriR/IdIJTFuPLMrWlgkLezeGkjK86IgAGiwXE6lFL+C+FIrcNmxNargQ0rs7KjJv+cdJBG8sc+Lb4kO4B2c5OGjHXIiCl/ct+3aj+xdT/shVsaXCkS0wV0NlNpvxLW1GyDu+9eiiCIitLnKf9xjVtl6OqKtRxmjuk2nxPdbYBTmfsil5m1jcxgeTnD3DPVQBvYFnOBNYioX4bUzZHRxsstc2INLyZq00DcB7mjAdICevYD29iwYVWnSy13uAsQ31l0/RdCviM+EpsJu1z7cAQyPmHIiIrS56IiIiIiIiLhY8h/5rv5Fc1wseQ/8138isO0KnT+IdVuwiIvKF+iFUPBWhW2za/plmpYij1I2jHd25rta7nNad46OJErXAfMQcLyaJf1zTdNfovvLYmnAsRV7cWX1tth8jy97w0s8V0jiMubkYB24KulF0ztIuJzsBBgxfVoideGoXBbsIMA7Oo5pAc2QGzlccxFxFjcHVRbuWcPSaZpkFWYjnZfLMGnLWvlcXbAR0O1u1pI6Eg46LAe6B0mzcoVY6sEth7bjXubCxz3NYIJ27iG9gy4D94VkIq9PGPbiO3NzM8rq7W2ZTqYP+DBIblDecBV/3BdMsVNMkitQS15DcleGSsLHFhigAcAfNlpH7irARFqxFc1qjqh1Jlb8FhW4Wgyi0yGiJOqKhPdIaLyrla/GMNsx8qQjpieDGxxPynRloH6BXvZeWsc4DJa1xA+MgZA6LXTifUdc4klr1zQfExjvFY2GZkLXuADpp5pejQBkebAJGCT16uwmuFftZAaPik7iF8/7WvY7CdgWkvcRkDRNwR6WMeam/ubtF5VKxecPGtS8uM//AIa+Wkj4syukB/RBSHu1cO2NS0zlVW75obEdhseQ0yBjJI3NaXEDdtlLup67cKT8N6UyjUrVI+ra8LIt2Mby0eO8j43O3OPzuKyCqVsc44o4husyJ4DT5Lo4XZLG7OGDfplgxxNyR56Kle4lJrdOwzTpqEsNB8s8ss01WZjmP5B2tZMSGbS+NnaD2nr1WK7ovDOozcRzWYqVmSA2KLhMyJ7oy1kFVryHAYIBa4H80q/0W8bXc2u6s1gBLYIvvMz1VR3s2x2EbhX1HENcHA2kQIDei8PEEbn1LTGguc6tO1rQMlznROAAHnJKpvuBcOX6epzS2qdivG6hLGHyxOY0vNio4NBcPKw1xx/2lXkiqUMa6lSfSAEO1XQxey2YjE0sQ4kGnMAaGeKpzux9zazZsu1HTmiSSQN74rhwZIXsAaJoS4hpJa0ZbkHLcjcXHEbn13i+aA0XVr+HDlOl7wkZM5hy0tdYMYaBjpvGCfj6krYdFbo7Xc1jWVGNfl0LhcLnYn2bY+q+rRqvp5/iDDY/d/Xqqs7i/c7m057r14NbZcwxwwNcH8hjsb3yOblplOA3DSQBnqS7DYt3cuGdQt6q6WtSszx97Qt3xRPe3c0PyMgYyMhX4ihT2tWbiDiDBJERuA5LbW9nMM/BDBtJDQZkak85XCAYa3PTxR/JUD3MOGNRg4gr2JqVmKBstwulfE9rAH1rLWEuIwAS5o/eFsCi0YXGuoMqMAHviDy109Vcx+ymYupRqOJHZuzCN9wb+iKjmV7Gly61TuaLZ1OHUpXyxz1o3v5wc97o2vlYxxYcuDunjMeCQDkFXiijhcV2MiJBjeRoZEEXU9obPGKykOylswYBEOEEEOkGypfuNaHe07UD74UbYM1NsVawSZoq0e8yugkLMtj3bR8W0txgb+l0IixjMUcTU7Rwg8tPms7M2e3A0exYSRJMmJvxiJ+wiIiqroLXb3R/3Yi/Z8H9+0qzVme6P+7EX7Pg/v2lWa9I2V+Up9F4b7RfzGt+oouqz2N/SR/1hdq6rPY39JH/AFhdBchnxBdqIiKKIiIiIiIiL21dTmjr2KrHAQ2nQOmbtBLjXc50WHHq3Be7s7V4kWHNDtfuLqTHuYZaY1HkRB9RIRERZUUREREREREXbXsSRlxje+Mua5jixzmFzHjDmOLT1YR2g9CupFgidVkEgyEREWVhERERERERERERERERFwseQ/8ANd/IrmuFjyH/AJrv5FYdoVOn8Q6rdhEReUL9EKqdb7s8FWzZrOoyuNaxNXLxOwBxhldGXAFvQEtz+9cNN7uNF7w2apYhYSAZGujmDM/fOaMO2j/tyfiBUU4N1GvV4r1GW1NHBELWqtMkrg1m51l+Bk9MlZbu98Q6TbqV2VZoLNptgOD4cOMcPLeJA6VoxtLjH4meuAfvV9T/AAGH7VlLsnHMAc4JtI9PmvPu+Mb/AA9XEfxDRkc4CmWtkgHjIN+iuqrYZKxksbg+ORjZI3tILXseA5rmkdrSCDn512KkeI9e1LSOH9C72nNeWVjuZmKGQmMt5kTSJ43bcNc3swpDwLrWvXrkNyzEYNHNZxaXd7N5mIsssSDPOy8+P4oDACMZHU8ipsxzWGpmbllwEm5ymLczuhfSUdu031W0Cx2eGEwJAzCZJ3AaEmPNWaipKHjHX9euTx6M6OpVg673tjzy3EiJ08kkbyJH7XEMY0YAIOcFx93CXHGq09UZpGubHule2OKw1rGuD5ekBBiAZLC84bnaHAnr2EKTtk1Wg3bmAksn3gP2+ahT9o8O9w91+RzsoqEe4TprM8phW+iqnunccX26hHo2jgC07YJZdrHv3yNEjYmCUGNjRHhznkHo773aSY7xjxXxRpEMVe3NGJJH8yK7FHXk3xta4SV3tdDsyC6J2drT0PV2eijsirUDbtBdcNJvHHomJ9o8PQNSWvLWGHOa2Wg/2zIvu4TvV8Iqx7p3FF+lommW61jl2LD6omk5UL94kpyyv8SSMsbl7WnoB2fF0Xn4E17iHULOn2ZInR6S2HbZlIrNdZkZVe187mdJdrrAyBE0NxjoepWtuzqhpdqXNAvqYuNw4k7lvdtuiMQMMGvLoabCQA7eYNgP6idOatZFTFfWuKtZsWDRxplaIgsZYhERLXF3LBfLC98kuGknbho6fGM5TuS8aajYv2tK1IslmrCYidjWNdvrzNiljdygGPb42QQB5BznPSVTZdRjC7M0loktBuAfl81ro7fo1KrWZHgOJDXuENcRwvPqFaaKiqHGXEVrVdQ0+nMyYiS5HCJYqzGVI4rIaJy5sQc8tYNgDtwJlBIcQuqpxxxJSvy6XOI7tyQtiha9sQEcsrWvjlY+FrQ+PY7JD8Aect2kLb3LV0zNmM0TeONwq/4pw1jkqZcxbmy2zDdYmSd0T+6vpFRdniziPRtQrR6rNHYhsFpcxrICx0bnhjzG+KNjmSMz2dnZ0Kzvdx4v1DTLFFtOcxMkjkfKzlQScwskYB40sbi3oSOnxqHdNU1GMa5pzAkEG1td30W78RUG0alV7Xt7MgOaQMwzab4jzVroqL4w4j4r00w3rL4YoJ5NrarWQSRROLTI2vL4vMyWtd1DyfFd4w6Lnr/EfFMlQ6zE6OnQIa+OBgryPbC5wYyV4mjLpGklvXI6HIaApjY9Qhpzsg2BzWnhpr0stTvaai0vaaVSWiSMt8v92th1g8tYtnjPWve6jZuiMS97sD+WX8sOy9rMb9p2+V8R7F5O51xMdWotuGEQbpJI+WJOaBy3Yzv2N7fyKM/8Z2rHC82qMLYLkbSwuY1r2CRlhkRe1koc3DmnOCDjcfiyuvhnie9Lwxa1CSfdcjZbcyblQN2mInZ9jbGIzj52/lUP4IiiQWjN2mWZPDSIiN8zPJbe9WuxLS1xyGiamXKIInWSc0xbLEc1ZqKjOEuIeKtXqPFSaMcqZ3MuSNrRueSxhbWjaItg2jxidufsrfGA6HPdxrja/ctWtN1LD7Fdj5GybGMe0wythmhlEWGOIL24IH3rsk9ErbJq02uOZpLdQDcc9P8AaYb2ioV302hjwH/C5zYaTw1N93BWqipfV+NNa1bU5aGhubBFXMgdMWxHeInbHTSSStcGxl+A1rBkggnPUN8N/jfiGtqVDT7cjYJBNWisbIqz2XI5bDQJ2uMZ2bmEtOwgZaejTkCTdj1TAzNmJyzcDmIUH+02GbJyPLc2UPDfdJmIBkfONDCvZERclfRKIcYdzzT9VsNs2jYEjYmwjlSNY3Yxz3joWHrmR3n+JYb4F9H+Vc+nZ/jVkIrbMfiGNDWvIA3SubV2Ngqry99JpJ1JFyq3+BfR/lXPp2f414dZ7jukMbEQbfjWazDmdvY+ZjT/AP1/EVayxvEXkQ/rlP8A3Ea2N2nip/7jvVajsLAC4ot9AoT8C+j/ACrn07P8afAvo/yrn07P8ashFjvPFeI71TuHZ/gs9Aq3+BfR/lXPp2f40+BfR/lXPp2f41ZCJ3nivEd6p3Ds/wAFnoFW/wAC+j/KufTs/wAafAvo/wAq59Oz/GrIRO88V4jvVO4dn+Cz0Crf4F9H+Vc+nZ/jT4F9H+Vc+nZ/jVkIneeK8R3qncOz/BZ6BVv8C+j/ACrn07P8afAvo/yrn07P8ashE7zxXiO9U7h2f4LPQKt/gX0f5Vz6dn+NPgX0f5Vz6dn+NWQid54rxHeqdw7P8FnoFW/wL6P8q59Oz/GnwL6P8q59Oz/GrIRO88V4jvVO4dn+Cz0Crf4F9H+Vc+nZ/jT4F9H+Vc+nZ/jVkIneeK8R3qncOz/BZ6BVv8C+j/KufTs/xp8C+j/KufTs/wAashE7zxXiO9U7h2f4LPQKt/gX0f5Vz6dn+NPgX0f5Vz6dn+NWQid54rxHeqdw7P8ABZ6BVv8AAvo/yrn07P8AGnwL6P8AKufTs/xqyETvPFeI71TuHZ/gs9Aq3+BfR/lXPp2f40+BfR/lXPp2f41ZCJ3nivEd6p3Ds/wWegVb/Avo/wAq59Oz/GnwL6P8q59Oz/GrIRO88V4jvVO4dn+Cz0Crf4F9H+Vc+nZ/jXn1HuM6O2GVwNzIikI+zs8zCfwatBebVftE/wChk/ocneeK8R3qsjYOzx/4W+gXpREVFdZa5cP6DX1LifUq1prnRG3qjyGPLDuZZk2+M3rjqrW03uVaJBI2UVTK5hy1s0skkeR53Rk7X/kcCPmWU0vgrT612TUYYnNtTOmfI8zSuaXWHF8p5bnbRkk9g6KRrr43aj6hApOcG5QCJi410K+b2XsClRa44hjHOL3OBibE21Cp/wB07/6bT/0839tqsapWMulxws6GTT2RNPZgvrBg/J2hfOLeFaWqMjZdjdI2JznMDZJI8FwAJJjcM9B51lqkDYmMjYMMjY1jRknDWANaMnqegCr1MU04enTGrSTyubK7Q2e9uNrV3Rle1oHGwIM/S6o/uAcQ1aBvU70kdSV0jHtdYc2JpdFvjlie9+Gse048UnJ3O+JefjS/Fq/FOnNoOEzYXVInSxncx3IsSWZpGOHR0bGOPUdCWHGeitLibue6VqMpnsVsTOxvlhkfE5+OmZAw7XuwAMkE4A6r2cKcHadpe4064ZI8bXyuc6SVzeh275CS1mQDtbgEgHC6DtpYftHYhodncIi2UEiJnWPJcVmw8Z2LMG9zOya4HMJzkAyBEQDzn131LqVxmmcZusW/Ehe/cJXAkNjsVDEyUf8Aa1+WE+YNf8S7/dCcUUbcNSrUnisvZM6eR8D2yxxtEbo2sMjCWlzi8nAJxs64yM5juta1p3fsVPWNLldB4pg1GOZzXCN7RzCxsbQXhjyQ6PcewOx1bmC8ajRpo6uncPQSWJ5bHMkl2TmV5Eb2MhBsAPx45ccAMG3J85HQwjRUfRrPY4ENAkRkgTcnd04rjbSe6hTxWGpVGEOeTlMirLiPdDYE7oIkRdS3u0f9N6N+ko/+PnVg8GTcrQqEobuMelV5A0dri2q1+0fOSP8A5XK/whVu0KdG8wysqsgxskkj+yxQGHcCwgkYc/p86zemUo60MNeIFsUEUcMbSS4tjjaGMBc7qTgDqVwa+KY6g2kNQ4nlBX12F2fVp4t+IJEOptaOII47vmqD4T1E62+zNrHEEtFrC3ZWjssqMe124kxNe7llrcBvRrndRk9mefcLEA4hsisXurivcEDpPLdCJ4RG5/QeOW7Seg6lWZL3K9EdOZzTxl28xNllbBuznpE12Gtz96MN82MdFkdN4H06tdN+vC6GwS7JjmlbFh7drmckO5ez/txgEAjsC6VbamHLHsYHAObAEABp8teq4mG9n8Y2rSqVSwlj5Lszi5w/+rDoNeIVZ9x3/qjWPzNS/wDJV191f/rmH8+D/wAeFZ+h8G0KVue9XicyzYEoleZZXhwmlbNJhjnFrcvY09B0X2fg6g/UBqjonG60tIk5soblkfKH2Pds8jp2LQ7aVI1nvgwaeTdrA56K0zYVcYanSlstr9obmMsk8Nb9OarD3Rv/AK7SvzH/AN6NPdG/+u0r8yT+9GrP4o4OoalJDLcidI+AERFssse0FwcchjgD1A7U4o4OoanJDLcidI+AERlssse0FwcejHAHqB2rGG2lSp9jIPuB4P8A9aRdSxuw69b+Jylv/UdTIkn+iJm3pEqG+6X+5dX9ox/7W2u7Xf8Aoxn7Ko/yrqa8V8N1NUhZBcjdJGyUTNDZHxkSBj2A5jIJG2R/T519scO1ZKI01zHGoIY4BHzHh3Ki27G8wHdkbG9c56LRTxrG0qTCDLX5j05K5W2XVfia9UEQ+nkHGYOttPVVLoX/AERc/Pk/3US9nB3/AEXe/RX/AOoqxK/B1COg/TGxOFOQkvj5shcSXiQ4kLt48Zo8650eE6UNCTTY43CpKJGvjMshcRL1f9kJ3DP5VuqbRpuDoBvVz+XrqqtHYtdhYSRbD9lqfi46afPkoh7m/wC5Ev6/N/ZrKNdyn/q3WPztV/8AIxq2+FuHaumQur02OjidI6UtdI+Q73Na0ndISQMMb0+ZeXR+DqFS5Pfgic21Y5xleZZXB3PlE0uGOdtbl4B6Doou2hTL67oPvi3rvv8A5U2bGrNp4RsiaRl2t7Ra37wqg7ieqwaTqWoVNQkZXe4crmzODIxLWleHMc93Ru7cXAnodnb1GePdJ16rf4i0w1HsmZBLShdMzqx7+/OYQx46PY0PHUdMlyyPFer6Ha1OaHW9Nm0+aPc022SyuMwYQ2Jz44IwZGOYPFkw7oGjOOzCsrUtQ1zTYNCruFOm6u+WbZINwjsGeaxI6Tx8bQ1gMmCSA0dNq7TA11U4h7XAllzbJ8MSCNZ4L5eoXsw4wVOoxzRVEAT2p9+YLSBEXJPktikRF8avTkREREWN4i8iH9cp/wC4jWSWN4i8iH9cp/7iNSbqsO0WSREUVlERERERERERERERERERERERERERERERERERERERERERERERERF5tV+0T/oZP6HL0rzar9on/Qyf0ORF6URERERERERERERERdVmtHK0sljZIw9rJGte0/la4YK6qOm14M8iCGHPbyomR5/LsAyvUizmMQo5GzMX4oiIsKSIiIiIiIiIiIiIiIiIiIiIiIi816hBOA2eGKYDsEsbJAPyB4OFzp1IoW7IYo4mdu2NjWNz+a0ALuRZzGIUcjZzRfiiIiwpIiIiIsbxF5EP65T/ANxGsksbxF5EP65T/wBxGpN1WHaLJIiKKyiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIi82q/aJ/0Mn9Dl6V5tV+0T/oZP6HIi9KIuq3YZFG+WRwZHGx0kj3HDWMY0ue5x8wABP7kRdqKkIe7RrFyGfUtK4VsXdEgdL/zsl6GtPPHBkTSw1HRmR4btd4rOZ1aQSHAgWZwlxpp+paVBrEUzYqU8ZeZLD2RCFzHuiljmcXbGvZK17DgkZb0JBBVmrhKtIS4b4sQYPAxMHkVpp12P0P3ynVSNF5NK1OtbjE1WxBZiJIEteWOaMkYyA+NxaT1Hn86hPdT7p1bR6T7NXvXUporlenPWjuxsfAZy8bpeW2R0bgWeS5oytVOi97sjRfRTfUa1uYmysFFEtN4msu1XVqlmCrX0/T4a8sN3v+s+SUSQRyzmxVEnMpsYXPG6QNDg0EdDlZqHiGg+WKBl6m+eZglhhbZgdLLGRkSRRh+6RmOu4AhYdTcPSbX1E7vsb1kPCyaLxXNXqQyxQTWa8U8/2iGSaKOWbHbyo3ODpP8A2grt069DZjEteaKeJ2Q2WGRksbi0lrsPYS04II/cowYlZkaL0Iq81PuklvElfh6tUjsF1cWbdt96KAVmGQxcuOAsJsTB2zLA5rvH6A4JExm1+iyw2o+7UZbdjbVdZhbYdnGNsJfvOcjsHnWx9F7YkaifLyUG1WmYOhjzWSReK5q1WF5jms14pBE6cxyTRxvELNxfNtc4HlDa7LuwbT8S6Nf1dtfT7V+MNnbBTntxhrwGTNigdM0NkaCA1waPGAPbnqoBpMKZcFlEVVdx/uxR67R1S5PUbQOlsbPLGLJnBrOglmbNvdDHtB5E47D5Gcr73Be62/ib3x5unt0/vBlJ+e+zY5gti07J3QR8trRXBz1yJPNjrYfgqzA8ub8MTcWnTr5LS3E03ZYOsx5K1EWN0jiChcc9lS7UtPj+2NrWYZ3R9ceO2J5LOvxrqn4n02MbpNQosbznV8vt12jvhuN0GS/7cNzcs7RuHRV8jpiFtzDWVl0Xh1fWKlNglt2q9WMnaJLM8UDC74g+VwBPzLur3oZIhPHLE+AtLxMyRjoiwZy8SNO0tGD1z5liDErMjRehFjKXEOnz8rkXqc3PdIyDlWYJOc+EAyti2PPMcwEZDc4yMrm7XKQsimblUWyMiqbEIskYzkQbt5GOvYs5HcEzDisgix97W6UDpGz26sLoYTYlbLYijdFACGmaQPcCyHJA3np1HVcdS4goVpI4rN2pXllAMUc9mGJ8oPQGNkjwXj8iBpO5Mw4rJIuE0rWNc9xw1jS5xPYGtGSfyYCpODuz6zZqTaxp/C8lnQ4TM4W36nBDZlgrue2edtTY542bH5aN3knr0ONtHDPqzli3Ega6C8XWupWazX5An9ld6LA6LxdRs6ZW1Yzsq07MMcwfbfHX5XMH2uVz3bGyNcHNIBIy04JHVZH33qd799981+9cB3fPOi732l2wHnbtmC4gZz2nC1FjgYI5eamHA6Fe1FDuPuL5KlYSaY2lfsCzVhmhk1GpVbFFZY6RsrpJpWt3FgDmszl4JIypDqmuUqr447VyrWfKcRMnsRQvlPZiNsjgXnPxLPZugHj99UziVkFjeIvIh/XKf+4jXo1PUa9WN01meGvC3ypZ5WQxtz2ZkkIaP9VjtQvwWa9eatNFYhdbqbZYJGSxu/5iPyXxktP7ijQdUcRos2iIoKSIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiLzar9on/Qyf0OXpXm1X7RP+hk/ociL0qP90rTJbujatTrjM9rTb1eEE4DpZq0kbGk+YFzgM/OpAikxxa4OG5YcJEKi+4p3UtCpcMVorlyvTtaZBNBaoTuEVwzQPkLhFWfiSZ7+hw0HDnlpwQQI/3RuIKmrzcEXdSqvocOXLN+Wevb2sr85rQ2hJaMZ5Yik6vaXHBillJ8Xcr11LgnRrNjvuxpOmz2sh3fE1KtJMXN8lxkfGXOcMDBJ6YWU1bS61uF1e1Xgs1343QWIo5oXbTkbo5GlpwR8S6IxdFtTtGtMkmb6SCPd6TIJ4KmcPULMhIgRFtYI1/Zazl0MWs8XDhIt7xHCsr5/ew7qrNTG3kmoYPEE4h5+wR/f8/HjA4h/GEPCzeFeHnac6n79Olpd9iCRhuFxjc6826wHeIBOGbN4x4sezxSVuHoOh0qEXJo1K1OHcXGKrBFXjLzgFxZE0AuOB17eixh4D0M80nRtKPPe2SfOn1DzZGuL2vkzF47w4l2T53E9pW+ntRrXAw60b7mBHvcfsc1qfgSREi87rCTNlSXEY//AJnuof8A69B/4NijXEPC+n0uF+DdTq1Y4dQm1bSHS3Wg98ScyKzMQ+UncWh8MJaOxvLaG4HRbQy8Oae99uV1Ck6TUIxDfkdVgL7sIjEIitvLM2YxGAza/I2jHYvljhrTpIIKsmn0n1ar45KtZ9WB0FaSIObE+CFzNkL2hzgC0AgOOO1QZtINywDbLPOGZf8Aam7B5pvx8pdP+lrzF7wSa7xm7iqSsy1HKxtHvyQRyRUWxSOgdpu4h3fXL73cOV4+Swt8p2bC9yNn/hLTs9vNv5/L3/YyufGnBGuWNSluVzw5eY5rBSk1fTR39pBbk5q2a8RdZAe5zwJC3B2fE4vlncn4OboGkVNLbMbHe4kL5i3ZzJJppJ5C1mTsYHSEAZJw0ZJOSmKxDH0MoNyWWmwytIMWEbvpa5xQoubVki3vX3mTPmqxGh1fhGs8upVMg4ddqEeYY8DUTciaLnk9LJDiOZ5WCeqq3SY+GncF6pLqjqv/ABMZrzpzZeBq41Hvk8gNa888RnxC/aNuefu6h2NvBo9QWjeFWsLph73NzkRd9GvuD+QbG3mGHc0O2ZxkA4Xis8IaTJaF6TTNPkuhzXC2+nXdZDm42uE7mb9wwMHOQsUtpBsAzYN0N/dm3Qz8lmpg5mIvm1HH6hUDJoA1biThOrrcckz5OEa8l+GV743TTxiy9zLWwhzjzg17m5ALmdemQb048rMh0HU4Y2hkcWkXY42DOGsZTlaxoz1wAAP3LLy6PUdaZddVrOuxxGGO26CI2o4SXExMsFvMbES952g48Y/GvVarxyxvilYySKRjo5I5Gh8ckbwWvY9jhhzC0kEHoQSqtfF9oWcG7t2s2/ZbqWHyB3E7/KFpRpdueho1OOADPFPD9jR4W4P2S/DxFLXJe4dje8r8g7O0NHnUorS09Nj7pDJ681ilBLolPvaCd1V8kZs3KsURnYCYojlgdgHLS5uDnC2YbwhpIbUYNL04MoSGWgwUq22lK6QTOlqN5eK0hla1+5mDuaD2hdzOGtOBuEafSB1DAvkVIAbwG/Hfh2f8z9sk+2bvtjvjKvv2qxxPum5k3/8AdpGnIR1VZmBc0D3tBGn/AKkfuZWsnATq7OMOFxV/4fiDq14SQ8PPkmayI6dZdGzUrTji1aO3d1aCNoJzlpXLhvhLTbeg8eX7NSKa5V1PXe9p5AXPr97wtsRmHJ+xu5jiSW43ANByAAtkaPBukQGuYdK06J1R0j6jo6VZjqz5dvNfXc2PML37W7nNwTtGc4Xpr8OadHDZrx0KTK918slyBlWBsNt87Qyd9mJrNs73tADi8EuA65UX7UEy0HQDW9nE/WFJuBt70b93EALWOxqVGY8K1bVfSpbcfC1WdtziW8+PR4YHmSLa2njbZtHkk5c5uREwZ8TLcFp9mX/gXWWwyHvH/i0R2nVWvZG3TpIqbnd7xvJdFA6YwYY4n7Zg5yc7Y2+DtImbVZLpenSMpNDabH0qz2VGjbhtZro8QNG1vRuB4rfiCx3FXCG6heg0YUdKtXZDNPMNOqTQ2pHn7MLsDo9tgyNLgXuy4E58bq0zZtKnYQfiBubCHE8yNdwtwKg7BOuZ3EWF9PJUjZi4dZxjwqOGnUXfYdQNhlGRslcP7wmFR0uxxaLLg2UPJ8chke771V5oWmMtaDLJbvcM0Lp1CSWxevG4ziiC+y5vOXRbpASW52sYQAXOOHtLhfvB3ctus1XTdRvs0OhFpLbZq0dAqywQz2LsIgms2XStbjxWtwxoONjBuwDmxn8I6U633+7TNPdeDg8XDTrm1vAwH88s37wOm7OVN20WUoDSTAF5kyHOMSbEXHGNLqIwbnyTAubaagD6KlNQ4Xravx0yrq0TbTBwrXnnjzLFFNOyzG0l7Btc6LdI54Y4Dq1hIy0KId0irSbq/Elpk3D2rNc4RX9O1vn6dqtPkQmER6PYs7GvO0ANmhccgQAA+KTtONHqC0bwq1u/TD3ubnIi76NfcH8g2NvM5O4B2zOMgHC8GucG6RelE93S9OuTABoms0q08u1vkt3ysLi0fF2KtS2llcJmA0CB1n6DQgrdUwcgxEzM/f1WF7kupwWOGtOsV6lhlfvDEVKWQ2ZhHCHxCBssu3ntIjwxztu5pZnGVRWhO0OPTp9Q4f4uv8Nub3zIND1K3WlEUsTnYibpz5C55kDG4cDM7xwDkgsG1UUbWNaxjQ1rQGta0BrWtaMBrQOgAAAwo/qPAmiWZzasaRpk9lzt7p5aNaSV7x2Oe98ZL3DA6nJ6LTQxbWOcSDBMwIPGxDgQdddfVbKuHLg2IkCOHpGnRa065r2o6zLwXZ1caW1tmjqMjBrccjNHsW4554RNZijIYXyVm05G9jC6duBh4aey3p/e/DHGrYb+lWqj7Gmyiro/fTqFG267F3wyB87Nha9og8WN7w0Rt8kFudpNa0Sleh73uVK1uuCCILMEU8Qc0Ya4RyNLQ4A9DjoukcMaaKZ0/wB76PeDsbqPekHejsPEgzW2cs+O1ruztaD2qyNqNAaA2ACDAiID81ue77haTgSZl0yDc63bH+/uVQHdV4Vo6bwlpLqkOyS5qWh2Lcznvklszuglc6aV0jjl5L3HpgDOAAAAMDxDT761/i9uoycNskEjI2niM2BNDQML+9pdKdG4ct3KdG4uj8YOdH8rrtFqOhUbMMdaxTq2K8Lo3QwTV4ZYYnRDbE6OJ7S1jmAkAgDHmXn17hTS78jJb2m0LksQxHJaqV7D2DO7a18rCWtz1x2ZUKW0so96ZvffctP0hSqYKdNLW3WBH1WtvE2mRv0fg2GxrmlTWa/vg/T2atWvnQ9VgbKxsPfE8kLWxCGu2OJvNADxMNp8YF0m7gGpVnzaxVh0+rSsQ6ho8lt2l3Tb0eeSSZ7WvpRNc6OqcMOWscchoBwY9ovTWdCpXYRXuU6tquC0iCzXiniaWjDS2ORpa0gdhA6LHSaJSoV4YKNStTh79qOMVWCKvGXmxGC4siaAXHA69qw/Hh9IsIMk+Ql2bdHoRrcRYLIwpa8OB3fSN8qRIiLlK8iIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIi82q/aJ/0Mn9Dl6V5tV+0T/oZP6HIi9KLQDw1eKvR/D/quo+0U8NXir0fw/wCq6j7RRFv+i0A8NXir0fw/6rqPtFPDV4q9H8P+q6j7RRFv+i0A8NXir0fw/wCq6j7RTw1eKvR/D/quo+0URb/otAPDV4q9H8P+q6j7RTw1eKvR/D/quo+0URb/AKLQDw1eKvR/D/quo+0U8NXir0fw/wCq6j7RRFv+i0A8NXir0fw/6rqPtFPDV4q9H8P+q6j7RRFv+i0A8NXir0fw/wCq6j7RTw1eKvR/D/quo+0URb/otAPDV4q9H8P+q6j7RTw1eKvR/D/quo+0URb/AKLQDw1eKvR/D/quo+0U8NXir0fw/wCq6j7RRFv+i0A8NXir0fw/6rqPtFPDV4q9H8P+q6j7RRFv+i0A8NXir0fw/wCq6j7RTw1eKvR/D/quo+0URb/otAPDV4q9H8P+q6j7RTw1eKvR/D/quo+0URb/AKLQDw1eKvR/D/quo+0U8NXir0fw/wCq6j7RRFv+i0A8NXir0fw/6rqPtFPDV4q9H8P+q6j7RRFv+i0A8NXir0fw/wCq6j7RTw1eKvR/D/quo+0URb/rG8ReRD+uU/8AcRrRLw1eKvR/D/quo+0V0XfdmcUShodQ0EBkkco21dQ8qJ4e0HOodmWhZBgrB0X6DItAPDV4q9H8P+q6j7RTw1eKvR/D/quo+0VhZW/6LQDw1eKvR/D/AKrqPtFPDV4q9H8P+q6j7RRFv+i0A8NXir0fw/6rqPtFPDV4q9H8P+q6j7RRFv8AotAPDV4q9H8P+q6j7RTw1eKvR/D/AKrqPtFEW/6LQDw1eKv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-       "            width=\"60%\"\n",
-       "            height=\"300\"\n",
-       "            src=\"https://www.youtube.com/embed/j4Xn8QFysmc\"\n",
-       "            frameborder=\"0\"\n",
-       "            allowfullscreen\n",
-       "        ></iframe>\n",
-       "        "
-      ],
-      "text/plain": [
-       "<IPython.lib.display.YouTubeVideo at 0x7f0b83dd0350>"
-      ]
-     },
-     "execution_count": 1,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from IPython.display import YouTubeVideo\n",
-    "YouTubeVideo(\"j4Xn8QFysmc\", width=\"60%\")"
+    "**Important**: Click on \"*Kernel*\" > \"*Restart Kernel and Clear All Outputs*\" *before* reading this chapter in [JupyterLab <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_jp.png\">](https://jupyterlab.readthedocs.io/en/stable/) (e.g., in the cloud on [MyBinder <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_mb.png\">](https://mybinder.org/v2/gh/webartifex/intro-to-python/master?urlpath=lab/tree/02_functions_00_content.ipynb))"
    ]
   },
   {
@@ -67,7 +30,7 @@
     }
    },
    "source": [
-    "In [Chapter 1](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/01_elements_00_lecture.ipynb#Example:-Averaging-Even-Numbers), we simply typed the code to calculate the average of the even numbers in a list of whole numbers into several code cells. Then, we executed them one after another. We had no way of *reusing* the code except for either executing cells multiple times. And, whenever we find ourselves doing repetitive manual work, we can be sure that there must be a way of automating what we are doing.\n",
+    "In [Chapter 1 <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/01_elements_00_content.ipynb#Example:-Averaging-all-even-Numbers-in-a-List), we simply typed the code to calculate the average of the even numbers in a list of whole numbers into several code cells. Then, we executed them one after another. We had no way of *reusing* the code except for either executing cells multiple times. And, whenever we find ourselves doing repetitive manual work, we can be sure that there must be a way of automating what we are doing.\n",
     "\n",
     "This chapter shows how Python offers language constructs that let us **define** functions ourselves that we may then **call** just like the built-in ones. Also, we look at how we can extend our Python installation with functionalities written by other people."
    ]
@@ -80,7 +43,7 @@
     }
    },
    "source": [
-    "## [Built-in Functions](https://docs.python.org/3/library/functions.html)"
+    "## [Built-in Functions <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/functions.html)"
    ]
   },
   {
@@ -91,14 +54,14 @@
     }
    },
    "source": [
-    "Python comes with plenty of useful functions built in, some of which we have already seen before (e.g., [print()](https://docs.python.org/3/library/functions.html#print), [sum()](https://docs.python.org/3/library/functions.html#sum), [len()](https://docs.python.org/3/library/functions.html#len), or [id()](https://docs.python.org/3/library/functions.html#id)). The [documentation](https://docs.python.org/3/library/functions.html) has the full list. Just as core Python itself, they are mostly implemented in C and thus very fast.\n",
+    "Python comes with plenty of useful functions built in, some of which we have already seen before (e.g., [print() <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/functions.html#print), [sum() <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/functions.html#sum), [len() <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/functions.html#len), or [id() <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/functions.html#id)). The [documentation <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/functions.html) has the full list. Just as core Python itself, they are mostly implemented in C and thus very fast.\n",
     "\n",
-    "Below, [sum()](https://docs.python.org/3/library/functions.html#sum) adds up all the elements in the `numbers` list while [len()](https://docs.python.org/3/library/functions.html#len) counts the number of elements in it."
+    "Below, [sum() <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/functions.html#sum) adds up all the elements in the `numbers` list while [len() <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/functions.html#len) counts the number of elements in it."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 1,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -111,7 +74,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 2,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -124,7 +87,7 @@
        "78"
       ]
      },
-     "execution_count": 3,
+     "execution_count": 2,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -135,7 +98,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 3,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -148,7 +111,7 @@
        "12"
       ]
      },
-     "execution_count": 4,
+     "execution_count": 3,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -165,14 +128,14 @@
     }
    },
    "source": [
-    "`sum` and `len` are *no* [keywords](https://docs.python.org/3/reference/lexical_analysis.html#keywords) like `for` or `if` but variables that reference *objects* in memory. Often, we hear people say that \"everything is an object in Python\" (e.g., this [question](https://stackoverflow.com/questions/40478536/in-python-what-does-it-mean-by-everything-is-an-object)). While this phrase may sound abstract in the beginning, it simply means that the entire memory is organized with \"bags\" of $0$s and $1$s, and there are even bags for the built-in functions. That is *not* true for many other languages (e.g., C or Java) and often a source of confusion for people coming to Python from another language.\n",
+    "`sum` and `len` are *no* [keywords <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/reference/lexical_analysis.html#keywords) like `for` or `if` but variables that reference *objects* in memory. Often, we hear people say that \"everything is an object in Python\" (e.g., this [question <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_so.png\">](https://stackoverflow.com/questions/40478536/in-python-what-does-it-mean-by-everything-is-an-object)). While this phrase may sound abstract in the beginning, it simply means that the entire memory is organized with \"bags\" of $0$s and $1$s, and there are even bags for the built-in functions. That is *not* true for many other languages (e.g., C or Java) and often a source of confusion for people coming to Python from another language.\n",
     "\n",
-    "The built-in [id()](https://docs.python.org/3/library/functions.html#id) function tells us where in memory a particular built-in function is stored."
+    "The built-in [id() <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/functions.html#id) function tells us where in memory a particular built-in function is stored."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 4,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -182,10 +145,10 @@
     {
      "data": {
       "text/plain": [
-       "139687475209792"
+       "140168648947264"
       ]
      },
-     "execution_count": 5,
+     "execution_count": 4,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -196,7 +159,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 5,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -206,10 +169,10 @@
     {
      "data": {
       "text/plain": [
-       "139687475208752"
+       "140168648946224"
       ]
      },
-     "execution_count": 6,
+     "execution_count": 5,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -226,12 +189,12 @@
     }
    },
    "source": [
-    "[type()](https://docs.python.org/3/library/functions.html#type) reveals that built-in functions like [sum()](https://docs.python.org/3/library/functions.html#sum) or [len()](https://docs.python.org/3/library/functions.html#len) are objects of type `builtin_function_or_method`."
+    "[type() <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/functions.html#type) reveals that built-in functions like [sum() <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/functions.html#sum) or [len() <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/functions.html#len) are objects of type `builtin_function_or_method`."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 6,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -244,7 +207,7 @@
        "builtin_function_or_method"
       ]
      },
-     "execution_count": 7,
+     "execution_count": 6,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -255,7 +218,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 7,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -268,7 +231,7 @@
        "builtin_function_or_method"
       ]
      },
-     "execution_count": 8,
+     "execution_count": 7,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -290,7 +253,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 8,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -300,10 +263,10 @@
     {
      "data": {
       "text/plain": [
-       "139687475208352"
+       "140168648945824"
       ]
      },
-     "execution_count": 9,
+     "execution_count": 8,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -314,7 +277,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 9,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -327,7 +290,7 @@
        "type"
       ]
      },
-     "execution_count": 10,
+     "execution_count": 9,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -344,16 +307,16 @@
     }
    },
    "source": [
-    "To execute a function, we **call** it with the **call operator** `()` as shown many times in [Chapter 1](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/01_elements_00_lecture.ipynb) and above.\n",
+    "To execute a function, we **call** it with the **call operator** `()` as shown many times in [Chapter 1 <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/01_elements_00_content.ipynb) and above.\n",
     "\n",
-    "If we are unsure whether a variable references a function or not, we can verify that with the built-in [callable()](https://docs.python.org/3/library/functions.html#callable) function.\n",
+    "If we are unsure whether a variable references a function or not, we can verify that with the built-in [callable() <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/functions.html#callable) function.\n",
     "\n",
     "Abstractly speaking, *any* object that can be called with the call operator `()` is a so-called **callable**. And, objects of type `builtin_function_or_method` are just one kind of examples thereof. We will see another one already in the next sub-section."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 10,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -366,7 +329,7 @@
        "True"
       ]
      },
-     "execution_count": 11,
+     "execution_count": 10,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -377,7 +340,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 11,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -390,7 +353,7 @@
        "True"
       ]
      },
-     "execution_count": 12,
+     "execution_count": 11,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -412,7 +375,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 12,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -425,7 +388,7 @@
        "False"
       ]
      },
-     "execution_count": 13,
+     "execution_count": 12,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -453,16 +416,16 @@
     }
    },
    "source": [
-    "The list of [built-in functions](https://docs.python.org/3/library/functions.html) in the documentation should really be named a list of built-in *callables*.\n",
+    "The list of [built-in functions <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/functions.html) in the documentation should really be named a list of built-in *callables*.\n",
     "\n",
-    "Besides the built-in functions, the list also features **constructors** for the built-in types. They may be used to **[cast](https://en.wikipedia.org/wiki/Type_conversion)** (i.e., \"convert\") any object as an object of a given type.\n",
+    "Besides the built-in functions, the list also features **constructors** for the built-in types. They may be used to **[cast <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_wiki.png\">](https://en.wikipedia.org/wiki/Type_conversion)** (i.e., \"convert\") any object as an object of a given type.\n",
     "\n",
-    "For example, to \"convert\" a `float` or a `str` into an `int` object, we use the [int()](https://docs.python.org/3/library/functions.html#int) built-in. Below, *new* `int` objects are created from the `7.0` and `\"7\"` objects that are *newly* created themselves before being processed by [int()](https://docs.python.org/3/library/functions.html#int) right away *without* ever being referenced by a variable."
+    "For example, to \"convert\" a `float` or a `str` into an `int` object, we use the [int() <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/functions.html#int) built-in. Below, *new* `int` objects are created from the `7.0` and `\"7\"` objects that are *newly* created themselves before being processed by [int() <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/functions.html#int) right away *without* ever being referenced by a variable."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 13,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -475,7 +438,7 @@
        "7"
       ]
      },
-     "execution_count": 14,
+     "execution_count": 13,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -486,7 +449,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 14,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -499,7 +462,7 @@
        "7"
       ]
      },
-     "execution_count": 15,
+     "execution_count": 14,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -516,12 +479,12 @@
     }
    },
    "source": [
-    "Casting an object as an `int` is different from rounding with the built-in [round()](https://docs.python.org/3/library/functions.html#round) function!"
+    "Casting an object as an `int` is different from rounding with the built-in [round() <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/functions.html#round) function!"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 15,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -534,7 +497,7 @@
        "7"
       ]
      },
-     "execution_count": 16,
+     "execution_count": 15,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -545,7 +508,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 16,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -558,7 +521,7 @@
        "8"
       ]
      },
-     "execution_count": 17,
+     "execution_count": 16,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -575,12 +538,12 @@
     }
    },
    "source": [
-    "Notice the subtle difference compared to the behavior of the `//` operator in [Chapter 1](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/01_elements_00_lecture.ipynb##%28Arithmetic#%29-Operators) that \"rounds\" towards minus infinity: [int()](https://docs.python.org/3/library/functions.html#int) always \"rounds\" towards `0`."
+    "Notice the subtle difference compared to the behavior of the `//` operator in [Chapter 1 <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/01_elements_00_content.ipynb##%28Arithmetic#%29-Operators) that \"rounds\" towards minus infinity: [int() <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/functions.html#int) always \"rounds\" towards `0`."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 17,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -593,7 +556,7 @@
        "-7"
       ]
      },
-     "execution_count": 18,
+     "execution_count": 17,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -615,7 +578,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 18,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -629,7 +592,7 @@
      "traceback": [
       "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
       "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-19-af421b358f21>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"seven\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;32m<ipython-input-18-af421b358f21>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"seven\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
       "\u001b[0;31mValueError\u001b[0m: invalid literal for int() with base 10: 'seven'"
      ]
     }
@@ -646,12 +609,12 @@
     }
    },
    "source": [
-    "We may also cast in the other direction with the [float()](https://docs.python.org/3/library/functions.html#float)  or [str()](https://docs.python.org/3/library/functions.html#func-str) built-ins."
+    "We may also cast in the other direction with the [float() <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/functions.html#float)  or [str() <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/functions.html#func-str) built-ins."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 19,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -664,7 +627,7 @@
        "7.0"
       ]
      },
-     "execution_count": 20,
+     "execution_count": 19,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -675,7 +638,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 20,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -688,7 +651,7 @@
        "'7'"
       ]
      },
-     "execution_count": 21,
+     "execution_count": 20,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -710,7 +673,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 21,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -720,10 +683,10 @@
     {
      "data": {
       "text/plain": [
-       "94868913087680"
+       "93973021516992"
       ]
      },
-     "execution_count": 22,
+     "execution_count": 21,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -734,7 +697,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 22,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -744,10 +707,10 @@
     {
      "data": {
       "text/plain": [
-       "94868913091584"
+       "93973021520896"
       ]
      },
-     "execution_count": 23,
+     "execution_count": 22,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -769,7 +732,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 23,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -782,7 +745,7 @@
        "type"
       ]
      },
-     "execution_count": 24,
+     "execution_count": 23,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -793,7 +756,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 24,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -806,7 +769,7 @@
        "type"
       ]
      },
-     "execution_count": 25,
+     "execution_count": 24,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -828,7 +791,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": 25,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -841,7 +804,7 @@
        "True"
       ]
      },
-     "execution_count": 26,
+     "execution_count": 25,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -852,7 +815,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": 26,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -865,7 +828,7 @@
        "True"
       ]
      },
-     "execution_count": 27,
+     "execution_count": 26,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -906,7 +869,7 @@
     }
    },
    "source": [
-    "We may create so-called *user-defined* **functions** with the `def` statement (cf., [reference](https://docs.python.org/3/reference/compound_stmts.html#function-definitions)). To extend an already familiar example, we reuse the introductory example from [Chapter 1](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/01_elements_00_lecture.ipynb#Best-Practices) in its final Pythonic version and transform it into the function `average_evens()` below. We replace the variable name `numbers` with `integers` for didactical purposes in the first couple of examples.\n",
+    "We may create so-called *user-defined* **functions** with the `def` statement (cf., [reference <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/reference/compound_stmts.html#function-definitions)). To extend an already familiar example, we reuse the introductory example from [Chapter 1 <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/01_elements_00_content.ipynb#Best-Practices) in its final Pythonic version and transform it into the function `average_evens()` below. We replace the variable name `numbers` with `integers` for didactical purposes in the first couple of examples.\n",
     "\n",
     "A function's **name** must be chosen according to the same naming rules as ordinary variables since Python manages function names like variables. In this book, we further adopt the convention of ending function names with parentheses `()` in text cells for faster comprehension when reading (i.e., `average_evens()` vs. `average_evens`). These are *not* part of the name but must always be written out in the `def` statement for syntactic reasons.\n",
     "\n",
@@ -914,16 +877,16 @@
     "\n",
     "The code block is also called a function's **body**, while the first line starting with `def` and ending with a colon is the **header**.\n",
     "\n",
-    "Together, the name and the list of parameters are also referred to as the function's **[signature](https://en.wikipedia.org/wiki/Type_signature)** (i.e., `average_evens(integers)` below).\n",
+    "Together, the name and the list of parameters are also referred to as the function's **[signature <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_wiki.png\">](https://en.wikipedia.org/wiki/Type_signature)** (i.e., `average_evens(integers)` below).\n",
     "\n",
-    "A function may specify an *explicit* **return value** (i.e., \"result\" or \"output\") with the `return` statement (cf., [reference](https://docs.python.org/3/reference/simple_stmts.html#the-return-statement)): Functions that have one are considered **fruitful**; otherwise, they are **void**. Functions of the latter kind are still useful because of their **side effects**. For example, the built-in [print()](https://docs.python.org/3/library/functions.html#print) function changes what we see on the screen. Strictly speaking, [print()](https://docs.python.org/3/library/functions.html#print) and other void functions also have an *implicit* return value, namely the `None` object.\n",
+    "A function may specify an *explicit* **return value** (i.e., \"result\" or \"output\") with the `return` statement (cf., [reference <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/reference/simple_stmts.html#the-return-statement)): Functions that have one are considered **fruitful**; otherwise, they are **void**. Functions of the latter kind are still useful because of their **side effects**. For example, the built-in [print() <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/functions.html#print) function changes what we see on the screen. Strictly speaking, [print() <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/functions.html#print) and other void functions also have an *implicit* return value, namely the `None` object.\n",
     "\n",
-    "A function should define a **docstring** that describes what it does in a short subject line, what parameters it expects (i.e., their types), and what it returns, if anything. A docstring is a syntactically valid multi-line string (i.e., type `str`) defined within **triple-double quotes** `\"\"\"`. Strings are covered in depth in [Chapter 6](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/06_text_00_lecture.ipynb#The-str-Type). Widely adopted standards for docstrings are [PEP 257](https://www.python.org/dev/peps/pep-0257/) and section 3.8 of [Google's Python Style Guide](https://github.com/google/styleguide/blob/gh-pages/pyguide.md)."
+    "A function should define a **docstring** that describes what it does in a short subject line, what parameters it expects (i.e., their types), and what it returns, if anything. A docstring is a syntactically valid multi-line string (i.e., type `str`) defined within **triple-double quotes** `\"\"\"`. Strings are covered in depth in [Chapter 6 <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/06_text_00_content.ipynb#The-str-Type). Widely adopted standards for docstrings are [PEP 257 <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://www.python.org/dev/peps/pep-0257/) and section 3.8 of [Google's Python Style Guide <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_gh.png\">](https://github.com/google/styleguide/blob/gh-pages/pyguide.md)."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": 27,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -958,7 +921,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
+   "execution_count": 28,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -971,7 +934,7 @@
        "<function __main__.average_evens(integers)>"
       ]
      },
-     "execution_count": 29,
+     "execution_count": 28,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -993,7 +956,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 30,
+   "execution_count": 29,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -1003,10 +966,10 @@
     {
      "data": {
       "text/plain": [
-       "139687370296784"
+       "140168546232512"
       ]
      },
-     "execution_count": 30,
+     "execution_count": 29,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1017,7 +980,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 31,
+   "execution_count": 30,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -1030,7 +993,7 @@
        "function"
       ]
      },
-     "execution_count": 31,
+     "execution_count": 30,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1049,12 +1012,12 @@
    "source": [
     "Its value may seem awkward at first: It consists of a location showing where the function is defined (i.e., `__main__` here, which is Python's way of saying \"in this notebook\") and the signature wrapped inside angle brackets `<` and `>`.\n",
     " \n",
-    "The angle brackets are a convention to indicate that the value may *not* be used as a *literal* (i.e., typed back into another code cell). Chapter 10 introduces the concept of a **text representation** of an object, which is related to the *semantic* meaning of an object's value as discussed in [Chapter 1](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/01_elements_00_lecture.ipynb#Value-/-\"Meaning\"), and the angle brackets convention is one such way to represent an object as text. When executed, the angle brackets cause a `SyntaxError` because Python expects the `<` operator to come with an operand on both sides (cf., [Chapter 3](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/03_conditionals_00_lecture.ipynb#Relational-Operators))."
+    "The angle brackets are a convention to indicate that the value may *not* be used as a *literal* (i.e., typed back into another code cell). Chapter 10 introduces the concept of a **text representation** of an object, which is related to the *semantic* meaning of an object's value as discussed in [Chapter 1 <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/01_elements_00_content.ipynb#Value-/-\"Meaning\"), and the angle brackets convention is one such way to represent an object as text. When executed, the angle brackets cause a `SyntaxError` because Python expects the `<` operator to come with an operand on both sides (cf., [Chapter 3 <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/03_conditionals_00_content.ipynb#Relational-Operators))."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 32,
+   "execution_count": 31,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -1063,10 +1026,10 @@
    "outputs": [
     {
      "ename": "SyntaxError",
-     "evalue": "invalid syntax (<ipython-input-32-7f49dff38622>, line 1)",
+     "evalue": "invalid syntax (<ipython-input-31-7f49dff38622>, line 1)",
      "output_type": "error",
      "traceback": [
-      "\u001b[0;36m  File \u001b[0;32m\"<ipython-input-32-7f49dff38622>\"\u001b[0;36m, line \u001b[0;32m1\u001b[0m\n\u001b[0;31m    <function __main__.average_evens(numbers)>\u001b[0m\n\u001b[0m    ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m invalid syntax\n"
+      "\u001b[0;36m  File \u001b[0;32m\"<ipython-input-31-7f49dff38622>\"\u001b[0;36m, line \u001b[0;32m1\u001b[0m\n\u001b[0;31m    <function __main__.average_evens(numbers)>\u001b[0m\n\u001b[0m    ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m invalid syntax\n"
      ]
     }
    ],
@@ -1087,7 +1050,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 33,
+   "execution_count": 32,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -1100,7 +1063,7 @@
        "True"
       ]
      },
-     "execution_count": 33,
+     "execution_count": 32,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1117,14 +1080,14 @@
     }
    },
    "source": [
-    "The built-in [help()](https://docs.python.org/3/library/functions.html#help) function shows a function's docstring.\n",
+    "The built-in [help() <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/functions.html#help) function shows a function's docstring.\n",
     "\n",
-    "Whenever we use code to analyze or obtain information on an object, we say that we **[introspect](https://en.wikipedia.org/wiki/Type_introspection)** it."
+    "Whenever we use code to analyze or obtain information on an object, we say that we **[introspect <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_wiki.png\">](https://en.wikipedia.org/wiki/Type_introspection)** it."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 34,
+   "execution_count": 33,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -1166,7 +1129,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 35,
+   "execution_count": 34,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -1185,7 +1148,7 @@
        "\n",
        "Returns:\n",
        "    average (float)\n",
-       "\u001b[0;31mFile:\u001b[0m      ~/repos/intro-to-python/<ipython-input-28-bcb78a3113d7>\n",
+       "\u001b[0;31mFile:\u001b[0m      ~/repos/intro-to-python/<ipython-input-27-bcb78a3113d7>\n",
        "\u001b[0;31mType:\u001b[0m      function\n"
       ]
      },
@@ -1210,7 +1173,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 36,
+   "execution_count": 35,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -1234,7 +1197,7 @@
        "\u001b[0;34m\u001b[0m    \u001b[0mevens\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mn\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mn\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mintegers\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mn\u001b[0m \u001b[0;34m%\u001b[0m \u001b[0;36m2\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\n",
        "\u001b[0;34m\u001b[0m    \u001b[0maverage\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msum\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mevens\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m/\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mevens\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n",
        "\u001b[0;34m\u001b[0m    \u001b[0;32mreturn\u001b[0m \u001b[0maverage\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-       "\u001b[0;31mFile:\u001b[0m      ~/repos/intro-to-python/<ipython-input-28-bcb78a3113d7>\n",
+       "\u001b[0;31mFile:\u001b[0m      ~/repos/intro-to-python/<ipython-input-27-bcb78a3113d7>\n",
        "\u001b[0;31mType:\u001b[0m      function\n"
       ]
      },
@@ -1254,12 +1217,12 @@
     }
    },
    "source": [
-    "[help()](https://docs.python.org/3/library/functions.html#help) and the `?`s also work for built-ins."
+    "[help() <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/functions.html#help) and the `?`s also work for built-ins."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 37,
+   "execution_count": 36,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -1310,7 +1273,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 38,
+   "execution_count": 37,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -1323,7 +1286,7 @@
        "7.0"
       ]
      },
-     "execution_count": 38,
+     "execution_count": 37,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1334,7 +1297,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 39,
+   "execution_count": 38,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -1347,7 +1310,7 @@
        "7.0"
       ]
      },
-     "execution_count": 39,
+     "execution_count": 38,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1369,7 +1332,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 40,
+   "execution_count": 39,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -1382,7 +1345,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 41,
+   "execution_count": 40,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -1395,7 +1358,7 @@
        "7.0"
       ]
      },
-     "execution_count": 41,
+     "execution_count": 40,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1439,7 +1402,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 42,
+   "execution_count": 41,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -1453,7 +1416,7 @@
      "traceback": [
       "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
       "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-42-756dfa02d4a8>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mintegers\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;32m<ipython-input-41-756dfa02d4a8>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mintegers\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
       "\u001b[0;31mNameError\u001b[0m: name 'integers' is not defined"
      ]
     }
@@ -1464,7 +1427,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 43,
+   "execution_count": 42,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -1478,7 +1441,7 @@
      "traceback": [
       "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
       "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-43-df246468d241>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mevens\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;32m<ipython-input-42-df246468d241>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mevens\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
       "\u001b[0;31mNameError\u001b[0m: name 'evens' is not defined"
      ]
     }
@@ -1489,7 +1452,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 44,
+   "execution_count": 43,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -1503,7 +1466,7 @@
      "traceback": [
       "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
       "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-44-c3fe9b4213f6>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0maverage\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;32m<ipython-input-43-c3fe9b4213f6>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0maverage\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
       "\u001b[0;31mNameError\u001b[0m: name 'average' is not defined"
      ]
     }
@@ -1520,7 +1483,7 @@
     }
    },
    "source": [
-    "[PythonTutor](http://pythontutor.com/visualize.html#code=numbers%20%3D%20%5B7,%2011,%208,%205,%203,%2012,%202,%206,%209,%2010,%201,%204%5D%0A%0Adef%20average_evens%28integers%29%3A%0A%20%20%20%20evens%20%3D%20%5Bn%20for%20n%20in%20integers%20if%20n%20%25%202%20%3D%3D%200%5D%0A%20%20%20%20average%20%3D%20sum%28evens%29%20/%20len%28evens%29%0A%20%20%20%20return%20average%0A%0Aresult%20%3D%20average_evens%28numbers%29&cumulative=false&curInstr=0&heapPrimitives=nevernest&mode=display&origin=opt-frontend.js&py=3&rawInputLstJSON=%5B%5D&textReferences=false) visualizes what happens in memory: To be precise, in the exact moment when the function call is initiated and `numbers` passed in as the `integers` argument, there are *two* references to the *same* `list` object (cf., steps 4-5 in the visualization). We also see how Python creates a *new* **frame** that holds the function's local scope (i.e., \"internal names\") in addition to the **global** frame. Frames are nothing but [namespaces](https://en.wikipedia.org/wiki/Namespace) to *isolate* the names of different **scopes** from each other. The list comprehension `[n for n in integers if n % 2 == 0]` constitutes yet another frame that is in scope as the `list` object assigned to `evens` is *being* created (cf., steps 6-20). When the function returns, only the global frame is left (cf., last step)."
+    "[PythonTutor <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](http://pythontutor.com/visualize.html#code=numbers%20%3D%20%5B7,%2011,%208,%205,%203,%2012,%202,%206,%209,%2010,%201,%204%5D%0A%0Adef%20average_evens%28integers%29%3A%0A%20%20%20%20evens%20%3D%20%5Bn%20for%20n%20in%20integers%20if%20n%20%25%202%20%3D%3D%200%5D%0A%20%20%20%20average%20%3D%20sum%28evens%29%20/%20len%28evens%29%0A%20%20%20%20return%20average%0A%0Aresult%20%3D%20average_evens%28numbers%29&cumulative=false&curInstr=0&heapPrimitives=nevernest&mode=display&origin=opt-frontend.js&py=3&rawInputLstJSON=%5B%5D&textReferences=false) visualizes what happens in memory: To be precise, in the exact moment when the function call is initiated and `numbers` passed in as the `integers` argument, there are *two* references to the *same* `list` object (cf., steps 4-5 in the visualization). We also see how Python creates a *new* **frame** that holds the function's local scope (i.e., \"internal names\") in addition to the **global** frame. Frames are nothing but [namespaces <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_wiki.png\">](https://en.wikipedia.org/wiki/Namespace) to *isolate* the names of different **scopes** from each other. The list comprehension `[n for n in integers if n % 2 == 0]` constitutes yet another frame that is in scope as the `list` object assigned to `evens` is *being* created (cf., steps 6-20). When the function returns, only the global frame is left (cf., last step)."
    ]
   },
   {
@@ -1547,7 +1510,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 45,
+   "execution_count": 44,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -1582,7 +1545,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 46,
+   "execution_count": 45,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -1595,7 +1558,7 @@
        "[7, 11, 8, 5, 3, 12, 2, 6, 9, 10, 1, 4]"
       ]
      },
-     "execution_count": 46,
+     "execution_count": 45,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1615,6 +1578,41 @@
     "Sometimes a function may return a correct solution for *some* inputs ..."
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "7.0"
+      ]
+     },
+     "execution_count": 46,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "average_wrong(numbers)  # correct by accident"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "... but still be wrong *in general*."
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 47,
@@ -1635,41 +1633,6 @@
      "output_type": "execute_result"
     }
    ],
-   "source": [
-    "average_wrong(numbers)  # correct by accident"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "source": [
-    "... but still be wrong *in general*."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 48,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "fragment"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "7.0"
-      ]
-     },
-     "execution_count": 48,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
    "source": [
     "average_wrong([123, 456, 789])"
    ]
@@ -1682,9 +1645,9 @@
     }
    },
    "source": [
-    "[PythonTutor](http://pythontutor.com/visualize.html#code=numbers%20%3D%20%5B7,%2011,%208,%205,%203,%2012,%202,%206,%209,%2010,%201,%204%5D%0A%0Adef%20average_wrong%28integers%29%3A%0A%20%20%20%20evens%20%3D%20%5Bn%20for%20n%20in%20numbers%20if%20n%20%25%202%20%3D%3D%200%5D%0A%20%20%20%20average%20%3D%20sum%28evens%29%20/%20len%28evens%29%0A%20%20%20%20return%20average%0A%0Aresult%20%3D%20average_wrong%28%5B123,%20456,%20789%5D%29&cumulative=false&curInstr=0&heapPrimitives=nevernest&mode=display&origin=opt-frontend.js&py=3&rawInputLstJSON=%5B%5D&textReferences=false) is again helpful at visualizing the error interactively: Creating the `list` object `evens` eventually references takes *16* computational steps, namely two for managing the list comprehension, one for setting up an empty `list` object, *twelve* for filling it with elements derived from `numbers` in the global scope (i.e., that is the error), and one to make `evens` reference it (cf., steps 6-21).\n",
+    "[PythonTutor <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](http://pythontutor.com/visualize.html#code=numbers%20%3D%20%5B7,%2011,%208,%205,%203,%2012,%202,%206,%209,%2010,%201,%204%5D%0A%0Adef%20average_wrong%28integers%29%3A%0A%20%20%20%20evens%20%3D%20%5Bn%20for%20n%20in%20numbers%20if%20n%20%25%202%20%3D%3D%200%5D%0A%20%20%20%20average%20%3D%20sum%28evens%29%20/%20len%28evens%29%0A%20%20%20%20return%20average%0A%0Aresult%20%3D%20average_wrong%28%5B123,%20456,%20789%5D%29&cumulative=false&curInstr=0&heapPrimitives=nevernest&mode=display&origin=opt-frontend.js&py=3&rawInputLstJSON=%5B%5D&textReferences=false) is again helpful at visualizing the error interactively: Creating the `list` object `evens` eventually references takes *16* computational steps, namely two for managing the list comprehension, one for setting up an empty `list` object, *twelve* for filling it with elements derived from `numbers` in the global scope (i.e., that is the error), and one to make `evens` reference it (cf., steps 6-21).\n",
     "\n",
-    "The frames logic shown by PythonTutor is the mechanism with which Python not only manages the names inside *one* function call but also for *many* potentially *simultaneous* calls, as revealed in [Chapter 4](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/04_iteration_00_lecture.ipynb#Trivial-Example:-Countdown). It is the reason why we may reuse the same names for the parameters and variables inside both `average_evens()` and `average_wrong()` without Python mixing them up. So, as we already read in the [Zen of Python](https://www.python.org/dev/peps/pep-0020/), \"namespaces are one honking great idea\" (cf., `import this`), and a frame is just a special kind of namespace."
+    "The frames logic shown by PythonTutor is the mechanism with which Python not only manages the names inside *one* function call but also for *many* potentially *simultaneous* calls, as revealed in [Chapter 4 <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/04_iteration_00_content.ipynb#Trivial-Example:-Countdown). It is the reason why we may reuse the same names for the parameters and variables inside both `average_evens()` and `average_wrong()` without Python mixing them up. So, as we already read in the [Zen of Python <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://www.python.org/dev/peps/pep-0020/), \"namespaces are one honking great idea\" (cf., `import this`), and a frame is just a special kind of namespace."
    ]
   },
   {
@@ -1708,12 +1671,12 @@
    "source": [
     "Code gets even more confusing when variables by the *same* name from *different* scopes collide. In particular, what should we expect to happen if a function \"changes\" a globally defined variable in its body?\n",
     "\n",
-    "`average_evens()` below works like `average_evens()` above except that it rounds the numbers in `integers` with the built-in [round()](https://docs.python.org/3/library/functions.html#round) function before filtering and averaging them. [round()](https://docs.python.org/3/library/functions.html#round) returns `int` objects independent of its argument being an `int` or a `float` object. On the first line in its body, `average_evens()` introduces a *local* variable `numbers` whose name collides with the one defined in the global scope."
+    "`average_evens()` below works like `average_evens()` above except that it rounds the numbers in `integers` with the built-in [round() <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/functions.html#round) function before filtering and averaging them. [round() <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/functions.html#round) returns `int` objects independent of its argument being an `int` or a `float` object. On the first line in its body, `average_evens()` introduces a *local* variable `numbers` whose name collides with the one defined in the global scope."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 49,
+   "execution_count": 48,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -1745,12 +1708,12 @@
     }
    },
    "source": [
-    "As a good practice, let's first \"verify\" that `average_evens()` is \"correct\" by calling it with inputs for which we can calculate the answer in our heads. Treating a function as a \"black box\" (i.e., input-output specification) when testing is also called [unit testing](https://en.wikipedia.org/wiki/Unit_testing) and plays an important role in modern software engineering."
+    "As a good practice, let's first \"verify\" that `average_evens()` is \"correct\" by calling it with inputs for which we can calculate the answer in our heads. Treating a function as a \"black box\" (i.e., input-output specification) when testing is also called [unit testing <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_wiki.png\">](https://en.wikipedia.org/wiki/Unit_testing) and plays an important role in modern software engineering."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 50,
+   "execution_count": 49,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -1763,7 +1726,7 @@
        "42.0"
       ]
      },
-     "execution_count": 50,
+     "execution_count": 49,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1780,12 +1743,12 @@
     }
    },
    "source": [
-    "Such tests are often and conveniently expressed with the `assert` statement (cf., [reference](https://docs.python.org/3/reference/simple_stmts.html#the-assert-statement)): If the expression following `assert` evaluates to `True`, nothing happens."
+    "Such tests are often and conveniently expressed with the `assert` statement (cf., [reference <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/reference/simple_stmts.html#the-assert-statement)): If the expression following `assert` evaluates to `True`, nothing happens."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 51,
+   "execution_count": 50,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -1809,7 +1772,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 52,
+   "execution_count": 51,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -1823,7 +1786,7 @@
      "traceback": [
       "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
       "\u001b[0;31mAssertionError\u001b[0m                            Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-52-f47586a118b9>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0;32massert\u001b[0m \u001b[0maverage_evens\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m40.0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m41.1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m42.2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m43.3\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m44.4\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m87.0\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;32m<ipython-input-51-f47586a118b9>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0;32massert\u001b[0m \u001b[0maverage_evens\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m40.0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m41.1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m42.2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m43.3\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m44.4\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m87.0\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
       "\u001b[0;31mAssertionError\u001b[0m: "
      ]
     }
@@ -1845,7 +1808,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 53,
+   "execution_count": 52,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -1858,7 +1821,7 @@
        "[7, 11, 8, 5, 3, 12, 2, 6, 9, 10, 1, 4]"
       ]
      },
-     "execution_count": 53,
+     "execution_count": 52,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1880,7 +1843,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 54,
+   "execution_count": 53,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -1893,7 +1856,7 @@
        "7.0"
       ]
      },
-     "execution_count": 54,
+     "execution_count": 53,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1915,7 +1878,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 55,
+   "execution_count": 54,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -1928,7 +1891,7 @@
        "[7, 11, 8, 5, 3, 12, 2, 6, 9, 10, 1, 4]"
       ]
      },
-     "execution_count": 55,
+     "execution_count": 54,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1947,7 +1910,7 @@
    "source": [
     "The reason why everything works is that *every* time we (re-)assign an object to a variable *inside* a function's body with the `=` statement, this is done in the *local* scope by default. There are ways to change variables existing in an outer scope from within a function, but this is a rather advanced topic.\n",
     "\n",
-    "[PythonTutor](http://pythontutor.com/visualize.html#code=numbers%20%3D%20%5B7,%2011,%208,%205,%203,%2012,%202,%206,%209,%2010,%201,%204%5D%0A%0Adef%20average_evens%28integers%29%3A%0A%20%20%20%20numbers%20%3D%20%5Bround%28n%29%20for%20n%20in%20integers%5D%0A%20%20%20%20evens%20%3D%20%5Bn%20for%20n%20in%20numbers%20if%20n%20%25%202%20%3D%3D%200%5D%0A%20%20%20%20average%20%3D%20sum%28evens%29%20/%20len%28evens%29%0A%20%20%20%20return%20average%0A%0Aresult%20%3D%20average_evens%28%5B40.0,%2041.1,%2042.2,%2043.3,%2044.4%5D%29&cumulative=false&curInstr=0&heapPrimitives=nevernest&mode=display&origin=opt-frontend.js&py=3&rawInputLstJSON=%5B%5D&textReferences=false) shows how *two* `numbers` variables exist in *different* scopes referencing *different* objects (cf., steps 14-25) when we execute `average_evens([40.0, 41.1, 42.2, 43.3, 44.4])`.\n",
+    "[PythonTutor <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](http://pythontutor.com/visualize.html#code=numbers%20%3D%20%5B7,%2011,%208,%205,%203,%2012,%202,%206,%209,%2010,%201,%204%5D%0A%0Adef%20average_evens%28integers%29%3A%0A%20%20%20%20numbers%20%3D%20%5Bround%28n%29%20for%20n%20in%20integers%5D%0A%20%20%20%20evens%20%3D%20%5Bn%20for%20n%20in%20numbers%20if%20n%20%25%202%20%3D%3D%200%5D%0A%20%20%20%20average%20%3D%20sum%28evens%29%20/%20len%28evens%29%0A%20%20%20%20return%20average%0A%0Aresult%20%3D%20average_evens%28%5B40.0,%2041.1,%2042.2,%2043.3,%2044.4%5D%29&cumulative=false&curInstr=0&heapPrimitives=nevernest&mode=display&origin=opt-frontend.js&py=3&rawInputLstJSON=%5B%5D&textReferences=false) shows how *two* `numbers` variables exist in *different* scopes referencing *different* objects (cf., steps 14-25) when we execute `average_evens([40.0, 41.1, 42.2, 43.3, 44.4])`.\n",
     "\n",
     "Variables whose names collide with the ones of variables in enclosing scopes - and the global scope is just the most enclosing scope - are said to **shadow** them.\n",
     "\n",
@@ -1958,7 +1921,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 56,
+   "execution_count": 55,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -1982,7 +1945,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 57,
+   "execution_count": 56,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -1995,7 +1958,7 @@
        "7.0"
       ]
      },
-     "execution_count": 57,
+     "execution_count": 56,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2006,7 +1969,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 58,
+   "execution_count": 57,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -2019,7 +1982,7 @@
        "[7, 11, 8, 5, 3, 12, 2, 6, 9, 10, 1, 4]"
       ]
      },
-     "execution_count": 58,
+     "execution_count": 57,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2036,7 +1999,7 @@
     }
    },
    "source": [
-    "[PythonTutor](http://pythontutor.com/visualize.html#code=numbers%20%3D%20%5B7,%2011,%208,%205,%203,%2012,%202,%206,%209,%2010,%201,%204%5D%0A%0Adef%20average_evens%28numbers%29%3A%0A%20%20%20%20evens%20%3D%20%5Bn%20for%20n%20in%20numbers%20if%20n%20%25%202%20%3D%3D%200%5D%0A%20%20%20%20average%20%3D%20sum%28evens%29%20/%20len%28evens%29%0A%20%20%20%20return%20average%0A%0Aresult%20%3D%20average_evens%28numbers%29&cumulative=false&curInstr=0&heapPrimitives=nevernest&mode=display&origin=opt-frontend.js&py=3&rawInputLstJSON=%5B%5D&textReferences=false) reveals that in this example there are *two* `numbers` variables in *different* scope referencing the *same* `list` object in memory (cf., steps 4-23)."
+    "[PythonTutor <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](http://pythontutor.com/visualize.html#code=numbers%20%3D%20%5B7,%2011,%208,%205,%203,%2012,%202,%206,%209,%2010,%201,%204%5D%0A%0Adef%20average_evens%28numbers%29%3A%0A%20%20%20%20evens%20%3D%20%5Bn%20for%20n%20in%20numbers%20if%20n%20%25%202%20%3D%3D%200%5D%0A%20%20%20%20average%20%3D%20sum%28evens%29%20/%20len%28evens%29%0A%20%20%20%20return%20average%0A%0Aresult%20%3D%20average_evens%28numbers%29&cumulative=false&curInstr=0&heapPrimitives=nevernest&mode=display&origin=opt-frontend.js&py=3&rawInputLstJSON=%5B%5D&textReferences=false) reveals that in this example there are *two* `numbers` variables in *different* scope referencing the *same* `list` object in memory (cf., steps 4-23)."
    ]
   },
   {
@@ -2058,12 +2021,12 @@
     }
    },
    "source": [
-    "So far, we have specified only one parameter in each of our user-defined functions. In [Chapter 1](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/01_elements_00_lecture.ipynb#%28Arithmetic%29-Operators), however, we saw the built-in [divmod()](https://docs.python.org/3/library/functions.html#divmod) function take two arguments. And, the order in which they are passed in matters! Whenever we call a function and list its arguments in a comma separated manner, we say that we pass in the arguments *by position* or refer to them as **positional arguments**."
+    "So far, we have specified only one parameter in each of our user-defined functions. In [Chapter 1 <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/01_elements_00_content.ipynb#%28Arithmetic%29-Operators), however, we saw the built-in [divmod() <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/functions.html#divmod) function take two arguments. And, the order in which they are passed in matters! Whenever we call a function and list its arguments in a comma separated manner, we say that we pass in the arguments *by position* or refer to them as **positional arguments**."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 59,
+   "execution_count": 58,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -2076,7 +2039,7 @@
        "(4, 2)"
       ]
      },
-     "execution_count": 59,
+     "execution_count": 58,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2087,7 +2050,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 60,
+   "execution_count": 59,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -2100,7 +2063,7 @@
        "(0, 10)"
       ]
      },
-     "execution_count": 60,
+     "execution_count": 59,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2122,7 +2085,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 61,
+   "execution_count": 60,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -2155,12 +2118,12 @@
     }
    },
    "source": [
-    "As with [divmod()](https://docs.python.org/3/library/functions.html#divmod), we may pass in the arguments by position."
+    "As with [divmod() <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/functions.html#divmod), we may pass in the arguments by position."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 62,
+   "execution_count": 61,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -2173,7 +2136,7 @@
        "14.0"
       ]
      },
-     "execution_count": 62,
+     "execution_count": 61,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2195,6 +2158,41 @@
     "Luckily, we may also pass in arguments *by name*. Then, we refer to them as **keyword arguments**."
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 62,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "14.0"
+      ]
+     },
+     "execution_count": 62,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "scaled_average_evens(numbers=numbers, scalar=2)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "When passing all arguments by name, we may do so in any order."
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 63,
@@ -2216,7 +2214,7 @@
     }
    ],
    "source": [
-    "scaled_average_evens(numbers=numbers, scalar=2)"
+    "scaled_average_evens(scalar=2, numbers=numbers)"
    ]
   },
   {
@@ -2227,7 +2225,7 @@
     }
    },
    "source": [
-    "When passing all arguments by name, we may do so in any order."
+    "We may even combine positional and keyword arguments in the same function call."
    ]
   },
   {
@@ -2250,41 +2248,6 @@
      "output_type": "execute_result"
     }
    ],
-   "source": [
-    "scaled_average_evens(scalar=2, numbers=numbers)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "source": [
-    "We may even combine positional and keyword arguments in the same function call."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 65,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "fragment"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "14.0"
-      ]
-     },
-     "execution_count": 65,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
    "source": [
     "scaled_average_evens(numbers, scalar=2)"
    ]
@@ -2302,7 +2265,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 66,
+   "execution_count": 65,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -2311,10 +2274,10 @@
    "outputs": [
     {
      "ename": "SyntaxError",
-     "evalue": "positional argument follows keyword argument (<ipython-input-66-b00eb6f72e84>, line 1)",
+     "evalue": "positional argument follows keyword argument (<ipython-input-65-b00eb6f72e84>, line 1)",
      "output_type": "error",
      "traceback": [
-      "\u001b[0;36m  File \u001b[0;32m\"<ipython-input-66-b00eb6f72e84>\"\u001b[0;36m, line \u001b[0;32m1\u001b[0m\n\u001b[0;31m    scaled_average_evens(numbers=numbers, 2)\u001b[0m\n\u001b[0m                                         ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m positional argument follows keyword argument\n"
+      "\u001b[0;36m  File \u001b[0;32m\"<ipython-input-65-b00eb6f72e84>\"\u001b[0;36m, line \u001b[0;32m1\u001b[0m\n\u001b[0;31m    scaled_average_evens(numbers=numbers, 2)\u001b[0m\n\u001b[0m                                         ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m positional argument follows keyword argument\n"
      ]
     }
    ],
@@ -2335,7 +2298,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 67,
+   "execution_count": 66,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -2349,7 +2312,7 @@
      "traceback": [
       "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
       "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-67-d910518345ec>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mscaled_average_evens\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnumbers\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;32m<ipython-input-66-d910518345ec>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mscaled_average_evens\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnumbers\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
       "\u001b[0;31mTypeError\u001b[0m: scaled_average_evens() missing 1 required positional argument: 'scalar'"
      ]
     }
@@ -2360,7 +2323,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 68,
+   "execution_count": 67,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -2374,7 +2337,7 @@
      "traceback": [
       "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
       "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-68-2e7ca1d2a555>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mscaled_average_evens\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnumbers\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m3\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;32m<ipython-input-67-2e7ca1d2a555>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mscaled_average_evens\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnumbers\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m3\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
       "\u001b[0;31mTypeError\u001b[0m: scaled_average_evens() takes 2 positional arguments but 3 were given"
      ]
     }
@@ -2409,7 +2372,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 69,
+   "execution_count": 68,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -2427,7 +2390,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 70,
+   "execution_count": 69,
    "metadata": {
     "slideshow": {
      "slide_type": "-"
@@ -2458,7 +2421,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 71,
+   "execution_count": 70,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -2473,7 +2436,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 72,
+   "execution_count": 71,
    "metadata": {
     "slideshow": {
      "slide_type": "-"
@@ -2502,7 +2465,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 73,
+   "execution_count": 72,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -2515,7 +2478,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 74,
+   "execution_count": 73,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -2550,7 +2513,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 75,
+   "execution_count": 74,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -2592,7 +2555,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 76,
+   "execution_count": 75,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -2605,7 +2568,7 @@
        "7.0"
       ]
      },
-     "execution_count": 76,
+     "execution_count": 75,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2614,6 +2577,30 @@
     "average_evens(numbers)"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 76,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "14.0"
+      ]
+     },
+     "execution_count": 76,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "average_evens(numbers, 2)"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 77,
@@ -2634,30 +2621,6 @@
      "output_type": "execute_result"
     }
    ],
-   "source": [
-    "average_evens(numbers, 2)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 78,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "fragment"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "14.0"
-      ]
-     },
-     "execution_count": 78,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
    "source": [
     "average_evens(numbers, scalar=2)"
    ]
@@ -2688,7 +2651,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 79,
+   "execution_count": 78,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -2726,7 +2689,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 80,
+   "execution_count": 79,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -2739,7 +2702,7 @@
        "7.0"
       ]
      },
-     "execution_count": 80,
+     "execution_count": 79,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2761,7 +2724,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 81,
+   "execution_count": 80,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -2774,7 +2737,7 @@
        "14.0"
       ]
      },
-     "execution_count": 81,
+     "execution_count": 80,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2796,7 +2759,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 82,
+   "execution_count": 81,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -2810,7 +2773,7 @@
      "traceback": [
       "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
       "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-82-12eb635366db>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0maverage_evens\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnumbers\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m2\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;32m<ipython-input-81-12eb635366db>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0maverage_evens\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnumbers\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m2\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
       "\u001b[0;31mTypeError\u001b[0m: average_evens() takes 1 positional argument but 2 were given"
      ]
     }
@@ -2840,18 +2803,18 @@
    "source": [
     "The `def` statement is a statement because of its *side effect* of creating a *new* name that references a *new* `function` object in memory.\n",
     "\n",
-    "We can thus think of it as doing *two* things **atomically** (i.e., either both of them happen or none). First, a `function` object is created that contains the concrete $0$s and $1$s that resemble the instructions we put into the function's body. In the context of a function, these $0$s and $1$s are also called **[byte code](https://en.wikipedia.org/wiki/Bytecode)**. Then, a name referencing the new `function` object is created.\n",
+    "We can thus think of it as doing *two* things **atomically** (i.e., either both of them happen or none). First, a `function` object is created that contains the concrete $0$s and $1$s that resemble the instructions we put into the function's body. In the context of a function, these $0$s and $1$s are also called **[byte code <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_wiki.png\">](https://en.wikipedia.org/wiki/Bytecode)**. Then, a name referencing the new `function` object is created.\n",
     "\n",
     "Only this second aspect makes `def` a statement: Merely creating a new object in memory without making it accessible for later reference does *not* constitute a side effect because the state the program is *not* changed. After all, if we cannot reference an object, how do we know it exists in the first place?\n",
     "\n",
-    "Python provides a `lambda` expression syntax that allows us to *only* create a `function` object in memory *without* making a name reference it (cf., [reference](https://docs.python.org/3/reference/expressions.html#lambda)). It starts with the keyword `lambda` followed by an optional listing of comma separated parameters, a mandatory colon, and *one* expression that serves as the return value of the resulting `function` object. Because it does *not* create a name referencing the object, we effectively create \"anonymous\" functions with it.\n",
+    "Python provides a `lambda` expression syntax that allows us to *only* create a `function` object in memory *without* making a name reference it (cf., [reference <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/reference/expressions.html#lambda)). It starts with the keyword `lambda` followed by an optional listing of comma separated parameters, a mandatory colon, and *one* expression that serves as the return value of the resulting `function` object. Because it does *not* create a name referencing the object, we effectively create \"anonymous\" functions with it.\n",
     "\n",
     "In the example, we create a `function` object that adds `3` to the only argument passed in as the parameter `x` and returns that sum."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 83,
+   "execution_count": 82,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -2864,7 +2827,7 @@
        "<function __main__.<lambda>(x)>"
       ]
      },
-     "execution_count": 83,
+     "execution_count": 82,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2890,7 +2853,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 84,
+   "execution_count": 83,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -2909,7 +2872,31 @@
     }
    },
    "source": [
-    "[type()](https://docs.python.org/3/library/functions.html#type) and [callable()](https://docs.python.org/3/library/functions.html#callable) confirm that `add_three` is indeed a callable `function` object."
+    "[type() <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/functions.html#type) and [callable() <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/functions.html#callable) confirm that `add_three` is indeed a callable `function` object."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 84,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "function"
+      ]
+     },
+     "execution_count": 84,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "type(add_three)"
    ]
   },
   {
@@ -2920,30 +2907,6 @@
      "slide_type": "skip"
     }
    },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "function"
-      ]
-     },
-     "execution_count": 85,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "type(add_three)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 86,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
    "outputs": [
     {
      "data": {
@@ -2951,7 +2914,7 @@
        "True"
       ]
      },
-     "execution_count": 86,
+     "execution_count": 85,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2971,6 +2934,41 @@
     "Now we may call `add_three()` as if we defined it with the `def` statement."
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 86,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "42"
+      ]
+     },
+     "execution_count": 86,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "add_three(39)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Alternatively, we could call an `function` object created with a `lambda` expression right away (i.e., without assigning it to a variable), which looks quite weird for now as we need *two* pairs of parentheses: The first one serves as a delimiter whereas the second represents the call operator."
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 87,
@@ -2991,41 +2989,6 @@
      "output_type": "execute_result"
     }
    ],
-   "source": [
-    "add_three(39)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "source": [
-    "Alternatively, we could call an `function` object created with a `lambda` expression right away (i.e., without assigning it to a variable), which looks quite weird for now as we need *two* pairs of parentheses: The first one serves as a delimiter whereas the second represents the call operator."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 88,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "42"
-      ]
-     },
-     "execution_count": 88,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
    "source": [
     "(lambda x: x + 3)(39)"
    ]
@@ -3040,7 +3003,7 @@
    "source": [
     "The main point of having functions without a reference to them is to use them in a situation where we know ahead of time that we use the function only *once*.\n",
     "\n",
-    "Popular applications of lambda expressions occur in combination with the **map-filter-reduce** paradigm (cf., [Chapter 8](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/08_mfr_00_lecture.ipynb#Lambda-Expressions))."
+    "Popular applications of lambda expressions occur in combination with the **map-filter-reduce** paradigm (cf., [Chapter 8 <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/08_mfr_00_content.ipynb#Lambda-Expressions))."
    ]
   },
   {
@@ -3062,13 +3025,13 @@
     }
    },
    "source": [
-    "So far, we have only used what we refer to as **core** Python in this book. By this, we mean all the syntactical rules as specified in the [language reference](https://docs.python.org/3/reference/) and a minimal set of about 50 built-in [functions](https://docs.python.org/3/library/functions.html). With this, we could already implement any algorithm or business logic we can think of!\n",
+    "So far, we have only used what we refer to as **core** Python in this book. By this, we mean all the syntactical rules as specified in the [language reference <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/reference/) and a minimal set of about 50 built-in [functions <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/functions.html). With this, we could already implement any algorithm or business logic we can think of!\n",
     "\n",
     "However, after our first couple of programs, we would already start seeing recurring patterns in the code we write. In other words, we would constantly be \"reinventing the wheel\" in each new project.\n",
     "\n",
     "Would it not be smarter to pull out the reusable components from our programs and put them into some project independent **library** of generically useful functionalities? Then we would only need a way of including these **utilities** in our projects.\n",
     "\n",
-    "As all programmers across all languages face this very same issue, most programming languages come with a so-called **[standard library](https://en.wikipedia.org/wiki/Standard_library)** that provides utilities to accomplish everyday tasks without much code. Examples are making an HTTP request to some website, open and read popular file types (e.g., CSV or Excel files), do something on a computer's file system, and many more."
+    "As all programmers across all languages face this very same issue, most programming languages come with a so-called **[standard library <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_wiki.png\">](https://en.wikipedia.org/wiki/Standard_library)** that provides utilities to accomplish everyday tasks without much code. Examples are making an HTTP request to some website, open and read popular file types (e.g., CSV or Excel files), do something on a computer's file system, and many more."
    ]
   },
   {
@@ -3090,13 +3053,13 @@
     }
    },
    "source": [
-    "Python also comes with a [standard library](https://docs.python.org/3/library/index.html) that is structured into coherent modules and packages for given topics: A **module** is just a plain text file with the file extension *.py* that contains Python code while a **package** is a folder that groups several related modules.\n",
+    "Python also comes with a [standard library <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/index.html) that is structured into coherent modules and packages for given topics: A **module** is just a plain text file with the file extension *.py* that contains Python code while a **package** is a folder that groups several related modules.\n",
     "\n",
-    "The code in the [standard library](https://docs.python.org/3/library/index.html) is contributed and maintained by many volunteers around the world. In contrast to so-called \"third-party\" packages (cf., the next section below), the Python core development team closely monitors and tests the code in the [standard library](https://docs.python.org/3/library/index.html). Consequently, we can be reasonably sure that anything provided by it works correctly independent of our computer's operating system and will most likely also be there in the next Python versions. Parts in the [standard library](https://docs.python.org/3/library/index.html) that are computationally expensive are often rewritten in C and, therefore, much faster than anything we could write in Python ourselves. So, whenever we can solve a problem with the help of the [standard library](https://docs.python.org/3/library/index.html), it is almost always the best way to do so as well.\n",
+    "The code in the [standard library <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/index.html) is contributed and maintained by many volunteers around the world. In contrast to so-called \"third-party\" packages (cf., the next section below), the Python core development team closely monitors and tests the code in the [standard library <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/index.html). Consequently, we can be reasonably sure that anything provided by it works correctly independent of our computer's operating system and will most likely also be there in the next Python versions. Parts in the [standard library <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/index.html) that are computationally expensive are often rewritten in C and, therefore, much faster than anything we could write in Python ourselves. So, whenever we can solve a problem with the help of the [standard library <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/index.html), it is almost always the best way to do so as well.\n",
     "\n",
-    "The [standard library](https://docs.python.org/3/library/index.html) has grown very big over the years, and we refer to the website [PYMOTW](https://pymotw.com/3/index.html) (i.e., \"Python Module of the Week\") that features well written introductory tutorials and how-to guides to most parts of the library. The same author also published a [book](https://www.amazon.com/Python-Standard-Library-Example-Developers/dp/0134291050/ref=as_li_ss_tl?ie=UTF8&qid=1493563121&sr=8-1&keywords=python+3+standard+library+by+example) that many Pythonistas keep on their shelf for reference. Knowing what is in the [standard library](https://docs.python.org/3/library/index.html) is quite valuable for solving real-world tasks quickly.\n",
+    "The [standard library <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/index.html) has grown very big over the years, and we refer to the website [PYMOTW](https://pymotw.com/3/index.html) (i.e., \"Python Module of the Week\") that features well written introductory tutorials and how-to guides to most parts of the library. The same author also published a [book](https://www.amazon.com/Python-Standard-Library-Example-Developers/dp/0134291050/ref=as_li_ss_tl?ie=UTF8&qid=1493563121&sr=8-1&keywords=python+3+standard+library+by+example) that many Pythonistas keep on their shelf for reference. Knowing what is in the [standard library <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/index.html) is quite valuable for solving real-world tasks quickly.\n",
     "\n",
-    "Throughout this book, we look at many modules and packages from the [standard library](https://docs.python.org/3/library/index.html) in more depth, starting with the [math](https://docs.python.org/3/library/math.html) and [random](https://docs.python.org/3/library/random.html) modules in this chapter."
+    "Throughout this book, we look at many modules and packages from the [standard library <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/index.html) in more depth, starting with the [math <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/math.html) and [random <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/random.html) modules in this chapter."
    ]
   },
   {
@@ -3107,7 +3070,7 @@
     }
    },
    "source": [
-    "#### [math](https://docs.python.org/3/library/math.html) Module"
+    "#### [math <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/math.html) Module"
    ]
   },
   {
@@ -3118,14 +3081,14 @@
     }
    },
    "source": [
-    "The [math](https://docs.python.org/3/library/math.html) module provides non-trivial mathematical functions like $sin(x)$ and constants like $\\pi$ or $\\text{e}$.\n",
+    "The [math <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/math.html) module provides non-trivial mathematical functions like $sin(x)$ and constants like $\\pi$ or $\\text{e}$.\n",
     "\n",
-    "To make functions and variables defined \"somewhere else\" available in our current program, we must first **import** them with the `import` statement (cf., [reference](https://docs.python.org/3/reference/simple_stmts.html#import)).  "
+    "To make functions and variables defined \"somewhere else\" available in our current program, we must first **import** them with the `import` statement (cf., [reference <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/reference/simple_stmts.html#import)).  "
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 89,
+   "execution_count": 88,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -3144,7 +3107,31 @@
     }
    },
    "source": [
-    "This creates the variable `math` that references a **[module object](https://docs.python.org/3/glossary.html#term-module)** (i.e., type `module`) in memory."
+    "This creates the variable `math` that references a **[module object <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/glossary.html#term-module)** (i.e., type `module`) in memory."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 89,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<module 'math' from '/home/webartifex/.pyenv/versions/anaconda3-2019.10/lib/python3.7/lib-dynload/math.cpython-37m-x86_64-linux-gnu.so'>"
+      ]
+     },
+     "execution_count": 89,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "math"
    ]
   },
   {
@@ -3159,7 +3146,7 @@
     {
      "data": {
       "text/plain": [
-       "<module 'math' from '/home/webartifex/.pyenv/versions/anaconda3-2019.10/lib/python3.7/lib-dynload/math.cpython-37m-x86_64-linux-gnu.so'>"
+       "140168646515504"
       ]
      },
      "execution_count": 90,
@@ -3168,7 +3155,7 @@
     }
    ],
    "source": [
-    "math"
+    "id(math)"
    ]
   },
   {
@@ -3179,30 +3166,6 @@
      "slide_type": "fragment"
     }
    },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "139687472778032"
-      ]
-     },
-     "execution_count": 91,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "id(math)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 92,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "fragment"
-    }
-   },
    "outputs": [
     {
      "data": {
@@ -3210,7 +3173,7 @@
        "module"
       ]
      },
-     "execution_count": 92,
+     "execution_count": 91,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3231,12 +3194,12 @@
     "\n",
     "Let's see what we can do with the `math` module.\n",
     "\n",
-    "The [dir()](https://docs.python.org/3/library/functions.html#dir) built-in function may also be used with an argument passed in. Ignoring the dunder-style names, `math` offers quite a lot of names. As we cannot know at this point if a listed name refers to a function or an ordinary variable, we use the more generic term **attribute** to mean either one of them."
+    "The [dir() <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/functions.html#dir) built-in function may also be used with an argument passed in. Ignoring the dunder-style names, `math` offers quite a lot of names. As we cannot know at this point if a listed name refers to a function or an ordinary variable, we use the more generic term **attribute** to mean either one of them."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 93,
+   "execution_count": 92,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -3304,7 +3267,7 @@
        " 'trunc']"
       ]
      },
-     "execution_count": 93,
+     "execution_count": 92,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3326,7 +3289,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 94,
+   "execution_count": 93,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -3339,7 +3302,7 @@
        "3.141592653589793"
       ]
      },
-     "execution_count": 94,
+     "execution_count": 93,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3350,7 +3313,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 95,
+   "execution_count": 94,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -3363,7 +3326,7 @@
        "2.718281828459045"
       ]
      },
-     "execution_count": 95,
+     "execution_count": 94,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3374,7 +3337,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 96,
+   "execution_count": 95,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -3387,7 +3350,7 @@
        "<function math.sqrt(x, /)>"
       ]
      },
-     "execution_count": 96,
+     "execution_count": 95,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3398,7 +3361,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 97,
+   "execution_count": 96,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -3423,7 +3386,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 98,
+   "execution_count": 97,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -3436,7 +3399,7 @@
        "1.4142135623730951"
       ]
      },
-     "execution_count": 98,
+     "execution_count": 97,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3455,14 +3418,14 @@
    "source": [
     "Observe how the arguments passed to functions do not need to be just variables or simple literals. Instead, we may pass in any *expression* that evaluates to a *new* object of the type the function expects.\n",
     "\n",
-    "So just as a reminder from the expression vs. statement discussion in [Chapter 1](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/01_elements_00_lecture.ipynb#Expressions): An expression is *any* syntactically correct combination of variables and literals with operators. And the call operator `()` is yet another operator. So both of the next two code cells are just expressions! They have no permanent side effects in memory. We may execute them as often as we want *without* changing the state of the program (i.e., this Jupyter notebook).\n",
+    "So just as a reminder from the expression vs. statement discussion in [Chapter 1 <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/01_elements_00_content.ipynb#Expressions): An expression is *any* syntactically correct combination of variables and literals with operators. And the call operator `()` is yet another operator. So both of the next two code cells are just expressions! They have no permanent side effects in memory. We may execute them as often as we want *without* changing the state of the program (i.e., this Jupyter notebook).\n",
     "\n",
-    "So, regarding the very next cell in particular: Although the `2 ** 2` creates a *new* object `4` in memory that is then immediately passed into the [math.sqrt()](https://docs.python.org/3/library/math.html#math.sqrt) function, once that function call returns, \"all is lost\" and the newly created `4` object is forgotten again, as well as the return value of [math.sqrt()](https://docs.python.org/3/library/math.html#math.sqrt)."
+    "So, regarding the very next cell in particular: Although the `2 ** 2` creates a *new* object `4` in memory that is then immediately passed into the [math.sqrt() <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/math.html#math.sqrt) function, once that function call returns, \"all is lost\" and the newly created `4` object is forgotten again, as well as the return value of [math.sqrt() <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/math.html#math.sqrt)."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 99,
+   "execution_count": 98,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -3475,7 +3438,7 @@
        "2.0"
       ]
      },
-     "execution_count": 99,
+     "execution_count": 98,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3497,7 +3460,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 100,
+   "execution_count": 99,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -3510,7 +3473,7 @@
        "10.0"
       ]
      },
-     "execution_count": 100,
+     "execution_count": 99,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3534,7 +3497,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 101,
+   "execution_count": 100,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -3547,7 +3510,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 102,
+   "execution_count": 101,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -3560,7 +3523,7 @@
        "4.0"
       ]
      },
-     "execution_count": 102,
+     "execution_count": 101,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3577,7 +3540,7 @@
     }
    },
    "source": [
-    "#### [random](https://docs.python.org/3/library/random.html) Module"
+    "#### [random <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/random.html) Module"
    ]
   },
   {
@@ -3588,12 +3551,12 @@
     }
    },
    "source": [
-    "Often, we need a random variable, for example, when we want to build a simulation. The [random](https://docs.python.org/3/library/random.html) module in the [standard library](https://docs.python.org/3/library/index.html) often suffices for that."
+    "Often, we need a random variable, for example, when we want to build a simulation. The [random <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/random.html) module in the [standard library <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/index.html) often suffices for that."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 103,
+   "execution_count": 102,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -3606,7 +3569,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 104,
+   "execution_count": 103,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -3619,7 +3582,7 @@
        "<module 'random' from '/home/webartifex/.pyenv/versions/anaconda3-2019.10/lib/python3.7/random.py'>"
       ]
      },
-     "execution_count": 104,
+     "execution_count": 103,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3636,12 +3599,12 @@
     }
    },
    "source": [
-    "Besides the usual dunder-style attributes, the built-in [dir()](https://docs.python.org/3/library/functions.html#dir) function lists some attributes in an upper case naming convention and many others starting with a *single* underscore `_`. To understand the former, we must wait until Chapter 10, while the latter is explained further below."
+    "Besides the usual dunder-style attributes, the built-in [dir() <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/functions.html#dir) function lists some attributes in an upper case naming convention and many others starting with a *single* underscore `_`. To understand the former, we must wait until Chapter 10, while the latter is explained further below."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 105,
+   "execution_count": 104,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -3715,7 +3678,7 @@
        " 'weibullvariate']"
       ]
      },
-     "execution_count": 105,
+     "execution_count": 104,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3732,12 +3695,12 @@
     }
    },
    "source": [
-    "The [random.random()](https://docs.python.org/3/library/random.html#random.random) function generates a uniformly distributed `float` number between $0$ (including) and $1$ (excluding)."
+    "The [random.random() <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/random.html#random.random) function generates a uniformly distributed `float` number between $0$ (including) and $1$ (excluding)."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 106,
+   "execution_count": 105,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -3750,7 +3713,7 @@
        "<function Random.random>"
       ]
      },
-     "execution_count": 106,
+     "execution_count": 105,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3761,7 +3724,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 107,
+   "execution_count": 106,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -3786,7 +3749,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 108,
+   "execution_count": 107,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -3796,10 +3759,10 @@
     {
      "data": {
       "text/plain": [
-       "0.5217159553088394"
+       "0.09249734497499318"
       ]
      },
-     "execution_count": 108,
+     "execution_count": 107,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3816,12 +3779,12 @@
     }
    },
    "source": [
-    "While we could build some conditional logic with an `if` statement to map the number generated by [random.random()](https://docs.python.org/3/library/random.html#random.random) to a finite set of elements manually, the [random.choice()](https://docs.python.org/3/library/random.html#random.choice) function provides a lot more **convenience** for us. We call it with, for example, the `numbers` list and it draws one element out of it with equal chance."
+    "While we could build some conditional logic with an `if` statement to map the number generated by [random.random() <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/random.html#random.random) to a finite set of elements manually, the [random.choice() <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/random.html#random.choice) function provides a lot more **convenience** for us. We call it with, for example, the `numbers` list and it draws one element out of it with equal chance."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 109,
+   "execution_count": 108,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -3831,10 +3794,10 @@
     {
      "data": {
       "text/plain": [
-       "<bound method Random.choice of <random.Random object at 0x5648648822a0>>"
+       "<bound method Random.choice of <random.Random object at 0x5577ce0920d0>>"
       ]
      },
-     "execution_count": 109,
+     "execution_count": 108,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3845,7 +3808,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 110,
+   "execution_count": 109,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -3870,7 +3833,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 111,
+   "execution_count": 110,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -3880,10 +3843,10 @@
     {
      "data": {
       "text/plain": [
-       "10"
+       "2"
       ]
      },
-     "execution_count": 111,
+     "execution_count": 110,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3900,14 +3863,14 @@
     }
    },
    "source": [
-    "To reproduce the *same* random numbers in a simulation each time we run it, we set the **[random seed](https://en.wikipedia.org/wiki/Random_seed)**. It is good practice to do that at the beginning of a program or notebook. It becomes essential when we employ randomized machine learning algorithms, like the [Random Forest](https://en.wikipedia.org/wiki/Random_forest), and want to obtain **reproducible** results for publication in academic journals.\n",
+    "To reproduce the *same* random numbers in a simulation each time we run it, we set the **[random seed <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_wiki.png\">](https://en.wikipedia.org/wiki/Random_seed)**. It is good practice to do that at the beginning of a program or notebook. It becomes essential when we employ randomized machine learning algorithms, like the [Random Forest <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_wiki.png\">](https://en.wikipedia.org/wiki/Random_forest), and want to obtain **reproducible** results for publication in academic journals.\n",
     "\n",
-    "The [random](https://docs.python.org/3/library/random.html) module provides the [random.seed()](https://docs.python.org/3/library/random.html#random.seed) function to do that."
+    "The [random <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/random.html) module provides the [random.seed() <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/random.html#random.seed) function to do that."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 112,
+   "execution_count": 111,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -3920,7 +3883,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 113,
+   "execution_count": 112,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -3933,7 +3896,7 @@
        "0.6394267984578837"
       ]
      },
-     "execution_count": 113,
+     "execution_count": 112,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3944,7 +3907,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 114,
+   "execution_count": 113,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -3957,7 +3920,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 115,
+   "execution_count": 114,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -3970,7 +3933,7 @@
        "0.6394267984578837"
       ]
      },
-     "execution_count": 115,
+     "execution_count": 114,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3998,9 +3961,9 @@
     }
    },
    "source": [
-    "As the Python community is based around open source, many developers publish their code, for example, on the Python Package Index [PyPI](https://pypi.org) from where anyone may download and install it for free using command-line based tools like [pip](https://pip.pypa.io/en/stable/) or [conda](https://conda.io/en/latest/). This way, we can always customize our Python installation even more. Managing many such packages is quite a deep topic on its own, sometimes fearfully called **[dependency hell](https://en.wikipedia.org/wiki/Dependency_hell)**.\n",
+    "As the Python community is based around open source, many developers publish their code, for example, on the Python Package Index [PyPI](https://pypi.org) from where anyone may download and install it for free using command-line based tools like [pip](https://pip.pypa.io/en/stable/) or [conda](https://conda.io/en/latest/). This way, we can always customize our Python installation even more. Managing many such packages is quite a deep topic on its own, sometimes fearfully called **[dependency hell <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_wiki.png\">](https://en.wikipedia.org/wiki/Dependency_hell)**.\n",
     "\n",
-    "The difference between the [standard library](https://docs.python.org/3/library/index.html) and such **third-party** packages is that in the first case, the code goes through a much more formalized review process and is officially endorsed by the Python core developers. Yet, many third-party projects also offer the highest quality standards and are also relied on by many businesses and researchers.\n",
+    "The difference between the [standard library <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/index.html) and such **third-party** packages is that in the first case, the code goes through a much more formalized review process and is officially endorsed by the Python core developers. Yet, many third-party projects also offer the highest quality standards and are also relied on by many businesses and researchers.\n",
     "\n",
     "Throughout this book, we will look at many third-party libraries, mostly from Python's [scientific stack](https://scipy.org/about.html), a tightly coupled set of third-party libraries for storing **big data** efficiently (e.g., [numpy](http://www.numpy.org/)), \"wrangling\" (e.g., [pandas](https://pandas.pydata.org/)) and visualizing them (e.g., [matplotlib](https://matplotlib.org/) or [seaborn](https://seaborn.pydata.org/)), fitting classical statistical models (e.g., [statsmodels](http://www.statsmodels.org/)), training machine learning models (e.g., [sklearn](http://scikit-learn.org/)), and much more.\n",
     "\n",
@@ -4028,14 +3991,14 @@
    "source": [
     "[numpy](http://www.numpy.org/) is the de-facto standard in the Python world for handling **array-like** data. That is a fancy word for data that can be put into a matrix or vector format.\n",
     "\n",
-    "As [numpy](http://www.numpy.org/) is *not* in the [standard library](https://docs.python.org/3/library/index.html), it must be *manually* installed, for example, with the [pip](https://pip.pypa.io/en/stable/) tool. As mentioned in [Chapter 0](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/00_intro_00_lecture.ipynb#Markdown-vs.-Code-Cells), to execute terminal commands from within a Jupyter notebook, we start a code cell with an exclamation mark.\n",
+    "As [numpy](http://www.numpy.org/) is *not* in the [standard library <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/index.html), it must be *manually* installed, for example, with the [pip](https://pip.pypa.io/en/stable/) tool. As mentioned in [Chapter 0 <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/00_intro_00_content.ipynb#Markdown-vs.-Code-Cells), to execute terminal commands from within a Jupyter notebook, we start a code cell with an exclamation mark.\n",
     "\n",
     "If you are running this notebook with an installation of the [Anaconda Distribution](https://www.anaconda.com/distribution/), then [numpy](http://www.numpy.org/) is probably already installed. Running the cell below confirms that."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 116,
+   "execution_count": 115,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -4067,7 +4030,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 117,
+   "execution_count": 116,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -4091,7 +4054,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 118,
+   "execution_count": 117,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -4104,7 +4067,7 @@
        "<module 'numpy' from '/home/webartifex/.pyenv/versions/anaconda3-2019.10/lib/python3.7/site-packages/numpy/__init__.py'>"
       ]
      },
-     "execution_count": 118,
+     "execution_count": 117,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -4126,7 +4089,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 119,
+   "execution_count": 118,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -4137,6 +4100,30 @@
     "vec = np.array(numbers)"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 119,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([ 7, 11,  8,  5,  3, 12,  2,  6,  9, 10,  1,  4])"
+      ]
+     },
+     "execution_count": 119,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "vec"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 120,
@@ -4145,30 +4132,6 @@
      "slide_type": "fragment"
     }
    },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([ 7, 11,  8,  5,  3, 12,  2,  6,  9, 10,  1,  4])"
-      ]
-     },
-     "execution_count": 120,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "vec"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 121,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "fragment"
-    }
-   },
    "outputs": [
     {
      "data": {
@@ -4176,7 +4139,7 @@
        "numpy.ndarray"
       ]
      },
-     "execution_count": 121,
+     "execution_count": 120,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -4193,14 +4156,14 @@
     }
    },
    "source": [
-    "[numpy](http://www.numpy.org/) somehow magically adds new behavior to Python's built-in arithmetic operators. For example, we may now [scalar-multiply](https://en.wikipedia.org/wiki/Scalar_multiplication) `vec`.\n",
+    "[numpy](http://www.numpy.org/) somehow magically adds new behavior to Python's built-in arithmetic operators. For example, we may now [scalar-multiply <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_wiki.png\">](https://en.wikipedia.org/wiki/Scalar_multiplication) `vec`.\n",
     "\n",
     "[numpy](http://www.numpy.org/)'s functions are implemented in highly optimized C code and, therefore, are fast, especially when dealing with bigger amounts of data."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 122,
+   "execution_count": 121,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -4213,7 +4176,7 @@
        "array([14, 22, 16, 10,  6, 24,  4, 12, 18, 20,  2,  8])"
       ]
      },
-     "execution_count": 122,
+     "execution_count": 121,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -4235,7 +4198,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 123,
+   "execution_count": 122,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -4248,7 +4211,7 @@
        "[7, 11, 8, 5, 3, 12, 2, 6, 9, 10, 1, 4, 7, 11, 8, 5, 3, 12, 2, 6, 9, 10, 1, 4]"
       ]
      },
-     "execution_count": 123,
+     "execution_count": 122,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -4265,7 +4228,31 @@
     }
    },
    "source": [
-    "[numpy](http://www.numpy.org/)'s `numpy.ndarray` objects integrate nicely with Python's built-in functions (e.g., [sum()](https://docs.python.org/3/library/functions.html#sum)) or functions from the [standard library](https://docs.python.org/3/library/index.html) (e.g., [random.choice()](https://docs.python.org/3/library/random.html#random.choice))."
+    "[numpy](http://www.numpy.org/)'s `numpy.ndarray` objects integrate nicely with Python's built-in functions (e.g., [sum() <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/functions.html#sum)) or functions from the [standard library <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/index.html) (e.g., [random.choice() <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/random.html#random.choice))."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 123,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "78"
+      ]
+     },
+     "execution_count": 123,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "sum(vec)"
    ]
   },
   {
@@ -4276,30 +4263,6 @@
      "slide_type": "fragment"
     }
    },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "78"
-      ]
-     },
-     "execution_count": 124,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "sum(vec)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 125,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "fragment"
-    }
-   },
    "outputs": [
     {
      "data": {
@@ -4307,7 +4270,7 @@
        "7"
       ]
      },
-     "execution_count": 125,
+     "execution_count": 124,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -4335,18 +4298,18 @@
     }
    },
    "source": [
-    "For sure, we can create local modules and packages. In the repository's main directory, there is a [*sample_module.py*](https://github.com/webartifex/intro-to-python/blob/master/sample_module.py) file that contains, among others, a function equivalent to the final version of `average_evens()`. To be realistic, this sample module is structured in a modular manner with several functions building on each other. It is best to skim over it *now* before reading on.\n",
+    "For sure, we can create local modules and packages. In the repository's main directory, there is a [*sample_module.py* <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_gh.png\">](https://github.com/webartifex/intro-to-python/blob/master/sample_module.py) file that contains, among others, a function equivalent to the final version of `average_evens()`. To be realistic, this sample module is structured in a modular manner with several functions building on each other. It is best to skim over it *now* before reading on.\n",
     "\n",
-    "To make code we put into a *.py* file available in our program, we import it as a module just as we did above with modules in the [standard library](https://docs.python.org/3/library/index.html) or third-party packages.\n",
+    "To make code we put into a *.py* file available in our program, we import it as a module just as we did above with modules in the [standard library <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/index.html) or third-party packages.\n",
     "\n",
-    "The *name* to be imported is the file's name except for the *.py* part. For this to work, the file's name *must* adhere to the *same* rules as hold for [variable names](https://docs.python.org/3/reference/lexical_analysis.html#identifiers) in general.\n",
+    "The *name* to be imported is the file's name except for the *.py* part. For this to work, the file's name *must* adhere to the *same* rules as hold for [variable names <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/reference/lexical_analysis.html#identifiers) in general.\n",
     "\n",
     "What happens during an import is as follows. When Python sees the `import sample_module` part, it first creates a *new* object of type `module` in memory. This is effectively an *empty* namespace. Then, it executes the imported file's code from top to bottom. Whatever variables are still defined at the end of this, are put into the module's namespace. Only if the file's code does *not* raise an error, will Python make a variable in our current location (i.e., `mod` here) reference the created `module` object. Otherwise, it is discarded. In essence, it is as if we copied and pasted the file's code in place of the import statement. If we import an already imported module again, Python is smart enough to avoid doing all this work all over and does nothing."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 126,
+   "execution_count": 125,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -4359,7 +4322,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 127,
+   "execution_count": 126,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -4372,7 +4335,7 @@
        "<module 'sample_module' from '/home/webartifex/repos/intro-to-python/sample_module.py'>"
       ]
      },
-     "execution_count": 127,
+     "execution_count": 126,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -4389,7 +4352,7 @@
     }
    },
    "source": [
-    "Disregarding the dunder-style attributes, `mod` defines the five attributes `_default_scalar`, `_scaled_average`, `average`, `average_evens`, and `average_odds`, which are exactly the ones we would expect from reading the [*sample_module.py*](https://github.com/webartifex/intro-to-python/blob/master/sample_module.py) file.\n",
+    "Disregarding the dunder-style attributes, `mod` defines the five attributes `_default_scalar`, `_scaled_average`, `average`, `average_evens`, and `average_odds`, which are exactly the ones we would expect from reading the [*sample_module.py* <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_gh.png\">](https://github.com/webartifex/intro-to-python/blob/master/sample_module.py) file.\n",
     "\n",
     "A convention when working with imported code is to *disregard* any attributes starting with an underscore `_`. These are considered **private** and constitute **implementation details** the author of the imported code might change in a future version of his software. We *must* not rely on them in any way.\n",
     "\n",
@@ -4398,7 +4361,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 128,
+   "execution_count": 127,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -4423,7 +4386,7 @@
        " 'average_odds']"
       ]
      },
-     "execution_count": 128,
+     "execution_count": 127,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -4445,7 +4408,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 129,
+   "execution_count": 128,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -4458,7 +4421,7 @@
        "<function sample_module.average_evens(numbers, *, scalar=1)>"
       ]
      },
-     "execution_count": 129,
+     "execution_count": 128,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -4469,7 +4432,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 130,
+   "execution_count": 129,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -4502,7 +4465,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 131,
+   "execution_count": 130,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -4515,7 +4478,7 @@
        "7.0"
       ]
      },
-     "execution_count": 131,
+     "execution_count": 130,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -4526,7 +4489,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 132,
+   "execution_count": 131,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -4539,7 +4502,7 @@
        "14.0"
       ]
      },
-     "execution_count": 132,
+     "execution_count": 131,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -4556,9 +4519,9 @@
     }
    },
    "source": [
-    "Packages are a generalization of modules, and we look at one in detail in Chapter 10. You may, however, already look at a [sample package](https://github.com/webartifex/intro-to-python/tree/master/sample_package) in the repository, which is nothing but a folder with *.py* files in it.\n",
+    "Packages are a generalization of modules, and we look at one in detail in Chapter 10. You may, however, already look at a [sample package <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_gh.png\">](https://github.com/webartifex/intro-to-python/tree/master/sample_package) in the repository, which is nothing but a folder with *.py* files in it.\n",
     "\n",
-    "As a further reading on modules and packages, we refer to the [official tutorial](https://docs.python.org/3/tutorial/modules.html)."
+    "As a further reading on modules and packages, we refer to the [official tutorial <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/tutorial/modules.html)."
    ]
   },
   {
@@ -4601,6 +4564,65 @@
     "\n",
     "Outside Jupyter notebooks, Python code is put into **modules** that are grouped in **packages**."
    ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "## Further Resources"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "A lecture-style **video presentation** of this chapter is integrated below (cf., the [video <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_yt.png\">](https://www.youtube.com/watch?v=j4Xn8QFysmc&list=PL-2JV1G3J10lQ2xokyQowcRJI5jjNfW7f) or the entire [playlist <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_yt.png\">](https://www.youtube.com/playlist?list=PL-2JV1G3J10lQ2xokyQowcRJI5jjNfW7f))."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 132,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/jpeg": 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+       "            width=\"60%\"\n",
+       "            height=\"300\"\n",
+       "            src=\"https://www.youtube.com/embed/j4Xn8QFysmc\"\n",
+       "            frameborder=\"0\"\n",
+       "            allowfullscreen\n",
+       "        ></iframe>\n",
+       "        "
+      ],
+      "text/plain": [
+       "<IPython.lib.display.YouTubeVideo at 0x7f7b72076f10>"
+      ]
+     },
+     "execution_count": 132,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from IPython.display import YouTubeVideo\n",
+    "YouTubeVideo(\"j4Xn8QFysmc\", width=\"60%\")"
+   ]
   }
  ],
  "metadata": {
diff --git a/02_functions_01_review.ipynb b/02_functions_01_review.ipynb
index 36a95ea..5f86d89 100644
--- a/02_functions_01_review.ipynb
+++ b/02_functions_01_review.ipynb
@@ -18,7 +18,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "The questions below assume that you have read [Chapter 2](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/02_functions_00_lecture.ipynb) in the book.\n",
+    "The questions below assume that you have read [Chapter 2 <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/02_functions_00_content.ipynb) in the book.\n",
     "\n",
     "Be concise in your answers! Most questions can be answered in *one* sentence."
    ]
@@ -188,7 +188,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "**Q11**: The [standard library](https://docs.python.org/3/library/index.html) is a collection of numerical tools often used in scientific computing, for example, advanced mathematical functions or utilities for simulation."
+    "**Q11**: The [standard library <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/index.html) is a collection of numerical tools often used in scientific computing, for example, advanced mathematical functions or utilities for simulation."
    ]
   },
   {
diff --git a/02_functions_02_exercises.ipynb b/02_functions_02_exercises.ipynb
index e749839..647ca8e 100644
--- a/02_functions_02_exercises.ipynb
+++ b/02_functions_02_exercises.ipynb
@@ -1,5 +1,12 @@
 {
  "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Important**: Click on \"*Kernel*\" > \"*Restart Kernel and Run All*\" *after* finishing the exercises in [JupyterLab <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_jp.png\">](https://jupyterlab.readthedocs.io/en/stable/) (e.g., in the cloud on [MyBinder <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_mb.png\">](https://mybinder.org/v2/gh/webartifex/intro-to-python/master?urlpath=lab/tree/02_functions_02_exercises.ipynb)) to ensure that your solution runs top to bottom *without* any errors"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -18,7 +25,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "The exercises below assume that you have read [Chapter 2](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/02_functions_00_lecture.ipynb) in the book.\n",
+    "The exercises below assume that you have read [Chapter 2 <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/02_functions_00_content.ipynb) in the book.\n",
     "\n",
     "The `...`'s in the code cells indicate where you need to fill in code snippets. The number of `...`'s within a code cell give you a rough idea of how many lines of code are needed to solve the task. You should not need to create any additional code cells for your final solution. However, you may want to use temporary code cells to try out some ideas."
    ]
@@ -34,7 +41,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "**Q1**: The [volume of a sphere](https://en.wikipedia.org/wiki/Sphere) is defined as $\\frac{4}{3} * \\pi * r^3$. Calculate this value for $r=10.0$ and round it to 10 digits after the comma. Use the [standard library](https://docs.python.org/3/library/index.html) to obtain a good approximation of $\\pi$."
+    "**Q1**: The [volume of a sphere <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_wiki.png\">](https://en.wikipedia.org/wiki/Sphere) is defined as $\\frac{4}{3} * \\pi * r^3$. Calculate this value for $r=10.0$ and round it to 10 digits after the comma. Use the [standard library <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/index.html) to obtain a good approximation of $\\pi$."
    ]
   },
   {
@@ -170,9 +177,9 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "**Q5**: Using the [range()](https://docs.python.org/3/library/functions.html#func-range) built-in, write a `for`-loop and calculate the volume of a sphere with `radius = 42.0` for all `digits` from `1` through `20`. Print out each volume on a separate line.\n",
+    "**Q5**: Using the [range() <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/functions.html#func-range) built-in, write a `for`-loop and calculate the volume of a sphere with `radius = 42.0` for all `digits` from `1` through `20`. Print out each volume on a separate line.\n",
     "\n",
-    "Note: This is the first task where you need to use the built-in [print()](https://docs.python.org/3/library/functions.html#print) function."
+    "Note: This is the first task where you need to use the built-in [print() <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/functions.html#print) function."
    ]
   },
   {
diff --git a/03_conditionals_00_lecture.ipynb b/03_conditionals_00_content.ipynb
similarity index 92%
rename from 03_conditionals_00_lecture.ipynb
rename to 03_conditionals_00_content.ipynb
index 61a1d3f..27bc436 100644
--- a/03_conditionals_00_lecture.ipynb
+++ b/03_conditionals_00_content.ipynb
@@ -8,44 +8,7 @@
     }
    },
    "source": [
-    "A **video presentation** of the contents in this chapter is shown below. A playlist with *all* chapters as videos is linked [here](https://www.youtube.com/playlist?list=PL-2JV1G3J10lQ2xokyQowcRJI5jjNfW7f)."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/jpeg": 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-       "            width=\"60%\"\n",
-       "            height=\"300\"\n",
-       "            src=\"https://www.youtube.com/embed/aDbblINzuGQ\"\n",
-       "            frameborder=\"0\"\n",
-       "            allowfullscreen\n",
-       "        ></iframe>\n",
-       "        "
-      ],
-      "text/plain": [
-       "<IPython.lib.display.YouTubeVideo at 0x7ff5d516f050>"
-      ]
-     },
-     "execution_count": 1,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from IPython.display import YouTubeVideo\n",
-    "YouTubeVideo(\"aDbblINzuGQ\", width=\"60%\")"
+    "**Important**: Click on \"*Kernel*\" > \"*Restart Kernel and Clear All Outputs*\" *before* reading this chapter in [JupyterLab <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_jp.png\">](https://jupyterlab.readthedocs.io/en/stable/) (e.g., in the cloud on [MyBinder <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_mb.png\">](https://mybinder.org/v2/gh/webartifex/intro-to-python/master?urlpath=lab/tree/03_conditionals_00_content.ipynb))"
    ]
   },
   {
@@ -67,7 +30,7 @@
     }
    },
    "source": [
-    "We analyzed every aspect of the `average_evens()` function in [Chapter 2](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/02_functions_00_lecture.ipynb) except for the `if`-related parts. While it does what we expect it to, there is a whole lot more to learn by taking it apart. In particular, the `if` may occur within both a **statement** or an **expression**, analogous as to how a noun in a natural language can be the subject of *or* an object in a sentence. What is common to both usages is that it leads to code being executed for *parts* of the input only. It is a way of controlling the **flow of execution** in a program.\n",
+    "We analyzed every aspect of the `average_evens()` function in [Chapter 2 <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/02_functions_00_content.ipynb) except for the `if`-related parts. While it does what we expect it to, there is a whole lot more to learn by taking it apart. In particular, the `if` may occur within both a **statement** or an **expression**, analogous as to how a noun in a natural language can be the subject of *or* an object in a sentence. What is common to both usages is that it leads to code being executed for *parts* of the input only. It is a way of controlling the **flow of execution** in a program.\n",
     "\n",
     "After deconstructing `if` in the first part of this chapter, we take a close look at a similar concept, namely handling **exceptions**."
    ]
@@ -91,14 +54,14 @@
     }
    },
    "source": [
-    "Any expression that is either true or not is called a **boolean expression**. It is such simple true-or-false observations about the world on which mathematicians, and originally philosophers, base their rules of reasoning: They are studied formally in the field of [propositional logic](https://en.wikipedia.org/wiki/Propositional_calculus).\n",
+    "Any expression that is either true or not is called a **boolean expression**. It is such simple true-or-false observations about the world on which mathematicians, and originally philosophers, base their rules of reasoning: They are studied formally in the field of [propositional logic <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_wiki.png\">](https://en.wikipedia.org/wiki/Propositional_calculus).\n",
     "\n",
     "A trivial example involves the equality operator `==` that evaluates to either `True` or `False` depending on its operands \"comparing equal\" or not."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 1,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -111,7 +74,7 @@
        "True"
       ]
      },
-     "execution_count": 2,
+     "execution_count": 1,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -122,7 +85,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 2,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -135,7 +98,7 @@
        "False"
       ]
      },
-     "execution_count": 3,
+     "execution_count": 2,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -157,7 +120,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 3,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -170,7 +133,7 @@
        "True"
       ]
      },
-     "execution_count": 4,
+     "execution_count": 3,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -187,12 +150,12 @@
     }
    },
    "source": [
-    "There are, however, cases where the `==` operator seems to not work intuitively. [Chapter 5](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/05_numbers_00_lecture.ipynb#Imprecision) provides more insights into this \"bug.\""
+    "There are, however, cases where the `==` operator seems to not work intuitively. [Chapter 5 <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/05_numbers_00_content.ipynb#Imprecision) provides more insights into this \"bug.\""
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 4,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -205,7 +168,7 @@
        "True"
       ]
      },
-     "execution_count": 5,
+     "execution_count": 4,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -238,7 +201,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 5,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -251,7 +214,7 @@
        "True"
       ]
      },
-     "execution_count": 6,
+     "execution_count": 5,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -260,6 +223,30 @@
     "True"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "94109522858624"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "id(True)"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 7,
@@ -268,30 +255,6 @@
      "slide_type": "fragment"
     }
    },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "94082696684160"
-      ]
-     },
-     "execution_count": 7,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "id(True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "fragment"
-    }
-   },
    "outputs": [
     {
      "data": {
@@ -299,7 +262,7 @@
        "bool"
       ]
      },
-     "execution_count": 8,
+     "execution_count": 7,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -310,7 +273,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 8,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -323,7 +286,7 @@
        "False"
       ]
      },
-     "execution_count": 9,
+     "execution_count": 8,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -332,6 +295,30 @@
     "False"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "94109522858592"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "id(False)"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 10,
@@ -340,30 +327,6 @@
      "slide_type": "fragment"
     }
    },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "94082696684128"
-      ]
-     },
-     "execution_count": 10,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "id(False)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "fragment"
-    }
-   },
    "outputs": [
     {
      "data": {
@@ -371,7 +334,7 @@
        "bool"
       ]
      },
-     "execution_count": 11,
+     "execution_count": 10,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -388,7 +351,7 @@
     }
    },
    "source": [
-    "Let's not confuse the boolean `False` with `None`, another built-in object! We saw the latter before in [Chapter 2](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/02_functions_00_lecture.ipynb#Function-Definitions) as the *implicit* return value of a function without a `return` statement.\n",
+    "Let's not confuse the boolean `False` with `None`, another built-in object! We saw the latter before in [Chapter 2 <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/02_functions_00_content.ipynb#Function-Definitions) as the *implicit* return value of a function without a `return` statement.\n",
     "\n",
     "We might think of `None` indicating a \"maybe\" or even an \"unknown\" answer; however, for Python, there are no \"maybe\" or \"unknown\" objects, as we see further below!\n",
     "\n",
@@ -397,7 +360,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 11,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -408,6 +371,30 @@
     "None"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "94109522845584"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "id(None)"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 13,
@@ -416,30 +403,6 @@
      "slide_type": "fragment"
     }
    },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "94082696671120"
-      ]
-     },
-     "execution_count": 13,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "id(None)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "fragment"
-    }
-   },
    "outputs": [
     {
      "data": {
@@ -447,7 +410,7 @@
        "NoneType"
       ]
      },
-     "execution_count": 14,
+     "execution_count": 13,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -475,14 +438,14 @@
     }
    },
    "source": [
-    "`True`, `False`, and `None` have the property that they each exist in memory only *once*. Objects designed this way are so-called **singletons**. This **[design pattern](https://en.wikipedia.org/wiki/Design_Patterns)** was originally developed to keep a program's memory usage at a minimum. It may only be employed in situations where we know that an object does *not* mutate its value (i.e., to reuse the bag analogy from [Chapter 1](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/01_elements_00_lecture.ipynb#Objects-vs.-Types-vs.-Values), no flipping of $0$s and $1$s in the bag is allowed). In languages \"closer\" to the memory like C, we would have to code this singleton logic ourselves, but Python has this built in for *some* types.\n",
+    "`True`, `False`, and `None` have the property that they each exist in memory only *once*. Objects designed this way are so-called **singletons**. This **[design pattern <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_wiki.png\">](https://en.wikipedia.org/wiki/Design_Patterns)** was originally developed to keep a program's memory usage at a minimum. It may only be employed in situations where we know that an object does *not* mutate its value (i.e., to reuse the bag analogy from [Chapter 1 <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/01_elements_00_content.ipynb#Objects-vs.-Types-vs.-Values), no flipping of $0$s and $1$s in the bag is allowed). In languages \"closer\" to the memory like C, we would have to code this singleton logic ourselves, but Python has this built in for *some* types.\n",
     "\n",
     "We verify this with either the `is` operator or by comparing memory addresses."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 14,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -495,7 +458,7 @@
        "True"
       ]
      },
-     "execution_count": 15,
+     "execution_count": 14,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -520,7 +483,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 15,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -533,7 +496,7 @@
        "False"
       ]
      },
-     "execution_count": 16,
+     "execution_count": 15,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -558,7 +521,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 16,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -571,7 +534,7 @@
        "[True, False, None, True, False, None]"
       ]
      },
-     "execution_count": 17,
+     "execution_count": 16,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -604,7 +567,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 17,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -617,7 +580,7 @@
        "False"
       ]
      },
-     "execution_count": 18,
+     "execution_count": 17,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -628,7 +591,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 18,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -641,7 +604,7 @@
        "True"
       ]
      },
-     "execution_count": 19,
+     "execution_count": 18,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -663,12 +626,36 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 19,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
     }
    },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "42 < 123"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
    "outputs": [
     {
      "data": {
@@ -682,7 +669,7 @@
     }
    ],
    "source": [
-    "42 < 123"
+    "42 <= 123"
    ]
   },
   {
@@ -697,7 +684,7 @@
     {
      "data": {
       "text/plain": [
-       "True"
+       "False"
       ]
      },
      "execution_count": 21,
@@ -706,7 +693,7 @@
     }
    ],
    "source": [
-    "42 <= 123"
+    "42 > 123"
    ]
   },
   {
@@ -729,30 +716,6 @@
      "output_type": "execute_result"
     }
    ],
-   "source": [
-    "42 > 123"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 23,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "fragment"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "False"
-      ]
-     },
-     "execution_count": 23,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
    "source": [
     "42 >= 123"
    ]
@@ -787,7 +750,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 23,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -807,12 +770,12 @@
     }
    },
    "source": [
-    "Relational operators have **[higher precedence](https://docs.python.org/3/reference/expressions.html#operator-precedence)** over logical operators. So the following expression means what we intuitively think it does."
+    "Relational operators have **[higher precedence <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/reference/expressions.html#operator-precedence)** over logical operators. So the following expression means what we intuitively think it does."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 24,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -825,7 +788,7 @@
        "True"
       ]
      },
-     "execution_count": 25,
+     "execution_count": 24,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -847,7 +810,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": 25,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -860,7 +823,7 @@
        "True"
       ]
      },
-     "execution_count": 26,
+     "execution_count": 25,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -882,12 +845,36 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": 26,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
     }
    },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 26,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "a <= 5 or not b > 100"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
    "outputs": [
     {
      "data": {
@@ -900,37 +887,13 @@
      "output_type": "execute_result"
     }
    ],
-   "source": [
-    "a <= 5 or not b > 100"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 28,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "fragment"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "True"
-      ]
-     },
-     "execution_count": 28,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
    "source": [
     "(a <= 5) or not (b > 100)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
+   "execution_count": 28,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -943,7 +906,7 @@
        "True"
       ]
      },
-     "execution_count": 29,
+     "execution_count": 28,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -960,14 +923,14 @@
     }
    },
    "source": [
-    "For even better readability, some practitioners suggest to *never* use the `>` and `>=` operators (cf., [source](https://llewellynfalco.blogspot.com/2016/02/dont-use-greater-than-sign-in.html); note that the included example is written in [Java](https://en.wikipedia.org/wiki/Java_%28programming_language%29) where `&&` means `and` and `||` means `or`).\n",
+    "For even better readability, some practitioners suggest to *never* use the `>` and `>=` operators (cf., [source](https://llewellynfalco.blogspot.com/2016/02/dont-use-greater-than-sign-in.html); note that the included example is written in [Java <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_wiki.png\">](https://en.wikipedia.org/wiki/Java_%28programming_language%29) where `&&` means `and` and `||` means `or`).\n",
     "\n",
     "We may **chain** operators if the expressions that contain them are combined with the `and` operator. For example, the following two cells implement the same logic, where the second is a lot easier to read."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 30,
+   "execution_count": 29,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -980,7 +943,7 @@
        "True"
       ]
      },
-     "execution_count": 30,
+     "execution_count": 29,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -991,7 +954,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 31,
+   "execution_count": 30,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -1004,7 +967,7 @@
        "True"
       ]
      },
-     "execution_count": 31,
+     "execution_count": 30,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1041,7 +1004,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 32,
+   "execution_count": 31,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -1054,7 +1017,7 @@
        "True"
       ]
      },
-     "execution_count": 32,
+     "execution_count": 31,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1071,7 +1034,31 @@
     }
    },
    "source": [
-    "Whenever we are unsure how Python evaluates a non-boolean expression in a boolean context, the [bool()](https://docs.python.org/3/library/functions.html#bool) built-in allows us to do it ourselves. [bool()](https://docs.python.org/3/library/functions.html#bool), like [int()](https://docs.python.org/3/library/functions.html#int), is yet another *constructor*."
+    "Whenever we are unsure how Python evaluates a non-boolean expression in a boolean context, the [bool() <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/functions.html#bool) built-in allows us to do it ourselves. [bool() <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/functions.html#bool), like [int() <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/functions.html#int), is yet another *constructor*."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 32,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "bool(a - 40)"
    ]
   },
   {
@@ -1082,30 +1069,6 @@
      "slide_type": "fragment"
     }
    },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "True"
-      ]
-     },
-     "execution_count": 33,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "bool(a - 40)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 34,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "fragment"
-    }
-   },
    "outputs": [
     {
      "data": {
@@ -1113,7 +1076,7 @@
        "False"
       ]
      },
-     "execution_count": 34,
+     "execution_count": 33,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1135,7 +1098,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 35,
+   "execution_count": 34,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -1148,7 +1111,7 @@
        "True"
       ]
      },
-     "execution_count": 35,
+     "execution_count": 34,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1168,6 +1131,41 @@
     "In a boolean context, `None` is cast as `False`! So, `None` is *not* a \"maybe\" answer but a \"no.\""
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "False"
+      ]
+     },
+     "execution_count": 35,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "bool(None)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Another good rule to know is that container types (e.g., `list`) evaluate to `False` whenever they are empty and `True` if they hold at least one element."
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 36,
@@ -1188,48 +1186,13 @@
      "output_type": "execute_result"
     }
    ],
-   "source": [
-    "bool(None)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "source": [
-    "Another good rule to know is that container types (e.g., `list`) evaluate to `False` whenever they are empty and `True` if they hold at least one element."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 37,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "slide"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "False"
-      ]
-     },
-     "execution_count": 37,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
    "source": [
     "bool([])"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 38,
+   "execution_count": 37,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -1242,7 +1205,7 @@
        "True"
       ]
      },
-     "execution_count": 38,
+     "execution_count": 37,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1264,7 +1227,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 39,
+   "execution_count": 38,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -1277,7 +1240,7 @@
        "False"
       ]
      },
-     "execution_count": 39,
+     "execution_count": 38,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1288,7 +1251,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 40,
+   "execution_count": 39,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -1301,7 +1264,7 @@
        "True"
       ]
      },
-     "execution_count": 40,
+     "execution_count": 39,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1340,7 +1303,7 @@
     }
    },
    "source": [
-    "When evaluating expressions involving the `and` and `or` operators, Python follows the **[short-circuiting](https://en.wikipedia.org/wiki/Short-circuit_evaluation)** strategy: Once it is clear what the overall truth value is, no more operands are evaluated, and the result is *immediately* returned.\n",
+    "When evaluating expressions involving the `and` and `or` operators, Python follows the **[short-circuiting <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_wiki.png\">](https://en.wikipedia.org/wiki/Short-circuit_evaluation)** strategy: Once it is clear what the overall truth value is, no more operands are evaluated, and the result is *immediately* returned.\n",
     "\n",
     "Also, if such expressions are evaluated in a non-boolean context, the result is returned as is and *not* cast as a `bool` type.\n",
     "\n",
@@ -1356,7 +1319,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 41,
+   "execution_count": 40,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -1383,7 +1346,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 42,
+   "execution_count": 41,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -1396,7 +1359,7 @@
        "1"
       ]
      },
-     "execution_count": 42,
+     "execution_count": 41,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1407,7 +1370,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 43,
+   "execution_count": 42,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -1422,6 +1385,30 @@
       "Arg: 1\n"
      ]
     },
+    {
+     "data": {
+      "text/plain": [
+       "1"
+      ]
+     },
+     "execution_count": 42,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "expr(0) or expr(1)  # both operands are evaluated"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
     {
      "data": {
       "text/plain": [
@@ -1433,37 +1420,13 @@
      "output_type": "execute_result"
     }
    ],
-   "source": [
-    "expr(0) or expr(1)  # both operands are evaluated"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 44,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "fragment"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "1"
-      ]
-     },
-     "execution_count": 44,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
    "source": [
     "1 or 2"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 45,
+   "execution_count": 44,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -1483,7 +1446,7 @@
        "1"
       ]
      },
-     "execution_count": 45,
+     "execution_count": 44,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1494,7 +1457,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 46,
+   "execution_count": 45,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -1507,7 +1470,7 @@
        "1"
       ]
      },
-     "execution_count": 46,
+     "execution_count": 45,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1518,7 +1481,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 47,
+   "execution_count": 46,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -1539,7 +1502,7 @@
        "1"
       ]
      },
-     "execution_count": 47,
+     "execution_count": 46,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1561,7 +1524,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 48,
+   "execution_count": 47,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -1574,7 +1537,7 @@
        "0"
       ]
      },
-     "execution_count": 48,
+     "execution_count": 47,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1585,7 +1548,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 49,
+   "execution_count": 48,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -1607,7 +1570,7 @@
        "0"
       ]
      },
-     "execution_count": 49,
+     "execution_count": 48,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1629,7 +1592,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 50,
+   "execution_count": 49,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -1642,7 +1605,7 @@
        "0"
       ]
      },
-     "execution_count": 50,
+     "execution_count": 49,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1653,7 +1616,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 51,
+   "execution_count": 50,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -1667,6 +1630,30 @@
       "Arg: 0\n"
      ]
     },
+    {
+     "data": {
+      "text/plain": [
+       "0"
+      ]
+     },
+     "execution_count": 50,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "expr(0) and expr(1)  # 1 is not evaluated"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 51,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
     {
      "data": {
       "text/plain": [
@@ -1679,7 +1666,7 @@
     }
    ],
    "source": [
-    "expr(0) and expr(1)  # 1 is not evaluated"
+    "1 and 0"
    ]
   },
   {
@@ -1687,10 +1674,18 @@
    "execution_count": 52,
    "metadata": {
     "slideshow": {
-     "slide_type": "fragment"
+     "slide_type": "skip"
     }
    },
    "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Arg: 1\n",
+      "Arg: 0\n"
+     ]
+    },
     {
      "data": {
       "text/plain": [
@@ -1702,45 +1697,13 @@
      "output_type": "execute_result"
     }
    ],
-   "source": [
-    "1 and 0"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 53,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Arg: 1\n",
-      "Arg: 0\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "0"
-      ]
-     },
-     "execution_count": 53,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
    "source": [
     "expr(1) and expr(0)  # both operands are evaluated"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 54,
+   "execution_count": 53,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -1753,7 +1716,7 @@
        "0"
       ]
      },
-     "execution_count": 54,
+     "execution_count": 53,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1764,7 +1727,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 55,
+   "execution_count": 54,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -1785,7 +1748,7 @@
        "0"
       ]
      },
-     "execution_count": 55,
+     "execution_count": 54,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1807,7 +1770,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 56,
+   "execution_count": 55,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -1820,7 +1783,7 @@
        "3"
       ]
      },
-     "execution_count": 56,
+     "execution_count": 55,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1831,7 +1794,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 57,
+   "execution_count": 56,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -1853,7 +1816,7 @@
        "3"
       ]
      },
-     "execution_count": 57,
+     "execution_count": 56,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1892,9 +1855,9 @@
     }
    },
    "source": [
-    "To write useful programs, we need to control the flow of execution, for example, to react to user input. The logic by which a program follows the rules from the \"real world\" is referred to as **[business logic](https://en.wikipedia.org/wiki/Business_logic)**.\n",
+    "To write useful programs, we need to control the flow of execution, for example, to react to user input. The logic by which a program follows the rules from the \"real world\" is referred to as **[business logic <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_wiki.png\">](https://en.wikipedia.org/wiki/Business_logic)**.\n",
     "\n",
-    "One language feature to do so is the `if` statement (cf., [reference](https://docs.python.org/3/reference/compound_stmts.html#the-if-statement)). It consists of:\n",
+    "One language feature to do so is the `if` statement (cf., [reference <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/reference/compound_stmts.html#the-if-statement)). It consists of:\n",
     "\n",
     "- *one* mandatory `if`-clause,\n",
     "- an *arbitrary* number of `elif`-clauses (i.e., \"else if\"), and\n",
@@ -1904,14 +1867,14 @@
     "\n",
     "In contrast to our intuitive interpretation in natural languages, only the code in *one* of the alternatives, also called **branches**, is executed. To be precise, it is always the code in the first clause whose condition evaluates to `True`.\n",
     "\n",
-    "In terms of syntax, the header lines end with a colon, and the code blocks are indented. Formally, any statement that is written across several lines is called a **[compound statement](https://docs.python.org/3/reference/compound_stmts.html#compound-statements)**, the code blocks are called **suites** and belong to one header line, and the term **clause** refers to a header line and its suite as a whole. So far, we have seen three compound statements: `for`, `if`, and `def`. On the contrary, **[simple statements](https://docs.python.org/3/reference/simple_stmts.html#simple-statements)**, for example, `=`, `del`, or `return`, are written on *one* line.\n",
+    "In terms of syntax, the header lines end with a colon, and the code blocks are indented. Formally, any statement that is written across several lines is called a **[compound statement <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/reference/compound_stmts.html#compound-statements)**, the code blocks are called **suites** and belong to one header line, and the term **clause** refers to a header line and its suite as a whole. So far, we have seen three compound statements: `for`, `if`, and `def`. On the contrary, **[simple statements <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/reference/simple_stmts.html#simple-statements)**, for example, `=`, `del`, or `return`, are written on *one* line.\n",
     "\n",
     "As an example, let's write code that checks if a randomly drawn number is divisible by `2`, `3`, both, or none. The code should print out a customized message for each of the *four* cases."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 58,
+   "execution_count": 57,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -1924,7 +1887,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 59,
+   "execution_count": 58,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -1937,7 +1900,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 60,
+   "execution_count": 59,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -1972,7 +1935,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 61,
+   "execution_count": 60,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -2024,7 +1987,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 62,
+   "execution_count": 61,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -2037,7 +2000,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 63,
+   "execution_count": 62,
    "metadata": {
     "code_folding": [],
     "slideshow": {
@@ -2090,7 +2053,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 64,
+   "execution_count": 63,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -2103,7 +2066,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 65,
+   "execution_count": 64,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -2157,7 +2120,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 66,
+   "execution_count": 65,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -2201,7 +2164,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 67,
+   "execution_count": 66,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -2214,7 +2177,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 68,
+   "execution_count": 67,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -2251,7 +2214,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 69,
+   "execution_count": 68,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -2264,7 +2227,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 70,
+   "execution_count": 69,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -2309,12 +2272,12 @@
    "source": [
     "We may **nest** `if` statements to control the flow of execution in a more granular way. Every additional layer, however, makes the code *less* readable, in particular, if we have more than one line per code block.\n",
     "\n",
-    "For example, the code cell below implements an [A/B Testing](https://en.wikipedia.org/wiki/A/B_testing) strategy where half the time a \"complex\" message is shown to a \"user\" while in the remaining times an \"easy\" message is shown. To do so, the code first \"tosses a coin\" and then checks a randomly drawn `number`."
+    "For example, the code cell below implements an [A/B Testing <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_wiki.png\">](https://en.wikipedia.org/wiki/A/B_testing) strategy where half the time a \"complex\" message is shown to a \"user\" while in the remaining times an \"easy\" message is shown. To do so, the code first \"tosses a coin\" and then checks a randomly drawn `number`."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 71,
+   "execution_count": 70,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -2327,7 +2290,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 72,
+   "execution_count": 71,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -2385,7 +2348,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 73,
+   "execution_count": 72,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -2398,7 +2361,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 74,
+   "execution_count": 73,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -2448,7 +2411,7 @@
     }
    },
    "source": [
-    "When an `if` statement assigns an object to a variable according to a true-or-false condition (i.e., a binary choice), there is a shortcut: We assign the variable the result of a so-called **[conditional expression](https://docs.python.org/3/reference/expressions.html#conditional-expressions)**, or `if` expression for short, instead.\n",
+    "When an `if` statement assigns an object to a variable according to a true-or-false condition (i.e., a binary choice), there is a shortcut: We assign the variable the result of a so-called **[conditional expression <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/reference/expressions.html#conditional-expressions)**, or `if` expression for short, instead.\n",
     "\n",
     "Think of a situation where we evaluate a piece-wise functional relationship $y = f(x)$ at a given $x$, for example:"
    ]
@@ -2483,7 +2446,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 75,
+   "execution_count": 74,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -2496,7 +2459,7 @@
        "3"
       ]
      },
-     "execution_count": 75,
+     "execution_count": 74,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2525,7 +2488,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 76,
+   "execution_count": 75,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -2538,7 +2501,7 @@
        "3"
       ]
      },
-     "execution_count": 76,
+     "execution_count": 75,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2559,12 +2522,12 @@
     }
    },
    "source": [
-    "In this example, however, the most elegant solution is to use the built-in [max()](https://docs.python.org/3/library/functions.html#max) function."
+    "In this example, however, the most elegant solution is to use the built-in [max() <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/functions.html#max) function."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 77,
+   "execution_count": 76,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -2577,7 +2540,7 @@
        "3"
       ]
      },
-     "execution_count": 77,
+     "execution_count": 76,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2616,7 +2579,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 78,
+   "execution_count": 77,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -2629,7 +2592,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 79,
+   "execution_count": 78,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -2643,7 +2606,7 @@
      "traceback": [
       "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
       "\u001b[0;31mZeroDivisionError\u001b[0m                         Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-79-7996bd5496ba>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0muser_input\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mrandom\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mchoice\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m3\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m4\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m5\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0;36m1\u001b[0m \u001b[0;34m/\u001b[0m \u001b[0muser_input\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;32m<ipython-input-78-7996bd5496ba>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0muser_input\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mrandom\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mchoice\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m3\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m4\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m5\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0;36m1\u001b[0m \u001b[0;34m/\u001b[0m \u001b[0muser_input\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
       "\u001b[0;31mZeroDivisionError\u001b[0m: division by zero"
      ]
     }
@@ -2662,14 +2625,14 @@
     }
    },
    "source": [
-    "With the compound `try` statement (cf., [reference](https://docs.python.org/3/reference/compound_stmts.html#the-try-statement)), we can **handle** any *runtime* error.\n",
+    "With the compound `try` statement (cf., [reference <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/reference/compound_stmts.html#the-try-statement)), we can **handle** any *runtime* error.\n",
     "\n",
     "In its simplest form, it comes with just two clauses: `try` and `except`. The following tells Python to execute the code in the `try`-clause, and if *anything* goes wrong, continue in the `except`-clause instead of **raising** an error to us. Of course, if nothing goes wrong, the `except`-clause is *not* executed."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 80,
+   "execution_count": 79,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -2682,7 +2645,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 81,
+   "execution_count": 80,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -2714,7 +2677,7 @@
     }
    },
    "source": [
-    "However, it is good practice *not* to handle *any* possible exception but only the ones we may *expect* from the code in the `try`-clause. The reason for that is that we do not want to risk *suppressing* an exception that we do *not* expect. Also, the code base becomes easier to understand as we communicate what could go wrong during execution in an *explicit* way to the (human) reader. Python comes with a lot of [built-in exceptions](https://docs.python.org/3/library/exceptions.html#concrete-exceptions) that we should familiarize ourselves with.\n",
+    "However, it is good practice *not* to handle *any* possible exception but only the ones we may *expect* from the code in the `try`-clause. The reason for that is that we do not want to risk *suppressing* an exception that we do *not* expect. Also, the code base becomes easier to understand as we communicate what could go wrong during execution in an *explicit* way to the (human) reader. Python comes with a lot of [built-in exceptions <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/exceptions.html#concrete-exceptions) that we should familiarize ourselves with.\n",
     "\n",
     "Another good practice is to always keep the code in the `try`-clause short to not *accidentally* handle an exception we do *not* want to handle.\n",
     "\n",
@@ -2723,7 +2686,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 82,
+   "execution_count": 81,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -2736,7 +2699,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 83,
+   "execution_count": 82,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -2777,7 +2740,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 84,
+   "execution_count": 83,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -2790,7 +2753,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 85,
+   "execution_count": 84,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -2845,6 +2808,66 @@
     "- a **conditional expression** is a short form of a conditional statement\n",
     "- **exception handling** is also a common way of **controlling** the **flow of execution**, in particular, if we have to be prepared for bad input data"
    ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "## Further Resources"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "A lecture-style **video presentation** of this chapter is integrated below (cf., the [video <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_yt.png\">](https://www.youtube.com/watch?v=aDbblINzuGQ&list=PL-2JV1G3J10lQ2xokyQowcRJI5jjNfW7f) or the entire [playlist <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_yt.png\">](https://www.youtube.com/playlist?list=PL-2JV1G3J10lQ2xokyQowcRJI5jjNfW7f))."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 85,
+   "metadata": {
+    "scrolled": true,
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/jpeg": 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+       "            width=\"60%\"\n",
+       "            height=\"300\"\n",
+       "            src=\"https://www.youtube.com/embed/aDbblINzuGQ\"\n",
+       "            frameborder=\"0\"\n",
+       "            allowfullscreen\n",
+       "        ></iframe>\n",
+       "        "
+      ],
+      "text/plain": [
+       "<IPython.lib.display.YouTubeVideo at 0x7fe4d01181d0>"
+      ]
+     },
+     "execution_count": 85,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from IPython.display import YouTubeVideo\n",
+    "YouTubeVideo(\"aDbblINzuGQ\", width=\"60%\")"
+   ]
   }
  ],
  "metadata": {
diff --git a/03_conditionals_01_review.ipynb b/03_conditionals_01_review.ipynb
index 5a5da9d..abe5db1 100644
--- a/03_conditionals_01_review.ipynb
+++ b/03_conditionals_01_review.ipynb
@@ -18,7 +18,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "The questions below assume that you have read [Chapter 3](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/03_conditionals_00_lecture.ipynb) in the book.\n",
+    "The questions below assume that you have read [Chapter 3 <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/03_conditionals_00_content.ipynb) in the book.\n",
     "\n",
     "Be concise in your answers! Most questions can be answered in *one* sentence."
    ]
diff --git a/03_conditionals_02_exercises.ipynb b/03_conditionals_02_exercises.ipynb
index 510b8cb..e71628d 100644
--- a/03_conditionals_02_exercises.ipynb
+++ b/03_conditionals_02_exercises.ipynb
@@ -1,5 +1,12 @@
 {
  "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Important**: Click on \"*Kernel*\" > \"*Restart Kernel and Run All*\" *after* finishing the exercises in [JupyterLab <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_jp.png\">](https://jupyterlab.readthedocs.io/en/stable/) (e.g., in the cloud on [MyBinder <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_mb.png\">](https://mybinder.org/v2/gh/webartifex/intro-to-python/master?urlpath=lab/tree/03_conditionals_02_exercises.ipynb)) to ensure that your solution runs top to bottom *without* any errors"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -18,7 +25,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "The exercises below assume that you have read [Chapter 3](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/03_conditionals_00_lecture.ipynb) in the book.\n",
+    "The exercises below assume that you have read [Chapter 3 <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/03_conditionals_00_content.ipynb) in the book.\n",
     "\n",
     "The `...`'s in the code cells indicate where you need to fill in code snippets. The number of `...`'s within a code cell give you a rough idea of how many lines of code are needed to solve the task. You should not need to create any additional code cells for your final solution. However, you may want to use temporary code cells to try out some ideas."
    ]
@@ -178,7 +185,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "The kids game [Fizz Buzz](https://en.wikipedia.org/wiki/Fizz_buzz) is said to be often used in job interviews for entry-level positions. However, opinions vary as to how good of a test it is (cf., [source](https://news.ycombinator.com/item?id=16446774)).\n",
+    "The kids game [Fizz Buzz <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_wiki.png\">](https://en.wikipedia.org/wiki/Fizz_buzz) is said to be often used in job interviews for entry-level positions. However, opinions vary as to how good of a test it is (cf., [source <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_hn.png\">](https://news.ycombinator.com/item?id=16446774)).\n",
     "\n",
     "In its simplest form, a group of people starts counting upwards in an alternating fashion. Whenever a number is divisible by $3$, the person must say \"Fizz\" instead of the number. The same holds for numbers divisible by $5$ when the person must say \"Buzz.\" If a number is divisible by both numbers, one must say \"FizzBuzz.\" Probably, this game would also make a good drinking game with the \"right\" beverages."
    ]
@@ -187,7 +194,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "**Q2.1**: First, create a list `numbers` with the numbers from 1 through 100. You could type all numbers manually, but there is, of course, a smarter way. The built-in [range()](https://docs.python.org/3/library/functions.html#func-range) may be useful here. Read how it works in the documentation. To make the output of [range()](https://docs.python.org/3/library/functions.html#func-range) a `list` object, you have to wrap it with the [list()](https://docs.python.org/3/library/functions.html#func-list) built-in (i.e., `list(range(...))`)."
+    "**Q2.1**: First, create a list `numbers` with the numbers from 1 through 100. You could type all numbers manually, but there is, of course, a smarter way. The built-in [range() <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/functions.html#func-range) may be useful here. Read how it works in the documentation. To make the output of [range() <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/functions.html#func-range) a `list` object, you have to wrap it with the [list() <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/functions.html#func-list) built-in (i.e., `list(range(...))`)."
    ]
   },
   {
@@ -205,7 +212,7 @@
    "source": [
     "**Q2.2**: Loop over the `numbers` list and *replace* numbers for which one of the two (or both) conditions apply with text strings `\"Fizz\"`, `\"Buzz\"`, or `\"FizzBuzz\"` using the indexing operator `[]` and the assignment statement `=`.\n",
     "\n",
-    "In [Chapter 1](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/01_elements_00_lecture.ipynb#Who-am-I?-And-how-many?), we saw that Python starts indexing with `0` as the first element. Keep that in mind.\n",
+    "In [Chapter 1 <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/01_elements_00_content.ipynb#Who-am-I?-And-how-many?), we saw that Python starts indexing with `0` as the first element. Keep that in mind.\n",
     "\n",
     "So in each iteration of the `for`-loop, you have to determine an `index` variable as well as check the actual `number` for its divisors.\n",
     "\n",
diff --git a/04_iteration_00_lecture.ipynb b/04_iteration_00_content.ipynb
similarity index 89%
rename from 04_iteration_00_lecture.ipynb
rename to 04_iteration_00_content.ipynb
index cba0152..89ceaba 100644
--- a/04_iteration_00_lecture.ipynb
+++ b/04_iteration_00_content.ipynb
@@ -8,44 +8,7 @@
     }
    },
    "source": [
-    "A **video presentation** of the contents in this chapter is shown below. A playlist with *all* chapters as videos is linked [here](https://www.youtube.com/playlist?list=PL-2JV1G3J10lQ2xokyQowcRJI5jjNfW7f)."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/jpeg": 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-       "            width=\"60%\"\n",
-       "            height=\"300\"\n",
-       "            src=\"https://www.youtube.com/embed/jT6hr4vOJks\"\n",
-       "            frameborder=\"0\"\n",
-       "            allowfullscreen\n",
-       "        ></iframe>\n",
-       "        "
-      ],
-      "text/plain": [
-       "<IPython.lib.display.YouTubeVideo at 0x7f432f422450>"
-      ]
-     },
-     "execution_count": 1,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from IPython.display import YouTubeVideo\n",
-    "YouTubeVideo(\"jT6hr4vOJks\", width=\"60%\")"
+    "**Important**: Click on \"*Kernel*\" > \"*Restart Kernel and Clear All Outputs*\" *before* reading this chapter in [JupyterLab <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_jp.png\">](https://jupyterlab.readthedocs.io/en/stable/) (e.g., in the cloud on [MyBinder <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_mb.png\">](https://mybinder.org/v2/gh/webartifex/intro-to-python/master?urlpath=lab/tree/04_iteration_00_content.ipynb))"
    ]
   },
   {
@@ -146,7 +109,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 1,
    "metadata": {
     "code_folding": [],
     "slideshow": {
@@ -170,7 +133,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 2,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -200,7 +163,7 @@
     }
    },
    "source": [
-    "As trivial as this seems, a lot of complexity is hidden in this implementation. In particular, the order in which objects are created and de-referenced in memory might not be apparent right away as [PythonTutor](http://pythontutor.com/visualize.html#code=def%20countdown%28n%29%3A%0A%20%20%20%20if%20n%20%3D%3D%200%3A%0A%20%20%20%20%20%20%20%20print%28%22Happy%20new%20Year!%22%29%0A%20%20%20%20else%3A%0A%20%20%20%20%20%20%20%20print%28n%29%0A%20%20%20%20%20%20%20%20countdown%28n%20-%201%29%0A%0Acountdown%283%29&cumulative=false&curInstr=0&heapPrimitives=nevernest&mode=display&origin=opt-frontend.js&py=3&rawInputLstJSON=%5B%5D&textReferences=false) shows: Each time `countdown()` is called, Python creates a *new* frame in the part of the memory where it manages all the names. This way, Python *isolates* all the different `n` variables from each other. As new frames are created until we reach the base case, after which the frames are destroyed in the *reversed* order, this is called a **[stack](https://en.wikipedia.org/wiki/Stack_(abstract_data_type))** of frames in computer science terminology. In simple words, a stack is a last-in-first-out (LIFO) task queue. Each frame has a single parent frame, namely the one whose recursive function call created it."
+    "As trivial as this seems, a lot of complexity is hidden in this implementation. In particular, the order in which objects are created and de-referenced in memory might not be apparent right away as [PythonTutor <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](http://pythontutor.com/visualize.html#code=def%20countdown%28n%29%3A%0A%20%20%20%20if%20n%20%3D%3D%200%3A%0A%20%20%20%20%20%20%20%20print%28%22Happy%20new%20Year!%22%29%0A%20%20%20%20else%3A%0A%20%20%20%20%20%20%20%20print%28n%29%0A%20%20%20%20%20%20%20%20countdown%28n%20-%201%29%0A%0Acountdown%283%29&cumulative=false&curInstr=0&heapPrimitives=nevernest&mode=display&origin=opt-frontend.js&py=3&rawInputLstJSON=%5B%5D&textReferences=false) shows: Each time `countdown()` is called, Python creates a *new* frame in the part of the memory where it manages all the names. This way, Python *isolates* all the different `n` variables from each other. As new frames are created until we reach the base case, after which the frames are destroyed in the *reversed* order, this is called a **[stack <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_wiki.png\">](https://en.wikipedia.org/wiki/Stack_(abstract_data_type))** of frames in computer science terminology. In simple words, a stack is a last-in-first-out (LIFO) task queue. Each frame has a single parent frame, namely the one whose recursive function call created it."
    ]
   },
   {
@@ -222,7 +185,7 @@
     }
    },
    "source": [
-    "Recursion plays a vital role in mathematics as well, and we likely know about it from some introductory course, for example, in [combinatorics](https://en.wikipedia.org/wiki/Combinatorics)."
+    "Recursion plays a vital role in mathematics as well, and we likely know about it from some introductory course, for example, in [combinatorics <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_wiki.png\">](https://en.wikipedia.org/wiki/Combinatorics)."
    ]
   },
   {
@@ -233,7 +196,7 @@
     }
    },
    "source": [
-    "#### Easy Example: [Factorial](https://en.wikipedia.org/wiki/Factorial)"
+    "#### Easy Example: [Factorial <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_wiki.png\">](https://en.wikipedia.org/wiki/Factorial)"
    ]
   },
   {
@@ -274,7 +237,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 3,
    "metadata": {
     "code_folding": [],
     "slideshow": {
@@ -308,14 +271,14 @@
     }
    },
    "source": [
-    "When we read such code, it is often easier not to follow every function call (i.e., `factorial(n - 1)` here) in one's mind but assume we receive a return value as specified in the documentation. Some call this approach a **[leap of faith](http://greenteapress.com/thinkpython2/html/thinkpython2007.html#sec75)**. We practice this already whenever we call built-in functions (e.g., [print()](https://docs.python.org/3/library/functions.html#print) or [len()](https://docs.python.org/3/library/functions.html#len)) where we would have to read C code in many cases.\n",
+    "When we read such code, it is often easier not to follow every function call (i.e., `factorial(n - 1)` here) in one's mind but assume we receive a return value as specified in the documentation. Some call this approach a **[leap of faith](http://greenteapress.com/thinkpython2/html/thinkpython2007.html#sec75)**. We practice this already whenever we call built-in functions (e.g., [print() <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/functions.html#print) or [len() <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/functions.html#len)) where we would have to read C code in many cases.\n",
     "\n",
-    "To visualize *all* the computational steps of the exemplary `factorial(3)`, we use [PythonTutor](http://pythontutor.com/visualize.html#code=def%20factorial%28n%29%3A%0A%20%20%20%20if%20n%20%3D%3D%200%3A%0A%20%20%20%20%20%20%20%20return%201%0A%20%20%20%20else%3A%0A%20%20%20%20%20%20%20%20recurse%20%3D%20factorial%28n%20-%201%29%0A%20%20%20%20%20%20%20%20result%20%3D%20n%20*%20recurse%0A%20%20%20%20%20%20%20%20return%20result%0A%0Asolution%20%3D%20factorial%283%29&cumulative=false&curInstr=0&heapPrimitives=nevernest&mode=display&origin=opt-frontend.js&py=3&rawInputLstJSON=%5B%5D&textReferences=false): The recursion again creates a stack of frames in memory. In contrast to the previous trivial example, each frame leaves a return value in memory after it is destroyed. This return value is then assigned to the `recurse` variable within the parent frame and used to compute `result`."
+    "To visualize *all* the computational steps of the exemplary `factorial(3)`, we use [PythonTutor <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](http://pythontutor.com/visualize.html#code=def%20factorial%28n%29%3A%0A%20%20%20%20if%20n%20%3D%3D%200%3A%0A%20%20%20%20%20%20%20%20return%201%0A%20%20%20%20else%3A%0A%20%20%20%20%20%20%20%20recurse%20%3D%20factorial%28n%20-%201%29%0A%20%20%20%20%20%20%20%20result%20%3D%20n%20*%20recurse%0A%20%20%20%20%20%20%20%20return%20result%0A%0Asolution%20%3D%20factorial%283%29&cumulative=false&curInstr=0&heapPrimitives=nevernest&mode=display&origin=opt-frontend.js&py=3&rawInputLstJSON=%5B%5D&textReferences=false): The recursion again creates a stack of frames in memory. In contrast to the previous trivial example, each frame leaves a return value in memory after it is destroyed. This return value is then assigned to the `recurse` variable within the parent frame and used to compute `result`."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 4,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -328,7 +291,7 @@
        "6"
       ]
      },
-     "execution_count": 5,
+     "execution_count": 4,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -339,7 +302,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 5,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -352,7 +315,7 @@
        "3628800"
       ]
      },
-     "execution_count": 6,
+     "execution_count": 5,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -371,12 +334,12 @@
    "source": [
     "A Pythonista would formulate `factorial()` in a more concise way using the so-called **early exit** pattern: No `else`-clause is needed as reaching a `return` statement ends a function call *immediately*. Furthermore, we do not need the temporary variables `recurse` and `result`.\n",
     "\n",
-    "As [PythonTutor](http://pythontutor.com/visualize.html#code=def%20factorial%28n%29%3A%0A%20%20%20%20if%20n%20%3D%3D%200%3A%0A%20%20%20%20%20%20%20%20return%201%0A%20%20%20%20return%20n%20*%20factorial%28n%20-%201%29%0A%0Asolution%20%3D%20factorial%283%29&cumulative=false&curInstr=0&heapPrimitives=nevernest&mode=display&origin=opt-frontend.js&py=3&rawInputLstJSON=%5B%5D&textReferences=false) shows, this implementation is more efficient as it only requires 18 computational steps instead of 24 to calculate `factorial(3)`, an improvement of 25 percent! "
+    "As [PythonTutor <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](http://pythontutor.com/visualize.html#code=def%20factorial%28n%29%3A%0A%20%20%20%20if%20n%20%3D%3D%200%3A%0A%20%20%20%20%20%20%20%20return%201%0A%20%20%20%20return%20n%20*%20factorial%28n%20-%201%29%0A%0Asolution%20%3D%20factorial%283%29&cumulative=false&curInstr=0&heapPrimitives=nevernest&mode=display&origin=opt-frontend.js&py=3&rawInputLstJSON=%5B%5D&textReferences=false) shows, this implementation is more efficient as it only requires 18 computational steps instead of 24 to calculate `factorial(3)`, an improvement of 25 percent! "
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 6,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -400,7 +363,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 7,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -413,7 +376,7 @@
        "6"
       ]
      },
-     "execution_count": 8,
+     "execution_count": 7,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -424,7 +387,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 8,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -437,7 +400,7 @@
        "3628800"
       ]
      },
-     "execution_count": 9,
+     "execution_count": 8,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -454,12 +417,12 @@
     }
    },
    "source": [
-    "Note that the [math](https://docs.python.org/3/library/math.html) module in the [standard library](https://docs.python.org/3/library/index.html) provides a [factorial()](https://docs.python.org/3/library/math.html#math.factorial) function as well, and we should, therefore, *never* implement it ourselves in a real codebase."
+    "Note that the [math <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/math.html) module in the [standard library <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/index.html) provides a [factorial() <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/math.html#math.factorial) function as well, and we should, therefore, *never* implement it ourselves in a real codebase."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 9,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -472,7 +435,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 10,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -499,7 +462,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 11,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -512,7 +475,7 @@
        "6"
       ]
      },
-     "execution_count": 12,
+     "execution_count": 11,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -523,7 +486,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 12,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -536,7 +499,7 @@
        "3628800"
       ]
      },
-     "execution_count": 13,
+     "execution_count": 12,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -553,7 +516,7 @@
     }
    },
    "source": [
-    "#### \"Involved\" Example: [Euclid's Algorithm](https://en.wikipedia.org/wiki/Euclidean_algorithm)"
+    "#### \"Involved\" Example: [Euclid's Algorithm <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_wiki.png\">](https://en.wikipedia.org/wiki/Euclidean_algorithm)"
    ]
   },
   {
@@ -569,7 +532,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 13,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -594,7 +557,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 14,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -607,7 +570,7 @@
        "4"
       ]
      },
-     "execution_count": 15,
+     "execution_count": 14,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -629,7 +592,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 15,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -642,7 +605,7 @@
        "9"
       ]
      },
-     "execution_count": 16,
+     "execution_count": 15,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -659,12 +622,12 @@
     }
    },
    "source": [
-    "As expected, for two [prime numbers](https://en.wikipedia.org/wiki/List_of_prime_numbers) the greatest common divisor is of course $1$."
+    "As expected, for two [prime numbers <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_wiki.png\">](https://en.wikipedia.org/wiki/List_of_prime_numbers) the greatest common divisor is of course $1$."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 16,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -677,7 +640,7 @@
        "1"
       ]
      },
-     "execution_count": 17,
+     "execution_count": 16,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -694,12 +657,12 @@
     }
    },
    "source": [
-    "The [math](https://docs.python.org/3/library/math.html) module in the [standard library](https://docs.python.org/3/library/index.html) provides a [gcd()](https://docs.python.org/3/library/math.html#math.gcd) function as well, and, therefore, we should again *never* implement it on our own."
+    "The [math <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/math.html) module in the [standard library <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/index.html) provides a [gcd() <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/math.html#math.gcd) function as well, and, therefore, we should again *never* implement it on our own."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 17,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -724,7 +687,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 18,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -737,7 +700,7 @@
        "4"
       ]
      },
-     "execution_count": 19,
+     "execution_count": 18,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -746,6 +709,30 @@
     "math.gcd(12, 4)"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "9"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "math.gcd(112233445566778899, 987654321)"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 20,
@@ -754,30 +741,6 @@
      "slide_type": "skip"
     }
    },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "9"
-      ]
-     },
-     "execution_count": 20,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "math.gcd(112233445566778899, 987654321)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
    "outputs": [
     {
      "data": {
@@ -785,7 +748,7 @@
        "1"
       ]
      },
-     "execution_count": 21,
+     "execution_count": 20,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -802,7 +765,7 @@
     }
    },
    "source": [
-    "#### \"Easy at first Glance\" Example: [Fibonacci Numbers](https://en.wikipedia.org/wiki/Fibonacci_number)"
+    "#### \"Easy at first Glance\" Example: [Fibonacci Numbers <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_wiki.png\">](https://en.wikipedia.org/wiki/Fibonacci_number)"
    ]
   },
   {
@@ -840,7 +803,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 21,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -866,7 +829,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 22,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -879,7 +842,7 @@
        "144"
       ]
      },
-     "execution_count": 23,
+     "execution_count": 22,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -907,18 +870,18 @@
     }
    },
    "source": [
-    "This implementation is *highly* **inefficient** as small Fibonacci numbers already take a very long time to compute. The reason for this is **exponential growth** in the number of function calls. As [PythonTutor](http://pythontutor.com/visualize.html#code=def%20fibonacci%28i%29%3A%0A%20%20%20%20if%20i%20%3D%3D%200%3A%0A%20%20%20%20%20%20%20%20return%200%0A%20%20%20%20elif%20i%20%3D%3D%201%3A%0A%20%20%20%20%20%20%20%20return%201%0A%20%20%20%20return%20fibonacci%28i%20-%201%29%20%2B%20fibonacci%28i%20-%202%29%0A%0Arv%20%3D%20fibonacci%285%29&cumulative=false&curInstr=0&heapPrimitives=nevernest&mode=display&origin=opt-frontend.js&py=3&rawInputLstJSON=%5B%5D&textReferences=false) shows, `fibonacci()` is called again and again with the same `i` arguments.\n",
+    "This implementation is *highly* **inefficient** as small Fibonacci numbers already take a very long time to compute. The reason for this is **exponential growth** in the number of function calls. As [PythonTutor <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](http://pythontutor.com/visualize.html#code=def%20fibonacci%28i%29%3A%0A%20%20%20%20if%20i%20%3D%3D%200%3A%0A%20%20%20%20%20%20%20%20return%200%0A%20%20%20%20elif%20i%20%3D%3D%201%3A%0A%20%20%20%20%20%20%20%20return%201%0A%20%20%20%20return%20fibonacci%28i%20-%201%29%20%2B%20fibonacci%28i%20-%202%29%0A%0Arv%20%3D%20fibonacci%285%29&cumulative=false&curInstr=0&heapPrimitives=nevernest&mode=display&origin=opt-frontend.js&py=3&rawInputLstJSON=%5B%5D&textReferences=false) shows, `fibonacci()` is called again and again with the same `i` arguments.\n",
     "\n",
-    "To understand this in detail, we have to study algorithms and data structures (e.g., with [this book](https://www.amazon.de/Introduction-Algorithms-Press-Thomas-Cormen/dp/0262033844/ref=sr_1_1?__mk_de_DE=%C3%85M%C3%85%C5%BD%C3%95%C3%91&crid=1JNE8U0VZGU0O&qid=1569837169&s=gateway&sprefix=algorithms+an%2Caps%2C180&sr=8-1)), a discipline within computer science, and dive into the analysis of **[time complexity of algorithms](https://en.wikipedia.org/wiki/Time_complexity)**.\n",
+    "To understand this in detail, we have to study algorithms and data structures (e.g., with [this book](https://www.amazon.de/Introduction-Algorithms-Press-Thomas-Cormen/dp/0262033844/ref=sr_1_1?__mk_de_DE=%C3%85M%C3%85%C5%BD%C3%95%C3%91&crid=1JNE8U0VZGU0O&qid=1569837169&s=gateway&sprefix=algorithms+an%2Caps%2C180&sr=8-1)), a discipline within computer science, and dive into the analysis of **[time complexity of algorithms <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_wiki.png\">](https://en.wikipedia.org/wiki/Time_complexity)**.\n",
     "\n",
-    "Luckily, in the Fibonacci case, the inefficiency can be resolved with a **caching** (i.e., \"reuse\") strategy from the field of **[dynamic programming](https://en.wikipedia.org/wiki/Dynamic_programming)**, namely **[memoization](https://en.wikipedia.org/wiki/Memoization)**. We do so in [Chapter 9](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/09_mappings_00_lecture.ipynb#Memoization), after introducing the `dict` data type.\n",
+    "Luckily, in the Fibonacci case, the inefficiency can be resolved with a **caching** (i.e., \"reuse\") strategy from the field of **[dynamic programming <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_wiki.png\">](https://en.wikipedia.org/wiki/Dynamic_programming)**, namely **[memoization <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_wiki.png\">](https://en.wikipedia.org/wiki/Memoization)**. We do so in [Chapter 9 <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/09_mappings_00_content.ipynb#Memoization), after introducing the `dict` data type.\n",
     "\n",
     "Let's measure the average run times for `fibonacci()` and varying `i` arguments with the `%%timeit` [cell magic](https://ipython.readthedocs.io/en/stable/interactive/magics.html#magic-timeit) that comes with Jupyter."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 23,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -929,7 +892,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "70.9 µs ± 22.5 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n"
+      "36.9 µs ± 2.06 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n"
      ]
     }
    ],
@@ -938,6 +901,28 @@
     "fibonacci(12)"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "11.2 ms ± 52.4 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n"
+     ]
+    }
+   ],
+   "source": [
+    "%%timeit -n 100\n",
+    "fibonacci(24)"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 25,
@@ -951,29 +936,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "11.3 ms ± 31.9 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n"
-     ]
-    }
-   ],
-   "source": [
-    "%%timeit -n 100\n",
-    "fibonacci(24)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 26,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "fragment"
-    }
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "3.65 s ± 0 ns per loop (mean ± std. dev. of 1 run, 1 loop each)\n"
+      "3.67 s ± 0 ns per loop (mean ± std. dev. of 1 run, 1 loop each)\n"
      ]
     }
    ],
@@ -984,7 +947,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": 26,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -995,7 +958,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "5.9 s ± 0 ns per loop (mean ± std. dev. of 1 run, 1 loop each)\n"
+      "5.86 s ± 0 ns per loop (mean ± std. dev. of 1 run, 1 loop each)\n"
      ]
     }
    ],
@@ -1030,7 +993,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": 27,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -1045,7 +1008,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
+   "execution_count": 28,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -1059,10 +1022,10 @@
      "traceback": [
       "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
       "\u001b[0;31mRecursionError\u001b[0m                            Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-29-62f686a9f8a1>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mrun_forever\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
-      "\u001b[0;32m<ipython-input-28-80575699c62c>\u001b[0m in \u001b[0;36mrun_forever\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mrun_forever\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m     \u001b[0;34m\"\"\"Also a pointless function should have a docstring.\"\"\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m     \u001b[0mrun_forever\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;32m<ipython-input-28-62f686a9f8a1>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mrun_forever\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;32m<ipython-input-27-80575699c62c>\u001b[0m in \u001b[0;36mrun_forever\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mrun_forever\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m     \u001b[0;34m\"\"\"Also a pointless function should have a docstring.\"\"\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m     \u001b[0mrun_forever\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
       "... last 1 frames repeated, from the frame below ...\n",
-      "\u001b[0;32m<ipython-input-28-80575699c62c>\u001b[0m in \u001b[0;36mrun_forever\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mrun_forever\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m     \u001b[0;34m\"\"\"Also a pointless function should have a docstring.\"\"\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m     \u001b[0mrun_forever\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;32m<ipython-input-27-80575699c62c>\u001b[0m in \u001b[0;36mrun_forever\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mrun_forever\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m     \u001b[0;34m\"\"\"Also a pointless function should have a docstring.\"\"\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m     \u001b[0mrun_forever\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
       "\u001b[0;31mRecursionError\u001b[0m: maximum recursion depth exceeded"
      ]
     }
@@ -1084,7 +1047,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 30,
+   "execution_count": 29,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -4058,10 +4021,10 @@
      "traceback": [
       "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
       "\u001b[0;31mRecursionError\u001b[0m                            Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-30-43e3ad331761>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mcountdown\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m3.1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
-      "\u001b[0;32m<ipython-input-2-10499518927d>\u001b[0m in \u001b[0;36mcountdown\u001b[0;34m(n)\u001b[0m\n\u001b[1;32m      9\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     10\u001b[0m         \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mn\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 11\u001b[0;31m         \u001b[0mcountdown\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mn\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;32m<ipython-input-29-43e3ad331761>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mcountdown\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m3.1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;32m<ipython-input-1-10499518927d>\u001b[0m in \u001b[0;36mcountdown\u001b[0;34m(n)\u001b[0m\n\u001b[1;32m      9\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     10\u001b[0m         \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mn\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 11\u001b[0;31m         \u001b[0mcountdown\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mn\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
       "... last 1 frames repeated, from the frame below ...\n",
-      "\u001b[0;32m<ipython-input-2-10499518927d>\u001b[0m in \u001b[0;36mcountdown\u001b[0;34m(n)\u001b[0m\n\u001b[1;32m      9\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     10\u001b[0m         \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mn\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 11\u001b[0;31m         \u001b[0mcountdown\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mn\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;32m<ipython-input-1-10499518927d>\u001b[0m in \u001b[0;36mcountdown\u001b[0;34m(n)\u001b[0m\n\u001b[1;32m      9\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     10\u001b[0m         \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mn\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 11\u001b[0;31m         \u001b[0mcountdown\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mn\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
       "\u001b[0;31mRecursionError\u001b[0m: maximum recursion depth exceeded while calling a Python object"
      ]
     }
@@ -4083,7 +4046,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 31,
+   "execution_count": 30,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -4097,10 +4060,10 @@
      "traceback": [
       "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
       "\u001b[0;31mRecursionError\u001b[0m                            Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-31-188b816be8b6>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mfactorial\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m3.1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
-      "\u001b[0;32m<ipython-input-7-88d8d933f651>\u001b[0m in \u001b[0;36mfactorial\u001b[0;34m(n)\u001b[0m\n\u001b[1;32m     10\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mn\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     11\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 12\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0mn\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0mfactorial\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mn\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;32m<ipython-input-30-188b816be8b6>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mfactorial\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m3.1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;32m<ipython-input-6-88d8d933f651>\u001b[0m in \u001b[0;36mfactorial\u001b[0;34m(n)\u001b[0m\n\u001b[1;32m     10\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mn\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     11\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 12\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0mn\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0mfactorial\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mn\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
       "... last 1 frames repeated, from the frame below ...\n",
-      "\u001b[0;32m<ipython-input-7-88d8d933f651>\u001b[0m in \u001b[0;36mfactorial\u001b[0;34m(n)\u001b[0m\n\u001b[1;32m     10\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mn\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     11\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 12\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0mn\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0mfactorial\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mn\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;32m<ipython-input-6-88d8d933f651>\u001b[0m in \u001b[0;36mfactorial\u001b[0;34m(n)\u001b[0m\n\u001b[1;32m     10\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mn\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     11\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 12\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0mn\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0mfactorial\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mn\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
       "\u001b[0;31mRecursionError\u001b[0m: maximum recursion depth exceeded in comparison"
      ]
     }
@@ -4143,14 +4106,14 @@
    "source": [
     "The missing type checking is *100% intentional* and considered a **[feature of rather than a bug](https://www.urbandictionary.com/define.php?term=It%27s%20not%20a%20bug%2C%20it%27s%20a%20feature)** in Python!\n",
     "\n",
-    "Pythonistas use the \"technical\" term **[duck typing](https://en.wikipedia.org/wiki/Duck_typing)** to express the idea of two objects of *different* types behaving in the *same* way in a given context. The colloquial saying goes, \"If it walks like a duck and it quacks like a duck, it must be a duck.\"\n",
+    "Pythonistas use the \"technical\" term **[duck typing <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_wiki.png\">](https://en.wikipedia.org/wiki/Duck_typing)** to express the idea of two objects of *different* types behaving in the *same* way in a given context. The colloquial saying goes, \"If it walks like a duck and it quacks like a duck, it must be a duck.\"\n",
     "\n",
     "For example, we could call `factorial()` with the `float` object `3.0`, and the recursion works out fine. So, because the `3.0` \"walks\" and \"quacks\" like a `3`, it \"must be\" a `3`."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 32,
+   "execution_count": 31,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -4163,7 +4126,7 @@
        "6.0"
       ]
      },
-     "execution_count": 32,
+     "execution_count": 31,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -4174,7 +4137,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 33,
+   "execution_count": 32,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -4187,7 +4150,7 @@
        "6"
       ]
      },
-     "execution_count": 33,
+     "execution_count": 32,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -4207,6 +4170,30 @@
     "We see similar behavior when we mix objects of types `int` and `float` with arithmetic operators. For example, `1 + 2.0` works because Python implicitly views the `1` as a `1.0` at runtime and then knows how to do floating-point arithmetic: Here, the `int` \"walks\" and \"quacks\" like a `float`."
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "3.0"
+      ]
+     },
+     "execution_count": 33,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "1 + 2.0"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 34,
@@ -4227,30 +4214,6 @@
      "output_type": "execute_result"
     }
    ],
-   "source": [
-    "1 + 2.0"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 35,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "fragment"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "3.0"
-      ]
-     },
-     "execution_count": 35,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
    "source": [
     "1.0 + 2.0"
    ]
@@ -4289,9 +4252,9 @@
     }
    },
    "source": [
-    "We use the built-in [isinstance()](https://docs.python.org/3/library/functions.html#isinstance) function to make sure `factorial()` is called with an `int` object as the argument. We further **validate** the **input** by verifying that the integer is non-negative.\n",
+    "We use the built-in [isinstance() <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/functions.html#isinstance) function to make sure `factorial()` is called with an `int` object as the argument. We further **validate** the **input** by verifying that the integer is non-negative.\n",
     "\n",
-    "Meanwhile, we also see how we manually raise exceptions with the `raise` statement (cf., [reference](https://docs.python.org/3/reference/simple_stmts.html#the-raise-statement)), another way of controlling the flow of execution.\n",
+    "Meanwhile, we also see how we manually raise exceptions with the `raise` statement (cf., [reference <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/reference/simple_stmts.html#the-raise-statement)), another way of controlling the flow of execution.\n",
     "\n",
     "The first two branches in the revised `factorial()` function act as **guardians** ensuring that the code does not produce *unexpected* runtime errors: Errors may be expected when mentioned in the docstring.\n",
     "\n",
@@ -4300,7 +4263,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 36,
+   "execution_count": 35,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -4343,7 +4306,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 37,
+   "execution_count": 36,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -4356,7 +4319,7 @@
        "1"
       ]
      },
-     "execution_count": 37,
+     "execution_count": 36,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -4367,7 +4330,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 38,
+   "execution_count": 37,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -4380,7 +4343,7 @@
        "6"
       ]
      },
-     "execution_count": 38,
+     "execution_count": 37,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -4402,7 +4365,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 39,
+   "execution_count": 38,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -4416,8 +4379,8 @@
      "traceback": [
       "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
       "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-39-188b816be8b6>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mfactorial\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m3.1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
-      "\u001b[0;32m<ipython-input-36-54b57bc2107d>\u001b[0m in \u001b[0;36mfactorial\u001b[0;34m(n)\u001b[0m\n\u001b[1;32m     13\u001b[0m     \"\"\"\n\u001b[1;32m     14\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mn\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mint\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 15\u001b[0;31m         \u001b[0;32mraise\u001b[0m \u001b[0mTypeError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Factorial is only defined for integers\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     16\u001b[0m     \u001b[0;32melif\u001b[0m \u001b[0mn\u001b[0m \u001b[0;34m<\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     17\u001b[0m         \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Factorial is not defined for negative integers\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m<ipython-input-38-188b816be8b6>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mfactorial\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m3.1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;32m<ipython-input-35-54b57bc2107d>\u001b[0m in \u001b[0;36mfactorial\u001b[0;34m(n)\u001b[0m\n\u001b[1;32m     13\u001b[0m     \"\"\"\n\u001b[1;32m     14\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mn\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mint\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 15\u001b[0;31m         \u001b[0;32mraise\u001b[0m \u001b[0mTypeError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Factorial is only defined for integers\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     16\u001b[0m     \u001b[0;32melif\u001b[0m \u001b[0mn\u001b[0m \u001b[0;34m<\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     17\u001b[0m         \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Factorial is not defined for negative integers\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
       "\u001b[0;31mTypeError\u001b[0m: Factorial is only defined for integers"
      ]
     }
@@ -4428,7 +4391,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 40,
+   "execution_count": 39,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -4442,8 +4405,8 @@
      "traceback": [
       "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
       "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-40-b06b2cada645>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mfactorial\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m42\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
-      "\u001b[0;32m<ipython-input-36-54b57bc2107d>\u001b[0m in \u001b[0;36mfactorial\u001b[0;34m(n)\u001b[0m\n\u001b[1;32m     15\u001b[0m         \u001b[0;32mraise\u001b[0m \u001b[0mTypeError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Factorial is only defined for integers\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     16\u001b[0m     \u001b[0;32melif\u001b[0m \u001b[0mn\u001b[0m \u001b[0;34m<\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 17\u001b[0;31m         \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Factorial is not defined for negative integers\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     18\u001b[0m     \u001b[0;32melif\u001b[0m \u001b[0mn\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     19\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m<ipython-input-39-b06b2cada645>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mfactorial\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m42\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;32m<ipython-input-35-54b57bc2107d>\u001b[0m in \u001b[0;36mfactorial\u001b[0;34m(n)\u001b[0m\n\u001b[1;32m     15\u001b[0m         \u001b[0;32mraise\u001b[0m \u001b[0mTypeError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Factorial is only defined for integers\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     16\u001b[0m     \u001b[0;32melif\u001b[0m \u001b[0mn\u001b[0m \u001b[0;34m<\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 17\u001b[0;31m         \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Factorial is not defined for negative integers\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     18\u001b[0m     \u001b[0;32melif\u001b[0m \u001b[0mn\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     19\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
       "\u001b[0;31mValueError\u001b[0m: Factorial is not defined for negative integers"
      ]
     }
@@ -4465,7 +4428,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 41,
+   "execution_count": 40,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -4479,8 +4442,8 @@
      "traceback": [
       "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
       "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-41-32f1a9e8cf49>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mfactorial\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m3.0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
-      "\u001b[0;32m<ipython-input-36-54b57bc2107d>\u001b[0m in \u001b[0;36mfactorial\u001b[0;34m(n)\u001b[0m\n\u001b[1;32m     13\u001b[0m     \"\"\"\n\u001b[1;32m     14\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mn\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mint\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 15\u001b[0;31m         \u001b[0;32mraise\u001b[0m \u001b[0mTypeError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Factorial is only defined for integers\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     16\u001b[0m     \u001b[0;32melif\u001b[0m \u001b[0mn\u001b[0m \u001b[0;34m<\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     17\u001b[0m         \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Factorial is not defined for negative integers\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m<ipython-input-40-32f1a9e8cf49>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mfactorial\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m3.0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;32m<ipython-input-35-54b57bc2107d>\u001b[0m in \u001b[0;36mfactorial\u001b[0;34m(n)\u001b[0m\n\u001b[1;32m     13\u001b[0m     \"\"\"\n\u001b[1;32m     14\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mn\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mint\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 15\u001b[0;31m         \u001b[0;32mraise\u001b[0m \u001b[0mTypeError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Factorial is only defined for integers\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     16\u001b[0m     \u001b[0;32melif\u001b[0m \u001b[0mn\u001b[0m \u001b[0;34m<\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     17\u001b[0m         \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Factorial is not defined for negative integers\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
       "\u001b[0;31mTypeError\u001b[0m: Factorial is only defined for integers"
      ]
     }
@@ -4508,12 +4471,12 @@
     }
    },
    "source": [
-    "A similar way to prevent an infinite recursion is to **cast** the **type** of the `n` argument with the built-in [int()](https://docs.python.org/3/library/functions.html#int) constructor."
+    "A similar way to prevent an infinite recursion is to **cast** the **type** of the `n` argument with the built-in [int() <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/functions.html#int) constructor."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 42,
+   "execution_count": 41,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -4555,12 +4518,36 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 43,
+   "execution_count": 42,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
     }
    },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "6"
+      ]
+     },
+     "execution_count": 42,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "factorial(3)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
    "outputs": [
     {
      "data": {
@@ -4574,7 +4561,18 @@
     }
    ],
    "source": [
-    "factorial(3)"
+    "factorial(3.0)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "However, if we now call `factorial()` with a non-integer `float` object like `3.1`, *no* error is raised. This is a potential source for *semantic* errors as the function runs for invalid input."
    ]
   },
   {
@@ -4597,41 +4595,6 @@
      "output_type": "execute_result"
     }
    ],
-   "source": [
-    "factorial(3.0)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "source": [
-    "However, if we now call `factorial()` with a non-integer `float` object like `3.1`, *no* error is raised. This is a potential source for *semantic* errors as the function runs for invalid input."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 45,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "fragment"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "6"
-      ]
-     },
-     "execution_count": 45,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
    "source": [
     "factorial(3.1)"
    ]
@@ -4649,7 +4612,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 46,
+   "execution_count": 45,
    "metadata": {
     "code_folding": [],
     "slideshow": {
@@ -4684,7 +4647,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 47,
+   "execution_count": 46,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -4697,7 +4660,7 @@
        "6"
       ]
      },
-     "execution_count": 47,
+     "execution_count": 46,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -4708,7 +4671,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 48,
+   "execution_count": 47,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -4722,8 +4685,8 @@
      "traceback": [
       "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
       "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-48-188b816be8b6>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mfactorial\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m3.1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
-      "\u001b[0;32m<ipython-input-46-07dc981a3422>\u001b[0m in \u001b[0;36mfactorial\u001b[0;34m(n)\u001b[0m\n\u001b[1;32m     13\u001b[0m     \"\"\"\n\u001b[1;32m     14\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mn\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0mint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mn\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 15\u001b[0;31m         \u001b[0;32mraise\u001b[0m \u001b[0mTypeError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"n is not integer-like; it has non-zero decimals\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     16\u001b[0m     \u001b[0mn\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mn\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     17\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m<ipython-input-47-188b816be8b6>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mfactorial\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m3.1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;32m<ipython-input-45-07dc981a3422>\u001b[0m in \u001b[0;36mfactorial\u001b[0;34m(n)\u001b[0m\n\u001b[1;32m     13\u001b[0m     \"\"\"\n\u001b[1;32m     14\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mn\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0mint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mn\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 15\u001b[0;31m         \u001b[0;32mraise\u001b[0m \u001b[0mTypeError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"n is not integer-like; it has non-zero decimals\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     16\u001b[0m     \u001b[0mn\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mn\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     17\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
       "\u001b[0;31mTypeError\u001b[0m: n is not integer-like; it has non-zero decimals"
      ]
     }
@@ -4740,12 +4703,12 @@
     }
    },
    "source": [
-    "However, using built-in constructors for type casting leads to another subtle inconsistency. As constructors are designed to take *any* object as their argument, they do not raise a `TypeError` when called with invalid input but a `ValueError` instead. So, if we, for example, called `factorial()` with `\"text\"` as the `n` argument, we see the `ValueError` raised by [int()](https://docs.python.org/3/library/functions.html#int) in a situation where a `TypeError` would be more appropriate."
+    "However, using built-in constructors for type casting leads to another subtle inconsistency. As constructors are designed to take *any* object as their argument, they do not raise a `TypeError` when called with invalid input but a `ValueError` instead. So, if we, for example, called `factorial()` with `\"text\"` as the `n` argument, we see the `ValueError` raised by [int() <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/functions.html#int) in a situation where a `TypeError` would be more appropriate."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 49,
+   "execution_count": 48,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -4759,8 +4722,8 @@
      "traceback": [
       "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
       "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-49-9a12eb120fc3>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mfactorial\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"text\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
-      "\u001b[0;32m<ipython-input-46-07dc981a3422>\u001b[0m in \u001b[0;36mfactorial\u001b[0;34m(n)\u001b[0m\n\u001b[1;32m     12\u001b[0m         \u001b[0mValueError\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mn\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0mnegative\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     13\u001b[0m     \"\"\"\n\u001b[0;32m---> 14\u001b[0;31m     \u001b[0;32mif\u001b[0m \u001b[0mn\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0mint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mn\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     15\u001b[0m         \u001b[0;32mraise\u001b[0m \u001b[0mTypeError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"n is not integer-like; it has non-zero decimals\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     16\u001b[0m     \u001b[0mn\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mn\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m<ipython-input-48-9a12eb120fc3>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mfactorial\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"text\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;32m<ipython-input-45-07dc981a3422>\u001b[0m in \u001b[0;36mfactorial\u001b[0;34m(n)\u001b[0m\n\u001b[1;32m     12\u001b[0m         \u001b[0mValueError\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mn\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0mnegative\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     13\u001b[0m     \"\"\"\n\u001b[0;32m---> 14\u001b[0;31m     \u001b[0;32mif\u001b[0m \u001b[0mn\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0mint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mn\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     15\u001b[0m         \u001b[0;32mraise\u001b[0m \u001b[0mTypeError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"n is not integer-like; it has non-zero decimals\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     16\u001b[0m     \u001b[0mn\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mn\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
       "\u001b[0;31mValueError\u001b[0m: invalid literal for int() with base 10: 'text'"
      ]
     }
@@ -4777,12 +4740,12 @@
     }
    },
    "source": [
-    "We could, of course, use a `try` statement to suppress the exceptions raised by [int()](https://docs.python.org/3/library/functions.html#int) and replace them with a custom `TypeError`. However, now the implementation as a whole is more about type checking than about the actual logic solving the problem. We took this example to the extreme on purpose. In practice, we rarely see such code!"
+    "We could, of course, use a `try` statement to suppress the exceptions raised by [int() <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/functions.html#int) and replace them with a custom `TypeError`. However, now the implementation as a whole is more about type checking than about the actual logic solving the problem. We took this example to the extreme on purpose. In practice, we rarely see such code!"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 50,
+   "execution_count": 49,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -4821,7 +4784,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 51,
+   "execution_count": 50,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -4835,8 +4798,8 @@
      "traceback": [
       "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
       "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-51-9a12eb120fc3>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mfactorial\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"text\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
-      "\u001b[0;32m<ipython-input-50-9ed22b5a2981>\u001b[0m in \u001b[0;36mfactorial\u001b[0;34m(n)\u001b[0m\n\u001b[1;32m     15\u001b[0m         \u001b[0mcasted_n\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mn\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     16\u001b[0m     \u001b[0;32mexcept\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 17\u001b[0;31m         \u001b[0;32mraise\u001b[0m \u001b[0mTypeError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"n cannot be casted as an integer\"\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     18\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     19\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mn\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0mcasted_n\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m<ipython-input-50-9a12eb120fc3>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mfactorial\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"text\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;32m<ipython-input-49-9ed22b5a2981>\u001b[0m in \u001b[0;36mfactorial\u001b[0;34m(n)\u001b[0m\n\u001b[1;32m     15\u001b[0m         \u001b[0mcasted_n\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mn\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     16\u001b[0m     \u001b[0;32mexcept\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 17\u001b[0;31m         \u001b[0;32mraise\u001b[0m \u001b[0mTypeError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"n cannot be casted as an integer\"\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     18\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     19\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mn\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0mcasted_n\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
       "\u001b[0;31mTypeError\u001b[0m: n cannot be casted as an integer"
      ]
     }
@@ -4847,7 +4810,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 52,
+   "execution_count": 51,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -4861,8 +4824,8 @@
      "traceback": [
       "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
       "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-52-188b816be8b6>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mfactorial\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m3.1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
-      "\u001b[0;32m<ipython-input-50-9ed22b5a2981>\u001b[0m in \u001b[0;36mfactorial\u001b[0;34m(n)\u001b[0m\n\u001b[1;32m     18\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     19\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mn\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0mcasted_n\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 20\u001b[0;31m             \u001b[0;32mraise\u001b[0m \u001b[0mTypeError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"n is not integer-like; it has non-zero decimals\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     21\u001b[0m         \u001b[0mn\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcasted_n\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     22\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m<ipython-input-51-188b816be8b6>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mfactorial\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m3.1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;32m<ipython-input-49-9ed22b5a2981>\u001b[0m in \u001b[0;36mfactorial\u001b[0;34m(n)\u001b[0m\n\u001b[1;32m     18\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     19\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mn\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0mcasted_n\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 20\u001b[0;31m             \u001b[0;32mraise\u001b[0m \u001b[0mTypeError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"n is not integer-like; it has non-zero decimals\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     21\u001b[0m         \u001b[0mn\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcasted_n\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     22\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
       "\u001b[0;31mTypeError\u001b[0m: n is not integer-like; it has non-zero decimals"
      ]
     }
@@ -4901,7 +4864,7 @@
     }
    },
    "source": [
-    "With everything *officially* introduced so far, Python would be what is called **[Turing complete](https://en.wikipedia.org/wiki/Turing_completeness)**. That means that anything that could be formulated as an algorithm could be expressed with all the language features we have seen. Note that, in particular, we have *not* yet formally *introduced* the `for` and `while` statements!"
+    "With everything *officially* introduced so far, Python would be what is called **[Turing complete <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_wiki.png\">](https://en.wikipedia.org/wiki/Turing_completeness)**. That means that anything that could be formulated as an algorithm could be expressed with all the language features we have seen. Note that, in particular, we have *not* yet formally *introduced* the `for` and `while` statements!"
    ]
   },
   {
@@ -4934,7 +4897,7 @@
     }
    },
    "source": [
-    "Whereas functions combined with `if` statements suffice to model any repetitive logic, Python comes with a compound `while` statement (cf., [reference](https://docs.python.org/3/reference/compound_stmts.html#the-while-statement)) that often makes it easier to implement iterative ideas.\n",
+    "Whereas functions combined with `if` statements suffice to model any repetitive logic, Python comes with a compound `while` statement (cf., [reference <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/reference/compound_stmts.html#the-while-statement)) that often makes it easier to implement iterative ideas.\n",
     "\n",
     "It consists of a header line with a boolean expression followed by an indented code block. Before the first and after every execution of the code block, the boolean expression is evaluated, and if it is (still) equal to `True`, the code block runs (again). Eventually, some variable referenced in the boolean expression is changed in the code block such that the condition becomes `False`.\n",
     "\n",
@@ -4965,7 +4928,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 53,
+   "execution_count": 52,
    "metadata": {
     "code_folding": [],
     "slideshow": {
@@ -4989,7 +4952,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 54,
+   "execution_count": 53,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -5019,7 +4982,7 @@
     }
    },
    "source": [
-    "As [PythonTutor](http://pythontutor.com/visualize.html#code=def%20countdown%28n%29%3A%0A%20%20%20%20while%20n%20!%3D%200%3A%0A%20%20%20%20%20%20%20%20print%28n%29%0A%20%20%20%20%20%20%20%20n%20-%3D%201%0A%0A%20%20%20%20print%28%22Happy%20new%20Year!%22%29%0A%0Acountdown%283%29&cumulative=false&curInstr=0&heapPrimitives=nevernest&mode=display&origin=opt-frontend.js&py=3&rawInputLstJSON=%5B%5D&textReferences=false) shows, there is a subtle but essential difference in the way a `while` statement is treated in memory: In short, `while` statements can *not* run into a `RecursionError` as only *one* frame is needed to manage the names. After all, there is only *one* function call to be made. For typical day-to-day applications, this difference is, however, not so important *unless* a problem instance becomes so big that a large (i.e., $> 3.000$) number of recursive calls must be made."
+    "As [PythonTutor <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](http://pythontutor.com/visualize.html#code=def%20countdown%28n%29%3A%0A%20%20%20%20while%20n%20!%3D%200%3A%0A%20%20%20%20%20%20%20%20print%28n%29%0A%20%20%20%20%20%20%20%20n%20-%3D%201%0A%0A%20%20%20%20print%28%22Happy%20new%20Year!%22%29%0A%0Acountdown%283%29&cumulative=false&curInstr=0&heapPrimitives=nevernest&mode=display&origin=opt-frontend.js&py=3&rawInputLstJSON=%5B%5D&textReferences=false) shows, there is a subtle but essential difference in the way a `while` statement is treated in memory: In short, `while` statements can *not* run into a `RecursionError` as only *one* frame is needed to manage the names. After all, there is only *one* function call to be made. For typical day-to-day applications, this difference is, however, not so important *unless* a problem instance becomes so big that a large (i.e., $> 3.000$) number of recursive calls must be made."
    ]
   },
   {
@@ -5030,7 +4993,7 @@
     }
    },
    "source": [
-    "#### \"Still involved\" Example: [Euclid's Algorithm](https://en.wikipedia.org/wiki/Euclidean_algorithm) (revisited)"
+    "#### \"Still involved\" Example: [Euclid's Algorithm <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_wiki.png\">](https://en.wikipedia.org/wiki/Euclidean_algorithm) (revisited)"
    ]
   },
   {
@@ -5048,7 +5011,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 55,
+   "execution_count": 54,
    "metadata": {
     "code_folding": [],
     "slideshow": {
@@ -5078,7 +5041,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 56,
+   "execution_count": 55,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -5091,7 +5054,7 @@
        "4"
       ]
      },
-     "execution_count": 56,
+     "execution_count": 55,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -5102,7 +5065,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 57,
+   "execution_count": 56,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -5115,7 +5078,7 @@
        "1"
       ]
      },
-     "execution_count": 57,
+     "execution_count": 56,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -5143,12 +5106,12 @@
     }
    },
    "source": [
-    "We also see that this implementation is a lot *less* efficient than its recursive counterpart which solves `gcd()` for the same two numbers $112233445566778899$ and $987654321$ within microseconds."
+    "We also see that this implementation is a lot *less* efficient than its recursive counterpart which solves `gcd()` for the same two numbers `112233445566778899` and `987654321` within microseconds."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 58,
+   "execution_count": 57,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -5159,7 +5122,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "5.18 s ± 0 ns per loop (mean ± std. dev. of 1 run, 1 loop each)\n"
+      "5.12 s ± 0 ns per loop (mean ± std. dev. of 1 run, 1 loop each)\n"
      ]
     }
    ],
@@ -5198,7 +5161,7 @@
     }
    },
    "source": [
-    "#### \"Mystery\" Example: [Collatz Conjecture](https://en.wikipedia.org/wiki/Collatz_conjecture)"
+    "#### \"Mystery\" Example: [Collatz Conjecture <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_wiki.png\">](https://en.wikipedia.org/wiki/Collatz_conjecture)"
    ]
   },
   {
@@ -5226,12 +5189,12 @@
     }
    },
    "source": [
-    "The function below implements this game. Does it always reach $1$? No one has proven it so far! We include some input validation as before because `collatz()` would for sure not terminate if we called it with a negative number. Further, the Collatz sequence also works for real numbers, but then we would have to study fractals (cf., [this](https://en.wikipedia.org/wiki/Collatz_conjecture#Iterating_on_real_or_complex_numbers)). So we restrict our example to integers only."
+    "The function below implements this game. Does it always reach $1$? No one has proven it so far! We include some input validation as before because `collatz()` would for sure not terminate if we called it with a negative number. Further, the Collatz sequence also works for real numbers, but then we would have to study fractals (cf., [this <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_wiki.png\">](https://en.wikipedia.org/wiki/Collatz_conjecture#Iterating_on_real_or_complex_numbers)). So we restrict our example to integers only."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 59,
+   "execution_count": 58,
    "metadata": {
     "code_folding": [],
     "slideshow": {
@@ -5274,7 +5237,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 60,
+   "execution_count": 59,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -5295,7 +5258,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 61,
+   "execution_count": 60,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -5316,7 +5279,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 62,
+   "execution_count": 61,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -5337,7 +5300,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 63,
+   "execution_count": 62,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -5377,14 +5340,14 @@
    "source": [
     "Recursion and the `while` statement are two sides of the same coin. Disregarding that in the case of recursion Python internally faces some additional burden for managing the stack of frames in memory, both approaches lead to the *same* computational steps in memory. More importantly, we can formulate any recursive implementation in an iterative way and vice versa despite one of the two ways often \"feeling\" a lot more natural given a particular problem.\n",
     "\n",
-    "So how does the compound `for` statement (cf., [reference](https://docs.python.org/3/reference/compound_stmts.html#the-for-statement)) in this book's very first example fit into this picture? It is a *redundant* language construct to provide a *shorter* and more *convenient* syntax for common applications of the `while` statement. In programming, such additions to a language are called **syntactic sugar**. A cup of tea tastes better with sugar, but we may drink tea without sugar too.\n",
+    "So how does the compound `for` statement (cf., [reference <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/reference/compound_stmts.html#the-for-statement)) in this book's very first example fit into this picture? It is a *redundant* language construct to provide a *shorter* and more *convenient* syntax for common applications of the `while` statement. In programming, such additions to a language are called **syntactic sugar**. A cup of tea tastes better with sugar, but we may drink tea without sugar too.\n",
     "\n",
     "Consider `elements` below. Without the `for` statement, we must manage a temporary **index variable**, `index`, to loop over all the elements and also obtain the individual elements with the `[]` operator in each iteration of the loop."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 64,
+   "execution_count": 63,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -5397,7 +5360,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 65,
+   "execution_count": 64,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -5431,12 +5394,12 @@
     }
    },
    "source": [
-    "The `for` statement, on the contrary, makes the actual business logic more apparent by stripping all the **[boilerplate code](https://en.wikipedia.org/wiki/Boilerplate_code)** away. The variable that is automatically set by Python in each iteration of the loop (i.e., `element` in the example) is called the **target variable**."
+    "The `for` statement, on the contrary, makes the actual business logic more apparent by stripping all the **[boilerplate code <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_wiki.png\">](https://en.wikipedia.org/wiki/Boilerplate_code)** away. The variable that is automatically set by Python in each iteration of the loop (i.e., `element` in the example) is called the **target variable**."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 66,
+   "execution_count": 65,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -5464,12 +5427,12 @@
     }
    },
    "source": [
-    "For sequences of integers, the [range()](https://docs.python.org/3/library/functions.html#func-range) built-in makes the `for` statement even more convenient: It creates a `list`-like object of type `range` that generates integers \"on the fly,\" and we look closely at the underlying effects in memory in [Chapter 8](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/08_mfr_00_lecture.ipynb#Mapping)."
+    "For sequences of integers, the [range() <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/functions.html#func-range) built-in makes the `for` statement even more convenient: It creates a `list`-like object of type `range` that generates integers \"on the fly,\" and we look closely at the underlying effects in memory in [Chapter 8 <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/08_mfr_00_content.ipynb#Mapping)."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 67,
+   "execution_count": 66,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -5491,7 +5454,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 68,
+   "execution_count": 67,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -5504,7 +5467,7 @@
        "range"
       ]
      },
-     "execution_count": 68,
+     "execution_count": 67,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -5521,12 +5484,12 @@
     }
    },
    "source": [
-    "[range()](https://docs.python.org/3/library/functions.html#func-range) takes optional `start` and `step` arguments that we use to customize the sequence of integers even more."
+    "[range() <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/functions.html#func-range) takes optional `start` and `step` arguments that we use to customize the sequence of integers even more."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 69,
+   "execution_count": 68,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -5548,7 +5511,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 70,
+   "execution_count": 69,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -5595,18 +5558,18 @@
     "\n",
     "Now, just as we classify objects by data type, we also classify these data types (e.g., `int`, `float`, `str`, or `list`) into **abstract concepts**.\n",
     "\n",
-    "We did this already in [Chapter 1](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/01_elements_00_lecture.ipynb#Who-am-I?-And-how-many?) when we described a `list` object as \"some sort of container that holds [...] references to other objects\". So, abstractly speaking, **containers** are any objects that are \"composed\" of other objects and also \"manage\" how these objects are organized. `list` objects, for example, have the property that they model an order associated with their elements. There exist, however, other container types, many of which do *not* come with an order. So, containers primarily \"contain\" other objects and have *nothing* to do with looping.\n",
+    "We did this already in [Chapter 1 <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/01_elements_00_content.ipynb#Who-am-I?-And-how-many?) when we described a `list` object as \"some sort of container that holds [...] references to other objects\". So, abstractly speaking, **containers** are any objects that are \"composed\" of other objects and also \"manage\" how these objects are organized. `list` objects, for example, have the property that they model an order associated with their elements. There exist, however, other container types, many of which do *not* come with an order. So, containers primarily \"contain\" other objects and have *nothing* to do with looping.\n",
     "\n",
     "On the contrary, the abstract concept of **iterables** is all about looping: Any object that we can loop over is, by definition, an iterable. So, `range` objects, for example, are iterables, even though they hold no references to other objects. Moreover, looping does *not* have to occur in a *predictable* order, although this is the case for both `list` and `range` objects.\n",
     "\n",
-    "Typically, containers are iterables, and iterables are containers. Yet, only because these two concepts coincide often, we must not think of them as the same. In [Chapter 7](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/07_sequences_00_lecture.ipynb#Collections-vs.-Sequences), we formalize these two concepts and introduce many more. Finally, Chapter 10 gives an explanation how abstract concepts are implemented and play together.\n",
+    "Typically, containers are iterables, and iterables are containers. Yet, only because these two concepts coincide often, we must not think of them as the same. In [Chapter 7 <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/07_sequences_00_content.ipynb#Collections-vs.-Sequences), we formalize these two concepts and introduce many more. Finally, Chapter 10 gives an explanation how abstract concepts are implemented and play together.\n",
     "\n",
     "Let's continue with `first_names` below as an example an illustrate what iterable containers are."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 71,
+   "execution_count": 70,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -5630,7 +5593,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 72,
+   "execution_count": 71,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -5643,7 +5606,7 @@
        "True"
       ]
      },
-     "execution_count": 72,
+     "execution_count": 71,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -5654,7 +5617,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 73,
+   "execution_count": 72,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -5667,7 +5630,7 @@
        "False"
       ]
      },
-     "execution_count": 73,
+     "execution_count": 72,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -5689,7 +5652,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 74,
+   "execution_count": 73,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -5702,7 +5665,7 @@
        "[0, 1, 2, 3, 4]"
       ]
      },
-     "execution_count": 74,
+     "execution_count": 73,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -5713,7 +5676,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 75,
+   "execution_count": 74,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -5726,7 +5689,7 @@
        "True"
       ]
      },
-     "execution_count": 75,
+     "execution_count": 74,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -5748,7 +5711,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 76,
+   "execution_count": 75,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -5776,12 +5739,12 @@
     }
    },
    "source": [
-    "If we must have an index variable in the loop's body, we use the [enumerate()](https://docs.python.org/3/library/functions.html#enumerate) built-in that takes an *iterable* as its argument and then generates a \"stream\" of \"pairs\" of an index variable, `i` below, and an object provided by the iterable, `name`, separated by a `,`. There is *no* need to ever revert to the `while` statement with an explicitly managed index variable to loop over an iterable object."
+    "If we must have an index variable in the loop's body, we use the [enumerate() <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/functions.html#enumerate) built-in that takes an *iterable* as its argument and then generates a \"stream\" of \"pairs\" of an index variable, `i` below, and an object provided by the iterable, `name`, separated by a `,`. There is *no* need to ever revert to the `while` statement with an explicitly managed index variable to loop over an iterable object."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 77,
+   "execution_count": 76,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -5809,12 +5772,12 @@
     }
    },
    "source": [
-    "[enumerate()](https://docs.python.org/3/library/functions.html#enumerate) takes an optional `start` argument."
+    "[enumerate() <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/functions.html#enumerate) takes an optional `start` argument."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 78,
+   "execution_count": 77,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -5842,12 +5805,12 @@
     }
    },
    "source": [
-    "The [zip()](https://docs.python.org/3/library/functions.html#zip) built-in allows us to combine the elements of two or more iterables in a *pairwise* fashion: It conceptually works like a zipper for a jacket."
+    "The [zip() <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/functions.html#zip) built-in allows us to combine the elements of two or more iterables in a *pairwise* fashion: It conceptually works like a zipper for a jacket."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 79,
+   "execution_count": 78,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -5860,7 +5823,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 80,
+   "execution_count": 79,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -5888,7 +5851,7 @@
     }
    },
    "source": [
-    "#### \"Hard at first Glance\" Example: [Fibonacci Numbers](https://en.wikipedia.org/wiki/Fibonacci_number) (revisited)"
+    "#### \"Hard at first Glance\" Example: [Fibonacci Numbers <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_wiki.png\">](https://en.wikipedia.org/wiki/Fibonacci_number) (revisited)"
    ]
   },
   {
@@ -5908,7 +5871,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 81,
+   "execution_count": 80,
    "metadata": {
     "code_folding": [],
     "slideshow": {
@@ -5940,7 +5903,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 82,
+   "execution_count": 81,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -5960,7 +5923,7 @@
        "144"
       ]
      },
-     "execution_count": 82,
+     "execution_count": 81,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -5993,7 +5956,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 83,
+   "execution_count": 82,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -6013,7 +5976,7 @@
        "218922995834555169026"
       ]
      },
-     "execution_count": 83,
+     "execution_count": 82,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -6030,7 +5993,7 @@
     }
    },
    "source": [
-    "#### Easy Example: [Factorial](https://en.wikipedia.org/wiki/Factorial) (revisited)"
+    "#### Easy Example: [Factorial <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_wiki.png\">](https://en.wikipedia.org/wiki/Factorial) (revisited)"
    ]
   },
   {
@@ -6046,7 +6009,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 84,
+   "execution_count": 83,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -6073,7 +6036,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 85,
+   "execution_count": 84,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -6093,7 +6056,7 @@
        "6"
       ]
      },
-     "execution_count": 85,
+     "execution_count": 84,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -6104,7 +6067,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 86,
+   "execution_count": 85,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -6124,7 +6087,7 @@
        "3628800"
       ]
      },
-     "execution_count": 86,
+     "execution_count": 85,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -6179,7 +6142,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 87,
+   "execution_count": 86,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -6207,7 +6170,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 88,
+   "execution_count": 87,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -6255,12 +6218,12 @@
     }
    },
    "source": [
-    "Python provides the `break` statement (cf., [reference](https://docs.python.org/3/reference/simple_stmts.html#the-break-statement)) that lets us stop a loop prematurely at any iteration. It is yet another means of controlling the flow of execution, and we say that we \"break out of a loop.\""
+    "Python provides the `break` statement (cf., [reference <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/reference/simple_stmts.html#the-break-statement)) that lets us stop a loop prematurely at any iteration. It is yet another means of controlling the flow of execution, and we say that we \"break out of a loop.\""
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 89,
+   "execution_count": 88,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -6320,14 +6283,14 @@
     }
    },
    "source": [
-    "To express the logic in a prettier way, we add an `else`-clause at the end of the `for`-loop (cf., [reference](https://docs.python.org/3/reference/compound_stmts.html#the-for-statement)). The `else`-clause is executed *only if* the `for`-loop is *not* stopped with a `break` statement *prematurely* (i.e., *before* reaching the *last* iteration in the loop). The word \"else\" implies a somewhat unintuitive meaning and may have better been named a `then`-clause. In most use cases, however, the `else`-clause logically goes together with some `if` statement in the loop's body.\n",
+    "To express the logic in a prettier way, we add an `else`-clause at the end of the `for`-loop (cf., [reference <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/reference/compound_stmts.html#the-for-statement)). The `else`-clause is executed *only if* the `for`-loop is *not* stopped with a `break` statement *prematurely* (i.e., *before* reaching the *last* iteration in the loop). The word \"else\" implies a somewhat unintuitive meaning and may have better been named a `then`-clause. In most use cases, however, the `else`-clause logically goes together with some `if` statement in the loop's body.\n",
     "\n",
     "Overall, the code's expressive power increases. Not many programming languages support an optional `else`-branching for the `for` and `while` statements, which turns out to be very useful in practice."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 90,
+   "execution_count": 89,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -6370,7 +6333,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 91,
+   "execution_count": 90,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -6403,12 +6366,12 @@
     }
    },
    "source": [
-    "Of course, if we choose the number an element's square has to pass to be larger, for example, to `200`, we have to loop over all `numbers`. There is *no way* to optimize this **[linear search](https://en.wikipedia.org/wiki/Linear_search)** further."
+    "Of course, if we choose the number an element's square has to pass to be larger, for example, to `200`, we have to loop over all `numbers`. There is *no way* to optimize this **[linear search <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_wiki.png\">](https://en.wikipedia.org/wiki/Linear_search)** further."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 92,
+   "execution_count": 91,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -6452,7 +6415,7 @@
     }
    },
    "source": [
-    "Often, we process some iterable with numeric data, for example, a list of `numbers` as in this book's introductory example in [Chapter 1](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/01_elements_00_lecture.ipynb#Example:-Averaging-Even-Numbers) or, more realistically, data from a CSV file with many rows and columns.\n",
+    "Often, we process some iterable with numeric data, for example, a list of `numbers` as in this book's introductory example in [Chapter 1 <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/01_elements_00_content.ipynb#Example:-Averaging-Even-Numbers) or, more realistically, data from a CSV file with many rows and columns.\n",
     "\n",
     "Processing numeric data usually comes down to operations that may be grouped into one of the following three categories:\n",
     "\n",
@@ -6460,7 +6423,7 @@
     "- **filtering**: throw away individual numbers (e.g., statistical outliers in a sample)\n",
     "- **reducing**: collect individual numbers into summary statistics\n",
     "\n",
-    "We study this **map-filter-reduce** paradigm extensively in [Chapter 8](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/08_mfr_00_lecture.ipynb) after introducing more advanced data types that are needed to work with \"big\" data.\n",
+    "We study this **map-filter-reduce** paradigm extensively in [Chapter 8 <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/08_mfr_00_content.ipynb) after introducing more advanced data types that are needed to work with \"big\" data.\n",
     "\n",
     "Here, we focus on *filtering out* some numbers in a `for`-loop."
    ]
@@ -6494,7 +6457,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 93,
+   "execution_count": 92,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -6507,7 +6470,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 94,
+   "execution_count": 93,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -6527,7 +6490,7 @@
        "370"
       ]
      },
-     "execution_count": 94,
+     "execution_count": 93,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -6554,7 +6517,7 @@
    "source": [
     "The above code is easy to read as it involves only two levels of indentation.\n",
     "\n",
-    "In general, code gets harder to comprehend the more **horizontal space** it occupies. It is commonly considered good practice to grow a program **vertically** rather than horizontally. Code compliant with [PEP 8](https://www.python.org/dev/peps/pep-0008/#maximum-line-length) requires us to use *at most* 79 characters in a line!\n",
+    "In general, code gets harder to comprehend the more **horizontal space** it occupies. It is commonly considered good practice to grow a program **vertically** rather than horizontally. Code compliant with [PEP 8 <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://www.python.org/dev/peps/pep-0008/#maximum-line-length) requires us to use *at most* 79 characters in a line!\n",
     "\n",
     "Consider the next example, whose implementation in code already starts to look unbalanced."
    ]
@@ -6589,7 +6552,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 95,
+   "execution_count": 94,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -6609,7 +6572,7 @@
        "227"
       ]
      },
-     "execution_count": 95,
+     "execution_count": 94,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -6635,7 +6598,7 @@
     }
    },
    "source": [
-    "With already three levels of indentation, less horizontal space is available for the actual code block. Of course, one could flatten the two `if` statements with the logical `and` operator, as shown in [Chapter 3](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/03_conditionals_00_lecture.ipynb#The-if-Statement). Then, however, we trade off horizontal space against a more \"complex\" `if` logic, and this is *not* a real improvement."
+    "With already three levels of indentation, less horizontal space is available for the actual code block. Of course, one could flatten the two `if` statements with the logical `and` operator, as shown in [Chapter 3 <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/03_conditionals_00_content.ipynb#The-if-Statement). Then, however, we trade off horizontal space against a more \"complex\" `if` logic, and this is *not* a real improvement."
    ]
   },
   {
@@ -6657,7 +6620,7 @@
     }
    },
    "source": [
-    "A Pythonista would instead make use of the `continue` statement (cf., [reference](https://docs.python.org/3/reference/simple_stmts.html#the-continue-statement)) that causes a loop to jump into the next iteration skipping the rest of the code block.\n",
+    "A Pythonista would instead make use of the `continue` statement (cf., [reference <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/reference/simple_stmts.html#the-continue-statement)) that causes a loop to jump into the next iteration skipping the rest of the code block.\n",
     "\n",
     "The revised code fragment below occupies more vertical space and less horizontal space: A *good* trade-off.\n",
     "\n",
@@ -6666,7 +6629,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 96,
+   "execution_count": 95,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -6686,7 +6649,7 @@
        "227"
       ]
      },
-     "execution_count": 96,
+     "execution_count": 95,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -6761,7 +6724,7 @@
    "source": [
     "Let's consider a game where we randomly choose a variable to be either \"Heads\" or \"Tails\" and the user of our program has to guess it.\n",
     "\n",
-    "Python provides the built-in [input()](https://docs.python.org/3/library/functions.html#input) function that prints a message to the user, called the **prompt**, and reads in what was typed in response as a `str` object. We use it to process a user's \"unreliable\" input to our program (i.e., a user might type in some invalid response). Further, we use the [random()](https://docs.python.org/3/library/random.html#random.random) function in the [random](https://docs.python.org/3/library/random.html) module to model the coin toss.\n",
+    "Python provides the built-in [input() <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/functions.html#input) function that prints a message to the user, called the **prompt**, and reads in what was typed in response as a `str` object. We use it to process a user's \"unreliable\" input to our program (i.e., a user might type in some invalid response). Further, we use the [random() <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/random.html#random.random) function in the [random <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/random.html) module to model the coin toss.\n",
     "\n",
     "A popular pattern to approach such **indefinite loops** is to go with a `while True` statement, which on its own would cause Python to enter into an infinite loop. Then, once a particular event occurs, we `break` out of the loop.\n",
     "\n",
@@ -6770,7 +6733,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 97,
+   "execution_count": 96,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -6783,7 +6746,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 98,
+   "execution_count": 97,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -6796,7 +6759,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 99,
+   "execution_count": 98,
    "metadata": {
     "code_folding": [],
     "slideshow": {
@@ -6902,12 +6865,12 @@
     "\n",
     "First, we divide the business logic into two functions `get_guess()` and `toss_coin()` that are controlled from within a `while`-loop.\n",
     "\n",
-    "`get_guess()` not only reads in the user's input but also implements a simple input validation pattern in that the [strip()](https://docs.python.org/3/library/stdtypes.html?highlight=__contains__#str.strip) and [lower()](https://docs.python.org/3/library/stdtypes.html?highlight=__contains__#str.lower) methods remove preceding and trailing whitespace and lower case the input ensuring that the user may spell the input in any possible way (e.g., all upper or lower case). Also, `get_guess()` checks if the user entered one of the two valid options. If so, it returns either `\"heads\"` or `\"tails\"`; if not, it returns `None`."
+    "`get_guess()` not only reads in the user's input but also implements a simple input validation pattern in that the [strip() <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/stdtypes.html?highlight=__contains__#str.strip) and [lower() <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/stdtypes.html?highlight=__contains__#str.lower) methods remove preceding and trailing whitespace and lower case the input ensuring that the user may spell the input in any possible way (e.g., all upper or lower case). Also, `get_guess()` checks if the user entered one of the two valid options. If so, it returns either `\"heads\"` or `\"tails\"`; if not, it returns `None`."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 100,
+   "execution_count": 99,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -6944,7 +6907,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 101,
+   "execution_count": 100,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -6982,7 +6945,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 102,
+   "execution_count": 101,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -6995,7 +6958,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 103,
+   "execution_count": 102,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -7053,7 +7016,7 @@
     }
    },
    "source": [
-    "Now, the program's business logic is expressed in a clearer way. More importantly, we can now change it more easily. For example, we could make the `toss_coin()` function base the tossing on a probability distribution other than the uniform (i.e., replace the [random.random()](https://docs.python.org/3/library/random.html#random.random) function with another one). In general, modular architecture leads to improved software maintenance."
+    "Now, the program's business logic is expressed in a clearer way. More importantly, we can now change it more easily. For example, we could make the `toss_coin()` function base the tossing on a probability distribution other than the uniform (i.e., replace the [random.random() <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/random.html#random.random) function with another one). In general, modular architecture leads to improved software maintenance."
    ]
   },
   {
@@ -7085,6 +7048,66 @@
     "\n",
     "**Iterables** are any **concrete data types** that support being looped over, for example, with the `for` statement. The idea behind iterables is an **abstract concept** that may or may not be implemented by any given concrete data type. For example, both `list` and `range` objects can be looped over. The `list` type is also a **container** as any given `list` objects \"contains\" references to other objects in memory. On the contrary, the `range` type does not reference any other object but instead creates *new* `int` objects \"on the fly\" (i.e., when being looped over)."
    ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "## Further Resources"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "A lecture-style **video presentation** of this chapter is integrated below (cf., the [video <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_yt.png\">](https://www.youtube.com/watch?v=jT6hr4vOJks&list=PL-2JV1G3J10lQ2xokyQowcRJI5jjNfW7f) or the entire [playlist <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_yt.png\">](https://www.youtube.com/playlist?list=PL-2JV1G3J10lQ2xokyQowcRJI5jjNfW7f))."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 103,
+   "metadata": {
+    "scrolled": true,
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
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+       "            width=\"60%\"\n",
+       "            height=\"300\"\n",
+       "            src=\"https://www.youtube.com/embed/jT6hr4vOJks\"\n",
+       "            frameborder=\"0\"\n",
+       "            allowfullscreen\n",
+       "        ></iframe>\n",
+       "        "
+      ],
+      "text/plain": [
+       "<IPython.lib.display.YouTubeVideo at 0x7f8cd4bec390>"
+      ]
+     },
+     "execution_count": 103,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from IPython.display import YouTubeVideo\n",
+    "YouTubeVideo(\"jT6hr4vOJks\", width=\"60%\")"
+   ]
   }
  ],
  "metadata": {
diff --git a/04_iteration_01_review.ipynb b/04_iteration_01_review.ipynb
index 283a72a..3caecb5 100644
--- a/04_iteration_01_review.ipynb
+++ b/04_iteration_01_review.ipynb
@@ -18,7 +18,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "The questions below assume that you have read [Chapter 4](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/04_iteration_00_lecture.ipynb) in the book.\n",
+    "The questions below assume that you have read [Chapter 4 <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/04_iteration_00_content.ipynb) in the book.\n",
     "\n",
     "Be concise in your answers! Most questions can be answered in *one* sentence."
    ]
diff --git a/04_iteration_02_exercises.ipynb b/04_iteration_02_exercises.ipynb
index d795aa5..8c0b4c0 100644
--- a/04_iteration_02_exercises.ipynb
+++ b/04_iteration_02_exercises.ipynb
@@ -1,5 +1,12 @@
 {
  "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Important**: Click on \"*Kernel*\" > \"*Restart Kernel and Run All*\" *after* finishing the exercises in [JupyterLab <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_jp.png\">](https://jupyterlab.readthedocs.io/en/stable/) (e.g., in the cloud on [MyBinder <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_mb.png\">](https://mybinder.org/v2/gh/webartifex/intro-to-python/master?urlpath=lab/tree/04_iteration_02_exercises.ipynb)) to ensure that your solution runs top to bottom *without* any errors"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -18,7 +25,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "The exercises below assume that you have read the \"*Recursion*\" part in [Chapter 4](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/04_iteration_00_lecture.ipynb) of the book.\n",
+    "The exercises below assume that you have read the \"*Recursion*\" part in [Chapter 4 <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/04_iteration_00_content.ipynb) of the book.\n",
     "\n",
     "The `...`'s in the code cells indicate where you need to fill in code snippets. The number of `...`'s within a code cell give you a rough idea of how many lines of code are needed to solve the task. You should not need to create any additional code cells for your final solution. However, you may want to use temporary code cells to try out some ideas."
    ]
@@ -34,7 +41,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "A popular example of a problem that is solved by recursion art the **[Towers of Hanoi](https://en.wikipedia.org/wiki/Tower_of_Hanoi)**.\n",
+    "A popular example of a problem that is solved by recursion art the **[Towers of Hanoi <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_wiki.png\">](https://en.wikipedia.org/wiki/Tower_of_Hanoi)**.\n",
     "\n",
     "In its basic version, a tower consisting of, for example, four disks with increasing radii, is placed on the left-most of **three** adjacent spots. In the following, we refer to the number of disks as $n$, so here $n = 4$.\n",
     "\n",
@@ -43,7 +50,7 @@
     "1. Disks can only be moved individually, and\n",
     "2. a disk with a larger radius must *never* be placed on a disk with a smaller one.\n",
     "\n",
-    "Although the **[Towers of Hanoi](https://en.wikipedia.org/wiki/Tower_of_Hanoi)** are a **classic** example, introduced by the mathematician [Édouard Lucas](https://en.wikipedia.org/wiki/%C3%89douard_Lucas) already in 1883, it is still **actively** researched as this scholarly [article](https://www.worldscientific.com/doi/abs/10.1142/S1793830919300017?journalCode=dmaa&) published in January 2019 shows.\n",
+    "Although the **[Towers of Hanoi <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_wiki.png\">](https://en.wikipedia.org/wiki/Tower_of_Hanoi)** are a **classic** example, introduced by the mathematician [Édouard Lucas <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_wiki.png\">](https://en.wikipedia.org/wiki/%C3%89douard_Lucas) already in 1883, it is still **actively** researched as this scholarly [article](https://www.worldscientific.com/doi/abs/10.1142/S1793830919300017?journalCode=dmaa&) published in January 2019 shows.\n",
     "\n",
     "Despite being so easy to formulate, the game is quite hard to solve.\n",
     "\n",
@@ -117,7 +124,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "**Q3**: The **[Towers of Hanoi](https://en.wikipedia.org/wiki/Tower_of_Hanoi)** problem is of **exponential growth**. What does that mean? What does that imply for large $n$?"
+    "**Q3**: The **[Towers of Hanoi <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_wiki.png\">](https://en.wikipedia.org/wiki/Tower_of_Hanoi)** problem is of **exponential growth**. What does that mean? What does that imply for large $n$?"
    ]
   },
   {
@@ -266,7 +273,7 @@
    "source": [
     "**Q7**: `sol()` calls itself *two* more times with the correct 2-tuples chosen from the three available spots `origin`, `intermediate`, and `destination`.\n",
     "\n",
-    "*In between* the two recursive function calls, use [print()](https://docs.python.org/3/library/functions.html#print) to print out from where to where the \"remaining and largest\" disk has to be moved!"
+    "*In between* the two recursive function calls, use [print() <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/functions.html#print) to print out from where to where the \"remaining and largest\" disk has to be moved!"
    ]
   },
   {
@@ -372,7 +379,7 @@
     "\n",
     "Figure out how the arguments are passed on in the two recursive `hanoi()` calls and finish `hanoi()`.\n",
     "\n",
-    "Hint: Do not forget to use [print()](https://docs.python.org/3/library/functions.html#print) to print out the moves!"
+    "Hint: Do not forget to use [print() <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/functions.html#print) to print out the moves!"
    ]
   },
   {
@@ -498,7 +505,7 @@
    "source": [
     "**Q13**: Complete the two recursive function calls with the same arguments as in `hanoi()`! Do *not* change the already filled in `offset` arguments!\n",
     "\n",
-    "Then, adjust the use of [print()](https://docs.python.org/3/library/functions.html#print) from above to print out the moves with their order number!"
+    "Then, adjust the use of [print() <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/functions.html#print) from above to print out the moves with their order number!"
    ]
   },
   {
@@ -548,7 +555,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Lastly, it is to be mentioned that for problem instances with a small `n_disks` argument, it is easier to collect all the moves first in a `list` object and then add the order number with the [enumerate()](https://docs.python.org/3/library/functions.html#enumerate) built-in."
+    "Lastly, it is to be mentioned that for problem instances with a small `n_disks` argument, it is easier to collect all the moves first in a `list` object and then add the order number with the [enumerate() <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/functions.html#enumerate) built-in."
    ]
   },
   {
@@ -562,7 +569,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "**Q15**: Conducting your own research on the internet, what can you say about generalizing the **[Towers of Hanoi](https://en.wikipedia.org/wiki/Tower_of_Hanoi)** problem to a setting with *more than three* landing spots?"
+    "**Q15**: Conducting your own research on the internet, what can you say about generalizing the **[Towers of Hanoi <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_wiki.png\">](https://en.wikipedia.org/wiki/Tower_of_Hanoi)** problem to a setting with *more than three* landing spots?"
    ]
   },
   {
diff --git a/04_iteration_03_exercises.ipynb b/04_iteration_03_exercises.ipynb
index 7d69c33..afc0606 100644
--- a/04_iteration_03_exercises.ipynb
+++ b/04_iteration_03_exercises.ipynb
@@ -1,5 +1,12 @@
 {
  "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Important**: Click on \"*Kernel*\" > \"*Restart Kernel and Run All*\" *after* finishing the exercises in [JupyterLab <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_jp.png\">](https://jupyterlab.readthedocs.io/en/stable/) (e.g., in the cloud on [MyBinder <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_mb.png\">](https://mybinder.org/v2/gh/webartifex/intro-to-python/master?urlpath=lab/tree/04_iteration_03_exercises.ipynb)) to ensure that your solution runs top to bottom *without* any errors"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -18,7 +25,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "The exercises below assume that you have read the \"*Looping*\" part in [Chapter 4](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/04_iteration_00_lecture.ipynb) of the book.\n",
+    "The exercises below assume that you have read the \"*Looping*\" part in [Chapter 4 <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/04_iteration_00_content.ipynb) of the book.\n",
     "\n",
     "The `...`'s in the code cells indicate where you need to fill in code snippets. The number of `...`'s within a code cell give you a rough idea of how many lines of code are needed to solve the task. You should not need to create any additional code cells for your final solution. However, you may want to use temporary code cells to try out some ideas."
    ]
@@ -34,9 +41,9 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "In this exercise, you will model the throwing of dice within the context of a guessing game similar to the one shown in the \"*Example: Guessing a Coin Toss*\" section in [Chapter 4](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/04_iteration_00_lecture.ipynb#Example:-Guessing-a-Coin-Toss).\n",
+    "In this exercise, you will model the throwing of dice within the context of a guessing game similar to the one shown in the \"*Example: Guessing a Coin Toss*\" section in [Chapter 4 <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/04_iteration_00_content.ipynb#Example:-Guessing-a-Coin-Toss).\n",
     "\n",
-    "As the game involves randomness, we import the [random](https://docs.python.org/3/library/random.html) module from the [standard library](https://docs.python.org/3/library/index.html). To follow best practices, we set the random seed as well."
+    "As the game involves randomness, we import the [random <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/random.html) module from the [standard library <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/index.html). To follow best practices, we set the random seed as well."
    ]
   },
   {
@@ -79,7 +86,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "**Q2**: What function from the [random](https://docs.python.org/3/library/random.html) module that we have seen already is useful for modeling a single throw of the `fair_die`? Write a simple expression (i.e., one function call) that draws one of the equally likely sides! Execute the cell a couple of times to \"see\" the probability distribution!"
+    "**Q2**: What function from the [random <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/random.html) module that we have seen already is useful for modeling a single throw of the `fair_die`? Write a simple expression (i.e., one function call) that draws one of the equally likely sides! Execute the cell a couple of times to \"see\" the probability distribution!"
    ]
   },
   {
@@ -124,9 +131,9 @@
    "source": [
     "`throws` contains the simulation results as absolute counts.\n",
     "\n",
-    "**Q4**: Complete the `for`-loop below to convert the counts in `throws` to relative frequencies stored in a `list` called `frequencies`! Round the frequencies to three decimals with the built-in [round()](https://docs.python.org/3/library/functions.html#round) function!\n",
+    "**Q4**: Complete the `for`-loop below to convert the counts in `throws` to relative frequencies stored in a `list` called `frequencies`! Round the frequencies to three decimals with the built-in [round() <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/functions.html#round) function!\n",
     "\n",
-    "Hints: Initialize `frequencies` just as `throws` above. How many iterations does the `for`-loop have? `6` or `100000`? You may want to obtain an `index` variable with the [enumerate()](https://docs.python.org/3/library/functions.html#enumerate) built-in."
+    "Hints: Initialize `frequencies` just as `throws` above. How many iterations does the `for`-loop have? `6` or `100000`? You may want to obtain an `index` variable with the [enumerate() <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/functions.html#enumerate) built-in."
    ]
   },
   {
@@ -206,7 +213,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "**Q8**: The built-in [input()](https://docs.python.org/3/library/functions.html#input) allows us to ask the user to enter a `guess`. What is the data type of the object returned by [input()](https://docs.python.org/3/library/functions.html#input)? Assume the user enters the `guess` as a number (i.e., \"1\", \"2\", ...) and not as a text (e.g., \"one\")."
+    "**Q8**: The built-in [input() <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/functions.html#input) allows us to ask the user to enter a `guess`. What is the data type of the object returned by [input() <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/functions.html#input)? Assume the user enters the `guess` as a number (i.e., \"1\", \"2\", ...) and not as a text (e.g., \"one\")."
    ]
   },
   {
diff --git a/05_numbers_00_lecture.ipynb b/05_numbers_00_content.ipynb
similarity index 89%
rename from 05_numbers_00_lecture.ipynb
rename to 05_numbers_00_content.ipynb
index 3f0bdb3..80e0c63 100644
--- a/05_numbers_00_lecture.ipynb
+++ b/05_numbers_00_content.ipynb
@@ -8,44 +8,7 @@
     }
    },
    "source": [
-    "A **video presentation** of the contents in this chapter is shown below. A playlist with *all* chapters as videos is linked [here](https://www.youtube.com/playlist?list=PL-2JV1G3J10lQ2xokyQowcRJI5jjNfW7f)."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/jpeg": 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-       "            width=\"60%\"\n",
-       "            height=\"300\"\n",
-       "            src=\"https://www.youtube.com/embed/nB00WGCnVjg\"\n",
-       "            frameborder=\"0\"\n",
-       "            allowfullscreen\n",
-       "        ></iframe>\n",
-       "        "
-      ],
-      "text/plain": [
-       "<IPython.lib.display.YouTubeVideo at 0x7f4b21935410>"
-      ]
-     },
-     "execution_count": 1,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from IPython.display import YouTubeVideo\n",
-    "YouTubeVideo(\"nB00WGCnVjg\", width=\"60%\")"
+    "**Important**: Click on \"*Kernel*\" > \"*Restart Kernel and Clear All Outputs*\" *before* reading this chapter in [JupyterLab <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_jp.png\">](https://jupyterlab.readthedocs.io/en/stable/) (e.g., in the cloud on [MyBinder <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_mb.png\">](https://mybinder.org/v2/gh/webartifex/intro-to-python/master?urlpath=lab/tree/05_numbers_00_content.ipynb))"
    ]
   },
   {
@@ -67,19 +30,19 @@
     }
    },
    "source": [
-    "After learning about the basic building blocks of expressing and structuring the business logic in programs, we focus our attention on the **data types** Python offers us, both built-in and available via the [standard library](https://docs.python.org/3/library/index.html) or third-party packages.\n",
+    "After learning about the basic building blocks of expressing and structuring the business logic in programs, we focus our attention on the **data types** Python offers us, both built-in and available via the [standard library <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/index.html) or third-party packages.\n",
     "\n",
-    "We start with the \"simple\" ones: Numeric types in this chapter and textual data in [Chapter 6](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/06_text_00_lecture.ipynb). An important fact that holds for all objects of these types is that they are **immutable**. To reuse the bag analogy from [Chapter 1](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/01_elements_00_lecture.ipynb#Objects-vs.-Types-vs.-Values), this means that the $0$s and $1$s making up an object's *value* cannot be changed once the bag is created in memory, implying that any operation with or method on the object creates a *new* object in a *different* memory location.\n",
+    "We start with the \"simple\" ones: Numeric types in this chapter and textual data in [Chapter 6 <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/06_text_00_content.ipynb). An important fact that holds for all objects of these types is that they are **immutable**. To reuse the bag analogy from [Chapter 1 <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/01_elements_00_content.ipynb#Objects-vs.-Types-vs.-Values), this means that the $0$s and $1$s making up an object's *value* cannot be changed once the bag is created in memory, implying that any operation with or method on the object creates a *new* object in a *different* memory location.\n",
     "\n",
-    "[Chapter 7](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/07_sequences_00_lecture.ipynb), [Chapter 8](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/08_mfr_00_lecture.ipynb), and [Chapter 9](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/09_mappings_00_lecture.ipynb) then cover the more \"complex\" data types, including, for example, the `list` type. Finally, Chapter 10 completes the picture by introducing language constructs to create custom types.\n",
+    "[Chapter 7 <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/07_sequences_00_content.ipynb), [Chapter 8 <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/08_mfr_00_content.ipynb), and [Chapter 9 <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/09_mappings_00_content.ipynb) then cover the more \"complex\" data types, including, for example, the `list` type. Finally, Chapter 10 completes the picture by introducing language constructs to create custom types.\n",
     "\n",
     "We have already seen many hints indicating that numbers are not as trivial to work with as it seems at first sight:\n",
     "\n",
-    "- [Chapter 1](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/01_elements_00_lecture.ipynb#%28Data%29-Type-%2F-%22Behavior%22) reveals that numbers may come in *different* data types (i.e., `int` vs. `float` so far),\n",
-    "- [Chapter 3](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/03_conditionals_00_lecture.ipynb#Boolean-Expressions) raises questions regarding the **limited precision** of `float` numbers (e.g., `42 == 42.000000000000001` evaluates to `True`), and\n",
-    "- [Chapter 4](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/04_iteration_00_lecture.ipynb#Infinite-Recursion) shows that sometimes a `float` \"walks\" and \"quacks\" like an `int`, whereas the reverse is true.\n",
+    "- [Chapter 1 <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/01_elements_00_content.ipynb#%28Data%29-Type-%2F-%22Behavior%22) reveals that numbers may come in *different* data types (i.e., `int` vs. `float` so far),\n",
+    "- [Chapter 3 <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/03_conditionals_00_content.ipynb#Boolean-Expressions) raises questions regarding the **limited precision** of `float` numbers (e.g., `42 == 42.000000000000001` evaluates to `True`), and\n",
+    "- [Chapter 4 <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/04_iteration_00_content.ipynb#Infinite-Recursion) shows that sometimes a `float` \"walks\" and \"quacks\" like an `int`, whereas the reverse is true.\n",
     "\n",
-    "This chapter introduces all the [built-in numeric types](https://docs.python.org/3/library/stdtypes.html#numeric-types-int-float-complex): `int`, `float`, and `complex`. To mitigate the limited precision of floating-point numbers, we also look at two replacements for the `float` type in the [standard library](https://docs.python.org/3/library/index.html), namely the `Decimal` type in the [decimals](https://docs.python.org/3/library/decimal.html#decimal.Decimal) and the `Fraction` type in the [fractions](https://docs.python.org/3/library/fractions.html#fractions.Fraction) module."
+    "This chapter introduces all the [built-in numeric types <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/stdtypes.html#numeric-types-int-float-complex): `int`, `float`, and `complex`. To mitigate the limited precision of floating-point numbers, we also look at two replacements for the `float` type in the [standard library <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/index.html), namely the `Decimal` type in the [decimals <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/decimal.html#decimal.Decimal) and the `Fraction` type in the [fractions <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/fractions.html#fractions.Fraction) module."
    ]
   },
   {
@@ -101,14 +64,14 @@
     }
    },
    "source": [
-    "The simplest numeric type is the `int` type: It behaves like an [integer in ordinary math](https://en.wikipedia.org/wiki/Integer) (i.e., the set $\\mathbb{Z}$) and supports operators in the way we saw in the section on arithmetic operators in [Chapter 1](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/01_elements_00_lecture.ipynb#%28Arithmetic%29-Operators).\n",
+    "The simplest numeric type is the `int` type: It behaves like an [integer in ordinary math <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_wiki.png\">](https://en.wikipedia.org/wiki/Integer) (i.e., the set $\\mathbb{Z}$) and supports operators in the way we saw in the section on arithmetic operators in [Chapter 1 <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/01_elements_00_content.ipynb#%28Arithmetic%29-Operators).\n",
     "\n",
     "One way to create `int` objects is by simply writing its value as a literal with the digits `0` to `9`."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 1,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -130,6 +93,30 @@
     "Just like any other object, the `42` has an identity, a type, and a value."
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "94263164110912"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "id(a)"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 3,
@@ -142,7 +129,7 @@
     {
      "data": {
       "text/plain": [
-       "94381541328960"
+       "int"
       ]
      },
      "execution_count": 3,
@@ -151,7 +138,7 @@
     }
    ],
    "source": [
-    "id(a)"
+    "type(a)"
    ]
   },
   {
@@ -162,30 +149,6 @@
      "slide_type": "fragment"
     }
    },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "int"
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "type(a)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "fragment"
-    }
-   },
    "outputs": [
     {
      "data": {
@@ -193,7 +156,7 @@
        "42"
       ]
      },
-     "execution_count": 5,
+     "execution_count": 4,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -215,7 +178,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 5,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -228,7 +191,7 @@
        "1000000"
       ]
      },
-     "execution_count": 6,
+     "execution_count": 5,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -250,7 +213,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 6,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -263,7 +226,7 @@
        "123456789"
       ]
      },
-     "execution_count": 7,
+     "execution_count": 6,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -285,7 +248,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 7,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -294,10 +257,10 @@
    "outputs": [
     {
      "ename": "SyntaxError",
-     "evalue": "invalid token (<ipython-input-8-9a7f0f99e867>, line 1)",
+     "evalue": "invalid token (<ipython-input-7-9a7f0f99e867>, line 1)",
      "output_type": "error",
      "traceback": [
-      "\u001b[0;36m  File \u001b[0;32m\"<ipython-input-8-9a7f0f99e867>\"\u001b[0;36m, line \u001b[0;32m1\u001b[0m\n\u001b[0;31m    042\u001b[0m\n\u001b[0m      ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m invalid token\n"
+      "\u001b[0;36m  File \u001b[0;32m\"<ipython-input-7-9a7f0f99e867>\"\u001b[0;36m, line \u001b[0;32m1\u001b[0m\n\u001b[0;31m    042\u001b[0m\n\u001b[0m      ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m invalid token\n"
      ]
     }
    ],
@@ -313,12 +276,12 @@
     }
    },
    "source": [
-    "Another way to create `int` objects is with the [int()](https://docs.python.org/3/library/functions.html#int) built-in that casts `float` or properly formatted `str` objects as integers. So, decimals are truncated (i.e., \"cut off\")."
+    "Another way to create `int` objects is with the [int() <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/functions.html#int) built-in that casts `float` or properly formatted `str` objects as integers. So, decimals are truncated (i.e., \"cut off\")."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 8,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -331,7 +294,7 @@
        "42"
       ]
      },
-     "execution_count": 9,
+     "execution_count": 8,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -342,7 +305,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 9,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -355,7 +318,7 @@
        "42"
       ]
      },
-     "execution_count": 10,
+     "execution_count": 9,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -372,12 +335,12 @@
     }
    },
    "source": [
-    "Whereas the floor division operator `//` effectively rounds towards negative infinity (cf., the \"*(Arithmetic) Operators*\" section in [Chapter 1](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/01_elements_00_lecture.ipynb#%28Arithmetic%29-Operators)), the [int()](https://docs.python.org/3/library/functions.html#int) built-in effectively rounds towards `0`."
+    "Whereas the floor division operator `//` effectively rounds towards negative infinity (cf., the \"*(Arithmetic) Operators*\" section in [Chapter 1 <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/01_elements_00_content.ipynb#%28Arithmetic%29-Operators)), the [int() <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/functions.html#int) built-in effectively rounds towards `0`."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 10,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -390,7 +353,7 @@
        "-42"
       ]
      },
-     "execution_count": 11,
+     "execution_count": 10,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -407,12 +370,12 @@
     }
    },
    "source": [
-    "When casting `str` objects as `int`, the [int()](https://docs.python.org/3/library/functions.html#int) built-in is less forgiving. We must not include any decimals as shows by the `ValueError`. Yet, leading and trailing whitespace is gracefully ignored."
+    "When casting `str` objects as `int`, the [int() <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/functions.html#int) built-in is less forgiving. We must not include any decimals as shows by the `ValueError`. Yet, leading and trailing whitespace is gracefully ignored."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 11,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -425,7 +388,7 @@
        "42"
       ]
      },
-     "execution_count": 12,
+     "execution_count": 11,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -436,7 +399,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 12,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -450,7 +413,7 @@
      "traceback": [
       "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
       "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-13-faae6b054331>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"42.0\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;32m<ipython-input-12-faae6b054331>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"42.0\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
       "\u001b[0;31mValueError\u001b[0m: invalid literal for int() with base 10: '42.0'"
      ]
     }
@@ -461,7 +424,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 13,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -474,7 +437,7 @@
        "42"
       ]
      },
-     "execution_count": 14,
+     "execution_count": 13,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -491,12 +454,12 @@
     }
    },
    "source": [
-    "The `int` type follows all rules we know from math, apart from one exception: Whereas mathematicians to this day argue what the term $0^0$ means (cf., this [article](https://en.wikipedia.org/wiki/Zero_to_the_power_of_zero)), programmers are pragmatic about this and simply define $0^0 = 1$."
+    "The `int` type follows all rules we know from math, apart from one exception: Whereas mathematicians to this day argue what the term $0^0$ means (cf., this [article <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_wiki.png\">](https://en.wikipedia.org/wiki/Zero_to_the_power_of_zero)), programmers are pragmatic about this and simply define $0^0 = 1$."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 14,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -509,7 +472,7 @@
        "1"
       ]
      },
-     "execution_count": 15,
+     "execution_count": 14,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -537,7 +500,7 @@
     }
    },
    "source": [
-    "As computers can only store $0$s and $1$s, `int` objects are nothing but that in memory as well. Consequently, computer scientists and engineers developed conventions as to how $0$s and $1$s are \"translated\" into integers, and one such convention is the **[binary representation](https://en.wikipedia.org/wiki/Binary_number)** of **non-negative integers**. Consider the integers from $0$ through $255$ that are encoded into $0$s and $1$s with the help of this table:"
+    "As computers can only store $0$s and $1$s, `int` objects are nothing but that in memory as well. Consequently, computer scientists and engineers developed conventions as to how $0$s and $1$s are \"translated\" into integers, and one such convention is the **[binary representation <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_wiki.png\">](https://en.wikipedia.org/wiki/Binary_number)** of **non-negative integers**. Consider the integers from $0$ through $255$ that are encoded into $0$s and $1$s with the help of this table:"
    ]
   },
   {
@@ -568,12 +531,12 @@
     "\n",
     "As each bit in the binary representation is one of two values, we say that this representation has a base of $2$. Often, the base is indicated with a subscript to avoid confusion. For example, we write $3_{10} = 00000011_2$ or $3_{10} = 11_2$ for short omitting leading $0$s. A subscript of $10$ implies a decimal number as we know it from elementary school.\n",
     "\n",
-    "We use the built-in [bin()](https://docs.python.org/3/library/functions.html#bin) function to obtain an `int` object's binary representation: It returns a `str` object starting with `\"0b\"` indicating the binary format and as many $0$s and $1$s as are necessary to encode the integer omitting leading $0$s."
+    "We use the built-in [bin() <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/functions.html#bin) function to obtain an `int` object's binary representation: It returns a `str` object starting with `\"0b\"` indicating the binary format and as many $0$s and $1$s as are necessary to encode the integer omitting leading $0$s."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 15,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -586,7 +549,7 @@
        "'0b11'"
       ]
      },
-     "execution_count": 16,
+     "execution_count": 15,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -603,7 +566,42 @@
     }
    },
    "source": [
-    "We may pass a `str` object formatted this way as the argument to the [int()](https://docs.python.org/3/library/functions.html#int) built-in, together with `base=2`, to create an `int` object, for example, with the value of `3`."
+    "We may pass a `str` object formatted this way as the argument to the [int() <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/functions.html#int) built-in, together with `base=2`, to create an `int` object, for example, with the value of `3`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "3"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "int(\"0b11\", base=2)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Moreover, we may also use the contents of the returned `str` object as a **literal** instead: Just like we type, for example, `3` without quotes (i.e., \"literally\") into a code cell to create the `int` object `3`, we may type `0b11` to obtain an `int` object with the same value."
    ]
   },
   {
@@ -626,41 +624,6 @@
      "output_type": "execute_result"
     }
    ],
-   "source": [
-    "int(\"0b11\", base=2)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "source": [
-    "Moreover, we may also use the contents of the returned `str` object as a **literal** instead: Just like we type, for example, `3` without quotes (i.e., \"literally\") into a code cell to create the `int` object `3`, we may type `0b11` to obtain an `int` object with the same value."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "fragment"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "3"
-      ]
-     },
-     "execution_count": 18,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
    "source": [
     "0b11"
    ]
@@ -678,7 +641,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 18,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -691,7 +654,7 @@
        "'0b1111011'"
       ]
      },
-     "execution_count": 19,
+     "execution_count": 18,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -713,7 +676,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 19,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -726,7 +689,7 @@
        "123"
       ]
      },
-     "execution_count": 20,
+     "execution_count": 19,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -748,7 +711,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 20,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -761,7 +724,7 @@
        "'0b0'"
       ]
      },
-     "execution_count": 21,
+     "execution_count": 20,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -770,6 +733,30 @@
     "bin(0)"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'0b1'"
+      ]
+     },
+     "execution_count": 21,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "bin(1)"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 22,
@@ -778,30 +765,6 @@
      "slide_type": "skip"
     }
    },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'0b1'"
-      ]
-     },
-     "execution_count": 22,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "bin(1)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 23,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
    "outputs": [
     {
      "data": {
@@ -809,7 +772,7 @@
        "'0b10'"
       ]
      },
-     "execution_count": 23,
+     "execution_count": 22,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -820,7 +783,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 23,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -833,7 +796,7 @@
        "'0b11111111'"
       ]
      },
-     "execution_count": 24,
+     "execution_count": 23,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -857,7 +820,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 24,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -870,7 +833,7 @@
        "'0b1100010101'"
       ]
      },
-     "execution_count": 25,
+     "execution_count": 24,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -921,7 +884,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": 25,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -934,7 +897,7 @@
        "3"
       ]
      },
-     "execution_count": 26,
+     "execution_count": 25,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -945,7 +908,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": 26,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -958,7 +921,7 @@
        "'0b1 + 0b10 = 0b11'"
       ]
      },
-     "execution_count": 27,
+     "execution_count": 26,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -980,7 +943,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": 27,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -993,7 +956,7 @@
        "4"
       ]
      },
-     "execution_count": 28,
+     "execution_count": 27,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1004,7 +967,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
+   "execution_count": 28,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -1017,7 +980,7 @@
        "'0b1 + 0b11 = 0b100'"
       ]
      },
-     "execution_count": 29,
+     "execution_count": 28,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1039,7 +1002,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 30,
+   "execution_count": 29,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -1052,7 +1015,7 @@
        "12"
       ]
      },
-     "execution_count": 30,
+     "execution_count": 29,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1061,6 +1024,30 @@
     "4 * 3"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'0b100 * 0b11 = 0b1100'"
+      ]
+     },
+     "execution_count": 30,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "bin(4) + \" * \" + bin(3) + \" = \" + bin(12)"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 31,
@@ -1073,7 +1060,7 @@
     {
      "data": {
       "text/plain": [
-       "'0b100 * 0b11 = 0b1100'"
+       "'0b100 * 0b1 = 0b100'"
       ]
      },
      "execution_count": 31,
@@ -1082,7 +1069,7 @@
     }
    ],
    "source": [
-    "bin(4) + \" * \" + bin(3) + \" = \" + bin(12)"
+    "bin(4) + \" * \" + bin(1) + \" = \" + bin(4)  # multiply with first digit"
    ]
   },
   {
@@ -1093,30 +1080,6 @@
      "slide_type": "fragment"
     }
    },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'0b100 * 0b1 = 0b100'"
-      ]
-     },
-     "execution_count": 32,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "bin(4) + \" * \" + bin(1) + \" = \" + bin(4)  # multiply with first digit"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 33,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "fragment"
-    }
-   },
    "outputs": [
     {
      "data": {
@@ -1124,7 +1087,7 @@
        "'0b100 * 0b10 = 0b1000'"
       ]
      },
-     "execution_count": 33,
+     "execution_count": 32,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1212,14 +1175,14 @@
     "\n",
     "$46_{10} = 2\\text{e}_{16} = 101110_2$\n",
     "\n",
-    "The built-in [hex()](https://docs.python.org/3/library/functions.html#hex) function creates a `str` object starting with `\"0x\"` representing an `int` object's hexadecimal representation. The length depends on how many groups of four bits are implied by the corresponding binary representation.\n",
+    "The built-in [hex() <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/functions.html#hex) function creates a `str` object starting with `\"0x\"` representing an `int` object's hexadecimal representation. The length depends on how many groups of four bits are implied by the corresponding binary representation.\n",
     "\n",
     "For `0` and `1`, the hexadecimal representation is similar to the binary one."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 34,
+   "execution_count": 33,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -1232,7 +1195,7 @@
        "'0x0'"
       ]
      },
-     "execution_count": 34,
+     "execution_count": 33,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1243,7 +1206,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 35,
+   "execution_count": 34,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -1256,7 +1219,7 @@
        "'0x1'"
       ]
      },
-     "execution_count": 35,
+     "execution_count": 34,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1278,7 +1241,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 36,
+   "execution_count": 35,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -1291,7 +1254,7 @@
        "'0x3'"
       ]
      },
-     "execution_count": 36,
+     "execution_count": 35,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1313,7 +1276,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 37,
+   "execution_count": 36,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -1326,7 +1289,7 @@
        "'0xa'"
       ]
      },
-     "execution_count": 37,
+     "execution_count": 36,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1337,7 +1300,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 38,
+   "execution_count": 37,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -1350,7 +1313,7 @@
        "'0xf'"
       ]
      },
-     "execution_count": 38,
+     "execution_count": 37,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1372,7 +1335,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 39,
+   "execution_count": 38,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -1385,7 +1348,7 @@
        "'0b1111011'"
       ]
      },
-     "execution_count": 39,
+     "execution_count": 38,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1396,7 +1359,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 40,
+   "execution_count": 39,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -1409,7 +1372,7 @@
        "'0x7b'"
       ]
      },
-     "execution_count": 40,
+     "execution_count": 39,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1426,7 +1389,42 @@
     }
    },
    "source": [
-    "To obtain a *new* `int` object with the value `123`, we call the [int()](https://docs.python.org/3/library/functions.html#int) built-in with a properly formatted `str` object and `base=16` as arguments."
+    "To obtain a *new* `int` object with the value `123`, we call the [int() <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/functions.html#int) built-in with a properly formatted `str` object and `base=16` as arguments."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "123"
+      ]
+     },
+     "execution_count": 40,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "int(\"0x7b\", base=16)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Alternatively, we could use a literal notation instead."
    ]
   },
   {
@@ -1449,41 +1447,6 @@
      "output_type": "execute_result"
     }
    ],
-   "source": [
-    "int(\"0x7b\", base=16)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "source": [
-    "Alternatively, we could use a literal notation instead."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 42,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "fragment"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "123"
-      ]
-     },
-     "execution_count": 42,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
    "source": [
     "0x7b"
    ]
@@ -1501,7 +1464,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 43,
+   "execution_count": 42,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -1514,7 +1477,7 @@
        "'0x0'"
       ]
      },
-     "execution_count": 43,
+     "execution_count": 42,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1525,7 +1488,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 44,
+   "execution_count": 43,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -1538,7 +1501,7 @@
        "'0xff'"
       ]
      },
-     "execution_count": 44,
+     "execution_count": 43,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1560,7 +1523,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 45,
+   "execution_count": 44,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -1573,7 +1536,7 @@
        "'0x315'"
       ]
      },
-     "execution_count": 45,
+     "execution_count": 44,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1590,7 +1553,7 @@
     }
    },
    "source": [
-    "For completeness sake, we mention that there is also the [oct()](https://docs.python.org/3/library/functions.html#oct) built-in to obtain an integer's **octal representation**. The logic is the same as for the hexadecimal representation, and we use *eight* instead of *sixteen* digits. That is the equivalent of viewing the binary representations in groups of three bits. As of today, octal representations have become less important, and the data science practitioner may probably live without them quite well."
+    "For completeness sake, we mention that there is also the [oct() <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/functions.html#oct) built-in to obtain an integer's **octal representation**. The logic is the same as for the hexadecimal representation, and we use *eight* instead of *sixteen* digits. That is the equivalent of viewing the binary representations in groups of three bits. As of today, octal representations have become less important, and the data science practitioner may probably live without them quite well."
    ]
   },
   {
@@ -1612,11 +1575,35 @@
     }
    },
    "source": [
-    "While there are conventions that model negative integers with $0$s and $1$s in memory (cf., [Two's Complement](https://en.wikipedia.org/wiki/Two%27s_complement)), Python manages that for us, and we do not look into the theory here for brevity. We have learned all that a practitioner needs to know about how integers are modeled in a computer. The \"*Further Resources*\" section at the end of this chapter provides a video tutorial on how the [Two's Complement](https://en.wikipedia.org/wiki/Two%27s_complement) idea works.\n",
+    "While there are conventions that model negative integers with $0$s and $1$s in memory (cf., [Two's Complement <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_wiki.png\">](https://en.wikipedia.org/wiki/Two%27s_complement)), Python manages that for us, and we do not look into the theory here for brevity. We have learned all that a practitioner needs to know about how integers are modeled in a computer. The \"*Further Resources*\" section at the end of this chapter provides a video tutorial on how the [Two's Complement <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_wiki.png\">](https://en.wikipedia.org/wiki/Two%27s_complement) idea works.\n",
     "\n",
     "The binary and hexadecimal representations of negative integers are identical to their positive counterparts except that they start with a minus sign `-`. However, as the video tutorial at the end of the chapter reveals, that is *not* how the bits are organized in memory."
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'-0b11'"
+      ]
+     },
+     "execution_count": 45,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "bin(-3)"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 46,
@@ -1629,7 +1616,7 @@
     {
      "data": {
       "text/plain": [
-       "'-0b11'"
+       "'-0x3'"
       ]
      },
      "execution_count": 46,
@@ -1638,7 +1625,7 @@
     }
    ],
    "source": [
-    "bin(-3)"
+    "hex(-3)"
    ]
   },
   {
@@ -1653,7 +1640,7 @@
     {
      "data": {
       "text/plain": [
-       "'-0x3'"
+       "'-0b11111111'"
       ]
      },
      "execution_count": 47,
@@ -1662,7 +1649,7 @@
     }
    ],
    "source": [
-    "hex(-3)"
+    "bin(-255)"
    ]
   },
   {
@@ -1673,30 +1660,6 @@
      "slide_type": "skip"
     }
    },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'-0b11111111'"
-      ]
-     },
-     "execution_count": 48,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "bin(-255)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 49,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
    "outputs": [
     {
      "data": {
@@ -1704,7 +1667,7 @@
        "'-0xff'"
       ]
      },
-     "execution_count": 49,
+     "execution_count": 48,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1737,7 +1700,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 50,
+   "execution_count": 49,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -1750,7 +1713,7 @@
        "1"
       ]
      },
-     "execution_count": 50,
+     "execution_count": 49,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1759,6 +1722,30 @@
     "True + False"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 50,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "42"
+      ]
+     },
+     "execution_count": 50,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "41 + True"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 51,
@@ -1767,30 +1754,6 @@
      "slide_type": "fragment"
     }
    },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "42"
-      ]
-     },
-     "execution_count": 51,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "41 + True"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 52,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "fragment"
-    }
-   },
    "outputs": [
     {
      "data": {
@@ -1798,7 +1761,7 @@
        "0.0"
       ]
      },
-     "execution_count": 52,
+     "execution_count": 51,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1815,12 +1778,12 @@
     }
    },
    "source": [
-    "We may explicitly cast `bool` objects as integers ourselves with the [int()](https://docs.python.org/3/library/functions.html#int) built-in."
+    "We may explicitly cast `bool` objects as integers ourselves with the [int() <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/functions.html#int) built-in."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 53,
+   "execution_count": 52,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -1833,7 +1796,7 @@
        "1"
       ]
      },
-     "execution_count": 53,
+     "execution_count": 52,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1844,7 +1807,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 54,
+   "execution_count": 53,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -1857,7 +1820,7 @@
        "0"
       ]
      },
-     "execution_count": 54,
+     "execution_count": 53,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1879,7 +1842,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 55,
+   "execution_count": 54,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -1892,7 +1855,7 @@
        "'0b1'"
       ]
      },
-     "execution_count": 55,
+     "execution_count": 54,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1903,7 +1866,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 56,
+   "execution_count": 55,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -1916,7 +1879,7 @@
        "'0b0'"
       ]
      },
-     "execution_count": 56,
+     "execution_count": 55,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1938,7 +1901,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 57,
+   "execution_count": 56,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -1951,7 +1914,7 @@
        "'0x1'"
       ]
      },
-     "execution_count": 57,
+     "execution_count": 56,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1962,7 +1925,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 58,
+   "execution_count": 57,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -1975,7 +1938,7 @@
        "'0x0'"
       ]
      },
-     "execution_count": 58,
+     "execution_count": 57,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1997,7 +1960,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 59,
+   "execution_count": 58,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -2011,7 +1974,7 @@
      "traceback": [
       "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
       "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-59-af2123a46eb2>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;32m<ipython-input-58-af2123a46eb2>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
       "\u001b[0;31mTypeError\u001b[0m: int() argument must be a string, a bytes-like object or a number, not 'NoneType'"
      ]
     }
@@ -2048,7 +2011,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 60,
+   "execution_count": 59,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -2061,7 +2024,7 @@
        "9"
       ]
      },
-     "execution_count": 60,
+     "execution_count": 59,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2070,6 +2033,30 @@
     "11 & 13"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 60,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'0b1011 & 0b1101'"
+      ]
+     },
+     "execution_count": 60,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "bin(11) + \" & \" + bin(13)  # to show the operands' bits"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 61,
@@ -2078,30 +2065,6 @@
      "slide_type": "skip"
     }
    },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'0b1011 & 0b1101'"
-      ]
-     },
-     "execution_count": 61,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "bin(11) + \" & \" + bin(13)  # to show the operands' bits"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 62,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
    "outputs": [
     {
      "data": {
@@ -2109,7 +2072,7 @@
        "'0b1001'"
       ]
      },
-     "execution_count": 62,
+     "execution_count": 61,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2131,7 +2094,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 63,
+   "execution_count": 62,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -2144,7 +2107,7 @@
        "9"
       ]
      },
-     "execution_count": 63,
+     "execution_count": 62,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2166,7 +2129,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 64,
+   "execution_count": 63,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -2179,7 +2142,7 @@
        "13"
       ]
      },
-     "execution_count": 64,
+     "execution_count": 63,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2188,6 +2151,30 @@
     "9 | 13"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 64,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'0b1001 | 0b1101'"
+      ]
+     },
+     "execution_count": 64,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "bin(9) + \" | \" + bin(13)  # to show the operands' bits"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 65,
@@ -2196,30 +2183,6 @@
      "slide_type": "skip"
     }
    },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'0b1001 | 0b1101'"
-      ]
-     },
-     "execution_count": 65,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "bin(9) + \" | \" + bin(13)  # to show the operands' bits"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 66,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
    "outputs": [
     {
      "data": {
@@ -2227,7 +2190,7 @@
        "'0b1101'"
       ]
      },
-     "execution_count": 66,
+     "execution_count": 65,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2249,7 +2212,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 67,
+   "execution_count": 66,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -2262,7 +2225,7 @@
        "13"
       ]
      },
-     "execution_count": 67,
+     "execution_count": 66,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2284,7 +2247,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 68,
+   "execution_count": 67,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -2297,7 +2260,7 @@
        "4"
       ]
      },
-     "execution_count": 68,
+     "execution_count": 67,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2306,6 +2269,30 @@
     "9 ^ 13"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 68,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'0b1001 ^ 0b1101'"
+      ]
+     },
+     "execution_count": 68,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "bin(9) + \" ^ \" + bin(13)  # to show the operands' bits"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 69,
@@ -2314,30 +2301,6 @@
      "slide_type": "skip"
     }
    },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'0b1001 ^ 0b1101'"
-      ]
-     },
-     "execution_count": 69,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "bin(9) + \" ^ \" + bin(13)  # to show the operands' bits"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 70,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
    "outputs": [
     {
      "data": {
@@ -2345,7 +2308,7 @@
        "'0b100'"
       ]
      },
-     "execution_count": 70,
+     "execution_count": 69,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2367,7 +2330,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 71,
+   "execution_count": 70,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -2380,7 +2343,7 @@
        "4"
       ]
      },
-     "execution_count": 71,
+     "execution_count": 70,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2397,14 +2360,14 @@
     }
    },
    "source": [
-    "The **bitwise NOT** operator `~`, sometimes also called **inversion** operator, is said to \"flip\" the $0$s into $1$s and the $1$s into $0$s. However, it is based on the aforementioned [Two's Complement](https://en.wikipedia.org/wiki/Two%27s_complement) convention and `~x = -(x + 1)` by definition (cf., the [reference](https://docs.python.org/3/reference/expressions.html#unary-arithmetic-and-bitwise-operations)). The full logic behind this, while actually quite simple, is considered out of scope in this book.\n",
+    "The **bitwise NOT** operator `~`, sometimes also called **inversion** operator, is said to \"flip\" the $0$s into $1$s and the $1$s into $0$s. However, it is based on the aforementioned [Two's Complement <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_wiki.png\">](https://en.wikipedia.org/wiki/Two%27s_complement) convention and `~x = -(x + 1)` by definition (cf., the [reference <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/reference/expressions.html#unary-arithmetic-and-bitwise-operations)). The full logic behind this, while actually quite simple, is considered out of scope in this book.\n",
     "\n",
     "We can at least verify the definition by comparing the binary representations of `7` and `-8`: They are indeed the same."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 72,
+   "execution_count": 71,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -2417,7 +2380,7 @@
        "-8"
       ]
      },
-     "execution_count": 72,
+     "execution_count": 71,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2426,6 +2389,30 @@
     "~7"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 72,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 72,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "~7 == -(7 + 1)  # = Two's Complement"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 73,
@@ -2438,7 +2425,7 @@
     {
      "data": {
       "text/plain": [
-       "True"
+       "'-0b1000'"
       ]
      },
      "execution_count": 73,
@@ -2447,7 +2434,7 @@
     }
    ],
    "source": [
-    "~7 == -(7 + 1)  # = Two's Complement"
+    "bin(~7)"
    ]
   },
   {
@@ -2470,30 +2457,6 @@
      "output_type": "execute_result"
     }
    ],
-   "source": [
-    "bin(~7)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 75,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'-0b1000'"
-      ]
-     },
-     "execution_count": 75,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
    "source": [
     "bin(-(7 + 1))"
    ]
@@ -2511,7 +2474,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 76,
+   "execution_count": 75,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -2524,7 +2487,7 @@
        "-1"
       ]
      },
-     "execution_count": 76,
+     "execution_count": 75,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2548,7 +2511,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 77,
+   "execution_count": 76,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -2561,7 +2524,7 @@
        "28"
       ]
      },
-     "execution_count": 77,
+     "execution_count": 76,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2570,6 +2533,30 @@
     "7 << 2"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 77,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'0b111'"
+      ]
+     },
+     "execution_count": 77,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "bin(7)"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 78,
@@ -2582,7 +2569,7 @@
     {
      "data": {
       "text/plain": [
-       "'0b111'"
+       "'0b11100'"
       ]
      },
      "execution_count": 78,
@@ -2591,7 +2578,7 @@
     }
    ],
    "source": [
-    "bin(7)"
+    "bin(7 << 2)"
    ]
   },
   {
@@ -2602,30 +2589,6 @@
      "slide_type": "skip"
     }
    },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'0b11100'"
-      ]
-     },
-     "execution_count": 79,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "bin(7 << 2)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 80,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
    "outputs": [
     {
      "data": {
@@ -2633,7 +2596,7 @@
        "28"
       ]
      },
-     "execution_count": 80,
+     "execution_count": 79,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2655,7 +2618,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 81,
+   "execution_count": 80,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -2668,7 +2631,7 @@
        "3"
       ]
      },
-     "execution_count": 81,
+     "execution_count": 80,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2677,6 +2640,30 @@
     "7 >> 1"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 81,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'0b111'"
+      ]
+     },
+     "execution_count": 81,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "bin(7)"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 82,
@@ -2689,7 +2676,7 @@
     {
      "data": {
       "text/plain": [
-       "'0b111'"
+       "'0b11'"
       ]
      },
      "execution_count": 82,
@@ -2698,7 +2685,7 @@
     }
    ],
    "source": [
-    "bin(7)"
+    "bin(7 >> 1)"
    ]
   },
   {
@@ -2709,30 +2696,6 @@
      "slide_type": "skip"
     }
    },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'0b11'"
-      ]
-     },
-     "execution_count": 83,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "bin(7 >> 1)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 84,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
    "outputs": [
     {
      "data": {
@@ -2740,7 +2703,7 @@
        "3"
       ]
      },
-     "execution_count": 84,
+     "execution_count": 83,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2768,18 +2731,18 @@
     }
    },
    "source": [
-    "As we have seen above, some assumptions need to be made as to how the $0$s and $1$s in a computer's memory are to be translated into numbers. This process becomes a lot more involved when we go beyond integers and model [real numbers](https://en.wikipedia.org/wiki/Real_number) (i.e., the set $\\mathbb{R}$) with possibly infinitely many digits to the right of the period like $1.23$.\n",
+    "As we have seen above, some assumptions need to be made as to how the $0$s and $1$s in a computer's memory are to be translated into numbers. This process becomes a lot more involved when we go beyond integers and model [real numbers <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_wiki.png\">](https://en.wikipedia.org/wiki/Real_number) (i.e., the set $\\mathbb{R}$) with possibly infinitely many digits to the right of the period like $1.23$.\n",
     "\n",
-    "The **[Institute of Electrical and Electronics Engineers](https://en.wikipedia.org/wiki/Institute_of_Electrical_and_Electronics_Engineers)** (IEEE, pronounced \"eye-triple-E\") is one of the important professional associations when it comes to standardizing all kinds of aspects regarding the implementation of soft- and hardware.\n",
+    "The **[Institute of Electrical and Electronics Engineers <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_wiki.png\">](https://en.wikipedia.org/wiki/Institute_of_Electrical_and_Electronics_Engineers)** (IEEE, pronounced \"eye-triple-E\") is one of the important professional associations when it comes to standardizing all kinds of aspects regarding the implementation of soft- and hardware.\n",
     "\n",
-    "The **[IEEE 754](https://en.wikipedia.org/wiki/IEEE_754)** standard defines the so-called **floating-point arithmetic** that is commonly used today by all major programming languages. The standard not only defines how the $0$s and $1$s are organized in memory but also, for example, how values are to be rounded, what happens in exceptional cases like divisions by zero, or what is a zero value in the first place.\n",
+    "The **[IEEE 754 <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_wiki.png\">](https://en.wikipedia.org/wiki/IEEE_754)** standard defines the so-called **floating-point arithmetic** that is commonly used today by all major programming languages. The standard not only defines how the $0$s and $1$s are organized in memory but also, for example, how values are to be rounded, what happens in exceptional cases like divisions by zero, or what is a zero value in the first place.\n",
     "\n",
     "In Python, the simplest way to create a `float` object is to use a literal notation with a dot `.` in it."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 85,
+   "execution_count": 84,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -2790,6 +2753,30 @@
     "b = 42.0"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 85,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "140466238252720"
+      ]
+     },
+     "execution_count": 85,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "id(b)"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 86,
@@ -2802,7 +2789,7 @@
     {
      "data": {
       "text/plain": [
-       "139960518791696"
+       "float"
       ]
      },
      "execution_count": 86,
@@ -2811,7 +2798,7 @@
     }
    ],
    "source": [
-    "id(b)"
+    "type(b)"
    ]
   },
   {
@@ -2822,30 +2809,6 @@
      "slide_type": "fragment"
     }
    },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "float"
-      ]
-     },
-     "execution_count": 87,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "type(b)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 88,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "fragment"
-    }
-   },
    "outputs": [
     {
      "data": {
@@ -2853,7 +2816,7 @@
        "42.0"
       ]
      },
-     "execution_count": 88,
+     "execution_count": 87,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2875,7 +2838,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 89,
+   "execution_count": 88,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -2888,7 +2851,7 @@
        "0.123456789"
       ]
      },
-     "execution_count": 89,
+     "execution_count": 88,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2905,7 +2868,31 @@
     }
    },
    "source": [
-    "In cases where the dot `.` is unnecessary from a mathematical point of view, we either need to end the number with it nevertheless or use the [float()](https://docs.python.org/3/library/functions.html#float) built-in to cast the number explicitly. [float()](https://docs.python.org/3/library/functions.html#float) can process any numeric object or a properly formatted `str` object."
+    "In cases where the dot `.` is unnecessary from a mathematical point of view, we either need to end the number with it nevertheless or use the [float() <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/functions.html#float) built-in to cast the number explicitly. [float() <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/functions.html#float) can process any numeric object or a properly formatted `str` object."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 89,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "42.0"
+      ]
+     },
+     "execution_count": 89,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "42."
    ]
   },
   {
@@ -2913,7 +2900,7 @@
    "execution_count": 90,
    "metadata": {
     "slideshow": {
-     "slide_type": "slide"
+     "slide_type": "fragment"
     }
    },
    "outputs": [
@@ -2929,7 +2916,7 @@
     }
    ],
    "source": [
-    "42."
+    "float(42)"
    ]
   },
   {
@@ -2952,30 +2939,6 @@
      "output_type": "execute_result"
     }
    ],
-   "source": [
-    "float(42)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 92,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "fragment"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "42.0"
-      ]
-     },
-     "execution_count": 92,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
    "source": [
     "float(\"42\")"
    ]
@@ -2993,7 +2956,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 93,
+   "execution_count": 92,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -3006,7 +2969,7 @@
        "42.87"
       ]
      },
-     "execution_count": 93,
+     "execution_count": 92,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3028,7 +2991,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 94,
+   "execution_count": 93,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -3042,7 +3005,7 @@
      "traceback": [
       "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
       "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-94-c7953b8b7956>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mfloat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"42. 87\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;32m<ipython-input-93-c7953b8b7956>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mfloat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"42. 87\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
       "\u001b[0;31mValueError\u001b[0m: could not convert string to float: '42. 87'"
      ]
     }
@@ -3064,7 +3027,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 95,
+   "execution_count": 94,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -3077,7 +3040,7 @@
        "0.3333333333333333"
       ]
      },
-     "execution_count": 95,
+     "execution_count": 94,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3099,7 +3062,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 96,
+   "execution_count": 95,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -3112,7 +3075,7 @@
        "42.0"
       ]
      },
-     "execution_count": 96,
+     "execution_count": 95,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3123,7 +3086,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 97,
+   "execution_count": 96,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -3136,7 +3099,7 @@
        "42.0"
       ]
      },
-     "execution_count": 97,
+     "execution_count": 96,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3169,7 +3132,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 98,
+   "execution_count": 97,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -3182,7 +3145,7 @@
        "1.23"
       ]
      },
-     "execution_count": 98,
+     "execution_count": 97,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3202,6 +3165,28 @@
     "Syntactically, `e` needs a `float` or `int` object in its literal notation on its left and an `int` object on its right, both without a space. Otherwise, we get a `SyntaxError`."
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 98,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "ename": "SyntaxError",
+     "evalue": "invalid syntax (<ipython-input-98-1b5daaac0077>, line 1)",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;36m  File \u001b[0;32m\"<ipython-input-98-1b5daaac0077>\"\u001b[0;36m, line \u001b[0;32m1\u001b[0m\n\u001b[0;31m    1.23 e0\u001b[0m\n\u001b[0m          ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m invalid syntax\n"
+     ]
+    }
+   ],
+   "source": [
+    "1.23 e0"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 99,
@@ -3213,15 +3198,15 @@
    "outputs": [
     {
      "ename": "SyntaxError",
-     "evalue": "invalid syntax (<ipython-input-99-1b5daaac0077>, line 1)",
+     "evalue": "invalid syntax (<ipython-input-99-a236c4a30231>, line 1)",
      "output_type": "error",
      "traceback": [
-      "\u001b[0;36m  File \u001b[0;32m\"<ipython-input-99-1b5daaac0077>\"\u001b[0;36m, line \u001b[0;32m1\u001b[0m\n\u001b[0;31m    1.23 e0\u001b[0m\n\u001b[0m          ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m invalid syntax\n"
+      "\u001b[0;36m  File \u001b[0;32m\"<ipython-input-99-a236c4a30231>\"\u001b[0;36m, line \u001b[0;32m1\u001b[0m\n\u001b[0;31m    1.23e 0\u001b[0m\n\u001b[0m        ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m invalid syntax\n"
      ]
     }
    ],
    "source": [
-    "1.23 e0"
+    "1.23e 0"
    ]
   },
   {
@@ -3235,32 +3220,10 @@
    "outputs": [
     {
      "ename": "SyntaxError",
-     "evalue": "invalid syntax (<ipython-input-100-a236c4a30231>, line 1)",
+     "evalue": "invalid syntax (<ipython-input-100-05b9072d76be>, line 1)",
      "output_type": "error",
      "traceback": [
-      "\u001b[0;36m  File \u001b[0;32m\"<ipython-input-100-a236c4a30231>\"\u001b[0;36m, line \u001b[0;32m1\u001b[0m\n\u001b[0;31m    1.23e 0\u001b[0m\n\u001b[0m        ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m invalid syntax\n"
-     ]
-    }
-   ],
-   "source": [
-    "1.23e 0"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 101,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "outputs": [
-    {
-     "ename": "SyntaxError",
-     "evalue": "invalid syntax (<ipython-input-101-05b9072d76be>, line 1)",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;36m  File \u001b[0;32m\"<ipython-input-101-05b9072d76be>\"\u001b[0;36m, line \u001b[0;32m1\u001b[0m\n\u001b[0;31m    1.23e0.0\u001b[0m\n\u001b[0m           ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m invalid syntax\n"
+      "\u001b[0;36m  File \u001b[0;32m\"<ipython-input-100-05b9072d76be>\"\u001b[0;36m, line \u001b[0;32m1\u001b[0m\n\u001b[0;31m    1.23e0.0\u001b[0m\n\u001b[0m           ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m invalid syntax\n"
      ]
     }
    ],
@@ -3281,7 +3244,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 102,
+   "execution_count": 101,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -3295,7 +3258,7 @@
      "traceback": [
       "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
       "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-102-abc4de6ccb5e>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0me0\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;32m<ipython-input-101-abc4de6ccb5e>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0me0\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
       "\u001b[0;31mNameError\u001b[0m: name 'e0' is not defined"
      ]
     }
@@ -3317,7 +3280,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 103,
+   "execution_count": 102,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -3330,7 +3293,7 @@
        "1.0"
       ]
      },
-     "execution_count": 103,
+     "execution_count": 102,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3352,7 +3315,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 104,
+   "execution_count": 103,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -3365,7 +3328,7 @@
        "1000.0"
       ]
      },
-     "execution_count": 104,
+     "execution_count": 103,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3387,7 +3350,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 105,
+   "execution_count": 104,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -3400,7 +3363,7 @@
        "0.001"
       ]
      },
-     "execution_count": 105,
+     "execution_count": 104,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3428,12 +3391,12 @@
     }
    },
    "source": [
-    "There are also three special values representing \"**not a number,**\" called `nan`, and positive or negative **infinity**, called `inf` or `-inf`, that are created by passing in the corresponding abbreviation as a `str` object to the [float()](https://docs.python.org/3/library/functions.html#float) built-in. These values could be used, for example, as the result of a mathematically undefined operation like division by zero or to model the value of a mathematical function as it goes to infinity."
+    "There are also three special values representing \"**not a number,**\" called `nan`, and positive or negative **infinity**, called `inf` or `-inf`, that are created by passing in the corresponding abbreviation as a `str` object to the [float() <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/functions.html#float) built-in. These values could be used, for example, as the result of a mathematically undefined operation like division by zero or to model the value of a mathematical function as it goes to infinity."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 106,
+   "execution_count": 105,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -3446,7 +3409,7 @@
        "nan"
       ]
      },
-     "execution_count": 106,
+     "execution_count": 105,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3457,12 +3420,36 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 107,
+   "execution_count": 106,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
     }
    },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "inf"
+      ]
+     },
+     "execution_count": 106,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "float(\"+inf\")  # also float(\"+infinity\") or float(\"infinity\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 107,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
    "outputs": [
     {
      "data": {
@@ -3476,7 +3463,7 @@
     }
    ],
    "source": [
-    "float(\"+inf\")  # also float(\"+infinity\") or float(\"infinity\")"
+    "float(\"inf\")  # also float(\"+inf\")"
    ]
   },
   {
@@ -3487,30 +3474,6 @@
      "slide_type": "fragment"
     }
    },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "inf"
-      ]
-     },
-     "execution_count": 108,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "float(\"inf\")  # also float(\"+inf\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 109,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "fragment"
-    }
-   },
    "outputs": [
     {
      "data": {
@@ -3518,7 +3481,7 @@
        "-inf"
       ]
      },
-     "execution_count": 109,
+     "execution_count": 108,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3535,12 +3498,12 @@
     }
    },
    "source": [
-    "`nan` objects *never* compare equal to *anything*, not even to themselves. This happens in accordance with the [IEEE 754](https://en.wikipedia.org/wiki/IEEE_754) standard."
+    "`nan` objects *never* compare equal to *anything*, not even to themselves. This happens in accordance with the [IEEE 754 <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_wiki.png\">](https://en.wikipedia.org/wiki/IEEE_754) standard."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 110,
+   "execution_count": 109,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -3553,7 +3516,7 @@
        "False"
       ]
      },
-     "execution_count": 110,
+     "execution_count": 109,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3570,12 +3533,12 @@
     }
    },
    "source": [
-    "Another caveat is that any arithmetic involving a `nan` object results in `nan`. In other words, the addition below **fails silently** as no error is raised. As this also happens in accordance with the [IEEE 754](https://en.wikipedia.org/wiki/IEEE_754) standard, we *need* to be aware of that and check any data we work with for any `nan` occurrences *before* doing any calculations."
+    "Another caveat is that any arithmetic involving a `nan` object results in `nan`. In other words, the addition below **fails silently** as no error is raised. As this also happens in accordance with the [IEEE 754 <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_wiki.png\">](https://en.wikipedia.org/wiki/IEEE_754) standard, we *need* to be aware of that and check any data we work with for any `nan` occurrences *before* doing any calculations."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 111,
+   "execution_count": 110,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -3588,7 +3551,7 @@
        "nan"
       ]
      },
-     "execution_count": 111,
+     "execution_count": 110,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3610,7 +3573,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 112,
+   "execution_count": 111,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -3623,7 +3586,7 @@
        "True"
       ]
      },
-     "execution_count": 112,
+     "execution_count": 111,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3645,7 +3608,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 113,
+   "execution_count": 112,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -3658,7 +3621,7 @@
        "inf"
       ]
      },
-     "execution_count": 113,
+     "execution_count": 112,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3669,7 +3632,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 114,
+   "execution_count": 113,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -3682,7 +3645,7 @@
        "True"
       ]
      },
-     "execution_count": 114,
+     "execution_count": 113,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3704,7 +3667,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 115,
+   "execution_count": 114,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -3717,7 +3680,7 @@
        "inf"
       ]
      },
-     "execution_count": 115,
+     "execution_count": 114,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3728,7 +3691,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 116,
+   "execution_count": 115,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -3741,7 +3704,7 @@
        "True"
       ]
      },
-     "execution_count": 116,
+     "execution_count": 115,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3763,7 +3726,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 117,
+   "execution_count": 116,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -3776,7 +3739,7 @@
        "inf"
       ]
      },
-     "execution_count": 117,
+     "execution_count": 116,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3787,7 +3750,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 118,
+   "execution_count": 117,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -3800,7 +3763,7 @@
        "True"
       ]
      },
-     "execution_count": 118,
+     "execution_count": 117,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3822,7 +3785,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 119,
+   "execution_count": 118,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -3835,7 +3798,7 @@
        "inf"
       ]
      },
-     "execution_count": 119,
+     "execution_count": 118,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3846,7 +3809,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 120,
+   "execution_count": 119,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -3859,7 +3822,7 @@
        "True"
       ]
      },
-     "execution_count": 120,
+     "execution_count": 119,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3879,6 +3842,30 @@
     "As a caveat, adding infinities of different signs is an *undefined operation* in math and results in a `nan` object. So, if we (accidentally or unknowingly) do this on a real dataset, we do *not* see any error messages, and our program may continue to run with non-meaningful results! This is another example of a piece of code **failing silently**."
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 120,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "nan"
+      ]
+     },
+     "execution_count": 120,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "float(\"inf\") + float(\"-inf\")"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 121,
@@ -3899,30 +3886,6 @@
      "output_type": "execute_result"
     }
    ],
-   "source": [
-    "float(\"inf\") + float(\"-inf\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 122,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "fragment"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "nan"
-      ]
-     },
-     "execution_count": 122,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
    "source": [
     "float(\"inf\") - float(\"inf\")"
    ]
@@ -3946,14 +3909,14 @@
     }
    },
    "source": [
-    "`float` objects are *inherently* imprecise, and there is *nothing* we can do about it! In particular, arithmetic operations with two `float` objects may result in \"weird\" rounding \"errors\" that are strictly deterministic and occur in accordance with the [IEEE 754](https://en.wikipedia.org/wiki/IEEE_754) standard.\n",
+    "`float` objects are *inherently* imprecise, and there is *nothing* we can do about it! In particular, arithmetic operations with two `float` objects may result in \"weird\" rounding \"errors\" that are strictly deterministic and occur in accordance with the [IEEE 754 <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_wiki.png\">](https://en.wikipedia.org/wiki/IEEE_754) standard.\n",
     "\n",
     "For example, let's add `1` to `1e15` and `1e16`, respectively. In the latter case, the `1` somehow gets \"lost.\""
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 123,
+   "execution_count": 122,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -3966,7 +3929,7 @@
        "1000000000000001.0"
       ]
      },
-     "execution_count": 123,
+     "execution_count": 122,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3977,7 +3940,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 124,
+   "execution_count": 123,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -3990,7 +3953,7 @@
        "1e+16"
       ]
      },
-     "execution_count": 124,
+     "execution_count": 123,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -4012,7 +3975,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 125,
+   "execution_count": 124,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -4023,6 +3986,30 @@
     "from math import sqrt"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 125,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "2.0000000000000004"
+      ]
+     },
+     "execution_count": 125,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "sqrt(2) ** 2"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 126,
@@ -4031,30 +4018,6 @@
      "slide_type": "fragment"
     }
    },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "2.0000000000000004"
-      ]
-     },
-     "execution_count": 126,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "sqrt(2) ** 2"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 127,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "fragment"
-    }
-   },
    "outputs": [
     {
      "data": {
@@ -4062,7 +4025,7 @@
        "0.30000000000000004"
       ]
      },
-     "execution_count": 127,
+     "execution_count": 126,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -4082,6 +4045,30 @@
     "This may become a problem if we rely on equality checks in our programs."
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 127,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "False"
+      ]
+     },
+     "execution_count": 127,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "sqrt(2) ** 2 == 2"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 128,
@@ -4102,30 +4089,6 @@
      "output_type": "execute_result"
     }
    ],
-   "source": [
-    "sqrt(2) ** 2 == 2"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 129,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "fragment"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "False"
-      ]
-     },
-     "execution_count": 129,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
    "source": [
     "0.1 + 0.2 == 0.3"
    ]
@@ -4143,7 +4106,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 130,
+   "execution_count": 129,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -4154,6 +4117,30 @@
     "threshold = 1e-15"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 130,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 130,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "abs((sqrt(2) ** 2) - 2) < threshold"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 131,
@@ -4174,30 +4161,6 @@
      "output_type": "execute_result"
     }
    ],
-   "source": [
-    "abs((sqrt(2) ** 2) - 2) < threshold"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 132,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "fragment"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "True"
-      ]
-     },
-     "execution_count": 132,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
    "source": [
     "abs((0.1 + 0.2) - 0.3) < threshold"
    ]
@@ -4210,14 +4173,14 @@
     }
    },
    "source": [
-    "The built-in [format()](https://docs.python.org/3/library/functions.html#format) function allows us to show the **significant digits** of a `float` number as they exist in memory to arbitrary precision. To exemplify it, let's view a couple of `float` objects with `50` digits. This analysis reveals that almost no `float` number is precise! After 14 or 15 digits \"weird\" things happen. As we see further below, the \"random\" digits ending the `float` numbers do *not* \"physically\" exist in memory! Rather, they are \"calculated\" by the [format()](https://docs.python.org/3/library/functions.html#format) function that is forced to show `50` digits.\n",
+    "The built-in [format() <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/functions.html#format) function allows us to show the **significant digits** of a `float` number as they exist in memory to arbitrary precision. To exemplify it, let's view a couple of `float` objects with `50` digits. This analysis reveals that almost no `float` number is precise! After 14 or 15 digits \"weird\" things happen. As we see further below, the \"random\" digits ending the `float` numbers do *not* \"physically\" exist in memory! Rather, they are \"calculated\" by the [format() <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/functions.html#format) function that is forced to show `50` digits.\n",
     "\n",
-    "The [format()](https://docs.python.org/3/library/functions.html#format) function is different from the [format()](https://docs.python.org/3/library/stdtypes.html#str.format) method on `str` objects introduced in the next chapter (cf., [Chapter 6](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/06_text_00_lecture.ipynb#format%28%29-Method)): Yet, both work with the so-called [format specification mini-language](https://docs.python.org/3/library/string.html#format-specification-mini-language): `\".50f\"` is the instruction to show `50` digits of a `float` number."
+    "The [format() <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/functions.html#format) function is different from the [format() <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/stdtypes.html#str.format) method on `str` objects introduced in the next chapter (cf., [Chapter 6 <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/06_text_00_content.ipynb#format%28%29-Method)): Yet, both work with the so-called [format specification mini-language <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/string.html#format-specification-mini-language): `\".50f\"` is the instruction to show `50` digits of a `float` number."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 133,
+   "execution_count": 132,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -4230,7 +4193,7 @@
        "'0.10000000000000000555111512312578270211815834045410'"
       ]
      },
-     "execution_count": 133,
+     "execution_count": 132,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -4239,6 +4202,30 @@
     "format(0.1, \".50f\")"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 133,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'0.20000000000000001110223024625156540423631668090820'"
+      ]
+     },
+     "execution_count": 133,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "format(0.2, \".50f\")"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 134,
@@ -4247,30 +4234,6 @@
      "slide_type": "fragment"
     }
    },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'0.20000000000000001110223024625156540423631668090820'"
-      ]
-     },
-     "execution_count": 134,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "format(0.2, \".50f\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 135,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "fragment"
-    }
-   },
    "outputs": [
     {
      "data": {
@@ -4278,7 +4241,7 @@
        "'0.29999999999999998889776975374843459576368331909180'"
       ]
      },
-     "execution_count": 135,
+     "execution_count": 134,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -4289,7 +4252,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 136,
+   "execution_count": 135,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -4302,7 +4265,7 @@
        "'0.33333333333333331482961625624739099293947219848633'"
       ]
      },
-     "execution_count": 136,
+     "execution_count": 135,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -4319,13 +4282,26 @@
     }
    },
    "source": [
-    "The [format()](https://docs.python.org/3/library/functions.html#format) function does *not* round a `float` object in the mathematical sense! It just allows us to show an arbitrary number of the digits as stored in memory, and it also does *not* change these.\n",
+    "The [format() <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/functions.html#format) function does *not* round a `float` object in the mathematical sense! It just allows us to show an arbitrary number of the digits as stored in memory, and it also does *not* change these.\n",
     "\n",
-    "On the contrary, the built-in [round()](https://docs.python.org/3/library/functions.html#round) function creates a *new* numeric object that is a rounded version of the one passed in as the argument. It adheres to the common rules of math.\n",
+    "On the contrary, the built-in [round() <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/functions.html#round) function creates a *new* numeric object that is a rounded version of the one passed in as the argument. It adheres to the common rules of math.\n",
     "\n",
     "For example, let's round `1 / 3` to five decimals. The obtained value for `roughly_a_third` is also *imprecise* but different from the \"exact\" representation of `1 / 3` above."
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 136,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "roughly_a_third = round(1 / 3, 5)"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 137,
@@ -4334,9 +4310,20 @@
      "slide_type": "fragment"
     }
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.33333"
+      ]
+     },
+     "execution_count": 137,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "roughly_a_third = round(1 / 3, 5)"
+    "roughly_a_third"
    ]
   },
   {
@@ -4347,30 +4334,6 @@
      "slide_type": "fragment"
     }
    },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "0.33333"
-      ]
-     },
-     "execution_count": 138,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "roughly_a_third"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 139,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "fragment"
-    }
-   },
    "outputs": [
     {
      "data": {
@@ -4378,7 +4341,7 @@
        "'0.33333000000000001517008740847813896834850311279297'"
       ]
      },
-     "execution_count": 139,
+     "execution_count": 138,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -4400,7 +4363,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 140,
+   "execution_count": 139,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -4413,7 +4376,7 @@
        "'0.12500000000000000000000000000000000000000000000000'"
       ]
      },
-     "execution_count": 140,
+     "execution_count": 139,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -4422,6 +4385,30 @@
     "format(0.125, \".50f\")"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 140,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'0.25000000000000000000000000000000000000000000000000'"
+      ]
+     },
+     "execution_count": 140,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "format(0.25, \".50f\")"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 141,
@@ -4430,30 +4417,6 @@
      "slide_type": "fragment"
     }
    },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'0.25000000000000000000000000000000000000000000000000'"
-      ]
-     },
-     "execution_count": 141,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "format(0.25, \".50f\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 142,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "fragment"
-    }
-   },
    "outputs": [
     {
      "data": {
@@ -4461,7 +4424,7 @@
        "True"
       ]
      },
-     "execution_count": 142,
+     "execution_count": 141,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -4489,7 +4452,7 @@
     }
    },
    "source": [
-    "To understand these subtleties, we need to look at the **[binary representation of floats](https://en.wikipedia.org/wiki/Double-precision_floating-point_format)** and review the basics of the **[IEEE 754](https://en.wikipedia.org/wiki/IEEE_754)** standard. On modern machines, floats are modeled in so-called double precision with $64$ bits that are grouped as in the figure below. The first bit determines the sign ($0$ for plus, $1$ for minus), the next $11$ bits represent an $exponent$ term, and the last $52$ bits resemble the actual significant digits, the so-called $fraction$ part. The three groups are put together like so:"
+    "To understand these subtleties, we need to look at the **[binary representation of floats <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_wiki.png\">](https://en.wikipedia.org/wiki/Double-precision_floating-point_format)** and review the basics of the **[IEEE 754 <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_wiki.png\">](https://en.wikipedia.org/wiki/IEEE_754)** standard. On modern machines, floats are modeled in so-called double precision with $64$ bits that are grouped as in the figure below. The first bit determines the sign ($0$ for plus, $1$ for minus), the next $11$ bits represent an $exponent$ term, and the last $52$ bits resemble the actual significant digits, the so-called $fraction$ part. The three groups are put together like so:"
    ]
   },
   {
@@ -4511,7 +4474,7 @@
     }
    },
    "source": [
-    "A $1.$ is implicitly prepended as the first digit, and both, $fraction$ and $exponent$, are stored in base $2$ representation (i.e., they both are interpreted like integers above). As $exponent$ is consequently non-negative, between $0_{10}$ and $2047_{10}$ to be precise, the $-1023$, called the exponent bias, centers the entire $2^{exponent-1023}$ term around $1$ and allows the period within the $1.fraction$ part be shifted into either direction by the same amount. Floating-point numbers received their name as the period, formally called the **[radix point](https://en.wikipedia.org/wiki/Radix_point)**, \"floats\" along the significant digits. As an aside, an $exponent$ of all $0$s or all $1$s is used to model the special values `nan` or `inf`.\n",
+    "A $1.$ is implicitly prepended as the first digit, and both, $fraction$ and $exponent$, are stored in base $2$ representation (i.e., they both are interpreted like integers above). As $exponent$ is consequently non-negative, between $0_{10}$ and $2047_{10}$ to be precise, the $-1023$, called the exponent bias, centers the entire $2^{exponent-1023}$ term around $1$ and allows the period within the $1.fraction$ part be shifted into either direction by the same amount. Floating-point numbers received their name as the period, formally called the **[radix point <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_wiki.png\">](https://en.wikipedia.org/wiki/Radix_point)**, \"floats\" along the significant digits. As an aside, an $exponent$ of all $0$s or all $1$s is used to model the special values `nan` or `inf`.\n",
     "\n",
     "As the standard defines the exponent part to come as a power of $2$, we now see why `0.125` is a *precise* float: It can be represented as a power of $2$, i.e., $0.125 = (-1)^0 * 1.0 * 2^{1020-1023} = 2^{-3} = \\frac{1}{8}$. In other words, the floating-point representation of $0.125_{10}$ is $0_2$, $1111111100_2 = 1020_{10}$, and $0_2$ for the three groups, respectively."
    ]
@@ -4541,14 +4504,14 @@
     "\n",
     "This [blog post](http://fabiensanglard.net/floating_point_visually_explained/) gives another neat and *visual* way as to how to think of floats. It also explains why floats become worse approximations of the reals as their absolute values increase.\n",
     "\n",
-    "The Python [documentation](https://docs.python.org/3/tutorial/floatingpoint.html) provides another good discussion of floats and the goodness of their approximations.\n",
+    "The Python [documentation <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/tutorial/floatingpoint.html) provides another good discussion of floats and the goodness of their approximations.\n",
     "\n",
-    "If we are interested in the exact bits behind a `float` object, we use the [hex()](https://docs.python.org/3/library/stdtypes.html#float.hex) method that returns a `str` object beginning with `\"0x1.\"` followed by the $fraction$ in hexadecimal notation and the $exponent$ as an integer after subtraction of $1023$ and separated by a `\"p\"`."
+    "If we are interested in the exact bits behind a `float` object, we use the [hex() <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/stdtypes.html#float.hex) method that returns a `str` object beginning with `\"0x1.\"` followed by the $fraction$ in hexadecimal notation and the $exponent$ as an integer after subtraction of $1023$ and separated by a `\"p\"`."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 143,
+   "execution_count": 142,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -4561,7 +4524,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 144,
+   "execution_count": 143,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -4574,7 +4537,7 @@
        "'0x1.0000000000000p-3'"
       ]
      },
-     "execution_count": 144,
+     "execution_count": 143,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -4591,12 +4554,12 @@
     }
    },
    "source": [
-    "Also, the [as_integer_ratio()](https://docs.python.org/3/library/stdtypes.html#float.as_integer_ratio) method returns the two smallest integers whose ratio best approximates a `float` object."
+    "Also, the [as_integer_ratio() <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/stdtypes.html#float.as_integer_ratio) method returns the two smallest integers whose ratio best approximates a `float` object."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 145,
+   "execution_count": 144,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -4609,7 +4572,7 @@
        "(1, 8)"
       ]
      },
-     "execution_count": 145,
+     "execution_count": 144,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -4620,7 +4583,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 146,
+   "execution_count": 145,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -4633,7 +4596,7 @@
        "'0x1.555475a31a4bep-2'"
       ]
      },
-     "execution_count": 146,
+     "execution_count": 145,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -4644,7 +4607,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 147,
+   "execution_count": 146,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -4657,7 +4620,7 @@
        "(3002369727582815, 9007199254740992)"
       ]
      },
-     "execution_count": 147,
+     "execution_count": 146,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -4679,7 +4642,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 148,
+   "execution_count": 147,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -4690,6 +4653,30 @@
     "zero = 0.0"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 148,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'0x0.0p+0'"
+      ]
+     },
+     "execution_count": 148,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "zero.hex()"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 149,
@@ -4698,30 +4685,6 @@
      "slide_type": "skip"
     }
    },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'0x0.0p+0'"
-      ]
-     },
-     "execution_count": 149,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "zero.hex()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 150,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
    "outputs": [
     {
      "data": {
@@ -4729,7 +4692,7 @@
        "(0, 1)"
       ]
      },
-     "execution_count": 150,
+     "execution_count": 149,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -4746,7 +4709,31 @@
     }
    },
    "source": [
-    "As seen in [Chapter 1](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/01_elements_00_lecture.ipynb#%28Data%29-Type-%2F-%22Behavior%22), the [is_integer()](https://docs.python.org/3/library/stdtypes.html#float.is_integer) method tells us if a `float` can be casted as an `int` object without any loss in precision."
+    "As seen in [Chapter 1 <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/01_elements_00_content.ipynb#%28Data%29-Type-%2F-%22Behavior%22), the [is_integer() <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/stdtypes.html#float.is_integer) method tells us if a `float` can be casted as an `int` object without any loss in precision."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 150,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "False"
+      ]
+     },
+     "execution_count": 150,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "roughly_a_third.is_integer()"
    ]
   },
   {
@@ -4757,30 +4744,6 @@
      "slide_type": "skip"
     }
    },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "False"
-      ]
-     },
-     "execution_count": 151,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "roughly_a_third.is_integer()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 152,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
    "outputs": [
     {
      "data": {
@@ -4788,7 +4751,7 @@
        "True"
       ]
      },
-     "execution_count": 152,
+     "execution_count": 151,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -4807,12 +4770,12 @@
     }
    },
    "source": [
-    "As the exact implementation of floats may vary and be dependent on a particular Python installation, we look up the [float_info](https://docs.python.org/3/library/sys.html#sys.float_info) attribute in the [sys](https://docs.python.org/3/library/sys.html) module in the [standard library](https://docs.python.org/3/library/index.html) to check the details. Usually, this is not necessary."
+    "As the exact implementation of floats may vary and be dependent on a particular Python installation, we look up the [float_info <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/sys.html#sys.float_info) attribute in the [sys <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/sys.html) module in the [standard library <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/index.html) to check the details. Usually, this is not necessary."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 153,
+   "execution_count": 152,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -4825,7 +4788,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 154,
+   "execution_count": 153,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -4838,7 +4801,7 @@
        "sys.float_info(max=1.7976931348623157e+308, max_exp=1024, max_10_exp=308, min=2.2250738585072014e-308, min_exp=-1021, min_10_exp=-307, dig=15, mant_dig=53, epsilon=2.220446049250313e-16, radix=2, rounds=1)"
       ]
      },
-     "execution_count": 154,
+     "execution_count": 153,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -4866,14 +4829,14 @@
     }
    },
    "source": [
-    "The [decimal](https://docs.python.org/3/library/decimal.html) module in the [standard library](https://docs.python.org/3/library/index.html) provides a [Decimal](https://docs.python.org/3/library/decimal.html#decimal.Decimal) type that may be used to represent any real number to a user-defined level of precision: \"User-defined\" does *not* mean an infinite or exact precision! The `Decimal` type merely allows us to work with a number of bits *different* from the $64$ as specified for the `float` type and also to customize the rounding rules and some other settings.\n",
+    "The [decimal <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/decimal.html) module in the [standard library <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/index.html) provides a [Decimal <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/decimal.html#decimal.Decimal) type that may be used to represent any real number to a user-defined level of precision: \"User-defined\" does *not* mean an infinite or exact precision! The `Decimal` type merely allows us to work with a number of bits *different* from the $64$ as specified for the `float` type and also to customize the rounding rules and some other settings.\n",
     "\n",
-    "We import the `Decimal` type and also the [getcontext()](https://docs.python.org/3/library/decimal.html#decimal.getcontext) function from the [decimal](https://docs.python.org/3/library/decimal.html) module."
+    "We import the `Decimal` type and also the [getcontext() <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/decimal.html#decimal.getcontext) function from the [decimal <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/decimal.html) module."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 155,
+   "execution_count": 154,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -4892,12 +4855,12 @@
     }
    },
    "source": [
-    "[getcontext()](https://docs.python.org/3/library/decimal.html#decimal.getcontext) shows us how the [decimal](https://docs.python.org/3/library/decimal.html) module is set up. By default, the precision is set to `28` significant digits, which is roughly twice as many as with `float` objects."
+    "[getcontext() <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/decimal.html#decimal.getcontext) shows us how the [decimal <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/decimal.html) module is set up. By default, the precision is set to `28` significant digits, which is roughly twice as many as with `float` objects."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 156,
+   "execution_count": 155,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -4910,7 +4873,7 @@
        "Context(prec=28, rounding=ROUND_HALF_EVEN, Emin=-999999, Emax=999999, capitals=1, clamp=0, flags=[], traps=[InvalidOperation, DivisionByZero, Overflow])"
       ]
      },
-     "execution_count": 156,
+     "execution_count": 155,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -4932,7 +4895,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 157,
+   "execution_count": 156,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -4945,7 +4908,7 @@
        "Decimal('42')"
       ]
      },
-     "execution_count": 157,
+     "execution_count": 156,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -4954,6 +4917,30 @@
     "Decimal(42)"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 157,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Decimal('0.1')"
+      ]
+     },
+     "execution_count": 157,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "Decimal(\"0.1\")"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 158,
@@ -4962,30 +4949,6 @@
      "slide_type": "fragment"
     }
    },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Decimal('0.1')"
-      ]
-     },
-     "execution_count": 158,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "Decimal(\"0.1\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 159,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "fragment"
-    }
-   },
    "outputs": [
     {
      "data": {
@@ -4993,7 +4956,7 @@
        "Decimal('0.001')"
       ]
      },
-     "execution_count": 159,
+     "execution_count": 158,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -5015,7 +4978,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 160,
+   "execution_count": 159,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -5028,7 +4991,7 @@
        "Decimal('0.1000000000000000055511151231257827021181583404541015625')"
       ]
      },
-     "execution_count": 160,
+     "execution_count": 159,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -5050,7 +5013,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 161,
+   "execution_count": 160,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -5063,7 +5026,7 @@
        "Decimal('0.3')"
       ]
      },
-     "execution_count": 161,
+     "execution_count": 160,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -5074,7 +5037,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 162,
+   "execution_count": 161,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -5087,7 +5050,7 @@
        "True"
       ]
      },
-     "execution_count": 162,
+     "execution_count": 161,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -5109,7 +5072,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 163,
+   "execution_count": 162,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -5122,7 +5085,7 @@
        "Decimal('0.30000')"
       ]
      },
-     "execution_count": 163,
+     "execution_count": 162,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -5133,7 +5096,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 164,
+   "execution_count": 163,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -5146,7 +5109,7 @@
        "True"
       ]
      },
-     "execution_count": 164,
+     "execution_count": 163,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -5168,7 +5131,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 165,
+   "execution_count": 164,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -5181,7 +5144,7 @@
        "Decimal('42')"
       ]
      },
-     "execution_count": 165,
+     "execution_count": 164,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -5192,7 +5155,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 166,
+   "execution_count": 165,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -5205,7 +5168,7 @@
        "Decimal('42.0')"
       ]
      },
-     "execution_count": 166,
+     "execution_count": 165,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -5216,7 +5179,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 167,
+   "execution_count": 166,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -5229,7 +5192,7 @@
        "Decimal('0.1')"
       ]
      },
-     "execution_count": 167,
+     "execution_count": 166,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -5246,7 +5209,31 @@
     }
    },
    "source": [
-    "To verify the precision, we apply the built-in [format()](https://docs.python.org/3/library/functions.html#format) function to the previous code cell and compare it with the same division resulting in a `float` object."
+    "To verify the precision, we apply the built-in [format() <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/functions.html#format) function to the previous code cell and compare it with the same division resulting in a `float` object."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 167,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'0.10000000000000000000000000000000000000000000000000'"
+      ]
+     },
+     "execution_count": 167,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "format(Decimal(1) / 10, \".50f\")"
    ]
   },
   {
@@ -5257,30 +5244,6 @@
      "slide_type": "skip"
     }
    },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'0.10000000000000000000000000000000000000000000000000'"
-      ]
-     },
-     "execution_count": 168,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "format(Decimal(1) / 10, \".50f\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 169,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
    "outputs": [
     {
      "data": {
@@ -5288,7 +5251,7 @@
        "'0.10000000000000000555111512312578270211815834045410'"
       ]
      },
-     "execution_count": 169,
+     "execution_count": 168,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -5310,7 +5273,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 170,
+   "execution_count": 169,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -5324,7 +5287,7 @@
      "traceback": [
       "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
       "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-170-a3cea145e6d1>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0;36m1.0\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0mDecimal\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m42\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;32m<ipython-input-169-a3cea145e6d1>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0;36m1.0\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0mDecimal\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m42\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
       "\u001b[0;31mTypeError\u001b[0m: unsupported operand type(s) for *: 'float' and 'decimal.Decimal'"
      ]
     }
@@ -5341,12 +5304,12 @@
     }
    },
    "source": [
-    "To preserve the precision for more advanced mathematical functions, `Decimal` objects come with many **methods bound** on them. For example, [ln()](https://docs.python.org/3/library/decimal.html#decimal.Decimal.ln) and [log10()](https://docs.python.org/3/library/decimal.html#decimal.Decimal.log10) take the logarithm while [sqrt()](https://docs.python.org/3/library/decimal.html#decimal.Decimal.sqrt) calculates the square root. The methods always return a *new* `Decimal` object. We must never use the functions in the [math](https://docs.python.org/3/library/math.html) module in the [standard library](https://docs.python.org/3/library/index.html) with `Decimal` objects as they do *not* preserve precision."
+    "To preserve the precision for more advanced mathematical functions, `Decimal` objects come with many **methods bound** on them. For example, [ln() <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/decimal.html#decimal.Decimal.ln) and [log10() <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/decimal.html#decimal.Decimal.log10) take the logarithm while [sqrt() <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/decimal.html#decimal.Decimal.sqrt) calculates the square root. The methods always return a *new* `Decimal` object. We must never use the functions in the [math <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/math.html) module in the [standard library <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/index.html) with `Decimal` objects as they do *not* preserve precision."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 171,
+   "execution_count": 170,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -5359,7 +5322,7 @@
        "Decimal('2')"
       ]
      },
-     "execution_count": 171,
+     "execution_count": 170,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -5370,7 +5333,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 172,
+   "execution_count": 171,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -5383,7 +5346,7 @@
        "Decimal('1.414213562373095048801688724')"
       ]
      },
-     "execution_count": 172,
+     "execution_count": 171,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -5400,14 +5363,14 @@
     }
    },
    "source": [
-    "The object returned by the [sqrt()](https://docs.python.org/3/library/decimal.html#decimal.Decimal.sqrt) method is still limited in precision: This must be so as, for example, $\\sqrt{2}$ is an **[irrational number](https://en.wikipedia.org/wiki/Irrational_number)** that *cannot* be expressed with absolute precision using *any* number of bits, even in theory.\n",
+    "The object returned by the [sqrt() <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/decimal.html#decimal.Decimal.sqrt) method is still limited in precision: This must be so as, for example, $\\sqrt{2}$ is an **[irrational number <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_wiki.png\">](https://en.wikipedia.org/wiki/Irrational_number)** that *cannot* be expressed with absolute precision using *any* number of bits, even in theory.\n",
     "\n",
     "We see this as raising $\\sqrt{2}$ to the power of $2$ results in an imprecise value as before!"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 173,
+   "execution_count": 172,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -5420,7 +5383,7 @@
        "Decimal('1.999999999999999999999999999')"
       ]
      },
-     "execution_count": 173,
+     "execution_count": 172,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -5439,14 +5402,14 @@
     }
    },
    "source": [
-    "However, the [quantize()](https://docs.python.org/3/library/decimal.html#decimal.Decimal.quantize) method allows us to [quantize](https://www.dictionary.com/browse/quantize) (i.e., \"round\") a `Decimal` number at any precision that is *smaller* than the set precision. It takes the number of decimals to the right of the period of the `Decimal` argument we pass in and rounds accordingly.\n",
+    "However, the [quantize() <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/decimal.html#decimal.Decimal.quantize) method allows us to [quantize](https://www.dictionary.com/browse/quantize) (i.e., \"round\") a `Decimal` number at any precision that is *smaller* than the set precision. It takes the number of decimals to the right of the period of the `Decimal` argument we pass in and rounds accordingly.\n",
     "\n",
     "For example, as the overall imprecise value of `two` still has an internal precision of `28` digits, we can correctly round it to *four* decimals (i.e., `Decimal(\"0.0000\")` has four decimals)."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 174,
+   "execution_count": 173,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -5459,7 +5422,7 @@
        "Decimal('2.0000')"
       ]
      },
-     "execution_count": 174,
+     "execution_count": 173,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -5481,7 +5444,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 175,
+   "execution_count": 174,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -5495,7 +5458,7 @@
      "traceback": [
       "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
       "\u001b[0;31mInvalidOperation\u001b[0m                          Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-175-5b555b105c8f>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mtwo\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mquantize\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mDecimal\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"1e-28\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;32m<ipython-input-174-5b555b105c8f>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mtwo\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mquantize\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mDecimal\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"1e-28\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
       "\u001b[0;31mInvalidOperation\u001b[0m: [<class 'decimal.InvalidOperation'>]"
      ]
     }
@@ -5515,6 +5478,41 @@
     "Consequently, with this little workaround $\\sqrt{2}^2 = 2$ works, even in Python."
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 175,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 175,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "two.quantize(Decimal(\"0.0000\")) == 2"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "The downside is that the entire expression is not as pretty as `sqrt(2) ** 2 == 2` from above."
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 176,
@@ -5535,41 +5533,6 @@
      "output_type": "execute_result"
     }
    ],
-   "source": [
-    "two.quantize(Decimal(\"0.0000\")) == 2"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "source": [
-    "The downside is that the entire expression is not as pretty as `sqrt(2) ** 2 == 2` from above."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 177,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "True"
-      ]
-     },
-     "execution_count": 177,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
    "source": [
     "(Decimal(2).sqrt() ** 2).quantize(Decimal(\"0.0000\")) == 2"
    ]
@@ -5587,7 +5550,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 178,
+   "execution_count": 177,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -5600,7 +5563,7 @@
        "Decimal('NaN')"
       ]
      },
-     "execution_count": 178,
+     "execution_count": 177,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -5622,7 +5585,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 179,
+   "execution_count": 178,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -5635,7 +5598,7 @@
        "False"
       ]
      },
-     "execution_count": 179,
+     "execution_count": 178,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -5657,7 +5620,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 180,
+   "execution_count": 179,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -5670,7 +5633,7 @@
        "Decimal('Infinity')"
       ]
      },
-     "execution_count": 180,
+     "execution_count": 179,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -5679,6 +5642,30 @@
     "Decimal(\"inf\")"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 180,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Decimal('-Infinity')"
+      ]
+     },
+     "execution_count": 180,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "Decimal(\"-inf\")"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 181,
@@ -5691,7 +5678,7 @@
     {
      "data": {
       "text/plain": [
-       "Decimal('-Infinity')"
+       "Decimal('Infinity')"
       ]
      },
      "execution_count": 181,
@@ -5700,7 +5687,7 @@
     }
    ],
    "source": [
-    "Decimal(\"-inf\")"
+    "Decimal(\"inf\") + 42"
    ]
   },
   {
@@ -5711,30 +5698,6 @@
      "slide_type": "skip"
     }
    },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Decimal('Infinity')"
-      ]
-     },
-     "execution_count": 182,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "Decimal(\"inf\") + 42"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 183,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
    "outputs": [
     {
      "data": {
@@ -5742,7 +5705,7 @@
        "True"
       ]
      },
-     "execution_count": 183,
+     "execution_count": 182,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -5762,6 +5725,31 @@
     "As with `float` objects, we cannot add infinities of different signs: Now, get a module-specific `InvalidOperation` exception instead of a `nan` value. Here, **failing loudly** is a good thing as it prevents us from working with invalid results."
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 183,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "ename": "InvalidOperation",
+     "evalue": "[<class 'decimal.InvalidOperation'>]",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mInvalidOperation\u001b[0m                          Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-183-87950ebbd6ef>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mDecimal\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"inf\"\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mDecimal\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"-inf\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;31mInvalidOperation\u001b[0m: [<class 'decimal.InvalidOperation'>]"
+     ]
+    }
+   ],
+   "source": [
+    "Decimal(\"inf\") + Decimal(\"-inf\")"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 184,
@@ -5778,32 +5766,7 @@
      "traceback": [
       "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
       "\u001b[0;31mInvalidOperation\u001b[0m                          Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-184-87950ebbd6ef>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mDecimal\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"inf\"\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mDecimal\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"-inf\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
-      "\u001b[0;31mInvalidOperation\u001b[0m: [<class 'decimal.InvalidOperation'>]"
-     ]
-    }
-   ],
-   "source": [
-    "Decimal(\"inf\") + Decimal(\"-inf\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 185,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "outputs": [
-    {
-     "ename": "InvalidOperation",
-     "evalue": "[<class 'decimal.InvalidOperation'>]",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mInvalidOperation\u001b[0m                          Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-185-996083f200c6>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mDecimal\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"inf\"\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0mDecimal\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"inf\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;32m<ipython-input-184-996083f200c6>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mDecimal\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"inf\"\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0mDecimal\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"inf\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
       "\u001b[0;31mInvalidOperation\u001b[0m: [<class 'decimal.InvalidOperation'>]"
      ]
     }
@@ -5820,7 +5783,7 @@
     }
    },
    "source": [
-    "For more information on the `Decimal` type, see the tutorial at [PYMOTW](https://pymotw.com/3/decimal/index.html) or the official [documentation](https://docs.python.org/3/library/decimal.html)."
+    "For more information on the `Decimal` type, see the tutorial at [PYMOTW](https://pymotw.com/3/decimal/index.html) or the official [documentation <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/decimal.html)."
    ]
   },
   {
@@ -5842,14 +5805,14 @@
     }
    },
    "source": [
-    "If the numbers in an application can be expressed as [rational numbers](https://en.wikipedia.org/wiki/Rational_number) (i.e., the set $\\mathbb{Q}$), we may model them as a [Fraction](https://docs.python.org/3/library/fractions.html#fractions.Fraction) type from the [fractions](https://docs.python.org/3/library/fractions.html) module in the [standard library](https://docs.python.org/3/library/index.html). As any fraction can always be formulated as the division of one integer by another, `Fraction` objects are inherently precise, just as `int` objects on their own. Further, we maintain the precision as long as we do not use them in a mathematical operation that could result in an irrational number (e.g., taking the square root).\n",
+    "If the numbers in an application can be expressed as [rational numbers <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_wiki.png\">](https://en.wikipedia.org/wiki/Rational_number) (i.e., the set $\\mathbb{Q}$), we may model them as a [Fraction <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/fractions.html#fractions.Fraction) type from the [fractions <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/fractions.html) module in the [standard library <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/index.html). As any fraction can always be formulated as the division of one integer by another, `Fraction` objects are inherently precise, just as `int` objects on their own. Further, we maintain the precision as long as we do not use them in a mathematical operation that could result in an irrational number (e.g., taking the square root).\n",
     "\n",
-    "We import the `Fraction` type from the [fractions](https://docs.python.org/3/library/fractions.html) module."
+    "We import the `Fraction` type from the [fractions <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/fractions.html) module."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 186,
+   "execution_count": 185,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -5871,6 +5834,30 @@
     "Among others, there are two simple ways to create a `Fraction` object: We either instantiate one with two `int` objects representing the numerator and denominator or with a `str` object. In the latter case, we have two options again and use either the format \"numerator/denominator\" (i.e., *without* any spaces) or the same format as for `float` and `Decimal` objects above."
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 186,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Fraction(1, 3)"
+      ]
+     },
+     "execution_count": 186,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "Fraction(1, 3)  # 1 / 3 with \"full\" precision"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 187,
@@ -5892,7 +5879,7 @@
     }
    ],
    "source": [
-    "Fraction(1, 3)  # 1 / 3 with \"full\" precision"
+    "Fraction(\"1/3\")  # 1 / 3 with \"full\" precision"
    ]
   },
   {
@@ -5903,30 +5890,6 @@
      "slide_type": "fragment"
     }
    },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Fraction(1, 3)"
-      ]
-     },
-     "execution_count": 188,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "Fraction(\"1/3\")  # 1 / 3 with \"full\" precision"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 189,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "fragment"
-    }
-   },
    "outputs": [
     {
      "data": {
@@ -5934,7 +5897,7 @@
        "Fraction(3333333333, 10000000000)"
       ]
      },
-     "execution_count": 189,
+     "execution_count": 188,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -5945,7 +5908,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 190,
+   "execution_count": 189,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -5958,7 +5921,7 @@
        "Fraction(3333333333, 10000000000)"
       ]
      },
-     "execution_count": 190,
+     "execution_count": 189,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -5978,6 +5941,30 @@
     "Only the lowest common denominator version is maintained after creation: For example, $\\frac{3}{2}$ and $\\frac{6}{4}$ are the same, and both become `Fraction(3, 2)`."
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 190,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Fraction(3, 2)"
+      ]
+     },
+     "execution_count": 190,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "Fraction(3, 2)"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 191,
@@ -5998,30 +5985,6 @@
      "output_type": "execute_result"
     }
    ],
-   "source": [
-    "Fraction(3, 2)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 192,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Fraction(3, 2)"
-      ]
-     },
-     "execution_count": 192,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
    "source": [
     "Fraction(6, 4)"
    ]
@@ -6039,7 +6002,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 193,
+   "execution_count": 192,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -6052,7 +6015,7 @@
        "Fraction(1, 10)"
       ]
      },
-     "execution_count": 193,
+     "execution_count": 192,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -6074,7 +6037,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 194,
+   "execution_count": 193,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -6087,7 +6050,7 @@
        "Fraction(3602879701896397, 36028797018963968)"
       ]
      },
-     "execution_count": 194,
+     "execution_count": 193,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -6109,7 +6072,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 195,
+   "execution_count": 194,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -6122,7 +6085,7 @@
        "Fraction(7, 4)"
       ]
      },
-     "execution_count": 195,
+     "execution_count": 194,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -6131,6 +6094,30 @@
     "Fraction(3, 2) + Fraction(1, 4)"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 195,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Fraction(1, 2)"
+      ]
+     },
+     "execution_count": 195,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "Fraction(5, 2) - 2"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 196,
@@ -6143,7 +6130,7 @@
     {
      "data": {
       "text/plain": [
-       "Fraction(1, 2)"
+       "Fraction(1, 1)"
       ]
      },
      "execution_count": 196,
@@ -6152,7 +6139,7 @@
     }
    ],
    "source": [
-    "Fraction(5, 2) - 2"
+    "3 * Fraction(1, 3)"
    ]
   },
   {
@@ -6175,30 +6162,6 @@
      "output_type": "execute_result"
     }
    ],
-   "source": [
-    "3 * Fraction(1, 3)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 198,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "fragment"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Fraction(1, 1)"
-      ]
-     },
-     "execution_count": 198,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
    "source": [
     "Fraction(3, 2) * Fraction(2, 3)"
    ]
@@ -6216,7 +6179,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 199,
+   "execution_count": 198,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -6229,7 +6192,7 @@
        "0.1"
       ]
      },
-     "execution_count": 199,
+     "execution_count": 198,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -6240,7 +6203,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 200,
+   "execution_count": 199,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -6253,7 +6216,7 @@
        "'0.10000000000000000555111512312578270211815834045410'"
       ]
      },
-     "execution_count": 200,
+     "execution_count": 199,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -6270,7 +6233,7 @@
     }
    },
    "source": [
-    "For more examples and discussions, see the tutorial at [PYMOTW](https://pymotw.com/3/fractions/index.html) or the official [documentation](https://docs.python.org/3/library/fractions.html)."
+    "For more examples and discussions, see the tutorial at [PYMOTW](https://pymotw.com/3/fractions/index.html) or the official [documentation <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/fractions.html)."
    ]
   },
   {
@@ -6303,11 +6266,11 @@
     }
    },
    "source": [
-    "Some mathematical equations cannot be solved if the solution has to be in the set of the real numbers $\\mathbb{R}$. For example, $x^2 = -1$ can be rearranged into $x = \\sqrt{-1}$, but the square root is not defined for negative numbers. To mitigate this, mathematicians introduced the concept of an [imaginary number](https://en.wikipedia.org/wiki/Imaginary_number) $\\textbf{i}$ that is *defined* as $\\textbf{i} = \\sqrt{-1}$ or often as the solution to the equation $\\textbf{i}^2 = -1$. So, the solution to $x = \\sqrt{-1}$ then becomes $x = \\textbf{i}$.\n",
+    "Some mathematical equations cannot be solved if the solution has to be in the set of the real numbers $\\mathbb{R}$. For example, $x^2 = -1$ can be rearranged into $x = \\sqrt{-1}$, but the square root is not defined for negative numbers. To mitigate this, mathematicians introduced the concept of an [imaginary number <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_wiki.png\">](https://en.wikipedia.org/wiki/Imaginary_number) $\\textbf{i}$ that is *defined* as $\\textbf{i} = \\sqrt{-1}$ or often as the solution to the equation $\\textbf{i}^2 = -1$. So, the solution to $x = \\sqrt{-1}$ then becomes $x = \\textbf{i}$.\n",
     "\n",
     "If we generalize the example equation into $(mx-n)^2 = -1 \\implies x = \\frac{1}{m}(\\sqrt{-1} + n)$ where $m$ and $n$ are constants chosen from the reals $\\mathbb{R}$, then the solution to the equation comes in the form $x = a + b\\textbf{i}$, the sum of a real number and an imaginary number, with $a=\\frac{n}{m}$ and $b = \\frac{1}{m}$.\n",
     "\n",
-    "Such \"compound\" numbers are called **[complex numbers](https://en.wikipedia.org/wiki/Complex_number)**, and the set of all such numbers is commonly denoted by $\\mathbb{C}$. The reals $\\mathbb{R}$ are a strict subset of $\\mathbb{C}$ with $b=0$. Further, $a$ is referred to as the **real part** and $b$ as the **imaginary part** of the complex number.\n",
+    "Such \"compound\" numbers are called **[complex numbers <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_wiki.png\">](https://en.wikipedia.org/wiki/Complex_number)**, and the set of all such numbers is commonly denoted by $\\mathbb{C}$. The reals $\\mathbb{R}$ are a strict subset of $\\mathbb{C}$ with $b=0$. Further, $a$ is referred to as the **real part** and $b$ as the **imaginary part** of the complex number.\n",
     "\n",
     "Complex numbers are often visualized in a plane like below, where the real part is depicted on the x-axis and the imaginary part on the y-axis."
    ]
@@ -6331,14 +6294,14 @@
     }
    },
    "source": [
-    "`complex` numbers are part of core Python. The simplest way to create one is to write an arithmetic expression with the literal `j` notation for $\\textbf{i}$. The `j` is commonly used in many engineering disciplines instead of the symbol $\\textbf{i}$ from math as $I$ in engineering more often than not means [electric current](https://en.wikipedia.org/wiki/Electric_current).\n",
+    "`complex` numbers are part of core Python. The simplest way to create one is to write an arithmetic expression with the literal `j` notation for $\\textbf{i}$. The `j` is commonly used in many engineering disciplines instead of the symbol $\\textbf{i}$ from math as $I$ in engineering more often than not means [electric current <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_wiki.png\">](https://en.wikipedia.org/wiki/Electric_current).\n",
     "\n",
     "For example, the answer to $x^2 = -1$ can be written in Python as `1j` like below. This creates a `complex` object with value `1j`. The same syntactic rules apply as with the above `e` notation: No spaces are allowed between the number and the `j`. The number may be any `int` or `float` literal; however, it is stored as a `float` internally. So, `complex` numbers suffer from the same imprecision as `float` numbers."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 201,
+   "execution_count": 200,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -6349,6 +6312,30 @@
     "x = 1j"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 201,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "140466238200560"
+      ]
+     },
+     "execution_count": 201,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "id(x)"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 202,
@@ -6361,7 +6348,7 @@
     {
      "data": {
       "text/plain": [
-       "139960519210800"
+       "complex"
       ]
      },
      "execution_count": 202,
@@ -6370,7 +6357,7 @@
     }
    ],
    "source": [
-    "id(x)"
+    "type(x)"
    ]
   },
   {
@@ -6381,30 +6368,6 @@
      "slide_type": "fragment"
     }
    },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "complex"
-      ]
-     },
-     "execution_count": 203,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "type(x)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 204,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "fragment"
-    }
-   },
    "outputs": [
     {
      "data": {
@@ -6412,7 +6375,7 @@
        "1j"
       ]
      },
-     "execution_count": 204,
+     "execution_count": 203,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -6434,7 +6397,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 205,
+   "execution_count": 204,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -6447,7 +6410,7 @@
        "True"
       ]
      },
-     "execution_count": 205,
+     "execution_count": 204,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -6469,7 +6432,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 206,
+   "execution_count": 205,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -6482,7 +6445,7 @@
        "(2+0.5j)"
       ]
      },
-     "execution_count": 206,
+     "execution_count": 205,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -6499,12 +6462,12 @@
     }
    },
    "source": [
-    "Alternatively, we may use the [complex()](https://docs.python.org/3/library/functions.html#complex) built-in: This takes two parameters where the second is optional and defaults to `0`. We may either call it with one or two arguments of any numeric type or a `str` object in the format of the previous code cell without any spaces."
+    "Alternatively, we may use the [complex() <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/functions.html#complex) built-in: This takes two parameters where the second is optional and defaults to `0`. We may either call it with one or two arguments of any numeric type or a `str` object in the format of the previous code cell without any spaces."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 207,
+   "execution_count": 206,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -6517,7 +6480,7 @@
        "(2+0.5j)"
       ]
      },
-     "execution_count": 207,
+     "execution_count": 206,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -6539,7 +6502,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 208,
+   "execution_count": 207,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -6552,7 +6515,7 @@
        "(2+0j)"
       ]
      },
-     "execution_count": 208,
+     "execution_count": 207,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -6569,7 +6532,31 @@
     }
    },
    "source": [
-    "The arguments to [complex()](https://docs.python.org/3/library/functions.html#complex) may be any numeric type or properly formated `str` object."
+    "The arguments to [complex() <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/functions.html#complex) may be any numeric type or properly formated `str` object."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 208,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(2+0.5j)"
+      ]
+     },
+     "execution_count": 208,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "complex(Decimal(\"2.0\"), Fraction(1, 2))"
    ]
   },
   {
@@ -6592,30 +6579,6 @@
      "output_type": "execute_result"
     }
    ],
-   "source": [
-    "complex(Decimal(\"2.0\"), Fraction(1, 2))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 210,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(2+0.5j)"
-      ]
-     },
-     "execution_count": 210,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
    "source": [
     "complex(\"2+0.5j\")"
    ]
@@ -6633,7 +6596,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 211,
+   "execution_count": 210,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -6645,6 +6608,30 @@
     "c2 = 3 + 4j"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 211,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(4+6j)"
+      ]
+     },
+     "execution_count": 211,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "c1 + c2"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 212,
@@ -6657,7 +6644,7 @@
     {
      "data": {
       "text/plain": [
-       "(4+6j)"
+       "(-2-2j)"
       ]
      },
      "execution_count": 212,
@@ -6666,7 +6653,7 @@
     }
    ],
    "source": [
-    "c1 + c2"
+    "c1 - c2"
    ]
   },
   {
@@ -6681,7 +6668,7 @@
     {
      "data": {
       "text/plain": [
-       "(-2-2j)"
+       "(2+2j)"
       ]
      },
      "execution_count": 213,
@@ -6690,7 +6677,7 @@
     }
    ],
    "source": [
-    "c1 - c2"
+    "c1 + 1"
    ]
   },
   {
@@ -6705,7 +6692,7 @@
     {
      "data": {
       "text/plain": [
-       "(2+2j)"
+       "(0.5-4j)"
       ]
      },
      "execution_count": 214,
@@ -6714,7 +6701,7 @@
     }
    ],
    "source": [
-    "c1 + 1"
+    "3.5 - c2"
    ]
   },
   {
@@ -6729,7 +6716,7 @@
     {
      "data": {
       "text/plain": [
-       "(0.5-4j)"
+       "(5+10j)"
       ]
      },
      "execution_count": 215,
@@ -6738,7 +6725,7 @@
     }
    ],
    "source": [
-    "3.5 - c2"
+    "5 * c1"
    ]
   },
   {
@@ -6753,7 +6740,7 @@
     {
      "data": {
       "text/plain": [
-       "(5+10j)"
+       "(0.5+0.6666666666666666j)"
       ]
      },
      "execution_count": 216,
@@ -6762,7 +6749,7 @@
     }
    ],
    "source": [
-    "5 * c1"
+    "c2 / 6"
    ]
   },
   {
@@ -6777,7 +6764,7 @@
     {
      "data": {
       "text/plain": [
-       "(0.5+0.6666666666666666j)"
+       "(-5+10j)"
       ]
      },
      "execution_count": 217,
@@ -6786,7 +6773,7 @@
     }
    ],
    "source": [
-    "c2 / 6"
+    "c1 * c2"
    ]
   },
   {
@@ -6797,30 +6784,6 @@
      "slide_type": "skip"
     }
    },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(-5+10j)"
-      ]
-     },
-     "execution_count": 218,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "c1 * c2"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 219,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
    "outputs": [
     {
      "data": {
@@ -6828,7 +6791,7 @@
        "(0.44+0.08j)"
       ]
      },
-     "execution_count": 219,
+     "execution_count": 218,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -6850,7 +6813,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 220,
+   "execution_count": 219,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -6863,7 +6826,7 @@
        "0.0"
       ]
      },
-     "execution_count": 220,
+     "execution_count": 219,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -6874,7 +6837,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 221,
+   "execution_count": 220,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -6887,7 +6850,7 @@
        "1.0"
       ]
      },
-     "execution_count": 221,
+     "execution_count": 220,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -6904,12 +6867,12 @@
     }
    },
    "source": [
-    "Also, a `conjugate()` method is bound to every `complex` object. The [complex conjugate](https://en.wikipedia.org/wiki/Complex_conjugate) is defined to be the complex number with identical real part but an imaginary part reversed in sign."
+    "Also, a `conjugate()` method is bound to every `complex` object. The [complex conjugate <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_wiki.png\">](https://en.wikipedia.org/wiki/Complex_conjugate) is defined to be the complex number with identical real part but an imaginary part reversed in sign."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 222,
+   "execution_count": 221,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -6922,7 +6885,7 @@
        "-1j"
       ]
      },
-     "execution_count": 222,
+     "execution_count": 221,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -6939,7 +6902,7 @@
     }
    },
    "source": [
-    "The [cmath](https://docs.python.org/3/library/cmath.html) module in the [standard library](https://docs.python.org/3/library/index.html) implements many of the functions from the [math](https://docs.python.org/3/library/math.html) module such that they work with complex numbers."
+    "The [cmath <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/cmath.html) module in the [standard library <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/index.html) implements many of the functions from the [math <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/math.html) module such that they work with complex numbers."
    ]
   },
   {
@@ -6961,15 +6924,15 @@
     }
    },
    "source": [
-    "Analogous to the discussion of *containers* and *iterables* in [Chapter 4](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/04_iteration_00_lecture.ipynb#Containers-vs.-Iterables), we contrast the *concrete* numeric data types in this chapter with the *abstract* ideas behind [numbers in mathematics](https://en.wikipedia.org/wiki/Number).\n",
+    "Analogous to the discussion of *containers* and *iterables* in [Chapter 4 <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/04_iteration_00_content.ipynb#Containers-vs.-Iterables), we contrast the *concrete* numeric data types in this chapter with the *abstract* ideas behind [numbers in mathematics <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_wiki.png\">](https://en.wikipedia.org/wiki/Number).\n",
     "\n",
-    "The figure below summarizes five *major* sets of [numbers in mathematics](https://en.wikipedia.org/wiki/Number) as we know them from high school:\n",
+    "The figure below summarizes five *major* sets of [numbers in mathematics <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_wiki.png\">](https://en.wikipedia.org/wiki/Number) as we know them from high school:\n",
     "\n",
-    "- $\\mathbb{N}$: [Natural numbers](https://en.wikipedia.org/wiki/Natural_number) are all non-negative count numbers, e.g., $0, 1, 2, ...$\n",
-    "- $\\mathbb{Z}$: [Integers](https://en.wikipedia.org/wiki/Integer) are all numbers *without* a fractional component, e.g., $-1, 0, 1, ...$\n",
-    "- $\\mathbb{Q}$: [Rational numbers](https://en.wikipedia.org/wiki/Rational_number) are all numbers that can be expressed as a quotient of two integers, e.g., $-\\frac{1}{2}, 0, \\frac{1}{2}, ...$\n",
-    "- $\\mathbb{R}$: [Real numbers](https://en.wikipedia.org/wiki/Real_number) are all numbers that can be represented as a distance along a line, and negative means \"reversed,\" e.g., $\\sqrt{2}, \\pi, \\text{e}, ...$\n",
-    "- $\\mathbb{C}$: [Complex numbers](https://en.wikipedia.org/wiki/Complex_number) are all numbers of the form $a + b\\textbf{i}$ where $a$ and $b$ are real numbers and $\\textbf{i}$ is the [imaginary number](https://en.wikipedia.org/wiki/Imaginary_number), e.g., $0, \\textbf{i}, 1 + \\textbf{i}, ...$\n",
+    "- $\\mathbb{N}$: [Natural numbers <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_wiki.png\">](https://en.wikipedia.org/wiki/Natural_number) are all non-negative count numbers, e.g., $0, 1, 2, ...$\n",
+    "- $\\mathbb{Z}$: [Integers <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_wiki.png\">](https://en.wikipedia.org/wiki/Integer) are all numbers *without* a fractional component, e.g., $-1, 0, 1, ...$\n",
+    "- $\\mathbb{Q}$: [Rational numbers <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_wiki.png\">](https://en.wikipedia.org/wiki/Rational_number) are all numbers that can be expressed as a quotient of two integers, e.g., $-\\frac{1}{2}, 0, \\frac{1}{2}, ...$\n",
+    "- $\\mathbb{R}$: [Real numbers <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_wiki.png\">](https://en.wikipedia.org/wiki/Real_number) are all numbers that can be represented as a distance along a line, and negative means \"reversed,\" e.g., $\\sqrt{2}, \\pi, \\text{e}, ...$\n",
+    "- $\\mathbb{C}$: [Complex numbers <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_wiki.png\">](https://en.wikipedia.org/wiki/Complex_number) are all numbers of the form $a + b\\textbf{i}$ where $a$ and $b$ are real numbers and $\\textbf{i}$ is the [imaginary number <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_wiki.png\">](https://en.wikipedia.org/wiki/Imaginary_number), e.g., $0, \\textbf{i}, 1 + \\textbf{i}, ...$\n",
     "\n",
     "In the listed order, the five sets are perfect subsets of the respective following sets, and $\\mathbb{C}$ is the largest set. To be precise, all sets are infinite, but they still have a different number of elements."
    ]
@@ -6999,18 +6962,18 @@
     "\n",
     "For the other types, in particular, the `float` type, the implications of their imprecision are discussed in detail above.\n",
     "\n",
-    "The abstract concepts behind the four outer-most mathematical sets are formalized in Python since [PEP 3141](https://www.python.org/dev/peps/pep-3141/) in 2007. The [numbers](https://docs.python.org/3/library/numbers.html) module in the [standard library](https://docs.python.org/3/library/index.html) defines what programmers call the **[numerical tower](https://en.wikipedia.org/wiki/Numerical_tower)**, a collection of five **[abstract data types](https://en.wikipedia.org/wiki/Abstract_data_type)**, or **abstract base classes** (ABCs) as they are called in Python jargon:\n",
+    "The abstract concepts behind the four outer-most mathematical sets are formalized in Python since [PEP 3141 <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://www.python.org/dev/peps/pep-3141/) in 2007. The [numbers <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/numbers.html) module in the [standard library <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/index.html) defines what programmers call the **[numerical tower <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_wiki.png\">](https://en.wikipedia.org/wiki/Numerical_tower)**, a collection of five **[abstract data types <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_wiki.png\">](https://en.wikipedia.org/wiki/Abstract_data_type)**, or **abstract base classes** (ABCs) as they are called in Python jargon:\n",
     "\n",
-    "- `Number`: \"any number\" (cf., [documentation](https://docs.python.org/3/library/numbers.html#numbers.Number))\n",
-    "- `Complex`: \"all complex numbers\" (cf., [documentation](https://docs.python.org/3/library/numbers.html#numbers.Complex))\n",
-    "- `Real`: \"all real numbers\" (cf., [documentation](https://docs.python.org/3/library/numbers.html#numbers.Real))\n",
-    "- `Rational`: \"all rational numbers\" (cf., [documentation](https://docs.python.org/3/library/numbers.html#numbers.Rational))\n",
-    "- `Integral`: \"all integers\" (cf., [documentation](https://docs.python.org/3/library/numbers.html#numbers.Integral))"
+    "- `Number`: \"any number\" (cf., [documentation <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/numbers.html#numbers.Number))\n",
+    "- `Complex`: \"all complex numbers\" (cf., [documentation <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/numbers.html#numbers.Complex))\n",
+    "- `Real`: \"all real numbers\" (cf., [documentation <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/numbers.html#numbers.Real))\n",
+    "- `Rational`: \"all rational numbers\" (cf., [documentation <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/numbers.html#numbers.Rational))\n",
+    "- `Integral`: \"all integers\" (cf., [documentation <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/numbers.html#numbers.Integral))"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 223,
+   "execution_count": 222,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -7023,7 +6986,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 224,
+   "execution_count": 223,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -7051,7 +7014,7 @@
        " 'abstractmethod']"
       ]
      },
-     "execution_count": 224,
+     "execution_count": 223,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -7081,14 +7044,14 @@
    "source": [
     "The primary purpose of ABCs is to classify the *concrete* data types and standardize how they behave. This guides us as the programmers in what kind of behavior we should expect from objects of a given data type. In this context, ABCs are not reflected in code but only in our heads.\n",
     "\n",
-    "For, example, as all numeric data types are `Complex` numbers in the abstract sense, they all work with the built-in [abs()](https://docs.python.org/3/library/functions.html#abs) function (cf., [documentation](https://docs.python.org/3/library/numbers.html#numbers.Complex)). While it is intuitively clear what the [absolute value](https://en.wikipedia.org/wiki/Absolute_value) (i.e., \"distance\" from $0$) of an integer, a fraction, or any real number is, [abs()](https://docs.python.org/3/library/functions.html#abs) calculates the equivalent of that for complex numbers. That concept is called the [magnitude](https://en.wikipedia.org/wiki/Magnitude_%28mathematics%29) of a number, and is really a *generalization* of the absolute value.\n",
+    "For, example, as all numeric data types are `Complex` numbers in the abstract sense, they all work with the built-in [abs() <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/functions.html#abs) function (cf., [documentation <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/numbers.html#numbers.Complex)). While it is intuitively clear what the [absolute value <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_wiki.png\">](https://en.wikipedia.org/wiki/Absolute_value) (i.e., \"distance\" from $0$) of an integer, a fraction, or any real number is, [abs() <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/functions.html#abs) calculates the equivalent of that for complex numbers. That concept is called the [magnitude <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_wiki.png\">](https://en.wikipedia.org/wiki/Magnitude_%28mathematics%29) of a number, and is really a *generalization* of the absolute value.\n",
     "\n",
-    "Relating back to the concept of **duck typing** mentioned in [Chapter 4](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/04_iteration_00_lecture.ipynb#Duck-Typing), `int`, `float`, and `complex` objects \"walk\" and \"quack\" alike in context of the [abs()](https://docs.python.org/3/library/functions.html#abs) function."
+    "Relating back to the concept of **duck typing** mentioned in [Chapter 4 <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/04_iteration_00_content.ipynb#Duck-Typing), `int`, `float`, and `complex` objects \"walk\" and \"quack\" alike in context of the [abs() <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/functions.html#abs) function."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 225,
+   "execution_count": 224,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -7101,7 +7064,7 @@
        "1"
       ]
      },
-     "execution_count": 225,
+     "execution_count": 224,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -7112,7 +7075,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 226,
+   "execution_count": 225,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -7125,7 +7088,7 @@
        "42.87"
       ]
      },
-     "execution_count": 226,
+     "execution_count": 225,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -7147,7 +7110,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 227,
+   "execution_count": 226,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -7160,7 +7123,7 @@
        "5.0"
       ]
      },
-     "execution_count": 227,
+     "execution_count": 226,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -7177,12 +7140,12 @@
     }
    },
    "source": [
-    "On the contrary, only `Real` numbers in the abstract sense may be rounded with the built-in [round()](https://docs.python.org/3/library/functions.html#round) function."
+    "On the contrary, only `Real` numbers in the abstract sense may be rounded with the built-in [round() <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/functions.html#round) function."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 228,
+   "execution_count": 227,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -7195,7 +7158,7 @@
        "100"
       ]
      },
-     "execution_count": 228,
+     "execution_count": 227,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -7206,7 +7169,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 229,
+   "execution_count": 228,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -7219,7 +7182,7 @@
        "42"
       ]
      },
-     "execution_count": 229,
+     "execution_count": 228,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -7236,12 +7199,12 @@
     }
    },
    "source": [
-    "`Complex` numbers are two-dimensional. So, rounding makes no sense here and leads to a `TypeError`. So, in the context of the [round()](https://docs.python.org/3/library/functions.html#round) function, `int` and `float` objects \"walk\" and \"quack\" alike whereas `complex` objects do not."
+    "`Complex` numbers are two-dimensional. So, rounding makes no sense here and leads to a `TypeError`. So, in the context of the [round() <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/functions.html#round) function, `int` and `float` objects \"walk\" and \"quack\" alike whereas `complex` objects do not."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 230,
+   "execution_count": 229,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -7255,7 +7218,7 @@
      "traceback": [
       "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
       "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-230-e40cd01fb15a>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mround\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m1\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;36m2j\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;32m<ipython-input-229-e40cd01fb15a>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mround\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m1\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;36m2j\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
       "\u001b[0;31mTypeError\u001b[0m: type complex doesn't define __round__ method"
      ]
     }
@@ -7285,12 +7248,12 @@
    "source": [
     "Another way to use ABCs is in place of a *concrete* data type.\n",
     "\n",
-    "For example, we may pass them as arguments to the built-in [isinstance()](https://docs.python.org/3/library/functions.html#isinstance) function and check in which of the five mathematical sets the object `1 / 10` is."
+    "For example, we may pass them as arguments to the built-in [isinstance() <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/functions.html#isinstance) function and check in which of the five mathematical sets the object `1 / 10` is."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 231,
+   "execution_count": 230,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -7303,7 +7266,7 @@
        "True"
       ]
      },
-     "execution_count": 231,
+     "execution_count": 230,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -7323,6 +7286,30 @@
     "A `float` object is a generic `Number` in the abstract sense but may also be seen as a `Complex` or `Real` number."
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 231,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 231,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "isinstance(1 / 10, numbers.Number)"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 232,
@@ -7344,7 +7331,7 @@
     }
    ],
    "source": [
-    "isinstance(1 / 10, numbers.Number)"
+    "isinstance(1 / 10, numbers.Complex)"
    ]
   },
   {
@@ -7367,30 +7354,6 @@
      "output_type": "execute_result"
     }
    ],
-   "source": [
-    "isinstance(1 / 10, numbers.Complex)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 234,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "fragment"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "True"
-      ]
-     },
-     "execution_count": 234,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
    "source": [
     "isinstance(1 / 10, numbers.Real)"
    ]
@@ -7408,7 +7371,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 235,
+   "execution_count": 234,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -7421,7 +7384,7 @@
        "False"
       ]
      },
-     "execution_count": 235,
+     "execution_count": 234,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -7443,7 +7406,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 236,
+   "execution_count": 235,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -7456,7 +7419,7 @@
        "True"
       ]
      },
-     "execution_count": 236,
+     "execution_count": 235,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -7473,9 +7436,9 @@
     }
    },
    "source": [
-    "Replacing *concrete* data types with ABCs is particularly valuable in the context of \"type checking:\" The revised version of the `factorial()` function below allows its user to take advantage of *duck typing*: If a real but non-integer argument `n` is passed in, `factorial()` tries to cast `n` as an `int` object with the [int()](https://docs.python.org/3/library/functions.html#int) built-in.\n",
+    "Replacing *concrete* data types with ABCs is particularly valuable in the context of \"type checking:\" The revised version of the `factorial()` function below allows its user to take advantage of *duck typing*: If a real but non-integer argument `n` is passed in, `factorial()` tries to cast `n` as an `int` object with the [int() <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/functions.html#int) built-in.\n",
     "\n",
-    "Two popular and distinguished Pythonistas, [Luciano Ramalho](https://github.com/ramalho) and [Alex Martelli](https://en.wikipedia.org/wiki/Alex_Martelli), coin the term **goose typing** to specifically mean using the built-in [isinstance()](https://docs.python.org/3/library/functions.html#isinstance) function with an ABC (cf., Chapter 11 in this [book](https://www.amazon.com/Fluent-Python-Concise-Effective-Programming/dp/1491946008) or this [summary](https://dgkim5360.github.io/blog/python/2017/07/duck-typing-vs-goose-typing-pythonic-interfaces/) thereof)."
+    "Two popular and distinguished Pythonistas, [Luciano Ramalho <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_gh.png\">](https://github.com/ramalho) and [Alex Martelli <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_wiki.png\">](https://en.wikipedia.org/wiki/Alex_Martelli), coin the term **goose typing** to specifically mean using the built-in [isinstance() <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/functions.html#isinstance) function with an ABC (cf., Chapter 11 in this [book](https://www.amazon.com/Fluent-Python-Concise-Effective-Programming/dp/1491946008) or this [summary](https://dgkim5360.github.io/blog/python/2017/07/duck-typing-vs-goose-typing-pythonic-interfaces/) thereof)."
    ]
   },
   {
@@ -7486,12 +7449,12 @@
     }
    },
    "source": [
-    "#### Example: [Factorial](https://en.wikipedia.org/wiki/Factorial) (revisited)"
+    "#### Example: [Factorial <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_wiki.png\">](https://en.wikipedia.org/wiki/Factorial) (revisited)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 237,
+   "execution_count": 236,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -7542,7 +7505,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 238,
+   "execution_count": 237,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -7555,7 +7518,7 @@
        "1"
       ]
      },
-     "execution_count": 238,
+     "execution_count": 237,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -7564,6 +7527,30 @@
     "factorial(0)"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 238,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "6"
+      ]
+     },
+     "execution_count": 238,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "factorial(3)"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 239,
@@ -7584,30 +7571,6 @@
      "output_type": "execute_result"
     }
    ],
-   "source": [
-    "factorial(3)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 240,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "fragment"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "6"
-      ]
-     },
-     "execution_count": 240,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
    "source": [
     "factorial(3.0)"
    ]
@@ -7625,7 +7588,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 241,
+   "execution_count": 240,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -7639,8 +7602,8 @@
      "traceback": [
       "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
       "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-241-188b816be8b6>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mfactorial\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m3.1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
-      "\u001b[0;32m<ipython-input-237-be6602f598f7>\u001b[0m in \u001b[0;36mfactorial\u001b[0;34m(n, strict)\u001b[0m\n\u001b[1;32m     17\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mn\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnumbers\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mReal\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     18\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mn\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0mint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mn\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mstrict\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 19\u001b[0;31m                 \u001b[0;32mraise\u001b[0m \u001b[0mTypeError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"n is not integer-like; it has non-zero decimals\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     20\u001b[0m             \u001b[0mn\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mn\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     21\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m<ipython-input-240-188b816be8b6>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mfactorial\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m3.1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;32m<ipython-input-236-be6602f598f7>\u001b[0m in \u001b[0;36mfactorial\u001b[0;34m(n, strict)\u001b[0m\n\u001b[1;32m     17\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mn\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnumbers\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mReal\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     18\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mn\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0mint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mn\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mstrict\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 19\u001b[0;31m                 \u001b[0;32mraise\u001b[0m \u001b[0mTypeError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"n is not integer-like; it has non-zero decimals\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     20\u001b[0m             \u001b[0mn\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mn\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     21\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
       "\u001b[0;31mTypeError\u001b[0m: n is not integer-like; it has non-zero decimals"
      ]
     }
@@ -7662,7 +7625,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 242,
+   "execution_count": 241,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -7675,7 +7638,7 @@
        "6"
       ]
      },
-     "execution_count": 242,
+     "execution_count": 241,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -7697,7 +7660,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 243,
+   "execution_count": 242,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -7711,8 +7674,8 @@
      "traceback": [
       "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
       "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-243-7296a74f5dcf>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mfactorial\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m1\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;36m2j\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
-      "\u001b[0;32m<ipython-input-237-be6602f598f7>\u001b[0m in \u001b[0;36mfactorial\u001b[0;34m(n, strict)\u001b[0m\n\u001b[1;32m     20\u001b[0m             \u001b[0mn\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mn\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     21\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 22\u001b[0;31m             \u001b[0;32mraise\u001b[0m \u001b[0mTypeError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Factorial is only defined for integers\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     23\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     24\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mn\u001b[0m \u001b[0;34m<\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m<ipython-input-242-7296a74f5dcf>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mfactorial\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m1\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;36m2j\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;32m<ipython-input-236-be6602f598f7>\u001b[0m in \u001b[0;36mfactorial\u001b[0;34m(n, strict)\u001b[0m\n\u001b[1;32m     20\u001b[0m             \u001b[0mn\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mn\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     21\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 22\u001b[0;31m             \u001b[0;32mraise\u001b[0m \u001b[0mTypeError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Factorial is only defined for integers\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     23\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     24\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mn\u001b[0m \u001b[0;34m<\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
       "\u001b[0;31mTypeError\u001b[0m: Factorial is only defined for integers"
      ]
     }
@@ -7745,7 +7708,7 @@
     "- `float`: the \"gold\" standard to approximate real numbers (i.e., $\\mathbb{R}$); inherently imprecise\n",
     "- `complex`: layer on top of the `float` type to approximate complex numbers (i.e., $\\mathbb{C}$); inherently imprecise\n",
     "\n",
-    "Furthermore, the [standard library](https://docs.python.org/3/library/index.html) provides two more types that can be used as substitutes for the `float` type:\n",
+    "Furthermore, the [standard library <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/index.html) provides two more types that can be used as substitutes for the `float` type:\n",
     "- `Decimal`: similar to `float` but allows customizing the precision; still inherently imprecise\n",
     "- `Fraction`: a near-perfect model for rational numbers (i.e., $\\mathbb{Q}$); built on top of the `int` type and therefore inherently precise\n",
     "\n",
@@ -7776,7 +7739,55 @@
     }
    },
    "source": [
-    "The two videos below show how addition and multiplication works with numbers in their binary representations. Subtraction is a bit more involved as we need to understand how negative numbers are represented in binary with the concept of [Two's Complement](https://en.wikipedia.org/wiki/Two%27s_complement) first. A video on that is shown further below. Division in binary is actually also quite simple."
+    "A lecture-style **video presentation** of this chapter is integrated below (cf., the [video <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_yt.png\">](https://www.youtube.com/watch?v=nB00WGCnVjg&list=PL-2JV1G3J10lQ2xokyQowcRJI5jjNfW7f) or the entire [playlist <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_yt.png\">](https://www.youtube.com/playlist?list=PL-2JV1G3J10lQ2xokyQowcRJI5jjNfW7f))."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 243,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/jpeg": 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+       "            width=\"60%\"\n",
+       "            height=\"300\"\n",
+       "            src=\"https://www.youtube.com/embed/nB00WGCnVjg\"\n",
+       "            frameborder=\"0\"\n",
+       "            allowfullscreen\n",
+       "        ></iframe>\n",
+       "        "
+      ],
+      "text/plain": [
+       "<IPython.lib.display.YouTubeVideo at 0x7fc0d836ddd0>"
+      ]
+     },
+     "execution_count": 243,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from IPython.display import YouTubeVideo\n",
+    "YouTubeVideo(\"nB00WGCnVjg\", width=\"60%\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "The two videos below show how addition and multiplication works with numbers in their binary representations. Subtraction is a bit more involved as we need to understand how negative numbers are represented in binary with the concept of [Two's Complement <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_wiki.png\">](https://en.wikipedia.org/wiki/Two%27s_complement) first. A video on that is shown further below. Division in binary is actually also quite simple."
    ]
   },
   {
@@ -7803,7 +7814,7 @@
        "        "
       ],
       "text/plain": [
-       "<IPython.lib.display.YouTubeVideo at 0x7f4b19022490>"
+       "<IPython.lib.display.YouTubeVideo at 0x7fc0d83afb10>"
       ]
      },
      "execution_count": 244,
@@ -7839,7 +7850,7 @@
        "        "
       ],
       "text/plain": [
-       "<IPython.lib.display.YouTubeVideo at 0x7f4b1901cc50>"
+       "<IPython.lib.display.YouTubeVideo at 0x7fc0d83b41d0>"
       ]
      },
      "execution_count": 245,
@@ -7859,7 +7870,7 @@
     }
    },
    "source": [
-    "The video below explains the idea behind [Two's Complement](https://en.wikipedia.org/wiki/Two%27s_complement). This is how most modern programming languages implement negative integers. The video also shows how subtraction in binary works."
+    "The video below explains the idea behind [Two's Complement <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_wiki.png\">](https://en.wikipedia.org/wiki/Two%27s_complement). This is how most modern programming languages implement negative integers. The video also shows how subtraction in binary works."
    ]
   },
   {
@@ -7886,7 +7897,7 @@
        "        "
       ],
       "text/plain": [
-       "<IPython.lib.display.YouTubeVideo at 0x7f4b190196d0>"
+       "<IPython.lib.display.YouTubeVideo at 0x7fc0d83aac50>"
       ]
      },
      "execution_count": 246,
@@ -7933,7 +7944,7 @@
        "        "
       ],
       "text/plain": [
-       "<IPython.lib.display.YouTubeVideo at 0x7f4b190160d0>"
+       "<IPython.lib.display.YouTubeVideo at 0x7fc0d83ad390>"
       ]
      },
      "execution_count": 247,
@@ -7953,7 +7964,7 @@
     }
    },
    "source": [
-    "Below is a short introduction to [complex numbers](https://en.wikipedia.org/wiki/Complex_number) by [MIT](https://www.mit.edu) professor [Gilbert Strang](https://en.wikipedia.org/wiki/Gilbert_Strang) aimed at high school students."
+    "Below is a short introduction to [complex numbers <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_wiki.png\">](https://en.wikipedia.org/wiki/Complex_number) by [MIT](https://www.mit.edu) professor [Gilbert Strang <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_wiki.png\">](https://en.wikipedia.org/wiki/Gilbert_Strang) aimed at high school students."
    ]
   },
   {
@@ -7980,7 +7991,7 @@
        "        "
       ],
       "text/plain": [
-       "<IPython.lib.display.YouTubeVideo at 0x7f4b1907e190>"
+       "<IPython.lib.display.YouTubeVideo at 0x7fc0d83d3290>"
       ]
      },
      "execution_count": 248,
diff --git a/05_numbers_01_review.ipynb b/05_numbers_01_review.ipynb
index 7b3ac58..f77ae74 100644
--- a/05_numbers_01_review.ipynb
+++ b/05_numbers_01_review.ipynb
@@ -18,7 +18,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "The questions below assume that you have read [Chapter 5](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/05_numbers_00_lecture.ipynb) in the book.\n",
+    "The questions below assume that you have read [Chapter 5 <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/05_numbers_00_content.ipynb) in the book.\n",
     "\n",
     "Be concise in your answers! Most questions can be answered in *one* sentence."
    ]
@@ -69,9 +69,9 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "**Q3**: Colors are commonly expressed in the **hexadecimal system** in websites (cf., the [HTML](https://en.wikipedia.org/wiki/HTML) and [CSS](https://en.wikipedia.org/wiki/Cascading_Style_Sheets) formats).\n",
+    "**Q3**: Colors are commonly expressed in the **hexadecimal system** in websites (cf., the [HTML <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_wiki.png\">](https://en.wikipedia.org/wiki/HTML) and [CSS <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_wiki.png\">](https://en.wikipedia.org/wiki/Cascading_Style_Sheets) formats).\n",
     "\n",
-    "For example, $#000000$, $#ff9900$, and $#ffffff$ turn out to be black, orange, and white. The six digits are read in *pairs of two* from left to right, and the *three pairs* correspond to the proportions of red, green, and blue mixed together. They reach from $0_{16} = 0_{10}$ for $0$% to $\\text{ff}_{16} = 255_{10}$ for $100$% (cf., this [article](https://en.wikipedia.org/wiki/RGB_color_model) for an in-depth discussion).\n",
+    "For example, $#000000$, $#ff9900$, and $#ffffff$ turn out to be black, orange, and white. The six digits are read in *pairs of two* from left to right, and the *three pairs* correspond to the proportions of red, green, and blue mixed together. They reach from $0_{16} = 0_{10}$ for $0$% to $\\text{ff}_{16} = 255_{10}$ for $100$% (cf., this [article <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_wiki.png\">](https://en.wikipedia.org/wiki/RGB_color_model) for an in-depth discussion).\n",
     "\n",
     "In percent, what are the proportions of red, green, and blue that make up orange? Calculate the three percentages separately! How many **bytes** are needed to encode a color? How many **bits** are that?"
    ]
@@ -129,7 +129,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "**Q7**: What data type, built-in or from the [standard library](https://docs.python.org/3/library/index.html), is best suited to represent the [transcendental numbers](https://en.wikipedia.org/wiki/Transcendental_number) $\\pi$ and $\\text{e}$?"
+    "**Q7**: What data type, built-in or from the [standard library <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/index.html), is best suited to represent the [transcendental numbers <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_wiki.png\">](https://en.wikipedia.org/wiki/Transcendental_number) $\\pi$ and $\\text{e}$?"
    ]
   },
   {
@@ -185,7 +185,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "**Q10**: With the built-in [round()](https://docs.python.org/3/library/functions.html#round) function, we obtain a *precise* representation for any `float` object if we can live with *less than* $15$ digits of precision."
+    "**Q10**: With the built-in [round() <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/functions.html#round) function, we obtain a *precise* representation for any `float` object if we can live with *less than* $15$ digits of precision."
    ]
   },
   {
@@ -213,7 +213,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "**Q12**: The [IEEE 754](https://en.wikipedia.org/wiki/IEEE_754) standard's **special values** provide no benefit in practice as we could always use a **[sentinel](https://en.wikipedia.org/wiki/Sentinel_value)** value (i.e., a \"dummy\"). For example, instead of `nan`, we can always use `0` to indicate a *missing* value."
+    "**Q12**: The [IEEE 754 <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_wiki.png\">](https://en.wikipedia.org/wiki/IEEE_754) standard's **special values** provide no benefit in practice as we could always use a **[sentinel <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_wiki.png\">](https://en.wikipedia.org/wiki/Sentinel_value)** value (i.e., a \"dummy\"). For example, instead of `nan`, we can always use `0` to indicate a *missing* value."
    ]
   },
   {
@@ -258,7 +258,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "**Q15**: From a practitioner's point of view, the built-in [format()](https://docs.python.org/3/library/functions.html#format) function does the *same* as the built-in [round()](https://docs.python.org/3/library/functions.html#round) function."
+    "**Q15**: From a practitioner's point of view, the built-in [format() <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/functions.html#format) function does the *same* as the built-in [round() <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/functions.html#round) function."
    ]
   },
   {
@@ -272,7 +272,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "**Q16**: The `Decimal` type from the [decimal](https://docs.python.org/3/library/decimal.html) module in the [standard library](https://docs.python.org/3/library/index.html) allows us to model the set of the real numbers $\\mathbb{R}$ *precisely*."
+    "**Q16**: The `Decimal` type from the [decimal <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/decimal.html) module in the [standard library <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/index.html) allows us to model the set of the real numbers $\\mathbb{R}$ *precisely*."
    ]
   },
   {
@@ -286,7 +286,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "**Q17**: The `Fraction` type from the [fractions](https://docs.python.org/3/library/fractions.html) module in the [standard library](https://docs.python.org/3/library/index.html) allows us to model the set of the rational numbers $\\mathbb{Q}$ *precisely*."
+    "**Q17**: The `Fraction` type from the [fractions <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/fractions.html) module in the [standard library <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/index.html) allows us to model the set of the rational numbers $\\mathbb{Q}$ *precisely*."
    ]
   },
   {
diff --git a/05_numbers_02_exercises.ipynb b/05_numbers_02_exercises.ipynb
index 8eaeaac..802c958 100644
--- a/05_numbers_02_exercises.ipynb
+++ b/05_numbers_02_exercises.ipynb
@@ -1,5 +1,12 @@
 {
  "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Important**: Click on \"*Kernel*\" > \"*Restart Kernel and Run All*\" *after* finishing the exercises in [JupyterLab <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_jp.png\">](https://jupyterlab.readthedocs.io/en/stable/) (e.g., in the cloud on [MyBinder <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_mb.png\">](https://mybinder.org/v2/gh/webartifex/intro-to-python/master?urlpath=lab/tree/05_numbers_02_exercises.ipynb)) to ensure that your solution runs top to bottom *without* any errors"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -18,7 +25,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "The exercises below assume that you have read [Chapter 5](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/05_numbers_00_lecture.ipynb) in the book.\n",
+    "The exercises below assume that you have read [Chapter 5 <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/05_numbers_00_content.ipynb) in the book.\n",
     "\n",
     "The `...`'s in the code cells indicate where you need to fill in code snippets. The number of `...`'s within a code cell give you a rough idea of how many lines of code are needed to solve the task. You should not need to create any additional code cells for your final solution. However, you may want to use temporary code cells to try out some ideas."
    ]
@@ -34,13 +41,13 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "The \"*Volume of a Sphere*\" problem in [Chapter 2's Exercises](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/02_functions_02_exercises.ipynb#Volume-of-a-Sphere) section revealed that we must consider the effects of the `float` type's imprecision.\n",
+    "The \"*Volume of a Sphere*\" problem in [Chapter 2's Exercises <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/02_functions_02_exercises.ipynb#Volume-of-a-Sphere) section revealed that we must consider the effects of the `float` type's imprecision.\n",
     "\n",
-    "This becomes even more important when we deal with numeric data modeling accounting or finance data (cf., [this comment](https://stackoverflow.com/a/24976426) on \"falsehoods programmers believe about money\").\n",
+    "This becomes even more important when we deal with numeric data modeling accounting or finance data (cf., [this comment <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_so.png\">](https://stackoverflow.com/a/24976426) on \"falsehoods programmers believe about money\").\n",
     "\n",
-    "In addition to the *inherent imprecision* of numbers in general, the topic of **[rounding numbers](https://en.wikipedia.org/wiki/Rounding)** is also not as trivial as we might expect! [This article](https://realpython.com/python-rounding/) summarizes everything the data science practitioner needs to know.\n",
+    "In addition to the *inherent imprecision* of numbers in general, the topic of **[rounding numbers <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_wiki.png\">](https://en.wikipedia.org/wiki/Rounding)** is also not as trivial as we might expect! [This article](https://realpython.com/python-rounding/) summarizes everything the data science practitioner needs to know.\n",
     "\n",
-    "In this exercise, we revisit the \"*Discounting Customer Orders*\" problem from [Chapter 3's Exercises](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/03_conditionals_02_exercises.ipynb#Discounting-Customer-Orders) section and make the `discounted_price()` function work *correctly* for real-life sales data."
+    "In this exercise, we revisit the \"*Discounting Customer Orders*\" problem from [Chapter 3's Exercises <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/03_conditionals_02_exercises.ipynb#Discounting-Customer-Orders) section and make the `discounted_price()` function work *correctly* for real-life sales data."
    ]
   },
   {
@@ -88,7 +95,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "**Q2**: The built-in [round()](https://docs.python.org/3/library/functions.html#round) function implements the \"**[round half to even](https://en.wikipedia.org/wiki/Rounding#Round_half_to_even)**\" strategy. Describe in one or two sentences what that means!"
+    "**Q2**: The built-in [round() <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/functions.html#round) function implements the \"**[round half to even <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_wiki.png\">](https://en.wikipedia.org/wiki/Rounding#Round_half_to_even)**\" strategy. Describe in one or two sentences what that means!"
    ]
   },
   {
@@ -102,9 +109,9 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "**Q3**: For the revised `discounted_price()` function, we have to tackle *two* issues: First, we have to replace the built-in `float` type with a data type that allows us to control the precision. Second, the discounted price should be rounded according to a more human-friendly rounding strategy, namely \"**[round half away from zero](https://en.wikipedia.org/wiki/Rounding#Round_half_away_from_zero)**.\"\n",
+    "**Q3**: For the revised `discounted_price()` function, we have to tackle *two* issues: First, we have to replace the built-in `float` type with a data type that allows us to control the precision. Second, the discounted price should be rounded according to a more human-friendly rounding strategy, namely \"**[round half away from zero <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_wiki.png\">](https://en.wikipedia.org/wiki/Rounding#Round_half_away_from_zero)**.\"\n",
     "\n",
-    "Describe in one or two sentences how \"**[round half away from zero](https://en.wikipedia.org/wiki/Rounding#Round_half_away_from_zero)**\" is more in line with how humans think of rounding!"
+    "Describe in one or two sentences how \"**[round half away from zero <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_wiki.png\">](https://en.wikipedia.org/wiki/Rounding#Round_half_away_from_zero)**\" is more in line with how humans think of rounding!"
    ]
   },
   {
@@ -118,9 +125,9 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "**Q4**: We use the `Decimal` type from the [decimal](https://docs.python.org/3/library/decimal.html) module in the [standard library](https://docs.python.org/3/library/index.html) to tackle *both* issues simultaneously.\n",
+    "**Q4**: We use the `Decimal` type from the [decimal <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/decimal.html) module in the [standard library <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/index.html) to tackle *both* issues simultaneously.\n",
     "\n",
-    "Assign `euro` a numeric object such that both `Decimal(\"1.5\")` and `Decimal(\"2.5\")` are rounded to `Decimal(\"2\")` (i.e., no decimal) with the [quantize()](https://docs.python.org/3/library/decimal.html#decimal.Decimal.quantize) method!"
+    "Assign `euro` a numeric object such that both `Decimal(\"1.5\")` and `Decimal(\"2.5\")` are rounded to `Decimal(\"2\")` (i.e., no decimal) with the [quantize() <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/decimal.html#decimal.Decimal.quantize) method!"
    ]
   },
   {
@@ -163,9 +170,9 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "**Q5**: Obviously, the two preceding code cells still [round half to even](https://en.wikipedia.org/wiki/Rounding#Round_half_to_even).\n",
+    "**Q5**: Obviously, the two preceding code cells still [round half to even <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_wiki.png\">](https://en.wikipedia.org/wiki/Rounding#Round_half_to_even).\n",
     "\n",
-    "The [decimal](https://docs.python.org/3/library/decimal.html) module defines a `ROUND_HALF_UP` flag that we can pass as the second argument to the [quantize()](https://docs.python.org/3/library/decimal.html#decimal.Decimal.quantize) method. Then, it [rounds half away from zero](https://en.wikipedia.org/wiki/Rounding#Round_half_away_from_zero).\n",
+    "The [decimal <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/decimal.html) module defines a `ROUND_HALF_UP` flag that we can pass as the second argument to the [quantize() <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/decimal.html#decimal.Decimal.quantize) method. Then, it [rounds half away from zero <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_wiki.png\">](https://en.wikipedia.org/wiki/Rounding#Round_half_away_from_zero).\n",
     "\n",
     "Add `ROUND_HALF_UP` to the code cells! `Decimal(\"2.5\")` should now be rounded to `Decimal(\"3\")`."
    ]
@@ -226,7 +233,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "**Q7**: Rewrite the function `discounted_price()` from [Chapter 3's Exercises](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/03_conditionals_02_exercises.ipynb#Discounting-Customer-Orders) section!\n",
+    "**Q7**: Rewrite the function `discounted_price()` from [Chapter 3's Exercises <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/03_conditionals_02_exercises.ipynb#Discounting-Customer-Orders) section!\n",
     "\n",
     "It takes the *positional* arguments `unit_price` and `quantity` and implements a discount scheme for a line item in a customer order as follows:\n",
     "\n",
@@ -237,7 +244,7 @@
     "\n",
     "The function then returns the overall price for the line item as a `Decimal` number with a precision of *two* decimals.\n",
     "\n",
-    "Enable **duck typing** by allowing the function to be called with various numeric types as the arguments, in particular, `quantity` may be a non-integer as well: Use an appropriate **abstract base class** from the [numbers](https://docs.python.org/3/library/numbers.html) module in the [standard library](https://docs.python.org/3/library/index.html) to verify the arguments' types and also that they are both positive!\n",
+    "Enable **duck typing** by allowing the function to be called with various numeric types as the arguments, in particular, `quantity` may be a non-integer as well: Use an appropriate **abstract base class** from the [numbers <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/numbers.html) module in the [standard library <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/index.html) to verify the arguments' types and also that they are both positive!\n",
     "\n",
     "Hint: It is considered a *best practice* to only round towards the *end* of the calculations."
    ]
diff --git a/06_text_00_lecture.ipynb b/06_text_00_content.ipynb
similarity index 88%
rename from 06_text_00_lecture.ipynb
rename to 06_text_00_content.ipynb
index 6a676d4..c988f3e 100644
--- a/06_text_00_lecture.ipynb
+++ b/06_text_00_content.ipynb
@@ -8,44 +8,7 @@
     }
    },
    "source": [
-    "A **video presentation** of the contents in this chapter is shown below. A playlist with *all* chapters as videos is linked [here](https://www.youtube.com/playlist?list=PL-2JV1G3J10lQ2xokyQowcRJI5jjNfW7f)."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/jpeg": 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8E7HRTObSfJPNNyngOEY5jG7sdpHxqELts2JJXbpZHyOwBukc57sDsG5xJx1K6lihSLMxcbkyY00A/YBSxeIbVytYIa1uUTc6lxnTeT0FkWR4ZH/PUv1ut/fjWOXZWmdG9kjDtfG5r2OGMtcwhzT16dCAtr25mkLRReGPa47iCs13RPuvqf6/a/vPWBXfftyTyyTzO3yyvdJI8gAue8lznENAAySewLoUaLCxjWncAFLE1BUqueNCSfUypnwS7vTT9T1RjWG1C+rVpyPY2QQPsOeZ5mseC3mctgDTjpud5iQeXFFg6hpFXU5gzvyO9Np88zY2Rmy3kMswvlEYDS9jSWZwMjtyvHwhdgfU1DTbE7awud7y1rEoeYY7FZ7jsm5bS5jJGPLd+Dt2g4K5cTWq9fT62l1547Tm2Zb1ueHf3vz3xtgiigdI1rpA2JuS7aBlwx58c4sPbzHvZheP6cvHSJm3G67jao/g4zDJ2ZBbI/7naGLazEGY+G0xIUUXtpavbgY6KG1ZhifnfHFPLHG/Iwd7GODXdOnULxIum5odYhfPse5hlpjoiL3aHpU92dteuzfK5r3AZwA2Nhe9znHo1oDT1PzDzrwoHCY3/f8AtCxwaHEWMiekT+49UWV1XWTPUoVOWGigyy0P3ZMvfE5nJLceJgnHacrFLOs4Wt94T6hJE+GvCYAwyxvYbHPdtBh3ABzQMEu7PGGMrXVNMFped9upt9Vvw7azg9tMGC33o/tEOvw+EHyhYJERblWRSTuZ6nBT1anZsv5cERmMj9j37d9aaNvixtLjlz2joPOo2vq11aYqMLDoQR6rdh67qFVtVurSCJ0sZXFo6D8i+oi2LSvq+IiIu2rA+V7Io2l8kj2xxsaMue97g1jWjzuJIH71xlYWuLXAhzSWuB7QQcEH58hTPuPasYNTqQtr1ZDPZjaZpYRJYia4FpEEhd9iyM9QPOVFdb/9VZ/WJv7jlXbWcaxpkWABnjJP+FdfhmNwzawdJLiCI0gA+eq8aIisKki4WPIf+a7+RXNcLHkP/Nd/IrDtCp0/iHVbsIiLyhfohERMoiIiZRERERERERERMplEREREREREREREREREREREREREREREREREWu3uj/uxF+z4P79pVmrM90f92Iv2fB/ftKs16Rsr8pT6Lw32i/mNb9RRERdBcZEWV4S0d2oXqtNrtvfErWF3aWMALpHAechjXkD4wFJWUtJ1Bl+GhVmrTU601utYdZfP37FVI5jZoXNAikfGd4DOgPTzeNWq4ptN2Ug7iSNwJgE/PSdCr2HwD6zMwIFyADMuIEkCARpGpAuBqoKiIrKooiIURERERERERT/uNa5aZeiotmIqzMuvkh2R4e5tKd4Jft39DGw4z5lAG9gWW4S1k6fbjtiMSmNszdhdsB50EsBO4A4wJM9nXCxIVanRy1nuAgEN8yC6f3Cv1sT2mFp0y4ktc+xmzSGRHKQ6w+qKa6Nenm0LWmyzSytik0hsbZJHyNjbzpxiMOJDBhreg+SPiUKWQp6tLFVtVGhnKuOrulJaS8Gs5749js4AzIc5Bz07FnEUu0aI1BafRwJ+QUMFiBReSTYteLc2OA+ZCx6EohCsKmrKuOo6ffraNJp1SxHtpxX7MrHm2+e3HG+WSvOH5gYwTM2tA+8PXrkQXiTTu87lqrku73sTQhxxlzY3uax5x0yWgH96nuqae3UtSrayyxVZSk7ynvPkswxvpSVo4o54ZIXOErpDyfF2tIcZG9cHKg3Fmoi5euWm5DZ7M0rARgiNz3GMEfK27crlYAnML3y+9+qfkdfKN0L6La7QGGwAzns4AvTjdGo+G/Gd8rFoi9ek2mQytkkrxWWtz9hmMgjcSCAXcp7XHB64zjouo4kCQJ5L59gBcATHPh6XXRNA9m3ex7N7BIze1zd7HeS9uR4zDg4I6dF1qZ91ycS2aEojjiEmjadIIohtiiD43uEcbfvWNzgD4gFDFqw9U1KYeRE7lvxtAUKzqYMgb+Ky3B+pspX6luRrnMrzMlc1mN7g09Q3cQM/lK8GoTCSaWQAgSSSPAPaA95cAcefquhfVPsxnz74hazWcaYpbgSfMgD6BfERFNakXCx5D/zXfyK5rhY8h/5rv5FYdoVOn8Q6rdhcZQdpx1ODgbi3Jx0G4Alv5QFyXVbic9ha2R8LjjEkYjL24IPQSsczrjHVp7T2dq8oX6IVVUKGo0RBFBWu12TPgieYBpLL9h0WnatJIbBdM6lLKJm1CbIZC6Q7Q7e1uBIdN0/WSa5szSCR8tsWpYu8hyoDacYIq/ibhCYY4sdDJ453EO7M3WaJXFsesTSOD5Yy1h0x7hJAQ2aMhtXIewuaHN7W5GcL1e9k3pG5/Bp/1NWX1idQPRV20gNJUU4L1LUn6hFXuyzGRlGR12L/AJA12ThmmcggVxzo5CZLflYY9xmLNzGs2+IaNqcE9mSnVfE+V87pbL+83WZGy6pXleyKVk7W3IjV745ZsRNkiaA0P3OcDOPeyb0jc/g0/wCprhHRkdu26nadscWO2t047XAAlrsVPFdgg4PxhY7a8gBZ7O15UXuU9fdAHMs2ROI3ANaNMj3EabYfG6RpD2Cbv4Vmu2v2ZDsYjLl0WWcRiKVwdYfLzGERRM0yMOcItQ5ghsTTPEdV0hobXPic9vLbujcHy7ZbZqPiAdJqlmNpfHGHPGmsBklkbFEwF1TBe+R7GAdpc9oHUhfYqUjy8M1O04xu2SBrdOcWP2tdsfip4rtr2nB64cPjQVbaD0Q0+Z9VHWVNZZZY1kk7a3f00rie852mGTUnSvY/fM2RkHeTmsj2hzmO5uW+LGD6LlW/7z045oJdRvllc2mSOqBvOcwunfYibLBBZgjkJxCx7Q4tjGQMvGe97JvSNz+DT/qa8WryR02tfb1qSqx7i1jrD9Kga5waXlrXS1QHODWudgeZpPmWO0JIgDyH2VnIBxUVj0S9CH4qXrRbBGzdPertmtV20NPgbTsSizhsvfEU8ryCWdLBa4mfD/Roeh3Y56jnQWBt5ThJJJXYymO+9Tmu1214rUojrPjnrxxxsdJhjawccwAtkVqSOKJ08utSRwMcxj5pH6UyJj5SxsTHSOq7Wuc6WIAE5JkZjtC5M2ugNpusTOqhjpDZDtLMAjZnfIZhV2bBtdl2cDBUjVcR/wAqIptB/wCFn0WCoMFhofBq88zCXgOi97HgmN2yQZbVPVrjgjzHoV3PpSNc1p1O0HPJDGlunBzy0Fzg0GplxABPTzBaMq3Zll0WJfp8zQXO1K2AASSWaeAAO0kmp0C5e9k/pG5/Bp/1NYjmszyWURYv3sn9I3P4NP8Aqae9k/pG5/Bp/wBTSOaTyWURYv3sn9I3P4NP+pp72T+kbn8Gn/U0jmk8llEWL97J/SNz+DT/AKmnvZP6Rufwaf8AU0jmk8llEWL97J/SNz+DT/qae9k/pG5/Bp/1NI5pPJZRFi/eyf0jc/g0/wCpp72T+kbn8Gn/AFNI5pPJZRFi/eyf0jc/g0/6mnvZP6Rufwaf9TSOaTyVFe6P+7EX7Pg/v2lWasL3Q1V7NWiDrM8p7wgO57awOOfa6fY4GjH7vOq55Tvwr/8ASL/GvR9lflafReI+0DQdoVr/ANR4rsRdfKd+Ff8A6Rf41wkDm7TzHHx2AgiPBDnAHsYD51flccMB3j5/4Us7l+ox1dYoTzODImzFj3k4awTRSQbnE+S0GUEnzAErPcLaFb0h+qWLsMkEVbT7laOWRpZFZsTgQ1467ndJg45dluQAOuOirxd0tmR7WMfI9zIxiNrnuc1g+JjScNH5FUr4U1HEg2IAPQEm3qQuhg9oNosALSS1xc0gxdwAvYyLNO7QjfbpREVxctFYOpa3PotbSoKIiY6zQi1G298MUptOtSSbIZTI0nksZHtDWkdHnz9VXym9kU9Vq6cZL9ejPRqto2W2RMd1aGR7oJqvKjcJpNkjgY8tOWjzEFUcY1pLM4lsmRE7jEgT/wAwutsx7mip2TofAymQD8QmCYvHPSd0rHd07Toq2pzsrsEcErYbMUY7I22IY5XMA7A0Pc8ADoBhRlZ7j7WI7+oT2IQ5sB5cUAd5XJgiZCwkeYuDN2PNuwsCt2FDhRYH6wJ9N/NVdommcTUNP4cxiNIndy4cl7tF0i1dl5NSCSxJguLY252tHQucexjckDJIGSB51w1bTLFSV0FmGSCVuCWSNLTg9jh5nNOD1GR0Kk1J5h4btPjO11rWIak5HQvgipyWGRnH3vMJOPypxKTLoeizPJMkcmoVWuJy4wMkjfEwk9drNzmgeYOwtIxL+0i2XNl5zlmeG6IjnO5WjgKfYEyc4YH7ssFwbHGYOaZ5RvUOWS4a0aW/ZjqwlrXP3Fz5DtjijjaXySyO+9Y1rSf9B2kLGqX9y4Zk1Rjeskuh6nHCB5TpTGwhrf8AuLWvW/FVDTpOc3WPv/Kq7PotrYhjHaE358vPReXWOGYWVX3KN+O/BBKyK1iCatJA6XIheY5uroXOaWh3TrgY7cRpS/g3A0niBzvI73os+bmvuDk/vy1x/cVEFDDOdL2uM5TE2/tBvEDfw0hTx1NgbTqMGXM2S0TAIc5tpJMGN5N53IiIrSoIvq+L26LpVi7M2vVidNM4EhjcDDWjLnOc4hrGD5TiB1Cw5waJJgKTGOe4NaJJ0A1K8SLKa/w/coGMWoTEJmudE8PiljkDSA7ZLC5zHEEjIzkZGe0LFrDHteMzTI4hZqUn03FrwQRuIg/NZ7jLVorbqRi34r6ZSqSb2hv2WvGWybcE5Zk9CsCiLFOmGNDQpV6zqry92pRS3gaKKGpqmpSQxWJKUdWKtFOwSQie5M6PnPjd0e6NkbiAcjLh8xESUr4JswyVdT02aeKsb0dV9eed2yBs9OZ0gjlf2RtkY943HoCB8wOjGT2XmJjhmE6cplW9mECuJiYdE/3ZHZdbfFETvXfxG5l7SYdTdDBDai1B+nzurxMhZZY+v3zDM+OMBglbtezIAyMZ82IapfxC+GppUGmNsV7ViS87ULD6sgmhhAg72hh5zfEkeQXuO3s6D4lEFjBiGGNJMdP8axy0ss7UM1RPxZW5o/ujfG/SeczeUXCx5D/zXfyK5rhY8h/5rv5FWnaFUafxDqt2Fwm3bXbA0v2naHEhpdjxQ4gEhucdgK5ovKF+iFWp7nt2MFsN4SB8cEszpBHBLNdEtY3S50VYsMFiKtG1xex56O3CQOIHvl4T1AMaGWWPAqwwOhmnncHPbLFJLOZWRhjpDE2SsN0JBaGOcHAvhM7RbziHlaexaoAeC7roWg2g2dkToWPbZs9IuTqUQYHRtj2bu+qpcWNbjkNI6xRkeetwRfbO+UTRwRyCQsggtSujqOc2wNkfMq75o382IOw6LAhZ0cGMa2x0T+Ics9i1YDWuHzNRhpxu2cuxp0pdzJdxbTvVrcuJQeZzXNhfhxOdzgSfOsCOCrbL752W5O93yxyMabUnNhMYHMJc+F8kxnAbE7bLFhkTOr+gbPUUW1nNELJpNJlQHSeE9RidSLpYcV5i5wNmWTbCTWMmQ2rGJ7D+XY8ePvZo5w3tny/fINQ0ua53hJK51V8W+SxHBO7c18tZ0boo52tG9rXvPjYbnaD07FnkWHVSTKCmBZQKxwNJCQaPIY2OWu6OB808cRhqWNGkrwucGP2BkemzNB2uwZyfv3k5kaHM6jbhkFcz25Zp3RNfOyux0rw7lRzxbJozho+ztDXCRzpA0HDFJEWTWcdUFJoUDh4X1Fs0doms6eN8u1j7UznNhdLpjm133RVEloFtSw4ySM3DmRM8cN3rHzcC6k5sQdYhMsTaxfYFmzzJxHBTimqOjlhkjjrONebxiH5Fh+WHfLzLMRSGIcFE0WlV/qHA1iWsYzM2SQxPiPPnme10TtMs1hA97YwHR99SwSF3LGRXY4t3NaBP2joOwfMOwfMF9Ra31C7VTawN0RERQU0RERERERERERERERERERFrt7o/7sRfs+D+/aVZqzPdH/diL9nwf37SrNekbK/KU+i8N9ov5jW/UUXVZ7G/pI/6wu1dVnsb+kj/AKwuguQz4gu1ERFFe7RNNNqXlCerX8UuMtudlaEAEDBkf9917Bk9D8SyPH2iR6dekqRSGVscVZ3MJB3ulrxSvc3aMbC55wPix1PasAexS/uwfdaX9Xo/7KuqrnOGIaJsWutzBb/ldBjKZwb3ZfeD2DNyIfb5BRBfV8XJjSSAASSQAACSSewADqT8ytLnriik2o8D34IpZHCu51dgltVorMMturGQDzLFdji5jcEZxnAOT0BIjK106zKglhB6LfXw1WgQKrSDz+/JSLhrW68dazQvRzSVLD4pg6u6Ns9exDkNliEg2PDmOLHB3mxghcOK9ahsMqVakUkVOiyVsImc188kk7xJPNM5gDQ5zg3xR0AaMfEMAih/DMz59+vKYiY4xZbDjqppdlaIiYExOaJ1ib/6ARerSr81WaOxXkdFNE7dHI3GWnBB6EEEEEgggggkEEFeVFuIBEHRVmuLSHNMEbws5rnFFm3EIHNrwQCTnOhqV4q0ck2NvOlEQHMkx0yVg0RRp020xDRAU61epWdmqEk80QFSLua6ZHc1ajXmAdE+Yukaeoe2GN85Y4edrhHg/MSpLoPEVnW/fKpcLZIH0bVqpFsY1tKes0SV+Q5jQWMDQWEffDt8+a1fFGm4gCQACTOgJItYzoTuV7CbPFZgJdBcS1oiZLQCZMiBdo0OvJVwpfwcdmk8QytOJRBp8AcO3k2boZYb+a5rWghRBZrhXXe8nTtkhFmtahdXtVy8x8yMkOa5kgB5crHgODsHz/HkbMUwvpw0TcGOMOBjziFp2fVbTrS8wCHCeGZpaDa9iQbX4XWWrnfw1MHHpBrELoc+Yy1XiRjc9jSAHYHnGVD1INf16GWrDQp1nVqkUzrLxLNz5rFlzOWJZXhjWt2x+IGtGMEk5z0j6xhmOAcSIlxMcPT181LH1GOcxrTOVoaSJgkTpMG0xpuREWT4Upss36NeQZjnuVoZBnGWSTMY8Z8x2kre9wa0uO5U6VM1Hhg1Jj1XjfUlEbZTFIInHDZSxwjcevRryNpPQ9h8y6Faui69ZvcQWNOnle/T7Ul2gaZJ73igijmbX5MXkRSMMMR3tAPQ/GqqHYq+HrueS14gwDYzZ09L2Ku43CU6TQ6m4kZnNuIu2J0JscwjeiIitLnouFjyH/mu/kVzXCx5D/zXfyKw7QqdP4h1W7CIi8oX6IREREREREREREREREREREREREREREREREREREREREREREREREREREWu3uj/ALsRfs+D+/aVZqzPdH/diL9nwf37SrNekbK/KU+i8N9ov5jW/UUXVZ7G/pI/6wu1dVnsb+kj/rC6C5DPiC7V6GUpTC+wGO5McjInyfeiSRr3MZ85LY3np2Y69oXnVgajq77fDb90VaFsOrwMYytAyuzrUlcXObGMOeT2u7eir16rmZYGpAPKVbweHZWD8xgtaSABrA+Sr9d1y1LM8yTSSSyENBfK90jyGgNaC55JIDQAPiAC6UW+BqqeYxCKQdzdjHavpof5Pftc9fO4SAsH8Yao+u2rO+KRksbiySN7ZI3jtY9jg5jhnzggH9yhWYXsc0bwR8ltw1UUqrXkSAQY6GVO+ApHP4kmEhOJ36s20D2OY6C294fnzb2t/eAq/b2D8imFzjZju+poNPgrXrsckVq5HLM7LZ8d8OgrvOyvJJjq4EkZdjGcqHqthWPDi5wizRFt03tPGBvsr2PrUyxtNjs3vPdNx8WWBeDPuyd0m0oi9ej6dNbnirQN3zTPDI29nU9pJ8zQAST5gCs3q/CLoa81mC5TvMqvYy2Kj5HOrmRxY15EkbeZCXjbvbnqfykb312McGuNz/x/ocSqlLB1ajDUa2QN/QSY3mBcxoLmyjKIi3KspRonBNyxSs6g9phqQVZbEczg1wnfGdoiY3eHAHD/AB8EDZ84UXUs7nQ8TWz5/eK5/eqqJqtRe81Hhx0IiBFo81fxVOkKNJ1MEEgzJmSDHAQOXzKy/Bus+99+rc2l4glDntHlOjcCyUNycbtjnYz58KSVJdN0xt+erebbfZqz1KMDIZ45IWWSGumsvlYGsfHGCA0F24nzDsgiJWwrajpJO4EDeAZg/PSNSmG2g6gzKADBJaTMtJEEiCBuGs3ARERWVQUrPBksel2NSsOYzYagrxRywyue2w/BfMI3O5Q2kYacOzuyBjrFFLeHPuHrv6bR/wC/ZUSVXDOeS8PMw7pbK08+Kv45lMNpOptiWSZMmc7xOg4Dci9Ol3HVp4LDMF8E0U7M9m+J7ZG5+bLQvMiskAiCqTXFpDhqFYcetaTWuz6zWnnfZk74lrae+sWd727THtc6azu5ckLDLIQGjJy3s25NeBEWihhxS3k6C/AaC0cTz4q1isa7EQCABJMNmJdEm5NzA5CLAIiIrCpouFjyH/mu/kVzXCx5D/zXfyKw7QqdP4h1W7CIi8oX6IUW7nfGLNYinkZA+AQSiIh7w8uJbuyNoGApTla78ITPj4Z158b3Me21Ww9jixw+y1wcOacjoSP3r2a9oNiHQaute+moOtiOo5recWwxxSljGMjaPGa9oc0l247iHEjLsru1tlM7UhrsozZQIJvAPpdfI4X2gqjDtc9mcin2jiCBbM4acbbtVcXEPE9ShLUhsue2S9IYq4axzw54dEzDiOjRmZnU/GVmsqi+6jWNqfhizJNOH6i2uJQyTayFxNEukrNx9hlJncS4Z6sZ8SyHHtW0Ne0fT6t6zBu09kHPdI978NFtj5ngECSwY2k7+h3YPmWnu1rmMh0EhxM6e6Tw6c+KsnblRlSrLJaHU2tg3OcA3m2+d3DmrkyipvierZit6PwxBetMhkZLYs2t+LEzZJrUpY6QHPRsUoA7CXNyDjC7e9ZuH9b0ytBdt2KWpExSQWpOaWvLgzeMANbh0kZBABw1wJIWvu4EWfcguAg3Am/ImCQFvO2iHHNTOUOaxzpHuudFo3gEgE+gKt/KjPBfFzNTm1CFsD4jQn5DnOeHCQ75mbmgAbR9i/8Asq/4c0yxxHd1O1Y1C5VjqWTBTiqTcrlbS/a4gggYa1hJGC4l3UYAWH4LM0Wm8W7pSZ43APmYdpdK19oPkaW+TlwJ6fGt7dmsDHtLpeMnH3cxHkbFVX7cquq03NYRTPaXJHvBjSerbi3EK/sr5lVRfty/8FCUSyCXkQnm8x4kz39GCeZndnHTtWO1rU7c1ThfS4rMsA1GCA2rDHuEzmbYm4Emc9jpCR98duemc6GbNc7+rRzmn/5Ek+m5W6u3GsA9wmabHgA6l7sob671dOVhdC4nqXbFytA57paMnKsBzHNDX75I8Ncejhuif1HxKL6DwRe06+01b8sulSwvjtQ27D32WyObIBJX2Q8sODuUd3inyx16KMdxnRmt1vWT3xaPeFp8bd0xIsh0tyDdcGPs7wBuB6eMSUbg6Jp1Hh85WgiBxMQR96ysVNp4kVqNM0suZzmuBM2DZlpGo36biOasjgjX7F9k7rGnzaeYpjExkxkJmZtB5reZDGdvXHQHs7VIcqh+F+JrdTQNZsslkdOL7YIZJHOkMQkETS5u8nBDS4jzZx2r063wpYoaOzWYtV1A6gyOtZlLrBdC/nujDmAOG5wbzB5ZcHbTkeN03VdmN7QjMGy7K0XMmAfIXHFVaG3X9iHBheQwvcZAgS4cgTY2EacVd2V9yqV411S1escLvisSVJL8LDI6FzgGPldX3uDM4ftLnbd3zLvq6fLo3Eun1YL1yxXvQvdMy1NzS4ls48bADSQ6Njg7GR1GSCc6hs33ZLveyuOWP7SZvpusrJ27/wBSG0yWBzGl0j+sAgxrvEq40RFy130RERERERERERERERFrt7o/7sRfs+D+/aVZqzPdH/diL9nwf37SrNekbK/KU+i8N9ov5jW/UUXVZ7G/pI/6wu1dVnsb+kj/AKwuguQz4gu1ZuLWWDSpdP2O3vvx2xJkbA1kD4iwjt3Zdn9ywiKD6YfE7jPop0qzqc5d4I8jqiIpn3J9J063ersuzPLzPiOmIN0c+1m8GWffhrMggs2nIHb1UK9YUaZeZtw+/wDS2YPCuxNVtJpAJMSTA+f7C53KGIucww5wHyj/ADK4LcFXIgwiLPXODtUhrd9y0pmVw1ry87dzGO7HviDuZGz53NAWBUGVWPu0g9DK2VaFSkQKjSJvcEW81Me4991GgfbDVvCH4+b3pNjb8+3euHc3+064T9q947QPyeaZa/Iz/wB2d2P3qNaXelrTRWIHmOaF7ZI3jGWub2dD0I8xB6EEg9qzOscXTWIJa7a1KpHYkZJZ7zgdC6y6M7mCYukd4jXEuDW7QD1wqVfDvc85dHZRPDKSfrbmupg8ZSZSbnJlheQI+LO0AdIIvy0uo6iIuguMvXp2pTVxOIX7BZgfWm8Vjt8Eha57PHaduSxvVuD07V5ERYDQDKkXuIAJsNOSLM6VwtqNuF1itSsTQtzmSOMkOLfKEY7ZSMEYaD16LDKcd0W7LUtaW2B7mChptB9baSAJHM5skoA6b3uPjH77ABVevUeHNYyJM66W6RxH+1dweHpOY+rVnK3KIbEy4njOgBOl7C2qhBC+KVd1usyLW9RZGAG88SYHZumijmf/APd7lFVso1O0ptfxAPqJVfFUDQrPpEzlcRPQwvTBemZFNAyRzYZzE6aMY2yGEudEXefxS5x/evNlCVaur69Ppmq1tHg2DT6/eNaxWMcTo7ffMUL7Us4c3L5H84jPm2jHnzpr1jTMMbJIJ1iwgcDe4AVrCYYV25qryGgtaLTdxJAiRAs4n9iSqqRZbjHT21NQu1mfa4LU0cYySRG2R3LBJ6khu3qsSrLHh7Q4aESqVWkaT3MdqCQfKyLIa1o89MwCwzY6xWjtMac7hFK6RrN4I8Vx5ZOPiIXTpWoTVZmWK8jopo92yRmNzdzXMdjPxtc4fvUq7rFmSaTSJpXl8suhUJJHu8p73vsuc4/OSStD6jxVa20GetvvirVGhTdhqlQk5mkW3QT6zyjzULREVlUUXCx5D/zXfyK5rhY8h/5rv5FYdoVOn8Q6rdhEReUL9EKsdJ7mc8Ok6lpptQufemikbKI3hsYjfG8hzc5JPLP+qzOt8GS2NBh0gTxtkjiqxmYtcWHvdzHEhuc9dn/ypqiuOx9Zzg4m+bNoNYA+gXLZsfCsYWBtizJqfhJJj1JvqoBxZwDPbraO2G0yG1pDYhHI6Mvie5jK4Ltucjx67CAc9pBXpt8HWZtV0vVJbMTnUqrIbDRG5pml2TiSSPrhjS6bIHmwpsq67vetW6NGtJUnfXe+4I3OjIBLDBM7acg9Mtaf3Ldha9es9tFpF8wBIH9Uzz4qtj8LhMLSfiXtJjK4gE3LIDTExIt13rKd0Hgt2oyVbVayad+m7MM4bvaW5Dtj25HY7JB6jxnghwPTwcP8C3HahFqer323Z67S2vHFEIooz42HHAAONzjgNHXBJOAFVuh6txZejM1Sa7PEHmMvYYsB7Q0lvjYOcOb/AKpa4y4n0qZnfcthpdktjtwxvimDcbgHbQSBludjgRkdRldZuzsSG9i2qwkAiP6gDqAYkL51+2sC54xNShVAJBzXyEjRxGbKSNxVk2+59qFe7as6RqYoxXnF9mJ8LZdrnOc5zog4EHxnvI8kt3EA4Xbwf3NjTratUntc6PUmhgka0iWNoEwD3lxw6T7I0/Flp+NSXgDiRmq0YrjG8tzi6OaLO7lzMOHtB87Tlrgfie3sWfXIrY3ENmk8wRANhPum0nUxHFfS4bZeCqZa9MEggke87KA8XgEwJkyICqQdzLV3UX6bJrDDSZgwQNgADnCUSjnPI5mwHc4M3OGdvZhZvXO526xQ0uFlrkXtKZEK9prCWFzBHnLMg43RMcD5iOw5IVgKKd1Hix+jU4rTIGzmSyyDY55jADoppN2Q05P2IDH/AHKVPG4mtUa1kTMiABJIgzoL75WqtsrAYWi99QHLlAJLnGGgyIuSIOkaLwcM8HX23majquo9+TQxmOCKFnJgZkObve1oa15w9/Tb2nOTgY+8OcF2qGr3L0NuM1L0sk1iu6I80udznsa2TOAGyyk5GMjphZLuacUO1ekbb4WwETyRbGvLxhgYd24tHU7vi8yqzuhcW6lBxDNVhuTR1xPSaImkbQ2SCs54GRnqXuP/ALirFGnia9WpRkAhpBECIBFhAjW4hVMVWwOEw9HEgOcC8FrpOaXA3JcQSCBBB3WhTjhzubNi07UNOtzNlZen5wfE1zHROAYY3Dd2ua+MH4j2FYt/c31eaCLTbOtNfpcRYBGyuGzOjiILIyT1AbgYDnvDSG9DtAFrIqY2lXBJkXM3AMGIkSLHoum7YeEc0NymAMtnOEtmcroIkTNioVr3A/OuaNPXkZDBpOxohLXOc6NjotrWuz0w2PGT8a7te4Sks6zp+qNmY1lOMsdEWuL3553Vrh0H20f6KXplaRjKoi+gI8nTP7lWDs2gZ93VzXG51bEekC2iIiKsr6IiIiIiIiIiIiIiIi1290f92Iv2fB/ftKs1Znuj/uxF+z4P79pVmvSNlflKfReG+0X8xrfqKLqs9jf0kf8AWF2rqs9jf0kf9YXQXIZ8QXaiIiiilPcmka3WtPc5wa0TOy5xAA+xSdpPQKLItdan2lNzOII9Qt+Fr9hWZVicrgY4wZXZP5TvznfzKyfBcDJNS06OQAskvVGPaRkOa6xG1zSPiIJH71iF21LD4pI5Y3bZInskjcO1r2ODmuGfOCAUqNJYWjhCxRqBtVrzoCD81YnCVqSbiyYSkvFq1qVaw1xJD65jsN5Ts9sbQyPA83Lb8SrZvYPyKcS8YUmzT6jWpTRapZZMC42GOp1prDCye1Xj5fMMrg6Qhr3EAyu7cYMHVXCMcHFxbHutEW1bM6Ta4HkujtKsxzAxr8xzvdInR2WNQDNiSN08ZRZLQNDt35RDUgkmduaHFjHFkQecB0zwNsTOh6ux2FY1ZbhKzJHdqCOR7A+1WDwx7mhw5zOjtp8YdT0Pxq1WLgwlusb1z8K1jqrW1Jgm8a/OV5+INONS1Zqlwea88sJeBtDjG8s3AEnAOOxeFZ7uifdfU/161/eesCsUHF1NrjqQP2WcWwMrPa3QOIHkVK9T4PNbSzflmhfK63DXZFXnhsNYx8MsjzM6IuAkJazADug3ZzkYiil9X/pux+2q/wDspVEFqwrnnNnMw4jhuCsbQZTb2ZptgFgMTN5PRFMoOI9NnZSk1Gtbks6fDFXZ3vLC2C3DXcXV2WeY0vjIB2FzMlw+LpiGotlWg2pEzbeDBWjDYt9CcsX1BEixka8Pu0r3a9qcl2zPamxzLEr5XAeS3cejG567WjDR8zQvCiLY1oaABoFoe9z3FzjJNyeZQqxLF3S7t2rq9i8IHsbVfepmCd88lioxjAKxa0xujlELBlzht3ElQGpWkme2OKN8sjs7WRtc97sAuOGtGTgAn8gK6VprUBVOpBAOkaHXWeHyVvC4t1AH3Q4Egw6YluhsRpJ8j0Xu1/UXW7Vm04bXWJ5Ztuc7OY8vDAfOGggZ+ZeFEW5rQ0ADQKo95e4udqTJ80WV4i1l13vPdG2PvOjXot2kne2uZCJDnsceYeg+JYpELASHHUKTarmtLQbGJ8tEREUlrRcLHkP/ADXfyK5rhY8h/wCa7+RWHaFTp/EOq3YRF0ajzuTL3vy+fy38jnbuTztp5XN2eNy9+3O3rjOF5SLr9DkwJXax4OcEHHbgg4/LhclSfcYu2a0uuWJu9m1IJJ5r5YJTMJouc/8A5YeSYcNl8rxvJ+dZCpxhxJcqTarUq0GUY+a5kEnNfYlihJEjmlrgJC0teOmzJYcA+fpVdlvbULQ4QIuTAkiY6/S64NDb9N9Jr3MdmOY5WiSA0wTutp52Eq3FVXumPubU/X2/7ewpzwLxCzVKMN1jDGZA5skZO7lyRuLHtDvvm5GQemQ4dB2KDe6Y+5tT9fb/ALews7MY5mNY12odB+abdqtq7KqVGGQWSDyMLh7n3VasGlSMns14Xm7M4NlmjjcWmKAA7XuBxkHr8y8PuhuIaFinWqwWIbFgWmzHkyMl5UbYZWO3uYSGkmRmGntxnzKIdz3uZyaxUdaZcZAGzvh2OhdISWMjdu3CQfhMYx5lLdM7hTRIDZ1AviB8ZkNflvd83MfI4M/hP7l2KowdHGGs+qcwM5QDr1XzGHdtPE7NbhaVAZC0DOXDTjCw/Cmn3W8J3LNaexWkjvvuRurzSQmSCKKGCxudGQSwBsrsfHAFJvc78TT2mXatqxNYljdHYjfPK+aQxvHLkYHSEkMa5jDjszKfjVm1NKrxVm044mtrNi5IiGdvLLS0tOersgnJPU5JPateu5+52i8TCrISG8+Wg9zu18cxArv6dgc8V3/kK006zcdSrti8528Y4fL5q3Vwz9k4nBuzHLHZu/tk7/nPRqkHd94ptx6hBTp2bMHKgDpBWnlidJLYd4rHiJw3kMZGRnP2047evg7sejarWqVTatmek3vSLZJM+WU3hWkM0zi9pO0uE+DvPRwH5PPwhF798VSWTh8MdiS4T16w1S2Op+XqKwI84ypz7pX7k1/2jF/trasMcMPWw+HAEx71t5+uvqqNZjsbhcZjHOOXNDACYhtukG3mFH+4BQ1QmGwyw0aU2aw2Wvvw50vJwHBnL6je6I+UPJ/1indgn5XElyXG7ly0pNucZ2VKrsZ82cdqtP3On3HP65P/AExKse6mAeKJwRkGxp4IPYQa1QEH5lPCVM+0asgWa4WtMOGvPmte0aHZ7Fw+Un3ntNzMEtOk6DkslxRR4rnhk1WeSxBEGmY14bToDXgALtwrRvG0Nb25y/A8bqCpT3A+NLV0z0bkrp3wxCeCZ5zKYw5scjJH9smHPjIccnxnZJ6YsriYf8lc/VbH9p6on3Nf3Wn/AGbN/uaapsrNxeCqlzGjLEZRELpVMM/Z21MOGVXu7SQ7MZn7meUKQd2XujWoLTtN055idGGixOwbpTLIA4QQ9CG4a5uXDxi52Bt2ndgLHCfFkNc3TatlzW810LdQnfaDQNxzHuw8gddgcT5sE9FjK20cWnvjs9/JfK+M238jt+93cvHzYWzKlia42eymymxplskkTKjgMI7bFWvVr1HDK4ta1pjLG+PTreVU/cT7oc9+R1C84PnbGZILGGtMrGY3xyBoAMgByHAdQ12eoy6L93bXr9fVnRV71yvH3tAdkNmeJgcd+XbI3gZPTqsN3NSz/ieDvfHK78umLbjbyeVZxtx97y//AIXf7of7sv8A1SD+T1cpYSkzaIytEOZmjcLrl19o4irsUl7yXNq5c03IAnXfqstqtHizVYzfaZ4IHN5lerDaMDjDjLS2JjgZHEdcv8Z2egwQF6+4TxzcluDTrc77Ec0cjq75nGSVkkbTIW8x3jPYWB5w4nGxuMDKu2sAGMHxNbj/AEC1r7kP/Utb9Nf/ANrbVPD1m4vDVmuY0BrZbA0sf8fuunjcK/Z2NwtRlV7jUflfmMgyWjSw3m260aLZhY7V9cpU9nfdqvWL87BPNHEX4xktDyCQMjJ82VkVQPdev162uTTSMrakJaBgdWkcSaMuwNY44BDXB2HgZz9lf5JLXHjbPwn8TUyX0Jtv5XsPNfUbZ2kcDQFURqBfQTvtc9B10BV9wSte1r2Oa9jgHNe0hzXNcMhzXDo5pHnC5qG9zFkNClS0uS3DNc73daDI37wYZpZJA+N3Y6IbsbvPgnsIUyVatTyPLRcTY8RxV7C1jVpNeRBgSJmDFx5IiItSsLXb3R/3Yi/Z8H9+0qzVme6P+7EX7Pg/v2lWa9I2V+Up9F4b7RfzGt+oouqz2N/SR/1hdq6rPY39JH/WF0FyGfEF2oiIooiIiIiIiIpIeDbjNOn1KeN9eGM1xC2RmHWRO/buZlwLGNBadxGHbxjz4jal/D0jjoeuAuJAl0fAJJA+zWOzPZ2D/RVsU97QC0/1NB6FwFvVXsBTpVHObUBPuPIgwAWsc6/HTiOciyiC7qdh0UkcrMb4pGSNyMjcxwc3I84yAulFYIkQVSaSDIXq1a8+1PNZlxzJ5XyybRhu+Rxc7A8wySvKiI0ACAsucXEuOpXobclEJriR/IdIJTFuPLMrWlgkLezeGkjK86IgAGiwXE6lFL+C+FIrcNmxNargQ0rs7KjJv+cdJBG8sc+Lb4kO4B2c5OGjHXIiCl/ct+3aj+xdT/shVsaXCkS0wV0NlNpvxLW1GyDu+9eiiCIitLnKf9xjVtl6OqKtRxmjuk2nxPdbYBTmfsil5m1jcxgeTnD3DPVQBvYFnOBNYioX4bUzZHRxsstc2INLyZq00DcB7mjAdICevYD29iwYVWnSy13uAsQ31l0/RdCviM+EpsJu1z7cAQyPmHIiIrS56IiIiIiIiLhY8h/5rv5Fc1wseQ/8138isO0KnT+IdVuwiIvKF+iFUPBWhW2za/plmpYij1I2jHd25rta7nNad46OJErXAfMQcLyaJf1zTdNfovvLYmnAsRV7cWX1tth8jy97w0s8V0jiMubkYB24KulF0ztIuJzsBBgxfVoideGoXBbsIMA7Oo5pAc2QGzlccxFxFjcHVRbuWcPSaZpkFWYjnZfLMGnLWvlcXbAR0O1u1pI6Eg46LAe6B0mzcoVY6sEth7bjXubCxz3NYIJ27iG9gy4D94VkIq9PGPbiO3NzM8rq7W2ZTqYP+DBIblDecBV/3BdMsVNMkitQS15DcleGSsLHFhigAcAfNlpH7irARFqxFc1qjqh1Jlb8FhW4Wgyi0yGiJOqKhPdIaLyrla/GMNsx8qQjpieDGxxPynRloH6BXvZeWsc4DJa1xA+MgZA6LXTifUdc4klr1zQfExjvFY2GZkLXuADpp5pejQBkebAJGCT16uwmuFftZAaPik7iF8/7WvY7CdgWkvcRkDRNwR6WMeam/ubtF5VKxecPGtS8uM//AIa+Wkj4syukB/RBSHu1cO2NS0zlVW75obEdhseQ0yBjJI3NaXEDdtlLup67cKT8N6UyjUrVI+ra8LIt2Mby0eO8j43O3OPzuKyCqVsc44o4husyJ4DT5Lo4XZLG7OGDfplgxxNyR56Kle4lJrdOwzTpqEsNB8s8ss01WZjmP5B2tZMSGbS+NnaD2nr1WK7ovDOozcRzWYqVmSA2KLhMyJ7oy1kFVryHAYIBa4H80q/0W8bXc2u6s1gBLYIvvMz1VR3s2x2EbhX1HENcHA2kQIDei8PEEbn1LTGguc6tO1rQMlznROAAHnJKpvuBcOX6epzS2qdivG6hLGHyxOY0vNio4NBcPKw1xx/2lXkiqUMa6lSfSAEO1XQxey2YjE0sQ4kGnMAaGeKpzux9zazZsu1HTmiSSQN74rhwZIXsAaJoS4hpJa0ZbkHLcjcXHEbn13i+aA0XVr+HDlOl7wkZM5hy0tdYMYaBjpvGCfj6krYdFbo7Xc1jWVGNfl0LhcLnYn2bY+q+rRqvp5/iDDY/d/Xqqs7i/c7m057r14NbZcwxwwNcH8hjsb3yOblplOA3DSQBnqS7DYt3cuGdQt6q6WtSszx97Qt3xRPe3c0PyMgYyMhX4ihT2tWbiDiDBJERuA5LbW9nMM/BDBtJDQZkak85XCAYa3PTxR/JUD3MOGNRg4gr2JqVmKBstwulfE9rAH1rLWEuIwAS5o/eFsCi0YXGuoMqMAHviDy109Vcx+ymYupRqOJHZuzCN9wb+iKjmV7Gly61TuaLZ1OHUpXyxz1o3v5wc97o2vlYxxYcuDunjMeCQDkFXiijhcV2MiJBjeRoZEEXU9obPGKykOylswYBEOEEEOkGypfuNaHe07UD74UbYM1NsVawSZoq0e8yugkLMtj3bR8W0txgb+l0IixjMUcTU7Rwg8tPms7M2e3A0exYSRJMmJvxiJ+wiIiqroLXb3R/3Yi/Z8H9+0qzVme6P+7EX7Pg/v2lWa9I2V+Up9F4b7RfzGt+oouqz2N/SR/1hdq6rPY39JH/AFhdBchnxBdqIiKKIiIiIiIiL21dTmjr2KrHAQ2nQOmbtBLjXc50WHHq3Be7s7V4kWHNDtfuLqTHuYZaY1HkRB9RIRERZUUREREREREXbXsSRlxje+Mua5jixzmFzHjDmOLT1YR2g9CupFgidVkEgyEREWVhERERERERERERERERFwseQ/8ANd/IrmuFjyH/AJrv5FYdoVOn8Q6rdhEReUL9EKqdb7s8FWzZrOoyuNaxNXLxOwBxhldGXAFvQEtz+9cNN7uNF7w2apYhYSAZGujmDM/fOaMO2j/tyfiBUU4N1GvV4r1GW1NHBELWqtMkrg1m51l+Bk9MlZbu98Q6TbqV2VZoLNptgOD4cOMcPLeJA6VoxtLjH4meuAfvV9T/AAGH7VlLsnHMAc4JtI9PmvPu+Mb/AA9XEfxDRkc4CmWtkgHjIN+iuqrYZKxksbg+ORjZI3tILXseA5rmkdrSCDn512KkeI9e1LSOH9C72nNeWVjuZmKGQmMt5kTSJ43bcNc3swpDwLrWvXrkNyzEYNHNZxaXd7N5mIsssSDPOy8+P4oDACMZHU8ipsxzWGpmbllwEm5ymLczuhfSUdu031W0Cx2eGEwJAzCZJ3AaEmPNWaipKHjHX9euTx6M6OpVg673tjzy3EiJ08kkbyJH7XEMY0YAIOcFx93CXHGq09UZpGubHule2OKw1rGuD5ekBBiAZLC84bnaHAnr2EKTtk1Wg3bmAksn3gP2+ahT9o8O9w91+RzsoqEe4TprM8phW+iqnunccX26hHo2jgC07YJZdrHv3yNEjYmCUGNjRHhznkHo773aSY7xjxXxRpEMVe3NGJJH8yK7FHXk3xta4SV3tdDsyC6J2drT0PV2eijsirUDbtBdcNJvHHomJ9o8PQNSWvLWGHOa2Wg/2zIvu4TvV8Iqx7p3FF+lommW61jl2LD6omk5UL94kpyyv8SSMsbl7WnoB2fF0Xn4E17iHULOn2ZInR6S2HbZlIrNdZkZVe187mdJdrrAyBE0NxjoepWtuzqhpdqXNAvqYuNw4k7lvdtuiMQMMGvLoabCQA7eYNgP6idOatZFTFfWuKtZsWDRxplaIgsZYhERLXF3LBfLC98kuGknbho6fGM5TuS8aajYv2tK1IslmrCYidjWNdvrzNiljdygGPb42QQB5BznPSVTZdRjC7M0loktBuAfl81ro7fo1KrWZHgOJDXuENcRwvPqFaaKiqHGXEVrVdQ0+nMyYiS5HCJYqzGVI4rIaJy5sQc8tYNgDtwJlBIcQuqpxxxJSvy6XOI7tyQtiha9sQEcsrWvjlY+FrQ+PY7JD8Aect2kLb3LV0zNmM0TeONwq/4pw1jkqZcxbmy2zDdYmSd0T+6vpFRdniziPRtQrR6rNHYhsFpcxrICx0bnhjzG+KNjmSMz2dnZ0Kzvdx4v1DTLFFtOcxMkjkfKzlQScwskYB40sbi3oSOnxqHdNU1GMa5pzAkEG1td30W78RUG0alV7Xt7MgOaQMwzab4jzVroqL4w4j4r00w3rL4YoJ5NrarWQSRROLTI2vL4vMyWtd1DyfFd4w6Lnr/EfFMlQ6zE6OnQIa+OBgryPbC5wYyV4mjLpGklvXI6HIaApjY9Qhpzsg2BzWnhpr0stTvaai0vaaVSWiSMt8v92th1g8tYtnjPWve6jZuiMS97sD+WX8sOy9rMb9p2+V8R7F5O51xMdWotuGEQbpJI+WJOaBy3Yzv2N7fyKM/8Z2rHC82qMLYLkbSwuY1r2CRlhkRe1koc3DmnOCDjcfiyuvhnie9Lwxa1CSfdcjZbcyblQN2mInZ9jbGIzj52/lUP4IiiQWjN2mWZPDSIiN8zPJbe9WuxLS1xyGiamXKIInWSc0xbLEc1ZqKjOEuIeKtXqPFSaMcqZ3MuSNrRueSxhbWjaItg2jxidufsrfGA6HPdxrja/ctWtN1LD7Fdj5GybGMe0wythmhlEWGOIL24IH3rsk9ErbJq02uOZpLdQDcc9P8AaYb2ioV302hjwH/C5zYaTw1N93BWqipfV+NNa1bU5aGhubBFXMgdMWxHeInbHTSSStcGxl+A1rBkggnPUN8N/jfiGtqVDT7cjYJBNWisbIqz2XI5bDQJ2uMZ2bmEtOwgZaejTkCTdj1TAzNmJyzcDmIUH+02GbJyPLc2UPDfdJmIBkfONDCvZERclfRKIcYdzzT9VsNs2jYEjYmwjlSNY3Yxz3joWHrmR3n+JYb4F9H+Vc+nZ/jVkIrbMfiGNDWvIA3SubV2Ngqry99JpJ1JFyq3+BfR/lXPp2f414dZ7jukMbEQbfjWazDmdvY+ZjT/AP1/EVayxvEXkQ/rlP8A3Ea2N2nip/7jvVajsLAC4ot9AoT8C+j/ACrn07P8afAvo/yrn07P8ashFjvPFeI71TuHZ/gs9Aq3+BfR/lXPp2f40+BfR/lXPp2f41ZCJ3nivEd6p3Ds/wAFnoFW/wAC+j/KufTs/wAafAvo/wAq59Oz/GrIRO88V4jvVO4dn+Cz0Crf4F9H+Vc+nZ/jT4F9H+Vc+nZ/jVkIneeK8R3qncOz/BZ6BVv8C+j/ACrn07P8afAvo/yrn07P8ashE7zxXiO9U7h2f4LPQKt/gX0f5Vz6dn+NPgX0f5Vz6dn+NWQid54rxHeqdw7P8FnoFW/wL6P8q59Oz/GnwL6P8q59Oz/GrIRO88V4jvVO4dn+Cz0Crf4F9H+Vc+nZ/jT4F9H+Vc+nZ/jVkIneeK8R3qncOz/BZ6BVv8C+j/KufTs/xp8C+j/KufTs/wAashE7zxXiO9U7h2f4LPQKt/gX0f5Vz6dn+NPgX0f5Vz6dn+NWQid54rxHeqdw7P8ABZ6BVv8AAvo/yrn07P8AGnwL6P8AKufTs/xqyETvPFeI71TuHZ/gs9Aq3+BfR/lXPp2f40+BfR/lXPp2f41ZCJ3nivEd6p3Ds/wWegVb/Avo/wAq59Oz/GnwL6P8q59Oz/GrIRO88V4jvVO4dn+Cz0Crf4F9H+Vc+nZ/jXn1HuM6O2GVwNzIikI+zs8zCfwatBebVftE/wChk/ocneeK8R3qsjYOzx/4W+gXpREVFdZa5cP6DX1LifUq1prnRG3qjyGPLDuZZk2+M3rjqrW03uVaJBI2UVTK5hy1s0skkeR53Rk7X/kcCPmWU0vgrT612TUYYnNtTOmfI8zSuaXWHF8p5bnbRkk9g6KRrr43aj6hApOcG5QCJi410K+b2XsClRa44hjHOL3OBibE21Cp/wB07/6bT/0839tqsapWMulxws6GTT2RNPZgvrBg/J2hfOLeFaWqMjZdjdI2JznMDZJI8FwAJJjcM9B51lqkDYmMjYMMjY1jRknDWANaMnqegCr1MU04enTGrSTyubK7Q2e9uNrV3Rle1oHGwIM/S6o/uAcQ1aBvU70kdSV0jHtdYc2JpdFvjlie9+Gse048UnJ3O+JefjS/Fq/FOnNoOEzYXVInSxncx3IsSWZpGOHR0bGOPUdCWHGeitLibue6VqMpnsVsTOxvlhkfE5+OmZAw7XuwAMkE4A6r2cKcHadpe4064ZI8bXyuc6SVzeh275CS1mQDtbgEgHC6DtpYftHYhodncIi2UEiJnWPJcVmw8Z2LMG9zOya4HMJzkAyBEQDzn131LqVxmmcZusW/Ehe/cJXAkNjsVDEyUf8Aa1+WE+YNf8S7/dCcUUbcNSrUnisvZM6eR8D2yxxtEbo2sMjCWlzi8nAJxs64yM5juta1p3fsVPWNLldB4pg1GOZzXCN7RzCxsbQXhjyQ6PcewOx1bmC8ajRpo6uncPQSWJ5bHMkl2TmV5Eb2MhBsAPx45ccAMG3J85HQwjRUfRrPY4ENAkRkgTcnd04rjbSe6hTxWGpVGEOeTlMirLiPdDYE7oIkRdS3u0f9N6N+ko/+PnVg8GTcrQqEobuMelV5A0dri2q1+0fOSP8A5XK/whVu0KdG8wysqsgxskkj+yxQGHcCwgkYc/p86zemUo60MNeIFsUEUcMbSS4tjjaGMBc7qTgDqVwa+KY6g2kNQ4nlBX12F2fVp4t+IJEOptaOII47vmqD4T1E62+zNrHEEtFrC3ZWjssqMe124kxNe7llrcBvRrndRk9mefcLEA4hsisXurivcEDpPLdCJ4RG5/QeOW7Seg6lWZL3K9EdOZzTxl28xNllbBuznpE12Gtz96MN82MdFkdN4H06tdN+vC6GwS7JjmlbFh7drmckO5ez/txgEAjsC6VbamHLHsYHAObAEABp8teq4mG9n8Y2rSqVSwlj5Lszi5w/+rDoNeIVZ9x3/qjWPzNS/wDJV191f/rmH8+D/wAeFZ+h8G0KVue9XicyzYEoleZZXhwmlbNJhjnFrcvY09B0X2fg6g/UBqjonG60tIk5soblkfKH2Pds8jp2LQ7aVI1nvgwaeTdrA56K0zYVcYanSlstr9obmMsk8Nb9OarD3Rv/AK7SvzH/AN6NPdG/+u0r8yT+9GrP4o4OoalJDLcidI+AERFssse0FwcchjgD1A7U4o4OoanJDLcidI+AERlssse0FwcejHAHqB2rGG2lSp9jIPuB4P8A9aRdSxuw69b+Jylv/UdTIkn+iJm3pEqG+6X+5dX9ox/7W2u7Xf8Aoxn7Ko/yrqa8V8N1NUhZBcjdJGyUTNDZHxkSBj2A5jIJG2R/T519scO1ZKI01zHGoIY4BHzHh3Ki27G8wHdkbG9c56LRTxrG0qTCDLX5j05K5W2XVfia9UEQ+nkHGYOttPVVLoX/AERc/Pk/3US9nB3/AEXe/RX/AOoqxK/B1COg/TGxOFOQkvj5shcSXiQ4kLt48Zo8650eE6UNCTTY43CpKJGvjMshcRL1f9kJ3DP5VuqbRpuDoBvVz+XrqqtHYtdhYSRbD9lqfi46afPkoh7m/wC5Ev6/N/ZrKNdyn/q3WPztV/8AIxq2+FuHaumQur02OjidI6UtdI+Q73Na0ndISQMMb0+ZeXR+DqFS5Pfgic21Y5xleZZXB3PlE0uGOdtbl4B6Doou2hTL67oPvi3rvv8A5U2bGrNp4RsiaRl2t7Ra37wqg7ieqwaTqWoVNQkZXe4crmzODIxLWleHMc93Ru7cXAnodnb1GePdJ16rf4i0w1HsmZBLShdMzqx7+/OYQx46PY0PHUdMlyyPFer6Ha1OaHW9Nm0+aPc022SyuMwYQ2Jz44IwZGOYPFkw7oGjOOzCsrUtQ1zTYNCruFOm6u+WbZINwjsGeaxI6Tx8bQ1gMmCSA0dNq7TA11U4h7XAllzbJ8MSCNZ4L5eoXsw4wVOoxzRVEAT2p9+YLSBEXJPktikRF8avTkREREWN4i8iH9cp/wC4jWSWN4i8iH9cp/7iNSbqsO0WSREUVlERERERERERERERERERERERERERERERERERERERERERERERERF5tV+0T/oZP6HL0rzar9on/Qyf0ORF6URERERERERERERERdVmtHK0sljZIw9rJGte0/la4YK6qOm14M8iCGHPbyomR5/LsAyvUizmMQo5GzMX4oiIsKSIiIiIiIiIiIiIiIiIiIiIiIi816hBOA2eGKYDsEsbJAPyB4OFzp1IoW7IYo4mdu2NjWNz+a0ALuRZzGIUcjZzRfiiIiwpIiIiIsbxF5EP65T/ANxGsksbxF5EP65T/wBxGpN1WHaLJIiKKyiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIi82q/aJ/0Mn9Dl6V5tV+0T/oZP6HIi9KIuq3YZFG+WRwZHGx0kj3HDWMY0ue5x8wABP7kRdqKkIe7RrFyGfUtK4VsXdEgdL/zsl6GtPPHBkTSw1HRmR4btd4rOZ1aQSHAgWZwlxpp+paVBrEUzYqU8ZeZLD2RCFzHuiljmcXbGvZK17DgkZb0JBBVmrhKtIS4b4sQYPAxMHkVpp12P0P3ynVSNF5NK1OtbjE1WxBZiJIEteWOaMkYyA+NxaT1Hn86hPdT7p1bR6T7NXvXUporlenPWjuxsfAZy8bpeW2R0bgWeS5oytVOi97sjRfRTfUa1uYmysFFEtN4msu1XVqlmCrX0/T4a8sN3v+s+SUSQRyzmxVEnMpsYXPG6QNDg0EdDlZqHiGg+WKBl6m+eZglhhbZgdLLGRkSRRh+6RmOu4AhYdTcPSbX1E7vsb1kPCyaLxXNXqQyxQTWa8U8/2iGSaKOWbHbyo3ODpP8A2grt069DZjEteaKeJ2Q2WGRksbi0lrsPYS04II/cowYlZkaL0Iq81PuklvElfh6tUjsF1cWbdt96KAVmGQxcuOAsJsTB2zLA5rvH6A4JExm1+iyw2o+7UZbdjbVdZhbYdnGNsJfvOcjsHnWx9F7YkaifLyUG1WmYOhjzWSReK5q1WF5jms14pBE6cxyTRxvELNxfNtc4HlDa7LuwbT8S6Nf1dtfT7V+MNnbBTntxhrwGTNigdM0NkaCA1waPGAPbnqoBpMKZcFlEVVdx/uxR67R1S5PUbQOlsbPLGLJnBrOglmbNvdDHtB5E47D5Gcr73Be62/ib3x5unt0/vBlJ+e+zY5gti07J3QR8trRXBz1yJPNjrYfgqzA8ub8MTcWnTr5LS3E03ZYOsx5K1EWN0jiChcc9lS7UtPj+2NrWYZ3R9ceO2J5LOvxrqn4n02MbpNQosbznV8vt12jvhuN0GS/7cNzcs7RuHRV8jpiFtzDWVl0Xh1fWKlNglt2q9WMnaJLM8UDC74g+VwBPzLur3oZIhPHLE+AtLxMyRjoiwZy8SNO0tGD1z5liDErMjRehFjKXEOnz8rkXqc3PdIyDlWYJOc+EAyti2PPMcwEZDc4yMrm7XKQsimblUWyMiqbEIskYzkQbt5GOvYs5HcEzDisgix97W6UDpGz26sLoYTYlbLYijdFACGmaQPcCyHJA3np1HVcdS4goVpI4rN2pXllAMUc9mGJ8oPQGNkjwXj8iBpO5Mw4rJIuE0rWNc9xw1jS5xPYGtGSfyYCpODuz6zZqTaxp/C8lnQ4TM4W36nBDZlgrue2edtTY542bH5aN3knr0ONtHDPqzli3Ega6C8XWupWazX5An9ld6LA6LxdRs6ZW1Yzsq07MMcwfbfHX5XMH2uVz3bGyNcHNIBIy04JHVZH33qd799981+9cB3fPOi732l2wHnbtmC4gZz2nC1FjgYI5eamHA6Fe1FDuPuL5KlYSaY2lfsCzVhmhk1GpVbFFZY6RsrpJpWt3FgDmszl4JIypDqmuUqr447VyrWfKcRMnsRQvlPZiNsjgXnPxLPZugHj99UziVkFjeIvIh/XKf+4jXo1PUa9WN01meGvC3ypZ5WQxtz2ZkkIaP9VjtQvwWa9eatNFYhdbqbZYJGSxu/5iPyXxktP7ijQdUcRos2iIoKSIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiLzar9on/Qyf0OXpXm1X7RP+hk/ociL0qP90rTJbujatTrjM9rTb1eEE4DpZq0kbGk+YFzgM/OpAikxxa4OG5YcJEKi+4p3UtCpcMVorlyvTtaZBNBaoTuEVwzQPkLhFWfiSZ7+hw0HDnlpwQQI/3RuIKmrzcEXdSqvocOXLN+Wevb2sr85rQ2hJaMZ5Yik6vaXHBillJ8Xcr11LgnRrNjvuxpOmz2sh3fE1KtJMXN8lxkfGXOcMDBJ6YWU1bS61uF1e1Xgs1343QWIo5oXbTkbo5GlpwR8S6IxdFtTtGtMkmb6SCPd6TIJ4KmcPULMhIgRFtYI1/Zazl0MWs8XDhIt7xHCsr5/ew7qrNTG3kmoYPEE4h5+wR/f8/HjA4h/GEPCzeFeHnac6n79Olpd9iCRhuFxjc6826wHeIBOGbN4x4sezxSVuHoOh0qEXJo1K1OHcXGKrBFXjLzgFxZE0AuOB17eixh4D0M80nRtKPPe2SfOn1DzZGuL2vkzF47w4l2T53E9pW+ntRrXAw60b7mBHvcfsc1qfgSREi87rCTNlSXEY//AJnuof8A69B/4NijXEPC+n0uF+DdTq1Y4dQm1bSHS3Wg98ScyKzMQ+UncWh8MJaOxvLaG4HRbQy8Oae99uV1Ck6TUIxDfkdVgL7sIjEIitvLM2YxGAza/I2jHYvljhrTpIIKsmn0n1ar45KtZ9WB0FaSIObE+CFzNkL2hzgC0AgOOO1QZtINywDbLPOGZf8Aam7B5pvx8pdP+lrzF7wSa7xm7iqSsy1HKxtHvyQRyRUWxSOgdpu4h3fXL73cOV4+Swt8p2bC9yNn/hLTs9vNv5/L3/YyufGnBGuWNSluVzw5eY5rBSk1fTR39pBbk5q2a8RdZAe5zwJC3B2fE4vlncn4OboGkVNLbMbHe4kL5i3ZzJJppJ5C1mTsYHSEAZJw0ZJOSmKxDH0MoNyWWmwytIMWEbvpa5xQoubVki3vX3mTPmqxGh1fhGs8upVMg4ddqEeYY8DUTciaLnk9LJDiOZ5WCeqq3SY+GncF6pLqjqv/ABMZrzpzZeBq41Hvk8gNa888RnxC/aNuefu6h2NvBo9QWjeFWsLph73NzkRd9GvuD+QbG3mGHc0O2ZxkA4Xis8IaTJaF6TTNPkuhzXC2+nXdZDm42uE7mb9wwMHOQsUtpBsAzYN0N/dm3Qz8lmpg5mIvm1HH6hUDJoA1biThOrrcckz5OEa8l+GV743TTxiy9zLWwhzjzg17m5ALmdemQb048rMh0HU4Y2hkcWkXY42DOGsZTlaxoz1wAAP3LLy6PUdaZddVrOuxxGGO26CI2o4SXExMsFvMbES952g48Y/GvVarxyxvilYySKRjo5I5Gh8ckbwWvY9jhhzC0kEHoQSqtfF9oWcG7t2s2/ZbqWHyB3E7/KFpRpdueho1OOADPFPD9jR4W4P2S/DxFLXJe4dje8r8g7O0NHnUorS09Nj7pDJ681ilBLolPvaCd1V8kZs3KsURnYCYojlgdgHLS5uDnC2YbwhpIbUYNL04MoSGWgwUq22lK6QTOlqN5eK0hla1+5mDuaD2hdzOGtOBuEafSB1DAvkVIAbwG/Hfh2f8z9sk+2bvtjvjKvv2qxxPum5k3/8AdpGnIR1VZmBc0D3tBGn/AKkfuZWsnATq7OMOFxV/4fiDq14SQ8PPkmayI6dZdGzUrTji1aO3d1aCNoJzlpXLhvhLTbeg8eX7NSKa5V1PXe9p5AXPr97wtsRmHJ+xu5jiSW43ANByAAtkaPBukQGuYdK06J1R0j6jo6VZjqz5dvNfXc2PML37W7nNwTtGc4Xpr8OadHDZrx0KTK918slyBlWBsNt87Qyd9mJrNs73tADi8EuA65UX7UEy0HQDW9nE/WFJuBt70b93EALWOxqVGY8K1bVfSpbcfC1WdtziW8+PR4YHmSLa2njbZtHkk5c5uREwZ8TLcFp9mX/gXWWwyHvH/i0R2nVWvZG3TpIqbnd7xvJdFA6YwYY4n7Zg5yc7Y2+DtImbVZLpenSMpNDabH0qz2VGjbhtZro8QNG1vRuB4rfiCx3FXCG6heg0YUdKtXZDNPMNOqTQ2pHn7MLsDo9tgyNLgXuy4E58bq0zZtKnYQfiBubCHE8yNdwtwKg7BOuZ3EWF9PJUjZi4dZxjwqOGnUXfYdQNhlGRslcP7wmFR0uxxaLLg2UPJ8chke771V5oWmMtaDLJbvcM0Lp1CSWxevG4ziiC+y5vOXRbpASW52sYQAXOOHtLhfvB3ctus1XTdRvs0OhFpLbZq0dAqywQz2LsIgms2XStbjxWtwxoONjBuwDmxn8I6U633+7TNPdeDg8XDTrm1vAwH88s37wOm7OVN20WUoDSTAF5kyHOMSbEXHGNLqIwbnyTAubaagD6KlNQ4Xravx0yrq0TbTBwrXnnjzLFFNOyzG0l7Btc6LdI54Y4Dq1hIy0KId0irSbq/Elpk3D2rNc4RX9O1vn6dqtPkQmER6PYs7GvO0ANmhccgQAA+KTtONHqC0bwq1u/TD3ubnIi76NfcH8g2NvM5O4B2zOMgHC8GucG6RelE93S9OuTABoms0q08u1vkt3ysLi0fF2KtS2llcJmA0CB1n6DQgrdUwcgxEzM/f1WF7kupwWOGtOsV6lhlfvDEVKWQ2ZhHCHxCBssu3ntIjwxztu5pZnGVRWhO0OPTp9Q4f4uv8Nub3zIND1K3WlEUsTnYibpz5C55kDG4cDM7xwDkgsG1UUbWNaxjQ1rQGta0BrWtaMBrQOgAAAwo/qPAmiWZzasaRpk9lzt7p5aNaSV7x2Oe98ZL3DA6nJ6LTQxbWOcSDBMwIPGxDgQdddfVbKuHLg2IkCOHpGnRa065r2o6zLwXZ1caW1tmjqMjBrccjNHsW4554RNZijIYXyVm05G9jC6duBh4aey3p/e/DHGrYb+lWqj7Gmyiro/fTqFG267F3wyB87Nha9og8WN7w0Rt8kFudpNa0Sleh73uVK1uuCCILMEU8Qc0Ya4RyNLQ4A9DjoukcMaaKZ0/wB76PeDsbqPekHejsPEgzW2cs+O1ruztaD2qyNqNAaA2ACDAiID81ue77haTgSZl0yDc63bH+/uVQHdV4Vo6bwlpLqkOyS5qWh2Lcznvklszuglc6aV0jjl5L3HpgDOAAAAMDxDT761/i9uoycNskEjI2niM2BNDQML+9pdKdG4ct3KdG4uj8YOdH8rrtFqOhUbMMdaxTq2K8Lo3QwTV4ZYYnRDbE6OJ7S1jmAkAgDHmXn17hTS78jJb2m0LksQxHJaqV7D2DO7a18rCWtz1x2ZUKW0so96ZvffctP0hSqYKdNLW3WBH1WtvE2mRv0fg2GxrmlTWa/vg/T2atWvnQ9VgbKxsPfE8kLWxCGu2OJvNADxMNp8YF0m7gGpVnzaxVh0+rSsQ6ho8lt2l3Tb0eeSSZ7WvpRNc6OqcMOWscchoBwY9ovTWdCpXYRXuU6tquC0iCzXiniaWjDS2ORpa0gdhA6LHSaJSoV4YKNStTh79qOMVWCKvGXmxGC4siaAXHA69qw/Hh9IsIMk+Ql2bdHoRrcRYLIwpa8OB3fSN8qRIiLlK8iIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIi82q/aJ/0Mn9Dl6V5tV+0T/oZP6HIi9KLQDw1eKvR/D/quo+0U8NXir0fw/wCq6j7RRFv+i0A8NXir0fw/6rqPtFPDV4q9H8P+q6j7RRFv+i0A8NXir0fw/wCq6j7RTw1eKvR/D/quo+0URb/otAPDV4q9H8P+q6j7RTw1eKvR/D/quo+0URb/AKLQDw1eKvR/D/quo+0U8NXir0fw/wCq6j7RRFv+i0A8NXir0fw/6rqPtFPDV4q9H8P+q6j7RRFv+i0A8NXir0fw/wCq6j7RTw1eKvR/D/quo+0URb/otAPDV4q9H8P+q6j7RTw1eKvR/D/quo+0URb/AKLQDw1eKvR/D/quo+0U8NXir0fw/wCq6j7RRFv+i0A8NXir0fw/6rqPtFPDV4q9H8P+q6j7RRFv+i0A8NXir0fw/wCq6j7RTw1eKvR/D/quo+0URb/otAPDV4q9H8P+q6j7RTw1eKvR/D/quo+0URb/AKLQDw1eKvR/D/quo+0U8NXir0fw/wCq6j7RRFv+i0A8NXir0fw/6rqPtFPDV4q9H8P+q6j7RRFv+i0A8NXir0fw/wCq6j7RTw1eKvR/D/quo+0URb/rG8ReRD+uU/8AcRrRLw1eKvR/D/quo+0V0XfdmcUShodQ0EBkkco21dQ8qJ4e0HOodmWhZBgrB0X6DItAPDV4q9H8P+q6j7RTw1eKvR/D/quo+0VhZW/6LQDw1eKvR/D/AKrqPtFPDV4q9H8P+q6j7RRFv+i0A8NXir0fw/6rqPtFPDV4q9H8P+q6j7RRFv8AotAPDV4q9H8P+q6j7RTw1eKvR/D/AKrqPtFEW/6LQDw1eKv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-       "            width=\"60%\"\n",
-       "            height=\"300\"\n",
-       "            src=\"https://www.youtube.com/embed/3WTRlgN09sM\"\n",
-       "            frameborder=\"0\"\n",
-       "            allowfullscreen\n",
-       "        ></iframe>\n",
-       "        "
-      ],
-      "text/plain": [
-       "<IPython.lib.display.YouTubeVideo at 0x7faec8a5e410>"
-      ]
-     },
-     "execution_count": 1,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from IPython.display import YouTubeVideo\n",
-    "YouTubeVideo(\"3WTRlgN09sM\", width=\"60%\")"
+    "**Important**: Click on \"*Kernel*\" > \"*Restart Kernel and Clear All Outputs*\" *before* reading this chapter in [JupyterLab <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_jp.png\">](https://jupyterlab.readthedocs.io/en/stable/) (e.g., in the cloud on [MyBinder <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_mb.png\">](https://mybinder.org/v2/gh/webartifex/intro-to-python/master?urlpath=lab/tree/06_text_00_content.ipynb))"
    ]
   },
   {
@@ -67,7 +30,7 @@
     }
    },
    "source": [
-    "In this chapter, we continue the study of the built-in data types. The next layer on top of numbers consists of **textual data** that are modeled primarily with the `str` type in Python. `str` objects are more complex than the numeric objects in [Chapter 5](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/05_numbers_00_lecture.ipynb) as they *consist* of an *arbitrary* and possibly large number of *individual* characters that may be chosen from *any* alphabet in the history of humankind. Luckily, Python abstracts away most of this complexity from us. However, after looking at the `str` type in great detail, we briefly introduce the `bytes` type at the end of this chapter, and learn how characters are modeled in memory."
+    "In this chapter, we continue the study of the built-in data types. The next layer on top of numbers consists of **textual data** that are modeled primarily with the `str` type in Python. `str` objects are more complex than the numeric objects in [Chapter 5 <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/05_numbers_00_content.ipynb) as they *consist* of an *arbitrary* and possibly large number of *individual* characters that may be chosen from *any* alphabet in the history of humankind. Luckily, Python abstracts away most of this complexity from us. However, after looking at the `str` type in great detail, we briefly introduce the `bytes` type at the end of this chapter, and learn how characters are modeled in memory."
    ]
   },
   {
@@ -94,7 +57,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 1,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -118,7 +81,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 2,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -128,10 +91,10 @@
     {
      "data": {
       "text/plain": [
-       "140388598371792"
+       "139935946644016"
       ]
      },
-     "execution_count": 3,
+     "execution_count": 2,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -142,7 +105,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 3,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -155,7 +118,7 @@
        "str"
       ]
      },
-     "execution_count": 4,
+     "execution_count": 3,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -174,12 +137,12 @@
    "source": [
     "As seen before, a `str` object evaluates to itself in a literal notation with enclosing **single quotes** `'`.\n",
     "\n",
-    "In [Chapter 1](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/01_elements_00_lecture.ipynb#Value-/-\"Meaning\"), we specify the double quotes `\"` convention this book follows. Yet, single quotes `'` and double quotes `\"` are *perfect* substitutes. We could use the reverse convention, as well. As [this discussion](https://stackoverflow.com/questions/56011/single-quotes-vs-double-quotes-in-python) shows, many programmers have *strong* opinions about such conventions. Consequently, the discussion was \"closed as not constructive\" by the moderators."
+    "In [Chapter 1 <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/01_elements_00_content.ipynb#Value-/-\"Meaning\"), we specify the double quotes `\"` convention this book follows. Yet, single quotes `'` and double quotes `\"` are *perfect* substitutes. We could use the reverse convention, as well. As [this discussion <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_so.png\">](https://stackoverflow.com/questions/56011/single-quotes-vs-double-quotes-in-python) shows, many programmers have *strong* opinions about such conventions. Consequently, the discussion was \"closed as not constructive\" by the moderators."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 4,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -192,7 +155,7 @@
        "'Lorem ipsum dolor sit amet.'"
       ]
      },
-     "execution_count": 5,
+     "execution_count": 4,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -213,12 +176,12 @@
     "\n",
     "Many proponents of the single quote `'` usage claim that double quotes `\"` cause more **visual noise** on the screen. However, this argument is also not convincing as, for example, one could claim that *two* single quotes `''` look so similar to *one* double quote `\"` that a reader may confuse an *empty* `str` object with a missing closing quote `\"`. With the double quotes `\"` convention we at least avoid such confusion (i.e., empty `str` objects are written as `\"\"`).\n",
     "\n",
-    "This discussion is an excellent example of a [flame war](https://en.wikipedia.org/wiki/Flaming_%28Internet%29#Flame_war) in the programming world: Everyone has an opinion and the discussion leads to *no* result."
+    "This discussion is an excellent example of a [flame war <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_wiki.png\">](https://en.wikipedia.org/wiki/Flaming_%28Internet%29#Flame_war) in the programming world: Everyone has an opinion and the discussion leads to *no* result."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 5,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -231,7 +194,7 @@
        "'Einstein said, \"If you can\\'t explain it, you don\\'t understand it.\"'"
       ]
      },
-     "execution_count": 6,
+     "execution_count": 5,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -242,7 +205,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 6,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -255,7 +218,7 @@
        "'Einstein said, \"If you can\\'t explain it, you don\\'t understand it.\"'"
       ]
      },
-     "execution_count": 7,
+     "execution_count": 6,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -274,12 +237,12 @@
    "source": [
     "An *important* fact to know is that enclosing quotes of either kind are *not* part of the `str` object's *value*! They are merely *syntax* indicating the literal notation.\n",
     "\n",
-    "So, printing out the sentence with the built-in [print()](https://docs.python.org/3/library/functions.html#print) function does the same in both cases."
+    "So, printing out the sentence with the built-in [print() <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/functions.html#print) function does the same in both cases."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 7,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -300,7 +263,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 8,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -327,12 +290,12 @@
     }
    },
    "source": [
-    "As an alternative to the literal notation, we may use the built-in [str()](https://docs.python.org/3/library/stdtypes.html#str) constructor to cast non-`str` objects as `str` ones. As Chapter 10 reveals, basically any object in Python has a **text representation**. Because of that we may also pass `list` objects, the boolean `True` and `False`, or `None` to [str()](https://docs.python.org/3/library/stdtypes.html#str)."
+    "As an alternative to the literal notation, we may use the built-in [str() <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/stdtypes.html#str) constructor to cast non-`str` objects as `str` ones. As Chapter 10 reveals, basically any object in Python has a **text representation**. Because of that we may also pass `list` objects, the boolean `True` and `False`, or `None` to [str() <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/stdtypes.html#str)."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 9,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -345,7 +308,7 @@
        "'42'"
       ]
      },
-     "execution_count": 10,
+     "execution_count": 9,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -354,6 +317,30 @@
     "str(42)"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'42.87'"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "str(42.87)"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 11,
@@ -366,7 +353,7 @@
     {
      "data": {
       "text/plain": [
-       "'42.87'"
+       "'[1, 2, 3]'"
       ]
      },
      "execution_count": 11,
@@ -375,7 +362,7 @@
     }
    ],
    "source": [
-    "str(42.87)"
+    "str([1, 2, 3])"
    ]
   },
   {
@@ -390,7 +377,7 @@
     {
      "data": {
       "text/plain": [
-       "'[1, 2, 3]'"
+       "'True'"
       ]
      },
      "execution_count": 12,
@@ -399,7 +386,7 @@
     }
    ],
    "source": [
-    "str([1, 2, 3])"
+    "str(True)"
    ]
   },
   {
@@ -407,14 +394,14 @@
    "execution_count": 13,
    "metadata": {
     "slideshow": {
-     "slide_type": "fragment"
+     "slide_type": "skip"
     }
    },
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "'True'"
+       "'False'"
       ]
      },
      "execution_count": 13,
@@ -423,7 +410,7 @@
     }
    ],
    "source": [
-    "str(True)"
+    "str(False)"
    ]
   },
   {
@@ -434,30 +421,6 @@
      "slide_type": "skip"
     }
    },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'False'"
-      ]
-     },
-     "execution_count": 14,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "str(False)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
    "outputs": [
     {
      "data": {
@@ -465,7 +428,7 @@
        "'None'"
       ]
      },
-     "execution_count": 15,
+     "execution_count": 14,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -493,12 +456,12 @@
     }
    },
    "source": [
-    "As shown in the \"*Guessing a Coin Toss*\" example in [Chapter 4](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/04_iteration_00_lecture.ipynb#Example:-Guessing-a-Coin-Toss), the built-in [input()](https://docs.python.org/3/library/functions.html#input) function displays a prompt to the user and returns whatever is entered as a `str` object. [input()](https://docs.python.org/3/library/functions.html#input) is in particular valuable when writing command-line tools."
+    "As shown in the \"*Guessing a Coin Toss*\" example in [Chapter 4 <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/04_iteration_00_content.ipynb#Example:-Guessing-a-Coin-Toss), the built-in [input() <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/functions.html#input) function displays a prompt to the user and returns whatever is entered as a `str` object. [input() <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/functions.html#input) is in particular valuable when writing command-line tools."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 15,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -509,7 +472,7 @@
      "name": "stdin",
      "output_type": "stream",
      "text": [
-      "Whatever you enter is put in a new string:  1\n"
+      "Whatever you enter is put in a new string:  123\n"
      ]
     }
    ],
@@ -517,6 +480,30 @@
     "user_input = input(\"Whatever you enter is put in a new string: \")"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "str"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "type(user_input)"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 17,
@@ -529,7 +516,7 @@
     {
      "data": {
       "text/plain": [
-       "str"
+       "'123'"
       ]
      },
      "execution_count": 17,
@@ -537,30 +524,6 @@
      "output_type": "execute_result"
     }
    ],
-   "source": [
-    "type(user_input)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "fragment"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'1'"
-      ]
-     },
-     "execution_count": 18,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
    "source": [
     "user_input"
    ]
@@ -584,12 +547,12 @@
     }
    },
    "source": [
-    "A more common situation where we obtain `str` objects is when reading the contents of a file with the [open()](https://docs.python.org/3/library/functions.html#open) built-in. In its simplest usage form, to open a [text file](https://en.wikipedia.org/wiki/Text_file) file, we pass in its path (i.e., \"filename\") as a `str` object."
+    "A more common situation where we obtain `str` objects is when reading the contents of a file with the [open() <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/functions.html#open) built-in. In its simplest usage form, to open a [text file <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_wiki.png\">](https://en.wikipedia.org/wiki/Text_file) file, we pass in its path (i.e., \"filename\") as a `str` object."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 18,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -608,7 +571,31 @@
     }
    },
    "source": [
-    "[open()](https://docs.python.org/3/library/functions.html#open) returns a **[proxy](https://en.wikipedia.org/wiki/Proxy_pattern)** object of type `TextIOWrapper` that allows us to interact with the file on disk. `mode='r'` shows that we opened the file in read-only mode and `encoding='UTF-8'` is explained in detail in the \"*The `bytes` Type*\" section at the end of this chapter."
+    "[open() <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/functions.html#open) returns a **[proxy <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_wiki.png\">](https://en.wikipedia.org/wiki/Proxy_pattern)** object of type `TextIOWrapper` that allows us to interact with the file on disk. `mode='r'` shows that we opened the file in read-only mode and `encoding='UTF-8'` is explained in detail in the \"*The `bytes` Type*\" section at the end of this chapter."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "_io.TextIOWrapper"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "type(file)"
    ]
   },
   {
@@ -619,30 +606,6 @@
      "slide_type": "fragment"
     }
    },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "_io.TextIOWrapper"
-      ]
-     },
-     "execution_count": 20,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "type(file)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "fragment"
-    }
-   },
    "outputs": [
     {
      "data": {
@@ -650,7 +613,7 @@
        "<_io.TextIOWrapper name='lorem_ipsum.txt' mode='r' encoding='UTF-8'>"
       ]
      },
-     "execution_count": 21,
+     "execution_count": 20,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -672,7 +635,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 21,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -685,7 +648,7 @@
        "True"
       ]
      },
-     "execution_count": 22,
+     "execution_count": 21,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -694,6 +657,30 @@
     "file.readable()"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "False"
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "file.writable()"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 23,
@@ -706,7 +693,7 @@
     {
      "data": {
       "text/plain": [
-       "False"
+       "'lorem_ipsum.txt'"
       ]
      },
      "execution_count": 23,
@@ -715,7 +702,7 @@
     }
    ],
    "source": [
-    "file.writable()"
+    "file.name"
    ]
   },
   {
@@ -726,30 +713,6 @@
      "slide_type": "fragment"
     }
    },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'lorem_ipsum.txt'"
-      ]
-     },
-     "execution_count": 24,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "file.name"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 25,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "fragment"
-    }
-   },
    "outputs": [
     {
      "data": {
@@ -757,7 +720,7 @@
        "'UTF-8'"
       ]
      },
-     "execution_count": 25,
+     "execution_count": 24,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -781,7 +744,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": 25,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -820,12 +783,12 @@
     }
    },
    "source": [
-    "Once we looped over the `file` object, it is **exhausted**: We can *not* loop over it a second time. So, the built-in [print()](https://docs.python.org/3/library/functions.html#print) function is *never* called in the code cell below!"
+    "Once we looped over the `file` object, it is **exhausted**: We can *not* loop over it a second time. So, the built-in [print() <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/functions.html#print) function is *never* called in the code cell below!"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": 26,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -850,7 +813,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": 27,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -863,7 +826,7 @@
        "'the 1960s with the release of Letraset sheets.\\n'"
       ]
      },
-     "execution_count": 28,
+     "execution_count": 27,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -874,7 +837,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
+   "execution_count": 28,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -887,7 +850,7 @@
        "str"
       ]
      },
-     "execution_count": 29,
+     "execution_count": 28,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -904,14 +867,14 @@
     }
    },
    "source": [
-    "An *important* observation is that the `file` object is still associated with an *open* **[file descriptor](https://en.wikipedia.org/wiki/File_descriptor)**. Without going into any technical details, we note that an operating system can only handle a *limited* number of \"open files\" at the same time, and, therefore, we should always *close* the file once we are done processing it.\n",
+    "An *important* observation is that the `file` object is still associated with an *open* **[file descriptor <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_wiki.png\">](https://en.wikipedia.org/wiki/File_descriptor)**. Without going into any technical details, we note that an operating system can only handle a *limited* number of \"open files\" at the same time, and, therefore, we should always *close* the file once we are done processing it.\n",
     "\n",
-    "`TextIOWrapper` objects have a `closed` attribute on them that indicates if the associated file descriptor is still open or has been closed. We can \"manually\" close any `TextIOWrapper` object with the [close()](https://docs.python.org/3/library/io.html#io.IOBase.close) method."
+    "`TextIOWrapper` objects have a `closed` attribute on them that indicates if the associated file descriptor is still open or has been closed. We can \"manually\" close any `TextIOWrapper` object with the [close() <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/io.html#io.IOBase.close) method."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 30,
+   "execution_count": 29,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -924,7 +887,7 @@
        "False"
       ]
      },
-     "execution_count": 30,
+     "execution_count": 29,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -935,7 +898,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 31,
+   "execution_count": 30,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -948,7 +911,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 32,
+   "execution_count": 31,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -961,7 +924,7 @@
        "True"
       ]
      },
-     "execution_count": 32,
+     "execution_count": 31,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -978,12 +941,12 @@
     }
    },
    "source": [
-    "The more Pythonic way is to use [open()](https://docs.python.org/3/library/functions.html#open) within the compound `with` statement (cf., [reference](https://docs.python.org/3/reference/compound_stmts.html#the-with-statement)): In the example below, the indented code block is said to be executed within the **context** of the `file` object that now plays the role of a **[context manager](https://docs.python.org/3/reference/datamodel.html#with-statement-context-managers)**. Many different kinds of context managers exist in Python with different applications and purposes. Context managers returned from [open()](https://docs.python.org/3/library/functions.html#open) mainly ensure that file descriptors get automatically closed after the last line in the code block is executed."
+    "The more Pythonic way is to use [open() <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/functions.html#open) within the compound `with` statement (cf., [reference <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/reference/compound_stmts.html#the-with-statement)): In the example below, the indented code block is said to be executed within the **context** of the `file` object that now plays the role of a **[context manager <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/reference/datamodel.html#with-statement-context-managers)**. Many different kinds of context managers exist in Python with different applications and purposes. Context managers returned from [open() <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/functions.html#open) mainly ensure that file descriptors get automatically closed after the last line in the code block is executed."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 33,
+   "execution_count": 32,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -1017,7 +980,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 34,
+   "execution_count": 33,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -1030,7 +993,7 @@
        "True"
       ]
      },
-     "execution_count": 34,
+     "execution_count": 33,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1047,12 +1010,12 @@
     }
    },
    "source": [
-    "Using syntax familiar from [Chapter 3](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/03_conditionals_00_lecture.ipynb#The-try-Statement) to explain what the `with open(...) as file:` does above, we provide an alternative formulation with a `try` statement below: The `finally`-branch is *always* executed, even if an exception is raised inside the `for`-loop. Therefore, `file` is sure to be closed too. However, this formulation is somewhat less expressive."
+    "Using syntax familiar from [Chapter 3 <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/03_conditionals_00_content.ipynb#The-try-Statement) to explain what the `with open(...) as file:` does above, we provide an alternative formulation with a `try` statement below: The `finally`-branch is *always* executed, even if an exception is raised inside the `for`-loop. Therefore, `file` is sure to be closed too. However, this formulation is somewhat less expressive."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 35,
+   "execution_count": 34,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -1089,7 +1052,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 36,
+   "execution_count": 35,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -1102,7 +1065,7 @@
        "True"
       ]
      },
-     "execution_count": 36,
+     "execution_count": 35,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1121,12 +1084,12 @@
    "source": [
     "As an alternative to reading the contents of a file by looping over a `TextIOWrapper` object, we may also call one of the methods they come with.\n",
     "\n",
-    "For example, the [read()](https://docs.python.org/3/library/io.html#io.TextIOBase.read) method takes a single `size` argument of type `int` and returns a `str` object with the specified number of characters."
+    "For example, the [read() <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/io.html#io.TextIOBase.read) method takes a single `size` argument of type `int` and returns a `str` object with the specified number of characters."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 37,
+   "execution_count": 36,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -1139,7 +1102,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 38,
+   "execution_count": 37,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -1152,6 +1115,41 @@
        "'Lorem Ipsum'"
       ]
      },
+     "execution_count": 37,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "file.read(11)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "When we call [read() <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/io.html#io.TextIOBase.read) again, the returned `str` object begins where the previous one left off. This is because `TextIOWrapper` objects like `file` simply store a position at which the associated file on disk is being read. In other words, `file` is like a **cursor** pointing into a file."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "' is simply '"
+      ]
+     },
      "execution_count": 38,
      "metadata": {},
      "output_type": "execute_result"
@@ -1169,47 +1167,12 @@
     }
    },
    "source": [
-    "When we call [read()](https://docs.python.org/3/library/io.html#io.TextIOBase.read) again, the returned `str` object begins where the previous one left off. This is because `TextIOWrapper` objects like `file` simply store a position at which the associated file on disk is being read. In other words, `file` is like a **cursor** pointing into a file."
+    "On the contrary, the [readline() <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/io.html#io.TextIOBase.readline) method keeps reading until it hits a **newline character**. These are shown in `str` objects as `\"\\n\"`."
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 39,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "' is simply '"
-      ]
-     },
-     "execution_count": 39,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "file.read(11)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "source": [
-    "On the contrary, the [readline()](https://docs.python.org/3/library/io.html#io.TextIOBase.readline) method keeps reading until it hits a **newline character**. These are shown in `str` objects as `\"\\n\"`."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 40,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -1222,6 +1185,41 @@
        "'dummy text of the printing and typesetting industry.\\n'"
       ]
      },
+     "execution_count": 39,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "file.readline()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "When we call [readline() <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/io.html#io.TextIOBase.readline) again, we obtain the next line."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "\"Lorem Ipsum has been the industry's standard dummy text ever since the 1500s\\n\""
+      ]
+     },
      "execution_count": 40,
      "metadata": {},
      "output_type": "execute_result"
@@ -1239,47 +1237,12 @@
     }
    },
    "source": [
-    "When we call [readline()](https://docs.python.org/3/library/io.html#io.TextIOBase.readline) again, we obtain the next line."
+    "Lastly, the [readlines() <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/io.html#io.IOBase.readlines) method returns a `list` object that holds *all* lines in the `file` from the current position to the end of the file. The latter position is often abbreviated as **EOF** in the documentation. Let's always remember that [readlines() <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/io.html#io.IOBase.readlines) has the potential to crash a computer with a `MemoryError`."
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 41,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "\"Lorem Ipsum has been the industry's standard dummy text ever since the 1500s\\n\""
-      ]
-     },
-     "execution_count": 41,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "file.readline()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "source": [
-    "Lastly, the [readlines()](https://docs.python.org/3/library/io.html#io.IOBase.readlines) method returns a `list` object that holds *all* lines in the `file` from the current position to the end of the file. The latter position is often abbreviated as **EOF** in the documentation. Let's always remember that [readlines()](https://docs.python.org/3/library/io.html#io.IOBase.readlines) has the potential to crash a computer with a `MemoryError`."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 42,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -1295,7 +1258,7 @@
        " 'the 1960s with the release of Letraset sheets.\\n']"
       ]
      },
-     "execution_count": 42,
+     "execution_count": 41,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1312,12 +1275,12 @@
     }
    },
    "source": [
-    "Calling [readlines()](https://docs.python.org/3/library/io.html#io.IOBase.readlines) a second time, is as pointless as looping over `file` a second time."
+    "Calling [readlines() <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/io.html#io.IOBase.readlines) a second time, is as pointless as looping over `file` a second time."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 43,
+   "execution_count": 42,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -1330,7 +1293,7 @@
        "[]"
       ]
      },
-     "execution_count": 43,
+     "execution_count": 42,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1341,7 +1304,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 44,
+   "execution_count": 43,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -1360,12 +1323,12 @@
     }
    },
    "source": [
-    "Because every `str` object created by reading the contents of a file in any of the ways shown in this section ends with a `\"\\n\"`, we see empty lines printed between each `line` in the `for`-loops above. To print the entire text without empty lines in between, we pass a `end=\"\"` argument to the [print()](https://docs.python.org/3/library/functions.html#print) function."
+    "Because every `str` object created by reading the contents of a file in any of the ways shown in this section ends with a `\"\\n\"`, we see empty lines printed between each `line` in the `for`-loops above. To print the entire text without empty lines in between, we pass a `end=\"\"` argument to the [print() <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/functions.html#print) function."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 45,
+   "execution_count": 44,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -1410,25 +1373,25 @@
     }
    },
    "source": [
-    "A **sequence** is yet another *abstract* concept (cf., the \"*Containers vs. Iterables*\" section in [Chapter 4](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/04_iteration_00_lecture.ipynb#Containers-vs.-Iterables)).\n",
+    "A **sequence** is yet another *abstract* concept (cf., the \"*Containers vs. Iterables*\" section in [Chapter 4 <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/04_iteration_00_content.ipynb#Containers-vs.-Iterables)).\n",
     "\n",
-    "It unifies *four* [orthogonal](https://en.wikipedia.org/wiki/Orthogonality) (i.e., \"independent\") concepts into one bigger idea: Any data type, such as `str`, is considered a sequence if it\n",
+    "It unifies *four* [orthogonal <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_wiki.png\">](https://en.wikipedia.org/wiki/Orthogonality) (i.e., \"independent\") concepts into one bigger idea: Any data type, such as `str`, is considered a sequence if it\n",
     "\n",
     "1. **contains**\n",
     "2. a **finite** number of other \"things\" that\n",
     "3. can be **iterated** over\n",
     "4. in a *predictable* **order**.\n",
     "\n",
-    "[Chapter 7](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/07_sequences_00_lecture.ipynb#Collections-vs.-Sequences) formalizes these concepts in great detail. Here, we keep our focus on the `str` type that historically received its name as it models a **[string of characters](https://en.wikipedia.org/wiki/String_%28computer_science%29)**, and *string* is simply another term for *sequence* in the computer science literature.\n",
+    "[Chapter 7 <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/07_sequences_00_content.ipynb#Collections-vs.-Sequences) formalizes these concepts in great detail. Here, we keep our focus on the `str` type that historically received its name as it models a **[string of characters <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_wiki.png\">](https://en.wikipedia.org/wiki/String_%28computer_science%29)**, and *string* is simply another term for *sequence* in the computer science literature.\n",
     "\n",
     "Another example of a sequence is the `list` type. Because of that, `str` objects may be treated like `list` objects in many situations.\n",
     "\n",
-    "Below, the built-in [len()](https://docs.python.org/3/library/functions.html#len) function tells us how many characters make up `text`. [len()](https://docs.python.org/3/library/functions.html#len) would not work with an \"infinite\" object. As anything modeled in a program must fit into a computer's finite memory, there cannot exist truly infinite objects; however, [Chapter 7](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/07_sequences_00_lecture.ipynb#Iterators-vs.-Iterables) introduces specialized iterable data types that can be used to model an *infinite* series of \"things\" and that, consequently, have no concept of \"length.\""
+    "Below, the built-in [len() <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/functions.html#len) function tells us how many characters make up `text`. [len() <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/functions.html#len) would not work with an \"infinite\" object. As anything modeled in a program must fit into a computer's finite memory, there cannot exist truly infinite objects; however, [Chapter 7 <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/07_sequences_00_content.ipynb#Iterators-vs.-Iterables) introduces specialized iterable data types that can be used to model an *infinite* series of \"things\" and that, consequently, have no concept of \"length.\""
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 46,
+   "execution_count": 45,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -1441,7 +1404,7 @@
        "'Lorem ipsum dolor sit amet.'"
       ]
      },
-     "execution_count": 46,
+     "execution_count": 45,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1452,7 +1415,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 47,
+   "execution_count": 46,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -1465,7 +1428,7 @@
        "27"
       ]
      },
-     "execution_count": 47,
+     "execution_count": 46,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1487,7 +1450,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 48,
+   "execution_count": 47,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -1515,12 +1478,12 @@
     }
    },
    "source": [
-    "With the [reversed()](https://docs.python.org/3/library/functions.html#reversed) built-in, we may loop over `text` in reversed order. Reversing `text` only works as it has a forward order to begin with."
+    "With the [reversed() <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/functions.html#reversed) built-in, we may loop over `text` in reversed order. Reversing `text` only works as it has a forward order to begin with."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 49,
+   "execution_count": 48,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -1555,12 +1518,36 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 50,
+   "execution_count": 49,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
     }
    },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 49,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "\"L\" in text"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 50,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
    "outputs": [
     {
      "data": {
@@ -1574,7 +1561,7 @@
     }
    ],
    "source": [
-    "\"L\" in text"
+    "\"ipsum\" in text"
    ]
   },
   {
@@ -1585,30 +1572,6 @@
      "slide_type": "fragment"
     }
    },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "True"
-      ]
-     },
-     "execution_count": 51,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "\"ipsum\" in text"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 52,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "fragment"
-    }
-   },
    "outputs": [
     {
      "data": {
@@ -1616,7 +1579,7 @@
        "False"
       ]
      },
-     "execution_count": 52,
+     "execution_count": 51,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1644,12 +1607,12 @@
     }
    },
    "source": [
-    "As `str` objects are *ordered* and *finite*, we may **index** into them to obtain individual characters with the **indexing operator** `[]`. This is analogous to how we obtained individual elements of a `list` object in [Chapter 1](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/01_elements_00_lecture.ipynb#Who-am-I?-And-how-many?)."
+    "As `str` objects are *ordered* and *finite*, we may **index** into them to obtain individual characters with the **indexing operator** `[]`. This is analogous to how we obtained individual elements of a `list` object in [Chapter 1 <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/01_elements_00_content.ipynb#Who-am-I?-And-how-many?)."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 53,
+   "execution_count": 52,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -1662,7 +1625,7 @@
        "'L'"
       ]
      },
-     "execution_count": 53,
+     "execution_count": 52,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1673,7 +1636,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 54,
+   "execution_count": 53,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -1686,7 +1649,7 @@
        "'o'"
       ]
      },
-     "execution_count": 54,
+     "execution_count": 53,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1708,7 +1671,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 55,
+   "execution_count": 54,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -1722,7 +1685,7 @@
      "traceback": [
       "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
       "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-55-31fb90bbe515>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mtext\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1.0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;32m<ipython-input-54-31fb90bbe515>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mtext\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1.0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
       "\u001b[0;31mTypeError\u001b[0m: string indices must be integers"
      ]
     }
@@ -1744,7 +1707,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 56,
+   "execution_count": 55,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -1757,7 +1720,7 @@
        "'.'"
       ]
      },
-     "execution_count": 56,
+     "execution_count": 55,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1779,7 +1742,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 57,
+   "execution_count": 56,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -1793,7 +1756,7 @@
      "traceback": [
       "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
       "\u001b[0;31mIndexError\u001b[0m                                Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-57-ffa3e1bc2f86>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mtext\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m27\u001b[0m\u001b[0;34m]\u001b[0m  \u001b[0;31m# == text[len(text)]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;32m<ipython-input-56-ffa3e1bc2f86>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mtext\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m27\u001b[0m\u001b[0;34m]\u001b[0m  \u001b[0;31m# == text[len(text)]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
       "\u001b[0;31mIndexError\u001b[0m: string index out of range"
      ]
     }
@@ -1829,7 +1792,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 58,
+   "execution_count": 57,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -1842,7 +1805,7 @@
        "'.'"
       ]
      },
-     "execution_count": 58,
+     "execution_count": 57,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1853,7 +1816,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 59,
+   "execution_count": 58,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -1866,7 +1829,7 @@
        "'L'"
       ]
      },
-     "execution_count": 59,
+     "execution_count": 58,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1886,6 +1849,30 @@
     "One reason why programmers like to start counting at `0` is that a positive index and its *corresponding* negative index always add up to the length of the sequence. Here, `6` and `21` add to `27`."
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 59,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'i'"
+      ]
+     },
+     "execution_count": 59,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "text[6]"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 60,
@@ -1906,30 +1893,6 @@
      "output_type": "execute_result"
     }
    ],
-   "source": [
-    "text[6]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 61,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'i'"
-      ]
-     },
-     "execution_count": 61,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
    "source": [
     "text[-21]"
    ]
@@ -1962,7 +1925,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 62,
+   "execution_count": 61,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -1975,7 +1938,7 @@
        "'Lorem'"
       ]
      },
-     "execution_count": 62,
+     "execution_count": 61,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1986,7 +1949,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 63,
+   "execution_count": 62,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -1999,7 +1962,7 @@
        "'dolor sit amet.'"
       ]
      },
-     "execution_count": 63,
+     "execution_count": 62,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2021,7 +1984,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 64,
+   "execution_count": 63,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -2034,7 +1997,7 @@
        "'Lorem'"
       ]
      },
-     "execution_count": 64,
+     "execution_count": 63,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2045,7 +2008,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 65,
+   "execution_count": 64,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -2058,7 +2021,7 @@
        "'dolor sit amet.'"
       ]
      },
-     "execution_count": 65,
+     "execution_count": 64,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2080,7 +2043,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 66,
+   "execution_count": 65,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -2093,7 +2056,7 @@
        "'Lorem ipsum dolor sit amet.'"
       ]
      },
-     "execution_count": 66,
+     "execution_count": 65,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2115,7 +2078,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 67,
+   "execution_count": 66,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -2128,7 +2091,7 @@
        "'Lorem ipsum sit amet.'"
       ]
      },
-     "execution_count": 67,
+     "execution_count": 66,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2150,7 +2113,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 68,
+   "execution_count": 67,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -2163,7 +2126,7 @@
        "'ipsum dolor'"
       ]
      },
-     "execution_count": 68,
+     "execution_count": 67,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2185,7 +2148,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 69,
+   "execution_count": 68,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -2198,7 +2161,7 @@
        "'Lorem ipsum dolor sit amet.'"
       ]
      },
-     "execution_count": 69,
+     "execution_count": 68,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2220,7 +2183,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 70,
+   "execution_count": 69,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -2233,7 +2196,7 @@
        "'Lorem ipsum dolor sit amet.'"
       ]
      },
-     "execution_count": 70,
+     "execution_count": 69,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2255,7 +2218,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 71,
+   "execution_count": 70,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -2268,7 +2231,7 @@
        "'Lrmismdlrstae.'"
       ]
      },
-     "execution_count": 71,
+     "execution_count": 70,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2290,7 +2253,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 72,
+   "execution_count": 71,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -2303,7 +2266,7 @@
        "'.tema tis rolod muspi meroL'"
       ]
      },
-     "execution_count": 72,
+     "execution_count": 71,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2331,16 +2294,16 @@
     }
    },
    "source": [
-    "Whereas elements of a `list` object *may* be *re-assigned*, as shortly hinted at in [Chapter 1](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/01_elements_00_lecture.ipynb#Who-am-I?-And-how-many?), this is *not* allowed for the individual characters of `str` objects. Once created, they can *not* be changed. Formally, we say that `str` objects are **immutable**. In that regard, they are like the numeric types in [Chapter 5](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/05_numbers_00_lecture.ipynb).\n",
+    "Whereas elements of a `list` object *may* be *re-assigned*, as shortly hinted at in [Chapter 1 <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/01_elements_00_content.ipynb#Who-am-I?-And-how-many?), this is *not* allowed for the individual characters of `str` objects. Once created, they can *not* be changed. Formally, we say that `str` objects are **immutable**. In that regard, they are like the numeric types in [Chapter 5 <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/05_numbers_00_content.ipynb).\n",
     "\n",
-    "On the contrary, objects that may be changed after creation, are called **mutable**. We already saw in [Chapter 1](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/01_elements_00_lecture.ipynb#Who-am-I?-And-how-many?) how mutable objects are more difficult to reason about for a beginner, in particular, if more than one variable references it. Yet, mutability does have its place in a programmer's toolbox, and we revisit this idea in the next chapters.\n",
+    "On the contrary, objects that may be changed after creation, are called **mutable**. We already saw in [Chapter 1 <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/01_elements_00_content.ipynb#Who-am-I?-And-how-many?) how mutable objects are more difficult to reason about for a beginner, in particular, if more than one variable references it. Yet, mutability does have its place in a programmer's toolbox, and we revisit this idea in the next chapters.\n",
     "\n",
     "The `TypeError` indicates that `str` objects are *immutable*: Assignment to an index or a slice are *not* supported."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 73,
+   "execution_count": 72,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -2354,7 +2317,7 @@
      "traceback": [
       "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
       "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-73-d8a1e6b0b786>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mtext\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m\"X\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;32m<ipython-input-72-d8a1e6b0b786>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mtext\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m\"X\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
       "\u001b[0;31mTypeError\u001b[0m: 'str' object does not support item assignment"
      ]
     }
@@ -2365,7 +2328,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 74,
+   "execution_count": 73,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -2379,7 +2342,7 @@
      "traceback": [
       "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
       "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-74-def135e84998>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mtext\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;36m5\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m\"random\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;32m<ipython-input-73-def135e84998>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mtext\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;36m5\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m\"random\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
       "\u001b[0;31mTypeError\u001b[0m: 'str' object does not support item assignment"
      ]
     }
@@ -2407,14 +2370,14 @@
     }
    },
    "source": [
-    "Objects of type `str` come with many **methods** bound on them (cf., the [documentation](https://docs.python.org/3/library/stdtypes.html#string-methods) for a full list). As seen before, they work like *normal* functions and are accessed via the **dot operator** `.`. Calling a method is also referred to as **method invocation**.\n",
+    "Objects of type `str` come with many **methods** bound on them (cf., the [documentation <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/stdtypes.html#string-methods) for a full list). As seen before, they work like *normal* functions and are accessed via the **dot operator** `.`. Calling a method is also referred to as **method invocation**.\n",
     "\n",
-    "The [find()](https://docs.python.org/3/library/stdtypes.html#str.find) method returns the index of the first occurrence of a character or a substring. If no match is found, it returns `-1`. A mirrored version searching from the right called [rfind()](https://docs.python.org/3/library/stdtypes.html#str.rfind) exists as well. The [index()](https://docs.python.org/3/library/stdtypes.html#str.index) and [rindex()](https://docs.python.org/3/library/stdtypes.html#str.rindex) methods work in the same way but raise a `ValueError` if no match is found. So, we can control if a search fails *silently* or *loudly*."
+    "The [find() <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/stdtypes.html#str.find) method returns the index of the first occurrence of a character or a substring. If no match is found, it returns `-1`. A mirrored version searching from the right called [rfind() <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/stdtypes.html#str.rfind) exists as well. The [index() <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/stdtypes.html#str.index) and [rindex() <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/stdtypes.html#str.rindex) methods work in the same way but raise a `ValueError` if no match is found. So, we can control if a search fails *silently* or *loudly*."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 75,
+   "execution_count": 74,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -2427,7 +2390,7 @@
        "'Lorem ipsum dolor sit amet.'"
       ]
      },
-     "execution_count": 75,
+     "execution_count": 74,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2436,6 +2399,30 @@
     "text"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 75,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "22"
+      ]
+     },
+     "execution_count": 75,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "text.find(\"a\")"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 76,
@@ -2448,7 +2435,7 @@
     {
      "data": {
       "text/plain": [
-       "22"
+       "-1"
       ]
      },
      "execution_count": 76,
@@ -2457,7 +2444,7 @@
     }
    ],
    "source": [
-    "text.find(\"a\")"
+    "text.find(\"b\")"
    ]
   },
   {
@@ -2468,30 +2455,6 @@
      "slide_type": "fragment"
     }
    },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "-1"
-      ]
-     },
-     "execution_count": 77,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "text.find(\"b\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 78,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "fragment"
-    }
-   },
    "outputs": [
     {
      "data": {
@@ -2499,7 +2462,7 @@
        "12"
       ]
      },
-     "execution_count": 78,
+     "execution_count": 77,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2516,7 +2479,31 @@
     }
    },
    "source": [
-    "[find()](https://docs.python.org/3/library/stdtypes.html#str.find) takes optional *start* and *end* arguments that allow us to find occurrences other than the first one."
+    "[find() <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/stdtypes.html#str.find) takes optional *start* and *end* arguments that allow us to find occurrences other than the first one."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 78,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "1"
+      ]
+     },
+     "execution_count": 78,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "text.find(\"o\")"
    ]
   },
   {
@@ -2531,7 +2518,7 @@
     {
      "data": {
       "text/plain": [
-       "1"
+       "13"
       ]
      },
      "execution_count": 79,
@@ -2540,7 +2527,7 @@
     }
    ],
    "source": [
-    "text.find(\"o\")"
+    "text.find(\"o\", 2)"
    ]
   },
   {
@@ -2551,30 +2538,6 @@
      "slide_type": "skip"
     }
    },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "13"
-      ]
-     },
-     "execution_count": 80,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "text.find(\"o\", 2)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 81,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
    "outputs": [
     {
      "data": {
@@ -2582,7 +2545,7 @@
        "-1"
       ]
      },
-     "execution_count": 81,
+     "execution_count": 80,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2599,12 +2562,12 @@
     }
    },
    "source": [
-    "The [count()](https://docs.python.org/3/library/stdtypes.html#str.count) method does what we expect."
+    "The [count() <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/stdtypes.html#str.count) method does what we expect."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 82,
+   "execution_count": 81,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -2617,7 +2580,7 @@
        "'Lorem ipsum dolor sit amet.'"
       ]
      },
-     "execution_count": 82,
+     "execution_count": 81,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2628,7 +2591,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 83,
+   "execution_count": 82,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -2641,7 +2604,7 @@
        "1"
       ]
      },
-     "execution_count": 83,
+     "execution_count": 82,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2658,12 +2621,12 @@
     }
    },
    "source": [
-    "As [count()](https://docs.python.org/3/library/stdtypes.html#str.count) is *case-sensitive*, we must **chain** it with the [lower()](https://docs.python.org/3/library/stdtypes.html#str.lower) method to get the count of all `\"L\"`s and `\"l\"`s."
+    "As [count() <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/stdtypes.html#str.count) is *case-sensitive*, we must **chain** it with the [lower() <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/stdtypes.html#str.lower) method to get the count of all `\"L\"`s and `\"l\"`s."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 84,
+   "execution_count": 83,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -2676,7 +2639,7 @@
        "2"
       ]
      },
-     "execution_count": 84,
+     "execution_count": 83,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2693,12 +2656,12 @@
     }
    },
    "source": [
-    "Alternatively, we can use the [upper()](https://docs.python.org/3/library/stdtypes.html#str.upper) method and search for `\"L\"`s."
+    "Alternatively, we can use the [upper() <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/stdtypes.html#str.upper) method and search for `\"L\"`s."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 85,
+   "execution_count": 84,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -2711,7 +2674,7 @@
        "2"
       ]
      },
-     "execution_count": 85,
+     "execution_count": 84,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2728,12 +2691,12 @@
     }
    },
    "source": [
-    "Because `str` objects are *immutable*, [upper()](https://docs.python.org/3/library/stdtypes.html#str.upper) and [lower()](https://docs.python.org/3/library/stdtypes.html#str.lower) return *new* `str` objects, even if they do *not* change the value of the original `str` object."
+    "Because `str` objects are *immutable*, [upper() <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/stdtypes.html#str.upper) and [lower() <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/stdtypes.html#str.lower) return *new* `str` objects, even if they do *not* change the value of the original `str` object."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 86,
+   "execution_count": 85,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -2746,7 +2709,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 87,
+   "execution_count": 86,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -2756,10 +2719,10 @@
     {
      "data": {
       "text/plain": [
-       "140388701131184"
+       "139936040767856"
       ]
      },
-     "execution_count": 87,
+     "execution_count": 86,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2770,7 +2733,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 88,
+   "execution_count": 87,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -2783,7 +2746,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 89,
+   "execution_count": 88,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -2793,10 +2756,10 @@
     {
      "data": {
       "text/plain": [
-       "140388590822320"
+       "139935945926704"
       ]
      },
-     "execution_count": 89,
+     "execution_count": 88,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2818,7 +2781,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 90,
+   "execution_count": 89,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -2831,7 +2794,7 @@
        "False"
       ]
      },
-     "execution_count": 90,
+     "execution_count": 89,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2842,7 +2805,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 91,
+   "execution_count": 90,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -2855,7 +2818,7 @@
        "True"
       ]
      },
-     "execution_count": 91,
+     "execution_count": 90,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2872,7 +2835,31 @@
     }
    },
    "source": [
-    "Besides [upper()](https://docs.python.org/3/library/stdtypes.html#str.upper) and [lower()](https://docs.python.org/3/library/stdtypes.html#str.lower) there exist also [title()](https://docs.python.org/3/library/stdtypes.html#str.title) and [swapcase()](https://docs.python.org/3/library/stdtypes.html#str.swapcase) methods."
+    "Besides [upper() <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/stdtypes.html#str.upper) and [lower() <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/stdtypes.html#str.lower) there exist also [title() <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/stdtypes.html#str.title) and [swapcase() <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/stdtypes.html#str.swapcase) methods."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 91,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'lorem ipsum dolor sit amet.'"
+      ]
+     },
+     "execution_count": 91,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "text.lower()"
    ]
   },
   {
@@ -2887,7 +2874,7 @@
     {
      "data": {
       "text/plain": [
-       "'lorem ipsum dolor sit amet.'"
+       "'LOREM IPSUM DOLOR SIT AMET.'"
       ]
      },
      "execution_count": 92,
@@ -2896,7 +2883,7 @@
     }
    ],
    "source": [
-    "text.lower()"
+    "text.upper()"
    ]
   },
   {
@@ -2911,7 +2898,7 @@
     {
      "data": {
       "text/plain": [
-       "'LOREM IPSUM DOLOR SIT AMET.'"
+       "'Lorem Ipsum Dolor Sit Amet.'"
       ]
      },
      "execution_count": 93,
@@ -2920,7 +2907,7 @@
     }
    ],
    "source": [
-    "text.upper()"
+    "text.title()"
    ]
   },
   {
@@ -2931,30 +2918,6 @@
      "slide_type": "skip"
     }
    },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'Lorem Ipsum Dolor Sit Amet.'"
-      ]
-     },
-     "execution_count": 94,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "text.title()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 95,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
    "outputs": [
     {
      "data": {
@@ -2962,7 +2925,7 @@
        "'lOREM IPSUM DOLOR SIT AMET.'"
       ]
      },
-     "execution_count": 95,
+     "execution_count": 94,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2979,14 +2942,14 @@
     }
    },
    "source": [
-    "Another popular string method is [split()](https://docs.python.org/3/library/stdtypes.html#str.split): It separates a longer `str` object into smaller ones collected in a `list` object. By default, groups of contiguous whitespace characters are used as the *separator*.\n",
+    "Another popular string method is [split() <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/stdtypes.html#str.split): It separates a longer `str` object into smaller ones collected in a `list` object. By default, groups of contiguous whitespace characters are used as the *separator*.\n",
     "\n",
-    "As an example, we use [split()](https://docs.python.org/3/library/stdtypes.html#str.split) to print out the individual words in `text` with more whitespace in between them."
+    "As an example, we use [split() <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/stdtypes.html#str.split) to print out the individual words in `text` with more whitespace in between them."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 96,
+   "execution_count": 95,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -2999,7 +2962,7 @@
        "['Lorem', 'ipsum', 'dolor', 'sit', 'amet.']"
       ]
      },
-     "execution_count": 96,
+     "execution_count": 95,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3010,7 +2973,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 97,
+   "execution_count": 96,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -3038,12 +3001,12 @@
     }
    },
    "source": [
-    "The opposite of splitting is done with the [join()](https://docs.python.org/3/library/stdtypes.html#str.join) method. It is typically invoked on a `str` object that represents a separator (e.g., `\" \"` or `\", \"`) and connects the elements provided by an *iterable* argument (e.g., `words` below) into one *new* `str` object."
+    "The opposite of splitting is done with the [join() <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/stdtypes.html#str.join) method. It is typically invoked on a `str` object that represents a separator (e.g., `\" \"` or `\", \"`) and connects the elements provided by an *iterable* argument (e.g., `words` below) into one *new* `str` object."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 98,
+   "execution_count": 97,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -3056,7 +3019,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 99,
+   "execution_count": 98,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -3069,7 +3032,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 100,
+   "execution_count": 99,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -3082,7 +3045,7 @@
        "'This will become a sentence.'"
       ]
      },
-     "execution_count": 100,
+     "execution_count": 99,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3104,7 +3067,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 101,
+   "execution_count": 100,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -3117,7 +3080,7 @@
        "'a b c d e'"
       ]
      },
-     "execution_count": 101,
+     "execution_count": 100,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3134,12 +3097,12 @@
     }
    },
    "source": [
-    "The [replace()](https://docs.python.org/3/library/stdtypes.html#str.replace) method creates a *new* `str` object with parts of the original `str` object potentially replaced."
+    "The [replace() <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/stdtypes.html#str.replace) method creates a *new* `str` object with parts of the original `str` object potentially replaced."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 102,
+   "execution_count": 101,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -3152,7 +3115,7 @@
        "'This is a sentence.'"
       ]
      },
-     "execution_count": 102,
+     "execution_count": 101,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3169,12 +3132,12 @@
     }
    },
    "source": [
-    "Note how `sentence` itself remains unchanged. Bound to an immutable object, [replace()](https://docs.python.org/3/library/stdtypes.html#str.replace) must create *new* objects."
+    "Note how `sentence` itself remains unchanged. Bound to an immutable object, [replace() <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/stdtypes.html#str.replace) must create *new* objects."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 103,
+   "execution_count": 102,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -3187,7 +3150,7 @@
        "'This will become a sentence.'"
       ]
      },
-     "execution_count": 103,
+     "execution_count": 102,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3204,12 +3167,12 @@
     }
    },
    "source": [
-    "As seen previously, the [strip()](https://docs.python.org/3/library/stdtypes.html#str.strip) method is often helpful in cleaning text data from unreliable sources like user input from unnecessary leading and trailing whitespace. The [lstrip()](https://docs.python.org/3/library/stdtypes.html#str.lstrip) and [rstrip()](https://docs.python.org/3/library/stdtypes.html#str.rstrip) methods are specialized versions of it."
+    "As seen previously, the [strip() <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/stdtypes.html#str.strip) method is often helpful in cleaning text data from unreliable sources like user input from unnecessary leading and trailing whitespace. The [lstrip() <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/stdtypes.html#str.lstrip) and [rstrip() <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/stdtypes.html#str.rstrip) methods are specialized versions of it."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 104,
+   "execution_count": 103,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -3222,7 +3185,7 @@
        "'text with whitespace'"
       ]
      },
-     "execution_count": 104,
+     "execution_count": 103,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3231,6 +3194,30 @@
     "\"  text with whitespace  \".strip()"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 104,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'text with whitespace  '"
+      ]
+     },
+     "execution_count": 104,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "\"  text with whitespace  \".lstrip()"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 105,
@@ -3239,30 +3226,6 @@
      "slide_type": "fragment"
     }
    },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'text with whitespace  '"
-      ]
-     },
-     "execution_count": 105,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "\"  text with whitespace  \".lstrip()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 106,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "fragment"
-    }
-   },
    "outputs": [
     {
      "data": {
@@ -3270,7 +3233,7 @@
        "'  text with whitespace'"
       ]
      },
-     "execution_count": 106,
+     "execution_count": 105,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3287,12 +3250,12 @@
     }
    },
    "source": [
-    "When justifying a `str` object for output, the [ljust()](https://docs.python.org/3/library/stdtypes.html#str.ljust) and [rjust()](https://docs.python.org/3/library/stdtypes.html#str.rjust) methods may be helpful."
+    "When justifying a `str` object for output, the [ljust() <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/stdtypes.html#str.ljust) and [rjust() <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/stdtypes.html#str.rjust) methods may be helpful."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 107,
+   "execution_count": 106,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -3305,7 +3268,7 @@
        "'This will become a sentence.            '"
       ]
      },
-     "execution_count": 107,
+     "execution_count": 106,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3316,7 +3279,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 108,
+   "execution_count": 107,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -3329,7 +3292,7 @@
        "'            This will become a sentence.'"
       ]
      },
-     "execution_count": 108,
+     "execution_count": 107,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3346,7 +3309,31 @@
     }
    },
    "source": [
-    "Similarly, the [zfill()](https://docs.python.org/3/library/stdtypes.html#str.zfill) method can be used to pad a `str` representation of a number with leading `0`s for justified output."
+    "Similarly, the [zfill() <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/stdtypes.html#str.zfill) method can be used to pad a `str` representation of a number with leading `0`s for justified output."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 108,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'0000042.87'"
+      ]
+     },
+     "execution_count": 108,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "\"42.87\".zfill(10)"
    ]
   },
   {
@@ -3357,30 +3344,6 @@
      "slide_type": "fragment"
     }
    },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'0000042.87'"
-      ]
-     },
-     "execution_count": 109,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "\"42.87\".zfill(10)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 110,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "fragment"
-    }
-   },
    "outputs": [
     {
      "data": {
@@ -3388,7 +3351,7 @@
        "'-000042.87'"
       ]
      },
-     "execution_count": 110,
+     "execution_count": 109,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3416,12 +3379,12 @@
     }
    },
    "source": [
-    "As mentioned in [Chapter 1](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/01_elements_00_lecture.ipynb#Operator-Overloading), the `+` and `*` operators are *overloaded* and used for **string concatenation**. They always create *new* `str` objects. That has nothing to do with the `str` type's immutability, but is the default behavior of operators."
+    "As mentioned in [Chapter 1 <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/01_elements_00_content.ipynb#Operator-Overloading), the `+` and `*` operators are *overloaded* and used for **string concatenation**. They always create *new* `str` objects. That has nothing to do with the `str` type's immutability, but is the default behavior of operators."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 111,
+   "execution_count": 110,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -3434,7 +3397,7 @@
        "'Hello Lore'"
       ]
      },
-     "execution_count": 111,
+     "execution_count": 110,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3445,7 +3408,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 112,
+   "execution_count": 111,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -3458,7 +3421,7 @@
        "'Lorem ipsum Lorem ipsum Lorem ipsum Lorem ipsum Lorem ipsum ...'"
       ]
      },
-     "execution_count": 112,
+     "execution_count": 111,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3491,7 +3454,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 113,
+   "execution_count": 112,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -3504,7 +3467,7 @@
        "True"
       ]
      },
-     "execution_count": 113,
+     "execution_count": 112,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3513,6 +3476,30 @@
     "\"Apple\" < \"Banana\""
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 113,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "False"
+      ]
+     },
+     "execution_count": 113,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "\"apple\" < \"Banana\""
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 114,
@@ -3521,30 +3508,6 @@
      "slide_type": "fragment"
     }
    },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "False"
-      ]
-     },
-     "execution_count": 114,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "\"apple\" < \"Banana\""
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 115,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "fragment"
-    }
-   },
    "outputs": [
     {
      "data": {
@@ -3552,7 +3515,7 @@
        "True"
       ]
      },
-     "execution_count": 115,
+     "execution_count": 114,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3574,7 +3537,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 116,
+   "execution_count": 115,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -3587,7 +3550,7 @@
        "True"
       ]
      },
-     "execution_count": 116,
+     "execution_count": 115,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3637,12 +3600,12 @@
     }
    },
    "source": [
-    "**[Formatted string literals](https://docs.python.org/3/reference/lexical_analysis.html#formatted-string-literals)**, of **f-strings** for short, are the least recently added (cf., [PEP 498](https://www.python.org/dev/peps/pep-0498/) in 2016) and most readable way: We simply prepend a `str` in its literal notation with an `f`, and put variables, or more generally, expressions, within curly braces `{}`. These are then filled in when the string literal is evaluated."
+    "**[Formatted string literals <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/reference/lexical_analysis.html#formatted-string-literals)**, of **f-strings** for short, are the least recently added (cf., [PEP 498 <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://www.python.org/dev/peps/pep-0498/) in 2016) and most readable way: We simply prepend a `str` in its literal notation with an `f`, and put variables, or more generally, expressions, within curly braces `{}`. These are then filled in when the string literal is evaluated."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 117,
+   "execution_count": 116,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -3656,7 +3619,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 118,
+   "execution_count": 117,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -3669,7 +3632,7 @@
        "'Hello Alexander! Good morning.'"
       ]
      },
-     "execution_count": 118,
+     "execution_count": 117,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3686,12 +3649,12 @@
     }
    },
    "source": [
-    "Separated by a colon `:`, various formatting options are available. In the beginning, the ability to round numbers for output may be particularly useful: This can be achieved by adding `:.2f` to the variable name inside the curly braces, which casts the number as a `float` and rounds it to two digits. The `:.2f` is a so-called format specifier, and there exists a whole **[format specification mini-language](https://docs.python.org/3/library/string.html#formatspec)** to govern how specifiers work."
+    "Separated by a colon `:`, various formatting options are available. In the beginning, the ability to round numbers for output may be particularly useful: This can be achieved by adding `:.2f` to the variable name inside the curly braces, which casts the number as a `float` and rounds it to two digits. The `:.2f` is a so-called format specifier, and there exists a whole **[format specification mini-language <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/string.html#formatspec)** to govern how specifiers work."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 119,
+   "execution_count": 118,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -3704,7 +3667,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 120,
+   "execution_count": 119,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -3717,7 +3680,7 @@
        "'Pi is 3.14'"
       ]
      },
-     "execution_count": 120,
+     "execution_count": 119,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3734,7 +3697,7 @@
     }
    },
    "source": [
-    "#### [format()](https://docs.python.org/3/library/stdtypes.html#str.format) Method"
+    "#### [format() <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/stdtypes.html#str.format) Method"
    ]
   },
   {
@@ -3745,12 +3708,12 @@
     }
    },
    "source": [
-    "`str` objects also provide a [format()](https://docs.python.org/3/library/stdtypes.html#str.format) method that accepts an arbitrary number of *positional* arguments that are inserted into the `str` object in the same order replacing empty curly brackets `{}`. String interpolation with the [format()](https://docs.python.org/3/library/stdtypes.html#str.format) method is a more traditional and probably the most common way as of today. While f-strings are the recommended way going forward, usage of the [format()](https://docs.python.org/3/library/stdtypes.html#str.format) method is likely not declining any time soon."
+    "`str` objects also provide a [format() <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/stdtypes.html#str.format) method that accepts an arbitrary number of *positional* arguments that are inserted into the `str` object in the same order replacing empty curly brackets `{}`. String interpolation with the [format() <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/stdtypes.html#str.format) method is a more traditional and probably the most common way as of today. While f-strings are the recommended way going forward, usage of the [format() <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/stdtypes.html#str.format) method is likely not declining any time soon."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 121,
+   "execution_count": 120,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -3763,7 +3726,7 @@
        "'Hello Alexander! Good morning.'"
       ]
      },
-     "execution_count": 121,
+     "execution_count": 120,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3785,7 +3748,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 122,
+   "execution_count": 121,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -3798,7 +3761,7 @@
        "'Good morning, Alexander'"
       ]
      },
-     "execution_count": 122,
+     "execution_count": 121,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3815,12 +3778,12 @@
     }
    },
    "source": [
-    "The [format()](https://docs.python.org/3/library/stdtypes.html#str.format) method may alternatively be used with *keyword* arguments as well. Then, we must put the keywords' names within the curly brackets."
+    "The [format() <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/stdtypes.html#str.format) method may alternatively be used with *keyword* arguments as well. Then, we must put the keywords' names within the curly brackets."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 123,
+   "execution_count": 122,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -3833,7 +3796,7 @@
        "'Hello Alexander! Good morning.'"
       ]
      },
-     "execution_count": 123,
+     "execution_count": 122,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3855,7 +3818,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 124,
+   "execution_count": 123,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -3868,7 +3831,7 @@
        "'Pi is 3.14'"
       ]
      },
-     "execution_count": 124,
+     "execution_count": 123,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3896,14 +3859,14 @@
     }
    },
    "source": [
-    "The `%` operator that we saw in the context of modulo division in [Chapter 1](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/01_elements_00_lecture.ipynb#%28Arithmetic%29-Operators) is overloaded with string interpolation when its first operand is a `str` object. The second operand consists of all expressions to be filled in. Format specifiers work with a `%` instead of curly braces and according to a different set of rules referred to as **[printf-style string formatting](https://docs.python.org/3/library/stdtypes.html#printf-style-string-formatting)**. So, `{:.2f}` becomes `%.2f`.\n",
+    "The `%` operator that we saw in the context of modulo division in [Chapter 1 <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/01_elements_00_content.ipynb#%28Arithmetic%29-Operators) is overloaded with string interpolation when its first operand is a `str` object. The second operand consists of all expressions to be filled in. Format specifiers work with a `%` instead of curly braces and according to a different set of rules referred to as **[printf-style string formatting <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/stdtypes.html#printf-style-string-formatting)**. So, `{:.2f}` becomes `%.2f`.\n",
     "\n",
-    "This way of string interpolation is the oldest and originates from the [C language](https://en.wikipedia.org/wiki/C_%28programming_language%29). It is still widely spread, but we should use one of the other two ways instead. We show it here mainly for completeness sake."
+    "This way of string interpolation is the oldest and originates from the [C language <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_wiki.png\">](https://en.wikipedia.org/wiki/C_%28programming_language%29). It is still widely spread, but we should use one of the other two ways instead. We show it here mainly for completeness sake."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 125,
+   "execution_count": 124,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -3916,7 +3879,7 @@
        "'Pi is 3.14'"
       ]
      },
-     "execution_count": 125,
+     "execution_count": 124,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3933,12 +3896,12 @@
     }
    },
    "source": [
-    "To insert more than one expression, we must list them in order and between parenthesis `(` and `)`. As [Chapter 7](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/07_sequences_00_lecture.ipynb#The-tuple-Type) reveals, this literal syntax creates an object of type `tuple`. Also, to format an expression as text, we use the format specifier `%s`."
+    "To insert more than one expression, we must list them in order and between parenthesis `(` and `)`. As [Chapter 7 <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/07_sequences_00_content.ipynb#The-tuple-Type) reveals, this literal syntax creates an object of type `tuple`. Also, to format an expression as text, we use the format specifier `%s`."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 126,
+   "execution_count": 125,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -3951,7 +3914,7 @@
        "'Hello Alexander! Good morning.'"
       ]
      },
-     "execution_count": 126,
+     "execution_count": 125,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3979,14 +3942,14 @@
     }
    },
    "source": [
-    "As previously seen, some characters have a special meaning when following the **escape character** `\"\\\"`. Besides escaping the kind of quote used as the `str` object's delimiter, `'` or `\"`, most of these **escape sequences** (i.e., `\"\\\"` with the subsequent character), act as a **control character** that moves the \"cursor\" in the output *without* generating any pixel on the screen. Because of that, we only see the effect of such escape sequences when used with the [print()](https://docs.python.org/3/library/functions.html#print) function. The [documentation](https://docs.python.org/3/reference/lexical_analysis.html#string-and-bytes-literals) lists all available escape sequences, of which we show the most important ones below.\n",
+    "As previously seen, some characters have a special meaning when following the **escape character** `\"\\\"`. Besides escaping the kind of quote used as the `str` object's delimiter, `'` or `\"`, most of these **escape sequences** (i.e., `\"\\\"` with the subsequent character), act as a **control character** that moves the \"cursor\" in the output *without* generating any pixel on the screen. Because of that, we only see the effect of such escape sequences when used with the [print() <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/functions.html#print) function. The [documentation <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/reference/lexical_analysis.html#string-and-bytes-literals) lists all available escape sequences, of which we show the most important ones below.\n",
     "\n",
-    "The most common escape sequence is `\"\\n\"` that \"prints\" a [newline character](https://en.wikipedia.org/wiki/Newline) that is also called the line feed character or LF for short."
+    "The most common escape sequence is `\"\\n\"` that \"prints\" a [newline character <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_wiki.png\">](https://en.wikipedia.org/wiki/Newline) that is also called the line feed character or LF for short."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 127,
+   "execution_count": 126,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -3999,7 +3962,7 @@
        "'This is a sentence\\nthat is printed\\non three lines.'"
       ]
      },
-     "execution_count": 127,
+     "execution_count": 126,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -4010,7 +3973,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 128,
+   "execution_count": 127,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -4039,12 +4002,12 @@
     }
    },
    "source": [
-    "`\"\\b\"` is the [backspace character](https://en.wikipedia.org/wiki/Backspace), or BS for short, that moves the cursor back by one character."
+    "`\"\\b\"` is the [backspace character <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_wiki.png\">](https://en.wikipedia.org/wiki/Backspace), or BS for short, that moves the cursor back by one character."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 129,
+   "execution_count": 128,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -4065,7 +4028,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 130,
+   "execution_count": 129,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -4092,12 +4055,12 @@
     }
    },
    "source": [
-    "Similarly, `\"\\r\"` is the [carriage return character](https://en.wikipedia.org/wiki/Carriage_return), or CR for short, that moves the cursor back to the beginning of the line."
+    "Similarly, `\"\\r\"` is the [carriage return character <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_wiki.png\">](https://en.wikipedia.org/wiki/Carriage_return), or CR for short, that moves the cursor back to the beginning of the line."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 131,
+   "execution_count": 130,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -4118,7 +4081,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 132,
+   "execution_count": 131,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -4150,7 +4113,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 133,
+   "execution_count": 132,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -4184,7 +4147,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 134,
+   "execution_count": 133,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -4228,7 +4191,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 135,
+   "execution_count": 134,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -4261,7 +4224,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 136,
+   "execution_count": 135,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -4270,10 +4233,10 @@
    "outputs": [
     {
      "ename": "SyntaxError",
-     "evalue": "(unicode error) 'unicodeescape' codec can't decode bytes in position 2-3: truncated \\UXXXXXXXX escape (<ipython-input-136-61308f43d404>, line 1)",
+     "evalue": "(unicode error) 'unicodeescape' codec can't decode bytes in position 2-3: truncated \\UXXXXXXXX escape (<ipython-input-135-61308f43d404>, line 1)",
      "output_type": "error",
      "traceback": [
-      "\u001b[0;36m  File \u001b[0;32m\"<ipython-input-136-61308f43d404>\"\u001b[0;36m, line \u001b[0;32m1\u001b[0m\n\u001b[0;31m    print(\"C:\\Users\\Administrator\\Desktop\\Project\")\u001b[0m\n\u001b[0m         ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m (unicode error) 'unicodeescape' codec can't decode bytes in position 2-3: truncated \\UXXXXXXXX escape\n"
+      "\u001b[0;36m  File \u001b[0;32m\"<ipython-input-135-61308f43d404>\"\u001b[0;36m, line \u001b[0;32m1\u001b[0m\n\u001b[0;31m    print(\"C:\\Users\\Administrator\\Desktop\\Project\")\u001b[0m\n\u001b[0m         ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m (unicode error) 'unicodeescape' codec can't decode bytes in position 2-3: truncated \\UXXXXXXXX escape\n"
      ]
     }
    ],
@@ -4294,7 +4257,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 137,
+   "execution_count": 136,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -4315,7 +4278,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 138,
+   "execution_count": 137,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -4347,7 +4310,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 139,
+   "execution_count": 138,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -4368,7 +4331,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 140,
+   "execution_count": 139,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -4408,7 +4371,7 @@
    "source": [
     "So far, we used the term **character** without any further consideration. In this section, we briefly look into what characters are and how they are modeled in software.\n",
     "\n",
-    "[Chapter 5](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/05_numbers_00_lecture.ipynb) gives us an idea on how individual **bits** are used to express all types of numbers, from \"simple\" `int` objects to \"complex\" `float` ones. To model characters, another **layer of abstraction** is put on top of whole numbers. So, just as bits are used to express integers, they themselves are used to express characters."
+    "[Chapter 5 <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/05_numbers_00_content.ipynb) gives us an idea on how individual **bits** are used to express all types of numbers, from \"simple\" `int` objects to \"complex\" `float` ones. To model characters, another **layer of abstraction** is put on top of whole numbers. So, just as bits are used to express integers, they themselves are used to express characters."
    ]
   },
   {
@@ -4430,14 +4393,14 @@
     }
    },
    "source": [
-    "Many conventions have been developed as to what integer is associated with which character. The most basic one that was also adopted around the world is the the so-called [American Standard Code for Information Interchange](https://en.wikipedia.org/wiki/ASCII), or **ASCII** for short. It uses 7 bits of information to map the unprintable control characters as well as the printable letters of the alphabet, numbers, and common symbols to the numbers `0` through `127`.\n",
+    "Many conventions have been developed as to what integer is associated with which character. The most basic one that was also adopted around the world is the the so-called [American Standard Code for Information Interchange <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_wiki.png\">](https://en.wikipedia.org/wiki/ASCII), or **ASCII** for short. It uses 7 bits of information to map the unprintable control characters as well as the printable letters of the alphabet, numbers, and common symbols to the numbers `0` through `127`.\n",
     "\n",
-    "A mapping from characters to numbers is referred to by the technical term **encoding**. We may use the built-in [ord()](https://docs.python.org/3/library/functions.html#ord) function to **encode** any single character. The inverse to that is the built-in [chr()](https://docs.python.org/3/library/functions.html#chr) function, which **decodes** a number into a character."
+    "A mapping from characters to numbers is referred to by the technical term **encoding**. We may use the built-in [ord() <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/functions.html#ord) function to **encode** any single character. The inverse to that is the built-in [chr() <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/functions.html#chr) function, which **decodes** a number into a character."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 141,
+   "execution_count": 140,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -4450,7 +4413,7 @@
        "65"
       ]
      },
-     "execution_count": 141,
+     "execution_count": 140,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -4461,7 +4424,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 142,
+   "execution_count": 141,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -4474,7 +4437,7 @@
        "'A'"
       ]
      },
-     "execution_count": 142,
+     "execution_count": 141,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -4496,7 +4459,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 143,
+   "execution_count": 142,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -4509,7 +4472,7 @@
        "10"
       ]
      },
-     "execution_count": 143,
+     "execution_count": 142,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -4520,7 +4483,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 144,
+   "execution_count": 143,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -4533,7 +4496,7 @@
        "'\\n'"
       ]
      },
-     "execution_count": 144,
+     "execution_count": 143,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -4559,7 +4522,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 145,
+   "execution_count": 144,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -4590,7 +4553,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 146,
+   "execution_count": 145,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -4621,7 +4584,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 147,
+   "execution_count": 146,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -4663,7 +4626,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 148,
+   "execution_count": 147,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -4681,7 +4644,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 149,
+   "execution_count": 148,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -4720,7 +4683,7 @@
     }
    },
    "source": [
-    "As the ASCII character set does not work for many languages other than English, various encodings were developed. Popular examples are [ISO 8859-1](https://en.wikipedia.org/wiki/ISO/IEC_8859-1) for western European letters or [Windows 1250](https://en.wikipedia.org/wiki/Windows-1250) for Latin ones. Many of these encodings use 8-bit numbers (i.e., `0` through `255`) to map the multitude of non-English letters (e.g., the German [umlauts](https://en.wikipedia.org/wiki/Umlaut_%28linguistics%29) `\"ä\"`, `\"ö\"`, `\"ü\"`, or `\"ß\"`)."
+    "As the ASCII character set does not work for many languages other than English, various encodings were developed. Popular examples are [ISO 8859-1 <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_wiki.png\">](https://en.wikipedia.org/wiki/ISO/IEC_8859-1) for western European letters or [Windows 1250 <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_wiki.png\">](https://en.wikipedia.org/wiki/Windows-1250) for Latin ones. Many of these encodings use 8-bit numbers (i.e., `0` through `255`) to map the multitude of non-English letters (e.g., the German [umlauts <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_wiki.png\">](https://en.wikipedia.org/wiki/Umlaut_%28linguistics%29) `\"ä\"`, `\"ö\"`, `\"ü\"`, or `\"ß\"`)."
    ]
   },
   {
@@ -4742,16 +4705,16 @@
     }
    },
    "source": [
-    "However, none of these specialized encodings can map *all* characters of *all* languages around the world from *all* times in human history. To achieve that, a truly global standard called **[Unicode](https://en.wikipedia.org/wiki/Unicode)** was developed and its first version released in 1991. Since then, Unicode has been amended with many other \"characters.\" The most popular among them being [emojis](https://en.wikipedia.org/wiki/Emoji) or the [Klingon](https://en.wikipedia.org/wiki/Klingon_scripts) language (from the science fiction series [Star Trek](https://en.wikipedia.org/wiki/Star_Trek)). In Unicode, every character is given an identity referred to as the **code point**. Code points are hexadecimal numbers from `0x0000` through `0x10ffff`, written as U+0000 and U+10FFFF outside of Python. Consequently, there exist at most $1,114,112$ code points, of which only about 10% are currently in use, allowing lots of room for new characters to be invented. The first `127` code points are identical to the ASCII encoding for reasons explained in the \"*The `bytes` Type*\" section further below. There exist plenty of lists of all Unicode characters on the web (e.g., [Wikipedia](https://en.wikipedia.org/wiki/List_of_Unicode_characters)).\n",
+    "However, none of these specialized encodings can map *all* characters of *all* languages around the world from *all* times in human history. To achieve that, a truly global standard called **[Unicode <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_wiki.png\">](https://en.wikipedia.org/wiki/Unicode)** was developed and its first version released in 1991. Since then, Unicode has been amended with many other \"characters.\" The most popular among them being [emojis <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_wiki.png\">](https://en.wikipedia.org/wiki/Emoji) or the [Klingon <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_wiki.png\">](https://en.wikipedia.org/wiki/Klingon_scripts) language (from the science fiction series [Star Trek <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_wiki.png\">](https://en.wikipedia.org/wiki/Star_Trek)). In Unicode, every character is given an identity referred to as the **code point**. Code points are hexadecimal numbers from `0x0000` through `0x10ffff`, written as U+0000 and U+10FFFF outside of Python. Consequently, there exist at most $1,114,112$ code points, of which only about 10% are currently in use, allowing lots of room for new characters to be invented. The first `127` code points are identical to the ASCII encoding for reasons explained in the \"*The `bytes` Type*\" section further below. There exist plenty of lists of all Unicode characters on the web (e.g., [Wikipedia <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_wiki.png\">](https://en.wikipedia.org/wiki/List_of_Unicode_characters)).\n",
     "\n",
     "All we need to know to print a character is its code point. Python uses the escape sequence `\"\\U\"` that is followed by eight hexadecimal digits. Underscore separators are unfortunately *not* allowed here.\n",
     "\n",
-    "So, to print a smiley, we just need to look up the corresponding number (e.g., [here](https://en.wikipedia.org/wiki/Emoji#Unicode_blocks))."
+    "So, to print a smiley, we just need to look up the corresponding number (e.g., [here <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_wiki.png\">](https://en.wikipedia.org/wiki/Emoji#Unicode_blocks))."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 150,
+   "execution_count": 149,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -4764,7 +4727,7 @@
        "'�'"
       ]
      },
-     "execution_count": 150,
+     "execution_count": 149,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -4786,7 +4749,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 151,
+   "execution_count": 150,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -4799,7 +4762,7 @@
        "'�'"
       ]
      },
-     "execution_count": 151,
+     "execution_count": 150,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -4819,6 +4782,30 @@
     "Whenever the code point can be expressed with just four hexadecimal digits, we may use the escape sequence `\"\\u\"` for brevity."
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 151,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'A'"
+      ]
+     },
+     "execution_count": 151,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "\"\\U00000041\"  # hex(65) == 0x41"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 152,
@@ -4840,7 +4827,18 @@
     }
    ],
    "source": [
-    "\"\\U00000041\"  # hex(65) == 0x41"
+    "\"\\u0041\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Analogously, if the code point can be expressed with two hexadecimal digits, we may use the escape sequence `\"\\x\"` for even conciser code."
    ]
   },
   {
@@ -4863,41 +4861,6 @@
      "output_type": "execute_result"
     }
    ],
-   "source": [
-    "\"\\u0041\""
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "source": [
-    "Analogously, if the code point can be expressed with two hexadecimal digits, we may use the escape sequence `\"\\x\"` for even conciser code."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 154,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "fragment"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'A'"
-      ]
-     },
-     "execution_count": 154,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
    "source": [
     "\"\\x41\""
    ]
@@ -4917,7 +4880,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 155,
+   "execution_count": 154,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -4930,7 +4893,7 @@
        "1"
       ]
      },
-     "execution_count": 155,
+     "execution_count": 154,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -4947,7 +4910,31 @@
     }
    },
    "source": [
-    "... what should `len(\"\\N{SNAKE}\")` evaluate to? As the idea of a snake is expressed as *one* \"character,\" [len()](https://docs.python.org/3/library/functions.html#len) also returns `1` here."
+    "... what should `len(\"\\N{SNAKE}\")` evaluate to? As the idea of a snake is expressed as *one* \"character,\" [len() <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/functions.html#len) also returns `1` here."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 155,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'�'"
+      ]
+     },
+     "execution_count": 155,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "\"\\N{SNAKE}\""
    ]
   },
   {
@@ -4958,30 +4945,6 @@
      "slide_type": "fragment"
     }
    },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'�'"
-      ]
-     },
-     "execution_count": 156,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "\"\\N{SNAKE}\""
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 157,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "fragment"
-    }
-   },
    "outputs": [
     {
      "data": {
@@ -4989,7 +4952,7 @@
        "1"
       ]
      },
-     "execution_count": 157,
+     "execution_count": 156,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -5006,7 +4969,31 @@
     }
    },
    "source": [
-    "Many of the built-in `str` methods also consider Unicode. For example, in contrast to [lower()](https://docs.python.org/3/library/stdtypes.html#str.lower), the [casefold()](https://docs.python.org/3/library/stdtypes.html#str.casefold) method knows that the German `\"Ăź\"` is commonly converted to `\"ss\"`. So, when searching for exact matches, normalizing text with [casefold()](https://docs.python.org/3/library/stdtypes.html#str.casefold) may yield better results than with [lower()](https://docs.python.org/3/library/stdtypes.html#str.lower)."
+    "Many of the built-in `str` methods also consider Unicode. For example, in contrast to [lower() <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/stdtypes.html#str.lower), the [casefold() <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/stdtypes.html#str.casefold) method knows that the German `\"Ăź\"` is commonly converted to `\"ss\"`. So, when searching for exact matches, normalizing text with [casefold() <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/stdtypes.html#str.casefold) may yield better results than with [lower() <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/stdtypes.html#str.lower)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 157,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'straĂźe'"
+      ]
+     },
+     "execution_count": 157,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "\"StraĂźe\".lower()"
    ]
   },
   {
@@ -5017,30 +5004,6 @@
      "slide_type": "skip"
     }
    },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'straĂźe'"
-      ]
-     },
-     "execution_count": 158,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "\"StraĂźe\".lower()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 159,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
    "outputs": [
     {
      "data": {
@@ -5048,7 +5011,7 @@
        "'strasse'"
       ]
      },
-     "execution_count": 159,
+     "execution_count": 158,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -5065,7 +5028,7 @@
     }
    },
    "source": [
-    "Many other methods like [isdecimal()](https://docs.python.org/3/library/stdtypes.html#str.isdecimal), [isdigit()](https://docs.python.org/3/library/stdtypes.html#str.isdigit), [isnumeric()](https://docs.python.org/3/library/stdtypes.html#str.isnumeric), [isprintable()](https://docs.python.org/3/library/stdtypes.html#str.isprintable), [isidentifier()](https://docs.python.org/3/library/stdtypes.html#str.isidentifier), and many more may be worthwhile to know for the data science practitioner, especially when it comes to data cleaning."
+    "Many other methods like [isdecimal() <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/stdtypes.html#str.isdecimal), [isdigit() <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/stdtypes.html#str.isdigit), [isnumeric() <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/stdtypes.html#str.isnumeric), [isprintable() <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/stdtypes.html#str.isprintable), [isidentifier() <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/stdtypes.html#str.isidentifier), and many more may be worthwhile to know for the data science practitioner, especially when it comes to data cleaning."
    ]
   },
   {
@@ -5087,12 +5050,12 @@
     }
    },
    "source": [
-    "Sometimes, it is convenient to split text across multiple lines in source code. For example, to make lines fit into the 79 characters requirement of [PEP 8](https://www.python.org/dev/peps/pep-0008/) or because the text consists of many lines and typing out `\"\\n\"` is tedious. However, using single double quotes `\"` around multiple lines results in a `SyntaxError`."
+    "Sometimes, it is convenient to split text across multiple lines in source code. For example, to make lines fit into the 79 characters requirement of [PEP 8 <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://www.python.org/dev/peps/pep-0008/) or because the text consists of many lines and typing out `\"\\n\"` is tedious. However, using single double quotes `\"` around multiple lines results in a `SyntaxError`."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 160,
+   "execution_count": 159,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -5101,10 +5064,10 @@
    "outputs": [
     {
      "ename": "SyntaxError",
-     "evalue": "EOL while scanning string literal (<ipython-input-160-4cef690f1f4a>, line 1)",
+     "evalue": "EOL while scanning string literal (<ipython-input-159-4cef690f1f4a>, line 1)",
      "output_type": "error",
      "traceback": [
-      "\u001b[0;36m  File \u001b[0;32m\"<ipython-input-160-4cef690f1f4a>\"\u001b[0;36m, line \u001b[0;32m1\u001b[0m\n\u001b[0;31m    \"\u001b[0m\n\u001b[0m     ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m EOL while scanning string literal\n"
+      "\u001b[0;36m  File \u001b[0;32m\"<ipython-input-159-4cef690f1f4a>\"\u001b[0;36m, line \u001b[0;32m1\u001b[0m\n\u001b[0;31m    \"\u001b[0m\n\u001b[0m     ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m EOL while scanning string literal\n"
      ]
     }
    ],
@@ -5127,7 +5090,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 161,
+   "execution_count": 160,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -5154,7 +5117,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 162,
+   "execution_count": 161,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -5167,7 +5130,7 @@
        "'\\nI am a multi-line string\\nconsisting of four lines.\\n'"
       ]
      },
-     "execution_count": 162,
+     "execution_count": 161,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -5178,7 +5141,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 163,
+   "execution_count": 162,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -5208,12 +5171,12 @@
     }
    },
    "source": [
-    "Using the [split()](https://docs.python.org/3/library/stdtypes.html#str.split) method with the optional `sep` argument, we confirm that `multi_line` consists of *four* lines with the first and last line being empty."
+    "Using the [split() <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/stdtypes.html#str.split) method with the optional `sep` argument, we confirm that `multi_line` consists of *four* lines with the first and last line being empty."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 164,
+   "execution_count": 163,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -5244,12 +5207,12 @@
     }
    },
    "source": [
-    "To mitigate that, we often see the [strip()](https://docs.python.org/3/library/stdtypes.html#bytes.strip) method in source code."
+    "To mitigate that, we often see the [strip() <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/stdtypes.html#bytes.strip) method in source code."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 165,
+   "execution_count": 164,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -5265,7 +5228,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 166,
+   "execution_count": 165,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -5312,7 +5275,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 167,
+   "execution_count": 166,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -5337,7 +5300,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 168,
+   "execution_count": 167,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -5347,10 +5310,10 @@
     {
      "data": {
       "text/plain": [
-       "140388598520624"
+       "139935946413808"
       ]
      },
-     "execution_count": 168,
+     "execution_count": 167,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -5361,7 +5324,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 169,
+   "execution_count": 168,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -5374,7 +5337,7 @@
        "bytes"
       ]
      },
-     "execution_count": 169,
+     "execution_count": 168,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -5391,12 +5354,12 @@
     }
    },
    "source": [
-    "It's value is given out in the literal bytes notation with a `b` prefix (cf., the [reference](https://docs.python.org/3/reference/lexical_analysis.html#string-and-bytes-literals)). Every byte is expressed in hexadecimal representation with the escape sequence `\"\\x\"`. This representation is commonly chosen as we can *not* tell what kind of information is hidden in the `data` by just looking at the bytes. Instead, we must be told by some other source how to **decode** the raw bytes into information we can interpret."
+    "It's value is given out in the literal bytes notation with a `b` prefix (cf., the [reference <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/reference/lexical_analysis.html#string-and-bytes-literals)). Every byte is expressed in hexadecimal representation with the escape sequence `\"\\x\"`. This representation is commonly chosen as we can *not* tell what kind of information is hidden in the `data` by just looking at the bytes. Instead, we must be told by some other source how to **decode** the raw bytes into information we can interpret."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 170,
+   "execution_count": 169,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -5409,7 +5372,7 @@
        "b'\\xf0\\x9f\\x82\\xa7\\xf0\\x9f\\x82\\xb7\\xf0\\x9f\\x83\\x97\\xf0\\x9f\\x83\\x8e\\xf0\\x9f\\x83\\x9e'"
       ]
      },
-     "execution_count": 170,
+     "execution_count": 169,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -5431,7 +5394,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 171,
+   "execution_count": 170,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -5444,7 +5407,7 @@
        "20"
       ]
      },
-     "execution_count": 171,
+     "execution_count": 170,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -5466,7 +5429,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 172,
+   "execution_count": 171,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -5499,7 +5462,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 173,
+   "execution_count": 172,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -5512,7 +5475,7 @@
        "158"
       ]
      },
-     "execution_count": 173,
+     "execution_count": 172,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -5534,7 +5497,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 174,
+   "execution_count": 173,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -5547,7 +5510,7 @@
        "b'\\xf0\\x82\\xf0\\x82\\xf0\\x83\\xf0\\x83\\xf0\\x83'"
       ]
      },
-     "execution_count": 174,
+     "execution_count": 173,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -5575,14 +5538,14 @@
     }
    },
    "source": [
-    "Luckily, `data` consists of bytes encoded with the [UTF-8](https://en.wikipedia.org/wiki/UTF-8) encoding. That is the most common way of mapping a Unicode character's code point to a sequence of bytes.\n",
+    "Luckily, `data` consists of bytes encoded with the [UTF-8 <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_wiki.png\">](https://en.wikipedia.org/wiki/UTF-8) encoding. That is the most common way of mapping a Unicode character's code point to a sequence of bytes.\n",
     "\n",
-    "To obtain a `str` object out of a given `bytes` object, we decode it with the `bytes` type's [decode()](https://docs.python.org/3/library/stdtypes.html#bytes.decode) method."
+    "To obtain a `str` object out of a given `bytes` object, we decode it with the `bytes` type's [decode() <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/stdtypes.html#bytes.decode) method."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 175,
+   "execution_count": 174,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -5595,7 +5558,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 176,
+   "execution_count": 175,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -5608,7 +5571,7 @@
        "str"
       ]
      },
-     "execution_count": 176,
+     "execution_count": 175,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -5625,12 +5588,12 @@
     }
    },
    "source": [
-    "So, `data` consisted of a [full house](https://en.wikipedia.org/wiki/List_of_poker_hands#Full_house) hand in a poker game."
+    "So, `data` consisted of a [full house <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_wiki.png\">](https://en.wikipedia.org/wiki/List_of_poker_hands#Full_house) hand in a poker game."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 177,
+   "execution_count": 176,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -5643,7 +5606,7 @@
        "'🂧🂷���'"
       ]
      },
-     "execution_count": 177,
+     "execution_count": 176,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -5660,12 +5623,12 @@
     }
    },
    "source": [
-    "To go the opposite direction and encode a given `str` object, we use the `str` type's [encode()](https://docs.python.org/3/library/stdtypes.html#str.encode) method."
+    "To go the opposite direction and encode a given `str` object, we use the `str` type's [encode() <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/stdtypes.html#str.encode) method."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 178,
+   "execution_count": 177,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -5678,7 +5641,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 179,
+   "execution_count": 178,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -5691,7 +5654,7 @@
        "b'Caf\\xc3\\xa9 Kastanient\\xc3\\xb6rtchen'"
       ]
      },
-     "execution_count": 179,
+     "execution_count": 178,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -5708,12 +5671,12 @@
     }
    },
    "source": [
-    "By default, [encode()](https://docs.python.org/3/library/stdtypes.html#str.encode) and [decode()](https://docs.python.org/3/library/stdtypes.html#bytes.decode) use an `encoding=\"utf-8\"` argument. We may use another encoding like, for example, `\"iso-8859-1\"`, which can deal with ASCII and western European letters."
+    "By default, [encode() <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/stdtypes.html#str.encode) and [decode() <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/stdtypes.html#bytes.decode) use an `encoding=\"utf-8\"` argument. We may use another encoding like, for example, `\"iso-8859-1\"`, which can deal with ASCII and western European letters."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 180,
+   "execution_count": 179,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -5726,7 +5689,7 @@
        "b'Caf\\xe9 Kastanient\\xf6rtchen'"
       ]
      },
-     "execution_count": 180,
+     "execution_count": 179,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -5748,7 +5711,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 181,
+   "execution_count": 180,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -5762,7 +5725,7 @@
      "traceback": [
       "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
       "\u001b[0;31mUnicodeDecodeError\u001b[0m                        Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-181-1630cf8a20cc>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mplace\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mencode\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"iso-8859-1\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdecode\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;32m<ipython-input-180-1630cf8a20cc>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mplace\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mencode\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"iso-8859-1\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdecode\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
       "\u001b[0;31mUnicodeDecodeError\u001b[0m: 'utf-8' codec can't decode byte 0xe9 in position 3: invalid continuation byte"
      ]
     }
@@ -5779,12 +5742,12 @@
     }
    },
    "source": [
-    "Not all encodings map all Unicode code points. For example `\"iso-8859-1\"` does not know Czech letters. Below, [encode()](https://docs.python.org/3/library/stdtypes.html#str.encode) raises a `UnicodeEncodeError` because of that."
+    "Not all encodings map all Unicode code points. For example `\"iso-8859-1\"` does not know Czech letters. Below, [encode() <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/stdtypes.html#str.encode) raises a `UnicodeEncodeError` because of that."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 182,
+   "execution_count": 181,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -5798,7 +5761,7 @@
      "traceback": [
       "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
       "\u001b[0;31mUnicodeEncodeError\u001b[0m                        Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-182-8dd3b1bfcf5d>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0;34m\"Dobrý den, přátelé!\"\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mencode\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"iso-8859-1\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;32m<ipython-input-181-8dd3b1bfcf5d>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0;34m\"Dobrý den, přátelé!\"\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mencode\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"iso-8859-1\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
       "\u001b[0;31mUnicodeEncodeError\u001b[0m: 'latin-1' codec can't encode character '\\u0159' in position 12: ordinal not in range(256)"
      ]
     }
@@ -5826,12 +5789,12 @@
     }
    },
    "source": [
-    "The [open()](https://docs.python.org/3/library/functions.html#open) function takes an optional `encoding` argument as well."
+    "The [open() <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/functions.html#open) function takes an optional `encoding` argument as well."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 183,
+   "execution_count": 182,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -5845,7 +5808,7 @@
      "traceback": [
       "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
       "\u001b[0;31mUnicodeDecodeError\u001b[0m                        Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-183-83a7d1245cc7>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;32mwith\u001b[0m \u001b[0mopen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"umlauts.txt\"\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mfile\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m     \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"\"\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mjoin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfile\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreadlines\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;32m<ipython-input-182-83a7d1245cc7>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;32mwith\u001b[0m \u001b[0mopen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"umlauts.txt\"\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mfile\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m     \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"\"\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mjoin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfile\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreadlines\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
       "\u001b[0;32m~/.pyenv/versions/anaconda3-2019.10/lib/python3.7/codecs.py\u001b[0m in \u001b[0;36mdecode\u001b[0;34m(self, input, final)\u001b[0m\n\u001b[1;32m    320\u001b[0m         \u001b[0;31m# decode input (taking the buffer into account)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    321\u001b[0m         \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbuffer\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0minput\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 322\u001b[0;31m         \u001b[0;34m(\u001b[0m\u001b[0mresult\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mconsumed\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_buffer_decode\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0merrors\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfinal\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    323\u001b[0m         \u001b[0;31m# keep undecoded input until the next call\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    324\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbuffer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mconsumed\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
       "\u001b[0;31mUnicodeDecodeError\u001b[0m: 'utf-8' codec can't decode byte 0xe4 in position 9: invalid continuation byte"
      ]
@@ -5858,7 +5821,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 184,
+   "execution_count": 183,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -5910,12 +5873,12 @@
    "source": [
     "A best practice is to *always* specify the `encoding`, especially on computers running on Windows (cf., the talk by �ukasz Langa in the \"*Further Resources*\" section below).\n",
     "\n",
-    "Below is the first example involving [open()](https://docs.python.org/3/library/functions.html#open) one last time: It shows how *all* the contents of a text file should be read into one `str` object."
+    "Below is the first example involving [open() <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/functions.html#open) one last time: It shows how *all* the contents of a text file should be read into one `str` object."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 185,
+   "execution_count": 184,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -5929,7 +5892,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 186,
+   "execution_count": 185,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -5942,7 +5905,7 @@
        "\"Lorem Ipsum is simply dummy text of the printing and typesetting industry.\\nLorem Ipsum has been the industry's standard dummy text ever since the 1500s\\nwhen an unknown printer took a galley of type and scrambled it to make a type\\nspecimen book. It has survived not only five centuries but also the leap into\\nelectronic typesetting, remaining essentially unchanged. It was popularised in\\nthe 1960s with the release of Letraset sheets.\\n\""
       ]
      },
-     "execution_count": 186,
+     "execution_count": 185,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -5953,7 +5916,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 187,
+   "execution_count": 186,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -6023,7 +5986,56 @@
     }
    },
    "source": [
-    "We refer to the official [Unicode HOWTO](https://docs.python.org/3/howto/unicode.html) in the Python documentation. Furthermore, the [unicodedata](https://docs.python.org/3/library/unicodedata.html) module in the [standard library](https://docs.python.org/3/library/index.html) provides a lot of utility functions around the Unicode standard.\n",
+    "A lecture-style **video presentation** of this chapter is integrated below (cf., the [video <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_yt.png\">](https://www.youtube.com/watch?v=3WTRlgN09sM&list=PL-2JV1G3J10lQ2xokyQowcRJI5jjNfW7f) or the entire [playlist <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_yt.png\">](https://www.youtube.com/playlist?list=PL-2JV1G3J10lQ2xokyQowcRJI5jjNfW7f))."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 187,
+   "metadata": {
+    "scrolled": true,
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
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+       "            width=\"60%\"\n",
+       "            height=\"300\"\n",
+       "            src=\"https://www.youtube.com/embed/3WTRlgN09sM\"\n",
+       "            frameborder=\"0\"\n",
+       "            allowfullscreen\n",
+       "        ></iframe>\n",
+       "        "
+      ],
+      "text/plain": [
+       "<IPython.lib.display.YouTubeVideo at 0x7f45605fab10>"
+      ]
+     },
+     "execution_count": 187,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from IPython.display import YouTubeVideo\n",
+    "YouTubeVideo(\"3WTRlgN09sM\", width=\"60%\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "We refer to the official [Unicode HOWTO <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/howto/unicode.html) in the Python documentation. Furthermore, the [unicodedata <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/unicodedata.html) module in the [standard library <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/index.html) provides a lot of utility functions around the Unicode standard.\n",
     "\n",
     "Next is a brief summary video by the YouTube channel [Computerphile](https://www.youtube.com/channel/UC9-y-6csu5WGm29I7JiwpnA) titled \"*Characters, Symbols and the Unicode Miracle*\"."
    ]
@@ -6052,7 +6064,7 @@
        "        "
       ],
       "text/plain": [
-       "<IPython.lib.display.YouTubeVideo at 0x7faec4157310>"
+       "<IPython.lib.display.YouTubeVideo at 0x7f456059dad0>"
       ]
      },
      "execution_count": 188,
@@ -6099,7 +6111,7 @@
        "        "
       ],
       "text/plain": [
-       "<IPython.lib.display.YouTubeVideo at 0x7faec41d5790>"
+       "<IPython.lib.display.YouTubeVideo at 0x7f4560598690>"
       ]
      },
      "execution_count": 189,
@@ -6146,7 +6158,7 @@
        "        "
       ],
       "text/plain": [
-       "<IPython.lib.display.YouTubeVideo at 0x7faec4167e10>"
+       "<IPython.lib.display.YouTubeVideo at 0x7f45605b2290>"
       ]
      },
      "execution_count": 190,
@@ -6193,7 +6205,7 @@
        "        "
       ],
       "text/plain": [
-       "<IPython.lib.display.YouTubeVideo at 0x7faec416bc50>"
+       "<IPython.lib.display.YouTubeVideo at 0x7f45605b2d10>"
       ]
      },
      "execution_count": 191,
@@ -6240,7 +6252,7 @@
        "        "
       ],
       "text/plain": [
-       "<IPython.lib.display.YouTubeVideo at 0x7faec4157e50>"
+       "<IPython.lib.display.YouTubeVideo at 0x7f45605a8810>"
       ]
      },
      "execution_count": 192,
@@ -6276,7 +6288,7 @@
        "        "
       ],
       "text/plain": [
-       "<IPython.lib.display.YouTubeVideo at 0x7faec416f6d0>"
+       "<IPython.lib.display.YouTubeVideo at 0x7f45605a8750>"
       ]
      },
      "execution_count": 193,
diff --git a/06_text_01_review.ipynb b/06_text_01_review.ipynb
index 8727868..51cce84 100644
--- a/06_text_01_review.ipynb
+++ b/06_text_01_review.ipynb
@@ -18,7 +18,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "The questions below assume that you have read [Chapter 6](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/06_text_00_lecture.ipynb) in the book.\n",
+    "The questions below assume that you have read [Chapter 6 <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/06_text_00_content.ipynb) in the book.\n",
     "\n",
     "Be concise in your answers! Most questions can be answered in *one* sentence."
    ]
diff --git a/06_text_02_exercises.ipynb b/06_text_02_exercises.ipynb
index d488f52..1eb7c19 100644
--- a/06_text_02_exercises.ipynb
+++ b/06_text_02_exercises.ipynb
@@ -1,5 +1,12 @@
 {
  "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Important**: Click on \"*Kernel*\" > \"*Restart Kernel and Run All*\" *after* finishing the exercises in [JupyterLab <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_jp.png\">](https://jupyterlab.readthedocs.io/en/stable/) (e.g., in the cloud on [MyBinder <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_mb.png\">](https://mybinder.org/v2/gh/webartifex/intro-to-python/master?urlpath=lab/tree/06_text_02_exercises.ipynb)) to ensure that your solution runs top to bottom *without* any errors"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -18,7 +25,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "The exercises below assume that you have read [Chapter 6](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/06_text_00_lecture.ipynb) in the book.\n",
+    "The exercises below assume that you have read [Chapter 6 <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/06_text_00_content.ipynb) in the book.\n",
     "\n",
     "The `...`'s in the code cells indicate where you need to fill in code snippets. The number of `...`'s within a code cell give you a rough idea of how many lines of code are needed to solve the task. You should not need to create any additional code cells for your final solution. However, you may want to use temporary code cells to try out some ideas."
    ]
@@ -34,11 +41,11 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "[Palindromes](https://en.wikipedia.org/wiki/Palindrome) are sequences of characters that read the same backward as forward. Examples are first names like \"Hannah\" or \"Otto,\" words like \"radar\" or \"level,\" or sentences like \"Was it a car or a cat I saw?\"\n",
+    "[Palindromes <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_wiki.png\">](https://en.wikipedia.org/wiki/Palindrome) are sequences of characters that read the same backward as forward. Examples are first names like \"Hannah\" or \"Otto,\" words like \"radar\" or \"level,\" or sentences like \"Was it a car or a cat I saw?\"\n",
     "\n",
     "In this exercise, you implement various functions that check if the given arguments are palindromes or not. We start with an iterative implementation and end with a recursive one.\n",
     "\n",
-    "Conceptually, the first function, `unpythonic_palindrome()`, is similar to the \"*Is the square of a number in `[7, 11, 8, 5, 3, 12, 2, 6, 9, 10, 1, 4]` greater than `100`?*\" example in [Chapter 4](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/04_iteration_00_lecture.ipynb#Example:-Is-the-square-of-a-number-in-[7,-11,-8,-5,-3,-12,-2,-6,-9,-10,-1,-4]-greater-than-100?): It assumes that the `text` argument is a palindrome (i.e., it initializes `is_palindrom` to `True`) and then checks in a `for`-loop if a pair of corresponding characters, `forward` and `backward`, contradicts that.\n",
+    "Conceptually, the first function, `unpythonic_palindrome()`, is similar to the \"*Is the square of a number in `[7, 11, 8, 5, 3, 12, 2, 6, 9, 10, 1, 4]` greater than `100`?*\" example in [Chapter 4 <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/04_iteration_00_content.ipynb#Example:-Is-the-square-of-a-number-in-[7,-11,-8,-5,-3,-12,-2,-6,-9,-10,-1,-4]-greater-than-100?): It assumes that the `text` argument is a palindrome (i.e., it initializes `is_palindrom` to `True`) and then checks in a `for`-loop if a pair of corresponding characters, `forward` and `backward`, contradicts that.\n",
     "\n",
     "**Q1**: How many iterations are needed in the `for`-loop? Take into account that `text` may contain an even or odd number of characters! Inside `unpythonic_palindrome()` below, write an expression whose result is assigned to `chars_to_check`!"
    ]
@@ -221,7 +228,7 @@
     "\n",
     "**Q5**: Copy your solutions to the previous questions into `almost_pythonic_palindrome()` below!\n",
     "\n",
-    "**Q6**: The [reversed()](https://docs.python.org/3/library/functions.html#reversed) and [zip()](https://docs.python.org/3/library/functions.html#zip) built-ins allow us to loop over the same `text` argument *in parallel* in both forward *and* backward order. Finish the `for` statement's header line to do just that!\n",
+    "**Q6**: The [reversed() <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/functions.html#reversed) and [zip() <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/functions.html#zip) built-ins allow us to loop over the same `text` argument *in parallel* in both forward *and* backward order. Finish the `for` statement's header line to do just that!\n",
     "\n",
     "Hint: You may need to slice the `text` argument."
    ]
@@ -454,7 +461,7 @@
    "source": [
     "**Q11**: Copy your code from `pythonic_palindrome()` above into `palindrome_ducks()` below and make the latter conform to *duck typing*!\n",
     "\n",
-    "Hints: You may want to use the [str()](https://docs.python.org/3/library/functions.html#func-str) built-in. You only need to add *one* short line of code."
+    "Hints: You may want to use the [str() <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/functions.html#func-str) built-in. You only need to add *one* short line of code."
    ]
   },
   {
@@ -576,7 +583,7 @@
    "source": [
     "**Q13**: Implement the final iterative version `is_a_palindrome()` below. Copy your solution from `palindrome_ducks()` above and add code that removes the \"special\" characters (and symbols) from the longer example palindromes above so that they are effectively ignored! Note that this processing should only be done if the `ignore_symbols` argument is set to `True`.\n",
     "\n",
-    "Hints: Use the [replace()](https://docs.python.org/3/library/stdtypes.html#str.replace) method on the `str` type to achieve that. You may want to do so within another `for`-loop."
+    "Hints: Use the [replace() <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/stdtypes.html#str.replace) method on the `str` type to achieve that. You may want to do so within another `for`-loop."
    ]
   },
   {
diff --git a/07_sequences_00_lecture.ipynb b/07_sequences_00_content.ipynb
similarity index 83%
rename from 07_sequences_00_lecture.ipynb
rename to 07_sequences_00_content.ipynb
index fc97607..011c37c 100644
--- a/07_sequences_00_lecture.ipynb
+++ b/07_sequences_00_content.ipynb
@@ -8,44 +8,7 @@
     }
    },
    "source": [
-    "A **video presentation** of the contents in this chapter is shown below. A playlist with *all* chapters as videos is linked [here](https://www.youtube.com/playlist?list=PL-2JV1G3J10lQ2xokyQowcRJI5jjNfW7f)."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/jpeg": 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-       "            width=\"60%\"\n",
-       "            height=\"300\"\n",
-       "            src=\"https://www.youtube.com/embed/nx2sCDoeC3I\"\n",
-       "            frameborder=\"0\"\n",
-       "            allowfullscreen\n",
-       "        ></iframe>\n",
-       "        "
-      ],
-      "text/plain": [
-       "<IPython.lib.display.YouTubeVideo at 0x7f46a9329390>"
-      ]
-     },
-     "execution_count": 1,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from IPython.display import YouTubeVideo\n",
-    "YouTubeVideo(\"nx2sCDoeC3I\", width=\"60%\")"
+    "**Important**: Click on \"*Kernel*\" > \"*Restart Kernel and Clear All Outputs*\" *before* reading this chapter in [JupyterLab <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_jp.png\">](https://jupyterlab.readthedocs.io/en/stable/) (e.g., in the cloud on [MyBinder <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_mb.png\">](https://mybinder.org/v2/gh/webartifex/intro-to-python/master?urlpath=lab/tree/07_sequences_00_content.ipynb))"
    ]
   },
   {
@@ -67,13 +30,13 @@
     }
    },
    "source": [
-    "We studied numbers (cf., [Chapter 5](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/05_numbers_00_lecture.ipynb)) and textual data (cf., [Chapter 6](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/06_text_00_lecture.ipynb)) first, mainly because objects of the presented data types are \"simple,\" for two reasons: First, they are *immutable*, and, as we saw in the \"*Who am I? And how many?*\" section in [Chapter 1](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/01_elements_00_lecture.ipynb#Who-am-I?-And-how-many?), mutable objects can quickly become hard to reason about. Second, they are \"flat\" in the sense that they are *not* composed of other objects.\n",
+    "We studied numbers (cf., [Chapter 5 <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/05_numbers_00_content.ipynb)) and textual data (cf., [Chapter 6 <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/06_text_00_content.ipynb)) first, mainly because objects of the presented data types are \"simple,\" for two reasons: First, they are *immutable*, and, as we saw in the \"*Who am I? And how many?*\" section in [Chapter 1 <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/01_elements_00_content.ipynb#Who-am-I?-And-how-many?), mutable objects can quickly become hard to reason about. Second, they are \"flat\" in the sense that they are *not* composed of other objects.\n",
     "\n",
     "The `str` type is a bit of a corner case in this regard. While one could argue that a longer `str` object, for example, `\"text\"`, is composed of individual characters, this is *not* the case in memory as the literal `\"text\"` only creates *one* object (i.e., one \"bag\" of $0$s and $1$s modeling all characters).\n",
     "\n",
-    "This chapter, [Chapter 8](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/08_mfr_00_lecture.ipynb), and [Chapter 9](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/09_mappings_00_lecture.ipynb) introduce various \"complex\" data types. While some are mutable and others are not, they all share that they are primarily used to \"manage,\" or structure, the memory in a program. Unsurprisingly, computer scientists refer to the ideas and theories behind these data types as **[data structures](https://en.wikipedia.org/wiki/Data_structure)**.\n",
+    "This chapter, [Chapter 8 <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/08_mfr_00_content.ipynb), and [Chapter 9 <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/09_mappings_00_content.ipynb) introduce various \"complex\" data types. While some are mutable and others are not, they all share that they are primarily used to \"manage,\" or structure, the memory in a program. Unsurprisingly, computer scientists refer to the ideas and theories behind these data types as **[data structures <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_wiki.png\">](https://en.wikipedia.org/wiki/Data_structure)**.\n",
     "\n",
-    "In this chapter, we focus on data types that model all kinds of sequential data. Examples of such data are [spreadsheets](https://en.wikipedia.org/wiki/Spreadsheet) or [matrices](https://en.wikipedia.org/wiki/Matrix_%28mathematics%29)/[vectors](https://en.wikipedia.org/wiki/Vector_%28mathematics_and_physics%29). Such formats share the property that they are composed of smaller units that come in a sequence of, for example, rows/columns/cells or elements/entries."
+    "In this chapter, we focus on data types that model all kinds of sequential data. Examples of such data are [spreadsheets <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_wiki.png\">](https://en.wikipedia.org/wiki/Spreadsheet) or [matrices <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_wiki.png\">](https://en.wikipedia.org/wiki/Matrix_%28mathematics%29) and [vectors <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_wiki.png\">](https://en.wikipedia.org/wiki/Vector_%28mathematics_and_physics%29). Such formats share the property that they are composed of smaller units that come in a sequence of, for example, rows/columns/cells or elements/entries."
    ]
   },
   {
@@ -95,16 +58,16 @@
     }
    },
    "source": [
-    "[Chapter 6](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/06_text_00_lecture.ipynb#A-\"String\"-of-Characters) already describes the **sequence** properties of `str` objects. In this section, we take a step back and study these properties one by one.\n",
+    "[Chapter 6 <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/06_text_00_content.ipynb#A-\"String\"-of-Characters) already describes the **sequence** properties of `str` objects. In this section, we take a step back and study these properties one by one.\n",
     "\n",
-    "The [collections.abc](https://docs.python.org/3/library/collections.abc.html) module in the [standard library](https://docs.python.org/3/library/index.html) defines a variety of **abstract base classes** (ABCs). We saw ABCs already in [Chapter 5](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/05_numbers_00_lecture.ipynb#The-Numerical-Tower), where we use the ones from the [numbers](https://docs.python.org/3/library/numbers.html) module in the [standard library](https://docs.python.org/3/library/index.html) to classify Python's numeric data types according to mathematical ideas. Now, we take the ABCs from the [collections.abc](https://docs.python.org/3/library/collections.abc.html) module to classify the data types in this chapter according to their behavior in various contexts.\n",
+    "The [collections.abc <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/collections.abc.html) module in the [standard library <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/index.html) defines a variety of **abstract base classes** (ABCs). We saw ABCs already in [Chapter 5 <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/05_numbers_00_content.ipynb#The-Numerical-Tower), where we use the ones from the [numbers <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/numbers.html) module in the [standard library <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/index.html) to classify Python's numeric data types according to mathematical ideas. Now, we take the ABCs from the [collections.abc <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/collections.abc.html) module to classify the data types in this chapter according to their behavior in various contexts.\n",
     "\n",
     "As an illustration, consider `numbers` and `text` below, two objects of *different* types."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 1,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -129,7 +92,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 2,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -151,7 +114,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 3,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -179,14 +142,14 @@
     }
    },
    "source": [
-    "In [Chapter 4](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/04_iteration_00_lecture.ipynb#Containers-vs.-Iterables), we referred to such types as *iterables*. That is *not* a proper [English](https://dictionary.cambridge.org/spellcheck/english-german/?q=iterable) word, even if it may sound like one at first sight. Yet, it is an official term in the Python world formalized with the `Iterable` ABC in the [collections.abc](https://docs.python.org/3/library/collections.abc.html) module.\n",
+    "In [Chapter 4 <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/04_iteration_00_content.ipynb#Containers-vs.-Iterables), we referred to such types as *iterables*. That is *not* a proper [English](https://dictionary.cambridge.org/spellcheck/english-german/?q=iterable) word, even if it may sound like one at first sight. Yet, it is an official term in the Python world formalized with the `Iterable` ABC in the [collections.abc <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/collections.abc.html) module.\n",
     "\n",
-    "For the data science practitioner, it is worthwhile to know such terms as, for example, the documentation on the [built-ins](https://docs.python.org/3/library/functions.html) uses them extensively: In simple words, any built-in that takes an argument called \"*iterable*\" may be called with *any* object that supports being looped over. Already familiar [built-ins](https://docs.python.org/3/library/functions.html) include [enumerate()](https://docs.python.org/3/library/functions.html#enumerate), [sum()](https://docs.python.org/3/library/functions.html#sum), or [zip()](https://docs.python.org/3/library/functions.html#zip). So, they do *not* require the argument to be of a certain data type (e.g., `list`); instead, any *iterable* type works."
+    "For the data science practitioner, it is worthwhile to know such terms as, for example, the documentation on the [built-ins <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/functions.html) uses them extensively: In simple words, any built-in that takes an argument called \"*iterable*\" may be called with *any* object that supports being looped over. Already familiar [built-ins <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/functions.html) include [enumerate() <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/functions.html#enumerate), [sum() <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/functions.html#sum), or [zip() <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/functions.html#zip). So, they do *not* require the argument to be of a certain data type (e.g., `list`); instead, any *iterable* type works."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 4,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -199,7 +162,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 5,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -212,7 +175,7 @@
        "collections.abc.Iterable"
       ]
      },
-     "execution_count": 6,
+     "execution_count": 5,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -229,14 +192,14 @@
     }
    },
    "source": [
-    "As seen in [Chapter 5](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/05_numbers_00_lecture.ipynb#Goose-Typing), we can use ABCs with the built-in [isinstance()](https://docs.python.org/3/library/functions.html#isinstance) function to check if an object supports a behavior.\n",
+    "As seen in [Chapter 5 <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/05_numbers_00_content.ipynb#Goose-Typing), we can use ABCs with the built-in [isinstance() <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/functions.html#isinstance) function to check if an object supports a behavior.\n",
     "\n",
     "So, let's \"ask\" Python if it can loop over `numbers` or `text`."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 6,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -249,7 +212,7 @@
        "True"
       ]
      },
-     "execution_count": 7,
+     "execution_count": 6,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -260,7 +223,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 7,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -273,7 +236,7 @@
        "True"
       ]
      },
-     "execution_count": 8,
+     "execution_count": 7,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -295,7 +258,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 8,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -308,7 +271,7 @@
        "False"
       ]
      },
-     "execution_count": 9,
+     "execution_count": 8,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -330,7 +293,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 9,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -344,7 +307,7 @@
      "traceback": [
       "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
       "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-10-8249bf25b5b7>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0;32mfor\u001b[0m \u001b[0mdigit\u001b[0m \u001b[0;32min\u001b[0m \u001b[0;36m999\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m     \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdigit\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m<ipython-input-9-8249bf25b5b7>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0;32mfor\u001b[0m \u001b[0mdigit\u001b[0m \u001b[0;32min\u001b[0m \u001b[0;36m999\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m     \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdigit\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
       "\u001b[0;31mTypeError\u001b[0m: 'int' object is not iterable"
      ]
     }
@@ -362,7 +325,7 @@
     }
    },
    "source": [
-    "Most of the data types in this and the next chapter exhibit three [orthogonal](https://en.wikipedia.org/wiki/Orthogonality) (i.e., \"independent\") behaviors, formalized by ABCs in the [collections.abc](https://docs.python.org/3/library/collections.abc.html) module as:\n",
+    "Most of the data types in this and the next chapter exhibit three [orthogonal <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_wiki.png\">](https://en.wikipedia.org/wiki/Orthogonality) (i.e., \"independent\") behaviors, formalized by ABCs in the [collections.abc <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/collections.abc.html) module as:\n",
     "- `Iterable`: An object may be looped over.\n",
     "- `Container`: An object \"contains\" references to other objects; a \"whole\" is composed of many \"parts.\"\n",
     "- `Sized`: The number of references to other objects, the \"parts,\" is *finite*.\n",
@@ -372,7 +335,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 10,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -385,7 +348,7 @@
        "False"
       ]
      },
-     "execution_count": 11,
+     "execution_count": 10,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -394,6 +357,41 @@
     "0 in numbers"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "\"l\" in text"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Alternatively, we could also check if `numbers` and `text` are `Container` types with [isinstance() <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/functions.html#isinstance)."
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 12,
@@ -415,18 +413,7 @@
     }
    ],
    "source": [
-    "\"l\" in text"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "source": [
-    "Alternatively, we could also check if `numbers` and `text` are `Container` types with [isinstance()](https://docs.python.org/3/library/functions.html#isinstance)."
+    "isinstance(numbers, abc.Container)"
    ]
   },
   {
@@ -449,30 +436,6 @@
      "output_type": "execute_result"
     }
    ],
-   "source": [
-    "isinstance(numbers, abc.Container)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "fragment"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "True"
-      ]
-     },
-     "execution_count": 14,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
    "source": [
     "isinstance(text, abc.Container)"
    ]
@@ -490,7 +453,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 14,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -503,7 +466,7 @@
        "False"
       ]
      },
-     "execution_count": 15,
+     "execution_count": 14,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -514,7 +477,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 15,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -528,7 +491,7 @@
      "traceback": [
       "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
       "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-16-b3e0b3c32aec>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0;36m9\u001b[0m \u001b[0;32min\u001b[0m \u001b[0;36m999\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;32m<ipython-input-15-b3e0b3c32aec>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0;36m9\u001b[0m \u001b[0;32min\u001b[0m \u001b[0;36m999\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
       "\u001b[0;31mTypeError\u001b[0m: argument of type 'int' is not iterable"
      ]
     }
@@ -545,12 +508,12 @@
     }
    },
    "source": [
-    "Analogously, being `Sized` types, we can pass `numbers` and `text` as the argument to the built-in [len()](https://docs.python.org/3/library/functions.html#len) function and obtain \"meaningful\" results. The exact meaning depends on the data type: For `numbers`, [len()](https://docs.python.org/3/library/functions.html#len) tells us how many elements are in the `list` object; for `text`, it tells us how many [Unicode characters](https://en.wikipedia.org/wiki/Unicode) make up the `str` object. *Abstractly* speaking, both data types exhibit the *same* behavior of *finiteness*."
+    "Analogously, being `Sized` types, we can pass `numbers` and `text` as the argument to the built-in [len() <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/functions.html#len) function and obtain \"meaningful\" results. The exact meaning depends on the data type: For `numbers`, [len() <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/functions.html#len) tells us how many elements are in the `list` object; for `text`, it tells us how many [Unicode characters <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_wiki.png\">](https://en.wikipedia.org/wiki/Unicode) make up the `str` object. *Abstractly* speaking, both data types exhibit the *same* behavior of *finiteness*."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 16,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -563,7 +526,7 @@
        "12"
       ]
      },
-     "execution_count": 17,
+     "execution_count": 16,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -572,6 +535,30 @@
     "len(numbers)"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "27"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "len(text)"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 18,
@@ -584,7 +571,7 @@
     {
      "data": {
       "text/plain": [
-       "27"
+       "True"
       ]
      },
      "execution_count": 18,
@@ -593,7 +580,7 @@
     }
    ],
    "source": [
-    "len(text)"
+    "isinstance(numbers, abc.Sized)"
    ]
   },
   {
@@ -616,30 +603,6 @@
      "output_type": "execute_result"
     }
    ],
-   "source": [
-    "isinstance(numbers, abc.Sized)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "fragment"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "True"
-      ]
-     },
-     "execution_count": 20,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
    "source": [
     "isinstance(text, abc.Sized)"
    ]
@@ -652,12 +615,12 @@
     }
    },
    "source": [
-    "On the contrary, even though `999` consists of three digits for humans, numeric objects in Python have no concept of a \"size\" or \"length,\" and the [len()](https://docs.python.org/3/library/functions.html#len) function raises a `TypeError`."
+    "On the contrary, even though `999` consists of three digits for humans, numeric objects in Python have no concept of a \"size\" or \"length,\" and the [len() <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/functions.html#len) function raises a `TypeError`."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 20,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -670,7 +633,7 @@
        "False"
       ]
      },
-     "execution_count": 21,
+     "execution_count": 20,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -681,7 +644,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 21,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -695,7 +658,7 @@
      "traceback": [
       "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
       "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-22-ae09c1aa5305>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m999\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;32m<ipython-input-21-ae09c1aa5305>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m999\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
       "\u001b[0;31mTypeError\u001b[0m: object of type 'int' has no len()"
      ]
     }
@@ -712,16 +675,16 @@
     }
    },
    "source": [
-    "These three behaviors are so essential that whenever they coincide for a data type, it is called a **collection**, formalized with the `Collection` ABC. That is where the [collections.abc](https://docs.python.org/3/library/collections.abc.html) module got its name from: It summarizes all ABCs related to collections; in particular, it defines a hierarchy of specialized kinds of collections.\n",
+    "These three behaviors are so essential that whenever they coincide for a data type, it is called a **collection**, formalized with the `Collection` ABC. That is where the [collections.abc <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/collections.abc.html) module got its name from: It summarizes all ABCs related to collections; in particular, it defines a hierarchy of specialized kinds of collections.\n",
     "\n",
-    "Without going into too much detail, one way to read the summary table at the beginning of the [collections.abc](https://docs.python.org/3/library/collections.abc.html) module's documention is as follows: The first column, titled \"ABC\", lists all collection-related ABCs in Python. The second column, titled \"Inherits from,\" indicates if the idea behind the ABC is *original* (e.g., the first row with the `Container` ABC has an empty \"Inherits from\" column) or a *combination* (e.g., the row with the `Collection` ABC has `Sized`, `Iterable`, and `Container` in the \"Inherits from\" column). The third and fourth columns list the methods that come with a data type following an ABC. We keep ignoring the methods named in the dunder style for now.\n",
+    "Without going into too much detail, one way to read the summary table at the beginning of the [collections.abc <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/collections.abc.html) module's documention is as follows: The first column, titled \"ABC\", lists all collection-related ABCs in Python. The second column, titled \"Inherits from,\" indicates if the idea behind the ABC is *original* (e.g., the first row with the `Container` ABC has an empty \"Inherits from\" column) or a *combination* (e.g., the row with the `Collection` ABC has `Sized`, `Iterable`, and `Container` in the \"Inherits from\" column). The third and fourth columns list the methods that come with a data type following an ABC. We keep ignoring the methods named in the dunder style for now.\n",
     "\n",
     "So, let's confirm that both `numbers` and `text` are collections."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 22,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -734,7 +697,7 @@
        "True"
       ]
      },
-     "execution_count": 23,
+     "execution_count": 22,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -745,7 +708,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 23,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -758,7 +721,7 @@
        "True"
       ]
      },
-     "execution_count": 24,
+     "execution_count": 23,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -775,14 +738,14 @@
     }
    },
    "source": [
-    "They share one more common behavior: When looping over them, we can *predict* the *order* of the elements or characters. The ABC in the [collections.abc](https://docs.python.org/3/library/collections.abc.html) module corresponding to this behavior is `Reversible`. While sounding unintuitive at first, it is evident that if something is reversible, it must have a forward order, to begin with.\n",
+    "They share one more common behavior: When looping over them, we can *predict* the *order* of the elements or characters. The ABC in the [collections.abc <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/collections.abc.html) module corresponding to this behavior is `Reversible`. While sounding unintuitive at first, it is evident that if something is reversible, it must have a forward order, to begin with.\n",
     "\n",
-    "The [reversed()](https://docs.python.org/3/library/functions.html#reversed) built-in allows us to loop over the elements or characters in reverse order."
+    "The [reversed() <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/functions.html#reversed) built-in allows us to loop over the elements or characters in reverse order."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 24,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -804,7 +767,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": 25,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -824,6 +787,30 @@
     "    print(character, end=\" \")"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 26,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "isinstance(numbers, abc.Reversible)"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 27,
@@ -845,7 +832,18 @@
     }
    ],
    "source": [
-    "isinstance(numbers, abc.Reversible)"
+    "isinstance(text, abc.Reversible)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Collections that exhibit this fourth behavior are referred to as **sequences**, formalized with the `Sequence` ABC in the [collections.abc <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/collections.abc.html) module."
    ]
   },
   {
@@ -853,7 +851,7 @@
    "execution_count": 28,
    "metadata": {
     "slideshow": {
-     "slide_type": "fragment"
+     "slide_type": "slide"
     }
    },
    "outputs": [
@@ -868,48 +866,13 @@
      "output_type": "execute_result"
     }
    ],
-   "source": [
-    "isinstance(text, abc.Reversible)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "source": [
-    "Collections that exhibit this fourth behavior are referred to as **sequences**, formalized with the `Sequence` ABC in the [collections.abc](https://docs.python.org/3/library/collections.abc.html) module."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 29,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "slide"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "True"
-      ]
-     },
-     "execution_count": 29,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
    "source": [
     "isinstance(numbers, abc.Sequence)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 30,
+   "execution_count": 29,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -922,7 +885,7 @@
        "True"
       ]
      },
-     "execution_count": 30,
+     "execution_count": 29,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -943,7 +906,7 @@
     "\n",
     "In Python-related documentations, the terms collection and sequence are heavily used, and the data science practitioner should always think of them in terms of the three or four behaviors they exhibit.\n",
     "\n",
-    "Data types that are collections but not sequences are covered in [Chapter 9](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/09_mappings_00_lecture.ipynb)."
+    "Data types that are collections but not sequences are covered in [Chapter 9 <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/09_mappings_00_content.ipynb)."
    ]
   },
   {
@@ -970,7 +933,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 31,
+   "execution_count": 30,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -983,7 +946,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 32,
+   "execution_count": 31,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -1007,7 +970,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 33,
+   "execution_count": 32,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -1026,12 +989,12 @@
     }
    },
    "source": [
-    "[PythonTutor](http://www.pythontutor.com/visualize.html#code=empty%20%3D%20%5B%5D%0Asimple%20%3D%20%5B40,%2050%5D%0Anested%20%3D%20%5Bempty,%2010,%2020.0,%20%22Thirty%22,%20simple%5D&cumulative=false&curInstr=0&heapPrimitives=nevernest&mode=display&origin=opt-frontend.js&py=3&rawInputLstJSON=%5B%5D&textReferences=false) shows how `nested` holds references to the `empty` and `simple` objects. Technically, it holds three more references to the `10`, `20.0`, and `\"Thirty\"` objects as well. However, to simplify the visualization, these three objects are shown right inside the `nested` object. That may be done because they are immutable and \"flat\" data types. In general, the $0$s and $1$s inside a `list` object in memory always constitute references to other objects only."
+    "[PythonTutor <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](http://pythontutor.com/visualize.html#code=empty%20%3D%20%5B%5D%0Asimple%20%3D%20%5B40,%2050%5D%0Anested%20%3D%20%5Bempty,%2010,%2020.0,%20%22Thirty%22,%20simple%5D&cumulative=false&curInstr=0&heapPrimitives=nevernest&mode=display&origin=opt-frontend.js&py=3&rawInputLstJSON=%5B%5D&textReferences=false) shows how `nested` holds references to the `empty` and `simple` objects. Technically, it holds three more references to the `10`, `20.0`, and `\"Thirty\"` objects as well. However, to simplify the visualization, these three objects are shown right inside the `nested` object. That may be done because they are immutable and \"flat\" data types. In general, the $0$s and $1$s inside a `list` object in memory always constitute references to other objects only."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 34,
+   "execution_count": 33,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -1044,7 +1007,7 @@
        "[[], 10, 20.0, 'Thirty', [40, 50]]"
       ]
      },
-     "execution_count": 34,
+     "execution_count": 33,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1066,7 +1029,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 35,
+   "execution_count": 34,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -1076,10 +1039,10 @@
     {
      "data": {
       "text/plain": [
-       "139941444092304"
+       "140483818052016"
       ]
      },
-     "execution_count": 35,
+     "execution_count": 34,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1090,7 +1053,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 36,
+   "execution_count": 35,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -1103,7 +1066,7 @@
        "list"
       ]
      },
-     "execution_count": 36,
+     "execution_count": 35,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1120,14 +1083,14 @@
     }
    },
    "source": [
-    "Alternatively, we use the built-in [list()](https://docs.python.org/3/library/functions.html#func-list) constructor to create a `list` object out of any (finite) *iterable* we pass to it as the argument.\n",
+    "Alternatively, we use the built-in [list() <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/functions.html#func-list) constructor to create a `list` object out of any (finite) *iterable* we pass to it as the argument.\n",
     "\n",
-    "For example, we can wrap the [range()](https://docs.python.org/3/library/functions.html#func-range) built-in with [list()](https://docs.python.org/3/library/functions.html#func-list): As described in [Chapter 4](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/04_iteration_00_lecture.ipynb#Containers-vs.-Iterables), `range` objects, like `range(1, 13)` below, are iterable and generate `int` objects \"on the fly\" (i.e., one by one). The [list()](https://docs.python.org/3/library/functions.html#func-list) around it acts like a `for`-loop and **materializes** twelve `int` objects in memory that then become the elements of the newly created `list` object. [PythonTutor](http://www.pythontutor.com/visualize.html#code=r%20%3D%20range%281,%2013%29%0Al%20%3D%20list%28range%281,%2013%29%29&cumulative=false&curInstr=0&heapPrimitives=nevernest&mode=display&origin=opt-frontend.js&py=3&rawInputLstJSON=%5B%5D&textReferences=false) shows this difference visually."
+    "For example, we can wrap the [range() <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/functions.html#func-range) built-in with [list() <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/functions.html#func-list): As described in [Chapter 4 <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/04_iteration_00_content.ipynb#Containers-vs.-Iterables), `range` objects, like `range(1, 13)` below, are iterable and generate `int` objects \"on the fly\" (i.e., one by one). The [list() <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/functions.html#func-list) around it acts like a `for`-loop and **materializes** twelve `int` objects in memory that then become the elements of the newly created `list` object. [PythonTutor <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](http://pythontutor.com/visualize.html#code=r%20%3D%20range%281,%2013%29%0Al%20%3D%20list%28range%281,%2013%29%29&cumulative=false&curInstr=0&heapPrimitives=nevernest&mode=display&origin=opt-frontend.js&py=3&rawInputLstJSON=%5B%5D&textReferences=false) shows this difference visually."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 37,
+   "execution_count": 36,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -1140,7 +1103,7 @@
        "[1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12]"
       ]
      },
-     "execution_count": 37,
+     "execution_count": 36,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1157,12 +1120,12 @@
     }
    },
    "source": [
-    "Beware of passing a `range` object over a \"big\" horizon as the argument to [list()](https://docs.python.org/3/library/functions.html#func-list) as that may lead to a `MemoryError` and the computer crashing."
+    "Beware of passing a `range` object over a \"big\" horizon as the argument to [list() <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/functions.html#func-list) as that may lead to a `MemoryError` and the computer crashing."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 38,
+   "execution_count": 37,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -1176,7 +1139,7 @@
      "traceback": [
       "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
       "\u001b[0;31mMemoryError\u001b[0m                               Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-38-dbb397cbb3b9>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mlist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m999_999_999_999\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;32m<ipython-input-37-dbb397cbb3b9>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mlist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m999_999_999_999\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
       "\u001b[0;31mMemoryError\u001b[0m: "
      ]
     }
@@ -1198,7 +1161,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 39,
+   "execution_count": 38,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -1211,7 +1174,7 @@
        "['i', 't', 'e', 'r', 'a', 'b', 'l', 'e']"
       ]
      },
-     "execution_count": 39,
+     "execution_count": 38,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1258,10 +1221,10 @@
     " - works with the `for` or `while` statements\n",
     "- `Reversible`:\n",
     " - the elements come in a *predictable* order that we may loop over in a forward or backward fashion\n",
-    " - works with the [reversed()](https://docs.python.org/3/library/functions.html#reversed) built-in\n",
+    " - works with the [reversed() <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/functions.html#reversed) built-in\n",
     "- `Sized`:\n",
     " - the number of elements is finite *and* known in advance\n",
-    " - works with the built-in [len()](https://docs.python.org/3/library/functions.html#len) function"
+    " - works with the built-in [len() <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/functions.html#len) function"
    ]
   },
   {
@@ -1277,7 +1240,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 40,
+   "execution_count": 39,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -1290,7 +1253,7 @@
        "5"
       ]
      },
-     "execution_count": 40,
+     "execution_count": 39,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1312,7 +1275,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 41,
+   "execution_count": 40,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -1323,11 +1286,11 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[]         139941463482272    <class 'list'>\n",
-      "10         94715524076608     <class 'int'>\n",
-      "20.0       139941444613712    <class 'float'>\n",
-      "Thirty     139941444470960    <class 'str'>\n",
-      "[40, 50]   139941444068128    <class 'list'>\n"
+      "[]         140483817280848    <class 'list'>\n",
+      "10         94627844305984     <class 'int'>\n",
+      "20.0       140483817093232    <class 'float'>\n",
+      "Thirty     140483698711280    <class 'str'>\n",
+      "[40, 50]   140483698705104    <class 'list'>\n"
      ]
     }
    ],
@@ -1338,7 +1301,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 42,
+   "execution_count": 41,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -1366,12 +1329,12 @@
     }
    },
    "source": [
-    "The `in` operator checks if a given object is \"contained\" in a `list` object. It uses the `==` operator behind the scenes (i.e., *not* the `is` operator) conducting a **[linear search](https://en.wikipedia.org/wiki/Linear_search)**: So, Python implicitly loops over *all* elements and only stops prematurely if an element evaluates equal to the searched object. A linear search may, therefore, be relatively *slow* for big `list` objects."
+    "The `in` operator checks if a given object is \"contained\" in a `list` object. It uses the `==` operator behind the scenes (i.e., *not* the `is` operator) conducting a **[linear search <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_wiki.png\">](https://en.wikipedia.org/wiki/Linear_search)**: So, Python implicitly loops over *all* elements and only stops prematurely if an element evaluates equal to the searched object. A linear search may, therefore, be relatively *slow* for big `list` objects."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 43,
+   "execution_count": 42,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -1384,7 +1347,7 @@
        "True"
       ]
      },
-     "execution_count": 43,
+     "execution_count": 42,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1406,7 +1369,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 44,
+   "execution_count": 43,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -1419,7 +1382,7 @@
        "True"
       ]
      },
-     "execution_count": 44,
+     "execution_count": 43,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1430,7 +1393,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 45,
+   "execution_count": 44,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -1443,7 +1406,7 @@
        "False"
       ]
      },
-     "execution_count": 45,
+     "execution_count": 44,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1478,7 +1441,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 46,
+   "execution_count": 45,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -1491,7 +1454,7 @@
        "[]"
       ]
      },
-     "execution_count": 46,
+     "execution_count": 45,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1513,7 +1476,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 47,
+   "execution_count": 46,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -1527,7 +1490,7 @@
      "traceback": [
       "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
       "\u001b[0;31mIndexError\u001b[0m                                Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-47-cb216f4a067e>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mnested\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m5\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;32m<ipython-input-46-cb216f4a067e>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mnested\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m5\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
       "\u001b[0;31mIndexError\u001b[0m: list index out of range"
      ]
     }
@@ -1549,7 +1512,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 48,
+   "execution_count": 47,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -1562,7 +1525,7 @@
        "[40, 50]"
       ]
      },
-     "execution_count": 48,
+     "execution_count": 47,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1573,7 +1536,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 49,
+   "execution_count": 48,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -1586,7 +1549,7 @@
        "50"
       ]
      },
-     "execution_count": 49,
+     "execution_count": 48,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1621,7 +1584,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 50,
+   "execution_count": 49,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -1634,7 +1597,7 @@
        "[10, 20.0, 'Thirty']"
       ]
      },
-     "execution_count": 50,
+     "execution_count": 49,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1656,7 +1619,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 51,
+   "execution_count": 50,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -1669,7 +1632,7 @@
        "[[], 20.0, [40, 50]]"
       ]
      },
-     "execution_count": 51,
+     "execution_count": 50,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1686,11 +1649,24 @@
     }
    },
    "source": [
-    "The literal notation with the colons `:` is *syntactic sugar*. It saves us from using the [slice()](https://docs.python.org/3/library/functions.html#slice) built-in to create `slice` objects. [slice()](https://docs.python.org/3/library/functions.html#slice) takes *start*, *stop*, and *step* arguments in the same way as the familiar [range()](https://docs.python.org/3/library/functions.html#func-range), and the `slice` objects it creates are used just as *indexes* above.\n",
+    "The literal notation with the colons `:` is *syntactic sugar*. It saves us from using the [slice() <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/functions.html#slice) built-in to create `slice` objects. [slice() <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/functions.html#slice) takes *start*, *stop*, and *step* arguments in the same way as the familiar [range() <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/functions.html#func-range), and the `slice` objects it creates are used just as *indexes* above.\n",
     "\n",
     "In most cases, the literal notation is more convenient to use; however, with `slice` objects, we can give names to slices and reuse them across several sequences."
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 51,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "middle = slice(1, 4)"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 52,
@@ -1699,9 +1675,20 @@
      "slide_type": "skip"
     }
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "slice"
+      ]
+     },
+     "execution_count": 52,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "middle = slice(1, 4)"
+    "type(middle)"
    ]
   },
   {
@@ -1716,7 +1703,7 @@
     {
      "data": {
       "text/plain": [
-       "slice"
+       "[10, 20.0, 'Thirty']"
       ]
      },
      "execution_count": 53,
@@ -1725,7 +1712,7 @@
     }
    ],
    "source": [
-    "type(middle)"
+    "nested[middle]"
    ]
   },
   {
@@ -1740,7 +1727,7 @@
     {
      "data": {
       "text/plain": [
-       "[10, 20.0, 'Thirty']"
+       "[11, 8, 5]"
       ]
      },
      "execution_count": 54,
@@ -1749,7 +1736,7 @@
     }
    ],
    "source": [
-    "nested[middle]"
+    "numbers[middle]"
    ]
   },
   {
@@ -1760,30 +1747,6 @@
      "slide_type": "skip"
     }
    },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[11, 8, 5]"
-      ]
-     },
-     "execution_count": 55,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "numbers[middle]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 56,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
    "outputs": [
     {
      "data": {
@@ -1791,7 +1754,7 @@
        "'ore'"
       ]
      },
-     "execution_count": 56,
+     "execution_count": 55,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1813,7 +1776,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 57,
+   "execution_count": 56,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -1826,7 +1789,7 @@
        "1"
       ]
      },
-     "execution_count": 57,
+     "execution_count": 56,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1837,7 +1800,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 58,
+   "execution_count": 57,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -1850,7 +1813,7 @@
        "4"
       ]
      },
-     "execution_count": 58,
+     "execution_count": 57,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1867,12 +1830,12 @@
     }
    },
    "source": [
-    "If not passed to [slice()](https://docs.python.org/3/library/functions.html#slice), these attributes default to `None`. That is why the cell below has no output."
+    "If not passed to [slice() <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/functions.html#slice), these attributes default to `None`. That is why the cell below has no output."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 59,
+   "execution_count": 58,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -1896,7 +1859,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 60,
+   "execution_count": 59,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -1909,7 +1872,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 61,
+   "execution_count": 60,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -1922,7 +1885,7 @@
        "[[], 10, 20.0, 'Thirty', [40, 50]]"
       ]
      },
-     "execution_count": 61,
+     "execution_count": 60,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1946,7 +1909,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 62,
+   "execution_count": 61,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -1959,7 +1922,7 @@
        "True"
       ]
      },
-     "execution_count": 62,
+     "execution_count": 61,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1970,7 +1933,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 63,
+   "execution_count": 62,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -1983,7 +1946,7 @@
        "False"
       ]
      },
-     "execution_count": 63,
+     "execution_count": 62,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2000,9 +1963,33 @@
     }
    },
    "source": [
-    "However, as [PythonTutor](http://pythontutor.com/visualize.html#code=nested%20%3D%20%5B%5B%5D,%2010,%2020.0,%20%22Thirty%22,%20%5B40,%2050%5D%5D%0Anested_copy%20%3D%20nested%5B%3A%5D&cumulative=false&curInstr=0&heapPrimitives=nevernest&mode=display&origin=opt-frontend.js&py=3&rawInputLstJSON=%5B%5D&textReferences=false) reveals, only the *references* to the elements are copied, and not the objects in `nested` themselves! Because of that, `nested_copy` is a so-called **[shallow copy](https://en.wikipedia.org/wiki/Object_copying#Shallow_copy)** of `nested`.\n",
+    "However, as [PythonTutor <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](http://pythontutor.com/visualize.html#code=nested%20%3D%20%5B%5B%5D,%2010,%2020.0,%20%22Thirty%22,%20%5B40,%2050%5D%5D%0Anested_copy%20%3D%20nested%5B%3A%5D&cumulative=false&curInstr=0&heapPrimitives=nevernest&mode=display&origin=opt-frontend.js&py=3&rawInputLstJSON=%5B%5D&textReferences=false) reveals, only the *references* to the elements are copied, and not the objects in `nested` themselves! Because of that, `nested_copy` is a so-called **[shallow copy <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_wiki.png\">](https://en.wikipedia.org/wiki/Object_copying#Shallow_copy)** of `nested`.\n",
     "\n",
-    "We could also see this with the [id()](https://docs.python.org/3/library/functions.html#id) function: The respective first elements in both `nested` and `nested_copy` are the *same* object, namely `empty`.  So, we have three ways of accessing the *same* address in memory. Also, we say that `nested` and `nested_copy` partially share the *same* state."
+    "We could also see this with the [id() <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/functions.html#id) function: The respective first elements in both `nested` and `nested_copy` are the *same* object, namely `empty`.  So, we have three ways of accessing the *same* address in memory. Also, we say that `nested` and `nested_copy` partially share the *same* state."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 63,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 63,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "nested[0] is nested_copy[0]"
    ]
   },
   {
@@ -2017,7 +2004,7 @@
     {
      "data": {
       "text/plain": [
-       "True"
+       "140483817280848"
       ]
      },
      "execution_count": 64,
@@ -2026,7 +2013,7 @@
     }
    ],
    "source": [
-    "nested[0] is nested_copy[0]"
+    "id(nested[0])"
    ]
   },
   {
@@ -2041,7 +2028,7 @@
     {
      "data": {
       "text/plain": [
-       "139941463482272"
+       "140483817280848"
       ]
      },
      "execution_count": 65,
@@ -2049,30 +2036,6 @@
      "output_type": "execute_result"
     }
    ],
-   "source": [
-    "id(nested[0])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 66,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "139941463482272"
-      ]
-     },
-     "execution_count": 66,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
    "source": [
     "id(nested_copy[0])"
    ]
@@ -2089,14 +2052,14 @@
     "\n",
     "As both the original `nested` object and its copy reference the *same* `list` objects in memory, any changes made to them are visible to both! Because of that, working with shallow copies can easily become confusing.\n",
     "\n",
-    "Instead of a shallow copy, we could also create a so-called **[deep copy](https://en.wikipedia.org/wiki/Object_copying#Deep_copy)** of `nested`: Then, the copying process recursively follows every reference in a nested data structure and creates copies of *every* object found.\n",
+    "Instead of a shallow copy, we could also create a so-called **[deep copy <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_wiki.png\">](https://en.wikipedia.org/wiki/Object_copying#Deep_copy)** of `nested`: Then, the copying process recursively follows every reference in a nested data structure and creates copies of *every* object found.\n",
     "\n",
-    "To explicitly create shallow or deep copies, the [copy](https://docs.python.org/3/library/copy.html) module in the [standard library](https://docs.python.org/3/library/index.html) provides two functions, [copy()](https://docs.python.org/3/library/copy.html#copy.copy) and [deepcopy()](https://docs.python.org/3/library/copy.html#copy.deepcopy). We must always remember that slicing creates *shallow* copies only."
+    "To explicitly create shallow or deep copies, the [copy <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/copy.html) module in the [standard library <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/index.html) provides two functions, [copy() <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/copy.html#copy.copy) and [deepcopy() <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/copy.html#copy.deepcopy). We must always remember that slicing creates *shallow* copies only."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 67,
+   "execution_count": 66,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -2109,7 +2072,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 68,
+   "execution_count": 67,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -2122,7 +2085,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 69,
+   "execution_count": 68,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -2135,7 +2098,7 @@
        "True"
       ]
      },
-     "execution_count": 69,
+     "execution_count": 68,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2152,7 +2115,31 @@
     }
    },
    "source": [
-    "Now, the first elements of `nested` and `nested_deep_copy` are *different* objects, and [PythonTutor](http://pythontutor.com/visualize.html#code=import%20copy%0Anested%20%3D%20%5B%5B%5D,%2010,%2020.0,%20%22Thirty%22,%20%5B40,%2050%5D%5D%0Anested_deep_copy%20%3D%20copy.deepcopy%28nested%29&cumulative=false&curInstr=0&heapPrimitives=nevernest&mode=display&origin=opt-frontend.js&py=3&rawInputLstJSON=%5B%5D&textReferences=false) shows that there are *six* `list` objects in memory."
+    "Now, the first elements of `nested` and `nested_deep_copy` are *different* objects, and [PythonTutor <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](http://pythontutor.com/visualize.html#code=import%20copy%0Anested%20%3D%20%5B%5B%5D,%2010,%2020.0,%20%22Thirty%22,%20%5B40,%2050%5D%5D%0Anested_deep_copy%20%3D%20copy.deepcopy%28nested%29&cumulative=false&curInstr=0&heapPrimitives=nevernest&mode=display&origin=opt-frontend.js&py=3&rawInputLstJSON=%5B%5D&textReferences=false) shows that there are *six* `list` objects in memory."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 69,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "False"
+      ]
+     },
+     "execution_count": 69,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "nested[0] is nested_deep_copy[0]"
    ]
   },
   {
@@ -2167,7 +2154,7 @@
     {
      "data": {
       "text/plain": [
-       "False"
+       "140483817280848"
       ]
      },
      "execution_count": 70,
@@ -2176,7 +2163,7 @@
     }
    ],
    "source": [
-    "nested[0] is nested_deep_copy[0]"
+    "id(nested[0])"
    ]
   },
   {
@@ -2191,7 +2178,7 @@
     {
      "data": {
       "text/plain": [
-       "139941463482272"
+       "140483818059328"
       ]
      },
      "execution_count": 71,
@@ -2199,30 +2186,6 @@
      "output_type": "execute_result"
     }
    ],
-   "source": [
-    "id(nested[0])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 72,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "139941444435792"
-      ]
-     },
-     "execution_count": 72,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
    "source": [
     "id(nested_deep_copy[0])"
    ]
@@ -2235,7 +2198,7 @@
     }
    },
    "source": [
-    "As this [StackOverflow question](https://stackoverflow.com/questions/184710/what-is-the-difference-between-a-deep-copy-and-a-shallow-copy) shows, understanding shallow and deep copies is a common source of confusion independent of the programming language."
+    "As this [StackOverflow question <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_so.png\">](https://stackoverflow.com/questions/184710/what-is-the-difference-between-a-deep-copy-and-a-shallow-copy) shows, understanding shallow and deep copies is a common source of confusion independent of the programming language."
    ]
   },
   {
@@ -2262,7 +2225,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 73,
+   "execution_count": 72,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -2275,7 +2238,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 74,
+   "execution_count": 73,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -2288,7 +2251,7 @@
        "[0, 10, 20.0, 'Thirty', [40, 50]]"
       ]
      },
-     "execution_count": 74,
+     "execution_count": 73,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2310,7 +2273,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 75,
+   "execution_count": 74,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -2321,6 +2284,30 @@
     "nested[:4] = [100, 100, 100]"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 75,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[100, 100, 100, [40, 50]]"
+      ]
+     },
+     "execution_count": 75,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "nested"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 76,
@@ -2329,30 +2316,6 @@
      "slide_type": "fragment"
     }
    },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[100, 100, 100, [40, 50]]"
-      ]
-     },
-     "execution_count": 76,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "nested"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 77,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "fragment"
-    }
-   },
    "outputs": [
     {
      "data": {
@@ -2360,7 +2323,7 @@
        "4"
       ]
      },
-     "execution_count": 77,
+     "execution_count": 76,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2382,7 +2345,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 78,
+   "execution_count": 77,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -2392,10 +2355,10 @@
     {
      "data": {
       "text/plain": [
-       "139941444092304"
+       "140483818052016"
       ]
      },
-     "execution_count": 78,
+     "execution_count": 77,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2417,7 +2380,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 79,
+   "execution_count": 78,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -2430,7 +2393,7 @@
        "[[], 10, 20.0, 'Thirty', [40, 50]]"
       ]
      },
-     "execution_count": 79,
+     "execution_count": 78,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2452,7 +2415,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 80,
+   "execution_count": 79,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -2465,7 +2428,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 81,
+   "execution_count": 80,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -2478,7 +2441,7 @@
        "[[], 10, 20.0, 'Thirty', [1, 2, 3]]"
       ]
      },
-     "execution_count": 81,
+     "execution_count": 80,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2500,7 +2463,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 82,
+   "execution_count": 81,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -2513,7 +2476,7 @@
        "[100, 100, 100, [1, 2, 3]]"
       ]
      },
-     "execution_count": 82,
+     "execution_count": 81,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2530,14 +2493,14 @@
     }
    },
    "source": [
-    "That is precisely the confusion we talked about above when we said that `nested_copy` is a *shallow* copy of `nested`. [PythonTutor](http://pythontutor.com/visualize.html#code=nested%20%3D%20%5B%5B%5D,%2010,%2020.0,%20%22Thirty%22,%20%5B40,%2050%5D%5D%0Anested_copy%20%3D%20nested%5B%3A%5D%0Anested%5B%3A4%5D%20%3D%20%5B100,%20100,%20100%5D%0Anested_copy%5B-1%5D%5B%3A%5D%20%3D%20%5B1,%202,%203%5D&cumulative=false&curInstr=0&heapPrimitives=nevernest&mode=display&origin=opt-frontend.js&py=3&rawInputLstJSON=%5B%5D&textReferences=false) shows how both reference the *same* nested `list` object that is changed *in place* from `[40, 50]` into `[1, 2, 3]`.\n",
+    "That is precisely the confusion we talked about above when we said that `nested_copy` is a *shallow* copy of `nested`. [PythonTutor <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](http://pythontutor.com/visualize.html#code=nested%20%3D%20%5B%5B%5D,%2010,%2020.0,%20%22Thirty%22,%20%5B40,%2050%5D%5D%0Anested_copy%20%3D%20nested%5B%3A%5D%0Anested%5B%3A4%5D%20%3D%20%5B100,%20100,%20100%5D%0Anested_copy%5B-1%5D%5B%3A%5D%20%3D%20%5B1,%202,%203%5D&cumulative=false&curInstr=0&heapPrimitives=nevernest&mode=display&origin=opt-frontend.js&py=3&rawInputLstJSON=%5B%5D&textReferences=false) shows how both reference the *same* nested `list` object that is changed *in place* from `[40, 50]` into `[1, 2, 3]`.\n",
     "\n",
     "Lastly, we use the `del` statement to remove an element."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 83,
+   "execution_count": 82,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -2550,7 +2513,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 84,
+   "execution_count": 83,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -2563,7 +2526,7 @@
        "[100, 100, 100]"
       ]
      },
-     "execution_count": 84,
+     "execution_count": 83,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2585,7 +2548,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 85,
+   "execution_count": 84,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -2598,7 +2561,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 86,
+   "execution_count": 85,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -2611,7 +2574,7 @@
        "[]"
       ]
      },
-     "execution_count": 86,
+     "execution_count": 85,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2628,7 +2591,7 @@
     }
    },
    "source": [
-    "Mutability for sequences is formalized by the `MutableSequence` ABC in the [collections.abc](https://docs.python.org/3/library/collections.abc.html) module."
+    "Mutability for sequences is formalized by the `MutableSequence` ABC in the [collections.abc <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/collections.abc.html) module."
    ]
   },
   {
@@ -2650,16 +2613,16 @@
     }
    },
    "source": [
-    "The `list` type is an essential data structure in any real-world Python application, and many typical `list`-related algorithms from computer science theory are already built into it at the C level (cf., the [documentation](https://docs.python.org/3/library/stdtypes.html#mutable-sequence-types) or the [tutorial](https://docs.python.org/3/tutorial/datastructures.html#more-on-lists) for a full overview; unfortunately, not all methods have direct links). So, understanding and applying the built-in methods of the `list` type not only speeds up the development process but also makes programs significantly faster.\n",
+    "The `list` type is an essential data structure in any real-world Python application, and many typical `list`-related algorithms from computer science theory are already built into it at the C level (cf., the [documentation <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/stdtypes.html#mutable-sequence-types) or the [tutorial <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/tutorial/datastructures.html#more-on-lists) for a full overview; unfortunately, not all methods have direct links). So, understanding and applying the built-in methods of the `list` type not only speeds up the development process but also makes programs significantly faster.\n",
     "\n",
-    "In contrast to the `str` type's methods in [Chapter 6](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/06_text_00_lecture.ipynb#String-Methods) (e.g., [upper()](https://docs.python.org/3/library/stdtypes.html#str.upper) or [lower()](https://docs.python.org/3/library/stdtypes.html#str.lower)), the `list` type's methods that mutate an object do so *in place*. That means they *never* create *new* `list` objects and return `None` to indicate that. So, we must *never* assign the return value of `list` methods to the variable holding the list!\n",
+    "In contrast to the `str` type's methods in [Chapter 6 <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/06_text_00_content.ipynb#String-Methods) (e.g., [upper() <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/stdtypes.html#str.upper) or [lower() <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/stdtypes.html#str.lower)), the `list` type's methods that mutate an object do so *in place*. That means they *never* create *new* `list` objects and return `None` to indicate that. So, we must *never* assign the return value of `list` methods to the variable holding the list!\n",
     "\n",
     "Let's look at the following `names` example."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 87,
+   "execution_count": 86,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -2683,7 +2646,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 88,
+   "execution_count": 87,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -2696,7 +2659,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 89,
+   "execution_count": 88,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -2709,7 +2672,7 @@
        "['Carl', 'Peter', 'Eckardt']"
       ]
      },
-     "execution_count": 89,
+     "execution_count": 88,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2731,7 +2694,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 90,
+   "execution_count": 89,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -2744,7 +2707,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 91,
+   "execution_count": 90,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -2757,7 +2720,7 @@
        "['Carl', 'Peter', 'Eckardt', 'Karl', 'Oliver']"
       ]
      },
-     "execution_count": 91,
+     "execution_count": 90,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2779,7 +2742,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 92,
+   "execution_count": 91,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -2792,7 +2755,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 93,
+   "execution_count": 92,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -2805,7 +2768,7 @@
        "['Carl', 'Berthold', 'Peter', 'Eckardt', 'Karl', 'Oliver']"
       ]
      },
-     "execution_count": 93,
+     "execution_count": 92,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2822,12 +2785,12 @@
     }
    },
    "source": [
-    "`list` objects may be sorted *in place* with the [sort()](https://docs.python.org/3/library/stdtypes.html#list.sort) method. That is different from the built-in [sorted()](https://docs.python.org/3/library/functions.html#sorted) function that takes any *finite* and *iterable* object and returns a *new* `list` object with the iterable's elements sorted!"
+    "`list` objects may be sorted *in place* with the [sort() <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/stdtypes.html#list.sort) method. That is different from the built-in [sorted() <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/functions.html#sorted) function that takes any *finite* and *iterable* object and returns a *new* `list` object with the iterable's elements sorted!"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 94,
+   "execution_count": 93,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -2840,7 +2803,7 @@
        "['Berthold', 'Carl', 'Eckardt', 'Karl', 'Oliver', 'Peter']"
       ]
      },
-     "execution_count": 94,
+     "execution_count": 93,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2862,7 +2825,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 95,
+   "execution_count": 94,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -2875,7 +2838,7 @@
        "['Carl', 'Berthold', 'Peter', 'Eckardt', 'Karl', 'Oliver']"
       ]
      },
-     "execution_count": 95,
+     "execution_count": 94,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2897,7 +2860,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 96,
+   "execution_count": 95,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -2910,7 +2873,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 97,
+   "execution_count": 96,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -2923,7 +2886,7 @@
        "['Berthold', 'Carl', 'Eckardt', 'Karl', 'Oliver', 'Peter']"
       ]
      },
-     "execution_count": 97,
+     "execution_count": 96,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2940,12 +2903,12 @@
     }
    },
    "source": [
-    "To sort in reverse order, we pass a keyword-only `reverse=True` argument to either the [sort()](https://docs.python.org/3/library/stdtypes.html#list.sort) method or the [sorted()](https://docs.python.org/3/library/functions.html#sorted) function."
+    "To sort in reverse order, we pass a keyword-only `reverse=True` argument to either the [sort() <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/stdtypes.html#list.sort) method or the [sorted() <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/functions.html#sorted) function."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 98,
+   "execution_count": 97,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -2958,7 +2921,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 99,
+   "execution_count": 98,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -2971,7 +2934,7 @@
        "['Peter', 'Oliver', 'Karl', 'Eckardt', 'Carl', 'Berthold']"
       ]
      },
-     "execution_count": 99,
+     "execution_count": 98,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2988,18 +2951,18 @@
     }
    },
    "source": [
-    "The [sort()](https://docs.python.org/3/library/stdtypes.html#list.sort) method and the [sorted()](https://docs.python.org/3/library/functions.html#sorted) function sort the elements in `names` in alphabetical order, forward or backward. However, that does *not* hold in general.\n",
+    "The [sort() <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/stdtypes.html#list.sort) method and the [sorted() <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/functions.html#sorted) function sort the elements in `names` in alphabetical order, forward or backward. However, that does *not* hold in general.\n",
     "\n",
-    "We mention above that `list` objects may contain objects of *any* type and even of *mixed* types. Because of that, the sorting is **[delegated](https://en.wikipedia.org/wiki/Delegation_(object-oriented_programming))** to the elements in a `list` object. In a way, Python \"asks\" the elements in a `list` object to sort themselves. As `names` contains only `str` objects, they are sorted according the the comparison rules explained in [Chapter 6](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/06_text_00_lecture.ipynb#String-Comparison).\n",
+    "We mention above that `list` objects may contain objects of *any* type and even of *mixed* types. Because of that, the sorting is **[delegated <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_wiki.png\">](https://en.wikipedia.org/wiki/Delegation_(object-oriented_programming))** to the elements in a `list` object. In a way, Python \"asks\" the elements in a `list` object to sort themselves. As `names` contains only `str` objects, they are sorted according the the comparison rules explained in [Chapter 6 <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/06_text_00_content.ipynb#String-Comparison).\n",
     "\n",
-    "To customize the sorting, we pass a keyword-only `key` argument to [sort()](https://docs.python.org/3/library/stdtypes.html#list.sort) or [sorted()](https://docs.python.org/3/library/functions.html#sorted), which must be a `function` object accepting *one* positional argument. Then, the elements in the `list` object are passed to that one by one, and the return values are used as the **sort keys**. The `key` argument is also a popular use case for `lambda` expressions.\n",
+    "To customize the sorting, we pass a keyword-only `key` argument to [sort() <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/stdtypes.html#list.sort) or [sorted() <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/functions.html#sorted), which must be a `function` object accepting *one* positional argument. Then, the elements in the `list` object are passed to that one by one, and the return values are used as the **sort keys**. The `key` argument is also a popular use case for `lambda` expressions.\n",
     "\n",
-    "For example, to sort `names` not by alphabet but by the names' lengths, we pass in a reference to the built-in [len()](https://docs.python.org/3/library/functions.html#len) function as `key=len`. Note that there are *no* parentheses after `len`!"
+    "For example, to sort `names` not by alphabet but by the names' lengths, we pass in a reference to the built-in [len() <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/functions.html#len) function as `key=len`. Note that there are *no* parentheses after `len`!"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 100,
+   "execution_count": 99,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -3018,14 +2981,14 @@
     }
    },
    "source": [
-    "If two names have the same length, their relative order is kept as is. That is why `\"Karl\"` comes before `\"Carl\" ` below. A [sorting algorithm](https://en.wikipedia.org/wiki/Sorting_algorithm) with that property is called **[stable](https://en.wikipedia.org/wiki/Sorting_algorithm#Stability)**.\n",
+    "If two names have the same length, their relative order is kept as is. That is why `\"Karl\"` comes before `\"Carl\" ` below. A [sorting algorithm <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_wiki.png\">](https://en.wikipedia.org/wiki/Sorting_algorithm) with that property is called **[stable <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_wiki.png\">](https://en.wikipedia.org/wiki/Sorting_algorithm#Stability)**.\n",
     "\n",
-    "Sorting is an important topic in programming, and we refer to the official [HOWTO](https://docs.python.org/3/howto/sorting.html) for a more comprehensive introduction."
+    "Sorting is an important topic in programming, and we refer to the official [HOWTO <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/howto/sorting.html) for a more comprehensive introduction."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 101,
+   "execution_count": 100,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -3038,7 +3001,7 @@
        "['Karl', 'Carl', 'Peter', 'Oliver', 'Eckardt', 'Berthold']"
       ]
      },
-     "execution_count": 101,
+     "execution_count": 100,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3060,7 +3023,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 102,
+   "execution_count": 101,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -3073,7 +3036,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 103,
+   "execution_count": 102,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -3086,7 +3049,7 @@
        "['Berthold', 'Eckardt', 'Oliver', 'Peter', 'Carl', 'Karl']"
       ]
      },
-     "execution_count": 103,
+     "execution_count": 102,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3108,7 +3071,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 104,
+   "execution_count": 103,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -3119,6 +3082,30 @@
     "removed = names.pop()"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 104,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Karl'"
+      ]
+     },
+     "execution_count": 104,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "removed"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 105,
@@ -3127,30 +3114,6 @@
      "slide_type": "fragment"
     }
    },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'Karl'"
-      ]
-     },
-     "execution_count": 105,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "removed"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 106,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "fragment"
-    }
-   },
    "outputs": [
     {
      "data": {
@@ -3158,7 +3121,7 @@
        "['Berthold', 'Eckardt', 'Oliver', 'Peter', 'Carl']"
       ]
      },
-     "execution_count": 106,
+     "execution_count": 105,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3182,7 +3145,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 107,
+   "execution_count": 106,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -3193,6 +3156,30 @@
     "removed = names.pop(1)"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 107,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Eckardt'"
+      ]
+     },
+     "execution_count": 107,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "removed"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 108,
@@ -3201,30 +3188,6 @@
      "slide_type": "fragment"
     }
    },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'Eckardt'"
-      ]
-     },
-     "execution_count": 108,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "removed"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 109,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "fragment"
-    }
-   },
    "outputs": [
     {
      "data": {
@@ -3232,7 +3195,7 @@
        "['Berthold', 'Oliver', 'Peter', 'Carl']"
       ]
      },
-     "execution_count": 109,
+     "execution_count": 108,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3254,7 +3217,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 110,
+   "execution_count": 109,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -3267,7 +3230,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 111,
+   "execution_count": 110,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -3280,7 +3243,7 @@
        "['Berthold', 'Oliver', 'Carl']"
       ]
      },
-     "execution_count": 111,
+     "execution_count": 110,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3302,7 +3265,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 112,
+   "execution_count": 111,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -3316,7 +3279,7 @@
      "traceback": [
       "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
       "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-112-6bb75d50cf06>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mnames\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mremove\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Peter\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;32m<ipython-input-111-6bb75d50cf06>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mnames\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mremove\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Peter\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
       "\u001b[0;31mValueError\u001b[0m: list.remove(x): x not in list"
      ]
     }
@@ -3338,7 +3301,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 113,
+   "execution_count": 112,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -3351,7 +3314,7 @@
        "['Berthold', 'Oliver', 'Carl']"
       ]
      },
-     "execution_count": 113,
+     "execution_count": 112,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3362,7 +3325,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 114,
+   "execution_count": 113,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -3375,7 +3338,7 @@
        "1"
       ]
      },
-     "execution_count": 114,
+     "execution_count": 113,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3386,7 +3349,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 115,
+   "execution_count": 114,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -3400,7 +3363,7 @@
      "traceback": [
       "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
       "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-115-8c741a6680be>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mnames\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Karl\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;32m<ipython-input-114-8c741a6680be>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mnames\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Karl\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
       "\u001b[0;31mValueError\u001b[0m: 'Karl' is not in list"
      ]
     }
@@ -3422,7 +3385,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 116,
+   "execution_count": 115,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -3435,7 +3398,7 @@
        "1"
       ]
      },
-     "execution_count": 116,
+     "execution_count": 115,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3446,7 +3409,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 117,
+   "execution_count": 116,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -3459,7 +3422,7 @@
        "0"
       ]
      },
-     "execution_count": 117,
+     "execution_count": 116,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3483,7 +3446,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 118,
+   "execution_count": 117,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -3496,7 +3459,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 119,
+   "execution_count": 118,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -3509,7 +3472,7 @@
        "['Berthold', 'Oliver', 'Carl']"
       ]
      },
-     "execution_count": 119,
+     "execution_count": 118,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3531,7 +3494,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 120,
+   "execution_count": 119,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -3544,7 +3507,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 121,
+   "execution_count": 120,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -3557,7 +3520,7 @@
        "[]"
       ]
      },
-     "execution_count": 121,
+     "execution_count": 120,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3574,7 +3537,7 @@
     }
    },
    "source": [
-    "Many methods introduced in this section are mentioned in the [collections.abc](https://docs.python.org/3/library/collections.abc.html) module's documentation as well: While the `index()` and `count()` methods come with any data type that is a `Sequence`, the `append()`, `extend()`, `insert()`, `reverse()`, `pop()`, and `remove()` methods are part of any `MutableSequence` type. The `sort()`, `copy()`, and `clear()` methods are `list`-specific.\n",
+    "Many methods introduced in this section are mentioned in the [collections.abc <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/collections.abc.html) module's documentation as well: While the `index()` and `count()` methods come with any data type that is a `Sequence`, the `append()`, `extend()`, `insert()`, `reverse()`, `pop()`, and `remove()` methods are part of any `MutableSequence` type. The `sort()`, `copy()`, and `clear()` methods are `list`-specific.\n",
     "\n",
     "So, being a sequence does not only imply the four *behaviors* specified above, but also means that a data type comes with certain standardized methods."
    ]
@@ -3603,7 +3566,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 122,
+   "execution_count": 121,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -3616,7 +3579,7 @@
        "['Berthold', 'Oliver', 'Carl']"
       ]
      },
-     "execution_count": 122,
+     "execution_count": 121,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3625,6 +3588,30 @@
     "names"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 122,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['Berthold', 'Oliver', 'Carl', 'Diedrich', 'Yves']"
+      ]
+     },
+     "execution_count": 122,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "names + [\"Diedrich\", \"Yves\"]"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 123,
@@ -3633,30 +3620,6 @@
      "slide_type": "fragment"
     }
    },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "['Berthold', 'Oliver', 'Carl', 'Diedrich', 'Yves']"
-      ]
-     },
-     "execution_count": 123,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "names + [\"Diedrich\", \"Yves\"]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 124,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "fragment"
-    }
-   },
    "outputs": [
     {
      "data": {
@@ -3664,7 +3627,7 @@
        "['Berthold', 'Oliver', 'Carl', 'Berthold', 'Oliver', 'Carl']"
       ]
      },
-     "execution_count": 124,
+     "execution_count": 123,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3681,14 +3644,14 @@
     }
    },
    "source": [
-    "Besides being an operator, the `*` symbol has a second syntactical use, as explained in [PEP 3132](https://www.python.org/dev/peps/pep-3132/) and [PEP 448](https://www.python.org/dev/peps/pep-0448/): It implements what is called **iterable unpacking**. It is *not* an operator syntactically but a notation that Python reads as a literal.\n",
+    "Besides being an operator, the `*` symbol has a second syntactical use, as explained in [PEP 3132 <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://www.python.org/dev/peps/pep-3132/) and [PEP 448 <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://www.python.org/dev/peps/pep-0448/): It implements what is called **iterable unpacking**. It is *not* an operator syntactically but a notation that Python reads as a literal.\n",
     "\n",
     "In the example, Python interprets the expression as if the elements of the iterable `names` were placed between `\"Achim\"` and `\"Xavier\"` one by one. So, we do not obtain a nested but a *flat* list."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 125,
+   "execution_count": 124,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -3701,7 +3664,7 @@
        "['Achim', 'Berthold', 'Oliver', 'Carl', 'Xavier']"
       ]
      },
-     "execution_count": 125,
+     "execution_count": 124,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3723,7 +3686,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 126,
+   "execution_count": 125,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -3736,7 +3699,7 @@
        "['Achim', 'Berthold', 'Oliver', 'Carl', 'Xavier']"
       ]
      },
-     "execution_count": 126,
+     "execution_count": 125,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3766,12 +3729,12 @@
    "source": [
     "The relational operators also work with `list` objects; yet another example of operator overloading.\n",
     "\n",
-    "Comparison is made in a pairwise fashion until the first pair of elements does not evaluate equal or one of the `list` objects ends. The exact comparison rules depend on the elements and not the `list` objects. As with [sort()](https://docs.python.org/3/library/stdtypes.html#list.sort) or [sorted()](https://docs.python.org/3/library/functions.html#sorted) above, comparison is *delegated* to the objects to be compared, and Python \"asks\" the elements in the two `list` objects to compare themselves. Usually, all elements are of the *same* type, and comparison is straightforward."
+    "Comparison is made in a pairwise fashion until the first pair of elements does not evaluate equal or one of the `list` objects ends. The exact comparison rules depend on the elements and not the `list` objects. As with [sort() <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/stdtypes.html#list.sort) or [sorted() <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/functions.html#sorted) above, comparison is *delegated* to the objects to be compared, and Python \"asks\" the elements in the two `list` objects to compare themselves. Usually, all elements are of the *same* type, and comparison is straightforward."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 127,
+   "execution_count": 126,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -3784,7 +3747,7 @@
        "['Berthold', 'Oliver', 'Carl']"
       ]
      },
-     "execution_count": 127,
+     "execution_count": 126,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -3793,6 +3756,30 @@
     "names"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 127,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 127,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "names == [\"Berthold\", \"Oliver\", \"Carl\"]"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 128,
@@ -3814,7 +3801,7 @@
     }
    ],
    "source": [
-    "names == [\"Berthold\", \"Oliver\", \"Carl\"]"
+    "names != [\"Berthold\", \"Oliver\", \"Karl\"]"
    ]
   },
   {
@@ -3838,7 +3825,18 @@
     }
    ],
    "source": [
-    "names != [\"Berthold\", \"Oliver\", \"Karl\"]"
+    "names < [\"Berthold\", \"Oliver\", \"Karl\"]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "If two `list` objects have a different number of elements and all overlapping elements compare equal, the shorter `list` object is considered \"smaller.\""
    ]
   },
   {
@@ -3861,41 +3859,6 @@
      "output_type": "execute_result"
     }
    ],
-   "source": [
-    "names < [\"Berthold\", \"Oliver\", \"Karl\"]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "source": [
-    "If two `list` objects have a different number of elements and all overlapping elements compare equal, the shorter `list` object is considered \"smaller.\""
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 131,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "fragment"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "True"
-      ]
-     },
-     "execution_count": 131,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
    "source": [
     "[\"Berthold\", \"Oliver\"] < names < [\"Berthold\", \"Oliver\", \"Carl\", \"Xavier\"]"
    ]
@@ -3926,7 +3889,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 132,
+   "execution_count": 131,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -3939,7 +3902,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 133,
+   "execution_count": 132,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -3963,7 +3926,20 @@
    "source": [
     "While this function is being executed, two variables, namely `letters` in the global scope and `arg` inside the function's local scope, reference the *same* `list` object in memory. Furthermore, the passed in `arg` is also the return value.\n",
     "\n",
-    "So, after the function call, `letters_with_xyz` and `letters` are **aliases** as well, referencing the *same* object. We can also visualize that with [PythonTutor](http://www.pythontutor.com/visualize.html#code=letters%20%3D%20%5B%22a%22,%20%22b%22,%20%22c%22%5D%0A%0Adef%20add_xyz%28arg%29%3A%0A%20%20%20%20arg.extend%28%5B%22x%22,%20%22y%22,%20%22z%22%5D%29%0A%20%20%20%20return%20arg%0A%0Aletters_with_xyz%20%3D%20add_xyz%28letters%29&cumulative=false&curInstr=0&heapPrimitives=nevernest&mode=display&origin=opt-frontend.js&py=3&rawInputLstJSON=%5B%5D&textReferences=false)."
+    "So, after the function call, `letters_with_xyz` and `letters` are **aliases** as well, referencing the *same* object. We can also visualize that with [PythonTutor <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](http://pythontutor.com/visualize.html#code=letters%20%3D%20%5B%22a%22,%20%22b%22,%20%22c%22%5D%0A%0Adef%20add_xyz%28arg%29%3A%0A%20%20%20%20arg.extend%28%5B%22x%22,%20%22y%22,%20%22z%22%5D%29%0A%20%20%20%20return%20arg%0A%0Aletters_with_xyz%20%3D%20add_xyz%28letters%29&cumulative=false&curInstr=0&heapPrimitives=nevernest&mode=display&origin=opt-frontend.js&py=3&rawInputLstJSON=%5B%5D&textReferences=false)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 133,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "letters_with_xyz = add_xyz(letters)"
    ]
   },
   {
@@ -3974,9 +3950,20 @@
      "slide_type": "fragment"
     }
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['a', 'b', 'c', 'x', 'y', 'z']"
+      ]
+     },
+     "execution_count": 134,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "letters_with_xyz = add_xyz(letters)"
+    "letters_with_xyz"
    ]
   },
   {
@@ -3999,30 +3986,6 @@
      "output_type": "execute_result"
     }
    ],
-   "source": [
-    "letters_with_xyz"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 136,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "fragment"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "['a', 'b', 'c', 'x', 'y', 'z']"
-      ]
-     },
-     "execution_count": 136,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
    "source": [
     "letters"
    ]
@@ -4037,14 +4000,14 @@
    "source": [
     "A better practice is to first create a copy of `arg` within the function that is then modified and returned. If we are sure that `arg` contains immutable elements only, we get away with a shallow copy. The downside of this approach is the higher amount of memory necessary.\n",
     "\n",
-    "The revised `add_xyz()` function below is more natural to reason about as it does *not* modify the passed in `arg` internally. [PythonTutor](http://www.pythontutor.com/visualize.html#code=letters%20%3D%20%5B%22a%22,%20%22b%22,%20%22c%22%5D%0A%0Adef%20add_xyz%28arg%29%3A%0A%20%20%20%20new_arg%20%3D%20arg%5B%3A%5D%0A%20%20%20%20new_arg.extend%28%5B%22x%22,%20%22y%22,%20%22z%22%5D%29%0A%20%20%20%20return%20new_arg%0A%0Aletters_with_xyz%20%3D%20add_xyz%28letters%29&cumulative=false&curInstr=0&heapPrimitives=nevernest&mode=display&origin=opt-frontend.js&py=3&rawInputLstJSON=%5B%5D&textReferences=false) shows that as well. This approach is following the **[functional programming](https://en.wikipedia.org/wiki/Functional_programming)** paradigm that is going through a \"renaissance\" currently. Two essential characteristics of functional programming are that a function *never* changes its inputs and *always* returns the same output given the same inputs.\n",
+    "The revised `add_xyz()` function below is more natural to reason about as it does *not* modify the passed in `arg` internally. [PythonTutor <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](http://pythontutor.com/visualize.html#code=letters%20%3D%20%5B%22a%22,%20%22b%22,%20%22c%22%5D%0A%0Adef%20add_xyz%28arg%29%3A%0A%20%20%20%20new_arg%20%3D%20arg%5B%3A%5D%0A%20%20%20%20new_arg.extend%28%5B%22x%22,%20%22y%22,%20%22z%22%5D%29%0A%20%20%20%20return%20new_arg%0A%0Aletters_with_xyz%20%3D%20add_xyz%28letters%29&cumulative=false&curInstr=0&heapPrimitives=nevernest&mode=display&origin=opt-frontend.js&py=3&rawInputLstJSON=%5B%5D&textReferences=false) shows that as well. This approach is following the **[functional programming <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_wiki.png\">](https://en.wikipedia.org/wiki/Functional_programming)** paradigm that is going through a \"renaissance\" currently. Two essential characteristics of functional programming are that a function *never* changes its inputs and *always* returns the same output given the same inputs.\n",
     "\n",
     "For a beginner, it is probably better to stick to this idea and not change any arguments as the original `add_xyz()` above. However, functions that modify and return the argument passed in are an important aspect of object-oriented programming, as explained in Chapter 10."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 137,
+   "execution_count": 136,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -4057,7 +4020,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 138,
+   "execution_count": 137,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -4074,7 +4037,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 139,
+   "execution_count": 138,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -4087,7 +4050,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 140,
+   "execution_count": 139,
    "metadata": {
     "scrolled": true,
     "slideshow": {
@@ -4101,7 +4064,7 @@
        "['a', 'b', 'c', 'x', 'y', 'z']"
       ]
      },
-     "execution_count": 140,
+     "execution_count": 139,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -4112,7 +4075,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 141,
+   "execution_count": 140,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -4125,7 +4088,7 @@
        "['a', 'b', 'c']"
       ]
      },
-     "execution_count": 141,
+     "execution_count": 140,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -4142,12 +4105,12 @@
     }
    },
    "source": [
-    "If we want to modify the argument passed in, it is best to return `None` and not `arg`, as does the final version of `add_xyz()` below. Then, the user of our function cannot accidentally create two aliases to the same object. That is also why the list methods above all return `None`. [PythonTutor](http://www.pythontutor.com/visualize.html#code=letters%20%3D%20%5B%22a%22,%20%22b%22,%20%22c%22%5D%0A%0Adef%20add_xyz%28arg%29%3A%0A%20%20%20%20arg.extend%28%5B%22x%22,%20%22y%22,%20%22z%22%5D%29%0A%20%20%20%20return%0A%0Aadd_xyz%28letters%29&cumulative=false&curInstr=0&heapPrimitives=nevernest&mode=display&origin=opt-frontend.js&py=3&rawInputLstJSON=%5B%5D&textReferences=false) shows how there is only *one* reference to `letters` after the function call."
+    "If we want to modify the argument passed in, it is best to return `None` and not `arg`, as does the final version of `add_xyz()` below. Then, the user of our function cannot accidentally create two aliases to the same object. That is also why the list methods above all return `None`. [PythonTutor <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](http://pythontutor.com/visualize.html#code=letters%20%3D%20%5B%22a%22,%20%22b%22,%20%22c%22%5D%0A%0Adef%20add_xyz%28arg%29%3A%0A%20%20%20%20arg.extend%28%5B%22x%22,%20%22y%22,%20%22z%22%5D%29%0A%20%20%20%20return%0A%0Aadd_xyz%28letters%29&cumulative=false&curInstr=0&heapPrimitives=nevernest&mode=display&origin=opt-frontend.js&py=3&rawInputLstJSON=%5B%5D&textReferences=false) shows how there is only *one* reference to `letters` after the function call."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 142,
+   "execution_count": 141,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -4160,7 +4123,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 143,
+   "execution_count": 142,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -4176,7 +4139,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 144,
+   "execution_count": 143,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -4189,7 +4152,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 145,
+   "execution_count": 144,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -4202,7 +4165,7 @@
        "['a', 'b', 'c', 'x', 'y', 'z']"
       ]
      },
-     "execution_count": 145,
+     "execution_count": 144,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -4224,7 +4187,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 146,
+   "execution_count": 145,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -4237,7 +4200,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 147,
+   "execution_count": 146,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -4250,7 +4213,7 @@
        "['a', 'b', 'c', 'x', 'y', 'z', 'x', 'y', 'z']"
       ]
      },
-     "execution_count": 147,
+     "execution_count": 146,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -4294,7 +4257,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 148,
+   "execution_count": 147,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -4307,7 +4270,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 149,
+   "execution_count": 148,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -4320,7 +4283,7 @@
        "(7, 11, 8, 5, 3, 12, 2, 6, 9, 10, 1, 4)"
       ]
      },
-     "execution_count": 149,
+     "execution_count": 148,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -4342,7 +4305,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 150,
+   "execution_count": 149,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -4355,7 +4318,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 151,
+   "execution_count": 150,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -4368,7 +4331,7 @@
        "(7, 11, 8, 5, 3, 12, 2, 6, 9, 10, 1, 4)"
       ]
      },
-     "execution_count": 151,
+     "execution_count": 150,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -4390,7 +4353,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 152,
+   "execution_count": 151,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -4400,10 +4363,10 @@
     {
      "data": {
       "text/plain": [
-       "139941463332656"
+       "140483698604784"
       ]
      },
-     "execution_count": 152,
+     "execution_count": 151,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -4414,7 +4377,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 153,
+   "execution_count": 152,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -4427,7 +4390,7 @@
        "tuple"
       ]
      },
-     "execution_count": 153,
+     "execution_count": 152,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -4449,7 +4412,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 154,
+   "execution_count": 153,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -4460,6 +4423,30 @@
     "empty_tuple = ()"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 154,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "()"
+      ]
+     },
+     "execution_count": 154,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "empty_tuple"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 155,
@@ -4468,30 +4455,6 @@
      "slide_type": "skip"
     }
    },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "()"
-      ]
-     },
-     "execution_count": 155,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "empty_tuple"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 156,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
    "outputs": [
     {
      "data": {
@@ -4499,7 +4462,7 @@
        "tuple"
       ]
      },
-     "execution_count": 156,
+     "execution_count": 155,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -4521,7 +4484,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 157,
+   "execution_count": 156,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -4532,6 +4495,30 @@
     "one_tuple = (1,)  # we could ommit the parentheses but not the comma"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 157,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(1,)"
+      ]
+     },
+     "execution_count": 157,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "one_tuple"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 158,
@@ -4540,30 +4527,6 @@
      "slide_type": "skip"
     }
    },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(1,)"
-      ]
-     },
-     "execution_count": 158,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "one_tuple"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 159,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
    "outputs": [
     {
      "data": {
@@ -4571,7 +4534,7 @@
        "tuple"
       ]
      },
-     "execution_count": 159,
+     "execution_count": 158,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -4582,7 +4545,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 160,
+   "execution_count": 159,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -4593,6 +4556,30 @@
     "no_tuple = (1)"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 160,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "1"
+      ]
+     },
+     "execution_count": 160,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "no_tuple"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 161,
@@ -4601,30 +4588,6 @@
      "slide_type": "skip"
     }
    },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "1"
-      ]
-     },
-     "execution_count": 161,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "no_tuple"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 162,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
    "outputs": [
     {
      "data": {
@@ -4632,7 +4595,7 @@
        "int"
       ]
      },
-     "execution_count": 162,
+     "execution_count": 161,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -4649,12 +4612,12 @@
     }
    },
    "source": [
-    "Alternatively, we may use the [tuple()](https://docs.python.org/3/library/functions.html#func-tuple) built-in that takes any iterable as its argument and creates a new `tuple` from its elements."
+    "Alternatively, we may use the [tuple() <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/functions.html#func-tuple) built-in that takes any iterable as its argument and creates a new `tuple` from its elements."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 163,
+   "execution_count": 162,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -4667,7 +4630,7 @@
        "(1,)"
       ]
      },
-     "execution_count": 163,
+     "execution_count": 162,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -4678,7 +4641,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 164,
+   "execution_count": 163,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -4691,7 +4654,7 @@
        "('i', 't', 'e', 'r', 'a', 'b', 'l', 'e')"
       ]
      },
-     "execution_count": 164,
+     "execution_count": 163,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -4726,7 +4689,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 165,
+   "execution_count": 164,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -4739,7 +4702,7 @@
        "12"
       ]
      },
-     "execution_count": 165,
+     "execution_count": 164,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -4761,7 +4724,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 166,
+   "execution_count": 165,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -4783,7 +4746,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 167,
+   "execution_count": 166,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -4816,7 +4779,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 168,
+   "execution_count": 167,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -4829,7 +4792,7 @@
        "False"
       ]
      },
-     "execution_count": 168,
+     "execution_count": 167,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -4838,6 +4801,30 @@
     "0 in numbers"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 168,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 168,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "1 in numbers"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 169,
@@ -4858,30 +4845,6 @@
      "output_type": "execute_result"
     }
    ],
-   "source": [
-    "1 in numbers"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 170,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "True"
-      ]
-     },
-     "execution_count": 170,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
    "source": [
     "1.0 in numbers  # in relies on == behind the scenes"
    ]
@@ -4899,7 +4862,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 171,
+   "execution_count": 170,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -4912,7 +4875,7 @@
        "7"
       ]
      },
-     "execution_count": 171,
+     "execution_count": 170,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -4923,7 +4886,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 172,
+   "execution_count": 171,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -4936,7 +4899,7 @@
        "4"
       ]
      },
-     "execution_count": 172,
+     "execution_count": 171,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -4947,7 +4910,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 173,
+   "execution_count": 172,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -4960,7 +4923,7 @@
        "(2, 6, 9, 10, 1, 4)"
       ]
      },
-     "execution_count": 173,
+     "execution_count": 172,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -4982,7 +4945,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 174,
+   "execution_count": 173,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -4996,7 +4959,7 @@
      "traceback": [
       "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
       "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-174-80fddf1304f0>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mnumbers\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m99\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;32m<ipython-input-173-80fddf1304f0>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mnumbers\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m99\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
       "\u001b[0;31mTypeError\u001b[0m: 'tuple' object does not support item assignment"
      ]
     }
@@ -5018,7 +4981,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 175,
+   "execution_count": 174,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -5031,7 +4994,7 @@
        "(7, 11, 8, 5, 3, 12, 2, 6, 9, 10, 1, 4, 99)"
       ]
      },
-     "execution_count": 175,
+     "execution_count": 174,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -5042,7 +5005,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 176,
+   "execution_count": 175,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -5055,7 +5018,7 @@
        "(7, 11, 8, 5, 3, 12, 2, 6, 9, 10, 1, 4, 7, 11, 8, 5, 3, 12, 2, 6, 9, 10, 1, 4)"
       ]
      },
-     "execution_count": 176,
+     "execution_count": 175,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -5077,7 +5040,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 177,
+   "execution_count": 176,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -5090,7 +5053,7 @@
        "0"
       ]
      },
-     "execution_count": 177,
+     "execution_count": 176,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -5101,7 +5064,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 178,
+   "execution_count": 177,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -5114,7 +5077,7 @@
        "10"
       ]
      },
-     "execution_count": 178,
+     "execution_count": 177,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -5136,7 +5099,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 179,
+   "execution_count": 178,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -5149,7 +5112,7 @@
        "(7, 11, 8, 5, 3, 12, 2, 6, 9, 10, 1, 4)"
       ]
      },
-     "execution_count": 179,
+     "execution_count": 178,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -5158,6 +5121,30 @@
     "numbers"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 179,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 179,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "numbers == (7, 11, 8, 5, 3, 12, 2, 6, 9, 10, 1, 4)"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 180,
@@ -5179,7 +5166,7 @@
     }
    ],
    "source": [
-    "numbers == (7, 11, 8, 5, 3, 12, 2, 6, 9, 10, 1, 4)"
+    "numbers != (99, 11, 8, 5, 3, 12, 2, 6, 9, 10, 1, 4)"
    ]
   },
   {
@@ -5202,30 +5189,6 @@
      "output_type": "execute_result"
     }
    ],
-   "source": [
-    "numbers != (99, 11, 8, 5, 3, 12, 2, 6, 9, 10, 1, 4)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 182,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "True"
-      ]
-     },
-     "execution_count": 182,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
    "source": [
     "numbers < (99, 11, 8, 5, 3, 12, 2, 6, 9, 10, 1, 4)"
    ]
@@ -5245,7 +5208,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 183,
+   "execution_count": 182,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -5258,7 +5221,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 184,
+   "execution_count": 183,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -5271,7 +5234,7 @@
        "(1, [2, 3, 4, 5, 6, 7, 8, 9, 10, 11], 12)"
       ]
      },
-     "execution_count": 184,
+     "execution_count": 183,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -5282,7 +5245,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 185,
+   "execution_count": 184,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -5295,7 +5258,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 186,
+   "execution_count": 185,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -5308,7 +5271,7 @@
        "(1, [99, 99, 99], 12)"
       ]
      },
-     "execution_count": 186,
+     "execution_count": 185,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -5343,7 +5306,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 187,
+   "execution_count": 186,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -5354,6 +5317,30 @@
     "n1, n2, n3, n4, n5, n6, n7, n8, n9, n10, n11, n12 = numbers"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 187,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "7"
+      ]
+     },
+     "execution_count": 187,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "n1"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 188,
@@ -5366,7 +5353,7 @@
     {
      "data": {
       "text/plain": [
-       "7"
+       "11"
       ]
      },
      "execution_count": 188,
@@ -5375,7 +5362,7 @@
     }
    ],
    "source": [
-    "n1"
+    "n2"
    ]
   },
   {
@@ -5386,30 +5373,6 @@
      "slide_type": "fragment"
     }
    },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "11"
-      ]
-     },
-     "execution_count": 189,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "n2"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 190,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "fragment"
-    }
-   },
    "outputs": [
     {
      "data": {
@@ -5417,7 +5380,7 @@
        "8"
       ]
      },
-     "execution_count": 190,
+     "execution_count": 189,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -5439,7 +5402,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 191,
+   "execution_count": 190,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -5453,7 +5416,7 @@
      "traceback": [
       "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
       "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-191-c5e79943681f>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mn1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn3\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn4\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn5\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn6\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn7\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn8\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn9\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn10\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn11\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnumbers\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;32m<ipython-input-190-c5e79943681f>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mn1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn3\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn4\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn5\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn6\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn7\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn8\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn9\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn10\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn11\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnumbers\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
       "\u001b[0;31mValueError\u001b[0m: too many values to unpack (expected 11)"
      ]
     }
@@ -5464,7 +5427,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 192,
+   "execution_count": 191,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -5478,7 +5441,7 @@
      "traceback": [
       "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
       "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-192-214f2de70baf>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mn1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn3\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn4\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn5\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn6\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn7\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn8\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn9\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn10\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn11\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn12\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn13\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnumbers\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;32m<ipython-input-191-214f2de70baf>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mn1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn3\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn4\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn5\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn6\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn7\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn8\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn9\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn10\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn11\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn12\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn13\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnumbers\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
       "\u001b[0;31mValueError\u001b[0m: not enough values to unpack (expected 13, got 12)"
      ]
     }
@@ -5502,7 +5465,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 193,
+   "execution_count": 192,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -5513,6 +5476,30 @@
     "first, *middle, last = numbers"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 193,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "7"
+      ]
+     },
+     "execution_count": 193,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "first"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 194,
@@ -5525,7 +5512,7 @@
     {
      "data": {
       "text/plain": [
-       "7"
+       "[11, 8, 5, 3, 12, 2, 6, 9, 10, 1]"
       ]
      },
      "execution_count": 194,
@@ -5534,7 +5521,7 @@
     }
    ],
    "source": [
-    "first"
+    "middle  # always a list!"
    ]
   },
   {
@@ -5545,30 +5532,6 @@
      "slide_type": "fragment"
     }
    },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[11, 8, 5, 3, 12, 2, 6, 9, 10, 1]"
-      ]
-     },
-     "execution_count": 195,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "middle  # always a list!"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 196,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "fragment"
-    }
-   },
    "outputs": [
     {
      "data": {
@@ -5576,7 +5539,7 @@
        "4"
       ]
      },
-     "execution_count": 196,
+     "execution_count": 195,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -5593,14 +5556,14 @@
     }
    },
    "source": [
-    "We already used unpacking before this section without knowing it. Whenever we write a `for`-loop over the [zip()](https://docs.python.org/3/library/functions.html#zip) built-in, that generates a new `tuple` object in each iteration, which we unpack by listing several loop variables.\n",
+    "We already used unpacking before this section without knowing it. Whenever we write a `for`-loop over the [zip() <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/functions.html#zip) built-in, that generates a new `tuple` object in each iteration, which we unpack by listing several loop variables.\n",
     "\n",
     "So, the `name, position` acts like a left-hand side of an `=` statement and unpacks the `tuple` objects generated from \"zipping\" the `names` list and the `positions` tuple together."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 197,
+   "execution_count": 196,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -5613,7 +5576,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 198,
+   "execution_count": 197,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -5643,12 +5606,12 @@
     }
    },
    "source": [
-    "Without unpacking, [zip()](https://docs.python.org/3/library/functions.html#zip) generates a series of `tuple` objects."
+    "Without unpacking, [zip() <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/functions.html#zip) generates a series of `tuple` objects."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 199,
+   "execution_count": 198,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -5678,12 +5641,12 @@
     }
    },
    "source": [
-    "Unpacking also works for nested objects. Below, we wrap [zip()](https://docs.python.org/3/library/functions.html#zip) with the [enumerate()](https://docs.python.org/3/library/functions.html#enumerate) built-in to have an index variable `number` inside the `for`-loop. In each iteration, a `tuple` object consisting of `number` and another `tuple` object is created. The inner one then holds the `name` and `position`."
+    "Unpacking also works for nested objects. Below, we wrap [zip() <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/functions.html#zip) with the [enumerate() <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/functions.html#enumerate) built-in to have an index variable `number` inside the `for`-loop. In each iteration, a `tuple` object consisting of `number` and another `tuple` object is created. The inner one then holds the `name` and `position`."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 200,
+   "execution_count": 199,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -5731,7 +5694,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 201,
+   "execution_count": 200,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -5756,7 +5719,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 202,
+   "execution_count": 201,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -5773,7 +5736,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 203,
+   "execution_count": 202,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -5786,7 +5749,7 @@
        "1"
       ]
      },
-     "execution_count": 203,
+     "execution_count": 202,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -5797,7 +5760,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 204,
+   "execution_count": 203,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -5810,7 +5773,7 @@
        "0"
       ]
      },
-     "execution_count": 204,
+     "execution_count": 203,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -5832,7 +5795,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 205,
+   "execution_count": 204,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -5845,7 +5808,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 206,
+   "execution_count": 205,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -5858,7 +5821,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 207,
+   "execution_count": 206,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -5871,7 +5834,7 @@
        "(1, 0)"
       ]
      },
-     "execution_count": 207,
+     "execution_count": 206,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -5888,7 +5851,7 @@
     }
    },
    "source": [
-    "##### Example: [Fibonacci Numbers](https://en.wikipedia.org/wiki/Fibonacci_number) (revisited)"
+    "##### Example: [Fibonacci Numbers <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_wiki.png\">](https://en.wikipedia.org/wiki/Fibonacci_number) (revisited)"
    ]
   },
   {
@@ -5899,12 +5862,12 @@
     }
    },
    "source": [
-    "Unpacking allows us to rewrite the iterative `fibonacci()` function from [Chapter 4](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/04_iteration_00_lecture.ipynb#\"Hard-at-first-Glance\"-Example:-Fibonacci-Numbers-%28revisited%29) in a concise way."
+    "Unpacking allows us to rewrite the iterative `fibonacci()` function from [Chapter 4 <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/04_iteration_00_content.ipynb#\"Hard-at-first-Glance\"-Example:-Fibonacci-Numbers-%28revisited%29) in a concise way."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 208,
+   "execution_count": 207,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -5931,7 +5894,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 209,
+   "execution_count": 208,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -5944,7 +5907,7 @@
        "144"
       ]
      },
-     "execution_count": 209,
+     "execution_count": 208,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -5981,7 +5944,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 210,
+   "execution_count": 209,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -6014,7 +5977,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 211,
+   "execution_count": 210,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -6027,7 +5990,7 @@
        "42"
       ]
      },
-     "execution_count": 211,
+     "execution_count": 210,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -6049,7 +6012,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 212,
+   "execution_count": 211,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -6062,7 +6025,7 @@
        "100"
       ]
      },
-     "execution_count": 212,
+     "execution_count": 211,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -6084,7 +6047,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 213,
+   "execution_count": 212,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -6098,8 +6061,8 @@
      "traceback": [
       "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
       "\u001b[0;31mIndexError\u001b[0m                                Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-213-640e0c632b8d>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mproduct\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
-      "\u001b[0;32m<ipython-input-210-02b0db9fbeab>\u001b[0m in \u001b[0;36mproduct\u001b[0;34m(*args)\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mproduct\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m     \u001b[0;34m\"\"\"Multiply all arguments.\"\"\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m     \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      4\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m     \u001b[0;32mfor\u001b[0m \u001b[0marg\u001b[0m \u001b[0;32min\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m<ipython-input-212-640e0c632b8d>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mproduct\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;32m<ipython-input-209-02b0db9fbeab>\u001b[0m in \u001b[0;36mproduct\u001b[0;34m(*args)\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mproduct\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m     \u001b[0;34m\"\"\"Multiply all arguments.\"\"\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m     \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      4\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m     \u001b[0;32mfor\u001b[0m \u001b[0marg\u001b[0m \u001b[0;32min\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
       "\u001b[0;31mIndexError\u001b[0m: tuple index out of range"
      ]
     }
@@ -6121,7 +6084,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 214,
+   "execution_count": 213,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -6134,7 +6097,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 215,
+   "execution_count": 214,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -6147,7 +6110,7 @@
        "[2, 5, 10]"
       ]
      },
-     "execution_count": 215,
+     "execution_count": 214,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -6167,6 +6130,41 @@
     "This error does not occur if we unpack `one_hundred` upon passing it as the argument."
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 215,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "100"
+      ]
+     },
+     "execution_count": 215,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "product(*one_hundred)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "That is the equivalent of writing out the following tedious expression. Yet, that does *not* scale for iterables with many elements in them."
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 216,
@@ -6187,41 +6185,6 @@
      "output_type": "execute_result"
     }
    ],
-   "source": [
-    "product(*one_hundred)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "source": [
-    "That is the equivalent of writing out the following tedious expression. Yet, that does *not* scale for iterables with many elements in them."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 217,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "fragment"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "100"
-      ]
-     },
-     "execution_count": 217,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
    "source": [
     "product(one_hundred[0], one_hundred[1], one_hundred[2])"
    ]
@@ -6234,14 +6197,14 @@
     }
    },
    "source": [
-    "In the \"*Packing & Unpacking with Functions*\" [exercise](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/07_sequences_02_exercises.ipynb#Packing-&-Unpacking-with-Functions) at the end of this chapter, we look at `product()` in more detail.\n",
+    "In the \"*Packing & Unpacking with Functions*\" [exercise <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/07_sequences_02_exercises.ipynb#Packing-&-Unpacking-with-Functions) at the end of this chapter, we look at `product()` in more detail.\n",
     "\n",
     "While we needed to unpack `one_hundred` above to avoid the semantic error, unpacking an argument in a function call may also be a convenience in general. For example, to print the elements of `one_hundred` in one line, we need to use a `for` statement, until now. With unpacking, we get away *without* a loop."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 218,
+   "execution_count": 217,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -6262,7 +6225,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 219,
+   "execution_count": 218,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -6284,7 +6247,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 220,
+   "execution_count": 219,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -6333,7 +6296,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 221,
+   "execution_count": 220,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -6354,12 +6317,12 @@
    "source": [
     "We implicitly assume that the first element represents the $x$ and the second the $y$ coordinate. While that follows intuitively from convention in math, we should at least add comments somewhere in the code to document this assumption.\n",
     "\n",
-    "A better way is to create a *custom* data type. While that is covered in depth in Chapter 10, the [collections](https://docs.python.org/3/library/collections.html) module in the [standard library](https://docs.python.org/3/library/index.html) provides a [namedtuple()](https://docs.python.org/3/library/collections.html#collections.namedtuple) **factory function** that creates \"simple\" custom data types on top of the standard `tuple` type."
+    "A better way is to create a *custom* data type. While that is covered in depth in Chapter 10, the [collections <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/collections.html) module in the [standard library <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/index.html) provides a [namedtuple() <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/collections.html#collections.namedtuple) **factory function** that creates \"simple\" custom data types on top of the standard `tuple` type."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 222,
+   "execution_count": 221,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -6378,12 +6341,12 @@
     }
    },
    "source": [
-    "[namedtuple()](https://docs.python.org/3/library/collections.html#collections.namedtuple) takes two arguments. The first argument is the name of the data type. That could be different from the variable `Point` we use to refer to the new type, but in most cases it is best to keep them in sync. The second argument is a sequence with the field names as `str` objects. The names' order corresponds to the one assumed in `current_position`."
+    "[namedtuple() <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/collections.html#collections.namedtuple) takes two arguments. The first argument is the name of the data type. That could be different from the variable `Point` we use to refer to the new type, but in most cases it is best to keep them in sync. The second argument is a sequence with the field names as `str` objects. The names' order corresponds to the one assumed in `current_position`."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 223,
+   "execution_count": 222,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -6402,7 +6365,31 @@
     }
    },
    "source": [
-    "The `Point` object is a so-called **class**. That is what it means if an object is of type `type`. It can be used as a **factory** to create *new* `tuple`-like objects of type `Point`. In a way, [namedtuple()](https://docs.python.org/3/library/collections.html#collections.namedtuple) gives us a way to create our own custom **constructors**."
+    "The `Point` object is a so-called **class**. That is what it means if an object is of type `type`. It can be used as a **factory** to create *new* `tuple`-like objects of type `Point`. In a way, [namedtuple() <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/collections.html#collections.namedtuple) gives us a way to create our own custom **constructors**."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 223,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "140483213000128"
+      ]
+     },
+     "execution_count": 223,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "id(Point)"
    ]
   },
   {
@@ -6413,30 +6400,6 @@
      "slide_type": "fragment"
     }
    },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "94715539542800"
-      ]
-     },
-     "execution_count": 224,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "id(Point)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 225,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "fragment"
-    }
-   },
    "outputs": [
     {
      "data": {
@@ -6444,7 +6407,7 @@
        "type"
       ]
      },
-     "execution_count": 225,
+     "execution_count": 224,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -6466,7 +6429,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 226,
+   "execution_count": 225,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -6479,7 +6442,7 @@
        "__main__.Point"
       ]
      },
-     "execution_count": 226,
+     "execution_count": 225,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -6501,7 +6464,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 227,
+   "execution_count": 226,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -6525,7 +6488,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 228,
+   "execution_count": 227,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -6538,7 +6501,7 @@
        "Point(x=4, y=2)"
       ]
      },
-     "execution_count": 228,
+     "execution_count": 227,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -6560,7 +6523,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 229,
+   "execution_count": 228,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -6570,10 +6533,10 @@
     {
      "data": {
       "text/plain": [
-       "139941443365056"
+       "140483698359504"
       ]
      },
-     "execution_count": 229,
+     "execution_count": 228,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -6584,7 +6547,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 230,
+   "execution_count": 229,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -6597,7 +6560,7 @@
        "__main__.Point"
       ]
      },
-     "execution_count": 230,
+     "execution_count": 229,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -6619,7 +6582,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 231,
+   "execution_count": 230,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -6632,7 +6595,7 @@
        "4"
       ]
      },
-     "execution_count": 231,
+     "execution_count": 230,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -6643,7 +6606,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 232,
+   "execution_count": 231,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -6656,7 +6619,7 @@
        "2"
       ]
      },
-     "execution_count": 232,
+     "execution_count": 231,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -6678,7 +6641,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 233,
+   "execution_count": 232,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -6692,7 +6655,7 @@
      "traceback": [
       "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
       "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-233-b30d0b930bac>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mcurrent_position\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mz\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;32m<ipython-input-232-b30d0b930bac>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mcurrent_position\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mz\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
       "\u001b[0;31mAttributeError\u001b[0m: 'Point' object has no attribute 'z'"
      ]
     }
@@ -6716,7 +6679,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 234,
+   "execution_count": 233,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -6729,7 +6692,7 @@
        "4"
       ]
      },
-     "execution_count": 234,
+     "execution_count": 233,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -6740,7 +6703,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 235,
+   "execution_count": 234,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -6753,7 +6716,7 @@
        "2"
       ]
      },
-     "execution_count": 235,
+     "execution_count": 234,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -6764,7 +6727,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 236,
+   "execution_count": 235,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -6787,7 +6750,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 237,
+   "execution_count": 236,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -6821,7 +6784,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 238,
+   "execution_count": 237,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -6834,7 +6797,7 @@
        "2"
       ]
      },
-     "execution_count": 238,
+     "execution_count": 237,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -6878,6 +6841,66 @@
     "\n",
     "On the contrary, `tuple` objects are like **immutable** lists: We can use them in place of any `list` object as long as we do *not* need to mutate it. Often, `tuple` objects are also used to model **records** of related **fields**."
    ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "## Further Resources"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "A lecture-style **video presentation** of this chapter is integrated below (cf., the [video <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_yt.png\">](https://www.youtube.com/watch?v=nx2sCDoeC3I&list=PL-2JV1G3J10lQ2xokyQowcRJI5jjNfW7f) or the entire [playlist <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_yt.png\">](https://www.youtube.com/playlist?list=PL-2JV1G3J10lQ2xokyQowcRJI5jjNfW7f))."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 238,
+   "metadata": {
+    "scrolled": true,
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/jpeg": 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+       "            width=\"60%\"\n",
+       "            height=\"300\"\n",
+       "            src=\"https://www.youtube.com/embed/nx2sCDoeC3I\"\n",
+       "            frameborder=\"0\"\n",
+       "            allowfullscreen\n",
+       "        ></iframe>\n",
+       "        "
+      ],
+      "text/plain": [
+       "<IPython.lib.display.YouTubeVideo at 0x7fc4e8f75950>"
+      ]
+     },
+     "execution_count": 238,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from IPython.display import YouTubeVideo\n",
+    "YouTubeVideo(\"nx2sCDoeC3I\", width=\"60%\")"
+   ]
   }
  ],
  "metadata": {
diff --git a/07_sequences_01_review.ipynb b/07_sequences_01_review.ipynb
index 7e9e5da..545d597 100644
--- a/07_sequences_01_review.ipynb
+++ b/07_sequences_01_review.ipynb
@@ -18,7 +18,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "The questions below assume that you have read [Chapter 7](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/07_sequences_00_lecture.ipynb) in the book.\n",
+    "The questions below assume that you have read [Chapter 7 <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/07_sequences_00_content.ipynb) in the book.\n",
     "\n",
     "Be concise in your answers! Most questions can be answered in *one* sentence."
    ]
@@ -41,7 +41,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "**Q1**: We have seen **containers** and **iterables** before in [Chapter 4](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/04_iteration_00_lecture.ipynb#Containers-vs.-Iterables). How do they relate to **sequences**? "
+    "**Q1**: We have seen **containers** and **iterables** before in [Chapter 4 <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/04_iteration_00_content.ipynb#Containers-vs.-Iterables). How do they relate to **sequences**? "
    ]
   },
   {
@@ -55,7 +55,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "**Q2**: What are **abstract base classes**? How can we make use of the ones from the [collections.abc](https://docs.python.org/3/library/collections.abc.html) module in the [standard library](https://docs.python.org/3/library/index.html)?"
+    "**Q2**: What are **abstract base classes**? How can we make use of the ones from the [collections.abc <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/collections.abc.html) module in the [standard library <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/index.html)?"
    ]
   },
   {
@@ -183,7 +183,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "**Q9**: `sequence` objects are *not* part of core Python but may be imported from the [standard library](https://docs.python.org/3/library/index.html)."
+    "**Q9**: `sequence` objects are *not* part of core Python but may be imported from the [standard library <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/index.html)."
    ]
   },
   {
@@ -197,7 +197,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "**Q10**: The built-in [sort()](https://docs.python.org/3/library/stdtypes.html#list.sort) function takes a *finite* **iterable** as its argument an returns a *new* `list` object. On the contrary, the [sorted()](https://docs.python.org/3/library/functions.html#sorted) method on `list` objects *mutates* them *in place*."
+    "**Q10**: The built-in [sort() <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/stdtypes.html#list.sort) function takes a *finite* **iterable** as its argument an returns a *new* `list` object. On the contrary, the [sorted() <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/functions.html#sorted) method on `list` objects *mutates* them *in place*."
    ]
   },
   {
diff --git a/07_sequences_02_exercises.ipynb b/07_sequences_02_exercises.ipynb
index b8d0a7b..2917663 100644
--- a/07_sequences_02_exercises.ipynb
+++ b/07_sequences_02_exercises.ipynb
@@ -1,5 +1,12 @@
 {
  "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Important**: Click on \"*Kernel*\" > \"*Restart Kernel and Run All*\" *after* finishing the exercises in [JupyterLab <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_jp.png\">](https://jupyterlab.readthedocs.io/en/stable/) (e.g., in the cloud on [MyBinder <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_mb.png\">](https://mybinder.org/v2/gh/webartifex/intro-to-python/master?urlpath=lab/tree/07_sequences_02_exercises.ipynb)) to ensure that your solution runs top to bottom *without* any errors"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -18,7 +25,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "The exercises below assume that you have read [Chapter 7](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/07_sequences_00_lecture.ipynb) in the book.\n",
+    "The exercises below assume that you have read [Chapter 7 <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/07_sequences_00_content.ipynb) in the book.\n",
     "\n",
     "The `...`'s in the code cells indicate where you need to fill in code snippets. The number of `...`'s within a code cell give you a rough idea of how many lines of code are needed to solve the task. You should not need to create any additional code cells for your final solution. However, you may want to use temporary code cells to try out some ideas."
    ]
@@ -85,7 +92,7 @@
    "source": [
     "**Q1.2**: Generalize `nested_sum()` into a function `mixed_sum()` that can process a \"mixed\" `list` object, which contains numbers and other `list` objects with numbers! Use `mixed_numbers` below for testing!\n",
     "\n",
-    "Hints: Use the built-in [isinstance()](https://docs.python.org/3/library/functions.html#isinstance) function to check how an element is to be processed. Get extra credit for adhering to *goose typing*, as explained in [Chapter 5](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/05_numbers_00_lecture.ipynb#Goose-Typing): Use some appropriate abstract base class (ABC) from the [collections.abc](https://docs.python.org/3/library/collections.abc.html) module in the [standard library](https://docs.python.org/3/library/index.html)."
+    "Hints: Use the built-in [isinstance() <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/functions.html#isinstance) function to check how an element is to be processed. Get extra credit for adhering to *goose typing*, as explained in [Chapter 5 <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/05_numbers_00_content.ipynb#Goose-Typing): Use some appropriate abstract base class (ABC) from the [collections.abc <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/collections.abc.html) module in the [standard library <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/index.html)."
    ]
   },
   {
@@ -147,7 +154,7 @@
    "source": [
     "**Q1.3.1**: Write a function `cum_sum()` that takes a `list` object with numbers as its argument and returns a *new* `list` object with the **cumulative sums** of these numbers! So, `sum_up` below, `[1, 2, 3, 4, 5]`, should return `[1, 3, 6, 10, 15]`.\n",
     "\n",
-    "Hint: The idea behind is similar to the [cumulative distribution function](https://en.wikipedia.org/wiki/Cumulative_distribution_function) from statistics."
+    "Hint: The idea behind is similar to the [cumulative distribution function <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_wiki.png\">](https://en.wikipedia.org/wiki/Cumulative_distribution_function) from statistics."
    ]
   },
   {
@@ -230,7 +237,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "In the \"*Function Definitions & Calls*\" section in [Chapter 7](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/07_sequences_00_lecture.ipynb#Function-Definitions-&-Calls), we define the following function `product()`. In this exercise, you will improve it by making it more \"user-friendly.\""
+    "In the \"*Function Definitions & Calls*\" section in [Chapter 7 <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/07_sequences_00_content.ipynb#Function-Definitions-&-Calls), we define the following function `product()`. In this exercise, you will improve it by making it more \"user-friendly.\""
    ]
   },
   {
@@ -324,7 +331,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "In [Chapter 7](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/07_sequences_00_lecture.ipynb#Function-Definitions-&-Calls), we also pass a `list` object, like `one_hundred`, to `product()`, and *no* exception is raised."
+    "In [Chapter 7 <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/07_sequences_00_content.ipynb#Function-Definitions-&-Calls), we also pass a `list` object, like `one_hundred`, to `product()`, and *no* exception is raised."
    ]
   },
   {
@@ -349,7 +356,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "**Q2.3**: What is wrong with that? What *kind* of error (cf., [Chapter 1](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/01_elements_00_lecture.ipynb#Formal-vs.-Natural-Languages)) is that conceptually? Describe precisely what happens to the passed in `one_hundred` in every line within `product()`!"
+    "**Q2.3**: What is wrong with that? What *kind* of error (cf., [Chapter 1 <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/01_elements_00_content.ipynb#Formal-vs.-Natural-Languages)) is that conceptually? Describe precisely what happens to the passed in `one_hundred` in every line within `product()`!"
    ]
   },
   {
@@ -468,7 +475,7 @@
    "source": [
     "**Q2.6**: Replace the `None` in `product()` above with something reasonable that does *not* cause exceptions! Ensure that `product(42)` and `product(2, 5, 10)` return a correct result.\n",
     "\n",
-    "Hints: It is ok if `product()` returns a result *different* from the `None` above. Look at the documentation of the built-in [sum()](https://docs.python.org/3/library/functions.html#sum) function for some inspiration."
+    "Hints: It is ok if `product()` returns a result *different* from the `None` above. Look at the documentation of the built-in [sum() <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/functions.html#sum) function for some inspiration."
    ]
   },
   {
@@ -527,7 +534,7 @@
    "source": [
     "**Q2.7**: Rewrite `product()` so that it takes a *keyword-only* argument `start`, defaulting to the above *default* or *start* value, and use `start` internally instead of `result`!\n",
     "\n",
-    "Hint: Remember that a *keyword-only* argument is any parameter specified in a function's header line after the first (and only) `*` (cf., [Chapter 2](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/02_functions_00_lecture.ipynb#Keyword-only-Arguments))."
+    "Hint: Remember that a *keyword-only* argument is any parameter specified in a function's header line after the first (and only) `*` (cf., [Chapter 2 <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/02_functions_00_content.ipynb#Keyword-only-Arguments))."
    ]
   },
   {
@@ -655,7 +662,7 @@
    "source": [
     "**Q2.9**: Rewrite the latest version of `product()` to check if the *only* positional argument is a *collection* type! If so, its elements are multiplied together. Otherwise, the logic remains the same.\n",
     "\n",
-    "Hints: Use the built-in [len()](https://docs.python.org/3/library/functions.html#len) and [isinstance()](https://docs.python.org/3/library/functions.html#isinstance) functions to check if there is only *one* positional argument and if it is a *collection* type. Use the *abstract base class* `Collection` from the [collections.abc](https://docs.python.org/3/library/collections.abc.html) module in the [standard library](https://docs.python.org/3/library/index.html). You may want to *re-assign* `args` inside the body."
+    "Hints: Use the built-in [len() <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/functions.html#len) and [isinstance() <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/functions.html#isinstance) functions to check if there is only *one* positional argument and if it is a *collection* type. Use the *abstract base class* `Collection` from the [collections.abc <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/collections.abc.html) module in the [standard library <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/index.html). You may want to *re-assign* `args` inside the body."
    ]
   },
   {
@@ -740,7 +747,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "**Side Note**: Above, we make `product()` work with a single *collection* type argument instead of a *sequence* type to keep it more generic: For example, we can pass in a `set` object, like `{2, 5, 10}` below, and `product()` continues to work correctly. The `set` type is introducted in [Chapter 9](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/09_mappings_00_lecture.ipynb#The-set-Type), and one essential difference to the `list` type is that objects of type `set` have *no* order regarding their elements. So, even though `[2, 5, 10]` and `{2, 5, 10}` look almost the same, the order implied in the literal notation gets lost in memory!"
+    "**Side Note**: Above, we make `product()` work with a single *collection* type argument instead of a *sequence* type to keep it more generic: For example, we can pass in a `set` object, like `{2, 5, 10}` below, and `product()` continues to work correctly. The `set` type is introducted in [Chapter 9 <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/09_mappings_00_content.ipynb#The-set-Type), and one essential difference to the `list` type is that objects of type `set` have *no* order regarding their elements. So, even though `[2, 5, 10]` and `{2, 5, 10}` look almost the same, the order implied in the literal notation gets lost in memory!"
    ]
   },
   {
@@ -774,7 +781,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Let's continue to improve `product()` and make it more Pythonic. It is always a good idea to mimic the behavior of built-ins when writing our own functions. And, [sum()](https://docs.python.org/3/library/functions.html#sum), for example, raises a `TypeError` if called *without* any arguments. It does *not* return the \"philosophical\" answer to adding *no* numbers, which would be `0`."
+    "Let's continue to improve `product()` and make it more Pythonic. It is always a good idea to mimic the behavior of built-ins when writing our own functions. And, [sum() <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/functions.html#sum), for example, raises a `TypeError` if called *without* any arguments. It does *not* return the \"philosophical\" answer to adding *no* numbers, which would be `0`."
    ]
   },
   {
diff --git a/08_mappings_00_lecture.ipynb b/08_mappings_00_lecture.ipynb
deleted file mode 100644
index 69ff961..0000000
--- a/08_mappings_00_lecture.ipynb
+++ /dev/null
@@ -1,8217 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "slide"
-    }
-   },
-   "source": [
-    "# Chapter 8: Mappings & Sets"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "source": [
-    "While [Chapter 7](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/07_sequences_00_lecture.ipynb) focuses on one special kind of *collection* types, namely *sequences*, this chapter introduces two more kinds: **Mappings** and **sets**. We present the data types belonging to these two groups in one chapter as they share the *same* underlying implementation at the C Level, known as **[hash tables](https://en.wikipedia.org/wiki/Hash_table)**.\n",
-    "\n",
-    "The most important mapping type in this chapter is the `dict` type that we have not yet seen before (cf, [documentation](https://docs.python.org/3/library/stdtypes.html#dict)). It is an essential part in a data science practitioner's toolbox for two reasons: First, Python employs `dict` objects basically \"everywhere\" internally. So, we must understand how they work to become better at Python in general. Second, after the many concepts related to *sequential* data, the ideas behind *mappings* enhance our general problem solving skills. As a concrete example, we look at the concept of **memoization** to complete our picture of *recursion*, as depicted in [Chapter 4](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/04_iteration_00_lecture.ipynb#Recursion). We end this chapter with a discussion of *set* types."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "slide"
-    }
-   },
-   "source": [
-    "## The `dict` Type"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "source": [
-    "A *mapping* is a one-to-one correspondence from a set of **keys** to a set of **values**. In other words, a *mapping* is a *collection* of **key-value pairs**, also called **items** for short.\n",
-    "\n",
-    "In the context of mappings, the term *value* has a meaning different from the general *value* that *every* object has: In the \"bag\" analogy from [Chapter 1](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/01_elements_00_lecture.ipynb#Value-/-\"Meaning\"), we descibe an object's value to be the concrete $0$s and $1$s it contains. Here, the terms *key* and *value* mean the *role* an object takes within a mapping. Both, *keys* and *values*, are real *objects* with a distinct *value*. So, the student should always remember the double meaning of the term *value* in this chapter!\n",
-    "\n",
-    "Let's continue with an example. To create a `dict` object, we commonly use the literal notation, `{..: .., ..: .., ...}`, and list all the items. `to_words` below maps the `int` objects `0`, `1`, and `2` to their English word equivalents, `\"zero\"`, `\"one\"`, and `\"two\"`, and `from_words` does the opposite. A stylistic side note: Pythonistas often expand `dict` or `list` definitions by writing each item or element on a line on their own. The commas `,` after the *last* items are *not* a mistake, as well, although they *may* be left out. Besides easier reading, such style has actual technical advantages (cf., [source](https://www.python.org/dev/peps/pep-0008/#when-to-use-trailing-commas)) that we do not go into detail about here."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "slide"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "to_words = {\n",
-    "    0: \"zero\",\n",
-    "    1: \"one\",\n",
-    "    2: \"two\",\n",
-    "}"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "-"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "from_words = {\n",
-    "    \"zero\": 0,\n",
-    "    \"one\": 1,\n",
-    "    \"two\": 2,\n",
-    "}"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "source": [
-    "As before, `dict` objects are objects on their own: They have an identity, a type, and a value. The latter is a *literal* that creates a *new* `dict` object with the *same* value when evaluated."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "fragment"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "139953979690352"
-      ]
-     },
-     "execution_count": 3,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "id(from_words)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "-"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "dict"
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "type(from_words)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "-"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'zero': 0, 'one': 1, 'two': 2}"
-      ]
-     },
-     "execution_count": 5,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from_words"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "source": [
-    "The [dict()](https://docs.python.org/3/library/functions.html#func-dict) built-in gives us an alternative way to create a `dict` object. It is versatile and can be used in different ways.\n",
-    "\n",
-    "First, we may pass it any *mapping* type, for example, a `dict` object, to obtain a *new* `dict` object. That is the easiest way to convert a more specialized mapping type, such as the `OrderedDict`, `defaultdict`, and `Counter` types introduced further below, into an \"ordinary\" `dict` object."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "slide"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'zero': 0, 'one': 1, 'two': 2}"
-      ]
-     },
-     "execution_count": 6,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "dict(from_words)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "source": [
-    "Second, we may pass it an *iterable* of *iterables* with *two* elements each. So, both of the following two code cells work: A `list` of `tuple` objects, or a `tuple` of `list` objects. More importantly, we could use an *iterator*, for example, a `generator` object, that produces the inner iterables \"on the fly.\""
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "-"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'zero': 0, 'one': 1, 'two': 2}"
-      ]
-     },
-     "execution_count": 7,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "dict([(\"zero\", 0), (\"one\", 1), (\"two\", 2)])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'zero': 0, 'one': 1, 'two': 2}"
-      ]
-     },
-     "execution_count": 8,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "dict(([\"zero\", 0], [\"one\", 1], [\"two\", 2]))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "source": [
-    "Lastly, [dict()](https://docs.python.org/3/library/functions.html#func-dict) may also be called with *keyword* arguments: The keywords become the keys and the arguments the values."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "-"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'zero': 0, 'one': 1, 'two': 2}"
-      ]
-     },
-     "execution_count": 9,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "dict(zero=0, one=1, two=2)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "source": [
-    "Keyword arguments may always be added in the first two cases as well. That is sometimes useful to take data as is and ensure that certain keys are *existent* and have a pre-defined value."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'zero': 0, 'one': 1, 'two': 2, 'three': 3}"
-      ]
-     },
-     "execution_count": 10,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "dict(from_words, three=3)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "slide"
-    }
-   },
-   "source": [
-    "### Nested Data"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "source": [
-    "Often, `dict` objects occur in a nested form and combined with other collection types, such as `list` or `tuple` objects, to model more complex \"objects\" from the real world.\n",
-    "\n",
-    "The reason for this popularity is that many modern [ReST APIs](https://en.wikipedia.org/wiki/Representational_state_transfer#Applied_to_Web_services) on the internet (e.g., [Google Maps API](https://cloud.google.com/maps-platform/), [Yelp API](https://www.yelp.com/developers/documentation/v3), [Twilio API](https://www.twilio.com/docs/usage/api)) provide their data in the popular [JSON](https://en.wikipedia.org/wiki/JSON) format, which looks almost like a combination of `dict` and `list` objects in Python. \n",
-    "\n",
-    "The `people` example below models three groups of people: Mathematicians, physicists, and programmers. Each person may have an arbitrary number of email addresses (e.g., [Leonhard Euler](https://en.wikipedia.org/wiki/Leonhard_Euler) has not lived long enough to get one, whereas [Guido](https://en.wikipedia.org/wiki/Guido_van_Rossum) has more than one for sure).\n",
-    "\n",
-    "`people` has many (implicit) structural assumptions built in. For example, there are a [one-to-many](https://en.wikipedia.org/wiki/One-to-many_%28data_model%29) relationship between people and their email addresses and a [one-to-one](https://en.wikipedia.org/wiki/One-to-one_%28data_model%29) relationship between each person and their name. It is important to understand that we determine this structure by choosing the data types involved in `people` and that it is impossible to model nested data without any (implicit) assumption about the structure. So, the data science practitioner should have a basic understanding of [database normalization](https://en.wikipedia.org/wiki/Database_normalization)."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "slide"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "people = {\n",
-    "    \"mathematicians\": [\n",
-    "        {\n",
-    "            \"name\": \"Gilbert Strang\",\n",
-    "            \"emails\": [\"gilbert@mit.edu\"],\n",
-    "        },\n",
-    "        {\n",
-    "            \"name\": \"Leonhard Euler\",\n",
-    "            \"emails\": [],\n",
-    "        },\n",
-    "    ],\n",
-    "    \"physicists\": [],\n",
-    "    \"programmers\": [\n",
-    "        {\n",
-    "            \"name\": \"Guido\",\n",
-    "            \"emails\": [\"guido@python.org\", \"guido@dropbox.com\"],\n",
-    "        },\n",
-    "    ],\n",
-    "}"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "source": [
-    "The literal notation of such a nested `dict` object may be hard to read."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'mathematicians': [{'name': 'Gilbert Strang', 'emails': ['gilbert@mit.edu']},\n",
-       "  {'name': 'Leonhard Euler', 'emails': []}],\n",
-       " 'physicists': [],\n",
-       " 'programmers': [{'name': 'Guido',\n",
-       "   'emails': ['guido@python.org', 'guido@dropbox.com']}]}"
-      ]
-     },
-     "execution_count": 12,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "people"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "source": [
-    "Luckily, the [pprint](https://docs.python.org/3/library/pprint.html) module in the [standard library](https://docs.python.org/3/library/index.html) provides a [pprint()](https://docs.python.org/3/library/pprint.html#pprint.pprint) function for \"pretty printing.\""
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "slide"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "from pprint import pprint"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "-"
-    }
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "{'mathematicians': [{'emails': ['gilbert@mit.edu'],\n",
-      "                     'name': 'Gilbert Strang'},\n",
-      "                    {'emails': [],\n",
-      "                     'name': 'Leonhard Euler'}],\n",
-      " 'physicists': [],\n",
-      " 'programmers': [{'emails': ['guido@python.org',\n",
-      "                             'guido@dropbox.com'],\n",
-      "                  'name': 'Guido'}]}\n"
-     ]
-    }
-   ],
-   "source": [
-    "pprint(people, indent=1, width=60)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "slide"
-    }
-   },
-   "source": [
-    "### Hash Tables & Key Hashability"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "source": [
-    "In [Chapter 0](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/00_intro_00_lecture.ipynb#Isn't-C-a-lot-faster?), we argue that a major advantage of using Python is that it takes care of the memory managment for us. In line with that, we have never talked about the C level implementation thus far in the book. However, the `dict` type, among others, exhibits some behaviors that may seem \"weird\" for a beginner. To built a solid intuition that enables the student to better \"predict\" how `dict` objects behave, we describe the underlying implementation details on a conceptual level (i.e., without C code).\n",
-    "\n",
-    "The first unintuitive behavior is that we may *not* use a *mutable* object as a key. That results in a `TypeError`."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "slide"
-    }
-   },
-   "outputs": [
-    {
-     "ename": "TypeError",
-     "evalue": "unhashable type: 'list'",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-15-de7a9b0b64a4>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m {\n\u001b[0;32m----> 2\u001b[0;31m     \u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m\"zero\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"one\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      3\u001b[0m }\n",
-      "\u001b[0;31mTypeError\u001b[0m: unhashable type: 'list'"
-     ]
-    }
-   ],
-   "source": [
-    "{\n",
-    "    [0, 1]: [\"zero\", \"one\"],\n",
-    "}"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "source": [
-    "Similarly surprising is that items with the *same* key get \"merged\" together. The resulting `dict` object seems to keep the position of the *first* mention of a key, while at the same time only the *last* mention of a value survives."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "fragment"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'zero': 3, 'one': 1, 'two': 2}"
-      ]
-     },
-     "execution_count": 16,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "{\n",
-    "    \"zero\": 0,\n",
-    "    \"one\": 1,\n",
-    "    \"two\": 2,\n",
-    "    \"zero\": 3,\n",
-    "}"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "source": [
-    "The reason for that is that the main building block behind the `dict` type is a [data structure](https://en.wikipedia.org/wiki/Data_structure) called a [hash table](https://en.wikipedia.org/wiki/Hash_table).\n",
-    "\n",
-    "Conceptually, when we create a *new* `dict` object, Python creates a \"bag\" in memory that takes significantly more space (i.e., $0$s and $1$s) than needed to store the references to all the key and value objects. This bag is a **contiguous** chunk of memory (i.e., all the $0$s and $1$s lie right next to each other) and divided into equally sized **buckets** that have just enough space to store *two* references each. These references go to an item's key and value objects. The buckets are labeled with *index* numbers, or \"integer addresses.\" Because Python knows how wide each bucket is and where the bag begins, it can jump directly into *any* bucket by calculating its **offset** from the start. It does not have to follow a reference to some \"random\" memory location once it has followed the reference to the `dict` object's \"start\" in memory.\n",
-    "\n",
-    "The figure below visualizes how we should think of hash tables. An empty `dict` object, created with the literal `{}`, still takes a lot of memory: It is essentially one big, contiguous, and empty table."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "slide"
-    }
-   },
-   "source": [
-    "|  Bucket |  0  |  1  |  2  |  3  |  4  |  5  |  6  |  7  |\n",
-    "|  :---:  |:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|\n",
-    "| **Key** |*...*|*...*|*...*|*...*|*...*|*...*|*...*|*...*|\n",
-    "|**Value**|*...*|*...*|*...*|*...*|*...*|*...*|*...*|*...*|"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "source": [
-    "To insert a key-value pair, the key must be translated into a bucket's address. As the first step to do so, the built-in [hash()](https://docs.python.org/3/library/functions.html#hash) function maps any **hashable** object to its **hash value**, a long `int` number, similar to the ones returned by the built-in [id()](https://docs.python.org/3/library/functions.html#id) function. This hash value is a summary of all the $0$s and $1$s that make up an object's value, and, according to the official [glossary](https://docs.python.org/3/glossary.html#term-hashable), an object is hashable *only if* \"it has a hash value which *never* changes during its *lifetime*.\" So, hashability implies immutability! This formal requirement is also absolutely useful to have: Without it, an object may have *several* addresses and, thus, we could not sort it into a *predictable* bucket. The exact logic behind [hash()](https://docs.python.org/3/library/functions.html#hash) is beyond the scope of this book.\n",
-    "\n",
-    "Let's calculate the hash value of `\"zero\"`. Because `str` objects are immutable, that works. Hash values have *no* semantic meaning. Also, everytime we re-start Python, we see *different* hash values for the *same* objects. That is a security measure, and we do not go into the technicalities here (cf. [source](https://docs.python.org/3/using/cmdline.html#envvar-PYTHONHASHSEED))."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "slide"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "-1033934764475250610"
-      ]
-     },
-     "execution_count": 17,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "hash(\"zero\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "source": [
-    "For numeric objects, we can sometimes predict the hash values. However, we must *never* interpret any meaning into them."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "-"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "0"
-      ]
-     },
-     "execution_count": 18,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "hash(0)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "230584300921369408"
-      ]
-     },
-     "execution_count": 19,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "hash(0.1)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "source": [
-    "The [glossary](https://docs.python.org/3/glossary.html#term-hashable) states a second requirement for hashability, namely that \"objects which *compare equal* must have the *same* hash value.\" The purpose of this is to ensure that if we put, for example, `1` as a key in a `dict` object, we can look it up later with `1.0`. In other words, we can look up keys by their object's value (i.e., in the meaning of [Chapter 1](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/01_elements_00_lecture.ipynb#Value-/-\"Meaning\")). The converse statement does *not* hold: Two objects *may* (accidentally) have the *same* hash value and *not* compare equal."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "True"
-      ]
-     },
-     "execution_count": 20,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "1 == 1.0"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "fragment"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "True"
-      ]
-     },
-     "execution_count": 21,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "hash(1) == hash(1.0)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "source": [
-    "Because `list` objects are not immutable, they are *never* hashable, as indicated by the `TypeError`."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 22,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "fragment"
-    }
-   },
-   "outputs": [
-    {
-     "ename": "TypeError",
-     "evalue": "unhashable type: 'list'",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-22-1ddb6259478e>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mhash\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
-      "\u001b[0;31mTypeError\u001b[0m: unhashable type: 'list'"
-     ]
-    }
-   ],
-   "source": [
-    "hash([0, 1])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "source": [
-    "If we need keys composed of several objects, we can use `tuple` objects instead. In general, the key object must be hashable as a whole. So, we must *never* put a *mutable* object in a `tuple` object used as a key, as well."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 23,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "fragment"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "3713080549409410656"
-      ]
-     },
-     "execution_count": 23,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "hash((0, 1))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "source": [
-    "There is no such restiction on objects inserted into `dict` objects as *values*."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 24,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "fragment"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{(0, 1): ['zero', 'one']}"
-      ]
-     },
-     "execution_count": 24,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "{\n",
-    "    (0, 1): [\"zero\", \"one\"],\n",
-    "}"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "source": [
-    "A natural question to ask is how does Python know how much memory it should reserve for a `dict` object's hash table. And, the answer is: Python allocates the memory according to some internal heuristics, and whenever a hash table is roughly 2/3 full, it creates a *new* one with twice the space, and re-inserts all items, one by one, from the *old* one. So, during its lifetime, a `dict` object may have several hash tables.\n",
-    "\n",
-    "Let's see how Python translates the keys' hash values into buckets and what happens if a hash table gets too crowded. Assume the now bigger `from_words` example ..."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 25,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "slide"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "from_words = {\n",
-    "    \"zero\": 0,\n",
-    "    \"one\": 1,\n",
-    "    \"two\": 2,\n",
-    "    \"three\": 3,\n",
-    "    \"four\": 4,\n",
-    "    \"five\": 5,\n",
-    "}"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "source": [
-    "... is to be stored in a hash table with eight buckets. Thus, to label the buckets, we need three bits (i.e., $2^3 = 8$).\n",
-    "\n",
-    "Once Python has obtained an object's hash value with [hash()](https://docs.python.org/3/library/functions.html#hash), that number's *least* significant bits in *binary* representation are used as the address. So, here, we cut off the last three digits. These digits could be converted back into an integer for nicer presentation, but Python itself does not need that.\n",
-    "\n",
-    "In summary, with a potentially infinite number of possible keys being mapped on a limited number of buckets, there is a chance that two or more keys end up in the *same* bucket. That is called a **hash collision** and sounds worse than it is. In such cases, Python uses a perturbation rule to rearrange the bits, and if the corresponding next bucket is empty, places an item there. The main disadvantage of that is that the nice offsetting logic from above breaks down, and Python needs more time on average to place items into a hash table. The remedy is to just use a bigger hash table as then the chance of colliding hashes decreases. Luckily, Python does all that for us in the background. So, the main cost we pay for that convenience is the *high* memory usage of `dict` objects.\n",
-    "\n",
-    "Another effect of this hashing logic is that items that have keys with the *same* value (i.e., $0$s and $1$s) end up in the *same* bucket, as well. The item that gets inserted last *overwrites* all previously inserted items. That is why the two `\"zero\"` keys get \"merged\" above.\n",
-    "\n",
-    "The code below shows how the key objects are transformed into their hash values in integer and binary representation, the least significant bits of the latter are cut off, and the buckets are obtained. Because we use `str` objects as the keys, their buckets are *unpredictable*. So, taking into account the randomization for security purposes from above, we may or may not see redundant buckets in the output. The chance is rather high."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 26,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "fragment"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "def buckets(mapping, *, bits):\n",
-    "    \"\"\"Calculate the bucket indices for a mapping's keys.\"\"\"\n",
-    "    for key in mapping:  # cf., next sub-section for details on looping\n",
-    "        hash_value = hash(key)\n",
-    "        binary = bin(hash_value)\n",
-    "        address = binary[-bits:]\n",
-    "        bucket = int(\"0b\" + address, base=2)\n",
-    "        print(key, hash_value, \"0b...\" + binary[-12:], address, bucket, sep=\"\\t\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 27,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "-"
-    }
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "zero\t-1033934764475250610\t0b...011110110010\t010\t2\n",
-      "one\t-3496683303036773129\t0b...001100001001\t001\t1\n",
-      "two\t-4468022785493412346\t0b...100111111010\t010\t2\n",
-      "three\t1491334499222274950\t0b...001110000110\t110\t6\n",
-      "four\t490235692124377396\t0b...000100110100\t100\t4\n",
-      "five\t-550474145318920636\t0b...000110111100\t100\t4\n"
-     ]
-    }
-   ],
-   "source": [
-    "buckets(from_words, bits=3)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "source": [
-    "With five of the six keys already inserted, the next insertion has a chance of more than 50% to be a hash collision."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "slide"
-    }
-   },
-   "source": [
-    "|  Bucket |   0   |  1  |   2   |   3    |  4  |    5    |  6  |    7   |\n",
-    "|  :---:  | :---: |:---:| :---: | :---:  |:---:|  :---:  |:---:|  :---: |\n",
-    "| **Key** |`\"one\"`|*...*|`\"two\"`|`\"four\"`|*...*|`\"three\"`|*...*|`\"zero\"`|\n",
-    "|**Value**|  `1`  |*...*|  `2`  |  `4`   |*...*|   `3`   |*...*|   `0`  |"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "source": [
-    "Luckily, in the given case, Python allocates sixteen buckets trading off memory against insertion speed."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "-"
-    }
-   },
-   "source": [
-    "|  Bucket |   0   |  1  |  2  |  3  |  4  |  5  |  6  |    7   |  8  |  9  |  10   |   11   |  12 |    13   |  14 |   15   |\n",
-    "|  :---:  | :---: |:---:|:---:|:---:|:---:|:---:|:---:|  :---: |:---:|:---:| :---: |  :---: |:---:|  :---:  |:---:|  :---: |\n",
-    "| **Key** |`\"one\"`|*...*|*...*|*...*|*...*|*...*|*...*|`\"five\"`|*...*|*...*|`\"two\"`|`\"four\"`|*...*|`\"three\"`|*...*|`\"zero\"`|\n",
-    "|**Value**|  `1`  |*...*|*...*|*...*|*...*|*...*|*...*|   `5`  |*...*|*...*|  `2`  |   `4`  |*...*|   `3`   |*...*|   `0`  |"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "source": [
-    "Sixteen buckets imply four bits (i.e., $2^4$) be cut off from the hash value's binary representation and used as the buckets' indices. It is unlikely we see redundant buckets in the code cell below."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 28,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "zero\t-1033934764475250610\t0b...011110110010\t0010\t2\n",
-      "one\t-3496683303036773129\t0b...001100001001\t1001\t9\n",
-      "two\t-4468022785493412346\t0b...100111111010\t1010\t10\n",
-      "three\t1491334499222274950\t0b...001110000110\t0110\t6\n",
-      "four\t490235692124377396\t0b...000100110100\t0100\t4\n",
-      "five\t-550474145318920636\t0b...000110111100\t1100\t12\n"
-     ]
-    }
-   ],
-   "source": [
-    "buckets(from_words, bits=4)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "source": [
-    "Although hash tables seem quite complex at first sight, they help us to make certain operations very fast as we see further below."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "slide"
-    }
-   },
-   "source": [
-    "### Mappings are Collections without \"Predictable\" Order"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "source": [
-    "In [Chapter 7](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/07_sequences_00_lecture.ipynb#Collections-vs.-Sequences), we show how *sequences* are a special kind of *collections*. The latter can be described as iterable containers with a finite number of elements.\n",
-    "\n",
-    "The `dict` type is a special kind of a *collection*, as well, as revealed with the `Collection` ABC from the [collections.abc](https://docs.python.org/3/library/collections.abc.html) module in the [standard library](https://docs.python.org/3/library/index.html)."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 29,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "import collections.abc as abc"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 30,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "True"
-      ]
-     },
-     "execution_count": 30,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "isinstance(to_words, abc.Collection)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 31,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "True"
-      ]
-     },
-     "execution_count": 31,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "isinstance(from_words, abc.Collection)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "source": [
-    "So, we may pass `to_words` or `from_words` to the built-in [len()](https://docs.python.org/3/library/functions.html#len) function to obtain the number of *items* they contain."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 32,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "slide"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "3"
-      ]
-     },
-     "execution_count": 32,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "len(to_words)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 33,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "-"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "6"
-      ]
-     },
-     "execution_count": 33,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "len(from_words)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "source": [
-    "In the terminology of the [collections.abc](https://docs.python.org/3/library/collections.abc.html) module, both are `Sized` objects."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 34,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "True"
-      ]
-     },
-     "execution_count": 34,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "isinstance(to_words, abc.Sized)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 35,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "True"
-      ]
-     },
-     "execution_count": 35,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "isinstance(from_words, abc.Sized)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "source": [
-    "`dict` objects may be looped over, for example, with the `for` statement. For technical reasons, we could *not* rely on the iteration order to be *predictable* in any form until Python 3.7 in 2018. Looping over the *same* `dict` objects multiple times during its lifetime could result in *different* iteration orders every time. That behavior is intentional as `dict` objects are optimized for use cases where order does not matter. Starting with Python 3.7, `dict` objects remember the order in that items are *inserted* (cf., [Python 3.7 release notes](https://www.python.org/downloads/release/python-370/)). A lot of research went into this preservation of order (cf., this [PyCon 2017 talk](https://www.youtube.com/watch?v=npw4s1QTmPg) by core developer [Raymond Hettinger](https://github.com/rhettinger)).\n",
-    "\n",
-    "Because of that, the order in the two `for`-loops below is the *same* as in the *source code* that defines `to_words` and `from_words` above. In that sense, it is \"*predictable*.\" However, if we fill `dict` objects with data from real-world sources, that kind of predictability is not really helpful as such data are not written as source code, and, thus, we consider the order of items in `dict` objects to be *unpredictable*. Further, when an \"insertion\" accidentally *updates* an item, the ordering remains unchanged."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 36,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Python 3.7.4\n"
-     ]
-    }
-   ],
-   "source": [
-    "!python --version  # the order in the for-loops is predictable only for Python 3.7 or higher"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "source": [
-    "By convention, iteration goes over the *keys* in the `dict` object only. The \"*Dictionary Methods*\" sub-section below shows how to loop over the *items* or the *values* instead."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 37,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "slide"
-    }
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "0\n",
-      "1\n",
-      "2\n"
-     ]
-    }
-   ],
-   "source": [
-    "for number in to_words:\n",
-    "    print(number)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 38,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "-"
-    }
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "zero\n",
-      "one\n",
-      "two\n",
-      "three\n",
-      "four\n",
-      "five\n"
-     ]
-    }
-   ],
-   "source": [
-    "for word in from_words:\n",
-    "    print(word)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "source": [
-    "Without a predictable *forward* order, `dict` objects are not *reversible* either.\n",
-    "\n",
-    "So, passing a `dict` object to the [reversed()](https://docs.python.org/3/library/functions.html#reversed) built-in raises a `TypeError` ..."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 39,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "slide"
-    }
-   },
-   "outputs": [
-    {
-     "ename": "TypeError",
-     "evalue": "'dict' object is not reversible",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-39-2d09207399cf>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0;32mfor\u001b[0m \u001b[0mnumber\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mreversed\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mto_words\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m     \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnumber\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;31mTypeError\u001b[0m: 'dict' object is not reversible"
-     ]
-    }
-   ],
-   "source": [
-    "for number in reversed(to_words):\n",
-    "    print(number)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 40,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "outputs": [
-    {
-     "ename": "TypeError",
-     "evalue": "'dict' object is not reversible",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-40-c723940a69be>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0;32mfor\u001b[0m \u001b[0mword\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mreversed\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfrom_words\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m     \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mword\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;31mTypeError\u001b[0m: 'dict' object is not reversible"
-     ]
-    }
-   ],
-   "source": [
-    "for word in reversed(from_words):\n",
-    "    print(word)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "source": [
-    "... and also `Reversible` ABC in the [collections.abc](https://docs.python.org/3/library/collections.abc.html) module confirms that."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 41,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "False"
-      ]
-     },
-     "execution_count": 41,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "isinstance(to_words, abc.Reversible)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 42,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "False"
-      ]
-     },
-     "execution_count": 42,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "isinstance(from_words, abc.Reversible)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "source": [
-    "Of course, we could use the built-in [sorted()](https://docs.python.org/3/library/functions.html#sorted) function to loop over, for example, `to_words` in a *predictable* order. However, that *materializes* a temporary `list` object in memory containing references to all the key objects *and* creates a *new* order according to some sorting criterion that has *nothing* to do with how the items are ordered inside the hash table."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 43,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "slide"
-    }
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "five\n",
-      "four\n",
-      "one\n",
-      "three\n",
-      "two\n",
-      "zero\n"
-     ]
-    }
-   ],
-   "source": [
-    "for word in sorted(from_words):\n",
-    "    print(word)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "source": [
-    "That approach may be combined with [reversed()](https://docs.python.org/3/library/functions.html#reversed)."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 44,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "fragment"
-    }
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "zero\n",
-      "two\n",
-      "three\n",
-      "one\n",
-      "four\n",
-      "five\n"
-     ]
-    }
-   ],
-   "source": [
-    "for word in reversed(sorted(from_words)):\n",
-    "    print(word)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "source": [
-    "To show the third behavior of *collection* types, we use the boolean `in` operator to check if a given and immutable object evaluates equal to a *key* in `to_words` or `from_words`."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 45,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "slide"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "True"
-      ]
-     },
-     "execution_count": 45,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "1 in to_words"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 46,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "-"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "True"
-      ]
-     },
-     "execution_count": 46,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "1.0 in to_words  # 1.0 is not contained but compares equal to a key that is"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 47,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "False"
-      ]
-     },
-     "execution_count": 47,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "10 in to_words"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 48,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "fragment"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "True"
-      ]
-     },
-     "execution_count": 48,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "\"one\" in from_words"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 49,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "-"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "False"
-      ]
-     },
-     "execution_count": 49,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "\"ten\" in from_words"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "source": [
-    "The `Container` ABC in the [collections.abc](https://docs.python.org/3/library/collections.abc.html) module may be used to \"ask\" Python to confirm that `to_words` and `from_words` are indeed *container* types."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 50,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "True"
-      ]
-     },
-     "execution_count": 50,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "isinstance(to_words, abc.Container)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 51,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "True"
-      ]
-     },
-     "execution_count": 51,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "isinstance(from_words, abc.Container)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "slide"
-    }
-   },
-   "source": [
-    "#### Membership Testing: Lists vs. Dictionaries"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "source": [
-    "Because of the [hash table](https://en.wikipedia.org/wiki/Hash_table) implementation, the `in` operator is *extremely* fast: With the hashing/offsetting described above, Python does *not* need to initiate a [linear search](https://en.wikipedia.org/wiki/Linear_search) as in the `list` case but immediately knows the only places in memory where the searched object must be located if present in the hash table at all. Conceptually, that is like comparing the searched object against all key objects with the `==` operator *without* actually doing it.\n",
-    "\n",
-    "To show the difference, we run a little experiment. For that, we create a `haystack`, a `list` object, with `10_000_001` elements in it, *one* of which is the `needle`, namely `42`. Once again, the [randint()](https://docs.python.org/3/library/random.html#random.randint) function in the [random](https://docs.python.org/3/library/random.html) module is helpful."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 52,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "slide"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "import random"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 53,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "random.seed(87)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 54,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "-"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "needle = 42"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 55,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "-"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "haystack = [random.randint(99, 9999) for _ in range(10_000_000)]\n",
-    "haystack.append(needle)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "source": [
-    "We put the elements in `haystack` in a *random* order with the [shuffle()](https://docs.python.org/3/library/random.html#random.shuffle) function in the [random](https://docs.python.org/3/library/random.html) module."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 56,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "-"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "random.shuffle(haystack)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 57,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "-"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[8126, 7370, 3735, 213, 7922, 1434, 8557, 9609, 9704, 9564]"
-      ]
-     },
-     "execution_count": 57,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "haystack[:10]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 58,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "-"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[7237, 886, 5945, 4014, 4998, 2055, 3531, 6919, 7875, 1944]"
-      ]
-     },
-     "execution_count": 58,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "haystack[-10:]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "source": [
-    "As modern computers are generally fast, we search the `needle` a total of `10` times."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 59,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "slide"
-    }
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "4.32 s ± 0 ns per loop (mean ± std. dev. of 1 run, 1 loop each)\n"
-     ]
-    }
-   ],
-   "source": [
-    "%%timeit -n 1 -r 1\n",
-    "for _ in range(10):\n",
-    "    needle in haystack"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "source": [
-    "Now, we convert the elements of `haystack` into the keys of `magic_haystack`, a `dict` object. We use `None` as a dummy value for all items."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 60,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "fragment"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "magic_haystack = dict((x, None) for x in haystack)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "source": [
-    "To show the *massive* effect of the hash table implementation, we search the `needle` not `10` but `10_000_000` times. The code cell still runs in only a fraction of the time its counterpart does above."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 61,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "-"
-    }
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "419 ms ± 0 ns per loop (mean ± std. dev. of 1 run, 1 loop each)\n"
-     ]
-    }
-   ],
-   "source": [
-    "%%timeit -n 1 -r 1\n",
-    "for _ in range(10_000_000):\n",
-    "    needle in magic_haystack"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "source": [
-    "However, there is no fast way to look up the values the keys are mapped to. To achieve that, we have to loop over *all* items and check for each value object if it evaluates equal to searched object. That is, by definition, a linear search, as well, and rather slow. In the context of `dict` objects, we call that a **reverse look-up**."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "slide"
-    }
-   },
-   "source": [
-    "### \"Indexing\" -> Key Look-up"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "source": [
-    "The same efficient key look-up executed in the background with the `in` operator is also behind the indexing operator `[]`. Instead of returning either `True` or `False`, it returns the value object the looked up key maps to.\n",
-    "\n",
-    "To show the similarity to indexing into `list` objects, we provide another example with `to_words_list` below."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 62,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "to_words_list = [\"zero\", \"one\", \"two\"]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "source": [
-    "Without the above definitions, we could not tell the difference between `to_words` and `to_words_list`: The usage of the `[]` is the same."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 63,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "slide"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'zero'"
-      ]
-     },
-     "execution_count": 63,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "to_words[0]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 64,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'one'"
-      ]
-     },
-     "execution_count": 64,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "to_words_list[1]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "source": [
-    "Because key objects can be of any immutable type and are, in particular, not constrained to just the `int` type, the word \"*indexing*\" is an understatement here. Therefore, in the context of `dict` objects, we view the `[]` operator as a generalization of the indexing operator and refer to it as the **(key) look-up** operator. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 65,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "fragment"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "2"
-      ]
-     },
-     "execution_count": 65,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from_words[\"two\"]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "source": [
-    "If a key is not in a `dict` object, Python raises a `KeyError`. A sequence type would raise an `IndexError` in this situation."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 66,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "fragment"
-    }
-   },
-   "outputs": [
-    {
-     "ename": "KeyError",
-     "evalue": "'drei'",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-66-8602ef11ff73>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mfrom_words\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"drei\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
-      "\u001b[0;31mKeyError\u001b[0m: 'drei'"
-     ]
-    }
-   ],
-   "source": [
-    "from_words[\"drei\"]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "source": [
-    "While `dict` objects support the `[]` operator to look up a *single* key, the more general concept of *slicing* is *not* available. That is in line with the idea that there is *no* predictable *order* associated with a `dict` object's keys, and slicing requires an order conceptually.\n",
-    "\n",
-    "To access \"lower\" levels in nested data, like `people`, we *chain* the look-up operator `[]`. For example, let's view all the mathematicians in `people`."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 67,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "slide"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[{'name': 'Gilbert Strang', 'emails': ['gilbert@mit.edu']},\n",
-       " {'name': 'Leonhard Euler', 'emails': []}]"
-      ]
-     },
-     "execution_count": 67,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "people[\"mathematicians\"]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "source": [
-    "Let's take the first mathematician on the list, ..."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 68,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "-"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'name': 'Gilbert Strang', 'emails': ['gilbert@mit.edu']}"
-      ]
-     },
-     "execution_count": 68,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "people[\"mathematicians\"][0]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "source": [
-    "... and output his name ..."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 69,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "-"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'Gilbert Strang'"
-      ]
-     },
-     "execution_count": 69,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "people[\"mathematicians\"][0][\"name\"]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "source": [
-    "... or all his emails."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 70,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "-"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "['gilbert@mit.edu']"
-      ]
-     },
-     "execution_count": 70,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "people[\"mathematicians\"][0][\"emails\"]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "slide"
-    }
-   },
-   "source": [
-    "### Mutability"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "source": [
-    "Analogous to `list` objects, we may mutate `dict` objects *in place*.\n",
-    "\n",
-    "For example, let's translate the English words in `to_words` to their German counterparts. Behind the scenes, Python determines the bucket of the objects passed to the `[]` operator, looks them up in the hash table, and, if present, *updates* their references to the mapped value objects."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 71,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "slide"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "to_words[0] = \"null\"\n",
-    "to_words[1] = \"eins\"\n",
-    "to_words[2] = \"zwei\""
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 72,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "-"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{0: 'null', 1: 'eins', 2: 'zwei'}"
-      ]
-     },
-     "execution_count": 72,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "to_words"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "source": [
-    "Let's add two more items. Again, Python determines their buckets, but this time finds them to be empty, and *inserts* the references to their key and value objects."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 73,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "slide"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "to_words[3] = \"drei\"\n",
-    "to_words[4] = \"vier\""
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 74,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "-"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{0: 'null', 1: 'eins', 2: 'zwei', 3: 'drei', 4: 'vier'}"
-      ]
-     },
-     "execution_count": 74,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "to_words"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "source": [
-    "None of these operations change the identity of the `to_words` object."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 75,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "139953979638320"
-      ]
-     },
-     "execution_count": 75,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "id(to_words)  # same memory location as before"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "source": [
-    "The `del` statement removes individual items. Python just removes the *two* references to the key and value objects in the corresponding bucket."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 76,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "slide"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "del to_words[0]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 77,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "-"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{1: 'eins', 2: 'zwei', 3: 'drei', 4: 'vier'}"
-      ]
-     },
-     "execution_count": 77,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "to_words"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "source": [
-    "We may also change parts of nested data, such as `people`.\n",
-    "\n",
-    "For example, let's add [Albert Einstein](https://en.wikipedia.org/wiki/Albert_Einstein) to the list of physicists, ..."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 78,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "slide"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "people[\"physicists\"].append({\"name\": \"Albert Einstein\"})"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "source": [
-    "... complete Guido's name, ..."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 79,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "-"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "people[\"programmers\"][0][\"name\"] = \"Guido van Rossum\""
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "source": [
-    "... and remove his work email because he retired."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 80,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "-"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "del people[\"programmers\"][0][\"emails\"][1]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "source": [
-    "Now, `people` looks like this."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 81,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "-"
-    }
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "{'mathematicians': [{'emails': ['gilbert@mit.edu'],\n",
-      "                     'name': 'Gilbert Strang'},\n",
-      "                    {'emails': [],\n",
-      "                     'name': 'Leonhard Euler'}],\n",
-      " 'physicists': [{'name': 'Albert Einstein'}],\n",
-      " 'programmers': [{'emails': ['guido@python.org'],\n",
-      "                  'name': 'Guido van Rossum'}]}\n"
-     ]
-    }
-   ],
-   "source": [
-    "pprint(people, indent=1, width=60)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "slide"
-    }
-   },
-   "source": [
-    "### Dictionary Methods"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "source": [
-    "`dict` objects come with many methods bound on them (cf., [documentation](https://docs.python.org/3/library/stdtypes.html#dict)), many of which are standardized by the `Mapping` and `MutableMapping` ABCs from the [collections.abc](https://docs.python.org/3/library/collections.abc.html) module. While the former requires a data type adhering to it to implement the [keys()](https://docs.python.org/3/library/stdtypes.html#dict.keys), [values()](https://docs.python.org/3/library/stdtypes.html#dict.values), [items()](https://docs.python.org/3/library/stdtypes.html#dict.items), and [get()](https://docs.python.org/3/library/stdtypes.html#dict.get) methods, which *never* mutate an object, the latter formalizes the [update()](https://docs.python.org/3/library/stdtypes.html#dict.update), [pop()](https://docs.python.org/3/library/stdtypes.html#dict.pop), [popitem()](https://docs.python.org/3/library/stdtypes.html#dict.popitem), [clear()](https://docs.python.org/3/library/stdtypes.html#dict.clear), and [setdefault()](https://docs.python.org/3/library/stdtypes.html#dict.setdefault) methods, which *may* do so."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 82,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "True"
-      ]
-     },
-     "execution_count": 82,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "isinstance(to_words, abc.Mapping)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 83,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "True"
-      ]
-     },
-     "execution_count": 83,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "isinstance(from_words, abc.Mapping)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "source": [
-    "While iteration over a mapping type goes over its keys, we may emphasize this explicitly by adding the [keys()](https://docs.python.org/3/library/stdtypes.html#dict.keys) method in the `for`-loop. Again, the iteration order is equivalent to the insertion order but still considered *unpredictable*."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 84,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "slide"
-    }
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "zero\n",
-      "one\n",
-      "two\n",
-      "three\n",
-      "four\n",
-      "five\n"
-     ]
-    }
-   ],
-   "source": [
-    "for word in from_words.keys():\n",
-    "    print(word)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "source": [
-    "[keys()](https://docs.python.org/3/library/stdtypes.html#dict.keys) returns an object of type `dict_keys`. That is a dynamic **view** inside the `from_words`'s hash table, which means it does *not* copy the references to the keys, and changes to `from_words` can be seen through it. View objects behave much like `dict` objects themselves."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 85,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "fragment"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "dict_keys(['zero', 'one', 'two', 'three', 'four', 'five'])"
-      ]
-     },
-     "execution_count": 85,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from_words.keys()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "source": [
-    "iews can be materialized with the [list()](https://docs.python.org/3/library/functions.html#func-list) built-in. However, that may introduce *semantic* errors into a program an the newly created `list` object has a \"*predictable*\" order (i.e., indexes `0`, `1`, ...) created from an *unpredictable* one."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 86,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "['zero', 'one', 'two', 'three', 'four', 'five']"
-      ]
-     },
-     "execution_count": 86,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "list(from_words.keys())"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "source": [
-    "To loop over the value objects instead, we use the [values()](https://docs.python.org/3/library/stdtypes.html#dict.values) method. That returns a *view* on the value objects inside `from_words` without copying the references to them."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 87,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "slide"
-    }
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "0\n",
-      "1\n",
-      "2\n",
-      "3\n",
-      "4\n",
-      "5\n"
-     ]
-    }
-   ],
-   "source": [
-    "for number in from_words.values():\n",
-    "    print(number)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 88,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "-"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "dict_values([0, 1, 2, 3, 4, 5])"
-      ]
-     },
-     "execution_count": 88,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from_words.values()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "source": [
-    "To loop over key-value pairs, we invoke the [items()](https://docs.python.org/3/library/stdtypes.html#dict.items) method. That returns a view on the key-value pairs as `tuple` objects, where the first element is the key and the second the value. Because of that, we use tuple unpacking in the `for`-loop."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 89,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "slide"
-    }
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "zero -> 0\n",
-      "one -> 1\n",
-      "two -> 2\n",
-      "three -> 3\n",
-      "four -> 4\n",
-      "five -> 5\n"
-     ]
-    }
-   ],
-   "source": [
-    "for word, number in from_words.items():\n",
-    "    print(f\"{word} -> {number}\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 90,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "-"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "dict_items([('zero', 0), ('one', 1), ('two', 2), ('three', 3), ('four', 4), ('five', 5)])"
-      ]
-     },
-     "execution_count": 90,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from_words.items()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "source": [
-    "Above, we see how the look-up operator fails *loudly* with a `KeyError` if a key is *not* in a `dict` object. For example, `to_words` does *not* have a key `0` any more."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 91,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "slide"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{1: 'eins', 2: 'zwei', 3: 'drei', 4: 'vier'}"
-      ]
-     },
-     "execution_count": 91,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "to_words"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 92,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "outputs": [
-    {
-     "ename": "KeyError",
-     "evalue": "0",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-92-30b825de37f0>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mto_words\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
-      "\u001b[0;31mKeyError\u001b[0m: 0"
-     ]
-    }
-   ],
-   "source": [
-    "to_words[0]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "source": [
-    "That may be mitigated with the [get()](https://docs.python.org/3/library/stdtypes.html#dict.get) method that takes two arguments: `key` and `default`. It returns the value object `key` maps to if it is in the `dict` object; otherwise, `default` is returned. If not provided, `default` is `None`."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 93,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "-"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'n/a'"
-      ]
-     },
-     "execution_count": 93,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "to_words.get(0, \"n/a\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 94,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "-"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "'eins'"
-      ]
-     },
-     "execution_count": 94,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "to_words.get(1, \"n/a\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "source": [
-    "`dict` objects are *mutable* as can be formally verified with the `MutableMapping` ABC from the [collections.abc](https://docs.python.org/3/library/collections.abc.html) module."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 95,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "True"
-      ]
-     },
-     "execution_count": 95,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "isinstance(to_words, abc.MutableMapping)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 96,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "True"
-      ]
-     },
-     "execution_count": 96,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "isinstance(from_words, abc.MutableMapping)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "source": [
-    "The [update()](https://docs.python.org/3/library/stdtypes.html#dict.update) method takes the items of another mapping and either inserts them or overwrites the ones with matching keys already in the `dict` objects. It may be used in the other two ways as the [dict()](https://docs.python.org/3/library/functions.html#func-dict) built-in allows, as well."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 97,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "slide"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{1: 'eins', 2: 'zwei', 3: 'drei', 4: 'vier'}"
-      ]
-     },
-     "execution_count": 97,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "to_words"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 98,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "-"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "to_spanish = {\n",
-    "    1: \"uno\",\n",
-    "    2: \"dos\",\n",
-    "    3: \"tres\",\n",
-    "    4: \"cuatro\",\n",
-    "    5: \"cinco\",  \n",
-    "}"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 99,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "fragment"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "to_words.update(to_spanish)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 100,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "-"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{1: 'uno', 2: 'dos', 3: 'tres', 4: 'cuatro', 5: 'cinco'}"
-      ]
-     },
-     "execution_count": 100,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "to_words"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "source": [
-    "In contrast to the `pop()` method of the `list` type, the [pop()](https://docs.python.org/3/library/stdtypes.html#dict.pop) method of the `dict` type *requires* a `key` argument to be passed. Then, it removes the corresponding key-value pair *and* returns the value object. If the `key` is not in the `dict` object, a `KeyError` is raised. With an optional `default` argument, that loud error may be suppressed and the `default` returned instead, just as with the [get()](https://docs.python.org/3/library/stdtypes.html#dict.get) method above."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 101,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "slide"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'zero': 0, 'one': 1, 'two': 2, 'three': 3, 'four': 4, 'five': 5}"
-      ]
-     },
-     "execution_count": 101,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from_words"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 102,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "-"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "number = from_words.pop(\"zero\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 103,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "-"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "0"
-      ]
-     },
-     "execution_count": 103,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "number"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 104,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "-"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'one': 1, 'two': 2, 'three': 3, 'four': 4, 'five': 5}"
-      ]
-     },
-     "execution_count": 104,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from_words"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 105,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "outputs": [
-    {
-     "ename": "KeyError",
-     "evalue": "'zero'",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-105-b91e52fa79fd>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mfrom_words\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpop\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"zero\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
-      "\u001b[0;31mKeyError\u001b[0m: 'zero'"
-     ]
-    }
-   ],
-   "source": [
-    "from_words.pop(\"zero\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 106,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "0"
-      ]
-     },
-     "execution_count": 106,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from_words.pop(\"zero\", 0)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "source": [
-    "Similar to the `pop()` method of the `list` type, the [popitem()](https://docs.python.org/3/library/stdtypes.html#dict.popitem) method of the `dict` type removes *and* returns an \"arbitrary\" key-value pair as a `tuple` object from a `dict` object. With the preservation of the insertion order in Python 3.7 and higher, this effectively becomes a \"last in, first out\" rule, just as with the `list` type. Once a `dict` object is empty, [popitem()](https://docs.python.org/3/library/stdtypes.html#dict.popitem) raises a `KeyError`."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 107,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "slide"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'one': 1, 'two': 2, 'three': 3, 'four': 4, 'five': 5}"
-      ]
-     },
-     "execution_count": 107,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from_words"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 108,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "-"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "word, number = from_words.popitem()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 109,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "-"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "('five', 5)"
-      ]
-     },
-     "execution_count": 109,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "word, number"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 110,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "-"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'one': 1, 'two': 2, 'three': 3, 'four': 4}"
-      ]
-     },
-     "execution_count": 110,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from_words"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "source": [
-    "The [clear()](https://docs.python.org/3/library/stdtypes.html#dict.clear) method removes all items but keeps the `dict` object alive in memory."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 111,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "to_words.clear()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 112,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{}"
-      ]
-     },
-     "execution_count": 112,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "to_words"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 113,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "from_words.clear()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 114,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{}"
-      ]
-     },
-     "execution_count": 114,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from_words"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "source": [
-    "The [setdefault()](https://docs.python.org/3/library/stdtypes.html#dict.setdefault) method may have a bit of an unfortunate name but is useful, in particular, with nested `list` objects. It takes two arguments, `key` and `default`, and returns the value mapped to `key` if `key` is in the `dict` object; otherwise, it inserts the `key`-`default` pair *and* returns a reference to the newly created value object. So, it is similar to the [get()](https://docs.python.org/3/library/stdtypes.html#dict.get) method above, but *mutates* the `dict` object.\n",
-    "\n",
-    "Consider the `people` example again and note hwo the `dict` object modeling \"Albert Einstein\" has *no* `\"emails\"` key in it."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 115,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "slide"
-    }
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "{'mathematicians': [{'emails': ['gilbert@mit.edu'],\n",
-      "                     'name': 'Gilbert Strang'},\n",
-      "                    {'emails': [],\n",
-      "                     'name': 'Leonhard Euler'}],\n",
-      " 'physicists': [{'name': 'Albert Einstein'}],\n",
-      " 'programmers': [{'emails': ['guido@python.org'],\n",
-      "                  'name': 'Guido van Rossum'}]}\n"
-     ]
-    }
-   ],
-   "source": [
-    "pprint(people, indent=1, width=60)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "source": [
-    "Let's say we want to append the imaginary emails `\"leonhard@math.org\"` and `\"albert@physics.org\"`. With the current \"messy\" structure, we cannot be sure if a `dict` object modeling a person has already a `\"emails\"` key or not. We could first use the `in` operator to check for that and create a new `list` object in a second step if one is missing. Third, we would finally append the new email.\n",
-    "\n",
-    "[setdefault()](https://docs.python.org/3/library/stdtypes.html#dict.setdefault) allows us to do all of the three steps at once. More importantly, behind the scenes Python only needs to make *one* key look-up instead of potentially three. For large nested data that could speed up the computations significantly.\n",
-    "\n",
-    "So, the first code cell below adds the email to the already existing empty `list` object, while the second one creates a new one."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 116,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "fragment"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "people[\"mathematicians\"][1].setdefault(\"emails\", []).append(\"leonhard@math.org\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 117,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "-"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "people[\"physicists\"][0].setdefault(\"emails\", []).append(\"albert@physics.org\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 118,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "fragment"
-    }
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "{'mathematicians': [{'emails': ['gilbert@mit.edu'],\n",
-      "                     'name': 'Gilbert Strang'},\n",
-      "                    {'emails': ['leonhard@math.org'],\n",
-      "                     'name': 'Leonhard Euler'}],\n",
-      " 'physicists': [{'emails': ['albert@physics.org'],\n",
-      "                 'name': 'Albert Einstein'}],\n",
-      " 'programmers': [{'emails': ['guido@python.org'],\n",
-      "                  'name': 'Guido van Rossum'}]}\n"
-     ]
-    }
-   ],
-   "source": [
-    "pprint(people, indent=1, width=60)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "source": [
-    "In addition to the standardized methods, `dict` objects come with a [copy()](https://docs.python.org/3/library/stdtypes.html#dict.copy) method on them that creates *shallow* copies."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 119,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "slide"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "guido = people[\"programmers\"][0].copy()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 120,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "-"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'name': 'Guido van Rossum', 'emails': ['guido@python.org']}"
-      ]
-     },
-     "execution_count": 120,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "guido"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "source": [
-    "If we mutate `guido`, for example, remove all his emails with the `clear()` method on the `list` type, these changes are also visible through `people`."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 121,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "fragment"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "guido[\"emails\"].clear()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 122,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "-"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'name': 'Guido van Rossum', 'emails': []}"
-      ]
-     },
-     "execution_count": 122,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "guido"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 123,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "fragment"
-    }
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "{'mathematicians': [{'emails': ['gilbert@mit.edu'],\n",
-      "                     'name': 'Gilbert Strang'},\n",
-      "                    {'emails': ['leonhard@math.org'],\n",
-      "                     'name': 'Leonhard Euler'}],\n",
-      " 'physicists': [{'emails': ['albert@physics.org'],\n",
-      "                 'name': 'Albert Einstein'}],\n",
-      " 'programmers': [{'emails': [],\n",
-      "                  'name': 'Guido van Rossum'}]}\n"
-     ]
-    }
-   ],
-   "source": [
-    "pprint(people, indent=1, width=60)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "slide"
-    }
-   },
-   "source": [
-    "### Packing & Unpacking (continued)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "source": [
-    "Just as a single `*` symbol is used for packing and unpacking iterables in [Chapter 7](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/07_sequences_00_lecture.ipynb#Packing-&-Unpacking), a double `**` symbol implements packing and unpacking for mappings.\n",
-    "\n",
-    "Let's say we have `to_words` and `more_words` as below and want to merge the items together into a *new* `dict` object."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 124,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "slide"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "to_words = {\n",
-    "    0: \"zero\",\n",
-    "    1: \"one\",\n",
-    "    2: \"two\",\n",
-    "}"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 125,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "-"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "more_words = {\n",
-    "    2: \"TWO\",  # upper case to illustrate a point\n",
-    "    3: \"three\",\n",
-    "    4: \"four\",\n",
-    "}"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "source": [
-    "By *unpacking* the items with `**`, the newly created `dict` object is first filled with the items from `to_words` and then from `more_words`. The item with the key `2` from `more_words` overwrites its counterpart from `to_words` as it is mentioned last."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 126,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "fragment"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{0: 'zero', 1: 'one', 2: 'TWO', 3: 'three', 4: 'four'}"
-      ]
-     },
-     "execution_count": 126,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "{**to_words, **more_words}"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "source": [
-    "With the [update()](https://docs.python.org/3/library/stdtypes.html#dict.update) method from above, we can only *mutate* one of the two `dict` objects in place. So, unpacking is *no* syntactic sugar in this context."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 127,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "to_words.update(more_words)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 128,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{0: 'zero', 1: 'one', 2: 'TWO', 3: 'three', 4: 'four'}"
-      ]
-     },
-     "execution_count": 128,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "to_words  # we do not want to change an existing object"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "source": [
-    "#### Function Definitions & Calls (continued)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "source": [
-    "Both, `*` and `**` may be used within the header line of a function definition, for example, as in `print_args1()` below. Here, *positional* arguments not captured by positional parameters are *packed* into the `tuple` object `args`, and *keyword* arguments not captured by keyword parameters are *packed* into the `dict` object `kwargs`.\n",
-    "\n",
-    "For `print_args1()`, all arguments are optional, and ..."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 129,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "def print_args1(*args, **kwargs):\n",
-    "    \"\"\"Print out all arguments passed in.\"\"\"\n",
-    "    for i, arg in enumerate(args):\n",
-    "        print(\"position\", i, arg)\n",
-    "    for key, value in kwargs.items():\n",
-    "        print(\"keyword\", key, value)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "source": [
-    "... we may pass whatever we want to it, or nothing at all."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 130,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "print_args1()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 131,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "position 0 a\n",
-      "position 1 b\n",
-      "position 2 c\n"
-     ]
-    }
-   ],
-   "source": [
-    "print_args1(\"a\", \"b\", \"c\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 132,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "keyword first 1\n",
-      "keyword second 2\n",
-      "keyword third 3\n"
-     ]
-    }
-   ],
-   "source": [
-    "print_args1(first=1, second=2, third=3)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 133,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "position 0 x\n",
-      "position 1 y\n",
-      "keyword flag True\n"
-     ]
-    }
-   ],
-   "source": [
-    "print_args1(\"x\", \"y\", flag=True)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "source": [
-    "The next example, `print_args2()`, requires the caller to pass one positional argument, captured in the `pos` parameter, and one keyword argument, captured in `key`."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 134,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "def print_args2(pos, *args, key, **kwargs):\n",
-    "    \"\"\"Print out all arguments passed in.\"\"\"\n",
-    "    print(\"required position\", pos)\n",
-    "    for i, arg in enumerate(args):\n",
-    "        print(\"optional position\", i, arg)\n",
-    "    print(\"required keyword\", key)\n",
-    "    for key, value in kwargs.items():\n",
-    "        print(\"optional keyword\", key, value)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "source": [
-    "If the caller does not respect that, a `TypeError` is raised."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 135,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "outputs": [
-    {
-     "ename": "TypeError",
-     "evalue": "print_args2() missing 1 required positional argument: 'pos'",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-135-21a06a55e707>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mprint_args2\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
-      "\u001b[0;31mTypeError\u001b[0m: print_args2() missing 1 required positional argument: 'pos'"
-     ]
-    }
-   ],
-   "source": [
-    "print_args2()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 136,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "outputs": [
-    {
-     "ename": "TypeError",
-     "evalue": "print_args2() missing 1 required keyword-only argument: 'key'",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-136-06c908f24fbb>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mprint_args2\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"p\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
-      "\u001b[0;31mTypeError\u001b[0m: print_args2() missing 1 required keyword-only argument: 'key'"
-     ]
-    }
-   ],
-   "source": [
-    "print_args2(\"p\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 137,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "required position p\n",
-      "required keyword k\n"
-     ]
-    }
-   ],
-   "source": [
-    "print_args2(\"p\", key=\"k\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 138,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "required position p\n",
-      "optional position 0 x\n",
-      "optional position 1 y\n",
-      "required keyword k\n",
-      "optional keyword flag True\n"
-     ]
-    }
-   ],
-   "source": [
-    "print_args2(\"p\", \"x\", \"y\", key=\"k\", flag=True)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "source": [
-    "Just as above when we merge `to_words` and `more_words`, we may use the `**` symbol to unpack the items of a mapping in a function call."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "slide"
-    }
-   },
-   "source": [
-    "### Dictionary Comprehensions"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "source": [
-    "Analogous to list comprehensions in [Chapter 7](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/07_sequences_00_lecture.ipynb#List-Comprehensions), **dictionary comprehensions**, or **dictcomps** for short, are a concise literal notation to derive new `dict` objects out of existing ones.\n",
-    "\n",
-    "For example, let's derive `from_words` from `to_words` below by swapping the keys and values."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 139,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "slide"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "to_words = {\n",
-    "    0: \"zero\",\n",
-    "    1: \"one\",\n",
-    "    2: \"two\",\n",
-    "}"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "source": [
-    "Without a dictionary comprehension, we would have to initialize an empty `dict` object, loop over the items of the original one, and insert the key-value pairs one by one in a \"reversed\" fashion as \"value-key\" pairs. That assumes that the values are unique as otherwise some would be merged."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 140,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "fragment"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'zero': 0, 'one': 1, 'two': 2}"
-      ]
-     },
-     "execution_count": 140,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from_words = {}\n",
-    "\n",
-    "for number, word in to_words.items():\n",
-    "    from_words[word] = number\n",
-    "\n",
-    "from_words"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "source": [
-    "While that code is correct, it is also unnecessarily verbose. The dictionary comprehension below works in the same way as list comprehensions except that entire expression is written within curly braces `{}` instead of brackets `[]`, and a colon `:` added to separate the keys from the values."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 141,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "fragment"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'zero': 0, 'one': 1, 'two': 2}"
-      ]
-     },
-     "execution_count": 141,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "{v: k for k, v in to_words.items()}"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "source": [
-    "We may filter out items with an `if`-clause and transform the remaining key and value objects.\n",
-    "\n",
-    "For no good reason, let's filter out all words starting with a `\"z\"` and upper case the remainin words."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 142,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "slide"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'ONE': 1, 'TWO': 2}"
-      ]
-     },
-     "execution_count": 142,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "{v.upper(): k for k, v in to_words.items() if not v.startswith(\"z\")}"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "source": [
-    "Multiple `for`- and/or `if`-clauses are allowed.\n",
-    "\n",
-    "For example, let's find all pairs of two numbers from `1` through `10` whose product is \"close\" to `50` (e.g., within a delta of `5`). The resulting `dict` object maps `tuple` to `int` objects."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 143,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "fragment"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{(5, 9): 45,\n",
-       " (5, 10): 50,\n",
-       " (6, 8): 48,\n",
-       " (6, 9): 54,\n",
-       " (7, 7): 49,\n",
-       " (8, 6): 48,\n",
-       " (9, 5): 45,\n",
-       " (9, 6): 54,\n",
-       " (10, 5): 50}"
-      ]
-     },
-     "execution_count": 143,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "one_to_ten = range(1, 11)\n",
-    "\n",
-    "{\n",
-    "    (x, y): x * y\n",
-    "    for x in one_to_ten for y in one_to_ten\n",
-    "    if abs(x * y - 50) <= 5\n",
-    "}"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "slide"
-    }
-   },
-   "source": [
-    "### Memoization"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "slide"
-    }
-   },
-   "source": [
-    "#### \"Easy at second Glance\" Example: [Fibonacci Numbers](https://en.wikipedia.org/wiki/Fibonacci_number) (revisited)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "source": [
-    "The *recursive* implementation of the [Fibonacci numbers](https://en.wikipedia.org/wiki/Fibonacci_number) in [Chapter 4](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/04_iteration_00_lecture.ipynb#\"Easy-at-first-Glance\"-Example:-Fibonacci-Numbers) takes long to compute for large Fibonacci numbers as the number of function calls grows exponentially.\n",
-    "\n",
-    "The graph below visualizes what the problem is and also suggests a solution: Instead of calculating the return value of the `fibonacci()` function for the *same* argument over and over again, it makes sense to **cache** the result and reuse it. This concept is called **[memoization](https://en.wikipedia.org/wiki/Memoization)** in the computer science literature."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "slide"
-    }
-   },
-   "source": [
-    "<img src=\"static/fibonacci_call_graph.png\" width=\"50%\" align=\"left\">"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "source": [
-    "Below is revision of the recursive `fibonacci()` implementation that uses a **globally** defined `dict` object `memo` to store intermediate results and look them up.\n",
-    "\n",
-    "To be precise, called with a valid `i`, the the revised `fibonacci()` function first checks if the `i`th Fibonacci number has already been calculated before. If yes, it is in the `memo` dictionary. That number is then returned immediately *without* any more calculations. If no, there is no corresponding entry in `memo` and a recursive function call must be made. The number obtained by recursion is then put into `memo`.\n",
-    "\n",
-    "When we follow the flow of execution closely, we realize that the intermediate results represented by the left-most path in the graph above are calculated first. `fibonacci(1)` (i.e., the left-most leaf node) is the first base case reached, followed immediately by `fibonacci(0)`. From that moment onwards, the flow of execution moves back up the left-most path while adding together the two corresponding child nodes.\n",
-    "\n",
-    "Effectively, this mirrors the *iterative* implementation in that the order of all computational steps are *identical*.\n",
-    "\n",
-    "We added a keyword-only argument `debug` that allows the caller to print out a message every time a `i` was *not* in the `memo`."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 144,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "slide"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "memo = {\n",
-    "    0: 0,\n",
-    "    1: 1,\n",
-    "}"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 145,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "-"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "import numbers  # for the goose typing"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 146,
-   "metadata": {
-    "code_folding": [],
-    "slideshow": {
-     "slide_type": "slide"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "def fibonacci(i, *, debug=False):\n",
-    "    \"\"\"Calculate the ith Fibonacci number.\n",
-    "\n",
-    "    Args:\n",
-    "        i (int): index of the Fibonacci number to calculate\n",
-    "        debug (bool): show non-cached calls; defaults to False\n",
-    "\n",
-    "    Returns:\n",
-    "        ith_fibonacci (int)\n",
-    "\n",
-    "    Raises:\n",
-    "        TypeError: if i is not an integer\n",
-    "        ValueError: if i is not positive\n",
-    "    \"\"\"\n",
-    "    if not isinstance(i, numbers.Integral):\n",
-    "        raise TypeError(\"i must be an integer\")\n",
-    "    elif i < 0:\n",
-    "        raise ValueError(\"i must be non-negative\")\n",
-    "\n",
-    "    if i in memo:\n",
-    "        return memo[i]\n",
-    "\n",
-    "    if debug:  # added for didactical purposes\n",
-    "        print(f\"fibonacci({i}) is calculated\")\n",
-    "\n",
-    "    recurse = fibonacci(i - 1, debug=debug) + fibonacci(i - 2, debug=debug)\n",
-    "    memo[i] = recurse\n",
-    "    return recurse"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 147,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "slide"
-    }
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "fibonacci(12) is calculated\n",
-      "fibonacci(11) is calculated\n",
-      "fibonacci(10) is calculated\n",
-      "fibonacci(9) is calculated\n",
-      "fibonacci(8) is calculated\n",
-      "fibonacci(7) is calculated\n",
-      "fibonacci(6) is calculated\n",
-      "fibonacci(5) is calculated\n",
-      "fibonacci(4) is calculated\n",
-      "fibonacci(3) is calculated\n",
-      "fibonacci(2) is calculated\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "144"
-      ]
-     },
-     "execution_count": 147,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "fibonacci(12, debug=True)  # = 13th number, 11 recursive calls necessary"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "source": [
-    "Now, calling `fibonacci()` has the *side effect* of growing the `memo` in the *global scope*. So, subsequent calls to `fibonacci()` need not calculate any Fibonacci number with an index `i` smaller than the maximum `i` used so far. Because of that, this `fibonacci()` is *not* a *pure* function."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 148,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "144"
-      ]
-     },
-     "execution_count": 148,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "fibonacci(12, debug=True)  # = 13th number, no recursive calls needed"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 149,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "fragment"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{0: 0,\n",
-       " 1: 1,\n",
-       " 2: 1,\n",
-       " 3: 2,\n",
-       " 4: 3,\n",
-       " 5: 5,\n",
-       " 6: 8,\n",
-       " 7: 13,\n",
-       " 8: 21,\n",
-       " 9: 34,\n",
-       " 10: 55,\n",
-       " 11: 89,\n",
-       " 12: 144}"
-      ]
-     },
-     "execution_count": 149,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "memo"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "source": [
-    "##### Efficiency of Algorithms (continued)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "source": [
-    "With memoization, the recursive `fibonacci()` implementation is as fast as its iterative counterpart, even for large numbers.\n",
-    "\n",
-    "The `%%timeit` magic, by default, runs a code cell seven times. Whereas in the first run, *new* Fibonacci numbers (i.e., intermediate results) are added to the `memo`, `fibonacci()` has no work to do in the subsequent six runs. `%%timeit` realizes this and tells us that \"an intermediate result is being cached.\""
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 150,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "fragment"
-    }
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "The slowest run took 183.70 times longer than the fastest. This could mean that an intermediate result is being cached.\n",
-      "21.4 µs ± 50.4 µs per loop (mean ± std. dev. of 7 runs, 1 loop each)\n"
-     ]
-    }
-   ],
-   "source": [
-    "%%timeit -n 1\n",
-    "fibonacci(99)  # = 100th number"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 151,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "-"
-    }
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "The slowest run took 1974.48 times longer than the fastest. This could mean that an intermediate result is being cached.\n",
-      "580 µs ± 1.41 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n"
-     ]
-    }
-   ],
-   "source": [
-    "%%timeit -n 1\n",
-    "fibonacci(999)  # = 1,000th number"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "source": [
-    "The iterative implementation still has an advantage as the `RecursionError` shows for even larger numbers.\n",
-    "\n",
-    "This exception occurs as Python must keep track of *every* function call *until* it has returned, and with large enough `i`, the recursion tree above grows too big. By default, Python has a limit of up to 3000 *simultaneous* function calls. So, theoretically this exception is not a bug in the narrow sense but the result of a \"security\" measure that is supposed to keep a computer from crashing. However, practically most high-level languages like Python incur such an overhead cost: It results from the fact that someone (i.e., Python) needs to manage each function call's *local scope*. With the `for`-loop in the iterative version, we do this managing ourselves."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 152,
-   "metadata": {
-    "scrolled": true,
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "outputs": [
-    {
-     "ename": "RecursionError",
-     "evalue": "maximum recursion depth exceeded in comparison",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mRecursionError\u001b[0m                            Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-152-cc76051ea9de>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mget_ipython\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrun_cell_magic\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'timeit'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'-n 1'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'fibonacci(9999)  # = 10,000th number\\n'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
-      "\u001b[0;32m~/.pyenv/versions/anaconda3-2019.10/lib/python3.7/site-packages/IPython/core/interactiveshell.py\u001b[0m in \u001b[0;36mrun_cell_magic\u001b[0;34m(self, magic_name, line, cell)\u001b[0m\n\u001b[1;32m   2357\u001b[0m             \u001b[0;32mwith\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbuiltin_trap\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2358\u001b[0m                 \u001b[0margs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mmagic_arg_s\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcell\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2359\u001b[0;31m                 \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2360\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mresult\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2361\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m</home/webartifex/.pyenv/versions/anaconda3-2019.10/lib/python3.7/site-packages/decorator.py:decorator-gen-60>\u001b[0m in \u001b[0;36mtimeit\u001b[0;34m(self, line, cell, local_ns)\u001b[0m\n",
-      "\u001b[0;32m~/.pyenv/versions/anaconda3-2019.10/lib/python3.7/site-packages/IPython/core/magic.py\u001b[0m in \u001b[0;36m<lambda>\u001b[0;34m(f, *a, **k)\u001b[0m\n\u001b[1;32m    185\u001b[0m     \u001b[0;31m# but it's overkill for just that one bit of state.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    186\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mmagic_deco\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marg\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 187\u001b[0;31m         \u001b[0mcall\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mlambda\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mk\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mk\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    188\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    189\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mcallable\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marg\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/.pyenv/versions/anaconda3-2019.10/lib/python3.7/site-packages/IPython/core/magics/execution.py\u001b[0m in \u001b[0;36mtimeit\u001b[0;34m(self, line, cell, local_ns)\u001b[0m\n\u001b[1;32m   1160\u001b[0m                     \u001b[0;32mbreak\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1161\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1162\u001b[0;31m         \u001b[0mall_runs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtimer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrepeat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrepeat\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnumber\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1163\u001b[0m         \u001b[0mbest\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mall_runs\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m/\u001b[0m \u001b[0mnumber\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1164\u001b[0m         \u001b[0mworst\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmax\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mall_runs\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m/\u001b[0m \u001b[0mnumber\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/.pyenv/versions/anaconda3-2019.10/lib/python3.7/timeit.py\u001b[0m in \u001b[0;36mrepeat\u001b[0;34m(self, repeat, number)\u001b[0m\n\u001b[1;32m    202\u001b[0m         \u001b[0mr\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    203\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrepeat\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 204\u001b[0;31m             \u001b[0mt\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtimeit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnumber\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    205\u001b[0m             \u001b[0mr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mt\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    206\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mr\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/.pyenv/versions/anaconda3-2019.10/lib/python3.7/site-packages/IPython/core/magics/execution.py\u001b[0m in \u001b[0;36mtimeit\u001b[0;34m(self, number)\u001b[0m\n\u001b[1;32m    167\u001b[0m         \u001b[0mgc\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdisable\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    168\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 169\u001b[0;31m             \u001b[0mtiming\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0minner\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mit\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtimer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    170\u001b[0m         \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    171\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mgcold\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m<magic-timeit>\u001b[0m in \u001b[0;36minner\u001b[0;34m(_it, _timer)\u001b[0m\n",
-      "\u001b[0;32m<ipython-input-146-223333299fa0>\u001b[0m in \u001b[0;36mfibonacci\u001b[0;34m(i, debug)\u001b[0m\n\u001b[1;32m     24\u001b[0m         \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34mf\"fibonacci({i}) is calculated\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     25\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 26\u001b[0;31m     \u001b[0mrecurse\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfibonacci\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mi\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdebug\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdebug\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mfibonacci\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mi\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0;36m2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdebug\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdebug\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     27\u001b[0m     \u001b[0mmemo\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mrecurse\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     28\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mrecurse\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "... last 1 frames repeated, from the frame below ...\n",
-      "\u001b[0;32m<ipython-input-146-223333299fa0>\u001b[0m in \u001b[0;36mfibonacci\u001b[0;34m(i, debug)\u001b[0m\n\u001b[1;32m     24\u001b[0m         \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34mf\"fibonacci({i}) is calculated\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     25\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 26\u001b[0;31m     \u001b[0mrecurse\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfibonacci\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mi\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdebug\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdebug\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mfibonacci\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mi\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0;36m2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdebug\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdebug\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     27\u001b[0m     \u001b[0mmemo\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mrecurse\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     28\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mrecurse\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;31mRecursionError\u001b[0m: maximum recursion depth exceeded in comparison"
-     ]
-    }
-   ],
-   "source": [
-    "%%timeit -n 1\n",
-    "fibonacci(9999)  # = 10,000th number"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "source": [
-    "We could \"hack\" a bit with Python's default configuration using the [sys](https://docs.python.org/3/library/sys.html) module in the [standard library](https://docs.python.org/3/library/index.html) and make it work anyhow. As we are good citizens, we reset everything to the defaults after our hack is completed."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 153,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "import sys"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 154,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "old_recursion_limit = sys.getrecursionlimit()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 155,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "3000"
-      ]
-     },
-     "execution_count": 155,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "old_recursion_limit"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 156,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "sys.setrecursionlimit(99999)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "source": [
-    "Computational speed is *not* the problem here."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 157,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "The slowest run took 27724.52 times longer than the fastest. This could mean that an intermediate result is being cached.\n",
-      "3.11 ms ± 7.61 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n"
-     ]
-    }
-   ],
-   "source": [
-    "%%timeit -n 1\n",
-    "fibonacci(9999)  # = 10,000th number"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 158,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "sys.setrecursionlimit(old_recursion_limit)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "slide"
-    }
-   },
-   "source": [
-    "#### \"Easy at third Glance\" Example: [Fibonacci Numbers](https://en.wikipedia.org/wiki/Fibonacci_number) (revisited)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "source": [
-    "It is considered *bad practice* to make a function and thereby its correctness dependent on a program's *global state*: For example, `memo` above could be \"manipulated.\"\n",
-    "\n",
-    "More often than not, such things happen by accident: Imagine we wrote two independent recursive functions with memoization built-in to solve different problems, and, unintentionally, we made both work with the *same* global `memo`. As a result, we would observe \"random\" bugs depending on the order in which we executed these functions. Such bugs are hard to track down in practice.\n",
-    "\n",
-    "A common pattern is to avoid global state and pass intermediate results \"down\" the recursion tree in a \"hidden\" argument. By convention, we prefix variable and argument names with a single leading underscore `_`, such as with `_memo` below, to indicate that a user of our code *must not* use it. Also, we make `_memo` a *keyword-only* argument to force ourselves to always explicitly name it in a function call. Because it is an **implementation detail**, the `_memo` argument is *not* even mentioned in the docstring.\n",
-    "\n",
-    "When initially called, `fibonacci()` creates a new `dict` object named `_memo` in its *local scope*. That is then shared \"internally\" between successive function calls by passing it on as an argument. Once the first call to `fibonacci()` returns, `_memo` is \"forgotten.\""
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 159,
-   "metadata": {
-    "code_folding": [],
-    "slideshow": {
-     "slide_type": "slide"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "def fibonacci(i, *, debug=False, _memo=None):\n",
-    "    \"\"\"Calculate the ith Fibonacci number.\n",
-    "\n",
-    "    Args:\n",
-    "        i (int): index of the Fibonacci number to calculate\n",
-    "        debug (bool): show non-cached calls; defaults to False\n",
-    "\n",
-    "    Returns:\n",
-    "        ith_fibonacci (int)\n",
-    "\n",
-    "    Raises:\n",
-    "        TypeError: if i is not an integer\n",
-    "        ValueError: if i is not positive\n",
-    "    \"\"\"\n",
-    "    if not isinstance(i, numbers.Integral):\n",
-    "        raise TypeError(\"i must be an integer\")\n",
-    "    elif i < 0:\n",
-    "        raise ValueError(\"i must be non-negative\")\n",
-    "\n",
-    "    if _memo is None:\n",
-    "        _memo = {\n",
-    "            0: 0,\n",
-    "            1: 1,\n",
-    "        }\n",
-    "\n",
-    "    if i in _memo:\n",
-    "        return _memo[i]\n",
-    "\n",
-    "    if debug:  # added for didactical purposes\n",
-    "        print(f\"fibonacci({i}) is calculated\")\n",
-    "\n",
-    "    recurse = (\n",
-    "        fibonacci(i - 1, debug=debug, _memo=_memo)\n",
-    "        + fibonacci(i - 2, debug=debug, _memo=_memo)\n",
-    "    )\n",
-    "    _memo[i] = recurse\n",
-    "    return recurse"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 160,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "slide"
-    }
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "fibonacci(12) is calculated\n",
-      "fibonacci(11) is calculated\n",
-      "fibonacci(10) is calculated\n",
-      "fibonacci(9) is calculated\n",
-      "fibonacci(8) is calculated\n",
-      "fibonacci(7) is calculated\n",
-      "fibonacci(6) is calculated\n",
-      "fibonacci(5) is calculated\n",
-      "fibonacci(4) is calculated\n",
-      "fibonacci(3) is calculated\n",
-      "fibonacci(2) is calculated\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "144"
-      ]
-     },
-     "execution_count": 160,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "fibonacci(12, debug=True)  # = 13th number, 11 recursive calls necessary"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "source": [
-    "Because `fibonacci()` is now independent from *global state*, the same eleven recursive function calls are made each time. So, this `fibonacci()` is a *pure* function."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 161,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "fibonacci(12) is calculated\n",
-      "fibonacci(11) is calculated\n",
-      "fibonacci(10) is calculated\n",
-      "fibonacci(9) is calculated\n",
-      "fibonacci(8) is calculated\n",
-      "fibonacci(7) is calculated\n",
-      "fibonacci(6) is calculated\n",
-      "fibonacci(5) is calculated\n",
-      "fibonacci(4) is calculated\n",
-      "fibonacci(3) is calculated\n",
-      "fibonacci(2) is calculated\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "144"
-      ]
-     },
-     "execution_count": 161,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "fibonacci(12, debug=True)  # = 13th number, still 11 recursive calls necessary"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "source": [
-    "##### Efficiency of Algorithms (continued)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "source": [
-    "Also, the runtime is now stable (i.e., no message that \"an intermediate result is being cached\"). The limitation with respect to the maximum number of simultaneous function calls still applies."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 162,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "138 µs ± 5.01 µs per loop (mean ± std. dev. of 7 runs, 1 loop each)\n"
-     ]
-    }
-   ],
-   "source": [
-    "%%timeit -n 1\n",
-    "fibonacci(99)  # = 100th number"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 163,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1.55 ms ± 56.7 µs per loop (mean ± std. dev. of 7 runs, 1 loop each)\n"
-     ]
-    }
-   ],
-   "source": [
-    "%%timeit -n 1\n",
-    "fibonacci(999)  # = 1,000th number"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 164,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "sys.setrecursionlimit(99999)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 165,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "20.8 ms ± 8.09 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n"
-     ]
-    }
-   ],
-   "source": [
-    "%%timeit -n 1\n",
-    "fibonacci(9999)  # = 10,000th number"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 166,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "sys.setrecursionlimit(old_recursion_limit)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "source": [
-    "### Specialized Mappings"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "source": [
-    "The [collections](https://docs.python.org/3/library/collections.html) module in the [standard library](https://docs.python.org/3/library/index.html) provides specialized mapping types for common enough use cases."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "source": [
-    "#### The `OrderedDict` Type"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "source": [
-    "The [OrderedDict](https://docs.python.org/3/library/collections.html#collections.OrderedDict) type may be used to create a `dict`-like object with the added feature of *explicitly* remembering the *insertion* order of its items.\n",
-    "\n",
-    "Let's look at a quick example: We create an `OrderedDict` object by passing an iterable of $2$-element iterables, one of the three ways to use [dict()](https://docs.python.org/3/library/functions.html#func-dict)."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 167,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "from collections import OrderedDict"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 168,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "ordered = OrderedDict([(\"first\", 1), (\"second\", 2), (\"third\", 3)])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 169,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "OrderedDict([('first', 1), ('second', 2), ('third', 3)])"
-      ]
-     },
-     "execution_count": 169,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "ordered"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 170,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "collections.OrderedDict"
-      ]
-     },
-     "execution_count": 170,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "type(ordered)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "source": [
-    "The iteration order is the insertion order, and ..."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 171,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "first 1\n",
-      "second 2\n",
-      "third 3\n"
-     ]
-    }
-   ],
-   "source": [
-    "for key, value in ordered.items():\n",
-    "    print(key, value)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "source": [
-    "... we may also loop over `ordered` in *reverse* order."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 172,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "third 3\n",
-      "second 2\n",
-      "first 1\n"
-     ]
-    }
-   ],
-   "source": [
-    "for key, value in reversed(ordered.items()):\n",
-    "    print(key, value)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "source": [
-    "For `OrderedDict` objects, the [popitem()](https://docs.python.org/3/library/collections.html#collections.OrderedDict.popitem) method takes an optional and boolean argument `last`. That allows us to remove either the *first* or *last* item inserted.\n",
-    "\n",
-    "Further, the [move_to_end()](https://docs.python.org/3/library/collections.html#collections.OrderedDict.move_to_end) method allows us to move any item by its key to the end of the order. It also takes an optional argument `last`."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 173,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "ordered.move_to_end(\"first\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 174,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "second 2\n",
-      "third 3\n",
-      "first 1\n"
-     ]
-    }
-   ],
-   "source": [
-    "for key, value in ordered.items():\n",
-    "    print(key, value)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 175,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "ordered.move_to_end(\"first\", last=False)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 176,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "first 1\n",
-      "second 2\n",
-      "third 3\n"
-     ]
-    }
-   ],
-   "source": [
-    "for key, value in ordered.items():\n",
-    "    print(key, value)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "source": [
-    "Otherwise, an `OrderedDict` object is no different than a normal `dict` one, starting with Python 3.7."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "source": [
-    "#### The `defaultdict` Type"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "source": [
-    "A more useful mapping is the [defaultdict](https://docs.python.org/3/library/collections.html#collections.defaultdict) type, which allows us to define a factory function. That creates default values whenever we look up a key that does not yet exist. Ordinary `dict` objects would throw a `KeyError` exception in such situations.\n",
-    "\n",
-    "Let's say we have a `list` with *records* of goals scored during a soccer game. The records consist of the fields \"Country,\" \"Player,\" and the \"Time\" when a goal was scored. Our task is to group the goals by player and/or country."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 177,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "goals = [\n",
-    "    (\"Germany\", \"MĂĽller\", 11), (\"Germany\", \"Klose\", 23),\n",
-    "    (\"Germany\", \"Kroos\", 24), (\"Germany\", \"Kroos\", 26),\n",
-    "    (\"Germany\", \"Khedira\", 29), (\"Germany\", \"SchĂĽrrle\", 69),\n",
-    "    (\"Germany\", \"SchĂĽrrle\", 79), (\"Brazil\", \"Oscar\", 90),\n",
-    "]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "source": [
-    "Using a normal `dict` object, we have to tediously check if a player has already scored a goal before. If not, we must create a *new* `list` object with the first time the player scored. Otherwise, we append the goal to an already existing `list` object."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 178,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'MĂĽller': [11],\n",
-       " 'Klose': [23],\n",
-       " 'Kroos': [24, 26],\n",
-       " 'Khedira': [29],\n",
-       " 'SchĂĽrrle': [69, 79],\n",
-       " 'Oscar': [90]}"
-      ]
-     },
-     "execution_count": 178,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "goals_by_player = {}\n",
-    "\n",
-    "for _, player, minute in goals:\n",
-    "    if player not in goals_by_player:\n",
-    "        goals_by_player[player] = [minute]\n",
-    "    else:\n",
-    "        goals_by_player[player].append(minute)\n",
-    "\n",
-    "goals_by_player"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "source": [
-    "We could replace the `if`-`else`-logic with the [setdefault()](https://docs.python.org/3/library/stdtypes.html#dict.setdefault) method mentioned above."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 179,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'MĂĽller': [11],\n",
-       " 'Klose': [23],\n",
-       " 'Kroos': [24, 26],\n",
-       " 'Khedira': [29],\n",
-       " 'SchĂĽrrle': [69, 79],\n",
-       " 'Oscar': [90]}"
-      ]
-     },
-     "execution_count": 179,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "goals_by_player = {}\n",
-    "\n",
-    "for _, player, minute in goals:\n",
-    "    goals_by_player.setdefault(player, []).append(minute)\n",
-    "\n",
-    "goals_by_player"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "source": [
-    "Instead, with a `defaultdict` object, we can portray the code fragment's intent in a concise form. We pass a reference to the [list()](https://docs.python.org/3/library/functions.html#func-list) built-in to `defaultdict`."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 180,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "from collections import defaultdict"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 181,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "defaultdict(list,\n",
-       "            {'MĂĽller': [11],\n",
-       "             'Klose': [23],\n",
-       "             'Kroos': [24, 26],\n",
-       "             'Khedira': [29],\n",
-       "             'SchĂĽrrle': [69, 79],\n",
-       "             'Oscar': [90]})"
-      ]
-     },
-     "execution_count": 181,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "goals_by_player = defaultdict(list)\n",
-    "\n",
-    "for _, player, minute in goals:\n",
-    "    goals_by_player[player].append(minute)\n",
-    "\n",
-    "goals_by_player"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 182,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "collections.defaultdict"
-      ]
-     },
-     "execution_count": 182,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "type(goals_by_player)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "source": [
-    "The factory function is stored in the `default_factory` attribute."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 183,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "list"
-      ]
-     },
-     "execution_count": 183,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "goals_by_player.default_factory"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "source": [
-    "If we want this code to produce a normal `dict` object, we pass `goals_by_player` to the [dict()](https://docs.python.org/3/library/functions.html#func-dict) built-in."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 184,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'MĂĽller': [11],\n",
-       " 'Klose': [23],\n",
-       " 'Kroos': [24, 26],\n",
-       " 'Khedira': [29],\n",
-       " 'SchĂĽrrle': [69, 79],\n",
-       " 'Oscar': [90]}"
-      ]
-     },
-     "execution_count": 184,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "dict(goals_by_player)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "source": [
-    "Being creative, we use a factory function that returns another [defaultdict](https://docs.python.org/3/library/collections.html#collections.defaultdict) with [list()](https://docs.python.org/3/library/functions.html#func-list) as its factory to group on the country and the player level simultaneously."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 185,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "defaultdict(<function __main__.<lambda>()>,\n",
-       "            {'Germany': defaultdict(list,\n",
-       "                         {'MĂĽller': [11],\n",
-       "                          'Klose': [23],\n",
-       "                          'Kroos': [24, 26],\n",
-       "                          'Khedira': [29],\n",
-       "                          'SchĂĽrrle': [69, 79]}),\n",
-       "             'Brazil': defaultdict(list, {'Oscar': [90]})})"
-      ]
-     },
-     "execution_count": 185,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "goals_by_country_and_player = defaultdict(lambda: defaultdict(list))\n",
-    "\n",
-    "for country, player, minute in goals:\n",
-    "    goals_by_country_and_player[country][player].append(minute)\n",
-    "\n",
-    "goals_by_country_and_player"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "source": [
-    "Conversion into a normal and nested `dict` object is now a bit tricky but can be achieved in one line with a comprehension."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 186,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "{'Brazil': {'Oscar': [90]},\n",
-      " 'Germany': {'Khedira': [29],\n",
-      "             'Klose': [23],\n",
-      "             'Kroos': [24, 26],\n",
-      "             'MĂĽller': [11],\n",
-      "             'SchĂĽrrle': [69, 79]}}\n"
-     ]
-    }
-   ],
-   "source": [
-    "pprint({country: dict(by_player) for country, by_player in goals_by_country_and_player.items()})"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "source": [
-    "#### The `Counter` Type"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "source": [
-    "A common task is to count the number of occurrences of elements in an iterable.\n",
-    "\n",
-    "The [Counter](https://docs.python.org/3/library/collections.html#collections.Counter) type provides an easy-to-use interface that can be called with any iterable and returns a `dict`-like object of type `Counter` that maps each unique elements to the number of times it occurs.\n",
-    "\n",
-    "To continue the previous example, let's create an overview that shows how many goals a player scorred. We use a generator expression as the argument to `Counter`."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 187,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[('Germany', 'MĂĽller', 11),\n",
-       " ('Germany', 'Klose', 23),\n",
-       " ('Germany', 'Kroos', 24),\n",
-       " ('Germany', 'Kroos', 26),\n",
-       " ('Germany', 'Khedira', 29),\n",
-       " ('Germany', 'SchĂĽrrle', 69),\n",
-       " ('Germany', 'SchĂĽrrle', 79),\n",
-       " ('Brazil', 'Oscar', 90)]"
-      ]
-     },
-     "execution_count": 187,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "goals"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 188,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "from collections import Counter"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 189,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "scorers = Counter(x[1] for x in goals)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 190,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Counter({'MĂĽller': 1,\n",
-       "         'Klose': 1,\n",
-       "         'Kroos': 2,\n",
-       "         'Khedira': 1,\n",
-       "         'SchĂĽrrle': 2,\n",
-       "         'Oscar': 1})"
-      ]
-     },
-     "execution_count": 190,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "scorers"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 191,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "collections.Counter"
-      ]
-     },
-     "execution_count": 191,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "type(scorers)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "source": [
-    "Now we can look up individual players. `scores` behaves like a normal dictionary with regard to key look-ups."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 192,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "1"
-      ]
-     },
-     "execution_count": 192,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "scorers[\"MĂĽller\"]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "source": [
-    "By default, it returns `0` if a key is not found. So, we do not have to handle a `KeyError`."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 193,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "0"
-      ]
-     },
-     "execution_count": 193,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "scorers[\"Lahm\"]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "source": [
-    "`Counter` objects have a [most_common()](https://docs.python.org/3/library/collections.html#collections.Counter.most_common) method that returns a `list` object containing $2$-element `tuple` objects, where the first element is the element from the original iterable and the second the number of occurrences. The `list` object is sorted in descending order of occurrences."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 194,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[('Kroos', 2), ('SchĂĽrrle', 2)]"
-      ]
-     },
-     "execution_count": 194,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "scorers.most_common(2)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "source": [
-    "We can increase the count of individual entries with the [update()](https://docs.python.org/3/library/collections.html#collections.Counter.update) method: That takes an *iterable* of the elements we want to count.\n",
-    "\n",
-    "Imagine if [Philipp Lahm](https://en.wikipedia.org/wiki/Philipp_Lahm) had also scored against Brazil."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 195,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "scorers.update([\"Lahm\"])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 196,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Counter({'MĂĽller': 1,\n",
-       "         'Klose': 1,\n",
-       "         'Kroos': 2,\n",
-       "         'Khedira': 1,\n",
-       "         'SchĂĽrrle': 2,\n",
-       "         'Oscar': 1,\n",
-       "         'Lahm': 1})"
-      ]
-     },
-     "execution_count": 196,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "scorers"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "source": [
-    "If we use a `str` object as the argument instead, each individual character is treated as an element to be updated. That is most likely not what we want."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 197,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "scorers.update(\"Lahm\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 198,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Counter({'MĂĽller': 1,\n",
-       "         'Klose': 1,\n",
-       "         'Kroos': 2,\n",
-       "         'Khedira': 1,\n",
-       "         'SchĂĽrrle': 2,\n",
-       "         'Oscar': 1,\n",
-       "         'Lahm': 1,\n",
-       "         'L': 1,\n",
-       "         'a': 1,\n",
-       "         'h': 1,\n",
-       "         'm': 1})"
-      ]
-     },
-     "execution_count": 198,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "scorers"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "source": [
-    "#### The `ChainMap` Type"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "source": [
-    "Consider `to_words`, `more_words`, and `even_more_words` below. Instead of merging the items of the three `dict` objects together into a *new* one, we want to create an object that behaves as if it contained all the unified items in it without materializing them in memory a second time."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 199,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "to_words = {\n",
-    "    0: \"zero\",\n",
-    "    1: \"one\",\n",
-    "    2: \"two\",\n",
-    "}"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 200,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "more_words = {\n",
-    "    2: \"TWO\",  # upper case to illustrate a point\n",
-    "    3: \"three\",\n",
-    "    4: \"four\",\n",
-    "}"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 201,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "even_more_words = {\n",
-    "    4: \"FOUR\",  # upper case to illustrate a point\n",
-    "    5: \"five\",\n",
-    "    6: \"six\",\n",
-    "}"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "source": [
-    "The [ChainMap](https://docs.python.org/3/library/collections.html#collections.ChainMap) type allows us to do precisely that."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 202,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "from collections import ChainMap"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "source": [
-    "We simply pass all mappings as positional arguments to `ChainMap` and obtain a **proxy** object that occupies almost no memory but gives us access to the union of all the items."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 203,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "chain = ChainMap(to_words, more_words, even_more_words)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "source": [
-    "Let's loop over the items in `chain` and see what is \"in\" it. The order is obviously *unpredictable* but all seven items we expected are there. Keys of later mappings do *not* overwrite earlier keys."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 204,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "4 four\n",
-      "5 five\n",
-      "6 six\n",
-      "2 two\n",
-      "3 three\n",
-      "0 zero\n",
-      "1 one\n"
-     ]
-    }
-   ],
-   "source": [
-    "for number, word in chain.items():\n",
-    "    print(number, word)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "source": [
-    "When looking up a non-existent key, `ChainMap` objects raise a `KeyError` just like normal `dict` objects would."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 205,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "outputs": [
-    {
-     "ename": "KeyError",
-     "evalue": "10",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-205-25fb5b5b2e5f>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mchain\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m10\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
-      "\u001b[0;32m~/.pyenv/versions/anaconda3-2019.10/lib/python3.7/collections/__init__.py\u001b[0m in \u001b[0;36m__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m    914\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    915\u001b[0m                 \u001b[0;32mpass\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 916\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__missing__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m            \u001b[0;31m# support subclasses that define __missing__\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    917\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    918\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdefault\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/.pyenv/versions/anaconda3-2019.10/lib/python3.7/collections/__init__.py\u001b[0m in \u001b[0;36m__missing__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m    906\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    907\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__missing__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 908\u001b[0;31m         \u001b[0;32mraise\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    909\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    910\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__getitem__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;31mKeyError\u001b[0m: 10"
-     ]
-    }
-   ],
-   "source": [
-    "chain[10]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "slide"
-    }
-   },
-   "source": [
-    "## The `set` Type"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "source": [
-    "Python's provides a built-in `set` type that resembles [mathematical sets](https://en.wikipedia.org/wiki/Set_%28mathematics%29): Each element may only be a member of a set once, and there is no *predictable* order regarding the elements (cf., [documentation](https://docs.python.org/3/library/stdtypes.html#set)).\n",
-    "\n",
-    "To create a set, we use the literal notation, `{..., ...}`, and list all the elements. The redundant `7` and `4` are discarded."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 206,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "slide"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "numbers = {7, 7, 11, 8, 5, 3, 12, 2, 6, 9, 10, 1, 4, 4}"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "source": [
-    "`set` objects are objects on their own."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 207,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "fragment"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "139953088728672"
-      ]
-     },
-     "execution_count": 207,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "id(numbers)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 208,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "-"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "set"
-      ]
-     },
-     "execution_count": 208,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "type(numbers)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 209,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "-"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12}"
-      ]
-     },
-     "execution_count": 209,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "numbers"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "source": [
-    "To create an empty set, we must use the [set()](https://docs.python.org/3/library/functions.html#func-set) built-in as empty curly brackets, `{}`, already creates an empty `dict` object."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 210,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "slide"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "empty_dict = {}"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 211,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "-"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "dict"
-      ]
-     },
-     "execution_count": 211,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "type(empty_dict)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 212,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "fragment"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "empty_set = set()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 213,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "-"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "set()"
-      ]
-     },
-     "execution_count": 213,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "empty_set"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "source": [
-    "The [set()](https://docs.python.org/3/library/functions.html#func-set) built-in takes any iterable and only keeps unique elements.\n",
-    "\n",
-    "For example, we obtain all unique letters of a long word like so."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 214,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "fragment"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'a', 'b', 'c', 'd', 'r'}"
-      ]
-     },
-     "execution_count": 214,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "set(\"abracadabra\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "slide"
-    }
-   },
-   "source": [
-    "### Sets are like \"Dictionaries without Values\""
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "source": [
-    "The curly brackets notation can be viewed as a hint that `dict` objects are conceptually generalizations of `set`  objects, and we think of `set` objects as a collection consisting of a `dict` object's keys with all the mapped values removed.\n",
-    "\n",
-    "Like `dict` objects, `set` objects are built on top of [hash tables](https://en.wikipedia.org/wiki/Hash_table), and, thus, each element must be a *hashable* (i.e., immutable at the very least) object and can only be contained in a set once due to the buckets logic."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 215,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "slide"
-    }
-   },
-   "outputs": [
-    {
-     "ename": "TypeError",
-     "evalue": "unhashable type: 'list'",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-215-4ac30fae8196>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0;34m{\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m2\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m3\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
-      "\u001b[0;31mTypeError\u001b[0m: unhashable type: 'list'"
-     ]
-    }
-   ],
-   "source": [
-    "{0, [1, 2], 3}"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "source": [
-    "[len()](https://docs.python.org/3/library/functions.html#len) tells us the number of elements in a `set` object, which is always `Sized`."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 216,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "slide"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "12"
-      ]
-     },
-     "execution_count": 216,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "len(numbers)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 217,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "True"
-      ]
-     },
-     "execution_count": 217,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "isinstance(numbers, abc.Sized)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "source": [
-    "We may loop over the elements in a `set` object, but we must keep in mind that there is no *predictable* order. In contrast to `dict` objects, the iteration order is also *not* guaranteed to be the insertion order. Because of the special hash values for `int` objects, `numbers` seems to be \"magically\" sorted, which is not the case."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 218,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "fragment"
-    }
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "1 2 3 4 5 6 7 8 9 10 11 12 "
-     ]
-    }
-   ],
-   "source": [
-    "for number in numbers:\n",
-    "    print(number, end=\" \")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "source": [
-    "`set` objects are `Iterable`."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 219,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "True"
-      ]
-     },
-     "execution_count": 219,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "isinstance(numbers, abc.Iterable)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "source": [
-    "`set` objects are not `Reversible`."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 220,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "fragment"
-    }
-   },
-   "outputs": [
-    {
-     "ename": "TypeError",
-     "evalue": "'set' object is not reversible",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-220-1095d1ef6c80>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0;32mfor\u001b[0m \u001b[0mnumber\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mreversed\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnumbers\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m     \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnumber\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mend\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\" \"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;31mTypeError\u001b[0m: 'set' object is not reversible"
-     ]
-    }
-   ],
-   "source": [
-    "for number in reversed(numbers):\n",
-    "    print(number, end=\" \")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 221,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "False"
-      ]
-     },
-     "execution_count": 221,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "isinstance(numbers, abc.Reversible)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "source": [
-    "The boolean `in` operator checks if a given and immutable object evaluates equal to an element in a `set` object. As with `dict` objects, membership testing is an *extremely* fast operation. Conceptually, it has the same result as conducting a linear search with the `==` operator behind the scenes."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 222,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "slide"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "False"
-      ]
-     },
-     "execution_count": 222,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "0 in numbers"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 223,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "-"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "True"
-      ]
-     },
-     "execution_count": 223,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "1 in numbers"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 224,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "-"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "True"
-      ]
-     },
-     "execution_count": 224,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "2.0 in numbers"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "source": [
-    "So, a `set` object is a `Container`. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 225,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "True"
-      ]
-     },
-     "execution_count": 225,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "isinstance(numbers, abc.Container)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "source": [
-    "There is a `Set` ABC in the [collections.abc](https://docs.python.org/3/library/collections.abc.html) module that formalizes, in particular, the operators supported by `set` objects. Furthermore, the ordinary `set` type is *mutable*, as expressed with the `MutableSet` ABC. The latter formalizes all the methods that mutate a `set` object."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 226,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "True"
-      ]
-     },
-     "execution_count": 226,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "isinstance(numbers, abc.Set)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 227,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "True"
-      ]
-     },
-     "execution_count": 227,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "isinstance(numbers, abc.MutableSet)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "slide"
-    }
-   },
-   "source": [
-    "### No Indexing / Key Look-up / Slicing"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "source": [
-    "As `set` objects come without a *predictable* order, indexing and slicing is not supported and results in a `TypeError`. In particular, as there are no values to be looked up, these operations are not *semantically* meaningful. Instead, we check membership via the `in` operator, as shown in the previous sub-section."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 228,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "slide"
-    }
-   },
-   "outputs": [
-    {
-     "ename": "TypeError",
-     "evalue": "'set' object is not subscriptable",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-228-7486ea9d37bc>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mnumbers\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
-      "\u001b[0;31mTypeError\u001b[0m: 'set' object is not subscriptable"
-     ]
-    }
-   ],
-   "source": [
-    "numbers[0]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 229,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "-"
-    }
-   },
-   "outputs": [
-    {
-     "ename": "TypeError",
-     "evalue": "'set' object is not subscriptable",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-229-4de4c0f84e99>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mnumbers\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
-      "\u001b[0;31mTypeError\u001b[0m: 'set' object is not subscriptable"
-     ]
-    }
-   ],
-   "source": [
-    "numbers[:]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "slide"
-    }
-   },
-   "source": [
-    "### Mutability & Set Methods"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "source": [
-    "Because the `[]` operator does not work for `set` objects, they are mutated maily via methods (cf., [documentation](https://docs.python.org/3/library/stdtypes.html#set)).\n",
-    "\n",
-    "We may add new elements to an existing `set` object with the [add()](https://docs.python.org/3/library/stdtypes.html#frozenset.add) method."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 230,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "slide"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "numbers.add(99)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 231,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "-"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 99}"
-      ]
-     },
-     "execution_count": 231,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "numbers"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "source": [
-    "The [update()](https://docs.python.org/3/library/stdtypes.html#frozenset.update) method takes an iterable and adds all its elements to a `set` object if they are not already contained in it."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 232,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "fragment"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "numbers.update(range(5))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 233,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "-"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 99}"
-      ]
-     },
-     "execution_count": 233,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "numbers"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "source": [
-    "To remove an element by value, we use the [remove()](https://docs.python.org/3/library/stdtypes.html#frozenset.remove) or [discard()](https://docs.python.org/3/library/stdtypes.html#frozenset.discard) methods. If the element to be removed is not in the `set` object, [remove()](https://docs.python.org/3/library/stdtypes.html#frozenset.remove) raises a loud `KeyError` while [discard()](https://docs.python.org/3/library/stdtypes.html#frozenset.discard) stays *silent*."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 234,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "slide"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "numbers.remove(99)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 235,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "outputs": [
-    {
-     "ename": "KeyError",
-     "evalue": "99",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-235-b23ddd7c18b2>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mnumbers\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mremove\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m99\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
-      "\u001b[0;31mKeyError\u001b[0m: 99"
-     ]
-    }
-   ],
-   "source": [
-    "numbers.remove(99)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 236,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "-"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "numbers.discard(0)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 237,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "numbers.discard(0)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 238,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "-"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12}"
-      ]
-     },
-     "execution_count": 238,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "numbers"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "source": [
-    "The [pop()](https://docs.python.org/3/library/stdtypes.html#frozenset.pop) method removes an *arbitrary* element. As not even the insertion order is tracked, that removes a \"random\" element in theory."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 239,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "1"
-      ]
-     },
-     "execution_count": 239,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "numbers.pop()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 240,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12}"
-      ]
-     },
-     "execution_count": 240,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "numbers"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "source": [
-    "The [clear()](https://docs.python.org/3/library/stdtypes.html#frozenset.clear) method discards all elements but keeps the `set` object alive."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 241,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "numbers.clear()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 242,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "set()"
-      ]
-     },
-     "execution_count": 242,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "numbers"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 243,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "139953088728672"
-      ]
-     },
-     "execution_count": 243,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "id(numbers)  # same memory location as before"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "slide"
-    }
-   },
-   "source": [
-    "### Set Operations"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "source": [
-    "The arithmetic and relational operators are overloaded with typical set operations from math. The operators have methods that do the equivalent. We omit them for brevity in this sub-section and only show them as comments in the code cells. Both the operators and the methods return *new* `set` objects without modifying the operands.\n",
-    "\n",
-    "We showcase the set operations with easy math examples."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 244,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "slide"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "numbers = set(range(1, 13))\n",
-    "zero = {0}\n",
-    "evens = set(range(2, 13, 2))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 245,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "-"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12}"
-      ]
-     },
-     "execution_count": 245,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "numbers"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 246,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "-"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{0}"
-      ]
-     },
-     "execution_count": 246,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "zero"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 247,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "-"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{2, 4, 6, 8, 10, 12}"
-      ]
-     },
-     "execution_count": 247,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "evens"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "source": [
-    "The bitwise OR operator `|` returns the union of two `set` objects."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 248,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "slide"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12}"
-      ]
-     },
-     "execution_count": 248,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "zero | numbers  # zero.union(numbers) or reversed order"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "source": [
-    "Of course, the operators may be *chained*."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 249,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "-"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12}"
-      ]
-     },
-     "execution_count": 249,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "zero | numbers | evens  # zero.union(numbers).union(evens) or any possible order"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "source": [
-    "To obtain an intersection of two or more `set` objects, we use the bitwise AND operator `&`."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 250,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "fragment"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "set()"
-      ]
-     },
-     "execution_count": 250,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "zero & numbers  #  zero.intersection(numbers) or reversed order"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 251,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "-"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{2, 4, 6, 8, 10, 12}"
-      ]
-     },
-     "execution_count": 251,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "numbers & evens  # numbers.intersection(evens) or reversed order"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "source": [
-    "To calculate a `set` object containing all elements that are in one but not the other `set` object, we use the minus operator `-`. This operation is *not* symmetric."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 252,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "slide"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{1, 3, 5, 7, 9, 11}"
-      ]
-     },
-     "execution_count": 252,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "numbers - evens  # numbers.difference(evens)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 253,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "-"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "set()"
-      ]
-     },
-     "execution_count": 253,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "evens - numbers  # evens.difference(numbers)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "source": [
-    "The *symmetric* difference is defined as the `set` object containing all elements that are in one but not both `set` objects. It is calculated with the bitwise XOR operator `^`."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 254,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "fragment"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{1, 3, 5, 7, 9, 11}"
-      ]
-     },
-     "execution_count": 254,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "numbers ^ evens  # numbers.symmetric_difference(evens)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 255,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "-"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{1, 3, 5, 7, 9, 11}"
-      ]
-     },
-     "execution_count": 255,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "evens ^ numbers  # evens.symmetric_difference(numbers)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "source": [
-    "The augmented versions of the four operators (e.g., `|` becomes `|=`) are also defined: They mutate the left operand *in place*."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "slide"
-    }
-   },
-   "source": [
-    "### Set Comprehensions"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "source": [
-    "Python provides a literal notation for **set comprehensions**, or **setcomps** for short, that works exactly like the one for dictionary comprehensions described above except that they use a single loop variable instead of a key-value pair.\n",
-    "\n",
-    "For example, let's create a new `set` object that consists of the squares of all the elements of `numbers`."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 256,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "slide"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{1, 4, 9, 16, 25, 36, 49, 64, 81, 100, 121, 144}"
-      ]
-     },
-     "execution_count": 256,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "{x ** 2 for x in numbers}"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "source": [
-    "As before, we may have multiple `for`- and/or `if`-clauses.\n",
-    "\n",
-    "For example, let's only keep the squares if they turn out to be an even number, or ..."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 257,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "-"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{4, 16, 36, 64, 100, 144}"
-      ]
-     },
-     "execution_count": 257,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "{x ** 2 for x in numbers if (x ** 2) % 2 == 0}"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "source": [
-    "... create a `set` object with all products obtained from the Cartesian product of `numbers` with itself as long as the products are greater than `80`."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 258,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "-"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{81, 84, 88, 90, 96, 99, 100, 108, 110, 120, 121, 132, 144}"
-      ]
-     },
-     "execution_count": 258,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "{x * y for x in numbers for y in numbers if x * y > 80}"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "source": [
-    "### The `frozenset` Type"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "source": [
-    "As `set` objects are mutable, they may *not* be used, for example, as keys in a `dict` object. Similar to how we replace `list` with `tuple` objects, we may often use a `frozenset` object instead of an ordinary one. The `frozenset` type is a built-in, and as `frozenset` objects are immutable, the only limitation is that we must specify *all* elements *upon* creation (cf., [documentation](https://docs.python.org/3/library/stdtypes.html#frozenset)).\n",
-    "\n",
-    "`frozenset` objects are created by passing an iterable to the [frozenset()](https://docs.python.org/3/library/functions.html#func-frozenset) built-in. Note that even though `frozenset` objects are hashable, their elements do still *not* come in a predictable order."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 259,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "frozenset({1, 2, 3})"
-      ]
-     },
-     "execution_count": 259,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "frozenset([1, 1, 2, 2, 3, 3])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 260,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "frozenset({1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12})"
-      ]
-     },
-     "execution_count": 260,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "frozenset(numbers)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "source": [
-    "## TL;DR"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "source": [
-    "`dict` objects are **mutable** one-to-one **mappings** from a set of **key** objects to a set of **value** objects. The association between a key-value pair is also called **item**.\n",
-    "\n",
-    "The items contained in a `dict` object have **no order** that is *predictable*.\n",
-    "\n",
-    "The underlying **data structure** of the `dict` type are **hash tables**. They make key look-ups *extremely* fast by converting the items' keys into *deterministic* hash values specifiying *precisely* one of a fixed number of equally \"wide\" buckets in which an item's references are stored. A limitation is that objects used as keys must be *immutable* (for technical reasons) and *unique* (for practical reasons).\n",
-    "\n",
-    "A `set` object is a **mutable** and **unordered collection** of **immutable** objects. The `set` type mimics sets we know from math."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "source": [
-    "## Further Resources"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "source": [
-    "Next, we list some conference talks that go into great detail regarding the workings of the `dict` type."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 261,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/jpeg": 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-      ],
-      "text/plain": [
-       "<IPython.lib.display.YouTubeVideo at 0x7f495e8601d0>"
-      ]
-     },
-     "execution_count": 261,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from IPython.display import YouTubeVideo\n",
-    "YouTubeVideo(\"C4Kc8xzcA68\", width=\"60%\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 262,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/jpeg": 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\n",
-      "text/html": [
-       "\n",
-       "        <iframe\n",
-       "            width=\"60%\"\n",
-       "            height=\"300\"\n",
-       "            src=\"https://www.youtube.com/embed/66P5FMkWoVU\"\n",
-       "            frameborder=\"0\"\n",
-       "            allowfullscreen\n",
-       "        ></iframe>\n",
-       "        "
-      ],
-      "text/plain": [
-       "<IPython.lib.display.YouTubeVideo at 0x7f495e2b4810>"
-      ]
-     },
-     "execution_count": 262,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "YouTubeVideo(\"66P5FMkWoVU\", width=\"60%\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 263,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/jpeg": 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-    "The `dict` type's order has been worked on in many PEPs in recent years:\n",
-    "- [PEP 412](https://www.python.org/dev/peps/pep-0412/): Key-Sharing Dictionary\n",
-    "- [PEP 468](https://www.python.org/dev/peps/pep-0468/): Preserving the order of \\*\\*kwargs in a function\n",
-    "- [PEP 520](https://www.python.org/dev/peps/pep-0520/): Preserving Class Attribute Definition Order"
-   ]
-  }
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-   "skip_h1_title": true,
-   "title_cell": "Table of Contents",
-   "title_sidebar": "Contents",
-   "toc_cell": false,
-   "toc_position": {
-    "height": "calc(100% - 180px)",
-    "left": "10px",
-    "top": "150px",
-    "width": "384px"
-   },
-   "toc_section_display": false,
-   "toc_window_display": false
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/08_mappings_01_review.ipynb b/08_mappings_01_review.ipynb
deleted file mode 100644
index 0ff62f9..0000000
--- a/08_mappings_01_review.ipynb
+++ /dev/null
@@ -1,261 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "\n",
-    "# Chapter 8: Mappings & Sets"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Content Review"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Read [Chapter 8](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/08_mappings_00_lecture.ipynb) of the book. Then, work through the questions below."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Essay Questions "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Answer the following questions *briefly*!"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**Q1**: `dict` objects are well-suited **to model** discrete mathematical **functions** and to approximate continuous ones. What property of dictionaries is the basis for that claim, and how does it relate to functions in the mathematical sense?"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    " "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**Q2**: Explain why **hash tables** are  a **trade-off** between **computational speed** and **memory** usage!"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    " "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**Q3:** The `dict` type is an **iterable** that **contains** a **finite** number of key-value pairs. Despite that, why is it *not* considered a **sequence**?"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    " "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**Q4**: Whereas *key* **look-ups** in a `dict` object run in so-called **[constant time](https://en.wikipedia.org/wiki/Time_complexity#Constant_time)** (i.e., *extremely* fast), that does not hold for *reverse* look-ups. Why is that?"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    " "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**Q5**: Why is it conceptually correct that the Python core developers do not implement **slicing** with the `[]` operator for `dict` objects?"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    " "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**Q6**: **Memoization** is an essential concept to know to solve problems in the real world. Together with the idea of **recursion**, it enables us to solve problems in a \"backwards\" fashion *effectively*.\n",
-    "\n",
-    "\n",
-    "Compare the **recursive** formulation of `fibonacci()` in [Chapter 8](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/08_mappings_00_lecture.ipynb#\"Easy-at-third-Glance\"-Example:-Fibonacci-Numbers-%28revisited%29), the \"*Easy at third Glance*\" example, with the **iterative** version in [Chapter 4](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/04_iteration_00_lecture.ipynb#\"Hard-at-first-Glance\"-Example:-Fibonacci-Numbers-%28revisited%29), the \"*Hard at first Glance*\" example!\n",
-    "\n",
-    "How are they similar and how do they differ?"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    " "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**Q7**: How are the `set` and the `dict` type related? How could we use the latter to mimic the former?"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    " "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### True / False Questions"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Motivate your answer with *one short* sentence!"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**Q8**: We may *not* put `dict` objects inside other `dict` objects because they are **mutable**."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    " "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**Q9**: **Mutable** objects (e.g., `list`) may generally *not* be used as keys in a `dict` object. However, if we collect, for example, `list` objects in a `tuple` object, the composite object becomes **hashable**."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    " "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**Q10**: **Mutability** of a `dict` object works until the underlying hash table becomes too crowded. Then, we cannot insert any items any more making the `dict` object effectively **immutable**. Luckily, that almost never happens in practice."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    " "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**Q11**: A `dict` object's [update()](https://docs.python.org/3/library/stdtypes.html#dict.update) method only inserts key-value pairs whose key is *not* yet in the `dict` object. So, it does *not* overwrite anything."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    " "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**Q12**: The `set` type is both a mapping and a sequence."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    " "
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.7.4"
-  },
-  "toc": {
-   "base_numbering": 1,
-   "nav_menu": {},
-   "number_sections": false,
-   "sideBar": true,
-   "skip_h1_title": true,
-   "title_cell": "Table of Contents",
-   "title_sidebar": "Contents",
-   "toc_cell": false,
-   "toc_position": {},
-   "toc_section_display": false,
-   "toc_window_display": false
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/08_mappings_02_exercises.ipynb b/08_mappings_02_exercises.ipynb
deleted file mode 100644
index 4db33ee..0000000
--- a/08_mappings_02_exercises.ipynb
+++ /dev/null
@@ -1,388 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "\n",
-    "# Chapter 8: Mappings & Sets"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Coding Exercises"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Read [Chapter 8](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/08_mappings_00_lecture.ipynb) of the book. Then, work through the exercises below."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Working with Nested Data"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Let's write some code to analyze the historic soccer game [Brazil vs. Germany](https://en.wikipedia.org/wiki/Brazil_v_Germany_%282014_FIFA_World_Cup%29) during the 2014 World Cup.\n",
-    "\n",
-    "Below, `players` consists of two nested `dict` objects, one for each team, that hold `tuple` objects (i.e., records) with information on the players. Besides the jersey number, name, and position, each `tuple` objects contains a `list` object with the times when the player scored."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "players = {\n",
-    "    \"Brazil\": [\n",
-    "        (12, \"JĂşlio CĂ©sar\", \"Goalkeeper\", []),\n",
-    "        (4, \"David Luiz\", \"Defender\", []),\n",
-    "        (6, \"Marcelo\", \"Defender\", []),\n",
-    "        (13, \"Dante\", \"Defender\", []),\n",
-    "        (23, \"Maicon\", \"Defender\", []),\n",
-    "        (5, \"Fernandinho\", \"Midfielder\", []),\n",
-    "        (7, \"Hulk\", \"Midfielder\", []),\n",
-    "        (8, \"Paulinho\", \"Midfielder\", []),\n",
-    "        (11, \"Oscar\", \"Midfielder\", [90]),\n",
-    "        (16, \"Ramires\", \"Midfielder\", []),\n",
-    "        (17, \"Luiz Gustavo\", \"Midfielder\", []),\n",
-    "        (19, \"Willian\", \"Midfielder\", []),\n",
-    "        (9, \"Fred\", \"Striker\", []),\n",
-    "    ],\n",
-    "    \"Germany\": [\n",
-    "        (1, \"Manuel Neuer\", \"Goalkeeper\", []),\n",
-    "        (4, \"Benedikt Höwedes\", \"Defender\", []),\n",
-    "        (5, \"Mats Hummels\", \"Defender\", []),\n",
-    "        (16, \"Philipp Lahm\", \"Defender\", []),\n",
-    "        (17, \"Per Mertesacker\", \"Defender\", []),\n",
-    "        (20, \"JĂ©rĂ´me Boateng\", \"Defender\", []),\n",
-    "        (6, \"Sami Khedira\", \"Midfielder\", [29]),\n",
-    "        (7, \"Bastian Schweinsteiger\", \"Midfielder\", []),\n",
-    "        (8, \"Mesut Ă–zil\", \"Midfielder\", []),\n",
-    "        (13, \"Thomas MĂĽller\", \"Midfielder\", [11]),\n",
-    "        (14, \"Julian Draxler\", \"Midfielder\", []),\n",
-    "        (18, \"Toni Kroos\", \"Midfielder\", [24, 26]),\n",
-    "        (9, \"André Schürrle\", \"Striker\", [69, 79]),\n",
-    "        (11, \"Miroslav Klose\", \"Striker\", [23]),\n",
-    "    ],\n",
-    "}"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**Q1.1**: Write a dictionary comprehension to derive a new `dict` object, called `brazilian_players`, that maps a Brazilian player's name to his position!"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "brazilian_players = {...}"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "brazilian_players"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**Q1.2**: Generalize the code fragment into a `get_players()` function: Passed a `team` name, it returns a `dict` object like `brazilian_players`. Verify that the function works for the German team!"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def get_players(team):\n",
-    "    ..."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "get_players(\"Germany\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Often, we are given a `dict` object like the one returned from `get_players()`: It is characterized by the observation that a large set of unique keys (i.e., the players' names) is mapped onto a smaller set of non-unique values (i.e., the positions).\n",
-    "\n",
-    "**Q1.3**: Create a generic `invert()` function that swaps the keys and values of a `mapping` argument passed to it and returns them in a *new* `dict` object! Ensure that *no* key gets lost. Verify your implementation with the `brazilian_players` dictionary!\n",
-    "\n",
-    "Hints: Think of this as a grouping operation. The *new* values are `list` or `tuple` objects that hold the original keys. You may want to use either the the [defaultdict](https://docs.python.org/3/library/collections.html#collections.defaultdict) type from the [collections](https://docs.python.org/3/library/collections.html) module in the [standard library](https://docs.python.org/3/library/index.html) or the [setdefault()](https://docs.python.org/3/library/stdtypes.html#dict.setdefault) method of the ordinary `dict` type."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def invert(mapping):\n",
-    "    ..."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "invert(brazilian_players)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**Q1.4**: Write a `score_at_minute()` function: It takes two arguments, `team` and `minute`, and returns the number of goals the `team` has scored up until this time in the game.\n",
-    "\n",
-    "Hints: The function may reference the global `players` for simplicity. Earn bonus points if you can write this in a one-line expression using some *reduction* function and a *generator expression*."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def score_at_minute(team, minute):\n",
-    "    ..."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The score at half time was:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "score_at_minute(\"Brazil\", 45)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "score_at_minute(\"Germany\", 45)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The final score was:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "score_at_minute(\"Brazil\", 90)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "score_at_minute(\"Germany\", 90)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**Q1.5**: Write a `goals_by_player()` function: It takes an argument like the global `players`, and returns a `dict` object mapping the players to the number of goals they scored.\n",
-    "\n",
-    "Hints: Do *not* \"hard code\" the names of the teams! Earn bonus points if you can solve it in a single dictionary comprehension."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def goals_by_player(players):\n",
-    "    ..."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "goals_by_player(players)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**Q1.6**: Write a *dictionary comprehension* to filter out the players who did *not* score from the preceding result. Then, write a *set comprehension* that does the same but discards the number of goals scored.\n",
-    "\n",
-    "Hints: Reference the `goals_by_player()` function from before."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "{...}"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "{...}"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**Q1.7**: Write a `all_goals()` function: It takes one argument like the global `players` and returns a `list` object containing $2$-element `tuple` objects, where the first element is the minute a player scored and the second his name. The list should be sorted by the time.\n",
-    "\n",
-    "Hints: You may want to use either the built-in [sorted()](https://docs.python.org/3/library/functions.html#sorted) function or the `list` type's [sort()](https://docs.python.org/3/library/stdtypes.html#list.sort) method. Earn bonus points if you can write a one-line expression with a *generator expression*."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def all_goals(players):\n",
-    "    ..."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "all_goals(players)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**Q1.8**: Lastly, write a `summary()` function: It takes one argument like the global `players` and prints out a concise report of the goals, the score at the half, and the final result.\n",
-    "\n",
-    "Hints: Use the `all_goals()` and `score_at_minute()` functions from before.\n",
-    "\n",
-    "The output should look similar to this:\n",
-    "```\n",
-    "12' Gerd MĂĽller scores\n",
-    "...\n",
-    "HALFTIME: TeamA 1 TeamB 2\n",
-    "77' Ronaldo scores\n",
-    "...\n",
-    "FINAL: TeamA 1 TeamB 3\n",
-    "```"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def summary(players):\n",
-    "    ..."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "summary(players)"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.7.4"
-  },
-  "toc": {
-   "base_numbering": 1,
-   "nav_menu": {},
-   "number_sections": false,
-   "sideBar": true,
-   "skip_h1_title": true,
-   "title_cell": "Table of Contents",
-   "title_sidebar": "Contents",
-   "toc_cell": false,
-   "toc_position": {},
-   "toc_section_display": false,
-   "toc_window_display": false
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/README.md b/README.md
index b46a9ff..42200f8 100644
--- a/README.md
+++ b/README.md
@@ -1,91 +1,143 @@
 # An Introduction to Python and Programming
 
-The purpose of this repository is to serve as an interactive "book" for a
+The purpose of this repository is to serve as an interactive book for a
 thorough introductory course on programming in the
-**[Python](https://www.python.org/)** language.
+**[Python <img height="12" style="display: inline-block" src="static/link_to_py.png">](https://www.python.org/)**
+language.
 
 The course's **main goal** is to **prepare** the student for **further
 studies** in the "field" of **data science**.
 
-The "chapters" are written in [Jupyter notebooks](https://jupyter-notebook.readthedocs.io/en/stable/)
-which are a de-facto standard for exchanging code and results among data
+The chapters are laid out in [Jupyter notebooks <img height="12" style="display: inline-block" src="static/link_to_jp.png">](https://jupyter-notebook.readthedocs.io/en/stable/)
+which are a de-facto standard for exchanging code and analyses among data
 science professionals and researchers.
-They can be viewed in a plain web browser with the help of
-[nbviewer](https://nbviewer.jupyter.org/):
+They can be viewed in a web browser either statically on [nbviewer <img height="12" style="display: inline-block" src="static/link_to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/tree/master/)
+or interactively (i.e., you can execute the code) on [mybinder <img height="12" style="display: inline-block" src="static/link_to_mb.png">](https://mybinder.org/v2/gh/webartifex/intro-to-python/master?urlpath=lab):
 
-- *Introduction*: Start up
-  ([video](https://www.youtube.com/watch?v=YTU8jaG27Xk)
-  | [lecture](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/00_intro_00_lecture.ipynb)
-  | [review](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/00_intro_01_review.ipynb)
-  | [exercises](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/00_intro_02_exercises.ipynb))
+- *Chapter 0*: Introduction
+  ([content <img height="12" style="display: inline-block" src="static/link_to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/00_intro_00_content.ipynb)
+   [<img height="12" style="display: inline-block" src="static/link_to_mb.png">](https://mybinder.org/v2/gh/webartifex/intro-to-python/master?urlpath=lab/tree/00_intro_00_content.ipynb)
+   [<img height="12" style="display: inline-block" src="static/link_to_yt.png">](https://www.youtube.com/watch?v=YTU8jaG27Xk&list=PL-2JV1G3J10lQ2xokyQowcRJI5jjNfW7f)
+   | [review <img height="12" style="display: inline-block" src="static/link_to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/00_intro_01_review.ipynb)
+   [<img height="12" style="display: inline-block" src="static/link_to_mb.png">](https://mybinder.org/v2/gh/webartifex/intro-to-python/master?urlpath=lab/tree/00_intro_01_review.ipynb)
+   | [exercises <img height="12" style="display: inline-block" src="static/link_to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/00_intro_02_exercises.ipynb)
+   [<img height="12" style="display: inline-block" src="static/link_to_mb.png">](https://mybinder.org/v2/gh/webartifex/intro-to-python/master?urlpath=lab/tree/00_intro_02_exercises.ipynb))
 - **Part A: Expressing Logic**
   - *Chapter 1*: Elements of a Program
-    ([video](https://www.youtube.com/watch?v=v0lk1Qfaw8Y)
-    | [lecture](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/01_elements_00_lecture.ipynb)
-    | [review](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/01_elements_01_review.ipynb)
-    | [exercises](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/01_elements_02_exercises.ipynb))
+    ([content <img height="12" style="display: inline-block" src="static/link_to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/01_elements_00_content.ipynb)
+     [<img height="12" style="display: inline-block" src="static/link_to_mb.png">](https://mybinder.org/v2/gh/webartifex/intro-to-python/master?urlpath=lab/tree/01_elements_00_content.ipynb)
+     [<img height="12" style="display: inline-block" src="static/link_to_yt.png">](https://www.youtube.com/watch?v=v0lk1Qfaw8Y&list=PL-2JV1G3J10lQ2xokyQowcRJI5jjNfW7f)
+     | [review <img height="12" style="display: inline-block" src="static/link_to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/01_elements_01_review.ipynb)
+     [<img height="12" style="display: inline-block" src="static/link_to_mb.png">](https://mybinder.org/v2/gh/webartifex/intro-to-python/master?urlpath=lab/tree/01_elements_01_review.ipynb)
+     | [exercises <img height="12" style="display: inline-block" src="static/link_to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/01_elements_02_exercises.ipynb)
+     [<img height="12" style="display: inline-block" src="static/link_to_mb.png">](https://mybinder.org/v2/gh/webartifex/intro-to-python/master?urlpath=lab/tree/01_elements_02_exercises.ipynb))
   - *Chapter 2*: Functions & Modularization
-    ([video](https://www.youtube.com/watch?v=j4Xn8QFysmc)
-    | [lecture](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/02_functions_00_lecture.ipynb)
-    | [review](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/02_functions_01_review.ipynb)
-    | [exercises](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/02_functions_02_exercises.ipynb))
+    ([content <img height="12" style="display: inline-block" src="static/link_to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/02_functions_00_content.ipynb)
+     [<img height="12" style="display: inline-block" src="static/link_to_mb.png">](https://mybinder.org/v2/gh/webartifex/intro-to-python/master?urlpath=lab/tree/02_functions_00_content.ipynb)
+     [<img height="12" style="display: inline-block" src="static/link_to_yt.png">](https://www.youtube.com/watch?v=j4Xn8QFysmc&list=PL-2JV1G3J10lQ2xokyQowcRJI5jjNfW7f)
+     | [review <img height="12" style="display: inline-block" src="static/link_to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/02_functions_01_review.ipynb)
+     [<img height="12" style="display: inline-block" src="static/link_to_mb.png">](https://mybinder.org/v2/gh/webartifex/intro-to-python/master?urlpath=lab/tree/02_functions_01_review.ipynb)
+     | [exercises <img height="12" style="display: inline-block" src="static/link_to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/02_functions_02_exercises.ipynb)
+     [<img height="12" style="display: inline-block" src="static/link_to_mb.png">](https://mybinder.org/v2/gh/webartifex/intro-to-python/master?urlpath=lab/tree/02_functions_02_exercises.ipynb))
   - *Chapter 3*: Conditionals & Exceptions
-    ([video](https://www.youtube.com/watch?v=aDbblINzuGQ)
-    | [lecture](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/03_conditionals_00_lecture.ipynb)
-    | [review](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/03_conditionals_01_review.ipynb)
-    | [exercises](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/03_conditionals_02_exercises.ipynb))
+    ([content <img height="12" style="display: inline-block" src="static/link_to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/03_conditionals_00_content.ipynb)
+     [<img height="12" style="display: inline-block" src="static/link_to_mb.png">](https://mybinder.org/v2/gh/webartifex/intro-to-python/master?urlpath=lab/tree/03_conditionals_00_content.ipynb)
+     [<img height="12" style="display: inline-block" src="static/link_to_yt.png">](https://www.youtube.com/watch?v=aDbblINzuGQ&list=PL-2JV1G3J10lQ2xokyQowcRJI5jjNfW7f)
+     | [review <img height="12" style="display: inline-block" src="static/link_to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/03_conditionals_01_review.ipynb)
+     [<img height="12" style="display: inline-block" src="static/link_to_mb.png">](https://mybinder.org/v2/gh/webartifex/intro-to-python/master?urlpath=lab/tree/03_conditionals_01_review.ipynb)
+     | [exercises <img height="12" style="display: inline-block" src="static/link_to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/03_conditionals_02_exercises.ipynb)
+     [<img height="12" style="display: inline-block" src="static/link_to_mb.png">](https://mybinder.org/v2/gh/webartifex/intro-to-python/master?urlpath=lab/tree/03_conditionals_02_exercises.ipynb))
   - *Chapter 4*: Recursion & Looping
-    ([video](https://www.youtube.com/watch?v=jT6hr4vOJks)
-    | [lecture](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/04_iteration_00_lecture.ipynb)
-    | [review](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/04_iteration_01_review.ipynb)
-    | [exercises 1](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/04_iteration_02_exercises.ipynb)
-    | [exercises 2](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/04_iteration_03_exercises.ipynb))
+    ([content <img height="12" style="display: inline-block" src="static/link_to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/04_iteration_00_content.ipynb)
+     [<img height="12" style="display: inline-block" src="static/link_to_mb.png">](https://mybinder.org/v2/gh/webartifex/intro-to-python/master?urlpath=lab/tree/04_iteration_00_content.ipynb)
+     [<img height="12" style="display: inline-block" src="static/link_to_yt.png">](https://www.youtube.com/watch?v=jT6hr4vOJks&list=PL-2JV1G3J10lQ2xokyQowcRJI5jjNfW7f)
+     | [review <img height="12" style="display: inline-block" src="static/link_to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/04_iteration_01_review.ipynb)
+     [<img height="12" style="display: inline-block" src="static/link_to_mb.png">](https://mybinder.org/v2/gh/webartifex/intro-to-python/master?urlpath=lab/tree/04_iteration_01_review.ipynb)
+     | [exercises 1 <img height="12" style="display: inline-block" src="static/link_to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/04_iteration_02_exercises.ipynb)
+     [<img height="12" style="display: inline-block" src="static/link_to_mb.png">](https://mybinder.org/v2/gh/webartifex/intro-to-python/master?urlpath=lab/tree/04_iteration_02_exercises.ipynb)
+     | [exercises 2 <img height="12" style="display: inline-block" src="static/link_to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/04_iteration_03_exercises.ipynb)
+     [<img height="12" style="display: inline-block" src="static/link_to_mb.png">](https://mybinder.org/v2/gh/webartifex/intro-to-python/master?urlpath=lab/tree/04_iteration_03_exercises.ipynb))
 - **Part B: Managing Data and Memory**
   - *Chapter 5*: Numbers & Bits
-    ([video](https://www.youtube.com/watch?v=nB00WGCnVjg)
-    | [lecture](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/05_numbers_00_lecture.ipynb)
-    | [review](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/05_numbers_01_review.ipynb)
-    | [exercises](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/05_numbers_02_exercises.ipynb))
+    ([content <img height="12" style="display: inline-block" src="static/link_to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/05_numbers_00_content.ipynb)
+     [<img height="12" style="display: inline-block" src="static/link_to_mb.png">](https://mybinder.org/v2/gh/webartifex/intro-to-python/master?urlpath=lab/tree/05_numbers_00_content.ipynb)
+     [<img height="12" style="display: inline-block" src="static/link_to_yt.png">](https://www.youtube.com/watch?v=nB00WGCnVjg&list=PL-2JV1G3J10lQ2xokyQowcRJI5jjNfW7f)
+     | [review <img height="12" style="display: inline-block" src="static/link_to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/05_numbers_01_review.ipynb)
+     [<img height="12" style="display: inline-block" src="static/link_to_mb.png">](https://mybinder.org/v2/gh/webartifex/intro-to-python/master?urlpath=lab/tree/05_numbers_01_review.ipynb)
+     | [exercises <img height="12" style="display: inline-block" src="static/link_to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/05_numbers_02_exercises.ipynb)
+     [<img height="12" style="display: inline-block" src="static/link_to_mb.png">](https://mybinder.org/v2/gh/webartifex/intro-to-python/master?urlpath=lab/tree/05_numbers_02_exercises.ipynb))
   - *Chapter 6*: Text & Bytes
-    ([video](https://www.youtube.com/watch?v=3WTRlgN09sM)
-    | [lecture](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/06_text_00_lecture.ipynb)
-    | [review](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/06_text_01_review.ipynb)
-    | [exercises](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/06_text_02_exercises.ipynb))
+    ([content <img height="12" style="display: inline-block" src="static/link_to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/06_text_00_content.ipynb)
+     [<img height="12" style="display: inline-block" src="static/link_to_mb.png">](https://mybinder.org/v2/gh/webartifex/intro-to-python/master?urlpath=lab/tree/06_text_00_content.ipynb)
+     [<img height="12" style="display: inline-block" src="static/link_to_yt.png">](https://www.youtube.com/watch?v=3WTRlgN09sM&list=PL-2JV1G3J10lQ2xokyQowcRJI5jjNfW7f)
+     | [review <img height="12" style="display: inline-block" src="static/link_to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/06_text_01_review.ipynb)
+     [<img height="12" style="display: inline-block" src="static/link_to_mb.png">](https://mybinder.org/v2/gh/webartifex/intro-to-python/master?urlpath=lab/tree/06_text_01_review.ipynb)
+     | [exercises <img height="12" style="display: inline-block" src="static/link_to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/06_text_02_exercises.ipynb)
+     [<img height="12" style="display: inline-block" src="static/link_to_mb.png">](https://mybinder.org/v2/gh/webartifex/intro-to-python/master?urlpath=lab/tree/06_text_02_exercises.ipynb))
   - *Chapter 7*: Sequential Data
-    ([video](https://www.youtube.com/watch?v=nx2sCDoeC3I)
-    | [lecture](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/07_sequences_00_lecture.ipynb)
-    | [review](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/07_sequences_01_review.ipynb)
-    | [exercises](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/07_sequences_02_exercises.ipynb))
+    ([content <img height="12" style="display: inline-block" src="static/link_to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/07_sequences_00_content.ipynb)
+     [<img height="12" style="display: inline-block" src="static/link_to_mb.png">](https://mybinder.org/v2/gh/webartifex/intro-to-python/master?urlpath=lab/tree/07_sequences_00_content.ipynb)
+     [<img height="12" style="display: inline-block" src="static/link_to_yt.png">](https://www.youtube.com/watch?v=nx2sCDoeC3I&list=PL-2JV1G3J10lQ2xokyQowcRJI5jjNfW7f)
+     | [review <img height="12" style="display: inline-block" src="static/link_to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/07_sequences_01_review.ipynb)
+     [<img height="12" style="display: inline-block" src="static/link_to_mb.png">](https://mybinder.org/v2/gh/webartifex/intro-to-python/master?urlpath=lab/tree/07_sequences_01_review.ipynb)
+     | [exercises <img height="12" style="display: inline-block" src="static/link_to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/07_sequences_02_exercises.ipynb)
+     [<img height="12" style="display: inline-block" src="static/link_to_mb.png">](https://mybinder.org/v2/gh/webartifex/intro-to-python/master?urlpath=lab/tree/07_sequences_02_exercises.ipynb))
   - *Chapter 8*: Map, Filter, & Reduce
-    ([video](https://www.youtube.com/watch?v=ePzzq2YBWjY)
-    | [lecture](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/08_mfr_00_lecture.ipynb)
-    | [review](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/08_mfr_01_review.ipynb)
-    | [exercises](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/08_mfr_02_exercises.ipynb))
+    ([content <img height="12" style="display: inline-block" src="static/link_to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/08_mfr_00_content.ipynb)
+     [<img height="12" style="display: inline-block" src="static/link_to_mb.png">](https://mybinder.org/v2/gh/webartifex/intro-to-python/master?urlpath=lab/tree/08_mfr_00_content.ipynb)
+     [<img height="12" style="display: inline-block" src="static/link_to_yt.png">](https://www.youtube.com/watch?v=ePzzq2YBWjY&list=PL-2JV1G3J10lQ2xokyQowcRJI5jjNfW7f)
+     | [review <img height="12" style="display: inline-block" src="static/link_to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/08_mfr_01_review.ipynb)
+     [<img height="12" style="display: inline-block" src="static/link_to_mb.png">](https://mybinder.org/v2/gh/webartifex/intro-to-python/master?urlpath=lab/tree/08_mfr_01_review.ipynb)
+     | [exercises <img height="12" style="display: inline-block" src="static/link_to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/08_mfr_02_exercises.ipynb)
+     [<img height="12" style="display: inline-block" src="static/link_to_mb.png">](https://mybinder.org/v2/gh/webartifex/intro-to-python/master?urlpath=lab/tree/08_mfr_02_exercises.ipynb))
   - *Chapter 9*: Mappings & Sets
-    ([video](https://www.youtube.com/watch?v=vbp0svA35TE)
-    | [lecture](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/08_mappings_00_lecture.ipynb)
-    | [review](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/08_mappings_01_review.ipynb)
-    | [exercises](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/08_mappings_02_exercises.ipynb))
+    ([content <img height="12" style="display: inline-block" src="static/link_to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/09_mappings_00_content.ipynb)
+     [<img height="12" style="display: inline-block" src="static/link_to_mb.png">](https://mybinder.org/v2/gh/webartifex/intro-to-python/master?urlpath=lab/tree/09_mappings_00_content.ipynb)
+     [<img height="12" style="display: inline-block" src="static/link_to_yt.png">](https://www.youtube.com/watch?v=vbp0svA35TE&list=PL-2JV1G3J10lQ2xokyQowcRJI5jjNfW7f)
+     | [review <img height="12" style="display: inline-block" src="static/link_to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/09_mappings_01_review.ipynb)
+     [<img height="12" style="display: inline-block" src="static/link_to_mb.png">](https://mybinder.org/v2/gh/webartifex/intro-to-python/master?urlpath=lab/tree/09_mappings_01_review.ipynb)
+     | [exercises <img height="12" style="display: inline-block" src="static/link_to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/09_mappings_02_exercises.ipynb)
+     [<img height="12" style="display: inline-block" src="static/link_to_mb.png">](https://mybinder.org/v2/gh/webartifex/intro-to-python/master?urlpath=lab/tree/09_mappings_02_exercises.ipynb))
+  - *Chapter 10*: Classes & Instances
+    ([content <img height="12" style="display: inline-block" src="static/link_to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/10_classes_00_content.ipynb)
+     [<img height="12" style="display: inline-block" src="static/link_to_mb.png">](https://mybinder.org/v2/gh/webartifex/intro-to-python/master?urlpath=lab/tree/10_classes_00_content.ipynb)
+     [<img height="12" style="display: inline-block" src="static/link_to_yt.png">](https://www.youtube.com/watch?v=ibDT0uOAOTI&list=PL-2JV1G3J10lQ2xokyQowcRJI5jjNfW7f)
+     | [review <img height="12" style="display: inline-block" src="static/link_to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/10_classes_01_review.ipynb)
+     [<img height="12" style="display: inline-block" src="static/link_to_mb.png">](https://mybinder.org/v2/gh/webartifex/intro-to-python/master?urlpath=lab/tree/10_classes_01_review.ipynb)
+     | [exercises <img height="12" style="display: inline-block" src="static/link_to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/10_classes_02_exercises.ipynb)
+     [<img height="12" style="display: inline-block" src="static/link_to_mb.png">](https://mybinder.org/v2/gh/webartifex/intro-to-python/master?urlpath=lab/tree/10_classes_02_exercises.ipynb))
 
-However, it is recommended that students **install Python and Jupyter
-locally** and run the code in the notebooks on their own.
-This way, the student can play with the code and learn more efficiently.
-Precise **installation instructions** are either in the [00th notebook](
-https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/00_intro_00_lecture.ipynb)
+However, it is recommended to **install Python and [Jupyter <img height="12" style="display: inline-block" src="static/link_to_jp.png">](https://jupyter.org)
+locally** and run the code in the notebooks on one's own machine in the [JupyterLab <img height="12" style="display: inline-block" src="static/link_to_jp.png">](https://jupyterlab.readthedocs.io/en/stable/)
+application.
+Precise **installation instructions** are either in [Chapter 0 <img height="12" style="display: inline-block" src="static/link_to_nb.png">](
+https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/00_intro_00_content.ipynb#Installation)
 or further below.
 
-A playlist with all video presentations of the individiual chapters can be found [here](https://www.youtube.com/playlist?list=PL-2JV1G3J10lQ2xokyQowcRJI5jjNfW7f).
 
-Feedback is encouraged and will be incorporated.
-Open an issue in the [issues tracker](https://github.com/webartifex/intro-to-python/issues)
-or initiate a [pull request](https://help.github.com/en/articles/about-pull-requests)
+### Feedback
+
+Feedback **is highly encouraged** and will be incorporated.
+Simply open an issue in the [issues tracker <img height="12" style="display: inline-block" src="static/link_to_gh.png">](https://github.com/webartifex/intro-to-python/issues)
+or initiate a [pull request <img height="12" style="display: inline-block" src="static/link_to_gh.png">](https://help.github.com/en/articles/about-pull-requests)
 if you are familiar with the concept.
+Simple issues that anyone can **help fix** are, for example,
+**spelling mistakes** or **broken links**.
+If you feel that some topic is missing entirely, you may also mention that.
+The materials here are considered a **permanent work-in-progress**.
 
-A "Show HN" post about this introduction was made on [Hacker News](https://news.ycombinator.com/item?id=22669084)
+A "Show HN" post about this course was made on [Hacker News <img height="12" style="display: inline-block" src="static/link_to_hn.png">](https://news.ycombinator.com/item?id=22669084)
 and some ideas for improvement were discussed there.
 
 
-## Prerequisites
+### Videos
+
+Presentations on the chapters are available either via the individual links to
+[YouTube <img height="12" style="display: inline-block" src="static/link_to_yt.png">](https://www.youtube.com)
+above or this [playlist <img height="12" style="display: inline-block" src="static/link_to_yt.png">](https://www.youtube.com/playlist?list=PL-2JV1G3J10lQ2xokyQowcRJI5jjNfW7f).
+
+
+### Prerequisites
 
 To be suitable for *total beginners*, there are *no* formal prerequisites.
 It is only expected that the student has:
@@ -93,7 +145,7 @@ It is only expected that the student has:
 - a *solid* understanding of the **English language**,
 - knowledge of **basic mathematics** from high school,
 - the ability to **think conceptually** and **reason logically**, and
-- the willingness to **invest 2-4 hours a day for a month**.
+- the willingness to **invest around 90 - 120 hours on this course**.
 
 
 ## Installation
@@ -109,8 +161,8 @@ Just go to the [download](https://www.anaconda.com/distribution/#download-sectio
 section and install the latest version (i.e., *2019-10* with Python 3.7 at the
 time of this writing) for your operating system.
 
-Then, among others, you will find an entry "Anaconda Navigator" in your start
-menu like below.
+Then, among others, you will find an entry "[Anaconda Navigator](https://docs.anaconda.com/anaconda/navigator/)"
+in your start menu like below.
 Click on it.
 
 <img src="static/anaconda_start_menu.png" width="30%">
@@ -123,9 +175,8 @@ Now, click on "JupyterLab."
 
 A new tab in your web browser opens with the website being "localhost" and some
 number (e.g., 8888).
-This is the [JupyterLab](https://jupyterlab.readthedocs.io/en/stable/)
-application that is used to display and run the Jupyter notebooks mentioned
-above.
+This is the [JupyterLab <img height="12" style="display: inline-block" src="static/link_to_jp.png">](https://jupyterlab.readthedocs.io/en/stable/)
+application that is used to display and run the notebooks mentioned above.
 On the left, you see the files and folders in your local user folder.
 This file browser works like any other.
 In the center, you have several options to launch (i.e., "create") new files.
@@ -144,14 +195,14 @@ JupyterLab.
 Python can also be installed in a "pure" way as obtained from its core
 development team (i.e., without any third-party packages installed).
 However, this may be too "advanced" for a beginner as it involves working
-with a [terminal emulator](https://en.wikipedia.org/wiki/Terminal_emulator),
+with a [terminal emulator <img height="12" style="display: inline-block" src="static/link_to_wiki.png">](https://en.wikipedia.org/wiki/Terminal_emulator),
 which looks like the one in the picture below and is used *without* a mouse by
 typing commands into it.
 
 <img src="static/terminal.png" width="50%" align="center">
 
 Assuming that you already have a working version of Python 3.7 or higher
-installed (cf., the official [download page](https://www.python.org/downloads/)),
+installed (cf., the official [download page <img height="12" style="display: inline-block" src="static/link_to_py.png">](https://www.python.org/downloads/)),
 the following summarizes the commands to be typed into a terminal emulator to
 get the course materials up and running on a local machine without the
 Anaconda Distribution.
@@ -167,7 +218,7 @@ This creates a new folder *intro-to-python* with all the materials of this
 repository in it.
 
 Inside this folder, it is recommended to create a so-called **virtual
-environment** with Python's [venv](https://docs.python.org/3/library/venv.html)
+environment** with Python's [venv <img height="12" style="display: inline-block" src="static/link_to_py.png">](https://docs.python.org/3/library/venv.html)
 module.
 This must only be done the first time.
 A virtual environment is a way of *isolating* the third-party packages
@@ -185,7 +236,7 @@ environment (*venv* is the name chosen before).
 
 - `source venv/bin/activate`
 
-This may change how the terminal's [command prompt](https://en.wikipedia.org/wiki/Command-line_interface#Command_prompt)
+This may change how the terminal's [command prompt <img height="12" style="display: inline-block" src="static/link_to_wiki.png">](https://en.wikipedia.org/wiki/Command-line_interface#Command_prompt)
 looks.
 
 [poetry](https://poetry.eustace.io/docs/) and [virtualenvwrapper](https://virtualenvwrapper.readthedocs.io/en/latest/)
@@ -193,7 +244,7 @@ are popular tools to automate the described management of virtual environments.
 
 After activation for the first time, you must install the project's
 **dependencies** (= the third-party packages needed to run the code), most
-notably [JupyterLab](https://pypi.org/project/jupyterlab/) in this project
+notably [JupyterLab <img height="12" style="display: inline-block" src="static/link_to_jp.png">](https://pypi.org/project/jupyterlab/) in this project
 (the "python -m" is often left out [but should not be](https://snarky.ca/why-you-should-use-python-m-pip/);
 if you have poetry installed, you may just type `poetry install` instead).
 
@@ -209,11 +260,11 @@ This opens a new tab in your web browser just as above.
 
 #### Interactive Presentation Mode & Live Coding
 
-The *requirements.txt* file also installs the [nbextensions](https://github.com/ipython-contrib/jupyter_contrib_nbextensions)
-for Jupyter notebooks, the [black](https://github.com/psf/black) code
-formatting tool (incl. the [blackcellmagic](https://github.com/csurfer/blackcellmagic)
-Jupyter extension) and the [RISE](https://github.com/damianavila/RISE) Jupyter
-extension.
+The *requirements.txt* file also installs the [nbextensions <img height="12" style="display: inline-block" src="static/link_to_gh.png">](https://github.com/ipython-contrib/jupyter_contrib_nbextensions)
+for Jupyter notebooks, the [black <img height="12" style="display: inline-block" src="static/link_to_gh.png">](https://github.com/psf/black)
+code formatting tool (incl. the [blackcellmagic <img height="12" style="display: inline-block" src="static/link_to_gh.png">](https://github.com/csurfer/blackcellmagic)
+Jupyter extension) and the [RISE <img height="12" style="display: inline-block" src="static/link_to_gh.png">](https://github.com/damianavila/RISE)
+Jupyter extension.
 With them, the instructor can easily re-format code in a class session and
 execute code in presentation mode.
 
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From d0729194bf8721ad3479981088040274ff8fc481 Mon Sep 17 00:00:00 2001
From: Alexander Hess <alexander@webartifex.biz>
Date: Fri, 3 Apr 2020 21:47:52 +0200
Subject: [PATCH 095/242] Add new chapter 8 on map, filter, & reduce

---
 04_iteration_00_content.ipynb |    2 +-
 05_numbers_00_content.ipynb   |    2 +-
 06_text_00_content.ipynb      |    4 +-
 07_sequences_00_content.ipynb |    6 +-
 08_mfr_00_content.ipynb       | 5312 +++++++++++++++++++++++++++++++++
 08_mfr_01_review.ipynb        |  222 ++
 08_mfr_02_exercises.ipynb     | 1202 ++++++++
 7 files changed, 6743 insertions(+), 7 deletions(-)
 create mode 100644 08_mfr_00_content.ipynb
 create mode 100644 08_mfr_01_review.ipynb
 create mode 100644 08_mfr_02_exercises.ipynb

diff --git a/04_iteration_00_content.ipynb b/04_iteration_00_content.ipynb
index 89ceaba..3976ad7 100644
--- a/04_iteration_00_content.ipynb
+++ b/04_iteration_00_content.ipynb
@@ -6415,7 +6415,7 @@
     }
    },
    "source": [
-    "Often, we process some iterable with numeric data, for example, a list of `numbers` as in this book's introductory example in [Chapter 1 <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/01_elements_00_content.ipynb#Example:-Averaging-Even-Numbers) or, more realistically, data from a CSV file with many rows and columns.\n",
+    "Often, we process some iterable with numeric data, for example, a list of `numbers` as in this book's introductory example in [Chapter 1 <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/01_elements_00_content.ipynb#Example:-Averaging-all-even-Numbers-in-a-List) or, more realistically, data from a CSV file with many rows and columns.\n",
     "\n",
     "Processing numeric data usually comes down to operations that may be grouped into one of the following three categories:\n",
     "\n",
diff --git a/05_numbers_00_content.ipynb b/05_numbers_00_content.ipynb
index 80e0c63..c2d6d1d 100644
--- a/05_numbers_00_content.ipynb
+++ b/05_numbers_00_content.ipynb
@@ -40,7 +40,7 @@
     "\n",
     "- [Chapter 1 <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/01_elements_00_content.ipynb#%28Data%29-Type-%2F-%22Behavior%22) reveals that numbers may come in *different* data types (i.e., `int` vs. `float` so far),\n",
     "- [Chapter 3 <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/03_conditionals_00_content.ipynb#Boolean-Expressions) raises questions regarding the **limited precision** of `float` numbers (e.g., `42 == 42.000000000000001` evaluates to `True`), and\n",
-    "- [Chapter 4 <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/04_iteration_00_content.ipynb#Infinite-Recursion) shows that sometimes a `float` \"walks\" and \"quacks\" like an `int`, whereas the reverse is true.\n",
+    "- [Chapter 4 <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/04_iteration_00_content.ipynb#Infinite-Recursion) shows that sometimes a `float` \"walks\" and \"quacks\" like an `int`, whereas the reverse is true in other cases.\n",
     "\n",
     "This chapter introduces all the [built-in numeric types <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/stdtypes.html#numeric-types-int-float-complex): `int`, `float`, and `complex`. To mitigate the limited precision of floating-point numbers, we also look at two replacements for the `float` type in the [standard library <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/index.html), namely the `Decimal` type in the [decimals <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/decimal.html#decimal.Decimal) and the `Fraction` type in the [fractions <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/fractions.html#fractions.Fraction) module."
    ]
diff --git a/06_text_00_content.ipynb b/06_text_00_content.ipynb
index c988f3e..36a991a 100644
--- a/06_text_00_content.ipynb
+++ b/06_text_00_content.ipynb
@@ -30,7 +30,7 @@
     }
    },
    "source": [
-    "In this chapter, we continue the study of the built-in data types. The next layer on top of numbers consists of **textual data** that are modeled primarily with the `str` type in Python. `str` objects are more complex than the numeric objects in [Chapter 5 <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/05_numbers_00_content.ipynb) as they *consist* of an *arbitrary* and possibly large number of *individual* characters that may be chosen from *any* alphabet in the history of humankind. Luckily, Python abstracts away most of this complexity from us. However, after looking at the `str` type in great detail, we briefly introduce the `bytes` type at the end of this chapter, and learn how characters are modeled in memory."
+    "In this chapter, we continue the study of the built-in data types. The next layer on top of numbers consists of **textual data** that are modeled primarily with the `str` type in Python. `str` objects are more complex than the numeric objects in [Chapter 5 <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/05_numbers_00_content.ipynb) as they *consist* of an *arbitrary* and possibly large number of *individual* characters that may be chosen from *any* alphabet in the history of humankind. Luckily, Python abstracts away most of this complexity from us. However, after looking at the `str` type in great detail, we briefly introduce the `bytes` type at the end of this chapter to understand how characters are modeled in memory."
    ]
   },
   {
@@ -1386,7 +1386,7 @@
     "\n",
     "Another example of a sequence is the `list` type. Because of that, `str` objects may be treated like `list` objects in many situations.\n",
     "\n",
-    "Below, the built-in [len() <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/functions.html#len) function tells us how many characters make up `text`. [len() <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/functions.html#len) would not work with an \"infinite\" object. As anything modeled in a program must fit into a computer's finite memory, there cannot exist truly infinite objects; however, [Chapter 7 <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/07_sequences_00_content.ipynb#Iterators-vs.-Iterables) introduces specialized iterable data types that can be used to model an *infinite* series of \"things\" and that, consequently, have no concept of \"length.\""
+    "Below, the built-in [len() <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/functions.html#len) function tells us how many characters make up `text`. [len() <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/functions.html#len) would not work with an \"infinite\" object. As anything modeled in a program must fit into a computer's finite memory, there cannot exist truly infinite objects; however, [Chapter 8 <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/08_mfr_00_content.ipynb#Iterators-vs.-Iterables) introduces specialized iterable data types that can be used to model an *infinite* series of \"things\" and that, consequently, have no concept of \"length.\""
    ]
   },
   {
diff --git a/07_sequences_00_content.ipynb b/07_sequences_00_content.ipynb
index 011c37c..ece7bd2 100644
--- a/07_sequences_00_content.ipynb
+++ b/07_sequences_00_content.ipynb
@@ -30,13 +30,13 @@
     }
    },
    "source": [
-    "We studied numbers (cf., [Chapter 5 <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/05_numbers_00_content.ipynb)) and textual data (cf., [Chapter 6 <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/06_text_00_content.ipynb)) first, mainly because objects of the presented data types are \"simple,\" for two reasons: First, they are *immutable*, and, as we saw in the \"*Who am I? And how many?*\" section in [Chapter 1 <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/01_elements_00_content.ipynb#Who-am-I?-And-how-many?), mutable objects can quickly become hard to reason about. Second, they are \"flat\" in the sense that they are *not* composed of other objects.\n",
+    "We studied numbers (cf., [Chapter 5 <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/05_numbers_00_content.ipynb)) and textual data (cf., [Chapter 6 <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/06_text_00_content.ipynb)) first mainly because objects of the presented data types are \"simple.\" That is so for two reasons: First, they are *immutable*, and, as we saw in the \"*Who am I? And how many?*\" section in [Chapter 1 <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/01_elements_00_content.ipynb#Who-am-I?-And-how-many?), mutable objects can quickly become hard to reason about. Second, they are \"flat\" in the sense that they are *not* composed of other objects.\n",
     "\n",
     "The `str` type is a bit of a corner case in this regard. While one could argue that a longer `str` object, for example, `\"text\"`, is composed of individual characters, this is *not* the case in memory as the literal `\"text\"` only creates *one* object (i.e., one \"bag\" of $0$s and $1$s modeling all characters).\n",
     "\n",
-    "This chapter, [Chapter 8 <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/08_mfr_00_content.ipynb), and [Chapter 9 <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/09_mappings_00_content.ipynb) introduce various \"complex\" data types. While some are mutable and others are not, they all share that they are primarily used to \"manage,\" or structure, the memory in a program. Unsurprisingly, computer scientists refer to the ideas and theories behind these data types as **[data structures <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_wiki.png\">](https://en.wikipedia.org/wiki/Data_structure)**.\n",
+    "This chapter, [Chapter 8 <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/08_mfr_00_content.ipynb), and [Chapter 9 <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/09_mappings_00_content.ipynb) introduce various \"complex\" data types. While some are mutable and others are not, they all share that they are primarily used to \"manage,\" or structure, the memory in a program (i.e., they provide references to other objects). Unsurprisingly, computer scientists refer to the ideas behind these data types as **[data structures <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_wiki.png\">](https://en.wikipedia.org/wiki/Data_structure)**.\n",
     "\n",
-    "In this chapter, we focus on data types that model all kinds of sequential data. Examples of such data are [spreadsheets <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_wiki.png\">](https://en.wikipedia.org/wiki/Spreadsheet) or [matrices <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_wiki.png\">](https://en.wikipedia.org/wiki/Matrix_%28mathematics%29) and [vectors <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_wiki.png\">](https://en.wikipedia.org/wiki/Vector_%28mathematics_and_physics%29). Such formats share the property that they are composed of smaller units that come in a sequence of, for example, rows/columns/cells or elements/entries."
+    "In this chapter, we focus on data types that model all kinds of sequential data. Examples of such data are [spreadsheets <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_wiki.png\">](https://en.wikipedia.org/wiki/Spreadsheet) or [matrices <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_wiki.png\">](https://en.wikipedia.org/wiki/Matrix_%28mathematics%29) and [vectors <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_wiki.png\">](https://en.wikipedia.org/wiki/Vector_%28mathematics_and_physics%29). These formats share the property that they are composed of smaller units that come in a sequence of, for example, rows/columns/cells or elements/entries."
    ]
   },
   {
diff --git a/08_mfr_00_content.ipynb b/08_mfr_00_content.ipynb
new file mode 100644
index 0000000..6054be5
--- /dev/null
+++ b/08_mfr_00_content.ipynb
@@ -0,0 +1,5312 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "**Important**: Click on \"*Kernel*\" > \"*Restart Kernel and Clear All Outputs*\" *before* reading this chapter in [JupyterLab <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_jp.png\">](https://jupyterlab.readthedocs.io/en/stable/) (e.g., in the cloud on [MyBinder <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_mb.png\">](https://mybinder.org/v2/gh/webartifex/intro-to-python/master?urlpath=lab/tree/08_mfr_00_content.ipynb))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "# Chapter 8: Map, Filter, & Reduce"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "In this chapter, we continue the study of sequential data by looking at memory efficient ways to process the elements in a sequence. That is an important topic for the data science practitioner who must be able to work with data that does *not* fit into a single computer's memory.\n",
+    "\n",
+    "As shown in [Chapter 4 <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/04_iteration_00_content.ipynb#Containers-vs.-Iterables), both the `list` objects `[0, 1, 2, 3, 4]` and `[1, 3, 5, 7, 9]` on the one side and the `range` objects `range(5)` and `range(1, 10, 2)` on the other side allow us to loop over the same numbers. However, the latter two only create *one* `int` object in every iteration while the former two create *all* `int` objects before the loop even starts. In this aspect, we consider `range` objects to be \"rules\" in memory that know how to calculate numbers *without* calculating them.\n",
+    "\n",
+    "In [Chapter 7 <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/07_sequences_00_content.ipynb#The-list-Type), we see how the built-in [list() <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/functions.html#func-list) constructor **materializes** the `range(1, 13)` object into the `list` object `[1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12]`. In other words, we make `range(1, 13)` calculate *all* numbers at once and store them in a `list` object for further processing.\n",
+    "\n",
+    "In many cases, however, it is not necessary to do that, and, in this chapter, we look at other types of \"rules\" in memory and how we can compose different \"rules\" together to implement bigger computations.\n",
+    "\n",
+    "Next, we take a step back and continue with a simple example involving the familiar `numbers` list. Then, we iteratively exchange `list` objects with \"rule\"-like objects *without* changing the overall computation at all. As computations involving sequential data are commonly classified into three categories **map**, **filter**, or **reduce**, we do so too for our `numbers` example."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "numbers = [7, 11, 8, 5, 3, 12, 2, 6, 9, 10, 1, 4]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "## Mapping"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "**Mapping** refers to the idea of applying a transformation to every element in a sequence.\n",
+    "\n",
+    "For example, let's square each element in `numbers` and add `1` to the squares. In essence, we apply the transformation $y := x^2 + 1$ expressed with the `transform()` function below."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "def transform(element):\n",
+    "    \"\"\"Map elements to their squares plus 1.\"\"\"\n",
+    "    return (element ** 2) + 1"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "With the syntax we know so far, we revert to a `for`-loop that iteratively appends the transformed elements to an initially empty `transformed_numbers` list."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "transformed_numbers = []\n",
+    "\n",
+    "for old in numbers:\n",
+    "    new = transform(old)\n",
+    "    transformed_numbers.append(new)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[50, 122, 65, 26, 10, 145, 5, 37, 82, 101, 2, 17]"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "transformed_numbers"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "As this kind of data processing is so common, Python provides the [map() <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/functions.html#map) built-in. In its simplest usage form, it takes two arguments: A transformation `function` that takes one positional argument and an `iterable`.\n",
+    "\n",
+    "We call [map() <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/functions.html#map) with a reference to the `transform()` function and the `numbers` list as the arguments and store the result in the variable `transformer` to inspect it."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "transformer = map(transform, numbers)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "We might expect to get back a materialized sequence (i.e., all elements exist in memory), and a `list` object would feel the most natural because of the type of the `numbers` argument. However, `transformer` is an object of type `map`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<map at 0x7f002518f190>"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "transformer"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "map"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "type(transformer)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Like `range` objects, `map` objects generate a series of objects \"on the fly\" (i.e., one by one), and we use the built-in [next() <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/functions.html#next) function to obtain the next object in line. So, we should think of a `map` object as a \"rule\" stored in memory that only knows how to calculate the next object of possibly *infinitely* many."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "50"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "next(transformer)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "122"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "next(transformer)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "65"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "next(transformer)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "It is essential to understand that by creating a `map` object with the [map() <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/functions.html#map) built-in, nothing happens in memory except the creation of the `map` object. In particular, no second `list` object derived from `numbers` is created. Also, we may view `range` objects as a special case of `map` objects: They are constrained to generating `int` objects only, and the `iterable` argument is replaced with `start`, `stop`, and `step` arguments.\n",
+    "\n",
+    "If we are sure that a `map` object generates a *finite* number of elements, we may materialize them into a `list` object with the built-in [list() <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/functions.html#func-list) constructor. In the example, this is the case as `transformer` is derived from a *finite* `list` object.\n",
+    "\n",
+    "In summary, instead of creating an empty list first and appending it in a `for`-loop as above, we write the following one-liner and obtain an equal `transformed_numbers` list."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "transformed_numbers = list(map(transform, numbers))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[50, 122, 65, 26, 10, 145, 5, 37, 82, 101, 2, 17]"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "transformed_numbers"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "## Filtering"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "**Filtering** refers to the idea of creating a subset of a sequence with a **boolean filter** `function` that indicates if an element should be kept (i.e., `True`) or not (i.e., `False`).\n",
+    "\n",
+    "In the example, let's only keep the even elements in `numbers`. The `is_even()` function implements that as a filter."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "def is_even(element):\n",
+    "    \"\"\"Filter out odd numbers.\"\"\"\n",
+    "    if element % 2 == 0:\n",
+    "        return True\n",
+    "    return False"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "As `element % 2 == 0` is already a boolean expression, we could shorten `is_even()` like so."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "def is_even(element):\n",
+    "    \"\"\"Filter out odd numbers.\"\"\"\n",
+    "    return element % 2 == 0"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "As before, we first use a `for`-loop that appends the elements to be kept iteratively to an initially empty `even_numbers` list."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "even_numbers = []\n",
+    "\n",
+    "for number in transformed_numbers:\n",
+    "    if is_even(number):\n",
+    "        even_numbers.append(number)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[50, 122, 26, 10, 82, 2]"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "even_numbers"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Analogously to the `map` object above, we use the [filter() <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/functions.html#filter) built-in to create an object of type `filter` and assign it to `evens`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "evens = filter(is_even, transformed_numbers)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<filter at 0x7f001cf8f090>"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "evens"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "filter"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "type(evens)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "`evens` works like `transformer` above: With the built-in [next() <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/functions.html#next) function we obtain the even numbers one by one. So, the \"next\" element in line is simply the next even `int` object the `filter` object encounters."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[50, 122, 65, 26, 10, 145, 5, 37, 82, 101, 2, 17]"
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "transformed_numbers"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "50"
+      ]
+     },
+     "execution_count": 21,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "next(evens)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "122"
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "next(evens)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "26"
+      ]
+     },
+     "execution_count": 23,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "next(evens)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "As above, we could create a materialized `list` object with the [list() <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/functions.html#func-list) constructor."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[50, 122, 26, 10, 82, 2]"
+      ]
+     },
+     "execution_count": 24,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "list(filter(is_even, transformed_numbers))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "We may also chain `map` and `filter` objects derived from the original `numbers` list."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[50, 122, 26, 10, 82, 2]"
+      ]
+     },
+     "execution_count": 25,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "list(\n",
+    "    filter(\n",
+    "        is_even,\n",
+    "        map(transform, numbers),\n",
+    "    )\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Using the [map() <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/functions.html#map) and [filter() <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/functions.html#filter) built-ins, we can quickly switch the order: Filter first and then transform the remaining elements. This variant equals the \"*A simple Filter*\" example in [Chapter 4 <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/04_iteration_00_content.ipynb#Example:-A-simple-Filter). On the contrary, code with `for`-loops and `if` statements is more tedious to adapt. Additionally, `map` and `filter` objects loop \"at the C level\" and are a lot faster because of that. Because of that, Pythonistas tend to *not* use explicit `for`-loops so much."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[65, 145, 5, 37, 101, 17]"
+      ]
+     },
+     "execution_count": 26,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "list(\n",
+    "    map(\n",
+    "        transform,\n",
+    "        filter(is_even, numbers),\n",
+    "    )\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "## Reducing"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Lastly, **reducing** sequential data means to summarize the elements into a single statistic.\n",
+    "\n",
+    "A simple example is the built-in [sum() <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/functions.html#sum) function."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "370"
+      ]
+     },
+     "execution_count": 27,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "sum(\n",
+    "    map(\n",
+    "        transform,\n",
+    "        filter(is_even, numbers),\n",
+    "    )\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Other straightforward examples are the built-in [min() <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/functions.html#min) or [max() <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/functions.html#max) functions."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "5"
+      ]
+     },
+     "execution_count": 28,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "min(map(transform, filter(is_even, numbers)))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "145"
+      ]
+     },
+     "execution_count": 29,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "max(map(transform, filter(is_even, numbers)))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "[sum() <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/functions.html#sum), [min() <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/functions.html#min), and [max() <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/functions.html#max) can be regarded as special cases.\n",
+    "\n",
+    "The generic way of reducing a sequence is to apply a function of *two* arguments on a rolling horizon: Its first argument is the reduction of the elements processed so far, and the second the next element to be reduced.\n",
+    "\n",
+    "For illustration, let's replicate [sum() <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/functions.html#sum) as such a function, called `sum_alt()`. Its implementation only adds two numbers."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "def sum_alt(sum_so_far, next_number):\n",
+    "    \"\"\"Reduce a sequence by addition.\"\"\"\n",
+    "    return sum_so_far + next_number"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Further, we create a *new* `map` object derived from `numbers` ..."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "evens_transformed = map(transform, filter(is_even, numbers))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "... and loop over all *but* the first element it generates. The latter is captured separately as the initial `result` with the [next() <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/functions.html#next) function. We know from above that `evens_transformed` generates *six* elements. That is why we see *five* growing `result` values resembling a [cumulative sum](http://mathworld.wolfram.com/CumulativeSum.html). The first `210` is the sum of the first two elements generated by `evens_transformed`, `65` and `145`.\n",
+    "\n",
+    "So, we also learn that `map` objects, and analogously `filter` objects, are *iterable* as we may loop over them."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "210 215 252 353 370 "
+     ]
+    }
+   ],
+   "source": [
+    "result = next(evens_transformed)\n",
+    "\n",
+    "for number in evens_transformed:\n",
+    "    result = sum_alt(result, number)\n",
+    "    print(result, end=\" \")  # line added for didactical purposes"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "The final `result` is the same `370` as above."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "370"
+      ]
+     },
+     "execution_count": 33,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "result"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "The [reduce() <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/functools.html#functools.reduce) function in the [functools <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/functools.html) module in the [standard library <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/index.html) provides more convenience (and speed) replacing the `for`-loop. It takes two arguments, `function` and `iterable`, in the same way as the [map() <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/functions.html#map) and [filter() <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/functions.html#filter) built-ins.\n",
+    "\n",
+    "[reduce() <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/functools.html#functools.reduce) is **[eager <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_wiki.png\">](https://en.wikipedia.org/wiki/Eager_evaluation)** meaning that all computations implied by the contained `map` and `filter` \"rules\" are executed immediately, and the code cell evaluates to `370`. On the contrary, [map() <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/functions.html#map) and [filter() <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/functions.html#filter) create **[lazy <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_wiki.png\">](https://en.wikipedia.org/wiki/Lazy_evaluation)** `map` and `filter` objects, and we have to use the [next() <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/functions.html#next) function to obtain the elements."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "from functools import reduce"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "370"
+      ]
+     },
+     "execution_count": 35,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "reduce(\n",
+    "    sum_alt,\n",
+    "    map(\n",
+    "        transform,\n",
+    "        filter(is_even, numbers),\n",
+    "    )\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "## Lambda Expressions"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "[map() <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/functions.html#map), [filter() <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/functions.html#filter), and [reduce() <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/functools.html#functools.reduce) take a `function` object as their first argument, and we defined `transform()`, `is_even()`, and `sum_alt()` to be used precisely for that.\n",
+    "\n",
+    "Often, such functions are used *only once* in a program. However, the primary purpose of functions is to *reuse* them. In such cases, it makes more sense to define them \"anonymously\" right at the position where the first argument goes.\n",
+    "\n",
+    "As mentioned in [Chapter 2 <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/02_functions_00_content.ipynb#Anonymous-Functions), we use `lambda` expressions to create `function` objects *without* a name referencing them.\n",
+    "\n",
+    "So, the above `sum_alt()` function could be rewritten as a `lambda` expression like so ..."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<function __main__.<lambda>(sum_so_far, next_number)>"
+      ]
+     },
+     "execution_count": 36,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "lambda sum_so_far, next_number: sum_so_far + next_number"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "... or even shorter."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<function __main__.<lambda>(x, y)>"
+      ]
+     },
+     "execution_count": 37,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "lambda x, y: x + y"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "With the new concepts in this section, we can rewrite the entire example in just a few lines of code *without* any `for`, `if`, and `def` statements. The resulting code is concise, easy to read, quick to modify, and even faster in execution. Most importantly, it is optimized to handle big amounts of data as *no* temporary `list` objects are materialized in memory."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "370"
+      ]
+     },
+     "execution_count": 38,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "numbers = [7, 11, 8, 5, 3, 12, 2, 6, 9, 10, 1, 4]\n",
+    "evens = filter(lambda x: x % 2 == 0, numbers)\n",
+    "transformed = map(lambda x: (x ** 2) + 1, evens)\n",
+    "sum(transformed)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "If `numbers` comes as a sorted sequence of whole numbers, we may use the [range() <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/functions.html#func-range) built-in and get away *without* any `list` object in memory at all!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "370"
+      ]
+     },
+     "execution_count": 39,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "numbers = range(1, 13)\n",
+    "evens = filter(lambda x: x % 2 == 0, numbers)\n",
+    "transformed = map(lambda x: (x ** 2) + 1, evens)\n",
+    "sum(transformed)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "To additionally save the temporary variables, `numbers`, `evens`, and `transformed`, we write the entire computation as *one* expression."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "370"
+      ]
+     },
+     "execution_count": 40,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "sum(\n",
+    "    map(\n",
+    "        lambda x: (x ** 2) + 1,\n",
+    "        filter(\n",
+    "            lambda x: x % 2 == 0,\n",
+    "            range(1, 13),\n",
+    "        )\n",
+    "    )\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "PythonTutor visualizes the differences in the number of computational steps and memory usage:\n",
+    "- [Version 1 <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](http://pythontutor.com/visualize.html#code=def%20is_even%28element%29%3A%0A%20%20%20%20if%20element%20%25%202%20%3D%3D%200%3A%0A%20%20%20%20%20%20%20%20return%20True%0A%20%20%20%20return%20False%0A%0Adef%20transform%28element%29%3A%0A%20%20%20%20return%20%28element%20**%202%29%20%2B%201%0A%0Anumbers%20%3D%20list%28range%281,%2013%29%29%0A%0Aevens%20%3D%20%5B%5D%0Afor%20number%20in%20numbers%3A%0A%20%20%20%20if%20is_even%28number%29%3A%0A%20%20%20%20%20%20%20%20evens.append%28number%29%0A%0Atransformed%20%3D%20%5B%5D%0Afor%20number%20in%20evens%3A%0A%20%20%20%20transformed.append%28transform%28number%29%29%0A%0Aresult%20%3D%20sum%28transformed%29&cumulative=false&curInstr=0&heapPrimitives=nevernest&mode=display&origin=opt-frontend.js&py=3&rawInputLstJSON=%5B%5D&textReferences=false): With `for`-loops, `if` statements, and named functions -> **116** steps and **3** `list` objects\n",
+    "- [Version 2 <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](http://pythontutor.com/visualize.html#code=numbers%20%3D%20range%281,%2013%29%0Aevens%20%3D%20filter%28lambda%20x%3A%20x%20%25%202%20%3D%3D%200,%20numbers%29%0Atransformed%20%3D%20map%28lambda%20x%3A%20%28x%20**%202%29%20%2B%201,%20evens%29%0Aresult%20%3D%20sum%28transformed%29&cumulative=false&curInstr=0&heapPrimitives=nevernest&mode=display&origin=opt-frontend.js&py=3&rawInputLstJSON=%5B%5D&textReferences=false): With named `map` and `filter` objects -> **58** steps and **no** `list` object\n",
+    "- [Version 3 <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](http://pythontutor.com/visualize.html#code=result%20%3D%20sum%28map%28lambda%20x%3A%20%28x%20**%202%29%20%2B%201,%20filter%28lambda%20x%3A%20x%20%25%202%20%3D%3D%200,%20range%281,%2013%29%29%29%29&cumulative=false&curInstr=0&heapPrimitives=nevernest&mode=display&origin=opt-frontend.js&py=3&rawInputLstJSON=%5B%5D&textReferences=false): Everything in *one* expression -> **55** steps and **no** `list` object\n",
+    "\n",
+    "Versions 2 and 3 are the same, except for the three additional steps required to create the temporary variables. The *major* downside of Version 1 is that, in the worst case, it may need *three times* the memory as compared to the other two versions!\n",
+    "\n",
+    "An experienced Pythonista would probably go with Version 2 in a production system to keep the code readable and maintainable.\n",
+    "\n",
+    "The map-filter-reduce paradigm has caught attention in recent years as it enables **[parallel computing <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_wiki.png\">](https://en.wikipedia.org/wiki/Parallel_computing)**, and this gets important when dealing with big amounts of data. The workings in the memory as shown in this section provide an idea why."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "## List Comprehensions"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "For [map() <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/functions.html#map) and [filter() <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/functions.html#filter), Python provides an alternative syntax appealing to people who like mathematical notations.\n",
+    "\n",
+    "Consider again the original \"*A simple Filter*\" example from [Chapter 4 <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/04_iteration_00_content.ipynb#Example:-A-simple-Filter), re-written such that both the mapping and the filtering are done in *one* `for`-loop instead of the *two* above."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "numbers = [7, 11, 8, 5, 3, 12, 2, 6, 9, 10, 1, 4]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "evens_transformed = []\n",
+    "\n",
+    "for number in numbers:\n",
+    "    if number % 2 == 0:\n",
+    "        evens_transformed.append((number ** 2) + 1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "370"
+      ]
+     },
+     "execution_count": 43,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "sum(evens_transformed)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "**List comprehensions**, or **listcomps** for short, are *expressions* to derive *new* `list` objects out of *existing* ones (cf., [reference <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/reference/expressions.html#displays-for-lists-sets-and-dictionaries)). Practically, this means that we place the `for` and `if` inside brackets `[` and `]`.\n",
+    "\n",
+    "So, the example can be written in a single *expression* like below replacing the compound `for` *statement* above."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[65, 145, 5, 37, 101, 17]"
+      ]
+     },
+     "execution_count": 44,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "[(n ** 2) + 1 for n in numbers if n % 2 == 0]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "A list comprehension may be used in place of any `list` object.\n",
+    "\n",
+    "For example, let's rewrite the \"*A simple Filter*\" example from [Chapter 4 <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/04_iteration_00_content.ipynb#Example:-A-simple-Filter) in just one line. As a caveat, the code below *materializes* all elements in memory *before* summing them up, and may, therefore, cause a `MemoryError` when executed with a bigger `numbers` list. We see with [PythonTutor <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](http://pythontutor.com/visualize.html#code=numbers%20%3D%20range%281,%2013%29%0Aresult%20%3D%20sum%28%5B%28n%20**%202%29%20%2B%201%20for%20n%20in%20numbers%20if%20n%20%25%202%20%3D%3D%200%5D%29&cumulative=false&curInstr=0&heapPrimitives=nevernest&mode=display&origin=opt-frontend.js&py=3&rawInputLstJSON=%5B%5D&textReferences=false) how a `list` object exists in memory at step 17 and then \"gets lost\" right after. As the next section shows, this downside may be mitigated."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "370"
+      ]
+     },
+     "execution_count": 45,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "sum([(n ** 2) + 1 for n in numbers if n % 2 == 0])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "#### Example: Nested Lists"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "List comprehensions may come with several `for`'s and `if`'s.\n",
+    "\n",
+    "The cell below creates a `list` object that contains three other `list` objects with a series of numbers in them. The first and last numbers in each inner `list` object are offset by `1`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "nested_numbers = [list(range(x, y + 1)) for x, y in zip([1, 2, 3], [7, 8, 9])]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[[1, 2, 3, 4, 5, 6, 7], [2, 3, 4, 5, 6, 7, 8], [3, 4, 5, 6, 7, 8, 9]]"
+      ]
+     },
+     "execution_count": 47,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "nested_numbers"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "To do something meaningful with the numbers, we have to remove the inner layer of `list` objects and **flatten** (i.e., \"un-nest\") the data.\n",
+    "\n",
+    "Without list comprehensions, we achieve that with two nested `for`-loops."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 48,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "flat_numbers = []\n",
+    "\n",
+    "for inner_numbers in nested_numbers:\n",
+    "    for number in inner_numbers:\n",
+    "        flat_numbers.append(number)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 49,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[1, 2, 3, 4, 5, 6, 7, 2, 3, 4, 5, 6, 7, 8, 3, 4, 5, 6, 7, 8, 9]"
+      ]
+     },
+     "execution_count": 49,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "flat_numbers"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "That translates into a list comprehension like below. The order of the `for`'s may be confusing at first but is the *same* as writing out the nested `for`-loops as above."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 50,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[1, 2, 3, 4, 5, 6, 7, 2, 3, 4, 5, 6, 7, 8, 3, 4, 5, 6, 7, 8, 9]"
+      ]
+     },
+     "execution_count": 50,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "[number for inner_numbers in nested_numbers for number in inner_numbers]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Now, we may use the `list` object resulting from the list comprehension in any way we want.\n",
+    "\n",
+    "For example, to add up the flattened numbers with [sum() <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/functions.html#sum). The same caveat holds as before: The `list` object passed into [sum() <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/functions.html#sum) is *materialized* with *all* its elements *before* the sum is calculated!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 51,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "105"
+      ]
+     },
+     "execution_count": 51,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "sum([number for inner_numbers in nested_numbers for number in inner_numbers])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "In this particular example, however, we can exploit the fact that any sum of numbers can be expressed as the sum of sums of mutually exclusive and collectively exhaustive subsets of these numbers and get away with just *one* `for` in the list comprehension."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 52,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "105"
+      ]
+     },
+     "execution_count": 52,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "sum([sum(inner_numbers) for inner_numbers in nested_numbers])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "#### Example: Working with Cartesian Products"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "A popular use case of nested list comprehensions is applying a transformation to each $n$-tuple in the [Cartesian product <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_wiki.png\">](https://en.wikipedia.org/wiki/Cartesian_product) created from elements of $n$ iterables. In the generic illustration below, a transformation $f(x, y)$ is applied to each $2$-tuple in the Cartesian product $X \\times Y$ where $x$ is an element in $X = \\{x_1, x_2, x_3\\}$ and $y$ is an element in $Y = \\{y_1, y_2, y_3\\}$."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "|$Y$ \\ $X$|    $x_1$     |    $x_2$     |    $x_3$     |\n",
+    "|---------|--------------|--------------|--------------|\n",
+    "|  $y_1$  |f($x_1$,$y_1$)|f($x_2$,$y_1$)|f($x_3$,$y_1$)|\n",
+    "|  $y_2$  |f($x_1$,$y_2$)|f($x_2$,$y_2$)|f($x_3$,$y_2$)|\n",
+    "|  $y_3$  |f($x_1$,$y_3$)|f($x_2$,$y_3$)|f($x_3$,$y_3$)|"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "For example, let's add each quotient obtained by taking the numerator $x$ from `[10, 20, 30]` and the denominator $y$ from `[40, 50, 60]` to `1`, and then find the overall product. This transformation can be described mathematically as the function $z = f(x, y)$ below."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "source": [
+    "$$z = f(x, y) = \\prod{ (1 + \\frac{x}{y} )}$$"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Further, the table below visualizes the calculations: The result is the product of *nine* entries."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "source": [
+    "|`y` \\ `x`|**10**|**20**|**30**|\n",
+    "|------|------|------|------|\n",
+    "|**40**| 1.25 | 1.50 | 1.75 |\n",
+    "|**50**| 1.20 | 1.40 | 1.60 |\n",
+    "|**60**| 1.17 | 1.33 | 1.50 |"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "To express that in Python, we start by creating two `list` objects, `first` and `second`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 53,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "first = [10, 20, 30]\n",
+    "second = [40, 50, 60]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "For a Cartesian product, we must loop over *all* possible $2$-tuples where one element is drawn from `first` and the other from `second`. We achieve that with two nested `for`-loops, in which we calculate each `quotient` and append it to an initially empty `cartesian_product` list."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 54,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[1.25, 1.2, 1.1666666666666667, 1.5, 1.4, 1.3333333333333333, 1.75, 1.6, 1.5]"
+      ]
+     },
+     "execution_count": 54,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "cartesian_product = []\n",
+    "\n",
+    "for numerator in first:\n",
+    "    for denominator in second:\n",
+    "        quotient = 1 + (numerator / denominator)\n",
+    "        cartesian_product.append(quotient)\n",
+    "\n",
+    "cartesian_product"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Next, we convert the two explicit `for`-loops into one list comprehensions and use `x` and `y` as shorter variable names."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 55,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[1.25, 1.2, 1.1666666666666667, 1.5, 1.4, 1.3333333333333333, 1.75, 1.6, 1.5]"
+      ]
+     },
+     "execution_count": 55,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "[1 + (x / y) for x in first for y in second]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "The order of the `for`'s is *important*: The list comprehension above divides numbers from `first` by numbers from `second`, whereas the list comprehension below does the opposite."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 56,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[5.0, 3.0, 2.333333333333333, 6.0, 3.5, 2.666666666666667, 7.0, 4.0, 3.0]"
+      ]
+     },
+     "execution_count": 56,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "[1 + (x / y) for x in second for y in first]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "To find the overall product, we *unpack* the list comprehension into the `product()` function from the \"*Function Definitions & Calls*\" sub-section in [Chapter 7 <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/07_sequences_00_content.ipynb#Function-Definitions-&-Calls)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 57,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "def product(*args):\n",
+    "    \"\"\"Multiply all arguments.\"\"\"\n",
+    "    result = args[0]\n",
+    "\n",
+    "    for arg in args[1:]:\n",
+    "        result *= arg\n",
+    "\n",
+    "    return result"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 58,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "20.58"
+      ]
+     },
+     "execution_count": 58,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "product(*[1 + (x / y) for x in first for y in second])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Alternatively, we use a `lambda` expression with the [reduce() <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/functools.html#functools.reduce) function from the [functools <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/functools.html) module."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 59,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "20.58"
+      ]
+     },
+     "execution_count": 59,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "reduce(lambda x, y: x * y, [1 + (x / y) for x in first for y in second])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "While this example is stylized, Cartesian products are hidden in many applications, and it shows how the various language features introduced in this chapter can be seamlessly combined to process sequential data."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "## Generator Expressions"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Because of the high memory consumption, Pythonistas avoid materialized `list` objects, and, thus, also list comprehensions, whenever possible. Instead, they prefer to work with **[generator expressions <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/reference/expressions.html#generator-expressions)**, or **genexps** for short. Syntactically, they work like list comprehensions except that parentheses, `(` and `)`, replace brackets, `[` and `]`.\n",
+    "\n",
+    "Let's go back to the original \"*A simple Filter*\" example from [Chapter 4 <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/04_iteration_00_content.ipynb#Example:-A-simple-Filter) one more time, apply the transformation $y := x^2 + 1$ to all even `numbers`, and sum them up."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 60,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "numbers = [7, 11, 8, 5, 3, 12, 2, 6, 9, 10, 1, 4]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "To filter and transform `numbers`, we wrote a list comprehension above ..."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 61,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[65, 145, 5, 37, 101, 17]"
+      ]
+     },
+     "execution_count": 61,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "[(n ** 2) + 1 for n in numbers if n % 2 == 0]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "... that now becomes a generator expression."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 62,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<generator object <genexpr> at 0x7f001cf938d0>"
+      ]
+     },
+     "execution_count": 62,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "((n ** 2) + 1 for n in numbers if n % 2 == 0)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "A generator expression evaluates to yet another \"rule\"-like object in memory that knows how to generate the individual objects in a series one by one. Whereas a list comprehension materializes its elements in memory *when* it is evaluated, the opposite holds true for generator expressions: *No* object is created in memory except the \"rule\" itself. Because of this behavior, we describe generator expressions as *lazy* and list comprehensions as *eager*.\n",
+    "\n",
+    "To materialize the elements specified by a generator expression, we use the [list() <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/functions.html#func-list) constructor as seen above."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 63,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[65, 145, 5, 37, 101, 17]"
+      ]
+     },
+     "execution_count": 63,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "list(((n ** 2) + 1 for n in numbers if n % 2 == 0))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Whenever a generator expression is the only argument in a function call, we may merge the double parentheses, `((` and `))`, into just `(` and `)`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 64,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[65, 145, 5, 37, 101, 17]"
+      ]
+     },
+     "execution_count": 64,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "list((n ** 2) + 1 for n in numbers if n % 2 == 0)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "A common use case is to reduce the elements into a single object instead, for example, by adding them up with [sum() <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/functions.html#sum) as in the original \"*A simple Filter*\" example. [PythonTutor <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](http://pythontutor.com/visualize.html#code=numbers%20%3D%20range%281,%2013%29%0Asum_with_list%20%3D%20sum%28%5B%28n%20**%202%29%20%2B%201%20for%20n%20in%20numbers%20if%20n%20%25%202%20%3D%3D%200%5D%29%0Asum_with_gen%20%3D%20sum%28%28n%20**%202%29%20%2B%201%20for%20n%20in%20numbers%20if%20n%20%25%202%20%3D%3D%200%29&cumulative=false&curInstr=0&heapPrimitives=nevernest&mode=display&origin=opt-frontend.js&py=3&rawInputLstJSON=%5B%5D&textReferences=false) shows how the code cell below does *not* create a temporary `list` object in memory whereas a list comprehension would do so (cf., step 17)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 65,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "370"
+      ]
+     },
+     "execution_count": 65,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "sum((n ** 2) + 1 for n in numbers if n % 2 == 0)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Let's assign the object to which the generator expression below evaluates to to a variable and inspect it."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 66,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "gen = ((n ** 2) + 1 for n in numbers if n % 2 == 0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 67,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<generator object <genexpr> at 0x7f001cf93b50>"
+      ]
+     },
+     "execution_count": 67,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "gen"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Unsurprisingly, generator expressions create objects of type `generator`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 68,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "generator"
+      ]
+     },
+     "execution_count": 68,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "type(gen)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "`generator` objects work just like the `map` and `filter` objects above. So, with the [next() <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/functions.html#next) function, we can generate elements one by one."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 69,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "65"
+      ]
+     },
+     "execution_count": 69,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "next(gen)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 70,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "145"
+      ]
+     },
+     "execution_count": 70,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "next(gen)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 71,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "5"
+      ]
+     },
+     "execution_count": 71,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "next(gen)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 72,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "37"
+      ]
+     },
+     "execution_count": 72,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "next(gen)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 73,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "101"
+      ]
+     },
+     "execution_count": 73,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "next(gen)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 74,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "17"
+      ]
+     },
+     "execution_count": 74,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "next(gen)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Once a `generator` object runs out of elements, it raises a `StopIteration` exception, and we say that the `generator` object is **exhausted**. To loop over its elements again, we must create a *new* one."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 75,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "ename": "StopIteration",
+     "evalue": "",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mStopIteration\u001b[0m                             Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-75-6e72e47198db>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mnext\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mgen\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;31mStopIteration\u001b[0m: "
+     ]
+    }
+   ],
+   "source": [
+    "next(gen)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 76,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "ename": "StopIteration",
+     "evalue": "",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mStopIteration\u001b[0m                             Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-76-6e72e47198db>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mnext\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mgen\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;31mStopIteration\u001b[0m: "
+     ]
+    }
+   ],
+   "source": [
+    "next(gen)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Calling the [next() <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/functions.html#next) function repeatedly with the *same* `generator` object as the argument is essentially what a `for` statement automates for us. So, `generator` objects are *iterable*. We look into this in detail further below in the \"*The `for` Statement (revisited)*\" section."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 77,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "65 145 5 37 101 17 "
+     ]
+    }
+   ],
+   "source": [
+    "for number in ((n ** 2) + 1 for n in numbers if n % 2 == 0):\n",
+    "    print(number, end=\" \")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "#### Example: Nested Lists (revisited)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "If we are only interested in a *reduction* of `nested_numbers` into a single statistic, as the overall sum in the \"*Nested Lists*\" example above, we should replace lists or list comprehensions with generator expressions wherever possible. The result is the *same*, but no intermediate `list` objects are materialized! That makes our code scale to large amounts of data.\n",
+    "\n",
+    "Let's adapt the example reuse `nested_numbers` from above."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 78,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[[1, 2, 3, 4, 5, 6, 7], [2, 3, 4, 5, 6, 7, 8], [3, 4, 5, 6, 7, 8, 9]]"
+      ]
+     },
+     "execution_count": 78,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "nested_numbers"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Compared to the implementation with the list comprehension above, we simply remove the brackets, `[` and `]`: The argument to [sum() <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/functions.html#sum) becomes a generator expression."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 79,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "105"
+      ]
+     },
+     "execution_count": 79,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "sum(number for inner_numbers in nested_numbers for number in inner_numbers)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "That also holds for the alternative formulation as a sum of sums."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 80,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "105"
+      ]
+     },
+     "execution_count": 80,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "sum(sum(inner_numbers) for inner_numbers in nested_numbers)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Because `nested_numbers` has an internal structure (i.e., the inner lists are series of consecutive integers), we can even provide an effectively **memoryless** implementation by expressing it as a generator expression derived from `range` objects. [PythonTutor <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](http://pythontutor.com/visualize.html#code=nested_numbers%20%3D%20%28%28range%28x,%20y%20%2B%201%29%29%20for%20x,%20y%20in%20zip%28range%281,%204%29,%20range%287,%2010%29%29%29%0Aresult%20%3D%20sum%28number%20for%20inner_numbers%20in%20nested_numbers%20for%20number%20in%20inner_numbers%29&cumulative=false&curInstr=0&heapPrimitives=nevernest&mode=display&origin=opt-frontend.js&py=3&rawInputLstJSON=%5B%5D&textReferences=false) confirms that no `list` objects materialize at any point in time."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 81,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "nested_numbers = ((range(x, y + 1)) for x, y in zip(range(1, 4), range(7, 10)))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 82,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<generator object <genexpr> at 0x7f001cfb10d0>"
+      ]
+     },
+     "execution_count": 82,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "nested_numbers"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 83,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "105"
+      ]
+     },
+     "execution_count": 83,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "sum(number for inner_numbers in nested_numbers for number in inner_numbers)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "We must be careful when assigning a `generator` object to a variable: If we use `nested_numbers` again, for example, in the alternative formulation below, [sum() <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/functions.html#sum) returns `0` because `nested_numbers` is exhausted after executing the previous code cell. [PythonTutor <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](http://pythontutor.com/visualize.html#code=nested_numbers%20%3D%20%28%28range%28x,%20y%20%2B%201%29%29%20for%20x,%20y%20in%20zip%28range%281,%204%29,%20range%287,%2010%29%29%29%0Aresult%20%3D%20sum%28number%20for%20inner_numbers%20in%20nested_numbers%20for%20number%20in%20inner_numbers%29%0Ano_result%20%3D%20sum%28sum%28inner_numbers%29%20for%20inner_numbers%20in%20nested_numbers%29&cumulative=false&curInstr=0&heapPrimitives=nevernest&mode=display&origin=opt-frontend.js&py=3&rawInputLstJSON=%5B%5D&textReferences=false) also shows that."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 84,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0"
+      ]
+     },
+     "execution_count": 84,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "sum(sum(inner_numbers) for inner_numbers in nested_numbers)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "#### Example: Working with Cartesian Products (revisited)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Let's also rewrite the \"*Working with Cartesian Products*\" example from above with generator expressions.\n",
+    "\n",
+    "As a first optimization, we replace the materialized `list` objects, `first` and `second`, with memoryless `range` objects."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 85,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "first = range(10, 31, 10)  # \"==\" [10, 20, 30]\n",
+    "second = range(40, 61, 10)  # \"==\" [40, 50, 60]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Now, the first of the two alternative solutions may be more appealing to many readers. In general, many practitioners seem to dislike `lambda` expressions.\n",
+    "\n",
+    "In the first solution, we *unpack* the elements produced by `(1 + (x / y) for x in first for y in second)` into the `product()` function from the \"*Function Definitions & Calls*\" sub-section in [Chapter 7 <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/07_sequences_00_content.ipynb#Function-Definitions-&-Calls). However, inside `product()`, the elements are *packed* into `args`, a *materialized* `tuple` object! So, all the memory efficiency gained by using a generator expression is lost! [PythonTutor <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](http://pythontutor.com/visualize.html#code=def%20product%28*args%29%3A%0A%20%20%20%20result%20%3D%20args%5B0%5D%0A%20%20%20%20for%20arg%20in%20args%5B1%3A%5D%3A%0A%20%20%20%20%20%20%20%20result%20*%3D%20arg%0A%20%20%20%20return%20result%0A%0Afirst%20%3D%20range%2810,%2031,%2010%29%0Asecond%20%3D%20range%2840,%2061,%2010%29%0A%0Aresult%20%3D%20product%28*%281%20%2B%20%28x%20/%20y%29%20for%20x%20in%20first%20for%20y%20in%20second%29%29&cumulative=false&curInstr=0&heapPrimitives=nevernest&mode=display&origin=opt-frontend.js&py=3&rawInputLstJSON=%5B%5D&textReferences=false) shows how a `tuple` object exists in steps 38-58."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 86,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "20.58"
+      ]
+     },
+     "execution_count": 86,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "product(*(1 + (x / y) for x in first for y in second))  # not memory efficient!"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "On the contrary, the second solution with the [reduce() <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/functools.html#functools.reduce) function from the [functools <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/functools.html) module and the `lambda` expression works *without* all elements materialized at the same time, and [PythonTutor <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](http://pythontutor.com/visualize.html#code=from%20functools%20import%20reduce%0A%0Afirst%20%3D%20range%2810,%2031,%2010%29%0Asecond%20%3D%20range%2840,%2061,%2010%29%0A%0Aresult%20%3D%20reduce%28%0A%20%20%20%20lambda%20x,%20y%3A%20x%20*%20y,%0A%20%20%20%20%281%20%2B%20%28x%20/%20y%29%20for%20x%20in%20first%20for%20y%20in%20second%29%0A%29&cumulative=false&curInstr=0&heapPrimitives=nevernest&mode=display&origin=opt-frontend.js&py=3&rawInputLstJSON=%5B%5D&textReferences=false) confirms that. So, only the second alternative is truly memory-efficient!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 87,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "20.58"
+      ]
+     },
+     "execution_count": 87,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "reduce(lambda x, y: x * y, (1 + (x / y) for x in first for y in second))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "In summary, we learn from this example that unpacking `generator` objects *may* be a *bad* idea."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "#### Example: Averaging all even Numbers in a List (revisited)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "With the new concepts in this chapter, let's rewrite the book's introductory \"*Averaging all even Numbers in a List*\" example from [Chapter 1 <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/01_elements_00_content.ipynb#Example:-Averaging-all-even-Numbers-in-a-List) such that it efficiently handles a large sequence of numbers. We continue from its latest implementation, the `average_evens()` function in the \"*Keyword-only Arguments*\" section in [Chapter 2 <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/02_functions_00_content.ipynb#Keyword-only-Arguments).\n",
+    "\n",
+    "We assume that `average_evens()` is called with a *finite* and *iterable* object that generates a **stream** of numeric objects that can be cast as `int` objects. After all, the idea of even and odd numbers makes sense only in the context of whole numbers.\n",
+    "\n",
+    "The generator expression `(round(n) for n in numbers)` implements the type casting, and when it is evaluated, *nothing* happens except that a `generator` object is assigned to `integers`. Then, with the [reduce() <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/functools.html#functools.reduce) function from the [functools <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/functools.html) module, we *simultaneously* add up *and* count the even numbers with the `generator` object to which the inner generator expression `((n, 1) for n in integers if n % 2 == 0)` evaluates to. That `generator` object takes the `integers` generator as its source and produces `tuple` objects consisting of the next *even* number in line and `1`. Two such `tuple` objects are then iteratively passed to the `function` object to which the `lambda` expression evaluates to. `x` represents the total and the count of the even numbers processed so far, while `y`'s first element, `y[0]`, is the next *even* number to be added to the running total. The result of calling [reduce() <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/functools.html#functools.reduce) is again a `tuple` object, namely the final `total` and `count`. Lastly, we calculate the simple average and scale it.\n",
+    "\n",
+    "In summary, this implementation of `average_evens()` does *not* keep materialized `list` objects internally like its predecessors in [Chapter 2 <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/02_functions_00_content.ipynb) but processes the elements of the `numbers` argument on a one-by-one basis."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 88,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "def average_evens(numbers, *, scalar=1):\n",
+    "    \"\"\"Calculate the average of all even numbers.\n",
+    "\n",
+    "    Args:\n",
+    "        numbers (iterable): a finite stream of the numbers to be averaged;\n",
+    "            if non-whole numbers are provided, they are rounded\n",
+    "        scalar (float, optional): multiplies the average; defaults to 1\n",
+    "\n",
+    "    Returns:\n",
+    "        float: (scaled) average\n",
+    "    \"\"\"\n",
+    "    integers = (round(n) for n in numbers)\n",
+    "    total, count = reduce(\n",
+    "        lambda x, y: (x[0] + y[0], x[1] + y[1]),\n",
+    "        ((n, 1) for n in integers if n % 2 == 0)\n",
+    "    )\n",
+    "    return scalar * total / count"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 89,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "7.0"
+      ]
+     },
+     "execution_count": 89,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "average_evens([7, 11, 8, 5, 3, 12, 2, 6, 9, 10, 1, 4], scalar=1)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "We may provide an optional `scalar` argument as before."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 90,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "14.0"
+      ]
+     },
+     "execution_count": 90,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "average_evens([7, 11, 8, 5, 3, 12, 2, 6, 9, 10, 1, 4], scalar=2)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "An argument with `float` objects works just as well."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 91,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "7.0"
+      ]
+     },
+     "execution_count": 91,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "average_evens([7., 11., 8., 5., 3., 12., 2., 6., 9., 10., 1., 4.])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "To show that `average_evens()` can process a large stream of data, we simulate `10_000_000` randomly drawn integers between `0` and `100` with the [randint() <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/random.html#random.randint) function from the [random <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/random.html) module. We use a generator expression derived from a `range` object as the `numbers` argument. So, at *no* point in time is there a materialized `list` or `tuple` object in memory. The result approaching `50` confirms that [randint() <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/random.html#random.randint) must be based on a uniform distribution."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 92,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "import random"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 93,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "random.seed(42)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 94,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "49.994081434519636"
+      ]
+     },
+     "execution_count": 94,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "average_evens(random.randint(0, 100) for _ in range(10_000_000))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "To show that `average_evens()` filters out odd numbers, we simulate another stream of `10_000_000` randomly drawn odd integers between `1` and `99`. As no function in the [random <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/random.html) module does that \"out of the box,\" we must be creative: Doubling a number drawn from `random.randint(0, 49)` results in an even number between `0` and `98`, and adding `1` makes it odd. Then, `average_evens()` raises a `TypeError`, essentially because `(int(n) for n in numbers)` does not generate any element."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 95,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "ename": "TypeError",
+     "evalue": "reduce() of empty sequence with no initial value",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-95-73a295f34553>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0maverage_evens\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m2\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0mrandom\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrandint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m49\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;36m1\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0m_\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m10_000_000\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;32m<ipython-input-88-29706a0855ba>\u001b[0m in \u001b[0;36maverage_evens\u001b[0;34m(numbers, scalar)\u001b[0m\n\u001b[1;32m     13\u001b[0m     total, count = reduce(\n\u001b[1;32m     14\u001b[0m         \u001b[0;32mlambda\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 15\u001b[0;31m         \u001b[0;34m(\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mn\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mn\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mintegers\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mn\u001b[0m \u001b[0;34m%\u001b[0m \u001b[0;36m2\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     16\u001b[0m     )\n\u001b[1;32m     17\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mscalar\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0mtotal\u001b[0m \u001b[0;34m/\u001b[0m \u001b[0mcount\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;31mTypeError\u001b[0m: reduce() of empty sequence with no initial value"
+     ]
+    }
+   ],
+   "source": [
+    "average_evens(2 * random.randint(0, 49) + 1 for _ in range(10_000_000))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "## \"Tuple Comprehensions\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "There is no syntax to derive *new* `tuple` objects out of existing ones. However, we can mimic such a construct by combining the built-in [tuple() <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/functions.html#func-tuple) constructor with a generator expression.\n",
+    "\n",
+    "So, to convert the list comprehension `[(n ** 2) + 1 for n in numbers if n % 2 == 0]` from above into a \"tuple comprehension,\" we write the following."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 96,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(65, 145, 5, 37, 101, 17)"
+      ]
+     },
+     "execution_count": 96,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "tuple((n ** 2) + 1 for n in numbers if n % 2 == 0)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "## Boolean Reducers"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Besides [min() <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/functions.html#min), [max() <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/functions.html#max), and [sum() <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/functions.html#sum), Python provides two boolean reduce functions: [all() <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/functions.html#all) and [any() <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/functions.html#any).\n",
+    "\n",
+    "Let's look at straightforward examples involving `numbers` again."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 97,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "numbers = [7, 11, 8, 5, 3, 12, 2, 6, 9, 10, 1, 4]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "[all() <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/functions.html#all) takes an `iterable` argument and returns `True` if *all* elements are *truthy*.\n",
+    "\n",
+    "For example, let's check if the square of each element in `numbers` is below `100` or `150`, respectively. We express the computation with a generator expression passed as the only argument to [all() <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/functions.html#all)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 98,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "False"
+      ]
+     },
+     "execution_count": 98,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "all(x ** 2 < 100 for x in numbers)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 99,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 99,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "all(x ** 2 < 150 for x in numbers)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "[all() <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/functions.html#all) can be viewed as syntactic sugar replacing a `for`-loop: Internally, [all() <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/functions.html#all) implements the *short-circuiting* strategy explained in [Chapter 3 <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/03_conditionals_00_content.ipynb#Short-Circuiting), and we mimic that by testing for the *opposite* condition in the `if` statement and stopping the `for`-loop early with the `break` statement. In the worst case, if `threshold` were, for example, `150`, we would loop over *all* elements in the `iterable` argument, which must be *finite* for the code to work. So, [all() <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/functions.html#all) is a *linear search* in disguise."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 100,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "False"
+      ]
+     },
+     "execution_count": 100,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "threshold = 100\n",
+    "\n",
+    "for number in numbers:\n",
+    "    if number ** 2 >= threshold:  # the opposite of what we are checking for\n",
+    "        all_below_threshold = False\n",
+    "        break\n",
+    "else:\n",
+    "    all_below_threshold = True\n",
+    "\n",
+    "all_below_threshold"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "The documentation of [all() <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/functions.html#all) shows in another way what it does with code: By placing a `return` statement in a `for`-loop's body inside a function, iteration is stopped prematurely once an element does *not* meet the condition. That is the familiar *early exit* pattern at work."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 101,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "def all_alt(iterable):\n",
+    "    \"\"\"Alternative implementation of the built-in all() function.\"\"\"\n",
+    "    for element in iterable:\n",
+    "        if not element:  # the opposite of what we are checking for\n",
+    "            return False\n",
+    "    return True"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 102,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "False"
+      ]
+     },
+     "execution_count": 102,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "all_alt(x ** 2 < 100 for x in numbers)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 103,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 103,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "all_alt(x ** 2 < 150 for x in numbers)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Similarly, [any() <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/functions.html#any) checks if *at least* one element in the `iterable` argument is *truthy*.\n",
+    "\n",
+    "To continue the example, let's check if the square of *any* element in `numbers` is above `100` or `150`, respectively."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 104,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 104,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "any(x ** 2 > 100 for x in numbers)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 105,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "False"
+      ]
+     },
+     "execution_count": 105,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "any(x ** 2 > 150 for x in numbers)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "The code cell below shows how [any() <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/functions.html#any) works internally: It also follows the *short-circuiting* strategy. Here, we do *not* need to check for the opposite condition."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 106,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 106,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "threshold = 100\n",
+    "\n",
+    "for number in numbers:\n",
+    "    if number ** 2 > threshold:\n",
+    "        any_above_threshold = True\n",
+    "        break\n",
+    "else:\n",
+    "    any_above_threshold = False\n",
+    "\n",
+    "any_above_threshold"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "The alternative formulation in the documentation of [any() <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/functions.html#any) is straightforward and also uses the early exit pattern."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 107,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "def any_alt(iterable):\n",
+    "    \"\"\"Alternative implementation of the built-in any() function.\"\"\"\n",
+    "    for element in iterable:\n",
+    "        if element:\n",
+    "            return True\n",
+    "    return False"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 108,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 108,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "any_alt(x ** 2 > 100 for x in numbers)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 109,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "False"
+      ]
+     },
+     "execution_count": 109,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "any_alt(x ** 2 > 150 for x in numbers)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "## Iterators vs. Iterables"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Similarly to how we classify different *concrete* data types like `list` or `str` by how they behave *abstractly* in a given context in [Chapter 7 <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/07_sequences_00_content.ipynb#Collections-vs.-Sequences), we also do so for the data types we have introduced in this chapter.\n",
+    "\n",
+    "Here, the `map`, `filter`, and `generator` types all behave like \"rules\" in memory that govern how objects are produced \"on the fly.\" Their main commonality is their support for the built-in [next() <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/functions.html#next) function. In computer science terminology, such data types are called **[iterators <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_wiki.png\">](https://en.wikipedia.org/wiki/Iterator)**, and the [collections.abc <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/collections.abc.html) module formalizes them with the `Iterator` ABC in Python.\n",
+    "\n",
+    "So, one example of an iterator is `evens_transformed` below, an object of type `generator`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 110,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "numbers = [7, 11, 8, 5, 3, 12, 2, 6, 9, 10, 1, 4]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 111,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "evens_transformed = ((x ** 2) + 1 for x in numbers if x % 2 == 0)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Let's first confirm that `evens_transformed` is indeed an `Iterator`, \"abstractly speaking.\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 112,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "import collections.abc as abc"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 113,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 113,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "isinstance(evens_transformed, abc.Iterator)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "In Python, iterators are *always* also iterables. The reverse is *not* true! To be precise, iterators are *specializations* of iterables. That is what the \"Inherits from\" column means in the [collections.abc <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/collections.abc.html) module's documentation."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 114,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 114,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "isinstance(evens_transformed, abc.Iterable)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Furthermore, we sharpen our definition of an *iterable* from [Chapter 7 <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/07_sequences_00_content.ipynb#Collections-vs.-Sequences): Just as we defined an *iterator* to be any object that supports the [next() <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/functions.html#next) function, we define an *iterable* to be any object that supports the built-in [iter() <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/functions.html#iter) function.\n",
+    "\n",
+    "The confused reader may now be wondering how the two concepts relate to each other.\n",
+    "\n",
+    "In short, the [iter() <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/functions.html#iter) function is the general way to create an *iterator* object out of a given *iterable* object. Then, the *iterator* object manages the iteration over the *iterable* object. In real-world code, we hardly ever see [iter() <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/functions.html#iter) as Python calls it for us in the background. \n",
+    "\n",
+    "For illustration, let's do that ourselves and create *two* iterators out of the iterable `numbers` and see what we can do with them."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 115,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "iterator1 = iter(numbers)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 116,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "iterator2 = iter(numbers)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "`iterator1` and `iterator2` are of type `list_iterator`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 117,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "list_iterator"
+      ]
+     },
+     "execution_count": 117,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "type(iterator1)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "*Iterators* are useful for only *one* thing: Get the next object from the associated *iterable*.\n",
+    "\n",
+    "By calling [next() <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/functions.html#next) three times with `iterator1` as the argument, we obtain the first three elements of `numbers`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 118,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(7, 11, 8)"
+      ]
+     },
+     "execution_count": 118,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "next(iterator1), next(iterator1), next(iterator1)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "`iterator1` and `iterator2` keep their *states* separate. So, we could loop over an *iterable* in parallel."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 119,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(5, 7)"
+      ]
+     },
+     "execution_count": 119,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "next(iterator1), next(iterator2)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "We can also play a \"trick\" and exchange some elements in `numbers`. `iterator1` and `iterator2` do *not* see these changes and present us with the new elements. So, *iterators* not only have state on their own but also keep this separate from the underlying *iterable*."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 120,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "numbers[1], numbers[4] = 99, 99"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 121,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(99, 99)"
+      ]
+     },
+     "execution_count": 121,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "next(iterator1), next(iterator2)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Let's re-assign the elements in `numbers` so that they are in order. Now, the numbers returned from [next() <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/functions.html#next) also tell us how often [next() <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/functions.html#next) was called with `iterator1` or `iterator2`. We conclude that `list_iterator` objects work by simply keeping track of the *last* index obtained from the underlying *iterable*."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 122,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "numbers[:] = list(range(1, 13))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 123,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12]"
+      ]
+     },
+     "execution_count": 123,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "numbers"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 124,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(6, 3)"
+      ]
+     },
+     "execution_count": 124,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "next(iterator1), next(iterator2)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "With the concepts introduced in this section, we can now understand the first sentence in the documentation on the [zip() <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/functions.html#zip) built-in better: \"Make an *iterator* that aggregates elements from each of the *iterables*.\"\n",
+    "\n",
+    "Because *iterators* are always also *iterables*, we may pass `iterator1` and `iterator2` as arguments to [zip() <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/functions.html#zip).\n",
+    "\n",
+    "The returned `zipper` object is of type `zip` and, \"abstractly speaking,\" an `Iterator` as well."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 125,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "zipper = zip(iterator1, iterator2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 126,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<zip at 0x7f001cfa7a50>"
+      ]
+     },
+     "execution_count": 126,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "zipper"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 127,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "zip"
+      ]
+     },
+     "execution_count": 127,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "type(zipper)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 128,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 128,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "isinstance(zipper, abc.Iterator)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "So far, we have always used [zip() <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/functions.html#zip) in a `for`-loop. That was our earlier definition of an *iterable*. Our revised definition in this section states that an *iterable* is an object that supports the [iter() <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/functions.html#iter) function. So, let's see what happens if we pass `zipper` to [iter() <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/functions.html#iter)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 129,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "zipper_iterator = iter(zipper)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 130,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<zip at 0x7f001cfa7a50>"
+      ]
+     },
+     "execution_count": 130,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "zipper_iterator"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "`zipper_iterator` references the *same* object as `zipper`! That is true for *iterators* in general: Any *iterator* created from an existing *iterator* with [iter() <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/functions.html#iter) is the *iterator* itself."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 131,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 131,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "zipper is zipper_iterator"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "The Python core developers made that design decision so that *iterators* may also be looped over.\n",
+    "\n",
+    "The `for`-loop below prints out only *six* more `tuple` objects derived from the now ordered `numbers` because the `iterator1` object hidden inside `zipper` already returned its first *six* elements. So, the respective first elements of the `tuple` objects printed range from `7` to `12`. Similarly, as `iterator2` already returned its first *three* elements from `numbers`, we see the respective second elements in the range from `4` to `9`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 132,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "7 and 4   8 and 5   9 and 6   10 and 7   11 and 8   12 and 9   "
+     ]
+    }
+   ],
+   "source": [
+    "for x, y in zipper:\n",
+    "    print(x, \"and\", y, end=\"   \")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "`zipper` is now *exhausted*. So, the `for`-loop below does *not* make any iteration at all."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 133,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "for x, y in zipper:\n",
+    "    raise RuntimeError(\"We won't see this error\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "We verify that `iterator1` is exhausted by passing it to [next() <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/functions.html#next) again, which raises a `StopIteration` exception."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 134,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "ename": "StopIteration",
+     "evalue": "",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mStopIteration\u001b[0m                             Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-134-0680f5ec8ce3>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mnext\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0miterator1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;31mStopIteration\u001b[0m: "
+     ]
+    }
+   ],
+   "source": [
+    "next(iterator1)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "On the contrary, `iterator2` is *not* yet exhausted."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 135,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "10"
+      ]
+     },
+     "execution_count": 135,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "next(iterator2)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Understanding *iterators* and *iterables* is helpful for any data science practitioner that deals with large amounts of data. Even without that, these two terms occur everywhere in Python-related texts and documentation. So, a beginner should regularly review this section until it becomes second nature."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "### The `for` Statement (revisited)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "In [Chapter 4 <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/04_iteration_00_content.ipynb#The-for-Statement), we argue that the `for` statement is syntactic sugar, replacing the `while` statement in many common scenarios. In particular, a `for`-loop saves us two tasks: Managing an index variable *and* obtaining the individual elements by indexing. In this sub-section, we look at a more realistic picture, using the new terminology as well.\n",
+    "\n",
+    "Let's print out the elements of a `list` object as the *iterable* being looped over."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 136,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "iterable = [0, 1, 2, 3, 4]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 137,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0 1 2 3 4 "
+     ]
+    }
+   ],
+   "source": [
+    "for element in iterable:\n",
+    "    print(element, end=\" \")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Our previous and equivalent formulation with a `while` statement is like so."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 138,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0 1 2 3 4 "
+     ]
+    }
+   ],
+   "source": [
+    "index = 0\n",
+    "\n",
+    "while index < len(iterable):\n",
+    "    element = iterable[index]\n",
+    "    print(element, end=\" \")\n",
+    "    index += 1\n",
+    "\n",
+    "del index"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "What actually happens behind the scenes in the Python interpreter is shown below.\n",
+    "\n",
+    "First, Python calls [iter() <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/functions.html#iter) with the `iterable` to be looped over as the argument. The returned `iterator` contains the entire logic of how the `iterable` is looped over. In particular, the `iterator` may or may not pick the `iterable`'s elements in a predictable order. That is up to the \"rule\" it models.\n",
+    "\n",
+    "Second, Python enters an *indefinite* `while`-loop. It tries to obtain the next element with [next() <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/functions.html#next). If that succeeds, the `for`-loop's code block is executed. Below, that code is placed within the `else`-clause that runs only if *no* exception is raised in the `try`-clause. Then, Python jumps into the next iteration and tries to obtain the next element from the `iterator`, and so on. Once the `iterator` is exhausted, it raises a `StopIteration` exception, and Python stops the `while`-loop with the `break` statement."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 139,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0 1 2 3 4 "
+     ]
+    }
+   ],
+   "source": [
+    "iterator = iter(iterable)\n",
+    "\n",
+    "while True:\n",
+    "    try:\n",
+    "        element = next(iterator)\n",
+    "    except StopIteration:\n",
+    "        break\n",
+    "    else:\n",
+    "        print(element, end=\" \")\n",
+    "\n",
+    "del iterator"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "### sorted() vs. reversed()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Now that we know the concept of an *iterator*, let's compare some of the built-ins introduced in [Chapter 7 <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/07_sequences_00_content.ipynb) in detail and make sure we understand what is going on in memory. This sub-section also serves as a summary of all the concepts in this chapter.\n",
+    "\n",
+    "We use two simple examples, `numbers` and `memoryless`: `numbers` creates *thirteen* objects in memory and `memoryless` only *one* (cf., [PythonTutor <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](http://pythontutor.com/visualize.html#code=numbers%20%3D%20%5B7,%2011,%208,%205,%203,%2012,%202,%206,%209,%2010,%201,%204%5D%0Amemoryless%20%3D%20range%281,%2013%29&cumulative=false&curInstr=2&heapPrimitives=nevernest&mode=display&origin=opt-frontend.js&py=3&rawInputLstJSON=%5B%5D&textReferences=false))."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 140,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "numbers = [7, 11, 8, 5, 3, 12, 2, 6, 9, 10, 1, 4]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 141,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "memoryless = range(1, 13)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "The [sorted() <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/functions.html#sorted) function takes a *finite* `iterable` argument and *materializes* its elements into a *new* `list` object that is returned.\n",
+    "\n",
+    "The argument may already be materialized, as is the case with `numbers`, but may also be an *iterable* without any objects in it, such as `memoryless`. In both cases, we end up with materialized `list` objects with the elements sorted in *forward* order (cf., [PythonTutor <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](http://pythontutor.com/visualize.html#code=numbers%20%3D%20%5B7,%2011,%208,%205,%203,%2012,%202,%206,%209,%2010,%201,%204%5D%0Amemoryless%20%3D%20range%281,%2013%29%0Aresult1%20%3D%20sorted%28numbers%29%0Aresult2%20%3D%20sorted%28memoryless%29&cumulative=false&curInstr=4&heapPrimitives=nevernest&mode=display&origin=opt-frontend.js&py=3&rawInputLstJSON=%5B%5D&textReferences=false))."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 142,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12]"
+      ]
+     },
+     "execution_count": 142,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "sorted(numbers)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 143,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12]"
+      ]
+     },
+     "execution_count": 143,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "sorted(memoryless)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "By adding a keyword-only argument `reverse=True`, the materialized `list` objects are sorted in *reverse* order (cf., [PythonTutor <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](http://pythontutor.com/visualize.html#code=numbers%20%3D%20%5B7,%2011,%208,%205,%203,%2012,%202,%206,%209,%2010,%201,%204%5D%0Amemoryless%20%3D%20range%281,%2013%29%0Aresult1%20%3D%20sorted%28numbers,%20reverse%3DTrue%29%0Aresult2%20%3D%20sorted%28memoryless,%20reverse%3DTrue%29&cumulative=false&curInstr=4&heapPrimitives=nevernest&mode=display&origin=opt-frontend.js&py=3&rawInputLstJSON=%5B%5D&textReferences=false))."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 144,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[12, 11, 10, 9, 8, 7, 6, 5, 4, 3, 2, 1]"
+      ]
+     },
+     "execution_count": 144,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "sorted(numbers, reverse=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 145,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[12, 11, 10, 9, 8, 7, 6, 5, 4, 3, 2, 1]"
+      ]
+     },
+     "execution_count": 145,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "sorted(memoryless, reverse=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "The order in `numbers` remains *unchanged*, and `memoryless` is still *not* materialized."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 146,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[7, 11, 8, 5, 3, 12, 2, 6, 9, 10, 1, 4]"
+      ]
+     },
+     "execution_count": 146,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "numbers"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 147,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "range(1, 13)"
+      ]
+     },
+     "execution_count": 147,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "memoryless"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "The [reversed() <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/functions.html#reversed) built-in takes a `sequence` argument and returns an *iterator*. The argument must be *finite* and *reversible* (i.e., *iterable* in *reverse* order) as otherwise [reversed() <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/functions.html#reversed) could neither determine the last element that becomes the first nor loop in a *predictable* backward fashion. [PythonTutor <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](http://pythontutor.com/visualize.html#code=numbers%20%3D%20%5B7,%2011,%208,%205,%203,%2012,%202,%206,%209,%2010,%201,%204%5D%0Amemoryless%20%3D%20range%281,%2013%29%0Aiterator1%20%3D%20reversed%28numbers%29%0Aiterator2%20%3D%20reversed%28memoryless%29&cumulative=false&curInstr=4&heapPrimitives=nevernest&mode=display&origin=opt-frontend.js&py=3&rawInputLstJSON=%5B%5D&textReferences=false) confirms that [reversed() <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/functions.html#reversed) does *not* materialize any elements but only returns an *iterator*.\n",
+    "\n",
+    "**Side Note**: Even though `range` objects, like `memoryless` here, do *not* \"contain\" references to other objects, they count as *sequence* types, and as such, they are also *container* types. The `in` operator works with `range` objects because we can always cast the object to be checked as an `int` and check if that lies within the `range` object's `start` and `stop` values, taking a potential `step` value into account (cf., this [blog post](https://treyhunner.com/2018/02/python-range-is-not-an-iterator/) for more details on the [range() <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/functions.html#func-range) built-in)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 148,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<list_reverseiterator at 0x7f002518b350>"
+      ]
+     },
+     "execution_count": 148,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "reversed(numbers)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 149,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<range_iterator at 0x7f001cfb2c60>"
+      ]
+     },
+     "execution_count": 149,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "reversed(memoryless)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "To materialize the elements, we can pass the returned *iterators* to, for example, the [list() <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/functions.html#func-list) or [tuple() <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/functions.html#func-tuple) constructors. That creates *new* `list` and `tuple` objects (cf., [PythonTutor <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](http://pythontutor.com/visualize.html#code=numbers%20%3D%20%5B7,%2011,%208,%205,%203,%2012,%202,%206,%209,%2010,%201,%204%5D%0Amemoryless%20%3D%20range%281,%2013%29%0Aresult1%20%3D%20list%28reversed%28numbers%29%29%0Aresult2%20%3D%20tuple%28reversed%28memoryless%29%29&cumulative=false&curInstr=4&heapPrimitives=nevernest&mode=display&origin=opt-frontend.js&py=3&rawInputLstJSON=%5B%5D&textReferences=false)).\n",
+    "\n",
+    "To reiterate some more new terminology from this chapter, we describe [reversed() <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/functions.html#reversed) as *lazy* whereas [list() <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/functions.html#func-list) and [tuple() <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/functions.html#func-tuple) are *eager*. The former has no significant side effect in memory, while the latter may require a lot of memory."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 150,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[4, 1, 10, 9, 6, 2, 12, 3, 5, 8, 11, 7]"
+      ]
+     },
+     "execution_count": 150,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "list(reversed(numbers))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 151,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(12, 11, 10, 9, 8, 7, 6, 5, 4, 3, 2, 1)"
+      ]
+     },
+     "execution_count": 151,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "tuple(reversed(memoryless))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Of course, we can also loop over the returned *iterators* instead.\n",
+    "\n",
+    "That works because *iterators* are always *iterable*; in particular, as the previous \"*The for Statement (revisited)*\" sub-section explains, the `for`-loops below call `iter(reversed(numbers))` and `iter(reversed(memoryless))` behind the scenes. However, the *iterators* returned by [iter() <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/functions.html#iter) are the *same* as the `reversed(numbers)` and `reversed(memoryless)` iterators passed in! In summary, the `for`-loops below involve many subtleties that together make Python the expressive language it is."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 152,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "4 1 10 9 6 2 12 3 5 8 11 7 "
+     ]
+    }
+   ],
+   "source": [
+    "for number in reversed(numbers):\n",
+    "    print(number, end=\" \")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 153,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "12 11 10 9 8 7 6 5 4 3 2 1 "
+     ]
+    }
+   ],
+   "source": [
+    "for element in reversed(memoryless):\n",
+    "    print(element, end=\" \")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "As with [sorted() <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/functions.html#sorted), the [reversed() <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/functions.html#reversed) built-in does *not* mutate its argument."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 154,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[7, 11, 8, 5, 3, 12, 2, 6, 9, 10, 1, 4]"
+      ]
+     },
+     "execution_count": 154,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "numbers"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 155,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "range(1, 13)"
+      ]
+     },
+     "execution_count": 155,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "memoryless"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "To point out the potentially obvious, we compare the results of *sorting* `numbers` in *reverse* order with *reversing* it: These are *different* concepts!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 156,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[7, 11, 8, 5, 3, 12, 2, 6, 9, 10, 1, 4]"
+      ]
+     },
+     "execution_count": 156,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "numbers"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 157,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[12, 11, 10, 9, 8, 7, 6, 5, 4, 3, 2, 1]"
+      ]
+     },
+     "execution_count": 157,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "sorted(numbers, reverse=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 158,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[4, 1, 10, 9, 6, 2, 12, 3, 5, 8, 11, 7]"
+      ]
+     },
+     "execution_count": 158,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "list(reversed(numbers))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Whereas both [sorted() <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/functions.html#sorted) and [reversed() <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/functions.html#reversed) do *not* mutate their arguments, the *mutable* `list` type comes with two methods, [sort() <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/stdtypes.html#list.sort) and `reverse()`, that implement the same logic but mutate an object, like `numbers` below, *in place*. To indicate that all changes occur *in place*, the [sort() <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/stdtypes.html#list.sort) and `reverse()` methods always return `None`, which is not shown in JupyterLab."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 159,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[7, 11, 8, 5, 3, 12, 2, 6, 9, 10, 1, 4]"
+      ]
+     },
+     "execution_count": 159,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "numbers"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "The `reverse()` method on the `list` type is *eager*, as opposed to the *lazy* [reversed() <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/functions.html#reversed) built-in. That means the mutations caused by the `reverse()` method are written into memory right away."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 160,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "numbers.reverse()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 161,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[4, 1, 10, 9, 6, 2, 12, 3, 5, 8, 11, 7]"
+      ]
+     },
+     "execution_count": 161,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "numbers"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "*Sorting* `numbers` in place occurs eagerly."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 162,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "numbers.sort(reverse=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 163,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[12, 11, 10, 9, 8, 7, 6, 5, 4, 3, 2, 1]"
+      ]
+     },
+     "execution_count": 163,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "numbers"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 164,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "numbers.sort()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 165,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12]"
+      ]
+     },
+     "execution_count": 165,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "numbers"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "## TL;DR"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "The operations we do with sequential data commonly follow the **map-filter-reduce paradigm**: We apply the same transformation to all elements, filter some of them out, and calculate summary statistics from the remaining ones.\n",
+    "\n",
+    "An essential idea in this chapter is that, in many situations, we need *not* have all the data **materialized** in memory. Instead, **iterators** allow us to process sequential data on a one-by-one basis.\n",
+    "\n",
+    "Examples for iterators are the `map`, `filter`, and `generator` types."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "## Further Resources"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "A lecture-style **video presentation** of this chapter is integrated below (cf., the [video <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_yt.png\">](https://www.youtube.com/watch?v=ePzzq2YBWjY&list=PL-2JV1G3J10lQ2xokyQowcRJI5jjNfW7f) or the entire [playlist <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_yt.png\">](https://www.youtube.com/playlist?list=PL-2JV1G3J10lQ2xokyQowcRJI5jjNfW7f))."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 166,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/jpeg": 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+       "            width=\"60%\"\n",
+       "            height=\"300\"\n",
+       "            src=\"https://www.youtube.com/embed/ePzzq2YBWjY\"\n",
+       "            frameborder=\"0\"\n",
+       "            allowfullscreen\n",
+       "        ></iframe>\n",
+       "        "
+      ],
+      "text/plain": [
+       "<IPython.lib.display.YouTubeVideo at 0x7f001cec40d0>"
+      ]
+     },
+     "execution_count": 166,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from IPython.display import YouTubeVideo\n",
+    "YouTubeVideo(\"ePzzq2YBWjY\", width=\"60%\")"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.4"
+  },
+  "livereveal": {
+   "auto_select": "code",
+   "auto_select_fragment": true,
+   "scroll": true,
+   "theme": "serif"
+  },
+  "toc": {
+   "base_numbering": 1,
+   "nav_menu": {},
+   "number_sections": false,
+   "sideBar": true,
+   "skip_h1_title": true,
+   "title_cell": "Table of Contents",
+   "title_sidebar": "Contents",
+   "toc_cell": false,
+   "toc_position": {
+    "height": "calc(100% - 180px)",
+    "left": "10px",
+    "top": "150px",
+    "width": "384px"
+   },
+   "toc_section_display": false,
+   "toc_window_display": false
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/08_mfr_01_review.ipynb b/08_mfr_01_review.ipynb
new file mode 100644
index 0000000..ac178ba
--- /dev/null
+++ b/08_mfr_01_review.ipynb
@@ -0,0 +1,222 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "\n",
+    "# Chapter 8: Map, Filter, & Reduce"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Content Review"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The questions below assume that you have read [Chapter 8 <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/08_mfr_00_content.ipynb) in the book.\n",
+    "\n",
+    "Be concise in your answers! Most questions can be answered in *one* sentence."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Essay Questions "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q1**: With the [map() <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/functions.html#map) and [filter() <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/functions.html#filter) built-ins and the [reduce() <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/functools.html#functools.reduce) function from the [functools <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/functools.html) module in the [standard library <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/index.html), we can replace many tedious `for`-loops and `if` statements. What are some advantages of doing so?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " < your answer >"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q2**: Looking at the `lambda` expression inside [reduce() <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/functools.html#functools.reduce) below, what [built-in function <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/functions.html) is mimicked here?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "```python\n",
+    "from functools import reduce\n",
+    "\n",
+    "numbers = [7, 11, 8, 5, 3, 12, 2, 6, 9, 10, 1, 4]\n",
+    "\n",
+    "reduce(lambda x, y: x if x > y else y, numbers)\n",
+    "```"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " < your answer >"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q3**: What is the primary use case of **list comprehensions**? Why do we describe them as **eager**?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " < your answer >"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q4**: **Generator expressions** may replace `list` objects and list comprehensions in many scenarios. When evaluated, they create a **lazy** `generator` object that does *not* **materialize** its elements right away. What do we mean by that? What does it mean for a `generator` object to be **exhausted**?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " < your answer >"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q5**: What does it mean for the **boolean reducers**, the built-in [all() <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/functions.html#all) and [any() <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/functions.html#any) functions, to follow the **short-circuiting** strategy?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " < your answer >"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q6**: What is an **iterator**? How does it relate to an **iterable**?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " < your answer >"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### True / False Questions"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Motivate your answer with *one short* sentence!"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q7**: `lambda` expressions are useful in the context of the **map-filter-reduce** paradigm, where we often do *not* re-use a `function` object more than once."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " < your answer >"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q8**: Using **generator expressions** in place of **list comprehensions** wherever possible is a good practice as it makes our programs use memory more efficiently."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " < your answer >"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q9**: Just as **list comprehensions** create `list` objects, **tuple comprehensions** create `tuple` objects."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " < your answer >"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.4"
+  },
+  "toc": {
+   "base_numbering": 1,
+   "nav_menu": {},
+   "number_sections": false,
+   "sideBar": true,
+   "skip_h1_title": true,
+   "title_cell": "Table of Contents",
+   "title_sidebar": "Contents",
+   "toc_cell": false,
+   "toc_position": {},
+   "toc_section_display": false,
+   "toc_window_display": false
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/08_mfr_02_exercises.ipynb b/08_mfr_02_exercises.ipynb
new file mode 100644
index 0000000..f9daaab
--- /dev/null
+++ b/08_mfr_02_exercises.ipynb
@@ -0,0 +1,1202 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Important**: Click on \"*Kernel*\" > \"*Restart Kernel and Run All*\" *after* finishing the exercises in [JupyterLab <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_jp.png\">](https://jupyterlab.readthedocs.io/en/stable/) (e.g., in the cloud on [MyBinder <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_mb.png\">](https://mybinder.org/v2/gh/webartifex/intro-to-python/master?urlpath=lab/tree/08_mfr_02_exercises.ipynb)) to ensure that your solution runs top to bottom *without* any errors"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Chapter 8: Map, Filter, & Reduce"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Coding Exercises"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The exercises below assume that you have read [Chapter 8 <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/08_mfr_00_content.ipynb) in the book.\n",
+    "\n",
+    "The `...`'s in the code cells indicate where you need to fill in code snippets. The number of `...`'s within a code cell give you a rough idea of how many lines of code are needed to solve the task. You should not need to create any additional code cells for your final solution. However, you may want to use temporary code cells to try out some ideas."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Packing & Unpacking with Functions (continued)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q1.1**: Copy your solution to **Q2.10** from the [Chapter 7 Exercises <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/07_sequences_02_exercises.ipynb#Packing-&-Unpacking-with-Functions) into the code cell below!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import collections.abc as abc"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def product(*args, ...):\n",
+    "    \"\"\"Multiply all arguments.\"\"\"\n",
+    "    ...\n",
+    "    ...\n",
+    "    ...\n",
+    "    ...\n",
+    "\n",
+    "    ...\n",
+    "    ...\n",
+    "\n",
+    "    return ..."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q1.2**: Verify that all test cases below work (i.e., the `assert` statements must *not* raise an `AssertionError`)!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "assert product(42) == 42"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "assert product(2, 5, 10) == 100"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "assert product(2, 5, 10, start=2) == 200"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "one_hundred = [2, 5, 10]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "assert product(one_hundred) == 100"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "assert product(*one_hundred) == 100"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q1.3**: Verify that `product()` raises a `TypeError` when called without any arguments!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "product()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This implementation of `product()` is convenient to use, in particular, because we can pass it any *collection* object with or without *unpacking* it.\n",
+    "\n",
+    "However, `product()` suffers from one last flaw: We cannot pass it a **stream** of data, as modeled, for example, with an *iterator* object that produces elements on a one-by-one basis.\n",
+    "\n",
+    "**Q1.4**: Click through the following example!\n",
+    "\n",
+    "The [*stream.py* <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_gh.png\">](https://github.com/webartifex/intro-to-python/blob/master/stream.py) module in the book's repository provides a `make_finite_stream()` function. It is a *factory* function creating objects of type `generator` that we use to model *streaming* data."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from stream import make_finite_stream"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "stream = make_finite_stream()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "stream"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "type(stream)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Being a `generator` object, `stream` is also an `Iterator` in the abstract sense."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "isinstance(stream, abc.Iterator)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "*Iterators* are good for only *one* thing: Giving us the \"next\" element in a series of possibly *infinitely* many objects. While the `stream` object is finite (i.e., execute the next code cell until you see a `StopIteration` exception), ..."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "next(stream)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "... it has *no* concept of a \"length:\" The built-in [len() <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/functions.html#len) function raises a `TypeError`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "len(stream)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can use the built-in [list() <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/functions.html#func-list) constructor to *materialize* the elements. However, in a real-world scenario, these may *not* fit into our machine's memory!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "list(stream)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "To be more realistic, `make_finite_stream()` creates `generator` objects producing a varying number of elements."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "list(make_finite_stream())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "list(make_finite_stream())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "list(make_finite_stream())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Let's see what happens if we pass an *iterator*, as created by `make_finite_stream()`, instead of a materialized *collection*, like `one_hundred`, to `product()`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "product(make_finite_stream())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q1.5**: What line causes the `TypeError`? What line is really the problem in `product()`? Hint: These may be different lines. Describe what happens on each line in the function's body until the exception is raised!"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " < your answer >"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q1.6**: Adapt `product()` one last time to make it work with *iterators* as well!\n",
+    "\n",
+    "Hints: This task is as easy as replacing `Collection` with something else. Which of the three behaviors of *collections* do *iterators* also exhibit? You may want to look at the documentations on the built-in [max() <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/functions.html#max), [min() <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/functions.html#min), and [sum() <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/functions.html#sum) functions: What kind of argument do they take?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def product(*args, ...):\n",
+    "    \"\"\"Multiply all arguments.\"\"\"\n",
+    "    ...\n",
+    "    ...\n",
+    "    ...\n",
+    "    ...\n",
+    "\n",
+    "    ...\n",
+    "    ...\n",
+    "\n",
+    "    return ..."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The final version of `product()` behaves like built-ins in edge cases (i.e., `sum()` also raises a `TypeError` when called without arguments), ..."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "product()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "... works with the arguments passed either separately as *positional* arguments, *packed* together into a single *collection* argument, or *unpacked*, ..."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "product(42)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "product(2, 5, 10)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "product([2, 5, 10])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "product(*[2, 5, 10])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "... and can handle *streaming* data with *indefinite* \"length.\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "product(make_finite_stream())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In real-world projects, the data science practitioner must decide if it is worthwhile to make a function usable in various different forms as we do in this exercise. This may be over-engineered.\n",
+    "\n",
+    "Yet, two lessons are important to take away:\n",
+    "- It is a good idea to *mimic* the behavior of *built-ins*, and\n",
+    "- make functions capable of working with *streaming* data."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Removing Outliers in Streaming Data"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Let's say we are given a `list` object with random integers like `sample` below, and we want to calculate some basic statistics on them."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sample = [\n",
+    "    45, 46, 40, 49, 36, 53, 49, 42, 25, 40, 39, 36, 38, 40, 40, 52, 36, 52, 40, 41,\n",
+    "    35, 29, 48, 43, 42, 30, 29, 33, 55, 33, 38, 50, 39, 56, 52, 28, 37, 56, 45, 37,\n",
+    "    41, 41, 37, 30, 51, 32, 23, 40, 53, 40, 45, 39, 99, 42, 34, 42, 34, 39, 39, 53,\n",
+    "    43, 37, 46, 36, 45, 42, 32, 38, 57, 34, 36, 44, 47, 51, 46, 39, 28, 40, 35, 46,\n",
+    "    41, 51, 41, 23, 46, 40, 40, 51, 50, 32, 47, 36, 38, 29, 32, 53, 34, 43, 39, 41,\n",
+    "    40, 34, 44, 40, 41, 43, 47, 57, 50, 42, 38, 25, 45, 41, 58, 37, 45, 55, 44, 53,\n",
+    "    82, 31, 45, 33, 32, 39, 46, 48, 42, 47, 40, 45, 51, 35, 31, 46, 40, 44, 61, 57,\n",
+    "    40, 36, 35, 55, 40, 56, 36, 35, 86, 36, 51, 40, 54, 50, 49, 36, 41, 37, 48, 41,\n",
+    "    42, 44, 40, 43, 51, 47, 46, 50, 40, 23, 40, 39, 28, 38, 42, 46, 46, 42, 46, 31,\n",
+    "    32, 40, 48, 27, 40, 40, 30, 32, 25, 31, 30, 43, 44, 29, 45, 41, 63, 32, 33, 58,\n",
+    "]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "len(sample)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q2.1**: `list` objects are **sequences**. What *four* behaviors do they always come with?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " < your answer >"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q2.2**: Write a function `mean()` that calculates the simple arithmetic mean of a given `sequence` with numbers!\n",
+    "\n",
+    "Hints: You can solve this task with [built-in functions <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/functions.html) only. A `for`-loop is *not* needed."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def mean(sequence):\n",
+    "    ..."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sample_mean = mean(sample)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sample_mean"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q2.3**: Write a function `std()` that calculates the [standard deviation <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_wiki.png\">](https://en.wikipedia.org/wiki/Standard_deviation) of a `sequence` of numbers! Integrate your `mean()` version from before and the [sqrt() <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/math.html#math.sqrt) function from the [math <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/math.html) module in the [standard library <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/index.html) provided to you below. Make sure `std()` calls `mean()` only *once* internally! Repeated calls to `mean()` would be a waste of computational resources.\n",
+    "\n",
+    "Hints: Parts of the code are probably too long to fit within the suggested 79 characters per line. So, use *temporary variables* inside your function. Instead of a `for`-loop, you may want to use a *list comprehension* or, even better, a memoryless *generator expression*."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from math import sqrt"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def std(sequence):\n",
+    "    ..."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sample_std = std(sample)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sample_std"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q2.4**: Complete `standardize()` below that takes a `sequence` of numbers and returns a `list` object with the **[z-scores <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_wiki.png\">](https://en.wikipedia.org/wiki/Standard_score)** of these numbers! A z-score is calculated by subtracting the mean and dividing by the standard deviation. Re-use `mean()` and `std()` from before. Again, ensure that `standardize()` calls `mean()` and `std()` only *once*! Further, round all z-scores with the built-in [round() <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/functions.html#round) function and pass on the keyword-only argument `digits` to it.\n",
+    "\n",
+    "Hint: You may want to use a *list comprehension* instead of a `for`-loop."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def standardize(sequence, *, digits=3):\n",
+    "    ..."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "z_scores = standardize(sample)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The [pprint() <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/pprint.html#pprint.pprint) function from the [pprint <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/pprint.html) module in the [standard library <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/index.html) allows us to \"pretty print\" long `list` objects compactly."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from pprint import pprint"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "pprint(z_scores, compact=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We know that `standardize()` works correctly if the resulting z-scores' mean and standard deviation approach `0` and `1` for a long enough `sequence`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "mean(z_scores), std(z_scores)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Even though `standardize()` calls `mean()` and `std()` only once each, `mean()` is called *twice*! That is so because `std()` internally also re-uses `mean()`!\n",
+    "\n",
+    "**Q2.5.1**: Rewrite `std()` to take an optional keyword-only argument `seq_mean`, defaulting to `None`. If provided, `seq_mean` is used instead of the result of calling `mean()`. Otherwise, the latter is called.\n",
+    "\n",
+    "Hint: You must check if `seq_mean` is still the default value."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def std(sequence, *, seq_mean=None):\n",
+    "    ..."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "`std()` continues to work as before."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sample_std = std(sample)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sample_std"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q2.5.2**: Now, rewrite `standardize()` to pass on the return value of `mean()` to `std()`! In summary, `standardize()` calculates the z-scores for the numbers in the `sequence` with as few computational steps as possible."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def standardize(sequence, *, digits=3):\n",
+    "    ..."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "z_scores = standardize(sample)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "mean(z_scores), std(z_scores)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q2.6**: With both `sample` and `z_scores` being materialized `list` objects, we can loop over pairs consisting of a number from `sample` and its corresponding z-score. Write a `for`-loop that prints out all the \"outliers,\" as which we define numbers with an absolute z-score above `1.96`. There are *four* of them in the `sample`.\n",
+    "\n",
+    "Hint: Use the [abs() <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/functions.html#abs) and [zip() <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/functions.html#zip) built-ins."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "..."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We provide a `stream` module with a `data` object that models an *infinite* **stream** of data (cf., the [*stream.py* <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_gh.png\">](https://github.com/webartifex/intro-to-python/blob/master/stream.py) file in the repository)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from stream import data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "`data` is of type `generator` and has *no* length."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "type(data)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "len(data)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Being a `generator`, it is an `Iterator` in the abstract sense ..."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import collections.abc as abc"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "isinstance(data, abc.Iterator)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "... and so the only thing we can do with it is to pass it to the built-in [next() <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/functions.html#next) function and go over the numbers it streams one by one."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "next(data)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q2.7**: What happens if you call `mean()` with `data` as the argument? What is the problem?\n",
+    "\n",
+    "Hints: If you try it out, you may have to press the \"Stop\" button in the toolbar at the top. Your computer should *not* crash, but you will *have to* restart this Jupyter notebook with \"Kernel\" > \"Restart\" and import `data` again."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " < your answer >"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "mean(data)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q2.8**: Write a function `take_sample()` that takes an `iterator` as its argument, like `data`, and creates a *materialized* `list` object out of its first `n` elements, defaulting to `1_000`!\n",
+    "\n",
+    "Hints: [next() <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/functions.html#next) and the [range() <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/functions.html#func-range) built-in may be helpful. You may want to use a *list comprehension* instead of a `for`-loop and write a one-liner. Audacious students may want to look at [isclice() <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/itertools.html#itertools.islice) in the [itertools <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/itertools.html) module in the [standard library <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/index.html)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def take_sample(iterator, *, n=1_000):\n",
+    "    ..."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We take a `new_sample` from the stream of `data`, and its statistics are similar to the initial `sample`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "new_sample = take_sample(data)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "len(new_sample)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "mean(new_sample)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "std(new_sample)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q2.9**: Convert `standardize()` into a *new* function `standardized()` that implements the *same* logic but works on a possibly *infinite* stream of data, provided as an `iterable`, instead of a *finite* `sequence`.\n",
+    "\n",
+    "To calculate a z-score, we need the stream's overall mean and standard deviation, and that is *impossible* to calculate if we do not know how long the stream is, and, in particular, if it is *infinite*. So, `standardized()` first takes a sample from the `iterable` internally, and uses the sample's mean and standard deviation to calculate the z-scores.\n",
+    "\n",
+    "Hint: `standardized()` *must* return a `generator` object. So, use a *generator expression* as the return value; unless you know about the `yield` statement already (cf., [reference <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/reference/simple_stmts.html#the-yield-statement))."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def standardized(iterable, *, digits=3):\n",
+    "    ..."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "`standardized()` works almost like `standardize()` except that we use it with [next() <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/functions.html#next) to obtain the z-scores one by one."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "z_scores = standardized(data)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "z_scores"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "type(z_scores)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "next(z_scores)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q2.10.1**: `standardized()` allows us to go over an *infinite* stream of z-scores. What we want to do instead is to loop over the stream's raw numbers and skip the outliers. In the remainder of this exercise, you look at the parts that make up the `skip_outliers()` function below to achieve precisely that.\n",
+    "\n",
+    "The first steps in `skip_outliers()` are the same as in `standardized()`: We take a `sample` from the stream of `data` and calculate its statistics."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sample = ...\n",
+    "seq_mean = ...\n",
+    "seq_std = ..."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q2.10.2**: Just as in `standardized()`, write a *generator expression* that produces z-scores one by one! However, instead of just generating a z-score, the resulting `generator` object should produce `tuple` objects consisting of a \"raw\" number from `data` and its z-score.\n",
+    "\n",
+    "Hint: Look at the revisited \"*Averaging Even Numbers*\" example in [Chapter 7 <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/07_sequences_00_content.ipynb#Example:-Averaging-all-even-Numbers-in-a-List-%28revisited%29) for some inspiration, which also contains a generator expression producing `tuple` objects."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "standardizer = (... for ... in data)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "`standardizer` should produce `tuple` objects."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "next(standardizer)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q2.10.3**: Write another generator expression that loops over `standardizer`. It contains an `if`-clause that keeps only numbers with an absolute z-score below the `threshold_z`. If you fancy, use *tuple unpacking*."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "threshold_z = 1.96"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "no_outliers = (... for ... in standardizer if ...)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "`no_outliers` should produce `int` objects."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "next(no_outliers)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q2.10.4**: Lastly, put everything together in the `skip_outliers()` function! Make sure you refer to `iterable` inside the function and not the global `data`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def skip_outliers(iterable, *, threshold_z=1.96):\n",
+    "    sample = ...\n",
+    "    seq_mean = ...\n",
+    "    seq_std = ...\n",
+    "    standardizer = ...\n",
+    "    no_outliers = ...\n",
+    "    return no_outliers"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now, we can create a `generator` object and loop over the `data` in the stream with outliers skipped. Instead of the default `1.96`, we use a `threshold_z` of only `0.05`: That filters out all numbers except `42`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "skipper = skip_outliers(data, threshold_z=0.05)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "skipper"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "type(skipper)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "next(skipper)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q2.11**: You implemented the functions `mean()`, `std()`, `standardize()`, `standardized()`, and `skip_outliers()`. Which of them are **eager**, and which are **lazy**? How do these two concepts relate to **finite** and **infinite** data?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " < your answer >"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.4"
+  },
+  "toc": {
+   "base_numbering": 1,
+   "nav_menu": {},
+   "number_sections": false,
+   "sideBar": true,
+   "skip_h1_title": true,
+   "title_cell": "Table of Contents",
+   "title_sidebar": "Contents",
+   "toc_cell": false,
+   "toc_position": {},
+   "toc_section_display": false,
+   "toc_window_display": false
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}

From 3ef07e57a1d4df6d7f91e9e215104d097d24a42f Mon Sep 17 00:00:00 2001
From: Alexander Hess <alexander@webartifex.biz>
Date: Thu, 9 Apr 2020 12:45:49 +0200
Subject: [PATCH 096/242] Refurbish the new new chapter 09 on maps

---
 09_mappings_00_content.ipynb   | 7404 ++++++++++++++++++++++++++++++++
 09_mappings_01_review.ipynb    |  255 ++
 09_mappings_02_exercises.ipynb |  631 +++
 3 files changed, 8290 insertions(+)
 create mode 100644 09_mappings_00_content.ipynb
 create mode 100644 09_mappings_01_review.ipynb
 create mode 100644 09_mappings_02_exercises.ipynb

diff --git a/09_mappings_00_content.ipynb b/09_mappings_00_content.ipynb
new file mode 100644
index 0000000..f08479b
--- /dev/null
+++ b/09_mappings_00_content.ipynb
@@ -0,0 +1,7404 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "# Chapter 8: Mappings & Sets"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "While [Chapter 7 <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/07_sequences_00_content.ipynb) focuses on one special kind of *collection* types, namely *sequences*, this chapter introduces two more kinds: **Mappings** and **sets**. Both are presented in this chapter as they share the *same* underlying implementation.\n",
+    "\n",
+    "The `dict` type (cf, [documentation <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/stdtypes.html#dict)) introduced in the next section is an essential part in a data scientist's toolbox for two reasons: First, Python employs `dict` objects basically \"everywhere\" internally. Second, after the many concepts involving *sequential* data, *mappings* provide a different perspective on data and enhance our general problem solving skills."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "## The `dict` Type"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "A *mapping* is a one-to-one correspondence from a set of **keys** to a set of **values**. In other words, a *mapping* is a *collection* of **key-value pairs**, also called **items** for short.\n",
+    "\n",
+    "In the context of mappings, the term *value* has a meaning different from the *value* every object has: In the \"bag\" analogy from [Chapter 1 <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/01_elements_00_content.ipynb#Value-/-\"Meaning\"), we describe an object's value to be the semantic meaning of the $0$s and $1$s it contains. Here, the terms *key* and *value* mean the *role* an object takes within a mapping. Both, *keys* and *values*, are *objects* on their own with distinct *values*.\n",
+    "\n",
+    "Let's continue with an example. To create a `dict` object, we commonly use the literal notation, `{..: .., ..: .., ...}`, and list all the items. `to_words` below maps the `int` objects `0`, `1`, and `2` to their English word equivalents, `\"zero\"`, `\"one\"`, and `\"two\"`, and `from_words` does the opposite. A stylistic side note: Pythonistas often expand `dict` or `list` definitions by writing each item or element on a line on their own. Also, the commas `,` after the respective *last* items, `2: \"two\"` and `\"two\": 2`, are *not* a mistake although they *may* be left out. Besides easier reading, such a style has technical advantages that we do not go into detail about here (cf., [source <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://www.python.org/dev/peps/pep-0008/#when-to-use-trailing-commas))."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "to_words = {\n",
+    "    0: \"zero\",\n",
+    "    1: \"one\",\n",
+    "    2: \"two\",\n",
+    "}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "from_words = {\n",
+    "    \"zero\": 0,\n",
+    "    \"one\": 1,\n",
+    "    \"two\": 2,\n",
+    "}"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "As before, `dict` objects are objects on their own: They have an identity, a type, and a value."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "140024292070576"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "id(to_words)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "dict"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "type(to_words)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{0: 'zero', 1: 'one', 2: 'two'}"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "to_words"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "140024292122864"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "id(from_words)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "dict"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "type(from_words)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'zero': 0, 'one': 1, 'two': 2}"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from_words"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "The built-in [dict() <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/functions.html#func-dict) constructor gives us an alternative way to create a `dict` object. It is versatile and can be used in different ways.\n",
+    "\n",
+    "First, we may pass it any *mapping* type, for example, a `dict` object, to obtain a *new* `dict` object. That is the easiest way to obtain a *shallow* copy of a `dict` object or convert any other mapping object into a `dict` one."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'zero': 0, 'one': 1, 'two': 2}"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "dict(from_words)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Second, we may pass it a *finite* `iterable` providing *iterables* with *two* elements each. So, both of the following two code cells work: A `list` of `tuple` objects, or a `tuple` of `list` objects. More importantly, we could use an *iterator*, for example, a `generator` object, that produces the inner iterables \"on the fly.\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'zero': 0, 'one': 1, 'two': 2}"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "dict([(\"zero\", 0), (\"one\", 1), (\"two\", 2)])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'zero': 0, 'one': 1, 'two': 2}"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "dict(([\"zero\", 0], [\"one\", 1], [\"two\", 2]))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Lastly, [dict() <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/functions.html#func-dict) may also be called with *keyword* arguments: The keywords become the keys and the arguments the values."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'zero': 0, 'one': 1, 'two': 2}"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "dict(zero=0, one=1, two=2)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "### Nested Data"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Often, `dict` objects occur in a nested form and combined with other collection types, such as `list` or `tuple` objects, to model more complex entities \"from the real world.\"\n",
+    "\n",
+    "The reason for this popularity is that many modern [ReST APIs <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_wiki.png\">](https://en.wikipedia.org/wiki/Representational_state_transfer#Applied_to_Web_services) on the internet (e.g., [Google Maps API](https://cloud.google.com/maps-platform/), [Yelp API](https://www.yelp.com/developers/documentation/v3), [Twilio API](https://www.twilio.com/docs/usage/api)) provide their data in the popular [JSON <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_wiki.png\">](https://en.wikipedia.org/wiki/JSON) format, which looks almost like a combination of `dict` and `list` objects in Python. \n",
+    "\n",
+    "The `people` example below models three groups of people: `\"mathematicians\"`, `\"physicists\"`, and `\"programmers\"`. Each person may have an arbitrary number of email addresses. In the example, [Leonhard Euler <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_wiki.png\">](https://en.wikipedia.org/wiki/Leonhard_Euler) has not lived long enough to get one whereas [Guido <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_wiki.png\">](https://en.wikipedia.org/wiki/Guido_van_Rossum) has more than one.\n",
+    "\n",
+    "`people` makes many implicit assumptions about the structure of the data. For example, there are a [one-to-many <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_wiki.png\">](https://en.wikipedia.org/wiki/One-to-many_%28data_model%29) relationship between a person and their email addresses and a [one-to-one <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_wiki.png\">](https://en.wikipedia.org/wiki/One-to-one_%28data_model%29) relationship between each person and their name."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "people = {\n",
+    "    \"mathematicians\": [\n",
+    "        {\n",
+    "            \"name\": \"Gilbert Strang\",\n",
+    "            \"emails\": [\"gilbert@mit.edu\"],\n",
+    "        },\n",
+    "        {\n",
+    "            \"name\": \"Leonhard Euler\",\n",
+    "            \"emails\": [],\n",
+    "        },\n",
+    "    ],\n",
+    "    \"physicists\": [],\n",
+    "    \"programmers\": [\n",
+    "        {\n",
+    "            \"name\": \"Guido\",\n",
+    "            \"emails\": [\"guido@python.org\", \"guido@dropbox.com\"],\n",
+    "        },\n",
+    "    ],\n",
+    "}"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "The literal notation of such a nested `dict` object may be hard to read ..."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'mathematicians': [{'name': 'Gilbert Strang', 'emails': ['gilbert@mit.edu']},\n",
+       "  {'name': 'Leonhard Euler', 'emails': []}],\n",
+       " 'physicists': [],\n",
+       " 'programmers': [{'name': 'Guido',\n",
+       "   'emails': ['guido@python.org', 'guido@dropbox.com']}]}"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "people"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "... but the [pprint <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/pprint.html) module in the [standard library <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/index.html) provides a [pprint() <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/pprint.html#pprint.pprint) function for \"pretty printing.\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "from pprint import pprint"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{'mathematicians': [{'emails': ['gilbert@mit.edu'],\n",
+      "                     'name': 'Gilbert Strang'},\n",
+      "                    {'emails': [],\n",
+      "                     'name': 'Leonhard Euler'}],\n",
+      " 'physicists': [],\n",
+      " 'programmers': [{'emails': ['guido@python.org',\n",
+      "                             'guido@dropbox.com'],\n",
+      "                  'name': 'Guido'}]}\n"
+     ]
+    }
+   ],
+   "source": [
+    "pprint(people, indent=1, width=60)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "### Hash Tables & (Key) Hashability"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "In [Chapter 0 <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/00_intro_00_content.ipynb#Isn't-C-a-lot-faster?), we argue that a major advantage of using Python is that it takes care of the memory managment for us. In line with that, we have never talked about the C level implementation thus far in the book. However, the `dict` type, among others, exhibits some behaviors that may seem \"weird\" for a beginner. To build some intuition, we describe the underlying implementation details on a conceptual level.\n",
+    "\n",
+    "The first unintuitive behavior is that we may *not* use a *mutable* object as a key. That results in a `TypeError`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "ename": "TypeError",
+     "evalue": "unhashable type: 'list'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-17-20a24130a135>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m {\n\u001b[0;32m----> 2\u001b[0;31m     \u001b[0;34m[\u001b[0m\u001b[0;34m\"zero\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"one\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      3\u001b[0m }\n",
+      "\u001b[0;31mTypeError\u001b[0m: unhashable type: 'list'"
+     ]
+    }
+   ],
+   "source": [
+    "{\n",
+    "    [\"zero\", \"one\"]: [0, 1],\n",
+    "}"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Similarly surprising is that items with the *same* key get merged together. The resulting `dict` object keeps the position of the *first* mention of the `\"zero\"` key while only the *last* mention of the corresponding values, `999`, survives."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'zero': 999, 'one': 1, 'two': 2}"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "{\n",
+    "    \"zero\": 0,\n",
+    "    \"one\": 1,\n",
+    "    \"two\": 2,\n",
+    "    \"zero\": 999,  # to illustrate a point\n",
+    "}"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "The reason for that is that the `dict` type is implemented with so-called [hash tables <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_wiki.png\">](https://en.wikipedia.org/wiki/Hash_table).\n",
+    "\n",
+    "Conceptually, when we create a *new* `dict` object, Python creates a \"bag\" in memory that takes significantly more space than needed to store the references to all the key and value objects. This bag is a **contiguous array** similar to the `list` type's implementation. Whereas in the `list` case the array is divided into equally sized *slots* capable of holding *one* reference, a `dict` object's array is divided into equally sized **buckets** with enough space to store *two* references each: One for an item's key and one for the mapped value. The buckets are labeled with *index* numbers. Because Python knows how wide each bucket, it can jump directly into *any* bucket by calculating its *offset* from the start.\n",
+    "\n",
+    "The figure below visualizes how we should think of hash tables. An empty `dict` object, created with the literal `{}`, still takes a lot of memory: It is essentially one big, contiguous, and empty table."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "|  Bucket |  0  |  1  |  2  |  3  |  4  |  5  |  6  |  7  |\n",
+    "|  :---:  |:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|\n",
+    "| **Key** |*...*|*...*|*...*|*...*|*...*|*...*|*...*|*...*|\n",
+    "|**Value**|*...*|*...*|*...*|*...*|*...*|*...*|*...*|*...*|"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "To insert a key-value pair, the key must be translated into a bucket's index.\n",
+    "\n",
+    "As the first step to do so, the built-in [hash() <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/functions.html#hash) function maps any **hashable** object to its **hash value**, a long and \"random\" `int` number, similar to the ones returned by the built-in [id() <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/functions.html#id) function. This hash value is a *summary* of all the $0$s and $1$s inside the object.\n",
+    "\n",
+    "According to the official [glossary <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/glossary.html#term-hashable), an object is hashable *only if* \"it has a hash value which *never* changes during its *lifetime*.\" So, hashability implies immutability! Without this formal requirement an object may end up in *different* buckets depending on its current value. As the name of the `dict` type (i.e., \"dictionary\") suggests, a primary purpose of it is to insert objects and look them up later on. Without a *unique* bucket, this is of course not doable. The exact logic behind [hash() <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/functions.html#hash) is beyond the scope of this book.\n",
+    "\n",
+    "Let's calculate the hash value of `\"zero\"`, an immutable `str` object. Hash values have *no* semantic meaning. Also, every time we re-start Python, we see *different* hash values for the *same* objects. That is a security measure, and we do not go into the technicalities here (cf. [source <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/using/cmdline.html#envvar-PYTHONHASHSEED))."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "2513450610271817024"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "hash(\"zero\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "For numeric objects, we can sometimes predict the hash values. However, we must *never* interpret any meaning into them."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0"
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "hash(0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0"
+      ]
+     },
+     "execution_count": 21,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "hash(0.0)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "The [glossary <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/glossary.html#term-hashable) states a second requirement for hashability, namely that \"objects which *compare equal* must have the *same* hash value.\" The purpose of this is to ensure that if we put, for example, `1` as a key in a `dict` object, we can look it up later with `1.0`. In other words, we can look up keys by their object's semantic value. The converse statement does *not* hold: Two objects *may* (accidentally) have the *same* hash value and *not* compare equal. However, that rarely happens."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "1 == 1.0"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 23,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "hash(1) == hash(1.0)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Because `list` objects are not immutable, they are *never* hashable, as indicated by the `TypeError`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "ename": "TypeError",
+     "evalue": "unhashable type: 'list'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-24-1afa4b4779b0>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mhash\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"zero\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"one\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;31mTypeError\u001b[0m: unhashable type: 'list'"
+     ]
+    }
+   ],
+   "source": [
+    "hash([\"zero\", \"one\"])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "If we need keys composed of several objects, we can use `tuple` objects instead."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "4408973713070187748"
+      ]
+     },
+     "execution_count": 25,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "hash((\"zero\", \"one\"))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "There is no such restiction on objects inserted into `dict` objects as *values*."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{('zero', 'one'): [0, 1]}"
+      ]
+     },
+     "execution_count": 26,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "{\n",
+    "    (\"zero\", \"one\"): [0, 1],\n",
+    "}"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "After obtaining the key object's hash value, Python must still convert that into a bucket index. We do not cover this step in technical detail but provide a conceptual description of it.\n",
+    "\n",
+    "The `buckets()` function below shows how we can obtain indexes from the binary representation of a hash value by simply extracting its least significant `bits` and interpreting them as index numbers. Alternatively, the hash value may also be divided with the `%` operator by the number of available buckets. We show this idea in the `buckets_alt()` function that takes the number of buckets, `n_buckets`, as its second argument."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "def buckets(mapping, *, bits):\n",
+    "    \"\"\"Calculate the bucket indices for a mapping's keys.\"\"\"\n",
+    "    for key in mapping:  # cf., next section for details on looping\n",
+    "        hash_value = hash(key)\n",
+    "        binary = bin(hash_value)\n",
+    "        address = binary[-bits:]\n",
+    "        bucket = int(\"0b\" + address, base=2)\n",
+    "        print(key, hash_value, \"0b...\" + binary[-8:], address, bucket, sep=\"\\t\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "def buckets_alt(mapping, *, n_buckets):\n",
+    "    \"\"\"Calculate the bucket indices for a mapping's keys.\"\"\"\n",
+    "    for key in mapping:  # cf., next section for details on looping\n",
+    "        hash_value = hash(key)\n",
+    "        bucket = hash_value % n_buckets\n",
+    "        print(key, hash_value, bucket, sep=\"\\t\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "With an infinite number of possible keys being mapped to a limited number of buckets, there is a realistic chance that two or more keys end up in the *same* bucket. That is called a **hash collision**. In such cases, Python uses a perturbation rule to rearrange the bits, and if the corresponding next bucket is empty, places an item there. Then, the nice offsetting logic from above breaks down and Python needs more time on average to place items into a hash table or look them up. The remedy is to use a bigger hash table as then the chance of collisions decreases. Python does all that for us in the background, and the main cost we pay for that is a *high* memory usage of `dict` objects in general.\n",
+    "\n",
+    "Because keys with the *same* semantic value have the *same* hash value, they end up in the *same* bucket. That is why the item that gets inserted last *overwrites* all previously inserted items whose keys compare equal, as we saw with the two `\"zero\"` keys above.\n",
+    "\n",
+    "Thus, to come up with indexes for 4 buckets, we need to extract 2 bits from the hash value (i.e., $2^2 = 4$)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "zero\t2513450610271817024\t0b...01000000\t00\t0\n",
+      "one\t-153603400453665003\t0b...11101011\t11\t3\n",
+      "two\t1981459398511907500\t0b...10101100\t00\t0\n"
+     ]
+    }
+   ],
+   "source": [
+    "buckets(from_words, bits=2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "zero\t2513450610271817024\t0\n",
+      "one\t-153603400453665003\t1\n",
+      "two\t1981459398511907500\t0\n"
+     ]
+    }
+   ],
+   "source": [
+    "buckets_alt(from_words, n_buckets=4)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Similarly, 3 bits provide indexes for 8 buckets (i.e., $2^3 = 8$) ..."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "zero\t2513450610271817024\t0b...01000000\t000\t0\n",
+      "one\t-153603400453665003\t0b...11101011\t011\t3\n",
+      "two\t1981459398511907500\t0b...10101100\t100\t4\n"
+     ]
+    }
+   ],
+   "source": [
+    "buckets(from_words, bits=3)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "zero\t2513450610271817024\t0\n",
+      "one\t-153603400453665003\t5\n",
+      "two\t1981459398511907500\t4\n"
+     ]
+    }
+   ],
+   "source": [
+    "buckets_alt(from_words, n_buckets=8)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "... while 4 bits do so for 16 buckets (i.e., $2^4 = 16$)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "zero\t2513450610271817024\t0b...01000000\t0000\t0\n",
+      "one\t-153603400453665003\t0b...11101011\t1011\t11\n",
+      "two\t1981459398511907500\t0b...10101100\t1100\t12\n"
+     ]
+    }
+   ],
+   "source": [
+    "buckets(from_words, bits=4)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "zero\t2513450610271817024\t0\n",
+      "one\t-153603400453665003\t5\n",
+      "two\t1981459398511907500\t12\n"
+     ]
+    }
+   ],
+   "source": [
+    "buckets_alt(from_words, n_buckets=16)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Python allocates the memory for a `dict` object's hash table according to some internal heuristics: Whenever a hash table is roughly 2/3 full, it creates a *new* one with twice the space, and re-inserts all items, one by one, from the *old* one. So, during its lifetime, a `dict` object may have several hash tables.\n",
+    "\n",
+    "Although hash tables seem quite complex at first sight, they help us to make certain operations very fast as we see further below."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "### Mappings are Collections without Predictable Order"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "In [Chapter 7 <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/07_sequences_00_content.ipynb#Collections-vs.-Sequences), we see how a *sequence* is a special kind of a *collection*, and that *collections* can be described as\n",
+    "- *iterable*\n",
+    "- *containers*\n",
+    "- with a *finite* number of elements.\n",
+    "\n",
+    "The `dict` type is another *collection* type. So, it has the three properties as well.\n",
+    "\n",
+    "For example, we may pass `to_words` or `from_words` to the built-in [len() <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/functions.html#len) function to obtain the number of *items* they contain. In the terminology of the [collections.abc <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/collections.abc.html) module in the [standard library <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/index.html), both are `Sized` objects."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "3"
+      ]
+     },
+     "execution_count": 35,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "len(to_words)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "3"
+      ]
+     },
+     "execution_count": 36,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "len(from_words)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Also, `dict` objects may be looped over, for example, with the `for` statement. So, in the terminology of the [collections.abc <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/collections.abc.html) module, they are `Iterable` objects.\n",
+    "\n",
+    "For technical reasons, we must *not* rely on the iteration order to be *predictable* in any form until Python 3.7 in 2018. Looping over the *same* `dict` objects multiple times during its lifetime could result in *different* iteration orders every time. That behavior is intentional as `dict` objects are optimized for use cases where order does not matter. Starting with Python 3.7, `dict` objects remember the order in that items are *inserted* (cf., [Python 3.7 release notes <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://www.python.org/downloads/release/python-370/)).\n",
+    "\n",
+    "Because of that, the order in the two `for`-loops below is the *same* as in the *source code* that defines `to_words` and `from_words` above. In that sense, it is \"*predictable*.\" However, if we fill `dict` objects with data from real-world sources, that kind of predictability is not helpful as such data are not written as source code, and, thus, we consider the order of items in `dict` objects to be *unpredictable*."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Python 3.7.4\n"
+     ]
+    }
+   ],
+   "source": [
+    "!python --version  # the order in the for-loops is predictable only for Python 3.7 or higher"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "By convention, iteration goes over the *keys* in the `dict` object only. The \"*Dictionary Methods*\" section below shows how to loop over the *items* or the *values* instead."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0\n",
+      "1\n",
+      "2\n"
+     ]
+    }
+   ],
+   "source": [
+    "for number in to_words:\n",
+    "    print(number)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "zero\n",
+      "one\n",
+      "two\n"
+     ]
+    }
+   ],
+   "source": [
+    "for word in from_words:\n",
+    "    print(word)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Without a predictable *forward* order, `dict` objects are not *reversible* either. So, passing a `dict` object to the [reversed() <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/functions.html#reversed) built-in raises a `TypeError`. In terminology of the [collections.abc <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/collections.abc.html) module, they are *no* `Reversible` objects."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "ename": "TypeError",
+     "evalue": "'dict' object is not reversible",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-40-2d09207399cf>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0;32mfor\u001b[0m \u001b[0mnumber\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mreversed\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mto_words\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m     \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnumber\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;31mTypeError\u001b[0m: 'dict' object is not reversible"
+     ]
+    }
+   ],
+   "source": [
+    "for number in reversed(to_words):\n",
+    "    print(number)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "ename": "TypeError",
+     "evalue": "'dict' object is not reversible",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-41-c723940a69be>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0;32mfor\u001b[0m \u001b[0mword\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mreversed\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfrom_words\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m     \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mword\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;31mTypeError\u001b[0m: 'dict' object is not reversible"
+     ]
+    }
+   ],
+   "source": [
+    "for word in reversed(from_words):\n",
+    "    print(word)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Of course, we could use the built-in [sorted() <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/functions.html#sorted) function to loop over, for example, `from_words` in a *predictable* order. However, that creates a temporary `list` object in memory and an order that has *nothing* to do with how the items are ordered inside the hash table."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "one\n",
+      "two\n",
+      "zero\n"
+     ]
+    }
+   ],
+   "source": [
+    "for word in sorted(from_words):\n",
+    "    print(word)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "To show the `Container` behavior of *collection* types, we use the boolean `in` operator to check if a given object evaluates equal to a *key* in `to_words` or `from_words`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 43,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "1.0 in to_words  # 1.0 is not a key but compares equal to a key"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "False"
+      ]
+     },
+     "execution_count": 44,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "-1 in to_words"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 45,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "\"one\" in from_words"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "False"
+      ]
+     },
+     "execution_count": 46,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "\"ten\" in from_words"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "#### Membership Testing: Lists vs. Dictionaries"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Because of the [hash table <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_wiki.png\">](https://en.wikipedia.org/wiki/Hash_table) implementation, the `in` operator is *extremely* fast: Python does *not* need to initiate a [linear search <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_wiki.png\">](https://en.wikipedia.org/wiki/Linear_search) as in the `list` case but immediately knows the only places in memory where the searched object must be located if present in the hash table at all. Conceptually, that is like comparing the searched object against *all* key objects with the `==` operator *without* doing it.\n",
+    "\n",
+    "To show the speed, we run an experiment. We create a `haystack`, a `list` object, with `10_000_001` elements in it, *one* of which is the `needle`, namely `42`. Once again, the [randint() <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/random.html#random.randint) function in the [random <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/random.html) module is helpful."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "import random"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 48,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "random.seed(87)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 49,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "needle = 42"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 50,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "haystack = [random.randint(99, 9999) for _ in range(10_000_000)]\n",
+    "haystack.append(needle)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "We put the elements in `haystack` in a *random* order with the [shuffle() <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/random.html#random.shuffle) function in the [random <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/random.html) module."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 51,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "random.shuffle(haystack)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 52,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[8126, 7370, 3735, 213, 7922, 1434, 8557, 9609, 9704, 9564]"
+      ]
+     },
+     "execution_count": 52,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "haystack[:10]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 53,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[7237, 886, 5945, 4014, 4998, 2055, 3531, 6919, 7875, 1944]"
+      ]
+     },
+     "execution_count": 53,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "haystack[-10:]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "As modern computers are generally fast, we search the `haystack` a total of `10` times."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 54,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "4.18 s ± 0 ns per loop (mean ± std. dev. of 1 run, 1 loop each)\n"
+     ]
+    }
+   ],
+   "source": [
+    "%%timeit -n 1 -r 1\n",
+    "for _ in range(10):\n",
+    "    needle in haystack"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Now, we convert the elements of the `haystack` into the keys of a `magic_haystack`, a `dict` object. We use `None` as a dummy value for all items."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 55,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "magic_haystack = dict((x, None) for x in haystack)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "To show the *massive* effect of the hash table implementation, we search the `magic_haystack` not `10` but `10_000_000` times. The code cell still runs in only a fraction of the time its counterpart does above."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 56,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "430 ms ± 0 ns per loop (mean ± std. dev. of 1 run, 1 loop each)\n"
+     ]
+    }
+   ],
+   "source": [
+    "%%timeit -n 1 -r 1\n",
+    "for _ in range(10_000_000):\n",
+    "    needle in magic_haystack"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "However, there is no fast way to look up the values the keys are mapped to. To achieve that, we have to loop over *all* items and check for each value object if it compares equal to the searched object. That is, by definition, a linear search, as well, and rather slow. In the context of `dict` objects, we call that a **reverse look-up**."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "### Indexing -> Key Look-up"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "The same efficient key look-up executed in the background with the `in` operator is also behind the indexing operator `[]`. Instead of returning either `True` or `False`, it returns the value object the looked up key maps to.\n",
+    "\n",
+    "To show the similarity to indexing into `list` objects, we provide another example with `to_words_list`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 57,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "to_words_list = [\"zero\", \"one\", \"two\"]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Without the above definitions, we could not tell the difference between `to_words` and `to_words_list`: The usage of the `[]` is the same."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 58,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'zero'"
+      ]
+     },
+     "execution_count": 58,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "to_words[0]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 59,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'zero'"
+      ]
+     },
+     "execution_count": 59,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "to_words_list[0]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Because key objects can be of any immutable type and are, in particular, not constrained to just the `int` type, the word \"*indexing*\" is an understatement here. Therefore, in the context of `dict` objects, we view the `[]` operator as a generalization of the indexing operator and refer to it as the **(key) look-up** operator. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 60,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "2"
+      ]
+     },
+     "execution_count": 60,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from_words[\"two\"]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "If a key is not in a `dict` object, Python raises a `KeyError`. A sequence type would raise an `IndexError` in this situation."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 61,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "ename": "KeyError",
+     "evalue": "'drei'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-61-8602ef11ff73>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mfrom_words\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"drei\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;31mKeyError\u001b[0m: 'drei'"
+     ]
+    }
+   ],
+   "source": [
+    "from_words[\"drei\"]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "While `dict` objects support the `[]` operator to look up a *single* key, the more general concept of *slicing* is *not* available. That is in line with the idea that there is *no* predictable *order* associated with a `dict` object's keys, and slicing requires an order.\n",
+    "\n",
+    "To access deeper levels in nested data, like `people`, we *chain* the look-up operator `[]`. For example, let's view all the `\"mathematicians\"` in `people`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 62,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[{'name': 'Gilbert Strang', 'emails': ['gilbert@mit.edu']},\n",
+       " {'name': 'Leonhard Euler', 'emails': []}]"
+      ]
+     },
+     "execution_count": 62,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "people[\"mathematicians\"]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Let's take the first mathematician on the list, ..."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 63,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'name': 'Gilbert Strang', 'emails': ['gilbert@mit.edu']}"
+      ]
+     },
+     "execution_count": 63,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "people[\"mathematicians\"][0]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "... and output his `\"name\"` ..."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 64,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Gilbert Strang'"
+      ]
+     },
+     "execution_count": 64,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "people[\"mathematicians\"][0][\"name\"]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "... or his `\"emails\"`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 65,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['gilbert@mit.edu']"
+      ]
+     },
+     "execution_count": 65,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "people[\"mathematicians\"][0][\"emails\"]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "### Mutability"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "We may mutate `dict` objects *in place*.\n",
+    "\n",
+    "For example, let's translate the English words in `to_words` to their German counterparts. Behind the scenes, Python determines the bucket of the objects passed to the `[]` operator, looks them up in the hash table, and, if present, *updates* the references to the mapped value objects."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 66,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{0: 'zero', 1: 'one', 2: 'two'}"
+      ]
+     },
+     "execution_count": 66,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "to_words"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 67,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "to_words[0] = \"null\"\n",
+    "to_words[1] = \"eins\"\n",
+    "to_words[2] = \"zwei\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 68,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{0: 'null', 1: 'eins', 2: 'zwei'}"
+      ]
+     },
+     "execution_count": 68,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "to_words"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Let's add two more items. Again, Python determines their buckets, but this time finds them to be empty, and *inserts* the references to their key and value objects."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 69,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "to_words[3] = \"drei\"\n",
+    "to_words[4] = \"vier\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 70,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{0: 'null', 1: 'eins', 2: 'zwei', 3: 'drei', 4: 'vier'}"
+      ]
+     },
+     "execution_count": 70,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "to_words"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "None of these operations change the identity of the `to_words` object."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 71,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "140024292070576"
+      ]
+     },
+     "execution_count": 71,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "id(to_words)  # same memory location as before"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "The `del` statement removes individual items. Python just removes the *two* references to the key and value objects in the corresponding bucket."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 72,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "del to_words[0]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 73,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{1: 'eins', 2: 'zwei', 3: 'drei', 4: 'vier'}"
+      ]
+     },
+     "execution_count": 73,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "to_words"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "We may also change parts of nested data, such as `people`.\n",
+    "\n",
+    "For example, let's add [Albert Einstein <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_wiki.png\">](https://en.wikipedia.org/wiki/Albert_Einstein) to the list of `\"physicists\"`, ..."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 74,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[]"
+      ]
+     },
+     "execution_count": 74,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "people[\"physicists\"]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 75,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "people[\"physicists\"].append({\"name\": \"Albert Einstein\"})"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "... complete Guido's `\"name\"`, ..."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 76,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'name': 'Guido', 'emails': ['guido@python.org', 'guido@dropbox.com']}"
+      ]
+     },
+     "execution_count": 76,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "people[\"programmers\"][0]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 77,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "people[\"programmers\"][0][\"name\"] = \"Guido van Rossum\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "... and remove his work email because he retired."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 78,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "del people[\"programmers\"][0][\"emails\"][1]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Now, `people` looks like this."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 79,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{'mathematicians': [{'emails': ['gilbert@mit.edu'],\n",
+      "                     'name': 'Gilbert Strang'},\n",
+      "                    {'emails': [],\n",
+      "                     'name': 'Leonhard Euler'}],\n",
+      " 'physicists': [{'name': 'Albert Einstein'}],\n",
+      " 'programmers': [{'emails': ['guido@python.org'],\n",
+      "                  'name': 'Guido van Rossum'}]}\n"
+     ]
+    }
+   ],
+   "source": [
+    "pprint(people, indent=1, width=60)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "### Dictionary Methods"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "`dict` objects come with many methods bound on them (cf., [documentation <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/stdtypes.html#dict)), many of which are standardized by the `Mapping` and `MutableMapping` ABCs from the [collections.abc <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/collections.abc.html) module. While the former requires the [keys() <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/stdtypes.html#dict.keys), [values() <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/stdtypes.html#dict.values), [items() <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/stdtypes.html#dict.items), and [get() <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/stdtypes.html#dict.get) methods, which *never* mutate an object, the latter formalizes the [update() <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/stdtypes.html#dict.update), [pop() <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/stdtypes.html#dict.pop), [popitem() <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/stdtypes.html#dict.popitem), [clear() <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/stdtypes.html#dict.clear), and [setdefault() <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/stdtypes.html#dict.setdefault) methods, which *may* do so."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 80,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "import collections.abc as abc"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 81,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 81,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "isinstance(from_words, abc.Mapping)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 82,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 82,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "isinstance(from_words, abc.MutableMapping)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "While iteration over a mapping type already goes over its keys, we may emphasize this explicitly by adding the [keys() <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/stdtypes.html#dict.keys) method in the `for`-loop. Again, the iteration order is equivalent to the insertion order but still considered *unpredictable*."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 83,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "zero\n",
+      "one\n",
+      "two\n"
+     ]
+    }
+   ],
+   "source": [
+    "for word in from_words.keys():\n",
+    "    print(word)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "[keys() <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/stdtypes.html#dict.keys) returns an object of type `dict_keys`. That is a dynamic **view** inside the `from_words`'s hash table, which means it does *not* copy the references to the keys, and changes to `from_words` can be seen through it. View objects behave much like `dict` objects themselves."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 84,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "dict_keys(['zero', 'one', 'two'])"
+      ]
+     },
+     "execution_count": 84,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from_words.keys()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Views can be materialized with the [list() <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/functions.html#func-list) built-in. However, that may introduce *semantic* errors into a program as the newly created `list` object has a \"*predictable*\" order (i.e., indexes `0`, `1`, ...) created from an *unpredictable* one."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 85,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['zero', 'one', 'two']"
+      ]
+     },
+     "execution_count": 85,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "list(from_words.keys())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "To loop over the value objects instead, we use the [values() <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/stdtypes.html#dict.values) method. That returns a *view* (i.e., type `dict_values`) on the value objects inside `from_words` without copying them."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 86,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0\n",
+      "1\n",
+      "2\n"
+     ]
+    }
+   ],
+   "source": [
+    "for number in from_words.values():\n",
+    "    print(number)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 87,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "dict_values([0, 1, 2])"
+      ]
+     },
+     "execution_count": 87,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from_words.values()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "To loop over key-value pairs, we invoke the [items() <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/stdtypes.html#dict.items) method. That returns a view (i.e., type `dict_items`) on the key-value pairs as `tuple` objects, where the first element is the key and the second the value. Because of that, we use tuple unpacking in the `for`-loop."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 88,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "zero -> 0\n",
+      "one -> 1\n",
+      "two -> 2\n"
+     ]
+    }
+   ],
+   "source": [
+    "for word, number in from_words.items():\n",
+    "    print(f\"{word} -> {number}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 89,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "dict_items([('zero', 0), ('one', 1), ('two', 2)])"
+      ]
+     },
+     "execution_count": 89,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from_words.items()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Above, we see how the look-up operator fails *loudly* with a `KeyError` if a key is *not* in a `dict` object. For example, `to_words` does *not* have a key `0` any more."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 90,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{1: 'eins', 2: 'zwei', 3: 'drei', 4: 'vier'}"
+      ]
+     },
+     "execution_count": 90,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "to_words"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 91,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "ename": "KeyError",
+     "evalue": "0",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-91-30b825de37f0>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mto_words\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;31mKeyError\u001b[0m: 0"
+     ]
+    }
+   ],
+   "source": [
+    "to_words[0]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "That may be mitigated with the [get() <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/stdtypes.html#dict.get) method that takes two arguments: `key` and `default`. It returns the value object `key` maps to if it is in the `dict` object; otherwise, `default` is returned. If not provided, `default` is `None`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 92,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'n/a'"
+      ]
+     },
+     "execution_count": 92,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "to_words.get(0, \"n/a\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 93,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'eins'"
+      ]
+     },
+     "execution_count": 93,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "to_words.get(1, \"n/a\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "The [update() <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/stdtypes.html#dict.update) method takes the items of another mapping and either inserts them or overwrites the ones with matching keys already in the `dict` objects. It may be used in the other two ways as the [dict() <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/functions.html#func-dict) constructor allows, as well."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 94,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "to_spanish = {\n",
+    "    0: \"cero\",\n",
+    "    1: \"uno\",\n",
+    "    2: \"dos\",\n",
+    "    3: \"tres\",\n",
+    "    4: \"cuatro\",\n",
+    "    5: \"cinco\",  \n",
+    "}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 95,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "to_words.update(to_spanish)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 96,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{1: 'uno', 2: 'dos', 3: 'tres', 4: 'cuatro', 0: 'cero', 5: 'cinco'}"
+      ]
+     },
+     "execution_count": 96,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "to_words"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "In contrast to the `pop()` method of the `list` type, the [pop() <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/stdtypes.html#dict.pop) method of the `dict` type *requires* a `key` argument to be passed. Then, it removes the corresponding key-value pair *and* returns the value object. If the `key` is not in the `dict` object, a `KeyError` is raised."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 97,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'zero': 0, 'one': 1, 'two': 2}"
+      ]
+     },
+     "execution_count": 97,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from_words"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 98,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "number = from_words.pop(\"zero\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 99,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0"
+      ]
+     },
+     "execution_count": 99,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "number"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 100,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'one': 1, 'two': 2}"
+      ]
+     },
+     "execution_count": 100,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from_words"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "With an optional `default` argument, the loud `KeyError` may be suppressed and the `default` returned instead, just as with the [get() <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/stdtypes.html#dict.get) method above."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 101,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "ename": "KeyError",
+     "evalue": "'zero'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-101-b91e52fa79fd>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mfrom_words\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpop\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"zero\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;31mKeyError\u001b[0m: 'zero'"
+     ]
+    }
+   ],
+   "source": [
+    "from_words.pop(\"zero\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 102,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0"
+      ]
+     },
+     "execution_count": 102,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from_words.pop(\"zero\", 0)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Similar to the `pop()` method of the `list` type, the [popitem() <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/stdtypes.html#dict.popitem) method of the `dict` type removes *and* returns an \"arbitrary\" key-value pair as a `tuple` object from a `dict` object. With the preservation of the insertion order in Python 3.7 and higher, this effectively becomes a \"last in, first out\" rule, just as with the `list` type. Once a `dict` object is empty, [popitem() <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/stdtypes.html#dict.popitem) raises a `KeyError`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 103,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "word, number = from_words.popitem()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 104,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "('two', 2)"
+      ]
+     },
+     "execution_count": 104,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "word, number"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 105,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'one': 1}"
+      ]
+     },
+     "execution_count": 105,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from_words"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "The [clear() <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/stdtypes.html#dict.clear) method removes all items but keeps the `dict` object alive in memory."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 106,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "to_words.clear()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 107,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{}"
+      ]
+     },
+     "execution_count": 107,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "to_words"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 108,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "from_words.clear()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 109,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{}"
+      ]
+     },
+     "execution_count": 109,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from_words"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "The [setdefault() <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/stdtypes.html#dict.setdefault) method may have a bit of an unfortunate name but is useful, in particular, with nested `list` objects. It takes two arguments, `key` and `default`, and returns the value mapped to `key` if `key` is in the `dict` object; otherwise, it inserts the `key`-`default` pair *and* returns a reference to the newly created value object. So, it is similar to the [get() <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/stdtypes.html#dict.get) method above but also *mutates* the `dict` object.\n",
+    "\n",
+    "Consider the `people` example again and note how the `dict` object modeling `\"Albert Einstein\"` has *no* `\"emails\"` key in it."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 110,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{'mathematicians': [{'emails': ['gilbert@mit.edu'],\n",
+      "                     'name': 'Gilbert Strang'},\n",
+      "                    {'emails': [],\n",
+      "                     'name': 'Leonhard Euler'}],\n",
+      " 'physicists': [{'name': 'Albert Einstein'}],\n",
+      " 'programmers': [{'emails': ['guido@python.org'],\n",
+      "                  'name': 'Guido van Rossum'}]}\n"
+     ]
+    }
+   ],
+   "source": [
+    "pprint(people, indent=1, width=60)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Let's say we want to append the imaginary emails `\"leonhard@math.org\"` and `\"albert@physics.org\"`. We cannot be sure if a `dict` object modeling a person has already a `\"emails\"` key or not. To play it safe, we could first use the `in` operator to check for that and create a new `list` object in a second step if one is missing. Then, we would finally append the new email.\n",
+    "\n",
+    "[setdefault() <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/stdtypes.html#dict.setdefault) allows us to do all of the three steps at once. More importantly, behind the scenes Python only needs to make *one* key look-up instead of potentially three. For large nested data, that could speed up the computations significantly.\n",
+    "\n",
+    "So, the first code cell below adds the email to the already existing empty `list` object, while the second one creates a new one first."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 111,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "people[\"mathematicians\"][1].setdefault(\"emails\", []).append(\"leonhard@math.org\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 112,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "people[\"physicists\"][0].setdefault(\"emails\", []).append(\"albert@physics.org\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 113,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{'mathematicians': [{'emails': ['gilbert@mit.edu'],\n",
+      "                     'name': 'Gilbert Strang'},\n",
+      "                    {'emails': ['leonhard@math.org'],\n",
+      "                     'name': 'Leonhard Euler'}],\n",
+      " 'physicists': [{'emails': ['albert@physics.org'],\n",
+      "                 'name': 'Albert Einstein'}],\n",
+      " 'programmers': [{'emails': ['guido@python.org'],\n",
+      "                  'name': 'Guido van Rossum'}]}\n"
+     ]
+    }
+   ],
+   "source": [
+    "pprint(people, indent=1, width=60)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "`dict` objects also come with a [copy() <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/stdtypes.html#dict.copy) method on them that creates *shallow* copies."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 114,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "guido = people[\"programmers\"][0].copy()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 115,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'name': 'Guido van Rossum', 'emails': ['guido@python.org']}"
+      ]
+     },
+     "execution_count": 115,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "guido"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "If we mutate `guido` and, for example, remove all his emails with the `clear()` method on the `list` type, these changes are also visible through `people`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 116,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "guido[\"emails\"].clear()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 117,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'name': 'Guido van Rossum', 'emails': []}"
+      ]
+     },
+     "execution_count": 117,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "guido"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 118,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{'mathematicians': [{'emails': ['gilbert@mit.edu'],\n",
+      "                     'name': 'Gilbert Strang'},\n",
+      "                    {'emails': ['leonhard@math.org'],\n",
+      "                     'name': 'Leonhard Euler'}],\n",
+      " 'physicists': [{'emails': ['albert@physics.org'],\n",
+      "                 'name': 'Albert Einstein'}],\n",
+      " 'programmers': [{'emails': [],\n",
+      "                  'name': 'Guido van Rossum'}]}\n"
+     ]
+    }
+   ],
+   "source": [
+    "pprint(people, indent=1, width=60)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "### Packing & Unpacking (continued)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Just as a single `*` symbol is used for packing and unpacking iterables in [Chapter 7 <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/07_sequences_00_content.ipynb#Packing-&-Unpacking), a double `**` symbol implements packing and unpacking for mappings.\n",
+    "\n",
+    "Let's say we have `to_words` and `more_words` as below and want to merge the items together into a *new* `dict` object."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 119,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "to_words = {\n",
+    "    0: \"zero\",\n",
+    "    1: \"one\",\n",
+    "    2: \"two\",\n",
+    "}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 120,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "more_words = {\n",
+    "    2: \"TWO\",  # to illustrate a point\n",
+    "    3: \"three\",\n",
+    "    4: \"four\",\n",
+    "}"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "By *unpacking* the items with `**`, the newly created `dict` object is first filled with the items from `to_words` and then from `more_words`. The item with the key `2` from `more_words` overwrites its counterpart from `to_words` as it is mentioned last."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 121,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{0: 'zero', 1: 'one', 2: 'TWO', 3: 'three', 4: 'four'}"
+      ]
+     },
+     "execution_count": 121,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "{**to_words, **more_words}"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "#### Function Definitions & Calls (continued)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Both, `*` and `**` may be used within the header line of a function definition, for example, as in `print_args1()` below. Here, *positional* arguments not captured by positional parameters are *packed* into the `tuple` object `args`, and *keyword* arguments not captured by keyword parameters are *packed* into the `dict` object `kwargs`.\n",
+    "\n",
+    "For `print_args1()`, all arguments are optional, and ..."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 122,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "def print_args1(*args, **kwargs):\n",
+    "    \"\"\"Print out all arguments passed in.\"\"\"\n",
+    "    for index, arg in enumerate(args):\n",
+    "        print(\"position\", index, arg)\n",
+    "\n",
+    "    for key, value in kwargs.items():\n",
+    "        print(\"keyword\", key, value)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "... we may pass whatever we want to it, or nothing at all."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 123,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "print_args1()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 124,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "position 0 a\n",
+      "position 1 b\n",
+      "position 2 c\n"
+     ]
+    }
+   ],
+   "source": [
+    "print_args1(\"a\", \"b\", \"c\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 125,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "keyword first 1\n",
+      "keyword second 2\n",
+      "keyword third 3\n"
+     ]
+    }
+   ],
+   "source": [
+    "print_args1(first=1, second=2, third=3)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 126,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "position 0 x\n",
+      "position 1 y\n",
+      "keyword flag True\n"
+     ]
+    }
+   ],
+   "source": [
+    "print_args1(\"x\", \"y\", flag=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "We may even unpack `dict` and `list` objects."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 127,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "flags = {\"flag\": True, \"another_flag\": False}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 128,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "keyword flag True\n",
+      "keyword another_flag False\n"
+     ]
+    }
+   ],
+   "source": [
+    "print_args1(**flags)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 129,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "position 0 42\n",
+      "position 1 87\n",
+      "keyword flag True\n",
+      "keyword another_flag False\n"
+     ]
+    }
+   ],
+   "source": [
+    "print_args1(*[42, 87], **flags)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "The next example, `print_args2()`, requires the caller to pass one positional argument, captured in the `positional` parameter, and one keyword argument, captured in `keyword`. Further, an optional keyword argument `default` may be passed in. Any other positional or keyword arguments are packed into either `args` or `kwargs`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 130,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "def print_args2(positional, *args, keyword, default=True, **kwargs):\n",
+    "    \"\"\"Print out all arguments passed in.\"\"\"\n",
+    "    print(\"required positional\", positional)\n",
+    "\n",
+    "    for index, arg in enumerate(args):\n",
+    "        print(\"optional positional\", index, arg)\n",
+    "\n",
+    "    print(\"required keyword\", keyword)\n",
+    "    print(\"default keyword\", default)\n",
+    "\n",
+    "    for key, value in kwargs.items():\n",
+    "        print(\"optional keyword\", key, value)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "If the caller does not respect that, a `TypeError` is raised."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 131,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "ename": "TypeError",
+     "evalue": "print_args2() missing 1 required positional argument: 'positional'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-131-21a06a55e707>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mprint_args2\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;31mTypeError\u001b[0m: print_args2() missing 1 required positional argument: 'positional'"
+     ]
+    }
+   ],
+   "source": [
+    "print_args2()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 132,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "ename": "TypeError",
+     "evalue": "print_args2() missing 1 required keyword-only argument: 'keyword'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-132-06c908f24fbb>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mprint_args2\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"p\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;31mTypeError\u001b[0m: print_args2() missing 1 required keyword-only argument: 'keyword'"
+     ]
+    }
+   ],
+   "source": [
+    "print_args2(\"p\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 133,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "required positional p\n",
+      "required keyword k\n",
+      "default keyword True\n"
+     ]
+    }
+   ],
+   "source": [
+    "print_args2(\"p\", keyword=\"k\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 134,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "required positional p\n",
+      "required keyword k\n",
+      "default keyword False\n"
+     ]
+    }
+   ],
+   "source": [
+    "print_args2(\"p\", keyword=\"k\", default=False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 135,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "required positional p\n",
+      "optional positional 0 x\n",
+      "optional positional 1 y\n",
+      "required keyword k\n",
+      "default keyword True\n",
+      "optional keyword flag True\n"
+     ]
+    }
+   ],
+   "source": [
+    "print_args2(\"p\", \"x\", \"y\", keyword=\"k\", flag=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 136,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "required positional p\n",
+      "optional positional 0 x\n",
+      "optional positional 1 y\n",
+      "required keyword k\n",
+      "default keyword False\n",
+      "optional keyword flag True\n"
+     ]
+    }
+   ],
+   "source": [
+    "print_args2(\"p\", \"x\", \"y\", keyword=\"k\", default=False, flag=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "As above, we may unpack `list` or `dict` objects in a function call."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 137,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "positionals = [\"x\", \"y\", \"z\"]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 138,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "required positional p\n",
+      "optional positional 0 x\n",
+      "optional positional 1 y\n",
+      "optional positional 2 z\n",
+      "required keyword k\n",
+      "default keyword False\n",
+      "optional keyword flag True\n",
+      "optional keyword another_flag False\n"
+     ]
+    }
+   ],
+   "source": [
+    "print_args2(\"p\", *positionals, keyword=\"k\", default=False, **flags)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "### Dictionary Comprehensions"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Analogous to list comprehensions in [Chapter 7 <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/07_sequences_00_content.ipynb#List-Comprehensions), **dictionary comprehensions** are a concise literal notation to derive new `dict` objects out of existing ones.\n",
+    "\n",
+    "For example, let's derive `from_words` out of `to_words` below by swapping the keys and values."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 139,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "to_words = {\n",
+    "    0: \"zero\",\n",
+    "    1: \"one\",\n",
+    "    2: \"two\",\n",
+    "}"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Without a dictionary comprehension, we would have to initialize an empty `dict` object, loop over the items of the original one, and insert the key-value pairs one by one in a reversed fashion as value-key pairs. That assumes that the values are unique as otherwise some would be merged."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 140,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'zero': 0, 'one': 1, 'two': 2}"
+      ]
+     },
+     "execution_count": 140,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from_words = {}\n",
+    "\n",
+    "for number, word in to_words.items():\n",
+    "    from_words[word] = number\n",
+    "\n",
+    "from_words"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "While that code is correct, it is also unnecessarily verbose. The dictionary comprehension below works in the same way as list comprehensions except that curly braces `{}` replace the brackets `[]` and a colon `:` is added to separate the keys from the values."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 141,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'zero': 0, 'one': 1, 'two': 2}"
+      ]
+     },
+     "execution_count": 141,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "{v: k for k, v in to_words.items()}"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "We may filter out items with an `if`-clause and transform the remaining key and value objects.\n",
+    "\n",
+    "For no good reason, let's filter out all words starting with a `\"z\"` and upper case the remainin words."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 142,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'ONE': 1, 'TWO': 2}"
+      ]
+     },
+     "execution_count": 142,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "{v.upper(): k for k, v in to_words.items() if not v.startswith(\"z\")}"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Multiple `for`- and/or `if`-clauses are allowed.\n",
+    "\n",
+    "For example, let's find all pairs of two numbers from `1` through `10` whose product is \"close\" to `50` (e.g., within a delta of `5`)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 143,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{(5, 9): 45,\n",
+       " (5, 10): 50,\n",
+       " (6, 8): 48,\n",
+       " (6, 9): 54,\n",
+       " (7, 7): 49,\n",
+       " (8, 6): 48,\n",
+       " (9, 5): 45,\n",
+       " (9, 6): 54,\n",
+       " (10, 5): 50}"
+      ]
+     },
+     "execution_count": 143,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "{\n",
+    "    (x, y): x * y\n",
+    "    for x in range(1, 11) for y in range(1, 11)\n",
+    "    if abs(x * y - 50) <= 5\n",
+    "}"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "### Memoization"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "#### \"Easy at second Glance\" Example: [Fibonacci Numbers <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_wiki.png\">](https://en.wikipedia.org/wiki/Fibonacci_number) (revisited)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "The *recursive* implementation of the [Fibonacci numbers <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_wiki.png\">](https://en.wikipedia.org/wiki/Fibonacci_number) in [Chapter 4 <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/04_iteration_00_content.ipynb#\"Easy-at-first-Glance\"-Example:-Fibonacci-Numbers) takes long to compute for large Fibonacci numbers as the number of function calls grows exponentially.\n",
+    "\n",
+    "The graph below visualizes what the problem is and also suggests a solution: Instead of calculating the return value of the `fibonacci()` function for the *same* argument over and over again, it makes sense to **cache** the result and reuse it. This concept is called **[memoization <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_wiki.png\">](https://en.wikipedia.org/wiki/Memoization)**."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "<img src=\"static/fibonacci_call_graph.png\" width=\"50%\" align=\"left\">"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Below is revision of the recursive `fibonacci()` implementation that uses a **globally** defined `dict` object `memo` to store intermediate results and look them up.\n",
+    "\n",
+    "To be precise, the the revised `fibonacci()` first checks if the `i`th Fibonacci number has already been calculated before. If yes, it is in the `memo`. That number is then returned immediately *without* any more calculations. If no, there is no corresponding entry in the `memo` and a recursive function call must be made. The number obtained by recursion is then put into the `memo`.\n",
+    "\n",
+    "When we follow the flow of execution closely, we realize that the intermediate results represented by the left-most path in the graph above are calculated first. `fibonacci(1)` (i.e., the left-most leaf node) is the first base case reached, followed immediately by `fibonacci(0)`. From that moment onwards, the flow of execution moves back up the left-most path while adding together the two corresponding child nodes. Effectively, this mirrors the *iterative* implementation in that the order of all computational steps are *identical*.\n",
+    "\n",
+    "We added a keyword-only argument `debug` that allows the caller to print out a message every time a `i` was *not* in the `memo`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 144,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "memo = {\n",
+    "    0: 0,\n",
+    "    1: 1,\n",
+    "}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 145,
+   "metadata": {
+    "code_folding": [],
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "def fibonacci(i, *, debug=False):\n",
+    "    \"\"\"Calculate the ith Fibonacci number.\n",
+    "\n",
+    "    Args:\n",
+    "        i (int): index of the Fibonacci number to calculate\n",
+    "        debug (bool): show non-cached calls; defaults to False\n",
+    "\n",
+    "    Returns:\n",
+    "        ith_fibonacci (int)\n",
+    "    \"\"\"\n",
+    "    if i in memo:\n",
+    "        return memo[i]\n",
+    "\n",
+    "    if debug:  # added for didactical purposes\n",
+    "        print(f\"fibonacci({i}) is calculated\")\n",
+    "\n",
+    "    recurse = fibonacci(i - 1, debug=debug) + fibonacci(i - 2, debug=debug)\n",
+    "    memo[i] = recurse\n",
+    "    return recurse"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 146,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "fibonacci(12) is calculated\n",
+      "fibonacci(11) is calculated\n",
+      "fibonacci(10) is calculated\n",
+      "fibonacci(9) is calculated\n",
+      "fibonacci(8) is calculated\n",
+      "fibonacci(7) is calculated\n",
+      "fibonacci(6) is calculated\n",
+      "fibonacci(5) is calculated\n",
+      "fibonacci(4) is calculated\n",
+      "fibonacci(3) is calculated\n",
+      "fibonacci(2) is calculated\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "144"
+      ]
+     },
+     "execution_count": 146,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "fibonacci(12, debug=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Now, calling `fibonacci()` has the *side effect* of growing the `memo` in the *global scope*. So, subsequent calls to `fibonacci()` need not calculate any Fibonacci number with an index `i` smaller than the maximum `i` used so far. Because of that, this `fibonacci()` is *not* a *pure* function."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 147,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "144"
+      ]
+     },
+     "execution_count": 147,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "fibonacci(12, debug=True)  # no more recursive calls needed"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 148,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{0: 0,\n",
+       " 1: 1,\n",
+       " 2: 1,\n",
+       " 3: 2,\n",
+       " 4: 3,\n",
+       " 5: 5,\n",
+       " 6: 8,\n",
+       " 7: 13,\n",
+       " 8: 21,\n",
+       " 9: 34,\n",
+       " 10: 55,\n",
+       " 11: 89,\n",
+       " 12: 144}"
+      ]
+     },
+     "execution_count": 148,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "memo"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "##### Efficiency of Algorithms (continued)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "With memoization, the recursive `fibonacci()` implementation is as fast as its iterative counterpart, even for large numbers.\n",
+    "\n",
+    "The `%%timeit` magic, by default, runs a code cell seven times. Whereas in the first run, *new* Fibonacci numbers (i.e., intermediate results) are added to the `memo`, `fibonacci()` has no work to do in the subsequent six runs. `%%timeit` realizes this and tells us that \"an intermediate result is being cached.\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 149,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The slowest run took 249.03 times longer than the fastest. This could mean that an intermediate result is being cached.\n",
+      "6.11 µs ± 14.5 µs per loop (mean ± std. dev. of 7 runs, 1 loop each)\n"
+     ]
+    }
+   ],
+   "source": [
+    "%%timeit -n 1\n",
+    "fibonacci(99)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 150,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The slowest run took 3416.49 times longer than the fastest. This could mean that an intermediate result is being cached.\n",
+      "82.7 µs ± 202 µs per loop (mean ± std. dev. of 7 runs, 1 loop each)\n"
+     ]
+    }
+   ],
+   "source": [
+    "%%timeit -n 1\n",
+    "fibonacci(999)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "The iterative implementation still has an advantage as the `RecursionError` shows for even larger `i`.\n",
+    "\n",
+    "This exception occurs as Python must keep track of *every* function call *until* it has returned, and with large enough `i`, the recursion tree above grows too big. By default, Python has a limit of up to 3000 *simultaneous* function calls. So, theoretically this exception is not a bug in the narrow sense but the result of a \"security\" measure that is supposed to keep a computer from crashing. However, practically most high-level languages like Python incur such an overhead cost: It results from the fact that someone (i.e., Python) needs to manage each function call's *local scope*. With the `for`-loop in the iterative version, we do this managing \"manually.\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 151,
+   "metadata": {
+    "scrolled": true,
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "ename": "RecursionError",
+     "evalue": "maximum recursion depth exceeded",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mRecursionError\u001b[0m                            Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-151-db4be504bbfd>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mget_ipython\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrun_cell_magic\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'timeit'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'-n 1'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'fibonacci(9999)\\n'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;32m~/.pyenv/versions/anaconda3-2019.10/lib/python3.7/site-packages/IPython/core/interactiveshell.py\u001b[0m in \u001b[0;36mrun_cell_magic\u001b[0;34m(self, magic_name, line, cell)\u001b[0m\n\u001b[1;32m   2357\u001b[0m             \u001b[0;32mwith\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbuiltin_trap\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2358\u001b[0m                 \u001b[0margs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mmagic_arg_s\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcell\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2359\u001b[0;31m                 \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2360\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mresult\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2361\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m</home/webartifex/.pyenv/versions/anaconda3-2019.10/lib/python3.7/site-packages/decorator.py:decorator-gen-60>\u001b[0m in \u001b[0;36mtimeit\u001b[0;34m(self, line, cell, local_ns)\u001b[0m\n",
+      "\u001b[0;32m~/.pyenv/versions/anaconda3-2019.10/lib/python3.7/site-packages/IPython/core/magic.py\u001b[0m in \u001b[0;36m<lambda>\u001b[0;34m(f, *a, **k)\u001b[0m\n\u001b[1;32m    185\u001b[0m     \u001b[0;31m# but it's overkill for just that one bit of state.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    186\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mmagic_deco\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marg\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 187\u001b[0;31m         \u001b[0mcall\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mlambda\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mk\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mk\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    188\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    189\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mcallable\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marg\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/.pyenv/versions/anaconda3-2019.10/lib/python3.7/site-packages/IPython/core/magics/execution.py\u001b[0m in \u001b[0;36mtimeit\u001b[0;34m(self, line, cell, local_ns)\u001b[0m\n\u001b[1;32m   1160\u001b[0m                     \u001b[0;32mbreak\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1161\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1162\u001b[0;31m         \u001b[0mall_runs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtimer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrepeat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrepeat\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnumber\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1163\u001b[0m         \u001b[0mbest\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mall_runs\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m/\u001b[0m \u001b[0mnumber\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1164\u001b[0m         \u001b[0mworst\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmax\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mall_runs\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m/\u001b[0m \u001b[0mnumber\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/.pyenv/versions/anaconda3-2019.10/lib/python3.7/timeit.py\u001b[0m in \u001b[0;36mrepeat\u001b[0;34m(self, repeat, number)\u001b[0m\n\u001b[1;32m    202\u001b[0m         \u001b[0mr\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    203\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrepeat\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 204\u001b[0;31m             \u001b[0mt\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtimeit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnumber\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    205\u001b[0m             \u001b[0mr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mt\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    206\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mr\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/.pyenv/versions/anaconda3-2019.10/lib/python3.7/site-packages/IPython/core/magics/execution.py\u001b[0m in \u001b[0;36mtimeit\u001b[0;34m(self, number)\u001b[0m\n\u001b[1;32m    167\u001b[0m         \u001b[0mgc\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdisable\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    168\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 169\u001b[0;31m             \u001b[0mtiming\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0minner\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mit\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtimer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    170\u001b[0m         \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    171\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mgcold\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m<magic-timeit>\u001b[0m in \u001b[0;36minner\u001b[0;34m(_it, _timer)\u001b[0m\n",
+      "\u001b[0;32m<ipython-input-145-dd55d928f4d4>\u001b[0m in \u001b[0;36mfibonacci\u001b[0;34m(i, debug)\u001b[0m\n\u001b[1;32m     15\u001b[0m         \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34mf\"fibonacci({i}) is calculated\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     16\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 17\u001b[0;31m     \u001b[0mrecurse\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfibonacci\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mi\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdebug\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdebug\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mfibonacci\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mi\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0;36m2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdebug\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdebug\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     18\u001b[0m     \u001b[0mmemo\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mrecurse\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     19\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mrecurse\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "... last 1 frames repeated, from the frame below ...\n",
+      "\u001b[0;32m<ipython-input-145-dd55d928f4d4>\u001b[0m in \u001b[0;36mfibonacci\u001b[0;34m(i, debug)\u001b[0m\n\u001b[1;32m     15\u001b[0m         \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34mf\"fibonacci({i}) is calculated\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     16\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 17\u001b[0;31m     \u001b[0mrecurse\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfibonacci\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mi\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdebug\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdebug\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mfibonacci\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mi\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0;36m2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdebug\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdebug\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     18\u001b[0m     \u001b[0mmemo\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mrecurse\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     19\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mrecurse\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;31mRecursionError\u001b[0m: maximum recursion depth exceeded"
+     ]
+    }
+   ],
+   "source": [
+    "%%timeit -n 1\n",
+    "fibonacci(9999)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "We could \"hack\" a bit with Python's default configuration using the [sys <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/sys.html) module in the [standard library <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/index.html) and make it work. As we are good citizens, we reset everything to the defaults after our hack is completed."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 152,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "import sys"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 153,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "old_recursion_limit = sys.getrecursionlimit()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 154,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "3000"
+      ]
+     },
+     "execution_count": 154,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "old_recursion_limit"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 155,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "sys.setrecursionlimit(99999)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Computational speed is *not* the problem here."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 156,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The slowest run took 46787.39 times longer than the fastest. This could mean that an intermediate result is being cached.\n",
+      "1.12 ms ± 2.73 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n"
+     ]
+    }
+   ],
+   "source": [
+    "%%timeit -n 1\n",
+    "fibonacci(9999)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 157,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "sys.setrecursionlimit(old_recursion_limit)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "## Specialized Mappings"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "The [collections <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/collections.html) module in the [standard library <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/index.html) provides specialized mapping types for common use cases."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "### The `defaultdict` Type"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "The [defaultdict <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/collections.html#collections.defaultdict) type allows us to define a factory function that creates default values whenever we look up a key that does not yet exist. Ordinary `dict` objects would throw a `KeyError` exception in such situations.\n",
+    "\n",
+    "Let's say we have a `list` with *records* of goals scored during a soccer game. The records consist of the fields \"Country,\" \"Player,\" and the \"Time\" when a goal was scored. Our task is to group the goals by player and/or country."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 158,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "goals = [\n",
+    "    (\"Germany\", \"MĂĽller\", 11), (\"Germany\", \"Klose\", 23),\n",
+    "    (\"Germany\", \"Kroos\", 24), (\"Germany\", \"Kroos\", 26),\n",
+    "    (\"Germany\", \"Khedira\", 29), (\"Germany\", \"SchĂĽrrle\", 69),\n",
+    "    (\"Germany\", \"SchĂĽrrle\", 79), (\"Brazil\", \"Oscar\", 90),\n",
+    "]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Using a normal `dict` object, we have to tediously check if a player has already scored a goal before. If not, we must create a *new* `list` object with the first time the player scored. Otherwise, we append the goal to an already existing `list` object."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 159,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'MĂĽller': [11],\n",
+       " 'Klose': [23],\n",
+       " 'Kroos': [24, 26],\n",
+       " 'Khedira': [29],\n",
+       " 'SchĂĽrrle': [69, 79],\n",
+       " 'Oscar': [90]}"
+      ]
+     },
+     "execution_count": 159,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "goals_by_player = {}\n",
+    "\n",
+    "for _, player, minute in goals:\n",
+    "    if player not in goals_by_player:\n",
+    "        goals_by_player[player] = [minute]\n",
+    "    else:\n",
+    "        goals_by_player[player].append(minute)\n",
+    "\n",
+    "goals_by_player"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Instead, with a `defaultdict` object, we can portray the code fragment's intent in a concise form. We pass a reference to the [list() <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/functions.html#func-list) built-in to `defaultdict`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 160,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "from collections import defaultdict"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 161,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "defaultdict(list,\n",
+       "            {'MĂĽller': [11],\n",
+       "             'Klose': [23],\n",
+       "             'Kroos': [24, 26],\n",
+       "             'Khedira': [29],\n",
+       "             'SchĂĽrrle': [69, 79],\n",
+       "             'Oscar': [90]})"
+      ]
+     },
+     "execution_count": 161,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "goals_by_player = defaultdict(list)\n",
+    "\n",
+    "for _, player, minute in goals:\n",
+    "    goals_by_player[player].append(minute)\n",
+    "\n",
+    "goals_by_player"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 162,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "collections.defaultdict"
+      ]
+     },
+     "execution_count": 162,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "type(goals_by_player)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "The factory function is stored in the `default_factory` attribute."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 163,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "list"
+      ]
+     },
+     "execution_count": 163,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "goals_by_player.default_factory"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "If we want this code to produce a normal `dict` object, we pass `goals_by_player` to the [dict() <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/functions.html#func-dict) constructor."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 164,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'MĂĽller': [11],\n",
+       " 'Klose': [23],\n",
+       " 'Kroos': [24, 26],\n",
+       " 'Khedira': [29],\n",
+       " 'SchĂĽrrle': [69, 79],\n",
+       " 'Oscar': [90]}"
+      ]
+     },
+     "execution_count": 164,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "dict(goals_by_player)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Being creative, we use a factory function that returns another [defaultdict <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/collections.html#collections.defaultdict) with [list() <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/functions.html#func-list) as its factory to group on the country and the player level simultaneously."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 165,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "defaultdict(<function __main__.<lambda>()>,\n",
+       "            {'Germany': defaultdict(list,\n",
+       "                         {'MĂĽller': [11],\n",
+       "                          'Klose': [23],\n",
+       "                          'Kroos': [24, 26],\n",
+       "                          'Khedira': [29],\n",
+       "                          'SchĂĽrrle': [69, 79]}),\n",
+       "             'Brazil': defaultdict(list, {'Oscar': [90]})})"
+      ]
+     },
+     "execution_count": 165,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "goals_by_country_and_player = defaultdict(lambda: defaultdict(list))\n",
+    "\n",
+    "for country, player, minute in goals:\n",
+    "    goals_by_country_and_player[country][player].append(minute)\n",
+    "\n",
+    "goals_by_country_and_player"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Conversion into a normal and nested `dict` object is now a bit tricky but can be achieved in one line with a comprehension."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 166,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{'Brazil': {'Oscar': [90]},\n",
+      " 'Germany': {'Khedira': [29],\n",
+      "             'Klose': [23],\n",
+      "             'Kroos': [24, 26],\n",
+      "             'MĂĽller': [11],\n",
+      "             'SchĂĽrrle': [69, 79]}}\n"
+     ]
+    }
+   ],
+   "source": [
+    "pprint({country: dict(by_player) for country, by_player in goals_by_country_and_player.items()})"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "### The `Counter` Type"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "A common task is to count the number of occurrences of elements in an iterable.\n",
+    "\n",
+    "The [Counter <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/collections.html#collections.Counter) type provides an easy-to-use interface that can be called with any iterable and returns a `dict`-like object of type `Counter` that maps each unique elements to the number of times it occurs.\n",
+    "\n",
+    "To continue the previous example, let's create an overview that shows how many goals a player scorred. We use a generator expression as the argument to `Counter`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 167,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[('Germany', 'MĂĽller', 11),\n",
+       " ('Germany', 'Klose', 23),\n",
+       " ('Germany', 'Kroos', 24),\n",
+       " ('Germany', 'Kroos', 26),\n",
+       " ('Germany', 'Khedira', 29),\n",
+       " ('Germany', 'SchĂĽrrle', 69),\n",
+       " ('Germany', 'SchĂĽrrle', 79),\n",
+       " ('Brazil', 'Oscar', 90)]"
+      ]
+     },
+     "execution_count": 167,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "goals"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 168,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "from collections import Counter"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 169,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "scorers = Counter(x[1] for x in goals)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 170,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Counter({'MĂĽller': 1,\n",
+       "         'Klose': 1,\n",
+       "         'Kroos': 2,\n",
+       "         'Khedira': 1,\n",
+       "         'SchĂĽrrle': 2,\n",
+       "         'Oscar': 1})"
+      ]
+     },
+     "execution_count": 170,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "scorers"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 171,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "collections.Counter"
+      ]
+     },
+     "execution_count": 171,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "type(scorers)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Now we can look up individual players. `scores` behaves like a normal dictionary with regard to key look-ups."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 172,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "1"
+      ]
+     },
+     "execution_count": 172,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "scorers[\"MĂĽller\"]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "By default, it returns `0` if a key is not found. So, we do not have to handle a `KeyError`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 173,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0"
+      ]
+     },
+     "execution_count": 173,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "scorers[\"Lahm\"]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "`Counter` objects have a [most_common() <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/collections.html#collections.Counter.most_common) method that returns a `list` object containing $2$-element `tuple` objects, where the first element is the element from the original iterable and the second the number of occurrences. The `list` object is sorted in descending order of occurrences."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 174,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[('Kroos', 2), ('SchĂĽrrle', 2)]"
+      ]
+     },
+     "execution_count": 174,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "scorers.most_common(2)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "We can increase the count of individual entries with the [update() <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/collections.html#collections.Counter.update) method: That takes an *iterable* of the elements we want to count.\n",
+    "\n",
+    "Imagine if [Philipp Lahm <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_wiki.png\">](https://en.wikipedia.org/wiki/Philipp_Lahm) had also scored against Brazil."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 175,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "scorers.update([\"Lahm\"])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 176,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Counter({'MĂĽller': 1,\n",
+       "         'Klose': 1,\n",
+       "         'Kroos': 2,\n",
+       "         'Khedira': 1,\n",
+       "         'SchĂĽrrle': 2,\n",
+       "         'Oscar': 1,\n",
+       "         'Lahm': 1})"
+      ]
+     },
+     "execution_count": 176,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "scorers"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "If we use a `str` object as the argument instead, each individual character is treated as an element to be updated. That is most likely not what we want."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 177,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "scorers.update(\"Lahm\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 178,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Counter({'MĂĽller': 1,\n",
+       "         'Klose': 1,\n",
+       "         'Kroos': 2,\n",
+       "         'Khedira': 1,\n",
+       "         'SchĂĽrrle': 2,\n",
+       "         'Oscar': 1,\n",
+       "         'Lahm': 1,\n",
+       "         'L': 1,\n",
+       "         'a': 1,\n",
+       "         'h': 1,\n",
+       "         'm': 1})"
+      ]
+     },
+     "execution_count": 178,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "scorers"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "### The `ChainMap` Type"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Consider `to_words`, `more_words`, and `even_more_words` below. Instead of merging the items of the three `dict` objects together into a *new* one, we want to create an object that behaves as if it contained all the unified items in it without materializing them in memory a second time."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 179,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "to_words = {\n",
+    "    0: \"zero\",\n",
+    "    1: \"one\",\n",
+    "    2: \"two\",\n",
+    "}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 180,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "more_words = {\n",
+    "    2: \"TWO\",  # to illustrate a point\n",
+    "    3: \"three\",\n",
+    "    4: \"four\",\n",
+    "}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 181,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "even_more_words = {\n",
+    "    4: \"FOUR\",  # to illustrate a point\n",
+    "    5: \"five\",\n",
+    "    6: \"six\",\n",
+    "}"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "The [ChainMap <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/collections.html#collections.ChainMap) type allows us to do precisely that."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 182,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "from collections import ChainMap"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "We simply pass all mappings as positional arguments to `ChainMap` and obtain a **proxy** object that occupies almost no memory but gives us access to the union of all the items."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 183,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "chain = ChainMap(to_words, more_words, even_more_words)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Let's loop over the items in `chain` and see what is \"in\" it. The order is obviously *unpredictable* but all seven items we expected are there. Keys of later mappings do *not* overwrite earlier keys."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 184,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "4 four\n",
+      "5 five\n",
+      "6 six\n",
+      "2 two\n",
+      "3 three\n",
+      "0 zero\n",
+      "1 one\n"
+     ]
+    }
+   ],
+   "source": [
+    "for number, word in chain.items():\n",
+    "    print(number, word)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "When looking up a non-existent key, `ChainMap` objects raise a `KeyError` just like normal `dict` objects would."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 185,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "ename": "KeyError",
+     "evalue": "10",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-185-25fb5b5b2e5f>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mchain\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m10\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;32m~/.pyenv/versions/anaconda3-2019.10/lib/python3.7/collections/__init__.py\u001b[0m in \u001b[0;36m__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m    914\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    915\u001b[0m                 \u001b[0;32mpass\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 916\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__missing__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m            \u001b[0;31m# support subclasses that define __missing__\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    917\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    918\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdefault\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/.pyenv/versions/anaconda3-2019.10/lib/python3.7/collections/__init__.py\u001b[0m in \u001b[0;36m__missing__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m    906\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    907\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__missing__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 908\u001b[0;31m         \u001b[0;32mraise\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    909\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    910\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__getitem__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;31mKeyError\u001b[0m: 10"
+     ]
+    }
+   ],
+   "source": [
+    "chain[10]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "## The `set` Type"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Python's provides a built-in `set` type that resembles [mathematical sets <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_wiki.png\">](https://en.wikipedia.org/wiki/Set_%28mathematics%29): Each element may only be a **member** of a set once, and there is no *predictable* order regarding the elements (cf., [documentation <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/stdtypes.html#set)).\n",
+    "\n",
+    "To create a set, we use the literal notation, `{..., ...}`, and list all the members. The redundant `7`s and `4`s below are discarded."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 186,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "numbers = {7, 7, 7, 7, 7, 11, 8, 5, 3, 12, 2, 6, 9, 10, 1, 4, 4, 4, 4, 4}"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "`set` objects are objects on their own."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 187,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "140024257046960"
+      ]
+     },
+     "execution_count": 187,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "id(numbers)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 188,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "set"
+      ]
+     },
+     "execution_count": 188,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "type(numbers)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 189,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12}"
+      ]
+     },
+     "execution_count": 189,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "numbers"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "To create an empty set, we must use the built-in [set() <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/functions.html#func-set) constructor as empty curly brackets, `{}`, already create an empty `dict` object."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 190,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "empty_dict = {}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 191,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "dict"
+      ]
+     },
+     "execution_count": 191,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "type(empty_dict)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 192,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "empty_set = set()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 193,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "set()"
+      ]
+     },
+     "execution_count": 193,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "empty_set"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "The [set() <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/functions.html#func-set) constructor takes any `iterable` and only keeps unique elements.\n",
+    "\n",
+    "For example, we obtain all unique letters of a long word like so."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 194,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'a', 'b', 'c', 'd', 'r'}"
+      ]
+     },
+     "execution_count": 194,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "set(\"abracadabra\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "### Sets are like Dictionaries without Values"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "The curly brackets notation can be viewed as a hint that `dict` objects are conceptually generalizations of `set`  objects, and we think of `set` objects as a collection consisting of a `dict` object's keys with all the mapped values removed.\n",
+    "\n",
+    "Like `dict` objects, `set` objects are built on top of [hash tables <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_wiki.png\">](https://en.wikipedia.org/wiki/Hash_table), and, thus, each element must be a *hashable* (i.e., immutable) object and can only be contained in a set once due to the buckets logic."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 195,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "ename": "TypeError",
+     "evalue": "unhashable type: 'list'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-195-4ac30fae8196>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0;34m{\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m2\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m3\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;31mTypeError\u001b[0m: unhashable type: 'list'"
+     ]
+    }
+   ],
+   "source": [
+    "{0, [1, 2], 3}"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "[len() <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/functions.html#len) tells us the number of elements in a `set` object."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 196,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "12"
+      ]
+     },
+     "execution_count": 196,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "len(numbers)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "We may loop over the elements in a `set` object, but we must keep in mind that there is no *predictable* order. In contrast to `dict` objects, the iteration order is also *not* guaranteed to be the insertion order. Because of the special hash values for `int` objects, `numbers` seems to be \"magically\" sorted, which is *not* the case."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 197,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "1 2 3 4 5 6 7 8 9 10 11 12 "
+     ]
+    }
+   ],
+   "source": [
+    "for number in numbers:  # beware the non-order!\n",
+    "    print(number, end=\" \")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "We confirm that `set` objects are not `Reversible`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 198,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "ename": "TypeError",
+     "evalue": "'set' object is not reversible",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-198-1095d1ef6c80>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0;32mfor\u001b[0m \u001b[0mnumber\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mreversed\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnumbers\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m     \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnumber\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mend\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\" \"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;31mTypeError\u001b[0m: 'set' object is not reversible"
+     ]
+    }
+   ],
+   "source": [
+    "for number in reversed(numbers):\n",
+    "    print(number, end=\" \")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "The boolean `in` operator checks if a given and immutable object compares equal to an element in a `set` object. As with `dict` objects, membership testing is an *extremely* fast operation. Conceptually, it has the same result as conducting a linear search with the `==` operator behind the scenes without doing it."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 199,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "False"
+      ]
+     },
+     "execution_count": 199,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "0 in numbers"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 200,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 200,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "1 in numbers"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 201,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 201,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "2.0 in numbers  # 2.0 is not a member itself but compares equal to a member"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "There is are `Set` ABC in the [collections.abc <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/collections.abc.html) module that formalizes, in particular, the operators supported by `set` objects (cf., the \"*Set Operations*\" section below). Further, the `MutableSet` ABC standardizes all the methods that mutate a `set` object in place (cf., the \"*Mutability & Set Methods*\" section below)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 202,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 202,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "isinstance(numbers, abc.Set)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 203,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 203,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "isinstance(numbers, abc.MutableSet)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "### No Indexing, Key Look-up, or Slicing"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "As `set` objects come without a *predictable* order, indexing and slicing are not supported and result in a `TypeError`. In particular, as there are no values to be looked up, these operations are not *semantically* meaningful. Instead, we check membership via the `in` operator, as shown in the previous sub-section."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 204,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12}"
+      ]
+     },
+     "execution_count": 204,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "numbers"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 205,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "ename": "TypeError",
+     "evalue": "'set' object is not subscriptable",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-205-7486ea9d37bc>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mnumbers\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;31mTypeError\u001b[0m: 'set' object is not subscriptable"
+     ]
+    }
+   ],
+   "source": [
+    "numbers[0]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 206,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "ename": "TypeError",
+     "evalue": "'set' object is not subscriptable",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-206-4de4c0f84e99>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mnumbers\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;31mTypeError\u001b[0m: 'set' object is not subscriptable"
+     ]
+    }
+   ],
+   "source": [
+    "numbers[:]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "### Mutability & Set Methods"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Because the `[]` operator does not work for `set` objects, they are mutated mainly via methods (cf., [documentation <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/stdtypes.html#set)).\n",
+    "\n",
+    "We may add new elements to an existing `set` object with the [add() <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/stdtypes.html#frozenset.add) method."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 207,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "numbers.add(999)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 208,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 999}"
+      ]
+     },
+     "execution_count": 208,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "numbers"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "The [update() <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/stdtypes.html#frozenset.update) method takes an iterable and adds all its elements to a `set` object if they are not already contained in it."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 209,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "numbers.update(range(5))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 210,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 999}"
+      ]
+     },
+     "execution_count": 210,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "numbers"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "To remove an element by value, we use the [remove() <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/stdtypes.html#frozenset.remove) or [discard() <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/stdtypes.html#frozenset.discard) methods. If the element to be removed is not in the `set` object, [remove() <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/stdtypes.html#frozenset.remove) raises a loud `KeyError` while [discard() <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/stdtypes.html#frozenset.discard) stays *silent*."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 211,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "numbers.remove(999)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 212,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "ename": "KeyError",
+     "evalue": "999",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-212-45d8b57b937d>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mnumbers\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mremove\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m999\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;31mKeyError\u001b[0m: 999"
+     ]
+    }
+   ],
+   "source": [
+    "numbers.remove(999)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 213,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "numbers.discard(0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 214,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "numbers.discard(0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 215,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12}"
+      ]
+     },
+     "execution_count": 215,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "numbers"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "The [pop() <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/stdtypes.html#frozenset.pop) method removes an *arbitrary* element. As not even the insertion order is tracked, that removes a \"random\" element."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 216,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "1"
+      ]
+     },
+     "execution_count": 216,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "numbers.pop()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 217,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12}"
+      ]
+     },
+     "execution_count": 217,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "numbers"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "The [clear() <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/stdtypes.html#frozenset.clear) method discards all elements but keeps the `set` object alive."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 218,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "numbers.clear()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 219,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "set()"
+      ]
+     },
+     "execution_count": 219,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "numbers"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 220,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "140024257046960"
+      ]
+     },
+     "execution_count": 220,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "id(numbers)  # same memory location as before"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "### Set Operations"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "The arithmetic and relational operators are overloaded with typical set operations from math. The operators have methods that do the equivalent. We omit them for brevity in this sub-section and only show them as comments in the code cells. Both the operators and the methods return *new* `set` objects without modifying the operands.\n",
+    "\n",
+    "We showcase the set operations with easy math examples."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 221,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "numbers = set(range(1, 13))\n",
+    "zero = {0}\n",
+    "evens = set(range(2, 13, 2))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 222,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12}"
+      ]
+     },
+     "execution_count": 222,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "numbers"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 223,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{0}"
+      ]
+     },
+     "execution_count": 223,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "zero"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 224,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{2, 4, 6, 8, 10, 12}"
+      ]
+     },
+     "execution_count": 224,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "evens"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "The bitwise OR operator `|` returns the union of two `set` objects."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 225,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12}"
+      ]
+     },
+     "execution_count": 225,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "zero | numbers  # zero.union(numbers)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Of course, the operators may be *chained*."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 226,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12}"
+      ]
+     },
+     "execution_count": 226,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "zero | numbers | evens  # zero.union(numbers).union(evens)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "To obtain an intersection of two or more `set` objects, we use the bitwise AND operator `&`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 227,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "set()"
+      ]
+     },
+     "execution_count": 227,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "zero & numbers  #  zero.intersection(numbers)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 228,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{2, 4, 6, 8, 10, 12}"
+      ]
+     },
+     "execution_count": 228,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "numbers & evens  # numbers.intersection(evens)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "To calculate a `set` object containing all elements that are in one but not the other `set` object, we use the minus operator `-`. This operation is *not* symmetric."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 229,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{1, 3, 5, 7, 9, 11}"
+      ]
+     },
+     "execution_count": 229,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "numbers - evens  # numbers.difference(evens)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 230,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "set()"
+      ]
+     },
+     "execution_count": 230,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "evens - numbers  # evens.difference(numbers)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "The *symmetric* difference is defined as the `set` object containing all elements that are in one but not both `set` objects. It is calculated with the bitwise XOR operator `^`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 231,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{1, 3, 5, 7, 9, 11}"
+      ]
+     },
+     "execution_count": 231,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "numbers ^ evens  # numbers.symmetric_difference(evens)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 232,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{1, 3, 5, 7, 9, 11}"
+      ]
+     },
+     "execution_count": 232,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "evens ^ numbers  # evens.symmetric_difference(numbers)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "The augmented versions of the four operators (e.g., `|` becomes `|=`) are also defined: They mutate the left operand *in place*."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "### Set Comprehensions"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Python provides a literal notation for **set comprehensions**, or **setcomps** for short, that works exactly like the one for dictionary comprehensions described above except that they use a single loop variable instead of a key-value pair.\n",
+    "\n",
+    "For example, let's create a new `set` object that consists of the squares of all the elements of `numbers`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 233,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{1, 4, 9, 16, 25, 36, 49, 64, 81, 100, 121, 144}"
+      ]
+     },
+     "execution_count": 233,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "{x ** 2 for x in numbers}"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "As before, we may have multiple `for`- and/or `if`-clauses.\n",
+    "\n",
+    "For example, let's only keep the squares if they turn out to be an even number, or ..."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 234,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{4, 16, 36, 64, 100, 144}"
+      ]
+     },
+     "execution_count": 234,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "{x ** 2 for x in numbers if (x ** 2) % 2 == 0}"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "... create a `set` object with all products obtained from the Cartesian product of `numbers` with itself as long as the products are greater than `80`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 235,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{81, 84, 88, 90, 96, 99, 100, 108, 110, 120, 121, 132, 144}"
+      ]
+     },
+     "execution_count": 235,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "{x * y for x in numbers for y in numbers if x * y > 80}"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "### The `frozenset` Type"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "As `set` objects are mutable, they may *not* be used, for example, as keys in a `dict` object. Similar to how we replace `list` with `tuple` objects, we may often use a `frozenset` object instead of an ordinary one. The `frozenset` type is a built-in, and as `frozenset` objects are immutable, the only limitation is that we must specify *all* elements *upon* creation (cf., [documentation <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/stdtypes.html#frozenset)).\n",
+    "\n",
+    "`frozenset` objects are created by passing an iterable to the built-in [frozenset() <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/functions.html#func-frozenset) constructor. Even though `frozenset` objects are hashable, their elements are *not* ordered."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 236,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "frozenset({1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12})"
+      ]
+     },
+     "execution_count": 236,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "frozenset([7, 7, 7, 7, 7, 11, 8, 5, 3, 12, 2, 6, 9, 10, 1, 4, 4, 4, 4, 4])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "## TL;DR"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "`dict` objects are **mutable** one-to-one **mappings** from a set of **key** objects to a set of **value** objects. The association between a key-value pair is also called **item**.\n",
+    "\n",
+    "The items contained in a `dict` object have **no order** that is *predictable*.\n",
+    "\n",
+    "The underlying **data structure** of the `dict` type are **hash tables**. They make key look-ups *extremely* fast by converting the items' keys into *deterministic* hash values specifiying *precisely* one of a fixed number of equally \"wide\" buckets in which an item's references are stored. A limitation is that objects used as keys must be *immutable* (for technical reasons) and *unique* (for practical reasons).\n",
+    "\n",
+    "A `set` object is a **mutable** and **unordered collection** of **immutable** objects. The `set` type mimics sets we know from math."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "## Further Resources"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "A lecture-style **video presentation** of this chapter is integrated below (cf., the [video <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_yt.png\">](https://www.youtube.com/watch?v=vbp0svA35TE&list=PL-2JV1G3J10lQ2xokyQowcRJI5jjNfW7f) or the entire [playlist <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_yt.png\">](https://www.youtube.com/playlist?list=PL-2JV1G3J10lQ2xokyQowcRJI5jjNfW7f))."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 237,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/jpeg": 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+       "            width=\"60%\"\n",
+       "            height=\"300\"\n",
+       "            src=\"https://www.youtube.com/embed/vbp0svA35TE\"\n",
+       "            frameborder=\"0\"\n",
+       "            allowfullscreen\n",
+       "        ></iframe>\n",
+       "        "
+      ],
+      "text/plain": [
+       "<IPython.lib.display.YouTubeVideo at 0x7f59bd0c3850>"
+      ]
+     },
+     "execution_count": 237,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from IPython.display import YouTubeVideo\n",
+    "YouTubeVideo(\"vbp0svA35TE\", width=\"60%\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Next, we list some conference talks that go into great detail regarding the workings of the `dict` type.\n",
+    "\n",
+    "[Brandon Rhodes](https://github.com/brandon-rhodes) explains in great detail in his PyCon 2010 & 2017 talks how dictionaries work \"under the hood.\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 238,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/jpeg": 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+      ],
+      "text/plain": [
+       "<IPython.lib.display.YouTubeVideo at 0x7f59bd7ba8d0>"
+      ]
+     },
+     "execution_count": 238,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "YouTubeVideo(\"C4Kc8xzcA68\", width=\"60%\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 239,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/jpeg": 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\n",
+      "text/html": [
+       "\n",
+       "        <iframe\n",
+       "            width=\"60%\"\n",
+       "            height=\"300\"\n",
+       "            src=\"https://www.youtube.com/embed/66P5FMkWoVU\"\n",
+       "            frameborder=\"0\"\n",
+       "            allowfullscreen\n",
+       "        ></iframe>\n",
+       "        "
+      ],
+      "text/plain": [
+       "<IPython.lib.display.YouTubeVideo at 0x7f59f00d92d0>"
+      ]
+     },
+     "execution_count": 239,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "YouTubeVideo(\"66P5FMkWoVU\", width=\"60%\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "The `dict` type's order has been worked on with many PEPs in recent years:\n",
+    "- [PEP 412 <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://www.python.org/dev/peps/pep-0412/): Key-Sharing Dictionary\n",
+    "- [PEP 468 <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://www.python.org/dev/peps/pep-0468/): Preserving the order of \\*\\*kwargs in a function\n",
+    "- [PEP 520 <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://www.python.org/dev/peps/pep-0520/): Preserving Class Attribute Definition Order\n",
+    "\n",
+    "[Raymond Hettinger](https://github.com/rhettinger), a Python core developer and one of the greatest Python teachers in the world, summaries the history of the `dict` type in his PyCon 2017 talk."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 240,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/jpeg": 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diff --git a/09_mappings_01_review.ipynb b/09_mappings_01_review.ipynb
new file mode 100644
index 0000000..b61463a
--- /dev/null
+++ b/09_mappings_01_review.ipynb
@@ -0,0 +1,255 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Chapter 9: Mappings & Sets"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Content Review"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The questions below assume that you have read [Chapter 9 <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/09_mappings_00_content.ipynb) in the book.\n",
+    "\n",
+    "Be concise in your answers! Most questions can be answered in *one* sentence."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Essay Questions "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q1**: `dict` objects are well-suited **to model** discrete mathematical **functions** and to approximate continuous ones. What property of dictionaries is the basis for that claim, and how does it relate to functions in the mathematical sense?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " < your answer >"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q2**: Explain why **hash tables** are  a **trade-off** between **computational speed** and **memory** usage!"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " < your answer >"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q3:** The `dict` type is an **iterable** that **contains** a **finite** number of key-value pairs. Despite that, why is it *not* considered a **sequence**?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " < your answer >"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q4**: Whereas *key* **look-ups** in a `dict` object run in so-called **[constant time <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_wiki.png\">](https://en.wikipedia.org/wiki/Time_complexity#Constant_time)** (i.e., *extremely* fast), that does not hold for *reverse* look-ups. Why is that?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " < your answer >"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q5**: Why is it conceptually correct that the Python core developers do not implement **slicing** with the `[]` operator for `dict` objects?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " < your answer >"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q6**: **Memoization** is an essential concept to know to solve problems in the real world. Together with the idea of **recursion**, it enables us to solve problems in a \"backwards\" fashion *effectively*.\n",
+    "\n",
+    "\n",
+    "Compare the **recursive** formulation of `fibonacci()` in [Chapter 8 <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/08_mappings_00_content.ipynb#\"Easy-at-third-Glance\"-Example:-Fibonacci-Numbers-%28revisited%29), the \"*Easy at second Glance*\" example, with the **iterative** version in [Chapter 4 <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/04_iteration_00_content.ipynb#\"Hard-at-first-Glance\"-Example:-Fibonacci-Numbers-%28revisited%29), the \"*Hard at first Glance*\" example!\n",
+    "\n",
+    "How are they similar and how do they differ?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " < your answer >"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q7**: How are the `set` and the `dict` type related? How could we use the latter to mimic the former?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " < your answer >"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### True / False Questions"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Motivate your answer with *one short* sentence!"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q8**: We may *not* put `dict` objects inside other `dict` objects because they are **mutable**."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " < your answer >"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q9**: **Mutable** objects (e.g., `list`) may generally *not* be used as keys in a `dict` object. However, if we collect, for example, `list` objects in a `tuple` object, the composite object becomes **hashable**."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " < your answer >"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q10**: **Mutability** of a `dict` object works until the underlying hash table becomes too crowded. Then, we cannot insert any items any more making the `dict` object effectively **immutable**. Luckily, that almost never happens in practice."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " < your answer >"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q11**: A `dict` object's [update() <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/stdtypes.html#dict.update) method only inserts key-value pairs whose key is *not* yet in the `dict` object. So, it does *not* overwrite anything."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " < your answer >"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q12**: The `set` type is both a mapping and a sequence."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " < your answer >"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.4"
+  },
+  "toc": {
+   "base_numbering": 1,
+   "nav_menu": {},
+   "number_sections": false,
+   "sideBar": true,
+   "skip_h1_title": true,
+   "title_cell": "Table of Contents",
+   "title_sidebar": "Contents",
+   "toc_cell": false,
+   "toc_position": {},
+   "toc_section_display": false,
+   "toc_window_display": false
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/09_mappings_02_exercises.ipynb b/09_mappings_02_exercises.ipynb
new file mode 100644
index 0000000..fa9e659
--- /dev/null
+++ b/09_mappings_02_exercises.ipynb
@@ -0,0 +1,631 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Important**: Click on \"*Kernel*\" > \"*Restart Kernel and Run All*\" *after* finishing the exercises in [JupyterLab <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_jp.png\">](https://jupyterlab.readthedocs.io/en/stable/) (e.g., in the cloud on [MyBinder <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_mb.png\">](https://mybinder.org/v2/gh/webartifex/intro-to-python/master?urlpath=lab/tree/09_mappings_02_exercises.ipynb)) to ensure that your solution runs top to bottom *without* any errors"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Chapter 9: Mappings & Sets"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Coding Exercises"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The exercises below assume that you have read [Chapter 9 <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/09_mappings_00_content.ipynb) in the book.\n",
+    "\n",
+    "The `...`'s in the code cells indicate where you need to fill in code snippets. The number of `...`'s within a code cell give you a rough idea of how many lines of code are needed to solve the task. You should not need to create any additional code cells for your final solution. However, you may want to use temporary code cells to try out some ideas."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Working with Nested Data"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Let's write some code to analyze the historic soccer game [Brazil vs. Germany <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_wiki.png\">](https://en.wikipedia.org/wiki/Brazil_v_Germany_%282014_FIFA_World_Cup%29) during the 2014 World Cup.\n",
+    "\n",
+    "Below, `players` consists of two nested `dict` objects, one for each team, that hold `tuple` objects (i.e., records) with information on the players. Besides the jersey number, name, and position, each `tuple` objects contains a `list` object with the times when the player scored."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "players = {\n",
+    "    \"Brazil\": [\n",
+    "        (12, \"JĂşlio CĂ©sar\", \"Goalkeeper\", []),\n",
+    "        (4, \"David Luiz\", \"Defender\", []),\n",
+    "        (6, \"Marcelo\", \"Defender\", []),\n",
+    "        (13, \"Dante\", \"Defender\", []),\n",
+    "        (23, \"Maicon\", \"Defender\", []),\n",
+    "        (5, \"Fernandinho\", \"Midfielder\", []),\n",
+    "        (7, \"Hulk\", \"Midfielder\", []),\n",
+    "        (8, \"Paulinho\", \"Midfielder\", []),\n",
+    "        (11, \"Oscar\", \"Midfielder\", [90]),\n",
+    "        (16, \"Ramires\", \"Midfielder\", []),\n",
+    "        (17, \"Luiz Gustavo\", \"Midfielder\", []),\n",
+    "        (19, \"Willian\", \"Midfielder\", []),\n",
+    "        (9, \"Fred\", \"Striker\", []),\n",
+    "    ],\n",
+    "    \"Germany\": [\n",
+    "        (1, \"Manuel Neuer\", \"Goalkeeper\", []),\n",
+    "        (4, \"Benedikt Höwedes\", \"Defender\", []),\n",
+    "        (5, \"Mats Hummels\", \"Defender\", []),\n",
+    "        (16, \"Philipp Lahm\", \"Defender\", []),\n",
+    "        (17, \"Per Mertesacker\", \"Defender\", []),\n",
+    "        (20, \"JĂ©rĂ´me Boateng\", \"Defender\", []),\n",
+    "        (6, \"Sami Khedira\", \"Midfielder\", [29]),\n",
+    "        (7, \"Bastian Schweinsteiger\", \"Midfielder\", []),\n",
+    "        (8, \"Mesut Ă–zil\", \"Midfielder\", []),\n",
+    "        (13, \"Thomas MĂĽller\", \"Midfielder\", [11]),\n",
+    "        (14, \"Julian Draxler\", \"Midfielder\", []),\n",
+    "        (18, \"Toni Kroos\", \"Midfielder\", [24, 26]),\n",
+    "        (9, \"André Schürrle\", \"Striker\", [69, 79]),\n",
+    "        (11, \"Miroslav Klose\", \"Striker\", [23]),\n",
+    "    ],\n",
+    "}"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q1.1**: Write a dictionary comprehension to derive a new `dict` object, called `brazilian_players`, that maps a Brazilian player's name to his position!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "brazilian_players = {...: ...}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "brazilian_players"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q1.2**: Generalize the code fragment into a `get_players()` function: Passed a `team` name, it returns a `dict` object like `brazilian_players`. Verify that the function works for the German team as well!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def get_players(team):\n",
+    "    \"\"\"Creates a dictionary mapping the players' names to their position.\"\"\"\n",
+    "    return {...: ...}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "get_players(\"Germany\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Often, we are given a `dict` object like the one returned from `get_players()`: Its main characteristic is that it maps a large set of unique keys (i.e., the players' names) onto a smaller set of non-unique values (i.e., the positions).\n",
+    "\n",
+    "**Q1.3**: Create a generic `invert()` function that swaps the keys and values of a `mapping` argument passed to it and returns them in a *new* `dict` object! Ensure that *no* key gets lost! Verify your implementation with the `brazilian_players` dictionary!\n",
+    "\n",
+    "Hints: Think of this as a grouping operation. The *new* values are `list` or `tuple` objects that hold the original keys. You may want to use either the [defaultdict <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/collections.html#collections.defaultdict) type from the [collections <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/collections.html) module in the [standard library <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/index.html) or the [setdefault() <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/stdtypes.html#dict.setdefault) method on the ordinary `dict` type."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def invert(mapping):\n",
+    "    \"\"\"Invert the keys and values of a mapping argument.\"\"\"\n",
+    "    ...\n",
+    "    ...\n",
+    "    ...\n",
+    "    return ..."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "invert(brazilian_players)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q1.4**: Write a `score_at_minute()` function: It takes two arguments, `team` and `minute`, and returns the number of goals the `team` has scored up until this time in the game.\n",
+    "\n",
+    "Hints: The function may reference the global `players` for simplicity. Earn bonus points if you can write this in a one-line expression using some *reduction* function and a *generator expression*."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def score_at_minute(team, minute):\n",
+    "    \"\"\"Determine the number of goals scored by a team until a given minute.\"\"\"\n",
+    "    ...\n",
+    "    ...\n",
+    "    ...\n",
+    "    ...\n",
+    "    ...\n",
+    "    return ..."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The score at half time was:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "score_at_minute(\"Brazil\", 45)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "score_at_minute(\"Germany\", 45)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The final score was:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "score_at_minute(\"Brazil\", 90)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "score_at_minute(\"Germany\", 90)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q1.5**: Write a `goals_by_player()` function that takes an argument like the global `players`, and returns a `dict` object mapping the players to the number of goals they scored!\n",
+    "\n",
+    "Hints: Do *not* \"hard code\" the names of the teams! Earn bonus points if you can solve it in a one-line dictionary comprehension."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def goals_by_player(players):\n",
+    "    \"\"\"Create a dictionary mapping the players' names to the number of goals.\"\"\"\n",
+    "    ...\n",
+    "    ...\n",
+    "    ...\n",
+    "    ...\n",
+    "    return ..."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "goals_by_player(players)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q1.6**: Write a *dictionary comprehension* to filter out the players who did *not* score from the preceding result. Then, write a *set comprehension* that does the same but discards the number of goals scored.\n",
+    "\n",
+    "Hints: Reference the `goals_by_player()` function from before."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "{...: ...}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "{...}"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q1.7**: Write a `all_goals()` function that takes one argument like the global `players` and returns a `list` object containing $2$-element `tuple` objects where the first element is the minute a player scored and the second his name! The list should be sorted by the time.\n",
+    "\n",
+    "Hints: You may want to use either the built-in [sorted() <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/functions.html#sorted) function or the `list` type's [sort() <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/stdtypes.html#list.sort) method. Earn bonus points if you can write a one-line expression with a *generator expression*."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def all_goals(players):\n",
+    "    \"\"\"Create a time table of the individual goals.\"\"\"\n",
+    "    ...\n",
+    "    ...\n",
+    "    ...\n",
+    "    ...\n",
+    "    ...\n",
+    "    return ..."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "all_goals(players)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q1.8**: Lastly, write a `summary()` function that takes one argument like the global `players` and prints out a concise report of the goals, the score at the half, and the final result.\n",
+    "\n",
+    "Hints: Use the `all_goals()` and `score_at_minute()` functions from before.\n",
+    "\n",
+    "The output should look similar to this:\n",
+    "```\n",
+    "12' Gerd MĂĽller scores\n",
+    "...\n",
+    "HALFTIME: TeamA 1 TeamB 2\n",
+    "77' Ronaldo scores\n",
+    "...\n",
+    "FINAL: TeamA 1 TeamB 3\n",
+    "```"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def summary(players):\n",
+    "    \"\"\"Create a written summary of the game.\"\"\"\n",
+    "    # Create two lists with the goals of either half.\n",
+    "    ...\n",
+    "    ...\n",
+    "    ...\n",
+    "\n",
+    "    # Print the goals of the first half.\n",
+    "    ...\n",
+    "    ...\n",
+    "\n",
+    "    # Print the half time score.\n",
+    "    ...\n",
+    "    ...\n",
+    "    ...\n",
+    "    ...\n",
+    "    ...\n",
+    "\n",
+    "    # Print the goals of the second half.\n",
+    "    ...\n",
+    "    ...\n",
+    "\n",
+    "    # Print the final score.\n",
+    "    ...\n",
+    "    ...\n",
+    "    ...\n",
+    "    ...\n",
+    "    ..."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "summary(players)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Memoization without Side Effects"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "It is considered *bad practice* to make a function and thereby its correctness dependent on a program's *global state*: For example, in the \"*Easy at second Glance: Fibonacci Numbers*\" section in [Chapter 9 <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/09_mappings_00_content.ipynb#\"Easy-at-second-Glance\"-Example:-Fibonacci-Numbers--%28revisited%29), we use a global `memo` to store the Fibonacci numbers that have already been calculated.\n",
+    "\n",
+    "That `memo` dictionary could be \"manipulated.\" More often than not, such things happen by accident: Imagine we wrote two independent recursive functions that both rely on memoization to solve different problems, and, unintentionally, we made both work with the *same* global `memo`. As a result, we would observe \"random\" bugs depending on the order in which we executed these functions. Such bugs are hard to track down in practice.\n",
+    "\n",
+    "A common remedy is to avoid global state and pass intermediate results \"down\" the recursion tree in a \"hidden\" argument. By convention, we prefix parameter names with a single leading underscore `_`, such as with `_memo` below, to indicate that the caller of our `fibonacci()` function *must not* use it. Also, we make `_memo` a *keyword-only* argument to force ourselves to always explicitly name it in a function call. Because it is an **implementation detail**, the `_memo` parameter is *not* mentioned in the docstring.\n",
+    "\n",
+    "Your task is to complete this version of `fibonacci()` so that the function works *without* any **side effects** in the global scope."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "#### \"Easy at third Glance\" Example: [Fibonacci Numbers <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_wiki.png\">](https://en.wikipedia.org/wiki/Fibonacci_number)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "code_folding": [],
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "def fibonacci(i, *, debug=False, _memo=None):\n",
+    "    \"\"\"Calculate the ith Fibonacci number.\n",
+    "\n",
+    "    Args:\n",
+    "        i (int): index of the Fibonacci number to calculate\n",
+    "        debug (bool): show non-cached calls; defaults to False\n",
+    "\n",
+    "    Returns:\n",
+    "        ith_fibonacci (int)\n",
+    "    \"\"\"\n",
+    "    # answer to Q2.1\n",
+    "    if ...:\n",
+    "        ... = {\n",
+    "            0: 0,\n",
+    "            1: 1,\n",
+    "        }\n",
+    "\n",
+    "    # answer to Q2.2\n",
+    "    if ...:\n",
+    "        return ...\n",
+    "\n",
+    "    if debug:  # added for didactical purposes\n",
+    "        print(f\"fibonacci({i}) is calculated\")\n",
+    "\n",
+    "    # answer to Q2.3\n",
+    "    recurse = (\n",
+    "        fibonacci(...)\n",
+    "        + fibonacci(...)\n",
+    "    )\n",
+    "    # answer to Q2.4\n",
+    "    ... = ...\n",
+    "    return ..."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "**Q2.1**: When `fibonacci()` is initially called, `_memo` is set to `None`. So, there is *no* `dict` object yet. Implement the *two* base cases in the first `if` statement!\n",
+    "\n",
+    "Hints: All you need to do is create a *new* `dict` object with the results for `i=0` and `i=1`. This object is then passed on in the recursive function calls. Use the `is` operator in the `if` statement."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q2.2**: When `fibonacci()` is called for non-base cases (i.e., `i > 1`), it first checks if the result is already in the `_memo`. Implement that step in the second `if` statement!\n",
+    "\n",
+    "Hint: Use the early exit pattern."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q2.3**: If `fibonacci()` is called for an `i` argument whose result is not yet in the `_memo`, it must calculate it with the usual recursive function calls. Fill in the arguments to the two recursive `fibonacci()` calls!\n",
+    "\n",
+    "Hint: You must pass on the hidden `_memo`."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q2.4**: Lastly, after the two recursive calls have returned, `fibonacci()` must store the `recurse` result for the given `i` in the `_memo` *before* returning it. Implement that logic!"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q2.5**: What happens to the hidden `_memo` after the initial call to `fibonacci()` returned? How many hidden `_memo` objects exist in memory during the entire computation?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " < your answer >"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Because `fibonacci()` is now independent of the *global state*, the same eleven recursive function calls are made each time! So, this `fibonacci()` is a **pure** function, meaning it has *no* side effects.\n",
+    "\n",
+    "**Q2.6**: Execute the following code cell a couple of times to observe that!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "fibonacci(12, debug=True)  # = 13th Fibonacci number -> 11 recursive calls necessary"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The runtime of `fibonacci()` is now stable: Ther is no message that \"an intermediate result is being cached\" as in [Chapter 9 <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/09_mappings_00_content.ipynb#\"Easy-at-second-Glance\"-Example:-Fibonacci-Numbers--%28revisited%29).\n",
+    "\n",
+    "**Q2.7**: Execute the following code cells a couple of times to observe that!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%%timeit -n 1\n",
+    "fibonacci(99)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%%timeit -n 1\n",
+    "fibonacci(999)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.4"
+  },
+  "toc": {
+   "base_numbering": 1,
+   "nav_menu": {},
+   "number_sections": false,
+   "sideBar": true,
+   "skip_h1_title": true,
+   "title_cell": "Table of Contents",
+   "title_sidebar": "Contents",
+   "toc_cell": false,
+   "toc_position": {},
+   "toc_section_display": false,
+   "toc_window_display": false
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}

From f6da894be9e60d461119b5341bbb9dd3acca10fe Mon Sep 17 00:00:00 2001
From: Alexander Hess <alexander@webartifex.biz>
Date: Wed, 15 Apr 2020 00:37:53 +0200
Subject: [PATCH 097/242] Bumb version number

---
 pyproject.toml | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/pyproject.toml b/pyproject.toml
index d4c3c99..2ef9137 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -1,6 +1,6 @@
 [tool.poetry]
 name = "intro-to-python"
-version = "0.6.8"
+version = "0.7.0"
 description = "An introduction to Python and programming for wanna-be data scientists"
 authors = ["Alexander Hess <alexander@webartifex.biz>"]
 license = "MIT"

From 2b8b0b4b16dcea7356370bc0494ee69bd1142d35 Mon Sep 17 00:00:00 2001
From: Alexander Hess <alexander@webartifex.biz>
Date: Thu, 16 Apr 2020 05:35:23 +0200
Subject: [PATCH 098/242] Add initial version of chapter 10

---
 01_elements_00_content.ipynb  |    8 +-
 02_functions_00_content.ipynb |    6 +-
 04_iteration_00_content.ipynb |    2 +-
 05_numbers_00_content.ipynb   |    2 +-
 06_text_00_content.ipynb      |    2 +-
 07_sequences_00_content.ipynb |    4 +-
 09_mappings_00_content.ipynb  |   13 +-
 10_classes_00_content.ipynb   | 7688 +++++++++++++++++++++++++++++++++
 8 files changed, 7712 insertions(+), 13 deletions(-)
 create mode 100644 10_classes_00_content.ipynb

diff --git a/01_elements_00_content.ipynb b/01_elements_00_content.ipynb
index 88d36b1..698947d 100644
--- a/01_elements_00_content.ipynb
+++ b/01_elements_00_content.ipynb
@@ -1343,7 +1343,7 @@
    "source": [
     "Different types imply different behaviors for the objects. The `b` object, for example, may be \"asked\" if it is a whole number with the [is_integer() <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/stdtypes.html#float.is_integer) \"functionality\" that comes with *every* `float` object.\n",
     "\n",
-    "Formally, we call such type-specific functionalities **methods** (i.e., as opposed to functions) and we look at them in detail in Chapter 10. For now, it suffices to know that we access them with the **dot operator** `.` on the object. Of course, `b` is a whole number, which the boolean object `True` tells us."
+    "Formally, we call such type-specific functionalities **methods** (i.e., as opposed to functions) and we look at them in detail in [Chapter 10 <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/10_classes_00_content.ipynb). For now, it suffices to know that we access them with the **dot operator** `.` on the object. Of course, `b` is a whole number, which the boolean object `True` tells us."
    ]
   },
   {
@@ -2474,7 +2474,7 @@
     }
    },
    "source": [
-    "Some variables magically exist when a Python process is started or are added by Jupyter. We may safely ignore the former until Chapter 10 and the latter for good."
+    "Some variables magically exist when a Python process is started or are added by Jupyter. We may safely ignore the former until [Chapter 10 <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/10_classes_00_content.ipynb) and the latter for good."
    ]
   },
   {
@@ -2898,7 +2898,7 @@
     }
    },
    "source": [
-    "Variables with leading and trailing double underscores, referred to as **dunder** in Python jargon, are used for built-in functionalities and to implement object-oriented features as we see in Chapter 10. We must *not* use this style for variables!"
+    "Variables with leading and trailing double underscores, referred to as **dunder** in Python jargon, are used for built-in functionalities and to implement object-oriented features as we see in [Chapter 10 <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/10_classes_00_content.ipynb). We must *not* use this style for variables!"
    ]
   },
   {
@@ -3734,7 +3734,7 @@
     "  - distinct and well-contained areas/parts of the memory that hold the actual data\n",
     "  - the concept by which Python manages the memory for us\n",
     "  - can be classified into objects of the same **type** (i.e., same abstract \"structure\" but different concrete data)\n",
-    "  - built-in objects (incl. **literals**) vs. user-defined objects (cf., Chapter 10)\n",
+    "  - built-in objects (incl. **literals**) vs. user-defined objects (cf., [Chapter 10 <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/10_classes_00_content.ipynb))\n",
     "  - e.g., `1`, `1.0`, and `\"one\"` are three different objects of distinct types that are also literals (i.e., by the way we type them into the command line Python knows what the value and type are)\n",
     "\n",
     "\n",
diff --git a/02_functions_00_content.ipynb b/02_functions_00_content.ipynb
index e810c48..5552ee6 100644
--- a/02_functions_00_content.ipynb
+++ b/02_functions_00_content.ipynb
@@ -1012,7 +1012,7 @@
    "source": [
     "Its value may seem awkward at first: It consists of a location showing where the function is defined (i.e., `__main__` here, which is Python's way of saying \"in this notebook\") and the signature wrapped inside angle brackets `<` and `>`.\n",
     " \n",
-    "The angle brackets are a convention to indicate that the value may *not* be used as a *literal* (i.e., typed back into another code cell). Chapter 10 introduces the concept of a **text representation** of an object, which is related to the *semantic* meaning of an object's value as discussed in [Chapter 1 <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/01_elements_00_content.ipynb#Value-/-\"Meaning\"), and the angle brackets convention is one such way to represent an object as text. When executed, the angle brackets cause a `SyntaxError` because Python expects the `<` operator to come with an operand on both sides (cf., [Chapter 3 <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/03_conditionals_00_content.ipynb#Relational-Operators))."
+    "The angle brackets are a convention to indicate that the value may *not* be used as a *literal* (i.e., typed back into another code cell). [Chapter 10 <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/10_classes_00_content.ipynb) introduces the concept of a **text representation** of an object, which is related to the *semantic* meaning of an object's value as discussed in [Chapter 1 <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/01_elements_00_content.ipynb#Value-/-\"Meaning\"), and the angle brackets convention is one such way to represent an object as text. When executed, the angle brackets cause a `SyntaxError` because Python expects the `<` operator to come with an operand on both sides (cf., [Chapter 3 <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/03_conditionals_00_content.ipynb#Relational-Operators))."
    ]
   },
   {
@@ -3599,7 +3599,7 @@
     }
    },
    "source": [
-    "Besides the usual dunder-style attributes, the built-in [dir() <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/functions.html#dir) function lists some attributes in an upper case naming convention and many others starting with a *single* underscore `_`. To understand the former, we must wait until Chapter 10, while the latter is explained further below."
+    "Besides the usual dunder-style attributes, the built-in [dir() <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/functions.html#dir) function lists some attributes in an upper case naming convention and many others starting with a *single* underscore `_`. To understand the former, we must wait until [Chapter 10 <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/10_classes_00_content.ipynb), while the latter is explained further below."
    ]
   },
   {
@@ -4519,7 +4519,7 @@
     }
    },
    "source": [
-    "Packages are a generalization of modules, and we look at one in detail in Chapter 10. You may, however, already look at a [sample package <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_gh.png\">](https://github.com/webartifex/intro-to-python/tree/master/sample_package) in the repository, which is nothing but a folder with *.py* files in it.\n",
+    "Packages are a generalization of modules, and we look at one in detail in [Chapter 10 <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/10_classes_00_content.ipynb). You may, however, already look at a [sample package <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_gh.png\">](https://github.com/webartifex/intro-to-python/tree/master/sample_package) in the repository, which is nothing but a folder with *.py* files in it.\n",
     "\n",
     "As a further reading on modules and packages, we refer to the [official tutorial <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/tutorial/modules.html)."
    ]
diff --git a/04_iteration_00_content.ipynb b/04_iteration_00_content.ipynb
index 3976ad7..aec2180 100644
--- a/04_iteration_00_content.ipynb
+++ b/04_iteration_00_content.ipynb
@@ -5562,7 +5562,7 @@
     "\n",
     "On the contrary, the abstract concept of **iterables** is all about looping: Any object that we can loop over is, by definition, an iterable. So, `range` objects, for example, are iterables, even though they hold no references to other objects. Moreover, looping does *not* have to occur in a *predictable* order, although this is the case for both `list` and `range` objects.\n",
     "\n",
-    "Typically, containers are iterables, and iterables are containers. Yet, only because these two concepts coincide often, we must not think of them as the same. In [Chapter 7 <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/07_sequences_00_content.ipynb#Collections-vs.-Sequences), we formalize these two concepts and introduce many more. Finally, Chapter 10 gives an explanation how abstract concepts are implemented and play together.\n",
+    "Typically, containers are iterables, and iterables are containers. Yet, only because these two concepts coincide often, we must not think of them as the same. In [Chapter 7 <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/07_sequences_00_content.ipynb#Collections-vs.-Sequences), we formalize these two concepts and introduce many more. Finally, [Chapter 10 <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/10_classes_00_content.ipynb) gives an explanation how abstract concepts are implemented and play together.\n",
     "\n",
     "Let's continue with `first_names` below as an example an illustrate what iterable containers are."
    ]
diff --git a/05_numbers_00_content.ipynb b/05_numbers_00_content.ipynb
index c2d6d1d..4046aa4 100644
--- a/05_numbers_00_content.ipynb
+++ b/05_numbers_00_content.ipynb
@@ -34,7 +34,7 @@
     "\n",
     "We start with the \"simple\" ones: Numeric types in this chapter and textual data in [Chapter 6 <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/06_text_00_content.ipynb). An important fact that holds for all objects of these types is that they are **immutable**. To reuse the bag analogy from [Chapter 1 <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/01_elements_00_content.ipynb#Objects-vs.-Types-vs.-Values), this means that the $0$s and $1$s making up an object's *value* cannot be changed once the bag is created in memory, implying that any operation with or method on the object creates a *new* object in a *different* memory location.\n",
     "\n",
-    "[Chapter 7 <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/07_sequences_00_content.ipynb), [Chapter 8 <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/08_mfr_00_content.ipynb), and [Chapter 9 <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/09_mappings_00_content.ipynb) then cover the more \"complex\" data types, including, for example, the `list` type. Finally, Chapter 10 completes the picture by introducing language constructs to create custom types.\n",
+    "[Chapter 7 <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/07_sequences_00_content.ipynb), [Chapter 8 <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/08_mfr_00_content.ipynb), and [Chapter 9 <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/09_mappings_00_content.ipynb) then cover the more \"complex\" data types, including, for example, the `list` type. Finally, [Chapter 10 <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/10_classes_00_content.ipynb) completes the picture by introducing language constructs to create custom types.\n",
     "\n",
     "We have already seen many hints indicating that numbers are not as trivial to work with as it seems at first sight:\n",
     "\n",
diff --git a/06_text_00_content.ipynb b/06_text_00_content.ipynb
index 36a991a..cc68262 100644
--- a/06_text_00_content.ipynb
+++ b/06_text_00_content.ipynb
@@ -290,7 +290,7 @@
     }
    },
    "source": [
-    "As an alternative to the literal notation, we may use the built-in [str() <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/stdtypes.html#str) constructor to cast non-`str` objects as `str` ones. As Chapter 10 reveals, basically any object in Python has a **text representation**. Because of that we may also pass `list` objects, the boolean `True` and `False`, or `None` to [str() <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/stdtypes.html#str)."
+    "As an alternative to the literal notation, we may use the built-in [str() <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/stdtypes.html#str) constructor to cast non-`str` objects as `str` ones. As [Chapter 10 <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/10_classes_00_content.ipynb) reveals, basically any object in Python has a **text representation**. Because of that we may also pass `list` objects, the boolean `True` and `False`, or `None` to [str() <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/stdtypes.html#str)."
    ]
   },
   {
diff --git a/07_sequences_00_content.ipynb b/07_sequences_00_content.ipynb
index ece7bd2..b68ac5e 100644
--- a/07_sequences_00_content.ipynb
+++ b/07_sequences_00_content.ipynb
@@ -4002,7 +4002,7 @@
     "\n",
     "The revised `add_xyz()` function below is more natural to reason about as it does *not* modify the passed in `arg` internally. [PythonTutor <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](http://pythontutor.com/visualize.html#code=letters%20%3D%20%5B%22a%22,%20%22b%22,%20%22c%22%5D%0A%0Adef%20add_xyz%28arg%29%3A%0A%20%20%20%20new_arg%20%3D%20arg%5B%3A%5D%0A%20%20%20%20new_arg.extend%28%5B%22x%22,%20%22y%22,%20%22z%22%5D%29%0A%20%20%20%20return%20new_arg%0A%0Aletters_with_xyz%20%3D%20add_xyz%28letters%29&cumulative=false&curInstr=0&heapPrimitives=nevernest&mode=display&origin=opt-frontend.js&py=3&rawInputLstJSON=%5B%5D&textReferences=false) shows that as well. This approach is following the **[functional programming <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_wiki.png\">](https://en.wikipedia.org/wiki/Functional_programming)** paradigm that is going through a \"renaissance\" currently. Two essential characteristics of functional programming are that a function *never* changes its inputs and *always* returns the same output given the same inputs.\n",
     "\n",
-    "For a beginner, it is probably better to stick to this idea and not change any arguments as the original `add_xyz()` above. However, functions that modify and return the argument passed in are an important aspect of object-oriented programming, as explained in Chapter 10."
+    "For a beginner, it is probably better to stick to this idea and not change any arguments as the original `add_xyz()` above. However, functions that modify and return the argument passed in are an important aspect of object-oriented programming, as explained in [Chapter 10 <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/10_classes_00_content.ipynb)."
    ]
   },
   {
@@ -6317,7 +6317,7 @@
    "source": [
     "We implicitly assume that the first element represents the $x$ and the second the $y$ coordinate. While that follows intuitively from convention in math, we should at least add comments somewhere in the code to document this assumption.\n",
     "\n",
-    "A better way is to create a *custom* data type. While that is covered in depth in Chapter 10, the [collections <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/collections.html) module in the [standard library <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/index.html) provides a [namedtuple() <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/collections.html#collections.namedtuple) **factory function** that creates \"simple\" custom data types on top of the standard `tuple` type."
+    "A better way is to create a *custom* data type. While that is covered in depth in [Chapter 10 <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/10_classes_00_content.ipynb), the [collections <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/collections.html) module in the [standard library <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/index.html) provides a [namedtuple() <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/collections.html#collections.namedtuple) **factory function** that creates \"simple\" custom data types on top of the standard `tuple` type."
    ]
   },
   {
diff --git a/09_mappings_00_content.ipynb b/09_mappings_00_content.ipynb
index f08479b..b66e0e8 100644
--- a/09_mappings_00_content.ipynb
+++ b/09_mappings_00_content.ipynb
@@ -1,5 +1,16 @@
 {
  "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "**Important**: Click on \"*Kernel*\" > \"*Restart Kernel and Clear All Outputs*\" *before* reading this chapter in [JupyterLab <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_jp.png\">](https://jupyterlab.readthedocs.io/en/stable/) (e.g., in the cloud on [MyBinder <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_mb.png\">](https://mybinder.org/v2/gh/webartifex/intro-to-python/master?urlpath=lab/tree/09_mappings_00_content.ipynb))"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {
@@ -8,7 +19,7 @@
     }
    },
    "source": [
-    "# Chapter 8: Mappings & Sets"
+    "# Chapter 9: Mappings & Sets"
    ]
   },
   {
diff --git a/10_classes_00_content.ipynb b/10_classes_00_content.ipynb
new file mode 100644
index 0000000..5af284a
--- /dev/null
+++ b/10_classes_00_content.ipynb
@@ -0,0 +1,7688 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "**Important**: Click on \"*Kernel*\" > \"*Restart Kernel and Clear All Outputs*\" *before* reading this chapter in [JupyterLab <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_jp.png\">](https://jupyterlab.readthedocs.io/en/stable/) (e.g., in the cloud on [MyBinder <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_mb.png\">](https://mybinder.org/v2/gh/webartifex/intro-to-python/master?urlpath=lab/tree/10_classes_00_content.ipynb))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "# Chapter 10: Classes & Instances"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "In contrast to all the built-in data types introduced in the previous chapters, **classes** allow us to create **user-defined data types**. They enable us to model **data** and its **associated behavior** in an *abstract* way. *Concrete* **instances** of these custom data types then **encapsulate** the **state** in a running program. Often, classes are **blueprints** modeling *\"real world things\"* (e.g., the vectors and matrices below).\n",
+    "\n",
+    "Classes and instances follow the **[object-oriented programming <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_wiki.png\">](https://en.wikipedia.org/wiki/Object-oriented_programming)** (OOP) paradigm where a *large program* is broken down into many *small components* (i.e., the objects) that *reuse* code. This way, a program that is too big for a programmer to fully comprehend as a whole becomes maintainable via its easier to understand individual pieces.\n",
+    "\n",
+    "**Side Note**: Often times, we see the terminology \"classes & objects\" used instead of \"classes & instances\" used on forums like [Stack Overflow <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_so.png\">](https://stackoverflow.com). In this book, we are more precise as both classes and instances are objects on their own in Python."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "## Example: Vectors & Matrices"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Neither core Python nor the standard library offer an implementation of common [linear algebra <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_wiki.png\">](https://en.wikipedia.org/wiki/Linear_algebra) functionalities. While the popular third-party library [numpy](http://www.numpy.org/) is the de-facto standard for that and is recommended to be used for real-life projects, we show how one could use Python's object-oriented language features to implement common matrix and vector operations throughout this chapter.\n",
+    "\n",
+    "Without classes, we could model a vector with a `tuple` or a `list` depending on if we want the vector to be mutable or not.\n",
+    "\n",
+    "Let's take the following vector $\\vec{y}$ as an example and model it as a `tuple`:"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "$\\vec{y} = \\begin{pmatrix} 1 \\\\ 2 \\\\ 3 \\end{pmatrix}$"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "y = (1, 2, 3)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(1, 2, 3)"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "y"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "We can extend this approach and model a matrix as either a `tuple` holding other `tuple`s or as a `list` holding other `list`s or as a mixture of both. Then, we must decide if the nested objects represent rows or columns. A common convention is to go with the former.\n",
+    "\n",
+    "For example, let's model the matrix $\\bf{X}$ below as a `list` of row `list`s:"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "$\\bf{X} = \\begin{bmatrix} 1 & 2 & 3 \\\\ 4 & 5 & 6 \\\\ 7 & 8 & 9 \\end{bmatrix}$"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "X = [[1, 2, 3], [4, 5, 6], [7, 8, 9]]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[[1, 2, 3], [4, 5, 6], [7, 8, 9]]"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "X"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "While this way of representing vectors and matrices in memory keeps things simple, we cannot \"work\" with them as Python does not know about the **semantics** (i.e., \"rules\") inherent to vectors and matrices modeled as `tuple`s and `list`s of `list`s.\n",
+    "\n",
+    "In the example, we should be able to multiply a matrix like $\\bf{X}$ with a vector like $\\vec{y}$ if their dimensions match. However, Python does not know how to do this and raises a `TypeError`."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "$\\bf{X} * \\vec{y} = \\begin{bmatrix} 1 & 2 & 3 \\\\ 4 & 5 & 6 \\\\ 7 & 8 & 9 \\end{bmatrix} * \\begin{pmatrix} 1 \\\\ 2 \\\\ 3 \\end{pmatrix} = \\begin{pmatrix} 14 \\\\ 32 \\\\ 50 \\end{pmatrix}$"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "ename": "TypeError",
+     "evalue": "can't multiply sequence by non-int of type 'tuple'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-5-c494e576a745>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mX\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;31mTypeError\u001b[0m: can't multiply sequence by non-int of type 'tuple'"
+     ]
+    }
+   ],
+   "source": [
+    "X * y"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "With Python's object-oriented features, we can \"teach\" it the rules of [linear algebra <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_wiki.png\">](https://en.wikipedia.org/wiki/Linear_algebra). By doing so, we create a **[domain-specific language <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_wiki.png\">](https://en.wikipedia.org/wiki/Domain-specific_language)** (DSL) that simplifies working with vectors and matrices."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "## Class Definition"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "The compound `class` statement creates a new variable that references a **class object** in memory.\n",
+    "\n",
+    "Following the *header* line, the indented *body* syntactically consists of function definitions (i.e., the `dummy_method()`) and variable assignments (i.e., the `dummy_variable`). Any code put here is executed just as if it were outside the `class` statement. The class object acts as a **namespace**, meaning that all the names do *not* exist in the global scope but may only be accessed with the dot operator `.` on the class object. In this context, the names are called **class attributes**.\n",
+    "\n",
+    "Within classes, functions are referred to as **methods** that are **bound** to *future* **instance objects**. This binding process means that Python *implicitly* inserts a reference to a *concrete* instance object as the first argument to any **method invocation** (i.e., \"function call\"). By convention, we call this parameter `self` as it references the instance object on which the method is invoked. Then, as the method is executed, we can set and access attributes via the dot operator `.` on `self`. That is how we manage the *state* of a *concrete* instance within a *generically* written class. At the same time, the code within a method is reused whenever we invoke a method on *any* instance.\n",
+    "\n",
+    "As indicated by [PEP 257 <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://www.python.org/dev/peps/pep-0257/) and also section 3.8.4 of the [Google Python Style Guide <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_gh.png\">](https://github.com/google/styleguide/blob/gh-pages/pyguide.md), we use docstrings to document relevant parts of the new data type. With respect to naming, classes are named according to the [CamelCase <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_wiki.png\">](https://en.wikipedia.org/wiki/Camel_case) convention while instances are treated like normal variables and named in [snake\\_case <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_wiki.png\">](https://en.wikipedia.org/wiki/Snake_case)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "code_folding": [],
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "class Vector:\n",
+    "    \"\"\"A standard one-dimensional vector from linear algebra.\"\"\"\n",
+    "\n",
+    "    dummy_variable = \"I am a vector\"\n",
+    "\n",
+    "    def dummy_method(self):\n",
+    "        \"\"\"A dummy method for illustration purposes.\"\"\"\n",
+    "        return self.dummy_variable"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "`Vector` is a full-fledged object on its own with an identity, a type, and a value."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "94199515148736"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "id(Vector)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Its type `type` indicates that it represents a user-defined data type and it evaluates to its fully qualified name.\n",
+    "\n",
+    "We have seen the type `type` before in the \"*Constructors*\" section in [Chapter 2 <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/02_functions_00_content.ipynb#Constructors) and also in the \"*The `namedtuple` Type*\" section in [Chapter 7 <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/07_sequences_00_content.ipynb#The-namedtuple-Type). In the latter case, we could also use a `Point` class but the [namedtuple() <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/collections.html#collections.namedtuple) function from the [collections <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/collections.html) module in the [standard library <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/index.html) is a convenient shortcut for custom data types that can be derived out of a plain `tuple`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "type"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "type(Vector)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "__main__.Vector"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "Vector"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "The docstrings are transformed into convenient help texts."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "\u001b[0;31mInit signature:\u001b[0m \u001b[0mVector\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+       "\u001b[0;31mDocstring:\u001b[0m      A standard one-dimensional vector from linear algebra.\n",
+       "\u001b[0;31mType:\u001b[0m           type\n",
+       "\u001b[0;31mSubclasses:\u001b[0m     \n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "Vector?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Help on class Vector in module __main__:\n",
+      "\n",
+      "class Vector(builtins.object)\n",
+      " |  A standard one-dimensional vector from linear algebra.\n",
+      " |  \n",
+      " |  Methods defined here:\n",
+      " |  \n",
+      " |  dummy_method(self)\n",
+      " |      A dummy method for illustration purposes.\n",
+      " |  \n",
+      " |  ----------------------------------------------------------------------\n",
+      " |  Data descriptors defined here:\n",
+      " |  \n",
+      " |  __dict__\n",
+      " |      dictionary for instance variables (if defined)\n",
+      " |  \n",
+      " |  __weakref__\n",
+      " |      list of weak references to the object (if defined)\n",
+      " |  \n",
+      " |  ----------------------------------------------------------------------\n",
+      " |  Data and other attributes defined here:\n",
+      " |  \n",
+      " |  dummy_variable = 'I am a vector'\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "help(Vector)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "We can use the built-in [vars() <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/functions.html#vars) function as an alternative to [dir() <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/functions.html#dir) to obtain a *brief* summary of the attributes on `Vector`. Whereas [vars() <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/functions.html#vars) returns a read-only `dict`-like overview on the *explicitly* defined attributes, [dir() <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/functions.html#dir) also shows *implicitly* added attributes in a `list`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "mappingproxy({'__module__': '__main__',\n",
+       "              '__doc__': 'A standard one-dimensional vector from linear algebra.',\n",
+       "              'dummy_variable': 'I am a vector',\n",
+       "              'dummy_method': <function __main__.Vector.dummy_method(self)>,\n",
+       "              '__dict__': <attribute '__dict__' of 'Vector' objects>,\n",
+       "              '__weakref__': <attribute '__weakref__' of 'Vector' objects>})"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "vars(Vector)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['__class__',\n",
+       " '__delattr__',\n",
+       " '__dict__',\n",
+       " '__dir__',\n",
+       " '__doc__',\n",
+       " '__eq__',\n",
+       " '__format__',\n",
+       " '__ge__',\n",
+       " '__getattribute__',\n",
+       " '__gt__',\n",
+       " '__hash__',\n",
+       " '__init__',\n",
+       " '__init_subclass__',\n",
+       " '__le__',\n",
+       " '__lt__',\n",
+       " '__module__',\n",
+       " '__ne__',\n",
+       " '__new__',\n",
+       " '__reduce__',\n",
+       " '__reduce_ex__',\n",
+       " '__repr__',\n",
+       " '__setattr__',\n",
+       " '__sizeof__',\n",
+       " '__str__',\n",
+       " '__subclasshook__',\n",
+       " '__weakref__',\n",
+       " 'dummy_method',\n",
+       " 'dummy_variable']"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "dir(Vector)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "With the dot operator `.` we access the class attributes."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'I am a vector'"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "Vector.dummy_variable"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<function __main__.Vector.dummy_method(self)>"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "Vector.dummy_method"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "However, invoking the `dummy_method()` raises a `TypeError`. That makes sense as the method expects a *concrete* instance passed in as the `self` argument. However, we have not yet created one."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "ename": "TypeError",
+     "evalue": "dummy_method() missing 1 required positional argument: 'self'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-16-829638f9c7cf>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mVector\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdummy_method\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;31mTypeError\u001b[0m: dummy_method() missing 1 required positional argument: 'self'"
+     ]
+    }
+   ],
+   "source": [
+    "Vector.dummy_method()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "## Instantiation"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "To create a *new* instance, we need to **instantiate** one.\n",
+    "\n",
+    "In the `class` statement, we implement a `__init__()` method that contains all the validation logic that we require a `Vector` instance to adhere to.\n",
+    "\n",
+    "`__init__()` is an example of a so-called **special method** that we may use to make new data types work with Python's language features seamlessly. Their naming follows the dunder convention. In this chapter, we introduce some of the more common special methods, and we refer to the [language reference <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/reference/datamodel.html) for an exhaustive list of all special methods. Special methods not *explicitly* defined in a class are *implicitly* added with a default implementation.\n",
+    "\n",
+    "The `__init__()` method (cf., [reference <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/reference/datamodel.html#object.__init__)) is responsible for **initializing** a *new* instance object immediately after its creation. That usually means setting up some **instance attributes**. In the example, a new vector is created from some finite and iterable object (e.g., a `tuple`) passed in as the `data` argument. The elements provided by the `data` argument are first cast as `float` objects and then stored in a `list` object named `_entries` on the new instance object. Together, the `float`s represent all the state encapsulated within an instance.\n",
+    "\n",
+    "A best practice is to *separate* the usage (i.e., \"behavior\") of a new data type from its implementation. By convention, attributes that should not be accessed from \"outside\" of an instance start with one leading underscore `_`. In the example, the instance attribute `_entries` is such an **implementation detail**: We could have decided to store a vector's entries in a `tuple` instead of a `list`. However, this decision should *not* affect how a `Vector` instance is to be used. Moreover, if we changed how the `_entries` are modeled later on, this must *not* break any existing code using `Vector`s. This idea is also known as **[information hiding <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_wiki.png\">](https://en.wikipedia.org/wiki/Information_hiding)** in software engineering."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "class Vector:\n",
+    "    \"\"\"A standard one-dimensional vector from linear algebra.\n",
+    "\n",
+    "    All entries are converted to floats.\n",
+    "    \"\"\"\n",
+    "\n",
+    "    def __init__(self, data):\n",
+    "        \"\"\"Initiate a new vector.\n",
+    "\n",
+    "        Args:\n",
+    "            data (iterable): the vector's entries;\n",
+    "                must be finite and have at least one element\n",
+    "\n",
+    "        Raises:\n",
+    "            ValueError: if the provided data do not have enough entries\n",
+    "        \"\"\"\n",
+    "        self._entries = list(float(x) for x in data)\n",
+    "        if len(self._entries) == 0:\n",
+    "            raise ValueError(\"a vector must have at least one entry\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "To create a `Vector` instance, we call the `Vector` class with the `()` operator. This call is forwarded to the `__init__()` method behind the scenes. That is what we mean by saying \"make new data types work with Python's language features seamlessly\" above: We use `Vector` just as any other built-in constructor."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "v = Vector([1, 2, 3])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "`v` is a full-fledged object as well."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "140613942015760"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "id(v)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Unsurprisingly, the type of `v` is `Vector`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "__main__.Vector"
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "type(v)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Every instance comes with a special `__class__` attribute that also references the corresponding class object."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "__main__.Vector"
+      ]
+     },
+     "execution_count": 21,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "v.__class__"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "`v`'s semantic \"value\" is not so clear yet. We fix this in the next section."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<__main__.Vector at 0x7fe33c107710>"
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "v"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Although the `__init__()` method defines *two* parameters, we must call it with only *one* `data` argument. As noted above, Python implicitly inserts a reference to the newly created instance object (i.e., `v`) as the first argument as `self`.\n",
+    "\n",
+    "Calling a class object with a wrong number of arguments leads to a generic `TypeError` ..."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "ename": "TypeError",
+     "evalue": "__init__() missing 1 required positional argument: 'data'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-23-bcda4006949f>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mVector\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;31mTypeError\u001b[0m: __init__() missing 1 required positional argument: 'data'"
+     ]
+    }
+   ],
+   "source": [
+    "Vector()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "ename": "TypeError",
+     "evalue": "__init__() takes 2 positional arguments but 4 were given",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-24-e00314c6a560>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mVector\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m3\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;31mTypeError\u001b[0m: __init__() takes 2 positional arguments but 4 were given"
+     ]
+    }
+   ],
+   "source": [
+    "Vector(1, 2, 3)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "... while creating a `Vector` instance from an iterable with no elements raises a custom `ValueError`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "ename": "ValueError",
+     "evalue": "a vector must have at least one entry",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-25-7261658e317e>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mVector\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;32m<ipython-input-17-b8f407c2a94e>\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, data)\u001b[0m\n\u001b[1;32m     17\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_entries\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfloat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mx\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     18\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_entries\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 19\u001b[0;31m             \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"a vector must have at least one entry\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;31mValueError\u001b[0m: a vector must have at least one entry"
+     ]
+    }
+   ],
+   "source": [
+    "Vector([])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Even though we can access the `_entries` attribute on the `v` object, we are not supposed to do that because of the underscore `_` convention. In other words, only an expression like `self._entries` within a method is allowed."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[1.0, 2.0, 3.0]"
+      ]
+     },
+     "execution_count": 26,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "v._entries  # by convention not allowed"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "## Text Representations"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "For all the built-in data types, an object's value is represented in a *literal notation*, implying that we can simply copy and paste the value into another code cell to create a *new* object with the *same* value.\n",
+    "\n",
+    "The exact representation of the value does *not* have to be identical to the one used to create the object. For example, we can create a `tuple` object without using parentheses and Python still outputs its value with `(` and `)`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "y = 1, 2, 3"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(1, 2, 3)"
+      ]
+     },
+     "execution_count": 28,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "y"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(1, 2, 3)"
+      ]
+     },
+     "execution_count": 29,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "(1, 2, 3)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "To control how objects of a user-defined data type are represented as text, we implement the `__repr__()` (cf., [reference <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/reference/datamodel.html#object.__repr__)) and `__str__()` (cf., [reference <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/reference/datamodel.html#object.__str__)) methods. Both take only a `self` argument and must return a `str` object."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "class Vector:\n",
+    "\n",
+    "    def __init__(self, data):\n",
+    "        self._entries = list(float(x) for x in data)\n",
+    "        # ...\n",
+    "\n",
+    "    def __repr__(self):\n",
+    "        args = \", \".join(f\"{x:.3f}\" for x in self._entries)\n",
+    "        return f\"Vector(({args}))\"\n",
+    "\n",
+    "    def __str__(self):\n",
+    "        first, last = self._entries[0], self._entries[-1]\n",
+    "        n_entries = len(self._entries)\n",
+    "        return f\"Vector({first:.1f}, ..., {last:.1f})[{n_entries:d}]\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Now, when `v` is evaluated in a code cell, we see the return value of the `__repr__()` method used as a \"literal\" notation.\n",
+    "\n",
+    "According to the specification, `__repr__()` should return a `str` object that, when entered in a code cell, creates a *new* instance with the *same* state (i.e., their `_entries` attributes compare equal) as the original one. In other words, it should return a text representation of the object optimized for direct consumption by the Python interpreter. That is often useful when debugging or logging large applications.\n",
+    "\n",
+    "Our implementation of `__repr__()` in the `Vector` class uses to a `tuple` notation for the `data` argument. So, even if we create `v` from a `list` object like `[1, 2, 3]` and even though the `_entries` are stored as a `list` object internally, a `Vector` instance's text representation \"defaults\" to `((` and `))` in the output. This decision is arbitrary and we could have used a `list` notation for the `data` argument as well."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "v = Vector([1, 2, 3])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Vector((1.000, 2.000, 3.000))"
+      ]
+     },
+     "execution_count": 32,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "v"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "If we copy and paste the value of the `v` object, we create a *new* `Vector` instance with the *same* state as `v`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Vector((1.000, 2.000, 3.000))"
+      ]
+     },
+     "execution_count": 33,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "Vector((1.000, 2.000, 3.000))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "To be more explicit, we could alternatively call the built-in [repr()]( https://docs.python.org/3/library/functions.html#repr) function with `v` as the argument."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Vector((1.000, 2.000, 3.000))'"
+      ]
+     },
+     "execution_count": 34,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "repr(v)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "On the contrary, the `__str__()` method should return a *human-readable* text representation of the object. We can use the built-in [str() <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/functions.html#func-str) and [print() <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/functions.html#print) functions to obtain this representation explicitly.\n",
+    "\n",
+    "For our `Vector` class, this representation only shows a vector's first and last entries followed by the number of entries in brackets. So, even for a `Vector` containing millions of entries, we could easily make sense of the representation. In other words, a human understands the semantic meaning of the $0$s and $1$s in memory."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Vector(1.0, ..., 3.0)[3]'"
+      ]
+     },
+     "execution_count": 35,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "str(v)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "The [print() <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/functions.html#print) function does not show the enclosing quotes."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Vector(1.0, ..., 3.0)[3]\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(v)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Below is a first implementation of the `Matrix` class that stores the entries internally as a list of lists."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "metadata": {
+    "code_folding": [],
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "class Matrix:\n",
+    "\n",
+    "    def __init__(self, data):\n",
+    "        self._entries = list(list(float(x) for x in r) for r in data)\n",
+    "        for row in self._entries[1:]:\n",
+    "            if len(row) != len(self._entries[0]):\n",
+    "                raise ValueError(\"rows must have the same number of entries\")\n",
+    "        if len(self._entries) == 0:\n",
+    "            raise ValueError(\"a matrix must have at least one entry\")\n",
+    "\n",
+    "    def __repr__(self):\n",
+    "        args = \", \".join(\"(\" + \", \".join(f\"{c:.3f}\" for c in r) + \",)\" for r in self._entries)\n",
+    "        return f\"Matrix(({args}))\"\n",
+    "\n",
+    "    def __str__(self):\n",
+    "        first, last = self._entries[0][0], self._entries[-1][-1]\n",
+    "        m, n = len(self._entries), len(self._entries[0])\n",
+    "        return f\"Matrix(({first:.1f}, ...), ..., (..., {last:.1f}))[{m:d}x{n:d}]\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "`Matrix` is of course a  full-fledged object as well."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "94199515162880"
+      ]
+     },
+     "execution_count": 38,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "id(Matrix)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "type"
+      ]
+     },
+     "execution_count": 39,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "type(Matrix)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "__main__.Matrix"
+      ]
+     },
+     "execution_count": 40,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "Matrix"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Let's create a new `Matrix` instance from a `list` of `tuple`s."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "m = Matrix([(1, 2, 3), (4, 5, 6), (7, 8, 9)])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "140613942007376"
+      ]
+     },
+     "execution_count": 42,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "id(m)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "__main__.Matrix"
+      ]
+     },
+     "execution_count": 43,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "type(m)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "The text representations work as above."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Matrix(((1.000, 2.000, 3.000,), (4.000, 5.000, 6.000,), (7.000, 8.000, 9.000,)))"
+      ]
+     },
+     "execution_count": 44,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "m"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Matrix((1.0, ...), ..., (..., 9.0))[3x3]\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(m)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Passing an invalid `data` argument when instantiating a `Matrix` results in the documented exceptions."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "ename": "ValueError",
+     "evalue": "a matrix must have at least one entry",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-46-87c8bd55bdc1>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mMatrix\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;32m<ipython-input-37-2c8575833cd9>\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, data)\u001b[0m\n\u001b[1;32m      7\u001b[0m                 \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"rows must have the same number of entries\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      8\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_entries\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 9\u001b[0;31m             \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"a matrix must have at least one entry\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     10\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     11\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__repr__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;31mValueError\u001b[0m: a matrix must have at least one entry"
+     ]
+    }
+   ],
+   "source": [
+    "Matrix(())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "ename": "ValueError",
+     "evalue": "rows must have the same number of entries",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-47-359f87edecdf>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mMatrix\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m3\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0;36m4\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m5\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;32m<ipython-input-37-2c8575833cd9>\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, data)\u001b[0m\n\u001b[1;32m      5\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0mrow\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_entries\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      6\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrow\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_entries\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 7\u001b[0;31m                 \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"rows must have the same number of entries\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      8\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_entries\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      9\u001b[0m             \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"a matrix must have at least one entry\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;31mValueError\u001b[0m: rows must have the same number of entries"
+     ]
+    }
+   ],
+   "source": [
+    "Matrix([(1, 2, 3), (4, 5)])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "## Computed Properties"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "After creation, a `Matrix` instance exhibits certain properties that depend only on the concrete `data` encapsulated in it and can *not* be set to some arbitrary value. For example, every `Matrix` instance implicitly has *two* dimensions: These are commonly denoted as $m$ and $n$ in math and represent the number of rows and columns.\n",
+    "\n",
+    "We design our `Matrix` class such that instances have two attributes, `n_rows` and `n_cols`, that are automatically set to the correct `int` objects. To do that, we use the [property() <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/functions.html#property) built-in with a special `@` syntax and implement **derived attributes** that are computed from an object's current state. They work like methods except that they do not need to be invoked with the call operator `()` but can be accessed as if they were instance variables.\n",
+    "\n",
+    "To reuse their code, we integrate the new properties already within the `__init__()` method."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 48,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "class Matrix:\n",
+    "\n",
+    "    def __init__(self, data):\n",
+    "        self._entries = list(list(float(x) for x in r) for r in data)\n",
+    "        for row in self._entries[1:]:\n",
+    "            if len(row) != self.n_cols:\n",
+    "                raise ValueError(\"rows must have the same number of entries\")\n",
+    "        if self.n_rows == 0:\n",
+    "            raise ValueError(\"a matrix must have at least one entry\")\n",
+    "\n",
+    "    def __repr__(self):\n",
+    "        args = \", \".join(\"(\" + \", \".join(f\"{c:.3f}\" for c in r) + \",)\" for r in self._entries)\n",
+    "        return f\"Matrix(({args}))\"\n",
+    "\n",
+    "    @property\n",
+    "    def n_rows(self):\n",
+    "        return len(self._entries)\n",
+    "\n",
+    "    @property\n",
+    "    def n_cols(self):\n",
+    "        return len(self._entries[0])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "The revised `m` models a $2 \\times 3$ matrix."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 49,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "m = Matrix([(1, 2, 3), (4, 5, 6)])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 50,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(2, 3)"
+      ]
+     },
+     "execution_count": 50,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "m.n_rows, m.n_cols"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "In its basic form, properties are *read-only* attributes. This makes sense for `Matrix` instances where we can *not* \"set\" how many rows and columns there are while keeping the `_entries` unchanged."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 51,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "ename": "AttributeError",
+     "evalue": "can't set attribute",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-51-f4cc4767ae11>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mm\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mn_rows\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m3\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;31mAttributeError\u001b[0m: can't set attribute"
+     ]
+    }
+   ],
+   "source": [
+    "m.n_rows = 3"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "## Instance Methods vs. Class Methods"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "The methods we have seen so far are all **instance methods**. The characteristic idea behind an instance method is that the behavior it provides either depends on the state of a concrete instance or mutates it. In other words, an instance method works with attributes on the `self` argument. If it does *not* need access to `self` to do its job, it is conceptually *not* an instance method and we should probably convert it into some other kind of method.\n",
+    "\n",
+    "An example of an instance method from linear algebra is the `transpose()` method below that switches the rows and columns of a `Matrix` instance and returns a *new* `Matrix` instance based off that. It is implemented by passing the *iterator* created with the [zip() <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/functions.html#zip) built-in as the `data` argument to the `Matrix` constructor. Without a concrete `Matrix`'s rows and columns, `transpose()` could not work, conceptually speaking."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 52,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "class Matrix:\n",
+    "\n",
+    "    def __init__(self, data):\n",
+    "        self._entries = list(list(float(x) for x in r) for r in data)\n",
+    "        # ...\n",
+    "\n",
+    "    def __repr__(self):\n",
+    "        args = \", \".join(\"(\" + \", \".join(f\"{c:.3f}\" for c in r) + \",)\" for r in self._entries)\n",
+    "        return f\"Matrix(({args}))\"\n",
+    "\n",
+    "    def transpose(self):\n",
+    "        return Matrix(zip(*self._entries))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "The `transpose()` method returns a *new* `Matrix` instance where the rows and columns are flipped."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 53,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "m = Matrix([(1, 2, 3), (4, 5, 6), (7, 8, 9)])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 54,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Matrix(((1.000, 2.000, 3.000,), (4.000, 5.000, 6.000,), (7.000, 8.000, 9.000,)))"
+      ]
+     },
+     "execution_count": 54,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "m"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 55,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Matrix(((1.000, 4.000, 7.000,), (2.000, 5.000, 8.000,), (3.000, 6.000, 9.000,)))"
+      ]
+     },
+     "execution_count": 55,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "m.transpose()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Two invocations of `transpose()` may be chained, which negates its overall effect but still creates a *new* instance object."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 56,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "n = m.transpose().transpose()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 57,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Matrix(((1.000, 2.000, 3.000,), (4.000, 5.000, 6.000,), (7.000, 8.000, 9.000,)))"
+      ]
+     },
+     "execution_count": 57,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 58,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "False"
+      ]
+     },
+     "execution_count": 58,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "m is n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Unintuitively, the comparison operator `==` returns a wrong result as `m` and `n` have `_entries` attributes that compare equal. We fix this in the \"*Operator Overloading*\" section below."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 59,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "False"
+      ]
+     },
+     "execution_count": 59,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "m == n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Sometimes, it is useful to attach functionality to a class object that does *not* depend on the state of a concrete instance but on the class as a whole. Such methods are called **class methods** and can be created with the [classmethod() <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/functions.html#classmethod) built-in combined with the `@` syntax. Then, Python adapts the binding process described above such that it implicitly inserts a reference to the class object itself when the method is invoked. By convention, we name this parameter `cls`. Class methods are commonly used to provide an alternative way to create instances, usually from a different kind of arguments.\n",
+    "\n",
+    "As an example, `from_columns()` expects an iterable of columns instead of rows as its `data` argument. It forwards the invocation to the `__init__()` method (i.e., what `cls(data)` does behind the scenes; `cls` references the *same* class object as `Matrix`), then calls the `transpose()` method on the newly created instance, and lastly returns the instance created by `transpose()`. Again, we are intelligently reusing a lot of code."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 60,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "class Matrix:\n",
+    "\n",
+    "    def __init__(self, data):\n",
+    "        self._entries = list(list(float(x) for x in r) for r in data)\n",
+    "        # ...\n",
+    "\n",
+    "    def __repr__(self):\n",
+    "        args = \", \".join(\"(\" + \", \".join(f\"{c:.3f}\" for c in r) + \",)\" for r in self._entries)\n",
+    "        return f\"Matrix(({args}))\"\n",
+    "\n",
+    "    def transpose(self):\n",
+    "        return Matrix(zip(*self._entries))\n",
+    "\n",
+    "    @classmethod\n",
+    "    def from_columns(cls, data):\n",
+    "        return cls(data).transpose()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "We use the alternative `from_columns()` constructor to create a `Matrix` equivalent to `m` above from a `list` of columns instead of rows."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 61,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "m = Matrix.from_columns([(1, 4, 7), (2, 5, 8), (3, 6, 9)])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 62,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Matrix(((1.000, 2.000, 3.000,), (4.000, 5.000, 6.000,), (7.000, 8.000, 9.000,)))"
+      ]
+     },
+     "execution_count": 62,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "m"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "There is also a [staticmethod() <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/functions.html#staticmethod) built-in to be used with the `@` syntax to define methods that are *independent* from both the class and instance objects but nevertheless related semantically to a class. In this case, the binding process is disabled an no argument is implicitly inserted upon a method's invocation. Such **static methods** are not really needed most of the time and we omit them here fore brevity."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "## Sequence Emulation"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "As discussed in detail in [Chapter 7 <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/07_sequences_00_content.ipynb#Collections-vs.-Sequences), a sequence is any finite and iterable container type with a *predictable* order of its elements such that we can label each element with an index in the range `0 <= index < len(sequence)`.\n",
+    "\n",
+    "To make `Vector` and `Matrix` instances emulate sequences, we implement the `__len__()` (cf., [reference <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/reference/datamodel.html#object.__len__)) and `__getitem__()` (cf., [reference <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/reference/datamodel.html#object.__getitem__)) methods . While the former returns the total number of elements in a container and is automatically invoked on any object passed to the built-in [len() <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/functions.html#len) function, the latter is invoked by the interpreter behind the scenes when we use the indexing operator `[]`.\n",
+    "\n",
+    "In the example, both `__len__()` and `__getitem__()` delegate parts of the work to the embedded `list` object named `_entries`. This is a design principle known as [delegation <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_wiki.png\">](https://en.wikipedia.org/wiki/Delegation_%28object-oriented_programming%29) in software engineering. We implicitly invoke the `__len__()` method inside the `__init__()` method already with `len(self)`. This not only reuses code but also ensures that we calculate the number of entries only in one way within the entire class."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 63,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "class Vector:\n",
+    "\n",
+    "    def __init__(self, data):\n",
+    "        self._entries = list(float(x) for x in data)\n",
+    "        if len(self) == 0:\n",
+    "            raise ValueError(\"a vector must have at least one entry\")\n",
+    "\n",
+    "    def __repr__(self):\n",
+    "        args = \", \".join(f\"{x:.3f}\" for x in self._entries)\n",
+    "        return f\"Vector(({args}))\"\n",
+    "\n",
+    "    def __len__(self):\n",
+    "        return len(self._entries)\n",
+    "\n",
+    "    def __getitem__(self, index):\n",
+    "        return self._entries[index]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Now, we may obtain the number of elements with [len() <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/functions.html#len) and index into `Vector` instances."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 64,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "v = Vector([1, 2, 3])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 65,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "3"
+      ]
+     },
+     "execution_count": 65,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "len(v)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 66,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "1.0"
+      ]
+     },
+     "execution_count": 66,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "v[0]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Negative indexes work \"out of the box\" because of the delegation to the `list` type."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 67,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "3.0"
+      ]
+     },
+     "execution_count": 67,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "v[-1]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Somehow \"magically\" we can loop over `v` with a `for` statement."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 68,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "1.0  2.0  3.0  "
+     ]
+    }
+   ],
+   "source": [
+    "for entry in v:\n",
+    "    print(entry, end=\"  \")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Behind the scenes, Python simply loops over the indexes implied by `len(v)` with the help of the [range() <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/functions.html#func-range) built-in and obtains each `entry` one by one. With sequence emulation, we should never have to do that \"manually.\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 69,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "1.0  2.0  3.0  "
+     ]
+    }
+   ],
+   "source": [
+    "for index in range(len(v)):\n",
+    "    entry = v[index]\n",
+    "    print(entry, end=\"  \")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "`v` may also be iterated over in reverse order with the [reversed() <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/functions.html#reversed) built-in."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 70,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "3.0  2.0  1.0  "
+     ]
+    }
+   ],
+   "source": [
+    "for entry in reversed(v):\n",
+    "    print(entry, end=\"  \")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Because of the iteration, membership testing with the `in` operator also comes \"for free.\" Here, Python compares the object to be searched to each element with the `==` operator and stops early once one compares equal. That constitutes a [linear search <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_wiki.png\">](https://en.wikipedia.org/wiki/Linear_search) as seen before."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 71,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 71,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "1 in v"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 72,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "False"
+      ]
+     },
+     "execution_count": 72,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "99 in v"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "So far, indexing is a *read-only* operation."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 73,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "ename": "TypeError",
+     "evalue": "'Vector' object does not support item assignment",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-73-ac550dc2349d>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mv\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m99\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;31mTypeError\u001b[0m: 'Vector' object does not support item assignment"
+     ]
+    }
+   ],
+   "source": [
+    "v[0] = 99"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Because a `Matrix` is two-dimensional, we must decide how we *flatten* the `_entries`. We *choose* the approach where we loop over the first row, then the second row, and so on. This is called a **[row major approach <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_wiki.png\">](https://en.wikipedia.org/wiki/Row-_and_column-major_order)**.\n",
+    "\n",
+    "In addition to indexing by `int` objects, we also implement indexing by 2-`tuple`s of `int`s where the first element indicates the row and the second the column. Deciding what to do inside a method depending on the *type* of an argument is known as **type dispatching**. We achieve that with the built-in [isinstance() <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/functions.html#isinstance) function.\n",
+    "\n",
+    "Lastly, we ensure that integer indexing also works with negative values as we are used to from sequences in general.\n",
+    "\n",
+    "Note how all of the methods work together:\n",
+    "- `__init__()`, `__len__()`, and `__getitem__()` reuse the `n_rows` and `n_cols` properties\n",
+    "- `__init__()` and `__getitem__()` invoke `__len__()` via the `len(self)` expressions"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 74,
+   "metadata": {
+    "code_folding": [],
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "class Matrix:\n",
+    "\n",
+    "    def __init__(self, data):\n",
+    "        self._entries = list(list(float(x) for x in r) for r in data)\n",
+    "        for row in self._entries[1:]:\n",
+    "            if len(row) != self.n_cols:\n",
+    "                raise ValueError(\"rows must have the same number of entries\")\n",
+    "        if len(self) == 0:\n",
+    "            raise ValueError(\"a matrix must have at least one entry\")\n",
+    "\n",
+    "    @property\n",
+    "    def n_rows(self):\n",
+    "        return len(self._entries)\n",
+    "\n",
+    "    @property\n",
+    "    def n_cols(self):\n",
+    "        return len(self._entries[0])\n",
+    "\n",
+    "    def __len__(self):\n",
+    "        return self.n_rows * self.n_cols\n",
+    "\n",
+    "    def __getitem__(self, index):\n",
+    "        if isinstance(index, int):\n",
+    "            if index < 0:\n",
+    "                index += len(self)\n",
+    "            if not (0 <= index < len(self)):\n",
+    "                raise IndexError(\"integer index out of range\")\n",
+    "            row, col = divmod(index, self.n_cols)\n",
+    "            return self._entries[row][col]\n",
+    "        elif (\n",
+    "            isinstance(index, tuple) and len(index) == 2\n",
+    "            and isinstance(index[0], int) and isinstance(index[1], int)\n",
+    "        ):\n",
+    "            return self._entries[index[0]][index[1]]\n",
+    "        raise TypeError(\"index must be either an int or a tuple of two int's\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Now, we may use a `Matrix` instance just like any other sequence ..."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 75,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "m = m = Matrix([(1, 2, 3), (4, 5, 6), (7, 8, 9)])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 76,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "9"
+      ]
+     },
+     "execution_count": 76,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "len(m)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 77,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "1.0"
+      ]
+     },
+     "execution_count": 77,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "m[0]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 78,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "9.0"
+      ]
+     },
+     "execution_count": 78,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "m[-1]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "... but also index in the two dimensions separately."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 79,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "3.0"
+      ]
+     },
+     "execution_count": 79,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "m[0, 2]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 80,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "9.0"
+      ]
+     },
+     "execution_count": 80,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "m[-1, -1]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "As before, Python figures out the iteration on its own ..."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 81,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "1.0  2.0  3.0  4.0  5.0  6.0  7.0  8.0  9.0  "
+     ]
+    }
+   ],
+   "source": [
+    "for entry in m:\n",
+    "    print(entry, end=\"  \")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 82,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "9.0  8.0  7.0  6.0  5.0  4.0  3.0  2.0  1.0  "
+     ]
+    }
+   ],
+   "source": [
+    "for entry in reversed(m):\n",
+    "    print(entry, end=\"  \")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "... and makes the `in` operator do a linear search."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 83,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 83,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "1 in m"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 84,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "False"
+      ]
+     },
+     "execution_count": 84,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "99 in m"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "### The Python Data Model"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Sequence emulation itself is *not* a property of object-oriented languages in general. Instead, it is a behavior any data type may or may not exhibit in Python.\n",
+    "\n",
+    "The collection of all such behaviors a programming language offers is commonly referred to as its **object model**. In Python, the term **data model** is used instead and all possible behaviors are documented in the [language reference <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/reference/datamodel.html), in particular, in the section on special methods. We can think of the data model as the collection of all the behaviors we can make our user-defined data types follow. Pythonistas also use the term **protocol** instead of behavior, for example, we may say that the `Vector` and `Matrix` classes follow the sequence protocol.\n",
+    "\n",
+    "So, merely defining the *two* `__len__()` and `__getitem__()` methods is enough to make instances of any user-defined type behave like the built-in sequences in [Chapter 7 <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/07_sequences_00_content.ipynb). Yet, there we defined sequences as all objects having the *four* properties of being finite, iterable, and ordered container types. And, these properties correspond to special methods by the names of `__len__()`, `__iter__()`, `__reversed__()`,  and `__contains__()` as we see in the next sub-section. Thus, Python \"magically\" knows how to derive the logic for the `__iter__()`, `__reversed__()`,  and `__contains__()` methods from the combination of the `__len__()` and `__getitem__()` methods. In general, while some special methods are related, others are not. Understanding these relationships means understanding the Python data model and vice versa. That is what every aspiring data scientist should aim for.\n",
+    "\n",
+    "On the contrary, we could also look at special methods individually. Whereas `__len__()` is invoked on the object passed to [len() <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/functions.html#len), Python \"translates\" the indexing operator applied on any name like `a[i]`, for example, into the method invocation `a.__getitem__(i)`. So, in both cases, the special methods are executed according to a deterministic rule of the language. In that sense, they act as some sort of syntactic sugar. Thus, they even work if only one of them is defined. For example, without `__len__()`, iteration with a `for`-loop still works but only in forward direction."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "### Alternative Iteration"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "When implementing the sequence protocol for our `Matrix` class, we had to make the assumption that the user of our class wants to iterate over the entries in a rows first fashion. While such assumptions can often be justified by referring to popular conventions (e.g., mathematicians usually look at matrices also in a \"row by column\" way), we could instead provide several iteration methods such that the user may choose one, just like `dict`s come with several built-in methods that provide iteration.\n",
+    "\n",
+    "In the revised `Matrix` class below, we add the `rows()`, `cols()`, and `entries()` methods that return *iterators* providing different and memory efficient ways of looping over the entries. `rows()` and `cols()` sequentially produce `Vector` instances representing individual rows and columns. This is in line with the popular idea in linear algebra to view matrices as a collection of either row or column vectors. Further, `entries()` by default produces the entries in the matrix one by one in a flat and row major fashion. Called with the optional `row_major=False` flag, it does the same in a column major fashion. The optional `reverse=True` flag allows iteration in backwards order.\n",
+    "\n",
+    "We also implement the `__iter__()` (cf., [reference <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/reference/datamodel.html#object.__iter__)) and `__reversed__()` (cf., [reference <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/reference/datamodel.html#object.__reversed__)) methods that immediately forward invocation to `entries()`. So, Python does not need to fall back to `__len__()` and `__getitem__()`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 85,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "class Matrix:\n",
+    "\n",
+    "    def __init__(self, data):\n",
+    "        self._entries = list(list(float(x) for x in r) for r in data)\n",
+    "        # ...\n",
+    "\n",
+    "    def __repr__(self):\n",
+    "        args = \", \".join(\"(\" + \", \".join(f\"{c:.3f}\" for c in r) + \",)\" for r in self._entries)\n",
+    "        return f\"Matrix(({args}))\"\n",
+    "\n",
+    "    @property\n",
+    "    def n_rows(self):\n",
+    "        return len(self._entries)\n",
+    "\n",
+    "    @property\n",
+    "    def n_cols(self):\n",
+    "        return len(self._entries[0])\n",
+    "\n",
+    "    def rows(self):\n",
+    "        return (Vector(r) for r in self._entries)\n",
+    "\n",
+    "    def cols(self):\n",
+    "        return (\n",
+    "            Vector(self._entries[r][c] for r in range(self.n_rows)) for c in range(self.n_cols)\n",
+    "        )\n",
+    "\n",
+    "    def entries(self, *, reverse=False, row_major=True):\n",
+    "        if reverse:\n",
+    "            rows, cols = (range(self.n_rows - 1, -1, -1), range(self.n_cols - 1, -1, -1))\n",
+    "        else:\n",
+    "            rows, cols = range(self.n_rows), range(self.n_cols)\n",
+    "        if row_major:\n",
+    "            return (self._entries[r][c] for r in rows for c in cols)\n",
+    "        return (self._entries[r][c] for c in cols for r in rows)\n",
+    "\n",
+    "    def __iter__(self):\n",
+    "        return self.entries()\n",
+    "\n",
+    "    def __reversed__(self):\n",
+    "        return self.entries(reverse=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "The revised version of `Vector` below also works without `__len__()` and `__getitem__()` methods and leaves the creation of memory efficient iterators up to the embedded `list` object in `_entries` by using the built-in [iter() <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/functions.html#iter) and [reversed() <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/functions.html#reversed) functions. Also, `__repr__()` now relies on the sequence protocol as the instance loops over \"itself\" with `for x in self`, a subtle reuse of code again. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 86,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "class Vector:\n",
+    "\n",
+    "    def __init__(self, data):\n",
+    "        self._entries = list(float(x) for x in data)\n",
+    "        # ...\n",
+    "\n",
+    "    def __repr__(self):\n",
+    "        args = \", \".join(f\"{x:.3f}\" for x in self)\n",
+    "        return f\"Vector(({args}))\"\n",
+    "\n",
+    "    def __iter__(self):\n",
+    "        return iter(self._entries)\n",
+    "\n",
+    "    def __reversed__(self):\n",
+    "        return reversed(self._entries)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 87,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "m = Matrix([(1, 2, 3), (4, 5, 6), (7, 8, 9)])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 88,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Matrix(((1.000, 2.000, 3.000,), (4.000, 5.000, 6.000,), (7.000, 8.000, 9.000,)))"
+      ]
+     },
+     "execution_count": 88,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "m"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Iteration works as before ..."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 89,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "1.0  2.0  3.0  4.0  5.0  6.0  7.0  8.0  9.0  "
+     ]
+    }
+   ],
+   "source": [
+    "for entry in m:\n",
+    "    print(entry, end=\"  \")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 90,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "9.0  8.0  7.0  6.0  5.0  4.0  3.0  2.0  1.0  "
+     ]
+    }
+   ],
+   "source": [
+    "for entry in reversed(m):\n",
+    "    print(entry, end=\"  \")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "... but now we have some ways of customizing it."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 91,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Vector((1.000, 2.000, 3.000))  Vector((4.000, 5.000, 6.000))  Vector((7.000, 8.000, 9.000))  "
+     ]
+    }
+   ],
+   "source": [
+    "for row_vector in m.rows():\n",
+    "    print(row_vector, end=\"  \")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 92,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Vector((1.000, 4.000, 7.000))  Vector((2.000, 5.000, 8.000))  Vector((3.000, 6.000, 9.000))  "
+     ]
+    }
+   ],
+   "source": [
+    "for col_vector in m.cols():\n",
+    "    print(col_vector, end=\"  \")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 93,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "1.0  2.0  3.0  4.0  5.0  6.0  7.0  8.0  9.0  "
+     ]
+    }
+   ],
+   "source": [
+    "for entry in m.entries():\n",
+    "    print(entry, end=\"  \")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 94,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "1.0  4.0  7.0  2.0  5.0  8.0  3.0  6.0  9.0  "
+     ]
+    }
+   ],
+   "source": [
+    "for entry in m.entries(row_major=False):\n",
+    "    print(entry, end=\"  \")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "## Mutability vs. Immutability"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "In the above implementations, the instance attribute `_entries` on a `Vector` or `Matrix` instances references either a `list` or a `list` of row `list`s , which is by the convention of the leading underscore `_` an implementation detail. If users of our classes adhere to this convention, `Vector` and `Matrix` instances can be regarded as *immutable*.\n",
+    "\n",
+    "In line with the implied immutability, we implemented the `transpose()` method such that it returns a *new* `Matrix` instance. Instead, we could make the method change the internal `self._entries` attribute *in place* as we do in the next example. To indicate this mutation to the user of the `Matrix` class clearly, the revised `transpose()` method returns `None`. That mirrors, for example, how the mutating methods of the built-in `list` type behave (cf., [Chapter 7 <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/07_sequences_00_content.ipynb#List-Methods)).\n",
+    "\n",
+    "Such decisions are better made consciously when designing a custom data type. The main trade-off is that immutable data types are typically easier to reason about when reading code whereas mutable data types tend to be more memory efficient and make programs faster as less copying operations take place in memory. However, this trade-off only becomes critical when we deal with big amounts of data."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 95,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "class Matrix:\n",
+    "\n",
+    "    def __init__(self, data):\n",
+    "        self._entries = list(list(float(x) for x in r) for r in data)\n",
+    "        # ...\n",
+    "\n",
+    "    def __repr__(self):\n",
+    "        args = \", \".join(\"(\" + \", \".join(f\"{c:.3f}\" for c in r) + \",)\" for r in self._entries)\n",
+    "        return f\"Matrix(({args}))\"\n",
+    "\n",
+    "    def transpose(self):\n",
+    "        self._entries = list(list(float(x) for x in r) for r in (zip(*self._entries)))\n",
+    "        return None"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 96,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "m = Matrix([(1, 2, 3), (4, 5, 6), (7, 8, 9)])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 97,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Matrix(((1.000, 2.000, 3.000,), (4.000, 5.000, 6.000,), (7.000, 8.000, 9.000,)))"
+      ]
+     },
+     "execution_count": 97,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "m"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Transposing `m` has *no* cell output ..."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 98,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "m.transpose()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "... so we must look at `m` again."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 99,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Matrix(((1.000, 4.000, 7.000,), (2.000, 5.000, 8.000,), (3.000, 6.000, 9.000,)))"
+      ]
+     },
+     "execution_count": 99,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "m"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "A downside of returning `None` is that we can *not* chain repeated invocations of `transpose()`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 100,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "ename": "AttributeError",
+     "evalue": "'NoneType' object has no attribute 'transpose'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-100-21b8354a80f8>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mm\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtranspose\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtranspose\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;31mAttributeError\u001b[0m: 'NoneType' object has no attribute 'transpose'"
+     ]
+    }
+   ],
+   "source": [
+    "m.transpose().transpose()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "### Enabling Method Chaining"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "To fix the missing method chaining, we end the `transpose()` method with `return self`, which returns a reference to the instance on which the method is invoked."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 101,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "class Matrix:\n",
+    "\n",
+    "    def __init__(self, data):\n",
+    "        self._entries = list(list(float(x) for x in r) for r in data)\n",
+    "        # ...\n",
+    "\n",
+    "    def __repr__(self):\n",
+    "        args = \", \".join(\"(\" + \", \".join(f\"{c:.3f}\" for c in r) + \",)\" for r in self._entries)\n",
+    "        return f\"Matrix(({args}))\"\n",
+    "\n",
+    "    def __iter__(self):  # adapted for brevity; uses parts of entries()\n",
+    "        rows, cols = range(len(self._entries)), range(len(self._entries[0]))\n",
+    "        return (self._entries[r][c] for r in rows for c in cols)\n",
+    "\n",
+    "    def transpose(self):\n",
+    "        self._entries = list(list(float(x) for x in r) for r in (zip(*self._entries)))\n",
+    "        return self"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 102,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "m = Matrix([(1, 2, 3), (4, 5, 6), (7, 8, 9)])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 103,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Matrix(((1.000, 2.000, 3.000,), (4.000, 5.000, 6.000,), (7.000, 8.000, 9.000,)))"
+      ]
+     },
+     "execution_count": 103,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "m"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "The downside of this approach is that a user may unknowingly end up with *two* references to the *same* instance. That can only be mitigated by clear documentation."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 104,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "n = m.transpose()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 105,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Matrix(((1.000, 4.000, 7.000,), (2.000, 5.000, 8.000,), (3.000, 6.000, 9.000,)))"
+      ]
+     },
+     "execution_count": 105,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "m"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 106,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Matrix(((1.000, 4.000, 7.000,), (2.000, 5.000, 8.000,), (3.000, 6.000, 9.000,)))"
+      ]
+     },
+     "execution_count": 106,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 107,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 107,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "m is n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "### Customizing Indexing"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Analogous to the `__getitem__()` method above, there are also the `__setitem__()` (cf., [reference <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/reference/datamodel.html#object.__setitem__)) and `__delitem__()` (cf., [reference <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/reference/datamodel.html#object.__delitem__)) methods that assign a new element to or delete an existing element from a sequence.\n",
+    "\n",
+    "Whereas deleting an individual entry in a `Vector` or `Matrix` instance may *not* really make sense semantically, we interpret this as setting the corresponding entry to \"unknown\" (i.e., `NaN`). Also, we implement changing individual entries via index assignment. Here, `__setitem__()` delegates the assignment to the embedded `list` object after casting the assigned value as a `float`. While the example below only allows indexing by an integer, it could be generalized to slicing as well."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 108,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "class Vector:\n",
+    "\n",
+    "    def __init__(self, data):\n",
+    "        self._entries = list(float(x) for x in data)\n",
+    "        # ...\n",
+    "\n",
+    "    def __repr__(self):\n",
+    "        args = \", \".join(f\"{x:.3f}\" for x in self)\n",
+    "        return f\"Vector(({args}))\"\n",
+    "\n",
+    "    def __getitem__(self, index):\n",
+    "        return self._entries[index]\n",
+    "\n",
+    "    def __setitem__(self, index, value):\n",
+    "        self._entries[index] = float(value)\n",
+    "\n",
+    "    def __delitem__(self, index):\n",
+    "        self._entries[index] = float(\"NaN\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 109,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "v = Vector([1, 2, 3])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 110,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Vector((1.000, 2.000, 3.000))"
+      ]
+     },
+     "execution_count": 110,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "v"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "`v` can now be changed in place."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 111,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "del v[0]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 112,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Vector((nan, 2.000, 3.000))"
+      ]
+     },
+     "execution_count": 112,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "v"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 113,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "v[0] = 99"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 114,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Vector((99.000, 2.000, 3.000))"
+      ]
+     },
+     "execution_count": 114,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "v"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "After this discourse on mutable `Vector` and `Matrix` classes, we continue with immutable classes in the rest of this chapter."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "## Polymorphism"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "A function is considered **polymorphic** if it can work with *different* data types. The main advantage is reuse of the function's code. Polymorphism goes hand in hand with the concept of [duck typing <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_wiki.png\">](https://en.wikipedia.org/wiki/Duck_typing) mentioned in previous chapters.\n",
+    "\n",
+    "We know polymorphic functions already: The built-in [sum() <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/functions.html#sum) function is a trivial example that works with all kinds of `iterable` arguments."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 115,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "10"
+      ]
+     },
+     "execution_count": 115,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "sum((1, 2, 3, 4))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 116,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "10"
+      ]
+     },
+     "execution_count": 116,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "sum([1, 2, 3, 4])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 117,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "10"
+      ]
+     },
+     "execution_count": 117,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "sum({1, 2, 3, 4})"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 118,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "10"
+      ]
+     },
+     "execution_count": 118,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "sum({1: 996, 2: 997, 3: 998, 4: 999})  # loops over the keys"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "As we implemented the `Vector` and `Matrix` classes to be iterable, we may pass them to [sum() <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/functions.html#sum) as well."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 119,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "10.0"
+      ]
+     },
+     "execution_count": 119,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "sum(Vector([1, 2, 3, 4]))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 120,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "10.0"
+      ]
+     },
+     "execution_count": 120,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "sum(Matrix([(1, 2), (3, 4)]))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "A polymorphic function with a semantic meaning in the context of linear algebra would be one that calculates the [Euclidean norm <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_wiki.png\">](https://en.wikipedia.org/wiki/Norm_%28mathematics%29#Euclidean_norm) for vectors, which is a generalization of the popular [Pythagorean theorem <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_wiki.png\">](https://en.wikipedia.org/wiki/Pythagorean_theorem). Extending the same kind of computation to a matrix results in the even more general [Frobenius norm <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_wiki.png\">](https://en.wikipedia.org/wiki/Matrix_norm#Frobenius_norm):"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "$$\\lVert \\bf{X} \\rVert_F = \\sqrt{ \\sum_{i=1}^m \\sum_{j=1}^n x_{ij}^2 }$$"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "The `norm()` function below can handle both a `Vector` or a `Matrix` instance and is therefore polymorphic. In this sense, `Vector` and `Matrix` instances \"walk\" and \"quack\" in a similar way. In particular they they both can provide all their entries as a flat sequence."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 121,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "import math"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 122,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "def norm(vector_or_matrix):\n",
+    "    \"\"\"Calculate the Frobenius or Euclidean norm of a matrix or vector.\n",
+    "\n",
+    "    Args:\n",
+    "        vector_or_matrix (Vector/Matrix): the entries whose squares\n",
+    "            are summed up\n",
+    "\n",
+    "    Returns:\n",
+    "        norm (float)\n",
+    "    \"\"\"\n",
+    "    return math.sqrt(sum(x ** 2 for x in vector_or_matrix))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "While `norm()` is intended to work with `Vector` or `Matrix` instances ..."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 123,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "5.477225575051661"
+      ]
+     },
+     "execution_count": 123,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "norm(Vector([1, 2, 3, 4]))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 124,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "5.477225575051661"
+      ]
+     },
+     "execution_count": 124,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "norm(Matrix([(1, 2), (3, 4)]))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "... it also works for any sequence of numbers."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 125,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "5.477225575051661"
+      ]
+     },
+     "execution_count": 125,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "norm([1, 2, 3, 4])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "An important criterion if different classes are compatible in the sense that the same polymorphic function can work with them is that they implement the same **interface**.\n",
+    "\n",
+    "Whereas many other programming languages formalize this [concept <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_wiki.png\">](https://en.wikipedia.org/wiki/Protocol_%28object-oriented_programming%29), in Python the term refers to the idea that different classes define the same attributes and implement the various protocols behind the special methods in a consistent way. This is what it means to \"walk\" and \"quack\" alike."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "## Representations of Data"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "source": [
+    "> \"If you change the way you look at things, the things you look at change.\"\n",
+    "> -- philosopher and personal coach [Dr. Wayne Dyer <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_wiki.png\">](https://en.wikipedia.org/wiki/Wayne_Dyer)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Sometimes, it is helpful to view a vector as a matrix with either one row or one column. On the contrary, such a matrix can always be interpreted as a vector again. Changing the representation of the same underlying data (i.e., the `_entries`) can be viewed as \"changing\" an object's data type, for which, however, there is no syntax.\n",
+    "\n",
+    "Thus, we implement the `as_matrix()` and `as_vector()` methods below that create *new* `Matrix` or `Vector` instances out of existing `Vector` or `Matrix` instances, respectively. Internally, both methods rely on the sequence protocol again (i.e., `for x in self`). Also, `as_matrix()` interprets the `Vector` instance as a column vector by default (i.e., the `column=True` default argument)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 126,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "class Vector:\n",
+    "\n",
+    "    def __init__(self, data):\n",
+    "        self._entries = list(float(x) for x in data)\n",
+    "        # ...\n",
+    "\n",
+    "    def __repr__(self):\n",
+    "        args = \", \".join(f\"{x:.3f}\" for x in self)\n",
+    "        return f\"Vector(({args}))\"\n",
+    "\n",
+    "    def __iter__(self):\n",
+    "        return iter(self._entries)\n",
+    "\n",
+    "    def as_matrix(self, *, column=True):\n",
+    "        if column:\n",
+    "            return Matrix([x] for x in self)\n",
+    "        return Matrix([(x for x in self)])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 127,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "class Matrix:\n",
+    "\n",
+    "    def __init__(self, data):\n",
+    "        self._entries = list(list(float(x) for x in r) for r in data)\n",
+    "        # ...\n",
+    "\n",
+    "    def __repr__(self):\n",
+    "        args = \", \".join(\"(\" + \", \".join(f\"{c:.3f}\" for c in r) + \",)\" for r in self._entries)\n",
+    "        return f\"Matrix(({args}))\"\n",
+    "\n",
+    "    @property\n",
+    "    def n_rows(self):\n",
+    "        return len(self._entries)\n",
+    "\n",
+    "    @property\n",
+    "    def n_cols(self):\n",
+    "        return len(self._entries[0])\n",
+    "\n",
+    "    def __iter__(self):  # adapted for brevity; uses parts of entries()\n",
+    "        return (self._entries[r][c] for r in range(self.n_rows) for c in range(self.n_cols))\n",
+    "\n",
+    "    def as_vector(self):\n",
+    "        if not (self.n_rows == 1 or self.n_cols == 1):\n",
+    "            raise RuntimeError(\"one dimension (m or n) must be 1\")\n",
+    "        return Vector(x for x in self)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 128,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "v = Vector([1, 2, 3])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 129,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Vector((1.000, 2.000, 3.000))"
+      ]
+     },
+     "execution_count": 129,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "v"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Let's interpret `v` as a column vector and create a matrix of dimension $3 \\times 1$."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 130,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "m = v.as_matrix()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 131,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Matrix(((1.000,), (2.000,), (3.000,)))"
+      ]
+     },
+     "execution_count": 131,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "m"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 132,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(3, 1)"
+      ]
+     },
+     "execution_count": 132,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "m.n_rows, m.n_cols"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "By chaining `as_matrix()` and `as_vector()` we get a *new* `Vector` instance back that is equivalent to the given `v`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 133,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Vector((1.000, 2.000, 3.000))"
+      ]
+     },
+     "execution_count": 133,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "v.as_matrix().as_vector()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "In the same way, we can also interpret `v` as a row vector and create a $1 \\times 3$ matrix."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 134,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "m = v.as_matrix(column=False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 135,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Matrix(((1.000, 2.000, 3.000,)))"
+      ]
+     },
+     "execution_count": 135,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "m"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 136,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(1, 3)"
+      ]
+     },
+     "execution_count": 136,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "m.n_rows, m.n_cols"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 137,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Vector((1.000, 2.000, 3.000))"
+      ]
+     },
+     "execution_count": 137,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "v.as_matrix(column=False).as_vector()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Interpreting a matrix as a vector only works if one of the two dimensions $m$ or $n$ is $1$. If this requirement is not satisfied, we get a `RuntimeError` as raised in `as_vector()` above."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 138,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "m = Matrix([(1, 2, 3), (4, 5, 6), (7, 8, 9)])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 139,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "ename": "RuntimeError",
+     "evalue": "one dimension (m or n) must be 1",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mRuntimeError\u001b[0m                              Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-139-625d8ddab141>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mm\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mas_vector\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;32m<ipython-input-127-6c3809cd776d>\u001b[0m in \u001b[0;36mas_vector\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m     22\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mas_vector\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     23\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mn_rows\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m1\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mn_cols\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 24\u001b[0;31m             \u001b[0;32mraise\u001b[0m \u001b[0mRuntimeError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"one dimension (m or n) must be 1\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     25\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mVector\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mx\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;31mRuntimeError\u001b[0m: one dimension (m or n) must be 1"
+     ]
+    }
+   ],
+   "source": [
+    "m.as_vector()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "## Operator Overloading"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Using special methods such as `__add__()`, `__sub__()`, `__mul__()`, and some others, user-defined data types can emulate numeric types (cf., [reference <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/reference/datamodel.html#emulating-numeric-types)): Then, `Vector` and `Matrix` instances can be added together, subtracted from one another, or be multiplied together. We use them to implement the arithmetic rules from linear algebra.\n",
+    "\n",
+    "The OOP concept behind this is **[operator overloading <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_wiki.png\">](https://en.wikipedia.org/wiki/Operator_overloading)** as first mentioned in the context of string concatenation in [Chapter 1 <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/01_elements_00_content.ipynb#Operator-Overloading)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "### Arithmetic Operators"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "To understand the protocol behind arithmetic operators, we first look at the simple case of how an `int` object and a `float` object are added. The expression `1 + 2.0` is \"translated\" by Python into an implicit method invocation of the form `1.__add__(2.0)`. This is why all the special methods behind binary operators take two arguments, `self` and, by convention, `other`. To allow binary operators to work with objects of *different* data types, Python expects the `__add__()` method on the `1` object to return `NotImplemented` if it does not know how to deal with the `2.0` object and then proceeds by invoking the *reverse* special method `2.0.__radd__(1)`. With this protocol, one can create *new* data types that know how to execute arithmetic operations with *existing* data types *without* having to change the latter. By convention, the result of a binary operation should always be a *new* instance object and *not* a mutation of an existing one."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Before implementing the arithmetic operators, we must first determine what other data types are allowed to interact with our `Vector` and `Matrix` instances and also how the two interact with each other. Conceptually, this is the same as to ask how strict we want the rules from linear algebra to be enforced in our model world. For example, while it is obvious that two vectors with the same number of entries may be added or subtracted, we could also allow a scalar value to be added to a vector. That seems awkward at first because it is an illegal operation in linear algebra. However, for convenience in our programs, we could interpret any scalar as a \"constants\" vector of the \"right size\" and add it to each entry in a `Vector`. This idea can be generalized into what is called **[broadcasting](https://docs.scipy.org/doc/numpy/user/basics.broadcasting.html)** in [numpy](https://docs.scipy.org/doc/numpy/index.html). We often see \"dirty hacks\" like this in code. They are no bugs but features supposed to make the user of a library more productive.\n",
+    "\n",
+    "In this chapter, we model the following binary arithmetic operations:"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "- **Addition** / **Subtraction**\n",
+    "  - `Vector` with `Vector` (if number of entries match; commutative)\n",
+    "  - `Matrix` with `Matrix` (if dimensions $m$ and $n$ match; commutative)\n",
+    "  - `Matrix` / `Vector` with scalar (the scalar is broadcasted; non-commutative for subtraction)\n",
+    "- **Multiplication**\n",
+    "  - `Vector` with `Vector` ([dot product <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_wiki.png\">](https://en.wikipedia.org/wiki/Dot_product) if number of entries match; commutative)\n",
+    "  - `Matrix` with `Vector` (if dimensions are compatible; vector interpreted as column vector; non-commutative)\n",
+    "  - `Vector` with `Matrix` (if dimensions are compatible; vector interpreted as row vector; non-commutative)\n",
+    "  - `Matrix` / `Vector` with scalar ([scalar multiplication <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_wiki.png\">](https://en.wikipedia.org/wiki/Scalar_multiplication); commutative)\n",
+    "  - `Matrix` with `Matrix` ([matrix-matrix multiplication <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_wiki.png\">](https://en.wikipedia.org/wiki/Matrix_multiplication) if dimensions are compatible; generally non-commutative)\n",
+    "- **Division**\n",
+    "  - `Matrix` / `Vector` by a scalar (inverse of [scalar multiplication <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_wiki.png\">](https://en.wikipedia.org/wiki/Scalar_multiplication); non-commutative)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "This listing shows the conceptual complexity behind the task of writing a \"little\" linear algebra library. Not to mention that some of the operations are [commutative <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_wiki.png\">](https://en.wikipedia.org/wiki/Commutative_property) while others are not.\n",
+    "\n",
+    "As the available special methods correspond to the high-level grouping in the listing, we must implement a lot of **type dispatching** within them. This is why you see the built-in [isinstance() <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/functions.html#isinstance) function in most of the methods below. We use it to check if the `other` argument passed in is a `Vector` or `Matrix` instance or a scalar.\n",
+    "\n",
+    "To check for the latter, we need to specify what data type constitutes a scalar. We use a goose typing strategy as explained in [Chapter 5 <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/05_numbers_00_content.ipynb#Goose-Typing): Any object that behaves like a `numbers.Number` from the [numbers <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/numbers.html) module in the [standard library <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/index.html) is considered a scalar.\n",
+    "\n",
+    "For example, the integer `1` is an instance of the built-in `int` type. At the same time, [isinstance() <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/functions.html#isinstance) also confirms that it is a `numbers.Number` in the abstract sense."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 140,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "import numbers"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 141,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 141,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "isinstance(1, int)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 142,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 142,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "isinstance(1, numbers.Number)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Now with all the preparation work done, let's look at a \"minimal\" implementation of `Vector` that supports all the arithmetic operations specified above. *None* of the special methods inside the `Vector` class is aware that the `Matrix` class exists! Thus, all operations involving at least one `Matrix` instance are implemented only in the `Matrix` class."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 143,
+   "metadata": {
+    "code_folding": [],
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "class Vector:\n",
+    "\n",
+    "    def __init__(self, data):\n",
+    "        self._entries = list(float(x) for x in data)\n",
+    "        # ...\n",
+    "\n",
+    "    def __repr__(self):\n",
+    "        args = \", \".join(f\"{x:.3f}\" for x in self)\n",
+    "        return f\"Vector(({args}))\"\n",
+    "\n",
+    "    def __len__(self):\n",
+    "        return len(self._entries)\n",
+    "\n",
+    "    def __iter__(self):\n",
+    "        return iter(self._entries)\n",
+    "\n",
+    "    def __add__(self, other):\n",
+    "        if isinstance(other, self.__class__):  # vector addition\n",
+    "            if len(self) != len(other):\n",
+    "                raise ValueError(\"vectors need to be of the same length\")\n",
+    "            return Vector(x + y for (x, y) in zip(self, other))\n",
+    "        elif isinstance(other, numbers.Number):  # broadcasting addition\n",
+    "            return Vector(x + other for x in self)\n",
+    "        return NotImplemented\n",
+    "\n",
+    "    def __radd__(self, other):\n",
+    "        return self + other\n",
+    "\n",
+    "    def __sub__(self, other):\n",
+    "        if isinstance(other, self.__class__):  # vector subtraction\n",
+    "            if len(self) != len(other):\n",
+    "                raise ValueError(\"vectors need to be of the same length\")\n",
+    "            return Vector(x - y for (x, y) in zip(self, other))\n",
+    "        elif isinstance(other, numbers.Number):  # broadcasting subtraction\n",
+    "            return Vector(x - other for x in self)\n",
+    "        return NotImplemented\n",
+    "\n",
+    "    def __rsub__(self, other):\n",
+    "        # Reverse vector subtraction is already handled in __sub__().\n",
+    "        if isinstance(other, numbers.Number):  # broadcasting subtraction\n",
+    "            return Vector(other - x for x in self)\n",
+    "        return NotImplemented\n",
+    "\n",
+    "    def __mul__(self, other):\n",
+    "        if isinstance(other, self.__class__):  # dot product\n",
+    "            if len(self) != len(other):\n",
+    "                raise ValueError(\"vectors need to be of the same length\")\n",
+    "            return sum(x * y for (x, y) in zip(self, other))\n",
+    "        elif isinstance(other, numbers.Number):  # scalar multiplication\n",
+    "            return Vector(x * other for x in self)\n",
+    "        return NotImplemented\n",
+    "\n",
+    "    def __rmul__(self, other):\n",
+    "        return self * other\n",
+    "\n",
+    "    def __truediv__(self, other):\n",
+    "        if isinstance(other, numbers.Number):\n",
+    "            return self * (1 / other)\n",
+    "        return NotImplemented\n",
+    "\n",
+    "    def as_matrix(self, *, column=True):\n",
+    "        if column:\n",
+    "            return Matrix([x] for x in self)\n",
+    "        return Matrix([(x for x in self)])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 144,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "v = Vector([1, 2, 3])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 145,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "w = Vector([4, 5])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "`__mul__()` implements both scalar multiplication and the dot product of two `Vector`s. As both operations are commutative, `__rmul__()` dispatches to `__mul__()`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 146,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Vector((2.000, 4.000, 6.000))"
+      ]
+     },
+     "execution_count": 146,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "2 * v"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 147,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Vector((3.000, 6.000, 9.000))"
+      ]
+     },
+     "execution_count": 147,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "v * 3"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 148,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "14.0"
+      ]
+     },
+     "execution_count": 148,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "v * v"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "If two `Vector`s do *not* have a matching number of entries, a `ValueError` is raised."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 149,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "ename": "ValueError",
+     "evalue": "vectors need to be of the same length",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-149-21fab2d5f12e>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mv\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0mw\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;32m<ipython-input-143-12852bd26164>\u001b[0m in \u001b[0;36m__mul__\u001b[0;34m(self, other)\u001b[0m\n\u001b[1;32m     45\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mother\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__class__\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m  \u001b[0;31m# dot product\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     46\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mother\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 47\u001b[0;31m                 \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"vectors need to be of the same length\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     48\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0msum\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0my\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mzip\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mother\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     49\u001b[0m         \u001b[0;32melif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mother\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnumbers\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mNumber\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m  \u001b[0;31m# scalar multiplication\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;31mValueError\u001b[0m: vectors need to be of the same length"
+     ]
+    }
+   ],
+   "source": [
+    "v * w"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "`__truediv__()` implements the ordinary division operator `/` while `__floordiv__()` would implement the integer division operator `//`. Here, `__truediv__()` dispatches to `__mul__()` after inverting the `other` argument."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 150,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Vector((0.333, 0.667, 1.000))"
+      ]
+     },
+     "execution_count": 150,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "v / 3"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "`__add__()` and `__sub__()` implement vector addition and subtraction according to standard linear algebra rules, meaning that both `Vector`s must have the same number of entries or a `ValueError` is raised. Furthermore, both methods are able to broadcast the `other` argument to the dimension of a `Vector` and then execute either vector addition or subtraction. As addition is commutative, `__radd__()` dispatches to `__add__()`. For now, we have to explicitly implement `__rsub__()`. Further below, we see how it can be re-factored to be commutative."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 151,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Vector((2.000, 4.000, 6.000))"
+      ]
+     },
+     "execution_count": 151,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "v + v"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 152,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Vector((0.000, 0.000, 0.000))"
+      ]
+     },
+     "execution_count": 152,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "v - v"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 153,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "ename": "ValueError",
+     "evalue": "vectors need to be of the same length",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-153-490ee3f2b9e8>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mv\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mw\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;32m<ipython-input-143-12852bd26164>\u001b[0m in \u001b[0;36m__add__\u001b[0;34m(self, other)\u001b[0m\n\u001b[1;32m     18\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mother\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__class__\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m  \u001b[0;31m# vector addition\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     19\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mother\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 20\u001b[0;31m                 \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"vectors need to be of the same length\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     21\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mVector\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0my\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mzip\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mother\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     22\u001b[0m         \u001b[0;32melif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mother\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnumbers\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mNumber\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m  \u001b[0;31m# broadcasting addition\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;31mValueError\u001b[0m: vectors need to be of the same length"
+     ]
+    }
+   ],
+   "source": [
+    "v + w"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 154,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Vector((101.000, 102.000, 103.000))"
+      ]
+     },
+     "execution_count": 154,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "v + 100"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 155,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Vector((99.000, 98.000, 97.000))"
+      ]
+     },
+     "execution_count": 155,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "100 - v"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "For `Matrix` instances, the implementation is a bit more involved as we need to distinguish between matrix-matrix, matrix-vector, vector-matrix, and scalar multiplication and check for compatible dimensions. To review the underlying rules, check this [article <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_wiki.png\">](https://en.wikipedia.org/wiki/Matrix_multiplication) or watch the video below."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 156,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/jpeg": 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\n",
+      "text/html": [
+       "\n",
+       "        <iframe\n",
+       "            width=\"60%\"\n",
+       "            height=\"300\"\n",
+       "            src=\"https://www.youtube.com/embed/OMA2Mwo0aZg\"\n",
+       "            frameborder=\"0\"\n",
+       "            allowfullscreen\n",
+       "        ></iframe>\n",
+       "        "
+      ],
+      "text/plain": [
+       "<IPython.lib.display.YouTubeVideo at 0x7fe33c0cbfd0>"
+      ]
+     },
+     "execution_count": 156,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from IPython.display import YouTubeVideo\n",
+    "YouTubeVideo(\"OMA2Mwo0aZg\", width=\"60%\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "To summarize the video, the multiplication of two matrices $\\bf{A}$ and $\\bf{B}$ with dimensions $m$ by $n$ and $n$ by $p$ yields a matrix $\\bf{C}$ with dimensions $m$ and $p$. To obtain an entry $c_{ij}$ of matrix $\\bf{C}$ where $i$ and $j$ represent the index labels of the rows and columns, we have to calculate the dot product of the $i$th row vector of $\\bf{A}$ with the $j$th column vector of $\\bf{B}$. So, it makes a difference if we multiply $\\bf{A}$ with $\\bf{B}$ from the right or left.\n",
+    "\n",
+    "When multiplying a `Matrix` with a `Vector`, we follow the convention that a `Vector` on the left is interpreted as a row vector and a `Vector` on the right as a column vector. The `Vector`'s length must match the `Matrix`'s corresponding dimension. As row and column vectors can be viewed as a `Matrix` as well, matrix-vector multiplication is a special case of matrix-matrix multiplication."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "In the updated `Matrix` class, the  `__add__()`, `__radd__()`, `__sub__()`, `__rsub__()`, and `__truediv__()` methods follow the same logic as in the `Vector` class above.\n",
+    "\n",
+    "Besides implementing scalar multiplication, `__mul__()` and `__rmul__()` are responsible for converting `Vector`s into `Matrix`s and back. In particular, all the different ways of performing a multiplication are reduced into *one* generic form, which is a matrix-matrix multiplication. That is achieved by the `_matrix_multiply()` method, another implementation detail."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 157,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "class Matrix:\n",
+    "\n",
+    "    def __init__(self, data):\n",
+    "        self._entries = list(list(float(x) for x in r) for r in data)\n",
+    "        # ...\n",
+    "\n",
+    "    def __repr__(self):\n",
+    "        args = \", \".join(\"(\" + \", \".join(f\"{c:.3f}\" for c in r) + \",)\" for r in self._entries)\n",
+    "        return f\"Matrix(({args}))\"\n",
+    "\n",
+    "    @property\n",
+    "    def n_rows(self):\n",
+    "        return len(self._entries)\n",
+    "\n",
+    "    @property\n",
+    "    def n_cols(self):\n",
+    "        return len(self._entries[0])\n",
+    "\n",
+    "    def rows(self):\n",
+    "        return (Vector(r) for r in self._entries)\n",
+    "\n",
+    "    def cols(self):\n",
+    "        return (\n",
+    "            Vector(self._entries[r][c] for r in range(self.n_rows)) for c in range(self.n_cols)\n",
+    "        )\n",
+    "\n",
+    "    def __iter__(self):  # adapted for brevity; uses parts of entries()\n",
+    "        return (self._entries[r][c] for r in range(self.n_rows) for c in range(self.n_cols))\n",
+    "\n",
+    "    def __add__(self, other):\n",
+    "        if isinstance(other, self.__class__):  # matrix addition\n",
+    "            if (self.n_rows != other.n_rows) or (self.n_cols != other.n_cols):\n",
+    "                raise ValueError(\"matrices need to be of the same dimensions\")\n",
+    "            return Matrix((s_col + o_col for (s_col, o_col) in zip(s_row, o_row))\n",
+    "                          for (s_row, o_row) in zip(self._entries, other._entries))\n",
+    "        elif isinstance(other, numbers.Number):  # broadcasting addition\n",
+    "            return Matrix((c + other for c in r) for r in self._entries)\n",
+    "        return NotImplemented\n",
+    "\n",
+    "    def __radd__(self, other):\n",
+    "        if isinstance(other, Vector):\n",
+    "            raise TypeError(\"vectors and matrices cannot be added\")\n",
+    "        return self + other\n",
+    "\n",
+    "    def __sub__(self, other):\n",
+    "        if isinstance(other, self.__class__):  # matrix subtraction\n",
+    "            if (self.n_rows != other.n_rows) or (self.n_cols != other.n_cols):\n",
+    "                raise ValueError(\"matrices need to be of the same dimensions\")\n",
+    "            return Matrix((s_col - o_col for (s_col, o_col) in zip(s_row, o_row))\n",
+    "                          for (s_row, o_row) in zip(self._entries, other._entries))\n",
+    "        elif isinstance(other, numbers.Number):  # broadcasting subtraction\n",
+    "            return Matrix((c - other for c in r) for r in self._entries)\n",
+    "        return NotImplemented\n",
+    "\n",
+    "    def __rsub__(self, other):\n",
+    "        if isinstance(other, Vector):\n",
+    "            raise TypeError(\"vectors and matrices cannot be subtracted\")\n",
+    "        # Reverse matrix subtraction is already handled in __sub__().\n",
+    "        if isinstance(other, numbers.Number):  # broadcasting subtraction\n",
+    "            return Matrix((other - c for c in r) for r in self._entries)\n",
+    "        return NotImplemented\n",
+    "    \n",
+    "    def _matrix_multiply(self, other):\n",
+    "        if self.n_cols != other.n_rows:\n",
+    "            raise ValueError(\"matrices need to have compatible dimensions\")\n",
+    "        return Matrix((rv * cv for cv in other.cols()) for rv in self.rows())\n",
+    "\n",
+    "    def __mul__(self, other):\n",
+    "        if isinstance(other, numbers.Number):\n",
+    "            return Matrix((x * other for x in r) for r in self._entries)\n",
+    "        elif isinstance(other, Vector):\n",
+    "            return self._matrix_multiply(other.as_matrix()).as_vector()\n",
+    "        elif isinstance(other, self.__class__):\n",
+    "            return self._matrix_multiply(other)\n",
+    "        return NotImplemented\n",
+    "\n",
+    "    def __rmul__(self, other):\n",
+    "        if isinstance(other, numbers.Number):\n",
+    "            return self * other\n",
+    "        elif isinstance(other, Vector):\n",
+    "            return other.as_matrix(column=False)._matrix_multiply(self).as_vector()\n",
+    "        return NotImplemented\n",
+    "\n",
+    "    def __truediv__(self, other):\n",
+    "        if isinstance(other, numbers.Number):\n",
+    "            return self * (1 / other)\n",
+    "        return NotImplemented\n",
+    "\n",
+    "    def as_vector(self):\n",
+    "        if not (self.n_rows == 1 or self.n_cols == 1):\n",
+    "            raise RuntimeError(\"one dimension (m or n) must be 1\")\n",
+    "        return Vector(x for x in self)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 158,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "m = Matrix([(1, 2, 3), (4, 5, 6), (7, 8, 9)])\n",
+    "n = Matrix([(10, 11, 12), (13, 14, 15)])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Scalar multiplication, addition, and subtraction work as before."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 159,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Matrix(((10.000, 20.000, 30.000,), (40.000, 50.000, 60.000,), (70.000, 80.000, 90.000,)))"
+      ]
+     },
+     "execution_count": 159,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "10 * m"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 160,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Matrix(((1.000, 2.000, 3.000,), (4.000, 5.000, 6.000,), (7.000, 8.000, 9.000,)))"
+      ]
+     },
+     "execution_count": 160,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "(2 * m + m * 3) / 5"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 161,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Matrix(((0.000, 0.000, 0.000,), (0.000, 0.000, 0.000,), (0.000, 0.000, 0.000,)))"
+      ]
+     },
+     "execution_count": 161,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "m - m"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Matrix-matrix multiplication works if the dimensions are compatible ..."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 162,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Matrix(((138.000, 171.000, 204.000,), (174.000, 216.000, 258.000,)))"
+      ]
+     },
+     "execution_count": 162,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "n * m"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "... and results in a `ValueError` otherwise."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 163,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "ename": "ValueError",
+     "evalue": "matrices need to have compatible dimensions",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-163-b57a49d097c9>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mm\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0mn\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;32m<ipython-input-157-f4e0bb37caf6>\u001b[0m in \u001b[0;36m__mul__\u001b[0;34m(self, other)\u001b[0m\n\u001b[1;32m     72\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_matrix_multiply\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mother\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mas_matrix\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mas_vector\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     73\u001b[0m         \u001b[0;32melif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mother\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__class__\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 74\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_matrix_multiply\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mother\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     75\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mNotImplemented\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     76\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m<ipython-input-157-f4e0bb37caf6>\u001b[0m in \u001b[0;36m_matrix_multiply\u001b[0;34m(self, other)\u001b[0m\n\u001b[1;32m     63\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_matrix_multiply\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mother\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     64\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mn_cols\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0mother\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mn_rows\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 65\u001b[0;31m             \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"matrices need to have compatible dimensions\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     66\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mMatrix\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrv\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0mcv\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mcv\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mother\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcols\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mrv\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrows\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     67\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;31mValueError\u001b[0m: matrices need to have compatible dimensions"
+     ]
+    }
+   ],
+   "source": [
+    "m * n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "The same holds for matrix-vector and vector-matrix multiplication. These operations always return `Vector` instances in line with standard linear algebra. If a `Vector`'s length is not compatible with the respective dimension of a `Matrix`, we receive a `ValueError`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 164,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Vector((14.000, 32.000, 50.000))"
+      ]
+     },
+     "execution_count": 164,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "m * v"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 165,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Vector((30.000, 36.000, 42.000))"
+      ]
+     },
+     "execution_count": 165,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "v * m"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 166,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Vector((68.000, 86.000))"
+      ]
+     },
+     "execution_count": 166,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "n * v"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 167,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "ename": "ValueError",
+     "evalue": "matrices need to have compatible dimensions",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-167-9faf2ee0ae54>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mv\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0mn\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;32m<ipython-input-157-f4e0bb37caf6>\u001b[0m in \u001b[0;36m__rmul__\u001b[0;34m(self, other)\u001b[0m\n\u001b[1;32m     79\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0mother\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     80\u001b[0m         \u001b[0;32melif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mother\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mVector\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 81\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mother\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mas_matrix\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcolumn\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_matrix_multiply\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mas_vector\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     82\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mNotImplemented\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     83\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m<ipython-input-157-f4e0bb37caf6>\u001b[0m in \u001b[0;36m_matrix_multiply\u001b[0;34m(self, other)\u001b[0m\n\u001b[1;32m     63\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_matrix_multiply\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mother\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     64\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mn_cols\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0mother\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mn_rows\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 65\u001b[0;31m             \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"matrices need to have compatible dimensions\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     66\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mMatrix\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrv\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0mcv\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mcv\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mother\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcols\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mrv\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrows\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     67\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;31mValueError\u001b[0m: matrices need to have compatible dimensions"
+     ]
+    }
+   ],
+   "source": [
+    "v * n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Lastly, the broadcasting addition and subtraction also work for our `Matrix` instances."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 168,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Matrix(((101.000, 102.000, 103.000,), (104.000, 105.000, 106.000,), (107.000, 108.000, 109.000,)))"
+      ]
+     },
+     "execution_count": 168,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "m + 100"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 169,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Matrix(((99.000, 98.000, 97.000,), (96.000, 95.000, 94.000,), (93.000, 92.000, 91.000,)))"
+      ]
+     },
+     "execution_count": 169,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "100 - m"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "We do not allow addition or subtraction of matrices with vectors and raise a `TypeError` instead. Alternatively, we could have implemented broadcasting here as well, just like [numpy](https://www.numpy.org/) does."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 170,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "ename": "TypeError",
+     "evalue": "vectors and matrices cannot be added",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-170-356d02c93f11>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mm\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mv\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;32m<ipython-input-143-12852bd26164>\u001b[0m in \u001b[0;36m__radd__\u001b[0;34m(self, other)\u001b[0m\n\u001b[1;32m     25\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     26\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__radd__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mother\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 27\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mother\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     28\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     29\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__sub__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mother\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m<ipython-input-157-f4e0bb37caf6>\u001b[0m in \u001b[0;36m__radd__\u001b[0;34m(self, other)\u001b[0m\n\u001b[1;32m     40\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__radd__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mother\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     41\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mother\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mVector\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 42\u001b[0;31m             \u001b[0;32mraise\u001b[0m \u001b[0mTypeError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"vectors and matrices cannot be added\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     43\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mother\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     44\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;31mTypeError\u001b[0m: vectors and matrices cannot be added"
+     ]
+    }
+   ],
+   "source": [
+    "m + v"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "### Relational Operators"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "As we have seen above, two different `Vector`s with the same `_entries` do *not* compare equal. The reason is that for user-defined types Python by default only assumes two instances to be equal if they are actually the same object. This is, of course, semantically wrong for our `Vector`s and `Matrix`s."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 171,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "v = Vector([1, 2, 3])\n",
+    "w = Vector([1, 2, 3])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 172,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "False"
+      ]
+     },
+     "execution_count": 172,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "v == w"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 173,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 173,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "v == v"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "We implement the `__eq__()` (cf., [reference <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/reference/datamodel.html#object.__eq__)) method to control how the binary comparison operator `==` is carried out. For brevity, we show this only for the `Vector` class. The `__eq__()` method exits early as soon as the first pair of entries does not match. Also, for reasons discussed in [Chapter 5 <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/05_numbers_00_content.ipynb#Imprecision), we compare the absolute difference of two corresponding entries to a very small `zero_threshold` that is stored as a class attribute. If the `Vector`s differ in their numbers of entries, we fail loudly and raise a `ValueError`. Alternatively, we could fail silently and return `False` instead."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 174,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "class Vector:\n",
+    "\n",
+    "    zero_threshold = 1e-12\n",
+    "\n",
+    "    def __init__(self, data):\n",
+    "        self._entries = list(float(x) for x in data)\n",
+    "        # ...\n",
+    "\n",
+    "    def __repr__(self):\n",
+    "        args = \", \".join(f\"{x:.3f}\" for x in self)\n",
+    "        return f\"Vector(({args}))\"\n",
+    "\n",
+    "    def __len__(self):\n",
+    "        return len(self._entries)\n",
+    "\n",
+    "    def __iter__(self):\n",
+    "        return iter(self._entries)\n",
+    "\n",
+    "    def __eq__(self, other):\n",
+    "        if isinstance(other, self.__class__):\n",
+    "            if len(self) != len(other):\n",
+    "                raise ValueError(\"vectors need to be of the same length\")\n",
+    "            for x, y in zip(self, other):\n",
+    "                if abs(x - y) > self.zero_threshold:\n",
+    "                    return False\n",
+    "            return True\n",
+    "        return NotImplemented"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 175,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "v = Vector([1, 2, 3])\n",
+    "w = Vector([1, 2, 3])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 176,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 176,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "v == w"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Besides `__eq__()`, there are other special methods to implement the `!=`, `<`, `>`, `<=`, and `>=` operators. However, Python implicitly creates the `!=` for us in that it just inverts the result of `__eq__()`. The remaining four operators are not semantically meaningful in the context of linear algebra."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "## Number Emulation"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Our `Vector` and `Matrix` classes do not fully behave like a `numbers.Number` in the abstract sense. Besides the not yet talked about but useful unary `+` and `-` operators, numbers in Python usually support being passed to built-in functions like [abs() <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/functions.html#abs), [bin() <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/functions.html#bin), [bool() <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/functions.html#bool), [complex() <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/functions.html#complex), [float() <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/functions.html#float), [hex() <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/functions.html#hex), [int() <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/functions.html#int), and others (cf., the [reference <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/reference/datamodel.html#emulating-numeric-types) for an exhaustive list).\n",
+    "\n",
+    "To see that our classes lack simple but expected behaviors, let's try to invert the signs of all entries in the vector `v`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 177,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Vector((1.000, 2.000, 3.000))"
+      ]
+     },
+     "execution_count": 177,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "v"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 178,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "ename": "TypeError",
+     "evalue": "bad operand type for unary -: 'Vector'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-178-c01df863c5f8>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0;34m-\u001b[0m\u001b[0mv\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;31mTypeError\u001b[0m: bad operand type for unary -: 'Vector'"
+     ]
+    }
+   ],
+   "source": [
+    "-v"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "For our example, we decide to implement the unary `+` and `-` operators, [abs() <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/functions.html#abs) as the Euclidean / Frobenius norm (i.e., the [magnitude <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_wiki.png\">](https://en.wikipedia.org/wiki/Magnitude_%28mathematics%29#Euclidean_vector_space)), [bool() <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/functions.html#bool) to check if that norm is greater than `0` or not, and [float() <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/functions.html#float) to obtain a single scalar if the vector or matrix consists of only one entry. To achieve that, we implement the `__pos__()`, `__neg__()`, `__abs__()`, `__bool__()`, and `__float__()` methods. For brevity, we do this only for the `Vector` class."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 179,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "class Vector:\n",
+    "\n",
+    "    def __init__(self, data):\n",
+    "        self._entries = list(float(x) for x in data)\n",
+    "        # ...\n",
+    "\n",
+    "    def __repr__(self):\n",
+    "        args = \", \".join(f\"{x:.3f}\" for x in self)\n",
+    "        return f\"Vector(({args}))\"\n",
+    "\n",
+    "    def __iter__(self):\n",
+    "        return iter(self._entries)\n",
+    "\n",
+    "    def __len__(self):\n",
+    "        return len(self._entries)\n",
+    "\n",
+    "    def __pos__(self):\n",
+    "        return self\n",
+    "\n",
+    "    def __neg__(self):\n",
+    "        return Vector(-x for x in self)\n",
+    "\n",
+    "    def __abs__(self):\n",
+    "        return norm(self)\n",
+    "\n",
+    "    def __bool__(self):\n",
+    "        return bool(abs(self))\n",
+    "\n",
+    "    def __float__(self):\n",
+    "        if len(self) != 1:\n",
+    "            raise RuntimeError(\"vector must have one entry to become a scalar\")\n",
+    "        return self._entries[0]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 180,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "v = Vector([1, 2, 3])\n",
+    "w = Vector([3, 4])\n",
+    "\n",
+    "z = Vector([0, 0])\n",
+    "s = Vector([42])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "The unary `+` operator is not only conceptually an identity operator but simply returns the instance itself."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 181,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Vector((1.000, 2.000, 3.000))"
+      ]
+     },
+     "execution_count": 181,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "+v"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 182,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 182,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "+v is v"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 183,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Vector((-3.000, -4.000))"
+      ]
+     },
+     "execution_count": 183,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "-w"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "The magnitude of a `Vector` is only `0` if *all* entries are `0`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 184,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "5.0"
+      ]
+     },
+     "execution_count": 184,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "abs(w)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 185,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.0"
+      ]
+     },
+     "execution_count": 185,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "abs(z)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Only an all `0`s `Vector` is `False` in a boolean context."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 186,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 186,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "bool(v)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 187,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "False"
+      ]
+     },
+     "execution_count": 187,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "bool(z)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Lastly, single entry `Vector`s can be casted as scalars."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 188,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "42.0"
+      ]
+     },
+     "execution_count": 188,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "float(s)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 189,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "ename": "RuntimeError",
+     "evalue": "vector must have one entry to become a scalar",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mRuntimeError\u001b[0m                              Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-189-8369cec552f3>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mfloat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mv\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;32m<ipython-input-179-01438fec4aa0>\u001b[0m in \u001b[0;36m__float__\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m     29\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__float__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     30\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 31\u001b[0;31m             \u001b[0;32mraise\u001b[0m \u001b[0mRuntimeError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"vector must have one entry to become a scalar\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     32\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_entries\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;31mRuntimeError\u001b[0m: vector must have one entry to become a scalar"
+     ]
+    }
+   ],
+   "source": [
+    "float(v)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "## The whole Picture"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Below are the two final implementations of the `Vector` and `Matrix` classes that integrate all of the functionalities introduced in this chapter. The code is cleaned up and fully documented.\n",
+    "\n",
+    "The `storage` and `typing` **class attributes** are added referencing the [list() <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/functions.html#func-list) and [float() <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/functions.html#float) built-ins. These class attributes are then used in the `__init__()` methods as defaults. They can be used just like instance attributes on the `self` object directly. If an attribute is not defined on an instance, Python first checks if it can fall back to the same name on the corresponding class object before it raises an `AttributeError`. If we want to refer to a class attribute right away, we could replace the `self.storage` and `self.typing` with `self.__class__.storage` and `self.__class__.typing` respectively.\n",
+    "\n",
+    "Also, we replace the text strings `\"Vector\"` and `\"Matrix\"` in the `__repr__()` and `__str__()` methods with `self.__class__.__name__`. This way, we only use the names of the classes in the `class` statements' header lines. Similarly, we replace all class instantiations of the form `Vector()` and `Matrix()` with `self.__class__()` to avoid using the classes' names again.\n",
+    "\n",
+    "Lastly, we adapt the `__sub__()` and `__rsub__()` methods to use the negation provided by `__neg__()` and then dispatch to `__add__()` for code reuse."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 190,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "class Vector:\n",
+    "    \"\"\"A standard one-dimensional vector from linear algebra.\n",
+    "\n",
+    "    Attributes:\n",
+    "        storage (callable): must return an iterable that is used\n",
+    "            to store the entries of the vector; defaults to list\n",
+    "        typing (callable): type casting applied to all vector\n",
+    "            entries upon creation; defaults to float\n",
+    "        zero_threshold (float): maximum difference allowed when\n",
+    "            comparing an entry to zero; defaults to 1e-12\n",
+    "    \"\"\"\n",
+    "\n",
+    "    storage = list\n",
+    "    typing = float\n",
+    "    zero_threshold = 1e-12\n",
+    "\n",
+    "    def __init__(self, data):\n",
+    "        \"\"\"Initiate a new vector.\n",
+    "\n",
+    "        Args:\n",
+    "            data (iterable): the vector's entries;\n",
+    "                must be finite and have at least one element\n",
+    "\n",
+    "        Raises:\n",
+    "            ValueError: if the provided data do not have enough entries\n",
+    "        \"\"\"\n",
+    "        self._entries = self.storage(self.typing(x) for x in data)\n",
+    "        if len(self) == 0:\n",
+    "            raise ValueError(\"a vector must have at least one entry\")\n",
+    "\n",
+    "    def __repr__(self):\n",
+    "        name, args = self.__class__.__name__, \", \".join(f\"{x:.3f}\" for x in self)\n",
+    "        return f\"{name}(({args}))\"\n",
+    "\n",
+    "    def __str__(self):\n",
+    "        name = self.__class__.__name__\n",
+    "        first, last, entries = self[0], self[-1], len(self)\n",
+    "        return f\"{name}({first:.1f}, ..., {last:.1f})[{entries:d}]\"\n",
+    "\n",
+    "    def __len__(self):\n",
+    "        return len(self._entries)\n",
+    "\n",
+    "    def __getitem__(self, index):\n",
+    "        return self._entries[index]\n",
+    "\n",
+    "    def __iter__(self):\n",
+    "        return iter(self._entries)\n",
+    "\n",
+    "    def __reversed__(self):\n",
+    "        return reversed(self._entries)\n",
+    "\n",
+    "    def __add__(self, other):\n",
+    "        if isinstance(other, self.__class__):\n",
+    "            if len(self) != len(other):\n",
+    "                raise ValueError(\"vectors need to be of the same length\")\n",
+    "            return self.__class__(x + y for (x, y) in zip(self, other))\n",
+    "        elif isinstance(other, numbers.Number):\n",
+    "            return self.__class__(x + other for x in self)\n",
+    "        return NotImplemented\n",
+    "\n",
+    "    def __radd__(self, other):\n",
+    "        return self + other\n",
+    "\n",
+    "    def __sub__(self, other):\n",
+    "        return self + (- other)\n",
+    "\n",
+    "    def __rsub__(self, other):\n",
+    "        return (- self) + other\n",
+    "\n",
+    "    def __mul__(self, other):\n",
+    "        if isinstance(other, self.__class__):\n",
+    "            if len(self) != len(other):\n",
+    "                raise ValueError(\"vectors need to be of the same length\")\n",
+    "            return sum(x * y for (x, y) in zip(self, other))\n",
+    "        elif isinstance(other, numbers.Number):\n",
+    "            return self.__class__(x * other for x in self)\n",
+    "        return NotImplemented\n",
+    "\n",
+    "    def __rmul__(self, other):\n",
+    "        return self * other\n",
+    "\n",
+    "    def __truediv__(self, other):\n",
+    "        if isinstance(other, numbers.Number):\n",
+    "            return self * (1 / other)\n",
+    "        return NotImplemented\n",
+    "\n",
+    "    def __eq__(self, other):\n",
+    "        if isinstance(other, self.__class__):\n",
+    "            if len(self) != len(other):\n",
+    "                raise ValueError(\"vectors need to be of the same length\")\n",
+    "            for x, y in zip(self, other):\n",
+    "                if abs(x - y) > self.zero_threshold:\n",
+    "                    return False\n",
+    "            return True\n",
+    "        return NotImplemented\n",
+    "\n",
+    "    def __pos__(self):\n",
+    "        return self\n",
+    "\n",
+    "    def __neg__(self):\n",
+    "        return self.__class__(-x for x in self)\n",
+    "\n",
+    "    def __abs__(self):\n",
+    "        \"\"\"The Euclidean norm of the vector.\"\"\"\n",
+    "        return norm(self)\n",
+    "\n",
+    "    def __bool__(self):\n",
+    "        return bool(abs(self))\n",
+    "\n",
+    "    def __float__(self):\n",
+    "        if len(self) != 1:\n",
+    "            raise RuntimeError(\"vector must have one entry to become a scalar\")\n",
+    "        return self[0]\n",
+    "\n",
+    "    def as_matrix(self, *, column=True):\n",
+    "        \"\"\"Convert the vector into a matrix.\n",
+    "\n",
+    "        Args:\n",
+    "            column (bool): if the vector should be interpreted as\n",
+    "                as a column vector or not; defaults to True\n",
+    "\n",
+    "        Returns:\n",
+    "            matrix (Matrix)\n",
+    "        \"\"\"\n",
+    "        if column:\n",
+    "            return Matrix([x] for x in self)\n",
+    "        return Matrix([(x for x in self)])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 191,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "class Matrix:\n",
+    "    \"\"\"A standard m-by-n-dimensional matrix from linear algebra.\n",
+    "\n",
+    "    Attributes:\n",
+    "        storage (callable): must return an iterable that is used\n",
+    "            to store the entries of the matrix; defaults to list\n",
+    "        typing (callable): type casting applied to all vector\n",
+    "            entries upon creation; defaults to float\n",
+    "        zero_threshold (float): maximum difference allowed when\n",
+    "            comparing an entry to zero; defaults to 1e-12\n",
+    "    \"\"\"\n",
+    "\n",
+    "    storage = list\n",
+    "    typing = float\n",
+    "    zero_threshold = 1e-12\n",
+    "\n",
+    "    def __init__(self, data):\n",
+    "        \"\"\"Initiate a new matrix.\n",
+    "\n",
+    "        Args:\n",
+    "            data (iterable of iterables): the matrix's entries;\n",
+    "                the inner iterables represent the rows;\n",
+    "                the number of column entries must be consistent across rows\n",
+    "                where the first row sets the standard;\n",
+    "                must be finite and have at least one element\n",
+    "\n",
+    "        Raises:\n",
+    "            ValueError:\n",
+    "                - if the number of columns is inconsistent across rows\n",
+    "                - if the provided data do not have enough entries\n",
+    "        \"\"\"\n",
+    "        self._entries = self.storage(\n",
+    "            self.storage(self.typing(x) for x in r) for r in data\n",
+    "        )\n",
+    "        for row in self._entries[1:]:\n",
+    "            if len(row) != self.n_cols:\n",
+    "                raise ValueError(\"rows must have the same number of entries\")\n",
+    "        if len(self) == 0:\n",
+    "            raise ValueError(\"a matrix must have at least one entry\")\n",
+    "\n",
+    "    @classmethod\n",
+    "    def from_columns(cls, data):\n",
+    "        \"\"\"Initiate a new matrix.\n",
+    "\n",
+    "        An alternative constructor for data provided in column-major order.\n",
+    "\n",
+    "        Args:\n",
+    "            data (iterable of iterables): the matrix's entries;\n",
+    "                the inner iterables represent the columns;\n",
+    "                the number of row entries must be consistent per column\n",
+    "                where the first column sets the standard;\n",
+    "                must be finite and have at least one element\n",
+    "\n",
+    "        Raises:\n",
+    "            ValueError:\n",
+    "                - if the number of rows is inconsistent across columns\n",
+    "                - if the provided data do not have enough entries\n",
+    "        \"\"\"\n",
+    "        return cls(data).transpose()\n",
+    "\n",
+    "    def __repr__(self):\n",
+    "        name = self.__class__.__name__\n",
+    "        args = \", \".join(\n",
+    "            \"(\" + \", \".join(f\"{c:.3f}\" for c in r) + \",)\" for r in self._entries\n",
+    "        )\n",
+    "        return f\"{name}(({args}))\"\n",
+    "\n",
+    "    def __str__(self):\n",
+    "        name = self.__class__.__name__\n",
+    "        first, last, m, n = self[0], self[-1], self.n_rows, self.n_cols\n",
+    "        return f\"{name}(({first:.1f}, ...), ..., (..., {last:.1f}))[{m:d}x{n:d}]\"\n",
+    "\n",
+    "    @property\n",
+    "    def n_rows(self):\n",
+    "        \"\"\"Number of rows in the matrix.\"\"\"\n",
+    "        return len(self._entries)\n",
+    "\n",
+    "    @property\n",
+    "    def n_cols(self):\n",
+    "        \"\"\"Number of columns in the matrix.\"\"\"\n",
+    "        return len(self._entries[0])\n",
+    "\n",
+    "    def __len__(self):\n",
+    "        return self.n_rows * self.n_cols\n",
+    "\n",
+    "    def __getitem__(self, index):\n",
+    "        if isinstance(index, int):\n",
+    "            if index < 0:\n",
+    "                index += len(self)\n",
+    "            if not (0 <= index < len(self)):\n",
+    "                raise IndexError(\"integer index out of range\")\n",
+    "            row, col = divmod(index, self.n_cols)\n",
+    "            return self._entries[row][col]\n",
+    "        elif (\n",
+    "            isinstance(index, tuple) and len(index) == 2\n",
+    "            and isinstance(index[0], int) and isinstance(index[1], int)\n",
+    "        ):\n",
+    "            return self._entries[index[0]][index[1]]\n",
+    "        raise TypeError(\"index must be either an int or a tuple of two int's\")\n",
+    "\n",
+    "    def rows(self):\n",
+    "        \"\"\"Loop over the rows of the matrix.\n",
+    "\n",
+    "        Returns:\n",
+    "            rows (Generator): produces Vectors representing\n",
+    "                individual rows of the matrix\n",
+    "        \"\"\"\n",
+    "        return (Vector(r) for r in self._entries)\n",
+    "\n",
+    "    def cols(self):\n",
+    "        \"\"\"Loop over the columns of the matrix.\n",
+    "\n",
+    "        Returns:\n",
+    "            columns (Generator): produces Vectors representing\n",
+    "                individual columns of the matrix\n",
+    "        \"\"\"\n",
+    "        return (\n",
+    "            Vector(self._entries[r][c] for r in range(self.n_rows))\n",
+    "            for c in range(self.n_cols)\n",
+    "        )\n",
+    "\n",
+    "    def entries(self, *, reverse=False, row_major=True):\n",
+    "        \"\"\"Loop over the entries of the matrix.\n",
+    "\n",
+    "        Args:\n",
+    "            reverse (bool): flag to loop backwards; defaults to False\n",
+    "            row_major (bool): flag to loop in row major order; defaults to False\n",
+    "\n",
+    "        Returns:\n",
+    "            entries (Generator): produces the entries of the matrix\n",
+    "        \"\"\"\n",
+    "        if reverse:\n",
+    "            rows = range(self.n_rows - 1, -1, -1)\n",
+    "            cols = range(self.n_cols - 1, -1, -1)\n",
+    "        else:\n",
+    "            rows, cols = range(self.n_rows), range(self.n_cols)\n",
+    "        if row_major:\n",
+    "            return (self._entries[r][c] for r in rows for c in cols)\n",
+    "        return (self._entries[r][c] for c in cols for r in rows)\n",
+    "\n",
+    "    def __iter__(self):\n",
+    "        return self.entries()\n",
+    "\n",
+    "    def __reversed__(self):\n",
+    "        return self.entries(reverse=True)\n",
+    "    \n",
+    "    def __add__(self, other):\n",
+    "        if isinstance(other, self.__class__):\n",
+    "            if (self.n_rows != other.n_rows) or (self.n_cols != other.n_cols):\n",
+    "                raise ValueError(\"matrices need to be of the same dimensions\")\n",
+    "            return self.__class__(\n",
+    "                (s_col + o_col for (s_col, o_col) in zip(s_row, o_row))\n",
+    "                for (s_row, o_row) in zip(self._entries, other._entries)\n",
+    "            )\n",
+    "        elif isinstance(other, numbers.Number):\n",
+    "            return self.__class__((c + other for c in r) for r in self._entries)\n",
+    "        return NotImplemented\n",
+    "\n",
+    "    def __radd__(self, other):\n",
+    "        if isinstance(other, Vector):\n",
+    "            raise TypeError(\"vectors and matrices cannot be added\")\n",
+    "        return self + other\n",
+    "\n",
+    "    def __sub__(self, other):\n",
+    "        return self + (-other)\n",
+    "\n",
+    "    def __rsub__(self, other):\n",
+    "        if isinstance(other, Vector):\n",
+    "            raise TypeError(\"vectors and matrices cannot be subtracted\")\n",
+    "        return (-self) + other\n",
+    "\n",
+    "    def _matrix_multiply(self, other):\n",
+    "        if self.n_cols != other.n_rows:\n",
+    "            raise ValueError(\"matrices need to have compatible dimensions\")\n",
+    "        return self.__class__(\n",
+    "            (rv * cv for cv in other.cols()) for rv in self.rows()\n",
+    "        )\n",
+    "\n",
+    "    def __mul__(self, other):\n",
+    "        if isinstance(other, numbers.Number):\n",
+    "            return self.__class__((x * other for x in r) for r in self._entries)\n",
+    "        elif isinstance(other, Vector):\n",
+    "            return self._matrix_multiply(other.as_matrix()).as_vector()\n",
+    "        elif isinstance(other, self.__class__):\n",
+    "            return self._matrix_multiply(other)\n",
+    "        return NotImplemented\n",
+    "\n",
+    "    def __rmul__(self, other):\n",
+    "        if isinstance(other, numbers.Number):\n",
+    "            return self * other\n",
+    "        elif isinstance(other, Vector):\n",
+    "            return (\n",
+    "                other.as_matrix(column=False)._matrix_multiply(self).as_vector()\n",
+    "            )\n",
+    "        return NotImplemented\n",
+    "\n",
+    "    def __truediv__(self, other):\n",
+    "        if isinstance(other, numbers.Number):\n",
+    "            return self * (1 / other)\n",
+    "        return NotImplemented\n",
+    "\n",
+    "    def __eq__(self, other):\n",
+    "        if isinstance(other, self.__class__):\n",
+    "            if (self.n_rows != other.n_rows) or (self.n_cols != other.n_cols):\n",
+    "                raise ValueError(\"matrices need to be of the same dimensions\")\n",
+    "            for x, y in zip(self, other):\n",
+    "                if abs(x - y) > self.zero_threshold:\n",
+    "                    return False\n",
+    "            return True\n",
+    "        return NotImplemented\n",
+    "\n",
+    "    def __pos__(self):\n",
+    "        return self\n",
+    "\n",
+    "    def __neg__(self):\n",
+    "        return self.__class__((-x for x in r) for r in self._entries)\n",
+    "\n",
+    "    def __abs__(self):\n",
+    "        \"\"\"The Frobenius norm of the matrix.\"\"\"\n",
+    "        return norm(self)\n",
+    "\n",
+    "    def __bool__(self):\n",
+    "        return bool(abs(self))\n",
+    "\n",
+    "    def __float__(self):\n",
+    "        if not (self.n_rows == 1 and self.n_cols == 1):\n",
+    "            raise RuntimeError(\"matrix must have one entry to become a scalar\")\n",
+    "        return self[0]\n",
+    "\n",
+    "    def as_vector(self):\n",
+    "        \"\"\"Cast the matrix as a one-dimensional vector.\n",
+    "\n",
+    "        Returns:\n",
+    "            vector (Vector)\n",
+    "\n",
+    "        Raises:\n",
+    "            RuntimeError: if not one of the two dimensions is 1\n",
+    "        \"\"\"\n",
+    "        if not (self.n_rows == 1 or self.n_cols == 1):\n",
+    "            raise RuntimeError(\"one dimension (m or n) must be 1\")\n",
+    "        return Vector(x for x in self)\n",
+    "\n",
+    "    def transpose(self):\n",
+    "        \"\"\"Switch the rows and columns of the matrix.\n",
+    "\n",
+    "        Returns:\n",
+    "            matrix (Matrix)\n",
+    "        \"\"\"\n",
+    "        return self.__class__(zip(*self._entries))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Let's do some math with bigger `Matrix` and `Vector` instances."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 192,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "import random"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 193,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "random.seed(42)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "We initialize `m` with random numbers in the range between `0` and `1000`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 194,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "m = Matrix((1000 * random.random() for _ in range(50)) for _ in range(100))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "We quickly lose track with all the numbers in the matrix, which is why we implemented the `__str__()` method as a summary representation."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 195,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Matrix(((639.427, 25.011, 275.029, 223.211, 736.471, 676.699, 892.180, 86.939, 421.922, 29.797, 218.638, 505.355, 26.536, 198.838, 649.884, 544.941, 220.441, 589.266, 809.430, 6.499, 805.819, 698.139, 340.251, 155.479, 957.213, 336.595, 92.746, 96.716, 847.494, 603.726, 807.128, 729.732, 536.228, 973.116, 378.534, 552.041, 829.405, 618.520, 861.707, 577.352, 704.572, 45.824, 227.898, 289.388, 79.792, 232.791, 101.001, 277.974, 635.684, 364.832,), (370.181, 209.507, 266.978, 936.655, 648.035, 609.131, 171.139, 729.127, 163.402, 379.455, 989.523, 640.000, 556.950, 684.614, 842.852, 776.000, 229.048, 32.100, 315.453, 267.741, 210.983, 942.910, 876.368, 314.678, 655.439, 395.632, 914.548, 458.852, 264.880, 246.628, 561.368, 262.742, 584.586, 897.823, 399.401, 219.321, 997.538, 509.526, 90.909, 47.116, 109.649, 627.446, 792.079, 422.160, 63.528, 381.619, 996.121, 529.114, 971.078, 860.780,), (11.481, 720.722, 681.710, 536.970, 266.825, 640.962, 111.552, 434.765, 453.724, 953.816, 875.853, 263.389, 500.586, 178.652, 912.628, 870.519, 298.445, 638.949, 608.970, 152.839, 762.511, 539.379, 778.626, 530.354, 0.572, 324.156, 19.477, 929.099, 878.722, 831.666, 307.514, 57.925, 878.010, 946.949, 85.653, 485.990, 69.213, 760.602, 765.834, 128.391, 475.282, 549.804, 265.057, 872.433, 423.138, 211.798, 539.296, 729.931, 201.151, 311.716,), (995.149, 649.878, 438.100, 517.576, 121.004, 224.697, 338.086, 588.309, 230.115, 220.217, 70.993, 631.103, 228.942, 905.420, 859.635, 70.857, 238.005, 668.978, 214.237, 132.312, 935.514, 571.043, 472.671, 784.619, 807.497, 190.410, 96.931, 431.051, 423.579, 467.025, 729.076, 673.365, 984.165, 98.418, 402.621, 339.303, 861.673, 248.656, 190.209, 448.614, 421.882, 278.545, 249.806, 923.266, 443.131, 861.349, 550.325, 50.588, 999.282, 836.028,), (968.996, 926.367, 848.696, 166.311, 485.641, 213.747, 401.040, 58.635, 378.973, 985.309, 265.203, 784.071, 455.008, 423.007, 957.318, 995.423, 555.768, 718.408, 154.797, 296.708, 968.709, 579.180, 542.195, 747.976, 57.165, 584.178, 502.850, 852.720, 157.433, 960.779, 80.111, 185.825, 595.035, 675.213, 235.204, 119.887, 890.287, 246.215, 594.519, 619.382, 419.225, 583.672, 522.783, 934.706, 204.259, 716.192, 238.686, 395.786, 671.690, 299.997,), (316.177, 751.864, 72.543, 458.286, 998.454, 996.096, 73.261, 213.154, 265.200, 933.259, 880.864, 879.270, 369.527, 157.747, 833.745, 703.540, 611.678, 987.233, 653.976, 7.823, 817.104, 299.379, 663.389, 938.930, 134.291, 115.429, 107.036, 553.224, 272.348, 604.830, 717.612, 203.597, 634.238, 263.984, 488.532, 905.336, 846.104, 92.298, 423.576, 276.680, 3.546, 771.119, 637.113, 261.955, 741.231, 551.680, 427.687, 9.670, 75.244, 883.106,), (903.929, 545.590, 834.595, 582.510, 148.094, 127.446, 308.258, 898.981, 796.122, 860.703, 898.925, 210.077, 249.530, 102.794, 780.116, 884.135, 406.377, 620.662, 154.553, 929.881, 864.606, 976.206, 810.772, 881.416, 24.786, 736.564, 332.185, 930.816, 802.235, 864.064, 810.749, 266.806, 787.375, 108.096, 872.167, 858.593, 222.434, 816.587, 460.303, 305.191, 795.345, 227.595, 23.664, 193.130, 328.262, 864.353, 966.889, 279.125, 641.482, 399.678,), (981.150, 536.216, 939.237, 115.342, 970.401, 178.568, 962.534, 265.466, 108.403, 434.564, 728.545, 313.677, 606.209, 511.423, 385.195, 576.588, 254.723, 708.785, 1.691, 925.575, 538.452, 719.430, 741.950, 670.629, 364.221, 69.974, 664.238, 330.200, 313.916, 848.015, 719.754, 300.322, 309.285, 408.393, 402.400, 295.655, 127.288, 420.446, 940.364, 677.318, 902.806, 615.515, 300.950, 547.937, 0.406, 286.914, 429.888, 579.985, 654.706, 464.988,), (442.160, 213.701, 473.186, 901.181, 796.025, 169.691, 84.796, 515.452, 632.941, 335.188, 818.423, 751.138, 672.796, 224.641, 199.130, 24.425, 244.843, 475.136, 849.738, 72.828, 414.441, 629.765, 194.435, 696.354, 494.377, 243.984, 656.058, 5.545, 750.964, 770.046, 106.587, 425.146, 175.887, 957.966, 517.958, 50.218, 249.198, 848.336, 456.462, 801.417, 667.578, 987.892, 595.452, 950.040, 891.426, 612.652, 719.274, 504.778, 830.569, 547.872,), (897.208, 743.655, 474.674, 259.192, 247.240, 637.661, 765.814, 521.300, 626.748, 274.597, 77.483, 285.728, 271.715, 319.710, 540.152, 138.374, 231.261, 693.950, 706.419, 64.229, 407.599, 542.611, 415.774, 206.834, 420.144, 904.838, 584.079, 695.523, 856.732, 765.595, 380.381, 5.896, 351.759, 753.475, 853.448, 953.430, 419.021, 747.516, 546.132, 603.253, 220.539, 219.422, 435.836, 29.025, 336.130, 679.142, 404.317, 165.045, 467.390, 127.628,), (622.257, 26.966, 394.020, 564.392, 27.102, 642.750, 135.699, 461.698, 50.285, 379.104, 211.660, 326.846, 761.230, 379.126, 752.010, 831.924, 252.272, 81.906, 19.383, 539.419, 999.908, 349.960, 650.144, 781.233, 651.755, 754.233, 949.612, 199.361, 20.380, 152.382, 126.221, 669.459, 563.970, 217.965, 699.465, 766.898, 167.789, 607.247, 747.926, 114.533, 819.301, 964.721, 108.099, 25.678, 311.957, 677.347, 958.173, 396.654, 715.015, 75.996,), (690.614, 627.242, 101.901, 772.481, 850.293, 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963.288, 276.508, 606.363, 194.525, 929.714, 574.907, 261.108, 407.135, 106.465, 72.386, 294.252, 947.015, 802.942, 956.709, 876.113, 813.147, 574.894, 694.192, 966.055, 563.169, 769.967, 757.473, 961.239, 458.848, 460.720, 586.827, 27.351, 116.268, 67.554, 633.662, 994.198, 676.913, 229.930, 315.731,), (955.446, 516.504, 9.723, 832.177, 248.258, 93.009, 673.698, 821.075, 76.029, 931.396, 476.555, 353.538, 894.316, 269.076, 947.118, 683.107, 909.927, 498.999, 199.667, 735.429, 872.742, 206.779, 202.767, 249.695, 618.597, 155.996, 106.689, 920.188, 675.812, 663.424, 613.827, 764.244, 541.474, 42.263, 482.292, 620.667, 492.340, 985.361, 897.000, 868.769, 490.805, 984.395, 916.007, 280.267, 222.093, 576.665, 54.148, 799.270, 478.320, 540.768,), (502.475, 393.717, 686.339, 174.800, 976.507, 698.245, 460.071, 689.211, 11.820, 210.752, 581.008, 325.436, 612.778, 259.701, 548.563, 237.139, 471.345, 613.084, 365.924, 498.770, 210.532, 700.713, 372.310, 854.326, 279.630, 179.889, 131.139, 575.893, 228.567, 99.870, 269.949, 235.805, 432.219, 380.883, 145.844, 959.148, 149.548, 805.668, 176.607, 499.598, 995.562, 849.390, 517.007, 720.560, 785.333, 300.034, 562.138, 567.835, 398.402, 690.543,), (60.036, 813.818, 476.539, 629.862, 449.855, 334.657, 360.962, 560.350, 931.845, 257.715, 19.835, 121.237, 867.369, 963.117, 198.958, 575.894, 649.131, 174.539, 780.309, 355.109, 673.193, 487.494, 736.526, 889.632, 381.071, 286.524, 631.712, 144.848, 167.576, 807.717, 337.388, 631.273, 571.638, 848.901, 71.344, 161.999, 228.218, 316.878, 291.356, 267.472, 644.644, 271.248, 444.898, 862.806, 363.165, 586.920, 965.526, 413.989, 183.886, 23.101,), (727.772, 662.023, 940.437, 700.697, 80.065, 166.910, 103.009, 63.792, 878.691, 548.434, 26.365, 397.013, 764.996, 83.251, 262.452, 155.274, 654.833, 885.471, 309.371, 247.397, 284.175, 626.481, 131.277, 838.518, 28.571, 663.549, 860.369, 325.201, 476.155, 974.982, 540.584, 272.414, 444.845, 969.542, 697.623, 177.037, 597.584, 631.048, 635.851, 563.984, 523.460, 639.528, 309.661, 347.168, 539.668, 804.708, 441.860, 365.783, 259.807, 303.573,), (0.092, 815.380, 847.946, 343.885, 469.198, 8.317, 922.088, 946.958, 477.067, 9.133, 430.435, 292.923, 231.215, 7.218, 373.644, 411.736, 560.554, 394.752, 163.269, 737.118, 389.723, 378.358, 262.980, 422.239, 239.562, 764.538, 909.954, 807.575, 684.640, 284.677, 742.927, 808.809, 412.904, 853.482, 182.407, 289.595, 636.893, 617.729, 272.134, 622.754, 187.789, 19.395, 49.632, 534.971, 185.938, 102.273, 269.195, 715.196, 727.107, 232.810,), (150.684, 493.580, 341.913, 311.577, 799.462, 997.963, 463.684, 791.425, 330.249, 843.546, 951.644, 56.038, 775.653, 71.385, 470.147, 192.192, 841.539, 817.251, 828.252, 121.962, 768.146, 248.962, 771.225, 442.961, 737.619, 33.510, 460.835, 770.996, 521.139, 982.116, 472.826, 681.442, 312.109, 323.063, 629.164, 42.186, 937.421, 520.926, 253.313, 638.520, 198.724, 887.520, 863.658, 217.788, 112.928, 632.909, 324.513, 167.360, 276.100, 119.650,), (789.144, 8.930, 41.993, 783.475, 475.271, 596.212, 371.399, 89.480, 157.698, 91.402, 618.645, 930.849, 996.973, 625.132, 59.775, 644.575, 701.328, 791.466, 125.827, 232.675, 981.676, 788.562, 756.698, 805.525, 438.719, 192.965, 689.250, 358.167, 134.732, 898.663, 485.302, 437.500, 294.715, 697.038, 189.776, 186.627, 347.665, 732.402, 275.324, 831.350, 914.474, 554.520, 68.877, 155.025, 295.673, 263.349, 367.113, 0.720, 638.026, 379.044,), (185.534, 18.720, 856.829, 776.017, 238.787, 721.004, 658.289, 538.957, 387.491, 524.070, 497.549, 551.468, 613.039, 322.600, 640.904, 110.464, 523.201, 66.680, 835.193, 129.774, 873.493, 202.079, 485.370, 98.754, 571.012, 836.300, 657.921, 525.821, 701.314, 255.939, 366.354, 605.954, 70.874, 930.211, 208.738, 491.406, 889.767, 79.957, 801.020, 60.700, 558.020, 938.244, 415.953, 369.069, 708.434, 823.643, 265.816, 40.653, 70.388, 303.905,), (944.603, 960.587, 97.119, 725.172, 531.661, 189.460, 526.192, 248.763, 625.325, 178.898, 749.194, 415.185, 105.514, 638.624, 357.963, 458.721, 665.242, 882.824, 166.809, 180.397, 401.557, 337.513, 158.176, 998.434, 443.012, 352.324, 315.155, 991.463, 324.654, 371.720, 764.184, 430.588, 725.868, 608.413, 563.145, 213.928, 765.708, 926.147, 254.089, 961.652, 447.500, 397.091, 726.313, 982.956, 595.763, 517.609, 993.412, 301.842, 300.334, 221.887,), (855.720, 21.701, 821.940, 689.719, 275.957, 553.663, 556.323, 925.922, 154.715, 37.736, 355.656, 138.408, 367.083, 582.156, 232.994, 811.074, 91.905, 399.798, 917.882, 734.266, 723.581, 792.904, 172.902, 825.705, 689.595, 576.232, 907.656, 595.231, 300.393, 730.789, 576.284, 78.477, 55.923, 770.902, 347.930, 817.142, 416.522, 867.831, 869.787, 226.718, 652.779, 602.302, 11.434, 777.419, 382.487, 304.783, 41.182, 539.930, 149.580, 502.402,), (220.797, 50.520, 731.579, 392.883, 445.616, 595.132, 504.729, 222.086, 289.783, 394.322, 132.189, 82.545, 571.441, 49.311, 399.191, 85.079, 501.823, 773.825, 130.375, 134.871, 559.296, 487.861, 652.248, 196.099, 615.997, 735.668, 246.246, 71.644, 776.772, 323.412, 924.138, 89.595, 671.748, 423.541, 348.308, 320.738, 593.877, 24.207, 304.819, 987.652, 616.221, 990.159, 442.210, 145.818, 44.878, 818.172, 199.685, 373.821, 757.734, 852.764,), (112.372, 54.538, 948.941, 926.730, 868.752, 820.134, 13.733, 693.795, 111.278, 450.062, 22.748, 209.010, 538.005, 203.801, 523.266, 258.658, 483.026, 729.924, 141.347, 698.755, 18.389, 583.005, 663.528, 43.482, 170.320, 284.014, 789.188, 617.965, 53.085, 654.758, 8.334, 388.645, 271.310, 852.083, 660.100, 864.282, 19.078, 867.410, 649.411, 231.169, 380.701, 976.612, 99.604, 315.456, 866.773, 531.561, 186.417, 500.651, 457.986, 926.350,), (21.488, 247.392, 529.415, 333.570, 393.400, 157.005, 346.780, 351.913, 625.231, 236.205, 978.244, 501.251, 811.893, 625.792, 878.677, 890.402, 814.859, 29.466, 554.939, 280.194, 151.708, 897.175, 656.932, 87.795, 382.207, 960.606, 612.448, 625.525, 227.428, 240.361, 152.749, 970.537, 909.735, 329.285, 542.234, 206.757, 138.577, 541.354, 800.105, 862.588, 308.998, 705.136, 523.805, 135.252, 995.686, 975.777, 145.152, 933.030, 917.112, 317.496,), (557.340, 948.602, 118.387, 317.598, 879.638, 727.080, 765.435, 880.132, 414.040, 411.252, 443.004, 933.769, 894.130, 933.250, 273.796, 779.108, 106.771, 184.746, 762.447, 611.982, 266.863, 567.043, 230.911, 232.183, 687.378, 359.261, 688.141, 476.602, 502.325, 604.712, 712.019, 373.959, 852.129, 491.446, 137.513, 193.260, 32.230, 764.539, 15.014, 269.859, 413.044, 742.367, 988.243, 757.777, 66.136, 927.103, 985.628, 867.129, 489.943, 324.896,), (457.545, 246.784, 404.867, 41.826, 735.332, 380.374, 312.897, 611.505, 742.468, 593.806, 525.231, 875.783, 786.920, 520.649, 451.610, 827.010, 42.416, 995.836, 518.703, 395.611, 735.170, 557.701, 516.127, 630.649, 49.295, 291.179, 398.040, 304.554, 827.621, 461.347, 422.462, 613.134, 54.547, 516.970, 142.240, 829.894, 451.698, 722.647, 113.172, 778.731, 937.842, 696.114, 135.307, 413.559, 450.886, 178.879, 590.315, 712.634, 201.932, 454.683,), (250.082, 691.717, 907.162, 796.486, 718.375, 123.544, 114.165, 449.398, 362.943, 523.817, 384.087, 791.067, 511.666, 949.754, 378.679, 380.607, 768.260, 912.253, 565.492, 659.530, 150.090, 868.819, 178.867, 712.047, 419.582, 309.518, 768.630, 446.258, 626.453, 117.554, 131.571, 202.852, 622.560, 253.111, 458.643, 855.411, 543.501, 5.751, 882.703, 237.878, 588.862, 475.712, 410.151, 79.405, 600.102, 244.713, 547.342, 619.766, 557.549, 825.253,), (48.919, 148.256, 653.277, 36.721, 853.829, 666.997, 838.014, 298.442, 920.385, 49.031, 416.957, 178.248, 672.090, 611.090, 691.514, 594.969, 787.308, 177.419, 455.338, 578.966, 931.480, 93.126, 299.294, 368.569, 378.939, 67.667, 427.324, 550.471, 292.833, 134.546, 694.619, 274.419, 527.053, 524.646, 695.829, 612.054, 109.302, 729.729, 628.979, 987.189, 483.663, 688.654, 933.892, 986.278, 287.187, 608.845, 316.489, 525.391, 995.024, 353.853,)))"
+      ]
+     },
+     "execution_count": 195,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "m"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 196,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Matrix((639.4, ...), ..., (..., 353.9))[100x50]\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(m)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 197,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "v = Vector(1000 * random.random() for _ in range(50))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 198,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Vector((129.713, 562.634, 519.706, 631.858, 492.504, 179.907, 609.406, 708.587, 979.258, 1.581, 23.987, 625.461, 117.926, 848.070, 799.564, 998.987, 414.041, 333.792, 560.416, 637.504, 11.297, 201.187, 281.627, 790.196, 307.773, 506.690, 323.924, 6.131, 685.836, 341.362, 724.397, 615.993, 29.117, 175.629, 330.515, 337.937, 672.473, 916.163, 797.254, 645.652, 481.496, 627.200, 892.058, 536.968, 335.110, 783.989, 413.953, 742.585, 835.106, 299.344))"
+      ]
+     },
+     "execution_count": 198,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "v"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 199,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Vector(129.7, ..., 299.3)[50]\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(v)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "The arithmetic works as before."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 200,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "w = m * v"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 201,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Vector(11378937.3, ..., 13593029.3)[100]\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(w)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "We can multiply `m` with its transpose or the other way round."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 202,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "n = m * m.transpose()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 203,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Matrix((14370711.3, ...), ..., (..., 16545418.2))[100x100]\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(n)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 204,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "o = m.transpose() * m"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 205,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Matrix((32618511.5, ...), ..., (..., 32339164.8))[50x50]\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(o)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "## Comparison with [numpy](https://www.numpy.org/)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "We started out in this chapter by realizing that Python provides us no good data type to model a vector $\\vec{v}$ or a matrix $\\bf{X}$. Then, we built up two custom data types `Vector` and `Matrix` that wrap a simple `list` object for $\\vec{v}$ and a `list` of `list`s for $\\bf{X}$ so that we can interact with their `_entries` in a \"natural\" (i.e., similar to how we write linear algebra tasks by hand) *and* Pythonic (i.e., how we typically interact with objects in Python) way. By doing this, we extend Python with our own little \"dialect\" or [DSL <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_wiki.png\">](https://en.wikipedia.org/wiki/Domain-specific_language).\n",
+    "\n",
+    "If we feel like sharing our linear algebra library with the world, we could easily do so on either [GitHub <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_gh.png\">](https://github.com) or [PyPI](https://pypi.org). However, for the domain of linear algebra this would be rather pointless as there is already a widely adopted library with [numpy](https://www.numpy.org/) that not only has a lot more features than ours but also is implemented in C, which makes it a lot faster with big data.\n",
+    "\n",
+    "Let's take a quick look at [numpy](https://www.numpy.org/) and compare it with our DSL using the example from the top."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 206,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "y = (1, 2, 3)\n",
+    "X = [[1, 2, 3], [4, 5, 6], [7, 8, 9]]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 207,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "ename": "TypeError",
+     "evalue": "can't multiply sequence by non-int of type 'tuple'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-207-c494e576a745>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mX\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;31mTypeError\u001b[0m: can't multiply sequence by non-int of type 'tuple'"
+     ]
+    }
+   ],
+   "source": [
+    "X * y"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "The creation of vectors and matrices is similar to our DSL. However, numpy uses the more general concept of an **n-dimensional array** where a vector is only a special case of a matrix and a matrix is yet another special case of an even higher dimensional structure."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 208,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "import numpy as np"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 209,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "y_arr = np.array(y)\n",
+    "X_arr = np.array(X)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 210,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "y_vec = Vector(y)\n",
+    "X_mat = Matrix(X)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "The text representations are very similar. However, the [numpy](https://www.numpy.org/) arrays keep the entries as `int`s."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 211,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([1, 2, 3])"
+      ]
+     },
+     "execution_count": 211,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "y_arr"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 212,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Vector((1.000, 2.000, 3.000))"
+      ]
+     },
+     "execution_count": 212,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "y_vec"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 213,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[1, 2, 3],\n",
+       "       [4, 5, 6],\n",
+       "       [7, 8, 9]])"
+      ]
+     },
+     "execution_count": 213,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "X_arr"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 214,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Matrix(((1.000, 2.000, 3.000,), (4.000, 5.000, 6.000,), (7.000, 8.000, 9.000,)))"
+      ]
+     },
+     "execution_count": 214,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "X_mat"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "[numpy](https://www.numpy.org/) arrays come with a `shape` instance attribute that returns a `tuple` with the dimensions."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 215,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(3,)"
+      ]
+     },
+     "execution_count": 215,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "y_arr.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 216,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(3, 3)"
+      ]
+     },
+     "execution_count": 216,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "X_arr.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 217,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(3, 3)"
+      ]
+     },
+     "execution_count": 217,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "X_mat.n_rows, X_mat.n_cols"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "The built-in [len() <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/functions.html#len) function does not return the number of entries in the array but the number of the rows instead. This is equivalent to the first element in the `shape` attribute."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 218,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "3"
+      ]
+     },
+     "execution_count": 218,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "len(y_arr)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 219,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "3"
+      ]
+     },
+     "execution_count": 219,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "len(y_vec)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 220,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "3"
+      ]
+     },
+     "execution_count": 220,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "len(X_arr)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 221,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "9"
+      ]
+     },
+     "execution_count": 221,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "len(X_mat)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "The `transpose()` method also exists for arrays."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 222,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[1, 4, 7],\n",
+       "       [2, 5, 8],\n",
+       "       [3, 6, 9]])"
+      ]
+     },
+     "execution_count": 222,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "X_arr.transpose()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 223,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Matrix(((1.000, 4.000, 7.000,), (2.000, 5.000, 8.000,), (3.000, 6.000, 9.000,)))"
+      ]
+     },
+     "execution_count": 223,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "X_mat.transpose()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "To perform matrix-matrix, matrix-vector, or vector-matrix multiplication in [numpy](https://www.numpy.org/), we use the `dot()` method. If we use the `*` operator on arrays, an entry-wise multiplication is performed."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 224,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([14, 32, 50])"
+      ]
+     },
+     "execution_count": 224,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "X_arr.dot(y_arr)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 225,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[ 1,  4,  9],\n",
+       "       [ 4, 10, 18],\n",
+       "       [ 7, 16, 27]])"
+      ]
+     },
+     "execution_count": 225,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "X_arr * y_arr"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 226,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Vector((14.000, 32.000, 50.000))"
+      ]
+     },
+     "execution_count": 226,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "X_mat * y_vec"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Scalar multiplication, however, works as expected."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 227,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([10, 20, 30])"
+      ]
+     },
+     "execution_count": 227,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "10 * y_arr"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 228,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Vector((10.000, 20.000, 30.000))"
+      ]
+     },
+     "execution_count": 228,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "10 * y_vec"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Because we implemented our classes to support the sequence protocol, numpy's one-dimensional arrays are actually able to work with them. Note that the `*` operator is applied on a per-entry basis."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 229,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([2., 4., 6.])"
+      ]
+     },
+     "execution_count": 229,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "y_arr + y_vec"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 230,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([1., 4., 9.])"
+      ]
+     },
+     "execution_count": 230,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "y_arr * y_vec"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 231,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "ename": "ValueError",
+     "evalue": "operands could not be broadcast together with shapes (3,3) (9,) ",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-231-89dcd380b2e7>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mX_arr\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mX_mat\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;31mValueError\u001b[0m: operands could not be broadcast together with shapes (3,3) (9,) "
+     ]
+    }
+   ],
+   "source": [
+    "X_arr + X_mat"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "We conclude that it is rather easy to extend Python in a way that makes the resulting application code read like core Python again. As there are many well established third-party packages out there, it is unlikely that we have to implement a fundamental library ourselves."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "## Further Resources"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "A lecture-style **video presentation** of this chapter is integrated below (cf., the [video <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_yt.png\">](https://www.youtube.com/watch?v=ibDT0uOAOTI&list=PL-2JV1G3J10lQ2xokyQowcRJI5jjNfW7f) or the entire [playlist <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_yt.png\">](https://www.youtube.com/playlist?list=PL-2JV1G3J10lQ2xokyQowcRJI5jjNfW7f))."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 232,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/jpeg": 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8E7HRTObSfJPNNyngOEY5jG7sdpHxqELts2JJXbpZHyOwBukc57sDsG5xJx1K6lihSLMxcbkyY00A/YBSxeIbVytYIa1uUTc6lxnTeT0FkWR4ZH/PUv1ut/fjWOXZWmdG9kjDtfG5r2OGMtcwhzT16dCAtr25mkLRReGPa47iCs13RPuvqf6/a/vPWBXfftyTyyTzO3yyvdJI8gAue8lznENAAySewLoUaLCxjWncAFLE1BUqueNCSfUypnwS7vTT9T1RjWG1C+rVpyPY2QQPsOeZ5mseC3mctgDTjpud5iQeXFFg6hpFXU5gzvyO9Np88zY2Rmy3kMswvlEYDS9jSWZwMjtyvHwhdgfU1DTbE7awud7y1rEoeYY7FZ7jsm5bS5jJGPLd+Dt2g4K5cTWq9fT62l1547Tm2Zb1ueHf3vz3xtgiigdI1rpA2JuS7aBlwx58c4sPbzHvZheP6cvHSJm3G67jao/g4zDJ2ZBbI/7naGLazEGY+G0xIUUXtpavbgY6KG1ZhifnfHFPLHG/Iwd7GODXdOnULxIum5odYhfPse5hlpjoiL3aHpU92dteuzfK5r3AZwA2Nhe9znHo1oDT1PzDzrwoHCY3/f8AtCxwaHEWMiekT+49UWV1XWTPUoVOWGigyy0P3ZMvfE5nJLceJgnHacrFLOs4Wt94T6hJE+GvCYAwyxvYbHPdtBh3ABzQMEu7PGGMrXVNMFped9upt9Vvw7azg9tMGC33o/tEOvw+EHyhYJERblWRSTuZ6nBT1anZsv5cERmMj9j37d9aaNvixtLjlz2joPOo2vq11aYqMLDoQR6rdh67qFVtVurSCJ0sZXFo6D8i+oi2LSvq+IiIu2rA+V7Io2l8kj2xxsaMue97g1jWjzuJIH71xlYWuLXAhzSWuB7QQcEH58hTPuPasYNTqQtr1ZDPZjaZpYRJYia4FpEEhd9iyM9QPOVFdb/9VZ/WJv7jlXbWcaxpkWABnjJP+FdfhmNwzawdJLiCI0gA+eq8aIisKki4WPIf+a7+RXNcLHkP/Nd/IrDtCp0/iHVbsIiLyhfohERMoiIiZRERERERERERMplEREREREREREREREREREREREREREREREREWu3uj/uxF+z4P79pVmrM90f92Iv2fB/ftKs16Rsr8pT6Lw32i/mNb9RRERdBcZEWV4S0d2oXqtNrtvfErWF3aWMALpHAechjXkD4wFJWUtJ1Bl+GhVmrTU601utYdZfP37FVI5jZoXNAikfGd4DOgPTzeNWq4ptN2Ug7iSNwJgE/PSdCr2HwD6zMwIFyADMuIEkCARpGpAuBqoKiIrKooiIURERERERERT/uNa5aZeiotmIqzMuvkh2R4e5tKd4Jft39DGw4z5lAG9gWW4S1k6fbjtiMSmNszdhdsB50EsBO4A4wJM9nXCxIVanRy1nuAgEN8yC6f3Cv1sT2mFp0y4ktc+xmzSGRHKQ6w+qKa6Nenm0LWmyzSytik0hsbZJHyNjbzpxiMOJDBhreg+SPiUKWQp6tLFVtVGhnKuOrulJaS8Gs5749js4AzIc5Bz07FnEUu0aI1BafRwJ+QUMFiBReSTYteLc2OA+ZCx6EohCsKmrKuOo6ffraNJp1SxHtpxX7MrHm2+e3HG+WSvOH5gYwTM2tA+8PXrkQXiTTu87lqrku73sTQhxxlzY3uax5x0yWgH96nuqae3UtSrayyxVZSk7ynvPkswxvpSVo4o54ZIXOErpDyfF2tIcZG9cHKg3Fmoi5euWm5DZ7M0rARgiNz3GMEfK27crlYAnML3y+9+qfkdfKN0L6La7QGGwAzns4AvTjdGo+G/Gd8rFoi9ek2mQytkkrxWWtz9hmMgjcSCAXcp7XHB64zjouo4kCQJ5L59gBcATHPh6XXRNA9m3ex7N7BIze1zd7HeS9uR4zDg4I6dF1qZ91ycS2aEojjiEmjadIIohtiiD43uEcbfvWNzgD4gFDFqw9U1KYeRE7lvxtAUKzqYMgb+Ky3B+pspX6luRrnMrzMlc1mN7g09Q3cQM/lK8GoTCSaWQAgSSSPAPaA95cAcefquhfVPsxnz74hazWcaYpbgSfMgD6BfERFNakXCx5D/zXfyK5rhY8h/5rv5FYdoVOn8Q6rdhcZQdpx1ODgbi3Jx0G4Alv5QFyXVbic9ha2R8LjjEkYjL24IPQSsczrjHVp7T2dq8oX6IVVUKGo0RBFBWu12TPgieYBpLL9h0WnatJIbBdM6lLKJm1CbIZC6Q7Q7e1uBIdN0/WSa5szSCR8tsWpYu8hyoDacYIq/ibhCYY4sdDJ453EO7M3WaJXFsesTSOD5Yy1h0x7hJAQ2aMhtXIewuaHN7W5GcL1e9k3pG5/Bp/1NWX1idQPRV20gNJUU4L1LUn6hFXuyzGRlGR12L/AJA12ThmmcggVxzo5CZLflYY9xmLNzGs2+IaNqcE9mSnVfE+V87pbL+83WZGy6pXleyKVk7W3IjV745ZsRNkiaA0P3OcDOPeyb0jc/g0/wCprhHRkdu26nadscWO2t047XAAlrsVPFdgg4PxhY7a8gBZ7O15UXuU9fdAHMs2ROI3ANaNMj3EabYfG6RpD2Cbv4Vmu2v2ZDsYjLl0WWcRiKVwdYfLzGERRM0yMOcItQ5ghsTTPEdV0hobXPic9vLbujcHy7ZbZqPiAdJqlmNpfHGHPGmsBklkbFEwF1TBe+R7GAdpc9oHUhfYqUjy8M1O04xu2SBrdOcWP2tdsfip4rtr2nB64cPjQVbaD0Q0+Z9VHWVNZZZY1kk7a3f00rie852mGTUnSvY/fM2RkHeTmsj2hzmO5uW+LGD6LlW/7z045oJdRvllc2mSOqBvOcwunfYibLBBZgjkJxCx7Q4tjGQMvGe97JvSNz+DT/qa8WryR02tfb1qSqx7i1jrD9Kga5waXlrXS1QHODWudgeZpPmWO0JIgDyH2VnIBxUVj0S9CH4qXrRbBGzdPertmtV20NPgbTsSizhsvfEU8ryCWdLBa4mfD/Roeh3Y56jnQWBt5ThJJJXYymO+9Tmu1214rUojrPjnrxxxsdJhjawccwAtkVqSOKJ08utSRwMcxj5pH6UyJj5SxsTHSOq7Wuc6WIAE5JkZjtC5M2ugNpusTOqhjpDZDtLMAjZnfIZhV2bBtdl2cDBUjVcR/wAqIptB/wCFn0WCoMFhofBq88zCXgOi97HgmN2yQZbVPVrjgjzHoV3PpSNc1p1O0HPJDGlunBzy0Fzg0GplxABPTzBaMq3Zll0WJfp8zQXO1K2AASSWaeAAO0kmp0C5e9k/pG5/Bp/1NYjmszyWURYv3sn9I3P4NP8Aqae9k/pG5/Bp/wBTSOaTyWURYv3sn9I3P4NP+pp72T+kbn8Gn/U0jmk8llEWL97J/SNz+DT/AKmnvZP6Rufwaf8AU0jmk8llEWL97J/SNz+DT/qae9k/pG5/Bp/1NI5pPJZRFi/eyf0jc/g0/wCpp72T+kbn8Gn/AFNI5pPJZRFi/eyf0jc/g0/6mnvZP6Rufwaf9TSOaTyVFe6P+7EX7Pg/v2lWasL3Q1V7NWiDrM8p7wgO57awOOfa6fY4GjH7vOq55Tvwr/8ASL/GvR9lflafReI+0DQdoVr/ANR4rsRdfKd+Ff8A6Rf41wkDm7TzHHx2AgiPBDnAHsYD51flccMB3j5/4Us7l+ox1dYoTzODImzFj3k4awTRSQbnE+S0GUEnzAErPcLaFb0h+qWLsMkEVbT7laOWRpZFZsTgQ1467ndJg45dluQAOuOirxd0tmR7WMfI9zIxiNrnuc1g+JjScNH5FUr4U1HEg2IAPQEm3qQuhg9oNosALSS1xc0gxdwAvYyLNO7QjfbpREVxctFYOpa3PotbSoKIiY6zQi1G298MUptOtSSbIZTI0nksZHtDWkdHnz9VXym9kU9Vq6cZL9ejPRqto2W2RMd1aGR7oJqvKjcJpNkjgY8tOWjzEFUcY1pLM4lsmRE7jEgT/wAwutsx7mip2TofAymQD8QmCYvHPSd0rHd07Toq2pzsrsEcErYbMUY7I22IY5XMA7A0Pc8ADoBhRlZ7j7WI7+oT2IQ5sB5cUAd5XJgiZCwkeYuDN2PNuwsCt2FDhRYH6wJ9N/NVdommcTUNP4cxiNIndy4cl7tF0i1dl5NSCSxJguLY252tHQucexjckDJIGSB51w1bTLFSV0FmGSCVuCWSNLTg9jh5nNOD1GR0Kk1J5h4btPjO11rWIak5HQvgipyWGRnH3vMJOPypxKTLoeizPJMkcmoVWuJy4wMkjfEwk9drNzmgeYOwtIxL+0i2XNl5zlmeG6IjnO5WjgKfYEyc4YH7ssFwbHGYOaZ5RvUOWS4a0aW/ZjqwlrXP3Fz5DtjijjaXySyO+9Y1rSf9B2kLGqX9y4Zk1Rjeskuh6nHCB5TpTGwhrf8AuLWvW/FVDTpOc3WPv/Kq7PotrYhjHaE358vPReXWOGYWVX3KN+O/BBKyK1iCatJA6XIheY5uroXOaWh3TrgY7cRpS/g3A0niBzvI73os+bmvuDk/vy1x/cVEFDDOdL2uM5TE2/tBvEDfw0hTx1NgbTqMGXM2S0TAIc5tpJMGN5N53IiIrSoIvq+L26LpVi7M2vVidNM4EhjcDDWjLnOc4hrGD5TiB1Cw5waJJgKTGOe4NaJJ0A1K8SLKa/w/coGMWoTEJmudE8PiljkDSA7ZLC5zHEEjIzkZGe0LFrDHteMzTI4hZqUn03FrwQRuIg/NZ7jLVorbqRi34r6ZSqSb2hv2WvGWybcE5Zk9CsCiLFOmGNDQpV6zqry92pRS3gaKKGpqmpSQxWJKUdWKtFOwSQie5M6PnPjd0e6NkbiAcjLh8xESUr4JswyVdT02aeKsb0dV9eed2yBs9OZ0gjlf2RtkY943HoCB8wOjGT2XmJjhmE6cplW9mECuJiYdE/3ZHZdbfFETvXfxG5l7SYdTdDBDai1B+nzurxMhZZY+v3zDM+OMBglbtezIAyMZ82IapfxC+GppUGmNsV7ViS87ULD6sgmhhAg72hh5zfEkeQXuO3s6D4lEFjBiGGNJMdP8axy0ss7UM1RPxZW5o/ujfG/SeczeUXCx5D/zXfyK5rhY8h/5rv5FWnaFUafxDqt2Fwm3bXbA0v2naHEhpdjxQ4gEhucdgK5ovKF+iFWp7nt2MFsN4SB8cEszpBHBLNdEtY3S50VYsMFiKtG1xex56O3CQOIHvl4T1AMaGWWPAqwwOhmnncHPbLFJLOZWRhjpDE2SsN0JBaGOcHAvhM7RbziHlaexaoAeC7roWg2g2dkToWPbZs9IuTqUQYHRtj2bu+qpcWNbjkNI6xRkeetwRfbO+UTRwRyCQsggtSujqOc2wNkfMq75o382IOw6LAhZ0cGMa2x0T+Ics9i1YDWuHzNRhpxu2cuxp0pdzJdxbTvVrcuJQeZzXNhfhxOdzgSfOsCOCrbL752W5O93yxyMabUnNhMYHMJc+F8kxnAbE7bLFhkTOr+gbPUUW1nNELJpNJlQHSeE9RidSLpYcV5i5wNmWTbCTWMmQ2rGJ7D+XY8ePvZo5w3tny/fINQ0ua53hJK51V8W+SxHBO7c18tZ0boo52tG9rXvPjYbnaD07FnkWHVSTKCmBZQKxwNJCQaPIY2OWu6OB808cRhqWNGkrwucGP2BkemzNB2uwZyfv3k5kaHM6jbhkFcz25Zp3RNfOyux0rw7lRzxbJozho+ztDXCRzpA0HDFJEWTWcdUFJoUDh4X1Fs0doms6eN8u1j7UznNhdLpjm133RVEloFtSw4ySM3DmRM8cN3rHzcC6k5sQdYhMsTaxfYFmzzJxHBTimqOjlhkjjrONebxiH5Fh+WHfLzLMRSGIcFE0WlV/qHA1iWsYzM2SQxPiPPnme10TtMs1hA97YwHR99SwSF3LGRXY4t3NaBP2joOwfMOwfMF9Ra31C7VTawN0RERQU0RERERERERERERERERERFrt7o/7sRfs+D+/aVZqzPdH/diL9nwf37SrNekbK/KU+i8N9ov5jW/UUXVZ7G/pI/6wu1dVnsb+kj/AKwuguQz4gu1ERFFe7RNNNqXlCerX8UuMtudlaEAEDBkf9917Bk9D8SyPH2iR6dekqRSGVscVZ3MJB3ulrxSvc3aMbC55wPix1PasAexS/uwfdaX9Xo/7KuqrnOGIaJsWutzBb/ldBjKZwb3ZfeD2DNyIfb5BRBfV8XJjSSAASSQAACSSewADqT8ytLnriik2o8D34IpZHCu51dgltVorMMturGQDzLFdji5jcEZxnAOT0BIjK106zKglhB6LfXw1WgQKrSDz+/JSLhrW68dazQvRzSVLD4pg6u6Ns9exDkNliEg2PDmOLHB3mxghcOK9ahsMqVakUkVOiyVsImc188kk7xJPNM5gDQ5zg3xR0AaMfEMAih/DMz59+vKYiY4xZbDjqppdlaIiYExOaJ1ib/6ARerSr81WaOxXkdFNE7dHI3GWnBB6EEEEEgggggkEEFeVFuIBEHRVmuLSHNMEbws5rnFFm3EIHNrwQCTnOhqV4q0ck2NvOlEQHMkx0yVg0RRp020xDRAU61epWdmqEk80QFSLua6ZHc1ajXmAdE+Yukaeoe2GN85Y4edrhHg/MSpLoPEVnW/fKpcLZIH0bVqpFsY1tKes0SV+Q5jQWMDQWEffDt8+a1fFGm4gCQACTOgJItYzoTuV7CbPFZgJdBcS1oiZLQCZMiBdo0OvJVwpfwcdmk8QytOJRBp8AcO3k2boZYb+a5rWghRBZrhXXe8nTtkhFmtahdXtVy8x8yMkOa5kgB5crHgODsHz/HkbMUwvpw0TcGOMOBjziFp2fVbTrS8wCHCeGZpaDa9iQbX4XWWrnfw1MHHpBrELoc+Yy1XiRjc9jSAHYHnGVD1INf16GWrDQp1nVqkUzrLxLNz5rFlzOWJZXhjWt2x+IGtGMEk5z0j6xhmOAcSIlxMcPT181LH1GOcxrTOVoaSJgkTpMG0xpuREWT4Upss36NeQZjnuVoZBnGWSTMY8Z8x2kre9wa0uO5U6VM1Hhg1Jj1XjfUlEbZTFIInHDZSxwjcevRryNpPQ9h8y6Faui69ZvcQWNOnle/T7Ul2gaZJ73igijmbX5MXkRSMMMR3tAPQ/GqqHYq+HrueS14gwDYzZ09L2Ku43CU6TQ6m4kZnNuIu2J0JscwjeiIitLnouFjyH/mu/kVzXCx5D/zXfyKw7QqdP4h1W7CIi8oX6IREREREREREREREREREREREREREREREREREREREREREREREREREREWu3uj/ALsRfs+D+/aVZqzPdH/diL9nwf37SrNekbK/KU+i8N9ov5jW/UUXVZ7G/pI/6wu1dVnsb+kj/rC6C5DPiC7V6GUpTC+wGO5McjInyfeiSRr3MZ85LY3np2Y69oXnVgajq77fDb90VaFsOrwMYytAyuzrUlcXObGMOeT2u7eir16rmZYGpAPKVbweHZWD8xgtaSABrA+Sr9d1y1LM8yTSSSyENBfK90jyGgNaC55JIDQAPiAC6UW+BqqeYxCKQdzdjHavpof5Pftc9fO4SAsH8Yao+u2rO+KRksbiySN7ZI3jtY9jg5jhnzggH9yhWYXsc0bwR8ltw1UUqrXkSAQY6GVO+ApHP4kmEhOJ36s20D2OY6C294fnzb2t/eAq/b2D8imFzjZju+poNPgrXrsckVq5HLM7LZ8d8OgrvOyvJJjq4EkZdjGcqHqthWPDi5wizRFt03tPGBvsr2PrUyxtNjs3vPdNx8WWBeDPuyd0m0oi9ej6dNbnirQN3zTPDI29nU9pJ8zQAST5gCs3q/CLoa81mC5TvMqvYy2Kj5HOrmRxY15EkbeZCXjbvbnqfykb312McGuNz/x/ocSqlLB1ajDUa2QN/QSY3mBcxoLmyjKIi3KspRonBNyxSs6g9phqQVZbEczg1wnfGdoiY3eHAHD/AB8EDZ84UXUs7nQ8TWz5/eK5/eqqJqtRe81Hhx0IiBFo81fxVOkKNJ1MEEgzJmSDHAQOXzKy/Bus+99+rc2l4glDntHlOjcCyUNycbtjnYz58KSVJdN0xt+erebbfZqz1KMDIZ45IWWSGumsvlYGsfHGCA0F24nzDsgiJWwrajpJO4EDeAZg/PSNSmG2g6gzKADBJaTMtJEEiCBuGs3ARERWVQUrPBksel2NSsOYzYagrxRywyue2w/BfMI3O5Q2kYacOzuyBjrFFLeHPuHrv6bR/wC/ZUSVXDOeS8PMw7pbK08+Kv45lMNpOptiWSZMmc7xOg4Dci9Ol3HVp4LDMF8E0U7M9m+J7ZG5+bLQvMiskAiCqTXFpDhqFYcetaTWuz6zWnnfZk74lrae+sWd727THtc6azu5ckLDLIQGjJy3s25NeBEWihhxS3k6C/AaC0cTz4q1isa7EQCABJMNmJdEm5NzA5CLAIiIrCpouFjyH/mu/kVzXCx5D/zXfyKw7QqdP4h1W7CIi8oX6IUW7nfGLNYinkZA+AQSiIh7w8uJbuyNoGApTla78ITPj4Z158b3Me21Ww9jixw+y1wcOacjoSP3r2a9oNiHQaute+moOtiOo5recWwxxSljGMjaPGa9oc0l247iHEjLsru1tlM7UhrsozZQIJvAPpdfI4X2gqjDtc9mcin2jiCBbM4acbbtVcXEPE9ShLUhsue2S9IYq4axzw54dEzDiOjRmZnU/GVmsqi+6jWNqfhizJNOH6i2uJQyTayFxNEukrNx9hlJncS4Z6sZ8SyHHtW0Ne0fT6t6zBu09kHPdI978NFtj5ngECSwY2k7+h3YPmWnu1rmMh0EhxM6e6Tw6c+KsnblRlSrLJaHU2tg3OcA3m2+d3DmrkyipvierZit6PwxBetMhkZLYs2t+LEzZJrUpY6QHPRsUoA7CXNyDjC7e9ZuH9b0ytBdt2KWpExSQWpOaWvLgzeMANbh0kZBABw1wJIWvu4EWfcguAg3Am/ImCQFvO2iHHNTOUOaxzpHuudFo3gEgE+gKt/KjPBfFzNTm1CFsD4jQn5DnOeHCQ75mbmgAbR9i/8Asq/4c0yxxHd1O1Y1C5VjqWTBTiqTcrlbS/a4gggYa1hJGC4l3UYAWH4LM0Wm8W7pSZ43APmYdpdK19oPkaW+TlwJ6fGt7dmsDHtLpeMnH3cxHkbFVX7cquq03NYRTPaXJHvBjSerbi3EK/sr5lVRfty/8FCUSyCXkQnm8x4kz39GCeZndnHTtWO1rU7c1ThfS4rMsA1GCA2rDHuEzmbYm4Emc9jpCR98duemc6GbNc7+rRzmn/5Ek+m5W6u3GsA9wmabHgA6l7sob671dOVhdC4nqXbFytA57paMnKsBzHNDX75I8Ncejhuif1HxKL6DwRe06+01b8sulSwvjtQ27D32WyObIBJX2Q8sODuUd3inyx16KMdxnRmt1vWT3xaPeFp8bd0xIsh0tyDdcGPs7wBuB6eMSUbg6Jp1Hh85WgiBxMQR96ysVNp4kVqNM0suZzmuBM2DZlpGo36biOasjgjX7F9k7rGnzaeYpjExkxkJmZtB5reZDGdvXHQHs7VIcqh+F+JrdTQNZsslkdOL7YIZJHOkMQkETS5u8nBDS4jzZx2r063wpYoaOzWYtV1A6gyOtZlLrBdC/nujDmAOG5wbzB5ZcHbTkeN03VdmN7QjMGy7K0XMmAfIXHFVaG3X9iHBheQwvcZAgS4cgTY2EacVd2V9yqV411S1escLvisSVJL8LDI6FzgGPldX3uDM4ftLnbd3zLvq6fLo3Eun1YL1yxXvQvdMy1NzS4ls48bADSQ6Njg7GR1GSCc6hs33ZLveyuOWP7SZvpusrJ27/wBSG0yWBzGl0j+sAgxrvEq40RFy130RERERERERERERERFrt7o/7sRfs+D+/aVZqzPdH/diL9nwf37SrNekbK/KU+i8N9ov5jW/UUXVZ7G/pI/6wu1dVnsb+kj/AKwuguQz4gu1ZuLWWDSpdP2O3vvx2xJkbA1kD4iwjt3Zdn9ywiKD6YfE7jPop0qzqc5d4I8jqiIpn3J9J063ersuzPLzPiOmIN0c+1m8GWffhrMggs2nIHb1UK9YUaZeZtw+/wDS2YPCuxNVtJpAJMSTA+f7C53KGIucww5wHyj/ADK4LcFXIgwiLPXODtUhrd9y0pmVw1ry87dzGO7HviDuZGz53NAWBUGVWPu0g9DK2VaFSkQKjSJvcEW81Me4991GgfbDVvCH4+b3pNjb8+3euHc3+064T9q947QPyeaZa/Iz/wB2d2P3qNaXelrTRWIHmOaF7ZI3jGWub2dD0I8xB6EEg9qzOscXTWIJa7a1KpHYkZJZ7zgdC6y6M7mCYukd4jXEuDW7QD1wqVfDvc85dHZRPDKSfrbmupg8ZSZSbnJlheQI+LO0AdIIvy0uo6iIuguMvXp2pTVxOIX7BZgfWm8Vjt8Eha57PHaduSxvVuD07V5ERYDQDKkXuIAJsNOSLM6VwtqNuF1itSsTQtzmSOMkOLfKEY7ZSMEYaD16LDKcd0W7LUtaW2B7mChptB9baSAJHM5skoA6b3uPjH77ABVevUeHNYyJM66W6RxH+1dweHpOY+rVnK3KIbEy4njOgBOl7C2qhBC+KVd1usyLW9RZGAG88SYHZumijmf/APd7lFVso1O0ptfxAPqJVfFUDQrPpEzlcRPQwvTBemZFNAyRzYZzE6aMY2yGEudEXefxS5x/evNlCVaur69Ppmq1tHg2DT6/eNaxWMcTo7ffMUL7Us4c3L5H84jPm2jHnzpr1jTMMbJIJ1iwgcDe4AVrCYYV25qryGgtaLTdxJAiRAs4n9iSqqRZbjHT21NQu1mfa4LU0cYySRG2R3LBJ6khu3qsSrLHh7Q4aESqVWkaT3MdqCQfKyLIa1o89MwCwzY6xWjtMac7hFK6RrN4I8Vx5ZOPiIXTpWoTVZmWK8jopo92yRmNzdzXMdjPxtc4fvUq7rFmSaTSJpXl8suhUJJHu8p73vsuc4/OSStD6jxVa20GetvvirVGhTdhqlQk5mkW3QT6zyjzULREVlUUXCx5D/zXfyK5rhY8h/5rv5FYdoVOn8Q6rdhEReUL9EKsdJ7mc8Ok6lpptQufemikbKI3hsYjfG8hzc5JPLP+qzOt8GS2NBh0gTxtkjiqxmYtcWHvdzHEhuc9dn/ypqiuOx9Zzg4m+bNoNYA+gXLZsfCsYWBtizJqfhJJj1JvqoBxZwDPbraO2G0yG1pDYhHI6Mvie5jK4Ltucjx67CAc9pBXpt8HWZtV0vVJbMTnUqrIbDRG5pml2TiSSPrhjS6bIHmwpsq67vetW6NGtJUnfXe+4I3OjIBLDBM7acg9Mtaf3Ldha9es9tFpF8wBIH9Uzz4qtj8LhMLSfiXtJjK4gE3LIDTExIt13rKd0Hgt2oyVbVayad+m7MM4bvaW5Dtj25HY7JB6jxnghwPTwcP8C3HahFqer323Z67S2vHFEIooz42HHAAONzjgNHXBJOAFVuh6txZejM1Sa7PEHmMvYYsB7Q0lvjYOcOb/AKpa4y4n0qZnfcthpdktjtwxvimDcbgHbQSBludjgRkdRldZuzsSG9i2qwkAiP6gDqAYkL51+2sC54xNShVAJBzXyEjRxGbKSNxVk2+59qFe7as6RqYoxXnF9mJ8LZdrnOc5zog4EHxnvI8kt3EA4Xbwf3NjTratUntc6PUmhgka0iWNoEwD3lxw6T7I0/Flp+NSXgDiRmq0YrjG8tzi6OaLO7lzMOHtB87Tlrgfie3sWfXIrY3ENmk8wRANhPum0nUxHFfS4bZeCqZa9MEggke87KA8XgEwJkyICqQdzLV3UX6bJrDDSZgwQNgADnCUSjnPI5mwHc4M3OGdvZhZvXO526xQ0uFlrkXtKZEK9prCWFzBHnLMg43RMcD5iOw5IVgKKd1Hix+jU4rTIGzmSyyDY55jADoppN2Q05P2IDH/AHKVPG4mtUa1kTMiABJIgzoL75WqtsrAYWi99QHLlAJLnGGgyIuSIOkaLwcM8HX23majquo9+TQxmOCKFnJgZkObve1oa15w9/Tb2nOTgY+8OcF2qGr3L0NuM1L0sk1iu6I80udznsa2TOAGyyk5GMjphZLuacUO1ekbb4WwETyRbGvLxhgYd24tHU7vi8yqzuhcW6lBxDNVhuTR1xPSaImkbQ2SCs54GRnqXuP/ALirFGnia9WpRkAhpBECIBFhAjW4hVMVWwOEw9HEgOcC8FrpOaXA3JcQSCBBB3WhTjhzubNi07UNOtzNlZen5wfE1zHROAYY3Dd2ua+MH4j2FYt/c31eaCLTbOtNfpcRYBGyuGzOjiILIyT1AbgYDnvDSG9DtAFrIqY2lXBJkXM3AMGIkSLHoum7YeEc0NymAMtnOEtmcroIkTNioVr3A/OuaNPXkZDBpOxohLXOc6NjotrWuz0w2PGT8a7te4Sks6zp+qNmY1lOMsdEWuL3553Vrh0H20f6KXplaRjKoi+gI8nTP7lWDs2gZ93VzXG51bEekC2iIiKsr6IiIiIiIiIiIiIiIi1290f92Iv2fB/ftKs1Znuj/uxF+z4P79pVmvSNlflKfReG+0X8xrfqKLqs9jf0kf8AWF2rqs9jf0kf9YXQXIZ8QXaiIiiilPcmka3WtPc5wa0TOy5xAA+xSdpPQKLItdan2lNzOII9Qt+Fr9hWZVicrgY4wZXZP5TvznfzKyfBcDJNS06OQAskvVGPaRkOa6xG1zSPiIJH71iF21LD4pI5Y3bZInskjcO1r2ODmuGfOCAUqNJYWjhCxRqBtVrzoCD81YnCVqSbiyYSkvFq1qVaw1xJD65jsN5Ts9sbQyPA83Lb8SrZvYPyKcS8YUmzT6jWpTRapZZMC42GOp1prDCye1Xj5fMMrg6Qhr3EAyu7cYMHVXCMcHFxbHutEW1bM6Ta4HkujtKsxzAxr8xzvdInR2WNQDNiSN08ZRZLQNDt35RDUgkmduaHFjHFkQecB0zwNsTOh6ux2FY1ZbhKzJHdqCOR7A+1WDwx7mhw5zOjtp8YdT0Pxq1WLgwlusb1z8K1jqrW1Jgm8a/OV5+INONS1Zqlwea88sJeBtDjG8s3AEnAOOxeFZ7uifdfU/161/eesCsUHF1NrjqQP2WcWwMrPa3QOIHkVK9T4PNbSzflmhfK63DXZFXnhsNYx8MsjzM6IuAkJazADug3ZzkYiil9X/pux+2q/wDspVEFqwrnnNnMw4jhuCsbQZTb2ZptgFgMTN5PRFMoOI9NnZSk1Gtbks6fDFXZ3vLC2C3DXcXV2WeY0vjIB2FzMlw+LpiGotlWg2pEzbeDBWjDYt9CcsX1BEixka8Pu0r3a9qcl2zPamxzLEr5XAeS3cejG567WjDR8zQvCiLY1oaABoFoe9z3FzjJNyeZQqxLF3S7t2rq9i8IHsbVfepmCd88lioxjAKxa0xujlELBlzht3ElQGpWkme2OKN8sjs7WRtc97sAuOGtGTgAn8gK6VprUBVOpBAOkaHXWeHyVvC4t1AH3Q4Egw6YluhsRpJ8j0Xu1/UXW7Vm04bXWJ5Ztuc7OY8vDAfOGggZ+ZeFEW5rQ0ADQKo95e4udqTJ80WV4i1l13vPdG2PvOjXot2kne2uZCJDnsceYeg+JYpELASHHUKTarmtLQbGJ8tEREUlrRcLHkP/ADXfyK5rhY8h/wCa7+RWHaFTp/EOq3YRF0ajzuTL3vy+fy38jnbuTztp5XN2eNy9+3O3rjOF5SLr9DkwJXax4OcEHHbgg4/LhclSfcYu2a0uuWJu9m1IJJ5r5YJTMJouc/8A5YeSYcNl8rxvJ+dZCpxhxJcqTarUq0GUY+a5kEnNfYlihJEjmlrgJC0teOmzJYcA+fpVdlvbULQ4QIuTAkiY6/S64NDb9N9Jr3MdmOY5WiSA0wTutp52Eq3FVXumPubU/X2/7ewpzwLxCzVKMN1jDGZA5skZO7lyRuLHtDvvm5GQemQ4dB2KDe6Y+5tT9fb/ALews7MY5mNY12odB+abdqtq7KqVGGQWSDyMLh7n3VasGlSMns14Xm7M4NlmjjcWmKAA7XuBxkHr8y8PuhuIaFinWqwWIbFgWmzHkyMl5UbYZWO3uYSGkmRmGntxnzKIdz3uZyaxUdaZcZAGzvh2OhdISWMjdu3CQfhMYx5lLdM7hTRIDZ1AviB8ZkNflvd83MfI4M/hP7l2KowdHGGs+qcwM5QDr1XzGHdtPE7NbhaVAZC0DOXDTjCw/Cmn3W8J3LNaexWkjvvuRurzSQmSCKKGCxudGQSwBsrsfHAFJvc78TT2mXatqxNYljdHYjfPK+aQxvHLkYHSEkMa5jDjszKfjVm1NKrxVm044mtrNi5IiGdvLLS0tOersgnJPU5JPateu5+52i8TCrISG8+Wg9zu18cxArv6dgc8V3/kK006zcdSrti8528Y4fL5q3Vwz9k4nBuzHLHZu/tk7/nPRqkHd94ptx6hBTp2bMHKgDpBWnlidJLYd4rHiJw3kMZGRnP2047evg7sejarWqVTatmek3vSLZJM+WU3hWkM0zi9pO0uE+DvPRwH5PPwhF798VSWTh8MdiS4T16w1S2Op+XqKwI84ypz7pX7k1/2jF/trasMcMPWw+HAEx71t5+uvqqNZjsbhcZjHOOXNDACYhtukG3mFH+4BQ1QmGwyw0aU2aw2Wvvw50vJwHBnL6je6I+UPJ/1indgn5XElyXG7ly0pNucZ2VKrsZ82cdqtP3On3HP65P/AExKse6mAeKJwRkGxp4IPYQa1QEH5lPCVM+0asgWa4WtMOGvPmte0aHZ7Fw+Un3ntNzMEtOk6DkslxRR4rnhk1WeSxBEGmY14bToDXgALtwrRvG0Nb25y/A8bqCpT3A+NLV0z0bkrp3wxCeCZ5zKYw5scjJH9smHPjIccnxnZJ6YsriYf8lc/VbH9p6on3Nf3Wn/AGbN/uaapsrNxeCqlzGjLEZRELpVMM/Z21MOGVXu7SQ7MZn7meUKQd2XujWoLTtN055idGGixOwbpTLIA4QQ9CG4a5uXDxi52Bt2ndgLHCfFkNc3TatlzW810LdQnfaDQNxzHuw8gddgcT5sE9FjK20cWnvjs9/JfK+M238jt+93cvHzYWzKlia42eymymxplskkTKjgMI7bFWvVr1HDK4ta1pjLG+PTreVU/cT7oc9+R1C84PnbGZILGGtMrGY3xyBoAMgByHAdQ12eoy6L93bXr9fVnRV71yvH3tAdkNmeJgcd+XbI3gZPTqsN3NSz/ieDvfHK78umLbjbyeVZxtx97y//AIXf7of7sv8A1SD+T1cpYSkzaIytEOZmjcLrl19o4irsUl7yXNq5c03IAnXfqstqtHizVYzfaZ4IHN5lerDaMDjDjLS2JjgZHEdcv8Z2egwQF6+4TxzcluDTrc77Ec0cjq75nGSVkkbTIW8x3jPYWB5w4nGxuMDKu2sAGMHxNbj/AEC1r7kP/Utb9Nf/ANrbVPD1m4vDVmuY0BrZbA0sf8fuunjcK/Z2NwtRlV7jUflfmMgyWjSw3m260aLZhY7V9cpU9nfdqvWL87BPNHEX4xktDyCQMjJ82VkVQPdev162uTTSMrakJaBgdWkcSaMuwNY44BDXB2HgZz9lf5JLXHjbPwn8TUyX0Jtv5XsPNfUbZ2kcDQFURqBfQTvtc9B10BV9wSte1r2Oa9jgHNe0hzXNcMhzXDo5pHnC5qG9zFkNClS0uS3DNc73daDI37wYZpZJA+N3Y6IbsbvPgnsIUyVatTyPLRcTY8RxV7C1jVpNeRBgSJmDFx5IiItSsLXb3R/3Yi/Z8H9+0qzVme6P+7EX7Pg/v2lWa9I2V+Up9F4b7RfzGt+oouqz2N/SR/1hdq6rPY39JH/WF0FyGfEF2oiIooiIiIiIiIpIeDbjNOn1KeN9eGM1xC2RmHWRO/buZlwLGNBadxGHbxjz4jal/D0jjoeuAuJAl0fAJJA+zWOzPZ2D/RVsU97QC0/1NB6FwFvVXsBTpVHObUBPuPIgwAWsc6/HTiOciyiC7qdh0UkcrMb4pGSNyMjcxwc3I84yAulFYIkQVSaSDIXq1a8+1PNZlxzJ5XyybRhu+Rxc7A8wySvKiI0ACAsucXEuOpXobclEJriR/IdIJTFuPLMrWlgkLezeGkjK86IgAGiwXE6lFL+C+FIrcNmxNargQ0rs7KjJv+cdJBG8sc+Lb4kO4B2c5OGjHXIiCl/ct+3aj+xdT/shVsaXCkS0wV0NlNpvxLW1GyDu+9eiiCIitLnKf9xjVtl6OqKtRxmjuk2nxPdbYBTmfsil5m1jcxgeTnD3DPVQBvYFnOBNYioX4bUzZHRxsstc2INLyZq00DcB7mjAdICevYD29iwYVWnSy13uAsQ31l0/RdCviM+EpsJu1z7cAQyPmHIiIrS56IiIiIiIiLhY8h/5rv5Fc1wseQ/8138isO0KnT+IdVuwiIvKF+iFUPBWhW2za/plmpYij1I2jHd25rta7nNad46OJErXAfMQcLyaJf1zTdNfovvLYmnAsRV7cWX1tth8jy97w0s8V0jiMubkYB24KulF0ztIuJzsBBgxfVoideGoXBbsIMA7Oo5pAc2QGzlccxFxFjcHVRbuWcPSaZpkFWYjnZfLMGnLWvlcXbAR0O1u1pI6Eg46LAe6B0mzcoVY6sEth7bjXubCxz3NYIJ27iG9gy4D94VkIq9PGPbiO3NzM8rq7W2ZTqYP+DBIblDecBV/3BdMsVNMkitQS15DcleGSsLHFhigAcAfNlpH7irARFqxFc1qjqh1Jlb8FhW4Wgyi0yGiJOqKhPdIaLyrla/GMNsx8qQjpieDGxxPynRloH6BXvZeWsc4DJa1xA+MgZA6LXTifUdc4klr1zQfExjvFY2GZkLXuADpp5pejQBkebAJGCT16uwmuFftZAaPik7iF8/7WvY7CdgWkvcRkDRNwR6WMeam/ubtF5VKxecPGtS8uM//AIa+Wkj4syukB/RBSHu1cO2NS0zlVW75obEdhseQ0yBjJI3NaXEDdtlLup67cKT8N6UyjUrVI+ra8LIt2Mby0eO8j43O3OPzuKyCqVsc44o4husyJ4DT5Lo4XZLG7OGDfplgxxNyR56Kle4lJrdOwzTpqEsNB8s8ss01WZjmP5B2tZMSGbS+NnaD2nr1WK7ovDOozcRzWYqVmSA2KLhMyJ7oy1kFVryHAYIBa4H80q/0W8bXc2u6s1gBLYIvvMz1VR3s2x2EbhX1HENcHA2kQIDei8PEEbn1LTGguc6tO1rQMlznROAAHnJKpvuBcOX6epzS2qdivG6hLGHyxOY0vNio4NBcPKw1xx/2lXkiqUMa6lSfSAEO1XQxey2YjE0sQ4kGnMAaGeKpzux9zazZsu1HTmiSSQN74rhwZIXsAaJoS4hpJa0ZbkHLcjcXHEbn13i+aA0XVr+HDlOl7wkZM5hy0tdYMYaBjpvGCfj6krYdFbo7Xc1jWVGNfl0LhcLnYn2bY+q+rRqvp5/iDDY/d/Xqqs7i/c7m057r14NbZcwxwwNcH8hjsb3yOblplOA3DSQBnqS7DYt3cuGdQt6q6WtSszx97Qt3xRPe3c0PyMgYyMhX4ihT2tWbiDiDBJERuA5LbW9nMM/BDBtJDQZkak85XCAYa3PTxR/JUD3MOGNRg4gr2JqVmKBstwulfE9rAH1rLWEuIwAS5o/eFsCi0YXGuoMqMAHviDy109Vcx+ymYupRqOJHZuzCN9wb+iKjmV7Gly61TuaLZ1OHUpXyxz1o3v5wc97o2vlYxxYcuDunjMeCQDkFXiijhcV2MiJBjeRoZEEXU9obPGKykOylswYBEOEEEOkGypfuNaHe07UD74UbYM1NsVawSZoq0e8yugkLMtj3bR8W0txgb+l0IixjMUcTU7Rwg8tPms7M2e3A0exYSRJMmJvxiJ+wiIiqroLXb3R/3Yi/Z8H9+0qzVme6P+7EX7Pg/v2lWa9I2V+Up9F4b7RfzGt+oouqz2N/SR/1hdq6rPY39JH/AFhdBchnxBdqIiKKIiIiIiIiL21dTmjr2KrHAQ2nQOmbtBLjXc50WHHq3Be7s7V4kWHNDtfuLqTHuYZaY1HkRB9RIRERZUUREREREREXbXsSRlxje+Mua5jixzmFzHjDmOLT1YR2g9CupFgidVkEgyEREWVhERERERERERERERERFwseQ/8ANd/IrmuFjyH/AJrv5FYdoVOn8Q6rdhEReUL9EKqdb7s8FWzZrOoyuNaxNXLxOwBxhldGXAFvQEtz+9cNN7uNF7w2apYhYSAZGujmDM/fOaMO2j/tyfiBUU4N1GvV4r1GW1NHBELWqtMkrg1m51l+Bk9MlZbu98Q6TbqV2VZoLNptgOD4cOMcPLeJA6VoxtLjH4meuAfvV9T/AAGH7VlLsnHMAc4JtI9PmvPu+Mb/AA9XEfxDRkc4CmWtkgHjIN+iuqrYZKxksbg+ORjZI3tILXseA5rmkdrSCDn512KkeI9e1LSOH9C72nNeWVjuZmKGQmMt5kTSJ43bcNc3swpDwLrWvXrkNyzEYNHNZxaXd7N5mIsssSDPOy8+P4oDACMZHU8ipsxzWGpmbllwEm5ymLczuhfSUdu031W0Cx2eGEwJAzCZJ3AaEmPNWaipKHjHX9euTx6M6OpVg673tjzy3EiJ08kkbyJH7XEMY0YAIOcFx93CXHGq09UZpGubHule2OKw1rGuD5ekBBiAZLC84bnaHAnr2EKTtk1Wg3bmAksn3gP2+ahT9o8O9w91+RzsoqEe4TprM8phW+iqnunccX26hHo2jgC07YJZdrHv3yNEjYmCUGNjRHhznkHo773aSY7xjxXxRpEMVe3NGJJH8yK7FHXk3xta4SV3tdDsyC6J2drT0PV2eijsirUDbtBdcNJvHHomJ9o8PQNSWvLWGHOa2Wg/2zIvu4TvV8Iqx7p3FF+lommW61jl2LD6omk5UL94kpyyv8SSMsbl7WnoB2fF0Xn4E17iHULOn2ZInR6S2HbZlIrNdZkZVe187mdJdrrAyBE0NxjoepWtuzqhpdqXNAvqYuNw4k7lvdtuiMQMMGvLoabCQA7eYNgP6idOatZFTFfWuKtZsWDRxplaIgsZYhERLXF3LBfLC98kuGknbho6fGM5TuS8aajYv2tK1IslmrCYidjWNdvrzNiljdygGPb42QQB5BznPSVTZdRjC7M0loktBuAfl81ro7fo1KrWZHgOJDXuENcRwvPqFaaKiqHGXEVrVdQ0+nMyYiS5HCJYqzGVI4rIaJy5sQc8tYNgDtwJlBIcQuqpxxxJSvy6XOI7tyQtiha9sQEcsrWvjlY+FrQ+PY7JD8Aect2kLb3LV0zNmM0TeONwq/4pw1jkqZcxbmy2zDdYmSd0T+6vpFRdniziPRtQrR6rNHYhsFpcxrICx0bnhjzG+KNjmSMz2dnZ0Kzvdx4v1DTLFFtOcxMkjkfKzlQScwskYB40sbi3oSOnxqHdNU1GMa5pzAkEG1td30W78RUG0alV7Xt7MgOaQMwzab4jzVroqL4w4j4r00w3rL4YoJ5NrarWQSRROLTI2vL4vMyWtd1DyfFd4w6Lnr/EfFMlQ6zE6OnQIa+OBgryPbC5wYyV4mjLpGklvXI6HIaApjY9Qhpzsg2BzWnhpr0stTvaai0vaaVSWiSMt8v92th1g8tYtnjPWve6jZuiMS97sD+WX8sOy9rMb9p2+V8R7F5O51xMdWotuGEQbpJI+WJOaBy3Yzv2N7fyKM/8Z2rHC82qMLYLkbSwuY1r2CRlhkRe1koc3DmnOCDjcfiyuvhnie9Lwxa1CSfdcjZbcyblQN2mInZ9jbGIzj52/lUP4IiiQWjN2mWZPDSIiN8zPJbe9WuxLS1xyGiamXKIInWSc0xbLEc1ZqKjOEuIeKtXqPFSaMcqZ3MuSNrRueSxhbWjaItg2jxidufsrfGA6HPdxrja/ctWtN1LD7Fdj5GybGMe0wythmhlEWGOIL24IH3rsk9ErbJq02uOZpLdQDcc9P8AaYb2ioV302hjwH/C5zYaTw1N93BWqipfV+NNa1bU5aGhubBFXMgdMWxHeInbHTSSStcGxl+A1rBkggnPUN8N/jfiGtqVDT7cjYJBNWisbIqz2XI5bDQJ2uMZ2bmEtOwgZaejTkCTdj1TAzNmJyzcDmIUH+02GbJyPLc2UPDfdJmIBkfONDCvZERclfRKIcYdzzT9VsNs2jYEjYmwjlSNY3Yxz3joWHrmR3n+JYb4F9H+Vc+nZ/jVkIrbMfiGNDWvIA3SubV2Ngqry99JpJ1JFyq3+BfR/lXPp2f414dZ7jukMbEQbfjWazDmdvY+ZjT/AP1/EVayxvEXkQ/rlP8A3Ea2N2nip/7jvVajsLAC4ot9AoT8C+j/ACrn07P8afAvo/yrn07P8ashFjvPFeI71TuHZ/gs9Aq3+BfR/lXPp2f40+BfR/lXPp2f41ZCJ3nivEd6p3Ds/wAFnoFW/wAC+j/KufTs/wAafAvo/wAq59Oz/GrIRO88V4jvVO4dn+Cz0Crf4F9H+Vc+nZ/jT4F9H+Vc+nZ/jVkIneeK8R3qncOz/BZ6BVv8C+j/ACrn07P8afAvo/yrn07P8ashE7zxXiO9U7h2f4LPQKt/gX0f5Vz6dn+NPgX0f5Vz6dn+NWQid54rxHeqdw7P8FnoFW/wL6P8q59Oz/GnwL6P8q59Oz/GrIRO88V4jvVO4dn+Cz0Crf4F9H+Vc+nZ/jT4F9H+Vc+nZ/jVkIneeK8R3qncOz/BZ6BVv8C+j/KufTs/xp8C+j/KufTs/wAashE7zxXiO9U7h2f4LPQKt/gX0f5Vz6dn+NPgX0f5Vz6dn+NWQid54rxHeqdw7P8ABZ6BVv8AAvo/yrn07P8AGnwL6P8AKufTs/xqyETvPFeI71TuHZ/gs9Aq3+BfR/lXPp2f40+BfR/lXPp2f41ZCJ3nivEd6p3Ds/wWegVb/Avo/wAq59Oz/GnwL6P8q59Oz/GrIRO88V4jvVO4dn+Cz0Crf4F9H+Vc+nZ/jXn1HuM6O2GVwNzIikI+zs8zCfwatBebVftE/wChk/ocneeK8R3qsjYOzx/4W+gXpREVFdZa5cP6DX1LifUq1prnRG3qjyGPLDuZZk2+M3rjqrW03uVaJBI2UVTK5hy1s0skkeR53Rk7X/kcCPmWU0vgrT612TUYYnNtTOmfI8zSuaXWHF8p5bnbRkk9g6KRrr43aj6hApOcG5QCJi410K+b2XsClRa44hjHOL3OBibE21Cp/wB07/6bT/0839tqsapWMulxws6GTT2RNPZgvrBg/J2hfOLeFaWqMjZdjdI2JznMDZJI8FwAJJjcM9B51lqkDYmMjYMMjY1jRknDWANaMnqegCr1MU04enTGrSTyubK7Q2e9uNrV3Rle1oHGwIM/S6o/uAcQ1aBvU70kdSV0jHtdYc2JpdFvjlie9+Gse048UnJ3O+JefjS/Fq/FOnNoOEzYXVInSxncx3IsSWZpGOHR0bGOPUdCWHGeitLibue6VqMpnsVsTOxvlhkfE5+OmZAw7XuwAMkE4A6r2cKcHadpe4064ZI8bXyuc6SVzeh275CS1mQDtbgEgHC6DtpYftHYhodncIi2UEiJnWPJcVmw8Z2LMG9zOya4HMJzkAyBEQDzn131LqVxmmcZusW/Ehe/cJXAkNjsVDEyUf8Aa1+WE+YNf8S7/dCcUUbcNSrUnisvZM6eR8D2yxxtEbo2sMjCWlzi8nAJxs64yM5juta1p3fsVPWNLldB4pg1GOZzXCN7RzCxsbQXhjyQ6PcewOx1bmC8ajRpo6uncPQSWJ5bHMkl2TmV5Eb2MhBsAPx45ccAMG3J85HQwjRUfRrPY4ENAkRkgTcnd04rjbSe6hTxWGpVGEOeTlMirLiPdDYE7oIkRdS3u0f9N6N+ko/+PnVg8GTcrQqEobuMelV5A0dri2q1+0fOSP8A5XK/whVu0KdG8wysqsgxskkj+yxQGHcCwgkYc/p86zemUo60MNeIFsUEUcMbSS4tjjaGMBc7qTgDqVwa+KY6g2kNQ4nlBX12F2fVp4t+IJEOptaOII47vmqD4T1E62+zNrHEEtFrC3ZWjssqMe124kxNe7llrcBvRrndRk9mefcLEA4hsisXurivcEDpPLdCJ4RG5/QeOW7Seg6lWZL3K9EdOZzTxl28xNllbBuznpE12Gtz96MN82MdFkdN4H06tdN+vC6GwS7JjmlbFh7drmckO5ez/txgEAjsC6VbamHLHsYHAObAEABp8teq4mG9n8Y2rSqVSwlj5Lszi5w/+rDoNeIVZ9x3/qjWPzNS/wDJV191f/rmH8+D/wAeFZ+h8G0KVue9XicyzYEoleZZXhwmlbNJhjnFrcvY09B0X2fg6g/UBqjonG60tIk5soblkfKH2Pds8jp2LQ7aVI1nvgwaeTdrA56K0zYVcYanSlstr9obmMsk8Nb9OarD3Rv/AK7SvzH/AN6NPdG/+u0r8yT+9GrP4o4OoalJDLcidI+AERFssse0FwcchjgD1A7U4o4OoanJDLcidI+AERlssse0FwcejHAHqB2rGG2lSp9jIPuB4P8A9aRdSxuw69b+Jylv/UdTIkn+iJm3pEqG+6X+5dX9ox/7W2u7Xf8Aoxn7Ko/yrqa8V8N1NUhZBcjdJGyUTNDZHxkSBj2A5jIJG2R/T519scO1ZKI01zHGoIY4BHzHh3Ki27G8wHdkbG9c56LRTxrG0qTCDLX5j05K5W2XVfia9UEQ+nkHGYOttPVVLoX/AERc/Pk/3US9nB3/AEXe/RX/AOoqxK/B1COg/TGxOFOQkvj5shcSXiQ4kLt48Zo8650eE6UNCTTY43CpKJGvjMshcRL1f9kJ3DP5VuqbRpuDoBvVz+XrqqtHYtdhYSRbD9lqfi46afPkoh7m/wC5Ev6/N/ZrKNdyn/q3WPztV/8AIxq2+FuHaumQur02OjidI6UtdI+Q73Na0ndISQMMb0+ZeXR+DqFS5Pfgic21Y5xleZZXB3PlE0uGOdtbl4B6Doou2hTL67oPvi3rvv8A5U2bGrNp4RsiaRl2t7Ra37wqg7ieqwaTqWoVNQkZXe4crmzODIxLWleHMc93Ru7cXAnodnb1GePdJ16rf4i0w1HsmZBLShdMzqx7+/OYQx46PY0PHUdMlyyPFer6Ha1OaHW9Nm0+aPc022SyuMwYQ2Jz44IwZGOYPFkw7oGjOOzCsrUtQ1zTYNCruFOm6u+WbZINwjsGeaxI6Tx8bQ1gMmCSA0dNq7TA11U4h7XAllzbJ8MSCNZ4L5eoXsw4wVOoxzRVEAT2p9+YLSBEXJPktikRF8avTkREREWN4i8iH9cp/wC4jWSWN4i8iH9cp/7iNSbqsO0WSREUVlERERERERERERERERERERERERERERERERERERERERERERERERF5tV+0T/oZP6HL0rzar9on/Qyf0ORF6URERERERERERERERdVmtHK0sljZIw9rJGte0/la4YK6qOm14M8iCGHPbyomR5/LsAyvUizmMQo5GzMX4oiIsKSIiIiIiIiIiIiIiIiIiIiIiIi816hBOA2eGKYDsEsbJAPyB4OFzp1IoW7IYo4mdu2NjWNz+a0ALuRZzGIUcjZzRfiiIiwpIiIiIsbxF5EP65T/ANxGsksbxF5EP65T/wBxGpN1WHaLJIiKKyiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIi82q/aJ/0Mn9Dl6V5tV+0T/oZP6HIi9KIuq3YZFG+WRwZHGx0kj3HDWMY0ue5x8wABP7kRdqKkIe7RrFyGfUtK4VsXdEgdL/zsl6GtPPHBkTSw1HRmR4btd4rOZ1aQSHAgWZwlxpp+paVBrEUzYqU8ZeZLD2RCFzHuiljmcXbGvZK17DgkZb0JBBVmrhKtIS4b4sQYPAxMHkVpp12P0P3ynVSNF5NK1OtbjE1WxBZiJIEteWOaMkYyA+NxaT1Hn86hPdT7p1bR6T7NXvXUporlenPWjuxsfAZy8bpeW2R0bgWeS5oytVOi97sjRfRTfUa1uYmysFFEtN4msu1XVqlmCrX0/T4a8sN3v+s+SUSQRyzmxVEnMpsYXPG6QNDg0EdDlZqHiGg+WKBl6m+eZglhhbZgdLLGRkSRRh+6RmOu4AhYdTcPSbX1E7vsb1kPCyaLxXNXqQyxQTWa8U8/2iGSaKOWbHbyo3ODpP8A2grt069DZjEteaKeJ2Q2WGRksbi0lrsPYS04II/cowYlZkaL0Iq81PuklvElfh6tUjsF1cWbdt96KAVmGQxcuOAsJsTB2zLA5rvH6A4JExm1+iyw2o+7UZbdjbVdZhbYdnGNsJfvOcjsHnWx9F7YkaifLyUG1WmYOhjzWSReK5q1WF5jms14pBE6cxyTRxvELNxfNtc4HlDa7LuwbT8S6Nf1dtfT7V+MNnbBTntxhrwGTNigdM0NkaCA1waPGAPbnqoBpMKZcFlEVVdx/uxR67R1S5PUbQOlsbPLGLJnBrOglmbNvdDHtB5E47D5Gcr73Be62/ib3x5unt0/vBlJ+e+zY5gti07J3QR8trRXBz1yJPNjrYfgqzA8ub8MTcWnTr5LS3E03ZYOsx5K1EWN0jiChcc9lS7UtPj+2NrWYZ3R9ceO2J5LOvxrqn4n02MbpNQosbznV8vt12jvhuN0GS/7cNzcs7RuHRV8jpiFtzDWVl0Xh1fWKlNglt2q9WMnaJLM8UDC74g+VwBPzLur3oZIhPHLE+AtLxMyRjoiwZy8SNO0tGD1z5liDErMjRehFjKXEOnz8rkXqc3PdIyDlWYJOc+EAyti2PPMcwEZDc4yMrm7XKQsimblUWyMiqbEIskYzkQbt5GOvYs5HcEzDisgix97W6UDpGz26sLoYTYlbLYijdFACGmaQPcCyHJA3np1HVcdS4goVpI4rN2pXllAMUc9mGJ8oPQGNkjwXj8iBpO5Mw4rJIuE0rWNc9xw1jS5xPYGtGSfyYCpODuz6zZqTaxp/C8lnQ4TM4W36nBDZlgrue2edtTY542bH5aN3knr0ONtHDPqzli3Ega6C8XWupWazX5An9ld6LA6LxdRs6ZW1Yzsq07MMcwfbfHX5XMH2uVz3bGyNcHNIBIy04JHVZH33qd799981+9cB3fPOi732l2wHnbtmC4gZz2nC1FjgYI5eamHA6Fe1FDuPuL5KlYSaY2lfsCzVhmhk1GpVbFFZY6RsrpJpWt3FgDmszl4JIypDqmuUqr447VyrWfKcRMnsRQvlPZiNsjgXnPxLPZugHj99UziVkFjeIvIh/XKf+4jXo1PUa9WN01meGvC3ypZ5WQxtz2ZkkIaP9VjtQvwWa9eatNFYhdbqbZYJGSxu/5iPyXxktP7ijQdUcRos2iIoKSIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiLzar9on/Qyf0OXpXm1X7RP+hk/ociL0qP90rTJbujatTrjM9rTb1eEE4DpZq0kbGk+YFzgM/OpAikxxa4OG5YcJEKi+4p3UtCpcMVorlyvTtaZBNBaoTuEVwzQPkLhFWfiSZ7+hw0HDnlpwQQI/3RuIKmrzcEXdSqvocOXLN+Wevb2sr85rQ2hJaMZ5Yik6vaXHBillJ8Xcr11LgnRrNjvuxpOmz2sh3fE1KtJMXN8lxkfGXOcMDBJ6YWU1bS61uF1e1Xgs1343QWIo5oXbTkbo5GlpwR8S6IxdFtTtGtMkmb6SCPd6TIJ4KmcPULMhIgRFtYI1/Zazl0MWs8XDhIt7xHCsr5/ew7qrNTG3kmoYPEE4h5+wR/f8/HjA4h/GEPCzeFeHnac6n79Olpd9iCRhuFxjc6826wHeIBOGbN4x4sezxSVuHoOh0qEXJo1K1OHcXGKrBFXjLzgFxZE0AuOB17eixh4D0M80nRtKPPe2SfOn1DzZGuL2vkzF47w4l2T53E9pW+ntRrXAw60b7mBHvcfsc1qfgSREi87rCTNlSXEY//AJnuof8A69B/4NijXEPC+n0uF+DdTq1Y4dQm1bSHS3Wg98ScyKzMQ+UncWh8MJaOxvLaG4HRbQy8Oae99uV1Ck6TUIxDfkdVgL7sIjEIitvLM2YxGAza/I2jHYvljhrTpIIKsmn0n1ar45KtZ9WB0FaSIObE+CFzNkL2hzgC0AgOOO1QZtINywDbLPOGZf8Aam7B5pvx8pdP+lrzF7wSa7xm7iqSsy1HKxtHvyQRyRUWxSOgdpu4h3fXL73cOV4+Swt8p2bC9yNn/hLTs9vNv5/L3/YyufGnBGuWNSluVzw5eY5rBSk1fTR39pBbk5q2a8RdZAe5zwJC3B2fE4vlncn4OboGkVNLbMbHe4kL5i3ZzJJppJ5C1mTsYHSEAZJw0ZJOSmKxDH0MoNyWWmwytIMWEbvpa5xQoubVki3vX3mTPmqxGh1fhGs8upVMg4ddqEeYY8DUTciaLnk9LJDiOZ5WCeqq3SY+GncF6pLqjqv/ABMZrzpzZeBq41Hvk8gNa888RnxC/aNuefu6h2NvBo9QWjeFWsLph73NzkRd9GvuD+QbG3mGHc0O2ZxkA4Xis8IaTJaF6TTNPkuhzXC2+nXdZDm42uE7mb9wwMHOQsUtpBsAzYN0N/dm3Qz8lmpg5mIvm1HH6hUDJoA1biThOrrcckz5OEa8l+GV743TTxiy9zLWwhzjzg17m5ALmdemQb048rMh0HU4Y2hkcWkXY42DOGsZTlaxoz1wAAP3LLy6PUdaZddVrOuxxGGO26CI2o4SXExMsFvMbES952g48Y/GvVarxyxvilYySKRjo5I5Gh8ckbwWvY9jhhzC0kEHoQSqtfF9oWcG7t2s2/ZbqWHyB3E7/KFpRpdueho1OOADPFPD9jR4W4P2S/DxFLXJe4dje8r8g7O0NHnUorS09Nj7pDJ681ilBLolPvaCd1V8kZs3KsURnYCYojlgdgHLS5uDnC2YbwhpIbUYNL04MoSGWgwUq22lK6QTOlqN5eK0hla1+5mDuaD2hdzOGtOBuEafSB1DAvkVIAbwG/Hfh2f8z9sk+2bvtjvjKvv2qxxPum5k3/8AdpGnIR1VZmBc0D3tBGn/AKkfuZWsnATq7OMOFxV/4fiDq14SQ8PPkmayI6dZdGzUrTji1aO3d1aCNoJzlpXLhvhLTbeg8eX7NSKa5V1PXe9p5AXPr97wtsRmHJ+xu5jiSW43ANByAAtkaPBukQGuYdK06J1R0j6jo6VZjqz5dvNfXc2PML37W7nNwTtGc4Xpr8OadHDZrx0KTK918slyBlWBsNt87Qyd9mJrNs73tADi8EuA65UX7UEy0HQDW9nE/WFJuBt70b93EALWOxqVGY8K1bVfSpbcfC1WdtziW8+PR4YHmSLa2njbZtHkk5c5uREwZ8TLcFp9mX/gXWWwyHvH/i0R2nVWvZG3TpIqbnd7xvJdFA6YwYY4n7Zg5yc7Y2+DtImbVZLpenSMpNDabH0qz2VGjbhtZro8QNG1vRuB4rfiCx3FXCG6heg0YUdKtXZDNPMNOqTQ2pHn7MLsDo9tgyNLgXuy4E58bq0zZtKnYQfiBubCHE8yNdwtwKg7BOuZ3EWF9PJUjZi4dZxjwqOGnUXfYdQNhlGRslcP7wmFR0uxxaLLg2UPJ8chke771V5oWmMtaDLJbvcM0Lp1CSWxevG4ziiC+y5vOXRbpASW52sYQAXOOHtLhfvB3ctus1XTdRvs0OhFpLbZq0dAqywQz2LsIgms2XStbjxWtwxoONjBuwDmxn8I6U633+7TNPdeDg8XDTrm1vAwH88s37wOm7OVN20WUoDSTAF5kyHOMSbEXHGNLqIwbnyTAubaagD6KlNQ4Xravx0yrq0TbTBwrXnnjzLFFNOyzG0l7Btc6LdI54Y4Dq1hIy0KId0irSbq/Elpk3D2rNc4RX9O1vn6dqtPkQmER6PYs7GvO0ANmhccgQAA+KTtONHqC0bwq1u/TD3ubnIi76NfcH8g2NvM5O4B2zOMgHC8GucG6RelE93S9OuTABoms0q08u1vkt3ysLi0fF2KtS2llcJmA0CB1n6DQgrdUwcgxEzM/f1WF7kupwWOGtOsV6lhlfvDEVKWQ2ZhHCHxCBssu3ntIjwxztu5pZnGVRWhO0OPTp9Q4f4uv8Nub3zIND1K3WlEUsTnYibpz5C55kDG4cDM7xwDkgsG1UUbWNaxjQ1rQGta0BrWtaMBrQOgAAAwo/qPAmiWZzasaRpk9lzt7p5aNaSV7x2Oe98ZL3DA6nJ6LTQxbWOcSDBMwIPGxDgQdddfVbKuHLg2IkCOHpGnRa065r2o6zLwXZ1caW1tmjqMjBrccjNHsW4554RNZijIYXyVm05G9jC6duBh4aey3p/e/DHGrYb+lWqj7Gmyiro/fTqFG267F3wyB87Nha9og8WN7w0Rt8kFudpNa0Sleh73uVK1uuCCILMEU8Qc0Ya4RyNLQ4A9DjoukcMaaKZ0/wB76PeDsbqPekHejsPEgzW2cs+O1ruztaD2qyNqNAaA2ACDAiID81ue77haTgSZl0yDc63bH+/uVQHdV4Vo6bwlpLqkOyS5qWh2Lcznvklszuglc6aV0jjl5L3HpgDOAAAAMDxDT761/i9uoycNskEjI2niM2BNDQML+9pdKdG4ct3KdG4uj8YOdH8rrtFqOhUbMMdaxTq2K8Lo3QwTV4ZYYnRDbE6OJ7S1jmAkAgDHmXn17hTS78jJb2m0LksQxHJaqV7D2DO7a18rCWtz1x2ZUKW0so96ZvffctP0hSqYKdNLW3WBH1WtvE2mRv0fg2GxrmlTWa/vg/T2atWvnQ9VgbKxsPfE8kLWxCGu2OJvNADxMNp8YF0m7gGpVnzaxVh0+rSsQ6ho8lt2l3Tb0eeSSZ7WvpRNc6OqcMOWscchoBwY9ovTWdCpXYRXuU6tquC0iCzXiniaWjDS2ORpa0gdhA6LHSaJSoV4YKNStTh79qOMVWCKvGXmxGC4siaAXHA69qw/Hh9IsIMk+Ql2bdHoRrcRYLIwpa8OB3fSN8qRIiLlK8iIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIiIi82q/aJ/0Mn9Dl6V5tV+0T/oZP6HIi9KLQDw1eKvR/D/quo+0U8NXir0fw/wCq6j7RRFv+i0A8NXir0fw/6rqPtFPDV4q9H8P+q6j7RRFv+i0A8NXir0fw/wCq6j7RTw1eKvR/D/quo+0URb/otAPDV4q9H8P+q6j7RTw1eKvR/D/quo+0URb/AKLQDw1eKvR/D/quo+0U8NXir0fw/wCq6j7RRFv+i0A8NXir0fw/6rqPtFPDV4q9H8P+q6j7RRFv+i0A8NXir0fw/wCq6j7RTw1eKvR/D/quo+0URb/otAPDV4q9H8P+q6j7RTw1eKvR/D/quo+0URb/AKLQDw1eKvR/D/quo+0U8NXir0fw/wCq6j7RRFv+i0A8NXir0fw/6rqPtFPDV4q9H8P+q6j7RRFv+i0A8NXir0fw/wCq6j7RTw1eKvR/D/quo+0URb/otAPDV4q9H8P+q6j7RTw1eKvR/D/quo+0URb/AKLQDw1eKvR/D/quo+0U8NXir0fw/wCq6j7RRFv+i0A8NXir0fw/6rqPtFPDV4q9H8P+q6j7RRFv+i0A8NXir0fw/wCq6j7RTw1eKvR/D/quo+0URb/rG8ReRD+uU/8AcRrRLw1eKvR/D/quo+0V0XfdmcUShodQ0EBkkco21dQ8qJ4e0HOodmWhZBgrB0X6DItAPDV4q9H8P+q6j7RTw1eKvR/D/quo+0VhZW/6LQDw1eKvR/D/AKrqPtFPDV4q9H8P+q6j7RRFv+i0A8NXir0fw/6rqPtFPDV4q9H8P+q6j7RRFv8AotAPDV4q9H8P+q6j7RTw1eKvR/D/AKrqPtFEW/6LQDw1eKv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+       "            width=\"60%\"\n",
+       "            height=\"300\"\n",
+       "            src=\"https://www.youtube.com/embed/ibDT0uOAOTI\"\n",
+       "            frameborder=\"0\"\n",
+       "            allowfullscreen\n",
+       "        ></iframe>\n",
+       "        "
+      ],
+      "text/plain": [
+       "<IPython.lib.display.YouTubeVideo at 0x7fe32c34c710>"
+      ]
+     },
+     "execution_count": 232,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from IPython.display import YouTubeVideo\n",
+    "YouTubeVideo(\"ibDT0uOAOTI\", width=\"60%\")"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.4"
+  },
+  "livereveal": {
+   "auto_select": "code",
+   "auto_select_fragment": true,
+   "scroll": true,
+   "theme": "serif"
+  },
+  "toc": {
+   "base_numbering": 1,
+   "nav_menu": {},
+   "number_sections": false,
+   "sideBar": true,
+   "skip_h1_title": true,
+   "title_cell": "Table of Contents",
+   "title_sidebar": "Contents",
+   "toc_cell": false,
+   "toc_position": {
+    "height": "calc(100% - 180px)",
+    "left": "10px",
+    "top": "150px",
+    "width": "384px"
+   },
+   "toc_section_display": false,
+   "toc_window_display": false
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}

From 44ecd10b58116c8d47343389dab349efb9dbab3d Mon Sep 17 00:00:00 2001
From: Alexander Hess <alexander@webartifex.biz>
Date: Fri, 17 Apr 2020 16:16:46 +0200
Subject: [PATCH 099/242] Add exercises for chapter 10

---
 10_classes_02_exercises.ipynb | 1502 +++++++++++++++++++++++++++++++++
 1 file changed, 1502 insertions(+)
 create mode 100644 10_classes_02_exercises.ipynb

diff --git a/10_classes_02_exercises.ipynb b/10_classes_02_exercises.ipynb
new file mode 100644
index 0000000..ee6f7cc
--- /dev/null
+++ b/10_classes_02_exercises.ipynb
@@ -0,0 +1,1502 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Important**: Click on \"*Kernel*\" > \"*Restart Kernel and Run All*\" *after* finishing the exercises in [JupyterLab <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_jp.png\">](https://jupyterlab.readthedocs.io/en/stable/) (e.g., in the cloud on [MyBinder <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_mb.png\">](https://mybinder.org/v2/gh/webartifex/intro-to-python/master?urlpath=lab/tree/10_classes_02_exercises.ipynb)) to ensure that your solution runs top to bottom *without* any errors"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Chapter 10: Classes & Instances"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The exercises below assume that you have read [Chapter 10 <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/10_classes_00_content.ipynb) in the book.\n",
+    "\n",
+    "The `...`'s in the code cells indicate where you need to fill in code snippets. The number of `...`'s within a code cell give you a rough idea of how many lines of code are needed to solve the task. You should not need to create any additional code cells for your final solution. However, you may want to use temporary code cells to try out some ideas."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Berlin Tourist Guide: A Traveling Salesman Problem"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This notebook is a hands-on and tutorial-like application to show how to load data from web services like [Google Maps](https://developers.google.com/maps) and use them to solve a logistics problem, namely a **[Traveling Salesman Problem <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_wiki.png\">](https://en.wikipedia.org/wiki/Traveling_salesman_problem)**.\n",
+    "\n",
+    "Imagine that a tourist lands at Berlin's [Tegel Airport <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_wiki.png\">](https://en.wikipedia.org/wiki/Berlin_Tegel_Airport) in the morning and has his \"connecting\" flight from [Schönefeld Airport <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_wiki.png\">](https://en.wikipedia.org/wiki/Berlin_Sch%C3%B6nefeld_Airport) in the evening. By the time, the flights were scheduled, the airline thought that there would be only one airport in Berlin.\n",
+    "\n",
+    "Having never been in Berlin before, the tourist wants to come up with a plan of sights that he can visit with a rental car on his way from Tegel to Schönefeld.\n",
+    "\n",
+    "With a bit of research, he creates a `list` of `sights` like below."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "arrival = \"Berlin Tegel Airport (TXL), Berlin\"\n",
+    "\n",
+    "sights = [\n",
+    "    \"Alexanderplatz, Berlin\",\n",
+    "    \"Brandenburger Tor, Pariser Platz, Berlin\",\n",
+    "    \"Checkpoint Charlie, FriedrichstraĂźe, Berlin\",\n",
+    "    \"Kottbusser Tor, Berlin\",\n",
+    "    \"Mauerpark, Berlin\",\n",
+    "    \"Siegessäule, Berlin\",\n",
+    "    \"Reichstag, Platz der Republik, Berlin\",\n",
+    "    \"Soho House Berlin, TorstraĂźe, Berlin\",\n",
+    "    \"Tempelhofer Feld, Berlin\",\n",
+    "]\n",
+    "\n",
+    "departure = \"Berlin Schönefeld Airport (SXF), Berlin\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "With just the street addresses, however, he cannot calculate a route. He needs `latitude`-`longitude` coordinates instead. While he could just open a site like [Google Maps](https://www.google.com/maps) in a web browser, he wonders if he can download the data with a bit of Python code using a [web API <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_wiki.png\">](https://en.wikipedia.org/wiki/Web_API) offered by [Google](https://www.google.com).\n",
+    "\n",
+    "So, in this notebook, we solve the entire problem with code. To keep everything clean and maintainable, we use Python's object-oriented features (cf., [Chapter 10 <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/master/10_classes_00_content.ipynb) to implement the solution."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Geocoding"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In order to obtain coordinates for the given street addresses above, a process called **geocoding**, we use the [Google Maps Geocoding API](https://developers.google.com/maps/documentation/geocoding/start).\n",
+    "\n",
+    "**Q1**: Familiarize yourself with this [documentation](https://developers.google.com/maps/documentation/geocoding/start), register a developer account, create a project, and [create an API key](https://console.cloud.google.com/apis/credentials) that is necessary for everything to work! Then, [enable the Geocoding API](https://console.developers.google.com/apis/library/geocoding-backend.googleapis.com) and link a [billing account](https://console.developers.google.com/billing)!\n",
+    "\n",
+    "Info: The first 200 Dollars per month are not charged (cf., [pricing page](https://cloud.google.com/maps-platform/pricing/)), so no costs will incur for this tutorial. You must sign up because Google simply wants to know the people using its services.\n",
+    "\n",
+    "**Q2**: Assign the API key as a `str` object to the `key` variable!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "key = \" < your API key goes here > \""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "To use external web services, our application needs to make HTTP requests just like web browsers do when surfing the web.\n",
+    "\n",
+    "We do not have to implement this on our own. Instead, we use the official Python Client for the Google Maps Services provided by Google in one of its corporate [GitHub repositories <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_gh.png\">](https://github.com/googlemaps).\n",
+    "\n",
+    "**Q3**: Familiarize yourself with the [googlemaps <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_gh.png\">](https://github.com/googlemaps/google-maps-services-python) package! Then, install it with the `pip` command line tool!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!pip install googlemaps"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q4**: Finish the following code cells and instantiate a `Client` object named `api`! Use the `key` from above. `api` provides us with a lot of methods to talk to the API."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import googlemaps"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "api = googlemaps.Client(...)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "api"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "type(api)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q5**: Execute the next code cell to list the methods and attributes on the `api` object!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "[x for x in dir(api) if not x.startswith(\"_\")]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "To obtain all kinds of information associated with a street address, we call the `geocode()` method with the address as the sole argument.\n",
+    "\n",
+    "For example, let's search for Brandenburg Gate. Its street address is `\"Brandenburger Tor, Pariser Platz, Berlin\"`.\n",
+    "\n",
+    "**Q6**: Execute the next code cell!\n",
+    "\n",
+    "Hint: If you get an error message, follow the instructions in it to debug it.\n",
+    "\n",
+    "If everything works, we receive a `list` with a single `dict` in it. That means the [Google Maps Geocoding API](https://developers.google.com/maps/documentation/geocoding/start) only knows about one place at the address. Unfortunately, the `dict` is pretty dense and hard to read."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "api.geocode(\"Brandenburger Tor, Pariser Platz, Berlin\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q7**: Capture the first and only search result in the `brandenburg_gate` variable and \"pretty print\" it with the help of the [pprint() <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/pprint.html#pprint.pprint) function in the [pprint <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/pprint.html) module in the [standard library <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/index.html)!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "response = api.geocode(\"Brandenburger Tor, Pariser Platz, Berlin\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "brandenburg_gate = ..."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The `dict` has several keys that are of use for us: `\"formatted_address\"` is a cleanly formatted version of the address. `\"geometry\"` is a nested `dict` with several `lat`-`lng` coordinates representing the place where `\"location\"` is the one we need for our calculations. Lastly, `\"place_id\"` is a unique identifier that allows us to obtain further information about the address from other Google APIs."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from pprint import pprint"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "pprint(brandenburg_gate)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### The `Place` Class"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "To keep our code readable and maintainable, we create a `Place` class to manage the API results in a clean way.\n",
+    "\n",
+    "The `__init__()` method takes a `street_address` (e.g., an element of `sights`) and a `client` argument (e.g., an object like `api`) and stores them on `self`. The place's `name` is parsed out of the `street_address` as well: It is the part before the first comma. Also, the instance attributes `latitude`, `longitude`, and `place_id` are initialized to `None`.\n",
+    "\n",
+    "**Q8**: Finish the `__init__()` method according to the description!\n",
+    "\n",
+    "The `sync_from_google()` method uses the internally kept `client` and synchronizes the place's state with the [Google Maps Geocoding API](https://developers.google.com/maps/documentation/geocoding/start). In particular, it updates the `address` with the `formatted_address` and stores the values for `latitude`, `longitude`, and `place_id`. It enables method chaining.\n",
+    "\n",
+    "**Q9**: Implement the `sync_from_google()` method according to the description!\n",
+    "\n",
+    "**Q10**: Add a read-only `location` property on the `Place` class that returns the `latitude` and `longitude` as a `tuple`!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "class Place:\n",
+    "    \"\"\"A place connected to the Google Maps Geocoding API.\"\"\"\n",
+    "\n",
+    "    # answer to Q8\n",
+    "    def __init__(self, street_address, *, client):\n",
+    "        \"\"\"Create a new place.\n",
+    "\n",
+    "        Args:\n",
+    "            street_address (str): street address of the place\n",
+    "            client (googlemaps.Client): access to the Google Maps Geocoding API\n",
+    "        \"\"\"\n",
+    "        ...\n",
+    "        ...\n",
+    "        ...\n",
+    "        ...\n",
+    "        ...\n",
+    "        ...\n",
+    "\n",
+    "    def __repr__(self):\n",
+    "        cls, name = self.__class__.__name__, self.name\n",
+    "        synced = \" [SYNCED]\" if self.place_id else \"\"\n",
+    "        return f\"<{cls}: {name}{synced}>\"\n",
+    "\n",
+    "    # answer to Q9\n",
+    "    def sync_from_google(self):\n",
+    "        \"\"\"Download the place's coordinates and other info.\"\"\"\n",
+    "        response = ...\n",
+    "        first_hit = ...\n",
+    "        ... = first_hit[...]\n",
+    "        ... = first_hit[...][...][...]\n",
+    "        ... = first_hit[...][...][...]\n",
+    "        ... = first_hit[...]\n",
+    "        return ...\n",
+    "\n",
+    "    # answer to Q10\n",
+    "    ...\n",
+    "    ...\n",
+    "    ..."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q11**: Verify that the instantiating a `Place` object works!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "brandenburg_gate = Place(\"Brandenburger Tor, Pariser Platz, Berlin\", client=api)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "brandenburg_gate"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q12**: What do the angle brackets `<` and `>` mean in the text representation?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " < your answer >"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now, we can obtain the geo-data from the [Google Maps Geocoding API](https://developers.google.com/maps/documentation/geocoding/start) in a clean way. As we enabled method chaining for `sync_from_google()`, we get back the instance after calling the method.\n",
+    "\n",
+    "**Q13**: Verify that the `sync_from_google()` method works!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "brandenburg_gate.sync_from_google()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "brandenburg_gate.address"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "brandenburg_gate.place_id"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "brandenburg_gate.location"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### The `Place` Class (continued): Batch  Synchronization"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q14**: Add an alternative constructor method named `from_addresses()` that takes an `addresses`, a `client`, and a `sync` argument! `addresses` is a finite iterable of `str` objects (e.g., like `sights`). The method returns a `list` of `Place`s, one for each `str` in `addresses`. All `Place`s are initialized with the same `client`. `sync` is a flag and defaults to `False`. If it is set to `True`, the alternative constructor invokes the `sync_from_google()` method on the `Place`s before returning them."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "code_folding": []
+   },
+   "outputs": [],
+   "source": [
+    "class Place:\n",
+    "    \"\"\"A place connected to the Google Maps Geocoding API.\"\"\"\n",
+    "\n",
+    "    # answers from above\n",
+    "\n",
+    "    # answer to Q14\n",
+    "    ...\n",
+    "    def from_addresses(cls, addresses, *, client, sync=False):\n",
+    "        \"\"\"Create new places in a batch.\n",
+    "\n",
+    "        Args:\n",
+    "            addresses (iterable of str's): the street addresses of the places\n",
+    "            client (googlemaps.Client): access to the Google Maps Geocoding API\n",
+    "        Returns:\n",
+    "            list of Places\n",
+    "        \"\"\"\n",
+    "        places = ...\n",
+    "        for address in addresses:\n",
+    "            place = ...\n",
+    "            if sync:\n",
+    "                ...\n",
+    "            ...\n",
+    "        return places"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q15**: Verify that the alternative constructor works with and without the `sync` flag!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "Place.from_addresses(sights, client=api)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "Place.from_addresses(sights, client=api, sync=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Visualization"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "For geo-data it always makes sense to plot them on a map. We use the third-party library [folium <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_gh.png\">](https://github.com/python-visualization/folium) to achieve that.\n",
+    "\n",
+    "**Q16**: Familiarize yourself with [folium <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_gh.png\">](https://github.com/python-visualization/folium) and install it with the `pip` command line tool!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!pip install folium"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q17**: Execute the code cells below to create an empty map of Berlin!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import folium"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "berlin = folium.Map(location=(52.513186, 13.3944349), zoom_start=14)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "type(berlin)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "`folium.Map` instances are shown as interactive maps in Jupyter notebooks whenever they are the last expression in a code cell."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "berlin"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In order to put something on the map, [folium <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_gh.png\">](https://github.com/python-visualization/folium) works with so-called `Marker` objects.\n",
+    "\n",
+    "**Q18**: Review its docstring and then create a marker `m` with the location data of Brandenburg Gate! Use the `brandenburg_gate` object from above!\n",
+    "\n",
+    "Hint: You may want to use HTML tags for the `popup` argument to format the text output on the map in a nicer way. So, instead of just passing `\"Brandenburger Tor\"` as the `popup` argument, you could use, for example, `\"<b>Brandenburger Tor</b><br/>(Pariser Platz, 10117 Berlin, Germany)\"`. Then, the name appears in bold and the street address is put on the next line. You could use an f-string to parametrize the argument."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "folium.Marker?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "m = folium.Marker(\n",
+    "    location=...,\n",
+    "    popup=...,\n",
+    "    tooltip=...,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "type(m)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q19**: Execute the next code cells that add `m` to the `berlin` map!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "m.add_to(berlin)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "berlin"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### The `Place` Class (continued): Marker Representation"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q20**: Finish the `as_marker()` method that returns a `Marker` instance when invoked on a `Place` instance! The method takes an optional `color` argument that uses [folium <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_gh.png\">](https://github.com/python-visualization/folium)'s `Icon` type to control the color of the marker."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "class Place:\n",
+    "    \"\"\"A place connected to the Google Maps Geocoding API.\"\"\"\n",
+    "\n",
+    "    # answers from above\n",
+    "\n",
+    "    # answer to Q20\n",
+    "    def as_marker(self, *, color=\"blue\"):\n",
+    "        \"\"\"Create a Marker representation of the place.\n",
+    "\n",
+    "        Args:\n",
+    "            color (str): color of the marker, defaults to \"blue\"\n",
+    "\n",
+    "        Returns:\n",
+    "            marker (folium.Marker)\n",
+    "\n",
+    "        Raises:\n",
+    "            RuntimeError: if the place is not synchronized with\n",
+    "                the Google Maps Geocoding API\n",
+    "        \"\"\"\n",
+    "        if not self.place_id:\n",
+    "            raise RuntimeError(\"must synchronize with Google first\")\n",
+    "        return folium.Marker(\n",
+    "            location=...,\n",
+    "            popup=...,\n",
+    "            tooltip=...,\n",
+    "            icon=folium.Icon(color=color),\n",
+    "        )"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q21**: Execute the next code cells that create a new `Place` and obtain a `Marker` for it!\n",
+    "\n",
+    "Notes: Without synchronization, we get a `RuntimeError`. `as_marker()` can be chained right after `sync_from_google()`"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "brandenburg_gate = Place(\"Brandenburger Tor, Pariser Platz, Berlin\", client=api)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "brandenburg_gate.as_marker()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "brandenburg_gate.sync_from_google().as_marker()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q22**: Use the alternative `from_addresses()` constructor to create a `list` named `places` with already synced `Place`s!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "places = Place.from_addresses(sights, client=api, sync=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "places"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### The `Map` Class"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "subslide"
+    }
+   },
+   "source": [
+    "To make [folium <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_gh.png\">](https://github.com/python-visualization/folium)'s `Map` class work even better with our `Place` instances, we write our own `Map` class wrapping [folium <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_gh.png\">](https://github.com/python-visualization/folium)'s. Then, we add further functionality to the class throughout this tutorial.\n",
+    "\n",
+    "The `__init__()` method takes mandatory `name`, `center`, `start`, `end`, and `places` arguments. `name` is there for convenience, `center` is the map's initial center, `start` and `end` are `Place` instances, and `places` is a finite iterable of `Place` instances. Also, `__init__()` accepts an optional `initial_zoom` argument defaulting to `12`.\n",
+    "\n",
+    "Upon initialization, a `folium.Map` instance is created and stored as an implementation detail `_map`. Also, `__init__()` puts markers for each place on the `_map` object: `\"green\"` and `\"red\"` markers for the `start` and `end` locations and `\"blue\"` ones for the `places` to be visited. To do that, `__init__()` invokes another `add_marker()` method on the `Map` class, once for every `Place` object. `add_marker()` itself invokes the `add_to()` method on the `folium.Marker` representation of a `Place` instance and enables method chaining.\n",
+    "\n",
+    "To keep the state in a `Map` instance consistent, all passed in arguments except `name` are treated as implementation details. Otherwise, a user of the `Map` class could, for example, change the `start` attribute, which would not be reflected in the internally kept `folium.Map` object.\n",
+    "\n",
+    "**Q23**: Implement the `__init__()` and `add_marker()` methods on the `Map` class as described!\n",
+    "\n",
+    "**Q24**: Add a `show()` method on the `Map` class that simply returns the internal `folium.Map` object!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "class Map:\n",
+    "    \"\"\"A map with plotting and routing capabilities.\"\"\"\n",
+    "\n",
+    "    # answer to Q23\n",
+    "    def __init__(self, name, center, start, end, places, initial_zoom=12):\n",
+    "        \"\"\"Create a new map.\n",
+    "\n",
+    "        Args:\n",
+    "            name (str): name of the map\n",
+    "            center (float, float): coordinates of the map's center\n",
+    "            start (Place): start of the tour\n",
+    "            end (Place): end of the tour\n",
+    "            places (iterable of Places): the places to be visitied\n",
+    "            initial_zoom (integer): zoom level according to folium's\n",
+    "                specifications; defaults to 12\n",
+    "        \"\"\"\n",
+    "        self.name = name\n",
+    "        ...\n",
+    "        ...\n",
+    "        ...\n",
+    "        ... = folium.Map(...)\n",
+    "\n",
+    "        # Add markers to the map.\n",
+    "        ...\n",
+    "        ...\n",
+    "        for place in places:\n",
+    "            ...\n",
+    "\n",
+    "    def __repr__(self):\n",
+    "        return f\"<Map: {self.name}>\"\n",
+    "\n",
+    "    # answer to Q24\n",
+    "    def show(self):\n",
+    "        \"\"\"Return a folium.Map representation of the map.\"\"\"\n",
+    "        return ...\n",
+    "\n",
+    "    # answer to Q23\n",
+    "    def add_marker(self, marker):\n",
+    "        \"\"\"Add a marker to the map.\n",
+    "\n",
+    "        Args:\n",
+    "            marker (folium.Marker): marker to be put on the map\n",
+    "        \"\"\"\n",
+    "        ...\n",
+    "        return ..."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Let's put all the sights, the two airports, and three more places, the [Bundeskanzleramt <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_wiki.png\">](https://en.wikipedia.org/wiki/German_Chancellery), the [Olympic Stadium <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_wiki.png\">](https://en.wikipedia.org/wiki/Olympiastadion_%28Berlin%29), and the [East Side Gallery <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_wiki.png\">](https://en.wikipedia.org/wiki/East_Side_Gallery), on the map.\n",
+    "\n",
+    "**Q25**: Execute the next code cells to create a map of Berlin with all the places on it!\n",
+    "\n",
+    "Note: Because we implemented method chaining everywhere, the code below is only *one* expression written over several lines. It almost looks like a self-explanatory and compact \"language\" on its own."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "berlin = (\n",
+    "    Map(\n",
+    "        \"Sights in Berlin\",\n",
+    "        center=(52.5015154, 13.4066838),\n",
+    "        start=Place(arrival, client=api).sync_from_google(),\n",
+    "        end=Place(departure, client=api).sync_from_google(),\n",
+    "        places=places,\n",
+    "        initial_zoom=10,\n",
+    "    )\n",
+    "    .add_marker(\n",
+    "        Place(\"Bundeskanzleramt, Willy-Brandt-StraĂźe, Berlin\", client=api)\n",
+    "        .sync_from_google()\n",
+    "        .as_marker(color=\"orange\")\n",
+    "    )\n",
+    "    .add_marker(\n",
+    "        Place(\"Olympiastadion Berlin\", client=api)\n",
+    "        .sync_from_google()\n",
+    "        .as_marker(color=\"orange\")\n",
+    "    )\n",
+    "    .add_marker(\n",
+    "        Place(\"East Side Gallery, Berlin\", client=api)\n",
+    "        .sync_from_google()\n",
+    "        .as_marker(color=\"orange\")\n",
+    "    )\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "berlin"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "berlin.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Distance Matrix"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Before we can find out the best order in which to visit all the sights, we must calculate the pairwise distances between all points. While Google also offers a [Directions API](https://developers.google.com/maps/documentation/directions/start) and a [Distance Matrix API](https://developers.google.com/maps/documentation/distance-matrix/start), we choose to calculate the air distances using the third-party library [geopy <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_gh.png\">](https://github.com/geopy/geopy).\n",
+    "\n",
+    "**Q26**: Familiarize yourself with the [documentation](https://geopy.readthedocs.io/en/stable/) and install [geopy <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_gh.png\">](https://github.com/geopy/geopy) with the `pip` command line tool!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!pip install geopy"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We use [geopy <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_gh.png\">](https://github.com/geopy/geopy) primarily for converting the `latitude`-`longitude` coordinates into a [distance matrix <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_wiki.png\">](https://en.wikipedia.org/wiki/Distance_matrix).\n",
+    "\n",
+    "Because the [earth is not flat <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_wiki.png\">](https://en.wikipedia.org/wiki/Flat_Earth), [geopy <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_gh.png\">](https://github.com/geopy/geopy) provides a `great_circle()` function that calculates the so-called [orthodromic distance <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_wiki.png\">](https://en.wikipedia.org/wiki/Great-circle_distance) between two places on a sphere."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from geopy.distance import great_circle"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q27**: For quick reference, read the docstring of `great_circle()` and execute the code cells below to calculate the distance between the `arrival` and the `departure`!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "great_circle?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "tegel = Place(arrival, client=api).sync_from_google()\n",
+    "schoenefeld = Place(departure, client=api).sync_from_google()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "great_circle(tegel.location, schoenefeld.location)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "great_circle(tegel.location, schoenefeld.location).km"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "great_circle(tegel.location, schoenefeld.location).meters"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### The `Place` Class (continued): Distance to another `Place`"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q28**: Finish the `distance_to()` method in the `Place` class that takes a `other` argument and returns the distance in meters! Adhere to the given docstring!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "class Place:\n",
+    "    \"\"\"A place connected to the Google Maps Geocoding API.\"\"\"\n",
+    "\n",
+    "    # answers from above\n",
+    "\n",
+    "    # answer to Q28\n",
+    "    def distance_to(self, other):\n",
+    "        \"\"\"Calculate the distance to another place in meters.\n",
+    "\n",
+    "        Args:\n",
+    "            other (Place): the other place to calculate the distance to\n",
+    "\n",
+    "        Returns:\n",
+    "            distance (int)\n",
+    "\n",
+    "        Raises:\n",
+    "            RuntimeError: if one of the places is not synchronized with\n",
+    "                the Google Maps Geocoding API\n",
+    "        \"\"\"\n",
+    "        if not self.place_id or not other.place_id:\n",
+    "            raise RuntimeError(\"must synchronize places with Google first\")\n",
+    "        return ..."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q29**: Execute the code cells below to test the new feature!\n",
+    "\n",
+    "Note: If done right, object-oriented code reads almost like plain English."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "tegel = Place(arrival, client=api).sync_from_google()\n",
+    "schoenefeld = Place(departure, client=api).sync_from_google()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "tegel.distance_to(schoenefeld)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q30**: Execute the next code cell to instantiate the `Place`s in `sights` again!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "places = Place.from_addresses(sights, client=api, sync=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "places"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### The `Map` Class (continued): Pairwise Distances"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now, we add a read-only `distances` property on our `Map` class. As we are working with air distances, these are *symmetric* which reduces the number of distances we must calculate.\n",
+    "\n",
+    "To do so, we use the [combinations() <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/itertools.html#itertools.combinations) generator function in the [itertools <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/itertools.html) module in the [standard library <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/index.html). That produces all possible `r`-`tuple`s from an `iterable` argument. `r` is `2` in our case as we are looking at `origin`-`destination` pairs.\n",
+    "\n",
+    "Let's first look at an easy example of [combinations() <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/itertools.html#itertools.combinations) to understand how it works: It gives us all the `2`-`tuple`s from a `list` of five `numbers` disregarding the order of the `tuple`s' elements."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import itertools"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "numbers = [1, 2, 3, 4, 5]\n",
+    "\n",
+    "for x, y in itertools.combinations(numbers, 2):\n",
+    "    print(x, y)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "`distances` uses the internal `_start`, `_end`, and `_places` attributes and creates a `dict` with the keys consisting of all pairs of `Place`s and the values being their distances in meters. As this operation is rather costly, we cache the distances the first time we calculate them into a hidden instance attribute `_distances`, which must be initialized with `None` in the `__init__()` method.\n",
+    "\n",
+    "**Q31**: Finish the `distances` property as described!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "class Map:\n",
+    "    \"\"\"A map with plotting and routing capabilities.\"\"\"\n",
+    "\n",
+    "    # answers from above with a tiny adjustment\n",
+    "\n",
+    "    # answer to Q31\n",
+    "    ...\n",
+    "    def distances(self):\n",
+    "        \"\"\"Return a dict with the pairwise distances of all places.\n",
+    "\n",
+    "        Implementation note: The results of the calculations are cached.\n",
+    "        \"\"\"\n",
+    "        if not self._distances:\n",
+    "            distances = ...\n",
+    "            all_pairs = itertools.combinations(\n",
+    "                ...,\n",
+    "                r=2,\n",
+    "            )\n",
+    "            for origin, destination in all_pairs:\n",
+    "                distance = ...\n",
+    "                distances[origin, destination] = distance\n",
+    "                distances[destination, origin] = distance\n",
+    "            self._distances = ...\n",
+    "        return ..."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We pretty print the total distance matrix."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "berlin = Map(\n",
+    "    \"Berlin\",\n",
+    "    center=(52.5015154, 13.4066838),\n",
+    "    start=Place(arrival, client=api).sync_from_google(),\n",
+    "    end=Place(departure, client=api).sync_from_google(),\n",
+    "    places=places,\n",
+    "    initial_zoom=10,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "pprint(berlin.distances)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "How can we be sure the matrix contains all possible pairs? As we have 9 `sights` plus the `start` and the `end` of the tour, we conclude that there must be `11 * 10 = 110` distances excluding the `0` distances of a `Place` to itself that are not in the distance matrix."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "n_places = len(places) + 2\n",
+    "\n",
+    "n_places * (n_places - 1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "len(berlin.distances)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Route Optimization"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Let us find the cost minimal order of traveling from the `arrival` airport to the `departure` airport while visiting all the `sights`.\n",
+    "\n",
+    "This problem can be expressed as finding the shortest so-called [Hamiltonian path <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_wiki.png\">](https://en.wikipedia.org/wiki/Hamiltonian_path) from the `start` to `end` on the `Map` (i.e., a path that visits each intermediate node exactly once). With the \"hack\" of assuming the distance of traveling from the `end` to the `start` to be `0` and thereby effectively merging the two airports into a single node, the problem can be viewed as a so-called [traveling salesman problem <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_wiki.png\">](https://en.wikipedia.org/wiki/Traveling_salesman_problem) (TSP).\n",
+    "\n",
+    "The TSP is a hard problem to solve but also well studied in the literature. Assuming symmetric distances, a TSP with $n$ nodes has $\\frac{(n-1)!}{2}$ possible routes. $(n-1)$ because any node can be the `start` / `end` and divided by $2$ as the problem is symmetric.\n",
+    "\n",
+    "Starting with about $n = 20$, the TSP is almost impossible to solve exactly in a reasonable amount of time. Luckily, we do not have that many `sights` to visit, and so we use a [brute force <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_wiki.png\">](https://en.wikipedia.org/wiki/Brute-force_search) approach and simply loop over all possible routes to find the shortest.\n",
+    "\n",
+    "In the case of our tourist, we \"only\" need to try out `181_440` possible routes because the two airports are effectively one node and $n$ becomes $10$."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import math"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "math.factorial(len(places) + 1 - 1) // 2"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Analyzing the problem a bit further, all we need is a list of [permutations <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_wiki.png\">](https://en.wikipedia.org/wiki/Permutation) of the sights as the two airports are always the first and last location.\n",
+    "\n",
+    "The [permutations() <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/itertools.html#itertools.permutations) generator function in the [itertools <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/itertools.html) module in the [standard library <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/index.html) helps us with the task. Let's see an example to understand how it works."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "numbers = [1, 2, 3]\n",
+    "\n",
+    "for permutation in itertools.permutations(numbers):\n",
+    "    print(permutation)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "However, if we use [permutations() <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/itertools.html#itertools.permutations) as is, we try out *redundant* routes. For example, transferred to our case, the tuples `(1, 2, 3)` and `(3, 2, 1)` represent the *same* route as the distances are symmetric and the tourist could be going in either direction. To obtain the *unique* routes, we use an `if`-clause in a \"hacky\" way by only accepting routes where the first node has a smaller value than the last. Thus, we keep, for example, `(1, 2, 3)` and discard `(3, 2, 1)`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "for permutation in itertools.permutations(numbers):\n",
+    "    if permutation[0] < permutation[-1]:\n",
+    "        print(permutation)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In order to compare `Place`s as numbers, we would have to implement, among others, the `__eq__()` special method. Otherwise, we get a `TypeError`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "Place(arrival, client=api) < Place(departure, client=api)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "As a quick and dirty solution, we use the `location` property on a `Place` to do the comparison."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "Place(arrival, client=api).location < Place(departure, client=api).location"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "As the code cell below shows, combining [permutations() <img height=\"12\" style=\"display: inline-block\" src=\"static/link_to_py.png\">](https://docs.python.org/3/library/itertools.html#itertools.permutations) with an `if`-clause results in the correct number of routes to be looped over."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sum(\n",
+    "    1\n",
+    "    for route in itertools.permutations(places)\n",
+    "    if route[0].location < route[-1].location\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "To implement the brute force algorithm, we split the logic into two methods.\n",
+    "\n",
+    "First, we create an `evaluate()` method that takes a `route` argument that is a sequence of `Place`s and returns the total distance of the route. Internally, this method uses the `distances` property repeatedly, which is why we built in caching above.\n",
+    "\n",
+    "**Q32**: Finish the `evaluate()` method as described!\n",
+    "\n",
+    "Second, we create a `brute_force()` method that needs no arguments. It loops over all possible routes to find the shortest. As the `start` and `end` of a route are fixed, we only need to look at `permutation`s of inner nodes. Each `permutation` can then be traversed in a forward and a backward order. `brute_force` enables method chaining as well.\n",
+    "\n",
+    "**Q33**: Finish the `brute_force()` method as described!"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### The `Map` Class (continued): Brute Forcing the TSP"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "class Map:\n",
+    "    \"\"\"A map with plotting and routing capabilities.\"\"\"\n",
+    "\n",
+    "    # answers from above\n",
+    "\n",
+    "    # answer to Q32\n",
+    "    def evaluate(self, route):\n",
+    "        \"\"\"Calculate the total distance of a route.\n",
+    "\n",
+    "        Args:\n",
+    "            route (sequence of Places): the ordered nodes in a tour\n",
+    "\n",
+    "        Returns:\n",
+    "            cost (int)\n",
+    "        \"\"\"\n",
+    "        cost = ...\n",
+    "        # Loop over all pairs of consecutive places.\n",
+    "        origin = ...\n",
+    "        for destination in ...:\n",
+    "            cost += self.distances[...]\n",
+    "            ...\n",
+    "\n",
+    "        return ...\n",
+    "\n",
+    "    # answer to Q33\n",
+    "    def brute_force(self):\n",
+    "        \"\"\"Calculate the shortest route by brute force.\n",
+    "\n",
+    "        The route is plotted on the folium.Map.\n",
+    "        \"\"\"\n",
+    "        # Assume a very high cost to begin with.\n",
+    "        min_cost = ...\n",
+    "        best_route = None\n",
+    "\n",
+    "        # Loop over all permutations of the intermediate nodes to visit.\n",
+    "        for permutation in ...:\n",
+    "            # Skip redundant permutations.\n",
+    "            if ...:\n",
+    "                ...\n",
+    "            # Travel through the routes in both directions.\n",
+    "            for route in (permutation, permutation[::-1]):\n",
+    "                # Add start and end to the route.\n",
+    "                route = (..., *route, ...)\n",
+    "                # Check if a route is cheaper than all routes seen before.\n",
+    "                cost = ...\n",
+    "                if ...:\n",
+    "                    min_cost = ...\n",
+    "                    best_route = ...\n",
+    "\n",
+    "        # Plot the route on the map\n",
+    "        folium.PolyLine(\n",
+    "            [x.location for x in best_route],\n",
+    "            color=\"orange\", weight=3, opacity=1\n",
+    "        ).add_to(self._map)\n",
+    "\n",
+    "        return ..."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q34**: Find the best route for our tourist by executing the code cells below!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "berlin = Map(\n",
+    "    \"Berlin\",\n",
+    "    center=(52.4915154, 13.4066838),\n",
+    "    start=Place(arrival, client=api).sync_from_google(),\n",
+    "    end=Place(departure, client=api).sync_from_google(),\n",
+    "    places=places,\n",
+    "    initial_zoom=12,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "berlin.brute_force().show()"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.4"
+  },
+  "toc": {
+   "base_numbering": 1,
+   "nav_menu": {},
+   "number_sections": false,
+   "sideBar": true,
+   "skip_h1_title": true,
+   "title_cell": "Table of Contents",
+   "title_sidebar": "Contents",
+   "toc_cell": false,
+   "toc_position": {
+    "height": "calc(100% - 180px)",
+    "left": "10px",
+    "top": "150px",
+    "width": "320px"
+   },
+   "toc_section_display": true,
+   "toc_window_display": false
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}

From 057a9229d517de381647630009e8d893d5b38d99 Mon Sep 17 00:00:00 2001
From: Alexander Hess <alexander@webartifex.biz>
Date: Wed, 30 Sep 2020 16:00:05 +0200
Subject: [PATCH 100/242] Initial commit

- add the following files:
  + LICENSE.txt => use MIT license as the project should be
                   accessible to anybody who wants to learn Python
  + README.md => provide a rough description of the project
  + pyproject.toml => initialize poetry and the add project metadata
  + poetry.lock => initialize poetry
  + .gitignore => hide pyenv and poetry artifacts
  + static/link/ => folder holding images for links
- for the pre-2020 version of the files, check out the "v0.0.0" tag
---
 .gitignore            |   2 ++
 LICENSE.txt           |  21 +++++++++++++++++++++
 README.md             |   7 +++++++
 poetry.lock           |   8 ++++++++
 pyproject.toml        |  16 ++++++++++++++++
 static/link/README.md |   2 ++
 static/link/to_py.png | Bin 0 -> 1308 bytes
 7 files changed, 56 insertions(+)
 create mode 100644 .gitignore
 create mode 100644 LICENSE.txt
 create mode 100644 README.md
 create mode 100644 poetry.lock
 create mode 100644 pyproject.toml
 create mode 100644 static/link/README.md
 create mode 100644 static/link/to_py.png

diff --git a/.gitignore b/.gitignore
new file mode 100644
index 0000000..35008af
--- /dev/null
+++ b/.gitignore
@@ -0,0 +1,2 @@
+.python-version
+.venv/
diff --git a/LICENSE.txt b/LICENSE.txt
new file mode 100644
index 0000000..0ec444a
--- /dev/null
+++ b/LICENSE.txt
@@ -0,0 +1,21 @@
+MIT License
+
+Copyright (c) 2018-2020 Alexander Hess [alexander@webartifex.biz]
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.
diff --git a/README.md b/README.md
new file mode 100644
index 0000000..4d95cf0
--- /dev/null
+++ b/README.md
@@ -0,0 +1,7 @@
+# An Introduction to Python and Programming
+
+This project is a thorough introductory course
+in programming with **[Python <img height="12" style="display: inline-block" src="static/link/to_py.png">](https://www.python.org/)**.
+
+The **main goal** is to **prepare** students
+for **further studies** in the "field" of **data science**.
diff --git a/poetry.lock b/poetry.lock
new file mode 100644
index 0000000..b9e1c09
--- /dev/null
+++ b/poetry.lock
@@ -0,0 +1,8 @@
+package = []
+
+[metadata]
+content-hash = "fafb334cb038533f851c23d0b63254223abf72ce4f02987e7064b0c95566699a"
+lock-version = "1.0"
+python-versions = "^3.8"
+
+[metadata.files]
diff --git a/pyproject.toml b/pyproject.toml
new file mode 100644
index 0000000..6f05059
--- /dev/null
+++ b/pyproject.toml
@@ -0,0 +1,16 @@
+[build-system]
+requires = ["poetry>=0.12"]
+build-backend = "poetry.masonry.api"
+
+[tool.poetry]
+name = "intro-to-python"
+version = "0.1.0.dev0"
+
+authors = ["Alexander Hess <alexander@webartifex.biz>"]
+description = "An intro to Python & programming for wanna-be data scientists"
+license = "MIT"
+
+[tool.poetry.dependencies]
+python = "^3.8"
+
+[tool.poetry.dev-dependencies]
diff --git a/static/link/README.md b/static/link/README.md
new file mode 100644
index 0000000..f5c9136
--- /dev/null
+++ b/static/link/README.md
@@ -0,0 +1,2 @@
+This folder contains small images
+that are used to enhance the links in the notebooks and markdown files.
diff --git a/static/link/to_py.png b/static/link/to_py.png
new file mode 100644
index 0000000000000000000000000000000000000000..53c60451f646449dce1043e762d615296f5e143c
GIT binary patch
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From 9fbfb9de8d71c21da45648e8b5e56edd6dc7a050 Mon Sep 17 00:00:00 2001
From: Alexander Hess <alexander@webartifex.biz>
Date: Wed, 30 Sep 2020 16:21:56 +0200
Subject: [PATCH 101/242] Add jupyterlab to the dependencies

---
 poetry.lock    | 1075 +++++++++++++++++++++++++++++++++++++++++++++++-
 pyproject.toml |    2 +
 2 files changed, 1075 insertions(+), 2 deletions(-)

diff --git a/poetry.lock b/poetry.lock
index b9e1c09..0024db9 100644
--- a/poetry.lock
+++ b/poetry.lock
@@ -1,8 +1,1079 @@
-package = []
+[[package]]
+category = "main"
+description = "Disable App Nap on OS X 10.9"
+marker = "sys_platform == \"darwin\" or platform_system == \"Darwin\""
+name = "appnope"
+optional = false
+python-versions = "*"
+version = "0.1.0"
+
+[[package]]
+category = "main"
+description = "The secure Argon2 password hashing algorithm."
+name = "argon2-cffi"
+optional = false
+python-versions = "*"
+version = "20.1.0"
+
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+    {file = "pyzmq-19.0.2-cp37-cp37m-win_amd64.whl", hash = "sha256:73483a2caaa0264ac717af33d6fb3f143d8379e60a422730ee8d010526ce1913"},
+    {file = "pyzmq-19.0.2-cp38-cp38-macosx_10_9_x86_64.whl", hash = "sha256:36ab114021c0cab1a423fe6689355e8f813979f2c750968833b318c1fa10a0fd"},
+    {file = "pyzmq-19.0.2-cp38-cp38-manylinux1_i686.whl", hash = "sha256:8b66b94fe6243d2d1d89bca336b2424399aac57932858b9a30309803ffc28112"},
+    {file = "pyzmq-19.0.2-cp38-cp38-manylinux1_x86_64.whl", hash = "sha256:654d3e06a4edc566b416c10293064732516cf8871a4522e0a2ba00cc2a2e600c"},
+    {file = "pyzmq-19.0.2-cp38-cp38-win32.whl", hash = "sha256:276ad604bffd70992a386a84bea34883e696a6b22e7378053e5d3227321d9702"},
+    {file = "pyzmq-19.0.2-cp38-cp38-win_amd64.whl", hash = "sha256:09d24a80ccb8cbda1af6ed8eb26b005b6743e58e9290566d2a6841f4e31fa8e0"},
+    {file = "pyzmq-19.0.2-pp27-pypy_73-macosx_10_9_x86_64.whl", hash = "sha256:c1a31cd42905b405530e92bdb70a8a56f048c8a371728b8acf9d746ecd4482c0"},
+    {file = "pyzmq-19.0.2-pp36-pypy36_pp73-macosx_10_9_x86_64.whl", hash = "sha256:a7e7f930039ee0c4c26e4dfee015f20bd6919cd8b97c9cd7afbde2923a5167b6"},
+    {file = "pyzmq-19.0.2.tar.gz", hash = "sha256:296540a065c8c21b26d63e3cea2d1d57902373b16e4256afe46422691903a438"},
+]
+requests = [
+    {file = "requests-2.24.0-py2.py3-none-any.whl", hash = "sha256:fe75cc94a9443b9246fc7049224f75604b113c36acb93f87b80ed42c44cbb898"},
+    {file = "requests-2.24.0.tar.gz", hash = "sha256:b3559a131db72c33ee969480840fff4bb6dd111de7dd27c8ee1f820f4f00231b"},
+]
+send2trash = [
+    {file = "Send2Trash-1.5.0-py3-none-any.whl", hash = "sha256:f1691922577b6fa12821234aeb57599d887c4900b9ca537948d2dac34aea888b"},
+    {file = "Send2Trash-1.5.0.tar.gz", hash = "sha256:60001cc07d707fe247c94f74ca6ac0d3255aabcb930529690897ca2a39db28b2"},
+]
+six = [
+    {file = "six-1.15.0-py2.py3-none-any.whl", hash = "sha256:8b74bedcbbbaca38ff6d7491d76f2b06b3592611af620f8426e82dddb04a5ced"},
+    {file = "six-1.15.0.tar.gz", hash = "sha256:30639c035cdb23534cd4aa2dd52c3bf48f06e5f4a941509c8bafd8ce11080259"},
+]
+terminado = [
+    {file = "terminado-0.9.1-py3-none-any.whl", hash = "sha256:c55f025beb06c2e2669f7ba5a04f47bb3304c30c05842d4981d8f0fc9ab3b4e3"},
+    {file = "terminado-0.9.1.tar.gz", hash = "sha256:3da72a155b807b01c9e8a5babd214e052a0a45a975751da3521a1c3381ce6d76"},
+]
+testpath = [
+    {file = "testpath-0.4.4-py2.py3-none-any.whl", hash = "sha256:bfcf9411ef4bf3db7579063e0546938b1edda3d69f4e1fb8756991f5951f85d4"},
+    {file = "testpath-0.4.4.tar.gz", hash = "sha256:60e0a3261c149755f4399a1fff7d37523179a70fdc3abdf78de9fc2604aeec7e"},
+]
+tornado = [
+    {file = "tornado-6.0.4-cp35-cp35m-win32.whl", hash = "sha256:5217e601700f24e966ddab689f90b7ea4bd91ff3357c3600fa1045e26d68e55d"},
+    {file = "tornado-6.0.4-cp35-cp35m-win_amd64.whl", hash = "sha256:c98232a3ac391f5faea6821b53db8db461157baa788f5d6222a193e9456e1740"},
+    {file = "tornado-6.0.4-cp36-cp36m-win32.whl", hash = "sha256:5f6a07e62e799be5d2330e68d808c8ac41d4a259b9cea61da4101b83cb5dc673"},
+    {file = "tornado-6.0.4-cp36-cp36m-win_amd64.whl", hash = "sha256:c952975c8ba74f546ae6de2e226ab3cc3cc11ae47baf607459a6728585bb542a"},
+    {file = "tornado-6.0.4-cp37-cp37m-win32.whl", hash = "sha256:2c027eb2a393d964b22b5c154d1a23a5f8727db6fda837118a776b29e2b8ebc6"},
+    {file = "tornado-6.0.4-cp37-cp37m-win_amd64.whl", hash = "sha256:5618f72e947533832cbc3dec54e1dffc1747a5cb17d1fd91577ed14fa0dc081b"},
+    {file = "tornado-6.0.4-cp38-cp38-win32.whl", hash = "sha256:22aed82c2ea340c3771e3babc5ef220272f6fd06b5108a53b4976d0d722bcd52"},
+    {file = "tornado-6.0.4-cp38-cp38-win_amd64.whl", hash = "sha256:c58d56003daf1b616336781b26d184023ea4af13ae143d9dda65e31e534940b9"},
+    {file = "tornado-6.0.4.tar.gz", hash = "sha256:0fe2d45ba43b00a41cd73f8be321a44936dc1aba233dee979f17a042b83eb6dc"},
+]
+traitlets = [
+    {file = "traitlets-5.0.4-py3-none-any.whl", hash = "sha256:9664ec0c526e48e7b47b7d14cd6b252efa03e0129011de0a9c1d70315d4309c3"},
+    {file = "traitlets-5.0.4.tar.gz", hash = "sha256:86c9351f94f95de9db8a04ad8e892da299a088a64fd283f9f6f18770ae5eae1b"},
+]
+urllib3 = [
+    {file = "urllib3-1.25.10-py2.py3-none-any.whl", hash = "sha256:e7983572181f5e1522d9c98453462384ee92a0be7fac5f1413a1e35c56cc0461"},
+    {file = "urllib3-1.25.10.tar.gz", hash = "sha256:91056c15fa70756691db97756772bb1eb9678fa585d9184f24534b100dc60f4a"},
+]
+wcwidth = [
+    {file = "wcwidth-0.2.5-py2.py3-none-any.whl", hash = "sha256:beb4802a9cebb9144e99086eff703a642a13d6a0052920003a230f3294bbe784"},
+    {file = "wcwidth-0.2.5.tar.gz", hash = "sha256:c4d647b99872929fdb7bdcaa4fbe7f01413ed3d98077df798530e5b04f116c83"},
+]
+webencodings = [
+    {file = "webencodings-0.5.1-py2.py3-none-any.whl", hash = "sha256:a0af1213f3c2226497a97e2b3aa01a7e4bee4f403f95be16fc9acd2947514a78"},
+    {file = "webencodings-0.5.1.tar.gz", hash = "sha256:b36a1c245f2d304965eb4e0a82848379241dc04b865afcc4aab16748587e1923"},
+]
diff --git a/pyproject.toml b/pyproject.toml
index 6f05059..35e025a 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -13,4 +13,6 @@ license = "MIT"
 [tool.poetry.dependencies]
 python = "^3.8"
 
+jupyterlab = "^2.2.8"
+
 [tool.poetry.dev-dependencies]

From 1351adaa621d01312d398aa00afc85fc332710e5 Mon Sep 17 00:00:00 2001
From: Alexander Hess <alexander@webartifex.biz>
Date: Wed, 30 Sep 2020 16:33:33 +0200
Subject: [PATCH 102/242] Ignore Jupyter's checkpoint files

---
 .gitignore | 1 +
 1 file changed, 1 insertion(+)

diff --git a/.gitignore b/.gitignore
index 35008af..79eb637 100644
--- a/.gitignore
+++ b/.gitignore
@@ -1,2 +1,3 @@
+**/.ipynb_checkpoints/
 .python-version
 .venv/

From fc87305922264f826bb9e75780d5b280adb6efeb Mon Sep 17 00:00:00 2001
From: Alexander Hess <alexander@webartifex.biz>
Date: Fri, 2 Oct 2020 15:18:40 +0200
Subject: [PATCH 103/242] Add nox to the develop dependencies

---
 poetry.lock    | 123 ++++++++++++++++++++++++++++++++++++++++++++++++-
 pyproject.toml |   2 +
 2 files changed, 124 insertions(+), 1 deletion(-)

diff --git a/poetry.lock b/poetry.lock
index 0024db9..2ac41a5 100644
--- a/poetry.lock
+++ b/poetry.lock
@@ -1,3 +1,11 @@
+[[package]]
+category = "dev"
+description = "A small Python module for determining appropriate platform-specific dirs, e.g. a \"user data dir\"."
+name = "appdirs"
+optional = false
+python-versions = "*"
+version = "1.4.4"
+
 [[package]]
 category = "main"
 description = "Disable App Nap on OS X 10.9"
@@ -7,6 +15,17 @@ optional = false
 python-versions = "*"
 version = "0.1.0"
 
+[[package]]
+category = "dev"
+description = "Bash tab completion for argparse"
+name = "argcomplete"
+optional = false
+python-versions = "*"
+version = "1.12.1"
+
+[package.extras]
+test = ["coverage", "flake8", "pexpect", "wheel"]
+
 [[package]]
 category = "main"
 description = "The secure Argon2 password hashing algorithm."
@@ -103,6 +122,17 @@ optional = false
 python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*, !=3.4.*"
 version = "0.4.3"
 
+[[package]]
+category = "dev"
+description = "Log formatting with colors!"
+name = "colorlog"
+optional = false
+python-versions = "*"
+version = "4.2.1"
+
+[package.dependencies]
+colorama = "*"
+
 [[package]]
 category = "main"
 description = "Decorators for Humans"
@@ -119,6 +149,14 @@ optional = false
 python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*, !=3.4.*"
 version = "0.6.0"
 
+[[package]]
+category = "dev"
+description = "Distribution utilities"
+name = "distlib"
+optional = false
+python-versions = "*"
+version = "0.3.1"
+
 [[package]]
 category = "main"
 description = "Discover and load entry points from installed packages."
@@ -127,6 +165,14 @@ optional = false
 python-versions = ">=2.7"
 version = "0.3"
 
+[[package]]
+category = "dev"
+description = "A platform independent file lock."
+name = "filelock"
+optional = false
+python-versions = "*"
+version = "3.0.12"
+
 [[package]]
 category = "main"
 description = "Internationalized Domain Names in Applications (IDNA)"
@@ -447,6 +493,23 @@ traitlets = ">=4.2.1"
 docs = ["sphinx", "nbsphinx", "sphinxcontrib-github-alt"]
 test = ["nose", "coverage", "requests", "nose-warnings-filters", "nbval", "nose-exclude", "selenium", "pytest", "pytest-cov", "requests-unixsocket"]
 
+[[package]]
+category = "dev"
+description = "Flexible test automation."
+name = "nox"
+optional = false
+python-versions = ">=3.5"
+version = "2020.8.22"
+
+[package.dependencies]
+argcomplete = ">=1.9.4,<2.0"
+colorlog = ">=2.6.1,<5.0.0"
+py = ">=1.4.0,<2.0.0"
+virtualenv = ">=14.0.0"
+
+[package.extras]
+tox_to_nox = ["jinja2", "tox"]
+
 [[package]]
 category = "main"
 description = "Core utilities for Python packages"
@@ -529,6 +592,14 @@ optional = false
 python-versions = "*"
 version = "0.6.0"
 
+[[package]]
+category = "dev"
+description = "library with cross-python path, ini-parsing, io, code, log facilities"
+name = "py"
+optional = false
+python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*"
+version = "1.9.0"
+
 [[package]]
 category = "main"
 description = "C parser in Python"
@@ -691,6 +762,24 @@ brotli = ["brotlipy (>=0.6.0)"]
 secure = ["certifi", "cryptography (>=1.3.4)", "idna (>=2.0.0)", "pyOpenSSL (>=0.14)", "ipaddress"]
 socks = ["PySocks (>=1.5.6,<1.5.7 || >1.5.7,<2.0)"]
 
+[[package]]
+category = "dev"
+description = "Virtual Python Environment builder"
+name = "virtualenv"
+optional = false
+python-versions = "!=3.0.*,!=3.1.*,!=3.2.*,!=3.3.*,>=2.7"
+version = "20.0.32"
+
+[package.dependencies]
+appdirs = ">=1.4.3,<2"
+distlib = ">=0.3.1,<1"
+filelock = ">=3.0.0,<4"
+six = ">=1.9.0,<2"
+
+[package.extras]
+docs = ["proselint (>=0.10.2)", "sphinx (>=3)", "sphinx-argparse (>=0.2.5)", "sphinx-rtd-theme (>=0.4.3)", "towncrier (>=19.9.0rc1)"]
+testing = ["coverage (>=5)", "coverage-enable-subprocess (>=1)", "flaky (>=3)", "pytest (>=4)", "pytest-env (>=0.6.2)", "pytest-freezegun (>=0.4.1)", "pytest-mock (>=2)", "pytest-randomly (>=1)", "pytest-timeout (>=1)", "pytest-xdist (>=1.31.0)", "packaging (>=20.0)", "xonsh (>=0.9.16)"]
+
 [[package]]
 category = "main"
 description = "Measures the displayed width of unicode strings in a terminal"
@@ -708,15 +797,23 @@ python-versions = "*"
 version = "0.5.1"
 
 [metadata]
-content-hash = "69a81b3b0db5478e6ec02790481e00bdcd688f9fc50bd86ba79be2028f9dc195"
+content-hash = "ff4ef02eb98bcdc61e34672f3684f54493b3e5522a84fc678bbf9847b10a3102"
 lock-version = "1.0"
 python-versions = "^3.8"
 
 [metadata.files]
+appdirs = [
+    {file = "appdirs-1.4.4-py2.py3-none-any.whl", hash = "sha256:a841dacd6b99318a741b166adb07e19ee71a274450e68237b4650ca1055ab128"},
+    {file = "appdirs-1.4.4.tar.gz", hash = "sha256:7d5d0167b2b1ba821647616af46a749d1c653740dd0d2415100fe26e27afdf41"},
+]
 appnope = [
     {file = "appnope-0.1.0-py2.py3-none-any.whl", hash = "sha256:5b26757dc6f79a3b7dc9fab95359328d5747fcb2409d331ea66d0272b90ab2a0"},
     {file = "appnope-0.1.0.tar.gz", hash = "sha256:8b995ffe925347a2138d7ac0fe77155e4311a0ea6d6da4f5128fe4b3cbe5ed71"},
 ]
+argcomplete = [
+    {file = "argcomplete-1.12.1-py2.py3-none-any.whl", hash = "sha256:5cd1ac4fc49c29d6016fc2cc4b19a3c08c3624544503495bf25989834c443898"},
+    {file = "argcomplete-1.12.1.tar.gz", hash = "sha256:849c2444c35bb2175aea74100ca5f644c29bf716429399c0f2203bb5d9a8e4e6"},
+]
 argon2-cffi = [
     {file = "argon2-cffi-20.1.0.tar.gz", hash = "sha256:d8029b2d3e4b4cea770e9e5a0104dd8fa185c1724a0f01528ae4826a6d25f97d"},
     {file = "argon2_cffi-20.1.0-cp27-cp27m-macosx_10_6_intel.whl", hash = "sha256:6ea92c980586931a816d61e4faf6c192b4abce89aa767ff6581e6ddc985ed003"},
@@ -801,6 +898,10 @@ colorama = [
     {file = "colorama-0.4.3-py2.py3-none-any.whl", hash = "sha256:7d73d2a99753107a36ac6b455ee49046802e59d9d076ef8e47b61499fa29afff"},
     {file = "colorama-0.4.3.tar.gz", hash = "sha256:e96da0d330793e2cb9485e9ddfd918d456036c7149416295932478192f4436a1"},
 ]
+colorlog = [
+    {file = "colorlog-4.2.1-py2.py3-none-any.whl", hash = "sha256:43597fd822ce705190fc997519342fdaaf44b9b47f896ece7aa153ed4b909c74"},
+    {file = "colorlog-4.2.1.tar.gz", hash = "sha256:75e55822c3a3387d721579241e776de2cf089c9ef9528b1f09e8b04d403ad118"},
+]
 decorator = [
     {file = "decorator-4.4.2-py2.py3-none-any.whl", hash = "sha256:41fa54c2a0cc4ba648be4fd43cff00aedf5b9465c9bf18d64325bc225f08f760"},
     {file = "decorator-4.4.2.tar.gz", hash = "sha256:e3a62f0520172440ca0dcc823749319382e377f37f140a0b99ef45fecb84bfe7"},
@@ -809,10 +910,18 @@ defusedxml = [
     {file = "defusedxml-0.6.0-py2.py3-none-any.whl", hash = "sha256:6687150770438374ab581bb7a1b327a847dd9c5749e396102de3fad4e8a3ef93"},
     {file = "defusedxml-0.6.0.tar.gz", hash = "sha256:f684034d135af4c6cbb949b8a4d2ed61634515257a67299e5f940fbaa34377f5"},
 ]
+distlib = [
+    {file = "distlib-0.3.1-py2.py3-none-any.whl", hash = "sha256:8c09de2c67b3e7deef7184574fc060ab8a793e7adbb183d942c389c8b13c52fb"},
+    {file = "distlib-0.3.1.zip", hash = "sha256:edf6116872c863e1aa9d5bb7cb5e05a022c519a4594dc703843343a9ddd9bff1"},
+]
 entrypoints = [
     {file = "entrypoints-0.3-py2.py3-none-any.whl", hash = "sha256:589f874b313739ad35be6e0cd7efde2a4e9b6fea91edcc34e58ecbb8dbe56d19"},
     {file = "entrypoints-0.3.tar.gz", hash = "sha256:c70dd71abe5a8c85e55e12c19bd91ccfeec11a6e99044204511f9ed547d48451"},
 ]
+filelock = [
+    {file = "filelock-3.0.12-py3-none-any.whl", hash = "sha256:929b7d63ec5b7d6b71b0fa5ac14e030b3f70b75747cef1b10da9b879fef15836"},
+    {file = "filelock-3.0.12.tar.gz", hash = "sha256:18d82244ee114f543149c66a6e0c14e9c4f8a1044b5cdaadd0f82159d6a6ff59"},
+]
 idna = [
     {file = "idna-2.10-py2.py3-none-any.whl", hash = "sha256:b97d804b1e9b523befed77c48dacec60e6dcb0b5391d57af6a65a312a90648c0"},
     {file = "idna-2.10.tar.gz", hash = "sha256:b307872f855b18632ce0c21c5e45be78c0ea7ae4c15c828c20788b26921eb3f6"},
@@ -924,6 +1033,10 @@ notebook = [
     {file = "notebook-6.1.4-py3-none-any.whl", hash = "sha256:07b6e8b8a61aa2f780fe9a97430470485bc71262bc5cae8521f1441b910d2c88"},
     {file = "notebook-6.1.4.tar.gz", hash = "sha256:687d01f963ea20360c0b904ee7a37c3d8cda553858c8d6e33fd0afd13e89de32"},
 ]
+nox = [
+    {file = "nox-2020.8.22-py3-none-any.whl", hash = "sha256:55f8cab16bcfaaea08b141c83bf2b7c779e943518d0de6cd9c38cd8da95d11ea"},
+    {file = "nox-2020.8.22.tar.gz", hash = "sha256:efa5adcf1134012f96bcd0a496ccebd4c9e9da53a831888a2a779462440eebcf"},
+]
 packaging = [
     {file = "packaging-20.4-py2.py3-none-any.whl", hash = "sha256:998416ba6962ae7fbd6596850b80e17859a5753ba17c32284f67bfff33784181"},
     {file = "packaging-20.4.tar.gz", hash = "sha256:4357f74f47b9c12db93624a82154e9b120fa8293699949152b22065d556079f8"},
@@ -955,6 +1068,10 @@ ptyprocess = [
     {file = "ptyprocess-0.6.0-py2.py3-none-any.whl", hash = "sha256:d7cc528d76e76342423ca640335bd3633420dc1366f258cb31d05e865ef5ca1f"},
     {file = "ptyprocess-0.6.0.tar.gz", hash = "sha256:923f299cc5ad920c68f2bc0bc98b75b9f838b93b599941a6b63ddbc2476394c0"},
 ]
+py = [
+    {file = "py-1.9.0-py2.py3-none-any.whl", hash = "sha256:366389d1db726cd2fcfc79732e75410e5fe4d31db13692115529d34069a043c2"},
+    {file = "py-1.9.0.tar.gz", hash = "sha256:9ca6883ce56b4e8da7e79ac18787889fa5206c79dcc67fb065376cd2fe03f342"},
+]
 pycparser = [
     {file = "pycparser-2.20-py2.py3-none-any.whl", hash = "sha256:7582ad22678f0fcd81102833f60ef8d0e57288b6b5fb00323d101be910e35705"},
     {file = "pycparser-2.20.tar.gz", hash = "sha256:2d475327684562c3a96cc71adf7dc8c4f0565175cf86b6d7a404ff4c771f15f0"},
@@ -1069,6 +1186,10 @@ urllib3 = [
     {file = "urllib3-1.25.10-py2.py3-none-any.whl", hash = "sha256:e7983572181f5e1522d9c98453462384ee92a0be7fac5f1413a1e35c56cc0461"},
     {file = "urllib3-1.25.10.tar.gz", hash = "sha256:91056c15fa70756691db97756772bb1eb9678fa585d9184f24534b100dc60f4a"},
 ]
+virtualenv = [
+    {file = "virtualenv-20.0.32-py2.py3-none-any.whl", hash = "sha256:9160a8f6196afcb8bb91405b5362651f302ee8e810fc471f5f9ce9a06b070298"},
+    {file = "virtualenv-20.0.32.tar.gz", hash = "sha256:3d427459dfe5ec3241a6bad046b1d10c0e445940e013c81946458987c7c7e255"},
+]
 wcwidth = [
     {file = "wcwidth-0.2.5-py2.py3-none-any.whl", hash = "sha256:beb4802a9cebb9144e99086eff703a642a13d6a0052920003a230f3294bbe784"},
     {file = "wcwidth-0.2.5.tar.gz", hash = "sha256:c4d647b99872929fdb7bdcaa4fbe7f01413ed3d98077df798530e5b04f116c83"},
diff --git a/pyproject.toml b/pyproject.toml
index 35e025a..c4fe109 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -16,3 +16,5 @@ python = "^3.8"
 jupyterlab = "^2.2.8"
 
 [tool.poetry.dev-dependencies]
+# Task runners
+nox = "^2020.8.22"

From 3b9fc59cb613b34e1b62ca3b271d84a4dc60c8ed Mon Sep 17 00:00:00 2001
From: Alexander Hess <alexander@webartifex.biz>
Date: Fri, 2 Oct 2020 15:34:31 +0200
Subject: [PATCH 104/242] Add noxfile

---
 noxfile.py | 13 +++++++++++++
 1 file changed, 13 insertions(+)
 create mode 100644 noxfile.py

diff --git a/noxfile.py b/noxfile.py
new file mode 100644
index 0000000..44afb33
--- /dev/null
+++ b/noxfile.py
@@ -0,0 +1,13 @@
+"""Configure nox as the task runner."""
+
+import nox
+
+
+PYTHON = "3.8"
+
+# Use a unified .cache/ folder for all develop tools.
+nox.options.envdir = ".cache/nox"
+
+# All tools except git and poetry are project dependencies.
+# Avoid accidental successes if the environment is not set up properly.
+nox.options.error_on_external_run = True

From d88dc0f91c5de32283954473d0ac5d3397d00ddb Mon Sep 17 00:00:00 2001
From: Alexander Hess <alexander@webartifex.biz>
Date: Fri, 2 Oct 2020 16:15:30 +0200
Subject: [PATCH 105/242] Add fix-branch-references task

- add nox session "fix-branch-references"
  + the session is a task executed in the normal develop environment
  + it rewrites all branch labels in links in *.ipynb and *.md files
---
 noxfile.py | 119 ++++++++++++++++++++++++++++++++++++++++++++++++++++-
 1 file changed, 117 insertions(+), 2 deletions(-)

diff --git a/noxfile.py b/noxfile.py
index 44afb33..e083aa7 100644
--- a/noxfile.py
+++ b/noxfile.py
@@ -1,9 +1,24 @@
-"""Configure nox as the task runner."""
+"""Configure nox as the task runner.
+
+Nox provides the following tasks:
+
+- "fix-branch-references": adjusts links with git branch references in
+  various files (e.g., Mardown or notebooks)
+
+"""
+
+import contextlib
+import glob
+import os
+import re
+import shutil
+import subprocess
+import tempfile
 
 import nox
 
 
-PYTHON = "3.8"
+REPOSITORY = "webartifex/intro-to-python"
 
 # Use a unified .cache/ folder for all develop tools.
 nox.options.envdir = ".cache/nox"
@@ -11,3 +26,103 @@ nox.options.envdir = ".cache/nox"
 # All tools except git and poetry are project dependencies.
 # Avoid accidental successes if the environment is not set up properly.
 nox.options.error_on_external_run = True
+
+
+@nox.session(name="fix-branch-references", venv_backend="none")
+def fix_branch_references(_session):
+    """Change git branch references.
+
+    Intended to be run as a pre-commit hook.
+
+    Many files in the project (e.g., README.md) contain links to resources on
+    github.com, nbviewer.jupyter.org, or mybinder.org that contain git branch
+    labels.
+
+    This task rewrites branch labels into either "main" or "develop".
+    """
+    # Glob patterns that expand into the files whose links are re-written.
+    paths = ["*.md", "**/*.ipynb"]
+
+    branch = (
+        subprocess.check_output(
+            ("git", "rev-parse", "--abbrev-ref", "HEAD"),
+        )
+        .decode()
+        .strip()
+    )
+    # If the current branch is only temporary and will be merged into "main", ...
+    if branch.startswith("release-") or branch.startswith("hotfix-"):
+        branch = "main"
+    # If the branch is not "main", we assume it is a feature branch.
+    elif branch != "main":
+        branch = "develop"
+
+    rewrites = [
+        {
+            "name": "github",
+            "pattern": re.compile(
+                fr"((((http)|(https))://github\.com/{REPOSITORY}/((blob)|(tree))/)([\w-]+)/)"
+            ),
+            "replacement": fr"\2{branch}/",
+        },
+        {
+            "name": "nbviewer",
+            "pattern": re.compile(
+                fr"((((http)|(https))://nbviewer\.jupyter\.org/github/{REPOSITORY}/((blob)|(tree))/)([\w-]+)/)",
+            ),
+            "replacement": fr"\2{branch}/",
+        },
+        {
+            "name": "mybinder",
+            "pattern": re.compile(
+                fr"((((http)|(https))://mybinder\.org/v2/gh/{REPOSITORY}/)([\w-]+)\?)",
+            ),
+            "replacement": fr"\2{branch}?",
+        },
+    ]
+
+    for expanded in _expand(*paths):
+        with _line_by_line_replace(expanded) as (old_file, new_file):
+            for line in old_file:
+                for rewrite in rewrites:
+                    line = re.sub(rewrite["pattern"], rewrite["replacement"], line)
+                new_file.write(line)
+
+
+def _expand(*patterns):
+    """Expand glob patterns into paths.
+
+    Args:
+        *patterns: the patterns to be expanded
+
+    Yields:
+        path: a single expanded path
+    """
+    for pattern in patterns:
+        yield from glob.glob(pattern.strip())
+
+
+@contextlib.contextmanager
+def _line_by_line_replace(path):
+    """Replace/change the lines in a file one by one.
+
+    This generator function yields two file handles, one to the current file
+    (i.e., `old_file`) and one to its replacement (i.e., `new_file`).
+
+    Usage: loop over the lines in `old_file` and write the files to be kept
+    to `new_file`. Files not written to `new_file` are removed!
+
+    Args:
+        path: the file whose lines are to be replaced
+
+    Yields:
+        old_file, new_file: handles to a file and its replacement
+    """
+    file_handle, new_file_path = tempfile.mkstemp()
+    with os.fdopen(file_handle, "w") as new_file:
+        with open(path) as old_file:
+            yield old_file, new_file
+
+    shutil.copymode(path, new_file_path)
+    os.remove(path)
+    shutil.move(new_file_path, path)

From b3b8a0d5ab1de3cb8bd8fe8df9399410f2c25e2e Mon Sep 17 00:00:00 2001
From: Alexander Hess <alexander@webartifex.biz>
Date: Fri, 2 Oct 2020 16:19:54 +0200
Subject: [PATCH 106/242] Add pre-commit to the develop dependencies

---
 poetry.lock    | 94 +++++++++++++++++++++++++++++++++++++++++++++++++-
 pyproject.toml |  1 +
 2 files changed, 94 insertions(+), 1 deletion(-)

diff --git a/poetry.lock b/poetry.lock
index 2ac41a5..e2a50ce 100644
--- a/poetry.lock
+++ b/poetry.lock
@@ -105,6 +105,14 @@ version = "1.14.3"
 [package.dependencies]
 pycparser = "*"
 
+[[package]]
+category = "dev"
+description = "Validate configuration and produce human readable error messages."
+name = "cfgv"
+optional = false
+python-versions = ">=3.6.1"
+version = "3.2.0"
+
 [[package]]
 category = "main"
 description = "Universal encoding detector for Python 2 and 3"
@@ -173,6 +181,17 @@ optional = false
 python-versions = "*"
 version = "3.0.12"
 
+[[package]]
+category = "dev"
+description = "File identification library for Python"
+name = "identify"
+optional = false
+python-versions = "!=3.0.*,!=3.1.*,!=3.2.*,!=3.3.*,>=2.7"
+version = "1.5.5"
+
+[package.extras]
+license = ["editdistance"]
+
 [[package]]
 category = "main"
 description = "Internationalized Domain Names in Applications (IDNA)"
@@ -465,6 +484,14 @@ optional = false
 python-versions = ">=3.5"
 version = "1.4.1"
 
+[[package]]
+category = "dev"
+description = "Node.js virtual environment builder"
+name = "nodeenv"
+optional = false
+python-versions = "*"
+version = "1.5.0"
+
 [[package]]
 category = "main"
 description = "A web-based notebook environment for interactive computing"
@@ -561,6 +588,22 @@ optional = false
 python-versions = "*"
 version = "0.7.5"
 
+[[package]]
+category = "dev"
+description = "A framework for managing and maintaining multi-language pre-commit hooks."
+name = "pre-commit"
+optional = false
+python-versions = ">=3.6.1"
+version = "2.7.1"
+
+[package.dependencies]
+cfgv = ">=2.0.0"
+identify = ">=1.0.0"
+nodeenv = ">=0.11.1"
+pyyaml = ">=5.1"
+toml = "*"
+virtualenv = ">=20.0.8"
+
 [[package]]
 category = "main"
 description = "Python client for the Prometheus monitoring system."
@@ -661,6 +704,14 @@ optional = false
 python-versions = "*"
 version = "0.5.7"
 
+[[package]]
+category = "dev"
+description = "YAML parser and emitter for Python"
+name = "pyyaml"
+optional = false
+python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*, !=3.4.*"
+version = "5.3.1"
+
 [[package]]
 category = "main"
 description = "Python bindings for 0MQ"
@@ -727,6 +778,14 @@ version = "0.4.4"
 [package.extras]
 test = ["pathlib2"]
 
+[[package]]
+category = "dev"
+description = "Python Library for Tom's Obvious, Minimal Language"
+name = "toml"
+optional = false
+python-versions = "*"
+version = "0.10.1"
+
 [[package]]
 category = "main"
 description = "Tornado is a Python web framework and asynchronous networking library, originally developed at FriendFeed."
@@ -797,7 +856,7 @@ python-versions = "*"
 version = "0.5.1"
 
 [metadata]
-content-hash = "ff4ef02eb98bcdc61e34672f3684f54493b3e5522a84fc678bbf9847b10a3102"
+content-hash = "cbabc76963596b4cb08138b7b42feef3ba509e9b10ea821a1a5d02675fd6c761"
 lock-version = "1.0"
 python-versions = "^3.8"
 
@@ -890,6 +949,10 @@ cffi = [
     {file = "cffi-1.14.3-cp39-cp39-win_amd64.whl", hash = "sha256:c150eaa3dadbb2b5339675b88d4573c1be3cb6f2c33a6c83387e10cc0bf05bd3"},
     {file = "cffi-1.14.3.tar.gz", hash = "sha256:f92f789e4f9241cd262ad7a555ca2c648a98178a953af117ef7fad46aa1d5591"},
 ]
+cfgv = [
+    {file = "cfgv-3.2.0-py2.py3-none-any.whl", hash = "sha256:32e43d604bbe7896fe7c248a9c2276447dbef840feb28fe20494f62af110211d"},
+    {file = "cfgv-3.2.0.tar.gz", hash = "sha256:cf22deb93d4bcf92f345a5c3cd39d3d41d6340adc60c78bbbd6588c384fda6a1"},
+]
 chardet = [
     {file = "chardet-3.0.4-py2.py3-none-any.whl", hash = "sha256:fc323ffcaeaed0e0a02bf4d117757b98aed530d9ed4531e3e15460124c106691"},
     {file = "chardet-3.0.4.tar.gz", hash = "sha256:84ab92ed1c4d4f16916e05906b6b75a6c0fb5db821cc65e70cbd64a3e2a5eaae"},
@@ -922,6 +985,10 @@ filelock = [
     {file = "filelock-3.0.12-py3-none-any.whl", hash = "sha256:929b7d63ec5b7d6b71b0fa5ac14e030b3f70b75747cef1b10da9b879fef15836"},
     {file = "filelock-3.0.12.tar.gz", hash = "sha256:18d82244ee114f543149c66a6e0c14e9c4f8a1044b5cdaadd0f82159d6a6ff59"},
 ]
+identify = [
+    {file = "identify-1.5.5-py2.py3-none-any.whl", hash = "sha256:da683bfb7669fa749fc7731f378229e2dbf29a1d1337cbde04106f02236eb29d"},
+    {file = "identify-1.5.5.tar.gz", hash = "sha256:7c22c384a2c9b32c5cc891d13f923f6b2653aa83e2d75d8f79be240d6c86c4f4"},
+]
 idna = [
     {file = "idna-2.10-py2.py3-none-any.whl", hash = "sha256:b97d804b1e9b523befed77c48dacec60e6dcb0b5391d57af6a65a312a90648c0"},
     {file = "idna-2.10.tar.gz", hash = "sha256:b307872f855b18632ce0c21c5e45be78c0ea7ae4c15c828c20788b26921eb3f6"},
@@ -1029,6 +1096,10 @@ nest-asyncio = [
     {file = "nest_asyncio-1.4.1-py3-none-any.whl", hash = "sha256:a4487c4f49f2d11a7bb89a512a6886b6a5045f47097f49815b2851aaa8599cf0"},
     {file = "nest_asyncio-1.4.1.tar.gz", hash = "sha256:b86c3193abda5b2eeccf8c79894bc71c680369a178f4b068514ac00720b14e01"},
 ]
+nodeenv = [
+    {file = "nodeenv-1.5.0-py2.py3-none-any.whl", hash = "sha256:5304d424c529c997bc888453aeaa6362d242b6b4631e90f3d4bf1b290f1c84a9"},
+    {file = "nodeenv-1.5.0.tar.gz", hash = "sha256:ab45090ae383b716c4ef89e690c41ff8c2b257b85b309f01f3654df3d084bd7c"},
+]
 notebook = [
     {file = "notebook-6.1.4-py3-none-any.whl", hash = "sha256:07b6e8b8a61aa2f780fe9a97430470485bc71262bc5cae8521f1441b910d2c88"},
     {file = "notebook-6.1.4.tar.gz", hash = "sha256:687d01f963ea20360c0b904ee7a37c3d8cda553858c8d6e33fd0afd13e89de32"},
@@ -1056,6 +1127,10 @@ pickleshare = [
     {file = "pickleshare-0.7.5-py2.py3-none-any.whl", hash = "sha256:9649af414d74d4df115d5d718f82acb59c9d418196b7b4290ed47a12ce62df56"},
     {file = "pickleshare-0.7.5.tar.gz", hash = "sha256:87683d47965c1da65cdacaf31c8441d12b8044cdec9aca500cd78fc2c683afca"},
 ]
+pre-commit = [
+    {file = "pre_commit-2.7.1-py2.py3-none-any.whl", hash = "sha256:810aef2a2ba4f31eed1941fc270e72696a1ad5590b9751839c90807d0fff6b9a"},
+    {file = "pre_commit-2.7.1.tar.gz", hash = "sha256:c54fd3e574565fe128ecc5e7d2f91279772ddb03f8729645fa812fe809084a70"},
+]
 prometheus-client = [
     {file = "prometheus_client-0.8.0-py2.py3-none-any.whl", hash = "sha256:983c7ac4b47478720db338f1491ef67a100b474e3bc7dafcbaefb7d0b8f9b01c"},
     {file = "prometheus_client-0.8.0.tar.gz", hash = "sha256:c6e6b706833a6bd1fd51711299edee907857be10ece535126a158f911ee80915"},
@@ -1117,6 +1192,19 @@ pywinpty = [
     {file = "pywinpty-0.5.7-cp38-cp38-win_amd64.whl", hash = "sha256:8fc5019ff3efb4f13708bd3b5ad327589c1a554cb516d792527361525a7cb78c"},
     {file = "pywinpty-0.5.7.tar.gz", hash = "sha256:2d7e9c881638a72ffdca3f5417dd1563b60f603e1b43e5895674c2a1b01f95a0"},
 ]
+pyyaml = [
+    {file = "PyYAML-5.3.1-cp27-cp27m-win32.whl", hash = "sha256:74809a57b329d6cc0fdccee6318f44b9b8649961fa73144a98735b0aaf029f1f"},
+    {file = "PyYAML-5.3.1-cp27-cp27m-win_amd64.whl", hash = "sha256:240097ff019d7c70a4922b6869d8a86407758333f02203e0fc6ff79c5dcede76"},
+    {file = "PyYAML-5.3.1-cp35-cp35m-win32.whl", hash = "sha256:4f4b913ca1a7319b33cfb1369e91e50354d6f07a135f3b901aca02aa95940bd2"},
+    {file = "PyYAML-5.3.1-cp35-cp35m-win_amd64.whl", hash = "sha256:cc8955cfbfc7a115fa81d85284ee61147059a753344bc51098f3ccd69b0d7e0c"},
+    {file = "PyYAML-5.3.1-cp36-cp36m-win32.whl", hash = "sha256:7739fc0fa8205b3ee8808aea45e968bc90082c10aef6ea95e855e10abf4a37b2"},
+    {file = "PyYAML-5.3.1-cp36-cp36m-win_amd64.whl", hash = "sha256:69f00dca373f240f842b2931fb2c7e14ddbacd1397d57157a9b005a6a9942648"},
+    {file = "PyYAML-5.3.1-cp37-cp37m-win32.whl", hash = "sha256:d13155f591e6fcc1ec3b30685d50bf0711574e2c0dfffd7644babf8b5102ca1a"},
+    {file = "PyYAML-5.3.1-cp37-cp37m-win_amd64.whl", hash = "sha256:73f099454b799e05e5ab51423c7bcf361c58d3206fa7b0d555426b1f4d9a3eaf"},
+    {file = "PyYAML-5.3.1-cp38-cp38-win32.whl", hash = "sha256:06a0d7ba600ce0b2d2fe2e78453a470b5a6e000a985dd4a4e54e436cc36b0e97"},
+    {file = "PyYAML-5.3.1-cp38-cp38-win_amd64.whl", hash = "sha256:95f71d2af0ff4227885f7a6605c37fd53d3a106fcab511b8860ecca9fcf400ee"},
+    {file = "PyYAML-5.3.1.tar.gz", hash = "sha256:b8eac752c5e14d3eca0e6dd9199cd627518cb5ec06add0de9d32baeee6fe645d"},
+]
 pyzmq = [
     {file = "pyzmq-19.0.2-cp27-cp27m-macosx_10_9_intel.whl", hash = "sha256:59f1e54627483dcf61c663941d94c4af9bf4163aec334171686cdaee67974fe5"},
     {file = "pyzmq-19.0.2-cp27-cp27m-win32.whl", hash = "sha256:c36ffe1e5aa35a1af6a96640d723d0d211c5f48841735c2aa8d034204e87eb87"},
@@ -1167,6 +1255,10 @@ testpath = [
     {file = "testpath-0.4.4-py2.py3-none-any.whl", hash = "sha256:bfcf9411ef4bf3db7579063e0546938b1edda3d69f4e1fb8756991f5951f85d4"},
     {file = "testpath-0.4.4.tar.gz", hash = "sha256:60e0a3261c149755f4399a1fff7d37523179a70fdc3abdf78de9fc2604aeec7e"},
 ]
+toml = [
+    {file = "toml-0.10.1-py2.py3-none-any.whl", hash = "sha256:bda89d5935c2eac546d648028b9901107a595863cb36bae0c73ac804a9b4ce88"},
+    {file = "toml-0.10.1.tar.gz", hash = "sha256:926b612be1e5ce0634a2ca03470f95169cf16f939018233a670519cb4ac58b0f"},
+]
 tornado = [
     {file = "tornado-6.0.4-cp35-cp35m-win32.whl", hash = "sha256:5217e601700f24e966ddab689f90b7ea4bd91ff3357c3600fa1045e26d68e55d"},
     {file = "tornado-6.0.4-cp35-cp35m-win_amd64.whl", hash = "sha256:c98232a3ac391f5faea6821b53db8db461157baa788f5d6222a193e9456e1740"},
diff --git a/pyproject.toml b/pyproject.toml
index c4fe109..c06108c 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -18,3 +18,4 @@ jupyterlab = "^2.2.8"
 [tool.poetry.dev-dependencies]
 # Task runners
 nox = "^2020.8.22"
+pre-commit = "^2.7.1"

From b2b29d9ec0774670149f43010affc8e5376b915d Mon Sep 17 00:00:00 2001
From: Alexander Hess <alexander@webartifex.biz>
Date: Fri, 2 Oct 2020 16:27:48 +0200
Subject: [PATCH 107/242] Configure pre-commit hooks

- run "fix-branch-references" before every commit
- add common pre-commit hooks from the pre-commit framework
---
 .pre-commit-config.yaml | 22 ++++++++++++++++++++++
 1 file changed, 22 insertions(+)
 create mode 100644 .pre-commit-config.yaml

diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml
new file mode 100644
index 0000000..40d4f46
--- /dev/null
+++ b/.pre-commit-config.yaml
@@ -0,0 +1,22 @@
+default_stages: [commit]
+fail_fast: true
+repos:
+- repo: local
+  hooks:
+  - id: fix-branch-references
+    name: Check for wrong branch references
+    entry: poetry run nox -s fix-branch-references --
+    language: system
+    stages: [commit, merge-commit]
+    types: [text]
+# Enable hooks provided by the pre-commit project to
+# enforce rules that local tools could not that easily.
+- repo: https://github.com/pre-commit/pre-commit-hooks
+  rev: v3.2.0
+  hooks:
+  - id: check-added-large-files
+    args: [--maxkb=250]
+  - id: check-merge-conflict
+  - id: no-commit-to-branch
+    args: [--branch, main]
+  - id: trailing-whitespace

From b0a5b496640503f63eb8447867d1daedc98cfbaf Mon Sep 17 00:00:00 2001
From: Alexander Hess <alexander@webartifex.biz>
Date: Fri, 2 Oct 2020 16:31:36 +0200
Subject: [PATCH 108/242] Add init-project task

- add a nox session "init-project"
  + it installs the pre-commit hooks
  + being a task, the session is run in the develop environment
---
 noxfile.py | 12 ++++++++++++
 1 file changed, 12 insertions(+)

diff --git a/noxfile.py b/noxfile.py
index e083aa7..184e1b5 100644
--- a/noxfile.py
+++ b/noxfile.py
@@ -2,6 +2,8 @@
 
 Nox provides the following tasks:
 
+- "init-project": install the pre-commit hooks
+
 - "fix-branch-references": adjusts links with git branch references in
   various files (e.g., Mardown or notebooks)
 
@@ -28,6 +30,16 @@ nox.options.envdir = ".cache/nox"
 nox.options.error_on_external_run = True
 
 
+@nox.session(name="init-project", venv_backend="none")
+def init_project(session):
+    """Install the pre-commit hooks."""
+    for type_ in (
+        "pre-commit",
+        "pre-merge-commit",
+    ):
+        session.run("poetry", "run", "pre-commit", "install", f"--hook-type={type_}")
+
+
 @nox.session(name="fix-branch-references", venv_backend="none")
 def fix_branch_references(_session):
     """Change git branch references.

From c67d26b78744ef2a0945447d91424f818ce4467b Mon Sep 17 00:00:00 2001
From: Alexander Hess <alexander@webartifex.biz>
Date: Mon, 12 Oct 2020 11:28:27 +0200
Subject: [PATCH 109/242] Pin the dependencies

- use Poetry 1.1
- re-oder the keys in poetry.lock
- upgrade transient dependencies:
  + colorlog
  + identify
  + nbconvert
  + virtualenv
---
 poetry.lock | 522 ++++++++++++++++++++++++++--------------------------
 1 file changed, 257 insertions(+), 265 deletions(-)

diff --git a/poetry.lock b/poetry.lock
index e2a50ce..6b2b916 100644
--- a/poetry.lock
+++ b/poetry.lock
@@ -1,85 +1,84 @@
 [[package]]
-category = "dev"
-description = "A small Python module for determining appropriate platform-specific dirs, e.g. a \"user data dir\"."
 name = "appdirs"
-optional = false
-python-versions = "*"
 version = "1.4.4"
-
-[[package]]
-category = "main"
-description = "Disable App Nap on OS X 10.9"
-marker = "sys_platform == \"darwin\" or platform_system == \"Darwin\""
-name = "appnope"
-optional = false
-python-versions = "*"
-version = "0.1.0"
-
-[[package]]
+description = "A small Python module for determining appropriate platform-specific dirs, e.g. a \"user data dir\"."
 category = "dev"
-description = "Bash tab completion for argparse"
-name = "argcomplete"
 optional = false
 python-versions = "*"
+
+[[package]]
+name = "appnope"
+version = "0.1.0"
+description = "Disable App Nap on OS X 10.9"
+category = "main"
+optional = false
+python-versions = "*"
+
+[[package]]
+name = "argcomplete"
 version = "1.12.1"
+description = "Bash tab completion for argparse"
+category = "dev"
+optional = false
+python-versions = "*"
 
 [package.extras]
 test = ["coverage", "flake8", "pexpect", "wheel"]
 
 [[package]]
-category = "main"
-description = "The secure Argon2 password hashing algorithm."
 name = "argon2-cffi"
+version = "20.1.0"
+description = "The secure Argon2 password hashing algorithm."
+category = "main"
 optional = false
 python-versions = "*"
-version = "20.1.0"
 
 [package.dependencies]
 cffi = ">=1.0.0"
 six = "*"
 
 [package.extras]
-dev = ["coverage (>=5.0.2)", "hypothesis", "pytest", "sphinx", "wheel", "pre-commit"]
+dev = ["coverage[toml] (>=5.0.2)", "hypothesis", "pytest", "sphinx", "wheel", "pre-commit"]
 docs = ["sphinx"]
-tests = ["coverage (>=5.0.2)", "hypothesis", "pytest"]
+tests = ["coverage[toml] (>=5.0.2)", "hypothesis", "pytest"]
 
 [[package]]
-category = "main"
-description = "Async generators and context managers for Python 3.5+"
 name = "async-generator"
+version = "1.10"
+description = "Async generators and context managers for Python 3.5+"
+category = "main"
 optional = false
 python-versions = ">=3.5"
-version = "1.10"
 
 [[package]]
-category = "main"
-description = "Classes Without Boilerplate"
 name = "attrs"
+version = "20.2.0"
+description = "Classes Without Boilerplate"
+category = "main"
 optional = false
 python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*"
-version = "20.2.0"
 
 [package.extras]
-dev = ["coverage (>=5.0.2)", "hypothesis", "pympler", "pytest (>=4.3.0)", "six", "zope.interface", "sphinx", "sphinx-rtd-theme", "pre-commit"]
+dev = ["coverage[toml] (>=5.0.2)", "hypothesis", "pympler", "pytest (>=4.3.0)", "six", "zope.interface", "sphinx", "sphinx-rtd-theme", "pre-commit"]
 docs = ["sphinx", "sphinx-rtd-theme", "zope.interface"]
-tests = ["coverage (>=5.0.2)", "hypothesis", "pympler", "pytest (>=4.3.0)", "six", "zope.interface"]
-tests_no_zope = ["coverage (>=5.0.2)", "hypothesis", "pympler", "pytest (>=4.3.0)", "six"]
+tests = ["coverage[toml] (>=5.0.2)", "hypothesis", "pympler", "pytest (>=4.3.0)", "six", "zope.interface"]
+tests_no_zope = ["coverage[toml] (>=5.0.2)", "hypothesis", "pympler", "pytest (>=4.3.0)", "six"]
 
 [[package]]
-category = "main"
-description = "Specifications for callback functions passed in to an API"
 name = "backcall"
+version = "0.2.0"
+description = "Specifications for callback functions passed in to an API"
+category = "main"
 optional = false
 python-versions = "*"
-version = "0.2.0"
 
 [[package]]
-category = "main"
-description = "An easy safelist-based HTML-sanitizing tool."
 name = "bleach"
+version = "3.2.1"
+description = "An easy safelist-based HTML-sanitizing tool."
+category = "main"
 optional = false
 python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*, !=3.4.*"
-version = "3.2.1"
 
 [package.dependencies]
 packaging = "*"
@@ -87,129 +86,128 @@ six = ">=1.9.0"
 webencodings = "*"
 
 [[package]]
-category = "main"
-description = "Python package for providing Mozilla's CA Bundle."
 name = "certifi"
+version = "2020.6.20"
+description = "Python package for providing Mozilla's CA Bundle."
+category = "main"
 optional = false
 python-versions = "*"
-version = "2020.6.20"
 
 [[package]]
-category = "main"
-description = "Foreign Function Interface for Python calling C code."
 name = "cffi"
+version = "1.14.3"
+description = "Foreign Function Interface for Python calling C code."
+category = "main"
 optional = false
 python-versions = "*"
-version = "1.14.3"
 
 [package.dependencies]
 pycparser = "*"
 
 [[package]]
-category = "dev"
-description = "Validate configuration and produce human readable error messages."
 name = "cfgv"
+version = "3.2.0"
+description = "Validate configuration and produce human readable error messages."
+category = "dev"
 optional = false
 python-versions = ">=3.6.1"
-version = "3.2.0"
 
 [[package]]
-category = "main"
-description = "Universal encoding detector for Python 2 and 3"
 name = "chardet"
+version = "3.0.4"
+description = "Universal encoding detector for Python 2 and 3"
+category = "main"
 optional = false
 python-versions = "*"
-version = "3.0.4"
 
 [[package]]
-category = "main"
-description = "Cross-platform colored terminal text."
-marker = "sys_platform == \"win32\""
 name = "colorama"
+version = "0.4.3"
+description = "Cross-platform colored terminal text."
+category = "main"
 optional = false
 python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*, !=3.4.*"
-version = "0.4.3"
 
 [[package]]
-category = "dev"
-description = "Log formatting with colors!"
 name = "colorlog"
+version = "4.4.0"
+description = "Log formatting with colors!"
+category = "dev"
 optional = false
 python-versions = "*"
-version = "4.2.1"
 
 [package.dependencies]
-colorama = "*"
+colorama = {version = "*", markers = "sys_platform == \"win32\""}
 
 [[package]]
-category = "main"
-description = "Decorators for Humans"
 name = "decorator"
+version = "4.4.2"
+description = "Decorators for Humans"
+category = "main"
 optional = false
 python-versions = ">=2.6, !=3.0.*, !=3.1.*"
-version = "4.4.2"
 
 [[package]]
-category = "main"
-description = "XML bomb protection for Python stdlib modules"
 name = "defusedxml"
+version = "0.6.0"
+description = "XML bomb protection for Python stdlib modules"
+category = "main"
 optional = false
 python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*, !=3.4.*"
-version = "0.6.0"
 
 [[package]]
-category = "dev"
-description = "Distribution utilities"
 name = "distlib"
+version = "0.3.1"
+description = "Distribution utilities"
+category = "dev"
 optional = false
 python-versions = "*"
-version = "0.3.1"
 
 [[package]]
-category = "main"
-description = "Discover and load entry points from installed packages."
 name = "entrypoints"
+version = "0.3"
+description = "Discover and load entry points from installed packages."
+category = "main"
 optional = false
 python-versions = ">=2.7"
-version = "0.3"
 
 [[package]]
-category = "dev"
-description = "A platform independent file lock."
 name = "filelock"
+version = "3.0.12"
+description = "A platform independent file lock."
+category = "dev"
 optional = false
 python-versions = "*"
-version = "3.0.12"
 
 [[package]]
-category = "dev"
-description = "File identification library for Python"
 name = "identify"
+version = "1.5.6"
+description = "File identification library for Python"
+category = "dev"
 optional = false
 python-versions = "!=3.0.*,!=3.1.*,!=3.2.*,!=3.3.*,>=2.7"
-version = "1.5.5"
 
 [package.extras]
 license = ["editdistance"]
 
 [[package]]
-category = "main"
-description = "Internationalized Domain Names in Applications (IDNA)"
 name = "idna"
+version = "2.10"
+description = "Internationalized Domain Names in Applications (IDNA)"
+category = "main"
 optional = false
 python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*"
-version = "2.10"
 
 [[package]]
-category = "main"
-description = "IPython Kernel for Jupyter"
 name = "ipykernel"
+version = "5.3.4"
+description = "IPython Kernel for Jupyter"
+category = "main"
 optional = false
 python-versions = ">=3.5"
-version = "5.3.4"
 
 [package.dependencies]
-appnope = "*"
+appnope = {version = "*", markers = "platform_system == \"Darwin\""}
 ipython = ">=5.0.0"
 jupyter-client = "*"
 tornado = ">=4.2"
@@ -219,24 +217,23 @@ traitlets = ">=4.1.0"
 test = ["pytest (!=5.3.4)", "pytest-cov", "flaky", "nose"]
 
 [[package]]
-category = "main"
-description = "IPython: Productive Interactive Computing"
 name = "ipython"
+version = "7.18.1"
+description = "IPython: Productive Interactive Computing"
+category = "main"
 optional = false
 python-versions = ">=3.7"
-version = "7.18.1"
 
 [package.dependencies]
-appnope = "*"
+appnope = {version = "*", markers = "sys_platform == \"darwin\""}
 backcall = "*"
-colorama = "*"
+colorama = {version = "*", markers = "sys_platform == \"win32\""}
 decorator = "*"
 jedi = ">=0.10"
-pexpect = ">4.3"
+pexpect = {version = ">4.3", markers = "sys_platform != \"win32\""}
 pickleshare = "*"
 prompt-toolkit = ">=2.0.0,<3.0.0 || >3.0.0,<3.0.1 || >3.0.1,<3.1.0"
 pygments = "*"
-setuptools = ">=18.5"
 traitlets = ">=4.2"
 
 [package.extras]
@@ -251,20 +248,20 @@ qtconsole = ["qtconsole"]
 test = ["nose (>=0.10.1)", "requests", "testpath", "pygments", "nbformat", "ipykernel", "numpy (>=1.14)"]
 
 [[package]]
-category = "main"
-description = "Vestigial utilities from IPython"
 name = "ipython-genutils"
+version = "0.2.0"
+description = "Vestigial utilities from IPython"
+category = "main"
 optional = false
 python-versions = "*"
-version = "0.2.0"
 
 [[package]]
-category = "main"
-description = "An autocompletion tool for Python that can be used for text editors."
 name = "jedi"
+version = "0.17.2"
+description = "An autocompletion tool for Python that can be used for text editors."
+category = "main"
 optional = false
 python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*, !=3.4.*"
-version = "0.17.2"
 
 [package.dependencies]
 parso = ">=0.7.0,<0.8.0"
@@ -274,12 +271,12 @@ qa = ["flake8 (3.7.9)"]
 testing = ["Django (<3.1)", "colorama", "docopt", "pytest (>=3.9.0,<5.0.0)"]
 
 [[package]]
-category = "main"
-description = "A very fast and expressive template engine."
 name = "jinja2"
+version = "2.11.2"
+description = "A very fast and expressive template engine."
+category = "main"
 optional = false
 python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*, !=3.4.*"
-version = "2.11.2"
 
 [package.dependencies]
 MarkupSafe = ">=0.23"
@@ -288,28 +285,27 @@ MarkupSafe = ">=0.23"
 i18n = ["Babel (>=0.8)"]
 
 [[package]]
-category = "main"
-description = "A Python implementation of the JSON5 data format."
 name = "json5"
+version = "0.9.5"
+description = "A Python implementation of the JSON5 data format."
+category = "main"
 optional = false
 python-versions = "*"
-version = "0.9.5"
 
 [package.extras]
 dev = ["hypothesis"]
 
 [[package]]
-category = "main"
-description = "An implementation of JSON Schema validation for Python"
 name = "jsonschema"
+version = "3.2.0"
+description = "An implementation of JSON Schema validation for Python"
+category = "main"
 optional = false
 python-versions = "*"
-version = "3.2.0"
 
 [package.dependencies]
 attrs = ">=17.4.0"
 pyrsistent = ">=0.14.0"
-setuptools = "*"
 six = ">=1.11.0"
 
 [package.extras]
@@ -317,12 +313,12 @@ format = ["idna", "jsonpointer (>1.13)", "rfc3987", "strict-rfc3339", "webcolors
 format_nongpl = ["idna", "jsonpointer (>1.13)", "webcolors", "rfc3986-validator (>0.1.0)", "rfc3339-validator"]
 
 [[package]]
-category = "main"
-description = "Jupyter protocol implementation and client libraries"
 name = "jupyter-client"
+version = "6.1.7"
+description = "Jupyter protocol implementation and client libraries"
+category = "main"
 optional = false
 python-versions = ">=3.5"
-version = "6.1.7"
 
 [package.dependencies]
 jupyter-core = ">=4.6.0"
@@ -335,24 +331,24 @@ traitlets = "*"
 test = ["ipykernel", "ipython", "mock", "pytest", "pytest-asyncio", "async-generator", "pytest-timeout"]
 
 [[package]]
-category = "main"
-description = "Jupyter core package. A base package on which Jupyter projects rely."
 name = "jupyter-core"
+version = "4.6.3"
+description = "Jupyter core package. A base package on which Jupyter projects rely."
+category = "main"
 optional = false
 python-versions = "!=3.0,!=3.1,!=3.2,!=3.3,!=3.4,>=2.7"
-version = "4.6.3"
 
 [package.dependencies]
-pywin32 = ">=1.0"
+pywin32 = {version = ">=1.0", markers = "sys_platform == \"win32\""}
 traitlets = "*"
 
 [[package]]
-category = "main"
-description = "The JupyterLab notebook server extension."
 name = "jupyterlab"
+version = "2.2.8"
+description = "The JupyterLab notebook server extension."
+category = "main"
 optional = false
 python-versions = ">=3.5"
-version = "2.2.8"
 
 [package.dependencies]
 jinja2 = ">=2.10"
@@ -365,23 +361,23 @@ docs = ["jsx-lexer", "recommonmark", "sphinx", "sphinx-rtd-theme", "sphinx-copyb
 test = ["pytest", "pytest-check-links", "requests", "wheel", "virtualenv"]
 
 [[package]]
-category = "main"
-description = "Pygments theme using JupyterLab CSS variables"
 name = "jupyterlab-pygments"
+version = "0.1.2"
+description = "Pygments theme using JupyterLab CSS variables"
+category = "main"
 optional = false
 python-versions = "*"
-version = "0.1.2"
 
 [package.dependencies]
 pygments = ">=2.4.1,<3"
 
 [[package]]
-category = "main"
-description = "JupyterLab Server"
 name = "jupyterlab-server"
+version = "1.2.0"
+description = "JupyterLab Server"
+category = "main"
 optional = false
 python-versions = ">=3.5"
-version = "1.2.0"
 
 [package.dependencies]
 jinja2 = ">=2.10"
@@ -394,28 +390,28 @@ requests = "*"
 test = ["pytest", "requests"]
 
 [[package]]
-category = "main"
-description = "Safely add untrusted strings to HTML/XML markup."
 name = "markupsafe"
+version = "1.1.1"
+description = "Safely add untrusted strings to HTML/XML markup."
+category = "main"
 optional = false
 python-versions = ">=2.7,!=3.0.*,!=3.1.*,!=3.2.*,!=3.3.*"
-version = "1.1.1"
 
 [[package]]
-category = "main"
-description = "The fastest markdown parser in pure Python"
 name = "mistune"
+version = "0.8.4"
+description = "The fastest markdown parser in pure Python"
+category = "main"
 optional = false
 python-versions = "*"
-version = "0.8.4"
 
 [[package]]
-category = "main"
-description = "A client library for executing notebooks. Formally nbconvert's ExecutePreprocessor."
 name = "nbclient"
+version = "0.5.0"
+description = "A client library for executing notebooks. Formally nbconvert's ExecutePreprocessor."
+category = "main"
 optional = false
 python-versions = ">=3.6"
-version = "0.5.0"
 
 [package.dependencies]
 async-generator = "*"
@@ -430,12 +426,12 @@ sphinx = ["Sphinx (>=1.7)", "sphinx-book-theme", "mock", "moto", "myst-parser"]
 test = ["codecov", "coverage", "ipython", "ipykernel", "ipywidgets", "pytest (>=4.1)", "pytest-cov (>=2.6.1)", "check-manifest", "flake8", "mypy", "tox", "bumpversion", "xmltodict", "pip (>=18.1)", "wheel (>=0.31.0)", "setuptools (>=38.6.0)", "twine (>=1.11.0)", "black"]
 
 [[package]]
-category = "main"
-description = "Converting Jupyter Notebooks"
 name = "nbconvert"
+version = "6.0.7"
+description = "Converting Jupyter Notebooks"
+category = "main"
 optional = false
 python-versions = ">=3.6"
-version = "6.0.6"
 
 [package.dependencies]
 bleach = "*"
@@ -460,12 +456,12 @@ test = ["pytest", "pytest-cov", "pytest-dependency", "ipykernel", "ipywidgets (>
 webpdf = ["pyppeteer (0.2.2)"]
 
 [[package]]
-category = "main"
-description = "The Jupyter Notebook format"
 name = "nbformat"
+version = "5.0.7"
+description = "The Jupyter Notebook format"
+category = "main"
 optional = false
 python-versions = ">=3.5"
-version = "5.0.7"
 
 [package.dependencies]
 ipython-genutils = "*"
@@ -477,31 +473,30 @@ traitlets = ">=4.1"
 test = ["pytest", "pytest-cov", "testpath"]
 
 [[package]]
-category = "main"
-description = "Patch asyncio to allow nested event loops"
 name = "nest-asyncio"
+version = "1.4.1"
+description = "Patch asyncio to allow nested event loops"
+category = "main"
 optional = false
 python-versions = ">=3.5"
-version = "1.4.1"
 
 [[package]]
-category = "dev"
-description = "Node.js virtual environment builder"
 name = "nodeenv"
+version = "1.5.0"
+description = "Node.js virtual environment builder"
+category = "dev"
 optional = false
 python-versions = "*"
-version = "1.5.0"
 
 [[package]]
-category = "main"
-description = "A web-based notebook environment for interactive computing"
 name = "notebook"
+version = "6.1.4"
+description = "A web-based notebook environment for interactive computing"
+category = "main"
 optional = false
 python-versions = ">=3.5"
-version = "6.1.4"
 
 [package.dependencies]
-Send2Trash = "*"
 argon2-cffi = "*"
 ipykernel = "*"
 ipython-genutils = "*"
@@ -512,6 +507,7 @@ nbconvert = "*"
 nbformat = "*"
 prometheus-client = "*"
 pyzmq = ">=17"
+Send2Trash = "*"
 terminado = ">=0.8.3"
 tornado = ">=5.0"
 traitlets = ">=4.2.1"
@@ -521,12 +517,12 @@ docs = ["sphinx", "nbsphinx", "sphinxcontrib-github-alt"]
 test = ["nose", "coverage", "requests", "nose-warnings-filters", "nbval", "nose-exclude", "selenium", "pytest", "pytest-cov", "requests-unixsocket"]
 
 [[package]]
-category = "dev"
-description = "Flexible test automation."
 name = "nox"
+version = "2020.8.22"
+description = "Flexible test automation."
+category = "dev"
 optional = false
 python-versions = ">=3.5"
-version = "2020.8.22"
 
 [package.dependencies]
 argcomplete = ">=1.9.4,<2.0"
@@ -538,63 +534,62 @@ virtualenv = ">=14.0.0"
 tox_to_nox = ["jinja2", "tox"]
 
 [[package]]
-category = "main"
-description = "Core utilities for Python packages"
 name = "packaging"
+version = "20.4"
+description = "Core utilities for Python packages"
+category = "main"
 optional = false
 python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*"
-version = "20.4"
 
 [package.dependencies]
 pyparsing = ">=2.0.2"
 six = "*"
 
 [[package]]
-category = "main"
-description = "Utilities for writing pandoc filters in python"
 name = "pandocfilters"
+version = "1.4.2"
+description = "Utilities for writing pandoc filters in python"
+category = "main"
 optional = false
 python-versions = "*"
-version = "1.4.2"
 
 [[package]]
-category = "main"
-description = "A Python Parser"
 name = "parso"
+version = "0.7.1"
+description = "A Python Parser"
+category = "main"
 optional = false
 python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*"
-version = "0.7.1"
 
 [package.extras]
 testing = ["docopt", "pytest (>=3.0.7)"]
 
 [[package]]
-category = "main"
-description = "Pexpect allows easy control of interactive console applications."
-marker = "sys_platform != \"win32\""
 name = "pexpect"
+version = "4.8.0"
+description = "Pexpect allows easy control of interactive console applications."
+category = "main"
 optional = false
 python-versions = "*"
-version = "4.8.0"
 
 [package.dependencies]
 ptyprocess = ">=0.5"
 
 [[package]]
-category = "main"
-description = "Tiny 'shelve'-like database with concurrency support"
 name = "pickleshare"
+version = "0.7.5"
+description = "Tiny 'shelve'-like database with concurrency support"
+category = "main"
 optional = false
 python-versions = "*"
-version = "0.7.5"
 
 [[package]]
-category = "dev"
-description = "A framework for managing and maintaining multi-language pre-commit hooks."
 name = "pre-commit"
+version = "2.7.1"
+description = "A framework for managing and maintaining multi-language pre-commit hooks."
+category = "dev"
 optional = false
 python-versions = ">=3.6.1"
-version = "2.7.1"
 
 [package.dependencies]
 cfgv = ">=2.0.0"
@@ -605,128 +600,125 @@ toml = "*"
 virtualenv = ">=20.0.8"
 
 [[package]]
-category = "main"
-description = "Python client for the Prometheus monitoring system."
 name = "prometheus-client"
+version = "0.8.0"
+description = "Python client for the Prometheus monitoring system."
+category = "main"
 optional = false
 python-versions = "*"
-version = "0.8.0"
 
 [package.extras]
 twisted = ["twisted"]
 
 [[package]]
-category = "main"
-description = "Library for building powerful interactive command lines in Python"
 name = "prompt-toolkit"
+version = "3.0.7"
+description = "Library for building powerful interactive command lines in Python"
+category = "main"
 optional = false
 python-versions = ">=3.6.1"
-version = "3.0.7"
 
 [package.dependencies]
 wcwidth = "*"
 
 [[package]]
-category = "main"
-description = "Run a subprocess in a pseudo terminal"
-marker = "sys_platform != \"win32\" or os_name != \"nt\""
 name = "ptyprocess"
+version = "0.6.0"
+description = "Run a subprocess in a pseudo terminal"
+category = "main"
 optional = false
 python-versions = "*"
-version = "0.6.0"
 
 [[package]]
-category = "dev"
-description = "library with cross-python path, ini-parsing, io, code, log facilities"
 name = "py"
-optional = false
-python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*"
 version = "1.9.0"
-
-[[package]]
-category = "main"
-description = "C parser in Python"
-name = "pycparser"
+description = "library with cross-python path, ini-parsing, io, code, log facilities"
+category = "dev"
 optional = false
 python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*"
-version = "2.20"
 
 [[package]]
+name = "pycparser"
+version = "2.20"
+description = "C parser in Python"
 category = "main"
-description = "Pygments is a syntax highlighting package written in Python."
+optional = false
+python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*"
+
+[[package]]
 name = "pygments"
+version = "2.7.1"
+description = "Pygments is a syntax highlighting package written in Python."
+category = "main"
 optional = false
 python-versions = ">=3.5"
-version = "2.7.1"
 
 [[package]]
-category = "main"
-description = "Python parsing module"
 name = "pyparsing"
+version = "2.4.7"
+description = "Python parsing module"
+category = "main"
 optional = false
 python-versions = ">=2.6, !=3.0.*, !=3.1.*, !=3.2.*"
-version = "2.4.7"
 
 [[package]]
-category = "main"
-description = "Persistent/Functional/Immutable data structures"
 name = "pyrsistent"
+version = "0.17.3"
+description = "Persistent/Functional/Immutable data structures"
+category = "main"
 optional = false
 python-versions = ">=3.5"
-version = "0.17.3"
 
 [[package]]
-category = "main"
-description = "Extensions to the standard Python datetime module"
 name = "python-dateutil"
+version = "2.8.1"
+description = "Extensions to the standard Python datetime module"
+category = "main"
 optional = false
 python-versions = "!=3.0.*,!=3.1.*,!=3.2.*,>=2.7"
-version = "2.8.1"
 
 [package.dependencies]
 six = ">=1.5"
 
 [[package]]
-category = "main"
-description = "Python for Window Extensions"
-marker = "sys_platform == \"win32\""
 name = "pywin32"
-optional = false
-python-versions = "*"
 version = "228"
-
-[[package]]
+description = "Python for Window Extensions"
+category = "main"
+optional = false
+python-versions = "*"
+
+[[package]]
+name = "pywinpty"
+version = "0.5.7"
+description = "Python bindings for the winpty library"
 category = "main"
-description = "Python bindings for the winpty library"
-marker = "os_name == \"nt\""
-name = "pywinpty"
 optional = false
 python-versions = "*"
-version = "0.5.7"
 
 [[package]]
-category = "dev"
-description = "YAML parser and emitter for Python"
 name = "pyyaml"
+version = "5.3.1"
+description = "YAML parser and emitter for Python"
+category = "dev"
 optional = false
 python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*, !=3.4.*"
-version = "5.3.1"
 
 [[package]]
-category = "main"
-description = "Python bindings for 0MQ"
 name = "pyzmq"
+version = "19.0.2"
+description = "Python bindings for 0MQ"
+category = "main"
 optional = false
 python-versions = ">=2.7,!=3.0.*,!=3.1.*,!=3.2.*"
-version = "19.0.2"
 
 [[package]]
-category = "main"
-description = "Python HTTP for Humans."
 name = "requests"
+version = "2.24.0"
+description = "Python HTTP for Humans."
+category = "main"
 optional = false
 python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*, !=3.4.*"
-version = "2.24.0"
 
 [package.dependencies]
 certifi = ">=2017.4.17"
@@ -739,68 +731,68 @@ security = ["pyOpenSSL (>=0.14)", "cryptography (>=1.3.4)"]
 socks = ["PySocks (>=1.5.6,<1.5.7 || >1.5.7)", "win-inet-pton"]
 
 [[package]]
-category = "main"
-description = "Send file to trash natively under Mac OS X, Windows and Linux."
 name = "send2trash"
+version = "1.5.0"
+description = "Send file to trash natively under Mac OS X, Windows and Linux."
+category = "main"
 optional = false
 python-versions = "*"
-version = "1.5.0"
 
 [[package]]
-category = "main"
-description = "Python 2 and 3 compatibility utilities"
 name = "six"
+version = "1.15.0"
+description = "Python 2 and 3 compatibility utilities"
+category = "main"
 optional = false
 python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*"
-version = "1.15.0"
 
 [[package]]
-category = "main"
-description = "Tornado websocket backend for the Xterm.js Javascript terminal emulator library."
 name = "terminado"
+version = "0.9.1"
+description = "Tornado websocket backend for the Xterm.js Javascript terminal emulator library."
+category = "main"
 optional = false
 python-versions = ">=3.6"
-version = "0.9.1"
 
 [package.dependencies]
-ptyprocess = "*"
-pywinpty = ">=0.5"
+ptyprocess = {version = "*", markers = "os_name != \"nt\""}
+pywinpty = {version = ">=0.5", markers = "os_name == \"nt\""}
 tornado = ">=4"
 
 [[package]]
-category = "main"
-description = "Test utilities for code working with files and commands"
 name = "testpath"
+version = "0.4.4"
+description = "Test utilities for code working with files and commands"
+category = "main"
 optional = false
 python-versions = "*"
-version = "0.4.4"
 
 [package.extras]
 test = ["pathlib2"]
 
 [[package]]
-category = "dev"
-description = "Python Library for Tom's Obvious, Minimal Language"
 name = "toml"
+version = "0.10.1"
+description = "Python Library for Tom's Obvious, Minimal Language"
+category = "dev"
 optional = false
 python-versions = "*"
-version = "0.10.1"
 
 [[package]]
-category = "main"
-description = "Tornado is a Python web framework and asynchronous networking library, originally developed at FriendFeed."
 name = "tornado"
+version = "6.0.4"
+description = "Tornado is a Python web framework and asynchronous networking library, originally developed at FriendFeed."
+category = "main"
 optional = false
 python-versions = ">= 3.5"
-version = "6.0.4"
 
 [[package]]
-category = "main"
-description = "Traitlets Python configuration system"
 name = "traitlets"
+version = "5.0.4"
+description = "Traitlets Python configuration system"
+category = "main"
 optional = false
 python-versions = ">=3.7"
-version = "5.0.4"
 
 [package.dependencies]
 ipython-genutils = "*"
@@ -809,12 +801,12 @@ ipython-genutils = "*"
 test = ["pytest"]
 
 [[package]]
-category = "main"
-description = "HTTP library with thread-safe connection pooling, file post, and more."
 name = "urllib3"
+version = "1.25.10"
+description = "HTTP library with thread-safe connection pooling, file post, and more."
+category = "main"
 optional = false
 python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*, !=3.4.*, <4"
-version = "1.25.10"
 
 [package.extras]
 brotli = ["brotlipy (>=0.6.0)"]
@@ -822,12 +814,12 @@ secure = ["certifi", "cryptography (>=1.3.4)", "idna (>=2.0.0)", "pyOpenSSL (>=0
 socks = ["PySocks (>=1.5.6,<1.5.7 || >1.5.7,<2.0)"]
 
 [[package]]
-category = "dev"
-description = "Virtual Python Environment builder"
 name = "virtualenv"
+version = "20.0.33"
+description = "Virtual Python Environment builder"
+category = "dev"
 optional = false
 python-versions = "!=3.0.*,!=3.1.*,!=3.2.*,!=3.3.*,>=2.7"
-version = "20.0.32"
 
 [package.dependencies]
 appdirs = ">=1.4.3,<2"
@@ -837,28 +829,28 @@ six = ">=1.9.0,<2"
 
 [package.extras]
 docs = ["proselint (>=0.10.2)", "sphinx (>=3)", "sphinx-argparse (>=0.2.5)", "sphinx-rtd-theme (>=0.4.3)", "towncrier (>=19.9.0rc1)"]
-testing = ["coverage (>=5)", "coverage-enable-subprocess (>=1)", "flaky (>=3)", "pytest (>=4)", "pytest-env (>=0.6.2)", "pytest-freezegun (>=0.4.1)", "pytest-mock (>=2)", "pytest-randomly (>=1)", "pytest-timeout (>=1)", "pytest-xdist (>=1.31.0)", "packaging (>=20.0)", "xonsh (>=0.9.16)"]
+testing = ["coverage (>=4)", "coverage-enable-subprocess (>=1)", "flaky (>=3)", "pytest (>=4)", "pytest-env (>=0.6.2)", "pytest-freezegun (>=0.4.1)", "pytest-mock (>=2)", "pytest-randomly (>=1)", "pytest-timeout (>=1)", "pytest-xdist (>=1.31.0)", "packaging (>=20.0)", "xonsh (>=0.9.16)"]
 
 [[package]]
-category = "main"
-description = "Measures the displayed width of unicode strings in a terminal"
 name = "wcwidth"
+version = "0.2.5"
+description = "Measures the displayed width of unicode strings in a terminal"
+category = "main"
 optional = false
 python-versions = "*"
-version = "0.2.5"
 
 [[package]]
-category = "main"
-description = "Character encoding aliases for legacy web content"
 name = "webencodings"
+version = "0.5.1"
+description = "Character encoding aliases for legacy web content"
+category = "main"
 optional = false
 python-versions = "*"
-version = "0.5.1"
 
 [metadata]
-content-hash = "cbabc76963596b4cb08138b7b42feef3ba509e9b10ea821a1a5d02675fd6c761"
-lock-version = "1.0"
+lock-version = "1.1"
 python-versions = "^3.8"
+content-hash = "cbabc76963596b4cb08138b7b42feef3ba509e9b10ea821a1a5d02675fd6c761"
 
 [metadata.files]
 appdirs = [
@@ -962,8 +954,8 @@ colorama = [
     {file = "colorama-0.4.3.tar.gz", hash = "sha256:e96da0d330793e2cb9485e9ddfd918d456036c7149416295932478192f4436a1"},
 ]
 colorlog = [
-    {file = "colorlog-4.2.1-py2.py3-none-any.whl", hash = "sha256:43597fd822ce705190fc997519342fdaaf44b9b47f896ece7aa153ed4b909c74"},
-    {file = "colorlog-4.2.1.tar.gz", hash = "sha256:75e55822c3a3387d721579241e776de2cf089c9ef9528b1f09e8b04d403ad118"},
+    {file = "colorlog-4.4.0-py2.py3-none-any.whl", hash = "sha256:f14f30f58e2ce6ef40b0088307cac7efb9ecff5605fa2267a2d29955f26aff23"},
+    {file = "colorlog-4.4.0.tar.gz", hash = "sha256:0272c537469ab1e63b9915535874d15b671963c9325db0c4891a2aeff97ce3d1"},
 ]
 decorator = [
     {file = "decorator-4.4.2-py2.py3-none-any.whl", hash = "sha256:41fa54c2a0cc4ba648be4fd43cff00aedf5b9465c9bf18d64325bc225f08f760"},
@@ -986,8 +978,8 @@ filelock = [
     {file = "filelock-3.0.12.tar.gz", hash = "sha256:18d82244ee114f543149c66a6e0c14e9c4f8a1044b5cdaadd0f82159d6a6ff59"},
 ]
 identify = [
-    {file = "identify-1.5.5-py2.py3-none-any.whl", hash = "sha256:da683bfb7669fa749fc7731f378229e2dbf29a1d1337cbde04106f02236eb29d"},
-    {file = "identify-1.5.5.tar.gz", hash = "sha256:7c22c384a2c9b32c5cc891d13f923f6b2653aa83e2d75d8f79be240d6c86c4f4"},
+    {file = "identify-1.5.6-py2.py3-none-any.whl", hash = "sha256:3139bf72d81dfd785b0a464e2776bd59bdc725b4cc10e6cf46b56a0db931c82e"},
+    {file = "identify-1.5.6.tar.gz", hash = "sha256:969d844b7a85d32a5f9ac4e163df6e846d73c87c8b75847494ee8f4bd2186421"},
 ]
 idna = [
     {file = "idna-2.10-py2.py3-none-any.whl", hash = "sha256:b97d804b1e9b523befed77c48dacec60e6dcb0b5391d57af6a65a312a90648c0"},
@@ -1085,8 +1077,8 @@ nbclient = [
     {file = "nbclient-0.5.0.tar.gz", hash = "sha256:8ad52d27ba144fca1402db014857e53c5a864a2f407be66ca9d74c3a56d6591d"},
 ]
 nbconvert = [
-    {file = "nbconvert-6.0.6-py3-none-any.whl", hash = "sha256:d8549f62e739a4d51f275c2932b1783ee5039dde07a2b71de70c0296a42c8394"},
-    {file = "nbconvert-6.0.6.tar.gz", hash = "sha256:68335477288aab8a9b9ec03002dce59b4eb1ca967116741ec218a4e78c129efd"},
+    {file = "nbconvert-6.0.7-py3-none-any.whl", hash = "sha256:39e9f977920b203baea0be67eea59f7b37a761caa542abe80f5897ce3cf6311d"},
+    {file = "nbconvert-6.0.7.tar.gz", hash = "sha256:cbbc13a86dfbd4d1b5dee106539de0795b4db156c894c2c5dc382062bbc29002"},
 ]
 nbformat = [
     {file = "nbformat-5.0.7-py3-none-any.whl", hash = "sha256:ea55c9b817855e2dfcd3f66d74857342612a60b1f09653440f4a5845e6e3523f"},
@@ -1279,8 +1271,8 @@ urllib3 = [
     {file = "urllib3-1.25.10.tar.gz", hash = "sha256:91056c15fa70756691db97756772bb1eb9678fa585d9184f24534b100dc60f4a"},
 ]
 virtualenv = [
-    {file = "virtualenv-20.0.32-py2.py3-none-any.whl", hash = "sha256:9160a8f6196afcb8bb91405b5362651f302ee8e810fc471f5f9ce9a06b070298"},
-    {file = "virtualenv-20.0.32.tar.gz", hash = "sha256:3d427459dfe5ec3241a6bad046b1d10c0e445940e013c81946458987c7c7e255"},
+    {file = "virtualenv-20.0.33-py2.py3-none-any.whl", hash = "sha256:35ecdeb58cfc2147bb0706f7cdef69a8f34f1b81b6d49568174e277932908b8f"},
+    {file = "virtualenv-20.0.33.tar.gz", hash = "sha256:a5e0d253fe138097c6559c906c528647254f437d1019af9d5a477b09bfa7300f"},
 ]
 wcwidth = [
     {file = "wcwidth-0.2.5-py2.py3-none-any.whl", hash = "sha256:beb4802a9cebb9144e99086eff703a642a13d6a0052920003a230f3294bbe784"},

From cd783a6ac7a044e5ab1bc54132f1d046f6fa3c6a Mon Sep 17 00:00:00 2001
From: Alexander Hess <alexander@webartifex.biz>
Date: Mon, 12 Oct 2020 12:02:40 +0200
Subject: [PATCH 110/242] Add prerequisites section

---
 README.md | 10 ++++++++++
 1 file changed, 10 insertions(+)

diff --git a/README.md b/README.md
index 4d95cf0..084a280 100644
--- a/README.md
+++ b/README.md
@@ -5,3 +5,13 @@ in programming with **[Python <img height="12" style="display: inline-block" src
 
 The **main goal** is to **prepare** students
 for **further studies** in the "field" of **data science**.
+
+
+### Prerequisites
+
+To be suitable for *beginners*, there are *no* formal prerequisites.
+It is only expected that the student has:
+- a *solid* understanding of the **English** language,
+- knowledge of **basic mathematics** from high school,
+- the ability to **think conceptually** and **reason logically**, and
+- the willingness to **invest** around **90-120 hours** on this course.

From f6fe8310392899efc96df007767e50bc57a21b1b Mon Sep 17 00:00:00 2001
From: Alexander Hess <alexander@webartifex.biz>
Date: Mon, 12 Oct 2020 15:21:39 +0200
Subject: [PATCH 111/242] Add installation sections

- add section "Installation"
  + explain how Anaconda is set up
  + include many screenshots
  + explain how the repo is downloaded as a zip archive
- add a section "Alternative Installation"
  + explain how git, poetry, and pyenv can be used
    to set up the project as well
  + explain what a CLI is (with screenshots)
---
 README.md                           | 159 ++++++++++++++++++++++++++++
 static/anaconda_download.png        | Bin 0 -> 81049 bytes
 static/anaconda_navigator.png       | Bin 0 -> 75999 bytes
 static/anaconda_start_menu.png      | Bin 0 -> 718473 bytes
 static/cli_install.png              | Bin 0 -> 61546 bytes
 static/cli_jupyter_lab.png          | Bin 0 -> 112032 bytes
 static/jupyter_lab.png              | Bin 0 -> 53263 bytes
 static/jupyter_notebook_blank.png   | Bin 0 -> 44093 bytes
 static/jupyter_notebook_example.png | Bin 0 -> 44702 bytes
 static/link/to_wiki.png             | Bin 0 -> 503 bytes
 static/repo_download.png            | Bin 0 -> 59439 bytes
 11 files changed, 159 insertions(+)
 create mode 100644 static/anaconda_download.png
 create mode 100644 static/anaconda_navigator.png
 create mode 100644 static/anaconda_start_menu.png
 create mode 100644 static/cli_install.png
 create mode 100644 static/cli_jupyter_lab.png
 create mode 100644 static/jupyter_lab.png
 create mode 100644 static/jupyter_notebook_blank.png
 create mode 100644 static/jupyter_notebook_example.png
 create mode 100644 static/link/to_wiki.png
 create mode 100644 static/repo_download.png

diff --git a/README.md b/README.md
index 084a280..dfbf6a3 100644
--- a/README.md
+++ b/README.md
@@ -15,3 +15,162 @@ It is only expected that the student has:
 - knowledge of **basic mathematics** from high school,
 - the ability to **think conceptually** and **reason logically**, and
 - the willingness to **invest** around **90-120 hours** on this course.
+
+
+## Getting started
+
+If you are a total beginner,
+    follow the instructions in the "Installation" section next.
+If you are familiar with
+    the [git](https://git-scm.com/)
+    and [poetry](https://python-poetry.org/docs/) command-line tools,
+    you may want to look at the "Alternative Installation" section further below.
+
+
+### Installation
+
+To follow this course, an installation of **Python 3.8** or higher is expected.
+
+A popular and beginner friendly way is
+    to install the [Anaconda Distribution](https://www.anaconda.com/products/individual)
+    that not only ships Python itself
+    but also comes pre-packaged with a lot of third-party libraries.
+
+<img src="static/anaconda_download.png" width="50%">
+
+Scroll down to the [download](https://www.anaconda.com/products/individual#Downloads) section
+    and install the latest version for your operating system
+    (i.e., *2020-07* with Python 3.8 at the time of this writing).
+
+After installation,
+    you find an entry "[Anaconda Navigator](https://docs.anaconda.com/anaconda/navigator/)"
+    in your start menu.
+Click on it.
+
+<img src="static/anaconda_start_menu.png" width="50%">
+
+A window opens giving you several options to start various applications.
+In the beginning, we will work mostly with [JupyterLab](https://jupyterlab.readthedocs.io/en/stable/).
+Click on "Launch".
+
+<img src="static/anaconda_navigator.png" width="50%">
+
+A new tab in your web browser opens:
+The website is "localhost" and some number (e.g., 8888).
+
+This is the [JupyterLab](https://jupyterlab.readthedocs.io/en/stable/) application
+    that is used to display the course materials.
+On the left, you see the files and folders on your computer.
+This file browser works like any other.
+In the center, you see several options to launch (i.e., "create") new files.
+
+<img src="static/jupyter_lab.png" width="50%">
+
+To check if your Python installation works,
+    double-click on the "Python 3" tile under the "Notebook" section.
+That opens a new [Jupyter notebook](https://jupyter-notebook.readthedocs.io/en/stable/)
+    named "Untitled.ipynb".
+
+<img src="static/jupyter_notebook_blank.png" width="50%">
+
+Enter some basic Python in the **code cell**, for example, `1 + 2`.
+Then, press the **Enter** key *while* holding down the **Control** key
+    (if that does not work, try with the **Shift** key)
+    to **execute** the snippet.
+The result of the calculation, `3` in the example, shows up below the cell.
+
+<img src="static/jupyter_notebook_example.png" width="50%">
+
+After setting up Python,
+    click on the green "Code" button on the top right on this website
+    to download the course materials.
+As a beginner, choosing "Download ZIP" is likely the easiest option.
+Then, unpack the ZIP file into a folder of your choice,
+    ideally somewhere within your personal user folder
+    so that the files show up right away in [JupyterLab](https://jupyterlab.readthedocs.io/en/stable/).
+
+<img src="static/repo_download.png" width="50%">
+
+
+### Alternative Installation (for Instructors)
+
+Python can also be installed in a "pure" way
+    obtained directly from its core development team [here](https://www.python.org/downloads/).
+Then, it comes *without* any third-party packages,
+    which is *not* a problem at all.
+Managing third-party packages can be automated to a large degree,
+    for example, with tools such as [poetry](https://python-poetry.org/docs/).
+
+However, this may be too "advanced" for a beginner
+    as it involves working with a [command-line interface <img height="12" style="display: inline-block" src="static/link/to_wiki.png">](https://en.wikipedia.org/wiki/Command-line_interface) (CLI),
+    also called a **terminal**,
+    which looks like the one below.
+It is used *without* a mouse by typing commands into it.
+The following instructions assume that
+    [git](https://git-scm.com/), [poetry](https://python-poetry.org/docs/),
+    and [pyenv](https://github.com/pyenv/pyenv) are installed.
+
+<img src="static/cli_install.png" width="50%" align="center">
+
+The screeshot above shows how this project can be set up in an alternative way
+    with the [zsh](https://en.wikipedia.org/wiki/Z_shell) CLI.
+
+First, the [git](https://git-scm.com/) tool is used
+    to **clone** the course materials as a **repository**
+    into a new folder called "*intro-to-python*"
+    that lives under a "*repos*" folder.
+
+- `git clone https://github.com/webartifex/intro-to-python.git`
+
+The `cd` command is used to "change directories".
+
+In the screenshot, the [pyenv](https://github.com/pyenv/pyenv) tool is used
+    to set the project's Python version.
+[pyenv](https://github.com/pyenv/pyenv)'s purpose is
+    to manage *many* parallel Python installations on the same computer.
+It is highly recommended for professional users;
+    however, any other way of installing Python works as well.
+
+- `pyenv local ...`
+
+On the contrary, the [poetry](https://python-poetry.org/docs/) tool is used
+    to manage third-party packages within the *same* Python installation
+    and, more importantly, on a per-project basis.
+So, for example,
+whereas "Project A" may depend on [numpy](https://numpy.org/) *v1.19*
+    from June 2020 be installed,
+    "Project B" may use *v1.14* from January 2018 instead
+    (cf., numpy's [release history](https://pypi.org/project/numpy/#history)).
+To achieve this per-project **isolation**,
+[poetry](https://python-poetry.org/docs/) uses so-called **virtual environments**
+    behind the scenes.
+While one could do that manually,
+    for example, by using Python's built-in
+        [venv <img height="12" style="display: inline-block" src="static/link/to_py.png">](https://docs.python.org/3/library/venv.html) module,
+    it is more convenient and reliable to have [poetry](https://python-poetry.org/docs/)
+        automate this.
+The following *one* command not only
+    creates a new virtual environment (manually: `python -m venv venv`)
+    and *activates* it (manually: `source venv/bin/activate`),
+    it also installs the versions of the project's third-party dependencies
+        as specified in the [poetry.lock](poetry.lock) file
+        (manually: `python -m pip install -r requirements.txt`
+         if a [requirements.txt](https://docs.python.org/3/tutorial/venv.html#managing-packages-with-pip)
+         file is used;
+         the `python -m` part is often left out [but should not be](https://snarky.ca/why-you-should-use-python-m-pip/)):
+
+- `poetry install`
+
+[poetry](https://python-poetry.org/docs/) is also used
+    to execute commands in the project's (virtual) environment.
+The command is then prefixed with `poetry run ...`.
+For example, to do the equivalent of clicking "Launch" in the Anaconda Navigator:
+
+- `poetry run jupyter lab`
+
+This opens a new tab in your web browser just as above.
+The command-line interface stays open in the background,
+    like in the screenshot below,
+    and prints log messages as we work in [JupyterLab](https://jupyterlab.readthedocs.io/en/stable/).
+
+<img src="static/cli_jupyter_lab.png" width="50%" align="center">
\ No newline at end of file
diff --git a/static/anaconda_download.png b/static/anaconda_download.png
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From 772f7f59d7e2098719536814fce9e8bfaac906bc Mon Sep 17 00:00:00 2001
From: Alexander Hess <alexander@webartifex.biz>
Date: Mon, 12 Oct 2020 15:29:31 +0200
Subject: [PATCH 112/242] Add author infos

---
 README.md | 11 ++++++++++-
 1 file changed, 10 insertions(+), 1 deletion(-)

diff --git a/README.md b/README.md
index dfbf6a3..58bbc94 100644
--- a/README.md
+++ b/README.md
@@ -173,4 +173,13 @@ The command-line interface stays open in the background,
     like in the screenshot below,
     and prints log messages as we work in [JupyterLab](https://jupyterlab.readthedocs.io/en/stable/).
 
-<img src="static/cli_jupyter_lab.png" width="50%" align="center">
\ No newline at end of file
+<img src="static/cli_jupyter_lab.png" width="50%" align="center">
+
+
+## About the Author
+
+Alexander Hess is a PhD student
+    at the Chair of Logistics Management at [WHU - Otto Beisheim School of Management](https://www.whu.edu)
+    where he conducts research on urban delivery platforms
+    and teaches coding courses based on Python in the BSc and MBA programs.
+Connect him on [LinkedIn](https://www.linkedin.com/in/webartifex).
\ No newline at end of file

From 3b730fb3d586b0d95ac5593f9610c5339ab9c27d Mon Sep 17 00:00:00 2001
From: Alexander Hess <alexander@webartifex.biz>
Date: Mon, 12 Oct 2020 21:32:57 +0200
Subject: [PATCH 113/242] Streamline text

---
 README.md | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/README.md b/README.md
index 58bbc94..7746018 100644
--- a/README.md
+++ b/README.md
@@ -1,7 +1,7 @@
 # An Introduction to Python and Programming
 
-This project is a thorough introductory course
-in programming with **[Python <img height="12" style="display: inline-block" src="static/link/to_py.png">](https://www.python.org/)**.
+This project is a *thorough* introductory course
+    in programming with **[Python <img height="12" style="display: inline-block" src="static/link/to_py.png">](https://www.python.org/)**.
 
 The **main goal** is to **prepare** students
 for **further studies** in the "field" of **data science**.

From 05df4c085785345471b1c92ec981526343dd18e5 Mon Sep 17 00:00:00 2001
From: Alexander Hess <alexander@webartifex.biz>
Date: Mon, 12 Oct 2020 21:38:27 +0200
Subject: [PATCH 114/242] Make course objectives more elaborate

---
 README.md | 19 ++++++++++++++++++-
 1 file changed, 18 insertions(+), 1 deletion(-)

diff --git a/README.md b/README.md
index 7746018..1752053 100644
--- a/README.md
+++ b/README.md
@@ -3,8 +3,25 @@
 This project is a *thorough* introductory course
     in programming with **[Python <img height="12" style="display: inline-block" src="static/link/to_py.png">](https://www.python.org/)**.
 
+
+### Objective
+
 The **main goal** is to **prepare** students
-for **further studies** in the "field" of **data science**.
+    for **further studies** in the "field" of **data science**,
+    including but not limited to topics such as:
+- algorithms & data structures
+- data cleaning & wrangling
+- data visualization
+- data engineering (incl. SQL databases)
+- data mining (incl. web scraping)
+- linear algebra
+- machine learning (incl. feature generation & deep learning)
+- optimization & (meta-)heuristics (incl. management science & operations research)
+- statistics & econometrics
+- quantitative finance (e.g., option valuation)
+- quantitative marketing (e.g., customer segmentation)
+- quantitative supply chain management (e.g., forecasting)
+- web development (incl. APIs)
 
 
 ### Prerequisites

From dff18c86a8b116ed96d1bdda19a8450b0e7e2191 Mon Sep 17 00:00:00 2001
From: Alexander Hess <alexander@webartifex.biz>
Date: Mon, 12 Oct 2020 21:40:46 +0200
Subject: [PATCH 115/242] Add contributing section

---
 README.md             |  15 +++++++++++++++
 static/link/to_gh.png | Bin 0 -> 1236 bytes
 static/link/to_hn.png | Bin 0 -> 632 bytes
 3 files changed, 15 insertions(+)
 create mode 100644 static/link/to_gh.png
 create mode 100644 static/link/to_hn.png

diff --git a/README.md b/README.md
index 1752053..595e7a3 100644
--- a/README.md
+++ b/README.md
@@ -193,6 +193,21 @@ The command-line interface stays open in the background,
 <img src="static/cli_jupyter_lab.png" width="50%" align="center">
 
 
+## Contributing
+
+Feedback **is highly encouraged** and will be incorporated.
+Open an issue in the [issues tracker <img height="12" style="display: inline-block" src="static/link/to_gh.png">](https://github.com/webartifex/intro-to-python/issues)
+    or initiate a [pull request <img height="12" style="display: inline-block" src="static/link/to_gh.png">](https://help.github.com/en/articles/about-pull-requests)
+    if you are familiar with the concept.
+Simple issues that *anyone* can **help fix** are, for example,
+    **spelling mistakes** or **broken links**.
+If you feel that some topic is missing entirely, you may also mention that.
+The materials here are considered a **permanent work-in-progress**.
+
+A "Show HN" post about this course was made on [Hacker News <img height="12" style="display: inline-block" src="static/link/to_hn.png">](https://news.ycombinator.com/item?id=22669084)
+    and some ideas for improvement were discussed there.
+
+
 ## About the Author
 
 Alexander Hess is a PhD student
diff --git a/static/link/to_gh.png b/static/link/to_gh.png
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From 363260625186875b2eca96225a24c14af07a6328 Mon Sep 17 00:00:00 2001
From: Alexander Hess <alexander@webartifex.biz>
Date: Mon, 12 Oct 2020 21:42:29 +0200
Subject: [PATCH 116/242] Add ToC section

---
 README.md             |  11 +++++++++++
 static/link/to_mb.png | Bin 0 -> 1222 bytes
 static/link/to_nb.png | Bin 0 -> 1138 bytes
 static/link/to_yt.png | Bin 0 -> 912 bytes
 4 files changed, 11 insertions(+)
 create mode 100644 static/link/to_mb.png
 create mode 100644 static/link/to_nb.png
 create mode 100644 static/link/to_yt.png

diff --git a/README.md b/README.md
index 595e7a3..aad3e4c 100644
--- a/README.md
+++ b/README.md
@@ -4,6 +4,17 @@ This project is a *thorough* introductory course
     in programming with **[Python <img height="12" style="display: inline-block" src="static/link/to_py.png">](https://www.python.org/)**.
 
 
+### Table of Contents
+
+The materials are designed to resemble a book.
+They can be viewed in a web browser
+    either statically (i.e., read-only) on [nbviewer <img height="12" style="display: inline-block" src="static/link/to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/tree/develop/)
+    or interactively (i.e., code can be executed) on [Binder <img height="12" style="display: inline-block" src="static/link/to_mb.png">](https://mybinder.org/v2/gh/webartifex/intro-to-python/develop?urlpath=lab).
+**Video** presentations on the content are available
+    either via the individual links to [YouTube <img height="12" style="display: inline-block" src="static/link/to_yt.png">](https://www.youtube.com) below
+    or this [playlist <img height="12" style="display: inline-block" src="static/link/to_yt.png">](https://www.youtube.com/playlist?list=PL-2JV1G3J10lQ2xokyQowcRJI5jjNfW7f).
+
+
 ### Objective
 
 The **main goal** is to **prepare** students
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From 1899a814c0d7026e9b9bff5697a76966e95e9c9b Mon Sep 17 00:00:00 2001
From: Alexander Hess <alexander@webartifex.biz>
Date: Mon, 12 Oct 2020 21:56:04 +0200
Subject: [PATCH 117/242] Add initial version of chapter 00

---
 README.md                                     |   7 +
 chapter_00_intro/00_content.ipynb             | 979 ++++++++++++++++++
 chapter_00_intro/static/cli_example.png       | Bin 0 -> 29502 bytes
 .../static/example_python_users.png           | Bin 0 -> 364253 bytes
 .../growth_of_major_programming_languages.png | Bin 0 -> 210392 bytes
 ...rowth_of_smaller_programming_languages.png | Bin 0 -> 175532 bytes
 chapter_00_intro/static/logo.png              | Bin 0 -> 7821 bytes
 chapter_00_intro/static/xkcd.png              | Bin 0 -> 90835 bytes
 static/link/to_so.png                         | Bin 0 -> 991 bytes
 9 files changed, 986 insertions(+)
 create mode 100644 chapter_00_intro/00_content.ipynb
 create mode 100644 chapter_00_intro/static/cli_example.png
 create mode 100644 chapter_00_intro/static/example_python_users.png
 create mode 100644 chapter_00_intro/static/growth_of_major_programming_languages.png
 create mode 100644 chapter_00_intro/static/growth_of_smaller_programming_languages.png
 create mode 100644 chapter_00_intro/static/logo.png
 create mode 100644 chapter_00_intro/static/xkcd.png
 create mode 100644 static/link/to_so.png

diff --git a/README.md b/README.md
index aad3e4c..458d934 100644
--- a/README.md
+++ b/README.md
@@ -14,6 +14,13 @@ They can be viewed in a web browser
     either via the individual links to [YouTube <img height="12" style="display: inline-block" src="static/link/to_yt.png">](https://www.youtube.com) below
     or this [playlist <img height="12" style="display: inline-block" src="static/link/to_yt.png">](https://www.youtube.com/playlist?list=PL-2JV1G3J10lQ2xokyQowcRJI5jjNfW7f).
 
+- *Chapter 0*: Introduction
+  (
+   [content <img height="12" style="display: inline-block" src="static/link/to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/chapter_00_intro/00_content.ipynb)
+   [<img height="12" style="display: inline-block" src="static/link/to_mb.png">](https://mybinder.org/v2/gh/webartifex/intro-to-python/develop?urlpath=lab/tree/chapter_00_intro/00_content.ipynb)
+   [<img height="12" style="display: inline-block" src="static/link/to_yt.png">](https://www.youtube.com/watch?v=YTU8jaG27Xk&list=PL-2JV1G3J10lQ2xokyQowcRJI5jjNfW7f)
+  )
+
 
 ### Objective
 
diff --git a/chapter_00_intro/00_content.ipynb b/chapter_00_intro/00_content.ipynb
new file mode 100644
index 0000000..b07b0c7
--- /dev/null
+++ b/chapter_00_intro/00_content.ipynb
@@ -0,0 +1,979 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "**Note**: Click on \"*Kernel*\" > \"*Restart Kernel and Clear All Outputs*\" in [JupyterLab](https://jupyterlab.readthedocs.io/en/stable/) *before* reading this notebook to reset its output. If you cannot run this file on your machine, you may want to open it [in the cloud <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_mb.png\">](https://mybinder.org/v2/gh/webartifex/intro-to-python/develop?urlpath=lab/tree/chapter_00_intro/00_content.ipynb)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "# An Introduction to Python and Programming"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "This book is a *thorough* introduction to programming in [Python <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://www.python.org/).\n",
+    "\n",
+    "It teaches the concepts behind and the syntax of the core Python language as defined by the [Python Software Foundation <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://www.python.org/psf/) in the official [language reference <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/reference/index.html). Furthermore, it introduces commonly used functionalities from the [standard library <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/index.html) and popular third-party libraries like [numpy <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://www.numpy.org/), [pandas <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://pandas.pydata.org/), [matplotlib <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://matplotlib.org/), and others."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "source": [
+    "<img src=\"static/logo.png\" width=\"15%\" align=\"left\">"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "## Prerequisites"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "There are *no* prerequisites for reading this book."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "## Objective"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "The **main goal** of this introduction is to **prepare** the student **for further studies** in the \"field\" of **data science**."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "### Why data science?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "The term **[data science <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_wiki.png\">](https://en.wikipedia.org/wiki/Data_science)** is rather vague and does *not* refer to an academic discipline. Instead, the term was popularized by the tech industry, who also coined non-meaningful job titles such as \"[rockstar](https://www.quora.com/Why-are-engineers-called-rockstars-and-ninjas)\" or \"[ninja developers](https://www.quora.com/Why-are-engineers-called-rockstars-and-ninjas).\" Most *serious* definitions describe the field as being **multi-disciplinary** *integrating* scientific methods, algorithms, and systems thinking to extract knowledge from structured and unstructured data, *and* also emphasize the importance of **[domain knowledge <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_wiki.png\">](https://en.wikipedia.org/wiki/Domain_knowledge)**.\n",
+    "\n",
+    "Recently, this integration aspect feeds back into the academic world. The [MIT](https://www.mit.edu/), for example, created the new [Stephen A. Schwarzman College of Computing](http://computing.mit.edu) for [artificial intelligence <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_wiki.png\">](https://en.wikipedia.org/wiki/Artificial_intelligence) with a 1 billion dollar initial investment where students undergo a \"bilingual\" curriculum with half the classes in quantitative and method-centric fields and the other half in domains such as biology, business, chemistry, politics, (art) history, or linguistics (cf., the [official Q&As](http://computing.mit.edu/faq/) or this [NYT article](https://www.nytimes.com/2018/10/15/technology/mit-college-artificial-intelligence.html)). Their strategists see a future where programming skills are just as naturally embedded into students' curricula as are nowadays subjects like calculus, statistics, or academic writing. Then, programming literacy is not just another \"nice to have\" skill but a prerequisite, or an enabler, to understanding more advanced topics in the actual domains studied."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "## Installation"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "To \"read\" this book in the most meaningful way, a working installation of **Python 3.8** with [JupyterLab](https://jupyterlab.readthedocs.io/en/stable/) is needed.\n",
+    "\n",
+    "For a tutorial on how to install Python on your computer, follow the instructions in the [README.md](https://github.com/webartifex/intro-to-python/blob/develop/README.md#installation) file in the project's [GitHub repository <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_gh.png\">](https://github.com/webartifex/intro-to-python). If you cannot install Python on your own machine, you may open the book interactively in the cloud with [Binder <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_mb.png\">](https://mybinder.org/v2/gh/webartifex/intro-to-python/develop?urlpath=lab)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "## Jupyter Notebooks"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "The document you are viewing is a so-called [Jupyter notebook](https://jupyter-notebook.readthedocs.io/en/stable/notebook.html), a file format introduced by the [Jupyter Project](https://jupyter.org/).\n",
+    "\n",
+    "\"Jupyter\" is an [acronym <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_wiki.png\">](https://en.wikipedia.org/wiki/Acronym) derived from the names of the three major programming languages **[Julia](https://julialang.org/)**, **[Python <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://www.python.org)**, and **[R](https://www.r-project.org/)**, all of which play significant roles in the world of data science. The Jupyter Project's idea is to serve as an integrating platform such that different programming languages and software packages can be used together within the same project.\n",
+    "\n",
+    "Jupyter notebooks have become a de-facto standard for communicating and exchanging results in the data science community - both in academia and business - and provide an alternative to command-line interface (CLI or \"terminal\") based ways of running Python code. As an example for the latter case, we could start the default [Python interpreter <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/tutorial/interpreter.html) that comes with every installation by typing the `python` command into a CLI (or `poetry run python` if the project is managed with the [poetry](https://python-poetry.org/docs/) CLI tool as explained in the [README.md](https://github.com/webartifex/intro-to-python/blob/develop/README.md#alternative-installation-for-instructors) file). Then, as the screenshot below shows, we could execute Python code like `1 + 2` or `print(\"Hello World\")` line by line simply by typing it following the `>>>` **prompt** and pressing the **Enter** key. For an introductory course, however, this would be rather tedious and probably scare off many beginners."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "<img src=\"static/cli_example.png\" width=\"50%\">"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "One reason for the popularity of Jupyter notebooks is that they allow mixing text with code in the same document. Text may be formatted with the [Markdown <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_gh.png\">](https://guides.github.com/features/mastering-markdown/) language and mathematical formulas typeset with [LaTeX](https://www.overleaf.com/learn/latex/Free_online_introduction_to_LaTeX_%28part_1%29). Moreover, we may include pictures, plots, and even videos. Because of these features, the notebooks developed for this book come in a self-contained \"tutorial\" style enabling students to simply read them from top to bottom while executing the code snippets.\n",
+    "\n",
+    "Other ways of running Python code are to use the [IPython <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://ipython.org/) CLI tool instead of the default interpreter or a full-fledged [Integrated Development Environment <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_wiki.png\">](https://en.wikipedia.org/wiki/Integrated_development_environment) (e.g., the commercial [PyCharm](https://www.jetbrains.com/pycharm/) or the free [Spyder <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_gh.png\">](https://github.com/spyder-ide/spyder) that comes with the Anaconda Distribution)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "### Markdown Cells vs. Code Cells"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "A Jupyter notebook consists of cells that have a type associated with them. So far, only cells of type \"Markdown\" have been used, which is the default way to present formatted text.\n",
+    "\n",
+    "The cells below are examples of \"Code\" cells containing actual Python code: They calculate the sum of `1` and `2` and print out `\"Hello World\"` when executed, respectively. To edit an existing code cell, enter into it with a mouse click. You are \"in\" a code cell if its frame is highlighted in blue. We call that the **edit mode**.\n",
+    "\n",
+    "There is also a **command mode** that you reach by hitting the **Escape** key. That un-highlights the frame. You are now \"out\" of but still \"on\" the cell. If you were already in command mode, hitting the Escape key does *nothing*.\n",
+    "\n",
+    "Using the **Enter** and **Escape** keys, you can now switch between the two modes.\n",
+    "\n",
+    "To **execute**, or \"run,\" a code cell, hold down the **Control** key and press **Enter**. Note how you do *not* go to the subsequent cell if you keep re-executing the cell you are on. Alternatively, you can hold the **Shift** key and press **Enter**, which executes a cell *and* places your focus on the subsequent cell or creates a new one if there is none."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "3"
+      ]
+     },
+     "execution_count": 1,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "1 + 2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Hello World\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"Hello World\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Similarly, a Markdown cell is also in either edit or command mode. For example, double-click on the text you are reading: This puts you into edit mode. Now, you could change the formatting (e.g., print a word in *italics* or **bold**) and \"execute\" the cell to render the text as specified.\n",
+    "\n",
+    "To change a cell's type, choose either \"Code\" or \"Markdown\" in the navigation bar at the top. Alternatively, you can press either the **Y** or **M** key in command mode.\n",
+    "\n",
+    "Sometimes, a code cell starts with an exclamation mark `!`. Then, the Jupyter notebook behaves as if the following command were typed directly into a terminal. The cell below asks the `python` CLI to show its version number and is *not* Python code but a command in the [Shell <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_wiki.png\">](https://en.wikipedia.org/wiki/Shell_%28computing%29) language. The `!` is useful to execute short CLI commands without leaving a Jupyter notebook."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Python 3.8.6\n"
+     ]
+    }
+   ],
+   "source": [
+    "!python --version"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "## Programming vs. Computer Science vs. IT"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "In this book, **programming** is defined as\n",
+    "- a *structured* way of *problem-solving*\n",
+    "- by *expressing* the steps of a *computation* or *process*\n",
+    "- and thereby *documenting* the process in a formal way.\n",
+    "\n",
+    "Programming is always *concrete* and based on a *particular case*. It exhibits elements of an *art* or a *craft* as we hear programmers call code \"beautiful\" or \"ugly\" or talk about the \"expressive\" power of an application."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "That is different from **computer science**, which is\n",
+    "- a field of study comparable to applied *mathematics* that\n",
+    "- asks *abstract* questions (e.g., \"Is something computable at all?\"),\n",
+    "- develops and analyses *algorithms* and *data structures*,\n",
+    "- and *proves* the *correctness* of a program.\n",
+    "\n",
+    "In a sense, a computer scientist does not need to know a programming language to work, and many computer scientists only know how to produce \"ugly\" looking code in the eyes of professional programmers."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "**IT** or **information technology** is a term that has many meanings to different people. Often, it has something to do with hardware or physical devices, both of which are out of scope for programmers and computer scientists. Sometimes, it refers to a [support function](https://en.wikipedia.org/wiki/Value_chain#Support_activities) within a company. Many computer scientists and programmers are more than happy if their printer and internet connection work as they often do not know a lot more about that than \"non-technical\" people."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "## Why Python?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "### What is Python?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Here is a brief history of and some background on Python (cf., also this [TechRepublic article](https://www.techrepublic.com/article/python-is-eating-the-world-how-one-developers-side-project-became-the-hottest-programming-language-on-the-planet/) for a more elaborate story):\n",
+    "\n",
+    "- [Guido van Rossum <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_wiki.png\">](https://en.wikipedia.org/wiki/Guido_van_Rossum) (Python’s **[Benevolent Dictator for Life <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_wiki.png\">](https://en.wikipedia.org/wiki/Benevolent_dictator_for_life)**) was bored during a week around Christmas 1989 and started Python as a hobby project \"that would keep \\[him\\] occupied\" for some days\n",
+    "- the idea was to create a **general-purpose** scripting **language** that would allow fast *prototyping* and would *run on every operating system*\n",
+    "- Python grew through the 90s as van Rossum promoted it via his \"Computer Programming for Everybody\" initiative that had the *goal to encourage a basic level of coding literacy* as an equal knowledge alongside English literacy and math skills\n",
+    "- to become more independent from its creator, the next major version **Python 2** - released in 2000 and still in heavy use as of today - was **open-source** from the get-go which attracted a *large and global community of programmers* that *contributed* their expertise and best practices in their free time to make Python even better\n",
+    "- **Python 3** resulted from a significant overhaul of the language in 2008 taking into account the *learnings from almost two decades*, streamlining the language, and getting ready for the age of **big data**\n",
+    "- the language is named after the sketch comedy group [Monty Python <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_wiki.png\">](https://en.wikipedia.org/wiki/Monty_Python)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "#### Summary"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Python is a **general-purpose** programming **language** that allows for *fast development*, is *easy to read*, **open-source**, long-established, unifies the knowledge of *hundreds of thousands of experts* around the world, runs on basically every machine, and can handle the complexities of applications involving **big data**."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "### Why open-source?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Couldn't a company like Google, Facebook, or Microsoft come up with a better programming language? The following is an argument on why this can likely not be the case.\n",
+    "\n",
+    "Wouldn't it be weird if professors and scholars of English literature and language studies dictated how we'd have to speak in day-to-day casual conversations or how authors of poesy and novels should use language constructs to achieve a particular type of mood? If you agree with that premise, it makes sense to assume that even programming languages should evolve in a \"natural\" way as users *use* the language over time and in *new* and *unpredictable* contexts creating new conventions."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Loose *communities* are the primary building block around which open-source software projects are built. Someone - like Guido - starts a project and makes it free to use for anybody (e.g., on a code-sharing platform like [GitHub <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_gh.png\">](https://github.com/)). People find it useful enough to solve one of their daily problems and start using it. They see how a project could be improved and provide new use cases (e.g., via the popularized concept of a [pull request <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_gh.png\">](https://help.github.com/articles/about-pull-requests/)). The project grows both in lines of code and people using it. After a while, people start local user groups to share their same interests and meet regularly (e.g., this is a big market for companies like [Meetup](https://www.meetup.com/) or non-profits like [PyData <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://pydata.org/)). Out of these local and usually monthly meetups grow yearly conferences on the country or even continental level (e.g., the original [PyCon <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://us.pycon.org/) in the US, [EuroPython <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://europython.eu/), or [PyCon.DE <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://de.pycon.org/)). The content presented at these conferences is made publicly available via GitHub and YouTube (e.g., [PyCon 2019 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://www.youtube.com/channel/UCxs2IIVXaEHHA4BtTiWZ2mQ) or [EuroPython <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](http://europython.tv/)) and serves as references on what people are working on and introductions to the endless number of specialized fields."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "While these communities are somewhat loose and continuously changing, smaller in-groups, often democratically organized and elected (e.g., the [Python Software Foundation <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://www.python.org/psf/)), take care of, for example, the development of the \"core\" Python language itself.\n",
+    "\n",
+    "Python itself is just a specification (i.e., a set of rules) as to what is allowed and what not: It must first be implemented (c.f., next section below). The current version of Python can always be looked up in the [Python Language Reference <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/reference/index.html). To make changes to that, anyone can make a so-called **[Python Enhancement Proposal <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://www.python.org/dev/peps/)**, or **PEP** for short, where it needs to be specified what exact changes are to be made and argued why that is a good thing to do. These PEPs are reviewed by the [core developers <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://devguide.python.org/coredev/) and interested people and are then either accepted, modified, or rejected if, for example, the change introduces internal inconsistencies. This process is similar to the **double-blind peer review** established in academia, just a lot more transparent. Many of the contributors even held or hold positions in academia, one more indicator of the high quality standards in the Python community. To learn more about PEPs, check out [PEP 1 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://www.python.org/dev/peps/pep-0001/) that describes the entire process.\n",
+    "\n",
+    "In total, no one single entity can control how the language evolves, and the users' needs and ideas always feed back to the language specification via a quality controlled and \"democratic\" process."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Besides being **free** as in \"free beer,\" a major benefit of open-source is that one can always *look up how something works in detail*: That is the literal meaning of *open* source and a difference to commercial languages (e.g., [MATLAB](https://www.mathworks.com/products/matlab.html)) as a programmer can always continue to *study best practices* or find out how things are implemented. Along this way, many *errors are uncovered*, as well. Furthermore, if one runs an open-source application, one can be reasonably sure that no bad people built in a \"backdoor.\" [Free software <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_wiki.png\">](https://en.wikipedia.org/wiki/Free_software) is consequently free of charge but brings *many other freedoms* with it, most notably the freedom to change the code."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "### Isn't C a lot faster?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "The default Python implementation is written in the C language.\n",
+    "\n",
+    "[C <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_wiki.png\">](https://en.wikipedia.org/wiki/C_%28programming_language%29) and [C++ <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_wiki.png\">](https://en.wikipedia.org/wiki/C%2B%2B) (cf., this [introduction](https://www.learncpp.com/)) are wide-spread and long-established (i.e., since the 1970s) programming languages employed in many mission-critical software systems (e.g., operating systems themselves, low latency databases and web servers, nuclear reactor control systems, airplanes, ...). They are fast, mainly because the programmer not only needs to come up with the **business logic** but also manage the computer's memory.\n",
+    "\n",
+    "In contrast, Python automatically manages the memory for the programmer. So, speed here is a trade-off between application run time and engineering/development time. Often, the program's run time is not that important: For example, what if C needs 0.001 seconds in a case where Python needs 0.1 seconds to do the same thing? When the requirements change and computing speed becomes an issue, the Python community offers many third-party libraries - usually also written in C - where specific problems can be solved in near-C time."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "#### Summary"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "While it is true that a language like C is a lot faster than Python when it comes to *pure* **computation time**, this does not matter in many cases as the *significantly shorter* **development cycles** are the more significant cost factor in a rapidly changing world."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "### Who uses it?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "<img src=\"static/example_python_users.png\" width=\"70%\">"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "While ad-hominem arguments are usually not the best kind of reasoning, we briefly look at some examples of who uses Python and leave it up to the reader to decide if this is convincing or not:\n",
+    "\n",
+    "- **[Massachusetts Institute of Technology](https://www.mit.edu/)**\n",
+    "  - teaches Python in its [introductory course](https://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-0001-introduction-to-computer-science-and-programming-in-python-fall-2016/) to computer science independent of the student's major\n",
+    "  - replaced the infamous course on the [Scheme](https://groups.csail.mit.edu/mac/projects/scheme/) language (cf., [source <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_hn.png\">](https://news.ycombinator.com/item?id=602307))\n",
+    "- **[Google](https://www.google.com/)**\n",
+    "  - used the strategy \"Python where we can, C++ where we must\" from its early days on to stay flexible in a rapidly changing environment (cf., [source <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_so.png\">](https://stackoverflow.com/questions/2560310/heavy-usage-of-python-at-google))\n",
+    "  - the very first web-crawler was written in Java and so difficult to maintain that it was rewritten in Python right away (cf., [source](https://www.amazon.com/Plex-Google-Thinks-Works-Shapes/dp/1416596585/ref=sr_1_1?ie=UTF8&qid=1539101827&sr=8-1&keywords=in+the+plex))\n",
+    "  - Guido van Rossom was hired by Google from 2005 to 2012 to advance the language there\n",
+    "- **[NASA](https://www.nasa.gov/)** open-sources many of its projects, often written in Python and regarding analyses with big data (cf., [source](https://code.nasa.gov/language/python/))\n",
+    "- **[Facebook](https://facebook.com/)** uses Python besides C++ and its legacy PHP (a language for building websites; the \"cool kid\" from the early 2000s)\n",
+    "- **[Instagram](https://instagram.com/)** operates the largest installation of the popular **web framework  [Django](https://www.djangoproject.com/)** (cf., [source](https://instagram-engineering.com/web-service-efficiency-at-instagram-with-python-4976d078e366))\n",
+    "- **[Spotify](https://spotify.com/)** bases its data science on Python (cf., [source](https://labs.spotify.com/2013/03/20/how-we-use-python-at-spotify/))\n",
+    "- **[Netflix](https://netflix.com/)** also runs its predictive models on Python (cf., [source](https://medium.com/netflix-techblog/python-at-netflix-86b6028b3b3e))\n",
+    "- **[Dropbox](https://dropbox.com/)** \"stole\" Guido van Rossom from Google to help scale the platform (cf., [source](https://medium.com/dropbox-makers/guido-van-rossum-on-finding-his-way-e018e8b5f6b1))\n",
+    "- **[JPMorgan Chase](https://www.jpmorganchase.com/)** requires new employees to learn Python as part of the onboarding process starting with the 2018 intake (cf., [source](https://www.ft.com/content/4c17d6ce-c8b2-11e8-ba8f-ee390057b8c9?segmentId=a7371401-027d-d8bf-8a7f-2a746e767d56))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "As images tell more than words, here are two plots of popular languages' \"market shares\" based on the number of questions asked on [Stack Overflow <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_so.png\">](https://stackoverflow.blog/2017/09/06/incredible-growth-python/), the most relevant platform for answering programming-related questions: As of late 2017, Python surpassed [Java](https://www.java.com/en/), heavily used in big corporates, and [JavaScript](https://developer.mozilla.org/en-US/docs/Web/JavaScript), the \"language of the internet\" that does everything in web browsers, in popularity. Two blog posts from \"technical\" people explain this in more depth to the layman: [Stack Overflow <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_so.png\">](https://stackoverflow.blog/2017/09/14/python-growing-quickly/) and [DataCamp](https://www.datacamp.com/community/blog/python-scientific-computing-case)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "<img src=\"static/growth_of_major_programming_languages.png\" width=\"50%\">"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "As the graph below shows, neither Google's very own language **[Go](https://golang.org/)** nor **[R](https://www.r-project.org/)**, a domain-specific language in the niche of statistics, can compete with Python's year-to-year growth."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "<img src=\"static/growth_of_smaller_programming_languages.png\" width=\"50%\">"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "[IEEE Sprectrum](https://spectrum.ieee.org/computing/software/the-top-programming-languages-2019) provides a more recent comparison of programming language's popularity. Even news and media outlets notice the recent popularity of Python: [Economist](https://www.economist.com/graphic-detail/2018/07/26/python-is-becoming-the-worlds-most-popular-coding-language), [Huffington Post](https://www.huffingtonpost.com/entry/why-python-is-the-best-programming-language-with-which_us_59ef8f62e4b04809c05011b9), [TechRepublic](https://www.techrepublic.com/article/why-python-is-so-popular-with-developers-3-reasons-the-language-has-exploded/), and [QZ](https://qz.com/1408660/the-rise-of-python-as-seen-through-a-decade-of-stack-overflow/)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "## How to learn Programming"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "source": [
+    "### ABC Rule"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "**A**lways **b**e **c**oding.\n",
+    "\n",
+    "Programming is more than just writing code into a text file. It means reading through parts of the [documentation <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/), blogs with best practices, and tutorials, or researching problems on [Stack Overflow <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_so.png\">](https://stackoverflow.com/) while trying to implement features in the application at hand. Also, it means using command-line tools to automate some part of the work or manage different versions of a program, for example, with **[git](https://git-scm.com/)**. In short, programming involves a lot of \"muscle memory,\" which can only be built and kept up through near-daily usage.\n",
+    "\n",
+    "Further, many aspects of software architecture and best practices can only be understood after having implemented some requirements for the very first time. Coding also means \"breaking\" things to find out what makes them work in the first place.\n",
+    "\n",
+    "Therefore, coding is learned best by just doing it for some time on a daily or at least a regular basis and not right before some task is due, just like learning a \"real\" language."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "source": [
+    "### The Maker's Schedule"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "[Y Combinator <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_hn.png\">](https://www.ycombinator.com/) co-founder [Paul Graham <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_wiki.png\">](https://en.wikipedia.org/wiki/Paul_Graham_%28programmer%29) wrote a very popular and often cited [article](http://www.paulgraham.com/makersschedule.html) where he divides every person into belonging to one of two groups:\n",
+    "\n",
+    "- **Managers**: People that need to organize things and command others (e.g., a \"boss\" or manager). Their schedule is usually organized by the hour or even 30-minute intervals.\n",
+    "- **Makers**: People that create things (e.g., programmers, artists, or writers). Such people think in half days or full days.\n",
+    "\n",
+    "Have you ever wondered why so many tech people work during nights and sleep at \"weird\" times? The reason is that many programming-related tasks require a \"flow\" state in one's mind that is hard to achieve when one can get interrupted, even if it is only for one short question. Graham describes that only knowing that one has an appointment in three hours can cause a programmer to not get into a flow state.\n",
+    "\n",
+    "As a result, do not set aside a certain amount of time for learning something but rather plan in an *entire evening* or a *rainy Sunday* where you can work on a problem in an *open end* setting. And do not be surprised anymore to hear \"I looked at it over the weekend\" from a programmer."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "source": [
+    "### Phase Iteration"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "When being asked the above question, most programmers answer something that can be classified into one of two broader groups.\n",
+    "\n",
+    "**1) Toy Problem, Case Study, or Prototype**: Pick some problem, break it down into smaller sub-problems, and solve them with an end in mind.\n",
+    "\n",
+    "**2) Books, Video Tutorials, and Courses**: Research the best book, blog, video, or tutorial for something and work it through from start to end.\n",
+    "\n",
+    "The truth is that you need to iterate between these two phases.\n",
+    "\n",
+    "Building a prototype always reveals issues no book or tutorial can think of before. Data is never as clean as it should be. An algorithm from a textbook must be adapted to a peculiar aspect of a case study. It is essential to learn to \"ship a product\" because only then will one have looked at all the aspects.\n",
+    "\n",
+    "The major downside of this approach is that one likely learns bad \"patterns\" overfitted to the case at hand, and one does not get the big picture or mental concepts behind a solution. This gap can be filled in by well-written books: For example, check the Python/programming books offered by [Packt](https://www.packtpub.com/packt/offers/free-learning/) or [O’Reilly](https://www.oreilly.com/)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "## Contents"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "**Part A: Expressing Logic**\n",
+    "\n",
+    "- What is a programming language? What kind of words exist?\n",
+    "  - *Chapter 1*: Elements of a Program\n",
+    "  - *Chapter 2*: Functions & Modularization\n",
+    "- What is the flow of execution? How can we form sentences from words?\n",
+    "  - *Chapter 3*: Conditionals & Exceptions\n",
+    "  - *Chapter 4*: Recursion & Looping"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "**Part B: Managing Data and Memory**\n",
+    "\n",
+    "- How is data stored in memory?\n",
+    "  - *Chapter 5*: Numbers & Bits\n",
+    "  - *Chapter 6*: Text & Bytes\n",
+    "  - *Chapter 7*: Sequential Data\n",
+    "  - *Chapter 8*: Map, Filter, & Reduce\n",
+    "  - *Chapter 9*: Mappings & Sets\n",
+    "  - *Chapter 10*: Arrays & Dataframes\n",
+    "- How can we create custom data types?\n",
+    "  - *Chapter 11*: Classes & Instances"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "## xkcd Comic"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "As with every good book, there has to be a [xkcd](https://xkcd.com/353/) comic somewhere."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "import antigravity"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "<img src=\"static/xkcd.png\" width=\"30%\">"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "## Further Resources"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "A lecture-style **video presentation** of this chapter is integrated below (cf., the [video <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_yt.png\">](https://www.youtube.com/watch?v=YTU8jaG27Xk&list=PL-2JV1G3J10lQ2xokyQowcRJI5jjNfW7f) or the entire [playlist <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_yt.png\">](https://www.youtube.com/playlist?list=PL-2JV1G3J10lQ2xokyQowcRJI5jjNfW7f))."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
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+       "            width=\"60%\"\n",
+       "            height=\"300\"\n",
+       "            src=\"https://www.youtube.com/embed/YTU8jaG27Xk\"\n",
+       "            frameborder=\"0\"\n",
+       "            allowfullscreen\n",
+       "        ></iframe>\n",
+       "        "
+      ],
+      "text/plain": [
+       "<IPython.lib.display.YouTubeVideo at 0x7f70900dc7f0>"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from IPython.display import YouTubeVideo\n",
+    "YouTubeVideo(\"YTU8jaG27Xk\", width=\"60%\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Also, to see some common shortcuts in [JupyterLab](https://jupyterlab.readthedocs.io/en/stable/), find a **video tutorial** below."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/jpeg": 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+       "            src=\"https://www.youtube.com/embed/5GGyKq1F_5c\"\n",
+       "            frameborder=\"0\"\n",
+       "            allowfullscreen\n",
+       "        ></iframe>\n",
+       "        "
+      ],
+      "text/plain": [
+       "<IPython.lib.display.YouTubeVideo at 0x7f70900dcf10>"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "YouTubeVideo(\"5GGyKq1F_5c\", width=\"60%\")"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.6"
+  },
+  "livereveal": {
+   "auto_select": "code",
+   "auto_select_fragment": true,
+   "scroll": true,
+   "theme": "serif"
+  },
+  "toc": {
+   "base_numbering": 1,
+   "nav_menu": {},
+   "number_sections": false,
+   "sideBar": true,
+   "skip_h1_title": true,
+   "title_cell": "Table of Contents",
+   "title_sidebar": "Contents",
+   "toc_cell": false,
+   "toc_position": {
+    "height": "calc(100% - 180px)",
+    "left": "10px",
+    "top": "150px",
+    "width": "384px"
+   },
+   "toc_section_display": false,
+   "toc_window_display": false
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/chapter_00_intro/static/cli_example.png b/chapter_00_intro/static/cli_example.png
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diff --git a/chapter_00_intro/static/xkcd.png b/chapter_00_intro/static/xkcd.png
new file mode 100644
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From 570d6cec1364a49a7bba925fc0306dcb2d24ae4e Mon Sep 17 00:00:00 2001
From: Alexander Hess <alexander@webartifex.biz>
Date: Mon, 12 Oct 2020 22:02:58 +0200
Subject: [PATCH 118/242] Add initial version of chapter 00's review

---
 README.md                        |   3 +
 chapter_00_intro/01_review.ipynb | 222 +++++++++++++++++++++++++++++++
 2 files changed, 225 insertions(+)
 create mode 100644 chapter_00_intro/01_review.ipynb

diff --git a/README.md b/README.md
index 458d934..2ed69f7 100644
--- a/README.md
+++ b/README.md
@@ -19,6 +19,9 @@ They can be viewed in a web browser
    [content <img height="12" style="display: inline-block" src="static/link/to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/chapter_00_intro/00_content.ipynb)
    [<img height="12" style="display: inline-block" src="static/link/to_mb.png">](https://mybinder.org/v2/gh/webartifex/intro-to-python/develop?urlpath=lab/tree/chapter_00_intro/00_content.ipynb)
    [<img height="12" style="display: inline-block" src="static/link/to_yt.png">](https://www.youtube.com/watch?v=YTU8jaG27Xk&list=PL-2JV1G3J10lQ2xokyQowcRJI5jjNfW7f)
+   |
+   [review <img height="12" style="display: inline-block" src="static/link/to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/chapter_00_intro/01_review.ipynb)
+   [<img height="12" style="display: inline-block" src="static/link/to_mb.png">](https://mybinder.org/v2/gh/webartifex/intro-to-python/develop?urlpath=lab/tree/chapter_00_intro/01_review.ipynb)
   )
 
 
diff --git a/chapter_00_intro/01_review.ipynb b/chapter_00_intro/01_review.ipynb
new file mode 100644
index 0000000..2ea6b98
--- /dev/null
+++ b/chapter_00_intro/01_review.ipynb
@@ -0,0 +1,222 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Chapter 0: Introduction"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Review"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The questions below assume that you have read [Chapter 0 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/chapter_00_intro/00_content.ipynb) in the book.\n",
+    "\n",
+    "Be concise in your answers! Most questions can be answered in *one* sentence."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Essay Questions "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q1**: Describe the difference between the terms **programming** and **computer science**!"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " < your answer >"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q2**: Explain what is a **pull request** and elaborate on how this concept fits a *distributed* organization of work!"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " < your answer >"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q3**: In what sense are **open-source** communities democracies? How are they near-perfect [meritocracies <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_wiki.png\">](https://en.wikipedia.org/wiki/Meritocracy)? How is open-source software development similar to academia?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " < your answer >"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q4**: What is a *significant* advantage of a \"slow\" programming language like Python over a faster one like C?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " < your answer >"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### True / False Questions"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Motivate your answer with *one short* sentence!"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q5**: Python has been the fastest-growing *major* programming language in recent years."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " < your answer >"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q6**: Python is named after a snake to emphasize its agility and fast development speed right in its name."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " < your answer >"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q7**: Python was initially designed for highly intensive numerical computing, in particular for use cases from physics and astronomy."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " < your answer >"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q8**: JavaScript is a subset of the Java language."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " < your answer >"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q9**: Python is **free software**. That means it does not cost anything."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " < your answer >"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q10**: The primary purpose of PEPs is to regulate how code should be documented and styled."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " < your answer >"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.6"
+  },
+  "toc": {
+   "base_numbering": 1,
+   "nav_menu": {},
+   "number_sections": false,
+   "sideBar": true,
+   "skip_h1_title": true,
+   "title_cell": "Table of Contents",
+   "title_sidebar": "Contents",
+   "toc_cell": false,
+   "toc_position": {},
+   "toc_section_display": false,
+   "toc_window_display": false
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}

From 0c754f1c7bd8519c0e6c8ebb702ea5f968b47f00 Mon Sep 17 00:00:00 2001
From: Alexander Hess <alexander@webartifex.biz>
Date: Mon, 12 Oct 2020 22:09:38 +0200
Subject: [PATCH 119/242] Add initial version of chapter 00's exercises

---
 README.md                           |   3 +
 chapter_00_intro/02_exercises.ipynb | 122 ++++++++++++++++++++++++++++
 2 files changed, 125 insertions(+)
 create mode 100644 chapter_00_intro/02_exercises.ipynb

diff --git a/README.md b/README.md
index 2ed69f7..66698ad 100644
--- a/README.md
+++ b/README.md
@@ -22,6 +22,9 @@ They can be viewed in a web browser
    |
    [review <img height="12" style="display: inline-block" src="static/link/to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/chapter_00_intro/01_review.ipynb)
    [<img height="12" style="display: inline-block" src="static/link/to_mb.png">](https://mybinder.org/v2/gh/webartifex/intro-to-python/develop?urlpath=lab/tree/chapter_00_intro/01_review.ipynb)
+   |
+   [exercises <img height="12" style="display: inline-block" src="static/link/to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/chapter_00_intro/02_exercises.ipynb)
+   [<img height="12" style="display: inline-block" src="static/link/to_mb.png">](https://mybinder.org/v2/gh/webartifex/intro-to-python/develop?urlpath=lab/tree/chapter_00_intro/02_exercises.ipynb)
   )
 
 
diff --git a/chapter_00_intro/02_exercises.ipynb b/chapter_00_intro/02_exercises.ipynb
new file mode 100644
index 0000000..28a2cbf
--- /dev/null
+++ b/chapter_00_intro/02_exercises.ipynb
@@ -0,0 +1,122 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Note**: Click on \"*Kernel*\" > \"*Restart Kernel and Run All*\" in [JupyterLab](https://jupyterlab.readthedocs.io/en/stable/) *after* finishing the exercises to ensure that your solution runs top to bottom *without* any errors. If you cannot run this file on your machine, you may want to open it [in the cloud <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_mb.png\">](https://mybinder.org/v2/gh/webartifex/intro-to-python/develop?urlpath=lab/tree/chapter_00_intro/02_exercises.ipynb)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Chapter 0: Introduction"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## \"Coding\" Exercises"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The exercises below assume that you have read [Chapter 0 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/chapter_00_intro/00_content.ipynb) in the book."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Mastering Markdown"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Briefly review GitHub's guide on [Mastering Markdown](https://guides.github.com/features/mastering-markdown/) and create nicely formatted \"text\" cells below!"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q1**: Check the latest [Bundesliga standings](https://www.bundesliga.com/en/bundesliga/table) and provide a table of the top three teams with the following four columns: rank, team name, games played, and points scored. Render the rank in **bold**, make the team name a clickable link (to the team's website), and put both the games played and points scored in *italics*. The header row should be visually different from the three rows with the teams' information."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " < your answer >"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q2**: The quote \"Education is what remains after one has forgotten what one has learned in school\" is attributed to Albert Einstein. Display the author and his quote appropriately!"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " < your answer >"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q3**: Integrate the image of the delicious dessert milk rice at this [URL](https://i.ytimg.com/vi/-BoSRlzy9c4/maxresdefault.jpg) into this notebook."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " < your answer >"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.6"
+  },
+  "toc": {
+   "base_numbering": 1,
+   "nav_menu": {},
+   "number_sections": false,
+   "sideBar": true,
+   "skip_h1_title": true,
+   "title_cell": "Table of Contents",
+   "title_sidebar": "Contents",
+   "toc_cell": false,
+   "toc_position": {},
+   "toc_section_display": false,
+   "toc_window_display": false
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}

From 955fb74673f2b4fa217227f293177f7531623758 Mon Sep 17 00:00:00 2001
From: Alexander Hess <alexander@webartifex.biz>
Date: Mon, 12 Oct 2020 22:47:50 +0200
Subject: [PATCH 120/242] Mention nox and init task

---
 README.md  | 11 ++++++++++-
 noxfile.py |  5 +++++
 2 files changed, 15 insertions(+), 1 deletion(-)

diff --git a/README.md b/README.md
index 66698ad..85f55ee 100644
--- a/README.md
+++ b/README.md
@@ -205,7 +205,16 @@ The following *one* command not only
 [poetry](https://python-poetry.org/docs/) is also used
     to execute commands in the project's (virtual) environment.
 The command is then prefixed with `poetry run ...`.
-For example, to do the equivalent of clicking "Launch" in the Anaconda Navigator:
+
+The project uses [nox](https://nox.thea.codes/en/stable/)
+    to manage various maintenance tasks.
+After cloning the repository and setting up the virual environment,
+    it is recommended to run the initialization task.
+That needs to be done only once.
+
+- `poetry run nox -s init-project`
+
+To do the equivalent of clicking "Launch" in the Anaconda Navigator:
 
 - `poetry run jupyter lab`
 
diff --git a/noxfile.py b/noxfile.py
index 184e1b5..e77cc26 100644
--- a/noxfile.py
+++ b/noxfile.py
@@ -39,6 +39,11 @@ def init_project(session):
     ):
         session.run("poetry", "run", "pre-commit", "install", f"--hook-type={type_}")
 
+    # Copy the extensions' JavaScript and CSS files into Jupyter's search directory.
+    session.run(
+        "poetry", "run", "jupyter", "contrib", "nbextension", "install", "--user"
+    )
+
 
 @nox.session(name="fix-branch-references", venv_backend="none")
 def fix_branch_references(_session):

From 35d20aae40a24dfdc3ceeaae7be9b117b913c73d Mon Sep 17 00:00:00 2001
From: Alexander Hess <alexander@webartifex.biz>
Date: Mon, 12 Oct 2020 22:49:53 +0200
Subject: [PATCH 121/242] Add dependencies for presentation mode

- RISE extension for Jupyter
- jupyter-contrib-nbextensions
---
 poetry.lock    | 178 +++++++++++++++++++++++++++++++++++++++++++++++--
 pyproject.toml |   4 ++
 2 files changed, 175 insertions(+), 7 deletions(-)

diff --git a/poetry.lock b/poetry.lock
index 6b2b916..bc75853 100644
--- a/poetry.lock
+++ b/poetry.lock
@@ -330,6 +330,48 @@ traitlets = "*"
 [package.extras]
 test = ["ipykernel", "ipython", "mock", "pytest", "pytest-asyncio", "async-generator", "pytest-timeout"]
 
+[[package]]
+name = "jupyter-contrib-core"
+version = "0.3.3"
+description = "Common utilities for jupyter-contrib projects."
+category = "dev"
+optional = false
+python-versions = "*"
+
+[package.dependencies]
+jupyter-core = "*"
+notebook = ">=4.0"
+tornado = "*"
+traitlets = "*"
+
+[package.extras]
+testing_utils = ["nose", "mock"]
+
+[[package]]
+name = "jupyter-contrib-nbextensions"
+version = "0.5.1"
+description = "A collection of Jupyter nbextensions."
+category = "dev"
+optional = false
+python-versions = "*"
+
+[package.dependencies]
+ipython-genutils = "*"
+jupyter-contrib-core = ">=0.3.3"
+jupyter-core = "*"
+jupyter-highlight-selected-word = ">=0.1.1"
+jupyter-latex-envs = ">=1.3.8"
+jupyter-nbextensions-configurator = ">=0.4.0"
+lxml = "*"
+nbconvert = ">=4.2"
+notebook = ">=4.0"
+pyyaml = "*"
+tornado = "*"
+traitlets = ">=4.1"
+
+[package.extras]
+test = ["nbformat", "nose", "pip", "requests", "mock"]
+
 [[package]]
 name = "jupyter-core"
 version = "4.6.3"
@@ -342,6 +384,48 @@ python-versions = "!=3.0,!=3.1,!=3.2,!=3.3,!=3.4,>=2.7"
 pywin32 = {version = ">=1.0", markers = "sys_platform == \"win32\""}
 traitlets = "*"
 
+[[package]]
+name = "jupyter-highlight-selected-word"
+version = "0.2.0"
+description = "Jupyter notebook extension that enables highlighting every instance of the current word in the notebook."
+category = "dev"
+optional = false
+python-versions = "*"
+
+[[package]]
+name = "jupyter-latex-envs"
+version = "1.4.6"
+description = "Jupyter notebook extension which supports (some) LaTeX environments within markdown cells. Also provides support for labels and crossreferences, document wide numbering, bibliography, and more..."
+category = "dev"
+optional = false
+python-versions = "*"
+
+[package.dependencies]
+ipython = "*"
+jupyter_core = "*"
+nbconvert = "*"
+notebook = ">=4.0"
+traitlets = ">=4.1"
+
+[[package]]
+name = "jupyter-nbextensions-configurator"
+version = "0.4.1"
+description = "jupyter serverextension providing configuration interfaces for nbextensions."
+category = "dev"
+optional = false
+python-versions = "*"
+
+[package.dependencies]
+jupyter_contrib_core = ">=0.3.3"
+jupyter_core = "*"
+notebook = ">=4.0"
+pyyaml = "*"
+tornado = "*"
+traitlets = "*"
+
+[package.extras]
+test = ["jupyter-contrib-core", "nose", "requests", "selenium", "mock"]
+
 [[package]]
 name = "jupyterlab"
 version = "2.2.8"
@@ -389,6 +473,20 @@ requests = "*"
 [package.extras]
 test = ["pytest", "requests"]
 
+[[package]]
+name = "lxml"
+version = "4.5.2"
+description = "Powerful and Pythonic XML processing library combining libxml2/libxslt with the ElementTree API."
+category = "dev"
+optional = false
+python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*, != 3.4.*"
+
+[package.extras]
+cssselect = ["cssselect (>=0.7)"]
+html5 = ["html5lib"]
+htmlsoup = ["beautifulsoup4"]
+source = ["Cython (>=0.29.7)"]
+
 [[package]]
 name = "markupsafe"
 version = "1.1.1"
@@ -612,7 +710,7 @@ twisted = ["twisted"]
 
 [[package]]
 name = "prompt-toolkit"
-version = "3.0.7"
+version = "3.0.8"
 description = "Library for building powerful interactive command lines in Python"
 category = "main"
 optional = false
@@ -730,6 +828,17 @@ urllib3 = ">=1.21.1,<1.25.0 || >1.25.0,<1.25.1 || >1.25.1,<1.26"
 security = ["pyOpenSSL (>=0.14)", "cryptography (>=1.3.4)"]
 socks = ["PySocks (>=1.5.6,<1.5.7 || >1.5.7)", "win-inet-pton"]
 
+[[package]]
+name = "rise"
+version = "5.6.1"
+description = "Reveal.js - Jupyter/IPython Slideshow Extension"
+category = "dev"
+optional = false
+python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*, <4"
+
+[package.dependencies]
+notebook = ">=5.5.0"
+
 [[package]]
 name = "send2trash"
 version = "1.5.0"
@@ -815,7 +924,7 @@ socks = ["PySocks (>=1.5.6,<1.5.7 || >1.5.7,<2.0)"]
 
 [[package]]
 name = "virtualenv"
-version = "20.0.33"
+version = "20.0.34"
 description = "Virtual Python Environment builder"
 category = "dev"
 optional = false
@@ -850,7 +959,7 @@ python-versions = "*"
 [metadata]
 lock-version = "1.1"
 python-versions = "^3.8"
-content-hash = "cbabc76963596b4cb08138b7b42feef3ba509e9b10ea821a1a5d02675fd6c761"
+content-hash = "1fd17e55acd76edd6126d5ee9223c0dd291bc2acacaaa242e80a3a2aaa5decce"
 
 [metadata.files]
 appdirs = [
@@ -1017,10 +1126,28 @@ jupyter-client = [
     {file = "jupyter_client-6.1.7-py3-none-any.whl", hash = "sha256:c958d24d6eacb975c1acebb68ac9077da61b5f5c040f22f6849928ad7393b950"},
     {file = "jupyter_client-6.1.7.tar.gz", hash = "sha256:49e390b36fe4b4226724704ea28d9fb903f1a3601b6882ce3105221cd09377a1"},
 ]
+jupyter-contrib-core = [
+    {file = "jupyter_contrib_core-0.3.3-py2.py3-none-any.whl", hash = "sha256:1ec81e275a8f5858d56b0c4c6cd85335aa8e915001b8657fe51c620c3cdde50f"},
+    {file = "jupyter_contrib_core-0.3.3.tar.gz", hash = "sha256:e65bc0e932ff31801003cef160a4665f2812efe26a53801925a634735e9a5794"},
+]
+jupyter-contrib-nbextensions = [
+    {file = "jupyter_contrib_nbextensions-0.5.1-py2.py3-none-any.whl", hash = "sha256:2c071f0aa208c569666f656bdc0f66906ca493cf9f06f46db6350db11030ff40"},
+    {file = "jupyter_contrib_nbextensions-0.5.1.tar.gz", hash = "sha256:eecd28ecc2fc410226c0a3d4932ed2fac4860ccf8d9e9b1b29548835a35b22ab"},
+]
 jupyter-core = [
     {file = "jupyter_core-4.6.3-py2.py3-none-any.whl", hash = "sha256:a4ee613c060fe5697d913416fc9d553599c05e4492d58fac1192c9a6844abb21"},
     {file = "jupyter_core-4.6.3.tar.gz", hash = "sha256:394fd5dd787e7c8861741880bdf8a00ce39f95de5d18e579c74b882522219e7e"},
 ]
+jupyter-highlight-selected-word = [
+    {file = "jupyter_highlight_selected_word-0.2.0-py2.py3-none-any.whl", hash = "sha256:9545dfa9cb057eebe3a5795604dcd3a5294ea18637e553f61a0b67c1b5903c58"},
+    {file = "jupyter_highlight_selected_word-0.2.0.tar.gz", hash = "sha256:9fa740424859a807950ca08d2bfd28a35154cd32dd6d50ac4e0950022adc0e7b"},
+]
+jupyter-latex-envs = [
+    {file = "jupyter_latex_envs-1.4.6.tar.gz", hash = "sha256:070a31eb2dc488bba983915879a7c2939247bf5c3b669b398bdb36a9b5343872"},
+]
+jupyter-nbextensions-configurator = [
+    {file = "jupyter_nbextensions_configurator-0.4.1.tar.gz", hash = "sha256:e5e86b5d9d898e1ffb30ebb08e4ad8696999f798fef3ff3262d7b999076e4e83"},
+]
 jupyterlab = [
     {file = "jupyterlab-2.2.8-py3-none-any.whl", hash = "sha256:95d0509557881cfa8a5fcdf225f2fca46faf1bc52fc56a28e0b72fcc594c90ab"},
     {file = "jupyterlab-2.2.8.tar.gz", hash = "sha256:c8377bee30504919c1e79949f9fe35443ab7f5c4be622c95307e8108410c8b8c"},
@@ -1033,6 +1160,39 @@ jupyterlab-server = [
     {file = "jupyterlab_server-1.2.0-py3-none-any.whl", hash = "sha256:55d256077bf13e5bc9e8fbd5aac51bef82f6315111cec6b712b9a5ededbba924"},
     {file = "jupyterlab_server-1.2.0.tar.gz", hash = "sha256:5431d9dde96659364b7cc877693d5d21e7b80cea7ae3959ecc2b87518e5f5d8c"},
 ]
+lxml = [
+    {file = "lxml-4.5.2-cp27-cp27m-macosx_10_9_x86_64.whl", hash = "sha256:74f48ec98430e06c1fa8949b49ebdd8d27ceb9df8d3d1c92e1fdc2773f003f20"},
+    {file = "lxml-4.5.2-cp27-cp27m-manylinux1_i686.whl", hash = "sha256:e70d4e467e243455492f5de463b72151cc400710ac03a0678206a5f27e79ddef"},
+    {file = "lxml-4.5.2-cp27-cp27m-manylinux1_x86_64.whl", hash = "sha256:7ad7906e098ccd30d8f7068030a0b16668ab8aa5cda6fcd5146d8d20cbaa71b5"},
+    {file = "lxml-4.5.2-cp27-cp27m-win32.whl", hash = "sha256:92282c83547a9add85ad658143c76a64a8d339028926d7dc1998ca029c88ea6a"},
+    {file = "lxml-4.5.2-cp27-cp27m-win_amd64.whl", hash = "sha256:05a444b207901a68a6526948c7cc8f9fe6d6f24c70781488e32fd74ff5996e3f"},
+    {file = "lxml-4.5.2-cp27-cp27mu-manylinux1_i686.whl", hash = "sha256:94150231f1e90c9595ccc80d7d2006c61f90a5995db82bccbca7944fd457f0f6"},
+    {file = "lxml-4.5.2-cp27-cp27mu-manylinux1_x86_64.whl", hash = "sha256:bea760a63ce9bba566c23f726d72b3c0250e2fa2569909e2d83cda1534c79443"},
+    {file = "lxml-4.5.2-cp35-cp35m-manylinux1_i686.whl", hash = "sha256:c3f511a3c58676147c277eff0224c061dd5a6a8e1373572ac817ac6324f1b1e0"},
+    {file = "lxml-4.5.2-cp35-cp35m-manylinux1_x86_64.whl", hash = "sha256:59daa84aef650b11bccd18f99f64bfe44b9f14a08a28259959d33676554065a1"},
+    {file = "lxml-4.5.2-cp35-cp35m-manylinux2014_aarch64.whl", hash = "sha256:c9d317efde4bafbc1561509bfa8a23c5cab66c44d49ab5b63ff690f5159b2304"},
+    {file = "lxml-4.5.2-cp35-cp35m-win32.whl", hash = "sha256:9dc9006dcc47e00a8a6a029eb035c8f696ad38e40a27d073a003d7d1443f5d88"},
+    {file = "lxml-4.5.2-cp35-cp35m-win_amd64.whl", hash = "sha256:08fc93257dcfe9542c0a6883a25ba4971d78297f63d7a5a26ffa34861ca78730"},
+    {file = "lxml-4.5.2-cp36-cp36m-macosx_10_9_x86_64.whl", hash = "sha256:121b665b04083a1e85ff1f5243d4a93aa1aaba281bc12ea334d5a187278ceaf1"},
+    {file = "lxml-4.5.2-cp36-cp36m-manylinux1_i686.whl", hash = "sha256:5591c4164755778e29e69b86e425880f852464a21c7bb53c7ea453bbe2633bbe"},
+    {file = "lxml-4.5.2-cp36-cp36m-manylinux1_x86_64.whl", hash = "sha256:cc411ad324a4486b142c41d9b2b6a722c534096963688d879ea6fa8a35028258"},
+    {file = "lxml-4.5.2-cp36-cp36m-manylinux2014_aarch64.whl", hash = "sha256:1fa21263c3aba2b76fd7c45713d4428dbcc7644d73dcf0650e9d344e433741b3"},
+    {file = "lxml-4.5.2-cp36-cp36m-win32.whl", hash = "sha256:786aad2aa20de3dbff21aab86b2fb6a7be68064cbbc0219bde414d3a30aa47ae"},
+    {file = "lxml-4.5.2-cp36-cp36m-win_amd64.whl", hash = "sha256:e1cacf4796b20865789083252186ce9dc6cc59eca0c2e79cca332bdff24ac481"},
+    {file = "lxml-4.5.2-cp37-cp37m-macosx_10_9_x86_64.whl", hash = "sha256:80a38b188d20c0524fe8959c8ce770a8fdf0e617c6912d23fc97c68301bb9aba"},
+    {file = "lxml-4.5.2-cp37-cp37m-manylinux1_i686.whl", hash = "sha256:ecc930ae559ea8a43377e8b60ca6f8d61ac532fc57efb915d899de4a67928efd"},
+    {file = "lxml-4.5.2-cp37-cp37m-manylinux1_x86_64.whl", hash = "sha256:a76979f728dd845655026ab991df25d26379a1a8fc1e9e68e25c7eda43004bed"},
+    {file = "lxml-4.5.2-cp37-cp37m-manylinux2014_aarch64.whl", hash = "sha256:cfd7c5dd3c35c19cec59c63df9571c67c6d6e5c92e0fe63517920e97f61106d1"},
+    {file = "lxml-4.5.2-cp37-cp37m-win32.whl", hash = "sha256:5a9c8d11aa2c8f8b6043d845927a51eb9102eb558e3f936df494e96393f5fd3e"},
+    {file = "lxml-4.5.2-cp37-cp37m-win_amd64.whl", hash = "sha256:4b4a111bcf4b9c948e020fd207f915c24a6de3f1adc7682a2d92660eb4e84f1a"},
+    {file = "lxml-4.5.2-cp38-cp38-macosx_10_9_x86_64.whl", hash = "sha256:5dd20538a60c4cc9a077d3b715bb42307239fcd25ef1ca7286775f95e9e9a46d"},
+    {file = "lxml-4.5.2-cp38-cp38-manylinux1_i686.whl", hash = "sha256:2b30aa2bcff8e958cd85d907d5109820b01ac511eae5b460803430a7404e34d7"},
+    {file = "lxml-4.5.2-cp38-cp38-manylinux1_x86_64.whl", hash = "sha256:aa8eba3db3d8761db161003e2d0586608092e217151d7458206e243be5a43843"},
+    {file = "lxml-4.5.2-cp38-cp38-manylinux2014_aarch64.whl", hash = "sha256:8f0ec6b9b3832e0bd1d57af41f9238ea7709bbd7271f639024f2fc9d3bb01293"},
+    {file = "lxml-4.5.2-cp38-cp38-win32.whl", hash = "sha256:107781b213cf7201ec3806555657ccda67b1fccc4261fb889ef7fc56976db81f"},
+    {file = "lxml-4.5.2-cp38-cp38-win_amd64.whl", hash = "sha256:f161af26f596131b63b236372e4ce40f3167c1b5b5d459b29d2514bd8c9dc9ee"},
+    {file = "lxml-4.5.2.tar.gz", hash = "sha256:cdc13a1682b2a6241080745b1953719e7fe0850b40a5c71ca574f090a1391df6"},
+]
 markupsafe = [
     {file = "MarkupSafe-1.1.1-cp27-cp27m-macosx_10_6_intel.whl", hash = "sha256:09027a7803a62ca78792ad89403b1b7a73a01c8cb65909cd876f7fcebd79b161"},
     {file = "MarkupSafe-1.1.1-cp27-cp27m-manylinux1_i686.whl", hash = "sha256:e249096428b3ae81b08327a63a485ad0878de3fb939049038579ac0ef61e17e7"},
@@ -1128,8 +1288,8 @@ prometheus-client = [
     {file = "prometheus_client-0.8.0.tar.gz", hash = "sha256:c6e6b706833a6bd1fd51711299edee907857be10ece535126a158f911ee80915"},
 ]
 prompt-toolkit = [
-    {file = "prompt_toolkit-3.0.7-py3-none-any.whl", hash = "sha256:83074ee28ad4ba6af190593d4d4c607ff525272a504eb159199b6dd9f950c950"},
-    {file = "prompt_toolkit-3.0.7.tar.gz", hash = "sha256:822f4605f28f7d2ba6b0b09a31e25e140871e96364d1d377667b547bb3bf4489"},
+    {file = "prompt_toolkit-3.0.8-py3-none-any.whl", hash = "sha256:7debb9a521e0b1ee7d2fe96ee4bd60ef03c6492784de0547337ca4433e46aa63"},
+    {file = "prompt_toolkit-3.0.8.tar.gz", hash = "sha256:25c95d2ac813909f813c93fde734b6e44406d1477a9faef7c915ff37d39c0a8c"},
 ]
 ptyprocess = [
     {file = "ptyprocess-0.6.0-py2.py3-none-any.whl", hash = "sha256:d7cc528d76e76342423ca640335bd3633420dc1366f258cb31d05e865ef5ca1f"},
@@ -1231,6 +1391,10 @@ requests = [
     {file = "requests-2.24.0-py2.py3-none-any.whl", hash = "sha256:fe75cc94a9443b9246fc7049224f75604b113c36acb93f87b80ed42c44cbb898"},
     {file = "requests-2.24.0.tar.gz", hash = "sha256:b3559a131db72c33ee969480840fff4bb6dd111de7dd27c8ee1f820f4f00231b"},
 ]
+rise = [
+    {file = "rise-5.6.1-py2.py3-none-any.whl", hash = "sha256:e9637ee5499ad7801474da53a2c830350a44b2192c2f113594e4426190e55ad4"},
+    {file = "rise-5.6.1.tar.gz", hash = "sha256:1343f068d01adc4dd0226d9b278ce93fc92f365d827431a57e8d5679eb39f4d6"},
+]
 send2trash = [
     {file = "Send2Trash-1.5.0-py3-none-any.whl", hash = "sha256:f1691922577b6fa12821234aeb57599d887c4900b9ca537948d2dac34aea888b"},
     {file = "Send2Trash-1.5.0.tar.gz", hash = "sha256:60001cc07d707fe247c94f74ca6ac0d3255aabcb930529690897ca2a39db28b2"},
@@ -1271,8 +1435,8 @@ urllib3 = [
     {file = "urllib3-1.25.10.tar.gz", hash = "sha256:91056c15fa70756691db97756772bb1eb9678fa585d9184f24534b100dc60f4a"},
 ]
 virtualenv = [
-    {file = "virtualenv-20.0.33-py2.py3-none-any.whl", hash = "sha256:35ecdeb58cfc2147bb0706f7cdef69a8f34f1b81b6d49568174e277932908b8f"},
-    {file = "virtualenv-20.0.33.tar.gz", hash = "sha256:a5e0d253fe138097c6559c906c528647254f437d1019af9d5a477b09bfa7300f"},
+    {file = "virtualenv-20.0.34-py2.py3-none-any.whl", hash = "sha256:4ecd607e7809bd7384039065639a8babc9fa562fe1b73b24095ce85b83d55fcf"},
+    {file = "virtualenv-20.0.34.tar.gz", hash = "sha256:4bf0e2bf99d33b123a895a5a244f0d7adb2a92171c6bbb31c3e2db235624abf1"},
 ]
 wcwidth = [
     {file = "wcwidth-0.2.5-py2.py3-none-any.whl", hash = "sha256:beb4802a9cebb9144e99086eff703a642a13d6a0052920003a230f3294bbe784"},
diff --git a/pyproject.toml b/pyproject.toml
index c06108c..e3b89bd 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -19,3 +19,7 @@ jupyterlab = "^2.2.8"
 # Task runners
 nox = "^2020.8.22"
 pre-commit = "^2.7.1"
+
+# Live coding during presentation mode
+jupyter-contrib-nbextensions = "^0.5.1"
+rise = "^5.6.1"

From f4c4eace8de81bc81aba6919f04a8e30ba607827 Mon Sep 17 00:00:00 2001
From: Alexander Hess <alexander@webartifex.biz>
Date: Mon, 12 Oct 2020 22:51:13 +0200
Subject: [PATCH 122/242] Add section on presentation mode

---
 README.md                    |  24 ++++++++++++++++++++++++
 static/presentation_mode.png | Bin 0 -> 102326 bytes
 2 files changed, 24 insertions(+)
 create mode 100644 static/presentation_mode.png

diff --git a/README.md b/README.md
index 85f55ee..208a5bd 100644
--- a/README.md
+++ b/README.md
@@ -226,6 +226,30 @@ The command-line interface stays open in the background,
 <img src="static/cli_jupyter_lab.png" width="50%" align="center">
 
 
+#### Interactive Presentation Mode & Live Coding
+
+`poetry install` also installs the
+    [RISE <img height="12" style="display: inline-block" src="static/link/to_gh.png">](https://github.com/damianavila/RISE)
+    extension for Jupyter.
+With that, the instructor can execute code in *presentation* mode during a class session.
+However, the RISE extension does *not* work in the more recent
+    [JupyterLab](https://jupyterlab.readthedocs.io/en/stable/) app
+    but only in the older [Jupyter Notebook](https://jupyter-notebook.readthedocs.io/en/stable/) app,
+    which comes with less features and a simpler [GUI](https://en.wikipedia.org/wiki/Graphical_user_interface).
+The instructor can start the latter with:
+
+- `poetry run jupyter notebook`
+
+This also opens a new tab in the web browser.
+After opening a notebook,
+    clicking on the button highlighted below
+    starts the presentation mode.
+
+<img src="static/presentation_mode.png" width="50%" align="center">
+
+Not all notebooks are designed for this presentation mode.
+
+
 ## Contributing
 
 Feedback **is highly encouraged** and will be incorporated.
diff --git a/static/presentation_mode.png b/static/presentation_mode.png
new file mode 100644
index 0000000000000000000000000000000000000000..898d8e142b374ebca7074f90efa6971437e21764
GIT binary patch
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From 694eea7786686603de66fbe58bdec1a2be60f0d9 Mon Sep 17 00:00:00 2001
From: Alexander Hess <alexander@webartifex.biz>
Date: Mon, 12 Oct 2020 22:52:43 +0200
Subject: [PATCH 123/242] Streamline slides

---
 chapter_00_intro/00_content.ipynb | 43 ++++++-------------------------
 1 file changed, 8 insertions(+), 35 deletions(-)

diff --git a/chapter_00_intro/00_content.ipynb b/chapter_00_intro/00_content.ipynb
index b07b0c7..ae68a81 100644
--- a/chapter_00_intro/00_content.ipynb
+++ b/chapter_00_intro/00_content.ipynb
@@ -94,7 +94,7 @@
    "cell_type": "markdown",
    "metadata": {
     "slideshow": {
-     "slide_type": "skip"
+     "slide_type": "fragment"
     }
    },
    "source": [
@@ -168,7 +168,7 @@
    "cell_type": "markdown",
    "metadata": {
     "slideshow": {
-     "slide_type": "skip"
+     "slide_type": "fragment"
     }
    },
    "source": [
@@ -220,45 +220,26 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": null,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
     }
    },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "3"
-      ]
-     },
-     "execution_count": 1,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "1 + 2"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": null,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
     }
    },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Hello World\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "print(\"Hello World\")"
    ]
@@ -286,15 +267,7 @@
      "slide_type": "skip"
     }
    },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Python 3.8.6\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "!python --version"
    ]
@@ -540,7 +513,7 @@
    "cell_type": "markdown",
    "metadata": {
     "slideshow": {
-     "slide_type": "skip"
+     "slide_type": "-"
     }
    },
    "source": [

From 6c2cc48d3ee4680b94fe332bbaa9a43dcda38cee Mon Sep 17 00:00:00 2001
From: Alexander Hess <alexander@webartifex.biz>
Date: Tue, 13 Oct 2020 10:41:55 +0200
Subject: [PATCH 124/242] Re-order exercises and review

---
 README.md                                                 | 8 ++++----
 .../{02_exercises.ipynb => 01_exercises.ipynb}            | 0
 chapter_00_intro/{01_review.ipynb => 02_review.ipynb}     | 0
 3 files changed, 4 insertions(+), 4 deletions(-)
 rename chapter_00_intro/{02_exercises.ipynb => 01_exercises.ipynb} (100%)
 rename chapter_00_intro/{01_review.ipynb => 02_review.ipynb} (100%)

diff --git a/README.md b/README.md
index 208a5bd..a9cb8c8 100644
--- a/README.md
+++ b/README.md
@@ -20,11 +20,11 @@ They can be viewed in a web browser
    [<img height="12" style="display: inline-block" src="static/link/to_mb.png">](https://mybinder.org/v2/gh/webartifex/intro-to-python/develop?urlpath=lab/tree/chapter_00_intro/00_content.ipynb)
    [<img height="12" style="display: inline-block" src="static/link/to_yt.png">](https://www.youtube.com/watch?v=YTU8jaG27Xk&list=PL-2JV1G3J10lQ2xokyQowcRJI5jjNfW7f)
    |
-   [review <img height="12" style="display: inline-block" src="static/link/to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/chapter_00_intro/01_review.ipynb)
-   [<img height="12" style="display: inline-block" src="static/link/to_mb.png">](https://mybinder.org/v2/gh/webartifex/intro-to-python/develop?urlpath=lab/tree/chapter_00_intro/01_review.ipynb)
+   [exercises <img height="12" style="display: inline-block" src="static/link/to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/chapter_00_intro/01_exercises.ipynb)
+   [<img height="12" style="display: inline-block" src="static/link/to_mb.png">](https://mybinder.org/v2/gh/webartifex/intro-to-python/develop?urlpath=lab/tree/chapter_00_intro/01_exercises.ipynb)
    |
-   [exercises <img height="12" style="display: inline-block" src="static/link/to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/chapter_00_intro/02_exercises.ipynb)
-   [<img height="12" style="display: inline-block" src="static/link/to_mb.png">](https://mybinder.org/v2/gh/webartifex/intro-to-python/develop?urlpath=lab/tree/chapter_00_intro/02_exercises.ipynb)
+   [review <img height="12" style="display: inline-block" src="static/link/to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/chapter_00_intro/02_review.ipynb)
+   [<img height="12" style="display: inline-block" src="static/link/to_mb.png">](https://mybinder.org/v2/gh/webartifex/intro-to-python/develop?urlpath=lab/tree/chapter_00_intro/02_review.ipynb)
   )
 
 
diff --git a/chapter_00_intro/02_exercises.ipynb b/chapter_00_intro/01_exercises.ipynb
similarity index 100%
rename from chapter_00_intro/02_exercises.ipynb
rename to chapter_00_intro/01_exercises.ipynb
diff --git a/chapter_00_intro/01_review.ipynb b/chapter_00_intro/02_review.ipynb
similarity index 100%
rename from chapter_00_intro/01_review.ipynb
rename to chapter_00_intro/02_review.ipynb

From 92dde01e0c51bf98a891f6c1480b79d1b3bf91f7 Mon Sep 17 00:00:00 2001
From: Alexander Hess <alexander@webartifex.biz>
Date: Tue, 13 Oct 2020 11:02:29 +0200
Subject: [PATCH 125/242] Streamline text

---
 README.md | 7 +++++--
 1 file changed, 5 insertions(+), 2 deletions(-)

diff --git a/README.md b/README.md
index a9cb8c8..f0e0087 100644
--- a/README.md
+++ b/README.md
@@ -6,8 +6,11 @@ This project is a *thorough* introductory course
 
 ### Table of Contents
 
-The materials are designed to resemble a book.
-They can be viewed in a web browser
+The materials are designed to resemble an *interactive* book.
+It is recommended
+    to follow the [installation instructions](https://github.com/webartifex/intro-to-python#installation) below
+    and work through the content on one's own computer.
+Alternatively, the content can be viewed in a web browser
     either statically (i.e., read-only) on [nbviewer <img height="12" style="display: inline-block" src="static/link/to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/tree/develop/)
     or interactively (i.e., code can be executed) on [Binder <img height="12" style="display: inline-block" src="static/link/to_mb.png">](https://mybinder.org/v2/gh/webartifex/intro-to-python/develop?urlpath=lab).
 **Video** presentations on the content are available

From 2dfc9540b68072ee13ead399c60e648145299acb Mon Sep 17 00:00:00 2001
From: Alexander Hess <alexander@webartifex.biz>
Date: Tue, 13 Oct 2020 11:10:37 +0200
Subject: [PATCH 126/242] Make YouTube references less prominent

- the current video lectures regard the old one-notebook-per-chapter
  structure of the project
- as the new structure involves several smaller notebooks per chapter,
  there is no one-on-one correspondence to the YouTube lectures any more
=> only reference the YouTube playlist once in the README.md
---
 README.md                         |  12 ++--
 chapter_00_intro/00_content.ipynb | 106 ------------------------------
 2 files changed, 8 insertions(+), 110 deletions(-)

diff --git a/README.md b/README.md
index f0e0087..18df65d 100644
--- a/README.md
+++ b/README.md
@@ -13,15 +13,11 @@ It is recommended
 Alternatively, the content can be viewed in a web browser
     either statically (i.e., read-only) on [nbviewer <img height="12" style="display: inline-block" src="static/link/to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/tree/develop/)
     or interactively (i.e., code can be executed) on [Binder <img height="12" style="display: inline-block" src="static/link/to_mb.png">](https://mybinder.org/v2/gh/webartifex/intro-to-python/develop?urlpath=lab).
-**Video** presentations on the content are available
-    either via the individual links to [YouTube <img height="12" style="display: inline-block" src="static/link/to_yt.png">](https://www.youtube.com) below
-    or this [playlist <img height="12" style="display: inline-block" src="static/link/to_yt.png">](https://www.youtube.com/playlist?list=PL-2JV1G3J10lQ2xokyQowcRJI5jjNfW7f).
 
 - *Chapter 0*: Introduction
   (
    [content <img height="12" style="display: inline-block" src="static/link/to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/chapter_00_intro/00_content.ipynb)
    [<img height="12" style="display: inline-block" src="static/link/to_mb.png">](https://mybinder.org/v2/gh/webartifex/intro-to-python/develop?urlpath=lab/tree/chapter_00_intro/00_content.ipynb)
-   [<img height="12" style="display: inline-block" src="static/link/to_yt.png">](https://www.youtube.com/watch?v=YTU8jaG27Xk&list=PL-2JV1G3J10lQ2xokyQowcRJI5jjNfW7f)
    |
    [exercises <img height="12" style="display: inline-block" src="static/link/to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/chapter_00_intro/01_exercises.ipynb)
    [<img height="12" style="display: inline-block" src="static/link/to_mb.png">](https://mybinder.org/v2/gh/webartifex/intro-to-python/develop?urlpath=lab/tree/chapter_00_intro/01_exercises.ipynb)
@@ -31,6 +27,14 @@ Alternatively, the content can be viewed in a web browser
   )
 
 
+#### Videos
+
+Presentations of the chapters are available on this [YouTube playlist <img height="12" style="display: inline-block" src="static/link/to_yt.png">](https://www.youtube.com/playlist?list=PL-2JV1G3J10lQ2xokyQowcRJI5jjNfW7f).
+The recordings are about 25 hours long in total
+    and were made in spring 2020
+    after a corresponding in-class Bachelor course was cancelled due to Corona.
+
+
 ### Objective
 
 The **main goal** is to **prepare** students
diff --git a/chapter_00_intro/00_content.ipynb b/chapter_00_intro/00_content.ipynb
index ae68a81..bbc1dce 100644
--- a/chapter_00_intro/00_content.ipynb
+++ b/chapter_00_intro/00_content.ipynb
@@ -796,112 +796,6 @@
    "source": [
     "<img src=\"static/xkcd.png\" width=\"30%\">"
    ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "source": [
-    "## Further Resources"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "source": [
-    "A lecture-style **video presentation** of this chapter is integrated below (cf., the [video <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_yt.png\">](https://www.youtube.com/watch?v=YTU8jaG27Xk&list=PL-2JV1G3J10lQ2xokyQowcRJI5jjNfW7f) or the entire [playlist <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_yt.png\">](https://www.youtube.com/playlist?list=PL-2JV1G3J10lQ2xokyQowcRJI5jjNfW7f))."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/jpeg": 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-       "            width=\"60%\"\n",
-       "            height=\"300\"\n",
-       "            src=\"https://www.youtube.com/embed/YTU8jaG27Xk\"\n",
-       "            frameborder=\"0\"\n",
-       "            allowfullscreen\n",
-       "        ></iframe>\n",
-       "        "
-      ],
-      "text/plain": [
-       "<IPython.lib.display.YouTubeVideo at 0x7f70900dc7f0>"
-      ]
-     },
-     "execution_count": 5,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from IPython.display import YouTubeVideo\n",
-    "YouTubeVideo(\"YTU8jaG27Xk\", width=\"60%\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "source": [
-    "Also, to see some common shortcuts in [JupyterLab](https://jupyterlab.readthedocs.io/en/stable/), find a **video tutorial** below."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/jpeg": 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-       "            width=\"60%\"\n",
-       "            height=\"300\"\n",
-       "            src=\"https://www.youtube.com/embed/5GGyKq1F_5c\"\n",
-       "            frameborder=\"0\"\n",
-       "            allowfullscreen\n",
-       "        ></iframe>\n",
-       "        "
-      ],
-      "text/plain": [
-       "<IPython.lib.display.YouTubeVideo at 0x7f70900dcf10>"
-      ]
-     },
-     "execution_count": 6,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "YouTubeVideo(\"5GGyKq1F_5c\", width=\"60%\")"
-   ]
   }
  ],
  "metadata": {

From 4c7f7f4387911ba83c2c0458749f3c28ba9febb1 Mon Sep 17 00:00:00 2001
From: Alexander Hess <alexander@webartifex.biz>
Date: Tue, 13 Oct 2020 11:15:33 +0200
Subject: [PATCH 127/242] Store notebooks with execution output

---
 chapter_00_intro/00_content.ipynb | 37 ++++++++++++++++++++++++++-----
 1 file changed, 32 insertions(+), 5 deletions(-)

diff --git a/chapter_00_intro/00_content.ipynb b/chapter_00_intro/00_content.ipynb
index bbc1dce..0d52716 100644
--- a/chapter_00_intro/00_content.ipynb
+++ b/chapter_00_intro/00_content.ipynb
@@ -220,26 +220,45 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 1,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
     }
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "3"
+      ]
+     },
+     "execution_count": 1,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "1 + 2"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 2,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
     }
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Hello World\n"
+     ]
+    }
+   ],
    "source": [
     "print(\"Hello World\")"
    ]
@@ -267,7 +286,15 @@
      "slide_type": "skip"
     }
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Python 3.8.6\n"
+     ]
+    }
+   ],
    "source": [
     "!python --version"
    ]

From 76fcfc54eace1db03134bb7c09fbc2606069f2f9 Mon Sep 17 00:00:00 2001
From: Alexander Hess <alexander@webartifex.biz>
Date: Tue, 13 Oct 2020 12:05:50 +0200
Subject: [PATCH 128/242] Streamline text

---
 chapter_00_intro/01_exercises.ipynb | 2 +-
 chapter_00_intro/02_review.ipynb    | 2 +-
 2 files changed, 2 insertions(+), 2 deletions(-)

diff --git a/chapter_00_intro/01_exercises.ipynb b/chapter_00_intro/01_exercises.ipynb
index 28a2cbf..7f51a1d 100644
--- a/chapter_00_intro/01_exercises.ipynb
+++ b/chapter_00_intro/01_exercises.ipynb
@@ -25,7 +25,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "The exercises below assume that you have read [Chapter 0 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/chapter_00_intro/00_content.ipynb) in the book."
+    "The exercises below assume that you have read [Chapter 0 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/chapter_00_intro/00_content.ipynb)."
    ]
   },
   {
diff --git a/chapter_00_intro/02_review.ipynb b/chapter_00_intro/02_review.ipynb
index 2ea6b98..0f75bfe 100644
--- a/chapter_00_intro/02_review.ipynb
+++ b/chapter_00_intro/02_review.ipynb
@@ -18,7 +18,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "The questions below assume that you have read [Chapter 0 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/chapter_00_intro/00_content.ipynb) in the book.\n",
+    "The questions below assume that you have read [Chapter 0 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/chapter_00_intro/00_content.ipynb).\n",
     "\n",
     "Be concise in your answers! Most questions can be answered in *one* sentence."
    ]

From 0bb99a13b1f6a578d85323efc4df9908c84d0660 Mon Sep 17 00:00:00 2001
From: Alexander Hess <alexander@webartifex.biz>
Date: Tue, 13 Oct 2020 12:08:58 +0200
Subject: [PATCH 129/242] Add initial version of chapter 01, part 1

---
 README.md                            |    6 +
 chapter_00_intro/00_content.ipynb    |    2 +-
 chapter_01_elements/00_content.ipynb | 2188 ++++++++++++++++++++++++++
 3 files changed, 2195 insertions(+), 1 deletion(-)
 create mode 100644 chapter_01_elements/00_content.ipynb

diff --git a/README.md b/README.md
index 18df65d..de59194 100644
--- a/README.md
+++ b/README.md
@@ -25,6 +25,12 @@ Alternatively, the content can be viewed in a web browser
    [review <img height="12" style="display: inline-block" src="static/link/to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/chapter_00_intro/02_review.ipynb)
    [<img height="12" style="display: inline-block" src="static/link/to_mb.png">](https://mybinder.org/v2/gh/webartifex/intro-to-python/develop?urlpath=lab/tree/chapter_00_intro/02_review.ipynb)
   )
+- **Part A: Expressing Logic**
+  - *Chapter 1*: Elements of a Program
+    (
+     [content 1 <img height="12" style="display: inline-block" src="static/link/to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/chapter_01_elements/00_content.ipynb)
+     [<img height="12" style="display: inline-block" src="static/link/to_mb.png">](https://mybinder.org/v2/gh/webartifex/intro-to-python/develop?urlpath=lab/tree/chapter_01_elements/00_content.ipynb)
+    )
 
 
 #### Videos
diff --git a/chapter_00_intro/00_content.ipynb b/chapter_00_intro/00_content.ipynb
index 0d52716..fc6a255 100644
--- a/chapter_00_intro/00_content.ipynb
+++ b/chapter_00_intro/00_content.ipynb
@@ -750,7 +750,7 @@
     "**Part A: Expressing Logic**\n",
     "\n",
     "- What is a programming language? What kind of words exist?\n",
-    "  - *Chapter 1*: Elements of a Program\n",
+    "  - *Chapter 1*: [Elements of a Program <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/chapter_01_elements/00_content.ipynb)\n",
     "  - *Chapter 2*: Functions & Modularization\n",
     "- What is the flow of execution? How can we form sentences from words?\n",
     "  - *Chapter 3*: Conditionals & Exceptions\n",
diff --git a/chapter_01_elements/00_content.ipynb b/chapter_01_elements/00_content.ipynb
new file mode 100644
index 0000000..ad71aa9
--- /dev/null
+++ b/chapter_01_elements/00_content.ipynb
@@ -0,0 +1,2188 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "**Note**: Click on \"*Kernel*\" > \"*Restart Kernel and Clear All Outputs*\" in [JupyterLab](https://jupyterlab.readthedocs.io/en/stable/) *before* reading this notebook to reset its output. If you cannot run this file on your machine, you may want to open it [in the cloud <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_mb.png\">](https://mybinder.org/v2/gh/webartifex/intro-to-python/develop?urlpath=lab/tree/chapter_01_elements/00_content.ipynb)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "# Chapter 1: Elements of a Program"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Do you remember how you first learned to speak in your mother tongue? Probably not. No one's memory goes back that far. Your earliest memory as a child should probably be around the age of three or four years old when you could already say simple things and interact with your environment. Although you did not know any grammar rules yet, other people just understood what you said. At least most of the time.\n",
+    "\n",
+    "It is intuitively best to take the very mindset of a small child when learning a new language. And a programming language is no different from that. This first chapter introduces simplistic examples and we accept them as they are *without* knowing any of the \"grammar\" rules yet. Then, we analyze them in parts and slowly build up our understanding.\n",
+    "\n",
+    "Consequently, if parts of this chapter do not make sense right away, let's not worry too much. Besides introducing the basic elements, it also serves as an outlook for what is to come. So, many terms and concepts used here are deconstructed in great detail in the following chapters."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "## Example: Averaging all even Numbers in a List"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "As our introductory example, we want to calculate the *average* of all *evens* in a **list** of whole numbers: `[7, 11, 8, 5, 3, 12, 2, 6, 9, 10, 1, 4]`.\n",
+    "\n",
+    "While we are used to finding an [analytical solution](https://math.stackexchange.com/questions/935405/what-s-the-difference-between-analytical-and-numerical-approaches-to-problems/935446#935446) in math (i.e., derive some equation with \"pen and paper\"), we solve this task *programmatically* instead.\n",
+    "\n",
+    "We start by creating a list called `numbers` that holds all the individual numbers between **brackets** `[` and `]`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "scrolled": true,
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "numbers = [7, 11, 8, 5, 3, 12, 2, 6, 9, 10, 1, 4]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "To verify that something happened in our computer's memory, we **reference** `numbers`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[7, 11, 8, 5, 3, 12, 2, 6, 9, 10, 1, 4]"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "numbers"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "So far, so good. Let's see how the desired **computation** could be expressed as a **sequence of instructions** in the next code cell.\n",
+    "\n",
+    "Intuitively, the line `for number in numbers` describes a \"loop\" over all the numbers in the `numbers` list, one at a time.\n",
+    "\n",
+    "The `if number % 2 == 0` may look confusing at first sight. Both `%` and `==` must have an unintuitive meaning here. Luckily, the **comment** in the same line after the `#` symbol has the answer: The program does something only for an even `number`.\n",
+    "\n",
+    "In particular, it increases `count` by `1` and adds the current `number` onto the [running <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_wiki.png\">](https://en.wikipedia.org/wiki/Running_total) `total`. Both `count` and `number` are **initialized** to `0` and the single `=` symbol reads as \"... is *set* equal to ...\". It cannot indicate a mathematical equation as, for example, `count` is generally *not* equal to `count + 1`.\n",
+    "\n",
+    "Lastly, the `average` is calculated as the ratio of the final **values** of `total` and `count`. Overall, we divide the sum of all even numbers by their count: This is nothing but the definition of an average.\n",
+    "\n",
+    "The lines of code \"within\" the `for` and `if` **statements** are **indented** and aligned with multiples of *four spaces*: This shows immediately how the lines relate to each other."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "count = 0  # initialize variables to keep track of the\n",
+    "total = 0  # running total and the count of even numbers\n",
+    "\n",
+    "for number in numbers:\n",
+    "    if number % 2 == 0:  # only work with even numbers\n",
+    "        count = count + 1\n",
+    "        total = total + number\n",
+    "\n",
+    "average = total / count"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "We do not see any **output** yet but obtain the value of `average` by referencing it again."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "7.0"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "average"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "## Output in a Jupyter Notebook"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Only two of the previous four code cells generate an **output** while two remained \"silent\" (i.e., nothing appears below the cell after running it).\n",
+    "\n",
+    "By default, Jupyter notebooks only show the value of the **expression** in the last line of a code cell. And, this output may also be suppressed by ending the last line with a semicolon `;`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'I am feeling great :-)'"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "\"Hello, World!\"\n",
+    "\"I am feeling great :-)\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "\"I am invisible!\";"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "To see any output other than that, we use the built-in [print() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#print) **function**. Here, the parentheses `()` indicate that we **call** (i.e., \"execute\") code written somewhere else."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Hello, World!\n",
+      "I am feeling great :-)\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"Hello, World!\")\n",
+    "print(\"I am feeling great :-)\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Outside Jupyter notebooks, the semicolon `;` is used as a **separator** between statements that must otherwise be on a line on their own. However, it is *not* considered good practice to use it as it makes code less readable."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Hello, World!\n",
+      "I am feeling great :-)\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"Hello, World!\"); print(\"I am feeling great :-)\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "## (Arithmetic) Operators"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Python comes with many built-in **[operators <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/reference/lexical_analysis.html#operators)**: They are **tokens** (i.e., \"symbols\") that have a special meaning to the Python interpreter.\n",
+    "\n",
+    "The arithmetic operators either \"operate\" with the number immediately following them, so-called **unary** operators (e.g., negation), or \"process\" the two numbers \"around\" them, so-called **binary** operators (e.g., addition).\n",
+    "\n",
+    "By definition, operators on their own have *no* permanent **side effects** in the computer's memory. Although the code cells in this section do indeed create *new* numbers in memory (e.g., `77 + 13` creates `90`), they are immediately \"forgotten\" as they are not stored in a **variable** like `numbers` or `average` above. We develop this thought further in the second part of this chapter when we compare **expressions** with **statements**.\n",
+    "\n",
+    "Let's see some examples of operators. We start with the binary `+` and the `-` operators for addition and subtraction. Binary operators mimic what mathematicians call [infix notation <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_wiki.png\">](https://en.wikipedia.org/wiki/Infix_notation) and have the expected meaning."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "90"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "77 + 13"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "8"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "101 - 93"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "The `-` operator may be used as a unary operator as well. Then, it unsurprisingly flips the sign of a number."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "-1"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "-1"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "When we compare the output of the `*` and `/` operators for multiplication and division, we note the subtle *difference* between the `42` and the `42.0`: They are the *same* number represented as a *different* **data type**."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "42"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "2 * 21"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "42.0"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "84 / 2"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "The so-called **floor division operator** `//` always \"rounds\" to an integer and is thus also called **integer division operator**. It is an example of an arithmetic operator we commonly do not know from high school mathematics."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "42"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "84 // 2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "42"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "85 // 2"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Even though it appears that the `//` operator **truncates** (i.e., \"cuts off\") the decimals so as to effectively round down (i.e., the `42.5` became `42` in the previous code cell), this is *not* the case: The result is always \"rounded\" towards minus infinity!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "-43"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "-85 // 2"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "To obtain the remainder of a division, we use the **modulo operator** `%`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "1"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "85 % 2"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "The remainder is `0` *only if* a number is *divisible* by another.\n",
+    "\n",
+    "A popular convention in both computer science and mathematics is to abbreviate \"only if\" as \"iff\", which is short for \"**[if and only if <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_wiki.png\">](https://en.wikipedia.org/wiki/If_and_only_if)**.\" The iff means that a remainder of `0` implies that a number is divisible by another but also that a number's being divisible by another implies a remainder of `0`. The implication goes in *both* directions!\n",
+    "\n",
+    "So, `49` is divisible by `7`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "49 % 7"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Modulo division is also useful if we want to extract the last couple of digits in a large integer."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "9"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "789 % 10"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "89"
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "789 % 100"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "The built-in [divmod() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#divmod) function combines the integer and modulo divisions into one step. However, grammatically this is *not* an operator but a function. Also, [divmod() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#divmod) returns a \"pair\" of integers and not a single one."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(4, 2)"
+      ]
+     },
+     "execution_count": 21,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "divmod(42, 10)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Raising a number to a power is performed with the **exponentiation operator** `**`. It is different from the `^` operator other programming languages may use and that also exists in Python with a *different* meaning."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "8"
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "2 ** 3"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "The standard [order of precedence <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/reference/expressions.html#operator-precedence) from mathematics applies (i.e., [PEMDAS](http://mathworld.wolfram.com/PEMDAS.html) rule) when several operators are combined."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "18"
+      ]
+     },
+     "execution_count": 23,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "3 ** 2 * 2 "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Parentheses help avoid confusion and take the role of a **delimiter** here."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "18"
+      ]
+     },
+     "execution_count": 24,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "(3 ** 2) * 2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "81"
+      ]
+     },
+     "execution_count": 25,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "3 ** (2 * 2)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Some programmers also use \"style\" conventions. For example, we might play with the **whitespace**, which is an umbrella term that refers to any non-printable sign like spaces, tabs, or the like. However, this is *not* a good practice and parentheses convey a much clearer picture."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "18"
+      ]
+     },
+     "execution_count": 26,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "3**2 * 2  # bad style; it is better to use parentheses here"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "There exist many non-mathematical operators that are introduced throughout this book, together with the concepts they implement. They often come in a form different from the unary and binary ones mentioned above."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "### Operator Overloading"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Python **overloads** certain operators. For example, you may not only \"add\" numbers but also text: This is called **concatenation**."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "greeting = \"Hi \"\n",
+    "audience = \"class\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Hi class'"
+      ]
+     },
+     "execution_count": 28,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "greeting + audience"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Multiplying text with a whole number also works."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Hi Hi Hi Hi Hi Hi Hi Hi Hi Hi '"
+      ]
+     },
+     "execution_count": 29,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "10 * greeting"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "## Objects vs. Types vs. Values"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Python is a so-called **object-oriented** language, which is a paradigm of organizing a program's memory.\n",
+    "\n",
+    "An **object** may be viewed as a \"bag\" of $0$s and $1$s in a given memory location. The $0$s and $1$s in a bag make up the object's **value**. There exist different **types** of bags, and each type comes with its own rules how the $0$s and $1$s are interpreted and may be worked with.\n",
+    "\n",
+    "So, an object *always* has *three* main characteristics. Let's look at the following examples and work them out."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "a = 42\n",
+    "b = 42.0\n",
+    "c = \"Python rocks\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "### Identity / \"Memory Location\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "The built-in [id() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#id) function shows an object's \"address\" in memory."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "94371758832672"
+      ]
+     },
+     "execution_count": 31,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "id(a)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "140673826512720"
+      ]
+     },
+     "execution_count": 32,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "id(b)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "140673817995952"
+      ]
+     },
+     "execution_count": 33,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "id(c)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "These addresses are *not* meaningful for anything other than checking if two variables reference the *same* object.\n",
+    "\n",
+    "Obviously, `a` and `b`  have the same *value* as revealed by the **equality operator** `==`: We say `a` and `b` \"evaluate equal.\" The resulting `True` - and the `False` further below - is yet another data type, a so-called **boolean**. We look into them in [Chapter 3 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/chapter_03_conditionals/00_content.ipynb#Boolean-Expressions)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 34,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "a == b"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "On the contrary, `a` and `b` are *different* objects as the **identity operator** `is` shows: They are stored at *different* addresses in the memory."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "False"
+      ]
+     },
+     "execution_count": 35,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "a is b"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "If we want to check the opposite case, we use the negated version of the `is` operator, namely `is not`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 36,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "a is not b"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "### (Data) Type / \"Behavior\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "The [type() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#type) built-in shows an object's type. For example, `a` is an integer (i.e., `int`) while `b` is a so-called [floating-point number <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_wiki.png\">](https://en.wikipedia.org/wiki/Floating-point_arithmetic) (i.e., `float`)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "int"
+      ]
+     },
+     "execution_count": 37,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "type(a)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "float"
+      ]
+     },
+     "execution_count": 38,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "type(b)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Different types imply different behaviors for the objects. The `b` object, for example, may be \"asked\" if it is a whole number with the [is_integer() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/stdtypes.html#float.is_integer) \"functionality\" that comes with *every* `float` object.\n",
+    "\n",
+    "Formally, we call such type-specific functionalities **methods** (i.e., as opposed to functions) and we look at them in detail in [Chapter 10 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/chapter_10_classes/00_content.ipynb). For now, it suffices to know that we access them with the **dot operator** `.` on the object. Of course, `b` is a whole number, which the boolean object `True` tells us."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 39,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "b.is_integer()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "For an `int` object, this [is_integer() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/stdtypes.html#float.is_integer) check does *not* make sense as we already know it is an `int`: We see the `AttributeError` below as `a` does not even know what `is_integer()` means."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "ename": "AttributeError",
+     "evalue": "'int' object has no attribute 'is_integer'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-40-7db0a38aefcc>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0ma\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mis_integer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;31mAttributeError\u001b[0m: 'int' object has no attribute 'is_integer'"
+     ]
+    }
+   ],
+   "source": [
+    "a.is_integer()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "The `c` object is a so-called **string** type (i.e., `str`), which is Python's way of representing text. Strings also come with peculiar behaviors, for example, to make a text lower or upper case."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "str"
+      ]
+     },
+     "execution_count": 41,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "type(c)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'python rocks'"
+      ]
+     },
+     "execution_count": 42,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "c.lower()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'PYTHON ROCKS'"
+      ]
+     },
+     "execution_count": 43,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "c.upper()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "### Value / (Semantic) \"Meaning\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Almost trivially, every object also has a value to which it **evaluates** when referenced. We think of the value as the **conceptual idea** of what the $0$s and $1$s in the bag mean to *humans*. In other words, an object's value regards its *semantic* meaning.\n",
+    "\n",
+    "For built-in data types, Python prints out an object's value as a so-called **[literal <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/reference/lexical_analysis.html#literals)**: This means that we may copy and paste the value back into a code cell and create a *new* object with the *same* value."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "42"
+      ]
+     },
+     "execution_count": 44,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "a"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "42.0"
+      ]
+     },
+     "execution_count": 45,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "b"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "In this book, we follow the convention of creating strings with **double quotes** `\"` instead of the **single quotes** `'` to which Python defaults in its *literal* notation for `str` objects. Both types of quotes may be used interchangeably. So, the `\"Python rocks\"` from above and `'Python rocks'` below create two objects that evaluate equal (i.e., `\"Python rocks\" == 'Python rocks'`)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Python rocks'"
+      ]
+     },
+     "execution_count": 46,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "c"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "## Formal vs. Natural Languages"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Just like the language of mathematics is good at expressing relationships among numbers and symbols, any programming language is just a formal language that is good at expressing computations.\n",
+    "\n",
+    "Formal languages come with their own \"grammatical rules\" called **syntax**."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "### Syntax Errors"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "If we do not follow the rules, the code cannot be **parsed** correctly, i.e., the program does not even start to run but **raises** a **syntax error** indicated as `SyntaxError` in the output. Computers are very dumb in the sense that the slightest syntax error leads to the machine not understanding our code.\n",
+    "\n",
+    "If we were to write an accounting program that adds up currencies, we would, for example, have to model dollar prices as `float` objects as the dollar symbol cannot be understood by Python."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "ename": "SyntaxError",
+     "evalue": "invalid syntax (<ipython-input-47-cafa82e54b9c>, line 1)",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;36m  File \u001b[0;32m\"<ipython-input-47-cafa82e54b9c>\"\u001b[0;36m, line \u001b[0;32m1\u001b[0m\n\u001b[0;31m    3.99 $ + 10.40 $\u001b[0m\n\u001b[0m         ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m invalid syntax\n"
+     ]
+    }
+   ],
+   "source": [
+    "3.99 $ + 10.40 $"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Python requires certain symbols at certain places (e.g., a `:` is missing here)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 48,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "ename": "SyntaxError",
+     "evalue": "invalid syntax (<ipython-input-48-499e4d0d0cbb>, line 1)",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;36m  File \u001b[0;32m\"<ipython-input-48-499e4d0d0cbb>\"\u001b[0;36m, line \u001b[0;32m1\u001b[0m\n\u001b[0;31m    for number in numbers\u001b[0m\n\u001b[0m                         ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m invalid syntax\n"
+     ]
+    }
+   ],
+   "source": [
+    "for number in numbers\n",
+    "    print(number)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Furthermore, it relies on whitespace (i.e., indentation), unlike many other programming languages. The `IndentationError` below is just a particular type of a `SyntaxError`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 49,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "ename": "IndentationError",
+     "evalue": "expected an indented block (<ipython-input-49-19398c5f89de>, line 2)",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;36m  File \u001b[0;32m\"<ipython-input-49-19398c5f89de>\"\u001b[0;36m, line \u001b[0;32m2\u001b[0m\n\u001b[0;31m    print(number)\u001b[0m\n\u001b[0m    ^\u001b[0m\n\u001b[0;31mIndentationError\u001b[0m\u001b[0;31m:\u001b[0m expected an indented block\n"
+     ]
+    }
+   ],
+   "source": [
+    "for number in numbers:\n",
+    "print(number)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "### Runtime Errors"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Syntax errors are easy to find as the code does *not* even run in the first place.\n",
+    "\n",
+    "However, there are also so-called **runtime errors** that occur whenever otherwise (i.e., syntactically) correct code does not run because of invalid input. Runtime errors are also often referred to as **exceptions**.\n",
+    "\n",
+    "This example does not work because just like in the \"real\" world, Python does not know how to divide by `0`. The syntactically correct code leads to a `ZeroDivisionError`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 50,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "ename": "ZeroDivisionError",
+     "evalue": "division by zero",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mZeroDivisionError\u001b[0m                         Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-50-bc757c3fda29>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0;36m1\u001b[0m \u001b[0;34m/\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;31mZeroDivisionError\u001b[0m: division by zero"
+     ]
+    }
+   ],
+   "source": [
+    "1 / 0"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "### Semantic Errors"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "So-called **semantic errors**, on the contrary, are hard to spot as they do *not* crash the program. The only way to find such errors is to run a program with test input for which we can predict the output. However, testing software is a whole discipline on its own and often very hard to do in practice.\n",
+    "\n",
+    "The cell below copies our first example from above with a \"tiny\" error. How fast could you have spotted it without the comment?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 51,
+   "metadata": {
+    "code_folding": [],
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "count = 0\n",
+    "total = 0\n",
+    "\n",
+    "for number in numbers:\n",
+    "    if number % 2 == 0:\n",
+    "        count = count + 1\n",
+    "        total = total + count  # count is wrong here, it should be number\n",
+    "\n",
+    "average = total / count"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 52,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "3.5"
+      ]
+     },
+     "execution_count": 52,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "average"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Systematically finding errors is called **debugging**. For the history of the term, see this [article <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_wiki.png\">](https://en.wikipedia.org/wiki/Debugging)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "## Best Practices"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Thus, adhering to just syntax rules is *never* enough. Over time, **best practices** and **style guides** were created to make it less likely for a developer to mess up a program and also to allow \"onboarding\" him as a contributor to an established code base, often called **legacy code**, faster. These rules are *not* enforced by Python itself: Badly styled code still runs. At the very least, Python programs should be styled according to [PEP 8 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://www.python.org/dev/peps/pep-0008/) and documented \"inline\" (i.e., in the code itself) according to [PEP 257 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://www.python.org/dev/peps/pep-0257/).\n",
+    "\n",
+    "An easier to read version of PEP 8 is [here](https://pep8.org/). The video below features a well known **[Pythonista](https://en.wiktionary.org/wiki/Pythonista)** talking about the importance of code style."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 53,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/jpeg": 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\n",
+      "text/html": [
+       "\n",
+       "        <iframe\n",
+       "            width=\"60%\"\n",
+       "            height=\"300\"\n",
+       "            src=\"https://www.youtube.com/embed/Hwckt4J96dI\"\n",
+       "            frameborder=\"0\"\n",
+       "            allowfullscreen\n",
+       "        ></iframe>\n",
+       "        "
+      ],
+      "text/plain": [
+       "<IPython.lib.display.YouTubeVideo at 0x7ff12d761280>"
+      ]
+     },
+     "execution_count": 53,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from IPython.display import YouTubeVideo\n",
+    "YouTubeVideo(\"Hwckt4J96dI\", width=\"60%\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "For example, while the above code to calculate the average of the even numbers in `[7, 11, 8, 5, 3, 12, 2, 6, 9, 10, 1, 4]` is correct, a Pythonista would rewrite it in a more \"Pythonic\" way and use the built-in [sum() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#sum) and [len() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#len) functions (cf., [Chapter 2 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/chapter_02_functions/00_content.ipynb#Built-in-Functions)) as well as a so-called **list comprehension** (cf., [Chapter 8 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/chapter_08_mfr/00_content.ipynb#List-Comprehensions)). Pythonic code runs faster in many cases and is less error-prone."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 54,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "numbers = [7, 11, 8, 5, 3, 12, 2, 6, 9, 10, 1, 4]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 55,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "evens = [n for n in numbers if n % 2 == 0]  # use of a list comprehension"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 56,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[8, 12, 2, 6, 10, 4]"
+      ]
+     },
+     "execution_count": 56,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "evens"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 57,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "average = sum(evens) / len(evens)  # use built-in functions"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 58,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "7.0"
+      ]
+     },
+     "execution_count": 58,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "average"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "To get a rough overview of the mindset of a typical Python programmer, look at these rules, also known as the **Zen of Python**, that are deemed so important that they are included in every Python installation."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 59,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The Zen of Python, by Tim Peters\n",
+      "\n",
+      "Beautiful is better than ugly.\n",
+      "Explicit is better than implicit.\n",
+      "Simple is better than complex.\n",
+      "Complex is better than complicated.\n",
+      "Flat is better than nested.\n",
+      "Sparse is better than dense.\n",
+      "Readability counts.\n",
+      "Special cases aren't special enough to break the rules.\n",
+      "Although practicality beats purity.\n",
+      "Errors should never pass silently.\n",
+      "Unless explicitly silenced.\n",
+      "In the face of ambiguity, refuse the temptation to guess.\n",
+      "There should be one-- and preferably only one --obvious way to do it.\n",
+      "Although that way may not be obvious at first unless you're Dutch.\n",
+      "Now is better than never.\n",
+      "Although never is often better than *right* now.\n",
+      "If the implementation is hard to explain, it's a bad idea.\n",
+      "If the implementation is easy to explain, it may be a good idea.\n",
+      "Namespaces are one honking great idea -- let's do more of those!\n"
+     ]
+    }
+   ],
+   "source": [
+    "import this"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "### Jupyter Notebook Aspects"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "#### The Order of Code Cells is arbitrary"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "We can run the code cells in a Jupyter notebook in *any* arbitrary order.\n",
+    "\n",
+    "That means, for example, that a variable defined towards the bottom could accidentally be referenced at the top of the notebook. This happens quickly when we iteratively built a program and go back and forth between cells.\n",
+    "\n",
+    "As a good practice, it is recommended to click on \"Kernel\" > \"Restart Kernel and Run All Cells\" in the navigation bar once a notebook is finished. That restarts the Python process forgetting all **state** (i.e., all variables) and ensures that the notebook runs top to bottom without any errors the next time it is opened."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "#### Notebooks are linear"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "While this book is built with Jupyter notebooks, it is crucial to understand that \"real\" programs are almost never \"linear\" (i.e., top to bottom) sequences of instructions but instead may take many different **flows of execution**.\n",
+    "\n",
+    "At the same time, for a beginner's course, it is often easier to code linearly.\n",
+    "\n",
+    "In real data science projects, one would probably employ a mixed approach and put reusable code into so-called Python modules (i.e., *.py* files; cf., [Chapter 2 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/chapter_02_functions/00_content.ipynb#Local-Modules-and-Packages)) and then use Jupyter notebooks to build up a linear report or storyline for an analysis."
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.6"
+  },
+  "livereveal": {
+   "auto_select": "code",
+   "auto_select_fragment": true,
+   "scroll": true,
+   "theme": "serif"
+  },
+  "toc": {
+   "base_numbering": 1,
+   "nav_menu": {},
+   "number_sections": false,
+   "sideBar": true,
+   "skip_h1_title": true,
+   "title_cell": "Table of Contents",
+   "title_sidebar": "Contents",
+   "toc_cell": false,
+   "toc_position": {
+    "height": "calc(100% - 180px)",
+    "left": "10px",
+    "top": "150px",
+    "width": "303.333px"
+   },
+   "toc_section_display": false,
+   "toc_window_display": false
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}

From 263565f8a129f3240ad146c4c1d4e13f1ccf5aa6 Mon Sep 17 00:00:00 2001
From: Alexander Hess <alexander@webartifex.biz>
Date: Tue, 13 Oct 2020 12:26:31 +0200
Subject: [PATCH 130/242] Add initial version of chapter 01's exercises, part 1

---
 README.md                              |   3 +
 chapter_01_elements/01_exercises.ipynb | 336 +++++++++++++++++++++++++
 2 files changed, 339 insertions(+)
 create mode 100644 chapter_01_elements/01_exercises.ipynb

diff --git a/README.md b/README.md
index de59194..51b053e 100644
--- a/README.md
+++ b/README.md
@@ -30,6 +30,9 @@ Alternatively, the content can be viewed in a web browser
     (
      [content 1 <img height="12" style="display: inline-block" src="static/link/to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/chapter_01_elements/00_content.ipynb)
      [<img height="12" style="display: inline-block" src="static/link/to_mb.png">](https://mybinder.org/v2/gh/webartifex/intro-to-python/develop?urlpath=lab/tree/chapter_01_elements/00_content.ipynb)
+     |
+     [exercises 1 <img height="12" style="display: inline-block" src="static/link/to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/chapter_01_elements/01_exercises.ipynb)
+     [<img height="12" style="display: inline-block" src="static/link/to_mb.png">](https://mybinder.org/v2/gh/webartifex/intro-to-python/develop?urlpath=lab/tree/chapter_01_elements/01_exercises.ipynb)
     )
 
 
diff --git a/chapter_01_elements/01_exercises.ipynb b/chapter_01_elements/01_exercises.ipynb
new file mode 100644
index 0000000..6e057b5
--- /dev/null
+++ b/chapter_01_elements/01_exercises.ipynb
@@ -0,0 +1,336 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Note**: Click on \"*Kernel*\" > \"*Restart Kernel and Run All*\" in [JupyterLab](https://jupyterlab.readthedocs.io/en/stable/) *after* finishing the exercises to ensure that your solution runs top to bottom *without* any errors. If you cannot run this file on your machine, you may want to open it [in the cloud <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_mb.png\">](https://mybinder.org/v2/gh/webartifex/intro-to-python/develop?urlpath=lab/tree/chapter_01_elements/01_exercises.ipynb)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Chapter 1: Elements of a Program"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Coding Exercises"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The exercises below assume that you have read the first part of [Chapter 1 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/chapter_01_elements/00_content.ipynb).\n",
+    "\n",
+    "The `...`'s in the code cells indicate where you need to fill in code snippets. The number of `...`'s within a code cell give you a rough idea of how many lines of code are needed to solve the task. You should not need to create any additional code cells for your final solution. However, you may want to use temporary code cells to try out some ideas."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### **Q1**: Printing Output"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q1.1**: *Concatenate* `greeting` and `audience` below with the `+` operator and print out the resulting message `\"Hello World\"` with only *one* call of the built-in [print() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#print) function!\n",
+    "\n",
+    "Hint: You may have to \"add\" a space character in between `greeting` and `audience`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "greeting = \"Hello\"\n",
+    "audience = \"World\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "print(...)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q1.2**: How is your answer to **Q1.1** an example of the concept of **operator overloading**?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " < your answer >"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q1.3**: Read the documentation on the built-in [print() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#print) function! How can you print the above message *without* concatenating `greeting` and `audience` first in *one* call of [print() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#print)?\n",
+    "\n",
+    "Hint: The `*objects` in the documentation implies that we can put several *expressions* (i.e., variables) separated by commas within the same call of the [print() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#print) function."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "print(...)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q1.4**: What does the `sep=\" \"` mean in the documentation on the built-in [print() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#print) function? Adjust and use it to print out the three names referenced by `first`, `second`, and `third` on *one* line separated by *commas* with only *one* call of the [print() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#print) function!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "first = \"Anthony\"\n",
+    "second = \"Berta\"\n",
+    "third = \"Christian\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "print(...)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q1.5**: Lastly, what does the `end=\"\\n\"` mean in the documentation? Adjust and use it within the `for`-loop to print the numbers `1` through `10` on *one* line with only *one* call of the [print() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#print) function!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "for number in [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]:\n",
+    "    print(...)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### **Q2**: Simple `for`-loops"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "`for`-loops are extremely versatile in Python. That is different from many other programming languages.\n",
+    "\n",
+    "As shown in the first example in [Chapter 1 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/chapter_01_elements/00_content.ipynb#Example:-Averaging-all-even-Numbers-in-a-List), we can create a `list` like `numbers` and loop over the numbers in it on a one-by-one basis."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "numbers = [7, 11, 8, 5, 3, 12, 2, 6, 9, 10, 1, 4]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q2.1**: Fill in the *condition* of the `if` statement such that only numbers divisible by `3` are printed! Adjust the call of the [print() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#print) function such that the `for`-loop prints out all the numbers on *one* line of output!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "for number in numbers:\n",
+    "    if ...:\n",
+    "        print(...)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Instead of looping over an *existing* object referenced by a variable like `numbers`, we may also create a *new* object within the `for` statement and loop over it directly. For example, below we write out the `list` object as a *literal*.\n",
+    "\n",
+    "Generically, the objects contained in a `list` objects are referred to as its **elements**. We reflect that in the name of the *target* variable `element` that is assigned a different number in every iteration of the `for`-loop. While we could use *any* syntactically valid name, it is best to choose one that makes sense in the context (e.g., `number` in `numbers`).\n",
+    "\n",
+    "**Q2.2**: Fill in the condition of the `if` statement such that only numbers consisting of *one* digit are printed out! As before, print out all the numbers on *one* line of output!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "for element in [7, 11, 8, 5, 3, 12, 2, 6, 9, 10, 1, 4]:\n",
+    "    if ...:\n",
+    "        print(...)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "An easy way to loop over a `list` object in a sorted manner, is to wrap it with the built-in [sorted() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#sorted) function.\n",
+    "\n",
+    "**Q2.3**: Fill in the condition of the `if` statement such that only odd numbers are printed out! Put all the numbers on *one* line of output!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "for number in sorted(numbers):\n",
+    "    if ...:\n",
+    "        print(...)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Whenever we want to loop over numbers representing a [series <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_wiki.png\">](https://en.wikipedia.org/wiki/Series_%28mathematics%29) in the mathematical sense (i.e., a rule to calculate the next number from its predecessor), we may be able to use the [range() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#func-range) built-in.\n",
+    "\n",
+    "For example, to loop over the whole numbers from `0` to `9` (both including) in order, we could write them out in a `list` like below.\n",
+    "\n",
+    "**Q2.4**: Fill in the call to the [print() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#print) function such that all the numbers are printed on *one* line ouf output!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "for number in [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]:\n",
+    "    print(...)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q2.5**: Read the documentation on the [range() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#func-range) built-in! It may be used with either one, two, or three expressions \"passed\" in. What do `start`, `stop`, and `step` mean? Fill in the calls to [range() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#func-range) and [print() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#print) to mimic the output of **Q2.4**!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "for number in range(...):\n",
+    "    print(...)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q2.6**: Fill in the calls to [range() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#func-range) and [print() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#print) to print out *all* numbers from `1` to `10` (both including)!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "for number in range(...):\n",
+    "    print(...)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q2.7**: Fill in the calls to [range() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#func-range) and [print() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#print) to print out the *even* numbers from `1` to `10` (both including)! Do *not* use an `if` statement to accomplish this!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "for number in range(...):\n",
+    "    print(...)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.6"
+  },
+  "toc": {
+   "base_numbering": 1,
+   "nav_menu": {},
+   "number_sections": false,
+   "sideBar": true,
+   "skip_h1_title": true,
+   "title_cell": "Table of Contents",
+   "title_sidebar": "Contents",
+   "toc_cell": false,
+   "toc_position": {},
+   "toc_section_display": false,
+   "toc_window_display": false
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}

From abeedd1265a5f88a4f5d2b9c78e370ca296fdcde Mon Sep 17 00:00:00 2001
From: Alexander Hess <alexander@webartifex.biz>
Date: Tue, 13 Oct 2020 15:10:15 +0200
Subject: [PATCH 131/242] Add initial version of chapter 01, part 2

---
 README.md                            |    3 +
 chapter_00_intro/00_content.ipynb    |    2 +-
 chapter_01_elements/02_content.ipynb | 1665 ++++++++++++++++++++++++++
 3 files changed, 1669 insertions(+), 1 deletion(-)
 create mode 100644 chapter_01_elements/02_content.ipynb

diff --git a/README.md b/README.md
index 51b053e..ef3422e 100644
--- a/README.md
+++ b/README.md
@@ -33,6 +33,9 @@ Alternatively, the content can be viewed in a web browser
      |
      [exercises 1 <img height="12" style="display: inline-block" src="static/link/to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/chapter_01_elements/01_exercises.ipynb)
      [<img height="12" style="display: inline-block" src="static/link/to_mb.png">](https://mybinder.org/v2/gh/webartifex/intro-to-python/develop?urlpath=lab/tree/chapter_01_elements/01_exercises.ipynb)
+     |
+     [content 2 <img height="12" style="display: inline-block" src="static/link/to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/chapter_01_elements/02_content.ipynb)
+     [<img height="12" style="display: inline-block" src="static/link/to_mb.png">](https://mybinder.org/v2/gh/webartifex/intro-to-python/develop?urlpath=lab/tree/chapter_01_elements/02_content.ipynb)
     )
 
 
diff --git a/chapter_00_intro/00_content.ipynb b/chapter_00_intro/00_content.ipynb
index fc6a255..219fbf3 100644
--- a/chapter_00_intro/00_content.ipynb
+++ b/chapter_00_intro/00_content.ipynb
@@ -496,7 +496,7 @@
     }
    },
    "source": [
-    "The default Python implementation is written in the C language.\n",
+    "The default Python implementation is written in the C language and called CPython. This is also what the Anaconda Distribution uses.\n",
     "\n",
     "[C <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_wiki.png\">](https://en.wikipedia.org/wiki/C_%28programming_language%29) and [C++ <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_wiki.png\">](https://en.wikipedia.org/wiki/C%2B%2B) (cf., this [introduction](https://www.learncpp.com/)) are wide-spread and long-established (i.e., since the 1970s) programming languages employed in many mission-critical software systems (e.g., operating systems themselves, low latency databases and web servers, nuclear reactor control systems, airplanes, ...). They are fast, mainly because the programmer not only needs to come up with the **business logic** but also manage the computer's memory.\n",
     "\n",
diff --git a/chapter_01_elements/02_content.ipynb b/chapter_01_elements/02_content.ipynb
new file mode 100644
index 0000000..ae9459c
--- /dev/null
+++ b/chapter_01_elements/02_content.ipynb
@@ -0,0 +1,1665 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "**Note**: Click on \"*Kernel*\" > \"*Restart Kernel and Clear All Outputs*\" in [JupyterLab](https://jupyterlab.readthedocs.io/en/stable/) *before* reading this notebook to reset its output. If you cannot run this file on your machine, you may want to open it [in the cloud <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_mb.png\">](https://mybinder.org/v2/gh/webartifex/intro-to-python/develop?urlpath=lab/tree/chapter_01_elements/02_content.ipynb)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "# Chapter 1: Elements of a Program (Part 2)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In this second part of Chapter 1, we look a bit closer into how the memory works and introduce a couple of \"theoretical\" terms."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "## Variables vs. Names vs. Identifiers vs. References"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "**Variables** are created with the **[assignment statement <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/reference/simple_stmts.html#assignment-statements)** `=`, which is *not* an operator because of its *side effect* of making a **[name <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/reference/lexical_analysis.html#identifiers)** reference an object in memory.\n",
+    "\n",
+    "We read the terms **variable**, **name**, and **identifier** used interchangebly in many Python-related texts. In this book, we adopt the following convention: First, we treat *name* and *identifier* as perfect synonyms but only use the term *name* in the text for clarity. Second, whereas *name* only refers to a string of letters, numbers, and some other symbols, a *variable* means the combination of a *name* and a *reference* to an object in memory."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "variable = 20.0"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "When used as a *literal*, a variable evaluates to the value of the object it references. Colloquially, we could say that `variable` evaluates to `20.0`, but this would not be an accurate description of what is going on in memory. We see some more colloquialisms in this section but should always relate this to what Python actually does in memory."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "20.0"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "variable"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "A variable may be **re-assigned** as often as we wish. Thereby, we could also assign an object of a *different* type. Because this is allowed, Python is said to be a **dynamically typed** language. On the contrary, a **statically typed** language like C also allows re-assignment but only with objects of the *same* type. This subtle distinction is one reason why Python is slower at execution than C: As it runs a program, it needs to figure out an object's type each time it is referenced."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "variable = 20"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "20"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "variable"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "If we want to re-assign a variable while referencing its \"old\" (i.e., current) object, we may also **update** it using a so-called **[augmented assignment statement <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/reference/simple_stmts.html#augmented-assignment-statements)** (i.e., *not* operator), as introduced with [PEP 203 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://www.python.org/dev/peps/pep-0203/): The currently mapped object is implicitly inserted as the first operand on the right-hand side."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "variable *= 4  # same as variable = variable * 4"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "80"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "variable"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "variable //= 2  # same as variable = variable // 2; \"//\" to retain the integer type"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "40"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "variable"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "variable += 2  # same as variable = variable + 2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "42"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "variable"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Variables are **[dereferenced <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/reference/simple_stmts.html#the-del-statement)** (i.e., \"deleted\") with the `del` statement. This does *not* delete the object a variable references but merely removes the variable's name from the \"global list of all names.\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "42"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "variable"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "del variable"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "If we refer to an unknown name, a *runtime* error occurs, namely a `NameError`. The `Name` in `NameError` gives a hint why we choose the term *name* over *identifier* above: Python uses it more often in its error messages."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "ename": "NameError",
+     "evalue": "name 'variable' is not defined",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-13-1748287bc46a>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mvariable\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;31mNameError\u001b[0m: name 'variable' is not defined"
+     ]
+    }
+   ],
+   "source": [
+    "variable"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Some variables magically exist when a Python process is started or are added by Jupyter. We may safely ignore the former until [Chapter 11 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/chapter_11_classes/00_content.ipynb) and the latter for good."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'__main__'"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "__name__"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "To see all defined names, the built-in function [dir() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#dir) is helpful."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['In',\n",
+       " 'Out',\n",
+       " '_',\n",
+       " '_10',\n",
+       " '_11',\n",
+       " '_14',\n",
+       " '_2',\n",
+       " '_4',\n",
+       " '_6',\n",
+       " '_8',\n",
+       " '__',\n",
+       " '___',\n",
+       " '__builtin__',\n",
+       " '__builtins__',\n",
+       " '__doc__',\n",
+       " '__loader__',\n",
+       " '__name__',\n",
+       " '__package__',\n",
+       " '__spec__',\n",
+       " '_dh',\n",
+       " '_i',\n",
+       " '_i1',\n",
+       " '_i10',\n",
+       " '_i11',\n",
+       " '_i12',\n",
+       " '_i13',\n",
+       " '_i14',\n",
+       " '_i15',\n",
+       " '_i2',\n",
+       " '_i3',\n",
+       " '_i4',\n",
+       " '_i5',\n",
+       " '_i6',\n",
+       " '_i7',\n",
+       " '_i8',\n",
+       " '_i9',\n",
+       " '_ih',\n",
+       " '_ii',\n",
+       " '_iii',\n",
+       " '_oh',\n",
+       " 'exit',\n",
+       " 'get_ipython',\n",
+       " 'quit',\n",
+       " 'register_readline_completion',\n",
+       " 'sys']"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "dir()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "### Naming Conventions"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "[Phil Karlton](https://skeptics.stackexchange.com/questions/19836/has-phil-karlton-ever-said-there-are-only-two-hard-things-in-computer-science) famously noted during his time at [Netscape <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_wiki.png\">](https://en.wikipedia.org/wiki/Netscape):\n",
+    "\n",
+    "> \"There are *two* hard problems in computer science: *naming things* and *cache invalidation* ... and *off-by-one* errors.\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Variable names may contain upper and lower case letters, numbers, and underscores (i.e., `_`) and be as long as we want them to be. However, they must not begin with a number. Also, they must not be any of Python's built-in **[keywords <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/reference/lexical_analysis.html#keywords)** like `for` or `if`.\n",
+    "\n",
+    "Variable names should be chosen such that they do not need any more documentation and are self-explanatory. A widespread convention is to use so-called **[snake\\_case <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_wiki.png\">](https://en.wikipedia.org/wiki/Snake_case)**: Keep everything lowercase and use underscores to separate words.\n",
+    "\n",
+    "See this [link <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_wiki.png\">](https://en.wikipedia.org/wiki/Naming_convention_%28programming%29#Python_and_Ruby) for a comparison of different naming conventions."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "#### Good examples"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "pi = 3.14"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "answer_to_everything = 42"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "my_name = \"Alexander\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "work_address = \"WHU, Burgplatz 2, Vallendar\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "#### Bad examples"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "PI = 3.14  # unless used as a \"global\" constant"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "answerToEverything = 42  # this is a style used in languages like Java"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "name = \"Alexander\"  # name of what?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "ename": "SyntaxError",
+     "evalue": "cannot assign to operator (<ipython-input-23-ec51dae29567>, line 1)",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;36m  File \u001b[0;32m\"<ipython-input-23-ec51dae29567>\"\u001b[0;36m, line \u001b[0;32m1\u001b[0m\n\u001b[0;31m    address@work = \"WHU, Burgplatz 2, Vallendar\"\u001b[0m\n\u001b[0m    ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m cannot assign to operator\n"
+     ]
+    }
+   ],
+   "source": [
+    "address@work = \"WHU, Burgplatz 2, Vallendar\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "If a variable name collides with a built-in name, we add a trailing underscore."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "type_ = \"student\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Variables with leading and trailing double underscores, referred to as **dunder** in Python jargon, are used for built-in functionalities and to implement object-oriented features as we see in [Chapter 11 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/chapter_11_classes/00_content.ipynb). We must *not* use this style for variables!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'__main__'"
+      ]
+     },
+     "execution_count": 25,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "__name__"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "## Who am I? And how many?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "It is *crucial* to understand that *several* variables may reference the *same* object in memory. Not having this in mind may lead to many hard to track down bugs.\n",
+    "\n",
+    "Let's make `b` reference whatever object `a` is referencing."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "a = 42"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "b = a"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "42"
+      ]
+     },
+     "execution_count": 28,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "b"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "For \"simple\" types like `int` or `float` this never causes troubles.\n",
+    "\n",
+    "Let's \"change the value\" of `a`. To be precise, let's create a *new* `87` object and make `a` reference it."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "a = 87"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "87"
+      ]
+     },
+     "execution_count": 30,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "a"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "`b` \"is still the same\" as before. To be precise, `b` still references the *same object* as before."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "42"
+      ]
+     },
+     "execution_count": 31,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "b"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "However, if a variable references an object of a more \"complex\" type (e.g., `list`), predicting the outcome of a code snippet may be unintuitive for a beginner."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "x = [1, 2, 3]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "list"
+      ]
+     },
+     "execution_count": 33,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "type(x)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "y = x"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[1, 2, 3]"
+      ]
+     },
+     "execution_count": 35,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "y"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Let's change the first element of `x`.\n",
+    "\n",
+    "[Chapter 7 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/chapter_07_sequences/00_content.ipynb#The-list-Type) discusses lists in more depth. For now, let's view a `list` object as some sort of **container** that holds an arbitrary number of references to other objects and treat the brackets `[]` attached to it as yet another operator, namely the **indexing operator**. So, `x[0]` instructs Python to first follow the reference from the global list of all names to the `x` object. Then, it follows the first reference it finds there to the `1` object we put in the list. The indexing operator must be an operator as we merely *read* the first element and do not change anything in memory permanently.\n",
+    "\n",
+    "Python **begins counting at 0**. This is not the case for many other languages, for example, [MATLAB <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_wiki.png\">](https://en.wikipedia.org/wiki/MATLAB), [R <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_wiki.png\">](https://en.wikipedia.org/wiki/R_%28programming_language%29), or [Stata <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_wiki.png\">](https://en.wikipedia.org/wiki/Stata). To understand why this makes sense, see this short [note](https://www.cs.utexas.edu/users/EWD/transcriptions/EWD08xx/EWD831.html) by one of the all-time greats in computer science, the late [Edsger Dijkstra <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_wiki.png\">](https://en.wikipedia.org/wiki/Edsger_W._Dijkstra)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "1"
+      ]
+     },
+     "execution_count": 36,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "x[0]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "To change the first entry in the list, we use the assignment statement `=` again. Here, this does *not* create a *new* variable, nor overwrite an existing one, but only changes the object referenced as the first element in `x`. As we only change parts of the `x` object, we say that we **mutate** its **state**. To use the bag analogy from above, we keep the same bag but \"flip\" some of the $0$s into $1$s and some of the $1$s into $0$s."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "x[0] = 99"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[99, 2, 3]"
+      ]
+     },
+     "execution_count": 38,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "x"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "The changes made to the object `x` is referencing can also be seen through the `y` variable!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[99, 2, 3]"
+      ]
+     },
+     "execution_count": 39,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "y"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "The difference in behavior illustrated in this sub-section has to do with the fact that `int` and `float` objects are **immutable** types while `list` objects are **mutable**.\n",
+    "\n",
+    "In the first case, an object cannot be changed \"in place\" once it is created in memory. When we assigned `87` to the already existing `a`, we did *not* change the $0$s and $1$s in the object `a` referenced before the assignment but created a new `int` object and made `a` reference it while the `b` variable is *not* affected.\n",
+    "\n",
+    "In the second case, `x[0] = 99` creates a *new* `int` object `99` and merely changes the first reference in the `x` list.\n",
+    "\n",
+    "In general, the assignment statement creates a new name and makes it reference whatever object is on the right-hand side *iff* the left-hand side is a *pure* name (i.e., it contains no operators like the indexing operator in the example). Otherwise, it *mutates* an already existing object. And, we must always expect that the latter may have more than one variable referencing it.\n",
+    "\n",
+    "Visualizing what is going on in memory with a tool like [PythonTutor <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](http://pythontutor.com/visualize.html#code=x%20%3D%20%5B1,%202,%203%5D%0Ay%20%3D%20x%0Ax%5B0%5D%20%3D%2099%0Aprint%28y%5B0%5D%29&cumulative=false&curInstr=0&heapPrimitives=nevernest&mode=display&origin=opt-frontend.js&py=3&rawInputLstJSON=%5B%5D&textReferences=false) may be helpful for a beginner."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## How Python reads \"Commands\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "As we saw in the previous `list` example, it is important to understand in what order Python executes the \"commands\" (= not an officially used term) we give it. In this last section of the chapter, we introduce a classification scheme and look at its implications."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "### Expressions"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "An **[expression <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/reference/expressions.html)** is any syntactically correct *combination* of *variables* and *literals* with *operators* that **evaluates** (i.e., \"becomes\") to an object. That object may already exist *before* the expression is parsed or created as a result thereof. The point is that *after* the expression is parsed, Python returns a reference to this object that may be used for further processing.\n",
+    "\n",
+    "In simple words, anything that may be used on the *right-hand* side of an assignment statement without creating a `SyntaxError` is an expression.\n",
+    "\n",
+    "What we have said about *individual* operators before, namely that they have *no* permanent side effects in memory, actually belongs here, to begin with: The absence of any *permanent* side effects is the characteristic property of expressions, and all the code cells in the \"*(Arithmetic) Operators*\" section in the [first part <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/chapter_01_elements/00_content.ipynb#%28Arithmetic%29-Operators) of this chapter are examples of expressions.\n",
+    "\n",
+    "The simplest possible expressions contain only one variable or literal. The output below a code cell is Jupyter's way of returning the reference to the object to us!\n",
+    "\n",
+    "Whereas `a` evaluates to the *existing* `87` object, ..."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "87"
+      ]
+     },
+     "execution_count": 40,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "a"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "... parsing the literal `42` creates a *new* `int` object and returns a reference to it (Note: for optimization reasons, the CPython implementation may already have a `42` object in memory)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "42"
+      ]
+     },
+     "execution_count": 41,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "42"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "For sure, we need to include operators to achieve something useful. Here, Python takes the existing `a` object, creates a new `42` object, creates the resulting `45` object, and returns a reference to that."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "45"
+      ]
+     },
+     "execution_count": 42,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "a - 42"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "The definition of an expression is **recursive**. So, the sub-expression `a - 42` is combined with the literal `9` by the operator `//` to form the full expression `(a - 42) // 9`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "5"
+      ]
+     },
+     "execution_count": 43,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "(a - 42) // 9"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Below, the variable `x` is combined with the literal `2` by the indexing operator `[]`. The resulting expression evaluates to the third element in the `x` list."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "3"
+      ]
+     },
+     "execution_count": 44,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "x[2]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "When not used as a delimiter, parentheses also constitute an operator, namely the **call operator** `()`. We saw this syntax before when we called built-in functions and methods."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "104"
+      ]
+     },
+     "execution_count": 45,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "sum(x)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "### Statements"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "A **[statement <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/reference/simple_stmts.html)** is any *single* command Python understands.\n",
+    "\n",
+    "So, what we mean with statement is a [strict superset <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_wiki.png\">](https://en.wikipedia.org/wiki/Subset) of what we mean with expression: All expressions are statements, but some statements are *not* expressions. That implies that all code cells in the previous \"*Expressions*\" section are also examples of statements.\n",
+    "\n",
+    "Unfortunately, many texts on Python use the two terms as mutually exclusive concepts by saying that a statement (always) *changes* the *state of a program* with a permanent *side effect* in memory (e.g., by creating a variable) whereas an expression does not. That is not an accurate way of distinguishing the two in all cases!\n",
+    "\n",
+    "So, often, the term statement is used in the meaning of \"a statement that is *not* an expression.\" We can identify such statements easily in Jupyter as the correspoding code cells have *no* output below them.\n",
+    "\n",
+    "While many statements, for example `=` and `del`, indeed have *permanent* side effects, ..."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "a = 21 * 2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "del a"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "... calling the built-in [print() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#print) function does *neither* change the memory *nor* evaluate to an object (disregarding the `None` object explained in [Chapter 2 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/chapter_02_functions/00_content.ipynb#Function-Definitions)). We could view changing the computer's screen as a side effect but this is outside of Python's memory!\n",
+    "\n",
+    "Also, the cell below has *no* output! It only looks like it does as Jupyter redirects whatever [print() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#print) writes to the \"screen\" to below a cell. We see a difference to the expressions above in that there are no brackets `[...]` next to the output showing the execution count number."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 48,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Don't I change the state of the computer's display?\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"Don't I change the state of the computer's display?\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Logical vs. Physical Lines"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "How many lines of code does the next cell constitute?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 49,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The answer is: 42\n"
+     ]
+    }
+   ],
+   "source": [
+    "result = 21 * 2; print(\"The answer is:\", result)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The answer depends on how we are counting. If we count the number of lines of written source code, the answer is *one*. This gives us the number of **physical lines**. On the contrary, if we count the number of statements separated by the `;`, the answer is *two*. This gives us the number of **logical lines**.\n",
+    "\n",
+    "While physical lines are what we, the humans, see, logical lines are how computers read. To align the two ways, it is a best practice to not write more than one logical lines on a single physical one. So, from Python's point of view, the cells above and below are the same. More importantly, the one above is *not* somehow \"magically\" faster."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 50,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The answer is: 42\n"
+     ]
+    }
+   ],
+   "source": [
+    "result = 21 * 2\n",
+    "print(\"The answer is:\", result)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "It is also possible to write a single logical line over several physical ones. The cell below is yet another physical representation of the same *two* logical lines. Any pair of delimiters, like `(` and `)` below, can be used to \"format\" the code in between with whitespace. The style guides mentioned before should still be taken into account (i.e., indent with 4 spaces)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 51,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The answer is: 42\n"
+     ]
+    }
+   ],
+   "source": [
+    "result = 21 * 2\n",
+    "print(\n",
+    "    \"The answer is:\",\n",
+    "    result\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Another situation, in which several physical lines are treated as a logical one, is with so-called **[compound statements <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/reference/compound_stmts.html)**. In contrast to simple statements like `=` and `del` above, the purpose of compound statements is to group other statements.\n",
+    "\n",
+    "We have seen two examples of compound statements already: `for` and `if`. They both consist of a **header** line ending with a `:` and an indented **(code) block** spanning an arbitrary number of logical lines.\n",
+    "\n",
+    "In the example, the first logical line spans the entire cell, the second logical line contains all physical lines except the first, and the third and fourth logical lines are just the third and fourth physical lines, respectively."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 52,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "2 -> 4\n"
+     ]
+    }
+   ],
+   "source": [
+    "for number in [1, 2, 3]:\n",
+    "    if number % 2 == 0:\n",
+    "        double = 2 * number \n",
+    "        print(number, \"->\", double)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "### Comments"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "A comment is a physical line that is *not* a logical line.\n",
+    "\n",
+    "We use the `#` symbol to write comments in plain text right into the code. Anything after the `#` until the end of the physical line is ignored by Python.\n",
+    "\n",
+    "As a good practice, comments should *not* describe *what* happens. This should be evident by reading the code. Otherwise, it is most likely badly written code. Rather, comments should describe *why* something happens.\n",
+    "\n",
+    "Comments may be added either at the end of a line of code, by convention separated with two spaces, or on a line on their own."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 53,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "distance = 891  # in meters\n",
+    "elapsed_time = 93  # in seconds\n",
+    "\n",
+    "# Calculate the speed in km/h.\n",
+    "speed = 3.6 * distance / elapsed_time"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "But let's think wisely if we need to use a comment.\n",
+    "The second cell is a lot more Pythonic."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 54,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "seconds = 365 * 24 * 60 * 60  # = seconds in the year"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 55,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "seconds_per_year = 365 * 24 * 60 * 60"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.6"
+  },
+  "livereveal": {
+   "auto_select": "code",
+   "auto_select_fragment": true,
+   "scroll": true,
+   "theme": "serif"
+  },
+  "toc": {
+   "base_numbering": 1,
+   "nav_menu": {},
+   "number_sections": false,
+   "sideBar": true,
+   "skip_h1_title": true,
+   "title_cell": "Table of Contents",
+   "title_sidebar": "Contents",
+   "toc_cell": false,
+   "toc_position": {
+    "height": "calc(100% - 180px)",
+    "left": "10px",
+    "top": "150px",
+    "width": "303.333px"
+   },
+   "toc_section_display": false,
+   "toc_window_display": false
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}

From 5163c34d534e33d46e2bddcabe13a220f0118a5f Mon Sep 17 00:00:00 2001
From: Alexander Hess <alexander@webartifex.biz>
Date: Tue, 13 Oct 2020 15:19:55 +0200
Subject: [PATCH 132/242] Add initial version of chapter 01's exercises, part 2

---
 README.md                              |   3 +
 chapter_01_elements/03_exercises.ipynb | 219 +++++++++++++++++++++++++
 2 files changed, 222 insertions(+)
 create mode 100644 chapter_01_elements/03_exercises.ipynb

diff --git a/README.md b/README.md
index ef3422e..07873b2 100644
--- a/README.md
+++ b/README.md
@@ -36,6 +36,9 @@ Alternatively, the content can be viewed in a web browser
      |
      [content 2 <img height="12" style="display: inline-block" src="static/link/to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/chapter_01_elements/02_content.ipynb)
      [<img height="12" style="display: inline-block" src="static/link/to_mb.png">](https://mybinder.org/v2/gh/webartifex/intro-to-python/develop?urlpath=lab/tree/chapter_01_elements/02_content.ipynb)
+     |
+     [exercises 2 <img height="12" style="display: inline-block" src="static/link/to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/chapter_01_elements/03_exercises.ipynb)
+     [<img height="12" style="display: inline-block" src="static/link/to_mb.png">](https://mybinder.org/v2/gh/webartifex/intro-to-python/develop?urlpath=lab/tree/chapter_01_elements/03_exercises.ipynb)
     )
 
 
diff --git a/chapter_01_elements/03_exercises.ipynb b/chapter_01_elements/03_exercises.ipynb
new file mode 100644
index 0000000..d9d0bbf
--- /dev/null
+++ b/chapter_01_elements/03_exercises.ipynb
@@ -0,0 +1,219 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Note**: Click on \"*Kernel*\" > \"*Restart Kernel and Run All*\" in [JupyterLab](https://jupyterlab.readthedocs.io/en/stable/) *after* finishing the exercises to ensure that your solution runs top to bottom *without* any errors. If you cannot run this file on your machine, you may want to open it [in the cloud <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_mb.png\">](https://mybinder.org/v2/gh/webartifex/intro-to-python/develop?urlpath=lab/tree/chapter_01_elements/03_exercises.ipynb)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Chapter 1: Elements of a Program (Part 2)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Coding Exercises"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The exercises below assume that you have read the second part of [Chapter 1 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/chapter_01_elements/02_content.ipynb).\n",
+    "\n",
+    "The `...`'s in the code cells indicate where you need to fill in code snippets. The number of `...`'s within a code cell give you a rough idea of how many lines of code are needed to solve the task. You should not need to create any additional code cells for your final solution. However, you may want to use temporary code cells to try out some ideas."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Python as a Calculator"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The [volume of a sphere <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_wiki.png\">](https://en.wikipedia.org/wiki/Sphere) is defined as $\\frac{4}{3} * \\pi * r^3$.\n",
+    "\n",
+    "**Q1**: Calculate it for `r = 2.88` and approximate $\\pi$ with `pi = 3.14`!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "pi = 3.14\n",
+    "r = 2.88"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "..."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "While Python may be used as a calculator, it behaves a bit differently compared to calculator apps that phones or computers come with and that we are accustomed to.\n",
+    "\n",
+    "A major difference is that Python \"forgets\" intermediate results that are not assigned to variables. On the contrary, the calculators we work with outside of programming always keep the last result and allow us to use it as the first input for the next calculation.\n",
+    "\n",
+    "One way to keep on working with intermediate results in Python is to write the entire calculation as just *one* big expression that is composed of many sub-expressions representing the individual steps in our overall calculation.\n",
+    "\n",
+    "**Q2.1**: Given `a` and `b` like below, subtract the smaller `a` from the larger `b`, divide the difference by `9`, and raise the result to the power of `2`! Use operators that preserve the `int` type of the final result! The entire calculations *must* be placed within *one* code cell.\n",
+    "\n",
+    "Hint: You may need to group sub-expressions with parentheses `(` and `)`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "a = 42\n",
+    "b = 87"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "..."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The code cell below contains nothing but a single underscore `_`. In both, a Python command-line prompt and Jupyter notebooks, the variable `_` is automatically updated and always references the object to which the *last* expression executed evaluated to.\n",
+    "\n",
+    "**Q2.2**: Execute the code cell below! It should evaluate to the *same* result as the previous code cell (i.e., your answer to **Q2.1** assuming you go through this notebook in order)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "_"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q2.3**: Implement the same overall calculation as in your answer to **Q2.1** in several independent steps (i.e., code cells)! Use only *one* operator per code cell!\n",
+    "\n",
+    "Hint: You should need *two* more code cells after the `b - a` one immediately below. If you *need* to use parentheses, you must be doing something wrong."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "b - a"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "_ ..."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "_ ..."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q3.1**: When answering the questions above, you should have used only **expressions** in the code cells. What are expressions syntactically?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " < your answer >"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q3.2**: The code cells that provide the numbers to work with contain **statements** that are *not* expressions. What are statements? How are they different from expressions?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " < your answer >"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.6"
+  },
+  "toc": {
+   "base_numbering": 1,
+   "nav_menu": {},
+   "number_sections": false,
+   "sideBar": true,
+   "skip_h1_title": true,
+   "title_cell": "Table of Contents",
+   "title_sidebar": "Contents",
+   "toc_cell": false,
+   "toc_position": {},
+   "toc_section_display": false,
+   "toc_window_display": false
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}

From 777da3568d7a539023cf909e1113f0c7558e2dd3 Mon Sep 17 00:00:00 2001
From: Alexander Hess <alexander@webartifex.biz>
Date: Tue, 13 Oct 2020 15:24:21 +0200
Subject: [PATCH 133/242] Add initial version of chapter 01's summary

---
 README.md                            |   2 +
 chapter_01_elements/04_summary.ipynb | 154 +++++++++++++++++++++++++++
 2 files changed, 156 insertions(+)
 create mode 100644 chapter_01_elements/04_summary.ipynb

diff --git a/README.md b/README.md
index 07873b2..425e21f 100644
--- a/README.md
+++ b/README.md
@@ -39,6 +39,8 @@ Alternatively, the content can be viewed in a web browser
      |
      [exercises 2 <img height="12" style="display: inline-block" src="static/link/to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/chapter_01_elements/03_exercises.ipynb)
      [<img height="12" style="display: inline-block" src="static/link/to_mb.png">](https://mybinder.org/v2/gh/webartifex/intro-to-python/develop?urlpath=lab/tree/chapter_01_elements/03_exercises.ipynb)
+     |
+     [summary <img height="12" style="display: inline-block" src="static/link/to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/chapter_01_elements/04_summary.ipynb)
     )
 
 
diff --git a/chapter_01_elements/04_summary.ipynb b/chapter_01_elements/04_summary.ipynb
new file mode 100644
index 0000000..9f52d19
--- /dev/null
+++ b/chapter_01_elements/04_summary.ipynb
@@ -0,0 +1,154 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "\n",
+    "# Chapter 1: Elements of a Program"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "## TL;DR"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "We end each chapter with a summary of the main points (i.e., **TL;DR** = \"too long; didn't read\"). The essence in this first chapter is that just as a sentence in a real language like English may be decomposed into its parts (e.g., subject, predicate, and objects), the same may be done with programming languages.\n",
+    "\n",
+    "- program\n",
+    "  - **sequence** of **instructions** that specify how to perform a computation (= a \"recipe\")\n",
+    "  - a \"black box\" that processes **inputs** and transforms them into meaningful **outputs** in a *deterministic* way\n",
+    "  - conceptually similar to a mathematical function $f$ that maps some input $x$ to an output $y = f(x)$\n",
+    "\n",
+    "\n",
+    "- input (examples)\n",
+    "  - data from a CSV file\n",
+    "  - text entered on a command line\n",
+    "  - data obtained from a database\n",
+    "  - etc.\n",
+    "\n",
+    "\n",
+    "- output (examples)\n",
+    "  - result of a computation (e.g., statistical summary of a sample dataset)\n",
+    "  - a \"side effect\" (e.g., a transformation of raw input data into cleaned data)\n",
+    "  - a physical \"behavior\" (e.g., a robot moving or a document printed)\n",
+    "  - etc.\n",
+    "\n",
+    "\n",
+    "- objects\n",
+    "  - distinct and well-contained areas/parts of the memory that hold the actual data\n",
+    "  - the concept by which Python manages the memory for us\n",
+    "  - can be classified into objects of the same **type** (i.e., same abstract \"structure\" but different concrete data)\n",
+    "  - built-in objects (incl. **literals**) vs. user-defined objects (cf., [Chapter 11 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/chapter_11_classes/00_content.ipynb))\n",
+    "  - e.g., `1`, `1.0`, and `\"one\"` are three different objects of distinct types that are also literals (i.e., by the way we type them into the command line Python knows what the value and type are)\n",
+    "\n",
+    "\n",
+    "- variables\n",
+    "  - storage of intermediate **state**\n",
+    "  - are **names** referencing **objects** in **memory**\n",
+    "  - e.g., `x = 1` creates the variable `x` that references the object `1`\n",
+    "\n",
+    "\n",
+    "- operators\n",
+    "  - special built-in symbols that perform operations with objects in memory\n",
+    "  - usually, operate with one or two objects\n",
+    "  - e.g., addition `+`, subtraction `-`, multiplication `*`, and division `/` all take two objects, whereas the negation `-` only takes one\n",
+    "\n",
+    "\n",
+    "- expressions\n",
+    "  - **combinations** of **variables** (incl. **literals**) and **operators**\n",
+    "  - do *not* change the involved objects/state of the program\n",
+    "  - evaluate to a **value** (i.e., the \"result\" of the expression, usually a new object)\n",
+    "  - e.g., `x + 2` evaluates to the (new) object `3` and `1 - 1.0` to `0.0`\n",
+    "\n",
+    "\n",
+    "- statements\n",
+    "  - instructions that **\"do\" something** and **have side effects** in memory\n",
+    "  - (re-)assign names to (different) objects, *change* an existing object *in place*, or, more conceptually, *change* the state of the program\n",
+    "  - usually, work with expressions\n",
+    "  - e.g., the assignment statement `=` makes a name reference an object\n",
+    "\n",
+    "\n",
+    "- comments\n",
+    "  - **prose** supporting a **human's understanding** of the program\n",
+    "  - ignored by Python\n",
+    "\n",
+    "\n",
+    "- functions (cf., [Chapter 2 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/chapter_02_functions/00_content.ipynb))\n",
+    "  - named sequences of instructions\n",
+    "  - the smaller parts in a larger program\n",
+    "  - make a program more modular and thus easier to understand\n",
+    "  - include [built-in functions <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html) like [print() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#print), [sum() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#sum), or [len() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#len)\n",
+    "\n",
+    "\n",
+    "- flow control (cf., [Chapter 3 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/chapter_03_conditionals/00_content.ipynb) and [Chapter 4 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/chapter_04_iteration/00_content.ipynb))\n",
+    "  - expression of **business logic** or an **algorithm**\n",
+    "  - conditional execution of parts of a program (e.g., `if` statements)\n",
+    "  - repetitive execution of parts of a program (e.g., `for`-loops)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.6"
+  },
+  "livereveal": {
+   "auto_select": "code",
+   "auto_select_fragment": true,
+   "scroll": true,
+   "theme": "serif"
+  },
+  "toc": {
+   "base_numbering": 1,
+   "nav_menu": {},
+   "number_sections": false,
+   "sideBar": true,
+   "skip_h1_title": true,
+   "title_cell": "Table of Contents",
+   "title_sidebar": "Contents",
+   "toc_cell": false,
+   "toc_position": {
+    "height": "calc(100% - 180px)",
+    "left": "10px",
+    "top": "150px",
+    "width": "303.333px"
+   },
+   "toc_section_display": false,
+   "toc_window_display": false
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}

From 6fb318d13aaae4c0c454bffa0d2297a9bc803f62 Mon Sep 17 00:00:00 2001
From: Alexander Hess <alexander@webartifex.biz>
Date: Tue, 13 Oct 2020 15:25:22 +0200
Subject: [PATCH 134/242] Add initial version of chapter 01's review

---
 README.md                           |   2 +
 chapter_01_elements/05_review.ipynb | 222 ++++++++++++++++++++++++++++
 2 files changed, 224 insertions(+)
 create mode 100644 chapter_01_elements/05_review.ipynb

diff --git a/README.md b/README.md
index 425e21f..d090fdb 100644
--- a/README.md
+++ b/README.md
@@ -41,6 +41,8 @@ Alternatively, the content can be viewed in a web browser
      [<img height="12" style="display: inline-block" src="static/link/to_mb.png">](https://mybinder.org/v2/gh/webartifex/intro-to-python/develop?urlpath=lab/tree/chapter_01_elements/03_exercises.ipynb)
      |
      [summary <img height="12" style="display: inline-block" src="static/link/to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/chapter_01_elements/04_summary.ipynb)
+     |
+     [review <img height="12" style="display: inline-block" src="static/link/to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/chapter_01_elements/05_review.ipynb)
     )
 
 
diff --git a/chapter_01_elements/05_review.ipynb b/chapter_01_elements/05_review.ipynb
new file mode 100644
index 0000000..d8ec024
--- /dev/null
+++ b/chapter_01_elements/05_review.ipynb
@@ -0,0 +1,222 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Chapter 1: Elements of a Program"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Content Review"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The questions below assume that you have read the [first <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/chapter_01_elements/00_content.ipynb) and the [second <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/chapter_01_elements/02_content.ipynb) part of Chapter 1.\n",
+    "\n",
+    "Be concise in your answers! Most questions can be answered in *one* sentence."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Essay Questions "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q1**: Elaborate on how **modulo division** might be a handy operation to know!"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " < your answer >"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q2**: What is a **dynamically typed** language? How does it differ from a **statically typed** language? What does that mean for Python?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " < your answer >"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q3**: Why is it useful to start counting at $0$?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " < your answer >"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q4**: What is **operator overloading**?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " < your answer >"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q5**: What are the basic **naming conventions** for variables? What happens if a name collides with one of Python's [keywords <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/reference/lexical_analysis.html#keywords)?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " < your answer >"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q6**: Advocates of the [functional programming <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_wiki.png\">](https://en.wikipedia.org/wiki/Functional_programming) paradigm suggest not to use **mutable** data types in a program. What are the advantages of that approach? What might be a downside?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " < your answer >"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### True / False Questions"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Motivate your answer with *one short* sentence!"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q7**: \"**dunder**\" refers to a group of Australian (i.e., \"down under\") geeks that work on core Python."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " < your answer >"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q8**: The **Zen of Python** talks about Indian genius programmers."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " < your answer >"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q9**: When NASA famously converted some measurements to the wrong unit and lost a Mars satellite in 1999 (cf., [source](https://www.wired.com/2010/11/1110mars-climate-observer-report/)), this is an example of a so-called **runtime error**."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " < your answer >"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q10**: [PEP 8 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://www.python.org/dev/peps/pep-0008/) suggests that developers use **8 spaces** per level of indentation."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " < your answer >"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.6"
+  },
+  "toc": {
+   "base_numbering": 1,
+   "nav_menu": {},
+   "number_sections": false,
+   "sideBar": true,
+   "skip_h1_title": true,
+   "title_cell": "Table of Contents",
+   "title_sidebar": "Contents",
+   "toc_cell": false,
+   "toc_position": {},
+   "toc_section_display": false,
+   "toc_window_display": false
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}

From ba9addd56272790b2599dbb59bbe0837d1fd3917 Mon Sep 17 00:00:00 2001
From: Alexander Hess <alexander@webartifex.biz>
Date: Tue, 13 Oct 2020 15:26:18 +0200
Subject: [PATCH 135/242] Chapter reviews are not interactive

---
 README.md | 1 -
 1 file changed, 1 deletion(-)

diff --git a/README.md b/README.md
index d090fdb..ff4e0b7 100644
--- a/README.md
+++ b/README.md
@@ -23,7 +23,6 @@ Alternatively, the content can be viewed in a web browser
    [<img height="12" style="display: inline-block" src="static/link/to_mb.png">](https://mybinder.org/v2/gh/webartifex/intro-to-python/develop?urlpath=lab/tree/chapter_00_intro/01_exercises.ipynb)
    |
    [review <img height="12" style="display: inline-block" src="static/link/to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/chapter_00_intro/02_review.ipynb)
-   [<img height="12" style="display: inline-block" src="static/link/to_mb.png">](https://mybinder.org/v2/gh/webartifex/intro-to-python/develop?urlpath=lab/tree/chapter_00_intro/02_review.ipynb)
   )
 - **Part A: Expressing Logic**
   - *Chapter 1*: Elements of a Program

From 5b6fd7122fc8625c725ca71f17957d0caf3a9416 Mon Sep 17 00:00:00 2001
From: Alexander Hess <alexander@webartifex.biz>
Date: Tue, 13 Oct 2020 15:28:02 +0200
Subject: [PATCH 136/242] Add initial version of chapter 01's further resources

---
 README.md                              |   3 +
 chapter_01_elements/06_resources.ipynb | 120 +++++++++++++++++++++++++
 2 files changed, 123 insertions(+)
 create mode 100644 chapter_01_elements/06_resources.ipynb

diff --git a/README.md b/README.md
index ff4e0b7..0c4e26d 100644
--- a/README.md
+++ b/README.md
@@ -42,6 +42,9 @@ Alternatively, the content can be viewed in a web browser
      [summary <img height="12" style="display: inline-block" src="static/link/to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/chapter_01_elements/04_summary.ipynb)
      |
      [review <img height="12" style="display: inline-block" src="static/link/to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/chapter_01_elements/05_review.ipynb)
+     |
+     [resources <img height="12" style="display: inline-block" src="static/link/to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/chapter_01_elements/06_resources.ipynb)
+     [<img height="12" style="display: inline-block" src="static/link/to_mb.png">](https://mybinder.org/v2/gh/webartifex/intro-to-python/develop?urlpath=lab/tree/chapter_01_elements/06_resources.ipynb)
     )
 
 
diff --git a/chapter_01_elements/06_resources.ipynb b/chapter_01_elements/06_resources.ipynb
new file mode 100644
index 0000000..bdc2e29
--- /dev/null
+++ b/chapter_01_elements/06_resources.ipynb
@@ -0,0 +1,120 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "\n",
+    "# Chapter 1: Elements of a Program"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "## Further Resources"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "This PyCon 2015 talk by [Ned Batchelder](https://nedbatchelder.com/), a well-known Pythonista and the organizer of the [Python User Group](https://www.meetup.com/bostonpython/) in Boston, summarizes all situations where some sort of assignment is done in Python and, thus, reviews how *variables are just names referencing objects*. The content is intermediate, and, thus, it might be worthwhile to come back to this talk at a later point in time. However, the contents should be known by everyone claiming to be proficient in Python."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
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+      "text/html": [
+       "\n",
+       "        <iframe\n",
+       "            width=\"60%\"\n",
+       "            height=\"300\"\n",
+       "            src=\"https://www.youtube.com/embed/_AEJHKGk9ns\"\n",
+       "            frameborder=\"0\"\n",
+       "            allowfullscreen\n",
+       "        ></iframe>\n",
+       "        "
+      ],
+      "text/plain": [
+       "<IPython.lib.display.YouTubeVideo at 0x7fa2fc050d60>"
+      ]
+     },
+     "execution_count": 1,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from IPython.display import YouTubeVideo\n",
+    "YouTubeVideo(\"_AEJHKGk9ns\", width=\"60%\")"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.6"
+  },
+  "livereveal": {
+   "auto_select": "code",
+   "auto_select_fragment": true,
+   "scroll": true,
+   "theme": "serif"
+  },
+  "toc": {
+   "base_numbering": 1,
+   "nav_menu": {},
+   "number_sections": false,
+   "sideBar": true,
+   "skip_h1_title": true,
+   "title_cell": "Table of Contents",
+   "title_sidebar": "Contents",
+   "toc_cell": false,
+   "toc_position": {
+    "height": "calc(100% - 180px)",
+    "left": "10px",
+    "top": "150px",
+    "width": "303.333px"
+   },
+   "toc_section_display": false,
+   "toc_window_display": false
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}

From 138bc12945589801cbfe22ca1f28b48b659f4814 Mon Sep 17 00:00:00 2001
From: Alexander Hess <alexander@webartifex.biz>
Date: Tue, 13 Oct 2020 15:36:35 +0200
Subject: [PATCH 137/242] Streamline slides

---
 chapter_01_elements/02_content.ipynb | 80 +++++++++++++++++++++++-----
 1 file changed, 66 insertions(+), 14 deletions(-)

diff --git a/chapter_01_elements/02_content.ipynb b/chapter_01_elements/02_content.ipynb
index ae9459c..ea72a59 100644
--- a/chapter_01_elements/02_content.ipynb
+++ b/chapter_01_elements/02_content.ipynb
@@ -24,7 +24,11 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
    "source": [
     "In this second part of Chapter 1, we look a bit closer into how the memory works and introduce a couple of \"theoretical\" terms."
    ]
@@ -1089,14 +1093,22 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
    "source": [
     "## How Python reads \"Commands\""
    ]
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
    "source": [
     "As we saw in the previous `list` example, it is important to understand in what order Python executes the \"commands\" (= not an officially used term) we give it. In this last section of the chapter, we introduce a classification scheme and look at its implications."
    ]
@@ -1157,7 +1169,11 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
    "source": [
     "... parsing the literal `42` creates a *new* `int` object and returns a reference to it (Note: for optimization reasons, the CPython implementation may already have a `42` object in memory)."
    ]
@@ -1400,7 +1416,7 @@
    "execution_count": 48,
    "metadata": {
     "slideshow": {
-     "slide_type": "skip"
+     "slide_type": "fragment"
     }
    },
    "outputs": [
@@ -1418,14 +1434,22 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
    "source": [
     "### Logical vs. Physical Lines"
    ]
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
    "source": [
     "How many lines of code does the next cell constitute?"
    ]
@@ -1433,7 +1457,11 @@
   {
    "cell_type": "code",
    "execution_count": 49,
-   "metadata": {},
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
    "outputs": [
     {
      "name": "stdout",
@@ -1449,7 +1477,11 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
    "source": [
     "The answer depends on how we are counting. If we count the number of lines of written source code, the answer is *one*. This gives us the number of **physical lines**. On the contrary, if we count the number of statements separated by the `;`, the answer is *two*. This gives us the number of **logical lines**.\n",
     "\n",
@@ -1459,7 +1491,11 @@
   {
    "cell_type": "code",
    "execution_count": 50,
-   "metadata": {},
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
    "outputs": [
     {
      "name": "stdout",
@@ -1476,7 +1512,11 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
    "source": [
     "It is also possible to write a single logical line over several physical ones. The cell below is yet another physical representation of the same *two* logical lines. Any pair of delimiters, like `(` and `)` below, can be used to \"format\" the code in between with whitespace. The style guides mentioned before should still be taken into account (i.e., indent with 4 spaces)."
    ]
@@ -1484,7 +1524,11 @@
   {
    "cell_type": "code",
    "execution_count": 51,
-   "metadata": {},
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
    "outputs": [
     {
      "name": "stdout",
@@ -1504,7 +1548,11 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
    "source": [
     "Another situation, in which several physical lines are treated as a logical one, is with so-called **[compound statements <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/reference/compound_stmts.html)**. In contrast to simple statements like `=` and `del` above, the purpose of compound statements is to group other statements.\n",
     "\n",
@@ -1516,7 +1564,11 @@
   {
    "cell_type": "code",
    "execution_count": 52,
-   "metadata": {},
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
    "outputs": [
     {
      "name": "stdout",

From 9b5e395a0e6668a6990c7dd7f7e6b656ca504dce Mon Sep 17 00:00:00 2001
From: Alexander Hess <alexander@webartifex.biz>
Date: Tue, 13 Oct 2020 20:10:46 +0200
Subject: [PATCH 138/242] Make notebooks bullet points in the ToC

---
 README.md | 63 ++++++++++++++++++++++++++++++-------------------------
 1 file changed, 34 insertions(+), 29 deletions(-)

diff --git a/README.md b/README.md
index 0c4e26d..4bcb4c6 100644
--- a/README.md
+++ b/README.md
@@ -15,37 +15,42 @@ Alternatively, the content can be viewed in a web browser
     or interactively (i.e., code can be executed) on [Binder <img height="12" style="display: inline-block" src="static/link/to_mb.png">](https://mybinder.org/v2/gh/webartifex/intro-to-python/develop?urlpath=lab).
 
 - *Chapter 0*: Introduction
-  (
-   [content <img height="12" style="display: inline-block" src="static/link/to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/chapter_00_intro/00_content.ipynb)
-   [<img height="12" style="display: inline-block" src="static/link/to_mb.png">](https://mybinder.org/v2/gh/webartifex/intro-to-python/develop?urlpath=lab/tree/chapter_00_intro/00_content.ipynb)
-   |
-   [exercises <img height="12" style="display: inline-block" src="static/link/to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/chapter_00_intro/01_exercises.ipynb)
-   [<img height="12" style="display: inline-block" src="static/link/to_mb.png">](https://mybinder.org/v2/gh/webartifex/intro-to-python/develop?urlpath=lab/tree/chapter_00_intro/01_exercises.ipynb)
-   |
-   [review <img height="12" style="display: inline-block" src="static/link/to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/chapter_00_intro/02_review.ipynb)
-  )
+  - [content <img height="12" style="display: inline-block" src="static/link/to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/chapter_00_intro/00_content.ipynb)
+    [<img height="12" style="display: inline-block" src="static/link/to_mb.png">](https://mybinder.org/v2/gh/webartifex/intro-to-python/develop?urlpath=lab/tree/chapter_00_intro/00_content.ipynb)
+    (Python's History & Background;
+     Open-source & Communities;
+     JupyterLab;
+     Programming vs. Computer Science;
+     Learning Tips)
+  - [exercises <img height="12" style="display: inline-block" src="static/link/to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/chapter_00_intro/01_exercises.ipynb)
+    [<img height="12" style="display: inline-block" src="static/link/to_mb.png">](https://mybinder.org/v2/gh/webartifex/intro-to-python/develop?urlpath=lab/tree/chapter_00_intro/01_exercises.ipynb)
+    (Mastering Markdown)
+  - [review <img height="12" style="display: inline-block" src="static/link/to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/chapter_00_intro/02_review.ipynb)
 - **Part A: Expressing Logic**
   - *Chapter 1*: Elements of a Program
-    (
-     [content 1 <img height="12" style="display: inline-block" src="static/link/to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/chapter_01_elements/00_content.ipynb)
-     [<img height="12" style="display: inline-block" src="static/link/to_mb.png">](https://mybinder.org/v2/gh/webartifex/intro-to-python/develop?urlpath=lab/tree/chapter_01_elements/00_content.ipynb)
-     |
-     [exercises 1 <img height="12" style="display: inline-block" src="static/link/to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/chapter_01_elements/01_exercises.ipynb)
-     [<img height="12" style="display: inline-block" src="static/link/to_mb.png">](https://mybinder.org/v2/gh/webartifex/intro-to-python/develop?urlpath=lab/tree/chapter_01_elements/01_exercises.ipynb)
-     |
-     [content 2 <img height="12" style="display: inline-block" src="static/link/to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/chapter_01_elements/02_content.ipynb)
-     [<img height="12" style="display: inline-block" src="static/link/to_mb.png">](https://mybinder.org/v2/gh/webartifex/intro-to-python/develop?urlpath=lab/tree/chapter_01_elements/02_content.ipynb)
-     |
-     [exercises 2 <img height="12" style="display: inline-block" src="static/link/to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/chapter_01_elements/03_exercises.ipynb)
-     [<img height="12" style="display: inline-block" src="static/link/to_mb.png">](https://mybinder.org/v2/gh/webartifex/intro-to-python/develop?urlpath=lab/tree/chapter_01_elements/03_exercises.ipynb)
-     |
-     [summary <img height="12" style="display: inline-block" src="static/link/to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/chapter_01_elements/04_summary.ipynb)
-     |
-     [review <img height="12" style="display: inline-block" src="static/link/to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/chapter_01_elements/05_review.ipynb)
-     |
-     [resources <img height="12" style="display: inline-block" src="static/link/to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/chapter_01_elements/06_resources.ipynb)
-     [<img height="12" style="display: inline-block" src="static/link/to_mb.png">](https://mybinder.org/v2/gh/webartifex/intro-to-python/develop?urlpath=lab/tree/chapter_01_elements/06_resources.ipynb)
-    )
+    - [content 1 <img height="12" style="display: inline-block" src="static/link/to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/chapter_01_elements/00_content.ipynb)
+      [<img height="12" style="display: inline-block" src="static/link/to_mb.png">](https://mybinder.org/v2/gh/webartifex/intro-to-python/develop?urlpath=lab/tree/chapter_01_elements/00_content.ipynb)
+      (A first Example: Averaging Even Numbers;
+       Operators;
+       Objects & Data Types;
+       Errors)
+    - [exercises 1 <img height="12" style="display: inline-block" src="static/link/to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/chapter_01_elements/01_exercises.ipynb)
+      [<img height="12" style="display: inline-block" src="static/link/to_mb.png">](https://mybinder.org/v2/gh/webartifex/intro-to-python/develop?urlpath=lab/tree/chapter_01_elements/01_exercises.ipynb)
+      (Printing Output;
+       Simple `for`-loops)
+    - [content 2 <img height="12" style="display: inline-block" src="static/link/to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/chapter_01_elements/02_content.ipynb)
+      [<img height="12" style="display: inline-block" src="static/link/to_mb.png">](https://mybinder.org/v2/gh/webartifex/intro-to-python/develop?urlpath=lab/tree/chapter_01_elements/02_content.ipynb)
+      (Memory in Detail;
+       Variables & References;
+       Mutability;
+       Expressions & Statements)
+    - [exercises 2 <img height="12" style="display: inline-block" src="static/link/to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/chapter_01_elements/03_exercises.ipynb)
+      [<img height="12" style="display: inline-block" src="static/link/to_mb.png">](https://mybinder.org/v2/gh/webartifex/intro-to-python/develop?urlpath=lab/tree/chapter_01_elements/03_exercises.ipynb)
+      (Python as a Calculator)
+    - [summary <img height="12" style="display: inline-block" src="static/link/to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/chapter_01_elements/04_summary.ipynb)
+    - [review <img height="12" style="display: inline-block" src="static/link/to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/chapter_01_elements/05_review.ipynb)
+    - [resources <img height="12" style="display: inline-block" src="static/link/to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/chapter_01_elements/06_resources.ipynb)
+      [<img height="12" style="display: inline-block" src="static/link/to_mb.png">](https://mybinder.org/v2/gh/webartifex/intro-to-python/develop?urlpath=lab/tree/chapter_01_elements/06_resources.ipynb)
 
 
 #### Videos

From 1cf468610e86115bbb3078953a8d9ceeca137fea Mon Sep 17 00:00:00 2001
From: Alexander Hess <alexander@webartifex.biz>
Date: Tue, 13 Oct 2020 23:19:17 +0200
Subject: [PATCH 139/242] Add initial version of chapter 02, part 1

---
 README.md                             |    8 +
 chapter_00_intro/00_content.ipynb     |    2 +-
 chapter_02_functions/00_content.ipynb | 3060 +++++++++++++++++++++++++
 3 files changed, 3069 insertions(+), 1 deletion(-)
 create mode 100644 chapter_02_functions/00_content.ipynb

diff --git a/README.md b/README.md
index 4bcb4c6..2065664 100644
--- a/README.md
+++ b/README.md
@@ -51,6 +51,14 @@ Alternatively, the content can be viewed in a web browser
     - [review <img height="12" style="display: inline-block" src="static/link/to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/chapter_01_elements/05_review.ipynb)
     - [resources <img height="12" style="display: inline-block" src="static/link/to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/chapter_01_elements/06_resources.ipynb)
       [<img height="12" style="display: inline-block" src="static/link/to_mb.png">](https://mybinder.org/v2/gh/webartifex/intro-to-python/develop?urlpath=lab/tree/chapter_01_elements/06_resources.ipynb)
+  - *Chapter 2*: Functions & Modularization
+    - [content 1 <img height="12" style="display: inline-block" src="static/link/to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/chapter_02_functions/00_content.ipynb)
+      [<img height="12" style="display: inline-block" src="static/link/to_mb.png">](https://mybinder.org/v2/gh/webartifex/intro-to-python/develop?urlpath=lab/tree/chapter_02_functions/00_content.ipynb)
+      (Built-in Functions & Constructors;
+       Function Definitions;
+       Function Calls & Scoping Rules;
+       Positional vs. Keyword Arguments;
+       Modularization)
 
 
 #### Videos
diff --git a/chapter_00_intro/00_content.ipynb b/chapter_00_intro/00_content.ipynb
index 219fbf3..b3f20a3 100644
--- a/chapter_00_intro/00_content.ipynb
+++ b/chapter_00_intro/00_content.ipynb
@@ -751,7 +751,7 @@
     "\n",
     "- What is a programming language? What kind of words exist?\n",
     "  - *Chapter 1*: [Elements of a Program <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/chapter_01_elements/00_content.ipynb)\n",
-    "  - *Chapter 2*: Functions & Modularization\n",
+    "  - *Chapter 2*: [Functions & Modularization <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/chapter_02_functions/00_content.ipynb)\n",
     "- What is the flow of execution? How can we form sentences from words?\n",
     "  - *Chapter 3*: Conditionals & Exceptions\n",
     "  - *Chapter 4*: Recursion & Looping"
diff --git a/chapter_02_functions/00_content.ipynb b/chapter_02_functions/00_content.ipynb
new file mode 100644
index 0000000..d7e21b5
--- /dev/null
+++ b/chapter_02_functions/00_content.ipynb
@@ -0,0 +1,3060 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "**Note**: Click on \"*Kernel*\" > \"*Restart Kernel and Clear All Outputs*\" in [JupyterLab](https://jupyterlab.readthedocs.io/en/stable/) *before* reading this notebook to reset its output. If you cannot run this file on your machine, you may want to open it [in the cloud <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_mb.png\">](https://mybinder.org/v2/gh/webartifex/intro-to-python/develop?urlpath=lab/tree/chapter_02_functions/00_content.ipynb)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "# Chapter 2: Functions & Modularization"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "In [Chapter 1 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/chapter_01_elements/00_content.ipynb#Example:-Averaging-all-even-Numbers-in-a-List), we simply typed the code to calculate the average of the even numbers in a list of whole numbers into several code cells. Then, we executed them one after another. We had no way of *reusing* the code except for either executing cells multiple times. And, whenever we find ourselves doing repetitive manual work, we can be sure that there must be a way of automating what we are doing.\n",
+    "\n",
+    "This chapter shows how Python offers language constructs that let us **define** functions ourselves that we may then **call** just like the built-in ones. Also, we look at how we can extend our Python installation with functionalities written by other people."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "## [Built-in Functions <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Python comes with plenty of useful functions built in, some of which we have already seen before (e.g., [print() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#print), [sum() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#sum), [len() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#len), or [id() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#id)). The [documentation <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html) has the full list. Just as core Python itself, they are mostly implemented in C and thus very fast.\n",
+    "\n",
+    "Below, [sum() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#sum) adds up all the elements in the `numbers` list while [len() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#len) counts the number of elements in it."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "numbers = [7, 11, 8, 5, 3, 12, 2, 6, 9, 10, 1, 4]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "78"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "sum(numbers)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "12"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "len(numbers)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "`sum` and `len` are *no* [keywords <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/reference/lexical_analysis.html#keywords) like `for` or `if` but variables that reference *objects* in memory. Often, we hear people say that \"everything is an object in Python\" (e.g., this [question <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_so.png\">](https://stackoverflow.com/questions/40478536/in-python-what-does-it-mean-by-everything-is-an-object)). While this phrase may sound abstract in the beginning, it simply means that the entire memory is organized with \"bags\" of $0$s and $1$s, and there are even bags for the built-in functions. That is *not* true for many other languages (e.g., C or Java) and often a source of confusion for people coming to Python from another language.\n",
+    "\n",
+    "The built-in [id() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#id) function tells us where in memory a particular built-in function is stored."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "139940703477088"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "id(sum)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "139940703476048"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "id(len)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "[type() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#type) reveals that built-in functions like [sum() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#sum) or [len() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#len) are objects of type `builtin_function_or_method`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "builtin_function_or_method"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "type(sum)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "builtin_function_or_method"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "type(len)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Python's object-oriented nature allows us to have functions work with themselves. While seemingly not useful from a beginner's point of view, that enables a lot of powerful programming styles later on."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "139940703475648"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "id(id)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "type"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "type(type)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "To execute a function, we **call** it with the **call operator** `()` as shown many times in [Chapter 1 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/chapter_01_elements/00_content.ipynb) and above.\n",
+    "\n",
+    "If we are unsure whether a variable references a function or not, we can verify that with the built-in [callable() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#callable) function.\n",
+    "\n",
+    "Abstractly speaking, *any* object that can be called with the call operator `()` is a so-called **callable**. And, objects of type `builtin_function_or_method` are just one kind of examples thereof. We will see another one already in the next sub-section."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "callable(sum)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "callable(len)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "`list` objects, for example, are *not* callable."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "False"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "callable(numbers)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "### Constructors"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "The list of [built-in functions <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html) in the documentation should really be named a list of built-in *callables*.\n",
+    "\n",
+    "Besides the built-in functions, the list also features **constructors** for the built-in types. They may be used to **[cast <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_wiki.png\">](https://en.wikipedia.org/wiki/Type_conversion)** (i.e., \"convert\") any object as an object of a given type.\n",
+    "\n",
+    "For example, to \"convert\" a `float` or a `str` into an `int` object, we use the [int() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#int) built-in. Below, *new* `int` objects are created from the `7.0` and `\"7\"` objects that are *newly* created themselves before being processed by [int() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#int) right away *without* ever being referenced by a variable."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "7"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "int(7.0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "7"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "int(\"7\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Casting an object as an `int` is different from rounding with the built-in [round() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#round) function!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "7"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "int(7.99)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "8"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "round(7.99)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Notice the subtle difference compared to the behavior of the `//` operator in [Chapter 1 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/chapter_01_elements/00_content.ipynb##%28Arithmetic#%29-Operators) that \"rounds\" towards minus infinity: [int() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#int) always \"rounds\" towards `0`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "-7"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "int(-7.99)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Not all conversions are valid and *runtime* errors may occur as the `ValueError` shows."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "ename": "ValueError",
+     "evalue": "invalid literal for int() with base 10: 'seven'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-18-af421b358f21>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"seven\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;31mValueError\u001b[0m: invalid literal for int() with base 10: 'seven'"
+     ]
+    }
+   ],
+   "source": [
+    "int(\"seven\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "We may also cast in the other direction with the [float() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#float)  or [str() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#func-str) built-ins."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "7.0"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "float(7)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'7'"
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "str(7)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Constructors are full-fledged objects as well."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "94623097764960"
+      ]
+     },
+     "execution_count": 21,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "id(int)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "94623097758720"
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "id(float)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "They are of type `type`, which is different from `builtin_function_or_method` above."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "type"
+      ]
+     },
+     "execution_count": 23,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "type(int)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "type"
+      ]
+     },
+     "execution_count": 24,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "type(float)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "As already noted, constructors are *callables*. In that regard, they behave the same as built-in functions. We may call them with the call operator `()`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 25,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "callable(int)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 26,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "callable(float)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "The attentive student may already have discovered that we refer to `builtin_function_or_method` objects as \"built-in functions\" and `type` objects as just \"built-ins.\" For a beginner, that difference is not so important. But, the ambitious student should already be aware that such subtleties exist.\n",
+    "\n",
+    "Next, let's look at a third kind of callables."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "##  Function Definitions"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "We may create so-called *user-defined* **functions** with the `def` statement (cf., [reference <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/reference/compound_stmts.html#function-definitions)). To extend an already familiar example, we reuse the introductory example from [Chapter 1 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/chapter_01_elements/00_content.ipynb#Best-Practices) in its final Pythonic version and transform it into the function `average_evens()` below. We replace the variable name `numbers` with `integers` for didactical purposes in the first couple of examples.\n",
+    "\n",
+    "A function's **name** must be chosen according to the same naming rules as ordinary variables since Python manages function names like variables. In this book, we further adopt the convention of ending function names with parentheses `()` in text cells for faster comprehension when reading (i.e., `average_evens()` vs. `average_evens`). These are *not* part of the name but must always be written out in the `def` statement for syntactic reasons.\n",
+    "\n",
+    "Functions may define an arbitrary number of **parameters** as inputs that can then be referenced within the indented **code block**: They are listed within the parentheses in the `def` statement (i.e., `integers` below). \n",
+    "\n",
+    "The code block is also called a function's **body**, while the first line starting with `def` and ending with a colon is the **header**.\n",
+    "\n",
+    "Together, the name and the list of parameters are also referred to as the function's **[signature <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_wiki.png\">](https://en.wikipedia.org/wiki/Type_signature)** (i.e., `average_evens(integers)` below).\n",
+    "\n",
+    "A function may specify an *explicit* **return value** (i.e., \"result\" or \"output\") with the `return` statement (cf., [reference <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/reference/simple_stmts.html#the-return-statement)): Functions that have one are considered **fruitful**; otherwise, they are **void**. Functions of the latter kind are still useful because of their **side effects**. For example, the built-in [print() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#print) function changes what we see on the screen. Strictly speaking, [print() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#print) and other void functions also have an *implicit* return value, namely the `None` object.\n",
+    "\n",
+    "A function should define a **docstring** that describes what it does in a short subject line, what parameters it expects (i.e., their types), and what it returns, if anything. A docstring is a syntactically valid multi-line string (i.e., type `str`) defined within **triple-double quotes** `\"\"\"`. Strings are covered in depth in [Chapter 6 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/chapter_06_text/00_content.ipynb#The-str-Type). Widely adopted standards for docstrings are [PEP 257 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://www.python.org/dev/peps/pep-0257/) and section 3.8 of [Google's Python Style Guide <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_gh.png\">](https://github.com/google/styleguide/blob/gh-pages/pyguide.md)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "def average_evens(integers):\n",
+    "    \"\"\"Calculate the average of all even numbers in a list.\n",
+    "\n",
+    "    Args:\n",
+    "        integers (list of int's): whole numbers to be averaged\n",
+    "\n",
+    "    Returns:\n",
+    "        average (float)\n",
+    "    \"\"\"\n",
+    "    evens = [n for n in integers if n % 2 == 0]\n",
+    "    average = sum(evens) / len(evens)\n",
+    "    return average"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Once defined, a function may be referenced just like any other variable by its name (i.e., *without* the parentheses)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<function __main__.average_evens(integers)>"
+      ]
+     },
+     "execution_count": 28,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "average_evens"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "This works as functions are full-fledged *objects*. So, `average_evens` is just a name referencing an object in memory with an **identity**, a **type**, namely `function`, and a **value**. In that regard, `average_evens` is *no* different from the variable `numbers` or the built-ins' names."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "139940571731424"
+      ]
+     },
+     "execution_count": 29,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "id(average_evens)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "function"
+      ]
+     },
+     "execution_count": 30,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "type(average_evens)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Its value may seem awkward at first: It consists of a location showing where the function is defined (i.e., `__main__` here, which is Python's way of saying \"in this notebook\") and the signature wrapped inside angle brackets `<` and `>`.\n",
+    " \n",
+    "The angle brackets are a convention to indicate that the value may *not* be used as a *literal* (i.e., typed back into another code cell). [Chapter 11 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/chapter_11_classes/00_content.ipynb) introduces the concept of a **text representation** of an object, which is related to the *semantic* meaning of an object's value as discussed in [Chapter 1 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/chapter_01_elements/00_content.ipynb#Value-/-\"Meaning\"), and the angle brackets convention is one such way to represent an object as text. When executed, the angle brackets cause a `SyntaxError` because Python expects the `<` operator to come with an operand on both sides (cf., [Chapter 3 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/chapter_03_conditionals/00_content.ipynb#Relational-Operators))."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "ename": "SyntaxError",
+     "evalue": "invalid syntax (<ipython-input-31-7f49dff38622>, line 1)",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;36m  File \u001b[0;32m\"<ipython-input-31-7f49dff38622>\"\u001b[0;36m, line \u001b[0;32m1\u001b[0m\n\u001b[0;31m    <function __main__.average_evens(numbers)>\u001b[0m\n\u001b[0m    ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m invalid syntax\n"
+     ]
+    }
+   ],
+   "source": [
+    "<function __main__.average_evens(numbers)>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "`average_evens` is, of course, callable. So, the `function` type is the third kind of callable in this chapter."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 32,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "callable(average_evens)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "The built-in [help() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#help) function shows a function's docstring.\n",
+    "\n",
+    "Whenever we use code to analyze or obtain information on an object, we say that we **[introspect <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_wiki.png\">](https://en.wikipedia.org/wiki/Type_introspection)** it."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Help on function average_evens in module __main__:\n",
+      "\n",
+      "average_evens(integers)\n",
+      "    Calculate the average of all even numbers in a list.\n",
+      "    \n",
+      "    Args:\n",
+      "        integers (list of int's): whole numbers to be averaged\n",
+      "    \n",
+      "    Returns:\n",
+      "        average (float)\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "help(average_evens)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "In JupyterLab, we can just as well add a question mark `?` to a function's name to achieve the same."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "\u001b[0;31mSignature:\u001b[0m \u001b[0maverage_evens\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mintegers\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+       "\u001b[0;31mDocstring:\u001b[0m\n",
+       "Calculate the average of all even numbers in a list.\n",
+       "\n",
+       "Args:\n",
+       "    integers (list of int's): whole numbers to be averaged\n",
+       "\n",
+       "Returns:\n",
+       "    average (float)\n",
+       "\u001b[0;31mFile:\u001b[0m      ~/repos/intro-to-python/chapter_02_functions/<ipython-input-27-bcb78a3113d7>\n",
+       "\u001b[0;31mType:\u001b[0m      function\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "average_evens?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Two question marks `??` show a function's source code."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "\u001b[0;31mSignature:\u001b[0m \u001b[0maverage_evens\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mintegers\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+       "\u001b[0;31mSource:\u001b[0m   \n",
+       "\u001b[0;32mdef\u001b[0m \u001b[0maverage_evens\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mintegers\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n",
+       "\u001b[0;34m\u001b[0m    \u001b[0;34m\"\"\"Calculate the average of all even numbers in a list.\u001b[0m\n",
+       "\u001b[0;34m\u001b[0m\n",
+       "\u001b[0;34m    Args:\u001b[0m\n",
+       "\u001b[0;34m        integers (list of int's): whole numbers to be averaged\u001b[0m\n",
+       "\u001b[0;34m\u001b[0m\n",
+       "\u001b[0;34m    Returns:\u001b[0m\n",
+       "\u001b[0;34m        average (float)\u001b[0m\n",
+       "\u001b[0;34m    \"\"\"\u001b[0m\u001b[0;34m\u001b[0m\n",
+       "\u001b[0;34m\u001b[0m    \u001b[0mevens\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mn\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mn\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mintegers\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mn\u001b[0m \u001b[0;34m%\u001b[0m \u001b[0;36m2\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\n",
+       "\u001b[0;34m\u001b[0m    \u001b[0maverage\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msum\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mevens\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m/\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mevens\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n",
+       "\u001b[0;34m\u001b[0m    \u001b[0;32mreturn\u001b[0m \u001b[0maverage\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+       "\u001b[0;31mFile:\u001b[0m      ~/repos/intro-to-python/chapter_02_functions/<ipython-input-27-bcb78a3113d7>\n",
+       "\u001b[0;31mType:\u001b[0m      function\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "average_evens??"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "[help() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#help) and the `?`s also work for built-ins."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Help on built-in function sum in module builtins:\n",
+      "\n",
+      "sum(iterable, /, start=0)\n",
+      "    Return the sum of a 'start' value (default: 0) plus an iterable of numbers\n",
+      "    \n",
+      "    When the iterable is empty, return the start value.\n",
+      "    This function is intended specifically for use with numeric values and may\n",
+      "    reject non-numeric types.\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "help(sum)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "## Function Calls"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Once defined, we may call a function with the call operator `()` as often as we wish. The formal parameters are then filled in by **passing** *expressions* (e.g., literals or variables) as **arguments** to the function within the parentheses."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "7.0"
+      ]
+     },
+     "execution_count": 37,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "average_evens([7, 11, 8, 5, 3, 12, 2, 6, 9, 10, 1, 4])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "7.0"
+      ]
+     },
+     "execution_count": 38,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "average_evens(numbers)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "A function call's return value is commonly assigned to a variable for subsequent use. Otherwise, we lose access to the returned object right away."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "result = average_evens(numbers)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "7.0"
+      ]
+     },
+     "execution_count": 40,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "result"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "## Scoping Rules"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "### Local Scope disappears"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "The parameters listed in a function's definition (i.e., `integers` in the example) and variables created *inside* it during execution (i.e., `evens` and `average`) are **local** to that function. That means they only reference an object in memory *while* the function is being executed and are dereferenced immediately when the function call returns. We say they go out of **scope**. That is why we see the `NameError`s below."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "ename": "NameError",
+     "evalue": "name 'integers' is not defined",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-41-756dfa02d4a8>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mintegers\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;31mNameError\u001b[0m: name 'integers' is not defined"
+     ]
+    }
+   ],
+   "source": [
+    "integers"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "ename": "NameError",
+     "evalue": "name 'evens' is not defined",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-42-df246468d241>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mevens\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;31mNameError\u001b[0m: name 'evens' is not defined"
+     ]
+    }
+   ],
+   "source": [
+    "evens"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "ename": "NameError",
+     "evalue": "name 'average' is not defined",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-43-c3fe9b4213f6>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0maverage\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;31mNameError\u001b[0m: name 'average' is not defined"
+     ]
+    }
+   ],
+   "source": [
+    "average"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "[PythonTutor <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](http://pythontutor.com/visualize.html#code=numbers%20%3D%20%5B7,%2011,%208,%205,%203,%2012,%202,%206,%209,%2010,%201,%204%5D%0A%0Adef%20average_evens%28integers%29%3A%0A%20%20%20%20evens%20%3D%20%5Bn%20for%20n%20in%20integers%20if%20n%20%25%202%20%3D%3D%200%5D%0A%20%20%20%20average%20%3D%20sum%28evens%29%20/%20len%28evens%29%0A%20%20%20%20return%20average%0A%0Aresult%20%3D%20average_evens%28numbers%29&cumulative=false&curInstr=0&heapPrimitives=nevernest&mode=display&origin=opt-frontend.js&py=3&rawInputLstJSON=%5B%5D&textReferences=false) visualizes what happens in memory: To be precise, in the exact moment when the function call is initiated and `numbers` passed in as the `integers` argument, there are *two* references to the *same* `list` object (cf., steps 4-5 in the visualization). We also see how Python creates a *new* **frame** that holds the function's local scope (i.e., \"internal names\") in addition to the **global** frame. Frames are nothing but [namespaces <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_wiki.png\">](https://en.wikipedia.org/wiki/Namespace) to *isolate* the names of different **scopes** from each other. The list comprehension `[n for n in integers if n % 2 == 0]` constitutes yet another frame that is in scope as the `list` object assigned to `evens` is *being* created (cf., steps 6-20). When the function returns, only the global frame is left (cf., last step)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "### Global Scope is everywhere"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "On the contrary, while a function is *being* executed, it may reference the variables of **enclosing scopes** (i.e., \"outside\" of it). This is a common source of *semantic* errors. Consider the following stylized and incorrect example `average_wrong()`. The error is hard to spot with eyes: The function never references the `integers` parameter but the `numbers` variable in the **global scope** instead."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "def average_wrong(integers):\n",
+    "    \"\"\"Calculate the average of all even numbers in a list.\n",
+    "\n",
+    "    Args:\n",
+    "        integers (list of int's): whole numbers to be averaged\n",
+    "\n",
+    "    Returns:\n",
+    "        average (float)\n",
+    "    \"\"\"\n",
+    "    evens = [n for n in numbers if n % 2 == 0]\n",
+    "    average = sum(evens) / len(evens)\n",
+    "    return average"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "`numbers` in the global scope is, of course, *not* changed by merely defining `average_wrong()`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[7, 11, 8, 5, 3, 12, 2, 6, 9, 10, 1, 4]"
+      ]
+     },
+     "execution_count": 45,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "numbers"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Sometimes a function may return a correct solution for *some* inputs ..."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "7.0"
+      ]
+     },
+     "execution_count": 46,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "average_wrong(numbers)  # correct by accident"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "... but still be wrong *in general*."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "7.0"
+      ]
+     },
+     "execution_count": 47,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "average_wrong([123, 456, 789])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "[PythonTutor <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](http://pythontutor.com/visualize.html#code=numbers%20%3D%20%5B7,%2011,%208,%205,%203,%2012,%202,%206,%209,%2010,%201,%204%5D%0A%0Adef%20average_wrong%28integers%29%3A%0A%20%20%20%20evens%20%3D%20%5Bn%20for%20n%20in%20numbers%20if%20n%20%25%202%20%3D%3D%200%5D%0A%20%20%20%20average%20%3D%20sum%28evens%29%20/%20len%28evens%29%0A%20%20%20%20return%20average%0A%0Aresult%20%3D%20average_wrong%28%5B123,%20456,%20789%5D%29&cumulative=false&curInstr=0&heapPrimitives=nevernest&mode=display&origin=opt-frontend.js&py=3&rawInputLstJSON=%5B%5D&textReferences=false) is again helpful at visualizing the error interactively: Creating the `list` object `evens` eventually references takes *16* computational steps, namely two for managing the list comprehension, one for setting up an empty `list` object, *twelve* for filling it with elements derived from `numbers` in the global scope (i.e., that is the error), and one to make `evens` reference it (cf., steps 6-21).\n",
+    "\n",
+    "The frames logic shown by PythonTutor is the mechanism with which Python not only manages the names inside *one* function call but also for *many* potentially *simultaneous* calls, as revealed in [Chapter 4 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/chapter_04_iteration/00_content.ipynb#Trivial-Example:-Countdown). It is the reason why we may reuse the same names for the parameters and variables inside both `average_evens()` and `average_wrong()` without Python mixing them up. So, as we already read in the [Zen of Python <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://www.python.org/dev/peps/pep-0020/), \"namespaces are one honking great idea\" (cf., `import this`), and a frame is just a special kind of namespace."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "### Shadowing"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Code gets even more confusing when variables by the *same* name from *different* scopes collide. In particular, what should we expect to happen if a function \"changes\" a globally defined variable in its body?\n",
+    "\n",
+    "`average_evens()` below works like `average_evens()` above except that it rounds the numbers in `integers` with the built-in [round() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#round) function before filtering and averaging them. [round() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#round) returns `int` objects independent of its argument being an `int` or a `float` object. On the first line in its body, `average_evens()` introduces a *local* variable `numbers` whose name collides with the one defined in the global scope."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 48,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "def average_evens(integers):\n",
+    "    \"\"\"Calculate the average of all even numbers in a list.\n",
+    "\n",
+    "    Args:\n",
+    "        integers (list of int's/float's): numbers to be averaged;\n",
+    "            if non-whole numbers are provided, they are rounded\n",
+    "\n",
+    "    Returns:\n",
+    "        average (float)\n",
+    "    \"\"\"\n",
+    "    numbers = [round(n) for n in integers]\n",
+    "    evens = [n for n in numbers if n % 2 == 0]\n",
+    "    average = sum(evens) / len(evens)\n",
+    "    return average"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "As a good practice, let's first \"verify\" that `average_evens()` is \"correct\" by calling it with inputs for which we can calculate the answer in our heads. Treating a function as a \"black box\" (i.e., input-output specification) when testing is also called [unit testing <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_wiki.png\">](https://en.wikipedia.org/wiki/Unit_testing) and plays an important role in modern software engineering."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 49,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "42.0"
+      ]
+     },
+     "execution_count": 49,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "average_evens([40.0, 41.1, 42.2, 43.3, 44.4])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Such tests are often and conveniently expressed with the `assert` statement (cf., [reference <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/reference/simple_stmts.html#the-assert-statement)): If the expression following `assert` evaluates to `True`, nothing happens."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 50,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "assert average_evens([40.0, 41.1, 42.2, 43.3, 44.4]) == 42.0"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "However, if the expression evaluates to `False`, an `AssertionError` is raised."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 51,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "ename": "AssertionError",
+     "evalue": "",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mAssertionError\u001b[0m                            Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-51-f47586a118b9>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0;32massert\u001b[0m \u001b[0maverage_evens\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m40.0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m41.1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m42.2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m43.3\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m44.4\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m87.0\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;31mAssertionError\u001b[0m: "
+     ]
+    }
+   ],
+   "source": [
+    "assert average_evens([40.0, 41.1, 42.2, 43.3, 44.4]) == 87.0"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Calling `average_evens()` leaves `numbers` in the global scope unchanged."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 52,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[7, 11, 8, 5, 3, 12, 2, 6, 9, 10, 1, 4]"
+      ]
+     },
+     "execution_count": 52,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "numbers"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "To add to the confusion, let's also pass the global `numbers` list as an argument to `average_evens()`. The return value is the same as before."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 53,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "7.0"
+      ]
+     },
+     "execution_count": 53,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "average_evens(numbers)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "In summary, Python is smart enough to keep all the involved `numbers` variables apart. So, the global `numbers` variable is still referencing the *same* `list` object as before."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 54,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[7, 11, 8, 5, 3, 12, 2, 6, 9, 10, 1, 4]"
+      ]
+     },
+     "execution_count": 54,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "numbers"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "The reason why everything works is that *every* time we (re-)assign an object to a variable *inside* a function's body with the `=` statement, this is done in the *local* scope by default. There are ways to change variables existing in an outer scope from within a function, but this is a rather advanced topic.\n",
+    "\n",
+    "[PythonTutor <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](http://pythontutor.com/visualize.html#code=numbers%20%3D%20%5B7,%2011,%208,%205,%203,%2012,%202,%206,%209,%2010,%201,%204%5D%0A%0Adef%20average_evens%28integers%29%3A%0A%20%20%20%20numbers%20%3D%20%5Bround%28n%29%20for%20n%20in%20integers%5D%0A%20%20%20%20evens%20%3D%20%5Bn%20for%20n%20in%20numbers%20if%20n%20%25%202%20%3D%3D%200%5D%0A%20%20%20%20average%20%3D%20sum%28evens%29%20/%20len%28evens%29%0A%20%20%20%20return%20average%0A%0Aresult%20%3D%20average_evens%28%5B40.0,%2041.1,%2042.2,%2043.3,%2044.4%5D%29&cumulative=false&curInstr=0&heapPrimitives=nevernest&mode=display&origin=opt-frontend.js&py=3&rawInputLstJSON=%5B%5D&textReferences=false) shows how *two* `numbers` variables exist in *different* scopes referencing *different* objects (cf., steps 14-25) when we execute `average_evens([40.0, 41.1, 42.2, 43.3, 44.4])`.\n",
+    "\n",
+    "Variables whose names collide with the ones of variables in enclosing scopes - and the global scope is just the most enclosing scope - are said to **shadow** them.\n",
+    "\n",
+    "While this is not a problem for Python, it may lead to less readable code for humans and should be avoided if possible. But, as the software engineering wisdom goes, \"[naming things](https://skeptics.stackexchange.com/questions/19836/has-phil-karlton-ever-said-there-are-only-two-hard-things-in-computer-science)\" is often considered a hard problem as well, and we have to be prepared to encounter shadowing variables.\n",
+    "\n",
+    "Shadowing also occurs if a parameter in the function definition goes by the same name as a variable in an outer scope. Below, `average_evens()` is identical to the first version in this chapter except that the parameter `integers` is now called `numbers` as well."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 55,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "def average_evens(numbers):\n",
+    "    \"\"\"Calculate the average of all even numbers in a list.\n",
+    "\n",
+    "    Args:\n",
+    "        numbers (list of int's): whole numbers to be averaged\n",
+    "\n",
+    "    Returns:\n",
+    "        average (float)\n",
+    "    \"\"\"\n",
+    "    evens = [n for n in numbers if n % 2 == 0]\n",
+    "    average = sum(evens) / len(evens)\n",
+    "    return average"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 56,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "7.0"
+      ]
+     },
+     "execution_count": 56,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "average_evens(numbers)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 57,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[7, 11, 8, 5, 3, 12, 2, 6, 9, 10, 1, 4]"
+      ]
+     },
+     "execution_count": 57,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "numbers"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "[PythonTutor <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](http://pythontutor.com/visualize.html#code=numbers%20%3D%20%5B7,%2011,%208,%205,%203,%2012,%202,%206,%209,%2010,%201,%204%5D%0A%0Adef%20average_evens%28numbers%29%3A%0A%20%20%20%20evens%20%3D%20%5Bn%20for%20n%20in%20numbers%20if%20n%20%25%202%20%3D%3D%200%5D%0A%20%20%20%20average%20%3D%20sum%28evens%29%20/%20len%28evens%29%0A%20%20%20%20return%20average%0A%0Aresult%20%3D%20average_evens%28numbers%29&cumulative=false&curInstr=0&heapPrimitives=nevernest&mode=display&origin=opt-frontend.js&py=3&rawInputLstJSON=%5B%5D&textReferences=false) reveals that in this example there are *two* `numbers` variables in *different* scope referencing the *same* `list` object in memory (cf., steps 4-23)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "## Positional vs. Keyword Arguments"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "So far, we have specified only one parameter in each of our user-defined functions. In [Chapter 1 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/chapter_01_elements/00_content.ipynb#%28Arithmetic%29-Operators), however, we saw the built-in [divmod() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#divmod) function take two arguments. And, the order in which they are passed in matters! Whenever we call a function and list its arguments in a comma separated manner, we say that we pass in the arguments *by position* or refer to them as **positional arguments**."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 58,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(4, 2)"
+      ]
+     },
+     "execution_count": 58,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "divmod(42, 10)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 59,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(0, 10)"
+      ]
+     },
+     "execution_count": 59,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "divmod(10, 42)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "For many functions, there is a natural order to the arguments: For example, for any kind of division passing the dividend first and the divisor second seems intuitive. But what if that is not the case in another setting? For example, let's create a close relative of the above `average_evens()` function that also scales the resulting average by a factor. What is more natural? Passing in `numbers` first? Or `scalar`? There is no obvious way and we continue with the first alternative for no concrete reason."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 60,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "def scaled_average_evens(numbers, scalar):\n",
+    "    \"\"\"Calculate the scaled average of all even numbers in a list.\n",
+    "\n",
+    "    Args:\n",
+    "        numbers (list of int's/float's): numbers to be averaged;\n",
+    "            if non-whole numbers are provided, they are rounded\n",
+    "        scalar (float): multiplies the average\n",
+    "\n",
+    "    Returns:\n",
+    "        scaled_average (float)\n",
+    "    \"\"\"\n",
+    "    numbers = [round(n) for n in numbers]\n",
+    "    evens = [n for n in numbers if n % 2 == 0]\n",
+    "    average = sum(evens) / len(evens)\n",
+    "    return scalar * average"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "As with [divmod() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#divmod), we may pass in the arguments by position."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 61,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "14.0"
+      ]
+     },
+     "execution_count": 61,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "scaled_average_evens(numbers, 2)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Now, this function call is a bit harder to understand as we always need to remember what the `2` means. This becomes even harder with more parameters.\n",
+    "\n",
+    "Luckily, we may also pass in arguments *by name*. Then, we refer to them as **keyword arguments**."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 62,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "14.0"
+      ]
+     },
+     "execution_count": 62,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "scaled_average_evens(numbers=numbers, scalar=2)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "When passing all arguments by name, we may do so in any order."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 63,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "14.0"
+      ]
+     },
+     "execution_count": 63,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "scaled_average_evens(scalar=2, numbers=numbers)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "We may even combine positional and keyword arguments in the same function call."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 64,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "14.0"
+      ]
+     },
+     "execution_count": 64,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "scaled_average_evens(numbers, scalar=2)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Unfortunately, there are ways to screw this up with a `SyntaxError`: If positional and keyword arguments are mixed, the keyword arguments *must* come last."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 65,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "ename": "SyntaxError",
+     "evalue": "positional argument follows keyword argument (<ipython-input-65-b00eb6f72e84>, line 1)",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;36m  File \u001b[0;32m\"<ipython-input-65-b00eb6f72e84>\"\u001b[0;36m, line \u001b[0;32m1\u001b[0m\n\u001b[0;31m    scaled_average_evens(numbers=numbers, 2)\u001b[0m\n\u001b[0m                                          ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m positional argument follows keyword argument\n"
+     ]
+    }
+   ],
+   "source": [
+    "scaled_average_evens(numbers=numbers, 2)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Similarly, we must always pass in the right number of arguments. Otherwise, a `TypeError` is raised."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 66,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "ename": "TypeError",
+     "evalue": "scaled_average_evens() missing 1 required positional argument: 'scalar'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-66-d910518345ec>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mscaled_average_evens\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnumbers\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;31mTypeError\u001b[0m: scaled_average_evens() missing 1 required positional argument: 'scalar'"
+     ]
+    }
+   ],
+   "source": [
+    "scaled_average_evens(numbers)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 67,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "ename": "TypeError",
+     "evalue": "scaled_average_evens() takes 2 positional arguments but 3 were given",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-67-2e7ca1d2a555>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mscaled_average_evens\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnumbers\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m3\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;31mTypeError\u001b[0m: scaled_average_evens() takes 2 positional arguments but 3 were given"
+     ]
+    }
+   ],
+   "source": [
+    "scaled_average_evens(numbers, 2, 3)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "### Modularization"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Defining `average_evens()` and `scaled_average_evens()` as above leads to a repetition of most of their code. That is *not* good as such a redundancy makes a code base hard to maintain in the long run: Whenever we change the logic in one function, we must *not* forget to do so for the other function as well. And, most likely, we forget about such issues in larger projects.\n",
+    "\n",
+    "Below, three of four lines in the functions' bodies are identical!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 68,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "def average_evens(numbers):\n",
+    "    \"\"\" ... ... ... \"\"\"\n",
+    "    numbers = [round(n) for n in numbers]\n",
+    "    evens = [n for n in numbers if n % 2 == 0]\n",
+    "    average = sum(evens) / len(evens)\n",
+    "    return average"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 69,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "def scaled_average_evens(numbers, scalar):\n",
+    "    \"\"\" ... ... ... \"\"\"\n",
+    "    numbers = [round(n) for n in numbers]\n",
+    "    evens = [n for n in numbers if n % 2 == 0]\n",
+    "    average = sum(evens) / len(evens)\n",
+    "    return scalar * average"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "A better way is to design related functions in a **modular** fashion such that they reuse each other's code.\n",
+    "\n",
+    "For example, as not scaling an average is just a special case of scaling it with `1`, we could redefine the two functions like below: In this version, the function resembling the *special* case, `average_evens()`, **forwards** the call to the more *general* function, `scaled_average_evens()`, passing a `scalar` argument of `1`. As the name `scaled_average_evens` within the body of `average_evens()` is looked up each time the function is *being* executed, we may define `average_evens()` before `scaled_average_evens()`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 70,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "def average_evens(numbers):\n",
+    "    \"\"\" ... ... ... \"\"\"\n",
+    "    return scaled_average_evens(numbers, scalar=1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 71,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "def scaled_average_evens(numbers, scalar):\n",
+    "    \"\"\" ... ... ... \"\"\"\n",
+    "    numbers = [round(n) for n in numbers]\n",
+    "    evens = [n for n in numbers if n % 2 == 0]\n",
+    "    average = sum(evens) / len(evens)\n",
+    "    return scalar * average"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "After **refactoring** the functions, it is a good idea to test them again."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 72,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "assert average_evens(numbers) == 7.0"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 73,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "assert scaled_average_evens(numbers, 2) == 14.0"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "### Default Arguments"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "*Assuming* that scaling the average occurs rarely, it may be a good idea to handle both cases in *one* function definition by providing a **default argument** of `1` for the `scalar` parameter."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 74,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "def average_evens(numbers, scalar=1):\n",
+    "    \"\"\"Calculate the average of all even numbers in a list.\n",
+    "\n",
+    "    Args:\n",
+    "        numbers (list of int's/float's): numbers to be averaged;\n",
+    "            if non-whole numbers are provided, they are rounded\n",
+    "        scalar (float, optional): multiplies the average; defaults to 1\n",
+    "\n",
+    "    Returns:\n",
+    "        scaled_average (float)\n",
+    "    \"\"\"\n",
+    "    numbers = [round(n) for n in numbers]\n",
+    "    evens = [n for n in numbers if n % 2 == 0]\n",
+    "    average = sum(evens) / len(evens)\n",
+    "    return scalar * average"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Now, we call the function with or without passing a `scalar` argument.\n",
+    "\n",
+    "If `scalar` is *not* passed in, it automatically takes the value `1`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 75,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "7.0"
+      ]
+     },
+     "execution_count": 75,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "average_evens(numbers)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "If `scalar` is passed in, this may be done as either a positional or a keyword argument. Which of the two calls where `scalar` is `2` is faster to understand in a larger program?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 76,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "14.0"
+      ]
+     },
+     "execution_count": 76,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "average_evens(numbers, 2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 77,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "14.0"
+      ]
+     },
+     "execution_count": 77,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "average_evens(numbers, scalar=2)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "### Keyword-only Arguments"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Because we *assumed* that scaling occurs rarely, we would prefer that our new version of `average_evens()` be called with a *keyword argument* whenever `scalar` is passed in. Then, a function call is never ambiguous when reading the source code.\n",
+    "\n",
+    "Python offers a **keyword-only** syntax when defining a function that *forces* a caller to pass the `scalar` argument *by name* if it is passed in at all: To do so, we place an asterisk `*` before the arguments that may only be passed in by name. Note that the keyword-only syntax also works *without* a default argument."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 78,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "def average_evens(numbers, *, scalar=1):\n",
+    "    \"\"\"Calculate the average of all even numbers in a list.\n",
+    "\n",
+    "    Args:\n",
+    "        numbers (list of int's/float's): numbers to be averaged;\n",
+    "            if non-whole numbers are provided, they are rounded\n",
+    "        scalar (float, optional): multiplies the average; defaults to 1\n",
+    "\n",
+    "    Returns:\n",
+    "        scaled_average (float)\n",
+    "    \"\"\"\n",
+    "    numbers = [round(n) for n in numbers]\n",
+    "    evens = [n for n in numbers if n % 2 == 0]\n",
+    "    average = sum(evens) / len(evens)\n",
+    "    return scalar * average"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "As before, we may call `average_evens()` without passing in an argument for the `scalar` parameter."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 79,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "7.0"
+      ]
+     },
+     "execution_count": 79,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "average_evens(numbers)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "If we call `average_evens()` with a `scalar` argument, we *must* use keyword notation."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 80,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "14.0"
+      ]
+     },
+     "execution_count": 80,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "average_evens(numbers, scalar=2)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "If instead we pass in `scalar` as a positional argument, we get a `TypeError`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 81,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "ename": "TypeError",
+     "evalue": "average_evens() takes 1 positional argument but 2 were given",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-81-12eb635366db>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0maverage_evens\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnumbers\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m2\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;31mTypeError\u001b[0m: average_evens() takes 1 positional argument but 2 were given"
+     ]
+    }
+   ],
+   "source": [
+    "average_evens(numbers, 2)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "## Anonymous Functions"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "The `def` statement is a statement because of its *side effect* of creating a *new* name that references a *new* `function` object in memory.\n",
+    "\n",
+    "We can thus think of it as doing *two* things **atomically** (i.e., either both of them happen or none). First, a `function` object is created that contains the concrete $0$s and $1$s that resemble the instructions we put into the function's body. In the context of a function, these $0$s and $1$s are also called **[byte code <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_wiki.png\">](https://en.wikipedia.org/wiki/Bytecode)**. Then, a name referencing the new `function` object is created.\n",
+    "\n",
+    "Only this second aspect makes `def` a statement: Merely creating a new object in memory without making it accessible for later reference does *not* constitute a side effect because the state the program is *not* changed. After all, if we cannot reference an object, how do we know it exists in the first place?\n",
+    "\n",
+    "Python provides a `lambda` expression syntax that allows us to *only* create a `function` object in memory *without* making a name reference it (cf., [reference <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/reference/expressions.html#lambda)). It starts with the keyword `lambda` followed by an optional listing of comma separated parameters, a mandatory colon, and *one* expression that serves as the return value of the resulting `function` object. Because it does *not* create a name referencing the object, we effectively create \"anonymous\" functions with it.\n",
+    "\n",
+    "In the example, we create a `function` object that adds `3` to the only argument passed in as the parameter `x` and returns that sum."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 82,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<function __main__.<lambda>(x)>"
+      ]
+     },
+     "execution_count": 82,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "lambda x: x + 3"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "If you think this is rather pointless to do, you are absolutely correct!\n",
+    "\n",
+    "We created a `function` object, dit *not* call it, and Python immediately forgot about it. So what's the point?\n",
+    "\n",
+    "To inspect the object created by a `lambda` expression, we use the simple `=` statement and assign it to the variable `add_three`, which is really `add_three()` as per our convention from above."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 83,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "add_three = lambda x: x + 3  # we could and should use def instead"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "[type() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#type) and [callable() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#callable) confirm that `add_three` is indeed a callable `function` object."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 84,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "function"
+      ]
+     },
+     "execution_count": 84,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "type(add_three)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 85,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 85,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "callable(add_three)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Now we may call `add_three()` as if we defined it with the `def` statement."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 86,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "42"
+      ]
+     },
+     "execution_count": 86,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "add_three(39)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Alternatively, we could call an `function` object created with a `lambda` expression right away (i.e., without assigning it to a variable), which looks quite weird for now as we need *two* pairs of parentheses: The first one serves as a delimiter whereas the second represents the call operator."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 87,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "42"
+      ]
+     },
+     "execution_count": 87,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "(lambda x: x + 3)(39)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "The main point of having functions without a reference to them is to use them in a situation where we know ahead of time that we use the function only *once*.\n",
+    "\n",
+    "Popular applications of lambda expressions occur in combination with the **map-filter-reduce** paradigm (cf., [Chapter 8 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/chapter_08_mfr/00_content.ipynb#Lambda-Expressions))."
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.6"
+  },
+  "livereveal": {
+   "auto_select": "code",
+   "auto_select_fragment": true,
+   "scroll": true,
+   "theme": "serif"
+  },
+  "toc": {
+   "base_numbering": 1,
+   "nav_menu": {},
+   "number_sections": false,
+   "sideBar": true,
+   "skip_h1_title": true,
+   "title_cell": "Table of Contents",
+   "title_sidebar": "Contents",
+   "toc_cell": false,
+   "toc_position": {
+    "height": "calc(100% - 180px)",
+    "left": "10px",
+    "top": "150px",
+    "width": "384px"
+   },
+   "toc_section_display": false,
+   "toc_window_display": false
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}

From 7119fd4c459623f25964e15ab1aaba61142fc537 Mon Sep 17 00:00:00 2001
From: Alexander Hess <alexander@webartifex.biz>
Date: Tue, 13 Oct 2020 23:29:08 +0200
Subject: [PATCH 140/242] Add initial version of chapter 02's exercises

---
 README.md                               |   3 +
 chapter_02_functions/01_exercises.ipynb | 257 ++++++++++++++++++++++++
 2 files changed, 260 insertions(+)
 create mode 100644 chapter_02_functions/01_exercises.ipynb

diff --git a/README.md b/README.md
index 2065664..23289c5 100644
--- a/README.md
+++ b/README.md
@@ -59,6 +59,9 @@ Alternatively, the content can be viewed in a web browser
        Function Calls & Scoping Rules;
        Positional vs. Keyword Arguments;
        Modularization)
+    - [exercises <img height="12" style="display: inline-block" src="static/link/to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/chapter_02_functions/01_exercises.ipynb)
+      [<img height="12" style="display: inline-block" src="static/link/to_mb.png">](https://mybinder.org/v2/gh/webartifex/intro-to-python/develop?urlpath=lab/tree/chapter_02_functions/01_exercises.ipynb)
+      (Volume of a Sphere)
 
 
 #### Videos
diff --git a/chapter_02_functions/01_exercises.ipynb b/chapter_02_functions/01_exercises.ipynb
new file mode 100644
index 0000000..664e8f0
--- /dev/null
+++ b/chapter_02_functions/01_exercises.ipynb
@@ -0,0 +1,257 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Note**: Click on \"*Kernel*\" > \"*Restart Kernel and Run All*\" in [JupyterLab](https://jupyterlab.readthedocs.io/en/stable/) *after* finishing the exercises to ensure that your solution runs top to bottom *without* any errors. If you cannot run this file on your machine, you may want to open it [in the cloud <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_mb.png\">](https://mybinder.org/v2/gh/webartifex/intro-to-python/develop?urlpath=lab/tree/chapter_02_functions/01_exercises.ipynb)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Chapter 2: Functions & Modularization"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Coding Exercises"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The exercises below assume that you have read the [first part <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/chapter_02_functions/00_content.ipynb) of Chapter 2.\n",
+    "\n",
+    "The `...`'s in the code cells indicate where you need to fill in code snippets. The number of `...`'s within a code cell give you a rough idea of how many lines of code are needed to solve the task. You should not need to create any additional code cells for your final solution. However, you may want to use temporary code cells to try out some ideas."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Volume of a Sphere"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q1**: The [volume of a sphere <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_wiki.png\">](https://en.wikipedia.org/wiki/Sphere) is defined as $\\frac{4}{3} * \\pi * r^3$. Calculate this value for $r=10.0$ and round it to 10 digits after the comma.\n",
+    "\n",
+    "Hints:\n",
+    "- use an appropriate approximation for $\\pi$\n",
+    "- you may use the [standard library <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/index.html) to do so if you have already looked at the [second part <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/chapter_02_functions/02_content.ipynb) of Chapter 2."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import ...  # you may drop this cell and use your own approximation for Pi"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "r = ..."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "..."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q2**: Encapsulate the logic into a function `sphere_volume()` that takes one *positional* argument `radius` and one *keyword-only* argument `digits` defaulting to `5`. The volume should be returned as a `float` object under *all* circumstances."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def sphere_volume(...):\n",
+    "    \"\"\"Calculate the volume of a sphere.\n",
+    "\n",
+    "    Args:\n",
+    "        radius (float): radius of the sphere\n",
+    "        digits (optional, int): number of digits\n",
+    "            for rounding the resulting volume\n",
+    "\n",
+    "    Returns:\n",
+    "        volume (float)\n",
+    "    \"\"\"\n",
+    "    return ..."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q3**: Evaluate the function with `radius = 100.0` and 1, 5, 10, 15, and 20 digits respectively."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "radius = ..."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sphere_volume(...)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sphere_volume(...)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sphere_volume(...)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sphere_volume(...)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sphere_volume(...)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q4**: What observation do you make?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " < your answer >"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q5**: Using the [range() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#func-range) built-in, write a `for`-loop and calculate the volume of a sphere with `radius = 42.0` for all `digits` from `1` through `20`. Print out each volume on a separate line.\n",
+    "\n",
+    "Note: This is the first task where you need to use the built-in [print() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#print) function."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "radius = ..."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "for ... in ...:\n",
+    "    ..."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q6**: What lesson do you learn about the `float` type?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " < your answer >"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.6"
+  },
+  "toc": {
+   "base_numbering": 1,
+   "nav_menu": {},
+   "number_sections": false,
+   "sideBar": true,
+   "skip_h1_title": true,
+   "title_cell": "Table of Contents",
+   "title_sidebar": "Contents",
+   "toc_cell": false,
+   "toc_position": {},
+   "toc_section_display": false,
+   "toc_window_display": false
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}

From ca1ccf21eb9b012f764bb898cbafb889d76205b0 Mon Sep 17 00:00:00 2001
From: Alexander Hess <alexander@webartifex.biz>
Date: Tue, 13 Oct 2020 23:39:22 +0200
Subject: [PATCH 141/242] Add numpy to the dependencies

---
 poetry.lock    | 38 +++++++++++++++++++++++++++++++++++++-
 pyproject.toml |  1 +
 2 files changed, 38 insertions(+), 1 deletion(-)

diff --git a/poetry.lock b/poetry.lock
index bc75853..dc2cc29 100644
--- a/poetry.lock
+++ b/poetry.lock
@@ -631,6 +631,14 @@ virtualenv = ">=14.0.0"
 [package.extras]
 tox_to_nox = ["jinja2", "tox"]
 
+[[package]]
+name = "numpy"
+version = "1.19.2"
+description = "NumPy is the fundamental package for array computing with Python."
+category = "main"
+optional = false
+python-versions = ">=3.6"
+
 [[package]]
 name = "packaging"
 version = "20.4"
@@ -959,7 +967,7 @@ python-versions = "*"
 [metadata]
 lock-version = "1.1"
 python-versions = "^3.8"
-content-hash = "1fd17e55acd76edd6126d5ee9223c0dd291bc2acacaaa242e80a3a2aaa5decce"
+content-hash = "abc30be36880f8b203c72895f70f906ef451fca6b4e5113891c9487bc95e02e5"
 
 [metadata.files]
 appdirs = [
@@ -1260,6 +1268,34 @@ nox = [
     {file = "nox-2020.8.22-py3-none-any.whl", hash = "sha256:55f8cab16bcfaaea08b141c83bf2b7c779e943518d0de6cd9c38cd8da95d11ea"},
     {file = "nox-2020.8.22.tar.gz", hash = "sha256:efa5adcf1134012f96bcd0a496ccebd4c9e9da53a831888a2a779462440eebcf"},
 ]
+numpy = [
+    {file = "numpy-1.19.2-cp36-cp36m-macosx_10_9_x86_64.whl", hash = "sha256:b594f76771bc7fc8a044c5ba303427ee67c17a09b36e1fa32bde82f5c419d17a"},
+    {file = "numpy-1.19.2-cp36-cp36m-manylinux1_i686.whl", hash = "sha256:e6ddbdc5113628f15de7e4911c02aed74a4ccff531842c583e5032f6e5a179bd"},
+    {file = "numpy-1.19.2-cp36-cp36m-manylinux1_x86_64.whl", hash = "sha256:3733640466733441295b0d6d3dcbf8e1ffa7e897d4d82903169529fd3386919a"},
+    {file = "numpy-1.19.2-cp36-cp36m-manylinux2010_i686.whl", hash = "sha256:4339741994c775396e1a274dba3609c69ab0f16056c1077f18979bec2a2c2e6e"},
+    {file = "numpy-1.19.2-cp36-cp36m-manylinux2010_x86_64.whl", hash = "sha256:7c6646314291d8f5ea900a7ea9c4261f834b5b62159ba2abe3836f4fa6705526"},
+    {file = "numpy-1.19.2-cp36-cp36m-manylinux2014_aarch64.whl", hash = "sha256:7118f0a9f2f617f921ec7d278d981244ba83c85eea197be7c5a4f84af80a9c3c"},
+    {file = "numpy-1.19.2-cp36-cp36m-win32.whl", hash = "sha256:9a3001248b9231ed73894c773142658bab914645261275f675d86c290c37f66d"},
+    {file = "numpy-1.19.2-cp36-cp36m-win_amd64.whl", hash = "sha256:967c92435f0b3ba37a4257c48b8715b76741410467e2bdb1097e8391fccfae15"},
+    {file = "numpy-1.19.2-cp37-cp37m-macosx_10_9_x86_64.whl", hash = "sha256:d526fa58ae4aead839161535d59ea9565863bb0b0bdb3cc63214613fb16aced4"},
+    {file = "numpy-1.19.2-cp37-cp37m-manylinux1_i686.whl", hash = "sha256:eb25c381d168daf351147713f49c626030dcff7a393d5caa62515d415a6071d8"},
+    {file = "numpy-1.19.2-cp37-cp37m-manylinux1_x86_64.whl", hash = "sha256:62139af94728d22350a571b7c82795b9d59be77fc162414ada6c8b6a10ef5d02"},
+    {file = "numpy-1.19.2-cp37-cp37m-manylinux2010_i686.whl", hash = "sha256:0c66da1d202c52051625e55a249da35b31f65a81cb56e4c69af0dfb8fb0125bf"},
+    {file = "numpy-1.19.2-cp37-cp37m-manylinux2010_x86_64.whl", hash = "sha256:2117536e968abb7357d34d754e3733b0d7113d4c9f1d921f21a3d96dec5ff716"},
+    {file = "numpy-1.19.2-cp37-cp37m-manylinux2014_aarch64.whl", hash = "sha256:54045b198aebf41bf6bf4088012777c1d11703bf74461d70cd350c0af2182e45"},
+    {file = "numpy-1.19.2-cp37-cp37m-win32.whl", hash = "sha256:aba1d5daf1144b956bc87ffb87966791f5e9f3e1f6fab3d7f581db1f5b598f7a"},
+    {file = "numpy-1.19.2-cp37-cp37m-win_amd64.whl", hash = "sha256:addaa551b298052c16885fc70408d3848d4e2e7352de4e7a1e13e691abc734c1"},
+    {file = "numpy-1.19.2-cp38-cp38-macosx_10_9_x86_64.whl", hash = "sha256:58d66a6b3b55178a1f8a5fe98df26ace76260a70de694d99577ddeab7eaa9a9d"},
+    {file = "numpy-1.19.2-cp38-cp38-manylinux1_i686.whl", hash = "sha256:59f3d687faea7a4f7f93bd9665e5b102f32f3fa28514f15b126f099b7997203d"},
+    {file = "numpy-1.19.2-cp38-cp38-manylinux1_x86_64.whl", hash = "sha256:cebd4f4e64cfe87f2039e4725781f6326a61f095bc77b3716502bed812b385a9"},
+    {file = "numpy-1.19.2-cp38-cp38-manylinux2010_i686.whl", hash = "sha256:c35a01777f81e7333bcf276b605f39c872e28295441c265cd0c860f4b40148c1"},
+    {file = "numpy-1.19.2-cp38-cp38-manylinux2010_x86_64.whl", hash = "sha256:d7ac33585e1f09e7345aa902c281bd777fdb792432d27fca857f39b70e5dd31c"},
+    {file = "numpy-1.19.2-cp38-cp38-manylinux2014_aarch64.whl", hash = "sha256:04c7d4ebc5ff93d9822075ddb1751ff392a4375e5885299445fcebf877f179d5"},
+    {file = "numpy-1.19.2-cp38-cp38-win32.whl", hash = "sha256:51ee93e1fac3fe08ef54ff1c7f329db64d8a9c5557e6c8e908be9497ac76374b"},
+    {file = "numpy-1.19.2-cp38-cp38-win_amd64.whl", hash = "sha256:1669ec8e42f169ff715a904c9b2105b6640f3f2a4c4c2cb4920ae8b2785dac65"},
+    {file = "numpy-1.19.2-pp36-pypy36_pp73-manylinux2010_x86_64.whl", hash = "sha256:0bfd85053d1e9f60234f28f63d4a5147ada7f432943c113a11afcf3e65d9d4c8"},
+    {file = "numpy-1.19.2.zip", hash = "sha256:0d310730e1e793527065ad7dde736197b705d0e4c9999775f212b03c44a8484c"},
+]
 packaging = [
     {file = "packaging-20.4-py2.py3-none-any.whl", hash = "sha256:998416ba6962ae7fbd6596850b80e17859a5753ba17c32284f67bfff33784181"},
     {file = "packaging-20.4.tar.gz", hash = "sha256:4357f74f47b9c12db93624a82154e9b120fa8293699949152b22065d556079f8"},
diff --git a/pyproject.toml b/pyproject.toml
index e3b89bd..588c1b4 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -14,6 +14,7 @@ license = "MIT"
 python = "^3.8"
 
 jupyterlab = "^2.2.8"
+numpy = "^1.19.2"
 
 [tool.poetry.dev-dependencies]
 # Task runners

From 559c8e7b71ca13045285d962c89ad18e0aedd8d2 Mon Sep 17 00:00:00 2001
From: Alexander Hess <alexander@webartifex.biz>
Date: Tue, 13 Oct 2020 23:40:11 +0200
Subject: [PATCH 142/242] Pin the dependencies

- upgrade colorama (transient dependency)
---
 poetry.lock | 5 ++---
 1 file changed, 2 insertions(+), 3 deletions(-)

diff --git a/poetry.lock b/poetry.lock
index dc2cc29..9da7a06 100644
--- a/poetry.lock
+++ b/poetry.lock
@@ -122,7 +122,7 @@ python-versions = "*"
 
 [[package]]
 name = "colorama"
-version = "0.4.3"
+version = "0.4.4"
 description = "Cross-platform colored terminal text."
 category = "main"
 optional = false
@@ -1067,8 +1067,7 @@ chardet = [
     {file = "chardet-3.0.4.tar.gz", hash = "sha256:84ab92ed1c4d4f16916e05906b6b75a6c0fb5db821cc65e70cbd64a3e2a5eaae"},
 ]
 colorama = [
-    {file = "colorama-0.4.3-py2.py3-none-any.whl", hash = "sha256:7d73d2a99753107a36ac6b455ee49046802e59d9d076ef8e47b61499fa29afff"},
-    {file = "colorama-0.4.3.tar.gz", hash = "sha256:e96da0d330793e2cb9485e9ddfd918d456036c7149416295932478192f4436a1"},
+    {file = "colorama-0.4.4-py2.py3-none-any.whl", hash = "sha256:9f47eda37229f68eee03b24b9748937c7dc3868f906e8ba69fbcbdd3bc5dc3e2"},
 ]
 colorlog = [
     {file = "colorlog-4.4.0-py2.py3-none-any.whl", hash = "sha256:f14f30f58e2ce6ef40b0088307cac7efb9ecff5605fa2267a2d29955f26aff23"},

From 917952176324d3877656a5b7c44d9b1dc8fa5742 Mon Sep 17 00:00:00 2001
From: Alexander Hess <alexander@webartifex.biz>
Date: Tue, 13 Oct 2020 23:58:55 +0200
Subject: [PATCH 143/242] Add initial version of chapter 02, part 2

---
 README.md                             |    5 +
 chapter_02_functions/02_content.ipynb | 1637 +++++++++++++++++++++++++
 chapter_02_functions/sample_module.py |   76 ++
 3 files changed, 1718 insertions(+)
 create mode 100644 chapter_02_functions/02_content.ipynb
 create mode 100644 chapter_02_functions/sample_module.py

diff --git a/README.md b/README.md
index 23289c5..05c8bce 100644
--- a/README.md
+++ b/README.md
@@ -62,6 +62,11 @@ Alternatively, the content can be viewed in a web browser
     - [exercises <img height="12" style="display: inline-block" src="static/link/to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/chapter_02_functions/01_exercises.ipynb)
       [<img height="12" style="display: inline-block" src="static/link/to_mb.png">](https://mybinder.org/v2/gh/webartifex/intro-to-python/develop?urlpath=lab/tree/chapter_02_functions/01_exercises.ipynb)
       (Volume of a Sphere)
+    - [content 2 <img height="12" style="display: inline-block" src="static/link/to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/chapter_02_functions/02_content.ipynb)
+      [<img height="12" style="display: inline-block" src="static/link/to_mb.png">](https://mybinder.org/v2/gh/webartifex/intro-to-python/develop?urlpath=lab/tree/chapter_02_functions/02_content.ipynb)
+      (Standard Library: `math` & `random` Modules;
+       Third-party Packages: `numpy` Library;
+       Writing one's own Modules)
 
 
 #### Videos
diff --git a/chapter_02_functions/02_content.ipynb b/chapter_02_functions/02_content.ipynb
new file mode 100644
index 0000000..87fd9f5
--- /dev/null
+++ b/chapter_02_functions/02_content.ipynb
@@ -0,0 +1,1637 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "**Note**: Click on \"*Kernel*\" > \"*Restart Kernel and Clear All Outputs*\" in [JupyterLab](https://jupyterlab.readthedocs.io/en/stable/) *before* reading this notebook to reset its output. If you cannot run this file on your machine, you may want to open it [in the cloud <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_mb.png\">](https://mybinder.org/v2/gh/webartifex/intro-to-python/develop?urlpath=lab/tree/chapter_02_functions/02_content.ipynb)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "# Chapter 2: Functions & Modularization (Part 2)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "So far, we have only used what we refer to as **core** Python in this book. By this, we mean all the syntactical rules as specified in the [language reference <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/reference/) and a minimal set of about 50 built-in [functions <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html). With this, we could already implement any algorithm or business logic we can think of!\n",
+    "\n",
+    "However, after our first couple of programs, we would already start seeing recurring patterns in the code we write. In other words, we would constantly be \"reinventing the wheel\" in each new project."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "## Extending Core Python"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Would it not be smarter to pull out the reusable components from our programs and put them into some project independent **library** of generically useful functionalities? Then we would only need a way of including these **utilities** in our projects.\n",
+    "\n",
+    "As all programmers across all languages face this very same issue, most programming languages come with a so-called **[standard library <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_wiki.png\">](https://en.wikipedia.org/wiki/Standard_library)** that provides utilities to accomplish everyday tasks without much code. Examples are making an HTTP request to some website, open and read popular file types (e.g., CSV or Excel files), do something on a computer's file system, and many more."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "### The Standard Library"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Python also comes with a [standard library <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/index.html) that is structured into coherent modules and packages for given topics: A **module** is just a plain text file with the file extension *.py* that contains Python code while a **package** is a folder that groups several related modules.\n",
+    "\n",
+    "The code in the [standard library <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/index.html) is contributed and maintained by many volunteers around the world. In contrast to so-called \"third-party\" packages (cf., the next section below), the Python core development team closely monitors and tests the code in the [standard library <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/index.html). Consequently, we can be reasonably sure that anything provided by it works correctly independent of our computer's operating system and will most likely also be there in the next Python versions. Parts in the [standard library <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/index.html) that are computationally expensive are often rewritten in C and, therefore, much faster than anything we could write in Python ourselves. So, whenever we can solve a problem with the help of the [standard library <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/index.html), it is almost always the best way to do so as well.\n",
+    "\n",
+    "The [standard library <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/index.html) has grown very big over the years, and we refer to the website [PYMOTW](https://pymotw.com/3/index.html) (i.e., \"Python Module of the Week\") that features well written introductory tutorials and how-to guides to most parts of the library. The same author also published a [book](https://www.amazon.com/Python-Standard-Library-Example-Developers/dp/0134291050/ref=as_li_ss_tl?ie=UTF8&qid=1493563121&sr=8-1&keywords=python+3+standard+library+by+example) that many Pythonistas keep on their shelf for reference. Knowing what is in the [standard library <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/index.html) is quite valuable for solving real-world tasks quickly.\n",
+    "\n",
+    "Throughout this book, we look at many modules and packages from the [standard library <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/index.html) in more depth, starting with the [math <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/math.html) and [random <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/random.html) modules in this chapter."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "#### [math <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/math.html) Module"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "The [math <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/math.html) module provides non-trivial mathematical functions like $sin(x)$ and constants like $\\pi$ or $\\text{e}$.\n",
+    "\n",
+    "To make functions and variables defined \"somewhere else\" available in our current program, we must first **import** them with the `import` statement (cf., [reference <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/reference/simple_stmts.html#import)).  "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "import math"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "This creates the variable `math` that references a **[module object <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/glossary.html#term-module)** (i.e., type `module`) in memory."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<module 'math' from '/home/webartifex/.pyenv/versions/3.8.6/lib/python3.8/lib-dynload/math.cpython-38-x86_64-linux-gnu.so'>"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "math"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "140177537558144"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "id(math)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "module"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "type(math)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "`module` objects serve as namespaces to organize the names inside a module. In this context, a namespace is nothing but a prefix that avoids collision with the variables already defined at the location where we import the module into.\n",
+    "\n",
+    "Let's see what we can do with the `math` module.\n",
+    "\n",
+    "The [dir() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#dir) built-in function may also be used with an argument passed in. Ignoring the dunder-style names, `math` offers quite a lot of names. As we cannot know at this point if a listed name refers to a function or an ordinary variable, we use the more generic term **attribute** to mean either one of them."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['__doc__',\n",
+       " '__file__',\n",
+       " '__loader__',\n",
+       " '__name__',\n",
+       " '__package__',\n",
+       " '__spec__',\n",
+       " 'acos',\n",
+       " 'acosh',\n",
+       " 'asin',\n",
+       " 'asinh',\n",
+       " 'atan',\n",
+       " 'atan2',\n",
+       " 'atanh',\n",
+       " 'ceil',\n",
+       " 'comb',\n",
+       " 'copysign',\n",
+       " 'cos',\n",
+       " 'cosh',\n",
+       " 'degrees',\n",
+       " 'dist',\n",
+       " 'e',\n",
+       " 'erf',\n",
+       " 'erfc',\n",
+       " 'exp',\n",
+       " 'expm1',\n",
+       " 'fabs',\n",
+       " 'factorial',\n",
+       " 'floor',\n",
+       " 'fmod',\n",
+       " 'frexp',\n",
+       " 'fsum',\n",
+       " 'gamma',\n",
+       " 'gcd',\n",
+       " 'hypot',\n",
+       " 'inf',\n",
+       " 'isclose',\n",
+       " 'isfinite',\n",
+       " 'isinf',\n",
+       " 'isnan',\n",
+       " 'isqrt',\n",
+       " 'ldexp',\n",
+       " 'lgamma',\n",
+       " 'log',\n",
+       " 'log10',\n",
+       " 'log1p',\n",
+       " 'log2',\n",
+       " 'modf',\n",
+       " 'nan',\n",
+       " 'perm',\n",
+       " 'pi',\n",
+       " 'pow',\n",
+       " 'prod',\n",
+       " 'radians',\n",
+       " 'remainder',\n",
+       " 'sin',\n",
+       " 'sinh',\n",
+       " 'sqrt',\n",
+       " 'tan',\n",
+       " 'tanh',\n",
+       " 'tau',\n",
+       " 'trunc']"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "dir(math)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Common mathematical constants and functions are now available via the dot operator `.` on the `math` object. This operator is sometimes also called the **attribute access operator**, in line with the just introduced term."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "3.141592653589793"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "math.pi"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "2.718281828459045"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "math.e"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<function math.sqrt(x, /)>"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "math.sqrt"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Help on built-in function sqrt in module math:\n",
+      "\n",
+      "sqrt(x, /)\n",
+      "    Return the square root of x.\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "help(math.sqrt)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "1.4142135623730951"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "math.sqrt(2)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Observe how the arguments passed to functions do not need to be just variables or simple literals. Instead, we may pass in any *expression* that evaluates to a *new* object of the type the function expects.\n",
+    "\n",
+    "So just as a reminder from the expression vs. statement discussion in [Chapter 1 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/chapter_01_elements/02_content.ipynb#Expressions): An expression is *any* syntactically correct combination of variables and literals with operators. And the call operator `()` is yet another operator. So both of the next two code cells are just expressions! They have no permanent side effects in memory. We may execute them as often as we want *without* changing the state of the program (i.e., this Jupyter notebook).\n",
+    "\n",
+    "So, regarding the very next cell in particular: Although the `2 ** 2` creates a *new* object `4` in memory that is then immediately passed into the [math.sqrt() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/math.html#math.sqrt) function, once that function call returns, \"all is lost\" and the newly created `4` object is forgotten again, as well as the return value of [math.sqrt() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/math.html#math.sqrt)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "2.0"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "math.sqrt(2 ** 2)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Even the **composition** of several function calls only constitutes another expression."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "10.0"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "math.sqrt(sum([99, 100, 101]) / 3)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "If we only need one particular function from a module, we may also use the alternative `from ... import ...` syntax.\n",
+    "\n",
+    "This does *not* create a module object but only makes a variable in our current location reference an object defined inside a module directly."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "from math import sqrt"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "4.0"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "sqrt(16)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "#### [random <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/random.html) Module"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Often, we need a random variable, for example, when we want to build a simulation. The [random <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/random.html) module in the [standard library <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/index.html) often suffices for that."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "import random"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<module 'random' from '/home/webartifex/.pyenv/versions/3.8.6/lib/python3.8/random.py'>"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "random"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Besides the usual dunder-style attributes, the built-in [dir() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#dir) function lists some attributes in an upper case naming convention and many others starting with a *single* underscore `_`. To understand the former, we must wait until [Chapter 11 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/chapter_11_classes/00_content.ipynb), while the latter is explained further below."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['BPF',\n",
+       " 'LOG4',\n",
+       " 'NV_MAGICCONST',\n",
+       " 'RECIP_BPF',\n",
+       " 'Random',\n",
+       " 'SG_MAGICCONST',\n",
+       " 'SystemRandom',\n",
+       " 'TWOPI',\n",
+       " '_Sequence',\n",
+       " '_Set',\n",
+       " '__all__',\n",
+       " '__builtins__',\n",
+       " '__cached__',\n",
+       " '__doc__',\n",
+       " '__file__',\n",
+       " '__loader__',\n",
+       " '__name__',\n",
+       " '__package__',\n",
+       " '__spec__',\n",
+       " '_accumulate',\n",
+       " '_acos',\n",
+       " '_bisect',\n",
+       " '_ceil',\n",
+       " '_cos',\n",
+       " '_e',\n",
+       " '_exp',\n",
+       " '_inst',\n",
+       " '_log',\n",
+       " '_os',\n",
+       " '_pi',\n",
+       " '_random',\n",
+       " '_repeat',\n",
+       " '_sha512',\n",
+       " '_sin',\n",
+       " '_sqrt',\n",
+       " '_test',\n",
+       " '_test_generator',\n",
+       " '_urandom',\n",
+       " '_warn',\n",
+       " 'betavariate',\n",
+       " 'choice',\n",
+       " 'choices',\n",
+       " 'expovariate',\n",
+       " 'gammavariate',\n",
+       " 'gauss',\n",
+       " 'getrandbits',\n",
+       " 'getstate',\n",
+       " 'lognormvariate',\n",
+       " 'normalvariate',\n",
+       " 'paretovariate',\n",
+       " 'randint',\n",
+       " 'random',\n",
+       " 'randrange',\n",
+       " 'sample',\n",
+       " 'seed',\n",
+       " 'setstate',\n",
+       " 'shuffle',\n",
+       " 'triangular',\n",
+       " 'uniform',\n",
+       " 'vonmisesvariate',\n",
+       " 'weibullvariate']"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "dir(random)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "The [random.random() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/random.html#random.random) function generates a uniformly distributed `float` number between $0$ (including) and $1$ (excluding)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<function Random.random()>"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "random.random"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Help on built-in function random:\n",
+      "\n",
+      "random() method of random.Random instance\n",
+      "    random() -> x in the interval [0, 1).\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "help(random.random)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.782609162553633"
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "random.random()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "While we could build some conditional logic with an `if` statement to map the number generated by [random.random() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/random.html#random.random) to a finite set of elements manually, the [random.choice() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/random.html#random.choice) function provides a lot more **convenience** for us. We call it with, for example, the `numbers` list and it draws one element out of it with equal chance."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<bound method Random.choice of <random.Random object at 0x561db35d9a60>>"
+      ]
+     },
+     "execution_count": 21,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "random.choice"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Help on method choice in module random:\n",
+      "\n",
+      "choice(seq) method of random.Random instance\n",
+      "    Choose a random element from a non-empty sequence.\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "help(random.choice)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "numbers = [7, 11, 8, 5, 3, 12, 2, 6, 9, 10, 1, 4]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "4"
+      ]
+     },
+     "execution_count": 24,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "random.choice(numbers)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "To reproduce the *same* random numbers in a simulation each time we run it, we set the **[random seed <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_wiki.png\">](https://en.wikipedia.org/wiki/Random_seed)**. It is good practice to do that at the beginning of a program or notebook. It becomes essential when we employ randomized machine learning algorithms, like the [Random Forest <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_wiki.png\">](https://en.wikipedia.org/wiki/Random_forest), and want to obtain **reproducible** results for publication in academic journals.\n",
+    "\n",
+    "The [random <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/random.html) module provides the [random.seed() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/random.html#random.seed) function to do that."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "random.seed(42)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.6394267984578837"
+      ]
+     },
+     "execution_count": 26,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "random.random()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "random.seed(42)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.6394267984578837"
+      ]
+     },
+     "execution_count": 28,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "random.random()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "### Third-party Packages"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "As the Python community is based around open source, many developers publish their code, for example, on the Python Package Index [PyPI](https://pypi.org) from where anyone may download and install it for free using command-line based tools like [pip](https://pip.pypa.io/en/stable/) or [conda](https://conda.io/en/latest/). This way, we can always customize our Python installation even more. Managing many such packages is quite a deep topic on its own, sometimes fearfully called **[dependency hell <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_wiki.png\">](https://en.wikipedia.org/wiki/Dependency_hell)**.\n",
+    "\n",
+    "The difference between the [standard library <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/index.html) and such **third-party** packages is that in the first case, the code goes through a much more formalized review process and is officially endorsed by the Python core developers. Yet, many third-party projects also offer the highest quality standards and are also relied on by many businesses and researchers.\n",
+    "\n",
+    "Throughout this book, we will look at many third-party libraries, mostly from Python's [scientific stack](https://scipy.org/about.html), a tightly coupled set of third-party libraries for storing **big data** efficiently (e.g., [numpy](http://www.numpy.org/)), \"wrangling\" (e.g., [pandas](https://pandas.pydata.org/)) and visualizing them (e.g., [matplotlib](https://matplotlib.org/) or [seaborn](https://seaborn.pydata.org/)), fitting classical statistical models (e.g., [statsmodels](http://www.statsmodels.org/)), training machine learning models (e.g., [sklearn](http://scikit-learn.org/)), and much more.\n",
+    "\n",
+    "Below, we briefly show how to install third-party libraries."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "#### [numpy](http://www.numpy.org/) Library"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "[numpy](http://www.numpy.org/) is the de-facto standard in the Python world for handling **array-like** data. That is a fancy word for data that can be put into a matrix or vector format.\n",
+    "\n",
+    "As [numpy](http://www.numpy.org/) is *not* in the [standard library <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/index.html), it must be *manually* installed, for example, with the [pip](https://pip.pypa.io/en/stable/) tool. As mentioned in [Chapter 0 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/chapter_00_intro/00_content.ipynb#Markdown-Cells-vs.-Code-Cells), to execute terminal commands from within a Jupyter notebook, we start a code cell with an exclamation mark.\n",
+    "\n",
+    "If you are running this notebook with an installation of the [Anaconda Distribution](https://www.anaconda.com/distribution/), then [numpy](http://www.numpy.org/) is probably already installed. Running the cell below confirms that."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Requirement already satisfied: numpy in /home/webartifex/repos/intro-to-python/.venv/lib/python3.8/site-packages (1.19.2)\n"
+     ]
+    }
+   ],
+   "source": [
+    "!pip install numpy"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "[numpy](http://www.numpy.org/) is conventionally imported with the shorter **idiomatic** name `np`. The `as` in the import statement changes the resulting variable name. It is a shortcut for the three lines `import numpy`, `np = numpy`, and `del numpy`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "import numpy as np"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "`np` is used in the same way as `math` or `random` above."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<module 'numpy' from '/home/webartifex/repos/intro-to-python/.venv/lib/python3.8/site-packages/numpy/__init__.py'>"
+      ]
+     },
+     "execution_count": 31,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "np"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Let's convert the above `numbers` list into a vector-like object of type `numpy.ndarray`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "vec = np.array(numbers)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([ 7, 11,  8,  5,  3, 12,  2,  6,  9, 10,  1,  4])"
+      ]
+     },
+     "execution_count": 33,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "vec"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "numpy.ndarray"
+      ]
+     },
+     "execution_count": 34,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "type(vec)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "[numpy](http://www.numpy.org/) somehow magically adds new behavior to Python's built-in arithmetic operators. For example, we may now [scalar-multiply <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_wiki.png\">](https://en.wikipedia.org/wiki/Scalar_multiplication) `vec`. Also, [numpy](http://www.numpy.org/)'s functions are implemented in highly optimized C code and, therefore, are fast, especially when dealing with bigger amounts of data."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([14, 22, 16, 10,  6, 24,  4, 12, 18, 20,  2,  8])"
+      ]
+     },
+     "execution_count": 35,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "2 * vec"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "This scalar multiplication would \"fail\" if we used a plain `list` object like `numbers` instead of an `numpy.ndarray` object like `vec`. The two types exhibit different **behavior** when used with the same operator, another example of **operator overloading**."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[7, 11, 8, 5, 3, 12, 2, 6, 9, 10, 1, 4, 7, 11, 8, 5, 3, 12, 2, 6, 9, 10, 1, 4]"
+      ]
+     },
+     "execution_count": 36,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "2 * numbers  # surprise, surprise"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "[numpy](http://www.numpy.org/)'s `numpy.ndarray` objects integrate nicely with Python's built-in functions (e.g., [sum() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#sum)) or functions from the [standard library <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/index.html) (e.g., [random.choice() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/random.html#random.choice))."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "78"
+      ]
+     },
+     "execution_count": 37,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "sum(vec)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "7"
+      ]
+     },
+     "execution_count": 38,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "random.choice(vec)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "### Local Modules and Packages"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "For sure, we can create local modules and packages. In the Chapter 2 directory, there is a [*sample_module.py* <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_gh.png\">](https://github.com/webartifex/intro-to-python/blob/develop/chapter_02_functions/sample_module.py) file that contains, among others, a function equivalent to the final version of `average_evens()`. To be realistic, this sample module is structured in a modular manner with several functions building on each other. It is best to skim over it *now* before reading on.\n",
+    "\n",
+    "To make code we put into a *.py* file available in our program, we import it as a module just as we did above with modules in the [standard library <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/index.html) or third-party packages.\n",
+    "\n",
+    "The `pwd` utility tells us in which directory Python is currently in. We refer to that as the **working directory**, and `pwd` reads \"print working directory.\" JupyterLab automatically sets this to the directory in which the notebook is in."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "/home/webartifex/repos/intro-to-python/chapter_02_functions\n"
+     ]
+    }
+   ],
+   "source": [
+    "!pwd"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "The *name* to be imported is the file's name except for the *.py* part. For this to work, the file's name *must* adhere to the *same* rules as hold for [variable names <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/reference/lexical_analysis.html#identifiers) in general.\n",
+    "\n",
+    "What happens during an import is as follows. When Python sees the `import sample_module` part, it first creates a *new* object of type `module` in memory. This is effectively an *empty* namespace. Then, it executes the imported file's code from top to bottom. Whatever variables are still defined at the end of this, are put into the module's namespace. Only if the file's code does *not* raise an error, will Python make a variable in our current location (i.e., `mod` here) reference the created `module` object. Otherwise, it is discarded. In essence, it is as if we copied and pasted the file's code in place of the import statement. If we import an already imported module again, Python is smart enough to avoid doing all this work all over and does nothing."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "import sample_module as mod"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<module 'sample_module' from '/home/webartifex/repos/intro-to-python/chapter_02_functions/sample_module.py'>"
+      ]
+     },
+     "execution_count": 41,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "mod"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Disregarding the dunder-style attributes, `mod` defines the attributes `_round_all`, `_scaled_average`, `average`, `average_evens`, and `average_odds`, which are exactly the ones we would expect from reading the [*sample_module.py* <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_gh.png\">](https://github.com/webartifex/intro-to-python/blob/develop/chapter_02_functions/sample_module.py) file.\n",
+    "\n",
+    "A convention when working with imported code is to *disregard* any attributes starting with a single underscore `_`. These are considered **private** and constitute **implementation details** the author of the imported code might change in a future version of his software. We *must not* rely on them in any way.\n",
+    "\n",
+    "In contrast, the three remaining **public** attributes are the functions `average()`, `average_evens()`, and `average_odds()` that we may use after the import."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['__builtins__',\n",
+       " '__cached__',\n",
+       " '__doc__',\n",
+       " '__file__',\n",
+       " '__loader__',\n",
+       " '__name__',\n",
+       " '__package__',\n",
+       " '__spec__',\n",
+       " '_round_all',\n",
+       " '_scaled_average',\n",
+       " 'average',\n",
+       " 'average_evens',\n",
+       " 'average_odds']"
+      ]
+     },
+     "execution_count": 42,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "dir(mod)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "We use the imported `mod.average_evens()` just like `average_evens()` defined in the [first part <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/chapter_02_functions/00_content.ipynb) of this chapter. The advantage we get from **modularization** with *.py* files is that we can now easily reuse functions across different Jupyter notebooks without redefining them again and again. Also, we can \"source out\" code that distracts from the storyline told in a notebook."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<function sample_module.average_evens(numbers, *, scalar=1)>"
+      ]
+     },
+     "execution_count": 43,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "mod.average_evens"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Help on function average_evens in module sample_module:\n",
+      "\n",
+      "average_evens(numbers, *, scalar=1)\n",
+      "    Calculate the average of all even numbers in a list.\n",
+      "    \n",
+      "    Args:\n",
+      "        numbers (list of int's/float's): numbers to be averaged;\n",
+      "            if non-whole numbers are provided, they are rounded\n",
+      "        scalar (float, optional): multiplies the average; defaults to 1\n",
+      "    \n",
+      "    Returns:\n",
+      "        scaled_average (float)\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "help(mod.average_evens)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "7.0"
+      ]
+     },
+     "execution_count": 45,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "mod.average_evens(numbers)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "14.0"
+      ]
+     },
+     "execution_count": 46,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "mod.average_evens(numbers, scalar=2)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Packages are a generalization of modules, and we look at one in detail in [Chapter 11 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/chapter_11_classes/00_content.ipynb). You may, however, already look at a [sample package <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_gh.png\">](https://github.com/webartifex/intro-to-python/tree/develop/chapter_11_classes/sample_package) in the repository, which is nothing but a folder with *.py* files in it.\n",
+    "\n",
+    "As a further reading on modules and packages, we refer to the [official tutorial <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/tutorial/modules.html)."
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.6"
+  },
+  "livereveal": {
+   "auto_select": "code",
+   "auto_select_fragment": true,
+   "scroll": true,
+   "theme": "serif"
+  },
+  "toc": {
+   "base_numbering": 1,
+   "nav_menu": {},
+   "number_sections": false,
+   "sideBar": true,
+   "skip_h1_title": true,
+   "title_cell": "Table of Contents",
+   "title_sidebar": "Contents",
+   "toc_cell": false,
+   "toc_position": {
+    "height": "calc(100% - 180px)",
+    "left": "10px",
+    "top": "150px",
+    "width": "384px"
+   },
+   "toc_section_display": false,
+   "toc_window_display": false
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/chapter_02_functions/sample_module.py b/chapter_02_functions/sample_module.py
new file mode 100644
index 0000000..3a097e1
--- /dev/null
+++ b/chapter_02_functions/sample_module.py
@@ -0,0 +1,76 @@
+"""This is a sample module.
+
+It defines three functions average(), average_evens(), and average_odds().
+The point is to show how we can put Python code in a .py file to be re-used
+in some other place.
+
+We should never forget to document the code as well, both on the module
+level (i.e., this docstring) but also in every function it defines.
+
+When imported, Python modules are executed top to bottom before the flow of
+execution returns to wherever they were imported into.
+
+An important convention is to prefix variables and functions that are not to
+be used outside the module with a single underscore "_". This way, we can
+design the code within a module in a modular fashion and only "export" what we
+want.
+
+Here, all three functions internally forward parts of their computations
+to the utility functions _round_all() and _scaled_average() that contain all
+the logic common to the three functions.
+
+While this example is stylized, it shows how Python modules are often
+designed.
+"""
+
+def _round_all(numbers):
+    """Internal utility function to round all numbers in a list."""
+    return [round(n) for n in numbers]
+
+
+def _scaled_average(numbers, scalar):
+    """Internal utility function to calculate scaled averages."""
+    average = sum(numbers) / len(numbers)
+    return scalar * average
+
+
+def average(numbers, *, scalar=1):
+    """Calculate the average of all numbers in a list.
+
+    Args:
+        numbers (list of int's/float's): numbers to be averaged;
+            if non-whole numbers are provided, they are rounded
+        scalar (float, optional): multiplies the average; defaults to 1
+
+    Returns:
+        scaled_average (float)
+    """
+    return _scaled_average(_round_all(numbers), scalar)
+
+
+def average_evens(numbers, *, scalar=1):
+    """Calculate the average of all even numbers in a list.
+
+    Args:
+        numbers (list of int's/float's): numbers to be averaged;
+            if non-whole numbers are provided, they are rounded
+        scalar (float, optional): multiplies the average; defaults to 1
+
+    Returns:
+        scaled_average (float)
+    """
+    return _scaled_average([n for n in _round_all(numbers) if n % 2 == 0], scalar)
+
+
+def average_odds(numbers, *, scalar=1):
+    """Calculate the average of all odd numbers in a list.
+
+    Args:
+        numbers (list of int's/float's): numbers to be averaged;
+            if non-whole numbers are provided, they are rounded
+        scalar (float, optional): multiplies the average; defaults to 1
+
+    Returns:
+        scaled_average (float)
+    """
+    return _scaled_average([n for n in _round_all(numbers) if n % 2 != 0], scalar)

From 54e63991a11a8a2ab0f2ae3e2f2ff2cdc04ee1b0 Mon Sep 17 00:00:00 2001
From: Alexander Hess <alexander@webartifex.biz>
Date: Wed, 14 Oct 2020 00:00:52 +0200
Subject: [PATCH 144/242] Add initial version of chapter 02's summary

---
 README.md                             |   1 +
 chapter_02_functions/03_summary.ipynb | 101 ++++++++++++++++++++++++++
 2 files changed, 102 insertions(+)
 create mode 100644 chapter_02_functions/03_summary.ipynb

diff --git a/README.md b/README.md
index 05c8bce..8278bff 100644
--- a/README.md
+++ b/README.md
@@ -67,6 +67,7 @@ Alternatively, the content can be viewed in a web browser
       (Standard Library: `math` & `random` Modules;
        Third-party Packages: `numpy` Library;
        Writing one's own Modules)
+    - [summary <img height="12" style="display: inline-block" src="static/link/to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/chapter_02_functions/03_summary.ipynb)
 
 
 #### Videos
diff --git a/chapter_02_functions/03_summary.ipynb b/chapter_02_functions/03_summary.ipynb
new file mode 100644
index 0000000..c5d712b
--- /dev/null
+++ b/chapter_02_functions/03_summary.ipynb
@@ -0,0 +1,101 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "# Chapter 2: Functions & Modularization"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "## TL;DR"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "A user-defined **function** is a **named sequence** of statements that perform a computation.\n",
+    "\n",
+    "Functions provide benefits as they\n",
+    "\n",
+    "- make programs easier to comprehend and debug for humans as they give names to the smaller parts of a larger program (i.e., they **modularize** a code base), and\n",
+    "- eliminate redundancies by allowing **reuse of code**.\n",
+    "\n",
+    "Functions are **defined** once with the `def` statement. Then, they may be **called** many times with the call operator `()`.\n",
+    "\n",
+    "They may process **parameterized** inputs, **passed** in as **arguments**, and output a **return value**.\n",
+    "\n",
+    "Arguments may be passed in by **position** or **keyword**. Some functions may even require **keyword-only** arguments.\n",
+    "\n",
+    "**Lambda expressions** create anonymous functions.\n",
+    "\n",
+    "Functions are a special kind of **callables**. Any object that may be **called** with the call operator `()` is a callable. Built-in functions and **constructors** are other kinds of callables.\n",
+    "\n",
+    "Core Python can be extended with code from either the **standard library** or **third-party** libraries.\n",
+    "\n",
+    "Outside Jupyter notebooks, Python code is put into **modules** that are grouped in **packages**."
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.6"
+  },
+  "livereveal": {
+   "auto_select": "code",
+   "auto_select_fragment": true,
+   "scroll": true,
+   "theme": "serif"
+  },
+  "toc": {
+   "base_numbering": 1,
+   "nav_menu": {},
+   "number_sections": false,
+   "sideBar": true,
+   "skip_h1_title": true,
+   "title_cell": "Table of Contents",
+   "title_sidebar": "Contents",
+   "toc_cell": false,
+   "toc_position": {
+    "height": "calc(100% - 180px)",
+    "left": "10px",
+    "top": "150px",
+    "width": "384px"
+   },
+   "toc_section_display": false,
+   "toc_window_display": false
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}

From 8a55f014a9a316cc27297ab38d5ec17144a6b4c1 Mon Sep 17 00:00:00 2001
From: Alexander Hess <alexander@webartifex.biz>
Date: Wed, 14 Oct 2020 00:02:49 +0200
Subject: [PATCH 145/242] Add initial version of chapter 02's review

---
 README.md                            |   1 +
 chapter_02_functions/04_review.ipynb | 236 +++++++++++++++++++++++++++
 2 files changed, 237 insertions(+)
 create mode 100644 chapter_02_functions/04_review.ipynb

diff --git a/README.md b/README.md
index 8278bff..35f4dbc 100644
--- a/README.md
+++ b/README.md
@@ -68,6 +68,7 @@ Alternatively, the content can be viewed in a web browser
        Third-party Packages: `numpy` Library;
        Writing one's own Modules)
     - [summary <img height="12" style="display: inline-block" src="static/link/to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/chapter_02_functions/03_summary.ipynb)
+    - [review <img height="12" style="display: inline-block" src="static/link/to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/chapter_02_functions/04_review.ipynb)
 
 
 #### Videos
diff --git a/chapter_02_functions/04_review.ipynb b/chapter_02_functions/04_review.ipynb
new file mode 100644
index 0000000..daa9d88
--- /dev/null
+++ b/chapter_02_functions/04_review.ipynb
@@ -0,0 +1,236 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Chapter 2: Functions & Modularization"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Content Review"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The questions below assume that you have read the [first <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/chapter_02_functions/00_content.ipynb) and the [second <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/chapter_02_functions/02_content.ipynb) part of Chapter 2.\n",
+    "\n",
+    "Be concise in your answers! Most questions can be answered in *one* sentence."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Essay Questions "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q1**: What behavior of the `def` statement makes it a **statement**? Is there a way to use an **expression** to create a function?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " < your answer >"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q2**: One of the first confusions of experienced programmers coming from other languages to Python regards the observation that **\"everything in Python is an object\"** (cf., this [discussion](https://www.reddit.com/r/learnpython/comments/8rypx9/everything_in_python_is_an_object/)). How does this relate to **functions**?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " < your answer >"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q3**: What does it mean for a variable to go out of **scope**?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " < your answer >"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q4**: How can a **global** variable be **shadowed**? Is this good or bad?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " < your answer >"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q5**: Explain the concept of **forwarding** a **function call**."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " < your answer >"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q6**: What are **keyword-only arguments** and when is it appropriate to use them?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " < your answer >"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q7**: What are **callables**? How do they relate to `function` objects?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " < your answer >"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### True / False Questions"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Motivate your answer with *one short* sentence!"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q8**: A mere function **call** is just an **expression**."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " < your answer >"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q9**: When using the `import` statement, we need to ensure that the imported attributes do *not* overwrite any already defined variables and functions."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " < your answer >"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q10:** Functions always have a name by which we can call them."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " < your answer >"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q11**: The [standard library <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/index.html) is a collection of numerical tools often used in scientific computing, for example, advanced mathematical functions or utilities for simulation."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " < your answer >"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.6"
+  },
+  "toc": {
+   "base_numbering": 1,
+   "nav_menu": {},
+   "number_sections": false,
+   "sideBar": true,
+   "skip_h1_title": true,
+   "title_cell": "Table of Contents",
+   "title_sidebar": "Contents",
+   "toc_cell": false,
+   "toc_position": {},
+   "toc_section_display": false,
+   "toc_window_display": false
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}

From d9011445690852d2dc3018ae471510456c24e4ab Mon Sep 17 00:00:00 2001
From: Alexander Hess <alexander@webartifex.biz>
Date: Wed, 14 Oct 2020 00:07:02 +0200
Subject: [PATCH 146/242] Streamline slides

---
 chapter_02_functions/00_content.ipynb |  6 +++++-
 chapter_02_functions/02_content.ipynb | 16 ++++++++++++----
 2 files changed, 17 insertions(+), 5 deletions(-)

diff --git a/chapter_02_functions/00_content.ipynb b/chapter_02_functions/00_content.ipynb
index d7e21b5..4d18040 100644
--- a/chapter_02_functions/00_content.ipynb
+++ b/chapter_02_functions/00_content.ipynb
@@ -2577,7 +2577,11 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
    "source": [
     "If `scalar` is passed in, this may be done as either a positional or a keyword argument. Which of the two calls where `scalar` is `2` is faster to understand in a larger program?"
    ]
diff --git a/chapter_02_functions/02_content.ipynb b/chapter_02_functions/02_content.ipynb
index 87fd9f5..4c18bb4 100644
--- a/chapter_02_functions/02_content.ipynb
+++ b/chapter_02_functions/02_content.ipynb
@@ -863,7 +863,11 @@
   {
    "cell_type": "code",
    "execution_count": 23,
-   "metadata": {},
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
    "outputs": [],
    "source": [
     "numbers = [7, 11, 8, 5, 3, 12, 2, 6, 9, 10, 1, 4]"
@@ -874,7 +878,7 @@
    "execution_count": 24,
    "metadata": {
     "slideshow": {
-     "slide_type": "fragment"
+     "slide_type": "-"
     }
    },
    "outputs": [
@@ -1344,7 +1348,11 @@
   {
    "cell_type": "code",
    "execution_count": 39,
-   "metadata": {},
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
    "outputs": [
     {
      "name": "stdout",
@@ -1376,7 +1384,7 @@
    "execution_count": 40,
    "metadata": {
     "slideshow": {
-     "slide_type": "slide"
+     "slide_type": "fragment"
     }
    },
    "outputs": [],

From 9ec2f5758017ff4701c8f74f782ad4658aeed23a Mon Sep 17 00:00:00 2001
From: Alexander Hess <alexander@webartifex.biz>
Date: Thu, 15 Oct 2020 15:10:22 +0200
Subject: [PATCH 147/242] Pin the dependencies

- upgrade transient dependencies:
  + nbformat
  + traitlets
  + virtualenv
---
 poetry.lock | 21 +++++++++++----------
 1 file changed, 11 insertions(+), 10 deletions(-)

diff --git a/poetry.lock b/poetry.lock
index 9da7a06..9f17fc7 100644
--- a/poetry.lock
+++ b/poetry.lock
@@ -555,7 +555,7 @@ webpdf = ["pyppeteer (0.2.2)"]
 
 [[package]]
 name = "nbformat"
-version = "5.0.7"
+version = "5.0.8"
 description = "The Jupyter Notebook format"
 category = "main"
 optional = false
@@ -568,7 +568,8 @@ jupyter-core = "*"
 traitlets = ">=4.1"
 
 [package.extras]
-test = ["pytest", "pytest-cov", "testpath"]
+fast = ["fastjsonschema"]
+test = ["fastjsonschema", "testpath", "pytest", "pytest-cov"]
 
 [[package]]
 name = "nest-asyncio"
@@ -905,7 +906,7 @@ python-versions = ">= 3.5"
 
 [[package]]
 name = "traitlets"
-version = "5.0.4"
+version = "5.0.5"
 description = "Traitlets Python configuration system"
 category = "main"
 optional = false
@@ -932,7 +933,7 @@ socks = ["PySocks (>=1.5.6,<1.5.7 || >1.5.7,<2.0)"]
 
 [[package]]
 name = "virtualenv"
-version = "20.0.34"
+version = "20.0.35"
 description = "Virtual Python Environment builder"
 category = "dev"
 optional = false
@@ -1248,8 +1249,8 @@ nbconvert = [
     {file = "nbconvert-6.0.7.tar.gz", hash = "sha256:cbbc13a86dfbd4d1b5dee106539de0795b4db156c894c2c5dc382062bbc29002"},
 ]
 nbformat = [
-    {file = "nbformat-5.0.7-py3-none-any.whl", hash = "sha256:ea55c9b817855e2dfcd3f66d74857342612a60b1f09653440f4a5845e6e3523f"},
-    {file = "nbformat-5.0.7.tar.gz", hash = "sha256:54d4d6354835a936bad7e8182dcd003ca3dc0cedfee5a306090e04854343b340"},
+    {file = "nbformat-5.0.8-py3-none-any.whl", hash = "sha256:aa9450c16d29286dc69b92ea4913c1bffe86488f90184445996ccc03a2f60382"},
+    {file = "nbformat-5.0.8.tar.gz", hash = "sha256:f545b22138865bfbcc6b1ffe89ed5a2b8e2dc5d4fe876f2ca60d8e6f702a30f8"},
 ]
 nest-asyncio = [
     {file = "nest_asyncio-1.4.1-py3-none-any.whl", hash = "sha256:a4487c4f49f2d11a7bb89a512a6886b6a5045f47097f49815b2851aaa8599cf0"},
@@ -1462,16 +1463,16 @@ tornado = [
     {file = "tornado-6.0.4.tar.gz", hash = "sha256:0fe2d45ba43b00a41cd73f8be321a44936dc1aba233dee979f17a042b83eb6dc"},
 ]
 traitlets = [
-    {file = "traitlets-5.0.4-py3-none-any.whl", hash = "sha256:9664ec0c526e48e7b47b7d14cd6b252efa03e0129011de0a9c1d70315d4309c3"},
-    {file = "traitlets-5.0.4.tar.gz", hash = "sha256:86c9351f94f95de9db8a04ad8e892da299a088a64fd283f9f6f18770ae5eae1b"},
+    {file = "traitlets-5.0.5-py3-none-any.whl", hash = "sha256:69ff3f9d5351f31a7ad80443c2674b7099df13cc41fc5fa6e2f6d3b0330b0426"},
+    {file = "traitlets-5.0.5.tar.gz", hash = "sha256:178f4ce988f69189f7e523337a3e11d91c786ded9360174a3d9ca83e79bc5396"},
 ]
 urllib3 = [
     {file = "urllib3-1.25.10-py2.py3-none-any.whl", hash = "sha256:e7983572181f5e1522d9c98453462384ee92a0be7fac5f1413a1e35c56cc0461"},
     {file = "urllib3-1.25.10.tar.gz", hash = "sha256:91056c15fa70756691db97756772bb1eb9678fa585d9184f24534b100dc60f4a"},
 ]
 virtualenv = [
-    {file = "virtualenv-20.0.34-py2.py3-none-any.whl", hash = "sha256:4ecd607e7809bd7384039065639a8babc9fa562fe1b73b24095ce85b83d55fcf"},
-    {file = "virtualenv-20.0.34.tar.gz", hash = "sha256:4bf0e2bf99d33b123a895a5a244f0d7adb2a92171c6bbb31c3e2db235624abf1"},
+    {file = "virtualenv-20.0.35-py2.py3-none-any.whl", hash = "sha256:0ebc633426d7468664067309842c81edab11ae97fcaf27e8ad7f5748c89b431b"},
+    {file = "virtualenv-20.0.35.tar.gz", hash = "sha256:2a72c80fa2ad8f4e2985c06e6fc12c3d60d060e410572f553c90619b0f6efaf3"},
 ]
 wcwidth = [
     {file = "wcwidth-0.2.5-py2.py3-none-any.whl", hash = "sha256:beb4802a9cebb9144e99086eff703a642a13d6a0052920003a230f3294bbe784"},

From 9df4d89e4a773e9780133c79d2cb5563afb32634 Mon Sep 17 00:00:00 2001
From: Alexander Hess <alexander@webartifex.biz>
Date: Thu, 15 Oct 2020 15:16:11 +0200
Subject: [PATCH 148/242] Streamline text

---
 README.md | 16 +++++++++-------
 1 file changed, 9 insertions(+), 7 deletions(-)

diff --git a/README.md b/README.md
index 35f4dbc..3193767 100644
--- a/README.md
+++ b/README.md
@@ -204,10 +204,10 @@ The following instructions assume that
 
 <img src="static/cli_install.png" width="50%" align="center">
 
-The screeshot above shows how this project can be set up in an alternative way
+The screenshot above shows how this project can be set up in an alternative way
     with the [zsh](https://en.wikipedia.org/wiki/Z_shell) CLI.
 
-First, the [git](https://git-scm.com/) tool is used
+First, [git](https://git-scm.com/) is used
     to **clone** the course materials as a **repository**
     into a new folder called "*intro-to-python*"
     that lives under a "*repos*" folder.
@@ -216,7 +216,7 @@ First, the [git](https://git-scm.com/) tool is used
 
 The `cd` command is used to "change directories".
 
-In the screenshot, the [pyenv](https://github.com/pyenv/pyenv) tool is used
+In the screenshot, [pyenv](https://github.com/pyenv/pyenv) is used
     to set the project's Python version.
 [pyenv](https://github.com/pyenv/pyenv)'s purpose is
     to manage *many* parallel Python installations on the same computer.
@@ -225,7 +225,7 @@ It is highly recommended for professional users;
 
 - `pyenv local ...`
 
-On the contrary, the [poetry](https://python-poetry.org/docs/) tool is used
+On the contrary, [poetry](https://python-poetry.org/docs/)'s purpose is
     to manage third-party packages within the *same* Python installation
     and, more importantly, on a per-project basis.
 So, for example,
@@ -255,7 +255,7 @@ The following *one* command not only
 
 [poetry](https://python-poetry.org/docs/) is also used
     to execute commands in the project's (virtual) environment.
-The command is then prefixed with `poetry run ...`.
+To do that, the command is prefixed with `poetry run ...`.
 
 The project uses [nox](https://nox.thea.codes/en/stable/)
     to manage various maintenance tasks.
@@ -286,7 +286,8 @@ With that, the instructor can execute code in *presentation* mode during a class
 However, the RISE extension does *not* work in the more recent
     [JupyterLab](https://jupyterlab.readthedocs.io/en/stable/) app
     but only in the older [Jupyter Notebook](https://jupyter-notebook.readthedocs.io/en/stable/) app,
-    which comes with less features and a simpler [GUI](https://en.wikipedia.org/wiki/Graphical_user_interface).
+    which comes with less features
+        and a simpler [GUI <img height="12" style="display: inline-block" src="static/link/to_gh.png">](https://en.wikipedia.org/wiki/Graphical_user_interface).
 The instructor can start the latter with:
 
 - `poetry run jupyter notebook`
@@ -322,4 +323,5 @@ Alexander Hess is a PhD student
     at the Chair of Logistics Management at [WHU - Otto Beisheim School of Management](https://www.whu.edu)
     where he conducts research on urban delivery platforms
     and teaches coding courses based on Python in the BSc and MBA programs.
-Connect him on [LinkedIn](https://www.linkedin.com/in/webartifex).
\ No newline at end of file
+
+Connect him on [LinkedIn](https://www.linkedin.com/in/webartifex).

From 086d0bafec616cc3aac9fdeaa5a5f6bb04ba2045 Mon Sep 17 00:00:00 2001
From: Alexander Hess <alexander@webartifex.biz>
Date: Thu, 15 Oct 2020 15:25:16 +0200
Subject: [PATCH 149/242] Restructure folders & notebooks

- drop 'chapter_' prefix for chapter folders
- drop numbers for ToC sub-chapters
- split exercises into one problem set per notebook
- adjust titles in notebooks => show sub-chapter titles on the same
  line as the actual chapter titles
---
 .../00_content.ipynb                          |   6 +-
 .../01_exercises.ipynb                        |  15 +-
 .../02_review.ipynb                           |  15 +-
 .../static/cli_example.png                    | Bin
 .../static/example_python_users.png           | Bin
 .../growth_of_major_programming_languages.png | Bin
 ...rowth_of_smaller_programming_languages.png | Bin
 .../static/logo.png                           | Bin
 .../static/xkcd.png                           | Bin
 .../00_content.ipynb                          |  10 +-
 01_elements/01_exercises.ipynb                | 172 +++++++++
 01_elements/02_exercises.ipynb                | 218 ++++++++++++
 .../03_content.ipynb                          |  14 +-
 .../04_exercises.ipynb                        |  15 +-
 .../05_summary.ipynb                          |  20 +-
 .../06_review.ipynb                           |  15 +-
 .../07_resources.ipynb                        |  16 +-
 .../00_content.ipynb                          |  24 +-
 .../01_exercises.ipynb                        |  26 +-
 .../02_content.ipynb                          |  58 +--
 .../03_summary.ipynb                          |  13 +-
 .../04_review.ipynb                           |  15 +-
 .../sample_module.py                          |   0
 README.md                                     |  56 +--
 chapter_01_elements/01_exercises.ipynb        | 336 ------------------
 25 files changed, 497 insertions(+), 547 deletions(-)
 rename {chapter_00_intro => 00_intro}/00_content.ipynb (99%)
 rename {chapter_00_intro => 00_intro}/01_exercises.ipynb (91%)
 rename {chapter_00_intro => 00_intro}/02_review.ipynb (94%)
 rename {chapter_00_intro => 00_intro}/static/cli_example.png (100%)
 rename {chapter_00_intro => 00_intro}/static/example_python_users.png (100%)
 rename {chapter_00_intro => 00_intro}/static/growth_of_major_programming_languages.png (100%)
 rename {chapter_00_intro => 00_intro}/static/growth_of_smaller_programming_languages.png (100%)
 rename {chapter_00_intro => 00_intro}/static/logo.png (100%)
 rename {chapter_00_intro => 00_intro}/static/xkcd.png (100%)
 rename {chapter_01_elements => 01_elements}/00_content.ipynb (98%)
 create mode 100644 01_elements/01_exercises.ipynb
 create mode 100644 01_elements/02_exercises.ipynb
 rename chapter_01_elements/02_content.ipynb => 01_elements/03_content.ipynb (96%)
 rename chapter_01_elements/03_exercises.ipynb => 01_elements/04_exercises.ipynb (91%)
 rename chapter_01_elements/04_summary.ipynb => 01_elements/05_summary.ipynb (90%)
 rename chapter_01_elements/05_review.ipynb => 01_elements/06_review.ipynb (90%)
 rename chapter_01_elements/06_resources.ipynb => 01_elements/07_resources.ipynb (98%)
 rename {chapter_02_functions => 02_functions}/00_content.ipynb (95%)
 rename {chapter_02_functions => 02_functions}/01_exercises.ipynb (92%)
 rename {chapter_02_functions => 02_functions}/02_content.ipynb (93%)
 rename {chapter_02_functions => 02_functions}/03_summary.ipynb (93%)
 rename {chapter_02_functions => 02_functions}/04_review.ipynb (90%)
 rename {chapter_02_functions => 02_functions}/sample_module.py (100%)
 delete mode 100644 chapter_01_elements/01_exercises.ipynb

diff --git a/chapter_00_intro/00_content.ipynb b/00_intro/00_content.ipynb
similarity index 99%
rename from chapter_00_intro/00_content.ipynb
rename to 00_intro/00_content.ipynb
index b3f20a3..75b5fb4 100644
--- a/chapter_00_intro/00_content.ipynb
+++ b/00_intro/00_content.ipynb
@@ -8,7 +8,7 @@
     }
    },
    "source": [
-    "**Note**: Click on \"*Kernel*\" > \"*Restart Kernel and Clear All Outputs*\" in [JupyterLab](https://jupyterlab.readthedocs.io/en/stable/) *before* reading this notebook to reset its output. If you cannot run this file on your machine, you may want to open it [in the cloud <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_mb.png\">](https://mybinder.org/v2/gh/webartifex/intro-to-python/develop?urlpath=lab/tree/chapter_00_intro/00_content.ipynb)."
+    "**Note**: Click on \"*Kernel*\" > \"*Restart Kernel and Clear All Outputs*\" in [JupyterLab](https://jupyterlab.readthedocs.io/en/stable/) *before* reading this notebook to reset its output. If you cannot run this file on your machine, you may want to open it [in the cloud <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_mb.png\">](https://mybinder.org/v2/gh/webartifex/intro-to-python/develop?urlpath=lab/tree/00_intro/00_content.ipynb)."
    ]
   },
   {
@@ -750,8 +750,8 @@
     "**Part A: Expressing Logic**\n",
     "\n",
     "- What is a programming language? What kind of words exist?\n",
-    "  - *Chapter 1*: [Elements of a Program <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/chapter_01_elements/00_content.ipynb)\n",
-    "  - *Chapter 2*: [Functions & Modularization <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/chapter_02_functions/00_content.ipynb)\n",
+    "  - *Chapter 1*: [Elements of a Program <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/01_elements/00_content.ipynb)\n",
+    "  - *Chapter 2*: [Functions & Modularization <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/02_functions/00_content.ipynb)\n",
     "- What is the flow of execution? How can we form sentences from words?\n",
     "  - *Chapter 3*: Conditionals & Exceptions\n",
     "  - *Chapter 4*: Recursion & Looping"
diff --git a/chapter_00_intro/01_exercises.ipynb b/00_intro/01_exercises.ipynb
similarity index 91%
rename from chapter_00_intro/01_exercises.ipynb
rename to 00_intro/01_exercises.ipynb
index 7f51a1d..c5082eb 100644
--- a/chapter_00_intro/01_exercises.ipynb
+++ b/00_intro/01_exercises.ipynb
@@ -4,35 +4,28 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "**Note**: Click on \"*Kernel*\" > \"*Restart Kernel and Run All*\" in [JupyterLab](https://jupyterlab.readthedocs.io/en/stable/) *after* finishing the exercises to ensure that your solution runs top to bottom *without* any errors. If you cannot run this file on your machine, you may want to open it [in the cloud <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_mb.png\">](https://mybinder.org/v2/gh/webartifex/intro-to-python/develop?urlpath=lab/tree/chapter_00_intro/02_exercises.ipynb)."
+    "**Note**: Click on \"*Kernel*\" > \"*Restart Kernel and Run All*\" in [JupyterLab](https://jupyterlab.readthedocs.io/en/stable/) *after* finishing the exercises to ensure that your solution runs top to bottom *without* any errors. If you cannot run this file on your machine, you may want to open it [in the cloud <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_mb.png\">](https://mybinder.org/v2/gh/webartifex/intro-to-python/develop?urlpath=lab/tree/00_intro/02_exercises.ipynb)."
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# Chapter 0: Introduction"
+    "# Chapter 0: Introduction (Coding Exercises)"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## \"Coding\" Exercises"
+    "The exercises below assume that you have read [Chapter 0 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/00_intro/00_content.ipynb)."
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "The exercises below assume that you have read [Chapter 0 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/chapter_00_intro/00_content.ipynb)."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Mastering Markdown"
+    "## Mastering Markdown"
    ]
   },
   {
diff --git a/chapter_00_intro/02_review.ipynb b/00_intro/02_review.ipynb
similarity index 94%
rename from chapter_00_intro/02_review.ipynb
rename to 00_intro/02_review.ipynb
index 0f75bfe..9a8d7c2 100644
--- a/chapter_00_intro/02_review.ipynb
+++ b/00_intro/02_review.ipynb
@@ -4,21 +4,14 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# Chapter 0: Introduction"
+    "# Chapter 0: Introduction (Review Questions)"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Review"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The questions below assume that you have read [Chapter 0 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/chapter_00_intro/00_content.ipynb).\n",
+    "The questions below assume that you have read [Chapter 0 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/00_intro/00_content.ipynb).\n",
     "\n",
     "Be concise in your answers! Most questions can be answered in *one* sentence."
    ]
@@ -27,7 +20,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### Essay Questions "
+    "## Essay Questions "
    ]
   },
   {
@@ -90,7 +83,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### True / False Questions"
+    "## True / False Questions"
    ]
   },
   {
diff --git a/chapter_00_intro/static/cli_example.png b/00_intro/static/cli_example.png
similarity index 100%
rename from chapter_00_intro/static/cli_example.png
rename to 00_intro/static/cli_example.png
diff --git a/chapter_00_intro/static/example_python_users.png b/00_intro/static/example_python_users.png
similarity index 100%
rename from chapter_00_intro/static/example_python_users.png
rename to 00_intro/static/example_python_users.png
diff --git a/chapter_00_intro/static/growth_of_major_programming_languages.png b/00_intro/static/growth_of_major_programming_languages.png
similarity index 100%
rename from chapter_00_intro/static/growth_of_major_programming_languages.png
rename to 00_intro/static/growth_of_major_programming_languages.png
diff --git a/chapter_00_intro/static/growth_of_smaller_programming_languages.png b/00_intro/static/growth_of_smaller_programming_languages.png
similarity index 100%
rename from chapter_00_intro/static/growth_of_smaller_programming_languages.png
rename to 00_intro/static/growth_of_smaller_programming_languages.png
diff --git a/chapter_00_intro/static/logo.png b/00_intro/static/logo.png
similarity index 100%
rename from chapter_00_intro/static/logo.png
rename to 00_intro/static/logo.png
diff --git a/chapter_00_intro/static/xkcd.png b/00_intro/static/xkcd.png
similarity index 100%
rename from chapter_00_intro/static/xkcd.png
rename to 00_intro/static/xkcd.png
diff --git a/chapter_01_elements/00_content.ipynb b/01_elements/00_content.ipynb
similarity index 98%
rename from chapter_01_elements/00_content.ipynb
rename to 01_elements/00_content.ipynb
index ad71aa9..6003bd9 100644
--- a/chapter_01_elements/00_content.ipynb
+++ b/01_elements/00_content.ipynb
@@ -8,7 +8,7 @@
     }
    },
    "source": [
-    "**Note**: Click on \"*Kernel*\" > \"*Restart Kernel and Clear All Outputs*\" in [JupyterLab](https://jupyterlab.readthedocs.io/en/stable/) *before* reading this notebook to reset its output. If you cannot run this file on your machine, you may want to open it [in the cloud <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_mb.png\">](https://mybinder.org/v2/gh/webartifex/intro-to-python/develop?urlpath=lab/tree/chapter_01_elements/00_content.ipynb)."
+    "**Note**: Click on \"*Kernel*\" > \"*Restart Kernel and Clear All Outputs*\" in [JupyterLab](https://jupyterlab.readthedocs.io/en/stable/) *before* reading this notebook to reset its output. If you cannot run this file on your machine, you may want to open it [in the cloud <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_mb.png\">](https://mybinder.org/v2/gh/webartifex/intro-to-python/develop?urlpath=lab/tree/01_elements/00_content.ipynb)."
    ]
   },
   {
@@ -1165,7 +1165,7 @@
    "source": [
     "These addresses are *not* meaningful for anything other than checking if two variables reference the *same* object.\n",
     "\n",
-    "Obviously, `a` and `b`  have the same *value* as revealed by the **equality operator** `==`: We say `a` and `b` \"evaluate equal.\" The resulting `True` - and the `False` further below - is yet another data type, a so-called **boolean**. We look into them in [Chapter 3 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/chapter_03_conditionals/00_content.ipynb#Boolean-Expressions)."
+    "Obviously, `a` and `b`  have the same *value* as revealed by the **equality operator** `==`: We say `a` and `b` \"evaluate equal.\" The resulting `True` - and the `False` further below - is yet another data type, a so-called **boolean**. We look into them in [Chapter 3 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/03_conditionals/00_content.ipynb#Boolean-Expressions)."
    ]
   },
   {
@@ -1342,7 +1342,7 @@
    "source": [
     "Different types imply different behaviors for the objects. The `b` object, for example, may be \"asked\" if it is a whole number with the [is_integer() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/stdtypes.html#float.is_integer) \"functionality\" that comes with *every* `float` object.\n",
     "\n",
-    "Formally, we call such type-specific functionalities **methods** (i.e., as opposed to functions) and we look at them in detail in [Chapter 10 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/chapter_10_classes/00_content.ipynb). For now, it suffices to know that we access them with the **dot operator** `.` on the object. Of course, `b` is a whole number, which the boolean object `True` tells us."
+    "Formally, we call such type-specific functionalities **methods** (i.e., as opposed to functions) and we look at them in detail in [Chapter 10 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/10_classes/00_content.ipynb). For now, it suffices to know that we access them with the **dot operator** `.` on the object. Of course, `b` is a whole number, which the boolean object `True` tells us."
    ]
   },
   {
@@ -1934,7 +1934,7 @@
     }
    },
    "source": [
-    "For example, while the above code to calculate the average of the even numbers in `[7, 11, 8, 5, 3, 12, 2, 6, 9, 10, 1, 4]` is correct, a Pythonista would rewrite it in a more \"Pythonic\" way and use the built-in [sum() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#sum) and [len() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#len) functions (cf., [Chapter 2 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/chapter_02_functions/00_content.ipynb#Built-in-Functions)) as well as a so-called **list comprehension** (cf., [Chapter 8 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/chapter_08_mfr/00_content.ipynb#List-Comprehensions)). Pythonic code runs faster in many cases and is less error-prone."
+    "For example, while the above code to calculate the average of the even numbers in `[7, 11, 8, 5, 3, 12, 2, 6, 9, 10, 1, 4]` is correct, a Pythonista would rewrite it in a more \"Pythonic\" way and use the built-in [sum() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#sum) and [len() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#len) functions (cf., [Chapter 2 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/02_functions/00_content.ipynb#Built-in-Functions)) as well as a so-called **list comprehension** (cf., [Chapter 8 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/08_mfr/00_content.ipynb#List-Comprehensions)). Pythonic code runs faster in many cases and is less error-prone."
    ]
   },
   {
@@ -2136,7 +2136,7 @@
     "\n",
     "At the same time, for a beginner's course, it is often easier to code linearly.\n",
     "\n",
-    "In real data science projects, one would probably employ a mixed approach and put reusable code into so-called Python modules (i.e., *.py* files; cf., [Chapter 2 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/chapter_02_functions/00_content.ipynb#Local-Modules-and-Packages)) and then use Jupyter notebooks to build up a linear report or storyline for an analysis."
+    "In real data science projects, one would probably employ a mixed approach and put reusable code into so-called Python modules (i.e., *.py* files; cf., [Chapter 2 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/02_functions/00_content.ipynb#Local-Modules-and-Packages)) and then use Jupyter notebooks to build up a linear report or storyline for an analysis."
    ]
   }
  ],
diff --git a/01_elements/01_exercises.ipynb b/01_elements/01_exercises.ipynb
new file mode 100644
index 0000000..d073a26
--- /dev/null
+++ b/01_elements/01_exercises.ipynb
@@ -0,0 +1,172 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Note**: Click on \"*Kernel*\" > \"*Restart Kernel and Run All*\" in [JupyterLab](https://jupyterlab.readthedocs.io/en/stable/) *after* finishing the exercises to ensure that your solution runs top to bottom *without* any errors. If you cannot run this file on your machine, you may want to open it [in the cloud <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_mb.png\">](https://mybinder.org/v2/gh/webartifex/intro-to-python/develop?urlpath=lab/tree/01_elements/01_exercises.ipynb)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Chapter 1: Elements of a Program (Coding Exercises)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The exercises below assume that you have read the [first part <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/01_elements/00_content.ipynb) of Chapter 1.\n",
+    "\n",
+    "The `...`'s in the code cells indicate where you need to fill in code snippets. The number of `...`'s within a code cell give you a rough idea of how many lines of code are needed to solve the task. You should not need to create any additional code cells for your final solution. However, you may want to use temporary code cells to try out some ideas."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Printing Output"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q1**: *Concatenate* `greeting` and `audience` below with the `+` operator and print out the resulting message `\"Hello World\"` with only *one* call of the built-in [print() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#print) function!\n",
+    "\n",
+    "Hint: You may have to \"add\" a space character in between `greeting` and `audience`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "greeting = \"Hello\"\n",
+    "audience = \"World\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "print(...)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q2**: How is your answer to **Q1** an example of the concept of **operator overloading**?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " < your answer >"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q3**: Read the documentation on the built-in [print() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#print) function! How can you print the above message *without* concatenating `greeting` and `audience` first in *one* call of [print() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#print)?\n",
+    "\n",
+    "Hint: The `*objects` in the documentation implies that we can put several *expressions* (i.e., variables) separated by commas within the same call of the [print() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#print) function."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "print(...)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q4**: What does the `sep=\" \"` mean in the documentation on the built-in [print() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#print) function? Adjust and use it to print out the three names referenced by `first`, `second`, and `third` on *one* line separated by *commas* with only *one* call of the [print() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#print) function!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "first = \"Anthony\"\n",
+    "second = \"Berta\"\n",
+    "third = \"Christian\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "print(...)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q5**: Lastly, what does the `end=\"\\n\"` mean in the documentation? Adjust and use it within the `for`-loop to print the numbers `1` through `10` on *one* line with only *one* call of the [print() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#print) function!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "for number in [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]:\n",
+    "    print(...)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.6"
+  },
+  "toc": {
+   "base_numbering": 1,
+   "nav_menu": {},
+   "number_sections": false,
+   "sideBar": true,
+   "skip_h1_title": true,
+   "title_cell": "Table of Contents",
+   "title_sidebar": "Contents",
+   "toc_cell": false,
+   "toc_position": {},
+   "toc_section_display": false,
+   "toc_window_display": false
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/01_elements/02_exercises.ipynb b/01_elements/02_exercises.ipynb
new file mode 100644
index 0000000..5ae269c
--- /dev/null
+++ b/01_elements/02_exercises.ipynb
@@ -0,0 +1,218 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Note**: Click on \"*Kernel*\" > \"*Restart Kernel and Run All*\" in [JupyterLab](https://jupyterlab.readthedocs.io/en/stable/) *after* finishing the exercises to ensure that your solution runs top to bottom *without* any errors. If you cannot run this file on your machine, you may want to open it [in the cloud <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_mb.png\">](https://mybinder.org/v2/gh/webartifex/intro-to-python/develop?urlpath=lab/tree/01_elements/02_exercises.ipynb)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Chapter 1: Elements of a Program (Coding Exercises)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The exercises below assume that you have read the [first part <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/01_elements/00_content.ipynb) of Chapter 1.\n",
+    "\n",
+    "The `...`'s in the code cells indicate where you need to fill in code snippets. The number of `...`'s within a code cell give you a rough idea of how many lines of code are needed to solve the task. You should not need to create any additional code cells for your final solution. However, you may want to use temporary code cells to try out some ideas."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Simple `for`-loops"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "`for`-loops are extremely versatile in Python. That is different from many other programming languages.\n",
+    "\n",
+    "As shown in the first example in [Chapter 1 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/01_elements/00_content.ipynb#Example:-Averaging-all-even-Numbers-in-a-List), we can create a `list` like `numbers` and loop over the numbers in it on a one-by-one basis."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "numbers = [7, 11, 8, 5, 3, 12, 2, 6, 9, 10, 1, 4]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q1**: Fill in the *condition* of the `if` statement such that only numbers divisible by `3` are printed! Adjust the call of the [print() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#print) function such that the `for`-loop prints out all the numbers on *one* line of output!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "for number in numbers:\n",
+    "    if ...:\n",
+    "        print(...)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Instead of looping over an *existing* object referenced by a variable like `numbers`, we may also create a *new* object within the `for` statement and loop over it directly. For example, below we write out the `list` object as a *literal*.\n",
+    "\n",
+    "Generically, the objects contained in a `list` objects are referred to as its **elements**. We reflect that in the name of the *target* variable `element` that is assigned a different number in every iteration of the `for`-loop. While we could use *any* syntactically valid name, it is best to choose one that makes sense in the context (e.g., `number` in `numbers`).\n",
+    "\n",
+    "**Q2**: Fill in the condition of the `if` statement such that only numbers consisting of *one* digit are printed out! As before, print out all the numbers on *one* line of output!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "for element in [7, 11, 8, 5, 3, 12, 2, 6, 9, 10, 1, 4]:\n",
+    "    if ...:\n",
+    "        print(...)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "An easy way to loop over a `list` object in a sorted manner, is to wrap it with the built-in [sorted() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#sorted) function.\n",
+    "\n",
+    "**Q3**: Fill in the condition of the `if` statement such that only odd numbers are printed out! Put all the numbers on *one* line of output!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "for number in sorted(numbers):\n",
+    "    if ...:\n",
+    "        print(...)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Whenever we want to loop over numbers representing a [series <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_wiki.png\">](https://en.wikipedia.org/wiki/Series_%28mathematics%29) in the mathematical sense (i.e., a rule to calculate the next number from its predecessor), we may be able to use the [range() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#func-range) built-in.\n",
+    "\n",
+    "For example, to loop over the whole numbers from `0` to `9` (both including) in order, we could write them out in a `list` like below.\n",
+    "\n",
+    "**Q4**: Fill in the call to the [print() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#print) function such that all the numbers are printed on *one* line ouf output!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "for number in [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]:\n",
+    "    print(...)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q5**: Read the documentation on the [range() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#func-range) built-in! It may be used with either one, two, or three expressions \"passed\" in. What do `start`, `stop`, and `step` mean? Fill in the calls to [range() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#func-range) and [print() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#print) to mimic the output of **Q4**!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "for number in range(...):\n",
+    "    print(...)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q6**: Fill in the calls to [range() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#func-range) and [print() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#print) to print out *all* numbers from `1` to `10` (both including)!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "for number in range(...):\n",
+    "    print(...)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q7**: Fill in the calls to [range() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#func-range) and [print() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#print) to print out the *even* numbers from `1` to `10` (both including)! Do *not* use an `if` statement to accomplish this!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "for number in range(...):\n",
+    "    print(...)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.6"
+  },
+  "toc": {
+   "base_numbering": 1,
+   "nav_menu": {},
+   "number_sections": false,
+   "sideBar": true,
+   "skip_h1_title": true,
+   "title_cell": "Table of Contents",
+   "title_sidebar": "Contents",
+   "toc_cell": false,
+   "toc_position": {},
+   "toc_section_display": false,
+   "toc_window_display": false
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/chapter_01_elements/02_content.ipynb b/01_elements/03_content.ipynb
similarity index 96%
rename from chapter_01_elements/02_content.ipynb
rename to 01_elements/03_content.ipynb
index ea72a59..74c3477 100644
--- a/chapter_01_elements/02_content.ipynb
+++ b/01_elements/03_content.ipynb
@@ -8,7 +8,7 @@
     }
    },
    "source": [
-    "**Note**: Click on \"*Kernel*\" > \"*Restart Kernel and Clear All Outputs*\" in [JupyterLab](https://jupyterlab.readthedocs.io/en/stable/) *before* reading this notebook to reset its output. If you cannot run this file on your machine, you may want to open it [in the cloud <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_mb.png\">](https://mybinder.org/v2/gh/webartifex/intro-to-python/develop?urlpath=lab/tree/chapter_01_elements/02_content.ipynb)."
+    "**Note**: Click on \"*Kernel*\" > \"*Restart Kernel and Clear All Outputs*\" in [JupyterLab](https://jupyterlab.readthedocs.io/en/stable/) *before* reading this notebook to reset its output. If you cannot run this file on your machine, you may want to open it [in the cloud <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_mb.png\">](https://mybinder.org/v2/gh/webartifex/intro-to-python/develop?urlpath=lab/tree/01_elements/03_content.ipynb)."
    ]
   },
   {
@@ -19,7 +19,7 @@
     }
    },
    "source": [
-    "# Chapter 1: Elements of a Program (Part 2)"
+    "# Chapter 1: Elements of a Program (continued)"
    ]
   },
   {
@@ -367,7 +367,7 @@
     }
    },
    "source": [
-    "Some variables magically exist when a Python process is started or are added by Jupyter. We may safely ignore the former until [Chapter 11 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/chapter_11_classes/00_content.ipynb) and the latter for good."
+    "Some variables magically exist when a Python process is started or are added by Jupyter. We may safely ignore the former until [Chapter 11 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/11_classes/00_content.ipynb) and the latter for good."
    ]
   },
   {
@@ -679,7 +679,7 @@
     }
    },
    "source": [
-    "Variables with leading and trailing double underscores, referred to as **dunder** in Python jargon, are used for built-in functionalities and to implement object-oriented features as we see in [Chapter 11 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/chapter_11_classes/00_content.ipynb). We must *not* use this style for variables!"
+    "Variables with leading and trailing double underscores, referred to as **dunder** in Python jargon, are used for built-in functionalities and to implement object-oriented features as we see in [Chapter 11 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/11_classes/00_content.ipynb). We must *not* use this style for variables!"
    ]
   },
   {
@@ -960,7 +960,7 @@
    "source": [
     "Let's change the first element of `x`.\n",
     "\n",
-    "[Chapter 7 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/chapter_07_sequences/00_content.ipynb#The-list-Type) discusses lists in more depth. For now, let's view a `list` object as some sort of **container** that holds an arbitrary number of references to other objects and treat the brackets `[]` attached to it as yet another operator, namely the **indexing operator**. So, `x[0]` instructs Python to first follow the reference from the global list of all names to the `x` object. Then, it follows the first reference it finds there to the `1` object we put in the list. The indexing operator must be an operator as we merely *read* the first element and do not change anything in memory permanently.\n",
+    "[Chapter 7 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/07_sequences/00_content.ipynb#The-list-Type) discusses lists in more depth. For now, let's view a `list` object as some sort of **container** that holds an arbitrary number of references to other objects and treat the brackets `[]` attached to it as yet another operator, namely the **indexing operator**. So, `x[0]` instructs Python to first follow the reference from the global list of all names to the `x` object. Then, it follows the first reference it finds there to the `1` object we put in the list. The indexing operator must be an operator as we merely *read* the first element and do not change anything in memory permanently.\n",
     "\n",
     "Python **begins counting at 0**. This is not the case for many other languages, for example, [MATLAB <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_wiki.png\">](https://en.wikipedia.org/wiki/MATLAB), [R <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_wiki.png\">](https://en.wikipedia.org/wiki/R_%28programming_language%29), or [Stata <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_wiki.png\">](https://en.wikipedia.org/wiki/Stata). To understand why this makes sense, see this short [note](https://www.cs.utexas.edu/users/EWD/transcriptions/EWD08xx/EWD831.html) by one of the all-time greats in computer science, the late [Edsger Dijkstra <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_wiki.png\">](https://en.wikipedia.org/wiki/Edsger_W._Dijkstra)."
    ]
@@ -1136,7 +1136,7 @@
     "\n",
     "In simple words, anything that may be used on the *right-hand* side of an assignment statement without creating a `SyntaxError` is an expression.\n",
     "\n",
-    "What we have said about *individual* operators before, namely that they have *no* permanent side effects in memory, actually belongs here, to begin with: The absence of any *permanent* side effects is the characteristic property of expressions, and all the code cells in the \"*(Arithmetic) Operators*\" section in the [first part <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/chapter_01_elements/00_content.ipynb#%28Arithmetic%29-Operators) of this chapter are examples of expressions.\n",
+    "What we have said about *individual* operators before, namely that they have *no* permanent side effects in memory, actually belongs here, to begin with: The absence of any *permanent* side effects is the characteristic property of expressions, and all the code cells in the \"*(Arithmetic) Operators*\" section in the [first part <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/01_elements/00_content.ipynb#%28Arithmetic%29-Operators) of this chapter are examples of expressions.\n",
     "\n",
     "The simplest possible expressions contain only one variable or literal. The output below a code cell is Jupyter's way of returning the reference to the object to us!\n",
     "\n",
@@ -1406,7 +1406,7 @@
     }
    },
    "source": [
-    "... calling the built-in [print() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#print) function does *neither* change the memory *nor* evaluate to an object (disregarding the `None` object explained in [Chapter 2 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/chapter_02_functions/00_content.ipynb#Function-Definitions)). We could view changing the computer's screen as a side effect but this is outside of Python's memory!\n",
+    "... calling the built-in [print() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#print) function does *neither* change the memory *nor* evaluate to an object (disregarding the `None` object explained in [Chapter 2 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/02_functions/00_content.ipynb#Function-Definitions)). We could view changing the computer's screen as a side effect but this is outside of Python's memory!\n",
     "\n",
     "Also, the cell below has *no* output! It only looks like it does as Jupyter redirects whatever [print() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#print) writes to the \"screen\" to below a cell. We see a difference to the expressions above in that there are no brackets `[...]` next to the output showing the execution count number."
    ]
diff --git a/chapter_01_elements/03_exercises.ipynb b/01_elements/04_exercises.ipynb
similarity index 91%
rename from chapter_01_elements/03_exercises.ipynb
rename to 01_elements/04_exercises.ipynb
index d9d0bbf..f1cca6d 100644
--- a/chapter_01_elements/03_exercises.ipynb
+++ b/01_elements/04_exercises.ipynb
@@ -4,28 +4,21 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "**Note**: Click on \"*Kernel*\" > \"*Restart Kernel and Run All*\" in [JupyterLab](https://jupyterlab.readthedocs.io/en/stable/) *after* finishing the exercises to ensure that your solution runs top to bottom *without* any errors. If you cannot run this file on your machine, you may want to open it [in the cloud <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_mb.png\">](https://mybinder.org/v2/gh/webartifex/intro-to-python/develop?urlpath=lab/tree/chapter_01_elements/03_exercises.ipynb)."
+    "**Note**: Click on \"*Kernel*\" > \"*Restart Kernel and Run All*\" in [JupyterLab](https://jupyterlab.readthedocs.io/en/stable/) *after* finishing the exercises to ensure that your solution runs top to bottom *without* any errors. If you cannot run this file on your machine, you may want to open it [in the cloud <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_mb.png\">](https://mybinder.org/v2/gh/webartifex/intro-to-python/develop?urlpath=lab/tree/01_elements/04_exercises.ipynb)."
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# Chapter 1: Elements of a Program (Part 2)"
+    "# Chapter 1: Elements of a Program (Coding Exercises)"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Coding Exercises"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The exercises below assume that you have read the second part of [Chapter 1 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/chapter_01_elements/02_content.ipynb).\n",
+    "The exercises below assume that you have read the [second part <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/01_elements/03_content.ipynb) Chapter 1.\n",
     "\n",
     "The `...`'s in the code cells indicate where you need to fill in code snippets. The number of `...`'s within a code cell give you a rough idea of how many lines of code are needed to solve the task. You should not need to create any additional code cells for your final solution. However, you may want to use temporary code cells to try out some ideas."
    ]
@@ -34,7 +27,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### Python as a Calculator"
+    "## Python as a Calculator"
    ]
   },
   {
diff --git a/chapter_01_elements/04_summary.ipynb b/01_elements/05_summary.ipynb
similarity index 90%
rename from chapter_01_elements/04_summary.ipynb
rename to 01_elements/05_summary.ipynb
index 9f52d19..c3b6339 100644
--- a/chapter_01_elements/04_summary.ipynb
+++ b/01_elements/05_summary.ipynb
@@ -8,19 +8,7 @@
     }
    },
    "source": [
-    "\n",
-    "# Chapter 1: Elements of a Program"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "source": [
-    "## TL;DR"
+    "# Chapter 1: Elements of a Program (TL;DR)"
    ]
   },
   {
@@ -57,7 +45,7 @@
     "  - distinct and well-contained areas/parts of the memory that hold the actual data\n",
     "  - the concept by which Python manages the memory for us\n",
     "  - can be classified into objects of the same **type** (i.e., same abstract \"structure\" but different concrete data)\n",
-    "  - built-in objects (incl. **literals**) vs. user-defined objects (cf., [Chapter 11 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/chapter_11_classes/00_content.ipynb))\n",
+    "  - built-in objects (incl. **literals**) vs. user-defined objects (cf., [Chapter 11 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/11_classes/00_content.ipynb))\n",
     "  - e.g., `1`, `1.0`, and `\"one\"` are three different objects of distinct types that are also literals (i.e., by the way we type them into the command line Python knows what the value and type are)\n",
     "\n",
     "\n",
@@ -92,14 +80,14 @@
     "  - ignored by Python\n",
     "\n",
     "\n",
-    "- functions (cf., [Chapter 2 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/chapter_02_functions/00_content.ipynb))\n",
+    "- functions (cf., [Chapter 2 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/02_functions/00_content.ipynb))\n",
     "  - named sequences of instructions\n",
     "  - the smaller parts in a larger program\n",
     "  - make a program more modular and thus easier to understand\n",
     "  - include [built-in functions <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html) like [print() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#print), [sum() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#sum), or [len() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#len)\n",
     "\n",
     "\n",
-    "- flow control (cf., [Chapter 3 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/chapter_03_conditionals/00_content.ipynb) and [Chapter 4 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/chapter_04_iteration/00_content.ipynb))\n",
+    "- flow control (cf., [Chapter 3 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/03_conditionals/00_content.ipynb) and [Chapter 4 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/04_iteration/00_content.ipynb))\n",
     "  - expression of **business logic** or an **algorithm**\n",
     "  - conditional execution of parts of a program (e.g., `if` statements)\n",
     "  - repetitive execution of parts of a program (e.g., `for`-loops)"
diff --git a/chapter_01_elements/05_review.ipynb b/01_elements/06_review.ipynb
similarity index 90%
rename from chapter_01_elements/05_review.ipynb
rename to 01_elements/06_review.ipynb
index d8ec024..d975603 100644
--- a/chapter_01_elements/05_review.ipynb
+++ b/01_elements/06_review.ipynb
@@ -4,21 +4,14 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# Chapter 1: Elements of a Program"
+    "# Chapter 1: Elements of a Program (Review Questions)"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Content Review"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The questions below assume that you have read the [first <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/chapter_01_elements/00_content.ipynb) and the [second <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/chapter_01_elements/02_content.ipynb) part of Chapter 1.\n",
+    "The questions below assume that you have read the [first <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/01_elements/00_content.ipynb) and the [second <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/01_elements/03_content.ipynb) part of Chapter 1.\n",
     "\n",
     "Be concise in your answers! Most questions can be answered in *one* sentence."
    ]
@@ -27,7 +20,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### Essay Questions "
+    "## Essay Questions "
    ]
   },
   {
@@ -118,7 +111,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### True / False Questions"
+    "## True / False Questions"
    ]
   },
   {
diff --git a/chapter_01_elements/06_resources.ipynb b/01_elements/07_resources.ipynb
similarity index 98%
rename from chapter_01_elements/06_resources.ipynb
rename to 01_elements/07_resources.ipynb
index bdc2e29..c73cc49 100644
--- a/chapter_01_elements/06_resources.ipynb
+++ b/01_elements/07_resources.ipynb
@@ -8,19 +8,7 @@
     }
    },
    "source": [
-    "\n",
-    "# Chapter 1: Elements of a Program"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "source": [
-    "## Further Resources"
+    "# Chapter 1: Elements of a Program (Further Resources)"
    ]
   },
   {
@@ -58,7 +46,7 @@
        "        "
       ],
       "text/plain": [
-       "<IPython.lib.display.YouTubeVideo at 0x7fa2fc050d60>"
+       "<IPython.lib.display.YouTubeVideo at 0x7ff1446acd30>"
       ]
      },
      "execution_count": 1,
diff --git a/chapter_02_functions/00_content.ipynb b/02_functions/00_content.ipynb
similarity index 95%
rename from chapter_02_functions/00_content.ipynb
rename to 02_functions/00_content.ipynb
index 4d18040..964ecaa 100644
--- a/chapter_02_functions/00_content.ipynb
+++ b/02_functions/00_content.ipynb
@@ -8,7 +8,7 @@
     }
    },
    "source": [
-    "**Note**: Click on \"*Kernel*\" > \"*Restart Kernel and Clear All Outputs*\" in [JupyterLab](https://jupyterlab.readthedocs.io/en/stable/) *before* reading this notebook to reset its output. If you cannot run this file on your machine, you may want to open it [in the cloud <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_mb.png\">](https://mybinder.org/v2/gh/webartifex/intro-to-python/develop?urlpath=lab/tree/chapter_02_functions/00_content.ipynb)."
+    "**Note**: Click on \"*Kernel*\" > \"*Restart Kernel and Clear All Outputs*\" in [JupyterLab](https://jupyterlab.readthedocs.io/en/stable/) *before* reading this notebook to reset its output. If you cannot run this file on your machine, you may want to open it [in the cloud <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_mb.png\">](https://mybinder.org/v2/gh/webartifex/intro-to-python/develop?urlpath=lab/tree/02_functions/00_content.ipynb)."
    ]
   },
   {
@@ -30,7 +30,7 @@
     }
    },
    "source": [
-    "In [Chapter 1 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/chapter_01_elements/00_content.ipynb#Example:-Averaging-all-even-Numbers-in-a-List), we simply typed the code to calculate the average of the even numbers in a list of whole numbers into several code cells. Then, we executed them one after another. We had no way of *reusing* the code except for either executing cells multiple times. And, whenever we find ourselves doing repetitive manual work, we can be sure that there must be a way of automating what we are doing.\n",
+    "In [Chapter 1 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/01_elements/00_content.ipynb#Example:-Averaging-all-even-Numbers-in-a-List), we simply typed the code to calculate the average of the even numbers in a list of whole numbers into several code cells. Then, we executed them one after another. We had no way of *reusing* the code except for either executing cells multiple times. And, whenever we find ourselves doing repetitive manual work, we can be sure that there must be a way of automating what we are doing.\n",
     "\n",
     "This chapter shows how Python offers language constructs that let us **define** functions ourselves that we may then **call** just like the built-in ones. Also, we look at how we can extend our Python installation with functionalities written by other people."
    ]
@@ -307,7 +307,7 @@
     }
    },
    "source": [
-    "To execute a function, we **call** it with the **call operator** `()` as shown many times in [Chapter 1 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/chapter_01_elements/00_content.ipynb) and above.\n",
+    "To execute a function, we **call** it with the **call operator** `()` as shown many times in [Chapter 1 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/01_elements/00_content.ipynb) and above.\n",
     "\n",
     "If we are unsure whether a variable references a function or not, we can verify that with the built-in [callable() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#callable) function.\n",
     "\n",
@@ -538,7 +538,7 @@
     }
    },
    "source": [
-    "Notice the subtle difference compared to the behavior of the `//` operator in [Chapter 1 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/chapter_01_elements/00_content.ipynb##%28Arithmetic#%29-Operators) that \"rounds\" towards minus infinity: [int() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#int) always \"rounds\" towards `0`."
+    "Notice the subtle difference compared to the behavior of the `//` operator in [Chapter 1 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/01_elements/00_content.ipynb##%28Arithmetic#%29-Operators) that \"rounds\" towards minus infinity: [int() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#int) always \"rounds\" towards `0`."
    ]
   },
   {
@@ -869,7 +869,7 @@
     }
    },
    "source": [
-    "We may create so-called *user-defined* **functions** with the `def` statement (cf., [reference <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/reference/compound_stmts.html#function-definitions)). To extend an already familiar example, we reuse the introductory example from [Chapter 1 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/chapter_01_elements/00_content.ipynb#Best-Practices) in its final Pythonic version and transform it into the function `average_evens()` below. We replace the variable name `numbers` with `integers` for didactical purposes in the first couple of examples.\n",
+    "We may create so-called *user-defined* **functions** with the `def` statement (cf., [reference <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/reference/compound_stmts.html#function-definitions)). To extend an already familiar example, we reuse the introductory example from [Chapter 1 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/01_elements/00_content.ipynb#Best-Practices) in its final Pythonic version and transform it into the function `average_evens()` below. We replace the variable name `numbers` with `integers` for didactical purposes in the first couple of examples.\n",
     "\n",
     "A function's **name** must be chosen according to the same naming rules as ordinary variables since Python manages function names like variables. In this book, we further adopt the convention of ending function names with parentheses `()` in text cells for faster comprehension when reading (i.e., `average_evens()` vs. `average_evens`). These are *not* part of the name but must always be written out in the `def` statement for syntactic reasons.\n",
     "\n",
@@ -881,7 +881,7 @@
     "\n",
     "A function may specify an *explicit* **return value** (i.e., \"result\" or \"output\") with the `return` statement (cf., [reference <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/reference/simple_stmts.html#the-return-statement)): Functions that have one are considered **fruitful**; otherwise, they are **void**. Functions of the latter kind are still useful because of their **side effects**. For example, the built-in [print() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#print) function changes what we see on the screen. Strictly speaking, [print() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#print) and other void functions also have an *implicit* return value, namely the `None` object.\n",
     "\n",
-    "A function should define a **docstring** that describes what it does in a short subject line, what parameters it expects (i.e., their types), and what it returns, if anything. A docstring is a syntactically valid multi-line string (i.e., type `str`) defined within **triple-double quotes** `\"\"\"`. Strings are covered in depth in [Chapter 6 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/chapter_06_text/00_content.ipynb#The-str-Type). Widely adopted standards for docstrings are [PEP 257 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://www.python.org/dev/peps/pep-0257/) and section 3.8 of [Google's Python Style Guide <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_gh.png\">](https://github.com/google/styleguide/blob/gh-pages/pyguide.md)."
+    "A function should define a **docstring** that describes what it does in a short subject line, what parameters it expects (i.e., their types), and what it returns, if anything. A docstring is a syntactically valid multi-line string (i.e., type `str`) defined within **triple-double quotes** `\"\"\"`. Strings are covered in depth in [Chapter 6 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/06_text/00_content.ipynb#The-str-Type). Widely adopted standards for docstrings are [PEP 257 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://www.python.org/dev/peps/pep-0257/) and section 3.8 of [Google's Python Style Guide <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_gh.png\">](https://github.com/google/styleguide/blob/gh-pages/pyguide.md)."
    ]
   },
   {
@@ -1012,7 +1012,7 @@
    "source": [
     "Its value may seem awkward at first: It consists of a location showing where the function is defined (i.e., `__main__` here, which is Python's way of saying \"in this notebook\") and the signature wrapped inside angle brackets `<` and `>`.\n",
     " \n",
-    "The angle brackets are a convention to indicate that the value may *not* be used as a *literal* (i.e., typed back into another code cell). [Chapter 11 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/chapter_11_classes/00_content.ipynb) introduces the concept of a **text representation** of an object, which is related to the *semantic* meaning of an object's value as discussed in [Chapter 1 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/chapter_01_elements/00_content.ipynb#Value-/-\"Meaning\"), and the angle brackets convention is one such way to represent an object as text. When executed, the angle brackets cause a `SyntaxError` because Python expects the `<` operator to come with an operand on both sides (cf., [Chapter 3 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/chapter_03_conditionals/00_content.ipynb#Relational-Operators))."
+    "The angle brackets are a convention to indicate that the value may *not* be used as a *literal* (i.e., typed back into another code cell). [Chapter 11 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/11_classes/00_content.ipynb) introduces the concept of a **text representation** of an object, which is related to the *semantic* meaning of an object's value as discussed in [Chapter 1 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/01_elements/00_content.ipynb#Value-/-\"Meaning\"), and the angle brackets convention is one such way to represent an object as text. When executed, the angle brackets cause a `SyntaxError` because Python expects the `<` operator to come with an operand on both sides (cf., [Chapter 3 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/03_conditionals/00_content.ipynb#Relational-Operators))."
    ]
   },
   {
@@ -1148,7 +1148,7 @@
        "\n",
        "Returns:\n",
        "    average (float)\n",
-       "\u001b[0;31mFile:\u001b[0m      ~/repos/intro-to-python/chapter_02_functions/<ipython-input-27-bcb78a3113d7>\n",
+       "\u001b[0;31mFile:\u001b[0m      ~/repos/intro-to-python/02_functions/<ipython-input-27-bcb78a3113d7>\n",
        "\u001b[0;31mType:\u001b[0m      function\n"
       ]
      },
@@ -1197,7 +1197,7 @@
        "\u001b[0;34m\u001b[0m    \u001b[0mevens\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mn\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mn\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mintegers\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mn\u001b[0m \u001b[0;34m%\u001b[0m \u001b[0;36m2\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\n",
        "\u001b[0;34m\u001b[0m    \u001b[0maverage\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msum\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mevens\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m/\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mevens\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n",
        "\u001b[0;34m\u001b[0m    \u001b[0;32mreturn\u001b[0m \u001b[0maverage\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-       "\u001b[0;31mFile:\u001b[0m      ~/repos/intro-to-python/chapter_02_functions/<ipython-input-27-bcb78a3113d7>\n",
+       "\u001b[0;31mFile:\u001b[0m      ~/repos/intro-to-python/02_functions/<ipython-input-27-bcb78a3113d7>\n",
        "\u001b[0;31mType:\u001b[0m      function\n"
       ]
      },
@@ -1647,7 +1647,7 @@
    "source": [
     "[PythonTutor <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](http://pythontutor.com/visualize.html#code=numbers%20%3D%20%5B7,%2011,%208,%205,%203,%2012,%202,%206,%209,%2010,%201,%204%5D%0A%0Adef%20average_wrong%28integers%29%3A%0A%20%20%20%20evens%20%3D%20%5Bn%20for%20n%20in%20numbers%20if%20n%20%25%202%20%3D%3D%200%5D%0A%20%20%20%20average%20%3D%20sum%28evens%29%20/%20len%28evens%29%0A%20%20%20%20return%20average%0A%0Aresult%20%3D%20average_wrong%28%5B123,%20456,%20789%5D%29&cumulative=false&curInstr=0&heapPrimitives=nevernest&mode=display&origin=opt-frontend.js&py=3&rawInputLstJSON=%5B%5D&textReferences=false) is again helpful at visualizing the error interactively: Creating the `list` object `evens` eventually references takes *16* computational steps, namely two for managing the list comprehension, one for setting up an empty `list` object, *twelve* for filling it with elements derived from `numbers` in the global scope (i.e., that is the error), and one to make `evens` reference it (cf., steps 6-21).\n",
     "\n",
-    "The frames logic shown by PythonTutor is the mechanism with which Python not only manages the names inside *one* function call but also for *many* potentially *simultaneous* calls, as revealed in [Chapter 4 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/chapter_04_iteration/00_content.ipynb#Trivial-Example:-Countdown). It is the reason why we may reuse the same names for the parameters and variables inside both `average_evens()` and `average_wrong()` without Python mixing them up. So, as we already read in the [Zen of Python <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://www.python.org/dev/peps/pep-0020/), \"namespaces are one honking great idea\" (cf., `import this`), and a frame is just a special kind of namespace."
+    "The frames logic shown by PythonTutor is the mechanism with which Python not only manages the names inside *one* function call but also for *many* potentially *simultaneous* calls, as revealed in [Chapter 4 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/04_iteration/00_content.ipynb#Trivial-Example:-Countdown). It is the reason why we may reuse the same names for the parameters and variables inside both `average_evens()` and `average_wrong()` without Python mixing them up. So, as we already read in the [Zen of Python <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://www.python.org/dev/peps/pep-0020/), \"namespaces are one honking great idea\" (cf., `import this`), and a frame is just a special kind of namespace."
    ]
   },
   {
@@ -2021,7 +2021,7 @@
     }
    },
    "source": [
-    "So far, we have specified only one parameter in each of our user-defined functions. In [Chapter 1 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/chapter_01_elements/00_content.ipynb#%28Arithmetic%29-Operators), however, we saw the built-in [divmod() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#divmod) function take two arguments. And, the order in which they are passed in matters! Whenever we call a function and list its arguments in a comma separated manner, we say that we pass in the arguments *by position* or refer to them as **positional arguments**."
+    "So far, we have specified only one parameter in each of our user-defined functions. In [Chapter 1 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/01_elements/00_content.ipynb#%28Arithmetic%29-Operators), however, we saw the built-in [divmod() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#divmod) function take two arguments. And, the order in which they are passed in matters! Whenever we call a function and list its arguments in a comma separated manner, we say that we pass in the arguments *by position* or refer to them as **positional arguments**."
    ]
   },
   {
@@ -3012,7 +3012,7 @@
    "source": [
     "The main point of having functions without a reference to them is to use them in a situation where we know ahead of time that we use the function only *once*.\n",
     "\n",
-    "Popular applications of lambda expressions occur in combination with the **map-filter-reduce** paradigm (cf., [Chapter 8 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/chapter_08_mfr/00_content.ipynb#Lambda-Expressions))."
+    "Popular applications of lambda expressions occur in combination with the **map-filter-reduce** paradigm (cf., [Chapter 8 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/08_mfr/00_content.ipynb#Lambda-Expressions))."
    ]
   }
  ],
diff --git a/chapter_02_functions/01_exercises.ipynb b/02_functions/01_exercises.ipynb
similarity index 92%
rename from chapter_02_functions/01_exercises.ipynb
rename to 02_functions/01_exercises.ipynb
index 664e8f0..795be04 100644
--- a/chapter_02_functions/01_exercises.ipynb
+++ b/02_functions/01_exercises.ipynb
@@ -4,28 +4,21 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "**Note**: Click on \"*Kernel*\" > \"*Restart Kernel and Run All*\" in [JupyterLab](https://jupyterlab.readthedocs.io/en/stable/) *after* finishing the exercises to ensure that your solution runs top to bottom *without* any errors. If you cannot run this file on your machine, you may want to open it [in the cloud <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_mb.png\">](https://mybinder.org/v2/gh/webartifex/intro-to-python/develop?urlpath=lab/tree/chapter_02_functions/01_exercises.ipynb)."
+    "**Note**: Click on \"*Kernel*\" > \"*Restart Kernel and Run All*\" in [JupyterLab](https://jupyterlab.readthedocs.io/en/stable/) *after* finishing the exercises to ensure that your solution runs top to bottom *without* any errors. If you cannot run this file on your machine, you may want to open it [in the cloud <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_mb.png\">](https://mybinder.org/v2/gh/webartifex/intro-to-python/develop?urlpath=lab/tree/02_functions/01_exercises.ipynb)."
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# Chapter 2: Functions & Modularization"
+    "# Chapter 2: Functions & Modularization (Coding Exercises)"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Coding Exercises"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The exercises below assume that you have read the [first part <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/chapter_02_functions/00_content.ipynb) of Chapter 2.\n",
+    "The exercises below assume that you have read the [first part <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/02_functions/00_content.ipynb) of Chapter 2.\n",
     "\n",
     "The `...`'s in the code cells indicate where you need to fill in code snippets. The number of `...`'s within a code cell give you a rough idea of how many lines of code are needed to solve the task. You should not need to create any additional code cells for your final solution. However, you may want to use temporary code cells to try out some ideas."
    ]
@@ -34,7 +27,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### Volume of a Sphere"
+    "## Volume of a Sphere"
    ]
   },
   {
@@ -45,7 +38,7 @@
     "\n",
     "Hints:\n",
     "- use an appropriate approximation for $\\pi$\n",
-    "- you may use the [standard library <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/index.html) to do so if you have already looked at the [second part <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/chapter_02_functions/02_content.ipynb) of Chapter 2."
+    "- you may use the [standard library <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/index.html) to do so if you have already looked at the [second part <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/02_functions/02_content.ipynb) of Chapter 2."
    ]
   },
   {
@@ -57,15 +50,6 @@
     "import ...  # you may drop this cell and use your own approximation for Pi"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "r = ..."
-   ]
-  },
   {
    "cell_type": "code",
    "execution_count": null,
diff --git a/chapter_02_functions/02_content.ipynb b/02_functions/02_content.ipynb
similarity index 93%
rename from chapter_02_functions/02_content.ipynb
rename to 02_functions/02_content.ipynb
index 4c18bb4..06f1765 100644
--- a/chapter_02_functions/02_content.ipynb
+++ b/02_functions/02_content.ipynb
@@ -8,7 +8,7 @@
     }
    },
    "source": [
-    "**Note**: Click on \"*Kernel*\" > \"*Restart Kernel and Clear All Outputs*\" in [JupyterLab](https://jupyterlab.readthedocs.io/en/stable/) *before* reading this notebook to reset its output. If you cannot run this file on your machine, you may want to open it [in the cloud <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_mb.png\">](https://mybinder.org/v2/gh/webartifex/intro-to-python/develop?urlpath=lab/tree/chapter_02_functions/02_content.ipynb)."
+    "**Note**: Click on \"*Kernel*\" > \"*Restart Kernel and Clear All Outputs*\" in [JupyterLab](https://jupyterlab.readthedocs.io/en/stable/) *before* reading this notebook to reset its output. If you cannot run this file on your machine, you may want to open it [in the cloud <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_mb.png\">](https://mybinder.org/v2/gh/webartifex/intro-to-python/develop?urlpath=lab/tree/02_functions/02_content.ipynb)."
    ]
   },
   {
@@ -19,7 +19,7 @@
     }
    },
    "source": [
-    "# Chapter 2: Functions & Modularization (Part 2)"
+    "# Chapter 2: Functions & Modularization (continued)"
    ]
   },
   {
@@ -32,28 +32,8 @@
    "source": [
     "So far, we have only used what we refer to as **core** Python in this book. By this, we mean all the syntactical rules as specified in the [language reference <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/reference/) and a minimal set of about 50 built-in [functions <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html). With this, we could already implement any algorithm or business logic we can think of!\n",
     "\n",
-    "However, after our first couple of programs, we would already start seeing recurring patterns in the code we write. In other words, we would constantly be \"reinventing the wheel\" in each new project."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "slide"
-    }
-   },
-   "source": [
-    "## Extending Core Python"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "source": [
+    "However, after our first couple of programs, we would already start seeing recurring patterns in the code we write. In other words, we would constantly be \"reinventing the wheel\" in each new project.\n",
+    "\n",
     "Would it not be smarter to pull out the reusable components from our programs and put them into some project independent **library** of generically useful functionalities? Then we would only need a way of including these **utilities** in our projects.\n",
     "\n",
     "As all programmers across all languages face this very same issue, most programming languages come with a so-called **[standard library <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_wiki.png\">](https://en.wikipedia.org/wiki/Standard_library)** that provides utilities to accomplish everyday tasks without much code. Examples are making an HTTP request to some website, open and read popular file types (e.g., CSV or Excel files), do something on a computer's file system, and many more."
@@ -67,7 +47,7 @@
     }
    },
    "source": [
-    "### The Standard Library"
+    "## The Standard Library"
    ]
   },
   {
@@ -95,7 +75,7 @@
     }
    },
    "source": [
-    "#### [math <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/math.html) Module"
+    "### [math <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/math.html) Module"
    ]
   },
   {
@@ -448,7 +428,7 @@
    "source": [
     "Observe how the arguments passed to functions do not need to be just variables or simple literals. Instead, we may pass in any *expression* that evaluates to a *new* object of the type the function expects.\n",
     "\n",
-    "So just as a reminder from the expression vs. statement discussion in [Chapter 1 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/chapter_01_elements/02_content.ipynb#Expressions): An expression is *any* syntactically correct combination of variables and literals with operators. And the call operator `()` is yet another operator. So both of the next two code cells are just expressions! They have no permanent side effects in memory. We may execute them as often as we want *without* changing the state of the program (i.e., this Jupyter notebook).\n",
+    "So just as a reminder from the expression vs. statement discussion in [Chapter 1 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/01_elements/03_content.ipynb#Expressions): An expression is *any* syntactically correct combination of variables and literals with operators. And the call operator `()` is yet another operator. So both of the next two code cells are just expressions! They have no permanent side effects in memory. We may execute them as often as we want *without* changing the state of the program (i.e., this Jupyter notebook).\n",
     "\n",
     "So, regarding the very next cell in particular: Although the `2 ** 2` creates a *new* object `4` in memory that is then immediately passed into the [math.sqrt() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/math.html#math.sqrt) function, once that function call returns, \"all is lost\" and the newly created `4` object is forgotten again, as well as the return value of [math.sqrt() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/math.html#math.sqrt)."
    ]
@@ -570,7 +550,7 @@
     }
    },
    "source": [
-    "#### [random <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/random.html) Module"
+    "### [random <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/random.html) Module"
    ]
   },
   {
@@ -629,7 +609,7 @@
     }
    },
    "source": [
-    "Besides the usual dunder-style attributes, the built-in [dir() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#dir) function lists some attributes in an upper case naming convention and many others starting with a *single* underscore `_`. To understand the former, we must wait until [Chapter 11 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/chapter_11_classes/00_content.ipynb), while the latter is explained further below."
+    "Besides the usual dunder-style attributes, the built-in [dir() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#dir) function lists some attributes in an upper case naming convention and many others starting with a *single* underscore `_`. To understand the former, we must wait until [Chapter 11 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/11_classes/00_content.ipynb), while the latter is explained further below."
    ]
   },
   {
@@ -992,7 +972,7 @@
     }
    },
    "source": [
-    "### Third-party Packages"
+    "## Third-party Packages"
    ]
   },
   {
@@ -1020,7 +1000,7 @@
     }
    },
    "source": [
-    "#### [numpy](http://www.numpy.org/) Library"
+    "### [numpy](http://www.numpy.org/) Library"
    ]
   },
   {
@@ -1033,7 +1013,7 @@
    "source": [
     "[numpy](http://www.numpy.org/) is the de-facto standard in the Python world for handling **array-like** data. That is a fancy word for data that can be put into a matrix or vector format.\n",
     "\n",
-    "As [numpy](http://www.numpy.org/) is *not* in the [standard library <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/index.html), it must be *manually* installed, for example, with the [pip](https://pip.pypa.io/en/stable/) tool. As mentioned in [Chapter 0 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/chapter_00_intro/00_content.ipynb#Markdown-Cells-vs.-Code-Cells), to execute terminal commands from within a Jupyter notebook, we start a code cell with an exclamation mark.\n",
+    "As [numpy](http://www.numpy.org/) is *not* in the [standard library <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/index.html), it must be *manually* installed, for example, with the [pip](https://pip.pypa.io/en/stable/) tool. As mentioned in [Chapter 0 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/00_intro/00_content.ipynb#Markdown-Cells-vs.-Code-Cells), to execute terminal commands from within a Jupyter notebook, we start a code cell with an exclamation mark.\n",
     "\n",
     "If you are running this notebook with an installation of the [Anaconda Distribution](https://www.anaconda.com/distribution/), then [numpy](http://www.numpy.org/) is probably already installed. Running the cell below confirms that."
    ]
@@ -1327,7 +1307,7 @@
     }
    },
    "source": [
-    "### Local Modules and Packages"
+    "## Local Modules and Packages"
    ]
   },
   {
@@ -1338,7 +1318,7 @@
     }
    },
    "source": [
-    "For sure, we can create local modules and packages. In the Chapter 2 directory, there is a [*sample_module.py* <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_gh.png\">](https://github.com/webartifex/intro-to-python/blob/develop/chapter_02_functions/sample_module.py) file that contains, among others, a function equivalent to the final version of `average_evens()`. To be realistic, this sample module is structured in a modular manner with several functions building on each other. It is best to skim over it *now* before reading on.\n",
+    "For sure, we can create local modules and packages. In the Chapter 2 directory, there is a [*sample_module.py* <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_gh.png\">](https://github.com/webartifex/intro-to-python/blob/develop/02_functions/sample_module.py) file that contains, among others, a function equivalent to the final version of `average_evens()`. To be realistic, this sample module is structured in a modular manner with several functions building on each other. It is best to skim over it *now* before reading on.\n",
     "\n",
     "To make code we put into a *.py* file available in our program, we import it as a module just as we did above with modules in the [standard library <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/index.html) or third-party packages.\n",
     "\n",
@@ -1358,7 +1338,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "/home/webartifex/repos/intro-to-python/chapter_02_functions\n"
+      "/home/webartifex/repos/intro-to-python/02_functions\n"
      ]
     }
    ],
@@ -1404,7 +1384,7 @@
     {
      "data": {
       "text/plain": [
-       "<module 'sample_module' from '/home/webartifex/repos/intro-to-python/chapter_02_functions/sample_module.py'>"
+       "<module 'sample_module' from '/home/webartifex/repos/intro-to-python/02_functions/sample_module.py'>"
       ]
      },
      "execution_count": 41,
@@ -1424,7 +1404,7 @@
     }
    },
    "source": [
-    "Disregarding the dunder-style attributes, `mod` defines the attributes `_round_all`, `_scaled_average`, `average`, `average_evens`, and `average_odds`, which are exactly the ones we would expect from reading the [*sample_module.py* <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_gh.png\">](https://github.com/webartifex/intro-to-python/blob/develop/chapter_02_functions/sample_module.py) file.\n",
+    "Disregarding the dunder-style attributes, `mod` defines the attributes `_round_all`, `_scaled_average`, `average`, `average_evens`, and `average_odds`, which are exactly the ones we would expect from reading the [*sample_module.py* <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_gh.png\">](https://github.com/webartifex/intro-to-python/blob/develop/02_functions/sample_module.py) file.\n",
     "\n",
     "A convention when working with imported code is to *disregard* any attributes starting with a single underscore `_`. These are considered **private** and constitute **implementation details** the author of the imported code might change in a future version of his software. We *must not* rely on them in any way.\n",
     "\n",
@@ -1475,7 +1455,7 @@
     }
    },
    "source": [
-    "We use the imported `mod.average_evens()` just like `average_evens()` defined in the [first part <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/chapter_02_functions/00_content.ipynb) of this chapter. The advantage we get from **modularization** with *.py* files is that we can now easily reuse functions across different Jupyter notebooks without redefining them again and again. Also, we can \"source out\" code that distracts from the storyline told in a notebook."
+    "We use the imported `mod.average_evens()` just like `average_evens()` defined in the [first part <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/02_functions/00_content.ipynb) of this chapter. The advantage we get from **modularization** with *.py* files is that we can now easily reuse functions across different Jupyter notebooks without redefining them again and again. Also, we can \"source out\" code that distracts from the storyline told in a notebook."
    ]
   },
   {
@@ -1591,7 +1571,7 @@
     }
    },
    "source": [
-    "Packages are a generalization of modules, and we look at one in detail in [Chapter 11 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/chapter_11_classes/00_content.ipynb). You may, however, already look at a [sample package <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_gh.png\">](https://github.com/webartifex/intro-to-python/tree/develop/chapter_11_classes/sample_package) in the repository, which is nothing but a folder with *.py* files in it.\n",
+    "Packages are a generalization of modules, and we look at one in detail in [Chapter 11 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/11_classes/00_content.ipynb). You may, however, already look at a [sample package <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_gh.png\">](https://github.com/webartifex/intro-to-python/tree/develop/11_classes/sample_package) in the repository, which is nothing but a folder with *.py* files in it.\n",
     "\n",
     "As a further reading on modules and packages, we refer to the [official tutorial <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/tutorial/modules.html)."
    ]
diff --git a/chapter_02_functions/03_summary.ipynb b/02_functions/03_summary.ipynb
similarity index 93%
rename from chapter_02_functions/03_summary.ipynb
rename to 02_functions/03_summary.ipynb
index c5d712b..2161f17 100644
--- a/chapter_02_functions/03_summary.ipynb
+++ b/02_functions/03_summary.ipynb
@@ -8,18 +8,7 @@
     }
    },
    "source": [
-    "# Chapter 2: Functions & Modularization"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "source": [
-    "## TL;DR"
+    "# Chapter 2: Functions & Modularization (TL;DR)"
    ]
   },
   {
diff --git a/chapter_02_functions/04_review.ipynb b/02_functions/04_review.ipynb
similarity index 90%
rename from chapter_02_functions/04_review.ipynb
rename to 02_functions/04_review.ipynb
index daa9d88..a85dade 100644
--- a/chapter_02_functions/04_review.ipynb
+++ b/02_functions/04_review.ipynb
@@ -4,21 +4,14 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# Chapter 2: Functions & Modularization"
+    "# Chapter 2: Functions & Modularization (Review Questions)"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Content Review"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The questions below assume that you have read the [first <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/chapter_02_functions/00_content.ipynb) and the [second <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/chapter_02_functions/02_content.ipynb) part of Chapter 2.\n",
+    "The questions below assume that you have read the [first <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/02_functions/00_content.ipynb) and the [second <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/02_functions/02_content.ipynb) part of Chapter 2.\n",
     "\n",
     "Be concise in your answers! Most questions can be answered in *one* sentence."
    ]
@@ -27,7 +20,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### Essay Questions "
+    "## Essay Questions "
    ]
   },
   {
@@ -132,7 +125,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### True / False Questions"
+    "## True / False Questions"
    ]
   },
   {
diff --git a/chapter_02_functions/sample_module.py b/02_functions/sample_module.py
similarity index 100%
rename from chapter_02_functions/sample_module.py
rename to 02_functions/sample_module.py
diff --git a/README.md b/README.md
index 3193767..658d95c 100644
--- a/README.md
+++ b/README.md
@@ -15,60 +15,62 @@ Alternatively, the content can be viewed in a web browser
     or interactively (i.e., code can be executed) on [Binder <img height="12" style="display: inline-block" src="static/link/to_mb.png">](https://mybinder.org/v2/gh/webartifex/intro-to-python/develop?urlpath=lab).
 
 - *Chapter 0*: Introduction
-  - [content <img height="12" style="display: inline-block" src="static/link/to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/chapter_00_intro/00_content.ipynb)
-    [<img height="12" style="display: inline-block" src="static/link/to_mb.png">](https://mybinder.org/v2/gh/webartifex/intro-to-python/develop?urlpath=lab/tree/chapter_00_intro/00_content.ipynb)
+  - [content <img height="12" style="display: inline-block" src="static/link/to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/00_intro/00_content.ipynb)
+    [<img height="12" style="display: inline-block" src="static/link/to_mb.png">](https://mybinder.org/v2/gh/webartifex/intro-to-python/develop?urlpath=lab/tree/00_intro/00_content.ipynb)
     (Python's History & Background;
      Open-source & Communities;
      JupyterLab;
      Programming vs. Computer Science;
      Learning Tips)
-  - [exercises <img height="12" style="display: inline-block" src="static/link/to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/chapter_00_intro/01_exercises.ipynb)
-    [<img height="12" style="display: inline-block" src="static/link/to_mb.png">](https://mybinder.org/v2/gh/webartifex/intro-to-python/develop?urlpath=lab/tree/chapter_00_intro/01_exercises.ipynb)
+  - [exercises <img height="12" style="display: inline-block" src="static/link/to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/00_intro/01_exercises.ipynb)
+    [<img height="12" style="display: inline-block" src="static/link/to_mb.png">](https://mybinder.org/v2/gh/webartifex/intro-to-python/develop?urlpath=lab/tree/00_intro/01_exercises.ipynb)
     (Mastering Markdown)
-  - [review <img height="12" style="display: inline-block" src="static/link/to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/chapter_00_intro/02_review.ipynb)
+  - [review questions <img height="12" style="display: inline-block" src="static/link/to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/00_intro/02_review.ipynb)
 - **Part A: Expressing Logic**
   - *Chapter 1*: Elements of a Program
-    - [content 1 <img height="12" style="display: inline-block" src="static/link/to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/chapter_01_elements/00_content.ipynb)
-      [<img height="12" style="display: inline-block" src="static/link/to_mb.png">](https://mybinder.org/v2/gh/webartifex/intro-to-python/develop?urlpath=lab/tree/chapter_01_elements/00_content.ipynb)
+    - [content <img height="12" style="display: inline-block" src="static/link/to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/01_elements/00_content.ipynb)
+      [<img height="12" style="display: inline-block" src="static/link/to_mb.png">](https://mybinder.org/v2/gh/webartifex/intro-to-python/develop?urlpath=lab/tree/01_elements/00_content.ipynb)
       (A first Example: Averaging Even Numbers;
        Operators;
        Objects & Data Types;
        Errors)
-    - [exercises 1 <img height="12" style="display: inline-block" src="static/link/to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/chapter_01_elements/01_exercises.ipynb)
-      [<img height="12" style="display: inline-block" src="static/link/to_mb.png">](https://mybinder.org/v2/gh/webartifex/intro-to-python/develop?urlpath=lab/tree/chapter_01_elements/01_exercises.ipynb)
-      (Printing Output;
-       Simple `for`-loops)
-    - [content 2 <img height="12" style="display: inline-block" src="static/link/to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/chapter_01_elements/02_content.ipynb)
-      [<img height="12" style="display: inline-block" src="static/link/to_mb.png">](https://mybinder.org/v2/gh/webartifex/intro-to-python/develop?urlpath=lab/tree/chapter_01_elements/02_content.ipynb)
+    - [exercises <img height="12" style="display: inline-block" src="static/link/to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/01_elements/01_exercises.ipynb)
+      [<img height="12" style="display: inline-block" src="static/link/to_mb.png">](https://mybinder.org/v2/gh/webartifex/intro-to-python/develop?urlpath=lab/tree/01_elements/01_exercises.ipynb)
+      (Printing Output)
+    - [exercises <img height="12" style="display: inline-block" src="static/link/to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/01_elements/02_exercises.ipynb)
+      [<img height="12" style="display: inline-block" src="static/link/to_mb.png">](https://mybinder.org/v2/gh/webartifex/intro-to-python/develop?urlpath=lab/tree/01_elements/02_exercises.ipynb)
+      (Simple `for`-loops)
+    - [content <img height="12" style="display: inline-block" src="static/link/to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/01_elements/03_content.ipynb)
+      [<img height="12" style="display: inline-block" src="static/link/to_mb.png">](https://mybinder.org/v2/gh/webartifex/intro-to-python/develop?urlpath=lab/tree/01_elements/03_content.ipynb)
       (Memory in Detail;
        Variables & References;
        Mutability;
        Expressions & Statements)
-    - [exercises 2 <img height="12" style="display: inline-block" src="static/link/to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/chapter_01_elements/03_exercises.ipynb)
-      [<img height="12" style="display: inline-block" src="static/link/to_mb.png">](https://mybinder.org/v2/gh/webartifex/intro-to-python/develop?urlpath=lab/tree/chapter_01_elements/03_exercises.ipynb)
+    - [exercises <img height="12" style="display: inline-block" src="static/link/to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/01_elements/03_exercises.ipynb)
+      [<img height="12" style="display: inline-block" src="static/link/to_mb.png">](https://mybinder.org/v2/gh/webartifex/intro-to-python/develop?urlpath=lab/tree/01_elements/03_exercises.ipynb)
       (Python as a Calculator)
-    - [summary <img height="12" style="display: inline-block" src="static/link/to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/chapter_01_elements/04_summary.ipynb)
-    - [review <img height="12" style="display: inline-block" src="static/link/to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/chapter_01_elements/05_review.ipynb)
-    - [resources <img height="12" style="display: inline-block" src="static/link/to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/chapter_01_elements/06_resources.ipynb)
-      [<img height="12" style="display: inline-block" src="static/link/to_mb.png">](https://mybinder.org/v2/gh/webartifex/intro-to-python/develop?urlpath=lab/tree/chapter_01_elements/06_resources.ipynb)
+    - [summary <img height="12" style="display: inline-block" src="static/link/to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/01_elements/04_summary.ipynb)
+    - [review questions <img height="12" style="display: inline-block" src="static/link/to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/01_elements/05_review.ipynb)
+    - [further resources <img height="12" style="display: inline-block" src="static/link/to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/01_elements/06_resources.ipynb)
+      [<img height="12" style="display: inline-block" src="static/link/to_mb.png">](https://mybinder.org/v2/gh/webartifex/intro-to-python/develop?urlpath=lab/tree/01_elements/06_resources.ipynb)
   - *Chapter 2*: Functions & Modularization
-    - [content 1 <img height="12" style="display: inline-block" src="static/link/to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/chapter_02_functions/00_content.ipynb)
-      [<img height="12" style="display: inline-block" src="static/link/to_mb.png">](https://mybinder.org/v2/gh/webartifex/intro-to-python/develop?urlpath=lab/tree/chapter_02_functions/00_content.ipynb)
+    - [content <img height="12" style="display: inline-block" src="static/link/to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/02_functions/00_content.ipynb)
+      [<img height="12" style="display: inline-block" src="static/link/to_mb.png">](https://mybinder.org/v2/gh/webartifex/intro-to-python/develop?urlpath=lab/tree/02_functions/00_content.ipynb)
       (Built-in Functions & Constructors;
        Function Definitions;
        Function Calls & Scoping Rules;
        Positional vs. Keyword Arguments;
        Modularization)
-    - [exercises <img height="12" style="display: inline-block" src="static/link/to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/chapter_02_functions/01_exercises.ipynb)
-      [<img height="12" style="display: inline-block" src="static/link/to_mb.png">](https://mybinder.org/v2/gh/webartifex/intro-to-python/develop?urlpath=lab/tree/chapter_02_functions/01_exercises.ipynb)
+    - [exercises <img height="12" style="display: inline-block" src="static/link/to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/02_functions/01_exercises.ipynb)
+      [<img height="12" style="display: inline-block" src="static/link/to_mb.png">](https://mybinder.org/v2/gh/webartifex/intro-to-python/develop?urlpath=lab/tree/02_functions/01_exercises.ipynb)
       (Volume of a Sphere)
-    - [content 2 <img height="12" style="display: inline-block" src="static/link/to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/chapter_02_functions/02_content.ipynb)
-      [<img height="12" style="display: inline-block" src="static/link/to_mb.png">](https://mybinder.org/v2/gh/webartifex/intro-to-python/develop?urlpath=lab/tree/chapter_02_functions/02_content.ipynb)
+    - [content <img height="12" style="display: inline-block" src="static/link/to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/02_functions/02_content.ipynb)
+      [<img height="12" style="display: inline-block" src="static/link/to_mb.png">](https://mybinder.org/v2/gh/webartifex/intro-to-python/develop?urlpath=lab/tree/02_functions/02_content.ipynb)
       (Standard Library: `math` & `random` Modules;
        Third-party Packages: `numpy` Library;
        Writing one's own Modules)
-    - [summary <img height="12" style="display: inline-block" src="static/link/to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/chapter_02_functions/03_summary.ipynb)
-    - [review <img height="12" style="display: inline-block" src="static/link/to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/chapter_02_functions/04_review.ipynb)
+    - [summary <img height="12" style="display: inline-block" src="static/link/to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/02_functions/03_summary.ipynb)
+    - [review questions <img height="12" style="display: inline-block" src="static/link/to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/02_functions/04_review.ipynb)
 
 
 #### Videos
diff --git a/chapter_01_elements/01_exercises.ipynb b/chapter_01_elements/01_exercises.ipynb
deleted file mode 100644
index 6e057b5..0000000
--- a/chapter_01_elements/01_exercises.ipynb
+++ /dev/null
@@ -1,336 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**Note**: Click on \"*Kernel*\" > \"*Restart Kernel and Run All*\" in [JupyterLab](https://jupyterlab.readthedocs.io/en/stable/) *after* finishing the exercises to ensure that your solution runs top to bottom *without* any errors. If you cannot run this file on your machine, you may want to open it [in the cloud <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_mb.png\">](https://mybinder.org/v2/gh/webartifex/intro-to-python/develop?urlpath=lab/tree/chapter_01_elements/01_exercises.ipynb)."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Chapter 1: Elements of a Program"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Coding Exercises"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The exercises below assume that you have read the first part of [Chapter 1 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/chapter_01_elements/00_content.ipynb).\n",
-    "\n",
-    "The `...`'s in the code cells indicate where you need to fill in code snippets. The number of `...`'s within a code cell give you a rough idea of how many lines of code are needed to solve the task. You should not need to create any additional code cells for your final solution. However, you may want to use temporary code cells to try out some ideas."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### **Q1**: Printing Output"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**Q1.1**: *Concatenate* `greeting` and `audience` below with the `+` operator and print out the resulting message `\"Hello World\"` with only *one* call of the built-in [print() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#print) function!\n",
-    "\n",
-    "Hint: You may have to \"add\" a space character in between `greeting` and `audience`."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "greeting = \"Hello\"\n",
-    "audience = \"World\""
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "print(...)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**Q1.2**: How is your answer to **Q1.1** an example of the concept of **operator overloading**?"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    " < your answer >"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**Q1.3**: Read the documentation on the built-in [print() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#print) function! How can you print the above message *without* concatenating `greeting` and `audience` first in *one* call of [print() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#print)?\n",
-    "\n",
-    "Hint: The `*objects` in the documentation implies that we can put several *expressions* (i.e., variables) separated by commas within the same call of the [print() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#print) function."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "print(...)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**Q1.4**: What does the `sep=\" \"` mean in the documentation on the built-in [print() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#print) function? Adjust and use it to print out the three names referenced by `first`, `second`, and `third` on *one* line separated by *commas* with only *one* call of the [print() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#print) function!"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "first = \"Anthony\"\n",
-    "second = \"Berta\"\n",
-    "third = \"Christian\""
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "print(...)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**Q1.5**: Lastly, what does the `end=\"\\n\"` mean in the documentation? Adjust and use it within the `for`-loop to print the numbers `1` through `10` on *one* line with only *one* call of the [print() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#print) function!"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "for number in [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]:\n",
-    "    print(...)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### **Q2**: Simple `for`-loops"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "`for`-loops are extremely versatile in Python. That is different from many other programming languages.\n",
-    "\n",
-    "As shown in the first example in [Chapter 1 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/chapter_01_elements/00_content.ipynb#Example:-Averaging-all-even-Numbers-in-a-List), we can create a `list` like `numbers` and loop over the numbers in it on a one-by-one basis."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "numbers = [7, 11, 8, 5, 3, 12, 2, 6, 9, 10, 1, 4]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**Q2.1**: Fill in the *condition* of the `if` statement such that only numbers divisible by `3` are printed! Adjust the call of the [print() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#print) function such that the `for`-loop prints out all the numbers on *one* line of output!"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "for number in numbers:\n",
-    "    if ...:\n",
-    "        print(...)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Instead of looping over an *existing* object referenced by a variable like `numbers`, we may also create a *new* object within the `for` statement and loop over it directly. For example, below we write out the `list` object as a *literal*.\n",
-    "\n",
-    "Generically, the objects contained in a `list` objects are referred to as its **elements**. We reflect that in the name of the *target* variable `element` that is assigned a different number in every iteration of the `for`-loop. While we could use *any* syntactically valid name, it is best to choose one that makes sense in the context (e.g., `number` in `numbers`).\n",
-    "\n",
-    "**Q2.2**: Fill in the condition of the `if` statement such that only numbers consisting of *one* digit are printed out! As before, print out all the numbers on *one* line of output!"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "for element in [7, 11, 8, 5, 3, 12, 2, 6, 9, 10, 1, 4]:\n",
-    "    if ...:\n",
-    "        print(...)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "An easy way to loop over a `list` object in a sorted manner, is to wrap it with the built-in [sorted() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#sorted) function.\n",
-    "\n",
-    "**Q2.3**: Fill in the condition of the `if` statement such that only odd numbers are printed out! Put all the numbers on *one* line of output!"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "for number in sorted(numbers):\n",
-    "    if ...:\n",
-    "        print(...)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Whenever we want to loop over numbers representing a [series <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_wiki.png\">](https://en.wikipedia.org/wiki/Series_%28mathematics%29) in the mathematical sense (i.e., a rule to calculate the next number from its predecessor), we may be able to use the [range() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#func-range) built-in.\n",
-    "\n",
-    "For example, to loop over the whole numbers from `0` to `9` (both including) in order, we could write them out in a `list` like below.\n",
-    "\n",
-    "**Q2.4**: Fill in the call to the [print() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#print) function such that all the numbers are printed on *one* line ouf output!"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "for number in [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]:\n",
-    "    print(...)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**Q2.5**: Read the documentation on the [range() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#func-range) built-in! It may be used with either one, two, or three expressions \"passed\" in. What do `start`, `stop`, and `step` mean? Fill in the calls to [range() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#func-range) and [print() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#print) to mimic the output of **Q2.4**!"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "for number in range(...):\n",
-    "    print(...)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**Q2.6**: Fill in the calls to [range() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#func-range) and [print() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#print) to print out *all* numbers from `1` to `10` (both including)!"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "for number in range(...):\n",
-    "    print(...)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "**Q2.7**: Fill in the calls to [range() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#func-range) and [print() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#print) to print out the *even* numbers from `1` to `10` (both including)! Do *not* use an `if` statement to accomplish this!"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "for number in range(...):\n",
-    "    print(...)"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.8.6"
-  },
-  "toc": {
-   "base_numbering": 1,
-   "nav_menu": {},
-   "number_sections": false,
-   "sideBar": true,
-   "skip_h1_title": true,
-   "title_cell": "Table of Contents",
-   "title_sidebar": "Contents",
-   "toc_cell": false,
-   "toc_position": {},
-   "toc_section_display": false,
-   "toc_window_display": false
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}

From c94574b86051ee758e0173ccb4a52cac828b290b Mon Sep 17 00:00:00 2001
From: Alexander Hess <alexander@webartifex.biz>
Date: Thu, 15 Oct 2020 15:29:58 +0200
Subject: [PATCH 150/242] Add initial version of chapter 03

---
 00_intro/00_content.ipynb        |    2 +-
 03_conditionals/00_content.ipynb | 2831 ++++++++++++++++++++++++++++++
 README.md                        |    8 +
 3 files changed, 2840 insertions(+), 1 deletion(-)
 create mode 100644 03_conditionals/00_content.ipynb

diff --git a/00_intro/00_content.ipynb b/00_intro/00_content.ipynb
index 75b5fb4..a2e8778 100644
--- a/00_intro/00_content.ipynb
+++ b/00_intro/00_content.ipynb
@@ -753,7 +753,7 @@
     "  - *Chapter 1*: [Elements of a Program <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/01_elements/00_content.ipynb)\n",
     "  - *Chapter 2*: [Functions & Modularization <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/02_functions/00_content.ipynb)\n",
     "- What is the flow of execution? How can we form sentences from words?\n",
-    "  - *Chapter 3*: Conditionals & Exceptions\n",
+    "  - *Chapter 3*: [Conditionals & Exceptions <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/03_conditionals/00_content.ipynb)\n",
     "  - *Chapter 4*: Recursion & Looping"
    ]
   },
diff --git a/03_conditionals/00_content.ipynb b/03_conditionals/00_content.ipynb
new file mode 100644
index 0000000..7a54f13
--- /dev/null
+++ b/03_conditionals/00_content.ipynb
@@ -0,0 +1,2831 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "**Note**: Click on \"*Kernel*\" > \"*Restart Kernel and Clear All Outputs*\" in [JupyterLab](https://jupyterlab.readthedocs.io/en/stable/) *before* reading this notebook to reset its output. If you cannot run this file on your machine, you may want to open it [in the cloud <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_mb.png\">](https://mybinder.org/v2/gh/webartifex/intro-to-python/develop?urlpath=lab/tree/03_conditionals/00_content.ipynb)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "# Chapter 3: Conditionals & Exceptions"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "We analyzed every aspect of the `average_evens()` function in [Chapter 2 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/02_functions/00_content.ipynb) except for the `if`-related parts. While it does what we expect it to, there is a whole lot more to learn by taking it apart. In particular, the `if` may occur within both a **statement** or an **expression**, analogous as to how a noun in a natural language can be the subject of *or* an object in a sentence. What is common to both usages is that it leads to code being executed for *parts* of the input only. It is a way of controlling the **flow of execution** in a program.\n",
+    "\n",
+    "After deconstructing `if` in the first part of this chapter, we take a close look at a similar concept, namely handling **exceptions**."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "## Boolean Expressions"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Any expression that is either true or not is called a **boolean expression**. It is such simple true-or-false observations about the world on which mathematicians, and originally philosophers, base their rules of reasoning: They are studied formally in the field of [propositional logic <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_wiki.png\">](https://en.wikipedia.org/wiki/Propositional_calculus).\n",
+    "\n",
+    "A trivial example involves the equality operator `==` that evaluates to either `True` or `False` depending on its operands \"comparing equal\" or not."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 1,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "42 == 42"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "False"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "42 == 123"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "The `==` operator handles objects of *different* types: Because of that, it implements a notion of equality in line with how humans think of things being equal or not. After all, `42` and `42.0` are different $0$s and $1$s for a computer and other programming languages may say `False` here! Technically, this is yet another example of operator overloading."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "42 == 42.0"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "There are, however, cases where the `==` operator seems to not work intuitively. [Chapter 5 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/05_numbers/00_content.ipynb#Imprecision) provides more insights into this \"bug.\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "42 == 42.000000000000001"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "## The `bool` Type"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "`True` and `False` are built-in *objects* of type `bool`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "True"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "94478067031520"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "id(True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "bool"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "type(True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "False"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "False"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "94478067031552"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "id(False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "bool"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "type(False)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Let's not confuse the boolean `False` with `None`, another built-in object! We saw the latter before in [Chapter 2 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/02_functions/00_content.ipynb#Function-Definitions) as the *implicit* return value of a function without a `return` statement.\n",
+    "\n",
+    "We might think of `None` indicating a \"maybe\" or even an \"unknown\" answer; however, for Python, there are no \"maybe\" or \"unknown\" objects, as we see further below!\n",
+    "\n",
+    "Whereas `False` is of type `bool`, `None` is of type `NoneType`. So, they are unrelated! On the contrary, as both `True` and `False` are of the same type, we could call them \"siblings.\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "None"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "94478066920032"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "id(None)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "NoneType"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "type(None)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "### Singletons"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "`True`, `False`, and `None` have the property that they each exist in memory only *once*. Objects designed this way are so-called **singletons**. This **[design pattern <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_wiki.png\">](https://en.wikipedia.org/wiki/Design_Patterns)** was originally developed to keep a program's memory usage at a minimum. It may only be employed in situations where we know that an object does *not* mutate its value (i.e., to reuse the bag analogy from [Chapter 1 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/01_elements/00_content.ipynb#Objects-vs.-Types-vs.-Values), no flipping of $0$s and $1$s in the bag is allowed). In languages \"closer\" to the memory like C, we would have to code this singleton logic ourselves, but Python has this built in for *some* types.\n",
+    "\n",
+    "We verify this with either the `is` operator or by comparing memory addresses."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "a = True\n",
+    "b = True\n",
+    "\n",
+    "a is b"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "To contrast this, we create *two* `789` objects."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "False"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "a = 789\n",
+    "b = 789\n",
+    "\n",
+    "a is b"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "So the following expression regards *four* objects in memory: *One* `list` object holding six references to *three* other objects."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[True, False, None, True, False, None]"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "[True, False, None, True, False, None]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "## Relational Operators"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "The equality operator is only one of several **relational** (i.e., \"comparison\") **operators** who all evaluate to a `bool` object."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "False"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "42 == 123"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "42 != 123  # \"not equal to\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "The \"less than\" `<` or \"greater than\" `>` operators mean \"*strictly* less than\" or \"*strictly* greater than\" but may be combined with the equality operator into just `<=` and `>=`. This is a shortcut for using the logical `or` operator as described in the next section."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "42 < 123"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "42 <= 123"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "False"
+      ]
+     },
+     "execution_count": 21,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "42 > 123"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "False"
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "42 >= 123"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "## Logical Operators"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Boolean expressions may be combined or negated with the **logical operators** `and`, `or`, and `not` to form new boolean expressions. This may be done repeatedly to obtain boolean expressions of arbitrary complexity.\n",
+    "\n",
+    "Their usage is similar to how the equivalent words are used in everyday English:\n",
+    "\n",
+    "- `and` evaluates to `True` if *both* operands evaluate to `True` and `False` otherwise,\n",
+    "- `or` evaluates to `True` if either one *or* both operands evaluate to `True` and `False` otherwise, and\n",
+    "- `not` evaluates to `True` if its *only* operand evaluates to `False` and vice versa."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "a = 42\n",
+    "b = 87"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Relational operators have **[higher precedence <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/reference/expressions.html#operator-precedence)** over logical operators. So the following expression means what we intuitively think it does."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 24,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "a > 5 and b <= 100"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "However, sometimes, it is good to use *parentheses* around each operand for clarity."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 25,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "(a > 5) and (b <= 100)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "This is especially so when several logical operators are combined."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 26,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "a <= 5 or not b > 100"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 27,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "(a <= 5) or not (b > 100)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 28,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "(a <= 5) or (not (b > 100))  # no need to \"over do\" it"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "For even better readability, some practitioners suggest to *never* use the `>` and `>=` operators (cf., [source](https://llewellynfalco.blogspot.com/2016/02/dont-use-greater-than-sign-in.html); note that the included example is written in [Java <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_wiki.png\">](https://en.wikipedia.org/wiki/Java_%28programming_language%29) where `&&` means `and` and `||` means `or`).\n",
+    "\n",
+    "We may **chain** operators if the expressions that contain them are combined with the `and` operator. For example, the following two cells implement the same logic, where the second is a lot easier to read."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 29,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "5 < a and a < 87"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 30,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "5 < a < 87"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "### Truthy vs. Falsy"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "The operands of a logical operator do not need to be *boolean* expressions but may be *any* expression. If an operand does *not* evaluate to an object of type `bool`, Python automatically casts it as such. Then, Pythonistas say that the expression is evaluated in a boolean context.\n",
+    "\n",
+    "For example, any non-zero numeric object is cast as `True`. While this behavior allows writing more concise and thus more \"beautiful\" code, it may also be a source of confusion.\n",
+    "\n",
+    "So, `(a - 40)` is cast as `True` and then the overall expression evaluates to `True` as well."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 31,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "(a - 40) and (b < 100)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Whenever we are unsure how Python evaluates a non-boolean expression in a boolean context, the [bool() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#bool) built-in allows us to do it ourselves. [bool() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#bool), like [int() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#int), is yet another *constructor*."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 32,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "bool(a - 40)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "False"
+      ]
+     },
+     "execution_count": 33,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "bool(a - 42)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Let's keep in mind that negative numbers also evaluate to `True`!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 34,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "bool(a - 44)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "In a boolean context, `None` is cast as `False`! So, `None` is *not* a \"maybe\" answer but a \"no.\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "False"
+      ]
+     },
+     "execution_count": 35,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "bool(None)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Another good rule to know is that container types (e.g., `list`) evaluate to `False` whenever they are empty and `True` if they hold at least one element."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "False"
+      ]
+     },
+     "execution_count": 36,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "bool([])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 37,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "bool([False])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "With `str` objects, the empty `\"\"` evaluates to `False`, and any other to `True`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "False"
+      ]
+     },
+     "execution_count": 38,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "bool(\"\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 39,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "bool(\"Lorem ipsum dolor sit amet.\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Pythonistas use the terms **truthy** or **falsy** to describe a non-boolean expression's behavior when evaluated in a boolean context."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "### Short-Circuiting"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "When evaluating expressions involving the `and` and `or` operators, Python follows the **[short-circuiting <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_wiki.png\">](https://en.wikipedia.org/wiki/Short-circuit_evaluation)** strategy: Once it is clear what the overall truth value is, no more operands are evaluated, and the result is *immediately* returned.\n",
+    "\n",
+    "Also, if such expressions are evaluated in a non-boolean context, the result is returned as is and *not* cast as a `bool` type.\n",
+    "\n",
+    "The two rules can be summarized as:\n",
+    "\n",
+    "- `a or b`: If `a` is truthy, it is returned *without* evaluating `b`. Otherwise, `b` is evaluated *and* returned.\n",
+    "- `a and b`: If `a` is falsy, it is returned *without* evaluating `b`. Otherwise, `b` is evaluated *and* returned.\n",
+    "\n",
+    "The rules may also be chained or combined.\n",
+    "\n",
+    "Let's look at a couple of examples below. To visualize which operands are evaluated, we define a helper function `expr()` that prints out the only argument it is passed before returning it."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "def expr(arg):\n",
+    "    \"\"\"Print and return the only argument.\"\"\"\n",
+    "    print(\"Arg:\", arg)\n",
+    "    return arg"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "With the `or` operator, the first truthy operand is returned."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "1"
+      ]
+     },
+     "execution_count": 41,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "0 or 1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Arg: 0\n",
+      "Arg: 1\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "1"
+      ]
+     },
+     "execution_count": 42,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "expr(0) or expr(1)  # both operands are evaluated"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "1"
+      ]
+     },
+     "execution_count": 43,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "1 or 2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Arg: 1\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "1"
+      ]
+     },
+     "execution_count": 44,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "expr(1) or expr(2)  # 2 is not evaluated"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "1"
+      ]
+     },
+     "execution_count": 45,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "0 or 1 or 2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Arg: 0\n",
+      "Arg: 1\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "1"
+      ]
+     },
+     "execution_count": 46,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "expr(0) or expr(1) or expr(2)  # 2 is not evaluated"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "If all operands are falsy, the last one is returned."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0"
+      ]
+     },
+     "execution_count": 47,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "False or [] or 0"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 48,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Arg: False\n",
+      "Arg: []\n",
+      "Arg: 0\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "0"
+      ]
+     },
+     "execution_count": 48,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "expr(False) or expr([]) or expr(0)  # all operands are evaluated"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "With the `and` operator, the first falsy operand is returned."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 49,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0"
+      ]
+     },
+     "execution_count": 49,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "0 and 1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 50,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Arg: 0\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "0"
+      ]
+     },
+     "execution_count": 50,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "expr(0) and expr(1)  # 1 is not evaluated"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 51,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0"
+      ]
+     },
+     "execution_count": 51,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "1 and 0"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 52,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Arg: 1\n",
+      "Arg: 0\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "0"
+      ]
+     },
+     "execution_count": 52,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "expr(1) and expr(0)  # both operands are evaluated"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 53,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0"
+      ]
+     },
+     "execution_count": 53,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "1 and 0 and 2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 54,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Arg: 1\n",
+      "Arg: 0\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "0"
+      ]
+     },
+     "execution_count": 54,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "expr(1) and expr(0) and expr(2)  # 2 is not evaluated"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "If all operands are truthy, the last one is returned."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 55,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "3"
+      ]
+     },
+     "execution_count": 55,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "1 and 2 and 3"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 56,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Arg: 1\n",
+      "Arg: 2\n",
+      "Arg: 3\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "3"
+      ]
+     },
+     "execution_count": 56,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "expr(1) and expr(2) and expr(3)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "The crucial takeaway is that Python does *not* necessarily evaluate *all* operands and, therefore, our code should never rely on that assumption."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "## The `if` Statement"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "To write useful programs, we need to control the flow of execution, for example, to react to user input. The logic by which a program follows the rules from the \"real world\" is referred to as **[business logic <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_wiki.png\">](https://en.wikipedia.org/wiki/Business_logic)**.\n",
+    "\n",
+    "One language feature to do so is the `if` statement (cf., [reference <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/reference/compound_stmts.html#the-if-statement)). It consists of:\n",
+    "\n",
+    "- *one* mandatory `if`-clause,\n",
+    "- an *arbitrary* number of `elif`-clauses (i.e., \"else if\"), and\n",
+    "- an *optional* `else`-clause.\n",
+    "\n",
+    "The `if`- and `elif`-clauses each specify one *boolean* expression, also called **condition** here, while the `else`-clause serves as the \"catch everything else\" case.\n",
+    "\n",
+    "In contrast to our intuitive interpretation in natural languages, only the code in *one* of the alternatives, also called **branches**, is executed. To be precise, it is always the code in the first clause whose condition evaluates to `True`.\n",
+    "\n",
+    "In terms of syntax, the header lines end with a colon, and the code blocks are indented. Formally, any statement that is written across several lines is called a **[compound statement <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/reference/compound_stmts.html#compound-statements)**, the code blocks are called **suites** and belong to one header line, and the term **clause** refers to a header line and its suite as a whole. So far, we have seen three compound statements: `for`, `if`, and `def`. On the contrary, **[simple statements <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/reference/simple_stmts.html#simple-statements)**, for example, `=`, `del`, or `return`, are written on *one* line.\n",
+    "\n",
+    "As an example, let's write code that checks if a randomly drawn number is divisible by `2`, `3`, both, or none. The code should print out a customized message for each of the *four* cases."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 57,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "numbers = [7, 11, 8, 5, 3, 12, 2, 6, 9, 10, 1, 4]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 58,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "import random"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 59,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "random.seed(789)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "#### \"Wrong Logic\" Example: Is the number divisible by `2`, `3`, both, or none?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "It turns out that translating this task into code is not so trivial. Whereas the code below looks right, it is *incorrect*. The reason is that the order of the `if`-, `elif`-, and `else`-clauses matters."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 60,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "6 is divisible by 2 only\n"
+     ]
+    }
+   ],
+   "source": [
+    "number = random.choice(numbers)\n",
+    "\n",
+    "if number % 2 == 0:\n",
+    "    print(number, \"is divisible by 2 only\")\n",
+    "elif number % 3 == 0:\n",
+    "    print(number, \"is divisible by 3 only\")\n",
+    "elif number % 2 == 0 and number % 3 == 0:\n",
+    "    print(number, \"is divisible by 2 and 3\")\n",
+    "else:\n",
+    "    print(number, \"is divisible by neither 2 nor 3\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "#### \"Correct Logic\" Example: Is the number divisible by `2`, `3`, both, or none?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "As a number divisible by both `2` and `3` is always a special (i.e., narrower) case of a number being divisible by either `2` or `3` on their own, we must check for that condition first. The order of the two latter cases is not important as they are mutually exclusive. Below is a correct implementation of the program."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 61,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "random.seed(789)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 62,
+   "metadata": {
+    "code_folding": [],
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "6 is divisible by 2 and 3\n"
+     ]
+    }
+   ],
+   "source": [
+    "number = random.choice(numbers)\n",
+    "\n",
+    "if number % 3 == 0 and number % 2 == 0:\n",
+    "    print(number, \"is divisible by 2 and 3\")\n",
+    "elif number % 3 == 0:\n",
+    "    print(number, \"is divisible by 3 only\")\n",
+    "elif number % 2 == 0:\n",
+    "    print(number, \"is divisible by 2 only\")\n",
+    "else:\n",
+    "    print(number, \"is divisible by neither 2 nor 3\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "#### \"Concise Logic\" Example: Is the number divisible by `2`, `3`, both, or none?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "A minor improvement could be to replace `number % 3 == 0 and number % 2 == 0` with the conciser `number % 6 == 0`. However, this has no effect on the order that is still essential for the code's correctness."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 63,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "random.seed(789)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 64,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "6 is divisible by 2 and 3\n"
+     ]
+    }
+   ],
+   "source": [
+    "number = random.choice(numbers)\n",
+    "\n",
+    "if number % 6 == 0:\n",
+    "    print(number, \"is divisible by 2 and 3\")\n",
+    "elif number % 3 == 0:\n",
+    "    print(number, \"is divisible by 3 only\")\n",
+    "elif number % 2 == 0:\n",
+    "    print(number, \"is divisible by 2 only\")\n",
+    "else:\n",
+    "    print(number, \"is divisible by neither 2 nor 3\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "#### Only the `if`-clause is mandatory"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Often, we only need a reduced form of the `if` statement.\n",
+    "\n",
+    "For example, below we **inject** code to print a message at random."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 65,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "You read this as often as you see heads when tossing a coin\n"
+     ]
+    }
+   ],
+   "source": [
+    "if random.random() > 0.5:\n",
+    "    print(\"You read this as often as you see heads when tossing a coin\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "#### Common Use Case: A binary Choice"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "More often than not, we model a **binary choice**. Then, we only need to write an `if`- and an `else`-clause."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 66,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "random.seed(789)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 67,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "6 is even\n"
+     ]
+    }
+   ],
+   "source": [
+    "number = random.choice(numbers)\n",
+    "\n",
+    "if number % 2 == 0:\n",
+    "    print(number, \"is even\")\n",
+    "else:\n",
+    "    print(number, \"is odd\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "To write the condition even conciser, we may take advantage of Python's implicit casting and leave out the `== 0`. However, then we *must* exchange the two suits! The `if`-clause below means \"If the `number` is odd\" in plain English. That is the opposite of the `if`-clause above."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 68,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "random.seed(789)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 69,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "6 is even\n"
+     ]
+    }
+   ],
+   "source": [
+    "number = random.choice(numbers)\n",
+    "\n",
+    "if number % 2:  # Note the opposite meaning!\n",
+    "    print(number, \"is odd\")\n",
+    "else:\n",
+    "    print(number, \"is even\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "#### \"Hard to read\" Example: Nesting `if` Statements"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "We may **nest** `if` statements to control the flow of execution in a more granular way. Every additional layer, however, makes the code *less* readable, in particular, if we have more than one line per code block.\n",
+    "\n",
+    "For example, the code cell below implements an [A/B Testing <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_wiki.png\">](https://en.wikipedia.org/wiki/A/B_testing) strategy where half the time a \"complex\" message is shown to a \"user\" while in the remaining times an \"easy\" message is shown. To do so, the code first \"tosses a coin\" and then checks a randomly drawn `number`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 70,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "random.seed(789)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 71,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "6 can be divided by 2 without a rest\n"
+     ]
+    }
+   ],
+   "source": [
+    "number = random.choice(numbers)\n",
+    "\n",
+    "# Coin is heads.\n",
+    "if random.random() > 0.5:\n",
+    "    if number % 2 == 0:\n",
+    "        print(number, \"can be divided by 2 without a rest\")\n",
+    "    else:\n",
+    "        print(number, \"divided by 2 results in a non-zero rest\")\n",
+    "# Coin is tails.\n",
+    "else:\n",
+    "    if number % 2 == 0:\n",
+    "        print(number, \"is even\")\n",
+    "    else:\n",
+    "        print(number, \"is odd\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "#### \"Easy to read\" Example: Flattening nested `if` Statements"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "A way to make this code more readable is to introduce **temporary variables** *in combination* with the `and` operator to **flatten** the branching logic. The `if` statement then reads almost like plain English. In contrast to many other languages, creating variables is a computationally *cheap* operation in Python (i.e., only a reference is created) and also helps to document the code *inline* with meaningful variable names.\n",
+    "\n",
+    "Flattening the logic *without* temporary variables could lead to *more* sub-expressions in the conditions be evaluated than necessary. Do you see why?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 72,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "random.seed(789)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 73,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "6 can be divided by 2 without a rest\n"
+     ]
+    }
+   ],
+   "source": [
+    "number = random.choice(numbers)\n",
+    "\n",
+    "coin_is_heads = random.random() > 0.5\n",
+    "number_is_even = number % 2 == 0\n",
+    "\n",
+    "if coin_is_heads and number_is_even:\n",
+    "    print(number, \"can be divided by 2 without a rest\")\n",
+    "elif coin_is_heads and not number_is_even:\n",
+    "    print(number, \"divided by 2 results in a non-zero rest\")\n",
+    "elif not coin_is_heads and number_is_even:\n",
+    "    print(number, \"is even\")\n",
+    "else:\n",
+    "    print(number, \"is odd\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "## The `if` Expression"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "When an `if` statement assigns an object to a variable according to a true-or-false condition (i.e., a binary choice), there is a shortcut: We assign the variable the result of a so-called **[conditional expression <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/reference/expressions.html#conditional-expressions)**, or `if` expression for short, instead.\n",
+    "\n",
+    "Think of a situation where we evaluate a piece-wise functional relationship $y = f(x)$ at a given $x$, for example:"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "$\n",
+    "y = f(x) =\n",
+    "\\begin{cases}\n",
+    "0, \\text{ if } x \\le 0 \\\\\n",
+    "x, \\text{ otherwise}\n",
+    "\\end{cases}\n",
+    "$"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Of course, we could use an `if` statement as above to do the job. Yet, this is rather lengthy."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 74,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "3"
+      ]
+     },
+     "execution_count": 74,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "x = 3\n",
+    "\n",
+    "if x <= 0:\n",
+    "    y = 0\n",
+    "else:\n",
+    "    y = x\n",
+    "\n",
+    "y"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "On the contrary, the `if` expression fits into one line. The main downside is a potential loss in readability, in particular, if the functional relationship is not that simple. Also, some practitioners do *not* like that the condition is in the middle of the expression."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 75,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "3"
+      ]
+     },
+     "execution_count": 75,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "x = 3\n",
+    "\n",
+    "y = 0 if x <= 0 else x\n",
+    "\n",
+    "y"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "In this example, however, the most elegant solution is to use the built-in [max() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#max) function."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 76,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "3"
+      ]
+     },
+     "execution_count": 76,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "x = 3\n",
+    "\n",
+    "y = max(0, x)\n",
+    "\n",
+    "y"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "## The `try` Statement"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "In the previous two chapters, we encountered a couple of *runtime* errors. A natural urge we might have after reading about conditional statements is to write code that somehow reacts to the occurrence of such exceptions.\n",
+    "\n",
+    "Consider a situation where we are given some user input that may contain values that cause problems. To illustrate this, we draw a random integer between `0` and `5`, and then divide by this number. Naturally, we see a `ZeroDivisionError` in 16.6% of the cases."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 77,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "random.seed(123)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 78,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "ename": "ZeroDivisionError",
+     "evalue": "division by zero",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mZeroDivisionError\u001b[0m                         Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-78-7996bd5496ba>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0muser_input\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mrandom\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mchoice\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m3\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m4\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m5\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0;36m1\u001b[0m \u001b[0;34m/\u001b[0m \u001b[0muser_input\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;31mZeroDivisionError\u001b[0m: division by zero"
+     ]
+    }
+   ],
+   "source": [
+    "user_input = random.choice([0, 1, 2, 3, 4, 5])\n",
+    "\n",
+    "1 / user_input"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "With the compound `try` statement (cf., [reference <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/reference/compound_stmts.html#the-try-statement)), we can **handle** any *runtime* error.\n",
+    "\n",
+    "In its simplest form, it comes with just two clauses: `try` and `except`. The following tells Python to execute the code in the `try`-clause, and if *anything* goes wrong, continue in the `except`-clause instead of **raising** an error to us. Of course, if nothing goes wrong, the `except`-clause is *not* executed."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 79,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "random.seed(123)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 80,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Something went wrong\n"
+     ]
+    }
+   ],
+   "source": [
+    "user_input = random.choice([0, 1, 2, 3, 4, 5])\n",
+    "\n",
+    "try:\n",
+    "    print(\"The result is\", 1 / user_input)\n",
+    "except:\n",
+    "    print(\"Something went wrong\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "However, it is good practice *not* to handle *any* possible exception but only the ones we may *expect* from the code in the `try`-clause. The reason for that is that we do not want to risk *suppressing* an exception that we do *not* expect. Also, the code base becomes easier to understand as we communicate what could go wrong during execution in an *explicit* way to the (human) reader. Python comes with a lot of [built-in exceptions <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/exceptions.html#concrete-exceptions) that we should familiarize ourselves with.\n",
+    "\n",
+    "Another good practice is to always keep the code in the `try`-clause short to not *accidentally* handle an exception we do *not* want to handle.\n",
+    "\n",
+    "In the example, we are dividing numbers and may expect a `ZeroDivisionError`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 81,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "random.seed(123)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 82,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Something went wrong\n"
+     ]
+    }
+   ],
+   "source": [
+    "user_input = random.choice([0, 1, 2, 3, 4, 5])\n",
+    "\n",
+    "try:\n",
+    "    print(\"The result is\", 1 / user_input)\n",
+    "except ZeroDivisionError:\n",
+    "    print(\"Something went wrong\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Often, we may have to run some code *independent* of an exception occurring, for example, to close a connection to a database. To achieve that, we add a `finally`-clause to the `try` statement.\n",
+    "\n",
+    "Similarly, we may have to run some code *only if* no exception occurs, but we do not want to put it in the `try`-clause as per the good practice mentioned above. To achieve that, we add an `else`-clause to the `try` statement.\n",
+    "\n",
+    "To showcase everything together, we look at one last example. It is randomized: So, run the cell several times and see for yourself."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 83,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "random.seed(123)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 84,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Oops. Division by 0. How does that work?\n",
+      "I am always printed\n"
+     ]
+    }
+   ],
+   "source": [
+    "user_input = random.choice([0, 1, 2, 3, 4, 5])\n",
+    "\n",
+    "try:\n",
+    "    result = 1 / user_input\n",
+    "except ZeroDivisionError:\n",
+    "    print(\"Oops. Division by 0. How does that work?\")\n",
+    "else:\n",
+    "    print(\"The result is\", result)\n",
+    "finally:\n",
+    "    print(\"I am always printed\")"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.6"
+  },
+  "livereveal": {
+   "auto_select": "code",
+   "auto_select_fragment": true,
+   "scroll": true,
+   "theme": "serif"
+  },
+  "toc": {
+   "base_numbering": 1,
+   "nav_menu": {},
+   "number_sections": false,
+   "sideBar": true,
+   "skip_h1_title": true,
+   "title_cell": "Table of Contents",
+   "title_sidebar": "Contents",
+   "toc_cell": false,
+   "toc_position": {
+    "height": "calc(100% - 180px)",
+    "left": "10px",
+    "top": "150px",
+    "width": "384px"
+   },
+   "toc_section_display": false,
+   "toc_window_display": false
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/README.md b/README.md
index 658d95c..be6a7e9 100644
--- a/README.md
+++ b/README.md
@@ -71,6 +71,14 @@ Alternatively, the content can be viewed in a web browser
        Writing one's own Modules)
     - [summary <img height="12" style="display: inline-block" src="static/link/to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/02_functions/03_summary.ipynb)
     - [review questions <img height="12" style="display: inline-block" src="static/link/to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/02_functions/04_review.ipynb)
+  - *Chapter 3*: Conditionals & Exceptions
+    - [content <img height="12" style="display: inline-block" src="static/link/to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/03_conditionals/00_content.ipynb)
+      [<img height="12" style="display: inline-block" src="static/link/to_mb.png">](https://mybinder.org/v2/gh/webartifex/intro-to-python/develop?urlpath=lab/tree/03_conditionals/00_content.ipynb)
+      (Boolean Expressions;
+       Relational Operators;
+       Logical Operators;
+       `if` statement;
+       Exception Handling)
 
 
 #### Videos

From ef438bd2bf2714e5ab2e6a9a4a2110b4bb6ccd44 Mon Sep 17 00:00:00 2001
From: Alexander Hess <alexander@webartifex.biz>
Date: Thu, 15 Oct 2020 15:31:07 +0200
Subject: [PATCH 151/242] Add initial version of chapter 03's exercises

---
 03_conditionals/01_exercises.ipynb | 205 +++++++++++++++++++++++++++++
 03_conditionals/02_exercises.ipynb | 140 ++++++++++++++++++++
 README.md                          |   6 +
 3 files changed, 351 insertions(+)
 create mode 100644 03_conditionals/01_exercises.ipynb
 create mode 100644 03_conditionals/02_exercises.ipynb

diff --git a/03_conditionals/01_exercises.ipynb b/03_conditionals/01_exercises.ipynb
new file mode 100644
index 0000000..513c67e
--- /dev/null
+++ b/03_conditionals/01_exercises.ipynb
@@ -0,0 +1,205 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Note**: Click on \"*Kernel*\" > \"*Restart Kernel and Run All*\" in [JupyterLab](https://jupyterlab.readthedocs.io/en/stable/) *after* finishing the exercises to ensure that your solution runs top to bottom *without* any errors. If you cannot run this file on your machine, you may want to open it [in the cloud <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_mb.png\">](https://mybinder.org/v2/gh/webartifex/intro-to-python/develop?urlpath=lab/tree/03_conditionals/01_exercises.ipynb)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Chapter 3: Conditionals & Exceptions (Coding Exercises)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The exercises below assume that you have read [Chapter 3 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/03_conditionals/00_content.ipynb).\n",
+    "\n",
+    "The `...`'s in the code cells indicate where you need to fill in code snippets. The number of `...`'s within a code cell give you a rough idea of how many lines of code are needed to solve the task. You should not need to create any additional code cells for your final solution. However, you may want to use temporary code cells to try out some ideas."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Discounting Customer Orders"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q1**: Write a function `discounted_price()` that takes the positional arguments `unit_price` (of type `float`) and `quantity` (of type `int`) and implements a discount scheme for a line item in a customer order as follows:\n",
+    "\n",
+    "- if the unit price is over 100 dollars, grant 10% relative discount\n",
+    "- if a customer orders more than 10 items, one in every five items is for free\n",
+    "\n",
+    "Only one of the two discounts is granted, whichever is better for the customer.\n",
+    "\n",
+    "The function should then return the overall price for the line item. Do not forget to round appropriately."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def discounted_price(unit_price, quantity):\n",
+    "    \"\"\"Calculate the price of a line item in an order.\n",
+    "\n",
+    "    Args:\n",
+    "        unit_price (float): price of one ordered item\n",
+    "        quantity (int): number of items ordered\n",
+    "\n",
+    "    Returns:\n",
+    "        line_item_price (float)\n",
+    "    \"\"\"\n",
+    "    ...\n",
+    "    ...\n",
+    "    ...\n",
+    "    ...\n",
+    "\n",
+    "    ...\n",
+    "    ...\n",
+    "    ...\n",
+    "    ...\n",
+    "    ...\n",
+    "\n",
+    "    return ..."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q2**: Calculate the final price for the following line items of an order:\n",
+    "- $7$ smartphones @ $99.00$ USD\n",
+    "- $3$ workstations @ $999.00$ USD\n",
+    "- $19$ GPUs @ $879.95$ USD\n",
+    "- $14$ Raspberry Pis @ $35.00$ USD"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "discounted_price(...)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "discounted_price(...)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "discounted_price(...)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "discounted_price(...)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q3**: Calculate the last two line items with order quantities of $20$ and $15$. What do you observe?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "discounted_price(...)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "discounted_price(...)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q4**: Looking at the `if`-`else`-logic in the function, why do you think the four example line items in **Q2** were chosen as they were?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " < your answer >"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.6"
+  },
+  "toc": {
+   "base_numbering": 1,
+   "nav_menu": {},
+   "number_sections": false,
+   "sideBar": true,
+   "skip_h1_title": true,
+   "title_cell": "Table of Contents",
+   "title_sidebar": "Contents",
+   "toc_cell": false,
+   "toc_position": {},
+   "toc_section_display": false,
+   "toc_window_display": false
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/03_conditionals/02_exercises.ipynb b/03_conditionals/02_exercises.ipynb
new file mode 100644
index 0000000..432ca73
--- /dev/null
+++ b/03_conditionals/02_exercises.ipynb
@@ -0,0 +1,140 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Note**: Click on \"*Kernel*\" > \"*Restart Kernel and Run All*\" in [JupyterLab](https://jupyterlab.readthedocs.io/en/stable/) *after* finishing the exercises to ensure that your solution runs top to bottom *without* any errors. If you cannot run this file on your machine, you may want to open it [in the cloud <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_mb.png\">](https://mybinder.org/v2/gh/webartifex/intro-to-python/develop?urlpath=lab/tree/03_conditionals/02_exercises.ipynb)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Chapter 3: Conditionals & Exceptions (Coding Exercises)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The exercises below assume that you have read [Chapter 3 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/03_conditionals/00_content.ipynb).\n",
+    "\n",
+    "The `...`'s in the code cells indicate where you need to fill in code snippets. The number of `...`'s within a code cell give you a rough idea of how many lines of code are needed to solve the task. You should not need to create any additional code cells for your final solution. However, you may want to use temporary code cells to try out some ideas."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Fizz Buzz"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The kids game [Fizz Buzz <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_wiki.png\">](https://en.wikipedia.org/wiki/Fizz_buzz) is said to be often used in job interviews for entry-level positions. However, opinions vary as to how good of a test it is (cf., [source <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_hn.png\">](https://news.ycombinator.com/item?id=16446774)).\n",
+    "\n",
+    "In its simplest form, a group of people starts counting upwards in an alternating fashion. Whenever a number is divisible by $3$, the person must say \"Fizz\" instead of the number. The same holds for numbers divisible by $5$ when the person must say \"Buzz.\" If a number is divisible by both numbers, one must say \"FizzBuzz.\" Probably, this game would also make a good drinking game with the \"right\" beverages."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q1**: First, create a list `numbers` with the numbers from 1 through 100. You could type all numbers manually, but there is, of course, a smarter way. The built-in [range() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#func-range) may be useful here. Read how it works in the documentation. To make the output of [range() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#func-range) a `list` object, you have to wrap it with the [list() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#func-list) built-in (i.e., `list(range(...))`)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "numbers = ..."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q2**: Loop over the `numbers` list and *replace* numbers for which one of the two (or both) conditions apply with text strings `\"Fizz\"`, `\"Buzz\"`, or `\"FizzBuzz\"` using the indexing operator `[]` and the assignment statement `=`.\n",
+    "\n",
+    "In [Chapter 1 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/01_elements/03_content.ipynb#Who-am-I?-And-how-many?), we saw that Python starts indexing with `0` as the first element. Keep that in mind.\n",
+    "\n",
+    "So in each iteration of the `for`-loop, you have to determine an `index` variable as well as check the actual `number` for its divisors.\n",
+    "\n",
+    "Hint: the order of the conditions is important!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "for number in numbers:\n",
+    "    ...\n",
+    "\n",
+    "    ...\n",
+    "    ...\n",
+    "    ...\n",
+    "    ...\n",
+    "    ...\n",
+    "    ..."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q3**: Create a loop that prints out either the number or any of the Fizz Buzz substitutes in `numbers`! Do it in such a way that the output is concise!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "for number in numbers:\n",
+    "    print(...)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.6"
+  },
+  "toc": {
+   "base_numbering": 1,
+   "nav_menu": {},
+   "number_sections": false,
+   "sideBar": true,
+   "skip_h1_title": true,
+   "title_cell": "Table of Contents",
+   "title_sidebar": "Contents",
+   "toc_cell": false,
+   "toc_position": {},
+   "toc_section_display": false,
+   "toc_window_display": false
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/README.md b/README.md
index be6a7e9..b9acb26 100644
--- a/README.md
+++ b/README.md
@@ -79,6 +79,12 @@ Alternatively, the content can be viewed in a web browser
        Logical Operators;
        `if` statement;
        Exception Handling)
+    - [exercises <img height="12" style="display: inline-block" src="static/link/to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/03_conditionals/01_exercises.ipynb)
+      [<img height="12" style="display: inline-block" src="static/link/to_mb.png">](https://mybinder.org/v2/gh/webartifex/intro-to-python/develop?urlpath=lab/tree/03_conditionals/01_exercises.ipynb)
+      (Discounting Customer Orders)
+    - [exercises <img height="12" style="display: inline-block" src="static/link/to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/03_conditionals/02_exercises.ipynb)
+      [<img height="12" style="display: inline-block" src="static/link/to_mb.png">](https://mybinder.org/v2/gh/webartifex/intro-to-python/develop?urlpath=lab/tree/03_conditionals/02_exercises.ipynb)
+      (Fizz Buzz)
 
 
 #### Videos

From fcde5176865a65f9a2db72789155630477deffad Mon Sep 17 00:00:00 2001
From: Alexander Hess <alexander@webartifex.biz>
Date: Thu, 15 Oct 2020 15:31:37 +0200
Subject: [PATCH 152/242] Add initial version of chapter 03's summary

---
 03_conditionals/03_summary.ipynb | 76 ++++++++++++++++++++++++++++++++
 README.md                        |  1 +
 2 files changed, 77 insertions(+)
 create mode 100644 03_conditionals/03_summary.ipynb

diff --git a/03_conditionals/03_summary.ipynb b/03_conditionals/03_summary.ipynb
new file mode 100644
index 0000000..86c8468
--- /dev/null
+++ b/03_conditionals/03_summary.ipynb
@@ -0,0 +1,76 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "# Chapter 3: Conditionals & Exceptions (TL;DR)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "- **boolean expressions** evaluate to either `True` or `False`\n",
+    "- **relational operators** (e.g., `==` or `!=`) compare operands according to \"human\" interpretations\n",
+    "- **logical operators** (e.g., `and` )combine boolean sub-expressions to more \"complex\" expressions\n",
+    "- the **conditional statement** (i.e., the `if` statement) allows **controlling** the **flow of execution** depending on some **conditions**\n",
+    "- a **conditional expression** is a short form of a conditional statement\n",
+    "- **exception handling** is also a common way of **controlling** the **flow of execution**, in particular, if we have to be prepared for bad input data"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.6"
+  },
+  "livereveal": {
+   "auto_select": "code",
+   "auto_select_fragment": true,
+   "scroll": true,
+   "theme": "serif"
+  },
+  "toc": {
+   "base_numbering": 1,
+   "nav_menu": {},
+   "number_sections": false,
+   "sideBar": true,
+   "skip_h1_title": true,
+   "title_cell": "Table of Contents",
+   "title_sidebar": "Contents",
+   "toc_cell": false,
+   "toc_position": {
+    "height": "calc(100% - 180px)",
+    "left": "10px",
+    "top": "150px",
+    "width": "384px"
+   },
+   "toc_section_display": false,
+   "toc_window_display": false
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/README.md b/README.md
index b9acb26..3390a5e 100644
--- a/README.md
+++ b/README.md
@@ -85,6 +85,7 @@ Alternatively, the content can be viewed in a web browser
     - [exercises <img height="12" style="display: inline-block" src="static/link/to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/03_conditionals/02_exercises.ipynb)
       [<img height="12" style="display: inline-block" src="static/link/to_mb.png">](https://mybinder.org/v2/gh/webartifex/intro-to-python/develop?urlpath=lab/tree/03_conditionals/02_exercises.ipynb)
       (Fizz Buzz)
+    - [summary <img height="12" style="display: inline-block" src="static/link/to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/03_conditionals/03_summary.ipynb)
 
 
 #### Videos

From 2ea34bf960bac328a149298e68b77314d5517896 Mon Sep 17 00:00:00 2001
From: Alexander Hess <alexander@webartifex.biz>
Date: Thu, 15 Oct 2020 15:32:08 +0200
Subject: [PATCH 153/242] Add initial version of chapter 03's review

---
 03_conditionals/04_review.ipynb | 201 ++++++++++++++++++++++++++++++++
 README.md                       |   1 +
 2 files changed, 202 insertions(+)
 create mode 100644 03_conditionals/04_review.ipynb

diff --git a/03_conditionals/04_review.ipynb b/03_conditionals/04_review.ipynb
new file mode 100644
index 0000000..78d9061
--- /dev/null
+++ b/03_conditionals/04_review.ipynb
@@ -0,0 +1,201 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Chapter 3: Conditionals & Exceptions (Review Questions)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The questions below assume that you have read [Chapter 3 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/03_conditionals/00_content.ipynb).\n",
+    "\n",
+    "Be concise in your answers! Most questions can be answered in *one* sentence."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Essay Questions "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q1**: What is the **singleton** design pattern? How many objects does the expression `[True, False, True, False]` generate in memory?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " < your answer >"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q2**: What do we mean when we talk about **truthy** and **falsy** expressions?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " < your answer >"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q3**: Describe in your own words the concept of **short-circuiting**! What does it imply for an individual sub-expression in a boolean expression?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " < your answer >"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q4**: Explain how the conceptual difference between a **statement** and an **expression** relates to the difference between a **conditional statement** and a **conditional expression**."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " < your answer >"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q5**: Why is the use of **temporary variables** encouraged in Python?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " < your answer >"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q6**: What does the `finally`-clause enforce in this code snippet? How can a `try` statement be useful *without* an `except`-clause?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "```python\n",
+    "try:\n",
+    "    print(\"Make a request to a service on the internet\")\n",
+    "finally:\n",
+    "    print(\"This could be clean-up code\")\n",
+    "```"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " < your answer >"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## True / False Questions"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Motivate your answer with *one short* sentence!"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q7**: The objects `True`, `False`, and `None` represent the idea of *yes*, *no*, and *maybe* answers in a natural language.\n",
+    "\n",
+    "Hint: you also respond with a code cell."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " < your answer >"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q8**: The `try` statement is useful for handling **syntax** errors."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " < your answer >"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.6"
+  },
+  "toc": {
+   "base_numbering": 1,
+   "nav_menu": {},
+   "number_sections": false,
+   "sideBar": true,
+   "skip_h1_title": true,
+   "title_cell": "Table of Contents",
+   "title_sidebar": "Contents",
+   "toc_cell": false,
+   "toc_position": {},
+   "toc_section_display": false,
+   "toc_window_display": false
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/README.md b/README.md
index 3390a5e..7328211 100644
--- a/README.md
+++ b/README.md
@@ -86,6 +86,7 @@ Alternatively, the content can be viewed in a web browser
       [<img height="12" style="display: inline-block" src="static/link/to_mb.png">](https://mybinder.org/v2/gh/webartifex/intro-to-python/develop?urlpath=lab/tree/03_conditionals/02_exercises.ipynb)
       (Fizz Buzz)
     - [summary <img height="12" style="display: inline-block" src="static/link/to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/03_conditionals/03_summary.ipynb)
+    - [review questions <img height="12" style="display: inline-block" src="static/link/to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/03_conditionals/04_review.ipynb)
 
 
 #### Videos

From 105cd574451acc656b5b1fad62efc71ee3443f52 Mon Sep 17 00:00:00 2001
From: Alexander Hess <alexander@webartifex.biz>
Date: Thu, 15 Oct 2020 15:41:11 +0200
Subject: [PATCH 154/242] Streamline slides

---
 03_conditionals/00_content.ipynb | 10 +++++-----
 1 file changed, 5 insertions(+), 5 deletions(-)

diff --git a/03_conditionals/00_content.ipynb b/03_conditionals/00_content.ipynb
index 7a54f13..5a6bc8e 100644
--- a/03_conditionals/00_content.ipynb
+++ b/03_conditionals/00_content.ipynb
@@ -228,7 +228,7 @@
    "execution_count": 6,
    "metadata": {
     "slideshow": {
-     "slide_type": "fragment"
+     "slide_type": "skip"
     }
    },
    "outputs": [
@@ -276,7 +276,7 @@
    "execution_count": 8,
    "metadata": {
     "slideshow": {
-     "slide_type": "slide"
+     "slide_type": "fragment"
     }
    },
    "outputs": [
@@ -300,7 +300,7 @@
    "execution_count": 9,
    "metadata": {
     "slideshow": {
-     "slide_type": "fragment"
+     "slide_type": "skip"
     }
    },
    "outputs": [
@@ -376,7 +376,7 @@
    "execution_count": 12,
    "metadata": {
     "slideshow": {
-     "slide_type": "fragment"
+     "slide_type": "skip"
     }
    },
    "outputs": [
@@ -610,7 +610,7 @@
     }
    ],
    "source": [
-    "42 != 123  # \"not equal to\""
+    "42 != 123"
    ]
   },
   {

From 93008de309abdad0ce449b4657d974dd4d629c11 Mon Sep 17 00:00:00 2001
From: Alexander Hess <alexander@webartifex.biz>
Date: Thu, 15 Oct 2020 18:12:43 +0200
Subject: [PATCH 155/242] Add initial version of chapter 04, part 1

---
 00_intro/00_content.ipynb     |    2 +-
 04_iteration/00_content.ipynb | 4919 +++++++++++++++++++++++++++++++++
 README.md                     |    8 +
 3 files changed, 4928 insertions(+), 1 deletion(-)
 create mode 100644 04_iteration/00_content.ipynb

diff --git a/00_intro/00_content.ipynb b/00_intro/00_content.ipynb
index a2e8778..dbe3f49 100644
--- a/00_intro/00_content.ipynb
+++ b/00_intro/00_content.ipynb
@@ -754,7 +754,7 @@
     "  - *Chapter 2*: [Functions & Modularization <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/02_functions/00_content.ipynb)\n",
     "- What is the flow of execution? How can we form sentences from words?\n",
     "  - *Chapter 3*: [Conditionals & Exceptions <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/03_conditionals/00_content.ipynb)\n",
-    "  - *Chapter 4*: Recursion & Looping"
+    "  - *Chapter 4*: [Recursion & Looping <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/04_iteration/00_content.ipynb)"
    ]
   },
   {
diff --git a/04_iteration/00_content.ipynb b/04_iteration/00_content.ipynb
new file mode 100644
index 0000000..580d340
--- /dev/null
+++ b/04_iteration/00_content.ipynb
@@ -0,0 +1,4919 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "**Note**: Click on \"*Kernel*\" > \"*Restart Kernel and Clear All Outputs*\" in [JupyterLab](https://jupyterlab.readthedocs.io/en/stable/) *before* reading this notebook to reset its output. If you cannot run this file on your machine, you may want to open it [in the cloud <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_mb.png\">](https://mybinder.org/v2/gh/webartifex/intro-to-python/develop?urlpath=lab/tree/04_iteration/00_content.ipynb)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "# Chapter 4: Recursion & Looping"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "While controlling the flow of execution with an `if` statement is a must-have building block in any programming language, it alone does not allow us to run a block of code repetitively, and we need to be able to do precisely that to write useful software.\n",
+    "\n",
+    "The `for` statement shown in some examples before might be the missing piece in the puzzle. However, we can live without it and postpone its official introduction until the second half of this chapter.\n",
+    "\n",
+    "Instead, we dive into the big idea of **iteration** by studying the concept of **recursion** first. This order is opposite to many other introductory books that only treat the latter as a nice-to-have artifact, if at all. Yet, understanding recursion sharpens one's mind and contributes to seeing problems from a different angle."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "## Recursion"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "A popular joke among programmers by an unknown author goes like this (cf., [discussion](https://www.quora.com/What-does-the-phrase-in-order-to-understand-recursion-you-must-first-understand-recursion-mean-to-you)):"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "> \"In order to understand **recursion**, you must first understand **recursion**.\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "A function that calls itself is **recursive**, and the process of executing such a function is called **recursion**.\n",
+    "\n",
+    "Recursive functions contain some form of a conditional check (e.g., `if` statement) to identify a **base case** that ends the recursion *when* reached. Otherwise, the function would keep calling itself forever.\n",
+    "\n",
+    "The meaning of the word *recursive* is similar to *circular*. However, a truly circular definition is not very helpful, and we think of a recursive function as kind of a \"circular function with a way out at the end.\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "### Trivial Example: Countdown"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "A rather trivial toy example illustrates the important aspects concretely: If called with any positive integer as its `n` argument, `countdown()` just prints that number and calls itself with the *new* `n` being the *old* `n` minus `1`. This continues until `countdown()` is called with `n=0`. Then, the flow of execution hits the base case, and the function calls stop."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "code_folding": [],
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "def countdown(n):\n",
+    "    \"\"\"Print a countdown until the party starts.\n",
+    "\n",
+    "    Args:\n",
+    "        n (int): seconds until the party begins\n",
+    "    \"\"\"\n",
+    "    if n == 0:\n",
+    "        print(\"Happy New Year!\")\n",
+    "    else:\n",
+    "        print(n)\n",
+    "        countdown(n - 1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "3\n",
+      "2\n",
+      "1\n",
+      "Happy New Year!\n"
+     ]
+    }
+   ],
+   "source": [
+    "countdown(3)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "As trivial as this seems, a lot of complexity is hidden in this implementation. In particular, the order in which objects are created and de-referenced in memory might not be apparent right away as [PythonTutor <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](http://pythontutor.com/visualize.html#code=def%20countdown%28n%29%3A%0A%20%20%20%20if%20n%20%3D%3D%200%3A%0A%20%20%20%20%20%20%20%20print%28%22Happy%20new%20Year!%22%29%0A%20%20%20%20else%3A%0A%20%20%20%20%20%20%20%20print%28n%29%0A%20%20%20%20%20%20%20%20countdown%28n%20-%201%29%0A%0Acountdown%283%29&cumulative=false&curInstr=0&heapPrimitives=nevernest&mode=display&origin=opt-frontend.js&py=3&rawInputLstJSON=%5B%5D&textReferences=false) shows: Each time `countdown()` is called, Python creates a *new* frame in the part of the memory where it manages all the names. This way, Python *isolates* all the different `n` variables from each other. As new frames are created until we reach the base case, after which the frames are destroyed in the *reversed* order, this is called a **[stack <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_wiki.png\">](https://en.wikipedia.org/wiki/Stack_(abstract_data_type))** of frames in computer science terminology. In simple words, a stack is a last-in-first-out (LIFO) task queue. Each frame has a single parent frame, namely the one whose recursive function call created it."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "## Recursion in Mathematics"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Recursion plays a vital role in mathematics as well, and we likely know about it from some introductory course, for example, in [combinatorics <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_wiki.png\">](https://en.wikipedia.org/wiki/Combinatorics)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "### Easy Example: [Factorial <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_wiki.png\">](https://en.wikipedia.org/wiki/Factorial)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "The factorial function, denoted with the $!$ symbol, is defined as follows for all non-negative integers:"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "$$0! = 1$$\n",
+    "$$n! = n*(n-1)!$$"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Whenever we find a recursive way of formulating an idea, we can immediately translate it into Python in a *naive* way (i.e., we create a *correct* program that may *not* be an *efficient* implementation yet).\n",
+    "\n",
+    "Below is a first version of `factorial()`: The `return` statement does not have to be a function's last code line, and we may indeed have several `return` statements as well."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "code_folding": [],
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "def factorial(n):\n",
+    "    \"\"\"Calculate the factorial of a number.\n",
+    "\n",
+    "    Args:\n",
+    "        n (int): number to calculate the factorial for\n",
+    "\n",
+    "    Returns:\n",
+    "        factorial (int)\n",
+    "    \"\"\"\n",
+    "    if n == 0:\n",
+    "        return 1\n",
+    "    else:\n",
+    "        recurse = factorial(n - 1)\n",
+    "        result = n * recurse\n",
+    "        return result"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "When we read such code, it is often easier not to follow every function call (i.e., `factorial(n - 1)` here) in one's mind but assume we receive a return value as specified in the documentation. Some call this approach a **[leap of faith](http://greenteapress.com/thinkpython2/html/thinkpython2007.html#sec75)**. We practice this already whenever we call built-in functions (e.g., [print() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#print) or [len() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#len)) where we would have to read C code in many cases.\n",
+    "\n",
+    "To visualize *all* the computational steps of the exemplary `factorial(3)`, we use [PythonTutor <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](http://pythontutor.com/visualize.html#code=def%20factorial%28n%29%3A%0A%20%20%20%20if%20n%20%3D%3D%200%3A%0A%20%20%20%20%20%20%20%20return%201%0A%20%20%20%20else%3A%0A%20%20%20%20%20%20%20%20recurse%20%3D%20factorial%28n%20-%201%29%0A%20%20%20%20%20%20%20%20result%20%3D%20n%20*%20recurse%0A%20%20%20%20%20%20%20%20return%20result%0A%0Asolution%20%3D%20factorial%283%29&cumulative=false&curInstr=0&heapPrimitives=nevernest&mode=display&origin=opt-frontend.js&py=3&rawInputLstJSON=%5B%5D&textReferences=false): The recursion again creates a stack of frames in memory. In contrast to the previous trivial example, each frame leaves a return value in memory after it is destroyed. This return value is then assigned to the `recurse` variable within the parent frame and used to compute `result`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "6"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "factorial(3)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "3628800"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "factorial(10)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "A Pythonista would formulate `factorial()` in a more concise way using the so-called **early exit** pattern: No `else`-clause is needed as reaching a `return` statement ends a function call *immediately*. Furthermore, we do not need the temporary variables `recurse` and `result`.\n",
+    "\n",
+    "As [PythonTutor <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](http://pythontutor.com/visualize.html#code=def%20factorial%28n%29%3A%0A%20%20%20%20if%20n%20%3D%3D%200%3A%0A%20%20%20%20%20%20%20%20return%201%0A%20%20%20%20return%20n%20*%20factorial%28n%20-%201%29%0A%0Asolution%20%3D%20factorial%283%29&cumulative=false&curInstr=0&heapPrimitives=nevernest&mode=display&origin=opt-frontend.js&py=3&rawInputLstJSON=%5B%5D&textReferences=false) shows, this implementation is more efficient as it only requires 18 computational steps instead of 24 to calculate `factorial(3)`, an improvement of 25 percent! "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "def factorial(n):\n",
+    "    \"\"\"Calculate the factorial of a number.\n",
+    "\n",
+    "    Args:\n",
+    "        n (int): number to calculate the factorial for\n",
+    "\n",
+    "    Returns:\n",
+    "        factorial (int)\n",
+    "    \"\"\"\n",
+    "    if n == 0:\n",
+    "        return 1\n",
+    "    return n * factorial(n - 1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "6"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "factorial(3)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "3628800"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "factorial(10)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Note that the [math <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/math.html) module in the [standard library <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/index.html) provides a [factorial() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/math.html#math.factorial) function as well, and we should, therefore, *never* implement it ourselves in a real codebase."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "import math"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Help on built-in function factorial in module math:\n",
+      "\n",
+      "factorial(x, /)\n",
+      "    Find x!.\n",
+      "    \n",
+      "    Raise a ValueError if x is negative or non-integral.\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "help(math.factorial)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "6"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "math.factorial(3)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "3628800"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "math.factorial(10)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "### \"Involved\" Example: [Euclid's Algorithm <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_wiki.png\">](https://en.wikipedia.org/wiki/Euclidean_algorithm)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "As famous philosopher Euclid already shows in his \"Elements\" (ca. 300 BC), the greatest common divisor of two integers, i.e., the largest number that divides both integers without a remainder, can be efficiently computed with the following code. This example illustrates that a recursive solution to a problem is not always easy to understand."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "def gcd(a, b):\n",
+    "    \"\"\"Calculate the greatest common divisor of two numbers.\n",
+    "\n",
+    "    Args:\n",
+    "        a (int): first number\n",
+    "        b (int): second number\n",
+    "\n",
+    "    Returns:\n",
+    "        gcd (int)\n",
+    "    \"\"\"\n",
+    "    if b == 0:\n",
+    "        return a \n",
+    "    return gcd(b, a % b)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "4"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "gcd(12, 4)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Euclid's algorithm is stunningly fast, even for large numbers. Its speed comes from the use of the modulo operator `%`. However, this is *not* true for recursion in general, which may result in slow programs if not applied correctly."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "9"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "gcd(112233445566778899, 987654321)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "As expected, for two [prime numbers <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_wiki.png\">](https://en.wikipedia.org/wiki/List_of_prime_numbers) the greatest common divisor is of course $1$."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "1"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "gcd(7, 7919)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "The [math <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/math.html) module in the [standard library <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/index.html) provides a [gcd() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/math.html#math.gcd) function as well, and, therefore, we should again *never* implement it on our own."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Help on built-in function gcd in module math:\n",
+      "\n",
+      "gcd(x, y, /)\n",
+      "    greatest common divisor of x and y\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "help(math.gcd)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "4"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "math.gcd(12, 4)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "9"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "math.gcd(112233445566778899, 987654321)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "1"
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "math.gcd(7, 7919)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "### \"Easy at first Glance\" Example: [Fibonacci Numbers <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_wiki.png\">](https://en.wikipedia.org/wiki/Fibonacci_number)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "The Fibonacci numbers are an infinite sequence of non-negative integers that are calculated such that every number is the sum of its two predecessors where the first two numbers of the sequence are defined to be $0$ and $1$. For example, the first 13 numbers are:"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "$0, 1, 1, 2, 3, 5, 8, 13, 21, 34, 55, 89, 144$"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Let's write a function `fibonacci()` that calculates the $i$th Fibonacci number where $0$ is the $0$th number. Looking at the numbers in a **backward** fashion (i.e., from right to left), we realize that the return value for `fibonacci(i)` can be reduced to the sum of the return values for `fibonacci(i - 1)` and `fibonacci(i - 2)` disregarding the *two* base cases."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "def fibonacci(i):\n",
+    "    \"\"\"Calculate the ith Fibonacci number.\n",
+    "\n",
+    "    Args:\n",
+    "        i (int): index of the Fibonacci number to calculate\n",
+    "\n",
+    "    Returns:\n",
+    "        ith_fibonacci (int)\n",
+    "    \"\"\"\n",
+    "    if i == 0:\n",
+    "        return 0\n",
+    "    elif i == 1:\n",
+    "        return 1\n",
+    "    return fibonacci(i - 1) + fibonacci(i - 2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "144"
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "fibonacci(12)  # = 13th number"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "##### Efficiency of Algorithms"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "This implementation is *highly* **inefficient** as small Fibonacci numbers already take a very long time to compute. The reason for this is **exponential growth** in the number of function calls. As [PythonTutor <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](http://pythontutor.com/visualize.html#code=def%20fibonacci%28i%29%3A%0A%20%20%20%20if%20i%20%3D%3D%200%3A%0A%20%20%20%20%20%20%20%20return%200%0A%20%20%20%20elif%20i%20%3D%3D%201%3A%0A%20%20%20%20%20%20%20%20return%201%0A%20%20%20%20return%20fibonacci%28i%20-%201%29%20%2B%20fibonacci%28i%20-%202%29%0A%0Arv%20%3D%20fibonacci%285%29&cumulative=false&curInstr=0&heapPrimitives=nevernest&mode=display&origin=opt-frontend.js&py=3&rawInputLstJSON=%5B%5D&textReferences=false) shows, `fibonacci()` is called again and again with the same `i` arguments.\n",
+    "\n",
+    "To understand this in detail, we have to study algorithms and data structures (e.g., with [this book](https://www.amazon.de/Introduction-Algorithms-Press-Thomas-Cormen/dp/0262033844/ref=sr_1_1?__mk_de_DE=%C3%85M%C3%85%C5%BD%C3%95%C3%91&crid=1JNE8U0VZGU0O&qid=1569837169&s=gateway&sprefix=algorithms+an%2Caps%2C180&sr=8-1)), a discipline within computer science, and dive into the analysis of **[time complexity of algorithms <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_wiki.png\">](https://en.wikipedia.org/wiki/Time_complexity)**.\n",
+    "\n",
+    "Luckily, in the Fibonacci case, the inefficiency can be resolved with a **caching** (i.e., \"reuse\") strategy from the field of **[dynamic programming <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_wiki.png\">](https://en.wikipedia.org/wiki/Dynamic_programming)**, namely **[memoization <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_wiki.png\">](https://en.wikipedia.org/wiki/Memoization)**. We do so in [Chapter 9 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/09_mappings/00_content.ipynb#Memoization), after introducing the `dict` data type.\n",
+    "\n",
+    "Let's measure the average run times for `fibonacci()` and varying `i` arguments with the `%%timeit` [cell magic](https://ipython.readthedocs.io/en/stable/interactive/magics.html#magic-timeit) that comes with Jupyter."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "38.9 µs ± 1.48 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n"
+     ]
+    }
+   ],
+   "source": [
+    "%%timeit -n 100\n",
+    "fibonacci(12)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "12.2 ms ± 77.4 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n"
+     ]
+    }
+   ],
+   "source": [
+    "%%timeit -n 100\n",
+    "fibonacci(24)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "3.89 s ± 0 ns per loop (mean ± std. dev. of 1 run, 1 loop each)\n"
+     ]
+    }
+   ],
+   "source": [
+    "%%timeit -n 1 -r 1\n",
+    "fibonacci(36)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "6.28 s ± 0 ns per loop (mean ± std. dev. of 1 run, 1 loop each)\n"
+     ]
+    }
+   ],
+   "source": [
+    "%%timeit -n 1 -r 1\n",
+    "fibonacci(37)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "## Infinite Recursion"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "If a recursion does not reach its base case, it theoretically runs forever. Luckily, Python detects that and saves the computer from crashing by raising a `RecursionError`.\n",
+    "\n",
+    "The simplest possible infinite recursion is generated like so."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "def run_forever():\n",
+    "    \"\"\"Also a pointless function should have a docstring.\"\"\"\n",
+    "    run_forever()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "ename": "RecursionError",
+     "evalue": "maximum recursion depth exceeded",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mRecursionError\u001b[0m                            Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-28-62f686a9f8a1>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mrun_forever\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;32m<ipython-input-27-80575699c62c>\u001b[0m in \u001b[0;36mrun_forever\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mrun_forever\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m     \u001b[0;34m\"\"\"Also a pointless function should have a docstring.\"\"\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m     \u001b[0mrun_forever\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "... last 1 frames repeated, from the frame below ...\n",
+      "\u001b[0;32m<ipython-input-27-80575699c62c>\u001b[0m in \u001b[0;36mrun_forever\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mrun_forever\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m     \u001b[0;34m\"\"\"Also a pointless function should have a docstring.\"\"\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m     \u001b[0mrun_forever\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;31mRecursionError\u001b[0m: maximum recursion depth exceeded"
+     ]
+    }
+   ],
+   "source": [
+    "run_forever()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "However, even the trivial `countdown()` function from above is not immune to infinite recursion. Let's call it with `3.1` instead of `3`. What goes wrong here?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "3.1\n",
+      "2.1\n",
+      "1.1\n",
+      "0.10000000000000009\n",
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+     ]
+    },
+    {
+     "ename": "RecursionError",
+     "evalue": "maximum recursion depth exceeded while calling a Python object",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mRecursionError\u001b[0m                            Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-29-43e3ad331761>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mcountdown\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m3.1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;32m<ipython-input-1-10499518927d>\u001b[0m in \u001b[0;36mcountdown\u001b[0;34m(n)\u001b[0m\n\u001b[1;32m      9\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     10\u001b[0m         \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mn\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 11\u001b[0;31m         \u001b[0mcountdown\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mn\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "... last 1 frames repeated, from the frame below ...\n",
+      "\u001b[0;32m<ipython-input-1-10499518927d>\u001b[0m in \u001b[0;36mcountdown\u001b[0;34m(n)\u001b[0m\n\u001b[1;32m      9\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     10\u001b[0m         \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mn\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 11\u001b[0;31m         \u001b[0mcountdown\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mn\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;31mRecursionError\u001b[0m: maximum recursion depth exceeded while calling a Python object"
+     ]
+    }
+   ],
+   "source": [
+    "countdown(3.1)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "In the same way, a `RecursionError` occurs if we call `factorial()` with `3.1` instead of `3`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "ename": "RecursionError",
+     "evalue": "maximum recursion depth exceeded in comparison",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mRecursionError\u001b[0m                            Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-30-188b816be8b6>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mfactorial\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m3.1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;32m<ipython-input-6-88d8d933f651>\u001b[0m in \u001b[0;36mfactorial\u001b[0;34m(n)\u001b[0m\n\u001b[1;32m     10\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mn\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     11\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 12\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0mn\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0mfactorial\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mn\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "... last 1 frames repeated, from the frame below ...\n",
+      "\u001b[0;32m<ipython-input-6-88d8d933f651>\u001b[0m in \u001b[0;36mfactorial\u001b[0;34m(n)\u001b[0m\n\u001b[1;32m     10\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mn\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     11\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 12\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0mn\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0mfactorial\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mn\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;31mRecursionError\u001b[0m: maximum recursion depth exceeded in comparison"
+     ]
+    }
+   ],
+   "source": [
+    "factorial(3.1)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "The infinite recursions could easily be avoided by replacing `n == 0` with `n <= 0` in both functions and thereby **generalizing** them. However, even then, calling either `countdown()` or `factorial()` with a non-integer number is still *semantically* wrong.\n",
+    "\n",
+    "Errors as above are a symptom of missing **type checking**: By design, Python allows us to pass in not only integers but objects of any type as arguments to the `countdown()` and `factorial()` functions. As long as the arguments \"behave\" like integers, we do not encounter any *runtime* errors. This is the case here as the two example functions only use the `-` and `*` operators internally, and, in the context of arithmetic, a `float` object behaves like an `int` object. So, the functions keep calling themselves until Python decides with a built-in heuristic that the recursion is likely not going to end and aborts the computations with a `RecursionError`. Strictly speaking, a `RecursionError` is, of course, a *runtime* error as well."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "## Duck Typing"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "The missing type checking is *100% intentional* and considered a **[feature of rather than a bug](https://www.urbandictionary.com/define.php?term=It%27s%20not%20a%20bug%2C%20it%27s%20a%20feature)** in Python!\n",
+    "\n",
+    "Pythonistas use the \"technical\" term **[duck typing <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_wiki.png\">](https://en.wikipedia.org/wiki/Duck_typing)** to express the idea of two objects of *different* types behaving in the *same* way in a given context. The colloquial saying goes, \"If it walks like a duck and it quacks like a duck, it must be a duck.\"\n",
+    "\n",
+    "For example, we could call `factorial()` with the `float` object `3.0`, and the recursion works out fine. So, because the `3.0` \"walks\" and \"quacks\" like a `3`, it \"must be\" a `3`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "6.0"
+      ]
+     },
+     "execution_count": 31,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "factorial(3.0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "6"
+      ]
+     },
+     "execution_count": 32,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "factorial(3)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "We see similar behavior when we mix objects of types `int` and `float` with arithmetic operators. For example, `1 + 2.0` works because Python implicitly views the `1` as a `1.0` at runtime and then knows how to do floating-point arithmetic: Here, the `int` \"walks\" and \"quacks\" like a `float`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "3.0"
+      ]
+     },
+     "execution_count": 33,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "1 + 2.0"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "3.0"
+      ]
+     },
+     "execution_count": 34,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "1.0 + 2.0"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "The important lesson is that we must expect our functions to be called with objects of *any* type at runtime, as opposed to the one type we had in mind when defining the function.\n",
+    "\n",
+    "Duck typing is possible because Python is a dynamically typed language. On the contrary, in statically typed languages like C, we *must* declare (i.e., \"specify\") the data type of every parameter in a function definition. Then, a `RecursionError` as for `countdown(3.1)` or `factorial(3.1)` above could not occur. For example, if we declared the `countdown()` and `factorial()` functions to only accept `int` objects, calling the functions with a `float` argument would immediately fail *syntactically*. As a downside, we would then lose the ability to call `factorial()` with `3.0`, which is *semantically* correct nevertheless.\n",
+    "\n",
+    "So, there is no black or white answer as to which of the two language designs is better. Yet, most professional programmers have strong opinions concerning duck typing, reaching from \"love\" to \"hate.\" This is another example of how programming is a subjective art rather than \"objective\" science. Python's design is probably more appealing to beginners who intuitively regard `3` and `3.0` as interchangeable."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "## Type Checking & Input Validation"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "We use the built-in [isinstance() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#isinstance) function to make sure `factorial()` is called with an `int` object as the argument. We further **validate** the **input** by verifying that the integer is non-negative.\n",
+    "\n",
+    "Meanwhile, we also see how we manually raise exceptions with the `raise` statement (cf., [reference <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/reference/simple_stmts.html#the-raise-statement)), another way of controlling the flow of execution.\n",
+    "\n",
+    "The first two branches in the revised `factorial()` function act as **guardians** ensuring that the code does not produce *unexpected* runtime errors: Errors may be expected when mentioned in the docstring.\n",
+    "\n",
+    "So, in essence, we are doing *two* things here: Besides checking the type, we also enforce **domain-specific** (i.e., mathematical here) rules concerning the non-negativity of `n`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "def factorial(n):\n",
+    "    \"\"\"Calculate the factorial of a number.\n",
+    "\n",
+    "    Args:\n",
+    "        n (int): number to calculate the factorial for; must be positive\n",
+    "\n",
+    "    Returns:\n",
+    "        factorial (int)\n",
+    "\n",
+    "    Raises:\n",
+    "        TypeError: if n is not an integer\n",
+    "        ValueError: if n is negative\n",
+    "    \"\"\"\n",
+    "    if not isinstance(n, int):\n",
+    "        raise TypeError(\"Factorial is only defined for integers\")\n",
+    "    elif n < 0:\n",
+    "        raise ValueError(\"Factorial is not defined for negative integers\")\n",
+    "    elif n == 0:\n",
+    "        return 1\n",
+    "    return n * factorial(n - 1)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "The revised `factorial()` function works like the old one."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "1"
+      ]
+     },
+     "execution_count": 36,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "factorial(0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "6"
+      ]
+     },
+     "execution_count": 37,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "factorial(3)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Instead of running into a situation of infinite recursion, we now receive specific error messages."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "ename": "TypeError",
+     "evalue": "Factorial is only defined for integers",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-38-188b816be8b6>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mfactorial\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m3.1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;32m<ipython-input-35-54b57bc2107d>\u001b[0m in \u001b[0;36mfactorial\u001b[0;34m(n)\u001b[0m\n\u001b[1;32m     13\u001b[0m     \"\"\"\n\u001b[1;32m     14\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mn\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mint\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 15\u001b[0;31m         \u001b[0;32mraise\u001b[0m \u001b[0mTypeError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Factorial is only defined for integers\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     16\u001b[0m     \u001b[0;32melif\u001b[0m \u001b[0mn\u001b[0m \u001b[0;34m<\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     17\u001b[0m         \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Factorial is not defined for negative integers\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;31mTypeError\u001b[0m: Factorial is only defined for integers"
+     ]
+    }
+   ],
+   "source": [
+    "factorial(3.1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "ename": "ValueError",
+     "evalue": "Factorial is not defined for negative integers",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-39-b06b2cada645>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mfactorial\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m42\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;32m<ipython-input-35-54b57bc2107d>\u001b[0m in \u001b[0;36mfactorial\u001b[0;34m(n)\u001b[0m\n\u001b[1;32m     15\u001b[0m         \u001b[0;32mraise\u001b[0m \u001b[0mTypeError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Factorial is only defined for integers\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     16\u001b[0m     \u001b[0;32melif\u001b[0m \u001b[0mn\u001b[0m \u001b[0;34m<\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 17\u001b[0;31m         \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Factorial is not defined for negative integers\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     18\u001b[0m     \u001b[0;32melif\u001b[0m \u001b[0mn\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     19\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;31mValueError\u001b[0m: Factorial is not defined for negative integers"
+     ]
+    }
+   ],
+   "source": [
+    "factorial(-42)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Forcing `n` to be an `int` is a very puritan way of handling the issues discussed above. So, we can *not* call `factorial()` with, for example, `3.0`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "ename": "TypeError",
+     "evalue": "Factorial is only defined for integers",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-40-32f1a9e8cf49>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mfactorial\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m3.0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;32m<ipython-input-35-54b57bc2107d>\u001b[0m in \u001b[0;36mfactorial\u001b[0;34m(n)\u001b[0m\n\u001b[1;32m     13\u001b[0m     \"\"\"\n\u001b[1;32m     14\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mn\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mint\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 15\u001b[0;31m         \u001b[0;32mraise\u001b[0m \u001b[0mTypeError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Factorial is only defined for integers\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     16\u001b[0m     \u001b[0;32melif\u001b[0m \u001b[0mn\u001b[0m \u001b[0;34m<\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     17\u001b[0m         \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Factorial is not defined for negative integers\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;31mTypeError\u001b[0m: Factorial is only defined for integers"
+     ]
+    }
+   ],
+   "source": [
+    "factorial(3.0)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "## Type Casting"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "A similar way to prevent an infinite recursion is to **cast** the **type** of the `n` argument with the built-in [int() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#int) constructor."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "def factorial(n):\n",
+    "    \"\"\"Calculate the factorial of a number.\n",
+    "\n",
+    "    Args:\n",
+    "        n (int): number to calculate the factorial for; must be positive\n",
+    "\n",
+    "    Returns:\n",
+    "        factorial (int)\n",
+    "\n",
+    "    Raises:\n",
+    "        TypeError: if n cannot be cast as an integer\n",
+    "        ValueError: if n is negative\n",
+    "    \"\"\"\n",
+    "    n = int(n)\n",
+    "    if n < 0:\n",
+    "        raise ValueError(\"Factorial is not defined for negative integers\")\n",
+    "    elif n == 0:\n",
+    "        return 1\n",
+    "    return n * factorial(n - 1)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "The not so strict type casting implements duck typing for `factorial()` as, for example, `3.0` \"walks\" and \"quacks\" like a `3`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "6"
+      ]
+     },
+     "execution_count": 42,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "factorial(3)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "6"
+      ]
+     },
+     "execution_count": 43,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "factorial(3.0)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "However, if we now call `factorial()` with a non-integer `float` object like `3.1`, *no* error is raised. This is a potential source for *semantic* errors as the function runs for invalid input."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "6"
+      ]
+     },
+     "execution_count": 44,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "factorial(3.1)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "We could adjust the type casting logic such that a `TypeError` is raised for `n` arguments with non-zero decimals. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "metadata": {
+    "code_folding": [],
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "def factorial(n):\n",
+    "    \"\"\"Calculate the factorial of a number.\n",
+    "\n",
+    "    Args:\n",
+    "        n (int): number to calculate the factorial for; must be positive\n",
+    "\n",
+    "    Returns:\n",
+    "        factorial (int)\n",
+    "\n",
+    "    Raises:\n",
+    "        TypeError: if n cannot be cast as an integer\n",
+    "        ValueError: if n is negative\n",
+    "    \"\"\"\n",
+    "    if n != int(n):\n",
+    "        raise TypeError(\"n is not integer-like; it has non-zero decimals\")\n",
+    "    n = int(n)\n",
+    "\n",
+    "    if n < 0:\n",
+    "        raise ValueError(\"Factorial is not defined for negative integers\")\n",
+    "    elif n == 0:\n",
+    "        return 1\n",
+    "    return n * factorial(n - 1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "6"
+      ]
+     },
+     "execution_count": 46,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "factorial(3.0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "ename": "TypeError",
+     "evalue": "n is not integer-like; it has non-zero decimals",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-47-188b816be8b6>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mfactorial\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m3.1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;32m<ipython-input-45-07dc981a3422>\u001b[0m in \u001b[0;36mfactorial\u001b[0;34m(n)\u001b[0m\n\u001b[1;32m     13\u001b[0m     \"\"\"\n\u001b[1;32m     14\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mn\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0mint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mn\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 15\u001b[0;31m         \u001b[0;32mraise\u001b[0m \u001b[0mTypeError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"n is not integer-like; it has non-zero decimals\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     16\u001b[0m     \u001b[0mn\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mn\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     17\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;31mTypeError\u001b[0m: n is not integer-like; it has non-zero decimals"
+     ]
+    }
+   ],
+   "source": [
+    "factorial(3.1)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "However, using built-in constructors for type casting leads to another subtle inconsistency. As constructors are designed to take *any* object as their argument, they do not raise a `TypeError` when called with invalid input but a `ValueError` instead. So, if we, for example, called `factorial()` with `\"text\"` as the `n` argument, we see the `ValueError` raised by [int() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#int) in a situation where a `TypeError` would be more appropriate."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 48,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "ename": "ValueError",
+     "evalue": "invalid literal for int() with base 10: 'text'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-48-9a12eb120fc3>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mfactorial\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"text\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;32m<ipython-input-45-07dc981a3422>\u001b[0m in \u001b[0;36mfactorial\u001b[0;34m(n)\u001b[0m\n\u001b[1;32m     12\u001b[0m         \u001b[0mValueError\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mn\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0mnegative\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     13\u001b[0m     \"\"\"\n\u001b[0;32m---> 14\u001b[0;31m     \u001b[0;32mif\u001b[0m \u001b[0mn\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0mint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mn\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     15\u001b[0m         \u001b[0;32mraise\u001b[0m \u001b[0mTypeError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"n is not integer-like; it has non-zero decimals\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     16\u001b[0m     \u001b[0mn\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mn\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;31mValueError\u001b[0m: invalid literal for int() with base 10: 'text'"
+     ]
+    }
+   ],
+   "source": [
+    "factorial(\"text\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "We could, of course, use a `try` statement to suppress the exceptions raised by [int() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#int) and replace them with a custom `TypeError`. However, now the implementation as a whole is more about type checking than about the actual logic solving the problem. We took this example to the extreme on purpose. In practice, we rarely see such code!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 49,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "def factorial(n):\n",
+    "    \"\"\"Calculate the factorial of a number.\n",
+    "\n",
+    "    Args:\n",
+    "        n (int): number to calculate the factorial for; must be positive\n",
+    "\n",
+    "    Returns:\n",
+    "        factorial (int)\n",
+    "\n",
+    "    Raises:\n",
+    "        TypeError: if n cannot be cast as an integer\n",
+    "        ValueError: if n is negative\n",
+    "    \"\"\"\n",
+    "    try:\n",
+    "        casted_n = int(n)\n",
+    "    except ValueError:\n",
+    "        raise TypeError(\"n cannot be casted as an integer\") from None\n",
+    "    else:\n",
+    "        if n != casted_n:\n",
+    "            raise TypeError(\"n is not integer-like; it has non-zero decimals\")\n",
+    "        n = casted_n\n",
+    "\n",
+    "    if n < 0:\n",
+    "        raise ValueError(\"Factorial is not defined for negative integers\")\n",
+    "    elif n == 0:\n",
+    "        return 1\n",
+    "    return n * factorial(n - 1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 50,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "ename": "TypeError",
+     "evalue": "n cannot be casted as an integer",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-50-9a12eb120fc3>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mfactorial\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"text\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;32m<ipython-input-49-9ed22b5a2981>\u001b[0m in \u001b[0;36mfactorial\u001b[0;34m(n)\u001b[0m\n\u001b[1;32m     15\u001b[0m         \u001b[0mcasted_n\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mn\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     16\u001b[0m     \u001b[0;32mexcept\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 17\u001b[0;31m         \u001b[0;32mraise\u001b[0m \u001b[0mTypeError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"n cannot be casted as an integer\"\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     18\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     19\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mn\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0mcasted_n\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;31mTypeError\u001b[0m: n cannot be casted as an integer"
+     ]
+    }
+   ],
+   "source": [
+    "factorial(\"text\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 51,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "ename": "TypeError",
+     "evalue": "n is not integer-like; it has non-zero decimals",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-51-188b816be8b6>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mfactorial\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m3.1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;32m<ipython-input-49-9ed22b5a2981>\u001b[0m in \u001b[0;36mfactorial\u001b[0;34m(n)\u001b[0m\n\u001b[1;32m     18\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     19\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mn\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0mcasted_n\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 20\u001b[0;31m             \u001b[0;32mraise\u001b[0m \u001b[0mTypeError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"n is not integer-like; it has non-zero decimals\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     21\u001b[0m         \u001b[0mn\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcasted_n\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     22\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;31mTypeError\u001b[0m: n is not integer-like; it has non-zero decimals"
+     ]
+    }
+   ],
+   "source": [
+    "factorial(3.1)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Which way we choose for **hardening** the `factorial()` function depends on the concrete circumstances. If we are the main caller of the function ourselves, we may choose to *not* do any of the approaches at all. After all, we should be able to call our own function in the correct way. The lesson is that just because Python has no static typing, this does *not* mean that we cannot do this \"manually.\" Yet, the idea behind a dynamically typed language is to *not* deal with the types too much at all."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "## Theory of Computation"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "With everything *officially* introduced so far, Python would be what is called **[Turing complete <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_wiki.png\">](https://en.wikipedia.org/wiki/Turing_completeness)**. That means that anything that could be formulated as an algorithm could be expressed with all the language features we have seen. Note that, in particular, we have *not* yet formally *introduced* the `for` and `while` statements!"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.6"
+  },
+  "livereveal": {
+   "auto_select": "code",
+   "auto_select_fragment": true,
+   "scroll": true,
+   "theme": "serif"
+  },
+  "toc": {
+   "base_numbering": 1,
+   "nav_menu": {},
+   "number_sections": false,
+   "sideBar": true,
+   "skip_h1_title": true,
+   "title_cell": "Table of Contents",
+   "title_sidebar": "Contents",
+   "toc_cell": false,
+   "toc_position": {
+    "height": "calc(100% - 180px)",
+    "left": "10px",
+    "top": "150px",
+    "width": "384px"
+   },
+   "toc_section_display": false,
+   "toc_window_display": false
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/README.md b/README.md
index 7328211..39fafd5 100644
--- a/README.md
+++ b/README.md
@@ -87,6 +87,14 @@ Alternatively, the content can be viewed in a web browser
       (Fizz Buzz)
     - [summary <img height="12" style="display: inline-block" src="static/link/to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/03_conditionals/03_summary.ipynb)
     - [review questions <img height="12" style="display: inline-block" src="static/link/to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/03_conditionals/04_review.ipynb)
+  - *Chapter 4*: Recursion & Looping
+    - [content <img height="12" style="display: inline-block" src="static/link/to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/04_iteration/00_content.ipynb)
+      [<img height="12" style="display: inline-block" src="static/link/to_mb.png">](https://mybinder.org/v2/gh/webartifex/intro-to-python/develop?urlpath=lab/tree/04_iteration/00_content.ipynb)
+      (Recursion;
+       Examples: Factorial, Euclid's Algorithm, & Fibonacci;
+       Duck Typing;
+       Type Casting & Checking;
+       Input Validation)
 
 
 #### Videos

From d770080fd37e5c6532e6bd09081cd9e89fd5114a Mon Sep 17 00:00:00 2001
From: Alexander Hess <alexander@webartifex.biz>
Date: Thu, 15 Oct 2020 18:17:02 +0200
Subject: [PATCH 156/242] Add initial version of chapter 04's exercises, part 1

---
 04_iteration/00_content.ipynb           |   2 +-
 04_iteration/01_exercises.ipynb         | 610 ++++++++++++++++++++++++
 04_iteration/static/towers_of_hanoi.gif | Bin 0 -> 197609 bytes
 README.md                               |   3 +
 4 files changed, 614 insertions(+), 1 deletion(-)
 create mode 100644 04_iteration/01_exercises.ipynb
 create mode 100644 04_iteration/static/towers_of_hanoi.gif

diff --git a/04_iteration/00_content.ipynb b/04_iteration/00_content.ipynb
index 580d340..f7d1e41 100644
--- a/04_iteration/00_content.ipynb
+++ b/04_iteration/00_content.ipynb
@@ -859,7 +859,7 @@
     }
    },
    "source": [
-    "##### Efficiency of Algorithms"
+    "#### Efficiency of Algorithms"
    ]
   },
   {
diff --git a/04_iteration/01_exercises.ipynb b/04_iteration/01_exercises.ipynb
new file mode 100644
index 0000000..cf4b0ce
--- /dev/null
+++ b/04_iteration/01_exercises.ipynb
@@ -0,0 +1,610 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Note**: Click on \"*Kernel*\" > \"*Restart Kernel and Run All*\" in [JupyterLab](https://jupyterlab.readthedocs.io/en/stable/) *after* finishing the exercises to ensure that your solution runs top to bottom *without* any errors. If you cannot run this file on your machine, you may want to open it [in the cloud <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_mb.png\">](https://mybinder.org/v2/gh/webartifex/intro-to-python/develop?urlpath=lab/tree/04_iteration/01_exercises.ipynb)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Chapter 4: Recursion & Looping (Coding Exercises)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The exercises below assume that you have read the [first part <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/04_iteration/00_content.ipynb) of Chapter 4.\n",
+    "\n",
+    "The `...`'s in the code cells indicate where you need to fill in code snippets. The number of `...`'s within a code cell give you a rough idea of how many lines of code are needed to solve the task. You should not need to create any additional code cells for your final solution. However, you may want to use temporary code cells to try out some ideas."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Towers of Hanoi"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "A popular example of a problem that is solved by recursion art the **[Towers of Hanoi <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_wiki.png\">](https://en.wikipedia.org/wiki/Tower_of_Hanoi)**.\n",
+    "\n",
+    "In its basic version, a tower consisting of, for example, four disks with increasing radii, is placed on the left-most of **three** adjacent spots. In the following, we refer to the number of disks as $n$, so here $n = 4$.\n",
+    "\n",
+    "The task is to move the entire tower to the right-most spot whereby **two rules** must be obeyed:\n",
+    "\n",
+    "1. Disks can only be moved individually, and\n",
+    "2. a disk with a larger radius must *never* be placed on a disk with a smaller one.\n",
+    "\n",
+    "Although the **[Towers of Hanoi <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_wiki.png\">](https://en.wikipedia.org/wiki/Tower_of_Hanoi)** are a **classic** example, introduced by the mathematician [Édouard Lucas <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_wiki.png\">](https://en.wikipedia.org/wiki/%C3%89douard_Lucas) already in 1883, it is still **actively** researched as this scholarly [article](https://www.worldscientific.com/doi/abs/10.1142/S1793830919300017?journalCode=dmaa&) published in January 2019 shows.\n",
+    "\n",
+    "Despite being so easy to formulate, the game is quite hard to solve.\n",
+    "\n",
+    "Below is an interactive illustration of the solution with the minimal number of moves for $n = 4$."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "<img src=\"static/towers_of_hanoi.gif\" width=\"60%\" align=\"left\">"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Watch the following video by [MIT](https://www.mit.edu/)'s professor [Richard Larson](https://idss.mit.edu/staff/richard-larson/) for a comprehensive introduction.\n",
+    "\n",
+    "The [MIT Blossoms Initiative](https://blossoms.mit.edu/) is primarily aimed at high school students and does not have any prerequisites.\n",
+    "\n",
+    "The video consists of three segments, the last of which is *not* necessary to have watched to solve the tasks below. So, watch the video until 37:55."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from IPython.display import YouTubeVideo\n",
+    "YouTubeVideo(\"UuIneNBbscc\", width=\"60%\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Video Review Questions"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q1**: Explain for the $n = 3$ case why it can be solved as a **recursion**!"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " < your answer >"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q2**: How does the number of minimal moves needed to solve a problem with three spots and $n$ disks grow as a function of $n$? How does this relate to the answer to **Q1**?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " < your answer >"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q3**: The **[Towers of Hanoi <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_wiki.png\">](https://en.wikipedia.org/wiki/Tower_of_Hanoi)** problem is of **exponential growth**. What does that mean? What does that imply for large $n$?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " < your answer >"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q4**: The video introduces the recursive relationship $Sol(4, 1, 3) = Sol(3, 1, 2) ~ \\bigoplus ~ Sol(1, 1, 3) ~ \\bigoplus ~ Sol(3, 2, 3)$. The $\\bigoplus$ is to be interpreted as some sort of \"plus\" operation. How does this \"plus\" operation work? How does this way of expressing the problem relate to the answer to **Q1**?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " < your answer >"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Naive Translation to Python"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "As most likely the first couple of tries will result in *semantic* errors, it is advisable to have some sort of **visualization tool** for the program's output: For example, an online version of the game can be found [here](https://www.mathsisfun.com/games/towerofhanoi.html)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Let's first **generalize** the mathematical relationship from above and then introduce the variable names used in our `sol()` implementation below.\n",
+    "\n",
+    "Unsurprisingly, the recursive relationship in the video may be generalized into:\n",
+    "\n",
+    "$Sol(n, o, d) = Sol(n-1, o, i) ~ \\bigoplus ~ Sol(1, o, d) ~ \\bigoplus ~ Sol(n-1, i, d)$\n",
+    "\n",
+    "$Sol(\\cdot)$ takes three \"arguments\" $n$, $o$, and $d$ and is defined with *three* references to itself that take modified versions of $n$, $o$, and $d$ in different orders. The middle reference, Sol(1, o, d), constitutes the \"end\" of the recursive definition: It is the problem of solving Towers of Hanoi for a \"tower\" of only one disk.\n",
+    "\n",
+    "While the first \"argument\" of $Sol(\\cdot)$ is a number that we refer to as `n_disks` below, the second and third \"arguments\" are merely **labels** for the spots, and we refer to the **roles** they take in a given problem as `origin` and `destination` below. Instead of labeling individual spots with the numbers `1`, `2`, and `3` as in the video, we may also call them `\"left\"`, `\"center\"`, and `\"right\"`. Both ways are equally correct! So, only the first \"argument\" of $Sol(\\cdot)$ is really a number!\n",
+    "\n",
+    "As an example, the notation $Sol(4, 1, 3)$ from above can then be \"translated\" into Python as either the function call `sol(4, 1, 3)` or `sol(4, \"left\", \"right\")`. This describes the problem of moving a tower consisting of `n_disks=4` disks from either the `origin=1` spot to the `destination=3` spot or from the `origin=\"left\"` spot to the `destination=\"right\"` spot.\n",
+    "\n",
+    "To adhere to the rules, an `intermediate` spot $i$ is needed. In `sol()` below, this is a temporary variable within a function call and *not* a parameter of the function itself."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In summary, to move a tower consisting of `n_disks` (= $n$) disks from an `origin` (= $o$) to a `destination` (= $d$), three steps must be executed:\n",
+    "\n",
+    "1. Move the tower's topmost `n_disks - 1` (= $n - 1$) disks from the `origin` (= $o$) to an `intermediate` (= $i$) spot (= **Sub-Problem 1**),\n",
+    "2. move the remaining and largest disk from the `origin` (= $o$) to the `destination` (= $d$), and\n",
+    "3. move the `n_disks - 1` (= $n - 1$) disks from the `intermediate` (= $i$) spot to the `destination` (= $d$) spot (= **Sub-Problem 2**).\n",
+    "\n",
+    "The two sub-problems themselves are solved via the same *recursive* logic."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Write your answers to **Q5** to **Q7** into the skeleton of `sol()` below.\n",
+    "\n",
+    "`sol()` takes three arguments `n_disks`, `origin`, and `destination` that mirror $n$, $o$, and $d$ above.\n",
+    "\n",
+    "For now, assume that all arguments to `sol()` are `int` objects! We generalize this into real labels further below in the `hanoi()` function.\n",
+    "\n",
+    "Once completed, `sol()` should *print* out all the moves in the correct order. For example, *print* `\"1 -> 3\"` to mean \"Move the top-most `n_disks - 1` disks from spot `1` to spot `3`.\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def sol(n_disks, origin, destination):\n",
+    "    \"\"\"A naive implementation of Towers of Hanoi.\n",
+    "\n",
+    "    This function prints out the moves to solve a Towers of Hanoi problem.\n",
+    "\n",
+    "    Args:\n",
+    "        n_disks (int): number of disks in the tower\n",
+    "        origin (int): spot of the tower at the start; 1, 2, or 3\n",
+    "        destination (int): spot of the tower at the end; 1, 2, or 3\n",
+    "    \"\"\"\n",
+    "    # answer to Q5\n",
+    "    ...\n",
+    "    ...\n",
+    "\n",
+    "    # answer to Q6\n",
+    "    ...\n",
+    "    ...\n",
+    "    ...\n",
+    "    ...\n",
+    "    ...\n",
+    "    ...\n",
+    "    ...\n",
+    "    ...\n",
+    "    ...\n",
+    "    ...\n",
+    "    ...\n",
+    "    ...\n",
+    "\n",
+    "    # answer to Q7\n",
+    "    ...\n",
+    "    ...\n",
+    "    ..."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q5**: What is the `n_disks` argument when the function reaches its **base case**? Check for the base case with a simple `if` statement and return from the function using the **early exit** pattern!\n",
+    "\n",
+    "Hint: The base case in the Python implementation may be slightly different than the one shown in the generalized mathematical relationship above!"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q6**: If not in the base case, `sol()` determines the `intermediate` spot given concrete `origin` and `destination` arguments. For example, if called with `origin=1` and `destination=2`, `intermediate` must be `3`.\n",
+    "\n",
+    "Add *one* compound `if` statement to `sol()` that has a branch for *every* possible `origin`-`destination`-pair that assigns the correct temporary spot to a variable `intermediate`.\n",
+    "\n",
+    "Hint: How many 2-tuples of 3 elements can there be if the order matters?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q7**: `sol()` calls itself *two* more times with the correct 2-tuples chosen from the three available spots `origin`, `intermediate`, and `destination`.\n",
+    "\n",
+    "*In between* the two recursive function calls, use [print() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#print) to print out from where to where the \"remaining and largest\" disk has to be moved!"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q8**: Execute the code cells below and confirm that the moves are correct!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sol(1, 1, 3)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sol(2, 1, 3)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sol(3, 1, 3)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sol(4, 1, 3)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Pythonic Refactoring"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The previous `sol()` implementation does the job, but the conditional statement is unnecessarily tedious. \n",
+    "\n",
+    "Let's create a concise `hanoi()` function that, in addition to a positional `n_disks` argument, takes three keyword-only arguments `origin`, `intermediate`, and `destination` with default values `\"left\"`, `\"center\"`, and `\"right\"`.\n",
+    "\n",
+    "Write your answers to **Q9** and **Q10** into the subsequent code cell and finalize `hanoi()`!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def hanoi(n_disks, *, origin=\"left\", intermediate=\"center\", destination=\"right\"):\n",
+    "    \"\"\"A Pythonic implementation of Towers of Hanoi.\n",
+    "\n",
+    "    This function prints out the moves to solve a Towers of Hanoi problem.\n",
+    "\n",
+    "    Args:\n",
+    "        n_disks (int): number of disks in the tower\n",
+    "        origin (str, optional): label for the spot of the tower at the start\n",
+    "        intermediate (str, optional): label for the intermediate spot\n",
+    "        destination (str, optional): label for the spot of the tower at the end\n",
+    "    \"\"\"\n",
+    "    # answer to Q9\n",
+    "    ...\n",
+    "    ...\n",
+    "\n",
+    "    # answer to Q10\n",
+    "    ...\n",
+    "    ...\n",
+    "    ..."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q9**: Copy the base case from `sol()`!"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q10**: Instead of conditional logic, `hanoi()` calls itself *two* times with the *three* arguments `origin`, `intermediate`, and `destination` passed on in a *different* order.\n",
+    "\n",
+    "Figure out how the arguments are passed on in the two recursive `hanoi()` calls and finish `hanoi()`.\n",
+    "\n",
+    "Hint: Do not forget to use [print() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#print) to print out the moves!"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q11**: Execute the code cells below and confirm that the moves are correct!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "hanoi(1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "hanoi(2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "hanoi(3)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "hanoi(4)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We could, of course, also use *numeric* labels for the three steps like so."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "hanoi(3, origin=1, intermediate=2, destination=3)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Passing a Value \"down\" the Recursion Tree"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The above `hanoi()` prints the optimal solution's moves in the correct order but fails to label each move with an order number. This is built in the `hanoi_ordered()` function below by passing on a \"private\" `_offset` argument \"down\" the recursion tree. The leading underscore `_` in the parameter name indicates that it is *not* to be used by the caller of the function. That is also why the parameter is *not* mentioned in the docstring.\n",
+    "\n",
+    "Write your answers to **Q12** and **Q13** into the subsequent code cell and finalize `hanoi_ordered()`! As the logic gets a bit \"involved,\" `hanoi_ordered()` below is almost finished."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def hanoi_ordered(n_disks, *, origin=\"left\", intermediate=\"center\", destination=\"right\", _offset=None):\n",
+    "    \"\"\"A Pythonic implementation of Towers of Hanoi.\n",
+    "\n",
+    "    This function prints out the moves to solve a Towers of Hanoi problem.\n",
+    "    Each move is labeled with an order number.\n",
+    "\n",
+    "    Args:\n",
+    "        n_disks (int): number of disks in the tower\n",
+    "        origin (str, optional): label for the spot of the tower at the start\n",
+    "        intermediate (str, optional): label for the intermediate spot\n",
+    "        destination (str, optional): label for the spot of the tower at the end\n",
+    "    \"\"\"\n",
+    "    # answer to Q12\n",
+    "    ...\n",
+    "    ...\n",
+    "\n",
+    "    total = (2 ** n_disks - 1)\n",
+    "    half = (2 ** (n_disks - 1) - 1)\n",
+    "    count = total - half\n",
+    "\n",
+    "    if _offset is not None:\n",
+    "        count += _offset\n",
+    "\n",
+    "    # answer to Q18\n",
+    "    hanoi_ordered(..., _offset=_offset)\n",
+    "    ...\n",
+    "    hanoi_ordered(..., _offset=count)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q12**: Copy the base case from the original `hanoi()`!"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q13**: Complete the two recursive function calls with the same arguments as in `hanoi()`! Do *not* change the already filled in `offset` arguments!\n",
+    "\n",
+    "Then, adjust the use of [print() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#print) from above to print out the moves with their order number!"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q14**: Execute the code cells below and confirm that the order numbers are correct!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "hanoi_ordered(1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "hanoi_ordered(2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "hanoi_ordered(3)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "hanoi_ordered(4)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Lastly, it is to be mentioned that for problem instances with a small `n_disks` argument, it is easier to collect all the moves first in a `list` object and then add the order number with the [enumerate() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#enumerate) built-in."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Open Question"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q15**: Conducting your own research on the internet, what can you say about generalizing the **[Towers of Hanoi <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_wiki.png\">](https://en.wikipedia.org/wiki/Tower_of_Hanoi)** problem to a setting with *more than three* landing spots?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " < your answer >"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.6"
+  },
+  "toc": {
+   "base_numbering": 1,
+   "nav_menu": {},
+   "number_sections": false,
+   "sideBar": true,
+   "skip_h1_title": true,
+   "title_cell": "Table of Contents",
+   "title_sidebar": "Contents",
+   "toc_cell": false,
+   "toc_position": {},
+   "toc_section_display": false,
+   "toc_window_display": false
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
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diff --git a/README.md b/README.md
index 39fafd5..6843c25 100644
--- a/README.md
+++ b/README.md
@@ -95,6 +95,9 @@ Alternatively, the content can be viewed in a web browser
        Duck Typing;
        Type Casting & Checking;
        Input Validation)
+    - [exercises <img height="12" style="display: inline-block" src="static/link/to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/04_iteration/01_exercises.ipynb)
+      [<img height="12" style="display: inline-block" src="static/link/to_mb.png">](https://mybinder.org/v2/gh/webartifex/intro-to-python/develop?urlpath=lab/tree/04_iteration/01_exercises.ipynb)
+      (Towers of Hanoi)
 
 
 #### Videos

From 76ef753ffa95b6bd45e617b5b609e04384b63a53 Mon Sep 17 00:00:00 2001
From: Alexander Hess <alexander@webartifex.biz>
Date: Thu, 15 Oct 2020 18:35:12 +0200
Subject: [PATCH 157/242] Add initial version of chapter 04, part 2

---
 04_iteration/02_content.ipynb | 1300 +++++++++++++++++++++++++++++++++
 README.md                     |    5 +
 2 files changed, 1305 insertions(+)
 create mode 100644 04_iteration/02_content.ipynb

diff --git a/04_iteration/02_content.ipynb b/04_iteration/02_content.ipynb
new file mode 100644
index 0000000..7fb3eb3
--- /dev/null
+++ b/04_iteration/02_content.ipynb
@@ -0,0 +1,1300 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "**Note**: Click on \"*Kernel*\" > \"*Restart Kernel and Clear All Outputs*\" in [JupyterLab](https://jupyterlab.readthedocs.io/en/stable/) *before* reading this notebook to reset its output. If you cannot run this file on your machine, you may want to open it [in the cloud <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_mb.png\">](https://mybinder.org/v2/gh/webartifex/intro-to-python/develop?urlpath=lab/tree/04_iteration/02_content.ipynb)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "# Chapter 4: Recursion & Looping (continued)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "After learning about the concept of **recursion** in the [first part <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/04_iteration/00_content.ipynb) of this chapter, we look at other ways of running code repeatedly, namely **looping** with the `for` and `while` statements. We start with the latter as it is more generic. Throughout this second part of the chapter, we revisit the same examples from the first part to show how recursion and looping are really two sides of the same coin."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "## The `while` Statement"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Whereas functions combined with `if` statements suffice to model any repetitive logic, Python comes with a compound `while` statement (cf., [reference <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/reference/compound_stmts.html#the-while-statement)) that often makes it easier to implement iterative ideas.\n",
+    "\n",
+    "It consists of a header line with a boolean expression followed by an indented code block. Before the first and after every execution of the code block, the boolean expression is evaluated, and if it is (still) equal to `True`, the code block runs (again). Eventually, some variable referenced in the boolean expression is changed in the code block such that the condition becomes `False`.\n",
+    "\n",
+    "If the condition is `False` before the first iteration, the entire code block is *never* executed. As the flow of control keeps \"looping\" (i.e., more formally, **iterating**) back to the beginning of the code block, this concept is also called a `while`-loop and each pass through the loop an **iteration**."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "### Trivial Example: Countdown (revisited)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Let's rewrite the `countdown()` example in an iterative style. We also build in **input validation** by allowing the function only to be called with strictly positive integers. As any positive integer hits $0$ at some point when iteratively decremented by $1$, `countdown()` is guaranteed to **terminate**. Also, the base case is now handled at the end of the function, which commonly happens with iterative solutions to problems."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "code_folding": [],
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "def countdown(n):\n",
+    "    \"\"\"Print a countdown until the party starts.\n",
+    "\n",
+    "    Args:\n",
+    "        n (int): seconds until the party begins; must be positive\n",
+    "    \"\"\"\n",
+    "    while n != 0:\n",
+    "        print(n)\n",
+    "        n -= 1\n",
+    "\n",
+    "    print(\"Happy new Year!\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "3\n",
+      "2\n",
+      "1\n",
+      "Happy new Year!\n"
+     ]
+    }
+   ],
+   "source": [
+    "countdown(3)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "As [PythonTutor <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](http://pythontutor.com/visualize.html#code=def%20countdown%28n%29%3A%0A%20%20%20%20while%20n%20!%3D%200%3A%0A%20%20%20%20%20%20%20%20print%28n%29%0A%20%20%20%20%20%20%20%20n%20-%3D%201%0A%0A%20%20%20%20print%28%22Happy%20new%20Year!%22%29%0A%0Acountdown%283%29&cumulative=false&curInstr=0&heapPrimitives=nevernest&mode=display&origin=opt-frontend.js&py=3&rawInputLstJSON=%5B%5D&textReferences=false) shows, there is a subtle but essential difference in the way a `while` statement is treated in memory: In short, `while` statements can *not* run into a `RecursionError` as only *one* frame is needed to manage the names. After all, there is only *one* function call to be made. For typical day-to-day applications, this difference is, however, not so important *unless* a problem instance becomes so big that a large (i.e., $> 3.000$) number of recursive calls must be made."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "### \"Still involved\" Example: [Euclid's Algorithm <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_wiki.png\">](https://en.wikipedia.org/wiki/Euclidean_algorithm) (revisited)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Finding the greatest common divisor of two numbers is still not so obvious when using a `while`-loop instead of a recursive formulation.\n",
+    "\n",
+    "The iterative implementation of `gcd()` below accepts any two strictly positive integers. As in any iteration through the loop, the smaller number is subtracted from the larger one, the two decremented values of `a` and `b` eventually become equal. Thus, this algorithm is also guaranteed to terminate. If one of the two numbers were negative or $0$ in the first place, `gcd()` would run forever, and not even Python could detect this. Try this out by removing the input validation and running the function with negative arguments!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "code_folding": [],
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "def gcd(a, b):\n",
+    "    \"\"\"Calculate the greatest common divisor of two numbers.\n",
+    "\n",
+    "    Args:\n",
+    "        a (int): first number; must be positive\n",
+    "        b (int): second number; must be positive\n",
+    "\n",
+    "    Returns:\n",
+    "        gcd (int)\n",
+    "    \"\"\"\n",
+    "    while a != b:\n",
+    "        if a > b:\n",
+    "            a -= b\n",
+    "        else:\n",
+    "            b -= a\n",
+    "\n",
+    "    return a"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "4"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "gcd(12, 4)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "1"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "gcd(7, 7919)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "#### Efficiency of Algorithms (continued)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "We also see that this implementation is a lot *less* efficient than its recursive counterpart which solves `gcd()` for the same two numbers `112233445566778899` and `987654321` within microseconds."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "5.32 s ± 0 ns per loop (mean ± std. dev. of 1 run, 1 loop each)\n"
+     ]
+    }
+   ],
+   "source": [
+    "%%timeit -n 1 -r 1\n",
+    "gcd(112233445566778899, 987654321)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "## Infinite Loops"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "As with recursion, we must ensure that the iteration ends. For the above `countdown()` and `gcd()` examples, we could \"prove\" (i.e., at least argue in favor) that some pre-defined **termination criterion** is reached eventually. However, this cannot be done in all cases, as the following example shows."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "### \"Mystery\" Example: [Collatz Conjecture <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_wiki.png\">](https://en.wikipedia.org/wiki/Collatz_conjecture)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "Let's play the following game:\n",
+    "- Think of any positive integer $n$.\n",
+    "- If $n$ is even, the next $n$ is half the old $n$.\n",
+    "- If $n$ is odd, multiply the old $n$ by $3$ and add $1$ to obtain the next $n$.\n",
+    "- Repeat these steps until you reach $1$.\n",
+    "\n",
+    "**Do we always reach the final $1$?**"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "The function below implements this game. Does it always reach $1$? No one has proven it so far! We include some input validation as before because `collatz()` would for sure not terminate if we called it with a negative number. Further, the Collatz sequence also works for real numbers, but then we would have to study fractals (cf., [this <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_wiki.png\">](https://en.wikipedia.org/wiki/Collatz_conjecture#Iterating_on_real_or_complex_numbers)). So we restrict our example to integers only."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "code_folding": [],
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "def collatz(n):\n",
+    "    \"\"\"Print a Collatz sequence in descending order.\n",
+    "\n",
+    "    Given a positive integer n, modify it according to these rules:\n",
+    "        - if n is even, the next n is half the previous one\n",
+    "        - if n is odd, the next n is 3 times the previous one plus 1\n",
+    "        - if n is 1, stop the iteration\n",
+    "\n",
+    "    Args:\n",
+    "        n (int): a positive number to start the Collatz sequence at\n",
+    "    \"\"\"\n",
+    "    while n != 1:\n",
+    "        print(n, end=\" \")\n",
+    "        if n % 2 == 0:\n",
+    "            n //= 2  # //= to preserve the int type\n",
+    "        else:\n",
+    "            n = 3 * n + 1\n",
+    "\n",
+    "    print(1)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Collatz sequences do not necessarily become longer with a larger initial `n`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "100 50 25 76 38 19 58 29 88 44 22 11 34 17 52 26 13 40 20 10 5 16 8 4 2 1\n"
+     ]
+    }
+   ],
+   "source": [
+    "collatz(100)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "1000 500 250 125 376 188 94 47 142 71 214 107 322 161 484 242 121 364 182 91 274 137 412 206 103 310 155 466 233 700 350 175 526 263 790 395 1186 593 1780 890 445 1336 668 334 167 502 251 754 377 1132 566 283 850 425 1276 638 319 958 479 1438 719 2158 1079 3238 1619 4858 2429 7288 3644 1822 911 2734 1367 4102 2051 6154 3077 9232 4616 2308 1154 577 1732 866 433 1300 650 325 976 488 244 122 61 184 92 46 23 70 35 106 53 160 80 40 20 10 5 16 8 4 2 1\n"
+     ]
+    }
+   ],
+   "source": [
+    "collatz(1000)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "10000 5000 2500 1250 625 1876 938 469 1408 704 352 176 88 44 22 11 34 17 52 26 13 40 20 10 5 16 8 4 2 1\n"
+     ]
+    }
+   ],
+   "source": [
+    "collatz(10000)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "100000 50000 25000 12500 6250 3125 9376 4688 2344 1172 586 293 880 440 220 110 55 166 83 250 125 376 188 94 47 142 71 214 107 322 161 484 242 121 364 182 91 274 137 412 206 103 310 155 466 233 700 350 175 526 263 790 395 1186 593 1780 890 445 1336 668 334 167 502 251 754 377 1132 566 283 850 425 1276 638 319 958 479 1438 719 2158 1079 3238 1619 4858 2429 7288 3644 1822 911 2734 1367 4102 2051 6154 3077 9232 4616 2308 1154 577 1732 866 433 1300 650 325 976 488 244 122 61 184 92 46 23 70 35 106 53 160 80 40 20 10 5 16 8 4 2 1\n"
+     ]
+    }
+   ],
+   "source": [
+    "collatz(100000)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "## The `for` Statement"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Recursion and the `while` statement are two sides of the same coin. Disregarding that in the case of recursion Python internally faces some additional burden for managing the stack of frames in memory, both approaches lead to the *same* computational steps in memory. More importantly, we can formulate any recursive implementation in an iterative way and vice versa despite one of the two ways often \"feeling\" a lot more natural given a particular problem.\n",
+    "\n",
+    "So how does the compound `for` statement (cf., [reference <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/reference/compound_stmts.html#the-for-statement)) in this book's very first example fit into this picture? It is a *redundant* language construct to provide a *shorter* and more *convenient* syntax for common applications of the `while` statement. In programming, such additions to a language are called **syntactic sugar**. A cup of tea tastes better with sugar, but we may drink tea without sugar too.\n",
+    "\n",
+    "Consider `elements` below. Without the `for` statement, we must manage a temporary **index variable**, `index`, to loop over all the elements and also obtain the individual elements with the `[]` operator in each iteration of the loop."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "elements = [0, 1, 2, 3, 4]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0 1 2 3 4 "
+     ]
+    }
+   ],
+   "source": [
+    "index = 0\n",
+    "\n",
+    "while index < len(elements):\n",
+    "    element = elements[index]\n",
+    "    print(element, end=\" \")\n",
+    "    index += 1\n",
+    "\n",
+    "del index"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "The `for` statement, on the contrary, makes the actual business logic more apparent by stripping all the **[boilerplate code <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_wiki.png\">](https://en.wikipedia.org/wiki/Boilerplate_code)** away. The variable that is automatically set by Python in each iteration of the loop (i.e., `element` in the example) is called the **target variable**."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0 1 2 3 4 "
+     ]
+    }
+   ],
+   "source": [
+    "for element in elements:\n",
+    "    print(element, end=\" \")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "For sequences of integers, the [range() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#func-range) built-in makes the `for` statement even more convenient: It creates a `list`-like object of type `range` that generates integers \"on the fly,\" and we look closely at the underlying effects in memory in [Chapter 8 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/08_mfr/00_content.ipynb#Mapping)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0 1 2 3 4 "
+     ]
+    }
+   ],
+   "source": [
+    "for element in range(5):\n",
+    "    print(element, end=\" \")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "range"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "type(range(5))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "[range() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#func-range) takes optional `start` and `step` arguments that we use to customize the sequence of integers even more."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "1 3 5 7 9 "
+     ]
+    }
+   ],
+   "source": [
+    "for element in [1, 3, 5, 7, 9]:\n",
+    "    print(element, end=\" \")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "1 3 5 7 9 "
+     ]
+    }
+   ],
+   "source": [
+    "for element in range(1, 10, 2):\n",
+    "    print(element, end=\" \")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "### Containers vs. Iterables"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "The essential difference between the above `list` objects, `[0, 1, 2, 3, 4]` and `[1, 3, 5, 7, 9]`, and the `range` objects, `range(5)` and `range(1, 10, 2)`, is that in the former case *six* objects are created in memory *before* the `for` statement starts running, *one* `list` holding references to *five* `int` objects, whereas in the latter case only *one* `range` object is created that **generates** `int` objects one at a time *while* the `for`-loop runs.\n",
+    "\n",
+    "However, we can loop over both of them. So a natural question to ask is why Python treats objects of *different*  types in the *same* way when used with a `for` statement.\n",
+    "\n",
+    "So far, the overarching storyline in this book goes like this: In Python, *everything* is an object. Besides its *identity* and *value*, every object is characterized by \"belonging\" to *one* data type that determines how the object behaves and what we may do with it.\n",
+    "\n",
+    "Now, just as we classify objects by data type, we also classify these data types (e.g., `int`, `float`, `str`, or `list`) into **abstract concepts**.\n",
+    "\n",
+    "We did this already in [Chapter 1 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/01_elements/03_content.ipynb#Who-am-I?-And-how-many?) when we described a `list` object as \"some sort of container that holds [...] references to other objects\". So, abstractly speaking, **containers** are any objects that are \"composed\" of other objects and also \"manage\" how these objects are organized. `list` objects, for example, have the property that they model an order associated with their elements. There exist, however, other container types, many of which do *not* come with an order. So, containers primarily \"contain\" other objects and have *nothing* to do with looping.\n",
+    "\n",
+    "On the contrary, the abstract concept of **iterables** is all about looping: Any object that we can loop over is, by definition, an iterable. So, `range` objects, for example, are iterables, even though they hold no references to other objects. Moreover, looping does *not* have to occur in a *predictable* order, although this is the case for both `list` and `range` objects.\n",
+    "\n",
+    "Typically, containers are iterables, and iterables are containers. Yet, only because these two concepts coincide often, we must not think of them as the same. In [Chapter 7 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/07_sequences/00_content.ipynb#Collections-vs.-Sequences), we formalize these two concepts and introduce many more. Finally, [Chapter 11 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/11_classes/00_content.ipynb) gives an explanation how abstract concepts are implemented and play together.\n",
+    "\n",
+    "Let's continue with `first_names` below as an example an illustrate what iterable containers are."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "first_names = [\"Achim\", \"Berthold\", \"Carl\", \"Diedrich\", \"Eckardt\"]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "The characteristic operator associated with container types is the `in` operator: It checks if a given object evaluates equal to at least one of the objects in the container. Colloquially, it checks if an object is \"contained\" in the container. Formally, this operation is called **membership testing**."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "\"Achim\" in first_names"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "False"
+      ]
+     },
+     "execution_count": 21,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "\"Alexander\" in first_names"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "The cell below shows the *exact* workings of the `in` operator: Although `3.0` is *not* contained in `elements`, it evaluates equal to the `3` that is, which is why the following expression evaluates to `True`. So, while we could colloquially say that `elements` \"contains\" `3.0`, it actually does not."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[0, 1, 2, 3, 4]"
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "elements"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 23,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "3.0 in elements"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Similarly, the characteristic operation of an iterable type is that it supports being looped over, for example, with the `for` statement."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Achim   Berthold   Carl   Diedrich   Eckardt   "
+     ]
+    }
+   ],
+   "source": [
+    "for name in first_names:\n",
+    "    print(name, end=\"   \")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "If we must have an index variable in the loop's body, we use the [enumerate() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#enumerate) built-in that takes an *iterable* as its argument and then generates a \"stream\" of \"pairs\" of an index variable, `i` below, and an object provided by the iterable, `name`, separated by a `,`. There is *no* need to ever revert to the `while` statement with an explicitly managed index variable to loop over an iterable object."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "1 > Achim   2 > Berthold   3 > Carl   4 > Diedrich   5 > Eckardt   "
+     ]
+    }
+   ],
+   "source": [
+    "for i, name in enumerate(first_names, start=1):\n",
+    "    print(i, \">\", name, end=\"   \")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "[enumerate() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#enumerate) takes an optional `start` argument."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "1 > Achim   2 > Berthold   3 > Carl   4 > Diedrich   5 > Eckardt   "
+     ]
+    }
+   ],
+   "source": [
+    "for i, name in enumerate(first_names, start=1):\n",
+    "    print(i, \">\", name, end=\"   \")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "The [zip() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#zip) built-in allows us to combine the elements of two or more iterables in a *pairwise* fashion: It conceptually works like a zipper for a jacket."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "last_names = [\"MĂĽller\", \"Meyer\", \"Mayer\", \"Schmitt\", \"Schmidt\"]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Achim MĂĽller   Berthold Meyer   Carl Mayer   Diedrich Schmitt   Eckardt Schmidt   "
+     ]
+    }
+   ],
+   "source": [
+    "for first_name, last_name in zip(first_names, last_names):\n",
+    "    print(first_name, last_name, end=\"   \")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "### \"Hard at first Glance\" Example: [Fibonacci Numbers <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_wiki.png\">](https://en.wikipedia.org/wiki/Fibonacci_number) (revisited)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "In contrast to its recursive counterpart, the iterative `fibonacci()` function below is somewhat harder to read. For example, it is not so obvious as to how many iterations through the `for`-loop we need to make when implementing it. There is an increased risk of making an *off-by-one* error. Moreover, we need to track a `temp` variable along.\n",
+    "\n",
+    "However, one advantage of calculating Fibonacci numbers in a **forward** fashion with a `for` statement is that we could list the entire sequence in ascending order as we calculate the desired number. To show this, we added `print()` statements in `fibonacci()` below.\n",
+    "\n",
+    "We do *not* need to store the index variable in the `for`-loop's header line: That is what the underscore variable `_` indicates; we \"throw it away.\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {
+    "code_folding": [],
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "def fibonacci(i):\n",
+    "    \"\"\"Calculate the ith Fibonacci number.\n",
+    "\n",
+    "    Args:\n",
+    "        i (int): index of the Fibonacci number to calculate\n",
+    "\n",
+    "    Returns:\n",
+    "        ith_fibonacci (int)\n",
+    "    \"\"\"\n",
+    "    a = 0\n",
+    "    b = 1\n",
+    "    print(a, b, sep=\" \", end=\" \")  # added for didactical purposes\n",
+    "    for _ in range(i - 1):\n",
+    "        temp = a + b\n",
+    "        a = b\n",
+    "        b = temp\n",
+    "        print(b, end=\" \")  # added for didactical purposes\n",
+    "\n",
+    "    return b"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0 1 1 2 3 5 8 13 21 34 55 89 144 "
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "144"
+      ]
+     },
+     "execution_count": 30,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "fibonacci(12)  # = 13th number"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "##### Efficiency of Algorithms (continued)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Another more important advantage is that now we may calculate even big Fibonacci numbers *efficiently*."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0 1 1 2 3 5 8 13 21 34 55 89 144 233 377 610 987 1597 2584 4181 6765 10946 17711 28657 46368 75025 121393 196418 317811 514229 832040 1346269 2178309 3524578 5702887 9227465 14930352 24157817 39088169 63245986 102334155 165580141 267914296 433494437 701408733 1134903170 1836311903 2971215073 4807526976 7778742049 12586269025 20365011074 32951280099 53316291173 86267571272 139583862445 225851433717 365435296162 591286729879 956722026041 1548008755920 2504730781961 4052739537881 6557470319842 10610209857723 17167680177565 27777890035288 44945570212853 72723460248141 117669030460994 190392490709135 308061521170129 498454011879264 806515533049393 1304969544928657 2111485077978050 3416454622906707 5527939700884757 8944394323791464 14472334024676221 23416728348467685 37889062373143906 61305790721611591 99194853094755497 160500643816367088 259695496911122585 420196140727489673 679891637638612258 1100087778366101931 1779979416004714189 2880067194370816120 4660046610375530309 7540113804746346429 12200160415121876738 19740274219868223167 31940434634990099905 51680708854858323072 83621143489848422977 135301852344706746049 218922995834555169026 "
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "218922995834555169026"
+      ]
+     },
+     "execution_count": 31,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "fibonacci(99)  # = 100th number"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "### Easy Example: [Factorial <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_wiki.png\">](https://en.wikipedia.org/wiki/Factorial) (revisited)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "The iterative `factorial()` implementation is comparable to its recursive counterpart when it comes to readability. One advantage of calculating the factorial in a forward fashion is that we could track the intermediate `product` as it grows."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "def factorial(n):\n",
+    "    \"\"\"Calculate the factorial of a number.\n",
+    "\n",
+    "    Args:\n",
+    "        n (int): number to calculate the factorial for, must be positive\n",
+    "\n",
+    "    Returns:\n",
+    "        factorial (int)\n",
+    "    \"\"\"\n",
+    "    product = 1  # because 0! = 1\n",
+    "    for i in range(1, n + 1):\n",
+    "        product *= i\n",
+    "        print(product, end=\" \")  # added for didactical purposes\n",
+    "\n",
+    "    return product"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "1 2 6 "
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "6"
+      ]
+     },
+     "execution_count": 33,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "factorial(3)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "1 2 6 24 120 720 5040 40320 362880 3628800 "
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "3628800"
+      ]
+     },
+     "execution_count": 34,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "factorial(10)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.6"
+  },
+  "livereveal": {
+   "auto_select": "code",
+   "auto_select_fragment": true,
+   "scroll": true,
+   "theme": "serif"
+  },
+  "toc": {
+   "base_numbering": 1,
+   "nav_menu": {},
+   "number_sections": false,
+   "sideBar": true,
+   "skip_h1_title": true,
+   "title_cell": "Table of Contents",
+   "title_sidebar": "Contents",
+   "toc_cell": false,
+   "toc_position": {
+    "height": "calc(100% - 180px)",
+    "left": "10px",
+    "top": "150px",
+    "width": "384px"
+   },
+   "toc_section_display": false,
+   "toc_window_display": false
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/README.md b/README.md
index 6843c25..911350d 100644
--- a/README.md
+++ b/README.md
@@ -98,6 +98,11 @@ Alternatively, the content can be viewed in a web browser
     - [exercises <img height="12" style="display: inline-block" src="static/link/to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/04_iteration/01_exercises.ipynb)
       [<img height="12" style="display: inline-block" src="static/link/to_mb.png">](https://mybinder.org/v2/gh/webartifex/intro-to-python/develop?urlpath=lab/tree/04_iteration/01_exercises.ipynb)
       (Towers of Hanoi)
+    - [content <img height="12" style="display: inline-block" src="static/link/to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/04_iteration/02_content.ipynb)
+      [<img height="12" style="display: inline-block" src="static/link/to_mb.png">](https://mybinder.org/v2/gh/webartifex/intro-to-python/develop?urlpath=lab/tree/04_iteration/02_content.ipynb)
+      (Looping with `while` & `for`;
+       Examples: Collatz Conjecture, Factorial, Euclid's Algorithm, & Fibonacci;
+       Containers vs. Iterables)
 
 
 #### Videos

From e9b166a618230ce9c7451dc9caaab46ad849bc11 Mon Sep 17 00:00:00 2001
From: Alexander Hess <alexander@webartifex.biz>
Date: Thu, 15 Oct 2020 18:54:15 +0200
Subject: [PATCH 158/242] Add initial version of chapter 04, part 3

---
 04_iteration/03_content.ipynb | 991 ++++++++++++++++++++++++++++++++++
 README.md                     |   4 +
 2 files changed, 995 insertions(+)
 create mode 100644 04_iteration/03_content.ipynb

diff --git a/04_iteration/03_content.ipynb b/04_iteration/03_content.ipynb
new file mode 100644
index 0000000..8cfb19f
--- /dev/null
+++ b/04_iteration/03_content.ipynb
@@ -0,0 +1,991 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "**Note**: Click on \"*Kernel*\" > \"*Restart Kernel and Clear All Outputs*\" in [JupyterLab](https://jupyterlab.readthedocs.io/en/stable/) *before* reading this notebook to reset its output. If you cannot run this file on your machine, you may want to open it [in the cloud <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_mb.png\">](https://mybinder.org/v2/gh/webartifex/intro-to-python/develop?urlpath=lab/tree/04_iteration/03_content.ipynb)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "# Chapter 4: Recursion & Looping (continued)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "While what we learned about the `for` and `while` statements in the [second part <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/04_iteration/02_content.ipynb) of this chapter suffices to translate any iterative algorithm into code, both come with some syntactic sugar to make life easier for the developer. This last part of the chapter shows how we can further customize the looping logic and introduces as \"trick\" for situations where we cannot come up with a stopping criterion in a `while`-loop."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "## Stopping Loops prematurely"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "This section introduces additional syntax to customize `for` and `while` statements in our code even further. They are mostly syntactic sugar in that they do not change how a program runs but make its code more readable. We illustrate them for the `for` statement only. However, everything presented in this section also works for the `while` statement."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "### Example: Is the square of a number in `[7, 11, 8, 5, 3, 12, 2, 6, 9, 10, 1, 4]` greater than `100`?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Let's say we have a list of `numbers` and want to check if the square of at least one of its elements is greater than `100`. So, conceptually, we are asking the question if a list of numbers as a whole satisfies a certain condition."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "numbers = [7, 11, 8, 5, 3, 12, 2, 6, 9, 10, 1, 4]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "A first naive implementation could look like this: We loop over *every* element in `numbers` and set an **indicator variable** `is_above`, initialized as `False`, to `True` once we encounter an element satisfying the condition.\n",
+    "\n",
+    "This implementation is *inefficient* as even if the *first* element in `numbers` has a square greater than `100`, we loop until the last element: This could take a long time for a big list.\n",
+    "\n",
+    "Moreover, we must initialize `is_above` *before* the `for`-loop and write an `if`-`else`-logic *after* it to check for the result. The actual business logic is *not* conveyed in a clear way."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "7   11   8   5   3   12   2   6   9   10   1   4   =>   at least one number satisfies the condition\n"
+     ]
+    }
+   ],
+   "source": [
+    "is_above = False\n",
+    "\n",
+    "for number in numbers:\n",
+    "    print(number, end=\"   \")  # added for didactical purposes\n",
+    "    if number ** 2 > 100:\n",
+    "        is_above = True\n",
+    "\n",
+    "if is_above:\n",
+    "    print(\"=>   at least one number satisfies the condition\")\n",
+    "else:\n",
+    "    print(\"=>   no number satisfies the condition\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "### The `break` Statement"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Python provides the `break` statement (cf., [reference <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/reference/simple_stmts.html#the-break-statement)) that lets us stop a loop prematurely at any iteration. It is yet another means of controlling the flow of execution, and we say that we \"break out of a loop.\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "7   11   =>   at least one number satisfies the condition\n"
+     ]
+    }
+   ],
+   "source": [
+    "is_above = False\n",
+    "\n",
+    "for number in numbers:\n",
+    "    print(number, end=\"   \")  # added for didactical purposes\n",
+    "    if number ** 2 > 100:\n",
+    "        is_above = True\n",
+    "        break\n",
+    "\n",
+    "if is_above:\n",
+    "    print(\"=>   at least one number satisfies the condition\")\n",
+    "else:\n",
+    "    print(\"=>   no number satisfies the condition\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "This is a computational improvement. However, the code still consists of *three* sections: Some initialization *before* the `for`-loop, the loop itself, and some finalizing logic. We prefer to convey the program's idea in *one* compound statement instead."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "### The `else`-clause"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "To express the logic in a prettier way, we add an `else`-clause at the end of the `for`-loop (cf., [reference <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/reference/compound_stmts.html#the-for-statement)). The `else`-clause is executed *only if* the `for`-loop is *not* stopped with a `break` statement *prematurely* (i.e., *before* reaching the *last* iteration in the loop). The word \"else\" implies a somewhat unintuitive meaning and may have better been named a `then`-clause. In most use cases, however, the `else`-clause logically goes together with some `if` statement in the loop's body.\n",
+    "\n",
+    "Overall, the code's expressive power increases. Not many programming languages support an optional `else`-branching for the `for` and `while` statements, which turns out to be very useful in practice."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "7   11   =>   at least one number satisfies the condition\n"
+     ]
+    }
+   ],
+   "source": [
+    "for number in numbers:\n",
+    "    print(number, end=\"   \")  # added for didactical purposes\n",
+    "    if number ** 2 > 100:\n",
+    "        is_above = True\n",
+    "        break\n",
+    "else:\n",
+    "    is_above = False\n",
+    "\n",
+    "if is_above:\n",
+    "    print(\"=>   at least one number satisfies the condition\")\n",
+    "else:\n",
+    "    print(\"=>   no number satisfies the condition\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Lastly, we incorporate the finalizing `if`-`else` logic into the `for`-loop, avoiding the `is_above` variable altogether."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "7   11   =>   at least one number satisfies the condition\n"
+     ]
+    }
+   ],
+   "source": [
+    "for number in numbers:\n",
+    "    print(number, end=\"   \")  # added for didactical purposes\n",
+    "    if number ** 2 > 100:\n",
+    "        print(\"=>   at least one number satisfies the condition\")\n",
+    "        break\n",
+    "else:\n",
+    "    print(\"=>   no number satisfies the condition\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Of course, if we choose the number an element's square has to pass to be larger, for example, to `200`, we have to loop over all `numbers`. There is *no way* to optimize this **[linear search <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_wiki.png\">](https://en.wikipedia.org/wiki/Linear_search)** further."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "7   11   8   5   3   12   2   6   9   10   1   4   =>   no number satisfies the condition\n"
+     ]
+    }
+   ],
+   "source": [
+    "for number in numbers:\n",
+    "    print(number, end=\"   \")  # added for didactical purposes\n",
+    "    if number ** 2 > 200:\n",
+    "        print(\"=>   at least one number satisfies the condition\")\n",
+    "        break\n",
+    "else:\n",
+    "    print(\"=>   no number satisfies the condition\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "## A first Glance at the **Map-Filter-Reduce** Paradigm"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Often, we process some iterable with numeric data, for example, a list of `numbers` as in this book's introductory example in [Chapter 1 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/01_elements/00_content.ipynb#Example:-Averaging-all-even-Numbers-in-a-List) or, more realistically, data from a CSV file with many rows and columns.\n",
+    "\n",
+    "Processing numeric data usually comes down to operations that may be grouped into one of the following three categories:\n",
+    "\n",
+    "- **mapping**: transform a number according to some functional relationship $y = f(x)$\n",
+    "- **filtering**: throw away individual numbers (e.g., statistical outliers in a sample)\n",
+    "- **reducing**: collect individual numbers into summary statistics\n",
+    "\n",
+    "We study this **map-filter-reduce** paradigm extensively in [Chapter 8 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/08_mfr/00_content.ipynb) after introducing more advanced data types that are needed to work with \"big\" data.\n",
+    "\n",
+    "Here, we focus on *filtering out* some numbers in a `for`-loop."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "### Example: A simple Filter"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "Calculate the sum of all even numbers in `[7, 11, 8, 5, 3, 12, 2, 6, 9, 10, 1, 4]` after squaring them and adding `1` to the squares:\n",
+    "\n",
+    "- \"*all*\" => **loop** over an iterable\n",
+    "- \"*even*\" => **filter** out the odd numbers\n",
+    "- \"*square and add $1$*\" => apply the **map** $y = f(x) = x^2 + 1$\n",
+    "- \"*sum*\" => **reduce** the remaining and mapped numbers to their sum"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "numbers = [7, 11, 8, 5, 3, 12, 2, 6, 9, 10, 1, 4]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "8 -> 65   12 -> 145   2 -> 5   6 -> 37   10 -> 101   4 -> 17   "
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "370"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "total = 0\n",
+    "\n",
+    "for number in numbers:\n",
+    "    if number % 2 == 0:  # only keep even numbers\n",
+    "        square = (number ** 2) + 1\n",
+    "        print(number, \"->\", square, end=\"   \")  # added for didactical purposes\n",
+    "        total += square\n",
+    "\n",
+    "total"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "The above code is easy to read as it involves only two levels of indentation.\n",
+    "\n",
+    "In general, code gets harder to comprehend the more **horizontal space** it occupies. It is commonly considered good practice to grow a program **vertically** rather than horizontally. Code compliant with [PEP 8 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://www.python.org/dev/peps/pep-0008/#maximum-line-length) requires us to use *at most* 79 characters in a line!\n",
+    "\n",
+    "Consider the next example, whose implementation in code already starts to look unbalanced."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "### Example: Several Filters"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "Calculate the sum of every third and even number in `[7, 11, 8, 5, 3, 12, 2, 6, 9, 10, 1, 4]` after squaring them and adding `1` to the squares:\n",
+    "\n",
+    "- \"*every*\" => **loop** over an iterable\n",
+    "- \"*third*\" => **filter** out all numbers except every third\n",
+    "- \"*even*\" => **filter** out the odd numbers\n",
+    "- \"*square and add $1$*\" => apply the **map** $y = f(x) = x^2 + 1$\n",
+    "- \"*sum*\" => **reduce** the remaining and mapped numbers to their sum"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "8 -> 65   12 -> 145   4 -> 17   "
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "227"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "total = 0\n",
+    "\n",
+    "for i, number in enumerate(numbers, start=1):\n",
+    "    if i % 3 == 0:  # only keep every third number\n",
+    "        if number % 2 == 0:  # only keep even numbers\n",
+    "            square = (number ** 2) + 1\n",
+    "            print(number, \"->\", square, end=\"   \")  # added for didactical purposes \n",
+    "            total += square\n",
+    "\n",
+    "total"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "With already three levels of indentation, less horizontal space is available for the actual code block. Of course, one could flatten the two `if` statements with the logical `and` operator, as shown in [Chapter 3 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/03_conditionals/00_content.ipynb#The-if-Statement). Then, however, we trade off horizontal space against a more \"complex\" `if` logic, and this is *not* a real improvement."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "### The `continue` Statement"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "A Pythonista would instead make use of the `continue` statement (cf., [reference <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/reference/simple_stmts.html#the-continue-statement)) that causes a loop to jump into the next iteration skipping the rest of the code block.\n",
+    "\n",
+    "The revised code fragment below occupies more vertical space and less horizontal space: A *good* trade-off.\n",
+    "\n",
+    "One caveat is that we need to negate the conditions in the `if` statements. Conceptually, we are now filtering \"out\" and not \"in.\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "8 -> 65   12 -> 145   4 -> 17   "
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "227"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "total = 0\n",
+    "\n",
+    "for i, number in enumerate(numbers, start=1):\n",
+    "    if i % 3 != 0:  # only keep every third number\n",
+    "        continue\n",
+    "    elif number % 2 != 0:  # only keep even numbers\n",
+    "        continue\n",
+    "\n",
+    "    square = (number ** 2) + 1\n",
+    "    print(number, \"->\", square, end=\"   \")  # added for didactical purposes \n",
+    "    total += square\n",
+    "\n",
+    "total"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "This is yet another illustration of why programming is an art. The two preceding code cells do the *same* with *identical* time complexity. However, the latter is arguably easier to read for a human, even more so when the business logic grows beyond two filters."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "## Indefinite Loops"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Sometimes we find ourselves in situations where we *cannot* know ahead of time how often or until which point in time a code block is to be executed."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "### Example: Guessing a Coin Toss"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Let's consider a game where we randomly choose a variable to be either \"Heads\" or \"Tails\" and the user of our program has to guess it.\n",
+    "\n",
+    "Python provides the built-in [input() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#input) function that prints a message to the user, called the **prompt**, and reads in what was typed in response as a `str` object. We use it to process a user's \"unreliable\" input to our program (i.e., a user might type in some invalid response). Further, we use the [random() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/random.html#random.random) function in the [random <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/random.html) module to model the coin toss.\n",
+    "\n",
+    "A popular pattern to approach such **indefinite loops** is to go with a `while True` statement, which on its own would cause Python to enter into an infinite loop. Then, once a particular event occurs, we `break` out of the loop.\n",
+    "\n",
+    "Let's look at a first and naive implementation."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "import random"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "random.seed(42)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {
+    "code_folding": [],
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdin",
+     "output_type": "stream",
+     "text": [
+      "Guess if the coin comes up as heads or tails:  heads\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Ooops, it was tails\n"
+     ]
+    },
+    {
+     "name": "stdin",
+     "output_type": "stream",
+     "text": [
+      "Guess if the coin comes up as heads or tails:  heads\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Yes, it was heads\n"
+     ]
+    }
+   ],
+   "source": [
+    "while True:\n",
+    "    guess = input(\"Guess if the coin comes up as heads or tails: \")\n",
+    "\n",
+    "    if random.random() < 0.5:\n",
+    "        if guess == \"heads\":\n",
+    "            print(\"Yes, it was heads\")\n",
+    "            break\n",
+    "        else:\n",
+    "            print(\"Ooops, it was heads\")\n",
+    "    else:\n",
+    "        if guess == \"tails\":\n",
+    "            print(\"Yes, it was tails\")\n",
+    "            break\n",
+    "        else:\n",
+    "            print(\"Ooops, it was tails\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "This version exhibits two *severe* issues where we should improve on:\n",
+    "\n",
+    "1. If a user enters *anything* other than `\"heads\"` or `\"tails\"`, for example, `\"Heads\"` or `\"Tails\"`, the program keeps running *without* the user knowing about the mistake!\n",
+    "2. The code *intermingles* the coin tossing with the processing of the user's input: Mixing *unrelated* business logic in the *same* code block makes a program harder to read and, more importantly, maintain in the long run."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "### Example: Guessing a Coin Toss (revisited)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Let's refactor the code and make it *modular*.\n",
+    "\n",
+    "First, we divide the business logic into two functions `get_guess()` and `toss_coin()` that are controlled from within a `while`-loop.\n",
+    "\n",
+    "`get_guess()` not only reads in the user's input but also implements a simple input validation pattern in that the [strip() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/stdtypes.html?highlight=__contains__#str.strip) and [lower() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/stdtypes.html?highlight=__contains__#str.lower) methods remove preceding and trailing whitespace and lower case the input ensuring that the user may spell the input in any possible way (e.g., all upper or lower case). Also, `get_guess()` checks if the user entered one of the two valid options. If so, it returns either `\"heads\"` or `\"tails\"`; if not, it returns `None`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "def get_guess():\n",
+    "    \"\"\"Process the user's input.\n",
+    "    \n",
+    "    Returns:\n",
+    "        guess (str / NoneType): either \"heads\" or \"tails\"\n",
+    "            if the input can be parsed and None otherwise\n",
+    "    \"\"\"\n",
+    "    guess = input(\"Guess if the coin comes up as heads or tails: \")\n",
+    "    # handle frequent cases of \"misspelled\" user input\n",
+    "    guess = guess.strip().lower()\n",
+    "\n",
+    "    if guess in [\"heads\", \"tails\"]:\n",
+    "        return guess\n",
+    "    return None"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "`toss_coin()` models a fair coin toss when called with default arguments."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "def toss_coin(p_heads=0.5):\n",
+    "    \"\"\"Simulate the tossing of a coin.\n",
+    "\n",
+    "    Args:\n",
+    "        p_heads (optional, float): probability that the coin comes up \"heads\";\n",
+    "            defaults to 0.5 resembling a fair coin\n",
+    "\n",
+    "    Returns:\n",
+    "        side_on_top (str): \"heads\" or \"tails\"\n",
+    "    \"\"\"\n",
+    "    if random.random() < p_heads:\n",
+    "        return \"heads\"\n",
+    "    return \"tails\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Second, we rewrite the `if`-`else`-logic to handle the case where `get_guess()` returns `None` explicitly: Whenever the user enters something invalid, a warning is shown, and another try is granted. We use the `is` operator and not the `==` operator as `None` is a singleton object.\n",
+    "\n",
+    "The `while`-loop takes on the role of **glue code** that manages how other parts of the program interact with each other."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "random.seed(42)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdin",
+     "output_type": "stream",
+     "text": [
+      "Guess if the coin comes up as heads or tails:  invalid\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Make sure to enter your guess correctly!\n"
+     ]
+    },
+    {
+     "name": "stdin",
+     "output_type": "stream",
+     "text": [
+      "Guess if the coin comes up as heads or tails:  Heads\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Yes, it was heads\n"
+     ]
+    }
+   ],
+   "source": [
+    "while True:\n",
+    "    guess = get_guess()\n",
+    "    result = toss_coin()\n",
+    "\n",
+    "    if guess is None:\n",
+    "        print(\"Make sure to enter your guess correctly!\")\n",
+    "    elif guess == result:\n",
+    "        print(\"Yes, it was\", result)\n",
+    "        break\n",
+    "    else:\n",
+    "        print(\"Ooops, it was\", result)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Now, the program's business logic is expressed in a clearer way. More importantly, we can now change it more easily. For example, we could make the `toss_coin()` function base the tossing on a probability distribution other than the uniform (i.e., replace the [random.random() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/random.html#random.random) function with another one). In general, modular architecture leads to improved software maintenance."
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.6"
+  },
+  "livereveal": {
+   "auto_select": "code",
+   "auto_select_fragment": true,
+   "scroll": true,
+   "theme": "serif"
+  },
+  "toc": {
+   "base_numbering": 1,
+   "nav_menu": {},
+   "number_sections": false,
+   "sideBar": true,
+   "skip_h1_title": true,
+   "title_cell": "Table of Contents",
+   "title_sidebar": "Contents",
+   "toc_cell": false,
+   "toc_position": {
+    "height": "calc(100% - 180px)",
+    "left": "10px",
+    "top": "150px",
+    "width": "384px"
+   },
+   "toc_section_display": false,
+   "toc_window_display": false
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/README.md b/README.md
index 911350d..2e7f606 100644
--- a/README.md
+++ b/README.md
@@ -103,6 +103,10 @@ Alternatively, the content can be viewed in a web browser
       (Looping with `while` & `for`;
        Examples: Collatz Conjecture, Factorial, Euclid's Algorithm, & Fibonacci;
        Containers vs. Iterables)
+    - [content <img height="12" style="display: inline-block" src="static/link/to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/04_iteration/03_content.ipynb)
+      [<img height="12" style="display: inline-block" src="static/link/to_mb.png">](https://mybinder.org/v2/gh/webartifex/intro-to-python/develop?urlpath=lab/tree/04_iteration/03_content.ipynb)
+      (Customizing Loops with `break` and `continue`;
+       Indefinite Loops)
 
 
 #### Videos

From b73df81315e7e1d52fede4cdb92efae9261cccc3 Mon Sep 17 00:00:00 2001
From: Alexander Hess <alexander@webartifex.biz>
Date: Thu, 15 Oct 2020 18:56:35 +0200
Subject: [PATCH 159/242] Add initial version of chapter 04's exercises, part 2

---
 04_iteration/04_exercises.ipynb | 403 ++++++++++++++++++++++++++++++++
 README.md                       |   3 +
 2 files changed, 406 insertions(+)
 create mode 100644 04_iteration/04_exercises.ipynb

diff --git a/04_iteration/04_exercises.ipynb b/04_iteration/04_exercises.ipynb
new file mode 100644
index 0000000..04654bc
--- /dev/null
+++ b/04_iteration/04_exercises.ipynb
@@ -0,0 +1,403 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Note**: Click on \"*Kernel*\" > \"*Restart Kernel and Run All*\" in [JupyterLab](https://jupyterlab.readthedocs.io/en/stable/) *after* finishing the exercises to ensure that your solution runs top to bottom *without* any errors. If you cannot run this file on your machine, you may want to open it [in the cloud <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_mb.png\">](https://mybinder.org/v2/gh/webartifex/intro-to-python/develop?urlpath=lab/tree/04_iteration/04_exercises.ipynb)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Chapter 4: Recursion & Looping (Coding Exercises)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The exercises below assume that you have read the [third part <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/04_iteration/03_content.ipynb) of Chapter 4.\n",
+    "\n",
+    "The `...`'s in the code cells indicate where you need to fill in code snippets. The number of `...`'s within a code cell give you a rough idea of how many lines of code are needed to solve the task. You should not need to create any additional code cells for your final solution. However, you may want to use temporary code cells to try out some ideas."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Throwing Dice"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In this exercise, you will model the throwing of dice within the context of a guessing game similar to the one shown in the \"*Example: Guessing a Coin Toss*\" section in [Chapter 4 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/04_iteration/03_content.ipynb#Example:-Guessing-a-Coin-Toss).\n",
+    "\n",
+    "As the game involves randomness, we import the [random <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/random.html) module from the [standard library <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/index.html). To follow best practices, we set the random seed as well."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import random"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "random.seed(42)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "A die has six sides that we labeled with integers `1` to `6` in this exercise. For a fair die, the probability for each side is the same.\n",
+    "\n",
+    "**Q1**: Model a `fair_die` as a `list` object!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "fair_die = ..."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q2**: What function from the [random <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/random.html) module that we have seen already is useful for modeling a single throw of the `fair_die`? Write a simple expression (i.e., one function call) that draws one of the equally likely sides! Execute the cell a couple of times to \"see\" the probability distribution!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "..."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Let's check if the `fair_die` is indeed fair. To do so, we create a little numerical experiment and throw the `fair_die` `100000` times. We track the six different outcomes in a `list` object called `throws` that we initialize with all `0`s for each outcome.\n",
+    "\n",
+    "**Q3**: Complete the `for`-loop below such that it runs `100000` times! In the body, use your answer to **Q2** to simulate a single throw of the `fair_die` and update the corresponding count in `throws`!\n",
+    "\n",
+    "Hints: You need to use the indexing operator `[]` and calculate an `index` in each iteration of the loop. Do do not actually need the target variable provided by the `for`-loop and may want to indicate that with an underscore `_`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "throws = [0, 0, 0, 0, 0, 0]\n",
+    "\n",
+    "for ... in ...:\n",
+    "    ...\n",
+    "    ...\n",
+    "    ...\n",
+    "\n",
+    "throws"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "`throws` contains the simulation results as absolute counts.\n",
+    "\n",
+    "**Q4**: Complete the `for`-loop below to convert the counts in `throws` to relative frequencies stored in a `list` called `frequencies`! Round the frequencies to three decimals with the built-in [round() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#round) function!\n",
+    "\n",
+    "Hints: Initialize `frequencies` just as `throws` above. How many iterations does the `for`-loop have? `6` or `100000`? You may want to obtain an `index` variable with the [enumerate() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#enumerate) built-in."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "frequencies = [0, 0, 0, 0, 0, 0]\n",
+    "\n",
+    "for ... in ...:\n",
+    "    ...\n",
+    "\n",
+    "frequencies"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q5**: How could we adapt the `list` object used above to model an `unfair_die` where `1` is as likely as `2`, `2` is twice as likely as `3`, and `3` is twice as likely as `4`, `5`, or `6`, who are all equally likely?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "unfair_die = ..."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q6**: Copy your solution to **Q2** for the `unfair_die`! Execute the cell a couple of times to \"see\" the probability distribution!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "..."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q7**: Copy and adapt your solutions to **Q3** and **Q4** to calculate the `frequencies` for the `unfair_die`!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "throws = [0, 0, 0, 0, 0, 0]\n",
+    "frequencies = [0, 0, 0, 0, 0, 0]\n",
+    "\n",
+    "for ... in ...:\n",
+    "    ...\n",
+    "    ...\n",
+    "    ...\n",
+    "\n",
+    "for ... in ...:\n",
+    "    ...\n",
+    "\n",
+    "frequencies"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q8**: The built-in [input() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#input) allows us to ask the user to enter a `guess`. What is the data type of the object returned by [input() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#input)? Assume the user enters the `guess` as a number (i.e., \"1\", \"2\", ...) and not as a text (e.g., \"one\")."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "guess = input(\"Guess the side of the die: \")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "guess"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "..."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q9**: Use a built-in constructor to cast `guess` as an `int` object!\n",
+    "\n",
+    "Hint: Simply wrap `guess` or `input(\"Guess the side of the die: \")` with the constructor you choose."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "..."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q10**: What type of error is raised if `guess` cannot be cast as an `int` object?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " < your answer >"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q11**: Write a `try` statement that catches the type of error (i.e., your answer to **Q10**) raised if the user's input cannot be cast as an `int` object! Print out some nice error message notifying the user of the bad input!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "try:\n",
+    "    ...\n",
+    "except ...:\n",
+    "    ..."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q12**: Write a function `get_guess()` that takes a user's input and checks if it is a valid side of the die! The function should *return* either an `int` object between `1` and `6` or `None` if the user enters something invalid.\n",
+    "\n",
+    "Hints: You may want to re-use the `try` statement from **Q11**. Instead of printing out an error message, you can also `return` directly from the `except`-clause (i.e., early exit) with `None`. So, the user can make *two* kinds of input errors and maybe you want to model that with two *distinct* `return None` statements. Also, you may want to allow the user to enter leading and trailing whitespace that gets removed without an error message."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def get_guess():\n",
+    "    \"\"\"Process the user's input.\n",
+    "    \n",
+    "    Returns:\n",
+    "        guess (int / NoneType): either 1, 2, 3, 4, 5 or 6\n",
+    "            if the input can be parsed and None otherwise\n",
+    "    \"\"\"\n",
+    "    ...\n",
+    "\n",
+    "    # Check if the user entered an integer.\n",
+    "    ...\n",
+    "    ...\n",
+    "    ...\n",
+    "    ...\n",
+    "\n",
+    "    # Check if the user entered a valid side.\n",
+    "    ...\n",
+    "    ...\n",
+    "    ..."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q13** Test your function for all *three* cases!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "get_guess()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q14**: Write an *indefinite* loop where in each iteration a `fair_die` is thrown and the user makes a guess! Print out an error message if the user does not enter something that can be understood as a number between `1` and `6`! The game should continue until the user makes a correct guess."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "...\n",
+    "...\n",
+    "...\n",
+    "\n",
+    "...\n",
+    "...\n",
+    "...\n",
+    "...\n",
+    "...\n",
+    "...\n",
+    "..."
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.6"
+  },
+  "toc": {
+   "base_numbering": 1,
+   "nav_menu": {},
+   "number_sections": false,
+   "sideBar": true,
+   "skip_h1_title": true,
+   "title_cell": "Table of Contents",
+   "title_sidebar": "Contents",
+   "toc_cell": false,
+   "toc_position": {},
+   "toc_section_display": false,
+   "toc_window_display": false
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/README.md b/README.md
index 2e7f606..209bbbc 100644
--- a/README.md
+++ b/README.md
@@ -107,6 +107,9 @@ Alternatively, the content can be viewed in a web browser
       [<img height="12" style="display: inline-block" src="static/link/to_mb.png">](https://mybinder.org/v2/gh/webartifex/intro-to-python/develop?urlpath=lab/tree/04_iteration/03_content.ipynb)
       (Customizing Loops with `break` and `continue`;
        Indefinite Loops)
+    - [exercises <img height="12" style="display: inline-block" src="static/link/to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/04_iteration/04_exercises.ipynb)
+      [<img height="12" style="display: inline-block" src="static/link/to_mb.png">](https://mybinder.org/v2/gh/webartifex/intro-to-python/develop?urlpath=lab/tree/04_iteration/04_exercises.ipynb)
+      (Throwing Dice)
 
 
 #### Videos

From 5958b386876f690e3d858f9448f0ad709bc77645 Mon Sep 17 00:00:00 2001
From: Alexander Hess <alexander@webartifex.biz>
Date: Thu, 15 Oct 2020 18:58:27 +0200
Subject: [PATCH 160/242] Add initial version of chapter 04's summary

---
 04_iteration/05_summary.ipynb | 79 +++++++++++++++++++++++++++++++++++
 README.md                     |  1 +
 2 files changed, 80 insertions(+)
 create mode 100644 04_iteration/05_summary.ipynb

diff --git a/04_iteration/05_summary.ipynb b/04_iteration/05_summary.ipynb
new file mode 100644
index 0000000..981bfec
--- /dev/null
+++ b/04_iteration/05_summary.ipynb
@@ -0,0 +1,79 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "# Chapter 4: Recursion & Looping (TL;DR)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "**Iteration** is about **running blocks of code repeatedly**.\n",
+    "\n",
+    "There are two redundant approaches to achieving that.\n",
+    "\n",
+    "First, we combine functions that call themselves with conditional statements. This concept is known as **recursion** and suffices to control the flow of execution in *every* way we desire. For a beginner, this approach of **backward** reasoning might not be intuitive, but it turns out to be a handy tool to have in one's toolbox.\n",
+    "\n",
+    "Second, the `while` and `for` statements are alternative and potentially more intuitive ways to express iteration as they correspond to a **forward** reasoning. The `for` statement is **syntactic sugar** that allows rewriting common occurrences of the `while` statement concisely. Python provides the `break` and `continue` statements as well as an optional `else`-clause that make working with the `for` and `while` statements even more convenient.\n",
+    "\n",
+    "**Iterables** are any **concrete data types** that support being looped over, for example, with the `for` statement. The idea behind iterables is an **abstract concept** that may or may not be implemented by any given concrete data type. For example, both `list` and `range` objects can be looped over. The `list` type is also a **container** as any given `list` objects \"contains\" references to other objects in memory. On the contrary, the `range` type does not reference any other object but instead creates *new* `int` objects \"on the fly\" (i.e., when being looped over)."
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.6"
+  },
+  "livereveal": {
+   "auto_select": "code",
+   "auto_select_fragment": true,
+   "scroll": true,
+   "theme": "serif"
+  },
+  "toc": {
+   "base_numbering": 1,
+   "nav_menu": {},
+   "number_sections": false,
+   "sideBar": true,
+   "skip_h1_title": true,
+   "title_cell": "Table of Contents",
+   "title_sidebar": "Contents",
+   "toc_cell": false,
+   "toc_position": {
+    "height": "calc(100% - 180px)",
+    "left": "10px",
+    "top": "150px",
+    "width": "384px"
+   },
+   "toc_section_display": false,
+   "toc_window_display": false
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/README.md b/README.md
index 209bbbc..f431c9a 100644
--- a/README.md
+++ b/README.md
@@ -110,6 +110,7 @@ Alternatively, the content can be viewed in a web browser
     - [exercises <img height="12" style="display: inline-block" src="static/link/to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/04_iteration/04_exercises.ipynb)
       [<img height="12" style="display: inline-block" src="static/link/to_mb.png">](https://mybinder.org/v2/gh/webartifex/intro-to-python/develop?urlpath=lab/tree/04_iteration/04_exercises.ipynb)
       (Throwing Dice)
+    - [summary <img height="12" style="display: inline-block" src="static/link/to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/04_iteration/05_summary.ipynb)
 
 
 #### Videos

From f557daee31c5f3fe3a1fd0970ca65d98b00d919d Mon Sep 17 00:00:00 2001
From: Alexander Hess <alexander@webartifex.biz>
Date: Thu, 15 Oct 2020 19:00:14 +0200
Subject: [PATCH 161/242] Add initial version of chapter 04's review

---
 04_iteration/06_review.ipynb | 268 +++++++++++++++++++++++++++++++++++
 README.md                    |   1 +
 2 files changed, 269 insertions(+)
 create mode 100644 04_iteration/06_review.ipynb

diff --git a/04_iteration/06_review.ipynb b/04_iteration/06_review.ipynb
new file mode 100644
index 0000000..b40647b
--- /dev/null
+++ b/04_iteration/06_review.ipynb
@@ -0,0 +1,268 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Chapter 4: Recursion & Looping (Review Questions)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The questions below assume that you have read the [first <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/04_iteration/00_content.ipynb), [second <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/04_iteration/02_content.ipynb), and the [third <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/04_iteration/03_content.ipynb) part of Chapter 4.\n",
+    "\n",
+    "Be concise in your answers! Most questions can be answered in *one* sentence."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Essay Questions "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q1**: Solving a problem by **recursion** is not only popular in computer science and math. Name some examples from the fields of business or economics where problems are also solved in a **backward** fashion!"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " < your answer >"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q2**: Explain what **duck typing** means! Why can it cause problems? Why is it [not a bug but a feature](https://www.urbandictionary.com/define.php?term=It%27s%20not%20a%20bug%2C%20it%27s%20a%20feature)?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " < your answer >"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q3**: What is **syntactic sugar**?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " < your answer >"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q4**: Describe in your own words why the **recursive** version of `fibonacci()`, the \"Easy at first Glance\" example in the chapter, is computationally **inefficient**! Why does the **iterative** version of `fibonacci()`, the \"Hard at first Glance\" example, run so much faster?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " < your answer >"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q5**: What is the conceptual difference between a **container** and a **list**?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " < your answer >"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q6**: What is a good use case for the `for`-loop's optional `else`-clause?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " < your answer >"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## True / False Questions"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Motivate your answer with *one short* sentence!"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q7**: When a **recursion** does **not** reach the base case, this is an example of the **early exit** strategy."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " < your answer >"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q8**: Any programming language **without** looping constructs like the `for` or `while` statements is **not** Turing complete."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " < your answer >"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q9**: A **recursive** formulation is the same as a **circular** one: The terms are **synonyms**."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " < your answer >"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q10**: Formulating a computational problem as a **recursion** results in an **efficient** implementation."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " < your answer >"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q11**: Whereas a **recursion** may accidentally result in a **never-ending** program, `while`-loops and `for`-loops are guaranteed to **terminate**."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " < your answer >"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q12**: Before writing **any** kind of **loop**, we **always** need to think about a **stopping criterion** ahead of time."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " < your answer >"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q13**: **Container** types such as `list` objects are characterized by their **support** for **being looped over**, for example as in:"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "```python\n",
+    "for element in container:\n",
+    "    # do something for every element\n",
+    "    ...\n",
+    "```"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " < your answer >"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.6"
+  },
+  "toc": {
+   "base_numbering": 1,
+   "nav_menu": {},
+   "number_sections": false,
+   "sideBar": true,
+   "skip_h1_title": true,
+   "title_cell": "Table of Contents",
+   "title_sidebar": "Contents",
+   "toc_cell": false,
+   "toc_position": {},
+   "toc_section_display": false,
+   "toc_window_display": false
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/README.md b/README.md
index f431c9a..bb5114f 100644
--- a/README.md
+++ b/README.md
@@ -111,6 +111,7 @@ Alternatively, the content can be viewed in a web browser
       [<img height="12" style="display: inline-block" src="static/link/to_mb.png">](https://mybinder.org/v2/gh/webartifex/intro-to-python/develop?urlpath=lab/tree/04_iteration/04_exercises.ipynb)
       (Throwing Dice)
     - [summary <img height="12" style="display: inline-block" src="static/link/to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/04_iteration/05_summary.ipynb)
+    - [review questions <img height="12" style="display: inline-block" src="static/link/to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/04_iteration/06_review.ipynb)
 
 
 #### Videos

From b3213e40458e9e8d7c17d6b2355a2fa7e5149391 Mon Sep 17 00:00:00 2001
From: Alexander Hess <alexander@webartifex.biz>
Date: Thu, 15 Oct 2020 23:32:24 +0200
Subject: [PATCH 162/242] Add initial version of chapter 05, part 1

---
 00_intro/00_content.ipynb   |    2 +-
 05_numbers/00_content.ipynb | 2454 +++++++++++++++++++++++++++++++++++
 README.md                   |    8 +
 3 files changed, 2463 insertions(+), 1 deletion(-)
 create mode 100644 05_numbers/00_content.ipynb

diff --git a/00_intro/00_content.ipynb b/00_intro/00_content.ipynb
index dbe3f49..d3e1fae 100644
--- a/00_intro/00_content.ipynb
+++ b/00_intro/00_content.ipynb
@@ -768,7 +768,7 @@
     "**Part B: Managing Data and Memory**\n",
     "\n",
     "- How is data stored in memory?\n",
-    "  - *Chapter 5*: Numbers & Bits\n",
+    "  - *Chapter 5*: [Numbers & Bits <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/05_numbers/00_content.ipynb)\n",
     "  - *Chapter 6*: Text & Bytes\n",
     "  - *Chapter 7*: Sequential Data\n",
     "  - *Chapter 8*: Map, Filter, & Reduce\n",
diff --git a/05_numbers/00_content.ipynb b/05_numbers/00_content.ipynb
new file mode 100644
index 0000000..0a106c4
--- /dev/null
+++ b/05_numbers/00_content.ipynb
@@ -0,0 +1,2454 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "**Note**: Click on \"*Kernel*\" > \"*Restart Kernel and Clear All Outputs*\" in [JupyterLab](https://jupyterlab.readthedocs.io/en/stable/) *before* reading this notebook to reset its output. If you cannot run this file on your machine, you may want to open it [in the cloud <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_mb.png\">](https://mybinder.org/v2/gh/webartifex/intro-to-python/develop?urlpath=lab/tree/05_numbers/00_content.ipynb)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "# Chapter 5: Numbers & Bits"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "After learning about the basic building blocks of expressing and structuring the business logic in programs, we focus our attention on the **data types** Python offers us, both built-in and available via the [standard library <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/index.html) or third-party packages.\n",
+    "\n",
+    "We start with the \"simple\" ones: Numeric types in this chapter and textual data in [Chapter 6 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/06_text/00_content.ipynb). An important fact that holds for all objects of these types is that they are **immutable**. To reuse the bag analogy from [Chapter 1 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/01_elements/00_content.ipynb#Objects-vs.-Types-vs.-Values), this means that the $0$s and $1$s making up an object's *value* cannot be changed once the bag is created in memory, implying that any operation with or method on the object creates a *new* object in a *different* memory location.\n",
+    "\n",
+    "[Chapter 7 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/07_sequences/00_content.ipynb), [Chapter 8 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/08_mfr/00_content.ipynb), [Chapter 9 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/09_mappings/00_content.ipynb), [Chapter 10 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/10_arrarys/00_content.ipynb) then cover the more \"complex\" data types, including, for example, the `list` type. Finally, [Chapter 11 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/11_classes/00_content.ipynb) completes the picture by introducing language constructs to create custom types.\n",
+    "\n",
+    "We have already seen many hints indicating that numbers are not as trivial to work with as it seems at first sight:\n",
+    "\n",
+    "- [Chapter 1 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/01_elements/00_content.ipynb#%28Data%29-Type-%2F-%22Behavior%22) reveals that numbers may come in *different* data types (i.e., `int` vs. `float` so far),\n",
+    "- [Chapter 3 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/03_conditionals/00_content.ipynb#Boolean-Expressions) raises questions regarding the **limited precision** of `float` numbers (e.g., `42 == 42.000000000000001` evaluates to `True`), and\n",
+    "- [Chapter 4 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/04_iteration/00_content.ipynb#Infinite-Recursion) shows that sometimes a `float` \"walks\" and \"quacks\" like an `int`, whereas the reverse is true in other cases.\n",
+    "\n",
+    "This chapter introduces all the [built-in numeric types <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/stdtypes.html#numeric-types-int-float-complex): `int`, `float`, and `complex`. To mitigate the limited precision of floating-point numbers, we also add an [Appendix <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/05_numbers/03_appendix.ipynb) where we look at two replacements for the `float` type in the [standard library <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/index.html), namely the `Decimal` type in the [decimals <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/decimal.html#decimal.Decimal) and the `Fraction` type in the [fractions <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/fractions.html#fractions.Fraction) module."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "## The `int` Type"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "The simplest numeric type is the `int` type: It behaves like an [integer in ordinary math <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_wiki.png\">](https://en.wikipedia.org/wiki/Integer) (i.e., the set $\\mathbb{Z}$) and supports operators in the way we saw in the section on arithmetic operators in [Chapter 1 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/01_elements/00_content.ipynb#%28Arithmetic%29-Operators).\n",
+    "\n",
+    "One way to create `int` objects is by simply writing its value as a literal with the digits `0` to `9`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "a = 42"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Just like any other object, the `42` has an identity, a type, and a value."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "94857615440928"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "id(a)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "int"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "type(a)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "42"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "a"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "A nice feature in newer Python versions is using underscores `_` as (thousands) separators in numeric literals. For example, `1_000_000` evaluates to `1000000` in memory; the `_`s are ignored by the interpreter."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "1000000"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "1_000_000"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "We may place the `_`s anywhere we want."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "123456789"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "1_2_3_4_5_6_7_8_9"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "It is syntactically invalid to write out leading `0`s in numeric literals. The reason for that will become apparent in the next section."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "ename": "SyntaxError",
+     "evalue": "leading zeros in decimal integer literals are not permitted; use an 0o prefix for octal integers (<ipython-input-7-9a7f0f99e867>, line 1)",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;36m  File \u001b[0;32m\"<ipython-input-7-9a7f0f99e867>\"\u001b[0;36m, line \u001b[0;32m1\u001b[0m\n\u001b[0;31m    042\u001b[0m\n\u001b[0m      ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m leading zeros in decimal integer literals are not permitted; use an 0o prefix for octal integers\n"
+     ]
+    }
+   ],
+   "source": [
+    "042"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Another way to create `int` objects is with the [int() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#int) built-in that casts `float` or properly formatted `str` objects as integers. Then, decimals are truncated (i.e., \"cut off\")."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "42"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "int(42.11)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "42"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "int(42.87)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Whereas the integer division operator `//` \"rounds\" towards negative infinity (cf., the \"*(Arithmetic) Operators*\" section in [Chapter 1 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/01_elements/00_content.ipynb#%28Arithmetic%29-Operators)), the [int() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#int) built-in rounds towards `0`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "-42"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "int(-42.87)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "When casting `str` objects as `int`s, the [int() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#int) built-in is less forgiving. We must not include any decimals as shown by the `ValueError`. Yet, leading and trailing whitespace is gracefully ignored."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "42"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "int(\"42\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "ename": "ValueError",
+     "evalue": "invalid literal for int() with base 10: '42.0'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-12-faae6b054331>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"42.0\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;31mValueError\u001b[0m: invalid literal for int() with base 10: '42.0'"
+     ]
+    }
+   ],
+   "source": [
+    "int(\"42.0\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "42"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "int(\" 42 \")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "The `int` type follows all rules we know from math, apart from one exception: Whereas mathematicians to this day argue what the term $0^0$ means (cf., this [article <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_wiki.png\">](https://en.wikipedia.org/wiki/Zero_to_the_power_of_zero)), programmers are pragmatic about this and simply define $0^0 = 1$."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "1"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "0 ** 0"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "## Binary Representations"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "As computers can only store $0$s and $1$s, `int` objects are nothing but that in memory as well. Consequently, computer scientists and engineers developed conventions as to how $0$s and $1$s are \"translated\" into integers, and one such convention is the **[binary representation <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_wiki.png\">](https://en.wikipedia.org/wiki/Binary_number)** of **non-negative integers**. Consider the integers from $0$ through $255$ that are encoded into $0$s and $1$s with the help of this table:"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "|Bit $i$|  7  |  6  |  5  |  4  |  3  |  2  |  1  |  0  |\n",
+    "|:------|:----|:----|:----|:----|:----|:----|:----|:----|\n",
+    "| Digit |$2^7$|$2^6$|$2^5$|$2^4$|$2^3$|$2^2$|$2^1$|$2^0$|\n",
+    "|  $=$  |$128$| $64$| $32$| $16$| $8$ | $4$ | $2$ | $1$ |"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "A number consists of exactly eight $0$s and $1$s that are read from right to left and referred to as the **bits** of the number. Each bit represents a distinct multiple of $2$, the **digit**. For sure, we start counting at $0$ again.\n",
+    "\n",
+    "To encode the integer $3$, for example, we need to find a combination of $0$s and $1$s such that the sum of digits marked with a $1$ is equal to the number we want to encode. In the example, we set all bits to $0$ except for the first ($i=0$) and second ($i=1$) as $2^0 + 2^1 = 1 + 2 = 3$. So the binary representation of $3$ is $00~00~00~11$. To borrow some terminology from linear algebra, the $3$ is a linear combination of the digits where the coefficients are either $0$ or $1$: $3 = 0*128 + 0*64 + 0*32 + 0*16 + 0*8 + 0*4 + 1*2 + 1*1$. It is *guaranteed* that there is exactly *one* such combination for each number between $0$ and $255$.\n",
+    "\n",
+    "As each bit in the binary representation is one of two values, we say that this representation has a base of $2$. Often, the base is indicated with a subscript to avoid confusion. For example, we write $3_{10} = 00000011_2$ or $3_{10} = 11_2$ for short omitting leading $0$s. A subscript of $10$ implies a decimal number as we know it from elementary school.\n",
+    "\n",
+    "We use the built-in [bin() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#bin) function to obtain an `int` object's binary representation: It returns a `str` object starting with `\"0b\"` indicating the binary format and as many $0$s and $1$s as are necessary to encode the integer omitting leading $0$s."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'0b11'"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "bin(3)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "We may pass a `str` object formatted this way as the argument to the [int() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#int) built-in, together with `base=2`, to create an `int` object, for example, with the value of `3`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "3"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "int(\"0b11\", base=2)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Moreover, we may also use the contents of the returned `str` object as a **literal** instead: Just like we type, for example, `3` without quotes (i.e., \"literally\") into a code cell to create the `int` object `3`, we may type `0b11` to obtain an `int` object with the same value."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "3"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "0b11"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Another example is the integer `123` that is the sum of $64 + 32 + 16 + 8 + 2 + 1$: Thus, its binary representation is the sequence of bits $01~11~10~11$, or to use our new notation, $123_{10} = 1111011_2$."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'0b1111011'"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "bin(123)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Analogous to typing `123` into a code cell, we may write `0b1111011`, or `0b_111_1011` to make use of the underscores, and create an `int` object with the value `123`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "123"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "0b_111_1011"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "`0` and `255` are the edge cases where we set all the bits to either $0$ or $1$."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'0b0'"
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "bin(0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'0b1'"
+      ]
+     },
+     "execution_count": 21,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "bin(1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'0b10'"
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "bin(2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'0b11111111'"
+      ]
+     },
+     "execution_count": 23,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "bin(255)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Groups of eight bits are also called a **byte**. As a byte can only represent non-negative integers up to $255$, the table above is extended conceptually with greater digits to the left to model integers beyond $255$. The memory management needed to implement this is built into Python, and we do not need to worry about it.\n",
+    "\n",
+    "For example, `789` is encoded with ten bits and $789_{10} = 1100010101_2$."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'0b1100010101'"
+      ]
+     },
+     "execution_count": 24,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "bin(789)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "To contrast this bits encoding with the familiar decimal system, we show an equivalent table with powers of $10$ as the digits:\n",
+    "\n",
+    "|Decimal|   3  |   2  |   1  |   0  |\n",
+    "|-------|------|------|------|------|\n",
+    "| Digit |$10^3$|$10^2$|$10^1$|$10^0$|\n",
+    "|  $=$  |$1000$| $100$| $10$ |  $1$ |\n",
+    "\n",
+    "Now, an integer is a linear combination of the digits where the coefficients are one of *ten* values, and the base is now $10$. For example, the number $123$ can be expressed as $0*1000 + 1*100 + 2*10 + 3*1$. So, the binary representation follows the same logic as the decimal system taught in elementary school. The decimal system is intuitive to us humans, mostly as we learn to count with our *ten* fingers. The $0$s and $1$s in a computer's memory are therefore no rocket science; they only feel unintuitive for a beginner."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "### Arithmetic with Bits"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Adding two numbers in their binary representations is straightforward and works just like we all learned addition in elementary school. Going from right to left, we add the individual digits, and ..."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "3"
+      ]
+     },
+     "execution_count": 25,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "1 + 2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'0b1 + 0b10 = 0b11'"
+      ]
+     },
+     "execution_count": 26,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "bin(1) + \" + \" + bin(2) + \" = \" + bin(3)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "... if any two digits add up to $2$, the resulting digit is $0$ and a $1$ carries over."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "4"
+      ]
+     },
+     "execution_count": 27,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "1 + 3"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'0b1 + 0b11 = 0b100'"
+      ]
+     },
+     "execution_count": 28,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "bin(1) + \" + \" + bin(3) + \" = \" + bin(4)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Multiplication is also quite easy. All we need to do is to multiply the left operand by all digits of the right operand separately and then add up the individual products, just like in elementary school."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "12"
+      ]
+     },
+     "execution_count": 29,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "4 * 3"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'0b100 * 0b11 = 0b1100'"
+      ]
+     },
+     "execution_count": 30,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "bin(4) + \" * \" + bin(3) + \" = \" + bin(12)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'0b100 * 0b1 = 0b100'"
+      ]
+     },
+     "execution_count": 31,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "bin(4) + \" * \" + bin(1) + \" = \" + bin(4)  # multiply with first digit"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'0b100 * 0b10 = 0b1000'"
+      ]
+     },
+     "execution_count": 32,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "bin(4) + \" * \" + bin(2) + \" = \" + bin(8)  # multiply with second digit"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "The [Further Resources <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/05_numbers/06_resources.ipynb) section at the end of this chapter provides video tutorials on addition and multiplication in binary. Subtraction and division are a bit more involved but essentially also easy to understand."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "## Hexadecimal Representations"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "While in the binary and decimal systems there are two and ten distinct coefficients per digit, another convenient representation, the **hexadecimal representation**, uses a base of $16$. It is convenient as one digit stores the same amount of information as *four* bits and the binary representation quickly becomes unreadable for larger numbers. The letters \"a\" through \"f\" are used as digits \"10\" through \"15\".\n",
+    "\n",
+    "The following table summarizes the relationship between the three systems:"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "|Decimal|Hexadecimal|Binary|$~~~~~~$|Decimal|Hexadecimal|Binary|$~~~~~~$|Decimal|Hexadecimal|Binary|$~~~~~~$|...|\n",
+    "|-------|-----------|------|--------|-------|-----------|------|--------|-------|-----------|------|--------|---|\n",
+    "|   0   |     0     | 0000 |$~~~~~~$|  16   |    10     | 10000|$~~~~~~$|  32   |    20     |100000|$~~~~~~$|...|\n",
+    "|   1   |     1     | 0001 |$~~~~~~$|  17   |    11     | 10001|$~~~~~~$|  33   |    21     |100001|$~~~~~~$|...|\n",
+    "|   2   |     2     | 0010 |$~~~~~~$|  18   |    12     | 10010|$~~~~~~$|  34   |    22     |100010|$~~~~~~$|...|\n",
+    "|   3   |     3     | 0011 |$~~~~~~$|  19   |    13     | 10011|$~~~~~~$|  35   |    23     |100011|$~~~~~~$|...|\n",
+    "|   4   |     4     | 0100 |$~~~~~~$|  20   |    14     | 10100|$~~~~~~$|  36   |    24     |100100|$~~~~~~$|...|\n",
+    "|   5   |     5     | 0101 |$~~~~~~$|  21   |    15     | 10101|$~~~~~~$|  37   |    25     |100101|$~~~~~~$|...|\n",
+    "|   6   |     6     | 0110 |$~~~~~~$|  22   |    16     | 10110|$~~~~~~$|  38   |    26     |100110|$~~~~~~$|...|\n",
+    "|   7   |     7     | 0111 |$~~~~~~$|  23   |    17     | 10111|$~~~~~~$|  39   |    27     |100111|$~~~~~~$|...|\n",
+    "|   8   |     8     | 1000 |$~~~~~~$|  24   |    18     | 11000|$~~~~~~$|  40   |    28     |101000|$~~~~~~$|...|\n",
+    "|   9   |     9     | 1001 |$~~~~~~$|  25   |    19     | 11001|$~~~~~~$|  41   |    29     |101001|$~~~~~~$|...|\n",
+    "|  10   |     a     | 1010 |$~~~~~~$|  26   |    1a     | 11010|$~~~~~~$|  42   |    2a     |101010|$~~~~~~$|...|\n",
+    "|  11   |     b     | 1011 |$~~~~~~$|  27   |    1b     | 11011|$~~~~~~$|  43   |    2b     |101011|$~~~~~~$|...|\n",
+    "|  12   |     c     | 1100 |$~~~~~~$|  28   |    1c     | 11100|$~~~~~~$|  44   |    2c     |101100|$~~~~~~$|...|\n",
+    "|  13   |     d     | 1101 |$~~~~~~$|  29   |    1d     | 11101|$~~~~~~$|  45   |    2d     |101101|$~~~~~~$|...|\n",
+    "|  14   |     e     | 1110 |$~~~~~~$|  30   |    1e     | 11110|$~~~~~~$|  46   |    2e     |101110|$~~~~~~$|...|\n",
+    "|  15   |     f     | 1111 |$~~~~~~$|  31   |    1f     | 11111|$~~~~~~$|  47   |    2f     |101111|$~~~~~~$|...|"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "To show more examples of the above subscript convention, we pick three random entries from the table:\n",
+    "\n",
+    "$11_{10} = \\text{b}_{16} = 1011_2$\n",
+    "\n",
+    "$25_{10} = 19_{16} = 11001_2$\n",
+    "\n",
+    "$46_{10} = 2\\text{e}_{16} = 101110_2$\n",
+    "\n",
+    "The built-in [hex() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#hex) function creates a `str` object starting with `\"0x\"` representing an `int` object's hexadecimal representation. The length depends on how many groups of four bits are implied by the corresponding binary representation.\n",
+    "\n",
+    "For `0` and `1`, the hexadecimal representation is similar to the binary one."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'0x0'"
+      ]
+     },
+     "execution_count": 33,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "hex(0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'0x1'"
+      ]
+     },
+     "execution_count": 34,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "hex(1)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Whereas `bin(3)` already requires two digits, one is enough for `hex(3)`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'0x3'"
+      ]
+     },
+     "execution_count": 35,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "hex(3)  # bin(3) => \"0b11\"; two digits needed"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "For `10` and `15`, we see the letter digits for the first time."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'0xa'"
+      ]
+     },
+     "execution_count": 36,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "hex(10)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'0xf'"
+      ]
+     },
+     "execution_count": 37,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "hex(15)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "The binary representation of `123`, `0b_111_1011`, can be viewed as *two* groups of four bits, $0111$ and $1011$, that are encoded as $7$ and $\\text{b}$ in hexadecimal (cf., table above)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'0b1111011'"
+      ]
+     },
+     "execution_count": 38,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "bin(123)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'0x7b'"
+      ]
+     },
+     "execution_count": 39,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "hex(123)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "To obtain a *new* `int` object with the value `123`, we call the [int() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#int) built-in with a properly formatted `str` object and `base=16` as arguments."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "123"
+      ]
+     },
+     "execution_count": 40,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "int(\"0x7b\", base=16)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Alternatively, we could use a literal notation instead."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "123"
+      ]
+     },
+     "execution_count": 41,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "0x7b"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Hexadecimals between $00_{16}$ and $\\text{ff}_{16}$ (i.e., $0_{10}$ and $255_{10}$) are commonly used to describe colors, for example, in web development but also graphics editors. See this [online tool](https://www.w3schools.com/colors/colors_hexadecimal.asp) for some more background."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'0x0'"
+      ]
+     },
+     "execution_count": 42,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "hex(0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'0xff'"
+      ]
+     },
+     "execution_count": 43,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "hex(255)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Just like binary representations, the hexadecimals extend to the left for larger numbers like `789`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'0x315'"
+      ]
+     },
+     "execution_count": 44,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "hex(789)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "For completeness sake, we mention that there is also the [oct() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#oct) built-in to obtain an integer's **octal representation**. The logic is the same as for the hexadecimal representation, and we use *eight* instead of *sixteen* digits. That is the equivalent of viewing the binary representations in groups of three bits. As of today, octal representations have become less important, and the data science practitioner may probably live without them quite well."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "## Negative Values"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "While there are conventions that model negative integers with $0$s and $1$s in memory (cf., [Two's Complement <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_wiki.png\">](https://en.wikipedia.org/wiki/Two%27s_complement)), Python manages that for us, and we do not look into the theory here for brevity. We have learned all that a practitioner needs to know about how integers are modeled in a computer. The [Further Resources <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/05_numbers/06_resources.ipynb) section at the end of this chapter provides a video tutorial on how the [Two's Complement <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_wiki.png\">](https://en.wikipedia.org/wiki/Two%27s_complement) idea works.\n",
+    "\n",
+    "The binary and hexadecimal representations of negative integers are identical to their positive counterparts except that they start with a minus sign `-`. However, as the video tutorial at the end of the chapter reveals, that is *not* how the bits are organized in memory."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'-0b11'"
+      ]
+     },
+     "execution_count": 45,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "bin(-3)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'-0x3'"
+      ]
+     },
+     "execution_count": 46,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "hex(-3)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'-0b11111111'"
+      ]
+     },
+     "execution_count": 47,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "bin(-255)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 48,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'-0xff'"
+      ]
+     },
+     "execution_count": 48,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "hex(-255)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "## Bitwise Operators"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Now that we know how integers are represented with $0$s and $1$s, we look at ways of working with the individual bits, in particular with the so-called **[bitwise operators](https://wiki.python.org/moin/BitwiseOperators)**: As the name suggests, the operators perform some operation on a bit by bit basis. They only work with and always return `int` objects.\n",
+    "\n",
+    "We keep this overview rather short as such \"low-level\" operations are not needed by the data science practitioner regularly. Yet, it is worthwhile to have heard about them as they form the basis of all of arithmetic in computers.\n",
+    "\n",
+    "The first operator is the **bitwise AND** operator `&`: It looks at the bits of its two operands, `11` and `13` in the example, in a pairwise fashion and if *both* operands have a $1$ in the *same* position, the resulting integer will have a $1$ in this position as well. Otherwise, the resulting integer will have a $0$ in this position. The binary representations of `11` and `13` both have $1$s in their respective first and fourth bits, which is why `bin(11 & 13)` evaluates to `Ob_1001` or `9`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 49,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "9"
+      ]
+     },
+     "execution_count": 49,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "11 & 13"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 50,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'0b1011 & 0b1101'"
+      ]
+     },
+     "execution_count": 50,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "bin(11) + \" & \" + bin(13)  # to show the operands' bits"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 51,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'0b1001'"
+      ]
+     },
+     "execution_count": 51,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "bin(11 & 13)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "`0b_1001` is the binary representation of `9`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 52,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "9"
+      ]
+     },
+     "execution_count": 52,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "0b_1001"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "The **bitwise OR** operator `|` evaluates to an `int` object whose bits are set to $1$ if the corresponding bits of either *one* or *both* operands are $1$. So in the example `9 | 13` only the second bit is $0$ for both operands, which is why the expression evaluates to `0b_1101` or `13`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 53,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "13"
+      ]
+     },
+     "execution_count": 53,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "9 | 13"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 54,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'0b1001 | 0b1101'"
+      ]
+     },
+     "execution_count": 54,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "bin(9) + \" | \" + bin(13)  # to show the operands' bits"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 55,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'0b1101'"
+      ]
+     },
+     "execution_count": 55,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "bin(9 | 13)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "`0b_1101` evaluates to an `int` object with the value `13`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 56,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "13"
+      ]
+     },
+     "execution_count": 56,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "0b_1101"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "The **bitwise XOR** operator `^` is a special case of the `|` operator in that it evaluates to an `int` object whose bits are set to $1$ if the corresponding bit of *exactly one* of the two operands is $1$. Colloquially, the \"X\" stands for \"exclusive.\" The `^` operator must *not* be confused with the exponentiation operator `**`! In the example, `9 ^ 13`, only the third bit differs between the two operands, which is why it evaluates to `0b_100` omitting the leading $0$."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 57,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "4"
+      ]
+     },
+     "execution_count": 57,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "9 ^ 13"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 58,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'0b1001 ^ 0b1101'"
+      ]
+     },
+     "execution_count": 58,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "bin(9) + \" ^ \" + bin(13)  # to show the operands' bits"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 59,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'0b100'"
+      ]
+     },
+     "execution_count": 59,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "bin(9 ^ 13)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "`0b_100` evaluates to an `int` object with the value `4`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 60,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "4"
+      ]
+     },
+     "execution_count": 60,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "0b_100"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "The **bitwise NOT** operator `~`, sometimes also called **inversion** operator, is said to \"flip\" the $0$s into $1$s and the $1$s into $0$s. However, it is based on the aforementioned [Two's Complement <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_wiki.png\">](https://en.wikipedia.org/wiki/Two%27s_complement) convention and `~x = -(x + 1)` by definition (cf., the [reference <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/reference/expressions.html#unary-arithmetic-and-bitwise-operations)). The full logic behind this, while actually quite simple, is considered out of scope in this book.\n",
+    "\n",
+    "We can at least verify the definition by comparing the binary representations of `7` and `-8`: They are indeed the same."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 61,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "-8"
+      ]
+     },
+     "execution_count": 61,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "~7"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 62,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 62,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "~7 == -(7 + 1)  # = Two's Complement"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 63,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'-0b1000'"
+      ]
+     },
+     "execution_count": 63,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "bin(~7)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 64,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'-0b1000'"
+      ]
+     },
+     "execution_count": 64,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "bin(-(7 + 1))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "`~x = -(x + 1)` can be reformulated as `~x + x = -1`, which is slightly easier to check."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 65,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "-1"
+      ]
+     },
+     "execution_count": 65,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "~7 + 7"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Lastly, the **bitwise left and right shift** operators, `<<` and `>>`, shift all the bits either to the left or to the right. This corresponds to multiplying or dividing an integer by powers of `2`.\n",
+    "\n",
+    "When shifting left, $0$s are filled in."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 66,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "28"
+      ]
+     },
+     "execution_count": 66,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "7 << 2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 67,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'0b111'"
+      ]
+     },
+     "execution_count": 67,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "bin(7)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 68,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'0b11100'"
+      ]
+     },
+     "execution_count": 68,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "bin(7 << 2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 69,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "28"
+      ]
+     },
+     "execution_count": 69,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "0b_1_1100"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "When shifting right, some bits are always lost."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 70,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "3"
+      ]
+     },
+     "execution_count": 70,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "7 >> 1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 71,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'0b111'"
+      ]
+     },
+     "execution_count": 71,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "bin(7)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 72,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'0b11'"
+      ]
+     },
+     "execution_count": 72,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "bin(7 >> 1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 73,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "3"
+      ]
+     },
+     "execution_count": 73,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "0b_11"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.6"
+  },
+  "livereveal": {
+   "auto_select": "code",
+   "auto_select_fragment": true,
+   "scroll": true,
+   "theme": "serif"
+  },
+  "toc": {
+   "base_numbering": 1,
+   "nav_menu": {},
+   "number_sections": false,
+   "sideBar": true,
+   "skip_h1_title": true,
+   "title_cell": "Table of Contents",
+   "title_sidebar": "Contents",
+   "toc_cell": false,
+   "toc_position": {
+    "height": "calc(100% - 180px)",
+    "left": "10px",
+    "top": "150px",
+    "width": "384px"
+   },
+   "toc_section_display": false,
+   "toc_window_display": false
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/README.md b/README.md
index bb5114f..1afc65c 100644
--- a/README.md
+++ b/README.md
@@ -112,6 +112,14 @@ Alternatively, the content can be viewed in a web browser
       (Throwing Dice)
     - [summary <img height="12" style="display: inline-block" src="static/link/to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/04_iteration/05_summary.ipynb)
     - [review questions <img height="12" style="display: inline-block" src="static/link/to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/04_iteration/06_review.ipynb)
+- **Part B: Managing Data and Memory**
+  - *Chapter 5*: Numbers & Bits
+    - [content <img height="12" style="display: inline-block" src="static/link/to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/05_numbers/00_content.ipynb)
+      [<img height="12" style="display: inline-block" src="static/link/to_mb.png">](https://mybinder.org/v2/gh/webartifex/intro-to-python/develop?urlpath=lab/tree/05_numbers/00_content.ipynb)
+      (`int` Type;
+       Binary & Hexadecimal Representations;
+       Bit Arithmetic;
+       Bitwise Operators)
 
 
 #### Videos

From 4d8d6fe240c2abfb46e23ba84b38ba62e31f0955 Mon Sep 17 00:00:00 2001
From: Alexander Hess <alexander@webartifex.biz>
Date: Thu, 15 Oct 2020 23:36:37 +0200
Subject: [PATCH 163/242] Fix wrong numbering and missing Binder link

---
 01_elements/07_resources.ipynb |  7 +++++++
 README.md                      | 12 ++++++------
 2 files changed, 13 insertions(+), 6 deletions(-)

diff --git a/01_elements/07_resources.ipynb b/01_elements/07_resources.ipynb
index c73cc49..9d46189 100644
--- a/01_elements/07_resources.ipynb
+++ b/01_elements/07_resources.ipynb
@@ -1,5 +1,12 @@
 {
  "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Note**: Click on \"*Kernel*\" > \"*Restart Kernel and Clear All Outputs*\" in [JupyterLab](https://jupyterlab.readthedocs.io/en/stable/) *before* reading this notebook to reset its output. If you cannot run this file on your machine, you may want to open it [in the cloud <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_mb.png\">](https://mybinder.org/v2/gh/webartifex/intro-to-python/develop?urlpath=lab/tree/01_elements/07_resources.ipynb)."
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {
diff --git a/README.md b/README.md
index 1afc65c..b553332 100644
--- a/README.md
+++ b/README.md
@@ -46,13 +46,13 @@ Alternatively, the content can be viewed in a web browser
        Variables & References;
        Mutability;
        Expressions & Statements)
-    - [exercises <img height="12" style="display: inline-block" src="static/link/to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/01_elements/03_exercises.ipynb)
-      [<img height="12" style="display: inline-block" src="static/link/to_mb.png">](https://mybinder.org/v2/gh/webartifex/intro-to-python/develop?urlpath=lab/tree/01_elements/03_exercises.ipynb)
+    - [exercises <img height="12" style="display: inline-block" src="static/link/to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/01_elements/04_exercises.ipynb)
+      [<img height="12" style="display: inline-block" src="static/link/to_mb.png">](https://mybinder.org/v2/gh/webartifex/intro-to-python/develop?urlpath=lab/tree/01_elements/04_exercises.ipynb)
       (Python as a Calculator)
-    - [summary <img height="12" style="display: inline-block" src="static/link/to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/01_elements/04_summary.ipynb)
-    - [review questions <img height="12" style="display: inline-block" src="static/link/to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/01_elements/05_review.ipynb)
-    - [further resources <img height="12" style="display: inline-block" src="static/link/to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/01_elements/06_resources.ipynb)
-      [<img height="12" style="display: inline-block" src="static/link/to_mb.png">](https://mybinder.org/v2/gh/webartifex/intro-to-python/develop?urlpath=lab/tree/01_elements/06_resources.ipynb)
+    - [summary <img height="12" style="display: inline-block" src="static/link/to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/01_elements/05_summary.ipynb)
+    - [review questions <img height="12" style="display: inline-block" src="static/link/to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/01_elements/06_review.ipynb)
+    - [further resources <img height="12" style="display: inline-block" src="static/link/to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/01_elements/07_resources.ipynb)
+      [<img height="12" style="display: inline-block" src="static/link/to_mb.png">](https://mybinder.org/v2/gh/webartifex/intro-to-python/develop?urlpath=lab/tree/01_elements/07_resources.ipynb)
   - *Chapter 2*: Functions & Modularization
     - [content <img height="12" style="display: inline-block" src="static/link/to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/02_functions/00_content.ipynb)
       [<img height="12" style="display: inline-block" src="static/link/to_mb.png">](https://mybinder.org/v2/gh/webartifex/intro-to-python/develop?urlpath=lab/tree/02_functions/00_content.ipynb)

From 1923297b0d76ac6b2c4865afddc4ddfdd1c0eb3c Mon Sep 17 00:00:00 2001
From: Alexander Hess <alexander@webartifex.biz>
Date: Thu, 15 Oct 2020 23:44:16 +0200
Subject: [PATCH 164/242] Add initial version of chapter 05, part 2

---
 05_numbers/01_content.ipynb | 2180 +++++++++++++++++++++++++++++++++++
 README.md                   |    7 +
 2 files changed, 2187 insertions(+)
 create mode 100644 05_numbers/01_content.ipynb

diff --git a/05_numbers/01_content.ipynb b/05_numbers/01_content.ipynb
new file mode 100644
index 0000000..af18f07
--- /dev/null
+++ b/05_numbers/01_content.ipynb
@@ -0,0 +1,2180 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "**Note**: Click on \"*Kernel*\" > \"*Restart Kernel and Clear All Outputs*\" in [JupyterLab](https://jupyterlab.readthedocs.io/en/stable/) *before* reading this notebook to reset its output. If you cannot run this file on your machine, you may want to open it [in the cloud <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_mb.png\">](https://mybinder.org/v2/gh/webartifex/intro-to-python/develop?urlpath=lab/tree/05_numbers/01_content.ipynb)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "# Chapter 5: Numbers & Bits (continued)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "In this second part of the chapter, we look at the `float` type in detail. It is probably the most commonly used one in all of data science, even across programming languages."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "## The `float` Type"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "As we have seen before, some assumptions need to be made as to how the $0$s and $1$s in a computer's memory are to be translated into numbers. This process becomes a lot more involved when we go beyond integers and model [real numbers <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_wiki.png\">](https://en.wikipedia.org/wiki/Real_number) (i.e., the set $\\mathbb{R}$) with possibly infinitely many digits to the right of the period like $1.23$.\n",
+    "\n",
+    "The **[Institute of Electrical and Electronics Engineers <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_wiki.png\">](https://en.wikipedia.org/wiki/Institute_of_Electrical_and_Electronics_Engineers)** (IEEE, pronounced \"eye-triple-E\") is one of the important professional associations when it comes to standardizing all kinds of aspects regarding the implementation of soft- and hardware.\n",
+    "\n",
+    "The **[IEEE 754 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_wiki.png\">](https://en.wikipedia.org/wiki/IEEE_754)** standard defines the so-called **floating-point arithmetic** that is commonly used today by all major programming languages. The standard not only defines how the $0$s and $1$s are organized in memory but also, for example, how values are to be rounded, what happens in exceptional cases like divisions by zero, or what is a zero value in the first place.\n",
+    "\n",
+    "In Python, the simplest way to create a `float` object is to use a literal notation with a dot `.` in it."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "b = 42.0"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "139923238853936"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "id(b)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "float"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "type(b)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "42.0"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "b"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "As with `int` literals, we may use underscores `_` to make longer `float` objects easier to read."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.123456789"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "0.123_456_789"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "In cases where the dot `.` is unnecessary from a mathematical point of view, we either need to end the number with it nevertheless or use the [float() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#float) built-in to cast the number explicitly. [float() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#float) can process any numeric object or a properly formatted `str` object."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "42.0"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "42."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "42.0"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "float(42)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "42.0"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "float(\"42\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Leading and trailing whitespace is ignored ..."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "42.87"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "float(\" 42.87 \")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "... but not whitespace in between."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "ename": "ValueError",
+     "evalue": "could not convert string to float: '42. 87'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-10-c7953b8b7956>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mfloat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"42. 87\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;31mValueError\u001b[0m: could not convert string to float: '42. 87'"
+     ]
+    }
+   ],
+   "source": [
+    "float(\"42. 87\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "`float` objects are implicitly created as the result of dividing an `int` object by another with the division operator `/`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.3333333333333333"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "1 / 3"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "In general, if we combine `float` and `int` objects in arithmetic operations, we always end up with a `float` type: Python uses the \"broader\" representation."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "42.0"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "40.0 + 2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "42.0"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "21 * 2.0"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "### Scientific Notation"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "`float` objects may also be created with the **scientific literal notation**: We use the symbol `e` to indicate powers of $10$, so $1.23 * 10^0$ translates into `1.23e0`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "1.23"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "1.23e0"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Syntactically, `e` needs a `float` or `int` object in its literal notation on its left and an `int` object on its right, both without a space. Otherwise, we get a `SyntaxError`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "ename": "SyntaxError",
+     "evalue": "invalid syntax (<ipython-input-15-1b5daaac0077>, line 1)",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;36m  File \u001b[0;32m\"<ipython-input-15-1b5daaac0077>\"\u001b[0;36m, line \u001b[0;32m1\u001b[0m\n\u001b[0;31m    1.23 e0\u001b[0m\n\u001b[0m         ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m invalid syntax\n"
+     ]
+    }
+   ],
+   "source": [
+    "1.23 e0"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "ename": "SyntaxError",
+     "evalue": "invalid syntax (<ipython-input-16-a236c4a30231>, line 1)",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;36m  File \u001b[0;32m\"<ipython-input-16-a236c4a30231>\"\u001b[0;36m, line \u001b[0;32m1\u001b[0m\n\u001b[0;31m    1.23e 0\u001b[0m\n\u001b[0m        ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m invalid syntax\n"
+     ]
+    }
+   ],
+   "source": [
+    "1.23e 0"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "ename": "SyntaxError",
+     "evalue": "invalid syntax (<ipython-input-17-05b9072d76be>, line 1)",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;36m  File \u001b[0;32m\"<ipython-input-17-05b9072d76be>\"\u001b[0;36m, line \u001b[0;32m1\u001b[0m\n\u001b[0;31m    1.23e0.0\u001b[0m\n\u001b[0m          ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m invalid syntax\n"
+     ]
+    }
+   ],
+   "source": [
+    "1.23e0.0"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "If we leave out the number to the left, Python raises a `NameError` as it unsuccessfully tries to look up a variable named `e0`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "ename": "NameError",
+     "evalue": "name 'e0' is not defined",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-18-abc4de6ccb5e>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0me0\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;31mNameError\u001b[0m: name 'e0' is not defined"
+     ]
+    }
+   ],
+   "source": [
+    "e0"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "So, to write $10^0$ in Python, we need to think of it as $1*10^0$ and write `1e0`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "1.0"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "1e0"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "To express thousands of something (i.e., $10^3$), we write `1e3`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "1000.0"
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "1e3  # = thousands"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Similarly, to express, for example, milliseconds (i.e., $10^{-3} s$), we write `1e-3`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.001"
+      ]
+     },
+     "execution_count": 21,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "1e-3  # = milli"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "## Special Values"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "There are also three special values representing \"**not a number,**\" called `nan`, and positive or negative **infinity**, called `inf` or `-inf`, that are created by passing in the corresponding abbreviation as a `str` object to the [float() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#float) built-in. These values could be used, for example, as the result of a mathematically undefined operation like division by zero or to model the value of a mathematical function as it goes to infinity."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "nan"
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "float(\"nan\")  # also float(\"NaN\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "inf"
+      ]
+     },
+     "execution_count": 23,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "float(\"+inf\")  # also float(\"+infinity\") or float(\"infinity\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "inf"
+      ]
+     },
+     "execution_count": 24,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "float(\"inf\")  # also float(\"+inf\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "-inf"
+      ]
+     },
+     "execution_count": 25,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "float(\"-inf\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "`nan` objects *never* compare equal to *anything*, not even to themselves. This happens in accordance with the [IEEE 754 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_wiki.png\">](https://en.wikipedia.org/wiki/IEEE_754) standard."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "False"
+      ]
+     },
+     "execution_count": 26,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "float(\"nan\") == float(\"nan\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Another caveat is that any arithmetic involving a `nan` object results in `nan`. In other words, the addition below **fails silently** as no error is raised. As this also happens in accordance with the [IEEE 754 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_wiki.png\">](https://en.wikipedia.org/wiki/IEEE_754) standard, we *need* to be aware of that and check any data we work with for any `nan` occurrences *before* doing any calculations."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "nan"
+      ]
+     },
+     "execution_count": 27,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "42 + float(\"nan\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "On the contrary, as two values go to infinity, there is no such concept as difference and *everything* compares equal."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 28,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "float(\"inf\") == float(\"inf\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Adding `42` to `inf` makes no difference."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "inf"
+      ]
+     },
+     "execution_count": 29,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "float(\"inf\") + 42"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 30,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "float(\"inf\") + 42 == float(\"inf\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "We observe the same for multiplication ..."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "inf"
+      ]
+     },
+     "execution_count": 31,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "42 * float(\"inf\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 32,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "42 * float(\"inf\") == float(\"inf\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "... and even exponentiation!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "inf"
+      ]
+     },
+     "execution_count": 33,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "float(\"inf\") ** 42"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 34,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "float(\"inf\") ** 42 == float(\"inf\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Although absolute differences become unmeaningful as we approach infinity, signs are still respected."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "inf"
+      ]
+     },
+     "execution_count": 35,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "-42 * float(\"-inf\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 36,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "-42 * float(\"-inf\") == float(\"inf\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "As a caveat, adding infinities of different signs is an *undefined operation* in math and results in a `nan` object. So, if we (accidentally or unknowingly) do this on a real dataset, we do *not* see any error messages, and our program may continue to run with non-meaningful results! This is another example of a piece of code **failing silently**."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "nan"
+      ]
+     },
+     "execution_count": 37,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "float(\"inf\") + float(\"-inf\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "nan"
+      ]
+     },
+     "execution_count": 38,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "float(\"inf\") - float(\"inf\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "## Imprecision"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "`float` objects are *inherently* imprecise, and there is *nothing* we can do about it! In particular, arithmetic operations with two `float` objects may result in \"weird\" rounding \"errors\" that are strictly deterministic and occur in accordance with the [IEEE 754 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_wiki.png\">](https://en.wikipedia.org/wiki/IEEE_754) standard.\n",
+    "\n",
+    "For example, let's add `1` to `1e15` and `1e16`, respectively. In the latter case, the `1` somehow gets \"lost.\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "1000000000000001.0"
+      ]
+     },
+     "execution_count": 39,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "1e15 + 1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "1e+16"
+      ]
+     },
+     "execution_count": 40,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "1e16 + 1"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Interactions between sufficiently large and small `float` objects are not the only source of imprecision."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "from math import sqrt"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "2.0000000000000004"
+      ]
+     },
+     "execution_count": 42,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "sqrt(2) ** 2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.30000000000000004"
+      ]
+     },
+     "execution_count": 43,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "0.1 + 0.2"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "This may become a problem if we rely on equality checks in our programs."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "False"
+      ]
+     },
+     "execution_count": 44,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "sqrt(2) ** 2 == 2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "False"
+      ]
+     },
+     "execution_count": 45,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "0.1 + 0.2 == 0.3"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "A popular workaround is to benchmark the absolute value of the difference between the two numbers to be checked for equality against a pre-defined `threshold` *sufficiently* close to `0`, for example, `1e-15`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "threshold = 1e-15"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 47,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "abs((sqrt(2) ** 2) - 2) < threshold"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 48,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 48,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "abs((0.1 + 0.2) - 0.3) < threshold"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "The built-in [format() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#format) function allows us to show the **significant digits** of a `float` number as they exist in memory to arbitrary precision. To exemplify it, let's view a couple of `float` objects with `50` digits. This analysis reveals that almost no `float` number is precise! After 14 or 15 digits \"weird\" things happen. As we see further below, the \"random\" digits ending the `float` numbers do *not* \"physically\" exist in memory! Rather, they are \"calculated\" by the [format() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#format) function that is forced to show `50` digits.\n",
+    "\n",
+    "The [format() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#format) function is different from the [format() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/stdtypes.html#str.format) method on `str` objects introduced in the next chapter (cf., [Chapter 6 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/06_text/00_content.ipynb#format%28%29-Method)): Yet, both work with the so-called [format specification mini-language <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/string.html#format-specification-mini-language): `\".50f\"` is the instruction to show `50` digits of a `float` number."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 49,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'0.10000000000000000555111512312578270211815834045410'"
+      ]
+     },
+     "execution_count": 49,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "format(0.1, \".50f\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 50,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'0.20000000000000001110223024625156540423631668090820'"
+      ]
+     },
+     "execution_count": 50,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "format(0.2, \".50f\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 51,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'0.29999999999999998889776975374843459576368331909180'"
+      ]
+     },
+     "execution_count": 51,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "format(0.3, \".50f\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 52,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'0.33333333333333331482961625624739099293947219848633'"
+      ]
+     },
+     "execution_count": 52,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "format(1 / 3, \".50f\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "The [format() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#format) function does *not* round a `float` object in the mathematical sense! It just allows us to show an arbitrary number of the digits as stored in memory, and it also does *not* change these.\n",
+    "\n",
+    "On the contrary, the built-in [round() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#round) function creates a *new* numeric object that is a rounded version of the one passed in as the argument. It adheres to the common rules of math.\n",
+    "\n",
+    "For example, let's round `1 / 3` to five decimals. The obtained value for `roughly_a_third` is also *imprecise* but different from the \"exact\" representation of `1 / 3` above."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 53,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "roughly_a_third = round(1 / 3, 5)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 54,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.33333"
+      ]
+     },
+     "execution_count": 54,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "roughly_a_third"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 55,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'0.33333000000000001517008740847813896834850311279297'"
+      ]
+     },
+     "execution_count": 55,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "format(roughly_a_third, \".50f\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Surprisingly, `0.125` and `0.25` appear to be *precise*, and equality comparison works without the `threshold` workaround: Both are powers of $2$ in disguise."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 56,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'0.12500000000000000000000000000000000000000000000000'"
+      ]
+     },
+     "execution_count": 56,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "format(0.125, \".50f\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 57,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'0.25000000000000000000000000000000000000000000000000'"
+      ]
+     },
+     "execution_count": 57,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "format(0.25, \".50f\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 58,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 58,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "0.125 + 0.125 == 0.25"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "## Binary Representations"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "To understand these subtleties, we need to look at the **[binary representation of floats <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_wiki.png\">](https://en.wikipedia.org/wiki/Double-precision_floating-point_format)** and review the basics of the **[IEEE 754 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_wiki.png\">](https://en.wikipedia.org/wiki/IEEE_754)** standard. On modern machines, floats are modeled in so-called double precision with $64$ bits that are grouped as in the figure below. The first bit determines the sign ($0$ for plus, $1$ for minus), the next $11$ bits represent an $exponent$ term, and the last $52$ bits resemble the actual significant digits, the so-called $fraction$ part. The three groups are put together like so:"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "$$float = (-1)^{sign} * 1.fraction * 2^{exponent-1023}$$"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "A $1.$ is implicitly prepended as the first digit, and both, $fraction$ and $exponent$, are stored in base $2$ representation (i.e., they both are interpreted like integers above). As $exponent$ is consequently non-negative, between $0_{10}$ and $2047_{10}$ to be precise, the $-1023$, called the exponent bias, centers the entire $2^{exponent-1023}$ term around $1$ and allows the period within the $1.fraction$ part be shifted into either direction by the same amount. Floating-point numbers received their name as the period, formally called the **[radix point <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_wiki.png\">](https://en.wikipedia.org/wiki/Radix_point)**, \"floats\" along the significant digits. As an aside, an $exponent$ of all $0$s or all $1$s is used to model the special values `nan` or `inf`.\n",
+    "\n",
+    "As the standard defines the exponent part to come as a power of $2$, we now see why `0.125` is a *precise* float: It can be represented as a power of $2$, i.e., $0.125 = (-1)^0 * 1.0 * 2^{1020-1023} = 2^{-3} = \\frac{1}{8}$. In other words, the floating-point representation of $0.125_{10}$ is $0_2$, $1111111100_2 = 1020_{10}$, and $0_2$ for the three groups, respectively."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "source": [
+    "<img src=\"static/floating_point.png\" width=\"85%\" align=\"center\">"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "The crucial fact for the data science practitioner to understand is that mapping the *infinite* set of the real numbers $\\mathbb{R}$ to a *finite* set of bits leads to the imprecisions shown above!\n",
+    "\n",
+    "So, floats are usually good approximations of real numbers only with their first $14$ or $15$ digits. If more precision is required, we need to revert to other data types such as a `Decimal` or a `Fraction`, as shown in the next two sections.\n",
+    "\n",
+    "This [blog post](http://fabiensanglard.net/floating_point_visually_explained/) gives another neat and *visual* way as to how to think of floats. It also explains why floats become worse approximations of the reals as their absolute values increase.\n",
+    "\n",
+    "The Python [documentation <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/tutorial/floatingpoint.html) provides another good discussion of floats and the goodness of their approximations.\n",
+    "\n",
+    "If we are interested in the exact bits behind a `float` object, we use the [hex() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/stdtypes.html#float.hex) method that returns a `str` object beginning with `\"0x1.\"` followed by the $fraction$ in hexadecimal notation and the $exponent$ as an integer after subtraction of $1023$ and separated by a `\"p\"`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 59,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "one_eighth = 1 / 8"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 60,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'0x1.0000000000000p-3'"
+      ]
+     },
+     "execution_count": 60,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "one_eighth.hex()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Also, the [as_integer_ratio() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/stdtypes.html#float.as_integer_ratio) method returns the two smallest integers whose ratio best approximates a `float` object."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 61,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(1, 8)"
+      ]
+     },
+     "execution_count": 61,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "one_eighth.as_integer_ratio()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 62,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'0x1.555475a31a4bep-2'"
+      ]
+     },
+     "execution_count": 62,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "roughly_a_third.hex()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 63,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(3002369727582815, 9007199254740992)"
+      ]
+     },
+     "execution_count": 63,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "roughly_a_third.as_integer_ratio()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "`0.0` is also a power of $2$ and thus a *precise* `float` number."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 64,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "zero = 0.0"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 65,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'0x0.0p+0'"
+      ]
+     },
+     "execution_count": 65,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "zero.hex()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 66,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(0, 1)"
+      ]
+     },
+     "execution_count": 66,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "zero.as_integer_ratio()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "As seen in [Chapter 1 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/01_elements/00_content.ipynb#%28Data%29-Type-%2F-%22Behavior%22), the [is_integer() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/stdtypes.html#float.is_integer) method tells us if a `float` can be casted as an `int` object without any loss in precision."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 67,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "False"
+      ]
+     },
+     "execution_count": 67,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "roughly_a_third.is_integer()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 68,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 68,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "one = roughly_a_third / roughly_a_third\n",
+    "\n",
+    "one.is_integer()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "As the exact implementation of floats may vary and be dependent on a particular Python installation, we look up the [float_info <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/sys.html#sys.float_info) attribute in the [sys <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/sys.html) module in the [standard library <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/index.html) to check the details. Usually, this is not necessary."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 69,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "import sys"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 70,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "sys.float_info(max=1.7976931348623157e+308, max_exp=1024, max_10_exp=308, min=2.2250738585072014e-308, min_exp=-1021, min_10_exp=-307, dig=15, mant_dig=53, epsilon=2.220446049250313e-16, radix=2, rounds=1)"
+      ]
+     },
+     "execution_count": 70,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "sys.float_info"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.6"
+  },
+  "livereveal": {
+   "auto_select": "code",
+   "auto_select_fragment": true,
+   "scroll": true,
+   "theme": "serif"
+  },
+  "toc": {
+   "base_numbering": 1,
+   "nav_menu": {},
+   "number_sections": false,
+   "sideBar": true,
+   "skip_h1_title": true,
+   "title_cell": "Table of Contents",
+   "title_sidebar": "Contents",
+   "toc_cell": false,
+   "toc_position": {
+    "height": "calc(100% - 180px)",
+    "left": "10px",
+    "top": "150px",
+    "width": "384px"
+   },
+   "toc_section_display": false,
+   "toc_window_display": false
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/README.md b/README.md
index b553332..66d7135 100644
--- a/README.md
+++ b/README.md
@@ -120,6 +120,13 @@ Alternatively, the content can be viewed in a web browser
        Binary & Hexadecimal Representations;
        Bit Arithmetic;
        Bitwise Operators)
+    - [content <img height="12" style="display: inline-block" src="static/link/to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/05_numbers/01_content.ipynb)
+      [<img height="12" style="display: inline-block" src="static/link/to_mb.png">](https://mybinder.org/v2/gh/webartifex/intro-to-python/develop?urlpath=lab/tree/05_numbers/01_content.ipynb)
+      (`float` Type;
+       Floating-point Standard;
+       Special Values;
+       Imprecision;
+       Binary & Hexadecimal Representations)
 
 
 #### Videos

From bf168d49706a404b6e7ab550b76e116ac23cf15b Mon Sep 17 00:00:00 2001
From: Alexander Hess <alexander@webartifex.biz>
Date: Fri, 16 Oct 2020 00:00:32 +0200
Subject: [PATCH 165/242] Add initial version of chapter 05, part 3

---
 05_numbers/02_content.ipynb | 1515 +++++++++++++++++++++++++++++++++++
 README.md                   |    5 +
 2 files changed, 1520 insertions(+)
 create mode 100644 05_numbers/02_content.ipynb

diff --git a/05_numbers/02_content.ipynb b/05_numbers/02_content.ipynb
new file mode 100644
index 0000000..89ff220
--- /dev/null
+++ b/05_numbers/02_content.ipynb
@@ -0,0 +1,1515 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "**Note**: Click on \"*Kernel*\" > \"*Restart Kernel and Clear All Outputs*\" in [JupyterLab](https://jupyterlab.readthedocs.io/en/stable/) *before* reading this notebook to reset its output. If you cannot run this file on your machine, you may want to open it [in the cloud <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_mb.png\">](https://mybinder.org/v2/gh/webartifex/intro-to-python/develop?urlpath=lab/tree/05_numbers/02_content.ipynb)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "# Chapter 5: Numbers & Bits (continued)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "In the third part of this chapter, we first look at the lesser known `complex` type. Then, we introduce a more abstract classification scheme for the numeric types."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "## The `complex` Type"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "**What is the solution to $x^2 = -1$ ?**"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Some mathematical equations cannot be solved if the solution has to be in the set of the real numbers $\\mathbb{R}$. For example, $x^2 = -1$ can be rearranged into $x = \\sqrt{-1}$, but the square root is not defined for negative numbers. To mitigate this, mathematicians introduced the concept of an [imaginary number <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_wiki.png\">](https://en.wikipedia.org/wiki/Imaginary_number) $\\textbf{i}$ that is *defined* as $\\textbf{i} = \\sqrt{-1}$ or often as the solution to the equation $\\textbf{i}^2 = -1$. So, the solution to $x = \\sqrt{-1}$ then becomes $x = \\textbf{i}$.\n",
+    "\n",
+    "If we generalize the example equation into $(mx-n)^2 = -1 \\implies x = \\frac{1}{m}(\\sqrt{-1} + n)$ where $m$ and $n$ are constants chosen from the reals $\\mathbb{R}$, then the solution to the equation comes in the form $x = a + b\\textbf{i}$, the sum of a real number and an imaginary number, with $a=\\frac{n}{m}$ and $b = \\frac{1}{m}$.\n",
+    "\n",
+    "Such \"compound\" numbers are called **[complex numbers <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_wiki.png\">](https://en.wikipedia.org/wiki/Complex_number)**, and the set of all such numbers is commonly denoted by $\\mathbb{C}$. The reals $\\mathbb{R}$ are a strict subset of $\\mathbb{C}$ with $b=0$. Further, $a$ is referred to as the **real part** and $b$ as the **imaginary part** of the complex number.\n",
+    "\n",
+    "Complex numbers are often visualized in a plane like below, where the real part is depicted on the x-axis and the imaginary part on the y-axis."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
+   "source": [
+    "<img src=\"static/complex_numbers.png\" width=\"25%\" align=\"center\">"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "`complex` numbers are part of core Python. The simplest way to create one is to write an arithmetic expression with the literal `j` notation for $\\textbf{i}$. The `j` is commonly used in many engineering disciplines instead of the symbol $\\textbf{i}$ from math as $I$ in engineering more often than not means [electric current <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_wiki.png\">](https://en.wikipedia.org/wiki/Electric_current).\n",
+    "\n",
+    "For example, the answer to $x^2 = -1$ can be written in Python as `1j` like below. This creates a `complex` object with value `1j`. The same syntactic rules apply as with the above `e` notation: No spaces are allowed between the number and the `j`. The number may be any `int` or `float` literal; however, it is stored as a `float` internally. So, `complex` numbers suffer from the same imprecision as `float` numbers."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "x = 1j"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "140084727448400"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "id(x)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "complex"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "type(x)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "1j"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "x"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "To verify that it solves the equation, let's raise it to the power of $2$."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "x ** 2 == -1"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Often, we write an expression of the form $a + b\\textbf{i}$."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(2+0.5j)"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "2 + 0.5j"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Alternatively, we may use the [complex() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#complex) built-in: This takes two parameters where the second is optional and defaults to `0`. We may either call it with one or two arguments of any numeric type or a `str` object in the format of the previous code cell without any spaces."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(2+0.5j)"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "complex(2, 0.5)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "By omitting the second argument, we set the imaginary part to $0$."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(2+0j)"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "complex(2)  "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "The arguments to [complex() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#complex) may be any numeric type or properly formated `str` object."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(2+0.5j)"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "complex(\"2+0.5j\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Arithmetic expressions work with `complex` numbers. They may be mixed with the other numeric types, and the result is always a `complex` number."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "c1 = 1 + 2j\n",
+    "c2 = 3 + 4j"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(4+6j)"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "c1 + c2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(-2-2j)"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "c1 - c2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(2+2j)"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "c1 + 1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(0.5-4j)"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "3.5 - c2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(5+10j)"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "5 * c1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(0.5+0.6666666666666666j)"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "c2 / 6"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(-5+10j)"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "c1 * c2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(0.44+0.08j)"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "c1 / c2"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "A `complex` number comes with two **attributes** `real` and `imag` that return the two parts as `float` objects on their own."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.0"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "x.real"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "1.0"
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "x.imag"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Also, a `conjugate()` method is bound to every `complex` object. The [complex conjugate <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_wiki.png\">](https://en.wikipedia.org/wiki/Complex_conjugate) is defined to be the complex number with identical real part but an imaginary part reversed in sign."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "-1j"
+      ]
+     },
+     "execution_count": 21,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "x.conjugate()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "The [cmath <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/cmath.html) module in the [standard library <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/index.html) implements many of the functions from the [math <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/math.html) module such that they work with complex numbers."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "## The Numerical Tower"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Analogous to the discussion of *containers* and *iterables* in [Chapter 4 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/04_iteration/02_content.ipynb#Containers-vs.-Iterables), we contrast the *concrete* numeric data types in this chapter with the *abstract* ideas behind [numbers in mathematics <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_wiki.png\">](https://en.wikipedia.org/wiki/Number).\n",
+    "\n",
+    "The figure below summarizes five *major* sets of [numbers in mathematics <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_wiki.png\">](https://en.wikipedia.org/wiki/Number) as we know them from high school:\n",
+    "\n",
+    "- $\\mathbb{N}$: [Natural numbers <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_wiki.png\">](https://en.wikipedia.org/wiki/Natural_number) are all non-negative count numbers, e.g., $0, 1, 2, ...$\n",
+    "- $\\mathbb{Z}$: [Integers <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_wiki.png\">](https://en.wikipedia.org/wiki/Integer) are all numbers *without* a fractional component, e.g., $-1, 0, 1, ...$\n",
+    "- $\\mathbb{Q}$: [Rational numbers <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_wiki.png\">](https://en.wikipedia.org/wiki/Rational_number) are all numbers that can be expressed as a quotient of two integers, e.g., $-\\frac{1}{2}, 0, \\frac{1}{2}, ...$\n",
+    "- $\\mathbb{R}$: [Real numbers <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_wiki.png\">](https://en.wikipedia.org/wiki/Real_number) are all numbers that can be represented as a distance along a line, and negative means \"reversed,\" e.g., $\\sqrt{2}, \\pi, \\text{e}, ...$\n",
+    "- $\\mathbb{C}$: [Complex numbers <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_wiki.png\">](https://en.wikipedia.org/wiki/Complex_number) are all numbers of the form $a + b\\textbf{i}$ where $a$ and $b$ are real numbers and $\\textbf{i}$ is the [imaginary number <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_wiki.png\">](https://en.wikipedia.org/wiki/Imaginary_number), e.g., $0, \\textbf{i}, 1 + \\textbf{i}, ...$\n",
+    "\n",
+    "In the listed order, the five sets are perfect subsets of the respective following sets, and $\\mathbb{C}$ is the largest set. To be precise, all sets are infinite, but they still have a different number of elements."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "<img src=\"static/numbers.png\" width=\"75%\" align=\"center\">"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "The data types introduced in this chapter and its appendix are all *(im)perfect* models of *abstract* mathematical ideas.\n",
+    "\n",
+    "The `int` and `Fraction` types are the models \"closest\" to the idea they implement: Whereas $\\mathbb{Z}$ and $\\mathbb{Q}$ are, by definition, infinite, every computer runs out of bits when representing sufficiently large integers or fractions with a sufficiently large number of decimals. However, within a system-dependent range, we can model an integer or fraction without any loss in precision.\n",
+    "\n",
+    "For the other types, in particular, the `float` type, the implications of their imprecision are discussed in detail above.\n",
+    "\n",
+    "The abstract concepts behind the four outer-most mathematical sets are formalized in Python since [PEP 3141 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://www.python.org/dev/peps/pep-3141/) in 2007. The [numbers <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/numbers.html) module in the [standard library <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/index.html) defines what programmers call the **[numerical tower <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_wiki.png\">](https://en.wikipedia.org/wiki/Numerical_tower)**, a collection of five **[abstract data types <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_wiki.png\">](https://en.wikipedia.org/wiki/Abstract_data_type)**, or **abstract base classes** (ABCs) as they are called in Python jargon:\n",
+    "\n",
+    "- `Number`: \"any number\" (cf., [documentation <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/numbers.html#numbers.Number))\n",
+    "- `Complex`: \"all complex numbers\" (cf., [documentation <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/numbers.html#numbers.Complex))\n",
+    "- `Real`: \"all real numbers\" (cf., [documentation <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/numbers.html#numbers.Real))\n",
+    "- `Rational`: \"all rational numbers\" (cf., [documentation <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/numbers.html#numbers.Rational))\n",
+    "- `Integral`: \"all integers\" (cf., [documentation <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/numbers.html#numbers.Integral))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "import numbers"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['ABCMeta',\n",
+       " 'Complex',\n",
+       " 'Integral',\n",
+       " 'Number',\n",
+       " 'Rational',\n",
+       " 'Real',\n",
+       " '__all__',\n",
+       " '__builtins__',\n",
+       " '__cached__',\n",
+       " '__doc__',\n",
+       " '__file__',\n",
+       " '__loader__',\n",
+       " '__name__',\n",
+       " '__package__',\n",
+       " '__spec__',\n",
+       " 'abstractmethod']"
+      ]
+     },
+     "execution_count": 23,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "dir(numbers)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "## Duck Typing (continued)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "The primary purpose of ABCs is to classify the *concrete* data types and standardize how they behave. This guides us as the programmers in what kind of behavior we should expect from objects of a given data type. In this context, ABCs are not reflected in code but only in our heads.\n",
+    "\n",
+    "For, example, as all numeric data types are `Complex` numbers in the abstract sense, they all work with the built-in [abs() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#abs) function (cf., [documentation <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/numbers.html#numbers.Complex)). While it is intuitively clear what the [absolute value <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_wiki.png\">](https://en.wikipedia.org/wiki/Absolute_value) (i.e., \"distance\" from $0$) of an integer, a fraction, or any real number is, [abs() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#abs) calculates the equivalent of that for complex numbers. That concept is called the [magnitude <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_wiki.png\">](https://en.wikipedia.org/wiki/Magnitude_%28mathematics%29) of a number, and is really a *generalization* of the absolute value.\n",
+    "\n",
+    "Relating back to the concept of **duck typing** mentioned in [Chapter 4 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/04_iteration/00_content.ipynb#Duck-Typing), `int`, `float`, and `complex` objects \"walk\" and \"quack\" alike in context of the [abs() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#abs) function."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "1"
+      ]
+     },
+     "execution_count": 24,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "abs(-1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "42.87"
+      ]
+     },
+     "execution_count": 25,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "abs(-42.87)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "The absolute value of a `complex` number $x$ is defined with the Pythagorean Theorem where $\\lVert x \\rVert = \\sqrt{a^2 + b^2}$ and $a$ and $b$ are the real and imaginary parts."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "5.0"
+      ]
+     },
+     "execution_count": 26,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "abs(3 + 4j)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "On the contrary, only `Real` numbers in the abstract sense may be rounded with the built-in [round() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#round) function."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "100"
+      ]
+     },
+     "execution_count": 27,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "round(123, -2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "42"
+      ]
+     },
+     "execution_count": 28,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "round(42.1)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "`Complex` numbers are two-dimensional. So, rounding makes no sense here and leads to a `TypeError`. So, in the context of the [round() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#round) function, `int` and `float` objects \"walk\" and \"quack\" alike whereas `complex` objects do not."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "ename": "TypeError",
+     "evalue": "type complex doesn't define __round__ method",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-29-e40cd01fb15a>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mround\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m1\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;36m2j\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;31mTypeError\u001b[0m: type complex doesn't define __round__ method"
+     ]
+    }
+   ],
+   "source": [
+    "round(1 + 2j)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "## Goose Typing"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Another way to use ABCs is in place of a *concrete* data type.\n",
+    "\n",
+    "For example, we may pass them as arguments to the built-in [isinstance() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#isinstance) function and check in which of the five mathematical sets the object `1 / 10` is."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 30,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "isinstance(1 / 10, float)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "A `float` object is a generic `Number` in the abstract sense but may also be seen as a `Complex` or `Real` number."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 31,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "isinstance(1 / 10, numbers.Number)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 32,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "isinstance(1 / 10, numbers.Complex)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 33,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "isinstance(1 / 10, numbers.Real)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Due to the `float` type's inherent imprecision, `1 / 10` is *not* a `Rational` number."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "False"
+      ]
+     },
+     "execution_count": 34,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "isinstance(1 / 10, numbers.Rational)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "However, if we model `1 / 10` as a `Fraction` object (cf., [Appendix <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/05_numbers/03_appendix.ipynb#The-Fraction-Type)), it is recognized as a `Rational` number."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from fractions import Fraction"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 36,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "isinstance(Fraction(\"1/10\"), numbers.Rational)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Replacing *concrete* data types with ABCs is particularly valuable in the context of \"type checking:\" The revised version of the `factorial()` function below allows its user to take advantage of *duck typing*: If a real but non-integer argument `n` is passed in, `factorial()` tries to cast `n` as an `int` object with the [int() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#int) built-in.\n",
+    "\n",
+    "Two popular and distinguished Pythonistas, [Luciano Ramalho <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_gh.png\">](https://github.com/ramalho) and [Alex Martelli <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_wiki.png\">](https://en.wikipedia.org/wiki/Alex_Martelli), coin the term **goose typing** to specifically mean using the built-in [isinstance() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#isinstance) function with an ABC (cf., Chapter 11 in this [book](https://www.amazon.com/Fluent-Python-Concise-Effective-Programming/dp/1491946008) or this [summary](https://dgkim5360.github.io/blog/python/2017/07/duck-typing-vs-goose-typing-pythonic-interfaces/) thereof)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "### Example: [Factorial <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_wiki.png\">](https://en.wikipedia.org/wiki/Factorial) (revisited)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "def factorial(n, *, strict=True):\n",
+    "    \"\"\"Calculate the factorial of a number.\n",
+    "\n",
+    "    Args:\n",
+    "        n (int): number to calculate the factorial for; must be positive\n",
+    "        strict (bool): if n must not contain decimals; defaults to True;\n",
+    "            if set to False, the decimals in n are ignored\n",
+    "\n",
+    "    Returns:\n",
+    "        factorial (int)\n",
+    "\n",
+    "    Raises:\n",
+    "        TypeError: if n is not an integer or cannot be cast as such\n",
+    "        ValueError: if n is negative\n",
+    "    \"\"\"\n",
+    "    if not isinstance(n, numbers.Integral):\n",
+    "        if isinstance(n, numbers.Real):\n",
+    "            if n != int(n) and strict:\n",
+    "                raise TypeError(\"n is not integer-like; it has non-zero decimals\")\n",
+    "            n = int(n)\n",
+    "        else:\n",
+    "            raise TypeError(\"Factorial is only defined for integers\")\n",
+    "\n",
+    "    if n < 0:\n",
+    "        raise ValueError(\"Factorial is not defined for negative integers\")\n",
+    "    elif n == 0:\n",
+    "        return 1\n",
+    "    return n * factorial(n - 1)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "`factorial()` works as before, but now also accepts, for example, `float` numbers."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "1"
+      ]
+     },
+     "execution_count": 38,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "factorial(0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "6"
+      ]
+     },
+     "execution_count": 39,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "factorial(3)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "6"
+      ]
+     },
+     "execution_count": 40,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "factorial(3.0)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "With the keyword-only argument `strict`, we can control whether or not a passed in `float` object may come with decimals that are then truncated. By default, this is not allowed and results in a `TypeError`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "ename": "TypeError",
+     "evalue": "n is not integer-like; it has non-zero decimals",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-41-188b816be8b6>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mfactorial\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m3.1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;32m<ipython-input-37-be6602f598f7>\u001b[0m in \u001b[0;36mfactorial\u001b[0;34m(n, strict)\u001b[0m\n\u001b[1;32m     17\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mn\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnumbers\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mReal\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     18\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mn\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0mint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mn\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mstrict\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 19\u001b[0;31m                 \u001b[0;32mraise\u001b[0m \u001b[0mTypeError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"n is not integer-like; it has non-zero decimals\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     20\u001b[0m             \u001b[0mn\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mn\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     21\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;31mTypeError\u001b[0m: n is not integer-like; it has non-zero decimals"
+     ]
+    }
+   ],
+   "source": [
+    "factorial(3.1)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "In non-strict mode, the passed in `3.1` is truncated into `3` resulting in a factorial of `6`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "6"
+      ]
+     },
+     "execution_count": 42,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "factorial(3.1, strict=False)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "For `complex` numbers, `factorial()` still raises a `TypeError` because they are neither an `Integral` nor a `Real` number."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "ename": "TypeError",
+     "evalue": "Factorial is only defined for integers",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-43-7296a74f5dcf>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mfactorial\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m1\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;36m2j\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;32m<ipython-input-37-be6602f598f7>\u001b[0m in \u001b[0;36mfactorial\u001b[0;34m(n, strict)\u001b[0m\n\u001b[1;32m     20\u001b[0m             \u001b[0mn\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mn\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     21\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 22\u001b[0;31m             \u001b[0;32mraise\u001b[0m \u001b[0mTypeError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Factorial is only defined for integers\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     23\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     24\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mn\u001b[0m \u001b[0;34m<\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;31mTypeError\u001b[0m: Factorial is only defined for integers"
+     ]
+    }
+   ],
+   "source": [
+    "factorial(1 + 2j)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.6"
+  },
+  "livereveal": {
+   "auto_select": "code",
+   "auto_select_fragment": true,
+   "scroll": true,
+   "theme": "serif"
+  },
+  "toc": {
+   "base_numbering": 1,
+   "nav_menu": {},
+   "number_sections": false,
+   "sideBar": true,
+   "skip_h1_title": true,
+   "title_cell": "Table of Contents",
+   "title_sidebar": "Contents",
+   "toc_cell": false,
+   "toc_position": {
+    "height": "calc(100% - 180px)",
+    "left": "10px",
+    "top": "150px",
+    "width": "384px"
+   },
+   "toc_section_display": false,
+   "toc_window_display": false
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/README.md b/README.md
index 66d7135..d9adfa3 100644
--- a/README.md
+++ b/README.md
@@ -127,6 +127,11 @@ Alternatively, the content can be viewed in a web browser
        Special Values;
        Imprecision;
        Binary & Hexadecimal Representations)
+    - [content <img height="12" style="display: inline-block" src="static/link/to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/05_numbers/02_content.ipynb)
+      [<img height="12" style="display: inline-block" src="static/link/to_mb.png">](https://mybinder.org/v2/gh/webartifex/intro-to-python/develop?urlpath=lab/tree/05_numbers/02_content.ipynb)
+      (`complex` Type;
+       Numerical Tower;
+       Duck vs. Goose Typing)
 
 
 #### Videos

From 586941fdbaa6c948d0aa8b92ee6c10866cb34416 Mon Sep 17 00:00:00 2001
From: Alexander Hess <alexander@webartifex.biz>
Date: Fri, 16 Oct 2020 00:10:13 +0200
Subject: [PATCH 166/242] Add initial version of chapter 05's appendix

---
 05_numbers/03_appendix.ipynb | 1504 ++++++++++++++++++++++++++++++++++
 README.md                    |    4 +
 2 files changed, 1508 insertions(+)
 create mode 100644 05_numbers/03_appendix.ipynb

diff --git a/05_numbers/03_appendix.ipynb b/05_numbers/03_appendix.ipynb
new file mode 100644
index 0000000..3db9422
--- /dev/null
+++ b/05_numbers/03_appendix.ipynb
@@ -0,0 +1,1504 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "**Note**: Click on \"*Kernel*\" > \"*Restart Kernel and Clear All Outputs*\" in [JupyterLab](https://jupyterlab.readthedocs.io/en/stable/) *before* reading this notebook to reset its output. If you cannot run this file on your machine, you may want to open it [in the cloud <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_mb.png\">](https://mybinder.org/v2/gh/webartifex/intro-to-python/develop?urlpath=lab/tree/05_numbers/03_appendix.ipynb)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "# Chapter 5: Numbers & Bits (Appendix)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In this appendix, we look at the `Decimal` and `Fraction` types that can be used as replacements for the built-in `float` type mitigating its imprecision. The content is put in an appendix as the data science practitioner can live without knowing about it for quite some time. Eventually, when working with financial data, for example, knowing how to not use the `float` type in a bad way pays off."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "## The `Decimal` Type"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "The [decimal <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/decimal.html) module in the [standard library <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/index.html) provides a [Decimal <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/decimal.html#decimal.Decimal) type that may be used to represent any real number to a user-defined level of precision: \"User-defined\" does *not* mean an infinite or exact precision! The `Decimal` type merely allows us to work with a number of bits *different* from the $64$ as specified for the `float` type and also to customize the rounding rules and some other settings.\n",
+    "\n",
+    "We import the `Decimal` type and also the [getcontext() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/decimal.html#decimal.getcontext) function from the [decimal <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/decimal.html) module."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "from decimal import Decimal, getcontext"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "[getcontext() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/decimal.html#decimal.getcontext) shows us how the [decimal <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/decimal.html) module is set up. By default, the precision is set to `28` significant digits, which is roughly twice as many as with `float` objects."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Context(prec=28, rounding=ROUND_HALF_EVEN, Emin=-999999, Emax=999999, capitals=1, clamp=0, flags=[], traps=[InvalidOperation, DivisionByZero, Overflow])"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "getcontext()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "The two simplest ways to create a `Decimal` object is to either **instantiate** it with an `int` or a `str` object consisting of all the significant digits. In the latter case, the scientific notation is also possible."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Decimal('42')"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "Decimal(42)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Decimal('0.1')"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "Decimal(\"0.1\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Decimal('0.001')"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "Decimal(\"1e-3\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "It is *not* a good idea to create a `Decimal` from a `float` object. If we did so, we would create a `Decimal` object that internally used extra bits to store the \"random\" digits that are not stored in the `float` object in the first place."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Decimal('0.1000000000000000055511151231257827021181583404541015625')"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "Decimal(0.1)  # do not do this!"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "With the `Decimal` type, the imprecisions in the arithmetic and equality comparisons go away."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Decimal('0.3')"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "Decimal(\"0.1\") + Decimal(\"0.2\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "Decimal(\"0.1\") + Decimal(\"0.2\") == Decimal(\"0.3\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "`Decimal` numbers *preserve* the **significant digits**, even in cases where this is not needed."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Decimal('0.30000')"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "Decimal(\"0.10000\") + Decimal(\"0.20000\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "Decimal(\"0.10000\") + Decimal(\"0.20000\") == Decimal(\"0.3\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Arithmetic operations between `Decimal` and `int` objects work as the latter are inherently precise: The results are *new* `Decimal` objects."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Decimal('42')"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "21 + Decimal(21)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Decimal('42.0')"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "10 * Decimal(\"4.2\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Decimal('0.1')"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "Decimal(1) / 10"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "To verify the precision, we apply the built-in [format() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#format) function to the previous code cell and compare it with the same division resulting in a `float` object."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'0.10000000000000000000000000000000000000000000000000'"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "format(Decimal(1) / 10, \".50f\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'0.10000000000000000555111512312578270211815834045410'"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "format(1 / 10, \".50f\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "However, mixing `Decimal` and `float` objects raises a `TypeError`: So, Python prevents us from potentially introducing imprecisions via innocent-looking arithmetic by **failing loudly**."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "ename": "TypeError",
+     "evalue": "unsupported operand type(s) for *: 'float' and 'decimal.Decimal'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-16-a3cea145e6d1>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0;36m1.0\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0mDecimal\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m42\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;31mTypeError\u001b[0m: unsupported operand type(s) for *: 'float' and 'decimal.Decimal'"
+     ]
+    }
+   ],
+   "source": [
+    "1.0 * Decimal(42)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "To preserve the precision for more advanced mathematical functions, `Decimal` objects come with many **methods bound** on them. For example, [ln() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/decimal.html#decimal.Decimal.ln) and [log10() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/decimal.html#decimal.Decimal.log10) take the logarithm while [sqrt() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/decimal.html#decimal.Decimal.sqrt) calculates the square root. The methods always return a *new* `Decimal` object. We must never use the functions in the [math <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/math.html) module in the [standard library <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/index.html) with `Decimal` objects as they do *not* preserve precision."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Decimal('2')"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "Decimal(100).log10()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Decimal('1.414213562373095048801688724')"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "Decimal(2).sqrt()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "The object returned by the [sqrt() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/decimal.html#decimal.Decimal.sqrt) method is still limited in precision: This must be so as, for example, $\\sqrt{2}$ is an **[irrational number <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_wiki.png\">](https://en.wikipedia.org/wiki/Irrational_number)** that *cannot* be expressed with absolute precision using *any* number of bits, even in theory.\n",
+    "\n",
+    "We see this as raising $\\sqrt{2}$ to the power of $2$ results in an imprecise value as before!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Decimal('1.999999999999999999999999999')"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "two = Decimal(2).sqrt() ** 2\n",
+    "\n",
+    "two"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "However, the [quantize() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/decimal.html#decimal.Decimal.quantize) method allows us to [quantize](https://www.dictionary.com/browse/quantize) (i.e., \"round\") a `Decimal` number at any precision that is *smaller* than the set precision. It takes the number of decimals to the right of the period of the `Decimal` argument we pass in and rounds accordingly.\n",
+    "\n",
+    "For example, as the overall imprecise value of `two` still has an internal precision of `28` digits, we can correctly round it to *four* decimals (i.e., `Decimal(\"0.0000\")` has four decimals)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Decimal('2.0000')"
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "two.quantize(Decimal(\"0.0000\"))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "We can never round a `Decimal` number and obtain a greater precision than before: The `InvalidOperation` exception tells us that *loudly*."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "ename": "InvalidOperation",
+     "evalue": "[<class 'decimal.InvalidOperation'>]",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mInvalidOperation\u001b[0m                          Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-21-5b555b105c8f>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mtwo\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mquantize\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mDecimal\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"1e-28\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;31mInvalidOperation\u001b[0m: [<class 'decimal.InvalidOperation'>]"
+     ]
+    }
+   ],
+   "source": [
+    "two.quantize(Decimal(\"1e-28\"))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Consequently, with this little workaround $\\sqrt{2}^2 = 2$ works, even in Python."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "two.quantize(Decimal(\"0.0000\")) == 2"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "The downside is that the entire expression is not as pretty as `sqrt(2) ** 2 == 2`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 23,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "(Decimal(2).sqrt() ** 2).quantize(Decimal(\"0.0000\")) == 2"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "`nan` and positive and negative `inf` exist as well, and the same remarks from the discussion of the `float` type apply."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Decimal('NaN')"
+      ]
+     },
+     "execution_count": 24,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "Decimal(\"nan\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "`Decimal(\"nan\")`s never compare equal to anything, not even to themselves."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "False"
+      ]
+     },
+     "execution_count": 25,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "Decimal(\"nan\") == Decimal(\"nan\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Infinity is larger than any concrete number."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Decimal('Infinity')"
+      ]
+     },
+     "execution_count": 26,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "Decimal(\"inf\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Decimal('-Infinity')"
+      ]
+     },
+     "execution_count": 27,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "Decimal(\"-inf\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Decimal('Infinity')"
+      ]
+     },
+     "execution_count": 28,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "Decimal(\"inf\") + 42"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 29,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "Decimal(\"inf\") + 42 == Decimal(\"inf\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "As with `float` objects, we cannot add infinities of different signs: Now, get a module-specific `InvalidOperation` exception instead of a `nan` value. Here, **failing loudly** is a good thing as it prevents us from working with invalid results."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "ename": "InvalidOperation",
+     "evalue": "[<class 'decimal.InvalidOperation'>]",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mInvalidOperation\u001b[0m                          Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-30-87950ebbd6ef>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mDecimal\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"inf\"\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mDecimal\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"-inf\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;31mInvalidOperation\u001b[0m: [<class 'decimal.InvalidOperation'>]"
+     ]
+    }
+   ],
+   "source": [
+    "Decimal(\"inf\") + Decimal(\"-inf\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "ename": "InvalidOperation",
+     "evalue": "[<class 'decimal.InvalidOperation'>]",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mInvalidOperation\u001b[0m                          Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-31-996083f200c6>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mDecimal\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"inf\"\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0mDecimal\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"inf\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;31mInvalidOperation\u001b[0m: [<class 'decimal.InvalidOperation'>]"
+     ]
+    }
+   ],
+   "source": [
+    "Decimal(\"inf\") - Decimal(\"inf\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "For more information on the `Decimal` type, see the tutorial at [PYMOTW](https://pymotw.com/3/decimal/index.html) or the official [documentation <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/decimal.html)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "## The `Fraction` Type"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "If the numbers in an application can be expressed as [rational numbers <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_wiki.png\">](https://en.wikipedia.org/wiki/Rational_number) (i.e., the set $\\mathbb{Q}$), we may model them as a [Fraction <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/fractions.html#fractions.Fraction) type from the [fractions <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/fractions.html) module in the [standard library <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/index.html). As any fraction can always be formulated as the division of one integer by another, `Fraction` objects are inherently precise, just as `int` objects on their own. Further, we maintain the precision as long as we do not use them in a mathematical operation that could result in an irrational number (e.g., taking the square root).\n",
+    "\n",
+    "We import the `Fraction` type from the [fractions <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/fractions.html) module."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "from fractions import Fraction"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Among others, there are two simple ways to create a `Fraction` object: We either instantiate one with two `int` objects representing the numerator and denominator or with a `str` object. In the latter case, we have two options again and use either the format \"numerator/denominator\" (i.e., *without* any spaces) or the same format as for `float` and `Decimal` objects."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Fraction(1, 3)"
+      ]
+     },
+     "execution_count": 33,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "Fraction(1, 3)  # 1 / 3 with \"full\" precision"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Fraction(1, 3)"
+      ]
+     },
+     "execution_count": 34,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "Fraction(\"1/3\")  # 1 / 3 with \"full\" precision"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Fraction(3333333333, 10000000000)"
+      ]
+     },
+     "execution_count": 35,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "Fraction(\"0.3333333333\")  # 1 / 3 with a precision of 10 significant digits"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Fraction(3333333333, 10000000000)"
+      ]
+     },
+     "execution_count": 36,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "Fraction(\"3333333333e-10\")  # scientific notation is also allowed"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Only the lowest common denominator version is maintained after creation: For example, $\\frac{3}{2}$ and $\\frac{6}{4}$ are the same, and both become `Fraction(3, 2)`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Fraction(3, 2)"
+      ]
+     },
+     "execution_count": 37,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "Fraction(3, 2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Fraction(3, 2)"
+      ]
+     },
+     "execution_count": 38,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "Fraction(6, 4)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "We could also cast a `Decimal` object as a `Fraction` object: This only makes sense as `Decimal` objects come with a pre-defined precision."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Fraction(1, 10)"
+      ]
+     },
+     "execution_count": 39,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "Fraction(Decimal(\"0.1\"))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "`float` objects may *syntactically* be cast as `Fraction` objects as well. However, then we create a `Fraction` object that precisely remembers the `float` object's imprecision: A *bad* idea!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Fraction(3602879701896397, 36028797018963968)"
+      ]
+     },
+     "execution_count": 40,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "Fraction(0.1)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "`Fraction` objects follow the arithmetic rules from middle school and may be mixed with `int` objects *without* any loss of precision. The result is always a *new* `Fraction` object."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Fraction(7, 4)"
+      ]
+     },
+     "execution_count": 41,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "Fraction(3, 2) + Fraction(1, 4)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Fraction(1, 2)"
+      ]
+     },
+     "execution_count": 42,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "Fraction(5, 2) - 2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Fraction(1, 1)"
+      ]
+     },
+     "execution_count": 43,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "3 * Fraction(1, 3)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Fraction(1, 1)"
+      ]
+     },
+     "execution_count": 44,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "Fraction(3, 2) * Fraction(2, 3)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "`Fraction` and `float` objects may also be mixed *syntactically*. However, then the results may exhibit imprecision again, even if we do not see them at first sight! This is another example of code **failing silently**."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.1"
+      ]
+     },
+     "execution_count": 45,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "10.0 * Fraction(1, 100)  # do not do this!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'0.10000000000000000555111512312578270211815834045410'"
+      ]
+     },
+     "execution_count": 46,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "format(10.0 * Fraction(1, 100), \".50f\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "For more examples and discussions, see the tutorial at [PYMOTW](https://pymotw.com/3/fractions/index.html) or the official [documentation <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/fractions.html)."
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.6"
+  },
+  "livereveal": {
+   "auto_select": "code",
+   "auto_select_fragment": true,
+   "scroll": true,
+   "theme": "serif"
+  },
+  "toc": {
+   "base_numbering": 1,
+   "nav_menu": {},
+   "number_sections": false,
+   "sideBar": true,
+   "skip_h1_title": true,
+   "title_cell": "Table of Contents",
+   "title_sidebar": "Contents",
+   "toc_cell": false,
+   "toc_position": {
+    "height": "calc(100% - 180px)",
+    "left": "10px",
+    "top": "150px",
+    "width": "384px"
+   },
+   "toc_section_display": false,
+   "toc_window_display": false
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/README.md b/README.md
index d9adfa3..4d3340b 100644
--- a/README.md
+++ b/README.md
@@ -132,6 +132,10 @@ Alternatively, the content can be viewed in a web browser
       (`complex` Type;
        Numerical Tower;
        Duck vs. Goose Typing)
+    - [appendix <img height="12" style="display: inline-block" src="static/link/to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/05_numbers/03_appendix.ipynb)
+      [<img height="12" style="display: inline-block" src="static/link/to_mb.png">](https://mybinder.org/v2/gh/webartifex/intro-to-python/develop?urlpath=lab/tree/05_numbers/03_appendix.ipynb)
+      (`Decimal` Type;
+       `Fraction` Type)
 
 
 #### Videos

From 50aa1804623869dd2fe292c9df2b6460abb4aa06 Mon Sep 17 00:00:00 2001
From: Alexander Hess <alexander@webartifex.biz>
Date: Fri, 16 Oct 2020 00:11:18 +0200
Subject: [PATCH 167/242] Add initial version of chapter 05's summary

---
 05_numbers/04_summary.ipynb | 85 +++++++++++++++++++++++++++++++++++++
 README.md                   |  1 +
 2 files changed, 86 insertions(+)
 create mode 100644 05_numbers/04_summary.ipynb

diff --git a/05_numbers/04_summary.ipynb b/05_numbers/04_summary.ipynb
new file mode 100644
index 0000000..45c7c29
--- /dev/null
+++ b/05_numbers/04_summary.ipynb
@@ -0,0 +1,85 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "# Chapter 5: Numbers & Bits (TL;DR)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "There exist three numeric types in core Python:\n",
+    "- `int`: a near-perfect model for whole numbers (i.e., $\\mathbb{Z}$); inherently precise\n",
+    "- `float`: the \"gold\" standard to approximate real numbers (i.e., $\\mathbb{R}$); inherently imprecise\n",
+    "- `complex`: layer on top of the `float` type to approximate complex numbers (i.e., $\\mathbb{C}$); inherently imprecise\n",
+    "\n",
+    "Furthermore, the [standard library <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/index.html) provides two more types that can be used as substitutes for the `float` type:\n",
+    "- `Decimal`: similar to `float` but allows customizing the precision; still inherently imprecise\n",
+    "- `Fraction`: a near-perfect model for rational numbers (i.e., $\\mathbb{Q}$); built on top of the `int` type and therefore inherently precise\n",
+    "\n",
+    "The *important* takeaways for the data science practitioner are:\n",
+    "\n",
+    "1. **Do not mix** precise and imprecise data types, and\n",
+    "2. actively expect `nan` results when working with `float` numbers as there are no **loud failures**.\n",
+    "\n",
+    "The **numerical tower** is Python's way of implementing various **abstract** ideas of what numbers are in mathematics."
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.6"
+  },
+  "livereveal": {
+   "auto_select": "code",
+   "auto_select_fragment": true,
+   "scroll": true,
+   "theme": "serif"
+  },
+  "toc": {
+   "base_numbering": 1,
+   "nav_menu": {},
+   "number_sections": false,
+   "sideBar": true,
+   "skip_h1_title": true,
+   "title_cell": "Table of Contents",
+   "title_sidebar": "Contents",
+   "toc_cell": false,
+   "toc_position": {
+    "height": "calc(100% - 180px)",
+    "left": "10px",
+    "top": "150px",
+    "width": "384px"
+   },
+   "toc_section_display": false,
+   "toc_window_display": false
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/README.md b/README.md
index 4d3340b..098db41 100644
--- a/README.md
+++ b/README.md
@@ -136,6 +136,7 @@ Alternatively, the content can be viewed in a web browser
       [<img height="12" style="display: inline-block" src="static/link/to_mb.png">](https://mybinder.org/v2/gh/webartifex/intro-to-python/develop?urlpath=lab/tree/05_numbers/03_appendix.ipynb)
       (`Decimal` Type;
        `Fraction` Type)
+    - [summary <img height="12" style="display: inline-block" src="static/link/to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/05_numbers/04_summary.ipynb)
 
 
 #### Videos

From dfaa1709353824ad2113742cc59b036539f81ea0 Mon Sep 17 00:00:00 2001
From: Alexander Hess <alexander@webartifex.biz>
Date: Fri, 16 Oct 2020 00:19:15 +0200
Subject: [PATCH 168/242] Add initial version of chapter 05's review

---
 05_numbers/05_review.ipynb | 327 +++++++++++++++++++++++++++++++++++++
 README.md                  |   1 +
 2 files changed, 328 insertions(+)
 create mode 100644 05_numbers/05_review.ipynb

diff --git a/05_numbers/05_review.ipynb b/05_numbers/05_review.ipynb
new file mode 100644
index 0000000..db49191
--- /dev/null
+++ b/05_numbers/05_review.ipynb
@@ -0,0 +1,327 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Chapter 5: Numbers & Bits (Review Questions)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The questions below assume that you have read the [first <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/05_numbers/00_content.ipynb), [second <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/05_numbers/01_content.ipynb), and the [third <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/05_numbers/02_content.ipynb) part of Chapter 5. Some questions regard the [Appendix <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/05_numbers/03_appendix.ipynb); that is indicated with a **\\***.\n",
+    "\n",
+    "Be concise in your answers! Most questions can be answered in *one* sentence."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Essay Questions "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Answer the following questions briefly with *at most* 300 characters per question!"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q1**: In what way is the **binary representation** of `int` objects *similar* to the **decimal system** taught in elementary school?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " < your answer >"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q2**: Why may objects of type `bool` be regarded a **numeric type** as well?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " < your answer >"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q3**: Colors are commonly expressed in the **hexadecimal system** in websites (cf., the [HTML <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_wiki.png\">](https://en.wikipedia.org/wiki/HTML) and [CSS <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_wiki.png\">](https://en.wikipedia.org/wiki/Cascading_Style_Sheets) formats).\n",
+    "\n",
+    "For example, $#000000$, $#ff9900$, and $#ffffff$ turn out to be black, orange, and white. The six digits are read in *pairs of two* from left to right, and the *three pairs* correspond to the proportions of red, green, and blue mixed together. They reach from $0_{16} = 0_{10}$ for $0$% to $\\text{ff}_{16} = 255_{10}$ for $100$% (cf., this [article <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_wiki.png\">](https://en.wikipedia.org/wiki/RGB_color_model) for an in-depth discussion).\n",
+    "\n",
+    "In percent, what are the proportions of red, green, and blue that make up orange? Calculate the three percentages separately! How many **bytes** are needed to encode a color? How many **bits** are that?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " < your answer >"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q4**: What does it mean for a code fragment to **fail silently**?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " < your answer >"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q5**: Explain why the mathematical set of all real numbers $\\mathbb{R}$ can only be **approximated** by floating-point numbers on a computer!"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " < your answer >"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q6**: How do we deal with a `float` object's imprecision if we need to **check for equality**?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " < your answer >"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q7**: What data type, built-in or from the [standard library <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/index.html), is best suited to represent the [transcendental numbers <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_wiki.png\">](https://en.wikipedia.org/wiki/Transcendental_number) $\\pi$ and $\\text{e}$?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " < your answer >"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q8**: How can **abstract base classes**, for example, as defined in the **numerical tower**, be helpful in enabling **duck typing**?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " < your answer >"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## True / False Questions"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Motivate your answer with *one short* sentence!"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q9**: The precision of `int` objects depends on how we choose to represent them in memory. For example, using a **hexadecimal representation** gives us $16^8$ digits whereas with a **binary representation** an `int` object can have *at most* $2^8$ digits."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " < your answer >"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q10**: With the built-in [round() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#round) function, we obtain a *precise* representation for any `float` object if we can live with *less than* $15$ digits of precision."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " < your answer >"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q11**: As most currencies operate with $2$ or $3$ decimals (e.g., EUR $9.99$), the `float` type's limitation of *at most* $15$ digits is *not* a problem in practice."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " < your answer >"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q12**: The [IEEE 754 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_wiki.png\">](https://en.wikipedia.org/wiki/IEEE_754) standard's **special values** provide no benefit in practice as we could always use a **[sentinel <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_wiki.png\">](https://en.wikipedia.org/wiki/Sentinel_value)** value (i.e., a \"dummy\"). For example, instead of `nan`, we can always use `0` to indicate a *missing* value."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " < your answer >"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q13**: The following code fragment raises an `InvalidOperation` exception. That is an example of code **failing loudly**.\n",
+    "```python\n",
+    "float(\"inf\") + float(\"-inf\")\n",
+    "```"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " < your answer >"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q14**: Python provides a `scientific` type (e.g., `1.23e4`) that is useful mainly to model problems in the domains of physics or astrophysics."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " < your answer >"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q15**: From a practitioner's point of view, the built-in [format() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#format) function does the *same* as the built-in [round() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#round) function."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " < your answer >"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q16\\***: The `Decimal` type from the [decimal <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/decimal.html) module in the [standard library <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/index.html) allows us to model the set of the real numbers $\\mathbb{R}$ *precisely*."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " < your answer >"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q17\\***: The `Fraction` type from the [fractions <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/fractions.html) module in the [standard library <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/index.html) allows us to model the set of the rational numbers $\\mathbb{Q}$ *precisely*."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " < your answer >"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.6"
+  },
+  "toc": {
+   "base_numbering": 1,
+   "nav_menu": {},
+   "number_sections": false,
+   "sideBar": true,
+   "skip_h1_title": true,
+   "title_cell": "Table of Contents",
+   "title_sidebar": "Contents",
+   "toc_cell": false,
+   "toc_position": {},
+   "toc_section_display": false,
+   "toc_window_display": false
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/README.md b/README.md
index 098db41..48c9171 100644
--- a/README.md
+++ b/README.md
@@ -137,6 +137,7 @@ Alternatively, the content can be viewed in a web browser
       (`Decimal` Type;
        `Fraction` Type)
     - [summary <img height="12" style="display: inline-block" src="static/link/to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/05_numbers/04_summary.ipynb)
+    - [review questions <img height="12" style="display: inline-block" src="static/link/to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/05_numbers/05_review.ipynb)
 
 
 #### Videos

From 57f615a33253eb01d9c44abffb9c77c059af3340 Mon Sep 17 00:00:00 2001
From: Alexander Hess <alexander@webartifex.biz>
Date: Fri, 16 Oct 2020 00:21:40 +0200
Subject: [PATCH 169/242] Add initial version of chapter 05's further resources

---
 05_numbers/06_resources.ipynb | 313 ++++++++++++++++++++++++++++++++++
 README.md                     |   2 +
 2 files changed, 315 insertions(+)
 create mode 100644 05_numbers/06_resources.ipynb

diff --git a/05_numbers/06_resources.ipynb b/05_numbers/06_resources.ipynb
new file mode 100644
index 0000000..87343f1
--- /dev/null
+++ b/05_numbers/06_resources.ipynb
@@ -0,0 +1,313 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Note**: Click on \"*Kernel*\" > \"*Restart Kernel and Clear All Outputs*\" in [JupyterLab](https://jupyterlab.readthedocs.io/en/stable/) *before* reading this notebook to reset its output. If you cannot run this file on your machine, you may want to open it [in the cloud <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_mb.png\">](https://mybinder.org/v2/gh/webartifex/intro-to-python/develop?urlpath=lab/tree/05_numbers/06_resources.ipynb)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "# Chapter 5: Numbers & Bits (Further Resources)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Working with Bits"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "The two videos below show how addition and multiplication works with numbers in their binary representations. Subtraction is a bit more involved as we need to understand how negative numbers are represented in binary with the concept of [Two's Complement <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_wiki.png\">](https://en.wikipedia.org/wiki/Two%27s_complement) first. A video on that is shown further below. Division in binary is actually also quite simple."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/jpeg": 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\n",
+      "text/html": [
+       "\n",
+       "        <iframe\n",
+       "            width=\"60%\"\n",
+       "            height=\"300\"\n",
+       "            src=\"https://www.youtube.com/embed/RgklPQ8rbkg\"\n",
+       "            frameborder=\"0\"\n",
+       "            allowfullscreen\n",
+       "        ></iframe>\n",
+       "        "
+      ],
+      "text/plain": [
+       "<IPython.lib.display.YouTubeVideo at 0x7f5405789fd0>"
+      ]
+     },
+     "execution_count": 1,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from IPython.display import YouTubeVideo\n",
+    "YouTubeVideo(\"RgklPQ8rbkg\", width=\"60%\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/jpeg": 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hFEYYZHPeGt5HsAA5vfuF99NeFuXi1jDpmtlK9fLQTyNbk8fPa6NeeClJccI52xxzCVrWOjJa0bO3G/Yq43hNVg0H4e+22mBtiOk/K3I3e66XIXQwVazvsk71K/5WKqHlDvz2vEHEWbEhlsW7WVnsSu25STTY3ISSyO2G27nucf70H182WgtTYabGTagzhzUuSZbbWcZLLxWbU9lEjWsmAbG1/tDDswDctcT3O5+Xgx5c8tqrEy5WjepwiK1PVZWtCcPfLDHFIPfjY5rWu6rRv322K298pl9LS/5M1/8AFLP/AJPMf9kpv07c/d6SCvPhV5TtTZmuy5bfFhq0w5QtvMkfdkafR/sbNjEw9/5xzHeh47EFcHxq8seoNN1X5FssOTx8G3tE9RsjJ6zSdurPWfvtCDtu9jnbbkkADdfnx58dNXWc7kYI8taoVsXk7dWrVxc0lONsdWxJCx0roXB9iRwZycZCRu47Bo2AuV5a9RWNTaOpT5bjZluw3aV5zmNaLMcc89UukYPd5PgDeXwLi47DfYB5n4XF2btiGpUhksWbcjYoIIWF8kkjjs1rWj1P/wC1ZbSXkt1BYhbJkMlSx0j2h3s0bJLsse4B4TOYWRNeDuDwc8dvUrr/ACC0KrNZWI7Aa+eni74oucB2sNnrwyvj5dxIazrA7fiukXd+ffVWpKmoK1eK5cpYw4+KSiKk89eCeXm4WnvfEQJZ2v4NIJPFpjOw59w6HUXk41fBLxqS4+/CRu2Vth1Z49O0kU7BxP5rnD71qXxZ8MczpexXrZaOKOW5AZ4RBM2dpjDzGd3N9DyHor3+S7N5q3pSOxnJZpOFuy2jbuPcZZcaxkTmTSTSHlIwSmy0Pcfoxt+AC8+9f6qyGXuyWb1+zkHN5RV57ry+QVmyPdE1rezYm+8XcWgDd7jt3KDH0REBERAREQEREBERAREQEREBERAREQEREBERAREQEREBERBknhZ/r3C/prG/vkKu98op9Van6fqfueQVFtCZCKplcbanJbDSyVKxM5oLiIobMUkhDR3cQ1p7BWd84fjnpjUuBgoYqxPLZiy1e05ktSaFvRjr243HnIAN+UzO33oMN8jfid8y50YyzJxx+pDFXJcTxgvtJbTm7nZoeXmFxA79SMk7MVzPF7VOO0jhctmGRRMntTGZkW3H23LTwRVoOYHdxLa8RcR34QPPwXlg1xHcHbbv27enosg1VrjOZWOGLJ5S9fjqfzEdy1NOyM7FvMNkcQZCDtzPvEdt0HN8PL01rU2Js2JDLPb1DQnsTP8ApSTTZCKSSR39Zz3OJ/KrpfKMfVal+sFX9yyKozoLIw08ti7c5LYKWTpWJ3NBcRFBZilkIaO7iGtPYeqs15xfHLTOpcFWoYmxNLZhy8Fp7Zas0DejHWuROIfIACeUzO33lBsL5OCVp05k2b+8zPyOI+Ia+hRDT/eWO/wVSvMhVli1dqNkjCxzs1dlDXdiY55nTxP/ADXRSMcPucFkHld8aX6Pvzumhks4zJsjberwuaJmPhLjDZriQhjpWh8jSwlocH+oIBVmNX+OfhDkNr9+rBk7kcYa2OxgXTWy1u5bCZbMIhc0EnYOk4jkftKDvvILXmj0dF1WPYJcldkh6jXND4ndICSPl9KMua/uO24cqWy/Xh364n+MK1nh55w8BM+83K15MXBFNG3ERwQus8qYjDdpzD2ZOHtc7i1oaGyMaC7iXOqB8+1HapOT5ltN2o/b+q5juQrHI+0czGAXcul34gE/D1QXi8//ANTpP0pR/bKq0+Qb65Qfo2//ANNq2f5vvHHSWf02/H4nJG1bdfqTCE0r8G8cRk5u52YGM7bjtvv3VY/BjXU2m85RzEUfW9ikcJ65dx69eaN0M8Yd+K8xvcWk7gOa07HbZBYn5SsH27T5+Hsd/v8AD+er/H+8f4qtnhMP+0GC/TmM/fYFeXIeZXwyyEFexf8Aw89RwsVql/DSWbNWfYHlDIYnwRTAgDmyT8Ud1UTWOtsJc1pDnaFSbH405ihemhk6bpN4rEUtuw2GHtEXlj39MOf7znHf3uLQt18oV9UY/wBN0v8Ao21rv5M36ep/zcN+3KLi+bvx30vqPTzcfirM0tpuTrWCyWpNC3pRR2GvPOQAb7yN7feuV8mb9PU/5uG/blEGnvOxE5uuc2XNIEnzc5hcCOTfmqizk3f6TeTXDcfFp+xbk+Ta0/Yac5lHxubWkZUpQSn6MsrXSz2Gs+J6bTBv/agd9jtsDxS8WdADO38LqzFwGbCOrtqZC1jxkY5IbVOtbIaYonT13B85aWAOaeDXb7ni3H9f+a/S2Jx3sWk6wszMidHTEdN1DGUyfR7oZGxySbFxcI2MAdsd3N+Iav8ANFmorfibRjjO/wA2WsFSkcNtjKLMdl4BB78Ta4n7Cxw+C315/wD6nSfpSj+2VUN03nSc5Uyd+ZznHMV71+y8F73H2xlizM4MG7nH33bNG5+AVq/N9446Sz+m34/E5I2rbr9SYQmlfg3jiMnN3OzAxnbcdt9+6CqfhrqqfB5fH5avv1MZbjnLRsOrEDxngJPo2SF0kZ+559F6R+KegsXrWhgrAc2WtXyNDLQybNLbGPkaHWKzt/8AZzQOZuP6UbPsIXl2F6PeTa7cp6FrWcu8RVavt9ipLLza6LExPfIHzF/fiHNsuZ8Ol0tu2yDVnyjGve2P05A/12yeRDSfTd8NKF23bbfryFp+yE/YtK+S769YL8/Ifwq8sF8VdXTZ7M5HLzbh2StPkjjcdzFA3aOtDuPXhAyJn/Cu+8teq6GD1RisrkHujp0XWzO+ON0rx1qFqBm0bBu78JKwdvtQWE+Uy+lpf8ma/wDilsD5PL6pTfp25+70loPzseLWB1ScH8zzSzfNgyXtPWrywcfafYOlx6gHPf2eT09Nh9qy7yheOul9N6fkx+VszxWX5SzZDIqk0zelJFWYw84wRvvG/t69kFbPFz6w579O5T9+nV/fIt9SMb/5jI/v9hee3iDkobmXytyAl0F7KXrMDnNLXGKe1LLGS092kscOxVsvK35gtJ4DTNLF5KzPFbrTXHSMjpzzMDZrcssZD42kH3HtQaD8E9P5LJ6zqVsZcfjrYyVqduQiHJ9aKv1pp3hh7S7xtczpu91/U4u90lXd8cvFXJaesVakelb2oastRssuQgB6TbAe6PpObDVlYJeLeZ34dpW8Qe+1YvJhoC3mdSWM5Wuvo1dP3HTGWONj57LrvtDG1WtlBZHG6Dqh7iCQHgNALuTNteJXnDhxGYyGMgwLrseLty0n2ZMj7I6Ses90VjaH2WXaMStc1pLtyG79t9gGp/H/AMxuq8pTkx3zTJp6jcYYrLZGWHWrETgWvgNmaKNrIHNOxaxgJG45bEg1rXo95f8AzCY/Wlizi5cRJTsR0pbT4pZYr1SWvHNDA9rpDHG7mTYj910fE+939Aai+cjR2NwmqrNbGxsgrWqte77JEAIqss/MSRRMH83GTH1Az0aJdhsAAA02iIgIiICIiAiIgIiICIiAiIgIiICIiAiIgIiICIiAiIgIiICIiAiIgIiICIiAiIgIiICIiArJeR/xTwGmXZw5m26qMk3GirxrWbHP2c3+rv7PG7ht14/pbb8u3oq2og2T5nNU0M1qvK5PHSmeldNLoTOikhL+jjqleTeOZrXt2lieO4G+2/xWtkRAREQdjpirUmu1Yr1g1KUlmFt201j5HQVi8daRkcbHOkkEfLZoadzt8Nyra+ZTzBabsaYGA0xPI8WW16UgZVsVY6uNrtG8LDYY0uLxHFFxaCOBk3I7A05RAREQEREBERBt7yxeNU2j707nwOt47KNiberRuDZmuhL+lYrl/u9VokkBY7YODttxsCLDZ/W/gjqSX2/KxsiuytD53TVsvSsOcWhv+kSYzaOeQBoG5e/0GxVG0QXaf4++G+k6ssWkMYbVmdnHnDBPXjeY9+mLl6//AKVMwFziA0P/ABu7d91T/XGp7uZyNrKX5OrbyEvVmeG8WjZoYyONo+jEyNrGNHwaxo7rpUQEREBERAREQEREBERAREQEREBERAREQEREBERAREQEREBERAREQEREG+PJBpXA5bUb4MzHDY6FCWehRtBphs2WyRNcHRu92cshdI/pHffYu2IYVkvn60ZpzE3cS7FV61G3dhtG/RosjhiEMZgFWwa0ZDa7nOdYbu1oD+mfiw76s8uHhz/KjNtxvtz8c6OrNcZbhi60jX13RcQ1vUZxO79+QduOIXceajwpdpXIVIpMpNlpcpVfZls2YTHKHMk6WznGWR0h2A7koNOorH+E/lgOodMR56DKuitWIrzoccaQkjfLUsTwMiM4nDh1DCO4Z25+h275Z4a+S2zYrRz53JmlNK0OOOoRsnkh5AHjNae7p9Ub7FrGuaNuzigqIisT5hfK7f01TkytC386Y6Bw9ra6Ho26jHENbK5rXOZYgDjs57eJbyaePEOc2uyDK/ByrDPqPT8E8Uc0NjP4qKeCZjZIpYpL8DJIpY3gtkjc0kFpBBBIKtf5+tE4LGaex82NxGOx80mdhifNQx9SpK+I0b7zG6SCNrnRlzGHiTtuxp+AVVvA760ab/WPD/xGur4+c3QOV1JisTjcXCJZ3Z6GWV8j+nBXgZSvMfPPJseEQdIwdgXEuAAJICDzdRXFr+R2UwbyajY2yQDwZjHOgDthu3qOtB7hvv73EfDsq1+LnhvldMZB2PyUbA4sEtexA5z69qEktEsD3AEgOBBa4BwI7j0JC3nkw8NNIZHSsduzj6GSvWp7UeSfdhhsy1XRzSMhgYJNzU/0YQyAt4uPV5b9xtTvxcxuNp53LVcVJ1cdVyNiKm8PMg6THkcGykkysad2B+55BgO533W8PLZ5d36lwj8mzP2cYLVielPVr1jIyaOHh2le2xH1Gnmfdc0gLRTtND5+OG6x4/PXzX7T0xvxFz2TrdLltvt73Hl926DG0VrNd+TLJ1n0WYnJNyHtlh0Np9uv7FFSjEbpG2ZHslkMke7Czi1vLk9m2+52y2r5IKPsxEufsG2W9pIqUTa7XfZ0XSl7wPz27/cgpMiz3xw8K8ppPIClf4Sx2GOlo3YOXRtQh3EkB43jmadg+M7lpcNi5pa53beBPgXnNWufJT6VbH1pujZyVpx6TZQ1j3QxRM9+ecRyMdx7NHJu7m7jcNWIrvVPJBjRDtLn7brHHtJFTgjhDvt6LpHOI3+HMKunmB8Ecro+eIWXsuULhc2pkoGOYx0jAHOgnicT7PY294N5ODm7lrjxeGhqxFu3yw+BUWs2ZNz8m/H/ADS+o0BlVtnq+1CwSSTMzhx6H378vuWV+G3lHymRv5GO9cFLGYrI2aUN0QcrGS9nldH1a1dz+MUJA7vc52zt2gP4uICs6K0mo/KzjY9S19PV9QvhlvYiXJVzboMmcejP0XQco7EYc9zBNINm+leTf4b6y8xngnc0bYpxy2mXq2ShkdBbjhdX/CwuaJoXxOe/ZwEkTgeR3D/hsUGqEXdaE01azOSpYuoB7Rk7UdeMu34M5n35ZOIJEbGBz3Ed+LCtu+YTy9x6PoVrk2bZdnvWxXr0o6Bgc4NjdJNMZXWXERsAYOzSSZox233AaIREQEREBERAREQEREBERAREQEREBERAREQEREBERAREQEREBERAREQEREBERBYf5Pv63/8As97/AJ6yyf5Sb/W+E/Rc3705Yx8n39b/AP2e9/z1lk/yk3+t8J+i5v3pyDfnkd+o+I/tcl/E7a8/Nba/zeZvHI5C/PNaEnUhcJXsZVIO7W1I2ENrMbsNgzb03O5JK9A/I79R8R/a5L+J215qoPT7wuuy5zQNWTIuNiTJadsQXHyHd046M1Z75Hernuazcn4kkrzBXpt5b/8A6f4r9DWf+eyvMlBmPgd9aNN/rHh/4jXV7/O7rzK4DTkUuKn9lsZLJRUJLTB+GhgfWtTvNd3pFMTXa3nsS0Odx2ds5tEPA760ab/WPD/xGurnfKLVJpNL0nxxPeytnq8k72Mc5sMZp3ohJKWjaNhkkjZyOw5SNHqQgq95f/FjPY/UeMkfkrdivev16uQr2rU88U0FqZkMj3Mlc78MwP5tf6gsHfYkG0vyhem4bOmYcgWDr4XIwlkuw5CC5vXmi39Q10nszj98TVTny96WtZjU2HqV43PDMhWtWnNBLYalWZk1iV7gNmNDG8QT2Lnsb6uCuX8oPqCKtpZlIuHWzOSrxxx7jl0qpNmaTb14tcyBpI+MzftQcr5P76nt/S179kKpi/68H9cj/F1c75P76nt/S179kKpi/wCvB/XI/wAXQXp85+qsliNK2LONsvqWZrdaqbMOwmZFMX9Xov8AWKQhu3NvvDc7EHYikfls1hk6WrsNLFamLstmaVO+HzSuFqG7YbWl9oBd+HIE7ntL99nNafUK4Xn/APqc/wDStH9sqo94FfWnTX6y4b+I1kFvvlIaUbsBibBaDLDmxCx+3dsc9K0+RoP2F1aI/wDAFUjwx8UdTYCO1Vwt2Su3KljZIWRRTkzfQZJXbKxxjsEHhyZ3II9S1pbcD5R76s439YYP4fkVzPJ/oLF4XSseo/ZW2snfpWr0s7WNksMgj6pjo0yRvFuyIcgO7nvO5Ia0AKuaW8PvE2a7FmauOzXtzJGyx5C46WGw8tdyHOS89r5Y3bdw7drgfiCrjedCiyzoXJyTMAlqfN9qMEAmKf22tE7ifgeE0rNx8HlVny3my11lLQgxMNao6zL06lOlS9utPLjsyPewH9eX72sbv/RCtD5uTIdA5kyfzhq0OrvsPf8AnClz7DsPe39EGn/kz/5rUv8AaYr/AJcgug85njnqGtnrGDxVybGVsSyATy1HdKxasTwRWi8zt9+OFrJWNDGlu5Dy7lu0N7/5M/8AmtS/2mK/5cgtG+cz6857+0o/wykgxfHeKWdbm8dnrV2a9exD6/SlsyFz314HOLqrn7b9J7JZmk+p6zz6lXr80OnK+rdFPu0R1n1q0Odxb2g8nsZCZJY+Ldy5z6kkw4f0wz4hebqvj8nvr327EWsDYeHTYOQzVWuO5fj7bi4sAJ3cI7BkB+AbYiHwQa8+Tr0P7Rk7+elZvHiIRTpOcAQbdtpM72H1Do6w4n7rgWDed3X3z1qeetE/lT06046vxO7XWGnlfl227O6/4LsSCKrD8VbbUUdHw60dlX0uDXMsZCaiC3bndyNmQY+NzAd5BDE6sw7bbspuPb4ea00jnuc97i98ji573kuc5zju5znHu5xJJ3P2oPyiIgIiICIiAiIgIiICIiAiIgIiICIiAiIgIiICIiAiIgIiICIiAiIgIiICIiDaHlk8SKelc586XIJ7EPsNit06nTMvKYxFrvwr2t4jpn4/Fdv5rvF2hq+9j7VGtZrMoU5K8jbnR5Oc+YyAs6L3DjsfjstMIgtj5efM/hdN6eo4e3j8hPPSfbc+asKpicLFyew3j1Zmu3DZQDuB3BVTkRBbjwq80uExGmaeEmx+RksU6EtZ80Iq9Fz5HTEObzmDuP4Qeo+BVR0RB33hxmocbmcRkZ2yOgxeWoXZ2QhrpXRVbUU8jYmvc1rpC1hABcBvtuR6q3urPOjiXT0m0MTbsUXumZl4skyrBI6F7WNi9kEU0rHvB6hLZOLSAG/jcmUkRBfKt5rdAY6sX4zFW45rHvPqVcbSpEybEj2mVkoj237cmdQ9/QqpPjh4pZLVmSN+7tFHEzo0aETi6GpByLuDSf5yVx2c+UgFx27BrWtbgaILReWbzJYfS2CGKuUL9iYXbFjq1RW6XGbp8W/hZWu5DgfgtCu1LD/KE5jpydA535yEXu9Xpe3e1cPXj1OHb123WLogtJ5mPMnh9U4J2Kp0L9eZ1ytYEtoVhFxhL+QPSlc7keQ27KvPhzm4sZmcTkpmPkixWWoXpY4uPUfHUtRTvZHyIbzLYyBuQNyF0KILKearzC4nV2Jq4+lSu1pauUjuOkuCuIzGyragLW9GVx58p2nuNtgV+vLR5nhpug3D5WnNcx9d8j6c9N0ftVYSv6j65imc1k0PUdI8Hm0tLyPeG3GtKILh6p82WnKrZptM6abDkrQcH371ShU4lwJMj20XvktHkfoukZvvuT8DjWvPM/Bm9Gz4K9TtHMW6taGe+32b2WaWvagmdYc1ha6MyNhJLGs2Dn7Dt3VYUQb88pfjfjdHMyzb1S3aOVfTdF7GINmCsLIdz60jfXrt2239Ctd+POs6+odQ5LMVopYIMi+u6OKxw6rRDUr1zz6bnN3LoXHsT2IWDogKw3kCw2Rn1W21VeY6uMpWHZN227ZYrDDFDVP9Z04jlH/lXH4KvKs95ZPHrS2kMNNWlo5Szk71mSzcmhhpCu4tHTq145X2GydBsY5buYSHTS7AjYIOx+US197TkaWnoZN4sQz2y+1pBBu2WbV2P+IdHVcXf/en12G1UF2+s9QWMrkbuStO5T5K3NZl7ucGmZ5eI2cu4jY0hjR8GsaPguoQEREBERAREQEREBERAREQEREBERAREQEREBERAREQEREBERAREQEREBERAREQEREBERAREQEREBERAREQEREBERAREQEREBERAREQEREBERAREQEREBERAREQEREBERAREQEREBERAREQEREBERAREQEREBERAREQEREBERAREQEREBERAREQEREBERAREQEREBERAREQEREBERAREQEREBERAREQEREBERAREQERAgKeP+Sy/Rfh7kMlxk4+zVj62ZmnZw/8GPs6X8vZvr3W7dKaKx2ObtDCJZXNIfYsNbJK7f6TR22Yz7mgffv6rkw4WWXd571T8S8n6fej+LP2x/zfl979FYiEW+daeFVO2HTUS2pOdz0tj7LIfzW7mH8rdx/VWmtQ4C3Ql6NuF0TvxSdiyQD1dHIPde309D8e+ymeGWHl2/S/W+U9Sx/c5fF88b5/3PrHVoiLD9YRNvgiAiIgIiICIiAiljSewBJAJ2Hc7NBc4/kABJ/IoQEREBERAREQEREBERAREQEREBERAREQEREBERAREQEREBERAREQEREBERAREQEREBERAREQF2mko2vv0WPaHMkvVWva4btc107A5rgexBBIXVrttG/6xx/6QqfvEas8uHmLrg5We1/st3SwV+aB1iCpPLXjcWOlhifI1rmtaSHCMEtADm99tgCtweXGTHCrZG8TbxsHq9QsEprcGdPhv3MXLqb/AH+vwXy8teajDLePc4CTqe1wNP47XNZFKG/e0xxn/wBQ/Yvp49aHidGMnViAmEscdqONoAm672xRy8f+96jmNJ+IfufTv2s8t3or516T6f8AsnLY+q8v8dkvVjfvq+886s8ffX/ihBWnzdmPFx9VryzeOmwyNfOGjrGJkQPL3vXj+NyWqPHnFS18TditQOimgFeRrJm8XsL5oeLwD3aSxx/ucVc/w70jWw9VrGtabD2B1u1t70km25Acfowt3IDfsG57klVN81mZZfizdqM8onurxwuHo6OGWtC14/quLC8fnp17xuM8SN8X0n9k5rgc1xctcTicSfDPElu7PrrtL8lffAdoOqdOAgEHUGL3BG4P+mw+oKtR5j/CG5qzX1GpXHs9Grp6jLlbzWDjXidkMoGtb22fakDHNY0/0HE9mFVY8BPrVpv9YMX++wr0Q1dr7Hfyhk0fee+o/P4Fk1G/XmMEr5bEt6rLVZM0h0NoMhbJE4ep5jseId1H0hV3zT+KeLoUW6I0vHFFj8e0V8pah4u6ro3c31I5diZD1hzmm33e/du+3PllnmL0xeyOg9DVsZQnu2TXxbujRrPnlDPmbZz3NiaS2PkW7uOwG43KrZ45+GOQ0rlZcfbBfA/eTHXWt2juVd9myD4Nlb2a9nq132tLXOtt4w+JWW0z4f6TnxEkcFrIUMPVNqSKOd0MTcUJnGKKYGIyOMbRu9rgAXdtyCApLqrS2UxUogyVC1QmeC5kd2vLA57Qdi+PqACRm/bk3cLttOeGWp8jE2ejg8lZgkbyjsQ0bBgkb6bxzFnCTv8A0SVaTx7yr9R+FmLz2QYw5CGzXk6sbQz8L7VNj53NA+g2RgDywduQb290bffTVTVGnMHhIszr6nplklcNx2KkxFS+8Rh3W4WJ5AJHcGzRsft7jPdHInuQptewd+C17DPTsw3eoyL2GavNHa6sm3Tj9ne0SdR3JuzdtzyG3qu9xHhnqe3LYgrYPJyy0X8LcbaFkOrycGyCKYOYOlKWPa4Mds4hwIGytb5waMTdZ6DshretZu1IppGDbmyDK03RDf1IBsS7fnLkecfxx1DpnN0Mfh5IK8TqMOStl9eGd1x0lmxD7PL1WnpxcKoBdHxeef0hsEFJ8tjbNSaStbgmq2IHBs1a1E+CaJxAcGyRSgOYdiDsR8QuKrafKQY+AW9O3mxtbPepXYp3j6T46z6skLXH8bibUvf+squ6VwdnJ3qmPqM6lnI2Yq0De+3UmeGBzy0Etjbvyc7bsGk/BBbT5P8A8NoHVsjqDIxsdDeY/D0GTgcJIpS1l5wDuzxI90dcbdyRM347KtvjdoeTTudyOKfyMdWwXU5HA/hacw6tWQO9HnpOa1xHYPY8fAq7/jF4YakGC09gNJGCGDBTVrNi3ZsNgkksUCyWq7gGEPc+0ZLL+wHNke3x2wrz6aAsXcLjtSurtiyGKihrZiKFwka2vZI22lA3kjhuPc1p7bttOJ227BTnS2lsrlZTDjcfbvysAMkdKtLYMYcdmuk6bT02bg+87Ydio1TpjKYqUQZKhaoTOBcyO7Xlrue0HYuj6jR1Gb/jN3CuNqjUUug/DnATafjhit6gbSfayRiZNtYu0XXJrG0gLJpfcEbOoC0MZ6HiAtBa/wDGPUusaWKwd2GvasfOLRBairRQ2btqUMggiLhtFA7eYg9MMDupHuBx7himP8JdXWIuvDp7Lvi4h7XjG2tntI3DogWbygj+gCsOtQSRSPilY6OWF7o5YpGuZJHIwlr2PY4bseHAgg9wQV6BYWxqLE5PC0M54hUGXZnUm/ycZhqz47MLnNr+ztvDhOJJS1zWSvDSX+jSOy6HJ6Kxt3xh3niY5kGGiyxge1pjnuwxMrROe0j3i3dkv50AJ377hW3wl8MNTjLYO8/A5MUm5nGSvsPx9kRCEW4HumdyZ/MBm5Mm3HYHus7+USja3VdTi0AHT1QniAP99yXc7fcP8lmurvMRqpmvW4aCSKrjINQwYl1GSpA908Jtx1pLL53tMwdI1zpG8HNAa5nY9ye+8atM0st4s6cpXmtlrHBRzyV5O7LBqSZi1HC5p7PYZImFzTuC1rgd90FSMb4Z6ns1Ber4PKT1HMD2WYcfZfHJGRuJIy1n4WPbvybuPvXQ4XC3r04rUqlm5YIe4VqdeWxOWxgmRwiiaXkNAJJ27bd16Ga41yylqTeXXuOxlPHSwMs6YnxMb3OiEbHTMmuulEomka7k17QGsBj2a4A88C0TewFvxYZdwNiGxXyGCs2LklTfo/OBZIycgbAB7o44Hu29XSOJ7koKlUPDnUc9I5GHC5KWiGGT2yOjYdCYx3MrXhmz4hsd3t3A2O57KcF4cakv1jdpYXJWqgG4s1qFmWJ43IJicxm0uxB34b7bd9lcXTXjRnpvE1+mjJCzCxz3aEdFleEcPY6M1hlgWA3rdUyQAceXANeQG7gFfnIeMmfr+JkWmI5IGYRlitRFBlWFo4zY+OcTCfj1WyNkk7Na4M4sA4/FBSzT2ksxkXzR4/GX776m3tEdGlZtPg5FzW9ZsDHGLcseBy2+ifsK4uGwV+7Y9kp07Nu0ee1SpXmnsfgwTJ+BiaX+6ASe3bY7q59fUMGA8X7lZvGGpquvUr2GghrPbrNaGWvLt8ZX24+P3m28991lmgPD2rpXP651VcYY6UIklx7+/evYhjyuRMTfj+HMcDdhvvFI0dj3Dz/zGMtUp5K1yvNUswECatahkgniLmhwEkUoD2Etc09x6OC4i7bWWoLOVyF3JWjysZK1LZl7khrpXlwY3fuI2ghoHwDQF1KAiIgIiICIiAiIgIiICIiAiIgIiICIiAiIgIiICIiAiIgIiIC5OKuOrzwzsALq00UzA76JdE8PaDt323aFxkRLJlNVZrw78S6luWF9eZ1LIRODo4nvDX8wP9hJ9GUevu+pBO7dlv6l40TOgEN6iydwdG7rQSmAvdFI2RpdG9jwHcmAkggeuwC85wT/AILYOlPFPIU2GKcC9G0bRmd5bMwjbYdYAl7fucCfvC55xccu2bxnMfh/nOR6s/SuJqXzhlqz+m+359/qt14heLFy9BJFtHQplh9o2k3e+Pb3mzWH8Q2Ij1AA37gkg7KsPiv4hVLNeXH0wZWzOYJrR3bGAyRr+MLSOT93MA5HYbb7b+qwPV+r8hk372Jdogd2VoiWws9Nvd39539Z25/Ise3KmXF7ax8O3yH4f4l42PN+oZ9fEniTxNflv7T+bvfDvOtxeXxeSfGZWYrJU7j4WENdI2tPHM5jXHsHEMIBP2rOvMr4rw6qzdXLUq89D2LH16rBJK0zCWC1asiZj4tuBBsN22O4LN1qhFwvUrEa58wmP1FpdmHz+Mms5irG41cxVkhiaLcYc2Cy5jmlzebOLZmN91/vlvD3eHReMnjTUz2mNP4GKlNXm09HTbNYkkjdFMa1A0z02tHIcnHl39AtKIg3ZlPGqpPoKtpAUZm2a0rHOvGRhgLW35LnZgHPcteG7fbv3WcXfMXpPMUMaNUaZlymUwce1d8czWVJpAxjXPl99rhHIY2OdE9krAW+h9FVtEG+PGDx9h1DlNL5R2OfWfpmdk9uBszXMnc23WsFldxbuxvGvsC74v8Au7435nvFGtq7MQZKtVlpx18ZDSMVh7Hvc6KxanMm8fYNIsAbf1D9q1WiDdXmi8aKmr/mf2alPT+ZobbJPaJI5OobPsu3DpjsG+zH19eY+xdH5bfEHF6YzBy1+jPefBWljosrviZ0JptmSTnqDu7omRg2P+1d9y1iiDYevPGPUeTyd2+zK5GnHdsySQ062QtRQ1od+MMDGRPazZkbWN3AHIgk9ys88G/MOcfi8th9RxXc7SzLXNYX2zJYhbNC6CzH1bRcem5gic0N24ua8/jdtAIgsR4L+YytQxDdO6lxLc5iIhtX3EUk0UbX9SOB8NkdOeNj+7HcmuZsANwG8fj4ueYKjbixVHTeDrYijgclBlazpoIOt7ZXkEsfSjg9ytGX7l7g5z5OwJaAQ6vqILW6k8yekLtirnZdKST6moQMZVmsWR7DDLG4vik3Y/eYRyOc9pdCHj0Dm9iNeeIXjzYt6vrarxVd1OWnWrwitacJWyhkckdiKXp7coZGyvZ22O2xGx220qiC3eT802k5Hty7dItdqSOJoit2BUfHFM1vBj/bA3rvawdgem12w4gt9Rqfxh8cbGX1Hi9SY6F+PuYihUha17hI02IJrM0rgB613+0uj4nuW8t/VacRBbex5mdF5Aw5HM6ObZzdZjQJmspzQufF3jPXn2kDAe4D2PLN+xO251d4deM9TG6ys6okxTYoLnte+Mxzw1sJsxhu7HSjZzi8F7js0F0jiA0bNGmUQbdwHi5Wra7fq51OZ1d967Z9hbIzrhtqpPWa3qEcOQMwcfzSFOV8Xa02vG6vFOYVm3q1n2EyM6/CCpFVLee3DkTGXf37LUKINr+K+uJdW6vgyOJglp2b1jGVcfFK9jpW24zDDA/k0cRvNxI9fRWT+UH12+nh6OAZIDZzUjbF/h2HsdQtIHEndrZbfEj17VnhVA8JdZu09l6uXZUhuS0Oq6GCy57Y+pJE+Jsh6Z35N6hI+8D7iPv4yeId3VGWmy1xjInyxwww1oi50VeGFnFscZf7xBeZJDv+NK78iDDUREBERAREQEREBERAREQEREBERAREQEREBERAREQEREBERAREQEUbpuglFG6boJRRum6CUUbpuglFG6boJRRum6CUUbpuglFG6boJRRum6CUUbpuglFG6boJRRum6CUUbpuglFG6boJRRum6CUUbpuglFG6boJRRum6CUUbpuglFG6boJRRum6CUUbpuglFG6boJRRum6CUUbpuglFG6boJRRum6CUUbpuglFG6boJRRum6CEREBERAREQEREBERAREQEREBERAREQEREBERAREQEREBERAREQEREBERAREQEREBERAREQEREBERAREQEREBERAREQEREBERAREQEREBERAREQEREBERAREQEREBERAREQEREBERAREQEREBERAREQEREBERAREQEREBERAREQEREBERAREQEREBERAREQEREBERAREQEREBERAREQEREBERAREQEREBERAREQEREBERAREQEREBERAREQEREBERAREQEREBERAREQEREBERAREQEREBERAREQEREBERAREQf/9k=\n",
+      "text/html": [
+       "\n",
+       "        <iframe\n",
+       "            width=\"60%\"\n",
+       "            height=\"300\"\n",
+       "            src=\"https://www.youtube.com/embed/xHWKYFhhtJQ\"\n",
+       "            frameborder=\"0\"\n",
+       "            allowfullscreen\n",
+       "        ></iframe>\n",
+       "        "
+      ],
+      "text/plain": [
+       "<IPython.lib.display.YouTubeVideo at 0x7f54057845b0>"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "YouTubeVideo(\"xHWKYFhhtJQ\", width=\"60%\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "The video below explains the idea behind [Two's Complement <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_wiki.png\">](https://en.wikipedia.org/wiki/Two%27s_complement). This is how most modern programming languages implement negative integers. The video also shows how subtraction in binary works."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/jpeg": 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+      "text/html": [
+       "\n",
+       "        <iframe\n",
+       "            width=\"60%\"\n",
+       "            height=\"300\"\n",
+       "            src=\"https://www.youtube.com/embed/4qH4unVtJkE\"\n",
+       "            frameborder=\"0\"\n",
+       "            allowfullscreen\n",
+       "        ></iframe>\n",
+       "        "
+      ],
+      "text/plain": [
+       "<IPython.lib.display.YouTubeVideo at 0x7f5405789730>"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "YouTubeVideo(\"4qH4unVtJkE\", width=\"60%\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## The Intuition behind Floating Point Numbers"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "This video by the YouTube channel [Computerphile](https://www.youtube.com/channel/UC9-y-6csu5WGm29I7JiwpnA) explains floating point numbers in an intuitive way with some numeric examples."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/jpeg": 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UjbgPvVRhv+mk/wC9yv8A7AYXf2vG/C4tFNUML8JwYt2csVrXVK27TubUT4mujxTOc3GHMuA83tfVXxzXzc//ABj/AHZfHKPzs/8Axj/dly6NdCaKLZ8W0dt1k0Dasu91gprCVzQSN49zo362vhw/CWm/Wstm2ehdDU0FRXbDq55fc2l9VBUlm83drl7C1jLdUONrG+F2dxYzendC+p2BsKaBjpo4YnsndEC/ATu4w1wbmOtG9veO1e+y+hkp9j7fmnjfDDLS7qKSVroxI4R1IIZiHWN5Yx/G3muR5puLy73alW3iq3VVVfTnqcT1GRwebe7U9u3iq3VVVfTk5/0Lp6R9SBtOWohpcD7upQC/efUF3Ndgbe+eE6DncX+m6FbJ2jHPFsqsrTVRQPnY2pwuiIYQCDaFlsRc1t75Yr2Nlv8AZ/BFRbCk2mzZ8O0qp9YafDPC2obDEAywZGWnAbkkkAOO8ZwHWuPs12xNWvmLti0+z4hEY/eIaVlK58hLD7uHYAX5WcQ29rNvwVdbrZrdLHa28dVVr2fLKfENfNOU8bklDjrFRtez5fU5j7DYKU7QaauWdtVHLD/Z7Iw3dSP+k3u/JY42yZl1dXZ5KwdOKHYRrKt1VVbQZVmWZ0rISzdifEbhoNPmMX7Xiqd7KertyiuR/wC9YdeOF4+3JTemHR2prNu1MEMDy6avkwF7XMiDCXO3j5CLMjwdbF+qFrkhertzcFsvivPujbNjvW7nNwWy+GkuvuiobBihfVwitkljpTL9M+AAzCPju8QsD22PcbWV66e9FKCLZMW0dmTVkjX1Ip/9pfG5paWSlxDWwsLXYo9c+KpG0tnyUtTJSy2M0MssDww4xjBwdQjXNdGrqST/AFRo4928yDaJJbgdjtiqs8Nrro1eTbPHJS4bSrimmnydetyOM8coz4ckq4ppp8lD9nexm7Q2nT0kzpGxyF+8MRAktHE99mFwIB6tr2Oqv+0OgeydmOczae0pzJI9xigo8Akihud0Z3mJ+J7mYb9Vgve2IZqsexuO23qLvnv3+7zZJX03u7a+0LnSsq25n/fPsmZZMmfbGbhHanxV9X3dkZ1lyajZGbhFRTdJXdvu7/sMPaL0O/s+sp4aSR88NZHHNSF1t4RK7Cxr7WBde2dh8QVpqehWx9m7ul2rtCpdWlrXT+6Fgp4S/QWNO8kcb3BIzsLrd7SZ2xSdGKiQ2a2hoJZDrYNFNI7TU2JXvtV6EVm0NpPq6KIVVPVR05ililiwdWJkfWc9zQxtxfFpYjPVccdTOUYRyT2WpXLhW06XXg4YarJJY45J7E1K5cK2nSVtV7if2k9AINl7OhqqepkqDLU4A68e4fTyMe+JzA0XxWaOtiIOLRtlzWzrZ3su0e1DYp2f0aoKOSVsskNWBI+O+DePbVSuawnMhuPDew+HRceecib5WZYLu+G5ZZMbblv5kr6Wk+D0fhWaWTE3KW/6pK+lpPjoR3tdYXvZYua64BvfgpU+jswbvFl7IAC03z/JegemRsDrgZ34IwOvbO6kHIjPOxXn943PvKEEdjHEm178V6yNxJtfLVb2iznAHXiiM5uF+P3ISRmMdna/avWxOtfOykQAZ52sSvGuuwi/NAaN061+FvRDonAAnQ6KRiFr3/u8Nu1a5HXjbnmDogMJIXC1+Oi8fC4EA68FtnfcRm+Y1Q54xNN+GaA1GN110r2TULnxPPw2kbLvM/oRGD9JpY5kC3Nw5qg7Lp97NFCDfG7B5ld16J0AjpDBGRE2XS5DA6OF4a8vlI6hkJcBiIb1TwGJcesyKMKNcMbkNukMt3DDa0hZOw6/RlgII/ddJ+9mP4a1tV5vZwNnDC8CxsJIQARfIlro2Ot+x2qw1FJI2MQyiPfRTBgLHsLHxyYnw2LNOsx3/LHHCq10hBJIIIOA4/12ZW8HWe7LtXhdz14rgsPsk2c+faEUjpN42OlgytkH2BIuRe2QyX0xst2EANF3Wy7uZ+xcT9i9MHRmcZb76c8gZX4yBza05DTqgfCuxUFS1osNR1s755cfX0XFnnumzrxRqKLJRwaGWci/CPq28Tw8ApbJqeM5kucP1nFzv+opG3FJr5H5ut0VGSLOkNv2AyMDsNziPnzSPBZxvqOz0gY0HdRacBgA8bG4UKfpJKf0cA8z26W7lqLWR5Xd3721u86/5rNtWwZ3OfEOzPfbXuyW6bfcy9JJ9CJWbSqnWIjtwPWjta2hLC7LXK3JUvpHLPCSSNbZcjchmhv8JIvblyVv2zt8NFhGB23Avb93K97ZZqvSRvqgci0G+uhGlgLXPLTmqyV9DaKpdCi7ZNRI0gteb6WtbLO5zyzAy7AqvtNzpWkmM6Z66k5/aV0uq2a6MMYL5DjkSOVtOY0VP2zQvbITa8biMYALyx5yuMGoufm6ptL9Uc22jGb3sch5ag+lkqqoMTMBJBYQ4E2yPWHHhqr/ALf2WcILbXzdbXLt4g9ipdVG4Ejj8QvyuMtLcvRaQdGE4le2pBiFze7dMj1NTkQM23Isc9Mgkj3McRieQTdgIBIfbmPEftZq2zsvbh9gPEC2mX6vIJPVbPEmdsMhOIAZAkWOO/1OriPku3Fkrqck4WJhSWuY3AZcjxI5lreGt+Kxmlc745SyPGLl+m7AzIDD1zawDb58cusmr6BzXEx3kswOkLPgvaxFtHtxENvxy5po/ZYy3kYLrMtlbGcDHvjZvG3wtJLb3dobLdZUnyYygyn0wJcXCImO4bGHhjzgY2wBfgtfCBcuA17U+paiQ5RAw6MyLAz/AJgFwePVTRtG90hs0SXsz9LDMwWGt45MPM2sW52U11K1xYZZMQi6rIoxYDAT8YLmxxdbEb/ipllTKKFFaqDU3zjc69/ju+1jbO+RzOlillbXvjdhADjGA19nXAfqWC2V23t4Hkrbtba8TSRo4gMO4GMBjA0WxxO+iyY0fCLNGWuJVSsrmSgs3YjBeHPm+N5OO4DDhbgbexw4cWWZWmK3y0Unx0MI+kDifhY3seWXOepcHXfxy7Smmx9o3lBLGAuIY8MaWZk2uQchl9irW1IWdZ7cIN8QaMZwDkcYacWYUnYMhGEXuM3DLQ6MF+/7Vu4qrRkyuspCRfLi63csXU5wg8D1Uxiw2HMAtt3rxrW7u3H4l3HKL30xAB5rN9Ied88OvFTsLcIGpBxLN2G9xxeHIVNlRtioNCzZxkBpGzGoZHhZdshB1fbFbrOyv9ZKBSnEWckwlY3IX+uXHxWb8GK/MFpKhRS6Ku5EYRj0VXz+vksOw/aHtKjp4qOCdgghYRGDHE5oYZDJq5ufXe4+KX9L+llbtYRR1kzXsgxvjDWMaAXgYj1BnkwJb1dL5YA268ZhxX4Ww9+VlhHS4lPeord5rkwjpMEcm9RW7zXIz6H9Lq7ZrS2jqMDHPvgc1kjcdtWCQdV1uLbaDks+lHTCv2o0R1VReIO/RsayNpLeLwwdc/vXSlmHuAfiC9YW6nUEnvurfLYt2/at3muSflMO/wBTat3mlYx6I9Kq3ZjXe5z4I5Ou6N7WSNuBbEGvHVdYWu22g5JvUe07ari1/vLRhYeqIobHELXcMPWOdwknR+GGSppY6qUwwOkZFUSiwMcL39Z7SWuDTbLFY2ve2Sm9OaOjp6t0ezp5KiDcR9Z72TFshviZvGRsa8WDTpxWM8GCWSpRTbV3tv8Aqc+XTaaWWpwTk1d7bX6vyVmMSRPE7XFsjXiUOYbPY4HE1wIzBvndXSX2qbVEe696bmwDEIYL998NsXbZVaUNsbcbeFl5M1pDQNR1brfJp8eSt0U66Wjpy6XFlrfFOulqzzZe0p6Stjq4pbVTJN8yV1pDjde7jj1OZz7Vcm+1nauI/Tx4gMzuYdO+yp8zGkstcnJvaeAGqa9K9hP2bVyUlTu96IwS6FxkjdjFxZxa08xpwWeXBgySSnFN9rq+DPNptPkmlkim+1pXS8ECk2xUxbQ/tBso98MklUXlrCHvlxbw4CMPWDnZW4qBUPkqZpaiR15JJHSyONhjke4ucbAWGZOSktw4geAGH0QA3MaC4cPBbqEU7S9v08HQoRi7S7V+ng2bc2zUVrII6mUPbRQimgAYxhZG0AAEsF3GzWi7v1Qr90c6E7aFLC6l2g2CCaJk8cTK2ZtmTNEgyjbha6zhkCueR4buJ4rXCwWPWd2BqxzYHKKjCl942vxaOfPp3KCjDavvG1+LR0b2qSMo9kUOyH1DaqtjmfV1T4yXBheZ3kPc7MuvP9YA9Qm2YXK305AB5qY3CG/rXPkUMwmP9u5V9Np/Sjtu+W2+nLdvjsW0mn9GG27bbbfTlu3x2Ib6Ui3G6zdRnIXvc4VKxNytckEO/FZNc0EW0JLvyW9HSQ3UxuM9eKxbTZ2vwxXUwtGTRfK7vNWGn6MCTZNTtYTYRBVNpNxguZA7c45N7j6mczerhPwuzVMmSMKcn1aX6voZ5MsYJOTq2l+r4RUxSm5F9Bi71jDS4r55BTWAby/ADzyzC9ia0EjO18QVzUgMprtJvovW0vVxX8FMhY0Ygb2K8aBa2fYgIjqWzMV/BePpbDXll3qWbYbcUPc23G5t6KoIclJbjxwnsKxdT6Z8cKlyvbwvm8OPgtT5G5Wv8RcfFANugULf7Qpi89UPxnX4WG50z0XbOk9TDvJWxulifFgd8W+Y9+7YQxhkDCzCSDZwzcw53XE/cptnTwPngki+sMYyLTYkXGV88x8Q4rptU+OqkdNfA2RjKi5IIeZIsclrcd4HZdhXna1bkmuh0aScX0Zt2PWyMlveRwktA9zxaxeRI15AOTmyRtI00ct+3HB30lsOIyZccfUL2HLXFi/hsocMrGiQghrwMzbBG/GwAMIe618myXt8TRbVaqyt3mIa3MeDjd5FhJlpiaT5heXOJ6mPqdv9llGIqGICwuGNub5WZbx/z5ro+zWhoBsCeZBuD4fB5qrdBKMNp42/qsjvfu4K7NrIocr+n28l5iVuzvT4JkEjjw9Mvsz4qNW1RbxztisAB/l3rR/rHCHYcu8E2sRyHdp2KPW1rJBcaXytkQe4Zngt+CxEl2icQyI5dYgA9hvcrF9c8aE25Zm549pWuaUZm9z28j3/AGKDJJqRn9XuHYPJRYB5MrhiAtft6/Zr+GqcSbSbSjXP9UWDL2uLC9zkNUnp6kRAuvc/FmLeXJUrpftm987Zl17kkH9Q9itFkjra/TBriSSLdfLiDcWtbjobdiq9Z0oacg42AORGguRqDn/mqdVSyODhe1zi88yk9ZQSFjS0E9QtIN7G0khzsf22jyWsY2ZTbXQu1X0gjJuCC4ai507RoOBSSunimJw/RyfEMsjcaW0t+JVQbsapBvYDWxxG4J4/ZxWyeCaO30biR1ibg3P7Fmt+06rR410TMXOXdEyspnNN7d+VwR2jXxvyWmwcMhbPvItwI5Xty0WMO1jpICRfPgRcWzvoVupImyHJ2nDM92iq011IRHwyNyYTgJDiLAD+Y5j+FQZo3OkcX7wYjiIwh8bzcHQam1+B58FYGM3RBtfxNiORuLEcNVH2kzetde9rZhmVxllfS17G1jwUwnyVnHgrL60t+jtGXHiDlkL/AFZMN+pc58AoNVVSOFt60fWY1jZGZ8LlrMz+8Sp8GyXGTdx7xxJzD43sI4l/UNiztaeKyqYhADicS0dd5YSQOvg3IecQBc4uBN8raNXfHanwcMrK1C/rZyNZ9Wxx4CeQDGuIHHTmvJSbFzbHCQ0lhEnkesBot9a02xAAx/ATYmzxwLr3Jv28Evme7KxxC+EC9yeGbL9fW2htftXZFWYNkVlU4OvlkeQOXK9s29qb7FbeaO2f0zLEm9r87nr5FJqhoBxWI5jM2PffVOujUotLI4D6GMzg34gEMsD/ALx49FpJccGbZEbTgtvxC2e6i3bbEpcDgARbMrPGLaZ4cN10mAvkphYEeK9kpQMGuYxJgXjDhsvZHAgDDogIHufXA4EYrrXUUwAuNCC7yumhkzFhotNYeqcsgx/2FH0Ky6HVOmY2Xsh0Tzs2nqKqWCGOOnLGCBkTbgzOjLC3eOOWJwc84Bm3PFVemOw6OpoRtnZzTTxb0QVlJazIpXYRii4AXkZ1W5dcEYbYVaPa10WnqZKaupo3VOCCOmlp4ml07es5zZWMbnK3rkENzFgdL2U9IaU7M2C6hqi1tZXVraxlKwh8kEQFPfe2yDvoDfXNwF7h2H5zSzWyE4TcpuVNW3xfKr2R8ro8q2Y5wm5zcqatvi+VXal3Imz9k7O2Zs2lrdo0r9oz1+OSngEkscUETO2N4xPwFriXYvjaAG4ST7UbL2dtXZ9bU7Po37Oqtnx+8SM3sskU0dnOwu3jyAcMT824cwPiup+0dkybU2Nsg0LW1DqNklPUQ7xjJGPIjacpS0ax89HtIRsHY82ytlbZm2g1lL73CKSli3kcksj8FQwZROI1maLXv1XG1gr+pcXPe/U31tvtuqq+3+TT1bTnvfqKdbd3bdVbfFexS+iNZRU+9dtDZ/8AaAkazcjfSwiDCXGQ2jc0SYgWZ52wdqY+13YcFHtHc0kLYI3U8Uro2GR7MRdI1xG9c4jJoyv9qVbC2NU17SyipzPu2N3zgY2Nj3heG3dK5oDnYH5fsO5K9e3DYVVJUy1sMDnU8VJHvJmGPqbuSV73YcVyMLwbtBXbkkoaqC3Vadq+O1cfk78uSMNZD6uqdrdxfFcdu4p9mvRejq6bajq7qbgUWCoxPY6mZJ7y6ZzGNe1r3OETB9IHeqY7DbsOrqo9mwbNlcJmvijrpJ6gTOfHE528tvLA9Q/UaP2PqqF0GkxbF6QnnTUWfPq1iT+zY4drbNsLMfOW9z3QyM8s1jlxznLNLdL6f3UnSuk+xzZseTJLPLdJbf3UnST2p3x7kfZkNNQbSliraU18MVRNRsZvZIMMrZw2OoJicC7Jrure3X7FfvahtfZsO0ZY67ZBrahscb3ziqqYrtLQQN3FI1vV00VD6XYW7Wrw7IM2hLKf3d+X3y+clc/ah0Tq67ajqmkp99DLDCyOUSwMj5HGXPyZmDeyzyxjLNjnkk0nF87mvHuUzRjLPiyZJNJwfO5pXx7nLqhrJJZHxR7qJz3yww4jIYYXEmOPeP6z8LbC7r6LTDCCczkRiZ3jgrB0s2R/Z9ZLSNlbVGAR4ZY2bvAXNDjG5j3O+G9teI+FQImgEktyPWt+o/jb54L3cbi4KUXafQ+gwyjKCadp9CHsSgbJURRPvaSqgYQOrjjklja4X1BsTmun9LNlbD2RUTOkpn1M0hYY6COapEFKzA3Nz97jJcQX9d7viAwW6ypGxP8A3mluLk1dNn/67LeKde1gD+26zK5w0/8A/ViXnalSnqYw3NLa26dXyv8AfJ5uqhPJqowUmo7ZN06umv8AeDR7RNiUZoaDauz4XUkVaZY305kfIxr43vYHtMjiWnHFILXtk3Jq0+0jo/BSM2U6kj3XvWzIKifrSSb2YsjL5TvHOwOdvNG2bpkm3SBt+i+yf2a2rZ5z7QcmvSvYFRtOh2FLQw+8Bmzo6aW0sDd3I2Knjsd88D4o3jswlcuLPKEoqUnSlONt9ldW316HJh1EscoKcntU5xtvsrq2+vQqmw+jtO7Ye062SPHVQ1FMyCbHIDCwvgDg2MOwuxCVw6wOgthsrbsXo/smCi2O+qod/U7RbHCPpqsCSWaRgdLII5g1mDeNADQNdL9ZsnanR/8As3o9tKF80Us75KWWdkGbKdxmpwyPEes44Re+FuuWIZui7R/QdDv+PB//AC0a556iWZvbJ05tcNrhRuvtZyZdTLPJ7ZS2vI1abXChf4sk7V6J7Kc3adFT0sjKqhpHVZqnTTveJC0zMiAc/A6PDhb8Hw8SRiUb2bQ0r+j9YNoOLaRu0TJLhJaX4IqJzYwWi/WeGts2xzyLdUzoettvpJH+tsv/AOxTN/8ArSv2d7IO0ej1ZStc2J8m0CYy++AyRQ0L2tNswHFtr566FYOcvS+uTq8btu2rpujnc5ek985VeKVt21dN0RdiwbH2zK6gp9mybOncyR1LUMlnkN4mmQ4mSSlp6jCbG+jsweskfs+6KQ1MtbJXl3uuzGmSpjhJD53tMgEYeOsIbQSkltjk0DDixNs/s86J1NDtBtdWQspKekiqnyySTQuHXp5YQGCJ7ic5MV3WFmnO5aHY+x+ua6fa7G7oy1n01JFUZRz4H1TsEg1cLTMu2x6pdyXVlzPHHJ6UnOKUXd3TbadPnt+Dty55YoZPRk5xSi7u6bbTpu64/BB2PVbDrqhlINjyUzp3ininZU1T3skkOGJ2F87gDiIHWDhnmo3Q3odB/blTs6rb71FBHUObnJEJLbndPO5e1wOGS9r6jsVtG0NqsfdnR3Z4LX4g9jaRpa5huHsc2TI3F79yU+zieaXpFVSVcW5qnQVAmiGkZBpgGjN1xhDTe5ve/FZrJkUJuMnW1/x7nfZrwZrLlUMkoSaWx/xqTvs14F+0KbYmySKSaldtapaf9qnM88DIX8Y4208ob1eWZyNznham6ZdGKSDbNNSQy7ujqpKNz3NkEnusNRLu5QJHF1w1oLgXX6rm3uq7E3J+WZc/PxK27O2W6olhpYhGJJpmRMMjxGzHIQLvedG+emWJerj08oLe5vo7vp966KvY9jFpJwSyPJJ8O7fHNc10Vex0nplsbZuzCcfRqSWnFgKplZWPY69h9IWzWidiJFjh7Fzfpq/Z8roJNmwS0xLHiqheXvjjeCN3u5JXucT8XG3w6dZdB2FSbe2c7dRwOqIGks3Uk0MsDmafROc9r2Ntwy1zCQe2vZkUE9I+OGKnnnpmz1lNAWmOKXL4d31b3xtu3qnBfiuPRy25FGUt7d8qTafHdNuv8nDoZuGVRlLe3dNTbT47p3X9rEOz+lj2A01W0VdPYNIlsZGAaWcfi8fAtVqpKeP3cOiMrYyA+COUFkgBJOBhdmRqRr3uulctRs7ZbWuiLNp15YHby1qaAmxGBhyDm8+scvqfCqnXdIZ5p2zyyElj8bWjJrM+A59uZXX6PqSuC2Lv2v7L/s9LAn6u/EnBd74T+y/74L8ALsZjBcbP6/UvkAWPz6hsSL9ykbOYHVlPG3Mb6Npv/ut3c9mlvBJqqTeytkxWZIGTg3yAIFxlmA0gj+FN+jEg9/g5jd3sf8P6Z/8A1Mt5rhyw2po+oxT6M7TtDpZ7jFhis6Sw42zA/D7VXD0wfKcy7HfPrPBJOouDpdQYdnurnb6UEMN8AJyIJuTa/NN6TY0MIyiYDzzJ8ybrzkoxO+MZS5AbWkLgQXZ2ab2Ohvkb/dxVm2Rtp5FidefL5ukzQwZZBbm2GYy7VSTTN4xrqWyKvD7Ak2HbmfX5ssaraAANiAb6jjb59FWA4+HyVrnnNr8vG33KiNNhN2ptO97HL4R4aG/kqxXEOOfPw0/yXtTV3JufH5+c1CknuddFePsVbSRm6yxBFlCrKsMFybJRUbWIv1g0Zci/M2GZ6ozW6i30MnJFjc5vNYSRtcMxceaqja9kmfvYbq7Mkaa36uEfks4q2SOzhI2aI8bg+T2Zeiv6bRClFjPaOyWuFwPQfcEroKbdyAHLVufLkc05pqsStu3I8QeCizMzuo3XwZzhRnV9XTj1u78e5YCUZnK4GWQ8gxz2k+ZW178s9QEvmbkoozqxdtDajWud+nAsXGMM6j7Z2EYGYvewxFQKnb29jAvgA+AyRQx58L3jwjI2s1v6yrHSyPDOSBoD9h+fBKqOow3H1T1hwseR7NfNexiwrbaPOyS5osM1XJjIldHKCNBjIA4WGG1rKJUYtWNhtxBiYQbfvBwJz+Gx/iWmJwkaRy0boRblw+e1bqeXDkL4h1Sw20HeNfBaLgxaINTI49clouAy8EMMEVs7BzadjW38FsA3Wz53XzlmZF1dMDAZHepb5KXKwOFsmt+MiwIcban4RitdaNqx22d1eFU9zs72Y8MEd7cfo3LeLtoyn0GUNON3iOt8lvFMC3TMKJBWgRgH/DD9OGQy8VLp64FuhaDxe0gH+PRdLgzE2yUrbHLMW9V6aUXblrqpFyRw4Labm3YqAiOgbkQDqfRaaykuHADIsP2JkRpkvS7O+WmHRQyGrHHTjpM920I3bMrSGe5QRyugILMYfM4MeHDDiaHcsrqn1bJHvfVVL5J3EjeukJe+2gcDwwjgmD4bO3kYAkHMZEcWH8VqrKshrhu3YpAW4cBdmRbUZEeKy0ukx4kklz0vi/ycum0ePAltXK4ulf5MIGyQ4pKeWeImzSaeV8ZLOF925uIZ9q014lkdHJVTz1LQSwGeWSXdE8RvDkHaeSn0AeyNrSM7C/YeS2hptYgWPPPVbenBSukb+lDdurkUQmWneRSzzU7phmYJZIrmO5GPdnrixPmVntPaVS+Etmrqs3DccU1VO9kguLgsc60jPwU6OjAN88hhFySB3X71ve24sQD3qZQxuSk0Hhg3uaVidtM9kTmxGQQzBm+iZI5gkAu9jJGXwytu4kB2lys9n0b2ti+KNzDiY5jiHsIN2FkjDdju26bOdla3ohzzlloja546lti59+osFBfrSEyOJe58jyS95JzL3HMntUyCSpiG7irKuKIM6kcdTMxgHJgDrNCkAONstOxbAxxN+zCqShGSporLDCSpqxXDs9rLuN3E2cSSSSXm5JJzJ7VuFG25Fu5MmxH5CyazO/FWL1QpjpBa9iDfIg2II0IIzBvxWRpC5xkldJLJJdz5JHGR5tkLvebnIAeATZjez9rRehptayiubFK7EklI90TWl0hjjeXsiMjzCx79XsjJwsdrnbiVIgjmha5tNU1MANnPZBNNGwk6ksa61+1NBGbWt6LaKdxGnLsVXji1TRR4oNU0IRs54YY97Lu5ntfO3eyYJXA3DpGXtI6+d3XW2WiJEV5JSISWwAyPIgzD/oRf6LrAHq20Cfe6k27FtNKeIUenHwPRj4RX/c5BLJLvZt5Kx7ZpN7JvJmOAxskkxYpGus3J19ByWh1A8NEcUkrIyRKYmSyMY+RuTZMANt40AWd2BWgw8beixYy+YsbctFKxR8D0YdKX/wAK9MyepjtNVVU8QItHNPM9lwcrsc618vRA2YATa4cOsxzCQWEaEEZg34pxLE5pMjRdp/SAC9rf3g+9YyEtu5wdY9YOAMjCP/TGSssMUqiuCI4oxVJcC9lXW2//ADCt/wD3U/8AUoscUjS6UTTCd2PHMJZBM/Fk+8oOI4h2pxSDeAloda+paWX7RiGixfT5Ec9VRYIR42r8ER0+OPSK59hEyhDWYQPqYrrVU0YI00AzToU5a2wvbtJPqTey0zRuItwWhoQpNqVowhu0K0N+G3vU+mlviSeqgMkhlle+V7nuxySPL3vsMruebngnc8LsvRRKmJ2XZoqQxwjzFJFIYYQdxSQglp2gglut1BnizdlponlZGb5+CW1DTftWhcc7BmEsBjy3kHWHPcO+Lt6rs/4nJxsyo3ZbNY/RyBr7W0u6MjMWAsVSKGrfBMyVt7tPmOIPZZXCJwc0mP8AQzMxsF72kZbesPHE2x/hcFw6rF38nbp8lcHe4A0QxEWsYw4W0zA0ske19rWJDRe3VJOTAeWmZXnROv32yqaQnMQ7gk5deImM+GQPikMdLJXykYjDTR3bhjJBkIvcyEZ636t14McVzpnv+rUEYVe3HAnrC41AjP8AV85JhsPpM2QhrzYnQ8D+CoPTerFLJNFEBFHAGZAYHzyO3dwCG62kvw6rHfsqJ0X2jvcLjm1we8Y7Y2PYc7H712S0dRtHNDVpy2ncYqhTqCh3wdbPJUjo/tMSxAh2IDq34gjgRwK7t7F+jxqqN1S8HC+R7Ga5iOwPhe/kV58sfPB2PLS5OFdI2OhlLDlmoAnzXZ/bZ0BIj94i+JmeXLwXDmsLT1gbgG/etYRrqZuRC2rO4m+ueFjL6v0WG09l+7UTpnfSTnAwE6Q7x9jYcMOvktlRQSGUStkYQLYGnh2/YmDp5JIHQyRxyAjCSCQQed+BvbPsXZgai+THPFyjwcrbVF1W+EAuwve0S9e7g05SYZBcNdrYgaq3xQAxRyNu2VwF8GhHE4DkB+I5Ib0bfJJjkLtdGAB575Lfcn8dBhbYgNFhaNlyBYcycXqt82aDXBy4MM07Yu2VORy8E7idvOCwo9nXztr1vn54JvS0gB+fNedJqztu0QXQZaKFVRWVnkpx8/Pckm1IreCqmVo5x04pcwbcMJPZn+KpMjC03HD5tbkundJafetHZ8/PcqtLscuJsPyzNl6umzqMeTiy4XKXAgpqg4hZoOjbjFmBkMgdbKwFz3R4bW/ZAtjA0v8Arm+fWvqfhU7ZexmROG8+L4rZEEc8xl/mmO0qUR5gZEa6+qZdUm+EaY9HxyVyJ2tgQ4A5E3B4Ht5ooTvoKqmzN2GVl/quiu+38uIeATDbse7ZBO0DC4mCZtrC4uWnLQ2uonRpoE5I0JxagXGdwRbSy3xTuO5HDqceyW02QR3po5ALlt7jmzMPHkp1C0Bz2AfRyRsfHfMHKxGfH8FD2MSBIzgHnyP+SYUdPZuEm7Q/FGRcPZfgD3/avWlKrTOIlUZtLJD9UBj4+wG12X5ZpluieH18lAo6TdkuvI5z9S83NuQy7vJMm4svRZTavgA2mzBsNCsvd9DbO3LtWYa+41vwWW6dfO91QGv3cAnLOwXohGI2HDlxstzYXX43XrYTnr2oCNDHkMtTmvBCbHLU8lKY3W3ivAHWyBsgI24JaRbhiXjqazW6ZnNSbOtloh0TyONvJAajTaXHHCsxCMshqbZLe2ndxW9tMcr3QEUU9y3JbG05vpn3dqkRTR7wxiRhlBwluMYweVtVKla8AlsZkeB8N7E8wL5XtwyTawQ20Jzyzy7VtZs/NxsvH1xDY5gCYHHBJIMjASbDeMtkMWRzyXtUyaCaMAumFTIWxg7sCPIHAXgNPEnFc6aOWkcMmDOCjAAyGpusYQwu3QGe73oyyey9rsdxzyWWzmye9y0wJad3v2Rzl5ub/SMxnEcOpDmnDh4ZdWPJSGOU7kEFs29jiJF46ki9RRG2WCaPrsPwlwW0NNzTBLpY2yM+jLHDNpsQbHSxGoPYiYtZYWBa14imcNYHutuzIzgx1x1u0JjQUDGbycA+61NqpkrAQ+mmIAlD7C7GusDf4cTXArRRUrqz3maAB0keOjLj1IaoAXiksRa9uqWOGEtI+Hq4YjhVt9gE8BErI3Ns2T9DJoC8ZmN+XUdbMc7Hkt9VsiQ2MTmMNtHx42HyLSD234qxUGy3OgjjmiGcYa+J5EwB/Uufja3x4KbHslwAAFgAGgaAAZADssudtJ8ArJ2ZYi44KNVbFikN3RNce7PxIzKuX9km+a8ds23eqKVAqDdnhos1oAADQAMh2AKCaMwnT6Jzw3sheeH/AA3Hyd3q7voraBRJ6EkPBaCCC0g5gg6iytGddQVd1AetktPuOQ5WN05hpHxOELrkG+4kP1wBfcvJ+u0A58Wjscs37NcpkqBWZqYYNNFGqKfLTgFZJtnEDsUOpo7BQCt1MBOHLK6g1VMRYkcSrHU0p5KBVUTtLIVKxtCkJcltXCBIbZ/VVnqqB17HVKqugN7cUQKrUQnrc7pl0Xqb7ylJ+J+OE3taZgy8HAlv8SwrKU3PqlEkbmm4yIPqkoqSounXJ3L2RSGajq4TcmGcSgHVjZmG47OtE7zTwU76UlwBAIzbhuzjg49TU559y1ewumabyn9JV0g3gAyJppLYyf1vpVeNv01jk3Ia5HhpwXzWWahlZ9Dp7eNWch6V7NdVuMscLA9wEU2MF7CAMn4Dhs7hi7kr2X0TMdt486YbMAYMHJltBpmunzsHBvO/E5qDLTXz0Cs9VKSo0WnjF3Qn2RRNi+jiaRfhqSdB3lfX/sqpxS7NpqYixjhGP/iHryX/AIyVwP2Z7AbJUiWQAhhxMB0J4L6Q6Nw/RclgpXPgpqUtnJV/aZWNtg+qeqRbmuHdK+ijTeVo1vpbNdc9pJ62QJtprw8FSqip+jAI81nOf1WaY41FI47U7KdEbH59Fiyn7wV0Kv2cJDccfnxSSs2QW8Pn5+5XWSzSivNjfzWbKQn4rnyTiOj537vkdyyZTAHO6WNrI1NS9nqFvZTcz3KbG1oGSxqDlw19FVhQF8xskW1XapttB9kg2m+6sik6Qgr88u1Ywsa3XIDrE5cNb/PBe1IzCjbbjduXWyv1fA6+i260iq4I/ur5pTK0gtJxAg5W4DyU6vhPuzr5lvWPZZVake+F1wTbiLq1bJqxM10Zt1gW+YVpx2v2NYq0I9v2OzCQf/iY2+ir+w3lrrkjM5ZEnwubJ7t14i2fBGbXdO9xB4hhfc+o9EnpYD8UfWFtAQbX4EYsQXo6bjH+rPI1z/5fwOKJgHWyzZhPaRplzTSFgwsz/aVa3xjZi8hzUujklw4iG24Nzuey/Ar1drfJ5Za2AWzIzItmpMEjCQWPDgOqbG9iNQe1J6A4mtcND5g8j23XuxISJqlt/wC8Dx/Ff8kULT9gWNh055/ks2jNtyMgo4jta/HtUljM7clQAyM58/JZ7nM5j4wgMKGxkkjkhU9bAOub6qLW1LIIxI65b8JwC5BOl88h+KlNpif81hU7O3sT4ycnsLfTXzUwq+SxEpK+OSMYSRc6PBZc2vYXyJ0NrqeW5HThZL9hwtkpt3IBeL/ZZmnnHkO8YbHxUmG8EjYpDijk/QSHN7Hj+5efrZfCVpLGraQJxgJwi+XYomzqh82ZpnBge9l2SRyG8ZseqS13hmmTn4TGC2Q7w4QQ0vAPDHgHUHatTITSzk6QVT8VxpDVZCx5NkFs/wBYdqjGk0769gQ4aCL3zeuEckVU0RY3sH0NSzWGQPF494ODrdZoTeoZ7neS+KC4bI0nG+AE2xxnUsuR1Hc8v1VH2/RAVNKLAmpk93njP99DkASNcUbiCHcE3rejc80Rpmzxbt1mvkkY8z7u4NrsOGQ5fFYf/UuhpPa5Phgg7UpmwufPbHSVAYyubwAeLNqmAd4DvAr3Zez3TRybPdJhmpnsqqGbXeQg3p5mn64b8J71dtnbEY2JkJAc1sYgIfnjYGBmY45LLYvRrdWifaSKF+OhkxPZUwA6wYhmWcL3zabEZKI54qLT7dAU7a+z5ZqnZ4NLO2eOctnkjad2yMlgL2VLOqW/E4DzGas+y+hrGGeSSSSc1ODeb4RgWivgsImtAOmbbaBXCnoidPQZplS7IJ1Hmscmqbiorj/bJXsVqj2JHHiLRbF1iLm2Pi+3BzuJ4qaygaOHLQKzxbHH+QUhuy2DX1K5nJklWFLpkvfczy496tgomDQA+F/VeOhHAfYFFla9yqHZrjbX7FgdlZq1PgOlgPUrU+icef2JY4Kq/ZrRe/rkoslK3O3oFbX7LHG32qNLs1o19ckssUjaOz2yxujcMjob2IINw9hHwOac79iV0zH3MMv6VgxAgWE8egmZ26At4O7C1X6WmbwHkPvSXbOzhM3ImOSM44ZRYmN4yvbi22RbxaStYST+llStTUGXgoFVRC2fNWGlvKHNcMM0f6aMaC98MkZ1fG6xIPYRqHLTU7PyuoknF0wVSrgaL25pZWx5gjkrdVUDR6apXV0rQQBxUAqlVBdxPMfclNZT2Iz4YSVbaqmHqWpRW0WZGdgFNkcFLracdYX44rpDXRjPvxK611AL65WxKtV1K3rdilEnXf8AR6qsTacXza+dn8EkRP8A3RBdW2002t9gv8lcC9gm1RDWNhJsHvLR4i3ne3qu91UwPevl/iOPZlfvyfQ6Ge/EvbgrNTAvKahLiE/ZR4tfnvTCmpREMR4cFxbnR6A59nlEBPHFyGN/YGd/bYeK7XRxtEYAIzXDug+0GxuqqlxA/R07CT+/JI3/ALE8k6d4TYSZLXDNR6nJqMDydGXvpHTxAXewOuFxj2h0Ge8poza/XYOHI2TTpB7QGFhu659exVWk6cxb4XyF875i3FWnNNl8OKUVRVqTbToZd1KCL6ZKywTiUA5eHzy+9Vz2w1cEohqaUtx74MkYLaPBs/55qN0b2icIN1lJd0dEVfUslVSDgMs0tkgOeSYsqw4KHVSjO3jy/C6tB2Q1QtlJGWSh1U3NbK6ayTzykqxRmFbNw5fISOrkubKfO5KX3J8VqjKRFqm5t/l9FF6QVIZHGwfEX4iOwA6+JTOtYkDtnulqnSSHqAhoHYAFrGurKRFk4vk1rj3A/cnPRyldHeRwIAB1U6QG1mNy8slq2nXiCB0rzYNFmMuc3n4R2m6tuc6ikaXtTbKf0pk3k27H9wzAL/Dd2cvqRn+yo2yKYukAIs34pOyNgu99/wB0H5K0RylxL3al5eHdps5wPZmpW0JNxSEjKSpGEA6sgBzP8TgP5e1exjhSUTwM2Tc3I3gtLADwGHxU+CcYBnoUpjjyvfhiUiNlhe+gxLts5Rrseua1jruAG+Nr5KdSTBtWc8pI8flYfiq9s+gAIc52LPEBbTtKZzZVEDwdSYj46fat00267oFsp5G9XO4zzU2NzbnPVUWto7ymMEgSgywgab4fGLeCb0LiIhLESRGz6aIkkEsykwXzjdxVXhVJpgtQlbpfl6LYJ29dLKFzZQxzT1XDEPnmpkTG8/rYVg1XAN0cosM9CV606Z8DksGNHPjh8l4w5E8lUGmem3bhNEL3s2pjGrwPhkYOL28uLbrR0lqonNgbFIHSuqYXBgObMFxmBmw3Iy7+SbxRXGp0xKVHRD4rDELZkNv5rfHlSdtdATG4AWW4eKmNDHDCRiab3BAIIPAjQhaKak0z1TWmpGi33rGySJQbHibPvmx/SWwhz3SSYG2tZge6zcrjqjiVZ6KmLss9O5eUMDb2HjYJ5Rxdn7Wf4BRKbfVkcHtFs/w04JzSULRmefH5svKOmcefgPvTek2cTrYepWQ+yNcDGjQX7ALBS42ngAPUqbTUDRrn6BS42sbp6feUJ57i9lI4638cvRbmbP7R4C6nF/IW7/wRu3Hn9iEcEUUQ7fsXjomDl6lSPczxt4m6zbSW1PkLITz4F73AaA+QCjSSdgHqmskLBr9ufktL3NGg8hZCOfIpkicef2fcor6TibD1TeWXst3/AJKHIxx7uwdXzQcCmWlA1/AJfO1ovl5Z+uieS0hOv4lRJqQZqSxUNt0hkwSRWjnivuZDmLH4oZgPjhdlcdxGYS2nqDM0gsMckZwTRHWN9uf12uGbXcWq4TxsHAfb6qvbdoS+00Fo6mMYWPf8EzNTBOBmY3Hj8Qcbj9reMlJbX+hUUVdPe6WVUIFuxOYqkTsNgY5IzgnhflJC/kebbZtd8JUarpBcdqo1ToqVqttwCU1hzJtqNFaKqmb9qVVTBwHApYKbtGM3vbTgqztGKxJsr1tGHU24aKtbUpdcuIUokRdFKz3avgkHCQO9V9PU0oJvfWzgvluSMRyh36rw5fRXRmo3tLTS3veFjf5Bg+4Lx/i0LSket8MnTcS3bPI/FQukNbZhFzn3+mazglwNKR1znTSBgzucPz88V4B7uPya/wC07UgjzG7mmlNtX7wMse02ZbwSyl2wJQSBI1w1EjLeuh81cJNkiOnOQzHz6qgOlMTnAm4BLbZX8Dqrx5NLRH2/VOtcNN/hANwB2lVY1U0UhdJKDH/hiNg8jbFfhqrPVzhwOXmq5WOz4eNitorimVlkroMujMf9o1FnRmOJjMTATmX6XPLK4t2qwzbBkpes0Et4gZ/JWj2dMwuMhGR6v+S6M6paQR1SON7qsjN5LZRYKrLW32hb96TqfsTfaGymEl0Y11tl6JPNTFvaOQVEJSsg7QjvoPnsSiaHPknMxtkdOChPiGZ73LVGMhNXiwSxjc9O5Nq+K7730boobQL8cuPNargzkRalmV+5QXQm+Nmo1HNMK1wtbtySGumfFISw5FmhzFxdXUXLoRFpPknVlcyniMszg0DgficeQHE9i5l0j206slz6sTf0cd/hvxPNyw6Q1z553GVxNiGgcGDiGjgPyS6G9wBoTn3L2tLpI4lu7nmavVOf0roNtmQmSSKIZFzy0n9QG1z/AAgOPgo3SKtE0zi3KNg3MbeDWMyYPIJj0cbeSSS3w007mHkd2WYyOPx2/iHJV5zdTfPVdWNc2cE2WOKYWPPDhW9kwt/DhKiNaLdtsSza3IcytCgyjqBkeS31043YI1jeJUtLRbLgcKnQtGQPEZ+KtF07A53zZRGRkW2ljPIn7uxb8BO83bhHvh9ILY2XOReziwpfRxtAaG3t8Nr6DvTSB7bnsU+o10Aw2QRFGI8yAMN9Mzcn7VOhkHriCXxPGLTK2JSYJB64VnJ27Kk+OUceeJbYpm2tbNQ42+pLVup4siT4IWGEVQLaZ2wqbDNfTjZQ4I228MV0zpw22XC2naqgnUwLiDY5J7s+m00/W56pZRfVFlYdnsJtb0UDga7PpG65p/RRsHL7Sluz6Qm2XmVYaGjHEqrBJp3gaMPjkp8BJ0t4C6KeBg1t4lTopmjT0Fgqk/dmEVM46i/efkqXHScz5fisW1B4Afas24nc/sHohHBtsxnK/bmVjJUchfvyH4obSnjYd2a2NhazM+p+7RCeSI9zzp6C6wMLjrfxP3KYZ2jS57sh6qPJM46WHqfVCOPJqFLzPksXQNGvqVkWvPP/ALF4aY8x28UC+xHe9g09B96izz9nmfwU99OBqT9gUWTAOXhmg5Fskjjp6BQJ4SdfUprNL2FQZpCfyzU2HQtmp/myXVLWj5um80RIOvj+ChzU2WfLgoJ+xUttUW8IljJinjGFkwF7s1MczL2lhv8AVuOwtKhuEhaN4Ghw13ZJZ3gvF+RsrTVRtF9OHaUrq3Z6cVpvbVMhlbqYSldWy35qw1LXE+aV1FOeOtj2oEVevOv4Ks7SvmrxXQAXPGyrm1GDrWHJSmQc/wBpMNyV2z2Q1O82bGDqx5b4ZEfeuT7VjOdhxV49h9dbfQHnjHgbfYfRcXxGG7C/Y7NFPblR1OWTI+KNgxNBM0lrM0vxJ0+/zWiqfwCWV1Ud2G6ZlxPPgPRfLH0sehaazarZQWj81ROkGznYjIBkTnfJStkVAElychrnw+QlHSnpAJJDnaMHAwDMnw4lbY4tsmCvqKJ6OS5JIaOd9Oyy0mKIOBcC4cc7X+fuTGj2bLUt3l2xxklv0jmMkyLBj3d8R1v1QdDyWUexBu77wk5tOhsRqCeB0y7V0KDJllxL3H3R/alIAG33bsrA6eBU/a1Y0NJDx2dy4tt3bsdNLNFvTJJE/AAIwQeoD+kGQOIkeCgUPtHLRhljJHffyNlqtJNq0jGc8fVM7PR7Ydaxz8V5LXAnTu/Bc02B05jmlDN3IxruqC8ZX4ZjgrkJb5hcuTFLG6kTFpq0MXt/yUGZ9suXyFMhlsMzcn58ksrpbn8URSRGqRdQJWjO2malVElhYHu71Alk4c1ojMhzZ3Hke8JTXx3t/KmlQ/I+igTZ2yWsHRSSOWbdZaolH7X3KGxxAy8z5J101hw1F/1mB323SQOGYtkV9JjdwR4eVVJlg2NMBTVcnEQBlhw3kjPuafNV7GLaZ2snGzG2oqx2dne7MHabyk5dw9UpwC3bhxXV4JKzFjts2X/SsxPkByWLRlpw9UYLAdqEG5tTl/1KQyqNwVGIyPhZbRq0IBnT1RuCmVPUZ35pLBJplnmp9O+5Z6oB5TVOd1LgqbckopnnNMIT9oUAYR1ZUqnqDayXw2GMqdSyCwHYboBlSuJ/7U7oQklI44Blqn1GCeHJVHA+oCMtFYqCfSw+5IdmwfACrNs+EZXVWORzQynLQeqc0gcef2BLqJ7Ry8E0gqOQ81A+4ypqc9g9VPhhbxufRKop3H8gpMbHH8yqjjwNGSsGlvDMrL3scB5myhMj5nyW9oYNfU/IQcm33hxy9AF6IiTc5dpWIqWjIei8dVE6ZeqUGl3JLYBxN/QfigyNGnpmogxP1ufs/BbWw8z5IF7Ixlm5Dz/ALS4vPO3YPvUvqt5eOq1yVA4An0QfdkJ9M48vErW+l5nyUl8rjy8BcrQ+Nx5+Jsg4IU8bRy8SoE8wGgPgLfaplc+KJ0Ucs0UTp3mKAPcGGaSxdu48fxvwgnD8WR5LCemaNc+8qzi66DkTzznlbvS+ZxdcA3t1SBw7+Sa1oaGuDSA7AWsNr2eQbE27bLjDOjMmz9l11U4Oj2xBMaoVbJS8PYyRhG7INpY5GB+NsgxdbMfCurT6eGRO5U7SS+4r3OizU57vVLq6nFiDcXBbe+YuNRbQ9qlbPrzU00M+m+ggnsOG9jD7c+K1VEJzPb4rnrbKn2HsUPoNtKWekLJ3GWeCpnpXyGwL92QWE244XAX/AGVNqg4n8FE6MxCHaW1qY8Zo61nDKoYS/wBSwJxVOAy7Dot9VFLI66On+SzK3V07s+aQ7QpznqrVVPN0jrKcm9+fFYJlSkbUiOdlr6D7TNJWxuOTHHCfHJONpQfEqrXR4bEHS6iUVOLiy0ZOLtH0K6QEAg3DhiB7+SW17cvntSL2d7bFXSCMn6WLqm+tuBVhmzHcvks+J45uLPp9PlU4pooHSmrqIiPdXbtx6ugeDjNtCuc7ZNa8FuLMPxYmAxyMcwk9VwPVz+xdjraTeOuRoVprNjRkOkw3xfGOR5+a6NLqPT7I6PRWRbZHMejfSnaNHFuI52zNdKSTVNc+WFzhm4SPd8Nxfj6qRTyyyiYz10wklkL5I4JLQyEsa0P3bBY5WFrD4VY6nYrLm2feM1sptltH1LW/zXbLUp80IfD4x4vgrGwegz6onE60YGIyG7BbPRgNyVYaToBSxm3Xd+0+3yOHFOopHgYGjCB9i3NDiRe5+tbhfwXPPVPomaPFjx+4sqOh0cTbwAkG175nwPJMqFj42gO4dX7k/ophbPkoe0pGrklNy6mDZClqcrJTNKSdeC21Eg1v4KHK/NSkZXyZTPyUKV6ykdYG6jzntV4oM11EoOX8Xz6rBsaw1KYUkV7/AMqvdFDnftHhzhk/fYfQj71VY47js+fVdE9pFJemv/hvY/wPU+/0VO2HTb0kvOGCLDLO7TK+TGc5HEEDuJ4L39LO8SPG1Mam7M9qS7qjpohrKZKh/cTu4x3YI2n+JJd6bW7MKnbVqzUSOfYNaLNjYNGRjJjB2WACj2y0ywYvFdUVSOJsZtebdi9LnWQbWyXuIWPeEAOc7itrXHILUSLeKzLhfssgJDXm45qXBObjmoAdp3KRC4YhnwQDemnddT6eZx4pNRyAcVPpJhY55oBtTuceKY0RNtcklp5hh1zTGkk6uqgFjonZDkn9BLoqxR2sM1ZdnW6ud1UFkoJHZKybOa4218VXdnStFtNVYKGp5KrFFjoYuZ8k4pg0fmq7Sznn5fN0zgBP5qB9h5HUtGnotgqzwFvVLoWdvkpLZGt5eOqqTyShM48T4ZLYyM9g7z+CiCrHevDVnsHqpI4GbWjiVtEzW8h6n8UpbI48/sCU7d2+yhmoYZmvtXTmlZJHmI5upuxIzXC4vtdullfHjlke2PUfZFudVjhdYmoccgfAfN1GjA45962e8NGnoqMl2bo4j3fatzYwNc/sUB1WeA+/8liHOdzP2KCOF0J0k7Rpn2C3+SiyVZ4ADvXjmhoLpHBoALib2AA1JedBZV3bPTrZlHfe19LiH93DIKqb/l0+JwW2LDkyOoKyeWJfbfRPqdjVLh+kpXwV8JA+CSnlF3g8C1j3lWDZc3vVNBUi1p6eGoGd/wBLGx/3qj7Z9osm04Kim2ZseurIpoJqczzWpYMEsbmF4eQ5p1OTnN0SX2d1W2arZ0Daap2fSU0QkpY3SRPnqvonm2NhDm9UEDh1bL2JaB/LJTkoOL7vs17E7eDqM1HzPl+KT7c2ZFNDLTyi8c0b4pBch5ZILGx4HPVIv9WNqyH6bpDN3QUFNCPPFYjwTvZuzZIoRHUVRqpATaZ8UcLyOAeIuoTrnkvLyY4YqcJpv2sVXQqFNsSspWMhh2o0wQsZFBHNs+N5EbBgYJJGTNLzYatso1J0gLqyXZ82795ihE+OC5hkYbB7cL+tDI3G3qOLuq4ZqX0l2RtSV5ip6+khpiTeYQyCtDCfgtdzCWjLE0s0+qtXRrotBs0Oe1zpqmX9NUzZyPzuQP1G4s7XPC5dZdE3B43Kck5Pokv6tk/crW2IjDtyklPwVlJNRm+m8iO8B78oh4p1UU/M3S32qAimiqowd5Q1UNUOeAPDHs7rlnkqt00lqzWxR09ZLHTV7A+ltgAE7WML4BLa7BJHZwDnYcRtorej8xGMrrhp/oKstNS0C/3JLWNJvaw70q2a6f3yejFXOXUzI5b1cdNNDMyQRn+6wyRu+kt8TmpiytG8MMzd1NYvAvjZMwayQSW+kHNtg4cs2uXLl07g6TshpiLaFMTf71Wdp0eRKutYRbxxKtbRZfEsUwLuhm0zQ1QN7Nd1SOztXZGyhwDm5gj57FwivitnxC6H7N9viaP3aR30jRkDx7u3TyXmfEtNvW9dUeh8P1G2Wx9GWl4z+dFsYETt1K0RE6HJfP8AK5PfUjyemB+oFBlpc/P51splTIQOPek1Q4niT5rSKbLb/cmsY0DM+q8dhGhuoLWdgTXZdEJPj+exW2GbZoDjw0WqcOd3fNk5qqZkdrC6WVkwzsQPCyKNGUmJanJQXuz8eKl1coOfDs5/elL3a9/zZapFDOV97rRJwWY4rAjM/PJSuC9GLGJ3s+DL/qS2miuVY6KLIKrZEkVzpXQCWCVp+sw/kuW9JzuD7jFk2I3kdpvpjq/921g39kDmu37Thyt8/wCa5f0w2JvnufHcTMb1mn++Y3izm5rQMuy69X4dlS+lnk67G+GihMa62SDit2fCthyNjkQTkViwjCbnjde0eYMmg2ussB5oLxYDkUbwZqoMsCMOmfyF5vAgOGXj6oDYAeeS2NBvqtOMeC2CUfY1AS4Qb6qdTanPRK2TBSoKgZ9qAdU/emNDJldIqaYZdiZUkwtZQC0UT+1WHZhJtxuqnQzj7FY9n1YysgLjs5mmasmzwMuPeqbQVeisGz5r21KoxwWymqAOPkmEVXy9VX6TvTSncB+aqORoyoJ4nuH5KRE09yXsqgOPksvfTwHmgddxvG0cbn0VH2pNX120ZaVskmzKCnAfvoReaqBtYxy6Xvfq3FrZ4lY2zF3En58lvYw8TZdGnzek20k379vcm/CE/Q/pBJ77tDZ08jp/dDC+CeSwmkglYHWm3Ywve0kDFYapf7Y5z7tQ1Onuu1KWc24C7wT/ADBqjdFMJ29tl2uGOlZryZGD6sTf2lRtm2TXRi12w78W5wvZL52Z6r0K2amL6XV/qie5bmuc7PPxP4qRGw8T5fmq50Z2xv6OkmA/SU0Ljc8d2A//AKgUyFU48fJeXmjUmvFlfpscNLRn6leurBwz9AlTLnXzKkRtHE39FiTb7G+WcuuDYggtLLZEHUEcUhoeg9BDJvYtnUcbrl+PcsJBPFgeLR/w2TvfNbyH2/jyWDqonT1/JaQySjwnRC47m/cjK+YHgPIcFyP2d9LqLZdLXU9VVRxGPadTu4wHySPZgiZcRxhxtijcL9iZ7Y6PbV2lK6Or2hFS0IOER7OEgkmZ/vHyZsPZdzexJfZH0apoqva0boY5ZKWrDIZJ2smmZHjqGCxeLA/Rg3aAvd00MUdPPfLf0bS/yzSPQby+1Bk1/cNl7Trvq444MEZ/ju77lYaDaL6mFsphmpnOGcM7QyeM3tZ4Y5w7btJ1TaQNA/H8NFFmnHD7F4+fLjmqhCv1tlXQvqIifzKgz0/MqdUVB5DxzS+ocX8/DJcpVUJ+kFAyaCaA/wB7DJFmf12EA+dj4LntFSnaGxY4RdtVSEsgdleGqo33hzOn0ZYP4l0qoiN75BUPoszdbR2tSm9t8ytjHD6YXkt2deIeC9HSzaxSr+FqX/TLq6K5FtDHVUu0z1WzRnZO0AP/AIarY8YN5+oHSBou7RuHmnm3dkieMtJMbgQ+GUfHDML4ZGePDi0kcVp6RRx0FVJO5rTs+vtT7RjIvHDOepFVPGmB18J7wVr2pQ1LIzHSVTY47YWCojM8kYtkIZg69v8AiB3er5nGUoyTr/ehLFuxKkVUBdJYSxPfT1LRoJo8iR+y4WPj2KJXRC3nopfR/YvucLo8RldI8yyOzALyAOoDoLAeq9qo+a4szj6j29OxD9inbSgyuk8Uj6aUSxkhzSHK1V7AAQq7tEXvkqdeGV6HWejG22V0IeD9IAGyM7fwTQWB05LhOxtrSUMokjOQOY4ELrGw+kUdXGDG76QaxnUc8l4es0Tg90eh7Wk1amtsuv8AcsFRYjkk1RTa281L947bcx88ESEW581xRPQsXQREHP8AP5/FMTU7oWBF7Zjkoj3jyKjVZ7R9+auiGzZVbScScyl1VV3vr8/ctVQc73socrych+aFaZjWScBoozVvLLrL3c2+SobRdIjhp0WW7UgRW5rZHDc2AVHIsb9lQcU+gb881EoILBMYo7qEUZArbHI6kFw7gRdVraVBvDfPXFfQg6gg8DdXGqh48bYfBL4aa5W0ZNdDGaTOebb6Jtnu9zXtfrjhaC9378d7E9rfJViPZlKXGLBUYhqZLRyDt3XLTiF36k2bcaKpe1Ghjhihdhbv3SFrHWzDA36TPlcsHiu/Drp/uswxaGE5pV1OMYRZazw7VjvEGTTsXttniUeuGXjhWYFj4LWJF66TioBvDRkexZBmY5KOJrL3fKbIolMI9VvhcL68UubKs2yqLJodQO1UozOaA4ZgDE8dnMdqRQ1RCa0tSC23MYVMWiGqLPs2QFjCOOncmHR6YsqpoS4luUsYJ0vqB2XKquwasgGI/wB2cu46Js6pw1NNLzJgf46fb6LaEVbiQdP2fIBZWCiqwqRs+cmysVBL2rmoclupKs9yYQyk8yq9RzAJpDWDvUD7jyHvsl3Sfbn9niCUxCSGSdkE7sVjAJPgktazhe+WSwZWHuS/pVLC+kljq5RHFIzDd5zD9Wlg1LsQBt2LXTxTmk1YtFybWN4G/K2i9FYe5c39nnSOWq3dMYhaCHDJOXWMgF2RPjiIucVhc96vkb2j81OoxSxTonkre0OjdWKqqqdn1MUZrsDZt+15fCWavgcOrxPBP9gdHm01K6mMjpXTB/vMryXvmklZgeSXnIWsLdik++DhmvDVHuV5aucqTfSv6dCLRVeh4ll2FNTwyuirKSSpghkBz3lPLvo2EHItcDgtbiVFovaXUz1NJS0lLATPAxxkqjJCw1LGE1MbCzKzSwgaqR0SJh2ptSm4Svj2jHfiJMpCP4njyUabYZjqpaQSbls8/wDa2yJrXEFdHnUUx/ZcOth/VvyXq7sW57op39S/VclkrL3s3pG4yimqYRTVRZjYBJvIJ2D4zTS4W4y3i1wDh3dZNBUOPE9w/Jcz9oW2D7nHGaaoj2mJ4X0+COR7I543C8kNQxuF7HNxC18XWzDV0CkrDu494AJCxjntZoHkDGB2XuvK1GFKKn5vj7dyGvIzjaTyC3MAHb36JX74eHz9y9EhdxJ+xcRHAzfVDv7lznoLMW7Z263TFMx//wA2Y/8A3Vdw3mfJVfZewZKbbVXXB0b6Wrpg0i9poZ49yALfXa7A83vxXoaXLGOLJF90q/Rosr7lncwnh4n5utb4eZWUtVyz7/hUSWUnj4D8l55TgJsI5D1Kg1Ew5Erc6M9y0yRDv9AhKbYvqZPD1VE2rSyRbbpqkRyGGqpX0U0gBLGSNvJHjIyGLBEAXciugTuaOX2n8UuqZu9a4szxt+6osuOoqrKRsjHNka1zCC17CA9hB1BByIUF1KyNoaGANaAxg4AAWAHgEyqJT2BL52E/ifm6zF+BbV25XzSatbkefYnk8dr3zSqseG3UDnuV+rpRbMcMSRbRhaBkFYK2YnIZfakVacjxVkCvbRiGeWij7BqXQ1kJjvm8NIHEFTKtrpDhAuTwCn09B7rHvHW35OEDUwi3P6rv+rJVzZIxjydOk02TPkUYL/8ADrk9GyVjJWdXeMD8tLkZi18s7qC+ikzsbj55+Cx9nlVvdn4TmYHviN9cBtIz/pf6J8QF8rkbiz6WWPY9r7FWnppOIPotDqZ2py8fyVse0FaH0zTwVfUZG1FW9z5nyQ2laNM/tViloG208lEfRDhwRSkyHFC7dAclqMOeWnJNY6O5Fyt7IG8k2kCaKkJ1HopbaRreGiaGMLU5iskDGGO9lPY2wWqnCkgLSKM2RJ2XRs+n6yluYLfOn4/mt2zmZ9isyjGFDSgD5+f8lxv2t7SE1eYmkbumZ7v2bz45SPGzf/TXXelu1hQUMs9xiaMMIP15n5MHnn+60r50leXuLiSXElxJzJJNyT23W2CPc9DQY+s2VGw9F4RkFhjKMZX0p8WbbIDR9q1F5XuMoTRmW6eq9AzK1l5XuMoDbYeq9aMytOMr0PKigb41NoZLG3YlrXFbInlQCxQMza4a/Ce0JrXuxQm2rSHjwVcpqnIXTimmxCx4jD5raMqaZBddkVwc2N/NgcT4Zp5DtJrGbx0gDP1r5Kg9F6n6Pdu1jeWeC3wQYt7Ql2EX94pjwtxZ3LWOJOTv/URR07Yu1Yp/0UjHdl8/I5qxUz+ZXL+h8xdHJSzj6SB+FjtHsBHULHjPXirNsXa797JSyn6WMB7HDLfQnR9uDuBVMuFJvb2I4L1DKB+aj7T2fT1RjfPCyV0fwE6js1zb2JbDIT+anRO7VhGTi7Qt9iZJFG4xkNEbov0bo+oWDizL6v7KnNkJ7e1LmSALcKoKJSb6j7jKI8z5fipDJAOSTe8nuWcbyfzUC12IPSSXcV1JtBg6g/2CqPDczH6J57GyHXtCfV7GztwyDIPZKwg2ex7DdkjHjNjmnioc8DZY3RyjE2QFr2cCCttFaGNseIuwjCHvtjIGlyNTa2a3nl3Rj5XH6E8jCNxPPvP2reztPklwqxwXnvR527lz/crwOWuaM/t+bLMVY70ma4n8/wA1vjHM+SgnnsMvej3favGkn8SorZGj80GsHDNBXkmYRxN1jJMB2dgUB9QTx8l41pPYqkX4N01TyChyOc7mfs/BSAwDXNaJqgDt7B82U0K8mh8R45eqjSwjPj89iynqj3epUGZxPM/Z+CjglNdjGeRo5eH5JXUVOth5qRUA8SlVW8BC3JEq5iePgEkrH5HQKXX1dr2SCsqSTYKVEUa6mYAFLxTPnJDR1QM3HIAcyVPhoC6zpSWsOdrdcj8O1b6p4EZDRhaBk0faeZ7VzajVxx8Llnt/DPgmXVu39MfP+CJHTRwg4MzxlOR7cH6vf8X7qS1zrkDhn9tvsumFZUXYAOQaeCWVWTgOwfaV5TyyyO5M+4w6HDpIbca+77ssvss2phraqlJykgZMzvidhd6SD+VdGlauOdEJN1taF5y3kMrB39U/ZddejluFTPBcNeD5zUtrNK/JgCiLVbJIwcwV43Vcmxme4ymjuPkKAXWPipzzkohiucRBtm0g/ar7SLMcPH5+1ehq2ho+cl470VkiLMSMlpXlY9wHVFzwC1NJ45FQlyRZvY+35LMTKMXLQ+TktEUbJr6i5TXZjtPmwVYieS62g+cl50m28KGlc5rgJZAYoOd7daTub+HNSotuhBOUtqK37YukW/mFLG68UBOO2j5zlIf4R1f51RKZt1g528JJv+sfzv4qXSRXz5fPLNdfEVR9DgxJVFdij8/3V6VrsUC694/ODYRovRw8VqN17mhJttojmtZujNCDbbM/PBeM4fvZrXndAugN7P6lkNFHF0NJUUST43a+CmU9QRxSgOK2xPKqyUWfZdVgkvwda/en0rHO3csf6WI4mdo4sVHgkOSs2w602w8QrQyuxKK6lsoes4VUJAkIwSRvyDwOB/Vc08Uw2VFK6uNVKGxNbHumNDg8kdtvFJqKaxuMidbcU6pJSt/WatL7FLLXDVqSyqKQU8nNT4ZgFiRyxxHKSpEZ7UoZVBbW1RVGOB2yQD81s96CSMmJ7VIjPMoLGfvJ7lmx5P4lQY3gLP3od6CvIyj7StrZQOSTe9HuWxjiUI+w2NZyXhqz3KA23E+S2tlaOQQmmTY3E5595UhnafJLPfBwufQINWTxt3KtEOhwJAOQ+1YPrOXqlbCT+JW5rRxN0HLN75i7XPsH4LAt55LwyW0UaadKJ2+TOVwH5qBV1Xao9dWAAkkADUk5DxVE6RdO6eG4bJvncocwO+Q9X7VO2i6XgtNbWa5qu7W2m1oJc5rRzeQB6rm+2enk8xLYrQg6BgxyfzuH/aF5sTYUtU7fVRkwfEN4SXv7r6BZzywgrZ04NJkzS2wVssMu3WzSbuLFKTxYOoO0vOVvNTtnuzuABbVx6+fEC+XisXwshjLI2gfVy8lqa7JeZn1sp8R4R9j8N+AY8X15vrfjsSZ5S424fP5rRWOFrXWAfZR5nLz2j6ZVFUiFI4aKJtHUHsw+S3VZ4qNUm7e75K1ic+SVojyyuY+KZvxQvD+8aOH8pK6v0d2mJ4WuB4Z965GXpj0c206jkGphccLxyz1W23cqPnviGJuW+P6nZYXmy9435Jbs+tbI0SNNwRiupe9XPtPMskBy8cVoL1li81O0WelYPKx3q0uebnPq8PzSiUzKYgKI53itlQ5QnTf0oolXI3vkUOScc0k2l0mhjm3BkG8uG9gJFwwv0xWzt2jmpdE7Fn6LV4ZLqjD1U2TXziNr3uya0Fzz2D5t4hcx6RbZdWTF5yYOoxvBjBoPvvzKa+0LbJH+ytP/ABu19rhn8IPr2KsUjNB8/at4Y9qtnr6HGkr7sl00V7fgmDyIxpd2TWDPN+XL50WqEBrb8h2ZDtzz/wAlNo4wPppBa36JhHwA/XPJ2qxk+59Bhx1wjmvP91engsMKA1fQn5YZ8PFenX+FYFq9woQZ5ZLzmscC8Lc0BsBC8aVgGr0MQGYK8NreKww6rzCgNuJZsco4CyIUUST4pMwm2x57OAuq806Jhsx3WCpXJa+C90U4yTelqlUaWVN6STtWhkWqCq7VNgmuq7TSphDUjmlAfwv7VKjkCQx1JW+OYlAPm1QCzFVySeMlSY3hVHIxbNftW+M80tFQBxXoqkHA5Y8D8SvfexwzScTE9q3xuPFQLYx95POyyY4n8VCjeAtwmQV5JrBzK3xuA0/NLhKvKitZE0vlkZG0cXkAeZKUSojgTLLern+2PaLTQ3EWKd37HUjv/wAR+o/dBXP+kftNqJbtZIIW/qwfH4yHrfy2VXKKNVibO27b6QQUgvPPHH9YMJu890Y6x8lzrpJ7VGC4pYr/AO9nyHhEw3P8RC5FJXTVDjhDnE9YuNyT2uJWyPYbznM63ZqfwWU86j14/qzeOBEvpD0wmqyd7M+QcIx1Ix/AOr45rDY2wpquznHdR8+JHYmewNhx4r4b2/XzueB7lbo7NFguXLq1/D/U9f4d8N9d3LiJD2PsKGmthbif/iPzPhyTYzW4qG+YBanzrzpSlN3I+uwYcWCO3GqJEz7grQH5rS6ZaN8bnRQom+8kSSqO5ywe/wCQtd1O0bwqDcFRGOyst2Lko0uRVkjOUu5GeLf9wWlzlvqefyO1RpfnitUjjyD3ov0lNJ1XXdFy4ju7Fftn7fimbeOQH61r5rjUi1Mmc03aSDzBI+xaekpHkZtOr+ng7ZPtNvNahtgDiuSRbalGRdi79fMKVF0hP1mHwN/Qp6COJ4siOo/2uCdVJFc0jVcsG3283eX5rezpQ0cX+X5qHhKKM/B0Koqr8dVAqqrgFTJOlLTwd6BQ6npQ/wDu2gdpzP4XUrEaLDJi3pzsstlMrPhccwSBYuN7i/6xJKj0fSN4pxT4nCzjdzXFj5GE3DHvHWtrxHBZ1FaZv0pLydc80pr6UYgWuHWPH6n8o0Xfje5bZHJn07wPfHldzfs91nhxF2j42uuWvB1466m6e0Fg1pve9tbdh71XKd4JDL9X+8P3DsvkrTs6G4D3ZRj4BzA+pnw/Cyx1XC5PV+DNzk5Jcf7yTaWO/wBI74dWA/XI0J/Z5d6jbU2nwC17SrOANuWnyPyUCjpt6bnT1XHGC/eke/PM/wByBWQQvcQy7FF3p7F7vT2fPiveo/MrJNwvSQou9PZ8+KN6ez58UoiyUXBeXCimU9i83hShZLBCMYUXensRvT2JRJJLhayMQUbeHsRvD2JRFkguC9xBRt4exG9PYlCySXJls0gAFJN4exb465w0DfI/iiiS2W2nmCZ09SqKza7xwj8j/Ut7OkEg+rF5P/rU0VOiU9QmNPIuYx9KphoyH+WT+tb2dNJx/dwfyyf+RRQOrQSBSmVAXIx06qP8On/kk/8AIs29Pqkf3dN/LN/5UoHX21SzbOTxXIG+0GpH91S/yTf+VbW+0eqH91SfyT/+ZRtIOwxuKkMcFxge0yr/AMKk/wCXP/5ll/7Tav8AwqT/AJc//mTayUjtjJVtbKuID2o1n+FSf8uf/wAy9/8AalWf4VH/AMuf/wAybS1ncmypVtbpVT0tw6UOd/hx9d/jbILiG0+nVZUZPkaG/qxh7GejrlJZtqvdyHdf8VDT7Fo7e51bbntKkNxCGwt/WP0kn9I9VSK/b8tS65MkzuBeSfIcFWm1RvcgO/ev9xTOh6SPh/RwwD+GT7d5dZTxza8/0RtHJBDWk2JPU5vO7Hbl6DMpxQdHoo83gyOGt9O8BV0dNZ733cH8sn/kWL+mMxzwQg9jZP8AyLhyafUy4tRXsbLPjRc3YWjC0AN4WAFildTIb27cPj+GirUnSmU/3cP8r/61jF0mkDg7BESOYf8A1qkPh2RdQ9TFnRdns3bQDqBn3nX57FufULn56aTf4cPlJ/Wtf+t0v+HF5P8A61Z6HIz3tP8AGNPigoK+PYvck/BYh6oh6WS/qReT/wCtA6WS/wCHF5P/AKlPyMzf9vYPf8F5D1qdJ3KmDpbN+pF5P/rWJ6VS/qReT/61HyOQft/B7/guZkXmJUv/AFok/Uj8nf1I/wBZ5P1WeT/61PyMx+38Hv8AguJd2LVIqn/rRJ/hxeT/AOpef6zSfqR+T/6kWhyEP49p35/BaCVBqGkG+o7NR3dnYkbukch+ozyd/UsD0hf+qzyd/UrrRzRlP41gfn8DeR1xf1/HkosgS121364Wjuv+K1narv1GeR/Fax00kc0/iuF+RndeXSo7RdyHr+K8/tB3Iev4q3oSMv2ni9xqCvC5LDtF3Iev4rz+0Hch6/ip9CQfxPF7jPEvCUu/tA8h6/ivPfzyHr+Kn0ZEftLETZ3WaSo08gb8GZdxvfDlmB48V4/aBIwlrbHUZ59+d1ppJ927FgY63B9y3yBzWsIOKODVaiOWSp8D/YWzMQDnAhupvq/u7O1OaqpAFhkAMIGQy0FlWndI5DlgZ5H+pR5NsvOob5H8VyZNNknK2ezg+K6fBj2Qv70ODm7P1Ke7JhsNAFSYtruBvhae+/4qfB0rkZpHF5P/AK1TLpMklSOjTfG9PCVyv8FcQhC9Q+NBCEIAQhCAEIQgBCEIAQhCAEIQgBCEIAQhCAEIQgBCEIAQhCAEIQgBCEIAQhCAEIQgBCEIAQhCAEIQgBCEIAQhCAEIQgBCEIAQhCAEIQgBCEIAQhCAEIQgBCEIAQhCAEIQgBCEIAQhCAEIQgBCEIAQhCAEIQgBCEIAQhCAEIQgBCEIAQhCAEIQgBCEIAQhCAEIQgBCEIAQhCAEIQgBCEIAQhCAEIQgBCEIAQhCAEIQgBCEIAQhCAEIQgBCEIAQhCAEIQgBCEIAQhCAEIQgBCEIAQhCAEIQgBCEIAQhCAEIQgBCEIAQhCAEIQgBCEIAQhCAEIQgBCEIAQhCAEIQgBCEIAQhCAEIQgBCEIAQhCAEIQgBCEIAQhCAEIQgBCEIAQhCA//Z\n",
+      "text/html": [
+       "\n",
+       "        <iframe\n",
+       "            width=\"60%\"\n",
+       "            height=\"300\"\n",
+       "            src=\"https://www.youtube.com/embed/PZRI1IfStY0\"\n",
+       "            frameborder=\"0\"\n",
+       "            allowfullscreen\n",
+       "        ></iframe>\n",
+       "        "
+      ],
+      "text/plain": [
+       "<IPython.lib.display.YouTubeVideo at 0x7f540577eb80>"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "YouTubeVideo(\"PZRI1IfStY0\", width=\"60%\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## An Introduction to Complex Numbers"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Below is a short introduction to [complex numbers <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_wiki.png\">](https://en.wikipedia.org/wiki/Complex_number) by [MIT](https://www.mit.edu) professor [Gilbert Strang <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_wiki.png\">](https://en.wikipedia.org/wiki/Gilbert_Strang) aimed at high school students."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/jpeg": 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+      "text/html": [
+       "\n",
+       "        <iframe\n",
+       "            width=\"60%\"\n",
+       "            height=\"300\"\n",
+       "            src=\"https://www.youtube.com/embed/Jkv-55ndVYY\"\n",
+       "            frameborder=\"0\"\n",
+       "            allowfullscreen\n",
+       "        ></iframe>\n",
+       "        "
+      ],
+      "text/plain": [
+       "<IPython.lib.display.YouTubeVideo at 0x7f5404f5b280>"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "YouTubeVideo(\"Jkv-55ndVYY\", width=\"60%\")"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.6"
+  },
+  "livereveal": {
+   "auto_select": "code",
+   "auto_select_fragment": true,
+   "scroll": true,
+   "theme": "serif"
+  },
+  "toc": {
+   "base_numbering": 1,
+   "nav_menu": {},
+   "number_sections": false,
+   "sideBar": true,
+   "skip_h1_title": true,
+   "title_cell": "Table of Contents",
+   "title_sidebar": "Contents",
+   "toc_cell": false,
+   "toc_position": {
+    "height": "calc(100% - 180px)",
+    "left": "10px",
+    "top": "150px",
+    "width": "384px"
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+   "toc_window_display": false
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/README.md b/README.md
index 48c9171..29198ed 100644
--- a/README.md
+++ b/README.md
@@ -138,6 +138,8 @@ Alternatively, the content can be viewed in a web browser
        `Fraction` Type)
     - [summary <img height="12" style="display: inline-block" src="static/link/to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/05_numbers/04_summary.ipynb)
     - [review questions <img height="12" style="display: inline-block" src="static/link/to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/05_numbers/05_review.ipynb)
+    - [further resources <img height="12" style="display: inline-block" src="static/link/to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/05_numbers/06_resources.ipynb)
+      [<img height="12" style="display: inline-block" src="static/link/to_mb.png">](https://mybinder.org/v2/gh/webartifex/intro-to-python/develop?urlpath=lab/tree/05_numbers/06_resources.ipynb)
 
 
 #### Videos

From 68fe189d89a78f6d069e4ccbcc3789beb8d0e81e Mon Sep 17 00:00:00 2001
From: Alexander Hess <alexander@webartifex.biz>
Date: Fri, 16 Oct 2020 00:28:15 +0200
Subject: [PATCH 170/242] Streamline slides

---
 05_numbers/02_content.ipynb  | 6 +++++-
 05_numbers/03_appendix.ipynb | 6 +++++-
 2 files changed, 10 insertions(+), 2 deletions(-)

diff --git a/05_numbers/02_content.ipynb b/05_numbers/02_content.ipynb
index 89ff220..e430e90 100644
--- a/05_numbers/02_content.ipynb
+++ b/05_numbers/02_content.ipynb
@@ -1180,7 +1180,11 @@
   {
    "cell_type": "code",
    "execution_count": 35,
-   "metadata": {},
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
    "outputs": [],
    "source": [
     "from fractions import Fraction"
diff --git a/05_numbers/03_appendix.ipynb b/05_numbers/03_appendix.ipynb
index 3db9422..c470e82 100644
--- a/05_numbers/03_appendix.ipynb
+++ b/05_numbers/03_appendix.ipynb
@@ -24,7 +24,11 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
    "source": [
     "In this appendix, we look at the `Decimal` and `Fraction` types that can be used as replacements for the built-in `float` type mitigating its imprecision. The content is put in an appendix as the data science practitioner can live without knowing about it for quite some time. Eventually, when working with financial data, for example, knowing how to not use the `float` type in a bad way pays off."
    ]

From 33f27da161bf6fccf3383e41df3bdaa73f9f8a33 Mon Sep 17 00:00:00 2001
From: Alexander Hess <alexander@webartifex.biz>
Date: Sat, 17 Oct 2020 16:51:57 +0200
Subject: [PATCH 171/242] Fix missing static files for chapter 05

---
 05_numbers/static/complex_numbers.png | Bin 0 -> 22878 bytes
 05_numbers/static/floating_point.png  | Bin 0 -> 15371 bytes
 05_numbers/static/numbers.png         | Bin 0 -> 90958 bytes
 3 files changed, 0 insertions(+), 0 deletions(-)
 create mode 100644 05_numbers/static/complex_numbers.png
 create mode 100644 05_numbers/static/floating_point.png
 create mode 100644 05_numbers/static/numbers.png

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HcmV?d00001


From 5eda6e8acfb72d0ef22ee6b4b42f3c548920960b Mon Sep 17 00:00:00 2001
From: Alexander Hess <alexander@webartifex.biz>
Date: Mon, 19 Oct 2020 11:00:39 +0200
Subject: [PATCH 172/242] Add initial version of chapter 06

---
 00_intro/00_content.ipynb |    2 +-
 06_text/00_content.ipynb  | 3970 +++++++++++++++++++++++++++++++++++++
 06_text/lorem_ipsum.txt   |    6 +
 README.md                 |    9 +
 4 files changed, 3986 insertions(+), 1 deletion(-)
 create mode 100644 06_text/00_content.ipynb
 create mode 100644 06_text/lorem_ipsum.txt

diff --git a/00_intro/00_content.ipynb b/00_intro/00_content.ipynb
index d3e1fae..de293c8 100644
--- a/00_intro/00_content.ipynb
+++ b/00_intro/00_content.ipynb
@@ -769,7 +769,7 @@
     "\n",
     "- How is data stored in memory?\n",
     "  - *Chapter 5*: [Numbers & Bits <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/05_numbers/00_content.ipynb)\n",
-    "  - *Chapter 6*: Text & Bytes\n",
+    "  - *Chapter 6*: [Text & Bytes <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/06_text/00_content.ipynb)\n",
     "  - *Chapter 7*: Sequential Data\n",
     "  - *Chapter 8*: Map, Filter, & Reduce\n",
     "  - *Chapter 9*: Mappings & Sets\n",
diff --git a/06_text/00_content.ipynb b/06_text/00_content.ipynb
new file mode 100644
index 0000000..7c9fff8
--- /dev/null
+++ b/06_text/00_content.ipynb
@@ -0,0 +1,3970 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "**Note**: Click on \"*Kernel*\" > \"*Restart Kernel and Clear All Outputs*\" in [JupyterLab](https://jupyterlab.readthedocs.io/en/stable/) *before* reading this notebook to reset its output. If you cannot run this file on your machine, you may want to open it [in the cloud <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_mb.png\">](https://mybinder.org/v2/gh/webartifex/intro-to-python/develop?urlpath=lab/tree/06_text/00_content.ipynb)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "# Chapter 6: Text & Bytes"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "In this chapter, we continue the study of the built-in data types. The next layer on top of numbers consists of **textual data** that are modeled primarily with the `str` type in Python. `str` objects are more complex than the numeric objects in [Chapter 5 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/05_numbers/00_content.ipynb) as they *consist* of an *arbitrary* and possibly large number of *individual* characters that may be chosen from *any* alphabet in the history of humankind. Luckily, Python abstracts away most of this complexity from us. However, after looking at the `str` type in great detail, we briefly introduce the `bytes` type at the end of this chapter to understand how characters are modeled in memory."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "## The `str` Type"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "To create a `str` object, we use the *literal* notation and type the text between enclosing **double quotes** `\"`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "text = \"Lorem ipsum dolor sit amet.\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Like everything in Python, `text` is an object with an *identity*, a *type*, and a *value*."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "140667764715968"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "id(text)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "str"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "type(text)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "As seen before, a `str` object evaluates to itself in a literal notation with enclosing **single quotes** `'`.\n",
+    "\n",
+    "In [Chapter 1 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/01_elements/00_content.ipynb#Value-/-(Semantic)-\"Meaning\"), we specify the double quotes `\"` convention this book follows. Yet, single quotes `'` and double quotes `\"` are *perfect* substitutes. We could use the reverse convention, as well. As [this discussion <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_so.png\">](https://stackoverflow.com/questions/56011/single-quotes-vs-double-quotes-in-python) shows, many programmers have *strong* opinions about such conventions. Consequently, the discussion was \"closed as not constructive\" by the moderators."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Lorem ipsum dolor sit amet.'"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "text"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "As the single quote `'` is often used in the English language as a shortener, we could make an argument in favor of using the double quotes `\"`: There are possibly fewer situations like the two code cells below, where we must **escape** the kind of quote used as the `str` object's delimiter with a backslash `\"\\\"` inside the text (cf., also the \"*Unicode & (Special) Characters*\" section further below). However, double quotes `\"` are often used as well, for example, to indicate a quote like the one by [Albert Einstein](https://de.wikipedia.org/wiki/Albert_Einstein) below. So, such arguments are not convincing.\n",
+    "\n",
+    "Many proponents of the single quote `'` usage claim that double quotes `\"` cause more **visual noise** on the screen. However, this argument is also not convincing as, for example, one could claim that *two* single quotes `''` look so similar to *one* double quote `\"` that a reader may confuse an *empty* `str` object with a missing closing quote `\"`. With the double quotes `\"` convention we at least avoid such confusion (i.e., empty `str` objects are written as `\"\"`).\n",
+    "\n",
+    "This discussion is an excellent example of a [flame war <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_wiki.png\">](https://en.wikipedia.org/wiki/Flaming_%28Internet%29#Flame_war) in the programming world: Everyone has an opinion and the discussion leads to *no* result."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Einstein said, \"If you can\\'t explain it, you don\\'t understand it.\"'"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "\"Einstein said, \\\"If you can't explain it, you don't understand it.\\\"\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Einstein said, \"If you can\\'t explain it, you don\\'t understand it.\"'"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "'Einstein said, \"If you can\\'t explain it, you don\\'t understand it.\"'"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "An *important* fact to know is that enclosing quotes of either kind are *not* part of the `str` object's *value*! They are merely *syntax* indicating the literal notation.\n",
+    "\n",
+    "So, printing out the sentence with the built-in [print() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#print) function does the same in both cases."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Einstein said, \"If you can't explain it, you don't understand it.\"\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"Einstein said, \\\"If you can't explain it, you don't understand it.\\\"\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Einstein said, \"If you can't explain it, you don't understand it.\"\n"
+     ]
+    }
+   ],
+   "source": [
+    "print('Einstein said, \"If you can\\'t explain it, you don\\'t understand it.\"')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "As an alternative to the literal notation, we may use the built-in [str() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/stdtypes.html#str) constructor to cast non-`str` objects as `str` ones. As [Chapter 11 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/11_classes/00_content.ipynb) reveals, basically any object in Python has a **text representation**. Because of that we may also pass `list` objects, the boolean `True` and `False`, or `None` to [str() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/stdtypes.html#str)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'42'"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "str(42)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'42.87'"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "str(42.87)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'[1, 2, 3]'"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "str([1, 2, 3])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'True'"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "str(True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'False'"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "str(False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'None'"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "str(None)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "### User Input"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "As shown in the \"*Guessing a Coin Toss*\" example in [Chapter 4 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/04_iteration/03_content.ipynb#Example:-Guessing-a-Coin-Toss), the built-in [input() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#input) function displays a prompt to the user and returns whatever is entered as a `str` object. [input() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#input) is in particular valuable when writing command-line tools."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdin",
+     "output_type": "stream",
+     "text": [
+      "Whatever you enter is put in a new string:  123\n"
+     ]
+    }
+   ],
+   "source": [
+    "user_input = input(\"Whatever you enter is put in a new string: \")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "str"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "type(user_input)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'123'"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "user_input"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "### Reading Files"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "A more common situation where we obtain `str` objects is when reading the contents of a file with the [open() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#open) built-in. In its simplest usage form, to open a [text file <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_wiki.png\">](https://en.wikipedia.org/wiki/Text_file) file, we pass in its path (i.e., \"filename\") as a `str` object."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "file = open(\"lorem_ipsum.txt\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "[open() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#open) returns a **[proxy <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_wiki.png\">](https://en.wikipedia.org/wiki/Proxy_pattern)** object of type `TextIOWrapper` that allows us to interact with the file on disk. `mode='r'` shows that we opened the file in read-only mode and `encoding='UTF-8'` is explained in detail in the [The `bytes` Type <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/06_text/01_content.ipynb#The-bytes-Type) section at the end of this chapter."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "_io.TextIOWrapper"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "type(file)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<_io.TextIOWrapper name='lorem_ipsum.txt' mode='r' encoding='UTF-8'>"
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "file"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "`TextIOWrapper` objects come with plenty of type-specific methods and attributes."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 21,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "file.readable()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "False"
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "file.writable()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'lorem_ipsum.txt'"
+      ]
+     },
+     "execution_count": 23,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "file.name"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'UTF-8'"
+      ]
+     },
+     "execution_count": 24,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "file.encoding"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "So far, we have not yet read anything from the file (i.e., from disk)! That is intentional as, for example, the file could contain more data than could fit into our computer's memory. Therefore, we have to explicitly instruct the `file` object to read some of or all the data in the file.\n",
+    "\n",
+    "One way to do that, is to simply loop over the `file` object with the `for` statement as shown next: In each iteration, `line` is assigned the next line in the file. Because we may loop over `TextIOWrapper` objects, they are *iterables*."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Lorem Ipsum is simply dummy text of the printing and typesetting industry.\n",
+      "\n",
+      "Lorem Ipsum has been the industry's standard dummy text ever since the 1500s\n",
+      "\n",
+      "when an unknown printer took a galley of type and scrambled it to make a type\n",
+      "\n",
+      "specimen book. It has survived not only five centuries but also the leap into\n",
+      "\n",
+      "electronic typesetting, remaining essentially unchanged. It was popularised in\n",
+      "\n",
+      "the 1960s with the release of Letraset sheets.\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "for line in file:\n",
+    "    print(line)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Once we looped over the `file` object, it is **exhausted**: We can *not* loop over it a second time. So, the built-in [print() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#print) function is *never* called in the code cell below!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "for line in file:\n",
+    "    print(line)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "After the `for`-loop, the `line` variable is still set and references the *last* line in the file. We verify that it is indeed a `str` object."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'the 1960s with the release of Letraset sheets.\\n'"
+      ]
+     },
+     "execution_count": 27,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "line"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "str"
+      ]
+     },
+     "execution_count": 28,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "type(line)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "An *important* observation is that the `file` object is still associated with an *open* **[file descriptor <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_wiki.png\">](https://en.wikipedia.org/wiki/File_descriptor)**. Without going into any technical details, we note that an operating system can only handle a *limited* number of \"open files\" at the same time, and, therefore, we should always *close* the file once we are done processing it.\n",
+    "\n",
+    "`TextIOWrapper` objects have a `closed` attribute on them that indicates if the associated file descriptor is still open or has been closed. We can \"manually\" close any `TextIOWrapper` object with the [close() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/io.html#io.IOBase.close) method."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "False"
+      ]
+     },
+     "execution_count": 29,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "file.closed"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "file.close()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 31,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "file.closed"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "The more Pythonic way is to use [open() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#open) within the compound `with` statement (cf., [reference <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/reference/compound_stmts.html#the-with-statement)): In the example below, the indented code block is said to be executed within the **context** of the `file` object that now plays the role of a **[context manager <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/reference/datamodel.html#with-statement-context-managers)**. Many different kinds of context managers exist in Python with different applications and purposes. Context managers returned from [open() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#open) mainly ensure that file descriptors get automatically closed after the last line in the code block is executed."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Lorem Ipsum is simply dummy text of the printing and typesetting industry.\n",
+      "\n",
+      "Lorem Ipsum has been the industry's standard dummy text ever since the 1500s\n",
+      "\n",
+      "when an unknown printer took a galley of type and scrambled it to make a type\n",
+      "\n",
+      "specimen book. It has survived not only five centuries but also the leap into\n",
+      "\n",
+      "electronic typesetting, remaining essentially unchanged. It was popularised in\n",
+      "\n",
+      "the 1960s with the release of Letraset sheets.\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "with open(\"lorem_ipsum.txt\") as file:\n",
+    "    for line in file:\n",
+    "        print(line)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 33,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "file.closed"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Using syntax familiar from [Chapter 3 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/03_conditionals/00_content.ipynb#The-try-Statement) to explain what the `with open(...) as file:` does above, we provide an alternative formulation with a `try` statement below: The `finally`-branch is *always* executed, even if an exception is raised inside the `for`-loop. Therefore, `file` is sure to be closed too. However, this formulation is somewhat less expressive."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Lorem Ipsum is simply dummy text of the printing and typesetting industry.\n",
+      "\n",
+      "Lorem Ipsum has been the industry's standard dummy text ever since the 1500s\n",
+      "\n",
+      "when an unknown printer took a galley of type and scrambled it to make a type\n",
+      "\n",
+      "specimen book. It has survived not only five centuries but also the leap into\n",
+      "\n",
+      "electronic typesetting, remaining essentially unchanged. It was popularised in\n",
+      "\n",
+      "the 1960s with the release of Letraset sheets.\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "try:\n",
+    "    file = open(\"lorem_ipsum.txt\")\n",
+    "    for line in file:\n",
+    "        print(line)\n",
+    "finally:\n",
+    "    file.close()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 35,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "file.closed"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "As an alternative to reading the contents of a file by looping over a `TextIOWrapper` object, we may also call one of the methods they come with.\n",
+    "\n",
+    "For example, the [read() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/io.html#io.TextIOBase.read) method takes a single `size` argument of type `int` and returns a `str` object with the specified number of characters."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "file = open(\"lorem_ipsum.txt\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Lorem Ipsum'"
+      ]
+     },
+     "execution_count": 37,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "file.read(11)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "When we call [read() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/io.html#io.TextIOBase.read) again, the returned `str` object begins where the previous one left off. This is because `TextIOWrapper` objects like `file` simply store a position at which the associated file on disk is being read. In other words, `file` is like a **cursor** pointing into a file."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "' is simply '"
+      ]
+     },
+     "execution_count": 38,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "file.read(11)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "On the contrary, the [readline() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/io.html#io.TextIOBase.readline) method keeps reading until it hits a **newline character**. These are shown in `str` objects as `\"\\n\"`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'dummy text of the printing and typesetting industry.\\n'"
+      ]
+     },
+     "execution_count": 39,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "file.readline()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "When we call [readline() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/io.html#io.TextIOBase.readline) again, we obtain the next line."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "\"Lorem Ipsum has been the industry's standard dummy text ever since the 1500s\\n\""
+      ]
+     },
+     "execution_count": 40,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "file.readline()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Lastly, the [readlines() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/io.html#io.IOBase.readlines) method returns a `list` object that holds *all* lines in the `file` from the current position to the end of the file. The latter position is often abbreviated as **EOF** in the documentation. Let's always remember that [readlines() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/io.html#io.IOBase.readlines) has the potential to crash a computer with a `MemoryError`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['when an unknown printer took a galley of type and scrambled it to make a type\\n',\n",
+       " 'specimen book. It has survived not only five centuries but also the leap into\\n',\n",
+       " 'electronic typesetting, remaining essentially unchanged. It was popularised in\\n',\n",
+       " 'the 1960s with the release of Letraset sheets.\\n']"
+      ]
+     },
+     "execution_count": 41,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "file.readlines()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Calling [readlines() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/io.html#io.IOBase.readlines) a second time, is as pointless as looping over `file` a second time."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[]"
+      ]
+     },
+     "execution_count": 42,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "file.readlines()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "file.close()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Because every `str` object created by reading the contents of a file in any of the ways shown in this section ends with a `\"\\n\"`, we see empty lines printed between each `line` in the `for`-loops above. To print the entire text without empty lines in between, we pass a `end=\"\"` argument to the [print() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#print) function."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Lorem Ipsum is simply dummy text of the printing and typesetting industry.\n",
+      "Lorem Ipsum has been the industry's standard dummy text ever since the 1500s\n",
+      "when an unknown printer took a galley of type and scrambled it to make a type\n",
+      "specimen book. It has survived not only five centuries but also the leap into\n",
+      "electronic typesetting, remaining essentially unchanged. It was popularised in\n",
+      "the 1960s with the release of Letraset sheets.\n"
+     ]
+    }
+   ],
+   "source": [
+    "with open(\"lorem_ipsum.txt\") as file:\n",
+    "    for line in file:\n",
+    "        print(line, end=\"\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "## A String of Characters"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "A **sequence** is yet another *abstract* concept (cf., the \"*Containers vs. Iterables*\" section in [Chapter 4 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/04_iteration/02_content.ipynb#Containers-vs.-Iterables)).\n",
+    "\n",
+    "It unifies *four* [orthogonal <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_wiki.png\">](https://en.wikipedia.org/wiki/Orthogonality) (i.e., \"independent\") concepts into one bigger idea: Any data type, such as `str`, is considered a sequence if it\n",
+    "\n",
+    "1. **contains**\n",
+    "2. a **finite** number of other objects that\n",
+    "3. can be **iterated** over\n",
+    "4. in a *predictable* **order**.\n",
+    "\n",
+    "[Chapter 7 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/07_sequences/00_content.ipynb#Collections-vs.-Sequences) formalizes these concepts in great detail. Here, we keep our focus on the `str` type that historically received its name as it models a **[string of characters <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_wiki.png\">](https://en.wikipedia.org/wiki/String_%28computer_science%29)**. *String* is simply another term for *sequence* in the computer science literature.\n",
+    "\n",
+    "Another example of a sequence is the `list` type. Because of that, `str` objects may be treated like `list` objects in many situations.\n",
+    "\n",
+    "Below, the built-in [len() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#len) function tells us how many characters make up `text`. [len() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#len) would not work with an \"infinite\" object. As anything modeled in a program must fit into a computer's finite memory, there cannot exist truly infinite objects; however, [Chapter 8 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/08_mfr/00_content.ipynb#Iterators-vs.-Iterables) introduces specialized iterable data types that can be used to model an *infinite* series of \"things\" and that, consequently, have no concept of \"length.\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Lorem ipsum dolor sit amet.'"
+      ]
+     },
+     "execution_count": 45,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "text"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "27"
+      ]
+     },
+     "execution_count": 46,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "len(text)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Being iterable, we may loop over `text` and do something with the individual characters, for example, print them out with extra space in between them. If it were not for the appropriately chosen name of the `text` variable, we could not tell what *concrete* type of object the `for` statement is looping over."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "L  o  r  e  m     i  p  s  u  m     d  o  l  o  r     s  i  t     a  m  e  t  .  "
+     ]
+    }
+   ],
+   "source": [
+    "for character in text:\n",
+    "    print(character, end=\"  \")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "With the [reversed() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#reversed) built-in, we may loop over `text` in reversed order. Reversing `text` only works as it has a forward order to begin with."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 48,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      ".  t  e  m  a     t  i  s     r  o  l  o  d     m  u  s  p  i     m  e  r  o  L  "
+     ]
+    }
+   ],
+   "source": [
+    "for character in reversed(text):\n",
+    "    print(character, end=\"  \")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Being a container, we may check if a given `str` object is contained in `text` with the `in` operator, which has *two* distinct usages: First, it checks if a *single* character is contained in a `str` object. Second, it may also check if a shorter `str` object, then called a **substring**, is contained in a longer one."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 49,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 49,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "\"L\" in text"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 50,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 50,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "\"ipsum\" in text"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 51,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "False"
+      ]
+     },
+     "execution_count": 51,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "\"veni, vidi, vici\" in text"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "## Indexing"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "As `str` objects are *ordered* and *finite*, we may **index** into them to obtain individual characters with the **indexing operator** `[]`. This is analogous to how we obtained individual elements of a `list` object in [Chapter 1 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/01_elements/03_content.ipynb#Who-am-I?-And-how-many?)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 52,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'L'"
+      ]
+     },
+     "execution_count": 52,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "text[0]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 53,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'o'"
+      ]
+     },
+     "execution_count": 53,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "text[1]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "The index must be of type `int`; othewise, we get a `TypeError`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 54,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "ename": "TypeError",
+     "evalue": "string indices must be integers",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-54-31fb90bbe515>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mtext\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1.0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;31mTypeError\u001b[0m: string indices must be integers"
+     ]
+    }
+   ],
+   "source": [
+    "text[1.0]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "The last index is one less than the above \"length\" of the `str` object as we start counting at `0`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 55,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'.'"
+      ]
+     },
+     "execution_count": 55,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "text[26]  # == text[len(text) - 1]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "An `IndexError` is raised whenever the index is out of range."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 56,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "ename": "IndexError",
+     "evalue": "string index out of range",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mIndexError\u001b[0m                                Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-56-ffa3e1bc2f86>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mtext\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m27\u001b[0m\u001b[0;34m]\u001b[0m  \u001b[0;31m# == text[len(text)]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;31mIndexError\u001b[0m: string index out of range"
+     ]
+    }
+   ],
+   "source": [
+    "text[27]  # == text[len(text)]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "We may use *negative* indexes to start counting from the end of the `str` object, as shown in the figure below. Note how this only works because sequences are *finite*."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "|   Index   | 0 | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10| 11| 12| 13| 14| 15| 16| 17| 18| 19| 20| 21| 22| 23| 24| 25| 26|\n",
+    "|:---------:|:-:|:-:|:-:|:-:|:-:|:-:|:-:|:-:|:-:|:-:|:-:|:-:|:-:|:-:|:-:|:-:|:-:|:-:|:-:|:-:|:-:|:-:|:-:|:-:|:-:|:-:|:-:|\n",
+    "|**Reverse**|-27|-26|-25|-24|-23|-22|-21|-20|-19|-18|-17|-16|-15|-14|-13|-12|-11|-10|-9 |-8 |-7 |-6 |-5 |-4 |-3 |-2 |-1 |\n",
+    "| **Character**  |`L`|`o`|`r`|`e`|`m`|` `|`i`|`p`|`s`|`u`|`m`|` `|`d`|`o`|`l`|`o`|`r`|` `|`s`|`i`|`t`|` `|`a`|`m`|`e`|`t`|`.`|"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 57,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'.'"
+      ]
+     },
+     "execution_count": 57,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "text[-1]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 58,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'L'"
+      ]
+     },
+     "execution_count": 58,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "text[-27]  # == text[-len(text)]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "One reason why programmers like to start counting at `0` is that a positive index and its *corresponding* negative index always add up to the length of the sequence. Here, `6` and `21` add to `27`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 59,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'i'"
+      ]
+     },
+     "execution_count": 59,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "text[6]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 60,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'i'"
+      ]
+     },
+     "execution_count": 60,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "text[-21]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "## Slicing"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "A **slice** is a substring of a `str` object.\n",
+    "\n",
+    "The **slicing operator** is a generalization of the indexing operator: We put one, two, or three integers within the brackets `[]`, separated by colons `:`. The three integers are then referred to as the *start*, *stop*, and *step* values.\n",
+    "\n",
+    "Let's start with two integers, *start* and *stop*. Whereas the character at the *start* position is included in the returned `str` object, the one at the *stop* position is not. If both *start* and *stop* are positive, the difference \"*stop* minus *start*\" tells us how many characters the resulting slice has. So, below, `5 - 0 == 5` implies that `\"Lorem\"` consists of `5` characters. So, colloquially speaking, `text[0:5]` means \"taking the first `5 - 0 == 5` characters of `text`.\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 61,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Lorem'"
+      ]
+     },
+     "execution_count": 61,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "text[0:5]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 62,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'dolor sit amet.'"
+      ]
+     },
+     "execution_count": 62,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "text[12:len(text)]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "If left out, *start* defaults to `0` and *stop* to the length of the `str` object (i.e., the end)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 63,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Lorem'"
+      ]
+     },
+     "execution_count": 63,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "text[:5]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 64,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'dolor sit amet.'"
+      ]
+     },
+     "execution_count": 64,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "text[12:]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Not including the character at the *stop* position makes working with individual slices easier as they add up to the original `str` object again (cf., the \"*String Operations*\" section below regarding the overloaded `+` operator)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 65,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Lorem ipsum dolor sit amet.'"
+      ]
+     },
+     "execution_count": 65,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "text[:5] + text[5:]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Slicing and indexing makes it easy to obtain shorter versions of the original `str` object. A common application would be to **parse** out meaningful substrings from raw text data."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 66,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Lorem ipsum sit amet.'"
+      ]
+     },
+     "execution_count": 66,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "text[:11] + text[-10:]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "By combining a positive *start* with a negative *stop* index, we specify both ends of the slice *relative* to the ends of the entire `str` object. So, colloquially speaking, `[6:-10]` below means \"drop the first six and last ten characters.\" The length of the resulting slice can then *not* be calculated from the indexes and depends only on the length of the original `str` object!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 67,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'ipsum dolor'"
+      ]
+     },
+     "execution_count": 67,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "text[6:-10]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "For convenience, the indexes do not need to lie within the range from `0` to `len(text)` when slicing. So, no `IndexError` is raised here."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 68,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Lorem ipsum dolor sit amet.'"
+      ]
+     },
+     "execution_count": 68,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "text[-999:999]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "By leaving out both *start* and *stop*, we take a \"full\" slice that is essentially a *copy* of the original `str` object."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 69,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Lorem ipsum dolor sit amet.'"
+      ]
+     },
+     "execution_count": 69,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "text[:]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "A *step* value of `i` can be used to obtain only every `i`th character."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 70,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Lrmismdlrstae.'"
+      ]
+     },
+     "execution_count": 70,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "text[::2]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "A negative *step* size of `-1` reverses the order of the characters."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 71,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'.tema tis rolod muspi meroL'"
+      ]
+     },
+     "execution_count": 71,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "text[::-1]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "## Immutability"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Whereas elements of a `list` object *may* be *re-assigned*, as shortly hinted at in [Chapter 1 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/01_elements/03_content.ipynb#Who-am-I?-And-how-many?), this is *not* allowed for the individual characters of `str` objects. Once created, they can *not* be changed. Formally, we say that `str` objects are **immutable**. In that regard, they are like the numeric types in [Chapter 5 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/05_numbers/00_content.ipynb).\n",
+    "\n",
+    "On the contrary, objects that may be changed after creation, are called **mutable**. We already saw in [Chapter 1 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/01_elements/03_content.ipynb#Who-am-I?-And-how-many?) how mutable objects are more difficult to reason about for a beginner, in particular, if more than one variable references it. Yet, mutability does have its place in a programmer's toolbox, and we revisit this idea in the next chapters.\n",
+    "\n",
+    "The `TypeError` indicates that `str` objects are *immutable*: Assignment to an index or a slice are *not* supported."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 72,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "ename": "TypeError",
+     "evalue": "'str' object does not support item assignment",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-72-d8a1e6b0b786>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mtext\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m\"X\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;31mTypeError\u001b[0m: 'str' object does not support item assignment"
+     ]
+    }
+   ],
+   "source": [
+    "text[0] = \"X\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 73,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "ename": "TypeError",
+     "evalue": "'str' object does not support item assignment",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-73-def135e84998>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mtext\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;36m5\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m\"random\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;31mTypeError\u001b[0m: 'str' object does not support item assignment"
+     ]
+    }
+   ],
+   "source": [
+    "text[:5] = \"random\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "## String Methods"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Objects of type `str` come with many **methods** bound on them (cf., the [documentation <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/stdtypes.html#string-methods) for a full list). As seen before, they work like *normal* functions and are accessed via the **dot operator** `.`. Calling a method is also referred to as **method invocation**.\n",
+    "\n",
+    "The [find() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/stdtypes.html#str.find) method returns the index of the first occurrence of a character or a substring. If no match is found, it returns `-1`. A mirrored version searching from the right called [rfind() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/stdtypes.html#str.rfind) exists as well. The [index() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/stdtypes.html#str.index) and [rindex() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/stdtypes.html#str.rindex) methods work in the same way but raise a `ValueError` if no match is found. So, we can control if a search fails *silently* or *loudly*."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 74,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Lorem ipsum dolor sit amet.'"
+      ]
+     },
+     "execution_count": 74,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "text"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 75,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "22"
+      ]
+     },
+     "execution_count": 75,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "text.find(\"a\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 76,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "-1"
+      ]
+     },
+     "execution_count": 76,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "text.find(\"b\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 77,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "12"
+      ]
+     },
+     "execution_count": 77,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "text.find(\"dolor\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "[find() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/stdtypes.html#str.find) takes optional *start* and *end* arguments that allow us to find occurrences other than the first one."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 78,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "1"
+      ]
+     },
+     "execution_count": 78,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "text.find(\"o\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 79,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "13"
+      ]
+     },
+     "execution_count": 79,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "text.find(\"o\", 2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 80,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "-1"
+      ]
+     },
+     "execution_count": 80,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "text.find(\"o\", 2, 12)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "The [count() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/stdtypes.html#str.count) method does what we expect."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 81,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Lorem ipsum dolor sit amet.'"
+      ]
+     },
+     "execution_count": 81,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "text"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 82,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "1"
+      ]
+     },
+     "execution_count": 82,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "text.count(\"l\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "As [count() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/stdtypes.html#str.count) is *case-sensitive*, we must **chain** it with the [lower() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/stdtypes.html#str.lower) method to get the count of all `\"L\"`s and `\"l\"`s."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 83,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "2"
+      ]
+     },
+     "execution_count": 83,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "text.lower().count(\"l\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Alternatively, we can use the [upper() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/stdtypes.html#str.upper) method and search for `\"L\"`s."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 84,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "2"
+      ]
+     },
+     "execution_count": 84,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "text.upper().count(\"L\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Because `str` objects are *immutable*, [upper() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/stdtypes.html#str.upper) and [lower() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/stdtypes.html#str.lower) return *new* `str` objects, even if they do *not* change the value of the original `str` object."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 85,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "example = \"random\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 86,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "140667840152112"
+      ]
+     },
+     "execution_count": 86,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "id(example)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 87,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "lower = example.lower()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 88,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "140667764453680"
+      ]
+     },
+     "execution_count": 88,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "id(lower)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "`example` and `lower` are *different* objects with the *same* value."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 89,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "False"
+      ]
+     },
+     "execution_count": 89,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "example is lower"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 90,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 90,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "example == lower"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Besides [upper() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/stdtypes.html#str.upper) and [lower() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/stdtypes.html#str.lower) there exist also [title() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/stdtypes.html#str.title) and [swapcase() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/stdtypes.html#str.swapcase) methods."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 91,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'lorem ipsum dolor sit amet.'"
+      ]
+     },
+     "execution_count": 91,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "text.lower()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 92,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'LOREM IPSUM DOLOR SIT AMET.'"
+      ]
+     },
+     "execution_count": 92,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "text.upper()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 93,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Lorem Ipsum Dolor Sit Amet.'"
+      ]
+     },
+     "execution_count": 93,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "text.title()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 94,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'lOREM IPSUM DOLOR SIT AMET.'"
+      ]
+     },
+     "execution_count": 94,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "text.swapcase()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Another popular string method is [split() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/stdtypes.html#str.split): It separates a longer `str` object into smaller ones collected in a `list` object. By default, groups of contiguous whitespace characters are used as the *separator*.\n",
+    "\n",
+    "As an example, we use [split() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/stdtypes.html#str.split) to print out the individual words in `text` with more whitespace in between them."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 95,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['Lorem', 'ipsum', 'dolor', 'sit', 'amet.']"
+      ]
+     },
+     "execution_count": 95,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "text.split()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 96,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Lorem   ipsum   dolor   sit   amet.   "
+     ]
+    }
+   ],
+   "source": [
+    "for word in text.split():\n",
+    "    print(word, end=\"   \")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "The opposite of splitting is done with the [join() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/stdtypes.html#str.join) method. It is typically invoked on a `str` object that represents a separator (e.g., `\" \"` or `\", \"`) and connects the elements provided by an *iterable* argument (e.g., `words` below) into one *new* `str` object."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 97,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "words = [\"This\", \"will\", \"become\", \"a\", \"sentence.\"]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 98,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "sentence = \" \".join(words)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 99,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'This will become a sentence.'"
+      ]
+     },
+     "execution_count": 99,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "sentence"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "As the `str` object `\"abcde\"` below is an *iterable* itself, its characters (!) are joined together with a space `\" \"` in between."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 100,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'a b c d e'"
+      ]
+     },
+     "execution_count": 100,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "\" \".join(\"abcde\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "The [replace() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/stdtypes.html#str.replace) method creates a *new* `str` object with parts of the original `str` object potentially replaced."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 101,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'This is a sentence.'"
+      ]
+     },
+     "execution_count": 101,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "sentence.replace(\"will become\", \"is\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Note how `sentence` itself remains unchanged. Bound to an immutable object, [replace() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/stdtypes.html#str.replace) must create *new* objects."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 102,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'This will become a sentence.'"
+      ]
+     },
+     "execution_count": 102,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "sentence"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "As seen previously, the [strip() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/stdtypes.html#str.strip) method is often helpful in cleaning text data from unreliable sources like user input from unnecessary leading and trailing whitespace. The [lstrip() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/stdtypes.html#str.lstrip) and [rstrip() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/stdtypes.html#str.rstrip) methods are specialized versions of it."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 103,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'text with whitespace'"
+      ]
+     },
+     "execution_count": 103,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "\"  text with whitespace  \".strip()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 104,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'text with whitespace  '"
+      ]
+     },
+     "execution_count": 104,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "\"  text with whitespace  \".lstrip()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 105,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'  text with whitespace'"
+      ]
+     },
+     "execution_count": 105,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "\"  text with whitespace  \".rstrip()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "When justifying a `str` object for output, the [ljust() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/stdtypes.html#str.ljust) and [rjust() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/stdtypes.html#str.rjust) methods may be helpful."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 106,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'This will become a sentence.            '"
+      ]
+     },
+     "execution_count": 106,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "sentence.ljust(40)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 107,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'            This will become a sentence.'"
+      ]
+     },
+     "execution_count": 107,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "sentence.rjust(40)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Similarly, the [zfill() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/stdtypes.html#str.zfill) method can be used to pad a `str` representation of a number with leading `0`s for justified output."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 108,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'0000042.87'"
+      ]
+     },
+     "execution_count": 108,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "\"42.87\".zfill(10)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 109,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'-000042.87'"
+      ]
+     },
+     "execution_count": 109,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "\"-42.87\".zfill(10)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "## String Operations"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "As mentioned in [Chapter 1 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/01_elements/00_content.ipynb#Operator-Overloading), the `+` and `*` operators are *overloaded* and used for **string concatenation**. They always create *new* `str` objects. That has nothing to do with the `str` type's immutability, but is the default behavior of operators."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 110,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Hello Lore'"
+      ]
+     },
+     "execution_count": 110,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "\"Hello \" + text[:4]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 111,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Lorem ipsum Lorem ipsum Lorem ipsum Lorem ipsum Lorem ipsum ...'"
+      ]
+     },
+     "execution_count": 111,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "5 * text[:12] + \"...\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "### String Comparison"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "The *relational* operators also work with `str` objects, another example of operator overloading. Comparison is done one character at a time in a pairwise fashion until the first pair differs or one operand ends. However, `str` objects are sorted in a \"weird\" way. For example, all upper case characters come before all lower case characters. The reason for that is given in the \"*Characters are Numbers with a Convention*\" sub-section in the [second part <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/06_text/02_content.ipynb#Characters-are-Numbers-with-a-Convention) of this chapter."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 112,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 112,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "\"Apple\" < \"Banana\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 113,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "False"
+      ]
+     },
+     "execution_count": 113,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "\"apple\" < \"Banana\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 114,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 114,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "\"apple\" < \"Banana\".lower()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Below is an example with typical German last names that shows how characters other than the first decide the ordering."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 115,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 115,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "\"Mai\" < \"Maier\" < \"Mayer\" < \"Meier\" < \"Meyer\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "## String Interpolation"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Often, we want to use `str` objects as drafts in the source code that are filled in with concrete text only at runtime. This approach is called **string interpolation**. There are three ways to do that in Python."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "### f-strings"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "**[Formatted string literals <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/reference/lexical_analysis.html#formatted-string-literals)**, of **f-strings** for short, are the least recently added (cf., [PEP 498 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://www.python.org/dev/peps/pep-0498/) in 2016) and most readable way: We simply prepend a `str` in its literal notation with an `f`, and put variables, or more generally, expressions, within curly braces `{}`. These are then filled in when the string literal is evaluated."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 116,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "name = \"Alexander\"\n",
+    "time_of_day = \"morning\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 117,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Hello Alexander! Good morning.'"
+      ]
+     },
+     "execution_count": 117,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "f\"Hello {name}! Good {time_of_day}.\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Separated by a colon `:`, various formatting options are available. In the beginning, the ability to round numbers for output may be particularly useful: This can be achieved by adding `:.2f` to the variable name inside the curly braces, which casts the number as a `float` and rounds it to two digits. The `:.2f` is a so-called format specifier, and there exists a whole **[format specification mini-language <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/string.html#formatspec)** to govern how specifiers work."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 118,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "pi = 3.141592653"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 119,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Pi is 3.14'"
+      ]
+     },
+     "execution_count": 119,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "f\"Pi is {pi:.2f}\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "### [format() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/stdtypes.html#str.format) Method"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "`str` objects also provide a [format() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/stdtypes.html#str.format) method that accepts an arbitrary number of *positional* arguments that are inserted into the `str` object in the same order replacing empty curly brackets `{}`. String interpolation with the [format() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/stdtypes.html#str.format) method is a more traditional and probably the most common way as of today. While f-strings are the recommended way going forward, usage of the [format() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/stdtypes.html#str.format) method is likely not declining any time soon."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 120,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Hello Alexander! Good morning.'"
+      ]
+     },
+     "execution_count": 120,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "\"Hello {}! Good {}.\".format(name, time_of_day)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "We may use index numbers inside the curly braces if the order is different in the `str` object."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 121,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Good morning, Alexander'"
+      ]
+     },
+     "execution_count": 121,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "\"Good {1}, {0}\".format(name, time_of_day)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "The [format() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/stdtypes.html#str.format) method may alternatively be used with *keyword* arguments as well. Then, we must put the keywords' names within the curly brackets."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 122,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Hello Alexander! Good morning.'"
+      ]
+     },
+     "execution_count": 122,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "\"Hello {name}! Good {time}.\".format(name=name, time=time_of_day)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Format specifiers work as in the f-string case."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 123,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Pi is 3.14'"
+      ]
+     },
+     "execution_count": 123,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "\"Pi is {:.2f}\".format(pi)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "### `%` Operator"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "The `%` operator that we saw in the context of modulo division in [Chapter 1 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/01_elements/00_content.ipynb#%28Arithmetic%29-Operators) is overloaded with string interpolation when its first operand is a `str` object. The second operand consists of all expressions to be filled in. Format specifiers work with a `%` instead of curly braces and according to a different set of rules referred to as **[printf-style string formatting <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/stdtypes.html#printf-style-string-formatting)**. So, `{:.2f}` becomes `%.2f`.\n",
+    "\n",
+    "This way of string interpolation is the oldest and originates from the [C language <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_wiki.png\">](https://en.wikipedia.org/wiki/C_%28programming_language%29). It is still widely spread, but we should use one of the other two ways instead. We show it here mainly for completeness sake."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 124,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Pi is 3.14'"
+      ]
+     },
+     "execution_count": 124,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "\"Pi is %.2f\" % pi"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "To insert more than one expression, we must list them in order and between parenthesis `(` and `)`. As [Chapter 7 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/07_sequences/00_content.ipynb#The-tuple-Type) reveals, this literal syntax creates an object of type `tuple`. Also, to format an expression as text, we use the format specifier `%s`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 125,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Hello Alexander! Good morning.'"
+      ]
+     },
+     "execution_count": 125,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "\"Hello %s! Good %s.\" % (name, time_of_day)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.6"
+  },
+  "livereveal": {
+   "auto_select": "code",
+   "auto_select_fragment": true,
+   "scroll": true,
+   "theme": "serif"
+  },
+  "toc": {
+   "base_numbering": 1,
+   "nav_menu": {},
+   "number_sections": false,
+   "sideBar": true,
+   "skip_h1_title": true,
+   "title_cell": "Table of Contents",
+   "title_sidebar": "Contents",
+   "toc_cell": false,
+   "toc_position": {
+    "height": "calc(100% - 180px)",
+    "left": "10px",
+    "top": "150px",
+    "width": "384px"
+   },
+   "toc_section_display": false,
+   "toc_window_display": false
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/06_text/lorem_ipsum.txt b/06_text/lorem_ipsum.txt
new file mode 100644
index 0000000..c0e21ab
--- /dev/null
+++ b/06_text/lorem_ipsum.txt
@@ -0,0 +1,6 @@
+Lorem Ipsum is simply dummy text of the printing and typesetting industry.
+Lorem Ipsum has been the industry's standard dummy text ever since the 1500s
+when an unknown printer took a galley of type and scrambled it to make a type
+specimen book. It has survived not only five centuries but also the leap into
+electronic typesetting, remaining essentially unchanged. It was popularised in
+the 1960s with the release of Letraset sheets.
diff --git a/README.md b/README.md
index 29198ed..d5d2970 100644
--- a/README.md
+++ b/README.md
@@ -140,6 +140,15 @@ Alternatively, the content can be viewed in a web browser
     - [review questions <img height="12" style="display: inline-block" src="static/link/to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/05_numbers/05_review.ipynb)
     - [further resources <img height="12" style="display: inline-block" src="static/link/to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/05_numbers/06_resources.ipynb)
       [<img height="12" style="display: inline-block" src="static/link/to_mb.png">](https://mybinder.org/v2/gh/webartifex/intro-to-python/develop?urlpath=lab/tree/05_numbers/06_resources.ipynb)
+  - *Chapter 6*: Text & Bytes
+    - [content <img height="12" style="display: inline-block" src="static/link/to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/06_numbers/00_content.ipynb)
+      [<img height="12" style="display: inline-block" src="static/link/to_mb.png">](https://mybinder.org/v2/gh/webartifex/intro-to-python/develop?urlpath=lab/tree/06_text/00_content.ipynb)
+      (`str` Type;
+       Reading Files;
+       Sequences;
+       Indexing & Slicing;
+       String Methods & Operations;
+       String Interpolation)
 
 
 #### Videos

From c6716db0b864779b603f64b125bba09509a8de45 Mon Sep 17 00:00:00 2001
From: Alexander Hess <alexander@webartifex.biz>
Date: Mon, 19 Oct 2020 11:34:08 +0200
Subject: [PATCH 173/242] Add initial version of chapter 06's exercises

---
 06_text/01_exercises.ipynb | 995 +++++++++++++++++++++++++++++++++++++
 README.md                  |   3 +
 2 files changed, 998 insertions(+)
 create mode 100644 06_text/01_exercises.ipynb

diff --git a/06_text/01_exercises.ipynb b/06_text/01_exercises.ipynb
new file mode 100644
index 0000000..0781f2b
--- /dev/null
+++ b/06_text/01_exercises.ipynb
@@ -0,0 +1,995 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Note**: Click on \"*Kernel*\" > \"*Restart Kernel and Run All*\" in [JupyterLab](https://jupyterlab.readthedocs.io/en/stable/) *after* finishing the exercises to ensure that your solution runs top to bottom *without* any errors. If you cannot run this file on your machine, you may want to open it [in the cloud <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_mb.png\">](https://mybinder.org/v2/gh/webartifex/intro-to-python/develop?urlpath=lab/tree/06_text/01_exercises.ipynb)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Chapter 6: Text & Bytes (Coding Exercises)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The exercises below assume that you have read the [first part <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/06_text/00_content.ipynb) of Chapter 6.\n",
+    "\n",
+    "The `...`'s in the code cells indicate where you need to fill in code snippets. The number of `...`'s within a code cell give you a rough idea of how many lines of code are needed to solve the task. You should not need to create any additional code cells for your final solution. However, you may want to use temporary code cells to try out some ideas."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Detecting Palindromes"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "[Palindromes <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_wiki.png\">](https://en.wikipedia.org/wiki/Palindrome) are sequences of characters that read the same backward as forward. Examples are first names like \"Hannah\" or \"Otto,\" words like \"radar\" or \"level,\" or sentences like \"Was it a car or a cat I saw?\"\n",
+    "\n",
+    "In this exercise, you implement various functions that check if the given arguments are palindromes or not. We start with an iterative implementation and end with a recursive one.\n",
+    "\n",
+    "Conceptually, the first function, `unpythonic_palindrome()`, is similar to the \"*Is the square of a number in `[7, 11, 8, 5, 3, 12, 2, 6, 9, 10, 1, 4]` greater than `100`?*\" example in [Chapter 4 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/04_iteration/03_content.ipynb#Example:-Is-the-square-of-a-number-in-[7,-11,-8,-5,-3,-12,-2,-6,-9,-10,-1,-4]-greater-than-100?): It assumes that the `text` argument is a palindrome (i.e., it initializes `is_palindrom` to `True`) and then checks in a `for`-loop if a pair of corresponding characters, `forward` and `backward`, contradicts that.\n",
+    "\n",
+    "**Q1**: How many iterations are needed in the `for`-loop? Take into account that `text` may contain an even or odd number of characters! Inside `unpythonic_palindrome()` below, write an expression whose result is assigned to `chars_to_check`!"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " < your answer > "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q2**: `forward_index` is the index going from left to right. How can we calculate `backward_index`, the index going from right to left, for a given `forward_index`? Write an expression whose result is assigned to `backward_index`! Then, use the indexing operator `[]` to obtain the two characters, `forward` and `backward`, from `text`."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " < your answer >"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q3**: Finish `unpythonic_palindrome()` below! Add code that adjusts `text` such that the function is case insensitive if the `ignore_case` argument is `True`! Make sure that the function returns once the first pair of corresponding characters does not match!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def unpythonic_palindrome(text, *, ignore_case=True):\n",
+    "    \"\"\"Check if a text is a palindrome or not.\n",
+    "\n",
+    "    Args:\n",
+    "        text (str): Text to be checked; must be an individual word\n",
+    "        ignore_case (bool): If the check is case insensitive; defaults to True\n",
+    "\n",
+    "    Returns:\n",
+    "        is_palindrome (bool)\n",
+    "    \"\"\"\n",
+    "    # answer to Q3\n",
+    "    is_palindrome = ...\n",
+    "    if ignore_case:\n",
+    "        ...\n",
+    "    # answer to Q1\n",
+    "    chars_to_check = ...\n",
+    "\n",
+    "    for forward_index in range(chars_to_check):\n",
+    "        # answer to Q2\n",
+    "        backward_index = ...\n",
+    "        forward = ...\n",
+    "        backward = ...\n",
+    "\n",
+    "        print(forward, \"and\", backward)  # added for didactical purposes\n",
+    "\n",
+    "        # answer to Q3\n",
+    "        if ...:\n",
+    "            is_palindrome = ...\n",
+    "            ...\n",
+    "\n",
+    "    return is_palindrome"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q4**: Ensure that `unpythonic_palindrome()` works for the provided test cases (i.e., the `assert` statements do *not* raise an `AssertionError`)! Also, for each of the test cases, provide a brief explanation of what makes them *unique*!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "assert unpythonic_palindrome(\"noon\") is True"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " < your explanation 1 >"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "assert unpythonic_palindrome(\"Hannah\") is True"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " < your explanation 2 >"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "assert unpythonic_palindrome(\"Hannah\", ignore_case=False) is False"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " < your explanation 3 >"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "assert unpythonic_palindrome(\"radar\") is True"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " < your explanation 4 >"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "assert unpythonic_palindrome(\"Hanna\") is False"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " < your explanation 5 >"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "assert unpythonic_palindrome(\"Warsaw\") is False"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " < your explanation 6 >"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "`unpythonic_palindrome()` is considered *not* Pythonic as it uses index variables to implement the looping logic. Instead, we should simply loop over an *iterable* object to work with its elements one by one.\n",
+    "\n",
+    "**Q5**: Copy your solutions to the previous questions into `almost_pythonic_palindrome()` below!\n",
+    "\n",
+    "**Q6**: The [reversed() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#reversed) and [zip() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#zip) built-ins allow us to loop over the same `text` argument *in parallel* in both forward *and* backward order. Finish the `for` statement's header line to do just that!\n",
+    "\n",
+    "Hint: You may need to slice the `text` argument."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def almost_pythonic_palindrome(text, *, ignore_case=True):\n",
+    "    \"\"\"Check if a text is a palindrome or not.\n",
+    "\n",
+    "    Args:\n",
+    "        text (str): Text to be checked; must be an individual word\n",
+    "        ignore_case (bool): If the check is case insensitive; defaults to True\n",
+    "\n",
+    "    Returns:\n",
+    "        is_palindrome (bool)\n",
+    "    \"\"\"\n",
+    "    # answers from above\n",
+    "    is_palindrome = ...\n",
+    "    if ignore_case:\n",
+    "        ...\n",
+    "    chars_to_check = ...\n",
+    "\n",
+    "    # answer to Q6\n",
+    "    for ... in ...:\n",
+    "\n",
+    "        print(forward, \"and\", backward)  # added for didactical purposes\n",
+    "\n",
+    "        # answers from above\n",
+    "        if ...:\n",
+    "            is_palindrome = ...\n",
+    "            ...\n",
+    "\n",
+    "    return is_palindrome"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q7**: Verify that the test cases work as before!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "assert almost_pythonic_palindrome(\"noon\") is True"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "assert almost_pythonic_palindrome(\"Hannah\") is True"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "assert almost_pythonic_palindrome(\"Hannah\", ignore_case=False) is False"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "assert almost_pythonic_palindrome(\"radar\") is True"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "assert almost_pythonic_palindrome(\"Hanna\") is False"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "assert almost_pythonic_palindrome(\"Warsaw\") is False"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q8**: `almost_pythonic_palindrome()` above may be made more Pythonic by removing the variable `is_palindrome` with the *early exit* pattern. Make the corresponding changes!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def pythonic_palindrome(text, *, ignore_case=True):\n",
+    "    \"\"\"Check if a text is a palindrome or not.\n",
+    "\n",
+    "    Args:\n",
+    "        text (str): Text to be checked; must be an individual word\n",
+    "        ignore_case (bool): If the check is case insensitive; defaults to True\n",
+    "\n",
+    "    Returns:\n",
+    "        is_palindrome (bool)\n",
+    "    \"\"\"\n",
+    "    # answers from above\n",
+    "    if ignore_case:\n",
+    "        ...\n",
+    "    chars_to_check = ...\n",
+    "\n",
+    "    for ... in ...:\n",
+    "\n",
+    "        print(forward, \"and\", backward)  # added for didactical purposes\n",
+    "\n",
+    "        if ...:\n",
+    "            # answer to Q8\n",
+    "            ...\n",
+    "\n",
+    "    # answer to Q8\n",
+    "    return ..."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q9**: Verify that the test cases still work!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "assert pythonic_palindrome(\"noon\") is True"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": [
+    "assert pythonic_palindrome(\"Hannah\") is True"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "assert pythonic_palindrome(\"Hannah\", ignore_case=False) is False"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "assert pythonic_palindrome(\"radar\") is True"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "assert pythonic_palindrome(\"Hanna\") is False"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "assert pythonic_palindrome(\"Warsaw\") is False"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q10**: `pythonic_palindrome()` is *not* able to check numeric palindromes. In addition to the string method that implements the case insensitivity and that essentially causes the `AttributeError`, what *abstract behaviors* are numeric data types, such as the `int` type in the example below, missing that would also cause runtime errors? "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "pythonic_palindrome(12321)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " < your answer >"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q11**: Copy your code from `pythonic_palindrome()` above into `palindrome_ducks()` below and make the latter conform to *duck typing*!\n",
+    "\n",
+    "Hints: You may want to use the [str() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#func-str) built-in. You only need to add *one* short line of code."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def palindrome_ducks(text, *, ignore_case=True):\n",
+    "    \"\"\"Check if a text is a palindrome or not.\n",
+    "\n",
+    "    Args:\n",
+    "        text (str): Text to be checked; must be an individual word\n",
+    "        ignore_case (bool): If the check is case insensitive; defaults to True\n",
+    "\n",
+    "    Returns:\n",
+    "        is_palindrome (bool)\n",
+    "    \"\"\"\n",
+    "    # answer to Q11\n",
+    "    ...\n",
+    "    # answers from above\n",
+    "    if ignore_case:\n",
+    "        text = ...\n",
+    "    chars_to_check = ...\n",
+    "\n",
+    "    for ... in ...:\n",
+    "        if ...:\n",
+    "            ...\n",
+    "\n",
+    "    return ..."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q12**: Verify that the two new test cases work as well!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "assert palindrome_ducks(12321) is True"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "assert palindrome_ducks(12345) is False"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "`palindrome_ducks()` can *not* process palindromes that consist of more than one word."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "palindrome_ducks(\"Never odd or even.\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "palindrome_ducks(\"Eva, can I stab bats in a cave?\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "palindrome_ducks(\"A man, a plan, a canal - Panama.\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "palindrome_ducks(\"A Santa lived as a devil at NASA!\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "palindrome_ducks(\"\"\"\n",
+    "    Dennis, Nell, Edna, Leon, Nedra, Anita, Rolf, Nora, Alice, Carol, Leo, Jane,\n",
+    "    Reed, Dena, Dale, Basil, Rae, Penny, Lana, Dave, Denny, Lena, Ida, Bernadette,\n",
+    "    Ben, Ray, Lila, Nina, Jo, Ira, Mara, Sara, Mario, Jan, Ina, Lily, Arne, Bette,\n",
+    "    Dan, Reba, Diane, Lynn, Ed, Eva, Dana, Lynne, Pearl, Isabel, Ada, Ned, Dee,\n",
+    "    Rena, Joel, Lora, Cecil, Aaron, Flora, Tina, Arden, Noel, and Ellen sinned.\n",
+    "\"\"\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q13**: Implement the final iterative version `is_a_palindrome()` below. Copy your solution from `palindrome_ducks()` above and add code that removes the \"special\" characters (and symbols) from the longer example palindromes above so that they are effectively ignored! Note that this processing should only be done if the `ignore_symbols` argument is set to `True`.\n",
+    "\n",
+    "Hints: Use the [replace() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/stdtypes.html#str.replace) method on the `str` type to achieve that. You may want to do so within another `for`-loop."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def is_a_palindrome(text, *, ignore_case=True, ignore_symbols=True):\n",
+    "    \"\"\"Check if a text is a palindrome or not.\n",
+    "\n",
+    "    Args:\n",
+    "        text (str): Text to be checked; may be multiple words\n",
+    "        ignore_case (bool): If the check is case insensitive; defaults to True\n",
+    "        ignore_symbols (bool): If special characters like \".\" or \"?\" and others\n",
+    "            are ignored; defaults to True\n",
+    "\n",
+    "    Returns:\n",
+    "        is_palindrome (bool)\n",
+    "    \"\"\"\n",
+    "    # answers from above\n",
+    "    ...\n",
+    "    if ignore_case:\n",
+    "        ...\n",
+    "    # answer to Q13\n",
+    "    if ignore_symbols:\n",
+    "        for ... in ...:\n",
+    "            ...\n",
+    "    # answers from above\n",
+    "    chars_to_check = ...\n",
+    "\n",
+    "    for ... in ...:\n",
+    "        if ...:\n",
+    "            ...\n",
+    "\n",
+    "    return ..."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q14**: Verify that all test cases below work!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "assert is_a_palindrome(\"noon\") is True"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": [
+    "assert is_a_palindrome(\"Hannah\") is True"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "assert is_a_palindrome(\"Hannah\", ignore_case=False) is False"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "assert is_a_palindrome(\"radar\") is True"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "assert is_a_palindrome(\"Hanna\") is False"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "assert is_a_palindrome(\"Warsaw\") is False"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "assert is_a_palindrome(12321) is True"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "assert is_a_palindrome(12345) is False"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "assert is_a_palindrome(\"Never odd or even.\") is True"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "assert is_a_palindrome(\"Never odd or even.\", ignore_symbols=False) is False"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "assert is_a_palindrome(\"Eva, can I stab bats in a cave?\") is True"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "assert is_a_palindrome(\"A man, a plan, a canal - Panama.\") is True"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "assert is_a_palindrome(\"A Santa lived as a devil at NASA!\") is True"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "assert is_a_palindrome(\"\"\"\n",
+    "    Dennis, Nell, Edna, Leon, Nedra, Anita, Rolf, Nora, Alice, Carol, Leo, Jane,\n",
+    "    Reed, Dena, Dale, Basil, Rae, Penny, Lana, Dave, Denny, Lena, Ida, Bernadette,\n",
+    "    Ben, Ray, Lila, Nina, Jo, Ira, Mara, Sara, Mario, Jan, Ina, Lily, Arne, Bette,\n",
+    "    Dan, Reba, Diane, Lynn, Ed, Eva, Dana, Lynne, Pearl, Isabel, Ada, Ned, Dee,\n",
+    "    Rena, Joel, Lora, Cecil, Aaron, Flora, Tina, Arden, Noel, and Ellen sinned.\n",
+    "\"\"\") is True"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now, let's look at a *recursive* formulation in `recursive_palindrome()` below.\n",
+    "\n",
+    "**Q15**: Copy the code from `is_a_palindrome()` that implements the duck typing, the case insensitivity, and the removal of special characters!\n",
+    "\n",
+    "The recursion becomes apparent if we remove the *first* and the *last* character from a given `text`: `text` can only be a palindrome if the two removed characters are the same *and* the remaining substring is a palindrome itself! So, the word `\"noon\"` has only *one* recursive call while `\"radar\"` has *two*.\n",
+    "\n",
+    "Further, `recursive_palindrome()` has *two* base cases of which only *one* is reached for a given `text`: First, if `recursive_palindrome()` is called with either an empty `\"\"` or a `text` argument with `len(text) == 1`, and, second, if the two removed characters are *not* the same.\n",
+    "\n",
+    "**Q16**: Implement the two base cases in `recursive_palindrome()`! Use the *early exit* pattern!\n",
+    "\n",
+    "**Q17**: Add the recursive call to `recursive_palindrome()` with a substring of `text`! Pass in the `ignore_case` and `ignore_symbols` arguments as `False`! This avoids unnecessary computations in the recursive calls. Why is that the case?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " < your answer >"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def recursive_palindrome(text, *, ignore_case=True, ignore_symbols=True):\n",
+    "    \"\"\"Check if a text is a palindrome or not.\n",
+    "\n",
+    "    Args:\n",
+    "        text (str): Text to be checked; may be multiple words\n",
+    "        ignore_case (bool): If the check is case insensitive; defaults to True\n",
+    "        ignore_symbols (bool): If special characters like \".\" or \"?\" and others\n",
+    "            are ignored; defaults to True\n",
+    "\n",
+    "    Returns:\n",
+    "        is_palindrome (bool)\n",
+    "    \"\"\"\n",
+    "    # answers from above\n",
+    "    ...\n",
+    "    if ignore_case:\n",
+    "        ...\n",
+    "    if ignore_symbols:\n",
+    "        for ... in ...:\n",
+    "            ...\n",
+    "\n",
+    "    # answer to Q16\n",
+    "    if ...:\n",
+    "        ...\n",
+    "    elif ...:\n",
+    "        ...\n",
+    "\n",
+    "    # answer to Q17\n",
+    "    return ..."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q18**: Lastly, verify that `recursive_palindrome()` passes all the test cases below!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "assert recursive_palindrome(\"noon\") is True"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": [
+    "assert recursive_palindrome(\"Hannah\") is True"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "assert recursive_palindrome(\"Hannah\", ignore_case=False) is False"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "assert recursive_palindrome(\"radar\") is True"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "assert recursive_palindrome(\"Hanna\") is False"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "assert recursive_palindrome(\"Warsaw\") is False"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "assert recursive_palindrome(12321) is True"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "assert recursive_palindrome(12345) is False"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "assert recursive_palindrome(\"Never odd or even.\") is True"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "assert recursive_palindrome(\"Never odd or even.\", ignore_symbols=False) is False"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "assert recursive_palindrome(\"Eva, can I stab bats in a cave?\") is True"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "assert recursive_palindrome(\"A man, a plan, a canal - Panama.\") is True"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "assert recursive_palindrome(\"A Santa lived as a devil at NASA!\") is True"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "assert recursive_palindrome(\"\"\"\n",
+    "    Dennis, Nell, Edna, Leon, Nedra, Anita, Rolf, Nora, Alice, Carol, Leo, Jane,\n",
+    "    Reed, Dena, Dale, Basil, Rae, Penny, Lana, Dave, Denny, Lena, Ida, Bernadette,\n",
+    "    Ben, Ray, Lila, Nina, Jo, Ira, Mara, Sara, Mario, Jan, Ina, Lily, Arne, Bette,\n",
+    "    Dan, Reba, Diane, Lynn, Ed, Eva, Dana, Lynne, Pearl, Isabel, Ada, Ned, Dee,\n",
+    "    Rena, Joel, Lora, Cecil, Aaron, Flora, Tina, Arden, Noel, and Ellen sinned.\n",
+    "\"\"\") is True"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.6"
+  },
+  "toc": {
+   "base_numbering": 1,
+   "nav_menu": {},
+   "number_sections": false,
+   "sideBar": true,
+   "skip_h1_title": true,
+   "title_cell": "Table of Contents",
+   "title_sidebar": "Contents",
+   "toc_cell": false,
+   "toc_position": {},
+   "toc_section_display": false,
+   "toc_window_display": false
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/README.md b/README.md
index d5d2970..430dbb9 100644
--- a/README.md
+++ b/README.md
@@ -149,6 +149,9 @@ Alternatively, the content can be viewed in a web browser
        Indexing & Slicing;
        String Methods & Operations;
        String Interpolation)
+    - [exercises <img height="12" style="display: inline-block" src="static/link/to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/06_text/01_exercises.ipynb)
+      [<img height="12" style="display: inline-block" src="static/link/to_mb.png">](https://mybinder.org/v2/gh/webartifex/intro-to-python/develop?urlpath=lab/tree/06_text/01_exercises.ipynb)
+      (Detecting Palindromes)
 
 
 #### Videos

From 3c91b00749cfcfa6998d3f71479893573f4f8b03 Mon Sep 17 00:00:00 2001
From: Alexander Hess <alexander@webartifex.biz>
Date: Mon, 19 Oct 2020 11:40:27 +0200
Subject: [PATCH 174/242] Add initial version of chapter 06, part 2

---
 06_text/02_content.ipynb | 2100 ++++++++++++++++++++++++++++++++++++++
 06_text/full_house.bin   |    1 +
 06_text/umlauts.txt      |   12 +
 README.md                |    7 +
 4 files changed, 2120 insertions(+)
 create mode 100644 06_text/02_content.ipynb
 create mode 100644 06_text/full_house.bin
 create mode 100644 06_text/umlauts.txt

diff --git a/06_text/02_content.ipynb b/06_text/02_content.ipynb
new file mode 100644
index 0000000..6ff2407
--- /dev/null
+++ b/06_text/02_content.ipynb
@@ -0,0 +1,2100 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "**Note**: Click on \"*Kernel*\" > \"*Restart Kernel and Clear All Outputs*\" in [JupyterLab](https://jupyterlab.readthedocs.io/en/stable/) *before* reading this notebook to reset its output. If you cannot run this file on your machine, you may want to open it [in the cloud <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_mb.png\">](https://mybinder.org/v2/gh/webartifex/intro-to-python/develop?urlpath=lab/tree/06_text/01_content.ipynb)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "# Chapter 6: Text & Bytes (continued)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "In this second part of the chapter, we look in more detail at how `str` objects work in memory, in particular how the $0$s and $1$s in the memory translate into characters."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "## Special Characters"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "As previously seen, some characters have a special meaning when following the **escape character** `\"\\\"`. Besides escaping the kind of quote used as the `str` object's delimiter, `'` or `\"`, most of these **escape sequences** (i.e., `\"\\\"` with the subsequent character), act as a **control character** that moves the \"cursor\" in the output *without* generating any pixel on the screen. Because of that, we only see the effect of such escape sequences when used with the [print() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#print) function. The [documentation <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/reference/lexical_analysis.html#string-and-bytes-literals) lists all available escape sequences, of which we show the most important ones below.\n",
+    "\n",
+    "The most common escape sequence is `\"\\n\"` that \"prints\" a [newline character <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_wiki.png\">](https://en.wikipedia.org/wiki/Newline) that is also called the line feed character or LF for short."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'This is a sentence\\nthat is printed\\non three lines.'"
+      ]
+     },
+     "execution_count": 1,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "\"This is a sentence\\nthat is printed\\non three lines.\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "This is a sentence\n",
+      "that is printed\n",
+      "on three lines.\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"This is a sentence\\nthat is printed\\non three lines.\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "`\"\\b\"` is the [backspace character <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_wiki.png\">](https://en.wikipedia.org/wiki/Backspace), or BS for short, that moves the cursor back by one character."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "ABX\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"ABC\\bX\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "ABXY\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"ABC\\bXY\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Similarly, `\"\\r\"` is the [carriage return character <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_wiki.png\">](https://en.wikipedia.org/wiki/Carriage_return), or CR for short, that moves the cursor back to the beginning of the line."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "XBC\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"ABC\\rX\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "XYC\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"ABC\\rXY\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "While Linux and modern MacOS systems use solely `\"\\n\"` to express a new line, Windows systems default to using `\"\\r\\n\"`. This may lead to \"weird\" bugs on software projects where people using both kind of operating systems collaborate."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "This is a sentence\n",
+      "that is printed\n",
+      "on three lines.\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"This is a sentence\\r\\nthat is printed\\r\\non three lines.\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "`\"\\t\"` makes the cursor \"jump\" in equidistant tab stops. That may be useful for formatting a program with lengthy and tabular results."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Jump\tfrom\ttab\tstop\tto\ttab\tstop.\n",
+      "The\tsecond\tline\tdoes\tso\ttoo.\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"Jump\\tfrom\\ttab\\tstop\\tto\\ttab\\tstop.\\nThe\\tsecond\\tline\\tdoes\\tso\\ttoo.\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "### Raw Strings"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Sometimes we do *not* want the backslash `\"\\\"` and its subsequent character be interpreted as an escape sequence. For example, let's print a typical installation path on a Windows systems. Obviously, the newline character `\"\\n\"` does *not* makes sense here."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "C:\\Programs\n",
+      "ew_application\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"C:\\Programs\\new_application\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Some `str` objects even produce a `SyntaxError` because the `\"\\U\"` can *not* be interpreted as a Unicode code point (cf., next section)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "ename": "SyntaxError",
+     "evalue": "(unicode error) 'unicodeescape' codec can't decode bytes in position 2-3: truncated \\UXXXXXXXX escape (<ipython-input-10-61308f43d404>, line 1)",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;36m  File \u001b[0;32m\"<ipython-input-10-61308f43d404>\"\u001b[0;36m, line \u001b[0;32m1\u001b[0m\n\u001b[0;31m    print(\"C:\\Users\\Administrator\\Desktop\\Project\")\u001b[0m\n\u001b[0m          ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m (unicode error) 'unicodeescape' codec can't decode bytes in position 2-3: truncated \\UXXXXXXXX escape\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"C:\\Users\\Administrator\\Desktop\\Project\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "A simple solution would be to escape the escape character with a *second* backslash `\"\\\"`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "C:\\Programs\\new_application\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"C:\\\\Programs\\\\new_application\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\Administrator\\Desktop\\Project\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"C:\\\\Users\\\\Administrator\\\\Desktop\\\\Project\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "However, this is tedious to remember and type. For such use cases, Python allows to prefix any string literal with a `r`. The literal is then interpreted in a \"raw\" way."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "C:\\Programs\\new_application\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(r\"C:\\Programs\\new_application\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\Administrator\\Desktop\\Project\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(r\"C:\\Users\\Administrator\\Desktop\\Project\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "## Characters are Numbers with a Convention"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "So far, we used the term **character** without any further consideration. In this section, we briefly look into what characters are and how they are modeled in software.\n",
+    "\n",
+    "[Chapter 5 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/05_numbers/00_content.ipynb) gives us an idea on how individual **bits** are used to express all types of numbers, from \"simple\" `int` objects to \"complex\" `float` ones. To model characters, another **layer of abstraction** is put on top of whole numbers. So, just as bits are used to express integers, they themselves are used to express characters."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "### ASCII"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Many conventions have been developed as to what integer is associated with which character. The most basic one that was also adopted around the world is the the so-called [American Standard Code for Information Interchange <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_wiki.png\">](https://en.wikipedia.org/wiki/ASCII), or **ASCII** for short. It uses 7 bits of information to map the unprintable control characters as well as the printable letters of the alphabet, numbers, and common symbols to the numbers `0` through `127`.\n",
+    "\n",
+    "A mapping from characters to numbers is referred to by the technical term **encoding**. We may use the built-in [ord() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#ord) function to **encode** any single character. The inverse to that is the built-in [chr() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#chr) function, which **decodes** a number into a character."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "65"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ord(\"A\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'A'"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "chr(65)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Of course, unprintable escape sequences like `\"\\n\"` count as only *one* character."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "10"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ord(\"\\n\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'\\n'"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "chr(10)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "In ASCII, the numbers `0` through `31` (and `127`) are mapped to all kinds of unprintable control characters. The decimal digits are encoded with the numbers `48` through `57`, the upper case letters with `65` through `90`, and the lower case letters with `97` through `122`. While this seems random as first, there is of course a \"sophisticated\" system behind it. That can immediately be seen when looking at the encoded numbers in their *binary* representations.\n",
+    "\n",
+    "For example, the digit `5` is mapped to the number `53` in ASCII. The binary representation of `53` is `0b_11_0101` and the least significant four bits, `0101`, mean $5$. Similarly, the letter `\"E\"` is the fifth letter in the alphabet. It is encoded with the number `69` in ASCII, which is `0b_100_0101` in binary. And, the least significant bits, `0_0101`, mean $5$. Analogously, `\"e\"` is encoded with `101` in ASCII, which is `0b_110_0101` in binary. And, the least significant bits, `0_0101`, mean $5$ again. This encoding was chosen mainly because programmers \"in the old days\" needed to implement these encodings \"by hand.\" Python abstracts that logic away from its users.\n",
+    "\n",
+    "This encoding scheme is also the cause for the \"weird\" sorting in the \"*String Comparison*\" section in the [first part <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/06_text/01_content.ipynb#String-Comparison) of this chapter, where `\"apple\"` comes *after* `\"Banana\"`. As `\"a\"` is encoded with `97` and `\"B\"` with `66`, `\"Banana\"` must of course be \"smaller\" than `\"apple\"` when comparison is done in a pairwise fashion of the individual characters."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "48 0b110000 ->  0\n",
+      "49 0b110001 ->  1\n",
+      "50 0b110010 ->  2\n",
+      "51 0b110011 ->  3\n",
+      "52 0b110100 ->  4\n",
+      "53 0b110101 ->  5\n",
+      "54 0b110110 ->  6\n",
+      "55 0b110111 ->  7\n",
+      "56 0b111000 ->  8\n",
+      "57 0b111001 ->  9\n"
+     ]
+    }
+   ],
+   "source": [
+    "for number in range(48, 58):\n",
+    "    print(number, bin(number), \"-> \", chr(number))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "65 0b1000001 ->  A\t66 0b1000010 ->  B\t67 0b1000011 ->  C\n",
+      "68 0b1000100 ->  D\t69 0b1000101 ->  E\t70 0b1000110 ->  F\n",
+      "71 0b1000111 ->  G\t72 0b1001000 ->  H\t73 0b1001001 ->  I\n",
+      "74 0b1001010 ->  J\t75 0b1001011 ->  K\t76 0b1001100 ->  L\n",
+      "77 0b1001101 ->  M\t78 0b1001110 ->  N\t79 0b1001111 ->  O\n",
+      "80 0b1010000 ->  P\t81 0b1010001 ->  Q\t82 0b1010010 ->  R\n",
+      "83 0b1010011 ->  S\t84 0b1010100 ->  T\t85 0b1010101 ->  U\n",
+      "86 0b1010110 ->  V\t87 0b1010111 ->  W\t88 0b1011000 ->  X\n",
+      "89 0b1011001 ->  Y\t90 0b1011010 ->  Z\t"
+     ]
+    }
+   ],
+   "source": [
+    "for i, number in enumerate(range(65, 91), start=1):\n",
+    "    end = \"\\n\" if i % 3 == 0 else \"\\t\"\n",
+    "    print(number, bin(number), \"-> \", chr(number), end=end)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      " 97 0b1100001 ->  a\t 98 0b1100010 ->  b\t 99 0b1100011 ->  c\n",
+      "100 0b1100100 ->  d\t101 0b1100101 ->  e\t102 0b1100110 ->  f\n",
+      "103 0b1100111 ->  g\t104 0b1101000 ->  h\t105 0b1101001 ->  i\n",
+      "106 0b1101010 ->  j\t107 0b1101011 ->  k\t108 0b1101100 ->  l\n",
+      "109 0b1101101 ->  m\t110 0b1101110 ->  n\t111 0b1101111 ->  o\n",
+      "112 0b1110000 ->  p\t113 0b1110001 ->  q\t114 0b1110010 ->  r\n",
+      "115 0b1110011 ->  s\t116 0b1110100 ->  t\t117 0b1110101 ->  u\n",
+      "118 0b1110110 ->  v\t119 0b1110111 ->  w\t120 0b1111000 ->  x\n",
+      "121 0b1111001 ->  y\t122 0b1111010 ->  z\t"
+     ]
+    }
+   ],
+   "source": [
+    "for i, number in enumerate(range(97, 123), start=1):\n",
+    "    end = \"\\n\" if i % 3 == 0 else \"\\t\"\n",
+    "    print(str(number).rjust(3), bin(number), \"-> \", chr(number), end=end)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "The remaining `symbols` encoded in ASCII are encoded with the numbers still unused, which is why they are scattered."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "symbols = (\n",
+    "    list(range(32, 48))\n",
+    "    + list(range(58, 65))\n",
+    "    + list(range(91, 97))\n",
+    "    + list(range(123, 127))\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      " 32   0b100000 ->   \t 33   0b100001 ->  !\t 34   0b100010 ->  \"\n",
+      " 35   0b100011 ->  #\t 36   0b100100 ->  $\t 37   0b100101 ->  %\n",
+      " 38   0b100110 ->  &\t 39   0b100111 ->  '\t 40   0b101000 ->  (\n",
+      " 41   0b101001 ->  )\t 42   0b101010 ->  *\t 43   0b101011 ->  +\n",
+      " 44   0b101100 ->  ,\t 45   0b101101 ->  -\t 46   0b101110 ->  .\n",
+      " 47   0b101111 ->  /\t 58   0b111010 ->  :\t 59   0b111011 ->  ;\n",
+      " 60   0b111100 ->  <\t 61   0b111101 ->  =\t 62   0b111110 ->  >\n",
+      " 63   0b111111 ->  ?\t 64  0b1000000 ->  @\t 91  0b1011011 ->  [\n",
+      " 92  0b1011100 ->  \\\t 93  0b1011101 ->  ]\t 94  0b1011110 ->  ^\n",
+      " 95  0b1011111 ->  _\t 96  0b1100000 ->  `\t123  0b1111011 ->  {\n",
+      "124  0b1111100 ->  |\t125  0b1111101 ->  }\t126  0b1111110 ->  ~\n"
+     ]
+    }
+   ],
+   "source": [
+    "for i, number in enumerate(symbols, start=1):\n",
+    "    end = \"\\n\" if i % 3 == 0 else \"\\t\"\n",
+    "    print(str(number).rjust(3), bin(number).rjust(10), \"-> \", chr(number), end=end)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "As the ASCII character set does not work for many languages other than English, various encodings were developed. Popular examples are [ISO 8859-1 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_wiki.png\">](https://en.wikipedia.org/wiki/ISO/IEC_8859-1) for western European letters or [Windows 1250 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_wiki.png\">](https://en.wikipedia.org/wiki/Windows-1250) for Latin ones. Many of these encodings use 8-bit numbers (i.e., `0` through `255`) to map the multitude of non-English letters (e.g., the German [umlauts <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_wiki.png\">](https://en.wikipedia.org/wiki/Umlaut_%28linguistics%29) `\"ä\"`, `\"ö\"`, `\"ü\"`, or `\"ß\"`)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "### Unicode"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "However, none of these specialized encodings can map *all* characters of *all* languages around the world from *all* times in human history. To achieve that, a truly global standard called **[Unicode <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_wiki.png\">](https://en.wikipedia.org/wiki/Unicode)** was developed and its first version released in 1991. Since then, Unicode has been amended with many other \"characters.\" The most popular among them being [emojis <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_wiki.png\">](https://en.wikipedia.org/wiki/Emoji) or the [Klingon <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_wiki.png\">](https://en.wikipedia.org/wiki/Klingon_scripts) language (from the science fiction series [Star Trek <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_wiki.png\">](https://en.wikipedia.org/wiki/Star_Trek)). In Unicode, every character is given an identity referred to as the **code point**. Code points are hexadecimal numbers from `0x0000` through `0x10ffff`, written as U+0000 and U+10FFFF outside of Python. Consequently, there exist at most $1,114,112$ code points, of which only about 10% are currently in use, allowing lots of room for new characters to be invented. The first `127` code points are identical to the ASCII encoding for reasons explained in the \"*The `bytes` Type*\" section further below. There exist plenty of lists of all Unicode characters on the web (e.g., [Wikipedia <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_wiki.png\">](https://en.wikipedia.org/wiki/List_of_Unicode_characters)).\n",
+    "\n",
+    "All we need to know to print a character is its code point. Python uses the escape sequence `\"\\U\"` that is followed by eight hexadecimal digits. Underscore separators are unfortunately *not* allowed here.\n",
+    "\n",
+    "So, to print a smiley, we just need to look up the corresponding number (e.g., [here <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_wiki.png\">](https://en.wikipedia.org/wiki/Emoji#Unicode_blocks))."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'�'"
+      ]
+     },
+     "execution_count": 24,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "\"\\U0001f604\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Every Unicode character also has a descriptive name that we can use with the escape sequence `\"\\N\"` and within curly braces `{}`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'�'"
+      ]
+     },
+     "execution_count": 25,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "\"\\N{FACE WITH TEARS OF JOY}\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Whenever the code point can be expressed with just four hexadecimal digits, we may use the escape sequence `\"\\u\"` for brevity."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'A'"
+      ]
+     },
+     "execution_count": 26,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "\"\\U00000041\"  # hex(65) == 0x41"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'A'"
+      ]
+     },
+     "execution_count": 27,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "\"\\u0041\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Analogously, if the code point can be expressed with two hexadecimal digits, we may use the escape sequence `\"\\x\"` for even conciser code."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'A'"
+      ]
+     },
+     "execution_count": 28,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "\"\\x41\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "As the `str` type is based on Unicode, a `str` object's behavior is more in line with how humans view text and not how it is expressed in source code.\n",
+    "\n",
+    "For example, while it is obvious that `len(\"A\")` evaluates to `1`, ..."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "1"
+      ]
+     },
+     "execution_count": 29,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "len(\"A\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "... what should `len(\"\\N{SNAKE}\")` evaluate to? As the idea of a snake is expressed as *one* \"character,\" [len() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#len) also returns `1` here."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'�'"
+      ]
+     },
+     "execution_count": 30,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "\"\\N{SNAKE}\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "1"
+      ]
+     },
+     "execution_count": 31,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "len(\"\\N{SNAKE}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Many of the built-in `str` methods also consider Unicode. For example, in contrast to [lower() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/stdtypes.html#str.lower), the [casefold() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/stdtypes.html#str.casefold) method knows that the German `\"Ăź\"` is commonly converted to `\"ss\"`. So, when searching for exact matches, normalizing text with [casefold() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/stdtypes.html#str.casefold) may yield better results than with [lower() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/stdtypes.html#str.lower)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'straĂźe'"
+      ]
+     },
+     "execution_count": 32,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "\"StraĂźe\".lower()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'strasse'"
+      ]
+     },
+     "execution_count": 33,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "\"StraĂźe\".casefold()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Many other methods like [isdecimal() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/stdtypes.html#str.isdecimal), [isdigit() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/stdtypes.html#str.isdigit), [isnumeric() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/stdtypes.html#str.isnumeric), [isprintable() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/stdtypes.html#str.isprintable), [isidentifier() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/stdtypes.html#str.isidentifier), and many more may be worthwhile to know for the data science practitioner, especially when it comes to data cleaning."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "## Multi-line Strings"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Sometimes, it is convenient to split text across multiple lines in source code. For example, to make lines fit into the 79 characters requirement of [PEP 8 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://www.python.org/dev/peps/pep-0008/) or because the text consists of many lines and typing out `\"\\n\"` is tedious. However, using single double quotes `\"` around multiple lines results in a `SyntaxError`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "ename": "SyntaxError",
+     "evalue": "EOL while scanning string literal (<ipython-input-34-4cef690f1f4a>, line 1)",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;36m  File \u001b[0;32m\"<ipython-input-34-4cef690f1f4a>\"\u001b[0;36m, line \u001b[0;32m1\u001b[0m\n\u001b[0;31m    \"\u001b[0m\n\u001b[0m     ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m EOL while scanning string literal\n"
+     ]
+    }
+   ],
+   "source": [
+    "\"\n",
+    "Do not break the lines like this\n",
+    "\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Instead, we may enclose a string literal with either **triple double** quotes `\"\"\"` or **triple single** quotes `'''`. Then, newline characters in the source code are converted into `\"\\n\"` characters in the resulting `str` object. Docstrings are precisely that, and, by convention, always written within triple double quotes `\"\"\"`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "multi_line = \"\"\"\n",
+    "I am a multi-line string\n",
+    "consisting of four lines.\n",
+    "\"\"\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "A caveat is that `\"\\n\"` characters are often inserted at the beginning or end of the text when we try to format the source code nicely."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'\\nI am a multi-line string\\nconsisting of four lines.\\n'"
+      ]
+     },
+     "execution_count": 36,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "multi_line"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "I am a multi-line string\n",
+      "consisting of four lines.\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(multi_line)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Using the [split() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/stdtypes.html#str.split) method with the optional `sep` argument, we confirm that `multi_line` consists of *four* lines with the first and last line being empty."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "1 \n",
+      "2 I am a multi-line string\n",
+      "3 consisting of four lines.\n",
+      "4 \n"
+     ]
+    }
+   ],
+   "source": [
+    "for i, line in enumerate(multi_line.split(\"\\n\"), start=1):\n",
+    "    print(i, line)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "To mitigate that, we often see the [strip() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/stdtypes.html#bytes.strip) method in source code."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "multi_line = \"\"\"\n",
+    "I am a multi-line string\n",
+    "consisting of two lines.\n",
+    "\"\"\".strip()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "1 I am a multi-line string\n",
+      "2 consisting of two lines.\n"
+     ]
+    }
+   ],
+   "source": [
+    "for i, line in enumerate(multi_line.split(\"\\n\"), start=1):\n",
+    "    print(i, line)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "## The `bytes` Type"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "To end this chapter, we want to briefly look at the `bytes` data type, which conceptually is a sequence of bytes. That data format is probably one of the most generic ways of exchanging data between any two programs or computers (e.g., a web browser obtains its data from a web server in this format).\n",
+    "\n",
+    "Let's open a binary file in read-only mode (i.e., `mode=\"rb\"`) and read in all of its contents."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "with open(\"full_house.bin\", mode=\"rb\") as binary_file:\n",
+    "    data = binary_file.read()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "`data` is an object of type `bytes`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "139880714782512"
+      ]
+     },
+     "execution_count": 42,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "id(data)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "bytes"
+      ]
+     },
+     "execution_count": 43,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "type(data)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "It's value is given out in the literal bytes notation with a `b` prefix (cf., the [reference <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/reference/lexical_analysis.html#string-and-bytes-literals)). Every byte is expressed in hexadecimal representation with the escape sequence `\"\\x\"`. This representation is commonly chosen as we can *not* tell what kind of information is hidden in the `data` by just looking at the bytes. Instead, we must be told by some other source how to **decode** the raw bytes into information we can interpret."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "b'\\xf0\\x9f\\x82\\xa7\\xf0\\x9f\\x82\\xb7\\xf0\\x9f\\x83\\x97\\xf0\\x9f\\x83\\x8e\\xf0\\x9f\\x83\\x9e'"
+      ]
+     },
+     "execution_count": 44,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "`bytes` objects work like `str` objects in many ways. In particular, they are *sequences* as well: The number of bytes is *finite* and we may *iterate* over them in *order*."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "20"
+      ]
+     },
+     "execution_count": 45,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "len(data)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Consisting of 8 bits, a single byte can always be interpreted as a whole number between `0` through `255`. That is exactly what we see when we loop over the `data` ..."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "240 159 130 167 240 159 130 183 240 159 131 151 240 159 131 142 240 159 131 158 "
+     ]
+    }
+   ],
+   "source": [
+    "for byte in data:\n",
+    "    print(byte, end=\" \")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "... or index into them."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "158"
+      ]
+     },
+     "execution_count": 47,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data[-1]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Slicing returns another `bytes` object."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 48,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "b'\\xf0\\x82\\xf0\\x82\\xf0\\x83\\xf0\\x83\\xf0\\x83'"
+      ]
+     },
+     "execution_count": 48,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data[::2]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "### Character Encodings"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Luckily, `data` consists of bytes encoded with the [UTF-8 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_wiki.png\">](https://en.wikipedia.org/wiki/UTF-8) encoding. That is the most common way of mapping a Unicode character's code point to a sequence of bytes.\n",
+    "\n",
+    "To obtain a `str` object out of a given `bytes` object, we decode it with the `bytes` type's [decode() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/stdtypes.html#bytes.decode) method."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 49,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "cards = data.decode()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 50,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "str"
+      ]
+     },
+     "execution_count": 50,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "type(cards)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "So, `data` consisted of a [full house <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_wiki.png\">](https://en.wikipedia.org/wiki/List_of_poker_hands#Full_house) hand in a poker game."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 51,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'🂧🂷���'"
+      ]
+     },
+     "execution_count": 51,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "cards"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "To go the opposite direction and encode a given `str` object, we use the `str` type's [encode() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/stdtypes.html#str.encode) method."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 52,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "place = \"Café Kastanientörtchen\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 53,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "b'Caf\\xc3\\xa9 Kastanient\\xc3\\xb6rtchen'"
+      ]
+     },
+     "execution_count": 53,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "place.encode()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "By default, [encode() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/stdtypes.html#str.encode) and [decode() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/stdtypes.html#bytes.decode) use an `encoding=\"utf-8\"` argument. We may use another encoding like, for example, `\"iso-8859-1\"`, which can deal with ASCII and western European letters."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 54,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "b'Caf\\xe9 Kastanient\\xf6rtchen'"
+      ]
+     },
+     "execution_count": 54,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "place.encode(\"iso-8859-1\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "However, we must use the *same* encoding for the decoding step as for the encoding step. Otherwise, a `UnicodeDecodeError` is raised."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 55,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "ename": "UnicodeDecodeError",
+     "evalue": "'utf-8' codec can't decode byte 0xe9 in position 3: invalid continuation byte",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mUnicodeDecodeError\u001b[0m                        Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-55-1630cf8a20cc>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mplace\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mencode\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"iso-8859-1\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdecode\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;31mUnicodeDecodeError\u001b[0m: 'utf-8' codec can't decode byte 0xe9 in position 3: invalid continuation byte"
+     ]
+    }
+   ],
+   "source": [
+    "place.encode(\"iso-8859-1\").decode()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Not all encodings map all Unicode code points. For example `\"iso-8859-1\"` does not know Czech letters. Below, [encode() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/stdtypes.html#str.encode) raises a `UnicodeEncodeError` because of that."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 56,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "ename": "UnicodeEncodeError",
+     "evalue": "'latin-1' codec can't encode character '\\u0159' in position 12: ordinal not in range(256)",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mUnicodeEncodeError\u001b[0m                        Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-56-8dd3b1bfcf5d>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0;34m\"Dobrý den, přátelé!\"\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mencode\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"iso-8859-1\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;31mUnicodeEncodeError\u001b[0m: 'latin-1' codec can't encode character '\\u0159' in position 12: ordinal not in range(256)"
+     ]
+    }
+   ],
+   "source": [
+    "\"Dobrý den, přátelé!\".encode(\"iso-8859-1\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "### Reading Files (continued)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "The [open() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#open) function takes an optional `encoding` argument as well."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 57,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "ename": "UnicodeDecodeError",
+     "evalue": "'utf-8' codec can't decode byte 0xe4 in position 9: invalid continuation byte",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mUnicodeDecodeError\u001b[0m                        Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-57-83a7d1245cc7>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;32mwith\u001b[0m \u001b[0mopen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"umlauts.txt\"\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mfile\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m     \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"\"\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mjoin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfile\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreadlines\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;32m~/.pyenv/versions/3.8.6/lib/python3.8/codecs.py\u001b[0m in \u001b[0;36mdecode\u001b[0;34m(self, input, final)\u001b[0m\n\u001b[1;32m    320\u001b[0m         \u001b[0;31m# decode input (taking the buffer into account)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    321\u001b[0m         \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbuffer\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0minput\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 322\u001b[0;31m         \u001b[0;34m(\u001b[0m\u001b[0mresult\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mconsumed\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_buffer_decode\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0merrors\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfinal\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    323\u001b[0m         \u001b[0;31m# keep undecoded input until the next call\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    324\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbuffer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mconsumed\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;31mUnicodeDecodeError\u001b[0m: 'utf-8' codec can't decode byte 0xe4 in position 9: invalid continuation byte"
+     ]
+    }
+   ],
+   "source": [
+    "with open(\"umlauts.txt\") as file:\n",
+    "    print(\"\".join(file.readlines()))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 58,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Lerchen-Lärchen-Ähnlichkeiten\n",
+      "fehlen. Dieses abzustreiten\n",
+      "mag im Klang der Worte liegen.\n",
+      "Merke, eine Lerch' kann fliegen,\n",
+      "Lärchen nicht, was kaum verwundert,\n",
+      "denn nicht eine unter hundert\n",
+      "ist geflĂĽgelt. Auch im Singen\n",
+      "sind die Bäume zu bezwingen.\n",
+      "Die Bätrachtung sollte reichen,\n",
+      "Rächtschreibfählern auszuweichen.\n",
+      "Leicht gälingt's, zu unterscheiden,\n",
+      "wär ist wär nun von dän beiden.\n"
+     ]
+    }
+   ],
+   "source": [
+    "with open(\"umlauts.txt\", encoding=\"iso-8859-1\") as file:\n",
+    "    print(\"\".join(file.readlines()))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "### Best Practice: Use UTF-8 explicitly"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "A best practice is to *always* specify the `encoding`, especially on computers running on Windows (cf., the talk by �ukasz Langa in the [Further Resources <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/06_text/05_resources.ipynb#Unicode)) section at the end of this chapter.\n",
+    "\n",
+    "Below is the first example involving [open() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#open) one last time: It shows how *all* the contents of a text file should be read into one `str` object."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 59,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "with open(\"lorem_ipsum.txt\", encoding=\"utf-8\") as file:\n",
+    "    content = \"\".join(file.readlines())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 60,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "\"Lorem Ipsum is simply dummy text of the printing and typesetting industry.\\nLorem Ipsum has been the industry's standard dummy text ever since the 1500s\\nwhen an unknown printer took a galley of type and scrambled it to make a type\\nspecimen book. It has survived not only five centuries but also the leap into\\nelectronic typesetting, remaining essentially unchanged. It was popularised in\\nthe 1960s with the release of Letraset sheets.\\n\""
+      ]
+     },
+     "execution_count": 60,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "content"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 61,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Lorem Ipsum is simply dummy text of the printing and typesetting industry.\n",
+      "Lorem Ipsum has been the industry's standard dummy text ever since the 1500s\n",
+      "when an unknown printer took a galley of type and scrambled it to make a type\n",
+      "specimen book. It has survived not only five centuries but also the leap into\n",
+      "electronic typesetting, remaining essentially unchanged. It was popularised in\n",
+      "the 1960s with the release of Letraset sheets.\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(content)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.6"
+  },
+  "livereveal": {
+   "auto_select": "code",
+   "auto_select_fragment": true,
+   "scroll": true,
+   "theme": "serif"
+  },
+  "toc": {
+   "base_numbering": 1,
+   "nav_menu": {},
+   "number_sections": false,
+   "sideBar": true,
+   "skip_h1_title": true,
+   "title_cell": "Table of Contents",
+   "title_sidebar": "Contents",
+   "toc_cell": false,
+   "toc_position": {
+    "height": "calc(100% - 180px)",
+    "left": "10px",
+    "top": "150px",
+    "width": "384px"
+   },
+   "toc_section_display": false,
+   "toc_window_display": false
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/06_text/full_house.bin b/06_text/full_house.bin
new file mode 100644
index 0000000..4ad5e35
--- /dev/null
+++ b/06_text/full_house.bin
@@ -0,0 +1 @@
+🂧🂷���
\ No newline at end of file
diff --git a/06_text/umlauts.txt b/06_text/umlauts.txt
new file mode 100644
index 0000000..9c0f911
--- /dev/null
+++ b/06_text/umlauts.txt
@@ -0,0 +1,12 @@
+Lerchen-Lärchen-Ähnlichkeiten
+fehlen. Dieses abzustreiten
+mag im Klang der Worte liegen.
+Merke, eine Lerch' kann fliegen,
+Lärchen nicht, was kaum verwundert,
+denn nicht eine unter hundert
+ist geflügelt. Auch im Singen
+sind die Bäume zu bezwingen.
+Die Bätrachtung sollte reichen,
+Rächtschreibfählern auszuweichen.
+Leicht gälingt's, zu unterscheiden,
+wär ist wär nun von dän beiden.
\ No newline at end of file
diff --git a/README.md b/README.md
index 430dbb9..fd12c45 100644
--- a/README.md
+++ b/README.md
@@ -152,6 +152,13 @@ Alternatively, the content can be viewed in a web browser
     - [exercises <img height="12" style="display: inline-block" src="static/link/to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/06_text/01_exercises.ipynb)
       [<img height="12" style="display: inline-block" src="static/link/to_mb.png">](https://mybinder.org/v2/gh/webartifex/intro-to-python/develop?urlpath=lab/tree/06_text/01_exercises.ipynb)
       (Detecting Palindromes)
+    - [content <img height="12" style="display: inline-block" src="static/link/to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/06_text/02_content.ipynb)
+      [<img height="12" style="display: inline-block" src="static/link/to_mb.png">](https://mybinder.org/v2/gh/webartifex/intro-to-python/develop?urlpath=lab/tree/06_text/02_content.ipynb)
+      (Special Characters;
+       ASCII & Unicode;
+       Multi-line Strings;
+       `bytes` Type;
+       Character Encodings)
 
 
 #### Videos

From 163f2cd98d310b1d9a4c2397b2a85822b8fe9950 Mon Sep 17 00:00:00 2001
From: Alexander Hess <alexander@webartifex.biz>
Date: Mon, 19 Oct 2020 11:41:41 +0200
Subject: [PATCH 175/242] Add initial version of chapter 06's summary

---
 06_text/03_summary.ipynb | 75 ++++++++++++++++++++++++++++++++++++++++
 README.md                |  1 +
 2 files changed, 76 insertions(+)
 create mode 100644 06_text/03_summary.ipynb

diff --git a/06_text/03_summary.ipynb b/06_text/03_summary.ipynb
new file mode 100644
index 0000000..bcb6506
--- /dev/null
+++ b/06_text/03_summary.ipynb
@@ -0,0 +1,75 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "# Chapter 6: Text & Bytes (TL;DR)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Textual data is modeled with the **immutable** `str` type.\n",
+    "\n",
+    "The `str` type supports *four* orthogonal **abstract concepts** that together constitute the idea of a **sequence**: Every `str` object is an *iterable container* of a *finite* number of *ordered* characters.\n",
+    "\n",
+    "A single **character** in a `str` object follows the idea of a **Unicode** character. It is mapped to a *unique* **code point** that is encoded into **bytes** with a dedicated character encoding, for example, **UTF-8**."
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.6"
+  },
+  "livereveal": {
+   "auto_select": "code",
+   "auto_select_fragment": true,
+   "scroll": true,
+   "theme": "serif"
+  },
+  "toc": {
+   "base_numbering": 1,
+   "nav_menu": {},
+   "number_sections": false,
+   "sideBar": true,
+   "skip_h1_title": true,
+   "title_cell": "Table of Contents",
+   "title_sidebar": "Contents",
+   "toc_cell": false,
+   "toc_position": {
+    "height": "calc(100% - 180px)",
+    "left": "10px",
+    "top": "150px",
+    "width": "384px"
+   },
+   "toc_section_display": false,
+   "toc_window_display": false
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/README.md b/README.md
index fd12c45..cccdf7b 100644
--- a/README.md
+++ b/README.md
@@ -159,6 +159,7 @@ Alternatively, the content can be viewed in a web browser
        Multi-line Strings;
        `bytes` Type;
        Character Encodings)
+    - [summary <img height="12" style="display: inline-block" src="static/link/to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/06_text/03_summary.ipynb)
 
 
 #### Videos

From a328d6c9e098c29a0255f0ddb84e26d3883603fe Mon Sep 17 00:00:00 2001
From: Alexander Hess <alexander@webartifex.biz>
Date: Mon, 19 Oct 2020 11:43:42 +0200
Subject: [PATCH 176/242] Add initial version of chapter 06's review

---
 06_text/04_review.ipynb | 199 ++++++++++++++++++++++++++++++++++++++++
 README.md               |   1 +
 2 files changed, 200 insertions(+)
 create mode 100644 06_text/04_review.ipynb

diff --git a/06_text/04_review.ipynb b/06_text/04_review.ipynb
new file mode 100644
index 0000000..9804bfc
--- /dev/null
+++ b/06_text/04_review.ipynb
@@ -0,0 +1,199 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Chapter 6: Text & Bytes (Review Questions)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The questions below assume that you have read the [first <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/06_text/00_content.ipynb) and [second <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/06_text/01_content.ipynb) part of Chapter 6.\n",
+    "\n",
+    "Be concise in your answers! Most questions can be answered in *one* sentence."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Essay Questions "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Answer the following questions *briefly*!"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q1**: In what sense is a **\"string\" of characters** a **sequence**? What is a **sequence** after all? A *concrete* **data type**, or something else?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " < your answer >"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q2**: What is a direct consequence of the `str` type's property of being an **ordered** sequence? What operations could we *not* do with it if it were *unordered*?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " < your answer >"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q3**: What does it mean for an object to be **immutable**? Discuss how we can verify the `str` type's immutability by comparing the two variables `example` and `full_slice` below. Are they pointing to the *same* object in memory?\n",
+    "```python\n",
+    "example = \"text\"\n",
+    "full_slice = example[:]\n",
+    "```\n",
+    "Hint: Find out what `[:]` does first!"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " < your answer >"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q4**: Describe in your own words what we mean by **string interpolation**!"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " < your answer >"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## True / False Questions"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Motivate your answer with *one short* sentence!"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q5**: **Triple-double** quotes `\"\"\"` and **triple-single** quotes `'''` create a *new* object of type `text` that models so-called **multi-line strings**."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " < your answer >"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q6**: A **substring** is a string that *subsumes* another string."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " < your answer >"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q7**: Indexing into a `str` object with a *negative* index **fails silently**: It does *neither* raise an error *nor* do anything useful."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " < your answer >"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q8**: We *cannot* assign a *different* character to an index or slice of a `str` object because it is **immutable**."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " < your answer >"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.6"
+  },
+  "toc": {
+   "base_numbering": 1,
+   "nav_menu": {},
+   "number_sections": false,
+   "sideBar": true,
+   "skip_h1_title": true,
+   "title_cell": "Table of Contents",
+   "title_sidebar": "Contents",
+   "toc_cell": false,
+   "toc_position": {},
+   "toc_section_display": false,
+   "toc_window_display": false
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/README.md b/README.md
index cccdf7b..bace325 100644
--- a/README.md
+++ b/README.md
@@ -160,6 +160,7 @@ Alternatively, the content can be viewed in a web browser
        `bytes` Type;
        Character Encodings)
     - [summary <img height="12" style="display: inline-block" src="static/link/to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/06_text/03_summary.ipynb)
+    - [review questions <img height="12" style="display: inline-block" src="static/link/to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/06_text/04_review.ipynb)
 
 
 #### Videos

From 1ef4b283ac477fce88ae23ef6f1db53b032ffb1d Mon Sep 17 00:00:00 2001
From: Alexander Hess <alexander@webartifex.biz>
Date: Mon, 19 Oct 2020 11:45:56 +0200
Subject: [PATCH 177/242] Add initial version of chapter 06's further resources

---
 06_text/05_resources.ipynb | 359 +++++++++++++++++++++++++++++++++++++
 README.md                  |   2 +
 2 files changed, 361 insertions(+)
 create mode 100644 06_text/05_resources.ipynb

diff --git a/06_text/05_resources.ipynb b/06_text/05_resources.ipynb
new file mode 100644
index 0000000..a5517f1
--- /dev/null
+++ b/06_text/05_resources.ipynb
@@ -0,0 +1,359 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "**Note**: Click on \"*Kernel*\" > \"*Restart Kernel and Clear All Outputs*\" in [JupyterLab](https://jupyterlab.readthedocs.io/en/stable/) *before* reading this notebook to reset its output. If you cannot run this file on your machine, you may want to open it [in the cloud <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_mb.png\">](https://mybinder.org/v2/gh/webartifex/intro-to-python/develop?urlpath=lab/tree/06_text/05_resources.ipynb)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "# Chapter 6: Text & Bytes (Further Resources)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Unicode"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "We refer to the official [Unicode HOWTO <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/howto/unicode.html) in the Python documentation. Furthermore, the [unicodedata <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/unicodedata.html) module in the [standard library <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/index.html) provides a lot of utility functions around the Unicode standard.\n",
+    "\n",
+    "Next is a brief summary video by the YouTube channel [Computerphile](https://www.youtube.com/channel/UC9-y-6csu5WGm29I7JiwpnA) titled \"*Characters, Symbols and the Unicode Miracle*\"."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/jpeg": 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\n",
+      "text/html": [
+       "\n",
+       "        <iframe\n",
+       "            width=\"60%\"\n",
+       "            height=\"300\"\n",
+       "            src=\"https://www.youtube.com/embed/MijmeoH9LT4\"\n",
+       "            frameborder=\"0\"\n",
+       "            allowfullscreen\n",
+       "        ></iframe>\n",
+       "        "
+      ],
+      "text/plain": [
+       "<IPython.lib.display.YouTubeVideo at 0x7f4128106fd0>"
+      ]
+     },
+     "execution_count": 1,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from IPython.display import YouTubeVideo\n",
+    "YouTubeVideo(\"MijmeoH9LT4\", width=\"60%\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "In his [PyCon Australia 2018](https://2018.pycon-au.org/) talk titled \"*Unicode and Python: The absolute minimum you need to know*\" [Raphaël Merx](https://www.linkedin.com/in/raphaelmerx/) explains some caveats and best practices regarding Unicode."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/jpeg": 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+      "text/html": [
+       "\n",
+       "        <iframe\n",
+       "            width=\"60%\"\n",
+       "            height=\"300\"\n",
+       "            src=\"https://www.youtube.com/embed/oXVmZGN6plY\"\n",
+       "            frameborder=\"0\"\n",
+       "            allowfullscreen\n",
+       "        ></iframe>\n",
+       "        "
+      ],
+      "text/plain": [
+       "<IPython.lib.display.YouTubeVideo at 0x7f4128106700>"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "YouTubeVideo(\"oXVmZGN6plY\", width=\"60%\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "In a similar talk at [PyCon 2017](https://us.pycon.org/2017/) titled \"*Unicode what is the big deal*\" [�ukasz Langa](https://www.linkedin.com/in/llanga/) provides further lessons learned regarding Unicode."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
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\n",
+      "text/html": [
+       "\n",
+       "        <iframe\n",
+       "            width=\"60%\"\n",
+       "            height=\"300\"\n",
+       "            src=\"https://www.youtube.com/embed/7m5JA3XaZ4k\"\n",
+       "            frameborder=\"0\"\n",
+       "            allowfullscreen\n",
+       "        ></iframe>\n",
+       "        "
+      ],
+      "text/plain": [
+       "<IPython.lib.display.YouTubeVideo at 0x7f41281004c0>"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "YouTubeVideo(\"7m5JA3XaZ4k\", width=\"60%\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "In a \"classic\" talk from PyCon 2012 titled \"*Pragmatic Unicode, or, How do I stop the pain?*\" [Ned Batchelder](https://nedbatchelder.com/) explains among others the concept of a \"Unicode Sandwich.\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/jpeg": 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\n",
+      "text/html": [
+       "\n",
+       "        <iframe\n",
+       "            width=\"60%\"\n",
+       "            height=\"300\"\n",
+       "            src=\"https://www.youtube.com/embed/sgHbC6udIqc\"\n",
+       "            frameborder=\"0\"\n",
+       "            allowfullscreen\n",
+       "        ></iframe>\n",
+       "        "
+      ],
+      "text/plain": [
+       "<IPython.lib.display.YouTubeVideo at 0x7f41280d8340>"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "YouTubeVideo(\"sgHbC6udIqc\", width=\"60%\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Character Encodings"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Lastly, in his entertaining talk at [PyCon.DE 2019](https://de.pycon.org/) titled \"*Your Name is Invalid!*\" [Miroslav Ĺ edivĂ˝](https://www.linkedin.com/in/%C5%A1ediv%C3%BD/) shows how hard it actually is to write software that can process any name a human can possibly have. Miroslav also gave a lightning talk where he shows how he uses only one keyboard for the 12 (!!!) languages he speaks."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/jpeg": 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+      "text/html": [
+       "\n",
+       "        <iframe\n",
+       "            width=\"60%\"\n",
+       "            height=\"300\"\n",
+       "            src=\"https://www.youtube.com/embed/pBuS7EUPnQA\"\n",
+       "            frameborder=\"0\"\n",
+       "            allowfullscreen\n",
+       "        ></iframe>\n",
+       "        "
+      ],
+      "text/plain": [
+       "<IPython.lib.display.YouTubeVideo at 0x7f4128114820>"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "YouTubeVideo(\"pBuS7EUPnQA\", width=\"60%\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/jpeg": 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+      "text/html": [
+       "\n",
+       "        <iframe\n",
+       "            width=\"60%\"\n",
+       "            height=\"300\"\n",
+       "            src=\"https://www.youtube.com/embed/-4QjII981sM\"\n",
+       "            frameborder=\"0\"\n",
+       "            allowfullscreen\n",
+       "        ></iframe>\n",
+       "        "
+      ],
+      "text/plain": [
+       "<IPython.lib.display.YouTubeVideo at 0x7f41280d8310>"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "YouTubeVideo(\"-4QjII981sM\", width=\"60%\")"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.6"
+  },
+  "livereveal": {
+   "auto_select": "code",
+   "auto_select_fragment": true,
+   "scroll": true,
+   "theme": "serif"
+  },
+  "toc": {
+   "base_numbering": 1,
+   "nav_menu": {},
+   "number_sections": false,
+   "sideBar": true,
+   "skip_h1_title": true,
+   "title_cell": "Table of Contents",
+   "title_sidebar": "Contents",
+   "toc_cell": false,
+   "toc_position": {
+    "height": "calc(100% - 180px)",
+    "left": "10px",
+    "top": "150px",
+    "width": "384px"
+   },
+   "toc_section_display": false,
+   "toc_window_display": false
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/README.md b/README.md
index bace325..82348a5 100644
--- a/README.md
+++ b/README.md
@@ -161,6 +161,8 @@ Alternatively, the content can be viewed in a web browser
        Character Encodings)
     - [summary <img height="12" style="display: inline-block" src="static/link/to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/06_text/03_summary.ipynb)
     - [review questions <img height="12" style="display: inline-block" src="static/link/to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/06_text/04_review.ipynb)
+    - [further resources <img height="12" style="display: inline-block" src="static/link/to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/06_text/05_resources.ipynb)
+      [<img height="12" style="display: inline-block" src="static/link/to_mb.png">](https://mybinder.org/v2/gh/webartifex/intro-to-python/develop?urlpath=lab/tree/06_text/05_resources.ipynb)
 
 
 #### Videos

From 84e05ab2988ddf8b861617faceadabfacefb5acf Mon Sep 17 00:00:00 2001
From: Alexander Hess <alexander@webartifex.biz>
Date: Mon, 19 Oct 2020 12:00:32 +0200
Subject: [PATCH 178/242] Move detailed ToC in a file on its own

---
 CONTENTS.md | 163 ++++++++++++++++++++++++++++++++++++++++++++++++++++
 README.md   | 150 +----------------------------------------------
 2 files changed, 166 insertions(+), 147 deletions(-)
 create mode 100644 CONTENTS.md

diff --git a/CONTENTS.md b/CONTENTS.md
new file mode 100644
index 0000000..50bf5f4
--- /dev/null
+++ b/CONTENTS.md
@@ -0,0 +1,163 @@
+# Table of Contents
+
+The materials are designed to resemble an *interactive* book.
+
+It is recommended
+    to follow the [installation instructions](https://github.com/webartifex/intro-to-python#installation)
+        in the [README.md](README.md) file
+    and work through the content on one's own computer.
+
+If this is not possible,
+    the files can be viewed in a web browser
+    either statically (i.e., read-only) on [nbviewer <img height="12" style="display: inline-block" src="static/link/to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/tree/develop/)
+    or interactively (i.e., code can be executed) on [Binder <img height="12" style="display: inline-block" src="static/link/to_mb.png">](https://mybinder.org/v2/gh/webartifex/intro-to-python/develop?urlpath=lab).
+
+- *Chapter 0*: Introduction
+  - [content <img height="12" style="display: inline-block" src="static/link/to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/00_intro/00_content.ipynb)
+    [<img height="12" style="display: inline-block" src="static/link/to_mb.png">](https://mybinder.org/v2/gh/webartifex/intro-to-python/develop?urlpath=lab/tree/00_intro/00_content.ipynb)
+    (Python's History & Background;
+     Open-source & Communities;
+     JupyterLab;
+     Programming vs. Computer Science;
+     Learning Tips)
+  - [exercises <img height="12" style="display: inline-block" src="static/link/to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/00_intro/01_exercises.ipynb)
+    [<img height="12" style="display: inline-block" src="static/link/to_mb.png">](https://mybinder.org/v2/gh/webartifex/intro-to-python/develop?urlpath=lab/tree/00_intro/01_exercises.ipynb)
+    (Mastering Markdown)
+  - [review questions <img height="12" style="display: inline-block" src="static/link/to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/00_intro/02_review.ipynb)
+- **Part A: Expressing Logic**
+  - *Chapter 1*: Elements of a Program
+    - [content <img height="12" style="display: inline-block" src="static/link/to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/01_elements/00_content.ipynb)
+      [<img height="12" style="display: inline-block" src="static/link/to_mb.png">](https://mybinder.org/v2/gh/webartifex/intro-to-python/develop?urlpath=lab/tree/01_elements/00_content.ipynb)
+      (A first Example: Averaging Even Numbers;
+       Operators;
+       Objects & Data Types;
+       Errors)
+    - [exercises <img height="12" style="display: inline-block" src="static/link/to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/01_elements/01_exercises.ipynb)
+      [<img height="12" style="display: inline-block" src="static/link/to_mb.png">](https://mybinder.org/v2/gh/webartifex/intro-to-python/develop?urlpath=lab/tree/01_elements/01_exercises.ipynb)
+      (Printing Output)
+    - [exercises <img height="12" style="display: inline-block" src="static/link/to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/01_elements/02_exercises.ipynb)
+      [<img height="12" style="display: inline-block" src="static/link/to_mb.png">](https://mybinder.org/v2/gh/webartifex/intro-to-python/develop?urlpath=lab/tree/01_elements/02_exercises.ipynb)
+      (Simple `for`-loops)
+    - [content <img height="12" style="display: inline-block" src="static/link/to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/01_elements/03_content.ipynb)
+      [<img height="12" style="display: inline-block" src="static/link/to_mb.png">](https://mybinder.org/v2/gh/webartifex/intro-to-python/develop?urlpath=lab/tree/01_elements/03_content.ipynb)
+      (Memory in Detail;
+       Variables & References;
+       Mutability;
+       Expressions & Statements)
+    - [exercises <img height="12" style="display: inline-block" src="static/link/to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/01_elements/04_exercises.ipynb)
+      [<img height="12" style="display: inline-block" src="static/link/to_mb.png">](https://mybinder.org/v2/gh/webartifex/intro-to-python/develop?urlpath=lab/tree/01_elements/04_exercises.ipynb)
+      (Python as a Calculator)
+    - [summary <img height="12" style="display: inline-block" src="static/link/to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/01_elements/05_summary.ipynb)
+    - [review questions <img height="12" style="display: inline-block" src="static/link/to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/01_elements/06_review.ipynb)
+    - [further resources <img height="12" style="display: inline-block" src="static/link/to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/01_elements/07_resources.ipynb)
+      [<img height="12" style="display: inline-block" src="static/link/to_mb.png">](https://mybinder.org/v2/gh/webartifex/intro-to-python/develop?urlpath=lab/tree/01_elements/07_resources.ipynb)
+  - *Chapter 2*: Functions & Modularization
+    - [content <img height="12" style="display: inline-block" src="static/link/to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/02_functions/00_content.ipynb)
+      [<img height="12" style="display: inline-block" src="static/link/to_mb.png">](https://mybinder.org/v2/gh/webartifex/intro-to-python/develop?urlpath=lab/tree/02_functions/00_content.ipynb)
+      (Built-in Functions & Constructors;
+       Function Definitions;
+       Function Calls & Scoping Rules;
+       Positional vs. Keyword Arguments;
+       Modularization)
+    - [exercises <img height="12" style="display: inline-block" src="static/link/to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/02_functions/01_exercises.ipynb)
+      [<img height="12" style="display: inline-block" src="static/link/to_mb.png">](https://mybinder.org/v2/gh/webartifex/intro-to-python/develop?urlpath=lab/tree/02_functions/01_exercises.ipynb)
+      (Volume of a Sphere)
+    - [content <img height="12" style="display: inline-block" src="static/link/to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/02_functions/02_content.ipynb)
+      [<img height="12" style="display: inline-block" src="static/link/to_mb.png">](https://mybinder.org/v2/gh/webartifex/intro-to-python/develop?urlpath=lab/tree/02_functions/02_content.ipynb)
+      (Standard Library: `math` & `random` Modules;
+       Third-party Packages: `numpy` Library;
+       Writing one's own Modules)
+    - [summary <img height="12" style="display: inline-block" src="static/link/to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/02_functions/03_summary.ipynb)
+    - [review questions <img height="12" style="display: inline-block" src="static/link/to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/02_functions/04_review.ipynb)
+  - *Chapter 3*: Conditionals & Exceptions
+    - [content <img height="12" style="display: inline-block" src="static/link/to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/03_conditionals/00_content.ipynb)
+      [<img height="12" style="display: inline-block" src="static/link/to_mb.png">](https://mybinder.org/v2/gh/webartifex/intro-to-python/develop?urlpath=lab/tree/03_conditionals/00_content.ipynb)
+      (Boolean Expressions;
+       Relational Operators;
+       Logical Operators;
+       `if` statement;
+       Exception Handling)
+    - [exercises <img height="12" style="display: inline-block" src="static/link/to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/03_conditionals/01_exercises.ipynb)
+      [<img height="12" style="display: inline-block" src="static/link/to_mb.png">](https://mybinder.org/v2/gh/webartifex/intro-to-python/develop?urlpath=lab/tree/03_conditionals/01_exercises.ipynb)
+      (Discounting Customer Orders)
+    - [exercises <img height="12" style="display: inline-block" src="static/link/to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/03_conditionals/02_exercises.ipynb)
+      [<img height="12" style="display: inline-block" src="static/link/to_mb.png">](https://mybinder.org/v2/gh/webartifex/intro-to-python/develop?urlpath=lab/tree/03_conditionals/02_exercises.ipynb)
+      (Fizz Buzz)
+    - [summary <img height="12" style="display: inline-block" src="static/link/to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/03_conditionals/03_summary.ipynb)
+    - [review questions <img height="12" style="display: inline-block" src="static/link/to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/03_conditionals/04_review.ipynb)
+  - *Chapter 4*: Recursion & Looping
+    - [content <img height="12" style="display: inline-block" src="static/link/to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/04_iteration/00_content.ipynb)
+      [<img height="12" style="display: inline-block" src="static/link/to_mb.png">](https://mybinder.org/v2/gh/webartifex/intro-to-python/develop?urlpath=lab/tree/04_iteration/00_content.ipynb)
+      (Recursion;
+       Examples: Factorial, Euclid's Algorithm, & Fibonacci;
+       Duck Typing;
+       Type Casting & Checking;
+       Input Validation)
+    - [exercises <img height="12" style="display: inline-block" src="static/link/to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/04_iteration/01_exercises.ipynb)
+      [<img height="12" style="display: inline-block" src="static/link/to_mb.png">](https://mybinder.org/v2/gh/webartifex/intro-to-python/develop?urlpath=lab/tree/04_iteration/01_exercises.ipynb)
+      (Towers of Hanoi)
+    - [content <img height="12" style="display: inline-block" src="static/link/to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/04_iteration/02_content.ipynb)
+      [<img height="12" style="display: inline-block" src="static/link/to_mb.png">](https://mybinder.org/v2/gh/webartifex/intro-to-python/develop?urlpath=lab/tree/04_iteration/02_content.ipynb)
+      (Looping with `while` & `for`;
+       Examples: Collatz Conjecture, Factorial, Euclid's Algorithm, & Fibonacci;
+       Containers vs. Iterables)
+    - [content <img height="12" style="display: inline-block" src="static/link/to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/04_iteration/03_content.ipynb)
+      [<img height="12" style="display: inline-block" src="static/link/to_mb.png">](https://mybinder.org/v2/gh/webartifex/intro-to-python/develop?urlpath=lab/tree/04_iteration/03_content.ipynb)
+      (Customizing Loops with `break` and `continue`;
+       Indefinite Loops)
+    - [exercises <img height="12" style="display: inline-block" src="static/link/to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/04_iteration/04_exercises.ipynb)
+      [<img height="12" style="display: inline-block" src="static/link/to_mb.png">](https://mybinder.org/v2/gh/webartifex/intro-to-python/develop?urlpath=lab/tree/04_iteration/04_exercises.ipynb)
+      (Throwing Dice)
+    - [summary <img height="12" style="display: inline-block" src="static/link/to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/04_iteration/05_summary.ipynb)
+    - [review questions <img height="12" style="display: inline-block" src="static/link/to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/04_iteration/06_review.ipynb)
+- **Part B: Managing Data and Memory**
+  - *Chapter 5*: Numbers & Bits
+    - [content <img height="12" style="display: inline-block" src="static/link/to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/05_numbers/00_content.ipynb)
+      [<img height="12" style="display: inline-block" src="static/link/to_mb.png">](https://mybinder.org/v2/gh/webartifex/intro-to-python/develop?urlpath=lab/tree/05_numbers/00_content.ipynb)
+      (`int` Type;
+       Binary & Hexadecimal Representations;
+       Bit Arithmetic;
+       Bitwise Operators)
+    - [content <img height="12" style="display: inline-block" src="static/link/to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/05_numbers/01_content.ipynb)
+      [<img height="12" style="display: inline-block" src="static/link/to_mb.png">](https://mybinder.org/v2/gh/webartifex/intro-to-python/develop?urlpath=lab/tree/05_numbers/01_content.ipynb)
+      (`float` Type;
+       Floating-point Standard;
+       Special Values;
+       Imprecision;
+       Binary & Hexadecimal Representations)
+    - [content <img height="12" style="display: inline-block" src="static/link/to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/05_numbers/02_content.ipynb)
+      [<img height="12" style="display: inline-block" src="static/link/to_mb.png">](https://mybinder.org/v2/gh/webartifex/intro-to-python/develop?urlpath=lab/tree/05_numbers/02_content.ipynb)
+      (`complex` Type;
+       Numerical Tower;
+       Duck vs. Goose Typing)
+    - [appendix <img height="12" style="display: inline-block" src="static/link/to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/05_numbers/03_appendix.ipynb)
+      [<img height="12" style="display: inline-block" src="static/link/to_mb.png">](https://mybinder.org/v2/gh/webartifex/intro-to-python/develop?urlpath=lab/tree/05_numbers/03_appendix.ipynb)
+      (`Decimal` Type;
+       `Fraction` Type)
+    - [summary <img height="12" style="display: inline-block" src="static/link/to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/05_numbers/04_summary.ipynb)
+    - [review questions <img height="12" style="display: inline-block" src="static/link/to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/05_numbers/05_review.ipynb)
+    - [further resources <img height="12" style="display: inline-block" src="static/link/to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/05_numbers/06_resources.ipynb)
+      [<img height="12" style="display: inline-block" src="static/link/to_mb.png">](https://mybinder.org/v2/gh/webartifex/intro-to-python/develop?urlpath=lab/tree/05_numbers/06_resources.ipynb)
+  - *Chapter 6*: Text & Bytes
+    - [content <img height="12" style="display: inline-block" src="static/link/to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/06_numbers/00_content.ipynb)
+      [<img height="12" style="display: inline-block" src="static/link/to_mb.png">](https://mybinder.org/v2/gh/webartifex/intro-to-python/develop?urlpath=lab/tree/06_text/00_content.ipynb)
+      (`str` Type;
+       Reading Files;
+       Sequences;
+       Indexing & Slicing;
+       String Methods & Operations;
+       String Interpolation)
+    - [exercises <img height="12" style="display: inline-block" src="static/link/to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/06_text/01_exercises.ipynb)
+      [<img height="12" style="display: inline-block" src="static/link/to_mb.png">](https://mybinder.org/v2/gh/webartifex/intro-to-python/develop?urlpath=lab/tree/06_text/01_exercises.ipynb)
+      (Detecting Palindromes)
+    - [content <img height="12" style="display: inline-block" src="static/link/to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/06_text/02_content.ipynb)
+      [<img height="12" style="display: inline-block" src="static/link/to_mb.png">](https://mybinder.org/v2/gh/webartifex/intro-to-python/develop?urlpath=lab/tree/06_text/02_content.ipynb)
+      (Special Characters;
+       ASCII & Unicode;
+       Multi-line Strings;
+       `bytes` Type;
+       Character Encodings)
+    - [summary <img height="12" style="display: inline-block" src="static/link/to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/06_text/03_summary.ipynb)
+    - [review questions <img height="12" style="display: inline-block" src="static/link/to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/06_text/04_review.ipynb)
+    - [further resources <img height="12" style="display: inline-block" src="static/link/to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/06_text/05_resources.ipynb)
+      [<img height="12" style="display: inline-block" src="static/link/to_mb.png">](https://mybinder.org/v2/gh/webartifex/intro-to-python/develop?urlpath=lab/tree/06_text/05_resources.ipynb)
\ No newline at end of file
diff --git a/README.md b/README.md
index 82348a5..db07011 100644
--- a/README.md
+++ b/README.md
@@ -6,163 +6,19 @@ This project is a *thorough* introductory course
 
 ### Table of Contents
 
-The materials are designed to resemble an *interactive* book.
-It is recommended
-    to follow the [installation instructions](https://github.com/webartifex/intro-to-python#installation) below
-    and work through the content on one's own computer.
-Alternatively, the content can be viewed in a web browser
-    either statically (i.e., read-only) on [nbviewer <img height="12" style="display: inline-block" src="static/link/to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/tree/develop/)
-    or interactively (i.e., code can be executed) on [Binder <img height="12" style="display: inline-block" src="static/link/to_mb.png">](https://mybinder.org/v2/gh/webartifex/intro-to-python/develop?urlpath=lab).
+The following is a high-level overview of the contents.
+For a more *detailed version* with **clickable links**
+    see the [CONTENTS.md](CONTENTS.md) file.
 
 - *Chapter 0*: Introduction
-  - [content <img height="12" style="display: inline-block" src="static/link/to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/00_intro/00_content.ipynb)
-    [<img height="12" style="display: inline-block" src="static/link/to_mb.png">](https://mybinder.org/v2/gh/webartifex/intro-to-python/develop?urlpath=lab/tree/00_intro/00_content.ipynb)
-    (Python's History & Background;
-     Open-source & Communities;
-     JupyterLab;
-     Programming vs. Computer Science;
-     Learning Tips)
-  - [exercises <img height="12" style="display: inline-block" src="static/link/to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/00_intro/01_exercises.ipynb)
-    [<img height="12" style="display: inline-block" src="static/link/to_mb.png">](https://mybinder.org/v2/gh/webartifex/intro-to-python/develop?urlpath=lab/tree/00_intro/01_exercises.ipynb)
-    (Mastering Markdown)
-  - [review questions <img height="12" style="display: inline-block" src="static/link/to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/00_intro/02_review.ipynb)
 - **Part A: Expressing Logic**
   - *Chapter 1*: Elements of a Program
-    - [content <img height="12" style="display: inline-block" src="static/link/to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/01_elements/00_content.ipynb)
-      [<img height="12" style="display: inline-block" src="static/link/to_mb.png">](https://mybinder.org/v2/gh/webartifex/intro-to-python/develop?urlpath=lab/tree/01_elements/00_content.ipynb)
-      (A first Example: Averaging Even Numbers;
-       Operators;
-       Objects & Data Types;
-       Errors)
-    - [exercises <img height="12" style="display: inline-block" src="static/link/to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/01_elements/01_exercises.ipynb)
-      [<img height="12" style="display: inline-block" src="static/link/to_mb.png">](https://mybinder.org/v2/gh/webartifex/intro-to-python/develop?urlpath=lab/tree/01_elements/01_exercises.ipynb)
-      (Printing Output)
-    - [exercises <img height="12" style="display: inline-block" src="static/link/to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/01_elements/02_exercises.ipynb)
-      [<img height="12" style="display: inline-block" src="static/link/to_mb.png">](https://mybinder.org/v2/gh/webartifex/intro-to-python/develop?urlpath=lab/tree/01_elements/02_exercises.ipynb)
-      (Simple `for`-loops)
-    - [content <img height="12" style="display: inline-block" src="static/link/to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/01_elements/03_content.ipynb)
-      [<img height="12" style="display: inline-block" src="static/link/to_mb.png">](https://mybinder.org/v2/gh/webartifex/intro-to-python/develop?urlpath=lab/tree/01_elements/03_content.ipynb)
-      (Memory in Detail;
-       Variables & References;
-       Mutability;
-       Expressions & Statements)
-    - [exercises <img height="12" style="display: inline-block" src="static/link/to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/01_elements/04_exercises.ipynb)
-      [<img height="12" style="display: inline-block" src="static/link/to_mb.png">](https://mybinder.org/v2/gh/webartifex/intro-to-python/develop?urlpath=lab/tree/01_elements/04_exercises.ipynb)
-      (Python as a Calculator)
-    - [summary <img height="12" style="display: inline-block" src="static/link/to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/01_elements/05_summary.ipynb)
-    - [review questions <img height="12" style="display: inline-block" src="static/link/to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/01_elements/06_review.ipynb)
-    - [further resources <img height="12" style="display: inline-block" src="static/link/to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/01_elements/07_resources.ipynb)
-      [<img height="12" style="display: inline-block" src="static/link/to_mb.png">](https://mybinder.org/v2/gh/webartifex/intro-to-python/develop?urlpath=lab/tree/01_elements/07_resources.ipynb)
   - *Chapter 2*: Functions & Modularization
-    - [content <img height="12" style="display: inline-block" src="static/link/to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/02_functions/00_content.ipynb)
-      [<img height="12" style="display: inline-block" src="static/link/to_mb.png">](https://mybinder.org/v2/gh/webartifex/intro-to-python/develop?urlpath=lab/tree/02_functions/00_content.ipynb)
-      (Built-in Functions & Constructors;
-       Function Definitions;
-       Function Calls & Scoping Rules;
-       Positional vs. Keyword Arguments;
-       Modularization)
-    - [exercises <img height="12" style="display: inline-block" src="static/link/to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/02_functions/01_exercises.ipynb)
-      [<img height="12" style="display: inline-block" src="static/link/to_mb.png">](https://mybinder.org/v2/gh/webartifex/intro-to-python/develop?urlpath=lab/tree/02_functions/01_exercises.ipynb)
-      (Volume of a Sphere)
-    - [content <img height="12" style="display: inline-block" src="static/link/to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/02_functions/02_content.ipynb)
-      [<img height="12" style="display: inline-block" src="static/link/to_mb.png">](https://mybinder.org/v2/gh/webartifex/intro-to-python/develop?urlpath=lab/tree/02_functions/02_content.ipynb)
-      (Standard Library: `math` & `random` Modules;
-       Third-party Packages: `numpy` Library;
-       Writing one's own Modules)
-    - [summary <img height="12" style="display: inline-block" src="static/link/to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/02_functions/03_summary.ipynb)
-    - [review questions <img height="12" style="display: inline-block" src="static/link/to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/02_functions/04_review.ipynb)
   - *Chapter 3*: Conditionals & Exceptions
-    - [content <img height="12" style="display: inline-block" src="static/link/to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/03_conditionals/00_content.ipynb)
-      [<img height="12" style="display: inline-block" src="static/link/to_mb.png">](https://mybinder.org/v2/gh/webartifex/intro-to-python/develop?urlpath=lab/tree/03_conditionals/00_content.ipynb)
-      (Boolean Expressions;
-       Relational Operators;
-       Logical Operators;
-       `if` statement;
-       Exception Handling)
-    - [exercises <img height="12" style="display: inline-block" src="static/link/to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/03_conditionals/01_exercises.ipynb)
-      [<img height="12" style="display: inline-block" src="static/link/to_mb.png">](https://mybinder.org/v2/gh/webartifex/intro-to-python/develop?urlpath=lab/tree/03_conditionals/01_exercises.ipynb)
-      (Discounting Customer Orders)
-    - [exercises <img height="12" style="display: inline-block" src="static/link/to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/03_conditionals/02_exercises.ipynb)
-      [<img height="12" style="display: inline-block" src="static/link/to_mb.png">](https://mybinder.org/v2/gh/webartifex/intro-to-python/develop?urlpath=lab/tree/03_conditionals/02_exercises.ipynb)
-      (Fizz Buzz)
-    - [summary <img height="12" style="display: inline-block" src="static/link/to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/03_conditionals/03_summary.ipynb)
-    - [review questions <img height="12" style="display: inline-block" src="static/link/to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/03_conditionals/04_review.ipynb)
   - *Chapter 4*: Recursion & Looping
-    - [content <img height="12" style="display: inline-block" src="static/link/to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/04_iteration/00_content.ipynb)
-      [<img height="12" style="display: inline-block" src="static/link/to_mb.png">](https://mybinder.org/v2/gh/webartifex/intro-to-python/develop?urlpath=lab/tree/04_iteration/00_content.ipynb)
-      (Recursion;
-       Examples: Factorial, Euclid's Algorithm, & Fibonacci;
-       Duck Typing;
-       Type Casting & Checking;
-       Input Validation)
-    - [exercises <img height="12" style="display: inline-block" src="static/link/to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/04_iteration/01_exercises.ipynb)
-      [<img height="12" style="display: inline-block" src="static/link/to_mb.png">](https://mybinder.org/v2/gh/webartifex/intro-to-python/develop?urlpath=lab/tree/04_iteration/01_exercises.ipynb)
-      (Towers of Hanoi)
-    - [content <img height="12" style="display: inline-block" src="static/link/to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/04_iteration/02_content.ipynb)
-      [<img height="12" style="display: inline-block" src="static/link/to_mb.png">](https://mybinder.org/v2/gh/webartifex/intro-to-python/develop?urlpath=lab/tree/04_iteration/02_content.ipynb)
-      (Looping with `while` & `for`;
-       Examples: Collatz Conjecture, Factorial, Euclid's Algorithm, & Fibonacci;
-       Containers vs. Iterables)
-    - [content <img height="12" style="display: inline-block" src="static/link/to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/04_iteration/03_content.ipynb)
-      [<img height="12" style="display: inline-block" src="static/link/to_mb.png">](https://mybinder.org/v2/gh/webartifex/intro-to-python/develop?urlpath=lab/tree/04_iteration/03_content.ipynb)
-      (Customizing Loops with `break` and `continue`;
-       Indefinite Loops)
-    - [exercises <img height="12" style="display: inline-block" src="static/link/to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/04_iteration/04_exercises.ipynb)
-      [<img height="12" style="display: inline-block" src="static/link/to_mb.png">](https://mybinder.org/v2/gh/webartifex/intro-to-python/develop?urlpath=lab/tree/04_iteration/04_exercises.ipynb)
-      (Throwing Dice)
-    - [summary <img height="12" style="display: inline-block" src="static/link/to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/04_iteration/05_summary.ipynb)
-    - [review questions <img height="12" style="display: inline-block" src="static/link/to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/04_iteration/06_review.ipynb)
 - **Part B: Managing Data and Memory**
   - *Chapter 5*: Numbers & Bits
-    - [content <img height="12" style="display: inline-block" src="static/link/to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/05_numbers/00_content.ipynb)
-      [<img height="12" style="display: inline-block" src="static/link/to_mb.png">](https://mybinder.org/v2/gh/webartifex/intro-to-python/develop?urlpath=lab/tree/05_numbers/00_content.ipynb)
-      (`int` Type;
-       Binary & Hexadecimal Representations;
-       Bit Arithmetic;
-       Bitwise Operators)
-    - [content <img height="12" style="display: inline-block" src="static/link/to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/05_numbers/01_content.ipynb)
-      [<img height="12" style="display: inline-block" src="static/link/to_mb.png">](https://mybinder.org/v2/gh/webartifex/intro-to-python/develop?urlpath=lab/tree/05_numbers/01_content.ipynb)
-      (`float` Type;
-       Floating-point Standard;
-       Special Values;
-       Imprecision;
-       Binary & Hexadecimal Representations)
-    - [content <img height="12" style="display: inline-block" src="static/link/to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/05_numbers/02_content.ipynb)
-      [<img height="12" style="display: inline-block" src="static/link/to_mb.png">](https://mybinder.org/v2/gh/webartifex/intro-to-python/develop?urlpath=lab/tree/05_numbers/02_content.ipynb)
-      (`complex` Type;
-       Numerical Tower;
-       Duck vs. Goose Typing)
-    - [appendix <img height="12" style="display: inline-block" src="static/link/to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/05_numbers/03_appendix.ipynb)
-      [<img height="12" style="display: inline-block" src="static/link/to_mb.png">](https://mybinder.org/v2/gh/webartifex/intro-to-python/develop?urlpath=lab/tree/05_numbers/03_appendix.ipynb)
-      (`Decimal` Type;
-       `Fraction` Type)
-    - [summary <img height="12" style="display: inline-block" src="static/link/to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/05_numbers/04_summary.ipynb)
-    - [review questions <img height="12" style="display: inline-block" src="static/link/to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/05_numbers/05_review.ipynb)
-    - [further resources <img height="12" style="display: inline-block" src="static/link/to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/05_numbers/06_resources.ipynb)
-      [<img height="12" style="display: inline-block" src="static/link/to_mb.png">](https://mybinder.org/v2/gh/webartifex/intro-to-python/develop?urlpath=lab/tree/05_numbers/06_resources.ipynb)
   - *Chapter 6*: Text & Bytes
-    - [content <img height="12" style="display: inline-block" src="static/link/to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/06_numbers/00_content.ipynb)
-      [<img height="12" style="display: inline-block" src="static/link/to_mb.png">](https://mybinder.org/v2/gh/webartifex/intro-to-python/develop?urlpath=lab/tree/06_text/00_content.ipynb)
-      (`str` Type;
-       Reading Files;
-       Sequences;
-       Indexing & Slicing;
-       String Methods & Operations;
-       String Interpolation)
-    - [exercises <img height="12" style="display: inline-block" src="static/link/to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/06_text/01_exercises.ipynb)
-      [<img height="12" style="display: inline-block" src="static/link/to_mb.png">](https://mybinder.org/v2/gh/webartifex/intro-to-python/develop?urlpath=lab/tree/06_text/01_exercises.ipynb)
-      (Detecting Palindromes)
-    - [content <img height="12" style="display: inline-block" src="static/link/to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/06_text/02_content.ipynb)
-      [<img height="12" style="display: inline-block" src="static/link/to_mb.png">](https://mybinder.org/v2/gh/webartifex/intro-to-python/develop?urlpath=lab/tree/06_text/02_content.ipynb)
-      (Special Characters;
-       ASCII & Unicode;
-       Multi-line Strings;
-       `bytes` Type;
-       Character Encodings)
-    - [summary <img height="12" style="display: inline-block" src="static/link/to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/06_text/03_summary.ipynb)
-    - [review questions <img height="12" style="display: inline-block" src="static/link/to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/06_text/04_review.ipynb)
-    - [further resources <img height="12" style="display: inline-block" src="static/link/to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/06_text/05_resources.ipynb)
-      [<img height="12" style="display: inline-block" src="static/link/to_mb.png">](https://mybinder.org/v2/gh/webartifex/intro-to-python/develop?urlpath=lab/tree/06_text/05_resources.ipynb)
 
 
 #### Videos

From f6704143953df44d506142b297cb57128837f72d Mon Sep 17 00:00:00 2001
From: Alexander Hess <alexander@webartifex.biz>
Date: Mon, 19 Oct 2020 18:35:13 +0200
Subject: [PATCH 179/242] Add initial version of chapter 07, part 1

---
 00_intro/00_content.ipynb     |   2 +-
 07_sequences/00_content.ipynb | 958 ++++++++++++++++++++++++++++++++++
 CONTENTS.md                   |   7 +-
 README.md                     |   1 +
 4 files changed, 966 insertions(+), 2 deletions(-)
 create mode 100644 07_sequences/00_content.ipynb

diff --git a/00_intro/00_content.ipynb b/00_intro/00_content.ipynb
index de293c8..a5d9e15 100644
--- a/00_intro/00_content.ipynb
+++ b/00_intro/00_content.ipynb
@@ -770,7 +770,7 @@
     "- How is data stored in memory?\n",
     "  - *Chapter 5*: [Numbers & Bits <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/05_numbers/00_content.ipynb)\n",
     "  - *Chapter 6*: [Text & Bytes <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/06_text/00_content.ipynb)\n",
-    "  - *Chapter 7*: Sequential Data\n",
+    "  - *Chapter 7*: [Sequential Data <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/07_sequences/00_content.ipynb)\n",
     "  - *Chapter 8*: Map, Filter, & Reduce\n",
     "  - *Chapter 9*: Mappings & Sets\n",
     "  - *Chapter 10*: Arrays & Dataframes\n",
diff --git a/07_sequences/00_content.ipynb b/07_sequences/00_content.ipynb
new file mode 100644
index 0000000..43ec596
--- /dev/null
+++ b/07_sequences/00_content.ipynb
@@ -0,0 +1,958 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "**Note**: Click on \"*Kernel*\" > \"*Restart Kernel and Clear All Outputs*\" in [JupyterLab](https://jupyterlab.readthedocs.io/en/stable/) *before* reading this notebook to reset its output. If you cannot run this file on your machine, you may want to open it [in the cloud <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_mb.png\">](https://mybinder.org/v2/gh/webartifex/intro-to-python/develop?urlpath=lab/tree/07_sequences/00_content.ipynb)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "# Chapter 7: Sequential Data"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "We studied numbers (cf., [Chapter 5 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/05_numbers/00_content.ipynb)) and textual data (cf., [Chapter 6 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/06_text/00_content.ipynb)) first mainly because objects of the presented data types are \"simple.\" That is so for two reasons: First, they are *immutable*, and, as we saw in the \"*Who am I? And how many?*\" section in [Chapter 1 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/01_elements/03_content.ipynb#Who-am-I?-And-how-many?), mutable objects can quickly become hard to reason about. Second, they are \"flat\" in the sense that they are *not* composed of other objects.\n",
+    "\n",
+    "The `str` type is a bit of a corner case in this regard. While one could argue that a longer `str` object, for example, `\"text\"`, is composed of individual characters, this is *not* the case in memory as the literal `\"text\"` only creates *one* object (i.e., one \"bag\" of $0$s and $1$s modeling all characters).\n",
+    "\n",
+    "This chapter, [Chapter 8 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/08_mfr/00_content.ipynb), [Chapter 9 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/09_mappings/00_content.ipynb), and [Chapter 10 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/10_arrays/00_content.ipynb) introduce various \"complex\" data types. While some are mutable and others are not, they all share that they are primarily used to \"manage,\" or structure, the memory in a program (i.e., they provide references to other objects). Unsurprisingly, computer scientists refer to the ideas behind these data types as **[data structures <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_wiki.png\">](https://en.wikipedia.org/wiki/Data_structure)**.\n",
+    "\n",
+    "In this chapter, we focus on data types that model all kinds of sequential data. Examples of such data are [spreadsheets <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_wiki.png\">](https://en.wikipedia.org/wiki/Spreadsheet) or [matrices <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_wiki.png\">](https://en.wikipedia.org/wiki/Matrix_%28mathematics%29) and [vectors <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_wiki.png\">](https://en.wikipedia.org/wiki/Vector_%28mathematics_and_physics%29). These formats share the property that they are composed of smaller units that come in a sequence of, for example, rows/columns/cells or elements/entries."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "## Collections vs. Sequences"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "[Chapter 6 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/06_text/00_content.ipynb#A-\"String\"-of-Characters) already describes the **sequence** properties of `str` objects. In this section, we take a step back and study these properties one by one.\n",
+    "\n",
+    "The [collections.abc <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/collections.abc.html) module in the [standard library <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/index.html) defines a variety of **abstract base classes** (ABCs). We saw ABCs already in [Chapter 5 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/05_numbers/02_content.ipynb#The-Numerical-Tower), where we use the ones from the [numbers <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/numbers.html) module in the [standard library <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/index.html) to classify Python's numeric data types according to mathematical ideas. Now, we take the ABCs from the [collections.abc <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/collections.abc.html) module to classify the data types in this chapter according to their behavior in various contexts.\n",
+    "\n",
+    "As an illustration, consider `numbers` and `text` below, two objects of *different* types."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "numbers = [7, 11, 8, 5, 3, 12, 2, 6, 9, 10, 1, 4]\n",
+    "text = \"Lorem ipsum dolor sit amet.\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Among others, one commonality between the two is that we may loop over them with the `for` statement. So, in the context of iteration, both exhibit the *same* behavior."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "7 11 8 5 3 12 2 6 9 10 1 4 "
+     ]
+    }
+   ],
+   "source": [
+    "for number in numbers:\n",
+    "    print(number, end=\" \")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "L o r e m   i p s u m   d o l o r   s i t   a m e t . "
+     ]
+    }
+   ],
+   "source": [
+    "for character in text:\n",
+    "    print(character, end=\" \")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "In [Chapter 4 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/04_iteration/02_content.ipynb#Containers-vs.-Iterables), we referred to such types as *iterables*. That is *not* a proper [English](https://dictionary.cambridge.org/spellcheck/english-german/?q=iterable) word, even if it may sound like one at first sight. Yet, it is an official term in the Python world formalized with the `Iterable` ABC in the [collections.abc <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/collections.abc.html) module.\n",
+    "\n",
+    "For the data science practitioner, it is worthwhile to know such terms as, for example, the documentation on the [built-ins <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html) uses them extensively: In simple words, any built-in that takes an argument called \"*iterable*\" may be called with *any* object that supports being looped over. Already familiar [built-ins <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html) include [enumerate() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#enumerate), [sum() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#sum), or [zip() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#zip). So, they do *not* require the argument to be of a certain data type (e.g., `list`); instead, any *iterable* type works."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "import collections.abc as abc"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "collections.abc.Iterable"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "abc.Iterable"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "As seen in [Chapter 5 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/05_numbers/02_content.ipynb#Goose-Typing), we can use ABCs with the built-in [isinstance() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#isinstance) function to check if an object supports a behavior.\n",
+    "\n",
+    "So, let's \"ask\" Python if it can loop over `numbers` or `text`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "isinstance(numbers, abc.Iterable)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "isinstance(text, abc.Iterable)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Contrary to `list` or `str` objects, numeric objects are *not* iterable."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "False"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "isinstance(999, abc.Iterable)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Instead of asking, we could try to loop over `999`, but this results in a `TypeError`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "ename": "TypeError",
+     "evalue": "'int' object is not iterable",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-9-8249bf25b5b7>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0;32mfor\u001b[0m \u001b[0mdigit\u001b[0m \u001b[0;32min\u001b[0m \u001b[0;36m999\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m     \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdigit\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;31mTypeError\u001b[0m: 'int' object is not iterable"
+     ]
+    }
+   ],
+   "source": [
+    "for digit in 999:\n",
+    "    print(digit)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Most of the data types in this chapter and [Chapter 9 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/09_mappings/00_content.ipynb) and [Chapter 10 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/10_arrays/00_content.ipynb) exhibit three [orthogonal <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_wiki.png\">](https://en.wikipedia.org/wiki/Orthogonality) (i.e., \"independent\") behaviors, formalized by ABCs in the [collections.abc <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/collections.abc.html) module as:\n",
+    "- `Iterable`: An object may be looped over.\n",
+    "- `Container`: An object \"contains\" references to other objects; a \"whole\" is composed of many \"parts.\"\n",
+    "- `Sized`: The number of references to other objects, the \"parts,\" is *finite*.\n",
+    "\n",
+    "The characteristical operation supported by `Container` types is the `in` operator for membership testing."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "False"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "0 in numbers"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "\"l\" in text"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Alternatively, we could also check if `numbers` and `text` are `Container` types with [isinstance() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#isinstance)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "isinstance(numbers, abc.Container)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "isinstance(text, abc.Container)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Numeric objects do *not* \"contain\" references to other objects, and that is why they are considered \"flat\" data types. The `in` operator raises a `TypeError`. Conceptually speaking, Python views numeric types as \"wholes\" without any \"parts.\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "False"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "isinstance(999, abc.Container)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "ename": "TypeError",
+     "evalue": "argument of type 'int' is not iterable",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-15-b3e0b3c32aec>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0;36m9\u001b[0m \u001b[0;32min\u001b[0m \u001b[0;36m999\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;31mTypeError\u001b[0m: argument of type 'int' is not iterable"
+     ]
+    }
+   ],
+   "source": [
+    "9 in 999"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Analogously, being `Sized` types, we can pass `numbers` and `text` as the argument to the built-in [len() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#len) function and obtain \"meaningful\" results. The exact meaning depends on the data type: For `numbers`, [len() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#len) tells us how many elements are in the `list` object; for `text`, it tells us how many [Unicode characters <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_wiki.png\">](https://en.wikipedia.org/wiki/Unicode) make up the `str` object. *Abstractly* speaking, both data types exhibit the *same* behavior of *finiteness*."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "12"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "len(numbers)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "27"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "len(text)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "isinstance(numbers, abc.Sized)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "isinstance(text, abc.Sized)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "On the contrary, even though `999` consists of three digits for humans, numeric objects in Python have no concept of a \"size\" or \"length,\" and the [len() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#len) function raises a `TypeError`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "False"
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "isinstance(999, abc.Sized)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "ename": "TypeError",
+     "evalue": "object of type 'int' has no len()",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-21-ae09c1aa5305>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m999\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;31mTypeError\u001b[0m: object of type 'int' has no len()"
+     ]
+    }
+   ],
+   "source": [
+    "len(999)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "These three behaviors are so essential that whenever they coincide for a data type, it is called a **collection**, formalized with the `Collection` ABC. That is where the [collections.abc <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/collections.abc.html) module got its name from: It summarizes all ABCs related to collections; in particular, it defines a hierarchy of specialized kinds of collections.\n",
+    "\n",
+    "Without going into too much detail, one way to read the summary table at the beginning of the [collections.abc <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/collections.abc.html) module's documention is as follows: The first column, titled \"ABC\", lists all collection-related ABCs in Python. The second column, titled \"Inherits from,\" indicates if the idea behind the ABC is *original* (e.g., the first row with the `Container` ABC has an empty \"Inherits from\" column) or a *combination* (e.g., the row with the `Collection` ABC has `Sized`, `Iterable`, and `Container` in the \"Inherits from\" column). The third and fourth columns list the methods that come with a data type following an ABC. We keep ignoring the methods named in the dunder style for now.\n",
+    "\n",
+    "So, let's confirm that both `numbers` and `text` are collections."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "isinstance(numbers, abc.Collection)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 23,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "isinstance(text, abc.Collection)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "They share one more common behavior: When looping over them, we can *predict* the *order* of the elements or characters. The ABC in the [collections.abc <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/collections.abc.html) module corresponding to this behavior is `Reversible`. While sounding unintuitive at first, it is evident that if something is reversible, it must have a forward order, to begin with.\n",
+    "\n",
+    "The [reversed() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#reversed) built-in allows us to loop over the elements or characters in reverse order."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "4 1 10 9 6 2 12 3 5 8 11 7 "
+     ]
+    }
+   ],
+   "source": [
+    "for number in reversed(numbers):\n",
+    "    print(number, end=\" \")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      ". t e m a   t i s   r o l o d   m u s p i   m e r o L "
+     ]
+    }
+   ],
+   "source": [
+    "for character in reversed(text):\n",
+    "    print(character, end=\" \")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 26,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "isinstance(numbers, abc.Reversible)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 27,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "isinstance(text, abc.Reversible)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Collections that exhibit this fourth behavior are referred to as **sequences**, formalized with the `Sequence` ABC in the [collections.abc <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/collections.abc.html) module."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 28,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "isinstance(numbers, abc.Sequence)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 29,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "isinstance(text, abc.Sequence)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "The data types introduced in this chapter are sequences. Nevertheless, we also look at some data types that are neither collections nor sequences but are still useful to model sequential data in practice in [Chapter 8 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/08_mfr/00_content.ipynb).\n",
+    "\n",
+    "In Python-related documentations, the terms collection and sequence are heavily used, and the data science practitioner should always think of them in terms of the three or four behaviors they exhibit.\n",
+    "\n",
+    "Data types that are collections but not sequences are covered in [Chapter 9 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/09_mappings/00_content.ipynb)."
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.6"
+  },
+  "livereveal": {
+   "auto_select": "code",
+   "auto_select_fragment": true,
+   "scroll": true,
+   "theme": "serif"
+  },
+  "toc": {
+   "base_numbering": 1,
+   "nav_menu": {},
+   "number_sections": false,
+   "sideBar": true,
+   "skip_h1_title": true,
+   "title_cell": "Table of Contents",
+   "title_sidebar": "Contents",
+   "toc_cell": false,
+   "toc_position": {
+    "height": "calc(100% - 180px)",
+    "left": "10px",
+    "top": "150px",
+    "width": "384px"
+   },
+   "toc_section_display": false,
+   "toc_window_display": false
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/CONTENTS.md b/CONTENTS.md
index 50bf5f4..fd55699 100644
--- a/CONTENTS.md
+++ b/CONTENTS.md
@@ -160,4 +160,9 @@ If this is not possible,
     - [summary <img height="12" style="display: inline-block" src="static/link/to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/06_text/03_summary.ipynb)
     - [review questions <img height="12" style="display: inline-block" src="static/link/to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/06_text/04_review.ipynb)
     - [further resources <img height="12" style="display: inline-block" src="static/link/to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/06_text/05_resources.ipynb)
-      [<img height="12" style="display: inline-block" src="static/link/to_mb.png">](https://mybinder.org/v2/gh/webartifex/intro-to-python/develop?urlpath=lab/tree/06_text/05_resources.ipynb)
\ No newline at end of file
+      [<img height="12" style="display: inline-block" src="static/link/to_mb.png">](https://mybinder.org/v2/gh/webartifex/intro-to-python/develop?urlpath=lab/tree/06_text/05_resources.ipynb)
+  - *Chapter 7*: Sequential Data
+    - [content <img height="12" style="display: inline-block" src="static/link/to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/07_sequences/00_content.ipynb)
+      [<img height="12" style="display: inline-block" src="static/link/to_mb.png">](https://mybinder.org/v2/gh/webartifex/intro-to-python/develop?urlpath=lab/tree/07_sequences/00_content.ipynb)
+      (Collections vs. Sequences;
+       ABCs: `Container`, `Iterable`, `Sized`, & `Reversible`)
\ No newline at end of file
diff --git a/README.md b/README.md
index db07011..e9b2add 100644
--- a/README.md
+++ b/README.md
@@ -19,6 +19,7 @@ For a more *detailed version* with **clickable links**
 - **Part B: Managing Data and Memory**
   - *Chapter 5*: Numbers & Bits
   - *Chapter 6*: Text & Bytes
+  - *Chapter 7*: Sequential Data
 
 
 #### Videos

From 049aeb104d016ee66ddea850076d3db9aeb0158b Mon Sep 17 00:00:00 2001
From: Alexander Hess <alexander@webartifex.biz>
Date: Mon, 19 Oct 2020 19:14:29 +0200
Subject: [PATCH 180/242] Prefix methods with a dot

---
 01_elements/00_content.ipynb  |  6 +++---
 04_iteration/03_content.ipynb |  2 +-
 05_numbers/01_content.ipynb   |  8 ++++----
 05_numbers/02_content.ipynb   |  4 ++--
 06_text/00_content.ipynb      | 34 +++++++++++++++++-----------------
 5 files changed, 27 insertions(+), 27 deletions(-)

diff --git a/01_elements/00_content.ipynb b/01_elements/00_content.ipynb
index 6003bd9..4d6c792 100644
--- a/01_elements/00_content.ipynb
+++ b/01_elements/00_content.ipynb
@@ -1340,9 +1340,9 @@
     }
    },
    "source": [
-    "Different types imply different behaviors for the objects. The `b` object, for example, may be \"asked\" if it is a whole number with the [is_integer() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/stdtypes.html#float.is_integer) \"functionality\" that comes with *every* `float` object.\n",
+    "Different types imply different behaviors for the objects. The `b` object, for example, may be \"asked\" if it is a whole number with the [.is_integer() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/stdtypes.html#float.is_integer) \"functionality\" that comes with *every* `float` object.\n",
     "\n",
-    "Formally, we call such type-specific functionalities **methods** (i.e., as opposed to functions) and we look at them in detail in [Chapter 10 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/10_classes/00_content.ipynb). For now, it suffices to know that we access them with the **dot operator** `.` on the object. Of course, `b` is a whole number, which the boolean object `True` tells us."
+    "Formally, we call such type-specific functionalities **methods** (i.e., as opposed to functions) and we look at them in detail in [Chapter 11 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/11_classes/00_content.ipynb). For now, it suffices to know that we access them with the **dot operator** `.` on the object. Of course, `b` is a whole number, which the boolean object `True` tells us."
    ]
   },
   {
@@ -1377,7 +1377,7 @@
     }
    },
    "source": [
-    "For an `int` object, this [is_integer() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/stdtypes.html#float.is_integer) check does *not* make sense as we already know it is an `int`: We see the `AttributeError` below as `a` does not even know what `is_integer()` means."
+    "For an `int` object, this [.is_integer() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/stdtypes.html#float.is_integer) check does *not* make sense as we already know it is an `int`: We see the `AttributeError` below as `a` does not even know what `is_integer()` means."
    ]
   },
   {
diff --git a/04_iteration/03_content.ipynb b/04_iteration/03_content.ipynb
index 8cfb19f..dafbe08 100644
--- a/04_iteration/03_content.ipynb
+++ b/04_iteration/03_content.ipynb
@@ -788,7 +788,7 @@
     "\n",
     "First, we divide the business logic into two functions `get_guess()` and `toss_coin()` that are controlled from within a `while`-loop.\n",
     "\n",
-    "`get_guess()` not only reads in the user's input but also implements a simple input validation pattern in that the [strip() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/stdtypes.html?highlight=__contains__#str.strip) and [lower() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/stdtypes.html?highlight=__contains__#str.lower) methods remove preceding and trailing whitespace and lower case the input ensuring that the user may spell the input in any possible way (e.g., all upper or lower case). Also, `get_guess()` checks if the user entered one of the two valid options. If so, it returns either `\"heads\"` or `\"tails\"`; if not, it returns `None`."
+    "`get_guess()` not only reads in the user's input but also implements a simple input validation pattern in that the [.strip() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/stdtypes.html?highlight=__contains__#str.strip) and [.lower() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/stdtypes.html?highlight=__contains__#str.lower) methods remove preceding and trailing whitespace and lower case the input ensuring that the user may spell the input in any possible way (e.g., all upper or lower case). Also, `get_guess()` checks if the user entered one of the two valid options. If so, it returns either `\"heads\"` or `\"tails\"`; if not, it returns `None`."
    ]
   },
   {
diff --git a/05_numbers/01_content.ipynb b/05_numbers/01_content.ipynb
index af18f07..00157ad 100644
--- a/05_numbers/01_content.ipynb
+++ b/05_numbers/01_content.ipynb
@@ -1827,7 +1827,7 @@
     "\n",
     "The Python [documentation <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/tutorial/floatingpoint.html) provides another good discussion of floats and the goodness of their approximations.\n",
     "\n",
-    "If we are interested in the exact bits behind a `float` object, we use the [hex() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/stdtypes.html#float.hex) method that returns a `str` object beginning with `\"0x1.\"` followed by the $fraction$ in hexadecimal notation and the $exponent$ as an integer after subtraction of $1023$ and separated by a `\"p\"`."
+    "If we are interested in the exact bits behind a `float` object, we use the [.hex() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/stdtypes.html#float.hex) method that returns a `str` object beginning with `\"0x1.\"` followed by the $fraction$ in hexadecimal notation and the $exponent$ as an integer after subtraction of $1023$ and separated by a `\"p\"`."
    ]
   },
   {
@@ -1875,7 +1875,7 @@
     }
    },
    "source": [
-    "Also, the [as_integer_ratio() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/stdtypes.html#float.as_integer_ratio) method returns the two smallest integers whose ratio best approximates a `float` object."
+    "Also, the [.as_integer_ratio() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/stdtypes.html#float.as_integer_ratio) method returns the two smallest integers whose ratio best approximates a `float` object."
    ]
   },
   {
@@ -2030,7 +2030,7 @@
     }
    },
    "source": [
-    "As seen in [Chapter 1 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/01_elements/00_content.ipynb#%28Data%29-Type-%2F-%22Behavior%22), the [is_integer() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/stdtypes.html#float.is_integer) method tells us if a `float` can be casted as an `int` object without any loss in precision."
+    "As seen in [Chapter 1 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/01_elements/00_content.ipynb#%28Data%29-Type-%2F-%22Behavior%22), the [.is_integer() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/stdtypes.html#float.is_integer) method tells us if a `float` can be casted as an `int` object without any loss in precision."
    ]
   },
   {
@@ -2091,7 +2091,7 @@
     }
    },
    "source": [
-    "As the exact implementation of floats may vary and be dependent on a particular Python installation, we look up the [float_info <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/sys.html#sys.float_info) attribute in the [sys <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/sys.html) module in the [standard library <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/index.html) to check the details. Usually, this is not necessary."
+    "As the exact implementation of floats may vary and be dependent on a particular Python installation, we look up the [.float_info <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/sys.html#sys.float_info) attribute in the [sys <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/sys.html) module in the [standard library <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/index.html) to check the details. Usually, this is not necessary."
    ]
   },
   {
diff --git a/05_numbers/02_content.ipynb b/05_numbers/02_content.ipynb
index e430e90..ae1b01d 100644
--- a/05_numbers/02_content.ipynb
+++ b/05_numbers/02_content.ipynb
@@ -581,7 +581,7 @@
     }
    },
    "source": [
-    "A `complex` number comes with two **attributes** `real` and `imag` that return the two parts as `float` objects on their own."
+    "A `complex` number comes with two **attributes** `.real` and `.imag` that return the two parts as `float` objects on their own."
    ]
   },
   {
@@ -640,7 +640,7 @@
     }
    },
    "source": [
-    "Also, a `conjugate()` method is bound to every `complex` object. The [complex conjugate <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_wiki.png\">](https://en.wikipedia.org/wiki/Complex_conjugate) is defined to be the complex number with identical real part but an imaginary part reversed in sign."
+    "Also, a `.conjugate()` method is bound to every `complex` object. The [complex conjugate <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_wiki.png\">](https://en.wikipedia.org/wiki/Complex_conjugate) is defined to be the complex number with identical real part but an imaginary part reversed in sign."
    ]
   },
   {
diff --git a/06_text/00_content.ipynb b/06_text/00_content.ipynb
index 7c9fff8..236942b 100644
--- a/06_text/00_content.ipynb
+++ b/06_text/00_content.ipynb
@@ -2370,7 +2370,7 @@
    "source": [
     "Objects of type `str` come with many **methods** bound on them (cf., the [documentation <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/stdtypes.html#string-methods) for a full list). As seen before, they work like *normal* functions and are accessed via the **dot operator** `.`. Calling a method is also referred to as **method invocation**.\n",
     "\n",
-    "The [find() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/stdtypes.html#str.find) method returns the index of the first occurrence of a character or a substring. If no match is found, it returns `-1`. A mirrored version searching from the right called [rfind() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/stdtypes.html#str.rfind) exists as well. The [index() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/stdtypes.html#str.index) and [rindex() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/stdtypes.html#str.rindex) methods work in the same way but raise a `ValueError` if no match is found. So, we can control if a search fails *silently* or *loudly*."
+    "The [.find() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/stdtypes.html#str.find) method returns the index of the first occurrence of a character or a substring. If no match is found, it returns `-1`. A mirrored version searching from the right called [.rfind() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/stdtypes.html#str.rfind) exists as well. The [.index() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/stdtypes.html#str.index) and [.rindex() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/stdtypes.html#str.rindex) methods work in the same way but raise a `ValueError` if no match is found. So, we can control if a search fails *silently* or *loudly*."
    ]
   },
   {
@@ -2477,7 +2477,7 @@
     }
    },
    "source": [
-    "[find() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/stdtypes.html#str.find) takes optional *start* and *end* arguments that allow us to find occurrences other than the first one."
+    "[.find() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/stdtypes.html#str.find) takes optional *start* and *end* arguments that allow us to find occurrences other than the first one."
    ]
   },
   {
@@ -2560,7 +2560,7 @@
     }
    },
    "source": [
-    "The [count() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/stdtypes.html#str.count) method does what we expect."
+    "The [.count() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/stdtypes.html#str.count) method does what we expect."
    ]
   },
   {
@@ -2619,7 +2619,7 @@
     }
    },
    "source": [
-    "As [count() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/stdtypes.html#str.count) is *case-sensitive*, we must **chain** it with the [lower() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/stdtypes.html#str.lower) method to get the count of all `\"L\"`s and `\"l\"`s."
+    "As [.count() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/stdtypes.html#str.count) is *case-sensitive*, we must **chain** it with the [.lower() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/stdtypes.html#str.lower) method to get the count of all `\"L\"`s and `\"l\"`s."
    ]
   },
   {
@@ -2654,7 +2654,7 @@
     }
    },
    "source": [
-    "Alternatively, we can use the [upper() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/stdtypes.html#str.upper) method and search for `\"L\"`s."
+    "Alternatively, we can use the [.upper() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/stdtypes.html#str.upper) method and search for `\"L\"`s."
    ]
   },
   {
@@ -2689,7 +2689,7 @@
     }
    },
    "source": [
-    "Because `str` objects are *immutable*, [upper() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/stdtypes.html#str.upper) and [lower() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/stdtypes.html#str.lower) return *new* `str` objects, even if they do *not* change the value of the original `str` object."
+    "Because `str` objects are *immutable*, [.upper() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/stdtypes.html#str.upper) and [.lower() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/stdtypes.html#str.lower) return *new* `str` objects, even if they do *not* change the value of the original `str` object."
    ]
   },
   {
@@ -2833,7 +2833,7 @@
     }
    },
    "source": [
-    "Besides [upper() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/stdtypes.html#str.upper) and [lower() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/stdtypes.html#str.lower) there exist also [title() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/stdtypes.html#str.title) and [swapcase() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/stdtypes.html#str.swapcase) methods."
+    "Besides [.upper() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/stdtypes.html#str.upper) and [.lower() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/stdtypes.html#str.lower) there exist also [.title() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/stdtypes.html#str.title) and [.swapcase() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/stdtypes.html#str.swapcase) methods."
    ]
   },
   {
@@ -2940,9 +2940,9 @@
     }
    },
    "source": [
-    "Another popular string method is [split() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/stdtypes.html#str.split): It separates a longer `str` object into smaller ones collected in a `list` object. By default, groups of contiguous whitespace characters are used as the *separator*.\n",
+    "Another popular string method is [.split() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/stdtypes.html#str.split): It separates a longer `str` object into smaller ones collected in a `list` object. By default, groups of contiguous whitespace characters are used as the *separator*.\n",
     "\n",
-    "As an example, we use [split() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/stdtypes.html#str.split) to print out the individual words in `text` with more whitespace in between them."
+    "As an example, we use [.split() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/stdtypes.html#str.split) to print out the individual words in `text` with more whitespace in between them."
    ]
   },
   {
@@ -2999,7 +2999,7 @@
     }
    },
    "source": [
-    "The opposite of splitting is done with the [join() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/stdtypes.html#str.join) method. It is typically invoked on a `str` object that represents a separator (e.g., `\" \"` or `\", \"`) and connects the elements provided by an *iterable* argument (e.g., `words` below) into one *new* `str` object."
+    "The opposite of splitting is done with the [.join() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/stdtypes.html#str.join) method. It is typically invoked on a `str` object that represents a separator (e.g., `\" \"` or `\", \"`) and connects the elements provided by an *iterable* argument (e.g., `words` below) into one *new* `str` object."
    ]
   },
   {
@@ -3095,7 +3095,7 @@
     }
    },
    "source": [
-    "The [replace() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/stdtypes.html#str.replace) method creates a *new* `str` object with parts of the original `str` object potentially replaced."
+    "The [.replace() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/stdtypes.html#str.replace) method creates a *new* `str` object with parts of the original `str` object potentially replaced."
    ]
   },
   {
@@ -3130,7 +3130,7 @@
     }
    },
    "source": [
-    "Note how `sentence` itself remains unchanged. Bound to an immutable object, [replace() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/stdtypes.html#str.replace) must create *new* objects."
+    "Note how `sentence` itself remains unchanged. Bound to an immutable object, [.replace() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/stdtypes.html#str.replace) must create *new* objects."
    ]
   },
   {
@@ -3165,7 +3165,7 @@
     }
    },
    "source": [
-    "As seen previously, the [strip() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/stdtypes.html#str.strip) method is often helpful in cleaning text data from unreliable sources like user input from unnecessary leading and trailing whitespace. The [lstrip() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/stdtypes.html#str.lstrip) and [rstrip() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/stdtypes.html#str.rstrip) methods are specialized versions of it."
+    "As seen previously, the [.strip() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/stdtypes.html#str.strip) method is often helpful in cleaning text data from unreliable sources like user input from unnecessary leading and trailing whitespace. The [.lstrip() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/stdtypes.html#str.lstrip) and [.rstrip() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/stdtypes.html#str.rstrip) methods are specialized versions of it."
    ]
   },
   {
@@ -3248,7 +3248,7 @@
     }
    },
    "source": [
-    "When justifying a `str` object for output, the [ljust() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/stdtypes.html#str.ljust) and [rjust() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/stdtypes.html#str.rjust) methods may be helpful."
+    "When justifying a `str` object for output, the [.ljust() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/stdtypes.html#str.ljust) and [.rjust() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/stdtypes.html#str.rjust) methods may be helpful."
    ]
   },
   {
@@ -3307,7 +3307,7 @@
     }
    },
    "source": [
-    "Similarly, the [zfill() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/stdtypes.html#str.zfill) method can be used to pad a `str` representation of a number with leading `0`s for justified output."
+    "Similarly, the [.zfill() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/stdtypes.html#str.zfill) method can be used to pad a `str` representation of a number with leading `0`s for justified output."
    ]
   },
   {
@@ -3706,7 +3706,7 @@
     }
    },
    "source": [
-    "`str` objects also provide a [format() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/stdtypes.html#str.format) method that accepts an arbitrary number of *positional* arguments that are inserted into the `str` object in the same order replacing empty curly brackets `{}`. String interpolation with the [format() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/stdtypes.html#str.format) method is a more traditional and probably the most common way as of today. While f-strings are the recommended way going forward, usage of the [format() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/stdtypes.html#str.format) method is likely not declining any time soon."
+    "`str` objects also provide a [.format() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/stdtypes.html#str.format) method that accepts an arbitrary number of *positional* arguments that are inserted into the `str` object in the same order replacing empty curly brackets `{}`. String interpolation with the [.format() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/stdtypes.html#str.format) method is a more traditional and probably the most common way as of today. While f-strings are the recommended way going forward, usage of the [.format() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/stdtypes.html#str.format) method is likely not declining any time soon."
    ]
   },
   {
@@ -3776,7 +3776,7 @@
     }
    },
    "source": [
-    "The [format() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/stdtypes.html#str.format) method may alternatively be used with *keyword* arguments as well. Then, we must put the keywords' names within the curly brackets."
+    "The [.format() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/stdtypes.html#str.format) method may alternatively be used with *keyword* arguments as well. Then, we must put the keywords' names within the curly brackets."
    ]
   },
   {

From d6bec8afa2bb493d53e185865485398ae065b067 Mon Sep 17 00:00:00 2001
From: Alexander Hess <alexander@webartifex.biz>
Date: Mon, 19 Oct 2020 19:15:05 +0200
Subject: [PATCH 181/242] Add initial version of chapter 07, part 2

---
 07_sequences/01_content.ipynb | 2995 +++++++++++++++++++++++++++++++++
 CONTENTS.md                   |    9 +-
 2 files changed, 3003 insertions(+), 1 deletion(-)
 create mode 100644 07_sequences/01_content.ipynb

diff --git a/07_sequences/01_content.ipynb b/07_sequences/01_content.ipynb
new file mode 100644
index 0000000..5123ac8
--- /dev/null
+++ b/07_sequences/01_content.ipynb
@@ -0,0 +1,2995 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "**Note**: Click on \"*Kernel*\" > \"*Restart Kernel and Clear All Outputs*\" in [JupyterLab](https://jupyterlab.readthedocs.io/en/stable/) *before* reading this notebook to reset its output. If you cannot run this file on your machine, you may want to open it [in the cloud <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_mb.png\">](https://mybinder.org/v2/gh/webartifex/intro-to-python/develop?urlpath=lab/tree/07_sequences/01_content.ipynb)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "# Chapter 7: Sequential Data (continued)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "In this second part of the chapter, we look closely at the built-in `list` type, which is probably the most commonly used sequence data type in practice."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "## The `list` Type"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "As already seen multiple times, to create a `list` object, we use the *literal notation* and list all elements within brackets `[` and `]`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "empty = []"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "simple = [40, 50]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "The elements do *not* need to be of the *same* type, and `list` objects may also be **nested**."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "nested = [empty, 10, 20.0, \"Thirty\", simple]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "[PythonTutor <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](http://pythontutor.com/visualize.html#code=empty%20%3D%20%5B%5D%0Asimple%20%3D%20%5B40,%2050%5D%0Anested%20%3D%20%5Bempty,%2010,%2020.0,%20%22Thirty%22,%20simple%5D&cumulative=false&curInstr=0&heapPrimitives=nevernest&mode=display&origin=opt-frontend.js&py=3&rawInputLstJSON=%5B%5D&textReferences=false) shows how `nested` holds references to the `empty` and `simple` objects. Technically, it holds three more references to the `10`, `20.0`, and `\"Thirty\"` objects as well. However, to simplify the visualization, these three objects are shown right inside the `nested` object. That may be done because they are immutable and \"flat\" data types. In general, the $0$s and $1$s inside a `list` object in memory always constitute references to other objects only."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[[], 10, 20.0, 'Thirty', [40, 50]]"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "nested"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Let's not forget that `nested` is an object on its own with an *identity* and *data type*."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "139688113982848"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "id(nested)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "list"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "type(nested)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Alternatively, we use the built-in [list() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#func-list) constructor to create a `list` object out of any (finite) *iterable* we pass to it as the argument.\n",
+    "\n",
+    "For example, we can wrap the [range() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#func-range) built-in with [list() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#func-list): As described in [Chapter 4 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/04_iteration/02_content.ipynb#Containers-vs.-Iterables), `range` objects, like `range(1, 13)` below, are iterable and generate `int` objects \"on the fly\" (i.e., one by one). The [list() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#func-list) around it acts like a `for`-loop and **materializes** twelve `int` objects in memory that then become the elements of the newly created `list` object. [PythonTutor <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](http://pythontutor.com/visualize.html#code=r%20%3D%20range%281,%2013%29%0Al%20%3D%20list%28range%281,%2013%29%29&cumulative=false&curInstr=0&heapPrimitives=nevernest&mode=display&origin=opt-frontend.js&py=3&rawInputLstJSON=%5B%5D&textReferences=false) shows this difference visually."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12]"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "list(range(1, 13))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Beware of passing a `range` object over a \"big\" horizon as the argument to [list() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#func-list) as that may lead to a `MemoryError` and the computer crashing."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "ename": "MemoryError",
+     "evalue": "",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mMemoryError\u001b[0m                               Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-8-dbb397cbb3b9>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mlist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m999_999_999_999\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;31mMemoryError\u001b[0m: "
+     ]
+    }
+   ],
+   "source": [
+    "list(range(999_999_999_999))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "As another example, we create a `list` object from a `str` object, which is iterable, as well. Then, the individual characters become the elements of the new `list` object!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['i', 't', 'e', 'r', 'a', 'b', 'l', 'e']"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "list(\"iterable\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "## Sequence Behaviors"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "`list` objects are *sequences*. To reiterate that, we briefly summarize the *four* behaviors of a sequence and provide some more `list`-specific details below:"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "- `Container`:\n",
+    " - holds references to other objects in memory (with their own *identity* and *type*)\n",
+    " - implements membership testing via the `in` operator\n",
+    "- `Iterable`:\n",
+    " - supports being looped over\n",
+    " - works with the `for` or `while` statements\n",
+    "- `Reversible`:\n",
+    " - the elements come in a *predictable* order that we may loop over in a forward or backward fashion\n",
+    " - works with the [reversed() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#reversed) built-in\n",
+    "- `Sized`:\n",
+    " - the number of elements is finite *and* known in advance\n",
+    " - works with the built-in [len() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#len) function"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "The \"length\" of `nested` is *five* because `empty` and `simple` count as *one* element each. In other words, `nested` holds five references to other objects, two of which are `list` objects."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "5"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "len(nested)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "With a `for`-loop, we can iterate over all elements in a *predictable* order, forward or backward. As `list` objects hold *references* to other *objects*, these have an *indentity* and may even be of *different* types; however, the latter observation is rarely, if ever, useful in practice."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[]         139688113991744    <class 'list'>\n",
+      "10         94291567220768     <class 'int'>\n",
+      "20.0       139688114016656    <class 'float'>\n",
+      "Thirty     139688114205808    <class 'str'>\n",
+      "[40, 50]   139688113982208    <class 'list'>\n"
+     ]
+    }
+   ],
+   "source": [
+    "for element in nested:\n",
+    "    print(str(element).ljust(10), str(id(element)).ljust(18), type(element))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[40, 50]     Thirty     20.0     10     []     "
+     ]
+    }
+   ],
+   "source": [
+    "for element in reversed(nested):\n",
+    "    print(element, end=\"     \")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "The `in` operator checks if a given object is \"contained\" in a `list` object. It uses the `==` operator behind the scenes (i.e., *not* the `is` operator) conducting a **[linear search <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_wiki.png\">](https://en.wikipedia.org/wiki/Linear_search)**: So, Python implicitly loops over *all* elements and only stops prematurely if an element evaluates equal to the searched object. A linear search may, therefore, be relatively *slow* for big `list` objects."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "10 in nested"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "`20` compares equal to the `20.0` in `nested`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "20 in nested"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "False"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "30 in nested"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "## Indexing"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Because of the *predictable* order and the *finiteness*, each element in a sequence can be labeled with a unique *index*, an `int` object in the range $0 \\leq \\text{index} < \\lvert \\text{sequence} \\rvert$.\n",
+    "\n",
+    "Brackets, `[` and `]`, are the literal syntax for accessing individual elements of any sequence type. In this book, we also call them the *indexing operator* in this context."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[]"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "nested[0]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "The last index is one less than `len(nested)`, and Python raises an `IndexError` if we look up an index that is not in the range."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "ename": "IndexError",
+     "evalue": "list index out of range",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mIndexError\u001b[0m                                Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-17-cb216f4a067e>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mnested\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m5\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;31mIndexError\u001b[0m: list index out of range"
+     ]
+    }
+   ],
+   "source": [
+    "nested[5]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Negative indices are used to count in reverse order from the end of a sequence, and brackets may be chained to access nested objects. So, to access the `50` inside `simple` via the `nested` object, we write `nested[-1][1]`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[40, 50]"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "nested[-1]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "50"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "nested[-1][1]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "## Slicing"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Slicing `list` objects works analogously to slicing `str` objects: We use the literal syntax with either one or two colons `:` inside the brackets `[]` to separate the *start*, *stop*, and *step* values. Slicing creates a *new* `list` object with the elements chosen from the original one.\n",
+    "\n",
+    "For example, to obtain the three elements in the \"middle\" of `nested`, we slice from `1` (including) to `4` (excluding)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[10, 20.0, 'Thirty']"
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "nested[1:4]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "To obtain \"every other\" element, we slice from beginning to end, defaulting to `0` and `len(nested)` when omitted, in steps of `2`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[[], 20.0, [40, 50]]"
+      ]
+     },
+     "execution_count": 21,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "nested[::2]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "The literal notation with the colons `:` is *syntactic sugar*. It saves us from using the [slice() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#slice) built-in to create `slice` objects. [slice() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#slice) takes *start*, *stop*, and *step* arguments in the same way as the familiar [range() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#func-range), and the `slice` objects it creates are used just as *indexes* above.\n",
+    "\n",
+    "In most cases, the literal notation is more convenient to use; however, with `slice` objects, we can give names to slices and reuse them across several sequences."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "middle = slice(1, 4)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "slice"
+      ]
+     },
+     "execution_count": 23,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "type(middle)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[10, 20.0, 'Thirty']"
+      ]
+     },
+     "execution_count": 24,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "nested[middle]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "`slice` objects come with three read-only attributes `start`, `stop`, and `step` on them."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "1"
+      ]
+     },
+     "execution_count": 25,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "middle.start"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "4"
+      ]
+     },
+     "execution_count": 26,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "middle.stop"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "If not passed to [slice() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#slice), these attributes default to `None`. That is why the cell below has no output."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "middle.step"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "A good trick to know is taking a \"full\" slice: This copies *all* elements of a `list` object into a *new* `list` object."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "nested_copy = nested[:]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[[], 10, 20.0, 'Thirty', [40, 50]]"
+      ]
+     },
+     "execution_count": 29,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "nested_copy"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "At first glance, `nested` and `nested_copy` seem to cause no pain. For `list` objects, the comparison operator `==` goes over the elements in both operands in a pairwise fashion and checks if they all evaluate equal (cf., the \"*List Comparison*\" section below for more details).\n",
+    "\n",
+    "We confirm that `nested` and `nested_copy` compare equal as could be expected but also that they are *different* objects."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 30,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "nested == nested_copy"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "False"
+      ]
+     },
+     "execution_count": 31,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "nested is nested_copy"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "However, as [PythonTutor <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](http://pythontutor.com/visualize.html#code=nested%20%3D%20%5B%5B%5D,%2010,%2020.0,%20%22Thirty%22,%20%5B40,%2050%5D%5D%0Anested_copy%20%3D%20nested%5B%3A%5D&cumulative=false&curInstr=0&heapPrimitives=nevernest&mode=display&origin=opt-frontend.js&py=3&rawInputLstJSON=%5B%5D&textReferences=false) reveals, only the *references* to the elements are copied, and not the objects in `nested` themselves! Because of that, `nested_copy` is a so-called **[shallow copy <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_wiki.png\">](https://en.wikipedia.org/wiki/Object_copying#Shallow_copy)** of `nested`.\n",
+    "\n",
+    "We could also see this with the [id() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#id) function: The respective first elements in both `nested` and `nested_copy` are the *same* object, namely `empty`.  So, we have three ways of accessing the *same* address in memory. Also, we say that `nested` and `nested_copy` partially share the *same* state."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 32,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "nested[0] is nested_copy[0]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "139688113991744"
+      ]
+     },
+     "execution_count": 33,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "id(nested[0])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "139688113991744"
+      ]
+     },
+     "execution_count": 34,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "id(nested_copy[0])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Knowing this becomes critical if the elements in a `list` object are mutable objects (i.e., we can change them *in place*), and this is the case with `nested` and `nested_copy`, as we see in the next section on \"*Mutability*\".\n",
+    "\n",
+    "As both the original `nested` object and its copy reference the *same* `list` objects in memory, any changes made to them are visible to both! Because of that, working with shallow copies can easily become confusing.\n",
+    "\n",
+    "Instead of a shallow copy, we could also create a so-called **[deep copy <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_wiki.png\">](https://en.wikipedia.org/wiki/Object_copying#Deep_copy)** of `nested`: Then, the copying process recursively follows every reference in a nested data structure and creates copies of *every* object found.\n",
+    "\n",
+    "To explicitly create shallow or deep copies, the [copy <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/copy.html) module in the [standard library <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/index.html) provides two functions, [copy() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/copy.html#copy.copy) and [deepcopy() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/copy.html#copy.deepcopy). We must always remember that slicing creates *shallow* copies only."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "import copy"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "nested_deep_copy = copy.deepcopy(nested)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 37,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "nested == nested_deep_copy"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Now, the first elements of `nested` and `nested_deep_copy` are *different* objects, and [PythonTutor <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](http://pythontutor.com/visualize.html#code=import%20copy%0Anested%20%3D%20%5B%5B%5D,%2010,%2020.0,%20%22Thirty%22,%20%5B40,%2050%5D%5D%0Anested_deep_copy%20%3D%20copy.deepcopy%28nested%29&cumulative=false&curInstr=0&heapPrimitives=nevernest&mode=display&origin=opt-frontend.js&py=3&rawInputLstJSON=%5B%5D&textReferences=false) shows that there are *six* `list` objects in memory."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "False"
+      ]
+     },
+     "execution_count": 38,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "nested[0] is nested_deep_copy[0]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "139688113991744"
+      ]
+     },
+     "execution_count": 39,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "id(nested[0])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "139688113233152"
+      ]
+     },
+     "execution_count": 40,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "id(nested_deep_copy[0])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "As this [StackOverflow question <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_so.png\">](https://stackoverflow.com/questions/184710/what-is-the-difference-between-a-deep-copy-and-a-shallow-copy) shows, understanding shallow and deep copies is a common source of confusion, independent of the programming language."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "## Mutability"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "In contrast to `str` objects, `list` objects are *mutable*: We may assign new elements to indices or slices and also remove elements. That changes the *references* in a `list` object. In general, if an object is *mutable*, we say that it may be changed *in place*."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "nested[0] = 0"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[0, 10, 20.0, 'Thirty', [40, 50]]"
+      ]
+     },
+     "execution_count": 42,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "nested"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "When we re-assign a slice, we can even change the size of the `list` object."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "nested[:4] = [100, 100, 100]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[100, 100, 100, [40, 50]]"
+      ]
+     },
+     "execution_count": 44,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "nested"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "4"
+      ]
+     },
+     "execution_count": 45,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "len(nested)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "The `list` object's identity does *not* change. That is the main point behind mutable objects."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "139688113982848"
+      ]
+     },
+     "execution_count": 46,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "id(nested)  # same memory location as before"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "`nested_copy` is unchanged!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[[], 10, 20.0, 'Thirty', [40, 50]]"
+      ]
+     },
+     "execution_count": 47,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "nested_copy"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Let's change the nested `[40, 50]` via `nested_copy` into `[1, 2, 3]` by replacing all its elements."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 48,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "nested_copy[-1][:] = [1, 2, 3]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 49,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[[], 10, 20.0, 'Thirty', [1, 2, 3]]"
+      ]
+     },
+     "execution_count": 49,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "nested_copy"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "That has a surprising side effect on `nested`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 50,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[100, 100, 100, [1, 2, 3]]"
+      ]
+     },
+     "execution_count": 50,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "nested"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "That is precisely the confusion we talked about above when we said that `nested_copy` is a *shallow* copy of `nested`. [PythonTutor <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](http://pythontutor.com/visualize.html#code=nested%20%3D%20%5B%5B%5D,%2010,%2020.0,%20%22Thirty%22,%20%5B40,%2050%5D%5D%0Anested_copy%20%3D%20nested%5B%3A%5D%0Anested%5B%3A4%5D%20%3D%20%5B100,%20100,%20100%5D%0Anested_copy%5B-1%5D%5B%3A%5D%20%3D%20%5B1,%202,%203%5D&cumulative=false&curInstr=0&heapPrimitives=nevernest&mode=display&origin=opt-frontend.js&py=3&rawInputLstJSON=%5B%5D&textReferences=false) shows how both reference the *same* nested `list` object that is changed *in place* from `[40, 50]` into `[1, 2, 3]`.\n",
+    "\n",
+    "Lastly, we use the `del` statement to remove an element."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 51,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "del nested[-1]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 52,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[100, 100, 100]"
+      ]
+     },
+     "execution_count": 52,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "nested"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "The `del` statement also works for slices. Here, we remove all references `nested` holds."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 53,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "del nested[:]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 54,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[]"
+      ]
+     },
+     "execution_count": 54,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "nested"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Mutability for sequences is formalized by the `MutableSequence` ABC in the [collections.abc <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/collections.abc.html) module."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "## List Methods"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "The `list` type is an essential data structure in any real-world Python application, and many typical `list`-related algorithms from computer science theory are already built into it at the C level (cf., the [documentation <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/stdtypes.html#mutable-sequence-types) or the [tutorial <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/tutorial/datastructures.html#more-on-lists) for a full overview; unfortunately, not all methods have direct links). So, understanding and applying the built-in methods of the `list` type not only speeds up the development process but also makes programs significantly faster.\n",
+    "\n",
+    "In contrast to the `str` type's methods in [Chapter 6 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/06_text/00_content.ipynb#String-Methods) (e.g., [.upper() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/stdtypes.html#str.upper) or [.lower() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/stdtypes.html#str.lower)), the `list` type's methods that mutate an object do so *in place*. That means they *never* create *new* `list` objects and return `None` to indicate that. So, we must *never* assign the return value of `list` methods to the variable holding the list!\n",
+    "\n",
+    "Let's look at the following `names` example."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 55,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "names = [\"Carl\", \"Peter\"]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "To add an object to the end of `names`, we use the `.append()` method. The code cell shows no output indicating that `None` must be the return value."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 56,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "names.append(\"Eckardt\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 57,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['Carl', 'Peter', 'Eckardt']"
+      ]
+     },
+     "execution_count": 57,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "names"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "With the `.extend()` method, we may also append multiple elements provided by an iterable. Here, the iterable is a `list` object itself holding two `str` objects."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 58,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "names.extend([\"Karl\", \"Oliver\"])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 59,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['Carl', 'Peter', 'Eckardt', 'Karl', 'Oliver']"
+      ]
+     },
+     "execution_count": 59,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "names"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Similar to `.append()`, we may add a new element at an arbitrary position with the `.insert()` method. `.insert()` takes two arguments, an *index* and the element to be inserted."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 60,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "names.insert(1, \"Berthold\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 61,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['Carl', 'Berthold', 'Peter', 'Eckardt', 'Karl', 'Oliver']"
+      ]
+     },
+     "execution_count": 61,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "names"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "`list` objects may be sorted *in place* with the [.sort() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/stdtypes.html#list.sort) method. That is different from the built-in [sorted() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#sorted) function that takes any *finite* and *iterable* object and returns a *new* `list` object with the iterable's elements sorted!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 62,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['Berthold', 'Carl', 'Eckardt', 'Karl', 'Oliver', 'Peter']"
+      ]
+     },
+     "execution_count": 62,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "sorted(names)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "As the previous code cell created a *new* `list` object, `names` is still unsorted."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 63,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['Carl', 'Berthold', 'Peter', 'Eckardt', 'Karl', 'Oliver']"
+      ]
+     },
+     "execution_count": 63,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "names"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Let's sort the elements in `names` instead."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 64,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "names.sort()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 65,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['Berthold', 'Carl', 'Eckardt', 'Karl', 'Oliver', 'Peter']"
+      ]
+     },
+     "execution_count": 65,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "names"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "To sort in reverse order, we pass a keyword-only `reverse=True` argument to either the [.sort() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/stdtypes.html#list.sort) method or the [sorted() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#sorted) function."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 66,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "names.sort(reverse=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 67,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['Peter', 'Oliver', 'Karl', 'Eckardt', 'Carl', 'Berthold']"
+      ]
+     },
+     "execution_count": 67,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "names"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "The [.sort() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/stdtypes.html#list.sort) method and the [sorted() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#sorted) function sort the elements in `names` in alphabetical order, forward or backward. However, that does *not* hold in general.\n",
+    "\n",
+    "We mention above that `list` objects may contain objects of *any* type and even of *mixed* types. Because of that, the sorting is **[delegated <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_wiki.png\">](https://en.wikipedia.org/wiki/Delegation_(object-oriented_programming))** to the elements in a `list` object. In a way, Python \"asks\" the elements in a `list` object to sort themselves. As `names` contains only `str` objects, they are sorted according the the comparison rules explained in [Chapter 6 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/06_text/00_content.ipynb#String-Comparison).\n",
+    "\n",
+    "To customize the sorting, we pass a keyword-only `key` argument to [.sort() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/stdtypes.html#list.sort) or [sorted() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#sorted), which must be a `function` object accepting *one* positional argument. Then, the elements in the `list` object are passed to that one by one, and the return values are used as the **sort keys**. The `key` argument is also a popular use case for `lambda` expressions.\n",
+    "\n",
+    "For example, to sort `names` not by alphabet but by the names' lengths, we pass in a reference to the built-in [len() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#len) function as `key=len`. Note that there are *no* parentheses after `len`!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 68,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "names.sort(key=len)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "If two names have the same length, their relative order is kept as is. That is why `\"Karl\"` comes before `\"Carl\" ` below. A [sorting algorithm <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_wiki.png\">](https://en.wikipedia.org/wiki/Sorting_algorithm) with that property is called **[stable <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_wiki.png\">](https://en.wikipedia.org/wiki/Sorting_algorithm#Stability)**.\n",
+    "\n",
+    "Sorting is an important topic in programming, and we refer to the official [HOWTO <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/howto/sorting.html) for a more comprehensive introduction."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 69,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['Karl', 'Carl', 'Peter', 'Oliver', 'Eckardt', 'Berthold']"
+      ]
+     },
+     "execution_count": 69,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "names"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "`.sort(reverse=True)` is different from the `.reverse()` method. Whereas the former applies some sorting rule in reverse order, the latter simply reverses the elements in a `list` object."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 70,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "names.reverse()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 71,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['Berthold', 'Eckardt', 'Oliver', 'Peter', 'Carl', 'Karl']"
+      ]
+     },
+     "execution_count": 71,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "names"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "The `.pop()` method removes the *last* element from a `list` object *and* returns it. Below we **capture** the `removed` element to show that the return value is not `None` as with all the methods introduced so far."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 72,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "removed = names.pop()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 73,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Karl'"
+      ]
+     },
+     "execution_count": 73,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "removed"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 74,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['Berthold', 'Eckardt', 'Oliver', 'Peter', 'Carl']"
+      ]
+     },
+     "execution_count": 74,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "names"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "`.pop()` takes an optional *index* argument and removes that instead.\n",
+    "\n",
+    "So, to remove the second element, `\"Eckhardt\"`, from `names`, we write this."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 75,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "removed = names.pop(1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 76,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Eckardt'"
+      ]
+     },
+     "execution_count": 76,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "removed"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 77,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['Berthold', 'Oliver', 'Peter', 'Carl']"
+      ]
+     },
+     "execution_count": 77,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "names"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Instead of removing an element by its index, we can also remove it by its value with the `.remove()` method. Behind the scenes, Python then compares the object to be removed, `\"Peter\"` in the example, sequentially to each element with the `==` operator and removes the *first* one that evaluates equal to it. `.remove()` does *not* return the removed element."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 78,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "names.remove(\"Peter\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 79,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['Berthold', 'Oliver', 'Carl']"
+      ]
+     },
+     "execution_count": 79,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "names"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Also, `.remove()` raises a `ValueError` if the value is not found."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 80,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "ename": "ValueError",
+     "evalue": "list.remove(x): x not in list",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-80-6bb75d50cf06>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mnames\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mremove\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Peter\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;31mValueError\u001b[0m: list.remove(x): x not in list"
+     ]
+    }
+   ],
+   "source": [
+    "names.remove(\"Peter\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "`list` objects implement an `.index()` method that returns the position of the first element that compares equal to its argument. It fails *loudly* with a `ValueError` if no element compares equal."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 81,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['Berthold', 'Oliver', 'Carl']"
+      ]
+     },
+     "execution_count": 81,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "names"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 82,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "1"
+      ]
+     },
+     "execution_count": 82,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "names.index(\"Oliver\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 83,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "ename": "ValueError",
+     "evalue": "'Karl' is not in list",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-83-8c741a6680be>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mnames\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Karl\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;31mValueError\u001b[0m: 'Karl' is not in list"
+     ]
+    }
+   ],
+   "source": [
+    "names.index(\"Karl\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "The `.count()` method returns the number of elements that compare equal to its argument."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 84,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "1"
+      ]
+     },
+     "execution_count": 84,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "names.count(\"Carl\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 85,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0"
+      ]
+     },
+     "execution_count": 85,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "names.count(\"Karl\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Two more methods, `.copy()` and `.clear()`, are *syntactic sugar* and replace working with slices.\n",
+    "\n",
+    "`.copy()` creates a *shallow* copy. So, `names.copy()` below does the same as taking a full slice with `names[:]`, and the caveats from above apply, too."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 86,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "names_copy = names.copy()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 87,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['Berthold', 'Oliver', 'Carl']"
+      ]
+     },
+     "execution_count": 87,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "names_copy"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "`.clear()` removes all references from a `list` object. So, `names_copy.clear()` is the same as `del names_copy[:]`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 88,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "names_copy.clear()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 89,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[]"
+      ]
+     },
+     "execution_count": 89,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "names_copy"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Many methods introduced in this section are mentioned in the [collections.abc <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/collections.abc.html) module's documentation as well: While the `.index()` and `.count()` methods come with any data type that is a `Sequence`, the `.append()`, `.extend()`, `.insert()`, `.reverse()`, `.pop()`, and `.remove()` methods are part of any `MutableSequence` type. The `.sort()`, `.copy()`, and `.clear()` methods are `list`-specific.\n",
+    "\n",
+    "So, being a sequence does not only imply the four *behaviors* specified above, but also means that a data type comes with certain standardized methods."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "## List Operations"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "As with `str` objects, the `+` and `*` operators are overloaded for concatenation and always return a *new* `list` object. The references in this newly created `list` object reference the *same* objects as the two original `list` objects. So, the same caveat as with *shallow* copies from above applies!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 90,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['Berthold', 'Oliver', 'Carl']"
+      ]
+     },
+     "execution_count": 90,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "names"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 91,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['Berthold', 'Oliver', 'Carl', 'Diedrich', 'Yves']"
+      ]
+     },
+     "execution_count": 91,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "names + [\"Diedrich\", \"Yves\"]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 92,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['Berthold', 'Oliver', 'Carl', 'Berthold', 'Oliver', 'Carl']"
+      ]
+     },
+     "execution_count": 92,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "2 * names"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Unpacking"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Besides being an operator, the `*` symbol has a second syntactical use, as explained in [PEP 3132 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://www.python.org/dev/peps/pep-3132/) and [PEP 448 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://www.python.org/dev/peps/pep-0448/): It implements what is called **iterable unpacking**. It is *not* an operator syntactically but a notation that Python reads as a literal.\n",
+    "\n",
+    "In the example, Python interprets the expression as if the elements of the iterable `names` were placed between `\"Achim\"` and `\"Xavier\"` one by one. So, we do not obtain a nested but a *flat* list."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 93,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['Achim', 'Berthold', 'Oliver', 'Carl', 'Xavier']"
+      ]
+     },
+     "execution_count": 93,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "[\"Achim\", *names, \"Xavier\"]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Effectively, Python reads that as if we wrote the following."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 94,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['Achim', 'Berthold', 'Oliver', 'Carl', 'Xavier']"
+      ]
+     },
+     "execution_count": 94,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "[\"Achim\", names[0], names[1], names[2], \"Xavier\"]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "### List Comparison"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "The relational operators also work with `list` objects; yet another example of operator overloading.\n",
+    "\n",
+    "Comparison is made in a pairwise fashion until the first pair of elements does not evaluate equal or one of the `list` objects ends. The exact comparison rules depend on the elements and not the `list` objects. As with [.sort() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/stdtypes.html#list.sort) or [sorted() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#sorted) above, comparison is *delegated* to the objects to be compared, and Python \"asks\" the elements in the two `list` objects to compare themselves. Usually, all elements are of the *same* type, and comparison is straightforward."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 95,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['Berthold', 'Oliver', 'Carl']"
+      ]
+     },
+     "execution_count": 95,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "names"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 96,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 96,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "names == [\"Berthold\", \"Oliver\", \"Carl\"]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 97,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 97,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "names != [\"Berthold\", \"Oliver\", \"Karl\"]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 98,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 98,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "names < [\"Berthold\", \"Oliver\", \"Karl\"]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "If two `list` objects have a different number of elements and all overlapping elements compare equal, the shorter `list` object is considered \"smaller.\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 99,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 99,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "[\"Berthold\", \"Oliver\"] < names < [\"Berthold\", \"Oliver\", \"Carl\", \"Xavier\"]"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.6"
+  },
+  "livereveal": {
+   "auto_select": "code",
+   "auto_select_fragment": true,
+   "scroll": true,
+   "theme": "serif"
+  },
+  "toc": {
+   "base_numbering": 1,
+   "nav_menu": {},
+   "number_sections": false,
+   "sideBar": true,
+   "skip_h1_title": true,
+   "title_cell": "Table of Contents",
+   "title_sidebar": "Contents",
+   "toc_cell": false,
+   "toc_position": {
+    "height": "calc(100% - 180px)",
+    "left": "10px",
+    "top": "150px",
+    "width": "384px"
+   },
+   "toc_section_display": false,
+   "toc_window_display": false
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/CONTENTS.md b/CONTENTS.md
index fd55699..bc87a09 100644
--- a/CONTENTS.md
+++ b/CONTENTS.md
@@ -165,4 +165,11 @@ If this is not possible,
     - [content <img height="12" style="display: inline-block" src="static/link/to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/07_sequences/00_content.ipynb)
       [<img height="12" style="display: inline-block" src="static/link/to_mb.png">](https://mybinder.org/v2/gh/webartifex/intro-to-python/develop?urlpath=lab/tree/07_sequences/00_content.ipynb)
       (Collections vs. Sequences;
-       ABCs: `Container`, `Iterable`, `Sized`, & `Reversible`)
\ No newline at end of file
+       ABCs: `Container`, `Iterable`, `Sized`, & `Reversible`)
+    - [content <img height="12" style="display: inline-block" src="static/link/to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/07_sequences/01_content.ipynb)
+      [<img height="12" style="display: inline-block" src="static/link/to_mb.png">](https://mybinder.org/v2/gh/webartifex/intro-to-python/develop?urlpath=lab/tree/07_sequences/01_content.ipynb)
+      (`list` Type;
+       Indexing & Slicing;
+       Shallow vs. Deep Copies;
+       List Methods & Operations;
+       Unpacking)
\ No newline at end of file

From 4dfc956f0efe028d20ffbbf0dd0eae0c2f97e05f Mon Sep 17 00:00:00 2001
From: Alexander Hess <alexander@webartifex.biz>
Date: Mon, 19 Oct 2020 19:20:28 +0200
Subject: [PATCH 182/242] Add initial version of chapter 07's exercises, part 1

---
 07_sequences/02_exercises.ipynb | 257 ++++++++++++++++++++++++++++++++
 CONTENTS.md                     |   5 +-
 2 files changed, 261 insertions(+), 1 deletion(-)
 create mode 100644 07_sequences/02_exercises.ipynb

diff --git a/07_sequences/02_exercises.ipynb b/07_sequences/02_exercises.ipynb
new file mode 100644
index 0000000..0ec7907
--- /dev/null
+++ b/07_sequences/02_exercises.ipynb
@@ -0,0 +1,257 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Note**: Click on \"*Kernel*\" > \"*Restart Kernel and Run All*\" in [JupyterLab](https://jupyterlab.readthedocs.io/en/stable/) *after* finishing the exercises to ensure that your solution runs top to bottom *without* any errors. If you cannot run this file on your machine, you may want to open it [in the cloud <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_mb.png\">](https://mybinder.org/v2/gh/webartifex/intro-to-python/develop?urlpath=lab/tree/07_sequences/02_exercises.ipynb)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Chapter 7: Sequential Data (Coding Exercises)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The exercises below assume that you have read the [second part <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/07_sequences/01_content.ipynb) of Chapter 7.\n",
+    "\n",
+    "The `...`'s in the code cells indicate where you need to fill in code snippets. The number of `...`'s within a code cell give you a rough idea of how many lines of code are needed to solve the task. You should not need to create any additional code cells for your final solution. However, you may want to use temporary code cells to try out some ideas."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Working with Lists"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q1**: Write a function `nested_sum()` that takes a `list` object as its argument, which contains other `list` objects with numbers, and adds up the numbers! Use `nested_numbers` below to test your function!\n",
+    "\n",
+    "Hint: You need at least one `for`-loop."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "nested_numbers = [[1, 2, 3], [4], [5], [6, 7], [8], [9]]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def nested_sum(list_of_lists):\n",
+    "    \"\"\"Add up numbers in nested lists.\n",
+    "    \n",
+    "    Args:\n",
+    "        list_of_lists (list): A list containing the lists with the numbers\n",
+    "    \n",
+    "    Returns:\n",
+    "        sum (int or float)\n",
+    "    \"\"\"\n",
+    "    ...\n",
+    "    ...\n",
+    "    ...\n",
+    "\n",
+    "    return ..."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "nested_sum(nested_numbers)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q2**: Generalize `nested_sum()` into a function `mixed_sum()` that can process a \"mixed\" `list` object, which contains numbers and other `list` objects with numbers! Use `mixed_numbers` below for testing!\n",
+    "\n",
+    "Hints: Use the built-in [isinstance() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#isinstance) function to check how an element is to be processed."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "mixed_numbers = [[1, 2, 3], 4, 5, [6, 7], 8, [9]]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import collections.abc as abc"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def mixed_sum(list_of_lists_or_numbers):\n",
+    "    \"\"\"Add up numbers in nested lists.\n",
+    "    \n",
+    "    Args:\n",
+    "        list_of_lists_or_numbers (list): A list containing both numbers and\n",
+    "            lists with numbers\n",
+    "    \n",
+    "    Returns:\n",
+    "        sum (int or float)\n",
+    "    \"\"\"\n",
+    "    ...\n",
+    "    ...\n",
+    "    ...\n",
+    "    ...\n",
+    "    ...\n",
+    "    ...\n",
+    "\n",
+    "    return ..."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "mixed_sum(mixed_numbers)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q3.1**: Write a function `cum_sum()` that takes a `list` object with numbers as its argument and returns a *new* `list` object with the **cumulative sums** of these numbers! So, `sum_up` below, `[1, 2, 3, 4, 5]`, should return `[1, 3, 6, 10, 15]`.\n",
+    "\n",
+    "Hint: The idea behind is similar to the [cumulative distribution function <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_wiki.png\">](https://en.wikipedia.org/wiki/Cumulative_distribution_function) from statistics."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sum_up = [1, 2, 3, 4, 5]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def cum_sum(numbers):\n",
+    "    \"\"\"Create the cumulative sums for some numbers.\n",
+    "\n",
+    "    Args:\n",
+    "        numbers (list): A list with numbers for that the cumulative sums\n",
+    "            are calculated\n",
+    "    \n",
+    "    Returns:\n",
+    "        cum_sums (list): A list with all the cumulative sums\n",
+    "    \"\"\"\n",
+    "    ...\n",
+    "    ...\n",
+    "\n",
+    "    ...\n",
+    "    ...\n",
+    "    ...\n",
+    "\n",
+    "    return ..."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "cum_sum(sum_up)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q3.2**: We should always make sure that our functions also work in corner cases. What happens if your implementation of `cum_sum()` is called with an empty list `[]`? Make sure it handles that case *without* crashing! What would be a good return value in this corner case?\n",
+    "\n",
+    "Hint: It is possible to write this without any extra input validation."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "cum_sum([])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " < your answer >"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.6"
+  },
+  "toc": {
+   "base_numbering": 1,
+   "nav_menu": {},
+   "number_sections": false,
+   "sideBar": true,
+   "skip_h1_title": true,
+   "title_cell": "Table of Contents",
+   "title_sidebar": "Contents",
+   "toc_cell": false,
+   "toc_position": {},
+   "toc_section_display": false,
+   "toc_window_display": false
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/CONTENTS.md b/CONTENTS.md
index bc87a09..73a5070 100644
--- a/CONTENTS.md
+++ b/CONTENTS.md
@@ -172,4 +172,7 @@ If this is not possible,
        Indexing & Slicing;
        Shallow vs. Deep Copies;
        List Methods & Operations;
-       Unpacking)
\ No newline at end of file
+       Unpacking)
+    - [exercises <img height="12" style="display: inline-block" src="static/link/to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/07_sequences/02_exercises.ipynb)
+      [<img height="12" style="display: inline-block" src="static/link/to_mb.png">](https://mybinder.org/v2/gh/webartifex/intro-to-python/develop?urlpath=lab/tree/07_sequences/02_exercises.ipynb)
+      (Working with Lists)
\ No newline at end of file

From 9a21de45390406a3ab385090451f2dee7776a5d2 Mon Sep 17 00:00:00 2001
From: Alexander Hess <alexander@webartifex.biz>
Date: Mon, 19 Oct 2020 19:45:22 +0200
Subject: [PATCH 183/242] Add initial version of chapter 07, part 3

---
 07_sequences/03_content.ipynb | 2494 +++++++++++++++++++++++++++++++++
 CONTENTS.md                   |   11 +-
 2 files changed, 2502 insertions(+), 3 deletions(-)
 create mode 100644 07_sequences/03_content.ipynb

diff --git a/07_sequences/03_content.ipynb b/07_sequences/03_content.ipynb
new file mode 100644
index 0000000..7b63f1e
--- /dev/null
+++ b/07_sequences/03_content.ipynb
@@ -0,0 +1,2494 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "**Note**: Click on \"*Kernel*\" > \"*Restart Kernel and Clear All Outputs*\" in [JupyterLab](https://jupyterlab.readthedocs.io/en/stable/) *before* reading this notebook to reset its output. If you cannot run this file on your machine, you may want to open it [in the cloud <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_mb.png\">](https://mybinder.org/v2/gh/webartifex/intro-to-python/develop?urlpath=lab/tree/07_sequences/03_content.ipynb)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "# Chapter 7: Sequential Data  (continued)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "In this third part of the chapter, we first look at a major implication of the `list` type's mutability. Then, we see how its close relative, the `tuple` type, can mitigate this. Lastly, we see how Python's syntax assumes sequential data at various places: for example, when unpacking iterables during a `for`-loop or an assignment, or when working with `function` objects."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "## Modifiers vs. Pure Functions"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "As `list` objects are mutable, the caller of a function can see the changes made to a `list` object passed to the function as an argument. That is often a surprising *side effect* and should be avoided.\n",
+    "\n",
+    "As an example, consider the `add_xyz()` function."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "letters = [\"a\", \"b\", \"c\"]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "def add_xyz(arg):\n",
+    "    \"\"\"Append letters to a list.\"\"\"\n",
+    "    arg.extend([\"x\", \"y\", \"z\"])\n",
+    "    return arg"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "While this function is being executed, two variables, namely `letters` in the global scope and `arg` inside the function's local scope, reference the *same* `list` object in memory. Furthermore, the passed in `arg` is also the return value.\n",
+    "\n",
+    "So, after the function call, `letters_with_xyz` and `letters` are **aliases** as well, referencing the *same* object. We can also visualize that with [PythonTutor <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](http://pythontutor.com/visualize.html#code=letters%20%3D%20%5B%22a%22,%20%22b%22,%20%22c%22%5D%0A%0Adef%20add_xyz%28arg%29%3A%0A%20%20%20%20arg.extend%28%5B%22x%22,%20%22y%22,%20%22z%22%5D%29%0A%20%20%20%20return%20arg%0A%0Aletters_with_xyz%20%3D%20add_xyz%28letters%29&cumulative=false&curInstr=0&heapPrimitives=nevernest&mode=display&origin=opt-frontend.js&py=3&rawInputLstJSON=%5B%5D&textReferences=false)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "letters_with_xyz = add_xyz(letters)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['a', 'b', 'c', 'x', 'y', 'z']"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "letters_with_xyz"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['a', 'b', 'c', 'x', 'y', 'z']"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "letters"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "A better practice is to first create a copy of `arg` within the function that is then modified and returned. If we are sure that `arg` contains immutable elements only, we get away with a shallow copy. The downside of this approach is the higher amount of memory necessary.\n",
+    "\n",
+    "The revised `add_xyz()` function below is more natural to reason about as it does *not* modify the passed in `arg` internally. [PythonTutor <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](http://pythontutor.com/visualize.html#code=letters%20%3D%20%5B%22a%22,%20%22b%22,%20%22c%22%5D%0A%0Adef%20add_xyz%28arg%29%3A%0A%20%20%20%20new_arg%20%3D%20arg%5B%3A%5D%0A%20%20%20%20new_arg.extend%28%5B%22x%22,%20%22y%22,%20%22z%22%5D%29%0A%20%20%20%20return%20new_arg%0A%0Aletters_with_xyz%20%3D%20add_xyz%28letters%29&cumulative=false&curInstr=0&heapPrimitives=nevernest&mode=display&origin=opt-frontend.js&py=3&rawInputLstJSON=%5B%5D&textReferences=false) shows that as well. This approach is following the **[functional programming <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_wiki.png\">](https://en.wikipedia.org/wiki/Functional_programming)** paradigm that is going through a \"renaissance\" currently. Two essential characteristics of functional programming are that a function *never* changes its inputs and *always* returns the same output given the same inputs.\n",
+    "\n",
+    "For a beginner, it is probably better to stick to this idea and not change any arguments as the original `add_xyz()` above. However, functions that modify and return the argument passed in are an important aspect of object-oriented programming, as explained in [Chapter 11 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/11_classes/00_content.ipynb)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "letters = [\"a\", \"b\", \"c\"]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "def add_xyz(arg):\n",
+    "    \"\"\"Create a new list from an existing one.\"\"\"\n",
+    "    new_arg = arg[:]\n",
+    "    new_arg.extend([\"x\", \"y\", \"z\"])\n",
+    "    return new_arg"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "letters_with_xyz = add_xyz(letters)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "scrolled": true,
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['a', 'b', 'c', 'x', 'y', 'z']"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "letters_with_xyz"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['a', 'b', 'c']"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "letters"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "If we want to modify the argument passed in, it is best to return `None` and not `arg`, as does the final version of `add_xyz()` below. Then, the user of our function cannot accidentally create two aliases to the same object. That is also why the list methods above all return `None`. [PythonTutor <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](http://pythontutor.com/visualize.html#code=letters%20%3D%20%5B%22a%22,%20%22b%22,%20%22c%22%5D%0A%0Adef%20add_xyz%28arg%29%3A%0A%20%20%20%20arg.extend%28%5B%22x%22,%20%22y%22,%20%22z%22%5D%29%0A%20%20%20%20return%0A%0Aadd_xyz%28letters%29&cumulative=false&curInstr=0&heapPrimitives=nevernest&mode=display&origin=opt-frontend.js&py=3&rawInputLstJSON=%5B%5D&textReferences=false) shows how there is only *one* reference to `letters` after the function call."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "letters = [\"a\", \"b\", \"c\"]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "def add_xyz(arg):\n",
+    "    \"\"\"Append letters to a list.\"\"\"\n",
+    "    arg.extend([\"x\", \"y\", \"z\"])\n",
+    "    return  # None"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "add_xyz(letters)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['a', 'b', 'c', 'x', 'y', 'z']"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "letters"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "If we call `add_xyz()` with `letters` as the argument again, we end up with an even longer `list` object."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "add_xyz(letters)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['a', 'b', 'c', 'x', 'y', 'z', 'x', 'y', 'z']"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "letters"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Functions that only work on the argument passed in are called **modifiers**. Their primary purpose is to change the **state** of the argument. On the contrary, functions that have *no* side effects on the arguments are said to be **pure**."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "## The `tuple` Type"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "To create a `tuple` object, we can use the same literal notation as for `list` objects *without* the brackets and list all elements."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "numbers = 7, 11, 8, 5, 3, 12, 2, 6, 9, 10, 1, 4"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(7, 11, 8, 5, 3, 12, 2, 6, 9, 10, 1, 4)"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "numbers"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "However, to be clearer, many Pythonistas write out the optional parentheses `(` and `)`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "numbers = (7, 11, 8, 5, 3, 12, 2, 6, 9, 10, 1, 4)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(7, 11, 8, 5, 3, 12, 2, 6, 9, 10, 1, 4)"
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "numbers"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "As before, `numbers` is an object on its own."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "140248673535456"
+      ]
+     },
+     "execution_count": 21,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "id(numbers)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "tuple"
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "type(numbers)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "While we could use empty parentheses `()` to create an empty `tuple` object ..."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "empty_tuple = ()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "()"
+      ]
+     },
+     "execution_count": 24,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "empty_tuple"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "tuple"
+      ]
+     },
+     "execution_count": 25,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "type(empty_tuple)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "... we must use a *trailing comma* to create a `tuple` object holding one element. If we forget the comma, the parentheses are interpreted as the grouping operator and effectively useless!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "one_tuple = (1,)  # we could ommit the parentheses but not the comma"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(1,)"
+      ]
+     },
+     "execution_count": 27,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "one_tuple"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "tuple"
+      ]
+     },
+     "execution_count": 28,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "type(one_tuple)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "no_tuple = (1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "1"
+      ]
+     },
+     "execution_count": 30,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "no_tuple"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "int"
+      ]
+     },
+     "execution_count": 31,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "type(no_tuple)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Alternatively, we may use the [tuple() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#func-tuple) built-in that takes any iterable as its argument and creates a new `tuple` from its elements."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(1,)"
+      ]
+     },
+     "execution_count": 32,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "tuple([1])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "('i', 't', 'e', 'r', 'a', 'b', 'l', 'e')"
+      ]
+     },
+     "execution_count": 33,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "tuple(\"iterable\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "## Tuples are like \"Immutable Lists\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Most operations involving `tuple` objects work in the same way as with `list` objects. The main difference is that `tuple` objects are *immutable*. So, if our program does not depend on mutability, we may and should use `tuple` and not `list` objects to model sequential data. That way, we avoid the pitfalls seen above.\n",
+    "\n",
+    "`tuple` objects are *sequences* exhibiting the familiar *four* behaviors. So, `numbers` holds a *finite* number of elements ..."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "12"
+      ]
+     },
+     "execution_count": 34,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "len(numbers)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "... that we can obtain individually by looping over it in a predictable *forward* or *reverse* order."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "7   11   8   5   3   12   2   6   9   10   1   4   "
+     ]
+    }
+   ],
+   "source": [
+    "for number in numbers:\n",
+    "    print(number, end=\"   \")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "4   1   10   9   6   2   12   3   5   8   11   7   "
+     ]
+    }
+   ],
+   "source": [
+    "for number in reversed(numbers):\n",
+    "    print(number, end=\"   \")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "To check if a given object is *contained* in `numbers`, we use the `in` operator and conduct a linear search."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "False"
+      ]
+     },
+     "execution_count": 37,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "0 in numbers"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 38,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "1 in numbers"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 39,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "1.0 in numbers  # in relies on == behind the scenes"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "We may index and slice with the `[]` operator. The latter returns *new* `tuple` objects."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "7"
+      ]
+     },
+     "execution_count": 40,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "numbers[0]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "4"
+      ]
+     },
+     "execution_count": 41,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "numbers[-1]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(2, 6, 9, 10, 1, 4)"
+      ]
+     },
+     "execution_count": 42,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "numbers[6:]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Index assignment does *not* work as tuples are *immutable* and results in a `TypeError`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "ename": "TypeError",
+     "evalue": "'tuple' object does not support item assignment",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-43-80fddf1304f0>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mnumbers\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m99\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;31mTypeError\u001b[0m: 'tuple' object does not support item assignment"
+     ]
+    }
+   ],
+   "source": [
+    "numbers[-1] = 99"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "The `+` and `*` operators work with `tuple` objects as well: They always create *new* `tuple` objects."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(7, 11, 8, 5, 3, 12, 2, 6, 9, 10, 1, 4, 99)"
+      ]
+     },
+     "execution_count": 44,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "numbers + (99,) "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(7, 11, 8, 5, 3, 12, 2, 6, 9, 10, 1, 4, 7, 11, 8, 5, 3, 12, 2, 6, 9, 10, 1, 4)"
+      ]
+     },
+     "execution_count": 45,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "2 * numbers"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Being immutable, `tuple` objects only provide the `.count()` and `.index()` methods of `Sequence` types. The `.append()`, `.extend()`, `.insert()`, `.reverse()`, `.pop()`, and `.remove()` methods of `MutableSequence` types are *not* available. The same holds for the `list`-specific `.sort()`, `.copy()`, and `.clear()` methods."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0"
+      ]
+     },
+     "execution_count": 46,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "numbers.count(0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "10"
+      ]
+     },
+     "execution_count": 47,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "numbers.index(1)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "The relational operators work in the *same* way as for `list` objects."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 48,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(7, 11, 8, 5, 3, 12, 2, 6, 9, 10, 1, 4)"
+      ]
+     },
+     "execution_count": 48,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "numbers"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 49,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 49,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "numbers == (7, 11, 8, 5, 3, 12, 2, 6, 9, 10, 1, 4)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 50,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 50,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "numbers != (99, 11, 8, 5, 3, 12, 2, 6, 9, 10, 1, 4)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 51,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 51,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "numbers < (99, 11, 8, 5, 3, 12, 2, 6, 9, 10, 1, 4)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "While `tuple` objects are immutable, this only relates to the references they hold. If a `tuple` object contains references to mutable objects, the entire nested structure is *not* immutable as a whole!\n",
+    "\n",
+    "Consider the following stylized example `not_immutable`: It contains *three* elements, `1`, `[2, ..., 11]`, and `12`, and the elements of the nested `list` object may be changed. While it is not practical to mix data types in a `tuple` object that is used as an \"immutable list,\" we want to make the point that the mere usage of the `tuple` type does *not* guarantee a nested object to be immutable as a whole."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 52,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "not_immutable = (1, [2, 3, 4, 5, 6, 7, 8, 9, 10, 11], 12)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 53,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(1, [2, 3, 4, 5, 6, 7, 8, 9, 10, 11], 12)"
+      ]
+     },
+     "execution_count": 53,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "not_immutable"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 54,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "not_immutable[1][:] = [99, 99, 99]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 55,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(1, [99, 99, 99], 12)"
+      ]
+     },
+     "execution_count": 55,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "not_immutable"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "## Packing & Unpacking"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "In the \"*List Operations*\" section in the [second part <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/07_sequences/01_content.ipynb#List-Operations) of this chapter, the `*` symbol **unpacks** the elements of a `list` object into another one. This idea of *iterable unpacking* is built into Python at various places, even *without* the `*` symbol.\n",
+    "\n",
+    "For example, we may write variables on the left-hand side of a `=` statement in a literal `tuple` style. Then,  any *finite* iterable on the right-hand side is unpacked. So, `numbers` is unpacked into *twelve* variables below."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 56,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "n1, n2, n3, n4, n5, n6, n7, n8, n9, n10, n11, n12 = numbers"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 57,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "7"
+      ]
+     },
+     "execution_count": 57,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "n1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 58,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "11"
+      ]
+     },
+     "execution_count": 58,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "n2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 59,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "8"
+      ]
+     },
+     "execution_count": 59,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "n3"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Having to type twelve variables on the left is already tedious. Furthermore, if the iterable on the right yields a number of elements *different* from the number of variables, we get a `ValueError`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 60,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "ename": "ValueError",
+     "evalue": "too many values to unpack (expected 11)",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-60-c5e79943681f>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mn1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn3\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn4\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn5\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn6\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn7\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn8\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn9\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn10\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn11\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnumbers\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;31mValueError\u001b[0m: too many values to unpack (expected 11)"
+     ]
+    }
+   ],
+   "source": [
+    "n1, n2, n3, n4, n5, n6, n7, n8, n9, n10, n11 = numbers"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 61,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "ename": "ValueError",
+     "evalue": "not enough values to unpack (expected 13, got 12)",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-61-214f2de70baf>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mn1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn3\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn4\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn5\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn6\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn7\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn8\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn9\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn10\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn11\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn12\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn13\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnumbers\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;31mValueError\u001b[0m: not enough values to unpack (expected 13, got 12)"
+     ]
+    }
+   ],
+   "source": [
+    "n1, n2, n3, n4, n5, n6, n7, n8, n9, n10, n11, n12, n13 = numbers"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "So, to make iterable unpacking useful, we prepend the `*` symbol to *one* of the variables on the left: That variable then becomes a `list` object holding the elements not captured by the other variables. We say that the excess elements from the iterable are **packed** into this variable.\n",
+    "\n",
+    "For example, let's get the `first` and `last` element of `numbers` and collect the rest in `middle`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 62,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "first, *middle, last = numbers"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 63,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "7"
+      ]
+     },
+     "execution_count": 63,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "first"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 64,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[11, 8, 5, 3, 12, 2, 6, 9, 10, 1]"
+      ]
+     },
+     "execution_count": 64,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "middle  # always a list!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 65,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "4"
+      ]
+     },
+     "execution_count": 65,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "last"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "We already used unpacking before this section without knowing it. Whenever we write a `for`-loop over the [zip() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#zip) built-in, that generates a new `tuple` object in each iteration that we unpack by listing several loop variables.\n",
+    "\n",
+    "So, the `name, position` below acts like a left-hand side of an `=` statement and unpacks the `tuple` objects generated from \"zipping\" the `names` list and the `positions` tuple together."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 66,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "names = [\"Berthold\", \"Oliver\", \"Carl\"]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 67,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "positions = (\"goalkeeper\", \"defender\", \"midfielder\", \"striker\", \"coach\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 68,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Berthold is a goalkeeper\n",
+      "Oliver is a defender\n",
+      "Carl is a midfielder\n"
+     ]
+    }
+   ],
+   "source": [
+    "for name, position in zip(names, positions):\n",
+    "    print(name, \"is a\", position)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Without unpacking, [zip() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#zip) generates a series of `tuple` objects."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 69,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'tuple'>   ('Berthold', 'goalkeeper')\n",
+      "<class 'tuple'>   ('Oliver', 'defender')\n",
+      "<class 'tuple'>   ('Carl', 'midfielder')\n"
+     ]
+    }
+   ],
+   "source": [
+    "for pair in zip(names, positions):\n",
+    "    print(type(pair), pair, sep=\"   \")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Unpacking also works for nested objects. Below, we wrap [zip() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#zip) with the [enumerate() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#enumerate) built-in to have an index variable `number` inside the `for`-loop. In each iteration, a `tuple` object consisting of `number` and another `tuple` object is created. The inner one then holds the `name` and `position`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 70,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Berthold (jersey #1) is a goalkeeper\n",
+      "Oliver (jersey #2) is a defender\n",
+      "Carl (jersey #3) is a midfielder\n"
+     ]
+    }
+   ],
+   "source": [
+    "for number, (name, position) in enumerate(zip(names, positions), start=1):\n",
+    "    print(f\"{name} (jersey #{number}) is a {position}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "### Swapping Variables"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "A popular use case of unpacking is **swapping** two variables.\n",
+    "\n",
+    "Consider `a` and `b` below."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 71,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "a = 0\n",
+    "b = 1"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Without unpacking, we must use a temporary variable `temp` to swap `a` and `b`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 72,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "temp = a\n",
+    "a = b\n",
+    "b = temp\n",
+    "\n",
+    "del temp"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 73,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "1"
+      ]
+     },
+     "execution_count": 73,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "a"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 74,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0"
+      ]
+     },
+     "execution_count": 74,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "b"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "With unpacking, the solution is more elegant. *All* expressions on the right-hand side are evaluated *before* any assignment takes place."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 75,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "a, b = 0, 1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 76,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "a, b = b, a"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 77,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(1, 0)"
+      ]
+     },
+     "execution_count": 77,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "a, b"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "#### Example: [Fibonacci Numbers <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_wiki.png\">](https://en.wikipedia.org/wiki/Fibonacci_number) (revisited)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Unpacking allows us to rewrite the iterative `fibonacci()` function from [Chapter 4 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/04_iteration/02_content.ipynb#\"Hard-at-first-Glance\"-Example:-Fibonacci-Numbers-%28revisited%29) in a concise way."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 78,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "def fibonacci(i):\n",
+    "    \"\"\"Calculate the ith Fibonacci number.\n",
+    "\n",
+    "    Args:\n",
+    "        i (int): index of the Fibonacci number to calculate\n",
+    "\n",
+    "    Returns:\n",
+    "        ith_fibonacci (int)\n",
+    "    \"\"\"\n",
+    "    a, b = 0, 1\n",
+    "\n",
+    "    for _ in range(i - 1):\n",
+    "        a, b = b, a + b\n",
+    "\n",
+    "    return b"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 79,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "144"
+      ]
+     },
+     "execution_count": 79,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "fibonacci(12)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "## Function Definitions & Calls"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "The concepts of packing and unpacking are also helpful when writing and using functions.\n",
+    "\n",
+    "For example, let's look at the `product()` function below. Its implementation suggests that `args` must be a sequence type. Otherwise, it would not make sense to index into it with `[0]` or take a slice with `[1:]`. In line with the function's name, the `for`-loop multiplies all elements of the `args` sequence. So, what does the `*` do in the header line, and what is the exact data type of `args`?\n",
+    "\n",
+    "The `*` is again *not* an operator in this context but a special syntax that makes Python *pack* all *positional* arguments passed to `product()` into a single `tuple` object called `args`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 80,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "def product(*args):\n",
+    "    \"\"\"Multiply all arguments.\"\"\"\n",
+    "    result = args[0]\n",
+    "\n",
+    "    for arg in args[1:]:\n",
+    "        result *= arg\n",
+    "\n",
+    "    return result"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "So, we can pass an *arbitrary* (i.e., also none) number of *positional* arguments to `product()`.\n",
+    "\n",
+    "The product of just one number is the number itself."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 81,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "42"
+      ]
+     },
+     "execution_count": 81,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "product(42)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Passing in several numbers works as expected."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 82,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "100"
+      ]
+     },
+     "execution_count": 82,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "product(2, 5, 10)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "However, this implementation of `product()` needs *at least* one argument passed in due to the expression `args[0]` used internally. Otherwise, we see a *runtime* error, namely an `IndexError`. We emphasize that this error is *not* caused in the header line."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 83,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "ename": "IndexError",
+     "evalue": "tuple index out of range",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mIndexError\u001b[0m                                Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-83-640e0c632b8d>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mproduct\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;32m<ipython-input-80-02b0db9fbeab>\u001b[0m in \u001b[0;36mproduct\u001b[0;34m(*args)\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mproduct\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m     \u001b[0;34m\"\"\"Multiply all arguments.\"\"\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m     \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      4\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m     \u001b[0;32mfor\u001b[0m \u001b[0marg\u001b[0m \u001b[0;32min\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;31mIndexError\u001b[0m: tuple index out of range"
+     ]
+    }
+   ],
+   "source": [
+    "product()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Another downside of this implementation is that we can easily generate *semantic* errors: For example, if we pass in an iterable object like the `one_hundred` list, *no* exception is raised. However, the return value is also not a numeric object as we expect. The reason for this is that during the function call, `args` becomes a `tuple` object holding *one* element, which is `one_hundred`, a `list` object. So, we created a nested structure by accident."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 84,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "one_hundred = [2, 5, 10]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 85,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[2, 5, 10]"
+      ]
+     },
+     "execution_count": 85,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "product(one_hundred)  # a semantic error!"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "This error does not occur if we unpack `one_hundred` upon passing it as the argument."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 86,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "100"
+      ]
+     },
+     "execution_count": 86,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "product(*one_hundred)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "That is the equivalent of writing out the following tedious expression. Yet, that does *not* scale for iterables with many elements in them."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 87,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "100"
+      ]
+     },
+     "execution_count": 87,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "product(one_hundred[0], one_hundred[1], one_hundred[2])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "In the \"*Packing & Unpacking with Functions*\" [exercise <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/07_sequences/04_exercises.ipynb), we look at `product()` in more detail.\n",
+    "\n",
+    "While we needed to unpack `one_hundred` above to avoid the semantic error, unpacking an argument in a function call may also be a convenience in general. For example, to print the elements of `one_hundred` in one line, we need to use a `for` statement, until now. With unpacking, we get away *without* a loop."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 88,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[2, 5, 10]\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(one_hundred)  # prints the tuple; we do not want that"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 89,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "2 5 10 "
+     ]
+    }
+   ],
+   "source": [
+    "for number in one_hundred:\n",
+    "    print(number, end=\" \")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 90,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "2 5 10\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(*one_hundred)  # replaces the for-loop"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.6"
+  },
+  "livereveal": {
+   "auto_select": "code",
+   "auto_select_fragment": true,
+   "scroll": true,
+   "theme": "serif"
+  },
+  "toc": {
+   "base_numbering": 1,
+   "nav_menu": {},
+   "number_sections": false,
+   "sideBar": true,
+   "skip_h1_title": true,
+   "title_cell": "Table of Contents",
+   "title_sidebar": "Contents",
+   "toc_cell": false,
+   "toc_position": {
+    "height": "calc(100% - 180px)",
+    "left": "10px",
+    "top": "150px",
+    "width": "384px"
+   },
+   "toc_section_display": false,
+   "toc_window_display": false
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/CONTENTS.md b/CONTENTS.md
index 73a5070..5abc9fb 100644
--- a/CONTENTS.md
+++ b/CONTENTS.md
@@ -171,8 +171,13 @@ If this is not possible,
       (`list` Type;
        Indexing & Slicing;
        Shallow vs. Deep Copies;
-       List Methods & Operations;
-       Unpacking)
+       List Methods & Operations)
     - [exercises <img height="12" style="display: inline-block" src="static/link/to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/07_sequences/02_exercises.ipynb)
       [<img height="12" style="display: inline-block" src="static/link/to_mb.png">](https://mybinder.org/v2/gh/webartifex/intro-to-python/develop?urlpath=lab/tree/07_sequences/02_exercises.ipynb)
-      (Working with Lists)
\ No newline at end of file
+      (Working with Lists)
+    - [content <img height="12" style="display: inline-block" src="static/link/to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/07_sequences/03_content.ipynb)
+      [<img height="12" style="display: inline-block" src="static/link/to_mb.png">](https://mybinder.org/v2/gh/webartifex/intro-to-python/develop?urlpath=lab/tree/07_sequences/03_content.ipynb)
+      (Modifiers vs. Pure Functions;
+       `tuple` Type;
+       Packing & Unpacking;
+       `*args` in Function Definitions)
\ No newline at end of file

From 98cb3bef933bc0b1a28ed9a1b32ef995482e2f42 Mon Sep 17 00:00:00 2001
From: Alexander Hess <alexander@webartifex.biz>
Date: Mon, 19 Oct 2020 19:49:34 +0200
Subject: [PATCH 184/242] Add initial version of chapter 07's exercises, part 2

---
 07_sequences/04_exercises.ipynb | 663 ++++++++++++++++++++++++++++++++
 CONTENTS.md                     |   5 +-
 2 files changed, 667 insertions(+), 1 deletion(-)
 create mode 100644 07_sequences/04_exercises.ipynb

diff --git a/07_sequences/04_exercises.ipynb b/07_sequences/04_exercises.ipynb
new file mode 100644
index 0000000..1d46524
--- /dev/null
+++ b/07_sequences/04_exercises.ipynb
@@ -0,0 +1,663 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Note**: Click on \"*Kernel*\" > \"*Restart Kernel and Run All*\" in [JupyterLab](https://jupyterlab.readthedocs.io/en/stable/) *after* finishing the exercises to ensure that your solution runs top to bottom *without* any errors. If you cannot run this file on your machine, you may want to open it [in the cloud <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_mb.png\">](https://mybinder.org/v2/gh/webartifex/intro-to-python/develop?urlpath=lab/tree/07_sequences/04_exercises.ipynb)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Chapter 7: Sequential Data (Coding Exercises)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The exercises below assume that you have read the [third part <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/07_sequences/03_content.ipynb) of Chapter 7.\n",
+    "\n",
+    "The `...`'s in the code cells indicate where you need to fill in code snippets. The number of `...`'s within a code cell give you a rough idea of how many lines of code are needed to solve the task. You should not need to create any additional code cells for your final solution. However, you may want to use temporary code cells to try out some ideas."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Packing & Unpacking with Functions"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In the \"*Function Definitions & Calls*\" section in [Chapter 7 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/07_sequences/03_content.ipynb#Function-Definitions-&-Calls), we define the following function `product()`. In this exercise, you will improve it by making it more \"user-friendly.\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def product(*args):\n",
+    "    \"\"\"Multiply all arguments.\"\"\"\n",
+    "    result = args[0]\n",
+    "\n",
+    "    for arg in args[1:]:\n",
+    "        result *= arg\n",
+    "\n",
+    "    return result"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The `*` in the function's header line *packs* all *positional* arguments passed to `product()` into one *iterable* called `args`.\n",
+    "\n",
+    "**Q1**: What is the data type of `args` within the function's body?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " < your answer >"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Because of the packing, we may call `product()` with an abitrary number of *positional* arguments: The product of just `42` remains `42`, while `2`, `5`, and `10` multiplied together result in `100`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "product(42)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "product(2, 5, 10)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "However, \"abitrary\" does not mean that we can pass *no* argument. If we do so, we get an `IndexError`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "product()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q2**: What line in the body of `product()` causes this exception? What is the exact problem?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " < your answer >"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In [Chapter 7 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/07_sequences/00_content.ipynb#Function-Definitions-&-Calls), we also pass a `list` object, like `one_hundred`, to `product()`, and *no* exception is raised."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "one_hundred = [2, 5, 10]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "product(one_hundred)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q3**: What is wrong with that? What *kind* of error (cf., [Chapter 1 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/01_elements/00_content.ipynb#Formal-vs.-Natural-Languages)) is that conceptually? Describe precisely what happens to the passed in `one_hundred` in every line within `product()`!"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " < your answer >"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Of course, one solution is to *unpack* `one_hundred` with the `*` symbol. We look at another solution further below."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "product(*one_hundred)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Let's continue with the issue when calling `product()` *without* any argument.\n",
+    "\n",
+    "This revised version of `product()` avoids the `IndexError` from before."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def product(*args):\n",
+    "    \"\"\"Multiply all arguments.\"\"\"\n",
+    "    result = None\n",
+    "\n",
+    "    for arg in args:\n",
+    "        result *= arg\n",
+    "\n",
+    "    return result"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "product()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q4**: Describe why no error occurs by going over every line in `product()`!"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " < your answer >"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Unfortunately, the new version cannot process any arguments we pass in any more."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "product(42)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "product(2, 5, 10)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q5**: What line causes troubles now? What is the exact problem?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " < your answer >"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q6**: Replace the `None` in `product()` above with something reasonable that does *not* cause exceptions! Ensure that `product(42)` and `product(2, 5, 10)` return a correct result.\n",
+    "\n",
+    "Hints: It is ok if `product()` returns a result *different* from the `None` above. Look at the documentation of the built-in [sum() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#sum) function for some inspiration."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def product(*args):\n",
+    "    \"\"\"Multiply all arguments.\"\"\"\n",
+    "    result = ...\n",
+    "\n",
+    "    for arg in args:\n",
+    "        result *= arg\n",
+    "\n",
+    "    return result"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "product(42)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "product(2, 5, 10)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now, calling `product()` without any arguments returns what we would best describe as a *default* or *start* value. To be \"philosophical,\" what is the product of *no* numbers? We know that the product of *one* number is just the number itself, but what could be a reasonable result when multiplying *no* numbers? The answer is what you use as the initial value of `result` above, and there is only *one* way to make `product(42)` and `product(2, 5, 10)` work."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "product()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q7**: Rewrite `product()` so that it takes a *keyword-only* argument `start`, defaulting to the above *default* or *start* value, and use `start` internally instead of `result`!\n",
+    "\n",
+    "Hint: Remember that a *keyword-only* argument is any parameter specified in a function's header line after the first and only `*` (cf., [Chapter 2 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/02_functions/00_content.ipynb#Keyword-only-Arguments))."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def product(*args, ...):\n",
+    "    \"\"\"Multiply all arguments.\"\"\"\n",
+    "    ...\n",
+    "    ...\n",
+    "\n",
+    "    return ..."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now, we can call `product()` with a truly arbitrary number of *positional* arguments."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "product(42)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "product(2, 5, 10)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "product()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Without any *positional* arguments but only the *keyword* argument `start`, for example, `start=0`, we can adjust the answer to the \"philosophical\" problem of multiplying *no* numbers. Because of the *keyword-only* syntax, there is *no* way to pass in a `start` number *without* naming it."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "product(start=0)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We could use `start` to inject a multiplier, for example, to double the outcomes."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "product(42, start=2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "product(2, 5, 10, start=2)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "There is still one issue left: Because of the function's name, a user of `product()` may assume that it is ok to pass a *collection* of numbers, like `one_hundred`, which are then multiplied."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "product(one_hundred)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q8**: What is a **collection**? How is that different from a **sequence**?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " < your answer >"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q9**: Rewrite the latest version of `product()` to check if the *only* positional argument is a *collection* type! If so, its elements are multiplied together. Otherwise, the logic remains the same.\n",
+    "\n",
+    "Hints: Use the built-in [len() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#len) and [isinstance() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#isinstance) functions to check if there is only *one* positional argument and if it is a *collection* type. Use the *abstract base class* `Collection` from the [collections.abc <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/collections.abc.html) module in the [standard library <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/index.html). You may want to *re-assign* `args` inside the body."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import collections.abc as abc"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def product(*args, ...):\n",
+    "    \"\"\"Multiply all arguments.\"\"\"\n",
+    "    ...\n",
+    "    ...\n",
+    "\n",
+    "    ...\n",
+    "    ...\n",
+    "\n",
+    "    return ..."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "All *five* code cells below now return correct results. We may unpack `one_hundred` or not."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "product(42)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "product(2, 5, 10)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "product()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "product(one_hundred)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "product(*one_hundred)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Side Note**: Above, we make `product()` work with a single *collection* type argument instead of a *sequence* type to keep it more generic: For example, we can pass in a `set` object, like `{2, 5, 10}` below, and `product()` continues to work correctly. The `set` type is introducted in [Chapter 9 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/09_mappings/00_content.ipynb#The-set-Type), and one essential difference to the `list` type is that objects of type `set` have *no* order regarding their elements. So, even though `[2, 5, 10]` and `{2, 5, 10}` look almost the same, the order implied in the literal notation gets lost in memory!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "product([2, 5, 10])  # the argument is a collection that is also a sequence"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "product({2, 5, 10})  # the argument is a collection that is NOT a sequence"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "isinstance({2, 5, 10}, abc.Sequence)  # sets are NO sequences"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Let's continue to improve `product()` and make it more Pythonic. It is always a good idea to mimic the behavior of built-ins when writing our own functions. And, [sum() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#sum), for example, raises a `TypeError` if called *without* any arguments. It does *not* return the \"philosophical\" answer to adding *no* numbers, which would be `0`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sum()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q10**: Adapt the latest version of `product()` to also raise a `TypeError` if called *without* any *positional* arguments!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def product(*args, ...):\n",
+    "    \"\"\"Multiply all arguments.\"\"\"\n",
+    "    ...\n",
+    "    ...\n",
+    "    ...\n",
+    "    ...\n",
+    "\n",
+    "    ...\n",
+    "    ...\n",
+    "\n",
+    "    return ..."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "product()"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.6"
+  },
+  "toc": {
+   "base_numbering": 1,
+   "nav_menu": {},
+   "number_sections": false,
+   "sideBar": true,
+   "skip_h1_title": true,
+   "title_cell": "Table of Contents",
+   "title_sidebar": "Contents",
+   "toc_cell": false,
+   "toc_position": {},
+   "toc_section_display": false,
+   "toc_window_display": false
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/CONTENTS.md b/CONTENTS.md
index 5abc9fb..e8568e6 100644
--- a/CONTENTS.md
+++ b/CONTENTS.md
@@ -180,4 +180,7 @@ If this is not possible,
       (Modifiers vs. Pure Functions;
        `tuple` Type;
        Packing & Unpacking;
-       `*args` in Function Definitions)
\ No newline at end of file
+       `*args` in Function Definitions)
+    - [exercises <img height="12" style="display: inline-block" src="static/link/to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/07_sequences/04_exercises.ipynb)
+      [<img height="12" style="display: inline-block" src="static/link/to_mb.png">](https://mybinder.org/v2/gh/webartifex/intro-to-python/develop?urlpath=lab/tree/07_sequences/04_exercises.ipynb)
+      (Packing & Unpacking with Functions)
\ No newline at end of file

From e34d8be113ae9c979163c8c78cf71cfdfa130ce4 Mon Sep 17 00:00:00 2001
From: Alexander Hess <alexander@webartifex.biz>
Date: Mon, 19 Oct 2020 19:57:18 +0200
Subject: [PATCH 185/242] Add initial version of chapter 07's appendix

---
 07_sequences/05_appendix.ipynb | 605 +++++++++++++++++++++++++++++++++
 CONTENTS.md                    |   7 +-
 2 files changed, 610 insertions(+), 2 deletions(-)
 create mode 100644 07_sequences/05_appendix.ipynb

diff --git a/07_sequences/05_appendix.ipynb b/07_sequences/05_appendix.ipynb
new file mode 100644
index 0000000..3c8c454
--- /dev/null
+++ b/07_sequences/05_appendix.ipynb
@@ -0,0 +1,605 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "**Note**: Click on \"*Kernel*\" > \"*Restart Kernel and Clear All Outputs*\" in [JupyterLab](https://jupyterlab.readthedocs.io/en/stable/) *before* reading this notebook to reset its output. If you cannot run this file on your machine, you may want to open it [in the cloud <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_mb.png\">](https://mybinder.org/v2/gh/webartifex/intro-to-python/develop?urlpath=lab/tree/07_sequences/05_appendix.ipynb)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "# Chapter 7: Sequential Data (Appendix)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "In the [third part <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/07_sequences/03_content.ipynb#Tuples-are-like-\"Immutable-Lists\") of the chapter, we proposed the idea that `tuple` objects are like \"immutable lists.\" Often, however, we use `tuple` objects to represent a **record** of related **fields**. Then, each element has a *semantic* meaning (i.e., a descriptive name).\n",
+    "\n",
+    "As an example, think of a spreadsheet with information on students in a course. Each row represents a record and holds all the data associated with an individual student. The columns (e.g., matriculation number, first name, last name) are the fields that may come as *different* data types (e.g., `int` for the matriculation number, `str` for the names).\n",
+    "\n",
+    "A simple way of modeling a single student is as a `tuple` object, for example, `(123456, \"John\", \"Doe\")`. A disadvantage of this approach is that we must remember the order and meaning of the elements/fields in the `tuple` object.\n",
+    "\n",
+    "An example from a different domain is the representation of $(x, y)$-points in the $x$-$y$-plane. Again, we could use a `tuple` object like `current_position` below to model the point $(4, 2)$."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "current_position = (4, 2)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "We implicitly assume that the first element represents the $x$ and the second the $y$ coordinate. While that follows intuitively from convention in math, we should at least add comments somewhere in the code to document this assumption."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## The `namedtuple` Type"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "A better way is to create a *custom* data type. While that is covered in depth in [Chapter 11 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/11_classes/00_content.ipynb), the [collections <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/collections.html) module in the [standard library <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/index.html) provides a [namedtuple() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/collections.html#collections.namedtuple) **factory function** that creates \"simple\" custom data types on top of the standard `tuple` type."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "from collections import namedtuple"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "[namedtuple() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/collections.html#collections.namedtuple) takes two arguments. The first argument is the name of the data type. That could be different from the variable `Point` we use to refer to the new type, but in most cases it is best to keep them in sync. The second argument is a sequence with the field names as `str` objects. The names' order corresponds to the one assumed in `current_position`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "Point = namedtuple(\"Point\", [\"x\", \"y\"])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "The `Point` object is a so-called **class**. That is what it means if an object is of type `type`. It can be used as a **factory** to create *new* `tuple`-like objects of type `Point`. In a way, [namedtuple() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/collections.html#collections.namedtuple) gives us a way to create our own custom **constructors**."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "94457911453856"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "id(Point)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "type"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "type(Point)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "The value of `Point` is just itself in a *literal notation*."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "__main__.Point"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "Point"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "We write `Point(4, 2)` to create a *new* object of type `Point`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "current_position = Point(4, 2)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Now, `current_position` has a somewhat nicer representation. In particular, the coordinates are named `x` and `y`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Point(x=4, y=2)"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "current_position"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "It is *not* a `tuple` any more but an object of type `Point`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "140376178109184"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "id(current_position)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "__main__.Point"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "type(current_position)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "We use the dot operator `.` to access the defined attributes."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "4"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "current_position.x"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "2"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "current_position.y"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "As before, we get an `AttributeError` if we try to access an undefined attribute."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "ename": "AttributeError",
+     "evalue": "'Point' object has no attribute 'z'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-13-b30d0b930bac>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mcurrent_position\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mz\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;31mAttributeError\u001b[0m: 'Point' object has no attribute 'z'"
+     ]
+    }
+   ],
+   "source": [
+    "current_position.z"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "`current_position` continues to work like a `tuple` object! That is why we can use `namedtuple` as a replacement for `tuple`. The underlying implementations exhibit the *same* computational efficiencies and memory usages.\n",
+    "\n",
+    "For example, we can index into or loop over `current_position` as it is still a sequence with the familiar four properties."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "4"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "current_position[0]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "2"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "current_position[1]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "4\n",
+      "2\n"
+     ]
+    }
+   ],
+   "source": [
+    "for number in current_position:\n",
+    "    print(number)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "2\n",
+      "4\n"
+     ]
+    }
+   ],
+   "source": [
+    "for number in reversed(current_position):\n",
+    "    print(number)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "2"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "len(current_position)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.6"
+  },
+  "livereveal": {
+   "auto_select": "code",
+   "auto_select_fragment": true,
+   "scroll": true,
+   "theme": "serif"
+  },
+  "toc": {
+   "base_numbering": 1,
+   "nav_menu": {},
+   "number_sections": false,
+   "sideBar": true,
+   "skip_h1_title": true,
+   "title_cell": "Table of Contents",
+   "title_sidebar": "Contents",
+   "toc_cell": false,
+   "toc_position": {
+    "height": "calc(100% - 180px)",
+    "left": "10px",
+    "top": "150px",
+    "width": "384px"
+   },
+   "toc_section_display": false,
+   "toc_window_display": false
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/CONTENTS.md b/CONTENTS.md
index e8568e6..bb9df6c 100644
--- a/CONTENTS.md
+++ b/CONTENTS.md
@@ -139,7 +139,7 @@ If this is not possible,
     - [further resources <img height="12" style="display: inline-block" src="static/link/to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/05_numbers/06_resources.ipynb)
       [<img height="12" style="display: inline-block" src="static/link/to_mb.png">](https://mybinder.org/v2/gh/webartifex/intro-to-python/develop?urlpath=lab/tree/05_numbers/06_resources.ipynb)
   - *Chapter 6*: Text & Bytes
-    - [content <img height="12" style="display: inline-block" src="static/link/to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/06_numbers/00_content.ipynb)
+    - [content <img height="12" style="display: inline-block" src="static/link/to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/06_text/00_content.ipynb)
       [<img height="12" style="display: inline-block" src="static/link/to_mb.png">](https://mybinder.org/v2/gh/webartifex/intro-to-python/develop?urlpath=lab/tree/06_text/00_content.ipynb)
       (`str` Type;
        Reading Files;
@@ -183,4 +183,7 @@ If this is not possible,
        `*args` in Function Definitions)
     - [exercises <img height="12" style="display: inline-block" src="static/link/to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/07_sequences/04_exercises.ipynb)
       [<img height="12" style="display: inline-block" src="static/link/to_mb.png">](https://mybinder.org/v2/gh/webartifex/intro-to-python/develop?urlpath=lab/tree/07_sequences/04_exercises.ipynb)
-      (Packing & Unpacking with Functions)
\ No newline at end of file
+      (Packing & Unpacking with Functions)
+    - [appendix <img height="12" style="display: inline-block" src="static/link/to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/07_sequences/05_appendix.ipynb)
+      [<img height="12" style="display: inline-block" src="static/link/to_mb.png">](https://mybinder.org/v2/gh/webartifex/intro-to-python/develop?urlpath=lab/tree/07_sequences/05_appendix.ipynb)
+      (`namedtuple` Type)
\ No newline at end of file

From eda9266a79774541da834260ef35a1b7d0474b9a Mon Sep 17 00:00:00 2001
From: Alexander Hess <alexander@webartifex.biz>
Date: Mon, 19 Oct 2020 20:01:04 +0200
Subject: [PATCH 186/242] Add initial version of chapter 07's summary

---
 07_sequences/06_summary.ipynb | 85 +++++++++++++++++++++++++++++++++++
 CONTENTS.md                   |  3 +-
 2 files changed, 87 insertions(+), 1 deletion(-)
 create mode 100644 07_sequences/06_summary.ipynb

diff --git a/07_sequences/06_summary.ipynb b/07_sequences/06_summary.ipynb
new file mode 100644
index 0000000..8115139
--- /dev/null
+++ b/07_sequences/06_summary.ipynb
@@ -0,0 +1,85 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "# Chapter 7: Sequential Data (TL;DR)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "**Sequences** are an *abstract* concept that summarizes *four* behaviors an object may or may not exhibit. Sequences are\n",
+    "- **finite** and\n",
+    "- **ordered**\n",
+    "- **containers** that we may\n",
+    "- **loop over**.\n",
+    "\n",
+    "Examples are the `list`, `tuple`, but also the `str` types.\n",
+    "\n",
+    "Objects that exhibit all behaviors *except* being ordered are referred to as **collections**.\n",
+    "\n",
+    "The objects inside a sequence are called its **elements** and may be labeled with a unique **index**, an `int` object in the range $0 \\leq \\text{index} < \\lvert \\text{sequence} \\rvert$.\n",
+    "\n",
+    "`list` objects are **mutable**. That means we can change the references to the other objects it contains, and, in particular, re-assign them.\n",
+    "\n",
+    "On the contrary, `tuple` objects are like **immutable** lists: We can use them in place of any `list` object as long as we do *not* need to mutate it. Often, `tuple` objects are also used to model **records** of related **fields**."
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.6"
+  },
+  "livereveal": {
+   "auto_select": "code",
+   "auto_select_fragment": true,
+   "scroll": true,
+   "theme": "serif"
+  },
+  "toc": {
+   "base_numbering": 1,
+   "nav_menu": {},
+   "number_sections": false,
+   "sideBar": true,
+   "skip_h1_title": true,
+   "title_cell": "Table of Contents",
+   "title_sidebar": "Contents",
+   "toc_cell": false,
+   "toc_position": {
+    "height": "calc(100% - 180px)",
+    "left": "10px",
+    "top": "150px",
+    "width": "384px"
+   },
+   "toc_section_display": false,
+   "toc_window_display": false
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/CONTENTS.md b/CONTENTS.md
index bb9df6c..ac2e795 100644
--- a/CONTENTS.md
+++ b/CONTENTS.md
@@ -186,4 +186,5 @@ If this is not possible,
       (Packing & Unpacking with Functions)
     - [appendix <img height="12" style="display: inline-block" src="static/link/to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/07_sequences/05_appendix.ipynb)
       [<img height="12" style="display: inline-block" src="static/link/to_mb.png">](https://mybinder.org/v2/gh/webartifex/intro-to-python/develop?urlpath=lab/tree/07_sequences/05_appendix.ipynb)
-      (`namedtuple` Type)
\ No newline at end of file
+      (`namedtuple` Type)
+    - [summary <img height="12" style="display: inline-block" src="static/link/to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/07_sequences/06_summary.ipynb)
\ No newline at end of file

From 2796007e39e35eaf2acb63ae07895658502cb8f6 Mon Sep 17 00:00:00 2001
From: Alexander Hess <alexander@webartifex.biz>
Date: Mon, 19 Oct 2020 20:03:02 +0200
Subject: [PATCH 187/242] Add initial version of chapter 07's review

---
 07_sequences/07_review.ipynb | 252 +++++++++++++++++++++++++++++++++++
 CONTENTS.md                  |   3 +-
 2 files changed, 254 insertions(+), 1 deletion(-)
 create mode 100644 07_sequences/07_review.ipynb

diff --git a/07_sequences/07_review.ipynb b/07_sequences/07_review.ipynb
new file mode 100644
index 0000000..de10156
--- /dev/null
+++ b/07_sequences/07_review.ipynb
@@ -0,0 +1,252 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Chapter 7: Sequential Data (Review Questions)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The questions below assume that you have read the [first <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/07_sequences/00_content.ipynb), [second <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/07_sequences/01_content.ipynb), and the [third <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/07_sequences/03_content.ipynb) part of Chapter 7. Some questions regard the [Appendix <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/07_sequences/05_appendix.ipynb); that is indicated with a **\\***.\n",
+    "\n",
+    "Be concise in your answers! Most questions can be answered in *one* sentence."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Essay Questions "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Answer the following questions *briefly*!"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q1**: We have seen **containers** and **iterables** before in [Chapter 4 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/04_iteration/02_content.ipynb#Containers-vs.-Iterables). How do they relate to **sequences**? "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " < your answer >"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q2**: What are **abstract base classes**? How can we make use of the ones from the [collections.abc <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/collections.abc.html) module in the [standard library <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/index.html)?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " < your answer >"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q3**: How are the *abstract behaviors* of **reversibility** and **finiteness** essential for *indexing* and *slicing* sequences?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " < your answer >"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q4**: Explain the difference between **mutable** and **immutable** objects in Python with the examples of the `list` and `tuple` types!"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " < your answer >"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q5**: What is the difference between a **shallow** and a **deep** copy of an object? How can one of them become a \"problem?\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " < your answer >"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q6**: Many **list methods** change `list` objects \"**in place**.\" What do we mean by that?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " < your answer >"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q7.1**: `tuple` objects have *two* primary usages. First, they can be used in place of `list` objects where **mutability** is *not* required. Second, we use them to model data **records**.\n",
+    "\n",
+    "Describe why `tuple` objects are a suitable replacement for `list` objects in general!"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " < your answer >"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q7.2\\***: What do we mean by a **record**? How are `tuple` objects suitable to model records? How can we integrate a **semantic meaning** when working with records into our code?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " < your answer >"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q8**: How is (iterable) **packing** and **unpacking** useful in the context of **function definitions** and **calls**?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " < your answer >"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## True / False Questions"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Motivate your answer with *one short* sentence!"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q9**: `sequence` objects are *not* part of core Python but may be imported from the [standard library <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/index.html)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " < your answer >"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q10**: The built-in [.sort() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/stdtypes.html#list.sort) function takes a *finite* **iterable** as its argument an returns a *new* `list` object. On the contrary, the [sorted() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#sorted) method on `list` objects *mutates* them *in place*."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " < your answer >"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q11**: Passing **mutable** objects as arguments to functions is not problematic because functions operate in a **local** scope without affecting the **global** scope."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " < your answer >"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.6"
+  },
+  "toc": {
+   "base_numbering": 1,
+   "nav_menu": {},
+   "number_sections": false,
+   "sideBar": true,
+   "skip_h1_title": true,
+   "title_cell": "Table of Contents",
+   "title_sidebar": "Contents",
+   "toc_cell": false,
+   "toc_position": {},
+   "toc_section_display": false,
+   "toc_window_display": false
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/CONTENTS.md b/CONTENTS.md
index ac2e795..bbc1dee 100644
--- a/CONTENTS.md
+++ b/CONTENTS.md
@@ -187,4 +187,5 @@ If this is not possible,
     - [appendix <img height="12" style="display: inline-block" src="static/link/to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/07_sequences/05_appendix.ipynb)
       [<img height="12" style="display: inline-block" src="static/link/to_mb.png">](https://mybinder.org/v2/gh/webartifex/intro-to-python/develop?urlpath=lab/tree/07_sequences/05_appendix.ipynb)
       (`namedtuple` Type)
-    - [summary <img height="12" style="display: inline-block" src="static/link/to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/07_sequences/06_summary.ipynb)
\ No newline at end of file
+    - [summary <img height="12" style="display: inline-block" src="static/link/to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/07_sequences/06_summary.ipynb)
+    - [review questions <img height="12" style="display: inline-block" src="static/link/to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/07_sequences/07_review.ipynb)

From 9f5500606131564c57dc0e500171b0f4473bab07 Mon Sep 17 00:00:00 2001
From: Alexander Hess <alexander@webartifex.biz>
Date: Mon, 19 Oct 2020 20:08:27 +0200
Subject: [PATCH 188/242] Streamline slides

---
 07_sequences/01_content.ipynb  | 6 +++++-
 07_sequences/05_appendix.ipynb | 6 +++++-
 2 files changed, 10 insertions(+), 2 deletions(-)

diff --git a/07_sequences/01_content.ipynb b/07_sequences/01_content.ipynb
index 5123ac8..1dd590a 100644
--- a/07_sequences/01_content.ipynb
+++ b/07_sequences/01_content.ipynb
@@ -2714,7 +2714,11 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
    "source": [
     "### Unpacking"
    ]
diff --git a/07_sequences/05_appendix.ipynb b/07_sequences/05_appendix.ipynb
index 3c8c454..09ebd0c 100644
--- a/07_sequences/05_appendix.ipynb
+++ b/07_sequences/05_appendix.ipynb
@@ -65,7 +65,11 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
    "source": [
     "## The `namedtuple` Type"
    ]

From 9257890614b470abfbb640859d379e882b805f58 Mon Sep 17 00:00:00 2001
From: Alexander Hess <alexander@webartifex.biz>
Date: Mon, 19 Oct 2020 20:12:06 +0200
Subject: [PATCH 189/242] Pin the dependencies

- upgrade transient dependencies:
  + lxml
  + nbclient
  + urllib3
---
 poetry.lock | 85 ++++++++++++++++++++++++++++-------------------------
 1 file changed, 45 insertions(+), 40 deletions(-)

diff --git a/poetry.lock b/poetry.lock
index 9f17fc7..8e454f7 100644
--- a/poetry.lock
+++ b/poetry.lock
@@ -475,7 +475,7 @@ test = ["pytest", "requests"]
 
 [[package]]
 name = "lxml"
-version = "4.5.2"
+version = "4.6.1"
 description = "Powerful and Pythonic XML processing library combining libxml2/libxslt with the ElementTree API."
 category = "dev"
 optional = false
@@ -505,8 +505,8 @@ python-versions = "*"
 
 [[package]]
 name = "nbclient"
-version = "0.5.0"
-description = "A client library for executing notebooks. Formally nbconvert's ExecutePreprocessor."
+version = "0.5.1"
+description = "A client library for executing notebooks. Formerly nbconvert's ExecutePreprocessor."
 category = "main"
 optional = false
 python-versions = ">=3.6"
@@ -920,7 +920,7 @@ test = ["pytest"]
 
 [[package]]
 name = "urllib3"
-version = "1.25.10"
+version = "1.25.11"
 description = "HTTP library with thread-safe connection pooling, file post, and more."
 category = "main"
 optional = false
@@ -928,7 +928,7 @@ python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*, !=3.4.*, <4"
 
 [package.extras]
 brotli = ["brotlipy (>=0.6.0)"]
-secure = ["certifi", "cryptography (>=1.3.4)", "idna (>=2.0.0)", "pyOpenSSL (>=0.14)", "ipaddress"]
+secure = ["pyOpenSSL (>=0.14)", "cryptography (>=1.3.4)", "idna (>=2.0.0)", "certifi", "ipaddress"]
 socks = ["PySocks (>=1.5.6,<1.5.7 || >1.5.7,<2.0)"]
 
 [[package]]
@@ -1169,37 +1169,42 @@ jupyterlab-server = [
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+    {file = "lxml-4.6.1-cp39-cp39-win32.whl", hash = "sha256:bb252f802f91f59767dcc559744e91efa9df532240a502befd874b54571417bd"},
+    {file = "lxml-4.6.1-cp39-cp39-win_amd64.whl", hash = "sha256:7ecaef52fd9b9535ae5f01a1dd2651f6608e4ec9dc136fc4dfe7ebe3c3ddb230"},
+    {file = "lxml-4.6.1.tar.gz", hash = "sha256:c152b2e93b639d1f36ec5a8ca24cde4a8eefb2b6b83668fcd8e83a67badcb367"},
 ]
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@@ -1241,8 +1246,8 @@ mistune = [
     {file = "mistune-0.8.4.tar.gz", hash = "sha256:59a3429db53c50b5c6bcc8a07f8848cb00d7dc8bdb431a4ab41920d201d4756e"},
 ]
 nbclient = [
-    {file = "nbclient-0.5.0-py3-none-any.whl", hash = "sha256:8a6e27ff581cee50895f44c41936ce02369674e85e2ad58643d8d4a6c36771b0"},
-    {file = "nbclient-0.5.0.tar.gz", hash = "sha256:8ad52d27ba144fca1402db014857e53c5a864a2f407be66ca9d74c3a56d6591d"},
+    {file = "nbclient-0.5.1-py3-none-any.whl", hash = "sha256:4d6b116187c795c99b9dba13d46e764d596574b14c296d60670c8dfe454db364"},
+    {file = "nbclient-0.5.1.tar.gz", hash = "sha256:01e2d726d16eaf2cde6db74a87e2451453547e8832d142f73f72fddcd4fe0250"},
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@@ -1467,8 +1472,8 @@ traitlets = [
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 ]
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-    {file = "urllib3-1.25.10.tar.gz", hash = "sha256:91056c15fa70756691db97756772bb1eb9678fa585d9184f24534b100dc60f4a"},
+    {file = "urllib3-1.25.11-py2.py3-none-any.whl", hash = "sha256:f5321fbe4bf3fefa0efd0bfe7fb14e90909eb62a48ccda331726b4319897dd5e"},
+    {file = "urllib3-1.25.11.tar.gz", hash = "sha256:8d7eaa5a82a1cac232164990f04874c594c9453ec55eef02eab885aa02fc17a2"},
 ]
 virtualenv = [
     {file = "virtualenv-20.0.35-py2.py3-none-any.whl", hash = "sha256:0ebc633426d7468664067309842c81edab11ae97fcaf27e8ad7f5748c89b431b"},

From 5d8af25aa63cefe2357c9cc417d6c4123f0765e3 Mon Sep 17 00:00:00 2001
From: Alexander Hess <alexander@webartifex.biz>
Date: Wed, 21 Oct 2020 16:58:20 +0200
Subject: [PATCH 190/242] Add initial version of chapter 08, part 1

---
 00_intro/00_content.ipynb |    2 +-
 08_mfr/00_content.ipynb   | 1377 +++++++++++++++++++++++++++++++++++++
 CONTENTS.md               |    7 +
 README.md                 |    1 +
 4 files changed, 1386 insertions(+), 1 deletion(-)
 create mode 100644 08_mfr/00_content.ipynb

diff --git a/00_intro/00_content.ipynb b/00_intro/00_content.ipynb
index a5d9e15..b1c99a6 100644
--- a/00_intro/00_content.ipynb
+++ b/00_intro/00_content.ipynb
@@ -771,7 +771,7 @@
     "  - *Chapter 5*: [Numbers & Bits <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/05_numbers/00_content.ipynb)\n",
     "  - *Chapter 6*: [Text & Bytes <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/06_text/00_content.ipynb)\n",
     "  - *Chapter 7*: [Sequential Data <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/07_sequences/00_content.ipynb)\n",
-    "  - *Chapter 8*: Map, Filter, & Reduce\n",
+    "  - *Chapter 8*: [Map, Filter, & Reduce <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/08_mfr/00_content.ipynb)\n",
     "  - *Chapter 9*: Mappings & Sets\n",
     "  - *Chapter 10*: Arrays & Dataframes\n",
     "- How can we create custom data types?\n",
diff --git a/08_mfr/00_content.ipynb b/08_mfr/00_content.ipynb
new file mode 100644
index 0000000..4cf1aa7
--- /dev/null
+++ b/08_mfr/00_content.ipynb
@@ -0,0 +1,1377 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "**Note**: Click on \"*Kernel*\" > \"*Restart Kernel and Clear All Outputs*\" in [JupyterLab](https://jupyterlab.readthedocs.io/en/stable/) *before* reading this notebook to reset its output. If you cannot run this file on your machine, you may want to open it [in the cloud <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_mb.png\">](https://mybinder.org/v2/gh/webartifex/intro-to-python/develop?urlpath=lab/tree/08_mfr/00_content.ipynb)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "# Chapter 8: Map, Filter, & Reduce"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "In this chapter, we continue the study of sequential data by looking at memory efficient ways to process the elements in a sequence. That is an important topic for the data science practitioner who must be able to work with data that does *not* fit into a single computer's memory.\n",
+    "\n",
+    "As shown in [Chapter 4 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/04_iteration/02_content.ipynb#Containers-vs.-Iterables), both the `list` objects `[0, 1, 2, 3, 4]` and `[1, 3, 5, 7, 9]` on the one side and the `range` objects `range(5)` and `range(1, 10, 2)` on the other side allow us to loop over the same numbers. However, the latter two only create *one* `int` object in every iteration while the former two create *all* `int` objects before the loop even starts. In this aspect, we consider `range` objects to be \"rules\" in memory that know how to calculate the numbers *without* calculating them.\n",
+    "\n",
+    "In [Chapter 7 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/07_sequences/01_content.ipynb#The-list-Type), we see how the built-in [list() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#func-list) constructor **materializes** the `range(1, 13)` object into the `list` object `[1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12]`. In other words, we make `range(1, 13)` calculate *all* numbers at once and store them in a `list` object for further processing.\n",
+    "\n",
+    "In many cases, however, it is not necessary to do that, and, in this chapter, we look at other types of \"rules\" in memory and how we can compose different \"rules\" together to implement bigger computations.\n",
+    "\n",
+    "Next, we take a step back and continue with a simple example involving the familiar `numbers` list. Then, we iteratively exchange `list` objects with \"rule\"-like objects *without* changing the overall computation at all. As computations involving sequential data are commonly classified into three categories **map**, **filter**, or **reduce**, we do so too for our `numbers` example."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "numbers = [7, 11, 8, 5, 3, 12, 2, 6, 9, 10, 1, 4]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "## Mapping"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "**Mapping** refers to the idea of applying a transformation to every element in a sequence.\n",
+    "\n",
+    "For example, let's square each element in `numbers` and add `1` to the squares. In essence, we apply the transformation $y := x^2 + 1$ as expressed with the `transform()` function below."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "def transform(element):\n",
+    "    \"\"\"Map elements to their squares plus 1.\"\"\"\n",
+    "    return (element ** 2) + 1"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "With the syntax we know so far, we revert to a `for`-loop that iteratively appends the transformed elements to an initially empty `transformed_numbers` list."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "transformed_numbers = []\n",
+    "\n",
+    "for old in numbers:\n",
+    "    new = transform(old)\n",
+    "    transformed_numbers.append(new)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[50, 122, 65, 26, 10, 145, 5, 37, 82, 101, 2, 17]"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "transformed_numbers"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "As this kind of data processing is so common, Python provides the [map() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#map) built-in. In its simplest usage form, it takes two arguments: A transformation `function` that takes exactly *one* positional argument and an `iterable` that provides the objects to be mapped.\n",
+    "\n",
+    "We call [map() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#map) with a reference to the `transform()` function and the `numbers` list as the arguments and store the result in the variable `transformer` to inspect it."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "transformer = map(transform, numbers)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "We might expect to get back a materialized sequence (i.e., all elements exist in memory), and a `list` object would feel the most natural because of the type of the `numbers` argument. However, `transformer` is an object of type `map`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<map at 0x7f778422fc10>"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "transformer"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "map"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "type(transformer)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Like `range` objects, `map` objects generate a series of objects \"on the fly\" (i.e., one by one), and we use the built-in [next() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#next) function to obtain the next object in line. So, we should think of a `map` object as a \"rule\" stored in memory that only knows how to calculate the next object of possibly *infinitely* many."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "50"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "next(transformer)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "122"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "next(transformer)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "65"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "next(transformer)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "It is essential to understand that by creating a `map` object with the [map() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#map) built-in, *nothing* happens in memory except the creation of the `map` object. In particular, no second `list` object derived from `numbers` is created. Also, we may view `range` objects as a special case of `map` objects: They are constrained to generating `int` objects only, and the `iterable` argument is replaced with `start`, `stop`, and `step` arguments.\n",
+    "\n",
+    "If we are sure that a `map` object generates a *finite* number of elements, we may materialize them into a `list` object with the built-in [list() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#func-list) constructor. Below, we \"pull out\" the remaining `int` objects from `transformer`, which itself is derived from a *finite* `list` object."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[26, 10, 145, 5, 37, 82, 101, 2, 17]"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "list(transformer)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "In summary, instead of creating an empty list first and appending it in a `for`-loop as above, we write the following one-liner and obtain an equal `transformed_numbers` list."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "transformed_numbers = list(map(transform, numbers))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[50, 122, 65, 26, 10, 145, 5, 37, 82, 101, 2, 17]"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "transformed_numbers"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "## Filtering"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "**Filtering** refers to the idea of creating a subset of a sequence with a **boolean filter** `function` that indicates if an element should be kept (i.e., `True`) or not (i.e., `False`).\n",
+    "\n",
+    "In the example, let's only keep the even elements in `numbers`. The `is_even()` function implements that as a filter."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "def is_even(element):\n",
+    "    \"\"\"Filter out odd numbers.\"\"\"\n",
+    "    if element % 2 == 0:\n",
+    "        return True\n",
+    "    return False"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "As `element % 2 == 0` is already a boolean expression, we could shorten `is_even()` like so."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "def is_even(element):\n",
+    "    \"\"\"Filter out odd numbers.\"\"\"\n",
+    "    return element % 2 == 0"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "As before, we first use a `for`-loop that appends the elements to be kept iteratively to an initially empty `even_numbers` list."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "even_numbers = []\n",
+    "\n",
+    "for number in transformed_numbers:\n",
+    "    if is_even(number):\n",
+    "        even_numbers.append(number)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[50, 122, 26, 10, 82, 2]"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "even_numbers"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Analogously to the `map` object above, we use the [filter() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#filter) built-in to create an object of type `filter` and assign it to `evens`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "evens = filter(is_even, transformed_numbers)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<filter at 0x7f778422fd30>"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "evens"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "filter"
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "type(evens)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "`evens` works like `transformer` above: With the built-in [next() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#next) function we obtain the even numbers one by one. So, the \"next\" element in line is simply the next even `int` object the `filter` object encounters."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[50, 122, 65, 26, 10, 145, 5, 37, 82, 101, 2, 17]"
+      ]
+     },
+     "execution_count": 21,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "transformed_numbers"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "50"
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "next(evens)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "122"
+      ]
+     },
+     "execution_count": 23,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "next(evens)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "26"
+      ]
+     },
+     "execution_count": 24,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "next(evens)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "As above, we could create a materialized `list` object with the [list() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#func-list) constructor."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[50, 122, 26, 10, 82, 2]"
+      ]
+     },
+     "execution_count": 25,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "list(filter(is_even, transformed_numbers))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "We may also chain `map` and `filter` objects derived from the original `numbers` list. As the entire cell is *one* big expression consisting of nested function calls, we read it from the inside out."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[50, 122, 26, 10, 82, 2]"
+      ]
+     },
+     "execution_count": 26,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "list(\n",
+    "    filter(\n",
+    "        is_even,\n",
+    "        map(transform, numbers),\n",
+    "    )\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Using the [map() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#map) and [filter() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#filter) built-ins, we can quickly switch the order: Filter first and then transform the remaining elements. This variant equals the \"*A simple Filter*\" example in [Chapter 4 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/04_iteration/03_content.ipynb#Example:-A-simple-Filter). On the contrary, code with `for`-loops and `if` statements is more tedious to adapt. Additionally, `map` and `filter` objects loop \"at the C level\" and are a lot faster because of that. Because of that, experienced Pythonistas tend to *not* use explicit `for`-loops so often."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[65, 145, 5, 37, 101, 17]"
+      ]
+     },
+     "execution_count": 27,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "list(\n",
+    "    map(\n",
+    "        transform,\n",
+    "        filter(is_even, numbers),\n",
+    "    )\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "## Reducing"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Lastly, **reducing** sequential data means to summarize the elements into a single statistic.\n",
+    "\n",
+    "A simple example is the built-in [sum() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#sum) function."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "370"
+      ]
+     },
+     "execution_count": 28,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "sum(\n",
+    "    map(\n",
+    "        transform,\n",
+    "        filter(is_even, numbers),\n",
+    "    )\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Other straightforward examples are the built-in [min() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#min) or [max() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#max) functions."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "5"
+      ]
+     },
+     "execution_count": 29,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "min(map(transform, filter(is_even, numbers)))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "145"
+      ]
+     },
+     "execution_count": 30,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "max(map(transform, filter(is_even, numbers)))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "[sum() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#sum), [min() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#min), and [max() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#max) can be regarded as special cases.\n",
+    "\n",
+    "The generic way of reducing a sequence is to apply a function of *two* arguments on a rolling horizon: Its first argument is the reduction of the elements processed so far, and the second the next element to be reduced.\n",
+    "\n",
+    "For illustration, let's replicate [sum() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#sum) as such a function, called `sum_alt()`. Its implementation only adds two numbers."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "def sum_alt(sum_so_far, next_number):\n",
+    "    \"\"\"Reduce a sequence by addition.\"\"\"\n",
+    "    return sum_so_far + next_number"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Further, we create a *new* `map` object derived from `numbers` ..."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "evens_transformed = map(transform, filter(is_even, numbers))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "... and loop over all *but* the first element it generates. The latter is captured separately as the initial `result` with the [next() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#next) function. We know from above that `evens_transformed` generates *six* elements. That is why we see *five* growing `result` values resembling a [cumulative sum](http://mathworld.wolfram.com/CumulativeSum.html). The first `210` is the sum of the first two elements generated by `evens_transformed`, `65` and `145`.\n",
+    "\n",
+    "So, we also learn that `map` objects, and analogously `filter` objects, are *iterable* as we may loop over them."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "210 215 252 353 370 "
+     ]
+    }
+   ],
+   "source": [
+    "result = next(evens_transformed)\n",
+    "\n",
+    "for number in evens_transformed:\n",
+    "    result = sum_alt(result, number)\n",
+    "    print(result, end=\" \")  # line added for didactical purposes"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "The final `result` is the same `370` as above."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "370"
+      ]
+     },
+     "execution_count": 34,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "result"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "The [reduce() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functools.html#functools.reduce) function in the [functools <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functools.html) module in the [standard library <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/index.html) provides more convenience (and speed) replacing the `for`-loop. It takes two arguments, `function` and `iterable`, in the same way as the [map() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#map) and [filter() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#filter) built-ins.\n",
+    "\n",
+    "[reduce() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functools.html#functools.reduce) is **[eager <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_wiki.png\">](https://en.wikipedia.org/wiki/Eager_evaluation)** meaning that all computations implied by the contained `map` and `filter` \"rules\" are executed immediately, and the code cell evaluates to `370`. On the contrary, [map() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#map) and [filter() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#filter) create **[lazy <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_wiki.png\">](https://en.wikipedia.org/wiki/Lazy_evaluation)** `map` and `filter` objects, and we have to use the [next() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#next) function to obtain the elements, one by one."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "from functools import reduce"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "370"
+      ]
+     },
+     "execution_count": 36,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "reduce(\n",
+    "    sum_alt,\n",
+    "    map(\n",
+    "        transform,\n",
+    "        filter(is_even, numbers),\n",
+    "    )\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "## Lambda Expressions"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "[map() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#map), [filter() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#filter), and [reduce() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functools.html#functools.reduce) take a `function` object as their first argument, and we defined `transform()`, `is_even()`, and `sum_alt()` to be used precisely for that.\n",
+    "\n",
+    "Often, such functions are used *only once* in a program. However, the primary purpose of functions is to *reuse* them. In such cases, it makes more sense to define them \"anonymously\" right at the position where the first argument goes.\n",
+    "\n",
+    "As mentioned in [Chapter 2 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/02_functions/00_content.ipynb#Anonymous-Functions), we use `lambda` expressions to create `function` objects *without* a name referencing them.\n",
+    "\n",
+    "So, the above `sum_alt()` function could be rewritten as a `lambda` expression like so ..."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<function __main__.<lambda>(sum_so_far, next_number)>"
+      ]
+     },
+     "execution_count": 37,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "lambda sum_so_far, next_number: sum_so_far + next_number"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "... or even shorter."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<function __main__.<lambda>(x, y)>"
+      ]
+     },
+     "execution_count": 38,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "lambda x, y: x + y"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "With the new concepts in this section, we can rewrite the entire example in just a few lines of code *without* any `for`, `if`, and `def` statements. The resulting code is concise, easy to read, quick to modify, and even faster in execution. Most importantly, it is optimized to handle big amounts of data as *no* temporary `list` objects are materialized in memory."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "370"
+      ]
+     },
+     "execution_count": 39,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "numbers = [7, 11, 8, 5, 3, 12, 2, 6, 9, 10, 1, 4]\n",
+    "evens = filter(lambda x: x % 2 == 0, numbers)\n",
+    "transformed = map(lambda x: (x ** 2) + 1, evens)\n",
+    "sum(transformed)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "If `numbers` comes as a sorted sequence of whole numbers, we may use the [range() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#func-range) built-in and get away *without* any materialized `list` object in memory at all!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "370"
+      ]
+     },
+     "execution_count": 40,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "numbers = range(1, 13)\n",
+    "evens = filter(lambda x: x % 2 == 0, numbers)\n",
+    "transformed = map(lambda x: (x ** 2) + 1, evens)\n",
+    "sum(transformed)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "To additionally save the temporary variables, `numbers`, `evens`, and `transformed`, we could write the entire computation as *one* expression."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "370"
+      ]
+     },
+     "execution_count": 41,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "sum(\n",
+    "    map(\n",
+    "        lambda x: (x ** 2) + 1,\n",
+    "        filter(\n",
+    "            lambda x: x % 2 == 0,\n",
+    "            range(1, 13),\n",
+    "        )\n",
+    "    )\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "PythonTutor visualizes the differences in the number of computational steps and memory usage:\n",
+    "- [Version 1 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](http://pythontutor.com/visualize.html#code=def%20is_even%28element%29%3A%0A%20%20%20%20if%20element%20%25%202%20%3D%3D%200%3A%0A%20%20%20%20%20%20%20%20return%20True%0A%20%20%20%20return%20False%0A%0Adef%20transform%28element%29%3A%0A%20%20%20%20return%20%28element%20**%202%29%20%2B%201%0A%0Anumbers%20%3D%20list%28range%281,%2013%29%29%0A%0Aevens%20%3D%20%5B%5D%0Afor%20number%20in%20numbers%3A%0A%20%20%20%20if%20is_even%28number%29%3A%0A%20%20%20%20%20%20%20%20evens.append%28number%29%0A%0Atransformed%20%3D%20%5B%5D%0Afor%20number%20in%20evens%3A%0A%20%20%20%20transformed.append%28transform%28number%29%29%0A%0Aresult%20%3D%20sum%28transformed%29&cumulative=false&curInstr=0&heapPrimitives=nevernest&mode=display&origin=opt-frontend.js&py=3&rawInputLstJSON=%5B%5D&textReferences=false): With `for`-loops, `if` statements, and named functions -> **116** steps and **3** `list` objects\n",
+    "- [Version 2 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](http://pythontutor.com/visualize.html#code=numbers%20%3D%20range%281,%2013%29%0Aevens%20%3D%20filter%28lambda%20x%3A%20x%20%25%202%20%3D%3D%200,%20numbers%29%0Atransformed%20%3D%20map%28lambda%20x%3A%20%28x%20**%202%29%20%2B%201,%20evens%29%0Aresult%20%3D%20sum%28transformed%29&cumulative=false&curInstr=0&heapPrimitives=nevernest&mode=display&origin=opt-frontend.js&py=3&rawInputLstJSON=%5B%5D&textReferences=false): With named `map` and `filter` objects -> **58** steps and **no** `list` object\n",
+    "- [Version 3 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](http://pythontutor.com/visualize.html#code=result%20%3D%20sum%28map%28lambda%20x%3A%20%28x%20**%202%29%20%2B%201,%20filter%28lambda%20x%3A%20x%20%25%202%20%3D%3D%200,%20range%281,%2013%29%29%29%29&cumulative=false&curInstr=0&heapPrimitives=nevernest&mode=display&origin=opt-frontend.js&py=3&rawInputLstJSON=%5B%5D&textReferences=false): Everything in *one* expression -> **55** steps and **no** `list` object\n",
+    "\n",
+    "Versions 2 and 3 are the same, except for the three additional steps required to create the temporary variables. The *major* downside of Version 1 is that, in the worst case, it may need *three times* the memory as compared to the other two versions!\n",
+    "\n",
+    "An experienced Pythonista would probably go with Version 2 in a production system to keep the code readable and maintainable.\n",
+    "\n",
+    "The map-filter-reduce paradigm has caught attention in recent years as it enables **[parallel computing <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_wiki.png\">](https://en.wikipedia.org/wiki/Parallel_computing)**, and this gets important when dealing with big amounts of data. The workings in the memory as shown in this section provide an idea why."
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
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+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.6"
+  },
+  "livereveal": {
+   "auto_select": "code",
+   "auto_select_fragment": true,
+   "scroll": true,
+   "theme": "serif"
+  },
+  "toc": {
+   "base_numbering": 1,
+   "nav_menu": {},
+   "number_sections": false,
+   "sideBar": true,
+   "skip_h1_title": true,
+   "title_cell": "Table of Contents",
+   "title_sidebar": "Contents",
+   "toc_cell": false,
+   "toc_position": {
+    "height": "calc(100% - 180px)",
+    "left": "10px",
+    "top": "150px",
+    "width": "384px"
+   },
+   "toc_section_display": false,
+   "toc_window_display": false
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/CONTENTS.md b/CONTENTS.md
index bbc1dee..476808d 100644
--- a/CONTENTS.md
+++ b/CONTENTS.md
@@ -189,3 +189,10 @@ If this is not possible,
       (`namedtuple` Type)
     - [summary <img height="12" style="display: inline-block" src="static/link/to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/07_sequences/06_summary.ipynb)
     - [review questions <img height="12" style="display: inline-block" src="static/link/to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/07_sequences/07_review.ipynb)
+  - *Chapter 8*: Map, Filter, & Reduce
+    - [content <img height="12" style="display: inline-block" src="static/link/to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/08_mfr/00_content.ipynb)
+      [<img height="12" style="display: inline-block" src="static/link/to_mb.png">](https://mybinder.org/v2/gh/webartifex/intro-to-python/develop?urlpath=lab/tree/08_mfr/00_content.ipynb)
+      (Mapping;
+       Filtering;
+       Reducing;
+       `lambda` Expression)
\ No newline at end of file
diff --git a/README.md b/README.md
index e9b2add..23b5521 100644
--- a/README.md
+++ b/README.md
@@ -20,6 +20,7 @@ For a more *detailed version* with **clickable links**
   - *Chapter 5*: Numbers & Bits
   - *Chapter 6*: Text & Bytes
   - *Chapter 7*: Sequential Data
+  - *Chapter 8*: Map, Filter, & Reduce
 
 
 #### Videos

From e63f5ae25a058635873544e2a0c1ace956dccc44 Mon Sep 17 00:00:00 2001
From: Alexander Hess <alexander@webartifex.biz>
Date: Wed, 21 Oct 2020 17:47:42 +0200
Subject: [PATCH 191/242] Add initial version of chapter 08, part 2

---
 08_mfr/01_content.ipynb | 2380 +++++++++++++++++++++++++++++++++++++++
 CONTENTS.md             |    8 +-
 2 files changed, 2387 insertions(+), 1 deletion(-)
 create mode 100644 08_mfr/01_content.ipynb

diff --git a/08_mfr/01_content.ipynb b/08_mfr/01_content.ipynb
new file mode 100644
index 0000000..2cf8720
--- /dev/null
+++ b/08_mfr/01_content.ipynb
@@ -0,0 +1,2380 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "**Note**: Click on \"*Kernel*\" > \"*Restart Kernel and Clear All Outputs*\" in [JupyterLab](https://jupyterlab.readthedocs.io/en/stable/) *before* reading this notebook to reset its output. If you cannot run this file on your machine, you may want to open it [in the cloud <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_mb.png\">](https://mybinder.org/v2/gh/webartifex/intro-to-python/develop?urlpath=lab/tree/08_mfr/01_content.ipynb)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "# Chapter 8: Map, Filter, & Reduce (continued)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "After introducing the Map-Filter-Reduce paradigm in the [first part <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/08_mfr/00_content.ipynb) of this chapter, we first see how `list` comprehensions can replace the [map() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#map) and [filter() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#filter) built-ins in many cases. Then, we learn how `generator` expressions are like `list` comprehensions *without* using the memory. We end this part with a short discussion of the built-in [all() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#all) and [any() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#any) functions."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "## `list` Comprehensions"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Consider again the original \"*A simple Filter*\" example from [Chapter 4 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/04_iteration/03_content.ipynb#Example:-A-simple-Filter), re-written such that both the mapping and the filtering are done in *one* `for`-loop instead of the *two* above."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "numbers = [7, 11, 8, 5, 3, 12, 2, 6, 9, 10, 1, 4]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "evens_transformed = []\n",
+    "\n",
+    "for number in numbers:\n",
+    "    if number % 2 == 0:\n",
+    "        evens_transformed.append((number ** 2) + 1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "370"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "sum(evens_transformed)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "**`list` comprehensions** are *expressions* to derive *new* `list` objects out of *existing* ones (cf., [reference <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/reference/expressions.html#displays-for-lists-sets-and-dictionaries)). Practically, this means that we place the `for` and `if` inside brackets `[` and `]`.\n",
+    "\n",
+    "So, the example can be written in a single *expression* like below replacing the compound `for` *statement* above."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[65, 145, 5, 37, 101, 17]"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "[(n ** 2) + 1 for n in numbers if n % 2 == 0]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "A list comprehension may always be used in a place where otherwise a `list` object would work.\n",
+    "\n",
+    "For example, let's rewrite the \"*A simple Filter*\" example from [Chapter 4 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/04_iteration/03_content.ipynb#Example:-A-simple-Filter) in just one line. As a caveat, the code below *materializes* all elements in memory *before* summing them up, and may, therefore, cause a `MemoryError` when executed with a bigger `numbers` list. We see with [PythonTutor <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](http://pythontutor.com/visualize.html#code=numbers%20%3D%20range%281,%2013%29%0Aresult%20%3D%20sum%28%5B%28n%20**%202%29%20%2B%201%20for%20n%20in%20numbers%20if%20n%20%25%202%20%3D%3D%200%5D%29&cumulative=false&curInstr=0&heapPrimitives=nevernest&mode=display&origin=opt-frontend.js&py=3&rawInputLstJSON=%5B%5D&textReferences=false) how a `list` object exists in memory at step 17 and then \"gets lost\" right after. As the next section shows, this downside may be mitigated."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "370"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "sum([(n ** 2) + 1 for n in numbers if n % 2 == 0])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "### Example: Nested Lists"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "List comprehensions may come with several `for`'s and `if`'s.\n",
+    "\n",
+    "The cell below creates a `list` object that contains three other `list` objects with a series of numbers in them. The first and last numbers in each inner `list` object are offset by `1`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "nested_numbers = [\n",
+    "    [1, 2, 3, 4, 5, 6, 7],\n",
+    "    [2, 3, 4, 5, 6, 7, 8],\n",
+    "    [3, 4, 5, 6, 7, 8, 9],\n",
+    "]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[[1, 2, 3, 4, 5, 6, 7], [2, 3, 4, 5, 6, 7, 8], [3, 4, 5, 6, 7, 8, 9]]"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "nested_numbers"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "To do something meaningful with the numbers, we have to remove the inner layer of `list` objects and **flatten** (i.e., \"un-nest\") the data.\n",
+    "\n",
+    "Without list comprehensions, we achieve that with two nested `for`-loops."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "flat_numbers = []\n",
+    "\n",
+    "for inner_numbers in nested_numbers:\n",
+    "    for number in inner_numbers:\n",
+    "        flat_numbers.append(number)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[1, 2, 3, 4, 5, 6, 7, 2, 3, 4, 5, 6, 7, 8, 3, 4, 5, 6, 7, 8, 9]"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "flat_numbers"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "That translates into a list comprehension like below. The order of the `for`'s may be confusing at first but is the *same* as writing out the nested `for`-loops as above."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[1, 2, 3, 4, 5, 6, 7, 2, 3, 4, 5, 6, 7, 8, 3, 4, 5, 6, 7, 8, 9]"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "[number for inner_numbers in nested_numbers for number in inner_numbers]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Now, we may use the `list` object resulting from the list comprehension in any way we want.\n",
+    "\n",
+    "For example, to add up the flattened numbers with [sum() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#sum). The same caveat holds as before: The `list` object passed into [sum() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#sum) is *materialized* with *all* its elements *before* the sum is calculated!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "105"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "sum([number for inner_numbers in nested_numbers for number in inner_numbers])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "In this particular example, however, we can exploit the fact that any sum of numbers can be expressed as the sum of sums of mutually exclusive and collectively exhaustive subsets of these numbers and get away with just *one* `for` in the list comprehension."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "105"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "sum([sum(inner_numbers) for inner_numbers in nested_numbers])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "### Example: Working with Cartesian Products"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "A popular use case of nested list comprehensions is applying a transformation to each $n$-tuple in the [Cartesian product <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_wiki.png\">](https://en.wikipedia.org/wiki/Cartesian_product) created from elements of $n$ iterables. In the generic illustration below, a transformation $f(x, y)$ is applied to each $2$-tuple in the Cartesian product $X \\times Y$ where $x$ is an element in $X = \\{x_1, x_2, x_3\\}$ and $y$ is an element in $Y = \\{y_1, y_2, y_3\\}$."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "|$Y$ \\ $X$|    $x_1$     |    $x_2$     |    $x_3$     |\n",
+    "|---------|--------------|--------------|--------------|\n",
+    "|  $y_1$  |f($x_1$,$y_1$)|f($x_2$,$y_1$)|f($x_3$,$y_1$)|\n",
+    "|  $y_2$  |f($x_1$,$y_2$)|f($x_2$,$y_2$)|f($x_3$,$y_2$)|\n",
+    "|  $y_3$  |f($x_1$,$y_3$)|f($x_2$,$y_3$)|f($x_3$,$y_3$)|"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "For example, let's add each quotient obtained by taking the numerator $x$ from `[10, 20, 30]` and the denominator $y$ from `[40, 50, 60]` to `1`, and then find the overall product. This transformation can be described mathematically as the function $z = f(x, y)$ below."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "source": [
+    "$$z = f(x, y) = \\prod{ (1 + \\frac{x}{y} )}$$"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Further, the table below visualizes the calculations: The result is the product of *nine* entries."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "source": [
+    "|`y` \\ `x`|**10**|**20**|**30**|\n",
+    "|------|------|------|------|\n",
+    "|**40**| 1.25 | 1.50 | 1.75 |\n",
+    "|**50**| 1.20 | 1.40 | 1.60 |\n",
+    "|**60**| 1.17 | 1.33 | 1.50 |"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "To express that in Python, we start by creating two `list` objects, `first` and `second`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "first = [10, 20, 30]\n",
+    "second = [40, 50, 60]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "For a Cartesian product, we must loop over *all* possible $2$-tuples where one element is drawn from `first` and the other from `second`. We achieve that with two nested `for`-loops, in which we calculate each `quotient` and append it to an initially empty `cartesian_product` list."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[1.25, 1.2, 1.1666666666666667, 1.5, 1.4, 1.3333333333333333, 1.75, 1.6, 1.5]"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "cartesian_product = []\n",
+    "\n",
+    "for numerator in first:\n",
+    "    for denominator in second:\n",
+    "        quotient = 1 + (numerator / denominator)\n",
+    "        cartesian_product.append(quotient)\n",
+    "\n",
+    "cartesian_product"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Next, we convert the two explicit `for`-loops into one list comprehensions and use `x` and `y` as shorter variable names."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[1.25, 1.2, 1.1666666666666667, 1.5, 1.4, 1.3333333333333333, 1.75, 1.6, 1.5]"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "[1 + (x / y) for x in first for y in second]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "The order of the `for`'s is *important*: The list comprehension above divides numbers from `first` by numbers from `second`, whereas the list comprehension below does the opposite."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[5.0, 3.0, 2.333333333333333, 6.0, 3.5, 2.666666666666667, 7.0, 4.0, 3.0]"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "[1 + (x / y) for x in second for y in first]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "To find the overall product, we *unpack* the list comprehension into the `product()` function from the \"*Function Definitions & Calls*\" sub-section in [Chapter 7 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/07_sequences/03_content.ipynb#Function-Definitions-&-Calls)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "def product(*args):\n",
+    "    \"\"\"Multiply all arguments.\"\"\"\n",
+    "    result = args[0]\n",
+    "\n",
+    "    for arg in args[1:]:\n",
+    "        result *= arg\n",
+    "\n",
+    "    return result"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "20.58"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "product(*[1 + (x / y) for x in first for y in second])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Alternatively, we use a `lambda` expression with the [reduce() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functools.html#functools.reduce) function from the [functools <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functools.html) module."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from functools import reduce"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "20.58"
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "reduce(lambda x, y: x * y, [1 + (x / y) for x in first for y in second])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "While this example is stylized, Cartesian products are hidden in many applications, and it shows how the various language features introduced in this chapter can be seamlessly combined to process sequential data."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "## `generator` Expressions"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Because of the high memory consumption, Pythonistas avoid materialized `list` objects, and, thus, also `list` comprehensions, whenever possible. Instead, they prefer to work with **[`generator` expressions <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/reference/expressions.html#generator-expressions)**. Syntactically, they work like list comprehensions except that parentheses, `(` and `)`, replace brackets, `[` and `]`.\n",
+    "\n",
+    "Let's go back to the original \"*A simple Filter*\" example from [Chapter 4 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/04_iteration/03_content.ipynb#Example:-A-simple-Filter) one more time, apply the transformation $y := x^2 + 1$ to all even `numbers`, and sum them up."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "numbers = [7, 11, 8, 5, 3, 12, 2, 6, 9, 10, 1, 4]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "To filter and transform `numbers`, we wrote a list comprehension above ..."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[65, 145, 5, 37, 101, 17]"
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "[(n ** 2) + 1 for n in numbers if n % 2 == 0]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "... that now becomes a `generator` expression."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<generator object <genexpr> at 0x7fcf703d84a0>"
+      ]
+     },
+     "execution_count": 23,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "((n ** 2) + 1 for n in numbers if n % 2 == 0)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "A `generator` expression evaluates to yet another \"rule\"-like object in memory that knows how to generate the individual objects in a series one by one. Whereas a `list` comprehension materializes its elements in memory *when* it is evaluated, the opposite holds true for `generator` expressions: *No* object is created in memory except the \"rule\" itself. Because of this behavior, we describe `generator` expressions as *lazy* and `list` comprehensions as *eager*.\n",
+    "\n",
+    "To materialize the elements specified by a `generator` expression, we use the [list() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#func-list) constructor as seen above."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[65, 145, 5, 37, 101, 17]"
+      ]
+     },
+     "execution_count": 24,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "list(((n ** 2) + 1 for n in numbers if n % 2 == 0))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Whenever a `generator` expression is the only argument in a function call, we may merge the double parentheses, `((` and `))`, into just `(` and `)`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[65, 145, 5, 37, 101, 17]"
+      ]
+     },
+     "execution_count": 25,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "list((n ** 2) + 1 for n in numbers if n % 2 == 0)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "A common use case is to reduce the elements into a single object instead, for example, by adding them up with [sum() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#sum) as in the original \"*A simple Filter*\" example. [PythonTutor <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](http://pythontutor.com/visualize.html#code=numbers%20%3D%20range%281,%2013%29%0Asum_with_list%20%3D%20sum%28%5B%28n%20**%202%29%20%2B%201%20for%20n%20in%20numbers%20if%20n%20%25%202%20%3D%3D%200%5D%29%0Asum_with_gen%20%3D%20sum%28%28n%20**%202%29%20%2B%201%20for%20n%20in%20numbers%20if%20n%20%25%202%20%3D%3D%200%29&cumulative=false&curInstr=0&heapPrimitives=nevernest&mode=display&origin=opt-frontend.js&py=3&rawInputLstJSON=%5B%5D&textReferences=false) shows how the code cell below does *not* create a temporary `list` object in memory whereas a `list` comprehension would do so (cf., step 17)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "370"
+      ]
+     },
+     "execution_count": 26,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "sum((n ** 2) + 1 for n in numbers if n % 2 == 0)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Let's assign the object to which the `generator` expression below evaluates to to a variable `gen` and inspect it."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "gen = ((n ** 2) + 1 for n in numbers if n % 2 == 0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<generator object <genexpr> at 0x7fcf703d8c10>"
+      ]
+     },
+     "execution_count": 28,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "gen"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Unsurprisingly, `generator` expressions create objects of type `generator`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "generator"
+      ]
+     },
+     "execution_count": 29,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "type(gen)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "`generator` objects work just like the `map` and `filter` objects in the [first part <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/08_mfr/00_content.ipynb) of this chapter. So, with the [next() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#next) function, we can generate elements one by one."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "65"
+      ]
+     },
+     "execution_count": 30,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "next(gen)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "145"
+      ]
+     },
+     "execution_count": 31,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "next(gen)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "5"
+      ]
+     },
+     "execution_count": 32,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "next(gen)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "37"
+      ]
+     },
+     "execution_count": 33,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "next(gen)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "101"
+      ]
+     },
+     "execution_count": 34,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "next(gen)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "17"
+      ]
+     },
+     "execution_count": 35,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "next(gen)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Once a `generator` object runs out of elements, it raises a `StopIteration` exception, and we say that the `generator` object is **exhausted**. To loop over its elements again, we must create a *new* one."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "ename": "StopIteration",
+     "evalue": "",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mStopIteration\u001b[0m                             Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-36-6e72e47198db>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mnext\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mgen\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;31mStopIteration\u001b[0m: "
+     ]
+    }
+   ],
+   "source": [
+    "next(gen)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "ename": "StopIteration",
+     "evalue": "",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mStopIteration\u001b[0m                             Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-37-6e72e47198db>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mnext\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mgen\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;31mStopIteration\u001b[0m: "
+     ]
+    }
+   ],
+   "source": [
+    "next(gen)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Calling the [next() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#next) function repeatedly with the *same* `generator` object as the argument is essentially what a `for`-loop automates for us. So, `generator` objects are *iterable*. We look into this in detail further below in the \"*The `for` Statement (revisited)*\" section."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "65 145 5 37 101 17 "
+     ]
+    }
+   ],
+   "source": [
+    "for number in ((n ** 2) + 1 for n in numbers if n % 2 == 0):\n",
+    "    print(number, end=\" \")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "### Example: Nested Lists (continued)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "If we are only interested in a *reduction* of `nested_numbers` into a single statistic, as the overall sum in the \"*Nested Lists*\" example above, we should replace `list` objects or `list` comprehensions with `generator` expressions wherever possible. The result is the *same*, but no intermediate `list` objects are materialized! That makes our code scale to large amounts of data.\n",
+    "\n",
+    "Let's adapt the example `nested_numbers` from above."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[[1, 2, 3, 4, 5, 6, 7], [2, 3, 4, 5, 6, 7, 8], [3, 4, 5, 6, 7, 8, 9]]"
+      ]
+     },
+     "execution_count": 39,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "nested_numbers"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Compared to the implementation with the list comprehension above, we simply remove the brackets, `[` and `]`: The argument to [sum() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#sum) becomes a generator expression."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "105"
+      ]
+     },
+     "execution_count": 40,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "sum(number for inner_numbers in nested_numbers for number in inner_numbers)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "That also holds for the alternative formulation as a sum of sums."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "105"
+      ]
+     },
+     "execution_count": 41,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "sum(sum(inner_numbers) for inner_numbers in nested_numbers)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Because `nested_numbers` has an internal structure (i.e., the inner `list` objects are series of consecutive `int` objects), we can even provide an effectively **memoryless** implementation by expressing it as a `generator` expression derived from `range` objects. [PythonTutor <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](http://pythontutor.com/visualize.html#code=nested_numbers%20%3D%20%28%28range%28x,%20y%20%2B%201%29%29%20for%20x,%20y%20in%20zip%28range%281,%204%29,%20range%287,%2010%29%29%29%0Aresult%20%3D%20sum%28number%20for%20inner_numbers%20in%20nested_numbers%20for%20number%20in%20inner_numbers%29&cumulative=false&curInstr=0&heapPrimitives=nevernest&mode=display&origin=opt-frontend.js&py=3&rawInputLstJSON=%5B%5D&textReferences=false) confirms that no `list` objects materialize at any point in time."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "nested_numbers = ((range(x, y + 1)) for x, y in zip(range(1, 4), range(7, 10)))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<generator object <genexpr> at 0x7fcf70347970>"
+      ]
+     },
+     "execution_count": 43,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "nested_numbers"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "105"
+      ]
+     },
+     "execution_count": 44,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "sum(number for inner_numbers in nested_numbers for number in inner_numbers)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "We must be careful when assigning a `generator` object to a variable: If we use `nested_numbers` again, for example, in the alternative formulation below, [sum() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#sum) returns `0` because `nested_numbers` is exhausted after executing the previous code cell. [PythonTutor <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](http://pythontutor.com/visualize.html#code=nested_numbers%20%3D%20%28%28range%28x,%20y%20%2B%201%29%29%20for%20x,%20y%20in%20zip%28range%281,%204%29,%20range%287,%2010%29%29%29%0Aresult%20%3D%20sum%28number%20for%20inner_numbers%20in%20nested_numbers%20for%20number%20in%20inner_numbers%29%0Ano_result%20%3D%20sum%28sum%28inner_numbers%29%20for%20inner_numbers%20in%20nested_numbers%29&cumulative=false&curInstr=0&heapPrimitives=nevernest&mode=display&origin=opt-frontend.js&py=3&rawInputLstJSON=%5B%5D&textReferences=false) also shows that."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0"
+      ]
+     },
+     "execution_count": 45,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "sum(sum(inner_numbers) for inner_numbers in nested_numbers)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "### Example: Working with Cartesian Products (continued)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Let's also rewrite the \"*Working with Cartesian Products*\" example from above with generator expressions.\n",
+    "\n",
+    "As a first optimization, we replace the materialized `list` objects, `first` and `second`, with memoryless `range` objects."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "first = range(10, 31, 10)  # \"==\" [10, 20, 30]\n",
+    "second = range(40, 61, 10)  # \"==\" [40, 50, 60]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Now, the first of the two alternative solutions may be more appealing to many readers. In general, many practitioners seem to dislike `lambda` expressions.\n",
+    "\n",
+    "In the first solution, we *unpack* the elements produced by `(1 + (x / y) for x in first for y in second)` into the `product()` function from the \"*Function Definitions & Calls*\" sub-section in [Chapter 7 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/07_sequences/03_content.ipynb#Function-Definitions-&-Calls). However, inside `product()`, the elements are *packed* into `args`, a *materialized* `tuple` object! So, all the memory efficiency gained by using a generator expression is lost! [PythonTutor <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](http://pythontutor.com/visualize.html#code=def%20product%28*args%29%3A%0A%20%20%20%20result%20%3D%20args%5B0%5D%0A%20%20%20%20for%20arg%20in%20args%5B1%3A%5D%3A%0A%20%20%20%20%20%20%20%20result%20*%3D%20arg%0A%20%20%20%20return%20result%0A%0Afirst%20%3D%20range%2810,%2031,%2010%29%0Asecond%20%3D%20range%2840,%2061,%2010%29%0A%0Aresult%20%3D%20product%28*%281%20%2B%20%28x%20/%20y%29%20for%20x%20in%20first%20for%20y%20in%20second%29%29&cumulative=false&curInstr=0&heapPrimitives=nevernest&mode=display&origin=opt-frontend.js&py=3&rawInputLstJSON=%5B%5D&textReferences=false) shows how a `tuple` object exists in steps 38-58."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "20.58"
+      ]
+     },
+     "execution_count": 47,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "product(*(1 + (x / y) for x in first for y in second))  # not memory efficient!"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "On the contrary, the second solution with the [reduce() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functools.html#functools.reduce) function from the [functools <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functools.html) module and the `lambda` expression works *without* the elements materialized at the same time, and [PythonTutor <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](http://pythontutor.com/visualize.html#code=from%20functools%20import%20reduce%0A%0Afirst%20%3D%20range%2810,%2031,%2010%29%0Asecond%20%3D%20range%2840,%2061,%2010%29%0A%0Aresult%20%3D%20reduce%28%0A%20%20%20%20lambda%20x,%20y%3A%20x%20*%20y,%0A%20%20%20%20%281%20%2B%20%28x%20/%20y%29%20for%20x%20in%20first%20for%20y%20in%20second%29%0A%29&cumulative=false&curInstr=0&heapPrimitives=nevernest&mode=display&origin=opt-frontend.js&py=3&rawInputLstJSON=%5B%5D&textReferences=false) confirms that. So, only the second alternative is truly memory-efficient!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 48,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "20.58"
+      ]
+     },
+     "execution_count": 48,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "reduce(lambda x, y: x * y, (1 + (x / y) for x in first for y in second))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "In summary, we learn from this example that unpacking `generator` objects *may* be a *bad* idea."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "### Example: Averaging all even Numbers in a List (revisited)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "With the new concepts in this chapter, let's rewrite the book's introductory \"*Averaging all even Numbers in a List*\" example from [Chapter 1 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/01_elements/00_content.ipynb#Example:-Averaging-all-even-Numbers-in-a-List) such that it efficiently handles a large sequence of numbers. We continue from its latest implementation, the `average_evens()` function in the \"*Keyword-only Arguments*\" section in [Chapter 2 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/02_functions/00_content.ipynb#Keyword-only-Arguments).\n",
+    "\n",
+    "We assume that `average_evens()` is called with a *finite* and *iterable* object that generates a **stream** of numeric objects that can be cast as `int` objects. After all, the idea of even and odd numbers makes sense only in the context of whole numbers.\n",
+    "\n",
+    "The `generator` expression `(round(n) for n in numbers)` implements the type casting, and, when it is evaluated during a function call, *nothing* happens except that a `generator` object is assigned to `integers`. Then, with the [reduce() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functools.html#functools.reduce) function from the [functools <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functools.html) module, we *simultaneously* add up *and* count the even numbers with the `generator` object to which the inner `generator` expression `((n, 1) for n in integers if n % 2 == 0)` evaluates to. That `generator` object takes the `integers` generator as its source and produces `tuple` objects consisting of the next *even* number in line and `1`. Two such `tuple` objects are then iteratively passed to the `function` object to which the `lambda` expression evaluates to. `x` represents the total and the count of the even numbers processed so far, while `y`'s first element, `y[0]`, is the next *even* number to be added to the running total. The result of calling [reduce() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functools.html#functools.reduce) is again a `tuple` object, namely the final `total` and `count`. Lastly, we calculate the simple average and scale it.\n",
+    "\n",
+    "In summary, this implementation of `average_evens()` does *not* keep materialized `list` objects internally like its predecessors in [Chapter 2 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/02_functions/00_content.ipynb) but processes the elements of the `numbers` argument on a one-by-one basis."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 49,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "def average_evens(numbers, *, scalar=1):\n",
+    "    \"\"\"Calculate the average of all even numbers.\n",
+    "\n",
+    "    Args:\n",
+    "        numbers (iterable): a finite stream of the numbers to be averaged;\n",
+    "            if non-whole numbers are provided, they are rounded\n",
+    "        scalar (float, optional): multiplies the average; defaults to 1\n",
+    "\n",
+    "    Returns:\n",
+    "        float: (scaled) average\n",
+    "    \"\"\"\n",
+    "    integers = (round(n) for n in numbers)\n",
+    "    total, count = reduce(\n",
+    "        lambda x, y: (x[0] + y[0], x[1] + y[1]),\n",
+    "        ((n, 1) for n in integers if n % 2 == 0)\n",
+    "    )\n",
+    "    return scalar * total / count"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 50,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "7.0"
+      ]
+     },
+     "execution_count": 50,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "average_evens([7, 11, 8, 5, 3, 12, 2, 6, 9, 10, 1, 4])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "We may provide an optional `scalar` argument as before."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 51,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "14.0"
+      ]
+     },
+     "execution_count": 51,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "average_evens([7, 11, 8, 5, 3, 12, 2, 6, 9, 10, 1, 4], scalar=2)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "An argument with `float` objects works just as well."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 52,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "7.0"
+      ]
+     },
+     "execution_count": 52,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "average_evens([7., 11., 8., 5., 3., 12., 2., 6., 9., 10., 1., 4.])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "To show that `average_evens()` can process a large stream of data, we simulate `10_000_000` randomly drawn integers between `0` and `100` with the [randint() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/random.html#random.randint) function from the [random <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/random.html) module. We use a `generator` expression derived from a `range` object as the `numbers` argument. So, at *no* point in time is there a materialized `list` object in memory. The result approaching `50` confirms that [randint() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/random.html#random.randint) must be based on a uniform distribution."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 53,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "import random"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 54,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "random.seed(42)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 55,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "49.994081434519636"
+      ]
+     },
+     "execution_count": 55,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "average_evens(random.randint(0, 100) for _ in range(10_000_000))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "To show that `average_evens()` filters out odd numbers, we simulate another stream of `10_000_000` randomly drawn odd integers between `1` and `99`. As no function in the [random <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/random.html) module does that \"out of the box,\" we must be creative: Doubling a number drawn from `random.randint(0, 49)` results in an even number between `0` and `98`, and adding `1` makes it odd. Then, `average_evens()` raises a `TypeError`, essentially because `(int(n) for n in numbers)` does not generate any element."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 56,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "ename": "TypeError",
+     "evalue": "reduce() of empty sequence with no initial value",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-56-73a295f34553>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0maverage_evens\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m2\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0mrandom\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrandint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m49\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;36m1\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0m_\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m10_000_000\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;32m<ipython-input-49-29706a0855ba>\u001b[0m in \u001b[0;36maverage_evens\u001b[0;34m(numbers, scalar)\u001b[0m\n\u001b[1;32m     11\u001b[0m     \"\"\"\n\u001b[1;32m     12\u001b[0m     \u001b[0mintegers\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mround\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mn\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mn\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mnumbers\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 13\u001b[0;31m     total, count = reduce(\n\u001b[0m\u001b[1;32m     14\u001b[0m         \u001b[0;32mlambda\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     15\u001b[0m         \u001b[0;34m(\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mn\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mn\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mintegers\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mn\u001b[0m \u001b[0;34m%\u001b[0m \u001b[0;36m2\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;31mTypeError\u001b[0m: reduce() of empty sequence with no initial value"
+     ]
+    }
+   ],
+   "source": [
+    "average_evens(2 * random.randint(0, 49) + 1 for _ in range(10_000_000))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "## `tuple` Comprehensions"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "There is no syntax to derive *new* `tuple` objects out of existing ones. However, we can mimic such a construct by combining the built-in [tuple() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#func-tuple) constructor with a `generator` expression.\n",
+    "\n",
+    "So, to convert the `list` comprehension `[(n ** 2) + 1 for n in numbers if n % 2 == 0]` from above into a \"`tuple` comprehension,\" we write the following."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 57,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(65, 145, 5, 37, 101, 17)"
+      ]
+     },
+     "execution_count": 57,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "tuple((n ** 2) + 1 for n in numbers if n % 2 == 0)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "## Boolean Reducers"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Besides [min() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#min), [max() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#max), and [sum() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#sum), Python provides two boolean reduce functions: [all() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#all) and [any() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#any).\n",
+    "\n",
+    "Let's look at straightforward examples involving `numbers` again."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 58,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "numbers = [7, 11, 8, 5, 3, 12, 2, 6, 9, 10, 1, 4]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "[all() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#all) takes an `iterable` argument and returns `True` if *all* elements are *truthy*.\n",
+    "\n",
+    "For example, let's check if the square of each element in `numbers` is below `100` or `150`, respectively. We express the computation with a `generator` expression passed as the only argument to [all() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#all)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 59,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "False"
+      ]
+     },
+     "execution_count": 59,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "all(x ** 2 < 100 for x in numbers)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 60,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 60,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "all(x ** 2 < 150 for x in numbers)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "[all() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#all) can be viewed as syntactic sugar replacing a `for`-loop: Internally, [all() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#all) implements the *short-circuiting* strategy explained in [Chapter 3 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/03_conditionals/00_content.ipynb#Short-Circuiting), and we mimic that by testing for the *opposite* condition in the `if` statement and stopping the `for`-loop early with the `break` statement. In the worst case, if `threshold` were, for example, `150`, we would loop over *all* elements in the `iterable` argument, which must be *finite* for the code to work. So, [all() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#all) is a *linear search* in disguise."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 61,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "False"
+      ]
+     },
+     "execution_count": 61,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "threshold = 100\n",
+    "\n",
+    "for number in numbers:\n",
+    "    if number ** 2 >= threshold:  # the opposite of what we are checking for\n",
+    "        all_below_threshold = False\n",
+    "        break\n",
+    "else:\n",
+    "    all_below_threshold = True\n",
+    "\n",
+    "all_below_threshold"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "The documentation of [all() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#all) shows in another way what it does with code: By placing a `return` statement in a `for`-loop's body inside a function, iteration is stopped prematurely once an element does *not* meet the condition. That is the familiar *early exit* pattern at work."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 62,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "def all_alt(iterable):\n",
+    "    \"\"\"Alternative implementation of the built-in all() function.\"\"\"\n",
+    "    for element in iterable:\n",
+    "        if not element:  # the opposite of what we are checking for\n",
+    "            return False\n",
+    "    return True"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 63,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "False"
+      ]
+     },
+     "execution_count": 63,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "all_alt(x ** 2 < 100 for x in numbers)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 64,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 64,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "all_alt(x ** 2 < 150 for x in numbers)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Similarly, [any() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#any) checks if *at least* one element in the `iterable` argument is *truthy*.\n",
+    "\n",
+    "To continue the example, let's check if the square of *any* element in `numbers` is above `100` or `150`, respectively."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 65,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 65,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "any(x ** 2 > 100 for x in numbers)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 66,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "False"
+      ]
+     },
+     "execution_count": 66,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "any(x ** 2 > 150 for x in numbers)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "The code cell below shows how [any() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#any) works internally: It also follows the *short-circuiting* strategy. Here, we do *not* need to check for the opposite condition."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 67,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 67,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "threshold = 100\n",
+    "\n",
+    "for number in numbers:\n",
+    "    if number ** 2 > threshold:\n",
+    "        any_above_threshold = True\n",
+    "        break\n",
+    "else:\n",
+    "    any_above_threshold = False\n",
+    "\n",
+    "any_above_threshold"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "The alternative formulation in the documentation of [any() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#any) is straightforward and also uses the early exit pattern."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 68,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "def any_alt(iterable):\n",
+    "    \"\"\"Alternative implementation of the built-in any() function.\"\"\"\n",
+    "    for element in iterable:\n",
+    "        if element:\n",
+    "            return True\n",
+    "    return False"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 69,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 69,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "any_alt(x ** 2 > 100 for x in numbers)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 70,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "False"
+      ]
+     },
+     "execution_count": 70,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "any_alt(x ** 2 > 150 for x in numbers)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.6"
+  },
+  "livereveal": {
+   "auto_select": "code",
+   "auto_select_fragment": true,
+   "scroll": true,
+   "theme": "serif"
+  },
+  "toc": {
+   "base_numbering": 1,
+   "nav_menu": {},
+   "number_sections": false,
+   "sideBar": true,
+   "skip_h1_title": true,
+   "title_cell": "Table of Contents",
+   "title_sidebar": "Contents",
+   "toc_cell": false,
+   "toc_position": {
+    "height": "calc(100% - 180px)",
+    "left": "10px",
+    "top": "150px",
+    "width": "384px"
+   },
+   "toc_section_display": false,
+   "toc_window_display": false
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/CONTENTS.md b/CONTENTS.md
index 476808d..ac3e1db 100644
--- a/CONTENTS.md
+++ b/CONTENTS.md
@@ -195,4 +195,10 @@ If this is not possible,
       (Mapping;
        Filtering;
        Reducing;
-       `lambda` Expression)
\ No newline at end of file
+       `lambda` Expression)
+    - [content <img height="12" style="display: inline-block" src="static/link/to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/08_mfr/01_content.ipynb)
+      [<img height="12" style="display: inline-block" src="static/link/to_mb.png">](https://mybinder.org/v2/gh/webartifex/intro-to-python/develop?urlpath=lab/tree/08_mfr/01_content.ipynb)
+      (`list` Comprehension;
+       `generator` Expression;
+       Streams of Data;
+       Boolean Reducers)
\ No newline at end of file

From f40c74f89e869826ea8131980a179a1bb89cd5e6 Mon Sep 17 00:00:00 2001
From: Alexander Hess <alexander@webartifex.biz>
Date: Wed, 21 Oct 2020 18:46:56 +0200
Subject: [PATCH 192/242] Explain what files are used and where they are

---
 CONTENTS.md | 15 +++++++++++++++
 1 file changed, 15 insertions(+)

diff --git a/CONTENTS.md b/CONTENTS.md
index ac3e1db..6291fb2 100644
--- a/CONTENTS.md
+++ b/CONTENTS.md
@@ -2,6 +2,21 @@
 
 The materials are designed to resemble an *interactive* book.
 
+The files come
+    primarily in the [Jupyter Notebook](https://jupyter-notebook.readthedocs.io/en/stable/)
+        format (i.e., \*.ipynb)
+    but also as [modules and packages <img height="12" style="display: inline-block" src="static/link/to_py.png">](https://docs.python.org/3/tutorial/modules.html)
+        (i.e., \*.py).
+Together with some other static files (e.g., images),
+    they are stored in one folder per chapter in this repository.
+They are to be opened
+    from within the [JupyterLab](https://jupyterlab.readthedocs.io/en/stable/) application,
+    even though other ways are certainly possible as well.
+Both the files and the folders
+    are appropriately named with prefixes
+    indicating the order in which they should be read
+        and starting with "00_".
+
 It is recommended
     to follow the [installation instructions](https://github.com/webartifex/intro-to-python#installation)
         in the [README.md](README.md) file

From f4f2cf476e74334e04dcaef54b4079f03952a4e1 Mon Sep 17 00:00:00 2001
From: Alexander Hess <alexander@webartifex.biz>
Date: Wed, 21 Oct 2020 18:48:59 +0200
Subject: [PATCH 193/242] Add initial version of chapter 08's exercises, part 1

---
 08_mfr/02_exercises.ipynb | 760 ++++++++++++++++++++++++++++++++++++++
 08_mfr/stream.py          |  51 +++
 CONTENTS.md               |   5 +-
 3 files changed, 815 insertions(+), 1 deletion(-)
 create mode 100644 08_mfr/02_exercises.ipynb
 create mode 100644 08_mfr/stream.py

diff --git a/08_mfr/02_exercises.ipynb b/08_mfr/02_exercises.ipynb
new file mode 100644
index 0000000..0911aaf
--- /dev/null
+++ b/08_mfr/02_exercises.ipynb
@@ -0,0 +1,760 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Note**: Click on \"*Kernel*\" > \"*Restart Kernel and Run All*\" in [JupyterLab](https://jupyterlab.readthedocs.io/en/stable/) *after* finishing the exercises to ensure that your solution runs top to bottom *without* any errors. If you cannot run this file on your machine, you may want to open it [in the cloud <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_mb.png\">](https://mybinder.org/v2/gh/webartifex/intro-to-python/develop?urlpath=lab/tree/08_mfr/02_exercises.ipynb)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Chapter 8: Map, Filter, & Reduce (Coding Exercises)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The exercises below assume that you have read the [first <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/08_mfr/00_content.ipynb) and [second <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/08_mfr/01_content.ipynb) part of Chapter 8.\n",
+    "\n",
+    "The `...`'s in the code cells indicate where you need to fill in code snippets. The number of `...`'s within a code cell give you a rough idea of how many lines of code are needed to solve the task. You should not need to create any additional code cells for your final solution. However, you may want to use temporary code cells to try out some ideas."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Removing Outliers in Streaming Data"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Let's say we are given a `list` object with random integers like `sample` below, and we want to calculate some basic statistics on them."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sample = [\n",
+    "    45, 46, 40, 49, 36, 53, 49, 42, 25, 40, 39, 36, 38, 40, 40, 52, 36, 52, 40, 41,\n",
+    "    35, 29, 48, 43, 42, 30, 29, 33, 55, 33, 38, 50, 39, 56, 52, 28, 37, 56, 45, 37,\n",
+    "    41, 41, 37, 30, 51, 32, 23, 40, 53, 40, 45, 39, 99, 42, 34, 42, 34, 39, 39, 53,\n",
+    "    43, 37, 46, 36, 45, 42, 32, 38, 57, 34, 36, 44, 47, 51, 46, 39, 28, 40, 35, 46,\n",
+    "    41, 51, 41, 23, 46, 40, 40, 51, 50, 32, 47, 36, 38, 29, 32, 53, 34, 43, 39, 41,\n",
+    "    40, 34, 44, 40, 41, 43, 47, 57, 50, 42, 38, 25, 45, 41, 58, 37, 45, 55, 44, 53,\n",
+    "    82, 31, 45, 33, 32, 39, 46, 48, 42, 47, 40, 45, 51, 35, 31, 46, 40, 44, 61, 57,\n",
+    "    40, 36, 35, 55, 40, 56, 36, 35, 86, 36, 51, 40, 54, 50, 49, 36, 41, 37, 48, 41,\n",
+    "    42, 44, 40, 43, 51, 47, 46, 50, 40, 23, 40, 39, 28, 38, 42, 46, 46, 42, 46, 31,\n",
+    "    32, 40, 48, 27, 40, 40, 30, 32, 25, 31, 30, 43, 44, 29, 45, 41, 63, 32, 33, 58,\n",
+    "]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "len(sample)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q1**: `list` objects are **sequences**. What *four* behaviors do they always come with?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " < your answer >"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q2**: Write a function `mean()` that calculates the simple arithmetic mean of a given `sequence` with numbers!\n",
+    "\n",
+    "Hints: You can solve this task with [built-in functions <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html) only. A `for`-loop is *not* needed."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def mean(sequence):\n",
+    "    ..."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sample_mean = mean(sample)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sample_mean"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q3**: Write a function `std()` that calculates the [standard deviation <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_wiki.png\">](https://en.wikipedia.org/wiki/Standard_deviation) of a `sequence` of numbers! Integrate your `mean()` version from before and the [sqrt() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/math.html#math.sqrt) function from the [math <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/math.html) module in the [standard library <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/index.html) provided to you below. Make sure `std()` calls `mean()` only *once* internally! Repeated calls to `mean()` would be a waste of computational resources.\n",
+    "\n",
+    "Hints: Parts of the code are probably too long to fit within the suggested 79 characters per line. So, use *temporary variables* inside your function. Instead of a `for`-loop, you may want to use a `list` comprehension or, even better, a memoryless `generator` expression."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from math import sqrt"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def std(sequence):\n",
+    "    ..."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sample_std = std(sample)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sample_std"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q4**: Complete `standardize()` below that takes a `sequence` of numbers and returns a `list` object with the **[z-scores <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_wiki.png\">](https://en.wikipedia.org/wiki/Standard_score)** of these numbers! A z-score is calculated by subtracting the mean and dividing by the standard deviation. Re-use `mean()` and `std()` from before. Again, ensure that `standardize()` calls `mean()` and `std()` only *once*! Further, round all z-scores with the built-in [round() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#round) function and pass on the keyword-only argument `digits` to it.\n",
+    "\n",
+    "Hint: You may want to use a `list` comprehension instead of a `for`-loop."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def standardize(sequence, *, digits=3):\n",
+    "    ..."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "z_scores = standardize(sample)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The [pprint() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/pprint.html#pprint.pprint) function from the [pprint <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/pprint.html) module in the [standard library <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/index.html) allows us to \"pretty print\" long `list` objects compactly."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from pprint import pprint"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "pprint(z_scores, compact=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We know that `standardize()` works correctly if the resulting z-scores' mean and standard deviation approach `0` and `1` for a long enough `sequence`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "mean(z_scores), std(z_scores)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Even though `standardize()` calls `mean()` and `std()` only once each, `mean()` is called *twice*! That is so because `std()` internally also re-uses `mean()`!\n",
+    "\n",
+    "**Q5.1**: Rewrite `std()` to take an optional keyword-only argument `seq_mean`, defaulting to `None`. If provided, `seq_mean` is used instead of the result of calling `mean()`. Otherwise, the latter is called.\n",
+    "\n",
+    "Hint: You must check if `seq_mean` is still the default value."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def std(sequence, *, seq_mean=None):\n",
+    "    ..."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "`std()` continues to work as before."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sample_std = std(sample)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sample_std"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q5.2**: Now, rewrite `standardize()` to pass on the return value of `mean()` to `std()`! In summary, `standardize()` calculates the z-scores for the numbers in the `sequence` with as few computational steps as possible."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def standardize(sequence, *, digits=3):\n",
+    "    ..."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "z_scores = standardize(sample)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "mean(z_scores), std(z_scores)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q6**: With both `sample` and `z_scores` being materialized `list` objects, we can loop over pairs consisting of a number from `sample` and its corresponding z-score. Write a `for`-loop that prints out all the \"outliers,\" as which we define numbers with an absolute z-score above `1.96`. There are *four* of them in the `sample`.\n",
+    "\n",
+    "Hint: Use the [abs() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#abs) and [zip() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#zip) built-ins."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "..."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We provide a `stream` module with a `data` object that models an *infinite* **stream** of data (cf., the [*stream.py* <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_gh.png\">](https://github.com/webartifex/intro-to-python/blob/develop/08_mfr/stream.py) file in the repository)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from stream import data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "`data` is of type `generator` and has *no* length."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "type(data)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "len(data)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "So, the only thing we can do with it is to pass it to the built-in [next() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#next) function and go over the numbers it streams one by one."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "next(data)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q7**: What happens if you call `mean()` with `data` as the argument? What is the problem?\n",
+    "\n",
+    "Hints: If you try it out, you may have to press the \"Stop\" button in the toolbar at the top. Your computer should *not* crash, but you will *have to* restart this Jupyter notebook with \"Kernel\" > \"Restart\" and import `data` again."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " < your answer >"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "mean(data)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q8**: Write a function `take_sample()` that takes an `iterable` as its argument, like `data`, and creates a *materialized* `list` object out of its first `n` elements, defaulting to `1_000`!\n",
+    "\n",
+    "Hints: [next() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#next) and the [range() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#func-range) built-in may be helpful. You may want to use a `list` comprehension instead of a `for`-loop and write a one-liner. Audacious students may want to look at [isclice() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/itertools.html#itertools.islice) in the [itertools <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/itertools.html) module in the [standard library <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/index.html)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def take_sample(iterable, *, n=1_000):\n",
+    "    ..."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We take a `new_sample` from the stream of `data`, and its statistics are similar to the initial `sample`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "new_sample = take_sample(data)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "len(new_sample)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "mean(new_sample)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "std(new_sample)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q9**: Convert `standardize()` into a *new* function `standardized()` that implements the *same* logic but works on a possibly *infinite* stream of data, provided as an `iterable`, instead of a *finite* `sequence`.\n",
+    "\n",
+    "To calculate a z-score, we need the stream's overall mean and standard deviation, and that is *impossible* to calculate if we do not know how long the stream is, and, in particular, if it is *infinite*. So, `standardized()` first takes a sample from the `iterable` internally, and uses the sample's mean and standard deviation to calculate the z-scores.\n",
+    "\n",
+    "Hint: `standardized()` *must* return a `generator` object. So, use a `generator` expression as the return value; unless you know about the `yield` statement already (cf., [reference <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/reference/simple_stmts.html#the-yield-statement))."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def standardized(iterable, *, digits=3):\n",
+    "    ..."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "`standardized()` works almost like `standardize()` except that we use it with [next() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#next) to obtain the z-scores one by one."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "z_scores = standardized(data)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "z_scores"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "type(z_scores)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "next(z_scores)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q10.1**: `standardized()` allows us to go over an *infinite* stream of z-scores. What we want to do instead is to loop over the stream's raw numbers and skip the outliers. In the remainder of this exercise, you look at the parts that make up the `skip_outliers()` function below to achieve precisely that.\n",
+    "\n",
+    "The first steps in `skip_outliers()` are the same as in `standardized()`: We take a `sample` from the stream of `data` and calculate its statistics."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sample = ...\n",
+    "seq_mean = ...\n",
+    "seq_std = ..."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q10.2**: Just as in `standardized()`, write a `generator` expression that produces z-scores one by one! However, instead of just generating a z-score, the resulting `generator` object should produce `tuple` objects consisting of a \"raw\" number from `data` and its z-score.\n",
+    "\n",
+    "Hint: Look at the revisited \"*Averaging Even Numbers*\" example in [Chapter 7 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/07_sequences/01_content.ipynb#Example:-Averaging-all-even-Numbers-in-a-List-%28revisited%29) for some inspiration, which also contains a `generator` expression producing `tuple` objects."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "standardizer = (... for ... in data)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "`standardizer` should produce `tuple` objects."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "next(standardizer)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q10.3**: Write another `generator` expression that loops over `standardizer`. It contains an `if`-clause that keeps only numbers with an absolute z-score below the `threshold_z`. If you fancy, use `tuple` unpacking."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "threshold_z = 1.96"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "no_outliers = (... for ... in standardizer if ...)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "`no_outliers` should produce `int` objects."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "next(no_outliers)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q10.4**: Lastly, put everything together in the `skip_outliers()` function! Make sure you refer to `iterable` inside the function and not the global `data`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def skip_outliers(iterable, *, threshold_z=1.96):\n",
+    "    sample = ...\n",
+    "    seq_mean = ...\n",
+    "    seq_std = ...\n",
+    "    standardizer = ...\n",
+    "    no_outliers = ...\n",
+    "    return no_outliers"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now, we can create a `generator` object and loop over the `data` in the stream with outliers skipped. Instead of the default `1.96`, we use a `threshold_z` of only `0.05`: That filters out all numbers except `42`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "skipper = skip_outliers(data, threshold_z=0.05)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "skipper"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "type(skipper)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "next(skipper)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q11**: You implemented the functions `mean()`, `std()`, `standardize()`, `standardized()`, and `skip_outliers()`. Which of them are **eager**, and which are **lazy**? How do these two concepts relate to **finite** and **infinite** data?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " < your answer >"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.6"
+  },
+  "toc": {
+   "base_numbering": 1,
+   "nav_menu": {},
+   "number_sections": false,
+   "sideBar": true,
+   "skip_h1_title": true,
+   "title_cell": "Table of Contents",
+   "title_sidebar": "Contents",
+   "toc_cell": false,
+   "toc_position": {},
+   "toc_section_display": false,
+   "toc_window_display": false
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/08_mfr/stream.py b/08_mfr/stream.py
new file mode 100644
index 0000000..6aa6cc7
--- /dev/null
+++ b/08_mfr/stream.py
@@ -0,0 +1,51 @@
+"""Simulation of random streams of data.
+
+This module defines:
+- a generator object `data` modeling an infinite stream of integers
+- a function `make_finite_stream()` that creates finite streams of data
+
+The probability distribution underlying the integers is Gaussian-like with a
+mean of 42 and a standard deviation of 8. The left tail of the distribution is
+cut off meaning that the streams only produce non-negative numbers. Further,
+one in a hundred random numbers has an increased chance to be an outlier.
+"""
+
+import itertools as _itertools
+import random as _random
+
+
+_random.seed(87)
+
+
+def _infinite_stream():
+    """Internal generator function to simulate an infinite stream of data."""
+    while True:
+        number = max(0, int(_random.gauss(42, 8)))
+        if _random.randint(1, 100) == 1:
+            number *= 2
+        yield number
+
+
+def make_finite_stream(min_=5, max_=15):
+    """Simulate a finite stream of data.
+
+    The returned stream is finite, but the number of elements to be produced
+    by it is still random. This default behavior may be turned off by passing
+    in `min_` and `max_` arguments with `min_ == max_`.
+
+    Args:
+        min_ (optional, int): minimum numbers in the stream; defaults to 5
+        max_ (optional, int): maximum numbers in the stream; defaults to 15
+
+    Returns:
+        finite_stream (generator)
+
+    Raises:
+        ValueError: if max_ < min_
+    """
+    stream = _infinite_stream()
+    n = _random.randint(min_, max_)
+    yield from _itertools.islice(stream, n)
+
+
+data = _infinite_stream()
diff --git a/CONTENTS.md b/CONTENTS.md
index 6291fb2..c377dbd 100644
--- a/CONTENTS.md
+++ b/CONTENTS.md
@@ -216,4 +216,7 @@ If this is not possible,
       (`list` Comprehension;
        `generator` Expression;
        Streams of Data;
-       Boolean Reducers)
\ No newline at end of file
+       Boolean Reducers)
+    - [exercises <img height="12" style="display: inline-block" src="static/link/to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/08_mfr/02_exercises.ipynb)
+      [<img height="12" style="display: inline-block" src="static/link/to_mb.png">](https://mybinder.org/v2/gh/webartifex/intro-to-python/develop?urlpath=lab/tree/08_mfr/02_exercises.ipynb)
+      (Removing Outliers in Streaming Data)
\ No newline at end of file

From 7ca4754140741034c538728eb36e72caa8dbf91e Mon Sep 17 00:00:00 2001
From: Alexander Hess <alexander@webartifex.biz>
Date: Wed, 21 Oct 2020 19:03:56 +0200
Subject: [PATCH 194/242] Add initial version of chapter 08's exercises, part 2

---
 08_mfr/03_exercises.ipynb | 455 ++++++++++++++++++++++++++++++++++++++
 CONTENTS.md               |   5 +-
 2 files changed, 459 insertions(+), 1 deletion(-)
 create mode 100644 08_mfr/03_exercises.ipynb

diff --git a/08_mfr/03_exercises.ipynb b/08_mfr/03_exercises.ipynb
new file mode 100644
index 0000000..b93761a
--- /dev/null
+++ b/08_mfr/03_exercises.ipynb
@@ -0,0 +1,455 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Note**: Click on \"*Kernel*\" > \"*Restart Kernel and Run All*\" in [JupyterLab](https://jupyterlab.readthedocs.io/en/stable/) *after* finishing the exercises to ensure that your solution runs top to bottom *without* any errors. If you cannot run this file on your machine, you may want to open it [in the cloud <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_mb.png\">](https://mybinder.org/v2/gh/webartifex/intro-to-python/develop?urlpath=lab/tree/08_mfr/03_exercises.ipynb)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Chapter 8: Map, Filter, & Reduce (Coding Exercises)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The exercises below assume that you have read the [first <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/08_mfr/00_content.ipynb) and [second <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/08_mfr/01_content.ipynb) part of Chapter 8.\n",
+    "\n",
+    "The `...`'s in the code cells indicate where you need to fill in code snippets. The number of `...`'s within a code cell give you a rough idea of how many lines of code are needed to solve the task. You should not need to create any additional code cells for your final solution. However, you may want to use temporary code cells to try out some ideas."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Packing & Unpacking with Functions (continued)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q1**: Copy your solution to **Q10** from the \"*Packing & Unpacking with Functions*\" exercise in [Chapter 7 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/07_sequences/04_exercises.ipynb) into the code cell below!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import collections.abc as abc"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def product(*args, ...):\n",
+    "    \"\"\"Multiply all arguments.\"\"\"\n",
+    "    ...\n",
+    "    ...\n",
+    "    ...\n",
+    "    ...\n",
+    "\n",
+    "    ...\n",
+    "    ...\n",
+    "\n",
+    "    return ..."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q2**: Verify that all test cases below work (i.e., the `assert` statements must *not* raise an `AssertionError`)!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "assert product(42) == 42"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "assert product(2, 5, 10) == 100"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "assert product(2, 5, 10, start=2) == 200"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "one_hundred = [2, 5, 10]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "assert product(one_hundred) == 100"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "assert product(*one_hundred) == 100"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q3**: Verify that `product()` raises a `TypeError` when called without any arguments!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "product()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This implementation of `product()` is convenient to use, in particular, because we can pass it any *collection* object with or without *unpacking* it.\n",
+    "\n",
+    "However, `product()` suffers from one last flaw: We cannot pass it a **stream** of data, as modeled, for example, with a `generator` object that produces elements on a one-by-one basis.\n",
+    "\n",
+    "**Q4**: Click through the following code cells and observe what they do!\n",
+    "\n",
+    "The [*stream.py* <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_gh.png\">](https://github.com/webartifex/intro-to-python/blob/develop/08_mfr/stream.py) module in the book's repository provides a `make_finite_stream()` function. It is a *factory* function creating objects of type `generator` that we use to model *streaming* data."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from stream import make_finite_stream"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data = make_finite_stream()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "type(data)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "`generator` objects are good for only *one* thing: Giving us the \"next\" element in a series of possibly *infinitely* many objects. While the `data` object is finite (i.e., execute the next code cell until you see a `StopIteration` exception), ..."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "next(data)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "... it has *no* concept of a \"length:\" The built-in [len() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#len) function raises a `TypeError`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "len(data)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can use the built-in [list() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#func-list) constructor to *materialize* all elements. However, in a real-world scenario, these may *not* fit into our machine's memory! If you get an empty `list` object below, you have to create a *new* `data` object above again."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "list(data)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "To be more realistic, `make_finite_stream()` creates `generator` objects producing a varying number of elements."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "list(make_finite_stream())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "list(make_finite_stream())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "list(make_finite_stream())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Let's see what happens if we pass a `generator` object, as created by `make_finite_stream()`, instead of a materialized *collection*, like `one_hundred`, to `product()`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "product(make_finite_stream())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q5**: What line causes the `TypeError`? What line is really the problem in `product()`? Hint: These may be different lines. Describe what happens on each line in the function's body until the exception is raised!"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " < your answer >"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q6**: Adapt `product()` one last time to make it work with `generator` objects, or more generallz *iterators*, as well!\n",
+    "\n",
+    "Hints: This task is as easy as replacing `Collection` with something else. Which of the three behaviors of *collections* do `generator` objects also exhibit? You may want to look at the documentations on the built-in [max() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#max), [min() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#min), and [sum() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#sum) functions: What kind of argument do they take?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def product(*args, ...):\n",
+    "    \"\"\"Multiply all arguments.\"\"\"\n",
+    "    ...\n",
+    "    ...\n",
+    "    ...\n",
+    "    ...\n",
+    "\n",
+    "    ...\n",
+    "    ...\n",
+    "\n",
+    "    return ..."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The final version of `product()` behaves like built-ins in edge cases (i.e., `sum()` also raises a `TypeError` when called without arguments), ..."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "product()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "... works with the arguments passed either separately as *positional* arguments, *packed* together into a single *collection* argument, or *unpacked*, ..."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "product(42)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "product(2, 5, 10)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "product([2, 5, 10])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "product(*[2, 5, 10])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "... and can handle *streaming* data with *indefinite* \"length.\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "product(make_finite_stream())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In real-world projects, the data science practitioner must decide if it is worthwhile to make a function usable in various different forms as we do in this exercise. This may be over-engineered.\n",
+    "\n",
+    "Yet, two lessons are important to take away:\n",
+    "- It is a good idea to *mimic* the behavior of *built-ins* when accepting arguments, and\n",
+    "- make functions capable of working with *streaming* data."
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.6"
+  },
+  "toc": {
+   "base_numbering": 1,
+   "nav_menu": {},
+   "number_sections": false,
+   "sideBar": true,
+   "skip_h1_title": true,
+   "title_cell": "Table of Contents",
+   "title_sidebar": "Contents",
+   "toc_cell": false,
+   "toc_position": {},
+   "toc_section_display": false,
+   "toc_window_display": false
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/CONTENTS.md b/CONTENTS.md
index c377dbd..971c890 100644
--- a/CONTENTS.md
+++ b/CONTENTS.md
@@ -219,4 +219,7 @@ If this is not possible,
        Boolean Reducers)
     - [exercises <img height="12" style="display: inline-block" src="static/link/to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/08_mfr/02_exercises.ipynb)
       [<img height="12" style="display: inline-block" src="static/link/to_mb.png">](https://mybinder.org/v2/gh/webartifex/intro-to-python/develop?urlpath=lab/tree/08_mfr/02_exercises.ipynb)
-      (Removing Outliers in Streaming Data)
\ No newline at end of file
+      (Removing Outliers in Streaming Data)
+    - [exercises <img height="12" style="display: inline-block" src="static/link/to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/08_mfr/03_exercises.ipynb)
+      [<img height="12" style="display: inline-block" src="static/link/to_mb.png">](https://mybinder.org/v2/gh/webartifex/intro-to-python/develop?urlpath=lab/tree/08_mfr/03_exercises.ipynb)
+      (Packing & Unpacking with Functions, continued)
\ No newline at end of file

From dc03b7ad64e8437bef1006b4979f9073cebfed2c Mon Sep 17 00:00:00 2001
From: Alexander Hess <alexander@webartifex.biz>
Date: Wed, 21 Oct 2020 19:17:43 +0200
Subject: [PATCH 195/242] Add initial version of chapter 08, part 3

---
 08_mfr/04_content.ipynb | 1672 +++++++++++++++++++++++++++++++++++++++
 CONTENTS.md             |    6 +-
 2 files changed, 1677 insertions(+), 1 deletion(-)
 create mode 100644 08_mfr/04_content.ipynb

diff --git a/08_mfr/04_content.ipynb b/08_mfr/04_content.ipynb
new file mode 100644
index 0000000..ff55376
--- /dev/null
+++ b/08_mfr/04_content.ipynb
@@ -0,0 +1,1672 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "**Note**: Click on \"*Kernel*\" > \"*Restart Kernel and Clear All Outputs*\" in [JupyterLab](https://jupyterlab.readthedocs.io/en/stable/) *before* reading this notebook to reset its output. If you cannot run this file on your machine, you may want to open it [in the cloud <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_mb.png\">](https://mybinder.org/v2/gh/webartifex/intro-to-python/develop?urlpath=lab/tree/08_mfr/04_content.ipynb)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "# Chapter 8: Map, Filter, & Reduce"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Similarly to how we classify different *concrete* data types like `list` or `str` by how they behave *abstractly* in a given context in [Chapter 7 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/07_sequences/00_content.ipynb#Collections-vs.-Sequences), we also do so for the data types we have introduced in this chapter."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "## Iterators vs. Iterables"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Here, the `map`, `filter`, and `generator` types all behave like \"rules\" in memory that govern how objects are produced \"on the fly.\" Their main commonality is their support for the built-in [next() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#next) function. In computer science terminology, such data types are called **[iterators <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_wiki.png\">](https://en.wikipedia.org/wiki/Iterator)**, and the [collections.abc <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/collections.abc.html) module formalizes them with the `Iterator` ABC in Python.\n",
+    "\n",
+    "So, one example of an iterator is `evens_transformed` below, an object of type `generator`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "numbers = [7, 11, 8, 5, 3, 12, 2, 6, 9, 10, 1, 4]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "evens_transformed = ((x ** 2) + 1 for x in numbers if x % 2 == 0)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Let's first confirm that `evens_transformed` is indeed an `Iterator`, \"abstractly speaking.\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "import collections.abc as abc"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "isinstance(evens_transformed, abc.Iterator)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "In Python, iterators are *always* also iterables. The reverse is *not* true! To be precise, iterators are *specializations* of iterables. That is what the \"Inherits from\" column means in the [collections.abc <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/collections.abc.html) module's documentation."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "isinstance(evens_transformed, abc.Iterable)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Furthermore, we sharpen our definition of an *iterable* from [Chapter 7 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/07_sequences/00_content.ipynb#Collections-vs.-Sequences): Just as we define an *iterator* to be any object that supports the [next() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#next) function, we define an *iterable* to be any object that supports the built-in [iter() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#iter) function.\n",
+    "\n",
+    "The confused reader may now be wondering how the two concepts relate to each other.\n",
+    "\n",
+    "In short, the [iter() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#iter) function is the general way to create an *iterator* object out of a given *iterable* object. Then, the *iterator* object manages the iteration over the *iterable* object. In real-world code, we hardly ever see [iter() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#iter) as Python calls it for us in the background. \n",
+    "\n",
+    "For illustration, let's do that ourselves and create *two* iterators out of the iterable `numbers` and see what we can do with them."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "iterator1 = iter(numbers)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "iterator2 = iter(numbers)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "`iterator1` and `iterator2` are of type `list_iterator`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "list_iterator"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "type(iterator1)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "*Iterators* are useful for only *one* thing: Get the next object from the associated *iterable*.\n",
+    "\n",
+    "By calling [next() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#next) three times with `iterator1` as the argument, we obtain the first three elements of `numbers`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(7, 11, 8)"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "next(iterator1), next(iterator1), next(iterator1)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "`iterator1` and `iterator2` keep their *states* separate. So, we could loop over the same *iterable* several times in parallel."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(5, 7)"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "next(iterator1), next(iterator2)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "We can also play a \"trick\" and exchange some elements in `numbers`. `iterator1` and `iterator2` do *not* see these changes and present us with the new elements. So, *iterators* not only have state on their own but also keep this separate from the underlying *iterable*."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "numbers[1], numbers[4] = 99, 99"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(99, 99)"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "next(iterator1), next(iterator2)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Let's re-assign the elements in `numbers` so that they are in order. After that, the numbers returned from [next() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#next) also tell us how often [next() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#next) was called with `iterator1` or `iterator2`. We conclude that `list_iterator` objects work by simply keeping track of the *last* index obtained from the underlying *iterable*."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "numbers[:] = list(range(1, 13))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12]"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "numbers"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(6, 3)"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "next(iterator1), next(iterator2)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "With the concepts introduced in this section, we can now understand the first sentence in the documentation on the [zip() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#zip) built-in better: \"Make an *iterator* that aggregates elements from each of the *iterables*.\"\n",
+    "\n",
+    "Because *iterators* are always also *iterables*, we may pass `iterator1` and `iterator2` as arguments to [zip() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#zip).\n",
+    "\n",
+    "The returned `zipper` object is of type `zip` and, \"abstractly speaking,\" an `Iterator` as well."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "zipper = zip(iterator1, iterator2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<zip at 0x7f2788118b00>"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "zipper"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "zip"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "type(zipper)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "isinstance(zipper, abc.Iterator)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "So far, we have always used [zip() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#zip) in a `for`-loop. That was our earlier definition of an *iterable*. Our revised definition in this section states that an *iterable* is an object that supports the [iter() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#iter) function. So, let's see what happens if we pass `zipper` to [iter() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#iter)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "zipper_iterator = iter(zipper)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<zip at 0x7f2788118b00>"
+      ]
+     },
+     "execution_count": 21,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "zipper_iterator"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "`zipper_iterator` references the *same* object as `zipper`! That is true for *iterators* in general: Any *iterator* created from an existing *iterator* with [iter() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#iter) is the *iterator* itself."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "zipper is zipper_iterator"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "The Python core developers made that design decision so that *iterators* may also be looped over.\n",
+    "\n",
+    "The `for`-loop below prints out only *six* more `tuple` objects derived from the now ordered `numbers` because the `iterator1` object hidden inside `zipper` already returned its first *six* elements. So, the respective first elements of the `tuple` objects printed range from `7` to `12`. Similarly, as `iterator2` already returned its first *three* elements from `numbers`, we see the respective second elements in the range from `4` to `9`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "7 and 4   8 and 5   9 and 6   10 and 7   11 and 8   12 and 9   "
+     ]
+    }
+   ],
+   "source": [
+    "for x, y in zipper:\n",
+    "    print(x, \"and\", y, end=\"   \")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "`zipper` is now *exhausted*. So, the `for`-loop below does *not* make any iteration at all."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "for x, y in zipper:\n",
+    "    raise RuntimeError(\"We won't see this error\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "We verify that `iterator1` is exhausted by passing it to [next() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#next) again, which raises a `StopIteration` exception."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "ename": "StopIteration",
+     "evalue": "",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mStopIteration\u001b[0m                             Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-25-0680f5ec8ce3>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mnext\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0miterator1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;31mStopIteration\u001b[0m: "
+     ]
+    }
+   ],
+   "source": [
+    "next(iterator1)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "On the contrary, `iterator2` is *not* yet exhausted."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "10"
+      ]
+     },
+     "execution_count": 26,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "next(iterator2)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Understanding *iterators* and *iterables* is helpful for any data science practitioner that deals with large amounts of data. Even without that, these two terms occur everywhere in Python-related texts and documentation. So, a beginner should regularly review this section until it becomes second nature."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "## The `for` Statement (revisited)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "In [Chapter 4 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/04_iteration/02_content.ipynb#The-for-Statement), we argue that the `for` statement is syntactic sugar, replacing the `while` statement in many common scenarios. In particular, a `for`-loop saves us two tasks: Managing an index variable *and* obtaining the individual elements by indexing. In this sub-section, we look at a more realistic picture, using the new terminology as well.\n",
+    "\n",
+    "Let's print out the elements of a `list` object as the *iterable* being looped over."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "iterable = [0, 1, 2, 3, 4]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0 1 2 3 4 "
+     ]
+    }
+   ],
+   "source": [
+    "for element in iterable:\n",
+    "    print(element, end=\" \")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Our previous and equivalent formulation with a `while` statement is like so."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0 1 2 3 4 "
+     ]
+    }
+   ],
+   "source": [
+    "index = 0\n",
+    "\n",
+    "while index < len(iterable):\n",
+    "    element = iterable[index]\n",
+    "    print(element, end=\" \")\n",
+    "    index += 1\n",
+    "\n",
+    "del index"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "What actually happens behind the scenes in the Python interpreter is shown below.\n",
+    "\n",
+    "First, Python calls [iter() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#iter) with the `iterable` to be looped over as the argument. The returned `iterator` contains the entire logic of how the `iterable` is looped over. In particular, the `iterator` may or may not pick the `iterable`'s elements in a predictable order. That is up to the \"rule\" it models.\n",
+    "\n",
+    "Second, Python enters an *indefinite* `while`-loop. It tries to obtain the next element with [next() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#next). If that succeeds, the `for`-loop's code block is executed. Below, that code is placed within the `else`-clause that runs only if *no* exception is raised in the `try`-clause. Then, Python jumps into the next iteration and tries to obtain the next element from the `iterator`, and so on. Once the `iterator` is exhausted, it raises a `StopIteration` exception, and Python stops the `while`-loop with the `break` statement."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0 1 2 3 4 "
+     ]
+    }
+   ],
+   "source": [
+    "iterator = iter(iterable)\n",
+    "\n",
+    "while True:\n",
+    "    try:\n",
+    "        element = next(iterator)\n",
+    "    except StopIteration:\n",
+    "        break\n",
+    "    else:\n",
+    "        print(element, end=\" \")\n",
+    "\n",
+    "del iterator"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "## sorted() vs. reversed()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Now that we know the concept of an *iterator*, let's compare some of the built-ins introduced in [Chapter 7 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/07_sequences/00_content.ipynb) in detail and make sure we understand what is going on in memory. This section also serves as a summary of all the concepts in this chapter.\n",
+    "\n",
+    "We use two simple examples, `numbers` and `memoryless`: `numbers` creates *thirteen* objects in memory and `memoryless` only *one* (cf., [PythonTutor <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](http://pythontutor.com/visualize.html#code=numbers%20%3D%20%5B7,%2011,%208,%205,%203,%2012,%202,%206,%209,%2010,%201,%204%5D%0Amemoryless%20%3D%20range%281,%2013%29&cumulative=false&curInstr=2&heapPrimitives=nevernest&mode=display&origin=opt-frontend.js&py=3&rawInputLstJSON=%5B%5D&textReferences=false))."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "numbers = [7, 11, 8, 5, 3, 12, 2, 6, 9, 10, 1, 4]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "memoryless = range(1, 13)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "The [sorted() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#sorted) function takes a *finite* `iterable` argument and *materializes* its elements into a *new* `list` object that is returned.\n",
+    "\n",
+    "The argument may already be materialized, as is the case with `numbers`, but may also be an *iterable* without any objects in it, such as `memoryless`. In both cases, we end up with materialized `list` objects with the elements sorted in *forward* order (cf., [PythonTutor <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](http://pythontutor.com/visualize.html#code=numbers%20%3D%20%5B7,%2011,%208,%205,%203,%2012,%202,%206,%209,%2010,%201,%204%5D%0Amemoryless%20%3D%20range%281,%2013%29%0Aresult1%20%3D%20sorted%28numbers%29%0Aresult2%20%3D%20sorted%28memoryless%29&cumulative=false&curInstr=4&heapPrimitives=nevernest&mode=display&origin=opt-frontend.js&py=3&rawInputLstJSON=%5B%5D&textReferences=false))."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12]"
+      ]
+     },
+     "execution_count": 33,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "sorted(numbers)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12]"
+      ]
+     },
+     "execution_count": 34,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "sorted(memoryless)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "By adding a keyword-only argument `reverse=True`, the materialized `list` objects are sorted in *reverse* order (cf., [PythonTutor <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](http://pythontutor.com/visualize.html#code=numbers%20%3D%20%5B7,%2011,%208,%205,%203,%2012,%202,%206,%209,%2010,%201,%204%5D%0Amemoryless%20%3D%20range%281,%2013%29%0Aresult1%20%3D%20sorted%28numbers,%20reverse%3DTrue%29%0Aresult2%20%3D%20sorted%28memoryless,%20reverse%3DTrue%29&cumulative=false&curInstr=4&heapPrimitives=nevernest&mode=display&origin=opt-frontend.js&py=3&rawInputLstJSON=%5B%5D&textReferences=false))."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[12, 11, 10, 9, 8, 7, 6, 5, 4, 3, 2, 1]"
+      ]
+     },
+     "execution_count": 35,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "sorted(numbers, reverse=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[12, 11, 10, 9, 8, 7, 6, 5, 4, 3, 2, 1]"
+      ]
+     },
+     "execution_count": 36,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "sorted(memoryless, reverse=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "The order in `numbers` remains *unchanged*, and `memoryless` is still *not* materialized."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[7, 11, 8, 5, 3, 12, 2, 6, 9, 10, 1, 4]"
+      ]
+     },
+     "execution_count": 37,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "numbers"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "range(1, 13)"
+      ]
+     },
+     "execution_count": 38,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "memoryless"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "The [reversed() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#reversed) built-in takes a `sequence` argument and returns an *iterator*. The argument must be *finite* and *reversible* (i.e., *iterable* in *reverse* order) as otherwise [reversed() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#reversed) could neither determine the last element that becomes the first nor loop in a *predictable* backward fashion. [PythonTutor <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](http://pythontutor.com/visualize.html#code=numbers%20%3D%20%5B7,%2011,%208,%205,%203,%2012,%202,%206,%209,%2010,%201,%204%5D%0Amemoryless%20%3D%20range%281,%2013%29%0Aiterator1%20%3D%20reversed%28numbers%29%0Aiterator2%20%3D%20reversed%28memoryless%29&cumulative=false&curInstr=4&heapPrimitives=nevernest&mode=display&origin=opt-frontend.js&py=3&rawInputLstJSON=%5B%5D&textReferences=false) confirms that [reversed() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#reversed) does *not* materialize any elements but only returns an *iterator*.\n",
+    "\n",
+    "**Side Note**: Even though `range` objects, like `memoryless` here, do *not* \"contain\" references to other objects, they count as *sequence* types, and as such, they are also *container* types. The `in` operator works with `range` objects because we can always cast the object to be checked as an `int` and check if that lies within the `range` object's `start` and `stop` values, taking a potential `step` value into account (cf., this [blog post](https://treyhunner.com/2018/02/python-range-is-not-an-iterator/) for more details on the [range() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#func-range) built-in)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<list_reverseiterator at 0x7f2788158880>"
+      ]
+     },
+     "execution_count": 39,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "reversed(numbers)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<range_iterator at 0x7f2788158fc0>"
+      ]
+     },
+     "execution_count": 40,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "reversed(memoryless)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "To materialize the elements, we can pass the returned *iterators* to, for example, the [list() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#func-list) or [tuple() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#func-tuple) constructors. That creates *new* `list` and `tuple` objects (cf., [PythonTutor <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](http://pythontutor.com/visualize.html#code=numbers%20%3D%20%5B7,%2011,%208,%205,%203,%2012,%202,%206,%209,%2010,%201,%204%5D%0Amemoryless%20%3D%20range%281,%2013%29%0Aresult1%20%3D%20list%28reversed%28numbers%29%29%0Aresult2%20%3D%20tuple%28reversed%28memoryless%29%29&cumulative=false&curInstr=4&heapPrimitives=nevernest&mode=display&origin=opt-frontend.js&py=3&rawInputLstJSON=%5B%5D&textReferences=false)).\n",
+    "\n",
+    "To reiterate some more new terminology from this chapter, we describe [reversed() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#reversed) as *lazy* whereas [list() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#func-list) and [tuple() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#func-tuple) are *eager*. The former has no significant side effect in memory, while the latter may require a lot of memory."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[4, 1, 10, 9, 6, 2, 12, 3, 5, 8, 11, 7]"
+      ]
+     },
+     "execution_count": 41,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "list(reversed(numbers))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(12, 11, 10, 9, 8, 7, 6, 5, 4, 3, 2, 1)"
+      ]
+     },
+     "execution_count": 42,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "tuple(reversed(memoryless))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Of course, we can also loop over the returned *iterators* instead.\n",
+    "\n",
+    "That works because *iterators* are always *iterable*; in particular, as the previous \"*The for Statement (revisited)*\" sub-section explains, the `for`-loops below call `iter(reversed(numbers))` and `iter(reversed(memoryless))` behind the scenes. However, the *iterators* returned by [iter() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#iter) are the *same* as the `reversed(numbers)` and `reversed(memoryless)` iterators passed in! In summary, the `for`-loops below involve many subtleties that together make Python the expressive language it is."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "4 1 10 9 6 2 12 3 5 8 11 7 "
+     ]
+    }
+   ],
+   "source": [
+    "for number in reversed(numbers):\n",
+    "    print(number, end=\" \")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "12 11 10 9 8 7 6 5 4 3 2 1 "
+     ]
+    }
+   ],
+   "source": [
+    "for element in reversed(memoryless):\n",
+    "    print(element, end=\" \")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "As with [sorted() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#sorted), the [reversed() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#reversed) built-in does *not* mutate its argument."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[7, 11, 8, 5, 3, 12, 2, 6, 9, 10, 1, 4]"
+      ]
+     },
+     "execution_count": 45,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "numbers"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "range(1, 13)"
+      ]
+     },
+     "execution_count": 46,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "memoryless"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "To point out the potentially obvious, we compare the results of *sorting* `numbers` in *reverse* order with *reversing* it: These are *different* concepts!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[7, 11, 8, 5, 3, 12, 2, 6, 9, 10, 1, 4]"
+      ]
+     },
+     "execution_count": 47,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "numbers"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 48,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[12, 11, 10, 9, 8, 7, 6, 5, 4, 3, 2, 1]"
+      ]
+     },
+     "execution_count": 48,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "sorted(numbers, reverse=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 49,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[4, 1, 10, 9, 6, 2, 12, 3, 5, 8, 11, 7]"
+      ]
+     },
+     "execution_count": 49,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "list(reversed(numbers))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Whereas both [sorted() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#sorted) and [reversed() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#reversed) do *not* mutate their arguments, the *mutable* `list` type comes with two methods, [sort() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/stdtypes.html#list.sort) and `reverse()`, that implement the same logic but mutate an object, like `numbers` below, *in place*. To indicate that all changes occur *in place*, the [sort() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/stdtypes.html#list.sort) and `reverse()` methods always return `None`, which is not shown in JupyterLab."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 50,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[7, 11, 8, 5, 3, 12, 2, 6, 9, 10, 1, 4]"
+      ]
+     },
+     "execution_count": 50,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "numbers"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "The `reverse()` method on the `list` type is *eager*, as opposed to the *lazy* [reversed() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#reversed) built-in. That means the mutations caused by the `reverse()` method are written into memory right away."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 51,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "numbers.reverse()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 52,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[4, 1, 10, 9, 6, 2, 12, 3, 5, 8, 11, 7]"
+      ]
+     },
+     "execution_count": 52,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "numbers"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "*Sorting* `numbers` in place occurs eagerly."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 53,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "numbers.sort(reverse=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 54,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[12, 11, 10, 9, 8, 7, 6, 5, 4, 3, 2, 1]"
+      ]
+     },
+     "execution_count": 54,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "numbers"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 55,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "numbers.sort()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 56,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12]"
+      ]
+     },
+     "execution_count": 56,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "numbers"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.6"
+  },
+  "livereveal": {
+   "auto_select": "code",
+   "auto_select_fragment": true,
+   "scroll": true,
+   "theme": "serif"
+  },
+  "toc": {
+   "base_numbering": 1,
+   "nav_menu": {},
+   "number_sections": false,
+   "sideBar": true,
+   "skip_h1_title": true,
+   "title_cell": "Table of Contents",
+   "title_sidebar": "Contents",
+   "toc_cell": false,
+   "toc_position": {
+    "height": "calc(100% - 180px)",
+    "left": "10px",
+    "top": "150px",
+    "width": "384px"
+   },
+   "toc_section_display": false,
+   "toc_window_display": false
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/CONTENTS.md b/CONTENTS.md
index 971c890..1309419 100644
--- a/CONTENTS.md
+++ b/CONTENTS.md
@@ -222,4 +222,8 @@ If this is not possible,
       (Removing Outliers in Streaming Data)
     - [exercises <img height="12" style="display: inline-block" src="static/link/to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/08_mfr/03_exercises.ipynb)
       [<img height="12" style="display: inline-block" src="static/link/to_mb.png">](https://mybinder.org/v2/gh/webartifex/intro-to-python/develop?urlpath=lab/tree/08_mfr/03_exercises.ipynb)
-      (Packing & Unpacking with Functions, continued)
\ No newline at end of file
+      (Packing & Unpacking with Functions, continued)
+    - [content <img height="12" style="display: inline-block" src="static/link/to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/08_mfr/04_content.ipynb)
+      [<img height="12" style="display: inline-block" src="static/link/to_mb.png">](https://mybinder.org/v2/gh/webartifex/intro-to-python/develop?urlpath=lab/tree/08_mfr/04_content.ipynb)
+      (Iterators vs. Iterables;
+       Example: `sorted()` vs. `reversed()`)
\ No newline at end of file

From 4d5b23ff03234d181688afe66c11b1dbb82c5871 Mon Sep 17 00:00:00 2001
From: Alexander Hess <alexander@webartifex.biz>
Date: Wed, 21 Oct 2020 19:19:20 +0200
Subject: [PATCH 196/242] Add initial version of chapter 08's summary

---
 08_mfr/05_summary.ipynb | 75 +++++++++++++++++++++++++++++++++++++++++
 CONTENTS.md             |  3 +-
 2 files changed, 77 insertions(+), 1 deletion(-)
 create mode 100644 08_mfr/05_summary.ipynb

diff --git a/08_mfr/05_summary.ipynb b/08_mfr/05_summary.ipynb
new file mode 100644
index 0000000..361fbd0
--- /dev/null
+++ b/08_mfr/05_summary.ipynb
@@ -0,0 +1,75 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "# Chapter 8: Map, Filter, & Reduce (TL;DR)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "The operations we do with sequential data commonly follow the **map-filter-reduce paradigm**: We apply the same transformation to all elements, filter some of them out, and calculate summary statistics from the remaining ones.\n",
+    "\n",
+    "An essential idea in this chapter is that, in many situations, we need *not* have all the data **materialized** in memory. Instead, **iterators** allow us to process sequential data on a one-by-one basis.\n",
+    "\n",
+    "Examples for iterators are the `map`, `filter`, and `generator` types."
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.6"
+  },
+  "livereveal": {
+   "auto_select": "code",
+   "auto_select_fragment": true,
+   "scroll": true,
+   "theme": "serif"
+  },
+  "toc": {
+   "base_numbering": 1,
+   "nav_menu": {},
+   "number_sections": false,
+   "sideBar": true,
+   "skip_h1_title": true,
+   "title_cell": "Table of Contents",
+   "title_sidebar": "Contents",
+   "toc_cell": false,
+   "toc_position": {
+    "height": "calc(100% - 180px)",
+    "left": "10px",
+    "top": "150px",
+    "width": "384px"
+   },
+   "toc_section_display": false,
+   "toc_window_display": false
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/CONTENTS.md b/CONTENTS.md
index 1309419..332a42f 100644
--- a/CONTENTS.md
+++ b/CONTENTS.md
@@ -226,4 +226,5 @@ If this is not possible,
     - [content <img height="12" style="display: inline-block" src="static/link/to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/08_mfr/04_content.ipynb)
       [<img height="12" style="display: inline-block" src="static/link/to_mb.png">](https://mybinder.org/v2/gh/webartifex/intro-to-python/develop?urlpath=lab/tree/08_mfr/04_content.ipynb)
       (Iterators vs. Iterables;
-       Example: `sorted()` vs. `reversed()`)
\ No newline at end of file
+       Example: `sorted()` vs. `reversed()`)
+    - [summary <img height="12" style="display: inline-block" src="static/link/to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/08_mfr/05_summary.ipynb)
\ No newline at end of file

From b1a55631147e8f4f6aa055265e4ba88d4bb40a51 Mon Sep 17 00:00:00 2001
From: Alexander Hess <alexander@webartifex.biz>
Date: Wed, 21 Oct 2020 19:22:08 +0200
Subject: [PATCH 197/242] Add initial version of chapter 08's review

---
 08_mfr/06_review.ipynb | 214 +++++++++++++++++++++++++++++++++++++++++
 CONTENTS.md            |   3 +-
 2 files changed, 216 insertions(+), 1 deletion(-)
 create mode 100644 08_mfr/06_review.ipynb

diff --git a/08_mfr/06_review.ipynb b/08_mfr/06_review.ipynb
new file mode 100644
index 0000000..2f3d29f
--- /dev/null
+++ b/08_mfr/06_review.ipynb
@@ -0,0 +1,214 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Chapter 8: Map, Filter, & Reduce (Review Questions)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The questions below assume that you have read the [first <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/08_mfr/00_content.ipynb), [second <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/08_mfr/01_content.ipynb), and [third <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/08_mfr/04_content.ipynb) part in Chapter 8.\n",
+    "\n",
+    "Be concise in your answers! Most questions can be answered in *one* sentence."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Essay Questions "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q1**: With the [map() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#map) and [filter() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#filter) built-ins and the [reduce() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functools.html#functools.reduce) function from the [functools <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functools.html) module in the [standard library <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/index.html), we can replace many tedious `for`-loops and `if` statements. What are some advantages of doing so?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " < your answer >"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q2**: Looking at the `lambda` expression inside [reduce() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functools.html#functools.reduce) below, what [built-in function <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html) is mimicked here?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "```python\n",
+    "from functools import reduce\n",
+    "\n",
+    "numbers = [7, 11, 8, 5, 3, 12, 2, 6, 9, 10, 1, 4]\n",
+    "\n",
+    "reduce(lambda x, y: x if x > y else y, numbers)\n",
+    "```"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " < your answer >"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q3**: What is the primary use case of **`list` comprehensions**? Why do we describe them as **eager**?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " < your answer >"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q4**: **`generator` expressions** may replace `list` objects and list comprehensions in many scenarios. When evaluated, they create a **lazy** `generator` object that does *not* **materialize** its elements right away. What do we mean by that? What does it mean for a `generator` object to be **exhausted**?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " < your answer >"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q5**: What does it mean for the **boolean reducers**, the built-in [all() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#all) and [any() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#any) functions, to follow the **short-circuiting** strategy?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " < your answer >"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q6**: What is an **iterator**? How does it relate to an **iterable**?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " < your answer >"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## True / False Questions"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Motivate your answer with *one short* sentence!"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q7**: `lambda` expressions are useful in the context of the **map-filter-reduce** paradigm, where we often do *not* re-use a `function` object more than once."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " < your answer >"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q8**: Using **`generator` expressions** in place of **`list` comprehensions** wherever possible is a good practice as it makes our programs use memory more efficiently."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " < your answer >"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q9**: Just as **`list` comprehensions** create `list` objects, **`tuple` comprehensions** create `tuple` objects."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " < your answer >"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.6"
+  },
+  "toc": {
+   "base_numbering": 1,
+   "nav_menu": {},
+   "number_sections": false,
+   "sideBar": true,
+   "skip_h1_title": true,
+   "title_cell": "Table of Contents",
+   "title_sidebar": "Contents",
+   "toc_cell": false,
+   "toc_position": {},
+   "toc_section_display": false,
+   "toc_window_display": false
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/CONTENTS.md b/CONTENTS.md
index 332a42f..bfa7f76 100644
--- a/CONTENTS.md
+++ b/CONTENTS.md
@@ -227,4 +227,5 @@ If this is not possible,
       [<img height="12" style="display: inline-block" src="static/link/to_mb.png">](https://mybinder.org/v2/gh/webartifex/intro-to-python/develop?urlpath=lab/tree/08_mfr/04_content.ipynb)
       (Iterators vs. Iterables;
        Example: `sorted()` vs. `reversed()`)
-    - [summary <img height="12" style="display: inline-block" src="static/link/to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/08_mfr/05_summary.ipynb)
\ No newline at end of file
+    - [summary <img height="12" style="display: inline-block" src="static/link/to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/08_mfr/05_summary.ipynb)
+    - [review questions <img height="12" style="display: inline-block" src="static/link/to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/08_mfr/06_review.ipynb)

From 814069e7fbfc31eaca0985f2e584a53e6642f3bb Mon Sep 17 00:00:00 2001
From: Alexander Hess <alexander@webartifex.biz>
Date: Wed, 21 Oct 2020 19:24:51 +0200
Subject: [PATCH 198/242] Streamline slides

---
 08_mfr/00_content.ipynb | 6 +++++-
 08_mfr/01_content.ipynb | 8 ++++++--
 2 files changed, 11 insertions(+), 3 deletions(-)

diff --git a/08_mfr/00_content.ipynb b/08_mfr/00_content.ipynb
index 4cf1aa7..1884515 100644
--- a/08_mfr/00_content.ipynb
+++ b/08_mfr/00_content.ipynb
@@ -329,7 +329,11 @@
   {
    "cell_type": "code",
    "execution_count": 11,
-   "metadata": {},
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
    "outputs": [
     {
      "data": {
diff --git a/08_mfr/01_content.ipynb b/08_mfr/01_content.ipynb
index 2cf8720..688d953 100644
--- a/08_mfr/01_content.ipynb
+++ b/08_mfr/01_content.ipynb
@@ -700,7 +700,11 @@
   {
    "cell_type": "code",
    "execution_count": 19,
-   "metadata": {},
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
    "outputs": [],
    "source": [
     "from functools import reduce"
@@ -711,7 +715,7 @@
    "execution_count": 20,
    "metadata": {
     "slideshow": {
-     "slide_type": "fragment"
+     "slide_type": "-"
     }
    },
    "outputs": [

From a52178732a67f51f84c260f603426ffd2528d732 Mon Sep 17 00:00:00 2001
From: Alexander Hess <alexander@webartifex.biz>
Date: Thu, 22 Oct 2020 18:19:34 +0200
Subject: [PATCH 199/242] Add initial version of chapter 09, part 1

---
 00_intro/00_content.ipynb    |    2 +-
 09_mappings/00_content.ipynb | 3773 ++++++++++++++++++++++++++++++++++
 CONTENTS.md                  |    8 +
 README.md                    |    1 +
 4 files changed, 3783 insertions(+), 1 deletion(-)
 create mode 100644 09_mappings/00_content.ipynb

diff --git a/00_intro/00_content.ipynb b/00_intro/00_content.ipynb
index b1c99a6..bc62059 100644
--- a/00_intro/00_content.ipynb
+++ b/00_intro/00_content.ipynb
@@ -772,7 +772,7 @@
     "  - *Chapter 6*: [Text & Bytes <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/06_text/00_content.ipynb)\n",
     "  - *Chapter 7*: [Sequential Data <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/07_sequences/00_content.ipynb)\n",
     "  - *Chapter 8*: [Map, Filter, & Reduce <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/08_mfr/00_content.ipynb)\n",
-    "  - *Chapter 9*: Mappings & Sets\n",
+    "  - *Chapter 9*: [Mappings & Sets <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/09_mappings/00_content.ipynb)\n",
     "  - *Chapter 10*: Arrays & Dataframes\n",
     "- How can we create custom data types?\n",
     "  - *Chapter 11*: Classes & Instances"
diff --git a/09_mappings/00_content.ipynb b/09_mappings/00_content.ipynb
new file mode 100644
index 0000000..b20e7ec
--- /dev/null
+++ b/09_mappings/00_content.ipynb
@@ -0,0 +1,3773 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "**Note**: Click on \"*Kernel*\" > \"*Restart Kernel and Clear All Outputs*\" in [JupyterLab](https://jupyterlab.readthedocs.io/en/stable/) *before* reading this notebook to reset its output. If you cannot run this file on your machine, you may want to open it [in the cloud <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_mb.png\">](https://mybinder.org/v2/gh/webartifex/intro-to-python/develop?urlpath=lab/tree/09_mappings/00_content.ipynb)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "# Chapter 9: Mappings & Sets"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "While [Chapter 7 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/07_sequences/00_content.ipynb) focuses on one special kind of *collection* types, namely *sequences*, this chapter introduces two more kinds: **Mappings** and **sets**. Both are presented in this chapter as they share the *same* underlying implementation.\n",
+    "\n",
+    "The `dict` type (cf, [documentation <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/stdtypes.html#dict)) introduced in the next section is an essential part in a data scientist's toolbox for two reasons: First, Python employs `dict` objects basically everywhere internally. Second, after the many concepts involving *sequential* data, *mappings* provide a different perspective on data and enhance our general problem solving skills."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "## The `dict` Type"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "A *mapping* is a one-to-one correspondence from a set of **keys** to a set of **values**. In other words, a *mapping* is a *collection* of **key-value pairs**, also called **items** for short.\n",
+    "\n",
+    "In the context of mappings, the term *value* has a meaning different from the *value* every object has: In the \"bag\" analogy from [Chapter 1 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/01_elements/00_content.ipynb#Value-/-%28Semantic%29-\"Meaning\"), we describe an object's value to be the semantic meaning of the $0$s and $1$s it contains. Here, the terms *key* and *value* mean the *role* an object takes within a mapping. Both, *keys* and *values*, are *objects* on their own with distinct *values*.\n",
+    "\n",
+    "Let's continue with an example. To create a `dict` object, we commonly use the literal notation, `{..: .., ..: .., ...}`, and list all the items. `to_words` below maps the `int` objects `0`, `1`, and `2` to their English word equivalents, `\"zero\"`, `\"one\"`, and `\"two\"`, and `from_words` does the opposite. A stylistic side note: Pythonistas often expand `dict` or `list` definitions by writing each item or element on a line on their own. Also, the commas `,` after the respective *last* items, `2: \"two\"` and `\"two\": 2`, are *not* a mistake although they *may* be left out. Besides easier reading, such a style has technical advantages that we do not go into detail about here (cf., [source <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://www.python.org/dev/peps/pep-0008/#when-to-use-trailing-commas))."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "to_words = {\n",
+    "    0: \"zero\",\n",
+    "    1: \"one\",\n",
+    "    2: \"two\",\n",
+    "}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "from_words = {\n",
+    "    \"zero\": 0,\n",
+    "    \"one\": 1,\n",
+    "    \"two\": 2,\n",
+    "}"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "As before, `dict` objects are objects on their own: They have an identity, a type, and a value."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "139936685526208"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "id(to_words)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "dict"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "type(to_words)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{0: 'zero', 1: 'one', 2: 'two'}"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "to_words"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "139936686018688"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "id(from_words)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "dict"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "type(from_words)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'zero': 0, 'one': 1, 'two': 2}"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from_words"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "The built-in [dict() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#func-dict) constructor gives us an alternative way to create a `dict` object. It is versatile and can be used in different ways.\n",
+    "\n",
+    "First, we may pass it any *mapping* type, for example, a `dict` object, to obtain a *new* `dict` object. That is the easiest way to obtain a *shallow* copy of a `dict` object or convert any other mapping object into a `dict` one."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'zero': 0, 'one': 1, 'two': 2}"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "dict(from_words)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Second, we may pass it a *finite* `iterable` providing *iterables* with *two* elements each. So, both of the following two code cells work: A `list` of `tuple` objects, or a `tuple` of `list` objects. More importantly, we could use an *iterator*, for example, a `generator` object, that produces the inner iterables \"on the fly.\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'zero': 0, 'one': 1, 'two': 2}"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "dict([(\"zero\", 0), (\"one\", 1), (\"two\", 2)])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'zero': 0, 'one': 1, 'two': 2}"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "dict(([\"zero\", 0], [\"one\", 1], [\"two\", 2]))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Lastly, [dict() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#func-dict) may also be called with *keyword* arguments: The keywords become the keys and the arguments the values."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'zero': 0, 'one': 1, 'two': 2}"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "dict(zero=0, one=1, two=2)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "## Nested Data"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Often, `dict` objects occur in a nested form and combined with other collection types, such as `list` or `tuple` objects, to model more complex entities \"from the real world.\"\n",
+    "\n",
+    "The reason for this popularity is that many modern [ReST APIs <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_wiki.png\">](https://en.wikipedia.org/wiki/Representational_state_transfer#Applied_to_Web_services) on the internet (e.g., [Google Maps API](https://cloud.google.com/maps-platform/), [Yelp API](https://www.yelp.com/developers/documentation/v3), [Twilio API](https://www.twilio.com/docs/usage/api)) provide their data in the popular [JSON <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_wiki.png\">](https://en.wikipedia.org/wiki/JSON) format, which looks almost like a combination of `dict` and `list` objects in Python. \n",
+    "\n",
+    "The `people` example below models three groups of people: `\"mathematicians\"`, `\"physicists\"`, and `\"programmers\"`. Each person may have an arbitrary number of email addresses. In the example, [Leonhard Euler <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_wiki.png\">](https://en.wikipedia.org/wiki/Leonhard_Euler) has not lived long enough to get one whereas [Guido <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_wiki.png\">](https://en.wikipedia.org/wiki/Guido_van_Rossum) has more than one.\n",
+    "\n",
+    "`people` makes many implicit assumptions about the structure of the data. For example, there are a [one-to-many <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_wiki.png\">](https://en.wikipedia.org/wiki/One-to-many_%28data_model%29) relationship between a person and their email addresses and a [one-to-one <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_wiki.png\">](https://en.wikipedia.org/wiki/One-to-one_%28data_model%29) relationship between each person and their name."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "people = {\n",
+    "    \"mathematicians\": [\n",
+    "        {\n",
+    "            \"name\": \"Gilbert Strang\",\n",
+    "            \"emails\": [\"gilbert@mit.edu\"],\n",
+    "        },\n",
+    "        {\n",
+    "            \"name\": \"Leonhard Euler\",\n",
+    "            \"emails\": [],\n",
+    "        },\n",
+    "    ],\n",
+    "    \"physicists\": [],\n",
+    "    \"programmers\": [\n",
+    "        {\n",
+    "            \"name\": \"Guido\",\n",
+    "            \"emails\": [\"guido@python.org\", \"guido@dropbox.com\"],\n",
+    "        },\n",
+    "    ],\n",
+    "}"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "The literal notation of such a nested `dict` object may be hard to read ..."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'mathematicians': [{'name': 'Gilbert Strang', 'emails': ['gilbert@mit.edu']},\n",
+       "  {'name': 'Leonhard Euler', 'emails': []}],\n",
+       " 'physicists': [],\n",
+       " 'programmers': [{'name': 'Guido',\n",
+       "   'emails': ['guido@python.org', 'guido@dropbox.com']}]}"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "people"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "... but the [pprint <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/pprint.html) module in the [standard library <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/index.html) provides a [pprint() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/pprint.html#pprint.pprint) function for \"pretty printing.\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "from pprint import pprint"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{'mathematicians': [{'emails': ['gilbert@mit.edu'],\n",
+      "                     'name': 'Gilbert Strang'},\n",
+      "                    {'emails': [],\n",
+      "                     'name': 'Leonhard Euler'}],\n",
+      " 'physicists': [],\n",
+      " 'programmers': [{'emails': ['guido@python.org',\n",
+      "                             'guido@dropbox.com'],\n",
+      "                  'name': 'Guido'}]}\n"
+     ]
+    }
+   ],
+   "source": [
+    "pprint(people, indent=1, width=60)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "## Hash Tables & (Key) Hashability"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "In [Chapter 0 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/00_intro/00_content.ipynb#Isn't-C-a-lot-faster?), we argue that a major advantage of using Python is that it takes care of the memory managment for us. In line with that, we have never talked about the C level implementation thus far in the book. However, the `dict` type, among others, exhibits some behaviors that may seem \"weird\" for a beginner. To build some intuition, we describe the underlying implementation details on a conceptual level.\n",
+    "\n",
+    "The first unintuitive behavior is that we may *not* use a *mutable* object as a key. That results in a `TypeError`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "ename": "TypeError",
+     "evalue": "unhashable type: 'list'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-17-20a24130a135>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m {\n\u001b[0m\u001b[1;32m      2\u001b[0m     \u001b[0;34m[\u001b[0m\u001b[0;34m\"zero\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"one\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m }\n",
+      "\u001b[0;31mTypeError\u001b[0m: unhashable type: 'list'"
+     ]
+    }
+   ],
+   "source": [
+    "{\n",
+    "    [\"zero\", \"one\"]: [0, 1],\n",
+    "}"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Similarly surprising is that items with the *same* key get merged together. The resulting `dict` object keeps the position of the *first* mention of the `\"zero\"` key while only the *last* mention of the corresponding values, `999`, survives."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'zero': 999, 'one': 1, 'two': 2}"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "{\n",
+    "    \"zero\": 0,\n",
+    "    \"one\": 1,\n",
+    "    \"two\": 2,\n",
+    "    \"zero\": 999,  # to illustrate a point\n",
+    "}"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "The reason for that is that the `dict` type is implemented with so-called [hash tables <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_wiki.png\">](https://en.wikipedia.org/wiki/Hash_table).\n",
+    "\n",
+    "Conceptually, when we create a *new* `dict` object, Python creates a \"bag\" in memory that takes significantly more space than needed to store the references to all the key and value objects. This bag is a **contiguous array** similar to the `list` type's implementation. Whereas in the `list` case the array is divided into equally sized *slots* capable of holding *one* reference, a `dict` object's array is divided into equally sized **buckets** with enough space to store *two* references each: One for an item's key and one for the mapped value. The buckets are labeled with *index* numbers. Because Python knows how wide each bucket, it can jump directly into *any* bucket by calculating its *offset* from the start.\n",
+    "\n",
+    "The figure below visualizes how we should think of hash tables. An empty `dict` object, created with the literal `{}`, still takes a lot of memory: It is essentially one big, contiguous, and empty table."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "|  Bucket |  0  |  1  |  2  |  3  |  4  |  5  |  6  |  7  |\n",
+    "|  :---:  |:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|\n",
+    "| **Key** |*...*|*...*|*...*|*...*|*...*|*...*|*...*|*...*|\n",
+    "|**Value**|*...*|*...*|*...*|*...*|*...*|*...*|*...*|*...*|"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "To insert a key-value pair, the key must be translated into a bucket's index.\n",
+    "\n",
+    "As the first step to do so, the built-in [hash() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#hash) function maps any **hashable** object to its **hash value**, a long and \"random\" `int` number, similar to the ones returned by the built-in [id() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#id) function. This hash value is a *summary* of all the $0$s and $1$s inside the object.\n",
+    "\n",
+    "According to the official [glossary <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/glossary.html#term-hashable), an object is hashable *only if* \"it has a hash value which *never* changes during its *lifetime*.\" So, hashability implies immutability! Without this formal requirement an object may end up in *different* buckets depending on its current value. As the name of the `dict` type (i.e., \"dictionary\") suggests, a primary purpose of it is to insert objects and look them up later on. Without a *unique* bucket, this is of course not doable. The exact logic behind [hash() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#hash) is beyond the scope of this book.\n",
+    "\n",
+    "Let's calculate the hash value of `\"zero\"`, an immutable `str` object. Hash values have *no* semantic meaning. Also, every time we re-start Python, we see *different* hash values for the *same* objects. That is a security measure, and we do not go into the technicalities here (cf. [source <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/using/cmdline.html#envvar-PYTHONHASHSEED))."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "-85344695604937002"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "hash(\"zero\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "For numeric objects, we can sometimes predict the hash values. However, we must *never* interpret any meaning into them."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0"
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "hash(0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0"
+      ]
+     },
+     "execution_count": 21,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "hash(0.0)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "The [glossary <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/glossary.html#term-hashable) states a second requirement for hashability, namely that \"objects which *compare equal* must have the *same* hash value.\" The purpose of this is to ensure that if we put, for example, `1` as a key in a `dict` object, we can look it up later with `1.0`. In other words, we can look up keys by their object's semantic value. The converse statement does *not* hold: Two objects *may* (accidentally) have the *same* hash value and *not* compare equal. However, that rarely happens."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "1 == 1.0"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 23,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "hash(1) == hash(1.0)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Because `list` objects are not immutable, they are *never* hashable, as indicated by the `TypeError`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "ename": "TypeError",
+     "evalue": "unhashable type: 'list'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-24-1afa4b4779b0>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mhash\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"zero\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"one\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;31mTypeError\u001b[0m: unhashable type: 'list'"
+     ]
+    }
+   ],
+   "source": [
+    "hash([\"zero\", \"one\"])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "If we need keys composed of several objects, we can use `tuple` objects instead."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "-1616807732336770172"
+      ]
+     },
+     "execution_count": 25,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "hash((\"zero\", \"one\"))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "There is no such restiction on objects inserted into `dict` objects as *values*."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{('zero', 'one'): [0, 1]}"
+      ]
+     },
+     "execution_count": 26,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "{\n",
+    "    (\"zero\", \"one\"): [0, 1],\n",
+    "}"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "After obtaining the key object's hash value, Python must still convert that into a bucket index. We do not cover this step in technical detail but provide a conceptual description of it.\n",
+    "\n",
+    "The `buckets()` function below shows how we can obtain indexes from the binary representation of a hash value by simply extracting its least significant `bits` and interpreting them as index numbers. Alternatively, the hash value may also be divided with the `%` operator by the number of available buckets. We show this idea in the `buckets_alt()` function that takes the number of buckets, `n_buckets`, as its second argument."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "def buckets(mapping, *, bits):\n",
+    "    \"\"\"Calculate the bucket indices for a mapping's keys.\"\"\"\n",
+    "    for key in mapping:  # cf., next section for details on looping\n",
+    "        hash_value = hash(key)\n",
+    "        binary = bin(hash_value)\n",
+    "        address = binary[-bits:]\n",
+    "        bucket = int(\"0b\" + address, base=2)\n",
+    "        print(key, hash_value, \"0b...\" + binary[-8:], address, bucket, sep=\"\\t\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "def buckets_alt(mapping, *, n_buckets):\n",
+    "    \"\"\"Calculate the bucket indices for a mapping's keys.\"\"\"\n",
+    "    for key in mapping:  # cf., next section for details on looping\n",
+    "        hash_value = hash(key)\n",
+    "        bucket = hash_value % n_buckets\n",
+    "        print(key, hash_value, bucket, sep=\"\\t\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "With an infinite number of possible keys being mapped to a limited number of buckets, there is a realistic chance that two or more keys end up in the *same* bucket. That is called a **hash collision**. In such cases, Python uses a perturbation rule to rearrange the bits, and if the corresponding next bucket is empty, places an item there. Then, the nice offsetting logic from above breaks down and Python needs more time on average to place items into a hash table or look them up. The remedy is to use a bigger hash table as then the chance of collisions decreases. Python does all that for us in the background, and the main cost we pay for that is a *high* memory usage of `dict` objects in general.\n",
+    "\n",
+    "Because keys with the *same* semantic value have the *same* hash value, they end up in the *same* bucket. That is why the item that gets inserted last *overwrites* all previously inserted items whose keys compare equal, as we saw with the two `\"zero\"` keys above.\n",
+    "\n",
+    "Thus, to come up with indexes for 4 buckets, we need to extract 2 bits from the hash value (i.e., $2^2 = 4$)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "zero\t-85344695604937002\t0b...00101010\t10\t2\n",
+      "one\t6414592332130781825\t0b...10000001\t01\t1\n",
+      "two\t4316247523642253857\t0b...00100001\t01\t1\n"
+     ]
+    }
+   ],
+   "source": [
+    "buckets(from_words, bits=2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "zero\t-85344695604937002\t2\n",
+      "one\t6414592332130781825\t1\n",
+      "two\t4316247523642253857\t1\n"
+     ]
+    }
+   ],
+   "source": [
+    "buckets_alt(from_words, n_buckets=4)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Similarly, 3 bits provide indexes for 8 buckets (i.e., $2^3 = 8$) ..."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "zero\t-85344695604937002\t0b...00101010\t010\t2\n",
+      "one\t6414592332130781825\t0b...10000001\t001\t1\n",
+      "two\t4316247523642253857\t0b...00100001\t001\t1\n"
+     ]
+    }
+   ],
+   "source": [
+    "buckets(from_words, bits=3)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "zero\t-85344695604937002\t6\n",
+      "one\t6414592332130781825\t1\n",
+      "two\t4316247523642253857\t1\n"
+     ]
+    }
+   ],
+   "source": [
+    "buckets_alt(from_words, n_buckets=8)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "... while 4 bits do so for 16 buckets (i.e., $2^4 = 16$)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "zero\t-85344695604937002\t0b...00101010\t1010\t10\n",
+      "one\t6414592332130781825\t0b...10000001\t0001\t1\n",
+      "two\t4316247523642253857\t0b...00100001\t0001\t1\n"
+     ]
+    }
+   ],
+   "source": [
+    "buckets(from_words, bits=4)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "zero\t-85344695604937002\t6\n",
+      "one\t6414592332130781825\t1\n",
+      "two\t4316247523642253857\t1\n"
+     ]
+    }
+   ],
+   "source": [
+    "buckets_alt(from_words, n_buckets=16)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Python allocates the memory for a `dict` object's hash table according to some internal heuristics: Whenever a hash table is roughly 2/3 full, it creates a *new* one with twice the space, and re-inserts all items, one by one, from the *old* one. So, during its lifetime, a `dict` object may have several hash tables.\n",
+    "\n",
+    "Although hash tables seem quite complex at first sight, they help us to make certain operations very fast as we see further below."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "## Mappings are Collections of Key-Value Pairs"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "In [Chapter 7 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/07_sequences/00_content.ipynb#Collections-vs.-Sequences), we see how a *sequence* is a special kind of a *collection*, and that collections can be described as\n",
+    "- *iterable*\n",
+    "- *containers*\n",
+    "- with a *finite* number of elements.\n",
+    "\n",
+    "The `dict` type is another *collection* type and has these three properties as well.\n",
+    "\n",
+    "For example, we may pass `to_words` or `from_words` to the built-in [len() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#len) function to obtain the number of *items* they contain. In the terminology of the [collections.abc <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/collections.abc.html) module in the [standard library <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/index.html), both are `Sized` objects."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "3"
+      ]
+     },
+     "execution_count": 35,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "len(to_words)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "3"
+      ]
+     },
+     "execution_count": 36,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "len(from_words)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Also, `dict` objects may be looped over, for example, with the `for` statement. So, in the terminology of the [collections.abc <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/collections.abc.html) module, they are `Iterable` objects.\n",
+    "\n",
+    "Regarding the *iteration order* things are not that easy, and programmers seem to often be confused about this (e.g., this [discussion](https://stackoverflow.com/questions/58413076/why-are-python-dictionaries-not-reversible-for-python3-7)). The confusion usually comes from one of two reasons:\n",
+    "1. The internal implementation of the `dict` type has been changed over the last couple of minor release versions, and the communication thereof in the official release notes was done only in a later version. In a nutshell, before Python 3.6, the core developers did not care about the iteration order at all as the goal was to optimize `dict` objects for computational speed, primarily regarding key look-up (cf., the \"Indexing -> Key Look-up\" section below). That meant that looping over the *same* `dict` object several times during its lifetime could have resulted in *different* iteration orders. In Python 3.6, it was discovered that it is possible to make `dict` objects remember the order in that items have been inserted *without* giving up any computational speed or memory (cf., [Raymond Hettinger](https://github.com/rhettinger)'s talk in the [Further Resources <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/09_mappings/08_resources.ipynb#History-of-the-dict-Type) section at the end of the chapter. However, that change was kept an *implementation detail* and *not* made official in the release notes. That was then done in Python 3.7's release notes (cf., [Python 3.7 release notes <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://www.python.org/downloads/release/python-370/)).\n",
+    "2. To make order an official part of a data type, it must adhere to the `Reversible` ABC in the [collections.abc <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/collections.abc.html) module and support the [reversed() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#reversed) built-in. Even though the items' order inside a `dict` is remembered for Python 3.6 and 3.7, `dict` objects are *not* `Reversible`. That was then changed in Python 3.8, but again *not* officially communicated (cf., [Python 3.8 release notes](https://www.python.org/downloads/release/python-380/)).\n",
+    "\n",
+    "In summary, we can say that depending on the exact Python version a `dict` object *may* remember the **insertion order** of its items.\n",
+    "\n",
+    "However, that order is only apparent to us (i.e., we could look it up) if we put the data stored in a `dict` object into the *source code* itself. Then, we say that we \"hard code\" the data in our program. That is often *not* useful as we want our software load the data to be processed, for example, from a file or a database.\n",
+    "\n",
+    "Therefore, we suggest and adopt the following *best practices* in this book:\n",
+    "- We *assume* that the items in a `dict` object are *not* in a **predictable order** and *never* make the correctness of the logic in our code dependent on it.\n",
+    "- Whenever we want or need to model data in `dict`-like objects with an *explicit* order, we use the [OrderedDict <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/collections.html#collections.OrderedDict) type in the [collections <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/collections.html) module in the [standard library <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/index.html).\n",
+    "\n",
+    "If you installed Python, as recommended, via the Anaconda Distribution, the order in the two `for`-loops below is the *same* as in the *source code* that defines `to_words` and `from_words` above. In that sense, it is *predictable*."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Python 3.8.6\n"
+     ]
+    }
+   ],
+   "source": [
+    "!python --version  # the order in the for-loops is predictable only for Python 3.7 or higher"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "By convention, iteration goes over the *keys* in the `dict` object only. The \"*Dictionary Methods*\" section below shows how to loop over the *items* or the *values* instead."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0\n",
+      "1\n",
+      "2\n"
+     ]
+    }
+   ],
+   "source": [
+    "for number in to_words:\n",
+    "    print(number)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "zero\n",
+      "one\n",
+      "two\n"
+     ]
+    }
+   ],
+   "source": [
+    "for word in from_words:\n",
+    "    print(word)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "For Python 3.8, `dict` objects are `Reversible` as well. So, passing a `dict` object to the [reversed() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#reversed) built-in works. However, for ealier Python versions, the two next cells raise a `TypeError`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "2\n",
+      "1\n",
+      "0\n"
+     ]
+    }
+   ],
+   "source": [
+    "for number in reversed(to_words):\n",
+    "    print(number)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "two\n",
+      "one\n",
+      "zero\n"
+     ]
+    }
+   ],
+   "source": [
+    "for word in reversed(from_words):\n",
+    "    print(word)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Of course, we may always use the built-in [sorted() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#sorted) function to loop over, for example, `from_words` in a *predictable* order. However, that creates a temporary `list` object in memory and an order that has *nothing* to do with how the items are ordered inside the `dict` object."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "one\n",
+      "two\n",
+      "zero\n"
+     ]
+    }
+   ],
+   "source": [
+    "for word in sorted(from_words):\n",
+    "    print(word)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "To show the `Container` behavior of *collection* types, we use the boolean `in` operator to check if a given object evaluates equal to a *key* in `to_words` or `from_words`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 43,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "1.0 in to_words  # 1.0 is not a key but compares equal to a key"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "False"
+      ]
+     },
+     "execution_count": 44,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "-1 in to_words"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 45,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "\"one\" in from_words"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "False"
+      ]
+     },
+     "execution_count": 46,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "\"ten\" in from_words"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "### Membership Testing: `list` vs. `dict`"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Because of the [hash table <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_wiki.png\">](https://en.wikipedia.org/wiki/Hash_table) implementation, the `in` operator is *extremely* fast: Python does *not* need to initiate a [linear search <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_wiki.png\">](https://en.wikipedia.org/wiki/Linear_search) as in the `list` case but immediately knows the only places in memory where the searched object must be located if present in the hash table at all. Then, the Python interpreter jumps right there in only *one* step. Because that is true no matter how many items are in the hash table, we call that a **constant time** operation.\n",
+    "\n",
+    "Conceptually, the overall behavior of the `in` operator is like comparing the searched object against *all* key objects with the `==` operator *without* doing it.\n",
+    "\n",
+    "To show the speed, we run an experiment. We create a `haystack`, a `list` object, with `10_000_001` elements in it, *one* of which is the `needle`, namely `42`. Once again, the [randint() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/random.html#random.randint) function in the [random <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/random.html) module is helpful."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "import random"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 48,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "random.seed(87)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 49,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "needle = 42"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 50,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "haystack = [random.randint(99, 9999) for _ in range(10_000_000)]\n",
+    "haystack.append(needle)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "We put the elements in `haystack` in a *random* order with the [shuffle() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/random.html#random.shuffle) function in the [random <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/random.html) module."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 51,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "random.shuffle(haystack)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 52,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[8126, 7370, 3735, 213, 7922, 1434, 8557, 9609, 9704, 9564]"
+      ]
+     },
+     "execution_count": 52,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "haystack[:10]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 53,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[7237, 886, 5945, 4014, 4998, 2055, 3531, 6919, 7875, 1944]"
+      ]
+     },
+     "execution_count": 53,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "haystack[-10:]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "As modern computers are generally fast, we search the `haystack` a total of `10` times."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 54,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "4.44 s ± 0 ns per loop (mean ± std. dev. of 1 run, 1 loop each)\n"
+     ]
+    }
+   ],
+   "source": [
+    "%%timeit -n 1 -r 1\n",
+    "for _ in range(10):\n",
+    "    needle in haystack"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Now, we convert the elements of the `haystack` into the keys of a `magic_haystack`, a `dict` object. We use `None` as a dummy value for all items."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 55,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "magic_haystack = dict((x, None) for x in haystack)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "To show the *massive* effect of the hash table implementation, we search the `magic_haystack` not `10` but `10_000_000` times. The code cell still runs in only a fraction of the time its counterpart does above."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 56,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "560 ms ± 0 ns per loop (mean ± std. dev. of 1 run, 1 loop each)\n"
+     ]
+    }
+   ],
+   "source": [
+    "%%timeit -n 1 -r 1\n",
+    "for _ in range(10_000_000):\n",
+    "    needle in magic_haystack"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "However, there is no fast way to look up the values the keys are mapped to. To achieve that, we have to loop over *all* items and check for each value object if it compares equal to the searched object. That is, by definition, a linear search, as well, and rather slow. In the context of `dict` objects, we call that a **reverse look-up**."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "## Indexing -> Key Look-up"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "The same efficient key look-up executed in the background with the `in` operator is also behind the indexing operator `[]`. Instead of returning either `True` or `False`, it returns the value object the looked up key maps to.\n",
+    "\n",
+    "To show the similarity to indexing into `list` objects, we provide another example with `to_words_list`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 57,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "to_words_list = [\"zero\", \"one\", \"two\"]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Without the above definitions, we could not tell the difference between `to_words` and `to_words_list`: The usage of the `[]` is the same."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 58,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'zero'"
+      ]
+     },
+     "execution_count": 58,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "to_words[0]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 59,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'zero'"
+      ]
+     },
+     "execution_count": 59,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "to_words_list[0]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Because key objects can be of any immutable type and are, in particular, not constrained to just the `int` type, the word \"*indexing*\" is an understatement here. Therefore, in the context of `dict` objects, we view the `[]` operator as a generalization of the indexing operator and refer to it as the **(key) look-up** operator. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 60,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "2"
+      ]
+     },
+     "execution_count": 60,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from_words[\"two\"]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "If a key is not in a `dict` object, Python raises a `KeyError`. A sequence type would raise an `IndexError` in this situation."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 61,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "ename": "KeyError",
+     "evalue": "'drei'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-61-8602ef11ff73>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mfrom_words\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"drei\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;31mKeyError\u001b[0m: 'drei'"
+     ]
+    }
+   ],
+   "source": [
+    "from_words[\"drei\"]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "While `dict` objects support the `[]` operator to look up a *single* key, the more general concept of *slicing* is *not* available. That is in line with the idea that there is *no* predictable *order* associated with a `dict` object's keys, and slicing requires an order.\n",
+    "\n",
+    "To access deeper levels in nested data, like `people`, we *chain* the look-up operator `[]`. For example, let's view all the `\"mathematicians\"` in `people`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 62,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[{'name': 'Gilbert Strang', 'emails': ['gilbert@mit.edu']},\n",
+       " {'name': 'Leonhard Euler', 'emails': []}]"
+      ]
+     },
+     "execution_count": 62,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "people[\"mathematicians\"]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Let's take the first mathematician on the list, ..."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 63,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'name': 'Gilbert Strang', 'emails': ['gilbert@mit.edu']}"
+      ]
+     },
+     "execution_count": 63,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "people[\"mathematicians\"][0]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "... and output his `\"name\"` ..."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 64,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Gilbert Strang'"
+      ]
+     },
+     "execution_count": 64,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "people[\"mathematicians\"][0][\"name\"]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "... or his `\"emails\"`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 65,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['gilbert@mit.edu']"
+      ]
+     },
+     "execution_count": 65,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "people[\"mathematicians\"][0][\"emails\"]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "## Mutability"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "We may mutate `dict` objects *in place*.\n",
+    "\n",
+    "For example, let's translate the English words in `to_words` to their German counterparts. Behind the scenes, Python determines the bucket of the objects passed to the `[]` operator, looks them up in the hash table, and, if present, *updates* the references to the mapped value objects."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 66,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{0: 'zero', 1: 'one', 2: 'two'}"
+      ]
+     },
+     "execution_count": 66,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "to_words"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 67,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "to_words[0] = \"null\"\n",
+    "to_words[1] = \"eins\"\n",
+    "to_words[2] = \"zwei\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 68,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{0: 'null', 1: 'eins', 2: 'zwei'}"
+      ]
+     },
+     "execution_count": 68,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "to_words"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Let's add two more items. Again, Python determines their buckets, but this time finds them to be empty, and *inserts* the references to their key and value objects."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 69,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "to_words[3] = \"drei\"\n",
+    "to_words[4] = \"vier\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 70,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{0: 'null', 1: 'eins', 2: 'zwei', 3: 'drei', 4: 'vier'}"
+      ]
+     },
+     "execution_count": 70,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "to_words"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "None of these operations change the identity of the `to_words` object."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 71,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "139936685526208"
+      ]
+     },
+     "execution_count": 71,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "id(to_words)  # same memory location as before"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "The `del` statement removes individual items. Python just removes the *two* references to the key and value objects in the corresponding bucket."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 72,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "del to_words[0]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 73,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{1: 'eins', 2: 'zwei', 3: 'drei', 4: 'vier'}"
+      ]
+     },
+     "execution_count": 73,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "to_words"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "We may also change parts of nested data, such as `people`.\n",
+    "\n",
+    "For example, let's add [Albert Einstein <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_wiki.png\">](https://en.wikipedia.org/wiki/Albert_Einstein) to the list of `\"physicists\"`, ..."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 74,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[]"
+      ]
+     },
+     "execution_count": 74,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "people[\"physicists\"]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 75,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "people[\"physicists\"].append({\"name\": \"Albert Einstein\"})"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "... complete Guido's `\"name\"`, ..."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 76,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'name': 'Guido', 'emails': ['guido@python.org', 'guido@dropbox.com']}"
+      ]
+     },
+     "execution_count": 76,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "people[\"programmers\"][0]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 77,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "people[\"programmers\"][0][\"name\"] = \"Guido van Rossum\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "... and remove his work email because he retired."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 78,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "del people[\"programmers\"][0][\"emails\"][1]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Now, `people` looks like this."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 79,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{'mathematicians': [{'emails': ['gilbert@mit.edu'],\n",
+      "                     'name': 'Gilbert Strang'},\n",
+      "                    {'emails': [],\n",
+      "                     'name': 'Leonhard Euler'}],\n",
+      " 'physicists': [{'name': 'Albert Einstein'}],\n",
+      " 'programmers': [{'emails': ['guido@python.org'],\n",
+      "                  'name': 'Guido van Rossum'}]}\n"
+     ]
+    }
+   ],
+   "source": [
+    "pprint(people, indent=1, width=60)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "## `dict` Methods"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "`dict` objects come with many methods bound on them (cf., [documentation <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/stdtypes.html#dict)), many of which are standardized by the `Mapping` and `MutableMapping` ABCs from the [collections.abc <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/collections.abc.html) module. While the former requires the [.keys() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/stdtypes.html#dict.keys), [.values() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/stdtypes.html#dict.values), [.items() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/stdtypes.html#dict.items), and [.get() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/stdtypes.html#dict.get) methods, which *never* mutate an object, the latter formalizes the [.update() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/stdtypes.html#dict.update), [.pop() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/stdtypes.html#dict.pop), [.popitem() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/stdtypes.html#dict.popitem), [.clear() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/stdtypes.html#dict.clear), and [.setdefault() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/stdtypes.html#dict.setdefault) methods, which *may* do so."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 80,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "import collections.abc as abc"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 81,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 81,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "isinstance(from_words, abc.Mapping)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 82,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 82,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "isinstance(from_words, abc.MutableMapping)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "While iteration over a mapping type already goes over its keys, we may emphasize this explicitly by adding the [.keys() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/stdtypes.html#dict.keys) method in the `for`-loop. Again, the iteration order is equivalent to the insertion order but still considered *unpredictable*."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 83,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "zero\n",
+      "one\n",
+      "two\n"
+     ]
+    }
+   ],
+   "source": [
+    "for word in from_words.keys():\n",
+    "    print(word)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "[.keys() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/stdtypes.html#dict.keys) returns an object of type `dict_keys`. That is a dynamic **view** inside the `from_words`'s hash table, which means it does *not* copy the references to the keys, and changes to `from_words` can be seen through it. View objects behave much like `dict` objects themselves."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 84,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "dict_keys(['zero', 'one', 'two'])"
+      ]
+     },
+     "execution_count": 84,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from_words.keys()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Views can be materialized with the [list() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#func-list) built-in. However, that may introduce *semantic* errors into a program as the newly created `list` object has a \"*predictable*\" order (i.e., indexes `0`, `1`, ...) created from an *unpredictable* one."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 85,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['zero', 'one', 'two']"
+      ]
+     },
+     "execution_count": 85,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "list(from_words.keys())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "To loop over the value objects instead, we use the [.values() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/stdtypes.html#dict.values) method. That returns a *view* (i.e., type `dict_values`) on the value objects inside `from_words` without copying them."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 86,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0\n",
+      "1\n",
+      "2\n"
+     ]
+    }
+   ],
+   "source": [
+    "for number in from_words.values():\n",
+    "    print(number)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 87,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "dict_values([0, 1, 2])"
+      ]
+     },
+     "execution_count": 87,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from_words.values()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "To loop over key-value pairs, we invoke the [.items() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/stdtypes.html#dict.items) method. That returns a view (i.e., type `dict_items`) on the key-value pairs as `tuple` objects, where the first element is the key and the second the value. Because of that, we use tuple unpacking in the `for`-loop."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 88,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "zero -> 0\n",
+      "one -> 1\n",
+      "two -> 2\n"
+     ]
+    }
+   ],
+   "source": [
+    "for word, number in from_words.items():\n",
+    "    print(f\"{word} -> {number}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 89,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "dict_items([('zero', 0), ('one', 1), ('two', 2)])"
+      ]
+     },
+     "execution_count": 89,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from_words.items()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Above, we see how the look-up operator fails *loudly* with a `KeyError` if a key is *not* in a `dict` object. For example, `to_words` does *not* have a key `0` any more."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 90,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{1: 'eins', 2: 'zwei', 3: 'drei', 4: 'vier'}"
+      ]
+     },
+     "execution_count": 90,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "to_words"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 91,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "ename": "KeyError",
+     "evalue": "0",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-91-30b825de37f0>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mto_words\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;31mKeyError\u001b[0m: 0"
+     ]
+    }
+   ],
+   "source": [
+    "to_words[0]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "That may be mitigated with the [.get() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/stdtypes.html#dict.get) method that takes two arguments: `key` and `default`. It returns the value object `key` maps to if it is in the `dict` object; otherwise, `default` is returned. If not provided, `default` is `None`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 92,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'n/a'"
+      ]
+     },
+     "execution_count": 92,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "to_words.get(0, \"n/a\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 93,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'eins'"
+      ]
+     },
+     "execution_count": 93,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "to_words.get(1, \"n/a\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "The [.update() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/stdtypes.html#dict.update) method takes the items of another mapping and either inserts them or overwrites the ones with matching keys already in the `dict` objects. It may be used in the other two ways as the [dict() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#func-dict) constructor allows, as well."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 94,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "to_spanish = {\n",
+    "    0: \"cero\",\n",
+    "    1: \"uno\",\n",
+    "    2: \"dos\",\n",
+    "    3: \"tres\",\n",
+    "    4: \"cuatro\",\n",
+    "    5: \"cinco\",  \n",
+    "}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 95,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "to_words.update(to_spanish)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 96,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{1: 'uno', 2: 'dos', 3: 'tres', 4: 'cuatro', 0: 'cero', 5: 'cinco'}"
+      ]
+     },
+     "execution_count": 96,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "to_words"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "In contrast to the `pop()` method of the `list` type, the [.pop() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/stdtypes.html#dict.pop) method of the `dict` type *requires* a `key` argument to be passed. Then, it removes the corresponding key-value pair *and* returns the value object. If the `key` is not in the `dict` object, a `KeyError` is raised."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 97,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'zero': 0, 'one': 1, 'two': 2}"
+      ]
+     },
+     "execution_count": 97,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from_words"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 98,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "number = from_words.pop(\"zero\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 99,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0"
+      ]
+     },
+     "execution_count": 99,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "number"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 100,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'one': 1, 'two': 2}"
+      ]
+     },
+     "execution_count": 100,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from_words"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "With an optional `default` argument, the loud `KeyError` may be suppressed and the `default` returned instead, just as with the [.get() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/stdtypes.html#dict.get) method above."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 101,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "ename": "KeyError",
+     "evalue": "'zero'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-101-b91e52fa79fd>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mfrom_words\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpop\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"zero\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;31mKeyError\u001b[0m: 'zero'"
+     ]
+    }
+   ],
+   "source": [
+    "from_words.pop(\"zero\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 102,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0"
+      ]
+     },
+     "execution_count": 102,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from_words.pop(\"zero\", 0)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Similar to the `pop()` method of the `list` type, the [.popitem() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/stdtypes.html#dict.popitem) method of the `dict` type removes *and* returns an \"arbitrary\" key-value pair as a `tuple` object from a `dict` object. With the preservation of the insertion order in Python 3.7 and higher, this effectively becomes a \"last in, first out\" rule, just as with the `list` type. Once a `dict` object is empty, [.popitem() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/stdtypes.html#dict.popitem) raises a `KeyError`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 103,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "word, number = from_words.popitem()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 104,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "('two', 2)"
+      ]
+     },
+     "execution_count": 104,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "word, number"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 105,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'one': 1}"
+      ]
+     },
+     "execution_count": 105,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from_words"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "The [.clear() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/stdtypes.html#dict.clear) method removes all items but keeps the `dict` object alive in memory."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 106,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "to_words.clear()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 107,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{}"
+      ]
+     },
+     "execution_count": 107,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "to_words"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 108,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "from_words.clear()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 109,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{}"
+      ]
+     },
+     "execution_count": 109,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from_words"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "The [.setdefault() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/stdtypes.html#dict.setdefault) method may have a bit of an unfortunate name but is useful, in particular, with nested `list` objects. It takes two arguments, `key` and `default`, and returns the value mapped to `key` if `key` is in the `dict` object; otherwise, it inserts the `key`-`default` pair *and* returns a reference to the newly created value object. So, it is similar to the [.get() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/stdtypes.html#dict.get) method above but also *mutates* the `dict` object.\n",
+    "\n",
+    "Consider the `people` example again and note how the `dict` object modeling `\"Albert Einstein\"` has *no* `\"emails\"` key in it."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 110,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{'mathematicians': [{'emails': ['gilbert@mit.edu'],\n",
+      "                     'name': 'Gilbert Strang'},\n",
+      "                    {'emails': [],\n",
+      "                     'name': 'Leonhard Euler'}],\n",
+      " 'physicists': [{'name': 'Albert Einstein'}],\n",
+      " 'programmers': [{'emails': ['guido@python.org'],\n",
+      "                  'name': 'Guido van Rossum'}]}\n"
+     ]
+    }
+   ],
+   "source": [
+    "pprint(people, indent=1, width=60)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Let's say we want to append the imaginary emails `\"leonhard@math.org\"` and `\"albert@physics.org\"`. We cannot be sure if a `dict` object modeling a person has already a `\"emails\"` key or not. To play it safe, we could first use the `in` operator to check for that and create a new `list` object in a second step if one is missing. Then, we would finally append the new email.\n",
+    "\n",
+    "[.setdefault() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/stdtypes.html#dict.setdefault) allows us to do all of the three steps at once. More importantly, behind the scenes Python only needs to make *one* key look-up instead of potentially three. For large nested data, that could speed up the computations significantly.\n",
+    "\n",
+    "So, the first code cell below adds the email to the already existing empty `list` object, while the second one creates a new one first."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 111,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "people[\"mathematicians\"][1].setdefault(\"emails\", []).append(\"leonhard@math.org\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 112,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "people[\"physicists\"][0].setdefault(\"emails\", []).append(\"albert@physics.org\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 113,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{'mathematicians': [{'emails': ['gilbert@mit.edu'],\n",
+      "                     'name': 'Gilbert Strang'},\n",
+      "                    {'emails': ['leonhard@math.org'],\n",
+      "                     'name': 'Leonhard Euler'}],\n",
+      " 'physicists': [{'emails': ['albert@physics.org'],\n",
+      "                 'name': 'Albert Einstein'}],\n",
+      " 'programmers': [{'emails': ['guido@python.org'],\n",
+      "                  'name': 'Guido van Rossum'}]}\n"
+     ]
+    }
+   ],
+   "source": [
+    "pprint(people, indent=1, width=60)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "`dict` objects also come with a [copy() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/stdtypes.html#dict.copy) method on them that creates *shallow* copies."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 114,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "guido = people[\"programmers\"][0].copy()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 115,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'name': 'Guido van Rossum', 'emails': ['guido@python.org']}"
+      ]
+     },
+     "execution_count": 115,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "guido"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "If we mutate `guido` and, for example, remove all his emails with the `.clear()` method on the `list` type, these changes are also visible through `people`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 116,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "guido[\"emails\"].clear()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 117,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'name': 'Guido van Rossum', 'emails': []}"
+      ]
+     },
+     "execution_count": 117,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "guido"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 118,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{'mathematicians': [{'emails': ['gilbert@mit.edu'],\n",
+      "                     'name': 'Gilbert Strang'},\n",
+      "                    {'emails': ['leonhard@math.org'],\n",
+      "                     'name': 'Leonhard Euler'}],\n",
+      " 'physicists': [{'emails': ['albert@physics.org'],\n",
+      "                 'name': 'Albert Einstein'}],\n",
+      " 'programmers': [{'emails': [],\n",
+      "                  'name': 'Guido van Rossum'}]}\n"
+     ]
+    }
+   ],
+   "source": [
+    "pprint(people, indent=1, width=60)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "## `dict` Comprehensions"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Analogous to `list` comprehensions in [Chapter 8 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/08_mfr/01_content.ipynb#list-Comprehensions), `dict` comprehensions are a concise literal notation to derive new `dict` objects out of existing ones.\n",
+    "\n",
+    "For example, let's derive `from_words` out of `to_words` below by swapping the keys and values."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 119,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "to_words = {\n",
+    "    0: \"zero\",\n",
+    "    1: \"one\",\n",
+    "    2: \"two\",\n",
+    "}"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Without a dictionary comprehension, we would have to initialize an empty `dict` object, loop over the items of the original one, and insert the key-value pairs one by one in a reversed fashion as value-key pairs. That assumes that the values are unique as otherwise some would be merged."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 120,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'zero': 0, 'one': 1, 'two': 2}"
+      ]
+     },
+     "execution_count": 120,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from_words = {}\n",
+    "\n",
+    "for number, word in to_words.items():\n",
+    "    from_words[word] = number\n",
+    "\n",
+    "from_words"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "While that code is correct, it is also unnecessarily verbose. The dictionary comprehension below works in the same way as list comprehensions except that curly braces `{}` replace the brackets `[]` and a colon `:` is added to separate the keys from the values."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 121,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'zero': 0, 'one': 1, 'two': 2}"
+      ]
+     },
+     "execution_count": 121,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "{v: k for k, v in to_words.items()}"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "We may filter out items with an `if`-clause and transform the remaining key and value objects.\n",
+    "\n",
+    "For no good reason, let's filter out all words starting with a `\"z\"` and upper case the remainin words."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 122,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'ONE': 1, 'TWO': 2}"
+      ]
+     },
+     "execution_count": 122,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "{v.upper(): k for k, v in to_words.items() if not v.startswith(\"z\")}"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Multiple `for`- and/or `if`-clauses are allowed.\n",
+    "\n",
+    "For example, let's find all pairs of two numbers from `1` through `10` whose product is \"close\" to `50` (e.g., within a delta of `5`)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 123,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{(5, 9): 45,\n",
+       " (5, 10): 50,\n",
+       " (6, 8): 48,\n",
+       " (6, 9): 54,\n",
+       " (7, 7): 49,\n",
+       " (8, 6): 48,\n",
+       " (9, 5): 45,\n",
+       " (9, 6): 54,\n",
+       " (10, 5): 50}"
+      ]
+     },
+     "execution_count": 123,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "{\n",
+    "    (x, y): x * y\n",
+    "    for x in range(1, 11) for y in range(1, 11)\n",
+    "    if abs(x * y - 50) <= 5\n",
+    "}"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.6"
+  },
+  "livereveal": {
+   "auto_select": "code",
+   "auto_select_fragment": true,
+   "scroll": true,
+   "theme": "serif"
+  },
+  "toc": {
+   "base_numbering": 1,
+   "nav_menu": {},
+   "number_sections": false,
+   "sideBar": true,
+   "skip_h1_title": true,
+   "title_cell": "Table of Contents",
+   "title_sidebar": "Contents",
+   "toc_cell": false,
+   "toc_position": {
+    "height": "calc(100% - 180px)",
+    "left": "10px",
+    "top": "150px",
+    "width": "384px"
+   },
+   "toc_section_display": false,
+   "toc_window_display": false
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/CONTENTS.md b/CONTENTS.md
index bfa7f76..9e3fbb6 100644
--- a/CONTENTS.md
+++ b/CONTENTS.md
@@ -229,3 +229,11 @@ If this is not possible,
        Example: `sorted()` vs. `reversed()`)
     - [summary <img height="12" style="display: inline-block" src="static/link/to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/08_mfr/05_summary.ipynb)
     - [review questions <img height="12" style="display: inline-block" src="static/link/to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/08_mfr/06_review.ipynb)
+  - *Chapter 9*: Mappings & Sets
+    - [content <img height="12" style="display: inline-block" src="static/link/to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/09_mappings/00_content.ipynb)
+      [<img height="12" style="display: inline-block" src="static/link/to_mb.png">](https://mybinder.org/v2/gh/webartifex/intro-to-python/develop?urlpath=lab/tree/09_mappings/00_content.ipynb)
+      (`dict` Type;
+       Nested Data;
+       Hash Tables;
+       `dict` Methods & Behavior;
+       `dict` Comprehension)
diff --git a/README.md b/README.md
index 23b5521..dd051e6 100644
--- a/README.md
+++ b/README.md
@@ -21,6 +21,7 @@ For a more *detailed version* with **clickable links**
   - *Chapter 6*: Text & Bytes
   - *Chapter 7*: Sequential Data
   - *Chapter 8*: Map, Filter, & Reduce
+  - *Chapter 9*: Mappings & Sets
 
 
 #### Videos

From f02a5f0e01410e8d3d7b024199cddf0ca9441568 Mon Sep 17 00:00:00 2001
From: Alexander Hess <alexander@webartifex.biz>
Date: Thu, 22 Oct 2020 18:26:17 +0200
Subject: [PATCH 200/242] Add initial version of chapter 09's exercises, part 1

---
 09_mappings/01_exercises.ipynb | 428 +++++++++++++++++++++++++++++++++
 CONTENTS.md                    |   3 +
 2 files changed, 431 insertions(+)
 create mode 100644 09_mappings/01_exercises.ipynb

diff --git a/09_mappings/01_exercises.ipynb b/09_mappings/01_exercises.ipynb
new file mode 100644
index 0000000..72b503d
--- /dev/null
+++ b/09_mappings/01_exercises.ipynb
@@ -0,0 +1,428 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Note**: Click on \"*Kernel*\" > \"*Restart Kernel and Run All*\" in [JupyterLab](https://jupyterlab.readthedocs.io/en/stable/) *after* finishing the exercises to ensure that your solution runs top to bottom *without* any errors. If you cannot run this file on your machine, you may want to open it [in the cloud <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_mb.png\">](https://mybinder.org/v2/gh/webartifex/intro-to-python/develop?urlpath=lab/tree/09_mappings/01_exercises.ipynb)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Chapter 9: Mappings & Sets (Coding Exercises)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The exercises below assume that you have read the [first part <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/09_mappings/00_content.ipynb) of Chapter 9.\n",
+    "\n",
+    "The `...`'s in the code cells indicate where you need to fill in code snippets. The number of `...`'s within a code cell give you a rough idea of how many lines of code are needed to solve the task. You should not need to create any additional code cells for your final solution. However, you may want to use temporary code cells to try out some ideas."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Working with Nested Data"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Let's write some code to analyze the historic soccer game [Brazil vs. Germany <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_wiki.png\">](https://en.wikipedia.org/wiki/Brazil_v_Germany_%282014_FIFA_World_Cup%29) during the 2014 World Cup.\n",
+    "\n",
+    "Below, `players` consists of two nested `dict` objects, one for each team, that hold `tuple` objects (i.e., records) with information on the players. Besides the jersey number, name, and position, each `tuple` objects contains a `list` object with the times when the player scored."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "players = {\n",
+    "    \"Brazil\": [\n",
+    "        (12, \"JĂşlio CĂ©sar\", \"Goalkeeper\", []),\n",
+    "        (4, \"David Luiz\", \"Defender\", []),\n",
+    "        (6, \"Marcelo\", \"Defender\", []),\n",
+    "        (13, \"Dante\", \"Defender\", []),\n",
+    "        (23, \"Maicon\", \"Defender\", []),\n",
+    "        (5, \"Fernandinho\", \"Midfielder\", []),\n",
+    "        (7, \"Hulk\", \"Midfielder\", []),\n",
+    "        (8, \"Paulinho\", \"Midfielder\", []),\n",
+    "        (11, \"Oscar\", \"Midfielder\", [90]),\n",
+    "        (16, \"Ramires\", \"Midfielder\", []),\n",
+    "        (17, \"Luiz Gustavo\", \"Midfielder\", []),\n",
+    "        (19, \"Willian\", \"Midfielder\", []),\n",
+    "        (9, \"Fred\", \"Striker\", []),\n",
+    "    ],\n",
+    "    \"Germany\": [\n",
+    "        (1, \"Manuel Neuer\", \"Goalkeeper\", []),\n",
+    "        (4, \"Benedikt Höwedes\", \"Defender\", []),\n",
+    "        (5, \"Mats Hummels\", \"Defender\", []),\n",
+    "        (16, \"Philipp Lahm\", \"Defender\", []),\n",
+    "        (17, \"Per Mertesacker\", \"Defender\", []),\n",
+    "        (20, \"JĂ©rĂ´me Boateng\", \"Defender\", []),\n",
+    "        (6, \"Sami Khedira\", \"Midfielder\", [29]),\n",
+    "        (7, \"Bastian Schweinsteiger\", \"Midfielder\", []),\n",
+    "        (8, \"Mesut Ă–zil\", \"Midfielder\", []),\n",
+    "        (13, \"Thomas MĂĽller\", \"Midfielder\", [11]),\n",
+    "        (14, \"Julian Draxler\", \"Midfielder\", []),\n",
+    "        (18, \"Toni Kroos\", \"Midfielder\", [24, 26]),\n",
+    "        (9, \"André Schürrle\", \"Striker\", [69, 79]),\n",
+    "        (11, \"Miroslav Klose\", \"Striker\", [23]),\n",
+    "    ],\n",
+    "}"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q1**: Write a dictionary comprehension to derive a new `dict` object, called `brazilian_players`, that maps a Brazilian player's name to his position!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "brazilian_players = {...: ...}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "brazilian_players"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q2**: Generalize the code fragment into a `get_players()` function: Passed a `team` name, it returns a `dict` object like `brazilian_players`. Verify that the function works for the German team as well!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def get_players(team):\n",
+    "    \"\"\"Creates a dictionary mapping the players' names to their position.\"\"\"\n",
+    "    return {...: ...}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "get_players(\"Germany\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Often, we are given a `dict` object like the one returned from `get_players()`: Its main characteristic is that it maps a large set of unique keys (i.e., the players' names) onto a smaller set of non-unique values (i.e., the positions).\n",
+    "\n",
+    "**Q3**: Create a generic `invert()` function that swaps the keys and values of a `mapping` argument passed to it and returns them in a *new* `dict` object! Ensure that *no* key gets lost! Verify your implementation with the `brazilian_players` dictionary!\n",
+    "\n",
+    "Hints: Think of this as a grouping operation. The *new* values are `list` or `tuple` objects that hold the original keys. You may want to use either the [defaultdict <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/collections.html#collections.defaultdict) type from the [collections <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/collections.html) module in the [standard library <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/index.html) or the [.setdefault() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/stdtypes.html#dict.setdefault) method on the ordinary `dict` type."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def invert(mapping):\n",
+    "    \"\"\"Invert the keys and values of a mapping argument.\"\"\"\n",
+    "    ...\n",
+    "    ...\n",
+    "    ...\n",
+    "    return ..."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "invert(brazilian_players)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q4**: Write a `score_at_minute()` function: It takes two arguments, `team` and `minute`, and returns the number of goals the `team` has scored up until this time in the game.\n",
+    "\n",
+    "Hints: The function may reference the global `players` for simplicity. Earn bonus points if you can write this in a one-line expression using some *reduction* function and a `generator` expression."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def score_at_minute(team, minute):\n",
+    "    \"\"\"Determine the number of goals scored by a team until a given minute.\"\"\"\n",
+    "    ...\n",
+    "    ...\n",
+    "    ...\n",
+    "    ...\n",
+    "    ...\n",
+    "    return ..."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The score at half time was:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "score_at_minute(\"Brazil\", 45)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "score_at_minute(\"Germany\", 45)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The final score was:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "score_at_minute(\"Brazil\", 90)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "score_at_minute(\"Germany\", 90)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q5**: Write a `goals_by_player()` function that takes an argument like the global `players`, and returns a `dict` object mapping the players to the number of goals they scored!\n",
+    "\n",
+    "Hints: Do *not* \"hard code\" the names of the teams! Earn bonus points if you can solve it in a one-line `dict` comprehension."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def goals_by_player(players):\n",
+    "    \"\"\"Create a dictionary mapping the players' names to the number of goals.\"\"\"\n",
+    "    ...\n",
+    "    ...\n",
+    "    ...\n",
+    "    ...\n",
+    "    return ..."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "goals_by_player(players)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q6**: Write a `dict` comprehension to filter out the players who did *not* score from the preceding result.\n",
+    "\n",
+    "Hints: Reference the `goals_by_player()` function from before."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "{...: ...}"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q7**: Write a `all_goals()` function that takes one argument like the global `players` and returns a `list` object containing $2$-element `tuple` objects where the first element is the minute a player scored and the second his name! The list should be sorted by the time.\n",
+    "\n",
+    "Hints: You may want to use either the built-in [sorted() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#sorted) function or the `list` type's [.sort() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/stdtypes.html#list.sort) method. Earn bonus points if you can write a one-line expression with a `generator` expression."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def all_goals(players):\n",
+    "    \"\"\"Create a time table of the individual goals.\"\"\"\n",
+    "    ...\n",
+    "    ...\n",
+    "    ...\n",
+    "    ...\n",
+    "    ...\n",
+    "    return ..."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "all_goals(players)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q8**: Lastly, write a `summary()` function that takes one argument like the global `players` and prints out a concise report of the goals, the score at the half, and the final result.\n",
+    "\n",
+    "Hints: Use the `all_goals()` and `score_at_minute()` functions from before.\n",
+    "\n",
+    "The output should look similar to this:\n",
+    "```\n",
+    "12' Gerd MĂĽller scores\n",
+    "...\n",
+    "HALFTIME: TeamA 1 TeamB 2\n",
+    "77' Ronaldo scores\n",
+    "...\n",
+    "FINAL: TeamA 1 TeamB 3\n",
+    "```"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def summary(players):\n",
+    "    \"\"\"Create a written summary of the game.\"\"\"\n",
+    "    # Create two lists with the goals of either half.\n",
+    "    ...\n",
+    "    ...\n",
+    "    ...\n",
+    "\n",
+    "    # Print the goals of the first half.\n",
+    "    ...\n",
+    "    ...\n",
+    "\n",
+    "    # Print the half time score.\n",
+    "    ...\n",
+    "    ...\n",
+    "    ...\n",
+    "    ...\n",
+    "    ...\n",
+    "\n",
+    "    # Print the goals of the second half.\n",
+    "    ...\n",
+    "    ...\n",
+    "\n",
+    "    # Print the final score.\n",
+    "    ...\n",
+    "    ...\n",
+    "    ...\n",
+    "    ...\n",
+    "    ..."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "summary(players)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.6"
+  },
+  "toc": {
+   "base_numbering": 1,
+   "nav_menu": {},
+   "number_sections": false,
+   "sideBar": true,
+   "skip_h1_title": true,
+   "title_cell": "Table of Contents",
+   "title_sidebar": "Contents",
+   "toc_cell": false,
+   "toc_position": {},
+   "toc_section_display": false,
+   "toc_window_display": false
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/CONTENTS.md b/CONTENTS.md
index 9e3fbb6..20adfea 100644
--- a/CONTENTS.md
+++ b/CONTENTS.md
@@ -237,3 +237,6 @@ If this is not possible,
        Hash Tables;
        `dict` Methods & Behavior;
        `dict` Comprehension)
+    - [exercises <img height="12" style="display: inline-block" src="static/link/to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/09_mappings/01_exercises.ipynb)
+      [<img height="12" style="display: inline-block" src="static/link/to_mb.png">](https://mybinder.org/v2/gh/webartifex/intro-to-python/develop?urlpath=lab/tree/09_mappings/01_exercises.ipynb)
+      (Working with Nested Data)

From 0fe9cca303634a63a6e8309e9587cc1189ccd4a9 Mon Sep 17 00:00:00 2001
From: Alexander Hess <alexander@webartifex.biz>
Date: Thu, 22 Oct 2020 18:31:16 +0200
Subject: [PATCH 201/242] Add initial version of chapter 09, part 2

---
 04_iteration/00_content.ipynb               |    2 +-
 09_mappings/02_content.ipynb                | 1201 +++++++++++++++++++
 09_mappings/static/fibonacci_call_graph.png |  Bin 0 -> 32706 bytes
 CONTENTS.md                                 |    4 +
 4 files changed, 1206 insertions(+), 1 deletion(-)
 create mode 100644 09_mappings/02_content.ipynb
 create mode 100644 09_mappings/static/fibonacci_call_graph.png

diff --git a/04_iteration/00_content.ipynb b/04_iteration/00_content.ipynb
index f7d1e41..0171c6c 100644
--- a/04_iteration/00_content.ipynb
+++ b/04_iteration/00_content.ipynb
@@ -874,7 +874,7 @@
     "\n",
     "To understand this in detail, we have to study algorithms and data structures (e.g., with [this book](https://www.amazon.de/Introduction-Algorithms-Press-Thomas-Cormen/dp/0262033844/ref=sr_1_1?__mk_de_DE=%C3%85M%C3%85%C5%BD%C3%95%C3%91&crid=1JNE8U0VZGU0O&qid=1569837169&s=gateway&sprefix=algorithms+an%2Caps%2C180&sr=8-1)), a discipline within computer science, and dive into the analysis of **[time complexity of algorithms <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_wiki.png\">](https://en.wikipedia.org/wiki/Time_complexity)**.\n",
     "\n",
-    "Luckily, in the Fibonacci case, the inefficiency can be resolved with a **caching** (i.e., \"reuse\") strategy from the field of **[dynamic programming <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_wiki.png\">](https://en.wikipedia.org/wiki/Dynamic_programming)**, namely **[memoization <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_wiki.png\">](https://en.wikipedia.org/wiki/Memoization)**. We do so in [Chapter 9 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/09_mappings/00_content.ipynb#Memoization), after introducing the `dict` data type.\n",
+    "Luckily, in the Fibonacci case, the inefficiency can be resolved with a **caching** (i.e., \"reuse\") strategy from the field of **[dynamic programming <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_wiki.png\">](https://en.wikipedia.org/wiki/Dynamic_programming)**, namely **[memoization <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_wiki.png\">](https://en.wikipedia.org/wiki/Memoization)**. We do so in [Chapter 9 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/09_mappings/02_content.ipynb#Memoization), after introducing the `dict` data type.\n",
     "\n",
     "Let's measure the average run times for `fibonacci()` and varying `i` arguments with the `%%timeit` [cell magic](https://ipython.readthedocs.io/en/stable/interactive/magics.html#magic-timeit) that comes with Jupyter."
    ]
diff --git a/09_mappings/02_content.ipynb b/09_mappings/02_content.ipynb
new file mode 100644
index 0000000..ae2c380
--- /dev/null
+++ b/09_mappings/02_content.ipynb
@@ -0,0 +1,1201 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "**Note**: Click on \"*Kernel*\" > \"*Restart Kernel and Clear All Outputs*\" in [JupyterLab](https://jupyterlab.readthedocs.io/en/stable/) *before* reading this notebook to reset its output. If you cannot run this file on your machine, you may want to open it [in the cloud <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_mb.png\">](https://mybinder.org/v2/gh/webartifex/intro-to-python/develop?urlpath=lab/tree/09_mappings/02_content.ipynb)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "# Chapter 9: Mappings & Sets (continued)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "After introducing the `dict` type in the [first part <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/09_mappings/00_content.ipynb) of this chapter, we first look at an extension of the packing and unpacking syntax that involves `dict` objects. Then, we see how mappings can help us write computationally more efficient implementations to recursive solutions of problems as introduced in [Chapter 4 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/04_iteration/00_content.ipynb#Recursion). In a way, this second part of the chapter \"finishes\" Chapter 4."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "## Packing & Unpacking (continued)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Just as a single `*` symbol is used for packing and unpacking iterables in [Chapter 7 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/07_sequences/03_content.ipynb#Packing-&-Unpacking), a double `**` symbol implements packing and unpacking for mappings.\n",
+    "\n",
+    "Let's say we have `to_words` and `more_words` as below and want to merge the items together into a *new* `dict` object."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "to_words = {\n",
+    "    0: \"zero\",\n",
+    "    1: \"one\",\n",
+    "    2: \"two\",\n",
+    "}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "more_words = {\n",
+    "    2: \"TWO\",  # to illustrate a point\n",
+    "    3: \"three\",\n",
+    "    4: \"four\",\n",
+    "}"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "By *unpacking* the items with `**`, the newly created `dict` object is first filled with the items from `to_words` and then from `more_words`. The item with the key `2` from `more_words` overwrites its counterpart from `to_words` as it is mentioned last."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{0: 'zero', 1: 'one', 2: 'TWO', 3: 'three', 4: 'four'}"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "{**to_words, **more_words}"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "### Function Definitions & Calls (continued)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Both, `*` and `**` may be used within the header line of a function definition, for example, as in `print_args1()` below. Here, *positional* arguments not captured by positional parameters are *packed* into the `tuple` object `args`, and *keyword* arguments not captured by keyword parameters are *packed* into the `dict` object `kwargs`.\n",
+    "\n",
+    "For `print_args1()`, all arguments are optional, and ..."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "def print_args1(*args, **kwargs):\n",
+    "    \"\"\"Print out all arguments passed in.\"\"\"\n",
+    "    for index, arg in enumerate(args):\n",
+    "        print(\"position\", index, arg)\n",
+    "\n",
+    "    for key, value in kwargs.items():\n",
+    "        print(\"keyword\", key, value)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "... we may pass whatever we want to it, or nothing at all."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "print_args1()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "position 0 a\n",
+      "position 1 b\n",
+      "position 2 c\n"
+     ]
+    }
+   ],
+   "source": [
+    "print_args1(\"a\", \"b\", \"c\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "keyword first 1\n",
+      "keyword second 2\n",
+      "keyword third 3\n"
+     ]
+    }
+   ],
+   "source": [
+    "print_args1(first=1, second=2, third=3)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "position 0 x\n",
+      "position 1 y\n",
+      "keyword flag True\n"
+     ]
+    }
+   ],
+   "source": [
+    "print_args1(\"x\", \"y\", flag=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "We may even unpack `dict` and `list` objects."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "flags = {\"flag\": True, \"another_flag\": False}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "keyword flag True\n",
+      "keyword another_flag False\n"
+     ]
+    }
+   ],
+   "source": [
+    "print_args1(**flags)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "position 0 42\n",
+      "position 1 87\n",
+      "keyword flag True\n",
+      "keyword another_flag False\n"
+     ]
+    }
+   ],
+   "source": [
+    "print_args1(*[42, 87], **flags)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "The next example, `print_args2()`, requires the caller to pass one positional argument, captured in the `positional` parameter, and one keyword argument, captured in `keyword`. Further, an optional keyword argument `default` may be passed in. Any other positional or keyword arguments are packed into either `args` or `kwargs`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "def print_args2(positional, *args, keyword, default=True, **kwargs):\n",
+    "    \"\"\"Print out all arguments passed in.\"\"\"\n",
+    "    print(\"required positional\", positional)\n",
+    "\n",
+    "    for index, arg in enumerate(args):\n",
+    "        print(\"optional positional\", index, arg)\n",
+    "\n",
+    "    print(\"required keyword\", keyword)\n",
+    "    print(\"default keyword\", default)\n",
+    "\n",
+    "    for key, value in kwargs.items():\n",
+    "        print(\"optional keyword\", key, value)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "If the caller does not respect that, a `TypeError` is raised."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "ename": "TypeError",
+     "evalue": "print_args2() missing 1 required positional argument: 'positional'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-13-21a06a55e707>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mprint_args2\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;31mTypeError\u001b[0m: print_args2() missing 1 required positional argument: 'positional'"
+     ]
+    }
+   ],
+   "source": [
+    "print_args2()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "ename": "TypeError",
+     "evalue": "print_args2() missing 1 required keyword-only argument: 'keyword'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-14-06c908f24fbb>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mprint_args2\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"p\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;31mTypeError\u001b[0m: print_args2() missing 1 required keyword-only argument: 'keyword'"
+     ]
+    }
+   ],
+   "source": [
+    "print_args2(\"p\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "required positional p\n",
+      "required keyword k\n",
+      "default keyword True\n"
+     ]
+    }
+   ],
+   "source": [
+    "print_args2(\"p\", keyword=\"k\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "required positional p\n",
+      "required keyword k\n",
+      "default keyword False\n"
+     ]
+    }
+   ],
+   "source": [
+    "print_args2(\"p\", keyword=\"k\", default=False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "required positional p\n",
+      "optional positional 0 x\n",
+      "optional positional 1 y\n",
+      "required keyword k\n",
+      "default keyword True\n",
+      "optional keyword flag True\n"
+     ]
+    }
+   ],
+   "source": [
+    "print_args2(\"p\", \"x\", \"y\", keyword=\"k\", flag=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "required positional p\n",
+      "optional positional 0 x\n",
+      "optional positional 1 y\n",
+      "required keyword k\n",
+      "default keyword False\n",
+      "optional keyword flag True\n"
+     ]
+    }
+   ],
+   "source": [
+    "print_args2(\"p\", \"x\", \"y\", keyword=\"k\", default=False, flag=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "As above, we may unpack `list` or `dict` objects in a function call."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "positionals = [\"x\", \"y\", \"z\"]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "required positional p\n",
+      "optional positional 0 x\n",
+      "optional positional 1 y\n",
+      "optional positional 2 z\n",
+      "required keyword k\n",
+      "default keyword False\n",
+      "optional keyword flag True\n",
+      "optional keyword another_flag False\n"
+     ]
+    }
+   ],
+   "source": [
+    "print_args2(\"p\", *positionals, keyword=\"k\", default=False, **flags)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "## Memoization"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "### \"Easy at first Glance\" Example: [Fibonacci Numbers <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_wiki.png\">](https://en.wikipedia.org/wiki/Fibonacci_number) (repeated)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "The *recursive* implementation of the [Fibonacci numbers <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_wiki.png\">](https://en.wikipedia.org/wiki/Fibonacci_number) in [Chapter 4 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/04_iteration/00_content.ipynb#\"Easy-at-first-Glance\"-Example:-Fibonacci-Numbers) takes long to compute for large Fibonacci numbers. For easier comparison, we show the old `fibonacci()` version here again."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def fibonacci(i):\n",
+    "    \"\"\"Calculate the ith Fibonacci number.\n",
+    "\n",
+    "    Args:\n",
+    "        i (int): index of the Fibonacci number to calculate\n",
+    "\n",
+    "    Returns:\n",
+    "        ith_fibonacci (int)\n",
+    "    \"\"\"\n",
+    "    if i == 0:\n",
+    "        return 0\n",
+    "    elif i == 1:\n",
+    "        return 1\n",
+    "    return fibonacci(i - 1) + fibonacci(i - 2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "144"
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "fibonacci(12)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Efficiency of Algorithms"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Timing the code cells below with the `%%timeit` magic shows how doubling the input (i.e., `12` becomes `24`) more than doubles how long it takes `fibonacci()` to calculate the solution. This is actually an understatement as we see the time go up by roughly a factor of $1000$ (i.e., from nano-seconds to milli-seconds). That is an example of **exponential growth**."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "40.1 µs ± 1.26 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n"
+     ]
+    }
+   ],
+   "source": [
+    "%%timeit -n 100\n",
+    "fibonacci(12)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "12.1 ms ± 149 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n"
+     ]
+    }
+   ],
+   "source": [
+    "%%timeit -n 100\n",
+    "fibonacci(24)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "The computation graph below visualizes what the problem is and also suggests a solution: In the recursive implementation, the same function calls are made over and over again. For example, in the visualization the call `fibonacci(3)`, shown as $F(3)$, is made *twice* when the actual goal is to calculate `fibonacci(5)`, shown as $F(5)$. This problem \"grows\" if the initial argument (i.e., `5` in the example) is chosen to be larger as we see with the many `fibonacci(2)`, `fibonacci(1)` and `fibonacci(0)` calls.\n",
+    "\n",
+    "Instead of calculating the return value of the `fibonacci()` function for the *same* argument over and over again, it makes sense to **cache** (i.e., \"store\") the result and reuse it. This concept is called **[memoization <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_wiki.png\">](https://en.wikipedia.org/wiki/Memoization)**."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "<img src=\"static/fibonacci_call_graph.png\" width=\"50%\" align=\"left\">"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### \"Easy at second Glance\" Example: [Fibonacci Numbers <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_wiki.png\">](https://en.wikipedia.org/wiki/Fibonacci_number) (revisited)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Below is a revision of the recursive `fibonacci()` implementation that uses a **globally** defined `dict` object, called `memo`, to store intermediate results and look them up.\n",
+    "\n",
+    "To be precise, the the revised `fibonacci()` first checks if the `i`th Fibonacci number has already been calculated before. If yes, it is in the `memo`. That number is then returned immediately *without* any more calculations. As `dict` objects are *optimized* for constant-time key look-ups, this takes essentially \"no\" time! With a `list` object, for example, the `in` operator would trigger a linear search, which takes longer the more elements are in the list. If the `i`th Fibonacci number has not been calculated before, there is no corresponding item in the `memo` and a recursive function call must be made. The result obtained by recursion is then inserted into the `memo`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "memo = {\n",
+    "    0: 0,\n",
+    "    1: 1,\n",
+    "}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {
+    "code_folding": [],
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "def fibonacci(i, *, debug=False):\n",
+    "    \"\"\"Calculate the ith Fibonacci number.\n",
+    "\n",
+    "    Args:\n",
+    "        i (int): index of the Fibonacci number to calculate\n",
+    "        debug (bool): show non-cached calls; defaults to False\n",
+    "\n",
+    "    Returns:\n",
+    "        ith_fibonacci (int)\n",
+    "    \"\"\"\n",
+    "    if i in memo:\n",
+    "        return memo[i]\n",
+    "\n",
+    "    if debug:  # added for didactical purposes\n",
+    "        print(f\"fibonacci({i}) is calculated\")\n",
+    "\n",
+    "    recurse = fibonacci(i - 1, debug=debug) + fibonacci(i - 2, debug=debug)\n",
+    "    memo[i] = recurse\n",
+    "    return recurse"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "When we follow the flow of execution closely, we realize that the intermediate results represented by the left-most path in the graph above are calculated first. `fibonacci(1)`, the left-most leaf node $F(1)$, is the first base case reached, followed immediately by `fibonacci(0)`. From that moment onwards, the flow of execution moves back up the left-most path while adding together the two corresponding child nodes. Effectively, this mirrors the *iterative* implementation in that the order of all computational steps are *identical* (cf., the \"*Hard at first Glance*\" example in [Chapter 4 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/04_iteration/02_content.ipynb#\"Hard-at-first-Glance\"-Example:-Fibonacci-Numbers--(revisited))).\n",
+    "\n",
+    "We added a keyword-only argument `debug` that allows the caller to print out a message every time a `i` was *not* in the `memo`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "fibonacci(12) is calculated\n",
+      "fibonacci(11) is calculated\n",
+      "fibonacci(10) is calculated\n",
+      "fibonacci(9) is calculated\n",
+      "fibonacci(8) is calculated\n",
+      "fibonacci(7) is calculated\n",
+      "fibonacci(6) is calculated\n",
+      "fibonacci(5) is calculated\n",
+      "fibonacci(4) is calculated\n",
+      "fibonacci(3) is calculated\n",
+      "fibonacci(2) is calculated\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "144"
+      ]
+     },
+     "execution_count": 27,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "fibonacci(12, debug=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Now, calling `fibonacci()` has the *side effect* of growing the `memo` in the *global scope*. So, subsequent calls to `fibonacci()` need not calculate any Fibonacci number with an index `i` smaller than the maximum `i` used so far. Because of that, this `fibonacci()` is *not* a *pure* function."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "144"
+      ]
+     },
+     "execution_count": 28,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "fibonacci(12, debug=True)  # no more recursive calls needed"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{0: 0,\n",
+       " 1: 1,\n",
+       " 2: 1,\n",
+       " 3: 2,\n",
+       " 4: 3,\n",
+       " 5: 5,\n",
+       " 6: 8,\n",
+       " 7: 13,\n",
+       " 8: 21,\n",
+       " 9: 34,\n",
+       " 10: 55,\n",
+       " 11: 89,\n",
+       " 12: 144}"
+      ]
+     },
+     "execution_count": 29,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "memo"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "#### Efficiency of Algorithms (continued)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "With memoization, the recursive `fibonacci()` implementation is as fast as its iterative counterpart, even for large numbers.\n",
+    "\n",
+    "The `%%timeit` magic, by default, runs a code cell seven times. Whereas in the first run, *new* Fibonacci numbers (i.e., intermediate results) are added to the `memo`, `fibonacci()` has no work to do in the subsequent six runs. `%%timeit` realizes this and tells us that \"an intermediate result is being cached.\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The slowest run took 252.65 times longer than the fastest. This could mean that an intermediate result is being cached.\n",
+      "6.68 µs ± 15.8 µs per loop (mean ± std. dev. of 7 runs, 1 loop each)\n"
+     ]
+    }
+   ],
+   "source": [
+    "%%timeit -n 1\n",
+    "fibonacci(99)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The slowest run took 3603.20 times longer than the fastest. This could mean that an intermediate result is being cached.\n",
+      "85.1 µs ± 208 µs per loop (mean ± std. dev. of 7 runs, 1 loop each)\n"
+     ]
+    }
+   ],
+   "source": [
+    "%%timeit -n 1\n",
+    "fibonacci(999)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "The iterative implementation still has an advantage as the `RecursionError` shows for larger `i`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "RecursionError",
+     "evalue": "maximum recursion depth exceeded",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mRecursionError\u001b[0m                            Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-32-db4be504bbfd>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mget_ipython\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrun_cell_magic\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'timeit'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'-n 1'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'fibonacci(9999)\\n'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;32m~/repos/intro-to-python/.venv/lib/python3.8/site-packages/IPython/core/interactiveshell.py\u001b[0m in \u001b[0;36mrun_cell_magic\u001b[0;34m(self, magic_name, line, cell)\u001b[0m\n\u001b[1;32m   2379\u001b[0m             \u001b[0;32mwith\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbuiltin_trap\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2380\u001b[0m                 \u001b[0margs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mmagic_arg_s\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcell\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2381\u001b[0;31m                 \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2382\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mresult\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2383\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m<decorator-gen-53>\u001b[0m in \u001b[0;36mtimeit\u001b[0;34m(self, line, cell, local_ns)\u001b[0m\n",
+      "\u001b[0;32m~/repos/intro-to-python/.venv/lib/python3.8/site-packages/IPython/core/magic.py\u001b[0m in \u001b[0;36m<lambda>\u001b[0;34m(f, *a, **k)\u001b[0m\n\u001b[1;32m    185\u001b[0m     \u001b[0;31m# but it's overkill for just that one bit of state.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    186\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mmagic_deco\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marg\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 187\u001b[0;31m         \u001b[0mcall\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mlambda\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mk\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mk\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    188\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    189\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mcallable\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marg\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/repos/intro-to-python/.venv/lib/python3.8/site-packages/IPython/core/magics/execution.py\u001b[0m in \u001b[0;36mtimeit\u001b[0;34m(self, line, cell, local_ns)\u001b[0m\n\u001b[1;32m   1171\u001b[0m                     \u001b[0;32mbreak\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1172\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1173\u001b[0;31m         \u001b[0mall_runs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtimer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrepeat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrepeat\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnumber\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1174\u001b[0m         \u001b[0mbest\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mall_runs\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m/\u001b[0m \u001b[0mnumber\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1175\u001b[0m         \u001b[0mworst\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmax\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mall_runs\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m/\u001b[0m \u001b[0mnumber\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/.pyenv/versions/3.8.6/lib/python3.8/timeit.py\u001b[0m in \u001b[0;36mrepeat\u001b[0;34m(self, repeat, number)\u001b[0m\n\u001b[1;32m    203\u001b[0m         \u001b[0mr\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    204\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrepeat\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 205\u001b[0;31m             \u001b[0mt\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtimeit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnumber\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    206\u001b[0m             \u001b[0mr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mt\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    207\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mr\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/repos/intro-to-python/.venv/lib/python3.8/site-packages/IPython/core/magics/execution.py\u001b[0m in \u001b[0;36mtimeit\u001b[0;34m(self, number)\u001b[0m\n\u001b[1;32m    167\u001b[0m         \u001b[0mgc\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdisable\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    168\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 169\u001b[0;31m             \u001b[0mtiming\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0minner\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mit\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtimer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    170\u001b[0m         \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    171\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mgcold\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m<magic-timeit>\u001b[0m in \u001b[0;36minner\u001b[0;34m(_it, _timer)\u001b[0m\n",
+      "\u001b[0;32m<ipython-input-26-dd55d928f4d4>\u001b[0m in \u001b[0;36mfibonacci\u001b[0;34m(i, debug)\u001b[0m\n\u001b[1;32m     15\u001b[0m         \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34mf\"fibonacci({i}) is calculated\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     16\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 17\u001b[0;31m     \u001b[0mrecurse\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfibonacci\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mi\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdebug\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdebug\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mfibonacci\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mi\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0;36m2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdebug\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdebug\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     18\u001b[0m     \u001b[0mmemo\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mrecurse\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     19\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mrecurse\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "... last 1 frames repeated, from the frame below ...\n",
+      "\u001b[0;32m<ipython-input-26-dd55d928f4d4>\u001b[0m in \u001b[0;36mfibonacci\u001b[0;34m(i, debug)\u001b[0m\n\u001b[1;32m     15\u001b[0m         \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34mf\"fibonacci({i}) is calculated\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     16\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 17\u001b[0;31m     \u001b[0mrecurse\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfibonacci\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mi\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdebug\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdebug\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mfibonacci\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mi\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0;36m2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdebug\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdebug\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     18\u001b[0m     \u001b[0mmemo\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mrecurse\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     19\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mrecurse\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;31mRecursionError\u001b[0m: maximum recursion depth exceeded"
+     ]
+    }
+   ],
+   "source": [
+    "%%timeit -n 1\n",
+    "fibonacci(9999)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "This exception occurs as Python must keep track of *every* function call *until* it has returned, and with large enough `i`, the recursion tree above grows too big. By default, Python has a limit of up to 3000 *simultaneous* function calls. So, theoretically this exception is not a bug in the narrow sense but the result of a \"security\" measure that is supposed to keep a computer from crashing. However, practically most high-level languages like Python incur such an overhead cost: It results from the fact that someone (i.e., Python) needs to manage each function call's *local scope*. With the `for`-loop in the iterative version, we do this managing as the programmer.\n",
+    "\n",
+    "We could \"hack\" a bit with Python's default configuration using the [sys <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/sys.html) module in the [standard library <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/index.html) and make it work. As we are good citizens, we reset everything to the defaults after our hack is completed."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "import sys"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "old_recursion_limit = sys.getrecursionlimit()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "3000"
+      ]
+     },
+     "execution_count": 35,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "old_recursion_limit"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "sys.setrecursionlimit(99999)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Computational speed is *not* the problem here."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The slowest run took 50532.39 times longer than the fastest. This could mean that an intermediate result is being cached.\n",
+      "1.21 ms ± 2.97 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n"
+     ]
+    }
+   ],
+   "source": [
+    "%%timeit -n 1\n",
+    "fibonacci(9999)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "sys.setrecursionlimit(old_recursion_limit)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.6"
+  },
+  "livereveal": {
+   "auto_select": "code",
+   "auto_select_fragment": true,
+   "scroll": true,
+   "theme": "serif"
+  },
+  "toc": {
+   "base_numbering": 1,
+   "nav_menu": {},
+   "number_sections": false,
+   "sideBar": true,
+   "skip_h1_title": true,
+   "title_cell": "Table of Contents",
+   "title_sidebar": "Contents",
+   "toc_cell": false,
+   "toc_position": {
+    "height": "calc(100% - 180px)",
+    "left": "10px",
+    "top": "150px",
+    "width": "384px"
+   },
+   "toc_section_display": false,
+   "toc_window_display": false
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
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zFB&bG)j(QOgvO~1D~!nH{^|69lhi8OF5BP;>ll7BzrJ`yEb7CkdvI62hoxS3%c@5R
zU8kEoHC!abWMIYmm!Jn`(T^Z~_P+2E@*INH&e&X^>6cn24iVPJ_)AucY~FKIg?|3%
zn@-4p=6u?iy@KQhQ?!b1N6^$2bFdd3es&L3!~e#lAZ<Y9&TV~!@5Z^~z7o>_T~+%z
zxOE?(nk(@Ad!LtYEXAo%^<MC#3g=?wAM7?r;w7VGWcXYaIs|uc<oud#x*2pY(s#?B
z@zwT}k1iU8XGzVLwaK$$CE9PgQ4rOzK#t8zYa9C`fKT<UOBX>Yq<9d7m7C)v*rr1p
zq^_0C!^6kApaNEq>^6dF>}TU5HrP+BPgzw+?g!-rEeYPxodunXL}h^xx=%{H<}st6
qrLOJ&5sZH9to?uAPCxlg3a4cFYrgxg*U)>EGT&?UCuR4^kpBe?tHRI#

literal 0
HcmV?d00001

diff --git a/CONTENTS.md b/CONTENTS.md
index 20adfea..4ca214f 100644
--- a/CONTENTS.md
+++ b/CONTENTS.md
@@ -240,3 +240,7 @@ If this is not possible,
     - [exercises <img height="12" style="display: inline-block" src="static/link/to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/09_mappings/01_exercises.ipynb)
       [<img height="12" style="display: inline-block" src="static/link/to_mb.png">](https://mybinder.org/v2/gh/webartifex/intro-to-python/develop?urlpath=lab/tree/09_mappings/01_exercises.ipynb)
       (Working with Nested Data)
+    - [content <img height="12" style="display: inline-block" src="static/link/to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/09_mappings/02_content.ipynb)
+      [<img height="12" style="display: inline-block" src="static/link/to_mb.png">](https://mybinder.org/v2/gh/webartifex/intro-to-python/develop?urlpath=lab/tree/09_mappings/02_content.ipynb)
+      (`**kwargs` in Function Definitions;
+       Memoization)

From 4f6c14822c72729cf1b989fe67b54cf9e929e8b8 Mon Sep 17 00:00:00 2001
From: Alexander Hess <alexander@webartifex.biz>
Date: Thu, 22 Oct 2020 18:33:59 +0200
Subject: [PATCH 202/242] Add initial version of chapter 09's exercises, part 2

---
 09_mappings/03_exercises.ipynb | 248 +++++++++++++++++++++++++++++++++
 CONTENTS.md                    |   3 +
 2 files changed, 251 insertions(+)
 create mode 100644 09_mappings/03_exercises.ipynb

diff --git a/09_mappings/03_exercises.ipynb b/09_mappings/03_exercises.ipynb
new file mode 100644
index 0000000..ea10e8f
--- /dev/null
+++ b/09_mappings/03_exercises.ipynb
@@ -0,0 +1,248 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Note**: Click on \"*Kernel*\" > \"*Restart Kernel and Run All*\" in [JupyterLab](https://jupyterlab.readthedocs.io/en/stable/) *after* finishing the exercises to ensure that your solution runs top to bottom *without* any errors. If you cannot run this file on your machine, you may want to open it [in the cloud <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_mb.png\">](https://mybinder.org/v2/gh/webartifex/intro-to-python/develop?urlpath=lab/tree/09_mappings/03_exercises.ipynb)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Chapter 9: Mappings & Sets (Coding Exercises)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The exercises below assume that you have read the [second part <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/09_mappings/02_content.ipynb) of Chapter 9.\n",
+    "\n",
+    "The `...`'s in the code cells indicate where you need to fill in code snippets. The number of `...`'s within a code cell give you a rough idea of how many lines of code are needed to solve the task. You should not need to create any additional code cells for your final solution. However, you may want to use temporary code cells to try out some ideas."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Memoization without Side Effects"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "It is considered *bad practice* to make a function and thereby its correctness dependent on a program's *global state*: For example, in the \"*Easy at second Glance: Fibonacci Numbers*\" section in [Chapter 9 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/09_mappings/02_content.ipynb#\"Easy-at-second-Glance\"-Example:-Fibonacci-Numbers--%28revisited%29), we use a global `memo` to store the Fibonacci numbers that have already been calculated.\n",
+    "\n",
+    "That `memo` dictionary could be \"manipulated.\" More often than not, such things happen by accident: Imagine we wrote two independent recursive functions that both rely on memoization to solve different problems, and, unintentionally, we made both work with the *same* global `memo`. As a result, we would observe \"random\" bugs depending on the order in which we executed these functions. Such bugs are hard to track down in practice.\n",
+    "\n",
+    "A common remedy is to avoid global state and pass intermediate results \"down\" the recursion tree in a \"hidden\" argument. By convention, we prefix parameter names with a single leading underscore `_`, such as with `_memo` below, to indicate that the caller of our `fibonacci()` function *must not* use it. Also, we make `_memo` a *keyword-only* argument to force ourselves to always explicitly name it in a function call. Because it is an **implementation detail**, the `_memo` parameter is *not* mentioned in the docstring.\n",
+    "\n",
+    "Your task is to complete this version of `fibonacci()` so that the function works *without* any **side effects** in the global scope."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "### \"Easy at third Glance\" Example: [Fibonacci Numbers <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_wiki.png\">](https://en.wikipedia.org/wiki/Fibonacci_number)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "code_folding": [],
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "def fibonacci(i, *, debug=False, _memo=None):\n",
+    "    \"\"\"Calculate the ith Fibonacci number.\n",
+    "\n",
+    "    Args:\n",
+    "        i (int): index of the Fibonacci number to calculate\n",
+    "        debug (bool): show non-cached calls; defaults to False\n",
+    "\n",
+    "    Returns:\n",
+    "        ith_fibonacci (int)\n",
+    "    \"\"\"\n",
+    "    # answer to Q1\n",
+    "    if ...:\n",
+    "        ... = {\n",
+    "            0: 0,\n",
+    "            1: 1,\n",
+    "        }\n",
+    "\n",
+    "    # answer to Q2\n",
+    "    if ...:\n",
+    "        return ...\n",
+    "\n",
+    "    if debug:  # added for didactical purposes\n",
+    "        print(f\"fibonacci({i}) is calculated\")\n",
+    "\n",
+    "    # answer to Q3\n",
+    "    recurse = (\n",
+    "        fibonacci(...)\n",
+    "        + fibonacci(...)\n",
+    "    )\n",
+    "    # answer to Q4\n",
+    "    ... = ...\n",
+    "    return ..."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "**Q1**: When `fibonacci()` is initially called, `_memo` is set to `None`. So, there is *no* `dict` object yet. Implement the *two* base cases in the first `if` statement!\n",
+    "\n",
+    "Hints: All you need to do is create a *new* `dict` object with the results for `i=0` and `i=1`. This object is then passed on in the recursive function calls. Use the `is` operator in the `if` statement."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q2**: When `fibonacci()` is called for non-base cases (i.e., `i > 1`), it first checks if the result is already in the `_memo`. Implement that step in the second `if` statement!\n",
+    "\n",
+    "Hint: Use the early exit pattern."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q3**: If `fibonacci()` is called for an `i` argument whose result is not yet in the `_memo`, it must calculate it with the usual recursive function calls. Fill in the arguments to the two recursive `fibonacci()` calls!\n",
+    "\n",
+    "Hint: You must pass on the hidden `_memo`."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q4**: Lastly, after the two recursive calls have returned, `fibonacci()` must store the `recurse` result for the given `i` in the `_memo` *before* returning it. Implement that logic!"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q5**: What happens to the hidden `_memo` after the initial call to `fibonacci()` returned? How many hidden `_memo` objects exist in memory during the entire computation?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " < your answer >"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Because `fibonacci()` is now independent of the *global state*, the same eleven recursive function calls are made each time! So, this `fibonacci()` is a **pure** function, meaning it has *no* side effects.\n",
+    "\n",
+    "**Q6**: Execute the following code cell a couple of times to observe that!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "fibonacci(12, debug=True)  # = 13th Fibonacci number -> 11 recursive calls necessary"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The runtime of `fibonacci()` is now stable: There is no message that \"an intermediate result is being cached\" as in [Chapter 9 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/09_mappings/02_content.ipynb#\"Easy-at-second-Glance\"-Example:-Fibonacci-Numbers--%28revisited%29).\n",
+    "\n",
+    "**Q7**: Execute the following code cells a couple of times to observe that!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%%timeit -n 1\n",
+    "fibonacci(99)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%%timeit -n 1\n",
+    "fibonacci(999)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.6"
+  },
+  "toc": {
+   "base_numbering": 1,
+   "nav_menu": {},
+   "number_sections": false,
+   "sideBar": true,
+   "skip_h1_title": true,
+   "title_cell": "Table of Contents",
+   "title_sidebar": "Contents",
+   "toc_cell": false,
+   "toc_position": {},
+   "toc_section_display": false,
+   "toc_window_display": false
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/CONTENTS.md b/CONTENTS.md
index 4ca214f..8941fed 100644
--- a/CONTENTS.md
+++ b/CONTENTS.md
@@ -244,3 +244,6 @@ If this is not possible,
       [<img height="12" style="display: inline-block" src="static/link/to_mb.png">](https://mybinder.org/v2/gh/webartifex/intro-to-python/develop?urlpath=lab/tree/09_mappings/02_content.ipynb)
       (`**kwargs` in Function Definitions;
        Memoization)
+    - [exercises <img height="12" style="display: inline-block" src="static/link/to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/09_mappings/03_exercises.ipynb)
+      [<img height="12" style="display: inline-block" src="static/link/to_mb.png">](https://mybinder.org/v2/gh/webartifex/intro-to-python/develop?urlpath=lab/tree/09_mappings/03_exercises.ipynb)
+      (Memoization without Side Effects)

From f7a8d4ed855eaa44d857aa9399851da17c5e6e50 Mon Sep 17 00:00:00 2001
From: Alexander Hess <alexander@webartifex.biz>
Date: Thu, 22 Oct 2020 18:37:48 +0200
Subject: [PATCH 203/242] Add initial version of chapter 09, part 3

---
 09_mappings/04_content.ipynb | 1601 ++++++++++++++++++++++++++++++++++
 CONTENTS.md                  |    6 +
 2 files changed, 1607 insertions(+)
 create mode 100644 09_mappings/04_content.ipynb

diff --git a/09_mappings/04_content.ipynb b/09_mappings/04_content.ipynb
new file mode 100644
index 0000000..97c3875
--- /dev/null
+++ b/09_mappings/04_content.ipynb
@@ -0,0 +1,1601 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "**Note**: Click on \"*Kernel*\" > \"*Restart Kernel and Clear All Outputs*\" in [JupyterLab](https://jupyterlab.readthedocs.io/en/stable/) *before* reading this notebook to reset its output. If you cannot run this file on your machine, you may want to open it [in the cloud <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_mb.png\">](https://mybinder.org/v2/gh/webartifex/intro-to-python/develop?urlpath=lab/tree/09_mappings/04_content.ipynb)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "# Chapter 9: Mappings & Sets (continued)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "In this part of the chapter, we look at the `set` type, a concrete example of the more abstract concept of *sets* as a specialization of collections."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "## The `set` Type"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Python's provides a built-in `set` type that resembles [mathematical sets <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_wiki.png\">](https://en.wikipedia.org/wiki/Set_%28mathematics%29): Each element may only be a **member** of a set once, and there is no *predictable* order regarding the elements (cf., [documentation <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/stdtypes.html#set)).\n",
+    "\n",
+    "To create a set, we use the literal notation, `{..., ...}`, and list all the members. The redundant `7`s and `4`s below are discarded."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "numbers = {7, 7, 7, 7, 7, 11, 8, 5, 3, 12, 2, 6, 9, 10, 1, 4, 4, 4, 4, 4}"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "`set` objects are objects on their own."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "139739115782880"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "id(numbers)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "set"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "type(numbers)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12}"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "numbers"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "To create an empty set, we must use the built-in [set() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#func-set) constructor as empty curly brackets, `{}`, already create an empty `dict` object."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "empty_dict = {}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "dict"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "type(empty_dict)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "empty_set = set()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "set()"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "empty_set"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "The [set() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#func-set) constructor takes any `iterable` and only keeps unique elements.\n",
+    "\n",
+    "For example, we obtain all unique letters of a long word like so."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'a', 'b', 'c', 'd', 'r'}"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "set(\"abracadabra\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "## Sets are like Mappings without Values"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "The curly brackets notation can be viewed as a hint that `dict` objects are conceptually generalizations of `set`  objects, and we think of `set` objects as a collection consisting of a `dict` object's keys with all the mapped values removed.\n",
+    "\n",
+    "Like `dict` objects, `set` objects are built on top of [hash tables <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_wiki.png\">](https://en.wikipedia.org/wiki/Hash_table), and, thus, each element must be a *hashable* (i.e., immutable) object and can only be contained in a set once due to the buckets logic."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "ename": "TypeError",
+     "evalue": "unhashable type: 'list'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-10-4ac30fae8196>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0;34m{\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m2\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m3\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;31mTypeError\u001b[0m: unhashable type: 'list'"
+     ]
+    }
+   ],
+   "source": [
+    "{0, [1, 2], 3}"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "[len() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#len) tells us the number of elements in a `set` object."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "12"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "len(numbers)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "We may loop over the elements in a `set` object, but we must keep in mind that there is no *predictable* order. In contrast to `dict` objects, the iteration order is also *not* guaranteed to be the insertion order. Because of the special hash values for `int` objects, `numbers` seems to be \"magically\" sorted, which is *not* the case."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "1 2 3 4 5 6 7 8 9 10 11 12 "
+     ]
+    }
+   ],
+   "source": [
+    "for number in numbers:  # beware the non-order!\n",
+    "    print(number, end=\" \")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "We confirm that `set` objects are not `Reversible`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "ename": "TypeError",
+     "evalue": "'set' object is not reversible",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-13-1095d1ef6c80>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0;32mfor\u001b[0m \u001b[0mnumber\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mreversed\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnumbers\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m     \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnumber\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mend\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\" \"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;31mTypeError\u001b[0m: 'set' object is not reversible"
+     ]
+    }
+   ],
+   "source": [
+    "for number in reversed(numbers):\n",
+    "    print(number, end=\" \")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "The boolean `in` operator checks if a given and immutable object compares equal to an element in a `set` object. As with `dict` objects, membership testing is an *extremely* fast operation. Conceptually, it has the same result as conducting a linear search with the `==` operator behind the scenes without doing it."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "False"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "0 in numbers"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "1 in numbers"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "2.0 in numbers  # 2.0 is not a member itself but compares equal to a member"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "There is are `Set` ABC in the [collections.abc <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/collections.abc.html) module that formalizes, in particular, the operators supported by `set` objects (cf., the \"*Set Operations*\" section below). Further, the `MutableSet` ABC standardizes all the methods that mutate a `set` object in place (cf., the \"*Mutability & Set Methods*\" section below)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import collections.abc as abc"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "isinstance(numbers, abc.Set)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "isinstance(numbers, abc.MutableSet)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "## No Indexing, Key Look-up, or Slicing"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "As `set` objects come without a *predictable* order, indexing and slicing are not supported and result in a `TypeError`. In particular, as there are no values to be looked up, these operations are not *semantically* meaningful. Instead, we check membership via the `in` operator, as shown in the previous sub-section."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12}"
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "numbers"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "ename": "TypeError",
+     "evalue": "'set' object is not subscriptable",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-21-7486ea9d37bc>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mnumbers\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;31mTypeError\u001b[0m: 'set' object is not subscriptable"
+     ]
+    }
+   ],
+   "source": [
+    "numbers[0]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "ename": "TypeError",
+     "evalue": "'set' object is not subscriptable",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-22-4de4c0f84e99>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mnumbers\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;31mTypeError\u001b[0m: 'set' object is not subscriptable"
+     ]
+    }
+   ],
+   "source": [
+    "numbers[:]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "## Mutability & `set` Methods"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Because the `[]` operator does not work for `set` objects, they are mutated mainly via methods (cf., [documentation <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/stdtypes.html#set)).\n",
+    "\n",
+    "We may add new elements to an existing `set` object with the [.add() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/stdtypes.html#frozenset.add) method."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "numbers.add(999)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 999}"
+      ]
+     },
+     "execution_count": 24,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "numbers"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "The [.update() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/stdtypes.html#frozenset.update) method takes an iterable and adds all its elements to a `set` object if they are not already contained in it."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "numbers.update(range(5))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 999}"
+      ]
+     },
+     "execution_count": 26,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "numbers"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "To remove an element by value, we use the [.remove() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/stdtypes.html#frozenset.remove) or [.discard() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/stdtypes.html#frozenset.discard) methods. If the element to be removed is not in the `set` object, [.remove() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/stdtypes.html#frozenset.remove) raises a loud `KeyError` while [.discard() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/stdtypes.html#frozenset.discard) stays *silent*."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "numbers.remove(999)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "ename": "KeyError",
+     "evalue": "999",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-28-45d8b57b937d>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mnumbers\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mremove\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m999\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;31mKeyError\u001b[0m: 999"
+     ]
+    }
+   ],
+   "source": [
+    "numbers.remove(999)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "numbers.discard(0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "numbers.discard(0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12}"
+      ]
+     },
+     "execution_count": 31,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "numbers"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "The [.pop() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/stdtypes.html#frozenset.pop) method removes an *arbitrary* element. As not even the insertion order is tracked, that removes a \"random\" element."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "1"
+      ]
+     },
+     "execution_count": 32,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "numbers.pop()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12}"
+      ]
+     },
+     "execution_count": 33,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "numbers"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "The [.clear() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/stdtypes.html#frozenset.clear) method discards all elements but keeps the `set` object alive."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "numbers.clear()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "set()"
+      ]
+     },
+     "execution_count": 35,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "numbers"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "139739115782880"
+      ]
+     },
+     "execution_count": 36,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "id(numbers)  # same memory location as before"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "## `set` Operations"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "The arithmetic and relational operators are overloaded with typical set operations from math. The operators have methods that do the equivalent. We omit them for brevity in this section and only show them as comments in the code cells. Both the operators and the methods return *new* `set` objects without modifying the operands.\n",
+    "\n",
+    "We showcase the set operations with easy math examples."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "numbers = set(range(1, 13))\n",
+    "zero = {0}\n",
+    "evens = set(range(2, 13, 2))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12}"
+      ]
+     },
+     "execution_count": 38,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "numbers"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{0}"
+      ]
+     },
+     "execution_count": 39,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "zero"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{2, 4, 6, 8, 10, 12}"
+      ]
+     },
+     "execution_count": 40,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "evens"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "The bitwise OR operator `|` returns the union of two `set` objects."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12}"
+      ]
+     },
+     "execution_count": 41,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "zero | numbers  # zero.union(numbers)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Of course, the operators may be *chained*."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12}"
+      ]
+     },
+     "execution_count": 42,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "zero | numbers | evens  # zero.union(numbers).union(evens)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "To obtain an intersection of two or more `set` objects, we use the bitwise AND operator `&`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "set()"
+      ]
+     },
+     "execution_count": 43,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "zero & numbers  #  zero.intersection(numbers)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{2, 4, 6, 8, 10, 12}"
+      ]
+     },
+     "execution_count": 44,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "numbers & evens  # numbers.intersection(evens)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "To calculate a `set` object containing all elements that are in one but not the other `set` object, we use the minus operator `-`. This operation is *not* symmetric."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{1, 3, 5, 7, 9, 11}"
+      ]
+     },
+     "execution_count": 45,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "numbers - evens  # numbers.difference(evens)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "set()"
+      ]
+     },
+     "execution_count": 46,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "evens - numbers  # evens.difference(numbers)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "The *symmetric* difference is defined as the `set` object containing all elements that are in one but not both `set` objects. It is calculated with the bitwise XOR operator `^`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{1, 3, 5, 7, 9, 11}"
+      ]
+     },
+     "execution_count": 47,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "numbers ^ evens  # numbers.symmetric_difference(evens)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 48,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{1, 3, 5, 7, 9, 11}"
+      ]
+     },
+     "execution_count": 48,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "evens ^ numbers  # evens.symmetric_difference(numbers)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "The augmented versions of the four operators (e.g., `|` becomes `|=`) are also defined: They mutate the left operand *in place*."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "## `set` Comprehensions"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Python provides a literal notation for `set` comprehensions that works exactly like the one for `dict` comprehensions described in the [first part <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/09_mappings/00_content.ipynb#dict-Comprehensions) of this chapter except that they use a single loop variable instead of a key-value pair.\n",
+    "\n",
+    "For example, let's create a new `set` object that consists of the squares of all the elements of `numbers`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 49,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{1, 4, 9, 16, 25, 36, 49, 64, 81, 100, 121, 144}"
+      ]
+     },
+     "execution_count": 49,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "{x ** 2 for x in numbers}"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "As before, we may have multiple `for`- and/or `if`-clauses.\n",
+    "\n",
+    "For example, let's only keep the squares if they turn out to be an even number, or ..."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 50,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{4, 16, 36, 64, 100, 144}"
+      ]
+     },
+     "execution_count": 50,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "{x ** 2 for x in numbers if (x ** 2) % 2 == 0}"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "... create a `set` object with all products obtained from the Cartesian product of `numbers` with itself as long as the products are greater than `80`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 51,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{81, 84, 88, 90, 96, 99, 100, 108, 110, 120, 121, 132, 144}"
+      ]
+     },
+     "execution_count": 51,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "{x * y for x in numbers for y in numbers if x * y > 80}"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "## The `frozenset` Type"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "As `set` objects are mutable, they may *not* be used, for example, as keys in a `dict` object. Similar to how we replace `list` with `tuple` objects, we may often use a `frozenset` object instead of an ordinary one. The `frozenset` type is a built-in, and as `frozenset` objects are immutable, the only limitation is that we must specify *all* elements *upon* creation (cf., [documentation <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/stdtypes.html#frozenset)).\n",
+    "\n",
+    "`frozenset` objects are created by passing an iterable to the built-in [frozenset() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#func-frozenset) constructor. Even though `frozenset` objects are hashable, their elements are *not* ordered."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 52,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "frozenset({1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12})"
+      ]
+     },
+     "execution_count": 52,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "frozenset([7, 7, 7, 7, 7, 11, 8, 5, 3, 12, 2, 6, 9, 10, 1, 4, 4, 4, 4, 4])"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.6"
+  },
+  "livereveal": {
+   "auto_select": "code",
+   "auto_select_fragment": true,
+   "scroll": true,
+   "theme": "serif"
+  },
+  "toc": {
+   "base_numbering": 1,
+   "nav_menu": {},
+   "number_sections": false,
+   "sideBar": true,
+   "skip_h1_title": true,
+   "title_cell": "Table of Contents",
+   "title_sidebar": "Contents",
+   "toc_cell": false,
+   "toc_position": {
+    "height": "calc(100% - 180px)",
+    "left": "10px",
+    "top": "150px",
+    "width": "384px"
+   },
+   "toc_section_display": false,
+   "toc_window_display": false
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/CONTENTS.md b/CONTENTS.md
index 8941fed..e2decdb 100644
--- a/CONTENTS.md
+++ b/CONTENTS.md
@@ -247,3 +247,9 @@ If this is not possible,
     - [exercises <img height="12" style="display: inline-block" src="static/link/to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/09_mappings/03_exercises.ipynb)
       [<img height="12" style="display: inline-block" src="static/link/to_mb.png">](https://mybinder.org/v2/gh/webartifex/intro-to-python/develop?urlpath=lab/tree/09_mappings/03_exercises.ipynb)
       (Memoization without Side Effects)
+    - [content <img height="12" style="display: inline-block" src="static/link/to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/09_mappings/04_content.ipynb)
+      [<img height="12" style="display: inline-block" src="static/link/to_mb.png">](https://mybinder.org/v2/gh/webartifex/intro-to-python/develop?urlpath=lab/tree/09_mappings/04_content.ipynb)
+      (`set` Type;
+       `set` Methods & Operations;
+       `set` Comprehension;
+       `frozenset` Type)

From 79c0e79d6357f4e29ea3fc7f558d9fe689a8f8a3 Mon Sep 17 00:00:00 2001
From: Alexander Hess <alexander@webartifex.biz>
Date: Thu, 22 Oct 2020 18:40:10 +0200
Subject: [PATCH 204/242] Add initial version of chapter 09's appendix

---
 09_mappings/05_appendix.ipynb | 970 ++++++++++++++++++++++++++++++++++
 CONTENTS.md                   |   5 +
 2 files changed, 975 insertions(+)
 create mode 100644 09_mappings/05_appendix.ipynb

diff --git a/09_mappings/05_appendix.ipynb b/09_mappings/05_appendix.ipynb
new file mode 100644
index 0000000..fbe47e3
--- /dev/null
+++ b/09_mappings/05_appendix.ipynb
@@ -0,0 +1,970 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "**Note**: Click on \"*Kernel*\" > \"*Restart Kernel and Clear All Outputs*\" in [JupyterLab](https://jupyterlab.readthedocs.io/en/stable/) *before* reading this notebook to reset its output. If you cannot run this file on your machine, you may want to open it [in the cloud <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_mb.png\">](https://mybinder.org/v2/gh/webartifex/intro-to-python/develop?urlpath=lab/tree/09_mappings/05_appendix.ipynb)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "# Chapter 9: Mappings & Sets (Appendix)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "The [collections <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/collections.html) module in the [standard library <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/index.html) provides specialized mapping types for common use cases."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "## The `defaultdict` Type"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "The [defaultdict <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/collections.html#collections.defaultdict) type allows us to define a factory function that creates default values whenever we look up a key that does not yet exist. Ordinary `dict` objects would throw a `KeyError` exception in such situations.\n",
+    "\n",
+    "Let's say we have a `list` with *records* of goals scored during a soccer game. The records consist of the fields \"Country,\" \"Player,\" and the \"Time\" when a goal was scored. Our task is to group the goals by player and/or country."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "goals = [\n",
+    "    (\"Germany\", \"MĂĽller\", 11), (\"Germany\", \"Klose\", 23),\n",
+    "    (\"Germany\", \"Kroos\", 24), (\"Germany\", \"Kroos\", 26),\n",
+    "    (\"Germany\", \"Khedira\", 29), (\"Germany\", \"SchĂĽrrle\", 69),\n",
+    "    (\"Germany\", \"SchĂĽrrle\", 79), (\"Brazil\", \"Oscar\", 90),\n",
+    "]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Using a normal `dict` object, we have to tediously check if a player has already scored a goal before. If not, we must create a *new* `list` object with the first time the player scored. Otherwise, we append the goal to an already existing `list` object."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'MĂĽller': [11],\n",
+       " 'Klose': [23],\n",
+       " 'Kroos': [24, 26],\n",
+       " 'Khedira': [29],\n",
+       " 'SchĂĽrrle': [69, 79],\n",
+       " 'Oscar': [90]}"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "goals_by_player = {}\n",
+    "\n",
+    "for _, player, minute in goals:\n",
+    "    if player not in goals_by_player:\n",
+    "        goals_by_player[player] = [minute]\n",
+    "    else:\n",
+    "        goals_by_player[player].append(minute)\n",
+    "\n",
+    "goals_by_player"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Instead, with a `defaultdict` object, we can portray the code fragment's intent in a concise form. We pass a reference to the [list() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#func-list) built-in to `defaultdict`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "from collections import defaultdict"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "defaultdict(list,\n",
+       "            {'MĂĽller': [11],\n",
+       "             'Klose': [23],\n",
+       "             'Kroos': [24, 26],\n",
+       "             'Khedira': [29],\n",
+       "             'SchĂĽrrle': [69, 79],\n",
+       "             'Oscar': [90]})"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "goals_by_player = defaultdict(list)\n",
+    "\n",
+    "for _, player, minute in goals:\n",
+    "    goals_by_player[player].append(minute)\n",
+    "\n",
+    "goals_by_player"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "collections.defaultdict"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "type(goals_by_player)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "A reference to the factory function is stored in the `default_factory` attribute."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "list"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "goals_by_player.default_factory"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "If we want this code to produce a normal `dict` object, we pass `goals_by_player` to the [dict() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#func-dict) constructor."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'MĂĽller': [11],\n",
+       " 'Klose': [23],\n",
+       " 'Kroos': [24, 26],\n",
+       " 'Khedira': [29],\n",
+       " 'SchĂĽrrle': [69, 79],\n",
+       " 'Oscar': [90]}"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "dict(goals_by_player)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Being creative, we use a factory function, created with a `lambda` expression, that returns another [defaultdict <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/collections.html#collections.defaultdict) with [list() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#func-list) as its factory to group on the country and the player level simultaneously."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "defaultdict(<function __main__.<lambda>()>,\n",
+       "            {'Germany': defaultdict(list,\n",
+       "                         {'MĂĽller': [11],\n",
+       "                          'Klose': [23],\n",
+       "                          'Kroos': [24, 26],\n",
+       "                          'Khedira': [29],\n",
+       "                          'SchĂĽrrle': [69, 79]}),\n",
+       "             'Brazil': defaultdict(list, {'Oscar': [90]})})"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "goals_by_country_and_player = defaultdict(lambda: defaultdict(list))\n",
+    "\n",
+    "for country, player, minute in goals:\n",
+    "    goals_by_country_and_player[country][player].append(minute)\n",
+    "\n",
+    "goals_by_country_and_player"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Conversion into a normal and nested `dict` object is now a bit tricky but can be achieved in one line with a comprehension."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'Germany': {'MĂĽller': [11],\n",
+       "  'Klose': [23],\n",
+       "  'Kroos': [24, 26],\n",
+       "  'Khedira': [29],\n",
+       "  'SchĂĽrrle': [69, 79]},\n",
+       " 'Brazil': {'Oscar': [90]}}"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "{country: dict(by_player) for country, by_player in goals_by_country_and_player.items()}"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "## The `Counter` Type"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "A common task is to count the number of occurrences of elements in an iterable.\n",
+    "\n",
+    "The [Counter <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/collections.html#collections.Counter) type provides an easy-to-use interface that can be called with any iterable and returns a `dict`-like object of type `Counter` that maps each unique elements to the number of times it occurs.\n",
+    "\n",
+    "To continue the previous example, let's create an overview that shows how many goals a player scorred. We use a generator expression as the argument to `Counter`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[('Germany', 'MĂĽller', 11),\n",
+       " ('Germany', 'Klose', 23),\n",
+       " ('Germany', 'Kroos', 24),\n",
+       " ('Germany', 'Kroos', 26),\n",
+       " ('Germany', 'Khedira', 29),\n",
+       " ('Germany', 'SchĂĽrrle', 69),\n",
+       " ('Germany', 'SchĂĽrrle', 79),\n",
+       " ('Brazil', 'Oscar', 90)]"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "goals"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "from collections import Counter"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "scorers = Counter(x[1] for x in goals)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Counter({'MĂĽller': 1,\n",
+       "         'Klose': 1,\n",
+       "         'Kroos': 2,\n",
+       "         'Khedira': 1,\n",
+       "         'SchĂĽrrle': 2,\n",
+       "         'Oscar': 1})"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "scorers"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "collections.Counter"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "type(scorers)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Now we can look up individual players. `scores` behaves like a normal dictionary with regard to key look-ups."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "1"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "scorers[\"MĂĽller\"]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "By default, it returns `0` if a key is not found. So, we do not have to handle a `KeyError`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "scorers[\"Lahm\"]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "`Counter` objects have a [.most_common() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/collections.html#collections.Counter.most_common) method that returns a `list` object containing $2$-element `tuple` objects, where the first element is the element from the original iterable and the second the number of occurrences. The `list` object is sorted in descending order of occurrences."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[('Kroos', 2), ('SchĂĽrrle', 2)]"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "scorers.most_common(2)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "We can increase the count of individual entries with the [.update() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/collections.html#collections.Counter.update) method: That takes an *iterable* of the elements we want to count.\n",
+    "\n",
+    "Imagine if [Philipp Lahm <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_wiki.png\">](https://en.wikipedia.org/wiki/Philipp_Lahm) had also scored against Brazil."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "scorers.update([\"Lahm\"])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Counter({'MĂĽller': 1,\n",
+       "         'Klose': 1,\n",
+       "         'Kroos': 2,\n",
+       "         'Khedira': 1,\n",
+       "         'SchĂĽrrle': 2,\n",
+       "         'Oscar': 1,\n",
+       "         'Lahm': 1})"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "scorers"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "If we use a `str` object as the argument instead, each individual character is treated as an element to be updated. That is most likely not what we want."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "scorers.update(\"Lahm\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Counter({'MĂĽller': 1,\n",
+       "         'Klose': 1,\n",
+       "         'Kroos': 2,\n",
+       "         'Khedira': 1,\n",
+       "         'SchĂĽrrle': 2,\n",
+       "         'Oscar': 1,\n",
+       "         'Lahm': 1,\n",
+       "         'L': 1,\n",
+       "         'a': 1,\n",
+       "         'h': 1,\n",
+       "         'm': 1})"
+      ]
+     },
+     "execution_count": 21,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "scorers"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "## The `ChainMap` Type"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Consider `to_words`, `more_words`, and `even_more_words` below. Instead of merging the items of the three `dict` objects together into a *new* one, we want to create an object that behaves as if it contained all the unified items in it without materializing them in memory a second time."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "to_words = {\n",
+    "    0: \"zero\",\n",
+    "    1: \"one\",\n",
+    "    2: \"two\",\n",
+    "}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "more_words = {\n",
+    "    2: \"TWO\",  # to illustrate a point\n",
+    "    3: \"three\",\n",
+    "    4: \"four\",\n",
+    "}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "even_more_words = {\n",
+    "    4: \"FOUR\",  # to illustrate a point\n",
+    "    5: \"five\",\n",
+    "    6: \"six\",\n",
+    "}"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "The [ChainMap <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/collections.html#collections.ChainMap) type allows us to do precisely that."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "from collections import ChainMap"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "We simply pass all mappings as positional arguments to `ChainMap` and obtain a **proxy** object that occupies almost no memory but gives us access to the union of all the items."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "chain = ChainMap(to_words, more_words, even_more_words)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Let's loop over the items in `chain` and see what is \"in\" it. The order is obviously *unpredictable* but all seven items we expected are there. Keys of later mappings do *not* overwrite earlier keys."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "4 four\n",
+      "5 five\n",
+      "6 six\n",
+      "2 two\n",
+      "3 three\n",
+      "0 zero\n",
+      "1 one\n"
+     ]
+    }
+   ],
+   "source": [
+    "for number, word in chain.items():\n",
+    "    print(number, word)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "When looking up a non-existent key, `ChainMap` objects raise a `KeyError` just like normal `dict` objects would."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "ename": "KeyError",
+     "evalue": "10",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-28-25fb5b5b2e5f>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mchain\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m10\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;32m~/.pyenv/versions/3.8.6/lib/python3.8/collections/__init__.py\u001b[0m in \u001b[0;36m__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m    896\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    897\u001b[0m                 \u001b[0;32mpass\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 898\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__missing__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m            \u001b[0;31m# support subclasses that define __missing__\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    899\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    900\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdefault\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m~/.pyenv/versions/3.8.6/lib/python3.8/collections/__init__.py\u001b[0m in \u001b[0;36m__missing__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m    888\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    889\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__missing__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 890\u001b[0;31m         \u001b[0;32mraise\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    891\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    892\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__getitem__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;31mKeyError\u001b[0m: 10"
+     ]
+    }
+   ],
+   "source": [
+    "chain[10]"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.6"
+  },
+  "livereveal": {
+   "auto_select": "code",
+   "auto_select_fragment": true,
+   "scroll": true,
+   "theme": "serif"
+  },
+  "toc": {
+   "base_numbering": 1,
+   "nav_menu": {},
+   "number_sections": false,
+   "sideBar": true,
+   "skip_h1_title": true,
+   "title_cell": "Table of Contents",
+   "title_sidebar": "Contents",
+   "toc_cell": false,
+   "toc_position": {
+    "height": "calc(100% - 180px)",
+    "left": "10px",
+    "top": "150px",
+    "width": "384px"
+   },
+   "toc_section_display": false,
+   "toc_window_display": false
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/CONTENTS.md b/CONTENTS.md
index e2decdb..76c1a8b 100644
--- a/CONTENTS.md
+++ b/CONTENTS.md
@@ -253,3 +253,8 @@ If this is not possible,
        `set` Methods & Operations;
        `set` Comprehension;
        `frozenset` Type)
+    - [appendix <img height="12" style="display: inline-block" src="static/link/to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/09_mappings/05_appendix.ipynb)
+      [<img height="12" style="display: inline-block" src="static/link/to_mb.png">](https://mybinder.org/v2/gh/webartifex/intro-to-python/develop?urlpath=lab/tree/09_mappings/05_appendix.ipynb)
+      (`defaultdict` Type;
+       `Counter` Type;
+       `ChainMap` Type)

From 8566a99c6af6ab1bd72842dd8d33eeb9c20b6af8 Mon Sep 17 00:00:00 2001
From: Alexander Hess <alexander@webartifex.biz>
Date: Thu, 22 Oct 2020 18:41:12 +0200
Subject: [PATCH 205/242] Add initial version of chapter 09's summary

---
 09_mappings/06_summary.ipynb | 77 ++++++++++++++++++++++++++++++++++++
 CONTENTS.md                  |  1 +
 2 files changed, 78 insertions(+)
 create mode 100644 09_mappings/06_summary.ipynb

diff --git a/09_mappings/06_summary.ipynb b/09_mappings/06_summary.ipynb
new file mode 100644
index 0000000..0b9c814
--- /dev/null
+++ b/09_mappings/06_summary.ipynb
@@ -0,0 +1,77 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "# Chapter 9: Mappings & Sets (TL;DR)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "`dict` objects are **mutable** one-to-one **mappings** from a set of **key** objects to a set of **value** objects. The association between a key-value pair is also called **item**.\n",
+    "\n",
+    "The items contained in a `dict` object have **no order** that is *predictable*.\n",
+    "\n",
+    "The underlying **data structure** of the `dict` type are **hash tables**. They make key look-ups *extremely* fast by converting the items' keys into *deterministic* hash values specifiying *precisely* one of a fixed number of equally \"wide\" buckets in which an item's references are stored. A limitation is that objects used as keys must be *immutable* (for technical reasons) and *unique* (for practical reasons).\n",
+    "\n",
+    "A `set` object is a **mutable** and **unordered collection** of **immutable** objects. The `set` type mimics sets we know from math."
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.6"
+  },
+  "livereveal": {
+   "auto_select": "code",
+   "auto_select_fragment": true,
+   "scroll": true,
+   "theme": "serif"
+  },
+  "toc": {
+   "base_numbering": 1,
+   "nav_menu": {},
+   "number_sections": false,
+   "sideBar": true,
+   "skip_h1_title": true,
+   "title_cell": "Table of Contents",
+   "title_sidebar": "Contents",
+   "toc_cell": false,
+   "toc_position": {
+    "height": "calc(100% - 180px)",
+    "left": "10px",
+    "top": "150px",
+    "width": "384px"
+   },
+   "toc_section_display": false,
+   "toc_window_display": false
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/CONTENTS.md b/CONTENTS.md
index 76c1a8b..28dcbc1 100644
--- a/CONTENTS.md
+++ b/CONTENTS.md
@@ -258,3 +258,4 @@ If this is not possible,
       (`defaultdict` Type;
        `Counter` Type;
        `ChainMap` Type)
+    - [summary <img height="12" style="display: inline-block" src="static/link/to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/09_mappings/06_summary.ipynb)

From e0aa914dc6512a4d3d09cdc6eadc5ed3722fdf36 Mon Sep 17 00:00:00 2001
From: Alexander Hess <alexander@webartifex.biz>
Date: Thu, 22 Oct 2020 18:47:40 +0200
Subject: [PATCH 206/242] Add initial version of chapter 09's review

---
 09_mappings/07_review.ipynb | 247 ++++++++++++++++++++++++++++++++++++
 CONTENTS.md                 |   1 +
 2 files changed, 248 insertions(+)
 create mode 100644 09_mappings/07_review.ipynb

diff --git a/09_mappings/07_review.ipynb b/09_mappings/07_review.ipynb
new file mode 100644
index 0000000..7e7722d
--- /dev/null
+++ b/09_mappings/07_review.ipynb
@@ -0,0 +1,247 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Chapter 9: Mappings & Sets (Review Questions)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The questions below assume that you have read the [first <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/09_mappings/00_content.ipynb), [second <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/09_mappings/02_content.ipynb), and [third <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/09_mappings/04_content.ipynb) part of Chapter 9.\n",
+    "\n",
+    "Be concise in your answers! Most questions can be answered in *one* sentence."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Essay Questions "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q1**: `dict` objects are well-suited **to model** discrete mathematical **functions** and to approximate continuous ones. What property of dictionaries is the basis for that claim, and how does it relate to functions in the mathematical sense?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " < your answer >"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q2**: Explain why **hash tables** are  a **trade-off** between **computational speed** and **memory** usage!"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " < your answer >"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q3:** The `dict` type is an **iterable** that **contains** a **finite** number of key-value pairs. Despite that, why is it *not* considered a **sequence**?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " < your answer >"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q4**: Whereas *key* **look-ups** in a `dict` object run in so-called **[constant time <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_wiki.png\">](https://en.wikipedia.org/wiki/Time_complexity#Constant_time)** (i.e., *extremely* fast), that does not hold for *reverse* look-ups. Why is that?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " < your answer >"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q5**: Why is it conceptually correct that the Python core developers do not implement **slicing** with the `[]` operator for `dict` objects?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " < your answer >"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q6**: **Memoization** is an essential concept to know to solve problems in the real world. Together with the idea of **recursion**, it enables us to solve problems in a \"backwards\" fashion *effectively*.\n",
+    "\n",
+    "Compare the **recursive** formulation of `fibonacci()` in [Chapter 9 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/09_mappings/02_content.ipynb#\"Easy-at-second-Glance\"-Example:-Fibonacci-Numbers--(revisited)), the \"*Easy at second Glance*\" example, with the **iterative** version in [Chapter 4 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/04_iteration/02_content.ipynb#\"Hard-at-first-Glance\"-Example:-Fibonacci-Numbers-%28revisited%29), the \"*Hard at first Glance*\" example!\n",
+    "\n",
+    "How are they similar and how do they differ? Also consider how the flow of execution behaves when the functions are being executed."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " < your answer >"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q7**: How are the `set` and the `dict` types related? How could we use the latter to mimic the former?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " < your answer >"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## True / False Questions"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Motivate your answer with *one short* sentence!"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q8**: We may *not* put `dict` objects inside other `dict` objects because they are **mutable**."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " < your answer >"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q9**: **Mutable** objects (e.g., `list`) may generally *not* be used as keys in a `dict` object. However, if we collect, for example, `list` objects in a `tuple` object, the composite object becomes **hashable**."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " < your answer >"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q10**: **Mutability** of a `dict` object works until the underlying hash table becomes too crowded. Then, we cannot insert any items any more making the `dict` object effectively **immutable**. Luckily, that almost never happens in practice."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " < your answer >"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q11**: A `dict` object's [.update() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/stdtypes.html#dict.update) method only inserts key-value pairs whose key is *not* yet in the `dict` object. So, it does *not* overwrite anything."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " < your answer >"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q12**: The `set` type is both a mapping and a sequence."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " < your answer >"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.6"
+  },
+  "toc": {
+   "base_numbering": 1,
+   "nav_menu": {},
+   "number_sections": false,
+   "sideBar": true,
+   "skip_h1_title": true,
+   "title_cell": "Table of Contents",
+   "title_sidebar": "Contents",
+   "toc_cell": false,
+   "toc_position": {},
+   "toc_section_display": false,
+   "toc_window_display": false
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/CONTENTS.md b/CONTENTS.md
index 28dcbc1..b7d6e4f 100644
--- a/CONTENTS.md
+++ b/CONTENTS.md
@@ -259,3 +259,4 @@ If this is not possible,
        `Counter` Type;
        `ChainMap` Type)
     - [summary <img height="12" style="display: inline-block" src="static/link/to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/09_mappings/06_summary.ipynb)
+    - [review questions <img height="12" style="display: inline-block" src="static/link/to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/09_mappings/07_review.ipynb)

From 7b339acd5421228d96cc1f3180df5a464112da0e Mon Sep 17 00:00:00 2001
From: Alexander Hess <alexander@webartifex.biz>
Date: Thu, 22 Oct 2020 18:49:35 +0200
Subject: [PATCH 207/242] Add initial version of chapter 09's further resources

---
 09_mappings/08_resources.ipynb | 228 +++++++++++++++++++++++++++++++++
 CONTENTS.md                    |   2 +
 2 files changed, 230 insertions(+)
 create mode 100644 09_mappings/08_resources.ipynb

diff --git a/09_mappings/08_resources.ipynb b/09_mappings/08_resources.ipynb
new file mode 100644
index 0000000..9b23588
--- /dev/null
+++ b/09_mappings/08_resources.ipynb
@@ -0,0 +1,228 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Note**: Click on \"*Kernel*\" > \"*Restart Kernel and Clear All Outputs*\" in [JupyterLab](https://jupyterlab.readthedocs.io/en/stable/) *before* reading this notebook to reset its output. If you cannot run this file on your machine, you may want to open it [in the cloud <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_mb.png\">](https://mybinder.org/v2/gh/webartifex/intro-to-python/develop?urlpath=lab/tree/09_mappings/08_resources.ipynb)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "# Chapter 9: Mappings & Sets (Further Resources)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Here, we list some conference talks that go into great detail regarding the workings of the `dict` type."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## How `dict` objects work"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "[Brandon Rhodes](https://github.com/brandon-rhodes) explains in great detail in his PyCon 2010 & 2017 talks how dictionaries work \"under the hood.\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/jpeg": 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+      ],
+      "text/plain": [
+       "<IPython.lib.display.YouTubeVideo at 0x7fee2cc0dfd0>"
+      ]
+     },
+     "execution_count": 1,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from IPython.display import YouTubeVideo\n",
+    "YouTubeVideo(\"C4Kc8xzcA68\", width=\"60%\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/jpeg": 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\n",
+      "text/html": [
+       "\n",
+       "        <iframe\n",
+       "            width=\"60%\"\n",
+       "            height=\"300\"\n",
+       "            src=\"https://www.youtube.com/embed/66P5FMkWoVU\"\n",
+       "            frameborder=\"0\"\n",
+       "            allowfullscreen\n",
+       "        ></iframe>\n",
+       "        "
+      ],
+      "text/plain": [
+       "<IPython.lib.display.YouTubeVideo at 0x7fee2cc08e80>"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "YouTubeVideo(\"66P5FMkWoVU\", width=\"60%\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## History of the `dict` Type"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "The `dict` type's order has been worked on with many PEPs in recent years:\n",
+    "- [PEP 412 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://www.python.org/dev/peps/pep-0412/): Key-Sharing Dictionary\n",
+    "- [PEP 468 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://www.python.org/dev/peps/pep-0468/): Preserving the order of \\*\\*kwargs in a function\n",
+    "- [PEP 520 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://www.python.org/dev/peps/pep-0520/): Preserving Class Attribute Definition Order\n",
+    "\n",
+    "[Raymond Hettinger](https://github.com/rhettinger), a Python core developer and one of the greatest Python teachers in the world, summaries the history of the `dict` type in his PyCon 2017 talk."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/jpeg": 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vO5qX7A7M0K77W5Wjc6Ky6NksrRLKGN000e8aj6FAItqMyZZJo+IJJQ0PMdRp1DBo3QGMgcvEvauWz7C8xi6DK8yP3aWu889Z+R5fQsamBxM2rzjFK+hNrRs2FylljAUoyzac5Saj7TSburZ23Qns+RsrZ0/5Ik/R2v6nqq8/5H//AJR/8LV0FnPsjMLI8gIiHAsFOTdIf8YfI689V9GztBwuBw8jwt3c3O45NN3+T8jroqMch1FG3SR922tnUqfxbQlUclSnZ1VPUtXdrNg7X42vNjYHSwRSODaoD3MaXgFjQdH6bzeXiUW2p2Bdj5obGFsTV5pZOG2J0nLU8w1sp5lh0+LJqPSsLYv550Yhey8Ym7ujXU3ADc+Lz4OvLRe29os24xul45MTxIwvptGjx28ohr8xVqlk7FUvcqRehaG3bRr0WKNfLmAr26SjNWlNtpJPSlm6b7GiX7G9ILnTe5+Xj7kutcGCRzeHHK7qaHg8onu7HDwT2acgthrnnavN2cg1gusiLoz4EvAEMwaetm8Oth/klZrZ/pFu1YGQERWWxjda+bf4u7+K1z2u0doOQJHYFYr5LlJZ0LX2q/5HLoZbpxbjO9tjtp+puxFq6v0sP/jKDT6WWS39jojqsrQ6T6kjmsNW21zuQDBFJ/8AcHRUp5PrwV3HR4o6NPKuGm82MtPgyeIsE/auoPKn5mD9XN3X8yvcbma9g7scnh/yHjdcfmB5O+hc/Pje1zqOlJK9jIIipvsRtOjpGNPiL2g/qJWTZglcqKOYqbXITeFqHiRoPY7dczdAPUeQP6iq+WvOkaY4yGDUhztdS4DloCOoFYN9fc0cZGs8IBpLt3wyfBAJ7SVRni6N/e1fJnocJknEqD9n3l3rzJwqGQk3YpXA6ERvIPidund+nXRY6vlS1gEoBcOW8CG73pII61j83adKNS4MhbzA1PM+MkfGdryACyeNpW18mV6WRcUqmmOr5rzLvYx3wUjf5Mmob1EAtbz08RIP7VnVDaDXsLZYpR6DzIcPER2jl1ejxqT1bzXMc92kYYAXuLhugaEkknqA07VNHE0n7MWTlHJuIi3VlHR9PDmXaKhRtxzsEkLxIx3U5vo6wR1gjxFVnEDmeQHMk8gB16n0K2mmro48oSi81qz7j1Fb0bsU4Lonh4ad06agg9nI89COYPb2K4RNPShOEoO0lZhEVJ9mMHQyRg+IvaD+olGzFK5VRGnXQjmD2jmiAIiIAiIgCIiAIiICw2h/BLH6J/8AZK1qtlbQ/glj9E/+yVrVVMRrOlgvdZ8SSbv/AGAB6SXHQD51Uc0tOjgWkdYOmo+dGPc3m0lp6tWkg/s7F5+s/OdT+sqp7Wd8jqt0eiVr59/oesHMfOFfPbr2DkdRqNRr6R4lZRfGHzj+tX6ykrqzK8akoSUo6Gj5YD2kHQBo0GgDW/FaATroB4196f8AQoDof++xUIYnBznFzQHdbI2PDTzBBO9IQDy5hoC1+7ZRWgsrNr51SrO0vDWfe4dSW7rdddTu6u0cAHAHXTQgDrBXzZHgn0aKqqdn4p/77VlGCTutppqYmpUSjJ3UdRTqXpYgWskcGu626jT5wHDQOB0Oqyt+TeY1wLy0yRFu8wOGhYfiho1d6de3VYFZVo/g8ZI0BlZzdK5rTpvjkR8mOzl1qtjI6EztZDqvPcNmsuJT4Duv4rP4h3lZV5AeR6/lJP4hw/ipP1fN9K+ZXDcdzZ8Vn+tSeUk9HzfN19q8hcNDzZ8eT/W5D/Fv7dOY9P0LmWPVFWiTvy/HHhwfFj3eWjtd4OHxPGQrHOE7zNd8ndd8cAO+UfpyA008X0K9oc3S6aO0fCfAmc7TQO5lx58v5CrD6fpJPbr289OZV7CRu7nnsuYhQhmbX+hHHEkaa6enRrtPocFXfC6TQl8o5aeEYyf/AEDTRXeYhGgeBodd0+nkTr8/L9qpw/FHzK3OjGTuzj4fKtejDNg+OmxbPrkDr3tBzPb85VJZFY+QcyPEStqVijKbm3J62fdbGQ3HtrTahsu81jwdHRSuY5scg7HaOI8E8io9ss2BnddG9BD3ZVtNPGe340LXFk7efIsBDHA9ol9ClWB/Cq/6aP8AtBR/p5x3c92G5H4IuV3xyEdr4Q2N+v50UkY//wA128nTnVpyoZzV9T7mcDKMaWHxEMVKClm61ZaUXvR9jY8tlbORMLI6FV4bWgYxrI3PHyO8xo0Jaz4V2vbIzsUo292+Zhp2RHHz2YW1H37k9d8DBSpssR13TGKQh0+j5GktZz0WV6NcT3Hi6kWmj3xieXXrMk/whB/NBaz/AGAont7s17q7QQ05LU1eq/Az92RQti3rtf3Tq61XySNJhjcQNXM0PLTVZ4qs5zsnoWhFbBYeEY3a16WSJ+3UIyHuZwZTaOTix7W7zAHRy412TF4a9dcRxyt069WFYnG9KkEkV2xLQv16tahcydadwge2/Tozdzzuha2T4GXibobHLpqHA9SqOw8H35w3N0cYbOyN17OWRjYHfnBkj26+JygmJ2lgsbK5HB12zzXqmBy77jGRO3assdmeAVJ9fCbbeXPc1mnMRPKrXLqhF7O4m7elKKOuZblCzSliy1HFWq8std5ruyEUc8FkywvLJIeFMxxA5jwlLdl802/DJMyN0YjuXqZa4gkuo25qjn8uxxhLgP5y0hmatTPxZJkUnHo5LafC14rMRcGOczAwQvdG8cyY5m8/SxT/AO5xfZds/C65r3W67lnWdQAe6Dk7Rm1A5A8Te6kuTOEUrmUqbesdlpMXJStwM3bvc92Th8G0/HNrvuNjiDuK1jRYZuvI0dz0WGwnSwbMViY4XJRtix8GUrsa6vYmt0rk3ApPiirvJY+R7ZS4P5METiTorettBTubR5ji24GS4nHy46nUfIGykSRMu5S7uOHJmra8QPirSHtUV6BpGyiy9k3HdBsni6dshsjWVrkDsi3uEtkHgTRwNg3mjtLz2pcno420ruJw7pMe52MLMRZfXybcLuWXTRsZHJmmyPZE1rma2DDHFI6Qs6gATpqnTRs2Javd9doZYp+E/cAHEr66v3hpo4xnw+fZxFiNjP4T941TrbSwDMxKOzfbjauNqans1ddsuH6FbZkja4FrgHNcC1zT1Oa4aEHxghbaFZ05KSKuKw8KkXGxpTJ5jHPpuu9yVhLJTbEyJsbWMZfMm5I8NZ2NaHyD0bqp7K0DDA10mvFkAc7UAOa082M9HLQn0krCYvZ1xzTMQ4l0Ud54cD+NDEDI5x9LoIx/vKeZX8In/TS/3jlsyhKVGn0ak3nty17NiGTlTxNZVcxRdOCjqXvLWy2VzVqyO0c3wdDqHEkcx2jTnrr2q2CkQGnIdQ5D0aLhHoJMur2YnMEceuj9CJpG/jachp2t1HM/OsHI4AFxIaBzJcQAPSSepZIhYew5rtWEEg6tJ7ObNdPH1FJzbIw9OOda2jaX0kprl269swa0O3mcmSAtDtRzO7qCsaKFjefLYhMrJDMYw+RxjYSXbxj3uXg9g/mr5rn+D7mmhbFoeWgI3SNQNfQV9TQQhm8JQ52jzudzygjd13dXObu6O9C52IWo9hkV6JrZnaPAtzQsQHSdrxvhpG+4l2mjt3QO/F8E/qVWpE9z95s0cfC+EDZNNCQGt3gC7w3eHyHoKpODd52nXHMYjyA18Au1GnZ6Ec9rWvJL9Rq4tboAWtic7k4tIB1aFN30P1NiprtD+y/1MviXPjl0dNHIyZnG3YwDukhoHU7wHeMKvk553uEUcLZYGFs8rXH5QMDuTvBIbGNdT9CxuHc0vaQX6lhcWu00AcGFvMNGp5n9Sv8AJQB8ZJ/E8MctSdORA5cuR1/2VjCn7DktZrxGLzcZGjJJp24lha7pjdxWRdywyBrS2KXRr3R7rHP8ADV2p/ajb1x4MTHuljOjpGPdvAsYd9wG8DujRp1I7NVbsJkrwy7x3S9wbGRzHMO3i4DQ6kEf7JVXHBxkIa7d1jk15E66D4vL+Vru/wC0s4TkqL0mOIoU3joXS1N/VXt+RksbctRTslbAwN3RHK3fJ3mPDXNO8W+C7RhcPHq5Z6fOSE+AGtb2ajU/SepRvCWDMx7ydAH8Pc0+MW6je1HLRoGmh/lhXGRAMT97q0Gumo5ajXq5rfhqko0zk5Zowq4uMLWehN+PkfWR2nnkY7cHwEbgyaePUEucDuho15s8F2pHWrJpBGo5g8wR1EFWl01GRtMTJnBrHOlY9zmNc7d1cI9PxdQOvxBVaQbuncbutJa4N1J03o2OI1PpJWNLETqS9o35SyVRw9FTpq2zx+ZlMVkX13AtJLNfDZ2OHbp4nelTmN4cA5vNrgHA+MEag/qWuVPcL+Dwfomf1BdPDyeo8li4JWZdoiK0UgiIgMT99GM85Y/16r+8T76MZ5yx/r1X94sF72Z/npv1eftae9mf56b9Xn7Wuj0OG33wKX274fNeZnfvoxnnLH+vVf3iffRjPOWP9eq/vFgvezP89N+rz9rT3sz/AD036vP2tOhw2++A+3fD5rzM1NtHintLXZHHOa4EOabtUgg9h+EVl3bgfyrFeuVf3isvezv89N+rz9rT3sz/AD036uP2tQ8PhX9/l6EqWPWqnzXmXvduB/KsV65V/eJ3bgfyrFeuVf3isvezP89N+rz9rT3sz/PTfq8/a06thd/l6E5+UNz8S8y97uwP5VivXKv7xfXulgvyzF+u1v3qsPezP89N+rz9rT3sz/PTfq8/a06thd/l6DPx+5+JeZf+6WC/LMX67W/ep7pYL8sxfrtb96rD3sz/AD036vP2tPezP89N+rz9rUdWwm/y9Bn4/c/EvMyHulg/yzF+uVv3q8ORwX5Xi/Xa371WHvZn+em/V5+1r33sz/PTfq8/a06thN7l6DPyhufiXmXnd2B/KsV65V/eKo7KYQtDDcxe6DqB3ZWAB9GknpKx3vZ3+em/V5+1p72Z/npv1eftaPC4R65cvQzhXyjF3jG393qX5yWD/LMZ69X/AHvpP60GRwfULmM7f9er9vX/ABqsPezP89N+rz9rT3sz/PTfq8/a1j1LBb3L0NnXcq9z/wA/UyUWWwjNd27jBvdZ7tranTq65V77r4X8txnrtX94sZ72d/npv1efta997M/z036vP2tSsLg1qly9DXOvlKfvRv4y9TISZPBuGjrmLI69Dcq/vF8jIYL8sxfrtb96rD3sz/PTfq8/a097M/z036vP2tT1bCb3L0MM/H7n4l5mQ90sH+WYv1yt+9Xwb2B/KsV65V/eKy97M/z036vP2tPezP8APTfq8/a06thN/l6DPyhufiXmX8WRwTXBzbeLa5pBaRcq6gjqI+ETL5HBXGtbat4qw1hLmCW3UeGkjQkaycjorD3sz/PTfq8/a1772Z/npv1eftazjRw0dKm19DGXXpe9Tv8AVeZmxtPixoBkscANAB3bV5D/AIiffPi9f85Y712r+r5RYP3sz/PTfq8/a097M/z036vP2tR0OG33wItjfhrivMzn3zYvr90sdr1a921ddPF8p1INpsWNSMljgTzJF2rzPUCfhOZWD97M/wA9N+rz9rT3sz/PTfq8/a06HDb74E/bvh815mbbtNiwNBkcaAOoC7VAHzDiL0bT4vsyWO7eq7V7eZ/jPGVg/ezP89N+rz9rT3sz/PTfq8/a06HDb74D7d8PmvMzLtocQSSb+MJcCHE26ZLgeRDiX8xp2FfY2mxfP/KWO5nU6XavMnrJ+E5lYP3sz/PTfq8/a097M/z036vP2tOhw2++A+3fD5rzM2NpsWNNMjjhy0/DavIDqHynUvr76MZ5yx/r1X94sF72Z/npv1eftae9mf56b9Xn7WnQ4bffAfbvh815l8zIYEWHXBaxItOGjrAtVOKRuhnN/E1+KAPoXsmQwTiXG3iiXEkk3KupJOpJ+E8asfezP89N+rz9rT3sz/PTfq8/a1MqOGlrm39BHr0fdp2+q8y87twP5VivXKv7xVvdfC/luM9drfvFjPezP89N+rz9rT3sz/PTfq8/a1h1bCb/AC9DLPyhufiXmZP3Xwv5bjPXa37xU/dDBa691YrXx911Nf18RWHvZn+em/V5+1r33sz/AD036vP2tOrYTe5egz8obn4l5l53dgdCO6sSAesC3VGvz6SL57qwH5Vi/Xa371WnvZ3+em/V5+1p72d/npv1eftah4TBvXLl6G2GKynD3Ytf3epdttbPjn3ViuZ1P8Mrcz1anWXmdO1ePs7PnXWziufX/DK3Ps8r4la+9mf56b9Xn7WnvZn+em/V5+1p1TB6s7l6E9bypfOs79+d6l5DcwDDqy1i2kDTUXa3V4vlVUlyeDcNHXMWR4u7a2n96sf72Z/npv1eftae9nf56b9XH7WnVMHqzuXoYyxGUnLOcXfvzvUum2dnxoBZxQA6h3ZW0HzDir6NzAa691Yr6Llb96rP3s7/AD036vP2tPezP89N+rz9rTqmD1Z3L0JeKym3nZrv353qXsF7As+JbxTdfFdrDr5n+N9CqS5TCOBa65iyD1juyt+8WO97M/z036vP2te+9mf56b9Xn7WnVcHvcvQxlXylKWc43ffnepcGfZ7q7pxXi/DK3V/xVViv4Fo0bbxQH9Mq/N5T0Kx97M/z036vP2tPezP89N+rj9rRYTBrVLl6Gc8VlSatKLa/7epkPdPB/lmL9cq/vFdxbSYpoDW5HHBoAAAu1QAB2fKLCe9nf56b9Xn7WvfezP8APTfq8/a1ksPhVqny9DQ5Y966fNeZnPvoxnnLH+vVf3iffRjPOWP9eq/vFgvezP8APTfq8/a097M/z036vP2tT0OG33wI+3fD5rzM799GM85Y/wBeq/vE++jGecsf69V/eLBe9mf56b9Xn7WnvZn+em/V5+1p0OG33wH274fNeZ0WiIqB6MIiIAiIgCIiAIiIAiIgCIiAIiIAiIgCIiAIiIAiIgCIiAIiIAiIgCIiAIiIAiIgCIiAIiIAiIgCIiAIiIAiIgCIiAIiIAiIgCIiAIiIAiIgCIiAIiIAiIgCIiAIiIAiIgCIiAIiIAiIgCIiAIiIAiIgCIiAIiIAiIgCIiAIiIAiIgCIiAIiIAiIgCIiAIiIAiIgCIiAIiIAiIgCIiAIiIAiIgCIiAIiIAiIgCIiAIi0n0/9Ns2y2Vw1Q0op6N4cW/Zc6QSU6zbUME0zGsG64NbLvc/EEBuxFpH7qPpxl2RjoNp0o79i4J55GyPc2KvUhMMQleYzvDfmsxNaerwX+hbB2y6SsDhpIYcrladGedofHDNKBIWOJaJCxvhMiLmuG+7QeC7nyQEtRRnavb/C4qtXuZDJ06tW3u9yzPmaWWQ9oe10BZqZWbrmu3m8tHAqDdDfS6zJ1NpMjkreOhxuJz16jVuxvEVZ+Ph4Xcs0kz5SyV8glbo5ugO+3Qc0Bt9FDsV0o7PWqFnJ18vSloUyxtuw2XRtYyODI+O0jfi3nEAFw589FQHS7sxw+N7uY4Q90SVOO6w1sBsQwxzyxCZ3gEtjljcSD+MgJwiwGxG2eKzcL7GJvV70MUhikfA/XhyAa7r2kbzSQQRr19i1lm+kvaSbafLbP4TGYmyMVXpWXy37lis+RlqCGTdHCjLd4PkI+gIDdiLVPRX010spWHumxmFyTMxNgJKM87JWvykPCBhq2GDdm3jK0AHQ6teBqACa3S/034XZ2rfkdPFcvUH1YpMZFMGWTJbcwsZvFpax3BMk2h7IXoDaCLRWQ6fKcefxkZv42PZy7gJ8m+69x3+6orlioYYp9/dfo+Es3A0nVjls/vgYT3LGa91KQxR6rxmYIC7fMe4HHnxd8Fu516jTRASZFEcb0m4Czjp8tBlqUmOqlrbNpso3K7nuaxjZwfCicS9mgcPxgvvZXpIwOVty0cdlqVy3CwySQV5myODAWhz2kcpGtLmg7uum8NUBK0WqOm7pGymIyez2KxNKlbs519+NpuzzQRROpsrSDwomk6ObK/s/FCssD0u3ZW7Q4zIUqmF2iwmOlvtZbsusYixX4LnQ3xYhaJe4mPMfEAGoEg566hoG5EUBi6TMbQw2LyGeymKqyX6sMnEryyGpZldGx8r6DZBxpa4LwddDoHt1KwvSH094LEPwv8KrW4sxNEBPDaj4dalJI+J2ReQCH12SRStIHPVjggNsIoHs1tm+bLZ6CxcwvubjYKFiDgTzC9WhnqmxPNluN8DFERo9jm6eCCT6L3Y/pN2fzEk0OMy1K5LXY6WaKGUGRsTC1r5gw+E+EFzQXt1Hht580BL0UExXTFstas1albO46azcIbWijsNc6R5eY2x+JkrnDRrHaE6jQcwrnN9KezlG+MZbzNCvfLmsNaSdoex7w0sZMfiwOcHNIDyPjBATJFFM10jYKldGOtZSpBfMtWEVHyfwh0twtFZjYgN5xfvDq8fNYXYfpBa7HZXIZrI4KOvj8tbpmzQsS9ywQRmEQQ3H2tC3I6zBrmN5Hfj05nRAbFRRXAdI2Cv0bGSqZWlNRqAm1ZE7Wx1gBvfwjiaGHlzG9pqvvYbpBwuc4vuTkqt4wacZkEmskYcSGudG4B4YSCA7TQ6FASdERAEWB6Q9pocNishlZxrHQqTWCzUNMj2MJjhaTy35JNxg9Lwol9zv0mS7TY2exbqDH5CnckqXaervg3bkc0MgD/DDHRSt6+1j0BstFgdtdscXhYBZyt6vRhe/hsfYkDTI/Qu3I2DwpHaAnRoPUVbP6QcG3GDMnK0Ri3cm3uOzgOfvFnDDtecu8C3hjnqDyQEnRRCj0m7Pz42bMRZak/G1nsjsW2yjh15JHxxsZMPjRPLpYwGuA+UaqOM6V9m7L7sdfNY+Z+PgktXBHO1whrw6cafeHJ8TNRvOZrpqNUBNUXHV/wC6M2isYvI7QY+1s+2tUyMdVmHfWtWLsdWxM6GrZtTCw1oc/cefB5HdPV1Lqj78MZv5KPu2DiYeNs2Ubv8AOjE+J87X2OXgNMUb3/M0oDOoolnOkvAUaNXJW8rTgpXRvU53y8rLdAd6BgG/KACCS0ctQvbnSTgIatO7LmMeynf4ncdp1mMV5zDrxgybXc1Zo4OB6i0g80BLEUX2d6Q8HkKVjI08pTmo1C4WrImayKtuN3nGcyacIbvPVy82M6RMHmW2H4zKU7jao1s8KUAwN8LSSRr9HNiO67R55eCeaAlKKJbH9JeAzFiWpjMtSu2YWl74YJg5+40hrpGD+NjBc0F7NR4QUtQBERAEREAREQBERAEREAREQBERAEREAXPPT3slJl9s9mqj4J3ULWG2jo3bEcL3RQNtY22yIyShu5G/icNzde1rV0MiA4R2l2Yz+Q2Wz9/KY+07J0ocHslRhbBYlsSVcRbgnvW2bzN+dk83CeZG8vgpOZW0dpi3CbVbW3Mxhb+Uq57FUY8RNVxr8jHI2Cn3PaxbjG09zSSS7ngu0BEQJ6wunUQHHOyWzGR2cPR/kM9jbtupQx+YrWWQVpb8uKs37FmxTdNWiaX73Bmhi008ExkdbQFg7uyGWubO52Wni8hBFF0iS5l+OOP3bjsSIiBwcbM3h2jGZYzwObfg3dgXcSIDivaPZ6S9jNscnVn2gyVm1h8dTk7p2ajw8FuUZGo+MV4q8YdZt14672uIbyFgczyWw9vdj42SdGsNbFtbXiyNea7HBRAhicKdUca21ke6x28xvhSdrfQukUQGkegzES1tsOkGQ1ZK9exZwTqzzA6KGfdq3jM6Fxbuy6Pf4Rb2u5qKWOj63lukLad/uhn8LX9z8UY7mKlNNttwq1WPiNiSEsmaw68m9RBXTKIDn7pe6H6eL2LuwYcTG5irLdpYrlmQ2LljI0SJbFqeTQcSw+syVvggDXd5LXb9kr+S6Ndpcx3JJPl9qcgzNugiY6aZlSLK13wQw/jvijrx2JGAfizaDxLpvpQ2Hr7Q0fc+1ayFWu6Vskwx1o1X2YwySN9Sy4NPFqSNkO9GevdYexSDEY+GpXgqVo2w160MVeCJvxYoYWNjijbrz3WsaB9CA552cazKba7M5KLH2hQGyViIPt4+aBsFmO7bgcx7Zo9IZiWyEA9YeCORWtcDsvfgxVS3Lir1jG4bpFvXrmNjpyOkOOdFVEFuvSc3WxXicZOTAR8I7sBI7aXwyZhc5oc0ubpvNDgXN16t4DmNfSgON9vsVaysHSHmcZi79fGZGjhalSKShPWnydytbqOmtQ0SzilrAHjfI/jD272mzHbPuh2z2Imr0XRQRbO3oLEsNUshiIqfAwzPYzdjO8XaNd4yt/ogOfvumbElTaXYbJ9x37VXHz5mS0aNOe4+JskNKNmrIWnQlx5a/wAl3iUayuLyWesbbbU+5d+hTk2Nv4HE1rtd0OQyDnRPnksdx/Kxt4kYa0EeEJmac9QOpHPAIBIBd8UEjU6eIdq+kByXs9Umw17YrN5XGX7OLi2KgxRENCa7JjcmNZXunqsYZIXSRP4W9p2kdQK+9t6McOI2OydTZi/icdU2sjyFjGthmu2q1Jz5HusS1WgvrxyFpdweobzB2hdYogORdvNksrlbfSizH1bQfkaWy81Euilg7rjrQ17FmvA5wAkkMUckZjHa7dPWsriiM3tDstZw2EyGMr4HDZOPLy2cbJj2RtnoCtVxbd9gFmSOXf8ABbqPhXEdRXUqIDizEbKW2bCbCtGMsMuw7ZQT2WilK21FCL2V1mnAj4jItwQeE7loI/QrPpzbk7UO2WP9zr1KeXLskqYzEbMxuqZWpFdjmbmchl2VXTSzvaXv1a9vhEDQhxA7fRAc+dG2zbZ9ustdt4/ixtwGCNS1aqFzGztgrmQ15ZmaCdpa3Xd5jRagt7GZmXZu/LFSv7tLpLt5azXjpce1JjxXrMFytRst3L7GOcdGHVp1dryBXcSIDkKvsrXyNHa+9KzanNx36eLiuiPBVsFNafWsNlhmxsG60W7NUQNL2lnMSADe1U3+53u5CfaC6ZYzlKMWIjji2lu7OnBZQSd0RBuGke6NpuxNjZvlwHXCz0a9DIgIXtfsLPkL8F2PPZvHshbC11GhYhipT8GZ8pdNG+EuLnh+47Qjkxq8qbCzszBypz+bkiMksnuQ+xAcWBJE6IRiEQcThtLt8De62hTN7gBqSAB1knQD6V6DrzHMHq8SA0N915SyeYZhdl8XAZHZa93Remmjn9z4qePbx2xXp4G70TJJd1w3SCe5dARqsR0c0NpMJtzLLma9F8G1VECabBQ3zjoLuKjDazrBtamu90IkZzOhNhunaukUQGiOnmCSntTsrn7NK1fw1CLKVrPctSS86jZswaQWn1oml5Y47o3wOXBHbugwPaXFskq4XOUtk8jRwtTbG1lchizFLPbuRTRwxR533Ld4UEYdGdK4HINBA3Tqus0QHFfSVh7eVo9IuXxmJv18bk2bNV6UL6M9efJ2aeQous24qJZxQxgEnhac99x694Daj9n3Rbc7JSw0XRVmbMWq9iSKqWV2ERScOCZ7Gbje3Rjl0AiA4ltbN3u9ZLWGPud1u2hdJ3OKc/dToheLw/giPiFm7z10Uw2xt2MTnOkmGfGZac7SYmo3EPo46xcisOixFqrKDJC0tj3JJ+e91cJ/o16qRAcU2dnctS+8XLSDJU6NbZluOksVcNHl58VfL53SPnx1iImISxSxx8QN1+DIWSw2xPDGxfBgy1upNtnfyMoyeIFJ0Eb21o+KaUQLKtJ8kBmYXBvy3UF2IiA426Rticpaf0nxUcfZfHLf2XuQ1ooHsZkYKvHmusqDd3bEgOkhDNSSwDmSApjtQ2vtRFtB972zV+ndfsu/Hty9mCbFNn3p45fcKKnMAyaRzIy0zdQ3QCQN3XphEByR0D4M28tstJNNtA23gqdiPuWfZevi6eODqfc89S3fY1rpw86iMneJ5k6EuXW6IgCIiAIiIAiIgCIiAIiIAiIgCIiAIiIAiIgCIiAIiIAiIgCIiAKGdM+3sWzeInyb4H25Gvhr1akbgx9m3YeI4Yd8g7jdSXF2h5MdoCeSmahnTPsFHtJiJsY+d9SQyQ2KttjQ91a3WkEkMvDJ0e3UFpbqOTnaEHmgIVjOlDPtyE+ByeHo08zNipMpiHRZB82PtcJxbLVsS8DfgmZoSSAQdx3o1i33A+IkGDlys1SuJclLK92T7qsWL+RfHcuMmN5kzd2HccGhu4Tvb7ydCTrNtjujPLHNe7+0OUq371fGvxlCGjUfUqwRyuLprMm/IXSWH6kaDQDed6NM/wBAewcmzWAp4aWwy0+q604zxsdGx/dFqawNGOJI0EoH0ICCdMfThkcBau64zFNpUeA5rL2dq1srl45HBs0mLx8Yc/dj11PF5kaEDsG0NpdtKtHBT597ZH1Ycd7oiMANlkY6ESxRDU6NkdvMbz7XLTe2v3PuTt2NpRUy2PjqbSzNszy3MY61k67muDxTgtCYBlPUEDlqBoANeZ23kdiGXNmzs/cl1bJiY8bNPC3TR7KzIePE1/Vo9oeGnxBAaByO02Zy20/Rxey2Kq42O1JlLlDue46zI6vZoQScK1G+FpgmawQO5ag8XsIIUlk+6GyPcUm0jMFE7ZGLIikbvd2mTdX7obU90WU+FucHiuDeETrry105rIbOdDmfF/Ze1lM5RtwbLieCrDBj5YZZ68lVlZsk9h053rG7HECNNPg+skkrHSfc95PuGTZpmdhbsjLkRdNQ0Scoyv3S22ccy3xeHwuK3e4xGuvPTTwUBlul/pb2iwVqvFDhcZeiyV1tTDRsycnuhf32g8QVG19I427zd55Og32anmqXSD063KWTt4mjSxUlrE0ILeVdkcvHjonWZ4G2Bj8bxWa2pixwIedBz0OnbVzfRPtG7aWfaKpmMUHtrmljILuMnstxlLQAsrBtkNbM8b29J1niyDkDovNrehO/Ll5c5Qt4d1zI06kGWr5fENyFN9qrDHCL1EF+/WduMA4fV16k9gHxB0638lbwlXA4evbdm8G/LRm7eNVtSSKxNXnincyE8SNj4XN1YNSS3qGpE66Cdv5No8W+5YqCjbrXbePuV2y8aNlmo8B/Dk08Jha5h/X19axGznRVPUzeGyz7laQYzAyYieOGjHSFmxLM+eW5HBXPBrNc95cY2jrc5ZjoS2Dl2eqX60tiOybmYv5Nro43RiNlwxkQkOJ3nN3D4XpCA1ptB0/ZcV9ocrjcFVtYPZ+7LjZbM+RMFuxahfFHJNFXbCW9ztdNEdCdSJGkc9WjNT9L2anzlbCYvB17jpcTjcvYsSXhAypWtlndBc17fhi3fDWtadSXa9QK0X0vtdjRtVsziMpNKM1k3XGYB+z2QfkpblmaqZm0b+6KzseRGCJTqd2vo0anePSWwXRvYpZ0ZyWxHo/Zuhhn1Awl8c1UwvfLxQ7dczVjhoPQgNdZX7p90ZtZCKhj5MDSyQx8jnZaJmbsRCZkMmQq4zc1fXDn6hhOpA6wNSM/0x9N+RwNi45uNxXcNJkEkfd+dq1clmGSkcV2Koxhzy2PXrk5kDkD1Kxx3QBdoWLMONuYQYm1eddAyWArZHJ02SSNfNTr2JncOWBwa5oMg1Ady581T226AMnbtbSGnlcdFU2kLJJ33MWbWSqmJoAqVrImDWVDpprpq0boA1GpA9ft3n7W3mIgpcE4a/gK+RjqyW5Y2voTysdNelibCQ3Ixh8jGxa6ERs8IaqU/dIZKrWm2S7ppm2Ztq8ZBXIt2KvctiTiNZZ0g5T7oLvg3cjqfSqMfRPk6+U2YytPI1GzYjC1MFko5qsj47dSExmeSpuyawzP0k3d7XTwOvmDJOmLo/lz0mBfHZZX9xs7SzDw+N0nHZULiYGbrvAc7X4xQGlunbpNymawm2UNHEQOwWLmlw9jIS3eHcfbrywcaaCpwtx8DHuZq0uBIe0jnq0SK30zz4/3LwWLrYyW1W2ex1+7Pl8pHjKsbX1oeDTgLmkzW3tId2ABzfTp97S9A+YfHtHjcbnKtXC7R3JslPXsY909uC5YdG+aKGdswYK73RMBcQSA1oHPwjebS9Bdnu2nlMZZxRuMxNTFX6+YxbcjRsipFHHHbhaXCSvOGxgcusBvVz1Ard/KzdwmFy+Nx9CCLJiwLVrOZetjsbi5qr5InwyTOHFtOfLE8MMbercJA10GKb90nxNnqWVZQrR27edds/J3RfDcRUstaZTbmyTIvCpmHck1aO1/MhupyWe6FMk6bZ69UyOJdfw1e3XmZbwsbcXO668vktwY+pI1law3eI1b8bdYSRodaOyXQlmcXhrGPrZynJYlzlnKyixi45cZka9mKsx9K/Rc/Vrd+DfDonDTe0HjAGxuiXanI5SvYfkaFao+GcxwWaF+HI47IwaeDapzx6PazUOBZIAfi+MgTVax6BOjCTZxuUfNPVdLlrjbb6mNrOpYqjux8MRUqz5HFoOp1cSOTYxpy57OQBERAEREAREQBERAEREAREQBERAEREAREQBERAEREAREQBERAEREAREQBERAEREAREQBERAEREAREQBERAEREAREQBERAEREAREQBERAEREAREQBERAEREAREQBERAEREAREQBERAEREAREQBERAEREAREQBERAEREAREQBERAEREAREQBERAEREAREQBERAEREAREQBERAEREAREQBERAEREAREQBERAEREAREQBERAEREAREQBERAEREAREQBERAEREAREQBERAEREAREQBERAEREAREQBERAEREAUDj20uBndUlWr3EcvLitGWJBbBblX4qOZsbodyUmVrXlgI5F3MkaGeFa6p9HfBdHdijpDKRZi9fFjc+XqXbtpz688vD4m+KlkAEdT4mdY112U837xXrZ+jMJJ9+eP4roeNJvtNhgcK1owyS1WyPswQT8LhWJ2Nil1jjJPwUnLwTp9XNscbFGyV9kcOSCC0x7I5ZGugtTRV68msbDyfJNGAOv4x6gdIxDstkW36tqzJA+Knkbt11l924+SWtPWyEEMTaT4+56nBZajb4BOohJ5EnWw2V2TksULLonsLJMjQGOfIySENwuIyrLVWEN0LiNO69x/U4SRHkCs8yHeaulq6radP5eegljdv8WS5plsMdHIyKZslC/GarpN3hG4HwfwSN++0tfLoCCSDoCryztbQjsmq+ZwkEsUD3iCw6vHYmDDDXlttj4EU7xJHpG5wPwsf8oa4rP7Kz2I84xj4QcmyBsG8X6MMVdkLuLozkN5pPg6q2vbL3nd1U2Gr3Bcvx3n2HySi3C3iwzz12wCLclc58Lg2XfG6JR4J3fCjNgZudVbOXj8/AzVTbTHS2G1mTSGR1ieoHGtabB3VWdK2at3S6Lg8ccGQhmupDdRqNFYv22hlvUKlQmVti1agmlfXssic2tUtSudUsvYIbBbNAxhLC7rcqFPZKwyGCMvhLos9cyjiHP07nsXLthjB4HywbZYCOrUO5q32f2XyED8RBJ3H3HhpJhHMyWY2bMJpWalcvhMIZDK1szN7wjro4jTqRKGn99/oQ5VdH02eHqSB20LW5OWhII42R0YLbZXSBpc6WexCY913LQCFp1/nqxxW20D4Hz2A5h90b9GCKvHNbln7isTQ78cNeMyP1ZCXndGg581WtbLMnyst2xFWngdQr1Y2SxiR7JYrFmV7g17N1rC2Zg1B/FKi56PJ4xDJHwXvr3szKytHcuY+J1TKWxYY0WabOJDNGIoRu6Fvyg8RCKg9YnKqtSvr/PyJEdvKZt160bLMzLFGe8yxDVtys3IHQtMYbHCS5/wp1A6i0NPhOANDD7fV7Faldk/g0FnGWslKyaOzx446oqOldGBDuzQtFnm8fG1YW7w10+MJspNUtYyeKOs2KCpkq1qIWLLzG+/ar3TLDLO0vtHiwPDuIW68XX+asI3YG+7G1aT31Gvq7PZTBte2WVzZH2Y6EVWwdYQWNLajnPbz03gBvLK1P8Af19DBzrL/Xh6kwr7aY57LL+O+NtSJliYz17Nf4CTfEc0QmjBsROMb2tdHrqRoF5BtrjnMneZZY+5hXM0c1S3BOw2nujrMFeWISvlkewtaxoJOrdBzGuF232blk7otcRjGx0KQZ8HPM7unH325CPfihYXvrudGxrt3noXclH4cXbzcuSs7sEZa7DGu6GxcFWaSg+5LNC282JkxBbZA40TdGl4+MWkGIwg1f8Aez1MpVaidtH7v6E4k24xrY2SGWbWSd9ZsIp3HWu6I4uO+B1RsPHZKIvhNHDqIPUdVJFBcHshNFPTsGOCAx3rFuyxty7ee8SY00IybNxu/NLyj11DQA0Aa6amdLXNRXum6i5te2ERFgbgiIgCIiAIiIAiIgCIiAIiIAiIgCIiAIiIAiIgCIiAIiIAiIgCIiAIiIAiIgCIiAIiIAiIgCIiAIiIAiIgCIiAIiIAiIgCIiAIiIAiIgCIiAIiIAiIgCIiAIiIAiIgCIiAIiIAiIgCIiAIiIAiIgCIiAIiIAiIgCIiAIiIAiIgCIiAIiIAiIgCIiAIiIAiIgCIiAIiIAiIgCIiAIiIAiIgCIiAIiIAiIgCIiAIiIAiIgCIiAIiIAiIgCIiAIiIAiIgCIiAIiIAiIgCIiAIiIAiIgCIiAIiIAiIgCIiAIiIAiIgCIiAIiIAiIgCIiAIiIAiIgCIiAIiIAiIgCIiAIiIAiIgCIiAIiIAiIgCIiAIiIAiIgCIiAIiIAiIgCIiAIiIAiIgCIiAIiIAiIgCIiAIiIAiIgCIiAIiIAiIgCIiAIiIAiIgCIiAIiIAiIgCIiAIiIAiIgCIiAIiIAiIgCIiAIiIAiIgOQu/1tH5ar6o32k7/W0flqvqjfaWrlfbOtBuUwQC11uqCCNQQZ4wQQeRBB6l6l4WklfNXA8BHHYhu2e+JsHv+7ReXqeqs9pO/wC7ReXqeqs9pdUN2Yxun+b6XqsHsJ97GN830vVYPYXJ63Q+GuXkeh7NxXxnz8zlfv8Au0Xl6nqrPaTv+7ReXqeqs9pdUfexjfN9L1WD2E+9jG+b6XqsHsJ1yh8NcvIns3FfGfPzOV+/7tF5ep6qz2k7/u0Xl6nqrPaXVH3sY3zfS9Vg9hPvYxvm+l6rB7CdcofDXLyHZuK+M+fmcr9/3aLy9T1VntL3v9bR+WqeqN9pdP5PZrHCCYihSBEUhB7lg5EMP8xcGwnwW/mj+pXML0Ne/sJWOblBYnCZt6rd79+w2p3+to/LVfVG+0nf62j8tV9Ub7S1cit9Vpbq4HN7QxG++JtHv9bR+Wq+qN9pO/1tH5ar6o32lq5E6rS3VwHaGI33xNo9/raPy1X1RvtJ3+to/LVfVG+0tXInVaW6uA7QxG++JtHv9bR+Wq+qN9pO/wBbR+Wq+qN9pauROq0t1cB2hiN98TaPf62j8tV9Ub7Sd/raPy1X1RvtLVyJ1WlurgO0MRvvibR7/W0flqvqjfaTv9bR+Wq+qN9pauROq0t1cB2hiN98TaPf62j8tV9Ub7Sd/raPy1X1RvtLVyJ1WlurgO0MRvvibR7/AFtH5ar6o32k7/W0flqvqjfaWrkTqtLdXAdoYjffE2j3+to/LVfVG+0nf62j8tV9Ub7S1cidVpbq4DtDEb74m0e/1tH5ar6o32k7/W0flqvqjfaWrkTqtLdXAdoYjffE2j3+to/LVfVG+0nf62j8tV9Ub7S1cidVpbq4DtDEb74m0e/1tH5ar6o32k7/AFtH5ar6o32lq5E6rS3VwHaGI33xNo9/raPy1X1RvtJ3+to/LVfVG+0tXInVaW6uA7QxG++JtHv9bR+Wq+qN9pO/1tH5ar6o32lq5E6rS3VwHaGI33xNo9/raPy1X1RvtJ3+to/LVfVG+0tXInVaW6uA7QxG++JtHv8AW0flqvqjfaTv9bR+Wq+qN9pauROq0t1cB2hiN98TaPf62j8tV9Ub7Sd/raPy1X1RvtLVyJ1WlurgO0MRvvibQ7/W0flqnqjfaXvf62j8tV9Ub7S1cUTqtLdXAnr+I33xNo9/raPy1X1RvtJ3+to/LVfVG+0tXInVaW6uBHaGI33xNo9/raPy1X1RvtJ3+to/LVfVG+0tXInVaW6uA7QxG++JtHv9bR+Wq+qN9pO/1tH5ar6o32lq5E6rS3VwHaGI33xNo9/raPy1X1RvtJ3+to/LVfVG+0tXInVaW6uA7QxG++JtHv8AW0flqvqjfaTv9bR+Wq+qN9pauROq0t1cB2hiN98TaPf62j8tV9Ub7Sd/raPy1X1RvtLVyJ1WlurgO0MRvvibR7/W0flqvqjfaTv9bR+Wq+qN9pauROq0t1cB2hiN98Qr/Zv8Opf0yr/fxqwV/s3+HUv6ZV/v41un7rK9L314nfzeperxvV9CwmWy7habThAL2wPt2nn+Jrgujia3/wAWWRr90nlpBL26a+RjFydkfRpTUVdl1lc3XrOEb3OfMQHNrwRyTzlpOgdwYWlzY9Rpvnl4ysaczk5BrBiCwdnd12Cvr4iBVEhA+fQqD4npHkhqVzFQluOOH92LU89uGOcwxvdE8yFlcNmmDY29QHYFXh6TZI7GWsSxmTGVqmKsV/CiimjkyETDFC4O5O4j5Bq9x0bwyrscHUV1mp2+fzS2Pve3Wc+WPpu3tNX7l8m9q+Wwlcuay8Q3pcNHKwcyKORZPLp2lsduCJrj6NVk9mNoquRje+u5wfE/hzwSsdDYrygamOeF43o3fsPZqtb3ekR191COBza00Oexte22rcjtwTVrUVh7Q2zCN2SN3DcHN0GhjKzuPYxu1LmV3Odw8K1uQdrvaym0w0eMR12OH3QQTz0PzLGeHsmpKztfR+ul69liaeKvJZss5Xtpty0LVtJzlfkJ/wBDJ/Ycvz3g+K380f1L9CMr8hP+hk/sOX57wfFb+aP6lcyT976fqcr+IvufX9D7REXZPMhERAEREAREQBERAEREAREQBERAEREAREQBERAEREAVxjqU1mVkFeKSeaQkRxRNL5HkNLiGtbzJ0aT9BVupt0Ef6SYn+kSf4adYVJZsG+5G2hBTqRi9rS5mO73+d8z5H1SX2U73+d8z5H1SX2V3RosI7azHCbufuhu/xRCXBkhhExcGCF1gN4LZt4hu4TrqdFxo5Tqy1RT4npZZBoR1za4HGXe/zvmfI+qS+yne/wA75nyPqkvsrulNFHas+5Gf8vUt58jhbvf53zPkfVJfZTvf53zPkfVJfZXcNy5FDw+I8M4sjYo9fxpHa7rB6Tof1K4UdrT7kR/L9LefI4StbDZqJj5ZcVfjjjY6SSR9aVrGMYC573OI5NABOvoUeXdXSj/mPM/+VZD/AAky4UC6GCxTrp3VrHIypgI4VxUW3cFerwr1Xdpy9hXx9OWxLHBBG+aaV27HFGN573aE7rWjrPL9ikPe6z3mfIeruVboX/0hxH9MZ/Yeu1spcZWgmsSa7kMT5X7oBduxtLjoCdNdAufjMZKjNRir3R2cm5MhiabnJtWduRxH3us95nyHq7k73We8z5D1dy6w2T6SaORvPx8cViKdjZHAyiF0T+EQH7kkErg7r1B6iOoqbKrLKVWDs4pHQhkGhNXU2+HkcM97rPeZ8h6u5O91nvM+Q9Xcu5tFhM5tNVqse5xMpjvY7Hyxw7jnxWMnZqVq4kDnANaO7YJD27rtQDyCw7Vn3Iz/AJepbz5eRxp3us95nyHq7k73We8z5D1dy7m0TRO1Z9yH8vUt58vI4Z73We8z5D1dyd7rPeZ8h6u5dzaJonas+5D+XqW8+XkcM97rPeZ8h6u5WmX2Ny1OF1i1jrleBhaHyywuZG0vcGMBcerVzmj6Qu8dFrP7p3/Rm9+lo/46utlLKc5zUWlpZpxGQqdOnKak9Cb2HHqIi7R5gK/2b/DqX9Mq/wB/GrBX+zf4dS/plX+/jWM/dZspe+vE7+HV9CgG0WUjxWadYu/B4/KUq1PutxPCr2qk1t7Yp3aaQxyR23aOPbG5T9vUFj7ktOzxKcoinD9WSQyMEkbuW8Y37w3C7d57p5rylKVm7q62n0KtByirOzWoiWN6PMfwdIbU74X4aTDMcJIXg1ZXvk4zXNj0dMN/QO6uQ5Lyx0a49sc7X2rLIZ6VOpOOLCxrnY8MFO4H8PejtR7gILTu+NqtMl0TYGN4dELVF00m61tO3ZZvyO1do2PUhoAa46NGgDSeQCQdDODfo+buu6CA5pnvTvaQRqCDG4ahXVVgnfpJf4r/ANd5z3Qna3Rx/wAn/wCTE5HMYSOSrUmy1rM3fdOrZrtritLJHPC4RNa41IWwxwAOcXtdz5vI5rYuyOzNXGRPZXD3PmeZbFiZ5ls2ZT1yTyu5vd6OodgX3s9szj8cC2lTr1tRo50UbQ9wHVvyabz/AKSswq1espaIXttvbT9FqLeGw7g86dr7LbPq9ZbZX5Cf9DJ/Ycvz3g+K380f1L9CMr8hP+hk/sOX57wfFb+aP6l0Mk/e+n6nE/iL7n1/Q+0RF2TzIREQBERAEREAREQBERAEREAREQBERAEREAREQBERAFNugj/STE/0iT/DTqEqbdBH+kmJ/pEn+GnWmv8A05eD/IsYT+tD/svzO07bHOje1rtxzmOa1w/FcQQHfQea1vWyc9bG1aNcWK2RqQiB1QY+ScWbDGtYHssObwhA6QF5n100eSdDqtmpovMU6mbrVz31Wk5u6djWMuTyTbe46e2brb9eFtNlX+ASUTwhNPxeDpu8Iyy8Te5OaG/zTnL16+2vK+sZZbYrWHPhfF4EczY3GMRjdAc7fAAbrz/apjoqdquyVj4pGhzJGuY9p6nNcC1wOnYQSudPCylVjPOaSd7d+lPv+VvBs6NbEKcM1Rs7Wvo7vD6999rNWS3J5bLI69i5ebDJjp4xbgMZbO515r9TwWuDCWRh2vIHUegZXosu3bD3m5ZsOdwo3yVpoJmOhn3nb5M/AZGwnUg1gHabrTqesy/B4GvTMjoRK58oY18k881mUsi3uFHxJ3lwjbvvIA/luPWSsmArleKlVzovR3f6KmCbo0pwmk3J6+7jf9CO9KX+Y8z/AOVZD/CTLhMLuzpS/wAx5n/yrIf4SZcJhdrJOqX0PL/xF70PBgr1eFerrrWeceol3Qv/AKQ4j+mM/sPXRHTVichcv42GB1llN7JBLNC2V8cD2nefJNwzutJYGhpfpz15rnfoX/0hxH9MZ/Yeu3J2BzXNI1DmkEa6agjQjXsXEypUlConDXmu3jpPU5CpqdCUXqzvI03stsEyhcN5li5am4T4mh4DgA8t1cSxu84gM0HzlZvbe7G3LYSvkBZlpy4bOzz1oYLlpr7Vexs8yCaWvSYXOdG2zaa15GgM/LmQpvica2J5dw3jmdC54dpry5aD+tV58TA+3BecwmzWr2qsMm84BsF2SpLYZuA7ri59GsdTzHDOnWV5fB0sS5uriZ50mrW2WXhY9PJUorMpKyNLtv5Ghi8n7otyzZZ9j6rashhvWnss125viCeeu1wrX2RT0DI+QgnTXU7pIyedwMjxlWirbL7ee2Qsh8MdoGWpHZwDbM0diEeDw3V7bnuaQWiMuOg0K2jtHg6+QhNa0Hvrv1E0DZZI47EZaWuhsCNwM1dwOjo3ciNQQQSEv4KrO8ySxbzyACeJI3qGg5Ndp1Lpr5mp32Gu5KE8V+xDNDffhY8048FrLk8fClwNB8TgxoL7GOF51zea3VokcCdN06WuFw96xDYivR35IWY3Kvx7ZXWhJG2TLXzitXcntyTMeKQG98I3Qa+FqTsf71qHkP8A3ZvbT71qHkP/AHZvbU2j3vh6kXl3c/QiroLEkED5obxkfTqNnLd8SumbXrmR4a6MujnBmeOWnxLPbyVzajc6R3DqXotRI7dj3mMl1BhgA1g3Wu+MfDI3ddTr4Osh+9ah5D/3ZvbXh2Tx/kP/AHZ/bT2f2vUXkYPCOmqymRlS5O6cdcpdvgGeRo5mEMieQwSOD9PjM5nRYX7pGbibK2pN1zN92Ofuva5j2712s7dc14DmuGuhBHYpmdkMcf8AVz/xp/3ih33S8bWbL3GNGjWyY9rRzOgbdrADU8zyW7D26WNu9FbG36Cd91/kcgIiL1R89Cv9m/w6l/TKv9/GrBX+zf4dS/plX+/jWM9TNlL314nfzeofMoxkqVvflZUZNA2TugyudNF3O8yQybr4C1xngmMxjJLQBzkPM9cnaeQXuq8jGWafRZRUkReDDufPBIa/Bhjsb7YJXxudH/BbMckjQx5a0Pe+DwWn8QnrJVpUxF2CGuyBrmCGvEXRNmaxslinqwRdZAhsh4Jd2CAajUqZar3VZ9LIw6CJB8lh8gQ6OMHf4MsRsskbG6UPx8jNTIZOIH92ODgAABo09aykGKlZc3tJOCJGvhewxlscQga18Ehkk4h3pRK86A68RpJ1HKSarzVS60nwsQqEU9e0t8r+Dz/oZf7BXDuG2IuT0obofXjry157Ebnmdz3R1JjWsaRV4HPLmScPUAdU7D49O4cqfgJ/0Mn9grjHZSG4MayZuUnq1RAWvY6Frq8bO7DHuAzTASgvsyyksB5lwGrhor+Tm0pW71+px8tRjKUE1fQ9VvkUR0eXd9sbp6LXPswVR8NNI0S2gw1S8wQEMjlEsJY53WJQeprt35q9Hl+aJ08L6ksbIxK4tllaQ014LbfBkhDiTXtVpdB1CYa6EODc3BgL7QGMy1ljY5YZdzueTfBpPNSKeKJkpdM1ppVNwN5uHAOng8qlLCZENZD7r3IY42Mhja6IBgjlknpMjY4W917uHHuuYCTuyQNG9yA6PSy3lwZxVh47YPivMge0uHkx9uanM6N8td5jkMYmDA8dYaZ4mucP5wGh5EEjmscrjJZGe3I6xYkdLNJzfI7TecdOtxaOZ9Kt1aje2nWc6ds55uoIiLIxCIiAIiIAiIgCIiAIiIAiIgCIiAIiIAiIgCymyecmxl2vfrtidNWe58bZmudES6N8fhtY4OI0eeo9gWLRQ0mrMyjJxd1rRvTA9Ne1N7idyUsTNwtzfG5JGdXtle0NbNkAZDuwzOIZroI3E8lf1ulHbSUgR4rHOLuFppHJz49p9OI88h1Gdjm69g0cdGkE6PwWft0eJ3LKIjLub54cTzqxsrGlrpGExncnmaS3TlI4LLw9ImZZu7t5w3dA34KuQ1oiZFuAGPQM3WN8Hq1Lj1klUJ4KN/ZjHmdWnlOVvbnO/wArG3Iek3bV7Q5uKxbm7rXEgEhodDLOBJ/lL4J/DheS12hGrAebmg2j+l/a4TSVzQxAlifWje067u/c17mDJfdLhy7+h5sJ6jqtUxbeZZgAbcc0BoZo2GuA4CGavrJpH8I8xTvaXO1J0YTzaNLX767/AHRJa4w48vC3pBBXBaIN3hMjAj3YmDdb4LNAdBrqVCwS2xjzJeU3bROfI3Ezpc2vI1bj8S7wBJo3Vx4bt/ck0GR14bhHIQ7qPDdp1LxnS9tcXNYKGH33CItZqd48ZrnxgNOS11LGOeR2BpJ0C01R2luwBginLdx7Xh25G6TeZM+wwuke3ffpLJK8BxPyr/GqkO1V5m5uzNbw93h6V64LC0yO326R+C93FlDiOviO11WXUo7q5kdpy358if7V9N2emgtY+1XxjG2K74JeFFO5witQfHjkbcMZJjlDgeY5halVW5ZkmeZJXF8jt3eefjPLWBgc4/jPIaNXHmTqTzVJWaVGNNeyrFCviZ1n7bbtqueFeoi2rWaG9Bktl8zLj7la9C1j5asolY2QOMbnAEaPDSDpz7FtT3xma017kxmnVrw7WnUe3ujr5H9RWmVl9n9oJ6Qc2MRua875bIxrwZBG5kLzvDqY5wkAGnONi1VsPCppkrss4bF1KXsxk4o2f74zN66dyY3e1004VrXXxad0a6r0fdF5v8jxug01+Ct6DXq1/hHLXktcDa2x8FpFWBhkErXbs5kLgSW78zp+K4DecNCfx3667ztb/H5K5kDIeNUje2StuRSl7Wzv32SthG88jc3qbHEHtI6gVXeEpLTmLiW1lGu9CqO/gibH7o3Naa9yYzQ9vDtaajn+UfMvPfG5v8lxep05cK129X+s/wDeqjmQkyFdtiQTYmaICSZzGg7pl4cbnPjicN7ijg6N1/lOPWeVw+S/xHO4mJMjZGFhkbIx0uvgb5kc/wCC3NG9Z7Trz1Cw6vR3VxZt63ifiPgjOe+Lzf5JjNPHwbfbqPynxg/qK898bmvyXF/8K19pUTbFejgjhdJj2VuJTdpvulc1rX8WAkPPNnwehA06jrzOpq1IbnE393FjfdLuuO+QHWXV5JXBgJL91kQ0H/ia6kEaz1ejuriR1zE774Ik/vjc119y4zT9Fa+0p743NfkuL/4Vrq9ZUbmZf3GtcMRzBP8AGtMZcA3UEO5PLWt5j+aOzlHLG1ViTm6KrrxGSkiFwcXMkEo1dv73XqNRz0c7t5qY4Wk9UVxMZ4/ER11HwRsg/dGZvtq4zs/irXbzH+s9qwe3fTLk8xRlx9mvRZDM6JznQR2GygwzMmbul85aNXRgHUdpUUZtXaAkaW13NkeXlr4i5rTwGVhuNLt1obGwADs1PZyWBC2QwlNO+baxoq5Rryjm57afyCIitnPCNOnMciOYI5EEcwQR1FEQFx3fY/KbPrE3tp3fY/KbPrE3toiw6OPcjZ00+98R3fY/KbPrE3tp3fY/KbPrE3toidHHuQ6afe+I7vsflNn1ib207vsflNn1ib20ROjj3IdNPvfEG9Y0/CLHp/hE3P0fH6lb+js7B2dnUPoH6giKVFLUYynKWt3Gv/x+waD6NOS9Lj4z1k9fadNT850H6giKSLniIikgIiIAiIgCIiAIiIAiIgCIiAIiIAiIgCIiAIiIAiIgCIiAIiIAiIgCIiAIiIAiIgCA6ft/aND+xEQHmibo8Q/UvEQHoCbo8QXiKAe6DxfsXqIgCIikBERAf//Z\n",
+      "text/html": [
+       "\n",
+       "        <iframe\n",
+       "            width=\"60%\"\n",
+       "            height=\"300\"\n",
+       "            src=\"https://www.youtube.com/embed/npw4s1QTmPg\"\n",
+       "            frameborder=\"0\"\n",
+       "            allowfullscreen\n",
+       "        ></iframe>\n",
+       "        "
+      ],
+      "text/plain": [
+       "<IPython.lib.display.YouTubeVideo at 0x7fee2cc02b50>"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "YouTubeVideo(\"npw4s1QTmPg\", width=\"60%\")"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.6"
+  },
+  "livereveal": {
+   "auto_select": "code",
+   "auto_select_fragment": true,
+   "scroll": true,
+   "theme": "serif"
+  },
+  "toc": {
+   "base_numbering": 1,
+   "nav_menu": {},
+   "number_sections": false,
+   "sideBar": true,
+   "skip_h1_title": true,
+   "title_cell": "Table of Contents",
+   "title_sidebar": "Contents",
+   "toc_cell": false,
+   "toc_position": {
+    "height": "calc(100% - 180px)",
+    "left": "10px",
+    "top": "150px",
+    "width": "384px"
+   },
+   "toc_section_display": false,
+   "toc_window_display": false
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/CONTENTS.md b/CONTENTS.md
index b7d6e4f..02fe78a 100644
--- a/CONTENTS.md
+++ b/CONTENTS.md
@@ -260,3 +260,5 @@ If this is not possible,
        `ChainMap` Type)
     - [summary <img height="12" style="display: inline-block" src="static/link/to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/09_mappings/06_summary.ipynb)
     - [review questions <img height="12" style="display: inline-block" src="static/link/to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/09_mappings/07_review.ipynb)
+    - [further resources <img height="12" style="display: inline-block" src="static/link/to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/09_mappings/08_resources.ipynb)
+      [<img height="12" style="display: inline-block" src="static/link/to_mb.png">](https://mybinder.org/v2/gh/webartifex/intro-to-python/develop?urlpath=lab/tree/09_mappings/08_resources.ipynb)

From f49e417988f8e7b53be11d6f979ca5564894f449 Mon Sep 17 00:00:00 2001
From: Alexander Hess <alexander@webartifex.biz>
Date: Thu, 22 Oct 2020 18:55:22 +0200
Subject: [PATCH 208/242] Streamline slides

---
 09_mappings/02_content.ipynb | 54 ++++++++++++++++++++++++++++++------
 09_mappings/04_content.ipynb |  6 +++-
 2 files changed, 50 insertions(+), 10 deletions(-)

diff --git a/09_mappings/02_content.ipynb b/09_mappings/02_content.ipynb
index ae2c380..78ac6ff 100644
--- a/09_mappings/02_content.ipynb
+++ b/09_mappings/02_content.ipynb
@@ -614,7 +614,11 @@
   {
    "cell_type": "code",
    "execution_count": 21,
-   "metadata": {},
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
    "outputs": [],
    "source": [
     "def fibonacci(i):\n",
@@ -636,7 +640,11 @@
   {
    "cell_type": "code",
    "execution_count": 22,
-   "metadata": {},
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
    "outputs": [
     {
      "data": {
@@ -655,14 +663,22 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
    "source": [
     "#### Efficiency of Algorithms"
    ]
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
    "source": [
     "Timing the code cells below with the `%%timeit` magic shows how doubling the input (i.e., `12` becomes `24`) more than doubles how long it takes `fibonacci()` to calculate the solution. This is actually an understatement as we see the time go up by roughly a factor of $1000$ (i.e., from nano-seconds to milli-seconds). That is an example of **exponential growth**."
    ]
@@ -670,7 +686,11 @@
   {
    "cell_type": "code",
    "execution_count": 23,
-   "metadata": {},
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
    "outputs": [
     {
      "name": "stdout",
@@ -688,7 +708,11 @@
   {
    "cell_type": "code",
    "execution_count": 24,
-   "metadata": {},
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
    "outputs": [
     {
      "name": "stdout",
@@ -729,7 +753,11 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
    "source": [
     "### \"Easy at second Glance\" Example: [Fibonacci Numbers <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_wiki.png\">](https://en.wikipedia.org/wiki/Fibonacci_number) (revisited)"
    ]
@@ -797,7 +825,11 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
    "source": [
     "When we follow the flow of execution closely, we realize that the intermediate results represented by the left-most path in the graph above are calculated first. `fibonacci(1)`, the left-most leaf node $F(1)$, is the first base case reached, followed immediately by `fibonacci(0)`. From that moment onwards, the flow of execution moves back up the left-most path while adding together the two corresponding child nodes. Effectively, this mirrors the *iterative* implementation in that the order of all computational steps are *identical* (cf., the \"*Hard at first Glance*\" example in [Chapter 4 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/04_iteration/02_content.ipynb#\"Hard-at-first-Glance\"-Example:-Fibonacci-Numbers--(revisited))).\n",
     "\n",
@@ -1000,7 +1032,11 @@
   {
    "cell_type": "code",
    "execution_count": 32,
-   "metadata": {},
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
    "outputs": [
     {
      "ename": "RecursionError",
diff --git a/09_mappings/04_content.ipynb b/09_mappings/04_content.ipynb
index 97c3875..06e0f7f 100644
--- a/09_mappings/04_content.ipynb
+++ b/09_mappings/04_content.ipynb
@@ -526,7 +526,11 @@
   {
    "cell_type": "code",
    "execution_count": 17,
-   "metadata": {},
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
    "outputs": [],
    "source": [
     "import collections.abc as abc"

From d874c83eb58f4e2b4c4fa3681121ae57ea8c1110 Mon Sep 17 00:00:00 2001
From: Alexander Hess <alexander@webartifex.biz>
Date: Mon, 26 Oct 2020 16:26:32 +0100
Subject: [PATCH 209/242] Add initial version of chapter 11, part 1

---
 00_intro/00_content.ipynb   |    2 +-
 11_classes/00_content.ipynb | 2171 +++++++++++++++++++++++++++++++++++
 CONTENTS.md                 |    8 +
 README.md                   |    1 +
 4 files changed, 2181 insertions(+), 1 deletion(-)
 create mode 100644 11_classes/00_content.ipynb

diff --git a/00_intro/00_content.ipynb b/00_intro/00_content.ipynb
index bc62059..2bc24f2 100644
--- a/00_intro/00_content.ipynb
+++ b/00_intro/00_content.ipynb
@@ -775,7 +775,7 @@
     "  - *Chapter 9*: [Mappings & Sets <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/09_mappings/00_content.ipynb)\n",
     "  - *Chapter 10*: Arrays & Dataframes\n",
     "- How can we create custom data types?\n",
-    "  - *Chapter 11*: Classes & Instances"
+    "  - *Chapter 11*: [Classes & Instances <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/11_classes/00_content.ipynb)"
    ]
   },
   {
diff --git a/11_classes/00_content.ipynb b/11_classes/00_content.ipynb
new file mode 100644
index 0000000..692af9c
--- /dev/null
+++ b/11_classes/00_content.ipynb
@@ -0,0 +1,2171 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "**Note**: Click on \"*Kernel*\" > \"*Restart Kernel and Clear All Outputs*\" in [JupyterLab](https://jupyterlab.readthedocs.io/en/stable/) *before* reading this notebook to reset its output. If you cannot run this file on your machine, you may want to open it [in the cloud <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_mb.png\">](https://mybinder.org/v2/gh/webartifex/intro-to-python/develop?urlpath=lab/tree/11_classes/00_content.ipynb)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "# Chapter 11: Classes & Instances"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "In contrast to all the built-in data types introduced in the previous chapters, **classes** allow us to create **user-defined data types**. They enable us to model **data** and its **associated behavior** in an *abstract* way. *Concrete* **instances** of these custom data types then **encapsulate** the **state** in a running program. Often, classes are **blueprints** modeling \"real world things.\"\n",
+    "\n",
+    "Classes and instances follow the **[object-oriented programming <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_wiki.png\">](https://en.wikipedia.org/wiki/Object-oriented_programming)** (OOP) paradigm where a *large program* is broken down into many *small components* (i.e., the objects) that *reuse* code. This way, a program that is too big for a programmer to fully comprehend as a whole becomes maintainable via its easier to understand individual pieces.\n",
+    "\n",
+    "Often, we see the terminology \"classes & objects\" used instead of \"classes & instances\" in Python related texts. In this book, we are more precise as *both* classes and instances are objects as specified already in the \"*Objects vs. Types vs. Values*\" section in [Chapter 1 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/01_elements/00_content.ipynb#Objects-vs.-Types-vs.-Values)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "## Example: Vectors & Matrices"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Neither core Python nor the standard library offer an implementation of common [linear algebra <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_wiki.png\">](https://en.wikipedia.org/wiki/Linear_algebra) functionalities. While we introduce the popular third-party library [numpy](http://www.numpy.org/) in [Chapter 10 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/10_arrarys/00_content.ipynb) as the de-facto standard for that and recommend to use it in real-life projects, we show how one could use Python's object-oriented language features to implement common matrix and vector operations throughout this chapter. Once we have achieved that, we compare our own library with [numpy](http://www.numpy.org/).\n",
+    "\n",
+    "Without classes, we could model a vector, for example, with a `tuple` or a `list` object, depending on if we want it to be mutable or not.\n",
+    "\n",
+    "Let's take the following vector $\\vec{x}$ as an example and model it as a `tuple`:"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "$\\vec{x} = \\begin{pmatrix} 1 \\\\ 2 \\\\ 3 \\end{pmatrix}$"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "x = (1, 2, 3)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(1, 2, 3)"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "x"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "We can extend this approach and model a matrix as either a `tuple` holding other `tuple`s or as a `list` holding other `list`s or as a mixture of both. Then, we *must* decide if the inner objects represent rows or columns. A common convention is to go with the former.\n",
+    "\n",
+    "For example, let's model the matrix $\\bf{A}$ below as a `list` of row `list`s:"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "$\\bf{A} = \\begin{bmatrix} 1 & 2 & 3 \\\\ 4 & 5 & 6 \\\\ 7 & 8 & 9 \\end{bmatrix}$"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "A = [[1, 2, 3], [4, 5, 6], [7, 8, 9]]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[[1, 2, 3], [4, 5, 6], [7, 8, 9]]"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "A"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "While this way of representing vectors and matrices in memory keeps things simple, we cannot work with them easily as Python does not know about the **semantics** (i.e., \"rules\") of vectors and matrices modeled as `tuple`s and `list`s of `list`s.\n",
+    "\n",
+    "For example, we should be able to multiply $\\bf{A}$ with $\\vec{x}$ if their dimensions match. However, Python does not know how to do this and raises a `TypeError`."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "$\\bf{A} * \\vec{x} = \\begin{bmatrix} 1 & 2 & 3 \\\\ 4 & 5 & 6 \\\\ 7 & 8 & 9 \\end{bmatrix} * \\begin{pmatrix} 1 \\\\ 2 \\\\ 3 \\end{pmatrix} = \\begin{pmatrix} 14 \\\\ 32 \\\\ 50 \\end{pmatrix}$"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "ename": "TypeError",
+     "evalue": "can't multiply sequence by non-int of type 'tuple'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-5-fd81d962f516>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mA\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;31mTypeError\u001b[0m: can't multiply sequence by non-int of type 'tuple'"
+     ]
+    }
+   ],
+   "source": [
+    "A * x"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Throughout this chapter, we \"teach\" Python the rules of [linear algebra <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_wiki.png\">](https://en.wikipedia.org/wiki/Linear_algebra)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "## Class Definition"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "The compound `class` statement creates a new variable that references a **class object** in memory.\n",
+    "\n",
+    "Following the *header* line, the indented *body* syntactically consists of function definitions (i.e., `.dummy_method()`) and variable assignments (i.e., `.dummy_variable`). Any code put here is executed just as if it were outside the `class` statement. However, the class object acts as a **namespace**, meaning that all the names do *not* exist in the global scope but may only be accessed with the dot operator `.` on the class object. In this context, the names are called **class attributes**.\n",
+    "\n",
+    "Within classes, functions are referred to as **methods** that are **bound** to *future* **instance objects**. This binding process means that Python *implicitly* inserts a reference to a *concrete* instance object as the first argument to any **method invocation** (i.e., \"function call\"). By convention, we call this parameter `self` as it references the instance object on which the method is invoked. Then, as the method is executed, we can set and access attributes via the dot operator `.` on `self`. That is how we manage the *state* of a *concrete* instance within a *generically* written class. At the same time, the code within a method is reused whenever we invoke a method on *any* instance.\n",
+    "\n",
+    "As indicated by [PEP 257 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://www.python.org/dev/peps/pep-0257/) and also section 3.8.4 of the [Google Python Style Guide <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_gh.png\">](https://github.com/google/styleguide/blob/gh-pages/pyguide.md#384-classes), we use docstrings to document relevant parts of the new data type. With respect to naming, classes are named according to the [CamelCase <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_wiki.png\">](https://en.wikipedia.org/wiki/Camel_case) convention while instances are treated like normal variables and named in [snake\\_case <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_wiki.png\">](https://en.wikipedia.org/wiki/Snake_case)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "code_folding": [],
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "class Vector:\n",
+    "    \"\"\"A standard one-dimensional vector from linear algebra.\"\"\"\n",
+    "\n",
+    "    dummy_variable = \"I am a vector\"\n",
+    "\n",
+    "    def dummy_method(self):\n",
+    "        \"\"\"A dummy method for illustration purposes.\"\"\"\n",
+    "        return self.dummy_variable"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "`Vector` is an object on its own with an identity, a type, and a value."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "94113690586816"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "id(Vector)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Its type is `type` indicating that it represents a user-defined data type and it evaluates to its fully qualified name (i.e., `__main__` as it is defined in this Jupyter notebook).\n",
+    "\n",
+    "We have seen the type `type` before in the \"*Constructors*\" section in [Chapter 2 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/02_functions/00_content.ipynb#Constructors) and also in the \"*The `namedtuple` Type*\" section in [Chapter 7's Appendix <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/07_sequences/05_appendix.ipynb#The-namedtuple-Type). In the latter case, we could also use a `Point` class but the [namedtuple() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/collections.html#collections.namedtuple) function from the [collections <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/collections.html) module in the [standard library <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/index.html) is a convenient shortcut to create custom data types that can be derived out of a plain `tuple`.\n",
+    "\n",
+    "In all examples, if an object's type is `type`, we can simply view it as a blueprint for a \"family\" of objects."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "type"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "type(Vector)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "__main__.Vector"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "Vector"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "The docstrings are transformed into convenient help texts."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "\u001b[0;31mInit signature:\u001b[0m \u001b[0mVector\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+       "\u001b[0;31mDocstring:\u001b[0m      A standard one-dimensional vector from linear algebra.\n",
+       "\u001b[0;31mType:\u001b[0m           type\n",
+       "\u001b[0;31mSubclasses:\u001b[0m     \n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "Vector?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Help on class Vector in module __main__:\n",
+      "\n",
+      "class Vector(builtins.object)\n",
+      " |  A standard one-dimensional vector from linear algebra.\n",
+      " |  \n",
+      " |  Methods defined here:\n",
+      " |  \n",
+      " |  dummy_method(self)\n",
+      " |      A dummy method for illustration purposes.\n",
+      " |  \n",
+      " |  ----------------------------------------------------------------------\n",
+      " |  Data descriptors defined here:\n",
+      " |  \n",
+      " |  __dict__\n",
+      " |      dictionary for instance variables (if defined)\n",
+      " |  \n",
+      " |  __weakref__\n",
+      " |      list of weak references to the object (if defined)\n",
+      " |  \n",
+      " |  ----------------------------------------------------------------------\n",
+      " |  Data and other attributes defined here:\n",
+      " |  \n",
+      " |  dummy_variable = 'I am a vector'\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "help(Vector)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "We can use the built-in [vars() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#vars) function as an alternative to [dir() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#dir) to obtain a *brief* summary of the attributes on `Vector`. Whereas [vars() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#vars) returns a read-only `dict`-like overview on mostly the *explicitly* defined attributes, [dir() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#dir) also shows all *implicitly* added attributes in a `list`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "mappingproxy({'__module__': '__main__',\n",
+       "              '__doc__': 'A standard one-dimensional vector from linear algebra.',\n",
+       "              'dummy_variable': 'I am a vector',\n",
+       "              'dummy_method': <function __main__.Vector.dummy_method(self)>,\n",
+       "              '__dict__': <attribute '__dict__' of 'Vector' objects>,\n",
+       "              '__weakref__': <attribute '__weakref__' of 'Vector' objects>})"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "vars(Vector)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['__class__',\n",
+       " '__delattr__',\n",
+       " '__dict__',\n",
+       " '__dir__',\n",
+       " '__doc__',\n",
+       " '__eq__',\n",
+       " '__format__',\n",
+       " '__ge__',\n",
+       " '__getattribute__',\n",
+       " '__gt__',\n",
+       " '__hash__',\n",
+       " '__init__',\n",
+       " '__init_subclass__',\n",
+       " '__le__',\n",
+       " '__lt__',\n",
+       " '__module__',\n",
+       " '__ne__',\n",
+       " '__new__',\n",
+       " '__reduce__',\n",
+       " '__reduce_ex__',\n",
+       " '__repr__',\n",
+       " '__setattr__',\n",
+       " '__sizeof__',\n",
+       " '__str__',\n",
+       " '__subclasshook__',\n",
+       " '__weakref__',\n",
+       " 'dummy_method',\n",
+       " 'dummy_variable']"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "dir(Vector)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "With the dot operator `.` we access the class attributes."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'I am a vector'"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "Vector.dummy_variable"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<function __main__.Vector.dummy_method(self)>"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "Vector.dummy_method"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "However, invoking the `.dummy_method()` raises a `TypeError`. That makes sense as the method expects a *concrete* instance passed in as the `self` argument. However, we have not yet created one."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "ename": "TypeError",
+     "evalue": "dummy_method() missing 1 required positional argument: 'self'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-16-829638f9c7cf>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mVector\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdummy_method\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;31mTypeError\u001b[0m: dummy_method() missing 1 required positional argument: 'self'"
+     ]
+    }
+   ],
+   "source": [
+    "Vector.dummy_method()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "## Instantiation"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "To create a *new* instance, we need to **instantiate** one.\n",
+    "\n",
+    "In the `class` statement, we see a `.__init__()` method that contains all the validation logic that we require a `Vector` instance to adhere to. In a way, this method serves as a constructor-like function.\n",
+    "\n",
+    "`.__init__()` is an example of a so-called **special method** that we use to make new data types integrate with Python's language features. Their naming follows the dunder convention. In this chapter, we introduce some of the more common special methods, and we refer to the [language reference <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/reference/datamodel.html) for an exhaustive list of all special methods. Special methods not *explicitly* defined in a class are *implicitly* added with a default implementation.\n",
+    "\n",
+    "The `.__init__()` method (cf., [reference <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/reference/datamodel.html#object.__init__)) is responsible for **initializing** a *new* instance object immediately after its creation. That usually means setting up some **instance attributes**. In the example, a new `Vector` instance is created from some sequence object (e.g., a `tuple` like `x`) passed in as the `data` argument. The elements provided by the `data` argument are first cast as `float` objects and then stored in a `list` object named `._entries` on the *new* instance object. Together, the `float`s represent the state encapsulated within an instance.\n",
+    "\n",
+    "A best practice is to *separate* the way we use a data type (i.e., its \"behavior\") from how we implement it. By convention, attributes that should not be accessed from \"outside\" of an instance start with one leading underscore `_`. In the example, the instance attribute `._entries` is such an **implementation detail**: We could have decided to store a `Vector`'s entries in a `tuple` instead of a `list`. However, this decision should *not* affect how a `Vector` instance is to be used. Moreover, if we changed how the `._entries` are modeled later on, this must *not* break any existing code using `Vector`s. This idea is also known as **[information hiding <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_wiki.png\">](https://en.wikipedia.org/wiki/Information_hiding)** in software engineering."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "class Vector:\n",
+    "    \"\"\"A standard one-dimensional vector from linear algebra.\n",
+    "\n",
+    "    All entries are converted to floats.\n",
+    "    \"\"\"\n",
+    "\n",
+    "    def __init__(self, data):\n",
+    "        \"\"\"Create a new vector.\n",
+    "\n",
+    "        Args:\n",
+    "            data (sequence): the vector's entries\n",
+    "\n",
+    "        Raises:\n",
+    "            ValueError: if no entries are provided\n",
+    "        \"\"\"\n",
+    "        self._entries = list(float(x) for x in data)\n",
+    "        if len(self._entries) == 0:\n",
+    "            raise ValueError(\"a vector must have at least one entry\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "To create a `Vector` instance, we call the `Vector` class with the `()` operator. This call is forwarded to the `.__init__()` method behind the scenes. That is what we mean by saying \"make new data types integrate with Python's language features\" above: We use `Vector` just as any other built-in constructor."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "v = Vector([1, 2, 3])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "`v` is an object as well."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "140306181183328"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "id(v)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Unsurprisingly, the type of `v` is `Vector`. That is the main point of this chapter."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "__main__.Vector"
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "type(v)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Every instance comes with a special `.__class__` attribute that also references the corresponding class object."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "__main__.Vector"
+      ]
+     },
+     "execution_count": 21,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "v.__class__"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "`v`'s semantic \"value\" is not so clear yet. We fix this in the next section."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<__main__.Vector at 0x7f9b9416d760>"
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "v"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Although the `.__init__()` method defines *two* parameters, we must call it with only *one* `data` argument. As noted above, Python implicitly inserts a reference to the newly created instance object (i.e., `v`) as the first argument as `self`.\n",
+    "\n",
+    "Calling a class object with a wrong number of arguments leads to generic `TypeError`s ..."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "ename": "TypeError",
+     "evalue": "__init__() missing 1 required positional argument: 'data'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-23-bcda4006949f>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mVector\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;31mTypeError\u001b[0m: __init__() missing 1 required positional argument: 'data'"
+     ]
+    }
+   ],
+   "source": [
+    "Vector()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "ename": "TypeError",
+     "evalue": "__init__() takes 2 positional arguments but 4 were given",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-24-e00314c6a560>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mVector\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m3\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;31mTypeError\u001b[0m: __init__() takes 2 positional arguments but 4 were given"
+     ]
+    }
+   ],
+   "source": [
+    "Vector(1, 2, 3)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "... while creating a `Vector` instance from an empty sequence raises a custom `ValueError`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "ename": "ValueError",
+     "evalue": "a vector must have at least one entry",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-25-7261658e317e>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mVector\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;32m<ipython-input-17-441d7f5df856>\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, data)\u001b[0m\n\u001b[1;32m     16\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_entries\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfloat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mx\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     17\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_entries\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 18\u001b[0;31m             \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"a vector must have at least one entry\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;31mValueError\u001b[0m: a vector must have at least one entry"
+     ]
+    }
+   ],
+   "source": [
+    "Vector([])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Even though we can access the `._entries` attribute on the `v` object (i.e., \"from outside\"), we are *not* supposed to do that because of the underscore `_` convention. In other words, we should access `._entries` only from within a method via `self`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[1.0, 2.0, 3.0]"
+      ]
+     },
+     "execution_count": 26,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "v._entries  # by convention not allowed"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "## Text Representations"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "For all the built-in data types, an object's value is represented in a *literal notation*, implying that we can simply copy and paste the value into another code cell to create a *new* object with the *same* value.\n",
+    "\n",
+    "The exact representation of the value does *not* have to be identical to the one used to create the object. For example, we can create a `tuple` object without using parentheses and Python still outputs its value with `(` and `)`. That was an *arbitrary* design decision by the core development team."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "x = 1, 2, 3"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(1, 2, 3)"
+      ]
+     },
+     "execution_count": 28,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "x"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(1, 2, 3)"
+      ]
+     },
+     "execution_count": 29,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "(1, 2, 3)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "To control how objects of a user-defined data type are represented as text, we implement the `.__repr__()` (cf., [reference <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/reference/datamodel.html#object.__repr__)) and `.__str__()` (cf., [reference <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/reference/datamodel.html#object.__str__)) methods. Both take only a `self` argument and must return a `str` object."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "class Vector:\n",
+    "\n",
+    "    def __init__(self, data):\n",
+    "        self._entries = list(float(x) for x in data)\n",
+    "        # ...\n",
+    "\n",
+    "    def __repr__(self):\n",
+    "        args = \", \".join(f\"{x:.3f}\" for x in self._entries)\n",
+    "        return f\"Vector(({args}))\"\n",
+    "\n",
+    "    def __str__(self):\n",
+    "        first, last = self._entries[0], self._entries[-1]\n",
+    "        n_entries = len(self._entries)\n",
+    "        return f\"Vector({first:.1f}, ..., {last:.1f})[{n_entries:d}]\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Now, when `v` is evaluated in a code cell, we see the return value of the `.__repr__()` method.\n",
+    "\n",
+    "According to the specification, `.__repr__()` should return a `str` object that, when used as a literal, creates a *new* instance with the *same* state (i.e., their `._entries` attributes compare equal) as the original one. In other words, it should return a **text representation** of the object optimized for direct consumption by the Python interpreter. That is often useful when debugging or logging large applications.\n",
+    "\n",
+    "Our implementation of `.__repr__()` in the `Vector` class uses to a `tuple` notation for the `data` argument. So, even if we create `v` from a `list` object like `[1, 2, 3]` and even though the `_entries` are stored as a `list` object internally, a `Vector` instance's text representation \"defaults\" to `((` and `))` in the output. This decision is arbitrary and we could have used a `list` notation for the `data` argument as well."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "v = Vector([1, 2, 3])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Vector((1.000, 2.000, 3.000))"
+      ]
+     },
+     "execution_count": 32,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "v"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "If we copy and paste the value of the `v` object into another code cell, we create a *new* `Vector` instance with the *same* state as `v`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Vector((1.000, 2.000, 3.000))"
+      ]
+     },
+     "execution_count": 33,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "Vector((1.000, 2.000, 3.000))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Alternatively, the built-in [repr()]( https://docs.python.org/3/library/functions.html#repr) function returns an object's value as a `str` object (i.e., with the quotes `'`)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Vector((1.000, 2.000, 3.000))'"
+      ]
+     },
+     "execution_count": 34,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "repr(v)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "On the contrary, the `.__str__()` method should return a *human-readable* text representation of the object, and we use the built-in [str() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#func-str) and [print() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#print) functions to obtain this representation explicitly.\n",
+    "\n",
+    "For our `Vector` class, this representation only shows a `Vector`'s first and last entries followed by the total number of entries in brackets. So, even for a `Vector` containing millions of entries, we could easily make sense of the representation.\n",
+    "\n",
+    "While [str() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#func-str) returns the text representation as a `str` object, ..."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Vector(1.0, ..., 3.0)[3]'"
+      ]
+     },
+     "execution_count": 35,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "str(v)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "... [print() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#print) does not show the enclosing quotes."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Vector(1.0, ..., 3.0)[3]\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(v)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "From a theoretical point of view, the text representation provided by `.__repr__()` contains all the information (i.e., the $0$s and $1$s in memory) that is needed to model something in a computer. In a way, it is a natural extension from the binary (cf., [Chapter 5 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/05_numbers/00_content.ipynb#Binary-Representations)), hexadecimal (cf., [Chapter 5 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/05_numbers/00_content.ipynb#Hexadecimal-Representations)), and `bytes` (cf., [Chapter 6 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/06_text/02_content.ipynb#The-bytes-Type)) representations of information. After all, just like Unicode characters are encoded in `bytes`, the more \"complex\" objects in this chapter are encoded in Unicode characters via their text representations."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### The `Matrix` Class"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Below is a first implementation of the `Matrix` class that stores the entries internally as a `list` of `list`s.\n",
+    "\n",
+    "The `.__init__()` method ensures that all the rows come with the same number of columns. Again, we do not allow `Matrix` instances without any entries."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "metadata": {
+    "code_folding": [],
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "class Matrix:\n",
+    "\n",
+    "    def __init__(self, data):\n",
+    "        self._entries = list(list(float(x) for x in r) for r in data)\n",
+    "        for row in self._entries[1:]:\n",
+    "            if len(row) != len(self._entries[0]):\n",
+    "                raise ValueError(\"rows must have the same number of entries\")\n",
+    "        if len(self._entries) == 0:\n",
+    "            raise ValueError(\"a matrix must have at least one entry\")\n",
+    "\n",
+    "    def __repr__(self):\n",
+    "        args = \", \".join(\"(\" + \", \".join(f\"{c:.3f}\" for c in r) + \",)\" for r in self._entries)\n",
+    "        return f\"Matrix(({args}))\"\n",
+    "\n",
+    "    def __str__(self):\n",
+    "        first, last = self._entries[0][0], self._entries[-1][-1]\n",
+    "        m, n = len(self._entries), len(self._entries[0])\n",
+    "        return f\"Matrix(({first:.1f}, ...), ..., (..., {last:.1f}))[{m:d}x{n:d}]\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "`Matrix` is an object as well."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "94113690738160"
+      ]
+     },
+     "execution_count": 38,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "id(Matrix)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "type"
+      ]
+     },
+     "execution_count": 39,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "type(Matrix)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "__main__.Matrix"
+      ]
+     },
+     "execution_count": 40,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "Matrix"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Let's create a new `Matrix` instance from a `list` of `tuple`s."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "m = Matrix([(1, 2, 3), (4, 5, 6), (7, 8, 9)])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "140306180401856"
+      ]
+     },
+     "execution_count": 42,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "id(m)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "__main__.Matrix"
+      ]
+     },
+     "execution_count": 43,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "type(m)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "The text representations work as above."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Matrix(((1.000, 2.000, 3.000,), (4.000, 5.000, 6.000,), (7.000, 8.000, 9.000,)))"
+      ]
+     },
+     "execution_count": 44,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "m"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Matrix((1.0, ...), ..., (..., 9.0))[3x3]\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(m)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Passing an invalid `data` argument when instantiating a `Matrix` results in the documented exceptions."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "ename": "ValueError",
+     "evalue": "a matrix must have at least one entry",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-46-87c8bd55bdc1>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mMatrix\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;32m<ipython-input-37-2c8575833cd9>\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, data)\u001b[0m\n\u001b[1;32m      7\u001b[0m                 \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"rows must have the same number of entries\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      8\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_entries\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 9\u001b[0;31m             \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"a matrix must have at least one entry\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     10\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     11\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__repr__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;31mValueError\u001b[0m: a matrix must have at least one entry"
+     ]
+    }
+   ],
+   "source": [
+    "Matrix(())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "ename": "ValueError",
+     "evalue": "rows must have the same number of entries",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-47-359f87edecdf>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mMatrix\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m3\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0;36m4\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m5\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;32m<ipython-input-37-2c8575833cd9>\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, data)\u001b[0m\n\u001b[1;32m      5\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0mrow\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_entries\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      6\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrow\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_entries\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 7\u001b[0;31m                 \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"rows must have the same number of entries\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      8\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_entries\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      9\u001b[0m             \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"a matrix must have at least one entry\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;31mValueError\u001b[0m: rows must have the same number of entries"
+     ]
+    }
+   ],
+   "source": [
+    "Matrix([(1, 2, 3), (4, 5)])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "## Instance Methods vs. Class Methods"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "The methods we have seen so far are all **instance methods**. The characteristic idea behind an instance method is that the behavior it provides either depends on the state of a concrete instance or mutates it. In other words, an instance method *always* works with attributes on the `self` argument. If a method does *not* need access to `self` to do its job, it is conceptually *not* an instance method and we should probably convert it into another kind of method as shown below.\n",
+    "\n",
+    "An example of an instance method from linear algebra is the `.transpose()` method below that switches the rows and columns of an *existing* `Matrix` instance and returns a *new* `Matrix` instance based off that. It is implemented by passing the *iterator* created with the [zip() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#zip) built-in as the `data` argument to the `Matrix` constructor: The expression `zip(*self._entries)` may be a bit hard to understand because of the involved unpacking but simply flips a `Vector`'s rows and columns. The built-in [list() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#func-list) constructor within the `.__init__()` method then materializes the iterator into the `._entries`. Without a concrete `Matrix`'s rows and columns, `.transpose()` does not make sense, conceptually speaking."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 48,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "class Matrix:\n",
+    "\n",
+    "    def __init__(self, data):\n",
+    "        self._entries = list(list(float(x) for x in r) for r in data)\n",
+    "        # ...\n",
+    "\n",
+    "    def __repr__(self):\n",
+    "        args = \", \".join(\"(\" + \", \".join(f\"{c:.3f}\" for c in r) + \",)\" for r in self._entries)\n",
+    "        return f\"Matrix(({args}))\"\n",
+    "\n",
+    "    def transpose(self):\n",
+    "        return Matrix(zip(*self._entries))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "The `.transpose()` method returns a *new* `Matrix` instance where the rows and columns are flipped."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 49,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "m = Matrix([(1, 2, 3), (4, 5, 6), (7, 8, 9)])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 50,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Matrix(((1.000, 2.000, 3.000,), (4.000, 5.000, 6.000,), (7.000, 8.000, 9.000,)))"
+      ]
+     },
+     "execution_count": 50,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "m"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 51,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Matrix(((1.000, 4.000, 7.000,), (2.000, 5.000, 8.000,), (3.000, 6.000, 9.000,)))"
+      ]
+     },
+     "execution_count": 51,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "m.transpose()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Two invocations of `.transpose()` may be chained, which negates its overall effect but still creates a *new* instance object (i.e., `m is n` is `False`)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 52,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "n = m.transpose().transpose()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 53,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Matrix(((1.000, 2.000, 3.000,), (4.000, 5.000, 6.000,), (7.000, 8.000, 9.000,)))"
+      ]
+     },
+     "execution_count": 53,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 54,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "False"
+      ]
+     },
+     "execution_count": 54,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "m is n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Unintuitively, the comparison operator `==` returns a wrong result as `m` and `n` have `_entries` attributes that compare equal. We fix this in the \"*Operator Overloading*\" section later in this chapter."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 55,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "False"
+      ]
+     },
+     "execution_count": 55,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "m == n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Sometimes, it is useful to attach functionality to a class object that does *not* depend on the state of a concrete instance but on the class as a whole. Such methods are called **class methods** and can be created with the [classmethod() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#classmethod) built-in combined with the `@` **decorator** syntax. Then, Python adapts the binding process described above such that it implicitly inserts a reference to the class object itself instead of the instance when the method is invoked. By convention, we name this parameter `cls`.\n",
+    "\n",
+    "Class methods are often used to provide an alternative way to create instances, usually from a different kind of arguments. As an example, `.from_columns()` expects a sequence of columns instead of rows as its `data` argument. It forwards the invocation to the `.__init__()` method (i.e., what `cls(data)` does; `cls` references the *same* class object as `Matrix`), then calls the `.transpose()` method on the newly created instance, and lastly returns the instance created by `.transpose()`. Again, we are intelligently *reusing* a lot of code."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 56,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "class Matrix:\n",
+    "\n",
+    "    def __init__(self, data):\n",
+    "        self._entries = list(list(float(x) for x in r) for r in data)\n",
+    "        # ...\n",
+    "\n",
+    "    def __repr__(self):\n",
+    "        args = \", \".join(\"(\" + \", \".join(f\"{c:.3f}\" for c in r) + \",)\" for r in self._entries)\n",
+    "        return f\"Matrix(({args}))\"\n",
+    "\n",
+    "    def transpose(self):\n",
+    "        return Matrix(zip(*self._entries))\n",
+    "\n",
+    "    @classmethod\n",
+    "    def from_columns(cls, data):\n",
+    "        return cls(data).transpose()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "We use the alternative `.from_columns()` constructor to create a `Matrix` equivalent to `m` above from a `list` of columns instead of rows."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 57,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "m = Matrix.from_columns([(1, 4, 7), (2, 5, 8), (3, 6, 9)])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 58,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Matrix(((1.000, 2.000, 3.000,), (4.000, 5.000, 6.000,), (7.000, 8.000, 9.000,)))"
+      ]
+     },
+     "execution_count": 58,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "m"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "There is also a [staticmethod() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#staticmethod) built-in to be used with the `@` syntax to define methods that are *independent* from both the class and instance objects but nevertheless related semantically to a class. In this case, the binding process is disabled an no argument is implicitly inserted upon a method's invocation. Such **static methods** are not really needed most of the time and we omit them here fore brevity."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "### Computed Properties"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "After creation, a `Matrix` instance exhibits certain properties that depend only on the concrete `data` encapsulated in it. For example, every `Matrix` instance implicitly has *two* dimensions: These are commonly denoted as $m$ and $n$ in math and represent the number of rows and columns.\n",
+    "\n",
+    "We would like our `Matrix` instances to have two attributes, `.n_rows` and `.n_cols`, that provide the correct dimensions as `int` objects. To achieve that, we implement two instance methods, `.n_rows()` and `.n_cols()`, and make them **derived attributes** by decorating them with the [property() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#property) built-in. They work like methods except that they do not need to be invoked with the call operator `()` but can be accessed as if they were instance variables.\n",
+    "\n",
+    "To reuse their code, we integrate the new properties already within the `.__init__()` method."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 59,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "class Matrix:\n",
+    "\n",
+    "    def __init__(self, data):\n",
+    "        self._entries = list(list(float(x) for x in r) for r in data)\n",
+    "        for row in self._entries[1:]:\n",
+    "            if len(row) != self.n_cols:\n",
+    "                raise ValueError(\"rows must have the same number of entries\")\n",
+    "        if self.n_rows == 0:\n",
+    "            raise ValueError(\"a matrix must have at least one entry\")\n",
+    "\n",
+    "    def __repr__(self):\n",
+    "        args = \", \".join(\"(\" + \", \".join(f\"{c:.3f}\" for c in r) + \",)\" for r in self._entries)\n",
+    "        return f\"Matrix(({args}))\"\n",
+    "\n",
+    "    @property\n",
+    "    def n_rows(self):\n",
+    "        return len(self._entries)\n",
+    "\n",
+    "    @property\n",
+    "    def n_cols(self):\n",
+    "        return len(self._entries[0])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "The revised `m` models a $2 \\times 3$ matrix."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 60,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "m = Matrix([(1, 2, 3), (4, 5, 6)])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 61,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(2, 3)"
+      ]
+     },
+     "execution_count": 61,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "m.n_rows, m.n_cols"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "In its basic form, properties are *read-only* attributes. This makes sense for `Matrix` instances where we can *not* \"set\" how many rows and columns there are while keeping the `_entries` unchanged."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 62,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "ename": "AttributeError",
+     "evalue": "can't set attribute",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-62-f4cc4767ae11>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mm\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mn_rows\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m3\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;31mAttributeError\u001b[0m: can't set attribute"
+     ]
+    }
+   ],
+   "source": [
+    "m.n_rows = 3"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.6"
+  },
+  "livereveal": {
+   "auto_select": "code",
+   "auto_select_fragment": true,
+   "scroll": true,
+   "theme": "serif"
+  },
+  "toc": {
+   "base_numbering": 1,
+   "nav_menu": {},
+   "number_sections": false,
+   "sideBar": true,
+   "skip_h1_title": true,
+   "title_cell": "Table of Contents",
+   "title_sidebar": "Contents",
+   "toc_cell": false,
+   "toc_position": {
+    "height": "calc(100% - 180px)",
+    "left": "10px",
+    "top": "150px",
+    "width": "384px"
+   },
+   "toc_section_display": false,
+   "toc_window_display": false
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/CONTENTS.md b/CONTENTS.md
index 02fe78a..a0f49df 100644
--- a/CONTENTS.md
+++ b/CONTENTS.md
@@ -262,3 +262,11 @@ If this is not possible,
     - [review questions <img height="12" style="display: inline-block" src="static/link/to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/09_mappings/07_review.ipynb)
     - [further resources <img height="12" style="display: inline-block" src="static/link/to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/09_mappings/08_resources.ipynb)
       [<img height="12" style="display: inline-block" src="static/link/to_mb.png">](https://mybinder.org/v2/gh/webartifex/intro-to-python/develop?urlpath=lab/tree/09_mappings/08_resources.ipynb)
+  - *Chapter 11*: Classes & Instances
+    - [content <img height="12" style="display: inline-block" src="static/link/to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/09_mappings/00_content.ipynb)
+      [<img height="12" style="display: inline-block" src="static/link/to_mb.png">](https://mybinder.org/v2/gh/webartifex/intro-to-python/develop?urlpath=lab/tree/11_classes/00_content.ipynb)
+      (`class` Statement;
+       Instantiation;
+       Text Representations;
+       Instance Methods vs. Class Methods;
+       Computed Properties)
diff --git a/README.md b/README.md
index dd051e6..49ae3d0 100644
--- a/README.md
+++ b/README.md
@@ -22,6 +22,7 @@ For a more *detailed version* with **clickable links**
   - *Chapter 7*: Sequential Data
   - *Chapter 8*: Map, Filter, & Reduce
   - *Chapter 9*: Mappings & Sets
+  - *Chapter 11*: Classes & Instances
 
 
 #### Videos

From fcf3410dddac205c029a7d5ae81ae1171b1abaf7 Mon Sep 17 00:00:00 2001
From: Alexander Hess <alexander@webartifex.biz>
Date: Mon, 26 Oct 2020 22:31:56 +0100
Subject: [PATCH 210/242] Add initial version of chapter 11's exercises

---
 11_classes/01_exercises.ipynb | 1502 +++++++++++++++++++++++++++++++++
 CONTENTS.md                   |    3 +
 2 files changed, 1505 insertions(+)
 create mode 100644 11_classes/01_exercises.ipynb

diff --git a/11_classes/01_exercises.ipynb b/11_classes/01_exercises.ipynb
new file mode 100644
index 0000000..915dbc9
--- /dev/null
+++ b/11_classes/01_exercises.ipynb
@@ -0,0 +1,1502 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Note**: Click on \"*Kernel*\" > \"*Restart Kernel and Run All*\" in [JupyterLab](https://jupyterlab.readthedocs.io/en/stable/) *after* finishing the exercises to ensure that your solution runs top to bottom *without* any errors. If you cannot run this file on your machine, you may want to open it [in the cloud <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_mb.png\">](https://mybinder.org/v2/gh/webartifex/intro-to-python/develop?urlpath=lab/tree/11_classes/01_exercises.ipynb)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Chapter 11: Classes & Instances (Coding Exercises)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The exercises below assume that you have read the [first part <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/11_classes/00_content.ipynb) of Chapter 11.\n",
+    "\n",
+    "The `...`'s in the code cells indicate where you need to fill in code snippets. The number of `...`'s within a code cell give you a rough idea of how many lines of code are needed to solve the task. You should not need to create any additional code cells for your final solution. However, you may want to use temporary code cells to try out some ideas."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Berlin Tourist Guide: A Traveling Salesman Problem"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This notebook is a hands-on and tutorial-like application to show how to load data from web services like [Google Maps](https://developers.google.com/maps) and use them to solve a logistics problem, namely a **[Traveling Salesman Problem <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_wiki.png\">](https://en.wikipedia.org/wiki/Traveling_salesman_problem)**.\n",
+    "\n",
+    "Imagine that a tourist lands at Berlin's [Tegel Airport <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_wiki.png\">](https://en.wikipedia.org/wiki/Berlin_Tegel_Airport) in the morning and has his \"connecting\" flight from [Schönefeld Airport <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_wiki.png\">](https://en.wikipedia.org/wiki/Berlin_Sch%C3%B6nefeld_Airport) in the evening. By the time, the flights were scheduled, the airline thought that there would be only one airport in Berlin.\n",
+    "\n",
+    "Having never been in Berlin before, the tourist wants to come up with a plan of sights that he can visit with a rental car on his way from Tegel to Schönefeld.\n",
+    "\n",
+    "With a bit of research, he creates a `list` of `sights` like below."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "arrival = \"Berlin Tegel Airport (TXL), Berlin\"\n",
+    "\n",
+    "sights = [\n",
+    "    \"Alexanderplatz, Berlin\",\n",
+    "    \"Brandenburger Tor, Pariser Platz, Berlin\",\n",
+    "    \"Checkpoint Charlie, FriedrichstraĂźe, Berlin\",\n",
+    "    \"Kottbusser Tor, Berlin\",\n",
+    "    \"Mauerpark, Berlin\",\n",
+    "    \"Siegessäule, Berlin\",\n",
+    "    \"Reichstag, Platz der Republik, Berlin\",\n",
+    "    \"Soho House Berlin, TorstraĂźe, Berlin\",\n",
+    "    \"Tempelhofer Feld, Berlin\",\n",
+    "]\n",
+    "\n",
+    "departure = \"Berlin Schönefeld Airport (SXF), Berlin\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "With just the street addresses, however, he cannot calculate a route. He needs `latitude`-`longitude` coordinates instead. While he could just open a site like [Google Maps](https://www.google.com/maps) in a web browser, he wonders if he can download the data with a bit of Python code using a [web API <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_wiki.png\">](https://en.wikipedia.org/wiki/Web_API) offered by [Google](https://www.google.com).\n",
+    "\n",
+    "So, in this notebook, we solve the entire problem with code."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Geocoding"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In order to obtain coordinates for the given street addresses above, a process called **geocoding**, we use the [Google Maps Geocoding API](https://developers.google.com/maps/documentation/geocoding/start).\n",
+    "\n",
+    "**Q1**: Familiarize yourself with this [documentation](https://developers.google.com/maps/documentation/geocoding/start), register a developer account, create a project, and [create an API key](https://console.cloud.google.com/apis/credentials) that is necessary for everything to work! Then, [enable the Geocoding API](https://console.developers.google.com/apis/library/geocoding-backend.googleapis.com) and link a [billing account](https://console.developers.google.com/billing)!\n",
+    "\n",
+    "Info: The first 200 Dollars per month are not charged (cf., [pricing page](https://cloud.google.com/maps-platform/pricing/)), so no costs will incur for this tutorial. You must sign up because Google simply wants to know the people using its services.\n",
+    "\n",
+    "**Q2**: Assign the API key as a `str` object to the `key` variable!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "key = \" < your API key goes here > \""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "To use external web services, our application needs to make HTTP requests just like web browsers do when surfing the web.\n",
+    "\n",
+    "We do not have to implement this on our own. Instead, we use the official Python Client for the Google Maps Services provided by Google in one of its corporate [GitHub repositories <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_gh.png\">](https://github.com/googlemaps).\n",
+    "\n",
+    "**Q3**: Familiarize yourself with the [googlemaps <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_gh.png\">](https://github.com/googlemaps/google-maps-services-python) package! Then, install it with the `pip` command line tool!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!pip install googlemaps"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q4**: Finish the following code cells and instantiate a `Client` object named `api`! Use the `key` from above. `api` provides us with a lot of methods to talk to the API."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import googlemaps"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "api = googlemaps.Client(...)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "api"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "type(api)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q5**: Execute the next code cell to list the methods and attributes on the `api` object!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "[x for x in dir(api) if not x.startswith(\"_\")]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "To obtain all kinds of information associated with a street address, we call the `geocode()` method with the address as the sole argument.\n",
+    "\n",
+    "For example, let's search for Brandenburg Gate. Its street address is `\"Brandenburger Tor, Pariser Platz, Berlin\"`.\n",
+    "\n",
+    "**Q6**: Execute the next code cell!\n",
+    "\n",
+    "Hint: If you get an error message, follow the instructions in it to debug it.\n",
+    "\n",
+    "If everything works, we receive a `list` with a single `dict` in it. That means the [Google Maps Geocoding API](https://developers.google.com/maps/documentation/geocoding/start) only knows about one place at the address. Unfortunately, the `dict` is pretty dense and hard to read."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "api.geocode(\"Brandenburger Tor, Pariser Platz, Berlin\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q7**: Capture the first and only search result in the `brandenburg_gate` variable and \"pretty print\" it with the help of the [pprint() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/pprint.html#pprint.pprint) function in the [pprint <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/pprint.html) module in the [standard library <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/index.html)!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "response = api.geocode(\"Brandenburger Tor, Pariser Platz, Berlin\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "brandenburg_gate = ..."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The `dict` has several keys that are of use for us: `\"formatted_address\"` is a cleanly formatted version of the address. `\"geometry\"` is a nested `dict` with several `lat`-`lng` coordinates representing the place where `\"location\"` is the one we need for our calculations. Lastly, `\"place_id\"` is a unique identifier that allows us to obtain further information about the address from other Google APIs."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from pprint import pprint"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "pprint(brandenburg_gate)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### The `Place` Class"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "To keep our code readable and maintainable, we create a `Place` class to manage the API results in a clean way.\n",
+    "\n",
+    "The `.__init__()` method takes a `street_address` (e.g., an element of `sights`) and a `client` argument (e.g., an object like `api`) and stores them on `self`. The place's `.name` is parsed out of the `street_address` as well: It is the part before the first comma. Also, the instance attributes `.latitude`, `.longitude`, and `.place_id` are initialized to `None`.\n",
+    "\n",
+    "**Q8**: Finish the `.__init__()` method according to the description!\n",
+    "\n",
+    "The `.sync_from_google()` method uses the internally kept `client` and synchronizes the place's state with the [Google Maps Geocoding API](https://developers.google.com/maps/documentation/geocoding/start). In particular, it updates the `.address` with the `formatted_address` and stores the values for `.latitude`, `.longitude`, and `.place_id`. It enables method chaining.\n",
+    "\n",
+    "**Q9**: Implement the `.sync_from_google()` method according to the description!\n",
+    "\n",
+    "**Q10**: Add a read-only `.location` property on the `Place` class that returns the `.latitude` and `.longitude` as a `tuple`!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "class Place:\n",
+    "    \"\"\"A place connected to the Google Maps Geocoding API.\"\"\"\n",
+    "\n",
+    "    # answer to Q8\n",
+    "    def __init__(self, street_address, *, client):\n",
+    "        \"\"\"Create a new place.\n",
+    "\n",
+    "        Args:\n",
+    "            street_address (str): street address of the place\n",
+    "            client (googlemaps.Client): access to the Google Maps Geocoding API\n",
+    "        \"\"\"\n",
+    "        ...\n",
+    "        ...\n",
+    "        ...\n",
+    "        ...\n",
+    "        ...\n",
+    "        ...\n",
+    "\n",
+    "    def __repr__(self):\n",
+    "        cls, name = self.__class__.__name__, self.name\n",
+    "        synced = \" [SYNCED]\" if self.place_id else \"\"\n",
+    "        return f\"<{cls}: {name}{synced}>\"\n",
+    "\n",
+    "    # answer to Q9\n",
+    "    def sync_from_google(self):\n",
+    "        \"\"\"Download the place's coordinates and other info.\"\"\"\n",
+    "        response = ...\n",
+    "        first_hit = ...\n",
+    "        ... = first_hit[...]\n",
+    "        ... = first_hit[...][...][...]\n",
+    "        ... = first_hit[...][...][...]\n",
+    "        ... = first_hit[...]\n",
+    "        return ...\n",
+    "\n",
+    "    # answer to Q10\n",
+    "    ...\n",
+    "    ...\n",
+    "    ..."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q11**: Verify that the instantiating a `Place` object works!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "brandenburg_gate = Place(\"Brandenburger Tor, Pariser Platz, Berlin\", client=api)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "brandenburg_gate"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q12**: What do the angle brackets `<` and `>` mean in the text representation?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " < your answer >"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now, we can obtain the geo-data from the [Google Maps Geocoding API](https://developers.google.com/maps/documentation/geocoding/start) in a clean way. As we enabled method chaining for `.sync_from_google()`, we get back the instance after calling the method.\n",
+    "\n",
+    "**Q13**: Verify that the `.sync_from_google()` method works!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "brandenburg_gate.sync_from_google()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "brandenburg_gate.address"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "brandenburg_gate.place_id"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "brandenburg_gate.location"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### The `Place` Class (continued): Batch  Synchronization"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q14**: Add an alternative constructor method named `.from_addresses()` that takes an `addresses`, a `client`, and a `sync` argument! `addresses` is a finite iterable of `str` objects (e.g., like `sights`). The method returns a `list` of `Place`s, one for each `str` in `addresses`. All `Place`s are initialized with the same `client`. `sync` is a flag and defaults to `False`. If it is set to `True`, the alternative constructor invokes the `.sync_from_google()` method on the `Place`s before returning them."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "code_folding": []
+   },
+   "outputs": [],
+   "source": [
+    "class Place:\n",
+    "    \"\"\"A place connected to the Google Maps Geocoding API.\"\"\"\n",
+    "\n",
+    "    # answers from above\n",
+    "\n",
+    "    # answer to Q14\n",
+    "    ...\n",
+    "    def from_addresses(cls, addresses, *, client, sync=False):\n",
+    "        \"\"\"Create new places in a batch.\n",
+    "\n",
+    "        Args:\n",
+    "            addresses (iterable of str's): the street addresses of the places\n",
+    "            client (googlemaps.Client): access to the Google Maps Geocoding API\n",
+    "        Returns:\n",
+    "            list of Places\n",
+    "        \"\"\"\n",
+    "        places = ...\n",
+    "        for address in addresses:\n",
+    "            place = ...\n",
+    "            if sync:\n",
+    "                ...\n",
+    "            ...\n",
+    "        return places"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q15**: Verify that the alternative constructor works with and without the `sync` flag!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "Place.from_addresses(sights, client=api)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "Place.from_addresses(sights, client=api, sync=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Visualization"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "For geo-data it always makes sense to plot them on a map. We use the third-party library [folium <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_gh.png\">](https://github.com/python-visualization/folium) to achieve that.\n",
+    "\n",
+    "**Q16**: Familiarize yourself with [folium <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_gh.png\">](https://github.com/python-visualization/folium) and install it with the `pip` command line tool!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!pip install folium"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q17**: Execute the code cells below to create an empty map of Berlin!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import folium"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "berlin = folium.Map(location=(52.513186, 13.3944349), zoom_start=14)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "type(berlin)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "`folium.Map` instances are shown as interactive maps in Jupyter notebooks whenever they are the last expression in a code cell."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "berlin"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In order to put something on the map, [folium <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_gh.png\">](https://github.com/python-visualization/folium) works with so-called `Marker` objects.\n",
+    "\n",
+    "**Q18**: Review its docstring and then create a marker `m` with the location data of Brandenburg Gate! Use the `brandenburg_gate` object from above!\n",
+    "\n",
+    "Hint: You may want to use HTML tags for the `popup` argument to format the text output on the map in a nicer way. So, instead of just passing `\"Brandenburger Tor\"` as the `popup` argument, you could use, for example, `\"<b>Brandenburger Tor</b><br/>(Pariser Platz, 10117 Berlin, Germany)\"`. Then, the name appears in bold and the street address is put on the next line. You could use an f-string to parametrize the argument."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "folium.Marker?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "m = folium.Marker(\n",
+    "    location=...,\n",
+    "    popup=...,\n",
+    "    tooltip=...,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "type(m)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q19**: Execute the next code cells that add `m` to the `berlin` map!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "m.add_to(berlin)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "berlin"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### The `Place` Class (continued): Marker Representation"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q20**: Finish the `.as_marker()` method that returns a `Marker` instance when invoked on a `Place` instance! The method takes an optional `color` argument that uses [folium <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_gh.png\">](https://github.com/python-visualization/folium)'s `Icon` type to control the color of the marker."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "class Place:\n",
+    "    \"\"\"A place connected to the Google Maps Geocoding API.\"\"\"\n",
+    "\n",
+    "    # answers from above\n",
+    "\n",
+    "    # answer to Q20\n",
+    "    def as_marker(self, *, color=\"blue\"):\n",
+    "        \"\"\"Create a Marker representation of the place.\n",
+    "\n",
+    "        Args:\n",
+    "            color (str): color of the marker, defaults to \"blue\"\n",
+    "\n",
+    "        Returns:\n",
+    "            marker (folium.Marker)\n",
+    "\n",
+    "        Raises:\n",
+    "            RuntimeError: if the place is not synchronized with\n",
+    "                the Google Maps Geocoding API\n",
+    "        \"\"\"\n",
+    "        if not self.place_id:\n",
+    "            raise RuntimeError(\"must synchronize with Google first\")\n",
+    "        return folium.Marker(\n",
+    "            location=...,\n",
+    "            popup=...,\n",
+    "            tooltip=...,\n",
+    "            icon=folium.Icon(color=color),\n",
+    "        )"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q21**: Execute the next code cells that create a new `Place` and obtain a `Marker` for it!\n",
+    "\n",
+    "Notes: Without synchronization, we get a `RuntimeError`. `.as_marker()` can be chained right after `.sync_from_google()`"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "brandenburg_gate = Place(\"Brandenburger Tor, Pariser Platz, Berlin\", client=api)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "brandenburg_gate.as_marker()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "brandenburg_gate.sync_from_google().as_marker()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q22**: Use the alternative `.from_addresses()` constructor to create a `list` named `places` with already synced `Place`s!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "places = Place.from_addresses(sights, client=api, sync=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "places"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### The `Map` Class"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "subslide"
+    }
+   },
+   "source": [
+    "To make [folium <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_gh.png\">](https://github.com/python-visualization/folium)'s `Map` class work even better with our `Place` instances, we write our own `Map` class wrapping [folium <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_gh.png\">](https://github.com/python-visualization/folium)'s. Then, we add further functionality to the class throughout this tutorial.\n",
+    "\n",
+    "The `.__init__()` method takes mandatory `name`, `center`, `start`, `end`, and `places` arguments. `name` is there for convenience, `center` is the map's initial center, `start` and `end` are `Place` instances, and `places` is a finite iterable of `Place` instances. Also, `.__init__()` accepts an optional `initial_zoom` argument defaulting to `12`.\n",
+    "\n",
+    "Upon initialization, a `folium.Map` instance is created and stored as an implementation detail `_map`. Also, `.__init__()` puts markers for each place on the `_map` object: `\"green\"` and `\"red\"` markers for the `start` and `end` locations and `\"blue\"` ones for the `places` to be visited. To do that, `.__init__()` invokes another `.add_marker()` method on the `Map` class, once for every `Place` object. `.add_marker()` itself invokes the `.add_to()` method on the `folium.Marker` representation of a `Place` instance and enables method chaining.\n",
+    "\n",
+    "To keep the state in a `Map` instance consistent, all passed in arguments except `name` are treated as implementation details. Otherwise, a user of the `Map` class could, for example, change the `start` attribute, which would not be reflected in the internally kept `folium.Map` object.\n",
+    "\n",
+    "**Q23**: Implement the `.__init__()` and `.add_marker()` methods on the `Map` class as described!\n",
+    "\n",
+    "**Q24**: Add a `.show()` method on the `Map` class that simply returns the internal `folium.Map` object!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "class Map:\n",
+    "    \"\"\"A map with plotting and routing capabilities.\"\"\"\n",
+    "\n",
+    "    # answer to Q23\n",
+    "    def __init__(self, name, center, start, end, places, initial_zoom=12):\n",
+    "        \"\"\"Create a new map.\n",
+    "\n",
+    "        Args:\n",
+    "            name (str): name of the map\n",
+    "            center (float, float): coordinates of the map's center\n",
+    "            start (Place): start of the tour\n",
+    "            end (Place): end of the tour\n",
+    "            places (iterable of Places): the places to be visitied\n",
+    "            initial_zoom (integer): zoom level according to folium's\n",
+    "                specifications; defaults to 12\n",
+    "        \"\"\"\n",
+    "        self.name = name\n",
+    "        ...\n",
+    "        ...\n",
+    "        ...\n",
+    "        ... = folium.Map(...)\n",
+    "\n",
+    "        # Add markers to the map.\n",
+    "        ...\n",
+    "        ...\n",
+    "        for place in places:\n",
+    "            ...\n",
+    "\n",
+    "    def __repr__(self):\n",
+    "        return f\"<Map: {self.name}>\"\n",
+    "\n",
+    "    # answer to Q24\n",
+    "    def show(self):\n",
+    "        \"\"\"Return a folium.Map representation of the map.\"\"\"\n",
+    "        return ...\n",
+    "\n",
+    "    # answer to Q23\n",
+    "    def add_marker(self, marker):\n",
+    "        \"\"\"Add a marker to the map.\n",
+    "\n",
+    "        Args:\n",
+    "            marker (folium.Marker): marker to be put on the map\n",
+    "        \"\"\"\n",
+    "        ...\n",
+    "        return ..."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Let's put all the sights, the two airports, and three more places, the [Bundeskanzleramt <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_wiki.png\">](https://en.wikipedia.org/wiki/German_Chancellery), the [Olympic Stadium <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_wiki.png\">](https://en.wikipedia.org/wiki/Olympiastadion_%28Berlin%29), and the [East Side Gallery <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_wiki.png\">](https://en.wikipedia.org/wiki/East_Side_Gallery), on the map.\n",
+    "\n",
+    "**Q25**: Execute the next code cells to create a map of Berlin with all the places on it!\n",
+    "\n",
+    "Note: Because we implemented method chaining everywhere, the code below is only *one* expression written over several lines. It almost looks like a self-explanatory and compact \"language\" on its own."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "berlin = (\n",
+    "    Map(\n",
+    "        \"Sights in Berlin\",\n",
+    "        center=(52.5015154, 13.4066838),\n",
+    "        start=Place(arrival, client=api).sync_from_google(),\n",
+    "        end=Place(departure, client=api).sync_from_google(),\n",
+    "        places=places,\n",
+    "        initial_zoom=10,\n",
+    "    )\n",
+    "    .add_marker(\n",
+    "        Place(\"Bundeskanzleramt, Willy-Brandt-StraĂźe, Berlin\", client=api)\n",
+    "        .sync_from_google()\n",
+    "        .as_marker(color=\"orange\")\n",
+    "    )\n",
+    "    .add_marker(\n",
+    "        Place(\"Olympiastadion Berlin\", client=api)\n",
+    "        .sync_from_google()\n",
+    "        .as_marker(color=\"orange\")\n",
+    "    )\n",
+    "    .add_marker(\n",
+    "        Place(\"East Side Gallery, Berlin\", client=api)\n",
+    "        .sync_from_google()\n",
+    "        .as_marker(color=\"orange\")\n",
+    "    )\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "berlin"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "berlin.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Distance Matrix"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Before we can find out the best order in which to visit all the sights, we must calculate the pairwise distances between all points. While Google also offers a [Directions API](https://developers.google.com/maps/documentation/directions/start) and a [Distance Matrix API](https://developers.google.com/maps/documentation/distance-matrix/start), we choose to calculate the air distances using the third-party library [geopy <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_gh.png\">](https://github.com/geopy/geopy).\n",
+    "\n",
+    "**Q26**: Familiarize yourself with the [documentation](https://geopy.readthedocs.io/en/stable/) and install [geopy <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_gh.png\">](https://github.com/geopy/geopy) with the `pip` command line tool!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "!pip install geopy"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We use [geopy <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_gh.png\">](https://github.com/geopy/geopy) primarily for converting the `latitude`-`longitude` coordinates into a [distance matrix <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_wiki.png\">](https://en.wikipedia.org/wiki/Distance_matrix).\n",
+    "\n",
+    "Because the [earth is not flat <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_wiki.png\">](https://en.wikipedia.org/wiki/Flat_Earth), [geopy <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_gh.png\">](https://github.com/geopy/geopy) provides a `great_circle()` function that calculates the so-called [orthodromic distance <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_wiki.png\">](https://en.wikipedia.org/wiki/Great-circle_distance) between two places on a sphere."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from geopy.distance import great_circle"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q27**: For quick reference, read the docstring of `great_circle()` and execute the code cells below to calculate the distance between the `arrival` and the `departure`!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "great_circle?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "tegel = Place(arrival, client=api).sync_from_google()\n",
+    "schoenefeld = Place(departure, client=api).sync_from_google()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "great_circle(tegel.location, schoenefeld.location)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "great_circle(tegel.location, schoenefeld.location).km"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "great_circle(tegel.location, schoenefeld.location).meters"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### The `Place` Class (continued): Distance to another `Place`"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q28**: Finish the `distance_to()` method in the `Place` class that takes a `other` argument and returns the distance in meters! Adhere to the given docstring!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "class Place:\n",
+    "    \"\"\"A place connected to the Google Maps Geocoding API.\"\"\"\n",
+    "\n",
+    "    # answers from above\n",
+    "\n",
+    "    # answer to Q28\n",
+    "    def distance_to(self, other):\n",
+    "        \"\"\"Calculate the distance to another place in meters.\n",
+    "\n",
+    "        Args:\n",
+    "            other (Place): the other place to calculate the distance to\n",
+    "\n",
+    "        Returns:\n",
+    "            distance (int)\n",
+    "\n",
+    "        Raises:\n",
+    "            RuntimeError: if one of the places is not synchronized with\n",
+    "                the Google Maps Geocoding API\n",
+    "        \"\"\"\n",
+    "        if not self.place_id or not other.place_id:\n",
+    "            raise RuntimeError(\"must synchronize places with Google first\")\n",
+    "        return ..."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q29**: Execute the code cells below to test the new feature!\n",
+    "\n",
+    "Note: If done right, object-oriented code reads almost like plain English."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "tegel = Place(arrival, client=api).sync_from_google()\n",
+    "schoenefeld = Place(departure, client=api).sync_from_google()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "tegel.distance_to(schoenefeld)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q30**: Execute the next code cell to instantiate the `Place`s in `sights` again!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "places = Place.from_addresses(sights, client=api, sync=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "places"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### The `Map` Class (continued): Pairwise Distances"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now, we add a read-only `distances` property on our `Map` class. As we are working with air distances, these are *symmetric* which reduces the number of distances we must calculate.\n",
+    "\n",
+    "To do so, we use the [combinations() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/itertools.html#itertools.combinations) generator function in the [itertools <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/itertools.html) module in the [standard library <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/index.html). That produces all possible `r`-`tuple`s from an `iterable` argument. `r` is `2` in our case as we are looking at `origin`-`destination` pairs.\n",
+    "\n",
+    "Let's first look at an easy example of [combinations() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/itertools.html#itertools.combinations) to understand how it works: It gives us all the `2`-`tuple`s from a `list` of five `numbers` disregarding the order of the `tuple`s' elements."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import itertools"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "numbers = [1, 2, 3, 4, 5]\n",
+    "\n",
+    "for x, y in itertools.combinations(numbers, 2):\n",
+    "    print(x, y)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "`distances` uses the internal `._start`, `._end`, and `._places` attributes and creates a `dict` with the keys consisting of all pairs of `Place`s and the values being their distances in meters. As this operation is rather costly, we cache the distances the first time we calculate them into a hidden instance attribute `._distances`, which must be initialized with `None` in the `.__init__()` method.\n",
+    "\n",
+    "**Q31**: Finish the `.distances` property as described!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "class Map:\n",
+    "    \"\"\"A map with plotting and routing capabilities.\"\"\"\n",
+    "\n",
+    "    # answers from above with a tiny adjustment\n",
+    "\n",
+    "    # answer to Q31\n",
+    "    ...\n",
+    "    def distances(self):\n",
+    "        \"\"\"Return a dict with the pairwise distances of all places.\n",
+    "\n",
+    "        Implementation note: The results of the calculations are cached.\n",
+    "        \"\"\"\n",
+    "        if not self._distances:\n",
+    "            distances = ...\n",
+    "            all_pairs = itertools.combinations(\n",
+    "                ...,\n",
+    "                r=2,\n",
+    "            )\n",
+    "            for origin, destination in all_pairs:\n",
+    "                distance = ...\n",
+    "                distances[origin, destination] = distance\n",
+    "                distances[destination, origin] = distance\n",
+    "            self._distances = ...\n",
+    "        return ..."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We pretty print the total distance matrix."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "berlin = Map(\n",
+    "    \"Berlin\",\n",
+    "    center=(52.5015154, 13.4066838),\n",
+    "    start=Place(arrival, client=api).sync_from_google(),\n",
+    "    end=Place(departure, client=api).sync_from_google(),\n",
+    "    places=places,\n",
+    "    initial_zoom=10,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "pprint(berlin.distances)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "How can we be sure the matrix contains all possible pairs? As we have 9 `sights` plus the `start` and the `end` of the tour, we conclude that there must be `11 * 10 = 110` distances excluding the `0` distances of a `Place` to itself that are not in the distance matrix."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "n_places = len(places) + 2\n",
+    "\n",
+    "n_places * (n_places - 1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "len(berlin.distances)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Route Optimization"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Let us find the cost minimal order of traveling from the `arrival` airport to the `departure` airport while visiting all the `sights`.\n",
+    "\n",
+    "This problem can be expressed as finding the shortest so-called [Hamiltonian path <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_wiki.png\">](https://en.wikipedia.org/wiki/Hamiltonian_path) from the `start` to `end` on the `Map` (i.e., a path that visits each intermediate node exactly once). With the \"hack\" of assuming the distance of traveling from the `end` to the `start` to be `0` and thereby effectively merging the two airports into a single node, the problem can be viewed as a so-called [traveling salesman problem <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_wiki.png\">](https://en.wikipedia.org/wiki/Traveling_salesman_problem) (TSP).\n",
+    "\n",
+    "The TSP is a hard problem to solve but also well studied in the literature. Assuming symmetric distances, a TSP with $n$ nodes has $\\frac{(n-1)!}{2}$ possible routes. $(n-1)$ because any node can be the `start` / `end` and divided by $2$ as the problem is symmetric.\n",
+    "\n",
+    "Starting with about $n = 20$, the TSP is almost impossible to solve exactly in a reasonable amount of time. Luckily, we do not have that many `sights` to visit, and so we use a [brute force <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_wiki.png\">](https://en.wikipedia.org/wiki/Brute-force_search) approach and simply loop over all possible routes to find the shortest.\n",
+    "\n",
+    "In the case of our tourist, we \"only\" need to try out `181_440` possible routes because the two airports are effectively one node and $n$ becomes $10$."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import math"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "math.factorial(len(places) + 1 - 1) // 2"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Analyzing the problem a bit further, all we need is a list of [permutations <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_wiki.png\">](https://en.wikipedia.org/wiki/Permutation) of the sights as the two airports are always the first and last location.\n",
+    "\n",
+    "The [permutations() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/itertools.html#itertools.permutations) generator function in the [itertools <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/itertools.html) module in the [standard library <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/index.html) helps us with the task. Let's see an example to understand how it works."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "numbers = [1, 2, 3]\n",
+    "\n",
+    "for permutation in itertools.permutations(numbers):\n",
+    "    print(permutation)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "However, if we use [permutations() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/itertools.html#itertools.permutations) as is, we try out *redundant* routes. For example, transferred to our case, the tuples `(1, 2, 3)` and `(3, 2, 1)` represent the *same* route as the distances are symmetric and the tourist could be going in either direction. To obtain the *unique* routes, we use an `if`-clause in a \"hacky\" way by only accepting routes where the first node has a smaller value than the last. Thus, we keep, for example, `(1, 2, 3)` and discard `(3, 2, 1)`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "for permutation in itertools.permutations(numbers):\n",
+    "    if permutation[0] < permutation[-1]:\n",
+    "        print(permutation)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In order to compare `Place`s as numbers, we would have to implement, among others, the `.__eq__()` special method. Otherwise, we get a `TypeError`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "Place(arrival, client=api) < Place(departure, client=api)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "As a quick and dirty solution, we use the `.location` property on a `Place` to do the comparison."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "Place(arrival, client=api).location < Place(departure, client=api).location"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "As the code cell below shows, combining [permutations() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/itertools.html#itertools.permutations) with an `if`-clause results in the correct number of routes to be looped over."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sum(\n",
+    "    1\n",
+    "    for route in itertools.permutations(places)\n",
+    "    if route[0].location < route[-1].location\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "To implement the brute force algorithm, we split the logic into two methods.\n",
+    "\n",
+    "First, we create an `.evaluate()` method that takes a `route` argument that is a sequence of `Place`s and returns the total distance of the route. Internally, this method uses the `.distances` property repeatedly, which is why we built in caching above.\n",
+    "\n",
+    "**Q32**: Finish the `.evaluate()` method as described!\n",
+    "\n",
+    "Second, we create a `.brute_force()` method that needs no arguments. It loops over all possible routes to find the shortest. As the `start` and `end` of a route are fixed, we only need to look at `permutation`s of inner nodes. Each `permutation` can then be traversed in a forward and a backward order. `.brute_force()` enables method chaining as well.\n",
+    "\n",
+    "**Q33**: Finish the `.brute_force()` method as described!"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### The `Map` Class (continued): Brute Forcing the TSP"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "class Map:\n",
+    "    \"\"\"A map with plotting and routing capabilities.\"\"\"\n",
+    "\n",
+    "    # answers from above\n",
+    "\n",
+    "    # answer to Q32\n",
+    "    def evaluate(self, route):\n",
+    "        \"\"\"Calculate the total distance of a route.\n",
+    "\n",
+    "        Args:\n",
+    "            route (sequence of Places): the ordered nodes in a tour\n",
+    "\n",
+    "        Returns:\n",
+    "            cost (int)\n",
+    "        \"\"\"\n",
+    "        cost = ...\n",
+    "        # Loop over all pairs of consecutive places.\n",
+    "        origin = ...\n",
+    "        for destination in ...:\n",
+    "            cost += self.distances[...]\n",
+    "            ...\n",
+    "\n",
+    "        return ...\n",
+    "\n",
+    "    # answer to Q33\n",
+    "    def brute_force(self):\n",
+    "        \"\"\"Calculate the shortest route by brute force.\n",
+    "\n",
+    "        The route is plotted on the folium.Map.\n",
+    "        \"\"\"\n",
+    "        # Assume a very high cost to begin with.\n",
+    "        min_cost = ...\n",
+    "        best_route = None\n",
+    "\n",
+    "        # Loop over all permutations of the intermediate nodes to visit.\n",
+    "        for permutation in ...:\n",
+    "            # Skip redundant permutations.\n",
+    "            if ...:\n",
+    "                ...\n",
+    "            # Travel through the routes in both directions.\n",
+    "            for route in (permutation, permutation[::-1]):\n",
+    "                # Add start and end to the route.\n",
+    "                route = (..., *route, ...)\n",
+    "                # Check if a route is cheaper than all routes seen before.\n",
+    "                cost = ...\n",
+    "                if ...:\n",
+    "                    min_cost = ...\n",
+    "                    best_route = ...\n",
+    "\n",
+    "        # Plot the route on the map\n",
+    "        folium.PolyLine(\n",
+    "            [x.location for x in best_route],\n",
+    "            color=\"orange\", weight=3, opacity=1\n",
+    "        ).add_to(self._map)\n",
+    "\n",
+    "        return ..."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q34**: Find the best route for our tourist by executing the code cells below!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "berlin = Map(\n",
+    "    \"Berlin\",\n",
+    "    center=(52.4915154, 13.4066838),\n",
+    "    start=Place(arrival, client=api).sync_from_google(),\n",
+    "    end=Place(departure, client=api).sync_from_google(),\n",
+    "    places=places,\n",
+    "    initial_zoom=12,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "berlin.brute_force().show()"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.6"
+  },
+  "toc": {
+   "base_numbering": 1,
+   "nav_menu": {},
+   "number_sections": false,
+   "sideBar": true,
+   "skip_h1_title": true,
+   "title_cell": "Table of Contents",
+   "title_sidebar": "Contents",
+   "toc_cell": false,
+   "toc_position": {
+    "height": "calc(100% - 180px)",
+    "left": "10px",
+    "top": "150px",
+    "width": "320px"
+   },
+   "toc_section_display": true,
+   "toc_window_display": false
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/CONTENTS.md b/CONTENTS.md
index a0f49df..aeb157a 100644
--- a/CONTENTS.md
+++ b/CONTENTS.md
@@ -270,3 +270,6 @@ If this is not possible,
        Text Representations;
        Instance Methods vs. Class Methods;
        Computed Properties)
+    - [exercises <img height="12" style="display: inline-block" src="static/link/to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/11_classes/01_exercises.ipynb)
+      [<img height="12" style="display: inline-block" src="static/link/to_mb.png">](https://mybinder.org/v2/gh/webartifex/intro-to-python/develop?urlpath=lab/tree/11_classes/01_exercises.ipynb)
+      (A Traveling Salesman Problem)

From da1940d8b6febe730e5e5861d3160f0fa389da23 Mon Sep 17 00:00:00 2001
From: Alexander Hess <alexander@webartifex.biz>
Date: Mon, 26 Oct 2020 23:34:42 +0100
Subject: [PATCH 211/242] Add initial version of chapter 11, part 2

---
 11_classes/02_content.ipynb | 1984 +++++++++++++++++++++++++++++++++++
 CONTENTS.md                 |    7 +
 2 files changed, 1991 insertions(+)
 create mode 100644 11_classes/02_content.ipynb

diff --git a/11_classes/02_content.ipynb b/11_classes/02_content.ipynb
new file mode 100644
index 0000000..1d99ecf
--- /dev/null
+++ b/11_classes/02_content.ipynb
@@ -0,0 +1,1984 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "**Note**: Click on \"*Kernel*\" > \"*Restart Kernel and Clear All Outputs*\" in [JupyterLab](https://jupyterlab.readthedocs.io/en/stable/) *before* reading this notebook to reset its output. If you cannot run this file on your machine, you may want to open it [in the cloud <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_mb.png\">](https://mybinder.org/v2/gh/webartifex/intro-to-python/develop?urlpath=lab/tree/11_classes/02_content.ipynb)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "# Chapter 11: Classes & Instances (continued)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "In this second part of the chapter, we learn how we make our `Vector` and `Matrix` instances behave like Python's built-in sequence types, for example, `list` or `tuple` objects."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "## Sequence Emulation"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "As discussed in detail in [Chapter 7 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/07_sequences/00_content.ipynb#Collections-vs.-Sequences), a sequence is any finite and iterable container type with a *predictable* order of its elements such that we can label each element with an index in the range `0 <= index < len(sequence)`.\n",
+    "\n",
+    "To make `Vector` and `Matrix` instances emulate sequences, we implement the `.__len__()` (cf., [reference <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/reference/datamodel.html#object.__len__)) and `.__getitem__()` (cf., [reference <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/reference/datamodel.html#object.__getitem__)) methods. While the former returns the total number of elements in a container and is automatically invoked on any object passed to the built-in [len() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#len) function, the latter is invoked by the interpreter behind the scenes when we use the indexing operator `[]`.\n",
+    "\n",
+    "In the example, both `.__len__()` and `.__getitem__()` delegate parts of the work to the embedded `list` object named `._entries`. This is a design principle known as [delegation <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_wiki.png\">](https://en.wikipedia.org/wiki/Delegation_%28object-oriented_programming%29) in software engineering. Also, we implicitly invoke the `.__len__()` method inside the `.__init__()` method already via the `len(self)` expression. This reuses code and also ensures that we calculate the number of entries in one way only within the entire class."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "class Vector:\n",
+    "\n",
+    "    def __init__(self, data):\n",
+    "        self._entries = list(float(x) for x in data)\n",
+    "        if len(self) == 0:\n",
+    "            raise ValueError(\"a vector must have at least one entry\")\n",
+    "\n",
+    "    def __repr__(self):\n",
+    "        args = \", \".join(f\"{x:.3f}\" for x in self._entries)\n",
+    "        return f\"Vector(({args}))\"\n",
+    "\n",
+    "    def __len__(self):\n",
+    "        return len(self._entries)\n",
+    "\n",
+    "    def __getitem__(self, index):\n",
+    "        return self._entries[index]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Now, we may obtain the number of elements with [len() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#len) and index into `Vector` instances."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "v = Vector([1, 2, 3])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "3"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "len(v)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "1.0"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "v[0]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Negative indexes work \"out of the box\" because of the delegation to the internal `list` object."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "3.0"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "v[-1]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Somehow \"magically\" we can loop over `v` with a `for` statement. This works as Python simply loops over the indexes implied by `len(v)` and obtains the entries one by one."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "1.0   2.0   3.0   "
+     ]
+    }
+   ],
+   "source": [
+    "for entry in v:\n",
+    "    print(entry, end=\"   \")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "`v` may also be looped over in reverse order with the [reversed() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#reversed) built-in."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "3.0   2.0   1.0   "
+     ]
+    }
+   ],
+   "source": [
+    "for entry in reversed(v):\n",
+    "    print(entry, end=\"   \")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Membership testing with the `in` operator also comes \"for free.\" Here, Python compares the object to be searched to each element with the `==` operator and stops early once one compares equal. That constitutes a [linear search <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_wiki.png\">](https://en.wikipedia.org/wiki/Linear_search) as seen before."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "1 in v"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "False"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "99 in v"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "So far, indexing is a *read-only* operation."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "ename": "TypeError",
+     "evalue": "'Vector' object does not support item assignment",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-10-ac550dc2349d>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mv\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m99\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;31mTypeError\u001b[0m: 'Vector' object does not support item assignment"
+     ]
+    }
+   ],
+   "source": [
+    "v[0] = 99"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Because a `Matrix` is two-dimensional, we must decide how we *flatten* the `._entries`. We *choose* to loop over the first row, then the second row, and so on. This is called a **[row major approach <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_wiki.png\">](https://en.wikipedia.org/wiki/Row-_and_column-major_order)**.\n",
+    "\n",
+    "In addition to indexing by `int` objects, we also implement indexing by 2-`tuple`s of `int`s where the first element indicates the row and the second the column. Deciding what to do inside a method depending on the *type* of an argument is known as **type dispatching**. We achieve that with the built-in [isinstance() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#isinstance) function.\n",
+    "\n",
+    "Lastly, we ensure that integer indexing also works with negative values as we are used to from sequences in general.\n",
+    "\n",
+    "Note how all of the methods work together:\n",
+    "- `.__init__()`, `.__len__()`, and `.__getitem__()` reuse the `.n_rows` and `.n_cols` properties, and\n",
+    "- `.__init__()` and `.__getitem__()` invoke `.__len__()` via the `len(self)` expressions."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {
+    "code_folding": [],
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "class Matrix:\n",
+    "\n",
+    "    def __init__(self, data):\n",
+    "        self._entries = list(list(float(x) for x in r) for r in data)\n",
+    "        for row in self._entries[1:]:\n",
+    "            if len(row) != self.n_cols:\n",
+    "                raise ValueError(\"rows must have the same number of entries\")\n",
+    "        if len(self) == 0:\n",
+    "            raise ValueError(\"a matrix must have at least one entry\")\n",
+    "\n",
+    "    @property\n",
+    "    def n_rows(self):\n",
+    "        return len(self._entries)\n",
+    "\n",
+    "    @property\n",
+    "    def n_cols(self):\n",
+    "        return len(self._entries[0])\n",
+    "\n",
+    "    def __len__(self):\n",
+    "        return self.n_rows * self.n_cols\n",
+    "\n",
+    "    def __getitem__(self, index):\n",
+    "        if isinstance(index, int):\n",
+    "            if index < 0:\n",
+    "                index += len(self)\n",
+    "            if not (0 <= index < len(self)):\n",
+    "                raise IndexError(\"integer index out of range\")\n",
+    "            row, col = divmod(index, self.n_cols)\n",
+    "            return self._entries[row][col]\n",
+    "        elif (\n",
+    "            isinstance(index, tuple) and len(index) == 2\n",
+    "            and isinstance(index[0], int) and isinstance(index[1], int)\n",
+    "        ):\n",
+    "            return self._entries[index[0]][index[1]]\n",
+    "        raise TypeError(\"index must be either an int or a tuple of two int's\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Now, we may use a `Matrix` instance just like any other sequence ..."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "m = Matrix([(1, 2, 3), (4, 5, 6), (7, 8, 9)])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "9"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "len(m)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "1.0"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "m[0]  # entry in the upper left corner"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "9.0"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "m[-1]  # entry in the lower right corner"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "... but also index in the two dimensions separately."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "3.0"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "m[0, 2]  # first row, third column"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "9.0"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "m[-1, -1]  # last row, last column / lower right corner"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "As before, Python figures out the iteration on its own ..."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "1.0   2.0   3.0   4.0   5.0   6.0   7.0   8.0   9.0   "
+     ]
+    }
+   ],
+   "source": [
+    "for entry in m:\n",
+    "    print(entry, end=\"   \")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "9.0   8.0   7.0   6.0   5.0   4.0   3.0   2.0   1.0   "
+     ]
+    }
+   ],
+   "source": [
+    "for entry in reversed(m):\n",
+    "    print(entry, end=\"   \")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "... and makes the `in` operator do a linear search."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "1 in m"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "False"
+      ]
+     },
+     "execution_count": 21,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "99 in m"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "### The Python Data Model"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Sequence emulation itself is *not* a property of object-oriented languages in general. Instead, it is a behavior any data type may or may not exhibit in Python.\n",
+    "\n",
+    "The collection of all such behaviors a programming language offers is commonly referred to as its **object model**. In Python, the term **data model** is used instead and all possible behaviors are documented in the [language reference <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/reference/datamodel.html), in particular, in the section on special methods. We can think of the data model as the collection of all the behaviors we can make our user-defined data types follow. Pythonistas also use the term **protocol** instead of behavior, for example, we may say that \"the `Vector` and `Matrix` classes follow the sequence protocol.\"\n",
+    "\n",
+    "So, merely defining the *two* `.__len__()` and `.__getitem__()` methods is enough to make instances of any user-defined type behave like the built-in sequences in [Chapter 7 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/07_sequences/00_content.ipynb). Yet, there we defined sequences as all objects having the *four* properties of being finite, iterable, and ordered container types. And, these properties correspond to special methods by the names of `.__len__()`, `.__iter__()`, `.__reversed__()`,  and `.__contains__()` as we see in the next section. Thus, Python \"magically\" knows how to derive the logic for the `.__iter__()`, `.__reversed__()`,  and `.__contains__()` methods from the combination of the `.__len__()` and `.__getitem__()` methods. In general, while some special methods are related, others are not. Understanding these relationships means understanding the Python data model and vice versa. That is what every aspiring data scientist should aim for.\n",
+    "\n",
+    "On the contrary, we could also look at special methods individually. Whereas `.__len__()` is invoked on the object passed to [len() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#len), Python \"translates\" the indexing operator applied on any name like `a[i]`, for example, into the method invocation `a.__getitem__(i)`. So, in both cases, the special methods are executed according to a deterministic rule of the language. In that sense, they act as some sort of syntactic sugar. Thus, they even work if only one of them is defined. For example, without `.__len__()`, iteration with a `for`-loop still works but only in forward direction."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "### More on Iteration"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "When implementing the sequence protocol for our `Matrix` class, we had to make the assumption that the user of our class wants to loop over the entries in a rows first fashion. While such assumptions can often be justified by referring to popular conventions (e.g., mathematicians usually look at matrices also in a \"row by column\" way), we could instead provide several iteration methods such that the user may choose one, just like `dict` objects come with several built-in methods that provide iteration.\n",
+    "\n",
+    "In the revised `Matrix` class below, we add the `.rows()`, `.cols()`, and `.entries()` methods that return `generator`s providing different and memory efficient ways of looping over the entries. `.rows()` and `.cols()` sequentially produce `Vector` instances representing individual rows and columns. This is in line with a popular idea in linear algebra to view a matrix as a collection of either row or column vectors. Further, `.entries()` by default produces the entries in the matrix one by one in a flat and row major fashion. Called with the optional `row_major=False` flag, it does the same in a column major fashion. The optional `reverse=True` flag allows iteration in backwards order.\n",
+    "\n",
+    "We also implement the `.__iter__()` (cf., [reference <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/reference/datamodel.html#object.__iter__)) and `.__reversed__()` (cf., [reference <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/reference/datamodel.html#object.__reversed__)) methods that immediately forward invocation to `.entries()`. So, Python does not need to fall back to `.__len__()` and `.__getitem__()` as we described above."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "class Matrix:\n",
+    "\n",
+    "    def __init__(self, data):\n",
+    "        self._entries = list(list(float(x) for x in r) for r in data)\n",
+    "        # ...\n",
+    "\n",
+    "    def __repr__(self):\n",
+    "        args = \", \".join(\"(\" + \", \".join(f\"{c:.3f}\" for c in r) + \",)\" for r in self._entries)\n",
+    "        return f\"Matrix(({args}))\"\n",
+    "\n",
+    "    @property\n",
+    "    def n_rows(self):\n",
+    "        return len(self._entries)\n",
+    "\n",
+    "    @property\n",
+    "    def n_cols(self):\n",
+    "        return len(self._entries[0])\n",
+    "\n",
+    "    def rows(self):\n",
+    "        return (Vector(r) for r in self._entries)\n",
+    "\n",
+    "    def cols(self):\n",
+    "        return (\n",
+    "            Vector(self._entries[r][c] for r in range(self.n_rows)) for c in range(self.n_cols)\n",
+    "        )\n",
+    "\n",
+    "    def entries(self, *, reverse=False, row_major=True):\n",
+    "        if reverse:\n",
+    "            rows, cols = (range(self.n_rows - 1, -1, -1), range(self.n_cols - 1, -1, -1))\n",
+    "        else:\n",
+    "            rows, cols = range(self.n_rows), range(self.n_cols)\n",
+    "        if row_major:\n",
+    "            return (self._entries[r][c] for r in rows for c in cols)\n",
+    "        return (self._entries[r][c] for c in cols for r in rows)\n",
+    "\n",
+    "    def __iter__(self):\n",
+    "        return self.entries()\n",
+    "\n",
+    "    def __reversed__(self):\n",
+    "        return self.entries(reverse=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "The revised version of `Vector` below also works without `.__len__()` and `.__getitem__()` methods and leaves the creation of memory efficient `generator`s up to the embedded `list` object in `._entries` by using the built-in [iter() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#iter) and [reversed() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#reversed) functions. Also, `.__repr__()` now relies on the sequence protocol as the instance loops over \"itself\" with `for x in self`, a subtle reuse of code again. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "class Vector:\n",
+    "\n",
+    "    def __init__(self, data):\n",
+    "        self._entries = list(float(x) for x in data)\n",
+    "        # ...\n",
+    "\n",
+    "    def __repr__(self):\n",
+    "        args = \", \".join(f\"{x:.3f}\" for x in self)\n",
+    "        return f\"Vector(({args}))\"\n",
+    "\n",
+    "    def __iter__(self):\n",
+    "        return iter(self._entries)\n",
+    "\n",
+    "    def __reversed__(self):\n",
+    "        return reversed(self._entries)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "m = Matrix([(1, 2, 3), (4, 5, 6), (7, 8, 9)])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Matrix(((1.000, 2.000, 3.000,), (4.000, 5.000, 6.000,), (7.000, 8.000, 9.000,)))"
+      ]
+     },
+     "execution_count": 25,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "m"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Iteration works as before ..."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "1.0   2.0   3.0   4.0   5.0   6.0   7.0   8.0   9.0   "
+     ]
+    }
+   ],
+   "source": [
+    "for entry in m:\n",
+    "    print(entry, end=\"   \")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "9.0   8.0   7.0   6.0   5.0   4.0   3.0   2.0   1.0   "
+     ]
+    }
+   ],
+   "source": [
+    "for entry in reversed(m):\n",
+    "    print(entry, end=\"   \")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "... but now we have some ways of customizing it."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Vector((1.000, 2.000, 3.000))   Vector((4.000, 5.000, 6.000))   Vector((7.000, 8.000, 9.000))   "
+     ]
+    }
+   ],
+   "source": [
+    "for row_vector in m.rows():\n",
+    "    print(row_vector, end=\"   \")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Vector((1.000, 4.000, 7.000))   Vector((2.000, 5.000, 8.000))   Vector((3.000, 6.000, 9.000))   "
+     ]
+    }
+   ],
+   "source": [
+    "for col_vector in m.cols():\n",
+    "    print(col_vector, end=\"   \")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "1.0   2.0   3.0   4.0   5.0   6.0   7.0   8.0   9.0   "
+     ]
+    }
+   ],
+   "source": [
+    "for entry in m.entries():\n",
+    "    print(entry, end=\"   \")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "1.0   4.0   7.0   2.0   5.0   8.0   3.0   6.0   9.0   "
+     ]
+    }
+   ],
+   "source": [
+    "for entry in m.entries(row_major=False):\n",
+    "    print(entry, end=\"   \")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "## Mutability vs. Immutability"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "In the above implementations, the instance attribute `._entries` on a `Vector` or `Matrix` instance references either a `list` or a `list` of row `list`s , which is by the convention of the leading underscore `_` an implementation detail. If users of our classes adhere to this convention, `Vector` and `Matrix` instances can be regarded as *immutable*.\n",
+    "\n",
+    "In line with the implied immutability, we implemented the `.transpose()` method such that it returns a *new* `Matrix` instance. Instead, we could make the method change the internal `self._entries` attribute *in place* as we do in the next example. To indicate this mutation to the user of the `Matrix` class clearly, the revised `.transpose()` method returns `None`. That mirrors, for example, how the mutating methods of the built-in `list` type behave (cf., [Chapter 7 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/07_sequences/01_content.ipynb#List-Methods)).\n",
+    "\n",
+    "Such decisions are better made consciously when designing a custom data type. The main trade-off is that immutable data types are typically easier to reason about when reading code whereas mutable data types tend to be more memory efficient and make programs faster as less copying operations take place in memory. However, this trade-off only becomes critical when we deal with big amounts of data."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "class Matrix:\n",
+    "\n",
+    "    def __init__(self, data):\n",
+    "        self._entries = list(list(float(x) for x in r) for r in data)\n",
+    "        # ...\n",
+    "\n",
+    "    def __repr__(self):\n",
+    "        args = \", \".join(\"(\" + \", \".join(f\"{c:.3f}\" for c in r) + \",)\" for r in self._entries)\n",
+    "        return f\"Matrix(({args}))\"\n",
+    "\n",
+    "    def transpose(self):\n",
+    "        self._entries = list(list(float(x) for x in r) for r in (zip(*self._entries)))\n",
+    "        return None"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "m = Matrix([(1, 2, 3), (4, 5, 6), (7, 8, 9)])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Matrix(((1.000, 2.000, 3.000,), (4.000, 5.000, 6.000,), (7.000, 8.000, 9.000,)))"
+      ]
+     },
+     "execution_count": 34,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "m"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Transposing `m` has *no* cell output ..."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "m.transpose()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "... so we must look at `m` again."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Matrix(((1.000, 4.000, 7.000,), (2.000, 5.000, 8.000,), (3.000, 6.000, 9.000,)))"
+      ]
+     },
+     "execution_count": 36,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "m"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "A downside of returning `None` is that we can *not* chain repeated invocations of `.transpose()`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "ename": "AttributeError",
+     "evalue": "'NoneType' object has no attribute 'transpose'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-37-21b8354a80f8>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mm\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtranspose\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtranspose\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;31mAttributeError\u001b[0m: 'NoneType' object has no attribute 'transpose'"
+     ]
+    }
+   ],
+   "source": [
+    "m.transpose().transpose()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "### Enabling Method Chaining"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "To fix the missing method chaining, we end the `.transpose()` method with `return self`, which returns a reference to the instance on which the method is invoked."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "class Matrix:\n",
+    "\n",
+    "    def __init__(self, data):\n",
+    "        self._entries = list(list(float(x) for x in r) for r in data)\n",
+    "        # ...\n",
+    "\n",
+    "    def __repr__(self):\n",
+    "        args = \", \".join(\"(\" + \", \".join(f\"{c:.3f}\" for c in r) + \",)\" for r in self._entries)\n",
+    "        return f\"Matrix(({args}))\"\n",
+    "\n",
+    "    def __iter__(self):  # adapted for brevity; uses parts of entries()\n",
+    "        rows, cols = range(len(self._entries)), range(len(self._entries[0]))\n",
+    "        return (self._entries[r][c] for r in rows for c in cols)\n",
+    "\n",
+    "    def transpose(self):\n",
+    "        self._entries = list(list(float(x) for x in r) for r in (zip(*self._entries)))\n",
+    "        return self"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "m = Matrix([(1, 2, 3), (4, 5, 6), (7, 8, 9)])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Matrix(((1.000, 2.000, 3.000,), (4.000, 5.000, 6.000,), (7.000, 8.000, 9.000,)))"
+      ]
+     },
+     "execution_count": 40,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "m"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "The downside of this approach is that a user may unknowingly end up with *two* references to the *same* instance. That can only be mitigated by clear documentation."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "n = m.transpose()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Matrix(((1.000, 4.000, 7.000,), (2.000, 5.000, 8.000,), (3.000, 6.000, 9.000,)))"
+      ]
+     },
+     "execution_count": 42,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "m"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Matrix(((1.000, 4.000, 7.000,), (2.000, 5.000, 8.000,), (3.000, 6.000, 9.000,)))"
+      ]
+     },
+     "execution_count": 43,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 44,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "m is n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "### More on Indexing"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Analogous to the `.__getitem__()` method above, there are also the `.__setitem__()` (cf., [reference <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/reference/datamodel.html#object.__setitem__)) and `.__delitem__()` (cf., [reference <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/reference/datamodel.html#object.__delitem__)) methods that assign a new element to or delete an existing element from a sequence.\n",
+    "\n",
+    "Whereas deleting an individual entry in a `Vector` or `Matrix` instance may *not* really make sense semantically, we interpret this as setting the corresponding entry to \"unknown\" (i.e., `NaN`). Also, we implement changing individual entries via index assignment. Here, `.__setitem__()` delegates the assignment to the embedded `list` object after casting the assigned value as a `float`. While the example below only allows indexing by an integer, it could be generalized to slicing as well."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "class Vector:\n",
+    "\n",
+    "    def __init__(self, data):\n",
+    "        self._entries = list(float(x) for x in data)\n",
+    "        # ...\n",
+    "\n",
+    "    def __repr__(self):\n",
+    "        args = \", \".join(f\"{x:.3f}\" for x in self)\n",
+    "        return f\"Vector(({args}))\"\n",
+    "\n",
+    "    def __getitem__(self, index):\n",
+    "        return self._entries[index]\n",
+    "\n",
+    "    def __setitem__(self, index, value):\n",
+    "        self._entries[index] = float(value)\n",
+    "\n",
+    "    def __delitem__(self, index):\n",
+    "        self._entries[index] = float(\"NaN\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "v = Vector([1, 2, 3])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Vector((1.000, 2.000, 3.000))"
+      ]
+     },
+     "execution_count": 47,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "v"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "`v` can now be changed in place."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 48,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "del v[0]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 49,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Vector((nan, 2.000, 3.000))"
+      ]
+     },
+     "execution_count": 49,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "v"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 50,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "v[0] = 99"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 51,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Vector((99.000, 2.000, 3.000))"
+      ]
+     },
+     "execution_count": 51,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "v"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "After this discussion of mutable `Vector` and `Matrix` classes, we continue with immutable classes in the rest of this chapter."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "## Polymorphism"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "A function is considered **polymorphic** if it can work with *different* data types. The main advantage is reuse of the function's code. Polymorphism goes hand in hand with the concept of [duck typing <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_wiki.png\">](https://en.wikipedia.org/wiki/Duck_typing), first mentioned in [Chapter 4 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/04_iteration/00_content.ipynb#Duck-Typing) in the context of input validation.\n",
+    "\n",
+    "We know polymorphic functions already: The built-in [sum() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#sum) function is a trivial example that works with all kinds of `iterable` arguments."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 52,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "10"
+      ]
+     },
+     "execution_count": 52,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "sum((1, 2, 3, 4))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 53,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "10"
+      ]
+     },
+     "execution_count": 53,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "sum([1, 2, 3, 4])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 54,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "10"
+      ]
+     },
+     "execution_count": 54,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "sum({1, 2, 3, 4})"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 55,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "10"
+      ]
+     },
+     "execution_count": 55,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "sum({1: 996, 2: 997, 3: 998, 4: 999})  # loops over the keys"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "As we implemented the `Vector` and `Matrix` classes to be iterable, we may pass them to [sum() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#sum) as well."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 56,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "10.0"
+      ]
+     },
+     "execution_count": 56,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "sum(Vector([1, 2, 3, 4]))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 57,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "10.0"
+      ]
+     },
+     "execution_count": 57,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "sum(Matrix([(1, 2), (3, 4)]))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "A polymorphic function with a semantic meaning in the context of linear algebra would be one that calculates the [Euclidean norm <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_wiki.png\">](https://en.wikipedia.org/wiki/Norm_%28mathematics%29#Euclidean_norm) for vectors, which is a generalization of the popular [Pythagorean theorem <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_wiki.png\">](https://en.wikipedia.org/wiki/Pythagorean_theorem). Extending the same kind of computation to a matrix results in the even more general [Frobenius norm <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_wiki.png\">](https://en.wikipedia.org/wiki/Matrix_norm#Frobenius_norm):"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "$$\\lVert \\bf{X} \\rVert_F = \\sqrt{ \\sum_{i=1}^m \\sum_{j=1}^n x_{ij}^2 }$$"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "The `norm()` function below can handle both a `Vector` or a `Matrix` instance and is therefore polymorphic. In this sense, `Vector` and `Matrix` instances \"walk\" and \"quack\" alike. In particular, they they both can provide all their entries as a flat sequence."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 58,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "import math"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 59,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "def norm(vector_or_matrix):\n",
+    "    \"\"\"Calculate the Frobenius or Euclidean norm of a matrix or vector.\n",
+    "\n",
+    "    Args:\n",
+    "        vector_or_matrix (Vector/Matrix): the entries whose squares\n",
+    "            are summed up\n",
+    "\n",
+    "    Returns:\n",
+    "        norm (float)\n",
+    "    \"\"\"\n",
+    "    return math.sqrt(sum(x ** 2 for x in vector_or_matrix))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "While `norm()` is intended to work with `Vector` or `Matrix` instances ..."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 60,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "5.477225575051661"
+      ]
+     },
+     "execution_count": 60,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "norm(Vector([1, 2, 3, 4]))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 61,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "5.477225575051661"
+      ]
+     },
+     "execution_count": 61,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "norm(Matrix([(1, 2), (3, 4)]))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "... it also works for any sequence of numbers."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 62,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "5.477225575051661"
+      ]
+     },
+     "execution_count": 62,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "norm([1, 2, 3, 4])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "An important criterion if different classes are compatible in the sense that the same polymorphic function can work with them is that they implement the same **interface**.\n",
+    "\n",
+    "Whereas many other programming languages formalize this [concept <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_wiki.png\">](https://en.wikipedia.org/wiki/Protocol_%28object-oriented_programming%29), in Python the term refers to the loose idea that different classes define the same attributes and implement the various protocols behind the special methods in a consistent way. This is what it means to \"walk\" and \"quack\" alike."
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.6"
+  },
+  "livereveal": {
+   "auto_select": "code",
+   "auto_select_fragment": true,
+   "scroll": true,
+   "theme": "serif"
+  },
+  "toc": {
+   "base_numbering": 1,
+   "nav_menu": {},
+   "number_sections": false,
+   "sideBar": true,
+   "skip_h1_title": true,
+   "title_cell": "Table of Contents",
+   "title_sidebar": "Contents",
+   "toc_cell": false,
+   "toc_position": {
+    "height": "calc(100% - 180px)",
+    "left": "10px",
+    "top": "150px",
+    "width": "384px"
+   },
+   "toc_section_display": false,
+   "toc_window_display": false
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/CONTENTS.md b/CONTENTS.md
index aeb157a..59b18c0 100644
--- a/CONTENTS.md
+++ b/CONTENTS.md
@@ -273,3 +273,10 @@ If this is not possible,
     - [exercises <img height="12" style="display: inline-block" src="static/link/to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/11_classes/01_exercises.ipynb)
       [<img height="12" style="display: inline-block" src="static/link/to_mb.png">](https://mybinder.org/v2/gh/webartifex/intro-to-python/develop?urlpath=lab/tree/11_classes/01_exercises.ipynb)
       (A Traveling Salesman Problem)
+    - [content <img height="12" style="display: inline-block" src="static/link/to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/11_classes/02_content.ipynb)
+      [<img height="12" style="display: inline-block" src="static/link/to_mb.png">](https://mybinder.org/v2/gh/webartifex/intro-to-python/develop?urlpath=lab/tree/11_classes/02_content.ipynb)
+      (Sequence Emulation & Iteration;
+       Python's Data Model;
+       (Im)mutable Data Types;
+       Method Chaining;
+       Polymorphism)

From c2e0deafde125a44f8b07d90ab2fb6b5461878ee Mon Sep 17 00:00:00 2001
From: Alexander Hess <alexander@webartifex.biz>
Date: Tue, 27 Oct 2020 11:00:34 +0100
Subject: [PATCH 212/242] Fix wrong link

---
 CONTENTS.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/CONTENTS.md b/CONTENTS.md
index 59b18c0..ca1718b 100644
--- a/CONTENTS.md
+++ b/CONTENTS.md
@@ -263,7 +263,7 @@ If this is not possible,
     - [further resources <img height="12" style="display: inline-block" src="static/link/to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/09_mappings/08_resources.ipynb)
       [<img height="12" style="display: inline-block" src="static/link/to_mb.png">](https://mybinder.org/v2/gh/webartifex/intro-to-python/develop?urlpath=lab/tree/09_mappings/08_resources.ipynb)
   - *Chapter 11*: Classes & Instances
-    - [content <img height="12" style="display: inline-block" src="static/link/to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/09_mappings/00_content.ipynb)
+    - [content <img height="12" style="display: inline-block" src="static/link/to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/11_classes/00_content.ipynb)
       [<img height="12" style="display: inline-block" src="static/link/to_mb.png">](https://mybinder.org/v2/gh/webartifex/intro-to-python/develop?urlpath=lab/tree/11_classes/00_content.ipynb)
       (`class` Statement;
        Instantiation;

From 2c83a883c5a9f550e7b36f08903cd4fc1b5c119c Mon Sep 17 00:00:00 2001
From: Alexander Hess <alexander@webartifex.biz>
Date: Tue, 27 Oct 2020 11:03:29 +0100
Subject: [PATCH 213/242] Add initial version of chapter 11, part 3

---
 11_classes/03_content.ipynb | 2367 +++++++++++++++++++++++++++++++++++
 CONTENTS.md                 |    4 +
 2 files changed, 2371 insertions(+)
 create mode 100644 11_classes/03_content.ipynb

diff --git a/11_classes/03_content.ipynb b/11_classes/03_content.ipynb
new file mode 100644
index 0000000..7b3a9ae
--- /dev/null
+++ b/11_classes/03_content.ipynb
@@ -0,0 +1,2367 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "**Note**: Click on \"*Kernel*\" > \"*Restart Kernel and Clear All Outputs*\" in [JupyterLab](https://jupyterlab.readthedocs.io/en/stable/) *before* reading this notebook to reset its output. If you cannot run this file on your machine, you may want to open it [in the cloud <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_mb.png\">](https://mybinder.org/v2/gh/webartifex/intro-to-python/develop?urlpath=lab/tree/11_classes/03_content.ipynb)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "# Chapter 11: Classes & Instances (continued)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "The implementations of our `Vector` and `Matrix` classes so far do not know any of the rules of [linear algebra <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_wiki.png\">](https://en.wikipedia.org/wiki/Linear_algebra). In this third part of the chapter, we add a couple of typical vector and matrix operations, for example, vector addition or matrix-vector multiplication, and some others. Before we do so, we briefly talk about how we can often model the *same* underlying data with a *different* data type."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "## Representations of Data"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "source": [
+    "> \"If you change the way you look at things, the things you look at change.\"\n",
+    "> -- philosopher and personal coach [Dr. Wayne Dyer <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_wiki.png\">](https://en.wikipedia.org/wiki/Wayne_Dyer)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Sometimes, it is helpful to view a `Vector` as a `Matrix` with either one row or one column. On the contrary, such a `Matrix` can always be interpreted as a `Vector` again. Changing the representation of the same underlying data (i.e., the `_entries`) can be viewed as \"changing\" an object's data type, for which, however, there is no built-in syntax.\n",
+    "\n",
+    "Thus, we implement the `.as_matrix()` and `.as_vector()` methods below that create *new* `Matrix` or `Vector` instances out of existing `Vector` or `Matrix` instances, respectively. Internally, both methods rely on the sequence protocol again (i.e., `for x in self`). Also, `.as_matrix()` interprets the `Vector` instance as a column vector by default (i.e., the `column=True` flag)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "class Vector:\n",
+    "\n",
+    "    def __init__(self, data):\n",
+    "        self._entries = list(float(x) for x in data)\n",
+    "        # ...\n",
+    "\n",
+    "    def __repr__(self):\n",
+    "        args = \", \".join(f\"{x:.3f}\" for x in self)\n",
+    "        return f\"Vector(({args}))\"\n",
+    "\n",
+    "    def __iter__(self):\n",
+    "        return iter(self._entries)\n",
+    "\n",
+    "    def as_matrix(self, *, column=True):\n",
+    "        if column:\n",
+    "            return Matrix([x] for x in self)\n",
+    "        return Matrix([(x for x in self)])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "class Matrix:\n",
+    "\n",
+    "    def __init__(self, data):\n",
+    "        self._entries = list(list(float(x) for x in r) for r in data)\n",
+    "        # ...\n",
+    "\n",
+    "    def __repr__(self):\n",
+    "        args = \", \".join(\"(\" + \", \".join(f\"{c:.3f}\" for c in r) + \",)\" for r in self._entries)\n",
+    "        return f\"Matrix(({args}))\"\n",
+    "\n",
+    "    @property\n",
+    "    def n_rows(self):\n",
+    "        return len(self._entries)\n",
+    "\n",
+    "    @property\n",
+    "    def n_cols(self):\n",
+    "        return len(self._entries[0])\n",
+    "\n",
+    "    def __iter__(self):  # adapted for brevity; uses parts of .entries()\n",
+    "        return (self._entries[r][c] for r in range(self.n_rows) for c in range(self.n_cols))\n",
+    "\n",
+    "    def as_vector(self):\n",
+    "        if not (self.n_rows == 1 or self.n_cols == 1):\n",
+    "            raise RuntimeError(\"one dimension (m or n) must be 1\")\n",
+    "        return Vector(x for x in self)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "v = Vector([1, 2, 3])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Vector((1.000, 2.000, 3.000))"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "v"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Let's interpret `v` as a column vector and create a matrix with dimension $3 \\times 1$."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "m = v.as_matrix()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Matrix(((1.000,), (2.000,), (3.000,)))"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "m"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(3, 1)"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "m.n_rows, m.n_cols"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "By chaining `.as_matrix()` and `.as_vector()` we get a *new* `Vector` instance back that is equivalent to the given `v`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Vector((1.000, 2.000, 3.000))"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "v.as_matrix().as_vector()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "In the same way, we can also interpret `v` as a row vector and create a $1 \\times 3$ matrix."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "m = v.as_matrix(column=False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Matrix(((1.000, 2.000, 3.000,)))"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "m"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(1, 3)"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "m.n_rows, m.n_cols"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Vector((1.000, 2.000, 3.000))"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "v.as_matrix(column=False).as_vector()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Interpreting a matrix as a vector only works if one of the two dimensions, $m$ or $n$, is $1$. If this requirement is not satisfied, we get the `RuntimeError` raised in `.as_vector()` above."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "m = Matrix([(1, 2, 3), (4, 5, 6), (7, 8, 9)])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "ename": "RuntimeError",
+     "evalue": "one dimension (m or n) must be 1",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mRuntimeError\u001b[0m                              Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-14-625d8ddab141>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mm\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mas_vector\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;32m<ipython-input-2-3a6f1abfe70b>\u001b[0m in \u001b[0;36mas_vector\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m     22\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mas_vector\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     23\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mn_rows\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m1\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mn_cols\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 24\u001b[0;31m             \u001b[0;32mraise\u001b[0m \u001b[0mRuntimeError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"one dimension (m or n) must be 1\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     25\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mVector\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mx\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;31mRuntimeError\u001b[0m: one dimension (m or n) must be 1"
+     ]
+    }
+   ],
+   "source": [
+    "m.as_vector()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "## Operator Overloading"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "By implementing special methods such as `.__add__()`, `.__sub__()`, `.__mul__()`, and some others, we can make user-defined data types emulate how numeric types operate with each other (cf., [reference <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/reference/datamodel.html#emulating-numeric-types)): Then, `Vector` and `Matrix` instances can be added together, subtracted from one another, or be multiplied together. We use them to implement the arithmetic rules from linear algebra.\n",
+    "\n",
+    "The OOP concept behind this is **[operator overloading <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_wiki.png\">](https://en.wikipedia.org/wiki/Operator_overloading)** as first mentioned in the context of `str` concatenation in [Chapter 1 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/01_elements/00_content.ipynb#Operator-Overloading)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "### Arithmetic Operators"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "To understand the protocol behind arithmetic operators, we first look at the simple case of how an `int` object and a `float` object are added. The expression `1 + 2.0` is \"translated\" by Python into a method invocation of the form `1.__add__(2.0)`. This is why all the special methods behind binary operators take two arguments, `self` and, by convention, `other`. To allow binary operators to work with objects of *different* data types, Python expects the `.__add__()` method on the `1` object to return `NotImplemented` if it does not know how to deal with the `2.0` object and then proceeds by invoking the *reverse* special method `2.0.__radd__(1)`. With this protocol, one can create *new* data types that know how to execute arithmetic operations with *existing* data types *without* having to change the latter. By convention, the result of a binary operation should always be a *new* instance object and *not* a mutation of an existing one."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "3.0"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "1 + 2.0"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Before implementing the arithmetic operators, we must first determine what other data types are allowed to interact with our `Vector` and `Matrix` instances and also how the two interact with each other. Conceptually, this is the same as to ask how strict we want the rules from linear algebra to be enforced in our model world. For example, while it is obvious that two vectors with the same number of entries may be added or subtracted, we could also allow a scalar value to be added to a vector. That seems awkward at first because it is an illegal operation in linear algebra. However, for convenience in our programs, we could interpret any scalar as a \"constants\" vector of the \"right size\" and add it to each entry in a `Vector`. This idea can be generalized into what is called **[broadcasting](https://docs.scipy.org/doc/numpy/user/basics.broadcasting.html)** in [numpy](https://docs.scipy.org/doc/numpy/index.html). We often see \"dirty hacks\" like this in code. They are no bugs but features supposed to make the user of a library more productive.\n",
+    "\n",
+    "In this chapter, we model the following binary arithmetic operations:"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "- **Addition** / **Subtraction**\n",
+    "  - `Vector` with `Vector` (if number of entries match; commutative)\n",
+    "  - `Matrix` with `Matrix` (if dimensions $m$ and $n$ match; commutative)\n",
+    "  - `Matrix` / `Vector` with scalar (the scalar is broadcasted; non-commutative for subtraction)\n",
+    "- **Multiplication**\n",
+    "  - `Vector` with `Vector` ([dot product <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_wiki.png\">](https://en.wikipedia.org/wiki/Dot_product) if number of entries match; commutative)\n",
+    "  - `Matrix` with `Vector` (if dimensions are compatible; vector interpreted as column vector; non-commutative)\n",
+    "  - `Vector` with `Matrix` (if dimensions are compatible; vector interpreted as row vector; non-commutative)\n",
+    "  - `Matrix` / `Vector` with scalar ([scalar multiplication <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_wiki.png\">](https://en.wikipedia.org/wiki/Scalar_multiplication); commutative)\n",
+    "  - `Matrix` with `Matrix` ([matrix-matrix multiplication <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_wiki.png\">](https://en.wikipedia.org/wiki/Matrix_multiplication) if dimensions are compatible; generally non-commutative)\n",
+    "- **Division**\n",
+    "  - `Matrix` / `Vector` by a scalar (inverse of [scalar multiplication <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_wiki.png\">](https://en.wikipedia.org/wiki/Scalar_multiplication); non-commutative)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "This listing shows the conceptual complexity behind the task of writing a \"little\" linear algebra library. Not to mention that some of the operations are [commutative <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_wiki.png\">](https://en.wikipedia.org/wiki/Commutative_property) while others are not.\n",
+    "\n",
+    "As the available special methods correspond to the high-level grouping in the listing, we must implement a lot of **type dispatching** within them. This is why you see the built-in [isinstance() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#isinstance) function in most of the methods below. We use it to check if the `other` argument passed in is a `Vector` or `Matrix` instance or a scalar."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "False"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "isinstance(m, Vector)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "isinstance(v, Vector)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "To check if `other` is a scalar, we need to specify what data type constitutes a scalar. We use a goose typing strategy as explained in [Chapter 5 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/05_numbers/02_content.ipynb#Goose-Typing): Any object that behaves like a `numbers.Number` from the [numbers <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/numbers.html) module in the [standard library <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/index.html) is considered a scalar.\n",
+    "\n",
+    "For example, the integer `1` is an instance of the built-in `int` type. At the same time, [isinstance() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#isinstance) also confirms that it is a `numbers.Number` in the abstract sense."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "isinstance(1, int)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "import numbers"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "isinstance(1, numbers.Number)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Now with all the preparation work done, let's look at a \"minimal\" implementation of `Vector` that supports all the arithmetic operations specified above. *None* of the special methods inside the `Vector` class is aware that the `Matrix` class exists! Thus, all operations involving at least one `Matrix` instance are implemented only in the `Matrix` class."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {
+    "code_folding": [],
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "class Vector:\n",
+    "\n",
+    "    def __init__(self, data):\n",
+    "        self._entries = list(float(x) for x in data)\n",
+    "        # ...\n",
+    "\n",
+    "    def __repr__(self):\n",
+    "        args = \", \".join(f\"{x:.3f}\" for x in self)\n",
+    "        return f\"Vector(({args}))\"\n",
+    "\n",
+    "    def __len__(self):\n",
+    "        return len(self._entries)\n",
+    "\n",
+    "    def __iter__(self):\n",
+    "        return iter(self._entries)\n",
+    "\n",
+    "    def __add__(self, other):\n",
+    "        if isinstance(other, self.__class__):  # vector addition\n",
+    "            if len(self) != len(other):\n",
+    "                raise ValueError(\"vectors need to be of the same length\")\n",
+    "            return Vector(x + y for (x, y) in zip(self, other))\n",
+    "        elif isinstance(other, numbers.Number):  # broadcasting addition\n",
+    "            return Vector(x + other for x in self)\n",
+    "        return NotImplemented\n",
+    "\n",
+    "    def __radd__(self, other):\n",
+    "        return self + other\n",
+    "\n",
+    "    def __sub__(self, other):\n",
+    "        if isinstance(other, self.__class__):  # vector subtraction\n",
+    "            if len(self) != len(other):\n",
+    "                raise ValueError(\"vectors need to be of the same length\")\n",
+    "            return Vector(x - y for (x, y) in zip(self, other))\n",
+    "        elif isinstance(other, numbers.Number):  # broadcasting subtraction\n",
+    "            return Vector(x - other for x in self)\n",
+    "        return NotImplemented\n",
+    "\n",
+    "    def __rsub__(self, other):\n",
+    "        # Reverse vector subtraction is already handled in __sub__().\n",
+    "        if isinstance(other, numbers.Number):  # broadcasting subtraction\n",
+    "            return Vector(other - x for x in self)\n",
+    "        return NotImplemented\n",
+    "\n",
+    "    def __mul__(self, other):\n",
+    "        if isinstance(other, self.__class__):  # dot product\n",
+    "            if len(self) != len(other):\n",
+    "                raise ValueError(\"vectors need to be of the same length\")\n",
+    "            return sum(x * y for (x, y) in zip(self, other))\n",
+    "        elif isinstance(other, numbers.Number):  # scalar multiplication\n",
+    "            return Vector(x * other for x in self)\n",
+    "        return NotImplemented\n",
+    "\n",
+    "    def __rmul__(self, other):\n",
+    "        return self * other\n",
+    "\n",
+    "    def __truediv__(self, other):\n",
+    "        if isinstance(other, numbers.Number):\n",
+    "            return self * (1 / other)\n",
+    "        return NotImplemented\n",
+    "\n",
+    "    def as_matrix(self, *, column=True):\n",
+    "        if column:\n",
+    "            return Matrix([x] for x in self)\n",
+    "        return Matrix([(x for x in self)])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "v = Vector([1, 2, 3])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "w = Vector([4, 5])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "`.__mul__()` implements both scalar multiplication and the dot product of two `Vector`s. As both operations are commutative, `.__rmul__()` dispatches to `.__mul__()` via the `self * other` expression."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Vector((2.000, 4.000, 6.000))"
+      ]
+     },
+     "execution_count": 24,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "2 * v"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Vector((3.000, 6.000, 9.000))"
+      ]
+     },
+     "execution_count": 25,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "v * 3"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "14.0"
+      ]
+     },
+     "execution_count": 26,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "v * v"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "If two `Vector`s do *not* have a matching number of entries, a `ValueError` is raised."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "ename": "ValueError",
+     "evalue": "vectors need to be of the same length",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-27-21fab2d5f12e>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mv\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0mw\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;32m<ipython-input-21-12852bd26164>\u001b[0m in \u001b[0;36m__mul__\u001b[0;34m(self, other)\u001b[0m\n\u001b[1;32m     45\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mother\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__class__\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m  \u001b[0;31m# dot product\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     46\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mother\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 47\u001b[0;31m                 \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"vectors need to be of the same length\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     48\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0msum\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0my\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mzip\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mother\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     49\u001b[0m         \u001b[0;32melif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mother\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnumbers\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mNumber\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m  \u001b[0;31m# scalar multiplication\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;31mValueError\u001b[0m: vectors need to be of the same length"
+     ]
+    }
+   ],
+   "source": [
+    "v * w"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "`.__truediv__()` implements the ordinary division operator `/` while `.__floordiv__()` would implement the integer division operator `//`. Here, `.__truediv__()` dispatches to `.__mul__()` after inverting the `other` argument via the `self * (1 / other)` expression."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Vector((0.333, 0.667, 1.000))"
+      ]
+     },
+     "execution_count": 28,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "v / 3"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "`.__add__()` and `.__sub__()` implement vector addition and subtraction according to standard linear algebra rules, meaning that both `Vector`s must have the same number of entries or a `ValueError` is raised. Furthermore, both methods are able to broadcast the `other` argument to the dimension of a `Vector` and then execute either vector addition or subtraction. As addition is commutative, `.__radd__()` dispatches to `.__add__()`. For now, we have to explicitly implement `.__rsub__()`. Further below, we see how it can be re-factored to be commutative."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Vector((2.000, 4.000, 6.000))"
+      ]
+     },
+     "execution_count": 29,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "v + v"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Vector((0.000, 0.000, 0.000))"
+      ]
+     },
+     "execution_count": 30,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "v - v"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "ename": "ValueError",
+     "evalue": "vectors need to be of the same length",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-31-490ee3f2b9e8>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mv\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mw\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;32m<ipython-input-21-12852bd26164>\u001b[0m in \u001b[0;36m__add__\u001b[0;34m(self, other)\u001b[0m\n\u001b[1;32m     18\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mother\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__class__\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m  \u001b[0;31m# vector addition\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     19\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mother\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 20\u001b[0;31m                 \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"vectors need to be of the same length\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     21\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mVector\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0my\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mzip\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mother\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     22\u001b[0m         \u001b[0;32melif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mother\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnumbers\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mNumber\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m  \u001b[0;31m# broadcasting addition\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;31mValueError\u001b[0m: vectors need to be of the same length"
+     ]
+    }
+   ],
+   "source": [
+    "v + w"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Vector((101.000, 102.000, 103.000))"
+      ]
+     },
+     "execution_count": 32,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "v + 100"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Vector((99.000, 98.000, 97.000))"
+      ]
+     },
+     "execution_count": 33,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "100 - v"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "For `Matrix` instances, the implementation is a bit more involved as we need to distinguish between matrix-matrix, matrix-vector, vector-matrix, and scalar multiplication and check for compatible dimensions. To review the underlying rules, check this [article <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_wiki.png\">](https://en.wikipedia.org/wiki/Matrix_multiplication) or watch the video below."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/jpeg": 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\n",
+      "text/html": [
+       "\n",
+       "        <iframe\n",
+       "            width=\"60%\"\n",
+       "            height=\"300\"\n",
+       "            src=\"https://www.youtube.com/embed/OMA2Mwo0aZg\"\n",
+       "            frameborder=\"0\"\n",
+       "            allowfullscreen\n",
+       "        ></iframe>\n",
+       "        "
+      ],
+      "text/plain": [
+       "<IPython.lib.display.YouTubeVideo at 0x7fa62c6bfa90>"
+      ]
+     },
+     "execution_count": 34,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from IPython.display import YouTubeVideo\n",
+    "YouTubeVideo(\"OMA2Mwo0aZg\", width=\"60%\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "To summarize the video, the multiplication of two matrices $\\bf{A}$ and $\\bf{B}$ with dimensions $m$ by $n$ and $n$ by $p$ yields a matrix $\\bf{C}$ with dimensions $m$ and $p$. To obtain an entry $c_{ij}$ of matrix $\\bf{C}$ where $i$ and $j$ represent the index labels of the rows and columns, we have to calculate the dot product of the $i$th row vector of $\\bf{A}$ with the $j$th column vector of $\\bf{B}$. So, it makes a difference if we multiply $\\bf{A}$ with $\\bf{B}$ from the right or left.\n",
+    "\n",
+    "When multiplying a `Matrix` with a `Vector`, we follow the convention that a `Vector` on the left is interpreted as a row vector and a `Vector` on the right as a column vector. The `Vector`'s length must match the `Matrix`'s corresponding dimension. As row and column vectors can be viewed as a `Matrix` as well, matrix-vector multiplication is a special case of matrix-matrix multiplication."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "In the revised `Matrix` class, the  `.__add__()`, `.__radd__()`, `.__sub__()`, `.__rsub__()`, and `.__truediv__()` methods follow the same logic as in the `Vector` class above.\n",
+    "\n",
+    "Besides implementing scalar multiplication, `.__mul__()` and `.__rmul__()` are responsible for converting `Vector`s into `Matrix`s and back. In particular, all the different ways of performing a multiplication are reduced into *one* generic form, which is a matrix-matrix multiplication. That is achieved by the `._matrix_multiply()` method, another implementation detail."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "class Matrix:\n",
+    "\n",
+    "    def __init__(self, data):\n",
+    "        self._entries = list(list(float(x) for x in r) for r in data)\n",
+    "        # ...\n",
+    "\n",
+    "    def __repr__(self):\n",
+    "        args = \", \".join(\"(\" + \", \".join(f\"{c:.3f}\" for c in r) + \",)\" for r in self._entries)\n",
+    "        return f\"Matrix(({args}))\"\n",
+    "\n",
+    "    @property\n",
+    "    def n_rows(self):\n",
+    "        return len(self._entries)\n",
+    "\n",
+    "    @property\n",
+    "    def n_cols(self):\n",
+    "        return len(self._entries[0])\n",
+    "\n",
+    "    def rows(self):\n",
+    "        return (Vector(r) for r in self._entries)\n",
+    "\n",
+    "    def cols(self):\n",
+    "        return (\n",
+    "            Vector(self._entries[r][c] for r in range(self.n_rows)) for c in range(self.n_cols)\n",
+    "        )\n",
+    "\n",
+    "    def __iter__(self):  # adapted for brevity; uses parts of entries()\n",
+    "        return (self._entries[r][c] for r in range(self.n_rows) for c in range(self.n_cols))\n",
+    "\n",
+    "    def __add__(self, other):\n",
+    "        if isinstance(other, self.__class__):  # matrix addition\n",
+    "            if (self.n_rows != other.n_rows) or (self.n_cols != other.n_cols):\n",
+    "                raise ValueError(\"matrices need to be of the same dimensions\")\n",
+    "            return Matrix((s_col + o_col for (s_col, o_col) in zip(s_row, o_row))\n",
+    "                          for (s_row, o_row) in zip(self._entries, other._entries))\n",
+    "        elif isinstance(other, numbers.Number):  # broadcasting addition\n",
+    "            return Matrix((c + other for c in r) for r in self._entries)\n",
+    "        return NotImplemented\n",
+    "\n",
+    "    def __radd__(self, other):\n",
+    "        if isinstance(other, Vector):\n",
+    "            raise TypeError(\"vectors and matrices cannot be added\")\n",
+    "        return self + other\n",
+    "\n",
+    "    def __sub__(self, other):\n",
+    "        if isinstance(other, self.__class__):  # matrix subtraction\n",
+    "            if (self.n_rows != other.n_rows) or (self.n_cols != other.n_cols):\n",
+    "                raise ValueError(\"matrices need to be of the same dimensions\")\n",
+    "            return Matrix((s_col - o_col for (s_col, o_col) in zip(s_row, o_row))\n",
+    "                          for (s_row, o_row) in zip(self._entries, other._entries))\n",
+    "        elif isinstance(other, numbers.Number):  # broadcasting subtraction\n",
+    "            return Matrix((c - other for c in r) for r in self._entries)\n",
+    "        return NotImplemented\n",
+    "\n",
+    "    def __rsub__(self, other):\n",
+    "        if isinstance(other, Vector):\n",
+    "            raise TypeError(\"vectors and matrices cannot be subtracted\")\n",
+    "        # Reverse matrix subtraction is already handled in __sub__().\n",
+    "        if isinstance(other, numbers.Number):  # broadcasting subtraction\n",
+    "            return Matrix((other - c for c in r) for r in self._entries)\n",
+    "        return NotImplemented\n",
+    "    \n",
+    "    def _matrix_multiply(self, other):\n",
+    "        if self.n_cols != other.n_rows:\n",
+    "            raise ValueError(\"matrices need to have compatible dimensions\")\n",
+    "        return Matrix((rv * cv for cv in other.cols()) for rv in self.rows())\n",
+    "\n",
+    "    def __mul__(self, other):\n",
+    "        if isinstance(other, numbers.Number):\n",
+    "            return Matrix((x * other for x in r) for r in self._entries)\n",
+    "        elif isinstance(other, Vector):\n",
+    "            return self._matrix_multiply(other.as_matrix()).as_vector()\n",
+    "        elif isinstance(other, self.__class__):\n",
+    "            return self._matrix_multiply(other)\n",
+    "        return NotImplemented\n",
+    "\n",
+    "    def __rmul__(self, other):\n",
+    "        if isinstance(other, numbers.Number):\n",
+    "            return self * other\n",
+    "        elif isinstance(other, Vector):\n",
+    "            return other.as_matrix(column=False)._matrix_multiply(self).as_vector()\n",
+    "        return NotImplemented\n",
+    "\n",
+    "    def __truediv__(self, other):\n",
+    "        if isinstance(other, numbers.Number):\n",
+    "            return self * (1 / other)\n",
+    "        return NotImplemented\n",
+    "\n",
+    "    def as_vector(self):\n",
+    "        if not (self.n_rows == 1 or self.n_cols == 1):\n",
+    "            raise RuntimeError(\"one dimension (m or n) must be 1\")\n",
+    "        return Vector(x for x in self)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "m = Matrix([(1, 2, 3), (4, 5, 6), (7, 8, 9)])\n",
+    "n = Matrix([(10, 11, 12), (13, 14, 15)])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Scalar multiplication, addition, and subtraction work as before."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Matrix(((10.000, 20.000, 30.000,), (40.000, 50.000, 60.000,), (70.000, 80.000, 90.000,)))"
+      ]
+     },
+     "execution_count": 37,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "10 * m"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Matrix(((1.000, 2.000, 3.000,), (4.000, 5.000, 6.000,), (7.000, 8.000, 9.000,)))"
+      ]
+     },
+     "execution_count": 38,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "(2 * m + m * 3) / 5"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Matrix(((0.000, 0.000, 0.000,), (0.000, 0.000, 0.000,), (0.000, 0.000, 0.000,)))"
+      ]
+     },
+     "execution_count": 39,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "m - m"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Matrix-matrix multiplication works if the dimensions are compatible ..."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Matrix(((138.000, 171.000, 204.000,), (174.000, 216.000, 258.000,)))"
+      ]
+     },
+     "execution_count": 40,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "n * m"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "... and results in a `ValueError` otherwise."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "ename": "ValueError",
+     "evalue": "matrices need to have compatible dimensions",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-41-b57a49d097c9>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mm\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0mn\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;32m<ipython-input-35-f4e0bb37caf6>\u001b[0m in \u001b[0;36m__mul__\u001b[0;34m(self, other)\u001b[0m\n\u001b[1;32m     72\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_matrix_multiply\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mother\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mas_matrix\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mas_vector\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     73\u001b[0m         \u001b[0;32melif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mother\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__class__\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 74\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_matrix_multiply\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mother\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     75\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mNotImplemented\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     76\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m<ipython-input-35-f4e0bb37caf6>\u001b[0m in \u001b[0;36m_matrix_multiply\u001b[0;34m(self, other)\u001b[0m\n\u001b[1;32m     63\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_matrix_multiply\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mother\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     64\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mn_cols\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0mother\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mn_rows\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 65\u001b[0;31m             \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"matrices need to have compatible dimensions\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     66\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mMatrix\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrv\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0mcv\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mcv\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mother\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcols\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mrv\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrows\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     67\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;31mValueError\u001b[0m: matrices need to have compatible dimensions"
+     ]
+    }
+   ],
+   "source": [
+    "m * n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "The same holds for matrix-vector and vector-matrix multiplication. These operations always return `Vector` instances in line with standard linear algebra. If a `Vector`'s length is not compatible with the respective dimension of a `Matrix`, we receive a `ValueError`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Vector((14.000, 32.000, 50.000))"
+      ]
+     },
+     "execution_count": 42,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "m * v"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Vector((30.000, 36.000, 42.000))"
+      ]
+     },
+     "execution_count": 43,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "v * m"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Vector((68.000, 86.000))"
+      ]
+     },
+     "execution_count": 44,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "n * v"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "ename": "ValueError",
+     "evalue": "matrices need to have compatible dimensions",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-45-9faf2ee0ae54>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mv\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0mn\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;32m<ipython-input-35-f4e0bb37caf6>\u001b[0m in \u001b[0;36m__rmul__\u001b[0;34m(self, other)\u001b[0m\n\u001b[1;32m     79\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0mother\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     80\u001b[0m         \u001b[0;32melif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mother\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mVector\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 81\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mother\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mas_matrix\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcolumn\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_matrix_multiply\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mas_vector\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     82\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mNotImplemented\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     83\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m<ipython-input-35-f4e0bb37caf6>\u001b[0m in \u001b[0;36m_matrix_multiply\u001b[0;34m(self, other)\u001b[0m\n\u001b[1;32m     63\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_matrix_multiply\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mother\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     64\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mn_cols\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0mother\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mn_rows\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 65\u001b[0;31m             \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"matrices need to have compatible dimensions\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     66\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mMatrix\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrv\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0mcv\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mcv\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mother\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcols\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mrv\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrows\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     67\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;31mValueError\u001b[0m: matrices need to have compatible dimensions"
+     ]
+    }
+   ],
+   "source": [
+    "v * n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Lastly, the broadcasting addition and subtraction also work for our `Matrix` instances."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Matrix(((101.000, 102.000, 103.000,), (104.000, 105.000, 106.000,), (107.000, 108.000, 109.000,)))"
+      ]
+     },
+     "execution_count": 46,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "m + 100"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Matrix(((99.000, 98.000, 97.000,), (96.000, 95.000, 94.000,), (93.000, 92.000, 91.000,)))"
+      ]
+     },
+     "execution_count": 47,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "100 - m"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "We do not allow addition or subtraction of matrices with vectors and raise a `TypeError` instead. Alternatively, we could have implemented broadcasting here as well, just like [numpy](https://www.numpy.org/) does."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 48,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "ename": "TypeError",
+     "evalue": "vectors and matrices cannot be added",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-48-356d02c93f11>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mm\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mv\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;32m<ipython-input-21-12852bd26164>\u001b[0m in \u001b[0;36m__radd__\u001b[0;34m(self, other)\u001b[0m\n\u001b[1;32m     25\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     26\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__radd__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mother\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 27\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mother\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     28\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     29\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__sub__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mother\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m<ipython-input-35-f4e0bb37caf6>\u001b[0m in \u001b[0;36m__radd__\u001b[0;34m(self, other)\u001b[0m\n\u001b[1;32m     40\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__radd__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mother\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     41\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mother\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mVector\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 42\u001b[0;31m             \u001b[0;32mraise\u001b[0m \u001b[0mTypeError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"vectors and matrices cannot be added\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     43\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mother\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     44\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;31mTypeError\u001b[0m: vectors and matrices cannot be added"
+     ]
+    }
+   ],
+   "source": [
+    "m + v"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "### Relational Operators"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "As we have seen before, two different `Vector`s with the same `._entries` do *not* compare equal. The reason is that for user-defined types Python by default only assumes two instances to be equal if they are actually the same object. This is, of course, semantically wrong for our `Vector`s and `Matrix`s."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 49,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "v = Vector([1, 2, 3])\n",
+    "w = Vector([1, 2, 3])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 50,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "False"
+      ]
+     },
+     "execution_count": 50,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "v == w"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 51,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 51,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "v == v"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "We implement the `.__eq__()` (cf., [reference <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/reference/datamodel.html#object.__eq__)) method to control how the comparison operator `==` is carried out. For brevity, we show this only for the `Vector` class. The `.__eq__()` method exits early as soon as the first pair of entries does not match. Also, for reasons discussed in [Chapter 5 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/05_numbers/01_content.ipynb#Imprecision), we compare the absolute difference of two corresponding entries to a very small `zero_threshold` that is stored as a class attribute shared among all `Vector` instances. If the `Vector`s differ in their numbers of entries, we fail loudly and raise a `ValueError`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 52,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "class Vector:\n",
+    "\n",
+    "    zero_threshold = 1e-12\n",
+    "\n",
+    "    def __init__(self, data):\n",
+    "        self._entries = list(float(x) for x in data)\n",
+    "        # ...\n",
+    "\n",
+    "    def __repr__(self):\n",
+    "        args = \", \".join(f\"{x:.3f}\" for x in self)\n",
+    "        return f\"Vector(({args}))\"\n",
+    "\n",
+    "    def __len__(self):\n",
+    "        return len(self._entries)\n",
+    "\n",
+    "    def __iter__(self):\n",
+    "        return iter(self._entries)\n",
+    "\n",
+    "    def __eq__(self, other):\n",
+    "        if isinstance(other, self.__class__):\n",
+    "            if len(self) != len(other):\n",
+    "                raise ValueError(\"vectors need to be of the same length\")\n",
+    "            for x, y in zip(self, other):\n",
+    "                if abs(x - y) > self.zero_threshold:\n",
+    "                    return False  # exit early if two corresponding entries differ\n",
+    "            return True\n",
+    "        return NotImplemented"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 53,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "v = Vector([1, 2, 3])\n",
+    "w = Vector([1, 2, 3])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 54,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 54,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "v == w"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Besides `.__eq__()`, there are other special methods to implement the `!=`, `<`, `>`, `<=`, and `>=` operators, the last four of which are not semantically meaningful in the context of linear algebra.\n",
+    "\n",
+    "Python implicitly creates the `!=` for us in that it just inverts the result of `.__eq__()`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 55,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "v = Vector([1, 2, 3])\n",
+    "w = Vector([1, 2, 4])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 56,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "False"
+      ]
+     },
+     "execution_count": 56,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "v == w"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 57,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 57,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "v != w"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "## Number Emulation"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Our `Vector` and `Matrix` classes do not fully behave like a `numbers.Number` in the abstract sense. Besides the not yet talked about but useful unary `+` and `-` operators, numbers in Python usually support being passed to built-in functions like [abs() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#abs), [bin() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#bin), [bool() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#bool), [complex() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#complex), [float() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#float), [hex() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#hex), [int() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#int), and others (cf., the [reference <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/reference/datamodel.html#emulating-numeric-types) for an exhaustive list).\n",
+    "\n",
+    "To see that our classes lack simple but expected behaviors, let's try to invert the signs of all entries in the vector `v` ..."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 58,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Vector((1.000, 2.000, 3.000))"
+      ]
+     },
+     "execution_count": 58,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "v"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 59,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "ename": "TypeError",
+     "evalue": "bad operand type for unary -: 'Vector'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-59-c01df863c5f8>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0;34m-\u001b[0m\u001b[0mv\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;31mTypeError\u001b[0m: bad operand type for unary -: 'Vector'"
+     ]
+    }
+   ],
+   "source": [
+    "-v"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "... or pass `v` to [abs() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#abs)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 60,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "TypeError",
+     "evalue": "bad operand type for abs(): 'Vector'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-60-09d4e6f30397>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mabs\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mv\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;31mTypeError\u001b[0m: bad operand type for abs(): 'Vector'"
+     ]
+    }
+   ],
+   "source": [
+    "abs(v)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "For our example, we decide to implement the unary `+` and `-` operators, [abs() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#abs) as the Euclidean / Frobenius norm (i.e., the [magnitude <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_wiki.png\">](https://en.wikipedia.org/wiki/Magnitude_%28mathematics%29#Euclidean_vector_space)), [bool() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#bool) to check if that norm is greater than `0` or not, and [float() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#float) to obtain a single scalar if the vector or matrix consists of only one entry. To achieve that, we implement the `.__pos__()`, `.__neg__()`, `.__abs__()`, `.__bool__()`, and `.__float__()` methods. For brevity, we do this only for the `Vector` class."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 61,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import math"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 62,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def norm(vector_or_matrix):\n",
+    "    \"\"\"Calculate the Frobenius or Euclidean norm of a matrix or vector.\n",
+    "    \n",
+    "    ...\n",
+    "    \"\"\"\n",
+    "    return math.sqrt(sum(x ** 2 for x in vector_or_matrix))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 63,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "class Vector:\n",
+    "\n",
+    "    def __init__(self, data):\n",
+    "        self._entries = list(float(x) for x in data)\n",
+    "        # ...\n",
+    "\n",
+    "    def __repr__(self):\n",
+    "        args = \", \".join(f\"{x:.3f}\" for x in self)\n",
+    "        return f\"Vector(({args}))\"\n",
+    "\n",
+    "    def __iter__(self):\n",
+    "        return iter(self._entries)\n",
+    "\n",
+    "    def __len__(self):\n",
+    "        return len(self._entries)\n",
+    "\n",
+    "    def __pos__(self):\n",
+    "        return self\n",
+    "\n",
+    "    def __neg__(self):\n",
+    "        return Vector(-x for x in self)\n",
+    "\n",
+    "    def __abs__(self):\n",
+    "        return norm(self)\n",
+    "\n",
+    "    def __bool__(self):\n",
+    "        return bool(abs(self))\n",
+    "\n",
+    "    def __float__(self):\n",
+    "        if len(self) != 1:\n",
+    "            raise RuntimeError(\"vector must have one entry to become a scalar\")\n",
+    "        return self._entries[0]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 64,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "v = Vector([1, 2, 3])\n",
+    "w = Vector([3, 4])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "The unary `+` operator is not only conceptually an identity operator but simply returns the instance itself."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 65,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Vector((1.000, 2.000, 3.000))"
+      ]
+     },
+     "execution_count": 65,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "+v"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 66,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 66,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "+v is v"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 67,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Vector((-3.000, -4.000))"
+      ]
+     },
+     "execution_count": 67,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "-w"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "The magnitude of a `Vector` is only `0` if *all* entries are `0`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 68,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "5.0"
+      ]
+     },
+     "execution_count": 68,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "abs(w)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 69,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "z = Vector([0, 0])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 70,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.0"
+      ]
+     },
+     "execution_count": 70,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "abs(z)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Only an all `0`s `Vector` is `False` in a boolean context. As mentioned in [Chapter 3 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/03_conditionals/00_content.ipynb#Truthy-vs.-Falsy), commonly we view an *empty* sequence as falsy; however, as we do not allow `Vector`s without any entries, we choose the all `0`s alternative. In that regard, the `Vector` class does not behave like the built-in sequence types."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 71,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "True"
+      ]
+     },
+     "execution_count": 71,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "bool(v)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 72,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "False"
+      ]
+     },
+     "execution_count": 72,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "bool(z)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Lastly, single entry `Vector`s can be casted as scalars."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 73,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "s = Vector([42])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 74,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "42.0"
+      ]
+     },
+     "execution_count": 74,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "float(s)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 75,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "ename": "RuntimeError",
+     "evalue": "vector must have one entry to become a scalar",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mRuntimeError\u001b[0m                              Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-75-8369cec552f3>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mfloat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mv\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;32m<ipython-input-63-01438fec4aa0>\u001b[0m in \u001b[0;36m__float__\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m     29\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__float__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     30\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 31\u001b[0;31m             \u001b[0;32mraise\u001b[0m \u001b[0mRuntimeError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"vector must have one entry to become a scalar\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     32\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_entries\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;31mRuntimeError\u001b[0m: vector must have one entry to become a scalar"
+     ]
+    }
+   ],
+   "source": [
+    "float(v)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.6"
+  },
+  "livereveal": {
+   "auto_select": "code",
+   "auto_select_fragment": true,
+   "scroll": true,
+   "theme": "serif"
+  },
+  "toc": {
+   "base_numbering": 1,
+   "nav_menu": {},
+   "number_sections": false,
+   "sideBar": true,
+   "skip_h1_title": true,
+   "title_cell": "Table of Contents",
+   "title_sidebar": "Contents",
+   "toc_cell": false,
+   "toc_position": {
+    "height": "calc(100% - 180px)",
+    "left": "10px",
+    "top": "150px",
+    "width": "384px"
+   },
+   "toc_section_display": false,
+   "toc_window_display": false
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/CONTENTS.md b/CONTENTS.md
index ca1718b..fed0b64 100644
--- a/CONTENTS.md
+++ b/CONTENTS.md
@@ -280,3 +280,7 @@ If this is not possible,
        (Im)mutable Data Types;
        Method Chaining;
        Polymorphism)
+    - [content <img height="12" style="display: inline-block" src="static/link/to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/11_classes/03_content.ipynb)
+      [<img height="12" style="display: inline-block" src="static/link/to_mb.png">](https://mybinder.org/v2/gh/webartifex/intro-to-python/develop?urlpath=lab/tree/11_classes/03_content.ipynb)
+      (Operator Overloading: Arithmetic & Relational Operators;
+       Number Emulation)

From 6bcfff481cc90c13938a68f55a516eeda64238b1 Mon Sep 17 00:00:00 2001
From: Alexander Hess <alexander@webartifex.biz>
Date: Tue, 27 Oct 2020 16:41:22 +0100
Subject: [PATCH 214/242] Add sample_package with linear algebra tools

- add sample_package:
  + __init__.py => high-level description and imports
  + matrix.py => define a Matrix class
  + vector.py => define a Vector class
  + utils.py => package-wide utilities
- streamline the code snippets in the chapter 11 notebooks
  to align with the sample_package
---
 11_classes/00_content.ipynb           |  10 +-
 11_classes/02_content.ipynb           |   7 +-
 11_classes/03_content.ipynb           |  59 ++--
 11_classes/sample_package/__init__.py |  30 ++
 11_classes/sample_package/matrix.py   | 416 ++++++++++++++++++++++++++
 11_classes/sample_package/utils.py    |  35 +++
 11_classes/sample_package/vector.py   | 253 ++++++++++++++++
 7 files changed, 770 insertions(+), 40 deletions(-)
 create mode 100644 11_classes/sample_package/__init__.py
 create mode 100644 11_classes/sample_package/matrix.py
 create mode 100644 11_classes/sample_package/utils.py
 create mode 100644 11_classes/sample_package/vector.py

diff --git a/11_classes/00_content.ipynb b/11_classes/00_content.ipynb
index 692af9c..6852453 100644
--- a/11_classes/00_content.ipynb
+++ b/11_classes/00_content.ipynb
@@ -272,7 +272,7 @@
    "outputs": [],
    "source": [
     "class Vector:\n",
-    "    \"\"\"A standard one-dimensional vector from linear algebra.\"\"\"\n",
+    "    \"\"\"A one-dimensional vector from linear algebra.\"\"\"\n",
     "\n",
     "    dummy_variable = \"I am a vector\"\n",
     "\n",
@@ -403,7 +403,7 @@
      "data": {
       "text/plain": [
        "\u001b[0;31mInit signature:\u001b[0m \u001b[0mVector\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-       "\u001b[0;31mDocstring:\u001b[0m      A standard one-dimensional vector from linear algebra.\n",
+       "\u001b[0;31mDocstring:\u001b[0m      A one-dimensional vector from linear algebra.\n",
        "\u001b[0;31mType:\u001b[0m           type\n",
        "\u001b[0;31mSubclasses:\u001b[0m     \n"
       ]
@@ -432,7 +432,7 @@
       "Help on class Vector in module __main__:\n",
       "\n",
       "class Vector(builtins.object)\n",
-      " |  A standard one-dimensional vector from linear algebra.\n",
+      " |  A one-dimensional vector from linear algebra.\n",
       " |  \n",
       " |  Methods defined here:\n",
       " |  \n",
@@ -484,7 +484,7 @@
      "data": {
       "text/plain": [
        "mappingproxy({'__module__': '__main__',\n",
-       "              '__doc__': 'A standard one-dimensional vector from linear algebra.',\n",
+       "              '__doc__': 'A one-dimensional vector from linear algebra.',\n",
        "              'dummy_variable': 'I am a vector',\n",
        "              'dummy_method': <function __main__.Vector.dummy_method(self)>,\n",
        "              '__dict__': <attribute '__dict__' of 'Vector' objects>,\n",
@@ -687,7 +687,7 @@
    "outputs": [],
    "source": [
     "class Vector:\n",
-    "    \"\"\"A standard one-dimensional vector from linear algebra.\n",
+    "    \"\"\"A one-dimensional vector from linear algebra.\n",
     "\n",
     "    All entries are converted to floats.\n",
     "    \"\"\"\n",
diff --git a/11_classes/02_content.ipynb b/11_classes/02_content.ipynb
index 1d99ecf..520339f 100644
--- a/11_classes/02_content.ipynb
+++ b/11_classes/02_content.ipynb
@@ -1815,17 +1815,16 @@
    },
    "outputs": [],
    "source": [
-    "def norm(vector_or_matrix):\n",
+    "def norm(vec_or_mat):\n",
     "    \"\"\"Calculate the Frobenius or Euclidean norm of a matrix or vector.\n",
     "\n",
     "    Args:\n",
-    "        vector_or_matrix (Vector/Matrix): the entries whose squares\n",
-    "            are summed up\n",
+    "        vec_or_mat (Vector / Matrix): object whose entries are squared and summed up\n",
     "\n",
     "    Returns:\n",
     "        norm (float)\n",
     "    \"\"\"\n",
-    "    return math.sqrt(sum(x ** 2 for x in vector_or_matrix))"
+    "    return math.sqrt(sum(x ** 2 for x in vec_or_mat))"
    ]
   },
   {
diff --git a/11_classes/03_content.ipynb b/11_classes/03_content.ipynb
index 7b3a9ae..9dc08a2 100644
--- a/11_classes/03_content.ipynb
+++ b/11_classes/03_content.ipynb
@@ -694,7 +694,7 @@
     "    def __add__(self, other):\n",
     "        if isinstance(other, self.__class__):  # vector addition\n",
     "            if len(self) != len(other):\n",
-    "                raise ValueError(\"vectors need to be of the same length\")\n",
+    "                raise ValueError(\"vectors must be of the same length\")\n",
     "            return Vector(x + y for (x, y) in zip(self, other))\n",
     "        elif isinstance(other, numbers.Number):  # broadcasting addition\n",
     "            return Vector(x + other for x in self)\n",
@@ -706,7 +706,7 @@
     "    def __sub__(self, other):\n",
     "        if isinstance(other, self.__class__):  # vector subtraction\n",
     "            if len(self) != len(other):\n",
-    "                raise ValueError(\"vectors need to be of the same length\")\n",
+    "                raise ValueError(\"vectors must be of the same length\")\n",
     "            return Vector(x - y for (x, y) in zip(self, other))\n",
     "        elif isinstance(other, numbers.Number):  # broadcasting subtraction\n",
     "            return Vector(x - other for x in self)\n",
@@ -721,7 +721,7 @@
     "    def __mul__(self, other):\n",
     "        if isinstance(other, self.__class__):  # dot product\n",
     "            if len(self) != len(other):\n",
-    "                raise ValueError(\"vectors need to be of the same length\")\n",
+    "                raise ValueError(\"vectors must be of the same length\")\n",
     "            return sum(x * y for (x, y) in zip(self, other))\n",
     "        elif isinstance(other, numbers.Number):  # scalar multiplication\n",
     "            return Vector(x * other for x in self)\n",
@@ -872,14 +872,14 @@
    "outputs": [
     {
      "ename": "ValueError",
-     "evalue": "vectors need to be of the same length",
+     "evalue": "vectors must be of the same length",
      "output_type": "error",
      "traceback": [
       "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
       "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
       "\u001b[0;32m<ipython-input-27-21fab2d5f12e>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mv\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0mw\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
-      "\u001b[0;32m<ipython-input-21-12852bd26164>\u001b[0m in \u001b[0;36m__mul__\u001b[0;34m(self, other)\u001b[0m\n\u001b[1;32m     45\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mother\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__class__\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m  \u001b[0;31m# dot product\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     46\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mother\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 47\u001b[0;31m                 \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"vectors need to be of the same length\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     48\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0msum\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0my\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mzip\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mother\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     49\u001b[0m         \u001b[0;32melif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mother\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnumbers\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mNumber\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m  \u001b[0;31m# scalar multiplication\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;31mValueError\u001b[0m: vectors need to be of the same length"
+      "\u001b[0;32m<ipython-input-21-e7b5dd8e219a>\u001b[0m in \u001b[0;36m__mul__\u001b[0;34m(self, other)\u001b[0m\n\u001b[1;32m     45\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mother\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__class__\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m  \u001b[0;31m# dot product\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     46\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mother\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 47\u001b[0;31m                 \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"vectors must be of the same length\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     48\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0msum\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0my\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mzip\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mother\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     49\u001b[0m         \u001b[0;32melif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mother\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnumbers\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mNumber\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m  \u001b[0;31m# scalar multiplication\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;31mValueError\u001b[0m: vectors must be of the same length"
      ]
     }
    ],
@@ -992,14 +992,14 @@
    "outputs": [
     {
      "ename": "ValueError",
-     "evalue": "vectors need to be of the same length",
+     "evalue": "vectors must be of the same length",
      "output_type": "error",
      "traceback": [
       "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
       "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
       "\u001b[0;32m<ipython-input-31-490ee3f2b9e8>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mv\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mw\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
-      "\u001b[0;32m<ipython-input-21-12852bd26164>\u001b[0m in \u001b[0;36m__add__\u001b[0;34m(self, other)\u001b[0m\n\u001b[1;32m     18\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mother\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__class__\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m  \u001b[0;31m# vector addition\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     19\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mother\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 20\u001b[0;31m                 \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"vectors need to be of the same length\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     21\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mVector\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0my\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mzip\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mother\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     22\u001b[0m         \u001b[0;32melif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mother\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnumbers\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mNumber\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m  \u001b[0;31m# broadcasting addition\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;31mValueError\u001b[0m: vectors need to be of the same length"
+      "\u001b[0;32m<ipython-input-21-e7b5dd8e219a>\u001b[0m in \u001b[0;36m__add__\u001b[0;34m(self, other)\u001b[0m\n\u001b[1;32m     18\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mother\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__class__\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m  \u001b[0;31m# vector addition\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     19\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mother\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 20\u001b[0;31m                 \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"vectors must be of the same length\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     21\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mVector\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0my\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mzip\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mother\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     22\u001b[0m         \u001b[0;32melif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mother\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnumbers\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mNumber\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m  \u001b[0;31m# broadcasting addition\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;31mValueError\u001b[0m: vectors must be of the same length"
      ]
     }
    ],
@@ -1171,7 +1171,7 @@
     "    def __add__(self, other):\n",
     "        if isinstance(other, self.__class__):  # matrix addition\n",
     "            if (self.n_rows != other.n_rows) or (self.n_cols != other.n_cols):\n",
-    "                raise ValueError(\"matrices need to be of the same dimensions\")\n",
+    "                raise ValueError(\"matrices must have the same dimensions\")\n",
     "            return Matrix((s_col + o_col for (s_col, o_col) in zip(s_row, o_row))\n",
     "                          for (s_row, o_row) in zip(self._entries, other._entries))\n",
     "        elif isinstance(other, numbers.Number):  # broadcasting addition\n",
@@ -1186,7 +1186,7 @@
     "    def __sub__(self, other):\n",
     "        if isinstance(other, self.__class__):  # matrix subtraction\n",
     "            if (self.n_rows != other.n_rows) or (self.n_cols != other.n_cols):\n",
-    "                raise ValueError(\"matrices need to be of the same dimensions\")\n",
+    "                raise ValueError(\"matrices must have the same dimensions\")\n",
     "            return Matrix((s_col - o_col for (s_col, o_col) in zip(s_row, o_row))\n",
     "                          for (s_row, o_row) in zip(self._entries, other._entries))\n",
     "        elif isinstance(other, numbers.Number):  # broadcasting subtraction\n",
@@ -1203,7 +1203,7 @@
     "    \n",
     "    def _matrix_multiply(self, other):\n",
     "        if self.n_cols != other.n_rows:\n",
-    "            raise ValueError(\"matrices need to have compatible dimensions\")\n",
+    "            raise ValueError(\"matrices must have compatible dimensions\")\n",
     "        return Matrix((rv * cv for cv in other.cols()) for rv in self.rows())\n",
     "\n",
     "    def __mul__(self, other):\n",
@@ -1387,15 +1387,15 @@
    "outputs": [
     {
      "ename": "ValueError",
-     "evalue": "matrices need to have compatible dimensions",
+     "evalue": "matrices must have compatible dimensions",
      "output_type": "error",
      "traceback": [
       "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
       "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
       "\u001b[0;32m<ipython-input-41-b57a49d097c9>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mm\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0mn\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
-      "\u001b[0;32m<ipython-input-35-f4e0bb37caf6>\u001b[0m in \u001b[0;36m__mul__\u001b[0;34m(self, other)\u001b[0m\n\u001b[1;32m     72\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_matrix_multiply\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mother\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mas_matrix\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mas_vector\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     73\u001b[0m         \u001b[0;32melif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mother\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__class__\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 74\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_matrix_multiply\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mother\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     75\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mNotImplemented\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     76\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m<ipython-input-35-f4e0bb37caf6>\u001b[0m in \u001b[0;36m_matrix_multiply\u001b[0;34m(self, other)\u001b[0m\n\u001b[1;32m     63\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_matrix_multiply\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mother\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     64\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mn_cols\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0mother\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mn_rows\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 65\u001b[0;31m             \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"matrices need to have compatible dimensions\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     66\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mMatrix\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrv\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0mcv\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mcv\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mother\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcols\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mrv\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrows\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     67\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;31mValueError\u001b[0m: matrices need to have compatible dimensions"
+      "\u001b[0;32m<ipython-input-35-def82d7b791b>\u001b[0m in \u001b[0;36m__mul__\u001b[0;34m(self, other)\u001b[0m\n\u001b[1;32m     72\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_matrix_multiply\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mother\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mas_matrix\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mas_vector\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     73\u001b[0m         \u001b[0;32melif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mother\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__class__\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 74\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_matrix_multiply\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mother\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     75\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mNotImplemented\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     76\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m<ipython-input-35-def82d7b791b>\u001b[0m in \u001b[0;36m_matrix_multiply\u001b[0;34m(self, other)\u001b[0m\n\u001b[1;32m     63\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_matrix_multiply\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mother\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     64\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mn_cols\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0mother\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mn_rows\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 65\u001b[0;31m             \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"matrices must have compatible dimensions\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     66\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mMatrix\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrv\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0mcv\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mcv\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mother\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcols\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mrv\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrows\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     67\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;31mValueError\u001b[0m: matrices must have compatible dimensions"
      ]
     }
    ],
@@ -1497,15 +1497,15 @@
    "outputs": [
     {
      "ename": "ValueError",
-     "evalue": "matrices need to have compatible dimensions",
+     "evalue": "matrices must have compatible dimensions",
      "output_type": "error",
      "traceback": [
       "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
       "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
       "\u001b[0;32m<ipython-input-45-9faf2ee0ae54>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mv\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0mn\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
-      "\u001b[0;32m<ipython-input-35-f4e0bb37caf6>\u001b[0m in \u001b[0;36m__rmul__\u001b[0;34m(self, other)\u001b[0m\n\u001b[1;32m     79\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0mother\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     80\u001b[0m         \u001b[0;32melif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mother\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mVector\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 81\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mother\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mas_matrix\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcolumn\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_matrix_multiply\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mas_vector\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     82\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mNotImplemented\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     83\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m<ipython-input-35-f4e0bb37caf6>\u001b[0m in \u001b[0;36m_matrix_multiply\u001b[0;34m(self, other)\u001b[0m\n\u001b[1;32m     63\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_matrix_multiply\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mother\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     64\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mn_cols\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0mother\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mn_rows\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 65\u001b[0;31m             \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"matrices need to have compatible dimensions\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     66\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mMatrix\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrv\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0mcv\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mcv\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mother\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcols\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mrv\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrows\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     67\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;31mValueError\u001b[0m: matrices need to have compatible dimensions"
+      "\u001b[0;32m<ipython-input-35-def82d7b791b>\u001b[0m in \u001b[0;36m__rmul__\u001b[0;34m(self, other)\u001b[0m\n\u001b[1;32m     79\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0mother\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     80\u001b[0m         \u001b[0;32melif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mother\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mVector\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 81\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mother\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mas_matrix\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcolumn\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_matrix_multiply\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mas_vector\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     82\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mNotImplemented\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     83\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m<ipython-input-35-def82d7b791b>\u001b[0m in \u001b[0;36m_matrix_multiply\u001b[0;34m(self, other)\u001b[0m\n\u001b[1;32m     63\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_matrix_multiply\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mother\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     64\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mn_cols\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0mother\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mn_rows\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 65\u001b[0;31m             \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"matrices must have compatible dimensions\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     66\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mMatrix\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrv\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0mcv\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mcv\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mother\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcols\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mrv\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrows\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     67\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;31mValueError\u001b[0m: matrices must have compatible dimensions"
      ]
     }
    ],
@@ -1736,7 +1736,7 @@
     "    def __eq__(self, other):\n",
     "        if isinstance(other, self.__class__):\n",
     "            if len(self) != len(other):\n",
-    "                raise ValueError(\"vectors need to be of the same length\")\n",
+    "                raise ValueError(\"vectors must be of the same length\")\n",
     "            for x, y in zip(self, other):\n",
     "                if abs(x - y) > self.zero_threshold:\n",
     "                    return False  # exit early if two corresponding entries differ\n",
@@ -1972,12 +1972,9 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "def norm(vector_or_matrix):\n",
-    "    \"\"\"Calculate the Frobenius or Euclidean norm of a matrix or vector.\n",
-    "    \n",
-    "    ...\n",
-    "    \"\"\"\n",
-    "    return math.sqrt(sum(x ** 2 for x in vector_or_matrix))"
+    "def norm(vec_or_mat):\n",
+    "    \"\"\"Calculate the Frobenius or Euclidean norm of a matrix or vector.\"\"\"\n",
+    "    return math.sqrt(sum(x ** 2 for x in vec_or_mat))"
    ]
   },
   {
@@ -2020,7 +2017,7 @@
     "\n",
     "    def __float__(self):\n",
     "        if len(self) != 1:\n",
-    "            raise RuntimeError(\"vector must have one entry to become a scalar\")\n",
+    "            raise RuntimeError(\"vector must have exactly one entry to become a scalar\")\n",
     "        return self._entries[0]"
    ]
   },
@@ -2303,14 +2300,14 @@
    "outputs": [
     {
      "ename": "RuntimeError",
-     "evalue": "vector must have one entry to become a scalar",
+     "evalue": "vector must have exactly one entry to become a scalar",
      "output_type": "error",
      "traceback": [
       "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
       "\u001b[0;31mRuntimeError\u001b[0m                              Traceback (most recent call last)",
       "\u001b[0;32m<ipython-input-75-8369cec552f3>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mfloat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mv\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
-      "\u001b[0;32m<ipython-input-63-01438fec4aa0>\u001b[0m in \u001b[0;36m__float__\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m     29\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__float__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     30\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 31\u001b[0;31m             \u001b[0;32mraise\u001b[0m \u001b[0mRuntimeError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"vector must have one entry to become a scalar\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     32\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_entries\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;31mRuntimeError\u001b[0m: vector must have one entry to become a scalar"
+      "\u001b[0;32m<ipython-input-63-67b90fc89e7c>\u001b[0m in \u001b[0;36m__float__\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m     29\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__float__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     30\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 31\u001b[0;31m             \u001b[0;32mraise\u001b[0m \u001b[0mRuntimeError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"vector must have exactly one entry to become a scalar\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     32\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_entries\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;31mRuntimeError\u001b[0m: vector must have exactly one entry to become a scalar"
      ]
     }
    ],
diff --git a/11_classes/sample_package/__init__.py b/11_classes/sample_package/__init__.py
new file mode 100644
index 0000000..1e8d12c
--- /dev/null
+++ b/11_classes/sample_package/__init__.py
@@ -0,0 +1,30 @@
+"""This package provides linear algebra functionalities.
+
+The package is split into three modules:
+- matrix: defines the Matrix class
+- vector: defines the Vector class
+- utils: defines the norm() function that is shared by Matrix and Vector
+         and package-wide constants
+
+The classes implement arithmetic operations involving vectors and matrices.
+
+See the docstrings in the modules and classes for further info.
+"""
+
+# Import the classes here so that they are available
+# from the package's top level. That means that a user
+# who imports this package with `import sample_package`
+# may then refer to, for example, the Matrix class with
+# simply `sample_package.Matrix` instead of the longer
+# `sample_package.matrix.Matrix`.
+from sample_package.matrix import Matrix
+from sample_package.vector import Vector
+
+
+# Define meta information for the package.
+# There are other (and more modern) ways of
+# doing this, but specifying the following
+# dunder variables here is the traditional way.
+__name__ = "linear_algebra_tools"
+__version__ = "0.1.0"  # see https://semver.org/ for how the format works
+__author__ = "Alexander Hess"
diff --git a/11_classes/sample_package/matrix.py b/11_classes/sample_package/matrix.py
new file mode 100644
index 0000000..b64e50d
--- /dev/null
+++ b/11_classes/sample_package/matrix.py
@@ -0,0 +1,416 @@
+"""This module defines a Matrix class."""
+
+import numbers
+
+# Note the import at the bottom of this file, and
+# see the comments about imports in the matrix module.
+from sample_package import utils
+
+
+class Matrix:
+    """An m-by-n-dimensional matrix from linear algebra.
+
+    All entries are converted to floats, or whatever is set in the typing attribute.
+
+    Attributes:
+        storage (callable): data type used to store the entries internally;
+            defaults to tuple
+        typing (callable): type casting applied to all entries upon creation;
+            defaults to float
+        zero_threshold (float): max. tolerance when comparing an entry to zero;
+            defaults to 1e-12
+    """
+
+    storage = utils.DEFAULT_ENTRIES_STORAGE
+    typing = utils.DEFAULT_ENTRY_TYPE
+    zero_threshold = utils.ZERO_THRESHOLD
+
+    def __init__(self, data):
+        """Create a new matrix.
+
+        Args:
+            data (sequence of sequences): the matrix's entries;
+                viewed as a sequence of the matrix's rows (i.e., row-major order);
+                use the .from_columns() class method if the data come as a sequence
+                of the matrix's columns (i.e., column-major order)
+
+        Raises:
+            ValueError:
+                - if no entries are provided
+                - if the number of columns is inconsistent across the rows
+        """
+        self._entries = self.storage(
+            self.storage(self.typing(x) for x in r) for r in data
+        )
+        for row in self._entries[1:]:
+            if len(row) != self.n_cols:
+                raise ValueError("rows must have the same number of entries")
+        if len(self) == 0:
+            raise ValueError("a matrix must have at least one entry")
+
+    @classmethod
+    def from_columns(cls, data):
+        """Create a new matrix.
+
+        This is an alternative constructor for data provided in column-major order.
+
+        Args:
+            data (sequence of sequences): the matrix's entries;
+                viewed as a sequence of the matrix's columns (i.e., column-major order);
+                use the normal constructor method if the data come as a sequence
+                of the matrix's rows (i.e., row-major order)
+
+        Raises:
+            ValueError:
+                - if no entries are provided
+                - if the number of rows is inconsistent across the columns
+        """
+        return cls(data).transpose()
+
+    @classmethod
+    def from_rows(cls, data):
+        """See docstring for .__init__()."""
+        # Some users may want to use this .from_rows() constructor
+        # to explicitly communicate that the data are in row-major order.
+        # Otherwise, this method is redundant.
+        return cls(data)
+
+    def __repr__(self):
+        """Text representation of a Matrix."""
+        name = self.__class__.__name__
+        args = ", ".join(
+            "(" + ", ".join(f"{c:.3f}" for c in r) + ",)" for r in self._entries
+        )
+        return f"{name}(({args}))"
+
+    def __str__(self):
+        """Human-readable text representation of a Matrix."""
+        name = self.__class__.__name__
+        first, last, m, n = self[0], self[-1], self.n_rows, self.n_cols
+        return f"{name}(({first:.1f}, ...), ..., (..., {last:.1f}))[{m:d}x{n:d}]"
+
+    @property
+    def n_rows(self):
+        """Number of rows in a Matrix."""
+        return len(self._entries)
+
+    @property
+    def n_cols(self):
+        """Number of columns in a Matrix."""
+        return len(self._entries[0])
+
+    def __len__(self):
+        """Number of entries in a Matrix."""
+        return self.n_rows * self.n_cols
+
+    def __getitem__(self, index):
+        """Obtain an individual entry of a Matrix.
+
+        Args:
+            index (int / tuple of int's): if index is an integer,
+                the Matrix is viewed as a sequence in row-major order;
+                if index is a tuple of integers, the first one refers to
+                the row and the second one to the column of the entry
+
+        Returns:
+            entry (Matrix.typing)
+
+        Example Usage:
+            >>> m = Matrix([(1, 2), (3, 4)])
+            >>> m[0]
+            1.0
+            >>> m[-1]
+            4.0
+            >>> m[0, 1]
+            3.0
+        """
+        # Sequence-like indexing (one-dimensional)
+        if isinstance(index, int):
+            if index < 0:
+                index += len(self)
+            if not (0 <= index < len(self)):
+                raise IndexError("integer index out of range")
+            row, col = divmod(index, self.n_cols)
+            return self._entries[row][col]
+        # Mathematical-like indexing (two-dimensional)
+        elif (
+            isinstance(index, tuple)
+            and len(index) == 2
+            and isinstance(index[0], int)
+            and isinstance(index[1], int)
+        ):
+            return self._entries[index[0]][index[1]]
+        raise TypeError("index must be either an int or a tuple of two int's")
+
+    def rows(self):
+        """Loop over a Matrix's rows.
+
+        Returns:
+            rows (generator): produces a Matrix's rows as Vectors
+        """
+        return (Vector(r) for r in self._entries)
+
+    def cols(self):
+        """Loop over a Matrix's columns.
+
+        Returns:
+            columns (generator): produces a Matrix's columns as Vectors
+        """
+        return (
+            Vector(self._entries[r][c] for r in range(self.n_rows))
+            for c in range(self.n_cols)
+        )
+
+    def entries(self, *, reverse=False, row_major=True):
+        """Loop over a Matrix's entries.
+
+        Args:
+            reverse (bool): flag to loop backwards; defaults to False
+            row_major (bool): flag to loop in row-major order; defaults to True
+
+        Returns:
+            entries (generator): produces a Matrix's entries
+        """
+        if reverse:
+            rows = range(self.n_rows - 1, -1, -1)
+            cols = range(self.n_cols - 1, -1, -1)
+        else:
+            rows, cols = range(self.n_rows), range(self.n_cols)
+        if row_major:
+            return (self._entries[r][c] for r in rows for c in cols)
+        return (self._entries[r][c] for c in cols for r in rows)
+
+    def __iter__(self):
+        """Loop over a Matrix's entries.
+
+        See .entries() for more customization options.
+        """
+        return self.entries()
+
+    def __reversed__(self):
+        """Loop over a Matrix's entries in reverse order.
+
+        See .entries() for more customization options.
+        """
+        return self.entries(reverse=True)
+
+    def __add__(self, other):
+        """Handle `self + other` and `other + self`.
+
+        This may be either matrix addition or broadcasting addition.
+
+        Example Usage:
+            >>> Matrix([(1, 2), (3, 4)]) + Matrix([(2, 3), (4, 5)])
+            Matrix(((3.0, 5.0), (7.0, 9.0)))
+
+            >>> Matrix([(1, 2), (3, 4)]) + 5
+            Matrix(((6.0, 7.0), (8.0, 9.0)))
+
+            >>> 10 + Matrix([(1, 2), (3, 4)])
+            Matrix(((11.0, 12.0), (13.0, 14.0)))
+        """
+        # Matrix addition
+        if isinstance(other, self.__class__):
+            if (self.n_rows != other.n_rows) or (self.n_cols != other.n_cols):
+                raise ValueError("matrices must have the same dimensions")
+            return self.__class__(
+                (s_col + o_col for (s_col, o_col) in zip(s_row, o_row))
+                for (s_row, o_row) in zip(self._entries, other._entries)
+            )
+        # Broadcasting addition
+        elif isinstance(other, numbers.Number):
+            return self.__class__((c + other for c in r) for r in self._entries)
+        return NotImplemented
+
+    def __radd__(self, other):
+        """See docstring for .__add__()."""
+        if isinstance(other, Vector):
+            raise TypeError("vectors and matrices cannot be added")
+        # As both matrix and broadcasting addition are commutative,
+        # we dispatch to .__add__().
+        return self + other
+
+    def __sub__(self, other):
+        """Handle `self - other` and `other - self`.
+
+        This may be either matrix subtraction or broadcasting subtraction.
+
+        Example Usage:
+            >>> Matrix([(2, 3), (4, 5)]) - Matrix([(1, 2), (3, 4)])
+            Matrix(((1.0, 1.0), (1.0, 1.0)))
+
+            >>> Matrix([(1, 2), (3, 4)]) - 1
+            Matrix(((0.0, 1.0), (2.0, 3.0)))
+
+            >>> 10 - Matrix([(1, 2), (3, 4)])
+            Matrix(((9.0, 8.0), (7.0, 6.0)))
+        """
+        # As subtraction is the inverse of addition,
+        # we first dispatch to .__neg__() to invert the signs of
+        # all entries in other and then dispatch to .__add__().
+        return self + (-other)
+
+    def __rsub__(self, other):
+        """See docstring for .__sub__()."""
+        if isinstance(other, Vector):
+            raise TypeError("vectors and matrices cannot be subtracted")
+        # Same comments as in .__sub__() apply
+        # with the roles of self and other swapped.
+        return (-self) + other
+
+    def _matrix_multiply(self, other):
+        """Internal utility method to multiply to Matrix instances."""
+        if self.n_cols != other.n_rows:
+            raise ValueError("matrices must have compatible dimensions")
+        # Matrix-matrix multiplication means that each entry of the resulting
+        # Matrix is the dot product of the respective row of the "left" Matrix
+        # and column of the "right" Matrix. So, the rows/columns are represented
+        # by the Vector instances provided by the .cols() and .rows() methods.
+        return self.__class__((rv * cv for cv in other.cols()) for rv in self.rows())
+
+    def __mul__(self, other):
+        """Handle `self * other` and `other * self`.
+
+        This may be either scalar multiplication, matrix-vector multiplication,
+        vector-matrix multiplication, or matrix-matrix multiplication.
+
+        Example Usage:
+            >>> Matrix([(1, 2), (3, 4)]) * Matrix([(1, 2), (3, 4)])
+            Matrix(((7.0, 10.0), (15.0, 22.0)))
+
+            >>> 2 * Matrix([(1, 2), (3, 4)])
+            Matrix(((2.0, 4.0), (6.0, 8.0)))
+
+            >>> Matrix([(1, 2), (3, 4)]) * 3
+            Matrix(((3.0, 6.0), (9.0, 12.0)))
+
+            Matrix-vector and vector-matrix multiplication are not commutative.
+
+            >>> Matrix([(1, 2), (3, 4)]) * Vector([5, 6])
+            Vector((17.0, 39.0))
+
+            >>> Vector([5, 6]) * Matrix([(1, 2), (3, 4)])
+            Vector((23.0, 34.0))
+        """
+        # Scalar multiplication
+        if isinstance(other, numbers.Number):
+            return self.__class__((x * other for x in r) for r in self._entries)
+        # Matrix-vector multiplication: Vector is a column Vector
+        elif isinstance(other, Vector):
+            # First, cast the other Vector as a Matrix, then do matrix-matrix
+            # multiplication, and lastly return the result as a Vector again.
+            return self._matrix_multiply(other.as_matrix()).as_vector()
+        # Matrix-matrix multiplication
+        elif isinstance(other, self.__class__):
+            return self._matrix_multiply(other)
+        return NotImplemented
+
+    def __rmul__(self, other):
+        """See docstring for .__mul__()."""
+        # As scalar multiplication is commutative, we dispatch to .__mul__().
+        if isinstance(other, numbers.Number):
+            return self * other
+        # Vector-matrix multiplication: Vector is a row Vector
+        elif isinstance(other, Vector):
+            return other.as_matrix(column=False)._matrix_multiply(self).as_vector()
+        return NotImplemented
+
+    def __truediv__(self, other):
+        """Handle `self / other`.
+
+        Divide a Matrix by a scalar.
+
+        Example Usage:
+            >>> Matrix([(1, 2), (3, 4)]) / 4
+            Matrix([(0.25, 0.5), (0.75, 1.0)])
+        """
+        # As scalar division division is the same as multiplication
+        # with the inverse, we dispatch to .__mul__().
+        if isinstance(other, numbers.Number):
+            return self * (1 / other)
+        return NotImplemented
+
+    def __eq__(self, other):
+        """Handle `self == other`.
+
+        Compare two Matrix instances for equality.
+
+        Example Usage:
+            >>> Matrix([(1, 2), (3, 4)]) == Matrix([(1, 2), (3, 4)])
+            True
+
+            >>> Matrix([(1, 2), (3, 4)]) == Matrix([(5, 6), (7, 8)])
+            False
+        """
+        if isinstance(other, self.__class__):
+            if (self.n_rows != other.n_rows) or (self.n_cols != other.n_cols):
+                raise ValueError("matrices must have the same dimensions")
+            for x, y in zip(self, other):
+                if abs(x - y) > self.zero_threshold:
+                    return False  # exit early if two corresponding entries differ
+            return True
+        return NotImplemented
+
+    def __pos__(self):
+        """Handle `+self`.
+
+        This is simply an identity operator returning the Matrix itself.
+        """
+        return self
+
+    def __neg__(self):
+        """Handle `-self`.
+
+        Negate all entries of a Matrix.
+        """
+        return self.__class__((-x for x in r) for r in self._entries)
+
+    def __abs__(self):
+        """The Frobenius norm of a Matrix."""
+        return utils.norm(self)  # use the norm() function shared with the Vector class
+
+    def __bool__(self):
+        """A Matrix is truthy if its Frobenius norm is strictly positive."""
+        return bool(abs(self))
+
+    def __float__(self):
+        """Cast a Matrix as a scalar.
+
+        Returns:
+            scalar (float)
+
+        Raises:
+            RuntimeError: if the Matrix has more than one entry
+        """
+        if not (self.n_rows == 1 and self.n_cols == 1):
+            raise RuntimeError("matrix must have exactly one entry to become a scalar")
+        return self[0]
+
+    def as_vector(self):
+        """Get a Vector representation of a Matrix.
+
+        Returns:
+            vector (Vector)
+
+        Raises:
+            RuntimeError: if one of the two dimensions, .n_rows or .n_cols, is not 1
+        """
+        if not (self.n_rows == 1 or self.n_cols == 1):
+            raise RuntimeError("one dimension (m or n) must be 1")
+        return Vector(x for x in self)
+
+    def transpose(self):
+        """Switch the rows and columns of a Matrix.
+
+        Returns:
+            matrix (Matrix)
+        """
+        return self.__class__(zip(*self._entries))
+
+
+# This import needs to be made here as otherwise an ImportError is raised.
+# That is so as both the matrix and vector modules import a class from each other.
+# We call that a circular import. Whereas Python handles "circular" references
+# (e.g., both the Matrix and Vector classes have methods that reference the
+# respective other class), that is forbidden for imports.
+from sample_package.vector import Vector
diff --git a/11_classes/sample_package/utils.py b/11_classes/sample_package/utils.py
new file mode 100644
index 0000000..2720372
--- /dev/null
+++ b/11_classes/sample_package/utils.py
@@ -0,0 +1,35 @@
+"""This module provides utilities for the whole package.
+
+The defined constants are used as defaults in the Vector and Matrix classes.
+
+The norm() function is shared by Vector.__abs__() and Matrix.__abs__().
+"""
+
+import math
+
+
+# Define constants (i.e., normal variables that are, by convention, named in UPPERCASE)
+# that are used as the defaults for class attributes within Vector and Matrix.
+DEFAULT_ENTRIES_STORAGE = tuple
+DEFAULT_ENTRY_TYPE = float
+ZERO_THRESHOLD = 1e-12
+
+
+def norm(vec_or_mat):
+    """Calculate the Frobenius or Euclidean norm of a matrix or vector.
+
+    Find more infos here: https://en.wikipedia.org/wiki/Matrix_norm#Frobenius_norm
+
+    Args:
+        vec_or_mat (Vector / Matrix): object whose entries are squared and summed up
+
+    Returns:
+        norm (float)
+
+    Example Usage:
+        As Vector and Matrix objects are by design non-empty sequences,
+        norm() may be called, for example, with `[3, 4]` as the argument:
+        >>> norm([3, 4])
+        5.0
+    """
+    return math.sqrt(sum(x ** 2 for x in vec_or_mat))
diff --git a/11_classes/sample_package/vector.py b/11_classes/sample_package/vector.py
new file mode 100644
index 0000000..1b6e305
--- /dev/null
+++ b/11_classes/sample_package/vector.py
@@ -0,0 +1,253 @@
+"""This module defines a Vector class."""
+
+# Imports from the standard library go first ...
+import numbers
+
+# ... and are followed by project-internal ones.
+# If third-party libraries are needed, they are
+# put into a group on their own in between.
+# Within a group, imports are sorted lexicographically.
+from sample_package import utils
+from sample_package.matrix import Matrix
+
+
+class Vector:
+    """A one-dimensional vector from linear algebra.
+
+    All entries are converted to floats, or whatever is set in the typing attribute.
+
+    Attributes:
+        storage (callable): data type used to store the entries internally;
+            defaults to tuple
+        typing (callable): type casting applied to all entries upon creation;
+            defaults to float
+        zero_threshold (float): max. tolerance when comparing an entry to zero;
+            defaults to 1e-12
+    """
+
+    storage = utils.DEFAULT_ENTRIES_STORAGE
+    typing = utils.DEFAULT_ENTRY_TYPE
+    zero_threshold = utils.ZERO_THRESHOLD
+
+    def __init__(self, data):
+        """Create a new vector.
+
+        Args:
+            data (sequence): the vector's entries
+
+        Raises:
+            ValueError: if no entries are provided
+
+        Example Usage:
+            >>> Vector([1, 2, 3])
+            Vector((1.0, 2.0, 3.0))
+
+            >>> Vector(range(3))
+            Vector((0.0, 1.0, 2.0))
+        """
+        self._entries = self.storage(self.typing(x) for x in data)
+        if len(self) == 0:
+            raise ValueError("a vector must have at least one entry")
+
+    def __repr__(self):
+        """Text representation of a Vector."""
+        name = self.__class__.__name__
+        args = ", ".join(f"{x:.3f}" for x in self)
+        return f"{name}(({args}))"
+
+    def __str__(self):
+        """Human-readable text representation of a Vector."""
+        name = self.__class__.__name__
+        first, last, n_entries = self[0], self[-1], len(self)
+        return f"{name}({first:.1f}, ..., {last:.1f})[{n_entries:d}]"
+
+    def __len__(self):
+        """Number of entries in a Vector."""
+        return len(self._entries)
+
+    def __getitem__(self, index):
+        """Obtain an individual entry of a Vector."""
+        if not isinstance(index, int):
+            raise TypeError("index must be an integer")
+        return self._entries[index]
+
+    def __iter__(self):
+        """Loop over a Vector's entries."""
+        return iter(self._entries)
+
+    def __reversed__(self):
+        """Loop over a Vector's entries in reverse order."""
+        return reversed(self._entries)
+
+    def __add__(self, other):
+        """Handle `self + other` and `other + self`.
+
+        This may be either vector addition or broadcasting addition.
+
+        Example Usage:
+            >>> Vector([1, 2, 3]) + Vector([2, 3, 4])
+            Vector((3, 5, 7))
+
+            >>> Vector([1, 2, 3]) + 4
+            Vector((5, 6, 7))
+
+            >>> 10 + Vector([1, 2, 3])
+            Vector((11, 12, 13))
+        """
+        # Vector addition
+        if isinstance(other, self.__class__):
+            if len(self) != len(other):
+                raise ValueError("vectors must be of the same length")
+            return self.__class__(x + y for (x, y) in zip(self, other))
+        # Broadcasting addition
+        elif isinstance(other, numbers.Number):
+            return self.__class__(x + other for x in self)
+        return NotImplemented
+
+    def __radd__(self, other):
+        """See docstring for .__add__()."""
+        # As both vector and broadcasting addition are commutative,
+        # we dispatch to .__add__().
+        return self + other
+
+    def __sub__(self, other):
+        """Handle `self - other` and `other - self`.
+
+        This may be either vector subtraction or broadcasting subtraction.
+
+        Example Usage:
+            >>> Vector([7, 8, 9]) - Vector([1, 2, 3])
+            Vector((6, 6, 6))
+
+            >>> Vector([1, 2, 3]) - 1
+            Vector((0, 1, 2))
+
+            >>> 10 - Vector([1, 2, 3])
+            Vector((9, 8, 7))
+        """
+        # As subtraction is the inverse of addition,
+        # we first dispatch to .__neg__() to invert the signs of
+        # all entries in other and then dispatch to .__add__().
+        return self + (-other)
+
+    def __rsub__(self, other):
+        """See docstring for .__sub__()."""
+        # Same comments as in .__sub__() apply
+        # with the roles of self and other swapped.
+        return (-self) + other
+
+    def __mul__(self, other):
+        """Handle `self * other` and `other * self`.
+
+        This may be either the dot product of two vectors or scalar multiplication.
+
+        Example Usage:
+            >>> Vector([1, 2, 3]) * Vector([2, 3, 4])
+            14
+
+            >>> 2 * Vector([1, 2, 3])
+            Vector((2.0, 4.0, 6.0))
+
+            >>> Vector([1, 2, 3]) * 3
+            Vector((3.0, 6.0, 9.0))
+        """
+        # Dot product
+        if isinstance(other, self.__class__):
+            if len(self) != len(other):
+                raise ValueError("vectors must be of the same length")
+            return sum(x * y for (x, y) in zip(self, other))
+        # Scalar multiplication
+        elif isinstance(other, numbers.Number):
+            return self.__class__(x * other for x in self)
+        return NotImplemented
+
+    def __rmul__(self, other):
+        """See docstring for .__mul__()."""
+        # As both dot product and scalar multiplication are commutative,
+        # we dispatch to .__mul__().
+        return self * other
+
+    def __truediv__(self, other):
+        """Handle `self / other`.
+
+        Divide a Vector by a scalar.
+
+        Example Usage:
+            >>> Vector([9, 6, 12]) / 3
+            Vector((3.0, 2.0, 4.0))
+        """
+        # As scalar division division is the same as multiplication
+        # with the inverse, we dispatch to .__mul__().
+        if isinstance(other, numbers.Number):
+            return self * (1 / other)
+        return NotImplemented
+
+    def __eq__(self, other):
+        """Handle `self == other`.
+
+        Compare two Vectors for equality.
+
+        Example Usage:
+            >>> Vector([1, 2, 3]) == Vector([1, 2, 3])
+            True
+
+            >>> Vector([1, 2, 3]) == Vector([4, 5, 6])
+            False
+        """
+        if isinstance(other, self.__class__):
+            if len(self) != len(other):
+                raise ValueError("vectors must be of the same length")
+            for x, y in zip(self, other):
+                if abs(x - y) > self.zero_threshold:
+                    return False  # exit early if two corresponding entries differ
+            return True
+        return NotImplemented
+
+    def __pos__(self):
+        """Handle `+self`.
+
+        This is simply an identity operator returning the Vector itself.
+        """
+        return self
+
+    def __neg__(self):
+        """Handle `-self`.
+
+        Negate all entries of a Vector.
+        """
+        return self.__class__(-x for x in self)
+
+    def __abs__(self):
+        """The Euclidean norm of a vector."""
+        return utils.norm(self)  # use the norm() function shared with the Matrix class
+
+    def __bool__(self):
+        """A Vector is truthy if its Euclidean norm is strictly positive."""
+        return bool(abs(self))
+
+    def __float__(self):
+        """Cast a Vector as a scalar.
+
+        Returns:
+            scalar (float)
+
+        Raises:
+            RuntimeError: if the Vector has more than one entry
+        """
+        if len(self) != 1:
+            raise RuntimeError("vector must have exactly one entry to become a scalar")
+        return self[0]
+
+    def as_matrix(self, *, column=True):
+        """Get a Matrix representation of a Vector.
+
+        Args:
+            column (bool): if the vector is interpreted as a
+                column vector or a row vector; defaults to True
+
+        Returns:
+            matrix (Matrix)
+        """
+        if column:
+            return Matrix([x] for x in self)
+        return Matrix([(x for x in self)])

From 3abae203cb3f770f3b28da9507e016a443d33e34 Mon Sep 17 00:00:00 2001
From: Alexander Hess <alexander@webartifex.biz>
Date: Tue, 27 Oct 2020 17:02:28 +0100
Subject: [PATCH 215/242] Pin the dependencies

- upgrade jupyterlab
- upgrade RISE
- upgrade transient dependencies:
  + nest-asyncio
  + notebook
  + pandocfilters
  + pygments
  + virtualenv
- pin transient dependencies:
  + flake8
  + socks
  + webpdf
---
 poetry.lock | 50 +++++++++++++++++++++++++-------------------------
 1 file changed, 25 insertions(+), 25 deletions(-)

diff --git a/poetry.lock b/poetry.lock
index 8e454f7..98d06c2 100644
--- a/poetry.lock
+++ b/poetry.lock
@@ -267,7 +267,7 @@ python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*, !=3.4.*"
 parso = ">=0.7.0,<0.8.0"
 
 [package.extras]
-qa = ["flake8 (3.7.9)"]
+qa = ["flake8 (==3.7.9)"]
 testing = ["Django (<3.1)", "colorama", "docopt", "pytest (>=3.9.0,<5.0.0)"]
 
 [[package]]
@@ -428,7 +428,7 @@ test = ["jupyter-contrib-core", "nose", "requests", "selenium", "mock"]
 
 [[package]]
 name = "jupyterlab"
-version = "2.2.8"
+version = "2.2.9"
 description = "The JupyterLab notebook server extension."
 category = "main"
 optional = false
@@ -547,11 +547,11 @@ testpath = "*"
 traitlets = ">=4.2"
 
 [package.extras]
-all = ["pytest", "pytest-cov", "pytest-dependency", "ipykernel", "ipywidgets (>=7)", "pyppeteer (0.2.2)", "tornado (>=4.0)", "sphinx (>=1.5.1)", "sphinx-rtd-theme", "nbsphinx (>=0.2.12)", "ipython"]
+all = ["pytest", "pytest-cov", "pytest-dependency", "ipykernel", "ipywidgets (>=7)", "pyppeteer (==0.2.2)", "tornado (>=4.0)", "sphinx (>=1.5.1)", "sphinx-rtd-theme", "nbsphinx (>=0.2.12)", "ipython"]
 docs = ["sphinx (>=1.5.1)", "sphinx-rtd-theme", "nbsphinx (>=0.2.12)", "ipython"]
 serve = ["tornado (>=4.0)"]
-test = ["pytest", "pytest-cov", "pytest-dependency", "ipykernel", "ipywidgets (>=7)", "pyppeteer (0.2.2)"]
-webpdf = ["pyppeteer (0.2.2)"]
+test = ["pytest", "pytest-cov", "pytest-dependency", "ipykernel", "ipywidgets (>=7)", "pyppeteer (==0.2.2)"]
+webpdf = ["pyppeteer (==0.2.2)"]
 
 [[package]]
 name = "nbformat"
@@ -573,7 +573,7 @@ test = ["fastjsonschema", "testpath", "pytest", "pytest-cov"]
 
 [[package]]
 name = "nest-asyncio"
-version = "1.4.1"
+version = "1.4.2"
 description = "Patch asyncio to allow nested event loops"
 category = "main"
 optional = false
@@ -654,11 +654,11 @@ six = "*"
 
 [[package]]
 name = "pandocfilters"
-version = "1.4.2"
+version = "1.4.3"
 description = "Utilities for writing pandoc filters in python"
 category = "main"
 optional = false
-python-versions = "*"
+python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*"
 
 [[package]]
 name = "parso"
@@ -754,7 +754,7 @@ python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*"
 
 [[package]]
 name = "pygments"
-version = "2.7.1"
+version = "2.7.2"
 description = "Pygments is a syntax highlighting package written in Python."
 category = "main"
 optional = false
@@ -835,18 +835,18 @@ urllib3 = ">=1.21.1,<1.25.0 || >1.25.0,<1.25.1 || >1.25.1,<1.26"
 
 [package.extras]
 security = ["pyOpenSSL (>=0.14)", "cryptography (>=1.3.4)"]
-socks = ["PySocks (>=1.5.6,<1.5.7 || >1.5.7)", "win-inet-pton"]
+socks = ["PySocks (>=1.5.6,!=1.5.7)", "win-inet-pton"]
 
 [[package]]
 name = "rise"
-version = "5.6.1"
+version = "5.7.0"
 description = "Reveal.js - Jupyter/IPython Slideshow Extension"
 category = "dev"
 optional = false
 python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*, <4"
 
 [package.dependencies]
-notebook = ">=5.5.0"
+notebook = ">=6.0"
 
 [[package]]
 name = "send2trash"
@@ -929,11 +929,11 @@ python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*, !=3.4.*, <4"
 [package.extras]
 brotli = ["brotlipy (>=0.6.0)"]
 secure = ["pyOpenSSL (>=0.14)", "cryptography (>=1.3.4)", "idna (>=2.0.0)", "certifi", "ipaddress"]
-socks = ["PySocks (>=1.5.6,<1.5.7 || >1.5.7,<2.0)"]
+socks = ["PySocks (>=1.5.6,!=1.5.7,<2.0)"]
 
 [[package]]
 name = "virtualenv"
-version = "20.0.35"
+version = "20.1.0"
 description = "Virtual Python Environment builder"
 category = "dev"
 optional = false
@@ -1157,8 +1157,8 @@ jupyter-nbextensions-configurator = [
     {file = "jupyter_nbextensions_configurator-0.4.1.tar.gz", hash = "sha256:e5e86b5d9d898e1ffb30ebb08e4ad8696999f798fef3ff3262d7b999076e4e83"},
 ]
 jupyterlab = [
-    {file = "jupyterlab-2.2.8-py3-none-any.whl", hash = "sha256:95d0509557881cfa8a5fcdf225f2fca46faf1bc52fc56a28e0b72fcc594c90ab"},
-    {file = "jupyterlab-2.2.8.tar.gz", hash = "sha256:c8377bee30504919c1e79949f9fe35443ab7f5c4be622c95307e8108410c8b8c"},
+    {file = "jupyterlab-2.2.9-py3-none-any.whl", hash = "sha256:59af02c26a15ec2d2862a15bc72e41ae304b406a0b0d3f4f705eeb7caf91902b"},
+    {file = "jupyterlab-2.2.9.tar.gz", hash = "sha256:3be8f8edea173753dd838c1b6d3bbcb6f5c801121f824a477025c1b6a1d33dc6"},
 ]
 jupyterlab-pygments = [
     {file = "jupyterlab_pygments-0.1.2-py2.py3-none-any.whl", hash = "sha256:abfb880fd1561987efaefcb2d2ac75145d2a5d0139b1876d5be806e32f630008"},
@@ -1258,8 +1258,8 @@ nbformat = [
     {file = "nbformat-5.0.8.tar.gz", hash = "sha256:f545b22138865bfbcc6b1ffe89ed5a2b8e2dc5d4fe876f2ca60d8e6f702a30f8"},
 ]
 nest-asyncio = [
-    {file = "nest_asyncio-1.4.1-py3-none-any.whl", hash = "sha256:a4487c4f49f2d11a7bb89a512a6886b6a5045f47097f49815b2851aaa8599cf0"},
-    {file = "nest_asyncio-1.4.1.tar.gz", hash = "sha256:b86c3193abda5b2eeccf8c79894bc71c680369a178f4b068514ac00720b14e01"},
+    {file = "nest_asyncio-1.4.2-py3-none-any.whl", hash = "sha256:c2d3bdc76ba235a7ad215128afe31d74a320d25790c50cd94685ec5ea221b94d"},
+    {file = "nest_asyncio-1.4.2.tar.gz", hash = "sha256:c614fcfaca72b1f04778bc0e73f49c84500b3d045c49d149fc46f1566643c175"},
 ]
 nodeenv = [
     {file = "nodeenv-1.5.0-py2.py3-none-any.whl", hash = "sha256:5304d424c529c997bc888453aeaa6362d242b6b4631e90f3d4bf1b290f1c84a9"},
@@ -1306,7 +1306,7 @@ packaging = [
     {file = "packaging-20.4.tar.gz", hash = "sha256:4357f74f47b9c12db93624a82154e9b120fa8293699949152b22065d556079f8"},
 ]
 pandocfilters = [
-    {file = "pandocfilters-1.4.2.tar.gz", hash = "sha256:b3dd70e169bb5449e6bc6ff96aea89c5eea8c5f6ab5e207fc2f521a2cf4a0da9"},
+    {file = "pandocfilters-1.4.3.tar.gz", hash = "sha256:bc63fbb50534b4b1f8ebe1860889289e8af94a23bff7445259592df25a3906eb"},
 ]
 parso = [
     {file = "parso-0.7.1-py2.py3-none-any.whl", hash = "sha256:97218d9159b2520ff45eb78028ba8b50d2bc61dcc062a9682666f2dc4bd331ea"},
@@ -1345,8 +1345,8 @@ pycparser = [
     {file = "pycparser-2.20.tar.gz", hash = "sha256:2d475327684562c3a96cc71adf7dc8c4f0565175cf86b6d7a404ff4c771f15f0"},
 ]
 pygments = [
-    {file = "Pygments-2.7.1-py3-none-any.whl", hash = "sha256:307543fe65c0947b126e83dd5a61bd8acbd84abec11f43caebaf5534cbc17998"},
-    {file = "Pygments-2.7.1.tar.gz", hash = "sha256:926c3f319eda178d1bd90851e4317e6d8cdb5e292a3386aac9bd75eca29cf9c7"},
+    {file = "Pygments-2.7.2-py3-none-any.whl", hash = "sha256:88a0bbcd659fcb9573703957c6b9cff9fab7295e6e76db54c9d00ae42df32773"},
+    {file = "Pygments-2.7.2.tar.gz", hash = "sha256:381985fcc551eb9d37c52088a32914e00517e57f4a21609f48141ba08e193fa0"},
 ]
 pyparsing = [
     {file = "pyparsing-2.4.7-py2.py3-none-any.whl", hash = "sha256:ef9d7589ef3c200abe66653d3f1ab1033c3c419ae9b9bdb1240a85b024efc88b"},
@@ -1433,8 +1433,8 @@ requests = [
     {file = "requests-2.24.0.tar.gz", hash = "sha256:b3559a131db72c33ee969480840fff4bb6dd111de7dd27c8ee1f820f4f00231b"},
 ]
 rise = [
-    {file = "rise-5.6.1-py2.py3-none-any.whl", hash = "sha256:e9637ee5499ad7801474da53a2c830350a44b2192c2f113594e4426190e55ad4"},
-    {file = "rise-5.6.1.tar.gz", hash = "sha256:1343f068d01adc4dd0226d9b278ce93fc92f365d827431a57e8d5679eb39f4d6"},
+    {file = "rise-5.7.0-py2.py3-none-any.whl", hash = "sha256:b500c7c3f7b09c8194a66feeffd60ead6da0132d9fa18a2bb7352587778ef482"},
+    {file = "rise-5.7.0.tar.gz", hash = "sha256:6c00721189e0b457ca40ab4eb0abef8edbba6c71bc04d7f04ad813a214ddea74"},
 ]
 send2trash = [
     {file = "Send2Trash-1.5.0-py3-none-any.whl", hash = "sha256:f1691922577b6fa12821234aeb57599d887c4900b9ca537948d2dac34aea888b"},
@@ -1476,8 +1476,8 @@ urllib3 = [
     {file = "urllib3-1.25.11.tar.gz", hash = "sha256:8d7eaa5a82a1cac232164990f04874c594c9453ec55eef02eab885aa02fc17a2"},
 ]
 virtualenv = [
-    {file = "virtualenv-20.0.35-py2.py3-none-any.whl", hash = "sha256:0ebc633426d7468664067309842c81edab11ae97fcaf27e8ad7f5748c89b431b"},
-    {file = "virtualenv-20.0.35.tar.gz", hash = "sha256:2a72c80fa2ad8f4e2985c06e6fc12c3d60d060e410572f553c90619b0f6efaf3"},
+    {file = "virtualenv-20.1.0-py2.py3-none-any.whl", hash = "sha256:b0011228208944ce71052987437d3843e05690b2f23d1c7da4263fde104c97a2"},
+    {file = "virtualenv-20.1.0.tar.gz", hash = "sha256:b8d6110f493af256a40d65e29846c69340a947669eec8ce784fcf3dd3af28380"},
 ]
 wcwidth = [
     {file = "wcwidth-0.2.5-py2.py3-none-any.whl", hash = "sha256:beb4802a9cebb9144e99086eff703a642a13d6a0052920003a230f3294bbe784"},

From a4c32f5811b604e211ea90088f64abd25f852364 Mon Sep 17 00:00:00 2001
From: Alexander Hess <alexander@webartifex.biz>
Date: Tue, 27 Oct 2020 17:07:07 +0100
Subject: [PATCH 216/242] Add xdoctest to the develop dependencies

---
 poetry.lock    | 24 +++++++++++++++++++++++-
 pyproject.toml |  3 +++
 2 files changed, 26 insertions(+), 1 deletion(-)

diff --git a/poetry.lock b/poetry.lock
index 98d06c2..e50d7f0 100644
--- a/poetry.lock
+++ b/poetry.lock
@@ -965,10 +965,28 @@ category = "main"
 optional = false
 python-versions = "*"
 
+[[package]]
+name = "xdoctest"
+version = "0.15.0"
+description = "A rewrite of the builtin doctest module"
+category = "dev"
+optional = false
+python-versions = "*"
+
+[package.dependencies]
+six = "*"
+
+[package.extras]
+all = ["six", "pytest", "pytest-cov", "codecov", "scikit-build", "cmake", "ninja", "pybind11", "pygments", "colorama", "nbformat", "nbconvert", "jupyter-client", "ipython", "ipykernel"]
+colors = ["pygments", "colorama"]
+jupyter = ["nbformat", "nbconvert", "jupyter-client", "ipython", "ipykernel"]
+optional = ["pygments", "colorama", "nbformat", "nbconvert", "jupyter-client", "ipython", "ipykernel"]
+tests = ["pytest", "pytest-cov", "codecov", "scikit-build", "cmake", "ninja", "pybind11", "nbformat", "nbconvert", "jupyter-client", "ipython", "ipykernel"]
+
 [metadata]
 lock-version = "1.1"
 python-versions = "^3.8"
-content-hash = "abc30be36880f8b203c72895f70f906ef451fca6b4e5113891c9487bc95e02e5"
+content-hash = "0483228a1bbf92c52f5045db05de80b951dbd7302db756ca6f65cd56b482b814"
 
 [metadata.files]
 appdirs = [
@@ -1487,3 +1505,7 @@ webencodings = [
     {file = "webencodings-0.5.1-py2.py3-none-any.whl", hash = "sha256:a0af1213f3c2226497a97e2b3aa01a7e4bee4f403f95be16fc9acd2947514a78"},
     {file = "webencodings-0.5.1.tar.gz", hash = "sha256:b36a1c245f2d304965eb4e0a82848379241dc04b865afcc4aab16748587e1923"},
 ]
+xdoctest = [
+    {file = "xdoctest-0.15.0-py2.py3-none-any.whl", hash = "sha256:695ea04303a48cbb319709270d43f7bae7f3de3701aec73f09d90a216499992e"},
+    {file = "xdoctest-0.15.0.tar.gz", hash = "sha256:7f0a184d403b69b166ebec1aadb13c98c96c59101e974ae2e4db4c3a803ec371"},
+]
diff --git a/pyproject.toml b/pyproject.toml
index 588c1b4..aa92bb6 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -21,6 +21,9 @@ numpy = "^1.19.2"
 nox = "^2020.8.22"
 pre-commit = "^2.7.1"
 
+# Testing
+xdoctest = "^0.15.0"
+
 # Live coding during presentation mode
 jupyter-contrib-nbextensions = "^0.5.1"
 rise = "^5.6.1"

From 4c123d5d01adc6f5ac5ecd7bd4f4c31c7a10174b Mon Sep 17 00:00:00 2001
From: Alexander Hess <alexander@webartifex.biz>
Date: Tue, 27 Oct 2020 17:38:20 +0100
Subject: [PATCH 217/242] Streamline the doctests in the sample_package

- add a couple of doctests
- fix failing existing doctests
---
 11_classes/sample_package/matrix.py | 45 ++++++++++++++++++++---------
 11_classes/sample_package/vector.py | 31 ++++++++++++--------
 2 files changed, 51 insertions(+), 25 deletions(-)

diff --git a/11_classes/sample_package/matrix.py b/11_classes/sample_package/matrix.py
index b64e50d..65f5fc1 100644
--- a/11_classes/sample_package/matrix.py
+++ b/11_classes/sample_package/matrix.py
@@ -38,6 +38,10 @@ class Matrix:
             ValueError:
                 - if no entries are provided
                 - if the number of columns is inconsistent across the rows
+
+        Example Usage:
+            >>> Matrix([(1, 2), (3, 4)])
+            Matrix(((1.000, 2.000,), (3.000, 4.000,)))
         """
         self._entries = self.storage(
             self.storage(self.typing(x) for x in r) for r in data
@@ -64,6 +68,10 @@ class Matrix:
             ValueError:
                 - if no entries are provided
                 - if the number of rows is inconsistent across the columns
+
+        Example Usage:
+            >>> Matrix.from_columns([(1, 2), (3, 4)])
+            Matrix(((1.000, 3.000,), (2.000, 4.000,)))
         """
         return cls(data).transpose()
 
@@ -122,7 +130,7 @@ class Matrix:
             >>> m[-1]
             4.0
             >>> m[0, 1]
-            3.0
+            2.0
         """
         # Sequence-like indexing (one-dimensional)
         if isinstance(index, int):
@@ -201,13 +209,13 @@ class Matrix:
 
         Example Usage:
             >>> Matrix([(1, 2), (3, 4)]) + Matrix([(2, 3), (4, 5)])
-            Matrix(((3.0, 5.0), (7.0, 9.0)))
+            Matrix(((3.000, 5.000,), (7.000, 9.000,)))
 
             >>> Matrix([(1, 2), (3, 4)]) + 5
-            Matrix(((6.0, 7.0), (8.0, 9.0)))
+            Matrix(((6.000, 7.000,), (8.000, 9.000,)))
 
             >>> 10 + Matrix([(1, 2), (3, 4)])
-            Matrix(((11.0, 12.0), (13.0, 14.0)))
+            Matrix(((11.000, 12.000,), (13.000, 14.000,)))
         """
         # Matrix addition
         if isinstance(other, self.__class__):
@@ -237,13 +245,13 @@ class Matrix:
 
         Example Usage:
             >>> Matrix([(2, 3), (4, 5)]) - Matrix([(1, 2), (3, 4)])
-            Matrix(((1.0, 1.0), (1.0, 1.0)))
+            Matrix(((1.000, 1.000,), (1.000, 1.000,)))
 
             >>> Matrix([(1, 2), (3, 4)]) - 1
-            Matrix(((0.0, 1.0), (2.0, 3.0)))
+            Matrix(((0.000, 1.000,), (2.000, 3.000,)))
 
             >>> 10 - Matrix([(1, 2), (3, 4)])
-            Matrix(((9.0, 8.0), (7.0, 6.0)))
+            Matrix(((9.000, 8.000,), (7.000, 6.000,)))
         """
         # As subtraction is the inverse of addition,
         # we first dispatch to .__neg__() to invert the signs of
@@ -276,21 +284,21 @@ class Matrix:
 
         Example Usage:
             >>> Matrix([(1, 2), (3, 4)]) * Matrix([(1, 2), (3, 4)])
-            Matrix(((7.0, 10.0), (15.0, 22.0)))
+            Matrix(((7.000, 10.000,), (15.000, 22.000,)))
 
             >>> 2 * Matrix([(1, 2), (3, 4)])
-            Matrix(((2.0, 4.0), (6.0, 8.0)))
+            Matrix(((2.000, 4.000,), (6.000, 8.000,)))
 
             >>> Matrix([(1, 2), (3, 4)]) * 3
-            Matrix(((3.0, 6.0), (9.0, 12.0)))
+            Matrix(((3.000, 6.000,), (9.000, 12.000,)))
 
             Matrix-vector and vector-matrix multiplication are not commutative.
 
             >>> Matrix([(1, 2), (3, 4)]) * Vector([5, 6])
-            Vector((17.0, 39.0))
+            Vector((17.000, 39.000))
 
             >>> Vector([5, 6]) * Matrix([(1, 2), (3, 4)])
-            Vector((23.0, 34.0))
+            Vector((23.000, 34.000))
         """
         # Scalar multiplication
         if isinstance(other, numbers.Number):
@@ -322,7 +330,7 @@ class Matrix:
 
         Example Usage:
             >>> Matrix([(1, 2), (3, 4)]) / 4
-            Matrix([(0.25, 0.5), (0.75, 1.0)])
+            Matrix(((0.250, 0.500,), (0.750, 1.000,)))
         """
         # As scalar division division is the same as multiplication
         # with the inverse, we dispatch to .__mul__().
@@ -394,6 +402,10 @@ class Matrix:
 
         Raises:
             RuntimeError: if one of the two dimensions, .n_rows or .n_cols, is not 1
+
+        Example Usage:
+            >>> Matrix([(1, 2, 3)]).as_vector()
+            Vector((1.000, 2.000, 3.000))
         """
         if not (self.n_rows == 1 or self.n_cols == 1):
             raise RuntimeError("one dimension (m or n) must be 1")
@@ -404,6 +416,13 @@ class Matrix:
 
         Returns:
             matrix (Matrix)
+
+        Example Usage:
+            >>> m = Matrix([(1, 2), (3, 4)])
+            >>> m
+            Matrix(((1.000, 2.000,), (3.000, 4.000,)))
+            >>> m.transpose()
+            Matrix(((1.000, 3.000,), (2.000, 4.000,)))
         """
         return self.__class__(zip(*self._entries))
 
diff --git a/11_classes/sample_package/vector.py b/11_classes/sample_package/vector.py
index 1b6e305..874d29a 100644
--- a/11_classes/sample_package/vector.py
+++ b/11_classes/sample_package/vector.py
@@ -40,10 +40,10 @@ class Vector:
 
         Example Usage:
             >>> Vector([1, 2, 3])
-            Vector((1.0, 2.0, 3.0))
+            Vector((1.000, 2.000, 3.000))
 
             >>> Vector(range(3))
-            Vector((0.0, 1.0, 2.0))
+            Vector((0.000, 1.000, 2.000))
         """
         self._entries = self.storage(self.typing(x) for x in data)
         if len(self) == 0:
@@ -86,13 +86,13 @@ class Vector:
 
         Example Usage:
             >>> Vector([1, 2, 3]) + Vector([2, 3, 4])
-            Vector((3, 5, 7))
+            Vector((3.000, 5.000, 7.000))
 
             >>> Vector([1, 2, 3]) + 4
-            Vector((5, 6, 7))
+            Vector((5.000, 6.000, 7.000))
 
             >>> 10 + Vector([1, 2, 3])
-            Vector((11, 12, 13))
+            Vector((11.000, 12.000, 13.000))
         """
         # Vector addition
         if isinstance(other, self.__class__):
@@ -117,13 +117,13 @@ class Vector:
 
         Example Usage:
             >>> Vector([7, 8, 9]) - Vector([1, 2, 3])
-            Vector((6, 6, 6))
+            Vector((6.000, 6.000, 6.000))
 
             >>> Vector([1, 2, 3]) - 1
-            Vector((0, 1, 2))
+            Vector((0.000, 1.000, 2.000))
 
             >>> 10 - Vector([1, 2, 3])
-            Vector((9, 8, 7))
+            Vector((9.000, 8.000, 7.000))
         """
         # As subtraction is the inverse of addition,
         # we first dispatch to .__neg__() to invert the signs of
@@ -143,13 +143,13 @@ class Vector:
 
         Example Usage:
             >>> Vector([1, 2, 3]) * Vector([2, 3, 4])
-            14
+            20.0
 
             >>> 2 * Vector([1, 2, 3])
-            Vector((2.0, 4.0, 6.0))
+            Vector((2.000, 4.000, 6.000))
 
             >>> Vector([1, 2, 3]) * 3
-            Vector((3.0, 6.0, 9.0))
+            Vector((3.000, 6.000, 9.000))
         """
         # Dot product
         if isinstance(other, self.__class__):
@@ -174,7 +174,7 @@ class Vector:
 
         Example Usage:
             >>> Vector([9, 6, 12]) / 3
-            Vector((3.0, 2.0, 4.0))
+            Vector((3.000, 2.000, 4.000))
         """
         # As scalar division division is the same as multiplication
         # with the inverse, we dispatch to .__mul__().
@@ -247,6 +247,13 @@ class Vector:
 
         Returns:
             matrix (Matrix)
+
+        Example Usage:
+            >>> v = Vector([1, 2, 3])
+            >>> v.as_matrix()
+            Matrix(((1.000,), (2.000,), (3.000,)))
+            >>> v.as_matrix(column=False)
+            Matrix(((1.000, 2.000, 3.000,)))
         """
         if column:
             return Matrix([x] for x in self)

From 8dc67c5bdd33bbd6466a4371ae2f19fa2f7303fd Mon Sep 17 00:00:00 2001
From: Alexander Hess <alexander@webartifex.biz>
Date: Tue, 27 Oct 2020 17:56:38 +0100
Subject: [PATCH 218/242] Add a nox session for running xdoctest

- add a session "doctests" to nox
- run it as a pre-commit hook
---
 .pre-commit-config.yaml |  6 ++++++
 noxfile.py              | 14 ++++++++++++++
 2 files changed, 20 insertions(+)

diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml
index 40d4f46..676b025 100644
--- a/.pre-commit-config.yaml
+++ b/.pre-commit-config.yaml
@@ -3,6 +3,12 @@ fail_fast: true
 repos:
 - repo: local
   hooks:
+  - id: doctests
+    name: Run the xdoctests in the source files
+    entry: poetry run nox -s doctests --
+    language: system
+    stages: [commit, merge-commit]
+    types: [python]
   - id: fix-branch-references
     name: Check for wrong branch references
     entry: poetry run nox -s fix-branch-references --
diff --git a/noxfile.py b/noxfile.py
index e77cc26..869cdab 100644
--- a/noxfile.py
+++ b/noxfile.py
@@ -4,6 +4,8 @@ Nox provides the following tasks:
 
 - "init-project": install the pre-commit hooks
 
+- "doctests": run the xdoctests in the source files
+
 - "fix-branch-references": adjusts links with git branch references in
   various files (e.g., Mardown or notebooks)
 
@@ -22,6 +24,11 @@ import nox
 
 REPOSITORY = "webartifex/intro-to-python"
 
+SRC_LOCATIONS = (
+    "02_functions/sample_module.py",
+    "11_classes/sample_package",
+)
+
 # Use a unified .cache/ folder for all develop tools.
 nox.options.envdir = ".cache/nox"
 
@@ -45,6 +52,13 @@ def init_project(session):
     )
 
 
+@nox.session(venv_backend="none")
+def doctests(session):
+    """Run the xdoctests in the source files."""
+    for location in SRC_LOCATIONS:
+        session.run("poetry", "run", "xdoctest", "--silent", location)
+
+
 @nox.session(name="fix-branch-references", venv_backend="none")
 def fix_branch_references(_session):
     """Change git branch references.

From 85b7fd274cdad5e79296ad2245d6868b796823cc Mon Sep 17 00:00:00 2001
From: Alexander Hess <alexander@webartifex.biz>
Date: Wed, 28 Oct 2020 16:18:15 +0100
Subject: [PATCH 219/242] Add initial version of chapter 11, part 4

---
 02_functions/02_content.ipynb         |    2 +-
 11_classes/00_content.ipynb           |  351 ++--
 11_classes/02_content.ipynb           |    2 +-
 11_classes/03_content.ipynb           |   26 +-
 11_classes/04_content.ipynb           | 2719 +++++++++++++++++++++++++
 11_classes/sample_package/__init__.py |    4 +
 11_classes/sample_package/matrix.py   |   28 +-
 11_classes/sample_package/vector.py   |   12 +-
 CONTENTS.md                           |    5 +
 9 files changed, 2927 insertions(+), 222 deletions(-)
 create mode 100644 11_classes/04_content.ipynb

diff --git a/02_functions/02_content.ipynb b/02_functions/02_content.ipynb
index 06f1765..5162e9c 100644
--- a/02_functions/02_content.ipynb
+++ b/02_functions/02_content.ipynb
@@ -1571,7 +1571,7 @@
     }
    },
    "source": [
-    "Packages are a generalization of modules, and we look at one in detail in [Chapter 11 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/11_classes/00_content.ipynb). You may, however, already look at a [sample package <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_gh.png\">](https://github.com/webartifex/intro-to-python/tree/develop/11_classes/sample_package) in the repository, which is nothing but a folder with *.py* files in it.\n",
+    "Packages are a generalization of modules, and we look at one in detail in [Chapter 11 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/11_classes/04_content.ipynb#Packages-vs.-Modules).\n",
     "\n",
     "As a further reading on modules and packages, we refer to the [official tutorial <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/tutorial/modules.html)."
    ]
diff --git a/11_classes/00_content.ipynb b/11_classes/00_content.ipynb
index 6852453..87a9bae 100644
--- a/11_classes/00_content.ipynb
+++ b/11_classes/00_content.ipynb
@@ -808,7 +808,7 @@
     }
    },
    "source": [
-    "Every instance comes with a special `.__class__` attribute that also references the corresponding class object."
+    "`v`'s semantic \"value\" is not so clear yet. We fix this in the next section."
    ]
   },
   {
@@ -819,41 +819,6 @@
      "slide_type": "fragment"
     }
    },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "__main__.Vector"
-      ]
-     },
-     "execution_count": 21,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "v.__class__"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "source": [
-    "`v`'s semantic \"value\" is not so clear yet. We fix this in the next section."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 22,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "fragment"
-    }
-   },
    "outputs": [
     {
      "data": {
@@ -861,7 +826,7 @@
        "<__main__.Vector at 0x7f9b9416d760>"
       ]
      },
-     "execution_count": 22,
+     "execution_count": 21,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -885,7 +850,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 22,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -910,7 +875,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 23,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -946,7 +911,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 24,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -983,7 +948,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": 25,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -996,7 +961,7 @@
        "[1.0, 2.0, 3.0]"
       ]
      },
-     "execution_count": 26,
+     "execution_count": 25,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1031,7 +996,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": 26,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -1042,6 +1007,30 @@
     "x = 1, 2, 3"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(1, 2, 3)"
+      ]
+     },
+     "execution_count": 27,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "x"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 28,
@@ -1062,30 +1051,6 @@
      "output_type": "execute_result"
     }
    ],
-   "source": [
-    "x"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 29,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "fragment"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(1, 2, 3)"
-      ]
-     },
-     "execution_count": 29,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
    "source": [
     "(1, 2, 3)"
    ]
@@ -1103,7 +1068,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 30,
+   "execution_count": 29,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -1144,7 +1109,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 31,
+   "execution_count": 30,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -1157,7 +1122,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 32,
+   "execution_count": 31,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -1170,7 +1135,7 @@
        "Vector((1.000, 2.000, 3.000))"
       ]
      },
-     "execution_count": 32,
+     "execution_count": 31,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1192,7 +1157,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 33,
+   "execution_count": 32,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -1205,7 +1170,7 @@
        "Vector((1.000, 2.000, 3.000))"
       ]
      },
-     "execution_count": 33,
+     "execution_count": 32,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1227,7 +1192,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 34,
+   "execution_count": 33,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -1240,7 +1205,7 @@
        "'Vector((1.000, 2.000, 3.000))'"
       ]
      },
-     "execution_count": 34,
+     "execution_count": 33,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1266,7 +1231,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 35,
+   "execution_count": 34,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -1279,7 +1244,7 @@
        "'Vector(1.0, ..., 3.0)[3]'"
       ]
      },
-     "execution_count": 35,
+     "execution_count": 34,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1301,7 +1266,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 36,
+   "execution_count": 35,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -1342,14 +1307,14 @@
     }
    },
    "source": [
-    "Below is a first implementation of the `Matrix` class that stores the entries internally as a `list` of `list`s.\n",
+    "Below is a first implementation of the `Matrix` class that stores the `._entries` internally as a `list` of `list`s.\n",
     "\n",
     "The `.__init__()` method ensures that all the rows come with the same number of columns. Again, we do not allow `Matrix` instances without any entries."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 37,
+   "execution_count": 36,
    "metadata": {
     "code_folding": [],
     "slideshow": {
@@ -1391,7 +1356,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 38,
+   "execution_count": 37,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -1404,7 +1369,7 @@
        "94113690738160"
       ]
      },
-     "execution_count": 38,
+     "execution_count": 37,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1413,6 +1378,30 @@
     "id(Matrix)"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "type"
+      ]
+     },
+     "execution_count": 38,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "type(Matrix)"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 39,
@@ -1421,30 +1410,6 @@
      "slide_type": "skip"
     }
    },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "type"
-      ]
-     },
-     "execution_count": 39,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "type(Matrix)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 40,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
    "outputs": [
     {
      "data": {
@@ -1452,7 +1417,7 @@
        "__main__.Matrix"
       ]
      },
-     "execution_count": 40,
+     "execution_count": 39,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1474,7 +1439,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 41,
+   "execution_count": 40,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -1487,7 +1452,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 42,
+   "execution_count": 41,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -1500,7 +1465,7 @@
        "140306180401856"
       ]
      },
-     "execution_count": 42,
+     "execution_count": 41,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1511,7 +1476,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 43,
+   "execution_count": 42,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -1524,7 +1489,7 @@
        "__main__.Matrix"
       ]
      },
-     "execution_count": 43,
+     "execution_count": 42,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1546,7 +1511,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 44,
+   "execution_count": 43,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -1559,7 +1524,7 @@
        "Matrix(((1.000, 2.000, 3.000,), (4.000, 5.000, 6.000,), (7.000, 8.000, 9.000,)))"
       ]
      },
-     "execution_count": 44,
+     "execution_count": 43,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1570,7 +1535,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 45,
+   "execution_count": 44,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -1602,7 +1567,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 46,
+   "execution_count": 45,
    "metadata": {
     "slideshow": {
      "slide_type": "skip"
@@ -1628,7 +1593,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 47,
+   "execution_count": 46,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -1673,12 +1638,14 @@
    "source": [
     "The methods we have seen so far are all **instance methods**. The characteristic idea behind an instance method is that the behavior it provides either depends on the state of a concrete instance or mutates it. In other words, an instance method *always* works with attributes on the `self` argument. If a method does *not* need access to `self` to do its job, it is conceptually *not* an instance method and we should probably convert it into another kind of method as shown below.\n",
     "\n",
-    "An example of an instance method from linear algebra is the `.transpose()` method below that switches the rows and columns of an *existing* `Matrix` instance and returns a *new* `Matrix` instance based off that. It is implemented by passing the *iterator* created with the [zip() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#zip) built-in as the `data` argument to the `Matrix` constructor: The expression `zip(*self._entries)` may be a bit hard to understand because of the involved unpacking but simply flips a `Vector`'s rows and columns. The built-in [list() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#func-list) constructor within the `.__init__()` method then materializes the iterator into the `._entries`. Without a concrete `Matrix`'s rows and columns, `.transpose()` does not make sense, conceptually speaking."
+    "An example of an instance method from linear algebra is the `.transpose()` method below that switches the rows and columns of an *existing* `Matrix` instance and returns a *new* `Matrix` instance based off that. It is implemented by passing the *iterator* created with the [zip() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#zip) built-in as the `data` argument to the `Matrix` constructor: The expression `zip(*self._entries)` may be a bit hard to understand because of the involved unpacking but simply flips a `Matrix`'s rows and columns. The built-in [list() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#func-list) constructor within the `.__init__()` method then materializes the iterator into the `._entries` attribute. Without a concrete `Matrix`'s rows and columns, `.transpose()` does not make sense, conceptually speaking.\n",
+    "\n",
+    "Also, we see that it is ok to reference a class from within one of its methods. While this seems trivial to some readers, others may find this confusing. The final versions of the `Vector` and `Matrix` classes in the [fourth part <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/11_classes/04_content.ipynb#The-final-Vector-and-Matrix-Classes) of this chapter show how this \"hard coded\" redundancy can be avoided."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 48,
+   "execution_count": 47,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -1713,7 +1680,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 49,
+   "execution_count": 48,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -1724,6 +1691,30 @@
     "m = Matrix([(1, 2, 3), (4, 5, 6), (7, 8, 9)])"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 49,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Matrix(((1.000, 2.000, 3.000,), (4.000, 5.000, 6.000,), (7.000, 8.000, 9.000,)))"
+      ]
+     },
+     "execution_count": 49,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "m"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 50,
@@ -1732,30 +1723,6 @@
      "slide_type": "fragment"
     }
    },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Matrix(((1.000, 2.000, 3.000,), (4.000, 5.000, 6.000,), (7.000, 8.000, 9.000,)))"
-      ]
-     },
-     "execution_count": 50,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "m"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 51,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "fragment"
-    }
-   },
    "outputs": [
     {
      "data": {
@@ -1763,7 +1730,7 @@
        "Matrix(((1.000, 4.000, 7.000,), (2.000, 5.000, 8.000,), (3.000, 6.000, 9.000,)))"
       ]
      },
-     "execution_count": 51,
+     "execution_count": 50,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1785,7 +1752,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 52,
+   "execution_count": 51,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -1796,6 +1763,30 @@
     "n = m.transpose().transpose()"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 52,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Matrix(((1.000, 2.000, 3.000,), (4.000, 5.000, 6.000,), (7.000, 8.000, 9.000,)))"
+      ]
+     },
+     "execution_count": 52,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "n"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 53,
@@ -1808,7 +1799,7 @@
     {
      "data": {
       "text/plain": [
-       "Matrix(((1.000, 2.000, 3.000,), (4.000, 5.000, 6.000,), (7.000, 8.000, 9.000,)))"
+       "False"
       ]
      },
      "execution_count": 53,
@@ -1817,7 +1808,18 @@
     }
    ],
    "source": [
-    "n"
+    "m is n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Unintuitively, the comparison operator `==` returns a wrong result as `m` and `n` have `_entries` attributes that compare equal. We fix this in the \"*Operator Overloading*\" section later in this chapter."
    ]
   },
   {
@@ -1840,41 +1842,6 @@
      "output_type": "execute_result"
     }
    ],
-   "source": [
-    "m is n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "slideshow": {
-     "slide_type": "skip"
-    }
-   },
-   "source": [
-    "Unintuitively, the comparison operator `==` returns a wrong result as `m` and `n` have `_entries` attributes that compare equal. We fix this in the \"*Operator Overloading*\" section later in this chapter."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 55,
-   "metadata": {
-    "slideshow": {
-     "slide_type": "fragment"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "False"
-      ]
-     },
-     "execution_count": 55,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
    "source": [
     "m == n"
    ]
@@ -1894,7 +1861,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 56,
+   "execution_count": 55,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -1933,7 +1900,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 57,
+   "execution_count": 56,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -1946,7 +1913,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 58,
+   "execution_count": 57,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -1959,7 +1926,7 @@
        "Matrix(((1.000, 2.000, 3.000,), (4.000, 5.000, 6.000,), (7.000, 8.000, 9.000,)))"
       ]
      },
-     "execution_count": 58,
+     "execution_count": 57,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2007,7 +1974,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 59,
+   "execution_count": 58,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -2051,7 +2018,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 60,
+   "execution_count": 59,
    "metadata": {
     "slideshow": {
      "slide_type": "slide"
@@ -2064,7 +2031,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 61,
+   "execution_count": 60,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
@@ -2077,7 +2044,7 @@
        "(2, 3)"
       ]
      },
-     "execution_count": 61,
+     "execution_count": 60,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -2099,7 +2066,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 62,
+   "execution_count": 61,
    "metadata": {
     "slideshow": {
      "slide_type": "fragment"
diff --git a/11_classes/02_content.ipynb b/11_classes/02_content.ipynb
index 520339f..644fad2 100644
--- a/11_classes/02_content.ipynb
+++ b/11_classes/02_content.ipynb
@@ -1577,7 +1577,7 @@
     }
    },
    "source": [
-    "After this discussion of mutable `Vector` and `Matrix` classes, we continue with immutable classes in the rest of this chapter."
+    "After this discussion of mutable `Vector` and `Matrix` classes, we continue with immutable implementations in the rest of this chapter. To lower the chance that we accidently design parts of our classes to be mutable, we replace the built-in [list() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#func-list) constructor with [tuple() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#func-tuple) in the `.__init__()` methods. As we learn in [Chapter 7 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/07_sequences/03_content.ipynb#Tuples-are-like-%22Immutable-Lists%22), `tuple`s are like immutable `list`s."
    ]
   },
   {
diff --git a/11_classes/03_content.ipynb b/11_classes/03_content.ipynb
index 9dc08a2..def4be5 100644
--- a/11_classes/03_content.ipynb
+++ b/11_classes/03_content.ipynb
@@ -82,7 +82,7 @@
     "class Vector:\n",
     "\n",
     "    def __init__(self, data):\n",
-    "        self._entries = list(float(x) for x in data)\n",
+    "        self._entries = tuple(float(x) for x in data)\n",
     "        # ...\n",
     "\n",
     "    def __repr__(self):\n",
@@ -111,7 +111,7 @@
     "class Matrix:\n",
     "\n",
     "    def __init__(self, data):\n",
-    "        self._entries = list(list(float(x) for x in r) for r in data)\n",
+    "        self._entries = tuple(tuple(float(x) for x in r) for r in data)\n",
     "        # ...\n",
     "\n",
     "    def __repr__(self):\n",
@@ -678,7 +678,7 @@
     "class Vector:\n",
     "\n",
     "    def __init__(self, data):\n",
-    "        self._entries = list(float(x) for x in data)\n",
+    "        self._entries = tuple(float(x) for x in data)\n",
     "        # ...\n",
     "\n",
     "    def __repr__(self):\n",
@@ -692,7 +692,7 @@
     "        return iter(self._entries)\n",
     "\n",
     "    def __add__(self, other):\n",
-    "        if isinstance(other, self.__class__):  # vector addition\n",
+    "        if isinstance(other, Vector):  # vector addition\n",
     "            if len(self) != len(other):\n",
     "                raise ValueError(\"vectors must be of the same length\")\n",
     "            return Vector(x + y for (x, y) in zip(self, other))\n",
@@ -704,7 +704,7 @@
     "        return self + other\n",
     "\n",
     "    def __sub__(self, other):\n",
-    "        if isinstance(other, self.__class__):  # vector subtraction\n",
+    "        if isinstance(other, Vector):  # vector subtraction\n",
     "            if len(self) != len(other):\n",
     "                raise ValueError(\"vectors must be of the same length\")\n",
     "            return Vector(x - y for (x, y) in zip(self, other))\n",
@@ -719,7 +719,7 @@
     "        return NotImplemented\n",
     "\n",
     "    def __mul__(self, other):\n",
-    "        if isinstance(other, self.__class__):  # dot product\n",
+    "        if isinstance(other, Vector):  # dot product\n",
     "            if len(self) != len(other):\n",
     "                raise ValueError(\"vectors must be of the same length\")\n",
     "            return sum(x * y for (x, y) in zip(self, other))\n",
@@ -1142,7 +1142,7 @@
     "class Matrix:\n",
     "\n",
     "    def __init__(self, data):\n",
-    "        self._entries = list(list(float(x) for x in r) for r in data)\n",
+    "        self._entries = tuple(tuple(float(x) for x in r) for r in data)\n",
     "        # ...\n",
     "\n",
     "    def __repr__(self):\n",
@@ -1169,7 +1169,7 @@
     "        return (self._entries[r][c] for r in range(self.n_rows) for c in range(self.n_cols))\n",
     "\n",
     "    def __add__(self, other):\n",
-    "        if isinstance(other, self.__class__):  # matrix addition\n",
+    "        if isinstance(other, Matrix):  # matrix addition\n",
     "            if (self.n_rows != other.n_rows) or (self.n_cols != other.n_cols):\n",
     "                raise ValueError(\"matrices must have the same dimensions\")\n",
     "            return Matrix((s_col + o_col for (s_col, o_col) in zip(s_row, o_row))\n",
@@ -1184,7 +1184,7 @@
     "        return self + other\n",
     "\n",
     "    def __sub__(self, other):\n",
-    "        if isinstance(other, self.__class__):  # matrix subtraction\n",
+    "        if isinstance(other, Matrix):  # matrix subtraction\n",
     "            if (self.n_rows != other.n_rows) or (self.n_cols != other.n_cols):\n",
     "                raise ValueError(\"matrices must have the same dimensions\")\n",
     "            return Matrix((s_col - o_col for (s_col, o_col) in zip(s_row, o_row))\n",
@@ -1211,7 +1211,7 @@
     "            return Matrix((x * other for x in r) for r in self._entries)\n",
     "        elif isinstance(other, Vector):\n",
     "            return self._matrix_multiply(other.as_matrix()).as_vector()\n",
-    "        elif isinstance(other, self.__class__):\n",
+    "        elif isinstance(other, Matrix):\n",
     "            return self._matrix_multiply(other)\n",
     "        return NotImplemented\n",
     "\n",
@@ -1720,7 +1720,7 @@
     "    zero_threshold = 1e-12\n",
     "\n",
     "    def __init__(self, data):\n",
-    "        self._entries = list(float(x) for x in data)\n",
+    "        self._entries = tuple(float(x) for x in data)\n",
     "        # ...\n",
     "\n",
     "    def __repr__(self):\n",
@@ -1734,7 +1734,7 @@
     "        return iter(self._entries)\n",
     "\n",
     "    def __eq__(self, other):\n",
-    "        if isinstance(other, self.__class__):\n",
+    "        if isinstance(other, Vector):\n",
     "            if len(self) != len(other):\n",
     "                raise ValueError(\"vectors must be of the same length\")\n",
     "            for x, y in zip(self, other):\n",
@@ -1990,7 +1990,7 @@
     "class Vector:\n",
     "\n",
     "    def __init__(self, data):\n",
-    "        self._entries = list(float(x) for x in data)\n",
+    "        self._entries = tuple(float(x) for x in data)\n",
     "        # ...\n",
     "\n",
     "    def __repr__(self):\n",
diff --git a/11_classes/04_content.ipynb b/11_classes/04_content.ipynb
new file mode 100644
index 0000000..51b0818
--- /dev/null
+++ b/11_classes/04_content.ipynb
@@ -0,0 +1,2719 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "**Note**: Click on \"*Kernel*\" > \"*Restart Kernel and Clear All Outputs*\" in [JupyterLab](https://jupyterlab.readthedocs.io/en/stable/) *before* reading this notebook to reset its output. If you cannot run this file on your machine, you may want to open it [in the cloud <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_mb.png\">](https://mybinder.org/v2/gh/webartifex/intro-to-python/develop?urlpath=lab/tree/11_classes/04_content.ipynb)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "# Chapter 11: Classes & Instances (continued)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "In this fourth part of the chapter, we finalize our `Vector` and `Matrix` classes. As both `class` definitions have become rather lengthy, we learn how we to organize them into a Python package and import them in this Jupyter notebook. "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Packages vs. Modules"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In [Chapter 2 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/02_functions/02_content.ipynb#Local-Modules-and-Packages), we introduce the concept of a Python module that is imported with the `import` statement. Essentially, a **module** is a single plain text \\*.py file on disk that contains Python code (e.g., [*sample_module.py* <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_gh.png\">](https://github.com/webartifex/intro-to-python/blob/develop/02_functions/sample_module.py) in [Chapter 2's folder <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_gh.png\">](https://github.com/webartifex/intro-to-python/tree/develop/02_functions)).\n",
+    "\n",
+    "Conceptually, a **package** is a generalization of a module whose code is split across several \\*.py to achieve a better organization of the individual parts. The \\*.py files are stored within a folder (e.g., [*sample_package* <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_gh.png\">](https://github.com/webartifex/intro-to-python/tree/develop/11_classes/sample_package) in [Chapter 11's folder <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_gh.png\">](https://github.com/webartifex/intro-to-python/tree/develop/11_classes)). In addition to that, a \"*\\_\\_init\\_\\_.py*\" file that may be empty must be put inside the folder. The latter is what the Python interpreter looks for to decide if a folder is a package or not.\n",
+    "\n",
+    "Let's look at an example with the final version of our `Vector` and `Matrix` classes.\n",
+    "\n",
+    "`!pwd` shows the location of this Jupyter notebook on the computer you are running [JupyterLab](https://jupyterlab.readthedocs.io/en/stable/) on: It is the local equivalent of [Chapter 11's folder <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_gh.png\">](https://github.com/webartifex/intro-to-python/tree/develop/11_classes) in this book's [GitHub repository <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_gh.png\">](https://github.com/webartifex/intro-to-python)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "/home/webartifex/repos/intro-to-python/11_classes\n"
+     ]
+    }
+   ],
+   "source": [
+    "!pwd"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "`!ls` lists all the files and folders in the current location: These are Chapter 11's Jupyter notebooks (i.e., the \\*.ipynb files) and the [*sample_package* <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_gh.png\">](https://github.com/webartifex/intro-to-python/tree/develop/11_classes/sample_package) folder. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "00_content.ipynb    02_content.ipynb  04_content.ipynb\n",
+      "01_exercises.ipynb  03_content.ipynb  sample_package\n"
+     ]
+    }
+   ],
+   "source": [
+    "!ls"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "If we run `!ls` with the `sample_package` folder as the argument, we see the folder's contents: Four \\*.py files. Alternatively, you can use [JupyterLab' File Browser](https://jupyterlab.readthedocs.io/en/stable/user/interface.html?highlight=file%20browser#left-sidebar) on the left to navigate into the package."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "__init__.py  matrix.py\tutils.py  vector.py\n"
+     ]
+    }
+   ],
+   "source": [
+    "!ls sample_package"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The package is organized such that the [*matrix.py* <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_gh.png\">](https://github.com/webartifex/intro-to-python/blob/develop/11_classes/sample_package/matrix.py) and [*vector.py* <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_gh.png\">](https://github.com/webartifex/intro-to-python/blob/develop/11_classes/sample_package/vector.py) modules each define just one class, `Matrix` and `Vector`. That is intentional as both classes consist of several hundred lines of code and comments.\n",
+    "\n",
+    "The [*utils.py* <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_gh.png\">](https://github.com/webartifex/intro-to-python/blob/develop/11_classes/sample_package/utils.py) module contains code that is shared by both classes. Such code snippets are commonly called \"utilities\" or \"helpers,\" which explains the module's name.\n",
+    "\n",
+    "Finally, the [*\\_\\_init\\_\\_.py* <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_gh.png\">](https://github.com/webartifex/intro-to-python/blob/develop/11_classes/sample_package/__init__.py) file contains mostly meta information and defines what objects should be importable from the package's top level.\n",
+    "\n",
+    "With the `import` statement, we can import the entire package just as we would import a module from the [standard library <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/index.html)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import sample_package as pkg"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The above cell runs the code in the [*\\_\\_init\\_\\_.py* <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_gh.png\">](https://github.com/webartifex/intro-to-python/blob/develop/11_classes/sample_package/__init__.py) file from top to bottom, which in turn runs the [*matrix.py* <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_gh.png\">](https://github.com/webartifex/intro-to-python/blob/develop/11_classes/sample_package/matrix.py), [*utils.py* <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_gh.png\">](https://github.com/webartifex/intro-to-python/blob/develop/11_classes/sample_package/utils.py), and [*vector.py* <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_gh.png\">](https://github.com/webartifex/intro-to-python/blob/develop/11_classes/sample_package/vector.py) modules (cf., look at the `import` statements in the four \\*.py files to get the idea). As both [*matrix.py* <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_gh.png\">](https://github.com/webartifex/intro-to-python/blob/develop/11_classes/sample_package/matrix.py) and [*vector.py* <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_gh.png\">](https://github.com/webartifex/intro-to-python/blob/develop/11_classes/sample_package/vector.py) depend on each other (i.e., the `Matrix` class needs the `Vector` class to work and vice versa), understanding the order in that the modules are executed is not trivial. Without going into detail, we mention that Python guarantees that each \\*.py file is run only once and figures out the order on its own. If Python is unable to do that, for example, due to unresolvable cirular imports, it aborts with an `ImportError`.\n",
+    "\n",
+    "Below, `pkg` is an object of type `module` ..."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<module 'sample_package' from '/home/webartifex/repos/intro-to-python/11_classes/sample_package/__init__.py'>"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pkg"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "module"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "type(pkg)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "... and we use the built-in [dir() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#dir) function to check what attributes `pkg` comes with."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['Matrix',\n",
+       " 'Vector',\n",
+       " '__all__',\n",
+       " '__author__',\n",
+       " '__builtins__',\n",
+       " '__cached__',\n",
+       " '__doc__',\n",
+       " '__file__',\n",
+       " '__loader__',\n",
+       " '__name__',\n",
+       " '__package__',\n",
+       " '__path__',\n",
+       " '__spec__',\n",
+       " '__version__',\n",
+       " 'matrix',\n",
+       " 'utils',\n",
+       " 'vector']"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "dir(pkg)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The package's meta information and documentation are automatically parsed from the [*\\_\\_init\\_\\_.py* <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_gh.png\">](https://github.com/webartifex/intro-to-python/blob/develop/11_classes/sample_package/__init__.py) file."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Help on package linear_algebra_tools:\n",
+      "\n",
+      "NAME\n",
+      "    linear_algebra_tools - This package provides linear algebra functionalities.\n",
+      "\n",
+      "DESCRIPTION\n",
+      "    The package is split into three modules:\n",
+      "    - matrix: defines the Matrix class\n",
+      "    - vector: defines the Vector class\n",
+      "    - utils: defines the norm() function that is shared by Matrix and Vector\n",
+      "             and package-wide constants\n",
+      "    \n",
+      "    The classes implement arithmetic operations involving vectors and matrices.\n",
+      "    \n",
+      "    See the docstrings in the modules and classes for further info.\n",
+      "\n",
+      "PACKAGE CONTENTS\n",
+      "    matrix\n",
+      "    utils\n",
+      "    vector\n",
+      "\n",
+      "CLASSES\n",
+      "    builtins.object\n",
+      "        sample_package.matrix.Matrix\n",
+      "        sample_package.vector.Vector\n",
+      "    \n",
+      "    class Matrix(builtins.object)\n",
+      "     |  Matrix(data)\n",
+      "     |  \n",
+      "     |  An m-by-n-dimensional matrix from linear algebra.\n",
+      "     |  \n",
+      "     |  All entries are converted to floats, or whatever is set in the typing attribute.\n",
+      "     |  \n",
+      "     |  Attributes:\n",
+      "     |      storage (callable): data type used to store the entries internally;\n",
+      "     |          defaults to tuple\n",
+      "     |      typing (callable): type casting applied to all entries upon creation;\n",
+      "     |          defaults to float\n",
+      "     |      vector_cls (vector.Vector): a reference to the Vector class to work with\n",
+      "     |      zero_threshold (float): max. tolerance when comparing an entry to zero;\n",
+      "     |          defaults to 1e-12\n",
+      "     |  \n",
+      "     |  Methods defined here:\n",
+      "     |  \n",
+      "     |  __abs__(self)\n",
+      "     |      The Frobenius norm of a Matrix.\n",
+      "     |  \n",
+      "     |  __add__(self, other)\n",
+      "     |      Handle `self + other` and `other + self`.\n",
+      "     |      \n",
+      "     |      This may be either matrix addition or broadcasting addition.\n",
+      "     |      \n",
+      "     |      Example Usage:\n",
+      "     |          >>> Matrix([(1, 2), (3, 4)]) + Matrix([(2, 3), (4, 5)])\n",
+      "     |          Matrix(((3.000, 5.000,), (7.000, 9.000,)))\n",
+      "     |      \n",
+      "     |          >>> Matrix([(1, 2), (3, 4)]) + 5\n",
+      "     |          Matrix(((6.000, 7.000,), (8.000, 9.000,)))\n",
+      "     |      \n",
+      "     |          >>> 10 + Matrix([(1, 2), (3, 4)])\n",
+      "     |          Matrix(((11.000, 12.000,), (13.000, 14.000,)))\n",
+      "     |  \n",
+      "     |  __bool__(self)\n",
+      "     |      A Matrix is truthy if its Frobenius norm is strictly positive.\n",
+      "     |  \n",
+      "     |  __eq__(self, other)\n",
+      "     |      Handle `self == other`.\n",
+      "     |      \n",
+      "     |      Compare two Matrix instances for equality.\n",
+      "     |      \n",
+      "     |      Example Usage:\n",
+      "     |          >>> Matrix([(1, 2), (3, 4)]) == Matrix([(1, 2), (3, 4)])\n",
+      "     |          True\n",
+      "     |      \n",
+      "     |          >>> Matrix([(1, 2), (3, 4)]) == Matrix([(5, 6), (7, 8)])\n",
+      "     |          False\n",
+      "     |  \n",
+      "     |  __float__(self)\n",
+      "     |      Cast a Matrix as a scalar.\n",
+      "     |      \n",
+      "     |      Returns:\n",
+      "     |          scalar (float)\n",
+      "     |      \n",
+      "     |      Raises:\n",
+      "     |          RuntimeError: if the Matrix has more than one entry\n",
+      "     |  \n",
+      "     |  __getitem__(self, index)\n",
+      "     |      Obtain an individual entry of a Matrix.\n",
+      "     |      \n",
+      "     |      Args:\n",
+      "     |          index (int / tuple of int's): if index is an integer,\n",
+      "     |              the Matrix is viewed as a sequence in row-major order;\n",
+      "     |              if index is a tuple of integers, the first one refers to\n",
+      "     |              the row and the second one to the column of the entry\n",
+      "     |      \n",
+      "     |      Returns:\n",
+      "     |          entry (Matrix.typing)\n",
+      "     |      \n",
+      "     |      Example Usage:\n",
+      "     |          >>> m = Matrix([(1, 2), (3, 4)])\n",
+      "     |          >>> m[0]\n",
+      "     |          1.0\n",
+      "     |          >>> m[-1]\n",
+      "     |          4.0\n",
+      "     |          >>> m[0, 1]\n",
+      "     |          2.0\n",
+      "     |  \n",
+      "     |  __init__(self, data)\n",
+      "     |      Create a new matrix.\n",
+      "     |      \n",
+      "     |      Args:\n",
+      "     |          data (sequence of sequences): the matrix's entries;\n",
+      "     |              viewed as a sequence of the matrix's rows (i.e., row-major order);\n",
+      "     |              use the .from_columns() class method if the data come as a sequence\n",
+      "     |              of the matrix's columns (i.e., column-major order)\n",
+      "     |      \n",
+      "     |      Raises:\n",
+      "     |          ValueError:\n",
+      "     |              - if no entries are provided\n",
+      "     |              - if the number of columns is inconsistent across the rows\n",
+      "     |      \n",
+      "     |      Example Usage:\n",
+      "     |          >>> Matrix([(1, 2), (3, 4)])\n",
+      "     |          Matrix(((1.000, 2.000,), (3.000, 4.000,)))\n",
+      "     |  \n",
+      "     |  __iter__(self)\n",
+      "     |      Loop over a Matrix's entries.\n",
+      "     |      \n",
+      "     |      See .entries() for more customization options.\n",
+      "     |  \n",
+      "     |  __len__(self)\n",
+      "     |      Number of entries in a Matrix.\n",
+      "     |  \n",
+      "     |  __mul__(self, other)\n",
+      "     |      Handle `self * other` and `other * self`.\n",
+      "     |      \n",
+      "     |      This may be either scalar multiplication, matrix-vector multiplication,\n",
+      "     |      vector-matrix multiplication, or matrix-matrix multiplication.\n",
+      "     |      \n",
+      "     |      Example Usage:\n",
+      "     |          >>> Matrix([(1, 2), (3, 4)]) * Matrix([(1, 2), (3, 4)])\n",
+      "     |          Matrix(((7.000, 10.000,), (15.000, 22.000,)))\n",
+      "     |      \n",
+      "     |          >>> 2 * Matrix([(1, 2), (3, 4)])\n",
+      "     |          Matrix(((2.000, 4.000,), (6.000, 8.000,)))\n",
+      "     |      \n",
+      "     |          >>> Matrix([(1, 2), (3, 4)]) * 3\n",
+      "     |          Matrix(((3.000, 6.000,), (9.000, 12.000,)))\n",
+      "     |      \n",
+      "     |          Matrix-vector and vector-matrix multiplication are not commutative.\n",
+      "     |      \n",
+      "     |          >>> Matrix([(1, 2), (3, 4)]) * Vector([5, 6])\n",
+      "     |          Vector((17.000, 39.000))\n",
+      "     |      \n",
+      "     |          >>> Vector([5, 6]) * Matrix([(1, 2), (3, 4)])\n",
+      "     |          Vector((23.000, 34.000))\n",
+      "     |  \n",
+      "     |  __neg__(self)\n",
+      "     |      Handle `-self`.\n",
+      "     |      \n",
+      "     |      Negate all entries of a Matrix.\n",
+      "     |  \n",
+      "     |  __pos__(self)\n",
+      "     |      Handle `+self`.\n",
+      "     |      \n",
+      "     |      This is simply an identity operator returning the Matrix itself.\n",
+      "     |  \n",
+      "     |  __radd__(self, other)\n",
+      "     |      See docstring for .__add__().\n",
+      "     |  \n",
+      "     |  __repr__(self)\n",
+      "     |      Text representation of a Matrix.\n",
+      "     |  \n",
+      "     |  __reversed__(self)\n",
+      "     |      Loop over a Matrix's entries in reverse order.\n",
+      "     |      \n",
+      "     |      See .entries() for more customization options.\n",
+      "     |  \n",
+      "     |  __rmul__(self, other)\n",
+      "     |      See docstring for .__mul__().\n",
+      "     |  \n",
+      "     |  __rsub__(self, other)\n",
+      "     |      See docstring for .__sub__().\n",
+      "     |  \n",
+      "     |  __str__(self)\n",
+      "     |      Human-readable text representation of a Matrix.\n",
+      "     |  \n",
+      "     |  __sub__(self, other)\n",
+      "     |      Handle `self - other` and `other - self`.\n",
+      "     |      \n",
+      "     |      This may be either matrix subtraction or broadcasting subtraction.\n",
+      "     |      \n",
+      "     |      Example Usage:\n",
+      "     |          >>> Matrix([(2, 3), (4, 5)]) - Matrix([(1, 2), (3, 4)])\n",
+      "     |          Matrix(((1.000, 1.000,), (1.000, 1.000,)))\n",
+      "     |      \n",
+      "     |          >>> Matrix([(1, 2), (3, 4)]) - 1\n",
+      "     |          Matrix(((0.000, 1.000,), (2.000, 3.000,)))\n",
+      "     |      \n",
+      "     |          >>> 10 - Matrix([(1, 2), (3, 4)])\n",
+      "     |          Matrix(((9.000, 8.000,), (7.000, 6.000,)))\n",
+      "     |  \n",
+      "     |  __truediv__(self, other)\n",
+      "     |      Handle `self / other`.\n",
+      "     |      \n",
+      "     |      Divide a Matrix by a scalar.\n",
+      "     |      \n",
+      "     |      Example Usage:\n",
+      "     |          >>> Matrix([(1, 2), (3, 4)]) / 4\n",
+      "     |          Matrix(((0.250, 0.500,), (0.750, 1.000,)))\n",
+      "     |  \n",
+      "     |  as_vector(self)\n",
+      "     |      Get a Vector representation of a Matrix.\n",
+      "     |      \n",
+      "     |      Returns:\n",
+      "     |          vector (vector.Vector)\n",
+      "     |      \n",
+      "     |      Raises:\n",
+      "     |          RuntimeError: if one of the two dimensions, .n_rows or .n_cols, is not 1\n",
+      "     |      \n",
+      "     |      Example Usage:\n",
+      "     |          >>> Matrix([(1, 2, 3)]).as_vector()\n",
+      "     |          Vector((1.000, 2.000, 3.000))\n",
+      "     |  \n",
+      "     |  cols(self)\n",
+      "     |      Loop over a Matrix's columns.\n",
+      "     |      \n",
+      "     |      Returns:\n",
+      "     |          columns (generator): produces a Matrix's columns as Vectors\n",
+      "     |  \n",
+      "     |  entries(self, *, reverse=False, row_major=True)\n",
+      "     |      Loop over a Matrix's entries.\n",
+      "     |      \n",
+      "     |      Args:\n",
+      "     |          reverse (bool): flag to loop backwards; defaults to False\n",
+      "     |          row_major (bool): flag to loop in row-major order; defaults to True\n",
+      "     |      \n",
+      "     |      Returns:\n",
+      "     |          entries (generator): produces a Matrix's entries\n",
+      "     |  \n",
+      "     |  rows(self)\n",
+      "     |      Loop over a Matrix's rows.\n",
+      "     |      \n",
+      "     |      Returns:\n",
+      "     |          rows (generator): produces a Matrix's rows as Vectors\n",
+      "     |  \n",
+      "     |  transpose(self)\n",
+      "     |      Switch the rows and columns of a Matrix.\n",
+      "     |      \n",
+      "     |      Returns:\n",
+      "     |          matrix (Matrix)\n",
+      "     |      \n",
+      "     |      Example Usage:\n",
+      "     |          >>> m = Matrix([(1, 2), (3, 4)])\n",
+      "     |          >>> m\n",
+      "     |          Matrix(((1.000, 2.000,), (3.000, 4.000,)))\n",
+      "     |          >>> m.transpose()\n",
+      "     |          Matrix(((1.000, 3.000,), (2.000, 4.000,)))\n",
+      "     |  \n",
+      "     |  ----------------------------------------------------------------------\n",
+      "     |  Class methods defined here:\n",
+      "     |  \n",
+      "     |  from_columns(data) from builtins.type\n",
+      "     |      Create a new matrix.\n",
+      "     |      \n",
+      "     |      This is an alternative constructor for data provided in column-major order.\n",
+      "     |      \n",
+      "     |      Args:\n",
+      "     |          data (sequence of sequences): the matrix's entries;\n",
+      "     |              viewed as a sequence of the matrix's columns (i.e., column-major order);\n",
+      "     |              use the normal constructor method if the data come as a sequence\n",
+      "     |              of the matrix's rows (i.e., row-major order)\n",
+      "     |      \n",
+      "     |      Raises:\n",
+      "     |          ValueError:\n",
+      "     |              - if no entries are provided\n",
+      "     |              - if the number of rows is inconsistent across the columns\n",
+      "     |      \n",
+      "     |      Example Usage:\n",
+      "     |          >>> Matrix.from_columns([(1, 2), (3, 4)])\n",
+      "     |          Matrix(((1.000, 3.000,), (2.000, 4.000,)))\n",
+      "     |  \n",
+      "     |  from_rows(data) from builtins.type\n",
+      "     |      See docstring for .__init__().\n",
+      "     |  \n",
+      "     |  ----------------------------------------------------------------------\n",
+      "     |  Readonly properties defined here:\n",
+      "     |  \n",
+      "     |  n_cols\n",
+      "     |      Number of columns in a Matrix.\n",
+      "     |  \n",
+      "     |  n_rows\n",
+      "     |      Number of rows in a Matrix.\n",
+      "     |  \n",
+      "     |  ----------------------------------------------------------------------\n",
+      "     |  Data descriptors defined here:\n",
+      "     |  \n",
+      "     |  __dict__\n",
+      "     |      dictionary for instance variables (if defined)\n",
+      "     |  \n",
+      "     |  __weakref__\n",
+      "     |      list of weak references to the object (if defined)\n",
+      "     |  \n",
+      "     |  ----------------------------------------------------------------------\n",
+      "     |  Data and other attributes defined here:\n",
+      "     |  \n",
+      "     |  __hash__ = None\n",
+      "     |  \n",
+      "     |  storage = <class 'tuple'>\n",
+      "     |      Built-in immutable sequence.\n",
+      "     |      \n",
+      "     |      If no argument is given, the constructor returns an empty tuple.\n",
+      "     |      If iterable is specified the tuple is initialized from iterable's items.\n",
+      "     |      \n",
+      "     |      If the argument is a tuple, the return value is the same object.\n",
+      "     |  \n",
+      "     |  typing = <class 'float'>\n",
+      "     |      Convert a string or number to a floating point number, if possible.\n",
+      "     |  \n",
+      "     |  vector_cls = <class 'sample_package.vector.Vector'>\n",
+      "     |      A one-dimensional vector from linear algebra.\n",
+      "     |      \n",
+      "     |      All entries are converted to floats, or whatever is set in the typing attribute.\n",
+      "     |      \n",
+      "     |      Attributes:\n",
+      "     |          matrix_cls (matrix.Matrix): a reference to the Matrix class to work with\n",
+      "     |          storage (callable): data type used to store the entries internally;\n",
+      "     |              defaults to tuple\n",
+      "     |          typing (callable): type casting applied to all entries upon creation;\n",
+      "     |              defaults to float\n",
+      "     |          zero_threshold (float): max. tolerance when comparing an entry to zero;\n",
+      "     |              defaults to 1e-12\n",
+      "     |  \n",
+      "     |  zero_threshold = 1e-12\n",
+      "    \n",
+      "    class Vector(builtins.object)\n",
+      "     |  Vector(data)\n",
+      "     |  \n",
+      "     |  A one-dimensional vector from linear algebra.\n",
+      "     |  \n",
+      "     |  All entries are converted to floats, or whatever is set in the typing attribute.\n",
+      "     |  \n",
+      "     |  Attributes:\n",
+      "     |      matrix_cls (matrix.Matrix): a reference to the Matrix class to work with\n",
+      "     |      storage (callable): data type used to store the entries internally;\n",
+      "     |          defaults to tuple\n",
+      "     |      typing (callable): type casting applied to all entries upon creation;\n",
+      "     |          defaults to float\n",
+      "     |      zero_threshold (float): max. tolerance when comparing an entry to zero;\n",
+      "     |          defaults to 1e-12\n",
+      "     |  \n",
+      "     |  Methods defined here:\n",
+      "     |  \n",
+      "     |  __abs__(self)\n",
+      "     |      The Euclidean norm of a vector.\n",
+      "     |  \n",
+      "     |  __add__(self, other)\n",
+      "     |      Handle `self + other` and `other + self`.\n",
+      "     |      \n",
+      "     |      This may be either vector addition or broadcasting addition.\n",
+      "     |      \n",
+      "     |      Example Usage:\n",
+      "     |          >>> Vector([1, 2, 3]) + Vector([2, 3, 4])\n",
+      "     |          Vector((3.000, 5.000, 7.000))\n",
+      "     |      \n",
+      "     |          >>> Vector([1, 2, 3]) + 4\n",
+      "     |          Vector((5.000, 6.000, 7.000))\n",
+      "     |      \n",
+      "     |          >>> 10 + Vector([1, 2, 3])\n",
+      "     |          Vector((11.000, 12.000, 13.000))\n",
+      "     |  \n",
+      "     |  __bool__(self)\n",
+      "     |      A Vector is truthy if its Euclidean norm is strictly positive.\n",
+      "     |  \n",
+      "     |  __eq__(self, other)\n",
+      "     |      Handle `self == other`.\n",
+      "     |      \n",
+      "     |      Compare two Vectors for equality.\n",
+      "     |      \n",
+      "     |      Example Usage:\n",
+      "     |          >>> Vector([1, 2, 3]) == Vector([1, 2, 3])\n",
+      "     |          True\n",
+      "     |      \n",
+      "     |          >>> Vector([1, 2, 3]) == Vector([4, 5, 6])\n",
+      "     |          False\n",
+      "     |  \n",
+      "     |  __float__(self)\n",
+      "     |      Cast a Vector as a scalar.\n",
+      "     |      \n",
+      "     |      Returns:\n",
+      "     |          scalar (float)\n",
+      "     |      \n",
+      "     |      Raises:\n",
+      "     |          RuntimeError: if the Vector has more than one entry\n",
+      "     |  \n",
+      "     |  __getitem__(self, index)\n",
+      "     |      Obtain an individual entry of a Vector.\n",
+      "     |  \n",
+      "     |  __init__(self, data)\n",
+      "     |      Create a new vector.\n",
+      "     |      \n",
+      "     |      Args:\n",
+      "     |          data (sequence): the vector's entries\n",
+      "     |      \n",
+      "     |      Raises:\n",
+      "     |          ValueError: if no entries are provided\n",
+      "     |      \n",
+      "     |      Example Usage:\n",
+      "     |          >>> Vector([1, 2, 3])\n",
+      "     |          Vector((1.000, 2.000, 3.000))\n",
+      "     |      \n",
+      "     |          >>> Vector(range(3))\n",
+      "     |          Vector((0.000, 1.000, 2.000))\n",
+      "     |  \n",
+      "     |  __iter__(self)\n",
+      "     |      Loop over a Vector's entries.\n",
+      "     |  \n",
+      "     |  __len__(self)\n",
+      "     |      Number of entries in a Vector.\n",
+      "     |  \n",
+      "     |  __mul__(self, other)\n",
+      "     |      Handle `self * other` and `other * self`.\n",
+      "     |      \n",
+      "     |      This may be either the dot product of two vectors or scalar multiplication.\n",
+      "     |      \n",
+      "     |      Example Usage:\n",
+      "     |          >>> Vector([1, 2, 3]) * Vector([2, 3, 4])\n",
+      "     |          20.0\n",
+      "     |      \n",
+      "     |          >>> 2 * Vector([1, 2, 3])\n",
+      "     |          Vector((2.000, 4.000, 6.000))\n",
+      "     |      \n",
+      "     |          >>> Vector([1, 2, 3]) * 3\n",
+      "     |          Vector((3.000, 6.000, 9.000))\n",
+      "     |  \n",
+      "     |  __neg__(self)\n",
+      "     |      Handle `-self`.\n",
+      "     |      \n",
+      "     |      Negate all entries of a Vector.\n",
+      "     |  \n",
+      "     |  __pos__(self)\n",
+      "     |      Handle `+self`.\n",
+      "     |      \n",
+      "     |      This is simply an identity operator returning the Vector itself.\n",
+      "     |  \n",
+      "     |  __radd__(self, other)\n",
+      "     |      See docstring for .__add__().\n",
+      "     |  \n",
+      "     |  __repr__(self)\n",
+      "     |      Text representation of a Vector.\n",
+      "     |  \n",
+      "     |  __reversed__(self)\n",
+      "     |      Loop over a Vector's entries in reverse order.\n",
+      "     |  \n",
+      "     |  __rmul__(self, other)\n",
+      "     |      See docstring for .__mul__().\n",
+      "     |  \n",
+      "     |  __rsub__(self, other)\n",
+      "     |      See docstring for .__sub__().\n",
+      "     |  \n",
+      "     |  __str__(self)\n",
+      "     |      Human-readable text representation of a Vector.\n",
+      "     |  \n",
+      "     |  __sub__(self, other)\n",
+      "     |      Handle `self - other` and `other - self`.\n",
+      "     |      \n",
+      "     |      This may be either vector subtraction or broadcasting subtraction.\n",
+      "     |      \n",
+      "     |      Example Usage:\n",
+      "     |          >>> Vector([7, 8, 9]) - Vector([1, 2, 3])\n",
+      "     |          Vector((6.000, 6.000, 6.000))\n",
+      "     |      \n",
+      "     |          >>> Vector([1, 2, 3]) - 1\n",
+      "     |          Vector((0.000, 1.000, 2.000))\n",
+      "     |      \n",
+      "     |          >>> 10 - Vector([1, 2, 3])\n",
+      "     |          Vector((9.000, 8.000, 7.000))\n",
+      "     |  \n",
+      "     |  __truediv__(self, other)\n",
+      "     |      Handle `self / other`.\n",
+      "     |      \n",
+      "     |      Divide a Vector by a scalar.\n",
+      "     |      \n",
+      "     |      Example Usage:\n",
+      "     |          >>> Vector([9, 6, 12]) / 3\n",
+      "     |          Vector((3.000, 2.000, 4.000))\n",
+      "     |  \n",
+      "     |  as_matrix(self, *, column=True)\n",
+      "     |      Get a Matrix representation of a Vector.\n",
+      "     |      \n",
+      "     |      Args:\n",
+      "     |          column (bool): if the vector is interpreted as a\n",
+      "     |              column vector or a row vector; defaults to True\n",
+      "     |      \n",
+      "     |      Returns:\n",
+      "     |          matrix (matrix.Matrix)\n",
+      "     |      \n",
+      "     |      Example Usage:\n",
+      "     |          >>> v = Vector([1, 2, 3])\n",
+      "     |          >>> v.as_matrix()\n",
+      "     |          Matrix(((1.000,), (2.000,), (3.000,)))\n",
+      "     |          >>> v.as_matrix(column=False)\n",
+      "     |          Matrix(((1.000, 2.000, 3.000,)))\n",
+      "     |  \n",
+      "     |  ----------------------------------------------------------------------\n",
+      "     |  Data descriptors defined here:\n",
+      "     |  \n",
+      "     |  __dict__\n",
+      "     |      dictionary for instance variables (if defined)\n",
+      "     |  \n",
+      "     |  __weakref__\n",
+      "     |      list of weak references to the object (if defined)\n",
+      "     |  \n",
+      "     |  ----------------------------------------------------------------------\n",
+      "     |  Data and other attributes defined here:\n",
+      "     |  \n",
+      "     |  __hash__ = None\n",
+      "     |  \n",
+      "     |  matrix_cls = <class 'sample_package.matrix.Matrix'>\n",
+      "     |      An m-by-n-dimensional matrix from linear algebra.\n",
+      "     |      \n",
+      "     |      All entries are converted to floats, or whatever is set in the typing attribute.\n",
+      "     |      \n",
+      "     |      Attributes:\n",
+      "     |          storage (callable): data type used to store the entries internally;\n",
+      "     |              defaults to tuple\n",
+      "     |          typing (callable): type casting applied to all entries upon creation;\n",
+      "     |              defaults to float\n",
+      "     |          vector_cls (vector.Vector): a reference to the Vector class to work with\n",
+      "     |          zero_threshold (float): max. tolerance when comparing an entry to zero;\n",
+      "     |              defaults to 1e-12\n",
+      "     |  \n",
+      "     |  storage = <class 'tuple'>\n",
+      "     |      Built-in immutable sequence.\n",
+      "     |      \n",
+      "     |      If no argument is given, the constructor returns an empty tuple.\n",
+      "     |      If iterable is specified the tuple is initialized from iterable's items.\n",
+      "     |      \n",
+      "     |      If the argument is a tuple, the return value is the same object.\n",
+      "     |  \n",
+      "     |  typing = <class 'float'>\n",
+      "     |      Convert a string or number to a floating point number, if possible.\n",
+      "     |  \n",
+      "     |  zero_threshold = 1e-12\n",
+      "\n",
+      "DATA\n",
+      "    __all__ = ['Matrix', 'Vector']\n",
+      "\n",
+      "VERSION\n",
+      "    0.1.0\n",
+      "\n",
+      "AUTHOR\n",
+      "    Alexander Hess\n",
+      "\n",
+      "FILE\n",
+      "    /home/webartifex/repos/intro-to-python/11_classes/sample_package/__init__.py\n",
+      "\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "help(pkg)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The meta information could also be accessed separately and individually."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'linear_algebra_tools'"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pkg.__name__"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'0.1.0'"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pkg.__version__  # follows the semantic versioning format"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We create `Vector` and `Matrix` instances in the usual way by calling the `Vector` and `Matrix` classes from the package's top level."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Vector((1.000, 2.000, 3.000))"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pkg.Vector([1, 2, 3])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Matrix(((1.000, 2.000, 3.000,), (4.000, 5.000, 6.000,), (7.000, 8.000, 9.000,)))"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pkg.Matrix([(1, 2, 3), (4, 5, 6), (7, 8, 9)])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "A common practice by package authors is to put all the objects on the package's top level that they want the package users to work with directly. That is achieved via the `import` statements in the [*\\_\\_init\\_\\_.py* <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_gh.png\">](https://github.com/webartifex/intro-to-python/blob/develop/11_classes/sample_package/__init__.py) file.\n",
+    "\n",
+    "However, users can always reach into a package and work with its internals.\n",
+    "\n",
+    "For example, the `Vector` and `Matrix` classes are also available via their **qualified name** (cf., [PEP 3155 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://www.python.org/dev/peps/pep-3155/)): First, we access the `vector` and `matrix` modules on `pkg`, and then the `Vector` and `Matrix` classes on the modules."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "sample_package.vector.Vector"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pkg.vector.Vector"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "sample_package.matrix.Matrix"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pkg.matrix.Matrix"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Also, let's import the [*utils.py* <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_gh.png\">](https://github.com/webartifex/intro-to-python/blob/develop/11_classes/sample_package/utils.py) module with the `norm()` function into the global scope. As this function is integrated into the `Vector.__abs__()` and `Matrix.__abs__()` methods, there is actually no need to work with it explicitly."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sample_package import utils"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Help on function norm in module sample_package.utils:\n",
+      "\n",
+      "norm(vec_or_mat)\n",
+      "    Calculate the Frobenius or Euclidean norm of a matrix or vector.\n",
+      "    \n",
+      "    Find more infos here: https://en.wikipedia.org/wiki/Matrix_norm#Frobenius_norm\n",
+      "    \n",
+      "    Args:\n",
+      "        vec_or_mat (Vector / Matrix): object whose entries are squared and summed up\n",
+      "    \n",
+      "    Returns:\n",
+      "        norm (float)\n",
+      "    \n",
+      "    Example Usage:\n",
+      "        As Vector and Matrix objects are by design non-empty sequences,\n",
+      "        norm() may be called, for example, with `[3, 4]` as the argument:\n",
+      "        >>> norm([3, 4])\n",
+      "        5.0\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "help(utils.norm)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Many tutorials on the internet begin by importing \"everything\" from a package into the global scope with `from ... import *`.\n",
+    "\n",
+    "That is commonly considered a *bad* practice as it may overwrite already existing variables. However, if the package's [*\\_\\_init\\_\\_.py* <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_gh.png\">](https://github.com/webartifex/intro-to-python/blob/develop/11_classes/sample_package/__init__.py) file defines an `__all__` attribute, a `list` with all the names to be \"exported,\" the **star import** is safe to be used, in particular, in *interactive* sessions like Jupyter notebooks. We emphasize that the star import should *not* be used *within* packages and modules as then it is not directly evident from a name where the corresponding object is defined.\n",
+    "\n",
+    "For more best practices regarding importing we refer to, among others, [Google's Python Style Guide](https://google.github.io/styleguide/pyguide.html#22-imports).\n",
+    "\n",
+    "The following `import` statement makes the `Vector` and `Matrix` classes available in the global scope."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sample_package import *"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "sample_package.vector.Vector"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "Vector"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "sample_package.matrix.Matrix"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "Matrix"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "For further information on modules and packages, we refer to the [official tutorial <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/tutorial/modules.html)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "## The final `Vector` and `Matrix` Classes"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The final implementations of the `Vector` and `Matrix` classes are in the [*matrix.py* <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_gh.png\">](https://github.com/webartifex/intro-to-python/blob/develop/11_classes/sample_package/matrix.py) and [*vector.py* <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_gh.png\">](https://github.com/webartifex/intro-to-python/blob/develop/11_classes/sample_package/vector.py) files: They integrate all of the functionalities introduced in this chapter. In addition, the code is cleaned up and fully documented, including examples of common usages.\n",
+    "\n",
+    "We strongly suggest the eager student go over the files in the [*sample_package* <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_gh.png\">](https://github.com/webartifex/intro-to-python/tree/develop/11_classes/sample_package) in detail at some point to understand what well-written and (re-)usable code looks like."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "v = Vector([1, 2, 3])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Vector((1.000, 2.000, 3.000))"
+      ]
+     },
+     "execution_count": 21,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "v"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "sample_package.vector.Vector"
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "type(v)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "m = Matrix([(1, 2, 3), (4, 5, 6), (7, 8, 9)])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Matrix(((1.000, 2.000, 3.000,), (4.000, 5.000, 6.000,), (7.000, 8.000, 9.000,)))"
+      ]
+     },
+     "execution_count": 24,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "m"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "sample_package.matrix.Matrix"
+      ]
+     },
+     "execution_count": 25,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "type(m)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Furthermore, the classes are designed for easier maintenence in the long-run.\n",
+    "\n",
+    "For example, the `Matrix/Vector.storage` and `Matrix/Vector.typing` class attributes replace the \"hard coded\" [tuple() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#func-tuple) and [float() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#float) built-ins in the `.__init__()` methods: As `self.storage` and `self.typing` are not defined on the *instances*, Python automatically looks them up on the *classes*."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "tuple"
+      ]
+     },
+     "execution_count": 26,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "Vector.storage"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "float"
+      ]
+     },
+     "execution_count": 27,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "Vector.typing"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "\u001b[0;31mSignature:\u001b[0m \u001b[0mVector\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__init__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+       "\u001b[0;31mSource:\u001b[0m   \n",
+       "    \u001b[0;32mdef\u001b[0m \u001b[0m__init__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n",
+       "\u001b[0;34m\u001b[0m        \u001b[0;34m\"\"\"Create a new vector.\u001b[0m\n",
+       "\u001b[0;34m\u001b[0m\n",
+       "\u001b[0;34m        Args:\u001b[0m\n",
+       "\u001b[0;34m            data (sequence): the vector's entries\u001b[0m\n",
+       "\u001b[0;34m\u001b[0m\n",
+       "\u001b[0;34m        Raises:\u001b[0m\n",
+       "\u001b[0;34m            ValueError: if no entries are provided\u001b[0m\n",
+       "\u001b[0;34m\u001b[0m\n",
+       "\u001b[0;34m        Example Usage:\u001b[0m\n",
+       "\u001b[0;34m            >>> Vector([1, 2, 3])\u001b[0m\n",
+       "\u001b[0;34m            Vector((1.000, 2.000, 3.000))\u001b[0m\n",
+       "\u001b[0;34m\u001b[0m\n",
+       "\u001b[0;34m            >>> Vector(range(3))\u001b[0m\n",
+       "\u001b[0;34m            Vector((0.000, 1.000, 2.000))\u001b[0m\n",
+       "\u001b[0;34m        \"\"\"\u001b[0m\u001b[0;34m\u001b[0m\n",
+       "\u001b[0;34m\u001b[0m        \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_entries\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstorage\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtyping\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mx\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n",
+       "\u001b[0;34m\u001b[0m        \u001b[0;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n",
+       "\u001b[0;34m\u001b[0m            \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"a vector must have at least one entry\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+       "\u001b[0;31mFile:\u001b[0m      ~/repos/intro-to-python/11_classes/sample_package/vector.py\n",
+       "\u001b[0;31mType:\u001b[0m      function\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "Vector.__init__??"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Both `Matrix/Vector.storage` and `Matrix/Vector.typing` themselves reference the `DEFAULT_ENTRIES_STORAGE` and `DEFAULT_ENTRY_TYPE` constants in the [*utils.py* <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_gh.png\">](https://github.com/webartifex/intro-to-python/blob/develop/11_classes/sample_package/utils.py) module. This way, we could, for example, change only the constants and thereby also change how the `._entries` are stored internally in both classes. Also, this single **[single source of truth <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_wiki.png\">](https://en.wikipedia.org/wiki/Single_source_of_truth)** ensures that both classes are consistent with each other at all times."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "tuple"
+      ]
+     },
+     "execution_count": 29,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "utils.DEFAULT_ENTRIES_STORAGE"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "float"
+      ]
+     },
+     "execution_count": 30,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "utils.DEFAULT_ENTRY_TYPE"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "For the same reasons, we also replace the \"hard coded\" references to the `Vector` and `Matrix` classes within the various methods.\n",
+    "\n",
+    "Every instance object has an automatically set `.__class__` attribute referencing its class."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "sample_package.matrix.Matrix"
+      ]
+     },
+     "execution_count": 31,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "m.__class__"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Of course, we could also use the [type() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#type) built-in instead."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "sample_package.matrix.Matrix"
+      ]
+     },
+     "execution_count": 32,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "type(m)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "So, for example, the `Matrix.transpose()` method makes a `self.__class__(...)` instead of a `Matrix(...)` call."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "\u001b[0;31mSignature:\u001b[0m \u001b[0mMatrix\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtranspose\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+       "\u001b[0;31mSource:\u001b[0m   \n",
+       "    \u001b[0;32mdef\u001b[0m \u001b[0mtranspose\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n",
+       "\u001b[0;34m\u001b[0m        \u001b[0;34m\"\"\"Switch the rows and columns of a Matrix.\u001b[0m\n",
+       "\u001b[0;34m\u001b[0m\n",
+       "\u001b[0;34m        Returns:\u001b[0m\n",
+       "\u001b[0;34m            matrix (Matrix)\u001b[0m\n",
+       "\u001b[0;34m\u001b[0m\n",
+       "\u001b[0;34m        Example Usage:\u001b[0m\n",
+       "\u001b[0;34m            >>> m = Matrix([(1, 2), (3, 4)])\u001b[0m\n",
+       "\u001b[0;34m            >>> m\u001b[0m\n",
+       "\u001b[0;34m            Matrix(((1.000, 2.000,), (3.000, 4.000,)))\u001b[0m\n",
+       "\u001b[0;34m            >>> m.transpose()\u001b[0m\n",
+       "\u001b[0;34m            Matrix(((1.000, 3.000,), (2.000, 4.000,)))\u001b[0m\n",
+       "\u001b[0;34m        \"\"\"\u001b[0m\u001b[0;34m\u001b[0m\n",
+       "\u001b[0;34m\u001b[0m        \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__class__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mzip\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_entries\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+       "\u001b[0;31mFile:\u001b[0m      ~/repos/intro-to-python/11_classes/sample_package/matrix.py\n",
+       "\u001b[0;31mType:\u001b[0m      function\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "Matrix.transpose??"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Whenever we need a `str` representation of a class's name, we use the `.__name__` attribute on the class, ..."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Matrix'"
+      ]
+     },
+     "execution_count": 34,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "Matrix.__name__"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "... or access it via the `.__class__` attribute on an instance."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Matrix'"
+      ]
+     },
+     "execution_count": 35,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "m.__class__.__name__"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "For example, the `.__repr__()` and `.__str__()` methods make use of that."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "\u001b[0;31mSignature:\u001b[0m \u001b[0mMatrix\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__repr__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+       "\u001b[0;31mSource:\u001b[0m   \n",
+       "    \u001b[0;32mdef\u001b[0m \u001b[0m__repr__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n",
+       "\u001b[0;34m\u001b[0m        \u001b[0;34m\"\"\"Text representation of a Matrix.\"\"\"\u001b[0m\u001b[0;34m\u001b[0m\n",
+       "\u001b[0;34m\u001b[0m        \u001b[0mname\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__class__\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__name__\u001b[0m\u001b[0;34m\u001b[0m\n",
+       "\u001b[0;34m\u001b[0m        \u001b[0margs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m\", \"\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mjoin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\u001b[0m\n",
+       "\u001b[0;34m\u001b[0m            \u001b[0;34m\"(\"\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;34m\", \"\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mjoin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34mf\"{c:.3f}\"\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mc\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mr\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;34m\",)\"\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mr\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_entries\u001b[0m\u001b[0;34m\u001b[0m\n",
+       "\u001b[0;34m\u001b[0m        \u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n",
+       "\u001b[0;34m\u001b[0m        \u001b[0;32mreturn\u001b[0m \u001b[0;34mf\"{name}(({args}))\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+       "\u001b[0;31mFile:\u001b[0m      ~/repos/intro-to-python/11_classes/sample_package/matrix.py\n",
+       "\u001b[0;31mType:\u001b[0m      function\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "Matrix.__repr__??"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In order to not have to \"hard code\" the name of *another* class (e.g., the `Vector.as_matrix()` method references the `Matrix` class), we apply the following \"hack:\" First, we store a reference to the other class as a class attribute (e.g., `Matrix.vector_cls` and `Vector.matrix_cls`), and then reference that attribute within the methods, just like `.storage` and `.typing` above."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "sample_package.vector.Vector"
+      ]
+     },
+     "execution_count": 37,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "Matrix.vector_cls"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "sample_package.matrix.Matrix"
+      ]
+     },
+     "execution_count": 38,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "Vector.matrix_cls"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "As an example, the `Vector.as_matrix()` method makes a `self.matrix_cls(...)` instead of a `Matrix(...)` call."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "\u001b[0;31mSignature:\u001b[0m \u001b[0mVector\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mas_matrix\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcolumn\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+       "\u001b[0;31mSource:\u001b[0m   \n",
+       "    \u001b[0;32mdef\u001b[0m \u001b[0mas_matrix\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcolumn\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n",
+       "\u001b[0;34m\u001b[0m        \u001b[0;34m\"\"\"Get a Matrix representation of a Vector.\u001b[0m\n",
+       "\u001b[0;34m\u001b[0m\n",
+       "\u001b[0;34m        Args:\u001b[0m\n",
+       "\u001b[0;34m            column (bool): if the vector is interpreted as a\u001b[0m\n",
+       "\u001b[0;34m                column vector or a row vector; defaults to True\u001b[0m\n",
+       "\u001b[0;34m\u001b[0m\n",
+       "\u001b[0;34m        Returns:\u001b[0m\n",
+       "\u001b[0;34m            matrix (matrix.Matrix)\u001b[0m\n",
+       "\u001b[0;34m\u001b[0m\n",
+       "\u001b[0;34m        Example Usage:\u001b[0m\n",
+       "\u001b[0;34m            >>> v = Vector([1, 2, 3])\u001b[0m\n",
+       "\u001b[0;34m            >>> v.as_matrix()\u001b[0m\n",
+       "\u001b[0;34m            Matrix(((1.000,), (2.000,), (3.000,)))\u001b[0m\n",
+       "\u001b[0;34m            >>> v.as_matrix(column=False)\u001b[0m\n",
+       "\u001b[0;34m            Matrix(((1.000, 2.000, 3.000,)))\u001b[0m\n",
+       "\u001b[0;34m        \"\"\"\u001b[0m\u001b[0;34m\u001b[0m\n",
+       "\u001b[0;34m\u001b[0m        \u001b[0;32mif\u001b[0m \u001b[0mcolumn\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n",
+       "\u001b[0;34m\u001b[0m            \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmatrix_cls\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mx\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n",
+       "\u001b[0;34m\u001b[0m        \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmatrix_cls\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mx\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+       "\u001b[0;31mFile:\u001b[0m      ~/repos/intro-to-python/11_classes/sample_package/vector.py\n",
+       "\u001b[0;31mType:\u001b[0m      function\n"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "Vector.as_matrix??"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "For completeness sake, we mention that in the final `Vector` and `Matrix` classes, the `.__sub__()` and `.__rsub__()` methods use the negation operator implemented in `.__neg__()` and then dispatch to `.__add__()` instead of implementing the subtraction logic themselves."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## \"Real-life\" Experiment"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Let's do some math with bigger `Matrix` and `Vector` instances."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "import random"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "random.seed(42)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "We initialize `m` as a $100x50$ dimensional `Matrix` with random numbers in the range between `0` and `1_000`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "m = Matrix((1_000 * random.random() for _ in range(50)) for _ in range(100))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "We quickly lose track with all the numbers in the `Matrix`, which is why we implemented the `__str__()` method as a summary representation."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Matrix(((639.427, 25.011, 275.029, 223.211, 736.471, 676.699, 892.180, 86.939, 421.922, 29.797, 218.638, 505.355, 26.536, 198.838, 649.884, 544.941, 220.441, 589.266, 809.430, 6.499, 805.819, 698.139, 340.251, 155.479, 957.213, 336.595, 92.746, 96.716, 847.494, 603.726, 807.128, 729.732, 536.228, 973.116, 378.534, 552.041, 829.405, 618.520, 861.707, 577.352, 704.572, 45.824, 227.898, 289.388, 79.792, 232.791, 101.001, 277.974, 635.684, 364.832,), (370.181, 209.507, 266.978, 936.655, 648.035, 609.131, 171.139, 729.127, 163.402, 379.455, 989.523, 640.000, 556.950, 684.614, 842.852, 776.000, 229.048, 32.100, 315.453, 267.741, 210.983, 942.910, 876.368, 314.678, 655.439, 395.632, 914.548, 458.852, 264.880, 246.628, 561.368, 262.742, 584.586, 897.823, 399.401, 219.321, 997.538, 509.526, 90.909, 47.116, 109.649, 627.446, 792.079, 422.160, 63.528, 381.619, 996.121, 529.114, 971.078, 860.780,), (11.481, 720.722, 681.710, 536.970, 266.825, 640.962, 111.552, 434.765, 453.724, 953.816, 875.853, 263.389, 500.586, 178.652, 912.628, 870.519, 298.445, 638.949, 608.970, 152.839, 762.511, 539.379, 778.626, 530.354, 0.572, 324.156, 19.477, 929.099, 878.722, 831.666, 307.514, 57.925, 878.010, 946.949, 85.653, 485.990, 69.213, 760.602, 765.834, 128.391, 475.282, 549.804, 265.057, 872.433, 423.138, 211.798, 539.296, 729.931, 201.151, 311.716,), (995.149, 649.878, 438.100, 517.576, 121.004, 224.697, 338.086, 588.309, 230.115, 220.217, 70.993, 631.103, 228.942, 905.420, 859.635, 70.857, 238.005, 668.978, 214.237, 132.312, 935.514, 571.043, 472.671, 784.619, 807.497, 190.410, 96.931, 431.051, 423.579, 467.025, 729.076, 673.365, 984.165, 98.418, 402.621, 339.303, 861.673, 248.656, 190.209, 448.614, 421.882, 278.545, 249.806, 923.266, 443.131, 861.349, 550.325, 50.588, 999.282, 836.028,), (968.996, 926.367, 848.696, 166.311, 485.641, 213.747, 401.040, 58.635, 378.973, 985.309, 265.203, 784.071, 455.008, 423.007, 957.318, 995.423, 555.768, 718.408, 154.797, 296.708, 968.709, 579.180, 542.195, 747.976, 57.165, 584.178, 502.850, 852.720, 157.433, 960.779, 80.111, 185.825, 595.035, 675.213, 235.204, 119.887, 890.287, 246.215, 594.519, 619.382, 419.225, 583.672, 522.783, 934.706, 204.259, 716.192, 238.686, 395.786, 671.690, 299.997,), (316.177, 751.864, 72.543, 458.286, 998.454, 996.096, 73.261, 213.154, 265.200, 933.259, 880.864, 879.270, 369.527, 157.747, 833.745, 703.540, 611.678, 987.233, 653.976, 7.823, 817.104, 299.379, 663.389, 938.930, 134.291, 115.429, 107.036, 553.224, 272.348, 604.830, 717.612, 203.597, 634.238, 263.984, 488.532, 905.336, 846.104, 92.298, 423.576, 276.680, 3.546, 771.119, 637.113, 261.955, 741.231, 551.680, 427.687, 9.670, 75.244, 883.106,), (903.929, 545.590, 834.595, 582.510, 148.094, 127.446, 308.258, 898.981, 796.122, 860.703, 898.925, 210.077, 249.530, 102.794, 780.116, 884.135, 406.377, 620.662, 154.553, 929.881, 864.606, 976.206, 810.772, 881.416, 24.786, 736.564, 332.185, 930.816, 802.235, 864.064, 810.749, 266.806, 787.375, 108.096, 872.167, 858.593, 222.434, 816.587, 460.303, 305.191, 795.345, 227.595, 23.664, 193.130, 328.262, 864.353, 966.889, 279.125, 641.482, 399.678,), (981.150, 536.216, 939.237, 115.342, 970.401, 178.568, 962.534, 265.466, 108.403, 434.564, 728.545, 313.677, 606.209, 511.423, 385.195, 576.588, 254.723, 708.785, 1.691, 925.575, 538.452, 719.430, 741.950, 670.629, 364.221, 69.974, 664.238, 330.200, 313.916, 848.015, 719.754, 300.322, 309.285, 408.393, 402.400, 295.655, 127.288, 420.446, 940.364, 677.318, 902.806, 615.515, 300.950, 547.937, 0.406, 286.914, 429.888, 579.985, 654.706, 464.988,), (442.160, 213.701, 473.186, 901.181, 796.025, 169.691, 84.796, 515.452, 632.941, 335.188, 818.423, 751.138, 672.796, 224.641, 199.130, 24.425, 244.843, 475.136, 849.738, 72.828, 414.441, 629.765, 194.435, 696.354, 494.377, 243.984, 656.058, 5.545, 750.964, 770.046, 106.587, 425.146, 175.887, 957.966, 517.958, 50.218, 249.198, 848.336, 456.462, 801.417, 667.578, 987.892, 595.452, 950.040, 891.426, 612.652, 719.274, 504.778, 830.569, 547.872,), (897.208, 743.655, 474.674, 259.192, 247.240, 637.661, 765.814, 521.300, 626.748, 274.597, 77.483, 285.728, 271.715, 319.710, 540.152, 138.374, 231.261, 693.950, 706.419, 64.229, 407.599, 542.611, 415.774, 206.834, 420.144, 904.838, 584.079, 695.523, 856.732, 765.595, 380.381, 5.896, 351.759, 753.475, 853.448, 953.430, 419.021, 747.516, 546.132, 603.253, 220.539, 219.422, 435.836, 29.025, 336.130, 679.142, 404.317, 165.045, 467.390, 127.628,), (622.257, 26.966, 394.020, 564.392, 27.102, 642.750, 135.699, 461.698, 50.285, 379.104, 211.660, 326.846, 761.230, 379.126, 752.010, 831.924, 252.272, 81.906, 19.383, 539.419, 999.908, 349.960, 650.144, 781.233, 651.755, 754.233, 949.612, 199.361, 20.380, 152.382, 126.221, 669.459, 563.970, 217.965, 699.465, 766.898, 167.789, 607.247, 747.926, 114.533, 819.301, 964.721, 108.099, 25.678, 311.957, 677.347, 958.173, 396.654, 715.015, 75.996,), (690.614, 627.242, 101.901, 772.481, 850.293, 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963.288, 276.508, 606.363, 194.525, 929.714, 574.907, 261.108, 407.135, 106.465, 72.386, 294.252, 947.015, 802.942, 956.709, 876.113, 813.147, 574.894, 694.192, 966.055, 563.169, 769.967, 757.473, 961.239, 458.848, 460.720, 586.827, 27.351, 116.268, 67.554, 633.662, 994.198, 676.913, 229.930, 315.731,), (955.446, 516.504, 9.723, 832.177, 248.258, 93.009, 673.698, 821.075, 76.029, 931.396, 476.555, 353.538, 894.316, 269.076, 947.118, 683.107, 909.927, 498.999, 199.667, 735.429, 872.742, 206.779, 202.767, 249.695, 618.597, 155.996, 106.689, 920.188, 675.812, 663.424, 613.827, 764.244, 541.474, 42.263, 482.292, 620.667, 492.340, 985.361, 897.000, 868.769, 490.805, 984.395, 916.007, 280.267, 222.093, 576.665, 54.148, 799.270, 478.320, 540.768,), (502.475, 393.717, 686.339, 174.800, 976.507, 698.245, 460.071, 689.211, 11.820, 210.752, 581.008, 325.436, 612.778, 259.701, 548.563, 237.139, 471.345, 613.084, 365.924, 498.770, 210.532, 700.713, 372.310, 854.326, 279.630, 179.889, 131.139, 575.893, 228.567, 99.870, 269.949, 235.805, 432.219, 380.883, 145.844, 959.148, 149.548, 805.668, 176.607, 499.598, 995.562, 849.390, 517.007, 720.560, 785.333, 300.034, 562.138, 567.835, 398.402, 690.543,), (60.036, 813.818, 476.539, 629.862, 449.855, 334.657, 360.962, 560.350, 931.845, 257.715, 19.835, 121.237, 867.369, 963.117, 198.958, 575.894, 649.131, 174.539, 780.309, 355.109, 673.193, 487.494, 736.526, 889.632, 381.071, 286.524, 631.712, 144.848, 167.576, 807.717, 337.388, 631.273, 571.638, 848.901, 71.344, 161.999, 228.218, 316.878, 291.356, 267.472, 644.644, 271.248, 444.898, 862.806, 363.165, 586.920, 965.526, 413.989, 183.886, 23.101,), (727.772, 662.023, 940.437, 700.697, 80.065, 166.910, 103.009, 63.792, 878.691, 548.434, 26.365, 397.013, 764.996, 83.251, 262.452, 155.274, 654.833, 885.471, 309.371, 247.397, 284.175, 626.481, 131.277, 838.518, 28.571, 663.549, 860.369, 325.201, 476.155, 974.982, 540.584, 272.414, 444.845, 969.542, 697.623, 177.037, 597.584, 631.048, 635.851, 563.984, 523.460, 639.528, 309.661, 347.168, 539.668, 804.708, 441.860, 365.783, 259.807, 303.573,), (0.092, 815.380, 847.946, 343.885, 469.198, 8.317, 922.088, 946.958, 477.067, 9.133, 430.435, 292.923, 231.215, 7.218, 373.644, 411.736, 560.554, 394.752, 163.269, 737.118, 389.723, 378.358, 262.980, 422.239, 239.562, 764.538, 909.954, 807.575, 684.640, 284.677, 742.927, 808.809, 412.904, 853.482, 182.407, 289.595, 636.893, 617.729, 272.134, 622.754, 187.789, 19.395, 49.632, 534.971, 185.938, 102.273, 269.195, 715.196, 727.107, 232.810,), (150.684, 493.580, 341.913, 311.577, 799.462, 997.963, 463.684, 791.425, 330.249, 843.546, 951.644, 56.038, 775.653, 71.385, 470.147, 192.192, 841.539, 817.251, 828.252, 121.962, 768.146, 248.962, 771.225, 442.961, 737.619, 33.510, 460.835, 770.996, 521.139, 982.116, 472.826, 681.442, 312.109, 323.063, 629.164, 42.186, 937.421, 520.926, 253.313, 638.520, 198.724, 887.520, 863.658, 217.788, 112.928, 632.909, 324.513, 167.360, 276.100, 119.650,), (789.144, 8.930, 41.993, 783.475, 475.271, 596.212, 371.399, 89.480, 157.698, 91.402, 618.645, 930.849, 996.973, 625.132, 59.775, 644.575, 701.328, 791.466, 125.827, 232.675, 981.676, 788.562, 756.698, 805.525, 438.719, 192.965, 689.250, 358.167, 134.732, 898.663, 485.302, 437.500, 294.715, 697.038, 189.776, 186.627, 347.665, 732.402, 275.324, 831.350, 914.474, 554.520, 68.877, 155.025, 295.673, 263.349, 367.113, 0.720, 638.026, 379.044,), (185.534, 18.720, 856.829, 776.017, 238.787, 721.004, 658.289, 538.957, 387.491, 524.070, 497.549, 551.468, 613.039, 322.600, 640.904, 110.464, 523.201, 66.680, 835.193, 129.774, 873.493, 202.079, 485.370, 98.754, 571.012, 836.300, 657.921, 525.821, 701.314, 255.939, 366.354, 605.954, 70.874, 930.211, 208.738, 491.406, 889.767, 79.957, 801.020, 60.700, 558.020, 938.244, 415.953, 369.069, 708.434, 823.643, 265.816, 40.653, 70.388, 303.905,), (944.603, 960.587, 97.119, 725.172, 531.661, 189.460, 526.192, 248.763, 625.325, 178.898, 749.194, 415.185, 105.514, 638.624, 357.963, 458.721, 665.242, 882.824, 166.809, 180.397, 401.557, 337.513, 158.176, 998.434, 443.012, 352.324, 315.155, 991.463, 324.654, 371.720, 764.184, 430.588, 725.868, 608.413, 563.145, 213.928, 765.708, 926.147, 254.089, 961.652, 447.500, 397.091, 726.313, 982.956, 595.763, 517.609, 993.412, 301.842, 300.334, 221.887,), (855.720, 21.701, 821.940, 689.719, 275.957, 553.663, 556.323, 925.922, 154.715, 37.736, 355.656, 138.408, 367.083, 582.156, 232.994, 811.074, 91.905, 399.798, 917.882, 734.266, 723.581, 792.904, 172.902, 825.705, 689.595, 576.232, 907.656, 595.231, 300.393, 730.789, 576.284, 78.477, 55.923, 770.902, 347.930, 817.142, 416.522, 867.831, 869.787, 226.718, 652.779, 602.302, 11.434, 777.419, 382.487, 304.783, 41.182, 539.930, 149.580, 502.402,), (220.797, 50.520, 731.579, 392.883, 445.616, 595.132, 504.729, 222.086, 289.783, 394.322, 132.189, 82.545, 571.441, 49.311, 399.191, 85.079, 501.823, 773.825, 130.375, 134.871, 559.296, 487.861, 652.248, 196.099, 615.997, 735.668, 246.246, 71.644, 776.772, 323.412, 924.138, 89.595, 671.748, 423.541, 348.308, 320.738, 593.877, 24.207, 304.819, 987.652, 616.221, 990.159, 442.210, 145.818, 44.878, 818.172, 199.685, 373.821, 757.734, 852.764,), (112.372, 54.538, 948.941, 926.730, 868.752, 820.134, 13.733, 693.795, 111.278, 450.062, 22.748, 209.010, 538.005, 203.801, 523.266, 258.658, 483.026, 729.924, 141.347, 698.755, 18.389, 583.005, 663.528, 43.482, 170.320, 284.014, 789.188, 617.965, 53.085, 654.758, 8.334, 388.645, 271.310, 852.083, 660.100, 864.282, 19.078, 867.410, 649.411, 231.169, 380.701, 976.612, 99.604, 315.456, 866.773, 531.561, 186.417, 500.651, 457.986, 926.350,), (21.488, 247.392, 529.415, 333.570, 393.400, 157.005, 346.780, 351.913, 625.231, 236.205, 978.244, 501.251, 811.893, 625.792, 878.677, 890.402, 814.859, 29.466, 554.939, 280.194, 151.708, 897.175, 656.932, 87.795, 382.207, 960.606, 612.448, 625.525, 227.428, 240.361, 152.749, 970.537, 909.735, 329.285, 542.234, 206.757, 138.577, 541.354, 800.105, 862.588, 308.998, 705.136, 523.805, 135.252, 995.686, 975.777, 145.152, 933.030, 917.112, 317.496,), (557.340, 948.602, 118.387, 317.598, 879.638, 727.080, 765.435, 880.132, 414.040, 411.252, 443.004, 933.769, 894.130, 933.250, 273.796, 779.108, 106.771, 184.746, 762.447, 611.982, 266.863, 567.043, 230.911, 232.183, 687.378, 359.261, 688.141, 476.602, 502.325, 604.712, 712.019, 373.959, 852.129, 491.446, 137.513, 193.260, 32.230, 764.539, 15.014, 269.859, 413.044, 742.367, 988.243, 757.777, 66.136, 927.103, 985.628, 867.129, 489.943, 324.896,), (457.545, 246.784, 404.867, 41.826, 735.332, 380.374, 312.897, 611.505, 742.468, 593.806, 525.231, 875.783, 786.920, 520.649, 451.610, 827.010, 42.416, 995.836, 518.703, 395.611, 735.170, 557.701, 516.127, 630.649, 49.295, 291.179, 398.040, 304.554, 827.621, 461.347, 422.462, 613.134, 54.547, 516.970, 142.240, 829.894, 451.698, 722.647, 113.172, 778.731, 937.842, 696.114, 135.307, 413.559, 450.886, 178.879, 590.315, 712.634, 201.932, 454.683,), (250.082, 691.717, 907.162, 796.486, 718.375, 123.544, 114.165, 449.398, 362.943, 523.817, 384.087, 791.067, 511.666, 949.754, 378.679, 380.607, 768.260, 912.253, 565.492, 659.530, 150.090, 868.819, 178.867, 712.047, 419.582, 309.518, 768.630, 446.258, 626.453, 117.554, 131.571, 202.852, 622.560, 253.111, 458.643, 855.411, 543.501, 5.751, 882.703, 237.878, 588.862, 475.712, 410.151, 79.405, 600.102, 244.713, 547.342, 619.766, 557.549, 825.253,), (48.919, 148.256, 653.277, 36.721, 853.829, 666.997, 838.014, 298.442, 920.385, 49.031, 416.957, 178.248, 672.090, 611.090, 691.514, 594.969, 787.308, 177.419, 455.338, 578.966, 931.480, 93.126, 299.294, 368.569, 378.939, 67.667, 427.324, 550.471, 292.833, 134.546, 694.619, 274.419, 527.053, 524.646, 695.829, 612.054, 109.302, 729.729, 628.979, 987.189, 483.663, 688.654, 933.892, 986.278, 287.187, 608.845, 316.489, 525.391, 995.024, 353.853,)))"
+      ]
+     },
+     "execution_count": 43,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "m"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Matrix((639.4, ...), ..., (..., 353.9))[100x50]\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(m)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Similarily, `v` is now a `Vector` with $50$ entries."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "v = Vector(1_000 * random.random() for _ in range(50))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Vector((129.713, 562.634, 519.706, 631.858, 492.504, 179.907, 609.406, 708.587, 979.258, 1.581, 23.987, 625.461, 117.926, 848.070, 799.564, 998.987, 414.041, 333.792, 560.416, 637.504, 11.297, 201.187, 281.627, 790.196, 307.773, 506.690, 323.924, 6.131, 685.836, 341.362, 724.397, 615.993, 29.117, 175.629, 330.515, 337.937, 672.473, 916.163, 797.254, 645.652, 481.496, 627.200, 892.058, 536.968, 335.110, 783.989, 413.953, 742.585, 835.106, 299.344))"
+      ]
+     },
+     "execution_count": 46,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "v"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Vector(129.7, ..., 299.3)[50]\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(v)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "The arithmetic works as before."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 48,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "w = m * v"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 49,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Vector(11378937.3, ..., 13593029.3)[100]\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(w)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "We can multiply `m` with its transpose or the other way round."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 50,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "n = m * m.transpose()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 51,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Matrix((14370711.3, ...), ..., (..., 16545418.2))[100x100]\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(n)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 52,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "o = m.transpose() * m"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 53,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Matrix((32618511.5, ...), ..., (..., 32339164.8))[50x50]\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(o)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "## Comparison with [numpy](https://www.numpy.org/)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "We started out in this chapter by realizing that Python provides us no good data type to model a vector $\\vec{x}$ or a matrix $\\bf{A}$. Then, we built up two custom data types, `Vector` and `Matrix`, that wrap a simple `tuple` object for $\\vec{x}$ and a `tuple` of `tuple`s for $\\bf{A}$ so that we can interact with their `._entries` in a \"natural\" way, which is similar to how we write linear algebra tasks by hand. By doing this, we extend Python with our own little \"dialect\" or **[domain-specific language <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_wiki.png\">](https://en.wikipedia.org/wiki/Domain-specific_language)** (DSL).\n",
+    "\n",
+    "If we feel like sharing our linear algebra library with the world, we could easily do so on either [GitHub <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_gh.png\">](https://github.com) or [PyPI](https://pypi.org). However, for the domain of linear algebra this would be rather pointless as there is already a widely adopted library with [numpy](https://www.numpy.org/) that not only has a lot more features than ours but also is implemented in C, which makes it a lot faster with big data.\n",
+    "\n",
+    "Let's model the example in the [first part <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/11_classes/00_content.ipynb#Example:-Vectors-&-Matrices) with both [numpy](https://www.numpy.org/) and our own DSL and compare them."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 54,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "x = (1, 2, 3)\n",
+    "A = [[1, 2, 3], [4, 5, 6], [7, 8, 9]]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 55,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "ename": "TypeError",
+     "evalue": "can't multiply sequence by non-int of type 'tuple'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-55-fd81d962f516>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mA\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;31mTypeError\u001b[0m: can't multiply sequence by non-int of type 'tuple'"
+     ]
+    }
+   ],
+   "source": [
+    "A * x"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "The creation of vectors and matrices is similar to our DSL. However, numpy uses the more general concept of an **n-dimensional array** (i.e., the `ndarray` type) where a vector is only a special case of a matrix and a matrix is yet another special case of an even higher dimensional structure."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 56,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "import numpy as np"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 57,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "x_arr = np.array(x)\n",
+    "A_arr = np.array(A)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 58,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "x_vec = Vector(x)\n",
+    "A_mat = Matrix(A)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "The text representations are very similar. However, [numpy](https://www.numpy.org/)'s `ndarray`s keep the entries as `int`s while our `Vector` and `Matrix` objects contain `float`s."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 59,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([1, 2, 3])"
+      ]
+     },
+     "execution_count": 59,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "x_arr"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 60,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Vector((1.000, 2.000, 3.000))"
+      ]
+     },
+     "execution_count": 60,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "x_vec"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 61,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[1, 2, 3],\n",
+       "       [4, 5, 6],\n",
+       "       [7, 8, 9]])"
+      ]
+     },
+     "execution_count": 61,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "A_arr"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 62,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Matrix(((1.000, 2.000, 3.000,), (4.000, 5.000, 6.000,), (7.000, 8.000, 9.000,)))"
+      ]
+     },
+     "execution_count": 62,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "A_mat"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "[numpy](https://www.numpy.org/)'s `ndarray`s come with a `.shape` instance attribute that returns a `tuple` with the dimensions ..."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 63,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(3,)"
+      ]
+     },
+     "execution_count": 63,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "x_arr.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 64,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(3, 3)"
+      ]
+     },
+     "execution_count": 64,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "A_arr.shape"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "... while `Matrix` objects come with `.n_rows` and `.n_cols` properties."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 65,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(3, 3)"
+      ]
+     },
+     "execution_count": 65,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "A_mat.n_rows, A_mat.n_cols"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "The built-in [len() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#len) function does not return the number of entries in an `ndarray` but the number of the rows instead. This is equivalent to the first element in the `.shape` attribute."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 66,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "3"
+      ]
+     },
+     "execution_count": 66,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "len(x_arr)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 67,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "3"
+      ]
+     },
+     "execution_count": 67,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "len(x_vec)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 68,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "3"
+      ]
+     },
+     "execution_count": 68,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "len(A_arr)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 69,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "9"
+      ]
+     },
+     "execution_count": 69,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "len(A_mat)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "The `.transpose()` method also exists for `ndarray`s."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 70,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[1, 4, 7],\n",
+       "       [2, 5, 8],\n",
+       "       [3, 6, 9]])"
+      ]
+     },
+     "execution_count": 70,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "A_arr.transpose()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 71,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Matrix(((1.000, 4.000, 7.000,), (2.000, 5.000, 8.000,), (3.000, 6.000, 9.000,)))"
+      ]
+     },
+     "execution_count": 71,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "A_mat.transpose()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "To perform matrix-matrix, matrix-vector, or vector-matrix multiplication in [numpy](https://www.numpy.org/), we use the `.dot()` method. If we use the `*` operator with `ndarray`s, an *entry-wise* multiplication is performed."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 72,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([14, 32, 50])"
+      ]
+     },
+     "execution_count": 72,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "A_arr.dot(x_arr)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 73,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[ 1,  4,  9],\n",
+       "       [ 4, 10, 18],\n",
+       "       [ 7, 16, 27]])"
+      ]
+     },
+     "execution_count": 73,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "A_arr * x_arr"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 74,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Vector((14.000, 32.000, 50.000))"
+      ]
+     },
+     "execution_count": 74,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "A_mat * x_vec"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Scalar multiplication, however, works as expected."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 75,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([10, 20, 30])"
+      ]
+     },
+     "execution_count": 75,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "10 * x_arr"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 76,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Vector((10.000, 20.000, 30.000))"
+      ]
+     },
+     "execution_count": 76,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "10 * x_vec"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "Because we implemented our classes to support the sequence protocol, [numpy](https://www.numpy.org/)'s *one*-dimensional `ndarray`s are actually able to work with them: The `*` operator is applied on a per-entry basis."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 77,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([2., 4., 6.])"
+      ]
+     },
+     "execution_count": 77,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "x_arr + x_vec"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 78,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([1., 4., 9.])"
+      ]
+     },
+     "execution_count": 78,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "x_arr * x_vec"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 79,
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
+   "outputs": [
+    {
+     "ename": "ValueError",
+     "evalue": "operands could not be broadcast together with shapes (3,3) (9,) ",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-79-032c970f8cfc>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mA_arr\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mA_mat\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;31mValueError\u001b[0m: operands could not be broadcast together with shapes (3,3) (9,) "
+     ]
+    }
+   ],
+   "source": [
+    "A_arr + A_mat"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "We conclude that it is rather easy to extend Python in a way that makes the resulting application code read like core Python again. As there are many well established third-party packages out there, it is unlikely that we have to implement a fundamental library ourselves. Yet, we can apply the concepts introduced in this chapter to organize the code in the applications we write."
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.6"
+  },
+  "livereveal": {
+   "auto_select": "code",
+   "auto_select_fragment": true,
+   "scroll": true,
+   "theme": "serif"
+  },
+  "toc": {
+   "base_numbering": 1,
+   "nav_menu": {},
+   "number_sections": false,
+   "sideBar": true,
+   "skip_h1_title": true,
+   "title_cell": "Table of Contents",
+   "title_sidebar": "Contents",
+   "toc_cell": false,
+   "toc_position": {
+    "height": "calc(100% - 180px)",
+    "left": "10px",
+    "top": "150px",
+    "width": "384px"
+   },
+   "toc_section_display": false,
+   "toc_window_display": false
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/11_classes/sample_package/__init__.py b/11_classes/sample_package/__init__.py
index 1e8d12c..8e87577 100644
--- a/11_classes/sample_package/__init__.py
+++ b/11_classes/sample_package/__init__.py
@@ -28,3 +28,7 @@ from sample_package.vector import Vector
 __name__ = "linear_algebra_tools"
 __version__ = "0.1.0"  # see https://semver.org/ for how the format works
 __author__ = "Alexander Hess"
+
+# Define what is imported with the "star import"
+# (i.e., with `from sample_package import *`).
+__all__ = ["Matrix", "Vector"]
diff --git a/11_classes/sample_package/matrix.py b/11_classes/sample_package/matrix.py
index 65f5fc1..3c82d26 100644
--- a/11_classes/sample_package/matrix.py
+++ b/11_classes/sample_package/matrix.py
@@ -17,12 +17,14 @@ class Matrix:
             defaults to tuple
         typing (callable): type casting applied to all entries upon creation;
             defaults to float
+        vector_cls (vector.Vector): a reference to the Vector class to work with
         zero_threshold (float): max. tolerance when comparing an entry to zero;
             defaults to 1e-12
     """
 
     storage = utils.DEFAULT_ENTRIES_STORAGE
     typing = utils.DEFAULT_ENTRY_TYPE
+    # the `vector_cls` attribute is set at the bottom of this file
     zero_threshold = utils.ZERO_THRESHOLD
 
     def __init__(self, data):
@@ -156,7 +158,7 @@ class Matrix:
         Returns:
             rows (generator): produces a Matrix's rows as Vectors
         """
-        return (Vector(r) for r in self._entries)
+        return (self.vector_cls(r) for r in self._entries)
 
     def cols(self):
         """Loop over a Matrix's columns.
@@ -165,7 +167,7 @@ class Matrix:
             columns (generator): produces a Matrix's columns as Vectors
         """
         return (
-            Vector(self._entries[r][c] for r in range(self.n_rows))
+            self.vector_cls(self._entries[r][c] for r in range(self.n_rows))
             for c in range(self.n_cols)
         )
 
@@ -232,7 +234,7 @@ class Matrix:
 
     def __radd__(self, other):
         """See docstring for .__add__()."""
-        if isinstance(other, Vector):
+        if isinstance(other, self.vector_cls):
             raise TypeError("vectors and matrices cannot be added")
         # As both matrix and broadcasting addition are commutative,
         # we dispatch to .__add__().
@@ -260,7 +262,7 @@ class Matrix:
 
     def __rsub__(self, other):
         """See docstring for .__sub__()."""
-        if isinstance(other, Vector):
+        if isinstance(other, self.vector_cls):
             raise TypeError("vectors and matrices cannot be subtracted")
         # Same comments as in .__sub__() apply
         # with the roles of self and other swapped.
@@ -294,6 +296,8 @@ class Matrix:
 
             Matrix-vector and vector-matrix multiplication are not commutative.
 
+            >>> from sample_package import Vector
+
             >>> Matrix([(1, 2), (3, 4)]) * Vector([5, 6])
             Vector((17.000, 39.000))
 
@@ -304,7 +308,7 @@ class Matrix:
         if isinstance(other, numbers.Number):
             return self.__class__((x * other for x in r) for r in self._entries)
         # Matrix-vector multiplication: Vector is a column Vector
-        elif isinstance(other, Vector):
+        elif isinstance(other, self.vector_cls):
             # First, cast the other Vector as a Matrix, then do matrix-matrix
             # multiplication, and lastly return the result as a Vector again.
             return self._matrix_multiply(other.as_matrix()).as_vector()
@@ -319,7 +323,7 @@ class Matrix:
         if isinstance(other, numbers.Number):
             return self * other
         # Vector-matrix multiplication: Vector is a row Vector
-        elif isinstance(other, Vector):
+        elif isinstance(other, self.vector_cls):
             return other.as_matrix(column=False)._matrix_multiply(self).as_vector()
         return NotImplemented
 
@@ -375,7 +379,7 @@ class Matrix:
 
     def __abs__(self):
         """The Frobenius norm of a Matrix."""
-        return utils.norm(self)  # use the norm() function shared with the Vector class
+        return utils.norm(self)  # uses the norm() function shared vector.Vector
 
     def __bool__(self):
         """A Matrix is truthy if its Frobenius norm is strictly positive."""
@@ -398,7 +402,7 @@ class Matrix:
         """Get a Vector representation of a Matrix.
 
         Returns:
-            vector (Vector)
+            vector (vector.Vector)
 
         Raises:
             RuntimeError: if one of the two dimensions, .n_rows or .n_cols, is not 1
@@ -409,7 +413,7 @@ class Matrix:
         """
         if not (self.n_rows == 1 or self.n_cols == 1):
             raise RuntimeError("one dimension (m or n) must be 1")
-        return Vector(x for x in self)
+        return self.vector_cls(x for x in self)
 
     def transpose(self):
         """Switch the rows and columns of a Matrix.
@@ -432,4 +436,8 @@ class Matrix:
 # We call that a circular import. Whereas Python handles "circular" references
 # (e.g., both the Matrix and Vector classes have methods that reference the
 # respective other class), that is forbidden for imports.
-from sample_package.vector import Vector
+from sample_package import vector
+
+# This attribute cannot be set in the class definition
+# as the vector module is only imported down here.
+Matrix.vector_cls = vector.Vector
diff --git a/11_classes/sample_package/vector.py b/11_classes/sample_package/vector.py
index 874d29a..21e3729 100644
--- a/11_classes/sample_package/vector.py
+++ b/11_classes/sample_package/vector.py
@@ -7,8 +7,8 @@ import numbers
 # If third-party libraries are needed, they are
 # put into a group on their own in between.
 # Within a group, imports are sorted lexicographically.
+from sample_package import matrix
 from sample_package import utils
-from sample_package.matrix import Matrix
 
 
 class Vector:
@@ -17,6 +17,7 @@ class Vector:
     All entries are converted to floats, or whatever is set in the typing attribute.
 
     Attributes:
+        matrix_cls (matrix.Matrix): a reference to the Matrix class to work with
         storage (callable): data type used to store the entries internally;
             defaults to tuple
         typing (callable): type casting applied to all entries upon creation;
@@ -25,6 +26,7 @@ class Vector:
             defaults to 1e-12
     """
 
+    matrix_cls = matrix.Matrix
     storage = utils.DEFAULT_ENTRIES_STORAGE
     typing = utils.DEFAULT_ENTRY_TYPE
     zero_threshold = utils.ZERO_THRESHOLD
@@ -219,7 +221,7 @@ class Vector:
 
     def __abs__(self):
         """The Euclidean norm of a vector."""
-        return utils.norm(self)  # use the norm() function shared with the Matrix class
+        return utils.norm(self)  # uses the norm() function shared matrix.Matrix
 
     def __bool__(self):
         """A Vector is truthy if its Euclidean norm is strictly positive."""
@@ -246,7 +248,7 @@ class Vector:
                 column vector or a row vector; defaults to True
 
         Returns:
-            matrix (Matrix)
+            matrix (matrix.Matrix)
 
         Example Usage:
             >>> v = Vector([1, 2, 3])
@@ -256,5 +258,5 @@ class Vector:
             Matrix(((1.000, 2.000, 3.000,)))
         """
         if column:
-            return Matrix([x] for x in self)
-        return Matrix([(x for x in self)])
+            return self.matrix_cls([x] for x in self)
+        return self.matrix_cls([(x for x in self)])
diff --git a/CONTENTS.md b/CONTENTS.md
index fed0b64..be3d163 100644
--- a/CONTENTS.md
+++ b/CONTENTS.md
@@ -284,3 +284,8 @@ If this is not possible,
       [<img height="12" style="display: inline-block" src="static/link/to_mb.png">](https://mybinder.org/v2/gh/webartifex/intro-to-python/develop?urlpath=lab/tree/11_classes/03_content.ipynb)
       (Operator Overloading: Arithmetic & Relational Operators;
        Number Emulation)
+    - [content <img height="12" style="display: inline-block" src="static/link/to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/11_classes/04_content.ipynb)
+      [<img height="12" style="display: inline-block" src="static/link/to_mb.png">](https://mybinder.org/v2/gh/webartifex/intro-to-python/develop?urlpath=lab/tree/11_classes/04_content.ipynb)
+      (Writing one's own Packages;
+       The final `Vector` & `Matrix` Classes;
+       Comparison with `numpy`)

From ca9df9e440c7521b0dc9975ed2e5ee7260efe73d Mon Sep 17 00:00:00 2001
From: Alexander Hess <alexander@webartifex.biz>
Date: Wed, 28 Oct 2020 16:57:48 +0100
Subject: [PATCH 220/242] Show only the necessary decimals

---
 11_classes/00_content.ipynb         |  32 ++++-----
 11_classes/02_content.ipynb         |  34 ++++-----
 11_classes/03_content.ipynb         |  60 ++++++++--------
 11_classes/04_content.ipynb         | 108 ++++++++++++++--------------
 11_classes/sample_package/matrix.py |  38 +++++-----
 11_classes/sample_package/vector.py |  30 ++++----
 6 files changed, 152 insertions(+), 150 deletions(-)

diff --git a/11_classes/00_content.ipynb b/11_classes/00_content.ipynb
index 87a9bae..2850088 100644
--- a/11_classes/00_content.ipynb
+++ b/11_classes/00_content.ipynb
@@ -1083,13 +1083,13 @@
     "        # ...\n",
     "\n",
     "    def __repr__(self):\n",
-    "        args = \", \".join(f\"{x:.3f}\" for x in self._entries)\n",
+    "        args = \", \".join(repr(x) for x in self._entries)\n",
     "        return f\"Vector(({args}))\"\n",
     "\n",
     "    def __str__(self):\n",
     "        first, last = self._entries[0], self._entries[-1]\n",
     "        n_entries = len(self._entries)\n",
-    "        return f\"Vector({first:.1f}, ..., {last:.1f})[{n_entries:d}]\""
+    "        return f\"Vector({first!r}, ..., {last!r})[{n_entries:d}]\""
    ]
   },
   {
@@ -1132,7 +1132,7 @@
     {
      "data": {
       "text/plain": [
-       "Vector((1.000, 2.000, 3.000))"
+       "Vector((1.0, 2.0, 3.0))"
       ]
      },
      "execution_count": 31,
@@ -1167,7 +1167,7 @@
     {
      "data": {
       "text/plain": [
-       "Vector((1.000, 2.000, 3.000))"
+       "Vector((1.0, 2.0, 3.0))"
       ]
      },
      "execution_count": 32,
@@ -1176,7 +1176,7 @@
     }
    ],
    "source": [
-    "Vector((1.000, 2.000, 3.000))"
+    "Vector((1.0, 2.0, 3.0))"
    ]
   },
   {
@@ -1202,7 +1202,7 @@
     {
      "data": {
       "text/plain": [
-       "'Vector((1.000, 2.000, 3.000))'"
+       "'Vector((1.0, 2.0, 3.0))'"
       ]
      },
      "execution_count": 33,
@@ -1334,13 +1334,13 @@
     "            raise ValueError(\"a matrix must have at least one entry\")\n",
     "\n",
     "    def __repr__(self):\n",
-    "        args = \", \".join(\"(\" + \", \".join(f\"{c:.3f}\" for c in r) + \",)\" for r in self._entries)\n",
+    "        args = \", \".join(\"(\" + \", \".join(repr(c) for c in r) + \",)\" for r in self._entries)\n",
     "        return f\"Matrix(({args}))\"\n",
     "\n",
     "    def __str__(self):\n",
     "        first, last = self._entries[0][0], self._entries[-1][-1]\n",
     "        m, n = len(self._entries), len(self._entries[0])\n",
-    "        return f\"Matrix(({first:.1f}, ...), ..., (..., {last:.1f}))[{m:d}x{n:d}]\""
+    "        return f\"Matrix(({first!r}, ...), ..., (..., {last!r}))[{m:d}x{n:d}]\""
    ]
   },
   {
@@ -1521,7 +1521,7 @@
     {
      "data": {
       "text/plain": [
-       "Matrix(((1.000, 2.000, 3.000,), (4.000, 5.000, 6.000,), (7.000, 8.000, 9.000,)))"
+       "Matrix(((1.0, 2.0, 3.0,), (4.0, 5.0, 6.0,), (7.0, 8.0, 9.0,)))"
       ]
      },
      "execution_count": 43,
@@ -1660,7 +1660,7 @@
     "        # ...\n",
     "\n",
     "    def __repr__(self):\n",
-    "        args = \", \".join(\"(\" + \", \".join(f\"{c:.3f}\" for c in r) + \",)\" for r in self._entries)\n",
+    "        args = \", \".join(\"(\" + \", \".join(repr(c) for c in r) + \",)\" for r in self._entries)\n",
     "        return f\"Matrix(({args}))\"\n",
     "\n",
     "    def transpose(self):\n",
@@ -1703,7 +1703,7 @@
     {
      "data": {
       "text/plain": [
-       "Matrix(((1.000, 2.000, 3.000,), (4.000, 5.000, 6.000,), (7.000, 8.000, 9.000,)))"
+       "Matrix(((1.0, 2.0, 3.0,), (4.0, 5.0, 6.0,), (7.0, 8.0, 9.0,)))"
       ]
      },
      "execution_count": 49,
@@ -1727,7 +1727,7 @@
     {
      "data": {
       "text/plain": [
-       "Matrix(((1.000, 4.000, 7.000,), (2.000, 5.000, 8.000,), (3.000, 6.000, 9.000,)))"
+       "Matrix(((1.0, 4.0, 7.0,), (2.0, 5.0, 8.0,), (3.0, 6.0, 9.0,)))"
       ]
      },
      "execution_count": 50,
@@ -1775,7 +1775,7 @@
     {
      "data": {
       "text/plain": [
-       "Matrix(((1.000, 2.000, 3.000,), (4.000, 5.000, 6.000,), (7.000, 8.000, 9.000,)))"
+       "Matrix(((1.0, 2.0, 3.0,), (4.0, 5.0, 6.0,), (7.0, 8.0, 9.0,)))"
       ]
      },
      "execution_count": 52,
@@ -1876,7 +1876,7 @@
     "        # ...\n",
     "\n",
     "    def __repr__(self):\n",
-    "        args = \", \".join(\"(\" + \", \".join(f\"{c:.3f}\" for c in r) + \",)\" for r in self._entries)\n",
+    "        args = \", \".join(\"(\" + \", \".join(repr(c) for c in r) + \",)\" for r in self._entries)\n",
     "        return f\"Matrix(({args}))\"\n",
     "\n",
     "    def transpose(self):\n",
@@ -1923,7 +1923,7 @@
     {
      "data": {
       "text/plain": [
-       "Matrix(((1.000, 2.000, 3.000,), (4.000, 5.000, 6.000,), (7.000, 8.000, 9.000,)))"
+       "Matrix(((1.0, 2.0, 3.0,), (4.0, 5.0, 6.0,), (7.0, 8.0, 9.0,)))"
       ]
      },
      "execution_count": 57,
@@ -1993,7 +1993,7 @@
     "            raise ValueError(\"a matrix must have at least one entry\")\n",
     "\n",
     "    def __repr__(self):\n",
-    "        args = \", \".join(\"(\" + \", \".join(f\"{c:.3f}\" for c in r) + \",)\" for r in self._entries)\n",
+    "        args = \", \".join(\"(\" + \", \".join(repr(c) for c in r) + \",)\" for r in self._entries)\n",
     "        return f\"Matrix(({args}))\"\n",
     "\n",
     "    @property\n",
diff --git a/11_classes/02_content.ipynb b/11_classes/02_content.ipynb
index 644fad2..90d650e 100644
--- a/11_classes/02_content.ipynb
+++ b/11_classes/02_content.ipynb
@@ -77,7 +77,7 @@
     "            raise ValueError(\"a vector must have at least one entry\")\n",
     "\n",
     "    def __repr__(self):\n",
-    "        args = \", \".join(f\"{x:.3f}\" for x in self._entries)\n",
+    "        args = \", \".join(repr(x) for x in self._entries)\n",
     "        return f\"Vector(({args}))\"\n",
     "\n",
     "    def __len__(self):\n",
@@ -762,7 +762,7 @@
     "        # ...\n",
     "\n",
     "    def __repr__(self):\n",
-    "        args = \", \".join(\"(\" + \", \".join(f\"{c:.3f}\" for c in r) + \",)\" for r in self._entries)\n",
+    "        args = \", \".join(\"(\" + \", \".join(repr(c) for c in r) + \",)\" for r in self._entries)\n",
     "        return f\"Matrix(({args}))\"\n",
     "\n",
     "    @property\n",
@@ -825,7 +825,7 @@
     "        # ...\n",
     "\n",
     "    def __repr__(self):\n",
-    "        args = \", \".join(f\"{x:.3f}\" for x in self)\n",
+    "        args = \", \".join(repr(x) for x in self)\n",
     "        return f\"Vector(({args}))\"\n",
     "\n",
     "    def __iter__(self):\n",
@@ -860,7 +860,7 @@
     {
      "data": {
       "text/plain": [
-       "Matrix(((1.000, 2.000, 3.000,), (4.000, 5.000, 6.000,), (7.000, 8.000, 9.000,)))"
+       "Matrix(((1.0, 2.0, 3.0,), (4.0, 5.0, 6.0,), (7.0, 8.0, 9.0,)))"
       ]
      },
      "execution_count": 25,
@@ -951,7 +951,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Vector((1.000, 2.000, 3.000))   Vector((4.000, 5.000, 6.000))   Vector((7.000, 8.000, 9.000))   "
+      "Vector((1.0, 2.0, 3.0))   Vector((4.0, 5.0, 6.0))   Vector((7.0, 8.0, 9.0))   "
      ]
     }
    ],
@@ -973,7 +973,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Vector((1.000, 4.000, 7.000))   Vector((2.000, 5.000, 8.000))   Vector((3.000, 6.000, 9.000))   "
+      "Vector((1.0, 4.0, 7.0))   Vector((2.0, 5.0, 8.0))   Vector((3.0, 6.0, 9.0))   "
      ]
     }
    ],
@@ -1069,7 +1069,7 @@
     "        # ...\n",
     "\n",
     "    def __repr__(self):\n",
-    "        args = \", \".join(\"(\" + \", \".join(f\"{c:.3f}\" for c in r) + \",)\" for r in self._entries)\n",
+    "        args = \", \".join(\"(\" + \", \".join(repr(c) for c in r) + \",)\" for r in self._entries)\n",
     "        return f\"Matrix(({args}))\"\n",
     "\n",
     "    def transpose(self):\n",
@@ -1102,7 +1102,7 @@
     {
      "data": {
       "text/plain": [
-       "Matrix(((1.000, 2.000, 3.000,), (4.000, 5.000, 6.000,), (7.000, 8.000, 9.000,)))"
+       "Matrix(((1.0, 2.0, 3.0,), (4.0, 5.0, 6.0,), (7.0, 8.0, 9.0,)))"
       ]
      },
      "execution_count": 34,
@@ -1161,7 +1161,7 @@
     {
      "data": {
       "text/plain": [
-       "Matrix(((1.000, 4.000, 7.000,), (2.000, 5.000, 8.000,), (3.000, 6.000, 9.000,)))"
+       "Matrix(((1.0, 4.0, 7.0,), (2.0, 5.0, 8.0,), (3.0, 6.0, 9.0,)))"
       ]
      },
      "execution_count": 36,
@@ -1248,7 +1248,7 @@
     "        # ...\n",
     "\n",
     "    def __repr__(self):\n",
-    "        args = \", \".join(\"(\" + \", \".join(f\"{c:.3f}\" for c in r) + \",)\" for r in self._entries)\n",
+    "        args = \", \".join(\"(\" + \", \".join(repr(c) for c in r) + \",)\" for r in self._entries)\n",
     "        return f\"Matrix(({args}))\"\n",
     "\n",
     "    def __iter__(self):  # adapted for brevity; uses parts of entries()\n",
@@ -1285,7 +1285,7 @@
     {
      "data": {
       "text/plain": [
-       "Matrix(((1.000, 2.000, 3.000,), (4.000, 5.000, 6.000,), (7.000, 8.000, 9.000,)))"
+       "Matrix(((1.0, 2.0, 3.0,), (4.0, 5.0, 6.0,), (7.0, 8.0, 9.0,)))"
       ]
      },
      "execution_count": 40,
@@ -1333,7 +1333,7 @@
     {
      "data": {
       "text/plain": [
-       "Matrix(((1.000, 4.000, 7.000,), (2.000, 5.000, 8.000,), (3.000, 6.000, 9.000,)))"
+       "Matrix(((1.0, 4.0, 7.0,), (2.0, 5.0, 8.0,), (3.0, 6.0, 9.0,)))"
       ]
      },
      "execution_count": 42,
@@ -1357,7 +1357,7 @@
     {
      "data": {
       "text/plain": [
-       "Matrix(((1.000, 4.000, 7.000,), (2.000, 5.000, 8.000,), (3.000, 6.000, 9.000,)))"
+       "Matrix(((1.0, 4.0, 7.0,), (2.0, 5.0, 8.0,), (3.0, 6.0, 9.0,)))"
       ]
      },
      "execution_count": 43,
@@ -1434,7 +1434,7 @@
     "        # ...\n",
     "\n",
     "    def __repr__(self):\n",
-    "        args = \", \".join(f\"{x:.3f}\" for x in self)\n",
+    "        args = \", \".join(repr(x) for x in self)\n",
     "        return f\"Vector(({args}))\"\n",
     "\n",
     "    def __getitem__(self, index):\n",
@@ -1472,7 +1472,7 @@
     {
      "data": {
       "text/plain": [
-       "Vector((1.000, 2.000, 3.000))"
+       "Vector((1.0, 2.0, 3.0))"
       ]
      },
      "execution_count": 47,
@@ -1520,7 +1520,7 @@
     {
      "data": {
       "text/plain": [
-       "Vector((nan, 2.000, 3.000))"
+       "Vector((nan, 2.0, 3.0))"
       ]
      },
      "execution_count": 49,
@@ -1557,7 +1557,7 @@
     {
      "data": {
       "text/plain": [
-       "Vector((99.000, 2.000, 3.000))"
+       "Vector((99.0, 2.0, 3.0))"
       ]
      },
      "execution_count": 51,
diff --git a/11_classes/03_content.ipynb b/11_classes/03_content.ipynb
index def4be5..5bd0fe2 100644
--- a/11_classes/03_content.ipynb
+++ b/11_classes/03_content.ipynb
@@ -86,7 +86,7 @@
     "        # ...\n",
     "\n",
     "    def __repr__(self):\n",
-    "        args = \", \".join(f\"{x:.3f}\" for x in self)\n",
+    "        args = \", \".join(repr(x) for x in self)\n",
     "        return f\"Vector(({args}))\"\n",
     "\n",
     "    def __iter__(self):\n",
@@ -115,7 +115,7 @@
     "        # ...\n",
     "\n",
     "    def __repr__(self):\n",
-    "        args = \", \".join(\"(\" + \", \".join(f\"{c:.3f}\" for c in r) + \",)\" for r in self._entries)\n",
+    "        args = \", \".join(\"(\" + \", \".join(repr(c) for c in r) + \",)\" for r in self._entries)\n",
     "        return f\"Matrix(({args}))\"\n",
     "\n",
     "    @property\n",
@@ -160,7 +160,7 @@
     {
      "data": {
       "text/plain": [
-       "Vector((1.000, 2.000, 3.000))"
+       "Vector((1.0, 2.0, 3.0))"
       ]
      },
      "execution_count": 4,
@@ -208,7 +208,7 @@
     {
      "data": {
       "text/plain": [
-       "Matrix(((1.000,), (2.000,), (3.000,)))"
+       "Matrix(((1.0,), (2.0,), (3.0,)))"
       ]
      },
      "execution_count": 6,
@@ -267,7 +267,7 @@
     {
      "data": {
       "text/plain": [
-       "Vector((1.000, 2.000, 3.000))"
+       "Vector((1.0, 2.0, 3.0))"
       ]
      },
      "execution_count": 8,
@@ -315,7 +315,7 @@
     {
      "data": {
       "text/plain": [
-       "Matrix(((1.000, 2.000, 3.000,)))"
+       "Matrix(((1.0, 2.0, 3.0,)))"
       ]
      },
      "execution_count": 10,
@@ -363,7 +363,7 @@
     {
      "data": {
       "text/plain": [
-       "Vector((1.000, 2.000, 3.000))"
+       "Vector((1.0, 2.0, 3.0))"
       ]
      },
      "execution_count": 12,
@@ -682,7 +682,7 @@
     "        # ...\n",
     "\n",
     "    def __repr__(self):\n",
-    "        args = \", \".join(f\"{x:.3f}\" for x in self)\n",
+    "        args = \", \".join(repr(x) for x in self)\n",
     "        return f\"Vector(({args}))\"\n",
     "\n",
     "    def __len__(self):\n",
@@ -790,7 +790,7 @@
     {
      "data": {
       "text/plain": [
-       "Vector((2.000, 4.000, 6.000))"
+       "Vector((2.0, 4.0, 6.0))"
       ]
      },
      "execution_count": 24,
@@ -814,7 +814,7 @@
     {
      "data": {
       "text/plain": [
-       "Vector((3.000, 6.000, 9.000))"
+       "Vector((3.0, 6.0, 9.0))"
       ]
      },
      "execution_count": 25,
@@ -910,7 +910,7 @@
     {
      "data": {
       "text/plain": [
-       "Vector((0.333, 0.667, 1.000))"
+       "Vector((0.3333333333333333, 0.6666666666666666, 1.0))"
       ]
      },
      "execution_count": 28,
@@ -945,7 +945,7 @@
     {
      "data": {
       "text/plain": [
-       "Vector((2.000, 4.000, 6.000))"
+       "Vector((2.0, 4.0, 6.0))"
       ]
      },
      "execution_count": 29,
@@ -969,7 +969,7 @@
     {
      "data": {
       "text/plain": [
-       "Vector((0.000, 0.000, 0.000))"
+       "Vector((0.0, 0.0, 0.0))"
       ]
      },
      "execution_count": 30,
@@ -1019,7 +1019,7 @@
     {
      "data": {
       "text/plain": [
-       "Vector((101.000, 102.000, 103.000))"
+       "Vector((101.0, 102.0, 103.0))"
       ]
      },
      "execution_count": 32,
@@ -1043,7 +1043,7 @@
     {
      "data": {
       "text/plain": [
-       "Vector((99.000, 98.000, 97.000))"
+       "Vector((99.0, 98.0, 97.0))"
       ]
      },
      "execution_count": 33,
@@ -1146,7 +1146,7 @@
     "        # ...\n",
     "\n",
     "    def __repr__(self):\n",
-    "        args = \", \".join(\"(\" + \", \".join(f\"{c:.3f}\" for c in r) + \",)\" for r in self._entries)\n",
+    "        args = \", \".join(\"(\" + \", \".join(repr(c) for c in r) + \",)\" for r in self._entries)\n",
     "        return f\"Matrix(({args}))\"\n",
     "\n",
     "    @property\n",
@@ -1270,7 +1270,7 @@
     {
      "data": {
       "text/plain": [
-       "Matrix(((10.000, 20.000, 30.000,), (40.000, 50.000, 60.000,), (70.000, 80.000, 90.000,)))"
+       "Matrix(((10.0, 20.0, 30.0,), (40.0, 50.0, 60.0,), (70.0, 80.0, 90.0,)))"
       ]
      },
      "execution_count": 37,
@@ -1294,7 +1294,7 @@
     {
      "data": {
       "text/plain": [
-       "Matrix(((1.000, 2.000, 3.000,), (4.000, 5.000, 6.000,), (7.000, 8.000, 9.000,)))"
+       "Matrix(((1.0, 2.0, 3.0,), (4.0, 5.0, 6.0,), (7.0, 8.0, 9.0,)))"
       ]
      },
      "execution_count": 38,
@@ -1318,7 +1318,7 @@
     {
      "data": {
       "text/plain": [
-       "Matrix(((0.000, 0.000, 0.000,), (0.000, 0.000, 0.000,), (0.000, 0.000, 0.000,)))"
+       "Matrix(((0.0, 0.0, 0.0,), (0.0, 0.0, 0.0,), (0.0, 0.0, 0.0,)))"
       ]
      },
      "execution_count": 39,
@@ -1353,7 +1353,7 @@
     {
      "data": {
       "text/plain": [
-       "Matrix(((138.000, 171.000, 204.000,), (174.000, 216.000, 258.000,)))"
+       "Matrix(((138.0, 171.0, 204.0,), (174.0, 216.0, 258.0,)))"
       ]
      },
      "execution_count": 40,
@@ -1426,7 +1426,7 @@
     {
      "data": {
       "text/plain": [
-       "Vector((14.000, 32.000, 50.000))"
+       "Vector((14.0, 32.0, 50.0))"
       ]
      },
      "execution_count": 42,
@@ -1450,7 +1450,7 @@
     {
      "data": {
       "text/plain": [
-       "Vector((30.000, 36.000, 42.000))"
+       "Vector((30.0, 36.0, 42.0))"
       ]
      },
      "execution_count": 43,
@@ -1474,7 +1474,7 @@
     {
      "data": {
       "text/plain": [
-       "Vector((68.000, 86.000))"
+       "Vector((68.0, 86.0))"
       ]
      },
      "execution_count": 44,
@@ -1536,7 +1536,7 @@
     {
      "data": {
       "text/plain": [
-       "Matrix(((101.000, 102.000, 103.000,), (104.000, 105.000, 106.000,), (107.000, 108.000, 109.000,)))"
+       "Matrix(((101.0, 102.0, 103.0,), (104.0, 105.0, 106.0,), (107.0, 108.0, 109.0,)))"
       ]
      },
      "execution_count": 46,
@@ -1560,7 +1560,7 @@
     {
      "data": {
       "text/plain": [
-       "Matrix(((99.000, 98.000, 97.000,), (96.000, 95.000, 94.000,), (93.000, 92.000, 91.000,)))"
+       "Matrix(((99.0, 98.0, 97.0,), (96.0, 95.0, 94.0,), (93.0, 92.0, 91.0,)))"
       ]
      },
      "execution_count": 47,
@@ -1724,7 +1724,7 @@
     "        # ...\n",
     "\n",
     "    def __repr__(self):\n",
-    "        args = \", \".join(f\"{x:.3f}\" for x in self)\n",
+    "        args = \", \".join(repr(x) for x in self)\n",
     "        return f\"Vector(({args}))\"\n",
     "\n",
     "    def __len__(self):\n",
@@ -1881,7 +1881,7 @@
     {
      "data": {
       "text/plain": [
-       "Vector((1.000, 2.000, 3.000))"
+       "Vector((1.0, 2.0, 3.0))"
       ]
      },
      "execution_count": 58,
@@ -1994,7 +1994,7 @@
     "        # ...\n",
     "\n",
     "    def __repr__(self):\n",
-    "        args = \", \".join(f\"{x:.3f}\" for x in self)\n",
+    "        args = \", \".join(repr(x) for x in self)\n",
     "        return f\"Vector(({args}))\"\n",
     "\n",
     "    def __iter__(self):\n",
@@ -2058,7 +2058,7 @@
     {
      "data": {
       "text/plain": [
-       "Vector((1.000, 2.000, 3.000))"
+       "Vector((1.0, 2.0, 3.0))"
       ]
      },
      "execution_count": 65,
@@ -2106,7 +2106,7 @@
     {
      "data": {
       "text/plain": [
-       "Vector((-3.000, -4.000))"
+       "Vector((-3.0, -4.0))"
       ]
      },
      "execution_count": 67,
diff --git a/11_classes/04_content.ipynb b/11_classes/04_content.ipynb
index 51b0818..959c160 100644
--- a/11_classes/04_content.ipynb
+++ b/11_classes/04_content.ipynb
@@ -303,13 +303,13 @@
       "     |      \n",
       "     |      Example Usage:\n",
       "     |          >>> Matrix([(1, 2), (3, 4)]) + Matrix([(2, 3), (4, 5)])\n",
-      "     |          Matrix(((3.000, 5.000,), (7.000, 9.000,)))\n",
+      "     |          Matrix(((3.0, 5.0,), (7.0, 9.0,)))\n",
       "     |      \n",
       "     |          >>> Matrix([(1, 2), (3, 4)]) + 5\n",
-      "     |          Matrix(((6.000, 7.000,), (8.000, 9.000,)))\n",
+      "     |          Matrix(((6.0, 7.0,), (8.0, 9.0,)))\n",
       "     |      \n",
       "     |          >>> 10 + Matrix([(1, 2), (3, 4)])\n",
-      "     |          Matrix(((11.000, 12.000,), (13.000, 14.000,)))\n",
+      "     |          Matrix(((11.0, 12.0,), (13.0, 14.0,)))\n",
       "     |  \n",
       "     |  __bool__(self)\n",
       "     |      A Matrix is truthy if its Frobenius norm is strictly positive.\n",
@@ -372,7 +372,7 @@
       "     |      \n",
       "     |      Example Usage:\n",
       "     |          >>> Matrix([(1, 2), (3, 4)])\n",
-      "     |          Matrix(((1.000, 2.000,), (3.000, 4.000,)))\n",
+      "     |          Matrix(((1.0, 2.0,), (3.0, 4.0,)))\n",
       "     |  \n",
       "     |  __iter__(self)\n",
       "     |      Loop over a Matrix's entries.\n",
@@ -390,21 +390,23 @@
       "     |      \n",
       "     |      Example Usage:\n",
       "     |          >>> Matrix([(1, 2), (3, 4)]) * Matrix([(1, 2), (3, 4)])\n",
-      "     |          Matrix(((7.000, 10.000,), (15.000, 22.000,)))\n",
+      "     |          Matrix(((7.0, 10.0,), (15.0, 22.0,)))\n",
       "     |      \n",
       "     |          >>> 2 * Matrix([(1, 2), (3, 4)])\n",
-      "     |          Matrix(((2.000, 4.000,), (6.000, 8.000,)))\n",
+      "     |          Matrix(((2.0, 4.0,), (6.0, 8.0,)))\n",
       "     |      \n",
       "     |          >>> Matrix([(1, 2), (3, 4)]) * 3\n",
-      "     |          Matrix(((3.000, 6.000,), (9.000, 12.000,)))\n",
+      "     |          Matrix(((3.0, 6.0,), (9.0, 12.0,)))\n",
       "     |      \n",
       "     |          Matrix-vector and vector-matrix multiplication are not commutative.\n",
       "     |      \n",
+      "     |          >>> from sample_package import Vector\n",
+      "     |      \n",
       "     |          >>> Matrix([(1, 2), (3, 4)]) * Vector([5, 6])\n",
-      "     |          Vector((17.000, 39.000))\n",
+      "     |          Vector((17.0, 39.0))\n",
       "     |      \n",
       "     |          >>> Vector([5, 6]) * Matrix([(1, 2), (3, 4)])\n",
-      "     |          Vector((23.000, 34.000))\n",
+      "     |          Vector((23.0, 34.0))\n",
       "     |  \n",
       "     |  __neg__(self)\n",
       "     |      Handle `-self`.\n",
@@ -443,13 +445,13 @@
       "     |      \n",
       "     |      Example Usage:\n",
       "     |          >>> Matrix([(2, 3), (4, 5)]) - Matrix([(1, 2), (3, 4)])\n",
-      "     |          Matrix(((1.000, 1.000,), (1.000, 1.000,)))\n",
+      "     |          Matrix(((1.0, 1.0,), (1.0, 1.0,)))\n",
       "     |      \n",
       "     |          >>> Matrix([(1, 2), (3, 4)]) - 1\n",
-      "     |          Matrix(((0.000, 1.000,), (2.000, 3.000,)))\n",
+      "     |          Matrix(((0.0, 1.0,), (2.0, 3.0,)))\n",
       "     |      \n",
       "     |          >>> 10 - Matrix([(1, 2), (3, 4)])\n",
-      "     |          Matrix(((9.000, 8.000,), (7.000, 6.000,)))\n",
+      "     |          Matrix(((9.0, 8.0,), (7.0, 6.0,)))\n",
       "     |  \n",
       "     |  __truediv__(self, other)\n",
       "     |      Handle `self / other`.\n",
@@ -458,7 +460,7 @@
       "     |      \n",
       "     |      Example Usage:\n",
       "     |          >>> Matrix([(1, 2), (3, 4)]) / 4\n",
-      "     |          Matrix(((0.250, 0.500,), (0.750, 1.000,)))\n",
+      "     |          Matrix(((0.25, 0.5,), (0.75, 1.0,)))\n",
       "     |  \n",
       "     |  as_vector(self)\n",
       "     |      Get a Vector representation of a Matrix.\n",
@@ -471,7 +473,7 @@
       "     |      \n",
       "     |      Example Usage:\n",
       "     |          >>> Matrix([(1, 2, 3)]).as_vector()\n",
-      "     |          Vector((1.000, 2.000, 3.000))\n",
+      "     |          Vector((1.0, 2.0, 3.0))\n",
       "     |  \n",
       "     |  cols(self)\n",
       "     |      Loop over a Matrix's columns.\n",
@@ -504,9 +506,9 @@
       "     |      Example Usage:\n",
       "     |          >>> m = Matrix([(1, 2), (3, 4)])\n",
       "     |          >>> m\n",
-      "     |          Matrix(((1.000, 2.000,), (3.000, 4.000,)))\n",
+      "     |          Matrix(((1.0, 2.0,), (3.0, 4.0,)))\n",
       "     |          >>> m.transpose()\n",
-      "     |          Matrix(((1.000, 3.000,), (2.000, 4.000,)))\n",
+      "     |          Matrix(((1.0, 3.0,), (2.0, 4.0,)))\n",
       "     |  \n",
       "     |  ----------------------------------------------------------------------\n",
       "     |  Class methods defined here:\n",
@@ -529,7 +531,7 @@
       "     |      \n",
       "     |      Example Usage:\n",
       "     |          >>> Matrix.from_columns([(1, 2), (3, 4)])\n",
-      "     |          Matrix(((1.000, 3.000,), (2.000, 4.000,)))\n",
+      "     |          Matrix(((1.0, 3.0,), (2.0, 4.0,)))\n",
       "     |  \n",
       "     |  from_rows(data) from builtins.type\n",
       "     |      See docstring for .__init__().\n",
@@ -612,13 +614,13 @@
       "     |      \n",
       "     |      Example Usage:\n",
       "     |          >>> Vector([1, 2, 3]) + Vector([2, 3, 4])\n",
-      "     |          Vector((3.000, 5.000, 7.000))\n",
+      "     |          Vector((3.0, 5.0, 7.0))\n",
       "     |      \n",
       "     |          >>> Vector([1, 2, 3]) + 4\n",
-      "     |          Vector((5.000, 6.000, 7.000))\n",
+      "     |          Vector((5.0, 6.0, 7.0))\n",
       "     |      \n",
       "     |          >>> 10 + Vector([1, 2, 3])\n",
-      "     |          Vector((11.000, 12.000, 13.000))\n",
+      "     |          Vector((11.0, 12.0, 13.0))\n",
       "     |  \n",
       "     |  __bool__(self)\n",
       "     |      A Vector is truthy if its Euclidean norm is strictly positive.\n",
@@ -658,10 +660,10 @@
       "     |      \n",
       "     |      Example Usage:\n",
       "     |          >>> Vector([1, 2, 3])\n",
-      "     |          Vector((1.000, 2.000, 3.000))\n",
+      "     |          Vector((1.0, 2.0, 3.0))\n",
       "     |      \n",
       "     |          >>> Vector(range(3))\n",
-      "     |          Vector((0.000, 1.000, 2.000))\n",
+      "     |          Vector((0.0, 1.0, 2.0))\n",
       "     |  \n",
       "     |  __iter__(self)\n",
       "     |      Loop over a Vector's entries.\n",
@@ -679,10 +681,10 @@
       "     |          20.0\n",
       "     |      \n",
       "     |          >>> 2 * Vector([1, 2, 3])\n",
-      "     |          Vector((2.000, 4.000, 6.000))\n",
+      "     |          Vector((2.0, 4.0, 6.0))\n",
       "     |      \n",
       "     |          >>> Vector([1, 2, 3]) * 3\n",
-      "     |          Vector((3.000, 6.000, 9.000))\n",
+      "     |          Vector((3.0, 6.0, 9.0))\n",
       "     |  \n",
       "     |  __neg__(self)\n",
       "     |      Handle `-self`.\n",
@@ -719,13 +721,13 @@
       "     |      \n",
       "     |      Example Usage:\n",
       "     |          >>> Vector([7, 8, 9]) - Vector([1, 2, 3])\n",
-      "     |          Vector((6.000, 6.000, 6.000))\n",
+      "     |          Vector((6.0, 6.0, 6.0))\n",
       "     |      \n",
       "     |          >>> Vector([1, 2, 3]) - 1\n",
-      "     |          Vector((0.000, 1.000, 2.000))\n",
+      "     |          Vector((0.0, 1.0, 2.0))\n",
       "     |      \n",
       "     |          >>> 10 - Vector([1, 2, 3])\n",
-      "     |          Vector((9.000, 8.000, 7.000))\n",
+      "     |          Vector((9.0, 8.0, 7.0))\n",
       "     |  \n",
       "     |  __truediv__(self, other)\n",
       "     |      Handle `self / other`.\n",
@@ -734,7 +736,7 @@
       "     |      \n",
       "     |      Example Usage:\n",
       "     |          >>> Vector([9, 6, 12]) / 3\n",
-      "     |          Vector((3.000, 2.000, 4.000))\n",
+      "     |          Vector((3.0, 2.0, 4.0))\n",
       "     |  \n",
       "     |  as_matrix(self, *, column=True)\n",
       "     |      Get a Matrix representation of a Vector.\n",
@@ -749,9 +751,9 @@
       "     |      Example Usage:\n",
       "     |          >>> v = Vector([1, 2, 3])\n",
       "     |          >>> v.as_matrix()\n",
-      "     |          Matrix(((1.000,), (2.000,), (3.000,)))\n",
+      "     |          Matrix(((1.0,), (2.0,), (3.0,)))\n",
       "     |          >>> v.as_matrix(column=False)\n",
-      "     |          Matrix(((1.000, 2.000, 3.000,)))\n",
+      "     |          Matrix(((1.0, 2.0, 3.0,)))\n",
       "     |  \n",
       "     |  ----------------------------------------------------------------------\n",
       "     |  Data descriptors defined here:\n",
@@ -876,7 +878,7 @@
     {
      "data": {
       "text/plain": [
-       "Vector((1.000, 2.000, 3.000))"
+       "Vector((1.0, 2.0, 3.0))"
       ]
      },
      "execution_count": 11,
@@ -896,7 +898,7 @@
     {
      "data": {
       "text/plain": [
-       "Matrix(((1.000, 2.000, 3.000,), (4.000, 5.000, 6.000,), (7.000, 8.000, 9.000,)))"
+       "Matrix(((1.0, 2.0, 3.0,), (4.0, 5.0, 6.0,), (7.0, 8.0, 9.0,)))"
       ]
      },
      "execution_count": 12,
@@ -1116,7 +1118,7 @@
     {
      "data": {
       "text/plain": [
-       "Vector((1.000, 2.000, 3.000))"
+       "Vector((1.0, 2.0, 3.0))"
       ]
      },
      "execution_count": 21,
@@ -1165,7 +1167,7 @@
     {
      "data": {
       "text/plain": [
-       "Matrix(((1.000, 2.000, 3.000,), (4.000, 5.000, 6.000,), (7.000, 8.000, 9.000,)))"
+       "Matrix(((1.0, 2.0, 3.0,), (4.0, 5.0, 6.0,), (7.0, 8.0, 9.0,)))"
       ]
      },
      "execution_count": 24,
@@ -1271,10 +1273,10 @@
        "\u001b[0;34m\u001b[0m\n",
        "\u001b[0;34m        Example Usage:\u001b[0m\n",
        "\u001b[0;34m            >>> Vector([1, 2, 3])\u001b[0m\n",
-       "\u001b[0;34m            Vector((1.000, 2.000, 3.000))\u001b[0m\n",
+       "\u001b[0;34m            Vector((1.0, 2.0, 3.0))\u001b[0m\n",
        "\u001b[0;34m\u001b[0m\n",
        "\u001b[0;34m            >>> Vector(range(3))\u001b[0m\n",
-       "\u001b[0;34m            Vector((0.000, 1.000, 2.000))\u001b[0m\n",
+       "\u001b[0;34m            Vector((0.0, 1.0, 2.0))\u001b[0m\n",
        "\u001b[0;34m        \"\"\"\u001b[0m\u001b[0;34m\u001b[0m\n",
        "\u001b[0;34m\u001b[0m        \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_entries\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstorage\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtyping\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mx\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n",
        "\u001b[0;34m\u001b[0m        \u001b[0;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n",
@@ -1420,9 +1422,9 @@
        "\u001b[0;34m        Example Usage:\u001b[0m\n",
        "\u001b[0;34m            >>> m = Matrix([(1, 2), (3, 4)])\u001b[0m\n",
        "\u001b[0;34m            >>> m\u001b[0m\n",
-       "\u001b[0;34m            Matrix(((1.000, 2.000,), (3.000, 4.000,)))\u001b[0m\n",
+       "\u001b[0;34m            Matrix(((1.0, 2.0,), (3.0, 4.0,)))\u001b[0m\n",
        "\u001b[0;34m            >>> m.transpose()\u001b[0m\n",
-       "\u001b[0;34m            Matrix(((1.000, 3.000,), (2.000, 4.000,)))\u001b[0m\n",
+       "\u001b[0;34m            Matrix(((1.0, 3.0,), (2.0, 4.0,)))\u001b[0m\n",
        "\u001b[0;34m        \"\"\"\u001b[0m\u001b[0;34m\u001b[0m\n",
        "\u001b[0;34m\u001b[0m        \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__class__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mzip\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_entries\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
        "\u001b[0;31mFile:\u001b[0m      ~/repos/intro-to-python/11_classes/sample_package/matrix.py\n",
@@ -1512,7 +1514,7 @@
        "\u001b[0;34m\u001b[0m        \u001b[0;34m\"\"\"Text representation of a Matrix.\"\"\"\u001b[0m\u001b[0;34m\u001b[0m\n",
        "\u001b[0;34m\u001b[0m        \u001b[0mname\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__class__\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__name__\u001b[0m\u001b[0;34m\u001b[0m\n",
        "\u001b[0;34m\u001b[0m        \u001b[0margs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m\", \"\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mjoin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\u001b[0m\n",
-       "\u001b[0;34m\u001b[0m            \u001b[0;34m\"(\"\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;34m\", \"\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mjoin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34mf\"{c:.3f}\"\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mc\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mr\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;34m\",)\"\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mr\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_entries\u001b[0m\u001b[0;34m\u001b[0m\n",
+       "\u001b[0;34m\u001b[0m            \u001b[0;34m\"(\"\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;34m\", \"\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mjoin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrepr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mc\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mc\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mr\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;34m\",)\"\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mr\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_entries\u001b[0m\u001b[0;34m\u001b[0m\n",
        "\u001b[0;34m\u001b[0m        \u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n",
        "\u001b[0;34m\u001b[0m        \u001b[0;32mreturn\u001b[0m \u001b[0;34mf\"{name}(({args}))\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
        "\u001b[0;31mFile:\u001b[0m      ~/repos/intro-to-python/11_classes/sample_package/matrix.py\n",
@@ -1604,9 +1606,9 @@
        "\u001b[0;34m        Example Usage:\u001b[0m\n",
        "\u001b[0;34m            >>> v = Vector([1, 2, 3])\u001b[0m\n",
        "\u001b[0;34m            >>> v.as_matrix()\u001b[0m\n",
-       "\u001b[0;34m            Matrix(((1.000,), (2.000,), (3.000,)))\u001b[0m\n",
+       "\u001b[0;34m            Matrix(((1.0,), (2.0,), (3.0,)))\u001b[0m\n",
        "\u001b[0;34m            >>> v.as_matrix(column=False)\u001b[0m\n",
-       "\u001b[0;34m            Matrix(((1.000, 2.000, 3.000,)))\u001b[0m\n",
+       "\u001b[0;34m            Matrix(((1.0, 2.0, 3.0,)))\u001b[0m\n",
        "\u001b[0;34m        \"\"\"\u001b[0m\u001b[0;34m\u001b[0m\n",
        "\u001b[0;34m\u001b[0m        \u001b[0;32mif\u001b[0m \u001b[0mcolumn\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n",
        "\u001b[0;34m\u001b[0m            \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmatrix_cls\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mx\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n",
@@ -1725,7 +1727,7 @@
     {
      "data": {
       "text/plain": [
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       ]
      },
      "execution_count": 43,
@@ -1750,7 +1752,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Matrix((639.4, ...), ..., (..., 353.9))[100x50]\n"
+      "Matrix((639.4267984578837, ...), ..., (..., 353.85334437419004))[100x50]\n"
      ]
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@@ -1790,7 +1792,7 @@
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       "text/plain": [
-       "Vector((129.713, 562.634, 519.706, 631.858, 492.504, 179.907, 609.406, 708.587, 979.258, 1.581, 23.987, 625.461, 117.926, 848.070, 799.564, 998.987, 414.041, 333.792, 560.416, 637.504, 11.297, 201.187, 281.627, 790.196, 307.773, 506.690, 323.924, 6.131, 685.836, 341.362, 724.397, 615.993, 29.117, 175.629, 330.515, 337.937, 672.473, 916.163, 797.254, 645.652, 481.496, 627.200, 892.058, 536.968, 335.110, 783.989, 413.953, 742.585, 835.106, 299.344))"
+       "Vector((129.7131375543026, 562.6343376666077, 519.7058001844938, 631.8576074968361, 492.50445371851595, 179.90723165619127, 609.4057577306154, 708.5870675033931, 979.2576595660704, 1.580917225831202, 23.986797518176004, 625.4607423896532, 117.92572392127187, 848.0697885701867, 799.5643512555062, 998.9870072501308, 414.04113952437183, 333.7922569338235, 560.4155049178265, 637.5035435880566, 11.297267978029769, 201.1871389300902, 281.62670325053864, 790.1955028462116, 307.77254924389706, 506.6896270960092, 323.9238393830701, 6.131262435292828, 685.8357886469579, 341.36158523000273, 724.3966428110118, 615.9933429964842, 29.117381811460618, 175.62908823799773, 330.51483209301, 337.936855273543, 672.4729573660214, 916.1630531735751, 797.2543838289249, 645.6522206645749, 481.4955231203607, 627.2004877076889, 892.0583267899182, 536.9675449723645, 335.10965453438513, 783.9890332085388, 413.95306533418574, 742.5846461655369, 835.1057359656187, 299.3437466393607))"
       ]
      },
      "execution_count": 46,
@@ -1815,7 +1817,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Vector(129.7, ..., 299.3)[50]\n"
+      "Vector(129.7131375543026, ..., 299.3437466393607)[50]\n"
      ]
     }
    ],
@@ -1860,7 +1862,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Vector(11378937.3, ..., 13593029.3)[100]\n"
+      "Vector(11378937.3105893, ..., 13593029.305789862)[100]\n"
      ]
     }
    ],
@@ -1905,7 +1907,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Matrix((14370711.3, ...), ..., (..., 16545418.2))[100x100]\n"
+      "Matrix((14370711.265265431, ...), ..., (..., 16545418.239505699))[100x100]\n"
      ]
     }
    ],
@@ -1939,7 +1941,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Matrix((32618511.5, ...), ..., (..., 32339164.8))[50x50]\n"
+      "Matrix((32618511.50703142, ...), ..., (..., 32339164.77803234))[50x50]\n"
      ]
     }
    ],
@@ -2111,7 +2113,7 @@
     {
      "data": {
       "text/plain": [
-       "Vector((1.000, 2.000, 3.000))"
+       "Vector((1.0, 2.0, 3.0))"
       ]
      },
      "execution_count": 60,
@@ -2161,7 +2163,7 @@
     {
      "data": {
       "text/plain": [
-       "Matrix(((1.000, 2.000, 3.000,), (4.000, 5.000, 6.000,), (7.000, 8.000, 9.000,)))"
+       "Matrix(((1.0, 2.0, 3.0,), (4.0, 5.0, 6.0,), (7.0, 8.0, 9.0,)))"
       ]
      },
      "execution_count": 62,
@@ -2419,7 +2421,7 @@
     {
      "data": {
       "text/plain": [
-       "Matrix(((1.000, 4.000, 7.000,), (2.000, 5.000, 8.000,), (3.000, 6.000, 9.000,)))"
+       "Matrix(((1.0, 4.0, 7.0,), (2.0, 5.0, 8.0,), (3.0, 6.0, 9.0,)))"
       ]
      },
      "execution_count": 71,
@@ -2504,7 +2506,7 @@
     {
      "data": {
       "text/plain": [
-       "Vector((14.000, 32.000, 50.000))"
+       "Vector((14.0, 32.0, 50.0))"
       ]
      },
      "execution_count": 74,
@@ -2563,7 +2565,7 @@
     {
      "data": {
       "text/plain": [
-       "Vector((10.000, 20.000, 30.000))"
+       "Vector((10.0, 20.0, 30.0))"
       ]
      },
      "execution_count": 76,
diff --git a/11_classes/sample_package/matrix.py b/11_classes/sample_package/matrix.py
index 3c82d26..50a683f 100644
--- a/11_classes/sample_package/matrix.py
+++ b/11_classes/sample_package/matrix.py
@@ -43,7 +43,7 @@ class Matrix:
 
         Example Usage:
             >>> Matrix([(1, 2), (3, 4)])
-            Matrix(((1.000, 2.000,), (3.000, 4.000,)))
+            Matrix(((1.0, 2.0,), (3.0, 4.0,)))
         """
         self._entries = self.storage(
             self.storage(self.typing(x) for x in r) for r in data
@@ -73,7 +73,7 @@ class Matrix:
 
         Example Usage:
             >>> Matrix.from_columns([(1, 2), (3, 4)])
-            Matrix(((1.000, 3.000,), (2.000, 4.000,)))
+            Matrix(((1.0, 3.0,), (2.0, 4.0,)))
         """
         return cls(data).transpose()
 
@@ -89,7 +89,7 @@ class Matrix:
         """Text representation of a Matrix."""
         name = self.__class__.__name__
         args = ", ".join(
-            "(" + ", ".join(f"{c:.3f}" for c in r) + ",)" for r in self._entries
+            "(" + ", ".join(repr(c) for c in r) + ",)" for r in self._entries
         )
         return f"{name}(({args}))"
 
@@ -97,7 +97,7 @@ class Matrix:
         """Human-readable text representation of a Matrix."""
         name = self.__class__.__name__
         first, last, m, n = self[0], self[-1], self.n_rows, self.n_cols
-        return f"{name}(({first:.1f}, ...), ..., (..., {last:.1f}))[{m:d}x{n:d}]"
+        return f"{name}(({first!r}, ...), ..., (..., {last!r}))[{m:d}x{n:d}]"
 
     @property
     def n_rows(self):
@@ -211,13 +211,13 @@ class Matrix:
 
         Example Usage:
             >>> Matrix([(1, 2), (3, 4)]) + Matrix([(2, 3), (4, 5)])
-            Matrix(((3.000, 5.000,), (7.000, 9.000,)))
+            Matrix(((3.0, 5.0,), (7.0, 9.0,)))
 
             >>> Matrix([(1, 2), (3, 4)]) + 5
-            Matrix(((6.000, 7.000,), (8.000, 9.000,)))
+            Matrix(((6.0, 7.0,), (8.0, 9.0,)))
 
             >>> 10 + Matrix([(1, 2), (3, 4)])
-            Matrix(((11.000, 12.000,), (13.000, 14.000,)))
+            Matrix(((11.0, 12.0,), (13.0, 14.0,)))
         """
         # Matrix addition
         if isinstance(other, self.__class__):
@@ -247,13 +247,13 @@ class Matrix:
 
         Example Usage:
             >>> Matrix([(2, 3), (4, 5)]) - Matrix([(1, 2), (3, 4)])
-            Matrix(((1.000, 1.000,), (1.000, 1.000,)))
+            Matrix(((1.0, 1.0,), (1.0, 1.0,)))
 
             >>> Matrix([(1, 2), (3, 4)]) - 1
-            Matrix(((0.000, 1.000,), (2.000, 3.000,)))
+            Matrix(((0.0, 1.0,), (2.0, 3.0,)))
 
             >>> 10 - Matrix([(1, 2), (3, 4)])
-            Matrix(((9.000, 8.000,), (7.000, 6.000,)))
+            Matrix(((9.0, 8.0,), (7.0, 6.0,)))
         """
         # As subtraction is the inverse of addition,
         # we first dispatch to .__neg__() to invert the signs of
@@ -286,23 +286,23 @@ class Matrix:
 
         Example Usage:
             >>> Matrix([(1, 2), (3, 4)]) * Matrix([(1, 2), (3, 4)])
-            Matrix(((7.000, 10.000,), (15.000, 22.000,)))
+            Matrix(((7.0, 10.0,), (15.0, 22.0,)))
 
             >>> 2 * Matrix([(1, 2), (3, 4)])
-            Matrix(((2.000, 4.000,), (6.000, 8.000,)))
+            Matrix(((2.0, 4.0,), (6.0, 8.0,)))
 
             >>> Matrix([(1, 2), (3, 4)]) * 3
-            Matrix(((3.000, 6.000,), (9.000, 12.000,)))
+            Matrix(((3.0, 6.0,), (9.0, 12.0,)))
 
             Matrix-vector and vector-matrix multiplication are not commutative.
 
             >>> from sample_package import Vector
 
             >>> Matrix([(1, 2), (3, 4)]) * Vector([5, 6])
-            Vector((17.000, 39.000))
+            Vector((17.0, 39.0))
 
             >>> Vector([5, 6]) * Matrix([(1, 2), (3, 4)])
-            Vector((23.000, 34.000))
+            Vector((23.0, 34.0))
         """
         # Scalar multiplication
         if isinstance(other, numbers.Number):
@@ -334,7 +334,7 @@ class Matrix:
 
         Example Usage:
             >>> Matrix([(1, 2), (3, 4)]) / 4
-            Matrix(((0.250, 0.500,), (0.750, 1.000,)))
+            Matrix(((0.25, 0.5,), (0.75, 1.0,)))
         """
         # As scalar division division is the same as multiplication
         # with the inverse, we dispatch to .__mul__().
@@ -409,7 +409,7 @@ class Matrix:
 
         Example Usage:
             >>> Matrix([(1, 2, 3)]).as_vector()
-            Vector((1.000, 2.000, 3.000))
+            Vector((1.0, 2.0, 3.0))
         """
         if not (self.n_rows == 1 or self.n_cols == 1):
             raise RuntimeError("one dimension (m or n) must be 1")
@@ -424,9 +424,9 @@ class Matrix:
         Example Usage:
             >>> m = Matrix([(1, 2), (3, 4)])
             >>> m
-            Matrix(((1.000, 2.000,), (3.000, 4.000,)))
+            Matrix(((1.0, 2.0,), (3.0, 4.0,)))
             >>> m.transpose()
-            Matrix(((1.000, 3.000,), (2.000, 4.000,)))
+            Matrix(((1.0, 3.0,), (2.0, 4.0,)))
         """
         return self.__class__(zip(*self._entries))
 
diff --git a/11_classes/sample_package/vector.py b/11_classes/sample_package/vector.py
index 21e3729..e9315c4 100644
--- a/11_classes/sample_package/vector.py
+++ b/11_classes/sample_package/vector.py
@@ -42,10 +42,10 @@ class Vector:
 
         Example Usage:
             >>> Vector([1, 2, 3])
-            Vector((1.000, 2.000, 3.000))
+            Vector((1.0, 2.0, 3.0))
 
             >>> Vector(range(3))
-            Vector((0.000, 1.000, 2.000))
+            Vector((0.0, 1.0, 2.0))
         """
         self._entries = self.storage(self.typing(x) for x in data)
         if len(self) == 0:
@@ -54,14 +54,14 @@ class Vector:
     def __repr__(self):
         """Text representation of a Vector."""
         name = self.__class__.__name__
-        args = ", ".join(f"{x:.3f}" for x in self)
+        args = ", ".join(repr(x) for x in self)
         return f"{name}(({args}))"
 
     def __str__(self):
         """Human-readable text representation of a Vector."""
         name = self.__class__.__name__
         first, last, n_entries = self[0], self[-1], len(self)
-        return f"{name}({first:.1f}, ..., {last:.1f})[{n_entries:d}]"
+        return f"{name}({first!r}, ..., {last!r})[{n_entries:d}]"
 
     def __len__(self):
         """Number of entries in a Vector."""
@@ -88,13 +88,13 @@ class Vector:
 
         Example Usage:
             >>> Vector([1, 2, 3]) + Vector([2, 3, 4])
-            Vector((3.000, 5.000, 7.000))
+            Vector((3.0, 5.0, 7.0))
 
             >>> Vector([1, 2, 3]) + 4
-            Vector((5.000, 6.000, 7.000))
+            Vector((5.0, 6.0, 7.0))
 
             >>> 10 + Vector([1, 2, 3])
-            Vector((11.000, 12.000, 13.000))
+            Vector((11.0, 12.0, 13.0))
         """
         # Vector addition
         if isinstance(other, self.__class__):
@@ -119,13 +119,13 @@ class Vector:
 
         Example Usage:
             >>> Vector([7, 8, 9]) - Vector([1, 2, 3])
-            Vector((6.000, 6.000, 6.000))
+            Vector((6.0, 6.0, 6.0))
 
             >>> Vector([1, 2, 3]) - 1
-            Vector((0.000, 1.000, 2.000))
+            Vector((0.0, 1.0, 2.0))
 
             >>> 10 - Vector([1, 2, 3])
-            Vector((9.000, 8.000, 7.000))
+            Vector((9.0, 8.0, 7.0))
         """
         # As subtraction is the inverse of addition,
         # we first dispatch to .__neg__() to invert the signs of
@@ -148,10 +148,10 @@ class Vector:
             20.0
 
             >>> 2 * Vector([1, 2, 3])
-            Vector((2.000, 4.000, 6.000))
+            Vector((2.0, 4.0, 6.0))
 
             >>> Vector([1, 2, 3]) * 3
-            Vector((3.000, 6.000, 9.000))
+            Vector((3.0, 6.0, 9.0))
         """
         # Dot product
         if isinstance(other, self.__class__):
@@ -176,7 +176,7 @@ class Vector:
 
         Example Usage:
             >>> Vector([9, 6, 12]) / 3
-            Vector((3.000, 2.000, 4.000))
+            Vector((3.0, 2.0, 4.0))
         """
         # As scalar division division is the same as multiplication
         # with the inverse, we dispatch to .__mul__().
@@ -253,9 +253,9 @@ class Vector:
         Example Usage:
             >>> v = Vector([1, 2, 3])
             >>> v.as_matrix()
-            Matrix(((1.000,), (2.000,), (3.000,)))
+            Matrix(((1.0,), (2.0,), (3.0,)))
             >>> v.as_matrix(column=False)
-            Matrix(((1.000, 2.000, 3.000,)))
+            Matrix(((1.0, 2.0, 3.0,)))
         """
         if column:
             return self.matrix_cls([x] for x in self)

From 8dd24414ac15b8b0951d36b6a93a9e8e38f4ddfa Mon Sep 17 00:00:00 2001
From: Alexander Hess <alexander@webartifex.biz>
Date: Wed, 28 Oct 2020 17:20:55 +0100
Subject: [PATCH 221/242] Streamline slides

---
 11_classes/00_content.ipynb |  12 +-
 11_classes/03_content.ipynb |  84 ++++++--
 11_classes/04_content.ipynb | 396 ++++++++++++++++++++++++++++++------
 3 files changed, 406 insertions(+), 86 deletions(-)

diff --git a/11_classes/00_content.ipynb b/11_classes/00_content.ipynb
index 2850088..34e3df3 100644
--- a/11_classes/00_content.ipynb
+++ b/11_classes/00_content.ipynb
@@ -1287,14 +1287,22 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
    "source": [
     "From a theoretical point of view, the text representation provided by `.__repr__()` contains all the information (i.e., the $0$s and $1$s in memory) that is needed to model something in a computer. In a way, it is a natural extension from the binary (cf., [Chapter 5 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/05_numbers/00_content.ipynb#Binary-Representations)), hexadecimal (cf., [Chapter 5 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/05_numbers/00_content.ipynb#Hexadecimal-Representations)), and `bytes` (cf., [Chapter 6 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/06_text/02_content.ipynb#The-bytes-Type)) representations of information. After all, just like Unicode characters are encoded in `bytes`, the more \"complex\" objects in this chapter are encoded in Unicode characters via their text representations."
    ]
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
    "source": [
     "### The `Matrix` Class"
    ]
diff --git a/11_classes/03_content.ipynb b/11_classes/03_content.ipynb
index 5bd0fe2..32491d8 100644
--- a/11_classes/03_content.ipynb
+++ b/11_classes/03_content.ipynb
@@ -260,7 +260,7 @@
    "execution_count": 8,
    "metadata": {
     "slideshow": {
-     "slide_type": "fragment"
+     "slide_type": "slide"
     }
    },
    "outputs": [
@@ -474,7 +474,11 @@
   {
    "cell_type": "code",
    "execution_count": 15,
-   "metadata": {},
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
    "outputs": [
     {
      "data": {
@@ -542,7 +546,11 @@
   {
    "cell_type": "code",
    "execution_count": 16,
-   "metadata": {},
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
    "outputs": [
     {
      "data": {
@@ -562,7 +570,11 @@
   {
    "cell_type": "code",
    "execution_count": 17,
-   "metadata": {},
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
    "outputs": [
     {
      "data": {
@@ -597,7 +609,7 @@
    "execution_count": 18,
    "metadata": {
     "slideshow": {
-     "slide_type": "slide"
+     "slide_type": "fragment"
     }
    },
    "outputs": [
@@ -1798,7 +1810,11 @@
   {
    "cell_type": "code",
    "execution_count": 55,
-   "metadata": {},
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
    "outputs": [],
    "source": [
     "v = Vector([1, 2, 3])\n",
@@ -1808,7 +1824,11 @@
   {
    "cell_type": "code",
    "execution_count": 56,
-   "metadata": {},
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
    "outputs": [
     {
      "data": {
@@ -1828,7 +1848,11 @@
   {
    "cell_type": "code",
    "execution_count": 57,
-   "metadata": {},
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
    "outputs": [
     {
      "data": {
@@ -1920,7 +1944,11 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
    "source": [
     "... or pass `v` to [abs() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#abs)."
    ]
@@ -1928,7 +1956,11 @@
   {
    "cell_type": "code",
    "execution_count": 60,
-   "metadata": {},
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
    "outputs": [
     {
      "ename": "TypeError",
@@ -1960,7 +1992,11 @@
   {
    "cell_type": "code",
    "execution_count": 61,
-   "metadata": {},
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
    "outputs": [],
    "source": [
     "import math"
@@ -1969,7 +2005,11 @@
   {
    "cell_type": "code",
    "execution_count": 62,
-   "metadata": {},
+   "metadata": {
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
    "outputs": [],
    "source": [
     "def norm(vec_or_mat):\n",
@@ -2051,7 +2091,7 @@
    "execution_count": 65,
    "metadata": {
     "slideshow": {
-     "slide_type": "slide"
+     "slide_type": "fragment"
     }
    },
    "outputs": [
@@ -2134,7 +2174,7 @@
    "execution_count": 68,
    "metadata": {
     "slideshow": {
-     "slide_type": "slide"
+     "slide_type": "fragment"
     }
    },
    "outputs": [
@@ -2156,7 +2196,11 @@
   {
    "cell_type": "code",
    "execution_count": 69,
-   "metadata": {},
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
    "outputs": [],
    "source": [
     "z = Vector([0, 0])"
@@ -2202,7 +2246,7 @@
    "execution_count": 71,
    "metadata": {
     "slideshow": {
-     "slide_type": "slide"
+     "slide_type": "fragment"
     }
    },
    "outputs": [
@@ -2259,7 +2303,11 @@
   {
    "cell_type": "code",
    "execution_count": 73,
-   "metadata": {},
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
    "outputs": [],
    "source": [
     "s = Vector([42])"
@@ -2270,7 +2318,7 @@
    "execution_count": 74,
    "metadata": {
     "slideshow": {
-     "slide_type": "slide"
+     "slide_type": "fragment"
     }
    },
    "outputs": [
diff --git a/11_classes/04_content.ipynb b/11_classes/04_content.ipynb
index 959c160..b495a9a 100644
--- a/11_classes/04_content.ipynb
+++ b/11_classes/04_content.ipynb
@@ -35,14 +35,22 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
    "source": [
     "## Packages vs. Modules"
    ]
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
    "source": [
     "In [Chapter 2 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/02_functions/02_content.ipynb#Local-Modules-and-Packages), we introduce the concept of a Python module that is imported with the `import` statement. Essentially, a **module** is a single plain text \\*.py file on disk that contains Python code (e.g., [*sample_module.py* <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_gh.png\">](https://github.com/webartifex/intro-to-python/blob/develop/02_functions/sample_module.py) in [Chapter 2's folder <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_gh.png\">](https://github.com/webartifex/intro-to-python/tree/develop/02_functions)).\n",
     "\n",
@@ -56,7 +64,11 @@
   {
    "cell_type": "code",
    "execution_count": 1,
-   "metadata": {},
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
    "outputs": [
     {
      "name": "stdout",
@@ -72,7 +84,11 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
    "source": [
     "`!ls` lists all the files and folders in the current location: These are Chapter 11's Jupyter notebooks (i.e., the \\*.ipynb files) and the [*sample_package* <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_gh.png\">](https://github.com/webartifex/intro-to-python/tree/develop/11_classes/sample_package) folder. "
    ]
@@ -80,7 +96,11 @@
   {
    "cell_type": "code",
    "execution_count": 2,
-   "metadata": {},
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
    "outputs": [
     {
      "name": "stdout",
@@ -97,7 +117,11 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
    "source": [
     "If we run `!ls` with the `sample_package` folder as the argument, we see the folder's contents: Four \\*.py files. Alternatively, you can use [JupyterLab' File Browser](https://jupyterlab.readthedocs.io/en/stable/user/interface.html?highlight=file%20browser#left-sidebar) on the left to navigate into the package."
    ]
@@ -105,7 +129,11 @@
   {
    "cell_type": "code",
    "execution_count": 3,
-   "metadata": {},
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
    "outputs": [
     {
      "name": "stdout",
@@ -121,7 +149,11 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
    "source": [
     "The package is organized such that the [*matrix.py* <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_gh.png\">](https://github.com/webartifex/intro-to-python/blob/develop/11_classes/sample_package/matrix.py) and [*vector.py* <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_gh.png\">](https://github.com/webartifex/intro-to-python/blob/develop/11_classes/sample_package/vector.py) modules each define just one class, `Matrix` and `Vector`. That is intentional as both classes consist of several hundred lines of code and comments.\n",
     "\n",
@@ -135,7 +167,11 @@
   {
    "cell_type": "code",
    "execution_count": 4,
-   "metadata": {},
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
    "outputs": [],
    "source": [
     "import sample_package as pkg"
@@ -143,7 +179,11 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
    "source": [
     "The above cell runs the code in the [*\\_\\_init\\_\\_.py* <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_gh.png\">](https://github.com/webartifex/intro-to-python/blob/develop/11_classes/sample_package/__init__.py) file from top to bottom, which in turn runs the [*matrix.py* <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_gh.png\">](https://github.com/webartifex/intro-to-python/blob/develop/11_classes/sample_package/matrix.py), [*utils.py* <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_gh.png\">](https://github.com/webartifex/intro-to-python/blob/develop/11_classes/sample_package/utils.py), and [*vector.py* <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_gh.png\">](https://github.com/webartifex/intro-to-python/blob/develop/11_classes/sample_package/vector.py) modules (cf., look at the `import` statements in the four \\*.py files to get the idea). As both [*matrix.py* <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_gh.png\">](https://github.com/webartifex/intro-to-python/blob/develop/11_classes/sample_package/matrix.py) and [*vector.py* <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_gh.png\">](https://github.com/webartifex/intro-to-python/blob/develop/11_classes/sample_package/vector.py) depend on each other (i.e., the `Matrix` class needs the `Vector` class to work and vice versa), understanding the order in that the modules are executed is not trivial. Without going into detail, we mention that Python guarantees that each \\*.py file is run only once and figures out the order on its own. If Python is unable to do that, for example, due to unresolvable cirular imports, it aborts with an `ImportError`.\n",
     "\n",
@@ -153,7 +193,11 @@
   {
    "cell_type": "code",
    "execution_count": 5,
-   "metadata": {},
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
    "outputs": [
     {
      "data": {
@@ -173,7 +217,11 @@
   {
    "cell_type": "code",
    "execution_count": 6,
-   "metadata": {},
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
    "outputs": [
     {
      "data": {
@@ -192,7 +240,11 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
    "source": [
     "... and we use the built-in [dir() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#dir) function to check what attributes `pkg` comes with."
    ]
@@ -200,7 +252,11 @@
   {
    "cell_type": "code",
    "execution_count": 7,
-   "metadata": {},
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
    "outputs": [
     {
      "data": {
@@ -235,7 +291,11 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
    "source": [
     "The package's meta information and documentation are automatically parsed from the [*\\_\\_init\\_\\_.py* <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_gh.png\">](https://github.com/webartifex/intro-to-python/blob/develop/11_classes/sample_package/__init__.py) file."
    ]
@@ -243,7 +303,11 @@
   {
    "cell_type": "code",
    "execution_count": 8,
-   "metadata": {},
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
    "outputs": [
     {
      "name": "stdout",
@@ -818,7 +882,11 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
    "source": [
     "The meta information could also be accessed separately and individually."
    ]
@@ -826,7 +894,11 @@
   {
    "cell_type": "code",
    "execution_count": 9,
-   "metadata": {},
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
    "outputs": [
     {
      "data": {
@@ -846,7 +918,11 @@
   {
    "cell_type": "code",
    "execution_count": 10,
-   "metadata": {},
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
    "outputs": [
     {
      "data": {
@@ -865,7 +941,11 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
    "source": [
     "We create `Vector` and `Matrix` instances in the usual way by calling the `Vector` and `Matrix` classes from the package's top level."
    ]
@@ -873,7 +953,11 @@
   {
    "cell_type": "code",
    "execution_count": 11,
-   "metadata": {},
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
    "outputs": [
     {
      "data": {
@@ -893,7 +977,11 @@
   {
    "cell_type": "code",
    "execution_count": 12,
-   "metadata": {},
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
    "outputs": [
     {
      "data": {
@@ -912,7 +1000,11 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
    "source": [
     "A common practice by package authors is to put all the objects on the package's top level that they want the package users to work with directly. That is achieved via the `import` statements in the [*\\_\\_init\\_\\_.py* <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_gh.png\">](https://github.com/webartifex/intro-to-python/blob/develop/11_classes/sample_package/__init__.py) file.\n",
     "\n",
@@ -924,7 +1016,11 @@
   {
    "cell_type": "code",
    "execution_count": 13,
-   "metadata": {},
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
    "outputs": [
     {
      "data": {
@@ -944,7 +1040,11 @@
   {
    "cell_type": "code",
    "execution_count": 14,
-   "metadata": {},
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
    "outputs": [
     {
      "data": {
@@ -963,7 +1063,11 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
    "source": [
     "Also, let's import the [*utils.py* <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_gh.png\">](https://github.com/webartifex/intro-to-python/blob/develop/11_classes/sample_package/utils.py) module with the `norm()` function into the global scope. As this function is integrated into the `Vector.__abs__()` and `Matrix.__abs__()` methods, there is actually no need to work with it explicitly."
    ]
@@ -971,7 +1075,11 @@
   {
    "cell_type": "code",
    "execution_count": 15,
-   "metadata": {},
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
    "outputs": [],
    "source": [
     "from sample_package import utils"
@@ -980,7 +1088,11 @@
   {
    "cell_type": "code",
    "execution_count": 16,
-   "metadata": {},
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
    "outputs": [
     {
      "name": "stdout",
@@ -1014,7 +1126,11 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
    "source": [
     "Many tutorials on the internet begin by importing \"everything\" from a package into the global scope with `from ... import *`.\n",
     "\n",
@@ -1028,7 +1144,11 @@
   {
    "cell_type": "code",
    "execution_count": 17,
-   "metadata": {},
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
    "outputs": [],
    "source": [
     "from sample_package import *"
@@ -1037,7 +1157,11 @@
   {
    "cell_type": "code",
    "execution_count": 18,
-   "metadata": {},
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
    "outputs": [
     {
      "data": {
@@ -1057,7 +1181,11 @@
   {
    "cell_type": "code",
    "execution_count": 19,
-   "metadata": {},
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
    "outputs": [
     {
      "data": {
@@ -1076,7 +1204,11 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
    "source": [
     "For further information on modules and packages, we refer to the [official tutorial <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/tutorial/modules.html)."
    ]
@@ -1094,7 +1226,11 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
    "source": [
     "The final implementations of the `Vector` and `Matrix` classes are in the [*matrix.py* <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_gh.png\">](https://github.com/webartifex/intro-to-python/blob/develop/11_classes/sample_package/matrix.py) and [*vector.py* <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_gh.png\">](https://github.com/webartifex/intro-to-python/blob/develop/11_classes/sample_package/vector.py) files: They integrate all of the functionalities introduced in this chapter. In addition, the code is cleaned up and fully documented, including examples of common usages.\n",
     "\n",
@@ -1104,7 +1240,11 @@
   {
    "cell_type": "code",
    "execution_count": 20,
-   "metadata": {},
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
    "outputs": [],
    "source": [
     "v = Vector([1, 2, 3])"
@@ -1113,7 +1253,11 @@
   {
    "cell_type": "code",
    "execution_count": 21,
-   "metadata": {},
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
    "outputs": [
     {
      "data": {
@@ -1133,7 +1277,11 @@
   {
    "cell_type": "code",
    "execution_count": 22,
-   "metadata": {},
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
    "outputs": [
     {
      "data": {
@@ -1153,7 +1301,11 @@
   {
    "cell_type": "code",
    "execution_count": 23,
-   "metadata": {},
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
    "outputs": [],
    "source": [
     "m = Matrix([(1, 2, 3), (4, 5, 6), (7, 8, 9)])"
@@ -1162,7 +1314,11 @@
   {
    "cell_type": "code",
    "execution_count": 24,
-   "metadata": {},
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
    "outputs": [
     {
      "data": {
@@ -1182,7 +1338,11 @@
   {
    "cell_type": "code",
    "execution_count": 25,
-   "metadata": {},
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
    "outputs": [
     {
      "data": {
@@ -1215,7 +1375,11 @@
   {
    "cell_type": "code",
    "execution_count": 26,
-   "metadata": {},
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
    "outputs": [
     {
      "data": {
@@ -1235,7 +1399,11 @@
   {
    "cell_type": "code",
    "execution_count": 27,
-   "metadata": {},
+   "metadata": {
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
    "outputs": [
     {
      "data": {
@@ -1255,7 +1423,11 @@
   {
    "cell_type": "code",
    "execution_count": 28,
-   "metadata": {},
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
    "outputs": [
     {
      "data": {
@@ -1295,7 +1467,11 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
    "source": [
     "Both `Matrix/Vector.storage` and `Matrix/Vector.typing` themselves reference the `DEFAULT_ENTRIES_STORAGE` and `DEFAULT_ENTRY_TYPE` constants in the [*utils.py* <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_gh.png\">](https://github.com/webartifex/intro-to-python/blob/develop/11_classes/sample_package/utils.py) module. This way, we could, for example, change only the constants and thereby also change how the `._entries` are stored internally in both classes. Also, this single **[single source of truth <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_wiki.png\">](https://en.wikipedia.org/wiki/Single_source_of_truth)** ensures that both classes are consistent with each other at all times."
    ]
@@ -1303,7 +1479,11 @@
   {
    "cell_type": "code",
    "execution_count": 29,
-   "metadata": {},
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
    "outputs": [
     {
      "data": {
@@ -1323,7 +1503,11 @@
   {
    "cell_type": "code",
    "execution_count": 30,
-   "metadata": {},
+   "metadata": {
+    "slideshow": {
+     "slide_type": "-"
+    }
+   },
    "outputs": [
     {
      "data": {
@@ -1342,7 +1526,11 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
    "source": [
     "For the same reasons, we also replace the \"hard coded\" references to the `Vector` and `Matrix` classes within the various methods.\n",
     "\n",
@@ -1352,7 +1540,11 @@
   {
    "cell_type": "code",
    "execution_count": 31,
-   "metadata": {},
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
    "outputs": [
     {
      "data": {
@@ -1371,7 +1563,11 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
    "source": [
     "Of course, we could also use the [type() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#type) built-in instead."
    ]
@@ -1379,7 +1575,11 @@
   {
    "cell_type": "code",
    "execution_count": 32,
-   "metadata": {},
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
    "outputs": [
     {
      "data": {
@@ -1398,7 +1598,11 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
    "source": [
     "So, for example, the `Matrix.transpose()` method makes a `self.__class__(...)` instead of a `Matrix(...)` call."
    ]
@@ -1406,7 +1610,11 @@
   {
    "cell_type": "code",
    "execution_count": 33,
-   "metadata": {},
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
    "outputs": [
     {
      "data": {
@@ -1441,7 +1649,11 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
    "source": [
     "Whenever we need a `str` representation of a class's name, we use the `.__name__` attribute on the class, ..."
    ]
@@ -1449,7 +1661,11 @@
   {
    "cell_type": "code",
    "execution_count": 34,
-   "metadata": {},
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
    "outputs": [
     {
      "data": {
@@ -1468,7 +1684,11 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
    "source": [
     "... or access it via the `.__class__` attribute on an instance."
    ]
@@ -1476,7 +1696,11 @@
   {
    "cell_type": "code",
    "execution_count": 35,
-   "metadata": {},
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
    "outputs": [
     {
      "data": {
@@ -1495,7 +1719,11 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
    "source": [
     "For example, the `.__repr__()` and `.__str__()` methods make use of that."
    ]
@@ -1503,7 +1731,11 @@
   {
    "cell_type": "code",
    "execution_count": 36,
-   "metadata": {},
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
    "outputs": [
     {
      "data": {
@@ -1531,7 +1763,11 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
    "source": [
     "In order to not have to \"hard code\" the name of *another* class (e.g., the `Vector.as_matrix()` method references the `Matrix` class), we apply the following \"hack:\" First, we store a reference to the other class as a class attribute (e.g., `Matrix.vector_cls` and `Vector.matrix_cls`), and then reference that attribute within the methods, just like `.storage` and `.typing` above."
    ]
@@ -1539,7 +1775,11 @@
   {
    "cell_type": "code",
    "execution_count": 37,
-   "metadata": {},
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
    "outputs": [
     {
      "data": {
@@ -1559,7 +1799,11 @@
   {
    "cell_type": "code",
    "execution_count": 38,
-   "metadata": {},
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
    "outputs": [
     {
      "data": {
@@ -1578,7 +1822,11 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
    "source": [
     "As an example, the `Vector.as_matrix()` method makes a `self.matrix_cls(...)` instead of a `Matrix(...)` call."
    ]
@@ -1586,7 +1834,11 @@
   {
    "cell_type": "code",
    "execution_count": 39,
-   "metadata": {},
+   "metadata": {
+    "slideshow": {
+     "slide_type": "fragment"
+    }
+   },
    "outputs": [
     {
      "data": {
@@ -1638,7 +1890,11 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
    "source": [
     "## \"Real-life\" Experiment"
    ]
@@ -1762,7 +2018,11 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
    "source": [
     "Similarily, `v` is now a `Vector` with $50$ entries."
    ]
@@ -2236,7 +2496,11 @@
   },
   {
    "cell_type": "markdown",
-   "metadata": {},
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
    "source": [
     "... while `Matrix` objects come with `.n_rows` and `.n_cols` properties."
    ]

From 30127b35533ecb0b32b5b85e1690e34708b93cc2 Mon Sep 17 00:00:00 2001
From: Alexander Hess <alexander@webartifex.biz>
Date: Wed, 28 Oct 2020 22:42:08 +0100
Subject: [PATCH 222/242] Add initial version of chapter 11's summary

---
 11_classes/05_summary.ipynb | 100 ++++++++++++++++++++++++++++++++++++
 CONTENTS.md                 |   1 +
 2 files changed, 101 insertions(+)
 create mode 100644 11_classes/05_summary.ipynb

diff --git a/11_classes/05_summary.ipynb b/11_classes/05_summary.ipynb
new file mode 100644
index 0000000..47baf9f
--- /dev/null
+++ b/11_classes/05_summary.ipynb
@@ -0,0 +1,100 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "slide"
+    }
+   },
+   "source": [
+    "# Chapter 11: Classes & Instances (TL;DR)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "slideshow": {
+     "slide_type": "skip"
+    }
+   },
+   "source": [
+    "With the `class` statement, we can create a *user-defined* data type that we also call a **class**.\n",
+    "\n",
+    "Then, to create new **instances** of the data type, we simply call the class, just as we do with the built-in constructors.\n",
+    "\n",
+    "Conceptually, a class is the **blueprint** defining the **behavior** each instance exhibits.\n",
+    "\n",
+    "In the example used throughout the chapter, the `Vector` and `Matrix` classes implement the linear algebra rules all `Vector` and `Matrix` instances follow *in general*. On the contrary, the instances **encapsulate** the **state** of *concrete* vectors and matrices.\n",
+    "\n",
+    "The `class` statement acts as a *namespace* that consists of simple *variable assignments* and *function definitions*:\n",
+    "1. Variables become the **class attributes** that are shared among all instances.\n",
+    "2. Functions may take a different role:\n",
+    "   - By default, they become **instance methods** by going through a **binding process** where a reference to the instance on which the method is *invoked* is passed in as the first argument. By convention, the corresponding parameter is called `self`; it embodies an instance's state: That means that instance methods set and get **instance attributes** on and from `self`.\n",
+    "   - They may be declared as **class methods**. Then, the binding process is adjusted such that the first argument passed in is a reference to the class itself and, by convention, named `cls`. A common use case is to design **alternative constructors**.\n",
+    "   - They may also be declared as **properties**. A use case for that are *derived* attributes that follow semantically from an instance's state.\n",
+    "   \n",
+    "The **Python Data Model** concerns what special methods (i.e., the ones with the dunder names) exists and how they work together.\n",
+    "\n",
+    "The instantiation process is controlled by the `.__init__()` method.\n",
+    "\n",
+    "The `__repr__()` and `__str__()` methods implement the **text representation** of an instance, which can be regarded as a Unicode encoded representation of all the state encapsulated in an instance.\n",
+    "\n",
+    "**Sequence emulation** means that a user-defined data type exhibits the same four properties as the built-in sequences, which are regarded as finite and iterable containers with a predictable order. The `.__len__()`, `.__iter__()`, `__reversed__()`, `__getitem__()`, and some others are used to implement the corresponding behaviors.\n",
+    "\n",
+    "Similarly, **number emulation** means that an instance of a user-defined data type behaves like a built-in number. For example, by implementing the `.__abs__()` method, an instance may be passed to the built-in [abs() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#abs) function.\n",
+    "\n",
+    "If different data types, built-in or user-defined, share a well-defined set of behaviors, a single function may be written to work with objects of all the data types. We describe such functions as **polymorphic**.\n",
+    "\n",
+    "Classes may specify how *operators* are **overloaded**. Examples for that are the `.__add__()`, `.__sub__()`, or `.__eq__()` methods.\n",
+    "\n",
+    "**Packages** are folders containing **modules** (i.e., \\*.py files) and a \"*\\_\\_init\\_\\_.py*\" file. We use them to design coherent libraries with reusable code."
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.6"
+  },
+  "livereveal": {
+   "auto_select": "code",
+   "auto_select_fragment": true,
+   "scroll": true,
+   "theme": "serif"
+  },
+  "toc": {
+   "base_numbering": 1,
+   "nav_menu": {},
+   "number_sections": false,
+   "sideBar": true,
+   "skip_h1_title": true,
+   "title_cell": "Table of Contents",
+   "title_sidebar": "Contents",
+   "toc_cell": false,
+   "toc_position": {
+    "height": "calc(100% - 180px)",
+    "left": "10px",
+    "top": "150px",
+    "width": "384px"
+   },
+   "toc_section_display": false,
+   "toc_window_display": false
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/CONTENTS.md b/CONTENTS.md
index be3d163..b545cb4 100644
--- a/CONTENTS.md
+++ b/CONTENTS.md
@@ -289,3 +289,4 @@ If this is not possible,
       (Writing one's own Packages;
        The final `Vector` & `Matrix` Classes;
        Comparison with `numpy`)
+    - [summary <img height="12" style="display: inline-block" src="static/link/to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/11_classes/05_summary.ipynb)

From 96ff64cfb5c3ff28aa1ae491f4762b196941f80c Mon Sep 17 00:00:00 2001
From: Alexander Hess <alexander@webartifex.biz>
Date: Wed, 28 Oct 2020 23:10:19 +0100
Subject: [PATCH 223/242] Add initial version of chapter 11's review

---
 11_classes/06_review.ipynb | 271 +++++++++++++++++++++++++++++++++++++
 CONTENTS.md                |   1 +
 2 files changed, 272 insertions(+)
 create mode 100644 11_classes/06_review.ipynb

diff --git a/11_classes/06_review.ipynb b/11_classes/06_review.ipynb
new file mode 100644
index 0000000..df7af79
--- /dev/null
+++ b/11_classes/06_review.ipynb
@@ -0,0 +1,271 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Chapter 11: Classes & Instances (Review Questions)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The questions below assume that you have read the [first <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/11_classes/00_content.ipynb), [second <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/11_classes/02_content.ipynb), [third <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/11_classes/03_content.ipynb), and [fourth <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/11_classes/04_content.ipynb) part of Chapter 11.\n",
+    "\n",
+    "Be concise in your answers! Most questions can be answered in *one* sentence."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Essay Questions "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q1**: How are **classes** a way to manage the **state** in a big program? How should we think of classes conceptually?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " < your answer >"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q2**: What do we mean with **instantiation**? How do **instances** relate to **classes**?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " < your answer >"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q3:** What is an **implementation detail**? Name two different examples of implementation details regarding the `Vector` and `Matrix` classes!"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " < your answer >"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q4**: How are **instance methods** different from **class methods**? How do **special methods** fit into the picture?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " < your answer >"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q5**: How do **mutability** and **immutability** come into play when designing a user-defined data type?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " < your answer >"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q6**: Explain the concept of **method chaining**!"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " < your answer >"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q7**: How can we implement **operator overloading** for a user-defined data type? When do we need to user *reverse* special methods?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " < your answer >"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## True / False Questions"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Motivate your answer with *one short* sentence!"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q8**: An instance's **text representation** is a `bytes` object with a special encoding."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " < your answer >"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q9**: Computed **properties** are special kinds of **instance methods**."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " < your answer >"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q10**: **Sequence emulation** means designing a user-defined data type around the built-in `list` or `tuple` types."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " < your answer >"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q11**: The **Python Data Model** can be regarded as the \"Theory\" or \"Mental Model\" behind the Python language."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " < your answer >"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q12**: **Polymorphism** means that two instances have the same data type, be it a built-in or user-defined one."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " < your answer >"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q13**: **Number emulation** means that two instances of the same user-defined data type can be added together."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " < your answer >"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Q14**: **Packages** are a good place to collect all the code to be reused in a data science project, for example, across different Jupyter notebooks."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    " < your answer >"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.6"
+  },
+  "toc": {
+   "base_numbering": 1,
+   "nav_menu": {},
+   "number_sections": false,
+   "sideBar": true,
+   "skip_h1_title": true,
+   "title_cell": "Table of Contents",
+   "title_sidebar": "Contents",
+   "toc_cell": false,
+   "toc_position": {},
+   "toc_section_display": false,
+   "toc_window_display": false
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/CONTENTS.md b/CONTENTS.md
index b545cb4..bdad0d7 100644
--- a/CONTENTS.md
+++ b/CONTENTS.md
@@ -290,3 +290,4 @@ If this is not possible,
        The final `Vector` & `Matrix` Classes;
        Comparison with `numpy`)
     - [summary <img height="12" style="display: inline-block" src="static/link/to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/11_classes/05_summary.ipynb)
+    - [review questions <img height="12" style="display: inline-block" src="static/link/to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/11_classes/06_review.ipynb)

From 950909547ccacfe6e5cee83dd5f47a4b4d8bc4ef Mon Sep 17 00:00:00 2001
From: Alexander Hess <alexander@webartifex.biz>
Date: Mon, 8 Apr 2024 15:44:35 +0200
Subject: [PATCH 224/242] Update the dependencies to up-to-date versions

---
 poetry.lock    | 2809 ++++++++++++++++++++++++++++++------------------
 pyproject.toml |   21 +-
 2 files changed, 1775 insertions(+), 1055 deletions(-)

diff --git a/poetry.lock b/poetry.lock
index e50d7f0..ac23fb3 100644
--- a/poetry.lock
+++ b/poetry.lock
@@ -1,1511 +1,2234 @@
+# This file is automatically @generated by Poetry 1.8.2 and should not be changed by hand.
+
 [[package]]
-name = "appdirs"
-version = "1.4.4"
-description = "A small Python module for determining appropriate platform-specific dirs, e.g. a \"user data dir\"."
-category = "dev"
+name = "anyio"
+version = "4.3.0"
+description = "High level compatibility layer for multiple asynchronous event loop implementations"
 optional = false
-python-versions = "*"
+python-versions = ">=3.8"
+files = [
+    {file = "anyio-4.3.0-py3-none-any.whl", hash = "sha256:048e05d0f6caeed70d731f3db756d35dcc1f35747c8c403364a8332c630441b8"},
+    {file = "anyio-4.3.0.tar.gz", hash = "sha256:f75253795a87df48568485fd18cdd2a3fa5c4f7c5be8e5e36637733fce06fed6"},
+]
+
+[package.dependencies]
+idna = ">=2.8"
+sniffio = ">=1.1"
+
+[package.extras]
+doc = ["Sphinx (>=7)", "packaging", "sphinx-autodoc-typehints (>=1.2.0)", "sphinx-rtd-theme"]
+test = ["anyio[trio]", "coverage[toml] (>=7)", "exceptiongroup (>=1.2.0)", "hypothesis (>=4.0)", "psutil (>=5.9)", "pytest (>=7.0)", "pytest-mock (>=3.6.1)", "trustme", "uvloop (>=0.17)"]
+trio = ["trio (>=0.23)"]
 
 [[package]]
 name = "appnope"
-version = "0.1.0"
-description = "Disable App Nap on OS X 10.9"
-category = "main"
+version = "0.1.4"
+description = "Disable App Nap on macOS >= 10.9"
 optional = false
-python-versions = "*"
+python-versions = ">=3.6"
+files = [
+    {file = "appnope-0.1.4-py2.py3-none-any.whl", hash = "sha256:502575ee11cd7a28c0205f379b525beefebab9d161b7c964670864014ed7213c"},
+    {file = "appnope-0.1.4.tar.gz", hash = "sha256:1de3860566df9caf38f01f86f65e0e13e379af54f9e4bee1e66b48f2efffd1ee"},
+]
 
 [[package]]
 name = "argcomplete"
-version = "1.12.1"
+version = "3.2.3"
 description = "Bash tab completion for argparse"
-category = "dev"
 optional = false
-python-versions = "*"
+python-versions = ">=3.8"
+files = [
+    {file = "argcomplete-3.2.3-py3-none-any.whl", hash = "sha256:c12355e0494c76a2a7b73e3a59b09024ca0ba1e279fb9ed6c1b82d5b74b6a70c"},
+    {file = "argcomplete-3.2.3.tar.gz", hash = "sha256:bf7900329262e481be5a15f56f19736b376df6f82ed27576fa893652c5de6c23"},
+]
 
 [package.extras]
-test = ["coverage", "flake8", "pexpect", "wheel"]
+test = ["coverage", "mypy", "pexpect", "ruff", "wheel"]
 
 [[package]]
 name = "argon2-cffi"
-version = "20.1.0"
-description = "The secure Argon2 password hashing algorithm."
-category = "main"
+version = "23.1.0"
+description = "Argon2 for Python"
 optional = false
-python-versions = "*"
+python-versions = ">=3.7"
+files = [
+    {file = "argon2_cffi-23.1.0-py3-none-any.whl", hash = "sha256:c670642b78ba29641818ab2e68bd4e6a78ba53b7eff7b4c3815ae16abf91c7ea"},
+    {file = "argon2_cffi-23.1.0.tar.gz", hash = "sha256:879c3e79a2729ce768ebb7d36d4609e3a78a4ca2ec3a9f12286ca057e3d0db08"},
+]
 
 [package.dependencies]
-cffi = ">=1.0.0"
-six = "*"
+argon2-cffi-bindings = "*"
 
 [package.extras]
-dev = ["coverage[toml] (>=5.0.2)", "hypothesis", "pytest", "sphinx", "wheel", "pre-commit"]
-docs = ["sphinx"]
-tests = ["coverage[toml] (>=5.0.2)", "hypothesis", "pytest"]
+dev = ["argon2-cffi[tests,typing]", "tox (>4)"]
+docs = ["furo", "myst-parser", "sphinx", "sphinx-copybutton", "sphinx-notfound-page"]
+tests = ["hypothesis", "pytest"]
+typing = ["mypy"]
 
 [[package]]
-name = "async-generator"
-version = "1.10"
-description = "Async generators and context managers for Python 3.5+"
-category = "main"
+name = "argon2-cffi-bindings"
+version = "21.2.0"
+description = "Low-level CFFI bindings for Argon2"
 optional = false
-python-versions = ">=3.5"
+python-versions = ">=3.6"
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 [package.dependencies]
 pycparser = "*"
 
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+    {file = "idna-3.6-py3-none-any.whl", hash = "sha256:c05567e9c24a6b9faaa835c4821bad0590fbb9d5779e7caa6e1cc4978e7eb24f"},
+    {file = "idna-3.6.tar.gz", hash = "sha256:9ecdbbd083b06798ae1e86adcbfe8ab1479cf864e4ee30fe4e46a003d12491ca"},
+]
 
 [[package]]
 name = "ipykernel"
-version = "5.3.4"
+version = "6.29.4"
 description = "IPython Kernel for Jupyter"
-category = "main"
 optional = false
-python-versions = ">=3.5"
+python-versions = ">=3.8"
+files = [
+    {file = "ipykernel-6.29.4-py3-none-any.whl", hash = "sha256:1181e653d95c6808039c509ef8e67c4126b3b3af7781496c7cbfb5ed938a27da"},
+    {file = "ipykernel-6.29.4.tar.gz", hash = "sha256:3d44070060f9475ac2092b760123fadf105d2e2493c24848b6691a7c4f42af5c"},
+]
 
 [package.dependencies]
 appnope = {version = "*", markers = "platform_system == \"Darwin\""}
-ipython = ">=5.0.0"
-jupyter-client = "*"
-tornado = ">=4.2"
-traitlets = ">=4.1.0"
+comm = ">=0.1.1"
+debugpy = ">=1.6.5"
+ipython = ">=7.23.1"
+jupyter-client = ">=6.1.12"
+jupyter-core = ">=4.12,<5.0.dev0 || >=5.1.dev0"
+matplotlib-inline = ">=0.1"
+nest-asyncio = "*"
+packaging = "*"
+psutil = "*"
+pyzmq = ">=24"
+tornado = ">=6.1"
+traitlets = ">=5.4.0"
 
 [package.extras]
-test = ["pytest (!=5.3.4)", "pytest-cov", "flaky", "nose"]
+cov = ["coverage[toml]", "curio", "matplotlib", "pytest-cov", "trio"]
+docs = ["myst-parser", "pydata-sphinx-theme", "sphinx", "sphinx-autodoc-typehints", "sphinxcontrib-github-alt", "sphinxcontrib-spelling", "trio"]
+pyqt5 = ["pyqt5"]
+pyside6 = ["pyside6"]
+test = ["flaky", "ipyparallel", "pre-commit", "pytest (>=7.0)", "pytest-asyncio (>=0.23.5)", "pytest-cov", "pytest-timeout"]
 
 [[package]]
 name = "ipython"
-version = "7.18.1"
+version = "8.23.0"
 description = "IPython: Productive Interactive Computing"
-category = "main"
 optional = false
-python-versions = ">=3.7"
+python-versions = ">=3.10"
+files = [
+    {file = "ipython-8.23.0-py3-none-any.whl", hash = "sha256:07232af52a5ba146dc3372c7bf52a0f890a23edf38d77caef8d53f9cdc2584c1"},
+    {file = "ipython-8.23.0.tar.gz", hash = "sha256:7468edaf4f6de3e1b912e57f66c241e6fd3c7099f2ec2136e239e142e800274d"},
+]
 
 [package.dependencies]
-appnope = {version = "*", markers = "sys_platform == \"darwin\""}
-backcall = "*"
 colorama = {version = "*", markers = "sys_platform == \"win32\""}
 decorator = "*"
-jedi = ">=0.10"
-pexpect = {version = ">4.3", markers = "sys_platform != \"win32\""}
-pickleshare = "*"
-prompt-toolkit = ">=2.0.0,<3.0.0 || >3.0.0,<3.0.1 || >3.0.1,<3.1.0"
-pygments = "*"
-traitlets = ">=4.2"
+jedi = ">=0.16"
+matplotlib-inline = "*"
+pexpect = {version = ">4.3", markers = "sys_platform != \"win32\" and sys_platform != \"emscripten\""}
+prompt-toolkit = ">=3.0.41,<3.1.0"
+pygments = ">=2.4.0"
+stack-data = "*"
+traitlets = ">=5.13.0"
+typing-extensions = {version = "*", markers = "python_version < \"3.12\""}
 
 [package.extras]
-all = ["Sphinx (>=1.3)", "ipykernel", "ipyparallel", "ipywidgets", "nbconvert", "nbformat", "nose (>=0.10.1)", "notebook", "numpy (>=1.14)", "pygments", "qtconsole", "requests", "testpath"]
-doc = ["Sphinx (>=1.3)"]
+all = ["ipython[black,doc,kernel,matplotlib,nbconvert,nbformat,notebook,parallel,qtconsole]", "ipython[test,test-extra]"]
+black = ["black"]
+doc = ["docrepr", "exceptiongroup", "ipykernel", "ipython[test]", "matplotlib", "setuptools (>=18.5)", "sphinx (>=1.3)", "sphinx-rtd-theme", "sphinxcontrib-jquery", "stack-data", "typing-extensions"]
 kernel = ["ipykernel"]
+matplotlib = ["matplotlib"]
 nbconvert = ["nbconvert"]
 nbformat = ["nbformat"]
-notebook = ["notebook", "ipywidgets"]
+notebook = ["ipywidgets", "notebook"]
 parallel = ["ipyparallel"]
 qtconsole = ["qtconsole"]
-test = ["nose (>=0.10.1)", "requests", "testpath", "pygments", "nbformat", "ipykernel", "numpy (>=1.14)"]
+test = ["pickleshare", "pytest (<8)", "pytest-asyncio (<0.22)", "testpath"]
+test-extra = ["curio", "ipython[test]", "matplotlib (!=3.2.0)", "nbformat", "numpy (>=1.23)", "pandas", "trio"]
 
 [[package]]
-name = "ipython-genutils"
-version = "0.2.0"
-description = "Vestigial utilities from IPython"
-category = "main"
+name = "isoduration"
+version = "20.11.0"
+description = "Operations with ISO 8601 durations"
 optional = false
-python-versions = "*"
+python-versions = ">=3.7"
+files = [
+    {file = "isoduration-20.11.0-py3-none-any.whl", hash = "sha256:b2904c2a4228c3d44f409c8ae8e2370eb21a26f7ac2ec5446df141dde3452042"},
+    {file = "isoduration-20.11.0.tar.gz", hash = "sha256:ac2f9015137935279eac671f94f89eb00584f940f5dc49462a0c4ee692ba1bd9"},
+]
+
+[package.dependencies]
+arrow = ">=0.15.0"
 
 [[package]]
 name = "jedi"
-version = "0.17.2"
+version = "0.19.1"
 description = "An autocompletion tool for Python that can be used for text editors."
-category = "main"
 optional = false
-python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*, !=3.4.*"
+python-versions = ">=3.6"
+files = [
+    {file = "jedi-0.19.1-py2.py3-none-any.whl", hash = "sha256:e983c654fe5c02867aef4cdfce5a2fbb4a50adc0af145f70504238f18ef5e7e0"},
+    {file = "jedi-0.19.1.tar.gz", hash = "sha256:cf0496f3651bc65d7174ac1b7d043eff454892c708a87d1b683e57b569927ffd"},
+]
 
 [package.dependencies]
-parso = ">=0.7.0,<0.8.0"
+parso = ">=0.8.3,<0.9.0"
 
 [package.extras]
-qa = ["flake8 (==3.7.9)"]
-testing = ["Django (<3.1)", "colorama", "docopt", "pytest (>=3.9.0,<5.0.0)"]
+docs = ["Jinja2 (==2.11.3)", "MarkupSafe (==1.1.1)", "Pygments (==2.8.1)", "alabaster (==0.7.12)", "babel (==2.9.1)", "chardet (==4.0.0)", "commonmark (==0.8.1)", "docutils (==0.17.1)", "future (==0.18.2)", "idna (==2.10)", "imagesize (==1.2.0)", "mock (==1.0.1)", "packaging (==20.9)", "pyparsing (==2.4.7)", "pytz (==2021.1)", "readthedocs-sphinx-ext (==2.1.4)", "recommonmark (==0.5.0)", "requests (==2.25.1)", "six (==1.15.0)", "snowballstemmer (==2.1.0)", "sphinx (==1.8.5)", "sphinx-rtd-theme (==0.4.3)", "sphinxcontrib-serializinghtml (==1.1.4)", "sphinxcontrib-websupport (==1.2.4)", "urllib3 (==1.26.4)"]
+qa = ["flake8 (==5.0.4)", "mypy (==0.971)", "types-setuptools (==67.2.0.1)"]
+testing = ["Django", "attrs", "colorama", "docopt", "pytest (<7.0.0)"]
 
 [[package]]
 name = "jinja2"
-version = "2.11.2"
+version = "3.1.3"
 description = "A very fast and expressive template engine."
-category = "main"
 optional = false
-python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*, !=3.4.*"
+python-versions = ">=3.7"
+files = [
+    {file = "Jinja2-3.1.3-py3-none-any.whl", hash = "sha256:7d6d50dd97d52cbc355597bd845fabfbac3f551e1f99619e39a35ce8c370b5fa"},
+    {file = "Jinja2-3.1.3.tar.gz", hash = "sha256:ac8bd6544d4bb2c9792bf3a159e80bba8fda7f07e81bc3aed565432d5925ba90"},
+]
 
 [package.dependencies]
-MarkupSafe = ">=0.23"
+MarkupSafe = ">=2.0"
 
 [package.extras]
-i18n = ["Babel (>=0.8)"]
+i18n = ["Babel (>=2.7)"]
 
 [[package]]
 name = "json5"
-version = "0.9.5"
+version = "0.9.24"
 description = "A Python implementation of the JSON5 data format."
-category = "main"
 optional = false
-python-versions = "*"
+python-versions = ">=3.8"
+files = [
+    {file = "json5-0.9.24-py3-none-any.whl", hash = "sha256:4ca101fd5c7cb47960c055ef8f4d0e31e15a7c6c48c3b6f1473fc83b6c462a13"},
+    {file = "json5-0.9.24.tar.gz", hash = "sha256:0c638399421da959a20952782800e5c1a78c14e08e1dc9738fa10d8ec14d58c8"},
+]
 
-[package.extras]
-dev = ["hypothesis"]
+[[package]]
+name = "jsonpointer"
+version = "2.4"
+description = "Identify specific nodes in a JSON document (RFC 6901)"
+optional = false
+python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*, !=3.4.*, !=3.5.*, !=3.6.*"
+files = [
+    {file = "jsonpointer-2.4-py2.py3-none-any.whl", hash = "sha256:15d51bba20eea3165644553647711d150376234112651b4f1811022aecad7d7a"},
+    {file = "jsonpointer-2.4.tar.gz", hash = "sha256:585cee82b70211fa9e6043b7bb89db6e1aa49524340dde8ad6b63206ea689d88"},
+]
 
 [[package]]
 name = "jsonschema"
-version = "3.2.0"
+version = "4.21.1"
 description = "An implementation of JSON Schema validation for Python"
-category = "main"
 optional = false
-python-versions = "*"
+python-versions = ">=3.8"
+files = [
+    {file = "jsonschema-4.21.1-py3-none-any.whl", hash = "sha256:7996507afae316306f9e2290407761157c6f78002dcf7419acb99822143d1c6f"},
+    {file = "jsonschema-4.21.1.tar.gz", hash = "sha256:85727c00279f5fa6bedbe6238d2aa6403bedd8b4864ab11207d07df3cc1b2ee5"},
+]
 
 [package.dependencies]
-attrs = ">=17.4.0"
-pyrsistent = ">=0.14.0"
-six = ">=1.11.0"
+attrs = ">=22.2.0"
+fqdn = {version = "*", optional = true, markers = "extra == \"format-nongpl\""}
+idna = {version = "*", optional = true, markers = "extra == \"format-nongpl\""}
+isoduration = {version = "*", optional = true, markers = "extra == \"format-nongpl\""}
+jsonpointer = {version = ">1.13", optional = true, markers = "extra == \"format-nongpl\""}
+jsonschema-specifications = ">=2023.03.6"
+referencing = ">=0.28.4"
+rfc3339-validator = {version = "*", optional = true, markers = "extra == \"format-nongpl\""}
+rfc3986-validator = {version = ">0.1.0", optional = true, markers = "extra == \"format-nongpl\""}
+rpds-py = ">=0.7.1"
+uri-template = {version = "*", optional = true, markers = "extra == \"format-nongpl\""}
+webcolors = {version = ">=1.11", optional = true, markers = "extra == \"format-nongpl\""}
 
 [package.extras]
-format = ["idna", "jsonpointer (>1.13)", "rfc3987", "strict-rfc3339", "webcolors"]
-format_nongpl = ["idna", "jsonpointer (>1.13)", "webcolors", "rfc3986-validator (>0.1.0)", "rfc3339-validator"]
+format = ["fqdn", "idna", "isoduration", "jsonpointer (>1.13)", "rfc3339-validator", "rfc3987", "uri-template", "webcolors (>=1.11)"]
+format-nongpl = ["fqdn", "idna", "isoduration", "jsonpointer (>1.13)", "rfc3339-validator", "rfc3986-validator (>0.1.0)", "uri-template", "webcolors (>=1.11)"]
+
+[[package]]
+name = "jsonschema-specifications"
+version = "2023.12.1"
+description = "The JSON Schema meta-schemas and vocabularies, exposed as a Registry"
+optional = false
+python-versions = ">=3.8"
+files = [
+    {file = "jsonschema_specifications-2023.12.1-py3-none-any.whl", hash = "sha256:87e4fdf3a94858b8a2ba2778d9ba57d8a9cafca7c7489c46ba0d30a8bc6a9c3c"},
+    {file = "jsonschema_specifications-2023.12.1.tar.gz", hash = "sha256:48a76787b3e70f5ed53f1160d2b81f586e4ca6d1548c5de7085d1682674764cc"},
+]
+
+[package.dependencies]
+referencing = ">=0.31.0"
 
 [[package]]
 name = "jupyter-client"
-version = "6.1.7"
+version = "8.6.1"
 description = "Jupyter protocol implementation and client libraries"
-category = "main"
 optional = false
-python-versions = ">=3.5"
+python-versions = ">=3.8"
+files = [
+    {file = "jupyter_client-8.6.1-py3-none-any.whl", hash = "sha256:3b7bd22f058434e3b9a7ea4b1500ed47de2713872288c0d511d19926f99b459f"},
+    {file = "jupyter_client-8.6.1.tar.gz", hash = "sha256:e842515e2bab8e19186d89fdfea7abd15e39dd581f94e399f00e2af5a1652d3f"},
+]
 
 [package.dependencies]
-jupyter-core = ">=4.6.0"
-python-dateutil = ">=2.1"
-pyzmq = ">=13"
-tornado = ">=4.1"
-traitlets = "*"
+jupyter-core = ">=4.12,<5.0.dev0 || >=5.1.dev0"
+python-dateutil = ">=2.8.2"
+pyzmq = ">=23.0"
+tornado = ">=6.2"
+traitlets = ">=5.3"
 
 [package.extras]
-test = ["ipykernel", "ipython", "mock", "pytest", "pytest-asyncio", "async-generator", "pytest-timeout"]
-
-[[package]]
-name = "jupyter-contrib-core"
-version = "0.3.3"
-description = "Common utilities for jupyter-contrib projects."
-category = "dev"
-optional = false
-python-versions = "*"
-
-[package.dependencies]
-jupyter-core = "*"
-notebook = ">=4.0"
-tornado = "*"
-traitlets = "*"
-
-[package.extras]
-testing_utils = ["nose", "mock"]
-
-[[package]]
-name = "jupyter-contrib-nbextensions"
-version = "0.5.1"
-description = "A collection of Jupyter nbextensions."
-category = "dev"
-optional = false
-python-versions = "*"
-
-[package.dependencies]
-ipython-genutils = "*"
-jupyter-contrib-core = ">=0.3.3"
-jupyter-core = "*"
-jupyter-highlight-selected-word = ">=0.1.1"
-jupyter-latex-envs = ">=1.3.8"
-jupyter-nbextensions-configurator = ">=0.4.0"
-lxml = "*"
-nbconvert = ">=4.2"
-notebook = ">=4.0"
-pyyaml = "*"
-tornado = "*"
-traitlets = ">=4.1"
-
-[package.extras]
-test = ["nbformat", "nose", "pip", "requests", "mock"]
+docs = ["ipykernel", "myst-parser", "pydata-sphinx-theme", "sphinx (>=4)", "sphinx-autodoc-typehints", "sphinxcontrib-github-alt", "sphinxcontrib-spelling"]
+test = ["coverage", "ipykernel (>=6.14)", "mypy", "paramiko", "pre-commit", "pytest", "pytest-cov", "pytest-jupyter[client] (>=0.4.1)", "pytest-timeout"]
 
 [[package]]
 name = "jupyter-core"
-version = "4.6.3"
+version = "5.7.2"
 description = "Jupyter core package. A base package on which Jupyter projects rely."
-category = "main"
 optional = false
-python-versions = "!=3.0,!=3.1,!=3.2,!=3.3,!=3.4,>=2.7"
+python-versions = ">=3.8"
+files = [
+    {file = "jupyter_core-5.7.2-py3-none-any.whl", hash = "sha256:4f7315d2f6b4bcf2e3e7cb6e46772eba760ae459cd1f59d29eb57b0a01bd7409"},
+    {file = "jupyter_core-5.7.2.tar.gz", hash = "sha256:aa5f8d32bbf6b431ac830496da7392035d6f61b4f54872f15c4bd2a9c3f536d9"},
+]
 
 [package.dependencies]
-pywin32 = {version = ">=1.0", markers = "sys_platform == \"win32\""}
-traitlets = "*"
-
-[[package]]
-name = "jupyter-highlight-selected-word"
-version = "0.2.0"
-description = "Jupyter notebook extension that enables highlighting every instance of the current word in the notebook."
-category = "dev"
-optional = false
-python-versions = "*"
-
-[[package]]
-name = "jupyter-latex-envs"
-version = "1.4.6"
-description = "Jupyter notebook extension which supports (some) LaTeX environments within markdown cells. Also provides support for labels and crossreferences, document wide numbering, bibliography, and more..."
-category = "dev"
-optional = false
-python-versions = "*"
-
-[package.dependencies]
-ipython = "*"
-jupyter_core = "*"
-nbconvert = "*"
-notebook = ">=4.0"
-traitlets = ">=4.1"
-
-[[package]]
-name = "jupyter-nbextensions-configurator"
-version = "0.4.1"
-description = "jupyter serverextension providing configuration interfaces for nbextensions."
-category = "dev"
-optional = false
-python-versions = "*"
-
-[package.dependencies]
-jupyter_contrib_core = ">=0.3.3"
-jupyter_core = "*"
-notebook = ">=4.0"
-pyyaml = "*"
-tornado = "*"
-traitlets = "*"
+platformdirs = ">=2.5"
+pywin32 = {version = ">=300", markers = "sys_platform == \"win32\" and platform_python_implementation != \"PyPy\""}
+traitlets = ">=5.3"
 
 [package.extras]
-test = ["jupyter-contrib-core", "nose", "requests", "selenium", "mock"]
+docs = ["myst-parser", "pydata-sphinx-theme", "sphinx-autodoc-typehints", "sphinxcontrib-github-alt", "sphinxcontrib-spelling", "traitlets"]
+test = ["ipykernel", "pre-commit", "pytest (<8)", "pytest-cov", "pytest-timeout"]
+
+[[package]]
+name = "jupyter-events"
+version = "0.10.0"
+description = "Jupyter Event System library"
+optional = false
+python-versions = ">=3.8"
+files = [
+    {file = "jupyter_events-0.10.0-py3-none-any.whl", hash = "sha256:4b72130875e59d57716d327ea70d3ebc3af1944d3717e5a498b8a06c6c159960"},
+    {file = "jupyter_events-0.10.0.tar.gz", hash = "sha256:670b8229d3cc882ec782144ed22e0d29e1c2d639263f92ca8383e66682845e22"},
+]
+
+[package.dependencies]
+jsonschema = {version = ">=4.18.0", extras = ["format-nongpl"]}
+python-json-logger = ">=2.0.4"
+pyyaml = ">=5.3"
+referencing = "*"
+rfc3339-validator = "*"
+rfc3986-validator = ">=0.1.1"
+traitlets = ">=5.3"
+
+[package.extras]
+cli = ["click", "rich"]
+docs = ["jupyterlite-sphinx", "myst-parser", "pydata-sphinx-theme", "sphinxcontrib-spelling"]
+test = ["click", "pre-commit", "pytest (>=7.0)", "pytest-asyncio (>=0.19.0)", "pytest-console-scripts", "rich"]
+
+[[package]]
+name = "jupyter-lsp"
+version = "2.2.4"
+description = "Multi-Language Server WebSocket proxy for Jupyter Notebook/Lab server"
+optional = false
+python-versions = ">=3.8"
+files = [
+    {file = "jupyter-lsp-2.2.4.tar.gz", hash = "sha256:5e50033149344065348e688608f3c6d654ef06d9856b67655bd7b6bac9ee2d59"},
+    {file = "jupyter_lsp-2.2.4-py3-none-any.whl", hash = "sha256:da61cb63a16b6dff5eac55c2699cc36eac975645adee02c41bdfc03bf4802e77"},
+]
+
+[package.dependencies]
+jupyter-server = ">=1.1.2"
+
+[[package]]
+name = "jupyter-server"
+version = "2.13.0"
+description = "The backend—i.e. core services, APIs, and REST endpoints—to Jupyter web applications."
+optional = false
+python-versions = ">=3.8"
+files = [
+    {file = "jupyter_server-2.13.0-py3-none-any.whl", hash = "sha256:77b2b49c3831fbbfbdb5048cef4350d12946191f833a24e5f83e5f8f4803e97b"},
+    {file = "jupyter_server-2.13.0.tar.gz", hash = "sha256:c80bfb049ea20053c3d9641c2add4848b38073bf79f1729cea1faed32fc1c78e"},
+]
+
+[package.dependencies]
+anyio = ">=3.1.0"
+argon2-cffi = "*"
+jinja2 = "*"
+jupyter-client = ">=7.4.4"
+jupyter-core = ">=4.12,<5.0.dev0 || >=5.1.dev0"
+jupyter-events = ">=0.9.0"
+jupyter-server-terminals = "*"
+nbconvert = ">=6.4.4"
+nbformat = ">=5.3.0"
+overrides = "*"
+packaging = "*"
+prometheus-client = "*"
+pywinpty = {version = "*", markers = "os_name == \"nt\""}
+pyzmq = ">=24"
+send2trash = ">=1.8.2"
+terminado = ">=0.8.3"
+tornado = ">=6.2.0"
+traitlets = ">=5.6.0"
+websocket-client = "*"
+
+[package.extras]
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-    {file = "toml-0.10.1.tar.gz", hash = "sha256:926b612be1e5ce0634a2ca03470f95169cf16f939018233a670519cb4ac58b0f"},
-]
-tornado = [
-    {file = "tornado-6.0.4-cp35-cp35m-win32.whl", hash = "sha256:5217e601700f24e966ddab689f90b7ea4bd91ff3357c3600fa1045e26d68e55d"},
-    {file = "tornado-6.0.4-cp35-cp35m-win_amd64.whl", hash = "sha256:c98232a3ac391f5faea6821b53db8db461157baa788f5d6222a193e9456e1740"},
-    {file = "tornado-6.0.4-cp36-cp36m-win32.whl", hash = "sha256:5f6a07e62e799be5d2330e68d808c8ac41d4a259b9cea61da4101b83cb5dc673"},
-    {file = "tornado-6.0.4-cp36-cp36m-win_amd64.whl", hash = "sha256:c952975c8ba74f546ae6de2e226ab3cc3cc11ae47baf607459a6728585bb542a"},
-    {file = "tornado-6.0.4-cp37-cp37m-win32.whl", hash = "sha256:2c027eb2a393d964b22b5c154d1a23a5f8727db6fda837118a776b29e2b8ebc6"},
-    {file = "tornado-6.0.4-cp37-cp37m-win_amd64.whl", hash = "sha256:5618f72e947533832cbc3dec54e1dffc1747a5cb17d1fd91577ed14fa0dc081b"},
-    {file = "tornado-6.0.4-cp38-cp38-win32.whl", hash = "sha256:22aed82c2ea340c3771e3babc5ef220272f6fd06b5108a53b4976d0d722bcd52"},
-    {file = "tornado-6.0.4-cp38-cp38-win_amd64.whl", hash = "sha256:c58d56003daf1b616336781b26d184023ea4af13ae143d9dda65e31e534940b9"},
-    {file = "tornado-6.0.4.tar.gz", hash = "sha256:0fe2d45ba43b00a41cd73f8be321a44936dc1aba233dee979f17a042b83eb6dc"},
-]
-traitlets = [
-    {file = "traitlets-5.0.5-py3-none-any.whl", hash = "sha256:69ff3f9d5351f31a7ad80443c2674b7099df13cc41fc5fa6e2f6d3b0330b0426"},
-    {file = "traitlets-5.0.5.tar.gz", hash = "sha256:178f4ce988f69189f7e523337a3e11d91c786ded9360174a3d9ca83e79bc5396"},
-]
-urllib3 = [
-    {file = "urllib3-1.25.11-py2.py3-none-any.whl", hash = "sha256:f5321fbe4bf3fefa0efd0bfe7fb14e90909eb62a48ccda331726b4319897dd5e"},
-    {file = "urllib3-1.25.11.tar.gz", hash = "sha256:8d7eaa5a82a1cac232164990f04874c594c9453ec55eef02eab885aa02fc17a2"},
-]
-virtualenv = [
-    {file = "virtualenv-20.1.0-py2.py3-none-any.whl", hash = "sha256:b0011228208944ce71052987437d3843e05690b2f23d1c7da4263fde104c97a2"},
-    {file = "virtualenv-20.1.0.tar.gz", hash = "sha256:b8d6110f493af256a40d65e29846c69340a947669eec8ce784fcf3dd3af28380"},
-]
-wcwidth = [
-    {file = "wcwidth-0.2.5-py2.py3-none-any.whl", hash = "sha256:beb4802a9cebb9144e99086eff703a642a13d6a0052920003a230f3294bbe784"},
-    {file = "wcwidth-0.2.5.tar.gz", hash = "sha256:c4d647b99872929fdb7bdcaa4fbe7f01413ed3d98077df798530e5b04f116c83"},
-]
-webencodings = [
+files = [
     {file = "webencodings-0.5.1-py2.py3-none-any.whl", hash = "sha256:a0af1213f3c2226497a97e2b3aa01a7e4bee4f403f95be16fc9acd2947514a78"},
     {file = "webencodings-0.5.1.tar.gz", hash = "sha256:b36a1c245f2d304965eb4e0a82848379241dc04b865afcc4aab16748587e1923"},
 ]
-xdoctest = [
-    {file = "xdoctest-0.15.0-py2.py3-none-any.whl", hash = "sha256:695ea04303a48cbb319709270d43f7bae7f3de3701aec73f09d90a216499992e"},
-    {file = "xdoctest-0.15.0.tar.gz", hash = "sha256:7f0a184d403b69b166ebec1aadb13c98c96c59101e974ae2e4db4c3a803ec371"},
+
+[[package]]
+name = "websocket-client"
+version = "1.7.0"
+description = "WebSocket client for Python with low level API options"
+optional = false
+python-versions = ">=3.8"
+files = [
+    {file = "websocket-client-1.7.0.tar.gz", hash = "sha256:10e511ea3a8c744631d3bd77e61eb17ed09304c413ad42cf6ddfa4c7787e8fe6"},
+    {file = "websocket_client-1.7.0-py3-none-any.whl", hash = "sha256:f4c3d22fec12a2461427a29957ff07d35098ee2d976d3ba244e688b8b4057588"},
 ]
+
+[package.extras]
+docs = ["Sphinx (>=6.0)", "sphinx-rtd-theme (>=1.1.0)"]
+optional = ["python-socks", "wsaccel"]
+test = ["websockets"]
+
+[[package]]
+name = "xdoctest"
+version = "1.1.3"
+description = "A rewrite of the builtin doctest module"
+optional = false
+python-versions = ">=3.6"
+files = [
+    {file = "xdoctest-1.1.3-py3-none-any.whl", hash = "sha256:9360535bd1a971ffc216d9613898cedceb81d0fd024587cc3c03c74d14c00a31"},
+    {file = "xdoctest-1.1.3.tar.gz", hash = "sha256:84e76a42a11a5926ff66d9d84c616bc101821099672550481ad96549cbdd02ae"},
+]
+
+[package.extras]
+all = ["IPython (>=7.10.0)", "IPython (>=7.23.1)", "Pygments (>=2.0.0)", "Pygments (>=2.4.1)", "attrs (>=19.2.0)", "colorama (>=0.4.1)", "debugpy (>=1.0.0)", "debugpy (>=1.0.0)", "debugpy (>=1.0.0)", "debugpy (>=1.3.0)", "debugpy (>=1.6.0)", "ipykernel (>=5.2.0)", "ipykernel (>=6.0.0)", "ipykernel (>=6.11.0)", "ipython-genutils (>=0.2.0)", "jedi (>=0.16)", "jinja2 (>=3.0.0)", "jupyter-client (>=6.1.5)", "jupyter-client (>=7.0.0)", "jupyter-core (>=4.7.0)", "nbconvert (>=6.0.0)", "nbconvert (>=6.1.0)", "pyflakes (>=2.2.0)", "pytest (>=4.6.0)", "pytest (>=4.6.0)", "pytest (>=6.2.5)", "pytest-cov (>=3.0.0)", "tomli (>=0.2.0)", "typing (>=3.7.4)"]
+all-strict = ["IPython (==7.10.0)", "IPython (==7.23.1)", "Pygments (==2.0.0)", "Pygments (==2.4.1)", "attrs (==19.2.0)", "colorama (==0.4.1)", "debugpy (==1.0.0)", "debugpy (==1.0.0)", "debugpy (==1.0.0)", "debugpy (==1.3.0)", "debugpy (==1.6.0)", "ipykernel (==5.2.0)", "ipykernel (==6.0.0)", "ipykernel (==6.11.0)", "ipython-genutils (==0.2.0)", "jedi (==0.16)", "jinja2 (==3.0.0)", "jupyter-client (==6.1.5)", "jupyter-client (==7.0.0)", "jupyter-core (==4.7.0)", "nbconvert (==6.0.0)", "nbconvert (==6.1.0)", "pyflakes (==2.2.0)", "pytest (==4.6.0)", "pytest (==4.6.0)", "pytest (==6.2.5)", "pytest-cov (==3.0.0)", "tomli (==0.2.0)", "typing (==3.7.4)"]
+colors = ["Pygments", "Pygments", "colorama"]
+jupyter = ["IPython", "IPython", "attrs", "debugpy", "debugpy", "debugpy", "debugpy", "debugpy", "ipykernel", "ipykernel", "ipykernel", "ipython-genutils", "jedi", "jinja2", "jupyter-client", "jupyter-client", "jupyter-core", "nbconvert", "nbconvert"]
+optional = ["IPython (>=7.10.0)", "IPython (>=7.23.1)", "Pygments (>=2.0.0)", "Pygments (>=2.4.1)", "attrs (>=19.2.0)", "colorama (>=0.4.1)", "debugpy (>=1.0.0)", "debugpy (>=1.0.0)", "debugpy (>=1.0.0)", "debugpy (>=1.3.0)", "debugpy (>=1.6.0)", "ipykernel (>=5.2.0)", "ipykernel (>=6.0.0)", "ipykernel (>=6.11.0)", "ipython-genutils (>=0.2.0)", "jedi (>=0.16)", "jinja2 (>=3.0.0)", "jupyter-client (>=6.1.5)", "jupyter-client (>=7.0.0)", "jupyter-core (>=4.7.0)", "nbconvert (>=6.0.0)", "nbconvert (>=6.1.0)", "pyflakes (>=2.2.0)", "tomli (>=0.2.0)"]
+optional-strict = ["IPython (==7.10.0)", "IPython (==7.23.1)", "Pygments (==2.0.0)", "Pygments (==2.4.1)", "attrs (==19.2.0)", "colorama (==0.4.1)", "debugpy (==1.0.0)", "debugpy (==1.0.0)", "debugpy (==1.0.0)", "debugpy (==1.3.0)", "debugpy (==1.6.0)", "ipykernel (==5.2.0)", "ipykernel (==6.0.0)", "ipykernel (==6.11.0)", "ipython-genutils (==0.2.0)", "jedi (==0.16)", "jinja2 (==3.0.0)", "jupyter-client (==6.1.5)", "jupyter-client (==7.0.0)", "jupyter-core (==4.7.0)", "nbconvert (==6.0.0)", "nbconvert (==6.1.0)", "pyflakes (==2.2.0)", "tomli (==0.2.0)"]
+tests = ["pytest (>=4.6.0)", "pytest (>=4.6.0)", "pytest (>=6.2.5)", "pytest-cov (>=3.0.0)", "typing (>=3.7.4)"]
+tests-binary = ["cmake", "cmake", "ninja", "ninja", "pybind11", "pybind11", "scikit-build", "scikit-build"]
+tests-binary-strict = ["cmake (==3.21.2)", "cmake (==3.25.0)", "ninja (==1.10.2)", "ninja (==1.11.1)", "pybind11 (==2.10.3)", "pybind11 (==2.7.1)", "scikit-build (==0.11.1)", "scikit-build (==0.16.1)"]
+tests-strict = ["pytest (==4.6.0)", "pytest (==4.6.0)", "pytest (==6.2.5)", "pytest-cov (==3.0.0)", "typing (==3.7.4)"]
+
+[metadata]
+lock-version = "2.0"
+python-versions = "^3.11"
+content-hash = "c6c80c1e03b497a4533d3c622b77740b172fa4818921210d88759b224a3654b4"
diff --git a/pyproject.toml b/pyproject.toml
index aa92bb6..4491fee 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -1,29 +1,26 @@
 [build-system]
-requires = ["poetry>=0.12"]
-build-backend = "poetry.masonry.api"
+requires = ["poetry-core"]
+build-backend = "poetry.core.masonry.api"
 
 [tool.poetry]
 name = "intro-to-python"
 version = "0.1.0.dev0"
+package-mode = false
 
 authors = ["Alexander Hess <alexander@webartifex.biz>"]
 description = "An intro to Python & programming for wanna-be data scientists"
 license = "MIT"
 
 [tool.poetry.dependencies]
-python = "^3.8"
+python = "^3.11"
 
-jupyterlab = "^2.2.8"
-numpy = "^1.19.2"
+jupyterlab = "^4.1.0"
+numpy = "^1.26.0"
 
 [tool.poetry.dev-dependencies]
 # Task runners
-nox = "^2020.8.22"
-pre-commit = "^2.7.1"
+nox = "^2024.3.2"
+pre-commit = "^3.7.0"
 
 # Testing
-xdoctest = "^0.15.0"
-
-# Live coding during presentation mode
-jupyter-contrib-nbextensions = "^0.5.1"
-rise = "^5.6.1"
+xdoctest = "^1.1.0"

From d6c8a5b8ac927a33ac1f9412a2ecddd1b845e793 Mon Sep 17 00:00:00 2001
From: Alexander Hess <alexander@webartifex.biz>
Date: Mon, 8 Apr 2024 15:54:29 +0200
Subject: [PATCH 225/242] Re-run Chapter 00 files with Python 3.12

---
 00_intro/00_content.ipynb   | 4 ++--
 00_intro/01_exercises.ipynb | 2 +-
 00_intro/02_review.ipynb    | 2 +-
 3 files changed, 4 insertions(+), 4 deletions(-)

diff --git a/00_intro/00_content.ipynb b/00_intro/00_content.ipynb
index 2bc24f2..8409944 100644
--- a/00_intro/00_content.ipynb
+++ b/00_intro/00_content.ipynb
@@ -291,7 +291,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Python 3.8.6\n"
+      "Python 3.12.2\n"
      ]
     }
    ],
@@ -841,7 +841,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.6"
+   "version": "3.12.2"
   },
   "livereveal": {
    "auto_select": "code",
diff --git a/00_intro/01_exercises.ipynb b/00_intro/01_exercises.ipynb
index c5082eb..272d5f9 100644
--- a/00_intro/01_exercises.ipynb
+++ b/00_intro/01_exercises.ipynb
@@ -94,7 +94,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.6"
+   "version": "3.12.2"
   },
   "toc": {
    "base_numbering": 1,
diff --git a/00_intro/02_review.ipynb b/00_intro/02_review.ipynb
index 9a8d7c2..0fa77c5 100644
--- a/00_intro/02_review.ipynb
+++ b/00_intro/02_review.ipynb
@@ -194,7 +194,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.6"
+   "version": "3.12.2"
   },
   "toc": {
    "base_numbering": 1,

From 8da18b86773b2aebd908df9084fbd79ca2d5a6a1 Mon Sep 17 00:00:00 2001
From: Alexander Hess <alexander@webartifex.biz>
Date: Mon, 8 Apr 2024 16:03:12 +0200
Subject: [PATCH 226/242] Re-run Chapter 01 files with Python 3.12

---
 01_elements/00_content.ipynb   | 16 ++++++++--------
 01_elements/01_exercises.ipynb |  2 +-
 01_elements/02_exercises.ipynb |  2 +-
 01_elements/03_content.ipynb   | 20 ++++++++++++++------
 01_elements/04_exercises.ipynb |  2 +-
 01_elements/05_summary.ipynb   |  2 +-
 01_elements/06_review.ipynb    |  2 +-
 01_elements/07_resources.ipynb |  2 +-
 8 files changed, 28 insertions(+), 20 deletions(-)

diff --git a/01_elements/00_content.ipynb b/01_elements/00_content.ipynb
index 4d6c792..3335486 100644
--- a/01_elements/00_content.ipynb
+++ b/01_elements/00_content.ipynb
@@ -1654,10 +1654,10 @@
    "outputs": [
     {
      "ename": "SyntaxError",
-     "evalue": "invalid syntax (<ipython-input-47-cafa82e54b9c>, line 1)",
+     "evalue": "invalid syntax (73631267.py, line 1)",
      "output_type": "error",
      "traceback": [
-      "\u001b[0;36m  File \u001b[0;32m\"<ipython-input-47-cafa82e54b9c>\"\u001b[0;36m, line \u001b[0;32m1\u001b[0m\n\u001b[0;31m    3.99 $ + 10.40 $\u001b[0m\n\u001b[0m         ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m invalid syntax\n"
+      "\u001b[0;36m  Cell \u001b[0;32mIn[47], line 1\u001b[0;36m\u001b[0m\n\u001b[0;31m    3.99 $ + 10.40 $\u001b[0m\n\u001b[0m         ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m invalid syntax\n"
      ]
     }
    ],
@@ -1687,10 +1687,10 @@
    "outputs": [
     {
      "ename": "SyntaxError",
-     "evalue": "invalid syntax (<ipython-input-48-499e4d0d0cbb>, line 1)",
+     "evalue": "expected ':' (2545637715.py, line 1)",
      "output_type": "error",
      "traceback": [
-      "\u001b[0;36m  File \u001b[0;32m\"<ipython-input-48-499e4d0d0cbb>\"\u001b[0;36m, line \u001b[0;32m1\u001b[0m\n\u001b[0;31m    for number in numbers\u001b[0m\n\u001b[0m                         ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m invalid syntax\n"
+      "\u001b[0;36m  Cell \u001b[0;32mIn[48], line 1\u001b[0;36m\u001b[0m\n\u001b[0;31m    for number in numbers\u001b[0m\n\u001b[0m                         ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m expected ':'\n"
      ]
     }
    ],
@@ -1721,10 +1721,10 @@
    "outputs": [
     {
      "ename": "IndentationError",
-     "evalue": "expected an indented block (<ipython-input-49-19398c5f89de>, line 2)",
+     "evalue": "expected an indented block after 'for' statement on line 1 (1544118965.py, line 2)",
      "output_type": "error",
      "traceback": [
-      "\u001b[0;36m  File \u001b[0;32m\"<ipython-input-49-19398c5f89de>\"\u001b[0;36m, line \u001b[0;32m2\u001b[0m\n\u001b[0;31m    print(number)\u001b[0m\n\u001b[0m    ^\u001b[0m\n\u001b[0;31mIndentationError\u001b[0m\u001b[0;31m:\u001b[0m expected an indented block\n"
+      "\u001b[0;36m  Cell \u001b[0;32mIn[49], line 2\u001b[0;36m\u001b[0m\n\u001b[0;31m    print(number)\u001b[0m\n\u001b[0m    ^\u001b[0m\n\u001b[0;31mIndentationError\u001b[0m\u001b[0;31m:\u001b[0m expected an indented block after 'for' statement on line 1\n"
      ]
     }
    ],
@@ -1775,7 +1775,7 @@
      "traceback": [
       "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
       "\u001b[0;31mZeroDivisionError\u001b[0m                         Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-50-bc757c3fda29>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0;36m1\u001b[0m \u001b[0;34m/\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "Cell \u001b[0;32mIn[50], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[38;5;241;43m1\u001b[39;49m\u001b[43m \u001b[49m\u001b[38;5;241;43m/\u001b[39;49m\u001b[43m \u001b[49m\u001b[38;5;241;43m0\u001b[39;49m\n",
       "\u001b[0;31mZeroDivisionError\u001b[0m: division by zero"
      ]
     }
@@ -2156,7 +2156,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.6"
+   "version": "3.12.2"
   },
   "livereveal": {
    "auto_select": "code",
diff --git a/01_elements/01_exercises.ipynb b/01_elements/01_exercises.ipynb
index d073a26..592d48a 100644
--- a/01_elements/01_exercises.ipynb
+++ b/01_elements/01_exercises.ipynb
@@ -151,7 +151,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.6"
+   "version": "3.12.2"
   },
   "toc": {
    "base_numbering": 1,
diff --git a/01_elements/02_exercises.ipynb b/01_elements/02_exercises.ipynb
index 5ae269c..2ff9c45 100644
--- a/01_elements/02_exercises.ipynb
+++ b/01_elements/02_exercises.ipynb
@@ -197,7 +197,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.6"
+   "version": "3.12.2"
   },
   "toc": {
    "base_numbering": 1,
diff --git a/01_elements/03_content.ipynb b/01_elements/03_content.ipynb
index 74c3477..e7886f8 100644
--- a/01_elements/03_content.ipynb
+++ b/01_elements/03_content.ipynb
@@ -350,7 +350,7 @@
      "traceback": [
       "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
       "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-13-1748287bc46a>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mvariable\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "Cell \u001b[0;32mIn[13], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mvariable\u001b[49m\n",
       "\u001b[0;31mNameError\u001b[0m: name 'variable' is not defined"
      ]
     }
@@ -419,6 +419,7 @@
       "text/plain": [
        "['In',\n",
        " 'Out',\n",
+       " 'Path',\n",
        " '_',\n",
        " '_10',\n",
        " '_11',\n",
@@ -435,6 +436,7 @@
        " '__loader__',\n",
        " '__name__',\n",
        " '__package__',\n",
+       " '__session__',\n",
        " '__spec__',\n",
        " '_dh',\n",
        " '_i',\n",
@@ -457,11 +459,17 @@
        " '_ii',\n",
        " '_iii',\n",
        " '_oh',\n",
+       " 'atexit',\n",
        " 'exit',\n",
        " 'get_ipython',\n",
+       " 'history',\n",
+       " 'history_path',\n",
+       " 'open',\n",
+       " 'os',\n",
        " 'quit',\n",
-       " 'register_readline_completion',\n",
-       " 'sys']"
+       " 'readline',\n",
+       " 'state_home',\n",
+       " 'write_history']"
       ]
      },
      "execution_count": 15,
@@ -636,10 +644,10 @@
    "outputs": [
     {
      "ename": "SyntaxError",
-     "evalue": "cannot assign to operator (<ipython-input-23-ec51dae29567>, line 1)",
+     "evalue": "cannot assign to expression here. Maybe you meant '==' instead of '='? (202417111.py, line 1)",
      "output_type": "error",
      "traceback": [
-      "\u001b[0;36m  File \u001b[0;32m\"<ipython-input-23-ec51dae29567>\"\u001b[0;36m, line \u001b[0;32m1\u001b[0m\n\u001b[0;31m    address@work = \"WHU, Burgplatz 2, Vallendar\"\u001b[0m\n\u001b[0m    ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m cannot assign to operator\n"
+      "\u001b[0;36m  Cell \u001b[0;32mIn[23], line 1\u001b[0;36m\u001b[0m\n\u001b[0;31m    address@work = \"WHU, Burgplatz 2, Vallendar\"\u001b[0m\n\u001b[0m    ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m cannot assign to expression here. Maybe you meant '==' instead of '='?\n"
      ]
     }
    ],
@@ -1685,7 +1693,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.6"
+   "version": "3.12.2"
   },
   "livereveal": {
    "auto_select": "code",
diff --git a/01_elements/04_exercises.ipynb b/01_elements/04_exercises.ipynb
index f1cca6d..3fa9301 100644
--- a/01_elements/04_exercises.ipynb
+++ b/01_elements/04_exercises.ipynb
@@ -191,7 +191,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.6"
+   "version": "3.12.2"
   },
   "toc": {
    "base_numbering": 1,
diff --git a/01_elements/05_summary.ipynb b/01_elements/05_summary.ipynb
index c3b6339..f57bb09 100644
--- a/01_elements/05_summary.ipynb
+++ b/01_elements/05_summary.ipynb
@@ -110,7 +110,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.6"
+   "version": "3.12.2"
   },
   "livereveal": {
    "auto_select": "code",
diff --git a/01_elements/06_review.ipynb b/01_elements/06_review.ipynb
index d975603..33ff535 100644
--- a/01_elements/06_review.ipynb
+++ b/01_elements/06_review.ipynb
@@ -194,7 +194,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.6"
+   "version": "3.12.2"
   },
   "toc": {
    "base_numbering": 1,
diff --git a/01_elements/07_resources.ipynb b/01_elements/07_resources.ipynb
index 9d46189..0ac2b87 100644
--- a/01_elements/07_resources.ipynb
+++ b/01_elements/07_resources.ipynb
@@ -83,7 +83,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.6"
+   "version": "3.12.2"
   },
   "livereveal": {
    "auto_select": "code",

From 7eed0841f19761f05153f2b5a8d529d37092f241 Mon Sep 17 00:00:00 2001
From: Alexander Hess <alexander@webartifex.biz>
Date: Mon, 8 Apr 2024 16:09:30 +0200
Subject: [PATCH 227/242] Re-run Chapter 02 files with Python 3.12

---
 02_functions/00_content.ipynb   | 48 ++++++++++++++++-----------------
 02_functions/01_exercises.ipynb |  2 +-
 02_functions/02_content.ipynb   | 36 +++++++++++++++++--------
 02_functions/03_summary.ipynb   |  2 +-
 02_functions/04_review.ipynb    |  2 +-
 5 files changed, 52 insertions(+), 38 deletions(-)

diff --git a/02_functions/00_content.ipynb b/02_functions/00_content.ipynb
index 964ecaa..cfc47d3 100644
--- a/02_functions/00_content.ipynb
+++ b/02_functions/00_content.ipynb
@@ -592,7 +592,7 @@
      "traceback": [
       "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
       "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-18-af421b358f21>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"seven\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "Cell \u001b[0;32mIn[18], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[38;5;28;43mint\u001b[39;49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mseven\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\n",
       "\u001b[0;31mValueError\u001b[0m: invalid literal for int() with base 10: 'seven'"
      ]
     }
@@ -1026,10 +1026,10 @@
    "outputs": [
     {
      "ename": "SyntaxError",
-     "evalue": "invalid syntax (<ipython-input-31-7f49dff38622>, line 1)",
+     "evalue": "invalid syntax (2246690741.py, line 1)",
      "output_type": "error",
      "traceback": [
-      "\u001b[0;36m  File \u001b[0;32m\"<ipython-input-31-7f49dff38622>\"\u001b[0;36m, line \u001b[0;32m1\u001b[0m\n\u001b[0;31m    <function __main__.average_evens(numbers)>\u001b[0m\n\u001b[0m    ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m invalid syntax\n"
+      "\u001b[0;36m  Cell \u001b[0;32mIn[31], line 1\u001b[0;36m\u001b[0m\n\u001b[0;31m    <function __main__.average_evens(numbers)>\u001b[0m\n\u001b[0m    ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m invalid syntax\n"
      ]
     }
    ],
@@ -1102,10 +1102,10 @@
       "\n",
       "average_evens(integers)\n",
       "    Calculate the average of all even numbers in a list.\n",
-      "    \n",
+      "\n",
       "    Args:\n",
       "        integers (list of int's): whole numbers to be averaged\n",
-      "    \n",
+      "\n",
       "    Returns:\n",
       "        average (float)\n",
       "\n"
@@ -1148,8 +1148,8 @@
        "\n",
        "Returns:\n",
        "    average (float)\n",
-       "\u001b[0;31mFile:\u001b[0m      ~/repos/intro-to-python/02_functions/<ipython-input-27-bcb78a3113d7>\n",
-       "\u001b[0;31mType:\u001b[0m      function\n"
+       "\u001b[0;31mFile:\u001b[0m      /tmp/ipykernel_152540/3598721284.py\n",
+       "\u001b[0;31mType:\u001b[0m      function"
       ]
      },
      "metadata": {},
@@ -1197,8 +1197,8 @@
        "\u001b[0;34m\u001b[0m    \u001b[0mevens\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mn\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mn\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mintegers\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mn\u001b[0m \u001b[0;34m%\u001b[0m \u001b[0;36m2\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\n",
        "\u001b[0;34m\u001b[0m    \u001b[0maverage\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msum\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mevens\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m/\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mevens\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n",
        "\u001b[0;34m\u001b[0m    \u001b[0;32mreturn\u001b[0m \u001b[0maverage\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-       "\u001b[0;31mFile:\u001b[0m      ~/repos/intro-to-python/02_functions/<ipython-input-27-bcb78a3113d7>\n",
-       "\u001b[0;31mType:\u001b[0m      function\n"
+       "\u001b[0;31mFile:\u001b[0m      /tmp/ipykernel_152540/3598721284.py\n",
+       "\u001b[0;31mType:\u001b[0m      function"
       ]
      },
      "metadata": {},
@@ -1237,7 +1237,7 @@
       "\n",
       "sum(iterable, /, start=0)\n",
       "    Return the sum of a 'start' value (default: 0) plus an iterable of numbers\n",
-      "    \n",
+      "\n",
       "    When the iterable is empty, return the start value.\n",
       "    This function is intended specifically for use with numeric values and may\n",
       "    reject non-numeric types.\n",
@@ -1416,7 +1416,7 @@
      "traceback": [
       "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
       "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-41-756dfa02d4a8>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mintegers\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "Cell \u001b[0;32mIn[41], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mintegers\u001b[49m\n",
       "\u001b[0;31mNameError\u001b[0m: name 'integers' is not defined"
      ]
     }
@@ -1441,7 +1441,7 @@
      "traceback": [
       "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
       "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-42-df246468d241>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mevens\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "Cell \u001b[0;32mIn[42], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mevens\u001b[49m\n",
       "\u001b[0;31mNameError\u001b[0m: name 'evens' is not defined"
      ]
     }
@@ -1466,7 +1466,7 @@
      "traceback": [
       "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
       "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-43-c3fe9b4213f6>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0maverage\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "Cell \u001b[0;32mIn[43], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43maverage\u001b[49m\n",
       "\u001b[0;31mNameError\u001b[0m: name 'average' is not defined"
      ]
     }
@@ -1483,7 +1483,7 @@
     }
    },
    "source": [
-    "[PythonTutor <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](http://pythontutor.com/visualize.html#code=numbers%20%3D%20%5B7,%2011,%208,%205,%203,%2012,%202,%206,%209,%2010,%201,%204%5D%0A%0Adef%20average_evens%28integers%29%3A%0A%20%20%20%20evens%20%3D%20%5Bn%20for%20n%20in%20integers%20if%20n%20%25%202%20%3D%3D%200%5D%0A%20%20%20%20average%20%3D%20sum%28evens%29%20/%20len%28evens%29%0A%20%20%20%20return%20average%0A%0Aresult%20%3D%20average_evens%28numbers%29&cumulative=false&curInstr=0&heapPrimitives=nevernest&mode=display&origin=opt-frontend.js&py=3&rawInputLstJSON=%5B%5D&textReferences=false) visualizes what happens in memory: To be precise, in the exact moment when the function call is initiated and `numbers` passed in as the `integers` argument, there are *two* references to the *same* `list` object (cf., steps 4-5 in the visualization). We also see how Python creates a *new* **frame** that holds the function's local scope (i.e., \"internal names\") in addition to the **global** frame. Frames are nothing but [namespaces <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_wiki.png\">](https://en.wikipedia.org/wiki/Namespace) to *isolate* the names of different **scopes** from each other. The list comprehension `[n for n in integers if n % 2 == 0]` constitutes yet another frame that is in scope as the `list` object assigned to `evens` is *being* created (cf., steps 6-20). When the function returns, only the global frame is left (cf., last step)."
+    "[PythonTutor <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](http://pythontutor.com/visualize.html#code=numbers%20%3D%20%5B7,%2011,%208,%205,%203,%2012,%202,%206,%209,%2010,%201,%204%5D%0A%0Adef%20average_evens%28integers%29%3A%0A%20%20%20%20evens%20%3D%20%5Bn%20for%20n%20in%20integers%20if%20n%20%25%202%20%3D%3D%200%5D%0A%20%20%20%20average%20%3D%20sum%28evens%29%20/%20len%28evens%29%0A%20%20%20%20return%20average%0A%0Aresult%20%3D%20average_evens%28numbers%29&cumulative=false&curstr=0&heapPrimitives=nevernest&mode=display&origin=opt-frontend.js&py=3&rawInputLstJSON=%5B%5D&textReferences=false) visualizes what happens in memory: To be precise, in the exact moment when the function call is initiated and `numbers` passed in as the `integers` argument, there are *two* references to the *same* `list` object (cf., steps 4-5 in the visualization). We also see how Python creates a *new* **frame** that holds the function's local scope (i.e., \"internal names\") in addition to the **global** frame. Frames are nothing but [namespaces <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_wiki.png\">](https://en.wikipedia.org/wiki/Namespace) to *isolate* the names of different **scopes** from each other. The list comprehension `[n for n in integers if n % 2 == 0]` constitutes yet another frame that is in scope as the `list` object assigned to `evens` is *being* created (cf., steps 6-20). When the function returns, only the global frame is left (cf., last step)."
    ]
   },
   {
@@ -1645,7 +1645,7 @@
     }
    },
    "source": [
-    "[PythonTutor <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](http://pythontutor.com/visualize.html#code=numbers%20%3D%20%5B7,%2011,%208,%205,%203,%2012,%202,%206,%209,%2010,%201,%204%5D%0A%0Adef%20average_wrong%28integers%29%3A%0A%20%20%20%20evens%20%3D%20%5Bn%20for%20n%20in%20numbers%20if%20n%20%25%202%20%3D%3D%200%5D%0A%20%20%20%20average%20%3D%20sum%28evens%29%20/%20len%28evens%29%0A%20%20%20%20return%20average%0A%0Aresult%20%3D%20average_wrong%28%5B123,%20456,%20789%5D%29&cumulative=false&curInstr=0&heapPrimitives=nevernest&mode=display&origin=opt-frontend.js&py=3&rawInputLstJSON=%5B%5D&textReferences=false) is again helpful at visualizing the error interactively: Creating the `list` object `evens` eventually references takes *16* computational steps, namely two for managing the list comprehension, one for setting up an empty `list` object, *twelve* for filling it with elements derived from `numbers` in the global scope (i.e., that is the error), and one to make `evens` reference it (cf., steps 6-21).\n",
+    "[PythonTutor <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](http://pythontutor.com/visualize.html#code=numbers%20%3D%20%5B7,%2011,%208,%205,%203,%2012,%202,%206,%209,%2010,%201,%204%5D%0A%0Adef%20average_wrong%28integers%29%3A%0A%20%20%20%20evens%20%3D%20%5Bn%20for%20n%20in%20numbers%20if%20n%20%25%202%20%3D%3D%200%5D%0A%20%20%20%20average%20%3D%20sum%28evens%29%20/%20len%28evens%29%0A%20%20%20%20return%20average%0A%0Aresult%20%3D%20average_wrong%28%5B123,%20456,%20789%5D%29&cumulative=false&curstr=0&heapPrimitives=nevernest&mode=display&origin=opt-frontend.js&py=3&rawInputLstJSON=%5B%5D&textReferences=false) is again helpful at visualizing the error interactively: Creating the `list` object `evens` eventually references takes *16* computational steps, namely two for managing the list comprehension, one for setting up an empty `list` object, *twelve* for filling it with elements derived from `numbers` in the global scope (i.e., that is the error), and one to make `evens` reference it (cf., steps 6-21).\n",
     "\n",
     "The frames logic shown by PythonTutor is the mechanism with which Python not only manages the names inside *one* function call but also for *many* potentially *simultaneous* calls, as revealed in [Chapter 4 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/04_iteration/00_content.ipynb#Trivial-Example:-Countdown). It is the reason why we may reuse the same names for the parameters and variables inside both `average_evens()` and `average_wrong()` without Python mixing them up. So, as we already read in the [Zen of Python <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://www.python.org/dev/peps/pep-0020/), \"namespaces are one honking great idea\" (cf., `import this`), and a frame is just a special kind of namespace."
    ]
@@ -1786,7 +1786,7 @@
      "traceback": [
       "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
       "\u001b[0;31mAssertionError\u001b[0m                            Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-51-f47586a118b9>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0;32massert\u001b[0m \u001b[0maverage_evens\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m40.0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m41.1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m42.2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m43.3\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m44.4\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m87.0\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "Cell \u001b[0;32mIn[51], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[38;5;28;01massert\u001b[39;00m average_evens([\u001b[38;5;241m40.0\u001b[39m, \u001b[38;5;241m41.1\u001b[39m, \u001b[38;5;241m42.2\u001b[39m, \u001b[38;5;241m43.3\u001b[39m, \u001b[38;5;241m44.4\u001b[39m]) \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m87.0\u001b[39m\n",
       "\u001b[0;31mAssertionError\u001b[0m: "
      ]
     }
@@ -1910,7 +1910,7 @@
    "source": [
     "The reason why everything works is that *every* time we (re-)assign an object to a variable *inside* a function's body with the `=` statement, this is done in the *local* scope by default. There are ways to change variables existing in an outer scope from within a function, but this is a rather advanced topic.\n",
     "\n",
-    "[PythonTutor <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](http://pythontutor.com/visualize.html#code=numbers%20%3D%20%5B7,%2011,%208,%205,%203,%2012,%202,%206,%209,%2010,%201,%204%5D%0A%0Adef%20average_evens%28integers%29%3A%0A%20%20%20%20numbers%20%3D%20%5Bround%28n%29%20for%20n%20in%20integers%5D%0A%20%20%20%20evens%20%3D%20%5Bn%20for%20n%20in%20numbers%20if%20n%20%25%202%20%3D%3D%200%5D%0A%20%20%20%20average%20%3D%20sum%28evens%29%20/%20len%28evens%29%0A%20%20%20%20return%20average%0A%0Aresult%20%3D%20average_evens%28%5B40.0,%2041.1,%2042.2,%2043.3,%2044.4%5D%29&cumulative=false&curInstr=0&heapPrimitives=nevernest&mode=display&origin=opt-frontend.js&py=3&rawInputLstJSON=%5B%5D&textReferences=false) shows how *two* `numbers` variables exist in *different* scopes referencing *different* objects (cf., steps 14-25) when we execute `average_evens([40.0, 41.1, 42.2, 43.3, 44.4])`.\n",
+    "[PythonTutor <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](http://pythontutor.com/visualize.html#code=numbers%20%3D%20%5B7,%2011,%208,%205,%203,%2012,%202,%206,%209,%2010,%201,%204%5D%0A%0Adef%20average_evens%28integers%29%3A%0A%20%20%20%20numbers%20%3D%20%5Bround%28n%29%20for%20n%20in%20integers%5D%0A%20%20%20%20evens%20%3D%20%5Bn%20for%20n%20in%20numbers%20if%20n%20%25%202%20%3D%3D%200%5D%0A%20%20%20%20average%20%3D%20sum%28evens%29%20/%20len%28evens%29%0A%20%20%20%20return%20average%0A%0Aresult%20%3D%20average_evens%28%5B40.0,%2041.1,%2042.2,%2043.3,%2044.4%5D%29&cumulative=false&curstr=0&heapPrimitives=nevernest&mode=display&origin=opt-frontend.js&py=3&rawInputLstJSON=%5B%5D&textReferences=false) shows how *two* `numbers` variables exist in *different* scopes referencing *different* objects (cf., steps 14-25) when we execute `average_evens([40.0, 41.1, 42.2, 43.3, 44.4])`.\n",
     "\n",
     "Variables whose names collide with the ones of variables in enclosing scopes - and the global scope is just the most enclosing scope - are said to **shadow** them.\n",
     "\n",
@@ -1999,7 +1999,7 @@
     }
    },
    "source": [
-    "[PythonTutor <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](http://pythontutor.com/visualize.html#code=numbers%20%3D%20%5B7,%2011,%208,%205,%203,%2012,%202,%206,%209,%2010,%201,%204%5D%0A%0Adef%20average_evens%28numbers%29%3A%0A%20%20%20%20evens%20%3D%20%5Bn%20for%20n%20in%20numbers%20if%20n%20%25%202%20%3D%3D%200%5D%0A%20%20%20%20average%20%3D%20sum%28evens%29%20/%20len%28evens%29%0A%20%20%20%20return%20average%0A%0Aresult%20%3D%20average_evens%28numbers%29&cumulative=false&curInstr=0&heapPrimitives=nevernest&mode=display&origin=opt-frontend.js&py=3&rawInputLstJSON=%5B%5D&textReferences=false) reveals that in this example there are *two* `numbers` variables in *different* scope referencing the *same* `list` object in memory (cf., steps 4-23)."
+    "[PythonTutor <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](http://pythontutor.com/visualize.html#code=numbers%20%3D%20%5B7,%2011,%208,%205,%203,%2012,%202,%206,%209,%2010,%201,%204%5D%0A%0Adef%20average_evens%28numbers%29%3A%0A%20%20%20%20evens%20%3D%20%5Bn%20for%20n%20in%20numbers%20if%20n%20%25%202%20%3D%3D%200%5D%0A%20%20%20%20average%20%3D%20sum%28evens%29%20/%20len%28evens%29%0A%20%20%20%20return%20average%0A%0Aresult%20%3D%20average_evens%28numbers%29&cumulative=false&curstr=0&heapPrimitives=nevernest&mode=display&origin=opt-frontend.js&py=3&rawInputLstJSON=%5B%5D&textReferences=false) reveals that in this example there are *two* `numbers` variables in *different* scope referencing the *same* `list` object in memory (cf., steps 4-23)."
    ]
   },
   {
@@ -2274,10 +2274,10 @@
    "outputs": [
     {
      "ename": "SyntaxError",
-     "evalue": "positional argument follows keyword argument (<ipython-input-65-b00eb6f72e84>, line 1)",
+     "evalue": "positional argument follows keyword argument (159253642.py, line 1)",
      "output_type": "error",
      "traceback": [
-      "\u001b[0;36m  File \u001b[0;32m\"<ipython-input-65-b00eb6f72e84>\"\u001b[0;36m, line \u001b[0;32m1\u001b[0m\n\u001b[0;31m    scaled_average_evens(numbers=numbers, 2)\u001b[0m\n\u001b[0m                                          ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m positional argument follows keyword argument\n"
+      "\u001b[0;36m  Cell \u001b[0;32mIn[65], line 1\u001b[0;36m\u001b[0m\n\u001b[0;31m    scaled_average_evens(numbers=numbers, 2)\u001b[0m\n\u001b[0m                                           ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m positional argument follows keyword argument\n"
      ]
     }
    ],
@@ -2312,7 +2312,7 @@
      "traceback": [
       "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
       "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-66-d910518345ec>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mscaled_average_evens\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnumbers\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "Cell \u001b[0;32mIn[66], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mscaled_average_evens\u001b[49m\u001b[43m(\u001b[49m\u001b[43mnumbers\u001b[49m\u001b[43m)\u001b[49m\n",
       "\u001b[0;31mTypeError\u001b[0m: scaled_average_evens() missing 1 required positional argument: 'scalar'"
      ]
     }
@@ -2337,7 +2337,7 @@
      "traceback": [
       "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
       "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-67-2e7ca1d2a555>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mscaled_average_evens\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnumbers\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m3\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "Cell \u001b[0;32mIn[67], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mscaled_average_evens\u001b[49m\u001b[43m(\u001b[49m\u001b[43mnumbers\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m2\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m3\u001b[39;49m\u001b[43m)\u001b[49m\n",
       "\u001b[0;31mTypeError\u001b[0m: scaled_average_evens() takes 2 positional arguments but 3 were given"
      ]
     }
@@ -2782,7 +2782,7 @@
      "traceback": [
       "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
       "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-81-12eb635366db>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0maverage_evens\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnumbers\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m2\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "Cell \u001b[0;32mIn[81], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43maverage_evens\u001b[49m\u001b[43m(\u001b[49m\u001b[43mnumbers\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m2\u001b[39;49m\u001b[43m)\u001b[49m\n",
       "\u001b[0;31mTypeError\u001b[0m: average_evens() takes 1 positional argument but 2 were given"
      ]
     }
@@ -3032,7 +3032,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.6"
+   "version": "3.12.2"
   },
   "livereveal": {
    "auto_select": "code",
diff --git a/02_functions/01_exercises.ipynb b/02_functions/01_exercises.ipynb
index 795be04..e4173c4 100644
--- a/02_functions/01_exercises.ipynb
+++ b/02_functions/01_exercises.ipynb
@@ -220,7 +220,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.6"
+   "version": "3.12.2"
   },
   "toc": {
    "base_numbering": 1,
diff --git a/02_functions/02_content.ipynb b/02_functions/02_content.ipynb
index 5162e9c..f0631a0 100644
--- a/02_functions/02_content.ipynb
+++ b/02_functions/02_content.ipynb
@@ -127,7 +127,7 @@
     {
      "data": {
       "text/plain": [
-       "<module 'math' from '/home/webartifex/.pyenv/versions/3.8.6/lib/python3.8/lib-dynload/math.cpython-38-x86_64-linux-gnu.so'>"
+       "<module 'math' from '/usr/lib64/python3.12/lib-dynload/math.cpython-312-x86_64-linux-gnu.so'>"
       ]
      },
      "execution_count": 2,
@@ -227,6 +227,7 @@
        " 'atan',\n",
        " 'atan2',\n",
        " 'atanh',\n",
+       " 'cbrt',\n",
        " 'ceil',\n",
        " 'comb',\n",
        " 'copysign',\n",
@@ -238,6 +239,7 @@
        " 'erf',\n",
        " 'erfc',\n",
        " 'exp',\n",
+       " 'exp2',\n",
        " 'expm1',\n",
        " 'fabs',\n",
        " 'factorial',\n",
@@ -254,6 +256,7 @@
        " 'isinf',\n",
        " 'isnan',\n",
        " 'isqrt',\n",
+       " 'lcm',\n",
        " 'ldexp',\n",
        " 'lgamma',\n",
        " 'log',\n",
@@ -262,6 +265,7 @@
        " 'log2',\n",
        " 'modf',\n",
        " 'nan',\n",
+       " 'nextafter',\n",
        " 'perm',\n",
        " 'pi',\n",
        " 'pow',\n",
@@ -271,10 +275,12 @@
        " 'sin',\n",
        " 'sinh',\n",
        " 'sqrt',\n",
+       " 'sumprod',\n",
        " 'tan',\n",
        " 'tanh',\n",
        " 'tau',\n",
-       " 'trunc']"
+       " 'trunc',\n",
+       " 'ulp']"
       ]
      },
      "execution_count": 5,
@@ -589,7 +595,7 @@
     {
      "data": {
       "text/plain": [
-       "<module 'random' from '/home/webartifex/.pyenv/versions/3.8.6/lib/python3.8/random.py'>"
+       "<module 'random' from '/usr/lib64/python3.12/random.py'>"
       ]
      },
      "execution_count": 16,
@@ -632,8 +638,8 @@
        " 'SG_MAGICCONST',\n",
        " 'SystemRandom',\n",
        " 'TWOPI',\n",
+       " '_ONE',\n",
        " '_Sequence',\n",
-       " '_Set',\n",
        " '__all__',\n",
        " '__builtins__',\n",
        " '__cached__',\n",
@@ -650,8 +656,14 @@
        " '_cos',\n",
        " '_e',\n",
        " '_exp',\n",
+       " '_fabs',\n",
+       " '_floor',\n",
+       " '_index',\n",
        " '_inst',\n",
+       " '_isfinite',\n",
+       " '_lgamma',\n",
        " '_log',\n",
+       " '_log2',\n",
        " '_os',\n",
        " '_pi',\n",
        " '_random',\n",
@@ -664,6 +676,7 @@
        " '_urandom',\n",
        " '_warn',\n",
        " 'betavariate',\n",
+       " 'binomialvariate',\n",
        " 'choice',\n",
        " 'choices',\n",
        " 'expovariate',\n",
@@ -674,6 +687,7 @@
        " 'lognormvariate',\n",
        " 'normalvariate',\n",
        " 'paretovariate',\n",
+       " 'randbytes',\n",
        " 'randint',\n",
        " 'random',\n",
        " 'randrange',\n",
@@ -1031,7 +1045,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Requirement already satisfied: numpy in /home/webartifex/repos/intro-to-python/.venv/lib/python3.8/site-packages (1.19.2)\n"
+      "Requirement already satisfied: numpy in /home/alexander/Repositories/intro-to-python/.venv/lib64/python3.12/site-packages (1.26.4)\n"
      ]
     }
    ],
@@ -1086,7 +1100,7 @@
     {
      "data": {
       "text/plain": [
-       "<module 'numpy' from '/home/webartifex/repos/intro-to-python/.venv/lib/python3.8/site-packages/numpy/__init__.py'>"
+       "<module 'numpy' from '/home/alexander/Repositories/intro-to-python/.venv/lib64/python3.12/site-packages/numpy/__init__.py'>"
       ]
      },
      "execution_count": 31,
@@ -1338,7 +1352,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "/home/webartifex/repos/intro-to-python/02_functions\n"
+      "/home/alexander/Repositories/intro-to-python/02_functions\n"
      ]
     }
    ],
@@ -1384,7 +1398,7 @@
     {
      "data": {
       "text/plain": [
-       "<module 'sample_module' from '/home/webartifex/repos/intro-to-python/02_functions/sample_module.py'>"
+       "<module 'sample_module' from '/home/alexander/Repositories/intro-to-python/02_functions/sample_module.py'>"
       ]
      },
      "execution_count": 41,
@@ -1499,12 +1513,12 @@
       "\n",
       "average_evens(numbers, *, scalar=1)\n",
       "    Calculate the average of all even numbers in a list.\n",
-      "    \n",
+      "\n",
       "    Args:\n",
       "        numbers (list of int's/float's): numbers to be averaged;\n",
       "            if non-whole numbers are provided, they are rounded\n",
       "        scalar (float, optional): multiplies the average; defaults to 1\n",
-      "    \n",
+      "\n",
       "    Returns:\n",
       "        scaled_average (float)\n",
       "\n"
@@ -1593,7 +1607,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.6"
+   "version": "3.12.2"
   },
   "livereveal": {
    "auto_select": "code",
diff --git a/02_functions/03_summary.ipynb b/02_functions/03_summary.ipynb
index 2161f17..f5efdad 100644
--- a/02_functions/03_summary.ipynb
+++ b/02_functions/03_summary.ipynb
@@ -58,7 +58,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.6"
+   "version": "3.12.2"
   },
   "livereveal": {
    "auto_select": "code",
diff --git a/02_functions/04_review.ipynb b/02_functions/04_review.ipynb
index a85dade..7ebc8fd 100644
--- a/02_functions/04_review.ipynb
+++ b/02_functions/04_review.ipynb
@@ -208,7 +208,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.6"
+   "version": "3.12.2"
   },
   "toc": {
    "base_numbering": 1,

From c0b09454bdcd920e98d417c5df28d1e1b4449d06 Mon Sep 17 00:00:00 2001
From: Alexander Hess <alexander@webartifex.biz>
Date: Mon, 8 Apr 2024 16:12:09 +0200
Subject: [PATCH 228/242] Re-run Chapter 03 files with Python 3.12

---
 03_conditionals/00_content.ipynb   | 4 ++--
 03_conditionals/01_exercises.ipynb | 2 +-
 03_conditionals/02_exercises.ipynb | 2 +-
 03_conditionals/03_summary.ipynb   | 2 +-
 03_conditionals/04_review.ipynb    | 2 +-
 5 files changed, 6 insertions(+), 6 deletions(-)

diff --git a/03_conditionals/00_content.ipynb b/03_conditionals/00_content.ipynb
index 5a6bc8e..62bf469 100644
--- a/03_conditionals/00_content.ipynb
+++ b/03_conditionals/00_content.ipynb
@@ -2606,7 +2606,7 @@
      "traceback": [
       "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
       "\u001b[0;31mZeroDivisionError\u001b[0m                         Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-78-7996bd5496ba>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0muser_input\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mrandom\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mchoice\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m3\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m4\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m5\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0;36m1\u001b[0m \u001b[0;34m/\u001b[0m \u001b[0muser_input\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "Cell \u001b[0;32mIn[78], line 3\u001b[0m\n\u001b[1;32m      1\u001b[0m user_input \u001b[38;5;241m=\u001b[39m random\u001b[38;5;241m.\u001b[39mchoice([\u001b[38;5;241m0\u001b[39m, \u001b[38;5;241m1\u001b[39m, \u001b[38;5;241m2\u001b[39m, \u001b[38;5;241m3\u001b[39m, \u001b[38;5;241m4\u001b[39m, \u001b[38;5;241m5\u001b[39m])\n\u001b[0;32m----> 3\u001b[0m \u001b[38;5;241;43m1\u001b[39;49m\u001b[43m \u001b[49m\u001b[38;5;241;43m/\u001b[39;49m\u001b[43m \u001b[49m\u001b[43muser_input\u001b[49m\n",
       "\u001b[0;31mZeroDivisionError\u001b[0m: division by zero"
      ]
     }
@@ -2799,7 +2799,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.6"
+   "version": "3.12.2"
   },
   "livereveal": {
    "auto_select": "code",
diff --git a/03_conditionals/01_exercises.ipynb b/03_conditionals/01_exercises.ipynb
index 513c67e..6bd0657 100644
--- a/03_conditionals/01_exercises.ipynb
+++ b/03_conditionals/01_exercises.ipynb
@@ -184,7 +184,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.6"
+   "version": "3.12.2"
   },
   "toc": {
    "base_numbering": 1,
diff --git a/03_conditionals/02_exercises.ipynb b/03_conditionals/02_exercises.ipynb
index 432ca73..4728719 100644
--- a/03_conditionals/02_exercises.ipynb
+++ b/03_conditionals/02_exercises.ipynb
@@ -119,7 +119,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.6"
+   "version": "3.12.2"
   },
   "toc": {
    "base_numbering": 1,
diff --git a/03_conditionals/03_summary.ipynb b/03_conditionals/03_summary.ipynb
index 86c8468..7d7b12d 100644
--- a/03_conditionals/03_summary.ipynb
+++ b/03_conditionals/03_summary.ipynb
@@ -44,7 +44,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.6"
+   "version": "3.12.2"
   },
   "livereveal": {
    "auto_select": "code",
diff --git a/03_conditionals/04_review.ipynb b/03_conditionals/04_review.ipynb
index 78d9061..e9890be 100644
--- a/03_conditionals/04_review.ipynb
+++ b/03_conditionals/04_review.ipynb
@@ -180,7 +180,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.6"
+   "version": "3.12.2"
   },
   "toc": {
    "base_numbering": 1,

From 2974f8322e0562606c9453a1bccd128f60492cf4 Mon Sep 17 00:00:00 2001
From: Alexander Hess <alexander@webartifex.biz>
Date: Mon, 8 Apr 2024 16:17:09 +0200
Subject: [PATCH 229/242] Re-run Chapter 04 files with Python 3.12

---
 04_iteration/00_content.ipynb   | 105 +++++++++++++++++++-------------
 04_iteration/01_exercises.ipynb |   2 +-
 04_iteration/02_content.ipynb   |   4 +-
 04_iteration/03_content.ipynb   |   2 +-
 04_iteration/04_exercises.ipynb |   2 +-
 04_iteration/05_summary.ipynb   |   2 +-
 04_iteration/06_review.ipynb    |   2 +-
 7 files changed, 71 insertions(+), 48 deletions(-)

diff --git a/04_iteration/00_content.ipynb b/04_iteration/00_content.ipynb
index 0171c6c..cc17d58 100644
--- a/04_iteration/00_content.ipynb
+++ b/04_iteration/00_content.ipynb
@@ -163,7 +163,7 @@
     }
    },
    "source": [
-    "As trivial as this seems, a lot of complexity is hidden in this implementation. In particular, the order in which objects are created and de-referenced in memory might not be apparent right away as [PythonTutor <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](http://pythontutor.com/visualize.html#code=def%20countdown%28n%29%3A%0A%20%20%20%20if%20n%20%3D%3D%200%3A%0A%20%20%20%20%20%20%20%20print%28%22Happy%20new%20Year!%22%29%0A%20%20%20%20else%3A%0A%20%20%20%20%20%20%20%20print%28n%29%0A%20%20%20%20%20%20%20%20countdown%28n%20-%201%29%0A%0Acountdown%283%29&cumulative=false&curInstr=0&heapPrimitives=nevernest&mode=display&origin=opt-frontend.js&py=3&rawInputLstJSON=%5B%5D&textReferences=false) shows: Each time `countdown()` is called, Python creates a *new* frame in the part of the memory where it manages all the names. This way, Python *isolates* all the different `n` variables from each other. As new frames are created until we reach the base case, after which the frames are destroyed in the *reversed* order, this is called a **[stack <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_wiki.png\">](https://en.wikipedia.org/wiki/Stack_(abstract_data_type))** of frames in computer science terminology. In simple words, a stack is a last-in-first-out (LIFO) task queue. Each frame has a single parent frame, namely the one whose recursive function call created it."
+    "As trivial as this seems, a lot of complexity is hidden in this implementation. In particular, the order in which objects are created and de-referenced in memory might not be apparent right away as [PythonTutor <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](http://pythontutor.com/visualize.html#code=def%20countdown%28n%29%3A%0A%20%20%20%20if%20n%20%3D%3D%200%3A%0A%20%20%20%20%20%20%20%20print%28%22Happy%20new%20Year!%22%29%0A%20%20%20%20else%3A%0A%20%20%20%20%20%20%20%20print%28n%29%0A%20%20%20%20%20%20%20%20countdown%28n%20-%201%29%0A%0Acountdown%283%29&cumulative=false&curstr=0&heapPrimitives=nevernest&mode=display&origin=opt-frontend.js&py=3&rawInputLstJSON=%5B%5D&textReferences=false) shows: Each time `countdown()` is called, Python creates a *new* frame in the part of the memory where it manages all the names. This way, Python *isolates* all the different `n` variables from each other. As new frames are created until we reach the base case, after which the frames are destroyed in the *reversed* order, this is called a **[stack <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_wiki.png\">](https://en.wikipedia.org/wiki/Stack_(abstract_data_type))** of frames in computer science terminology. In simple words, a stack is a last-in-first-out (LIFO) task queue. Each frame has a single parent frame, namely the one whose recursive function call created it."
    ]
   },
   {
@@ -273,7 +273,7 @@
    "source": [
     "When we read such code, it is often easier not to follow every function call (i.e., `factorial(n - 1)` here) in one's mind but assume we receive a return value as specified in the documentation. Some call this approach a **[leap of faith](http://greenteapress.com/thinkpython2/html/thinkpython2007.html#sec75)**. We practice this already whenever we call built-in functions (e.g., [print() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#print) or [len() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#len)) where we would have to read C code in many cases.\n",
     "\n",
-    "To visualize *all* the computational steps of the exemplary `factorial(3)`, we use [PythonTutor <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](http://pythontutor.com/visualize.html#code=def%20factorial%28n%29%3A%0A%20%20%20%20if%20n%20%3D%3D%200%3A%0A%20%20%20%20%20%20%20%20return%201%0A%20%20%20%20else%3A%0A%20%20%20%20%20%20%20%20recurse%20%3D%20factorial%28n%20-%201%29%0A%20%20%20%20%20%20%20%20result%20%3D%20n%20*%20recurse%0A%20%20%20%20%20%20%20%20return%20result%0A%0Asolution%20%3D%20factorial%283%29&cumulative=false&curInstr=0&heapPrimitives=nevernest&mode=display&origin=opt-frontend.js&py=3&rawInputLstJSON=%5B%5D&textReferences=false): The recursion again creates a stack of frames in memory. In contrast to the previous trivial example, each frame leaves a return value in memory after it is destroyed. This return value is then assigned to the `recurse` variable within the parent frame and used to compute `result`."
+    "To visualize *all* the computational steps of the exemplary `factorial(3)`, we use [PythonTutor <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](http://pythontutor.com/visualize.html#code=def%20factorial%28n%29%3A%0A%20%20%20%20if%20n%20%3D%3D%200%3A%0A%20%20%20%20%20%20%20%20return%201%0A%20%20%20%20else%3A%0A%20%20%20%20%20%20%20%20recurse%20%3D%20factorial%28n%20-%201%29%0A%20%20%20%20%20%20%20%20result%20%3D%20n%20*%20recurse%0A%20%20%20%20%20%20%20%20return%20result%0A%0Asolution%20%3D%20factorial%283%29&cumulative=false&curstr=0&heapPrimitives=nevernest&mode=display&origin=opt-frontend.js&py=3&rawInputLstJSON=%5B%5D&textReferences=false): The recursion again creates a stack of frames in memory. In contrast to the previous trivial example, each frame leaves a return value in memory after it is destroyed. This return value is then assigned to the `recurse` variable within the parent frame and used to compute `result`."
    ]
   },
   {
@@ -334,7 +334,7 @@
    "source": [
     "A Pythonista would formulate `factorial()` in a more concise way using the so-called **early exit** pattern: No `else`-clause is needed as reaching a `return` statement ends a function call *immediately*. Furthermore, we do not need the temporary variables `recurse` and `result`.\n",
     "\n",
-    "As [PythonTutor <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](http://pythontutor.com/visualize.html#code=def%20factorial%28n%29%3A%0A%20%20%20%20if%20n%20%3D%3D%200%3A%0A%20%20%20%20%20%20%20%20return%201%0A%20%20%20%20return%20n%20*%20factorial%28n%20-%201%29%0A%0Asolution%20%3D%20factorial%283%29&cumulative=false&curInstr=0&heapPrimitives=nevernest&mode=display&origin=opt-frontend.js&py=3&rawInputLstJSON=%5B%5D&textReferences=false) shows, this implementation is more efficient as it only requires 18 computational steps instead of 24 to calculate `factorial(3)`, an improvement of 25 percent! "
+    "As [PythonTutor <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](http://pythontutor.com/visualize.html#code=def%20factorial%28n%29%3A%0A%20%20%20%20if%20n%20%3D%3D%200%3A%0A%20%20%20%20%20%20%20%20return%201%0A%20%20%20%20return%20n%20*%20factorial%28n%20-%201%29%0A%0Asolution%20%3D%20factorial%283%29&cumulative=false&curstr=0&heapPrimitives=nevernest&mode=display&origin=opt-frontend.js&py=3&rawInputLstJSON=%5B%5D&textReferences=false) shows, this implementation is more efficient as it only requires 18 computational steps instead of 24 to calculate `factorial(3)`, an improvement of 25 percent! "
    ]
   },
   {
@@ -448,9 +448,9 @@
      "text": [
       "Help on built-in function factorial in module math:\n",
       "\n",
-      "factorial(x, /)\n",
-      "    Find x!.\n",
-      "    \n",
+      "factorial(n, /)\n",
+      "    Find n!.\n",
+      "\n",
       "    Raise a ValueError if x is negative or non-integral.\n",
       "\n"
      ]
@@ -675,8 +675,8 @@
      "text": [
       "Help on built-in function gcd in module math:\n",
       "\n",
-      "gcd(x, y, /)\n",
-      "    greatest common divisor of x and y\n",
+      "gcd(*integers)\n",
+      "    Greatest Common Divisor.\n",
       "\n"
      ]
     }
@@ -870,7 +870,7 @@
     }
    },
    "source": [
-    "This implementation is *highly* **inefficient** as small Fibonacci numbers already take a very long time to compute. The reason for this is **exponential growth** in the number of function calls. As [PythonTutor <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](http://pythontutor.com/visualize.html#code=def%20fibonacci%28i%29%3A%0A%20%20%20%20if%20i%20%3D%3D%200%3A%0A%20%20%20%20%20%20%20%20return%200%0A%20%20%20%20elif%20i%20%3D%3D%201%3A%0A%20%20%20%20%20%20%20%20return%201%0A%20%20%20%20return%20fibonacci%28i%20-%201%29%20%2B%20fibonacci%28i%20-%202%29%0A%0Arv%20%3D%20fibonacci%285%29&cumulative=false&curInstr=0&heapPrimitives=nevernest&mode=display&origin=opt-frontend.js&py=3&rawInputLstJSON=%5B%5D&textReferences=false) shows, `fibonacci()` is called again and again with the same `i` arguments.\n",
+    "This implementation is *highly* **inefficient** as small Fibonacci numbers already take a very long time to compute. The reason for this is **exponential growth** in the number of function calls. As [PythonTutor <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](http://pythontutor.com/visualize.html#code=def%20fibonacci%28i%29%3A%0A%20%20%20%20if%20i%20%3D%3D%200%3A%0A%20%20%20%20%20%20%20%20return%200%0A%20%20%20%20elif%20i%20%3D%3D%201%3A%0A%20%20%20%20%20%20%20%20return%201%0A%20%20%20%20return%20fibonacci%28i%20-%201%29%20%2B%20fibonacci%28i%20-%202%29%0A%0Arv%20%3D%20fibonacci%285%29&cumulative=false&curstr=0&heapPrimitives=nevernest&mode=display&origin=opt-frontend.js&py=3&rawInputLstJSON=%5B%5D&textReferences=false) shows, `fibonacci()` is called again and again with the same `i` arguments.\n",
     "\n",
     "To understand this in detail, we have to study algorithms and data structures (e.g., with [this book](https://www.amazon.de/Introduction-Algorithms-Press-Thomas-Cormen/dp/0262033844/ref=sr_1_1?__mk_de_DE=%C3%85M%C3%85%C5%BD%C3%95%C3%91&crid=1JNE8U0VZGU0O&qid=1569837169&s=gateway&sprefix=algorithms+an%2Caps%2C180&sr=8-1)), a discipline within computer science, and dive into the analysis of **[time complexity of algorithms <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_wiki.png\">](https://en.wikipedia.org/wiki/Time_complexity)**.\n",
     "\n",
@@ -1022,10 +1022,11 @@
      "traceback": [
       "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
       "\u001b[0;31mRecursionError\u001b[0m                            Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-28-62f686a9f8a1>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mrun_forever\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
-      "\u001b[0;32m<ipython-input-27-80575699c62c>\u001b[0m in \u001b[0;36mrun_forever\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mrun_forever\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m     \u001b[0;34m\"\"\"Also a pointless function should have a docstring.\"\"\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m     \u001b[0mrun_forever\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
-      "... last 1 frames repeated, from the frame below ...\n",
-      "\u001b[0;32m<ipython-input-27-80575699c62c>\u001b[0m in \u001b[0;36mrun_forever\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mrun_forever\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m     \u001b[0;34m\"\"\"Also a pointless function should have a docstring.\"\"\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m     \u001b[0mrun_forever\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "Cell \u001b[0;32mIn[28], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mrun_forever\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n",
+      "Cell \u001b[0;32mIn[27], line 3\u001b[0m, in \u001b[0;36mrun_forever\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mrun_forever\u001b[39m():\n\u001b[1;32m      2\u001b[0m \u001b[38;5;250m    \u001b[39m\u001b[38;5;124;03m\"\"\"Also a pointless function should have a docstring.\"\"\"\u001b[39;00m\n\u001b[0;32m----> 3\u001b[0m     \u001b[43mrun_forever\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n",
+      "Cell \u001b[0;32mIn[27], line 3\u001b[0m, in \u001b[0;36mrun_forever\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mrun_forever\u001b[39m():\n\u001b[1;32m      2\u001b[0m \u001b[38;5;250m    \u001b[39m\u001b[38;5;124;03m\"\"\"Also a pointless function should have a docstring.\"\"\"\u001b[39;00m\n\u001b[0;32m----> 3\u001b[0m     \u001b[43mrun_forever\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n",
+      "    \u001b[0;31m[... skipping similar frames: run_forever at line 3 (2974 times)]\u001b[0m\n",
+      "Cell \u001b[0;32mIn[27], line 3\u001b[0m, in \u001b[0;36mrun_forever\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mrun_forever\u001b[39m():\n\u001b[1;32m      2\u001b[0m \u001b[38;5;250m    \u001b[39m\u001b[38;5;124;03m\"\"\"Also a pointless function should have a docstring.\"\"\"\u001b[39;00m\n\u001b[0;32m----> 3\u001b[0m     \u001b[43mrun_forever\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n",
       "\u001b[0;31mRecursionError\u001b[0m: maximum recursion depth exceeded"
      ]
     }
@@ -4014,21 +4015,42 @@
       "-2949.9\n",
       "-2950.9\n",
       "-2951.9\n",
-      "-2952.9\n"
+      "-2952.9\n",
+      "-2953.9\n",
+      "-2954.9\n",
+      "-2955.9\n",
+      "-2956.9\n",
+      "-2957.9\n",
+      "-2958.9\n",
+      "-2959.9\n",
+      "-2960.9\n",
+      "-2961.9\n",
+      "-2962.9\n",
+      "-2963.9\n",
+      "-2964.9\n",
+      "-2965.9\n",
+      "-2966.9\n",
+      "-2967.9\n",
+      "-2968.9\n",
+      "-2969.9\n",
+      "-2970.9\n"
      ]
     },
     {
      "ename": "RecursionError",
-     "evalue": "maximum recursion depth exceeded while calling a Python object",
+     "evalue": "maximum recursion depth exceeded",
      "output_type": "error",
      "traceback": [
       "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
       "\u001b[0;31mRecursionError\u001b[0m                            Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-29-43e3ad331761>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mcountdown\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m3.1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
-      "\u001b[0;32m<ipython-input-1-10499518927d>\u001b[0m in \u001b[0;36mcountdown\u001b[0;34m(n)\u001b[0m\n\u001b[1;32m      9\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     10\u001b[0m         \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mn\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 11\u001b[0;31m         \u001b[0mcountdown\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mn\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
-      "... last 1 frames repeated, from the frame below ...\n",
-      "\u001b[0;32m<ipython-input-1-10499518927d>\u001b[0m in \u001b[0;36mcountdown\u001b[0;34m(n)\u001b[0m\n\u001b[1;32m      9\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     10\u001b[0m         \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mn\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 11\u001b[0;31m         \u001b[0mcountdown\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mn\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
-      "\u001b[0;31mRecursionError\u001b[0m: maximum recursion depth exceeded while calling a Python object"
+      "Cell \u001b[0;32mIn[29], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mcountdown\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m3.1\u001b[39;49m\u001b[43m)\u001b[49m\n",
+      "Cell \u001b[0;32mIn[1], line 11\u001b[0m, in \u001b[0;36mcountdown\u001b[0;34m(n)\u001b[0m\n\u001b[1;32m      9\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m     10\u001b[0m     \u001b[38;5;28mprint\u001b[39m(n)\n\u001b[0;32m---> 11\u001b[0m     \u001b[43mcountdown\u001b[49m\u001b[43m(\u001b[49m\u001b[43mn\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m-\u001b[39;49m\u001b[43m \u001b[49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m)\u001b[49m\n",
+      "Cell \u001b[0;32mIn[1], line 11\u001b[0m, in \u001b[0;36mcountdown\u001b[0;34m(n)\u001b[0m\n\u001b[1;32m      9\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m     10\u001b[0m     \u001b[38;5;28mprint\u001b[39m(n)\n\u001b[0;32m---> 11\u001b[0m     \u001b[43mcountdown\u001b[49m\u001b[43m(\u001b[49m\u001b[43mn\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m-\u001b[39;49m\u001b[43m \u001b[49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m)\u001b[49m\n",
+      "    \u001b[0;31m[... skipping similar frames: countdown at line 11 (2972 times)]\u001b[0m\n",
+      "Cell \u001b[0;32mIn[1], line 11\u001b[0m, in \u001b[0;36mcountdown\u001b[0;34m(n)\u001b[0m\n\u001b[1;32m      9\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m     10\u001b[0m     \u001b[38;5;28mprint\u001b[39m(n)\n\u001b[0;32m---> 11\u001b[0m     \u001b[43mcountdown\u001b[49m\u001b[43m(\u001b[49m\u001b[43mn\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m-\u001b[39;49m\u001b[43m \u001b[49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m)\u001b[49m\n",
+      "Cell \u001b[0;32mIn[1], line 10\u001b[0m, in \u001b[0;36mcountdown\u001b[0;34m(n)\u001b[0m\n\u001b[1;32m      8\u001b[0m     \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mHappy New Year!\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m      9\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m---> 10\u001b[0m     \u001b[38;5;28;43mprint\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mn\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     11\u001b[0m     countdown(n \u001b[38;5;241m-\u001b[39m \u001b[38;5;241m1\u001b[39m)\n",
+      "File \u001b[0;32m~/Repositories/intro-to-python/.venv/lib/python3.12/site-packages/ipykernel/iostream.py:664\u001b[0m, in \u001b[0;36mOutStream.write\u001b[0;34m(self, string)\u001b[0m\n\u001b[1;32m    655\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mwrite\u001b[39m(\u001b[38;5;28mself\u001b[39m, string: \u001b[38;5;28mstr\u001b[39m) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m Optional[\u001b[38;5;28mint\u001b[39m]:  \u001b[38;5;66;03m# type:ignore[override]\u001b[39;00m\n\u001b[1;32m    656\u001b[0m \u001b[38;5;250m    \u001b[39m\u001b[38;5;124;03m\"\"\"Write to current stream after encoding if necessary\u001b[39;00m\n\u001b[1;32m    657\u001b[0m \n\u001b[1;32m    658\u001b[0m \u001b[38;5;124;03m    Returns\u001b[39;00m\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    662\u001b[0m \n\u001b[1;32m    663\u001b[0m \u001b[38;5;124;03m    \"\"\"\u001b[39;00m\n\u001b[0;32m--> 664\u001b[0m     parent \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mparent_header\u001b[49m\n\u001b[1;32m    666\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(string, \u001b[38;5;28mstr\u001b[39m):\n\u001b[1;32m    667\u001b[0m         msg \u001b[38;5;241m=\u001b[39m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mwrite() argument must be str, not \u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mtype\u001b[39m(string)\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m  \u001b[38;5;66;03m# type:ignore[unreachable]\u001b[39;00m\n",
+      "\u001b[0;31mRecursionError\u001b[0m: maximum recursion depth exceeded"
      ]
     }
    ],
@@ -4058,16 +4080,17 @@
    "outputs": [
     {
      "ename": "RecursionError",
-     "evalue": "maximum recursion depth exceeded in comparison",
+     "evalue": "maximum recursion depth exceeded",
      "output_type": "error",
      "traceback": [
       "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
       "\u001b[0;31mRecursionError\u001b[0m                            Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-30-188b816be8b6>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mfactorial\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m3.1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
-      "\u001b[0;32m<ipython-input-6-88d8d933f651>\u001b[0m in \u001b[0;36mfactorial\u001b[0;34m(n)\u001b[0m\n\u001b[1;32m     10\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mn\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     11\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 12\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0mn\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0mfactorial\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mn\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
-      "... last 1 frames repeated, from the frame below ...\n",
-      "\u001b[0;32m<ipython-input-6-88d8d933f651>\u001b[0m in \u001b[0;36mfactorial\u001b[0;34m(n)\u001b[0m\n\u001b[1;32m     10\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mn\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     11\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 12\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0mn\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0mfactorial\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mn\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
-      "\u001b[0;31mRecursionError\u001b[0m: maximum recursion depth exceeded in comparison"
+      "Cell \u001b[0;32mIn[30], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mfactorial\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m3.1\u001b[39;49m\u001b[43m)\u001b[49m\n",
+      "Cell \u001b[0;32mIn[6], line 12\u001b[0m, in \u001b[0;36mfactorial\u001b[0;34m(n)\u001b[0m\n\u001b[1;32m     10\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m n \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m0\u001b[39m:\n\u001b[1;32m     11\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;241m1\u001b[39m\n\u001b[0;32m---> 12\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m n \u001b[38;5;241m*\u001b[39m \u001b[43mfactorial\u001b[49m\u001b[43m(\u001b[49m\u001b[43mn\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m-\u001b[39;49m\u001b[43m \u001b[49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m)\u001b[49m\n",
+      "Cell \u001b[0;32mIn[6], line 12\u001b[0m, in \u001b[0;36mfactorial\u001b[0;34m(n)\u001b[0m\n\u001b[1;32m     10\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m n \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m0\u001b[39m:\n\u001b[1;32m     11\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;241m1\u001b[39m\n\u001b[0;32m---> 12\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m n \u001b[38;5;241m*\u001b[39m \u001b[43mfactorial\u001b[49m\u001b[43m(\u001b[49m\u001b[43mn\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m-\u001b[39;49m\u001b[43m \u001b[49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m)\u001b[49m\n",
+      "    \u001b[0;31m[... skipping similar frames: factorial at line 12 (2974 times)]\u001b[0m\n",
+      "Cell \u001b[0;32mIn[6], line 12\u001b[0m, in \u001b[0;36mfactorial\u001b[0;34m(n)\u001b[0m\n\u001b[1;32m     10\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m n \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m0\u001b[39m:\n\u001b[1;32m     11\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;241m1\u001b[39m\n\u001b[0;32m---> 12\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m n \u001b[38;5;241m*\u001b[39m \u001b[43mfactorial\u001b[49m\u001b[43m(\u001b[49m\u001b[43mn\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m-\u001b[39;49m\u001b[43m \u001b[49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m)\u001b[49m\n",
+      "\u001b[0;31mRecursionError\u001b[0m: maximum recursion depth exceeded"
      ]
     }
    ],
@@ -4382,8 +4405,8 @@
      "traceback": [
       "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
       "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-38-188b816be8b6>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mfactorial\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m3.1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
-      "\u001b[0;32m<ipython-input-35-54b57bc2107d>\u001b[0m in \u001b[0;36mfactorial\u001b[0;34m(n)\u001b[0m\n\u001b[1;32m     13\u001b[0m     \"\"\"\n\u001b[1;32m     14\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mn\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mint\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 15\u001b[0;31m         \u001b[0;32mraise\u001b[0m \u001b[0mTypeError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Factorial is only defined for integers\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     16\u001b[0m     \u001b[0;32melif\u001b[0m \u001b[0mn\u001b[0m \u001b[0;34m<\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     17\u001b[0m         \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Factorial is not defined for negative integers\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "Cell \u001b[0;32mIn[38], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mfactorial\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m3.1\u001b[39;49m\u001b[43m)\u001b[49m\n",
+      "Cell \u001b[0;32mIn[35], line 15\u001b[0m, in \u001b[0;36mfactorial\u001b[0;34m(n)\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"Calculate the factorial of a number.\u001b[39;00m\n\u001b[1;32m      3\u001b[0m \n\u001b[1;32m      4\u001b[0m \u001b[38;5;124;03mArgs:\u001b[39;00m\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m     12\u001b[0m \u001b[38;5;124;03m    ValueError: if n is negative\u001b[39;00m\n\u001b[1;32m     13\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m     14\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(n, \u001b[38;5;28mint\u001b[39m):\n\u001b[0;32m---> 15\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mTypeError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mFactorial is only defined for integers\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m     16\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m n \u001b[38;5;241m<\u001b[39m \u001b[38;5;241m0\u001b[39m:\n\u001b[1;32m     17\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mFactorial is not defined for negative integers\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n",
       "\u001b[0;31mTypeError\u001b[0m: Factorial is only defined for integers"
      ]
     }
@@ -4408,8 +4431,8 @@
      "traceback": [
       "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
       "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-39-b06b2cada645>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mfactorial\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m42\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
-      "\u001b[0;32m<ipython-input-35-54b57bc2107d>\u001b[0m in \u001b[0;36mfactorial\u001b[0;34m(n)\u001b[0m\n\u001b[1;32m     15\u001b[0m         \u001b[0;32mraise\u001b[0m \u001b[0mTypeError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Factorial is only defined for integers\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     16\u001b[0m     \u001b[0;32melif\u001b[0m \u001b[0mn\u001b[0m \u001b[0;34m<\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 17\u001b[0;31m         \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Factorial is not defined for negative integers\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     18\u001b[0m     \u001b[0;32melif\u001b[0m \u001b[0mn\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     19\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "Cell \u001b[0;32mIn[39], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mfactorial\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m-\u001b[39;49m\u001b[38;5;241;43m42\u001b[39;49m\u001b[43m)\u001b[49m\n",
+      "Cell \u001b[0;32mIn[35], line 17\u001b[0m, in \u001b[0;36mfactorial\u001b[0;34m(n)\u001b[0m\n\u001b[1;32m     15\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mTypeError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mFactorial is only defined for integers\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m     16\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m n \u001b[38;5;241m<\u001b[39m \u001b[38;5;241m0\u001b[39m:\n\u001b[0;32m---> 17\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mFactorial is not defined for negative integers\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m     18\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m n \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m0\u001b[39m:\n\u001b[1;32m     19\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;241m1\u001b[39m\n",
       "\u001b[0;31mValueError\u001b[0m: Factorial is not defined for negative integers"
      ]
     }
@@ -4445,8 +4468,8 @@
      "traceback": [
       "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
       "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-40-32f1a9e8cf49>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mfactorial\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m3.0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
-      "\u001b[0;32m<ipython-input-35-54b57bc2107d>\u001b[0m in \u001b[0;36mfactorial\u001b[0;34m(n)\u001b[0m\n\u001b[1;32m     13\u001b[0m     \"\"\"\n\u001b[1;32m     14\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mn\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mint\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 15\u001b[0;31m         \u001b[0;32mraise\u001b[0m \u001b[0mTypeError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Factorial is only defined for integers\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     16\u001b[0m     \u001b[0;32melif\u001b[0m \u001b[0mn\u001b[0m \u001b[0;34m<\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     17\u001b[0m         \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Factorial is not defined for negative integers\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "Cell \u001b[0;32mIn[40], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mfactorial\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m3.0\u001b[39;49m\u001b[43m)\u001b[49m\n",
+      "Cell \u001b[0;32mIn[35], line 15\u001b[0m, in \u001b[0;36mfactorial\u001b[0;34m(n)\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"Calculate the factorial of a number.\u001b[39;00m\n\u001b[1;32m      3\u001b[0m \n\u001b[1;32m      4\u001b[0m \u001b[38;5;124;03mArgs:\u001b[39;00m\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m     12\u001b[0m \u001b[38;5;124;03m    ValueError: if n is negative\u001b[39;00m\n\u001b[1;32m     13\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m     14\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(n, \u001b[38;5;28mint\u001b[39m):\n\u001b[0;32m---> 15\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mTypeError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mFactorial is only defined for integers\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m     16\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m n \u001b[38;5;241m<\u001b[39m \u001b[38;5;241m0\u001b[39m:\n\u001b[1;32m     17\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mFactorial is not defined for negative integers\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n",
       "\u001b[0;31mTypeError\u001b[0m: Factorial is only defined for integers"
      ]
     }
@@ -4688,8 +4711,8 @@
      "traceback": [
       "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
       "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-47-188b816be8b6>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mfactorial\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m3.1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
-      "\u001b[0;32m<ipython-input-45-07dc981a3422>\u001b[0m in \u001b[0;36mfactorial\u001b[0;34m(n)\u001b[0m\n\u001b[1;32m     13\u001b[0m     \"\"\"\n\u001b[1;32m     14\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mn\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0mint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mn\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 15\u001b[0;31m         \u001b[0;32mraise\u001b[0m \u001b[0mTypeError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"n is not integer-like; it has non-zero decimals\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     16\u001b[0m     \u001b[0mn\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mn\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     17\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
+      "Cell \u001b[0;32mIn[47], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mfactorial\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m3.1\u001b[39;49m\u001b[43m)\u001b[49m\n",
+      "Cell \u001b[0;32mIn[45], line 15\u001b[0m, in \u001b[0;36mfactorial\u001b[0;34m(n)\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"Calculate the factorial of a number.\u001b[39;00m\n\u001b[1;32m      3\u001b[0m \n\u001b[1;32m      4\u001b[0m \u001b[38;5;124;03mArgs:\u001b[39;00m\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m     12\u001b[0m \u001b[38;5;124;03m    ValueError: if n is negative\u001b[39;00m\n\u001b[1;32m     13\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m     14\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m n \u001b[38;5;241m!=\u001b[39m \u001b[38;5;28mint\u001b[39m(n):\n\u001b[0;32m---> 15\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mTypeError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mn is not integer-like; it has non-zero decimals\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m     16\u001b[0m n \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mint\u001b[39m(n)\n\u001b[1;32m     18\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m n \u001b[38;5;241m<\u001b[39m \u001b[38;5;241m0\u001b[39m:\n",
       "\u001b[0;31mTypeError\u001b[0m: n is not integer-like; it has non-zero decimals"
      ]
     }
@@ -4725,8 +4748,8 @@
      "traceback": [
       "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
       "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-48-9a12eb120fc3>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mfactorial\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"text\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
-      "\u001b[0;32m<ipython-input-45-07dc981a3422>\u001b[0m in \u001b[0;36mfactorial\u001b[0;34m(n)\u001b[0m\n\u001b[1;32m     12\u001b[0m         \u001b[0mValueError\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mn\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0mnegative\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     13\u001b[0m     \"\"\"\n\u001b[0;32m---> 14\u001b[0;31m     \u001b[0;32mif\u001b[0m \u001b[0mn\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0mint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mn\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     15\u001b[0m         \u001b[0;32mraise\u001b[0m \u001b[0mTypeError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"n is not integer-like; it has non-zero decimals\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     16\u001b[0m     \u001b[0mn\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mn\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "Cell \u001b[0;32mIn[48], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mfactorial\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mtext\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\n",
+      "Cell \u001b[0;32mIn[45], line 14\u001b[0m, in \u001b[0;36mfactorial\u001b[0;34m(n)\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mfactorial\u001b[39m(n):\n\u001b[1;32m      2\u001b[0m \u001b[38;5;250m    \u001b[39m\u001b[38;5;124;03m\"\"\"Calculate the factorial of a number.\u001b[39;00m\n\u001b[1;32m      3\u001b[0m \n\u001b[1;32m      4\u001b[0m \u001b[38;5;124;03m    Args:\u001b[39;00m\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m     12\u001b[0m \u001b[38;5;124;03m        ValueError: if n is negative\u001b[39;00m\n\u001b[1;32m     13\u001b[0m \u001b[38;5;124;03m    \"\"\"\u001b[39;00m\n\u001b[0;32m---> 14\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m n \u001b[38;5;241m!=\u001b[39m \u001b[38;5;28;43mint\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mn\u001b[49m\u001b[43m)\u001b[49m:\n\u001b[1;32m     15\u001b[0m         \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mTypeError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mn is not integer-like; it has non-zero decimals\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m     16\u001b[0m     n \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mint\u001b[39m(n)\n",
       "\u001b[0;31mValueError\u001b[0m: invalid literal for int() with base 10: 'text'"
      ]
     }
@@ -4801,8 +4824,8 @@
      "traceback": [
       "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
       "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-50-9a12eb120fc3>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mfactorial\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"text\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
-      "\u001b[0;32m<ipython-input-49-9ed22b5a2981>\u001b[0m in \u001b[0;36mfactorial\u001b[0;34m(n)\u001b[0m\n\u001b[1;32m     15\u001b[0m         \u001b[0mcasted_n\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mn\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     16\u001b[0m     \u001b[0;32mexcept\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 17\u001b[0;31m         \u001b[0;32mraise\u001b[0m \u001b[0mTypeError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"n cannot be casted as an integer\"\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     18\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     19\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mn\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0mcasted_n\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "Cell \u001b[0;32mIn[50], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mfactorial\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mtext\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\n",
+      "Cell \u001b[0;32mIn[49], line 17\u001b[0m, in \u001b[0;36mfactorial\u001b[0;34m(n)\u001b[0m\n\u001b[1;32m     15\u001b[0m     casted_n \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mint\u001b[39m(n)\n\u001b[1;32m     16\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m:\n\u001b[0;32m---> 17\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mTypeError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mn cannot be casted as an integer\u001b[39m\u001b[38;5;124m\"\u001b[39m) \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m     18\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m     19\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m n \u001b[38;5;241m!=\u001b[39m casted_n:\n",
       "\u001b[0;31mTypeError\u001b[0m: n cannot be casted as an integer"
      ]
     }
@@ -4827,8 +4850,8 @@
      "traceback": [
       "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
       "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-51-188b816be8b6>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mfactorial\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m3.1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
-      "\u001b[0;32m<ipython-input-49-9ed22b5a2981>\u001b[0m in \u001b[0;36mfactorial\u001b[0;34m(n)\u001b[0m\n\u001b[1;32m     18\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     19\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mn\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0mcasted_n\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 20\u001b[0;31m             \u001b[0;32mraise\u001b[0m \u001b[0mTypeError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"n is not integer-like; it has non-zero decimals\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     21\u001b[0m         \u001b[0mn\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcasted_n\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     22\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
+      "Cell \u001b[0;32mIn[51], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mfactorial\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m3.1\u001b[39;49m\u001b[43m)\u001b[49m\n",
+      "Cell \u001b[0;32mIn[49], line 20\u001b[0m, in \u001b[0;36mfactorial\u001b[0;34m(n)\u001b[0m\n\u001b[1;32m     18\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m     19\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m n \u001b[38;5;241m!=\u001b[39m casted_n:\n\u001b[0;32m---> 20\u001b[0m         \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mTypeError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mn is not integer-like; it has non-zero decimals\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m     21\u001b[0m     n \u001b[38;5;241m=\u001b[39m casted_n\n\u001b[1;32m     23\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m n \u001b[38;5;241m<\u001b[39m \u001b[38;5;241m0\u001b[39m:\n",
       "\u001b[0;31mTypeError\u001b[0m: n is not integer-like; it has non-zero decimals"
      ]
     }
@@ -4887,7 +4910,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.6"
+   "version": "3.12.2"
   },
   "livereveal": {
    "auto_select": "code",
diff --git a/04_iteration/01_exercises.ipynb b/04_iteration/01_exercises.ipynb
index cf4b0ce..28304a9 100644
--- a/04_iteration/01_exercises.ipynb
+++ b/04_iteration/01_exercises.ipynb
@@ -589,7 +589,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.6"
+   "version": "3.12.2"
   },
   "toc": {
    "base_numbering": 1,
diff --git a/04_iteration/02_content.ipynb b/04_iteration/02_content.ipynb
index 7fb3eb3..35ac2cb 100644
--- a/04_iteration/02_content.ipynb
+++ b/04_iteration/02_content.ipynb
@@ -137,7 +137,7 @@
     }
    },
    "source": [
-    "As [PythonTutor <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](http://pythontutor.com/visualize.html#code=def%20countdown%28n%29%3A%0A%20%20%20%20while%20n%20!%3D%200%3A%0A%20%20%20%20%20%20%20%20print%28n%29%0A%20%20%20%20%20%20%20%20n%20-%3D%201%0A%0A%20%20%20%20print%28%22Happy%20new%20Year!%22%29%0A%0Acountdown%283%29&cumulative=false&curInstr=0&heapPrimitives=nevernest&mode=display&origin=opt-frontend.js&py=3&rawInputLstJSON=%5B%5D&textReferences=false) shows, there is a subtle but essential difference in the way a `while` statement is treated in memory: In short, `while` statements can *not* run into a `RecursionError` as only *one* frame is needed to manage the names. After all, there is only *one* function call to be made. For typical day-to-day applications, this difference is, however, not so important *unless* a problem instance becomes so big that a large (i.e., $> 3.000$) number of recursive calls must be made."
+    "As [PythonTutor <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](http://pythontutor.com/visualize.html#code=def%20countdown%28n%29%3A%0A%20%20%20%20while%20n%20!%3D%200%3A%0A%20%20%20%20%20%20%20%20print%28n%29%0A%20%20%20%20%20%20%20%20n%20-%3D%201%0A%0A%20%20%20%20print%28%22Happy%20new%20Year!%22%29%0A%0Acountdown%283%29&cumulative=false&curstr=0&heapPrimitives=nevernest&mode=display&origin=opt-frontend.js&py=3&rawInputLstJSON=%5B%5D&textReferences=false) shows, there is a subtle but essential difference in the way a `while` statement is treated in memory: In short, `while` statements can *not* run into a `RecursionError` as only *one* frame is needed to manage the names. After all, there is only *one* function call to be made. For typical day-to-day applications, this difference is, however, not so important *unless* a problem instance becomes so big that a large (i.e., $> 3.000$) number of recursive calls must be made."
    ]
   },
   {
@@ -1268,7 +1268,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.6"
+   "version": "3.12.2"
   },
   "livereveal": {
    "auto_select": "code",
diff --git a/04_iteration/03_content.ipynb b/04_iteration/03_content.ipynb
index dafbe08..d9bb92f 100644
--- a/04_iteration/03_content.ipynb
+++ b/04_iteration/03_content.ipynb
@@ -959,7 +959,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.6"
+   "version": "3.12.2"
   },
   "livereveal": {
    "auto_select": "code",
diff --git a/04_iteration/04_exercises.ipynb b/04_iteration/04_exercises.ipynb
index 04654bc..8bd97ff 100644
--- a/04_iteration/04_exercises.ipynb
+++ b/04_iteration/04_exercises.ipynb
@@ -382,7 +382,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.6"
+   "version": "3.12.2"
   },
   "toc": {
    "base_numbering": 1,
diff --git a/04_iteration/05_summary.ipynb b/04_iteration/05_summary.ipynb
index 981bfec..18d287d 100644
--- a/04_iteration/05_summary.ipynb
+++ b/04_iteration/05_summary.ipynb
@@ -47,7 +47,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.6"
+   "version": "3.12.2"
   },
   "livereveal": {
    "auto_select": "code",
diff --git a/04_iteration/06_review.ipynb b/04_iteration/06_review.ipynb
index b40647b..2043dcc 100644
--- a/04_iteration/06_review.ipynb
+++ b/04_iteration/06_review.ipynb
@@ -247,7 +247,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.6"
+   "version": "3.12.2"
   },
   "toc": {
    "base_numbering": 1,

From 4444d197f1a22ba1aa050b7954e34426093cc9bf Mon Sep 17 00:00:00 2001
From: Alexander Hess <alexander@webartifex.biz>
Date: Mon, 8 Apr 2024 16:26:28 +0200
Subject: [PATCH 230/242] Re-run Chapter 05 files with Python 3.12

---
 05_numbers/00_content.ipynb   |  8 ++++----
 05_numbers/01_content.ipynb   | 18 +++++++++---------
 05_numbers/02_content.ipynb   | 12 ++++++------
 05_numbers/03_appendix.ipynb  | 10 +++++-----
 05_numbers/04_summary.ipynb   |  2 +-
 05_numbers/05_review.ipynb    |  2 +-
 05_numbers/06_resources.ipynb |  7 ++++++-
 7 files changed, 32 insertions(+), 27 deletions(-)

diff --git a/05_numbers/00_content.ipynb b/05_numbers/00_content.ipynb
index 0a106c4..d04c106 100644
--- a/05_numbers/00_content.ipynb
+++ b/05_numbers/00_content.ipynb
@@ -257,10 +257,10 @@
    "outputs": [
     {
      "ename": "SyntaxError",
-     "evalue": "leading zeros in decimal integer literals are not permitted; use an 0o prefix for octal integers (<ipython-input-7-9a7f0f99e867>, line 1)",
+     "evalue": "leading zeros in decimal integer literals are not permitted; use an 0o prefix for octal integers (1481240458.py, line 1)",
      "output_type": "error",
      "traceback": [
-      "\u001b[0;36m  File \u001b[0;32m\"<ipython-input-7-9a7f0f99e867>\"\u001b[0;36m, line \u001b[0;32m1\u001b[0m\n\u001b[0;31m    042\u001b[0m\n\u001b[0m      ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m leading zeros in decimal integer literals are not permitted; use an 0o prefix for octal integers\n"
+      "\u001b[0;36m  Cell \u001b[0;32mIn[7], line 1\u001b[0;36m\u001b[0m\n\u001b[0;31m    042\u001b[0m\n\u001b[0m    ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m leading zeros in decimal integer literals are not permitted; use an 0o prefix for octal integers\n"
      ]
     }
    ],
@@ -413,7 +413,7 @@
      "traceback": [
       "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
       "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-12-faae6b054331>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"42.0\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "Cell \u001b[0;32mIn[12], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[38;5;28;43mint\u001b[39;49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43m42.0\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\n",
       "\u001b[0;31mValueError\u001b[0m: invalid literal for int() with base 10: '42.0'"
      ]
     }
@@ -2422,7 +2422,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.6"
+   "version": "3.12.2"
   },
   "livereveal": {
    "auto_select": "code",
diff --git a/05_numbers/01_content.ipynb b/05_numbers/01_content.ipynb
index 00157ad..af3b6c0 100644
--- a/05_numbers/01_content.ipynb
+++ b/05_numbers/01_content.ipynb
@@ -326,7 +326,7 @@
      "traceback": [
       "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
       "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-10-c7953b8b7956>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mfloat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"42. 87\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "Cell \u001b[0;32mIn[10], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[38;5;28;43mfloat\u001b[39;49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43m42. 87\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\n",
       "\u001b[0;31mValueError\u001b[0m: could not convert string to float: '42. 87'"
      ]
     }
@@ -497,10 +497,10 @@
    "outputs": [
     {
      "ename": "SyntaxError",
-     "evalue": "invalid syntax (<ipython-input-15-1b5daaac0077>, line 1)",
+     "evalue": "invalid syntax (1434619204.py, line 1)",
      "output_type": "error",
      "traceback": [
-      "\u001b[0;36m  File \u001b[0;32m\"<ipython-input-15-1b5daaac0077>\"\u001b[0;36m, line \u001b[0;32m1\u001b[0m\n\u001b[0;31m    1.23 e0\u001b[0m\n\u001b[0m         ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m invalid syntax\n"
+      "\u001b[0;36m  Cell \u001b[0;32mIn[15], line 1\u001b[0;36m\u001b[0m\n\u001b[0;31m    1.23 e0\u001b[0m\n\u001b[0m         ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m invalid syntax\n"
      ]
     }
    ],
@@ -519,10 +519,10 @@
    "outputs": [
     {
      "ename": "SyntaxError",
-     "evalue": "invalid syntax (<ipython-input-16-a236c4a30231>, line 1)",
+     "evalue": "invalid decimal literal (3971374856.py, line 1)",
      "output_type": "error",
      "traceback": [
-      "\u001b[0;36m  File \u001b[0;32m\"<ipython-input-16-a236c4a30231>\"\u001b[0;36m, line \u001b[0;32m1\u001b[0m\n\u001b[0;31m    1.23e 0\u001b[0m\n\u001b[0m        ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m invalid syntax\n"
+      "\u001b[0;36m  Cell \u001b[0;32mIn[16], line 1\u001b[0;36m\u001b[0m\n\u001b[0;31m    1.23e 0\u001b[0m\n\u001b[0m       ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m invalid decimal literal\n"
      ]
     }
    ],
@@ -541,10 +541,10 @@
    "outputs": [
     {
      "ename": "SyntaxError",
-     "evalue": "invalid syntax (<ipython-input-17-05b9072d76be>, line 1)",
+     "evalue": "invalid syntax (2795275998.py, line 1)",
      "output_type": "error",
      "traceback": [
-      "\u001b[0;36m  File \u001b[0;32m\"<ipython-input-17-05b9072d76be>\"\u001b[0;36m, line \u001b[0;32m1\u001b[0m\n\u001b[0;31m    1.23e0.0\u001b[0m\n\u001b[0m          ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m invalid syntax\n"
+      "\u001b[0;36m  Cell \u001b[0;32mIn[17], line 1\u001b[0;36m\u001b[0m\n\u001b[0;31m    1.23e0.0\u001b[0m\n\u001b[0m          ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m invalid syntax\n"
      ]
     }
    ],
@@ -579,7 +579,7 @@
      "traceback": [
       "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
       "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-18-abc4de6ccb5e>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0me0\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "Cell \u001b[0;32mIn[18], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43me0\u001b[49m\n",
       "\u001b[0;31mNameError\u001b[0m: name 'e0' is not defined"
      ]
     }
@@ -2148,7 +2148,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.6"
+   "version": "3.12.2"
   },
   "livereveal": {
    "auto_select": "code",
diff --git a/05_numbers/02_content.ipynb b/05_numbers/02_content.ipynb
index ae1b01d..3f24e6a 100644
--- a/05_numbers/02_content.ipynb
+++ b/05_numbers/02_content.ipynb
@@ -991,7 +991,7 @@
      "traceback": [
       "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
       "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-29-e40cd01fb15a>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mround\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m1\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;36m2j\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "Cell \u001b[0;32mIn[29], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[38;5;28;43mround\u001b[39;49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m \u001b[49m\u001b[38;5;241;43m+\u001b[39;49m\u001b[43m \u001b[49m\u001b[38;5;241;43m2\u001b[39;49m\u001b[43mj\u001b[49m\u001b[43m)\u001b[49m\n",
       "\u001b[0;31mTypeError\u001b[0m: type complex doesn't define __round__ method"
      ]
     }
@@ -1388,8 +1388,8 @@
      "traceback": [
       "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
       "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-41-188b816be8b6>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mfactorial\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m3.1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
-      "\u001b[0;32m<ipython-input-37-be6602f598f7>\u001b[0m in \u001b[0;36mfactorial\u001b[0;34m(n, strict)\u001b[0m\n\u001b[1;32m     17\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mn\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnumbers\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mReal\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     18\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mn\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0mint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mn\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mstrict\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 19\u001b[0;31m                 \u001b[0;32mraise\u001b[0m \u001b[0mTypeError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"n is not integer-like; it has non-zero decimals\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     20\u001b[0m             \u001b[0mn\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mn\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     21\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "Cell \u001b[0;32mIn[41], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mfactorial\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m3.1\u001b[39;49m\u001b[43m)\u001b[49m\n",
+      "Cell \u001b[0;32mIn[37], line 19\u001b[0m, in \u001b[0;36mfactorial\u001b[0;34m(n, strict)\u001b[0m\n\u001b[1;32m     17\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(n, numbers\u001b[38;5;241m.\u001b[39mReal):\n\u001b[1;32m     18\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m n \u001b[38;5;241m!=\u001b[39m \u001b[38;5;28mint\u001b[39m(n) \u001b[38;5;129;01mand\u001b[39;00m strict:\n\u001b[0;32m---> 19\u001b[0m         \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mTypeError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mn is not integer-like; it has non-zero decimals\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m     20\u001b[0m     n \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mint\u001b[39m(n)\n\u001b[1;32m     21\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n",
       "\u001b[0;31mTypeError\u001b[0m: n is not integer-like; it has non-zero decimals"
      ]
     }
@@ -1460,8 +1460,8 @@
      "traceback": [
       "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
       "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-43-7296a74f5dcf>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mfactorial\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m1\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;36m2j\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
-      "\u001b[0;32m<ipython-input-37-be6602f598f7>\u001b[0m in \u001b[0;36mfactorial\u001b[0;34m(n, strict)\u001b[0m\n\u001b[1;32m     20\u001b[0m             \u001b[0mn\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mn\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     21\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 22\u001b[0;31m             \u001b[0;32mraise\u001b[0m \u001b[0mTypeError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Factorial is only defined for integers\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     23\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     24\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mn\u001b[0m \u001b[0;34m<\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "Cell \u001b[0;32mIn[43], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mfactorial\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m \u001b[49m\u001b[38;5;241;43m+\u001b[39;49m\u001b[43m \u001b[49m\u001b[38;5;241;43m2\u001b[39;49m\u001b[43mj\u001b[49m\u001b[43m)\u001b[49m\n",
+      "Cell \u001b[0;32mIn[37], line 22\u001b[0m, in \u001b[0;36mfactorial\u001b[0;34m(n, strict)\u001b[0m\n\u001b[1;32m     20\u001b[0m         n \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mint\u001b[39m(n)\n\u001b[1;32m     21\u001b[0m     \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m---> 22\u001b[0m         \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mTypeError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mFactorial is only defined for integers\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m     24\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m n \u001b[38;5;241m<\u001b[39m \u001b[38;5;241m0\u001b[39m:\n\u001b[1;32m     25\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mFactorial is not defined for negative integers\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n",
       "\u001b[0;31mTypeError\u001b[0m: Factorial is only defined for integers"
      ]
     }
@@ -1487,7 +1487,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.6"
+   "version": "3.12.2"
   },
   "livereveal": {
    "auto_select": "code",
diff --git a/05_numbers/03_appendix.ipynb b/05_numbers/03_appendix.ipynb
index c470e82..9551b70 100644
--- a/05_numbers/03_appendix.ipynb
+++ b/05_numbers/03_appendix.ipynb
@@ -510,7 +510,7 @@
      "traceback": [
       "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
       "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-16-a3cea145e6d1>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0;36m1.0\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0mDecimal\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m42\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "Cell \u001b[0;32mIn[16], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[38;5;241;43m1.0\u001b[39;49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43m \u001b[49m\u001b[43mDecimal\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m42\u001b[39;49m\u001b[43m)\u001b[49m\n",
       "\u001b[0;31mTypeError\u001b[0m: unsupported operand type(s) for *: 'float' and 'decimal.Decimal'"
      ]
     }
@@ -681,7 +681,7 @@
      "traceback": [
       "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
       "\u001b[0;31mInvalidOperation\u001b[0m                          Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-21-5b555b105c8f>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mtwo\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mquantize\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mDecimal\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"1e-28\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "Cell \u001b[0;32mIn[21], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mtwo\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mquantize\u001b[49m\u001b[43m(\u001b[49m\u001b[43mDecimal\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43m1e-28\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\n",
       "\u001b[0;31mInvalidOperation\u001b[0m: [<class 'decimal.InvalidOperation'>]"
      ]
     }
@@ -964,7 +964,7 @@
      "traceback": [
       "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
       "\u001b[0;31mInvalidOperation\u001b[0m                          Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-30-87950ebbd6ef>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mDecimal\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"inf\"\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mDecimal\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"-inf\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "Cell \u001b[0;32mIn[30], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mDecimal\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43minf\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m+\u001b[39;49m\u001b[43m \u001b[49m\u001b[43mDecimal\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43m-inf\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\n",
       "\u001b[0;31mInvalidOperation\u001b[0m: [<class 'decimal.InvalidOperation'>]"
      ]
     }
@@ -989,7 +989,7 @@
      "traceback": [
       "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
       "\u001b[0;31mInvalidOperation\u001b[0m                          Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-31-996083f200c6>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mDecimal\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"inf\"\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0mDecimal\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"inf\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "Cell \u001b[0;32mIn[31], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mDecimal\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43minf\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m-\u001b[39;49m\u001b[43m \u001b[49m\u001b[43mDecimal\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43minf\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\n",
       "\u001b[0;31mInvalidOperation\u001b[0m: [<class 'decimal.InvalidOperation'>]"
      ]
     }
@@ -1476,7 +1476,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.6"
+   "version": "3.12.2"
   },
   "livereveal": {
    "auto_select": "code",
diff --git a/05_numbers/04_summary.ipynb b/05_numbers/04_summary.ipynb
index 45c7c29..ad1ebde 100644
--- a/05_numbers/04_summary.ipynb
+++ b/05_numbers/04_summary.ipynb
@@ -53,7 +53,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.6"
+   "version": "3.12.2"
   },
   "livereveal": {
    "auto_select": "code",
diff --git a/05_numbers/05_review.ipynb b/05_numbers/05_review.ipynb
index db49191..a911fb0 100644
--- a/05_numbers/05_review.ipynb
+++ b/05_numbers/05_review.ipynb
@@ -306,7 +306,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.6"
+   "version": "3.12.2"
   },
   "toc": {
    "base_numbering": 1,
diff --git a/05_numbers/06_resources.ipynb b/05_numbers/06_resources.ipynb
index 87343f1..5fa8cfa 100644
--- a/05_numbers/06_resources.ipynb
+++ b/05_numbers/06_resources.ipynb
@@ -56,6 +56,7 @@
        "            src=\"https://www.youtube.com/embed/RgklPQ8rbkg\"\n",
        "            frameborder=\"0\"\n",
        "            allowfullscreen\n",
+       "            \n",
        "        ></iframe>\n",
        "        "
       ],
@@ -93,6 +94,7 @@
        "            src=\"https://www.youtube.com/embed/xHWKYFhhtJQ\"\n",
        "            frameborder=\"0\"\n",
        "            allowfullscreen\n",
+       "            \n",
        "        ></iframe>\n",
        "        "
       ],
@@ -140,6 +142,7 @@
        "            src=\"https://www.youtube.com/embed/4qH4unVtJkE\"\n",
        "            frameborder=\"0\"\n",
        "            allowfullscreen\n",
+       "            \n",
        "        ></iframe>\n",
        "        "
       ],
@@ -194,6 +197,7 @@
        "            src=\"https://www.youtube.com/embed/PZRI1IfStY0\"\n",
        "            frameborder=\"0\"\n",
        "            allowfullscreen\n",
+       "            \n",
        "        ></iframe>\n",
        "        "
       ],
@@ -248,6 +252,7 @@
        "            src=\"https://www.youtube.com/embed/Jkv-55ndVYY\"\n",
        "            frameborder=\"0\"\n",
        "            allowfullscreen\n",
+       "            \n",
        "        ></iframe>\n",
        "        "
       ],
@@ -281,7 +286,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.6"
+   "version": "3.12.2"
   },
   "livereveal": {
    "auto_select": "code",

From 7999b808a964f866a393bdd96d7c6a17b739b5d3 Mon Sep 17 00:00:00 2001
From: Alexander Hess <alexander@webartifex.biz>
Date: Mon, 8 Apr 2024 16:29:22 +0200
Subject: [PATCH 231/242] Re-run Chapter 06 files with Python 3.12

---
 06_text/00_content.ipynb   | 14 +++++++-------
 06_text/01_exercises.ipynb |  2 +-
 06_text/02_content.ipynb   | 28 +++++++++++++++++++---------
 06_text/03_summary.ipynb   |  2 +-
 06_text/04_review.ipynb    |  2 +-
 06_text/05_resources.ipynb | 14 ++++++++++----
 6 files changed, 39 insertions(+), 23 deletions(-)

diff --git a/06_text/00_content.ipynb b/06_text/00_content.ipynb
index 236942b..f1f6871 100644
--- a/06_text/00_content.ipynb
+++ b/06_text/00_content.ipynb
@@ -1678,13 +1678,13 @@
    "outputs": [
     {
      "ename": "TypeError",
-     "evalue": "string indices must be integers",
+     "evalue": "string indices must be integers, not 'float'",
      "output_type": "error",
      "traceback": [
       "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
       "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-54-31fb90bbe515>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mtext\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1.0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
-      "\u001b[0;31mTypeError\u001b[0m: string indices must be integers"
+      "Cell \u001b[0;32mIn[54], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mtext\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;241;43m1.0\u001b[39;49m\u001b[43m]\u001b[49m\n",
+      "\u001b[0;31mTypeError\u001b[0m: string indices must be integers, not 'float'"
      ]
     }
    ],
@@ -1754,7 +1754,7 @@
      "traceback": [
       "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
       "\u001b[0;31mIndexError\u001b[0m                                Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-56-ffa3e1bc2f86>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mtext\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m27\u001b[0m\u001b[0;34m]\u001b[0m  \u001b[0;31m# == text[len(text)]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "Cell \u001b[0;32mIn[56], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mtext\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;241;43m27\u001b[39;49m\u001b[43m]\u001b[49m  \u001b[38;5;66;03m# == text[len(text)]\u001b[39;00m\n",
       "\u001b[0;31mIndexError\u001b[0m: string index out of range"
      ]
     }
@@ -2315,7 +2315,7 @@
      "traceback": [
       "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
       "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-72-d8a1e6b0b786>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mtext\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m\"X\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "Cell \u001b[0;32mIn[72], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mtext\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;241;43m0\u001b[39;49m\u001b[43m]\u001b[49m \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mX\u001b[39m\u001b[38;5;124m\"\u001b[39m\n",
       "\u001b[0;31mTypeError\u001b[0m: 'str' object does not support item assignment"
      ]
     }
@@ -2340,7 +2340,7 @@
      "traceback": [
       "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
       "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-73-def135e84998>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mtext\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;36m5\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m\"random\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "Cell \u001b[0;32mIn[73], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mtext\u001b[49m\u001b[43m[\u001b[49m\u001b[43m:\u001b[49m\u001b[38;5;241;43m5\u001b[39;49m\u001b[43m]\u001b[49m \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mrandom\u001b[39m\u001b[38;5;124m\"\u001b[39m\n",
       "\u001b[0;31mTypeError\u001b[0m: 'str' object does not support item assignment"
      ]
     }
@@ -3938,7 +3938,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.6"
+   "version": "3.12.2"
   },
   "livereveal": {
    "auto_select": "code",
diff --git a/06_text/01_exercises.ipynb b/06_text/01_exercises.ipynb
index 0781f2b..89dc687 100644
--- a/06_text/01_exercises.ipynb
+++ b/06_text/01_exercises.ipynb
@@ -974,7 +974,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.6"
+   "version": "3.12.2"
   },
   "toc": {
    "base_numbering": 1,
diff --git a/06_text/02_content.ipynb b/06_text/02_content.ipynb
index 6ff2407..fc5efa3 100644
--- a/06_text/02_content.ipynb
+++ b/06_text/02_content.ipynb
@@ -315,6 +315,16 @@
       "C:\\Programs\n",
       "ew_application\n"
      ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "<>:1: SyntaxWarning: invalid escape sequence '\\P'\n",
+      "<>:1: SyntaxWarning: invalid escape sequence '\\P'\n",
+      "/tmp/ipykernel_159416/1102122489.py:1: SyntaxWarning: invalid escape sequence '\\P'\n",
+      "  print(\"C:\\Programs\\new_application\")\n"
+     ]
     }
    ],
    "source": [
@@ -343,10 +353,10 @@
    "outputs": [
     {
      "ename": "SyntaxError",
-     "evalue": "(unicode error) 'unicodeescape' codec can't decode bytes in position 2-3: truncated \\UXXXXXXXX escape (<ipython-input-10-61308f43d404>, line 1)",
+     "evalue": "(unicode error) 'unicodeescape' codec can't decode bytes in position 2-3: truncated \\UXXXXXXXX escape (2296736867.py, line 1)",
      "output_type": "error",
      "traceback": [
-      "\u001b[0;36m  File \u001b[0;32m\"<ipython-input-10-61308f43d404>\"\u001b[0;36m, line \u001b[0;32m1\u001b[0m\n\u001b[0;31m    print(\"C:\\Users\\Administrator\\Desktop\\Project\")\u001b[0m\n\u001b[0m          ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m (unicode error) 'unicodeescape' codec can't decode bytes in position 2-3: truncated \\UXXXXXXXX escape\n"
+      "\u001b[0;36m  Cell \u001b[0;32mIn[10], line 1\u001b[0;36m\u001b[0m\n\u001b[0;31m    print(\"C:\\Users\\Administrator\\Desktop\\Project\")\u001b[0m\n\u001b[0m          ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m (unicode error) 'unicodeescape' codec can't decode bytes in position 2-3: truncated \\UXXXXXXXX escape\n"
      ]
     }
    ],
@@ -1174,10 +1184,10 @@
    "outputs": [
     {
      "ename": "SyntaxError",
-     "evalue": "EOL while scanning string literal (<ipython-input-34-4cef690f1f4a>, line 1)",
+     "evalue": "unterminated string literal (detected at line 1) (2682216481.py, line 1)",
      "output_type": "error",
      "traceback": [
-      "\u001b[0;36m  File \u001b[0;32m\"<ipython-input-34-4cef690f1f4a>\"\u001b[0;36m, line \u001b[0;32m1\u001b[0m\n\u001b[0;31m    \"\u001b[0m\n\u001b[0m     ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m EOL while scanning string literal\n"
+      "\u001b[0;36m  Cell \u001b[0;32mIn[34], line 1\u001b[0;36m\u001b[0m\n\u001b[0;31m    \"\u001b[0m\n\u001b[0m    ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m unterminated string literal (detected at line 1)\n"
      ]
     }
    ],
@@ -1835,7 +1845,7 @@
      "traceback": [
       "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
       "\u001b[0;31mUnicodeDecodeError\u001b[0m                        Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-55-1630cf8a20cc>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mplace\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mencode\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"iso-8859-1\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdecode\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "Cell \u001b[0;32mIn[55], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mplace\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mencode\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43miso-8859-1\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdecode\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n",
       "\u001b[0;31mUnicodeDecodeError\u001b[0m: 'utf-8' codec can't decode byte 0xe9 in position 3: invalid continuation byte"
      ]
     }
@@ -1871,7 +1881,7 @@
      "traceback": [
       "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
       "\u001b[0;31mUnicodeEncodeError\u001b[0m                        Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-56-8dd3b1bfcf5d>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0;34m\"Dobrý den, přátelé!\"\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mencode\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"iso-8859-1\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "Cell \u001b[0;32mIn[56], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mDobrý den, přátelé!\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mencode\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43miso-8859-1\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\n",
       "\u001b[0;31mUnicodeEncodeError\u001b[0m: 'latin-1' codec can't encode character '\\u0159' in position 12: ordinal not in range(256)"
      ]
     }
@@ -1918,8 +1928,8 @@
      "traceback": [
       "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
       "\u001b[0;31mUnicodeDecodeError\u001b[0m                        Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-57-83a7d1245cc7>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;32mwith\u001b[0m \u001b[0mopen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"umlauts.txt\"\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mfile\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m     \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"\"\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mjoin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfile\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreadlines\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
-      "\u001b[0;32m~/.pyenv/versions/3.8.6/lib/python3.8/codecs.py\u001b[0m in \u001b[0;36mdecode\u001b[0;34m(self, input, final)\u001b[0m\n\u001b[1;32m    320\u001b[0m         \u001b[0;31m# decode input (taking the buffer into account)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    321\u001b[0m         \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbuffer\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0minput\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 322\u001b[0;31m         \u001b[0;34m(\u001b[0m\u001b[0mresult\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mconsumed\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_buffer_decode\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0merrors\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfinal\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    323\u001b[0m         \u001b[0;31m# keep undecoded input until the next call\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    324\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbuffer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mconsumed\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "Cell \u001b[0;32mIn[57], line 2\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m \u001b[38;5;28mopen\u001b[39m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mumlauts.txt\u001b[39m\u001b[38;5;124m\"\u001b[39m) \u001b[38;5;28;01mas\u001b[39;00m file:\n\u001b[0;32m----> 2\u001b[0m     \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;241m.\u001b[39mjoin(\u001b[43mfile\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mreadlines\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m))\n",
+      "File \u001b[0;32m<frozen codecs>:322\u001b[0m, in \u001b[0;36mdecode\u001b[0;34m(self, input, final)\u001b[0m\n",
       "\u001b[0;31mUnicodeDecodeError\u001b[0m: 'utf-8' codec can't decode byte 0xe4 in position 9: invalid continuation byte"
      ]
     }
@@ -2068,7 +2078,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.6"
+   "version": "3.12.2"
   },
   "livereveal": {
    "auto_select": "code",
diff --git a/06_text/03_summary.ipynb b/06_text/03_summary.ipynb
index bcb6506..f3ac4d7 100644
--- a/06_text/03_summary.ipynb
+++ b/06_text/03_summary.ipynb
@@ -43,7 +43,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.6"
+   "version": "3.12.2"
   },
   "livereveal": {
    "auto_select": "code",
diff --git a/06_text/04_review.ipynb b/06_text/04_review.ipynb
index 9804bfc..b92cb56 100644
--- a/06_text/04_review.ipynb
+++ b/06_text/04_review.ipynb
@@ -178,7 +178,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.6"
+   "version": "3.12.2"
   },
   "toc": {
    "base_numbering": 1,
diff --git a/06_text/05_resources.ipynb b/06_text/05_resources.ipynb
index a5517f1..24882f9 100644
--- a/06_text/05_resources.ipynb
+++ b/06_text/05_resources.ipynb
@@ -62,6 +62,7 @@
        "            src=\"https://www.youtube.com/embed/MijmeoH9LT4\"\n",
        "            frameborder=\"0\"\n",
        "            allowfullscreen\n",
+       "            \n",
        "        ></iframe>\n",
        "        "
       ],
@@ -87,7 +88,7 @@
     }
    },
    "source": [
-    "In his [PyCon Australia 2018](https://2018.pycon-au.org/) talk titled \"*Unicode and Python: The absolute minimum you need to know*\" [Raphaël Merx](https://www.linkedin.com/in/raphaelmerx/) explains some caveats and best practices regarding Unicode."
+    "In his [PyCon Australia 2018](https://2018.pycon-au.org/) talk titled \"*Unicode and Python: The absolute minimum you need to know*\" [Raphaël Merx](https://www.linkedin.com/raphaelmerx/) explains some caveats and best practices regarding Unicode."
    ]
   },
   {
@@ -110,6 +111,7 @@
        "            src=\"https://www.youtube.com/embed/oXVmZGN6plY\"\n",
        "            frameborder=\"0\"\n",
        "            allowfullscreen\n",
+       "            \n",
        "        ></iframe>\n",
        "        "
       ],
@@ -134,7 +136,7 @@
     }
    },
    "source": [
-    "In a similar talk at [PyCon 2017](https://us.pycon.org/2017/) titled \"*Unicode what is the big deal*\" [�ukasz Langa](https://www.linkedin.com/in/llanga/) provides further lessons learned regarding Unicode."
+    "In a similar talk at [PyCon 2017](https://us.pycon.org/2017/) titled \"*Unicode what is the big deal*\" [�ukasz Langa](https://www.linkedin.com/llanga/) provides further lessons learned regarding Unicode."
    ]
   },
   {
@@ -157,6 +159,7 @@
        "            src=\"https://www.youtube.com/embed/7m5JA3XaZ4k\"\n",
        "            frameborder=\"0\"\n",
        "            allowfullscreen\n",
+       "            \n",
        "        ></iframe>\n",
        "        "
       ],
@@ -204,6 +207,7 @@
        "            src=\"https://www.youtube.com/embed/sgHbC6udIqc\"\n",
        "            frameborder=\"0\"\n",
        "            allowfullscreen\n",
+       "            \n",
        "        ></iframe>\n",
        "        "
       ],
@@ -235,7 +239,7 @@
     }
    },
    "source": [
-    "Lastly, in his entertaining talk at [PyCon.DE 2019](https://de.pycon.org/) titled \"*Your Name is Invalid!*\" [Miroslav Ĺ edivĂ˝](https://www.linkedin.com/in/%C5%A1ediv%C3%BD/) shows how hard it actually is to write software that can process any name a human can possibly have. Miroslav also gave a lightning talk where he shows how he uses only one keyboard for the 12 (!!!) languages he speaks."
+    "Lastly, in his entertaining talk at [PyCon.DE 2019](https://de.pycon.org/) titled \"*Your Name is Invalid!*\" [Miroslav Ĺ edivĂ˝](https://www.linkedin.com/%C5%A1ediv%C3%BD/) shows how hard it actually is to write software that can process any name a human can possibly have. Miroslav also gave a lightning talk where he shows how he uses only one keyboard for the 12 (!!!) languages he speaks."
    ]
   },
   {
@@ -258,6 +262,7 @@
        "            src=\"https://www.youtube.com/embed/pBuS7EUPnQA\"\n",
        "            frameborder=\"0\"\n",
        "            allowfullscreen\n",
+       "            \n",
        "        ></iframe>\n",
        "        "
       ],
@@ -294,6 +299,7 @@
        "            src=\"https://www.youtube.com/embed/-4QjII981sM\"\n",
        "            frameborder=\"0\"\n",
        "            allowfullscreen\n",
+       "            \n",
        "        ></iframe>\n",
        "        "
       ],
@@ -327,7 +333,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.6"
+   "version": "3.12.2"
   },
   "livereveal": {
    "auto_select": "code",

From ba1349f796d876b9024d905afa4445b591d09e45 Mon Sep 17 00:00:00 2001
From: Alexander Hess <alexander@webartifex.biz>
Date: Mon, 8 Apr 2024 16:34:43 +0200
Subject: [PATCH 232/242] Re-run Chapter 07 files with Python 3.12

---
 07_sequences/00_content.ipynb   |  8 ++++----
 07_sequences/01_content.ipynb   | 20 ++++++++++----------
 07_sequences/02_exercises.ipynb |  2 +-
 07_sequences/03_content.ipynb   | 20 ++++++++++----------
 07_sequences/04_exercises.ipynb |  2 +-
 07_sequences/05_appendix.ipynb  |  4 ++--
 07_sequences/06_summary.ipynb   |  2 +-
 07_sequences/07_review.ipynb    |  2 +-
 8 files changed, 30 insertions(+), 30 deletions(-)

diff --git a/07_sequences/00_content.ipynb b/07_sequences/00_content.ipynb
index 43ec596..c37450b 100644
--- a/07_sequences/00_content.ipynb
+++ b/07_sequences/00_content.ipynb
@@ -307,7 +307,7 @@
      "traceback": [
       "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
       "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-9-8249bf25b5b7>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0;32mfor\u001b[0m \u001b[0mdigit\u001b[0m \u001b[0;32min\u001b[0m \u001b[0;36m999\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m     \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdigit\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "Cell \u001b[0;32mIn[9], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[38;5;28;43;01mfor\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mdigit\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;129;43;01min\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;241;43m999\u001b[39;49m\u001b[43m:\u001b[49m\n\u001b[1;32m      2\u001b[0m \u001b[43m    \u001b[49m\u001b[38;5;28;43mprint\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mdigit\u001b[49m\u001b[43m)\u001b[49m\n",
       "\u001b[0;31mTypeError\u001b[0m: 'int' object is not iterable"
      ]
     }
@@ -491,7 +491,7 @@
      "traceback": [
       "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
       "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-15-b3e0b3c32aec>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0;36m9\u001b[0m \u001b[0;32min\u001b[0m \u001b[0;36m999\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "Cell \u001b[0;32mIn[15], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[38;5;241;43m9\u001b[39;49m\u001b[43m \u001b[49m\u001b[38;5;129;43;01min\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;241;43m999\u001b[39;49m\n",
       "\u001b[0;31mTypeError\u001b[0m: argument of type 'int' is not iterable"
      ]
     }
@@ -658,7 +658,7 @@
      "traceback": [
       "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
       "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-21-ae09c1aa5305>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m999\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "Cell \u001b[0;32mIn[21], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[38;5;28;43mlen\u001b[39;49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m999\u001b[39;49m\u001b[43m)\u001b[49m\n",
       "\u001b[0;31mTypeError\u001b[0m: object of type 'int' has no len()"
      ]
     }
@@ -926,7 +926,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.6"
+   "version": "3.12.2"
   },
   "livereveal": {
    "auto_select": "code",
diff --git a/07_sequences/01_content.ipynb b/07_sequences/01_content.ipynb
index 1dd590a..fb6d54d 100644
--- a/07_sequences/01_content.ipynb
+++ b/07_sequences/01_content.ipynb
@@ -113,7 +113,7 @@
     }
    },
    "source": [
-    "[PythonTutor <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](http://pythontutor.com/visualize.html#code=empty%20%3D%20%5B%5D%0Asimple%20%3D%20%5B40,%2050%5D%0Anested%20%3D%20%5Bempty,%2010,%2020.0,%20%22Thirty%22,%20simple%5D&cumulative=false&curInstr=0&heapPrimitives=nevernest&mode=display&origin=opt-frontend.js&py=3&rawInputLstJSON=%5B%5D&textReferences=false) shows how `nested` holds references to the `empty` and `simple` objects. Technically, it holds three more references to the `10`, `20.0`, and `\"Thirty\"` objects as well. However, to simplify the visualization, these three objects are shown right inside the `nested` object. That may be done because they are immutable and \"flat\" data types. In general, the $0$s and $1$s inside a `list` object in memory always constitute references to other objects only."
+    "[PythonTutor <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](http://pythontutor.com/visualize.html#code=empty%20%3D%20%5B%5D%0Asimple%20%3D%20%5B40,%2050%5D%0Anested%20%3D%20%5Bempty,%2010,%2020.0,%20%22Thirty%22,%20simple%5D&cumulative=false&curstr=0&heapPrimitives=nevernest&mode=display&origin=opt-frontend.js&py=3&rawInputLstJSON=%5B%5D&textReferences=false) shows how `nested` holds references to the `empty` and `simple` objects. Technically, it holds three more references to the `10`, `20.0`, and `\"Thirty\"` objects as well. However, to simplify the visualization, these three objects are shown right inside the `nested` object. That may be done because they are immutable and \"flat\" data types. In general, the $0$s and $1$s inside a `list` object in memory always constitute references to other objects only."
    ]
   },
   {
@@ -209,7 +209,7 @@
    "source": [
     "Alternatively, we use the built-in [list() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#func-list) constructor to create a `list` object out of any (finite) *iterable* we pass to it as the argument.\n",
     "\n",
-    "For example, we can wrap the [range() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#func-range) built-in with [list() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#func-list): As described in [Chapter 4 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/04_iteration/02_content.ipynb#Containers-vs.-Iterables), `range` objects, like `range(1, 13)` below, are iterable and generate `int` objects \"on the fly\" (i.e., one by one). The [list() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#func-list) around it acts like a `for`-loop and **materializes** twelve `int` objects in memory that then become the elements of the newly created `list` object. [PythonTutor <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](http://pythontutor.com/visualize.html#code=r%20%3D%20range%281,%2013%29%0Al%20%3D%20list%28range%281,%2013%29%29&cumulative=false&curInstr=0&heapPrimitives=nevernest&mode=display&origin=opt-frontend.js&py=3&rawInputLstJSON=%5B%5D&textReferences=false) shows this difference visually."
+    "For example, we can wrap the [range() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#func-range) built-in with [list() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#func-list): As described in [Chapter 4 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/04_iteration/02_content.ipynb#Containers-vs.-Iterables), `range` objects, like `range(1, 13)` below, are iterable and generate `int` objects \"on the fly\" (i.e., one by one). The [list() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#func-list) around it acts like a `for`-loop and **materializes** twelve `int` objects in memory that then become the elements of the newly created `list` object. [PythonTutor <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](http://pythontutor.com/visualize.html#code=r%20%3D%20range%281,%2013%29%0Al%20%3D%20list%28range%281,%2013%29%29&cumulative=false&curstr=0&heapPrimitives=nevernest&mode=display&origin=opt-frontend.js&py=3&rawInputLstJSON=%5B%5D&textReferences=false) shows this difference visually."
    ]
   },
   {
@@ -263,7 +263,7 @@
      "traceback": [
       "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
       "\u001b[0;31mMemoryError\u001b[0m                               Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-8-dbb397cbb3b9>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mlist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m999_999_999_999\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "Cell \u001b[0;32mIn[8], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[38;5;28;43mlist\u001b[39;49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mrange\u001b[39;49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m999_999_999_999\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\n",
       "\u001b[0;31mMemoryError\u001b[0m: "
      ]
     }
@@ -614,7 +614,7 @@
      "traceback": [
       "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
       "\u001b[0;31mIndexError\u001b[0m                                Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-17-cb216f4a067e>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mnested\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m5\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "Cell \u001b[0;32mIn[17], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mnested\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;241;43m5\u001b[39;49m\u001b[43m]\u001b[49m\n",
       "\u001b[0;31mIndexError\u001b[0m: list index out of range"
      ]
     }
@@ -1039,7 +1039,7 @@
     }
    },
    "source": [
-    "However, as [PythonTutor <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](http://pythontutor.com/visualize.html#code=nested%20%3D%20%5B%5B%5D,%2010,%2020.0,%20%22Thirty%22,%20%5B40,%2050%5D%5D%0Anested_copy%20%3D%20nested%5B%3A%5D&cumulative=false&curInstr=0&heapPrimitives=nevernest&mode=display&origin=opt-frontend.js&py=3&rawInputLstJSON=%5B%5D&textReferences=false) reveals, only the *references* to the elements are copied, and not the objects in `nested` themselves! Because of that, `nested_copy` is a so-called **[shallow copy <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_wiki.png\">](https://en.wikipedia.org/wiki/Object_copying#Shallow_copy)** of `nested`.\n",
+    "However, as [PythonTutor <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](http://pythontutor.com/visualize.html#code=nested%20%3D%20%5B%5B%5D,%2010,%2020.0,%20%22Thirty%22,%20%5B40,%2050%5D%5D%0Anested_copy%20%3D%20nested%5B%3A%5D&cumulative=false&curstr=0&heapPrimitives=nevernest&mode=display&origin=opt-frontend.js&py=3&rawInputLstJSON=%5B%5D&textReferences=false) reveals, only the *references* to the elements are copied, and not the objects in `nested` themselves! Because of that, `nested_copy` is a so-called **[shallow copy <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_wiki.png\">](https://en.wikipedia.org/wiki/Object_copying#Shallow_copy)** of `nested`.\n",
     "\n",
     "We could also see this with the [id() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#id) function: The respective first elements in both `nested` and `nested_copy` are the *same* object, namely `empty`.  So, we have three ways of accessing the *same* address in memory. Also, we say that `nested` and `nested_copy` partially share the *same* state."
    ]
@@ -1191,7 +1191,7 @@
     }
    },
    "source": [
-    "Now, the first elements of `nested` and `nested_deep_copy` are *different* objects, and [PythonTutor <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](http://pythontutor.com/visualize.html#code=import%20copy%0Anested%20%3D%20%5B%5B%5D,%2010,%2020.0,%20%22Thirty%22,%20%5B40,%2050%5D%5D%0Anested_deep_copy%20%3D%20copy.deepcopy%28nested%29&cumulative=false&curInstr=0&heapPrimitives=nevernest&mode=display&origin=opt-frontend.js&py=3&rawInputLstJSON=%5B%5D&textReferences=false) shows that there are *six* `list` objects in memory."
+    "Now, the first elements of `nested` and `nested_deep_copy` are *different* objects, and [PythonTutor <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](http://pythontutor.com/visualize.html#code=import%20copy%0Anested%20%3D%20%5B%5B%5D,%2010,%2020.0,%20%22Thirty%22,%20%5B40,%2050%5D%5D%0Anested_deep_copy%20%3D%20copy.deepcopy%28nested%29&cumulative=false&curstr=0&heapPrimitives=nevernest&mode=display&origin=opt-frontend.js&py=3&rawInputLstJSON=%5B%5D&textReferences=false) shows that there are *six* `list` objects in memory."
    ]
   },
   {
@@ -1569,7 +1569,7 @@
     }
    },
    "source": [
-    "That is precisely the confusion we talked about above when we said that `nested_copy` is a *shallow* copy of `nested`. [PythonTutor <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](http://pythontutor.com/visualize.html#code=nested%20%3D%20%5B%5B%5D,%2010,%2020.0,%20%22Thirty%22,%20%5B40,%2050%5D%5D%0Anested_copy%20%3D%20nested%5B%3A%5D%0Anested%5B%3A4%5D%20%3D%20%5B100,%20100,%20100%5D%0Anested_copy%5B-1%5D%5B%3A%5D%20%3D%20%5B1,%202,%203%5D&cumulative=false&curInstr=0&heapPrimitives=nevernest&mode=display&origin=opt-frontend.js&py=3&rawInputLstJSON=%5B%5D&textReferences=false) shows how both reference the *same* nested `list` object that is changed *in place* from `[40, 50]` into `[1, 2, 3]`.\n",
+    "That is precisely the confusion we talked about above when we said that `nested_copy` is a *shallow* copy of `nested`. [PythonTutor <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](http://pythontutor.com/visualize.html#code=nested%20%3D%20%5B%5B%5D,%2010,%2020.0,%20%22Thirty%22,%20%5B40,%2050%5D%5D%0Anested_copy%20%3D%20nested%5B%3A%5D%0Anested%5B%3A4%5D%20%3D%20%5B100,%20100,%20100%5D%0Anested_copy%5B-1%5D%5B%3A%5D%20%3D%20%5B1,%202,%203%5D&cumulative=false&curstr=0&heapPrimitives=nevernest&mode=display&origin=opt-frontend.js&py=3&rawInputLstJSON=%5B%5D&textReferences=false) shows how both reference the *same* nested `list` object that is changed *in place* from `[40, 50]` into `[1, 2, 3]`.\n",
     "\n",
     "Lastly, we use the `del` statement to remove an element."
    ]
@@ -2355,7 +2355,7 @@
      "traceback": [
       "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
       "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-80-6bb75d50cf06>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mnames\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mremove\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Peter\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "Cell \u001b[0;32mIn[80], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mnames\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mremove\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mPeter\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\n",
       "\u001b[0;31mValueError\u001b[0m: list.remove(x): x not in list"
      ]
     }
@@ -2439,7 +2439,7 @@
      "traceback": [
       "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
       "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-83-8c741a6680be>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mnames\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Karl\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "Cell \u001b[0;32mIn[83], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mnames\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mindex\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mKarl\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\n",
       "\u001b[0;31mValueError\u001b[0m: 'Karl' is not in list"
      ]
     }
@@ -2967,7 +2967,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.6"
+   "version": "3.12.2"
   },
   "livereveal": {
    "auto_select": "code",
diff --git a/07_sequences/02_exercises.ipynb b/07_sequences/02_exercises.ipynb
index 0ec7907..2709373 100644
--- a/07_sequences/02_exercises.ipynb
+++ b/07_sequences/02_exercises.ipynb
@@ -236,7 +236,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.6"
+   "version": "3.12.2"
   },
   "toc": {
    "base_numbering": 1,
diff --git a/07_sequences/03_content.ipynb b/07_sequences/03_content.ipynb
index 7b63f1e..817951f 100644
--- a/07_sequences/03_content.ipynb
+++ b/07_sequences/03_content.ipynb
@@ -96,7 +96,7 @@
    "source": [
     "While this function is being executed, two variables, namely `letters` in the global scope and `arg` inside the function's local scope, reference the *same* `list` object in memory. Furthermore, the passed in `arg` is also the return value.\n",
     "\n",
-    "So, after the function call, `letters_with_xyz` and `letters` are **aliases** as well, referencing the *same* object. We can also visualize that with [PythonTutor <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](http://pythontutor.com/visualize.html#code=letters%20%3D%20%5B%22a%22,%20%22b%22,%20%22c%22%5D%0A%0Adef%20add_xyz%28arg%29%3A%0A%20%20%20%20arg.extend%28%5B%22x%22,%20%22y%22,%20%22z%22%5D%29%0A%20%20%20%20return%20arg%0A%0Aletters_with_xyz%20%3D%20add_xyz%28letters%29&cumulative=false&curInstr=0&heapPrimitives=nevernest&mode=display&origin=opt-frontend.js&py=3&rawInputLstJSON=%5B%5D&textReferences=false)."
+    "So, after the function call, `letters_with_xyz` and `letters` are **aliases** as well, referencing the *same* object. We can also visualize that with [PythonTutor <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](http://pythontutor.com/visualize.html#code=letters%20%3D%20%5B%22a%22,%20%22b%22,%20%22c%22%5D%0A%0Adef%20add_xyz%28arg%29%3A%0A%20%20%20%20arg.extend%28%5B%22x%22,%20%22y%22,%20%22z%22%5D%29%0A%20%20%20%20return%20arg%0A%0Aletters_with_xyz%20%3D%20add_xyz%28letters%29&cumulative=false&curstr=0&heapPrimitives=nevernest&mode=display&origin=opt-frontend.js&py=3&rawInputLstJSON=%5B%5D&textReferences=false)."
    ]
   },
   {
@@ -170,7 +170,7 @@
    "source": [
     "A better practice is to first create a copy of `arg` within the function that is then modified and returned. If we are sure that `arg` contains immutable elements only, we get away with a shallow copy. The downside of this approach is the higher amount of memory necessary.\n",
     "\n",
-    "The revised `add_xyz()` function below is more natural to reason about as it does *not* modify the passed in `arg` internally. [PythonTutor <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](http://pythontutor.com/visualize.html#code=letters%20%3D%20%5B%22a%22,%20%22b%22,%20%22c%22%5D%0A%0Adef%20add_xyz%28arg%29%3A%0A%20%20%20%20new_arg%20%3D%20arg%5B%3A%5D%0A%20%20%20%20new_arg.extend%28%5B%22x%22,%20%22y%22,%20%22z%22%5D%29%0A%20%20%20%20return%20new_arg%0A%0Aletters_with_xyz%20%3D%20add_xyz%28letters%29&cumulative=false&curInstr=0&heapPrimitives=nevernest&mode=display&origin=opt-frontend.js&py=3&rawInputLstJSON=%5B%5D&textReferences=false) shows that as well. This approach is following the **[functional programming <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_wiki.png\">](https://en.wikipedia.org/wiki/Functional_programming)** paradigm that is going through a \"renaissance\" currently. Two essential characteristics of functional programming are that a function *never* changes its inputs and *always* returns the same output given the same inputs.\n",
+    "The revised `add_xyz()` function below is more natural to reason about as it does *not* modify the passed in `arg` internally. [PythonTutor <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](http://pythontutor.com/visualize.html#code=letters%20%3D%20%5B%22a%22,%20%22b%22,%20%22c%22%5D%0A%0Adef%20add_xyz%28arg%29%3A%0A%20%20%20%20new_arg%20%3D%20arg%5B%3A%5D%0A%20%20%20%20new_arg.extend%28%5B%22x%22,%20%22y%22,%20%22z%22%5D%29%0A%20%20%20%20return%20new_arg%0A%0Aletters_with_xyz%20%3D%20add_xyz%28letters%29&cumulative=false&curstr=0&heapPrimitives=nevernest&mode=display&origin=opt-frontend.js&py=3&rawInputLstJSON=%5B%5D&textReferences=false) shows that as well. This approach is following the **[functional programming <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_wiki.png\">](https://en.wikipedia.org/wiki/Functional_programming)** paradigm that is going through a \"renaissance\" currently. Two essential characteristics of functional programming are that a function *never* changes its inputs and *always* returns the same output given the same inputs.\n",
     "\n",
     "For a beginner, it is probably better to stick to this idea and not change any arguments as the original `add_xyz()` above. However, functions that modify and return the argument passed in are an important aspect of object-oriented programming, as explained in [Chapter 11 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/11_classes/00_content.ipynb)."
    ]
@@ -275,7 +275,7 @@
     }
    },
    "source": [
-    "If we want to modify the argument passed in, it is best to return `None` and not `arg`, as does the final version of `add_xyz()` below. Then, the user of our function cannot accidentally create two aliases to the same object. That is also why the list methods above all return `None`. [PythonTutor <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](http://pythontutor.com/visualize.html#code=letters%20%3D%20%5B%22a%22,%20%22b%22,%20%22c%22%5D%0A%0Adef%20add_xyz%28arg%29%3A%0A%20%20%20%20arg.extend%28%5B%22x%22,%20%22y%22,%20%22z%22%5D%29%0A%20%20%20%20return%0A%0Aadd_xyz%28letters%29&cumulative=false&curInstr=0&heapPrimitives=nevernest&mode=display&origin=opt-frontend.js&py=3&rawInputLstJSON=%5B%5D&textReferences=false) shows how there is only *one* reference to `letters` after the function call."
+    "If we want to modify the argument passed in, it is best to return `None` and not `arg`, as does the final version of `add_xyz()` below. Then, the user of our function cannot accidentally create two aliases to the same object. That is also why the list methods above all return `None`. [PythonTutor <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](http://pythontutor.com/visualize.html#code=letters%20%3D%20%5B%22a%22,%20%22b%22,%20%22c%22%5D%0A%0Adef%20add_xyz%28arg%29%3A%0A%20%20%20%20arg.extend%28%5B%22x%22,%20%22y%22,%20%22z%22%5D%29%0A%20%20%20%20return%0A%0Aadd_xyz%28letters%29&cumulative=false&curstr=0&heapPrimitives=nevernest&mode=display&origin=opt-frontend.js&py=3&rawInputLstJSON=%5B%5D&textReferences=false) shows how there is only *one* reference to `letters` after the function call."
    ]
   },
   {
@@ -1129,7 +1129,7 @@
      "traceback": [
       "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
       "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-43-80fddf1304f0>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mnumbers\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m99\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "Cell \u001b[0;32mIn[43], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mnumbers\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;241;43m-\u001b[39;49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m]\u001b[49m \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m99\u001b[39m\n",
       "\u001b[0;31mTypeError\u001b[0m: 'tuple' object does not support item assignment"
      ]
     }
@@ -1170,7 +1170,7 @@
     }
    ],
    "source": [
-    "numbers + (99,) "
+    "numbers + (99,)"
    ]
   },
   {
@@ -1586,7 +1586,7 @@
      "traceback": [
       "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
       "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-60-c5e79943681f>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mn1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn3\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn4\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn5\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn6\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn7\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn8\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn9\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn10\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn11\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnumbers\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "Cell \u001b[0;32mIn[60], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m n1, n2, n3, n4, n5, n6, n7, n8, n9, n10, n11 \u001b[38;5;241m=\u001b[39m numbers\n",
       "\u001b[0;31mValueError\u001b[0m: too many values to unpack (expected 11)"
      ]
     }
@@ -1611,7 +1611,7 @@
      "traceback": [
       "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
       "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-61-214f2de70baf>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mn1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn3\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn4\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn5\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn6\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn7\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn8\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn9\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn10\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn11\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn12\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn13\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnumbers\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "Cell \u001b[0;32mIn[61], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m n1, n2, n3, n4, n5, n6, n7, n8, n9, n10, n11, n12, n13 \u001b[38;5;241m=\u001b[39m numbers\n",
       "\u001b[0;31mValueError\u001b[0m: not enough values to unpack (expected 13, got 12)"
      ]
     }
@@ -2240,8 +2240,8 @@
      "traceback": [
       "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
       "\u001b[0;31mIndexError\u001b[0m                                Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-83-640e0c632b8d>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mproduct\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
-      "\u001b[0;32m<ipython-input-80-02b0db9fbeab>\u001b[0m in \u001b[0;36mproduct\u001b[0;34m(*args)\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mproduct\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m     \u001b[0;34m\"\"\"Multiply all arguments.\"\"\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m     \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      4\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m     \u001b[0;32mfor\u001b[0m \u001b[0marg\u001b[0m \u001b[0;32min\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "Cell \u001b[0;32mIn[83], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mproduct\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n",
+      "Cell \u001b[0;32mIn[80], line 3\u001b[0m, in \u001b[0;36mproduct\u001b[0;34m(*args)\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mproduct\u001b[39m(\u001b[38;5;241m*\u001b[39margs):\n\u001b[1;32m      2\u001b[0m \u001b[38;5;250m    \u001b[39m\u001b[38;5;124;03m\"\"\"Multiply all arguments.\"\"\"\u001b[39;00m\n\u001b[0;32m----> 3\u001b[0m     result \u001b[38;5;241m=\u001b[39m \u001b[43margs\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;241;43m0\u001b[39;49m\u001b[43m]\u001b[49m\n\u001b[1;32m      5\u001b[0m     \u001b[38;5;28;01mfor\u001b[39;00m arg \u001b[38;5;129;01min\u001b[39;00m args[\u001b[38;5;241m1\u001b[39m:]:\n\u001b[1;32m      6\u001b[0m         result \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m=\u001b[39m arg\n",
       "\u001b[0;31mIndexError\u001b[0m: tuple index out of range"
      ]
     }
@@ -2462,7 +2462,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.6"
+   "version": "3.12.2"
   },
   "livereveal": {
    "auto_select": "code",
diff --git a/07_sequences/04_exercises.ipynb b/07_sequences/04_exercises.ipynb
index 1d46524..6df6a33 100644
--- a/07_sequences/04_exercises.ipynb
+++ b/07_sequences/04_exercises.ipynb
@@ -642,7 +642,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.6"
+   "version": "3.12.2"
   },
   "toc": {
    "base_numbering": 1,
diff --git a/07_sequences/05_appendix.ipynb b/07_sequences/05_appendix.ipynb
index 09ebd0c..b7fb1f6 100644
--- a/07_sequences/05_appendix.ipynb
+++ b/07_sequences/05_appendix.ipynb
@@ -420,7 +420,7 @@
      "traceback": [
       "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
       "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-13-b30d0b930bac>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mcurrent_position\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mz\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "Cell \u001b[0;32mIn[13], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mcurrent_position\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mz\u001b[49m\n",
       "\u001b[0;31mAttributeError\u001b[0m: 'Point' object has no attribute 'z'"
      ]
     }
@@ -577,7 +577,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.6"
+   "version": "3.12.2"
   },
   "livereveal": {
    "auto_select": "code",
diff --git a/07_sequences/06_summary.ipynb b/07_sequences/06_summary.ipynb
index 8115139..043ed03 100644
--- a/07_sequences/06_summary.ipynb
+++ b/07_sequences/06_summary.ipynb
@@ -53,7 +53,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.6"
+   "version": "3.12.2"
   },
   "livereveal": {
    "auto_select": "code",
diff --git a/07_sequences/07_review.ipynb b/07_sequences/07_review.ipynb
index de10156..44b7766 100644
--- a/07_sequences/07_review.ipynb
+++ b/07_sequences/07_review.ipynb
@@ -231,7 +231,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.6"
+   "version": "3.12.2"
   },
   "toc": {
    "base_numbering": 1,

From 6a7bf98768ba0eea21d42e69a9dba36f67492d7d Mon Sep 17 00:00:00 2001
From: Alexander Hess <alexander@webartifex.biz>
Date: Mon, 8 Apr 2024 16:43:43 +0200
Subject: [PATCH 233/242] Re-run Chapter 08 files with Python 3.12

---
 08_mfr/00_content.ipynb   |  4 ++--
 08_mfr/01_content.ipynb   | 26 +++++++++++++-------------
 08_mfr/02_exercises.ipynb |  2 +-
 08_mfr/03_exercises.ipynb |  2 +-
 08_mfr/04_content.ipynb   | 14 +++++++-------
 08_mfr/05_summary.ipynb   |  2 +-
 08_mfr/06_review.ipynb    |  2 +-
 7 files changed, 26 insertions(+), 26 deletions(-)

diff --git a/08_mfr/00_content.ipynb b/08_mfr/00_content.ipynb
index 1884515..bcb7f04 100644
--- a/08_mfr/00_content.ipynb
+++ b/08_mfr/00_content.ipynb
@@ -1321,7 +1321,7 @@
    },
    "source": [
     "PythonTutor visualizes the differences in the number of computational steps and memory usage:\n",
-    "- [Version 1 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](http://pythontutor.com/visualize.html#code=def%20is_even%28element%29%3A%0A%20%20%20%20if%20element%20%25%202%20%3D%3D%200%3A%0A%20%20%20%20%20%20%20%20return%20True%0A%20%20%20%20return%20False%0A%0Adef%20transform%28element%29%3A%0A%20%20%20%20return%20%28element%20**%202%29%20%2B%201%0A%0Anumbers%20%3D%20list%28range%281,%2013%29%29%0A%0Aevens%20%3D%20%5B%5D%0Afor%20number%20in%20numbers%3A%0A%20%20%20%20if%20is_even%28number%29%3A%0A%20%20%20%20%20%20%20%20evens.append%28number%29%0A%0Atransformed%20%3D%20%5B%5D%0Afor%20number%20in%20evens%3A%0A%20%20%20%20transformed.append%28transform%28number%29%29%0A%0Aresult%20%3D%20sum%28transformed%29&cumulative=false&curInstr=0&heapPrimitives=nevernest&mode=display&origin=opt-frontend.js&py=3&rawInputLstJSON=%5B%5D&textReferences=false): With `for`-loops, `if` statements, and named functions -> **116** steps and **3** `list` objects\n",
+    "- [Version 1 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](http://pythontutor.com/visualize.html#code=def%20is_even%28element%29%3A%0A%20%20%20%20if%20element%20%25%202%20%3D%3D%200%3A%0A%20%20%20%20%20%20%20%20return%20True%0A%20%20%20%20return%20False%0A%0Adef%20transform%28element%29%3A%0A%20%20%20%20return%20%28element%20**%202%29%20%2B%201%0A%0Anumbers%20%3D%20list%28range%281,%2013%29%29%0A%0Aevens%20%3D%20%5B%5D%0Afor%20number%20in%20numbers%3A%0A%20%20%20%20if%20is_even%28number%29%3A%0A%20%20%20%20%20%20%20%20evens.append%28number%29%0A%0Atransformed%20%3D%20%5B%5D%0Afor%20number%20in%20evens%3A%0A%20%20%20%20transformed.append%28transform%28number%29%29%0A%0Aresult%20%3D%20sum%28transformed%29&cumulative=false&curstr=0&heapPrimitives=nevernest&mode=display&origin=opt-frontend.js&py=3&rawInputLstJSON=%5B%5D&textReferences=false): With `for`-loops, `if` statements, and named functions -> **116** steps and **3** `list` objects\n",
     "- [Version 2 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](http://pythontutor.com/visualize.html#code=numbers%20%3D%20range%281,%2013%29%0Aevens%20%3D%20filter%28lambda%20x%3A%20x%20%25%202%20%3D%3D%200,%20numbers%29%0Atransformed%20%3D%20map%28lambda%20x%3A%20%28x%20**%202%29%20%2B%201,%20evens%29%0Aresult%20%3D%20sum%28transformed%29&cumulative=false&curInstr=0&heapPrimitives=nevernest&mode=display&origin=opt-frontend.js&py=3&rawInputLstJSON=%5B%5D&textReferences=false): With named `map` and `filter` objects -> **58** steps and **no** `list` object\n",
     "- [Version 3 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](http://pythontutor.com/visualize.html#code=result%20%3D%20sum%28map%28lambda%20x%3A%20%28x%20**%202%29%20%2B%201,%20filter%28lambda%20x%3A%20x%20%25%202%20%3D%3D%200,%20range%281,%2013%29%29%29%29&cumulative=false&curInstr=0&heapPrimitives=nevernest&mode=display&origin=opt-frontend.js&py=3&rawInputLstJSON=%5B%5D&textReferences=false): Everything in *one* expression -> **55** steps and **no** `list` object\n",
     "\n",
@@ -1349,7 +1349,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.6"
+   "version": "3.12.2"
   },
   "livereveal": {
    "auto_select": "code",
diff --git a/08_mfr/01_content.ipynb b/08_mfr/01_content.ipynb
index 688d953..a92bd6e 100644
--- a/08_mfr/01_content.ipynb
+++ b/08_mfr/01_content.ipynb
@@ -156,7 +156,7 @@
    "source": [
     "A list comprehension may always be used in a place where otherwise a `list` object would work.\n",
     "\n",
-    "For example, let's rewrite the \"*A simple Filter*\" example from [Chapter 4 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/04_iteration/03_content.ipynb#Example:-A-simple-Filter) in just one line. As a caveat, the code below *materializes* all elements in memory *before* summing them up, and may, therefore, cause a `MemoryError` when executed with a bigger `numbers` list. We see with [PythonTutor <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](http://pythontutor.com/visualize.html#code=numbers%20%3D%20range%281,%2013%29%0Aresult%20%3D%20sum%28%5B%28n%20**%202%29%20%2B%201%20for%20n%20in%20numbers%20if%20n%20%25%202%20%3D%3D%200%5D%29&cumulative=false&curInstr=0&heapPrimitives=nevernest&mode=display&origin=opt-frontend.js&py=3&rawInputLstJSON=%5B%5D&textReferences=false) how a `list` object exists in memory at step 17 and then \"gets lost\" right after. As the next section shows, this downside may be mitigated."
+    "For example, let's rewrite the \"*A simple Filter*\" example from [Chapter 4 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/04_iteration/03_content.ipynb#Example:-A-simple-Filter) in just one line. As a caveat, the code below *materializes* all elements in memory *before* summing them up, and may, therefore, cause a `MemoryError` when executed with a bigger `numbers` list. We see with [PythonTutor <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](http://pythontutor.com/visualize.html#code=numbers%20%3D%20range%281,%2013%29%0Aresult%20%3D%20sum%28%5B%28n%20**%202%29%20%2B%201%20for%20n%20in%20numbers%20if%20n%20%25%202%20%3D%3D%200%5D%29&cumulative=false&curstr=0&heapPrimitives=nevernest&mode=display&origin=opt-frontend.js&py=3&rawInputLstJSON=%5B%5D&textReferences=false) how a `list` object exists in memory at step 17 and then \"gets lost\" right after. As the next section shows, this downside may be mitigated."
    ]
   },
   {
@@ -932,7 +932,7 @@
     }
    },
    "source": [
-    "A common use case is to reduce the elements into a single object instead, for example, by adding them up with [sum() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#sum) as in the original \"*A simple Filter*\" example. [PythonTutor <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](http://pythontutor.com/visualize.html#code=numbers%20%3D%20range%281,%2013%29%0Asum_with_list%20%3D%20sum%28%5B%28n%20**%202%29%20%2B%201%20for%20n%20in%20numbers%20if%20n%20%25%202%20%3D%3D%200%5D%29%0Asum_with_gen%20%3D%20sum%28%28n%20**%202%29%20%2B%201%20for%20n%20in%20numbers%20if%20n%20%25%202%20%3D%3D%200%29&cumulative=false&curInstr=0&heapPrimitives=nevernest&mode=display&origin=opt-frontend.js&py=3&rawInputLstJSON=%5B%5D&textReferences=false) shows how the code cell below does *not* create a temporary `list` object in memory whereas a `list` comprehension would do so (cf., step 17)."
+    "A common use case is to reduce the elements into a single object instead, for example, by adding them up with [sum() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#sum) as in the original \"*A simple Filter*\" example. [PythonTutor <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](http://pythontutor.com/visualize.html#code=numbers%20%3D%20range%281,%2013%29%0Asum_with_list%20%3D%20sum%28%5B%28n%20**%202%29%20%2B%201%20for%20n%20in%20numbers%20if%20n%20%25%202%20%3D%3D%200%5D%29%0Asum_with_gen%20%3D%20sum%28%28n%20**%202%29%20%2B%201%20for%20n%20in%20numbers%20if%20n%20%25%202%20%3D%3D%200%29&cumulative=false&curstr=0&heapPrimitives=nevernest&mode=display&origin=opt-frontend.js&py=3&rawInputLstJSON=%5B%5D&textReferences=false) shows how the code cell below does *not* create a temporary `list` object in memory whereas a `list` comprehension would do so (cf., step 17)."
    ]
   },
   {
@@ -1224,7 +1224,7 @@
      "traceback": [
       "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
       "\u001b[0;31mStopIteration\u001b[0m                             Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-36-6e72e47198db>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mnext\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mgen\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "Cell \u001b[0;32mIn[36], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[38;5;28;43mnext\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mgen\u001b[49m\u001b[43m)\u001b[49m\n",
       "\u001b[0;31mStopIteration\u001b[0m: "
      ]
     }
@@ -1249,7 +1249,7 @@
      "traceback": [
       "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
       "\u001b[0;31mStopIteration\u001b[0m                             Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-37-6e72e47198db>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mnext\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mgen\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "Cell \u001b[0;32mIn[37], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[38;5;28;43mnext\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mgen\u001b[49m\u001b[43m)\u001b[49m\n",
       "\u001b[0;31mStopIteration\u001b[0m: "
      ]
     }
@@ -1417,7 +1417,7 @@
     }
    },
    "source": [
-    "Because `nested_numbers` has an internal structure (i.e., the inner `list` objects are series of consecutive `int` objects), we can even provide an effectively **memoryless** implementation by expressing it as a `generator` expression derived from `range` objects. [PythonTutor <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](http://pythontutor.com/visualize.html#code=nested_numbers%20%3D%20%28%28range%28x,%20y%20%2B%201%29%29%20for%20x,%20y%20in%20zip%28range%281,%204%29,%20range%287,%2010%29%29%29%0Aresult%20%3D%20sum%28number%20for%20inner_numbers%20in%20nested_numbers%20for%20number%20in%20inner_numbers%29&cumulative=false&curInstr=0&heapPrimitives=nevernest&mode=display&origin=opt-frontend.js&py=3&rawInputLstJSON=%5B%5D&textReferences=false) confirms that no `list` objects materialize at any point in time."
+    "Because `nested_numbers` has an internal structure (i.e., the inner `list` objects are series of consecutive `int` objects), we can even provide an effectively **memoryless** implementation by expressing it as a `generator` expression derived from `range` objects. [PythonTutor <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](http://pythontutor.com/visualize.html#code=nested_numbers%20%3D%20%28%28range%28x,%20y%20%2B%201%29%29%20for%20x,%20y%20in%20zip%28range%281,%204%29,%20range%287,%2010%29%29%29%0Aresult%20%3D%20sum%28number%20for%20inner_numbers%20in%20nested_numbers%20for%20number%20in%20inner_numbers%29&cumulative=false&curstr=0&heapPrimitives=nevernest&mode=display&origin=opt-frontend.js&py=3&rawInputLstJSON=%5B%5D&textReferences=false) confirms that no `list` objects materialize at any point in time."
    ]
   },
   {
@@ -1489,7 +1489,7 @@
     }
    },
    "source": [
-    "We must be careful when assigning a `generator` object to a variable: If we use `nested_numbers` again, for example, in the alternative formulation below, [sum() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#sum) returns `0` because `nested_numbers` is exhausted after executing the previous code cell. [PythonTutor <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](http://pythontutor.com/visualize.html#code=nested_numbers%20%3D%20%28%28range%28x,%20y%20%2B%201%29%29%20for%20x,%20y%20in%20zip%28range%281,%204%29,%20range%287,%2010%29%29%29%0Aresult%20%3D%20sum%28number%20for%20inner_numbers%20in%20nested_numbers%20for%20number%20in%20inner_numbers%29%0Ano_result%20%3D%20sum%28sum%28inner_numbers%29%20for%20inner_numbers%20in%20nested_numbers%29&cumulative=false&curInstr=0&heapPrimitives=nevernest&mode=display&origin=opt-frontend.js&py=3&rawInputLstJSON=%5B%5D&textReferences=false) also shows that."
+    "We must be careful when assigning a `generator` object to a variable: If we use `nested_numbers` again, for example, in the alternative formulation below, [sum() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#sum) returns `0` because `nested_numbers` is exhausted after executing the previous code cell. [PythonTutor <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](http://pythontutor.com/visualize.html#code=nested_numbers%20%3D%20%28%28range%28x,%20y%20%2B%201%29%29%20for%20x,%20y%20in%20zip%28range%281,%204%29,%20range%287,%2010%29%29%29%0Aresult%20%3D%20sum%28number%20for%20inner_numbers%20in%20nested_numbers%20for%20number%20in%20inner_numbers%29%0Ano_result%20%3D%20sum%28sum%28inner_numbers%29%20for%20inner_numbers%20in%20nested_numbers%29&cumulative=false&curstr=0&heapPrimitives=nevernest&mode=display&origin=opt-frontend.js&py=3&rawInputLstJSON=%5B%5D&textReferences=false) also shows that."
    ]
   },
   {
@@ -1564,7 +1564,7 @@
    "source": [
     "Now, the first of the two alternative solutions may be more appealing to many readers. In general, many practitioners seem to dislike `lambda` expressions.\n",
     "\n",
-    "In the first solution, we *unpack* the elements produced by `(1 + (x / y) for x in first for y in second)` into the `product()` function from the \"*Function Definitions & Calls*\" sub-section in [Chapter 7 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/07_sequences/03_content.ipynb#Function-Definitions-&-Calls). However, inside `product()`, the elements are *packed* into `args`, a *materialized* `tuple` object! So, all the memory efficiency gained by using a generator expression is lost! [PythonTutor <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](http://pythontutor.com/visualize.html#code=def%20product%28*args%29%3A%0A%20%20%20%20result%20%3D%20args%5B0%5D%0A%20%20%20%20for%20arg%20in%20args%5B1%3A%5D%3A%0A%20%20%20%20%20%20%20%20result%20*%3D%20arg%0A%20%20%20%20return%20result%0A%0Afirst%20%3D%20range%2810,%2031,%2010%29%0Asecond%20%3D%20range%2840,%2061,%2010%29%0A%0Aresult%20%3D%20product%28*%281%20%2B%20%28x%20/%20y%29%20for%20x%20in%20first%20for%20y%20in%20second%29%29&cumulative=false&curInstr=0&heapPrimitives=nevernest&mode=display&origin=opt-frontend.js&py=3&rawInputLstJSON=%5B%5D&textReferences=false) shows how a `tuple` object exists in steps 38-58."
+    "In the first solution, we *unpack* the elements produced by `(1 + (x / y) for x in first for y in second)` into the `product()` function from the \"*Function Definitions & Calls*\" sub-section in [Chapter 7 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/07_sequences/03_content.ipynb#Function-Definitions-&-Calls). However, inside `product()`, the elements are *packed* into `args`, a *materialized* `tuple` object! So, all the memory efficiency gained by using a generator expression is lost! [PythonTutor <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](http://pythontutor.com/visualize.html#code=def%20product%28*args%29%3A%0A%20%20%20%20result%20%3D%20args%5B0%5D%0A%20%20%20%20for%20arg%20in%20args%5B1%3A%5D%3A%0A%20%20%20%20%20%20%20%20result%20*%3D%20arg%0A%20%20%20%20return%20result%0A%0Afirst%20%3D%20range%2810,%2031,%2010%29%0Asecond%20%3D%20range%2840,%2061,%2010%29%0A%0Aresult%20%3D%20product%28*%281%20%2B%20%28x%20/%20y%29%20for%20x%20in%20first%20for%20y%20in%20second%29%29&cumulative=false&curstr=0&heapPrimitives=nevernest&mode=display&origin=opt-frontend.js&py=3&rawInputLstJSON=%5B%5D&textReferences=false) shows how a `tuple` object exists in steps 38-58."
    ]
   },
   {
@@ -1599,7 +1599,7 @@
     }
    },
    "source": [
-    "On the contrary, the second solution with the [reduce() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functools.html#functools.reduce) function from the [functools <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functools.html) module and the `lambda` expression works *without* the elements materialized at the same time, and [PythonTutor <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](http://pythontutor.com/visualize.html#code=from%20functools%20import%20reduce%0A%0Afirst%20%3D%20range%2810,%2031,%2010%29%0Asecond%20%3D%20range%2840,%2061,%2010%29%0A%0Aresult%20%3D%20reduce%28%0A%20%20%20%20lambda%20x,%20y%3A%20x%20*%20y,%0A%20%20%20%20%281%20%2B%20%28x%20/%20y%29%20for%20x%20in%20first%20for%20y%20in%20second%29%0A%29&cumulative=false&curInstr=0&heapPrimitives=nevernest&mode=display&origin=opt-frontend.js&py=3&rawInputLstJSON=%5B%5D&textReferences=false) confirms that. So, only the second alternative is truly memory-efficient!"
+    "On the contrary, the second solution with the [reduce() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functools.html#functools.reduce) function from the [functools <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functools.html) module and the `lambda` expression works *without* the elements materialized at the same time, and [PythonTutor <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](http://pythontutor.com/visualize.html#code=from%20functools%20import%20reduce%0A%0Afirst%20%3D%20range%2810,%2031,%2010%29%0Asecond%20%3D%20range%2840,%2061,%2010%29%0A%0Aresult%20%3D%20reduce%28%0A%20%20%20%20lambda%20x,%20y%3A%20x%20*%20y,%0A%20%20%20%20%281%20%2B%20%28x%20/%20y%29%20for%20x%20in%20first%20for%20y%20in%20second%29%0A%29&cumulative=false&curstr=0&heapPrimitives=nevernest&mode=display&origin=opt-frontend.js&py=3&rawInputLstJSON=%5B%5D&textReferences=false) confirms that. So, only the second alternative is truly memory-efficient!"
    ]
   },
   {
@@ -1871,14 +1871,14 @@
    "outputs": [
     {
      "ename": "TypeError",
-     "evalue": "reduce() of empty sequence with no initial value",
+     "evalue": "reduce() of empty iterable with no initial value",
      "output_type": "error",
      "traceback": [
       "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
       "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-56-73a295f34553>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0maverage_evens\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m2\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0mrandom\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrandint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m49\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;36m1\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0m_\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m10_000_000\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
-      "\u001b[0;32m<ipython-input-49-29706a0855ba>\u001b[0m in \u001b[0;36maverage_evens\u001b[0;34m(numbers, scalar)\u001b[0m\n\u001b[1;32m     11\u001b[0m     \"\"\"\n\u001b[1;32m     12\u001b[0m     \u001b[0mintegers\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mround\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mn\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mn\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mnumbers\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 13\u001b[0;31m     total, count = reduce(\n\u001b[0m\u001b[1;32m     14\u001b[0m         \u001b[0;32mlambda\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     15\u001b[0m         \u001b[0;34m(\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mn\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mn\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mintegers\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mn\u001b[0m \u001b[0;34m%\u001b[0m \u001b[0;36m2\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;31mTypeError\u001b[0m: reduce() of empty sequence with no initial value"
+      "Cell \u001b[0;32mIn[56], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43maverage_evens\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m2\u001b[39;49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43m \u001b[49m\u001b[43mrandom\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrandint\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m0\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m49\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m+\u001b[39;49m\u001b[43m \u001b[49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mfor\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43m_\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;129;43;01min\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;28;43mrange\u001b[39;49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m10_000_000\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\n",
+      "Cell \u001b[0;32mIn[49], line 13\u001b[0m, in \u001b[0;36maverage_evens\u001b[0;34m(numbers, scalar)\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"Calculate the average of all even numbers.\u001b[39;00m\n\u001b[1;32m      3\u001b[0m \n\u001b[1;32m      4\u001b[0m \u001b[38;5;124;03mArgs:\u001b[39;00m\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m     10\u001b[0m \u001b[38;5;124;03m    float: (scaled) average\u001b[39;00m\n\u001b[1;32m     11\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m     12\u001b[0m integers \u001b[38;5;241m=\u001b[39m (\u001b[38;5;28mround\u001b[39m(n) \u001b[38;5;28;01mfor\u001b[39;00m n \u001b[38;5;129;01min\u001b[39;00m numbers)\n\u001b[0;32m---> 13\u001b[0m total, count \u001b[38;5;241m=\u001b[39m \u001b[43mreduce\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m     14\u001b[0m \u001b[43m    \u001b[49m\u001b[38;5;28;43;01mlambda\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mx\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43my\u001b[49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43m(\u001b[49m\u001b[43mx\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;241;43m0\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m+\u001b[39;49m\u001b[43m \u001b[49m\u001b[43my\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;241;43m0\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mx\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m+\u001b[39;49m\u001b[43m \u001b[49m\u001b[43my\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m     15\u001b[0m \u001b[43m    \u001b[49m\u001b[43m(\u001b[49m\u001b[43m(\u001b[49m\u001b[43mn\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mfor\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mn\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;129;43;01min\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mintegers\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mif\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mn\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m%\u001b[39;49m\u001b[43m \u001b[49m\u001b[38;5;241;43m2\u001b[39;49m\u001b[43m \u001b[49m\u001b[38;5;241;43m==\u001b[39;49m\u001b[43m \u001b[49m\u001b[38;5;241;43m0\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m     16\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     17\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m scalar \u001b[38;5;241m*\u001b[39m total \u001b[38;5;241m/\u001b[39m count\n",
+      "\u001b[0;31mTypeError\u001b[0m: reduce() of empty iterable with no initial value"
      ]
     }
    ],
@@ -2352,7 +2352,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.6"
+   "version": "3.12.2"
   },
   "livereveal": {
    "auto_select": "code",
diff --git a/08_mfr/02_exercises.ipynb b/08_mfr/02_exercises.ipynb
index 0911aaf..f6d81f8 100644
--- a/08_mfr/02_exercises.ipynb
+++ b/08_mfr/02_exercises.ipynb
@@ -739,7 +739,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.6"
+   "version": "3.12.2"
   },
   "toc": {
    "base_numbering": 1,
diff --git a/08_mfr/03_exercises.ipynb b/08_mfr/03_exercises.ipynb
index b93761a..d4b0518 100644
--- a/08_mfr/03_exercises.ipynb
+++ b/08_mfr/03_exercises.ipynb
@@ -434,7 +434,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.6"
+   "version": "3.12.2"
   },
   "toc": {
    "base_numbering": 1,
diff --git a/08_mfr/04_content.ipynb b/08_mfr/04_content.ipynb
index ff55376..8a3fd51 100644
--- a/08_mfr/04_content.ipynb
+++ b/08_mfr/04_content.ipynb
@@ -706,7 +706,7 @@
      "traceback": [
       "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
       "\u001b[0;31mStopIteration\u001b[0m                             Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-25-0680f5ec8ce3>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mnext\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0miterator1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "Cell \u001b[0;32mIn[25], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[38;5;28;43mnext\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43miterator1\u001b[49m\u001b[43m)\u001b[49m\n",
       "\u001b[0;31mStopIteration\u001b[0m: "
      ]
     }
@@ -926,7 +926,7 @@
    "source": [
     "Now that we know the concept of an *iterator*, let's compare some of the built-ins introduced in [Chapter 7 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/07_sequences/00_content.ipynb) in detail and make sure we understand what is going on in memory. This section also serves as a summary of all the concepts in this chapter.\n",
     "\n",
-    "We use two simple examples, `numbers` and `memoryless`: `numbers` creates *thirteen* objects in memory and `memoryless` only *one* (cf., [PythonTutor <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](http://pythontutor.com/visualize.html#code=numbers%20%3D%20%5B7,%2011,%208,%205,%203,%2012,%202,%206,%209,%2010,%201,%204%5D%0Amemoryless%20%3D%20range%281,%2013%29&cumulative=false&curInstr=2&heapPrimitives=nevernest&mode=display&origin=opt-frontend.js&py=3&rawInputLstJSON=%5B%5D&textReferences=false))."
+    "We use two simple examples, `numbers` and `memoryless`: `numbers` creates *thirteen* objects in memory and `memoryless` only *one* (cf., [PythonTutor <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](http://pythontutor.com/visualize.html#code=numbers%20%3D%20%5B7,%2011,%208,%205,%203,%2012,%202,%206,%209,%2010,%201,%204%5D%0Amemoryless%20%3D%20range%281,%2013%29&cumulative=false&curstr=2&heapPrimitives=nevernest&mode=display&origin=opt-frontend.js&py=3&rawInputLstJSON=%5B%5D&textReferences=false))."
    ]
   },
   {
@@ -965,7 +965,7 @@
    "source": [
     "The [sorted() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#sorted) function takes a *finite* `iterable` argument and *materializes* its elements into a *new* `list` object that is returned.\n",
     "\n",
-    "The argument may already be materialized, as is the case with `numbers`, but may also be an *iterable* without any objects in it, such as `memoryless`. In both cases, we end up with materialized `list` objects with the elements sorted in *forward* order (cf., [PythonTutor <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](http://pythontutor.com/visualize.html#code=numbers%20%3D%20%5B7,%2011,%208,%205,%203,%2012,%202,%206,%209,%2010,%201,%204%5D%0Amemoryless%20%3D%20range%281,%2013%29%0Aresult1%20%3D%20sorted%28numbers%29%0Aresult2%20%3D%20sorted%28memoryless%29&cumulative=false&curInstr=4&heapPrimitives=nevernest&mode=display&origin=opt-frontend.js&py=3&rawInputLstJSON=%5B%5D&textReferences=false))."
+    "The argument may already be materialized, as is the case with `numbers`, but may also be an *iterable* without any objects in it, such as `memoryless`. In both cases, we end up with materialized `list` objects with the elements sorted in *forward* order (cf., [PythonTutor <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](http://pythontutor.com/visualize.html#code=numbers%20%3D%20%5B7,%2011,%208,%205,%203,%2012,%202,%206,%209,%2010,%201,%204%5D%0Amemoryless%20%3D%20range%281,%2013%29%0Aresult1%20%3D%20sorted%28numbers%29%0Aresult2%20%3D%20sorted%28memoryless%29&cumulative=false&curstr=4&heapPrimitives=nevernest&mode=display&origin=opt-frontend.js&py=3&rawInputLstJSON=%5B%5D&textReferences=false))."
    ]
   },
   {
@@ -1024,7 +1024,7 @@
     }
    },
    "source": [
-    "By adding a keyword-only argument `reverse=True`, the materialized `list` objects are sorted in *reverse* order (cf., [PythonTutor <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](http://pythontutor.com/visualize.html#code=numbers%20%3D%20%5B7,%2011,%208,%205,%203,%2012,%202,%206,%209,%2010,%201,%204%5D%0Amemoryless%20%3D%20range%281,%2013%29%0Aresult1%20%3D%20sorted%28numbers,%20reverse%3DTrue%29%0Aresult2%20%3D%20sorted%28memoryless,%20reverse%3DTrue%29&cumulative=false&curInstr=4&heapPrimitives=nevernest&mode=display&origin=opt-frontend.js&py=3&rawInputLstJSON=%5B%5D&textReferences=false))."
+    "By adding a keyword-only argument `reverse=True`, the materialized `list` objects are sorted in *reverse* order (cf., [PythonTutor <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](http://pythontutor.com/visualize.html#code=numbers%20%3D%20%5B7,%2011,%208,%205,%203,%2012,%202,%206,%209,%2010,%201,%204%5D%0Amemoryless%20%3D%20range%281,%2013%29%0Aresult1%20%3D%20sorted%28numbers,%20reverse%3DTrue%29%0Aresult2%20%3D%20sorted%28memoryless,%20reverse%3DTrue%29&cumulative=false&curstr=4&heapPrimitives=nevernest&mode=display&origin=opt-frontend.js&py=3&rawInputLstJSON=%5B%5D&textReferences=false))."
    ]
   },
   {
@@ -1142,7 +1142,7 @@
     }
    },
    "source": [
-    "The [reversed() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#reversed) built-in takes a `sequence` argument and returns an *iterator*. The argument must be *finite* and *reversible* (i.e., *iterable* in *reverse* order) as otherwise [reversed() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#reversed) could neither determine the last element that becomes the first nor loop in a *predictable* backward fashion. [PythonTutor <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](http://pythontutor.com/visualize.html#code=numbers%20%3D%20%5B7,%2011,%208,%205,%203,%2012,%202,%206,%209,%2010,%201,%204%5D%0Amemoryless%20%3D%20range%281,%2013%29%0Aiterator1%20%3D%20reversed%28numbers%29%0Aiterator2%20%3D%20reversed%28memoryless%29&cumulative=false&curInstr=4&heapPrimitives=nevernest&mode=display&origin=opt-frontend.js&py=3&rawInputLstJSON=%5B%5D&textReferences=false) confirms that [reversed() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#reversed) does *not* materialize any elements but only returns an *iterator*.\n",
+    "The [reversed() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#reversed) built-in takes a `sequence` argument and returns an *iterator*. The argument must be *finite* and *reversible* (i.e., *iterable* in *reverse* order) as otherwise [reversed() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#reversed) could neither determine the last element that becomes the first nor loop in a *predictable* backward fashion. [PythonTutor <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](http://pythontutor.com/visualize.html#code=numbers%20%3D%20%5B7,%2011,%208,%205,%203,%2012,%202,%206,%209,%2010,%201,%204%5D%0Amemoryless%20%3D%20range%281,%2013%29%0Aiterator1%20%3D%20reversed%28numbers%29%0Aiterator2%20%3D%20reversed%28memoryless%29&cumulative=false&curstr=4&heapPrimitives=nevernest&mode=display&origin=opt-frontend.js&py=3&rawInputLstJSON=%5B%5D&textReferences=false) confirms that [reversed() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#reversed) does *not* materialize any elements but only returns an *iterator*.\n",
     "\n",
     "**Side Note**: Even though `range` objects, like `memoryless` here, do *not* \"contain\" references to other objects, they count as *sequence* types, and as such, they are also *container* types. The `in` operator works with `range` objects because we can always cast the object to be checked as an `int` and check if that lies within the `range` object's `start` and `stop` values, taking a potential `step` value into account (cf., this [blog post](https://treyhunner.com/2018/02/python-range-is-not-an-iterator/) for more details on the [range() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#func-range) built-in)."
    ]
@@ -1203,7 +1203,7 @@
     }
    },
    "source": [
-    "To materialize the elements, we can pass the returned *iterators* to, for example, the [list() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#func-list) or [tuple() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#func-tuple) constructors. That creates *new* `list` and `tuple` objects (cf., [PythonTutor <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](http://pythontutor.com/visualize.html#code=numbers%20%3D%20%5B7,%2011,%208,%205,%203,%2012,%202,%206,%209,%2010,%201,%204%5D%0Amemoryless%20%3D%20range%281,%2013%29%0Aresult1%20%3D%20list%28reversed%28numbers%29%29%0Aresult2%20%3D%20tuple%28reversed%28memoryless%29%29&cumulative=false&curInstr=4&heapPrimitives=nevernest&mode=display&origin=opt-frontend.js&py=3&rawInputLstJSON=%5B%5D&textReferences=false)).\n",
+    "To materialize the elements, we can pass the returned *iterators* to, for example, the [list() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#func-list) or [tuple() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#func-tuple) constructors. That creates *new* `list` and `tuple` objects (cf., [PythonTutor <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](http://pythontutor.com/visualize.html#code=numbers%20%3D%20%5B7,%2011,%208,%205,%203,%2012,%202,%206,%209,%2010,%201,%204%5D%0Amemoryless%20%3D%20range%281,%2013%29%0Aresult1%20%3D%20list%28reversed%28numbers%29%29%0Aresult2%20%3D%20tuple%28reversed%28memoryless%29%29&cumulative=false&curstr=4&heapPrimitives=nevernest&mode=display&origin=opt-frontend.js&py=3&rawInputLstJSON=%5B%5D&textReferences=false)).\n",
     "\n",
     "To reiterate some more new terminology from this chapter, we describe [reversed() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#reversed) as *lazy* whereas [list() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#func-list) and [tuple() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#func-tuple) are *eager*. The former has no significant side effect in memory, while the latter may require a lot of memory."
    ]
@@ -1640,7 +1640,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.6"
+   "version": "3.12.2"
   },
   "livereveal": {
    "auto_select": "code",
diff --git a/08_mfr/05_summary.ipynb b/08_mfr/05_summary.ipynb
index 361fbd0..28859bc 100644
--- a/08_mfr/05_summary.ipynb
+++ b/08_mfr/05_summary.ipynb
@@ -43,7 +43,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.6"
+   "version": "3.12.2"
   },
   "livereveal": {
    "auto_select": "code",
diff --git a/08_mfr/06_review.ipynb b/08_mfr/06_review.ipynb
index 2f3d29f..faf8ab8 100644
--- a/08_mfr/06_review.ipynb
+++ b/08_mfr/06_review.ipynb
@@ -193,7 +193,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.6"
+   "version": "3.12.2"
   },
   "toc": {
    "base_numbering": 1,

From 88c67873dd313646a7690067d79148d34d8b9cbf Mon Sep 17 00:00:00 2001
From: Alexander Hess <alexander@webartifex.biz>
Date: Mon, 8 Apr 2024 16:51:05 +0200
Subject: [PATCH 234/242] Re-run Chapter 09 files with Python 3.12

---
 09_mappings/00_content.ipynb   | 14 +++++++-------
 09_mappings/01_exercises.ipynb |  2 +-
 09_mappings/02_content.ipynb   | 27 +++++++++++++--------------
 09_mappings/03_exercises.ipynb |  2 +-
 09_mappings/04_content.ipynb   | 12 ++++++------
 09_mappings/05_appendix.ipynb  | 32 ++++++++++++++++----------------
 09_mappings/06_summary.ipynb   |  2 +-
 09_mappings/07_review.ipynb    |  2 +-
 09_mappings/08_resources.ipynb |  5 ++++-
 9 files changed, 50 insertions(+), 48 deletions(-)

diff --git a/09_mappings/00_content.ipynb b/09_mappings/00_content.ipynb
index b20e7ec..4e97131 100644
--- a/09_mappings/00_content.ipynb
+++ b/09_mappings/00_content.ipynb
@@ -571,7 +571,7 @@
      "traceback": [
       "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
       "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-17-20a24130a135>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m {\n\u001b[0m\u001b[1;32m      2\u001b[0m     \u001b[0;34m[\u001b[0m\u001b[0;34m\"zero\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"one\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m }\n",
+      "Cell \u001b[0;32mIn[17], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m {\n\u001b[1;32m      2\u001b[0m     [\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mzero\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mone\u001b[39m\u001b[38;5;124m\"\u001b[39m]: [\u001b[38;5;241m0\u001b[39m, \u001b[38;5;241m1\u001b[39m],\n\u001b[1;32m      3\u001b[0m }\n",
       "\u001b[0;31mTypeError\u001b[0m: unhashable type: 'list'"
      ]
     }
@@ -837,7 +837,7 @@
      "traceback": [
       "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
       "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-24-1afa4b4779b0>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mhash\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"zero\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"one\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "Cell \u001b[0;32mIn[24], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[38;5;28;43mhash\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mzero\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mone\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m)\u001b[49m\n",
       "\u001b[0;31mTypeError\u001b[0m: unhashable type: 'list'"
      ]
     }
@@ -1272,7 +1272,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Python 3.8.6\n"
+      "Python 3.12.2\n"
      ]
     }
    ],
@@ -1952,7 +1952,7 @@
      "traceback": [
       "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
       "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-61-8602ef11ff73>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mfrom_words\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"drei\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "Cell \u001b[0;32mIn[61], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mfrom_words\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mdrei\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\n",
       "\u001b[0;31mKeyError\u001b[0m: 'drei'"
      ]
     }
@@ -2840,7 +2840,7 @@
      "traceback": [
       "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
       "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-91-30b825de37f0>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mto_words\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "Cell \u001b[0;32mIn[91], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mto_words\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;241;43m0\u001b[39;49m\u001b[43m]\u001b[49m\n",
       "\u001b[0;31mKeyError\u001b[0m: 0"
      ]
     }
@@ -3099,7 +3099,7 @@
      "traceback": [
       "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
       "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-101-b91e52fa79fd>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mfrom_words\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpop\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"zero\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "Cell \u001b[0;32mIn[101], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mfrom_words\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mpop\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mzero\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\n",
       "\u001b[0;31mKeyError\u001b[0m: 'zero'"
      ]
     }
@@ -3741,7 +3741,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.6"
+   "version": "3.12.2"
   },
   "livereveal": {
    "auto_select": "code",
diff --git a/09_mappings/01_exercises.ipynb b/09_mappings/01_exercises.ipynb
index 72b503d..82eae0d 100644
--- a/09_mappings/01_exercises.ipynb
+++ b/09_mappings/01_exercises.ipynb
@@ -407,7 +407,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.6"
+   "version": "3.12.2"
   },
   "toc": {
    "base_numbering": 1,
diff --git a/09_mappings/02_content.ipynb b/09_mappings/02_content.ipynb
index 78ac6ff..e0b9083 100644
--- a/09_mappings/02_content.ipynb
+++ b/09_mappings/02_content.ipynb
@@ -394,7 +394,7 @@
      "traceback": [
       "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
       "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-13-21a06a55e707>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mprint_args2\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "Cell \u001b[0;32mIn[13], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mprint_args2\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n",
       "\u001b[0;31mTypeError\u001b[0m: print_args2() missing 1 required positional argument: 'positional'"
      ]
     }
@@ -419,7 +419,7 @@
      "traceback": [
       "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
       "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-14-06c908f24fbb>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mprint_args2\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"p\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "Cell \u001b[0;32mIn[14], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mprint_args2\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mp\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\n",
       "\u001b[0;31mTypeError\u001b[0m: print_args2() missing 1 required keyword-only argument: 'keyword'"
      ]
     }
@@ -1045,17 +1045,16 @@
      "traceback": [
       "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
       "\u001b[0;31mRecursionError\u001b[0m                            Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-32-db4be504bbfd>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mget_ipython\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrun_cell_magic\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'timeit'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'-n 1'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'fibonacci(9999)\\n'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
-      "\u001b[0;32m~/repos/intro-to-python/.venv/lib/python3.8/site-packages/IPython/core/interactiveshell.py\u001b[0m in \u001b[0;36mrun_cell_magic\u001b[0;34m(self, magic_name, line, cell)\u001b[0m\n\u001b[1;32m   2379\u001b[0m             \u001b[0;32mwith\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbuiltin_trap\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2380\u001b[0m                 \u001b[0margs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mmagic_arg_s\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcell\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2381\u001b[0;31m                 \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2382\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mresult\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2383\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m<decorator-gen-53>\u001b[0m in \u001b[0;36mtimeit\u001b[0;34m(self, line, cell, local_ns)\u001b[0m\n",
-      "\u001b[0;32m~/repos/intro-to-python/.venv/lib/python3.8/site-packages/IPython/core/magic.py\u001b[0m in \u001b[0;36m<lambda>\u001b[0;34m(f, *a, **k)\u001b[0m\n\u001b[1;32m    185\u001b[0m     \u001b[0;31m# but it's overkill for just that one bit of state.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    186\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mmagic_deco\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marg\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 187\u001b[0;31m         \u001b[0mcall\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mlambda\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mk\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mk\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    188\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    189\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mcallable\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marg\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/repos/intro-to-python/.venv/lib/python3.8/site-packages/IPython/core/magics/execution.py\u001b[0m in \u001b[0;36mtimeit\u001b[0;34m(self, line, cell, local_ns)\u001b[0m\n\u001b[1;32m   1171\u001b[0m                     \u001b[0;32mbreak\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1172\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1173\u001b[0;31m         \u001b[0mall_runs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtimer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrepeat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrepeat\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnumber\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1174\u001b[0m         \u001b[0mbest\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mall_runs\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m/\u001b[0m \u001b[0mnumber\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1175\u001b[0m         \u001b[0mworst\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmax\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mall_runs\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m/\u001b[0m \u001b[0mnumber\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/.pyenv/versions/3.8.6/lib/python3.8/timeit.py\u001b[0m in \u001b[0;36mrepeat\u001b[0;34m(self, repeat, number)\u001b[0m\n\u001b[1;32m    203\u001b[0m         \u001b[0mr\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    204\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrepeat\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 205\u001b[0;31m             \u001b[0mt\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtimeit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnumber\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    206\u001b[0m             \u001b[0mr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mt\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    207\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mr\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/repos/intro-to-python/.venv/lib/python3.8/site-packages/IPython/core/magics/execution.py\u001b[0m in \u001b[0;36mtimeit\u001b[0;34m(self, number)\u001b[0m\n\u001b[1;32m    167\u001b[0m         \u001b[0mgc\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdisable\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    168\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 169\u001b[0;31m             \u001b[0mtiming\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0minner\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mit\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtimer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    170\u001b[0m         \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    171\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mgcold\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m<magic-timeit>\u001b[0m in \u001b[0;36minner\u001b[0;34m(_it, _timer)\u001b[0m\n",
-      "\u001b[0;32m<ipython-input-26-dd55d928f4d4>\u001b[0m in \u001b[0;36mfibonacci\u001b[0;34m(i, debug)\u001b[0m\n\u001b[1;32m     15\u001b[0m         \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34mf\"fibonacci({i}) is calculated\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     16\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 17\u001b[0;31m     \u001b[0mrecurse\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfibonacci\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mi\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdebug\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdebug\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mfibonacci\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mi\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0;36m2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdebug\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdebug\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     18\u001b[0m     \u001b[0mmemo\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mrecurse\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     19\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mrecurse\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "... last 1 frames repeated, from the frame below ...\n",
-      "\u001b[0;32m<ipython-input-26-dd55d928f4d4>\u001b[0m in \u001b[0;36mfibonacci\u001b[0;34m(i, debug)\u001b[0m\n\u001b[1;32m     15\u001b[0m         \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34mf\"fibonacci({i}) is calculated\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     16\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 17\u001b[0;31m     \u001b[0mrecurse\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfibonacci\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mi\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdebug\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdebug\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mfibonacci\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mi\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0;36m2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdebug\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdebug\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     18\u001b[0m     \u001b[0mmemo\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mrecurse\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     19\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mrecurse\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "Cell \u001b[0;32mIn[32], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mget_ipython\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrun_cell_magic\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mtimeit\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43m-n 1\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mfibonacci(9999)\u001b[39;49m\u001b[38;5;130;43;01m\\n\u001b[39;49;00m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m)\u001b[49m\n",
+      "File \u001b[0;32m~/Repositories/intro-to-python/.venv/lib/python3.12/site-packages/IPython/core/interactiveshell.py:2541\u001b[0m, in \u001b[0;36mInteractiveShell.run_cell_magic\u001b[0;34m(self, magic_name, line, cell)\u001b[0m\n\u001b[1;32m   2539\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mbuiltin_trap:\n\u001b[1;32m   2540\u001b[0m     args \u001b[38;5;241m=\u001b[39m (magic_arg_s, cell)\n\u001b[0;32m-> 2541\u001b[0m     result \u001b[38;5;241m=\u001b[39m \u001b[43mfn\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   2543\u001b[0m \u001b[38;5;66;03m# The code below prevents the output from being displayed\u001b[39;00m\n\u001b[1;32m   2544\u001b[0m \u001b[38;5;66;03m# when using magics with decorator @output_can_be_silenced\u001b[39;00m\n\u001b[1;32m   2545\u001b[0m \u001b[38;5;66;03m# when the last Python token in the expression is a ';'.\u001b[39;00m\n\u001b[1;32m   2546\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mgetattr\u001b[39m(fn, magic\u001b[38;5;241m.\u001b[39mMAGIC_OUTPUT_CAN_BE_SILENCED, \u001b[38;5;28;01mFalse\u001b[39;00m):\n",
+      "File \u001b[0;32m~/Repositories/intro-to-python/.venv/lib/python3.12/site-packages/IPython/core/magics/execution.py:1189\u001b[0m, in \u001b[0;36mExecutionMagics.timeit\u001b[0;34m(self, line, cell, local_ns)\u001b[0m\n\u001b[1;32m   1186\u001b[0m         \u001b[38;5;28;01mif\u001b[39;00m time_number \u001b[38;5;241m>\u001b[39m\u001b[38;5;241m=\u001b[39m \u001b[38;5;241m0.2\u001b[39m:\n\u001b[1;32m   1187\u001b[0m             \u001b[38;5;28;01mbreak\u001b[39;00m\n\u001b[0;32m-> 1189\u001b[0m all_runs \u001b[38;5;241m=\u001b[39m \u001b[43mtimer\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrepeat\u001b[49m\u001b[43m(\u001b[49m\u001b[43mrepeat\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mnumber\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1190\u001b[0m best \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mmin\u001b[39m(all_runs) \u001b[38;5;241m/\u001b[39m number\n\u001b[1;32m   1191\u001b[0m worst \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mmax\u001b[39m(all_runs) \u001b[38;5;241m/\u001b[39m number\n",
+      "File \u001b[0;32m/usr/lib64/python3.12/timeit.py:208\u001b[0m, in \u001b[0;36mTimer.repeat\u001b[0;34m(self, repeat, number)\u001b[0m\n\u001b[1;32m    206\u001b[0m r \u001b[38;5;241m=\u001b[39m []\n\u001b[1;32m    207\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m i \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mrange\u001b[39m(repeat):\n\u001b[0;32m--> 208\u001b[0m     t \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mtimeit\u001b[49m\u001b[43m(\u001b[49m\u001b[43mnumber\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    209\u001b[0m     r\u001b[38;5;241m.\u001b[39mappend(t)\n\u001b[1;32m    210\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m r\n",
+      "File \u001b[0;32m~/Repositories/intro-to-python/.venv/lib/python3.12/site-packages/IPython/core/magics/execution.py:173\u001b[0m, in \u001b[0;36mTimer.timeit\u001b[0;34m(self, number)\u001b[0m\n\u001b[1;32m    171\u001b[0m gc\u001b[38;5;241m.\u001b[39mdisable()\n\u001b[1;32m    172\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 173\u001b[0m     timing \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43minner\u001b[49m\u001b[43m(\u001b[49m\u001b[43mit\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mtimer\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    174\u001b[0m \u001b[38;5;28;01mfinally\u001b[39;00m:\n\u001b[1;32m    175\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m gcold:\n",
+      "File \u001b[0;32m<magic-timeit>:1\u001b[0m, in \u001b[0;36minner\u001b[0;34m(_it, _timer)\u001b[0m\n",
+      "Cell \u001b[0;32mIn[26], line 17\u001b[0m, in \u001b[0;36mfibonacci\u001b[0;34m(i, debug)\u001b[0m\n\u001b[1;32m     14\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m debug:  \u001b[38;5;66;03m# added for didactical purposes\u001b[39;00m\n\u001b[1;32m     15\u001b[0m     \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mfibonacci(\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mi\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m) is calculated\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m---> 17\u001b[0m recurse \u001b[38;5;241m=\u001b[39m \u001b[43mfibonacci\u001b[49m\u001b[43m(\u001b[49m\u001b[43mi\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m-\u001b[39;49m\u001b[43m \u001b[49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdebug\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdebug\u001b[49m\u001b[43m)\u001b[49m \u001b[38;5;241m+\u001b[39m fibonacci(i \u001b[38;5;241m-\u001b[39m \u001b[38;5;241m2\u001b[39m, debug\u001b[38;5;241m=\u001b[39mdebug)\n\u001b[1;32m     18\u001b[0m memo[i] \u001b[38;5;241m=\u001b[39m recurse\n\u001b[1;32m     19\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m recurse\n",
+      "Cell \u001b[0;32mIn[26], line 17\u001b[0m, in \u001b[0;36mfibonacci\u001b[0;34m(i, debug)\u001b[0m\n\u001b[1;32m     14\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m debug:  \u001b[38;5;66;03m# added for didactical purposes\u001b[39;00m\n\u001b[1;32m     15\u001b[0m     \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mfibonacci(\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mi\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m) is calculated\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m---> 17\u001b[0m recurse \u001b[38;5;241m=\u001b[39m \u001b[43mfibonacci\u001b[49m\u001b[43m(\u001b[49m\u001b[43mi\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m-\u001b[39;49m\u001b[43m \u001b[49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdebug\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdebug\u001b[49m\u001b[43m)\u001b[49m \u001b[38;5;241m+\u001b[39m fibonacci(i \u001b[38;5;241m-\u001b[39m \u001b[38;5;241m2\u001b[39m, debug\u001b[38;5;241m=\u001b[39mdebug)\n\u001b[1;32m     18\u001b[0m memo[i] \u001b[38;5;241m=\u001b[39m recurse\n\u001b[1;32m     19\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m recurse\n",
+      "    \u001b[0;31m[... skipping similar frames: fibonacci at line 17 (2969 times)]\u001b[0m\n",
+      "Cell \u001b[0;32mIn[26], line 17\u001b[0m, in \u001b[0;36mfibonacci\u001b[0;34m(i, debug)\u001b[0m\n\u001b[1;32m     14\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m debug:  \u001b[38;5;66;03m# added for didactical purposes\u001b[39;00m\n\u001b[1;32m     15\u001b[0m     \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mfibonacci(\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mi\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m) is calculated\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m---> 17\u001b[0m recurse \u001b[38;5;241m=\u001b[39m \u001b[43mfibonacci\u001b[49m\u001b[43m(\u001b[49m\u001b[43mi\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m-\u001b[39;49m\u001b[43m \u001b[49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdebug\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdebug\u001b[49m\u001b[43m)\u001b[49m \u001b[38;5;241m+\u001b[39m fibonacci(i \u001b[38;5;241m-\u001b[39m \u001b[38;5;241m2\u001b[39m, debug\u001b[38;5;241m=\u001b[39mdebug)\n\u001b[1;32m     18\u001b[0m memo[i] \u001b[38;5;241m=\u001b[39m recurse\n\u001b[1;32m     19\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m recurse\n",
       "\u001b[0;31mRecursionError\u001b[0m: maximum recursion depth exceeded"
      ]
     }
@@ -1205,7 +1204,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.6"
+   "version": "3.12.2"
   },
   "livereveal": {
    "auto_select": "code",
diff --git a/09_mappings/03_exercises.ipynb b/09_mappings/03_exercises.ipynb
index ea10e8f..1ef6bf8 100644
--- a/09_mappings/03_exercises.ipynb
+++ b/09_mappings/03_exercises.ipynb
@@ -227,7 +227,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.6"
+   "version": "3.12.2"
   },
   "toc": {
    "base_numbering": 1,
diff --git a/09_mappings/04_content.ipynb b/09_mappings/04_content.ipynb
index 06e0f7f..6b97bf7 100644
--- a/09_mappings/04_content.ipynb
+++ b/09_mappings/04_content.ipynb
@@ -315,7 +315,7 @@
      "traceback": [
       "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
       "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-10-4ac30fae8196>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0;34m{\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m2\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m3\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "Cell \u001b[0;32mIn[10], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m {\u001b[38;5;241m0\u001b[39m, [\u001b[38;5;241m1\u001b[39m, \u001b[38;5;241m2\u001b[39m], \u001b[38;5;241m3\u001b[39m}\n",
       "\u001b[0;31mTypeError\u001b[0m: unhashable type: 'list'"
      ]
     }
@@ -419,7 +419,7 @@
      "traceback": [
       "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
       "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-13-1095d1ef6c80>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0;32mfor\u001b[0m \u001b[0mnumber\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mreversed\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnumbers\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m     \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnumber\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mend\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\" \"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "Cell \u001b[0;32mIn[13], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m number \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28;43mreversed\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mnumbers\u001b[49m\u001b[43m)\u001b[49m:\n\u001b[1;32m      2\u001b[0m     \u001b[38;5;28mprint\u001b[39m(number, end\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m \u001b[39m\u001b[38;5;124m\"\u001b[39m)\n",
       "\u001b[0;31mTypeError\u001b[0m: 'set' object is not reversible"
      ]
     }
@@ -646,7 +646,7 @@
      "traceback": [
       "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
       "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-21-7486ea9d37bc>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mnumbers\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "Cell \u001b[0;32mIn[21], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mnumbers\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;241;43m0\u001b[39;49m\u001b[43m]\u001b[49m\n",
       "\u001b[0;31mTypeError\u001b[0m: 'set' object is not subscriptable"
      ]
     }
@@ -671,7 +671,7 @@
      "traceback": [
       "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
       "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-22-4de4c0f84e99>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mnumbers\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "Cell \u001b[0;32mIn[22], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mnumbers\u001b[49m\u001b[43m[\u001b[49m\u001b[43m:\u001b[49m\u001b[43m]\u001b[49m\n",
       "\u001b[0;31mTypeError\u001b[0m: 'set' object is not subscriptable"
      ]
     }
@@ -829,7 +829,7 @@
      "traceback": [
       "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
       "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-28-45d8b57b937d>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mnumbers\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mremove\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m999\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "Cell \u001b[0;32mIn[28], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mnumbers\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mremove\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m999\u001b[39;49m\u001b[43m)\u001b[49m\n",
       "\u001b[0;31mKeyError\u001b[0m: 999"
      ]
     }
@@ -1573,7 +1573,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.6"
+   "version": "3.12.2"
   },
   "livereveal": {
    "auto_select": "code",
diff --git a/09_mappings/05_appendix.ipynb b/09_mappings/05_appendix.ipynb
index fbe47e3..95050b6 100644
--- a/09_mappings/05_appendix.ipynb
+++ b/09_mappings/05_appendix.ipynb
@@ -463,11 +463,11 @@
     {
      "data": {
       "text/plain": [
-       "Counter({'MĂĽller': 1,\n",
-       "         'Klose': 1,\n",
-       "         'Kroos': 2,\n",
-       "         'Khedira': 1,\n",
+       "Counter({'Kroos': 2,\n",
        "         'SchĂĽrrle': 2,\n",
+       "         'MĂĽller': 1,\n",
+       "         'Klose': 1,\n",
+       "         'Khedira': 1,\n",
        "         'Oscar': 1})"
       ]
      },
@@ -647,11 +647,11 @@
     {
      "data": {
       "text/plain": [
-       "Counter({'MĂĽller': 1,\n",
-       "         'Klose': 1,\n",
-       "         'Kroos': 2,\n",
-       "         'Khedira': 1,\n",
+       "Counter({'Kroos': 2,\n",
        "         'SchĂĽrrle': 2,\n",
+       "         'MĂĽller': 1,\n",
+       "         'Klose': 1,\n",
+       "         'Khedira': 1,\n",
        "         'Oscar': 1,\n",
        "         'Lahm': 1})"
       ]
@@ -701,11 +701,11 @@
     {
      "data": {
       "text/plain": [
-       "Counter({'MĂĽller': 1,\n",
-       "         'Klose': 1,\n",
-       "         'Kroos': 2,\n",
-       "         'Khedira': 1,\n",
+       "Counter({'Kroos': 2,\n",
        "         'SchĂĽrrle': 2,\n",
+       "         'MĂĽller': 1,\n",
+       "         'Klose': 1,\n",
+       "         'Khedira': 1,\n",
        "         'Oscar': 1,\n",
        "         'Lahm': 1,\n",
        "         'L': 1,\n",
@@ -910,9 +910,9 @@
      "traceback": [
       "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
       "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-28-25fb5b5b2e5f>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mchain\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m10\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
-      "\u001b[0;32m~/.pyenv/versions/3.8.6/lib/python3.8/collections/__init__.py\u001b[0m in \u001b[0;36m__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m    896\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    897\u001b[0m                 \u001b[0;32mpass\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 898\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__missing__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m            \u001b[0;31m# support subclasses that define __missing__\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    899\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    900\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdefault\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/.pyenv/versions/3.8.6/lib/python3.8/collections/__init__.py\u001b[0m in \u001b[0;36m__missing__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m    888\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    889\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__missing__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 890\u001b[0;31m         \u001b[0;32mraise\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    891\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    892\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__getitem__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "Cell \u001b[0;32mIn[28], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mchain\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;241;43m10\u001b[39;49m\u001b[43m]\u001b[49m\n",
+      "File \u001b[0;32m/usr/lib64/python3.12/collections/__init__.py:1014\u001b[0m, in \u001b[0;36mChainMap.__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m   1012\u001b[0m     \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m:\n\u001b[1;32m   1013\u001b[0m         \u001b[38;5;28;01mpass\u001b[39;00m\n\u001b[0;32m-> 1014\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[38;5;21;43m__missing__\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mkey\u001b[49m\u001b[43m)\u001b[49m\n",
+      "File \u001b[0;32m/usr/lib64/python3.12/collections/__init__.py:1006\u001b[0m, in \u001b[0;36mChainMap.__missing__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m   1005\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m__missing__\u001b[39m(\u001b[38;5;28mself\u001b[39m, key):\n\u001b[0;32m-> 1006\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m(key)\n",
       "\u001b[0;31mKeyError\u001b[0m: 10"
      ]
     }
@@ -938,7 +938,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.6"
+   "version": "3.12.2"
   },
   "livereveal": {
    "auto_select": "code",
diff --git a/09_mappings/06_summary.ipynb b/09_mappings/06_summary.ipynb
index 0b9c814..ef620b7 100644
--- a/09_mappings/06_summary.ipynb
+++ b/09_mappings/06_summary.ipynb
@@ -45,7 +45,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.6"
+   "version": "3.12.2"
   },
   "livereveal": {
    "auto_select": "code",
diff --git a/09_mappings/07_review.ipynb b/09_mappings/07_review.ipynb
index 7e7722d..a484886 100644
--- a/09_mappings/07_review.ipynb
+++ b/09_mappings/07_review.ipynb
@@ -226,7 +226,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.6"
+   "version": "3.12.2"
   },
   "toc": {
    "base_numbering": 1,
diff --git a/09_mappings/08_resources.ipynb b/09_mappings/08_resources.ipynb
index 9b23588..c68eef1 100644
--- a/09_mappings/08_resources.ipynb
+++ b/09_mappings/08_resources.ipynb
@@ -67,6 +67,7 @@
        "            src=\"https://www.youtube.com/embed/C4Kc8xzcA68\"\n",
        "            frameborder=\"0\"\n",
        "            allowfullscreen\n",
+       "            \n",
        "        ></iframe>\n",
        "        "
       ],
@@ -104,6 +105,7 @@
        "            src=\"https://www.youtube.com/embed/66P5FMkWoVU\"\n",
        "            frameborder=\"0\"\n",
        "            allowfullscreen\n",
+       "            \n",
        "        ></iframe>\n",
        "        "
       ],
@@ -163,6 +165,7 @@
        "            src=\"https://www.youtube.com/embed/npw4s1QTmPg\"\n",
        "            frameborder=\"0\"\n",
        "            allowfullscreen\n",
+       "            \n",
        "        ></iframe>\n",
        "        "
       ],
@@ -196,7 +199,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.6"
+   "version": "3.12.2"
   },
   "livereveal": {
    "auto_select": "code",

From 1902eb2a503d245862c322f8e0baea888413cdac Mon Sep 17 00:00:00 2001
From: Alexander Hess <alexander@webartifex.biz>
Date: Mon, 8 Apr 2024 16:53:41 +0200
Subject: [PATCH 235/242] Add new Chapter 10 on Arrays and DataFrames

---
 10_arrays/TODO.md | 1 +
 CONTENTS.md       | 1 +
 README.md         | 1 +
 3 files changed, 3 insertions(+)
 create mode 100644 10_arrays/TODO.md

diff --git a/10_arrays/TODO.md b/10_arrays/TODO.md
new file mode 100644
index 0000000..9902b8f
--- /dev/null
+++ b/10_arrays/TODO.md
@@ -0,0 +1 @@
+Here, a new section on `numpy` Arrays and, maybe, `pandas` DataFrames will be added.
\ No newline at end of file
diff --git a/CONTENTS.md b/CONTENTS.md
index bdad0d7..46e16ce 100644
--- a/CONTENTS.md
+++ b/CONTENTS.md
@@ -262,6 +262,7 @@ If this is not possible,
     - [review questions <img height="12" style="display: inline-block" src="static/link/to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/09_mappings/07_review.ipynb)
     - [further resources <img height="12" style="display: inline-block" src="static/link/to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/09_mappings/08_resources.ipynb)
       [<img height="12" style="display: inline-block" src="static/link/to_mb.png">](https://mybinder.org/v2/gh/webartifex/intro-to-python/develop?urlpath=lab/tree/09_mappings/08_resources.ipynb)
+  - *Chapter 10*: Arrays & Dataframes
   - *Chapter 11*: Classes & Instances
     - [content <img height="12" style="display: inline-block" src="static/link/to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/11_classes/00_content.ipynb)
       [<img height="12" style="display: inline-block" src="static/link/to_mb.png">](https://mybinder.org/v2/gh/webartifex/intro-to-python/develop?urlpath=lab/tree/11_classes/00_content.ipynb)
diff --git a/README.md b/README.md
index 49ae3d0..f3fffd8 100644
--- a/README.md
+++ b/README.md
@@ -22,6 +22,7 @@ For a more *detailed version* with **clickable links**
   - *Chapter 7*: Sequential Data
   - *Chapter 8*: Map, Filter, & Reduce
   - *Chapter 9*: Mappings & Sets
+  - *Chapter 10*: Arrays & Dataframes
   - *Chapter 11*: Classes & Instances
 
 

From c206a75f3cb01f1bdd5fafa7a42354e631d77417 Mon Sep 17 00:00:00 2001
From: Alexander Hess <alexander@webartifex.biz>
Date: Mon, 8 Apr 2024 17:03:29 +0200
Subject: [PATCH 236/242] Re-run Chapter 11 files with Python 3.12

---
 11_classes/00_content.ipynb   |  61 +++---
 11_classes/01_exercises.ipynb |   2 +-
 11_classes/02_content.ipynb   |   6 +-
 11_classes/03_content.ipynb   |  41 ++--
 11_classes/04_content.ipynb   | 390 +++++++++++++++++-----------------
 11_classes/05_summary.ipynb   |   2 +-
 11_classes/06_review.ipynb    |   2 +-
 7 files changed, 256 insertions(+), 248 deletions(-)

diff --git a/11_classes/00_content.ipynb b/11_classes/00_content.ipynb
index 34e3df3..b910095 100644
--- a/11_classes/00_content.ipynb
+++ b/11_classes/00_content.ipynb
@@ -212,7 +212,7 @@
      "traceback": [
       "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
       "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-5-fd81d962f516>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mA\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "Cell \u001b[0;32mIn[5], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mA\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43m \u001b[49m\u001b[43mx\u001b[49m\n",
       "\u001b[0;31mTypeError\u001b[0m: can't multiply sequence by non-int of type 'tuple'"
      ]
     }
@@ -405,7 +405,7 @@
        "\u001b[0;31mInit signature:\u001b[0m \u001b[0mVector\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
        "\u001b[0;31mDocstring:\u001b[0m      A one-dimensional vector from linear algebra.\n",
        "\u001b[0;31mType:\u001b[0m           type\n",
-       "\u001b[0;31mSubclasses:\u001b[0m     \n"
+       "\u001b[0;31mSubclasses:\u001b[0m     "
       ]
      },
      "metadata": {},
@@ -433,24 +433,24 @@
       "\n",
       "class Vector(builtins.object)\n",
       " |  A one-dimensional vector from linear algebra.\n",
-      " |  \n",
+      " |\n",
       " |  Methods defined here:\n",
-      " |  \n",
+      " |\n",
       " |  dummy_method(self)\n",
       " |      A dummy method for illustration purposes.\n",
-      " |  \n",
+      " |\n",
       " |  ----------------------------------------------------------------------\n",
       " |  Data descriptors defined here:\n",
-      " |  \n",
+      " |\n",
       " |  __dict__\n",
-      " |      dictionary for instance variables (if defined)\n",
-      " |  \n",
+      " |      dictionary for instance variables\n",
+      " |\n",
       " |  __weakref__\n",
-      " |      list of weak references to the object (if defined)\n",
-      " |  \n",
+      " |      list of weak references to the object\n",
+      " |\n",
       " |  ----------------------------------------------------------------------\n",
       " |  Data and other attributes defined here:\n",
-      " |  \n",
+      " |\n",
       " |  dummy_variable = 'I am a vector'\n",
       "\n"
      ]
@@ -521,6 +521,7 @@
        " '__format__',\n",
        " '__ge__',\n",
        " '__getattribute__',\n",
+       " '__getstate__',\n",
        " '__gt__',\n",
        " '__hash__',\n",
        " '__init__',\n",
@@ -632,13 +633,13 @@
    "outputs": [
     {
      "ename": "TypeError",
-     "evalue": "dummy_method() missing 1 required positional argument: 'self'",
+     "evalue": "Vector.dummy_method() missing 1 required positional argument: 'self'",
      "output_type": "error",
      "traceback": [
       "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
       "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-16-829638f9c7cf>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mVector\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdummy_method\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
-      "\u001b[0;31mTypeError\u001b[0m: dummy_method() missing 1 required positional argument: 'self'"
+      "Cell \u001b[0;32mIn[16], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mVector\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdummy_method\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n",
+      "\u001b[0;31mTypeError\u001b[0m: Vector.dummy_method() missing 1 required positional argument: 'self'"
      ]
     }
    ],
@@ -859,13 +860,13 @@
    "outputs": [
     {
      "ename": "TypeError",
-     "evalue": "__init__() missing 1 required positional argument: 'data'",
+     "evalue": "Vector.__init__() missing 1 required positional argument: 'data'",
      "output_type": "error",
      "traceback": [
       "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
       "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-23-bcda4006949f>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mVector\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
-      "\u001b[0;31mTypeError\u001b[0m: __init__() missing 1 required positional argument: 'data'"
+      "Cell \u001b[0;32mIn[22], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mVector\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n",
+      "\u001b[0;31mTypeError\u001b[0m: Vector.__init__() missing 1 required positional argument: 'data'"
      ]
     }
    ],
@@ -884,13 +885,13 @@
    "outputs": [
     {
      "ename": "TypeError",
-     "evalue": "__init__() takes 2 positional arguments but 4 were given",
+     "evalue": "Vector.__init__() takes 2 positional arguments but 4 were given",
      "output_type": "error",
      "traceback": [
       "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
       "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-24-e00314c6a560>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mVector\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m3\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
-      "\u001b[0;31mTypeError\u001b[0m: __init__() takes 2 positional arguments but 4 were given"
+      "Cell \u001b[0;32mIn[23], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mVector\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m2\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m3\u001b[39;49m\u001b[43m)\u001b[49m\n",
+      "\u001b[0;31mTypeError\u001b[0m: Vector.__init__() takes 2 positional arguments but 4 were given"
      ]
     }
    ],
@@ -925,8 +926,8 @@
      "traceback": [
       "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
       "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-25-7261658e317e>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mVector\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
-      "\u001b[0;32m<ipython-input-17-441d7f5df856>\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, data)\u001b[0m\n\u001b[1;32m     16\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_entries\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfloat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mx\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     17\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_entries\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 18\u001b[0;31m             \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"a vector must have at least one entry\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "Cell \u001b[0;32mIn[24], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mVector\u001b[49m\u001b[43m(\u001b[49m\u001b[43m[\u001b[49m\u001b[43m]\u001b[49m\u001b[43m)\u001b[49m\n",
+      "Cell \u001b[0;32mIn[17], line 18\u001b[0m, in \u001b[0;36mVector.__init__\u001b[0;34m(self, data)\u001b[0m\n\u001b[1;32m     16\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_entries \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mlist\u001b[39m(\u001b[38;5;28mfloat\u001b[39m(x) \u001b[38;5;28;01mfor\u001b[39;00m x \u001b[38;5;129;01min\u001b[39;00m data)\n\u001b[1;32m     17\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_entries) \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m0\u001b[39m:\n\u001b[0;32m---> 18\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124ma vector must have at least one entry\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n",
       "\u001b[0;31mValueError\u001b[0m: a vector must have at least one entry"
      ]
     }
@@ -1589,8 +1590,8 @@
      "traceback": [
       "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
       "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-46-87c8bd55bdc1>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mMatrix\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
-      "\u001b[0;32m<ipython-input-37-2c8575833cd9>\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, data)\u001b[0m\n\u001b[1;32m      7\u001b[0m                 \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"rows must have the same number of entries\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      8\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_entries\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 9\u001b[0;31m             \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"a matrix must have at least one entry\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     10\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     11\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__repr__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "Cell \u001b[0;32mIn[45], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mMatrix\u001b[49m\u001b[43m(\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\n",
+      "Cell \u001b[0;32mIn[36], line 9\u001b[0m, in \u001b[0;36mMatrix.__init__\u001b[0;34m(self, data)\u001b[0m\n\u001b[1;32m      7\u001b[0m         \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mrows must have the same number of entries\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m      8\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_entries) \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m0\u001b[39m:\n\u001b[0;32m----> 9\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124ma matrix must have at least one entry\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n",
       "\u001b[0;31mValueError\u001b[0m: a matrix must have at least one entry"
      ]
     }
@@ -1615,8 +1616,8 @@
      "traceback": [
       "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
       "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-47-359f87edecdf>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mMatrix\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m3\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0;36m4\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m5\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
-      "\u001b[0;32m<ipython-input-37-2c8575833cd9>\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, data)\u001b[0m\n\u001b[1;32m      5\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0mrow\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_entries\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      6\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrow\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_entries\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 7\u001b[0;31m                 \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"rows must have the same number of entries\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      8\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_entries\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      9\u001b[0m             \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"a matrix must have at least one entry\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "Cell \u001b[0;32mIn[46], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mMatrix\u001b[49m\u001b[43m(\u001b[49m\u001b[43m[\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m2\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m3\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m4\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m5\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m]\u001b[49m\u001b[43m)\u001b[49m\n",
+      "Cell \u001b[0;32mIn[36], line 7\u001b[0m, in \u001b[0;36mMatrix.__init__\u001b[0;34m(self, data)\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m row \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_entries[\u001b[38;5;241m1\u001b[39m:]:\n\u001b[1;32m      6\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(row) \u001b[38;5;241m!=\u001b[39m \u001b[38;5;28mlen\u001b[39m(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_entries[\u001b[38;5;241m0\u001b[39m]):\n\u001b[0;32m----> 7\u001b[0m         \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mrows must have the same number of entries\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m      8\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_entries) \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m0\u001b[39m:\n\u001b[1;32m      9\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124ma matrix must have at least one entry\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n",
       "\u001b[0;31mValueError\u001b[0m: rows must have the same number of entries"
      ]
     }
@@ -2083,13 +2084,13 @@
    "outputs": [
     {
      "ename": "AttributeError",
-     "evalue": "can't set attribute",
+     "evalue": "property 'n_rows' of 'Matrix' object has no setter",
      "output_type": "error",
      "traceback": [
       "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
       "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-62-f4cc4767ae11>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mm\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mn_rows\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m3\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
-      "\u001b[0;31mAttributeError\u001b[0m: can't set attribute"
+      "Cell \u001b[0;32mIn[61], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mm\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mn_rows\u001b[49m \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m3\u001b[39m\n",
+      "\u001b[0;31mAttributeError\u001b[0m: property 'n_rows' of 'Matrix' object has no setter"
      ]
     }
    ],
@@ -2114,7 +2115,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.6"
+   "version": "3.12.2"
   },
   "livereveal": {
    "auto_select": "code",
diff --git a/11_classes/01_exercises.ipynb b/11_classes/01_exercises.ipynb
index 915dbc9..cdc755c 100644
--- a/11_classes/01_exercises.ipynb
+++ b/11_classes/01_exercises.ipynb
@@ -1476,7 +1476,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.6"
+   "version": "3.12.2"
   },
   "toc": {
    "base_numbering": 1,
diff --git a/11_classes/02_content.ipynb b/11_classes/02_content.ipynb
index 90d650e..fb8a6bd 100644
--- a/11_classes/02_content.ipynb
+++ b/11_classes/02_content.ipynb
@@ -346,7 +346,7 @@
      "traceback": [
       "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
       "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-10-ac550dc2349d>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mv\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m99\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "Cell \u001b[0;32mIn[10], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mv\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;241;43m0\u001b[39;49m\u001b[43m]\u001b[49m \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m99\u001b[39m\n",
       "\u001b[0;31mTypeError\u001b[0m: 'Vector' object does not support item assignment"
      ]
     }
@@ -1200,7 +1200,7 @@
      "traceback": [
       "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
       "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-37-21b8354a80f8>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mm\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtranspose\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtranspose\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "Cell \u001b[0;32mIn[37], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mm\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mtranspose\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mtranspose\u001b[49m()\n",
       "\u001b[0;31mAttributeError\u001b[0m: 'NoneType' object has no attribute 'transpose'"
      ]
     }
@@ -1951,7 +1951,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.6"
+   "version": "3.12.2"
   },
   "livereveal": {
    "auto_select": "code",
diff --git a/11_classes/03_content.ipynb b/11_classes/03_content.ipynb
index 32491d8..3d40e39 100644
--- a/11_classes/03_content.ipynb
+++ b/11_classes/03_content.ipynb
@@ -415,8 +415,8 @@
      "traceback": [
       "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
       "\u001b[0;31mRuntimeError\u001b[0m                              Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-14-625d8ddab141>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mm\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mas_vector\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
-      "\u001b[0;32m<ipython-input-2-3a6f1abfe70b>\u001b[0m in \u001b[0;36mas_vector\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m     22\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mas_vector\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     23\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mn_rows\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m1\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mn_cols\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 24\u001b[0;31m             \u001b[0;32mraise\u001b[0m \u001b[0mRuntimeError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"one dimension (m or n) must be 1\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     25\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mVector\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mx\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "Cell \u001b[0;32mIn[14], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mm\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mas_vector\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n",
+      "Cell \u001b[0;32mIn[2], line 24\u001b[0m, in \u001b[0;36mMatrix.as_vector\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m     22\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mas_vector\u001b[39m(\u001b[38;5;28mself\u001b[39m):\n\u001b[1;32m     23\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m (\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mn_rows \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m1\u001b[39m \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mn_cols \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m1\u001b[39m):\n\u001b[0;32m---> 24\u001b[0m         \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mRuntimeError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mone dimension (m or n) must be 1\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m     25\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m Vector(x \u001b[38;5;28;01mfor\u001b[39;00m x \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m)\n",
       "\u001b[0;31mRuntimeError\u001b[0m: one dimension (m or n) must be 1"
      ]
     }
@@ -889,8 +889,8 @@
      "traceback": [
       "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
       "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-27-21fab2d5f12e>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mv\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0mw\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
-      "\u001b[0;32m<ipython-input-21-e7b5dd8e219a>\u001b[0m in \u001b[0;36m__mul__\u001b[0;34m(self, other)\u001b[0m\n\u001b[1;32m     45\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mother\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__class__\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m  \u001b[0;31m# dot product\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     46\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mother\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 47\u001b[0;31m                 \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"vectors must be of the same length\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     48\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0msum\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0my\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mzip\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mother\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     49\u001b[0m         \u001b[0;32melif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mother\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnumbers\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mNumber\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m  \u001b[0;31m# scalar multiplication\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "Cell \u001b[0;32mIn[27], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mv\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43m \u001b[49m\u001b[43mw\u001b[49m\n",
+      "Cell \u001b[0;32mIn[21], line 47\u001b[0m, in \u001b[0;36mVector.__mul__\u001b[0;34m(self, other)\u001b[0m\n\u001b[1;32m     45\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(other, Vector):  \u001b[38;5;66;03m# dot product\u001b[39;00m\n\u001b[1;32m     46\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(\u001b[38;5;28mself\u001b[39m) \u001b[38;5;241m!=\u001b[39m \u001b[38;5;28mlen\u001b[39m(other):\n\u001b[0;32m---> 47\u001b[0m         \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mvectors must be of the same length\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m     48\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28msum\u001b[39m(x \u001b[38;5;241m*\u001b[39m y \u001b[38;5;28;01mfor\u001b[39;00m (x, y) \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mzip\u001b[39m(\u001b[38;5;28mself\u001b[39m, other))\n\u001b[1;32m     49\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(other, numbers\u001b[38;5;241m.\u001b[39mNumber):  \u001b[38;5;66;03m# scalar multiplication\u001b[39;00m\n",
       "\u001b[0;31mValueError\u001b[0m: vectors must be of the same length"
      ]
     }
@@ -1009,8 +1009,8 @@
      "traceback": [
       "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
       "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-31-490ee3f2b9e8>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mv\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mw\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
-      "\u001b[0;32m<ipython-input-21-e7b5dd8e219a>\u001b[0m in \u001b[0;36m__add__\u001b[0;34m(self, other)\u001b[0m\n\u001b[1;32m     18\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mother\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__class__\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m  \u001b[0;31m# vector addition\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     19\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mother\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 20\u001b[0;31m                 \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"vectors must be of the same length\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     21\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mVector\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0my\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mzip\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mother\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     22\u001b[0m         \u001b[0;32melif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mother\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnumbers\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mNumber\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m  \u001b[0;31m# broadcasting addition\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "Cell \u001b[0;32mIn[31], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mv\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m+\u001b[39;49m\u001b[43m \u001b[49m\u001b[43mw\u001b[49m\n",
+      "Cell \u001b[0;32mIn[21], line 20\u001b[0m, in \u001b[0;36mVector.__add__\u001b[0;34m(self, other)\u001b[0m\n\u001b[1;32m     18\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(other, Vector):  \u001b[38;5;66;03m# vector addition\u001b[39;00m\n\u001b[1;32m     19\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(\u001b[38;5;28mself\u001b[39m) \u001b[38;5;241m!=\u001b[39m \u001b[38;5;28mlen\u001b[39m(other):\n\u001b[0;32m---> 20\u001b[0m         \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mvectors must be of the same length\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m     21\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m Vector(x \u001b[38;5;241m+\u001b[39m y \u001b[38;5;28;01mfor\u001b[39;00m (x, y) \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mzip\u001b[39m(\u001b[38;5;28mself\u001b[39m, other))\n\u001b[1;32m     22\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(other, numbers\u001b[38;5;241m.\u001b[39mNumber):  \u001b[38;5;66;03m# broadcasting addition\u001b[39;00m\n",
       "\u001b[0;31mValueError\u001b[0m: vectors must be of the same length"
      ]
     }
@@ -1098,6 +1098,7 @@
        "            src=\"https://www.youtube.com/embed/OMA2Mwo0aZg\"\n",
        "            frameborder=\"0\"\n",
        "            allowfullscreen\n",
+       "            \n",
        "        ></iframe>\n",
        "        "
       ],
@@ -1404,9 +1405,9 @@
      "traceback": [
       "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
       "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-41-b57a49d097c9>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mm\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0mn\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
-      "\u001b[0;32m<ipython-input-35-def82d7b791b>\u001b[0m in \u001b[0;36m__mul__\u001b[0;34m(self, other)\u001b[0m\n\u001b[1;32m     72\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_matrix_multiply\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mother\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mas_matrix\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mas_vector\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     73\u001b[0m         \u001b[0;32melif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mother\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__class__\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 74\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_matrix_multiply\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mother\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     75\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mNotImplemented\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     76\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m<ipython-input-35-def82d7b791b>\u001b[0m in \u001b[0;36m_matrix_multiply\u001b[0;34m(self, other)\u001b[0m\n\u001b[1;32m     63\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_matrix_multiply\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mother\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     64\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mn_cols\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0mother\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mn_rows\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 65\u001b[0;31m             \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"matrices must have compatible dimensions\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     66\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mMatrix\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrv\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0mcv\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mcv\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mother\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcols\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mrv\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrows\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     67\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
+      "Cell \u001b[0;32mIn[41], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mm\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43m \u001b[49m\u001b[43mn\u001b[49m\n",
+      "Cell \u001b[0;32mIn[35], line 74\u001b[0m, in \u001b[0;36mMatrix.__mul__\u001b[0;34m(self, other)\u001b[0m\n\u001b[1;32m     72\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_matrix_multiply(other\u001b[38;5;241m.\u001b[39mas_matrix())\u001b[38;5;241m.\u001b[39mas_vector()\n\u001b[1;32m     73\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(other, Matrix):\n\u001b[0;32m---> 74\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_matrix_multiply\u001b[49m\u001b[43m(\u001b[49m\u001b[43mother\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     75\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mNotImplemented\u001b[39m\n",
+      "Cell \u001b[0;32mIn[35], line 65\u001b[0m, in \u001b[0;36mMatrix._matrix_multiply\u001b[0;34m(self, other)\u001b[0m\n\u001b[1;32m     63\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_matrix_multiply\u001b[39m(\u001b[38;5;28mself\u001b[39m, other):\n\u001b[1;32m     64\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mn_cols \u001b[38;5;241m!=\u001b[39m other\u001b[38;5;241m.\u001b[39mn_rows:\n\u001b[0;32m---> 65\u001b[0m         \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mmatrices must have compatible dimensions\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m     66\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m Matrix((rv \u001b[38;5;241m*\u001b[39m cv \u001b[38;5;28;01mfor\u001b[39;00m cv \u001b[38;5;129;01min\u001b[39;00m other\u001b[38;5;241m.\u001b[39mcols()) \u001b[38;5;28;01mfor\u001b[39;00m rv \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mrows())\n",
       "\u001b[0;31mValueError\u001b[0m: matrices must have compatible dimensions"
      ]
     }
@@ -1514,9 +1515,9 @@
      "traceback": [
       "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
       "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-45-9faf2ee0ae54>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mv\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0mn\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
-      "\u001b[0;32m<ipython-input-35-def82d7b791b>\u001b[0m in \u001b[0;36m__rmul__\u001b[0;34m(self, other)\u001b[0m\n\u001b[1;32m     79\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0mother\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     80\u001b[0m         \u001b[0;32melif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mother\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mVector\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 81\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mother\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mas_matrix\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcolumn\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_matrix_multiply\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mas_vector\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     82\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mNotImplemented\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     83\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m<ipython-input-35-def82d7b791b>\u001b[0m in \u001b[0;36m_matrix_multiply\u001b[0;34m(self, other)\u001b[0m\n\u001b[1;32m     63\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_matrix_multiply\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mother\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     64\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mn_cols\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0mother\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mn_rows\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 65\u001b[0;31m             \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"matrices must have compatible dimensions\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     66\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mMatrix\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrv\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0mcv\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mcv\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mother\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcols\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mrv\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrows\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     67\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
+      "Cell \u001b[0;32mIn[45], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mv\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43m \u001b[49m\u001b[43mn\u001b[49m\n",
+      "Cell \u001b[0;32mIn[35], line 81\u001b[0m, in \u001b[0;36mMatrix.__rmul__\u001b[0;34m(self, other)\u001b[0m\n\u001b[1;32m     79\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m \u001b[38;5;241m*\u001b[39m other\n\u001b[1;32m     80\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(other, Vector):\n\u001b[0;32m---> 81\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mother\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mas_matrix\u001b[49m\u001b[43m(\u001b[49m\u001b[43mcolumn\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_matrix_multiply\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m)\u001b[49m\u001b[38;5;241m.\u001b[39mas_vector()\n\u001b[1;32m     82\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mNotImplemented\u001b[39m\n",
+      "Cell \u001b[0;32mIn[35], line 65\u001b[0m, in \u001b[0;36mMatrix._matrix_multiply\u001b[0;34m(self, other)\u001b[0m\n\u001b[1;32m     63\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_matrix_multiply\u001b[39m(\u001b[38;5;28mself\u001b[39m, other):\n\u001b[1;32m     64\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mn_cols \u001b[38;5;241m!=\u001b[39m other\u001b[38;5;241m.\u001b[39mn_rows:\n\u001b[0;32m---> 65\u001b[0m         \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mmatrices must have compatible dimensions\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m     66\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m Matrix((rv \u001b[38;5;241m*\u001b[39m cv \u001b[38;5;28;01mfor\u001b[39;00m cv \u001b[38;5;129;01min\u001b[39;00m other\u001b[38;5;241m.\u001b[39mcols()) \u001b[38;5;28;01mfor\u001b[39;00m rv \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mrows())\n",
       "\u001b[0;31mValueError\u001b[0m: matrices must have compatible dimensions"
      ]
     }
@@ -1611,9 +1612,9 @@
      "traceback": [
       "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
       "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-48-356d02c93f11>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mm\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mv\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
-      "\u001b[0;32m<ipython-input-21-12852bd26164>\u001b[0m in \u001b[0;36m__radd__\u001b[0;34m(self, other)\u001b[0m\n\u001b[1;32m     25\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     26\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__radd__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mother\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 27\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mother\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     28\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     29\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__sub__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mother\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m<ipython-input-35-f4e0bb37caf6>\u001b[0m in \u001b[0;36m__radd__\u001b[0;34m(self, other)\u001b[0m\n\u001b[1;32m     40\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__radd__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mother\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     41\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mother\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mVector\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 42\u001b[0;31m             \u001b[0;32mraise\u001b[0m \u001b[0mTypeError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"vectors and matrices cannot be added\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     43\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mother\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     44\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
+      "Cell \u001b[0;32mIn[48], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mm\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m+\u001b[39;49m\u001b[43m \u001b[49m\u001b[43mv\u001b[49m\n",
+      "Cell \u001b[0;32mIn[21], line 27\u001b[0m, in \u001b[0;36mVector.__radd__\u001b[0;34m(self, other)\u001b[0m\n\u001b[1;32m     26\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m__radd__\u001b[39m(\u001b[38;5;28mself\u001b[39m, other):\n\u001b[0;32m---> 27\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m \u001b[49m\u001b[38;5;241;43m+\u001b[39;49m\u001b[43m \u001b[49m\u001b[43mother\u001b[49m\n",
+      "Cell \u001b[0;32mIn[35], line 42\u001b[0m, in \u001b[0;36mMatrix.__radd__\u001b[0;34m(self, other)\u001b[0m\n\u001b[1;32m     40\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m__radd__\u001b[39m(\u001b[38;5;28mself\u001b[39m, other):\n\u001b[1;32m     41\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(other, Vector):\n\u001b[0;32m---> 42\u001b[0m         \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mTypeError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mvectors and matrices cannot be added\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m     43\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m \u001b[38;5;241m+\u001b[39m other\n",
       "\u001b[0;31mTypeError\u001b[0m: vectors and matrices cannot be added"
      ]
     }
@@ -1933,7 +1934,7 @@
      "traceback": [
       "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
       "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-59-c01df863c5f8>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0;34m-\u001b[0m\u001b[0mv\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "Cell \u001b[0;32mIn[59], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[38;5;241;43m-\u001b[39;49m\u001b[43mv\u001b[49m\n",
       "\u001b[0;31mTypeError\u001b[0m: bad operand type for unary -: 'Vector'"
      ]
     }
@@ -1969,7 +1970,7 @@
      "traceback": [
       "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
       "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-60-09d4e6f30397>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mabs\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mv\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "Cell \u001b[0;32mIn[60], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[38;5;28;43mabs\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mv\u001b[49m\u001b[43m)\u001b[49m\n",
       "\u001b[0;31mTypeError\u001b[0m: bad operand type for abs(): 'Vector'"
      ]
     }
@@ -2353,8 +2354,8 @@
      "traceback": [
       "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
       "\u001b[0;31mRuntimeError\u001b[0m                              Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-75-8369cec552f3>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mfloat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mv\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
-      "\u001b[0;32m<ipython-input-63-67b90fc89e7c>\u001b[0m in \u001b[0;36m__float__\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m     29\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__float__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     30\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 31\u001b[0;31m             \u001b[0;32mraise\u001b[0m \u001b[0mRuntimeError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"vector must have exactly one entry to become a scalar\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     32\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_entries\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "Cell \u001b[0;32mIn[75], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[38;5;28;43mfloat\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mv\u001b[49m\u001b[43m)\u001b[49m\n",
+      "Cell \u001b[0;32mIn[63], line 31\u001b[0m, in \u001b[0;36mVector.__float__\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m     29\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m__float__\u001b[39m(\u001b[38;5;28mself\u001b[39m):\n\u001b[1;32m     30\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(\u001b[38;5;28mself\u001b[39m) \u001b[38;5;241m!=\u001b[39m \u001b[38;5;241m1\u001b[39m:\n\u001b[0;32m---> 31\u001b[0m         \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mRuntimeError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mvector must have exactly one entry to become a scalar\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m     32\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_entries[\u001b[38;5;241m0\u001b[39m]\n",
       "\u001b[0;31mRuntimeError\u001b[0m: vector must have exactly one entry to become a scalar"
      ]
     }
@@ -2380,7 +2381,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.6"
+   "version": "3.12.2"
   },
   "livereveal": {
    "auto_select": "code",
diff --git a/11_classes/04_content.ipynb b/11_classes/04_content.ipynb
index b495a9a..9f563fe 100644
--- a/11_classes/04_content.ipynb
+++ b/11_classes/04_content.ipynb
@@ -74,7 +74,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "/home/webartifex/repos/intro-to-python/11_classes\n"
+      "/home/alexander/Repositories/intro-to-python/11_classes\n"
      ]
     }
    ],
@@ -106,8 +106,8 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "00_content.ipynb    02_content.ipynb  04_content.ipynb\n",
-      "01_exercises.ipynb  03_content.ipynb  sample_package\n"
+      "00_content.ipynb    02_content.ipynb  04_content.ipynb\t06_review.ipynb\n",
+      "01_exercises.ipynb  03_content.ipynb  05_summary.ipynb\tsample_package\n"
      ]
     }
    ],
@@ -202,7 +202,7 @@
     {
      "data": {
       "text/plain": [
-       "<module 'sample_package' from '/home/webartifex/repos/intro-to-python/11_classes/sample_package/__init__.py'>"
+       "<module 'sample_package' from '/home/alexander/Repositories/intro-to-python/11_classes/sample_package/__init__.py'>"
       ]
      },
      "execution_count": 5,
@@ -324,9 +324,9 @@
       "    - vector: defines the Vector class\n",
       "    - utils: defines the norm() function that is shared by Matrix and Vector\n",
       "             and package-wide constants\n",
-      "    \n",
+      "\n",
       "    The classes implement arithmetic operations involving vectors and matrices.\n",
-      "    \n",
+      "\n",
       "    See the docstrings in the modules and classes for further info.\n",
       "\n",
       "PACKAGE CONTENTS\n",
@@ -338,14 +338,14 @@
       "    builtins.object\n",
       "        sample_package.matrix.Matrix\n",
       "        sample_package.vector.Vector\n",
-      "    \n",
+      "\n",
       "    class Matrix(builtins.object)\n",
       "     |  Matrix(data)\n",
-      "     |  \n",
+      "     |\n",
       "     |  An m-by-n-dimensional matrix from linear algebra.\n",
-      "     |  \n",
+      "     |\n",
       "     |  All entries are converted to floats, or whatever is set in the typing attribute.\n",
-      "     |  \n",
+      "     |\n",
       "     |  Attributes:\n",
       "     |      storage (callable): data type used to store the entries internally;\n",
       "     |          defaults to tuple\n",
@@ -354,63 +354,63 @@
       "     |      vector_cls (vector.Vector): a reference to the Vector class to work with\n",
       "     |      zero_threshold (float): max. tolerance when comparing an entry to zero;\n",
       "     |          defaults to 1e-12\n",
-      "     |  \n",
+      "     |\n",
       "     |  Methods defined here:\n",
-      "     |  \n",
+      "     |\n",
       "     |  __abs__(self)\n",
       "     |      The Frobenius norm of a Matrix.\n",
-      "     |  \n",
+      "     |\n",
       "     |  __add__(self, other)\n",
       "     |      Handle `self + other` and `other + self`.\n",
-      "     |      \n",
+      "     |\n",
       "     |      This may be either matrix addition or broadcasting addition.\n",
-      "     |      \n",
+      "     |\n",
       "     |      Example Usage:\n",
       "     |          >>> Matrix([(1, 2), (3, 4)]) + Matrix([(2, 3), (4, 5)])\n",
       "     |          Matrix(((3.0, 5.0,), (7.0, 9.0,)))\n",
-      "     |      \n",
+      "     |\n",
       "     |          >>> Matrix([(1, 2), (3, 4)]) + 5\n",
       "     |          Matrix(((6.0, 7.0,), (8.0, 9.0,)))\n",
-      "     |      \n",
+      "     |\n",
       "     |          >>> 10 + Matrix([(1, 2), (3, 4)])\n",
       "     |          Matrix(((11.0, 12.0,), (13.0, 14.0,)))\n",
-      "     |  \n",
+      "     |\n",
       "     |  __bool__(self)\n",
       "     |      A Matrix is truthy if its Frobenius norm is strictly positive.\n",
-      "     |  \n",
+      "     |\n",
       "     |  __eq__(self, other)\n",
       "     |      Handle `self == other`.\n",
-      "     |      \n",
+      "     |\n",
       "     |      Compare two Matrix instances for equality.\n",
-      "     |      \n",
+      "     |\n",
       "     |      Example Usage:\n",
       "     |          >>> Matrix([(1, 2), (3, 4)]) == Matrix([(1, 2), (3, 4)])\n",
       "     |          True\n",
-      "     |      \n",
+      "     |\n",
       "     |          >>> Matrix([(1, 2), (3, 4)]) == Matrix([(5, 6), (7, 8)])\n",
       "     |          False\n",
-      "     |  \n",
+      "     |\n",
       "     |  __float__(self)\n",
       "     |      Cast a Matrix as a scalar.\n",
-      "     |      \n",
+      "     |\n",
       "     |      Returns:\n",
       "     |          scalar (float)\n",
-      "     |      \n",
+      "     |\n",
       "     |      Raises:\n",
       "     |          RuntimeError: if the Matrix has more than one entry\n",
-      "     |  \n",
+      "     |\n",
       "     |  __getitem__(self, index)\n",
       "     |      Obtain an individual entry of a Matrix.\n",
-      "     |      \n",
+      "     |\n",
       "     |      Args:\n",
       "     |          index (int / tuple of int's): if index is an integer,\n",
       "     |              the Matrix is viewed as a sequence in row-major order;\n",
       "     |              if index is a tuple of integers, the first one refers to\n",
       "     |              the row and the second one to the column of the entry\n",
-      "     |      \n",
+      "     |\n",
       "     |      Returns:\n",
       "     |          entry (Matrix.typing)\n",
-      "     |      \n",
+      "     |\n",
       "     |      Example Usage:\n",
       "     |          >>> m = Matrix([(1, 2), (3, 4)])\n",
       "     |          >>> m[0]\n",
@@ -419,226 +419,228 @@
       "     |          4.0\n",
       "     |          >>> m[0, 1]\n",
       "     |          2.0\n",
-      "     |  \n",
+      "     |\n",
       "     |  __init__(self, data)\n",
       "     |      Create a new matrix.\n",
-      "     |      \n",
+      "     |\n",
       "     |      Args:\n",
       "     |          data (sequence of sequences): the matrix's entries;\n",
       "     |              viewed as a sequence of the matrix's rows (i.e., row-major order);\n",
       "     |              use the .from_columns() class method if the data come as a sequence\n",
       "     |              of the matrix's columns (i.e., column-major order)\n",
-      "     |      \n",
+      "     |\n",
       "     |      Raises:\n",
       "     |          ValueError:\n",
       "     |              - if no entries are provided\n",
       "     |              - if the number of columns is inconsistent across the rows\n",
-      "     |      \n",
+      "     |\n",
       "     |      Example Usage:\n",
       "     |          >>> Matrix([(1, 2), (3, 4)])\n",
       "     |          Matrix(((1.0, 2.0,), (3.0, 4.0,)))\n",
-      "     |  \n",
+      "     |\n",
       "     |  __iter__(self)\n",
       "     |      Loop over a Matrix's entries.\n",
-      "     |      \n",
+      "     |\n",
       "     |      See .entries() for more customization options.\n",
-      "     |  \n",
+      "     |\n",
       "     |  __len__(self)\n",
       "     |      Number of entries in a Matrix.\n",
-      "     |  \n",
+      "     |\n",
       "     |  __mul__(self, other)\n",
       "     |      Handle `self * other` and `other * self`.\n",
-      "     |      \n",
+      "     |\n",
       "     |      This may be either scalar multiplication, matrix-vector multiplication,\n",
       "     |      vector-matrix multiplication, or matrix-matrix multiplication.\n",
-      "     |      \n",
+      "     |\n",
       "     |      Example Usage:\n",
       "     |          >>> Matrix([(1, 2), (3, 4)]) * Matrix([(1, 2), (3, 4)])\n",
       "     |          Matrix(((7.0, 10.0,), (15.0, 22.0,)))\n",
-      "     |      \n",
+      "     |\n",
       "     |          >>> 2 * Matrix([(1, 2), (3, 4)])\n",
       "     |          Matrix(((2.0, 4.0,), (6.0, 8.0,)))\n",
-      "     |      \n",
+      "     |\n",
       "     |          >>> Matrix([(1, 2), (3, 4)]) * 3\n",
       "     |          Matrix(((3.0, 6.0,), (9.0, 12.0,)))\n",
-      "     |      \n",
+      "     |\n",
       "     |          Matrix-vector and vector-matrix multiplication are not commutative.\n",
-      "     |      \n",
+      "     |\n",
       "     |          >>> from sample_package import Vector\n",
-      "     |      \n",
+      "     |\n",
       "     |          >>> Matrix([(1, 2), (3, 4)]) * Vector([5, 6])\n",
       "     |          Vector((17.0, 39.0))\n",
-      "     |      \n",
+      "     |\n",
       "     |          >>> Vector([5, 6]) * Matrix([(1, 2), (3, 4)])\n",
       "     |          Vector((23.0, 34.0))\n",
-      "     |  \n",
+      "     |\n",
       "     |  __neg__(self)\n",
       "     |      Handle `-self`.\n",
-      "     |      \n",
+      "     |\n",
       "     |      Negate all entries of a Matrix.\n",
-      "     |  \n",
+      "     |\n",
       "     |  __pos__(self)\n",
       "     |      Handle `+self`.\n",
-      "     |      \n",
+      "     |\n",
       "     |      This is simply an identity operator returning the Matrix itself.\n",
-      "     |  \n",
+      "     |\n",
       "     |  __radd__(self, other)\n",
       "     |      See docstring for .__add__().\n",
-      "     |  \n",
+      "     |\n",
       "     |  __repr__(self)\n",
       "     |      Text representation of a Matrix.\n",
-      "     |  \n",
+      "     |\n",
       "     |  __reversed__(self)\n",
       "     |      Loop over a Matrix's entries in reverse order.\n",
-      "     |      \n",
+      "     |\n",
       "     |      See .entries() for more customization options.\n",
-      "     |  \n",
+      "     |\n",
       "     |  __rmul__(self, other)\n",
       "     |      See docstring for .__mul__().\n",
-      "     |  \n",
+      "     |\n",
       "     |  __rsub__(self, other)\n",
       "     |      See docstring for .__sub__().\n",
-      "     |  \n",
+      "     |\n",
       "     |  __str__(self)\n",
       "     |      Human-readable text representation of a Matrix.\n",
-      "     |  \n",
+      "     |\n",
       "     |  __sub__(self, other)\n",
       "     |      Handle `self - other` and `other - self`.\n",
-      "     |      \n",
+      "     |\n",
       "     |      This may be either matrix subtraction or broadcasting subtraction.\n",
-      "     |      \n",
+      "     |\n",
       "     |      Example Usage:\n",
       "     |          >>> Matrix([(2, 3), (4, 5)]) - Matrix([(1, 2), (3, 4)])\n",
       "     |          Matrix(((1.0, 1.0,), (1.0, 1.0,)))\n",
-      "     |      \n",
+      "     |\n",
       "     |          >>> Matrix([(1, 2), (3, 4)]) - 1\n",
       "     |          Matrix(((0.0, 1.0,), (2.0, 3.0,)))\n",
-      "     |      \n",
+      "     |\n",
       "     |          >>> 10 - Matrix([(1, 2), (3, 4)])\n",
       "     |          Matrix(((9.0, 8.0,), (7.0, 6.0,)))\n",
-      "     |  \n",
+      "     |\n",
       "     |  __truediv__(self, other)\n",
       "     |      Handle `self / other`.\n",
-      "     |      \n",
+      "     |\n",
       "     |      Divide a Matrix by a scalar.\n",
-      "     |      \n",
+      "     |\n",
       "     |      Example Usage:\n",
       "     |          >>> Matrix([(1, 2), (3, 4)]) / 4\n",
       "     |          Matrix(((0.25, 0.5,), (0.75, 1.0,)))\n",
-      "     |  \n",
+      "     |\n",
       "     |  as_vector(self)\n",
       "     |      Get a Vector representation of a Matrix.\n",
-      "     |      \n",
+      "     |\n",
       "     |      Returns:\n",
       "     |          vector (vector.Vector)\n",
-      "     |      \n",
+      "     |\n",
       "     |      Raises:\n",
       "     |          RuntimeError: if one of the two dimensions, .n_rows or .n_cols, is not 1\n",
-      "     |      \n",
+      "     |\n",
       "     |      Example Usage:\n",
       "     |          >>> Matrix([(1, 2, 3)]).as_vector()\n",
       "     |          Vector((1.0, 2.0, 3.0))\n",
-      "     |  \n",
+      "     |\n",
       "     |  cols(self)\n",
       "     |      Loop over a Matrix's columns.\n",
-      "     |      \n",
+      "     |\n",
       "     |      Returns:\n",
       "     |          columns (generator): produces a Matrix's columns as Vectors\n",
-      "     |  \n",
+      "     |\n",
       "     |  entries(self, *, reverse=False, row_major=True)\n",
       "     |      Loop over a Matrix's entries.\n",
-      "     |      \n",
+      "     |\n",
       "     |      Args:\n",
       "     |          reverse (bool): flag to loop backwards; defaults to False\n",
       "     |          row_major (bool): flag to loop in row-major order; defaults to True\n",
-      "     |      \n",
+      "     |\n",
       "     |      Returns:\n",
       "     |          entries (generator): produces a Matrix's entries\n",
-      "     |  \n",
+      "     |\n",
       "     |  rows(self)\n",
       "     |      Loop over a Matrix's rows.\n",
-      "     |      \n",
+      "     |\n",
       "     |      Returns:\n",
       "     |          rows (generator): produces a Matrix's rows as Vectors\n",
-      "     |  \n",
+      "     |\n",
       "     |  transpose(self)\n",
       "     |      Switch the rows and columns of a Matrix.\n",
-      "     |      \n",
+      "     |\n",
       "     |      Returns:\n",
       "     |          matrix (Matrix)\n",
-      "     |      \n",
+      "     |\n",
       "     |      Example Usage:\n",
       "     |          >>> m = Matrix([(1, 2), (3, 4)])\n",
       "     |          >>> m\n",
       "     |          Matrix(((1.0, 2.0,), (3.0, 4.0,)))\n",
       "     |          >>> m.transpose()\n",
       "     |          Matrix(((1.0, 3.0,), (2.0, 4.0,)))\n",
-      "     |  \n",
+      "     |\n",
       "     |  ----------------------------------------------------------------------\n",
       "     |  Class methods defined here:\n",
-      "     |  \n",
+      "     |\n",
       "     |  from_columns(data) from builtins.type\n",
       "     |      Create a new matrix.\n",
-      "     |      \n",
+      "     |\n",
       "     |      This is an alternative constructor for data provided in column-major order.\n",
-      "     |      \n",
+      "     |\n",
       "     |      Args:\n",
       "     |          data (sequence of sequences): the matrix's entries;\n",
       "     |              viewed as a sequence of the matrix's columns (i.e., column-major order);\n",
       "     |              use the normal constructor method if the data come as a sequence\n",
       "     |              of the matrix's rows (i.e., row-major order)\n",
-      "     |      \n",
+      "     |\n",
       "     |      Raises:\n",
       "     |          ValueError:\n",
       "     |              - if no entries are provided\n",
       "     |              - if the number of rows is inconsistent across the columns\n",
-      "     |      \n",
+      "     |\n",
       "     |      Example Usage:\n",
       "     |          >>> Matrix.from_columns([(1, 2), (3, 4)])\n",
       "     |          Matrix(((1.0, 3.0,), (2.0, 4.0,)))\n",
-      "     |  \n",
+      "     |\n",
       "     |  from_rows(data) from builtins.type\n",
       "     |      See docstring for .__init__().\n",
-      "     |  \n",
+      "     |\n",
       "     |  ----------------------------------------------------------------------\n",
       "     |  Readonly properties defined here:\n",
-      "     |  \n",
+      "     |\n",
       "     |  n_cols\n",
       "     |      Number of columns in a Matrix.\n",
-      "     |  \n",
+      "     |\n",
       "     |  n_rows\n",
       "     |      Number of rows in a Matrix.\n",
-      "     |  \n",
+      "     |\n",
       "     |  ----------------------------------------------------------------------\n",
       "     |  Data descriptors defined here:\n",
-      "     |  \n",
+      "     |\n",
       "     |  __dict__\n",
-      "     |      dictionary for instance variables (if defined)\n",
-      "     |  \n",
+      "     |      dictionary for instance variables\n",
+      "     |\n",
       "     |  __weakref__\n",
-      "     |      list of weak references to the object (if defined)\n",
-      "     |  \n",
+      "     |      list of weak references to the object\n",
+      "     |\n",
       "     |  ----------------------------------------------------------------------\n",
       "     |  Data and other attributes defined here:\n",
-      "     |  \n",
+      "     |\n",
       "     |  __hash__ = None\n",
-      "     |  \n",
+      "     |\n",
       "     |  storage = <class 'tuple'>\n",
       "     |      Built-in immutable sequence.\n",
-      "     |      \n",
+      "     |\n",
       "     |      If no argument is given, the constructor returns an empty tuple.\n",
       "     |      If iterable is specified the tuple is initialized from iterable's items.\n",
-      "     |      \n",
+      "     |\n",
       "     |      If the argument is a tuple, the return value is the same object.\n",
-      "     |  \n",
+      "     |\n",
+      "     |\n",
       "     |  typing = <class 'float'>\n",
       "     |      Convert a string or number to a floating point number, if possible.\n",
-      "     |  \n",
+      "     |\n",
+      "     |\n",
       "     |  vector_cls = <class 'sample_package.vector.Vector'>\n",
       "     |      A one-dimensional vector from linear algebra.\n",
-      "     |      \n",
+      "     |\n",
       "     |      All entries are converted to floats, or whatever is set in the typing attribute.\n",
-      "     |      \n",
+      "     |\n",
       "     |      Attributes:\n",
       "     |          matrix_cls (matrix.Matrix): a reference to the Matrix class to work with\n",
       "     |          storage (callable): data type used to store the entries internally;\n",
@@ -647,16 +649,17 @@
       "     |              defaults to float\n",
       "     |          zero_threshold (float): max. tolerance when comparing an entry to zero;\n",
       "     |              defaults to 1e-12\n",
-      "     |  \n",
+      "     |\n",
+      "     |\n",
       "     |  zero_threshold = 1e-12\n",
-      "    \n",
+      "\n",
       "    class Vector(builtins.object)\n",
       "     |  Vector(data)\n",
-      "     |  \n",
+      "     |\n",
       "     |  A one-dimensional vector from linear algebra.\n",
-      "     |  \n",
+      "     |\n",
       "     |  All entries are converted to floats, or whatever is set in the typing attribute.\n",
-      "     |  \n",
+      "     |\n",
       "     |  Attributes:\n",
       "     |      matrix_cls (matrix.Matrix): a reference to the Matrix class to work with\n",
       "     |      storage (callable): data type used to store the entries internally;\n",
@@ -665,179 +668,179 @@
       "     |          defaults to float\n",
       "     |      zero_threshold (float): max. tolerance when comparing an entry to zero;\n",
       "     |          defaults to 1e-12\n",
-      "     |  \n",
+      "     |\n",
       "     |  Methods defined here:\n",
-      "     |  \n",
+      "     |\n",
       "     |  __abs__(self)\n",
       "     |      The Euclidean norm of a vector.\n",
-      "     |  \n",
+      "     |\n",
       "     |  __add__(self, other)\n",
       "     |      Handle `self + other` and `other + self`.\n",
-      "     |      \n",
+      "     |\n",
       "     |      This may be either vector addition or broadcasting addition.\n",
-      "     |      \n",
+      "     |\n",
       "     |      Example Usage:\n",
       "     |          >>> Vector([1, 2, 3]) + Vector([2, 3, 4])\n",
       "     |          Vector((3.0, 5.0, 7.0))\n",
-      "     |      \n",
+      "     |\n",
       "     |          >>> Vector([1, 2, 3]) + 4\n",
       "     |          Vector((5.0, 6.0, 7.0))\n",
-      "     |      \n",
+      "     |\n",
       "     |          >>> 10 + Vector([1, 2, 3])\n",
       "     |          Vector((11.0, 12.0, 13.0))\n",
-      "     |  \n",
+      "     |\n",
       "     |  __bool__(self)\n",
       "     |      A Vector is truthy if its Euclidean norm is strictly positive.\n",
-      "     |  \n",
+      "     |\n",
       "     |  __eq__(self, other)\n",
       "     |      Handle `self == other`.\n",
-      "     |      \n",
+      "     |\n",
       "     |      Compare two Vectors for equality.\n",
-      "     |      \n",
+      "     |\n",
       "     |      Example Usage:\n",
       "     |          >>> Vector([1, 2, 3]) == Vector([1, 2, 3])\n",
       "     |          True\n",
-      "     |      \n",
+      "     |\n",
       "     |          >>> Vector([1, 2, 3]) == Vector([4, 5, 6])\n",
       "     |          False\n",
-      "     |  \n",
+      "     |\n",
       "     |  __float__(self)\n",
       "     |      Cast a Vector as a scalar.\n",
-      "     |      \n",
+      "     |\n",
       "     |      Returns:\n",
       "     |          scalar (float)\n",
-      "     |      \n",
+      "     |\n",
       "     |      Raises:\n",
       "     |          RuntimeError: if the Vector has more than one entry\n",
-      "     |  \n",
+      "     |\n",
       "     |  __getitem__(self, index)\n",
       "     |      Obtain an individual entry of a Vector.\n",
-      "     |  \n",
+      "     |\n",
       "     |  __init__(self, data)\n",
       "     |      Create a new vector.\n",
-      "     |      \n",
+      "     |\n",
       "     |      Args:\n",
       "     |          data (sequence): the vector's entries\n",
-      "     |      \n",
+      "     |\n",
       "     |      Raises:\n",
       "     |          ValueError: if no entries are provided\n",
-      "     |      \n",
+      "     |\n",
       "     |      Example Usage:\n",
       "     |          >>> Vector([1, 2, 3])\n",
       "     |          Vector((1.0, 2.0, 3.0))\n",
-      "     |      \n",
+      "     |\n",
       "     |          >>> Vector(range(3))\n",
       "     |          Vector((0.0, 1.0, 2.0))\n",
-      "     |  \n",
+      "     |\n",
       "     |  __iter__(self)\n",
       "     |      Loop over a Vector's entries.\n",
-      "     |  \n",
+      "     |\n",
       "     |  __len__(self)\n",
       "     |      Number of entries in a Vector.\n",
-      "     |  \n",
+      "     |\n",
       "     |  __mul__(self, other)\n",
       "     |      Handle `self * other` and `other * self`.\n",
-      "     |      \n",
+      "     |\n",
       "     |      This may be either the dot product of two vectors or scalar multiplication.\n",
-      "     |      \n",
+      "     |\n",
       "     |      Example Usage:\n",
       "     |          >>> Vector([1, 2, 3]) * Vector([2, 3, 4])\n",
       "     |          20.0\n",
-      "     |      \n",
+      "     |\n",
       "     |          >>> 2 * Vector([1, 2, 3])\n",
       "     |          Vector((2.0, 4.0, 6.0))\n",
-      "     |      \n",
+      "     |\n",
       "     |          >>> Vector([1, 2, 3]) * 3\n",
       "     |          Vector((3.0, 6.0, 9.0))\n",
-      "     |  \n",
+      "     |\n",
       "     |  __neg__(self)\n",
       "     |      Handle `-self`.\n",
-      "     |      \n",
+      "     |\n",
       "     |      Negate all entries of a Vector.\n",
-      "     |  \n",
+      "     |\n",
       "     |  __pos__(self)\n",
       "     |      Handle `+self`.\n",
-      "     |      \n",
+      "     |\n",
       "     |      This is simply an identity operator returning the Vector itself.\n",
-      "     |  \n",
+      "     |\n",
       "     |  __radd__(self, other)\n",
       "     |      See docstring for .__add__().\n",
-      "     |  \n",
+      "     |\n",
       "     |  __repr__(self)\n",
       "     |      Text representation of a Vector.\n",
-      "     |  \n",
+      "     |\n",
       "     |  __reversed__(self)\n",
       "     |      Loop over a Vector's entries in reverse order.\n",
-      "     |  \n",
+      "     |\n",
       "     |  __rmul__(self, other)\n",
       "     |      See docstring for .__mul__().\n",
-      "     |  \n",
+      "     |\n",
       "     |  __rsub__(self, other)\n",
       "     |      See docstring for .__sub__().\n",
-      "     |  \n",
+      "     |\n",
       "     |  __str__(self)\n",
       "     |      Human-readable text representation of a Vector.\n",
-      "     |  \n",
+      "     |\n",
       "     |  __sub__(self, other)\n",
       "     |      Handle `self - other` and `other - self`.\n",
-      "     |      \n",
+      "     |\n",
       "     |      This may be either vector subtraction or broadcasting subtraction.\n",
-      "     |      \n",
+      "     |\n",
       "     |      Example Usage:\n",
       "     |          >>> Vector([7, 8, 9]) - Vector([1, 2, 3])\n",
       "     |          Vector((6.0, 6.0, 6.0))\n",
-      "     |      \n",
+      "     |\n",
       "     |          >>> Vector([1, 2, 3]) - 1\n",
       "     |          Vector((0.0, 1.0, 2.0))\n",
-      "     |      \n",
+      "     |\n",
       "     |          >>> 10 - Vector([1, 2, 3])\n",
       "     |          Vector((9.0, 8.0, 7.0))\n",
-      "     |  \n",
+      "     |\n",
       "     |  __truediv__(self, other)\n",
       "     |      Handle `self / other`.\n",
-      "     |      \n",
+      "     |\n",
       "     |      Divide a Vector by a scalar.\n",
-      "     |      \n",
+      "     |\n",
       "     |      Example Usage:\n",
       "     |          >>> Vector([9, 6, 12]) / 3\n",
       "     |          Vector((3.0, 2.0, 4.0))\n",
-      "     |  \n",
+      "     |\n",
       "     |  as_matrix(self, *, column=True)\n",
       "     |      Get a Matrix representation of a Vector.\n",
-      "     |      \n",
+      "     |\n",
       "     |      Args:\n",
       "     |          column (bool): if the vector is interpreted as a\n",
       "     |              column vector or a row vector; defaults to True\n",
-      "     |      \n",
+      "     |\n",
       "     |      Returns:\n",
       "     |          matrix (matrix.Matrix)\n",
-      "     |      \n",
+      "     |\n",
       "     |      Example Usage:\n",
       "     |          >>> v = Vector([1, 2, 3])\n",
       "     |          >>> v.as_matrix()\n",
       "     |          Matrix(((1.0,), (2.0,), (3.0,)))\n",
       "     |          >>> v.as_matrix(column=False)\n",
       "     |          Matrix(((1.0, 2.0, 3.0,)))\n",
-      "     |  \n",
+      "     |\n",
       "     |  ----------------------------------------------------------------------\n",
       "     |  Data descriptors defined here:\n",
-      "     |  \n",
+      "     |\n",
       "     |  __dict__\n",
-      "     |      dictionary for instance variables (if defined)\n",
-      "     |  \n",
+      "     |      dictionary for instance variables\n",
+      "     |\n",
       "     |  __weakref__\n",
-      "     |      list of weak references to the object (if defined)\n",
-      "     |  \n",
+      "     |      list of weak references to the object\n",
+      "     |\n",
       "     |  ----------------------------------------------------------------------\n",
       "     |  Data and other attributes defined here:\n",
-      "     |  \n",
+      "     |\n",
       "     |  __hash__ = None\n",
-      "     |  \n",
+      "     |\n",
       "     |  matrix_cls = <class 'sample_package.matrix.Matrix'>\n",
       "     |      An m-by-n-dimensional matrix from linear algebra.\n",
-      "     |      \n",
+      "     |\n",
       "     |      All entries are converted to floats, or whatever is set in the typing attribute.\n",
-      "     |      \n",
+      "     |\n",
       "     |      Attributes:\n",
       "     |          storage (callable): data type used to store the entries internally;\n",
       "     |              defaults to tuple\n",
@@ -846,18 +849,21 @@
       "     |          vector_cls (vector.Vector): a reference to the Vector class to work with\n",
       "     |          zero_threshold (float): max. tolerance when comparing an entry to zero;\n",
       "     |              defaults to 1e-12\n",
-      "     |  \n",
+      "     |\n",
+      "     |\n",
       "     |  storage = <class 'tuple'>\n",
       "     |      Built-in immutable sequence.\n",
-      "     |      \n",
+      "     |\n",
       "     |      If no argument is given, the constructor returns an empty tuple.\n",
       "     |      If iterable is specified the tuple is initialized from iterable's items.\n",
-      "     |      \n",
+      "     |\n",
       "     |      If the argument is a tuple, the return value is the same object.\n",
-      "     |  \n",
+      "     |\n",
+      "     |\n",
       "     |  typing = <class 'float'>\n",
       "     |      Convert a string or number to a floating point number, if possible.\n",
-      "     |  \n",
+      "     |\n",
+      "     |\n",
       "     |  zero_threshold = 1e-12\n",
       "\n",
       "DATA\n",
@@ -870,7 +876,7 @@
       "    Alexander Hess\n",
       "\n",
       "FILE\n",
-      "    /home/webartifex/repos/intro-to-python/11_classes/sample_package/__init__.py\n",
+      "    /home/alexander/Repositories/intro-to-python/11_classes/sample_package/__init__.py\n",
       "\n",
       "\n"
      ]
@@ -1102,15 +1108,15 @@
       "\n",
       "norm(vec_or_mat)\n",
       "    Calculate the Frobenius or Euclidean norm of a matrix or vector.\n",
-      "    \n",
+      "\n",
       "    Find more infos here: https://en.wikipedia.org/wiki/Matrix_norm#Frobenius_norm\n",
-      "    \n",
+      "\n",
       "    Args:\n",
       "        vec_or_mat (Vector / Matrix): object whose entries are squared and summed up\n",
-      "    \n",
+      "\n",
       "    Returns:\n",
       "        norm (float)\n",
-      "    \n",
+      "\n",
       "    Example Usage:\n",
       "        As Vector and Matrix objects are by design non-empty sequences,\n",
       "        norm() may be called, for example, with `[3, 4]` as the argument:\n",
@@ -1453,8 +1459,8 @@
        "\u001b[0;34m\u001b[0m        \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_entries\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstorage\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtyping\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mx\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n",
        "\u001b[0;34m\u001b[0m        \u001b[0;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n",
        "\u001b[0;34m\u001b[0m            \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"a vector must have at least one entry\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-       "\u001b[0;31mFile:\u001b[0m      ~/repos/intro-to-python/11_classes/sample_package/vector.py\n",
-       "\u001b[0;31mType:\u001b[0m      function\n"
+       "\u001b[0;31mFile:\u001b[0m      ~/Repositories/intro-to-python/11_classes/sample_package/vector.py\n",
+       "\u001b[0;31mType:\u001b[0m      function"
       ]
      },
      "metadata": {},
@@ -1635,8 +1641,8 @@
        "\u001b[0;34m            Matrix(((1.0, 3.0,), (2.0, 4.0,)))\u001b[0m\n",
        "\u001b[0;34m        \"\"\"\u001b[0m\u001b[0;34m\u001b[0m\n",
        "\u001b[0;34m\u001b[0m        \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__class__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mzip\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_entries\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-       "\u001b[0;31mFile:\u001b[0m      ~/repos/intro-to-python/11_classes/sample_package/matrix.py\n",
-       "\u001b[0;31mType:\u001b[0m      function\n"
+       "\u001b[0;31mFile:\u001b[0m      ~/Repositories/intro-to-python/11_classes/sample_package/matrix.py\n",
+       "\u001b[0;31mType:\u001b[0m      function"
       ]
      },
      "metadata": {},
@@ -1748,9 +1754,9 @@
        "\u001b[0;34m\u001b[0m        \u001b[0margs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m\", \"\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mjoin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\u001b[0m\n",
        "\u001b[0;34m\u001b[0m            \u001b[0;34m\"(\"\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;34m\", \"\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mjoin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrepr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mc\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mc\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mr\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;34m\",)\"\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mr\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_entries\u001b[0m\u001b[0;34m\u001b[0m\n",
        "\u001b[0;34m\u001b[0m        \u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n",
-       "\u001b[0;34m\u001b[0m        \u001b[0;32mreturn\u001b[0m \u001b[0;34mf\"{name}(({args}))\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-       "\u001b[0;31mFile:\u001b[0m      ~/repos/intro-to-python/11_classes/sample_package/matrix.py\n",
-       "\u001b[0;31mType:\u001b[0m      function\n"
+       "\u001b[0;34m\u001b[0m        \u001b[0;32mreturn\u001b[0m \u001b[0;34mf\"\u001b[0m\u001b[0;34m{\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m((\u001b[0m\u001b[0;34m{\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m))\u001b[0m\u001b[0;34m\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+       "\u001b[0;31mFile:\u001b[0m      ~/Repositories/intro-to-python/11_classes/sample_package/matrix.py\n",
+       "\u001b[0;31mType:\u001b[0m      function"
       ]
      },
      "metadata": {},
@@ -1865,8 +1871,8 @@
        "\u001b[0;34m\u001b[0m        \u001b[0;32mif\u001b[0m \u001b[0mcolumn\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n",
        "\u001b[0;34m\u001b[0m            \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmatrix_cls\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mx\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n",
        "\u001b[0;34m\u001b[0m        \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmatrix_cls\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mx\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-       "\u001b[0;31mFile:\u001b[0m      ~/repos/intro-to-python/11_classes/sample_package/vector.py\n",
-       "\u001b[0;31mType:\u001b[0m      function\n"
+       "\u001b[0;31mFile:\u001b[0m      ~/Repositories/intro-to-python/11_classes/sample_package/vector.py\n",
+       "\u001b[0;31mType:\u001b[0m      function"
       ]
      },
      "metadata": {},
@@ -2122,7 +2128,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Vector(11378937.3105893, ..., 13593029.305789862)[100]\n"
+      "Vector(11378937.310589302, ..., 13593029.305789862)[100]\n"
      ]
     }
    ],
@@ -2167,7 +2173,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Matrix((14370711.265265431, ...), ..., (..., 16545418.239505699))[100x100]\n"
+      "Matrix((14370711.26526542, ...), ..., (..., 16545418.239505697))[100x100]\n"
      ]
     }
    ],
@@ -2201,7 +2207,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Matrix((32618511.50703142, ...), ..., (..., 32339164.77803234))[50x50]\n"
+      "Matrix((32618511.507031415, ...), ..., (..., 32339164.778032355))[50x50]\n"
      ]
     }
    ],
@@ -2265,7 +2271,7 @@
      "traceback": [
       "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
       "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-55-fd81d962f516>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mA\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "Cell \u001b[0;32mIn[55], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mA\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43m \u001b[49m\u001b[43mx\u001b[49m\n",
       "\u001b[0;31mTypeError\u001b[0m: can't multiply sequence by non-int of type 'tuple'"
      ]
     }
@@ -2916,7 +2922,7 @@
      "traceback": [
       "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
       "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-79-032c970f8cfc>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mA_arr\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mA_mat\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "Cell \u001b[0;32mIn[79], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mA_arr\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m+\u001b[39;49m\u001b[43m \u001b[49m\u001b[43mA_mat\u001b[49m\n",
       "\u001b[0;31mValueError\u001b[0m: operands could not be broadcast together with shapes (3,3) (9,) "
      ]
     }
@@ -2953,7 +2959,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.6"
+   "version": "3.12.2"
   },
   "livereveal": {
    "auto_select": "code",
diff --git a/11_classes/05_summary.ipynb b/11_classes/05_summary.ipynb
index 47baf9f..163f711 100644
--- a/11_classes/05_summary.ipynb
+++ b/11_classes/05_summary.ipynb
@@ -68,7 +68,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.6"
+   "version": "3.12.2"
   },
   "livereveal": {
    "auto_select": "code",
diff --git a/11_classes/06_review.ipynb b/11_classes/06_review.ipynb
index df7af79..0f54544 100644
--- a/11_classes/06_review.ipynb
+++ b/11_classes/06_review.ipynb
@@ -250,7 +250,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.6"
+   "version": "3.12.2"
   },
   "toc": {
    "base_numbering": 1,

From 1ee63daeb9179a5c286a3862724df19b241f1f5a Mon Sep 17 00:00:00 2001
From: Alexander Hess <alexander@webartifex.biz>
Date: Mon, 8 Apr 2024 17:27:53 +0200
Subject: [PATCH 237/242] Rename exercise notebooks more clearly

Add a short title/description into the filename of a notebook
holding exercises. In the past, students often did not find
the exercise files right away in class.
---
 ...ises.ipynb => 01_exercises_markdown.ipynb} |  0
 ...ercises.ipynb => 01_exercises_print.ipynb} |  0
 ...ses.ipynb => 02_exercises_for-loops.ipynb} |  0
 ...es.ipynb => 04_exercises_calculator.ipynb} |  0
 ...ipynb => 01_exercises_sphere-volume.ipynb} |  0
 ...ses.ipynb => 01_exercises_discounts.ipynb} |  0
 ...ses.ipynb => 02_exercises_fizz-buzz.ipynb} |  0
 ....ipynb => 01_exercises_hanoi-towers.ipynb} |  0
 ...xercises.ipynb => 04_exercises_dice.ipynb} |  0
 ...s.ipynb => 01_exercises_palindromes.ipynb} |  0
 ...ercises.ipynb => 02_exercises_lists.ipynb} |  0
 ...es.ipynb => 04_exercises_un-packing.ipynb} |  0
 ...ises.ipynb => 02_exercises_outliers.ipynb} |  0
 ...es.ipynb => 03_exercises_un-packing.ipynb} |  0
 ...s.ipynb => 01_exercises_nested-data.ipynb} |  0
 ...ses.ipynb => 03_exercises_fibonacci.ipynb} |  0
 ...exercises.ipynb => 01_exercises_tsp.ipynb} |  0
 CONTENTS.md                                   | 68 +++++++++----------
 18 files changed, 34 insertions(+), 34 deletions(-)
 rename 00_intro/{01_exercises.ipynb => 01_exercises_markdown.ipynb} (100%)
 rename 01_elements/{01_exercises.ipynb => 01_exercises_print.ipynb} (100%)
 rename 01_elements/{02_exercises.ipynb => 02_exercises_for-loops.ipynb} (100%)
 rename 01_elements/{04_exercises.ipynb => 04_exercises_calculator.ipynb} (100%)
 rename 02_functions/{01_exercises.ipynb => 01_exercises_sphere-volume.ipynb} (100%)
 rename 03_conditionals/{01_exercises.ipynb => 01_exercises_discounts.ipynb} (100%)
 rename 03_conditionals/{02_exercises.ipynb => 02_exercises_fizz-buzz.ipynb} (100%)
 rename 04_iteration/{01_exercises.ipynb => 01_exercises_hanoi-towers.ipynb} (100%)
 rename 04_iteration/{04_exercises.ipynb => 04_exercises_dice.ipynb} (100%)
 rename 06_text/{01_exercises.ipynb => 01_exercises_palindromes.ipynb} (100%)
 rename 07_sequences/{02_exercises.ipynb => 02_exercises_lists.ipynb} (100%)
 rename 07_sequences/{04_exercises.ipynb => 04_exercises_un-packing.ipynb} (100%)
 rename 08_mfr/{02_exercises.ipynb => 02_exercises_outliers.ipynb} (100%)
 rename 08_mfr/{03_exercises.ipynb => 03_exercises_un-packing.ipynb} (100%)
 rename 09_mappings/{01_exercises.ipynb => 01_exercises_nested-data.ipynb} (100%)
 rename 09_mappings/{03_exercises.ipynb => 03_exercises_fibonacci.ipynb} (100%)
 rename 11_classes/{01_exercises.ipynb => 01_exercises_tsp.ipynb} (100%)

diff --git a/00_intro/01_exercises.ipynb b/00_intro/01_exercises_markdown.ipynb
similarity index 100%
rename from 00_intro/01_exercises.ipynb
rename to 00_intro/01_exercises_markdown.ipynb
diff --git a/01_elements/01_exercises.ipynb b/01_elements/01_exercises_print.ipynb
similarity index 100%
rename from 01_elements/01_exercises.ipynb
rename to 01_elements/01_exercises_print.ipynb
diff --git a/01_elements/02_exercises.ipynb b/01_elements/02_exercises_for-loops.ipynb
similarity index 100%
rename from 01_elements/02_exercises.ipynb
rename to 01_elements/02_exercises_for-loops.ipynb
diff --git a/01_elements/04_exercises.ipynb b/01_elements/04_exercises_calculator.ipynb
similarity index 100%
rename from 01_elements/04_exercises.ipynb
rename to 01_elements/04_exercises_calculator.ipynb
diff --git a/02_functions/01_exercises.ipynb b/02_functions/01_exercises_sphere-volume.ipynb
similarity index 100%
rename from 02_functions/01_exercises.ipynb
rename to 02_functions/01_exercises_sphere-volume.ipynb
diff --git a/03_conditionals/01_exercises.ipynb b/03_conditionals/01_exercises_discounts.ipynb
similarity index 100%
rename from 03_conditionals/01_exercises.ipynb
rename to 03_conditionals/01_exercises_discounts.ipynb
diff --git a/03_conditionals/02_exercises.ipynb b/03_conditionals/02_exercises_fizz-buzz.ipynb
similarity index 100%
rename from 03_conditionals/02_exercises.ipynb
rename to 03_conditionals/02_exercises_fizz-buzz.ipynb
diff --git a/04_iteration/01_exercises.ipynb b/04_iteration/01_exercises_hanoi-towers.ipynb
similarity index 100%
rename from 04_iteration/01_exercises.ipynb
rename to 04_iteration/01_exercises_hanoi-towers.ipynb
diff --git a/04_iteration/04_exercises.ipynb b/04_iteration/04_exercises_dice.ipynb
similarity index 100%
rename from 04_iteration/04_exercises.ipynb
rename to 04_iteration/04_exercises_dice.ipynb
diff --git a/06_text/01_exercises.ipynb b/06_text/01_exercises_palindromes.ipynb
similarity index 100%
rename from 06_text/01_exercises.ipynb
rename to 06_text/01_exercises_palindromes.ipynb
diff --git a/07_sequences/02_exercises.ipynb b/07_sequences/02_exercises_lists.ipynb
similarity index 100%
rename from 07_sequences/02_exercises.ipynb
rename to 07_sequences/02_exercises_lists.ipynb
diff --git a/07_sequences/04_exercises.ipynb b/07_sequences/04_exercises_un-packing.ipynb
similarity index 100%
rename from 07_sequences/04_exercises.ipynb
rename to 07_sequences/04_exercises_un-packing.ipynb
diff --git a/08_mfr/02_exercises.ipynb b/08_mfr/02_exercises_outliers.ipynb
similarity index 100%
rename from 08_mfr/02_exercises.ipynb
rename to 08_mfr/02_exercises_outliers.ipynb
diff --git a/08_mfr/03_exercises.ipynb b/08_mfr/03_exercises_un-packing.ipynb
similarity index 100%
rename from 08_mfr/03_exercises.ipynb
rename to 08_mfr/03_exercises_un-packing.ipynb
diff --git a/09_mappings/01_exercises.ipynb b/09_mappings/01_exercises_nested-data.ipynb
similarity index 100%
rename from 09_mappings/01_exercises.ipynb
rename to 09_mappings/01_exercises_nested-data.ipynb
diff --git a/09_mappings/03_exercises.ipynb b/09_mappings/03_exercises_fibonacci.ipynb
similarity index 100%
rename from 09_mappings/03_exercises.ipynb
rename to 09_mappings/03_exercises_fibonacci.ipynb
diff --git a/11_classes/01_exercises.ipynb b/11_classes/01_exercises_tsp.ipynb
similarity index 100%
rename from 11_classes/01_exercises.ipynb
rename to 11_classes/01_exercises_tsp.ipynb
diff --git a/CONTENTS.md b/CONTENTS.md
index 46e16ce..cd8228b 100644
--- a/CONTENTS.md
+++ b/CONTENTS.md
@@ -35,8 +35,8 @@ If this is not possible,
      JupyterLab;
      Programming vs. Computer Science;
      Learning Tips)
-  - [exercises <img height="12" style="display: inline-block" src="static/link/to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/00_intro/01_exercises.ipynb)
-    [<img height="12" style="display: inline-block" src="static/link/to_mb.png">](https://mybinder.org/v2/gh/webartifex/intro-to-python/develop?urlpath=lab/tree/00_intro/01_exercises.ipynb)
+  - [exercises <img height="12" style="display: inline-block" src="static/link/to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/00_intro/01_exercises_markdown.ipynb)
+    [<img height="12" style="display: inline-block" src="static/link/to_mb.png">](https://mybinder.org/v2/gh/webartifex/intro-to-python/develop?urlpath=lab/tree/00_intro/01_exercises_markdown.ipynb)
     (Mastering Markdown)
   - [review questions <img height="12" style="display: inline-block" src="static/link/to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/00_intro/02_review.ipynb)
 - **Part A: Expressing Logic**
@@ -47,11 +47,11 @@ If this is not possible,
        Operators;
        Objects & Data Types;
        Errors)
-    - [exercises <img height="12" style="display: inline-block" src="static/link/to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/01_elements/01_exercises.ipynb)
-      [<img height="12" style="display: inline-block" src="static/link/to_mb.png">](https://mybinder.org/v2/gh/webartifex/intro-to-python/develop?urlpath=lab/tree/01_elements/01_exercises.ipynb)
+    - [exercises <img height="12" style="display: inline-block" src="static/link/to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/01_elements/01_exercises_print.ipynb)
+      [<img height="12" style="display: inline-block" src="static/link/to_mb.png">](https://mybinder.org/v2/gh/webartifex/intro-to-python/develop?urlpath=lab/tree/01_elements/01_exercises_print.ipynb)
       (Printing Output)
-    - [exercises <img height="12" style="display: inline-block" src="static/link/to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/01_elements/02_exercises.ipynb)
-      [<img height="12" style="display: inline-block" src="static/link/to_mb.png">](https://mybinder.org/v2/gh/webartifex/intro-to-python/develop?urlpath=lab/tree/01_elements/02_exercises.ipynb)
+    - [exercises <img height="12" style="display: inline-block" src="static/link/to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/01_elements/02_exercises_for-loops.ipynb)
+      [<img height="12" style="display: inline-block" src="static/link/to_mb.png">](https://mybinder.org/v2/gh/webartifex/intro-to-python/develop?urlpath=lab/tree/01_elements/02_exercises_for-loops.ipynb)
       (Simple `for`-loops)
     - [content <img height="12" style="display: inline-block" src="static/link/to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/01_elements/03_content.ipynb)
       [<img height="12" style="display: inline-block" src="static/link/to_mb.png">](https://mybinder.org/v2/gh/webartifex/intro-to-python/develop?urlpath=lab/tree/01_elements/03_content.ipynb)
@@ -59,8 +59,8 @@ If this is not possible,
        Variables & References;
        Mutability;
        Expressions & Statements)
-    - [exercises <img height="12" style="display: inline-block" src="static/link/to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/01_elements/04_exercises.ipynb)
-      [<img height="12" style="display: inline-block" src="static/link/to_mb.png">](https://mybinder.org/v2/gh/webartifex/intro-to-python/develop?urlpath=lab/tree/01_elements/04_exercises.ipynb)
+    - [exercises <img height="12" style="display: inline-block" src="static/link/to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/01_elements/04_exercises_calculator.ipynb)
+      [<img height="12" style="display: inline-block" src="static/link/to_mb.png">](https://mybinder.org/v2/gh/webartifex/intro-to-python/develop?urlpath=lab/tree/01_elements/04_exercises_calculator.ipynb)
       (Python as a Calculator)
     - [summary <img height="12" style="display: inline-block" src="static/link/to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/01_elements/05_summary.ipynb)
     - [review questions <img height="12" style="display: inline-block" src="static/link/to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/01_elements/06_review.ipynb)
@@ -74,8 +74,8 @@ If this is not possible,
        Function Calls & Scoping Rules;
        Positional vs. Keyword Arguments;
        Modularization)
-    - [exercises <img height="12" style="display: inline-block" src="static/link/to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/02_functions/01_exercises.ipynb)
-      [<img height="12" style="display: inline-block" src="static/link/to_mb.png">](https://mybinder.org/v2/gh/webartifex/intro-to-python/develop?urlpath=lab/tree/02_functions/01_exercises.ipynb)
+    - [exercises <img height="12" style="display: inline-block" src="static/link/to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/02_functions/01_exercises_sphere-volume.ipynb)
+      [<img height="12" style="display: inline-block" src="static/link/to_mb.png">](https://mybinder.org/v2/gh/webartifex/intro-to-python/develop?urlpath=lab/tree/02_functions/01_exercises_sphere-volume.ipynb)
       (Volume of a Sphere)
     - [content <img height="12" style="display: inline-block" src="static/link/to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/02_functions/02_content.ipynb)
       [<img height="12" style="display: inline-block" src="static/link/to_mb.png">](https://mybinder.org/v2/gh/webartifex/intro-to-python/develop?urlpath=lab/tree/02_functions/02_content.ipynb)
@@ -92,11 +92,11 @@ If this is not possible,
        Logical Operators;
        `if` statement;
        Exception Handling)
-    - [exercises <img height="12" style="display: inline-block" src="static/link/to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/03_conditionals/01_exercises.ipynb)
-      [<img height="12" style="display: inline-block" src="static/link/to_mb.png">](https://mybinder.org/v2/gh/webartifex/intro-to-python/develop?urlpath=lab/tree/03_conditionals/01_exercises.ipynb)
+    - [exercises <img height="12" style="display: inline-block" src="static/link/to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/03_conditionals/01_exercises_discounts.ipynb)
+      [<img height="12" style="display: inline-block" src="static/link/to_mb.png">](https://mybinder.org/v2/gh/webartifex/intro-to-python/develop?urlpath=lab/tree/03_conditionals/01_exercises_discounts.ipynb)
       (Discounting Customer Orders)
-    - [exercises <img height="12" style="display: inline-block" src="static/link/to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/03_conditionals/02_exercises.ipynb)
-      [<img height="12" style="display: inline-block" src="static/link/to_mb.png">](https://mybinder.org/v2/gh/webartifex/intro-to-python/develop?urlpath=lab/tree/03_conditionals/02_exercises.ipynb)
+    - [exercises <img height="12" style="display: inline-block" src="static/link/to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/03_conditionals/02_exercises_fizz-buzz.ipynb)
+      [<img height="12" style="display: inline-block" src="static/link/to_mb.png">](https://mybinder.org/v2/gh/webartifex/intro-to-python/develop?urlpath=lab/tree/03_conditionals/02_exercises_fizz-buzz.ipynb)
       (Fizz Buzz)
     - [summary <img height="12" style="display: inline-block" src="static/link/to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/03_conditionals/03_summary.ipynb)
     - [review questions <img height="12" style="display: inline-block" src="static/link/to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/03_conditionals/04_review.ipynb)
@@ -108,8 +108,8 @@ If this is not possible,
        Duck Typing;
        Type Casting & Checking;
        Input Validation)
-    - [exercises <img height="12" style="display: inline-block" src="static/link/to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/04_iteration/01_exercises.ipynb)
-      [<img height="12" style="display: inline-block" src="static/link/to_mb.png">](https://mybinder.org/v2/gh/webartifex/intro-to-python/develop?urlpath=lab/tree/04_iteration/01_exercises.ipynb)
+    - [exercises <img height="12" style="display: inline-block" src="static/link/to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/04_iteration/01_exercises_hanoi-towers.ipynb)
+      [<img height="12" style="display: inline-block" src="static/link/to_mb.png">](https://mybinder.org/v2/gh/webartifex/intro-to-python/develop?urlpath=lab/tree/04_iteration/01_exercises_hanoi-towers.ipynb)
       (Towers of Hanoi)
     - [content <img height="12" style="display: inline-block" src="static/link/to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/04_iteration/02_content.ipynb)
       [<img height="12" style="display: inline-block" src="static/link/to_mb.png">](https://mybinder.org/v2/gh/webartifex/intro-to-python/develop?urlpath=lab/tree/04_iteration/02_content.ipynb)
@@ -120,8 +120,8 @@ If this is not possible,
       [<img height="12" style="display: inline-block" src="static/link/to_mb.png">](https://mybinder.org/v2/gh/webartifex/intro-to-python/develop?urlpath=lab/tree/04_iteration/03_content.ipynb)
       (Customizing Loops with `break` and `continue`;
        Indefinite Loops)
-    - [exercises <img height="12" style="display: inline-block" src="static/link/to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/04_iteration/04_exercises.ipynb)
-      [<img height="12" style="display: inline-block" src="static/link/to_mb.png">](https://mybinder.org/v2/gh/webartifex/intro-to-python/develop?urlpath=lab/tree/04_iteration/04_exercises.ipynb)
+    - [exercises <img height="12" style="display: inline-block" src="static/link/to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/04_iteration/04_exercises_dice.ipynb)
+      [<img height="12" style="display: inline-block" src="static/link/to_mb.png">](https://mybinder.org/v2/gh/webartifex/intro-to-python/develop?urlpath=lab/tree/04_iteration/04_exercises_dice.ipynb)
       (Throwing Dice)
     - [summary <img height="12" style="display: inline-block" src="static/link/to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/04_iteration/05_summary.ipynb)
     - [review questions <img height="12" style="display: inline-block" src="static/link/to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/04_iteration/06_review.ipynb)
@@ -162,8 +162,8 @@ If this is not possible,
        Indexing & Slicing;
        String Methods & Operations;
        String Interpolation)
-    - [exercises <img height="12" style="display: inline-block" src="static/link/to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/06_text/01_exercises.ipynb)
-      [<img height="12" style="display: inline-block" src="static/link/to_mb.png">](https://mybinder.org/v2/gh/webartifex/intro-to-python/develop?urlpath=lab/tree/06_text/01_exercises.ipynb)
+    - [exercises <img height="12" style="display: inline-block" src="static/link/to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/06_text/01_exercises_palindromes.ipynb)
+      [<img height="12" style="display: inline-block" src="static/link/to_mb.png">](https://mybinder.org/v2/gh/webartifex/intro-to-python/develop?urlpath=lab/tree/06_text/01_exercises_palindromes.ipynb)
       (Detecting Palindromes)
     - [content <img height="12" style="display: inline-block" src="static/link/to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/06_text/02_content.ipynb)
       [<img height="12" style="display: inline-block" src="static/link/to_mb.png">](https://mybinder.org/v2/gh/webartifex/intro-to-python/develop?urlpath=lab/tree/06_text/02_content.ipynb)
@@ -187,8 +187,8 @@ If this is not possible,
        Indexing & Slicing;
        Shallow vs. Deep Copies;
        List Methods & Operations)
-    - [exercises <img height="12" style="display: inline-block" src="static/link/to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/07_sequences/02_exercises.ipynb)
-      [<img height="12" style="display: inline-block" src="static/link/to_mb.png">](https://mybinder.org/v2/gh/webartifex/intro-to-python/develop?urlpath=lab/tree/07_sequences/02_exercises.ipynb)
+    - [exercises <img height="12" style="display: inline-block" src="static/link/to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/07_sequences/02_exercises_lists.ipynb)
+      [<img height="12" style="display: inline-block" src="static/link/to_mb.png">](https://mybinder.org/v2/gh/webartifex/intro-to-python/develop?urlpath=lab/tree/07_sequences/02_exercises_lists.ipynb)
       (Working with Lists)
     - [content <img height="12" style="display: inline-block" src="static/link/to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/07_sequences/03_content.ipynb)
       [<img height="12" style="display: inline-block" src="static/link/to_mb.png">](https://mybinder.org/v2/gh/webartifex/intro-to-python/develop?urlpath=lab/tree/07_sequences/03_content.ipynb)
@@ -196,8 +196,8 @@ If this is not possible,
        `tuple` Type;
        Packing & Unpacking;
        `*args` in Function Definitions)
-    - [exercises <img height="12" style="display: inline-block" src="static/link/to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/07_sequences/04_exercises.ipynb)
-      [<img height="12" style="display: inline-block" src="static/link/to_mb.png">](https://mybinder.org/v2/gh/webartifex/intro-to-python/develop?urlpath=lab/tree/07_sequences/04_exercises.ipynb)
+    - [exercises <img height="12" style="display: inline-block" src="static/link/to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/07_sequences/04_exercises_un-packing.ipynb)
+      [<img height="12" style="display: inline-block" src="static/link/to_mb.png">](https://mybinder.org/v2/gh/webartifex/intro-to-python/develop?urlpath=lab/tree/07_sequences/04_exercises_un-packing.ipynb)
       (Packing & Unpacking with Functions)
     - [appendix <img height="12" style="display: inline-block" src="static/link/to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/07_sequences/05_appendix.ipynb)
       [<img height="12" style="display: inline-block" src="static/link/to_mb.png">](https://mybinder.org/v2/gh/webartifex/intro-to-python/develop?urlpath=lab/tree/07_sequences/05_appendix.ipynb)
@@ -217,11 +217,11 @@ If this is not possible,
        `generator` Expression;
        Streams of Data;
        Boolean Reducers)
-    - [exercises <img height="12" style="display: inline-block" src="static/link/to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/08_mfr/02_exercises.ipynb)
-      [<img height="12" style="display: inline-block" src="static/link/to_mb.png">](https://mybinder.org/v2/gh/webartifex/intro-to-python/develop?urlpath=lab/tree/08_mfr/02_exercises.ipynb)
+    - [exercises <img height="12" style="display: inline-block" src="static/link/to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/08_mfr/02_exercises_outliers.ipynb)
+      [<img height="12" style="display: inline-block" src="static/link/to_mb.png">](https://mybinder.org/v2/gh/webartifex/intro-to-python/develop?urlpath=lab/tree/08_mfr/02_exercises_outliers.ipynb)
       (Removing Outliers in Streaming Data)
-    - [exercises <img height="12" style="display: inline-block" src="static/link/to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/08_mfr/03_exercises.ipynb)
-      [<img height="12" style="display: inline-block" src="static/link/to_mb.png">](https://mybinder.org/v2/gh/webartifex/intro-to-python/develop?urlpath=lab/tree/08_mfr/03_exercises.ipynb)
+    - [exercises <img height="12" style="display: inline-block" src="static/link/to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/08_mfr/03_exercises_un-packing.ipynb)
+      [<img height="12" style="display: inline-block" src="static/link/to_mb.png">](https://mybinder.org/v2/gh/webartifex/intro-to-python/develop?urlpath=lab/tree/08_mfr/03_exercises_un-packing.ipynb)
       (Packing & Unpacking with Functions, continued)
     - [content <img height="12" style="display: inline-block" src="static/link/to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/08_mfr/04_content.ipynb)
       [<img height="12" style="display: inline-block" src="static/link/to_mb.png">](https://mybinder.org/v2/gh/webartifex/intro-to-python/develop?urlpath=lab/tree/08_mfr/04_content.ipynb)
@@ -237,15 +237,15 @@ If this is not possible,
        Hash Tables;
        `dict` Methods & Behavior;
        `dict` Comprehension)
-    - [exercises <img height="12" style="display: inline-block" src="static/link/to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/09_mappings/01_exercises.ipynb)
-      [<img height="12" style="display: inline-block" src="static/link/to_mb.png">](https://mybinder.org/v2/gh/webartifex/intro-to-python/develop?urlpath=lab/tree/09_mappings/01_exercises.ipynb)
+    - [exercises <img height="12" style="display: inline-block" src="static/link/to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/09_mappings/01_exercises_nested-data.ipynb)
+      [<img height="12" style="display: inline-block" src="static/link/to_mb.png">](https://mybinder.org/v2/gh/webartifex/intro-to-python/develop?urlpath=lab/tree/09_mappings/01_exercises_nested-data.ipynb)
       (Working with Nested Data)
     - [content <img height="12" style="display: inline-block" src="static/link/to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/09_mappings/02_content.ipynb)
       [<img height="12" style="display: inline-block" src="static/link/to_mb.png">](https://mybinder.org/v2/gh/webartifex/intro-to-python/develop?urlpath=lab/tree/09_mappings/02_content.ipynb)
       (`**kwargs` in Function Definitions;
        Memoization)
-    - [exercises <img height="12" style="display: inline-block" src="static/link/to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/09_mappings/03_exercises.ipynb)
-      [<img height="12" style="display: inline-block" src="static/link/to_mb.png">](https://mybinder.org/v2/gh/webartifex/intro-to-python/develop?urlpath=lab/tree/09_mappings/03_exercises.ipynb)
+    - [exercises <img height="12" style="display: inline-block" src="static/link/to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/09_mappings/03_exercises_fibonacci.ipynb)
+      [<img height="12" style="display: inline-block" src="static/link/to_mb.png">](https://mybinder.org/v2/gh/webartifex/intro-to-python/develop?urlpath=lab/tree/09_mappings/03_exercises_fibonacci.ipynb)
       (Memoization without Side Effects)
     - [content <img height="12" style="display: inline-block" src="static/link/to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/09_mappings/04_content.ipynb)
       [<img height="12" style="display: inline-block" src="static/link/to_mb.png">](https://mybinder.org/v2/gh/webartifex/intro-to-python/develop?urlpath=lab/tree/09_mappings/04_content.ipynb)
@@ -271,8 +271,8 @@ If this is not possible,
        Text Representations;
        Instance Methods vs. Class Methods;
        Computed Properties)
-    - [exercises <img height="12" style="display: inline-block" src="static/link/to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/11_classes/01_exercises.ipynb)
-      [<img height="12" style="display: inline-block" src="static/link/to_mb.png">](https://mybinder.org/v2/gh/webartifex/intro-to-python/develop?urlpath=lab/tree/11_classes/01_exercises.ipynb)
+    - [exercises <img height="12" style="display: inline-block" src="static/link/to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/11_classes/01_exercises_tsp.ipynb)
+      [<img height="12" style="display: inline-block" src="static/link/to_mb.png">](https://mybinder.org/v2/gh/webartifex/intro-to-python/develop?urlpath=lab/tree/11_classes/01_exercises_tsp.ipynb)
       (A Traveling Salesman Problem)
     - [content <img height="12" style="display: inline-block" src="static/link/to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/11_classes/02_content.ipynb)
       [<img height="12" style="display: inline-block" src="static/link/to_mb.png">](https://mybinder.org/v2/gh/webartifex/intro-to-python/develop?urlpath=lab/tree/11_classes/02_content.ipynb)

From da43bc3d97f081aeabaab856d93b5f87f6f00d72 Mon Sep 17 00:00:00 2001
From: Alexander Hess <alexander@webartifex.biz>
Date: Mon, 8 Apr 2024 17:45:13 +0200
Subject: [PATCH 238/242] Update links and versions

---
 README.md | 12 ++++++------
 1 file changed, 6 insertions(+), 6 deletions(-)

diff --git a/README.md b/README.md
index f3fffd8..35aa76e 100644
--- a/README.md
+++ b/README.md
@@ -28,7 +28,7 @@ For a more *detailed version* with **clickable links**
 
 #### Videos
 
-Presentations of the chapters are available on this [YouTube playlist <img height="12" style="display: inline-block" src="static/link/to_yt.png">](https://www.youtube.com/playlist?list=PL-2JV1G3J10lQ2xokyQowcRJI5jjNfW7f).
+Presentations of the chapters are available on this [YouTube playlist <img height="12" style="display: inline-block" src="static/link/to_yt.png">](https://www.youtube.com/playlist?list=PL-2JV1G3J10kRUPgP7EwLhyeN5lOZW2kH).
 The recordings are about 25 hours long in total
     and were made in spring 2020
     after a corresponding in-class Bachelor course was cancelled due to Corona.
@@ -76,21 +76,21 @@ If you are familiar with
 
 ### Installation
 
-To follow this course, an installation of **Python 3.8** or higher is expected.
+To follow this course, an installation of **Python 3.11** or higher is expected.
 
 A popular and beginner friendly way is
-    to install the [Anaconda Distribution](https://www.anaconda.com/products/individual)
+    to install the [Anaconda Distribution](https://www.anaconda.com/download)
     that not only ships Python itself
     but also comes pre-packaged with a lot of third-party libraries.
 
 <img src="static/anaconda_download.png" width="50%">
 
-Scroll down to the [download](https://www.anaconda.com/products/individual#Downloads) section
+Scroll down to the "Anaconda Installers" section
     and install the latest version for your operating system
-    (i.e., *2020-07* with Python 3.8 at the time of this writing).
+    (i.e., *2024-02* with Python 3.11 at the time of this writing).
 
 After installation,
-    you find an entry "[Anaconda Navigator](https://docs.anaconda.com/anaconda/navigator/)"
+    you find an entry "[Anaconda Navigator](https://docs.anaconda.com/free/navigator/)"
     in your start menu.
 Click on it.
 

From 110bb3b38ee2f0741f610ee6f5d2bc362d645768 Mon Sep 17 00:00:00 2001
From: Alexander Hess <alexander@webartifex.biz>
Date: Mon, 8 Apr 2024 17:45:35 +0200
Subject: [PATCH 239/242] Remove the instuctor's presentation mode

Reason: RISE only works in the older Jupyter Notebook app
and not in the more commonly used JupyterLab app
---
 README.md  | 27 +--------------------------
 noxfile.py |  5 -----
 2 files changed, 1 insertion(+), 31 deletions(-)

diff --git a/README.md b/README.md
index 35aa76e..c1b0db9 100644
--- a/README.md
+++ b/README.md
@@ -139,7 +139,7 @@ Then, unpack the ZIP file into a folder of your choice,
 <img src="static/repo_download.png" width="50%">
 
 
-### Alternative Installation (for Instructors)
+### Alternative Installation (for Instructors using Linux)
 
 Python can also be installed in a "pure" way
     obtained directly from its core development team [here](https://www.python.org/downloads/).
@@ -232,31 +232,6 @@ The command-line interface stays open in the background,
 <img src="static/cli_jupyter_lab.png" width="50%" align="center">
 
 
-#### Interactive Presentation Mode & Live Coding
-
-`poetry install` also installs the
-    [RISE <img height="12" style="display: inline-block" src="static/link/to_gh.png">](https://github.com/damianavila/RISE)
-    extension for Jupyter.
-With that, the instructor can execute code in *presentation* mode during a class session.
-However, the RISE extension does *not* work in the more recent
-    [JupyterLab](https://jupyterlab.readthedocs.io/en/stable/) app
-    but only in the older [Jupyter Notebook](https://jupyter-notebook.readthedocs.io/en/stable/) app,
-    which comes with less features
-        and a simpler [GUI <img height="12" style="display: inline-block" src="static/link/to_gh.png">](https://en.wikipedia.org/wiki/Graphical_user_interface).
-The instructor can start the latter with:
-
-- `poetry run jupyter notebook`
-
-This also opens a new tab in the web browser.
-After opening a notebook,
-    clicking on the button highlighted below
-    starts the presentation mode.
-
-<img src="static/presentation_mode.png" width="50%" align="center">
-
-Not all notebooks are designed for this presentation mode.
-
-
 ## Contributing
 
 Feedback **is highly encouraged** and will be incorporated.
diff --git a/noxfile.py b/noxfile.py
index 869cdab..03e40cb 100644
--- a/noxfile.py
+++ b/noxfile.py
@@ -46,11 +46,6 @@ def init_project(session):
     ):
         session.run("poetry", "run", "pre-commit", "install", f"--hook-type={type_}")
 
-    # Copy the extensions' JavaScript and CSS files into Jupyter's search directory.
-    session.run(
-        "poetry", "run", "jupyter", "contrib", "nbextension", "install", "--user"
-    )
-
 
 @nox.session(venv_backend="none")
 def doctests(session):

From e3ef1aec29ebe16db911abd68a318e5a2273b0c6 Mon Sep 17 00:00:00 2001
From: Alexander Hess <alexander@webartifex.biz>
Date: Mon, 8 Apr 2024 17:54:41 +0200
Subject: [PATCH 240/242] Update copyright timespan

---
 LICENSE.txt | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/LICENSE.txt b/LICENSE.txt
index 0ec444a..19656d3 100644
--- a/LICENSE.txt
+++ b/LICENSE.txt
@@ -1,6 +1,6 @@
 MIT License
 
-Copyright (c) 2018-2020 Alexander Hess [alexander@webartifex.biz]
+Copyright (c) 2018-2024 Alexander Hess [alexander@webartifex.biz]
 
 Permission is hereby granted, free of charge, to any person obtaining a copy
 of this software and associated documentation files (the "Software"), to deal

From c2a1a8dcc1b00281409d94447074c84e117037bf Mon Sep 17 00:00:00 2001
From: Alexander Hess <alexander@webartifex.biz>
Date: Mon, 8 Apr 2024 17:56:40 +0200
Subject: [PATCH 241/242] Format with black

---
 noxfile.py | 12 ++++++------
 1 file changed, 6 insertions(+), 6 deletions(-)

diff --git a/noxfile.py b/noxfile.py
index 03e40cb..7a54a68 100644
--- a/noxfile.py
+++ b/noxfile.py
@@ -87,23 +87,23 @@ def fix_branch_references(_session):
         {
             "name": "github",
             "pattern": re.compile(
-                fr"((((http)|(https))://github\.com/{REPOSITORY}/((blob)|(tree))/)([\w-]+)/)"
+                rf"((((http)|(https))://github\.com/{REPOSITORY}/((blob)|(tree))/)([\w-]+)/)"
             ),
-            "replacement": fr"\2{branch}/",
+            "replacement": rf"\2{branch}/",
         },
         {
             "name": "nbviewer",
             "pattern": re.compile(
-                fr"((((http)|(https))://nbviewer\.jupyter\.org/github/{REPOSITORY}/((blob)|(tree))/)([\w-]+)/)",
+                rf"((((http)|(https))://nbviewer\.jupyter\.org/github/{REPOSITORY}/((blob)|(tree))/)([\w-]+)/)",
             ),
-            "replacement": fr"\2{branch}/",
+            "replacement": rf"\2{branch}/",
         },
         {
             "name": "mybinder",
             "pattern": re.compile(
-                fr"((((http)|(https))://mybinder\.org/v2/gh/{REPOSITORY}/)([\w-]+)\?)",
+                rf"((((http)|(https))://mybinder\.org/v2/gh/{REPOSITORY}/)([\w-]+)\?)",
             ),
-            "replacement": fr"\2{branch}?",
+            "replacement": rf"\2{branch}?",
         },
     ]
 

From 94e5112f10fe85098ced441f2ddd437c6f9f0739 Mon Sep 17 00:00:00 2001
From: Alexander Hess <alexander@webartifex.biz>
Date: Mon, 8 Apr 2024 22:13:31 +0200
Subject: [PATCH 242/242] Release 0.1.0

After refurbishing the project we prepare a new relaease.
There are no changes with respect to the contents as compared to v0.0.0
that are noteworthy release notes.
---
 00_intro/00_content.ipynb                     |  26 +-
 00_intro/01_exercises_markdown.ipynb          |   4 +-
 00_intro/02_review.ipynb                      |   2 +-
 01_elements/00_content.ipynb                  |  10 +-
 01_elements/01_exercises_print.ipynb          |   4 +-
 01_elements/02_exercises_for-loops.ipynb      |   6 +-
 01_elements/03_content.ipynb                  |  12 +-
 01_elements/04_exercises_calculator.ipynb     |   4 +-
 01_elements/05_summary.ipynb                  |   6 +-
 01_elements/06_review.ipynb                   |   2 +-
 01_elements/07_resources.ipynb                |   2 +-
 02_functions/00_content.ipynb                 |  20 +-
 02_functions/01_exercises_sphere-volume.ipynb |   6 +-
 02_functions/02_content.ipynb                 |  16 +-
 02_functions/04_review.ipynb                  |   2 +-
 03_conditionals/00_content.ipynb              |  10 +-
 03_conditionals/01_exercises_discounts.ipynb  |   4 +-
 03_conditionals/02_exercises_fizz-buzz.ipynb  |   6 +-
 03_conditionals/04_review.ipynb               |   2 +-
 04_iteration/00_content.ipynb                 |   4 +-
 04_iteration/01_exercises_hanoi-towers.ipynb  |   4 +-
 04_iteration/02_content.ipynb                 |  10 +-
 04_iteration/03_content.ipynb                 |  10 +-
 04_iteration/04_exercises_dice.ipynb          |   6 +-
 04_iteration/06_review.ipynb                  |   2 +-
 05_numbers/00_content.ipynb                   |  22 +-
 05_numbers/01_content.ipynb                   |   6 +-
 05_numbers/02_content.ipynb                   |   8 +-
 05_numbers/03_appendix.ipynb                  |   2 +-
 05_numbers/05_review.ipynb                    |   2 +-
 05_numbers/06_resources.ipynb                 |   2 +-
 06_text/00_content.ipynb                      |  34 +--
 06_text/01_exercises_palindromes.ipynb        |   6 +-
 06_text/02_content.ipynb                      |   8 +-
 06_text/04_review.ipynb                       |   2 +-
 06_text/05_resources.ipynb                    |   2 +-
 07_sequences/00_content.ipynb                 |  20 +-
 07_sequences/01_content.ipynb                 |   8 +-
 07_sequences/02_exercises_lists.ipynb         |   4 +-
 07_sequences/03_content.ipynb                 |  10 +-
 07_sequences/04_exercises_un-packing.ipynb    |  14 +-
 07_sequences/05_appendix.ipynb                |   6 +-
 07_sequences/07_review.ipynb                  |   4 +-
 08_mfr/00_content.ipynb                       |  10 +-
 08_mfr/01_content.ipynb                       |  22 +-
 08_mfr/02_exercises_outliers.ipynb            |   8 +-
 08_mfr/03_exercises_un-packing.ipynb          |   8 +-
 08_mfr/04_content.ipynb                       |  10 +-
 08_mfr/06_review.ipynb                        |   2 +-
 09_mappings/00_content.ipynb                  |  14 +-
 09_mappings/01_exercises_nested-data.ipynb    |   4 +-
 09_mappings/02_content.ipynb                  |  10 +-
 09_mappings/03_exercises_fibonacci.ipynb      |   8 +-
 09_mappings/04_content.ipynb                  |   4 +-
 09_mappings/05_appendix.ipynb                 |   2 +-
 09_mappings/07_review.ipynb                   |   4 +-
 09_mappings/08_resources.ipynb                |   2 +-
 11_classes/00_content.ipynb                   |  12 +-
 11_classes/01_exercises_tsp.ipynb             |   4 +-
 11_classes/02_content.ipynb                   |  12 +-
 11_classes/03_content.ipynb                   |  10 +-
 11_classes/04_content.ipynb                   |  34 +--
 11_classes/06_review.ipynb                    |   2 +-
 CONTENTS.md                                   | 250 +++++++++---------
 pyproject.toml                                |   2 +-
 65 files changed, 387 insertions(+), 387 deletions(-)

diff --git a/00_intro/00_content.ipynb b/00_intro/00_content.ipynb
index 8409944..0b6ba58 100644
--- a/00_intro/00_content.ipynb
+++ b/00_intro/00_content.ipynb
@@ -8,7 +8,7 @@
     }
    },
    "source": [
-    "**Note**: Click on \"*Kernel*\" > \"*Restart Kernel and Clear All Outputs*\" in [JupyterLab](https://jupyterlab.readthedocs.io/en/stable/) *before* reading this notebook to reset its output. If you cannot run this file on your machine, you may want to open it [in the cloud <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_mb.png\">](https://mybinder.org/v2/gh/webartifex/intro-to-python/develop?urlpath=lab/tree/00_intro/00_content.ipynb)."
+    "**Note**: Click on \"*Kernel*\" > \"*Restart Kernel and Clear All Outputs*\" in [JupyterLab](https://jupyterlab.readthedocs.io/en/stable/) *before* reading this notebook to reset its output. If you cannot run this file on your machine, you may want to open it [in the cloud <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_mb.png\">](https://mybinder.org/v2/gh/webartifex/intro-to-python/main?urlpath=lab/tree/00_intro/00_content.ipynb)."
    ]
   },
   {
@@ -135,7 +135,7 @@
    "source": [
     "To \"read\" this book in the most meaningful way, a working installation of **Python 3.8** with [JupyterLab](https://jupyterlab.readthedocs.io/en/stable/) is needed.\n",
     "\n",
-    "For a tutorial on how to install Python on your computer, follow the instructions in the [README.md](https://github.com/webartifex/intro-to-python/blob/develop/README.md#installation) file in the project's [GitHub repository <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_gh.png\">](https://github.com/webartifex/intro-to-python). If you cannot install Python on your own machine, you may open the book interactively in the cloud with [Binder <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_mb.png\">](https://mybinder.org/v2/gh/webartifex/intro-to-python/develop?urlpath=lab)."
+    "For a tutorial on how to install Python on your computer, follow the instructions in the [README.md](https://github.com/webartifex/intro-to-python/blob/main/README.md#installation) file in the project's [GitHub repository <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_gh.png\">](https://github.com/webartifex/intro-to-python). If you cannot install Python on your own machine, you may open the book interactively in the cloud with [Binder <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_mb.png\">](https://mybinder.org/v2/gh/webartifex/intro-to-python/main?urlpath=lab)."
    ]
   },
   {
@@ -161,7 +161,7 @@
     "\n",
     "\"Jupyter\" is an [acronym <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_wiki.png\">](https://en.wikipedia.org/wiki/Acronym) derived from the names of the three major programming languages **[Julia](https://julialang.org/)**, **[Python <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://www.python.org)**, and **[R](https://www.r-project.org/)**, all of which play significant roles in the world of data science. The Jupyter Project's idea is to serve as an integrating platform such that different programming languages and software packages can be used together within the same project.\n",
     "\n",
-    "Jupyter notebooks have become a de-facto standard for communicating and exchanging results in the data science community - both in academia and business - and provide an alternative to command-line interface (CLI or \"terminal\") based ways of running Python code. As an example for the latter case, we could start the default [Python interpreter <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/tutorial/interpreter.html) that comes with every installation by typing the `python` command into a CLI (or `poetry run python` if the project is managed with the [poetry](https://python-poetry.org/docs/) CLI tool as explained in the [README.md](https://github.com/webartifex/intro-to-python/blob/develop/README.md#alternative-installation-for-instructors) file). Then, as the screenshot below shows, we could execute Python code like `1 + 2` or `print(\"Hello World\")` line by line simply by typing it following the `>>>` **prompt** and pressing the **Enter** key. For an introductory course, however, this would be rather tedious and probably scare off many beginners."
+    "Jupyter notebooks have become a de-facto standard for communicating and exchanging results in the data science community - both in academia and business - and provide an alternative to command-line interface (CLI or \"terminal\") based ways of running Python code. As an example for the latter case, we could start the default [Python interpreter <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/tutorial/interpreter.html) that comes with every installation by typing the `python` command into a CLI (or `poetry run python` if the project is managed with the [poetry](https://python-poetry.org/docs/) CLI tool as explained in the [README.md](https://github.com/webartifex/intro-to-python/blob/main/README.md#alternative-installation-for-instructors) file). Then, as the screenshot below shows, we could execute Python code like `1 + 2` or `print(\"Hello World\")` line by line simply by typing it following the `>>>` **prompt** and pressing the **Enter** key. For an introductory course, however, this would be rather tedious and probably scare off many beginners."
    ]
   },
   {
@@ -750,11 +750,11 @@
     "**Part A: Expressing Logic**\n",
     "\n",
     "- What is a programming language? What kind of words exist?\n",
-    "  - *Chapter 1*: [Elements of a Program <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/01_elements/00_content.ipynb)\n",
-    "  - *Chapter 2*: [Functions & Modularization <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/02_functions/00_content.ipynb)\n",
+    "  - *Chapter 1*: [Elements of a Program <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/main/01_elements/00_content.ipynb)\n",
+    "  - *Chapter 2*: [Functions & Modularization <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/main/02_functions/00_content.ipynb)\n",
     "- What is the flow of execution? How can we form sentences from words?\n",
-    "  - *Chapter 3*: [Conditionals & Exceptions <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/03_conditionals/00_content.ipynb)\n",
-    "  - *Chapter 4*: [Recursion & Looping <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/04_iteration/00_content.ipynb)"
+    "  - *Chapter 3*: [Conditionals & Exceptions <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/main/03_conditionals/00_content.ipynb)\n",
+    "  - *Chapter 4*: [Recursion & Looping <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/main/04_iteration/00_content.ipynb)"
    ]
   },
   {
@@ -768,14 +768,14 @@
     "**Part B: Managing Data and Memory**\n",
     "\n",
     "- How is data stored in memory?\n",
-    "  - *Chapter 5*: [Numbers & Bits <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/05_numbers/00_content.ipynb)\n",
-    "  - *Chapter 6*: [Text & Bytes <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/06_text/00_content.ipynb)\n",
-    "  - *Chapter 7*: [Sequential Data <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/07_sequences/00_content.ipynb)\n",
-    "  - *Chapter 8*: [Map, Filter, & Reduce <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/08_mfr/00_content.ipynb)\n",
-    "  - *Chapter 9*: [Mappings & Sets <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/09_mappings/00_content.ipynb)\n",
+    "  - *Chapter 5*: [Numbers & Bits <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/main/05_numbers/00_content.ipynb)\n",
+    "  - *Chapter 6*: [Text & Bytes <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/main/06_text/00_content.ipynb)\n",
+    "  - *Chapter 7*: [Sequential Data <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/main/07_sequences/00_content.ipynb)\n",
+    "  - *Chapter 8*: [Map, Filter, & Reduce <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/main/08_mfr/00_content.ipynb)\n",
+    "  - *Chapter 9*: [Mappings & Sets <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/main/09_mappings/00_content.ipynb)\n",
     "  - *Chapter 10*: Arrays & Dataframes\n",
     "- How can we create custom data types?\n",
-    "  - *Chapter 11*: [Classes & Instances <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/11_classes/00_content.ipynb)"
+    "  - *Chapter 11*: [Classes & Instances <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/main/11_classes/00_content.ipynb)"
    ]
   },
   {
diff --git a/00_intro/01_exercises_markdown.ipynb b/00_intro/01_exercises_markdown.ipynb
index 272d5f9..d80f9d8 100644
--- a/00_intro/01_exercises_markdown.ipynb
+++ b/00_intro/01_exercises_markdown.ipynb
@@ -4,7 +4,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "**Note**: Click on \"*Kernel*\" > \"*Restart Kernel and Run All*\" in [JupyterLab](https://jupyterlab.readthedocs.io/en/stable/) *after* finishing the exercises to ensure that your solution runs top to bottom *without* any errors. If you cannot run this file on your machine, you may want to open it [in the cloud <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_mb.png\">](https://mybinder.org/v2/gh/webartifex/intro-to-python/develop?urlpath=lab/tree/00_intro/02_exercises.ipynb)."
+    "**Note**: Click on \"*Kernel*\" > \"*Restart Kernel and Run All*\" in [JupyterLab](https://jupyterlab.readthedocs.io/en/stable/) *after* finishing the exercises to ensure that your solution runs top to bottom *without* any errors. If you cannot run this file on your machine, you may want to open it [in the cloud <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_mb.png\">](https://mybinder.org/v2/gh/webartifex/intro-to-python/main?urlpath=lab/tree/00_intro/02_exercises.ipynb)."
    ]
   },
   {
@@ -18,7 +18,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "The exercises below assume that you have read [Chapter 0 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/00_intro/00_content.ipynb)."
+    "The exercises below assume that you have read [Chapter 0 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/main/00_intro/00_content.ipynb)."
    ]
   },
   {
diff --git a/00_intro/02_review.ipynb b/00_intro/02_review.ipynb
index 0fa77c5..bf3a39a 100644
--- a/00_intro/02_review.ipynb
+++ b/00_intro/02_review.ipynb
@@ -11,7 +11,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "The questions below assume that you have read [Chapter 0 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/00_intro/00_content.ipynb).\n",
+    "The questions below assume that you have read [Chapter 0 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/main/00_intro/00_content.ipynb).\n",
     "\n",
     "Be concise in your answers! Most questions can be answered in *one* sentence."
    ]
diff --git a/01_elements/00_content.ipynb b/01_elements/00_content.ipynb
index 3335486..af2d022 100644
--- a/01_elements/00_content.ipynb
+++ b/01_elements/00_content.ipynb
@@ -8,7 +8,7 @@
     }
    },
    "source": [
-    "**Note**: Click on \"*Kernel*\" > \"*Restart Kernel and Clear All Outputs*\" in [JupyterLab](https://jupyterlab.readthedocs.io/en/stable/) *before* reading this notebook to reset its output. If you cannot run this file on your machine, you may want to open it [in the cloud <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_mb.png\">](https://mybinder.org/v2/gh/webartifex/intro-to-python/develop?urlpath=lab/tree/01_elements/00_content.ipynb)."
+    "**Note**: Click on \"*Kernel*\" > \"*Restart Kernel and Clear All Outputs*\" in [JupyterLab](https://jupyterlab.readthedocs.io/en/stable/) *before* reading this notebook to reset its output. If you cannot run this file on your machine, you may want to open it [in the cloud <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_mb.png\">](https://mybinder.org/v2/gh/webartifex/intro-to-python/main?urlpath=lab/tree/01_elements/00_content.ipynb)."
    ]
   },
   {
@@ -1165,7 +1165,7 @@
    "source": [
     "These addresses are *not* meaningful for anything other than checking if two variables reference the *same* object.\n",
     "\n",
-    "Obviously, `a` and `b`  have the same *value* as revealed by the **equality operator** `==`: We say `a` and `b` \"evaluate equal.\" The resulting `True` - and the `False` further below - is yet another data type, a so-called **boolean**. We look into them in [Chapter 3 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/03_conditionals/00_content.ipynb#Boolean-Expressions)."
+    "Obviously, `a` and `b`  have the same *value* as revealed by the **equality operator** `==`: We say `a` and `b` \"evaluate equal.\" The resulting `True` - and the `False` further below - is yet another data type, a so-called **boolean**. We look into them in [Chapter 3 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/main/03_conditionals/00_content.ipynb#Boolean-Expressions)."
    ]
   },
   {
@@ -1342,7 +1342,7 @@
    "source": [
     "Different types imply different behaviors for the objects. The `b` object, for example, may be \"asked\" if it is a whole number with the [.is_integer() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/stdtypes.html#float.is_integer) \"functionality\" that comes with *every* `float` object.\n",
     "\n",
-    "Formally, we call such type-specific functionalities **methods** (i.e., as opposed to functions) and we look at them in detail in [Chapter 11 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/11_classes/00_content.ipynb). For now, it suffices to know that we access them with the **dot operator** `.` on the object. Of course, `b` is a whole number, which the boolean object `True` tells us."
+    "Formally, we call such type-specific functionalities **methods** (i.e., as opposed to functions) and we look at them in detail in [Chapter 11 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/main/11_classes/00_content.ipynb). For now, it suffices to know that we access them with the **dot operator** `.` on the object. Of course, `b` is a whole number, which the boolean object `True` tells us."
    ]
   },
   {
@@ -1934,7 +1934,7 @@
     }
    },
    "source": [
-    "For example, while the above code to calculate the average of the even numbers in `[7, 11, 8, 5, 3, 12, 2, 6, 9, 10, 1, 4]` is correct, a Pythonista would rewrite it in a more \"Pythonic\" way and use the built-in [sum() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#sum) and [len() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#len) functions (cf., [Chapter 2 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/02_functions/00_content.ipynb#Built-in-Functions)) as well as a so-called **list comprehension** (cf., [Chapter 8 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/08_mfr/00_content.ipynb#List-Comprehensions)). Pythonic code runs faster in many cases and is less error-prone."
+    "For example, while the above code to calculate the average of the even numbers in `[7, 11, 8, 5, 3, 12, 2, 6, 9, 10, 1, 4]` is correct, a Pythonista would rewrite it in a more \"Pythonic\" way and use the built-in [sum() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#sum) and [len() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#len) functions (cf., [Chapter 2 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/main/02_functions/00_content.ipynb#Built-in-Functions)) as well as a so-called **list comprehension** (cf., [Chapter 8 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/main/08_mfr/00_content.ipynb#List-Comprehensions)). Pythonic code runs faster in many cases and is less error-prone."
    ]
   },
   {
@@ -2136,7 +2136,7 @@
     "\n",
     "At the same time, for a beginner's course, it is often easier to code linearly.\n",
     "\n",
-    "In real data science projects, one would probably employ a mixed approach and put reusable code into so-called Python modules (i.e., *.py* files; cf., [Chapter 2 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/02_functions/00_content.ipynb#Local-Modules-and-Packages)) and then use Jupyter notebooks to build up a linear report or storyline for an analysis."
+    "In real data science projects, one would probably employ a mixed approach and put reusable code into so-called Python modules (i.e., *.py* files; cf., [Chapter 2 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/main/02_functions/00_content.ipynb#Local-Modules-and-Packages)) and then use Jupyter notebooks to build up a linear report or storyline for an analysis."
    ]
   }
  ],
diff --git a/01_elements/01_exercises_print.ipynb b/01_elements/01_exercises_print.ipynb
index 592d48a..68432ee 100644
--- a/01_elements/01_exercises_print.ipynb
+++ b/01_elements/01_exercises_print.ipynb
@@ -4,7 +4,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "**Note**: Click on \"*Kernel*\" > \"*Restart Kernel and Run All*\" in [JupyterLab](https://jupyterlab.readthedocs.io/en/stable/) *after* finishing the exercises to ensure that your solution runs top to bottom *without* any errors. If you cannot run this file on your machine, you may want to open it [in the cloud <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_mb.png\">](https://mybinder.org/v2/gh/webartifex/intro-to-python/develop?urlpath=lab/tree/01_elements/01_exercises.ipynb)."
+    "**Note**: Click on \"*Kernel*\" > \"*Restart Kernel and Run All*\" in [JupyterLab](https://jupyterlab.readthedocs.io/en/stable/) *after* finishing the exercises to ensure that your solution runs top to bottom *without* any errors. If you cannot run this file on your machine, you may want to open it [in the cloud <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_mb.png\">](https://mybinder.org/v2/gh/webartifex/intro-to-python/main?urlpath=lab/tree/01_elements/01_exercises.ipynb)."
    ]
   },
   {
@@ -18,7 +18,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "The exercises below assume that you have read the [first part <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/01_elements/00_content.ipynb) of Chapter 1.\n",
+    "The exercises below assume that you have read the [first part <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/main/01_elements/00_content.ipynb) of Chapter 1.\n",
     "\n",
     "The `...`'s in the code cells indicate where you need to fill in code snippets. The number of `...`'s within a code cell give you a rough idea of how many lines of code are needed to solve the task. You should not need to create any additional code cells for your final solution. However, you may want to use temporary code cells to try out some ideas."
    ]
diff --git a/01_elements/02_exercises_for-loops.ipynb b/01_elements/02_exercises_for-loops.ipynb
index 2ff9c45..fab3299 100644
--- a/01_elements/02_exercises_for-loops.ipynb
+++ b/01_elements/02_exercises_for-loops.ipynb
@@ -4,7 +4,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "**Note**: Click on \"*Kernel*\" > \"*Restart Kernel and Run All*\" in [JupyterLab](https://jupyterlab.readthedocs.io/en/stable/) *after* finishing the exercises to ensure that your solution runs top to bottom *without* any errors. If you cannot run this file on your machine, you may want to open it [in the cloud <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_mb.png\">](https://mybinder.org/v2/gh/webartifex/intro-to-python/develop?urlpath=lab/tree/01_elements/02_exercises.ipynb)."
+    "**Note**: Click on \"*Kernel*\" > \"*Restart Kernel and Run All*\" in [JupyterLab](https://jupyterlab.readthedocs.io/en/stable/) *after* finishing the exercises to ensure that your solution runs top to bottom *without* any errors. If you cannot run this file on your machine, you may want to open it [in the cloud <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_mb.png\">](https://mybinder.org/v2/gh/webartifex/intro-to-python/main?urlpath=lab/tree/01_elements/02_exercises.ipynb)."
    ]
   },
   {
@@ -18,7 +18,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "The exercises below assume that you have read the [first part <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/01_elements/00_content.ipynb) of Chapter 1.\n",
+    "The exercises below assume that you have read the [first part <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/main/01_elements/00_content.ipynb) of Chapter 1.\n",
     "\n",
     "The `...`'s in the code cells indicate where you need to fill in code snippets. The number of `...`'s within a code cell give you a rough idea of how many lines of code are needed to solve the task. You should not need to create any additional code cells for your final solution. However, you may want to use temporary code cells to try out some ideas."
    ]
@@ -36,7 +36,7 @@
    "source": [
     "`for`-loops are extremely versatile in Python. That is different from many other programming languages.\n",
     "\n",
-    "As shown in the first example in [Chapter 1 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/01_elements/00_content.ipynb#Example:-Averaging-all-even-Numbers-in-a-List), we can create a `list` like `numbers` and loop over the numbers in it on a one-by-one basis."
+    "As shown in the first example in [Chapter 1 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/main/01_elements/00_content.ipynb#Example:-Averaging-all-even-Numbers-in-a-List), we can create a `list` like `numbers` and loop over the numbers in it on a one-by-one basis."
    ]
   },
   {
diff --git a/01_elements/03_content.ipynb b/01_elements/03_content.ipynb
index e7886f8..a438ab7 100644
--- a/01_elements/03_content.ipynb
+++ b/01_elements/03_content.ipynb
@@ -8,7 +8,7 @@
     }
    },
    "source": [
-    "**Note**: Click on \"*Kernel*\" > \"*Restart Kernel and Clear All Outputs*\" in [JupyterLab](https://jupyterlab.readthedocs.io/en/stable/) *before* reading this notebook to reset its output. If you cannot run this file on your machine, you may want to open it [in the cloud <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_mb.png\">](https://mybinder.org/v2/gh/webartifex/intro-to-python/develop?urlpath=lab/tree/01_elements/03_content.ipynb)."
+    "**Note**: Click on \"*Kernel*\" > \"*Restart Kernel and Clear All Outputs*\" in [JupyterLab](https://jupyterlab.readthedocs.io/en/stable/) *before* reading this notebook to reset its output. If you cannot run this file on your machine, you may want to open it [in the cloud <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_mb.png\">](https://mybinder.org/v2/gh/webartifex/intro-to-python/main?urlpath=lab/tree/01_elements/03_content.ipynb)."
    ]
   },
   {
@@ -367,7 +367,7 @@
     }
    },
    "source": [
-    "Some variables magically exist when a Python process is started or are added by Jupyter. We may safely ignore the former until [Chapter 11 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/11_classes/00_content.ipynb) and the latter for good."
+    "Some variables magically exist when a Python process is started or are added by Jupyter. We may safely ignore the former until [Chapter 11 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/main/11_classes/00_content.ipynb) and the latter for good."
    ]
   },
   {
@@ -687,7 +687,7 @@
     }
    },
    "source": [
-    "Variables with leading and trailing double underscores, referred to as **dunder** in Python jargon, are used for built-in functionalities and to implement object-oriented features as we see in [Chapter 11 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/11_classes/00_content.ipynb). We must *not* use this style for variables!"
+    "Variables with leading and trailing double underscores, referred to as **dunder** in Python jargon, are used for built-in functionalities and to implement object-oriented features as we see in [Chapter 11 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/main/11_classes/00_content.ipynb). We must *not* use this style for variables!"
    ]
   },
   {
@@ -968,7 +968,7 @@
    "source": [
     "Let's change the first element of `x`.\n",
     "\n",
-    "[Chapter 7 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/07_sequences/00_content.ipynb#The-list-Type) discusses lists in more depth. For now, let's view a `list` object as some sort of **container** that holds an arbitrary number of references to other objects and treat the brackets `[]` attached to it as yet another operator, namely the **indexing operator**. So, `x[0]` instructs Python to first follow the reference from the global list of all names to the `x` object. Then, it follows the first reference it finds there to the `1` object we put in the list. The indexing operator must be an operator as we merely *read* the first element and do not change anything in memory permanently.\n",
+    "[Chapter 7 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/main/07_sequences/00_content.ipynb#The-list-Type) discusses lists in more depth. For now, let's view a `list` object as some sort of **container** that holds an arbitrary number of references to other objects and treat the brackets `[]` attached to it as yet another operator, namely the **indexing operator**. So, `x[0]` instructs Python to first follow the reference from the global list of all names to the `x` object. Then, it follows the first reference it finds there to the `1` object we put in the list. The indexing operator must be an operator as we merely *read* the first element and do not change anything in memory permanently.\n",
     "\n",
     "Python **begins counting at 0**. This is not the case for many other languages, for example, [MATLAB <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_wiki.png\">](https://en.wikipedia.org/wiki/MATLAB), [R <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_wiki.png\">](https://en.wikipedia.org/wiki/R_%28programming_language%29), or [Stata <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_wiki.png\">](https://en.wikipedia.org/wiki/Stata). To understand why this makes sense, see this short [note](https://www.cs.utexas.edu/users/EWD/transcriptions/EWD08xx/EWD831.html) by one of the all-time greats in computer science, the late [Edsger Dijkstra <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_wiki.png\">](https://en.wikipedia.org/wiki/Edsger_W._Dijkstra)."
    ]
@@ -1144,7 +1144,7 @@
     "\n",
     "In simple words, anything that may be used on the *right-hand* side of an assignment statement without creating a `SyntaxError` is an expression.\n",
     "\n",
-    "What we have said about *individual* operators before, namely that they have *no* permanent side effects in memory, actually belongs here, to begin with: The absence of any *permanent* side effects is the characteristic property of expressions, and all the code cells in the \"*(Arithmetic) Operators*\" section in the [first part <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/01_elements/00_content.ipynb#%28Arithmetic%29-Operators) of this chapter are examples of expressions.\n",
+    "What we have said about *individual* operators before, namely that they have *no* permanent side effects in memory, actually belongs here, to begin with: The absence of any *permanent* side effects is the characteristic property of expressions, and all the code cells in the \"*(Arithmetic) Operators*\" section in the [first part <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/main/01_elements/00_content.ipynb#%28Arithmetic%29-Operators) of this chapter are examples of expressions.\n",
     "\n",
     "The simplest possible expressions contain only one variable or literal. The output below a code cell is Jupyter's way of returning the reference to the object to us!\n",
     "\n",
@@ -1414,7 +1414,7 @@
     }
    },
    "source": [
-    "... calling the built-in [print() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#print) function does *neither* change the memory *nor* evaluate to an object (disregarding the `None` object explained in [Chapter 2 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/02_functions/00_content.ipynb#Function-Definitions)). We could view changing the computer's screen as a side effect but this is outside of Python's memory!\n",
+    "... calling the built-in [print() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#print) function does *neither* change the memory *nor* evaluate to an object (disregarding the `None` object explained in [Chapter 2 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/main/02_functions/00_content.ipynb#Function-Definitions)). We could view changing the computer's screen as a side effect but this is outside of Python's memory!\n",
     "\n",
     "Also, the cell below has *no* output! It only looks like it does as Jupyter redirects whatever [print() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#print) writes to the \"screen\" to below a cell. We see a difference to the expressions above in that there are no brackets `[...]` next to the output showing the execution count number."
    ]
diff --git a/01_elements/04_exercises_calculator.ipynb b/01_elements/04_exercises_calculator.ipynb
index 3fa9301..5b66384 100644
--- a/01_elements/04_exercises_calculator.ipynb
+++ b/01_elements/04_exercises_calculator.ipynb
@@ -4,7 +4,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "**Note**: Click on \"*Kernel*\" > \"*Restart Kernel and Run All*\" in [JupyterLab](https://jupyterlab.readthedocs.io/en/stable/) *after* finishing the exercises to ensure that your solution runs top to bottom *without* any errors. If you cannot run this file on your machine, you may want to open it [in the cloud <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_mb.png\">](https://mybinder.org/v2/gh/webartifex/intro-to-python/develop?urlpath=lab/tree/01_elements/04_exercises.ipynb)."
+    "**Note**: Click on \"*Kernel*\" > \"*Restart Kernel and Run All*\" in [JupyterLab](https://jupyterlab.readthedocs.io/en/stable/) *after* finishing the exercises to ensure that your solution runs top to bottom *without* any errors. If you cannot run this file on your machine, you may want to open it [in the cloud <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_mb.png\">](https://mybinder.org/v2/gh/webartifex/intro-to-python/main?urlpath=lab/tree/01_elements/04_exercises.ipynb)."
    ]
   },
   {
@@ -18,7 +18,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "The exercises below assume that you have read the [second part <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/01_elements/03_content.ipynb) Chapter 1.\n",
+    "The exercises below assume that you have read the [second part <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/main/01_elements/03_content.ipynb) Chapter 1.\n",
     "\n",
     "The `...`'s in the code cells indicate where you need to fill in code snippets. The number of `...`'s within a code cell give you a rough idea of how many lines of code are needed to solve the task. You should not need to create any additional code cells for your final solution. However, you may want to use temporary code cells to try out some ideas."
    ]
diff --git a/01_elements/05_summary.ipynb b/01_elements/05_summary.ipynb
index f57bb09..e5a7dd0 100644
--- a/01_elements/05_summary.ipynb
+++ b/01_elements/05_summary.ipynb
@@ -45,7 +45,7 @@
     "  - distinct and well-contained areas/parts of the memory that hold the actual data\n",
     "  - the concept by which Python manages the memory for us\n",
     "  - can be classified into objects of the same **type** (i.e., same abstract \"structure\" but different concrete data)\n",
-    "  - built-in objects (incl. **literals**) vs. user-defined objects (cf., [Chapter 11 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/11_classes/00_content.ipynb))\n",
+    "  - built-in objects (incl. **literals**) vs. user-defined objects (cf., [Chapter 11 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/main/11_classes/00_content.ipynb))\n",
     "  - e.g., `1`, `1.0`, and `\"one\"` are three different objects of distinct types that are also literals (i.e., by the way we type them into the command line Python knows what the value and type are)\n",
     "\n",
     "\n",
@@ -80,14 +80,14 @@
     "  - ignored by Python\n",
     "\n",
     "\n",
-    "- functions (cf., [Chapter 2 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/02_functions/00_content.ipynb))\n",
+    "- functions (cf., [Chapter 2 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/main/02_functions/00_content.ipynb))\n",
     "  - named sequences of instructions\n",
     "  - the smaller parts in a larger program\n",
     "  - make a program more modular and thus easier to understand\n",
     "  - include [built-in functions <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html) like [print() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#print), [sum() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#sum), or [len() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#len)\n",
     "\n",
     "\n",
-    "- flow control (cf., [Chapter 3 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/03_conditionals/00_content.ipynb) and [Chapter 4 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/04_iteration/00_content.ipynb))\n",
+    "- flow control (cf., [Chapter 3 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/main/03_conditionals/00_content.ipynb) and [Chapter 4 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/main/04_iteration/00_content.ipynb))\n",
     "  - expression of **business logic** or an **algorithm**\n",
     "  - conditional execution of parts of a program (e.g., `if` statements)\n",
     "  - repetitive execution of parts of a program (e.g., `for`-loops)"
diff --git a/01_elements/06_review.ipynb b/01_elements/06_review.ipynb
index 33ff535..60c9360 100644
--- a/01_elements/06_review.ipynb
+++ b/01_elements/06_review.ipynb
@@ -11,7 +11,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "The questions below assume that you have read the [first <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/01_elements/00_content.ipynb) and the [second <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/01_elements/03_content.ipynb) part of Chapter 1.\n",
+    "The questions below assume that you have read the [first <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/main/01_elements/00_content.ipynb) and the [second <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/main/01_elements/03_content.ipynb) part of Chapter 1.\n",
     "\n",
     "Be concise in your answers! Most questions can be answered in *one* sentence."
    ]
diff --git a/01_elements/07_resources.ipynb b/01_elements/07_resources.ipynb
index 0ac2b87..0bf7edb 100644
--- a/01_elements/07_resources.ipynb
+++ b/01_elements/07_resources.ipynb
@@ -4,7 +4,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "**Note**: Click on \"*Kernel*\" > \"*Restart Kernel and Clear All Outputs*\" in [JupyterLab](https://jupyterlab.readthedocs.io/en/stable/) *before* reading this notebook to reset its output. If you cannot run this file on your machine, you may want to open it [in the cloud <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_mb.png\">](https://mybinder.org/v2/gh/webartifex/intro-to-python/develop?urlpath=lab/tree/01_elements/07_resources.ipynb)."
+    "**Note**: Click on \"*Kernel*\" > \"*Restart Kernel and Clear All Outputs*\" in [JupyterLab](https://jupyterlab.readthedocs.io/en/stable/) *before* reading this notebook to reset its output. If you cannot run this file on your machine, you may want to open it [in the cloud <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_mb.png\">](https://mybinder.org/v2/gh/webartifex/intro-to-python/main?urlpath=lab/tree/01_elements/07_resources.ipynb)."
    ]
   },
   {
diff --git a/02_functions/00_content.ipynb b/02_functions/00_content.ipynb
index cfc47d3..a40b1e6 100644
--- a/02_functions/00_content.ipynb
+++ b/02_functions/00_content.ipynb
@@ -8,7 +8,7 @@
     }
    },
    "source": [
-    "**Note**: Click on \"*Kernel*\" > \"*Restart Kernel and Clear All Outputs*\" in [JupyterLab](https://jupyterlab.readthedocs.io/en/stable/) *before* reading this notebook to reset its output. If you cannot run this file on your machine, you may want to open it [in the cloud <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_mb.png\">](https://mybinder.org/v2/gh/webartifex/intro-to-python/develop?urlpath=lab/tree/02_functions/00_content.ipynb)."
+    "**Note**: Click on \"*Kernel*\" > \"*Restart Kernel and Clear All Outputs*\" in [JupyterLab](https://jupyterlab.readthedocs.io/en/stable/) *before* reading this notebook to reset its output. If you cannot run this file on your machine, you may want to open it [in the cloud <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_mb.png\">](https://mybinder.org/v2/gh/webartifex/intro-to-python/main?urlpath=lab/tree/02_functions/00_content.ipynb)."
    ]
   },
   {
@@ -30,7 +30,7 @@
     }
    },
    "source": [
-    "In [Chapter 1 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/01_elements/00_content.ipynb#Example:-Averaging-all-even-Numbers-in-a-List), we simply typed the code to calculate the average of the even numbers in a list of whole numbers into several code cells. Then, we executed them one after another. We had no way of *reusing* the code except for either executing cells multiple times. And, whenever we find ourselves doing repetitive manual work, we can be sure that there must be a way of automating what we are doing.\n",
+    "In [Chapter 1 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/main/01_elements/00_content.ipynb#Example:-Averaging-all-even-Numbers-in-a-List), we simply typed the code to calculate the average of the even numbers in a list of whole numbers into several code cells. Then, we executed them one after another. We had no way of *reusing* the code except for either executing cells multiple times. And, whenever we find ourselves doing repetitive manual work, we can be sure that there must be a way of automating what we are doing.\n",
     "\n",
     "This chapter shows how Python offers language constructs that let us **define** functions ourselves that we may then **call** just like the built-in ones. Also, we look at how we can extend our Python installation with functionalities written by other people."
    ]
@@ -307,7 +307,7 @@
     }
    },
    "source": [
-    "To execute a function, we **call** it with the **call operator** `()` as shown many times in [Chapter 1 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/01_elements/00_content.ipynb) and above.\n",
+    "To execute a function, we **call** it with the **call operator** `()` as shown many times in [Chapter 1 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/main/01_elements/00_content.ipynb) and above.\n",
     "\n",
     "If we are unsure whether a variable references a function or not, we can verify that with the built-in [callable() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#callable) function.\n",
     "\n",
@@ -538,7 +538,7 @@
     }
    },
    "source": [
-    "Notice the subtle difference compared to the behavior of the `//` operator in [Chapter 1 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/01_elements/00_content.ipynb##%28Arithmetic#%29-Operators) that \"rounds\" towards minus infinity: [int() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#int) always \"rounds\" towards `0`."
+    "Notice the subtle difference compared to the behavior of the `//` operator in [Chapter 1 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/main/01_elements/00_content.ipynb##%28Arithmetic#%29-Operators) that \"rounds\" towards minus infinity: [int() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#int) always \"rounds\" towards `0`."
    ]
   },
   {
@@ -869,7 +869,7 @@
     }
    },
    "source": [
-    "We may create so-called *user-defined* **functions** with the `def` statement (cf., [reference <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/reference/compound_stmts.html#function-definitions)). To extend an already familiar example, we reuse the introductory example from [Chapter 1 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/01_elements/00_content.ipynb#Best-Practices) in its final Pythonic version and transform it into the function `average_evens()` below. We replace the variable name `numbers` with `integers` for didactical purposes in the first couple of examples.\n",
+    "We may create so-called *user-defined* **functions** with the `def` statement (cf., [reference <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/reference/compound_stmts.html#function-definitions)). To extend an already familiar example, we reuse the introductory example from [Chapter 1 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/main/01_elements/00_content.ipynb#Best-Practices) in its final Pythonic version and transform it into the function `average_evens()` below. We replace the variable name `numbers` with `integers` for didactical purposes in the first couple of examples.\n",
     "\n",
     "A function's **name** must be chosen according to the same naming rules as ordinary variables since Python manages function names like variables. In this book, we further adopt the convention of ending function names with parentheses `()` in text cells for faster comprehension when reading (i.e., `average_evens()` vs. `average_evens`). These are *not* part of the name but must always be written out in the `def` statement for syntactic reasons.\n",
     "\n",
@@ -881,7 +881,7 @@
     "\n",
     "A function may specify an *explicit* **return value** (i.e., \"result\" or \"output\") with the `return` statement (cf., [reference <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/reference/simple_stmts.html#the-return-statement)): Functions that have one are considered **fruitful**; otherwise, they are **void**. Functions of the latter kind are still useful because of their **side effects**. For example, the built-in [print() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#print) function changes what we see on the screen. Strictly speaking, [print() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#print) and other void functions also have an *implicit* return value, namely the `None` object.\n",
     "\n",
-    "A function should define a **docstring** that describes what it does in a short subject line, what parameters it expects (i.e., their types), and what it returns, if anything. A docstring is a syntactically valid multi-line string (i.e., type `str`) defined within **triple-double quotes** `\"\"\"`. Strings are covered in depth in [Chapter 6 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/06_text/00_content.ipynb#The-str-Type). Widely adopted standards for docstrings are [PEP 257 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://www.python.org/dev/peps/pep-0257/) and section 3.8 of [Google's Python Style Guide <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_gh.png\">](https://github.com/google/styleguide/blob/gh-pages/pyguide.md)."
+    "A function should define a **docstring** that describes what it does in a short subject line, what parameters it expects (i.e., their types), and what it returns, if anything. A docstring is a syntactically valid multi-line string (i.e., type `str`) defined within **triple-double quotes** `\"\"\"`. Strings are covered in depth in [Chapter 6 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/main/06_text/00_content.ipynb#The-str-Type). Widely adopted standards for docstrings are [PEP 257 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://www.python.org/dev/peps/pep-0257/) and section 3.8 of [Google's Python Style Guide <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_gh.png\">](https://github.com/google/styleguide/blob/gh-pages/pyguide.md)."
    ]
   },
   {
@@ -1012,7 +1012,7 @@
    "source": [
     "Its value may seem awkward at first: It consists of a location showing where the function is defined (i.e., `__main__` here, which is Python's way of saying \"in this notebook\") and the signature wrapped inside angle brackets `<` and `>`.\n",
     " \n",
-    "The angle brackets are a convention to indicate that the value may *not* be used as a *literal* (i.e., typed back into another code cell). [Chapter 11 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/11_classes/00_content.ipynb) introduces the concept of a **text representation** of an object, which is related to the *semantic* meaning of an object's value as discussed in [Chapter 1 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/01_elements/00_content.ipynb#Value-/-\"Meaning\"), and the angle brackets convention is one such way to represent an object as text. When executed, the angle brackets cause a `SyntaxError` because Python expects the `<` operator to come with an operand on both sides (cf., [Chapter 3 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/03_conditionals/00_content.ipynb#Relational-Operators))."
+    "The angle brackets are a convention to indicate that the value may *not* be used as a *literal* (i.e., typed back into another code cell). [Chapter 11 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/main/11_classes/00_content.ipynb) introduces the concept of a **text representation** of an object, which is related to the *semantic* meaning of an object's value as discussed in [Chapter 1 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/main/01_elements/00_content.ipynb#Value-/-\"Meaning\"), and the angle brackets convention is one such way to represent an object as text. When executed, the angle brackets cause a `SyntaxError` because Python expects the `<` operator to come with an operand on both sides (cf., [Chapter 3 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/main/03_conditionals/00_content.ipynb#Relational-Operators))."
    ]
   },
   {
@@ -1647,7 +1647,7 @@
    "source": [
     "[PythonTutor <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](http://pythontutor.com/visualize.html#code=numbers%20%3D%20%5B7,%2011,%208,%205,%203,%2012,%202,%206,%209,%2010,%201,%204%5D%0A%0Adef%20average_wrong%28integers%29%3A%0A%20%20%20%20evens%20%3D%20%5Bn%20for%20n%20in%20numbers%20if%20n%20%25%202%20%3D%3D%200%5D%0A%20%20%20%20average%20%3D%20sum%28evens%29%20/%20len%28evens%29%0A%20%20%20%20return%20average%0A%0Aresult%20%3D%20average_wrong%28%5B123,%20456,%20789%5D%29&cumulative=false&curstr=0&heapPrimitives=nevernest&mode=display&origin=opt-frontend.js&py=3&rawInputLstJSON=%5B%5D&textReferences=false) is again helpful at visualizing the error interactively: Creating the `list` object `evens` eventually references takes *16* computational steps, namely two for managing the list comprehension, one for setting up an empty `list` object, *twelve* for filling it with elements derived from `numbers` in the global scope (i.e., that is the error), and one to make `evens` reference it (cf., steps 6-21).\n",
     "\n",
-    "The frames logic shown by PythonTutor is the mechanism with which Python not only manages the names inside *one* function call but also for *many* potentially *simultaneous* calls, as revealed in [Chapter 4 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/04_iteration/00_content.ipynb#Trivial-Example:-Countdown). It is the reason why we may reuse the same names for the parameters and variables inside both `average_evens()` and `average_wrong()` without Python mixing them up. So, as we already read in the [Zen of Python <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://www.python.org/dev/peps/pep-0020/), \"namespaces are one honking great idea\" (cf., `import this`), and a frame is just a special kind of namespace."
+    "The frames logic shown by PythonTutor is the mechanism with which Python not only manages the names inside *one* function call but also for *many* potentially *simultaneous* calls, as revealed in [Chapter 4 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/main/04_iteration/00_content.ipynb#Trivial-Example:-Countdown). It is the reason why we may reuse the same names for the parameters and variables inside both `average_evens()` and `average_wrong()` without Python mixing them up. So, as we already read in the [Zen of Python <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://www.python.org/dev/peps/pep-0020/), \"namespaces are one honking great idea\" (cf., `import this`), and a frame is just a special kind of namespace."
    ]
   },
   {
@@ -2021,7 +2021,7 @@
     }
    },
    "source": [
-    "So far, we have specified only one parameter in each of our user-defined functions. In [Chapter 1 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/01_elements/00_content.ipynb#%28Arithmetic%29-Operators), however, we saw the built-in [divmod() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#divmod) function take two arguments. And, the order in which they are passed in matters! Whenever we call a function and list its arguments in a comma separated manner, we say that we pass in the arguments *by position* or refer to them as **positional arguments**."
+    "So far, we have specified only one parameter in each of our user-defined functions. In [Chapter 1 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/main/01_elements/00_content.ipynb#%28Arithmetic%29-Operators), however, we saw the built-in [divmod() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#divmod) function take two arguments. And, the order in which they are passed in matters! Whenever we call a function and list its arguments in a comma separated manner, we say that we pass in the arguments *by position* or refer to them as **positional arguments**."
    ]
   },
   {
@@ -3012,7 +3012,7 @@
    "source": [
     "The main point of having functions without a reference to them is to use them in a situation where we know ahead of time that we use the function only *once*.\n",
     "\n",
-    "Popular applications of lambda expressions occur in combination with the **map-filter-reduce** paradigm (cf., [Chapter 8 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/08_mfr/00_content.ipynb#Lambda-Expressions))."
+    "Popular applications of lambda expressions occur in combination with the **map-filter-reduce** paradigm (cf., [Chapter 8 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/main/08_mfr/00_content.ipynb#Lambda-Expressions))."
    ]
   }
  ],
diff --git a/02_functions/01_exercises_sphere-volume.ipynb b/02_functions/01_exercises_sphere-volume.ipynb
index e4173c4..49f957e 100644
--- a/02_functions/01_exercises_sphere-volume.ipynb
+++ b/02_functions/01_exercises_sphere-volume.ipynb
@@ -4,7 +4,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "**Note**: Click on \"*Kernel*\" > \"*Restart Kernel and Run All*\" in [JupyterLab](https://jupyterlab.readthedocs.io/en/stable/) *after* finishing the exercises to ensure that your solution runs top to bottom *without* any errors. If you cannot run this file on your machine, you may want to open it [in the cloud <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_mb.png\">](https://mybinder.org/v2/gh/webartifex/intro-to-python/develop?urlpath=lab/tree/02_functions/01_exercises.ipynb)."
+    "**Note**: Click on \"*Kernel*\" > \"*Restart Kernel and Run All*\" in [JupyterLab](https://jupyterlab.readthedocs.io/en/stable/) *after* finishing the exercises to ensure that your solution runs top to bottom *without* any errors. If you cannot run this file on your machine, you may want to open it [in the cloud <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_mb.png\">](https://mybinder.org/v2/gh/webartifex/intro-to-python/main?urlpath=lab/tree/02_functions/01_exercises.ipynb)."
    ]
   },
   {
@@ -18,7 +18,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "The exercises below assume that you have read the [first part <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/02_functions/00_content.ipynb) of Chapter 2.\n",
+    "The exercises below assume that you have read the [first part <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/main/02_functions/00_content.ipynb) of Chapter 2.\n",
     "\n",
     "The `...`'s in the code cells indicate where you need to fill in code snippets. The number of `...`'s within a code cell give you a rough idea of how many lines of code are needed to solve the task. You should not need to create any additional code cells for your final solution. However, you may want to use temporary code cells to try out some ideas."
    ]
@@ -38,7 +38,7 @@
     "\n",
     "Hints:\n",
     "- use an appropriate approximation for $\\pi$\n",
-    "- you may use the [standard library <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/index.html) to do so if you have already looked at the [second part <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/02_functions/02_content.ipynb) of Chapter 2."
+    "- you may use the [standard library <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/index.html) to do so if you have already looked at the [second part <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/main/02_functions/02_content.ipynb) of Chapter 2."
    ]
   },
   {
diff --git a/02_functions/02_content.ipynb b/02_functions/02_content.ipynb
index f0631a0..5e663cc 100644
--- a/02_functions/02_content.ipynb
+++ b/02_functions/02_content.ipynb
@@ -8,7 +8,7 @@
     }
    },
    "source": [
-    "**Note**: Click on \"*Kernel*\" > \"*Restart Kernel and Clear All Outputs*\" in [JupyterLab](https://jupyterlab.readthedocs.io/en/stable/) *before* reading this notebook to reset its output. If you cannot run this file on your machine, you may want to open it [in the cloud <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_mb.png\">](https://mybinder.org/v2/gh/webartifex/intro-to-python/develop?urlpath=lab/tree/02_functions/02_content.ipynb)."
+    "**Note**: Click on \"*Kernel*\" > \"*Restart Kernel and Clear All Outputs*\" in [JupyterLab](https://jupyterlab.readthedocs.io/en/stable/) *before* reading this notebook to reset its output. If you cannot run this file on your machine, you may want to open it [in the cloud <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_mb.png\">](https://mybinder.org/v2/gh/webartifex/intro-to-python/main?urlpath=lab/tree/02_functions/02_content.ipynb)."
    ]
   },
   {
@@ -434,7 +434,7 @@
    "source": [
     "Observe how the arguments passed to functions do not need to be just variables or simple literals. Instead, we may pass in any *expression* that evaluates to a *new* object of the type the function expects.\n",
     "\n",
-    "So just as a reminder from the expression vs. statement discussion in [Chapter 1 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/01_elements/03_content.ipynb#Expressions): An expression is *any* syntactically correct combination of variables and literals with operators. And the call operator `()` is yet another operator. So both of the next two code cells are just expressions! They have no permanent side effects in memory. We may execute them as often as we want *without* changing the state of the program (i.e., this Jupyter notebook).\n",
+    "So just as a reminder from the expression vs. statement discussion in [Chapter 1 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/main/01_elements/03_content.ipynb#Expressions): An expression is *any* syntactically correct combination of variables and literals with operators. And the call operator `()` is yet another operator. So both of the next two code cells are just expressions! They have no permanent side effects in memory. We may execute them as often as we want *without* changing the state of the program (i.e., this Jupyter notebook).\n",
     "\n",
     "So, regarding the very next cell in particular: Although the `2 ** 2` creates a *new* object `4` in memory that is then immediately passed into the [math.sqrt() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/math.html#math.sqrt) function, once that function call returns, \"all is lost\" and the newly created `4` object is forgotten again, as well as the return value of [math.sqrt() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/math.html#math.sqrt)."
    ]
@@ -615,7 +615,7 @@
     }
    },
    "source": [
-    "Besides the usual dunder-style attributes, the built-in [dir() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#dir) function lists some attributes in an upper case naming convention and many others starting with a *single* underscore `_`. To understand the former, we must wait until [Chapter 11 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/11_classes/00_content.ipynb), while the latter is explained further below."
+    "Besides the usual dunder-style attributes, the built-in [dir() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#dir) function lists some attributes in an upper case naming convention and many others starting with a *single* underscore `_`. To understand the former, we must wait until [Chapter 11 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/main/11_classes/00_content.ipynb), while the latter is explained further below."
    ]
   },
   {
@@ -1027,7 +1027,7 @@
    "source": [
     "[numpy](http://www.numpy.org/) is the de-facto standard in the Python world for handling **array-like** data. That is a fancy word for data that can be put into a matrix or vector format.\n",
     "\n",
-    "As [numpy](http://www.numpy.org/) is *not* in the [standard library <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/index.html), it must be *manually* installed, for example, with the [pip](https://pip.pypa.io/en/stable/) tool. As mentioned in [Chapter 0 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/00_intro/00_content.ipynb#Markdown-Cells-vs.-Code-Cells), to execute terminal commands from within a Jupyter notebook, we start a code cell with an exclamation mark.\n",
+    "As [numpy](http://www.numpy.org/) is *not* in the [standard library <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/index.html), it must be *manually* installed, for example, with the [pip](https://pip.pypa.io/en/stable/) tool. As mentioned in [Chapter 0 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/main/00_intro/00_content.ipynb#Markdown-Cells-vs.-Code-Cells), to execute terminal commands from within a Jupyter notebook, we start a code cell with an exclamation mark.\n",
     "\n",
     "If you are running this notebook with an installation of the [Anaconda Distribution](https://www.anaconda.com/distribution/), then [numpy](http://www.numpy.org/) is probably already installed. Running the cell below confirms that."
    ]
@@ -1332,7 +1332,7 @@
     }
    },
    "source": [
-    "For sure, we can create local modules and packages. In the Chapter 2 directory, there is a [*sample_module.py* <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_gh.png\">](https://github.com/webartifex/intro-to-python/blob/develop/02_functions/sample_module.py) file that contains, among others, a function equivalent to the final version of `average_evens()`. To be realistic, this sample module is structured in a modular manner with several functions building on each other. It is best to skim over it *now* before reading on.\n",
+    "For sure, we can create local modules and packages. In the Chapter 2 directory, there is a [*sample_module.py* <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_gh.png\">](https://github.com/webartifex/intro-to-python/blob/main/02_functions/sample_module.py) file that contains, among others, a function equivalent to the final version of `average_evens()`. To be realistic, this sample module is structured in a modular manner with several functions building on each other. It is best to skim over it *now* before reading on.\n",
     "\n",
     "To make code we put into a *.py* file available in our program, we import it as a module just as we did above with modules in the [standard library <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/index.html) or third-party packages.\n",
     "\n",
@@ -1418,7 +1418,7 @@
     }
    },
    "source": [
-    "Disregarding the dunder-style attributes, `mod` defines the attributes `_round_all`, `_scaled_average`, `average`, `average_evens`, and `average_odds`, which are exactly the ones we would expect from reading the [*sample_module.py* <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_gh.png\">](https://github.com/webartifex/intro-to-python/blob/develop/02_functions/sample_module.py) file.\n",
+    "Disregarding the dunder-style attributes, `mod` defines the attributes `_round_all`, `_scaled_average`, `average`, `average_evens`, and `average_odds`, which are exactly the ones we would expect from reading the [*sample_module.py* <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_gh.png\">](https://github.com/webartifex/intro-to-python/blob/main/02_functions/sample_module.py) file.\n",
     "\n",
     "A convention when working with imported code is to *disregard* any attributes starting with a single underscore `_`. These are considered **private** and constitute **implementation details** the author of the imported code might change in a future version of his software. We *must not* rely on them in any way.\n",
     "\n",
@@ -1469,7 +1469,7 @@
     }
    },
    "source": [
-    "We use the imported `mod.average_evens()` just like `average_evens()` defined in the [first part <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/02_functions/00_content.ipynb) of this chapter. The advantage we get from **modularization** with *.py* files is that we can now easily reuse functions across different Jupyter notebooks without redefining them again and again. Also, we can \"source out\" code that distracts from the storyline told in a notebook."
+    "We use the imported `mod.average_evens()` just like `average_evens()` defined in the [first part <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/main/02_functions/00_content.ipynb) of this chapter. The advantage we get from **modularization** with *.py* files is that we can now easily reuse functions across different Jupyter notebooks without redefining them again and again. Also, we can \"source out\" code that distracts from the storyline told in a notebook."
    ]
   },
   {
@@ -1585,7 +1585,7 @@
     }
    },
    "source": [
-    "Packages are a generalization of modules, and we look at one in detail in [Chapter 11 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/11_classes/04_content.ipynb#Packages-vs.-Modules).\n",
+    "Packages are a generalization of modules, and we look at one in detail in [Chapter 11 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/main/11_classes/04_content.ipynb#Packages-vs.-Modules).\n",
     "\n",
     "As a further reading on modules and packages, we refer to the [official tutorial <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/tutorial/modules.html)."
    ]
diff --git a/02_functions/04_review.ipynb b/02_functions/04_review.ipynb
index 7ebc8fd..f3b7ab9 100644
--- a/02_functions/04_review.ipynb
+++ b/02_functions/04_review.ipynb
@@ -11,7 +11,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "The questions below assume that you have read the [first <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/02_functions/00_content.ipynb) and the [second <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/02_functions/02_content.ipynb) part of Chapter 2.\n",
+    "The questions below assume that you have read the [first <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/main/02_functions/00_content.ipynb) and the [second <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/main/02_functions/02_content.ipynb) part of Chapter 2.\n",
     "\n",
     "Be concise in your answers! Most questions can be answered in *one* sentence."
    ]
diff --git a/03_conditionals/00_content.ipynb b/03_conditionals/00_content.ipynb
index 62bf469..8797d0a 100644
--- a/03_conditionals/00_content.ipynb
+++ b/03_conditionals/00_content.ipynb
@@ -8,7 +8,7 @@
     }
    },
    "source": [
-    "**Note**: Click on \"*Kernel*\" > \"*Restart Kernel and Clear All Outputs*\" in [JupyterLab](https://jupyterlab.readthedocs.io/en/stable/) *before* reading this notebook to reset its output. If you cannot run this file on your machine, you may want to open it [in the cloud <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_mb.png\">](https://mybinder.org/v2/gh/webartifex/intro-to-python/develop?urlpath=lab/tree/03_conditionals/00_content.ipynb)."
+    "**Note**: Click on \"*Kernel*\" > \"*Restart Kernel and Clear All Outputs*\" in [JupyterLab](https://jupyterlab.readthedocs.io/en/stable/) *before* reading this notebook to reset its output. If you cannot run this file on your machine, you may want to open it [in the cloud <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_mb.png\">](https://mybinder.org/v2/gh/webartifex/intro-to-python/main?urlpath=lab/tree/03_conditionals/00_content.ipynb)."
    ]
   },
   {
@@ -30,7 +30,7 @@
     }
    },
    "source": [
-    "We analyzed every aspect of the `average_evens()` function in [Chapter 2 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/02_functions/00_content.ipynb) except for the `if`-related parts. While it does what we expect it to, there is a whole lot more to learn by taking it apart. In particular, the `if` may occur within both a **statement** or an **expression**, analogous as to how a noun in a natural language can be the subject of *or* an object in a sentence. What is common to both usages is that it leads to code being executed for *parts* of the input only. It is a way of controlling the **flow of execution** in a program.\n",
+    "We analyzed every aspect of the `average_evens()` function in [Chapter 2 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/main/02_functions/00_content.ipynb) except for the `if`-related parts. While it does what we expect it to, there is a whole lot more to learn by taking it apart. In particular, the `if` may occur within both a **statement** or an **expression**, analogous as to how a noun in a natural language can be the subject of *or* an object in a sentence. What is common to both usages is that it leads to code being executed for *parts* of the input only. It is a way of controlling the **flow of execution** in a program.\n",
     "\n",
     "After deconstructing `if` in the first part of this chapter, we take a close look at a similar concept, namely handling **exceptions**."
    ]
@@ -150,7 +150,7 @@
     }
    },
    "source": [
-    "There are, however, cases where the `==` operator seems to not work intuitively. [Chapter 5 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/05_numbers/00_content.ipynb#Imprecision) provides more insights into this \"bug.\""
+    "There are, however, cases where the `==` operator seems to not work intuitively. [Chapter 5 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/main/05_numbers/00_content.ipynb#Imprecision) provides more insights into this \"bug.\""
    ]
   },
   {
@@ -351,7 +351,7 @@
     }
    },
    "source": [
-    "Let's not confuse the boolean `False` with `None`, another built-in object! We saw the latter before in [Chapter 2 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/02_functions/00_content.ipynb#Function-Definitions) as the *implicit* return value of a function without a `return` statement.\n",
+    "Let's not confuse the boolean `False` with `None`, another built-in object! We saw the latter before in [Chapter 2 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/main/02_functions/00_content.ipynb#Function-Definitions) as the *implicit* return value of a function without a `return` statement.\n",
     "\n",
     "We might think of `None` indicating a \"maybe\" or even an \"unknown\" answer; however, for Python, there are no \"maybe\" or \"unknown\" objects, as we see further below!\n",
     "\n",
@@ -438,7 +438,7 @@
     }
    },
    "source": [
-    "`True`, `False`, and `None` have the property that they each exist in memory only *once*. Objects designed this way are so-called **singletons**. This **[design pattern <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_wiki.png\">](https://en.wikipedia.org/wiki/Design_Patterns)** was originally developed to keep a program's memory usage at a minimum. It may only be employed in situations where we know that an object does *not* mutate its value (i.e., to reuse the bag analogy from [Chapter 1 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/01_elements/00_content.ipynb#Objects-vs.-Types-vs.-Values), no flipping of $0$s and $1$s in the bag is allowed). In languages \"closer\" to the memory like C, we would have to code this singleton logic ourselves, but Python has this built in for *some* types.\n",
+    "`True`, `False`, and `None` have the property that they each exist in memory only *once*. Objects designed this way are so-called **singletons**. This **[design pattern <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_wiki.png\">](https://en.wikipedia.org/wiki/Design_Patterns)** was originally developed to keep a program's memory usage at a minimum. It may only be employed in situations where we know that an object does *not* mutate its value (i.e., to reuse the bag analogy from [Chapter 1 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/main/01_elements/00_content.ipynb#Objects-vs.-Types-vs.-Values), no flipping of $0$s and $1$s in the bag is allowed). In languages \"closer\" to the memory like C, we would have to code this singleton logic ourselves, but Python has this built in for *some* types.\n",
     "\n",
     "We verify this with either the `is` operator or by comparing memory addresses."
    ]
diff --git a/03_conditionals/01_exercises_discounts.ipynb b/03_conditionals/01_exercises_discounts.ipynb
index 6bd0657..9764fd6 100644
--- a/03_conditionals/01_exercises_discounts.ipynb
+++ b/03_conditionals/01_exercises_discounts.ipynb
@@ -4,7 +4,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "**Note**: Click on \"*Kernel*\" > \"*Restart Kernel and Run All*\" in [JupyterLab](https://jupyterlab.readthedocs.io/en/stable/) *after* finishing the exercises to ensure that your solution runs top to bottom *without* any errors. If you cannot run this file on your machine, you may want to open it [in the cloud <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_mb.png\">](https://mybinder.org/v2/gh/webartifex/intro-to-python/develop?urlpath=lab/tree/03_conditionals/01_exercises.ipynb)."
+    "**Note**: Click on \"*Kernel*\" > \"*Restart Kernel and Run All*\" in [JupyterLab](https://jupyterlab.readthedocs.io/en/stable/) *after* finishing the exercises to ensure that your solution runs top to bottom *without* any errors. If you cannot run this file on your machine, you may want to open it [in the cloud <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_mb.png\">](https://mybinder.org/v2/gh/webartifex/intro-to-python/main?urlpath=lab/tree/03_conditionals/01_exercises.ipynb)."
    ]
   },
   {
@@ -18,7 +18,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "The exercises below assume that you have read [Chapter 3 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/03_conditionals/00_content.ipynb).\n",
+    "The exercises below assume that you have read [Chapter 3 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/main/03_conditionals/00_content.ipynb).\n",
     "\n",
     "The `...`'s in the code cells indicate where you need to fill in code snippets. The number of `...`'s within a code cell give you a rough idea of how many lines of code are needed to solve the task. You should not need to create any additional code cells for your final solution. However, you may want to use temporary code cells to try out some ideas."
    ]
diff --git a/03_conditionals/02_exercises_fizz-buzz.ipynb b/03_conditionals/02_exercises_fizz-buzz.ipynb
index 4728719..5b0ce28 100644
--- a/03_conditionals/02_exercises_fizz-buzz.ipynb
+++ b/03_conditionals/02_exercises_fizz-buzz.ipynb
@@ -4,7 +4,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "**Note**: Click on \"*Kernel*\" > \"*Restart Kernel and Run All*\" in [JupyterLab](https://jupyterlab.readthedocs.io/en/stable/) *after* finishing the exercises to ensure that your solution runs top to bottom *without* any errors. If you cannot run this file on your machine, you may want to open it [in the cloud <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_mb.png\">](https://mybinder.org/v2/gh/webartifex/intro-to-python/develop?urlpath=lab/tree/03_conditionals/02_exercises.ipynb)."
+    "**Note**: Click on \"*Kernel*\" > \"*Restart Kernel and Run All*\" in [JupyterLab](https://jupyterlab.readthedocs.io/en/stable/) *after* finishing the exercises to ensure that your solution runs top to bottom *without* any errors. If you cannot run this file on your machine, you may want to open it [in the cloud <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_mb.png\">](https://mybinder.org/v2/gh/webartifex/intro-to-python/main?urlpath=lab/tree/03_conditionals/02_exercises.ipynb)."
    ]
   },
   {
@@ -18,7 +18,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "The exercises below assume that you have read [Chapter 3 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/03_conditionals/00_content.ipynb).\n",
+    "The exercises below assume that you have read [Chapter 3 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/main/03_conditionals/00_content.ipynb).\n",
     "\n",
     "The `...`'s in the code cells indicate where you need to fill in code snippets. The number of `...`'s within a code cell give you a rough idea of how many lines of code are needed to solve the task. You should not need to create any additional code cells for your final solution. However, you may want to use temporary code cells to try out some ideas."
    ]
@@ -61,7 +61,7 @@
    "source": [
     "**Q2**: Loop over the `numbers` list and *replace* numbers for which one of the two (or both) conditions apply with text strings `\"Fizz\"`, `\"Buzz\"`, or `\"FizzBuzz\"` using the indexing operator `[]` and the assignment statement `=`.\n",
     "\n",
-    "In [Chapter 1 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/01_elements/03_content.ipynb#Who-am-I?-And-how-many?), we saw that Python starts indexing with `0` as the first element. Keep that in mind.\n",
+    "In [Chapter 1 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/main/01_elements/03_content.ipynb#Who-am-I?-And-how-many?), we saw that Python starts indexing with `0` as the first element. Keep that in mind.\n",
     "\n",
     "So in each iteration of the `for`-loop, you have to determine an `index` variable as well as check the actual `number` for its divisors.\n",
     "\n",
diff --git a/03_conditionals/04_review.ipynb b/03_conditionals/04_review.ipynb
index e9890be..67e9dd3 100644
--- a/03_conditionals/04_review.ipynb
+++ b/03_conditionals/04_review.ipynb
@@ -11,7 +11,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "The questions below assume that you have read [Chapter 3 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/03_conditionals/00_content.ipynb).\n",
+    "The questions below assume that you have read [Chapter 3 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/main/03_conditionals/00_content.ipynb).\n",
     "\n",
     "Be concise in your answers! Most questions can be answered in *one* sentence."
    ]
diff --git a/04_iteration/00_content.ipynb b/04_iteration/00_content.ipynb
index cc17d58..362583b 100644
--- a/04_iteration/00_content.ipynb
+++ b/04_iteration/00_content.ipynb
@@ -8,7 +8,7 @@
     }
    },
    "source": [
-    "**Note**: Click on \"*Kernel*\" > \"*Restart Kernel and Clear All Outputs*\" in [JupyterLab](https://jupyterlab.readthedocs.io/en/stable/) *before* reading this notebook to reset its output. If you cannot run this file on your machine, you may want to open it [in the cloud <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_mb.png\">](https://mybinder.org/v2/gh/webartifex/intro-to-python/develop?urlpath=lab/tree/04_iteration/00_content.ipynb)."
+    "**Note**: Click on \"*Kernel*\" > \"*Restart Kernel and Clear All Outputs*\" in [JupyterLab](https://jupyterlab.readthedocs.io/en/stable/) *before* reading this notebook to reset its output. If you cannot run this file on your machine, you may want to open it [in the cloud <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_mb.png\">](https://mybinder.org/v2/gh/webartifex/intro-to-python/main?urlpath=lab/tree/04_iteration/00_content.ipynb)."
    ]
   },
   {
@@ -874,7 +874,7 @@
     "\n",
     "To understand this in detail, we have to study algorithms and data structures (e.g., with [this book](https://www.amazon.de/Introduction-Algorithms-Press-Thomas-Cormen/dp/0262033844/ref=sr_1_1?__mk_de_DE=%C3%85M%C3%85%C5%BD%C3%95%C3%91&crid=1JNE8U0VZGU0O&qid=1569837169&s=gateway&sprefix=algorithms+an%2Caps%2C180&sr=8-1)), a discipline within computer science, and dive into the analysis of **[time complexity of algorithms <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_wiki.png\">](https://en.wikipedia.org/wiki/Time_complexity)**.\n",
     "\n",
-    "Luckily, in the Fibonacci case, the inefficiency can be resolved with a **caching** (i.e., \"reuse\") strategy from the field of **[dynamic programming <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_wiki.png\">](https://en.wikipedia.org/wiki/Dynamic_programming)**, namely **[memoization <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_wiki.png\">](https://en.wikipedia.org/wiki/Memoization)**. We do so in [Chapter 9 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/09_mappings/02_content.ipynb#Memoization), after introducing the `dict` data type.\n",
+    "Luckily, in the Fibonacci case, the inefficiency can be resolved with a **caching** (i.e., \"reuse\") strategy from the field of **[dynamic programming <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_wiki.png\">](https://en.wikipedia.org/wiki/Dynamic_programming)**, namely **[memoization <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_wiki.png\">](https://en.wikipedia.org/wiki/Memoization)**. We do so in [Chapter 9 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/main/09_mappings/02_content.ipynb#Memoization), after introducing the `dict` data type.\n",
     "\n",
     "Let's measure the average run times for `fibonacci()` and varying `i` arguments with the `%%timeit` [cell magic](https://ipython.readthedocs.io/en/stable/interactive/magics.html#magic-timeit) that comes with Jupyter."
    ]
diff --git a/04_iteration/01_exercises_hanoi-towers.ipynb b/04_iteration/01_exercises_hanoi-towers.ipynb
index 28304a9..b29212e 100644
--- a/04_iteration/01_exercises_hanoi-towers.ipynb
+++ b/04_iteration/01_exercises_hanoi-towers.ipynb
@@ -4,7 +4,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "**Note**: Click on \"*Kernel*\" > \"*Restart Kernel and Run All*\" in [JupyterLab](https://jupyterlab.readthedocs.io/en/stable/) *after* finishing the exercises to ensure that your solution runs top to bottom *without* any errors. If you cannot run this file on your machine, you may want to open it [in the cloud <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_mb.png\">](https://mybinder.org/v2/gh/webartifex/intro-to-python/develop?urlpath=lab/tree/04_iteration/01_exercises.ipynb)."
+    "**Note**: Click on \"*Kernel*\" > \"*Restart Kernel and Run All*\" in [JupyterLab](https://jupyterlab.readthedocs.io/en/stable/) *after* finishing the exercises to ensure that your solution runs top to bottom *without* any errors. If you cannot run this file on your machine, you may want to open it [in the cloud <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_mb.png\">](https://mybinder.org/v2/gh/webartifex/intro-to-python/main?urlpath=lab/tree/04_iteration/01_exercises.ipynb)."
    ]
   },
   {
@@ -18,7 +18,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "The exercises below assume that you have read the [first part <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/04_iteration/00_content.ipynb) of Chapter 4.\n",
+    "The exercises below assume that you have read the [first part <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/main/04_iteration/00_content.ipynb) of Chapter 4.\n",
     "\n",
     "The `...`'s in the code cells indicate where you need to fill in code snippets. The number of `...`'s within a code cell give you a rough idea of how many lines of code are needed to solve the task. You should not need to create any additional code cells for your final solution. However, you may want to use temporary code cells to try out some ideas."
    ]
diff --git a/04_iteration/02_content.ipynb b/04_iteration/02_content.ipynb
index 35ac2cb..1d41ed7 100644
--- a/04_iteration/02_content.ipynb
+++ b/04_iteration/02_content.ipynb
@@ -8,7 +8,7 @@
     }
    },
    "source": [
-    "**Note**: Click on \"*Kernel*\" > \"*Restart Kernel and Clear All Outputs*\" in [JupyterLab](https://jupyterlab.readthedocs.io/en/stable/) *before* reading this notebook to reset its output. If you cannot run this file on your machine, you may want to open it [in the cloud <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_mb.png\">](https://mybinder.org/v2/gh/webartifex/intro-to-python/develop?urlpath=lab/tree/04_iteration/02_content.ipynb)."
+    "**Note**: Click on \"*Kernel*\" > \"*Restart Kernel and Clear All Outputs*\" in [JupyterLab](https://jupyterlab.readthedocs.io/en/stable/) *before* reading this notebook to reset its output. If you cannot run this file on your machine, you may want to open it [in the cloud <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_mb.png\">](https://mybinder.org/v2/gh/webartifex/intro-to-python/main?urlpath=lab/tree/04_iteration/02_content.ipynb)."
    ]
   },
   {
@@ -30,7 +30,7 @@
     }
    },
    "source": [
-    "After learning about the concept of **recursion** in the [first part <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/04_iteration/00_content.ipynb) of this chapter, we look at other ways of running code repeatedly, namely **looping** with the `for` and `while` statements. We start with the latter as it is more generic. Throughout this second part of the chapter, we revisit the same examples from the first part to show how recursion and looping are really two sides of the same coin."
+    "After learning about the concept of **recursion** in the [first part <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/main/04_iteration/00_content.ipynb) of this chapter, we look at other ways of running code repeatedly, namely **looping** with the `for` and `while` statements. We start with the latter as it is more generic. Throughout this second part of the chapter, we revisit the same examples from the first part to show how recursion and looping are really two sides of the same coin."
    ]
   },
   {
@@ -582,7 +582,7 @@
     }
    },
    "source": [
-    "For sequences of integers, the [range() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#func-range) built-in makes the `for` statement even more convenient: It creates a `list`-like object of type `range` that generates integers \"on the fly,\" and we look closely at the underlying effects in memory in [Chapter 8 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/08_mfr/00_content.ipynb#Mapping)."
+    "For sequences of integers, the [range() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#func-range) built-in makes the `for` statement even more convenient: It creates a `list`-like object of type `range` that generates integers \"on the fly,\" and we look closely at the underlying effects in memory in [Chapter 8 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/main/08_mfr/00_content.ipynb#Mapping)."
    ]
   },
   {
@@ -713,11 +713,11 @@
     "\n",
     "Now, just as we classify objects by data type, we also classify these data types (e.g., `int`, `float`, `str`, or `list`) into **abstract concepts**.\n",
     "\n",
-    "We did this already in [Chapter 1 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/01_elements/03_content.ipynb#Who-am-I?-And-how-many?) when we described a `list` object as \"some sort of container that holds [...] references to other objects\". So, abstractly speaking, **containers** are any objects that are \"composed\" of other objects and also \"manage\" how these objects are organized. `list` objects, for example, have the property that they model an order associated with their elements. There exist, however, other container types, many of which do *not* come with an order. So, containers primarily \"contain\" other objects and have *nothing* to do with looping.\n",
+    "We did this already in [Chapter 1 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/main/01_elements/03_content.ipynb#Who-am-I?-And-how-many?) when we described a `list` object as \"some sort of container that holds [...] references to other objects\". So, abstractly speaking, **containers** are any objects that are \"composed\" of other objects and also \"manage\" how these objects are organized. `list` objects, for example, have the property that they model an order associated with their elements. There exist, however, other container types, many of which do *not* come with an order. So, containers primarily \"contain\" other objects and have *nothing* to do with looping.\n",
     "\n",
     "On the contrary, the abstract concept of **iterables** is all about looping: Any object that we can loop over is, by definition, an iterable. So, `range` objects, for example, are iterables, even though they hold no references to other objects. Moreover, looping does *not* have to occur in a *predictable* order, although this is the case for both `list` and `range` objects.\n",
     "\n",
-    "Typically, containers are iterables, and iterables are containers. Yet, only because these two concepts coincide often, we must not think of them as the same. In [Chapter 7 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/07_sequences/00_content.ipynb#Collections-vs.-Sequences), we formalize these two concepts and introduce many more. Finally, [Chapter 11 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/11_classes/00_content.ipynb) gives an explanation how abstract concepts are implemented and play together.\n",
+    "Typically, containers are iterables, and iterables are containers. Yet, only because these two concepts coincide often, we must not think of them as the same. In [Chapter 7 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/main/07_sequences/00_content.ipynb#Collections-vs.-Sequences), we formalize these two concepts and introduce many more. Finally, [Chapter 11 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/main/11_classes/00_content.ipynb) gives an explanation how abstract concepts are implemented and play together.\n",
     "\n",
     "Let's continue with `first_names` below as an example an illustrate what iterable containers are."
    ]
diff --git a/04_iteration/03_content.ipynb b/04_iteration/03_content.ipynb
index d9bb92f..0eb9d25 100644
--- a/04_iteration/03_content.ipynb
+++ b/04_iteration/03_content.ipynb
@@ -8,7 +8,7 @@
     }
    },
    "source": [
-    "**Note**: Click on \"*Kernel*\" > \"*Restart Kernel and Clear All Outputs*\" in [JupyterLab](https://jupyterlab.readthedocs.io/en/stable/) *before* reading this notebook to reset its output. If you cannot run this file on your machine, you may want to open it [in the cloud <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_mb.png\">](https://mybinder.org/v2/gh/webartifex/intro-to-python/develop?urlpath=lab/tree/04_iteration/03_content.ipynb)."
+    "**Note**: Click on \"*Kernel*\" > \"*Restart Kernel and Clear All Outputs*\" in [JupyterLab](https://jupyterlab.readthedocs.io/en/stable/) *before* reading this notebook to reset its output. If you cannot run this file on your machine, you may want to open it [in the cloud <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_mb.png\">](https://mybinder.org/v2/gh/webartifex/intro-to-python/main?urlpath=lab/tree/04_iteration/03_content.ipynb)."
    ]
   },
   {
@@ -30,7 +30,7 @@
     }
    },
    "source": [
-    "While what we learned about the `for` and `while` statements in the [second part <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/04_iteration/02_content.ipynb) of this chapter suffices to translate any iterative algorithm into code, both come with some syntactic sugar to make life easier for the developer. This last part of the chapter shows how we can further customize the looping logic and introduces as \"trick\" for situations where we cannot come up with a stopping criterion in a `while`-loop."
+    "While what we learned about the `for` and `while` statements in the [second part <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/main/04_iteration/02_content.ipynb) of this chapter suffices to translate any iterative algorithm into code, both come with some syntactic sugar to make life easier for the developer. This last part of the chapter shows how we can further customize the looping logic and introduces as \"trick\" for situations where we cannot come up with a stopping criterion in a `while`-loop."
    ]
   },
   {
@@ -352,7 +352,7 @@
     }
    },
    "source": [
-    "Often, we process some iterable with numeric data, for example, a list of `numbers` as in this book's introductory example in [Chapter 1 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/01_elements/00_content.ipynb#Example:-Averaging-all-even-Numbers-in-a-List) or, more realistically, data from a CSV file with many rows and columns.\n",
+    "Often, we process some iterable with numeric data, for example, a list of `numbers` as in this book's introductory example in [Chapter 1 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/main/01_elements/00_content.ipynb#Example:-Averaging-all-even-Numbers-in-a-List) or, more realistically, data from a CSV file with many rows and columns.\n",
     "\n",
     "Processing numeric data usually comes down to operations that may be grouped into one of the following three categories:\n",
     "\n",
@@ -360,7 +360,7 @@
     "- **filtering**: throw away individual numbers (e.g., statistical outliers in a sample)\n",
     "- **reducing**: collect individual numbers into summary statistics\n",
     "\n",
-    "We study this **map-filter-reduce** paradigm extensively in [Chapter 8 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/08_mfr/00_content.ipynb) after introducing more advanced data types that are needed to work with \"big\" data.\n",
+    "We study this **map-filter-reduce** paradigm extensively in [Chapter 8 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/main/08_mfr/00_content.ipynb) after introducing more advanced data types that are needed to work with \"big\" data.\n",
     "\n",
     "Here, we focus on *filtering out* some numbers in a `for`-loop."
    ]
@@ -535,7 +535,7 @@
     }
    },
    "source": [
-    "With already three levels of indentation, less horizontal space is available for the actual code block. Of course, one could flatten the two `if` statements with the logical `and` operator, as shown in [Chapter 3 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/03_conditionals/00_content.ipynb#The-if-Statement). Then, however, we trade off horizontal space against a more \"complex\" `if` logic, and this is *not* a real improvement."
+    "With already three levels of indentation, less horizontal space is available for the actual code block. Of course, one could flatten the two `if` statements with the logical `and` operator, as shown in [Chapter 3 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/main/03_conditionals/00_content.ipynb#The-if-Statement). Then, however, we trade off horizontal space against a more \"complex\" `if` logic, and this is *not* a real improvement."
    ]
   },
   {
diff --git a/04_iteration/04_exercises_dice.ipynb b/04_iteration/04_exercises_dice.ipynb
index 8bd97ff..9c3feae 100644
--- a/04_iteration/04_exercises_dice.ipynb
+++ b/04_iteration/04_exercises_dice.ipynb
@@ -4,7 +4,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "**Note**: Click on \"*Kernel*\" > \"*Restart Kernel and Run All*\" in [JupyterLab](https://jupyterlab.readthedocs.io/en/stable/) *after* finishing the exercises to ensure that your solution runs top to bottom *without* any errors. If you cannot run this file on your machine, you may want to open it [in the cloud <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_mb.png\">](https://mybinder.org/v2/gh/webartifex/intro-to-python/develop?urlpath=lab/tree/04_iteration/04_exercises.ipynb)."
+    "**Note**: Click on \"*Kernel*\" > \"*Restart Kernel and Run All*\" in [JupyterLab](https://jupyterlab.readthedocs.io/en/stable/) *after* finishing the exercises to ensure that your solution runs top to bottom *without* any errors. If you cannot run this file on your machine, you may want to open it [in the cloud <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_mb.png\">](https://mybinder.org/v2/gh/webartifex/intro-to-python/main?urlpath=lab/tree/04_iteration/04_exercises.ipynb)."
    ]
   },
   {
@@ -18,7 +18,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "The exercises below assume that you have read the [third part <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/04_iteration/03_content.ipynb) of Chapter 4.\n",
+    "The exercises below assume that you have read the [third part <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/main/04_iteration/03_content.ipynb) of Chapter 4.\n",
     "\n",
     "The `...`'s in the code cells indicate where you need to fill in code snippets. The number of `...`'s within a code cell give you a rough idea of how many lines of code are needed to solve the task. You should not need to create any additional code cells for your final solution. However, you may want to use temporary code cells to try out some ideas."
    ]
@@ -34,7 +34,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "In this exercise, you will model the throwing of dice within the context of a guessing game similar to the one shown in the \"*Example: Guessing a Coin Toss*\" section in [Chapter 4 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/04_iteration/03_content.ipynb#Example:-Guessing-a-Coin-Toss).\n",
+    "In this exercise, you will model the throwing of dice within the context of a guessing game similar to the one shown in the \"*Example: Guessing a Coin Toss*\" section in [Chapter 4 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/main/04_iteration/03_content.ipynb#Example:-Guessing-a-Coin-Toss).\n",
     "\n",
     "As the game involves randomness, we import the [random <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/random.html) module from the [standard library <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/index.html). To follow best practices, we set the random seed as well."
    ]
diff --git a/04_iteration/06_review.ipynb b/04_iteration/06_review.ipynb
index 2043dcc..eeb19a5 100644
--- a/04_iteration/06_review.ipynb
+++ b/04_iteration/06_review.ipynb
@@ -11,7 +11,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "The questions below assume that you have read the [first <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/04_iteration/00_content.ipynb), [second <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/04_iteration/02_content.ipynb), and the [third <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/04_iteration/03_content.ipynb) part of Chapter 4.\n",
+    "The questions below assume that you have read the [first <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/main/04_iteration/00_content.ipynb), [second <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/main/04_iteration/02_content.ipynb), and the [third <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/main/04_iteration/03_content.ipynb) part of Chapter 4.\n",
     "\n",
     "Be concise in your answers! Most questions can be answered in *one* sentence."
    ]
diff --git a/05_numbers/00_content.ipynb b/05_numbers/00_content.ipynb
index d04c106..405c540 100644
--- a/05_numbers/00_content.ipynb
+++ b/05_numbers/00_content.ipynb
@@ -8,7 +8,7 @@
     }
    },
    "source": [
-    "**Note**: Click on \"*Kernel*\" > \"*Restart Kernel and Clear All Outputs*\" in [JupyterLab](https://jupyterlab.readthedocs.io/en/stable/) *before* reading this notebook to reset its output. If you cannot run this file on your machine, you may want to open it [in the cloud <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_mb.png\">](https://mybinder.org/v2/gh/webartifex/intro-to-python/develop?urlpath=lab/tree/05_numbers/00_content.ipynb)."
+    "**Note**: Click on \"*Kernel*\" > \"*Restart Kernel and Clear All Outputs*\" in [JupyterLab](https://jupyterlab.readthedocs.io/en/stable/) *before* reading this notebook to reset its output. If you cannot run this file on your machine, you may want to open it [in the cloud <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_mb.png\">](https://mybinder.org/v2/gh/webartifex/intro-to-python/main?urlpath=lab/tree/05_numbers/00_content.ipynb)."
    ]
   },
   {
@@ -32,17 +32,17 @@
    "source": [
     "After learning about the basic building blocks of expressing and structuring the business logic in programs, we focus our attention on the **data types** Python offers us, both built-in and available via the [standard library <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/index.html) or third-party packages.\n",
     "\n",
-    "We start with the \"simple\" ones: Numeric types in this chapter and textual data in [Chapter 6 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/06_text/00_content.ipynb). An important fact that holds for all objects of these types is that they are **immutable**. To reuse the bag analogy from [Chapter 1 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/01_elements/00_content.ipynb#Objects-vs.-Types-vs.-Values), this means that the $0$s and $1$s making up an object's *value* cannot be changed once the bag is created in memory, implying that any operation with or method on the object creates a *new* object in a *different* memory location.\n",
+    "We start with the \"simple\" ones: Numeric types in this chapter and textual data in [Chapter 6 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/main/06_text/00_content.ipynb). An important fact that holds for all objects of these types is that they are **immutable**. To reuse the bag analogy from [Chapter 1 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/main/01_elements/00_content.ipynb#Objects-vs.-Types-vs.-Values), this means that the $0$s and $1$s making up an object's *value* cannot be changed once the bag is created in memory, implying that any operation with or method on the object creates a *new* object in a *different* memory location.\n",
     "\n",
-    "[Chapter 7 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/07_sequences/00_content.ipynb), [Chapter 8 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/08_mfr/00_content.ipynb), [Chapter 9 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/09_mappings/00_content.ipynb), [Chapter 10 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/10_arrarys/00_content.ipynb) then cover the more \"complex\" data types, including, for example, the `list` type. Finally, [Chapter 11 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/11_classes/00_content.ipynb) completes the picture by introducing language constructs to create custom types.\n",
+    "[Chapter 7 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/main/07_sequences/00_content.ipynb), [Chapter 8 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/main/08_mfr/00_content.ipynb), [Chapter 9 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/main/09_mappings/00_content.ipynb), [Chapter 10 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/main/10_arrarys/00_content.ipynb) then cover the more \"complex\" data types, including, for example, the `list` type. Finally, [Chapter 11 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/main/11_classes/00_content.ipynb) completes the picture by introducing language constructs to create custom types.\n",
     "\n",
     "We have already seen many hints indicating that numbers are not as trivial to work with as it seems at first sight:\n",
     "\n",
-    "- [Chapter 1 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/01_elements/00_content.ipynb#%28Data%29-Type-%2F-%22Behavior%22) reveals that numbers may come in *different* data types (i.e., `int` vs. `float` so far),\n",
-    "- [Chapter 3 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/03_conditionals/00_content.ipynb#Boolean-Expressions) raises questions regarding the **limited precision** of `float` numbers (e.g., `42 == 42.000000000000001` evaluates to `True`), and\n",
-    "- [Chapter 4 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/04_iteration/00_content.ipynb#Infinite-Recursion) shows that sometimes a `float` \"walks\" and \"quacks\" like an `int`, whereas the reverse is true in other cases.\n",
+    "- [Chapter 1 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/main/01_elements/00_content.ipynb#%28Data%29-Type-%2F-%22Behavior%22) reveals that numbers may come in *different* data types (i.e., `int` vs. `float` so far),\n",
+    "- [Chapter 3 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/main/03_conditionals/00_content.ipynb#Boolean-Expressions) raises questions regarding the **limited precision** of `float` numbers (e.g., `42 == 42.000000000000001` evaluates to `True`), and\n",
+    "- [Chapter 4 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/main/04_iteration/00_content.ipynb#Infinite-Recursion) shows that sometimes a `float` \"walks\" and \"quacks\" like an `int`, whereas the reverse is true in other cases.\n",
     "\n",
-    "This chapter introduces all the [built-in numeric types <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/stdtypes.html#numeric-types-int-float-complex): `int`, `float`, and `complex`. To mitigate the limited precision of floating-point numbers, we also add an [Appendix <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/05_numbers/03_appendix.ipynb) where we look at two replacements for the `float` type in the [standard library <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/index.html), namely the `Decimal` type in the [decimals <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/decimal.html#decimal.Decimal) and the `Fraction` type in the [fractions <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/fractions.html#fractions.Fraction) module."
+    "This chapter introduces all the [built-in numeric types <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/stdtypes.html#numeric-types-int-float-complex): `int`, `float`, and `complex`. To mitigate the limited precision of floating-point numbers, we also add an [Appendix <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/main/05_numbers/03_appendix.ipynb) where we look at two replacements for the `float` type in the [standard library <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/index.html), namely the `Decimal` type in the [decimals <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/decimal.html#decimal.Decimal) and the `Fraction` type in the [fractions <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/fractions.html#fractions.Fraction) module."
    ]
   },
   {
@@ -64,7 +64,7 @@
     }
    },
    "source": [
-    "The simplest numeric type is the `int` type: It behaves like an [integer in ordinary math <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_wiki.png\">](https://en.wikipedia.org/wiki/Integer) (i.e., the set $\\mathbb{Z}$) and supports operators in the way we saw in the section on arithmetic operators in [Chapter 1 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/01_elements/00_content.ipynb#%28Arithmetic%29-Operators).\n",
+    "The simplest numeric type is the `int` type: It behaves like an [integer in ordinary math <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_wiki.png\">](https://en.wikipedia.org/wiki/Integer) (i.e., the set $\\mathbb{Z}$) and supports operators in the way we saw in the section on arithmetic operators in [Chapter 1 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/main/01_elements/00_content.ipynb#%28Arithmetic%29-Operators).\n",
     "\n",
     "One way to create `int` objects is by simply writing its value as a literal with the digits `0` to `9`."
    ]
@@ -335,7 +335,7 @@
     }
    },
    "source": [
-    "Whereas the integer division operator `//` \"rounds\" towards negative infinity (cf., the \"*(Arithmetic) Operators*\" section in [Chapter 1 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/01_elements/00_content.ipynb#%28Arithmetic%29-Operators)), the [int() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#int) built-in rounds towards `0`."
+    "Whereas the integer division operator `//` \"rounds\" towards negative infinity (cf., the \"*(Arithmetic) Operators*\" section in [Chapter 1 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/main/01_elements/00_content.ipynb#%28Arithmetic%29-Operators)), the [int() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#int) built-in rounds towards `0`."
    ]
   },
   {
@@ -1104,7 +1104,7 @@
     }
    },
    "source": [
-    "The [Further Resources <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/05_numbers/06_resources.ipynb) section at the end of this chapter provides video tutorials on addition and multiplication in binary. Subtraction and division are a bit more involved but essentially also easy to understand."
+    "The [Further Resources <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/main/05_numbers/06_resources.ipynb) section at the end of this chapter provides video tutorials on addition and multiplication in binary. Subtraction and division are a bit more involved but essentially also easy to understand."
    ]
   },
   {
@@ -1575,7 +1575,7 @@
     }
    },
    "source": [
-    "While there are conventions that model negative integers with $0$s and $1$s in memory (cf., [Two's Complement <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_wiki.png\">](https://en.wikipedia.org/wiki/Two%27s_complement)), Python manages that for us, and we do not look into the theory here for brevity. We have learned all that a practitioner needs to know about how integers are modeled in a computer. The [Further Resources <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/05_numbers/06_resources.ipynb) section at the end of this chapter provides a video tutorial on how the [Two's Complement <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_wiki.png\">](https://en.wikipedia.org/wiki/Two%27s_complement) idea works.\n",
+    "While there are conventions that model negative integers with $0$s and $1$s in memory (cf., [Two's Complement <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_wiki.png\">](https://en.wikipedia.org/wiki/Two%27s_complement)), Python manages that for us, and we do not look into the theory here for brevity. We have learned all that a practitioner needs to know about how integers are modeled in a computer. The [Further Resources <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/main/05_numbers/06_resources.ipynb) section at the end of this chapter provides a video tutorial on how the [Two's Complement <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_wiki.png\">](https://en.wikipedia.org/wiki/Two%27s_complement) idea works.\n",
     "\n",
     "The binary and hexadecimal representations of negative integers are identical to their positive counterparts except that they start with a minus sign `-`. However, as the video tutorial at the end of the chapter reveals, that is *not* how the bits are organized in memory."
    ]
diff --git a/05_numbers/01_content.ipynb b/05_numbers/01_content.ipynb
index af3b6c0..265ae0b 100644
--- a/05_numbers/01_content.ipynb
+++ b/05_numbers/01_content.ipynb
@@ -8,7 +8,7 @@
     }
    },
    "source": [
-    "**Note**: Click on \"*Kernel*\" > \"*Restart Kernel and Clear All Outputs*\" in [JupyterLab](https://jupyterlab.readthedocs.io/en/stable/) *before* reading this notebook to reset its output. If you cannot run this file on your machine, you may want to open it [in the cloud <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_mb.png\">](https://mybinder.org/v2/gh/webartifex/intro-to-python/develop?urlpath=lab/tree/05_numbers/01_content.ipynb)."
+    "**Note**: Click on \"*Kernel*\" > \"*Restart Kernel and Clear All Outputs*\" in [JupyterLab](https://jupyterlab.readthedocs.io/en/stable/) *before* reading this notebook to reset its output. If you cannot run this file on your machine, you may want to open it [in the cloud <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_mb.png\">](https://mybinder.org/v2/gh/webartifex/intro-to-python/main?urlpath=lab/tree/05_numbers/01_content.ipynb)."
    ]
   },
   {
@@ -1496,7 +1496,7 @@
    "source": [
     "The built-in [format() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#format) function allows us to show the **significant digits** of a `float` number as they exist in memory to arbitrary precision. To exemplify it, let's view a couple of `float` objects with `50` digits. This analysis reveals that almost no `float` number is precise! After 14 or 15 digits \"weird\" things happen. As we see further below, the \"random\" digits ending the `float` numbers do *not* \"physically\" exist in memory! Rather, they are \"calculated\" by the [format() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#format) function that is forced to show `50` digits.\n",
     "\n",
-    "The [format() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#format) function is different from the [format() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/stdtypes.html#str.format) method on `str` objects introduced in the next chapter (cf., [Chapter 6 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/06_text/00_content.ipynb#format%28%29-Method)): Yet, both work with the so-called [format specification mini-language <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/string.html#format-specification-mini-language): `\".50f\"` is the instruction to show `50` digits of a `float` number."
+    "The [format() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#format) function is different from the [format() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/stdtypes.html#str.format) method on `str` objects introduced in the next chapter (cf., [Chapter 6 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/main/06_text/00_content.ipynb#format%28%29-Method)): Yet, both work with the so-called [format specification mini-language <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/string.html#format-specification-mini-language): `\".50f\"` is the instruction to show `50` digits of a `float` number."
    ]
   },
   {
@@ -2030,7 +2030,7 @@
     }
    },
    "source": [
-    "As seen in [Chapter 1 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/01_elements/00_content.ipynb#%28Data%29-Type-%2F-%22Behavior%22), the [.is_integer() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/stdtypes.html#float.is_integer) method tells us if a `float` can be casted as an `int` object without any loss in precision."
+    "As seen in [Chapter 1 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/main/01_elements/00_content.ipynb#%28Data%29-Type-%2F-%22Behavior%22), the [.is_integer() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/stdtypes.html#float.is_integer) method tells us if a `float` can be casted as an `int` object without any loss in precision."
    ]
   },
   {
diff --git a/05_numbers/02_content.ipynb b/05_numbers/02_content.ipynb
index 3f24e6a..701a414 100644
--- a/05_numbers/02_content.ipynb
+++ b/05_numbers/02_content.ipynb
@@ -8,7 +8,7 @@
     }
    },
    "source": [
-    "**Note**: Click on \"*Kernel*\" > \"*Restart Kernel and Clear All Outputs*\" in [JupyterLab](https://jupyterlab.readthedocs.io/en/stable/) *before* reading this notebook to reset its output. If you cannot run this file on your machine, you may want to open it [in the cloud <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_mb.png\">](https://mybinder.org/v2/gh/webartifex/intro-to-python/develop?urlpath=lab/tree/05_numbers/02_content.ipynb)."
+    "**Note**: Click on \"*Kernel*\" > \"*Restart Kernel and Clear All Outputs*\" in [JupyterLab](https://jupyterlab.readthedocs.io/en/stable/) *before* reading this notebook to reset its output. If you cannot run this file on your machine, you may want to open it [in the cloud <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_mb.png\">](https://mybinder.org/v2/gh/webartifex/intro-to-python/main?urlpath=lab/tree/05_numbers/02_content.ipynb)."
    ]
   },
   {
@@ -697,7 +697,7 @@
     }
    },
    "source": [
-    "Analogous to the discussion of *containers* and *iterables* in [Chapter 4 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/04_iteration/02_content.ipynb#Containers-vs.-Iterables), we contrast the *concrete* numeric data types in this chapter with the *abstract* ideas behind [numbers in mathematics <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_wiki.png\">](https://en.wikipedia.org/wiki/Number).\n",
+    "Analogous to the discussion of *containers* and *iterables* in [Chapter 4 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/main/04_iteration/02_content.ipynb#Containers-vs.-Iterables), we contrast the *concrete* numeric data types in this chapter with the *abstract* ideas behind [numbers in mathematics <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_wiki.png\">](https://en.wikipedia.org/wiki/Number).\n",
     "\n",
     "The figure below summarizes five *major* sets of [numbers in mathematics <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_wiki.png\">](https://en.wikipedia.org/wiki/Number) as we know them from high school:\n",
     "\n",
@@ -819,7 +819,7 @@
     "\n",
     "For, example, as all numeric data types are `Complex` numbers in the abstract sense, they all work with the built-in [abs() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#abs) function (cf., [documentation <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/numbers.html#numbers.Complex)). While it is intuitively clear what the [absolute value <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_wiki.png\">](https://en.wikipedia.org/wiki/Absolute_value) (i.e., \"distance\" from $0$) of an integer, a fraction, or any real number is, [abs() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#abs) calculates the equivalent of that for complex numbers. That concept is called the [magnitude <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_wiki.png\">](https://en.wikipedia.org/wiki/Magnitude_%28mathematics%29) of a number, and is really a *generalization* of the absolute value.\n",
     "\n",
-    "Relating back to the concept of **duck typing** mentioned in [Chapter 4 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/04_iteration/00_content.ipynb#Duck-Typing), `int`, `float`, and `complex` objects \"walk\" and \"quack\" alike in context of the [abs() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#abs) function."
+    "Relating back to the concept of **duck typing** mentioned in [Chapter 4 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/main/04_iteration/00_content.ipynb#Duck-Typing), `int`, `float`, and `complex` objects \"walk\" and \"quack\" alike in context of the [abs() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#abs) function."
    ]
   },
   {
@@ -1174,7 +1174,7 @@
     }
    },
    "source": [
-    "However, if we model `1 / 10` as a `Fraction` object (cf., [Appendix <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/05_numbers/03_appendix.ipynb#The-Fraction-Type)), it is recognized as a `Rational` number."
+    "However, if we model `1 / 10` as a `Fraction` object (cf., [Appendix <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/main/05_numbers/03_appendix.ipynb#The-Fraction-Type)), it is recognized as a `Rational` number."
    ]
   },
   {
diff --git a/05_numbers/03_appendix.ipynb b/05_numbers/03_appendix.ipynb
index 9551b70..4ab003b 100644
--- a/05_numbers/03_appendix.ipynb
+++ b/05_numbers/03_appendix.ipynb
@@ -8,7 +8,7 @@
     }
    },
    "source": [
-    "**Note**: Click on \"*Kernel*\" > \"*Restart Kernel and Clear All Outputs*\" in [JupyterLab](https://jupyterlab.readthedocs.io/en/stable/) *before* reading this notebook to reset its output. If you cannot run this file on your machine, you may want to open it [in the cloud <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_mb.png\">](https://mybinder.org/v2/gh/webartifex/intro-to-python/develop?urlpath=lab/tree/05_numbers/03_appendix.ipynb)."
+    "**Note**: Click on \"*Kernel*\" > \"*Restart Kernel and Clear All Outputs*\" in [JupyterLab](https://jupyterlab.readthedocs.io/en/stable/) *before* reading this notebook to reset its output. If you cannot run this file on your machine, you may want to open it [in the cloud <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_mb.png\">](https://mybinder.org/v2/gh/webartifex/intro-to-python/main?urlpath=lab/tree/05_numbers/03_appendix.ipynb)."
    ]
   },
   {
diff --git a/05_numbers/05_review.ipynb b/05_numbers/05_review.ipynb
index a911fb0..9c0a424 100644
--- a/05_numbers/05_review.ipynb
+++ b/05_numbers/05_review.ipynb
@@ -11,7 +11,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "The questions below assume that you have read the [first <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/05_numbers/00_content.ipynb), [second <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/05_numbers/01_content.ipynb), and the [third <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/05_numbers/02_content.ipynb) part of Chapter 5. Some questions regard the [Appendix <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/05_numbers/03_appendix.ipynb); that is indicated with a **\\***.\n",
+    "The questions below assume that you have read the [first <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/main/05_numbers/00_content.ipynb), [second <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/main/05_numbers/01_content.ipynb), and the [third <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/main/05_numbers/02_content.ipynb) part of Chapter 5. Some questions regard the [Appendix <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/main/05_numbers/03_appendix.ipynb); that is indicated with a **\\***.\n",
     "\n",
     "Be concise in your answers! Most questions can be answered in *one* sentence."
    ]
diff --git a/05_numbers/06_resources.ipynb b/05_numbers/06_resources.ipynb
index 5fa8cfa..76db27c 100644
--- a/05_numbers/06_resources.ipynb
+++ b/05_numbers/06_resources.ipynb
@@ -4,7 +4,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "**Note**: Click on \"*Kernel*\" > \"*Restart Kernel and Clear All Outputs*\" in [JupyterLab](https://jupyterlab.readthedocs.io/en/stable/) *before* reading this notebook to reset its output. If you cannot run this file on your machine, you may want to open it [in the cloud <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_mb.png\">](https://mybinder.org/v2/gh/webartifex/intro-to-python/develop?urlpath=lab/tree/05_numbers/06_resources.ipynb)."
+    "**Note**: Click on \"*Kernel*\" > \"*Restart Kernel and Clear All Outputs*\" in [JupyterLab](https://jupyterlab.readthedocs.io/en/stable/) *before* reading this notebook to reset its output. If you cannot run this file on your machine, you may want to open it [in the cloud <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_mb.png\">](https://mybinder.org/v2/gh/webartifex/intro-to-python/main?urlpath=lab/tree/05_numbers/06_resources.ipynb)."
    ]
   },
   {
diff --git a/06_text/00_content.ipynb b/06_text/00_content.ipynb
index f1f6871..57c34fa 100644
--- a/06_text/00_content.ipynb
+++ b/06_text/00_content.ipynb
@@ -8,7 +8,7 @@
     }
    },
    "source": [
-    "**Note**: Click on \"*Kernel*\" > \"*Restart Kernel and Clear All Outputs*\" in [JupyterLab](https://jupyterlab.readthedocs.io/en/stable/) *before* reading this notebook to reset its output. If you cannot run this file on your machine, you may want to open it [in the cloud <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_mb.png\">](https://mybinder.org/v2/gh/webartifex/intro-to-python/develop?urlpath=lab/tree/06_text/00_content.ipynb)."
+    "**Note**: Click on \"*Kernel*\" > \"*Restart Kernel and Clear All Outputs*\" in [JupyterLab](https://jupyterlab.readthedocs.io/en/stable/) *before* reading this notebook to reset its output. If you cannot run this file on your machine, you may want to open it [in the cloud <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_mb.png\">](https://mybinder.org/v2/gh/webartifex/intro-to-python/main?urlpath=lab/tree/06_text/00_content.ipynb)."
    ]
   },
   {
@@ -30,7 +30,7 @@
     }
    },
    "source": [
-    "In this chapter, we continue the study of the built-in data types. The next layer on top of numbers consists of **textual data** that are modeled primarily with the `str` type in Python. `str` objects are more complex than the numeric objects in [Chapter 5 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/05_numbers/00_content.ipynb) as they *consist* of an *arbitrary* and possibly large number of *individual* characters that may be chosen from *any* alphabet in the history of humankind. Luckily, Python abstracts away most of this complexity from us. However, after looking at the `str` type in great detail, we briefly introduce the `bytes` type at the end of this chapter to understand how characters are modeled in memory."
+    "In this chapter, we continue the study of the built-in data types. The next layer on top of numbers consists of **textual data** that are modeled primarily with the `str` type in Python. `str` objects are more complex than the numeric objects in [Chapter 5 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/main/05_numbers/00_content.ipynb) as they *consist* of an *arbitrary* and possibly large number of *individual* characters that may be chosen from *any* alphabet in the history of humankind. Luckily, Python abstracts away most of this complexity from us. However, after looking at the `str` type in great detail, we briefly introduce the `bytes` type at the end of this chapter to understand how characters are modeled in memory."
    ]
   },
   {
@@ -137,7 +137,7 @@
    "source": [
     "As seen before, a `str` object evaluates to itself in a literal notation with enclosing **single quotes** `'`.\n",
     "\n",
-    "In [Chapter 1 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/01_elements/00_content.ipynb#Value-/-(Semantic)-\"Meaning\"), we specify the double quotes `\"` convention this book follows. Yet, single quotes `'` and double quotes `\"` are *perfect* substitutes. We could use the reverse convention, as well. As [this discussion <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_so.png\">](https://stackoverflow.com/questions/56011/single-quotes-vs-double-quotes-in-python) shows, many programmers have *strong* opinions about such conventions. Consequently, the discussion was \"closed as not constructive\" by the moderators."
+    "In [Chapter 1 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/main/01_elements/00_content.ipynb#Value-/-(Semantic)-\"Meaning\"), we specify the double quotes `\"` convention this book follows. Yet, single quotes `'` and double quotes `\"` are *perfect* substitutes. We could use the reverse convention, as well. As [this discussion <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_so.png\">](https://stackoverflow.com/questions/56011/single-quotes-vs-double-quotes-in-python) shows, many programmers have *strong* opinions about such conventions. Consequently, the discussion was \"closed as not constructive\" by the moderators."
    ]
   },
   {
@@ -290,7 +290,7 @@
     }
    },
    "source": [
-    "As an alternative to the literal notation, we may use the built-in [str() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/stdtypes.html#str) constructor to cast non-`str` objects as `str` ones. As [Chapter 11 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/11_classes/00_content.ipynb) reveals, basically any object in Python has a **text representation**. Because of that we may also pass `list` objects, the boolean `True` and `False`, or `None` to [str() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/stdtypes.html#str)."
+    "As an alternative to the literal notation, we may use the built-in [str() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/stdtypes.html#str) constructor to cast non-`str` objects as `str` ones. As [Chapter 11 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/main/11_classes/00_content.ipynb) reveals, basically any object in Python has a **text representation**. Because of that we may also pass `list` objects, the boolean `True` and `False`, or `None` to [str() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/stdtypes.html#str)."
    ]
   },
   {
@@ -456,7 +456,7 @@
     }
    },
    "source": [
-    "As shown in the \"*Guessing a Coin Toss*\" example in [Chapter 4 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/04_iteration/03_content.ipynb#Example:-Guessing-a-Coin-Toss), the built-in [input() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#input) function displays a prompt to the user and returns whatever is entered as a `str` object. [input() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#input) is in particular valuable when writing command-line tools."
+    "As shown in the \"*Guessing a Coin Toss*\" example in [Chapter 4 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/main/04_iteration/03_content.ipynb#Example:-Guessing-a-Coin-Toss), the built-in [input() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#input) function displays a prompt to the user and returns whatever is entered as a `str` object. [input() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#input) is in particular valuable when writing command-line tools."
    ]
   },
   {
@@ -571,7 +571,7 @@
     }
    },
    "source": [
-    "[open() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#open) returns a **[proxy <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_wiki.png\">](https://en.wikipedia.org/wiki/Proxy_pattern)** object of type `TextIOWrapper` that allows us to interact with the file on disk. `mode='r'` shows that we opened the file in read-only mode and `encoding='UTF-8'` is explained in detail in the [The `bytes` Type <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/06_text/01_content.ipynb#The-bytes-Type) section at the end of this chapter."
+    "[open() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#open) returns a **[proxy <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_wiki.png\">](https://en.wikipedia.org/wiki/Proxy_pattern)** object of type `TextIOWrapper` that allows us to interact with the file on disk. `mode='r'` shows that we opened the file in read-only mode and `encoding='UTF-8'` is explained in detail in the [The `bytes` Type <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/main/06_text/01_content.ipynb#The-bytes-Type) section at the end of this chapter."
    ]
   },
   {
@@ -1010,7 +1010,7 @@
     }
    },
    "source": [
-    "Using syntax familiar from [Chapter 3 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/03_conditionals/00_content.ipynb#The-try-Statement) to explain what the `with open(...) as file:` does above, we provide an alternative formulation with a `try` statement below: The `finally`-branch is *always* executed, even if an exception is raised inside the `for`-loop. Therefore, `file` is sure to be closed too. However, this formulation is somewhat less expressive."
+    "Using syntax familiar from [Chapter 3 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/main/03_conditionals/00_content.ipynb#The-try-Statement) to explain what the `with open(...) as file:` does above, we provide an alternative formulation with a `try` statement below: The `finally`-branch is *always* executed, even if an exception is raised inside the `for`-loop. Therefore, `file` is sure to be closed too. However, this formulation is somewhat less expressive."
    ]
   },
   {
@@ -1373,7 +1373,7 @@
     }
    },
    "source": [
-    "A **sequence** is yet another *abstract* concept (cf., the \"*Containers vs. Iterables*\" section in [Chapter 4 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/04_iteration/02_content.ipynb#Containers-vs.-Iterables)).\n",
+    "A **sequence** is yet another *abstract* concept (cf., the \"*Containers vs. Iterables*\" section in [Chapter 4 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/main/04_iteration/02_content.ipynb#Containers-vs.-Iterables)).\n",
     "\n",
     "It unifies *four* [orthogonal <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_wiki.png\">](https://en.wikipedia.org/wiki/Orthogonality) (i.e., \"independent\") concepts into one bigger idea: Any data type, such as `str`, is considered a sequence if it\n",
     "\n",
@@ -1382,11 +1382,11 @@
     "3. can be **iterated** over\n",
     "4. in a *predictable* **order**.\n",
     "\n",
-    "[Chapter 7 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/07_sequences/00_content.ipynb#Collections-vs.-Sequences) formalizes these concepts in great detail. Here, we keep our focus on the `str` type that historically received its name as it models a **[string of characters <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_wiki.png\">](https://en.wikipedia.org/wiki/String_%28computer_science%29)**. *String* is simply another term for *sequence* in the computer science literature.\n",
+    "[Chapter 7 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/main/07_sequences/00_content.ipynb#Collections-vs.-Sequences) formalizes these concepts in great detail. Here, we keep our focus on the `str` type that historically received its name as it models a **[string of characters <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_wiki.png\">](https://en.wikipedia.org/wiki/String_%28computer_science%29)**. *String* is simply another term for *sequence* in the computer science literature.\n",
     "\n",
     "Another example of a sequence is the `list` type. Because of that, `str` objects may be treated like `list` objects in many situations.\n",
     "\n",
-    "Below, the built-in [len() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#len) function tells us how many characters make up `text`. [len() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#len) would not work with an \"infinite\" object. As anything modeled in a program must fit into a computer's finite memory, there cannot exist truly infinite objects; however, [Chapter 8 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/08_mfr/00_content.ipynb#Iterators-vs.-Iterables) introduces specialized iterable data types that can be used to model an *infinite* series of \"things\" and that, consequently, have no concept of \"length.\""
+    "Below, the built-in [len() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#len) function tells us how many characters make up `text`. [len() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#len) would not work with an \"infinite\" object. As anything modeled in a program must fit into a computer's finite memory, there cannot exist truly infinite objects; however, [Chapter 8 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/main/08_mfr/00_content.ipynb#Iterators-vs.-Iterables) introduces specialized iterable data types that can be used to model an *infinite* series of \"things\" and that, consequently, have no concept of \"length.\""
    ]
   },
   {
@@ -1605,7 +1605,7 @@
     }
    },
    "source": [
-    "As `str` objects are *ordered* and *finite*, we may **index** into them to obtain individual characters with the **indexing operator** `[]`. This is analogous to how we obtained individual elements of a `list` object in [Chapter 1 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/01_elements/03_content.ipynb#Who-am-I?-And-how-many?)."
+    "As `str` objects are *ordered* and *finite*, we may **index** into them to obtain individual characters with the **indexing operator** `[]`. This is analogous to how we obtained individual elements of a `list` object in [Chapter 1 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/main/01_elements/03_content.ipynb#Who-am-I?-And-how-many?)."
    ]
   },
   {
@@ -2292,9 +2292,9 @@
     }
    },
    "source": [
-    "Whereas elements of a `list` object *may* be *re-assigned*, as shortly hinted at in [Chapter 1 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/01_elements/03_content.ipynb#Who-am-I?-And-how-many?), this is *not* allowed for the individual characters of `str` objects. Once created, they can *not* be changed. Formally, we say that `str` objects are **immutable**. In that regard, they are like the numeric types in [Chapter 5 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/05_numbers/00_content.ipynb).\n",
+    "Whereas elements of a `list` object *may* be *re-assigned*, as shortly hinted at in [Chapter 1 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/main/01_elements/03_content.ipynb#Who-am-I?-And-how-many?), this is *not* allowed for the individual characters of `str` objects. Once created, they can *not* be changed. Formally, we say that `str` objects are **immutable**. In that regard, they are like the numeric types in [Chapter 5 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/main/05_numbers/00_content.ipynb).\n",
     "\n",
-    "On the contrary, objects that may be changed after creation, are called **mutable**. We already saw in [Chapter 1 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/01_elements/03_content.ipynb#Who-am-I?-And-how-many?) how mutable objects are more difficult to reason about for a beginner, in particular, if more than one variable references it. Yet, mutability does have its place in a programmer's toolbox, and we revisit this idea in the next chapters.\n",
+    "On the contrary, objects that may be changed after creation, are called **mutable**. We already saw in [Chapter 1 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/main/01_elements/03_content.ipynb#Who-am-I?-And-how-many?) how mutable objects are more difficult to reason about for a beginner, in particular, if more than one variable references it. Yet, mutability does have its place in a programmer's toolbox, and we revisit this idea in the next chapters.\n",
     "\n",
     "The `TypeError` indicates that `str` objects are *immutable*: Assignment to an index or a slice are *not* supported."
    ]
@@ -3377,7 +3377,7 @@
     }
    },
    "source": [
-    "As mentioned in [Chapter 1 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/01_elements/00_content.ipynb#Operator-Overloading), the `+` and `*` operators are *overloaded* and used for **string concatenation**. They always create *new* `str` objects. That has nothing to do with the `str` type's immutability, but is the default behavior of operators."
+    "As mentioned in [Chapter 1 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/main/01_elements/00_content.ipynb#Operator-Overloading), the `+` and `*` operators are *overloaded* and used for **string concatenation**. They always create *new* `str` objects. That has nothing to do with the `str` type's immutability, but is the default behavior of operators."
    ]
   },
   {
@@ -3447,7 +3447,7 @@
     }
    },
    "source": [
-    "The *relational* operators also work with `str` objects, another example of operator overloading. Comparison is done one character at a time in a pairwise fashion until the first pair differs or one operand ends. However, `str` objects are sorted in a \"weird\" way. For example, all upper case characters come before all lower case characters. The reason for that is given in the \"*Characters are Numbers with a Convention*\" sub-section in the [second part <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/06_text/02_content.ipynb#Characters-are-Numbers-with-a-Convention) of this chapter."
+    "The *relational* operators also work with `str` objects, another example of operator overloading. Comparison is done one character at a time in a pairwise fashion until the first pair differs or one operand ends. However, `str` objects are sorted in a \"weird\" way. For example, all upper case characters come before all lower case characters. The reason for that is given in the \"*Characters are Numbers with a Convention*\" sub-section in the [second part <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/main/06_text/02_content.ipynb#Characters-are-Numbers-with-a-Convention) of this chapter."
    ]
   },
   {
@@ -3857,7 +3857,7 @@
     }
    },
    "source": [
-    "The `%` operator that we saw in the context of modulo division in [Chapter 1 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/01_elements/00_content.ipynb#%28Arithmetic%29-Operators) is overloaded with string interpolation when its first operand is a `str` object. The second operand consists of all expressions to be filled in. Format specifiers work with a `%` instead of curly braces and according to a different set of rules referred to as **[printf-style string formatting <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/stdtypes.html#printf-style-string-formatting)**. So, `{:.2f}` becomes `%.2f`.\n",
+    "The `%` operator that we saw in the context of modulo division in [Chapter 1 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/main/01_elements/00_content.ipynb#%28Arithmetic%29-Operators) is overloaded with string interpolation when its first operand is a `str` object. The second operand consists of all expressions to be filled in. Format specifiers work with a `%` instead of curly braces and according to a different set of rules referred to as **[printf-style string formatting <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/stdtypes.html#printf-style-string-formatting)**. So, `{:.2f}` becomes `%.2f`.\n",
     "\n",
     "This way of string interpolation is the oldest and originates from the [C language <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_wiki.png\">](https://en.wikipedia.org/wiki/C_%28programming_language%29). It is still widely spread, but we should use one of the other two ways instead. We show it here mainly for completeness sake."
    ]
@@ -3894,7 +3894,7 @@
     }
    },
    "source": [
-    "To insert more than one expression, we must list them in order and between parenthesis `(` and `)`. As [Chapter 7 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/07_sequences/00_content.ipynb#The-tuple-Type) reveals, this literal syntax creates an object of type `tuple`. Also, to format an expression as text, we use the format specifier `%s`."
+    "To insert more than one expression, we must list them in order and between parenthesis `(` and `)`. As [Chapter 7 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/main/07_sequences/00_content.ipynb#The-tuple-Type) reveals, this literal syntax creates an object of type `tuple`. Also, to format an expression as text, we use the format specifier `%s`."
    ]
   },
   {
diff --git a/06_text/01_exercises_palindromes.ipynb b/06_text/01_exercises_palindromes.ipynb
index 89dc687..b8a8c33 100644
--- a/06_text/01_exercises_palindromes.ipynb
+++ b/06_text/01_exercises_palindromes.ipynb
@@ -4,7 +4,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "**Note**: Click on \"*Kernel*\" > \"*Restart Kernel and Run All*\" in [JupyterLab](https://jupyterlab.readthedocs.io/en/stable/) *after* finishing the exercises to ensure that your solution runs top to bottom *without* any errors. If you cannot run this file on your machine, you may want to open it [in the cloud <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_mb.png\">](https://mybinder.org/v2/gh/webartifex/intro-to-python/develop?urlpath=lab/tree/06_text/01_exercises.ipynb)."
+    "**Note**: Click on \"*Kernel*\" > \"*Restart Kernel and Run All*\" in [JupyterLab](https://jupyterlab.readthedocs.io/en/stable/) *after* finishing the exercises to ensure that your solution runs top to bottom *without* any errors. If you cannot run this file on your machine, you may want to open it [in the cloud <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_mb.png\">](https://mybinder.org/v2/gh/webartifex/intro-to-python/main?urlpath=lab/tree/06_text/01_exercises.ipynb)."
    ]
   },
   {
@@ -18,7 +18,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "The exercises below assume that you have read the [first part <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/06_text/00_content.ipynb) of Chapter 6.\n",
+    "The exercises below assume that you have read the [first part <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/main/06_text/00_content.ipynb) of Chapter 6.\n",
     "\n",
     "The `...`'s in the code cells indicate where you need to fill in code snippets. The number of `...`'s within a code cell give you a rough idea of how many lines of code are needed to solve the task. You should not need to create any additional code cells for your final solution. However, you may want to use temporary code cells to try out some ideas."
    ]
@@ -38,7 +38,7 @@
     "\n",
     "In this exercise, you implement various functions that check if the given arguments are palindromes or not. We start with an iterative implementation and end with a recursive one.\n",
     "\n",
-    "Conceptually, the first function, `unpythonic_palindrome()`, is similar to the \"*Is the square of a number in `[7, 11, 8, 5, 3, 12, 2, 6, 9, 10, 1, 4]` greater than `100`?*\" example in [Chapter 4 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/04_iteration/03_content.ipynb#Example:-Is-the-square-of-a-number-in-[7,-11,-8,-5,-3,-12,-2,-6,-9,-10,-1,-4]-greater-than-100?): It assumes that the `text` argument is a palindrome (i.e., it initializes `is_palindrom` to `True`) and then checks in a `for`-loop if a pair of corresponding characters, `forward` and `backward`, contradicts that.\n",
+    "Conceptually, the first function, `unpythonic_palindrome()`, is similar to the \"*Is the square of a number in `[7, 11, 8, 5, 3, 12, 2, 6, 9, 10, 1, 4]` greater than `100`?*\" example in [Chapter 4 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/main/04_iteration/03_content.ipynb#Example:-Is-the-square-of-a-number-in-[7,-11,-8,-5,-3,-12,-2,-6,-9,-10,-1,-4]-greater-than-100?): It assumes that the `text` argument is a palindrome (i.e., it initializes `is_palindrom` to `True`) and then checks in a `for`-loop if a pair of corresponding characters, `forward` and `backward`, contradicts that.\n",
     "\n",
     "**Q1**: How many iterations are needed in the `for`-loop? Take into account that `text` may contain an even or odd number of characters! Inside `unpythonic_palindrome()` below, write an expression whose result is assigned to `chars_to_check`!"
    ]
diff --git a/06_text/02_content.ipynb b/06_text/02_content.ipynb
index fc5efa3..887deb5 100644
--- a/06_text/02_content.ipynb
+++ b/06_text/02_content.ipynb
@@ -8,7 +8,7 @@
     }
    },
    "source": [
-    "**Note**: Click on \"*Kernel*\" > \"*Restart Kernel and Clear All Outputs*\" in [JupyterLab](https://jupyterlab.readthedocs.io/en/stable/) *before* reading this notebook to reset its output. If you cannot run this file on your machine, you may want to open it [in the cloud <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_mb.png\">](https://mybinder.org/v2/gh/webartifex/intro-to-python/develop?urlpath=lab/tree/06_text/01_content.ipynb)."
+    "**Note**: Click on \"*Kernel*\" > \"*Restart Kernel and Clear All Outputs*\" in [JupyterLab](https://jupyterlab.readthedocs.io/en/stable/) *before* reading this notebook to reset its output. If you cannot run this file on your machine, you may want to open it [in the cloud <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_mb.png\">](https://mybinder.org/v2/gh/webartifex/intro-to-python/main?urlpath=lab/tree/06_text/01_content.ipynb)."
    ]
   },
   {
@@ -491,7 +491,7 @@
    "source": [
     "So far, we used the term **character** without any further consideration. In this section, we briefly look into what characters are and how they are modeled in software.\n",
     "\n",
-    "[Chapter 5 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/05_numbers/00_content.ipynb) gives us an idea on how individual **bits** are used to express all types of numbers, from \"simple\" `int` objects to \"complex\" `float` ones. To model characters, another **layer of abstraction** is put on top of whole numbers. So, just as bits are used to express integers, they themselves are used to express characters."
+    "[Chapter 5 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/main/05_numbers/00_content.ipynb) gives us an idea on how individual **bits** are used to express all types of numbers, from \"simple\" `int` objects to \"complex\" `float` ones. To model characters, another **layer of abstraction** is put on top of whole numbers. So, just as bits are used to express integers, they themselves are used to express characters."
    ]
   },
   {
@@ -637,7 +637,7 @@
     "\n",
     "For example, the digit `5` is mapped to the number `53` in ASCII. The binary representation of `53` is `0b_11_0101` and the least significant four bits, `0101`, mean $5$. Similarly, the letter `\"E\"` is the fifth letter in the alphabet. It is encoded with the number `69` in ASCII, which is `0b_100_0101` in binary. And, the least significant bits, `0_0101`, mean $5$. Analogously, `\"e\"` is encoded with `101` in ASCII, which is `0b_110_0101` in binary. And, the least significant bits, `0_0101`, mean $5$ again. This encoding was chosen mainly because programmers \"in the old days\" needed to implement these encodings \"by hand.\" Python abstracts that logic away from its users.\n",
     "\n",
-    "This encoding scheme is also the cause for the \"weird\" sorting in the \"*String Comparison*\" section in the [first part <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/06_text/01_content.ipynb#String-Comparison) of this chapter, where `\"apple\"` comes *after* `\"Banana\"`. As `\"a\"` is encoded with `97` and `\"B\"` with `66`, `\"Banana\"` must of course be \"smaller\" than `\"apple\"` when comparison is done in a pairwise fashion of the individual characters."
+    "This encoding scheme is also the cause for the \"weird\" sorting in the \"*String Comparison*\" section in the [first part <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/main/06_text/01_content.ipynb#String-Comparison) of this chapter, where `\"apple\"` comes *after* `\"Banana\"`. As `\"a\"` is encoded with `97` and `\"B\"` with `66`, `\"Banana\"` must of course be \"smaller\" than `\"apple\"` when comparison is done in a pairwise fashion of the individual characters."
    ]
   },
   {
@@ -1991,7 +1991,7 @@
     }
    },
    "source": [
-    "A best practice is to *always* specify the `encoding`, especially on computers running on Windows (cf., the talk by �ukasz Langa in the [Further Resources <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/06_text/05_resources.ipynb#Unicode)) section at the end of this chapter.\n",
+    "A best practice is to *always* specify the `encoding`, especially on computers running on Windows (cf., the talk by �ukasz Langa in the [Further Resources <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/main/06_text/05_resources.ipynb#Unicode)) section at the end of this chapter.\n",
     "\n",
     "Below is the first example involving [open() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#open) one last time: It shows how *all* the contents of a text file should be read into one `str` object."
    ]
diff --git a/06_text/04_review.ipynb b/06_text/04_review.ipynb
index b92cb56..17dd0a5 100644
--- a/06_text/04_review.ipynb
+++ b/06_text/04_review.ipynb
@@ -11,7 +11,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "The questions below assume that you have read the [first <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/06_text/00_content.ipynb) and [second <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/06_text/01_content.ipynb) part of Chapter 6.\n",
+    "The questions below assume that you have read the [first <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/main/06_text/00_content.ipynb) and [second <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/main/06_text/01_content.ipynb) part of Chapter 6.\n",
     "\n",
     "Be concise in your answers! Most questions can be answered in *one* sentence."
    ]
diff --git a/06_text/05_resources.ipynb b/06_text/05_resources.ipynb
index 24882f9..425c6cf 100644
--- a/06_text/05_resources.ipynb
+++ b/06_text/05_resources.ipynb
@@ -8,7 +8,7 @@
     }
    },
    "source": [
-    "**Note**: Click on \"*Kernel*\" > \"*Restart Kernel and Clear All Outputs*\" in [JupyterLab](https://jupyterlab.readthedocs.io/en/stable/) *before* reading this notebook to reset its output. If you cannot run this file on your machine, you may want to open it [in the cloud <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_mb.png\">](https://mybinder.org/v2/gh/webartifex/intro-to-python/develop?urlpath=lab/tree/06_text/05_resources.ipynb)."
+    "**Note**: Click on \"*Kernel*\" > \"*Restart Kernel and Clear All Outputs*\" in [JupyterLab](https://jupyterlab.readthedocs.io/en/stable/) *before* reading this notebook to reset its output. If you cannot run this file on your machine, you may want to open it [in the cloud <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_mb.png\">](https://mybinder.org/v2/gh/webartifex/intro-to-python/main?urlpath=lab/tree/06_text/05_resources.ipynb)."
    ]
   },
   {
diff --git a/07_sequences/00_content.ipynb b/07_sequences/00_content.ipynb
index c37450b..8f8984f 100644
--- a/07_sequences/00_content.ipynb
+++ b/07_sequences/00_content.ipynb
@@ -8,7 +8,7 @@
     }
    },
    "source": [
-    "**Note**: Click on \"*Kernel*\" > \"*Restart Kernel and Clear All Outputs*\" in [JupyterLab](https://jupyterlab.readthedocs.io/en/stable/) *before* reading this notebook to reset its output. If you cannot run this file on your machine, you may want to open it [in the cloud <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_mb.png\">](https://mybinder.org/v2/gh/webartifex/intro-to-python/develop?urlpath=lab/tree/07_sequences/00_content.ipynb)."
+    "**Note**: Click on \"*Kernel*\" > \"*Restart Kernel and Clear All Outputs*\" in [JupyterLab](https://jupyterlab.readthedocs.io/en/stable/) *before* reading this notebook to reset its output. If you cannot run this file on your machine, you may want to open it [in the cloud <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_mb.png\">](https://mybinder.org/v2/gh/webartifex/intro-to-python/main?urlpath=lab/tree/07_sequences/00_content.ipynb)."
    ]
   },
   {
@@ -30,11 +30,11 @@
     }
    },
    "source": [
-    "We studied numbers (cf., [Chapter 5 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/05_numbers/00_content.ipynb)) and textual data (cf., [Chapter 6 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/06_text/00_content.ipynb)) first mainly because objects of the presented data types are \"simple.\" That is so for two reasons: First, they are *immutable*, and, as we saw in the \"*Who am I? And how many?*\" section in [Chapter 1 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/01_elements/03_content.ipynb#Who-am-I?-And-how-many?), mutable objects can quickly become hard to reason about. Second, they are \"flat\" in the sense that they are *not* composed of other objects.\n",
+    "We studied numbers (cf., [Chapter 5 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/main/05_numbers/00_content.ipynb)) and textual data (cf., [Chapter 6 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/main/06_text/00_content.ipynb)) first mainly because objects of the presented data types are \"simple.\" That is so for two reasons: First, they are *immutable*, and, as we saw in the \"*Who am I? And how many?*\" section in [Chapter 1 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/main/01_elements/03_content.ipynb#Who-am-I?-And-how-many?), mutable objects can quickly become hard to reason about. Second, they are \"flat\" in the sense that they are *not* composed of other objects.\n",
     "\n",
     "The `str` type is a bit of a corner case in this regard. While one could argue that a longer `str` object, for example, `\"text\"`, is composed of individual characters, this is *not* the case in memory as the literal `\"text\"` only creates *one* object (i.e., one \"bag\" of $0$s and $1$s modeling all characters).\n",
     "\n",
-    "This chapter, [Chapter 8 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/08_mfr/00_content.ipynb), [Chapter 9 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/09_mappings/00_content.ipynb), and [Chapter 10 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/10_arrays/00_content.ipynb) introduce various \"complex\" data types. While some are mutable and others are not, they all share that they are primarily used to \"manage,\" or structure, the memory in a program (i.e., they provide references to other objects). Unsurprisingly, computer scientists refer to the ideas behind these data types as **[data structures <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_wiki.png\">](https://en.wikipedia.org/wiki/Data_structure)**.\n",
+    "This chapter, [Chapter 8 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/main/08_mfr/00_content.ipynb), [Chapter 9 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/main/09_mappings/00_content.ipynb), and [Chapter 10 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/main/10_arrays/00_content.ipynb) introduce various \"complex\" data types. While some are mutable and others are not, they all share that they are primarily used to \"manage,\" or structure, the memory in a program (i.e., they provide references to other objects). Unsurprisingly, computer scientists refer to the ideas behind these data types as **[data structures <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_wiki.png\">](https://en.wikipedia.org/wiki/Data_structure)**.\n",
     "\n",
     "In this chapter, we focus on data types that model all kinds of sequential data. Examples of such data are [spreadsheets <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_wiki.png\">](https://en.wikipedia.org/wiki/Spreadsheet) or [matrices <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_wiki.png\">](https://en.wikipedia.org/wiki/Matrix_%28mathematics%29) and [vectors <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_wiki.png\">](https://en.wikipedia.org/wiki/Vector_%28mathematics_and_physics%29). These formats share the property that they are composed of smaller units that come in a sequence of, for example, rows/columns/cells or elements/entries."
    ]
@@ -58,9 +58,9 @@
     }
    },
    "source": [
-    "[Chapter 6 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/06_text/00_content.ipynb#A-\"String\"-of-Characters) already describes the **sequence** properties of `str` objects. In this section, we take a step back and study these properties one by one.\n",
+    "[Chapter 6 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/main/06_text/00_content.ipynb#A-\"String\"-of-Characters) already describes the **sequence** properties of `str` objects. In this section, we take a step back and study these properties one by one.\n",
     "\n",
-    "The [collections.abc <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/collections.abc.html) module in the [standard library <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/index.html) defines a variety of **abstract base classes** (ABCs). We saw ABCs already in [Chapter 5 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/05_numbers/02_content.ipynb#The-Numerical-Tower), where we use the ones from the [numbers <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/numbers.html) module in the [standard library <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/index.html) to classify Python's numeric data types according to mathematical ideas. Now, we take the ABCs from the [collections.abc <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/collections.abc.html) module to classify the data types in this chapter according to their behavior in various contexts.\n",
+    "The [collections.abc <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/collections.abc.html) module in the [standard library <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/index.html) defines a variety of **abstract base classes** (ABCs). We saw ABCs already in [Chapter 5 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/main/05_numbers/02_content.ipynb#The-Numerical-Tower), where we use the ones from the [numbers <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/numbers.html) module in the [standard library <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/index.html) to classify Python's numeric data types according to mathematical ideas. Now, we take the ABCs from the [collections.abc <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/collections.abc.html) module to classify the data types in this chapter according to their behavior in various contexts.\n",
     "\n",
     "As an illustration, consider `numbers` and `text` below, two objects of *different* types."
    ]
@@ -142,7 +142,7 @@
     }
    },
    "source": [
-    "In [Chapter 4 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/04_iteration/02_content.ipynb#Containers-vs.-Iterables), we referred to such types as *iterables*. That is *not* a proper [English](https://dictionary.cambridge.org/spellcheck/english-german/?q=iterable) word, even if it may sound like one at first sight. Yet, it is an official term in the Python world formalized with the `Iterable` ABC in the [collections.abc <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/collections.abc.html) module.\n",
+    "In [Chapter 4 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/main/04_iteration/02_content.ipynb#Containers-vs.-Iterables), we referred to such types as *iterables*. That is *not* a proper [English](https://dictionary.cambridge.org/spellcheck/english-german/?q=iterable) word, even if it may sound like one at first sight. Yet, it is an official term in the Python world formalized with the `Iterable` ABC in the [collections.abc <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/collections.abc.html) module.\n",
     "\n",
     "For the data science practitioner, it is worthwhile to know such terms as, for example, the documentation on the [built-ins <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html) uses them extensively: In simple words, any built-in that takes an argument called \"*iterable*\" may be called with *any* object that supports being looped over. Already familiar [built-ins <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html) include [enumerate() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#enumerate), [sum() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#sum), or [zip() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#zip). So, they do *not* require the argument to be of a certain data type (e.g., `list`); instead, any *iterable* type works."
    ]
@@ -192,7 +192,7 @@
     }
    },
    "source": [
-    "As seen in [Chapter 5 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/05_numbers/02_content.ipynb#Goose-Typing), we can use ABCs with the built-in [isinstance() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#isinstance) function to check if an object supports a behavior.\n",
+    "As seen in [Chapter 5 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/main/05_numbers/02_content.ipynb#Goose-Typing), we can use ABCs with the built-in [isinstance() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#isinstance) function to check if an object supports a behavior.\n",
     "\n",
     "So, let's \"ask\" Python if it can loop over `numbers` or `text`."
    ]
@@ -325,7 +325,7 @@
     }
    },
    "source": [
-    "Most of the data types in this chapter and [Chapter 9 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/09_mappings/00_content.ipynb) and [Chapter 10 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/10_arrays/00_content.ipynb) exhibit three [orthogonal <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_wiki.png\">](https://en.wikipedia.org/wiki/Orthogonality) (i.e., \"independent\") behaviors, formalized by ABCs in the [collections.abc <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/collections.abc.html) module as:\n",
+    "Most of the data types in this chapter and [Chapter 9 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/main/09_mappings/00_content.ipynb) and [Chapter 10 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/main/10_arrays/00_content.ipynb) exhibit three [orthogonal <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_wiki.png\">](https://en.wikipedia.org/wiki/Orthogonality) (i.e., \"independent\") behaviors, formalized by ABCs in the [collections.abc <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/collections.abc.html) module as:\n",
     "- `Iterable`: An object may be looped over.\n",
     "- `Container`: An object \"contains\" references to other objects; a \"whole\" is composed of many \"parts.\"\n",
     "- `Sized`: The number of references to other objects, the \"parts,\" is *finite*.\n",
@@ -902,11 +902,11 @@
     }
    },
    "source": [
-    "The data types introduced in this chapter are sequences. Nevertheless, we also look at some data types that are neither collections nor sequences but are still useful to model sequential data in practice in [Chapter 8 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/08_mfr/00_content.ipynb).\n",
+    "The data types introduced in this chapter are sequences. Nevertheless, we also look at some data types that are neither collections nor sequences but are still useful to model sequential data in practice in [Chapter 8 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/main/08_mfr/00_content.ipynb).\n",
     "\n",
     "In Python-related documentations, the terms collection and sequence are heavily used, and the data science practitioner should always think of them in terms of the three or four behaviors they exhibit.\n",
     "\n",
-    "Data types that are collections but not sequences are covered in [Chapter 9 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/09_mappings/00_content.ipynb)."
+    "Data types that are collections but not sequences are covered in [Chapter 9 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/main/09_mappings/00_content.ipynb)."
    ]
   }
  ],
diff --git a/07_sequences/01_content.ipynb b/07_sequences/01_content.ipynb
index fb6d54d..d267d48 100644
--- a/07_sequences/01_content.ipynb
+++ b/07_sequences/01_content.ipynb
@@ -8,7 +8,7 @@
     }
    },
    "source": [
-    "**Note**: Click on \"*Kernel*\" > \"*Restart Kernel and Clear All Outputs*\" in [JupyterLab](https://jupyterlab.readthedocs.io/en/stable/) *before* reading this notebook to reset its output. If you cannot run this file on your machine, you may want to open it [in the cloud <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_mb.png\">](https://mybinder.org/v2/gh/webartifex/intro-to-python/develop?urlpath=lab/tree/07_sequences/01_content.ipynb)."
+    "**Note**: Click on \"*Kernel*\" > \"*Restart Kernel and Clear All Outputs*\" in [JupyterLab](https://jupyterlab.readthedocs.io/en/stable/) *before* reading this notebook to reset its output. If you cannot run this file on your machine, you may want to open it [in the cloud <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_mb.png\">](https://mybinder.org/v2/gh/webartifex/intro-to-python/main?urlpath=lab/tree/07_sequences/01_content.ipynb)."
    ]
   },
   {
@@ -209,7 +209,7 @@
    "source": [
     "Alternatively, we use the built-in [list() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#func-list) constructor to create a `list` object out of any (finite) *iterable* we pass to it as the argument.\n",
     "\n",
-    "For example, we can wrap the [range() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#func-range) built-in with [list() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#func-list): As described in [Chapter 4 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/04_iteration/02_content.ipynb#Containers-vs.-Iterables), `range` objects, like `range(1, 13)` below, are iterable and generate `int` objects \"on the fly\" (i.e., one by one). The [list() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#func-list) around it acts like a `for`-loop and **materializes** twelve `int` objects in memory that then become the elements of the newly created `list` object. [PythonTutor <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](http://pythontutor.com/visualize.html#code=r%20%3D%20range%281,%2013%29%0Al%20%3D%20list%28range%281,%2013%29%29&cumulative=false&curstr=0&heapPrimitives=nevernest&mode=display&origin=opt-frontend.js&py=3&rawInputLstJSON=%5B%5D&textReferences=false) shows this difference visually."
+    "For example, we can wrap the [range() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#func-range) built-in with [list() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#func-list): As described in [Chapter 4 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/main/04_iteration/02_content.ipynb#Containers-vs.-Iterables), `range` objects, like `range(1, 13)` below, are iterable and generate `int` objects \"on the fly\" (i.e., one by one). The [list() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#func-list) around it acts like a `for`-loop and **materializes** twelve `int` objects in memory that then become the elements of the newly created `list` object. [PythonTutor <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](http://pythontutor.com/visualize.html#code=r%20%3D%20range%281,%2013%29%0Al%20%3D%20list%28range%281,%2013%29%29&cumulative=false&curstr=0&heapPrimitives=nevernest&mode=display&origin=opt-frontend.js&py=3&rawInputLstJSON=%5B%5D&textReferences=false) shows this difference visually."
    ]
   },
   {
@@ -1691,7 +1691,7 @@
    "source": [
     "The `list` type is an essential data structure in any real-world Python application, and many typical `list`-related algorithms from computer science theory are already built into it at the C level (cf., the [documentation <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/stdtypes.html#mutable-sequence-types) or the [tutorial <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/tutorial/datastructures.html#more-on-lists) for a full overview; unfortunately, not all methods have direct links). So, understanding and applying the built-in methods of the `list` type not only speeds up the development process but also makes programs significantly faster.\n",
     "\n",
-    "In contrast to the `str` type's methods in [Chapter 6 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/06_text/00_content.ipynb#String-Methods) (e.g., [.upper() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/stdtypes.html#str.upper) or [.lower() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/stdtypes.html#str.lower)), the `list` type's methods that mutate an object do so *in place*. That means they *never* create *new* `list` objects and return `None` to indicate that. So, we must *never* assign the return value of `list` methods to the variable holding the list!\n",
+    "In contrast to the `str` type's methods in [Chapter 6 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/main/06_text/00_content.ipynb#String-Methods) (e.g., [.upper() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/stdtypes.html#str.upper) or [.lower() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/stdtypes.html#str.lower)), the `list` type's methods that mutate an object do so *in place*. That means they *never* create *new* `list` objects and return `None` to indicate that. So, we must *never* assign the return value of `list` methods to the variable holding the list!\n",
     "\n",
     "Let's look at the following `names` example."
    ]
@@ -2029,7 +2029,7 @@
    "source": [
     "The [.sort() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/stdtypes.html#list.sort) method and the [sorted() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#sorted) function sort the elements in `names` in alphabetical order, forward or backward. However, that does *not* hold in general.\n",
     "\n",
-    "We mention above that `list` objects may contain objects of *any* type and even of *mixed* types. Because of that, the sorting is **[delegated <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_wiki.png\">](https://en.wikipedia.org/wiki/Delegation_(object-oriented_programming))** to the elements in a `list` object. In a way, Python \"asks\" the elements in a `list` object to sort themselves. As `names` contains only `str` objects, they are sorted according the the comparison rules explained in [Chapter 6 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/06_text/00_content.ipynb#String-Comparison).\n",
+    "We mention above that `list` objects may contain objects of *any* type and even of *mixed* types. Because of that, the sorting is **[delegated <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_wiki.png\">](https://en.wikipedia.org/wiki/Delegation_(object-oriented_programming))** to the elements in a `list` object. In a way, Python \"asks\" the elements in a `list` object to sort themselves. As `names` contains only `str` objects, they are sorted according the the comparison rules explained in [Chapter 6 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/main/06_text/00_content.ipynb#String-Comparison).\n",
     "\n",
     "To customize the sorting, we pass a keyword-only `key` argument to [.sort() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/stdtypes.html#list.sort) or [sorted() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#sorted), which must be a `function` object accepting *one* positional argument. Then, the elements in the `list` object are passed to that one by one, and the return values are used as the **sort keys**. The `key` argument is also a popular use case for `lambda` expressions.\n",
     "\n",
diff --git a/07_sequences/02_exercises_lists.ipynb b/07_sequences/02_exercises_lists.ipynb
index 2709373..7d5fab5 100644
--- a/07_sequences/02_exercises_lists.ipynb
+++ b/07_sequences/02_exercises_lists.ipynb
@@ -4,7 +4,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "**Note**: Click on \"*Kernel*\" > \"*Restart Kernel and Run All*\" in [JupyterLab](https://jupyterlab.readthedocs.io/en/stable/) *after* finishing the exercises to ensure that your solution runs top to bottom *without* any errors. If you cannot run this file on your machine, you may want to open it [in the cloud <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_mb.png\">](https://mybinder.org/v2/gh/webartifex/intro-to-python/develop?urlpath=lab/tree/07_sequences/02_exercises.ipynb)."
+    "**Note**: Click on \"*Kernel*\" > \"*Restart Kernel and Run All*\" in [JupyterLab](https://jupyterlab.readthedocs.io/en/stable/) *after* finishing the exercises to ensure that your solution runs top to bottom *without* any errors. If you cannot run this file on your machine, you may want to open it [in the cloud <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_mb.png\">](https://mybinder.org/v2/gh/webartifex/intro-to-python/main?urlpath=lab/tree/07_sequences/02_exercises.ipynb)."
    ]
   },
   {
@@ -18,7 +18,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "The exercises below assume that you have read the [second part <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/07_sequences/01_content.ipynb) of Chapter 7.\n",
+    "The exercises below assume that you have read the [second part <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/main/07_sequences/01_content.ipynb) of Chapter 7.\n",
     "\n",
     "The `...`'s in the code cells indicate where you need to fill in code snippets. The number of `...`'s within a code cell give you a rough idea of how many lines of code are needed to solve the task. You should not need to create any additional code cells for your final solution. However, you may want to use temporary code cells to try out some ideas."
    ]
diff --git a/07_sequences/03_content.ipynb b/07_sequences/03_content.ipynb
index 817951f..bf7e929 100644
--- a/07_sequences/03_content.ipynb
+++ b/07_sequences/03_content.ipynb
@@ -8,7 +8,7 @@
     }
    },
    "source": [
-    "**Note**: Click on \"*Kernel*\" > \"*Restart Kernel and Clear All Outputs*\" in [JupyterLab](https://jupyterlab.readthedocs.io/en/stable/) *before* reading this notebook to reset its output. If you cannot run this file on your machine, you may want to open it [in the cloud <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_mb.png\">](https://mybinder.org/v2/gh/webartifex/intro-to-python/develop?urlpath=lab/tree/07_sequences/03_content.ipynb)."
+    "**Note**: Click on \"*Kernel*\" > \"*Restart Kernel and Clear All Outputs*\" in [JupyterLab](https://jupyterlab.readthedocs.io/en/stable/) *before* reading this notebook to reset its output. If you cannot run this file on your machine, you may want to open it [in the cloud <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_mb.png\">](https://mybinder.org/v2/gh/webartifex/intro-to-python/main?urlpath=lab/tree/07_sequences/03_content.ipynb)."
    ]
   },
   {
@@ -172,7 +172,7 @@
     "\n",
     "The revised `add_xyz()` function below is more natural to reason about as it does *not* modify the passed in `arg` internally. [PythonTutor <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](http://pythontutor.com/visualize.html#code=letters%20%3D%20%5B%22a%22,%20%22b%22,%20%22c%22%5D%0A%0Adef%20add_xyz%28arg%29%3A%0A%20%20%20%20new_arg%20%3D%20arg%5B%3A%5D%0A%20%20%20%20new_arg.extend%28%5B%22x%22,%20%22y%22,%20%22z%22%5D%29%0A%20%20%20%20return%20new_arg%0A%0Aletters_with_xyz%20%3D%20add_xyz%28letters%29&cumulative=false&curstr=0&heapPrimitives=nevernest&mode=display&origin=opt-frontend.js&py=3&rawInputLstJSON=%5B%5D&textReferences=false) shows that as well. This approach is following the **[functional programming <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_wiki.png\">](https://en.wikipedia.org/wiki/Functional_programming)** paradigm that is going through a \"renaissance\" currently. Two essential characteristics of functional programming are that a function *never* changes its inputs and *always* returns the same output given the same inputs.\n",
     "\n",
-    "For a beginner, it is probably better to stick to this idea and not change any arguments as the original `add_xyz()` above. However, functions that modify and return the argument passed in are an important aspect of object-oriented programming, as explained in [Chapter 11 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/11_classes/00_content.ipynb)."
+    "For a beginner, it is probably better to stick to this idea and not change any arguments as the original `add_xyz()` above. However, functions that modify and return the argument passed in are an important aspect of object-oriented programming, as explained in [Chapter 11 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/main/11_classes/00_content.ipynb)."
    ]
   },
   {
@@ -1469,7 +1469,7 @@
     }
    },
    "source": [
-    "In the \"*List Operations*\" section in the [second part <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/07_sequences/01_content.ipynb#List-Operations) of this chapter, the `*` symbol **unpacks** the elements of a `list` object into another one. This idea of *iterable unpacking* is built into Python at various places, even *without* the `*` symbol.\n",
+    "In the \"*List Operations*\" section in the [second part <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/main/07_sequences/01_content.ipynb#List-Operations) of this chapter, the `*` symbol **unpacks** the elements of a `list` object into another one. This idea of *iterable unpacking* is built into Python at various places, even *without* the `*` symbol.\n",
     "\n",
     "For example, we may write variables on the left-hand side of a `=` statement in a literal `tuple` style. Then,  any *finite* iterable on the right-hand side is unpacked. So, `numbers` is unpacked into *twelve* variables below."
    ]
@@ -2041,7 +2041,7 @@
     }
    },
    "source": [
-    "Unpacking allows us to rewrite the iterative `fibonacci()` function from [Chapter 4 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/04_iteration/02_content.ipynb#\"Hard-at-first-Glance\"-Example:-Fibonacci-Numbers-%28revisited%29) in a concise way."
+    "Unpacking allows us to rewrite the iterative `fibonacci()` function from [Chapter 4 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/main/04_iteration/02_content.ipynb#\"Hard-at-first-Glance\"-Example:-Fibonacci-Numbers-%28revisited%29) in a concise way."
    ]
   },
   {
@@ -2376,7 +2376,7 @@
     }
    },
    "source": [
-    "In the \"*Packing & Unpacking with Functions*\" [exercise <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/07_sequences/04_exercises.ipynb), we look at `product()` in more detail.\n",
+    "In the \"*Packing & Unpacking with Functions*\" [exercise <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/main/07_sequences/04_exercises.ipynb), we look at `product()` in more detail.\n",
     "\n",
     "While we needed to unpack `one_hundred` above to avoid the semantic error, unpacking an argument in a function call may also be a convenience in general. For example, to print the elements of `one_hundred` in one line, we need to use a `for` statement, until now. With unpacking, we get away *without* a loop."
    ]
diff --git a/07_sequences/04_exercises_un-packing.ipynb b/07_sequences/04_exercises_un-packing.ipynb
index 6df6a33..3f783a2 100644
--- a/07_sequences/04_exercises_un-packing.ipynb
+++ b/07_sequences/04_exercises_un-packing.ipynb
@@ -4,7 +4,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "**Note**: Click on \"*Kernel*\" > \"*Restart Kernel and Run All*\" in [JupyterLab](https://jupyterlab.readthedocs.io/en/stable/) *after* finishing the exercises to ensure that your solution runs top to bottom *without* any errors. If you cannot run this file on your machine, you may want to open it [in the cloud <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_mb.png\">](https://mybinder.org/v2/gh/webartifex/intro-to-python/develop?urlpath=lab/tree/07_sequences/04_exercises.ipynb)."
+    "**Note**: Click on \"*Kernel*\" > \"*Restart Kernel and Run All*\" in [JupyterLab](https://jupyterlab.readthedocs.io/en/stable/) *after* finishing the exercises to ensure that your solution runs top to bottom *without* any errors. If you cannot run this file on your machine, you may want to open it [in the cloud <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_mb.png\">](https://mybinder.org/v2/gh/webartifex/intro-to-python/main?urlpath=lab/tree/07_sequences/04_exercises.ipynb)."
    ]
   },
   {
@@ -18,7 +18,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "The exercises below assume that you have read the [third part <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/07_sequences/03_content.ipynb) of Chapter 7.\n",
+    "The exercises below assume that you have read the [third part <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/main/07_sequences/03_content.ipynb) of Chapter 7.\n",
     "\n",
     "The `...`'s in the code cells indicate where you need to fill in code snippets. The number of `...`'s within a code cell give you a rough idea of how many lines of code are needed to solve the task. You should not need to create any additional code cells for your final solution. However, you may want to use temporary code cells to try out some ideas."
    ]
@@ -34,7 +34,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "In the \"*Function Definitions & Calls*\" section in [Chapter 7 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/07_sequences/03_content.ipynb#Function-Definitions-&-Calls), we define the following function `product()`. In this exercise, you will improve it by making it more \"user-friendly.\""
+    "In the \"*Function Definitions & Calls*\" section in [Chapter 7 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/main/07_sequences/03_content.ipynb#Function-Definitions-&-Calls), we define the following function `product()`. In this exercise, you will improve it by making it more \"user-friendly.\""
    ]
   },
   {
@@ -128,7 +128,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "In [Chapter 7 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/07_sequences/00_content.ipynb#Function-Definitions-&-Calls), we also pass a `list` object, like `one_hundred`, to `product()`, and *no* exception is raised."
+    "In [Chapter 7 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/main/07_sequences/00_content.ipynb#Function-Definitions-&-Calls), we also pass a `list` object, like `one_hundred`, to `product()`, and *no* exception is raised."
    ]
   },
   {
@@ -153,7 +153,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "**Q3**: What is wrong with that? What *kind* of error (cf., [Chapter 1 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/01_elements/00_content.ipynb#Formal-vs.-Natural-Languages)) is that conceptually? Describe precisely what happens to the passed in `one_hundred` in every line within `product()`!"
+    "**Q3**: What is wrong with that? What *kind* of error (cf., [Chapter 1 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/main/01_elements/00_content.ipynb#Formal-vs.-Natural-Languages)) is that conceptually? Describe precisely what happens to the passed in `one_hundred` in every line within `product()`!"
    ]
   },
   {
@@ -331,7 +331,7 @@
    "source": [
     "**Q7**: Rewrite `product()` so that it takes a *keyword-only* argument `start`, defaulting to the above *default* or *start* value, and use `start` internally instead of `result`!\n",
     "\n",
-    "Hint: Remember that a *keyword-only* argument is any parameter specified in a function's header line after the first and only `*` (cf., [Chapter 2 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/02_functions/00_content.ipynb#Keyword-only-Arguments))."
+    "Hint: Remember that a *keyword-only* argument is any parameter specified in a function's header line after the first and only `*` (cf., [Chapter 2 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/main/02_functions/00_content.ipynb#Keyword-only-Arguments))."
    ]
   },
   {
@@ -544,7 +544,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "**Side Note**: Above, we make `product()` work with a single *collection* type argument instead of a *sequence* type to keep it more generic: For example, we can pass in a `set` object, like `{2, 5, 10}` below, and `product()` continues to work correctly. The `set` type is introducted in [Chapter 9 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/09_mappings/00_content.ipynb#The-set-Type), and one essential difference to the `list` type is that objects of type `set` have *no* order regarding their elements. So, even though `[2, 5, 10]` and `{2, 5, 10}` look almost the same, the order implied in the literal notation gets lost in memory!"
+    "**Side Note**: Above, we make `product()` work with a single *collection* type argument instead of a *sequence* type to keep it more generic: For example, we can pass in a `set` object, like `{2, 5, 10}` below, and `product()` continues to work correctly. The `set` type is introducted in [Chapter 9 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/main/09_mappings/00_content.ipynb#The-set-Type), and one essential difference to the `list` type is that objects of type `set` have *no* order regarding their elements. So, even though `[2, 5, 10]` and `{2, 5, 10}` look almost the same, the order implied in the literal notation gets lost in memory!"
    ]
   },
   {
diff --git a/07_sequences/05_appendix.ipynb b/07_sequences/05_appendix.ipynb
index b7fb1f6..f34771d 100644
--- a/07_sequences/05_appendix.ipynb
+++ b/07_sequences/05_appendix.ipynb
@@ -8,7 +8,7 @@
     }
    },
    "source": [
-    "**Note**: Click on \"*Kernel*\" > \"*Restart Kernel and Clear All Outputs*\" in [JupyterLab](https://jupyterlab.readthedocs.io/en/stable/) *before* reading this notebook to reset its output. If you cannot run this file on your machine, you may want to open it [in the cloud <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_mb.png\">](https://mybinder.org/v2/gh/webartifex/intro-to-python/develop?urlpath=lab/tree/07_sequences/05_appendix.ipynb)."
+    "**Note**: Click on \"*Kernel*\" > \"*Restart Kernel and Clear All Outputs*\" in [JupyterLab](https://jupyterlab.readthedocs.io/en/stable/) *before* reading this notebook to reset its output. If you cannot run this file on your machine, you may want to open it [in the cloud <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_mb.png\">](https://mybinder.org/v2/gh/webartifex/intro-to-python/main?urlpath=lab/tree/07_sequences/05_appendix.ipynb)."
    ]
   },
   {
@@ -30,7 +30,7 @@
     }
    },
    "source": [
-    "In the [third part <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/07_sequences/03_content.ipynb#Tuples-are-like-\"Immutable-Lists\") of the chapter, we proposed the idea that `tuple` objects are like \"immutable lists.\" Often, however, we use `tuple` objects to represent a **record** of related **fields**. Then, each element has a *semantic* meaning (i.e., a descriptive name).\n",
+    "In the [third part <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/main/07_sequences/03_content.ipynb#Tuples-are-like-\"Immutable-Lists\") of the chapter, we proposed the idea that `tuple` objects are like \"immutable lists.\" Often, however, we use `tuple` objects to represent a **record** of related **fields**. Then, each element has a *semantic* meaning (i.e., a descriptive name).\n",
     "\n",
     "As an example, think of a spreadsheet with information on students in a course. Each row represents a record and holds all the data associated with an individual student. The columns (e.g., matriculation number, first name, last name) are the fields that may come as *different* data types (e.g., `int` for the matriculation number, `str` for the names).\n",
     "\n",
@@ -82,7 +82,7 @@
     }
    },
    "source": [
-    "A better way is to create a *custom* data type. While that is covered in depth in [Chapter 11 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/11_classes/00_content.ipynb), the [collections <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/collections.html) module in the [standard library <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/index.html) provides a [namedtuple() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/collections.html#collections.namedtuple) **factory function** that creates \"simple\" custom data types on top of the standard `tuple` type."
+    "A better way is to create a *custom* data type. While that is covered in depth in [Chapter 11 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/main/11_classes/00_content.ipynb), the [collections <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/collections.html) module in the [standard library <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/index.html) provides a [namedtuple() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/collections.html#collections.namedtuple) **factory function** that creates \"simple\" custom data types on top of the standard `tuple` type."
    ]
   },
   {
diff --git a/07_sequences/07_review.ipynb b/07_sequences/07_review.ipynb
index 44b7766..57927bd 100644
--- a/07_sequences/07_review.ipynb
+++ b/07_sequences/07_review.ipynb
@@ -11,7 +11,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "The questions below assume that you have read the [first <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/07_sequences/00_content.ipynb), [second <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/07_sequences/01_content.ipynb), and the [third <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/07_sequences/03_content.ipynb) part of Chapter 7. Some questions regard the [Appendix <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/07_sequences/05_appendix.ipynb); that is indicated with a **\\***.\n",
+    "The questions below assume that you have read the [first <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/main/07_sequences/00_content.ipynb), [second <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/main/07_sequences/01_content.ipynb), and the [third <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/main/07_sequences/03_content.ipynb) part of Chapter 7. Some questions regard the [Appendix <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/main/07_sequences/05_appendix.ipynb); that is indicated with a **\\***.\n",
     "\n",
     "Be concise in your answers! Most questions can be answered in *one* sentence."
    ]
@@ -34,7 +34,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "**Q1**: We have seen **containers** and **iterables** before in [Chapter 4 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/04_iteration/02_content.ipynb#Containers-vs.-Iterables). How do they relate to **sequences**? "
+    "**Q1**: We have seen **containers** and **iterables** before in [Chapter 4 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/main/04_iteration/02_content.ipynb#Containers-vs.-Iterables). How do they relate to **sequences**? "
    ]
   },
   {
diff --git a/08_mfr/00_content.ipynb b/08_mfr/00_content.ipynb
index bcb7f04..4a1ae86 100644
--- a/08_mfr/00_content.ipynb
+++ b/08_mfr/00_content.ipynb
@@ -8,7 +8,7 @@
     }
    },
    "source": [
-    "**Note**: Click on \"*Kernel*\" > \"*Restart Kernel and Clear All Outputs*\" in [JupyterLab](https://jupyterlab.readthedocs.io/en/stable/) *before* reading this notebook to reset its output. If you cannot run this file on your machine, you may want to open it [in the cloud <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_mb.png\">](https://mybinder.org/v2/gh/webartifex/intro-to-python/develop?urlpath=lab/tree/08_mfr/00_content.ipynb)."
+    "**Note**: Click on \"*Kernel*\" > \"*Restart Kernel and Clear All Outputs*\" in [JupyterLab](https://jupyterlab.readthedocs.io/en/stable/) *before* reading this notebook to reset its output. If you cannot run this file on your machine, you may want to open it [in the cloud <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_mb.png\">](https://mybinder.org/v2/gh/webartifex/intro-to-python/main?urlpath=lab/tree/08_mfr/00_content.ipynb)."
    ]
   },
   {
@@ -32,9 +32,9 @@
    "source": [
     "In this chapter, we continue the study of sequential data by looking at memory efficient ways to process the elements in a sequence. That is an important topic for the data science practitioner who must be able to work with data that does *not* fit into a single computer's memory.\n",
     "\n",
-    "As shown in [Chapter 4 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/04_iteration/02_content.ipynb#Containers-vs.-Iterables), both the `list` objects `[0, 1, 2, 3, 4]` and `[1, 3, 5, 7, 9]` on the one side and the `range` objects `range(5)` and `range(1, 10, 2)` on the other side allow us to loop over the same numbers. However, the latter two only create *one* `int` object in every iteration while the former two create *all* `int` objects before the loop even starts. In this aspect, we consider `range` objects to be \"rules\" in memory that know how to calculate the numbers *without* calculating them.\n",
+    "As shown in [Chapter 4 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/main/04_iteration/02_content.ipynb#Containers-vs.-Iterables), both the `list` objects `[0, 1, 2, 3, 4]` and `[1, 3, 5, 7, 9]` on the one side and the `range` objects `range(5)` and `range(1, 10, 2)` on the other side allow us to loop over the same numbers. However, the latter two only create *one* `int` object in every iteration while the former two create *all* `int` objects before the loop even starts. In this aspect, we consider `range` objects to be \"rules\" in memory that know how to calculate the numbers *without* calculating them.\n",
     "\n",
-    "In [Chapter 7 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/07_sequences/01_content.ipynb#The-list-Type), we see how the built-in [list() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#func-list) constructor **materializes** the `range(1, 13)` object into the `list` object `[1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12]`. In other words, we make `range(1, 13)` calculate *all* numbers at once and store them in a `list` object for further processing.\n",
+    "In [Chapter 7 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/main/07_sequences/01_content.ipynb#The-list-Type), we see how the built-in [list() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#func-list) constructor **materializes** the `range(1, 13)` object into the `list` object `[1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12]`. In other words, we make `range(1, 13)` calculate *all* numbers at once and store them in a `list` object for further processing.\n",
     "\n",
     "In many cases, however, it is not necessary to do that, and, in this chapter, we look at other types of \"rules\" in memory and how we can compose different \"rules\" together to implement bigger computations.\n",
     "\n",
@@ -779,7 +779,7 @@
     }
    },
    "source": [
-    "Using the [map() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#map) and [filter() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#filter) built-ins, we can quickly switch the order: Filter first and then transform the remaining elements. This variant equals the \"*A simple Filter*\" example in [Chapter 4 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/04_iteration/03_content.ipynb#Example:-A-simple-Filter). On the contrary, code with `for`-loops and `if` statements is more tedious to adapt. Additionally, `map` and `filter` objects loop \"at the C level\" and are a lot faster because of that. Because of that, experienced Pythonistas tend to *not* use explicit `for`-loops so often."
+    "Using the [map() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#map) and [filter() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#filter) built-ins, we can quickly switch the order: Filter first and then transform the remaining elements. This variant equals the \"*A simple Filter*\" example in [Chapter 4 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/main/04_iteration/03_content.ipynb#Example:-A-simple-Filter). On the contrary, code with `for`-loops and `if` statements is more tedious to adapt. Additionally, `map` and `filter` objects loop \"at the C level\" and are a lot faster because of that. Because of that, experienced Pythonistas tend to *not* use explicit `for`-loops so often."
    ]
   },
   {
@@ -1129,7 +1129,7 @@
     "\n",
     "Often, such functions are used *only once* in a program. However, the primary purpose of functions is to *reuse* them. In such cases, it makes more sense to define them \"anonymously\" right at the position where the first argument goes.\n",
     "\n",
-    "As mentioned in [Chapter 2 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/02_functions/00_content.ipynb#Anonymous-Functions), we use `lambda` expressions to create `function` objects *without* a name referencing them.\n",
+    "As mentioned in [Chapter 2 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/main/02_functions/00_content.ipynb#Anonymous-Functions), we use `lambda` expressions to create `function` objects *without* a name referencing them.\n",
     "\n",
     "So, the above `sum_alt()` function could be rewritten as a `lambda` expression like so ..."
    ]
diff --git a/08_mfr/01_content.ipynb b/08_mfr/01_content.ipynb
index a92bd6e..38300c0 100644
--- a/08_mfr/01_content.ipynb
+++ b/08_mfr/01_content.ipynb
@@ -8,7 +8,7 @@
     }
    },
    "source": [
-    "**Note**: Click on \"*Kernel*\" > \"*Restart Kernel and Clear All Outputs*\" in [JupyterLab](https://jupyterlab.readthedocs.io/en/stable/) *before* reading this notebook to reset its output. If you cannot run this file on your machine, you may want to open it [in the cloud <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_mb.png\">](https://mybinder.org/v2/gh/webartifex/intro-to-python/develop?urlpath=lab/tree/08_mfr/01_content.ipynb)."
+    "**Note**: Click on \"*Kernel*\" > \"*Restart Kernel and Clear All Outputs*\" in [JupyterLab](https://jupyterlab.readthedocs.io/en/stable/) *before* reading this notebook to reset its output. If you cannot run this file on your machine, you may want to open it [in the cloud <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_mb.png\">](https://mybinder.org/v2/gh/webartifex/intro-to-python/main?urlpath=lab/tree/08_mfr/01_content.ipynb)."
    ]
   },
   {
@@ -30,7 +30,7 @@
     }
    },
    "source": [
-    "After introducing the Map-Filter-Reduce paradigm in the [first part <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/08_mfr/00_content.ipynb) of this chapter, we first see how `list` comprehensions can replace the [map() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#map) and [filter() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#filter) built-ins in many cases. Then, we learn how `generator` expressions are like `list` comprehensions *without* using the memory. We end this part with a short discussion of the built-in [all() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#all) and [any() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#any) functions."
+    "After introducing the Map-Filter-Reduce paradigm in the [first part <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/main/08_mfr/00_content.ipynb) of this chapter, we first see how `list` comprehensions can replace the [map() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#map) and [filter() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#filter) built-ins in many cases. Then, we learn how `generator` expressions are like `list` comprehensions *without* using the memory. We end this part with a short discussion of the built-in [all() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#all) and [any() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#any) functions."
    ]
   },
   {
@@ -52,7 +52,7 @@
     }
    },
    "source": [
-    "Consider again the original \"*A simple Filter*\" example from [Chapter 4 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/04_iteration/03_content.ipynb#Example:-A-simple-Filter), re-written such that both the mapping and the filtering are done in *one* `for`-loop instead of the *two* above."
+    "Consider again the original \"*A simple Filter*\" example from [Chapter 4 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/main/04_iteration/03_content.ipynb#Example:-A-simple-Filter), re-written such that both the mapping and the filtering are done in *one* `for`-loop instead of the *two* above."
    ]
   },
   {
@@ -156,7 +156,7 @@
    "source": [
     "A list comprehension may always be used in a place where otherwise a `list` object would work.\n",
     "\n",
-    "For example, let's rewrite the \"*A simple Filter*\" example from [Chapter 4 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/04_iteration/03_content.ipynb#Example:-A-simple-Filter) in just one line. As a caveat, the code below *materializes* all elements in memory *before* summing them up, and may, therefore, cause a `MemoryError` when executed with a bigger `numbers` list. We see with [PythonTutor <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](http://pythontutor.com/visualize.html#code=numbers%20%3D%20range%281,%2013%29%0Aresult%20%3D%20sum%28%5B%28n%20**%202%29%20%2B%201%20for%20n%20in%20numbers%20if%20n%20%25%202%20%3D%3D%200%5D%29&cumulative=false&curstr=0&heapPrimitives=nevernest&mode=display&origin=opt-frontend.js&py=3&rawInputLstJSON=%5B%5D&textReferences=false) how a `list` object exists in memory at step 17 and then \"gets lost\" right after. As the next section shows, this downside may be mitigated."
+    "For example, let's rewrite the \"*A simple Filter*\" example from [Chapter 4 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/main/04_iteration/03_content.ipynb#Example:-A-simple-Filter) in just one line. As a caveat, the code below *materializes* all elements in memory *before* summing them up, and may, therefore, cause a `MemoryError` when executed with a bigger `numbers` list. We see with [PythonTutor <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](http://pythontutor.com/visualize.html#code=numbers%20%3D%20range%281,%2013%29%0Aresult%20%3D%20sum%28%5B%28n%20**%202%29%20%2B%201%20for%20n%20in%20numbers%20if%20n%20%25%202%20%3D%3D%200%5D%29&cumulative=false&curstr=0&heapPrimitives=nevernest&mode=display&origin=opt-frontend.js&py=3&rawInputLstJSON=%5B%5D&textReferences=false) how a `list` object exists in memory at step 17 and then \"gets lost\" right after. As the next section shows, this downside may be mitigated."
    ]
   },
   {
@@ -639,7 +639,7 @@
     }
    },
    "source": [
-    "To find the overall product, we *unpack* the list comprehension into the `product()` function from the \"*Function Definitions & Calls*\" sub-section in [Chapter 7 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/07_sequences/03_content.ipynb#Function-Definitions-&-Calls)."
+    "To find the overall product, we *unpack* the list comprehension into the `product()` function from the \"*Function Definitions & Calls*\" sub-section in [Chapter 7 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/main/07_sequences/03_content.ipynb#Function-Definitions-&-Calls)."
    ]
   },
   {
@@ -766,7 +766,7 @@
    "source": [
     "Because of the high memory consumption, Pythonistas avoid materialized `list` objects, and, thus, also `list` comprehensions, whenever possible. Instead, they prefer to work with **[`generator` expressions <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/reference/expressions.html#generator-expressions)**. Syntactically, they work like list comprehensions except that parentheses, `(` and `)`, replace brackets, `[` and `]`.\n",
     "\n",
-    "Let's go back to the original \"*A simple Filter*\" example from [Chapter 4 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/04_iteration/03_content.ipynb#Example:-A-simple-Filter) one more time, apply the transformation $y := x^2 + 1$ to all even `numbers`, and sum them up."
+    "Let's go back to the original \"*A simple Filter*\" example from [Chapter 4 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/main/04_iteration/03_content.ipynb#Example:-A-simple-Filter) one more time, apply the transformation $y := x^2 + 1$ to all even `numbers`, and sum them up."
    ]
   },
   {
@@ -1050,7 +1050,7 @@
     }
    },
    "source": [
-    "`generator` objects work just like the `map` and `filter` objects in the [first part <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/08_mfr/00_content.ipynb) of this chapter. So, with the [next() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#next) function, we can generate elements one by one."
+    "`generator` objects work just like the `map` and `filter` objects in the [first part <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/main/08_mfr/00_content.ipynb) of this chapter. So, with the [next() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#next) function, we can generate elements one by one."
    ]
   },
   {
@@ -1564,7 +1564,7 @@
    "source": [
     "Now, the first of the two alternative solutions may be more appealing to many readers. In general, many practitioners seem to dislike `lambda` expressions.\n",
     "\n",
-    "In the first solution, we *unpack* the elements produced by `(1 + (x / y) for x in first for y in second)` into the `product()` function from the \"*Function Definitions & Calls*\" sub-section in [Chapter 7 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/07_sequences/03_content.ipynb#Function-Definitions-&-Calls). However, inside `product()`, the elements are *packed* into `args`, a *materialized* `tuple` object! So, all the memory efficiency gained by using a generator expression is lost! [PythonTutor <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](http://pythontutor.com/visualize.html#code=def%20product%28*args%29%3A%0A%20%20%20%20result%20%3D%20args%5B0%5D%0A%20%20%20%20for%20arg%20in%20args%5B1%3A%5D%3A%0A%20%20%20%20%20%20%20%20result%20*%3D%20arg%0A%20%20%20%20return%20result%0A%0Afirst%20%3D%20range%2810,%2031,%2010%29%0Asecond%20%3D%20range%2840,%2061,%2010%29%0A%0Aresult%20%3D%20product%28*%281%20%2B%20%28x%20/%20y%29%20for%20x%20in%20first%20for%20y%20in%20second%29%29&cumulative=false&curstr=0&heapPrimitives=nevernest&mode=display&origin=opt-frontend.js&py=3&rawInputLstJSON=%5B%5D&textReferences=false) shows how a `tuple` object exists in steps 38-58."
+    "In the first solution, we *unpack* the elements produced by `(1 + (x / y) for x in first for y in second)` into the `product()` function from the \"*Function Definitions & Calls*\" sub-section in [Chapter 7 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/main/07_sequences/03_content.ipynb#Function-Definitions-&-Calls). However, inside `product()`, the elements are *packed* into `args`, a *materialized* `tuple` object! So, all the memory efficiency gained by using a generator expression is lost! [PythonTutor <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](http://pythontutor.com/visualize.html#code=def%20product%28*args%29%3A%0A%20%20%20%20result%20%3D%20args%5B0%5D%0A%20%20%20%20for%20arg%20in%20args%5B1%3A%5D%3A%0A%20%20%20%20%20%20%20%20result%20*%3D%20arg%0A%20%20%20%20return%20result%0A%0Afirst%20%3D%20range%2810,%2031,%2010%29%0Asecond%20%3D%20range%2840,%2061,%2010%29%0A%0Aresult%20%3D%20product%28*%281%20%2B%20%28x%20/%20y%29%20for%20x%20in%20first%20for%20y%20in%20second%29%29&cumulative=false&curstr=0&heapPrimitives=nevernest&mode=display&origin=opt-frontend.js&py=3&rawInputLstJSON=%5B%5D&textReferences=false) shows how a `tuple` object exists in steps 38-58."
    ]
   },
   {
@@ -1656,13 +1656,13 @@
     }
    },
    "source": [
-    "With the new concepts in this chapter, let's rewrite the book's introductory \"*Averaging all even Numbers in a List*\" example from [Chapter 1 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/01_elements/00_content.ipynb#Example:-Averaging-all-even-Numbers-in-a-List) such that it efficiently handles a large sequence of numbers. We continue from its latest implementation, the `average_evens()` function in the \"*Keyword-only Arguments*\" section in [Chapter 2 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/02_functions/00_content.ipynb#Keyword-only-Arguments).\n",
+    "With the new concepts in this chapter, let's rewrite the book's introductory \"*Averaging all even Numbers in a List*\" example from [Chapter 1 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/main/01_elements/00_content.ipynb#Example:-Averaging-all-even-Numbers-in-a-List) such that it efficiently handles a large sequence of numbers. We continue from its latest implementation, the `average_evens()` function in the \"*Keyword-only Arguments*\" section in [Chapter 2 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/main/02_functions/00_content.ipynb#Keyword-only-Arguments).\n",
     "\n",
     "We assume that `average_evens()` is called with a *finite* and *iterable* object that generates a **stream** of numeric objects that can be cast as `int` objects. After all, the idea of even and odd numbers makes sense only in the context of whole numbers.\n",
     "\n",
     "The `generator` expression `(round(n) for n in numbers)` implements the type casting, and, when it is evaluated during a function call, *nothing* happens except that a `generator` object is assigned to `integers`. Then, with the [reduce() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functools.html#functools.reduce) function from the [functools <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functools.html) module, we *simultaneously* add up *and* count the even numbers with the `generator` object to which the inner `generator` expression `((n, 1) for n in integers if n % 2 == 0)` evaluates to. That `generator` object takes the `integers` generator as its source and produces `tuple` objects consisting of the next *even* number in line and `1`. Two such `tuple` objects are then iteratively passed to the `function` object to which the `lambda` expression evaluates to. `x` represents the total and the count of the even numbers processed so far, while `y`'s first element, `y[0]`, is the next *even* number to be added to the running total. The result of calling [reduce() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functools.html#functools.reduce) is again a `tuple` object, namely the final `total` and `count`. Lastly, we calculate the simple average and scale it.\n",
     "\n",
-    "In summary, this implementation of `average_evens()` does *not* keep materialized `list` objects internally like its predecessors in [Chapter 2 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/02_functions/00_content.ipynb) but processes the elements of the `numbers` argument on a one-by-one basis."
+    "In summary, this implementation of `average_evens()` does *not* keep materialized `list` objects internally like its predecessors in [Chapter 2 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/main/02_functions/00_content.ipynb) but processes the elements of the `numbers` argument on a one-by-one basis."
    ]
   },
   {
@@ -2040,7 +2040,7 @@
     }
    },
    "source": [
-    "[all() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#all) can be viewed as syntactic sugar replacing a `for`-loop: Internally, [all() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#all) implements the *short-circuiting* strategy explained in [Chapter 3 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/03_conditionals/00_content.ipynb#Short-Circuiting), and we mimic that by testing for the *opposite* condition in the `if` statement and stopping the `for`-loop early with the `break` statement. In the worst case, if `threshold` were, for example, `150`, we would loop over *all* elements in the `iterable` argument, which must be *finite* for the code to work. So, [all() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#all) is a *linear search* in disguise."
+    "[all() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#all) can be viewed as syntactic sugar replacing a `for`-loop: Internally, [all() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#all) implements the *short-circuiting* strategy explained in [Chapter 3 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/main/03_conditionals/00_content.ipynb#Short-Circuiting), and we mimic that by testing for the *opposite* condition in the `if` statement and stopping the `for`-loop early with the `break` statement. In the worst case, if `threshold` were, for example, `150`, we would loop over *all* elements in the `iterable` argument, which must be *finite* for the code to work. So, [all() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#all) is a *linear search* in disguise."
    ]
   },
   {
diff --git a/08_mfr/02_exercises_outliers.ipynb b/08_mfr/02_exercises_outliers.ipynb
index f6d81f8..1ad6876 100644
--- a/08_mfr/02_exercises_outliers.ipynb
+++ b/08_mfr/02_exercises_outliers.ipynb
@@ -4,7 +4,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "**Note**: Click on \"*Kernel*\" > \"*Restart Kernel and Run All*\" in [JupyterLab](https://jupyterlab.readthedocs.io/en/stable/) *after* finishing the exercises to ensure that your solution runs top to bottom *without* any errors. If you cannot run this file on your machine, you may want to open it [in the cloud <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_mb.png\">](https://mybinder.org/v2/gh/webartifex/intro-to-python/develop?urlpath=lab/tree/08_mfr/02_exercises.ipynb)."
+    "**Note**: Click on \"*Kernel*\" > \"*Restart Kernel and Run All*\" in [JupyterLab](https://jupyterlab.readthedocs.io/en/stable/) *after* finishing the exercises to ensure that your solution runs top to bottom *without* any errors. If you cannot run this file on your machine, you may want to open it [in the cloud <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_mb.png\">](https://mybinder.org/v2/gh/webartifex/intro-to-python/main?urlpath=lab/tree/08_mfr/02_exercises.ipynb)."
    ]
   },
   {
@@ -18,7 +18,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "The exercises below assume that you have read the [first <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/08_mfr/00_content.ipynb) and [second <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/08_mfr/01_content.ipynb) part of Chapter 8.\n",
+    "The exercises below assume that you have read the [first <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/main/08_mfr/00_content.ipynb) and [second <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/main/08_mfr/01_content.ipynb) part of Chapter 8.\n",
     "\n",
     "The `...`'s in the code cells indicate where you need to fill in code snippets. The number of `...`'s within a code cell give you a rough idea of how many lines of code are needed to solve the task. You should not need to create any additional code cells for your final solution. However, you may want to use temporary code cells to try out some ideas."
    ]
@@ -335,7 +335,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "We provide a `stream` module with a `data` object that models an *infinite* **stream** of data (cf., the [*stream.py* <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_gh.png\">](https://github.com/webartifex/intro-to-python/blob/develop/08_mfr/stream.py) file in the repository)."
+    "We provide a `stream` module with a `data` object that models an *infinite* **stream** of data (cf., the [*stream.py* <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_gh.png\">](https://github.com/webartifex/intro-to-python/blob/main/08_mfr/stream.py) file in the repository)."
    ]
   },
   {
@@ -574,7 +574,7 @@
    "source": [
     "**Q10.2**: Just as in `standardized()`, write a `generator` expression that produces z-scores one by one! However, instead of just generating a z-score, the resulting `generator` object should produce `tuple` objects consisting of a \"raw\" number from `data` and its z-score.\n",
     "\n",
-    "Hint: Look at the revisited \"*Averaging Even Numbers*\" example in [Chapter 7 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/07_sequences/01_content.ipynb#Example:-Averaging-all-even-Numbers-in-a-List-%28revisited%29) for some inspiration, which also contains a `generator` expression producing `tuple` objects."
+    "Hint: Look at the revisited \"*Averaging Even Numbers*\" example in [Chapter 7 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/main/07_sequences/01_content.ipynb#Example:-Averaging-all-even-Numbers-in-a-List-%28revisited%29) for some inspiration, which also contains a `generator` expression producing `tuple` objects."
    ]
   },
   {
diff --git a/08_mfr/03_exercises_un-packing.ipynb b/08_mfr/03_exercises_un-packing.ipynb
index d4b0518..5723ec6 100644
--- a/08_mfr/03_exercises_un-packing.ipynb
+++ b/08_mfr/03_exercises_un-packing.ipynb
@@ -4,7 +4,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "**Note**: Click on \"*Kernel*\" > \"*Restart Kernel and Run All*\" in [JupyterLab](https://jupyterlab.readthedocs.io/en/stable/) *after* finishing the exercises to ensure that your solution runs top to bottom *without* any errors. If you cannot run this file on your machine, you may want to open it [in the cloud <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_mb.png\">](https://mybinder.org/v2/gh/webartifex/intro-to-python/develop?urlpath=lab/tree/08_mfr/03_exercises.ipynb)."
+    "**Note**: Click on \"*Kernel*\" > \"*Restart Kernel and Run All*\" in [JupyterLab](https://jupyterlab.readthedocs.io/en/stable/) *after* finishing the exercises to ensure that your solution runs top to bottom *without* any errors. If you cannot run this file on your machine, you may want to open it [in the cloud <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_mb.png\">](https://mybinder.org/v2/gh/webartifex/intro-to-python/main?urlpath=lab/tree/08_mfr/03_exercises.ipynb)."
    ]
   },
   {
@@ -18,7 +18,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "The exercises below assume that you have read the [first <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/08_mfr/00_content.ipynb) and [second <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/08_mfr/01_content.ipynb) part of Chapter 8.\n",
+    "The exercises below assume that you have read the [first <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/main/08_mfr/00_content.ipynb) and [second <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/main/08_mfr/01_content.ipynb) part of Chapter 8.\n",
     "\n",
     "The `...`'s in the code cells indicate where you need to fill in code snippets. The number of `...`'s within a code cell give you a rough idea of how many lines of code are needed to solve the task. You should not need to create any additional code cells for your final solution. However, you may want to use temporary code cells to try out some ideas."
    ]
@@ -34,7 +34,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "**Q1**: Copy your solution to **Q10** from the \"*Packing & Unpacking with Functions*\" exercise in [Chapter 7 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/07_sequences/04_exercises.ipynb) into the code cell below!"
+    "**Q1**: Copy your solution to **Q10** from the \"*Packing & Unpacking with Functions*\" exercise in [Chapter 7 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/main/07_sequences/04_exercises.ipynb) into the code cell below!"
    ]
   },
   {
@@ -152,7 +152,7 @@
     "\n",
     "**Q4**: Click through the following code cells and observe what they do!\n",
     "\n",
-    "The [*stream.py* <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_gh.png\">](https://github.com/webartifex/intro-to-python/blob/develop/08_mfr/stream.py) module in the book's repository provides a `make_finite_stream()` function. It is a *factory* function creating objects of type `generator` that we use to model *streaming* data."
+    "The [*stream.py* <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_gh.png\">](https://github.com/webartifex/intro-to-python/blob/main/08_mfr/stream.py) module in the book's repository provides a `make_finite_stream()` function. It is a *factory* function creating objects of type `generator` that we use to model *streaming* data."
    ]
   },
   {
diff --git a/08_mfr/04_content.ipynb b/08_mfr/04_content.ipynb
index 8a3fd51..a04f016 100644
--- a/08_mfr/04_content.ipynb
+++ b/08_mfr/04_content.ipynb
@@ -8,7 +8,7 @@
     }
    },
    "source": [
-    "**Note**: Click on \"*Kernel*\" > \"*Restart Kernel and Clear All Outputs*\" in [JupyterLab](https://jupyterlab.readthedocs.io/en/stable/) *before* reading this notebook to reset its output. If you cannot run this file on your machine, you may want to open it [in the cloud <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_mb.png\">](https://mybinder.org/v2/gh/webartifex/intro-to-python/develop?urlpath=lab/tree/08_mfr/04_content.ipynb)."
+    "**Note**: Click on \"*Kernel*\" > \"*Restart Kernel and Clear All Outputs*\" in [JupyterLab](https://jupyterlab.readthedocs.io/en/stable/) *before* reading this notebook to reset its output. If you cannot run this file on your machine, you may want to open it [in the cloud <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_mb.png\">](https://mybinder.org/v2/gh/webartifex/intro-to-python/main?urlpath=lab/tree/08_mfr/04_content.ipynb)."
    ]
   },
   {
@@ -30,7 +30,7 @@
     }
    },
    "source": [
-    "Similarly to how we classify different *concrete* data types like `list` or `str` by how they behave *abstractly* in a given context in [Chapter 7 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/07_sequences/00_content.ipynb#Collections-vs.-Sequences), we also do so for the data types we have introduced in this chapter."
+    "Similarly to how we classify different *concrete* data types like `list` or `str` by how they behave *abstractly* in a given context in [Chapter 7 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/main/07_sequences/00_content.ipynb#Collections-vs.-Sequences), we also do so for the data types we have introduced in this chapter."
    ]
   },
   {
@@ -174,7 +174,7 @@
     }
    },
    "source": [
-    "Furthermore, we sharpen our definition of an *iterable* from [Chapter 7 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/07_sequences/00_content.ipynb#Collections-vs.-Sequences): Just as we define an *iterator* to be any object that supports the [next() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#next) function, we define an *iterable* to be any object that supports the built-in [iter() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#iter) function.\n",
+    "Furthermore, we sharpen our definition of an *iterable* from [Chapter 7 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/main/07_sequences/00_content.ipynb#Collections-vs.-Sequences): Just as we define an *iterator* to be any object that supports the [next() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#next) function, we define an *iterable* to be any object that supports the built-in [iter() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#iter) function.\n",
     "\n",
     "The confused reader may now be wondering how the two concepts relate to each other.\n",
     "\n",
@@ -780,7 +780,7 @@
     }
    },
    "source": [
-    "In [Chapter 4 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/04_iteration/02_content.ipynb#The-for-Statement), we argue that the `for` statement is syntactic sugar, replacing the `while` statement in many common scenarios. In particular, a `for`-loop saves us two tasks: Managing an index variable *and* obtaining the individual elements by indexing. In this sub-section, we look at a more realistic picture, using the new terminology as well.\n",
+    "In [Chapter 4 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/main/04_iteration/02_content.ipynb#The-for-Statement), we argue that the `for` statement is syntactic sugar, replacing the `while` statement in many common scenarios. In particular, a `for`-loop saves us two tasks: Managing an index variable *and* obtaining the individual elements by indexing. In this sub-section, we look at a more realistic picture, using the new terminology as well.\n",
     "\n",
     "Let's print out the elements of a `list` object as the *iterable* being looped over."
    ]
@@ -924,7 +924,7 @@
     }
    },
    "source": [
-    "Now that we know the concept of an *iterator*, let's compare some of the built-ins introduced in [Chapter 7 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/07_sequences/00_content.ipynb) in detail and make sure we understand what is going on in memory. This section also serves as a summary of all the concepts in this chapter.\n",
+    "Now that we know the concept of an *iterator*, let's compare some of the built-ins introduced in [Chapter 7 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/main/07_sequences/00_content.ipynb) in detail and make sure we understand what is going on in memory. This section also serves as a summary of all the concepts in this chapter.\n",
     "\n",
     "We use two simple examples, `numbers` and `memoryless`: `numbers` creates *thirteen* objects in memory and `memoryless` only *one* (cf., [PythonTutor <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](http://pythontutor.com/visualize.html#code=numbers%20%3D%20%5B7,%2011,%208,%205,%203,%2012,%202,%206,%209,%2010,%201,%204%5D%0Amemoryless%20%3D%20range%281,%2013%29&cumulative=false&curstr=2&heapPrimitives=nevernest&mode=display&origin=opt-frontend.js&py=3&rawInputLstJSON=%5B%5D&textReferences=false))."
    ]
diff --git a/08_mfr/06_review.ipynb b/08_mfr/06_review.ipynb
index faf8ab8..0ebf0b5 100644
--- a/08_mfr/06_review.ipynb
+++ b/08_mfr/06_review.ipynb
@@ -11,7 +11,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "The questions below assume that you have read the [first <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/08_mfr/00_content.ipynb), [second <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/08_mfr/01_content.ipynb), and [third <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/08_mfr/04_content.ipynb) part in Chapter 8.\n",
+    "The questions below assume that you have read the [first <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/main/08_mfr/00_content.ipynb), [second <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/main/08_mfr/01_content.ipynb), and [third <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/main/08_mfr/04_content.ipynb) part in Chapter 8.\n",
     "\n",
     "Be concise in your answers! Most questions can be answered in *one* sentence."
    ]
diff --git a/09_mappings/00_content.ipynb b/09_mappings/00_content.ipynb
index 4e97131..9926f40 100644
--- a/09_mappings/00_content.ipynb
+++ b/09_mappings/00_content.ipynb
@@ -8,7 +8,7 @@
     }
    },
    "source": [
-    "**Note**: Click on \"*Kernel*\" > \"*Restart Kernel and Clear All Outputs*\" in [JupyterLab](https://jupyterlab.readthedocs.io/en/stable/) *before* reading this notebook to reset its output. If you cannot run this file on your machine, you may want to open it [in the cloud <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_mb.png\">](https://mybinder.org/v2/gh/webartifex/intro-to-python/develop?urlpath=lab/tree/09_mappings/00_content.ipynb)."
+    "**Note**: Click on \"*Kernel*\" > \"*Restart Kernel and Clear All Outputs*\" in [JupyterLab](https://jupyterlab.readthedocs.io/en/stable/) *before* reading this notebook to reset its output. If you cannot run this file on your machine, you may want to open it [in the cloud <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_mb.png\">](https://mybinder.org/v2/gh/webartifex/intro-to-python/main?urlpath=lab/tree/09_mappings/00_content.ipynb)."
    ]
   },
   {
@@ -30,7 +30,7 @@
     }
    },
    "source": [
-    "While [Chapter 7 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/07_sequences/00_content.ipynb) focuses on one special kind of *collection* types, namely *sequences*, this chapter introduces two more kinds: **Mappings** and **sets**. Both are presented in this chapter as they share the *same* underlying implementation.\n",
+    "While [Chapter 7 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/main/07_sequences/00_content.ipynb) focuses on one special kind of *collection* types, namely *sequences*, this chapter introduces two more kinds: **Mappings** and **sets**. Both are presented in this chapter as they share the *same* underlying implementation.\n",
     "\n",
     "The `dict` type (cf, [documentation <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/stdtypes.html#dict)) introduced in the next section is an essential part in a data scientist's toolbox for two reasons: First, Python employs `dict` objects basically everywhere internally. Second, after the many concepts involving *sequential* data, *mappings* provide a different perspective on data and enhance our general problem solving skills."
    ]
@@ -56,7 +56,7 @@
    "source": [
     "A *mapping* is a one-to-one correspondence from a set of **keys** to a set of **values**. In other words, a *mapping* is a *collection* of **key-value pairs**, also called **items** for short.\n",
     "\n",
-    "In the context of mappings, the term *value* has a meaning different from the *value* every object has: In the \"bag\" analogy from [Chapter 1 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/01_elements/00_content.ipynb#Value-/-%28Semantic%29-\"Meaning\"), we describe an object's value to be the semantic meaning of the $0$s and $1$s it contains. Here, the terms *key* and *value* mean the *role* an object takes within a mapping. Both, *keys* and *values*, are *objects* on their own with distinct *values*.\n",
+    "In the context of mappings, the term *value* has a meaning different from the *value* every object has: In the \"bag\" analogy from [Chapter 1 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/main/01_elements/00_content.ipynb#Value-/-%28Semantic%29-\"Meaning\"), we describe an object's value to be the semantic meaning of the $0$s and $1$s it contains. Here, the terms *key* and *value* mean the *role* an object takes within a mapping. Both, *keys* and *values*, are *objects* on their own with distinct *values*.\n",
     "\n",
     "Let's continue with an example. To create a `dict` object, we commonly use the literal notation, `{..: .., ..: .., ...}`, and list all the items. `to_words` below maps the `int` objects `0`, `1`, and `2` to their English word equivalents, `\"zero\"`, `\"one\"`, and `\"two\"`, and `from_words` does the opposite. A stylistic side note: Pythonistas often expand `dict` or `list` definitions by writing each item or element on a line on their own. Also, the commas `,` after the respective *last* items, `2: \"two\"` and `\"two\": 2`, are *not* a mistake although they *may* be left out. Besides easier reading, such a style has technical advantages that we do not go into detail about here (cf., [source <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://www.python.org/dev/peps/pep-0008/#when-to-use-trailing-commas))."
    ]
@@ -550,7 +550,7 @@
     }
    },
    "source": [
-    "In [Chapter 0 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/00_intro/00_content.ipynb#Isn't-C-a-lot-faster?), we argue that a major advantage of using Python is that it takes care of the memory managment for us. In line with that, we have never talked about the C level implementation thus far in the book. However, the `dict` type, among others, exhibits some behaviors that may seem \"weird\" for a beginner. To build some intuition, we describe the underlying implementation details on a conceptual level.\n",
+    "In [Chapter 0 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/main/00_intro/00_content.ipynb#Isn't-C-a-lot-faster?), we argue that a major advantage of using Python is that it takes care of the memory managment for us. In line with that, we have never talked about the C level implementation thus far in the book. However, the `dict` type, among others, exhibits some behaviors that may seem \"weird\" for a beginner. To build some intuition, we describe the underlying implementation details on a conceptual level.\n",
     "\n",
     "The first unintuitive behavior is that we may *not* use a *mutable* object as a key. That results in a `TypeError`."
    ]
@@ -1176,7 +1176,7 @@
     }
    },
    "source": [
-    "In [Chapter 7 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/07_sequences/00_content.ipynb#Collections-vs.-Sequences), we see how a *sequence* is a special kind of a *collection*, and that collections can be described as\n",
+    "In [Chapter 7 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/main/07_sequences/00_content.ipynb#Collections-vs.-Sequences), we see how a *sequence* is a special kind of a *collection*, and that collections can be described as\n",
     "- *iterable*\n",
     "- *containers*\n",
     "- with a *finite* number of elements.\n",
@@ -1245,7 +1245,7 @@
     "Also, `dict` objects may be looped over, for example, with the `for` statement. So, in the terminology of the [collections.abc <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/collections.abc.html) module, they are `Iterable` objects.\n",
     "\n",
     "Regarding the *iteration order* things are not that easy, and programmers seem to often be confused about this (e.g., this [discussion](https://stackoverflow.com/questions/58413076/why-are-python-dictionaries-not-reversible-for-python3-7)). The confusion usually comes from one of two reasons:\n",
-    "1. The internal implementation of the `dict` type has been changed over the last couple of minor release versions, and the communication thereof in the official release notes was done only in a later version. In a nutshell, before Python 3.6, the core developers did not care about the iteration order at all as the goal was to optimize `dict` objects for computational speed, primarily regarding key look-up (cf., the \"Indexing -> Key Look-up\" section below). That meant that looping over the *same* `dict` object several times during its lifetime could have resulted in *different* iteration orders. In Python 3.6, it was discovered that it is possible to make `dict` objects remember the order in that items have been inserted *without* giving up any computational speed or memory (cf., [Raymond Hettinger](https://github.com/rhettinger)'s talk in the [Further Resources <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/09_mappings/08_resources.ipynb#History-of-the-dict-Type) section at the end of the chapter. However, that change was kept an *implementation detail* and *not* made official in the release notes. That was then done in Python 3.7's release notes (cf., [Python 3.7 release notes <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://www.python.org/downloads/release/python-370/)).\n",
+    "1. The internal implementation of the `dict` type has been changed over the last couple of minor release versions, and the communication thereof in the official release notes was done only in a later version. In a nutshell, before Python 3.6, the core developers did not care about the iteration order at all as the goal was to optimize `dict` objects for computational speed, primarily regarding key look-up (cf., the \"Indexing -> Key Look-up\" section below). That meant that looping over the *same* `dict` object several times during its lifetime could have resulted in *different* iteration orders. In Python 3.6, it was discovered that it is possible to make `dict` objects remember the order in that items have been inserted *without* giving up any computational speed or memory (cf., [Raymond Hettinger](https://github.com/rhettinger)'s talk in the [Further Resources <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/main/09_mappings/08_resources.ipynb#History-of-the-dict-Type) section at the end of the chapter. However, that change was kept an *implementation detail* and *not* made official in the release notes. That was then done in Python 3.7's release notes (cf., [Python 3.7 release notes <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://www.python.org/downloads/release/python-370/)).\n",
     "2. To make order an official part of a data type, it must adhere to the `Reversible` ABC in the [collections.abc <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/collections.abc.html) module and support the [reversed() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#reversed) built-in. Even though the items' order inside a `dict` is remembered for Python 3.6 and 3.7, `dict` objects are *not* `Reversible`. That was then changed in Python 3.8, but again *not* officially communicated (cf., [Python 3.8 release notes](https://www.python.org/downloads/release/python-380/)).\n",
     "\n",
     "In summary, we can say that depending on the exact Python version a `dict` object *may* remember the **insertion order** of its items.\n",
@@ -3541,7 +3541,7 @@
     }
    },
    "source": [
-    "Analogous to `list` comprehensions in [Chapter 8 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/08_mfr/01_content.ipynb#list-Comprehensions), `dict` comprehensions are a concise literal notation to derive new `dict` objects out of existing ones.\n",
+    "Analogous to `list` comprehensions in [Chapter 8 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/main/08_mfr/01_content.ipynb#list-Comprehensions), `dict` comprehensions are a concise literal notation to derive new `dict` objects out of existing ones.\n",
     "\n",
     "For example, let's derive `from_words` out of `to_words` below by swapping the keys and values."
    ]
diff --git a/09_mappings/01_exercises_nested-data.ipynb b/09_mappings/01_exercises_nested-data.ipynb
index 82eae0d..fe7ae76 100644
--- a/09_mappings/01_exercises_nested-data.ipynb
+++ b/09_mappings/01_exercises_nested-data.ipynb
@@ -4,7 +4,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "**Note**: Click on \"*Kernel*\" > \"*Restart Kernel and Run All*\" in [JupyterLab](https://jupyterlab.readthedocs.io/en/stable/) *after* finishing the exercises to ensure that your solution runs top to bottom *without* any errors. If you cannot run this file on your machine, you may want to open it [in the cloud <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_mb.png\">](https://mybinder.org/v2/gh/webartifex/intro-to-python/develop?urlpath=lab/tree/09_mappings/01_exercises.ipynb)."
+    "**Note**: Click on \"*Kernel*\" > \"*Restart Kernel and Run All*\" in [JupyterLab](https://jupyterlab.readthedocs.io/en/stable/) *after* finishing the exercises to ensure that your solution runs top to bottom *without* any errors. If you cannot run this file on your machine, you may want to open it [in the cloud <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_mb.png\">](https://mybinder.org/v2/gh/webartifex/intro-to-python/main?urlpath=lab/tree/09_mappings/01_exercises.ipynb)."
    ]
   },
   {
@@ -18,7 +18,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "The exercises below assume that you have read the [first part <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/09_mappings/00_content.ipynb) of Chapter 9.\n",
+    "The exercises below assume that you have read the [first part <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/main/09_mappings/00_content.ipynb) of Chapter 9.\n",
     "\n",
     "The `...`'s in the code cells indicate where you need to fill in code snippets. The number of `...`'s within a code cell give you a rough idea of how many lines of code are needed to solve the task. You should not need to create any additional code cells for your final solution. However, you may want to use temporary code cells to try out some ideas."
    ]
diff --git a/09_mappings/02_content.ipynb b/09_mappings/02_content.ipynb
index e0b9083..b2fe6a0 100644
--- a/09_mappings/02_content.ipynb
+++ b/09_mappings/02_content.ipynb
@@ -8,7 +8,7 @@
     }
    },
    "source": [
-    "**Note**: Click on \"*Kernel*\" > \"*Restart Kernel and Clear All Outputs*\" in [JupyterLab](https://jupyterlab.readthedocs.io/en/stable/) *before* reading this notebook to reset its output. If you cannot run this file on your machine, you may want to open it [in the cloud <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_mb.png\">](https://mybinder.org/v2/gh/webartifex/intro-to-python/develop?urlpath=lab/tree/09_mappings/02_content.ipynb)."
+    "**Note**: Click on \"*Kernel*\" > \"*Restart Kernel and Clear All Outputs*\" in [JupyterLab](https://jupyterlab.readthedocs.io/en/stable/) *before* reading this notebook to reset its output. If you cannot run this file on your machine, you may want to open it [in the cloud <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_mb.png\">](https://mybinder.org/v2/gh/webartifex/intro-to-python/main?urlpath=lab/tree/09_mappings/02_content.ipynb)."
    ]
   },
   {
@@ -30,7 +30,7 @@
     }
    },
    "source": [
-    "After introducing the `dict` type in the [first part <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/09_mappings/00_content.ipynb) of this chapter, we first look at an extension of the packing and unpacking syntax that involves `dict` objects. Then, we see how mappings can help us write computationally more efficient implementations to recursive solutions of problems as introduced in [Chapter 4 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/04_iteration/00_content.ipynb#Recursion). In a way, this second part of the chapter \"finishes\" Chapter 4."
+    "After introducing the `dict` type in the [first part <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/main/09_mappings/00_content.ipynb) of this chapter, we first look at an extension of the packing and unpacking syntax that involves `dict` objects. Then, we see how mappings can help us write computationally more efficient implementations to recursive solutions of problems as introduced in [Chapter 4 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/main/04_iteration/00_content.ipynb#Recursion). In a way, this second part of the chapter \"finishes\" Chapter 4."
    ]
   },
   {
@@ -52,7 +52,7 @@
     }
    },
    "source": [
-    "Just as a single `*` symbol is used for packing and unpacking iterables in [Chapter 7 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/07_sequences/03_content.ipynb#Packing-&-Unpacking), a double `**` symbol implements packing and unpacking for mappings.\n",
+    "Just as a single `*` symbol is used for packing and unpacking iterables in [Chapter 7 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/main/07_sequences/03_content.ipynb#Packing-&-Unpacking), a double `**` symbol implements packing and unpacking for mappings.\n",
     "\n",
     "Let's say we have `to_words` and `more_words` as below and want to merge the items together into a *new* `dict` object."
    ]
@@ -608,7 +608,7 @@
     }
    },
    "source": [
-    "The *recursive* implementation of the [Fibonacci numbers <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_wiki.png\">](https://en.wikipedia.org/wiki/Fibonacci_number) in [Chapter 4 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/04_iteration/00_content.ipynb#\"Easy-at-first-Glance\"-Example:-Fibonacci-Numbers) takes long to compute for large Fibonacci numbers. For easier comparison, we show the old `fibonacci()` version here again."
+    "The *recursive* implementation of the [Fibonacci numbers <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_wiki.png\">](https://en.wikipedia.org/wiki/Fibonacci_number) in [Chapter 4 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/main/04_iteration/00_content.ipynb#\"Easy-at-first-Glance\"-Example:-Fibonacci-Numbers) takes long to compute for large Fibonacci numbers. For easier comparison, we show the old `fibonacci()` version here again."
    ]
   },
   {
@@ -831,7 +831,7 @@
     }
    },
    "source": [
-    "When we follow the flow of execution closely, we realize that the intermediate results represented by the left-most path in the graph above are calculated first. `fibonacci(1)`, the left-most leaf node $F(1)$, is the first base case reached, followed immediately by `fibonacci(0)`. From that moment onwards, the flow of execution moves back up the left-most path while adding together the two corresponding child nodes. Effectively, this mirrors the *iterative* implementation in that the order of all computational steps are *identical* (cf., the \"*Hard at first Glance*\" example in [Chapter 4 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/04_iteration/02_content.ipynb#\"Hard-at-first-Glance\"-Example:-Fibonacci-Numbers--(revisited))).\n",
+    "When we follow the flow of execution closely, we realize that the intermediate results represented by the left-most path in the graph above are calculated first. `fibonacci(1)`, the left-most leaf node $F(1)$, is the first base case reached, followed immediately by `fibonacci(0)`. From that moment onwards, the flow of execution moves back up the left-most path while adding together the two corresponding child nodes. Effectively, this mirrors the *iterative* implementation in that the order of all computational steps are *identical* (cf., the \"*Hard at first Glance*\" example in [Chapter 4 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/main/04_iteration/02_content.ipynb#\"Hard-at-first-Glance\"-Example:-Fibonacci-Numbers--(revisited))).\n",
     "\n",
     "We added a keyword-only argument `debug` that allows the caller to print out a message every time a `i` was *not* in the `memo`."
    ]
diff --git a/09_mappings/03_exercises_fibonacci.ipynb b/09_mappings/03_exercises_fibonacci.ipynb
index 1ef6bf8..c9738fe 100644
--- a/09_mappings/03_exercises_fibonacci.ipynb
+++ b/09_mappings/03_exercises_fibonacci.ipynb
@@ -4,7 +4,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "**Note**: Click on \"*Kernel*\" > \"*Restart Kernel and Run All*\" in [JupyterLab](https://jupyterlab.readthedocs.io/en/stable/) *after* finishing the exercises to ensure that your solution runs top to bottom *without* any errors. If you cannot run this file on your machine, you may want to open it [in the cloud <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_mb.png\">](https://mybinder.org/v2/gh/webartifex/intro-to-python/develop?urlpath=lab/tree/09_mappings/03_exercises.ipynb)."
+    "**Note**: Click on \"*Kernel*\" > \"*Restart Kernel and Run All*\" in [JupyterLab](https://jupyterlab.readthedocs.io/en/stable/) *after* finishing the exercises to ensure that your solution runs top to bottom *without* any errors. If you cannot run this file on your machine, you may want to open it [in the cloud <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_mb.png\">](https://mybinder.org/v2/gh/webartifex/intro-to-python/main?urlpath=lab/tree/09_mappings/03_exercises.ipynb)."
    ]
   },
   {
@@ -18,7 +18,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "The exercises below assume that you have read the [second part <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/09_mappings/02_content.ipynb) of Chapter 9.\n",
+    "The exercises below assume that you have read the [second part <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/main/09_mappings/02_content.ipynb) of Chapter 9.\n",
     "\n",
     "The `...`'s in the code cells indicate where you need to fill in code snippets. The number of `...`'s within a code cell give you a rough idea of how many lines of code are needed to solve the task. You should not need to create any additional code cells for your final solution. However, you may want to use temporary code cells to try out some ideas."
    ]
@@ -38,7 +38,7 @@
     }
    },
    "source": [
-    "It is considered *bad practice* to make a function and thereby its correctness dependent on a program's *global state*: For example, in the \"*Easy at second Glance: Fibonacci Numbers*\" section in [Chapter 9 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/09_mappings/02_content.ipynb#\"Easy-at-second-Glance\"-Example:-Fibonacci-Numbers--%28revisited%29), we use a global `memo` to store the Fibonacci numbers that have already been calculated.\n",
+    "It is considered *bad practice* to make a function and thereby its correctness dependent on a program's *global state*: For example, in the \"*Easy at second Glance: Fibonacci Numbers*\" section in [Chapter 9 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/main/09_mappings/02_content.ipynb#\"Easy-at-second-Glance\"-Example:-Fibonacci-Numbers--%28revisited%29), we use a global `memo` to store the Fibonacci numbers that have already been calculated.\n",
     "\n",
     "That `memo` dictionary could be \"manipulated.\" More often than not, such things happen by accident: Imagine we wrote two independent recursive functions that both rely on memoization to solve different problems, and, unintentionally, we made both work with the *same* global `memo`. As a result, we would observe \"random\" bugs depending on the order in which we executed these functions. Such bugs are hard to track down in practice.\n",
     "\n",
@@ -185,7 +185,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "The runtime of `fibonacci()` is now stable: There is no message that \"an intermediate result is being cached\" as in [Chapter 9 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/09_mappings/02_content.ipynb#\"Easy-at-second-Glance\"-Example:-Fibonacci-Numbers--%28revisited%29).\n",
+    "The runtime of `fibonacci()` is now stable: There is no message that \"an intermediate result is being cached\" as in [Chapter 9 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/main/09_mappings/02_content.ipynb#\"Easy-at-second-Glance\"-Example:-Fibonacci-Numbers--%28revisited%29).\n",
     "\n",
     "**Q7**: Execute the following code cells a couple of times to observe that!"
    ]
diff --git a/09_mappings/04_content.ipynb b/09_mappings/04_content.ipynb
index 6b97bf7..3f1f1d5 100644
--- a/09_mappings/04_content.ipynb
+++ b/09_mappings/04_content.ipynb
@@ -8,7 +8,7 @@
     }
    },
    "source": [
-    "**Note**: Click on \"*Kernel*\" > \"*Restart Kernel and Clear All Outputs*\" in [JupyterLab](https://jupyterlab.readthedocs.io/en/stable/) *before* reading this notebook to reset its output. If you cannot run this file on your machine, you may want to open it [in the cloud <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_mb.png\">](https://mybinder.org/v2/gh/webartifex/intro-to-python/develop?urlpath=lab/tree/09_mappings/04_content.ipynb)."
+    "**Note**: Click on \"*Kernel*\" > \"*Restart Kernel and Clear All Outputs*\" in [JupyterLab](https://jupyterlab.readthedocs.io/en/stable/) *before* reading this notebook to reset its output. If you cannot run this file on your machine, you may want to open it [in the cloud <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_mb.png\">](https://mybinder.org/v2/gh/webartifex/intro-to-python/main?urlpath=lab/tree/09_mappings/04_content.ipynb)."
    ]
   },
   {
@@ -1407,7 +1407,7 @@
     }
    },
    "source": [
-    "Python provides a literal notation for `set` comprehensions that works exactly like the one for `dict` comprehensions described in the [first part <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/09_mappings/00_content.ipynb#dict-Comprehensions) of this chapter except that they use a single loop variable instead of a key-value pair.\n",
+    "Python provides a literal notation for `set` comprehensions that works exactly like the one for `dict` comprehensions described in the [first part <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/main/09_mappings/00_content.ipynb#dict-Comprehensions) of this chapter except that they use a single loop variable instead of a key-value pair.\n",
     "\n",
     "For example, let's create a new `set` object that consists of the squares of all the elements of `numbers`."
    ]
diff --git a/09_mappings/05_appendix.ipynb b/09_mappings/05_appendix.ipynb
index 95050b6..0b774af 100644
--- a/09_mappings/05_appendix.ipynb
+++ b/09_mappings/05_appendix.ipynb
@@ -8,7 +8,7 @@
     }
    },
    "source": [
-    "**Note**: Click on \"*Kernel*\" > \"*Restart Kernel and Clear All Outputs*\" in [JupyterLab](https://jupyterlab.readthedocs.io/en/stable/) *before* reading this notebook to reset its output. If you cannot run this file on your machine, you may want to open it [in the cloud <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_mb.png\">](https://mybinder.org/v2/gh/webartifex/intro-to-python/develop?urlpath=lab/tree/09_mappings/05_appendix.ipynb)."
+    "**Note**: Click on \"*Kernel*\" > \"*Restart Kernel and Clear All Outputs*\" in [JupyterLab](https://jupyterlab.readthedocs.io/en/stable/) *before* reading this notebook to reset its output. If you cannot run this file on your machine, you may want to open it [in the cloud <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_mb.png\">](https://mybinder.org/v2/gh/webartifex/intro-to-python/main?urlpath=lab/tree/09_mappings/05_appendix.ipynb)."
    ]
   },
   {
diff --git a/09_mappings/07_review.ipynb b/09_mappings/07_review.ipynb
index a484886..7aed88f 100644
--- a/09_mappings/07_review.ipynb
+++ b/09_mappings/07_review.ipynb
@@ -11,7 +11,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "The questions below assume that you have read the [first <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/09_mappings/00_content.ipynb), [second <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/09_mappings/02_content.ipynb), and [third <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/09_mappings/04_content.ipynb) part of Chapter 9.\n",
+    "The questions below assume that you have read the [first <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/main/09_mappings/00_content.ipynb), [second <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/main/09_mappings/02_content.ipynb), and [third <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/main/09_mappings/04_content.ipynb) part of Chapter 9.\n",
     "\n",
     "Be concise in your answers! Most questions can be answered in *one* sentence."
    ]
@@ -99,7 +99,7 @@
    "source": [
     "**Q6**: **Memoization** is an essential concept to know to solve problems in the real world. Together with the idea of **recursion**, it enables us to solve problems in a \"backwards\" fashion *effectively*.\n",
     "\n",
-    "Compare the **recursive** formulation of `fibonacci()` in [Chapter 9 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/09_mappings/02_content.ipynb#\"Easy-at-second-Glance\"-Example:-Fibonacci-Numbers--(revisited)), the \"*Easy at second Glance*\" example, with the **iterative** version in [Chapter 4 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/04_iteration/02_content.ipynb#\"Hard-at-first-Glance\"-Example:-Fibonacci-Numbers-%28revisited%29), the \"*Hard at first Glance*\" example!\n",
+    "Compare the **recursive** formulation of `fibonacci()` in [Chapter 9 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/main/09_mappings/02_content.ipynb#\"Easy-at-second-Glance\"-Example:-Fibonacci-Numbers--(revisited)), the \"*Easy at second Glance*\" example, with the **iterative** version in [Chapter 4 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/main/04_iteration/02_content.ipynb#\"Hard-at-first-Glance\"-Example:-Fibonacci-Numbers-%28revisited%29), the \"*Hard at first Glance*\" example!\n",
     "\n",
     "How are they similar and how do they differ? Also consider how the flow of execution behaves when the functions are being executed."
    ]
diff --git a/09_mappings/08_resources.ipynb b/09_mappings/08_resources.ipynb
index c68eef1..b9dd3da 100644
--- a/09_mappings/08_resources.ipynb
+++ b/09_mappings/08_resources.ipynb
@@ -4,7 +4,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "**Note**: Click on \"*Kernel*\" > \"*Restart Kernel and Clear All Outputs*\" in [JupyterLab](https://jupyterlab.readthedocs.io/en/stable/) *before* reading this notebook to reset its output. If you cannot run this file on your machine, you may want to open it [in the cloud <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_mb.png\">](https://mybinder.org/v2/gh/webartifex/intro-to-python/develop?urlpath=lab/tree/09_mappings/08_resources.ipynb)."
+    "**Note**: Click on \"*Kernel*\" > \"*Restart Kernel and Clear All Outputs*\" in [JupyterLab](https://jupyterlab.readthedocs.io/en/stable/) *before* reading this notebook to reset its output. If you cannot run this file on your machine, you may want to open it [in the cloud <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_mb.png\">](https://mybinder.org/v2/gh/webartifex/intro-to-python/main?urlpath=lab/tree/09_mappings/08_resources.ipynb)."
    ]
   },
   {
diff --git a/11_classes/00_content.ipynb b/11_classes/00_content.ipynb
index b910095..696eed2 100644
--- a/11_classes/00_content.ipynb
+++ b/11_classes/00_content.ipynb
@@ -8,7 +8,7 @@
     }
    },
    "source": [
-    "**Note**: Click on \"*Kernel*\" > \"*Restart Kernel and Clear All Outputs*\" in [JupyterLab](https://jupyterlab.readthedocs.io/en/stable/) *before* reading this notebook to reset its output. If you cannot run this file on your machine, you may want to open it [in the cloud <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_mb.png\">](https://mybinder.org/v2/gh/webartifex/intro-to-python/develop?urlpath=lab/tree/11_classes/00_content.ipynb)."
+    "**Note**: Click on \"*Kernel*\" > \"*Restart Kernel and Clear All Outputs*\" in [JupyterLab](https://jupyterlab.readthedocs.io/en/stable/) *before* reading this notebook to reset its output. If you cannot run this file on your machine, you may want to open it [in the cloud <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_mb.png\">](https://mybinder.org/v2/gh/webartifex/intro-to-python/main?urlpath=lab/tree/11_classes/00_content.ipynb)."
    ]
   },
   {
@@ -34,7 +34,7 @@
     "\n",
     "Classes and instances follow the **[object-oriented programming <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_wiki.png\">](https://en.wikipedia.org/wiki/Object-oriented_programming)** (OOP) paradigm where a *large program* is broken down into many *small components* (i.e., the objects) that *reuse* code. This way, a program that is too big for a programmer to fully comprehend as a whole becomes maintainable via its easier to understand individual pieces.\n",
     "\n",
-    "Often, we see the terminology \"classes & objects\" used instead of \"classes & instances\" in Python related texts. In this book, we are more precise as *both* classes and instances are objects as specified already in the \"*Objects vs. Types vs. Values*\" section in [Chapter 1 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/01_elements/00_content.ipynb#Objects-vs.-Types-vs.-Values)."
+    "Often, we see the terminology \"classes & objects\" used instead of \"classes & instances\" in Python related texts. In this book, we are more precise as *both* classes and instances are objects as specified already in the \"*Objects vs. Types vs. Values*\" section in [Chapter 1 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/main/01_elements/00_content.ipynb#Objects-vs.-Types-vs.-Values)."
    ]
   },
   {
@@ -56,7 +56,7 @@
     }
    },
    "source": [
-    "Neither core Python nor the standard library offer an implementation of common [linear algebra <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_wiki.png\">](https://en.wikipedia.org/wiki/Linear_algebra) functionalities. While we introduce the popular third-party library [numpy](http://www.numpy.org/) in [Chapter 10 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/10_arrarys/00_content.ipynb) as the de-facto standard for that and recommend to use it in real-life projects, we show how one could use Python's object-oriented language features to implement common matrix and vector operations throughout this chapter. Once we have achieved that, we compare our own library with [numpy](http://www.numpy.org/).\n",
+    "Neither core Python nor the standard library offer an implementation of common [linear algebra <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_wiki.png\">](https://en.wikipedia.org/wiki/Linear_algebra) functionalities. While we introduce the popular third-party library [numpy](http://www.numpy.org/) in [Chapter 10 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/main/10_arrarys/00_content.ipynb) as the de-facto standard for that and recommend to use it in real-life projects, we show how one could use Python's object-oriented language features to implement common matrix and vector operations throughout this chapter. Once we have achieved that, we compare our own library with [numpy](http://www.numpy.org/).\n",
     "\n",
     "Without classes, we could model a vector, for example, with a `tuple` or a `list` object, depending on if we want it to be mutable or not.\n",
     "\n",
@@ -326,7 +326,7 @@
    "source": [
     "Its type is `type` indicating that it represents a user-defined data type and it evaluates to its fully qualified name (i.e., `__main__` as it is defined in this Jupyter notebook).\n",
     "\n",
-    "We have seen the type `type` before in the \"*Constructors*\" section in [Chapter 2 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/02_functions/00_content.ipynb#Constructors) and also in the \"*The `namedtuple` Type*\" section in [Chapter 7's Appendix <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/07_sequences/05_appendix.ipynb#The-namedtuple-Type). In the latter case, we could also use a `Point` class but the [namedtuple() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/collections.html#collections.namedtuple) function from the [collections <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/collections.html) module in the [standard library <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/index.html) is a convenient shortcut to create custom data types that can be derived out of a plain `tuple`.\n",
+    "We have seen the type `type` before in the \"*Constructors*\" section in [Chapter 2 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/main/02_functions/00_content.ipynb#Constructors) and also in the \"*The `namedtuple` Type*\" section in [Chapter 7's Appendix <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/main/07_sequences/05_appendix.ipynb#The-namedtuple-Type). In the latter case, we could also use a `Point` class but the [namedtuple() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/collections.html#collections.namedtuple) function from the [collections <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/collections.html) module in the [standard library <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/index.html) is a convenient shortcut to create custom data types that can be derived out of a plain `tuple`.\n",
     "\n",
     "In all examples, if an object's type is `type`, we can simply view it as a blueprint for a \"family\" of objects."
    ]
@@ -1294,7 +1294,7 @@
     }
    },
    "source": [
-    "From a theoretical point of view, the text representation provided by `.__repr__()` contains all the information (i.e., the $0$s and $1$s in memory) that is needed to model something in a computer. In a way, it is a natural extension from the binary (cf., [Chapter 5 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/05_numbers/00_content.ipynb#Binary-Representations)), hexadecimal (cf., [Chapter 5 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/05_numbers/00_content.ipynb#Hexadecimal-Representations)), and `bytes` (cf., [Chapter 6 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/06_text/02_content.ipynb#The-bytes-Type)) representations of information. After all, just like Unicode characters are encoded in `bytes`, the more \"complex\" objects in this chapter are encoded in Unicode characters via their text representations."
+    "From a theoretical point of view, the text representation provided by `.__repr__()` contains all the information (i.e., the $0$s and $1$s in memory) that is needed to model something in a computer. In a way, it is a natural extension from the binary (cf., [Chapter 5 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/main/05_numbers/00_content.ipynb#Binary-Representations)), hexadecimal (cf., [Chapter 5 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/main/05_numbers/00_content.ipynb#Hexadecimal-Representations)), and `bytes` (cf., [Chapter 6 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/main/06_text/02_content.ipynb#The-bytes-Type)) representations of information. After all, just like Unicode characters are encoded in `bytes`, the more \"complex\" objects in this chapter are encoded in Unicode characters via their text representations."
    ]
   },
   {
@@ -1649,7 +1649,7 @@
     "\n",
     "An example of an instance method from linear algebra is the `.transpose()` method below that switches the rows and columns of an *existing* `Matrix` instance and returns a *new* `Matrix` instance based off that. It is implemented by passing the *iterator* created with the [zip() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#zip) built-in as the `data` argument to the `Matrix` constructor: The expression `zip(*self._entries)` may be a bit hard to understand because of the involved unpacking but simply flips a `Matrix`'s rows and columns. The built-in [list() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#func-list) constructor within the `.__init__()` method then materializes the iterator into the `._entries` attribute. Without a concrete `Matrix`'s rows and columns, `.transpose()` does not make sense, conceptually speaking.\n",
     "\n",
-    "Also, we see that it is ok to reference a class from within one of its methods. While this seems trivial to some readers, others may find this confusing. The final versions of the `Vector` and `Matrix` classes in the [fourth part <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/11_classes/04_content.ipynb#The-final-Vector-and-Matrix-Classes) of this chapter show how this \"hard coded\" redundancy can be avoided."
+    "Also, we see that it is ok to reference a class from within one of its methods. While this seems trivial to some readers, others may find this confusing. The final versions of the `Vector` and `Matrix` classes in the [fourth part <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/main/11_classes/04_content.ipynb#The-final-Vector-and-Matrix-Classes) of this chapter show how this \"hard coded\" redundancy can be avoided."
    ]
   },
   {
diff --git a/11_classes/01_exercises_tsp.ipynb b/11_classes/01_exercises_tsp.ipynb
index cdc755c..fbdbc1f 100644
--- a/11_classes/01_exercises_tsp.ipynb
+++ b/11_classes/01_exercises_tsp.ipynb
@@ -4,7 +4,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "**Note**: Click on \"*Kernel*\" > \"*Restart Kernel and Run All*\" in [JupyterLab](https://jupyterlab.readthedocs.io/en/stable/) *after* finishing the exercises to ensure that your solution runs top to bottom *without* any errors. If you cannot run this file on your machine, you may want to open it [in the cloud <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_mb.png\">](https://mybinder.org/v2/gh/webartifex/intro-to-python/develop?urlpath=lab/tree/11_classes/01_exercises.ipynb)."
+    "**Note**: Click on \"*Kernel*\" > \"*Restart Kernel and Run All*\" in [JupyterLab](https://jupyterlab.readthedocs.io/en/stable/) *after* finishing the exercises to ensure that your solution runs top to bottom *without* any errors. If you cannot run this file on your machine, you may want to open it [in the cloud <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_mb.png\">](https://mybinder.org/v2/gh/webartifex/intro-to-python/main?urlpath=lab/tree/11_classes/01_exercises.ipynb)."
    ]
   },
   {
@@ -18,7 +18,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "The exercises below assume that you have read the [first part <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/11_classes/00_content.ipynb) of Chapter 11.\n",
+    "The exercises below assume that you have read the [first part <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/main/11_classes/00_content.ipynb) of Chapter 11.\n",
     "\n",
     "The `...`'s in the code cells indicate where you need to fill in code snippets. The number of `...`'s within a code cell give you a rough idea of how many lines of code are needed to solve the task. You should not need to create any additional code cells for your final solution. However, you may want to use temporary code cells to try out some ideas."
    ]
diff --git a/11_classes/02_content.ipynb b/11_classes/02_content.ipynb
index fb8a6bd..5ab6d81 100644
--- a/11_classes/02_content.ipynb
+++ b/11_classes/02_content.ipynb
@@ -8,7 +8,7 @@
     }
    },
    "source": [
-    "**Note**: Click on \"*Kernel*\" > \"*Restart Kernel and Clear All Outputs*\" in [JupyterLab](https://jupyterlab.readthedocs.io/en/stable/) *before* reading this notebook to reset its output. If you cannot run this file on your machine, you may want to open it [in the cloud <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_mb.png\">](https://mybinder.org/v2/gh/webartifex/intro-to-python/develop?urlpath=lab/tree/11_classes/02_content.ipynb)."
+    "**Note**: Click on \"*Kernel*\" > \"*Restart Kernel and Clear All Outputs*\" in [JupyterLab](https://jupyterlab.readthedocs.io/en/stable/) *before* reading this notebook to reset its output. If you cannot run this file on your machine, you may want to open it [in the cloud <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_mb.png\">](https://mybinder.org/v2/gh/webartifex/intro-to-python/main?urlpath=lab/tree/11_classes/02_content.ipynb)."
    ]
   },
   {
@@ -52,7 +52,7 @@
     }
    },
    "source": [
-    "As discussed in detail in [Chapter 7 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/07_sequences/00_content.ipynb#Collections-vs.-Sequences), a sequence is any finite and iterable container type with a *predictable* order of its elements such that we can label each element with an index in the range `0 <= index < len(sequence)`.\n",
+    "As discussed in detail in [Chapter 7 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/main/07_sequences/00_content.ipynb#Collections-vs.-Sequences), a sequence is any finite and iterable container type with a *predictable* order of its elements such that we can label each element with an index in the range `0 <= index < len(sequence)`.\n",
     "\n",
     "To make `Vector` and `Matrix` instances emulate sequences, we implement the `.__len__()` (cf., [reference <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/reference/datamodel.html#object.__len__)) and `.__getitem__()` (cf., [reference <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/reference/datamodel.html#object.__getitem__)) methods. While the former returns the total number of elements in a container and is automatically invoked on any object passed to the built-in [len() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#len) function, the latter is invoked by the interpreter behind the scenes when we use the indexing operator `[]`.\n",
     "\n",
@@ -714,7 +714,7 @@
     "\n",
     "The collection of all such behaviors a programming language offers is commonly referred to as its **object model**. In Python, the term **data model** is used instead and all possible behaviors are documented in the [language reference <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/reference/datamodel.html), in particular, in the section on special methods. We can think of the data model as the collection of all the behaviors we can make our user-defined data types follow. Pythonistas also use the term **protocol** instead of behavior, for example, we may say that \"the `Vector` and `Matrix` classes follow the sequence protocol.\"\n",
     "\n",
-    "So, merely defining the *two* `.__len__()` and `.__getitem__()` methods is enough to make instances of any user-defined type behave like the built-in sequences in [Chapter 7 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/07_sequences/00_content.ipynb). Yet, there we defined sequences as all objects having the *four* properties of being finite, iterable, and ordered container types. And, these properties correspond to special methods by the names of `.__len__()`, `.__iter__()`, `.__reversed__()`,  and `.__contains__()` as we see in the next section. Thus, Python \"magically\" knows how to derive the logic for the `.__iter__()`, `.__reversed__()`,  and `.__contains__()` methods from the combination of the `.__len__()` and `.__getitem__()` methods. In general, while some special methods are related, others are not. Understanding these relationships means understanding the Python data model and vice versa. That is what every aspiring data scientist should aim for.\n",
+    "So, merely defining the *two* `.__len__()` and `.__getitem__()` methods is enough to make instances of any user-defined type behave like the built-in sequences in [Chapter 7 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/main/07_sequences/00_content.ipynb). Yet, there we defined sequences as all objects having the *four* properties of being finite, iterable, and ordered container types. And, these properties correspond to special methods by the names of `.__len__()`, `.__iter__()`, `.__reversed__()`,  and `.__contains__()` as we see in the next section. Thus, Python \"magically\" knows how to derive the logic for the `.__iter__()`, `.__reversed__()`,  and `.__contains__()` methods from the combination of the `.__len__()` and `.__getitem__()` methods. In general, while some special methods are related, others are not. Understanding these relationships means understanding the Python data model and vice versa. That is what every aspiring data scientist should aim for.\n",
     "\n",
     "On the contrary, we could also look at special methods individually. Whereas `.__len__()` is invoked on the object passed to [len() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#len), Python \"translates\" the indexing operator applied on any name like `a[i]`, for example, into the method invocation `a.__getitem__(i)`. So, in both cases, the special methods are executed according to a deterministic rule of the language. In that sense, they act as some sort of syntactic sugar. Thus, they even work if only one of them is defined. For example, without `.__len__()`, iteration with a `for`-loop still works but only in forward direction."
    ]
@@ -1047,7 +1047,7 @@
    "source": [
     "In the above implementations, the instance attribute `._entries` on a `Vector` or `Matrix` instance references either a `list` or a `list` of row `list`s , which is by the convention of the leading underscore `_` an implementation detail. If users of our classes adhere to this convention, `Vector` and `Matrix` instances can be regarded as *immutable*.\n",
     "\n",
-    "In line with the implied immutability, we implemented the `.transpose()` method such that it returns a *new* `Matrix` instance. Instead, we could make the method change the internal `self._entries` attribute *in place* as we do in the next example. To indicate this mutation to the user of the `Matrix` class clearly, the revised `.transpose()` method returns `None`. That mirrors, for example, how the mutating methods of the built-in `list` type behave (cf., [Chapter 7 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/07_sequences/01_content.ipynb#List-Methods)).\n",
+    "In line with the implied immutability, we implemented the `.transpose()` method such that it returns a *new* `Matrix` instance. Instead, we could make the method change the internal `self._entries` attribute *in place* as we do in the next example. To indicate this mutation to the user of the `Matrix` class clearly, the revised `.transpose()` method returns `None`. That mirrors, for example, how the mutating methods of the built-in `list` type behave (cf., [Chapter 7 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/main/07_sequences/01_content.ipynb#List-Methods)).\n",
     "\n",
     "Such decisions are better made consciously when designing a custom data type. The main trade-off is that immutable data types are typically easier to reason about when reading code whereas mutable data types tend to be more memory efficient and make programs faster as less copying operations take place in memory. However, this trade-off only becomes critical when we deal with big amounts of data."
    ]
@@ -1577,7 +1577,7 @@
     }
    },
    "source": [
-    "After this discussion of mutable `Vector` and `Matrix` classes, we continue with immutable implementations in the rest of this chapter. To lower the chance that we accidently design parts of our classes to be mutable, we replace the built-in [list() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#func-list) constructor with [tuple() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#func-tuple) in the `.__init__()` methods. As we learn in [Chapter 7 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/07_sequences/03_content.ipynb#Tuples-are-like-%22Immutable-Lists%22), `tuple`s are like immutable `list`s."
+    "After this discussion of mutable `Vector` and `Matrix` classes, we continue with immutable implementations in the rest of this chapter. To lower the chance that we accidently design parts of our classes to be mutable, we replace the built-in [list() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#func-list) constructor with [tuple() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#func-tuple) in the `.__init__()` methods. As we learn in [Chapter 7 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/main/07_sequences/03_content.ipynb#Tuples-are-like-%22Immutable-Lists%22), `tuple`s are like immutable `list`s."
    ]
   },
   {
@@ -1599,7 +1599,7 @@
     }
    },
    "source": [
-    "A function is considered **polymorphic** if it can work with *different* data types. The main advantage is reuse of the function's code. Polymorphism goes hand in hand with the concept of [duck typing <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_wiki.png\">](https://en.wikipedia.org/wiki/Duck_typing), first mentioned in [Chapter 4 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/04_iteration/00_content.ipynb#Duck-Typing) in the context of input validation.\n",
+    "A function is considered **polymorphic** if it can work with *different* data types. The main advantage is reuse of the function's code. Polymorphism goes hand in hand with the concept of [duck typing <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_wiki.png\">](https://en.wikipedia.org/wiki/Duck_typing), first mentioned in [Chapter 4 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/main/04_iteration/00_content.ipynb#Duck-Typing) in the context of input validation.\n",
     "\n",
     "We know polymorphic functions already: The built-in [sum() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#sum) function is a trivial example that works with all kinds of `iterable` arguments."
    ]
diff --git a/11_classes/03_content.ipynb b/11_classes/03_content.ipynb
index 3d40e39..8689e63 100644
--- a/11_classes/03_content.ipynb
+++ b/11_classes/03_content.ipynb
@@ -8,7 +8,7 @@
     }
    },
    "source": [
-    "**Note**: Click on \"*Kernel*\" > \"*Restart Kernel and Clear All Outputs*\" in [JupyterLab](https://jupyterlab.readthedocs.io/en/stable/) *before* reading this notebook to reset its output. If you cannot run this file on your machine, you may want to open it [in the cloud <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_mb.png\">](https://mybinder.org/v2/gh/webartifex/intro-to-python/develop?urlpath=lab/tree/11_classes/03_content.ipynb)."
+    "**Note**: Click on \"*Kernel*\" > \"*Restart Kernel and Clear All Outputs*\" in [JupyterLab](https://jupyterlab.readthedocs.io/en/stable/) *before* reading this notebook to reset its output. If you cannot run this file on your machine, you may want to open it [in the cloud <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_mb.png\">](https://mybinder.org/v2/gh/webartifex/intro-to-python/main?urlpath=lab/tree/11_classes/03_content.ipynb)."
    ]
   },
   {
@@ -446,7 +446,7 @@
    "source": [
     "By implementing special methods such as `.__add__()`, `.__sub__()`, `.__mul__()`, and some others, we can make user-defined data types emulate how numeric types operate with each other (cf., [reference <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/reference/datamodel.html#emulating-numeric-types)): Then, `Vector` and `Matrix` instances can be added together, subtracted from one another, or be multiplied together. We use them to implement the arithmetic rules from linear algebra.\n",
     "\n",
-    "The OOP concept behind this is **[operator overloading <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_wiki.png\">](https://en.wikipedia.org/wiki/Operator_overloading)** as first mentioned in the context of `str` concatenation in [Chapter 1 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/01_elements/00_content.ipynb#Operator-Overloading)."
+    "The OOP concept behind this is **[operator overloading <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_wiki.png\">](https://en.wikipedia.org/wiki/Operator_overloading)** as first mentioned in the context of `str` concatenation in [Chapter 1 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/main/01_elements/00_content.ipynb#Operator-Overloading)."
    ]
   },
   {
@@ -599,7 +599,7 @@
     }
    },
    "source": [
-    "To check if `other` is a scalar, we need to specify what data type constitutes a scalar. We use a goose typing strategy as explained in [Chapter 5 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/05_numbers/02_content.ipynb#Goose-Typing): Any object that behaves like a `numbers.Number` from the [numbers <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/numbers.html) module in the [standard library <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/index.html) is considered a scalar.\n",
+    "To check if `other` is a scalar, we need to specify what data type constitutes a scalar. We use a goose typing strategy as explained in [Chapter 5 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/main/05_numbers/02_content.ipynb#Goose-Typing): Any object that behaves like a `numbers.Number` from the [numbers <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/numbers.html) module in the [standard library <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/index.html) is considered a scalar.\n",
     "\n",
     "For example, the integer `1` is an instance of the built-in `int` type. At the same time, [isinstance() <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/functions.html#isinstance) also confirms that it is a `numbers.Number` in the abstract sense."
    ]
@@ -1715,7 +1715,7 @@
     }
    },
    "source": [
-    "We implement the `.__eq__()` (cf., [reference <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/reference/datamodel.html#object.__eq__)) method to control how the comparison operator `==` is carried out. For brevity, we show this only for the `Vector` class. The `.__eq__()` method exits early as soon as the first pair of entries does not match. Also, for reasons discussed in [Chapter 5 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/05_numbers/01_content.ipynb#Imprecision), we compare the absolute difference of two corresponding entries to a very small `zero_threshold` that is stored as a class attribute shared among all `Vector` instances. If the `Vector`s differ in their numbers of entries, we fail loudly and raise a `ValueError`."
+    "We implement the `.__eq__()` (cf., [reference <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/reference/datamodel.html#object.__eq__)) method to control how the comparison operator `==` is carried out. For brevity, we show this only for the `Vector` class. The `.__eq__()` method exits early as soon as the first pair of entries does not match. Also, for reasons discussed in [Chapter 5 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/main/05_numbers/01_content.ipynb#Imprecision), we compare the absolute difference of two corresponding entries to a very small `zero_threshold` that is stored as a class attribute shared among all `Vector` instances. If the `Vector`s differ in their numbers of entries, we fail loudly and raise a `ValueError`."
    ]
   },
   {
@@ -2239,7 +2239,7 @@
     }
    },
    "source": [
-    "Only an all `0`s `Vector` is `False` in a boolean context. As mentioned in [Chapter 3 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/03_conditionals/00_content.ipynb#Truthy-vs.-Falsy), commonly we view an *empty* sequence as falsy; however, as we do not allow `Vector`s without any entries, we choose the all `0`s alternative. In that regard, the `Vector` class does not behave like the built-in sequence types."
+    "Only an all `0`s `Vector` is `False` in a boolean context. As mentioned in [Chapter 3 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/main/03_conditionals/00_content.ipynb#Truthy-vs.-Falsy), commonly we view an *empty* sequence as falsy; however, as we do not allow `Vector`s without any entries, we choose the all `0`s alternative. In that regard, the `Vector` class does not behave like the built-in sequence types."
    ]
   },
   {
diff --git a/11_classes/04_content.ipynb b/11_classes/04_content.ipynb
index 9f563fe..684d227 100644
--- a/11_classes/04_content.ipynb
+++ b/11_classes/04_content.ipynb
@@ -8,7 +8,7 @@
     }
    },
    "source": [
-    "**Note**: Click on \"*Kernel*\" > \"*Restart Kernel and Clear All Outputs*\" in [JupyterLab](https://jupyterlab.readthedocs.io/en/stable/) *before* reading this notebook to reset its output. If you cannot run this file on your machine, you may want to open it [in the cloud <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_mb.png\">](https://mybinder.org/v2/gh/webartifex/intro-to-python/develop?urlpath=lab/tree/11_classes/04_content.ipynb)."
+    "**Note**: Click on \"*Kernel*\" > \"*Restart Kernel and Clear All Outputs*\" in [JupyterLab](https://jupyterlab.readthedocs.io/en/stable/) *before* reading this notebook to reset its output. If you cannot run this file on your machine, you may want to open it [in the cloud <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_mb.png\">](https://mybinder.org/v2/gh/webartifex/intro-to-python/main?urlpath=lab/tree/11_classes/04_content.ipynb)."
    ]
   },
   {
@@ -52,13 +52,13 @@
     }
    },
    "source": [
-    "In [Chapter 2 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/02_functions/02_content.ipynb#Local-Modules-and-Packages), we introduce the concept of a Python module that is imported with the `import` statement. Essentially, a **module** is a single plain text \\*.py file on disk that contains Python code (e.g., [*sample_module.py* <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_gh.png\">](https://github.com/webartifex/intro-to-python/blob/develop/02_functions/sample_module.py) in [Chapter 2's folder <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_gh.png\">](https://github.com/webartifex/intro-to-python/tree/develop/02_functions)).\n",
+    "In [Chapter 2 <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/main/02_functions/02_content.ipynb#Local-Modules-and-Packages), we introduce the concept of a Python module that is imported with the `import` statement. Essentially, a **module** is a single plain text \\*.py file on disk that contains Python code (e.g., [*sample_module.py* <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_gh.png\">](https://github.com/webartifex/intro-to-python/blob/main/02_functions/sample_module.py) in [Chapter 2's folder <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_gh.png\">](https://github.com/webartifex/intro-to-python/tree/main/02_functions)).\n",
     "\n",
-    "Conceptually, a **package** is a generalization of a module whose code is split across several \\*.py to achieve a better organization of the individual parts. The \\*.py files are stored within a folder (e.g., [*sample_package* <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_gh.png\">](https://github.com/webartifex/intro-to-python/tree/develop/11_classes/sample_package) in [Chapter 11's folder <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_gh.png\">](https://github.com/webartifex/intro-to-python/tree/develop/11_classes)). In addition to that, a \"*\\_\\_init\\_\\_.py*\" file that may be empty must be put inside the folder. The latter is what the Python interpreter looks for to decide if a folder is a package or not.\n",
+    "Conceptually, a **package** is a generalization of a module whose code is split across several \\*.py to achieve a better organization of the individual parts. The \\*.py files are stored within a folder (e.g., [*sample_package* <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_gh.png\">](https://github.com/webartifex/intro-to-python/tree/main/11_classes/sample_package) in [Chapter 11's folder <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_gh.png\">](https://github.com/webartifex/intro-to-python/tree/main/11_classes)). In addition to that, a \"*\\_\\_init\\_\\_.py*\" file that may be empty must be put inside the folder. The latter is what the Python interpreter looks for to decide if a folder is a package or not.\n",
     "\n",
     "Let's look at an example with the final version of our `Vector` and `Matrix` classes.\n",
     "\n",
-    "`!pwd` shows the location of this Jupyter notebook on the computer you are running [JupyterLab](https://jupyterlab.readthedocs.io/en/stable/) on: It is the local equivalent of [Chapter 11's folder <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_gh.png\">](https://github.com/webartifex/intro-to-python/tree/develop/11_classes) in this book's [GitHub repository <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_gh.png\">](https://github.com/webartifex/intro-to-python)."
+    "`!pwd` shows the location of this Jupyter notebook on the computer you are running [JupyterLab](https://jupyterlab.readthedocs.io/en/stable/) on: It is the local equivalent of [Chapter 11's folder <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_gh.png\">](https://github.com/webartifex/intro-to-python/tree/main/11_classes) in this book's [GitHub repository <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_gh.png\">](https://github.com/webartifex/intro-to-python)."
    ]
   },
   {
@@ -90,7 +90,7 @@
     }
    },
    "source": [
-    "`!ls` lists all the files and folders in the current location: These are Chapter 11's Jupyter notebooks (i.e., the \\*.ipynb files) and the [*sample_package* <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_gh.png\">](https://github.com/webartifex/intro-to-python/tree/develop/11_classes/sample_package) folder. "
+    "`!ls` lists all the files and folders in the current location: These are Chapter 11's Jupyter notebooks (i.e., the \\*.ipynb files) and the [*sample_package* <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_gh.png\">](https://github.com/webartifex/intro-to-python/tree/main/11_classes/sample_package) folder. "
    ]
   },
   {
@@ -155,11 +155,11 @@
     }
    },
    "source": [
-    "The package is organized such that the [*matrix.py* <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_gh.png\">](https://github.com/webartifex/intro-to-python/blob/develop/11_classes/sample_package/matrix.py) and [*vector.py* <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_gh.png\">](https://github.com/webartifex/intro-to-python/blob/develop/11_classes/sample_package/vector.py) modules each define just one class, `Matrix` and `Vector`. That is intentional as both classes consist of several hundred lines of code and comments.\n",
+    "The package is organized such that the [*matrix.py* <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_gh.png\">](https://github.com/webartifex/intro-to-python/blob/main/11_classes/sample_package/matrix.py) and [*vector.py* <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_gh.png\">](https://github.com/webartifex/intro-to-python/blob/main/11_classes/sample_package/vector.py) modules each define just one class, `Matrix` and `Vector`. That is intentional as both classes consist of several hundred lines of code and comments.\n",
     "\n",
-    "The [*utils.py* <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_gh.png\">](https://github.com/webartifex/intro-to-python/blob/develop/11_classes/sample_package/utils.py) module contains code that is shared by both classes. Such code snippets are commonly called \"utilities\" or \"helpers,\" which explains the module's name.\n",
+    "The [*utils.py* <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_gh.png\">](https://github.com/webartifex/intro-to-python/blob/main/11_classes/sample_package/utils.py) module contains code that is shared by both classes. Such code snippets are commonly called \"utilities\" or \"helpers,\" which explains the module's name.\n",
     "\n",
-    "Finally, the [*\\_\\_init\\_\\_.py* <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_gh.png\">](https://github.com/webartifex/intro-to-python/blob/develop/11_classes/sample_package/__init__.py) file contains mostly meta information and defines what objects should be importable from the package's top level.\n",
+    "Finally, the [*\\_\\_init\\_\\_.py* <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_gh.png\">](https://github.com/webartifex/intro-to-python/blob/main/11_classes/sample_package/__init__.py) file contains mostly meta information and defines what objects should be importable from the package's top level.\n",
     "\n",
     "With the `import` statement, we can import the entire package just as we would import a module from the [standard library <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_py.png\">](https://docs.python.org/3/library/index.html)."
    ]
@@ -185,7 +185,7 @@
     }
    },
    "source": [
-    "The above cell runs the code in the [*\\_\\_init\\_\\_.py* <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_gh.png\">](https://github.com/webartifex/intro-to-python/blob/develop/11_classes/sample_package/__init__.py) file from top to bottom, which in turn runs the [*matrix.py* <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_gh.png\">](https://github.com/webartifex/intro-to-python/blob/develop/11_classes/sample_package/matrix.py), [*utils.py* <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_gh.png\">](https://github.com/webartifex/intro-to-python/blob/develop/11_classes/sample_package/utils.py), and [*vector.py* <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_gh.png\">](https://github.com/webartifex/intro-to-python/blob/develop/11_classes/sample_package/vector.py) modules (cf., look at the `import` statements in the four \\*.py files to get the idea). As both [*matrix.py* <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_gh.png\">](https://github.com/webartifex/intro-to-python/blob/develop/11_classes/sample_package/matrix.py) and [*vector.py* <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_gh.png\">](https://github.com/webartifex/intro-to-python/blob/develop/11_classes/sample_package/vector.py) depend on each other (i.e., the `Matrix` class needs the `Vector` class to work and vice versa), understanding the order in that the modules are executed is not trivial. Without going into detail, we mention that Python guarantees that each \\*.py file is run only once and figures out the order on its own. If Python is unable to do that, for example, due to unresolvable cirular imports, it aborts with an `ImportError`.\n",
+    "The above cell runs the code in the [*\\_\\_init\\_\\_.py* <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_gh.png\">](https://github.com/webartifex/intro-to-python/blob/main/11_classes/sample_package/__init__.py) file from top to bottom, which in turn runs the [*matrix.py* <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_gh.png\">](https://github.com/webartifex/intro-to-python/blob/main/11_classes/sample_package/matrix.py), [*utils.py* <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_gh.png\">](https://github.com/webartifex/intro-to-python/blob/main/11_classes/sample_package/utils.py), and [*vector.py* <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_gh.png\">](https://github.com/webartifex/intro-to-python/blob/main/11_classes/sample_package/vector.py) modules (cf., look at the `import` statements in the four \\*.py files to get the idea). As both [*matrix.py* <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_gh.png\">](https://github.com/webartifex/intro-to-python/blob/main/11_classes/sample_package/matrix.py) and [*vector.py* <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_gh.png\">](https://github.com/webartifex/intro-to-python/blob/main/11_classes/sample_package/vector.py) depend on each other (i.e., the `Matrix` class needs the `Vector` class to work and vice versa), understanding the order in that the modules are executed is not trivial. Without going into detail, we mention that Python guarantees that each \\*.py file is run only once and figures out the order on its own. If Python is unable to do that, for example, due to unresolvable cirular imports, it aborts with an `ImportError`.\n",
     "\n",
     "Below, `pkg` is an object of type `module` ..."
    ]
@@ -297,7 +297,7 @@
     }
    },
    "source": [
-    "The package's meta information and documentation are automatically parsed from the [*\\_\\_init\\_\\_.py* <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_gh.png\">](https://github.com/webartifex/intro-to-python/blob/develop/11_classes/sample_package/__init__.py) file."
+    "The package's meta information and documentation are automatically parsed from the [*\\_\\_init\\_\\_.py* <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_gh.png\">](https://github.com/webartifex/intro-to-python/blob/main/11_classes/sample_package/__init__.py) file."
    ]
   },
   {
@@ -1012,7 +1012,7 @@
     }
    },
    "source": [
-    "A common practice by package authors is to put all the objects on the package's top level that they want the package users to work with directly. That is achieved via the `import` statements in the [*\\_\\_init\\_\\_.py* <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_gh.png\">](https://github.com/webartifex/intro-to-python/blob/develop/11_classes/sample_package/__init__.py) file.\n",
+    "A common practice by package authors is to put all the objects on the package's top level that they want the package users to work with directly. That is achieved via the `import` statements in the [*\\_\\_init\\_\\_.py* <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_gh.png\">](https://github.com/webartifex/intro-to-python/blob/main/11_classes/sample_package/__init__.py) file.\n",
     "\n",
     "However, users can always reach into a package and work with its internals.\n",
     "\n",
@@ -1075,7 +1075,7 @@
     }
    },
    "source": [
-    "Also, let's import the [*utils.py* <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_gh.png\">](https://github.com/webartifex/intro-to-python/blob/develop/11_classes/sample_package/utils.py) module with the `norm()` function into the global scope. As this function is integrated into the `Vector.__abs__()` and `Matrix.__abs__()` methods, there is actually no need to work with it explicitly."
+    "Also, let's import the [*utils.py* <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_gh.png\">](https://github.com/webartifex/intro-to-python/blob/main/11_classes/sample_package/utils.py) module with the `norm()` function into the global scope. As this function is integrated into the `Vector.__abs__()` and `Matrix.__abs__()` methods, there is actually no need to work with it explicitly."
    ]
   },
   {
@@ -1140,7 +1140,7 @@
    "source": [
     "Many tutorials on the internet begin by importing \"everything\" from a package into the global scope with `from ... import *`.\n",
     "\n",
-    "That is commonly considered a *bad* practice as it may overwrite already existing variables. However, if the package's [*\\_\\_init\\_\\_.py* <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_gh.png\">](https://github.com/webartifex/intro-to-python/blob/develop/11_classes/sample_package/__init__.py) file defines an `__all__` attribute, a `list` with all the names to be \"exported,\" the **star import** is safe to be used, in particular, in *interactive* sessions like Jupyter notebooks. We emphasize that the star import should *not* be used *within* packages and modules as then it is not directly evident from a name where the corresponding object is defined.\n",
+    "That is commonly considered a *bad* practice as it may overwrite already existing variables. However, if the package's [*\\_\\_init\\_\\_.py* <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_gh.png\">](https://github.com/webartifex/intro-to-python/blob/main/11_classes/sample_package/__init__.py) file defines an `__all__` attribute, a `list` with all the names to be \"exported,\" the **star import** is safe to be used, in particular, in *interactive* sessions like Jupyter notebooks. We emphasize that the star import should *not* be used *within* packages and modules as then it is not directly evident from a name where the corresponding object is defined.\n",
     "\n",
     "For more best practices regarding importing we refer to, among others, [Google's Python Style Guide](https://google.github.io/styleguide/pyguide.html#22-imports).\n",
     "\n",
@@ -1238,9 +1238,9 @@
     }
    },
    "source": [
-    "The final implementations of the `Vector` and `Matrix` classes are in the [*matrix.py* <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_gh.png\">](https://github.com/webartifex/intro-to-python/blob/develop/11_classes/sample_package/matrix.py) and [*vector.py* <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_gh.png\">](https://github.com/webartifex/intro-to-python/blob/develop/11_classes/sample_package/vector.py) files: They integrate all of the functionalities introduced in this chapter. In addition, the code is cleaned up and fully documented, including examples of common usages.\n",
+    "The final implementations of the `Vector` and `Matrix` classes are in the [*matrix.py* <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_gh.png\">](https://github.com/webartifex/intro-to-python/blob/main/11_classes/sample_package/matrix.py) and [*vector.py* <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_gh.png\">](https://github.com/webartifex/intro-to-python/blob/main/11_classes/sample_package/vector.py) files: They integrate all of the functionalities introduced in this chapter. In addition, the code is cleaned up and fully documented, including examples of common usages.\n",
     "\n",
-    "We strongly suggest the eager student go over the files in the [*sample_package* <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_gh.png\">](https://github.com/webartifex/intro-to-python/tree/develop/11_classes/sample_package) in detail at some point to understand what well-written and (re-)usable code looks like."
+    "We strongly suggest the eager student go over the files in the [*sample_package* <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_gh.png\">](https://github.com/webartifex/intro-to-python/tree/main/11_classes/sample_package) in detail at some point to understand what well-written and (re-)usable code looks like."
    ]
   },
   {
@@ -1479,7 +1479,7 @@
     }
    },
    "source": [
-    "Both `Matrix/Vector.storage` and `Matrix/Vector.typing` themselves reference the `DEFAULT_ENTRIES_STORAGE` and `DEFAULT_ENTRY_TYPE` constants in the [*utils.py* <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_gh.png\">](https://github.com/webartifex/intro-to-python/blob/develop/11_classes/sample_package/utils.py) module. This way, we could, for example, change only the constants and thereby also change how the `._entries` are stored internally in both classes. Also, this single **[single source of truth <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_wiki.png\">](https://en.wikipedia.org/wiki/Single_source_of_truth)** ensures that both classes are consistent with each other at all times."
+    "Both `Matrix/Vector.storage` and `Matrix/Vector.typing` themselves reference the `DEFAULT_ENTRIES_STORAGE` and `DEFAULT_ENTRY_TYPE` constants in the [*utils.py* <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_gh.png\">](https://github.com/webartifex/intro-to-python/blob/main/11_classes/sample_package/utils.py) module. This way, we could, for example, change only the constants and thereby also change how the `._entries` are stored internally in both classes. Also, this single **[single source of truth <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_wiki.png\">](https://en.wikipedia.org/wiki/Single_source_of_truth)** ensures that both classes are consistent with each other at all times."
    ]
   },
   {
@@ -2238,7 +2238,7 @@
     "\n",
     "If we feel like sharing our linear algebra library with the world, we could easily do so on either [GitHub <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_gh.png\">](https://github.com) or [PyPI](https://pypi.org). However, for the domain of linear algebra this would be rather pointless as there is already a widely adopted library with [numpy](https://www.numpy.org/) that not only has a lot more features than ours but also is implemented in C, which makes it a lot faster with big data.\n",
     "\n",
-    "Let's model the example in the [first part <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/11_classes/00_content.ipynb#Example:-Vectors-&-Matrices) with both [numpy](https://www.numpy.org/) and our own DSL and compare them."
+    "Let's model the example in the [first part <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/main/11_classes/00_content.ipynb#Example:-Vectors-&-Matrices) with both [numpy](https://www.numpy.org/) and our own DSL and compare them."
    ]
   },
   {
diff --git a/11_classes/06_review.ipynb b/11_classes/06_review.ipynb
index 0f54544..53cb6bb 100644
--- a/11_classes/06_review.ipynb
+++ b/11_classes/06_review.ipynb
@@ -11,7 +11,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "The questions below assume that you have read the [first <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/11_classes/00_content.ipynb), [second <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/11_classes/02_content.ipynb), [third <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/11_classes/03_content.ipynb), and [fourth <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/11_classes/04_content.ipynb) part of Chapter 11.\n",
+    "The questions below assume that you have read the [first <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/main/11_classes/00_content.ipynb), [second <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/main/11_classes/02_content.ipynb), [third <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/main/11_classes/03_content.ipynb), and [fourth <img height=\"12\" style=\"display: inline-block\" src=\"../static/link/to_nb.png\">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/main/11_classes/04_content.ipynb) part of Chapter 11.\n",
     "\n",
     "Be concise in your answers! Most questions can be answered in *one* sentence."
    ]
diff --git a/CONTENTS.md b/CONTENTS.md
index cd8228b..c958a72 100644
--- a/CONTENTS.md
+++ b/CONTENTS.md
@@ -24,271 +24,271 @@ It is recommended
 
 If this is not possible,
     the files can be viewed in a web browser
-    either statically (i.e., read-only) on [nbviewer <img height="12" style="display: inline-block" src="static/link/to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/tree/develop/)
-    or interactively (i.e., code can be executed) on [Binder <img height="12" style="display: inline-block" src="static/link/to_mb.png">](https://mybinder.org/v2/gh/webartifex/intro-to-python/develop?urlpath=lab).
+    either statically (i.e., read-only) on [nbviewer <img height="12" style="display: inline-block" src="static/link/to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/tree/main/)
+    or interactively (i.e., code can be executed) on [Binder <img height="12" style="display: inline-block" src="static/link/to_mb.png">](https://mybinder.org/v2/gh/webartifex/intro-to-python/main?urlpath=lab).
 
 - *Chapter 0*: Introduction
-  - [content <img height="12" style="display: inline-block" src="static/link/to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/00_intro/00_content.ipynb)
-    [<img height="12" style="display: inline-block" src="static/link/to_mb.png">](https://mybinder.org/v2/gh/webartifex/intro-to-python/develop?urlpath=lab/tree/00_intro/00_content.ipynb)
+  - [content <img height="12" style="display: inline-block" src="static/link/to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/main/00_intro/00_content.ipynb)
+    [<img height="12" style="display: inline-block" src="static/link/to_mb.png">](https://mybinder.org/v2/gh/webartifex/intro-to-python/main?urlpath=lab/tree/00_intro/00_content.ipynb)
     (Python's History & Background;
      Open-source & Communities;
      JupyterLab;
      Programming vs. Computer Science;
      Learning Tips)
-  - [exercises <img height="12" style="display: inline-block" src="static/link/to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/00_intro/01_exercises_markdown.ipynb)
-    [<img height="12" style="display: inline-block" src="static/link/to_mb.png">](https://mybinder.org/v2/gh/webartifex/intro-to-python/develop?urlpath=lab/tree/00_intro/01_exercises_markdown.ipynb)
+  - [exercises <img height="12" style="display: inline-block" src="static/link/to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/main/00_intro/01_exercises_markdown.ipynb)
+    [<img height="12" style="display: inline-block" src="static/link/to_mb.png">](https://mybinder.org/v2/gh/webartifex/intro-to-python/main?urlpath=lab/tree/00_intro/01_exercises_markdown.ipynb)
     (Mastering Markdown)
-  - [review questions <img height="12" style="display: inline-block" src="static/link/to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/00_intro/02_review.ipynb)
+  - [review questions <img height="12" style="display: inline-block" src="static/link/to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/main/00_intro/02_review.ipynb)
 - **Part A: Expressing Logic**
   - *Chapter 1*: Elements of a Program
-    - [content <img height="12" style="display: inline-block" src="static/link/to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/01_elements/00_content.ipynb)
-      [<img height="12" style="display: inline-block" src="static/link/to_mb.png">](https://mybinder.org/v2/gh/webartifex/intro-to-python/develop?urlpath=lab/tree/01_elements/00_content.ipynb)
+    - [content <img height="12" style="display: inline-block" src="static/link/to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/main/01_elements/00_content.ipynb)
+      [<img height="12" style="display: inline-block" src="static/link/to_mb.png">](https://mybinder.org/v2/gh/webartifex/intro-to-python/main?urlpath=lab/tree/01_elements/00_content.ipynb)
       (A first Example: Averaging Even Numbers;
        Operators;
        Objects & Data Types;
        Errors)
-    - [exercises <img height="12" style="display: inline-block" src="static/link/to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/01_elements/01_exercises_print.ipynb)
-      [<img height="12" style="display: inline-block" src="static/link/to_mb.png">](https://mybinder.org/v2/gh/webartifex/intro-to-python/develop?urlpath=lab/tree/01_elements/01_exercises_print.ipynb)
+    - [exercises <img height="12" style="display: inline-block" src="static/link/to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/main/01_elements/01_exercises_print.ipynb)
+      [<img height="12" style="display: inline-block" src="static/link/to_mb.png">](https://mybinder.org/v2/gh/webartifex/intro-to-python/main?urlpath=lab/tree/01_elements/01_exercises_print.ipynb)
       (Printing Output)
-    - [exercises <img height="12" style="display: inline-block" src="static/link/to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/01_elements/02_exercises_for-loops.ipynb)
-      [<img height="12" style="display: inline-block" src="static/link/to_mb.png">](https://mybinder.org/v2/gh/webartifex/intro-to-python/develop?urlpath=lab/tree/01_elements/02_exercises_for-loops.ipynb)
+    - [exercises <img height="12" style="display: inline-block" src="static/link/to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/main/01_elements/02_exercises_for-loops.ipynb)
+      [<img height="12" style="display: inline-block" src="static/link/to_mb.png">](https://mybinder.org/v2/gh/webartifex/intro-to-python/main?urlpath=lab/tree/01_elements/02_exercises_for-loops.ipynb)
       (Simple `for`-loops)
-    - [content <img height="12" style="display: inline-block" src="static/link/to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/01_elements/03_content.ipynb)
-      [<img height="12" style="display: inline-block" src="static/link/to_mb.png">](https://mybinder.org/v2/gh/webartifex/intro-to-python/develop?urlpath=lab/tree/01_elements/03_content.ipynb)
+    - [content <img height="12" style="display: inline-block" src="static/link/to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/main/01_elements/03_content.ipynb)
+      [<img height="12" style="display: inline-block" src="static/link/to_mb.png">](https://mybinder.org/v2/gh/webartifex/intro-to-python/main?urlpath=lab/tree/01_elements/03_content.ipynb)
       (Memory in Detail;
        Variables & References;
        Mutability;
        Expressions & Statements)
-    - [exercises <img height="12" style="display: inline-block" src="static/link/to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/01_elements/04_exercises_calculator.ipynb)
-      [<img height="12" style="display: inline-block" src="static/link/to_mb.png">](https://mybinder.org/v2/gh/webartifex/intro-to-python/develop?urlpath=lab/tree/01_elements/04_exercises_calculator.ipynb)
+    - [exercises <img height="12" style="display: inline-block" src="static/link/to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/main/01_elements/04_exercises_calculator.ipynb)
+      [<img height="12" style="display: inline-block" src="static/link/to_mb.png">](https://mybinder.org/v2/gh/webartifex/intro-to-python/main?urlpath=lab/tree/01_elements/04_exercises_calculator.ipynb)
       (Python as a Calculator)
-    - [summary <img height="12" style="display: inline-block" src="static/link/to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/01_elements/05_summary.ipynb)
-    - [review questions <img height="12" style="display: inline-block" src="static/link/to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/01_elements/06_review.ipynb)
-    - [further resources <img height="12" style="display: inline-block" src="static/link/to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/01_elements/07_resources.ipynb)
-      [<img height="12" style="display: inline-block" src="static/link/to_mb.png">](https://mybinder.org/v2/gh/webartifex/intro-to-python/develop?urlpath=lab/tree/01_elements/07_resources.ipynb)
+    - [summary <img height="12" style="display: inline-block" src="static/link/to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/main/01_elements/05_summary.ipynb)
+    - [review questions <img height="12" style="display: inline-block" src="static/link/to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/main/01_elements/06_review.ipynb)
+    - [further resources <img height="12" style="display: inline-block" src="static/link/to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/main/01_elements/07_resources.ipynb)
+      [<img height="12" style="display: inline-block" src="static/link/to_mb.png">](https://mybinder.org/v2/gh/webartifex/intro-to-python/main?urlpath=lab/tree/01_elements/07_resources.ipynb)
   - *Chapter 2*: Functions & Modularization
-    - [content <img height="12" style="display: inline-block" src="static/link/to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/02_functions/00_content.ipynb)
-      [<img height="12" style="display: inline-block" src="static/link/to_mb.png">](https://mybinder.org/v2/gh/webartifex/intro-to-python/develop?urlpath=lab/tree/02_functions/00_content.ipynb)
+    - [content <img height="12" style="display: inline-block" src="static/link/to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/main/02_functions/00_content.ipynb)
+      [<img height="12" style="display: inline-block" src="static/link/to_mb.png">](https://mybinder.org/v2/gh/webartifex/intro-to-python/main?urlpath=lab/tree/02_functions/00_content.ipynb)
       (Built-in Functions & Constructors;
        Function Definitions;
        Function Calls & Scoping Rules;
        Positional vs. Keyword Arguments;
        Modularization)
-    - [exercises <img height="12" style="display: inline-block" src="static/link/to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/02_functions/01_exercises_sphere-volume.ipynb)
-      [<img height="12" style="display: inline-block" src="static/link/to_mb.png">](https://mybinder.org/v2/gh/webartifex/intro-to-python/develop?urlpath=lab/tree/02_functions/01_exercises_sphere-volume.ipynb)
+    - [exercises <img height="12" style="display: inline-block" src="static/link/to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/main/02_functions/01_exercises_sphere-volume.ipynb)
+      [<img height="12" style="display: inline-block" src="static/link/to_mb.png">](https://mybinder.org/v2/gh/webartifex/intro-to-python/main?urlpath=lab/tree/02_functions/01_exercises_sphere-volume.ipynb)
       (Volume of a Sphere)
-    - [content <img height="12" style="display: inline-block" src="static/link/to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/02_functions/02_content.ipynb)
-      [<img height="12" style="display: inline-block" src="static/link/to_mb.png">](https://mybinder.org/v2/gh/webartifex/intro-to-python/develop?urlpath=lab/tree/02_functions/02_content.ipynb)
+    - [content <img height="12" style="display: inline-block" src="static/link/to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/main/02_functions/02_content.ipynb)
+      [<img height="12" style="display: inline-block" src="static/link/to_mb.png">](https://mybinder.org/v2/gh/webartifex/intro-to-python/main?urlpath=lab/tree/02_functions/02_content.ipynb)
       (Standard Library: `math` & `random` Modules;
        Third-party Packages: `numpy` Library;
        Writing one's own Modules)
-    - [summary <img height="12" style="display: inline-block" src="static/link/to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/02_functions/03_summary.ipynb)
-    - [review questions <img height="12" style="display: inline-block" src="static/link/to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/02_functions/04_review.ipynb)
+    - [summary <img height="12" style="display: inline-block" src="static/link/to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/main/02_functions/03_summary.ipynb)
+    - [review questions <img height="12" style="display: inline-block" src="static/link/to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/main/02_functions/04_review.ipynb)
   - *Chapter 3*: Conditionals & Exceptions
-    - [content <img height="12" style="display: inline-block" src="static/link/to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/03_conditionals/00_content.ipynb)
-      [<img height="12" style="display: inline-block" src="static/link/to_mb.png">](https://mybinder.org/v2/gh/webartifex/intro-to-python/develop?urlpath=lab/tree/03_conditionals/00_content.ipynb)
+    - [content <img height="12" style="display: inline-block" src="static/link/to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/main/03_conditionals/00_content.ipynb)
+      [<img height="12" style="display: inline-block" src="static/link/to_mb.png">](https://mybinder.org/v2/gh/webartifex/intro-to-python/main?urlpath=lab/tree/03_conditionals/00_content.ipynb)
       (Boolean Expressions;
        Relational Operators;
        Logical Operators;
        `if` statement;
        Exception Handling)
-    - [exercises <img height="12" style="display: inline-block" src="static/link/to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/03_conditionals/01_exercises_discounts.ipynb)
-      [<img height="12" style="display: inline-block" src="static/link/to_mb.png">](https://mybinder.org/v2/gh/webartifex/intro-to-python/develop?urlpath=lab/tree/03_conditionals/01_exercises_discounts.ipynb)
+    - [exercises <img height="12" style="display: inline-block" src="static/link/to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/main/03_conditionals/01_exercises_discounts.ipynb)
+      [<img height="12" style="display: inline-block" src="static/link/to_mb.png">](https://mybinder.org/v2/gh/webartifex/intro-to-python/main?urlpath=lab/tree/03_conditionals/01_exercises_discounts.ipynb)
       (Discounting Customer Orders)
-    - [exercises <img height="12" style="display: inline-block" src="static/link/to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/03_conditionals/02_exercises_fizz-buzz.ipynb)
-      [<img height="12" style="display: inline-block" src="static/link/to_mb.png">](https://mybinder.org/v2/gh/webartifex/intro-to-python/develop?urlpath=lab/tree/03_conditionals/02_exercises_fizz-buzz.ipynb)
+    - [exercises <img height="12" style="display: inline-block" src="static/link/to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/main/03_conditionals/02_exercises_fizz-buzz.ipynb)
+      [<img height="12" style="display: inline-block" src="static/link/to_mb.png">](https://mybinder.org/v2/gh/webartifex/intro-to-python/main?urlpath=lab/tree/03_conditionals/02_exercises_fizz-buzz.ipynb)
       (Fizz Buzz)
-    - [summary <img height="12" style="display: inline-block" src="static/link/to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/03_conditionals/03_summary.ipynb)
-    - [review questions <img height="12" style="display: inline-block" src="static/link/to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/03_conditionals/04_review.ipynb)
+    - [summary <img height="12" style="display: inline-block" src="static/link/to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/main/03_conditionals/03_summary.ipynb)
+    - [review questions <img height="12" style="display: inline-block" src="static/link/to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/main/03_conditionals/04_review.ipynb)
   - *Chapter 4*: Recursion & Looping
-    - [content <img height="12" style="display: inline-block" src="static/link/to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/04_iteration/00_content.ipynb)
-      [<img height="12" style="display: inline-block" src="static/link/to_mb.png">](https://mybinder.org/v2/gh/webartifex/intro-to-python/develop?urlpath=lab/tree/04_iteration/00_content.ipynb)
+    - [content <img height="12" style="display: inline-block" src="static/link/to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/main/04_iteration/00_content.ipynb)
+      [<img height="12" style="display: inline-block" src="static/link/to_mb.png">](https://mybinder.org/v2/gh/webartifex/intro-to-python/main?urlpath=lab/tree/04_iteration/00_content.ipynb)
       (Recursion;
        Examples: Factorial, Euclid's Algorithm, & Fibonacci;
        Duck Typing;
        Type Casting & Checking;
        Input Validation)
-    - [exercises <img height="12" style="display: inline-block" src="static/link/to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/04_iteration/01_exercises_hanoi-towers.ipynb)
-      [<img height="12" style="display: inline-block" src="static/link/to_mb.png">](https://mybinder.org/v2/gh/webartifex/intro-to-python/develop?urlpath=lab/tree/04_iteration/01_exercises_hanoi-towers.ipynb)
+    - [exercises <img height="12" style="display: inline-block" src="static/link/to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/main/04_iteration/01_exercises_hanoi-towers.ipynb)
+      [<img height="12" style="display: inline-block" src="static/link/to_mb.png">](https://mybinder.org/v2/gh/webartifex/intro-to-python/main?urlpath=lab/tree/04_iteration/01_exercises_hanoi-towers.ipynb)
       (Towers of Hanoi)
-    - [content <img height="12" style="display: inline-block" src="static/link/to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/04_iteration/02_content.ipynb)
-      [<img height="12" style="display: inline-block" src="static/link/to_mb.png">](https://mybinder.org/v2/gh/webartifex/intro-to-python/develop?urlpath=lab/tree/04_iteration/02_content.ipynb)
+    - [content <img height="12" style="display: inline-block" src="static/link/to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/main/04_iteration/02_content.ipynb)
+      [<img height="12" style="display: inline-block" src="static/link/to_mb.png">](https://mybinder.org/v2/gh/webartifex/intro-to-python/main?urlpath=lab/tree/04_iteration/02_content.ipynb)
       (Looping with `while` & `for`;
        Examples: Collatz Conjecture, Factorial, Euclid's Algorithm, & Fibonacci;
        Containers vs. Iterables)
-    - [content <img height="12" style="display: inline-block" src="static/link/to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/04_iteration/03_content.ipynb)
-      [<img height="12" style="display: inline-block" src="static/link/to_mb.png">](https://mybinder.org/v2/gh/webartifex/intro-to-python/develop?urlpath=lab/tree/04_iteration/03_content.ipynb)
+    - [content <img height="12" style="display: inline-block" src="static/link/to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/main/04_iteration/03_content.ipynb)
+      [<img height="12" style="display: inline-block" src="static/link/to_mb.png">](https://mybinder.org/v2/gh/webartifex/intro-to-python/main?urlpath=lab/tree/04_iteration/03_content.ipynb)
       (Customizing Loops with `break` and `continue`;
        Indefinite Loops)
-    - [exercises <img height="12" style="display: inline-block" src="static/link/to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/04_iteration/04_exercises_dice.ipynb)
-      [<img height="12" style="display: inline-block" src="static/link/to_mb.png">](https://mybinder.org/v2/gh/webartifex/intro-to-python/develop?urlpath=lab/tree/04_iteration/04_exercises_dice.ipynb)
+    - [exercises <img height="12" style="display: inline-block" src="static/link/to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/main/04_iteration/04_exercises_dice.ipynb)
+      [<img height="12" style="display: inline-block" src="static/link/to_mb.png">](https://mybinder.org/v2/gh/webartifex/intro-to-python/main?urlpath=lab/tree/04_iteration/04_exercises_dice.ipynb)
       (Throwing Dice)
-    - [summary <img height="12" style="display: inline-block" src="static/link/to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/04_iteration/05_summary.ipynb)
-    - [review questions <img height="12" style="display: inline-block" src="static/link/to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/04_iteration/06_review.ipynb)
+    - [summary <img height="12" style="display: inline-block" src="static/link/to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/main/04_iteration/05_summary.ipynb)
+    - [review questions <img height="12" style="display: inline-block" src="static/link/to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/main/04_iteration/06_review.ipynb)
 - **Part B: Managing Data and Memory**
   - *Chapter 5*: Numbers & Bits
-    - [content <img height="12" style="display: inline-block" src="static/link/to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/05_numbers/00_content.ipynb)
-      [<img height="12" style="display: inline-block" src="static/link/to_mb.png">](https://mybinder.org/v2/gh/webartifex/intro-to-python/develop?urlpath=lab/tree/05_numbers/00_content.ipynb)
+    - [content <img height="12" style="display: inline-block" src="static/link/to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/main/05_numbers/00_content.ipynb)
+      [<img height="12" style="display: inline-block" src="static/link/to_mb.png">](https://mybinder.org/v2/gh/webartifex/intro-to-python/main?urlpath=lab/tree/05_numbers/00_content.ipynb)
       (`int` Type;
        Binary & Hexadecimal Representations;
        Bit Arithmetic;
        Bitwise Operators)
-    - [content <img height="12" style="display: inline-block" src="static/link/to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/05_numbers/01_content.ipynb)
-      [<img height="12" style="display: inline-block" src="static/link/to_mb.png">](https://mybinder.org/v2/gh/webartifex/intro-to-python/develop?urlpath=lab/tree/05_numbers/01_content.ipynb)
+    - [content <img height="12" style="display: inline-block" src="static/link/to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/main/05_numbers/01_content.ipynb)
+      [<img height="12" style="display: inline-block" src="static/link/to_mb.png">](https://mybinder.org/v2/gh/webartifex/intro-to-python/main?urlpath=lab/tree/05_numbers/01_content.ipynb)
       (`float` Type;
        Floating-point Standard;
        Special Values;
        Imprecision;
        Binary & Hexadecimal Representations)
-    - [content <img height="12" style="display: inline-block" src="static/link/to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/05_numbers/02_content.ipynb)
-      [<img height="12" style="display: inline-block" src="static/link/to_mb.png">](https://mybinder.org/v2/gh/webartifex/intro-to-python/develop?urlpath=lab/tree/05_numbers/02_content.ipynb)
+    - [content <img height="12" style="display: inline-block" src="static/link/to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/main/05_numbers/02_content.ipynb)
+      [<img height="12" style="display: inline-block" src="static/link/to_mb.png">](https://mybinder.org/v2/gh/webartifex/intro-to-python/main?urlpath=lab/tree/05_numbers/02_content.ipynb)
       (`complex` Type;
        Numerical Tower;
        Duck vs. Goose Typing)
-    - [appendix <img height="12" style="display: inline-block" src="static/link/to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/05_numbers/03_appendix.ipynb)
-      [<img height="12" style="display: inline-block" src="static/link/to_mb.png">](https://mybinder.org/v2/gh/webartifex/intro-to-python/develop?urlpath=lab/tree/05_numbers/03_appendix.ipynb)
+    - [appendix <img height="12" style="display: inline-block" src="static/link/to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/main/05_numbers/03_appendix.ipynb)
+      [<img height="12" style="display: inline-block" src="static/link/to_mb.png">](https://mybinder.org/v2/gh/webartifex/intro-to-python/main?urlpath=lab/tree/05_numbers/03_appendix.ipynb)
       (`Decimal` Type;
        `Fraction` Type)
-    - [summary <img height="12" style="display: inline-block" src="static/link/to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/05_numbers/04_summary.ipynb)
-    - [review questions <img height="12" style="display: inline-block" src="static/link/to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/05_numbers/05_review.ipynb)
-    - [further resources <img height="12" style="display: inline-block" src="static/link/to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/05_numbers/06_resources.ipynb)
-      [<img height="12" style="display: inline-block" src="static/link/to_mb.png">](https://mybinder.org/v2/gh/webartifex/intro-to-python/develop?urlpath=lab/tree/05_numbers/06_resources.ipynb)
+    - [summary <img height="12" style="display: inline-block" src="static/link/to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/main/05_numbers/04_summary.ipynb)
+    - [review questions <img height="12" style="display: inline-block" src="static/link/to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/main/05_numbers/05_review.ipynb)
+    - [further resources <img height="12" style="display: inline-block" src="static/link/to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/main/05_numbers/06_resources.ipynb)
+      [<img height="12" style="display: inline-block" src="static/link/to_mb.png">](https://mybinder.org/v2/gh/webartifex/intro-to-python/main?urlpath=lab/tree/05_numbers/06_resources.ipynb)
   - *Chapter 6*: Text & Bytes
-    - [content <img height="12" style="display: inline-block" src="static/link/to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/06_text/00_content.ipynb)
-      [<img height="12" style="display: inline-block" src="static/link/to_mb.png">](https://mybinder.org/v2/gh/webartifex/intro-to-python/develop?urlpath=lab/tree/06_text/00_content.ipynb)
+    - [content <img height="12" style="display: inline-block" src="static/link/to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/main/06_text/00_content.ipynb)
+      [<img height="12" style="display: inline-block" src="static/link/to_mb.png">](https://mybinder.org/v2/gh/webartifex/intro-to-python/main?urlpath=lab/tree/06_text/00_content.ipynb)
       (`str` Type;
        Reading Files;
        Sequences;
        Indexing & Slicing;
        String Methods & Operations;
        String Interpolation)
-    - [exercises <img height="12" style="display: inline-block" src="static/link/to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/06_text/01_exercises_palindromes.ipynb)
-      [<img height="12" style="display: inline-block" src="static/link/to_mb.png">](https://mybinder.org/v2/gh/webartifex/intro-to-python/develop?urlpath=lab/tree/06_text/01_exercises_palindromes.ipynb)
+    - [exercises <img height="12" style="display: inline-block" src="static/link/to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/main/06_text/01_exercises_palindromes.ipynb)
+      [<img height="12" style="display: inline-block" src="static/link/to_mb.png">](https://mybinder.org/v2/gh/webartifex/intro-to-python/main?urlpath=lab/tree/06_text/01_exercises_palindromes.ipynb)
       (Detecting Palindromes)
-    - [content <img height="12" style="display: inline-block" src="static/link/to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/06_text/02_content.ipynb)
-      [<img height="12" style="display: inline-block" src="static/link/to_mb.png">](https://mybinder.org/v2/gh/webartifex/intro-to-python/develop?urlpath=lab/tree/06_text/02_content.ipynb)
+    - [content <img height="12" style="display: inline-block" src="static/link/to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/main/06_text/02_content.ipynb)
+      [<img height="12" style="display: inline-block" src="static/link/to_mb.png">](https://mybinder.org/v2/gh/webartifex/intro-to-python/main?urlpath=lab/tree/06_text/02_content.ipynb)
       (Special Characters;
        ASCII & Unicode;
        Multi-line Strings;
        `bytes` Type;
        Character Encodings)
-    - [summary <img height="12" style="display: inline-block" src="static/link/to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/06_text/03_summary.ipynb)
-    - [review questions <img height="12" style="display: inline-block" src="static/link/to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/06_text/04_review.ipynb)
-    - [further resources <img height="12" style="display: inline-block" src="static/link/to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/06_text/05_resources.ipynb)
-      [<img height="12" style="display: inline-block" src="static/link/to_mb.png">](https://mybinder.org/v2/gh/webartifex/intro-to-python/develop?urlpath=lab/tree/06_text/05_resources.ipynb)
+    - [summary <img height="12" style="display: inline-block" src="static/link/to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/main/06_text/03_summary.ipynb)
+    - [review questions <img height="12" style="display: inline-block" src="static/link/to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/main/06_text/04_review.ipynb)
+    - [further resources <img height="12" style="display: inline-block" src="static/link/to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/main/06_text/05_resources.ipynb)
+      [<img height="12" style="display: inline-block" src="static/link/to_mb.png">](https://mybinder.org/v2/gh/webartifex/intro-to-python/main?urlpath=lab/tree/06_text/05_resources.ipynb)
   - *Chapter 7*: Sequential Data
-    - [content <img height="12" style="display: inline-block" src="static/link/to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/07_sequences/00_content.ipynb)
-      [<img height="12" style="display: inline-block" src="static/link/to_mb.png">](https://mybinder.org/v2/gh/webartifex/intro-to-python/develop?urlpath=lab/tree/07_sequences/00_content.ipynb)
+    - [content <img height="12" style="display: inline-block" src="static/link/to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/main/07_sequences/00_content.ipynb)
+      [<img height="12" style="display: inline-block" src="static/link/to_mb.png">](https://mybinder.org/v2/gh/webartifex/intro-to-python/main?urlpath=lab/tree/07_sequences/00_content.ipynb)
       (Collections vs. Sequences;
        ABCs: `Container`, `Iterable`, `Sized`, & `Reversible`)
-    - [content <img height="12" style="display: inline-block" src="static/link/to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/07_sequences/01_content.ipynb)
-      [<img height="12" style="display: inline-block" src="static/link/to_mb.png">](https://mybinder.org/v2/gh/webartifex/intro-to-python/develop?urlpath=lab/tree/07_sequences/01_content.ipynb)
+    - [content <img height="12" style="display: inline-block" src="static/link/to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/main/07_sequences/01_content.ipynb)
+      [<img height="12" style="display: inline-block" src="static/link/to_mb.png">](https://mybinder.org/v2/gh/webartifex/intro-to-python/main?urlpath=lab/tree/07_sequences/01_content.ipynb)
       (`list` Type;
        Indexing & Slicing;
        Shallow vs. Deep Copies;
        List Methods & Operations)
-    - [exercises <img height="12" style="display: inline-block" src="static/link/to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/07_sequences/02_exercises_lists.ipynb)
-      [<img height="12" style="display: inline-block" src="static/link/to_mb.png">](https://mybinder.org/v2/gh/webartifex/intro-to-python/develop?urlpath=lab/tree/07_sequences/02_exercises_lists.ipynb)
+    - [exercises <img height="12" style="display: inline-block" src="static/link/to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/main/07_sequences/02_exercises_lists.ipynb)
+      [<img height="12" style="display: inline-block" src="static/link/to_mb.png">](https://mybinder.org/v2/gh/webartifex/intro-to-python/main?urlpath=lab/tree/07_sequences/02_exercises_lists.ipynb)
       (Working with Lists)
-    - [content <img height="12" style="display: inline-block" src="static/link/to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/07_sequences/03_content.ipynb)
-      [<img height="12" style="display: inline-block" src="static/link/to_mb.png">](https://mybinder.org/v2/gh/webartifex/intro-to-python/develop?urlpath=lab/tree/07_sequences/03_content.ipynb)
+    - [content <img height="12" style="display: inline-block" src="static/link/to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/main/07_sequences/03_content.ipynb)
+      [<img height="12" style="display: inline-block" src="static/link/to_mb.png">](https://mybinder.org/v2/gh/webartifex/intro-to-python/main?urlpath=lab/tree/07_sequences/03_content.ipynb)
       (Modifiers vs. Pure Functions;
        `tuple` Type;
        Packing & Unpacking;
        `*args` in Function Definitions)
-    - [exercises <img height="12" style="display: inline-block" src="static/link/to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/07_sequences/04_exercises_un-packing.ipynb)
-      [<img height="12" style="display: inline-block" src="static/link/to_mb.png">](https://mybinder.org/v2/gh/webartifex/intro-to-python/develop?urlpath=lab/tree/07_sequences/04_exercises_un-packing.ipynb)
+    - [exercises <img height="12" style="display: inline-block" src="static/link/to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/main/07_sequences/04_exercises_un-packing.ipynb)
+      [<img height="12" style="display: inline-block" src="static/link/to_mb.png">](https://mybinder.org/v2/gh/webartifex/intro-to-python/main?urlpath=lab/tree/07_sequences/04_exercises_un-packing.ipynb)
       (Packing & Unpacking with Functions)
-    - [appendix <img height="12" style="display: inline-block" src="static/link/to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/07_sequences/05_appendix.ipynb)
-      [<img height="12" style="display: inline-block" src="static/link/to_mb.png">](https://mybinder.org/v2/gh/webartifex/intro-to-python/develop?urlpath=lab/tree/07_sequences/05_appendix.ipynb)
+    - [appendix <img height="12" style="display: inline-block" src="static/link/to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/main/07_sequences/05_appendix.ipynb)
+      [<img height="12" style="display: inline-block" src="static/link/to_mb.png">](https://mybinder.org/v2/gh/webartifex/intro-to-python/main?urlpath=lab/tree/07_sequences/05_appendix.ipynb)
       (`namedtuple` Type)
-    - [summary <img height="12" style="display: inline-block" src="static/link/to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/07_sequences/06_summary.ipynb)
-    - [review questions <img height="12" style="display: inline-block" src="static/link/to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/07_sequences/07_review.ipynb)
+    - [summary <img height="12" style="display: inline-block" src="static/link/to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/main/07_sequences/06_summary.ipynb)
+    - [review questions <img height="12" style="display: inline-block" src="static/link/to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/main/07_sequences/07_review.ipynb)
   - *Chapter 8*: Map, Filter, & Reduce
-    - [content <img height="12" style="display: inline-block" src="static/link/to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/08_mfr/00_content.ipynb)
-      [<img height="12" style="display: inline-block" src="static/link/to_mb.png">](https://mybinder.org/v2/gh/webartifex/intro-to-python/develop?urlpath=lab/tree/08_mfr/00_content.ipynb)
+    - [content <img height="12" style="display: inline-block" src="static/link/to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/main/08_mfr/00_content.ipynb)
+      [<img height="12" style="display: inline-block" src="static/link/to_mb.png">](https://mybinder.org/v2/gh/webartifex/intro-to-python/main?urlpath=lab/tree/08_mfr/00_content.ipynb)
       (Mapping;
        Filtering;
        Reducing;
        `lambda` Expression)
-    - [content <img height="12" style="display: inline-block" src="static/link/to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/08_mfr/01_content.ipynb)
-      [<img height="12" style="display: inline-block" src="static/link/to_mb.png">](https://mybinder.org/v2/gh/webartifex/intro-to-python/develop?urlpath=lab/tree/08_mfr/01_content.ipynb)
+    - [content <img height="12" style="display: inline-block" src="static/link/to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/main/08_mfr/01_content.ipynb)
+      [<img height="12" style="display: inline-block" src="static/link/to_mb.png">](https://mybinder.org/v2/gh/webartifex/intro-to-python/main?urlpath=lab/tree/08_mfr/01_content.ipynb)
       (`list` Comprehension;
        `generator` Expression;
        Streams of Data;
        Boolean Reducers)
-    - [exercises <img height="12" style="display: inline-block" src="static/link/to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/08_mfr/02_exercises_outliers.ipynb)
-      [<img height="12" style="display: inline-block" src="static/link/to_mb.png">](https://mybinder.org/v2/gh/webartifex/intro-to-python/develop?urlpath=lab/tree/08_mfr/02_exercises_outliers.ipynb)
+    - [exercises <img height="12" style="display: inline-block" src="static/link/to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/main/08_mfr/02_exercises_outliers.ipynb)
+      [<img height="12" style="display: inline-block" src="static/link/to_mb.png">](https://mybinder.org/v2/gh/webartifex/intro-to-python/main?urlpath=lab/tree/08_mfr/02_exercises_outliers.ipynb)
       (Removing Outliers in Streaming Data)
-    - [exercises <img height="12" style="display: inline-block" src="static/link/to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/08_mfr/03_exercises_un-packing.ipynb)
-      [<img height="12" style="display: inline-block" src="static/link/to_mb.png">](https://mybinder.org/v2/gh/webartifex/intro-to-python/develop?urlpath=lab/tree/08_mfr/03_exercises_un-packing.ipynb)
+    - [exercises <img height="12" style="display: inline-block" src="static/link/to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/main/08_mfr/03_exercises_un-packing.ipynb)
+      [<img height="12" style="display: inline-block" src="static/link/to_mb.png">](https://mybinder.org/v2/gh/webartifex/intro-to-python/main?urlpath=lab/tree/08_mfr/03_exercises_un-packing.ipynb)
       (Packing & Unpacking with Functions, continued)
-    - [content <img height="12" style="display: inline-block" src="static/link/to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/08_mfr/04_content.ipynb)
-      [<img height="12" style="display: inline-block" src="static/link/to_mb.png">](https://mybinder.org/v2/gh/webartifex/intro-to-python/develop?urlpath=lab/tree/08_mfr/04_content.ipynb)
+    - [content <img height="12" style="display: inline-block" src="static/link/to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/main/08_mfr/04_content.ipynb)
+      [<img height="12" style="display: inline-block" src="static/link/to_mb.png">](https://mybinder.org/v2/gh/webartifex/intro-to-python/main?urlpath=lab/tree/08_mfr/04_content.ipynb)
       (Iterators vs. Iterables;
        Example: `sorted()` vs. `reversed()`)
-    - [summary <img height="12" style="display: inline-block" src="static/link/to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/08_mfr/05_summary.ipynb)
-    - [review questions <img height="12" style="display: inline-block" src="static/link/to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/08_mfr/06_review.ipynb)
+    - [summary <img height="12" style="display: inline-block" src="static/link/to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/main/08_mfr/05_summary.ipynb)
+    - [review questions <img height="12" style="display: inline-block" src="static/link/to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/main/08_mfr/06_review.ipynb)
   - *Chapter 9*: Mappings & Sets
-    - [content <img height="12" style="display: inline-block" src="static/link/to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/09_mappings/00_content.ipynb)
-      [<img height="12" style="display: inline-block" src="static/link/to_mb.png">](https://mybinder.org/v2/gh/webartifex/intro-to-python/develop?urlpath=lab/tree/09_mappings/00_content.ipynb)
+    - [content <img height="12" style="display: inline-block" src="static/link/to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/main/09_mappings/00_content.ipynb)
+      [<img height="12" style="display: inline-block" src="static/link/to_mb.png">](https://mybinder.org/v2/gh/webartifex/intro-to-python/main?urlpath=lab/tree/09_mappings/00_content.ipynb)
       (`dict` Type;
        Nested Data;
        Hash Tables;
        `dict` Methods & Behavior;
        `dict` Comprehension)
-    - [exercises <img height="12" style="display: inline-block" src="static/link/to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/09_mappings/01_exercises_nested-data.ipynb)
-      [<img height="12" style="display: inline-block" src="static/link/to_mb.png">](https://mybinder.org/v2/gh/webartifex/intro-to-python/develop?urlpath=lab/tree/09_mappings/01_exercises_nested-data.ipynb)
+    - [exercises <img height="12" style="display: inline-block" src="static/link/to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/main/09_mappings/01_exercises_nested-data.ipynb)
+      [<img height="12" style="display: inline-block" src="static/link/to_mb.png">](https://mybinder.org/v2/gh/webartifex/intro-to-python/main?urlpath=lab/tree/09_mappings/01_exercises_nested-data.ipynb)
       (Working with Nested Data)
-    - [content <img height="12" style="display: inline-block" src="static/link/to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/09_mappings/02_content.ipynb)
-      [<img height="12" style="display: inline-block" src="static/link/to_mb.png">](https://mybinder.org/v2/gh/webartifex/intro-to-python/develop?urlpath=lab/tree/09_mappings/02_content.ipynb)
+    - [content <img height="12" style="display: inline-block" src="static/link/to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/main/09_mappings/02_content.ipynb)
+      [<img height="12" style="display: inline-block" src="static/link/to_mb.png">](https://mybinder.org/v2/gh/webartifex/intro-to-python/main?urlpath=lab/tree/09_mappings/02_content.ipynb)
       (`**kwargs` in Function Definitions;
        Memoization)
-    - [exercises <img height="12" style="display: inline-block" src="static/link/to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/09_mappings/03_exercises_fibonacci.ipynb)
-      [<img height="12" style="display: inline-block" src="static/link/to_mb.png">](https://mybinder.org/v2/gh/webartifex/intro-to-python/develop?urlpath=lab/tree/09_mappings/03_exercises_fibonacci.ipynb)
+    - [exercises <img height="12" style="display: inline-block" src="static/link/to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/main/09_mappings/03_exercises_fibonacci.ipynb)
+      [<img height="12" style="display: inline-block" src="static/link/to_mb.png">](https://mybinder.org/v2/gh/webartifex/intro-to-python/main?urlpath=lab/tree/09_mappings/03_exercises_fibonacci.ipynb)
       (Memoization without Side Effects)
-    - [content <img height="12" style="display: inline-block" src="static/link/to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/09_mappings/04_content.ipynb)
-      [<img height="12" style="display: inline-block" src="static/link/to_mb.png">](https://mybinder.org/v2/gh/webartifex/intro-to-python/develop?urlpath=lab/tree/09_mappings/04_content.ipynb)
+    - [content <img height="12" style="display: inline-block" src="static/link/to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/main/09_mappings/04_content.ipynb)
+      [<img height="12" style="display: inline-block" src="static/link/to_mb.png">](https://mybinder.org/v2/gh/webartifex/intro-to-python/main?urlpath=lab/tree/09_mappings/04_content.ipynb)
       (`set` Type;
        `set` Methods & Operations;
        `set` Comprehension;
        `frozenset` Type)
-    - [appendix <img height="12" style="display: inline-block" src="static/link/to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/09_mappings/05_appendix.ipynb)
-      [<img height="12" style="display: inline-block" src="static/link/to_mb.png">](https://mybinder.org/v2/gh/webartifex/intro-to-python/develop?urlpath=lab/tree/09_mappings/05_appendix.ipynb)
+    - [appendix <img height="12" style="display: inline-block" src="static/link/to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/main/09_mappings/05_appendix.ipynb)
+      [<img height="12" style="display: inline-block" src="static/link/to_mb.png">](https://mybinder.org/v2/gh/webartifex/intro-to-python/main?urlpath=lab/tree/09_mappings/05_appendix.ipynb)
       (`defaultdict` Type;
        `Counter` Type;
        `ChainMap` Type)
-    - [summary <img height="12" style="display: inline-block" src="static/link/to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/09_mappings/06_summary.ipynb)
-    - [review questions <img height="12" style="display: inline-block" src="static/link/to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/09_mappings/07_review.ipynb)
-    - [further resources <img height="12" style="display: inline-block" src="static/link/to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/09_mappings/08_resources.ipynb)
-      [<img height="12" style="display: inline-block" src="static/link/to_mb.png">](https://mybinder.org/v2/gh/webartifex/intro-to-python/develop?urlpath=lab/tree/09_mappings/08_resources.ipynb)
+    - [summary <img height="12" style="display: inline-block" src="static/link/to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/main/09_mappings/06_summary.ipynb)
+    - [review questions <img height="12" style="display: inline-block" src="static/link/to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/main/09_mappings/07_review.ipynb)
+    - [further resources <img height="12" style="display: inline-block" src="static/link/to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/main/09_mappings/08_resources.ipynb)
+      [<img height="12" style="display: inline-block" src="static/link/to_mb.png">](https://mybinder.org/v2/gh/webartifex/intro-to-python/main?urlpath=lab/tree/09_mappings/08_resources.ipynb)
   - *Chapter 10*: Arrays & Dataframes
   - *Chapter 11*: Classes & Instances
-    - [content <img height="12" style="display: inline-block" src="static/link/to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/11_classes/00_content.ipynb)
-      [<img height="12" style="display: inline-block" src="static/link/to_mb.png">](https://mybinder.org/v2/gh/webartifex/intro-to-python/develop?urlpath=lab/tree/11_classes/00_content.ipynb)
+    - [content <img height="12" style="display: inline-block" src="static/link/to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/main/11_classes/00_content.ipynb)
+      [<img height="12" style="display: inline-block" src="static/link/to_mb.png">](https://mybinder.org/v2/gh/webartifex/intro-to-python/main?urlpath=lab/tree/11_classes/00_content.ipynb)
       (`class` Statement;
        Instantiation;
        Text Representations;
        Instance Methods vs. Class Methods;
        Computed Properties)
-    - [exercises <img height="12" style="display: inline-block" src="static/link/to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/11_classes/01_exercises_tsp.ipynb)
-      [<img height="12" style="display: inline-block" src="static/link/to_mb.png">](https://mybinder.org/v2/gh/webartifex/intro-to-python/develop?urlpath=lab/tree/11_classes/01_exercises_tsp.ipynb)
+    - [exercises <img height="12" style="display: inline-block" src="static/link/to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/main/11_classes/01_exercises_tsp.ipynb)
+      [<img height="12" style="display: inline-block" src="static/link/to_mb.png">](https://mybinder.org/v2/gh/webartifex/intro-to-python/main?urlpath=lab/tree/11_classes/01_exercises_tsp.ipynb)
       (A Traveling Salesman Problem)
-    - [content <img height="12" style="display: inline-block" src="static/link/to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/11_classes/02_content.ipynb)
-      [<img height="12" style="display: inline-block" src="static/link/to_mb.png">](https://mybinder.org/v2/gh/webartifex/intro-to-python/develop?urlpath=lab/tree/11_classes/02_content.ipynb)
+    - [content <img height="12" style="display: inline-block" src="static/link/to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/main/11_classes/02_content.ipynb)
+      [<img height="12" style="display: inline-block" src="static/link/to_mb.png">](https://mybinder.org/v2/gh/webartifex/intro-to-python/main?urlpath=lab/tree/11_classes/02_content.ipynb)
       (Sequence Emulation & Iteration;
        Python's Data Model;
        (Im)mutable Data Types;
        Method Chaining;
        Polymorphism)
-    - [content <img height="12" style="display: inline-block" src="static/link/to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/11_classes/03_content.ipynb)
-      [<img height="12" style="display: inline-block" src="static/link/to_mb.png">](https://mybinder.org/v2/gh/webartifex/intro-to-python/develop?urlpath=lab/tree/11_classes/03_content.ipynb)
+    - [content <img height="12" style="display: inline-block" src="static/link/to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/main/11_classes/03_content.ipynb)
+      [<img height="12" style="display: inline-block" src="static/link/to_mb.png">](https://mybinder.org/v2/gh/webartifex/intro-to-python/main?urlpath=lab/tree/11_classes/03_content.ipynb)
       (Operator Overloading: Arithmetic & Relational Operators;
        Number Emulation)
-    - [content <img height="12" style="display: inline-block" src="static/link/to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/11_classes/04_content.ipynb)
-      [<img height="12" style="display: inline-block" src="static/link/to_mb.png">](https://mybinder.org/v2/gh/webartifex/intro-to-python/develop?urlpath=lab/tree/11_classes/04_content.ipynb)
+    - [content <img height="12" style="display: inline-block" src="static/link/to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/main/11_classes/04_content.ipynb)
+      [<img height="12" style="display: inline-block" src="static/link/to_mb.png">](https://mybinder.org/v2/gh/webartifex/intro-to-python/main?urlpath=lab/tree/11_classes/04_content.ipynb)
       (Writing one's own Packages;
        The final `Vector` & `Matrix` Classes;
        Comparison with `numpy`)
-    - [summary <img height="12" style="display: inline-block" src="static/link/to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/11_classes/05_summary.ipynb)
-    - [review questions <img height="12" style="display: inline-block" src="static/link/to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/develop/11_classes/06_review.ipynb)
+    - [summary <img height="12" style="display: inline-block" src="static/link/to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/main/11_classes/05_summary.ipynb)
+    - [review questions <img height="12" style="display: inline-block" src="static/link/to_nb.png">](https://nbviewer.jupyter.org/github/webartifex/intro-to-python/blob/main/11_classes/06_review.ipynb)
diff --git a/pyproject.toml b/pyproject.toml
index 4491fee..8d26fc8 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -4,7 +4,7 @@ build-backend = "poetry.core.masonry.api"
 
 [tool.poetry]
 name = "intro-to-python"
-version = "0.1.0.dev0"
+version = "0.1.0"
 package-mode = false
 
 authors = ["Alexander Hess <alexander@webartifex.biz>"]